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+import warnings +from typing import Any, Iterator + +from mlflow.deployments import BaseDeploymentClient +from mlflow.deployments.constants import ( + MLFLOW_DEPLOYMENT_CLIENT_REQUEST_RETRY_CODES, +) +from mlflow.environment_variables import ( + MLFLOW_DEPLOYMENT_PREDICT_TIMEOUT, + MLFLOW_HTTP_REQUEST_TIMEOUT, +) +from mlflow.exceptions import MlflowException +from mlflow.utils import AttrDict +from mlflow.utils.annotations import deprecated +from mlflow.utils.databricks_utils import get_databricks_host_creds +from mlflow.utils.rest_utils import augmented_raise_for_status, http_request + + +class DatabricksEndpoint(AttrDict): + """ + A dictionary-like object representing a Databricks serving endpoint. + + .. code-block:: python + + endpoint = DatabricksEndpoint( + { + "name": "chat", + "creator": "alice@company.com", + "creation_timestamp": 0, + "last_updated_timestamp": 0, + "state": {...}, + "config": {...}, + "tags": [...], + "id": "88fd3f75a0d24b0380ddc40484d7a31b", + } + ) + assert endpoint.name == "chat" + """ + + +class DatabricksDeploymentClient(BaseDeploymentClient): + """ + Client for interacting with Databricks serving endpoints. + + Example: + + First, set up credentials for authentication: + + .. code-block:: bash + + export DATABRICKS_HOST=... + export DATABRICKS_TOKEN=... + + .. seealso:: + + See https://docs.databricks.com/en/dev-tools/auth.html for other authentication methods. + + Then, create a deployment client and use it to interact with Databricks serving endpoints: + + .. code-block:: python + + from mlflow.deployments import get_deploy_client + + client = get_deploy_client("databricks") + endpoints = client.list_endpoints() + assert endpoints == [ + { + "name": "chat", + "creator": "alice@company.com", + "creation_timestamp": 0, + "last_updated_timestamp": 0, + "state": {...}, + "config": {...}, + "tags": [...], + "id": "88fd3f75a0d24b0380ddc40484d7a31b", + }, + ] + """ + + def create_deployment(self, name, model_uri, flavor=None, config=None, endpoint=None): + """ + .. warning:: + + This method is not implemented for `DatabricksDeploymentClient`. + """ + raise NotImplementedError + + def update_deployment(self, name, model_uri=None, flavor=None, config=None, endpoint=None): + """ + .. warning:: + + This method is not implemented for `DatabricksDeploymentClient`. + """ + raise NotImplementedError + + def delete_deployment(self, name, config=None, endpoint=None): + """ + .. warning:: + + This method is not implemented for `DatabricksDeploymentClient`. + """ + raise NotImplementedError + + def list_deployments(self, endpoint=None): + """ + .. warning:: + + This method is not implemented for `DatabricksDeploymentClient`. + """ + raise NotImplementedError + + def get_deployment(self, name, endpoint=None): + """ + .. warning:: + + This method is not implemented for `DatabricksDeploymentClient`. + """ + raise NotImplementedError + + def _call_endpoint( + self, + *, + method: str, + prefix: str = "/api/2.0", + route: str | None = None, + json_body: dict[str, Any] | None = None, + timeout: int | None = None, + ): + call_kwargs = {} + if method.lower() == "get": + call_kwargs["params"] = json_body + else: + call_kwargs["json"] = json_body + + response = http_request( + host_creds=get_databricks_host_creds(self.target_uri), + endpoint=posixpath.join(prefix, "serving-endpoints", route or ""), + method=method, + timeout=MLFLOW_HTTP_REQUEST_TIMEOUT.get() if timeout is None else timeout, + raise_on_status=False, + retry_codes=MLFLOW_DEPLOYMENT_CLIENT_REQUEST_RETRY_CODES, + extra_headers={"X-Databricks-Endpoints-API-Client": "Databricks Deployment Client"}, + **call_kwargs, + ) + augmented_raise_for_status(response) + return DatabricksEndpoint(response.json()) + + def _call_endpoint_stream( + self, + *, + method: str, + prefix: str = "/api/2.0", + route: str | None = None, + json_body: dict[str, Any] | None = None, + timeout: int | None = None, + ) -> Iterator[str]: + call_kwargs = {} + if method.lower() == "get": + call_kwargs["params"] = json_body + else: + call_kwargs["json"] = json_body + + response = http_request( + host_creds=get_databricks_host_creds(self.target_uri), + endpoint=posixpath.join(prefix, "serving-endpoints", route or ""), + method=method, + timeout=MLFLOW_HTTP_REQUEST_TIMEOUT.get() if timeout is None else timeout, + raise_on_status=False, + retry_codes=MLFLOW_DEPLOYMENT_CLIENT_REQUEST_RETRY_CODES, + extra_headers={"X-Databricks-Endpoints-API-Client": "Databricks Deployment Client"}, + stream=True, # Receive response content in streaming way. + **call_kwargs, + ) + augmented_raise_for_status(response) + + # Streaming response content are composed of multiple lines. + # Each line format depends on specific endpoint + # Explicitly set the encoding to `utf-8` so the `decode_unicode` in the next line + # will decode correctly + response.encoding = "utf-8" + return ( + line.strip() + for line in response.iter_lines(decode_unicode=True) + if line.strip() # filter out keep-alive new lines + ) + + def predict(self, deployment_name=None, inputs=None, endpoint=None): + """ + Query a serving endpoint with the provided model inputs. + See https://docs.databricks.com/api/workspace/servingendpoints/query for request/response + schema. + + Args: + deployment_name: Unused. + inputs: A dictionary containing the model inputs to query. + endpoint: The name of the serving endpoint to query. + + Returns: + A :py:class:`DatabricksEndpoint` object containing the query response. + + Example: + + .. code-block:: python + + from mlflow.deployments import get_deploy_client + + client = get_deploy_client("databricks") + response = client.predict( + endpoint="chat", + inputs={ + "messages": [ + {"role": "user", "content": "Hello!"}, + ], + }, + ) + assert response == { + "id": "chatcmpl-8OLm5kfqBAJD8CpsMANESWKpLSLXY", + "object": "chat.completion", + "created": 1700814265, + "model": "gpt-4-0613", + "choices": [ + { + "index": 0, + "message": { + "role": "assistant", + "content": "Hello! How can I assist you today?", + }, + "finish_reason": "stop", + } + ], + "usage": { + "prompt_tokens": 9, + "completion_tokens": 9, + "total_tokens": 18, + }, + } + """ + return self._call_endpoint( + method="POST", + prefix="/", + route=posixpath.join(endpoint, "invocations"), + json_body=inputs, + timeout=MLFLOW_DEPLOYMENT_PREDICT_TIMEOUT.get(), + ) + + def predict_stream( + self, deployment_name=None, inputs=None, endpoint=None + ) -> Iterator[dict[str, Any]]: + """ + Submit a query to a configured provider endpoint, and get streaming response + + Args: + deployment_name: Unused. + inputs: The inputs to the query, as a dictionary. + endpoint: The name of the endpoint to query. + + Returns: + An iterator of dictionary containing the response from the endpoint. + + Example: + + .. code-block:: python + + from mlflow.deployments import get_deploy_client + + client = get_deploy_client("databricks") + chunk_iter = client.predict_stream( + endpoint="databricks-llama-2-70b-chat", + inputs={ + "messages": [{"role": "user", "content": "Hello!"}], + "temperature": 0.0, + "n": 1, + "max_tokens": 500, + }, + ) + for chunk in chunk_iter: + print(chunk) + # Example: + # { + # "id": "82a834f5-089d-4fc0-ad6c-db5c7d6a6129", + # "object": "chat.completion.chunk", + # "created": 1712133837, + # "model": "llama-2-70b-chat-030424", + # "choices": [ + # { + # "index": 0, "delta": {"role": "assistant", "content": "Hello"}, + # "finish_reason": None, + # } + # ], + # "usage": {"prompt_tokens": 11, "completion_tokens": 1, "total_tokens": 12}, + # } + """ + inputs = inputs or {} + + # Add stream=True param in request body to get streaming response + # See https://docs.databricks.com/api/workspace/servingendpoints/query#stream + chunk_line_iter = self._call_endpoint_stream( + method="POST", + prefix="/", + route=posixpath.join(endpoint, "invocations"), + json_body={**inputs, "stream": True}, + timeout=MLFLOW_DEPLOYMENT_PREDICT_TIMEOUT.get(), + ) + + for line in chunk_line_iter: + splits = line.split(":", 1) + if len(splits) < 2: + raise MlflowException( + f"Unknown response format: '{line}', " + "expected 'data: ' for streaming response." + ) + key, value = splits + if key != "data": + raise MlflowException( + f"Unknown response format with key '{key}'. " + f"Expected 'data: ' for streaming response, got '{line}'." + ) + + value = value.strip() + if value == "[DONE]": + # Databricks endpoint streaming response ends with + # a line of "data: [DONE]" + return + + yield json.loads(value) + + def create_endpoint(self, name=None, config=None, route_optimized=False): + """ + Create a new serving endpoint with the provided name and configuration. + + See https://docs.databricks.com/api/workspace/servingendpoints/create for request/response + schema. + + Args: + name: The name of the serving endpoint to create. + + .. warning:: + Deprecated. Include `name` in `config` instead. + + config: A dictionary containing either the full API request payload + or the configuration of the serving endpoint to create. + route_optimized: A boolean which defines whether databricks serving endpoint + is optimized for routing traffic. Only used in the deprecated approach. + + .. warning:: + Deprecated. Include `route_optimized` in `config` instead. + + Returns: + A :py:class:`DatabricksEndpoint` object containing the request response. + + Example: + + .. code-block:: python + + from mlflow.deployments import get_deploy_client + + client = get_deploy_client("databricks") + endpoint = client.create_endpoint( + config={ + "name": "test", + "config": { + "served_entities": [ + { + "external_model": { + "name": "gpt-4", + "provider": "openai", + "task": "llm/v1/chat", + "openai_config": { + "openai_api_key": "{{secrets/scope/key}}", + }, + }, + } + ], + "route_optimized": True, + }, + }, + ) + assert endpoint == { + "name": "test", + "creator": "alice@company.com", + "creation_timestamp": 0, + "last_updated_timestamp": 0, + "state": {...}, + "config": {...}, + "tags": [...], + "id": "88fd3f75a0d24b0380ddc40484d7a31b", + "permission_level": "CAN_MANAGE", + "route_optimized": False, + "task": "llm/v1/chat", + "endpoint_type": "EXTERNAL_MODEL", + "creator_display_name": "Alice", + "creator_kind": "User", + } + + """ + warnings_list = [] + + if config and "config" in config: + # Using new style: full API request payload + payload = config.copy() + + # Validate name conflicts + if "name" in payload: + if name is not None: + if payload["name"] == name: + warnings_list.append( + "Passing 'name' as a parameter is deprecated. " + "Please specify 'name' only within the config dictionary." + ) + else: + raise MlflowException( + f"Name mismatch. Found '{name}' as parameter and '{payload['name']}' " + "in config. Please specify 'name' only within the config dictionary " + "as this parameter is deprecated." + ) + else: + if name is None: + raise MlflowException( + "The 'name' field is required. Please specify it within the config " + "dictionary." + ) + payload["name"] = name + warnings_list.append( + "Passing 'name' as a parameter is deprecated. " + "Please specify 'name' within the config dictionary." + ) + + # Validate route_optimized conflicts + if "route_optimized" in payload: + if route_optimized is not None: + if payload["route_optimized"] != route_optimized: + raise MlflowException( + "Conflicting 'route_optimized' values found. " + "Please specify 'route_optimized' only within the config dictionary " + "as this parameter is deprecated." + ) + warnings_list.append( + "Passing 'route_optimized' as a parameter is deprecated. " + "Please specify 'route_optimized' only within the config dictionary." + ) + else: + if route_optimized: + payload["route_optimized"] = route_optimized + warnings_list.append( + "Passing 'route_optimized' as a parameter is deprecated. " + "Please specify 'route_optimized' within the config dictionary." + ) + else: + # Handle legacy format (backwards compatibility) + warnings_list.append( + "Passing 'name', 'config', and 'route_optimized' as separate parameters is " + "deprecated. Please pass the full API request payload as a single dictionary " + "in the 'config' parameter." + ) + config = config.copy() if config else {} # avoid mutating config + extras = {} + for key in ("tags", "rate_limits"): + if tags := config.pop(key, None): + extras[key] = tags + payload = {"name": name, "config": config, "route_optimized": route_optimized, **extras} + + if warnings_list: + warnings.warn("\n".join(warnings_list), UserWarning) + + return self._call_endpoint(method="POST", json_body=payload) + + @deprecated( + alternative=( + "update_endpoint_config, update_endpoint_tags, update_endpoint_rate_limits, " + "or update_endpoint_ai_gateway" + ) + ) + def update_endpoint(self, endpoint, config=None): + """ + Update a specified serving endpoint with the provided configuration. + See https://docs.databricks.com/api/workspace/servingendpoints/updateconfig for + request/response schema. + + Args: + endpoint: The name of the serving endpoint to update. + config: A dictionary containing the configuration of the serving endpoint to update. + + Returns: + A :py:class:`DatabricksEndpoint` object containing the request response. + + Example: + + .. code-block:: python + + from mlflow.deployments import get_deploy_client + + client = get_deploy_client("databricks") + endpoint = client.update_endpoint( + endpoint="chat", + config={ + "served_entities": [ + { + "name": "test", + "external_model": { + "name": "gpt-4", + "provider": "openai", + "task": "llm/v1/chat", + "openai_config": { + "openai_api_key": "{{secrets/scope/key}}", + }, + }, + } + ], + }, + ) + assert endpoint == { + "name": "chat", + "creator": "alice@company.com", + "creation_timestamp": 0, + "last_updated_timestamp": 0, + "state": {...}, + "config": {...}, + "tags": [...], + "id": "88fd3f75a0d24b0380ddc40484d7a31b", + } + + rate_limits = client.update_endpoint( + endpoint="chat", + config={ + "rate_limits": [ + { + "key": "user", + "renewal_period": "minute", + "calls": 10, + } + ], + }, + ) + assert rate_limits == { + "rate_limits": [ + { + "key": "user", + "renewal_period": "minute", + "calls": 10, + } + ], + } + """ + warnings.warn( + "The `update_endpoint` method is deprecated. Use the specific update methods—" + "`update_endpoint_config`, `update_endpoint_tags`, `update_endpoint_rate_limits`, " + "`update_endpoint_ai_gateway`—instead.", + UserWarning, + ) + + if list(config) == ["rate_limits"]: + return self._call_endpoint( + method="PUT", route=posixpath.join(endpoint, "rate-limits"), json_body=config + ) + else: + return self._call_endpoint( + method="PUT", route=posixpath.join(endpoint, "config"), json_body=config + ) + + def update_endpoint_config(self, endpoint, config): + """ + Update the configuration of a specified serving endpoint. See + https://docs.databricks.com/api/workspace/servingendpoints/updateconfig for request/response + request/response schema. + + Args: + endpoint: The name of the serving endpoint to update. + config: A dictionary containing the configuration of the serving endpoint to update. + + Returns: + A :py:class:`DatabricksEndpoint` object containing the request response. + + Example: + + .. code-block:: python + + from mlflow.deployments import get_deploy_client + + client = get_deploy_client("databricks") + updated_endpoint = client.update_endpoint_config( + endpoint="test", + config={ + "served_entities": [ + { + "name": "gpt-4o-mini", + "external_model": { + "name": "gpt-4o-mini", + "provider": "openai", + "task": "llm/v1/chat", + "openai_config": { + "openai_api_key": "{{secrets/scope/key}}", + }, + }, + } + ] + }, + ) + assert updated_endpoint == { + "name": "test", + "creator": "alice@company.com", + "creation_timestamp": 1729527763000, + "last_updated_timestamp": 1729530896000, + "state": {"ready": "READY", "config_update": "NOT_UPDATING"}, + "config": {...}, + "id": "44b258fb39804564b37603d8d14b853e", + "permission_level": "CAN_MANAGE", + "route_optimized": False, + "task": "llm/v1/chat", + "endpoint_type": "EXTERNAL_MODEL", + "creator_display_name": "Alice", + "creator_kind": "User", + } + """ + + return self._call_endpoint( + method="PUT", route=posixpath.join(endpoint, "config"), json_body=config + ) + + def update_endpoint_tags(self, endpoint, config): + """ + Update the tags of a specified serving endpoint. See + https://docs.databricks.com/api/workspace/servingendpoints/patch for request/response + schema. + + Args: + endpoint: The name of the serving endpoint to update. + config: A dictionary containing tags to add and/or remove. + + Returns: + A :py:class:`DatabricksEndpoint` object containing the request response. + + Example: + + .. code-block:: python + + from mlflow.deployments import get_deploy_client + + client = get_deploy_client("databricks") + updated_tags = client.update_endpoint_tags( + endpoint="test", config={"add_tags": [{"key": "project", "value": "test"}]} + ) + assert updated_tags == {"tags": [{"key": "project", "value": "test"}]} + """ + return self._call_endpoint( + method="PATCH", route=posixpath.join(endpoint, "tags"), json_body=config + ) + + def update_endpoint_rate_limits(self, endpoint, config): + """ + Update the rate limits of a specified serving endpoint. + See https://docs.databricks.com/api/workspace/servingendpoints/put for request/response + schema. + + Args: + endpoint: The name of the serving endpoint to update. + config: A dictionary containing the updated rate limit configuration. + + Returns: + A :py:class:`DatabricksEndpoint` object containing the updated rate limits. + + Example: + + .. code-block:: python + + from mlflow.deployments import get_deploy_client + + client = get_deploy_client("databricks") + name = "databricks-dbrx-instruct" + rate_limits = { + "rate_limits": [{"calls": 10, "key": "endpoint", "renewal_period": "minute"}] + } + updated_rate_limits = client.update_endpoint_rate_limits( + endpoint=name, config=rate_limits + ) + assert updated_rate_limits == { + "rate_limits": [{"calls": 10, "key": "endpoint", "renewal_period": "minute"}] + } + """ + return self._call_endpoint( + method="PUT", route=posixpath.join(endpoint, "rate-limits"), json_body=config + ) + + def update_endpoint_ai_gateway(self, endpoint, config): + """ + Update the AI Gateway configuration of a specified serving endpoint. + + Args: + endpoint (str): The name of the serving endpoint to update. + config (dict): A dictionary containing the AI Gateway configuration to update. + + Returns: + dict: A dictionary containing the updated AI Gateway configuration. + + Example: + + .. code-block:: python + + from mlflow.deployments import get_deploy_client + + client = get_deploy_client("databricks") + name = "test" + + gateway_config = { + "usage_tracking_config": {"enabled": True}, + "inference_table_config": { + "enabled": True, + "catalog_name": "my_catalog", + "schema_name": "my_schema", + }, + } + + updated_gateway = client.update_endpoint_ai_gateway( + endpoint=name, config=gateway_config + ) + assert updated_gateway == { + "usage_tracking_config": {"enabled": True}, + "inference_table_config": { + "catalog_name": "my_catalog", + "schema_name": "my_schema", + "table_name_prefix": "test", + "enabled": True, + }, + } + """ + return self._call_endpoint( + method="PUT", route=posixpath.join(endpoint, "ai-gateway"), json_body=config + ) + + def delete_endpoint(self, endpoint): + """ + Delete a specified serving endpoint. + See https://docs.databricks.com/api/workspace/servingendpoints/delete for request/response + schema. + + Args: + endpoint: The name of the serving endpoint to delete. + + Returns: + A DatabricksEndpoint object containing the request response. + + Example: + + .. code-block:: python + + from mlflow.deployments import get_deploy_client + + client = get_deploy_client("databricks") + client.delete_endpoint(endpoint="chat") + """ + return self._call_endpoint(method="DELETE", route=endpoint) + + def list_endpoints(self): + """ + Retrieve all serving endpoints. + + See https://docs.databricks.com/api/workspace/servingendpoints/list for request/response + schema. + + Returns: + A list of :py:class:`DatabricksEndpoint` objects containing the request response. + + Example: + + .. code-block:: python + + from mlflow.deployments import get_deploy_client + + client = get_deploy_client("databricks") + endpoints = client.list_endpoints() + assert endpoints == [ + { + "name": "chat", + "creator": "alice@company.com", + "creation_timestamp": 0, + "last_updated_timestamp": 0, + "state": {...}, + "config": {...}, + "tags": [...], + "id": "88fd3f75a0d24b0380ddc40484d7a31b", + }, + ] + + """ + return self._call_endpoint(method="GET").endpoints + + def get_endpoint(self, endpoint): + """ + Get a specified serving endpoint. + See https://docs.databricks.com/api/workspace/servingendpoints/get for request/response + schema. + + Args: + endpoint: The name of the serving endpoint to get. + + Returns: + A DatabricksEndpoint object containing the request response. + + Example: + + .. code-block:: python + + from mlflow.deployments import get_deploy_client + + client = get_deploy_client("databricks") + endpoint = client.get_endpoint(endpoint="chat") + assert endpoint == { + "name": "chat", + "creator": "alice@company.com", + "creation_timestamp": 0, + "last_updated_timestamp": 0, + "state": {...}, + "config": {...}, + "tags": [...], + "id": "88fd3f75a0d24b0380ddc40484d7a31b", + } + """ + return self._call_endpoint(method="GET", route=endpoint) + + +def run_local(name, model_uri, flavor=None, config=None): + pass + + +def target_help(): + pass diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/deployments/databricks/__pycache__/__init__.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/deployments/databricks/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..1893bfd4a98ac9863e209c6bddf89c804faa1151 Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/deployments/databricks/__pycache__/__init__.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/deployments/mlflow/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/deployments/mlflow/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..28a03937400a3dfb631d1e92baef85f3131bc1fe --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/deployments/mlflow/__init__.py @@ -0,0 +1,329 @@ +from typing import TYPE_CHECKING, Any + +import requests + +from mlflow import MlflowException +from mlflow.deployments import BaseDeploymentClient +from mlflow.deployments.constants import ( + MLFLOW_DEPLOYMENT_CLIENT_REQUEST_RETRY_CODES, +) +from mlflow.deployments.server.constants import ( + MLFLOW_DEPLOYMENTS_CRUD_ENDPOINT_BASE, + MLFLOW_DEPLOYMENTS_ENDPOINTS_BASE, + MLFLOW_DEPLOYMENTS_QUERY_SUFFIX, +) +from mlflow.deployments.utils import resolve_endpoint_url +from mlflow.environment_variables import ( + MLFLOW_DEPLOYMENT_PREDICT_TIMEOUT, + MLFLOW_HTTP_REQUEST_TIMEOUT, +) +from mlflow.protos.databricks_pb2 import BAD_REQUEST +from mlflow.store.entities.paged_list import PagedList +from mlflow.utils.credentials import get_default_host_creds +from mlflow.utils.rest_utils import augmented_raise_for_status, http_request +from mlflow.utils.uri import join_paths + +if TYPE_CHECKING: + from mlflow.deployments.server.config import Endpoint + + +class MlflowDeploymentClient(BaseDeploymentClient): + """ + Client for interacting with the MLflow AI Gateway. + + Example: + + First, start the MLflow AI Gateway: + + .. code-block:: bash + + mlflow gateway start --config-path path/to/config.yaml + + Then, create a client and use it to interact with the server: + + .. code-block:: python + + from mlflow.deployments import get_deploy_client + + client = get_deploy_client("http://localhost:5000") + endpoints = client.list_endpoints() + assert [e.dict() for e in endpoints] == [ + { + "name": "chat", + "endpoint_type": "llm/v1/chat", + "model": {"name": "gpt-4o-mini", "provider": "openai"}, + "endpoint_url": "http://localhost:5000/gateway/chat/invocations", + }, + ] + """ + + def create_deployment(self, name, model_uri, flavor=None, config=None, endpoint=None): + """ + .. warning:: + This method is not implemented for `MlflowDeploymentClient`. + """ + raise NotImplementedError + + def update_deployment(self, name, model_uri=None, flavor=None, config=None, endpoint=None): + """ + .. warning:: + This method is not implemented for `MlflowDeploymentClient`. + """ + raise NotImplementedError + + def delete_deployment(self, name, config=None, endpoint=None): + """ + .. warning:: + This method is not implemented for `MlflowDeploymentClient`. + """ + raise NotImplementedError + + def list_deployments(self, endpoint=None): + """ + .. warning:: + This method is not implemented for `MlflowDeploymentClient`. + """ + raise NotImplementedError + + def get_deployment(self, name, endpoint=None): + """ + .. warning:: + This method is not implemented for `MLflowDeploymentClient`. + """ + raise NotImplementedError + + def create_endpoint(self, name, config=None): + """ + .. warning:: + This method is not implemented for `MlflowDeploymentClient`. + """ + raise NotImplementedError + + def update_endpoint(self, endpoint, config=None): + """ + .. warning:: + This method is not implemented for `MlflowDeploymentClient`. + """ + raise NotImplementedError + + def delete_endpoint(self, endpoint): + """ + .. warning:: + This method is not implemented for `MlflowDeploymentClient`. + """ + raise NotImplementedError + + def _call_endpoint( + self, + method: str, + route: str, + json_body: str | None = None, + timeout: int | None = None, + ): + call_kwargs = {} + if method.lower() == "get": + call_kwargs["params"] = json_body + else: + call_kwargs["json"] = json_body + + response = http_request( + host_creds=get_default_host_creds(self.target_uri), + endpoint=route, + method=method, + timeout=MLFLOW_HTTP_REQUEST_TIMEOUT.get() if timeout is None else timeout, + retry_codes=MLFLOW_DEPLOYMENT_CLIENT_REQUEST_RETRY_CODES, + raise_on_status=False, + **call_kwargs, + ) + augmented_raise_for_status(response) + return response.json() + + def get_endpoint(self, endpoint) -> "Endpoint": + """ + Gets a specified endpoint configured for the MLflow AI Gateway. + + Args: + endpoint: The name of the endpoint to retrieve. + + Returns: + An `Endpoint` object representing the endpoint. + + Example: + + .. code-block:: python + + from mlflow.deployments import get_deploy_client + + client = get_deploy_client("http://localhost:5000") + endpoint = client.get_endpoint(endpoint="chat") + assert endpoint.dict() == { + "name": "chat", + "endpoint_type": "llm/v1/chat", + "model": {"name": "gpt-4o-mini", "provider": "openai"}, + "endpoint_url": "http://localhost:5000/gateway/chat/invocations", + } + """ + # Delayed import to avoid importing mlflow.gateway in the module scope + from mlflow.deployments.server.config import Endpoint + + route = join_paths(MLFLOW_DEPLOYMENTS_CRUD_ENDPOINT_BASE, endpoint) + response = self._call_endpoint("GET", route) + return Endpoint( + **{ + **response, + "endpoint_url": resolve_endpoint_url(self.target_uri, response["endpoint_url"]), + } + ) + + def _list_endpoints(self, page_token=None) -> "PagedList[Endpoint]": + # Delayed import to avoid importing mlflow.gateway in the module scope + from mlflow.deployments.server.config import Endpoint + + params = None if page_token is None else {"page_token": page_token} + response_json = self._call_endpoint( + "GET", MLFLOW_DEPLOYMENTS_CRUD_ENDPOINT_BASE, json_body=params + ) + routes = [ + Endpoint( + **{ + **resp, + "endpoint_url": resolve_endpoint_url( + self.target_uri, + resp["endpoint_url"], + ), + } + ) + for resp in response_json.get("endpoints", []) + ] + next_page_token = response_json.get("next_page_token") + return PagedList(routes, next_page_token) + + def list_endpoints(self) -> "list[Endpoint]": + """ + List endpoints configured for the MLflow AI Gateway. + + Returns: + A list of ``Endpoint`` objects. + + Example: + + .. code-block:: python + + from mlflow.deployments import get_deploy_client + + client = get_deploy_client("http://localhost:5000") + + endpoints = client.list_endpoints() + assert [e.dict() for e in endpoints] == [ + { + "name": "chat", + "endpoint_type": "llm/v1/chat", + "model": {"name": "gpt-4o-mini", "provider": "openai"}, + "endpoint_url": "http://localhost:5000/gateway/chat/invocations", + }, + ] + + """ + endpoints = [] + next_page_token = None + while True: + page = self._list_endpoints(next_page_token) + endpoints.extend(page) + next_page_token = page.token + if next_page_token is None: + break + return endpoints + + def predict(self, deployment_name=None, inputs=None, endpoint=None) -> dict[str, Any]: + """ + Submit a query to a configured provider endpoint. + + Args: + deployment_name: Unused. + inputs: The inputs to the query, as a dictionary. + endpoint: The name of the endpoint to query. + + Returns: + A dictionary containing the response from the endpoint. + + Example: + + .. code-block:: python + + from mlflow.deployments import get_deploy_client + + client = get_deploy_client("http://localhost:5000") + + response = client.predict( + endpoint="chat", + inputs={"messages": [{"role": "user", "content": "Hello"}]}, + ) + assert response == { + "id": "chatcmpl-8OLoQuaeJSLybq3NBoe0w5eyqjGb9", + "object": "chat.completion", + "created": 1700814410, + "model": "gpt-4o-mini", + "choices": [ + { + "index": 0, + "message": { + "role": "assistant", + "content": "Hello! How can I assist you today?", + }, + "finish_reason": "stop", + } + ], + "usage": { + "prompt_tokens": 9, + "completion_tokens": 9, + "total_tokens": 18, + }, + } + + Additional parameters that are valid for a given provider and endpoint configuration can be + included with the request as shown below, using an openai completions endpoint request as + an example: + + .. code-block:: python + + from mlflow.deployments import get_deploy_client + + client = get_deploy_client("http://localhost:5000") + client.predict( + endpoint="completions", + inputs={ + "prompt": "Hello!", + "temperature": 0.3, + "max_tokens": 500, + }, + ) + """ + query_route = join_paths( + MLFLOW_DEPLOYMENTS_ENDPOINTS_BASE, endpoint, MLFLOW_DEPLOYMENTS_QUERY_SUFFIX + ) + try: + return self._call_endpoint( + "POST", query_route, inputs, MLFLOW_DEPLOYMENT_PREDICT_TIMEOUT.get() + ) + except MlflowException as e: + if isinstance(e.__cause__, requests.exceptions.Timeout): + raise MlflowException( + message=( + "The provider has timed out while generating a response to your " + "query. Please evaluate the available parameters for the query " + "that you are submitting. Some parameter values and inputs can " + "increase the computation time beyond the allowable route " + f"timeout of {MLFLOW_DEPLOYMENT_PREDICT_TIMEOUT} " + "seconds." + ), + error_code=BAD_REQUEST, + ) + raise e + + +def run_local(name, model_uri, flavor=None, config=None): + pass + + +def target_help(): + pass diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/deployments/mlflow/__pycache__/__init__.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/deployments/mlflow/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..96efd6abd23a9d9cbe2d1188f8700dd7281a031e Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/deployments/mlflow/__pycache__/__init__.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/deployments/openai/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/deployments/openai/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..154ebe3464e2cb79f43c70f2c218c9dd84a517bb --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/deployments/openai/__init__.py @@ -0,0 +1,252 @@ +import os + +from mlflow.deployments import BaseDeploymentClient +from mlflow.exceptions import MlflowException +from mlflow.protos.databricks_pb2 import INVALID_PARAMETER_VALUE +from mlflow.utils.openai_utils import ( + _OAITokenHolder, + _OpenAIApiConfig, + _OpenAIEnvVar, +) +from mlflow.utils.rest_utils import augmented_raise_for_status + + +class OpenAIDeploymentClient(BaseDeploymentClient): + """ + Client for interacting with OpenAI endpoints. + + Example: + + First, set up credentials for authentication: + + .. code-block:: bash + + export OPENAI_API_KEY=... + + .. seealso:: + + See https://mlflow.org/docs/latest/python_api/openai/index.html for other authentication + methods. + + Then, create a deployment client and use it to interact with OpenAI endpoints: + + .. code-block:: python + + from mlflow.deployments import get_deploy_client + + client = get_deploy_client("openai") + client.predict( + endpoint="gpt-4o-mini", + inputs={ + "messages": [ + {"role": "user", "content": "Hello!"}, + ], + }, + ) + """ + + def create_deployment(self, name, model_uri, flavor=None, config=None, endpoint=None): + """ + .. warning:: + + This method is not implemented for `OpenAIDeploymentClient`. + """ + raise NotImplementedError + + def update_deployment(self, name, model_uri=None, flavor=None, config=None, endpoint=None): + """ + .. warning:: + + This method is not implemented for `OpenAIDeploymentClient`. + """ + raise NotImplementedError + + def delete_deployment(self, name, config=None, endpoint=None): + """ + .. warning:: + + This method is not implemented for `OpenAIDeploymentClient`. + """ + raise NotImplementedError + + def list_deployments(self, endpoint=None): + """ + .. warning:: + + This method is not implemented for `OpenAIDeploymentClient`. + """ + raise NotImplementedError + + def get_deployment(self, name, endpoint=None): + """ + .. warning:: + + This method is not implemented for `OpenAIDeploymentClient`. + """ + raise NotImplementedError + + def predict(self, deployment_name=None, inputs=None, endpoint=None): + """Query an OpenAI endpoint. + See https://platform.openai.com/docs/api-reference for more information. + + Args: + deployment_name: Unused. + inputs: A dictionary containing the model inputs to query. + endpoint: The name of the endpoint to query. + + Returns: + A dictionary containing the model outputs. + + """ + _check_openai_key() + + api_config = _get_api_config_without_openai_dep() + api_token = _OAITokenHolder(api_config.api_type) + api_token.refresh() + + if api_config.api_type in ("azure", "azure_ad", "azuread"): + from openai import AzureOpenAI + + client = AzureOpenAI( + api_key=api_token.token, + azure_endpoint=api_config.api_base, + api_version=api_config.api_version, + azure_deployment=api_config.deployment_id, + max_retries=api_config.max_retries, + timeout=api_config.timeout, + ) + else: + from openai import OpenAI + + client = OpenAI( + api_key=api_token.token, + base_url=api_config.api_base, + max_retries=api_config.max_retries, + timeout=api_config.timeout, + ) + + return client.chat.completions.create( + messages=inputs["messages"], model=endpoint + ).model_dump() + + def create_endpoint(self, name, config=None): + """ + .. warning:: + + This method is not implemented for `OpenAIDeploymentClient`. + """ + raise NotImplementedError + + def update_endpoint(self, endpoint, config=None): + """ + .. warning:: + + This method is not implemented for `OpenAIDeploymentClient`. + """ + raise NotImplementedError + + def delete_endpoint(self, endpoint): + """ + .. warning:: + + This method is not implemented for `OpenAIDeploymentClient`. + """ + raise NotImplementedError + + def list_endpoints(self): + """ + List the currently available models. + """ + + _check_openai_key() + + api_config = _get_api_config_without_openai_dep() + import requests + + if api_config.api_type in ("azure", "azure_ad", "azuread"): + raise NotImplementedError( + "List endpoints is not implemented for Azure OpenAI API", + ) + else: + api_key = os.environ["OPENAI_API_KEY"] + request_header = {"Authorization": f"Bearer {api_key}"} + + response = requests.get( + "https://api.openai.com/v1/models", + headers=request_header, + ) + + augmented_raise_for_status(response) + + return response.json() + + def get_endpoint(self, endpoint): + """ + Get information about a specific model. + """ + + _check_openai_key() + + api_config = _get_api_config_without_openai_dep() + import requests + + if api_config.api_type in ("azure", "azure_ad", "azuread"): + raise NotImplementedError( + "Get endpoint is not implemented for Azure OpenAI API", + ) + else: + api_key = os.environ["OPENAI_API_KEY"] + request_header = {"Authorization": f"Bearer {api_key}"} + + response = requests.get( + f"https://api.openai.com/v1/models/{endpoint}", + headers=request_header, + ) + + augmented_raise_for_status(response) + + return response.json() + + +def run_local(name, model_uri, flavor=None, config=None): + pass + + +def target_help(): + pass + + +def _get_api_config_without_openai_dep() -> _OpenAIApiConfig: + """ + Gets the parameters and configuration of the OpenAI API connected to. + """ + api_type = os.getenv(_OpenAIEnvVar.OPENAI_API_TYPE.value) + api_version = os.getenv(_OpenAIEnvVar.OPENAI_API_VERSION.value) + api_base = os.getenv(_OpenAIEnvVar.OPENAI_API_BASE.value, None) + deployment_id = os.getenv(_OpenAIEnvVar.OPENAI_DEPLOYMENT_NAME.value, None) + if api_type in ("azure", "azure_ad", "azuread"): + batch_size = 16 + max_tokens_per_minute = 60_000 + else: + # The maximum batch size is 2048: + # https://github.com/openai/openai-python/blob/b82a3f7e4c462a8a10fa445193301a3cefef9a4a/openai/embeddings_utils.py#L43 + # We use a smaller batch size to be safe. + batch_size = 1024 + max_tokens_per_minute = 90_000 + return _OpenAIApiConfig( + api_type=api_type, + batch_size=batch_size, + max_requests_per_minute=3_500, + max_tokens_per_minute=max_tokens_per_minute, + api_base=api_base, + api_version=api_version, + deployment_id=deployment_id, + ) + + +def _check_openai_key(): + if "OPENAI_API_KEY" not in os.environ: + raise MlflowException( + "OPENAI_API_KEY environment variable not set", + error_code=INVALID_PARAMETER_VALUE, + ) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/deployments/openai/__pycache__/__init__.cpython-312.pyc 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b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/deployments/server/config.py new file mode 100644 index 0000000000000000000000000000000000000000..722fa0edc4e2b4686ae4f33fac94c31bd9a41b4d --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/deployments/server/config.py @@ -0,0 +1,24 @@ +from mlflow.gateway.base_models import ResponseModel +from mlflow.gateway.config import Limit, RouteModelInfo + + +class Endpoint(ResponseModel): + name: str + endpoint_type: str + model: RouteModelInfo + endpoint_url: str + limit: Limit | None + + class Config: + schema_extra = { + "example": { + "name": "openai-completions", + "endpoint_type": "llm/v1/completions", + "model": { + "name": "gpt-4o-mini", + "provider": "openai", + }, + "endpoint_url": "/endpoints/completions/invocations", + "limit": {"calls": 1, "key": None, "renewal_period": "minute"}, + } + } diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/deployments/server/constants.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/deployments/server/constants.py new file mode 100644 index 0000000000000000000000000000000000000000..e3123607b72f2d7bd501fabf2c6af53d701909f8 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/deployments/server/constants.py @@ -0,0 +1,6 @@ +MLFLOW_DEPLOYMENTS_HEALTH_ENDPOINT = "/health" +MLFLOW_DEPLOYMENTS_CRUD_ENDPOINT_BASE = "/api/2.0/endpoints/" +MLFLOW_DEPLOYMENTS_LIMITS_BASE = "/api/2.0/endpoints/limits/" +MLFLOW_DEPLOYMENTS_ENDPOINTS_BASE = "/endpoints/" +MLFLOW_DEPLOYMENTS_QUERY_SUFFIX = "/invocations" +MLFLOW_DEPLOYMENTS_LIST_ENDPOINTS_PAGE_SIZE = 3000 diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/diviner/__pycache__/__init__.cpython-312.pyc 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diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/entities/model_registry/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/entities/model_registry/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..a7a0393b4b3c7a4b9f97528344c9a8193f8838ef --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/entities/model_registry/__init__.py @@ -0,0 +1,29 @@ +from mlflow.entities.model_registry.model_version import ModelVersion +from mlflow.entities.model_registry.model_version_deployment_job_state import ( + ModelVersionDeploymentJobState, +) +from mlflow.entities.model_registry.model_version_search import ModelVersionSearch +from mlflow.entities.model_registry.model_version_tag import ModelVersionTag +from mlflow.entities.model_registry.prompt import Prompt +from mlflow.entities.model_registry.prompt_version import PromptVersion +from mlflow.entities.model_registry.registered_model import RegisteredModel +from mlflow.entities.model_registry.registered_model_alias import RegisteredModelAlias +from mlflow.entities.model_registry.registered_model_deployment_job_state import ( + RegisteredModelDeploymentJobState, +) +from mlflow.entities.model_registry.registered_model_search import RegisteredModelSearch +from mlflow.entities.model_registry.registered_model_tag import RegisteredModelTag + +__all__ = [ + "Prompt", + "PromptVersion", + "RegisteredModel", + "ModelVersion", + "RegisteredModelAlias", + "RegisteredModelTag", + "ModelVersionTag", + "RegisteredModelSearch", + "ModelVersionSearch", + "ModelVersionDeploymentJobState", + "RegisteredModelDeploymentJobState", +] diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/entities/model_registry/__pycache__/__init__.cpython-312.pyc 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+ return dict(self) == dict(other) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/entities/model_registry/model_version.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/entities/model_registry/model_version.py new file mode 100644 index 0000000000000000000000000000000000000000..c203fb61d99ffe40a510e21e3ccb6dd3819567d1 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/entities/model_registry/model_version.py @@ -0,0 +1,241 @@ +from mlflow.entities.logged_model_parameter import LoggedModelParameter as ModelParam +from mlflow.entities.metric import Metric +from mlflow.entities.model_registry._model_registry_entity import _ModelRegistryEntity +from mlflow.entities.model_registry.model_version_deployment_job_state import ( + ModelVersionDeploymentJobState, +) +from mlflow.entities.model_registry.model_version_status import ModelVersionStatus +from mlflow.entities.model_registry.model_version_tag import ModelVersionTag +from mlflow.protos.model_registry_pb2 import ModelVersion as ProtoModelVersion +from mlflow.protos.model_registry_pb2 import ModelVersionTag as ProtoModelVersionTag + + +class ModelVersion(_ModelRegistryEntity): + """ + MLflow entity for Model Version. + """ + + def __init__( + self, + name: str, + version: str, + creation_timestamp: int, + last_updated_timestamp: int | None = None, + description: str | None = None, + user_id: str | None = None, + current_stage: str | None = None, + source: str | None = None, + run_id: str | None = None, + status: str = ModelVersionStatus.to_string(ModelVersionStatus.READY), + status_message: str | None = None, + tags: list[ModelVersionTag] | None = None, + run_link: str | None = None, + aliases: list[str] | None = None, + # TODO: Make model_id a required field + # (currently optional to minimize breakages during prototype development) + model_id: str | None = None, + params: list[ModelParam] | None = None, + metrics: list[Metric] | None = None, + deployment_job_state: ModelVersionDeploymentJobState | None = None, + ): + super().__init__() + self._name: str = name + self._version: str = version + self._creation_time: int = creation_timestamp + self._last_updated_timestamp: int | None = last_updated_timestamp + self._description: str | None = description + self._user_id: str | None = user_id + self._current_stage: str | None = current_stage + self._source: str | None = source + self._run_id: str | None = run_id + self._run_link: str | None = run_link + self._status: str = status + self._status_message: str | None = status_message + self._tags: dict[str, str] = {tag.key: tag.value for tag in (tags or [])} + self._aliases: list[str] = aliases or [] + self._model_id: str | None = model_id + self._params: list[ModelParam] | None = params + self._metrics: list[Metric] | None = metrics + self._deployment_job_state: ModelVersionDeploymentJobState | None = deployment_job_state + + @property + def name(self) -> str: + """String. Unique name within Model Registry.""" + return self._name + + @name.setter + def name(self, new_name: str): + self._name = new_name + + @property + def version(self) -> str: + """Version""" + return self._version + + @property + def creation_timestamp(self) -> int: + """Integer. Model version creation timestamp (milliseconds since the Unix epoch).""" + return self._creation_time + + @property + def last_updated_timestamp(self) -> int | None: + """Integer. Timestamp of last update for this model version (milliseconds since the Unix + epoch). + """ + return self._last_updated_timestamp + + @last_updated_timestamp.setter + def last_updated_timestamp(self, updated_timestamp: int): + self._last_updated_timestamp = updated_timestamp + + @property + def description(self) -> str | None: + """String. Description""" + return self._description + + @description.setter + def description(self, description: str): + self._description = description + + @property + def user_id(self) -> str | None: + """String. User ID that created this model version.""" + return self._user_id + + @property + def current_stage(self) -> str | None: + """String. Current stage of this model version.""" + return self._current_stage + + @current_stage.setter + def current_stage(self, stage: str): + self._current_stage = stage + + @property + def source(self) -> str | None: + """String. Source path for the model.""" + return self._source + + @property + def run_id(self) -> str | None: + """String. MLflow run ID that generated this model.""" + return self._run_id + + @property + def run_link(self) -> str | None: + """String. MLflow run link referring to the exact run that generated this model version.""" + return self._run_link + + @property + def status(self) -> str: + """String. Current Model Registry status for this model.""" + return self._status + + @property + def status_message(self) -> str | None: + """String. Descriptive message for error status conditions.""" + return self._status_message + + @property + def tags(self) -> dict[str, str]: + """Dictionary of tag key (string) -> tag value for the current model version.""" + return self._tags + + @property + def aliases(self) -> list[str]: + """List of aliases (string) for the current model version.""" + return self._aliases + + @aliases.setter + def aliases(self, aliases: list[str]): + self._aliases = aliases + + @property + def model_id(self) -> str | None: + """String. ID of the model associated with this version.""" + return self._model_id + + @property + def params(self) -> list[ModelParam] | None: + """List of parameters associated with this model version.""" + return self._params + + @property + def metrics(self) -> list[Metric] | None: + """List of metrics associated with this model version.""" + return self._metrics + + @property + def deployment_job_state(self) -> ModelVersionDeploymentJobState | None: + """Deployment job state for the current model version.""" + return self._deployment_job_state + + @classmethod + def _properties(cls) -> list[str]: + # aggregate with base class properties since cls.__dict__ does not do it automatically + return sorted(cls._get_properties_helper()) + + def _add_tag(self, tag: ModelVersionTag): + self._tags[tag.key] = tag.value + + # proto mappers + @classmethod + def from_proto(cls, proto) -> "ModelVersion": + # input: mlflow.protos.model_registry_pb2.ModelVersion + # returns: ModelVersion entity + model_version = cls( + proto.name, + proto.version, + proto.creation_timestamp, + proto.last_updated_timestamp, + proto.description if proto.HasField("description") else None, + proto.user_id, + proto.current_stage, + proto.source, + proto.run_id if proto.HasField("run_id") else None, + ModelVersionStatus.to_string(proto.status), + proto.status_message if proto.HasField("status_message") else None, + run_link=proto.run_link, + aliases=proto.aliases, + deployment_job_state=ModelVersionDeploymentJobState.from_proto( + proto.deployment_job_state + ), + ) + for tag in proto.tags: + model_version._add_tag(ModelVersionTag.from_proto(tag)) + # TODO: Include params, metrics, and model ID in proto + return model_version + + def to_proto(self): + # input: ModelVersion entity + # returns mlflow.protos.model_registry_pb2.ModelVersion + model_version = ProtoModelVersion() + model_version.name = self.name + model_version.version = str(self.version) + model_version.creation_timestamp = self.creation_timestamp + if self.last_updated_timestamp is not None: + model_version.last_updated_timestamp = self.last_updated_timestamp + if self.description is not None: + model_version.description = self.description + if self.user_id is not None: + model_version.user_id = self.user_id + if self.current_stage is not None: + model_version.current_stage = self.current_stage + if self.source is not None: + model_version.source = str(self.source) + if self.run_id is not None: + model_version.run_id = str(self.run_id) + if self.run_link is not None: + model_version.run_link = str(self.run_link) + if self.status is not None: + model_version.status = ModelVersionStatus.from_string(self.status) + if self.status_message: + model_version.status_message = self.status_message + model_version.tags.extend( + [ProtoModelVersionTag(key=key, value=value) for key, value in self._tags.items()] + ) + model_version.aliases.extend(self.aliases) + if self.deployment_job_state is not None: + ModelVersionDeploymentJobState.to_proto(self.deployment_job_state) + # TODO: Include params, metrics, and model ID in proto + return model_version diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/entities/model_registry/model_version_deployment_job_run_state.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/entities/model_registry/model_version_deployment_job_run_state.py new file mode 100644 index 0000000000000000000000000000000000000000..4e58b3c0a0660e0bfe46bb33f1c49fd090ce7c51 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/entities/model_registry/model_version_deployment_job_run_state.py @@ -0,0 +1,44 @@ +from mlflow.protos.databricks_uc_registry_messages_pb2 import ( + ModelVersionDeploymentJobState as ProtoModelVersionDeploymentJobState, +) + + +class ModelVersionDeploymentJobRunState: + """Enum for model version deployment state of an + :py:class:`mlflow.entities.model_registry.ModelVersion`. + """ + + NO_VALID_DEPLOYMENT_JOB_FOUND = ProtoModelVersionDeploymentJobState.DeploymentJobRunState.Value( + "NO_VALID_DEPLOYMENT_JOB_FOUND" + ) + RUNNING = ProtoModelVersionDeploymentJobState.DeploymentJobRunState.Value("RUNNING") + SUCCEEDED = ProtoModelVersionDeploymentJobState.DeploymentJobRunState.Value("SUCCEEDED") + FAILED = ProtoModelVersionDeploymentJobState.DeploymentJobRunState.Value("FAILED") + PENDING = ProtoModelVersionDeploymentJobState.DeploymentJobRunState.Value("PENDING") + _STRING_TO_STATE = { + k: ProtoModelVersionDeploymentJobState.DeploymentJobRunState.Value(k) + for k in ProtoModelVersionDeploymentJobState.DeploymentJobRunState.keys() + } + _STATE_TO_STRING = {value: key for key, value in _STRING_TO_STATE.items()} + + @staticmethod + def from_string(state_str): + if state_str not in ModelVersionDeploymentJobRunState._STRING_TO_STATE: + raise Exception( + f"Could not get deployment job run state corresponding to string {state_str}. " + f"Valid state strings: {ModelVersionDeploymentJobRunState.all_states()}" + ) + return ModelVersionDeploymentJobRunState._STRING_TO_STATE[state_str] + + @staticmethod + def to_string(state): + if state not in ModelVersionDeploymentJobRunState._STATE_TO_STRING: + raise Exception( + f"Could not get string corresponding to deployment job run {state}. " + f"Valid states: {ModelVersionDeploymentJobRunState.all_states()}" + ) + return ModelVersionDeploymentJobRunState._STATE_TO_STRING[state] + + @staticmethod + def all_states(): + return list(ModelVersionDeploymentJobRunState._STATE_TO_STRING.keys()) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/entities/model_registry/model_version_deployment_job_state.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/entities/model_registry/model_version_deployment_job_state.py new file mode 100644 index 0000000000000000000000000000000000000000..b0621ae31ccb6d2ed7b934922244ec386b87f22d --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/entities/model_registry/model_version_deployment_job_state.py @@ -0,0 +1,70 @@ +from mlflow.entities.model_registry._model_registry_entity import _ModelRegistryEntity +from mlflow.entities.model_registry.model_version_deployment_job_run_state import ( + ModelVersionDeploymentJobRunState, +) +from mlflow.entities.model_registry.registered_model_deployment_job_state import ( + RegisteredModelDeploymentJobState, +) +from mlflow.protos.databricks_uc_registry_messages_pb2 import ( + ModelVersionDeploymentJobState as ProtoModelVersionDeploymentJobState, +) + + +class ModelVersionDeploymentJobState(_ModelRegistryEntity): + """Deployment Job state object associated with a model version.""" + + def __init__(self, job_id, run_id, job_state, run_state, current_task_name): + self._job_id = job_id + self._run_id = run_id + self._job_state = job_state + self._run_state = run_state + self._current_task_name = current_task_name + + def __eq__(self, other): + if type(other) is type(self): + return self.__dict__ == other.__dict__ + return False + + @property + def job_id(self): + return self._job_id + + @property + def run_id(self): + return self._run_id + + @property + def job_state(self): + return self._job_state + + @property + def run_state(self): + return self._run_state + + @property + def current_task_name(self): + return self._current_task_name + + @classmethod + def from_proto(cls, proto): + return cls( + job_id=proto.job_id, + run_id=proto.run_id, + job_state=RegisteredModelDeploymentJobState.to_string(proto.job_state), + run_state=ModelVersionDeploymentJobRunState.to_string(proto.run_state), + current_task_name=proto.current_task_name, + ) + + def to_proto(self): + state = ProtoModelVersionDeploymentJobState() + if self.job_id is not None: + state.job_id = self.job_id + if self.run_id is not None: + state.run_id = self.run_id + if self.job_state is not None: + state.job_state = RegisteredModelDeploymentJobState.from_string(self.job_state) + if self.run_state is not None: + state.run_state = ModelVersionDeploymentJobRunState.from_string(self.run_state) + if self.current_task_name is not None: + state.current_task_name = self.current_task_name + return state diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/entities/model_registry/model_version_search.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/entities/model_registry/model_version_search.py new file mode 100644 index 0000000000000000000000000000000000000000..e940c6990d2b99d41c123e19ce6b0292a25f8a8f --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/entities/model_registry/model_version_search.py @@ -0,0 +1,25 @@ +from mlflow.entities.model_registry import ModelVersion + + +class ModelVersionSearch(ModelVersion): + def __init__(self, *args, **kwargs): + kwargs["tags"] = [] + kwargs["aliases"] = [] + super().__init__(*args, **kwargs) + + def tags(self): + raise Exception( + "UC Model Versions gathered through search_model_versions do not have tags. " + "Please use get_model_version to obtain an individual version's tags." + ) + + def aliases(self): + raise Exception( + "UC Model Versions gathered through search_model_versions do not have aliases. " + "Please use get_model_version to obtain an individual version's aliases." + ) + + def __eq__(self, other): + if type(other) in {type(self), ModelVersion}: + return self.__dict__ == other.__dict__ + return False diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/entities/model_registry/model_version_stages.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/entities/model_registry/model_version_stages.py new file mode 100644 index 0000000000000000000000000000000000000000..3b7d1d0f26422aeea5c0b58e1c82a1324b5764e5 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/entities/model_registry/model_version_stages.py @@ -0,0 +1,25 @@ +from mlflow.exceptions import MlflowException +from mlflow.protos.databricks_pb2 import INVALID_PARAMETER_VALUE + +STAGE_NONE = "None" +STAGE_STAGING = "Staging" +STAGE_PRODUCTION = "Production" +STAGE_ARCHIVED = "Archived" + +STAGE_DELETED_INTERNAL = "Deleted_Internal" + +ALL_STAGES = [STAGE_NONE, STAGE_STAGING, STAGE_PRODUCTION, STAGE_ARCHIVED] +DEFAULT_STAGES_FOR_GET_LATEST_VERSIONS = [STAGE_STAGING, STAGE_PRODUCTION] +_CANONICAL_MAPPING = {stage.lower(): stage for stage in ALL_STAGES} + + +def get_canonical_stage(stage): + key = stage.lower() + if key not in _CANONICAL_MAPPING: + raise MlflowException( + "Invalid Model Version stage: {}. Value must be one of {}.".format( + stage, ", ".join(ALL_STAGES) + ), + INVALID_PARAMETER_VALUE, + ) + return _CANONICAL_MAPPING[key] diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/entities/model_registry/model_version_status.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/entities/model_registry/model_version_status.py new file mode 100644 index 0000000000000000000000000000000000000000..63afd86336bed0eb010738b7fc6b6ff5cf78b5b3 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/entities/model_registry/model_version_status.py @@ -0,0 +1,35 @@ +from mlflow.protos.model_registry_pb2 import ModelVersionStatus as ProtoModelVersionStatus + + +class ModelVersionStatus: + """Enum for status of an :py:class:`mlflow.entities.model_registry.ModelVersion`.""" + + PENDING_REGISTRATION = ProtoModelVersionStatus.Value("PENDING_REGISTRATION") + FAILED_REGISTRATION = ProtoModelVersionStatus.Value("FAILED_REGISTRATION") + READY = ProtoModelVersionStatus.Value("READY") + _STRING_TO_STATUS = { + k: ProtoModelVersionStatus.Value(k) for k in ProtoModelVersionStatus.keys() + } + _STATUS_TO_STRING = {value: key for key, value in _STRING_TO_STATUS.items()} + + @staticmethod + def from_string(status_str): + if status_str not in ModelVersionStatus._STRING_TO_STATUS: + raise Exception( + f"Could not get model version status corresponding to string {status_str}. " + f"Valid status strings: {list(ModelVersionStatus._STRING_TO_STATUS.keys())}" + ) + return ModelVersionStatus._STRING_TO_STATUS[status_str] + + @staticmethod + def to_string(status): + if status not in ModelVersionStatus._STATUS_TO_STRING: + raise Exception( + f"Could not get string corresponding to model version status {status}. " + f"Valid statuses: {list(ModelVersionStatus._STATUS_TO_STRING.keys())}" + ) + return ModelVersionStatus._STATUS_TO_STRING[status] + + @staticmethod + def all_status(): + return list(ModelVersionStatus._STATUS_TO_STRING.keys()) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/entities/model_registry/model_version_tag.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/entities/model_registry/model_version_tag.py new file mode 100644 index 0000000000000000000000000000000000000000..9f00e9dc6247c382f7d6e12d9cab2fd611d986ff --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/entities/model_registry/model_version_tag.py @@ -0,0 +1,35 @@ +from mlflow.entities.model_registry._model_registry_entity import _ModelRegistryEntity +from mlflow.protos.model_registry_pb2 import ModelVersionTag as ProtoModelVersionTag + + +class ModelVersionTag(_ModelRegistryEntity): + """Tag object associated with a model version.""" + + def __init__(self, key, value): + self._key = key + self._value = value + + def __eq__(self, other): + if type(other) is type(self): + return self.__dict__ == other.__dict__ + return False + + @property + def key(self): + """String name of the tag.""" + return self._key + + @property + def value(self): + """String value of the tag.""" + return self._value + + @classmethod + def from_proto(cls, proto): + return cls(proto.key, proto.value) + + def to_proto(self): + tag = ProtoModelVersionTag() + tag.key = self.key + tag.value = self.value + return tag diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/entities/model_registry/prompt.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/entities/model_registry/prompt.py new file mode 100644 index 0000000000000000000000000000000000000000..c33b3bccb61d5e96e4dc422ce9b1db45a11caaf5 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/entities/model_registry/prompt.py @@ -0,0 +1,71 @@ +""" +Prompt entity for MLflow Model Registry. + +This represents a prompt in the registry with its metadata, without version-specific +content like template text. For version-specific content, use PromptVersion. +""" + + +class Prompt: + """ + Entity representing a prompt in the MLflow Model Registry. + + This contains prompt-level information (name, description, tags) but not version-specific + content. To access version-specific content like the template, use PromptVersion. + """ + + def __init__( + self, + name: str, + description: str | None = None, + creation_timestamp: int | None = None, + tags: dict[str, str] | None = None, + ): + """ + Construct a Prompt entity. + + Args: + name: Name of the prompt. + description: Description of the prompt. + creation_timestamp: Timestamp when the prompt was created. + tags: Prompt-level metadata as key-value pairs. + """ + self._name = name + self._description = description + self._creation_timestamp = creation_timestamp + self._tags = tags or {} + + @property + def name(self) -> str: + """The name of the prompt.""" + return self._name + + @property + def description(self) -> str | None: + """The description of the prompt.""" + return self._description + + @property + def creation_timestamp(self) -> int | None: + """The creation timestamp of the prompt.""" + return self._creation_timestamp + + @property + def tags(self) -> dict[str, str]: + """Prompt-level metadata as key-value pairs.""" + return self._tags.copy() + + def __eq__(self, other) -> bool: + if not isinstance(other, Prompt): + return False + return ( + self.name == other.name + and self.description == other.description + and self.creation_timestamp == other.creation_timestamp + and self.tags == other.tags + ) + + def __repr__(self) -> str: + return ( + f"" + ) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/entities/model_registry/prompt_version.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/entities/model_registry/prompt_version.py new file mode 100644 index 0000000000000000000000000000000000000000..6a788b2d754a0c8c46aab5f1ae2a091a799d74fd --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/entities/model_registry/prompt_version.py @@ -0,0 +1,369 @@ +from __future__ import annotations + +import json +import re +from typing import Any + +from pydantic import BaseModel, ValidationError + +from mlflow.entities.model_registry._model_registry_entity import _ModelRegistryEntity +from mlflow.entities.model_registry.model_version_tag import ModelVersionTag +from mlflow.exceptions import MlflowException +from mlflow.prompt.constants import ( + IS_PROMPT_TAG_KEY, + PROMPT_TEMPLATE_VARIABLE_PATTERN, + PROMPT_TEXT_DISPLAY_LIMIT, + PROMPT_TEXT_TAG_KEY, + PROMPT_TYPE_CHAT, + PROMPT_TYPE_TAG_KEY, + PROMPT_TYPE_TEXT, + RESPONSE_FORMAT_TAG_KEY, +) + +# Alias type +PromptVersionTag = ModelVersionTag + + +def _is_reserved_tag(key: str) -> bool: + return key in { + IS_PROMPT_TAG_KEY, + PROMPT_TEXT_TAG_KEY, + PROMPT_TYPE_TAG_KEY, + RESPONSE_FORMAT_TAG_KEY, + } + + +class PromptVersion(_ModelRegistryEntity): + """ + An entity representing a specific version of a prompt with its template content. + + Args: + name: The name of the prompt. + version: The version number of the prompt. + template: The template content of the prompt. Can be either: + + - A string containing text with variables enclosed in double curly braces, + e.g. {{variable}}, which will be replaced with actual values by the `format` method. + MLflow uses the same variable naming rules as Jinja2: + https://jinja.palletsprojects.com/en/stable/api/#notes-on-identifiers + - A list of dictionaries representing chat messages, where each message has + 'role' and 'content' keys (e.g., [{"role": "user", "content": "Hello {{name}}"}]) + + response_format: Optional Pydantic class or dictionary defining the expected response + structure. This can be used to specify the schema for structured outputs. + commit_message: The commit message for the prompt version. Optional. + creation_timestamp: Timestamp of the prompt creation. Optional. + tags: A dictionary of tags associated with the **prompt version**. + This is useful for storing version-specific information, such as the author of + the changes. Optional. + aliases: List of aliases for this prompt version. Optional. + last_updated_timestamp: Timestamp of last update. Optional. + user_id: User ID that created this prompt version. Optional. + + """ + + def __init__( + self, + name: str, + version: int, + template: str | list[dict[str, Any]], + commit_message: str | None = None, + creation_timestamp: int | None = None, + tags: dict[str, str] | None = None, + aliases: list[str] | None = None, + last_updated_timestamp: int | None = None, + user_id: str | None = None, + response_format: BaseModel | dict[str, Any] | None = None, + ): + from mlflow.types.chat import ChatMessage + + super().__init__() + + # Core PromptVersion attributes + self._name: str = name + self._version: str = str(version) # Store as string internally + self._creation_time: int = creation_timestamp or 0 + + # Initialize tags first + tags = tags or {} + + # Determine prompt type and set it + if isinstance(template, list) and len(template) > 0: + try: + for msg in template: + ChatMessage.model_validate(msg) + except ValidationError as e: + raise ValueError("Template must be a list of dicts with role and content") from e + self._prompt_type = PROMPT_TYPE_CHAT + tags[PROMPT_TYPE_TAG_KEY] = PROMPT_TYPE_CHAT + else: + self._prompt_type = PROMPT_TYPE_TEXT + tags[PROMPT_TYPE_TAG_KEY] = PROMPT_TYPE_TEXT + + # Store template text as a tag + tags[PROMPT_TEXT_TAG_KEY] = template if isinstance(template, str) else json.dumps(template) + tags[IS_PROMPT_TAG_KEY] = "true" + + if response_format: + tags[RESPONSE_FORMAT_TAG_KEY] = json.dumps( + self.convert_response_format_to_dict(response_format) + ) + + # Store the tags dict + self._tags: dict[str, str] = tags + + template_text = template if isinstance(template, str) else json.dumps(template) + self._variables = set(PROMPT_TEMPLATE_VARIABLE_PATTERN.findall(template_text)) + self._last_updated_timestamp: int | None = last_updated_timestamp + self._description: str | None = commit_message + self._user_id: str | None = user_id + self._aliases: list[str] = aliases or [] + + def __repr__(self) -> str: + if self.is_text_prompt: + text = ( + self.template[:PROMPT_TEXT_DISPLAY_LIMIT] + "..." + if len(self.template) > PROMPT_TEXT_DISPLAY_LIMIT + else self.template + ) + else: + message = json.dumps(self.template) + text = ( + message[:PROMPT_TEXT_DISPLAY_LIMIT] + "..." + if len(message) > PROMPT_TEXT_DISPLAY_LIMIT + else message + ) + return f"PromptVersion(name={self.name}, version={self.version}, template={text})" + + # Core PromptVersion properties + @property + def template(self) -> str | list[dict[str, Any]]: + """ + Return the template content of the prompt. + + Returns: + Either a string (for text prompts) or a list of chat message dictionaries + (for chat prompts) with 'role' and 'content' keys. + """ + if self.is_text_prompt: + return self._tags[PROMPT_TEXT_TAG_KEY] + else: + return json.loads(self._tags[PROMPT_TEXT_TAG_KEY]) + + @property + def is_text_prompt(self) -> bool: + """ + Return True if the prompt is a text prompt, False if it's a chat prompt. + + Returns: + True for text prompts (string templates), False for chat prompts (list of messages). + """ + return self._prompt_type == PROMPT_TYPE_TEXT + + @property + def response_format(self) -> dict[str, Any] | None: + """ + Return the response format specification for the prompt. + + Returns: + A dictionary defining the expected response structure, or None if no + response format is specified. This can be used to validate or structure + the output from LLM calls. + """ + if RESPONSE_FORMAT_TAG_KEY not in self._tags: + return None + return json.loads(self._tags[RESPONSE_FORMAT_TAG_KEY]) + + def to_single_brace_format(self) -> str | list[dict[str, Any]]: + """ + Convert the template to single brace format. This is useful for integrating with other + systems that use single curly braces for variable replacement, such as LangChain's prompt + template. + + Returns: + The template with variables converted from {{variable}} to {variable} format. + For text prompts, returns a string. For chat prompts, returns a list of messages. + """ + t = self.template if self.is_text_prompt else json.dumps(self.template) + for var in self.variables: + t = re.sub(r"\{\{\s*" + var + r"\s*\}\}", "{" + var + "}", t) + return t if self.is_text_prompt else json.loads(t) + + @staticmethod + def convert_response_format_to_dict( + response_format: BaseModel | dict[str, Any], + ) -> dict[str, Any]: + """ + Convert a response format specification to a dictionary representation. + + Args: + response_format: Either a Pydantic BaseModel class or a dictionary defining + the response structure. + + Returns: + A dictionary representation of the response format. If a Pydantic class is + provided, returns its JSON schema. If a dictionary is provided, returns it as-is. + """ + if isinstance(response_format, type) and issubclass(response_format, BaseModel): + return response_format.model_json_schema() + else: + return response_format + + @property + def variables(self) -> set[str]: + """ + Return a list of variables in the template text. + The value must be enclosed in double curly braces, e.g. {{variable}}. + """ + return self._variables + + @property + def commit_message(self) -> str | None: + """ + Return the commit message of the prompt version. + """ + return self.description + + @property + def tags(self) -> dict[str, str]: + """ + Return the version-level tags. + """ + return {key: value for key, value in self._tags.items() if not _is_reserved_tag(key)} + + @property + def uri(self) -> str: + """Return the URI of the prompt.""" + return f"prompts:/{self.name}/{self.version}" + + @property + def name(self) -> str: + """String. Unique name within Model Registry.""" + return self._name + + @name.setter + def name(self, new_name: str): + self._name = new_name + + @property + def version(self) -> int: + """Version""" + return int(self._version) + + @property + def creation_timestamp(self) -> int: + """Integer. Prompt version creation timestamp (milliseconds since the Unix epoch).""" + return self._creation_time + + @property + def last_updated_timestamp(self) -> int | None: + """Integer. Timestamp of last update for this prompt version (milliseconds since the Unix + epoch). + """ + return self._last_updated_timestamp + + @last_updated_timestamp.setter + def last_updated_timestamp(self, updated_timestamp: int): + self._last_updated_timestamp = updated_timestamp + + @property + def description(self) -> str | None: + """String. Description""" + return self._description + + @description.setter + def description(self, description: str): + self._description = description + + @property + def user_id(self) -> str | None: + """String. User ID that created this prompt version.""" + return self._user_id + + @property + def aliases(self) -> list[str]: + """List of aliases (string) for the current prompt version.""" + return self._aliases + + @aliases.setter + def aliases(self, aliases: list[str]): + self._aliases = aliases + + # Methods + @classmethod + def _properties(cls) -> list[str]: + # aggregate with base class properties since cls.__dict__ does not do it automatically + return sorted(cls._get_properties_helper()) + + def _add_tag(self, tag: ModelVersionTag): + self._tags[tag.key] = tag.value + + def format( + self, allow_partial: bool = False, **kwargs + ) -> PromptVersion | str | list[dict[str, Any]]: + """ + Format the template with the given keyword arguments. + By default, it raises an error if there are missing variables. To format + the prompt partially, set `allow_partial=True`. + + Example: + + .. code-block:: python + + # Text prompt formatting + prompt = PromptVersion("my-prompt", 1, "Hello, {{title}} {{name}}!") + formatted = prompt.format(title="Ms", name="Alice") + print(formatted) + # Output: "Hello, Ms Alice!" + + # Chat prompt formatting + chat_prompt = PromptVersion( + "assistant", + 1, + [ + {"role": "system", "content": "You are a {{style}} assistant."}, + {"role": "user", "content": "{{question}}"}, + ], + ) + formatted = chat_prompt.format(style="friendly", question="How are you?") + print(formatted) + # Output: [{"role": "system", "content": "You are a friendly assistant."}, + # {"role": "user", "content": "How are you?"}] + + # Partial formatting + formatted = prompt.format(title="Ms", allow_partial=True) + print(formatted) + # Output: PromptVersion(name=my-prompt, version=1, template="Hello, Ms {{name}}!") + + + Args: + allow_partial: If True, allow partial formatting of the prompt text. + If False, raise an error if there are missing variables. + kwargs: Keyword arguments to replace the variables in the template. + """ + input_keys = set(kwargs.keys()) + template_str = self.template if self.is_text_prompt else json.dumps(self.template) + for key, value in kwargs.items(): + template_str = re.sub(r"\{\{\s*" + key + r"\s*\}\}", str(value), template_str) + template = template_str if self.is_text_prompt else json.loads(template_str) + + if missing_keys := self.variables - input_keys: + if not allow_partial: + raise MlflowException.invalid_parameter_value( + f"Missing variables: {missing_keys}. To partially format the prompt, " + "set `allow_partial=True`." + ) + else: + return PromptVersion( + name=self.name, + version=int(self.version), + template=template, + response_format=self.response_format, + commit_message=self.commit_message, + creation_timestamp=self.creation_timestamp, + tags=self.tags, + aliases=self.aliases, + last_updated_timestamp=self.last_updated_timestamp, + user_id=self.user_id, + ) + return template diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/entities/model_registry/registered_model.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/entities/model_registry/registered_model.py new file mode 100644 index 0000000000000000000000000000000000000000..b3befa3b7fde3b5326d55f023d3be0fbfdffaa04 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/entities/model_registry/registered_model.py @@ -0,0 +1,179 @@ +from mlflow.entities.model_registry._model_registry_entity import _ModelRegistryEntity +from mlflow.entities.model_registry.model_version import ModelVersion +from mlflow.entities.model_registry.prompt_version import IS_PROMPT_TAG_KEY +from mlflow.entities.model_registry.registered_model_alias import RegisteredModelAlias +from mlflow.entities.model_registry.registered_model_deployment_job_state import ( + RegisteredModelDeploymentJobState, +) +from mlflow.entities.model_registry.registered_model_tag import RegisteredModelTag +from mlflow.protos.model_registry_pb2 import RegisteredModel as ProtoRegisteredModel +from mlflow.protos.model_registry_pb2 import RegisteredModelAlias as ProtoRegisteredModelAlias +from mlflow.protos.model_registry_pb2 import RegisteredModelTag as ProtoRegisteredModelTag + + +class RegisteredModel(_ModelRegistryEntity): + """ + MLflow entity for Registered Model. + """ + + def __init__( + self, + name, + creation_timestamp=None, + last_updated_timestamp=None, + description=None, + latest_versions=None, + tags=None, + aliases=None, + deployment_job_id=None, + deployment_job_state=None, + ): + # Constructor is called only from within the system by various backend stores. + super().__init__() + self._name = name + self._creation_time = creation_timestamp + self._last_updated_timestamp = last_updated_timestamp + self._description = description + self._latest_version = latest_versions + self._tags = {tag.key: tag.value for tag in (tags or [])} + self._aliases = {alias.alias: alias.version for alias in (aliases or [])} + self._deployment_job_id = deployment_job_id + self._deployment_job_state = deployment_job_state + + @property + def name(self): + """String. Registered model name.""" + return self._name + + @name.setter + def name(self, new_name): + self._name = new_name + + @property + def creation_timestamp(self): + """Integer. Model version creation timestamp (milliseconds since the Unix epoch).""" + return self._creation_time + + @property + def last_updated_timestamp(self): + """Integer. Timestamp of last update for this model version (milliseconds since the Unix + epoch). + """ + return self._last_updated_timestamp + + @last_updated_timestamp.setter + def last_updated_timestamp(self, updated_timestamp): + self._last_updated_timestamp = updated_timestamp + + @property + def description(self): + """String. Description""" + return self._description + + @description.setter + def description(self, description): + self._description = description + + @property + def latest_versions(self): + """List of the latest :py:class:`mlflow.entities.model_registry.ModelVersion` instances + for each stage. + """ + return self._latest_version + + @latest_versions.setter + def latest_versions(self, latest_versions): + self._latest_version = latest_versions + + @property + def tags(self): + """Dictionary of tag key (string) -> tag value for the current registered model.""" + # Remove the is_prompt tag as it should not be user-facing + return {k: v for k, v in self._tags.items() if k != IS_PROMPT_TAG_KEY} + + def _is_prompt(self): + """Check if the registered model is a prompt.""" + return self._tags.get(IS_PROMPT_TAG_KEY, "false").lower() == "true" + + @property + def aliases(self): + """Dictionary of aliases (string) -> version for the current registered model.""" + return self._aliases + + @classmethod + def _properties(cls): + # aggregate with base class properties since cls.__dict__ does not do it automatically + return sorted(cls._get_properties_helper()) + + def _add_tag(self, tag): + self._tags[tag.key] = tag.value + + def _add_alias(self, alias): + self._aliases[alias.alias] = alias.version + + @property + def deployment_job_id(self): + """Deployment job ID for the current registered model.""" + return self._deployment_job_id + + @deployment_job_id.setter + def deployment_job_id(self, deployment_job_id): + self._deployment_job_id = deployment_job_id + + @property + def deployment_job_state(self): + """Deployment job state for the current registered model.""" + return self._deployment_job_state + + # proto mappers + @classmethod + def from_proto(cls, proto): + # input: mlflow.protos.model_registry_pb2.RegisteredModel + # returns RegisteredModel entity + registered_model = cls( + proto.name, + proto.creation_timestamp, + proto.last_updated_timestamp, + proto.description, + [ModelVersion.from_proto(mvd) for mvd in proto.latest_versions], + ) + for tag in proto.tags: + registered_model._add_tag(RegisteredModelTag.from_proto(tag)) + for alias in proto.aliases: + registered_model._add_alias(RegisteredModelAlias.from_proto(alias)) + registered_model._deployment_job_id = proto.deployment_job_id + registered_model._deployment_job_state = RegisteredModelDeploymentJobState.to_string( + proto.deployment_job_state + ) + return registered_model + + def to_proto(self): + # returns mlflow.protos.model_registry_pb2.RegisteredModel + rmd = ProtoRegisteredModel() + rmd.name = self.name + if self.creation_timestamp is not None: + rmd.creation_timestamp = self.creation_timestamp + if self.last_updated_timestamp: + rmd.last_updated_timestamp = self.last_updated_timestamp + if self.description: + rmd.description = self.description + if self.latest_versions is not None: + rmd.latest_versions.extend( + [model_version.to_proto() for model_version in self.latest_versions] + ) + if self.deployment_job_id: + rmd.deployment_job_id = self.deployment_job_id + if self.deployment_job_state: + rmd.deployment_job_state = RegisteredModelDeploymentJobState.from_string( + self.deployment_job_state + ) + rmd.tags.extend( + [ProtoRegisteredModelTag(key=key, value=value) for key, value in self._tags.items()] + ) + rmd.aliases.extend( + [ + ProtoRegisteredModelAlias(alias=alias, version=str(version)) + for alias, version in self._aliases.items() + ] + ) + return rmd diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/entities/model_registry/registered_model_alias.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/entities/model_registry/registered_model_alias.py new file mode 100644 index 0000000000000000000000000000000000000000..42ee135713449057ac5118c18cbe16065982d31f --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/entities/model_registry/registered_model_alias.py @@ -0,0 +1,35 @@ +from mlflow.entities.model_registry._model_registry_entity import _ModelRegistryEntity +from mlflow.protos.model_registry_pb2 import RegisteredModelAlias as ProtoRegisteredModelAlias + + +class RegisteredModelAlias(_ModelRegistryEntity): + """Alias object associated with a registered model.""" + + def __init__(self, alias, version): + self._alias = alias + self._version = version + + def __eq__(self, other): + if type(other) is type(self): + return self.__dict__ == other.__dict__ + return False + + @property + def alias(self): + """String name of the alias.""" + return self._alias + + @property + def version(self): + """String model version number that the alias points to.""" + return self._version + + @classmethod + def from_proto(cls, proto): + return cls(proto.alias, proto.version) + + def to_proto(self): + alias_proto = ProtoRegisteredModelAlias() + alias_proto.alias = self.alias + alias_proto.version = self.version + return alias_proto diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/entities/model_registry/registered_model_deployment_job_state.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/entities/model_registry/registered_model_deployment_job_state.py new file mode 100644 index 0000000000000000000000000000000000000000..99b1f69824b9d42042d057316ada0185f67277f9 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/entities/model_registry/registered_model_deployment_job_state.py @@ -0,0 +1,39 @@ +from mlflow.protos.databricks_uc_registry_messages_pb2 import DeploymentJobConnection + + +class RegisteredModelDeploymentJobState: + """Enum for registered model deployment state of an + :py:class:`mlflow.entities.model_registry.RegisteredModel`. + """ + + NOT_SET_UP = DeploymentJobConnection.State.Value("NOT_SET_UP") + CONNECTED = DeploymentJobConnection.State.Value("CONNECTED") + NOT_FOUND = DeploymentJobConnection.State.Value("NOT_FOUND") + REQUIRED_PARAMETERS_CHANGED = DeploymentJobConnection.State.Value("REQUIRED_PARAMETERS_CHANGED") + _STRING_TO_STATE = { + k: DeploymentJobConnection.State.Value(k) for k in DeploymentJobConnection.State.keys() + } + _STATE_TO_STRING = {value: key for key, value in _STRING_TO_STATE.items()} + + @staticmethod + def from_string(state_str): + if state_str not in RegisteredModelDeploymentJobState._STRING_TO_STATE: + raise Exception( + f"Could not get deployment job connection state corresponding to string " + f"{state_str}. " + f"Valid state strings: {RegisteredModelDeploymentJobState.all_states()}" + ) + return RegisteredModelDeploymentJobState._STRING_TO_STATE[state_str] + + @staticmethod + def to_string(state): + if state not in RegisteredModelDeploymentJobState._STATE_TO_STRING: + raise Exception( + f"Could not get string corresponding to deployment job connection {state}. " + f"Valid states: {RegisteredModelDeploymentJobState.all_states()}" + ) + return RegisteredModelDeploymentJobState._STATE_TO_STRING[state] + + @staticmethod + def all_states(): + return list(RegisteredModelDeploymentJobState._STATE_TO_STRING.keys()) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/entities/model_registry/registered_model_search.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/entities/model_registry/registered_model_search.py new file mode 100644 index 0000000000000000000000000000000000000000..77a79ea69286bd4a06239d2e5cc4a4d42a02faa7 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/entities/model_registry/registered_model_search.py @@ -0,0 +1,25 @@ +from mlflow.entities.model_registry import RegisteredModel + + +class RegisteredModelSearch(RegisteredModel): + def __init__(self, *args, **kwargs): + kwargs["tags"] = [] + kwargs["aliases"] = [] + super().__init__(*args, **kwargs) + + def tags(self): + raise Exception( + "UC Registered Models gathered through search_registered_models do not have tags. " + "Please use get_registered_model to obtain an individual model's tags." + ) + + def aliases(self): + raise Exception( + "UC Registered Models gathered through search_registered_models do not have aliases. " + "Please use get_registered_model to obtain an individual model's aliases." + ) + + def __eq__(self, other): + if type(other) in {type(self), RegisteredModel}: + return self.__dict__ == other.__dict__ + return False diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/entities/model_registry/registered_model_tag.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/entities/model_registry/registered_model_tag.py new file mode 100644 index 0000000000000000000000000000000000000000..f98b53cd877d8f64294472f132a29a17d004be52 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/entities/model_registry/registered_model_tag.py @@ -0,0 +1,35 @@ +from mlflow.entities.model_registry._model_registry_entity import _ModelRegistryEntity +from mlflow.protos.model_registry_pb2 import RegisteredModelTag as ProtoRegisteredModelTag + + +class RegisteredModelTag(_ModelRegistryEntity): + """Tag object associated with a registered model.""" + + def __init__(self, key, value): + self._key = key + self._value = value + + def __eq__(self, other): + if type(other) is type(self): + return self.__dict__ == other.__dict__ + return False + + @property + def key(self): + """String name of the tag.""" + return self._key + + @property + def value(self): + """String value of the tag.""" + return self._value + + @classmethod + def from_proto(cls, proto): + return cls(proto.key, proto.value) + + def to_proto(self): + tag = ProtoRegisteredModelTag() + tag.key = self.key + tag.value = self.value + return tag diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/entities/span.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/entities/span.py new file mode 100644 index 0000000000000000000000000000000000000000..4357af632c6fb447a4d0e35bc69ee36990cdff14 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/entities/span.py @@ -0,0 +1,779 @@ +import base64 +import json +import logging +from functools import lru_cache +from typing import Any, Union + +from google.protobuf.json_format import MessageToDict, ParseDict +from google.protobuf.struct_pb2 import Value +from opentelemetry.sdk.resources import Resource as _OTelResource +from opentelemetry.sdk.trace import Event as OTelEvent +from opentelemetry.sdk.trace import ReadableSpan as OTelReadableSpan +from opentelemetry.trace import NonRecordingSpan, SpanContext, TraceFlags +from opentelemetry.trace import Span as OTelSpan + +import mlflow +from mlflow.entities.span_event import SpanEvent +from mlflow.entities.span_status import SpanStatus, SpanStatusCode +from mlflow.exceptions import MlflowException +from mlflow.protos.databricks_pb2 import INVALID_PARAMETER_VALUE +from mlflow.protos.databricks_trace_server_pb2 import Span as ProtoSpan +from mlflow.tracing.constant import SpanAttributeKey +from mlflow.tracing.utils import ( + TraceJSONEncoder, + build_otel_context, + decode_id, + encode_span_id, + encode_trace_id, +) +from mlflow.tracing.utils.processor import apply_span_processors + +_logger = logging.getLogger(__name__) + + +# Not using enum as we want to allow custom span type string. +class SpanType: + """ + Predefined set of span types. + """ + + LLM = "LLM" + CHAIN = "CHAIN" + AGENT = "AGENT" + TOOL = "TOOL" + CHAT_MODEL = "CHAT_MODEL" + RETRIEVER = "RETRIEVER" + PARSER = "PARSER" + EMBEDDING = "EMBEDDING" + RERANKER = "RERANKER" + MEMORY = "MEMORY" + UNKNOWN = "UNKNOWN" + + +def create_mlflow_span( + otel_span: Any, trace_id: str, span_type: str | None = None +) -> Union["Span", "LiveSpan", "NoOpSpan"]: + """ + Factory function to create a span object. + + When creating a MLflow span object from the OpenTelemetry span, the factory function + should always be used to ensure the correct span object is created. + """ + if not otel_span or isinstance(otel_span, NonRecordingSpan): + return NoOpSpan() + + if isinstance(otel_span, OTelSpan): + return LiveSpan(otel_span, trace_id, span_type) + + if isinstance(otel_span, OTelReadableSpan): + return Span(otel_span) + + raise MlflowException( + "The `otel_span` argument must be an instance of one of valid " + f"OpenTelemetry span classes, but got {type(otel_span)}.", + INVALID_PARAMETER_VALUE, + ) + + +class Span: + """ + A span object. A span represents a unit of work or operation and is the building + block of Traces. + + This Span class represents immutable span data that is already finished and persisted. + The "live" span that is being created and updated during the application runtime is + represented by the :py:class:`LiveSpan ` subclass. + """ + + def __init__(self, otel_span: OTelReadableSpan): + if not isinstance(otel_span, OTelReadableSpan): + raise MlflowException( + "The `otel_span` argument for the Span class must be an instance of ReadableSpan, " + f"but got {type(otel_span)}.", + INVALID_PARAMETER_VALUE, + ) + + self._span = otel_span + # Since the span is immutable, we can cache the attributes to avoid the redundant + # deserialization of the attribute values. + self._attributes = _CachedSpanAttributesRegistry(otel_span) + + @property + @lru_cache(maxsize=1) + def trace_id(self) -> str: + """The trace ID of the span, a unique identifier for the trace it belongs to.""" + return self.get_attribute(SpanAttributeKey.REQUEST_ID) + + @property + def request_id(self) -> str: + """Deprecated. Use `trace_id` instead.""" + return self.trace_id + + @property + def span_id(self) -> str: + """The ID of the span. This is only unique within a trace.""" + return encode_span_id(self._span.context.span_id) + + @property + def name(self) -> str: + """The name of the span.""" + return self._span.name + + @property + def start_time_ns(self) -> int: + """The start time of the span in nanosecond.""" + return self._span._start_time + + @property + def end_time_ns(self) -> int | None: + """The end time of the span in nanosecond.""" + return self._span._end_time + + @property + def parent_id(self) -> str | None: + """The span ID of the parent span.""" + if self._span.parent is None: + return None + return encode_span_id(self._span.parent.span_id) + + @property + def status(self) -> SpanStatus: + """The status of the span.""" + return SpanStatus.from_otel_status(self._span.status) + + @property + def inputs(self) -> Any: + """The input values of the span.""" + return self.get_attribute(SpanAttributeKey.INPUTS) + + @property + def outputs(self) -> Any: + """The output values of the span.""" + return self.get_attribute(SpanAttributeKey.OUTPUTS) + + @property + def span_type(self) -> str: + """The type of the span.""" + return self.get_attribute(SpanAttributeKey.SPAN_TYPE) + + @property + def _trace_id(self) -> str: + """ + The OpenTelemetry trace ID of the span. Note that this should not be exposed to + the user, instead, use trace_id property as an unique identifier for a trace. + """ + return encode_trace_id(self._span.context.trace_id) + + @property + def attributes(self) -> dict[str, Any]: + """ + Get all attributes of the span. + + Returns: + A dictionary of all attributes of the span. + """ + return self._attributes.get_all() + + @property + def events(self) -> list[SpanEvent]: + """ + Get all events of the span. + + Returns: + A list of all events of the span. + """ + return [ + SpanEvent( + name=event.name, + timestamp=event.timestamp, + # Convert from OpenTelemetry's BoundedAttributes class to a simple dict + # to avoid the serialization issue due to having a lock object. + attributes=dict(event.attributes), + ) + for event in self._span.events + ] + + def __repr__(self): + return ( + f"{type(self).__name__}(name={self.name!r}, trace_id={self.trace_id!r}, " + f"span_id={self.span_id!r}, parent_id={self.parent_id!r})" + ) + + def get_attribute(self, key: str) -> Any | None: + """ + Get a single attribute value from the span. + + Args: + key: The key of the attribute to get. + + Returns: + The value of the attribute if it exists, otherwise None. + """ + return self._attributes.get(key) + + def to_dict(self) -> dict[str, Any]: + d = MessageToDict( + self.to_proto(), + preserving_proto_field_name=True, + ) + # Casting fields types as MessageToDict convert everything to string + d["start_time_unix_nano"] = self.start_time_ns + d["end_time_unix_nano"] = self.end_time_ns + for i, event in enumerate(d.get("events", [])): + event["time_unix_nano"] = self.events[i].timestamp + event["attributes"] = self.events[i].attributes + return d + + @classmethod + def from_dict(cls, data: dict[str, Any]) -> "Span": + """Create a Span object from the given dictionary.""" + try: + # Try to deserialize the span using the v3 schema + request_id = data.get("attributes", {}).get(SpanAttributeKey.REQUEST_ID) + if not request_id: + raise MlflowException( + f"The {SpanAttributeKey.REQUEST_ID} attribute is empty or missing.", + INVALID_PARAMETER_VALUE, + ) + + if Span._is_span_v2_schema(data): + return cls.from_dict_v2(data) + + trace_id = _decode_id_from_byte(data["trace_id"]) + span_id = _decode_id_from_byte(data["span_id"]) + # Parent ID always exists in proto (empty string) even if the span is a root span. + parent_id = ( + _decode_id_from_byte(data["parent_span_id"]) if data["parent_span_id"] else None + ) + + end_time_ns = data.get("end_time_unix_nano") + end_time_ns = int(end_time_ns) if end_time_ns else None + + otel_span = OTelReadableSpan( + name=data["name"], + context=build_otel_context(trace_id, span_id), + parent=build_otel_context(trace_id, parent_id) if parent_id else None, + start_time=int(data["start_time_unix_nano"]), + end_time=end_time_ns, + attributes=data["attributes"], + status=SpanStatus( + status_code=SpanStatusCode.from_proto_status_code(data["status"]["code"]), + description=data["status"].get("message"), + ).to_otel_status(), + # Setting an empty resource explicitly. Otherwise OTel create a new Resource by + # Resource.create(), which introduces a significant overhead in some environments. + # https://github.com/mlflow/mlflow/issues/15625 + resource=_OTelResource.get_empty(), + events=[ + OTelEvent( + name=event["name"], + timestamp=int(event["time_unix_nano"]), + attributes=event.get("attributes", {}), + ) + for event in data.get("events", []) + ], + ) + return cls(otel_span) + except Exception as e: + raise MlflowException( + "Failed to create a Span object from the given dictionary", + INVALID_PARAMETER_VALUE, + ) from e + + @staticmethod + def _is_span_v2_schema(data: dict[str, Any]) -> bool: + return "context" in data + + @classmethod + def from_dict_v2(cls, data: dict[str, Any]) -> "Span": + """Create a Span object from the given dictionary in v2 schema.""" + trace_id = decode_id(data["context"]["trace_id"]) + span_id = decode_id(data["context"]["span_id"]) + parent_id = decode_id(data["parent_id"]) if data["parent_id"] else None + + otel_span = OTelReadableSpan( + name=data["name"], + context=build_otel_context(trace_id, span_id), + parent=build_otel_context(trace_id, parent_id) if parent_id else None, + start_time=data["start_time"], + end_time=data["end_time"], + attributes=data["attributes"], + status=SpanStatus(data["status_code"], data["status_message"]).to_otel_status(), + # Setting an empty resource explicitly. Otherwise OTel create a new Resource by + # Resource.create(), which introduces a significant overhead in some environments. + # https://github.com/mlflow/mlflow/issues/15625 + resource=_OTelResource.get_empty(), + events=[ + OTelEvent( + name=event["name"], + timestamp=event["timestamp"], + attributes=event["attributes"], + ) + for event in data["events"] + ], + ) + return cls(otel_span) + + def to_proto(self): + """Convert into OTLP compatible proto object to sent to the Databricks Trace Server.""" + otel_status = self._span.status + status = ProtoSpan.Status( + code=otel_status.status_code.value, + message=otel_status.description, + ) + parent = _encode_span_id_to_byte(self._span.parent.span_id) if self._span.parent else b"" + + # NB: This is a workaround that some DBX internal code pass float timestamp + start_time_unix_nano = int(self._span.start_time) if self._span.start_time else None + end_time_unix_nano = int(self._span.end_time) if self._span.end_time else None + + return ProtoSpan( + trace_id=_encode_trace_id_to_byte(self._span.context.trace_id), + span_id=_encode_span_id_to_byte(self._span.context.span_id), + trace_state=self._span.context.trace_state or "", + parent_span_id=parent, + name=self.name, + start_time_unix_nano=start_time_unix_nano, + end_time_unix_nano=end_time_unix_nano, + events=[event.to_proto() for event in self.events], + status=status, + attributes={k: ParseDict(v, Value()) for k, v in self._span.attributes.items()}, + ) + + +def _encode_span_id_to_byte(span_id: int | None) -> bytes: + # https://github.com/open-telemetry/opentelemetry-python/blob/e01fa0c77a7be0af77d008a888c2b6a707b05c3d/exporter/opentelemetry-exporter-otlp-proto-common/src/opentelemetry/exporter/otlp/proto/common/_internal/__init__.py#L131 + return span_id.to_bytes(length=8, byteorder="big", signed=False) + + +def _encode_trace_id_to_byte(trace_id: int) -> bytes: + # https://github.com/open-telemetry/opentelemetry-python/blob/e01fa0c77a7be0af77d008a888c2b6a707b05c3d/exporter/opentelemetry-exporter-otlp-proto-common/src/opentelemetry/exporter/otlp/proto/common/_internal/__init__.py#L135 + return trace_id.to_bytes(length=16, byteorder="big", signed=False) + + +def _decode_id_from_byte(trace_or_span_id_b64: str) -> int: + # Decoding the base64 encoded trace or span ID to bytes and then converting it to int. + bytes = base64.b64decode(trace_or_span_id_b64) + return int.from_bytes(bytes, byteorder="big", signed=False) + + +class LiveSpan(Span): + """ + A "live" version of the :py:class:`Span ` class. + + The live spans are those being created and updated during the application runtime. + When users start a new span using the tracing APIs within their code, this live span + object is returned to get and set the span attributes, status, events, and etc. + """ + + def __init__( + self, + otel_span: OTelSpan, + trace_id: str, + span_type: str = SpanType.UNKNOWN, + ): + """ + The `otel_span` argument takes an instance of OpenTelemetry Span class, which is + indeed a subclass of ReadableSpan. Thanks to this, the getter methods of the Span + class can be reused without any modification. + + Note that the constructor doesn't call the super().__init__ method, because the Span + initialization logic is a bit different from the immutable span. + """ + if not isinstance(otel_span, OTelReadableSpan): + raise MlflowException( + "The `otel_span` argument for the LiveSpan class must be an instance of " + f"trace.Span, but got {type(otel_span)}.", + INVALID_PARAMETER_VALUE, + ) + + self._span = otel_span + self._attributes = _SpanAttributesRegistry(otel_span) + self._attributes.set(SpanAttributeKey.REQUEST_ID, trace_id) + self._attributes.set(SpanAttributeKey.SPAN_TYPE, span_type) + + def set_span_type(self, span_type: str): + """Set the type of the span.""" + self.set_attribute(SpanAttributeKey.SPAN_TYPE, span_type) + + def set_inputs(self, inputs: Any): + """Set the input values to the span.""" + self.set_attribute(SpanAttributeKey.INPUTS, inputs) + + def set_outputs(self, outputs: Any): + """Set the output values to the span.""" + self.set_attribute(SpanAttributeKey.OUTPUTS, outputs) + + def set_attributes(self, attributes: dict[str, Any]): + """ + Set the attributes to the span. The attributes must be a dictionary of key-value pairs. + This method is additive, i.e. it will add new attributes to the existing ones. If an + attribute with the same key already exists, it will be overwritten. + """ + if not isinstance(attributes, dict): + _logger.warning( + f"Attributes must be a dictionary, but got {type(attributes)}. Skipping." + ) + return + + for key, value in attributes.items(): + self.set_attribute(key, value) + + def set_attribute(self, key: str, value: Any): + """Set a single attribute to the span.""" + self._attributes.set(key, value) + + def set_status(self, status: SpanStatusCode | str): + """ + Set the status of the span. + + Args: + status: The status of the span. This can be a + :py:class:`SpanStatus ` object or a string representing + of the status code defined in + :py:class:`SpanStatusCode ` + e.g. ``"OK"``, ``"ERROR"``. + """ + if isinstance(status, str): + status = SpanStatus(status) + + # NB: We need to set the OpenTelemetry native StatusCode, because span's set_status + # method only accepts a StatusCode enum in their definition. + # https://github.com/open-telemetry/opentelemetry-python/blob/8ed71b15fb8fc9534529da8ce4a21e686248a8f3/opentelemetry-sdk/src/opentelemetry/sdk/trace/__init__.py#L949 + # Working around this is possible, but requires some hack to handle automatic status + # propagation mechanism, so here we just use the native object that meets our + # current requirements at least. Nevertheless, declaring the new class extending + # the OpenTelemetry Status class so users code doesn't have to import the OTel's + # StatusCode object, which makes future migration easier. + self._span.set_status(status.to_otel_status()) + + def add_event(self, event: SpanEvent): + """ + Add an event to the span. + + Args: + event: The event to add to the span. This should be a + :py:class:`SpanEvent ` object. + """ + self._span.add_event(event.name, event.attributes, event.timestamp) + + def record_exception(self, exception: str | Exception): + """ + Record an exception on the span, adding an exception event and setting span status to ERROR. + + Args: + exception: The exception to record. Can be an Exception instance or a string + describing the exception. + """ + if isinstance(exception, Exception): + self.add_event(SpanEvent.from_exception(exception)) + elif isinstance(exception, str): + self.add_event(SpanEvent.from_exception(Exception(exception))) + else: + raise MlflowException( + "The `exception` parameter must be an Exception instance or a string.", + INVALID_PARAMETER_VALUE, + ) + + self.set_status( + SpanStatus( + status_code=SpanStatusCode.ERROR, + description=f"{type(exception).__name__}: {exception}", + ) + ) + + def end( + self, + outputs: Any | None = None, + attributes: dict[str, Any] | None = None, + status: SpanStatus | str | None = None, + end_time_ns: int | None = None, + ): + """ + End the span. + + outputs: Outputs to set on the span. + attributes: A dictionary of attributes to set on the span. If the span already has + attributes, the new attributes will be merged with the existing ones. If the same + key already exists, the new value will overwrite the old one. + status: The status of the span. This can be a + :py:class:`SpanStatus ` object or a string + representing the status code defined in + :py:class:`SpanStatusCode ` + e.g. ``"OK"``, ``"ERROR"``. The default status is OK. + end_time_ns: The end time of the span in nano seconds since the UNIX epoch. + If not provided, the current time will be used. + + :meta private: + """ + try: + self.set_attributes(attributes or {}) + if outputs is not None: + self.set_outputs(outputs) + if status is not None: + self.set_status(status) + + # NB: In OpenTelemetry, status code remains UNSET if not explicitly set + # by the user. However, there is not way to set the status when using + # @mlflow.trace decorator. Therefore, we just automatically set the status + # to OK if it is not ERROR. + if self.status.status_code != SpanStatusCode.ERROR: + self.set_status(SpanStatus(SpanStatusCode.OK)) + + # Apply span processors + apply_span_processors(self) + + self._span.end(end_time=end_time_ns) + + except Exception as e: + _logger.warning( + f"Failed to end span {self.span_id}: {e}. " + "For full traceback, set logging level to debug.", + exc_info=_logger.isEnabledFor(logging.DEBUG), + ) + + def from_dict(cls, data: dict[str, Any]) -> "Span": + raise NotImplementedError("The `from_dict` method is not supported for the LiveSpan class.") + + def to_immutable_span(self) -> "Span": + """ + Downcast the live span object to the immutable span. + + :meta private: + """ + # All state of the live span is already persisted in the OpenTelemetry span object. + return Span(self._span) + + @classmethod + def from_immutable_span( + cls, + span: Span, + parent_span_id: str | None = None, + trace_id: str | None = None, + experiment_id: str | None = None, + otel_trace_id: str | None = None, + end_trace: bool = True, + ) -> "LiveSpan": + """ + Create a new LiveSpan object from the given immutable span by + cloning the underlying OpenTelemetry span within current context. + + This is particularly useful when we merging a remote trace into the current trace. + We cannot merge the remote trace directly, because it is already stored as an immutable + span, meaning that we cannot update metadata like trace ID, parent span ID, + which are necessary for merging the trace. + + Args: + span: The immutable span object to clone. + parent_span_id: The parent span ID of the new span. + If it is None, the span will be created as a root span. + trace_id: The trace ID to be set on the new span. Specify this if you want to + create the new span with a particular trace ID. + experiment_id: The experiment ID to be set on the new span. If not specified, the + experiment ID will be set to the current experiment ID. + otel_trace_id: The OpenTelemetry trace ID of the new span in hex encoded format. + If not specified, the newly generated trace ID will be used. + end_trace: Whether to end the trace after cloning the span. Default is True. + + Returns: + The new LiveSpan object with the same state as the original span. + + :meta private: + """ + from mlflow.tracing.trace_manager import InMemoryTraceManager + + trace_manager = InMemoryTraceManager.get_instance() + parent_span = trace_manager.get_span_from_id(trace_id, parent_span_id) + + # Create a new span with the same name, parent, and start time + otel_span = mlflow.tracing.provider.start_detached_span( + name=span.name, + parent=parent_span._span if parent_span else None, + start_time_ns=span.start_time_ns, + experiment_id=experiment_id, + ) + # The latter one from attributes is the newly generated trace ID by the span processor. + trace_id = trace_id or json.loads(otel_span.attributes.get(SpanAttributeKey.REQUEST_ID)) + clone_span = LiveSpan(otel_span, trace_id, span.span_type) + + # Copy all the attributes, inputs, outputs, and events from the original span + clone_span.set_status(span.status) + clone_span.set_attributes( + {k: v for k, v in span.attributes.items() if k != SpanAttributeKey.REQUEST_ID} + ) + if span.inputs: + clone_span.set_inputs(span.inputs) + if span.outputs: + clone_span.set_outputs(span.outputs) + for event in span.events: + clone_span.add_event(event) + + # Update trace ID and span ID + context = span._span.get_span_context() + clone_span._span._context = SpanContext( + # Override otel_trace_id if provided, otherwise use the new trace ID + trace_id=decode_id(otel_trace_id) if otel_trace_id else otel_span.context.trace_id, + # Re-use same span ID as their ID space is local to the trace + span_id=context.span_id, + is_remote=context.is_remote, + # Override trace flag as if it is sampled within current context. + trace_flags=TraceFlags(TraceFlags.SAMPLED), + ) + + if end_trace: + clone_span.end(end_time_ns=span.end_time_ns) + + return clone_span + + +NO_OP_SPAN_TRACE_ID = "MLFLOW_NO_OP_SPAN_TRACE_ID" + + +class NoOpSpan(Span): + """ + No-op implementation of the Span interface. + + This instance should be returned from the mlflow.start_span context manager when span + creation fails. This class should have exactly the same interface as the Span so that + user's setter calls do not raise runtime errors. + + E.g. + + .. code-block:: python + + with mlflow.start_span("span_name") as span: + # Even if the span creation fails, the following calls should pass. + span.set_inputs({"x": 1}) + # Do something + + """ + + def __init__(self): + self._span = NonRecordingSpan(context=None) + self._attributes = {} + + @property + def trace_id(self): + """ + No-op span returns a special trace ID to distinguish it from the real spans. + """ + return NO_OP_SPAN_TRACE_ID + + @property + def span_id(self): + return None + + @property + def name(self): + return None + + @property + def start_time_ns(self): + return None + + @property + def end_time_ns(self): + return None + + @property + def context(self): + return None + + @property + def parent_id(self): + return None + + @property + def status(self): + return None + + @property + def _trace_id(self): + return None + + def set_inputs(self, inputs: dict[str, Any]): + pass + + def set_outputs(self, outputs: dict[str, Any]): + pass + + def set_attributes(self, attributes: dict[str, Any]): + pass + + def set_attribute(self, key: str, value: Any): + pass + + def set_status(self, status: SpanStatus): + pass + + def add_event(self, event: SpanEvent): + pass + + def record_exception(self, exception: str | Exception): + pass + + def end( + self, + outputs: Any | None = None, + attributes: dict[str, Any] | None = None, + status: SpanStatus | str | None = None, + end_time_ns: int | None = None, + ): + pass + + +class _SpanAttributesRegistry: + """ + A utility class to manage the span attributes. + + In MLflow users can add arbitrary key-value pairs to the span attributes, however, + OpenTelemetry only allows a limited set of types to be stored in the attribute values. + Therefore, we serialize all values into JSON string before storing them in the span. + This class provides simple getter and setter methods to interact with the span attributes + without worrying about the serde process. + """ + + def __init__(self, otel_span: OTelSpan): + self._span = otel_span + + def get_all(self) -> dict[str, Any]: + return {key: self.get(key) for key in self._span.attributes.keys()} + + def get(self, key: str): + serialized_value = self._span.attributes.get(key) + if serialized_value: + try: + return json.loads(serialized_value) + except Exception: + # If failed to deserialize (e.g., string value is directly set to OTel span), + # return the original value as is. + return serialized_value + + def set(self, key: str, value: Any): + if not isinstance(key, str): + _logger.warning(f"Attribute key must be a string, but got {type(key)}. Skipping.") + return + + # NB: OpenTelemetry attribute can store not only string but also a few primitives like + # int, float, bool, and list of them. However, we serialize all into JSON string here + # for the simplicity in deserialization process. + self._span.set_attribute(key, json.dumps(value, cls=TraceJSONEncoder, ensure_ascii=False)) + + +class _CachedSpanAttributesRegistry(_SpanAttributesRegistry): + """ + A cache-enabled version of the SpanAttributesRegistry. + + The caching helps to avoid the redundant deserialization of the attribute, however, it does + not handle the value change well. Therefore, this class should only be used for the persisted + spans that are immutable, and thus implemented as a subclass of _SpanAttributesRegistry. + """ + + @lru_cache(maxsize=128) + def get(self, key: str): + return super().get(key) + + def set(self, key: str, value: Any): + raise MlflowException( + "The attributes of the immutable span must not be updated.", INVALID_PARAMETER_VALUE + ) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/entities/trace_info_v2.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/entities/trace_info_v2.py new file mode 100644 index 0000000000000000000000000000000000000000..f3cef361f7f28637830d54df78fcb99a39adb949 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/entities/trace_info_v2.py @@ -0,0 +1,162 @@ +from dataclasses import asdict, dataclass, field +from typing import Any + +from mlflow.entities._mlflow_object import _MlflowObject +from mlflow.entities.assessment import Assessment +from mlflow.entities.trace_info import TraceInfo +from mlflow.entities.trace_location import TraceLocation +from mlflow.entities.trace_status import TraceStatus +from mlflow.protos.service_pb2 import TraceInfo as ProtoTraceInfo +from mlflow.protos.service_pb2 import TraceRequestMetadata as ProtoTraceRequestMetadata +from mlflow.protos.service_pb2 import TraceTag as ProtoTraceTag +from mlflow.tracing.constant import TRACE_SCHEMA_VERSION_KEY + + +def _truncate_request_metadata(d: dict[str, Any]) -> dict[str, str]: + from mlflow.tracing.constant import MAX_CHARS_IN_TRACE_INFO_METADATA + + return { + k[:MAX_CHARS_IN_TRACE_INFO_METADATA]: str(v)[:MAX_CHARS_IN_TRACE_INFO_METADATA] + for k, v in d.items() + } + + +def _truncate_tags(d: dict[str, Any]) -> dict[str, str]: + from mlflow.tracing.constant import ( + MAX_CHARS_IN_TRACE_INFO_TAGS_KEY, + MAX_CHARS_IN_TRACE_INFO_TAGS_VALUE, + ) + + return { + k[:MAX_CHARS_IN_TRACE_INFO_TAGS_KEY]: str(v)[:MAX_CHARS_IN_TRACE_INFO_TAGS_VALUE] + for k, v in d.items() + } + + +@dataclass +class TraceInfoV2(_MlflowObject): + """Metadata about a trace. + + Args: + request_id: id of the trace. + experiment_id: id of the experiment. + timestamp_ms: start time of the trace, in milliseconds. + execution_time_ms: duration of the trace, in milliseconds. + status: status of the trace. + request_metadata: Key-value pairs associated with the trace. Request metadata are designed + for immutable values like run ID associated with the trace. + tags: Tags associated with the trace. Tags are designed for mutable values like trace name, + that can be updated by the users after the trace is created, unlike request_metadata. + """ + + request_id: str + experiment_id: str + timestamp_ms: int + execution_time_ms: int | None + status: TraceStatus + request_metadata: dict[str, str] = field(default_factory=dict) + tags: dict[str, str] = field(default_factory=dict) + assessments: list[Assessment] = field(default_factory=list) + + def __post_init__(self): + self.request_metadata[TRACE_SCHEMA_VERSION_KEY] = "2" + + def __eq__(self, other): + if type(other) is type(self): + return self.__dict__ == other.__dict__ + return False + + @property + def trace_id(self) -> str: + """Returns the trace ID of the trace info.""" + return self.request_id + + def to_proto(self): + proto = ProtoTraceInfo() + proto.request_id = self.request_id + proto.experiment_id = self.experiment_id + proto.timestamp_ms = self.timestamp_ms + # NB: Proto setter does not support nullable fields (even with 'optional' keyword), + # so we substitute None with 0 for execution_time_ms. This should be not too confusing + # as we only put None when starting a trace i.e. the execution time is actually 0. + proto.execution_time_ms = self.execution_time_ms or 0 + proto.status = self.status.to_proto() + + request_metadata = [] + for key, value in _truncate_request_metadata(self.request_metadata).items(): + attr = ProtoTraceRequestMetadata() + attr.key = key + attr.value = value + request_metadata.append(attr) + proto.request_metadata.extend(request_metadata) + + tags = [] + for key, value in _truncate_tags(self.tags).items(): + tag = ProtoTraceTag() + tag.key = key + tag.value = str(value) + tags.append(tag) + + proto.tags.extend(tags) + return proto + + @classmethod + def from_proto(cls, proto, assessments=None): + return cls( + request_id=proto.request_id, + experiment_id=proto.experiment_id, + timestamp_ms=proto.timestamp_ms, + execution_time_ms=proto.execution_time_ms, + status=TraceStatus.from_proto(proto.status), + request_metadata={attr.key: attr.value for attr in proto.request_metadata}, + tags={tag.key: tag.value for tag in proto.tags}, + assessments=assessments or [], + ) + + def to_dict(self): + """ + Convert trace info to a dictionary for persistence. + Update status field to the string value for serialization. + """ + trace_info_dict = asdict(self) + trace_info_dict["status"] = self.status.value + # Client request ID field is only added for internal use, and should not be + # serialized for V2 TraceInfo. + trace_info_dict.pop("client_request_id", None) + return trace_info_dict + + @classmethod + def from_dict(cls, trace_info_dict): + """ + Convert trace info dictionary to TraceInfo object. + """ + if "status" not in trace_info_dict: + raise ValueError("status is required in trace info dictionary.") + trace_info_dict["status"] = TraceStatus(trace_info_dict["status"]) + return cls(**trace_info_dict) + + def to_v3(self, request: str | None = None, response: str | None = None) -> TraceInfo: + return TraceInfo( + trace_id=self.request_id, + trace_location=TraceLocation.from_experiment_id(self.experiment_id), + request_preview=request, + response_preview=response, + request_time=self.timestamp_ms, + execution_duration=self.execution_time_ms, + state=self.status.to_state(), + trace_metadata=self.request_metadata.copy(), + tags=self.tags, + assessments=self.assessments, + ) + + @classmethod + def from_v3(cls, trace_info: TraceInfo) -> "TraceInfoV2": + return cls( + request_id=trace_info.trace_id, + experiment_id=trace_info.experiment_id, + timestamp_ms=trace_info.request_time, + execution_time_ms=trace_info.execution_duration, + status=TraceStatus.from_state(trace_info.state), + request_metadata=trace_info.trace_metadata.copy(), + tags=trace_info.tags, + ) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/entities/trace_location.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/entities/trace_location.py new file mode 100644 index 0000000000000000000000000000000000000000..c4ff3e53a47a61bcb0c34bce478813dec22f3e61 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/entities/trace_location.py @@ -0,0 +1,173 @@ +from dataclasses import dataclass +from enum import Enum +from typing import Any + +from mlflow.entities._mlflow_object import _MlflowObject +from mlflow.exceptions import MlflowException +from mlflow.protos import service_pb2 as pb + + +@dataclass +class MlflowExperimentLocation(_MlflowObject): + """ + Represents the location of an MLflow experiment. + + Args: + experiment_id: The ID of the MLflow experiment where the trace is stored. + """ + + experiment_id: str + + def to_proto(self): + return pb.TraceLocation.MlflowExperimentLocation(experiment_id=self.experiment_id) + + @classmethod + def from_proto(cls, proto) -> "MlflowExperimentLocation": + return cls(experiment_id=proto.experiment_id) + + def to_dict(self) -> dict[str, Any]: + return {"experiment_id": self.experiment_id} + + @classmethod + def from_dict(cls, d: dict[str, Any]) -> "MlflowExperimentLocation": + return cls(experiment_id=d["experiment_id"]) + + +@dataclass +class InferenceTableLocation(_MlflowObject): + """ + Represents the location of a Databricks inference table. + + Args: + full_table_name: The fully qualified name of the inference table where + the trace is stored, in the format of `..`. + """ + + full_table_name: str + + def to_proto(self): + return pb.TraceLocation.InferenceTableLocation(full_table_name=self.full_table_name) + + @classmethod + def from_proto(cls, proto) -> "InferenceTableLocation": + return cls(full_table_name=proto.full_table_name) + + def to_dict(self) -> dict[str, Any]: + return {"full_table_name": self.full_table_name} + + @classmethod + def from_dict(cls, d: dict[str, Any]) -> "InferenceTableLocation": + return cls(full_table_name=d["full_table_name"]) + + +class TraceLocationType(str, Enum): + TRACE_LOCATION_TYPE_UNSPECIFIED = "TRACE_LOCATION_TYPE_UNSPECIFIED" + MLFLOW_EXPERIMENT = "MLFLOW_EXPERIMENT" + INFERENCE_TABLE = "INFERENCE_TABLE" + + def to_proto(self): + return pb.TraceLocation.TraceLocationType.Value(self) + + @classmethod + def from_proto(cls, proto: int) -> "TraceLocationType": + return TraceLocationType(pb.TraceLocation.TraceLocationType.Name(proto)) + + @classmethod + def from_dict(cls, d: dict[str, Any]) -> "TraceLocationType": + return cls(d["type"]) + + +@dataclass +class TraceLocation(_MlflowObject): + """ + Represents the location where the trace is stored. + + Currently, MLflow supports two types of trace locations: + + - MLflow experiment: The trace is stored in an MLflow experiment. + - Inference table: The trace is stored in a Databricks inference table. + + Args: + type: The type of the trace location, should be one of the + :py:class:`TraceLocationType` enum values. + mlflow_experiment: The MLflow experiment location. Set this when the + location type is MLflow experiment. + inference_table: The inference table location. Set this when the + location type is Databricks Inference table. + """ + + type: TraceLocationType + mlflow_experiment: MlflowExperimentLocation | None = None + inference_table: InferenceTableLocation | None = None + + def __post_init__(self) -> None: + if self.mlflow_experiment is not None and self.inference_table is not None: + raise MlflowException.invalid_parameter_value( + "Only one of mlflow_experiment or inference_table can be provided." + ) + + if (self.mlflow_experiment and self.type != TraceLocationType.MLFLOW_EXPERIMENT) or ( + self.inference_table and self.type != TraceLocationType.INFERENCE_TABLE + ): + raise MlflowException.invalid_parameter_value( + f"Trace location type {type} does not match the provided location " + f"{self.mlflow_experiment or self.inference_table}." + ) + + def to_dict(self) -> dict[str, Any]: + d = {"type": self.type.value} + if self.mlflow_experiment: + d["mlflow_experiment"] = self.mlflow_experiment.to_dict() + elif self.inference_table: + d["inference_table"] = self.inference_table.to_dict() + return d + + @classmethod + def from_dict(cls, d: dict[str, Any]) -> "TraceLocation": + return cls( + type=TraceLocationType(d["type"]), + mlflow_experiment=( + MlflowExperimentLocation.from_dict(v) if (v := d.get("mlflow_experiment")) else None + ), + inference_table=( + InferenceTableLocation.from_dict(v) if (v := d.get("inference_table")) else None + ), + ) + + def to_proto(self): + if self.mlflow_experiment: + return pb.TraceLocation( + type=self.type.to_proto(), + mlflow_experiment=self.mlflow_experiment.to_proto(), + ) + elif self.inference_table: + return pb.TraceLocation( + type=self.type.to_proto(), + inference_table=self.inference_table.to_proto(), + ) + + else: + return pb.TraceLocation(type=self.type.to_proto()) + + @classmethod + def from_proto(cls, proto) -> "TraceLocation": + type_ = TraceLocationType.from_proto(proto.type) + if proto.WhichOneof("identifier") == "mlflow_experiment": + return cls( + type=type_, + mlflow_experiment=MlflowExperimentLocation.from_proto(proto.mlflow_experiment), + ) + elif proto.WhichOneof("identifier") == "inference_table": + return cls( + type=type_, + inference_table=InferenceTableLocation.from_proto(proto.inference_table), + ) + else: + return cls(type=type_) + + @classmethod + def from_experiment_id(cls, experiment_id: str) -> "TraceLocation": + return cls( + type=TraceLocationType.MLFLOW_EXPERIMENT, + mlflow_experiment=MlflowExperimentLocation(experiment_id=experiment_id), + ) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/entities/trace_state.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/entities/trace_state.py new file mode 100644 index 0000000000000000000000000000000000000000..4820e430f924896f7bf953daf27dc16afaab00f3 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/entities/trace_state.py @@ -0,0 +1,39 @@ +from enum import Enum + +from opentelemetry import trace as trace_api + +from mlflow.protos import service_pb2 as pb + + +class TraceState(str, Enum): + """Enum representing the state of a trace. + + - ``STATE_UNSPECIFIED``: Unspecified trace state. + - ``OK``: Trace successfully completed. + - ``ERROR``: Trace encountered an error. + - ``IN_PROGRESS``: Trace is currently in progress. + """ + + STATE_UNSPECIFIED = "STATE_UNSPECIFIED" + OK = "OK" + ERROR = "ERROR" + IN_PROGRESS = "IN_PROGRESS" + + def to_proto(self): + return pb.TraceInfoV3.State.Value(self) + + @classmethod + def from_proto(cls, proto: int) -> "TraceState": + return TraceState(pb.TraceInfoV3.State.Name(proto)) + + @staticmethod + def from_otel_status(otel_status: trace_api.Status): + """Convert OpenTelemetry status code to MLflow TraceState.""" + return _OTEL_STATUS_CODE_TO_MLFLOW[otel_status.status_code] + + +_OTEL_STATUS_CODE_TO_MLFLOW = { + trace_api.StatusCode.OK: TraceState.OK, + trace_api.StatusCode.ERROR: TraceState.ERROR, + trace_api.StatusCode.UNSET: TraceState.STATE_UNSPECIFIED, +} diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/entities/trace_status.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/entities/trace_status.py new file mode 100644 index 0000000000000000000000000000000000000000..19a35df2f4a5ce2f17f38bd5bcc0fe52b547b223 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/entities/trace_status.py @@ -0,0 +1,68 @@ +from enum import Enum + +from opentelemetry import trace as trace_api + +from mlflow.entities.trace_state import TraceState +from mlflow.protos.service_pb2 import TraceStatus as ProtoTraceStatus +from mlflow.utils.annotations import deprecated + + +@deprecated(alternative="mlflow.entities.trace_state.TraceState") +class TraceStatus(str, Enum): + """Enum for status of an :py:class:`mlflow.entities.TraceInfo`.""" + + UNSPECIFIED = "TRACE_STATUS_UNSPECIFIED" + OK = "OK" + ERROR = "ERROR" + IN_PROGRESS = "IN_PROGRESS" + + def to_state(self) -> TraceState: + if self == TraceStatus.UNSPECIFIED: + return TraceState.STATE_UNSPECIFIED + elif self == TraceStatus.OK: + return TraceState.OK + elif self == TraceStatus.ERROR: + return TraceState.ERROR + elif self == TraceStatus.IN_PROGRESS: + return TraceState.IN_PROGRESS + raise ValueError(f"Unknown TraceStatus: {self}") + + @classmethod + def from_state(cls, state: TraceState) -> "TraceStatus": + if state == TraceState.STATE_UNSPECIFIED: + return cls.UNSPECIFIED + elif state == TraceState.OK: + return cls.OK + elif state == TraceState.ERROR: + return cls.ERROR + elif state == TraceState.IN_PROGRESS: + return cls.IN_PROGRESS + raise ValueError(f"Unknown TraceState: {state}") + + def to_proto(self): + return ProtoTraceStatus.Value(self) + + @staticmethod + def from_proto(proto_status): + return TraceStatus(ProtoTraceStatus.Name(proto_status)) + + @staticmethod + def from_otel_status(otel_status: trace_api.Status): + return _OTEL_STATUS_CODE_TO_MLFLOW[otel_status.status_code] + + @classmethod + def pending_statuses(cls): + """Traces in pending statuses can be updated to any statuses.""" + return {cls.IN_PROGRESS} + + @classmethod + def end_statuses(cls): + """Traces in end statuses cannot be updated to any statuses.""" + return {cls.UNSPECIFIED, cls.OK, cls.ERROR} + + +_OTEL_STATUS_CODE_TO_MLFLOW = { + trace_api.StatusCode.OK: TraceStatus.OK, + trace_api.StatusCode.ERROR: TraceStatus.ERROR, + trace_api.StatusCode.UNSET: TraceStatus.UNSPECIFIED, +} diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/evaluation/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/evaluation/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..6a67de31078e76ca04997be5c0acb2c700aa8f15 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/evaluation/__init__.py @@ -0,0 +1,16 @@ +""" +THE 'mlflow.evaluation` MODULE IS LEGACY AND WILL BE REMOVED SOON. PLEASE DO NOT USE THESE CLASSES +IN NEW CODE. INSTEAD, USE `mlflow/entities/assessment.py` FOR ASSESSMENT CLASSES. +""" + +from mlflow.evaluation.assessment import Assessment, AssessmentSource, AssessmentSourceType +from mlflow.evaluation.evaluation import Evaluation +from mlflow.evaluation.fluent import log_evaluations + +__all__ = [ + "Assessment", + "AssessmentSource", + "AssessmentSourceType", + "Evaluation", + "log_evaluations", +] diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/evaluation/__pycache__/__init__.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/evaluation/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..40c7c20de31e7bcd8defdf5496a405c5cdcf1c47 Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/evaluation/__pycache__/__init__.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/evaluation/__pycache__/assessment.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/evaluation/__pycache__/assessment.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..1b6fb1b88bc35bbf677b30fe1cffdd615236943a Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/evaluation/__pycache__/assessment.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/evaluation/__pycache__/evaluation.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/evaluation/__pycache__/evaluation.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..d47ba0797ca0fbfc84c1d004098c866651ea982c Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/evaluation/__pycache__/evaluation.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/evaluation/__pycache__/evaluation_tag.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/evaluation/__pycache__/evaluation_tag.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..ca423250e8738c638b2edfd8f0267fc5de440b63 Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/evaluation/__pycache__/evaluation_tag.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/evaluation/__pycache__/fluent.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/evaluation/__pycache__/fluent.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..47c0e5a6b2aafdc0bbfbfe5312b5008d5bc5d367 Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/evaluation/__pycache__/fluent.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/evaluation/__pycache__/utils.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/evaluation/__pycache__/utils.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..7a43d6343649e1f22d1c446318e42899b9efa3ad Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/evaluation/__pycache__/utils.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/evaluation/assessment.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/evaluation/assessment.py new file mode 100644 index 0000000000000000000000000000000000000000..b616b3a2c3d9db423ede77a5b8d615002af921ba --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/evaluation/assessment.py @@ -0,0 +1,369 @@ +""" +THE 'mlflow.evaluation` MODULE IS LEGACY AND WILL BE REMOVED SOON. PLEASE DO NOT USE THESE CLASSES +IN NEW CODE. INSTEAD, USE `mlflow/entities/assessment.py` FOR ASSESSMENT CLASSES. +""" + +import numbers +import time +from typing import Any + +from mlflow.entities._mlflow_object import _MlflowObject +from mlflow.entities.assessment import AssessmentSource, AssessmentSourceType +from mlflow.exceptions import MlflowException +from mlflow.protos.databricks_pb2 import INVALID_PARAMETER_VALUE +from mlflow.utils.annotations import deprecated + + +@deprecated(since="3.0.0", alternative="mlflow.entities.Assessment") +class AssessmentEntity(_MlflowObject): + """ + Assessment data associated with an evaluation. + """ + + def __init__( + self, + evaluation_id: str, + name: str, + source: AssessmentSource, + timestamp: int, + boolean_value: bool | None = None, + numeric_value: float | None = None, + string_value: str | None = None, + rationale: str | None = None, + metadata: dict[str, str] | None = None, + error_code: str | None = None, + error_message: str | None = None, + span_id: str | None = None, + ): + """Construct a new mlflow.evaluation.AssessmentEntity instance. + + Args: + evaluation_id: The ID of the evaluation with which the assessment is associated. + name: The name of the assessment. + source: The source of the assessment (AssessmentSource instance). + timestamp: The timestamp when the assessment was given. + boolean_value: The boolean assessment value, if applicable. + numeric_value: The numeric assessment value, if applicable. + string_value: The string assessment value, if applicable. + rationale: The rationale / justification for the value. + metadata: Additional metadata for the assessment, e.g. the index of the chunk in the + retrieved documents that the assessment applies to. + error_code: An error code representing any issues encountered during the assessment. + error_message: A descriptive error message representing any issues encountered during + the assessment. + span_id: The span ID of the span within the Trace, that the assessment is evaluating, + e.g. if you are evaluating a retrieval span for document recall, you can + specify the span ID of the retrieval span here. This field is only supported + by mlflow.log_feedback. + """ + self._evaluation_id = evaluation_id + self._name = name + self._source = source + self._timestamp = timestamp + self._boolean_value = boolean_value + self._numeric_value = numeric_value + self._string_value = string_value + self._rationale = rationale + self._metadata = metadata or {} + self._error_code = error_code + self._error_message = error_message + self._span_id = span_id + + if error_message is not None and ( + boolean_value is not None or numeric_value is not None or string_value is not None + ): + raise MlflowException( + "error_message cannot be specified when boolean_value, numeric_value, " + "or string_value is specified.", + INVALID_PARAMETER_VALUE, + ) + + if (self._boolean_value, self._string_value, self._numeric_value, self._error_code).count( + None + ) != 3: + raise MlflowException( + "Exactly one of boolean_value, numeric_value, string_value, or error_code must be " + "specified for an assessment.", + INVALID_PARAMETER_VALUE, + ) + + @property + def evaluation_id(self) -> str: + """The evaluation ID.""" + return self._evaluation_id + + @property + def name(self) -> str: + """The name of the assessment.""" + return self._name + + @property + def timestamp(self) -> int: + """The timestamp of the assessment.""" + return self._timestamp + + @property + def boolean_value(self) -> bool | None: + """The boolean assessment value.""" + return self._boolean_value + + @property + def numeric_value(self) -> float | None: + """The numeric assessment value.""" + return self._numeric_value + + @property + def string_value(self) -> str | None: + """The string assessment value.""" + return self._string_value + + @property + def rationale(self) -> str | None: + """The rationale / justification for the assessment.""" + return self._rationale + + @property + def source(self) -> AssessmentSource: + """The source of the assessment.""" + return self._source + + @property + def metadata(self) -> dict[str, Any]: + """The metadata associated with the assessment.""" + return self._metadata + + @property + def error_code(self) -> str | None: + """The error code.""" + return self._error_code + + @property + def error_message(self) -> str | None: + """The error message.""" + return self._error_message + + @property + def span_id(self) -> str | None: + """The span ID of the span within the Trace, that the assessment is evaluating.""" + return self._span_id + + def __eq__(self, __o): + if isinstance(__o, self.__class__): + return self.to_dictionary() == __o.to_dictionary() + return False + + def to_dictionary(self) -> dict[str, Any]: + return { + "evaluation_id": self.evaluation_id, + "name": self.name, + "source": self.source.to_dictionary(), + "timestamp": self.timestamp, + "boolean_value": self.boolean_value, + "numeric_value": self.numeric_value, + "string_value": self.string_value, + "rationale": self.rationale, + "metadata": self.metadata, + "error_code": self.error_code, + "error_message": self.error_message, + "span_id": self.span_id, + } + + @classmethod + def from_dictionary(cls, assessment_dict: dict[str, Any]) -> "AssessmentEntity": + """ + Create an Assessment object from a dictionary. + + Args: + assessment_dict (dict): Dictionary containing assessment information. + + Returns: + Assessment: The Assessment object created from the dictionary. + """ + return cls( + evaluation_id=assessment_dict["evaluation_id"], + name=assessment_dict["name"], + source=AssessmentSource.from_dictionary(assessment_dict["source"]), + timestamp=assessment_dict["timestamp"], + boolean_value=assessment_dict.get("boolean_value"), + numeric_value=assessment_dict.get("numeric_value"), + string_value=assessment_dict.get("string_value"), + rationale=assessment_dict.get("rationale"), + metadata=assessment_dict.get("metadata"), + error_code=assessment_dict.get("error_code"), + error_message=assessment_dict.get("error_message"), + span_id=assessment_dict.get("span_id"), + ) + + +@deprecated(since="3.0.0", alternative="mlflow.entities.Assessment") +class Assessment(_MlflowObject): + """ + Assessment data associated with an evaluation result. + + Assessment is an enriched output from the evaluation that provides more context, + such as the rationale, source, and metadata for the evaluation result. + + Example: + + .. code-block:: python + + from mlflow.evaluation import Assessment + + assessment = Assessment( + name="answer_correctness", + value=0.5, + rationale="The answer is partially correct.", + ) + """ + + def __init__( + self, + name: str, + source: AssessmentSource | None = None, + value: bool | float | str | None = None, + rationale: str | None = None, + metadata: dict[str, Any] | None = None, + error_code: str | None = None, + error_message: str | None = None, + ): + """Construct a new Assessment instance. + + Args: + name: The name of the piece of assessment. + source: The source of the assessment (AssessmentSource instance). + value: The value of the assessment. This can be a boolean, numeric, or string value. + rationale: The rationale / justification for the value. + metadata: Additional metadata for the assessment, e.g. the index of the chunk in the + retrieved documents that the assessment applies to. + error_code: An error code representing any issues encountered during the assessment. + error_message: A descriptive error message representing any issues encountered during + the assessment. + """ + if (value is None) == (error_code is None): + raise MlflowException( + "Exactly one of value or error_code must be specified for an assessment.", + INVALID_PARAMETER_VALUE, + ) + + if value is not None and error_message is not None: + raise MlflowException( + "error_message cannot be specified when value is specified.", + INVALID_PARAMETER_VALUE, + ) + + self._name = name + self._source = source or AssessmentSource( + source_type=AssessmentSourceType.SOURCE_TYPE_UNSPECIFIED, + source_id="unknown", + ) + self._value = value + self._rationale = rationale + self._metadata = metadata or {} + self._error_code = error_code + self._error_message = error_message + + self._boolean_value = None + self._numeric_value = None + self._string_value = None + if isinstance(value, bool): + self._boolean_value = value + elif isinstance(value, numbers.Number): + self._numeric_value = float(value) + elif value is not None: + self._string_value = str(value) + + @property + def name(self) -> str: + """The name of the assessment.""" + return self._name + + @property + def value(self) -> bool | float | str: + """The assessment value.""" + return self._value + + @property + def rationale(self) -> str | None: + """The rationale / justification for the assessment.""" + return self._rationale + + @property + def source(self) -> AssessmentSource: + """The source of the assessment.""" + return self._source + + @property + def metadata(self) -> dict[str, Any]: + """The metadata associated with the assessment.""" + return self._metadata + + @property + def error_code(self) -> str | None: + """The error code.""" + return self._error_code + + @property + def error_message(self) -> str | None: + """The error message.""" + return self._error_message + + def __eq__(self, __o): + if isinstance(__o, self.__class__): + return self.to_dictionary() == __o.to_dictionary() + return False + + def to_dictionary(self) -> dict[str, Any]: + return { + "name": self.name, + "source": self.source.to_dictionary() if self.source is not None else None, + "value": self.value, + "rationale": self.rationale, + "metadata": self.metadata, + "error_code": self.error_code, + "error_message": self.error_message, + } + + @classmethod + def from_dictionary(cls, assessment_dict: dict[str, Any]) -> "Assessment": + """ + Create an Assessment object from a dictionary. + + Args: + assessment_dict (dict): Dictionary containing assessment information. + + Returns: + Assessment: The Assessment object created from the dictionary. + """ + return cls( + name=assessment_dict["name"], + source=AssessmentSource.from_dictionary(assessment_dict["source"]), + value=assessment_dict.get("value"), + rationale=assessment_dict.get("rationale"), + metadata=assessment_dict.get("metadata"), + error_code=assessment_dict.get("error_code"), + error_message=assessment_dict.get("error_message"), + ) + + def _to_entity(self, evaluation_id: str) -> AssessmentEntity: + # We require that the source be specified for an assessment before sending it to the backend + if self._source is None: + raise MlflowException( + message=( + f"Assessment source must be specified." + f"Got empty source for assessment with name {self._name}" + ), + error_code=INVALID_PARAMETER_VALUE, + ) + return AssessmentEntity( + evaluation_id=evaluation_id, + name=self._name, + source=self._source, + timestamp=int(time.time() * 1000), + boolean_value=self._boolean_value, + numeric_value=self._numeric_value, + string_value=self._string_value, + rationale=self._rationale, + metadata=self._metadata, + error_code=self._error_code, + error_message=self._error_message, + ) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/evaluation/evaluation.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/evaluation/evaluation.py new file mode 100644 index 0000000000000000000000000000000000000000..9d46176958312b1aab9defc38dc331ae2435ab1e --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/evaluation/evaluation.py @@ -0,0 +1,411 @@ +""" +THE 'mlflow.evaluation` MODULE IS LEGACY AND WILL BE REMOVED IN MLFLOW 3.0. +For assessment functionality, use `mlflow.entities.assessment` for assessment classes and +`mlflow.tracing.assessments` for assessment APIs. There are no alternatives for Evaluation and +EvaluationEntity objects and related APIs. +""" + +import hashlib +import json +from typing import Any + +from mlflow.entities._mlflow_object import _MlflowObject +from mlflow.entities.metric import Metric +from mlflow.evaluation.assessment import Assessment, AssessmentEntity +from mlflow.evaluation.evaluation_tag import ( + EvaluationTag, # Assuming EvaluationTag is in this module +) +from mlflow.tracing.utils import TraceJSONEncoder +from mlflow.utils.annotations import deprecated + + +@deprecated(since="3.0.0") +class EvaluationEntity(_MlflowObject): + """ + Evaluation result data, including inputs, outputs, targets, assessments, and more. + """ + + def __init__( + self, + evaluation_id: str, + run_id: str, + inputs_id: str, + inputs: dict[str, Any], + outputs: dict[str, Any] | None = None, + request_id: str | None = None, + targets: dict[str, Any] | None = None, + error_code: str | None = None, + error_message: str | None = None, + assessments: list[AssessmentEntity] | None = None, + metrics: list[Metric] | None = None, + tags: list[EvaluationTag] | None = None, + ): + """ + Construct a new mlflow.evaluation.EvaluationEntity instance. + + Args: + evaluation_id: A unique identifier for the evaluation. + run_id: The ID of the MLflow Run containing the Evaluation. + inputs_id: A unique identifier for the input names and values for evaluation. + inputs: Input names and values for evaluation. + outputs: Outputs obtained during inference. + request_id: The ID of an MLflow Trace corresponding to the inputs and outputs. + targets: Expected values that the model should produce during inference. + error_code: An error code representing any issues encountered during the evaluation. + error_message: A descriptive error message representing any issues encountered during + the evaluation. + assessments: Assessments for the evaluation. + metrics: Objective numerical metrics for the evaluation, e.g., "number of input tokens", + "number of output tokens". + tags: List of tags associated with the evaluation. + """ + self._evaluation_id = evaluation_id + self._run_id = run_id + self._inputs_id = inputs_id + self._inputs = inputs + self._outputs = outputs + self._request_id = request_id + self._targets = targets + self._error_code = error_code + self._error_message = error_message + self._assessments = assessments + self._metrics = metrics + self._tags = tags + + @property + def evaluation_id(self) -> str: + """The evaluation ID.""" + return self._evaluation_id + + @property + def run_id(self) -> str: + """The ID of the MLflow Run containing the evaluation""" + return self._run_id + + @property + def inputs_id(self) -> str: + """The evaluation inputs ID.""" + return self._inputs_id + + @property + def inputs(self) -> dict[str, Any]: + """The evaluation inputs.""" + return self._inputs + + @property + def outputs(self) -> dict[str, Any] | None: + """The evaluation outputs.""" + return self._outputs + + @property + def request_id(self) -> str | None: + """The evaluation request ID.""" + return self._request_id + + @property + def targets(self) -> dict[str, Any] | None: + """The evaluation targets.""" + return self._targets + + @property + def error_code(self) -> str | None: + """The evaluation error code.""" + return self._error_code + + @property + def error_message(self) -> str | None: + """The evaluation error message.""" + return self._error_message + + @property + def assessments(self) -> list[AssessmentEntity] | None: + """The evaluation assessments.""" + return self._assessments + + @property + def metrics(self) -> list[Metric] | None: + """The evaluation metrics.""" + return self._metrics + + @property + def tags(self) -> list[EvaluationTag] | None: + """The evaluation tags.""" + return self._tags + + def __eq__(self, __o): + if isinstance(__o, self.__class__): + return self.to_dictionary() == __o.to_dictionary() + return False + + def to_dictionary(self) -> dict[str, Any]: + """ + Convert the Evaluation object to a dictionary. + + Returns: + dict: The Evaluation object represented as a dictionary. + """ + evaluation_dict = { + "evaluation_id": self.evaluation_id, + "run_id": self.run_id, + "inputs_id": self.inputs_id, + "inputs": self.inputs, + "outputs": self.outputs, + "request_id": self.request_id, + "targets": self.targets, + "error_code": self.error_code, + "error_message": self.error_message, + "assessments": [assess.to_dictionary() for assess in self.assessments] + if self.assessments + else None, + "metrics": [metric.to_dictionary() for metric in self.metrics] + if self.metrics + else None, + "tags": [tag.to_dictionary() for tag in self.tags] if self.tags else None, + } + return {k: v for k, v in evaluation_dict.items() if v is not None} + + @classmethod + def from_dictionary(cls, evaluation_dict: dict[str, Any]): + """ + Create an Evaluation object from a dictionary. + + Args: + evaluation_dict (dict): Dictionary containing evaluation information. + + Returns: + Evaluation: The Evaluation object created from the dictionary. + """ + assessments = None + if "assessments" in evaluation_dict: + assessments = [ + AssessmentEntity.from_dictionary(assess) + for assess in evaluation_dict["assessments"] + ] + metrics = None + if "metrics" in evaluation_dict: + metrics = [Metric.from_dictionary(metric) for metric in evaluation_dict["metrics"]] + tags = None + if "tags" in evaluation_dict: + tags = [EvaluationTag(tag["key"], tag["value"]) for tag in evaluation_dict["tags"]] + return cls( + evaluation_id=evaluation_dict["evaluation_id"], + run_id=evaluation_dict["run_id"], + inputs_id=evaluation_dict["inputs_id"], + inputs=evaluation_dict["inputs"], + outputs=evaluation_dict.get("outputs"), + request_id=evaluation_dict.get("request_id"), + targets=evaluation_dict.get("targets"), + error_code=evaluation_dict.get("error_code"), + error_message=evaluation_dict.get("error_message"), + assessments=assessments, + metrics=metrics, + tags=tags, + ) + + +@deprecated(since="3.0.0") +class Evaluation(_MlflowObject): + """ + Evaluation result data. + """ + + def __init__( + self, + inputs: dict[str, Any], + outputs: dict[str, Any] | None = None, + inputs_id: str | None = None, + request_id: str | None = None, + targets: dict[str, Any] | None = None, + error_code: str | None = None, + error_message: str | None = None, + assessments: list[Assessment] | None = None, + metrics: dict[str, float] | list[Metric] | None = None, + tags: dict[str, str] | None = None, + ): + """ + Construct a new Evaluation instance. + + Args: + inputs: Input names and values for evaluation. + outputs: Outputs obtained during inference. + inputs_id: A unique identifier for the input names and values for evaluation. + request_id: The ID of an MLflow Trace corresponding to the inputs and outputs. + targets: Expected values that the model should produce during inference. + error_code: An error code representing any issues encountered during the evaluation. + error_message: A descriptive error message representing any issues encountered during + the evaluation. + assessments: Assessments for the evaluation. + metrics: Objective numerical metrics for the evaluation, e.g., "number of input tokens", + "number of output tokens". + tags: Dictionary of tags associated with the evaluation. + """ + if isinstance(metrics, dict): + metrics = [ + Metric(key=key, value=value, timestamp=0, step=0) for key, value in metrics.items() + ] + if isinstance(tags, dict): + tags = [EvaluationTag(key=str(key), value=str(value)) for key, value in tags.items()] + + self._inputs = inputs + self._outputs = outputs + self._inputs_id = inputs_id or _generate_inputs_id(inputs) + self._request_id = request_id + self._targets = targets + self._error_code = error_code + self._error_message = error_message + self._assessments = assessments + self._metrics = metrics + self._tags = tags + + @property + def inputs_id(self) -> str: + """The evaluation inputs ID.""" + return self._inputs_id + + @property + def inputs(self) -> dict[str, Any]: + """The evaluation inputs.""" + return self._inputs + + @property + def outputs(self) -> dict[str, Any] | None: + """The evaluation outputs.""" + return self._outputs + + @property + def request_id(self) -> str | None: + """The evaluation request ID.""" + return self._request_id + + @property + def targets(self) -> dict[str, Any] | None: + """The evaluation targets.""" + return self._targets + + @property + def error_code(self) -> str | None: + """The evaluation error code.""" + return self._error_code + + @property + def error_message(self) -> str | None: + """The evaluation error message.""" + return self._error_message + + @property + def assessments(self) -> list[Assessment] | None: + """The evaluation assessments.""" + return self._assessments + + @property + def metrics(self) -> list[Metric] | None: + """The evaluation metrics.""" + return self._metrics + + @property + def tags(self) -> dict[str, str] | None: + """The evaluation tags.""" + return self._tags + + def __eq__(self, __o): + if isinstance(__o, self.__class__): + return self.to_dictionary() == __o.to_dictionary() + return False + + def _to_entity(self, run_id: str, evaluation_id: str) -> EvaluationEntity: + """ + Convert the Evaluation object to an EvaluationEntity object. + + Returns: + EvaluationEntity: An EvaluationEntity object. + """ + return EvaluationEntity( + evaluation_id=evaluation_id, + run_id=run_id, + inputs_id=self.inputs_id, + inputs=self.inputs, + outputs=self.outputs, + request_id=self.request_id, + targets=self.targets, + error_code=self.error_code, + error_message=self.error_message, + assessments=[assess._to_entity(evaluation_id) for assess in self.assessments] + if self.assessments + else None, + metrics=self.metrics, + tags=self.tags, + ) + + def to_dictionary(self) -> dict[str, Any]: + """ + Convert the Evaluation object to a dictionary. + + Returns: + dict: The Evaluation object represented as a dictionary. + """ + evaluation_dict = { + "inputs_id": self.inputs_id, + "inputs": self.inputs, + "outputs": self.outputs, + "request_id": self.request_id, + "targets": self.targets, + "error_code": self.error_code, + "error_message": self.error_message, + "assessments": [assess.to_dictionary() for assess in self.assessments] + if self.assessments + else None, + "metrics": [metric.to_dictionary() for metric in self.metrics] + if self.metrics + else None, + "tags": [tag.to_dictionary() for tag in self.tags] if self.tags else None, + } + return {k: v for k, v in evaluation_dict.items() if v is not None} + + @classmethod + def from_dictionary(cls, evaluation_dict: dict[str, Any]): + """ + Create an Evaluation object from a dictionary. + + Args: + evaluation_dict (dict): Dictionary containing evaluation information. + + Returns: + Evaluation: The Evaluation object created from the dictionary. + """ + assessments = None + if "assessments" in evaluation_dict: + assessments = [ + Assessment.from_dictionary(assess) for assess in evaluation_dict["assessments"] + ] + metrics = None + if "metrics" in evaluation_dict: + metrics = [Metric.from_dictionary(metric) for metric in evaluation_dict["metrics"]] + tags = None + if "tags" in evaluation_dict: + tags = [EvaluationTag(tag["key"], tag["value"]) for tag in evaluation_dict["tags"]] + return cls( + inputs_id=evaluation_dict["inputs_id"], + inputs=evaluation_dict["inputs"], + outputs=evaluation_dict.get("outputs"), + request_id=evaluation_dict.get("request_id"), + targets=evaluation_dict.get("targets"), + error_code=evaluation_dict.get("error_code"), + error_message=evaluation_dict.get("error_message"), + assessments=assessments, + metrics=metrics, + tags=tags, + ) + + +def _generate_inputs_id(inputs: dict[str, Any]) -> str: + """ + Generates a unique identifier for the inputs. + + Args: + inputs (Dict[str, Any]): Input fields used by the model to compute outputs. + + Returns: + str: A unique identifier for the inputs. + """ + inputs_json = json.dumps(inputs, sort_keys=True, cls=TraceJSONEncoder) + return hashlib.sha256(inputs_json.encode("utf-8")).hexdigest() diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/evaluation/evaluation_tag.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/evaluation/evaluation_tag.py new file mode 100644 index 0000000000000000000000000000000000000000..01b9f2905015c4e4e1eac3f53a4923dc15f4d9a0 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/evaluation/evaluation_tag.py @@ -0,0 +1,61 @@ +""" +THE 'mlflow.evaluation` MODULE IS LEGACY AND WILL BE REMOVED SOON. PLEASE DO NOT USE THESE CLASSES +IN NEW CODE. INSTEAD, USE `mlflow/entities/assessment.py` FOR ASSESSMENT CLASSES. +""" + +from mlflow.entities._mlflow_object import _MlflowObject +from mlflow.utils.annotations import deprecated + + +@deprecated(since="3.0.0") +class EvaluationTag(_MlflowObject): + """Key-value tag associated with an evaluation.""" + + def __init__(self, key, value): + self._key = key + self._value = value + + def __eq__(self, other): + if type(other) is type(self): + return self.__dict__ == other.__dict__ + return False + + @property + def key(self): + """String name of the tag.""" + return self._key + + @property + def value(self): + """String value of the tag.""" + return self._value + + def to_dictionary(self) -> dict[str, str]: + """ + Convert the EvaluationTag object to a dictionary. + + Returns: + dict: The EvaluationTag object represented as a dictionary. + """ + return { + "key": self.key, + "value": self.value, + } + + @classmethod + def from_dictionary(cls, evaluation_tag_dict: dict[str, str]): + """ + Create an EvaluationTag object from a dictionary. + + Args: + evaluation_tag_dict (dict): Dictionary containing evaluation tag information. + + Returns: + Evaluation: The EvaluationTag object created from the dictionary. + """ + key = evaluation_tag_dict["key"] + value = evaluation_tag_dict["value"] + return cls( + key=key, + value=value, + ) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/evaluation/fluent.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/evaluation/fluent.py new file mode 100644 index 0000000000000000000000000000000000000000..c627ff2d58b2dc183d08b6c8b0d3f2d3dedd00f8 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/evaluation/fluent.py @@ -0,0 +1,47 @@ +""" +THE 'mlflow.evaluation` MODULE IS LEGACY AND WILL BE REMOVED SOON. PLEASE DO NOT USE THESE CLASSES +IN NEW CODE. INSTEAD, USE `mlflow/entities/assessment.py` FOR ASSESSMENT CLASSES. +""" + +import uuid + +from mlflow.evaluation.evaluation import Evaluation, EvaluationEntity +from mlflow.evaluation.utils import evaluations_to_dataframes +from mlflow.tracking.client import MlflowClient +from mlflow.tracking.fluent import _get_or_start_run +from mlflow.utils.annotations import deprecated + + +@deprecated(since="3.0.0") +def log_evaluations( + *, evaluations: list[Evaluation], run_id: str | None = None +) -> list[EvaluationEntity]: + """ + Logs one or more evaluations to an MLflow Run. + + Args: + evaluations (List[Evaluation]): List of one or more MLflow Evaluation objects. + run_id (Optional[str]): ID of the MLflow Run to log the evaluation. If unspecified, the + current active run is used, or a new run is started. + + Returns: + List[EvaluationEntity]: The logged Evaluation objects. + """ + run_id = run_id if run_id is not None else _get_or_start_run().info.run_id + if not evaluations: + return [] + + client = MlflowClient() + evaluation_entities = [ + evaluation._to_entity(run_id=run_id, evaluation_id=uuid.uuid4().hex) + for evaluation in evaluations + ] + evaluations_df, metrics_df, assessments_df, tags_df = evaluations_to_dataframes( + evaluation_entities + ) + client.log_table(run_id=run_id, data=evaluations_df, artifact_file="_evaluations.json") + client.log_table(run_id=run_id, data=metrics_df, artifact_file="_metrics.json") + client.log_table(run_id=run_id, data=assessments_df, artifact_file="_assessments.json") + client.log_table(run_id=run_id, data=tags_df, artifact_file="_tags.json") + + return evaluation_entities diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/evaluation/utils.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/evaluation/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..abf2bff280c7cc9c3b6af05a6be09e15a99d748d --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/evaluation/utils.py @@ -0,0 +1,201 @@ +""" +THE 'mlflow.evaluation` MODULE IS LEGACY AND WILL BE REMOVED SOON. PLEASE DO NOT USE THESE CLASSES +IN NEW CODE. INSTEAD, USE `mlflow/entities/assessment.py` FOR ASSESSMENT CLASSES. +""" + +import pandas as pd + +from mlflow.evaluation.evaluation import EvaluationEntity as EvaluationEntity +from mlflow.utils.annotations import deprecated + + +@deprecated(since="3.0.0") +def evaluations_to_dataframes( + evaluations: list[EvaluationEntity], +) -> tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame, pd.DataFrame]: + """ + Converts a list of Evaluation entities to four separate DataFrames: one for main evaluation + data (excluding assessments and metrics), one for metrics, one for assessments, and one for + tags. + + Args: + evaluations (List[Evaluation]): List of Evaluation entities. + + Returns: + Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame, pd.DataFrame]: A tuple of four DataFrames + containing evaluation data, metrics data, assessments data, and tags data. + """ + evaluations_data = [] + metrics_data = [] + assessments_data = [] + tags_data = [] + + for evaluation in evaluations: + eval_dict = evaluation.to_dictionary() + + # Extract assessment and metrics + assessments_list = eval_dict.pop("assessments", []) + metrics_list = eval_dict.pop("metrics", []) + tags_list = eval_dict.pop("tags", []) + + evaluations_data.append(eval_dict) + + for metric_dict in metrics_list: + metric_dict["evaluation_id"] = eval_dict["evaluation_id"] + # Remove 'step' key if it exists, since it is not valid for evaluation metrics + metric_dict.pop("step", None) + metrics_data.append(metric_dict) + + for assess_dict in assessments_list: + assess_dict["evaluation_id"] = eval_dict["evaluation_id"] + assessments_data.append(assess_dict) + + for tag_dict in tags_list: + tag_dict["evaluation_id"] = eval_dict["evaluation_id"] + tags_data.append(tag_dict) + + evaluations_df = ( + _apply_schema_to_dataframe( + pd.DataFrame(evaluations_data), _get_evaluations_dataframe_schema() + ) + if evaluations_data + else _get_empty_evaluations_dataframe() + ) + metrics_df = ( + _apply_schema_to_dataframe(pd.DataFrame(metrics_data), _get_metrics_dataframe_schema()) + if metrics_data + else _get_empty_metrics_dataframe() + ) + assessments_df = ( + _apply_schema_to_dataframe( + pd.DataFrame(assessments_data), _get_assessments_dataframe_schema() + ) + if assessments_data + else _get_empty_assessments_dataframe() + ) + tags_df = ( + _apply_schema_to_dataframe(pd.DataFrame(tags_data), _get_tags_dataframe_schema()) + if tags_data + else _get_empty_tags_dataframe() + ) + + return evaluations_df, metrics_df, assessments_df, tags_df + + +def _get_evaluations_dataframe_schema() -> dict[str, str]: + """ + Returns the pandas schema for the evaluation DataFrame. + """ + return { + "evaluation_id": "string", + "run_id": "string", + "inputs_id": "string", + "inputs": "object", + "outputs": "object", + "request_id": "object", + "targets": "object", + "error_code": "object", + "error_message": "object", + } + + +def _get_empty_evaluations_dataframe() -> pd.DataFrame: + """ + Creates an empty DataFrame with columns for evaluation data. + """ + schema = _get_evaluations_dataframe_schema() + df = pd.DataFrame(columns=schema.keys()) + return _apply_schema_to_dataframe(df, schema) + + +def _get_assessments_dataframe_schema() -> dict[str, str]: + """ + Returns the pandas schema for the assessments DataFrame. + """ + return { + "evaluation_id": "string", + "name": "string", + "source": "object", + "timestamp": "int64", + "boolean_value": "object", + "numeric_value": "object", + "string_value": "object", + "rationale": "object", + "metadata": "object", + "error_code": "object", + "error_message": "object", + "span_id": "object", + } + + +def _get_empty_assessments_dataframe() -> pd.DataFrame: + """ + Creates an empty DataFrame with columns for evaluation assessments data. + """ + schema = _get_assessments_dataframe_schema() + df = pd.DataFrame(columns=schema.keys()) + return _apply_schema_to_dataframe(df, schema) + + +def _get_metrics_dataframe_schema() -> dict[str, str]: + """ + Returns the pandas schema for the metrics DataFrame. + """ + return { + "evaluation_id": "string", + "key": "string", + "value": "float64", + "timestamp": "int64", + "model_id": "string", + "dataset_name": "string", + "dataset_digest": "string", + "run_id": "string", + } + + +def _get_empty_metrics_dataframe() -> pd.DataFrame: + """ + Creates an empty DataFrame with columns for evaluation metric data. + """ + schema = _get_metrics_dataframe_schema() + df = pd.DataFrame(columns=schema.keys()) + return _apply_schema_to_dataframe(df, schema) + + +def _get_tags_dataframe_schema() -> dict[str, str]: + """ + Returns the pandas schema for the tags DataFrame. + """ + return { + "evaluation_id": "string", + "key": "string", + "value": "string", + } + + +def _get_empty_tags_dataframe() -> pd.DataFrame: + """ + Creates an empty DataFrame with columns for evaluation tags data. + """ + schema = _get_tags_dataframe_schema() + df = pd.DataFrame(columns=schema.keys()) + return _apply_schema_to_dataframe(df, schema) + + +def _apply_schema_to_dataframe(df: pd.DataFrame, schema: dict[str, str]) -> pd.DataFrame: + """ + Applies a schema to a DataFrame. + + Args: + df (pd.DataFrame): DataFrame to apply the schema to. + schema (Dict[str, Any]): Schema to apply. + + Returns: + pd.DataFrame: DataFrame with schema applied. + """ + for column in df.columns: + df[column] = df[column].astype(schema[column]) + # By default, null values are represented as `pd.NA` in pandas when reading a dataframe from + # JSON. However, MLflow entities use `None` to represent null values. Accordingly, we convert + # instances of pd.NA to None so that DataFrame rows can be parsed as MLflow entities + return df.replace(pd.NA, None) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gateway/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gateway/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e5c303232cc99c6e87de07222e130969f4d24b88 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gateway/__init__.py @@ -0,0 +1,6 @@ +from mlflow.gateway.utils import get_gateway_uri, set_gateway_uri + +__all__ = [ + "get_gateway_uri", + "set_gateway_uri", +] diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gateway/__pycache__/__init__.cpython-312.pyc 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HTTPException, Request +from fastapi.openapi.docs import get_swagger_ui_html +from fastapi.responses import FileResponse, RedirectResponse +from pydantic import BaseModel +from slowapi import Limiter, _rate_limit_exceeded_handler +from slowapi.errors import RateLimitExceeded +from slowapi.util import get_remote_address + +from mlflow.deployments.server.config import Endpoint +from mlflow.deployments.server.constants import ( + MLFLOW_DEPLOYMENTS_CRUD_ENDPOINT_BASE, + MLFLOW_DEPLOYMENTS_ENDPOINTS_BASE, + MLFLOW_DEPLOYMENTS_HEALTH_ENDPOINT, + MLFLOW_DEPLOYMENTS_LIMITS_BASE, + MLFLOW_DEPLOYMENTS_LIST_ENDPOINTS_PAGE_SIZE, + MLFLOW_DEPLOYMENTS_QUERY_SUFFIX, +) +from mlflow.environment_variables import ( + MLFLOW_GATEWAY_CONFIG, + MLFLOW_GATEWAY_RATE_LIMITS_STORAGE_URI, +) +from mlflow.exceptions import MlflowException +from mlflow.gateway.base_models import SetLimitsModel +from mlflow.gateway.config import ( + GatewayConfig, + LimitsConfig, + Route, + RouteConfig, + RouteType, + _load_route_config, +) +from mlflow.gateway.constants import ( + MLFLOW_GATEWAY_CRUD_ROUTE_BASE, + MLFLOW_GATEWAY_HEALTH_ENDPOINT, + MLFLOW_GATEWAY_LIMITS_BASE, + MLFLOW_GATEWAY_ROUTE_BASE, + MLFLOW_GATEWAY_SEARCH_ROUTES_PAGE_SIZE, + MLFLOW_QUERY_SUFFIX, +) +from mlflow.gateway.exceptions import AIGatewayException +from mlflow.gateway.providers import get_provider +from mlflow.gateway.schemas import chat, completions, embeddings +from mlflow.gateway.utils import SearchRoutesToken, make_streaming_response +from mlflow.version import VERSION + + +class GatewayAPI(FastAPI): + def __init__(self, config: GatewayConfig, limiter: Limiter, *args: Any, **kwargs: Any): + super().__init__(*args, **kwargs) + self.state.limiter = limiter + self.add_exception_handler(RateLimitExceeded, _rate_limit_exceeded_handler) + self.dynamic_routes: dict[str, RouteConfig] = {} + self.set_dynamic_routes(config, limiter) + + def set_dynamic_routes(self, config: GatewayConfig, limiter: Limiter) -> None: + self.dynamic_routes.clear() + for route in config.endpoints: + # TODO: Remove deployments server URLs after deprecation window elapses + self.add_api_route( + path=( + MLFLOW_DEPLOYMENTS_ENDPOINTS_BASE + route.name + MLFLOW_DEPLOYMENTS_QUERY_SUFFIX + ), + endpoint=_route_type_to_endpoint(route, limiter, "deployments"), + methods=["POST"], + ) + self.add_api_route( + path=f"{MLFLOW_GATEWAY_ROUTE_BASE}{route.name}{MLFLOW_QUERY_SUFFIX}", + endpoint=_route_type_to_endpoint(route, limiter, "gateway"), + methods=["POST"], + include_in_schema=False, + ) + self.dynamic_routes[route.name] = route + + def get_dynamic_route(self, route_name: str) -> Route | None: + return r.to_route() if (r := self.dynamic_routes.get(route_name)) else None + + +def _translate_http_exception(func): + """ + Decorator for translating MLflow exceptions to HTTP exceptions + """ + + @functools.wraps(func) + async def wrapper(*args, **kwargs): + try: + return await func(*args, **kwargs) + except AIGatewayException as e: + raise HTTPException(status_code=e.status_code, detail=e.detail) + + return wrapper + + +def _create_chat_endpoint(config: RouteConfig): + prov = get_provider(config.model.provider)(config) + + # https://slowapi.readthedocs.io/en/latest/#limitations-and-known-issues + @_translate_http_exception + async def _chat( + request: Request, payload: chat.RequestPayload + ) -> chat.ResponsePayload | chat.StreamResponsePayload: + if payload.stream: + return await make_streaming_response(prov.chat_stream(payload)) + else: + return await prov.chat(payload) + + return _chat + + +def _create_completions_endpoint(config: RouteConfig): + prov = get_provider(config.model.provider)(config) + + @_translate_http_exception + async def _completions( + request: Request, payload: completions.RequestPayload + ) -> completions.ResponsePayload | completions.StreamResponsePayload: + if payload.stream: + return await make_streaming_response(prov.completions_stream(payload)) + else: + return await prov.completions(payload) + + return _completions + + +def _create_embeddings_endpoint(config: RouteConfig): + prov = get_provider(config.model.provider)(config) + + @_translate_http_exception + async def _embeddings( + request: Request, payload: embeddings.RequestPayload + ) -> embeddings.ResponsePayload: + return await prov.embeddings(payload) + + return _embeddings + + +async def _custom(request: Request): + return request.json() + + +def _route_type_to_endpoint(config: RouteConfig, limiter: Limiter, key: str): + provider_to_factory = { + RouteType.LLM_V1_CHAT: _create_chat_endpoint, + RouteType.LLM_V1_COMPLETIONS: _create_completions_endpoint, + RouteType.LLM_V1_EMBEDDINGS: _create_embeddings_endpoint, + } + if factory := provider_to_factory.get(config.endpoint_type): + handler = factory(config) + if limit := config.limit: + limit_value = f"{limit.calls}/{limit.renewal_period}" + handler.__name__ = f"{handler.__name__}_{config.name}_{key}" + return limiter.limit(limit_value)(handler) + else: + return handler + + raise HTTPException( + status_code=404, + detail=f"Unexpected route type {config.endpoint_type!r} for route {config.name!r}.", + ) + + +class HealthResponse(BaseModel): + status: str + + +class ListEndpointsResponse(BaseModel): + endpoints: list[Endpoint] + next_page_token: str | None = None + + class Config: + schema_extra = { + "example": { + "endpoints": [ + { + "name": "openai-chat", + "endpoint_type": "llm/v1/chat", + "model": { + "name": "gpt-4o-mini", + "provider": "openai", + }, + "limit": {"calls": 1, "key": None, "renewal_period": "minute"}, + }, + { + "name": "anthropic-completions", + "endpoint_type": "llm/v1/completions", + "model": { + "name": "claude-instant-100k", + "provider": "anthropic", + }, + }, + { + "name": "cohere-embeddings", + "endpoint_type": "llm/v1/embeddings", + "model": { + "name": "embed-english-v2.0", + "provider": "cohere", + }, + }, + ], + "next_page_token": "eyJpbmRleCI6IDExfQ==", + } + } + + +class SearchRoutesResponse(BaseModel): + routes: list[Route] + next_page_token: str | None = None + + class Config: + schema_extra = { + "example": { + "endpoints": [ + { + "name": "openai-chat", + "route_type": "llm/v1/chat", + "model": { + "name": "gpt-4o-mini", + "provider": "openai", + }, + }, + { + "name": "anthropic-completions", + "route_type": "llm/v1/completions", + "model": { + "name": "claude-instant-100k", + "provider": "anthropic", + }, + }, + { + "name": "cohere-embeddings", + "route_type": "llm/v1/embeddings", + "model": { + "name": "embed-english-v2.0", + "provider": "cohere", + }, + }, + ], + "next_page_token": "eyJpbmRleCI6IDExfQ==", + } + } + + +def create_app_from_config(config: GatewayConfig) -> GatewayAPI: + """ + Create the GatewayAPI app from the gateway configuration. + """ + limiter = Limiter( + key_func=get_remote_address, storage_uri=MLFLOW_GATEWAY_RATE_LIMITS_STORAGE_URI.get() + ) + app = GatewayAPI( + config=config, + limiter=limiter, + title="MLflow AI Gateway", + description="The core deployments API for reverse proxy interface using remote inference " + "endpoints within MLflow", + version=VERSION, + docs_url=None, + ) + + @app.get("/", include_in_schema=False) + async def index(): + return RedirectResponse(url="/docs") + + @app.get("/favicon.ico", include_in_schema=False) + async def favicon(): + for directory in ["build", "public"]: + favicon_file = Path(__file__).parent.parent.joinpath( + "server", "js", directory, "favicon.ico" + ) + if favicon_file.exists(): + return FileResponse(favicon_file) + raise HTTPException(status_code=404, detail="favicon.ico not found") + + @app.get("/docs", include_in_schema=False) + async def docs(): + return get_swagger_ui_html( + openapi_url="/openapi.json", + title="MLflow AI Gateway", + swagger_favicon_url="/favicon.ico", + ) + + # TODO: Remove deployments server URLs after deprecation window elapses + @app.get(MLFLOW_DEPLOYMENTS_HEALTH_ENDPOINT) + @app.get(MLFLOW_GATEWAY_HEALTH_ENDPOINT, include_in_schema=False) + async def health() -> HealthResponse: + return {"status": "OK"} + + # TODO: Remove deployments server URLs after deprecation window elapses + @app.get(MLFLOW_DEPLOYMENTS_CRUD_ENDPOINT_BASE + "{endpoint_name}") + async def get_endpoint(endpoint_name: str) -> Endpoint: + if matched := app.get_dynamic_route(endpoint_name): + return matched.to_endpoint() + + raise HTTPException( + status_code=404, + detail=f"The endpoint '{endpoint_name}' is not present or active on the server. Please " + "verify the endpoint name.", + ) + + @app.get(MLFLOW_GATEWAY_CRUD_ROUTE_BASE + "{route_name}", include_in_schema=False) + async def get_route(route_name: str) -> Route: + if matched := app.get_dynamic_route(route_name): + return matched + + raise HTTPException( + status_code=404, + detail=f"The route '{route_name}' is not present or active on the server. Please " + "verify the route name.", + ) + + # TODO: Remove deployments server URLs after deprecation window elapses + @app.get(MLFLOW_DEPLOYMENTS_CRUD_ENDPOINT_BASE) + async def list_endpoints(page_token: str | None = None) -> ListEndpointsResponse: + start_idx = SearchRoutesToken.decode(page_token).index if page_token is not None else 0 + + end_idx = start_idx + MLFLOW_DEPLOYMENTS_LIST_ENDPOINTS_PAGE_SIZE + routes = list(app.dynamic_routes.values()) + result = { + "endpoints": [route.to_route().to_endpoint() for route in routes[start_idx:end_idx]] + } + if len(routes[end_idx:]) > 0: + next_page_token = SearchRoutesToken(index=end_idx) + result["next_page_token"] = next_page_token.encode() + + return result + + @app.get(MLFLOW_GATEWAY_CRUD_ROUTE_BASE, include_in_schema=False) + async def search_routes(page_token: str | None = None) -> SearchRoutesResponse: + start_idx = SearchRoutesToken.decode(page_token).index if page_token is not None else 0 + + end_idx = start_idx + MLFLOW_GATEWAY_SEARCH_ROUTES_PAGE_SIZE + routes = list(app.dynamic_routes.values()) + result = {"routes": [r.to_route() for r in routes[start_idx:end_idx]]} + if len(routes[end_idx:]) > 0: + next_page_token = SearchRoutesToken(index=end_idx) + result["next_page_token"] = next_page_token.encode() + + return result + + # TODO: Remove deployments server URLs after deprecation window elapses + @app.get(MLFLOW_DEPLOYMENTS_LIMITS_BASE + "{endpoint}") + @app.get(MLFLOW_GATEWAY_LIMITS_BASE + "{endpoint}", include_in_schema=False) + async def get_limits(endpoint: str) -> LimitsConfig: + raise HTTPException(status_code=501, detail="The get_limits API is not available yet.") + + # TODO: Remove deployments server URLs after deprecation window elapses + @app.post(MLFLOW_DEPLOYMENTS_LIMITS_BASE) + @app.post(MLFLOW_GATEWAY_LIMITS_BASE, include_in_schema=False) + async def set_limits(payload: SetLimitsModel) -> LimitsConfig: + raise HTTPException(status_code=501, detail="The set_limits API is not available yet.") + + def _look_up_route(name: str) -> Route | None: + if r := app.dynamic_routes.get(name): + return r + + raise HTTPException( + status_code=400, + detail=f"Route {name} not found in the configuration.", + ) + + @app.post("/v1/chat/completions") + async def openai_chat_handler( + request: Request, payload: chat.RequestPayload + ) -> chat.ResponsePayload: + route = _look_up_route(payload.model) + if route.endpoint_type != RouteType.LLM_V1_CHAT: + raise HTTPException( + status_code=400, + detail=f"Endpoint {route.name!r} is not a chat endpoint.", + ) + + prov = get_provider(route.model.provider)(route) + payload.model = None # provider rejects a request with model field, must be set to None + if payload.stream: + return await make_streaming_response(prov.chat_stream(payload)) + else: + return await prov.chat(payload) + + @app.post("/v1/completions") + async def openai_completions_handler( + request: Request, payload: completions.RequestPayload + ) -> completions.ResponsePayload: + route = _look_up_route(payload.model) + if route.endpoint_type != RouteType.LLM_V1_COMPLETIONS: + raise HTTPException( + status_code=400, + detail=f"Endpoint {route.name!r} is not a completions endpoint.", + ) + + prov = get_provider(route.model.provider)(route) + payload.model = None # provider rejects a request with model field, must be set to None + if payload.stream: + return await make_streaming_response(prov.completions_stream(payload)) + else: + return await prov.completions(payload) + + @app.post("/v1/embeddings") + async def openai_embeddings_handler( + request: Request, payload: embeddings.RequestPayload + ) -> embeddings.ResponsePayload: + route = _look_up_route(payload.model) + if route.endpoint_type != RouteType.LLM_V1_EMBEDDINGS: + raise HTTPException( + status_code=400, + detail=f"Endpoint {route.name!r} is not an embeddings endpoint.", + ) + + prov = get_provider(route.model.provider)(route) + payload.model = None # provider rejects a request with model field, must be set to None + return await prov.embeddings(payload) + + return app + + +def create_app_from_path(config_path: str | Path) -> GatewayAPI: + """ + Load the path and generate the GatewayAPI app instance. + """ + config = _load_route_config(config_path) + return create_app_from_config(config) + + +def create_app_from_env() -> GatewayAPI: + """ + Load the path from the environment variable and generate the GatewayAPI app instance. + """ + if config_path := MLFLOW_GATEWAY_CONFIG.get(): + return create_app_from_path(config_path) + + raise MlflowException( + f"Environment variable {MLFLOW_GATEWAY_CONFIG!r} is not set. " + "Please set it to the path of the gateway configuration file." + ) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gateway/base_models.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gateway/base_models.py new file mode 100644 index 0000000000000000000000000000000000000000..6d2a4941e80728721fe16931649b970889acbc82 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gateway/base_models.py @@ -0,0 +1,60 @@ +from typing import Any + +from pydantic import BaseModel + + +class RequestModel( + BaseModel, + # Allow extra fields for pydantic request models, e.g. to support + # vendor-specific embeddings parameters + extra="allow", +): + """ + A pydantic model representing Gateway request data, such as a chat or completions request + """ + + +class ResponseModel( + BaseModel, + # Ignore extra fields for pydantic response models to ensure a consistent response + # experience for clients across different backends + extra="ignore", +): + """ + A pydantic model representing Gateway response data, such as information about a Gateway + Route returned in response to a GetRoute request + """ + + +class ConfigModel( + BaseModel, + # Ignore extra fields for pydantic config models, since they are unused + extra="ignore", +): + """ + A pydantic model representing Gateway configuration data, such as an OpenAI completions + route definition including route name, model name, API keys, etc. + """ + + +class LimitModel( + BaseModel, + # Ignore extra fields for pydantic limit models, since they are unused + extra="ignore", +): + """ + A pydantic model representing Gateway Limit data, such as renewal period, limit + key, limit value, etc. + """ + + +class SetLimitsModel( + BaseModel, + # Ignore extra fields for pydantic limit models, since they are unused + extra="ignore", +): + route: str + limits: list[dict[str, Any]] + """ + A pydantic model representing Gateway SetLimits request body, containing route and limits. + """ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gateway/cli.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gateway/cli.py new file mode 100644 index 0000000000000000000000000000000000000000..7ac51cc29eb65e4fc506631e269633f154b9f0cb --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gateway/cli.py @@ -0,0 +1,49 @@ +import click + +from mlflow.environment_variables import MLFLOW_GATEWAY_CONFIG +from mlflow.gateway.config import _validate_config +from mlflow.gateway.runner import run_app +from mlflow.utils.os import is_windows + + +def validate_config_path(_ctx, _param, value): + try: + _validate_config(value) + return value + except Exception as e: + raise click.BadParameter(str(e)) + + +@click.group("gateway", help="Manage the MLflow Gateway service") +def commands(): + pass + + +# TODO: add telemetry decorator +@commands.command("start", help="Start the MLflow Gateway service") +@click.option( + "--config-path", + envvar=MLFLOW_GATEWAY_CONFIG.name, + callback=validate_config_path, + required=True, + help="The path to the gateway configuration file.", +) +@click.option( + "--host", + default="127.0.0.1", + help="The network address to listen on (default: 127.0.0.1).", +) +@click.option( + "--port", + default=5000, + help="The port to listen on (default: 5000).", +) +@click.option( + "--workers", + default=2, + help="The number of workers.", +) +def start(config_path: str, host: str, port: str, workers: int): + if is_windows(): + raise click.ClickException("MLflow AI Gateway does not support Windows.") + run_app(config_path=config_path, host=host, port=port, workers=workers) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gateway/config.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gateway/config.py new file mode 100644 index 0000000000000000000000000000000000000000..9ccafe71a83eed54ca768d9f08577bae66710c7f --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gateway/config.py @@ -0,0 +1,519 @@ +import json +import logging +import os +import pathlib +from enum import Enum +from pathlib import Path +from typing import Any + +import pydantic +import yaml +from packaging.version import Version +from pydantic import ConfigDict, ValidationError +from pydantic.json import pydantic_encoder + +from mlflow.exceptions import MlflowException +from mlflow.gateway.base_models import ConfigModel, LimitModel, ResponseModel +from mlflow.gateway.constants import ( + MLFLOW_AI_GATEWAY_MOSAICML_CHAT_SUPPORTED_MODEL_PREFIXES, + MLFLOW_GATEWAY_ROUTE_BASE, + MLFLOW_QUERY_SUFFIX, +) +from mlflow.gateway.utils import ( + check_configuration_deprecated_fields, + check_configuration_route_name_collisions, + is_valid_ai21labs_model, + is_valid_endpoint_name, + is_valid_mosiacml_chat_model, +) +from mlflow.utils.pydantic_utils import IS_PYDANTIC_V2_OR_NEWER, field_validator, model_validator + +_logger = logging.getLogger(__name__) + +if IS_PYDANTIC_V2_OR_NEWER: + from pydantic import SerializeAsAny + + +class Provider(str, Enum): + OPENAI = "openai" + ANTHROPIC = "anthropic" + COHERE = "cohere" + AI21LABS = "ai21labs" + MLFLOW_MODEL_SERVING = "mlflow-model-serving" + MOSAICML = "mosaicml" + HUGGINGFACE_TEXT_GENERATION_INFERENCE = "huggingface-text-generation-inference" + PALM = "palm" + GEMINI = "gemini" + BEDROCK = "bedrock" + AMAZON_BEDROCK = "amazon-bedrock" # an alias for bedrock + # Note: The following providers are only supported on Databricks + DATABRICKS_MODEL_SERVING = "databricks-model-serving" + DATABRICKS = "databricks" + MISTRAL = "mistral" + TOGETHERAI = "togetherai" + + @classmethod + def values(cls): + return {p.value for p in cls} + + +class TogetherAIConfig(ConfigModel): + togetherai_api_key: str + + @field_validator("togetherai_api_key", mode="before") + def validate_togetherai_api_key(cls, value): + return _resolve_api_key_from_input(value) + + +class RouteType(str, Enum): + LLM_V1_COMPLETIONS = "llm/v1/completions" + LLM_V1_CHAT = "llm/v1/chat" + LLM_V1_EMBEDDINGS = "llm/v1/embeddings" + + +class CohereConfig(ConfigModel): + cohere_api_key: str + + @field_validator("cohere_api_key", mode="before") + def validate_cohere_api_key(cls, value): + return _resolve_api_key_from_input(value) + + +class AI21LabsConfig(ConfigModel): + ai21labs_api_key: str + + @field_validator("ai21labs_api_key", mode="before") + def validate_ai21labs_api_key(cls, value): + return _resolve_api_key_from_input(value) + + +class MosaicMLConfig(ConfigModel): + mosaicml_api_key: str + mosaicml_api_base: str | None = None + + @field_validator("mosaicml_api_key", mode="before") + def validate_mosaicml_api_key(cls, value): + return _resolve_api_key_from_input(value) + + +class OpenAIAPIType(str, Enum): + OPENAI = "openai" + AZURE = "azure" + AZUREAD = "azuread" + + @classmethod + def _missing_(cls, value): + """ + Implements case-insensitive matching of API type strings + """ + for api_type in cls: + if api_type.value == value.lower(): + return api_type + + raise MlflowException.invalid_parameter_value(f"Invalid OpenAI API type '{value}'") + + +class OpenAIConfig(ConfigModel): + openai_api_key: str + openai_api_type: OpenAIAPIType = OpenAIAPIType.OPENAI + openai_api_base: str | None = None + openai_api_version: str | None = None + openai_deployment_name: str | None = None + openai_organization: str | None = None + + @field_validator("openai_api_key", mode="before") + def validate_openai_api_key(cls, value): + return _resolve_api_key_from_input(value) + + @classmethod + def _validate_field_compatibility(cls, info: dict[str, Any]): + if not isinstance(info, dict): + return info + api_type = (info.get("openai_api_type") or OpenAIAPIType.OPENAI).lower() + if api_type == OpenAIAPIType.OPENAI: + if info.get("openai_deployment_name") is not None: + raise MlflowException.invalid_parameter_value( + f"OpenAI route configuration can only specify a value for " + f"'openai_deployment_name' if 'openai_api_type' is '{OpenAIAPIType.AZURE}' " + f"or '{OpenAIAPIType.AZUREAD}'. Found type: '{api_type}'" + ) + if info.get("openai_api_base") is None: + info["openai_api_base"] = "https://api.openai.com/v1" + elif api_type in (OpenAIAPIType.AZURE, OpenAIAPIType.AZUREAD): + if info.get("openai_organization") is not None: + raise MlflowException.invalid_parameter_value( + f"OpenAI route configuration can only specify a value for " + f"'openai_organization' if 'openai_api_type' is '{OpenAIAPIType.OPENAI}'" + ) + base_url = info.get("openai_api_base") + deployment_name = info.get("openai_deployment_name") + api_version = info.get("openai_api_version") + if (base_url, deployment_name, api_version).count(None) > 0: + raise MlflowException.invalid_parameter_value( + f"OpenAI route configuration must specify 'openai_api_base', " + f"'openai_deployment_name', and 'openai_api_version' if 'openai_api_type' is " + f"'{OpenAIAPIType.AZURE}' or '{OpenAIAPIType.AZUREAD}'." + ) + else: + raise MlflowException.invalid_parameter_value(f"Invalid OpenAI API type '{api_type}'") + + return info + + @model_validator(mode="before") + def validate_field_compatibility(cls, info: dict[str, Any]): + return cls._validate_field_compatibility(info) + + +class AnthropicConfig(ConfigModel): + anthropic_api_key: str + anthropic_version: str = "2023-06-01" + + @field_validator("anthropic_api_key", mode="before") + def validate_anthropic_api_key(cls, value): + return _resolve_api_key_from_input(value) + + +class PaLMConfig(ConfigModel): + palm_api_key: str + + @field_validator("palm_api_key", mode="before") + def validate_palm_api_key(cls, value): + return _resolve_api_key_from_input(value) + + +class GeminiConfig(ConfigModel): + gemini_api_key: str + + @field_validator("gemini_api_key", mode="before") + def validate_gemini_api_key(cls, value): + return _resolve_api_key_from_input(value) + + +class MlflowModelServingConfig(ConfigModel): + model_server_url: str + + # Workaround to suppress warning that Pydantic raises when a field name starts with "model_". + # https://github.com/mlflow/mlflow/issues/10335 + model_config = pydantic.ConfigDict(protected_namespaces=()) + + +class HuggingFaceTextGenerationInferenceConfig(ConfigModel): + hf_server_url: str + + +class AWSBaseConfig(pydantic.BaseModel): + aws_region: str | None = None + + +class AWSRole(AWSBaseConfig): + aws_role_arn: str + session_length_seconds: int = 15 * 60 + + +class AWSIdAndKey(AWSBaseConfig): + aws_access_key_id: str + aws_secret_access_key: str + aws_session_token: str | None = None + + +class AmazonBedrockConfig(ConfigModel): + # order here is important, at least for pydantic<2 + aws_config: AWSRole | AWSIdAndKey | AWSBaseConfig + + +class MistralConfig(ConfigModel): + mistral_api_key: str + + @field_validator("mistral_api_key", mode="before") + def validate_mistral_api_key(cls, value): + return _resolve_api_key_from_input(value) + + +class ModelInfo(ResponseModel): + name: str | None = None + provider: Provider + + +def _resolve_api_key_from_input(api_key_input): + """ + Resolves the provided API key. + + Input formats accepted: + + - Path to a file as a string which will have the key loaded from it + - environment variable name that stores the api key + - the api key itself + """ + + if not isinstance(api_key_input, str): + raise MlflowException.invalid_parameter_value( + "The api key provided is not a string. Please provide either an environment " + "variable key, a path to a file containing the api key, or the api key itself" + ) + + # try reading as an environment variable + if api_key_input.startswith("$"): + env_var_name = api_key_input[1:] + if env_var := os.getenv(env_var_name): + return env_var + else: + raise MlflowException.invalid_parameter_value( + f"Environment variable {env_var_name!r} is not set" + ) + + # try reading from a local path + file = pathlib.Path(api_key_input) + try: + if file.is_file(): + return file.read_text() + except OSError: + # `is_file` throws an OSError if `api_key_input` exceeds the maximum filename length + # (e.g., 255 characters on Unix). + pass + + # if the key itself is passed, return + return api_key_input + + +class Model(ConfigModel): + name: str | None = None + provider: str | Provider + if IS_PYDANTIC_V2_OR_NEWER: + config: SerializeAsAny[ConfigModel] | None = None + else: + config: ConfigModel | None = None + + @field_validator("provider", mode="before") + def validate_provider(cls, value): + from mlflow.gateway.provider_registry import provider_registry + + if isinstance(value, Provider): + return value + formatted_value = value.replace("-", "_").upper() + if formatted_value in Provider.__members__: + return Provider[formatted_value] + if value in provider_registry.keys(): + return value + raise MlflowException.invalid_parameter_value(f"The provider '{value}' is not supported.") + + @classmethod + def _validate_config(cls, val, context): + from mlflow.gateway.provider_registry import provider_registry + + # For Pydantic v2: 'context' is a ValidationInfo object with a 'data' attribute. + # For Pydantic v1: 'context' is dict-like 'values'. + if IS_PYDANTIC_V2_OR_NEWER: + provider = context.data.get("provider") + else: + provider = context.get("provider") if context else None + + if provider: + config_type = provider_registry.get(provider).CONFIG_TYPE + return config_type(**val) if isinstance(val, dict) else val + raise MlflowException.invalid_parameter_value( + "A provider must be provided for each gateway route." + ) + + @field_validator("config", mode="before") + def validate_config(cls, info, values): + return cls._validate_config(info, values) + + +class AliasedConfigModel(ConfigModel): + """ + Enables use of field aliases in a configuration model for backwards compatibility + """ + + if Version(pydantic.__version__) >= Version("2.0"): + model_config = ConfigDict(populate_by_name=True) + else: + + class Config: + allow_population_by_field_name = True + + +class Limit(LimitModel): + calls: int + key: str | None = None + renewal_period: str + + +class LimitsConfig(ConfigModel): + limits: list[Limit] | None = [] + + +class RouteConfig(AliasedConfigModel): + name: str + endpoint_type: RouteType + model: Model + limit: Limit | None = None + + @field_validator("name") + def validate_endpoint_name(cls, route_name): + if not is_valid_endpoint_name(route_name): + raise MlflowException.invalid_parameter_value( + "The route name provided contains disallowed characters for a url endpoint. " + f"'{route_name}' is invalid. Names cannot contain spaces or any non " + "alphanumeric characters other than hyphen and underscore." + ) + return route_name + + @field_validator("model", mode="before") + def validate_model(cls, model): + if model: + model_instance = Model(**model) + if model_instance.provider in Provider.values() and model_instance.config is None: + raise MlflowException.invalid_parameter_value( + "A config must be supplied when setting a provider. The provider entry for " + f"{model_instance.provider} is incorrect." + ) + return model + + @model_validator(mode="after", skip_on_failure=True) + def validate_route_type_and_model_name(cls, values): + if IS_PYDANTIC_V2_OR_NEWER: + route_type = values.endpoint_type + model = values.model + else: + route_type = values.get("endpoint_type") + model = values.get("model") + if ( + model + and model.provider == "mosaicml" + and route_type == RouteType.LLM_V1_CHAT + and not is_valid_mosiacml_chat_model(model.name) + ): + raise MlflowException.invalid_parameter_value( + f"An invalid model has been specified for the chat route. '{model.name}'. " + f"Ensure the model selected starts with one of: " + f"{MLFLOW_AI_GATEWAY_MOSAICML_CHAT_SUPPORTED_MODEL_PREFIXES}" + ) + if model and model.provider == "ai21labs" and not is_valid_ai21labs_model(model.name): + raise MlflowException.invalid_parameter_value( + f"An Unsupported AI21Labs model has been specified: '{model.name}'. " + f"Please see documentation for supported models." + ) + return values + + @field_validator("endpoint_type", mode="before") + def validate_route_type(cls, value): + if value in RouteType._value2member_map_: + return value + raise MlflowException.invalid_parameter_value(f"The route_type '{value}' is not supported.") + + @field_validator("limit", mode="before") + def validate_limit(cls, value): + from limits import parse + + if value: + limit = Limit(**value) + try: + parse(f"{limit.calls}/{limit.renewal_period}") + except ValueError: + raise MlflowException.invalid_parameter_value( + "Failed to parse the rate limit configuration." + "Please make sure limit.calls is a positive number and" + "limit.renewal_period is a right granularity" + ) + + return value + + def to_route(self) -> "Route": + return Route( + name=self.name, + route_type=self.endpoint_type, + model=RouteModelInfo( + name=self.model.name, + provider=self.model.provider, + ), + route_url=f"{MLFLOW_GATEWAY_ROUTE_BASE}{self.name}{MLFLOW_QUERY_SUFFIX}", + limit=self.limit, + ) + + +class RouteModelInfo(ResponseModel): + name: str | None = None + # Use `str` instead of `Provider` enum to allow gateway backends such as Databricks to + # support new providers without breaking the gateway client. + provider: str + + +_ROUTE_EXTRA_SCHEMA = { + "example": { + "name": "openai-completions", + "route_type": "llm/v1/completions", + "model": { + "name": "gpt-4o-mini", + "provider": "openai", + }, + "route_url": "/gateway/routes/completions/invocations", + } +} + + +class Route(ConfigModel): + name: str + route_type: str + model: RouteModelInfo + route_url: str + limit: Limit | None = None + + class Config: + if IS_PYDANTIC_V2_OR_NEWER: + json_schema_extra = _ROUTE_EXTRA_SCHEMA + else: + schema_extra = _ROUTE_EXTRA_SCHEMA + + def to_endpoint(self): + from mlflow.deployments.server.config import Endpoint + + return Endpoint( + name=self.name, + endpoint_type=self.route_type, + model=self.model, + endpoint_url=self.route_url, + limit=self.limit, + ) + + +class GatewayConfig(AliasedConfigModel): + endpoints: list[RouteConfig] + + +def _load_route_config(path: str | Path) -> GatewayConfig: + """ + Reads the gateway configuration yaml file from the storage location and returns an instance + of the configuration RouteConfig class + """ + if isinstance(path, str): + path = Path(path) + try: + configuration = yaml.safe_load(path.read_text()) + except Exception as e: + raise MlflowException.invalid_parameter_value( + f"The file at {path} is not a valid yaml file" + ) from e + check_configuration_deprecated_fields(configuration) + check_configuration_route_name_collisions(configuration) + try: + return GatewayConfig(**configuration) + except ValidationError as e: + raise MlflowException.invalid_parameter_value( + f"The gateway configuration is invalid: {e}" + ) from e + + +def _save_route_config(config: GatewayConfig, path: str | Path) -> None: + if isinstance(path, str): + path = Path(path) + path.write_text(yaml.safe_dump(json.loads(json.dumps(config.dict(), default=pydantic_encoder)))) + + +def _validate_config(config_path: str) -> GatewayConfig: + if not os.path.exists(config_path): + raise MlflowException.invalid_parameter_value(f"{config_path} does not exist") + + try: + return _load_route_config(config_path) + except ValidationError as e: + raise MlflowException.invalid_parameter_value(f"Invalid gateway configuration: {e}") from e diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gateway/constants.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gateway/constants.py new file mode 100644 index 0000000000000000000000000000000000000000..d5061e5a73be3208777d107c3140562f00669e0d --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gateway/constants.py @@ -0,0 +1,40 @@ +MLFLOW_GATEWAY_HEALTH_ENDPOINT = "/health" +MLFLOW_GATEWAY_CRUD_ROUTE_BASE = "/api/2.0/gateway/routes/" +MLFLOW_GATEWAY_LIMITS_BASE = "/api/2.0/gateway/limits/" +MLFLOW_GATEWAY_ROUTE_BASE = "/gateway/" +MLFLOW_QUERY_SUFFIX = "/invocations" +MLFLOW_GATEWAY_SEARCH_ROUTES_PAGE_SIZE = 3000 + +# Specifies the timeout for the Gateway server to declare a request submitted to a provider has +# timed out. +MLFLOW_GATEWAY_ROUTE_TIMEOUT_SECONDS = 300 + +# Abridged retryable error codes for the interface to the Gateway Server. +# These are modified from the standard MLflow Tracking server retry codes for the MLflowClient to +# remove timeouts from the list of the retryable conditions. A long-running timeout with +# retries for the proxied providers generally indicates an issue with the underlying query or +# the model being served having issues responding to the query due to parameter configuration. +MLFLOW_GATEWAY_CLIENT_QUERY_RETRY_CODES = frozenset( + [ + 429, # Too many requests + 500, # Server Error + 502, # Bad Gateway + 503, # Service Unavailable + ] +) + +# Provider constants +MLFLOW_AI_GATEWAY_ANTHROPIC_MAXIMUM_MAX_TOKENS = 1_000_000 +# Max for Claude 3.5 Sonnet. Newer models have higher limits. +# https://docs.anthropic.com/en/docs/about-claude/models/overview#model-comparison-table +MLFLOW_AI_GATEWAY_ANTHROPIC_DEFAULT_MAX_TOKENS = 8192 + +# MLflow model serving constants +MLFLOW_SERVING_RESPONSE_KEY = "predictions" + +# MosaicML constants +# MosaicML supported chat model names +# These validated names are used for the MosaicML provider due to the need to perform prompt +# translations prior to sending a request payload to their chat endpoints. +# to reduce the need to case-match, supported model prefixes are lowercase. +MLFLOW_AI_GATEWAY_MOSAICML_CHAT_SUPPORTED_MODEL_PREFIXES = ["llama2"] diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gateway/exceptions.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gateway/exceptions.py new file mode 100644 index 0000000000000000000000000000000000000000..b1a75a573e1184c2a2ee14184825ef3cace020e4 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gateway/exceptions.py @@ -0,0 +1,14 @@ +class AIGatewayConfigException(Exception): + pass + + +class AIGatewayException(Exception): + """ + A custom exception class for handling exceptions raised by the AI Gateway. + This will be transformed into an HTTPException before being returned to the client. + """ + + def __init__(self, status_code: int, detail: str): + self.status_code = status_code + self.detail = detail + super().__init__(detail) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gateway/provider_registry.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gateway/provider_registry.py new file mode 100644 index 0000000000000000000000000000000000000000..4a0cf8ffd22d7445097afa8014fcbc1072214f96 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gateway/provider_registry.py @@ -0,0 +1,71 @@ +from mlflow import MlflowException +from mlflow.gateway.config import Provider +from mlflow.gateway.providers import BaseProvider +from mlflow.utils.plugins import get_entry_points + + +class ProviderRegistry: + def __init__(self): + self._providers: dict[str | Provider, type[BaseProvider]] = {} + + def register(self, name: str, provider: type[BaseProvider]): + if name in self._providers: + raise MlflowException.invalid_parameter_value( + f"Provider {name} is already registered: {self._providers[name]}" + ) + self._providers[name] = provider + + def get(self, name: str) -> type[BaseProvider]: + if name not in self._providers: + raise MlflowException.invalid_parameter_value(f"Provider {name} not found") + return self._providers[name] + + def keys(self): + return list(self._providers.keys()) + + +def _register_default_providers(registry: ProviderRegistry): + from mlflow.gateway.providers.ai21labs import AI21LabsProvider + from mlflow.gateway.providers.anthropic import AnthropicProvider + from mlflow.gateway.providers.bedrock import AmazonBedrockProvider + from mlflow.gateway.providers.cohere import CohereProvider + from mlflow.gateway.providers.gemini import GeminiProvider + from mlflow.gateway.providers.huggingface import HFTextGenerationInferenceServerProvider + from mlflow.gateway.providers.mistral import MistralProvider + from mlflow.gateway.providers.mlflow import MlflowModelServingProvider + from mlflow.gateway.providers.mosaicml import MosaicMLProvider + from mlflow.gateway.providers.openai import OpenAIProvider + from mlflow.gateway.providers.palm import PaLMProvider + from mlflow.gateway.providers.togetherai import TogetherAIProvider + + registry.register(Provider.OPENAI, OpenAIProvider) + registry.register(Provider.ANTHROPIC, AnthropicProvider) + registry.register(Provider.COHERE, CohereProvider) + registry.register(Provider.AI21LABS, AI21LabsProvider) + registry.register(Provider.MOSAICML, MosaicMLProvider) + registry.register(Provider.PALM, PaLMProvider) + registry.register(Provider.GEMINI, GeminiProvider) + registry.register(Provider.MLFLOW_MODEL_SERVING, MlflowModelServingProvider) + registry.register(Provider.BEDROCK, AmazonBedrockProvider) + registry.register(Provider.AMAZON_BEDROCK, AmazonBedrockProvider) + registry.register( + Provider.HUGGINGFACE_TEXT_GENERATION_INFERENCE, HFTextGenerationInferenceServerProvider + ) + registry.register(Provider.MISTRAL, MistralProvider) + registry.register(Provider.TOGETHERAI, TogetherAIProvider) + + +def _register_plugin_providers(registry: ProviderRegistry): + providers = get_entry_points("mlflow.gateway.providers") + for p in providers: + cls = p.load() + registry.register(p.name, cls) + + +def is_supported_provider(name: str) -> bool: + return name in provider_registry.keys() + + +provider_registry = ProviderRegistry() +_register_default_providers(provider_registry) +_register_plugin_providers(provider_registry) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gateway/providers/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gateway/providers/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..944a9be368f82d17be1807eb9094c4c196f4242d --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gateway/providers/__init__.py @@ -0,0 +1,8 @@ +from mlflow.gateway.config import Provider +from mlflow.gateway.providers.base import BaseProvider + + +def get_provider(provider: Provider) -> type[BaseProvider]: + from mlflow.gateway.provider_registry import provider_registry + + return provider_registry.get(provider) diff --git 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b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gateway/providers/ai21labs.py new file mode 100644 index 0000000000000000000000000000000000000000..5734ea8ae3ccfa3fb9a5bf38649057ea2029f0b0 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gateway/providers/ai21labs.py @@ -0,0 +1,87 @@ +import time + +from mlflow.gateway.config import AI21LabsConfig, RouteConfig +from mlflow.gateway.exceptions import AIGatewayException +from mlflow.gateway.providers.base import BaseProvider +from mlflow.gateway.providers.utils import rename_payload_keys, send_request +from mlflow.gateway.schemas import completions + + +class AI21LabsProvider(BaseProvider): + NAME = "AI21Labs" + CONFIG_TYPE = AI21LabsConfig + + def __init__(self, config: RouteConfig) -> None: + super().__init__(config) + if config.model.config is None or not isinstance(config.model.config, AI21LabsConfig): + raise TypeError(f"Unexpected config type {config.model.config}") + self.ai21labs_config: AI21LabsConfig = config.model.config + self.headers = {"Authorization": f"Bearer {self.ai21labs_config.ai21labs_api_key}"} + self.base_url = f"https://api.ai21.com/studio/v1/{self.config.model.name}/" + + async def completions(self, payload: completions.RequestPayload) -> completions.ResponsePayload: + from fastapi.encoders import jsonable_encoder + + payload = jsonable_encoder(payload, exclude_none=True) + self.check_for_model_field(payload) + key_mapping = { + "stop": "stopSequences", + "n": "numResults", + "max_tokens": "maxTokens", + } + for k1, k2 in key_mapping.items(): + if k2 in payload: + raise AIGatewayException( + status_code=422, detail=f"Invalid parameter {k2}. Use {k1} instead." + ) + if payload.get("stream", False): + raise AIGatewayException( + status_code=422, + detail="Setting the 'stream' parameter to 'true' is not supported with the MLflow " + "Gateway.", + ) + payload = rename_payload_keys(payload, key_mapping) + resp = await send_request( + headers=self.headers, + base_url=self.base_url, + path="complete", + payload=payload, + ) + # Response example (https://docs.ai21.com/reference/j2-complete-ref) + # ``` + # { + # "id": "7921a78e-d905-c9df-27e3-88e4831e3c3b", + # "prompt": { + # "text": "I will" + # }, + # "completions": [ + # { + # "data": { + # "text": " complete this" + # }, + # "finishReason": { + # "reason": "length", + # "length": 2 + # } + # } + # ] + # } + # ``` + return completions.ResponsePayload( + created=int(time.time()), + object="text_completion", + model=self.config.model.name, + choices=[ + completions.Choice( + index=idx, + text=c["data"]["text"], + finish_reason=c["finishReason"]["reason"], + ) + for idx, c in enumerate(resp["completions"]) + ], + usage=completions.CompletionsUsage( + prompt_tokens=None, + completion_tokens=None, + total_tokens=None, + ), + ) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gateway/providers/anthropic.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gateway/providers/anthropic.py new file mode 100644 index 0000000000000000000000000000000000000000..afbf1c60fd472f3f35efaf2e46f96aa3ecd53513 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gateway/providers/anthropic.py @@ -0,0 +1,357 @@ +import json +import time +from typing import AsyncIterable + +from mlflow.gateway.config import AnthropicConfig, RouteConfig +from mlflow.gateway.constants import ( + MLFLOW_AI_GATEWAY_ANTHROPIC_DEFAULT_MAX_TOKENS, + MLFLOW_AI_GATEWAY_ANTHROPIC_MAXIMUM_MAX_TOKENS, +) +from mlflow.gateway.exceptions import AIGatewayException +from mlflow.gateway.providers.base import BaseProvider, ProviderAdapter +from mlflow.gateway.providers.utils import rename_payload_keys, send_request, send_stream_request +from mlflow.gateway.schemas import chat, completions + + +class AnthropicAdapter(ProviderAdapter): + @classmethod + def chat_to_model(cls, payload, config): + key_mapping = {"stop": "stop_sequences"} + payload["model"] = config.model.name + payload = rename_payload_keys(payload, key_mapping) + + if "top_p" in payload and "temperature" in payload: + raise AIGatewayException( + status_code=422, detail="Cannot set both 'temperature' and 'top_p' parameters." + ) + + max_tokens = payload.get("max_tokens", MLFLOW_AI_GATEWAY_ANTHROPIC_DEFAULT_MAX_TOKENS) + if max_tokens > MLFLOW_AI_GATEWAY_ANTHROPIC_MAXIMUM_MAX_TOKENS: + raise AIGatewayException( + status_code=422, + detail="Invalid value for max_tokens: cannot exceed " + f"{MLFLOW_AI_GATEWAY_ANTHROPIC_MAXIMUM_MAX_TOKENS}.", + ) + payload["max_tokens"] = max_tokens + + if payload.pop("n", 1) != 1: + raise AIGatewayException( + status_code=422, + detail="'n' must be '1' for the Anthropic provider. Received value: '{n}'.", + ) + + # Cohere uses `system` to set the system message + # we concatenate all system messages from the user with a newline + system_messages = [m for m in payload["messages"] if m["role"] == "system"] + if system_messages: + payload["system"] = "\n".join(m["content"] for m in system_messages) + + # remaining messages are chat history + # we want to include only user and assistant messages + payload["messages"] = [m for m in payload["messages"] if m["role"] in ("user", "assistant")] + + # The range of Anthropic's temperature is 0-1, but ours is 0-2, so we halve it + if "temperature" in payload: + payload["temperature"] = 0.5 * payload["temperature"] + + return payload + + @classmethod + def model_to_chat(cls, resp, config): + # API reference: https://docs.anthropic.com/en/api/messages#body-messages + # + # Example response: + # ``` + # { + # "content": [ + # { + # "text": "Blue is often seen as a calming and soothing color.", + # "type": "text" + # }, + # { + # "source": { + # "type": "base64", + # "media_type": "image/jpeg", + # "data": "/9j/4AAQSkZJRg...", + # "type": "image", + # } + # } + # ], + # "id": "msg_013Zva2CMHLNnXjNJJKqJ2EF", + # "model": "claude-2.1", + # "role": "assistant", + # "stop_reason": "end_turn", + # "stop_sequence": null, + # "type": "message", + # "usage": { + # "input_tokens": 10, + # "output_tokens": 25 + # } + # } + # ``` + from mlflow.anthropic.chat import convert_message_to_mlflow_chat + + stop_reason = "length" if resp["stop_reason"] == "max_tokens" else "stop" + + return chat.ResponsePayload( + id=resp["id"], + created=int(time.time()), + object="chat.completion", + model=resp["model"], + choices=[ + chat.Choice( + index=0, + # TODO: Remove this casting once + # https://github.com/mlflow/mlflow/pull/14160 is merged + message=chat.ResponseMessage( + **convert_message_to_mlflow_chat(resp).model_dump_compat() + ), + finish_reason=stop_reason, + ) + ], + usage=chat.ChatUsage( + prompt_tokens=resp["usage"]["input_tokens"], + completion_tokens=resp["usage"]["output_tokens"], + total_tokens=resp["usage"]["input_tokens"] + resp["usage"]["output_tokens"], + ), + ) + + @classmethod + def chat_streaming_to_model(cls, payload, config): + return cls.chat_to_model(payload, config) + + @classmethod + def model_to_chat_streaming(cls, resp, config): + content = resp.get("delta") or resp.get("content_block") or {} + if (stop_reason := content.get("stop_reason")) is not None: + stop_reason = "length" if stop_reason == "max_tokens" else "stop" + return chat.StreamResponsePayload( + id=resp["id"], + created=int(time.time()), + model=resp["model"], + choices=[ + chat.StreamChoice( + index=resp["index"], + finish_reason=stop_reason, + delta=chat.StreamDelta( + role=None, + content=content.get("text"), + ), + ) + ], + ) + + @classmethod + def model_to_completions(cls, resp, config): + stop_reason = "stop" if resp["stop_reason"] == "stop_sequence" else "length" + + return completions.ResponsePayload( + created=int(time.time()), + object="text_completion", + model=resp["model"], + choices=[ + completions.Choice( + index=0, + text=resp["completion"], + finish_reason=stop_reason, + ) + ], + usage=completions.CompletionsUsage( + prompt_tokens=None, + completion_tokens=None, + total_tokens=None, + ), + ) + + @classmethod + def completions_to_model(cls, payload, config): + key_mapping = {"max_tokens": "max_tokens_to_sample", "stop": "stop_sequences"} + + payload["model"] = config.model.name + + if "top_p" in payload: + raise AIGatewayException( + status_code=422, + detail="Cannot set both 'temperature' and 'top_p' parameters. " + "Please use only the temperature parameter for your query.", + ) + max_tokens = payload.get("max_tokens", MLFLOW_AI_GATEWAY_ANTHROPIC_DEFAULT_MAX_TOKENS) + + if max_tokens > MLFLOW_AI_GATEWAY_ANTHROPIC_MAXIMUM_MAX_TOKENS: + raise AIGatewayException( + status_code=422, + detail="Invalid value for max_tokens: cannot exceed " + f"{MLFLOW_AI_GATEWAY_ANTHROPIC_MAXIMUM_MAX_TOKENS}.", + ) + + payload["max_tokens"] = max_tokens + + if payload.get("stream", False): + raise AIGatewayException( + status_code=422, + detail="Setting the 'stream' parameter to 'true' is not supported with the MLflow " + "Gateway.", + ) + n = payload.pop("n", 1) + if n != 1: + raise AIGatewayException( + status_code=422, + detail=f"'n' must be '1' for the Anthropic provider. Received value: '{n}'.", + ) + + payload = rename_payload_keys(payload, key_mapping) + + if payload["prompt"].startswith("Human: "): + payload["prompt"] = "\n\n" + payload["prompt"] + + if not payload["prompt"].startswith("\n\nHuman: "): + payload["prompt"] = "\n\nHuman: " + payload["prompt"] + + if not payload["prompt"].endswith("\n\nAssistant:"): + payload["prompt"] = payload["prompt"] + "\n\nAssistant:" + + # The range of Anthropic's temperature is 0-1, but ours is 0-2, so we halve it + if "temperature" in payload: + payload["temperature"] = 0.5 * payload["temperature"] + + return payload + + @classmethod + def embeddings_to_model(cls, payload, config): + raise NotImplementedError + + @classmethod + def model_to_embeddings(cls, resp, config): + raise NotImplementedError + + +class AnthropicProvider(BaseProvider, AnthropicAdapter): + NAME = "Anthropic" + CONFIG_TYPE = AnthropicConfig + + def __init__(self, config: RouteConfig) -> None: + super().__init__(config) + if config.model.config is None or not isinstance(config.model.config, AnthropicConfig): + raise TypeError(f"Invalid config type {config.model.config}") + self.anthropic_config: AnthropicConfig = config.model.config + + @property + def headers(self) -> dict[str, str]: + return { + "x-api-key": self.anthropic_config.anthropic_api_key, + "anthropic-version": self.anthropic_config.anthropic_version, + } + + @property + def base_url(self) -> str: + return "https://api.anthropic.com/v1" + + @property + def adapter_class(self) -> type[ProviderAdapter]: + return AnthropicAdapter + + def get_endpoint_url(self, route_type: str) -> str: + if route_type == "llm/v1/chat": + return f"{self.base_url}/messages" + elif route_type == "llm/v1/completions": + return f"{self.base_url}/complete" + else: + raise ValueError(f"Invalid route type {route_type}") + + async def chat_stream( + self, payload: chat.RequestPayload + ) -> AsyncIterable[chat.StreamResponsePayload]: + from fastapi.encoders import jsonable_encoder + + payload = jsonable_encoder(payload, exclude_none=True) + self.check_for_model_field(payload) + stream = send_stream_request( + headers=self.headers, + base_url=self.base_url, + path="messages", + payload=AnthropicAdapter.chat_streaming_to_model(payload, self.config), + ) + + indices = [] + metadata = {} + async for chunk in stream: + chunk = chunk.strip() + if not chunk: + continue + + # No handling on "event" lines + prefix, content = chunk.split(b":", 1) + if prefix != b"data": + continue + + # See https://docs.anthropic.com/claude/reference/messages-streaming + resp = json.loads(content.decode("utf-8")) + + # response id and model are only present in `message_start` + if resp["type"] == "message_start": + metadata["id"] = resp["message"]["id"] + metadata["model"] = resp["message"]["model"] + continue + + if resp["type"] not in ( + "message_delta", + "content_block_start", + "content_block_delta", + ): + continue + + index = resp.get("index") + if index is not None and index not in indices: + indices.append(index) + + resp.update(metadata) + if resp["type"] == "message_delta": + for index in indices: + yield AnthropicAdapter.model_to_chat_streaming( + {**resp, "index": index}, + self.config, + ) + else: + yield AnthropicAdapter.model_to_chat_streaming(resp, self.config) + + async def chat(self, payload: chat.RequestPayload) -> chat.ResponsePayload: + from fastapi.encoders import jsonable_encoder + + payload = jsonable_encoder(payload, exclude_none=True) + self.check_for_model_field(payload) + resp = await send_request( + headers=self.headers, + base_url=self.base_url, + path="messages", + payload=AnthropicAdapter.chat_to_model(payload, self.config), + ) + return AnthropicAdapter.model_to_chat(resp, self.config) + + async def completions(self, payload: completions.RequestPayload) -> completions.ResponsePayload: + from fastapi.encoders import jsonable_encoder + + payload = jsonable_encoder(payload, exclude_none=True) + self.check_for_model_field(payload) + + resp = await send_request( + headers=self.headers, + base_url=self.base_url, + path="complete", + payload=AnthropicAdapter.completions_to_model(payload, self.config), + ) + + # Example response: + # Documentation: https://docs.anthropic.com/claude/reference/complete_post + # ``` + # { + # "completion": " Hello! My name is Claude." + # "stop_reason": "stop_sequence", + # "model": "claude-instant-1.1", + # "truncated": False, + # "stop": None, + # "log_id": "dee173f87ddf1357da639dee3c38d833", + # "exception": None, + # } + # ``` + + return AnthropicAdapter.model_to_completions(resp, self.config) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gateway/providers/base.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gateway/providers/base.py new file mode 100644 index 0000000000000000000000000000000000000000..db89037e4175999a1ceec2793e55a704d7ae0f3b --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gateway/providers/base.py @@ -0,0 +1,127 @@ +from abc import ABC, abstractmethod +from typing import AsyncIterable + +from mlflow.gateway.base_models import ConfigModel +from mlflow.gateway.config import RouteConfig +from mlflow.gateway.exceptions import AIGatewayException +from mlflow.gateway.schemas import chat, completions, embeddings +from mlflow.utils.annotations import developer_stable + + +@developer_stable +class BaseProvider(ABC): + """ + Base class for MLflow Gateway providers. + """ + + NAME: str = "" + SUPPORTED_ROUTE_TYPES: tuple[str, ...] + CONFIG_TYPE: type[ConfigModel] + + def __init__(self, config: RouteConfig): + if self.NAME == "": + raise ValueError( + f"{self.__class__.__name__} is a subclass of BaseProvider and must " + f"override 'NAME' attribute as a non-empty string." + ) + + if not hasattr(self, "CONFIG_TYPE") or not issubclass(self.CONFIG_TYPE, ConfigModel): + raise ValueError( + f"{self.__class__.__name__} is a subclass of BaseProvider and must " + f"override 'CONFIG_TYPE' attribute as a subclass of ConfigModel." + ) + + self.config = config + + async def chat_stream( + self, payload: chat.RequestPayload + ) -> AsyncIterable[chat.StreamResponsePayload]: + raise AIGatewayException( + status_code=501, + detail=f"The chat streaming route is not implemented for {self.NAME} models.", + ) + + async def chat(self, payload: chat.RequestPayload) -> chat.ResponsePayload: + raise AIGatewayException( + status_code=501, + detail=f"The chat route is not implemented for {self.NAME} models.", + ) + + async def completions_stream( + self, payload: completions.RequestPayload + ) -> AsyncIterable[completions.StreamResponsePayload]: + raise AIGatewayException( + status_code=501, + detail=f"The completions streaming route is not implemented for {self.NAME} models.", + ) + + async def completions(self, payload: completions.RequestPayload) -> completions.ResponsePayload: + raise AIGatewayException( + status_code=501, + detail=f"The completions route is not implemented for {self.NAME} models.", + ) + + async def embeddings(self, payload: embeddings.RequestPayload) -> embeddings.ResponsePayload: + raise AIGatewayException( + status_code=501, + detail=f"The embeddings route is not implemented for {self.NAME} models.", + ) + + @staticmethod + def check_for_model_field(payload): + if "model" in payload: + raise AIGatewayException( + status_code=422, + detail="The parameter 'model' is not permitted to be passed. The route being " + "queried already defines a model instance.", + ) + + +class ProviderAdapter(ABC): + @classmethod + @abstractmethod + def model_to_embeddings(cls, resp, config): ... + + @classmethod + @abstractmethod + def model_to_completions(cls, resp, config): ... + + @classmethod + def model_to_completions_streaming(cls, resp, config): + raise NotImplementedError + + @classmethod + @abstractmethod + def completions_to_model(cls, payload, config): ... + + @classmethod + def completions_streaming_to_model(cls, payload, config): + raise NotImplementedError + + @classmethod + def model_to_chat(cls, resp, config): + raise NotImplementedError + + @classmethod + def model_to_chat_streaming(cls, resp, config): + raise NotImplementedError + + @classmethod + def chat_to_model(cls, payload, config): + raise NotImplementedError + + @classmethod + def chat_streaming_to_model(cls, payload, config): + raise NotImplementedError + + @classmethod + @abstractmethod + def embeddings_to_model(cls, payload, config): ... + + @classmethod + def check_keys_against_mapping(cls, mapping, payload): + for k1, k2 in mapping.items(): + if k2 in payload: + raise AIGatewayException( + status_code=400, detail=f"Invalid parameter {k2}. Use {k1} instead." + ) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gateway/providers/bedrock.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gateway/providers/bedrock.py new file mode 100644 index 0000000000000000000000000000000000000000..f3c96230aa949d3bcc2851a54aae60444c440baa --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gateway/providers/bedrock.py @@ -0,0 +1,295 @@ +import json +import time +from enum import Enum + +from mlflow.gateway.config import AmazonBedrockConfig, AWSIdAndKey, AWSRole, RouteConfig +from mlflow.gateway.constants import ( + MLFLOW_AI_GATEWAY_ANTHROPIC_DEFAULT_MAX_TOKENS, +) +from mlflow.gateway.exceptions import AIGatewayConfigException, AIGatewayException +from mlflow.gateway.providers.anthropic import AnthropicAdapter +from mlflow.gateway.providers.base import BaseProvider, ProviderAdapter +from mlflow.gateway.providers.cohere import CohereAdapter +from mlflow.gateway.providers.utils import rename_payload_keys +from mlflow.gateway.schemas import completions + +AWS_BEDROCK_ANTHROPIC_MAXIMUM_MAX_TOKENS = 8191 + + +class AmazonBedrockAnthropicAdapter(AnthropicAdapter): + @classmethod + def chat_to_model(cls, payload, config): + payload = super().chat_to_model(payload, config) + # "model" keys are not supported in Bedrock" + payload.pop("model", None) + return payload + + @classmethod + def completions_to_model(cls, payload, config): + payload = super().completions_to_model(payload, config) + + if "\n\nHuman:" not in payload.get("stop_sequences", []): + payload.setdefault("stop_sequences", []).append("\n\nHuman:") + + payload["max_tokens_to_sample"] = min( + payload.get("max_tokens_to_sample", MLFLOW_AI_GATEWAY_ANTHROPIC_DEFAULT_MAX_TOKENS), + AWS_BEDROCK_ANTHROPIC_MAXIMUM_MAX_TOKENS, + ) + + # "model" keys are not supported in Bedrock" + payload.pop("model", None) + return payload + + @classmethod + def model_to_completions(cls, payload, config): + payload["model"] = config.model.name + return super().model_to_completions(payload, config) + + +class AWSTitanAdapter(ProviderAdapter): + # TODO handle top_p, top_k, etc. + @classmethod + def completions_to_model(cls, payload, config): + n = payload.pop("n", 1) + if n != 1: + raise AIGatewayException( + status_code=422, + detail=f"'n' must be '1' for AWS Titan models. Received value: '{n}'.", + ) + + # The range of Titan's temperature is 0-1, but ours is 0-2, so we halve it + if "temperature" in payload: + payload["temperature"] = 0.5 * payload["temperature"] + return { + "inputText": payload.pop("prompt"), + "textGenerationConfig": rename_payload_keys( + payload, {"max_tokens": "maxTokenCount", "stop": "stopSequences"} + ), + } + + @classmethod + def model_to_completions(cls, resp, config): + return completions.ResponsePayload( + created=int(time.time()), + object="text_completion", + model=config.model.name, + choices=[ + completions.Choice( + index=idx, + text=candidate.get("outputText"), + finish_reason=None, + ) + for idx, candidate in enumerate(resp.get("results", [])) + ], + usage=completions.CompletionsUsage( + prompt_tokens=None, + completion_tokens=None, + total_tokens=None, + ), + ) + + @classmethod + def embeddings_to_model(cls, payload, config): + raise NotImplementedError + + @classmethod + def model_to_embeddings(cls, resp, config): + raise NotImplementedError + + +class AI21Adapter(ProviderAdapter): + # TODO handle top_p, top_k, etc. + @classmethod + def completions_to_model(cls, payload, config): + return rename_payload_keys( + payload, + { + "stop": "stopSequences", + "n": "numResults", + "max_tokens": "maxTokens", + }, + ) + + @classmethod + def model_to_completions(cls, resp, config): + return completions.ResponsePayload( + created=int(time.time()), + object="text_completion", + model=config.model.name, + choices=[ + completions.Choice( + index=idx, + text=candidate.get("data", {}).get("text"), + finish_reason=None, + ) + for idx, candidate in enumerate(resp.get("completions", [])) + ], + usage=completions.CompletionsUsage( + prompt_tokens=None, + completion_tokens=None, + total_tokens=None, + ), + ) + + @classmethod + def embeddings_to_model(cls, payload, config): + raise NotImplementedError + + @classmethod + def model_to_embeddings(cls, resp, config): + raise NotImplementedError + + +class AmazonBedrockModelProvider(Enum): + AMAZON = "amazon" + COHERE = "cohere" + AI21 = "ai21" + ANTHROPIC = "anthropic" + + @property + def adapter_class(self) -> type[ProviderAdapter]: + return AWS_MODEL_PROVIDER_TO_ADAPTER.get(self) + + @classmethod + def of_str(cls, name: str): + name = name.lower() + + for opt in cls: + if opt.name.lower() in name or opt.value.lower() in name: + return opt + + +AWS_MODEL_PROVIDER_TO_ADAPTER = { + AmazonBedrockModelProvider.COHERE: CohereAdapter, + AmazonBedrockModelProvider.ANTHROPIC: AmazonBedrockAnthropicAdapter, + AmazonBedrockModelProvider.AMAZON: AWSTitanAdapter, + AmazonBedrockModelProvider.AI21: AI21Adapter, +} + + +class AmazonBedrockProvider(BaseProvider): + NAME = "Amazon Bedrock" + CONFIG_TYPE = AmazonBedrockConfig + + def __init__(self, config: RouteConfig): + super().__init__(config) + + if config.model.config is None or not isinstance(config.model.config, AmazonBedrockConfig): + raise TypeError(f"Invalid config type {config.model.config}") + self.bedrock_config: AmazonBedrockConfig = config.model.config + self._client = None + self._client_created = 0 + + def _client_expired(self): + if not isinstance(self.bedrock_config.aws_config, AWSRole): + return False + + return ( + (time.monotonic_ns() - self._client_created) + >= (self.bedrock_config.aws_config.session_length_seconds) * 1_000_000_000, + ) + + def get_bedrock_client(self): + import boto3 + import botocore.exceptions + + if self._client is not None and not self._client_expired(): + return self._client + + session = boto3.Session(**self._construct_session_args()) + + try: + self._client = session.client( + service_name="bedrock-runtime", **self._construct_client_args(session) + ) + self._client_created = time.monotonic_ns() + return self._client + except botocore.exceptions.UnknownServiceError as e: + raise AIGatewayConfigException( + "Cannot create Amazon Bedrock client; ensure boto3/botocore " + "linked from the Amazon Bedrock user guide are installed. " + "Otherwise likely missing credentials or accessing account without to " + "Amazon Bedrock Private Preview" + ) from e + + def _construct_session_args(self): + session_args = { + "region_name": self.bedrock_config.aws_config.aws_region, + } + + return {k: v for k, v in session_args.items() if v} + + def _construct_client_args(self, session): + aws_config = self.bedrock_config.aws_config + + if isinstance(aws_config, AWSRole): + role = session.client(service_name="sts").assume_role( + RoleArn=aws_config.aws_role_arn, + RoleSessionName="ai-gateway-bedrock", + DurationSeconds=aws_config.session_length_seconds, + ) + return { + "aws_access_key_id": role["Credentials"]["AccessKeyId"], + "aws_secret_access_key": role["Credentials"]["SecretAccessKey"], + "aws_session_token": role["Credentials"]["SessionToken"], + } + elif isinstance(aws_config, AWSIdAndKey): + return { + "aws_access_key_id": aws_config.aws_access_key_id, + "aws_secret_access_key": aws_config.aws_secret_access_key, + "aws_session_token": aws_config.aws_session_token, + } + else: + return {} + + @property + def _underlying_provider(self): + if (not self.config.model.name) or "." not in self.config.model.name: + return None + return AmazonBedrockModelProvider.of_str(self.config.model.name) + + @property + def adapter_class(self) -> type[ProviderAdapter]: + provider = self._underlying_provider + if not provider: + raise AIGatewayException( + status_code=422, + detail=f"Unknown Amazon Bedrock model type {self._underlying_provider}", + ) + adapter = provider.adapter_class + if not adapter: + raise AIGatewayException( + status_code=422, + detail=f"Don't know how to handle {self._underlying_provider} for Amazon Bedrock", + ) + return adapter + + def _request(self, body): + import botocore.exceptions + + try: + response = self.get_bedrock_client().invoke_model( + body=json.dumps(body).encode(), + modelId=self.config.model.name, + # defaults + # save=False, + accept="application/json", + contentType="application/json", + ) + return json.loads(response.get("body").read()) + + # TODO work though botocore.exceptions to make this catchable. + # except botocore.exceptions.ValidationException as e: + # raise HTTPException(status_code=422, detail=str(e)) from e + + except botocore.exceptions.ReadTimeoutError as e: + raise AIGatewayException(status_code=408) from e + + async def completions(self, payload: completions.RequestPayload) -> completions.ResponsePayload: + from fastapi.encoders import jsonable_encoder + + self.check_for_model_field(payload) + payload = jsonable_encoder(payload, exclude_none=True, exclude_defaults=True) + payload = self.adapter_class.completions_to_model(payload, self.config) + response = self._request(payload) + return self.adapter_class.model_to_completions(response, self.config) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gateway/providers/cohere.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gateway/providers/cohere.py new file mode 100644 index 0000000000000000000000000000000000000000..d4bc08c0560254cb57154a125d0b855258d4179e --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gateway/providers/cohere.py @@ -0,0 +1,457 @@ +import json +import time +from typing import Any, AsyncGenerator, AsyncIterable + +from mlflow.gateway.config import CohereConfig, RouteConfig +from mlflow.gateway.exceptions import AIGatewayException +from mlflow.gateway.providers.base import BaseProvider, ProviderAdapter +from mlflow.gateway.providers.utils import rename_payload_keys, send_request, send_stream_request +from mlflow.gateway.schemas import chat, completions, embeddings + + +class CohereAdapter(ProviderAdapter): + @staticmethod + def _scale_temperature(payload): + # The range of Cohere's temperature is 0-5, but ours is 0-2, so we scale it. + if temperature := payload.get("temperature"): + payload["temperature"] = 2.5 * temperature + return payload + + @classmethod + def model_to_completions(cls, resp, config): + # Response example (https://docs.cohere.com/reference/generate) + # ``` + # { + # "id": "string", + # "generations": [ + # { + # "id": "string", + # "text": "string" + # } + # ], + # "prompt": "string" + # } + # ``` + return completions.ResponsePayload( + created=int(time.time()), + object="text_completion", + model=config.model.name, + choices=[ + completions.Choice( + index=idx, + text=c["text"], + finish_reason=None, + ) + for idx, c in enumerate(resp["generations"]) + ], + usage=completions.CompletionsUsage( + prompt_tokens=None, + completion_tokens=None, + total_tokens=None, + ), + ) + + @classmethod + def model_to_completions_streaming(cls, resp, config): + # Response example (https://docs.cohere.com/reference/generate) + # + # Streaming chunks: + # ``` + # {"index":0,"text":" Hi","is_finished":false,"event_type":"text-generation"} + # ``` + # ``` + # {"index":1,"text":" Hi","is_finished":false,"event_type":"text-generation"} + # ``` + # notes: "index" is only present if "num_generations" > 1 + # + # Final chunk: + # ``` + # {"is_finished":true,"event_type":"stream-end","finish_reason":"COMPLETE", + # "response":{"id":"b32a70c5-8c91-4f96-958f-d942801ed22f", + # "generations":[ + # { + # "id":"5d5d0851-35ac-4c25-a9a9-2fbb391bd415", + # "index":0, + # "text":" Hi there! How can I assist you today? ", + # "finish_reason":"COMPLETE" + # }, + # { + # "id":"0a24787f-504e-470e-a088-0bf801a2c72d", + # "index":1, + # "text":" Hi there, how can I assist you today? ", + # "finish_reason":"COMPLETE" + # } + # ], + # "prompt":"Hello" + # }} + # ``` + response = resp.get("response") + return completions.StreamResponsePayload( + id=response["id"] if response else None, + created=int(time.time()), + model=config.model.name, + choices=[ + completions.StreamChoice( + index=resp.get("index", 0), + finish_reason=resp.get("finish_reason"), + text=resp.get("text"), + ) + ], + usage=completions.CompletionsUsage( + prompt_tokens=None, + completion_tokens=None, + total_tokens=None, + ), + ) + + @classmethod + def model_to_embeddings(cls, resp, config): + # Response example (https://docs.cohere.com/reference/embed): + # ``` + # { + # "id": "bc57846a-3e56-4327-8acc-588ca1a37b8a", + # "texts": [ + # "hello world" + # ], + # "embeddings": [ + # [ + # 3.25, + # 0.7685547, + # 2.65625, + # ... + # -0.30126953, + # -2.3554688, + # 1.2597656 + # ] + # ], + # "meta": [ + # { + # "api_version": [ + # { + # "version": "1" + # } + # ] + # } + # ] + # } + # ``` + return embeddings.ResponsePayload( + data=[ + embeddings.EmbeddingObject( + embedding=output, + index=idx, + ) + for idx, output in enumerate(resp["embeddings"]) + ], + model=config.model.name, + usage=embeddings.EmbeddingsUsage( + prompt_tokens=None, + total_tokens=None, + ), + ) + + @classmethod + def completions_to_model(cls, payload, config): + key_mapping = { + "stop": "stop_sequences", + "n": "num_generations", + } + cls.check_keys_against_mapping(key_mapping, payload) + payload = cls._scale_temperature(payload) + return rename_payload_keys(payload, key_mapping) + + @classmethod + def completions_streaming_to_model(cls, payload, config): + return cls.completions_to_model(payload, config) + + @classmethod + def embeddings_to_model(cls, payload, config): + key_mapping = {"input": "texts"} + for k1, k2 in key_mapping.items(): + if k2 in payload: + raise AIGatewayException( + status_code=422, detail=f"Invalid parameter {k2}. Use {k1} instead." + ) + return rename_payload_keys(payload, key_mapping) + + @classmethod + def chat_to_model(cls, payload, config): + if payload["n"] != 1: + raise AIGatewayException( + status_code=422, + detail=f"Parameter n must be 1 for Cohere chat, got {payload['n']}.", + ) + del payload["n"] + + if "stop" in payload: + raise AIGatewayException( + status_code=422, + detail="Parameter stop is not supported for Cohere chat.", + ) + payload = cls._scale_temperature(payload) + + messages = payload.pop("messages") + last_message = messages.pop() # pydantic enforces min_length=1 + if last_message["role"] != "user": + raise AIGatewayException( + status_code=422, + detail=f"Last message must be from user, got {last_message['role']}.", + ) + payload["message"] = last_message["content"] + + # Cohere uses `preamble_override` to set the system message + # we concatenate all system messages from the user with a newline + system_messages = [m for m in messages if m["role"] == "system"] + if len(system_messages) > 0: + payload["preamble_override"] = "\n".join(m["content"] for m in system_messages) + + # remaining messages are chat history + # we want to include only user and assistant messages + messages = [m for m in messages if m["role"] in ("user", "assistant")] + if messages: + payload["chat_history"] = [ + { + "role": "USER" if m["role"] == "user" else "CHATBOT", + "message": m["content"], + } + for m in messages + ] + return payload + + @classmethod + def chat_streaming_to_model(cls, payload, config): + return cls.chat_to_model(payload, config) + + @classmethod + def model_to_chat(cls, resp, config): + # Response example (https://docs.cohere.com/reference/chat) + # ``` + # { + # "response_id": "string", + # "text": "string", + # "generation_id": "string", + # "token_count": { + # "prompt_tokens": 0, + # "response_tokens": 0, + # "total_tokens": 0, + # "billed_tokens": 0 + # }, + # "meta": { + # "api_version": { + # "version": "1" + # }, + # "billed_units": { + # "input_tokens": 0, + # "output_tokens": 0 + # } + # }, + # "tool_inputs": null + # } + # ``` + return chat.ResponsePayload( + id=resp["response_id"], + object="chat.completion", + created=int(time.time()), + model=config.model.name, + choices=[ + chat.Choice( + index=0, + message=chat.ResponseMessage( + role="assistant", + content=resp["text"], + ), + finish_reason=None, + ), + ], + usage=chat.ChatUsage( + prompt_tokens=resp["token_count"]["prompt_tokens"], + completion_tokens=resp["token_count"]["response_tokens"], + total_tokens=resp["token_count"]["total_tokens"], + ), + ) + + @classmethod + def model_to_chat_streaming(cls, resp, config): + # Response example (https://docs.cohere.com/reference/chat) + # Streaming chunks: + # ``` + # { + # "is_finished":false, + # "event_type":"stream-start", + # "generation_id":"string" + # } + # {"is_finished":false,"event_type":"text-generation","text":"How"} + # {"is_finished":false,"event_type":"text-generation","text":" are"} + # {"is_finished":false,"event_type":"text-generation","text":" you"} + # { + # "is_finished":true, + # "event_type":"stream-end", + # "response":{ + # "response_id":"string", + # "text":"How are you", + # "generation_id":"string", + # "token_count":{ + # "prompt_tokens":83,"response_tokens":63,"total_tokens":146,"billed_tokens":128 + # }, + # "tool_inputs":null + # }, + # "finish_reason":"COMPLETE" + # } + # ``` + response = resp.get("response") + return chat.StreamResponsePayload( + # first chunk has "generation_id" but not "response_id" + id=response["response_id"] if response else None, + created=int(time.time()), + model=config.model.name, + choices=[ + chat.StreamChoice( + index=0, + finish_reason=resp.get("finish_reason"), + delta=chat.StreamDelta( + role=None, + content=resp.get("text"), + ), + ) + ], + usage=chat.ChatUsage( + prompt_tokens=response["token_count"]["prompt_tokens"] if response else None, + completion_tokens=response["token_count"]["response_tokens"] if response else None, + total_tokens=response["token_count"]["total_tokens"] if response else None, + ), + ) + + +class CohereProvider(BaseProvider): + NAME = "Cohere" + CONFIG_TYPE = CohereConfig + + def __init__(self, config: RouteConfig) -> None: + super().__init__(config) + if config.model.config is None or not isinstance(config.model.config, CohereConfig): + raise TypeError(f"Unexpected config type {config.model.config}") + self.cohere_config: CohereConfig = config.model.config + + @property + def headers(self) -> dict[str, str]: + return {"Authorization": f"Bearer {self.cohere_config.cohere_api_key}"} + + @property + def base_url(self) -> str: + return "https://api.cohere.ai/v1" + + @property + def adapter_class(self) -> type[ProviderAdapter]: + return CohereAdapter + + def get_endpoint_url(self, route_type: str) -> str: + if route_type == "llm/v1/chat": + return f"{self.base_url}/chat" + elif route_type == "llm/v1/completions": + return f"{self.base_url}/generate" + elif route_type == "llm/v1/embeddings": + return f"{self.base_url}/embed" + else: + raise ValueError(f"Invalid route type {route_type}") + + async def _request(self, path: str, payload: dict[str, Any]) -> dict[str, Any]: + return await send_request( + headers=self.headers, + base_url=self.base_url, + path=path, + payload=payload, + ) + + def _stream_request(self, path: str, payload: dict[str, Any]) -> AsyncGenerator[bytes, None]: + return send_stream_request( + headers=self.headers, + base_url=self.base_url, + path=path, + payload=payload, + ) + + async def chat_stream( + self, payload: chat.RequestPayload + ) -> AsyncIterable[chat.StreamResponsePayload]: + from fastapi.encoders import jsonable_encoder + + payload = jsonable_encoder(payload, exclude_none=True) + self.check_for_model_field(payload) + stream = self._stream_request( + "chat", + { + "model": self.config.model.name, + **CohereAdapter.chat_streaming_to_model(payload, self.config), + }, + ) + async for chunk in stream: + if not chunk: + continue + + resp = json.loads(chunk) + if resp["event_type"] == "stream-start": + continue + yield CohereAdapter.model_to_chat_streaming(resp, self.config) + + async def chat(self, payload: chat.RequestPayload) -> chat.ResponsePayload: + from fastapi.encoders import jsonable_encoder + + payload = jsonable_encoder(payload, exclude_none=True) + self.check_for_model_field(payload) + resp = await self._request( + "chat", + { + "model": self.config.model.name, + **CohereAdapter.chat_to_model(payload, self.config), + }, + ) + return CohereAdapter.model_to_chat(resp, self.config) + + async def completions_stream( + self, payload: completions.RequestPayload + ) -> AsyncIterable[completions.StreamResponsePayload]: + from fastapi.encoders import jsonable_encoder + + payload = jsonable_encoder(payload, exclude_none=True) + self.check_for_model_field(payload) + stream = self._stream_request( + "generate", + { + "model": self.config.model.name, + **CohereAdapter.completions_streaming_to_model(payload, self.config), + }, + ) + async for chunk in stream: + if not chunk: + continue + + resp = json.loads(chunk) + yield CohereAdapter.model_to_completions_streaming(resp, self.config) + + async def completions(self, payload: completions.RequestPayload) -> completions.ResponsePayload: + from fastapi.encoders import jsonable_encoder + + payload = jsonable_encoder(payload, exclude_none=True) + self.check_for_model_field(payload) + resp = await self._request( + "generate", + { + "model": self.config.model.name, + **CohereAdapter.completions_to_model(payload, self.config), + }, + ) + return CohereAdapter.model_to_completions(resp, self.config) + + async def embeddings(self, payload: embeddings.RequestPayload) -> embeddings.ResponsePayload: + from fastapi.encoders import jsonable_encoder + + payload = jsonable_encoder(payload, exclude_none=True) + self.check_for_model_field(payload) + resp = await self._request( + "embed", + { + "model": self.config.model.name, + **CohereAdapter.embeddings_to_model(payload, self.config), + }, + ) + return CohereAdapter.model_to_embeddings(resp, self.config) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gateway/providers/gemini.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gateway/providers/gemini.py new file mode 100644 index 0000000000000000000000000000000000000000..dff23d8d1c241b3dea9b3d8ec15b05b78992d931 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gateway/providers/gemini.py @@ -0,0 +1,430 @@ +import time +from typing import Any + +from mlflow.gateway.config import GeminiConfig, RouteConfig +from mlflow.gateway.exceptions import AIGatewayException +from mlflow.gateway.providers.base import BaseProvider, ProviderAdapter +from mlflow.gateway.providers.utils import rename_payload_keys, send_request +from mlflow.gateway.schemas import chat, completions, embeddings + +GENERATION_CONFIG_KEY_MAPPING = { + "stop": "stopSequences", + "n": "candidateCount", + "max_tokens": "maxOutputTokens", + "top_k": "topK", + "top_p": "topP", +} + +GENERATION_CONFIGS = [ + "temperature", + "stopSequences", + "candidateCount", + "maxOutputTokens", + "topK", + "topP", +] + + +class GeminiAdapter(ProviderAdapter): + @classmethod + def chat_to_model(cls, payload, config): + # Documentation: https://ai.google.dev/api/generate-content + # Example payload for the chat API. + # + # { + # "contents": [ + # { + # "role": "user", + # "parts": [ + # { + # "text": "System: You are an advanced AI Assistant" + # } + # ] + # }, + # { + # "role": "user", + # "parts": [ + # { + # "text": "Please give the code for addition of two numbers" + # } + # ] + # } + # ], + # "generationConfig": { + # "temperature": 0.1, + # "topP": 1, + # "stopSequences": ["\n"], + # "candidateCount": 1, + # "maxOutputTokens": 100, + # "topK": 40 + # } + # } + + for k1, k2 in GENERATION_CONFIG_KEY_MAPPING.items(): + if k2 in payload: + raise AIGatewayException( + status_code=422, detail=f"Invalid parameter {k2}. Use {k1} instead." + ) + + if "top_p" in payload and payload["top_p"] > 1: + raise AIGatewayException( + status_code=422, detail="top_p should be less than or equal to 1" + ) + + payload = rename_payload_keys(payload, GENERATION_CONFIG_KEY_MAPPING) + + contents = [] + system_message = None + for message in payload["messages"]: + role = message["role"] + + if role == "assistant": + role = "model" + elif role == "system": + if system_message is None: + system_message = {"parts": []} + system_message["parts"].append({"text": message["content"]}) + continue + + contents.append({"role": role, "parts": [{"text": message["content"]}]}) + + gemini_payload = {"contents": contents} + + if system_message: + gemini_payload["system_instruction"] = system_message + + generation_config = {k: v for k, v in payload.items() if k in GENERATION_CONFIGS} + + if generation_config: + gemini_payload["generationConfig"] = generation_config + + return gemini_payload + + @classmethod + def model_to_chat(cls, resp, config): + # Documentation: https://ai.google.dev/api/generate-content + # + # Example Response: + # { + # "candidates": [ + # { + # "content": { + # "parts": [ + # { + # "text": "Blue is often seen as a calming and soothing color." + # } + # ] + # }, + # "finishReason": "stop" + # } + # ], + # "usageMetadata": { + # "promptTokenCount": 10, + # "candidatesTokenCount": 10, + # "totalTokenCount": 20 + # } + # } + choices = [] + for idx, candidate in enumerate(resp.get("candidates", [])): + content = "" + if parts := candidate.get("content", {}).get("parts", None): + content = parts[0].get("text", None) + if not content: + continue + + finish_reason = candidate.get("finishReason", "stop") + if finish_reason == "MAX_TOKENS": + finish_reason = "length" + + choices.append( + chat.Choice( + index=idx, + message=chat.ResponseMessage( + role="assistant", + content=content, + ), + finish_reason=finish_reason, + ) + ) + + usage_metadata = resp.get("usageMetadata", {}) + + return chat.ResponsePayload( + id=f"gemini-chat-{int(time.time())}", + created=int(time.time()), + object="chat.completion", + model=config.model.name, + choices=choices, + usage=chat.ChatUsage( + prompt_tokens=usage_metadata.get("promptTokenCount", None), + completion_tokens=usage_metadata.get("candidatesTokenCount", None), + total_tokens=usage_metadata.get("totalTokenCount", None), + ), + ) + + @classmethod + def completions_to_model(cls, payload, config): + # Documentation: https://ai.google.dev/api/generate-content + # Example payload for the completions API. + # + # { + # "prompt": "What is the world record for flapjack consumption in a single sitting?", + # "temperature": 0.1, + # "topP": 1, + # "stop": ["\n"], + # "n": 1, + # "max_tokens": 100, + # "top_k": 40, + # } + + chat_payload = {"messages": [{"role": "user", "content": payload.pop("prompt")}], **payload} + if system_message := payload.pop("system_prompt", None): + chat_payload["messages"].insert(0, {"role": "system", "content": system_message}) + return cls.chat_to_model(chat_payload, config) + + @classmethod + def model_to_completions(cls, resp, config): + # Documentation: https://ai.google.dev/api/generate-content + # + # Example Response: + # { + # "candidates": [ + # { + # "content": { + # "parts": [ + # { + # "text": "Blue is often seen as a calming and soothing color." + # } + # ] + # }, + # "finishReason": "stop" + # } + # ], + # "usageMetadata": { + # "promptTokenCount": 10, + # "candidatesTokenCount": 10, + # "totalTokenCount": 20 + # } + # } + choices = [] + + for idx, candidate in enumerate(resp.get("candidates", [])): + text = "" + if parts := candidate.get("content", {}).get("parts", None): + text = parts[0].get("text", None) + if not text: + continue + + finish_reason = candidate.get("finishReason", "stop") + if finish_reason == "MAX_TOKENS": + finish_reason = "length" + + choices.append( + completions.Choice( + index=idx, + text=text, + finish_reason=finish_reason, + ) + ) + + usage_metadata = resp.get("usageMetadata", {}) + + return completions.ResponsePayload( + created=int(time.time()), + object="text_completion", + model=config.model.name, + choices=choices, + usage=completions.CompletionsUsage( + prompt_tokens=usage_metadata.get("promptTokenCount", None), + completion_tokens=usage_metadata.get("candidatesTokenCount", None), + total_tokens=usage_metadata.get("totalTokenCount", None), + ), + ) + + @classmethod + def embeddings_to_model(cls, payload, config): + # Example payload for the embedding API. + # Documentation: https://ai.google.dev/api/embeddings#v1beta.ContentEmbedding + # + # { + # "requests": [ + # { + # "model": "models/text-embedding-004", + # "content": { + # "parts": [ + # { + # "text": "What is the meaning of life?" + # } + # ] + # } + # }, + # { + # "model": "models/text-embedding-004", + # "content": { + # "parts": [ + # { + # "text": "How much wood would a woodchuck chuck?" + # } + # ] + # } + # }, + # { + # "model": "models/text-embedding-004", + # "content": { + # "parts": [ + # { + # "text": "How does the brain work?" + # } + # ] + # } + # } + # ] + # } + + texts = payload["input"] + if isinstance(texts, str): + texts = [texts] + return ( + {"content": {"parts": [{"text": texts[0]}]}} + if len(texts) == 1 + else { + "requests": [ + {"model": f"models/{config.model.name}", "content": {"parts": [{"text": text}]}} + for text in texts + ] + } + ) + + @classmethod + def model_to_embeddings(cls, resp, config): + # Documentation: https://ai.google.dev/api/embeddings#v1beta.ContentEmbedding + # + # Example Response: + # { + # "embeddings": [ + # { + # "values": [ + # 3.25, + # 0.7685547, + # 2.65625, + # ..., + # -0.30126953, + # -2.3554688, + # 1.2597656 + # ] + # } + # ] + # } + + data = [ + embeddings.EmbeddingObject(embedding=item.get("values", []), index=i) + for i, item in enumerate(resp.get("embeddings") or [resp.get("embedding", {})]) + ] + + # Create and return response payload directly + return embeddings.ResponsePayload( + data=data, + model=config.model.name, + usage=embeddings.EmbeddingsUsage( + prompt_tokens=None, + total_tokens=None, + ), + ) + + +class GeminiProvider(BaseProvider): + NAME = "Gemini" + CONFIG_TYPE = GeminiConfig + + def __init__(self, config: RouteConfig) -> None: + super().__init__(config) + if config.model.config is None or not isinstance(config.model.config, GeminiConfig): + raise TypeError(f"Unexpected config type {config.model.config}") + self.gemini_config: GeminiConfig = config.model.config + + @property + def headers(self): + return {"x-goog-api-key": self.gemini_config.gemini_api_key} + + @property + def base_url(self): + return "https://generativelanguage.googleapis.com/v1beta/models" + + @property + def adapter_class(self): + return GeminiAdapter + + async def _request(self, path: str, payload: dict[str, Any]) -> dict[str, Any]: + return await send_request( + headers=self.headers, + base_url=self.base_url, + path=path, + payload=payload, + ) + + async def embeddings(self, payload: embeddings.RequestPayload) -> embeddings.ResponsePayload: + from fastapi.encoders import jsonable_encoder + + payload = jsonable_encoder(payload, exclude_none=True) + self.check_for_model_field(payload) + + embedding_payload = self.adapter_class.embeddings_to_model(payload, self.config) + # Documentation: https://ai.google.dev/api/embeddings + + # Use the batch endpoint if payload contains "requests" + if "requests" in embedding_payload: + endpoint_suffix = ":batchEmbedContents" + else: + endpoint_suffix = ":embedContent" + + resp = await self._request( + f"{self.config.model.name}{endpoint_suffix}", + embedding_payload, + ) + return self.adapter_class.model_to_embeddings(resp, self.config) + + async def completions(self, payload: completions.RequestPayload) -> completions.ResponsePayload: + from fastapi.encoders import jsonable_encoder + + payload = jsonable_encoder(payload, exclude_none=True) + self.check_for_model_field(payload) + + completions_payload = self.adapter_class.completions_to_model(payload, self.config) + # Documentation: https://ai.google.dev/api/generate-content + + if payload.get("stream", False): + # TODO: Implement streaming for completions + raise AIGatewayException( + status_code=422, + detail="Streaming is not yet supported for completions with Gemini AI Gateway", + ) + + resp = await self._request( + f"{self.config.model.name}:generateContent", + completions_payload, + ) + + return self.adapter_class.model_to_completions(resp, self.config) + + async def chat(self, payload: chat.RequestPayload) -> chat.ResponsePayload: + from fastapi.encoders import jsonable_encoder + + payload = jsonable_encoder(payload, exclude_none=True) + self.check_for_model_field(payload) + + chat_payload = self.adapter_class.chat_to_model(payload, self.config) + # Documentation: https://ai.google.dev/api/generate-content + + if payload.get("stream", False): + # TODO: Implement streaming for chat completions + raise AIGatewayException( + status_code=422, + detail="Streaming is not yet supported for chat completions with Gemini AI Gateway", + ) + + resp = await self._request( + f"{self.config.model.name}:generateContent", + chat_payload, + ) + + return self.adapter_class.model_to_chat(resp, self.config) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gateway/providers/huggingface.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gateway/providers/huggingface.py new file mode 100644 index 0000000000000000000000000000000000000000..d931e0368a742d3cc3528f4b850f63b048ef00f5 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gateway/providers/huggingface.py @@ -0,0 +1,115 @@ +import time +from typing import Any + +from mlflow.gateway.config import HuggingFaceTextGenerationInferenceConfig, RouteConfig +from mlflow.gateway.exceptions import AIGatewayException +from mlflow.gateway.providers.base import BaseProvider +from mlflow.gateway.providers.utils import ( + rename_payload_keys, + send_request, +) +from mlflow.gateway.schemas import completions + + +class HFTextGenerationInferenceServerProvider(BaseProvider): + NAME = "Hugging Face Text Generation Inference" + CONFIG_TYPE = HuggingFaceTextGenerationInferenceConfig + + def __init__(self, config: RouteConfig) -> None: + super().__init__(config) + if config.model.config is None or not isinstance( + config.model.config, HuggingFaceTextGenerationInferenceConfig + ): + raise TypeError(f"Unexpected config type {config.model.config}") + self.huggingface_config: HuggingFaceTextGenerationInferenceConfig = config.model.config + self.headers = {"Content-Type": "application/json"} + + async def _request(self, path: str, payload: dict[str, Any]) -> dict[str, Any]: + return await send_request( + headers=self.headers, + base_url=self.huggingface_config.hf_server_url, + path=path, + payload=payload, + ) + + async def completions(self, payload: completions.RequestPayload) -> completions.ResponsePayload: + from fastapi.encoders import jsonable_encoder + + payload = jsonable_encoder(payload, exclude_none=True) + self.check_for_model_field(payload) + key_mapping = { + "max_tokens": "max_new_tokens", + } + for k1, k2 in key_mapping.items(): + if k2 in payload: + raise AIGatewayException( + status_code=422, detail=f"Invalid parameter {k2}. Use {k1} instead." + ) + + # HF TGI does not support generating multiple candidates. + n = payload.pop("n", 1) + if n != 1: + raise AIGatewayException( + status_code=422, + detail="'n' must be '1' for the Text Generation Inference provider." + f"Received value: '{n}'.", + ) + prompt = payload.pop("prompt") + parameters = rename_payload_keys(payload, key_mapping) + + # The range of HF TGI's temperature is 0-100, but ours is 0-2, so we multiply + # by 50 + payload["temperature"] = 50 * payload["temperature"] + # HF TGI does not support 0 temperature + parameters["temperature"] = max(payload["temperature"], 1e-3) + parameters["details"] = True + parameters["decoder_input_details"] = True + + final_payload = {"inputs": prompt, "parameters": parameters} + + resp = await self._request( + "generate", + final_payload, + ) + + # Example Response: + # Documentation: https://huggingface.github.io/text-generation-inference/#/Text%20Generation%20Inference/compat_generate + # {'details': {'best_of_sequences': [{'finish_reason': 'length', + # 'generated_text': 'test', + # 'generated_tokens': 1, + # 'prefill': [{'id': 0, 'logprob': -0.34, 'text': 'test'}], + # 'seed': 42, + # 'tokens': [{'id': 0, 'logprob': -0.34, 'special': False, 'text': 'test'}], + # 'top_tokens': [[{'id': 0, + # 'logprob': -0.34, + # 'special': False, + # 'text': 'test'}]]}], + # 'finish_reason': 'length', + # 'generated_tokens': 1, + # 'prefill': [{'id': 0, 'logprob': -0.34, 'text': 'test'}], + # 'seed': 42, + # 'tokens': [{'id': 0, 'logprob': -0.34, 'special': False, 'text': 'test'}], + # 'top_tokens': [[{'id': 0, + # 'logprob': -0.34, + # 'special': False, + # 'text': 'test'}]]}, + # 'generated_text': 'test'} + output_tokens = resp["details"]["generated_tokens"] + input_tokens = len(resp["details"]["prefill"]) + return completions.ResponsePayload( + created=int(time.time()), + object="text_completion", + model=self.config.model.name, + choices=[ + completions.Choice( + index=0, + text=resp["generated_text"], + finish_reason=resp["details"]["finish_reason"], + ) + ], + usage=completions.CompletionsUsage( + prompt_tokens=input_tokens, + completion_tokens=output_tokens, + total_tokens=input_tokens + output_tokens, + ), + ) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gateway/providers/mistral.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gateway/providers/mistral.py new file mode 100644 index 0000000000000000000000000000000000000000..97fa403118421d9bb90de355cc4e42d133253984 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gateway/providers/mistral.py @@ -0,0 +1,206 @@ +import time +from typing import Any + +from mlflow.gateway.config import MistralConfig, RouteConfig +from mlflow.gateway.providers.base import BaseProvider, ProviderAdapter +from mlflow.gateway.providers.utils import send_request +from mlflow.gateway.schemas import chat, completions, embeddings + + +class MistralAdapter(ProviderAdapter): + @classmethod + def model_to_completions(cls, resp, config): + # Response example (https://docs.mistral.ai/api/#operation/createChatCompletion) + # ``` + # { + # "id": "string", + # "object": "string", + # "created": "integer", + # "model": "string", + # "choices": [ + # { + # "index": "integer", + # "message": { + # "role": "string", + # "content": "string" + # }, + # "finish_reason": "string", + # } + # ], + # "usage": + # { + # "prompt_tokens": "integer", + # "completion_tokens": "integer", + # "total_tokens": "integer", + # } + # } + # ``` + return completions.ResponsePayload( + created=int(time.time()), + object="text_completion", + model=config.model.name, + choices=[ + completions.Choice( + index=idx, + text=c["message"]["content"], + finish_reason=c["finish_reason"], + ) + for idx, c in enumerate(resp["choices"]) + ], + usage=completions.CompletionsUsage( + prompt_tokens=resp["usage"]["prompt_tokens"], + completion_tokens=resp["usage"]["completion_tokens"], + total_tokens=resp["usage"]["total_tokens"], + ), + ) + + @classmethod + def model_to_chat(cls, resp, config): + # Response example (https://docs.mistral.ai/api/#operation/createChatCompletion) + return chat.ResponsePayload( + id=resp["id"], + object=resp["object"], + created=resp["created"], + model=resp["model"], + choices=[ + chat.Choice( + index=idx, + message=chat.ResponseMessage( + role=c["message"]["role"], + content=c["message"].get("content"), + tool_calls=( + (calls := c["message"].get("tool_calls")) + and [chat.ToolCall(**c) for c in calls] + ), + ), + finish_reason=c.get("finish_reason"), + ) + for idx, c in enumerate(resp["choices"]) + ], + usage=chat.ChatUsage( + prompt_tokens=resp["usage"]["prompt_tokens"], + completion_tokens=resp["usage"]["completion_tokens"], + total_tokens=resp["usage"]["total_tokens"], + ), + ) + + @classmethod + def model_to_embeddings(cls, resp, config): + # Response example (https://docs.mistral.ai/api/#operation/createEmbedding): + # ``` + # { + # "id": "string", + # "object": "string", + # "data": [ + # { + # "object": "string", + # "embedding": + # [ + # float, + # float + # ] + # "index": "integer", + # } + # ], + # "model": "string", + # "usage": + # { + # "prompt_tokens": "integer", + # "total_tokens": "integer", + # } + # } + # ``` + return embeddings.ResponsePayload( + data=[ + embeddings.EmbeddingObject( + embedding=data["embedding"], + index=data["index"], + ) + for data in resp["data"] + ], + model=config.model.name, + usage=embeddings.EmbeddingsUsage( + prompt_tokens=resp["usage"]["prompt_tokens"], + total_tokens=resp["usage"]["total_tokens"], + ), + ) + + @classmethod + def completions_to_model(cls, payload, config): + payload["model"] = config.model.name + payload.pop("stop", None) + payload.pop("n", None) + payload["messages"] = [{"role": "user", "content": payload.pop("prompt")}] + + # The range of Mistral's temperature is 0-1, but ours is 0-2, so we scale it. + if "temperature" in payload: + payload["temperature"] = 0.5 * payload["temperature"] + + return payload + + @classmethod + def chat_to_model(cls, payload, config): + return {"model": config.model.name, **payload} + + @classmethod + def embeddings_to_model(cls, payload, config): + return {"model": config.model.name, **payload} + + +class MistralProvider(BaseProvider): + NAME = "Mistral" + CONFIG_TYPE = MistralConfig + + def __init__(self, config: RouteConfig) -> None: + super().__init__(config) + if config.model.config is None or not isinstance(config.model.config, MistralConfig): + raise TypeError(f"Unexpected config type {config.model.config}") + self.mistral_config: MistralConfig = config.model.config + + @property + def headers(self) -> dict[str, str]: + return {"Authorization": f"Bearer {self.mistral_config.mistral_api_key}"} + + @property + def base_url(self) -> str: + return "https://api.mistral.ai/v1" + + @property + def adapter_class(self) -> type[ProviderAdapter]: + return MistralAdapter + + def get_endpoint_url(self, route_type: str) -> str: + if route_type == "llm/v1/chat": + return f"{self.base_url}/chat/completions" + else: + raise ValueError(f"Invalid route type {route_type}") + + async def _request(self, path: str, payload: dict[str, Any]) -> dict[str, Any]: + return await send_request( + headers=self.headers, + base_url=self.base_url, + path=path, + payload=payload, + ) + + async def completions(self, payload: completions.RequestPayload) -> completions.ResponsePayload: + from fastapi.encoders import jsonable_encoder + + payload = jsonable_encoder(payload, exclude_none=True) + self.check_for_model_field(payload) + resp = await self._request( + "chat/completions", + MistralAdapter.completions_to_model(payload, self.config), + ) + return MistralAdapter.model_to_completions(resp, self.config) + + async def embeddings(self, payload: embeddings.RequestPayload) -> embeddings.ResponsePayload: + from fastapi.encoders import jsonable_encoder + + payload = jsonable_encoder(payload, exclude_none=True) + self.check_for_model_field(payload) + resp = await self._request( + "embeddings", + MistralAdapter.embeddings_to_model(payload, self.config), + ) + return MistralAdapter.model_to_embeddings(resp, self.config) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gateway/providers/mlflow.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gateway/providers/mlflow.py new file mode 100644 index 0000000000000000000000000000000000000000..c0b22d410b876b6fb4be297ddcc206b8ee6173a3 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gateway/providers/mlflow.py @@ -0,0 +1,238 @@ +import time + +from pydantic import BaseModel, StrictFloat, StrictStr, ValidationError + +from mlflow.gateway.config import MlflowModelServingConfig, RouteConfig +from mlflow.gateway.constants import MLFLOW_SERVING_RESPONSE_KEY +from mlflow.gateway.exceptions import AIGatewayException +from mlflow.gateway.providers.base import BaseProvider +from mlflow.gateway.providers.utils import send_request +from mlflow.gateway.schemas import chat, completions, embeddings +from mlflow.utils.pydantic_utils import field_validator + + +class ServingTextResponse(BaseModel): + predictions: list[StrictStr] + + @field_validator("predictions", mode="before") + def extract_choices(cls, predictions): + if isinstance(predictions, list) and not predictions: + raise ValueError("The input list is empty") + if isinstance(predictions, dict): + if "choices" not in predictions and len(predictions) > 1: + raise ValueError( + "The dict format is invalid for this route type. Ensure the served model " + "returns a dict key containing 'choices'" + ) + if len(predictions) == 1: + predictions = next(iter(predictions.values())) + else: + predictions = predictions.get("choices", predictions) + if not predictions: + raise ValueError("The input list is empty") + return predictions + + +class EmbeddingsResponse(BaseModel): + predictions: list[list[StrictFloat]] + + @field_validator("predictions", mode="before") + def validate_predictions(cls, predictions): + if isinstance(predictions, list) and not predictions: + raise ValueError("The input list is empty") + if isinstance(predictions, list) and all( + isinstance(item, list) and not item for item in predictions + ): + raise ValueError("One or more lists in the returned prediction response are empty") + elif all(isinstance(item, float) for item in predictions): + return [predictions] + else: + return predictions + + +class MlflowModelServingProvider(BaseProvider): + NAME = "MLflow Model Serving" + CONFIG_TYPE = MlflowModelServingConfig + + def __init__(self, config: RouteConfig) -> None: + super().__init__(config) + if config.model.config is None or not isinstance( + config.model.config, MlflowModelServingConfig + ): + raise TypeError(f"Invalid config type {config.model.config}") + self.mlflow_config: MlflowModelServingConfig = config.model.config + self.headers = {"Content-Type": "application/json"} + + @staticmethod + def _extract_mlflow_response_key(response): + if MLFLOW_SERVING_RESPONSE_KEY not in response: + raise AIGatewayException( + status_code=502, + detail=f"The response is missing the required key: {MLFLOW_SERVING_RESPONSE_KEY}.", + ) + return response[MLFLOW_SERVING_RESPONSE_KEY] + + @staticmethod + def _process_payload(payload, key): + from fastapi.encoders import jsonable_encoder + + payload = jsonable_encoder(payload, exclude_none=True) + + input_data = payload.pop(key, None) + request_payload = {"inputs": input_data if isinstance(input_data, list) else [input_data]} + + if payload: + request_payload["params"] = payload + + return request_payload + + @staticmethod + def _process_completions_response_for_mlflow_serving(response): + try: + validated_response = ServingTextResponse(**response) + inference_data = validated_response.predictions + except ValidationError as e: + raise AIGatewayException(status_code=502, detail=str(e)) + + return [ + completions.Choice(index=idx, text=entry, finish_reason=None) + for idx, entry in enumerate(inference_data) + ] + + async def completions(self, payload: completions.RequestPayload) -> completions.ResponsePayload: + # Example request to MLflow REST API server for completions: + # { + # "inputs": ["hi", "hello", "bye"], + # "params": { + # "temperature": 0.5, + # "top_k": 3, + # } + # } + + resp = await send_request( + headers=self.headers, + base_url=self.mlflow_config.model_server_url, + path="invocations", + payload=self._process_payload(payload, "prompt"), + ) + + # Example response: + # {"predictions": ["hello", "hi", "goodbye"]} + + return completions.ResponsePayload( + created=int(time.time()), + object="text_completion", + model=self.config.model.name, + choices=self._process_completions_response_for_mlflow_serving(resp), + usage=completions.CompletionsUsage( + prompt_tokens=None, + completion_tokens=None, + total_tokens=None, + ), + ) + + def _process_chat_response_for_mlflow_serving(self, response): + try: + validated_response = ServingTextResponse(**response) + inference_data = validated_response.predictions + except ValidationError as e: + raise AIGatewayException(status_code=502, detail=str(e)) + + return [ + {"message": {"role": "assistant", "content": entry}, "metadata": {}} + for entry in inference_data + ] + + async def chat(self, payload: chat.RequestPayload) -> chat.ResponsePayload: + # Example request to MLflow REST API for chat: + # { + # "inputs": ["question"], + # "params": ["temperature": 0.2], + # } + + payload = self._process_payload(payload, "messages") + + query_count = len(payload["inputs"]) + if query_count > 1: + raise AIGatewayException( + status_code=422, + detail="MLflow chat models are only capable of processing a single query at a " + f"time. The request submitted consists of {query_count} queries.", + ) + + payload["inputs"] = [payload["inputs"][0]["content"]] + + resp = await send_request( + headers=self.headers, + base_url=self.mlflow_config.model_server_url, + path="invocations", + payload=payload, + ) + + # Example response: + # {"predictions": ["answer"]} + + return chat.ResponsePayload( + created=int(time.time()), + model=self.config.model.name, + choices=[ + chat.Choice( + index=idx, + message=chat.ResponseMessage( + role=c["message"]["role"], content=c["message"]["content"] + ), + finish_reason=None, + ) + for idx, c in enumerate(self._process_chat_response_for_mlflow_serving(resp)) + ], + usage=chat.ChatUsage( + prompt_tokens=None, + completion_tokens=None, + total_tokens=None, + ), + ) + + def _process_embeddings_response_for_mlflow_serving(self, response): + try: + validated_response = EmbeddingsResponse(**response) + inference_data = validated_response.predictions + except ValidationError as e: + raise AIGatewayException(status_code=502, detail=str(e)) + + return inference_data + + async def embeddings(self, payload: embeddings.RequestPayload) -> embeddings.ResponsePayload: + # Example request to MLflow REST API server for embeddings: + # { + # "inputs": ["a sentence", "another sentence"], + # "params": { + # "output_value": "token_embeddings", + # } + # } + + resp = await send_request( + headers=self.headers, + base_url=self.mlflow_config.model_server_url, + path="invocations", + payload=self._process_payload(payload, "input"), + ) + + # Example response: + # {"predictions": [[0.100, -0.234, 0.002, ...], [0.222, -0.111, 0.134, ...]]} + + return embeddings.ResponsePayload( + data=[ + embeddings.EmbeddingObject( + embedding=embedding, + index=idx, + ) + for idx, embedding in enumerate( + self._process_embeddings_response_for_mlflow_serving(resp) + ) + ], + model=self.config.model.name, + usage=embeddings.EmbeddingsUsage( + prompt_tokens=None, + total_tokens=None, + ), + ) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gateway/providers/mosaicml.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gateway/providers/mosaicml.py new file mode 100644 index 0000000000000000000000000000000000000000..fd089800301cf3ec0d41e5846ad4410eb5bfafeb --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gateway/providers/mosaicml.py @@ -0,0 +1,301 @@ +import time +from contextlib import contextmanager +from typing import Any + +from mlflow.exceptions import MlflowException +from mlflow.gateway.config import MosaicMLConfig, RouteConfig +from mlflow.gateway.exceptions import AIGatewayException +from mlflow.gateway.providers.base import BaseProvider +from mlflow.gateway.providers.utils import rename_payload_keys, send_request +from mlflow.gateway.schemas import chat, completions, embeddings + + +class MosaicMLProvider(BaseProvider): + NAME = "MosaicML" + CONFIG_TYPE = MosaicMLConfig + + def __init__(self, config: RouteConfig) -> None: + super().__init__(config) + if config.model.config is None or not isinstance(config.model.config, MosaicMLConfig): + raise TypeError(f"Unexpected config type {config.model.config}") + self.mosaicml_config: MosaicMLConfig = config.model.config + + async def _request(self, model: str, payload: dict[str, Any]) -> dict[str, Any]: + headers = {"Authorization": f"{self.mosaicml_config.mosaicml_api_key}"} + return await send_request( + headers=headers, + base_url=self.mosaicml_config.mosaicml_api_base + or "https://models.hosted-on.mosaicml.hosting", + path=model + "/v1/predict", + payload=payload, + ) + + # NB: as this parser performs no blocking operations, we are intentionally not defining it + # as async due to the overhead of spawning an additional thread if we did. + @staticmethod + def _parse_chat_messages_to_prompt(messages: list[chat.RequestMessage]) -> str: + """ + This parser is based on the format described in + https://huggingface.co/blog/llama2#how-to-prompt-llama-2 . + The expected format is: + "[INST] <> + {{ system_prompt }} + <> + + {{ user_msg_1 }} [/INST] {{ model_answer_1 }} + [INST] {{ user_msg_2 }} [/INST]" + """ + prompt = "" # Always start with an opening tag + for m in messages: + if m.role == "system" or m.role == "user": + inst = m.content + + # Wrap system messages in <> tags + if m.role == "system": + inst = f"<> {inst} <>" + + # Close the [INST] tag + inst += " [/INST]" + + # If the previous message was a system/user message, + # remove previous closing [/INST] tag + if prompt.endswith("[/INST]"): + prompt = prompt[:-7] + # Otherwise, add an opening [INST] tag + else: + inst = f"[INST] {inst}" + prompt += inst + elif m.role == "assistant": + # Add statement closing/opening tags by default + prompt += f" {m.content} " + else: + raise MlflowException.invalid_parameter_value( + f"Invalid role {m.role} inputted. Must be one of 'system', " + "'user', or 'assistant'.", + ) + + # Remove the last tags if they exist to allow for + # assistant completion prompts. + if prompt.endswith(""): + prompt = prompt[:-7] + return prompt + + async def chat(self, payload: chat.RequestPayload) -> chat.ResponsePayload: + from fastapi.encoders import jsonable_encoder + + # Extract the List[RequestMessage] from the RequestPayload + messages = payload.messages + payload = jsonable_encoder(payload, exclude_none=True) + # remove the messages from the remaining configuration items + payload.pop("messages", None) + self.check_for_model_field(payload) + key_mapping = { + "max_tokens": "max_new_tokens", + } + for k1, k2 in key_mapping.items(): + if k2 in payload: + raise AIGatewayException( + status_code=422, detail=f"Invalid parameter {k2}. Use {k1} instead." + ) + payload = rename_payload_keys(payload, key_mapping) + + # Handle 'prompt' field in payload + try: + prompt = [self._parse_chat_messages_to_prompt(messages)] + except MlflowException as e: + raise AIGatewayException( + status_code=422, detail=f"An invalid request structure was submitted. {e.message}" + ) + # Construct final payload structure + final_payload = {"inputs": prompt, "parameters": payload} + + # Input data structure for Mosaic Text Completion endpoint + # + # {"inputs": [prompt], + # { + # "parameters": { + # "temperature": 0.2 + # } + # } + # } + with custom_token_allowance_exceeded_handling(): + resp = await self._request( + self.config.model.name, + final_payload, + ) + # Response example + # (https://docs.mosaicml.com/en/latest/inference.html#text-completion-models) + # ``` + # { + # "outputs": [ + # "string", + # ], + # } + # ``` + return chat.ResponsePayload( + created=int(time.time()), + model=self.config.model.name, + choices=[ + chat.Choice( + index=idx, + message=chat.ResponseMessage(role="assistant", content=c), + finish_reason=None, + ) + for idx, c in enumerate(resp["outputs"]) + ], + usage=chat.ChatUsage( + prompt_tokens=None, + completion_tokens=None, + total_tokens=None, + ), + ) + + async def completions(self, payload: completions.RequestPayload) -> completions.ResponsePayload: + from fastapi.encoders import jsonable_encoder + + payload = jsonable_encoder(payload, exclude_none=True) + self.check_for_model_field(payload) + key_mapping = { + "max_tokens": "max_new_tokens", + } + for k1, k2 in key_mapping.items(): + if k2 in payload: + raise AIGatewayException( + status_code=422, detail=f"Invalid parameter {k2}. Use {k1} instead." + ) + payload = rename_payload_keys(payload, key_mapping) + + # Handle 'prompt' field in payload + prompt = payload.pop("prompt") + if isinstance(prompt, str): + prompt = [prompt] + + # Construct final payload structure + final_payload = {"inputs": prompt, "parameters": payload} + + # Input data structure for Mosaic Text Completion endpoint + # + # {"inputs": [prompt], + # { + # "parameters": { + # "temperature": 0.2 + # } + # } + # } + + with custom_token_allowance_exceeded_handling(): + resp = await self._request( + self.config.model.name, + final_payload, + ) + + # Response example + # (https://docs.mosaicml.com/en/latest/inference.html#text-completion-models) + # ``` + # { + # "outputs": [ + # "string", + # ], + # } + # ``` + return completions.ResponsePayload( + created=int(time.time()), + object="text_completion", + model=self.config.model.name, + choices=[ + completions.Choice( + index=idx, + text=c, + finish_reason=None, + ) + for idx, c in enumerate(resp["outputs"]) + ], + usage=completions.CompletionsUsage( + prompt_tokens=None, + completion_tokens=None, + total_tokens=None, + ), + ) + + async def embeddings(self, payload: embeddings.RequestPayload) -> embeddings.ResponsePayload: + from fastapi.encoders import jsonable_encoder + + payload = jsonable_encoder(payload, exclude_none=True) + self.check_for_model_field(payload) + key_mapping = {"input": "inputs"} + for k1, k2 in key_mapping.items(): + if k2 in payload: + raise AIGatewayException( + status_code=422, detail=f"Invalid parameter {k2}. Use {k1} instead." + ) + payload = rename_payload_keys(payload, key_mapping) + + # Ensure 'inputs' is a list of strings + if isinstance(payload["inputs"], str): + payload["inputs"] = [payload["inputs"]] + + resp = await self._request( + self.config.model.name, + payload, + ) + # Response example + # (https://docs.mosaicml.com/en/latest/inference.html#text-embedding-models): + # ``` + # { + # "outputs": [ + # [ + # 3.25, + # 0.7685547, + # 2.65625, + # ... + # -0.30126953, + # -2.3554688, + # 1.2597656 + # ] + # ] + # } + # ``` + return embeddings.ResponsePayload( + data=[ + embeddings.EmbeddingObject( + embedding=output, + index=idx, + ) + for idx, output in enumerate(resp["outputs"]) + ], + model=self.config.model.name, + usage=embeddings.EmbeddingsUsage( + prompt_tokens=None, + total_tokens=None, + ), + ) + + +@contextmanager +def custom_token_allowance_exceeded_handling(): + """ + Context manager handler for specific error messages that are incorrectly set as server-side + errors, but are in actuality an issue with the request sent to the external provider. + """ + from fastapi import HTTPException + + try: + yield + except HTTPException as e: + status_code = e.status_code + detail = e.detail or {} + + if ( + status_code == 500 + and detail + and any( + detail.get("message", "").startswith(x) + for x in ( + "Error: max output tokens is limited to", + "Error: prompt token count", + ) + ) + ): + raise HTTPException(status_code=422, detail=detail) + else: + raise diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gateway/providers/openai.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gateway/providers/openai.py new file mode 100644 index 0000000000000000000000000000000000000000..76821d20540318d52db6a570aba0fa00479ae8c8 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gateway/providers/openai.py @@ -0,0 +1,600 @@ +import json +import os +from typing import TYPE_CHECKING, AsyncIterable +from urllib.parse import urlparse, urlunparse + +from mlflow.environment_variables import MLFLOW_ENABLE_UC_FUNCTIONS +from mlflow.exceptions import MlflowException +from mlflow.gateway.config import OpenAIAPIType, OpenAIConfig, RouteConfig +from mlflow.gateway.exceptions import AIGatewayException +from mlflow.gateway.providers.base import BaseProvider, ProviderAdapter +from mlflow.gateway.providers.utils import send_request, send_stream_request +from mlflow.gateway.schemas import chat, completions, embeddings +from mlflow.gateway.uc_function_utils import ( + _UC_FUNCTION, + TokenUsageAccumulator, + execute_function, + get_func_schema, + join_uc_functions, + parse_uc_functions, + prepend_uc_functions, +) +from mlflow.gateway.utils import handle_incomplete_chunks, strip_sse_prefix +from mlflow.utils.uri import append_to_uri_path, append_to_uri_query_params + +if TYPE_CHECKING: + from databricks.sdk import FunctionInfo + + +# To mock the WorkspaceClient in tests +def _get_workspace_client(): + try: + from databricks.sdk import WorkspaceClient + + return WorkspaceClient() + except ImportError: + raise AIGatewayException( + message="Databricks SDK is required to use Unity Catalog integration", + error_code=404, + ) + + +class OpenAIAdapter(ProviderAdapter): + @classmethod + def chat_to_model(cls, payload, config): + return cls._add_model_to_payload_if_necessary(payload, config) + + @classmethod + def completion_to_model(cls, payload, config): + return cls._add_model_to_payload_if_necessary(payload, config) + + @classmethod + def embeddings_to_model(cls, payload, config): + return cls._add_model_to_payload_if_necessary(payload, config) + + @classmethod + def _add_model_to_payload_if_necessary(cls, payload, config): + # NB: For Azure OpenAI, the deployment name (which is included in the URL) specifies + # the model; it is not specified in the payload. For OpenAI outside of Azure, the + # model is always specified in the payload + if config.model.config.openai_api_type not in (OpenAIAPIType.AZURE, OpenAIAPIType.AZUREAD): + return {"model": config.model.name, **payload} + else: + return payload + + @classmethod + def model_to_chat(cls, resp, config): + # Response example (https://platform.openai.com/docs/api-reference/chat/create) + # ``` + # { + # "id":"chatcmpl-abc123", + # "object":"chat.completion", + # "created":1677858242, + # "model":"gpt-4o-mini", + # "usage":{ + # "prompt_tokens":13, + # "completion_tokens":7, + # "total_tokens":20 + # }, + # "choices":[ + # { + # "message":{ + # "role":"assistant", + # "content":"\n\nThis is a test!" + # }, + # "finish_reason":"stop", + # "index":0 + # } + # ] + # } + # ``` + return chat.ResponsePayload( + id=resp["id"], + object=resp["object"], + created=resp["created"], + model=resp["model"], + choices=[ + chat.Choice( + index=idx, + message=chat.ResponseMessage( + role=c["message"]["role"], + content=c["message"].get("content"), + tool_calls=( + (calls := c["message"].get("tool_calls")) + and [chat.ToolCall(**c) for c in calls] + ), + ), + finish_reason=c.get("finish_reason"), + ) + for idx, c in enumerate(resp["choices"]) + ], + usage=chat.ChatUsage( + prompt_tokens=resp["usage"]["prompt_tokens"], + completion_tokens=resp["usage"]["completion_tokens"], + total_tokens=resp["usage"]["total_tokens"], + ), + ) + + @classmethod + def model_to_chat_streaming(cls, resp, config): + return chat.StreamResponsePayload( + id=resp["id"], + object=resp["object"], + created=resp["created"], + model=resp["model"], + choices=[ + chat.StreamChoice( + index=c["index"], + finish_reason=c["finish_reason"], + delta=chat.StreamDelta( + role=c["delta"].get("role"), content=c["delta"].get("content") + ), + ) + for c in resp["choices"] + ], + ) + + @classmethod + def model_to_completions(cls, resp, config): + # Response example (https://platform.openai.com/docs/api-reference/completions/create) + # ``` + # { + # "id": "cmpl-uqkvlQyYK7bGYrRHQ0eXlWi7", + # "object": "text_completion", + # "created": 1589478378, + # "model": "text-davinci-003", + # "choices": [ + # { + # "text": "\n\nThis is indeed a test", + # "index": 0, + # "logprobs": null, + # "finish_reason": "length" + # } + # ], + # "usage": { + # "prompt_tokens": 5, + # "completion_tokens": 7, + # "total_tokens": 12 + # } + # } + # ``` + return completions.ResponsePayload( + id=resp["id"], + # The chat models response from OpenAI is of object type "chat.completion". Since + # we're using the completions response format here, we hardcode the "text_completion" + # object type in the response instead + object="text_completion", + created=resp["created"], + model=resp["model"], + choices=[ + completions.Choice( + index=idx, + text=c.get("message", {}).get("content") or c.get("text") or "", + finish_reason=c["finish_reason"], + ) + for idx, c in enumerate(resp["choices"]) + ], + usage=completions.CompletionsUsage( + prompt_tokens=resp["usage"]["prompt_tokens"], + completion_tokens=resp["usage"]["completion_tokens"], + total_tokens=resp["usage"]["total_tokens"], + ), + ) + + @classmethod + def model_to_completions_streaming(cls, resp, config): + return completions.StreamResponsePayload( + id=resp["id"], + # The chat models response from OpenAI is of object type "chat.completion.chunk". + # Since we're using the completions response format here, we hardcode the + # "text_completion_chunk" object type in the response instead + object="text_completion_chunk", + created=resp["created"], + model=resp["model"], + choices=[ + completions.StreamChoice( + index=c["index"], + finish_reason=c["finish_reason"], + text=c["delta"].get("content"), + ) + for c in resp["choices"] + ], + ) + + @classmethod + def model_to_embeddings(cls, resp, config): + # Response example (https://platform.openai.com/docs/api-reference/embeddings/create): + # ``` + # { + # "object": "list", + # "data": [ + # { + # "object": "embedding", + # "embedding": [ + # 0.0023064255, + # -0.009327292, + # .... (1536 floats total for ada-002) + # -0.0028842222, + # ], + # "index": 0 + # } + # ], + # "model": "text-embedding-ada-002", + # "usage": { + # "prompt_tokens": 8, + # "total_tokens": 8 + # } + # } + # ``` + return embeddings.ResponsePayload( + data=[ + embeddings.EmbeddingObject( + embedding=d["embedding"], + index=idx, + ) + for idx, d in enumerate(resp["data"]) + ], + model=resp["model"], + usage=embeddings.EmbeddingsUsage( + prompt_tokens=resp["usage"]["prompt_tokens"], + total_tokens=resp["usage"]["total_tokens"], + ), + ) + + +class OpenAIProvider(BaseProvider): + NAME = "OpenAI" + CONFIG_TYPE = OpenAIConfig + + def __init__(self, config: RouteConfig) -> None: + super().__init__(config) + if config.model.config is None or not isinstance(config.model.config, OpenAIConfig): + # Should be unreachable + raise MlflowException.invalid_parameter_value( + "Invalid config type {config.model.config}" + ) + self.openai_config: OpenAIConfig = config.model.config + + @property + def base_url(self): + api_type = self.openai_config.openai_api_type + if api_type == OpenAIAPIType.OPENAI: + base_url = self.openai_config.openai_api_base or "https://api.openai.com/v1" + if (api_version := self.openai_config.openai_api_version) is not None: + return append_to_uri_query_params(base_url, ("api-version", api_version)) + else: + return base_url + elif api_type in (OpenAIAPIType.AZURE, OpenAIAPIType.AZUREAD): + openai_url = append_to_uri_path( + self.openai_config.openai_api_base, + "openai", + "deployments", + self.openai_config.openai_deployment_name, + ) + return append_to_uri_query_params( + openai_url, + ("api-version", self.openai_config.openai_api_version), + ) + else: + raise MlflowException.invalid_parameter_value( + f"Invalid OpenAI API type '{self.openai_config.openai_api_type}'" + ) + + @property + def headers(self): + api_type = self.openai_config.openai_api_type + if api_type == OpenAIAPIType.OPENAI: + headers = { + "Authorization": f"Bearer {self.openai_config.openai_api_key}", + } + if org := self.openai_config.openai_organization: + headers["OpenAI-Organization"] = org + return headers + elif api_type == OpenAIAPIType.AZUREAD: + return { + "Authorization": f"Bearer {self.openai_config.openai_api_key}", + } + elif api_type == OpenAIAPIType.AZURE: + return { + "api-key": self.openai_config.openai_api_key, + } + else: + raise MlflowException.invalid_parameter_value( + f"Invalid OpenAI API type '{self.openai_config.openai_api_type}'" + ) + + @property + def adapter_class(self): + return OpenAIAdapter + + def get_endpoint_url(self, route_type: str) -> str: + if route_type == "llm/v1/chat": + route_path = "chat/completions" + elif route_type == "llm/v1/completions": + route_path = "completions" + elif route_type == "llm/v1/embeddings": + route_path = "embeddings" + else: + raise ValueError(f"Invalid route type {route_type}") + + # Append the route path to the base URL. Note that we cannot simply append the route path + # at the end of the base URL because it has query parameters for the Azure OpenAI case. + parsed_base_url = urlparse(self.base_url) + return urlunparse(parsed_base_url._replace(path=f"{parsed_base_url.path}/{route_path}")) + + async def chat_stream( + self, payload: chat.RequestPayload + ) -> AsyncIterable[chat.StreamResponsePayload]: + from fastapi.encoders import jsonable_encoder + + payload = jsonable_encoder(payload, exclude_none=True) + self.check_for_model_field(payload) + + stream = send_stream_request( + headers=self.headers, + base_url=self.base_url, + path="chat/completions", + payload=self.adapter_class.chat_to_model(payload, self.config), + ) + + async for chunk in handle_incomplete_chunks(stream): + chunk = chunk.strip() + if not chunk: + continue + + data = strip_sse_prefix(chunk.decode("utf-8")) + if data == "[DONE]": + return + + resp = json.loads(data) + yield OpenAIAdapter.model_to_chat_streaming(resp, self.config) + + async def _chat(self, payload: chat.RequestPayload) -> chat.ResponsePayload: + from fastapi.encoders import jsonable_encoder + + payload = jsonable_encoder(payload, exclude_none=True) + self.check_for_model_field(payload) + + return await send_request( + headers=self.headers, + base_url=self.base_url, + path="chat/completions", + payload=self.adapter_class.chat_to_model(payload, self.config), + ) + + async def _chat_uc_function(self, payload: chat.RequestPayload) -> chat.ResponsePayload: + workspace_client = _get_workspace_client() + warehouse_id = os.environ.get("DATABRICKS_WAREHOUSE_ID") + if warehouse_id is None: + raise AIGatewayException( + status_code=400, + detail="DATABRICKS_WAREHOUSE_ID environment variable is not set", + ) + + from fastapi.encoders import jsonable_encoder + + payload = jsonable_encoder(payload, exclude_none=True) + self.check_for_model_field(payload) + + token_usage_accumulator = TokenUsageAccumulator() + user_tool_messages = [m for m in payload["messages"] if m["role"] == "tool"] + user_tool_calls = next( + (m["tool_calls"] for m in payload["messages"] if "tool_calls" in m), None + ) + if ( + user_tool_messages + and user_tool_calls + and (result := parse_uc_functions(payload["messages"][0]["content"])) + ): + uc_func_calls, uc_func_messages = result + messages = [ + *[m for m in payload["messages"] if m["role"] == "tool" or "tool_calls" in m], + # Join UC function calls and user tool calls + { + "role": "assistant", + "content": None, + "tool_calls": uc_func_calls + user_tool_calls, + }, + *uc_func_messages, + *user_tool_messages, + ] + resp = await send_request( + headers=self.headers, + base_url=self.base_url, + path="chat/completions", + payload=self.adapter_class.chat_to_model( + { + **payload, + "messages": messages, + }, + self.config, + ), + ) + token_usage_accumulator.update(resp.get("usage", {})) + elif any(t["type"] == _UC_FUNCTION for t in payload.get("tools", [])): + updated_tools = [] + uc_func_mapping: dict[str, "FunctionInfo"] = {} + for tool in payload.get("tools", []): + if tool["type"] == _UC_FUNCTION: + function_name = tool[_UC_FUNCTION]["name"] + function = workspace_client.functions.get(function_name) + param_metadata = get_func_schema(function) + t = { + "type": "function", + "function": param_metadata, + } + uc_func_mapping[t["function"]["name"]] = function + updated_tools.append(t) + else: + updated_tools.append(tool) + + payload["tools"] = updated_tools + + messages = payload.pop("messages", []) + uc_func_calls = [] + user_tool_calls = [] + resp = None + for _ in range(20): # loop until we get a response without tool_calls + resp = await send_request( + headers=self.headers, + base_url=self.base_url, + path="chat/completions", + payload=self.adapter_class.chat_to_model( + { + **payload, + "messages": messages, + }, + self.config, + ), + ) + token_usage_accumulator.update(resp.get("usage", {})) + # TODO to support n > 1. + assistant_msg = resp["choices"][0]["message"] + tool_calls = assistant_msg.get("tool_calls") + if tool_calls is None: + if uc_func_calls: + original_content = resp["choices"][0]["message"]["content"] + resp["choices"][0]["message"]["content"] = prepend_uc_functions( + original_content, uc_func_calls + ) + + if user_tool_calls: + # Is this line unreachable? + resp["choices"][0]["message"]["tool_calls"] = user_tool_calls + + break + + tool_messages = [] + for tool_call in tool_calls: + func = tool_call["function"] + parameters = json.loads(func["arguments"]) + if func_info := uc_func_mapping.get(func["name"]): + result = execute_function( + ws=workspace_client, + warehouse_id=warehouse_id, + function=function, + parameters=parameters, + ) + tool_messages.append( + { + "role": "tool", + "tool_call_id": tool_call["id"], + "content": result.to_json(), + } + ) + + uc_func_calls.append( + ( + { + "id": tool_call["id"], + "name": func_info.full_name, + "arguments": func["arguments"], + }, + { + "tool_call_id": tool_call["id"], + "content": result.to_json(), + }, + ) + ) + else: + user_tool_calls.append( + { + "id": tool_call["id"], + "type": "function", + "function": { + "name": func["name"], + "arguments": func["arguments"], + }, + } + ) + + if message_content := assistant_msg.pop("content", None): + messages.append({"role": "assistant", "content": message_content}) + messages += [assistant_msg, *tool_messages] + + if user_tool_calls: + # We can't go on without a response from the user, so we break here + if uc_func_calls: + resp["choices"][0]["message"]["content"] = join_uc_functions(uc_func_calls) + + resp["choices"][0]["message"]["tool_calls"] = user_tool_calls + break + else: + raise AIGatewayException( + status_code=500, + detail="Max iterations reached", + ) + else: + # No UC functions to execute + resp = await send_request( + headers=self.headers, + base_url=self.base_url, + path="chat/completions", + payload=self.adapter_class.chat_to_model(payload, self.config), + ) + token_usage_accumulator.update(resp.get("usage", {})) + + # Update the token usage + resp["usage"].update(token_usage_accumulator.dict()) + + return resp + + async def chat(self, payload: chat.RequestPayload) -> chat.ResponsePayload: + if MLFLOW_ENABLE_UC_FUNCTIONS.get(): + resp = await self._chat_uc_function(payload) + else: + resp = await self._chat(payload) + + return OpenAIAdapter.model_to_chat(resp, self.config) + + async def completions_stream( + self, payload: completions.RequestPayload + ) -> AsyncIterable[completions.StreamResponsePayload]: + from fastapi.encoders import jsonable_encoder + + payload = jsonable_encoder(payload, exclude_none=True) + self.check_for_model_field(payload) + stream = send_stream_request( + headers=self.headers, + base_url=self.base_url, + path="completions", + payload=OpenAIAdapter.completion_to_model(payload, self.config), + ) + + async for chunk in handle_incomplete_chunks(stream): + chunk = chunk.strip() + if not chunk: + continue + + data = strip_sse_prefix(chunk.decode("utf-8")) + if data == "[DONE]": + return + + resp = json.loads(data) + yield OpenAIAdapter.model_to_completions_streaming(resp, self.config) + + async def completions(self, payload: completions.RequestPayload) -> completions.ResponsePayload: + from fastapi.encoders import jsonable_encoder + + payload = jsonable_encoder(payload, exclude_none=True) + self.check_for_model_field(payload) + resp = await send_request( + headers=self.headers, + base_url=self.base_url, + path="completions", + payload=OpenAIAdapter.completion_to_model(payload, self.config), + ) + return OpenAIAdapter.model_to_completions(resp, self.config) + + async def embeddings(self, payload: embeddings.RequestPayload) -> embeddings.ResponsePayload: + from fastapi.encoders import jsonable_encoder + + payload = jsonable_encoder(payload, exclude_none=True) + self.check_for_model_field(payload) + resp = await send_request( + headers=self.headers, + base_url=self.base_url, + path="embeddings", + payload=OpenAIAdapter.embeddings_to_model(payload, self.config), + ) + return OpenAIAdapter.model_to_embeddings(resp, self.config) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gateway/providers/palm.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gateway/providers/palm.py new file mode 100644 index 0000000000000000000000000000000000000000..b99039a85a7157f551be27dcac0d1c795584115f --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gateway/providers/palm.py @@ -0,0 +1,219 @@ +import time +from typing import Any + +from mlflow.gateway.config import PaLMConfig, RouteConfig +from mlflow.gateway.exceptions import AIGatewayException +from mlflow.gateway.providers.base import BaseProvider +from mlflow.gateway.providers.utils import rename_payload_keys, send_request +from mlflow.gateway.schemas import chat, completions, embeddings + + +class PaLMProvider(BaseProvider): + NAME = "PaLM" + CONFIG_TYPE = PaLMConfig + + def __init__(self, config: RouteConfig) -> None: + super().__init__(config) + if config.model.config is None or not isinstance(config.model.config, PaLMConfig): + raise TypeError(f"Unexpected config type {config.model.config}") + self.palm_config: PaLMConfig = config.model.config + + async def _request(self, path: str, payload: dict[str, Any]) -> dict[str, Any]: + headers = {"x-goog-api-key": self.palm_config.palm_api_key} + return await send_request( + headers=headers, + base_url="https://generativelanguage.googleapis.com/v1beta3/models/", + path=path, + payload=payload, + ) + + async def chat(self, payload: chat.RequestPayload) -> chat.ResponsePayload: + from fastapi.encoders import jsonable_encoder + + payload = jsonable_encoder(payload, exclude_none=True) + self.check_for_model_field(payload) + if "max_tokens" in payload or "maxOutputTokens" in payload: + raise AIGatewayException( + status_code=422, detail="Max tokens is not supported for PaLM chat." + ) + key_mapping = { + "stop": "stopSequences", + "n": "candidateCount", + } + for k1, k2 in key_mapping.items(): + if k2 in payload: + raise AIGatewayException( + status_code=422, detail=f"Invalid parameter {k2}. Use {k1} instead." + ) + payload = rename_payload_keys(payload, key_mapping) + # The range of PaLM's temperature is 0-1, but ours is 0-2, so we halve it + payload["temperature"] = 0.5 * payload["temperature"] + + # Replace 'role' with 'author' in payload + for m in payload["messages"]: + m["author"] = m.pop("role") + + # Map 'messages', 'examples, and 'context' to 'prompt' + prompt = {"messages": payload.pop("messages")} + if "examples" in payload: + prompt["examples"] = payload.pop("examples") + if "context" in payload: + prompt["context"] = payload.pop("context") + payload["prompt"] = prompt + + resp = await self._request( + f"{self.config.model.name}:generateMessage", + payload, + ) + + # Response example + # (https://developers.generativeai.google/api/rest/generativelanguage/models/generateMessage) + # ``` + # { + # "candidates": [ + # { + # "author": "1", + # "content": "Hi there! How can I help you today?" + # } + # ], + # "messages": [ + # { + # "author": "0", + # "content": "hi" + # } + # ] + # } + # ``` + return chat.ResponsePayload( + created=int(time.time()), + model=self.config.model.name, + choices=[ + chat.Choice( + index=idx, + message=chat.ResponseMessage(role=c["author"], content=c["content"]), + finish_reason=None, + ) + for idx, c in enumerate(resp["candidates"]) + ], + usage=chat.ChatUsage( + prompt_tokens=None, + completion_tokens=None, + total_tokens=None, + ), + ) + + async def completions(self, payload: completions.RequestPayload) -> completions.ResponsePayload: + from fastapi.encoders import jsonable_encoder + + payload = jsonable_encoder(payload, exclude_none=True) + self.check_for_model_field(payload) + key_mapping = { + "stop": "stopSequences", + "n": "candidateCount", + "max_tokens": "maxOutputTokens", + } + for k1, k2 in key_mapping.items(): + if k2 in payload: + raise AIGatewayException( + status_code=422, detail=f"Invalid parameter {k2}. Use {k1} instead." + ) + payload = rename_payload_keys(payload, key_mapping) + # The range of PaLM's temperature is 0-1, but ours is 0-2, so we halve it + payload["temperature"] = 0.5 * payload["temperature"] + payload["prompt"] = {"text": payload["prompt"]} + resp = await self._request( + f"{self.config.model.name}:generateText", + payload, + ) + # Response example (https://developers.generativeai.google/api/rest/generativelanguage/models/generateText) + # ``` + # { + # "candidates": [ + # { + # "output": "Once upon a time, there was a young girl named Lily...", + # "safetyRatings": [ + # { + # "category": "HARM_CATEGORY_DEROGATORY", + # "probability": "NEGLIGIBLE" + # }, ... + # ] + # { + # "output": "Once upon a time, there was a young boy named Billy...", + # "safetyRatings": [ + # ... + # ] + # } + # ] + # } + # ``` + return completions.ResponsePayload( + created=int(time.time()), + object="text_completion", + model=self.config.model.name, + choices=[ + completions.Choice( + index=idx, + text=c["output"], + finish_reason=None, + ) + for idx, c in enumerate(resp["candidates"]) + ], + usage=completions.CompletionsUsage( + prompt_tokens=None, + completion_tokens=None, + total_tokens=None, + ), + ) + + async def embeddings(self, payload: embeddings.RequestPayload) -> embeddings.ResponsePayload: + from fastapi.encoders import jsonable_encoder + + payload = jsonable_encoder(payload, exclude_none=True) + self.check_for_model_field(payload) + key_mapping = { + "input": "texts", + } + for k1, k2 in key_mapping.items(): + if k2 in payload: + raise AIGatewayException( + status_code=422, detail=f"Invalid parameter {k2}. Use {k1} instead." + ) + payload = rename_payload_keys(payload, key_mapping) + + resp = await self._request( + f"{self.config.model.name}:batchEmbedText", + payload, + ) + + # Batch-text response example (https://developers.generativeai.google/api/rest/generativelanguage/models/batchEmbedText): + # ``` + # { + # "embeddings": [ + # { + # "value": [ + # 3.25, + # 0.7685547, + # 2.65625, + # ... + # -0.30126953, + # -2.3554688, + # 1.2597656 + # ] + # } + # ] + # } + # ``` + return embeddings.ResponsePayload( + data=[ + embeddings.EmbeddingObject( + embedding=embedding["value"], + index=idx, + ) + for idx, embedding in enumerate(resp["embeddings"]) + ], + model=self.config.model.name, + usage=embeddings.EmbeddingsUsage( + prompt_tokens=None, + total_tokens=None, + ), + ) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gateway/providers/togetherai.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gateway/providers/togetherai.py new file mode 100644 index 0000000000000000000000000000000000000000..ae2bc4ade18046a9b1db20e2a6a80247a0f5772b --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gateway/providers/togetherai.py @@ -0,0 +1,448 @@ +import json +from typing import Any, AsyncGenerator, AsyncIterable + +from mlflow.exceptions import MlflowException +from mlflow.gateway.config import RouteConfig, TogetherAIConfig +from mlflow.gateway.exceptions import AIGatewayException +from mlflow.gateway.providers.base import BaseProvider, ProviderAdapter +from mlflow.gateway.providers.utils import rename_payload_keys, send_request, send_stream_request +from mlflow.gateway.schemas import chat as chat_schema +from mlflow.gateway.schemas import completions as completions_schema +from mlflow.gateway.schemas import embeddings as embeddings_schema +from mlflow.gateway.utils import strip_sse_prefix + + +class TogetherAIAdapter(ProviderAdapter): + @classmethod + def model_to_embeddings(cls, resp, config): + # Response example: (https://docs.together.ai/docs/embeddings-rest) + # ``` + # { + # "object": "list", + # "data": [ + # { + # "object": "embedding", + # "embedding": [ + # 0.44990748, + # -0.2521129, + # ... + # -0.43091708, + # 0.214978 + # ], + # "index": 0 + # } + # ], + # "model": "togethercomputer/m2-bert-80M-8k-retrieval", + # "request_id": "840fc1b5bb2830cb-SEA" + # } + # ``` + return embeddings_schema.ResponsePayload( + data=[ + embeddings_schema.EmbeddingObject( + embedding=item["embedding"], + index=item["index"], + ) + for item in resp["data"] + ], + model=config.model.name, + usage=embeddings_schema.EmbeddingsUsage(prompt_tokens=None, total_tokens=None), + ) + + @classmethod + def model_to_completions(cls, resp, config): + # Example response (https://docs.together.ai/reference/completions): + # { + # "id": "8447f286bbdb67b3-SJC", + # "choices": [ + # { + # "text": "Example text." + # } + # ], + # "usage": { + # "prompt_tokens": 16, + # "completion_tokens": 78, + # "total_tokens": 94 + # }, + # "created": 1705089226, + # "model": "mistralai/Mixtral-8x7B-Instruct-v0.1", + # "object": "text_completion" + # } + + return completions_schema.ResponsePayload( + id=resp["id"], + created=resp["created"], + model=config.model.name, + choices=[ + completions_schema.Choice( + index=idx, + text=c["text"], + finish_reason=None, + ) + for idx, c in enumerate(resp["choices"]) + ], + usage=completions_schema.CompletionsUsage( + prompt_tokens=resp["usage"]["prompt_tokens"], + completion_tokens=resp["usage"]["completion_tokens"], + total_tokens=resp["usage"]["total_tokens"], + ), + ) + + @classmethod + def model_to_completions_streaming(cls, resp, config): + # Response example (after manually calling API): + # + # {'id': '86d8d6e06df86f61-ATH', 'object': 'completion.chunk', + # 'created': 1711977238, 'choices': [{'index': 0, 'text': ' ', + # 'logprobs': None, 'finish_reason': None, 'delta': {'token_id': 2287, 'content': ' '}}], + # 'model': 'mistralai/Mixtral-8x7B-v0.1', 'usage': None} + # + # {'id': '86d8d6e06df86f61-ATH', 'object': 'completion.chunk', + # 'created': 1711977238, 'choices': [{'index': 0, 'text': ' "', 'logprobs': None, + # 'finish_reason': None, 'delta': {'token_id': 345, 'content': ' "'}}], + # 'model': 'mistralai/Mixtral-8x7B-v0.1', 'usage': None} + # + # "{'id': '86d8d6e06df86f61-ATH', 'object': 'completion.chunk', + # 'created': 1711977238, 'choices': [{'index': 0, 'text': 'name', 'logprobs': None, + # 'finish_reason': None, 'delta': {'token_id': 861, 'content': 'name'}}], + # 'model': 'mistralai/Mixtral-8x7B-v0.1', 'usage': None} + # + # LAST CHUNK + # {'id': '86d8d6e06df86f61-ATH', 'object': 'completion.chunk', + # 'created': 1711977238, 'choices': [{'index': 0, 'text': '":', 'logprobs': None, + # 'finish_reason': 'length', 'delta': {'token_id': 1264, 'content': '":'}}], + # 'model': 'mistralai/Mixtral-8x7B-v0.1', + # 'usage': {'prompt_tokens': 17, 'completion_tokens': 200, 'total_tokens': 217}} + # ":[DONE] + + return completions_schema.StreamResponsePayload( + id=resp.get("id"), + created=resp.get("created"), + model=config.model.name, + choices=[ + completions_schema.StreamChoice( + index=idx, + # TODO this is questionable since the finish reason comes from togetherai api + finish_reason=choice.get("finish_reason"), + text=choice.get("text"), + ) + for idx, choice in enumerate(resp.get("choices", [])) + ], + # usage is not included in OpenAI StreamResponsePayload + ) + + @classmethod + def completions_to_model(cls, payload, config): + key_mapping = { + # TogetherAI uses logprobs + # OpenAI uses top_logprobs + "top_logprobs": "logprobs" + } + + # in openAI API the logprobs parameter + # is a boolean flag. + # Insert this here to prevent the user from mixing up the APIs + logprobs_in_payload_condition = "logprobs" in payload and not isinstance( + payload["logprobs"], int + ) + + if logprobs_in_payload_condition: + raise AIGatewayException( + status_code=422, + detail="Wrong type for logprobs. It should be an 32bit integer.", + ) + + openai_top_logprobs_in_payload_condition = "top_logprobs" in payload and not isinstance( + payload["top_logprobs"], int + ) + + if openai_top_logprobs_in_payload_condition: + raise AIGatewayException( + status_code=422, + detail="Wrong type for top_logprobs. It should a 32bit integer.", + ) + + payload = rename_payload_keys(payload, key_mapping) + return {"model": config.model.name, **payload} + + @classmethod + def completions_streaming_to_model(cls, payload, config): + # parameters for streaming completions are the same as the standard completions + return TogetherAIAdapter.completions_to_model(payload, config) + + @classmethod + def model_to_chat(cls, resp, config): + # Example response (https://docs.together.ai/reference/chat-completions): + # { + # "id": "8448080b880415ea-SJC", + # "choices": [ + # { + # "message": { + # "role": "assistant", + # "content": "example" + # } + # } + # ], + # "usage": { + # "prompt_tokens": 31, + # "completion_tokens": 455, + # "total_tokens": 486 + # }, + # "created": 1705090115, + # "model": "mistralai/Mixtral-8x7B-Instruct-v0.1", + # "object": "chat.completion" + # } + return chat_schema.ResponsePayload( + id=resp["id"], + object="chat.completion", + created=resp["created"], + model=config.model.name, + choices=[ + chat_schema.Choice( + index=idx, + message=chat_schema.ResponseMessage( + role="assistant", + content=c["message"]["content"], + ), + finish_reason=None, + ) + for idx, c in enumerate(resp["choices"]) + ], + usage=chat_schema.ChatUsage( + prompt_tokens=resp["usage"]["prompt_tokens"], + completion_tokens=resp["usage"]["completion_tokens"], + total_tokens=resp["usage"]["total_tokens"], + ), + ) + + @classmethod + def model_to_chat_streaming(cls, resp, config): + # Response example (after running API manually): + # + # {'id': '86f2cfd18f6b38ca-ATH', 'object': 'chat.completion.chunk', + # 'created': 1712249578, 'choices': [{'index': 0, 'text': ' The', 'logprobs': None, + # 'finish_reason': None, 'delta': {'token_id': 415, 'content': ' The'}}], + # 'model': 'mistralai/Mixtral-8x7B-v0.1', 'usage': None} + # + # {'id': '86f2cfd18f6b38ca-ATH', 'object': 'chat.completion.chunk', + # 'created': 1712249578, 'choices': [{'index': 0, 'text': ' City', 'logprobs': None, + # 'finish_reason': None, 'delta': {'token_id': 3805, 'content': ' City'}}], + # 'model': 'mistralai/Mixtral-8x7B-v0.1', 'usage': None} + # + # {'id': '86f2cfd18f6b38ca-ATH', 'object': 'chat.completion.chunk', + # 'created': 1712249578, 'choices': [{'index': 0, 'text': ' of', 'logprobs': None, + # 'finish_reason': None, 'delta': {'token_id': 302, 'content': ' of'}}], + # 'model': 'mistralai/Mixtral-8x7B-v0.1', 'usage': None} + # + # LAST CHUNK + # {'id': '86f2cfd18f6b38ca-ATH', 'object': 'chat.completion.chunk', + # 'created': 1712249578, 'choices': [{'index': 0, 'text': ' Paris', 'logprobs': None, + # 'finish_reason': 'length', 'delta': {'token_id': 5465, 'content': ' Paris'}}], + # 'model': 'mistralai/Mixtral-8x7B-v0.1', + # 'usage': {'prompt_tokens': 93, 'completion_tokens': 100, 'total_tokens': 193}} + + return chat_schema.StreamResponsePayload( + id=resp["id"], + model=config.model.name, + object="chat.completion.chunk", + created=resp["created"], + choices=[ + chat_schema.StreamChoice( + index=idx, + finish_reason=choice.get("finish_reason"), + delta=chat_schema.StreamDelta( + role=None, + content=choice.get("text"), + ), + ) + # Added enumerate and a default empty list + for idx, choice in enumerate(resp.get("choices", [])) + ], + usage=resp.get("usage"), + ) + + @classmethod + def chat_to_model(cls, payload, config): + # completions and chat endpoint contain the same parameters + return TogetherAIAdapter.completions_to_model(payload, config) + + @classmethod + def chat_streaming_to_model(cls, payload, config): + # streaming and standard chat contain the same parameters + return TogetherAIAdapter.chat_to_model(payload, config) + + @classmethod + def embeddings_to_model(cls, payload, config): + # Example request (https://docs.together.ai/reference/embeddings): + # curl --request POST \ + # --url https://api.together.xyz/v1/embeddings \ + # --header 'accept: application/json' \ + # --header 'content-type: application/json' \ + # --data ' + # { + # "model": "togethercomputer/m2-bert-80M-8k-retrieval", + # "input": "Our solar system orbits the Milky Way galaxy at about 515,000 mph" + # } + + # This is just to keep the interface consistent the adapter + # class is not needed here as the togetherai request similar + # to the openAI one. + + return payload + + +class TogetherAIProvider(BaseProvider): + NAME = "TogetherAI" + CONFIG_TYPE = TogetherAIConfig + + def __init__(self, config: RouteConfig) -> None: + super().__init__(config) + if config.model.config is None or not isinstance(config.model.config, TogetherAIConfig): + # Should be unreachable + raise MlflowException.invalid_parameter_value( + f"Invalid config type {config.model.config}" + ) + self.togetherai_config: TogetherAIConfig = config.model.config + + @property + def base_url(self): + # togetherai seems to support only this url + return "https://api.together.xyz/v1" + + @property + def headers(self): + return {"Authorization": f"Bearer {self.togetherai_config.togetherai_api_key}"} + + @property + def adapter_class(self) -> type[ProviderAdapter]: + return TogetherAIAdapter + + def get_endpoint_url(self, route_type: str) -> str: + if route_type == "llm/v1/chat": + return f"{self.base_url}/chat/completions" + elif route_type == "llm/v1/completions": + return f"{self.base_url}/completions" + elif route_type == "llm/v1/embeddings": + return f"{self.base_url}/embeddings" + else: + raise ValueError(f"Invalid route type {route_type}") + + async def _request(self, path: str, payload: dict[str, Any]) -> dict[str, Any]: + return await send_request( + headers=self.headers, + base_url=self.base_url, + path=path, + payload=payload, + ) + + async def _stream_request( + self, path: str, payload: dict[str, Any] + ) -> AsyncGenerator[bytes, None]: + return send_stream_request( + headers=self.headers, + base_url=self.base_url, + path=path, + payload=payload, + ) + + async def embeddings( + self, payload: embeddings_schema.RequestPayload + ) -> embeddings_schema.ResponsePayload: + from fastapi.encoders import jsonable_encoder + + payload = jsonable_encoder(payload, exclude_none=True) + + resp = await self._request( + path="embeddings", + payload=TogetherAIAdapter.embeddings_to_model(payload, self.config), + ) + + return TogetherAIAdapter.model_to_embeddings(resp, self.config) + + async def completions_stream( + self, payload: completions_schema.RequestPayload + ) -> AsyncIterable[completions_schema.StreamResponsePayload]: + from fastapi.encoders import jsonable_encoder + + payload = jsonable_encoder(payload, exclude_none=True) + + if not payload.get("max_tokens"): + raise AIGatewayException( + status_code=422, + detail=( + "max_tokens is not present in payload." + "It is a required parameter for TogetherAI completions." + ), + ) + + stream = await self._stream_request( + path="completions", + payload=TogetherAIAdapter.completions_streaming_to_model(payload, self.config), + ) + + async for chunk in stream: + chunk = chunk.strip() + if not chunk: + continue + + chunk = strip_sse_prefix(chunk.decode("utf-8")) + if chunk == "[DONE]": + return + + resp = json.loads(chunk) + yield TogetherAIAdapter.model_to_completions_streaming(resp, self.config) + + async def completions( + self, payload: completions_schema.RequestPayload + ) -> completions_schema.ResponsePayload: + from fastapi.encoders import jsonable_encoder + + payload = jsonable_encoder(payload, exclude_none=True) + + if not payload.get("max_tokens"): + raise AIGatewayException( + status_code=422, + detail=( + "max_tokens is not present in payload." + "It is a required parameter for TogetherAI completions." + ), + ) + + resp = await self._request( + path="completions", payload=TogetherAIAdapter.completions_to_model(payload, self.config) + ) + + return TogetherAIAdapter.model_to_completions(resp, self.config) + + async def chat_stream(self, payload: chat_schema.RequestPayload) -> chat_schema.ResponsePayload: + from fastapi.encoders import jsonable_encoder + + payload = jsonable_encoder(payload, exclude_none=True) + + stream = await self._stream_request( + path="chat/completions", + payload=TogetherAIAdapter.chat_streaming_to_model(payload, self.config), + ) + + async for chunk in stream: + chunk = chunk.strip() + if not chunk: + continue + + chunk = strip_sse_prefix(chunk.decode("utf-8")) + if chunk == "[DONE]": + return + + resp = json.loads(chunk) + yield TogetherAIAdapter.model_to_chat_streaming(resp, self.config) + + async def chat(self, payload: chat_schema.RequestPayload) -> chat_schema.ResponsePayload: + from fastapi.encoders import jsonable_encoder + + payload = jsonable_encoder(payload, exclude_none=True) + + resp = await self._request( + path="chat/completions", + payload=TogetherAIAdapter.chat_to_model(payload, self.config), + ) + + return TogetherAIAdapter.model_to_chat(resp, self.config) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gateway/providers/utils.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gateway/providers/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..366deed61f7eca5782d44a14bfcd1adacaf1c81d --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gateway/providers/utils.py @@ -0,0 +1,96 @@ +from contextlib import asynccontextmanager +from typing import Any, AsyncGenerator + +from mlflow.gateway.constants import ( + MLFLOW_GATEWAY_ROUTE_TIMEOUT_SECONDS, +) +from mlflow.utils.uri import append_to_uri_path + + +@asynccontextmanager +async def _aiohttp_post(headers: dict[str, str], base_url: str, path: str, payload: dict[str, Any]): + import aiohttp + + async with aiohttp.ClientSession(headers=headers) as session: + url = append_to_uri_path(base_url, path) + timeout = aiohttp.ClientTimeout(total=MLFLOW_GATEWAY_ROUTE_TIMEOUT_SECONDS) + async with session.post(url, json=payload, timeout=timeout) as response: + yield response + + +async def send_request(headers: dict[str, str], base_url: str, path: str, payload: dict[str, Any]): + """ + Send an HTTP request to a specific URL path with given headers and payload. + + Args: + headers: The headers to include in the request. + base_url: The base URL where the request will be sent. + path: The specific path of the URL to which the request will be sent. + payload: The payload (or data) to be included in the request. + + Returns: + The server's response as a JSON object. + + Raises: + HTTPException if the HTTP request fails. + """ + import aiohttp + from fastapi import HTTPException + + async with _aiohttp_post(headers, base_url, path, payload) as response: + content_type = response.headers.get("Content-Type") + if content_type and "application/json" in content_type: + js = await response.json() + elif content_type and "text/plain" in content_type: + js = {"message": await response.text()} + else: + raise HTTPException( + status_code=502, + detail=f"The returned data type from the route service is not supported. " + f"Received content type: {content_type}", + ) + try: + response.raise_for_status() + except aiohttp.ClientResponseError as e: + detail = js.get("error", {}).get("message", e.message) if "error" in js else js + raise HTTPException(status_code=e.status, detail=detail) + return js + + +async def send_stream_request( + headers: dict[str, str], base_url: str, path: str, payload: dict[str, Any] +) -> AsyncGenerator[bytes, None]: + """ + Send an HTTP request to a specific URL path with given headers and payload. + + Args: + headers: The headers to include in the request. + base_url: The base URL where the request will be sent. + path: The specific path of the URL to which the request will be sent. + payload: The payload (or data) to be included in the request. + + Returns: + The server's response as a JSON object. + + Raises: + HTTPException if the HTTP request fails. + """ + async with _aiohttp_post(headers, base_url, path, payload) as response: + async for line in response.content: + yield line + + +def rename_payload_keys(payload: dict[str, Any], mapping: dict[str, str]) -> dict[str, Any]: + """Rename payload keys based on the specified mapping. If a key is not present in the + mapping, the key and its value will remain unchanged. + + Args: + payload: The original dictionary to transform. + mapping: A dictionary where each key-value pair represents a mapping from the old + key to the new key. + + Returns: + A new dictionary containing the transformed keys. + + """ + return {mapping.get(k, k): v for k, v in payload.items()} diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gateway/runner.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gateway/runner.py new file mode 100644 index 0000000000000000000000000000000000000000..c5442e6681ec4b2c8d6db526c3b18e10a566795e --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gateway/runner.py @@ -0,0 +1,106 @@ +import logging +import os +import subprocess +import sys +from typing import Generator + +from watchfiles import watch + +from mlflow.environment_variables import MLFLOW_GATEWAY_CONFIG +from mlflow.gateway import app +from mlflow.gateway.config import _load_route_config +from mlflow.gateway.utils import kill_child_processes + +_logger = logging.getLogger(__name__) + + +def monitor_config(config_path: str) -> Generator[None, None, None]: + with open(config_path) as f: + prev_config = f.read() + + for changes in watch(os.path.dirname(config_path)): + if not any((path == config_path) for _, path in changes): + continue + + if not os.path.exists(config_path): + _logger.warning(f"{config_path} deleted") + continue + + with open(config_path) as f: + config = f.read() + if config == prev_config: + continue + + try: + _load_route_config(config_path) + except Exception as e: + _logger.warning("Invalid configuration: %s", e) + continue + else: + prev_config = config + + yield + + +class Runner: + def __init__( + self, + config_path: str, + host: str, + port: int, + workers: int, + ) -> None: + self.config_path = config_path + self.host = host + self.port = port + self.workers = workers + self.process = None + + def start(self) -> None: + self.process = subprocess.Popen( + [ + sys.executable, + "-m", + "gunicorn", + "--bind", + f"{self.host}:{self.port}", + "--workers", + str(self.workers), + "--worker-class", + "uvicorn.workers.UvicornWorker", + f"{app.__name__}:create_app_from_env()", + ], + env={ + **os.environ, + MLFLOW_GATEWAY_CONFIG.name: self.config_path, + }, + ) + + def stop(self) -> None: + if self.process is not None: + self.process.terminate() + self.process.wait() + self.process = None + + def reload(self) -> None: + kill_child_processes(self.process.pid) + + def __enter__(self): + self.start() + return self + + def __exit__(self, exc_type, exc_val, exc_tb): + self.stop() + + +def run_app(config_path: str, host: str, port: int, workers: int) -> None: + config_path = os.path.abspath(os.path.normpath(os.path.expanduser(config_path))) + with Runner( + config_path=config_path, + host=host, + port=port, + workers=workers, + ) as runner: + for _ in monitor_config(config_path): + _logger.info("Configuration updated, reloading workers") + runner.reload() diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gateway/schemas/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gateway/schemas/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..341cc1a65077c3099a7566cec3cde73acc10bdd8 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gateway/schemas/__init__.py @@ -0,0 +1,3 @@ +from mlflow.gateway.schemas import chat, completions, embeddings + +__all__ = ["chat", "completions", "embeddings"] diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gateway/schemas/__pycache__/__init__.cpython-312.pyc 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MLflow AI Gateway's chat endpoint. + +The schemas must be compatible with OpenAI's Chat Completion API. +https://platform.openai.com/docs/api-reference/chat + +NB: These Pydantic models just alias the models defined in mlflow.types.chat to avoid code + duplication, but with the addition of RequestModel and ResponseModel base classes. +""" + +from typing import Literal + +from pydantic import Field + +from mlflow.gateway.base_models import RequestModel, ResponseModel + +# Import marked with noqa is for backward compatibility +from mlflow.types.chat import ( + ChatChoice, + ChatChoiceDelta, + ChatChunkChoice, + ChatCompletionChunk, + ChatCompletionRequest, + ChatCompletionResponse, + ChatMessage, + ChatUsage, # noqa F401 + Function, # noqa F401 + FunctionToolDefinition, + ToolCall, # noqa F401 +) +from mlflow.utils import IS_PYDANTIC_V2_OR_NEWER + +# NB: `import x as y` does not work and will cause a Pydantic error. +StreamDelta = ChatChoiceDelta +StreamChoice = ChatChunkChoice +RequestMessage = ChatMessage + + +class UnityCatalogFunctionToolDefinition(RequestModel): + name: str + + +class ChatToolWithUC(RequestModel): + """ + A tool definition for the chat endpoint with Unity Catalog integration. + The Gateway request accepts a special tool type 'uc_function' for Unity Catalog integration. + https://mlflow.org/docs/latest/llms/deployments/uc_integration.html + """ + + type: Literal["function", "uc_function"] + function: FunctionToolDefinition | None = None + uc_function: UnityCatalogFunctionToolDefinition | None = None + + +_REQUEST_PAYLOAD_EXTRA_SCHEMA = { + "messages": [ + {"role": "system", "content": "You are a helpful assistant."}, + {"role": "user", "content": "Hello!"}, + ], + "temperature": 0.0, + "max_tokens": 64, + "stop": ["END"], + "n": 1, + "stream": False, +} + + +class RequestPayload(ChatCompletionRequest, RequestModel): + messages: list[RequestMessage] = ( + Field(..., min_length=1) if IS_PYDANTIC_V2_OR_NEWER else Field(..., min_items=1) + ) + tools: list[ChatToolWithUC] | None = None + + class Config: + if IS_PYDANTIC_V2_OR_NEWER: + json_schema_extra = _REQUEST_PAYLOAD_EXTRA_SCHEMA + else: + schema_extra = _REQUEST_PAYLOAD_EXTRA_SCHEMA + + +_RESPONSE_PAYLOAD_EXTRA_SCHEMA = { + "example": { + "id": "3cdb958c-e4cc-4834-b52b-1d1a7f324714", + "object": "chat.completion", + "created": 1700173217, + "model": "llama-2-70b-chat-hf", + "choices": [ + { + "index": 0, + "message": {"role": "assistant", "content": "Hello! I am an AI assistant"}, + "finish_reason": "stop", + } + ], + "usage": {"prompt_tokens": 10, "completion_tokens": 8, "total_tokens": 18}, + } +} + + +class ResponseMessage(ChatMessage, ResponseModel): + # Override the `tool_call_id` field to be excluded from the response. + # This is a band-aid solution to avoid exposing the tool_call_id in the response, + # while we use the same ChatMessage model for both request and response. + tool_call_id: str | None = Field(None, exclude=True) + + +class Choice(ChatChoice, ResponseModel): + # Override the `message` field to use the ResponseMessage model. + message: ResponseMessage + + +class ResponsePayload(ChatCompletionResponse, ResponseModel): + # Override the `choices` field to use the Choice model + choices: list[Choice] + + class Config: + if IS_PYDANTIC_V2_OR_NEWER: + json_schema_extra = _RESPONSE_PAYLOAD_EXTRA_SCHEMA + else: + schema_extra = _RESPONSE_PAYLOAD_EXTRA_SCHEMA + + +_STREAM_RESPONSE_PAYLOAD_EXTRA_SCHEMA = { + "example": { + "id": "3cdb958c-e4cc-4834-b52b-1d1a7f324714", + "object": "chat.completion", + "created": 1700173217, + "model": "llama-2-70b-chat-hf", + "choices": [ + { + "index": 6, + "finish_reason": "stop", + "delta": {"role": "assistant", "content": "you?"}, + } + ], + } +} + + +class StreamResponsePayload(ChatCompletionChunk, ResponseModel): + class Config: + if IS_PYDANTIC_V2_OR_NEWER: + json_schema_extra = _STREAM_RESPONSE_PAYLOAD_EXTRA_SCHEMA + else: + schema_extra = _STREAM_RESPONSE_PAYLOAD_EXTRA_SCHEMA diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gateway/schemas/completions.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gateway/schemas/completions.py new file mode 100644 index 0000000000000000000000000000000000000000..e23f34e7cf1baad651d8767247ae56921a3c1a4c --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gateway/schemas/completions.py @@ -0,0 +1,107 @@ +from mlflow.gateway.base_models import RequestModel, ResponseModel +from mlflow.types.chat import BaseRequestPayload +from mlflow.utils import IS_PYDANTIC_V2_OR_NEWER + +_REQUEST_PAYLOAD_EXTRA_SCHEMA = { + "example": { + "prompt": "hello", + "temperature": 0.0, + "max_tokens": 64, + "stop": ["END"], + "n": 1, + } +} + + +class RequestPayload(BaseRequestPayload, RequestModel): + prompt: str + model: str | None = None + + class Config: + if IS_PYDANTIC_V2_OR_NEWER: + json_schema_extra = _REQUEST_PAYLOAD_EXTRA_SCHEMA + else: + schema_extra = _REQUEST_PAYLOAD_EXTRA_SCHEMA + + +class Choice(ResponseModel): + index: int + text: str + finish_reason: str | None = None + + +class CompletionsUsage(ResponseModel): + prompt_tokens: int | None = None + completion_tokens: int | None = None + total_tokens: int | None = None + + +_RESPONSE_PAYLOAD_EXTRA_SCHEMA = { + "example": { + "id": "cmpl-123", + "object": "text_completion", + "created": 1589478378, + "model": "gpt-4", + "choices": [ + {"text": "Hello! I am an AI Assistant!", "index": 0, "finish_reason": "length"} + ], + "usage": {"prompt_tokens": 5, "completion_tokens": 7, "total_tokens": 12}, + } +} + + +class ResponsePayload(ResponseModel): + id: str | None = None + object: str = "text_completion" + created: int + model: str + choices: list[Choice] + usage: CompletionsUsage + + class Config: + if IS_PYDANTIC_V2_OR_NEWER: + json_schema_extra = _RESPONSE_PAYLOAD_EXTRA_SCHEMA + else: + schema_extra = _RESPONSE_PAYLOAD_EXTRA_SCHEMA + + +class StreamDelta(ResponseModel): + role: str | None = None + content: str | None = None + + +class StreamChoice(ResponseModel): + index: int + finish_reason: str | None = None + text: str | None = None + + +_STREAM_RESPONSE_PAYLOAD_EXTRA_SCHEMA = { + "example": { + "id": "cmpl-123", + "object": "text_completion", + "created": 1589478378, + "model": "gpt-4", + "choices": [ + { + "index": 6, + "finish_reason": "stop", + "delta": {"role": "assistant", "content": "you?"}, + } + ], + } +} + + +class StreamResponsePayload(ResponseModel): + id: str | None = None + object: str = "text_completion_chunk" + created: int + model: str + choices: list[StreamChoice] + + class Config: + if IS_PYDANTIC_V2_OR_NEWER: + json_schema_extra = _STREAM_RESPONSE_PAYLOAD_EXTRA_SCHEMA + else: + schema_extra = _STREAM_RESPONSE_PAYLOAD_EXTRA_SCHEMA diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gateway/schemas/embeddings.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gateway/schemas/embeddings.py new file mode 100644 index 0000000000000000000000000000000000000000..3fb56e82ba044d551bafa8b50ef3936f44a02cab --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gateway/schemas/embeddings.py @@ -0,0 +1,95 @@ +from mlflow.gateway.base_models import RequestModel, ResponseModel +from mlflow.utils import IS_PYDANTIC_V2_OR_NEWER + +_REQUEST_PAYLOAD_EXTRA_SCHEMA = { + "example": { + "input": ["hello", "world"], + } +} + + +class RequestPayload(RequestModel): + input: str | list[str] | list[int] | list[list[int]] + + class Config: + if IS_PYDANTIC_V2_OR_NEWER: + json_schema_extra = _REQUEST_PAYLOAD_EXTRA_SCHEMA + else: + schema_extra = _REQUEST_PAYLOAD_EXTRA_SCHEMA + + +class EmbeddingObject(ResponseModel): + object: str = "embedding" + embedding: list[float] | str + index: int + + +class EmbeddingsUsage(ResponseModel): + prompt_tokens: int | None = None + total_tokens: int | None = None + + +_RESPONSE_PAYLOAD_EXTRA_SCHEMA = { + "object": "list", + "data": [ + { + "object": "embedding", + "index": 0, + "embedding": [ + 0.017291732, + -0.017291732, + 0.014577783, + -0.02902633, + -0.037271563, + 0.019333655, + -0.023055641, + -0.007359971, + -0.015818445, + -0.030654699, + 0.008348623, + 0.018312693, + -0.017149571, + -0.0044424757, + -0.011165961, + 0.01018377, + ], + }, + { + "object": "embedding", + "index": 1, + "embedding": [ + 0.0060126893, + -0.008691099, + -0.0040095365, + 0.019889368, + 0.036211833, + -0.0013270887, + 0.013401738, + -0.0036735237, + -0.0049594184, + 0.035229642, + -0.03435084, + 0.019798903, + -0.0006110424, + 0.0073793563, + 0.005657291, + 0.022487005, + ], + }, + ], + "model": "text-embedding-ada-002-v2", + "usage": {"prompt_tokens": 400, "total_tokens": 400}, +} + + +class ResponsePayload(ResponseModel): + object: str = "list" + data: list[EmbeddingObject] + model: str + usage: EmbeddingsUsage + + class Config: + if IS_PYDANTIC_V2_OR_NEWER: + json_schema_extra = _RESPONSE_PAYLOAD_EXTRA_SCHEMA + else: + schema_extra = _RESPONSE_PAYLOAD_EXTRA_SCHEMA diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gateway/uc_function_utils.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gateway/uc_function_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..c2789239b3a8a175aeadc7528997ab888e40e52f --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gateway/uc_function_utils.py @@ -0,0 +1,321 @@ +# TODO: Move this in mlflow/gateway/utils/uc_functions.py + +import json +import re +from dataclasses import dataclass +from io import StringIO +from typing import TYPE_CHECKING, Any, Literal + +if TYPE_CHECKING: + from databricks.sdk import WorkspaceClient + from databricks.sdk.service.catalog import FunctionInfo, FunctionParameterInfo + from databricks.sdk.service.sql import StatementParameterListItem + + +_UC_FUNCTION = "uc_function" + + +def uc_type_to_json_schema_type(uc_type_json: str | dict[str, Any]) -> dict[str, Any]: + """ + Converts the JSON representation of a Unity Catalog data type to the corresponding JSON schema + type. The conversion is lossy because we do not need to convert it back. + """ + # See https://docs.databricks.com/en/sql/language-manual/sql-ref-datatypes.html + # The actual type name in type_json is different from the corresponding SQL type name. + spark_struct_field_mapping = { + "long": {"type": "integer"}, + "binary": {"type": "string"}, + "boolean": {"type": "boolean"}, + "date": {"type": "string", "format": "date"}, + "double": {"type": "number"}, + "float": {"type": "number"}, + "integer": {"type": "integer"}, + "void": {"type": "null"}, + "short": {"type": "integer"}, + "string": {"type": "string"}, + "timestamp": {"type": "string", "format": "date-time"}, + "timestamp_ntz": {"type": "string", "format": "date-time"}, + "byte": {"type": "integer"}, + } + if isinstance(uc_type_json, str): + if t := spark_struct_field_mapping.get(uc_type_json): + return t + else: + if uc_type_json.startswith("decimal"): + return {"type": "number"} + elif uc_type_json.startswith("interval"): + raise TypeError(f"Type {uc_type_json} is not supported.") + else: + raise TypeError(f"Unknown type {uc_type_json}. Try upgrading this package.") + else: + assert isinstance(uc_type_json, dict) + type = uc_type_json["type"] + if type == "array": + element_type = uc_type_to_json_schema_type(uc_type_json["elementType"]) + return {"type": "array", "items": element_type} + elif type == "map": + key_type = uc_type_json["keyType"] + if key_type != "string": + raise TypeError(f"Only support STRING key type for MAP but got {key_type}.") + value_type = uc_type_to_json_schema_type(uc_type_json["valueType"]) + return { + "type": "object", + "additionalProperties": value_type, + } + elif type == "struct": + properties = {} + for field in uc_type_json["fields"]: + properties[field["name"]] = uc_type_to_json_schema_type(field["type"]) + return {"type": "object", "properties": properties} + else: + raise TypeError(f"Unknown type {uc_type_json}. Try upgrading this package.") + + +def extract_param_metadata(p: "FunctionParameterInfo") -> dict[str, Any]: + type_json = json.loads(p.type_json)["type"] + json_schema_type = uc_type_to_json_schema_type(type_json) + json_schema_type["name"] = p.name + json_schema_type["description"] = ( + (p.comment or "") + f" (default: {p.parameter_default})" if p.parameter_default else "" + ) + return json_schema_type + + +def get_func_schema(func: "FunctionInfo") -> dict[str, Any]: + parameters = func.input_params.parameters if func.input_params else [] + return { + "description": func.comment, + "name": _get_tool_name(func), + "parameters": { + "type": "object", + "properties": {p.name: extract_param_metadata(p) for p in parameters}, + "required": [p.name for p in parameters if p.parameter_default is None], + }, + } + + +@dataclass +class ParameterizedStatement: + statement: str + parameters: list["StatementParameterListItem"] + + +@dataclass +class FunctionExecutionResult: + """ + Result of executing a function. + We always use a string to present the result value for AI model to consume. + """ + + error: str | None = None + format: Literal["SCALAR", "CSV"] | None = None + value: str | None = None + truncated: bool | None = None + + def to_json(self) -> str: + data = {k: v for (k, v) in self.__dict__.items() if v is not None} + return json.dumps(data) + + +def is_scalar(function: "FunctionInfo") -> bool: + """ + Returns True if the function returns a single row instead of a table. + """ + from databricks.sdk.service.catalog import ColumnTypeName + + return function.data_type != ColumnTypeName.TABLE_TYPE + + +def get_execute_function_sql_stmt( + function: "FunctionInfo", + json_params: dict[str, Any], +) -> ParameterizedStatement: + from databricks.sdk.service.catalog import ColumnTypeName + from databricks.sdk.service.sql import StatementParameterListItem + + parts = [] + output_params = [] + if is_scalar(function): + parts.append(f"SELECT {function.full_name}(") + else: + parts.append(f"SELECT * FROM {function.full_name}(") + if function.input_params is None or function.input_params.parameters is None: + assert not json_params, "Function has no parameters but parameters were provided." + else: + args = [] + use_named_args = False + for p in function.input_params.parameters: + if p.name not in json_params: + if p.parameter_default is not None: + use_named_args = True + else: + raise ValueError(f"Parameter {p.name} is required but not provided.") + else: + arg_clause = "" + if use_named_args: + arg_clause += f"{p.name} => " + json_value = json_params[p.name] + if p.type_name in ( + ColumnTypeName.ARRAY, + ColumnTypeName.MAP, + ColumnTypeName.STRUCT, + ): + # Use from_json to restore values of complex types. + json_value_str = json.dumps(json_value) + # TODO: parametrize type + arg_clause += f"from_json(:{p.name}, '{p.type_text}')" + output_params.append( + StatementParameterListItem(name=p.name, value=json_value_str) + ) + elif p.type_name == ColumnTypeName.BINARY: + # Use ubbase64 to restore binary values. + arg_clause += f"unbase64(:{p.name})" + output_params.append(StatementParameterListItem(name=p.name, value=json_value)) + else: + arg_clause += f":{p.name}" + output_params.append( + StatementParameterListItem(name=p.name, value=json_value, type=p.type_text) + ) + args.append(arg_clause) + parts.append(",".join(args)) + parts.append(")") + # TODO: check extra params in kwargs + statement = "".join(parts) + return ParameterizedStatement(statement=statement, parameters=output_params) + + +def execute_function( + ws: "WorkspaceClient", + warehouse_id: str, + function: "FunctionInfo", + parameters: dict[str, Any], +) -> FunctionExecutionResult: + """ + Execute a function with the given arguments and return the result. + """ + try: + import pandas as pd + except ImportError as e: + raise ImportError( + "Could not import pandas python package. Please install it with `pip install pandas`." + ) from e + from databricks.sdk.service.sql import StatementState + + # TODO: async so we can run functions in parallel + parameterized_statement = get_execute_function_sql_stmt(function, parameters) + # TODO: make limits and wait timeout configurable + response = ws.statement_execution.execute_statement( + statement=parameterized_statement.statement, + warehouse_id=warehouse_id, + parameters=parameterized_statement.parameters, + wait_timeout="30s", + row_limit=100, + byte_limit=4096, + ) + status = response.status + assert status is not None, f"Statement execution failed: {response}" + if status.state != StatementState.SUCCEEDED: + error = status.error + assert error is not None, "Statement execution failed but no error message was provided." + return FunctionExecutionResult(error=f"{error.error_code}: {error.message}") + manifest = response.manifest + assert manifest is not None + truncated = manifest.truncated + result = response.result + assert result is not None, "Statement execution succeeded but no result was provided." + data_array = result.data_array + if is_scalar(function): + value = None + if data_array and len(data_array) > 0 and len(data_array[0]) > 0: + value = str(data_array[0][0]) # type: ignore + return FunctionExecutionResult(format="SCALAR", value=value, truncated=truncated) + else: + schema = manifest.schema + assert schema is not None and schema.columns is not None, ( + "Statement execution succeeded but no schema was provided." + ) + columns = [c.name for c in schema.columns] + if data_array is None: + data_array = [] + pdf = pd.DataFrame.from_records(data_array, columns=columns) + csv_buffer = StringIO() + pdf.to_csv(csv_buffer, index=False) + return FunctionExecutionResult( + format="CSV", value=csv_buffer.getvalue(), truncated=truncated + ) + + +def join_uc_functions(uc_functions: list[dict[str, Any]]): + calls = [ + f""" + +{json.dumps(request, indent=2)} + + + +{json.dumps(result, indent=2)} + +""".strip() + for (request, result) in uc_functions + ] + return "\n\n".join(calls) + + +def _get_tool_name(function: "FunctionInfo") -> str: + # The maximum function name length OpenAI supports is 64 characters. + return f"{function.catalog_name}__{function.schema_name}__{function.name}"[-64:] + + +@dataclass +class ParseResult: + tool_calls: list[dict[str, Any]] + tool_messages: list[dict[str, Any]] + + +_UC_REGEX = re.compile( + r""" + +(?P.*?) + + + +(?P.*?) + +""", + re.DOTALL, +) + + +def parse_uc_functions(content) -> ParseResult | None: + tool_calls = [] + tool_messages = [] + for m in _UC_REGEX.finditer(content): + c = m.group("uc_function_call") + g = m.group("uc_function_result") + tool_calls.append(json.loads(c)) + tool_messages.append(json.loads(g)) + + return ParseResult(tool_calls, tool_messages) if tool_calls else None + + +@dataclass +class TokenUsageAccumulator: + prompt_tokens: int = 0 + completions_tokens: int = 0 + total_tokens: int = 0 + + def update(self, usage_dict): + self.prompt_tokens += usage_dict.get("prompt_tokens", 0) + self.completions_tokens += usage_dict.get("completion_tokens", 0) + self.total_tokens += usage_dict.get("total_tokens", 0) + + def dict(self): + return { + "prompt_tokens": self.prompt_tokens, + "completion_tokens": self.completions_tokens, + "total_tokens": self.total_tokens, + } + + +def prepend_uc_functions(content, uc_functions): + return join_uc_functions(uc_functions) + "\n\n" + content diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gateway/utils.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gateway/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..2afe38955a007d32b76658a2a8e8c50e490d7e72 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gateway/utils.py @@ -0,0 +1,305 @@ +import base64 +import functools +import inspect +import json +import logging +import posixpath +import re +import textwrap +import warnings +from typing import Any, AsyncGenerator +from urllib.parse import urlparse + +from mlflow.environment_variables import MLFLOW_GATEWAY_URI +from mlflow.exceptions import MlflowException +from mlflow.gateway.constants import MLFLOW_AI_GATEWAY_MOSAICML_CHAT_SUPPORTED_MODEL_PREFIXES +from mlflow.utils.uri import append_to_uri_path + +_logger = logging.getLogger(__name__) +_gateway_uri: str | None = None + + +def is_valid_endpoint_name(name: str) -> bool: + """ + Check whether a string contains any URL reserved characters, spaces, or characters other + than alphanumeric, underscore, hyphen, and dot. + + Returns True if the string doesn't contain any of these characters. + """ + return bool(re.fullmatch(r"[\w\-\.]+", name)) + + +def check_configuration_route_name_collisions(config): + if "routes" in config: + raise MlflowException.invalid_parameter_value( + "The 'routes' field is not supported in the configuration file, " + "use 'endpoints' instead." + ) + routes = config.get("endpoints") or [] + if len(routes) < 2: + return + names = [route["name"] for route in routes] + if len(names) != len(set(names)): + raise MlflowException.invalid_parameter_value( + "Duplicate names found in endpoint configurations. Please remove the duplicate endpoint" + " name from the configuration to ensure that endpoints are created properly." + ) + + +def check_configuration_deprecated_fields(config): + routes = config.get("endpoints", []) + for route in routes: + if "route_type" in route: + raise MlflowException.invalid_parameter_value( + "The 'route_type' configuration key is not supported in the configuration file. " + "Use 'endpoint_type' instead." + ) + + +def kill_child_processes(parent_pid): + """ + Gracefully terminate or kill child processes from a main process + """ + import psutil + + parent = psutil.Process(parent_pid) + for child in parent.children(recursive=True): + try: + child.terminate() + except psutil.NoSuchProcess: + pass + _, still_alive = psutil.wait_procs(parent.children(), timeout=3) + for p in still_alive: + p.kill() + + +def _is_valid_uri(uri: str): + """ + Evaluates the basic structure of a provided gateway uri to determine if the scheme and + netloc are provided + """ + if uri == "databricks": + return True + try: + parsed = urlparse(uri) + return parsed.scheme == "databricks" or all([parsed.scheme, parsed.netloc]) + except ValueError: + return False + + +def _get_indent(s: str) -> str: + for l in s.splitlines(): + if l.startswith(" "): + return " " * (len(l) - len(l.lstrip())) + return "" + + +def _prepend(docstring: str | None, text: str) -> str: + if not docstring: + return text + + indent = _get_indent(docstring) + return f""" +{textwrap.indent(text, indent)} + +{docstring} +""" + + +def gateway_deprecated(obj): + msg = ( + "MLflow AI gateway is deprecated and has been replaced by the deployments API for " + "generative AI. See https://mlflow.org/docs/latest/llms/gateway/migration.html for " + "migration." + ) + warning = f""" +.. warning:: + + {msg} +""".strip() + if inspect.isclass(obj): + original = obj.__init__ + + @functools.wraps(original) + def wrapper(*args, **kwargs): + warnings.warn(msg, FutureWarning, stacklevel=2) + return original(*args, **kwargs) + + obj.__init__ = wrapper + obj.__init__.__doc__ = _prepend(obj.__init__.__doc__, warning) + return obj + else: + + @functools.wraps(obj) + def wrapper(*args, **kwargs): + warnings.warn(msg, FutureWarning, stacklevel=2) + return obj(*args, **kwargs) + + wrapper.__doc__ = _prepend(obj.__doc__, warning) + + return wrapper + + +@gateway_deprecated +def set_gateway_uri(gateway_uri: str): + """Sets the uri of a configured and running MLflow AI Gateway server in a global context. + Providing a valid uri and calling this function is required in order to use the MLflow + AI Gateway fluent APIs. + + Args: + gateway_uri: The full uri of a running MLflow AI Gateway server or, if running on + Databricks, "databricks". + """ + if not _is_valid_uri(gateway_uri): + raise MlflowException.invalid_parameter_value( + "The gateway uri provided is missing required elements. Ensure that the schema " + "and netloc are provided." + ) + + global _gateway_uri + _gateway_uri = gateway_uri + + +@gateway_deprecated +def get_gateway_uri() -> str: + """ + Returns the currently set MLflow AI Gateway server uri iff set. + If the Gateway uri has not been set by using ``set_gateway_uri``, an ``MlflowException`` + is raised. + """ + if _gateway_uri is not None: + return _gateway_uri + elif uri := MLFLOW_GATEWAY_URI.get(): + return uri + else: + raise MlflowException( + "No Gateway server uri has been set. Please either set the MLflow Gateway URI via " + "`mlflow.gateway.set_gateway_uri()` or set the environment variable " + f"{MLFLOW_GATEWAY_URI} to the running Gateway API server's uri" + ) + + +def assemble_uri_path(paths: list[str]) -> str: + """Assemble a correct URI path from a list of path parts. + + Args: + paths: A list of strings representing parts of a URI path. + + Returns: + A string representing the complete assembled URI path. + + """ + stripped_paths = [path.strip("/").lstrip("/") for path in paths if path] + return "/" + posixpath.join(*stripped_paths) if stripped_paths else "/" + + +def resolve_route_url(base_url: str, route: str) -> str: + """ + Performs a validation on whether the returned value is a fully qualified url (as the case + with Databricks) or requires the assembly of a fully qualified url by appending the + Route return route_url to the base url of the AI Gateway server. + + Args: + base_url: The base URL. Should include the scheme and domain, e.g., + ``http://127.0.0.1:6000``. + route: The route to be appended to the base URL, e.g., ``/api/2.0/gateway/routes/`` or, + in the case of Databricks, the fully qualified url. + + Returns: + The complete URL, either directly returned or formed and returned by joining the + base URL and the route path. + """ + return route if _is_valid_uri(route) else append_to_uri_path(base_url, route) + + +class SearchRoutesToken: + def __init__(self, index: int): + self._index = index + + @property + def index(self): + return self._index + + @classmethod + def decode(cls, encoded_token: str): + try: + decoded_token = base64.b64decode(encoded_token) + parsed_token = json.loads(decoded_token) + index = int(parsed_token.get("index")) + except Exception as e: + raise MlflowException.invalid_parameter_value( + f"Invalid SearchRoutes token: {encoded_token}. The index is not defined as a " + "value that can be represented as a positive integer." + ) from e + + if index < 0: + raise MlflowException.invalid_parameter_value( + f"Invalid SearchRoutes token: {encoded_token}. The index cannot be negative." + ) + + return cls(index=index) + + def encode(self) -> str: + token_json = json.dumps( + { + "index": self.index, + } + ) + encoded_token_bytes = base64.b64encode(bytes(token_json, "utf-8")) + return encoded_token_bytes.decode("utf-8") + + +def is_valid_mosiacml_chat_model(model_name: str) -> bool: + return any( + model_name.lower().startswith(supported) + for supported in MLFLOW_AI_GATEWAY_MOSAICML_CHAT_SUPPORTED_MODEL_PREFIXES + ) + + +def is_valid_ai21labs_model(model_name: str) -> bool: + return model_name in {"j2-ultra", "j2-mid", "j2-light"} + + +def strip_sse_prefix(s: str) -> str: + # https://html.spec.whatwg.org/multipage/server-sent-events.html + return re.sub(r"^data:\s+", "", s) + + +def to_sse_chunk(data: str) -> str: + # https://html.spec.whatwg.org/multipage/server-sent-events.html + return f"data: {data}\n\n" + + +def _find_boundary(buffer: bytes) -> int: + try: + return buffer.index(b"\n") + except ValueError: + return -1 + + +async def handle_incomplete_chunks( + stream: AsyncGenerator[bytes, Any], +) -> AsyncGenerator[bytes, Any]: + """ + Wraps a streaming response and handles incomplete chunks from the server. + See https://community.openai.com/t/incomplete-stream-chunks-for-completions-api/383520 + for more information. + """ + buffer = b"" + async for chunk in stream: + buffer += chunk + while (boundary := _find_boundary(buffer)) != -1: + yield buffer[:boundary] + buffer = buffer[boundary + 1 :] + + +async def make_streaming_response(resp): + from starlette.responses import StreamingResponse + + if isinstance(resp, AsyncGenerator): + return StreamingResponse( + (to_sse_chunk(d.json()) async for d in resp), + media_type="text/event-stream", + ) + else: + return await resp diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gemini/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gemini/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..976662bc5c79dc9e708dcb3a0a40bbbbb5323095 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gemini/__init__.py @@ -0,0 +1,92 @@ +""" +The ``mlflow.gemini`` module provides an API for tracing the interaction with Gemini models. +""" + +from mlflow.gemini.autolog import ( + async_patched_class_call, + patched_class_call, + patched_module_call, +) +from mlflow.utils.autologging_utils import autologging_integration, safe_patch + +FLAVOR_NAME = "gemini" + + +@autologging_integration(FLAVOR_NAME) +def autolog( + log_traces: bool = True, + disable: bool = False, + silent: bool = False, +): + """ + Enables (or disables) and configures autologging from Gemini to MLflow. + Currently, both legacy SDK google-generativeai and new SDK google-genai are supported. + Both synchronous and asynchronous calls are supported for the new SDK. + + Args: + log_traces: If ``True``, traces are logged for Gemini models. + If ``False``, no traces are collected during inference. Default to ``True``. + disable: If ``True``, disables the Gemini autologging. Default to ``False``. + silent: If ``True``, suppress all event logs and warnings from MLflow during Gemini + autologging. If ``False``, show all events and warnings. + """ + try: + from google import generativeai + + for method in ["generate_content", "count_tokens"]: + safe_patch( + FLAVOR_NAME, + generativeai.GenerativeModel, + method, + patched_class_call, + ) + + safe_patch( + FLAVOR_NAME, + generativeai.ChatSession, + "send_message", + patched_class_call, + ) + + safe_patch( + FLAVOR_NAME, + generativeai, + "embed_content", + patched_module_call, + ) + except ImportError: + pass + + try: + from google import genai + + # Since the genai SDK calls "_generate_content" iteratively within "generate_content", + # we need to patch both "generate_content" and "_generate_content". + for method in ["generate_content", "_generate_content", "count_tokens", "embed_content"]: + safe_patch( + FLAVOR_NAME, + genai.models.Models, + method, + patched_class_call, + ) + safe_patch( + FLAVOR_NAME, + genai.models.AsyncModels, + method, + async_patched_class_call, + ) + + safe_patch( + FLAVOR_NAME, + genai.chats.Chat, + "send_message", + patched_class_call, + ) + safe_patch( + FLAVOR_NAME, + genai.chats.AsyncChat, + "send_message", + async_patched_class_call, + ) + except ImportError: + pass diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gemini/__pycache__/__init__.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gemini/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..c5761ef3a6836a13a224c39f039371ab6e2c2043 Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gemini/__pycache__/__init__.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gemini/__pycache__/autolog.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gemini/__pycache__/autolog.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..6cf68fb26c833ba6369a30c99e23c9565e8e808e Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gemini/__pycache__/autolog.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gemini/__pycache__/chat.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gemini/__pycache__/chat.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..00d2aa373e52ada432690a08d381ae29b9390439 Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gemini/__pycache__/chat.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gemini/autolog.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gemini/autolog.py new file mode 100644 index 0000000000000000000000000000000000000000..94543251a0acb78bdcbc28a31056a194ef8300af --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gemini/autolog.py @@ -0,0 +1,228 @@ +import inspect +import logging + +import mlflow +import mlflow.gemini +from mlflow.entities import SpanType +from mlflow.gemini.chat import ( + convert_gemini_func_to_mlflow_chat_tool, +) +from mlflow.tracing.constant import SpanAttributeKey +from mlflow.tracing.provider import detach_span_from_context, set_span_in_context +from mlflow.tracing.utils import construct_full_inputs, set_span_chat_tools +from mlflow.utils.autologging_utils.config import AutoLoggingConfig + +try: + # This is for supporting the previous Google GenAI SDK + # https://github.com/google-gemini/generative-ai-python + from google import generativeai + + has_generativeai = True +except ImportError: + has_generativeai = False + +try: + from google import genai + + has_genai = True +except ImportError: + has_genai = False + +_logger = logging.getLogger(__name__) + + +def patched_class_call(original, self, *args, **kwargs): + """ + This method is used for patching class methods of gemini SDKs. + This patch creates a span and set input and output of the original method to the span. + """ + with TracingSession(original, self, args, kwargs) as manager: + output = original(self, *args, **kwargs) + manager.output = output + return output + + +async def async_patched_class_call(original, self, *args, **kwargs): + """ + This method is used for patching async class methods of gemini SDKs. + This patch creates a span and set input and output of the original method to the span. + """ + async with TracingSession(original, self, args, kwargs) as manager: + output = await original(self, *args, **kwargs) + manager.output = output + return output + + +class TracingSession: + """Context manager for handling MLflow spans in both sync and async contexts.""" + + def __init__(self, original, instance, args, kwargs): + self.original = original + self.instance = instance + self.inputs = construct_full_inputs(original, instance, *args, **kwargs) + + # These attributes are set outside the constructor. + self.span = None + self.token = None + self.output = None + + def __enter__(self): + return self._enter_impl() + + def __exit__(self, exc_type, exc_val, exc_tb): + self._exit_impl(exc_type, exc_val, exc_tb) + + async def __aenter__(self): + return self._enter_impl() + + async def __aexit__(self, exc_type, exc_val, exc_tb): + self._exit_impl(exc_type, exc_val, exc_tb) + + def _enter_impl(self): + config = AutoLoggingConfig.init(flavor_name=mlflow.gemini.FLAVOR_NAME) + if not config.log_traces: + return self + + self.span = mlflow.start_span_no_context( + name=f"{self.instance.__class__.__name__}.{self.original.__name__}", + span_type=_get_span_type(self.original.__name__), + inputs=self.inputs, + attributes={SpanAttributeKey.MESSAGE_FORMAT: "gemini"}, + ) + if has_generativeai and isinstance(self.instance, generativeai.GenerativeModel): + _log_generativeai_tool_definition(self.instance, self.span) + + if _is_genai_model_or_chat(self.instance): + _log_genai_tool_definition(self.instance, self.inputs, self.span) + + # Attach the span to the current context. This is necessary because single Gemini + # SDK call might create multiple child spans. + self.token = set_span_in_context(self.span) + return self + + def _exit_impl(self, exc_type, exc_val, exc_tb) -> None: + if not self.span: + return + + # Detach span from the context at first. This must not be interrupted by any exception, + # otherwise the span context will leak and pollute other traces created next. + detach_span_from_context(self.token) + + if exc_val: + self.span.record_exception(exc_val) + + # need to convert the response of generate_content for better visualization + outputs = self.output.to_dict() if hasattr(self.output, "to_dict") else self.output + self.span.end(outputs=outputs) + + +def _is_genai_model_or_chat(instance) -> bool: + return has_genai and isinstance( + instance, + ( + genai.models.Models, + genai.chats.Chat, + genai.models.AsyncModels, + genai.chats.AsyncChat, + ), + ) + + +def patched_module_call(original, *args, **kwargs): + """ + This method is used for patching standalone functions of the google.generativeai module. + This patch creates a span and set input and output of the original function to the span. + """ + config = AutoLoggingConfig.init(flavor_name=mlflow.gemini.FLAVOR_NAME) + if not config.log_traces: + return original(*args, **kwargs) + + with mlflow.start_span( + name=f"{original.__name__}", + span_type=_get_span_type(original.__name__), + ) as span: + inputs = _construct_full_inputs(original, *args, **kwargs) + span.set_inputs(inputs) + span.set_attribute(SpanAttributeKey.MESSAGE_FORMAT, "gemini") + result = original(*args, **kwargs) + # need to convert the response of generate_content for better visualization + outputs = result.to_dict() if hasattr(result, "to_dict") else result + span.set_outputs(outputs) + + return result + + +def _get_keys(dic, keys): + for key in keys: + if key in dic: + return dic[key] + + return None + + +def _log_generativeai_tool_definition(model, span): + """ + This method extract tool definition from generativeai tool type. + """ + # when tools are not passed + if not getattr(model, "_tools", None): + return + + try: + set_span_chat_tools( + span, + [ + convert_gemini_func_to_mlflow_chat_tool(func) + for func in model._tools.to_proto()[0].function_declarations + ], + ) + except Exception as e: + _logger.warning(f"Failed to set tool definitions for {span}. Error: {e}") + + +def _log_genai_tool_definition(model, inputs, span): + """ + This method extract tool definition from genai tool type. + """ + config = inputs.get("config") + tools = getattr(config, "tools", None) + if not tools: + return + # Here, we use an internal function of gemini library to convert callable to Tool schema to + # avoid having the same logic on mlflow side and there is no public attribute for Tool schema. + # https://github.com/googleapis/python-genai/blob/01b15e32d3823a58d25534bb6eea93f30bf82219/google/genai/_transformers.py#L662 + tools = genai._transformers.t_tools(model._api_client, tools) + + try: + set_span_chat_tools( + span, + [ + convert_gemini_func_to_mlflow_chat_tool(function_declaration) + for tool in tools + for function_declaration in tool.function_declarations + ], + ) + except Exception as e: + _logger.warning(f"Failed to set tool definitions for {span}. Error: {e}") + + +def _get_span_type(task_name: str) -> str: + span_type_mapping = { + "generate_content": SpanType.LLM, + "_generate_content": SpanType.LLM, + "send_message": SpanType.CHAT_MODEL, + "count_tokens": SpanType.LLM, + "embed_content": SpanType.EMBEDDING, + } + return span_type_mapping.get(task_name, SpanType.UNKNOWN) + + +def _construct_full_inputs(func, *args, **kwargs): + signature = inspect.signature(func) + # this method does not create copy. So values should not be mutated directly + arguments = signature.bind_partial(*args, **kwargs).arguments + + if "self" in arguments: + arguments.pop("self") + + return arguments diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gemini/chat.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gemini/chat.py new file mode 100644 index 0000000000000000000000000000000000000000..499fcd0a95ef34dfe74bed6a849e38eb767346f9 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/gemini/chat.py @@ -0,0 +1,112 @@ +import json +import logging +from typing import TYPE_CHECKING + +from mlflow.types.chat import ( + ChatTool, + Function, + FunctionParams, + FunctionToolDefinition, + ParamProperty, + ToolCall, +) + +if TYPE_CHECKING: + from google import genai + +_logger = logging.getLogger(__name__) + + +def convert_gemini_func_to_mlflow_chat_tool( + function_def: "genai.types.FunctionDeclaration", +) -> ChatTool: + """ + Convert Gemini function definition into MLflow's standard format (OpenAI compatible). + Ref: https://ai.google.dev/gemini-api/docs/function-calling + + Args: + function_def: A genai.types.FunctionDeclaration or genai.protos.FunctionDeclaration object + representing a function definition. + + Returns: + ChatTool: MLflow's standard tool definition object. + """ + return ChatTool( + type="function", + function=FunctionToolDefinition( + name=function_def.name, + description=function_def.description, + parameters=_convert_gemini_function_param_to_mlflow_function_param( + function_def.parameters + ), + ), + ) + + +def convert_gemini_func_call_to_mlflow_tool_call( + func_call: "genai.types.FunctionCall", +) -> ToolCall: + """ + Convert Gemini function call into MLflow's standard format (OpenAI compatible). + Ref: https://ai.google.dev/gemini-api/docs/function-calling + + Args: + func_call: A genai.types.FunctionCall or genai.protos.FunctionCall object + representing a single func call. + + Returns: + ToolCall: MLflow's standard tool call object. + """ + # original args object is not json serializable + args = func_call.args or {} + + return ToolCall( + # Gemini does not have func call id + id=func_call.name, + type="function", + function=Function(name=func_call.name, arguments=json.dumps(dict(args))), + ) + + +def _convert_gemini_param_property_to_mlflow_param_property(param_property) -> ParamProperty: + """ + Convert Gemini parameter property definition into MLflow's standard format (OpenAI compatible). + Ref: https://ai.google.dev/gemini-api/docs/function-calling + + Args: + param_property: A genai.types.Schema or genai.protos.Schema object + representing a parameter property. + + Returns: + ParamProperty: MLflow's standard param property object. + """ + type_name = param_property.type + type_name = type_name.name.lower() if hasattr(type_name, "name") else type_name.lower() + return ParamProperty( + description=param_property.description, + enum=param_property.enum, + type=type_name, + ) + + +def _convert_gemini_function_param_to_mlflow_function_param( + function_params: "genai.types.Schema", +) -> FunctionParams: + """ + Convert Gemini function parameter definition into MLflow's standard format (OpenAI compatible). + Ref: https://ai.google.dev/gemini-api/docs/function-calling + + Args: + function_params: A genai.types.Schema or genai.protos.Schema object + representing function parameters. + + Returns: + FunctionParams: MLflow's standard function parameter object. + """ + return FunctionParams( + properties={ + k: _convert_gemini_param_property_to_mlflow_param_property(v) + for k, v in function_params.properties.items() + }, + required=function_params.required, + ) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..0d7818d93b86b5e40f408b51f34929bf3b6304e1 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/__init__.py @@ -0,0 +1,61 @@ +from mlflow.genai import ( + datasets, + judges, + scorers, +) +from mlflow.genai.datasets import ( + create_dataset, + delete_dataset, + get_dataset, +) +from mlflow.genai.evaluation import evaluate, to_predict_fn +from mlflow.genai.labeling import ( + Agent, + LabelingSession, + ReviewApp, + create_labeling_session, + delete_labeling_session, + get_labeling_session, + get_labeling_sessions, + get_review_app, +) +from mlflow.genai.optimize import optimize_prompt +from mlflow.genai.prompts import ( + delete_prompt_alias, + load_prompt, + register_prompt, + search_prompts, + set_prompt_alias, +) +from mlflow.genai.scheduled_scorers import ( + ScorerScheduleConfig, +) +from mlflow.genai.scorers import Scorer, scorer + +__all__ = [ + "datasets", + "evaluate", + "to_predict_fn", + "Scorer", + "scorer", + "judges", + "scorers", + "create_dataset", + "delete_dataset", + "get_dataset", + "load_prompt", + "register_prompt", + "search_prompts", + "delete_prompt_alias", + "set_prompt_alias", + "optimize_prompt", + "ScorerScheduleConfig", + "Agent", + "LabelingSession", + "ReviewApp", + "get_review_app", + "create_labeling_session", + "get_labeling_sessions", + "get_labeling_session", + "delete_labeling_session", +] diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/__pycache__/__init__.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 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b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/datasets/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..95342f9b9bd3450e534825c0b2de9391d56270f5 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/datasets/__init__.py @@ -0,0 +1,65 @@ +""" +Databricks Agent Datasets Python SDK. For more details see Databricks Agent Evaluation: + + +The API docs can be found here: + +""" + +from mlflow.genai.datasets.evaluation_dataset import EvaluationDataset + +_ERROR_MSG = ( + "The `databricks-agents` package is required to use `mlflow.genai.datasets`. " + "Please install it with `pip install databricks-agents`." +) + + +def create_dataset( + uc_table_name: str, experiment_id: str | list[str] | None = None +) -> "EvaluationDataset": + """Create a dataset with the given name and associate it with the given experiment. + + Args: + uc_table_name: The UC table name of the dataset. + experiment_id: The ID of the experiment to associate the dataset with. If not provided, + the current experiment is inferred from the environment. + """ + try: + from databricks.agents.datasets import create_dataset + except ImportError as e: + raise ImportError(_ERROR_MSG) from e + return EvaluationDataset(create_dataset(uc_table_name, experiment_id)) + + +def delete_dataset(uc_table_name: str) -> None: + """Delete the dataset with the given name. + + Args: + uc_table_name: The UC table name of the dataset. + """ + try: + from databricks.agents.datasets import delete_dataset + except ImportError: + raise ImportError(_ERROR_MSG) from None + return delete_dataset(uc_table_name) + + +def get_dataset(uc_table_name: str) -> "EvaluationDataset": + """Get the dataset with the given name. + + Args: + uc_table_name: The UC table name of the dataset. + """ + try: + from databricks.agents.datasets import get_dataset + except ImportError as e: + raise ImportError(_ERROR_MSG) from e + return EvaluationDataset(get_dataset(uc_table_name)) + + +__all__ = [ + "create_dataset", + "delete_dataset", + "get_dataset", + "EvaluationDataset", +] diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/datasets/__pycache__/__init__.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/datasets/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..ecf631d3672882839f89218073266d40bfa715b8 Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/datasets/__pycache__/__init__.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/datasets/__pycache__/databricks_evaluation_dataset_source.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/datasets/__pycache__/databricks_evaluation_dataset_source.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..d4e8e1e2715aeb9f5f148de3b26e439398b5d89c Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/datasets/__pycache__/databricks_evaluation_dataset_source.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/datasets/__pycache__/evaluation_dataset.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/datasets/__pycache__/evaluation_dataset.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..d7ad112c69c8df9601b9bcdecbc646c7d097c606 Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/datasets/__pycache__/evaluation_dataset.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/datasets/databricks_evaluation_dataset_source.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/datasets/databricks_evaluation_dataset_source.py new file mode 100644 index 0000000000000000000000000000000000000000..a826f48ec11b921bf989b95e7cc62eca8a5539dc --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/datasets/databricks_evaluation_dataset_source.py @@ -0,0 +1,77 @@ +from typing import Any + +from mlflow.data.dataset_source import DatasetSource + + +class DatabricksEvaluationDatasetSource(DatasetSource): + """ + Represents a Databricks Evaluation Dataset source. + + This source is used for datasets managed by the Databricks agents SDK. + """ + + def __init__(self, table_name: str, dataset_id: str): + """ + Args: + table_name: The three-level UC table name of the dataset + dataset_id: The unique identifier of the dataset + """ + self._table_name = table_name + self._dataset_id = dataset_id + + @property + def table_name(self) -> str: + """The UC table name of the dataset.""" + return self._table_name + + @property + def dataset_id(self) -> str: + """The unique identifier of the dataset.""" + return self._dataset_id + + @staticmethod + def _get_source_type() -> str: + return "databricks_evaluation_dataset" + + def load(self, **kwargs) -> Any: + """ + Loads the dataset from the source. + + This method is not implemented as the dataset should be loaded through + the databricks.agents.datasets API. + """ + raise NotImplementedError( + "Loading a Databricks Evaluation Dataset from source is not supported" + ) + + @staticmethod + def _can_resolve(raw_source: dict[str, Any]) -> bool: + """ + Determines whether the source can be resolved from a dictionary representation. + """ + # Resolution from a dictionary representation is not supported for Databricks Evaluation + # Datasets + return False + + @classmethod + def _resolve(cls, raw_source: dict[str, Any]): + """ + Resolves the source from a dictionary representation. + """ + raise NotImplementedError("Resolution from a source dictionary is not supported") + + def to_dict(self) -> dict[str, Any]: + """ + Returns a dictionary representation of the source. + """ + return { + "table_name": self._table_name, + "dataset_id": self._dataset_id, + } + + @classmethod + def from_dict(cls, source_dict: dict[str, Any]) -> "DatabricksEvaluationDatasetSource": + """ + Creates an instance from a dictionary representation. + """ + return cls(table_name=source_dict["table_name"], dataset_id=source_dict["dataset_id"]) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/datasets/evaluation_dataset.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/datasets/evaluation_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..a733fde6d94e6123c2e0a87bde6a3eca630f8ff1 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/datasets/evaluation_dataset.py @@ -0,0 +1,132 @@ +from typing import TYPE_CHECKING, Any, Union + +from mlflow.data import Dataset +from mlflow.data.dataset_source import DatasetSource +from mlflow.data.digest_utils import compute_pandas_digest +from mlflow.data.evaluation_dataset import EvaluationDataset as LegacyEvaluationDataset +from mlflow.data.pyfunc_dataset_mixin import PyFuncConvertibleDatasetMixin +from mlflow.entities import Dataset as DatasetEntity +from mlflow.genai.datasets.databricks_evaluation_dataset_source import ( + DatabricksEvaluationDatasetSource, +) + +if TYPE_CHECKING: + import pandas as pd + import pyspark + from databricks.agents.datasets import Dataset as ManagedDataset + + +class EvaluationDataset(Dataset, PyFuncConvertibleDatasetMixin): + """ + A dataset for storing evaluation records (inputs and expectations). + + Currently, this class is only supported for Databricks managed datasets. + To use this class, you must have the `databricks-agents` package installed. + """ + + def __init__(self, dataset: "ManagedDataset"): + self._dataset = dataset + self._df = None + self._digest = None + + @property + def dataset_id(self) -> str: + """The unique identifier of the dataset.""" + return self._dataset.dataset_id + + @property + def digest(self) -> str | None: + """String digest (hash) of the dataset provided by the caller that uniquely identifies""" + # NB: The managed Dataset entity in Agent SDK doesn't propagate the digest + # information. So we compute the digest of the dataframe view. + if self._digest is None: + self._digest = self._dataset.digest or compute_pandas_digest(self.to_df()) + return self._digest + + @property + def name(self) -> str | None: + """The UC table name of the dataset.""" + return self._dataset.name + + @property + def schema(self) -> str | None: + """The schema of the dataset.""" + return self._dataset.schema + + @property + def profile(self) -> str | None: + """The profile of the dataset, summary statistics.""" + return self._dataset.profile + + @property + def source(self) -> DatasetSource: + """Source information for the dataset.""" + return DatabricksEvaluationDatasetSource(table_name=self.name, dataset_id=self.dataset_id) + + @property + def source_type(self) -> str | None: + """The type of the dataset source, e.g. "databricks-uc-table", "DBFS", "S3", ...""" + return self._dataset.source_type + + @property + def create_time(self) -> str | None: + """The time the dataset was created.""" + return self._dataset.create_time + + @property + def created_by(self) -> str | None: + """The user who created the dataset.""" + return self._dataset.created_by + + @property + def last_update_time(self) -> str | None: + """The time the dataset was last updated.""" + return self._dataset.last_update_time + + @property + def last_updated_by(self) -> str | None: + """The user who last updated the dataset.""" + return self._dataset.last_updated_by + + def set_profile(self, profile: str) -> "EvaluationDataset": + """Set the profile of the dataset.""" + dataset = self._dataset.set_profile(profile) + return EvaluationDataset(dataset) + + def merge_records( + self, + records: Union[list[dict[str, Any]], "pd.DataFrame", "pyspark.sql.DataFrame"], + ) -> "EvaluationDataset": + """Merge records into the dataset.""" + dataset = self._dataset.merge_records(records) + return EvaluationDataset(dataset) + + def to_df(self) -> "pd.DataFrame": + """Convert the dataset to a pandas DataFrame.""" + # Cache the dataframe view to avoid re-fetching the records many times + if self._df is None: + self._df = self._dataset.to_df() + return self._df + + def to_evaluation_dataset(self, path=None, feature_names=None) -> LegacyEvaluationDataset: + """ + Converts the dataset to the legacy EvaluationDataset for model evaluation. Required + for use with mlflow.evaluate(). + """ + return LegacyEvaluationDataset( + data=self.to_df(), + path=path, + feature_names=feature_names, + name=self.name, + digest=self.digest, + ) + + def _to_mlflow_entity(self) -> DatasetEntity: + return DatasetEntity( + name=self.name, + digest=self.digest, + source_type=self.source_type, + source=self.source.to_json(), + schema=self.schema, + profile=self.profile, + ) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/evaluation/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/evaluation/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..4ee11ac3f986565e8577e3c793b58a0378704a43 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/evaluation/__init__.py @@ -0,0 +1,3 @@ +from mlflow.genai.evaluation.base import evaluate, 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b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/evaluation/base.py @@ -0,0 +1,494 @@ +import logging +import os +import time +import warnings +from contextlib import nullcontext +from typing import TYPE_CHECKING, Any, Callable + +import mlflow +from mlflow.data.dataset import Dataset +from mlflow.entities.dataset_input import DatasetInput +from mlflow.entities.logged_model_input import LoggedModelInput +from mlflow.environment_variables import MLFLOW_GENAI_EVAL_MAX_WORKERS +from mlflow.exceptions import MlflowException +from mlflow.genai.datasets import EvaluationDataset +from mlflow.genai.evaluation.constant import InputDatasetColumn +from mlflow.genai.evaluation.utils import ( + _convert_scorer_to_legacy_metric, + _convert_to_eval_set, +) +from mlflow.genai.scorers import Scorer +from mlflow.genai.scorers.builtin_scorers import GENAI_CONFIG_NAME, BuiltInScorer +from mlflow.genai.scorers.validation import valid_data_for_builtin_scorers, validate_scorers +from mlflow.genai.utils.display_utils import display_evaluation_output +from mlflow.genai.utils.trace_utils import clean_up_extra_traces, convert_predict_fn +from mlflow.models.evaluation.base import ( + EvaluationResult, + _is_model_deployment_endpoint_uri, + _start_run_or_reuse_active_run, +) +from mlflow.models.evaluation.utils.trace import configure_autologging_for_evaluation +from mlflow.tracing.constant import ( + DATABRICKS_OPTIONS_KEY, + DATABRICKS_OUTPUT_KEY, + RETURN_TRACE_OPTION_KEY, +) +from mlflow.tracing.utils.copy import copy_trace_to_experiment +from mlflow.tracking.client import MlflowClient +from mlflow.tracking.fluent import _set_active_model +from mlflow.utils.annotations import experimental +from mlflow.utils.mlflow_tags import MLFLOW_RUN_IS_EVALUATION +from mlflow.utils.uri import is_databricks_uri + +if TYPE_CHECKING: + from mlflow.genai.evaluation.utils import EvaluationDatasetTypes + + +logger = logging.getLogger(__name__) + + +@experimental(version="3.0.0") +def evaluate( + data: "EvaluationDatasetTypes", + scorers: list[Scorer], + predict_fn: Callable[..., Any] | None = None, + model_id: str | None = None, +) -> EvaluationResult: + """ + Evaluate the performance of a generative AI model/application using specified + data and scorers. + + This function allows you to evaluate a model's performance on a given dataset + using various scoring criteria. It supports both built-in scorers provided by + MLflow and custom scorers. The evaluation results include metrics and detailed + per-row assessments. + + There are three different ways to use this function: + + **1. Use Traces to evaluate the model/application.** + + The `data` parameter takes a DataFrame with `trace` column, which contains a + single trace object corresponding to the prediction for the row. This dataframe + is easily obtained from the existing traces stored in MLflow, by using the + :py:func:`mlflow.search_traces` function. + + .. code-block:: python + + import mlflow + from mlflow.genai.scorers import Correctness, Safety + import pandas as pd + + # model_id is a string starting with "m-", e.g. "m-074689226d3b40bfbbdf4c3ff35832cd" + trace_df = mlflow.search_traces(model_id="") + + mlflow.genai.evaluate( + data=trace_df, + scorers=[Correctness(), Safety()], + ) + + Built-in scorers will understand the model inputs, outputs, and other intermediate + information e.g. retrieved context, from the trace object. You can also access to + the trace object from the custom scorer function by using the `trace` parameter. + + .. code-block:: python + + from mlflow.genai.scorers import scorer + + + @scorer + def faster_than_one_second(inputs, outputs, trace): + return trace.info.execution_duration < 1000 + + **2. Use DataFrame or dictionary with "inputs", "outputs", "expectations" columns.** + + Alternatively, you can pass inputs, outputs, and expectations (ground truth) as + a column in the dataframe (or equivalent list of dictionaries). + + .. code-block:: python + + import mlflow + from mlflow.genai.scorers import Correctness + import pandas as pd + + data = pd.DataFrame( + [ + { + "inputs": {"question": "What is MLflow?"}, + "outputs": "MLflow is an ML platform", + "expectations": "MLflow is an ML platform", + }, + { + "inputs": {"question": "What is Spark?"}, + "outputs": "I don't know", + "expectations": "Spark is a data engine", + }, + ] + ) + + mlflow.genai.evaluate( + data=data, + scorers=[Correctness()], + ) + + **3. Pass `predict_fn` and input samples (and optionally expectations).** + + If you want to generate the outputs and traces on-the-fly from your input samples, + you can pass a callable to the `predict_fn` parameter. In this case, MLflow will + pass the inputs to the `predict_fn` as keyword arguments. Therefore, the "inputs" + column must be a dictionary with the parameter names as keys. + + .. code-block:: python + + import mlflow + from mlflow.genai.scorers import Correctness, Safety + import openai + + # Create a dataframe with input samples + data = pd.DataFrame( + [ + {"inputs": {"question": "What is MLflow?"}}, + {"inputs": {"question": "What is Spark?"}}, + ] + ) + + + # Define a predict function to evaluate. The "inputs" column will be + # passed to the prediction function as keyword arguments. + def predict_fn(question: str) -> str: + response = openai.OpenAI().chat.completions.create( + model="gpt-4o-mini", + messages=[{"role": "user", "content": question}], + ) + return response.choices[0].message.content + + + mlflow.genai.evaluate( + data=data, + predict_fn=predict_fn, + scorers=[Correctness(), Safety()], + ) + + Args: + data: Dataset for the evaluation. Must be one of the following formats: + + * An EvaluationDataset entity + * Pandas DataFrame + * Spark DataFrame + * List of dictionaries + + The dataset must include either of the following columns: + + 1. `trace` column that contains a single trace object corresponding + to the prediction for the row. + + If this column is present, MLflow extracts inputs, outputs, assessments, + and other intermediate information e.g. retrieved context, from the trace + object and uses them for scoring. When this column is present, the + `predict_fn` parameter must not be provided. + + 2. `inputs`, `outputs`, `expectations` columns. + + Alternatively, you can pass inputs, outputs, and expectations(ground + truth) as a column in the dataframe (or equivalent list of dictionaries). + + - inputs (required): Column containing inputs for evaluation. The value + must be a dictionary. When `predict_fn` is provided, MLflow will pass + the inputs to the `predict_fn` as keyword arguments. For example, + + * predict_fn: `def predict_fn(question: str, context: str) -> str` + * inputs: `{"question": "What is MLflow?", "context": "MLflow is an ML platform"}` + * `predict_fn` will receive "What is MLflow?" as the first argument + (`question`) and "MLflow is an ML platform" as the second argument (`context`) + + - outputs (optional): Column containing model or app outputs. + If this column is present, `predict_fn` must not be provided. + + - expectations (optional): Column containing a dictionary of ground truths. + + For list of dictionaries, each dict should follow the above schema. + + scorers: A list of Scorer objects that produces evaluation scores from + inputs, outputs, and other additional contexts. MLflow provides pre-defined + scorers, but you can also define custom ones. + + predict_fn: The target function to be evaluated. The specified function will be + executed for each row in the input dataset, and outputs will be used for + scoring. + + The function must emit a single trace per call. If it doesn't, decorate + the function with @mlflow.trace decorator to ensure a trace to be emitted. + + model_id: Optional model identifier (e.g. "m-074689226d3b40bfbbdf4c3ff35832cd") + to associate with the evaluation results. Can be also set globally via the + :py:func:`mlflow.set_active_model` function. + + Returns: + An :py:class:`mlflow.models.EvaluationResult~` object. + + Note: + This function is only supported on Databricks. The tracking URI must be + set to Databricks. + + .. warning:: + + This function is not thread-safe. Please do not use it in multi-threaded + environments. + """ + is_managed_dataset = isinstance(data, EvaluationDataset) + + scorers = validate_scorers(scorers) + # convert into a pandas dataframe with expected evaluation set schema + df = data.to_df() if is_managed_dataset else _convert_to_eval_set(data) + + builtin_scorers = [scorer for scorer in scorers if isinstance(scorer, BuiltInScorer)] + valid_data_for_builtin_scorers(df, builtin_scorers, predict_fn) + + # "request" column must exist after conversion + input_key = "inputs" if is_managed_dataset else "request" + sample_input = df.iloc[0][input_key] + + # Only check 'inputs' column when it is not derived from the trace object + if "trace" not in df.columns and not isinstance(sample_input, dict): + raise MlflowException.invalid_parameter_value( + "The 'inputs' column must be a dictionary of field names and values. " + "For example: {'query': 'What is MLflow?'}" + ) + + # If the input dataset is a managed dataset, we pass the original dataset + # to the evaluate function to preserve metadata like dataset name. + data = data if is_managed_dataset else df + + if predict_fn: + predict_fn = convert_predict_fn(predict_fn=predict_fn, sample_input=sample_input) + + eval_start_time = int(time.time() * 1000) + + if is_databricks_uri(mlflow.get_tracking_uri()): + result = _evaluate_dbx(data, scorers, predict_fn, model_id) + else: + result = _evaluate_oss(data, scorers, predict_fn, model_id) + + # Clean up noisy traces generated during evaluation + clean_up_extra_traces(result.run_id, eval_start_time) + + return result + + +def _evaluate_oss(data, scorers, predict_fn, model_id): + from mlflow.genai.evaluation import harness + + if isinstance(data, EvaluationDataset): + mlflow_dataset = data + df = data.to_df() + else: + # Rename 'request' / 'response' column back to 'inputs' / 'outputs'. + # This is a temporary hack to avoid branching _convert_to_eval_set() + # into OSS and DBX implementation. + data = data.rename( + columns={ + "request": InputDatasetColumn.INPUTS, + "response": InputDatasetColumn.OUTPUTS, + } + ) + mlflow_dataset = mlflow.data.from_pandas(df=data) + df = data + + with ( + _start_run_or_reuse_active_run() as run_id, + _set_active_model(model_id=model_id) if model_id else nullcontext(), + # NB: Auto-logging should be enabled outside the thread pool to avoid race conditions. + configure_autologging_for_evaluation(enable_tracing=True), + ): + _log_dataset_input(mlflow_dataset, run_id, model_id) + + # NB: Set this tag before run finishes to suppress the generic run URL printing. + MlflowClient().set_tag(run_id, MLFLOW_RUN_IS_EVALUATION, "true") + + result = harness.run( + predict_fn=predict_fn, + eval_df=df, + scorers=scorers, + run_id=run_id, + ) + + try: + display_evaluation_output(run_id) + except Exception: + logger.debug("Failed to display summary and usage instructions", exc_info=True) + + return result + + +def _evaluate_dbx(data, scorers, predict_fn, model_id): + """In Databricks, we run GenAI evaluation using databricks-agents package and + the mlflow.evaluate() function. This is a temporary migration state and we will + eventually unify this into OSS flow. + """ + + # NB: The "RAG_EVAL_MAX_WORKERS" env var is used in the DBX agent harness, but is + # deprecated in favor of the new "MLFLOW_GENAI_EVAL_MAX_WORKERS" env var. The old + # one is not publicly documented, but we keep it for backward compatibility. + if MLFLOW_GENAI_EVAL_MAX_WORKERS.is_set() and "RAG_EVAL_MAX_WORKERS" not in os.environ: + os.environ["RAG_EVAL_MAX_WORKERS"] = str(MLFLOW_GENAI_EVAL_MAX_WORKERS.get()) + elif "RAG_EVAL_MAX_WORKERS" in os.environ: + logger.warning( + "The `RAG_EVAL_MAX_WORKERS` environment variable is deprecated. " + "Please use `MLFLOW_GENAI_EVAL_MAX_WORKERS` instead." + ) + + with warnings.catch_warnings(): + warnings.filterwarnings( + "ignore", + message=r"Hint: Inferred schema contains integer column\(s\).*", + category=UserWarning, + ) + # Suppress numpy warning about ragged nested sequences. This is raised when passing + # a dataset that contains complex object to mlflow.evaluate(). MLflow converts data + # into numpy array to compute dataset digest, which triggers the warning. + warnings.filterwarnings( + "ignore", + message=r"Creating an ndarray from ragged nested sequences", + module="mlflow.data.evaluation_dataset", + ) + + return mlflow.models.evaluate( + model=predict_fn, + data=data, + evaluator_config={GENAI_CONFIG_NAME: {"metrics": []}}, # Turn off the default metrics + # Scorers are passed to the eval harness as extra metrics + extra_metrics=[_convert_scorer_to_legacy_metric(_scorer) for _scorer in scorers], + model_type=GENAI_CONFIG_NAME, + model_id=model_id, + _called_from_genai_evaluate=True, + ) + + +def _log_dataset_input( + data: Dataset, + run_id: str, + model_id: str | None = None, +): + client = MlflowClient() + dataset_input = DatasetInput(dataset=data._to_mlflow_entity()) + client.log_inputs( + run_id=run_id, + datasets=[dataset_input], + models=[LoggedModelInput(model_id=model_id)] if model_id else None, + ) + + +@experimental(version="3.0.0") +def to_predict_fn(endpoint_uri: str) -> Callable[..., Any]: + """ + Convert an endpoint URI to a predict function. + + Args: + endpoint_uri: The endpoint URI to convert. + + Returns: + A predict function that can be used to make predictions. + + Example: + + The following example assumes that the model serving endpoint accepts a JSON + object with a `messages` key. Please adjust the input based on the actual + schema of the model serving endpoint. + + .. code-block:: python + + from mlflow.genai.scorers import get_all_scorers + + data = [ + { + "inputs": { + "messages": [ + {"role": "system", "content": "You are a helpful assistant."}, + {"role": "user", "content": "What is MLflow?"}, + ] + } + }, + { + "inputs": { + "messages": [ + {"role": "system", "content": "You are a helpful assistant."}, + {"role": "user", "content": "What is Spark?"}, + ] + } + }, + ] + predict_fn = mlflow.genai.to_predict_fn("endpoints:/chat") + mlflow.genai.evaluate( + data=data, + predict_fn=predict_fn, + scorers=get_all_scorers(), + ) + + You can also directly invoke the function to validate if the endpoint works + properly with your input schema. + + .. code-block:: python + + predict_fn(**data[0]["inputs"]) + """ + if not _is_model_deployment_endpoint_uri(endpoint_uri): + raise ValueError( + f"Invalid endpoint URI: {endpoint_uri}. The endpoint URI must be a valid model " + f"deployment endpoint URI." + ) + + from mlflow.deployments import get_deploy_client + from mlflow.metrics.genai.model_utils import _parse_model_uri + + client = get_deploy_client("databricks") + _, endpoint = _parse_model_uri(endpoint_uri) + endpoint_info = client.get_endpoint(endpoint) + + # Databricks Foundation Model API does not allow passing "databricks_options" in the payload, + # so we need to handle this case separately. + is_fmapi = False + if isinstance(endpoint_info, dict): + is_fmapi = endpoint_info.get("endpoint_type") == "FOUNDATION_MODEL_API" + + # NB: Wrap the function to show better docstring and change signature to `model_inputs` + # to unnamed keyword arguments. This is necessary because we pass input samples as + # keyword arguments to the predict function. + def predict_fn(**kwargs): + start_time_ms = int(time.time_ns() / 1e6) + # Inject `{"databricks_options": {"return_trace": True}}` to the input payload + # to return the trace in the response. + databricks_options = {DATABRICKS_OPTIONS_KEY: {RETURN_TRACE_OPTION_KEY: True}} + payload = kwargs if is_fmapi else {**kwargs, **databricks_options} + result = client.predict(endpoint=endpoint, inputs=payload) + end_time_ms = int(time.time_ns() / 1e6) + + # If the endpoint returns a trace, copy it to the current experiment. + if trace_dict := result.pop(DATABRICKS_OUTPUT_KEY, {}).get("trace"): + try: + copy_trace_to_experiment(trace_dict) + return result + except Exception: + logger.debug( + "Failed to copy trace from the endpoint response to the current experiment. " + "Trace will only have a root span with request and response.", + exc_info=True, + ) + + # If the endpoint doesn't return a trace, manually create a trace with request/response. + mlflow.log_trace( + name="predict", + request=kwargs, + response=result, + start_time_ms=start_time_ms, + execution_time_ms=end_time_ms - start_time_ms, + ) + return result + + predict_fn.__doc__ = f""" +A wrapper function for invoking the model serving endpoint `{endpoint_uri}`. + +Args: + **kwargs: The input samples to be passed to the model serving endpoint. + For example, if the endpoint accepts a JSON object with a `messages` key, + the function also expects to get `messages` as an argument. + """ + return predict_fn diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/evaluation/constant.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/evaluation/constant.py new file mode 100644 index 0000000000000000000000000000000000000000..06f2af868aa528e6cf4eca4a2e3875c1e6220789 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/evaluation/constant.py @@ -0,0 +1,44 @@ +class AgentEvaluationReserverKey: + """ + Expectation column names that are used by Agent Evaluation. + Ref: https://docs.databricks.com/aws/en/generative-ai/agent-evaluation/evaluation-schema + """ + + EXPECTED_RESPONSE = "expected_response" + EXPECTED_RETRIEVED_CONTEXT = "expected_retrieved_context" + EXPECTED_FACTS = "expected_facts" + GUIDELINES = "guidelines" + + @classmethod + def get_all(cls) -> set[str]: + return { + cls.EXPECTED_RESPONSE, + cls.EXPECTED_RETRIEVED_CONTEXT, + cls.EXPECTED_FACTS, + cls.GUIDELINES, + } + + +# A column name for storing custom expectations dictionary in Agent Evaluation. +AGENT_EVAL_CUSTOM_EXPECTATION_KEY = "custom_expected" + + +# Input dataset column names +class InputDatasetColumn: + REQUEST_ID = "request_id" + INPUTS = "inputs" + REQUEST = "request" + RESPONSE = "response" + OUTPUTS = "outputs" + EXPECTATIONS = "expectations" + TRACE = "trace" + + +# Result Dataframe column names +class ResultDataFrameColumn: + REQUEST_ID = "request_id" + INPUTS = "inputs" + OUTPUTS = "outputs" + EXPECTATIONS = "expectations" + TRACE = "trace" + ERROR_MESSAGE = "error_message" diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/evaluation/context.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/evaluation/context.py new file mode 100644 index 0000000000000000000000000000000000000000..5c3a260f7c53f00bfa572a7518680cde4b5840c1 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/evaluation/context.py @@ -0,0 +1,145 @@ +""" +Introduces main Context class and the framework to specify different specialized +contexts. +""" + +import functools +from abc import ABC, abstractmethod +from typing import Callable, ParamSpec, TypeVar + +import mlflow +from mlflow.tracking.context import registry as context_registry +from mlflow.utils.mlflow_tags import MLFLOW_USER + +P = ParamSpec("P") +R = TypeVar("R") + + +class Context(ABC): + """ + Abstract class for execution context. + Context is stateless and should NOT be used to store information related to specific eval run. + """ + + @abstractmethod + def get_mlflow_experiment_id(self) -> str | None: + """ + Get the current MLflow experiment ID, or None if not running within an MLflow experiment. + """ + + @abstractmethod + def get_mlflow_run_id(self) -> str | None: + """ + Gets the MLflow RunId, or None if not running within an MLflow run. + """ + + @abstractmethod + def get_user_name(self) -> str: + """ + Get the current user's name. + """ + + +class NoneContext(Context): + """ + A context that does nothing. + """ + + def get_mlflow_experiment_id(self) -> str | None: + raise NotImplementedError("Context is not set") + + def get_mlflow_run_id(self) -> str | None: + raise NotImplementedError("Context is not set") + + def get_user_name(self) -> str: + raise NotImplementedError("Context is not set") + + +class RealContext(Context): + """ + Context for eval execution. + + NOTE: This class is not covered by unit tests and is meant to be tested through + smoke tests that run this code on an actual Databricks cluster. + """ + + def __init__(self): + self._run_id = None + self._context_tags = context_registry.resolve_tags() + + def get_mlflow_experiment_id(self) -> str | None: + # Note `_get_experiment_id` is thread-safe + return mlflow.tracking.fluent._get_experiment_id() + + def get_mlflow_run_id(self) -> str | None: + """ + Gets the MLflow run_id the evaluation harness is running under. + + Warning: This run_id may not be active. This happens when `get_mlflow_run_id` is called from + a different thread than the one that started the MLflow run. + """ + # First check if a run ID is specified explicitly by the parent thread + if self._run_id: + return self._run_id + + # Otherwise fall back to the active run in the current thread + if run := mlflow.active_run(): + return run.info.run_id + + return None + + def set_mlflow_run_id(self, run_id: str) -> None: + """ + Set the MLflow run ID explicitly. + + This method should be called when running code in a different thread than the one that + started the MLflow run. It sets the run ID in a thread-local variable so that it can be + accessed from the thread. + """ + self._run_id = run_id + + def get_user_name(self) -> str: + return self._context_tags.get(MLFLOW_USER, "unknown") + + +# Context is a singleton. +_context_singleton = NoneContext() + + +def context_is_active() -> bool: + """ + Check if a context is active. + """ + return not isinstance(get_context(), NoneContext) + + +def get_context() -> Context: + """ + Get the context. + """ + return _context_singleton + + +def eval_context(func: Callable[P, R]) -> Callable[P, R]: + """ + Decorator for wrapping all eval APIs with setup and closure logic. + + Sets up a context singleton with RealContext if there isn't one already. + """ + + @functools.wraps(func) + def wrapper(*args: P.args, **kwargs: P.kwargs) -> R: + # Set up the context singleton if it doesn't exist + if not context_is_active(): + global _context_singleton + _context_singleton = RealContext() + + return func(*args, **kwargs) + + return wrapper + + +def _set_context(context: Context) -> None: + """SHOULD ONLY BE USED FOR TESTING.""" + global _context_singleton + _context_singleton = context diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/evaluation/entities.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/evaluation/entities.py new file mode 100644 index 0000000000000000000000000000000000000000..6bda6369f58e109b573dc7c722c56cd76fc3b79c --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/evaluation/entities.py @@ -0,0 +1,168 @@ +"""Entities for evaluation.""" + +import hashlib +import json +from dataclasses import dataclass, field +from typing import Any + +import pandas as pd + +from mlflow.entities.assessment import Expectation, Feedback +from mlflow.entities.assessment_source import AssessmentSource, AssessmentSourceType +from mlflow.entities.trace import Trace +from mlflow.exceptions import MlflowException +from mlflow.genai.evaluation.constant import InputDatasetColumn, ResultDataFrameColumn +from mlflow.genai.evaluation.context import get_context +from mlflow.genai.evaluation.utils import is_none_or_nan + + +@dataclass +class EvalItem: + """Represents a row in the evaluation dataset.""" + + """Unique identifier for the eval item.""" + request_id: str + + """Raw input to the model/application when `evaluate` is called.""" + inputs: dict[str, Any] + + """Raw output from the model/application.""" + outputs: Any + + """Expectations from the eval item.""" + expectations: dict[str, Any] + + """Trace of the model invocation.""" + trace: Trace | None = None + + """Error message if the model invocation fails.""" + error_message: str | None = None + + @classmethod + def from_dataset_row(cls, row: dict[str, Any]) -> "EvalItem": + """ + Create an EvalItem from a row of input Pandas Dataframe row. + """ + inputs = cls._parse_inputs(row.get(InputDatasetColumn.INPUTS)) + outputs = row.get(InputDatasetColumn.OUTPUTS) + + # Get the request ID from the row, or generate a new unique ID if not present. + request_id = row.get(InputDatasetColumn.REQUEST_ID) + if is_none_or_nan(request_id): + request_id = hashlib.sha256(str(inputs).encode()).hexdigest() + + # Extract trace column from the dataset. + trace = row.get(InputDatasetColumn.TRACE) + if is_none_or_nan(trace): + trace = None + else: + trace = trace if isinstance(trace, Trace) else Trace.from_json(trace) + + # Extract expectations column from the dataset. + expectations = row.get(InputDatasetColumn.EXPECTATIONS, {}) + + return cls( + request_id=request_id, + inputs=inputs, + outputs=outputs, + expectations=expectations, + trace=trace, + ) + + @classmethod + def _parse_inputs(cls, data: str | dict[str, Any]) -> dict[str, Any]: + # The inputs can be either a dictionary or JSON-serialized version of it. + if isinstance(data, dict): + return data + elif isinstance(data, str): # JSON-serialized string + try: + return json.loads(data) + except Exception as e: + raise MlflowException.invalid_parameter_value( + "Failed to parse inputs as JSON." + ) from e + + raise MlflowException.invalid_parameter_value( + f"inputs must be a dictionary or JSON serializable: {type(data)}" + ) + + def get_expectation_assessments(self) -> list[Expectation]: + """Get the expectations as a list of Expectation objects.""" + expectations = [] + for name, value in self.expectations.items(): + source_id = get_context().get_user_name() + expectations.append( + Expectation( + trace_id=self.trace.info.trace_id, + name=name, + source=AssessmentSource( + source_type=AssessmentSourceType.HUMAN, + source_id=source_id or "unknown", + ), + value=value, + ) + ) + return expectations + + def to_dict(self) -> dict[str, Any]: + inputs = { + ResultDataFrameColumn.REQUEST_ID: self.request_id, + ResultDataFrameColumn.INPUTS: self.inputs, + ResultDataFrameColumn.OUTPUTS: self.outputs, + ResultDataFrameColumn.TRACE: self.trace.to_json(), + ResultDataFrameColumn.EXPECTATIONS: self.expectations, + ResultDataFrameColumn.ERROR_MESSAGE: self.error_message, + } + return {k: v for k, v in inputs.items() if v is not None} + + +@dataclass +class EvalResult: + """Holds the result of the evaluation for an eval item.""" + + eval_item: EvalItem + """A collection of assessments from scorers.""" + assessments: list[Feedback] = field(default_factory=list) + """Error message encountered in processing the eval item.""" + eval_error: str | None = None + + def to_pd_series(self) -> pd.Series: + """Converts the EvalResult to a flattened pd.Series.""" + inputs = self.eval_item.to_dict() + assessments = self.get_assessments_dict() + + # Merge dictionaries and convert to pd.Series + return pd.Series(inputs | assessments) + + def get_assessments_dict(self) -> dict[str, Any]: + result = {} + for assessment in self.assessments: + if not isinstance(assessment, Feedback): + continue + + result |= { + f"{assessment.name}/value": assessment.value, + f"{assessment.name}/rationale": assessment.rationale, + f"{assessment.name}/error_message": assessment.error_message, + f"{assessment.name}/error_code": assessment.error_code, + } + + return result + + +@dataclass +class EvaluationResult: + run_id: str + metrics: dict[str, float] + result_df: pd.DataFrame + + def __repr__(self) -> str: + metrics_str = "\n ".join([f"{k}: {v}" for k, v in self.metrics.items()]) + return ( + "EvaluationResult(\n" + f" run_id: {self.run_id}\n" + " metrics:\n" + f" {metrics_str}\n" + f" result_df: [{len(self.result_df)} rows x {len(self.result_df.columns)} cols]\n" + ")" + ) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/evaluation/harness.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/evaluation/harness.py new file mode 100644 index 0000000000000000000000000000000000000000..7f2402033bde11ed5f02f548178c9ab36325ab32 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/evaluation/harness.py @@ -0,0 +1,206 @@ +"""Entry point to the evaluation harness""" + +from __future__ import annotations + +import logging +import traceback +import uuid +from concurrent.futures import ThreadPoolExecutor, as_completed +from typing import Any, Callable + +import pandas as pd + +import mlflow +from mlflow.entities.assessment import Assessment, Feedback +from mlflow.entities.assessment_error import AssessmentError +from mlflow.entities.trace import Trace +from mlflow.environment_variables import MLFLOW_GENAI_EVAL_MAX_WORKERS +from mlflow.genai.evaluation import context +from mlflow.genai.evaluation.entities import EvalItem, EvalResult, EvaluationResult +from mlflow.genai.evaluation.utils import ( + complete_eval_futures_with_progress_base, + make_code_type_assessment_source, + standardize_scorer_value, +) +from mlflow.genai.scorers.aggregation import compute_aggregated_metrics +from mlflow.genai.scorers.base import Scorer +from mlflow.genai.utils.trace_utils import create_minimal_trace +from mlflow.pyfunc.context import Context, set_prediction_context +from mlflow.tracing.constant import AssessmentMetadataKey + +_logger = logging.getLogger(__name__) + + +@context.eval_context +def run( + *, + eval_df: pd.DataFrame, + predict_fn=None, + scorers=None, + run_id: str | None = None, +) -> EvaluationResult: + """ + Runs GenAI evaluation harness to the given dataset. + + The overall flow is: + + 1. Convert the dataset to a list of EvalItem objects + 2. Run the prediction and scoring for each EvalItem in parallel + a. If predict_fn is provided, invoke the predict_fn for the EvalItem + b. If predict_fn is not provided, create a dummy trace for the EvalItem + c. Execute the scorers to compute assessments. + d. Log the assessments to the trace. + 3. Compute the aggregated metrics from the assessments. + """ + eval_items = [EvalItem.from_dataset_row(row) for row in eval_df.to_dict(orient="records")] + + run_id = context.get_context().get_mlflow_run_id() if run_id is None else run_id + + with ThreadPoolExecutor( + max_workers=MLFLOW_GENAI_EVAL_MAX_WORKERS.get(), + thread_name_prefix="MlflowGenAIEvalHarness", + ) as executor: + futures = [ + executor.submit( + _run_single, + eval_item=eval_item, + scorers=scorers, + predict_fn=predict_fn, + run_id=run_id, + ) + for eval_item in eval_items + ] + eval_results = complete_eval_futures_with_progress_base(futures) + + # Aggregate metrics and log to MLflow run + aggregated_metrics = compute_aggregated_metrics(eval_results, scorers=scorers) + mlflow.log_metrics(aggregated_metrics) + + eval_results_df = pd.DataFrame([result.to_pd_series() for result in eval_results]) + return EvaluationResult( + run_id=run_id, + result_df=eval_results_df, + metrics=aggregated_metrics, + ) + + +def _run_single( + eval_item: EvalItem, + scorers: list[Scorer], + run_id: str | None, + predict_fn: Callable[..., Any] | None = None, +) -> EvalResult: + """Run the logic of the eval harness for a single eval item.""" + # Set the MLflow run ID in the context for this thread + if run_id: + # Manually set the mlflow_run_id for this context to be the same as was set in + # the parent thread. This is required because MLflow runs are thread-local. + ctx = context.get_context() + ctx.set_mlflow_run_id(run_id) + + # TODO: Support another pattern that are currently supported in the DBX agent harness, + # which is when traces are given as dataset + if predict_fn: + # NB: Setting prediction context let us retrieve the trace by a custom ID. Setting + # is_evaluate=True disables async trace logging to make sure the trace is available. + eval_request_id = str(uuid.uuid4()) + with set_prediction_context(Context(request_id=eval_request_id, is_evaluate=True)): + try: + eval_item.outputs = predict_fn(eval_item.inputs) + except Exception as e: + eval_item.error_message = ( + f"Failed to invoke the predict_fn with {eval_item.inputs}: {e}" + ) + + eval_item.trace = mlflow.get_trace(eval_request_id, silent=True) + else: + # When static dataset (a pair of inputs and outputs) is given, we create a minimal + # trace with root span only, to log the assessments on it. + minimal_trace = create_minimal_trace(eval_item) + eval_item.trace = minimal_trace + + # Execute the scorers + assessments = _compute_eval_scores(eval_item=eval_item, scorers=scorers) + assessments.extend(eval_item.get_expectation_assessments()) + eval_result = EvalResult(eval_item=eval_item, assessments=assessments) + + try: + logged_trace = _log_assessments( + run_id=run_id, + trace=eval_item.trace, + assessments=eval_result.assessments, + ) + eval_result.eval_item.trace = logged_trace + except Exception as e: + # Failures in logging to MLflow should not fail the entire harness run + _logger.warning(f"Failed to log trace and assessments to MLflow: {e}") + + return eval_result + + +def _compute_eval_scores( + *, + eval_item: EvalItem, + scorers: list[Scorer], +) -> list[Feedback]: + """Compute the per-eval-item scores.""" + if not scorers: + return [] + + def run_scorer(scorer): + try: + value = scorer.run( + inputs=eval_item.inputs, + outputs=eval_item.outputs, + expectations=eval_item.expectations, + trace=eval_item.trace, + ) + return standardize_scorer_value(scorer.name, value) + except Exception as e: + error_assessment = Feedback( + name=scorer.name, + source=make_code_type_assessment_source(scorer.name), + error=AssessmentError( + error_code="SCORER_ERROR", + error_message=str(e), + stack_trace=traceback.format_exc(), + ), + ) + return [error_assessment] + + # Use a thread pool to run scorers in parallel + with ThreadPoolExecutor( + max_workers=len(scorers), + thread_name_prefix="MlflowGenAIEvalScorer", + ) as executor: + futures = [executor.submit(run_scorer, scorer) for scorer in scorers] + + try: + results = [future.result() for future in as_completed(futures)] + except KeyboardInterrupt: + # Cancel pending futures + executor.shutdown(cancel_futures=True) + raise + + # Flatten list[list[Assessment]] into a single list[Assessment] + return [assessment for sublist in results for assessment in sublist] + + +def _log_assessments( + run_id: str | None, + trace: Trace, + assessments: list[Assessment], +) -> Trace: + for assessment in assessments: + # Ensure that if we created a new trace, that the updated trace_id is reflected in + # the assessments. + assessment.trace_id = trace.info.trace_id + if run_id is not None: + assessment.metadata = { + **(assessment.metadata or {}), + AssessmentMetadataKey.SOURCE_RUN_ID: run_id, + } + mlflow.log_assessment(trace_id=assessment.trace_id, assessment=assessment) + + # Get the trace to fetch newly created assessments. + return mlflow.get_trace(trace.info.trace_id) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/evaluation/utils.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/evaluation/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..9f8e1c0fbc95008f8f70dd2757abec70fdd835b0 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/evaluation/utils.py @@ -0,0 +1,369 @@ +import json +import logging +import math +from concurrent.futures import Future, as_completed +from typing import TYPE_CHECKING, Any, Collection + +from mlflow.entities import Assessment, Trace +from mlflow.entities.assessment import DEFAULT_FEEDBACK_NAME, Feedback +from mlflow.entities.assessment_source import AssessmentSource, AssessmentSourceType +from mlflow.exceptions import MlflowException +from mlflow.genai.evaluation.constant import ( + AgentEvaluationReserverKey, +) +from mlflow.genai.scorers import Scorer +from mlflow.models import EvaluationMetric + +try: + # `pandas` is not required for `mlflow-skinny`. + import pandas as pd +except ImportError: + pass + +if TYPE_CHECKING: + from mlflow.genai.datasets import EvaluationDataset + from mlflow.genai.evaluation.entities import EvalResult + + try: + import pyspark.sql.dataframe + + EvaluationDatasetTypes = ( + pd.DataFrame | pyspark.sql.dataframe.DataFrame | list[dict] | EvaluationDataset + ) + except ImportError: + EvaluationDatasetTypes = pd.DataFrame | list[dict] | EvaluationDataset + + +_logger = logging.getLogger(__name__) + +USER_DEFINED_ASSESSMENT_NAME_KEY = "_user_defined_assessment_name" +PGBAR_FORMAT = ( + "{l_bar}{bar}| {n_fmt}/{total_fmt} [Elapsed: {elapsed}, Remaining: {remaining}] {postfix}" +) + + +def _convert_eval_set_to_df(data: "EvaluationDatasetTypes") -> "pd.DataFrame": + """ + Takes in a dataset in the format that `mlflow.genai.evaluate()` expects and + converts it into a pandas DataFrame. + """ + if isinstance(data, list): + # validate that every item in the list is a dict and has inputs as key + for item in data: + if not isinstance(item, dict): + raise MlflowException.invalid_parameter_value( + "Every item in the list must be a dictionary." + ) + df = pd.DataFrame(data) + elif isinstance(data, pd.DataFrame): + # Data is already a pd DataFrame, just copy it + df = data.copy() + else: + try: + from mlflow.utils.spark_utils import get_spark_dataframe_type + + if isinstance(data, get_spark_dataframe_type()): + df = _deserialize_inputs_and_expectations_column(data.toPandas()) + else: + raise MlflowException.invalid_parameter_value( + "Invalid type for parameter `data`. Expected a list of dictionaries, " + f"a pandas DataFrame, or a Spark DataFrame. Got: {type(data)}" + ) + except ImportError: + raise ImportError( + "The `pyspark` package is required to use mlflow.genai.evaluate() " + "Please install it with `pip install pyspark`." + ) + + if len(df) == 0: + raise MlflowException.invalid_parameter_value( + "The dataset is empty. Please provide a non-empty dataset." + ) + + if not any(col in df.columns for col in ("trace", "inputs")): + raise MlflowException.invalid_parameter_value( + "Either `inputs` or `trace` column is required in the dataset. Please provide inputs " + "for every datapoint or provide a trace." + ) + + return df + + +def _convert_to_eval_set(data: "EvaluationDatasetTypes") -> "pd.DataFrame": + """ + Takes in a dataset in the multiple format that mlflow.genai.evaluate() expects and converts it + into standardized Pandas DataFrame. + The expected schema can be found at: + https://docs.databricks.com/aws/en/generative-ai/agent-evaluation/evaluation-schema + + NB: The harness secretly support 'expectations' column as well. It accepts a dictionary of + expectations, which is same as the schema that mlflow.genai.evaluate() expects. + Therefore, we can simply pass through expectations column. + """ + column_mapping = { + "inputs": "request", + "outputs": "response", + } + + df = _convert_eval_set_to_df(data) + + return ( + df.rename(columns=column_mapping) + .pipe(_deserialize_trace_column_if_needed) + .pipe(_extract_request_response_from_trace) + .pipe(_extract_expectations_from_trace) + ) + + +def _deserialize_inputs_and_expectations_column(df: "pd.DataFrame") -> "pd.DataFrame": + """ + Deserialize the `inputs` and `expectations` string columns from the dataframe. + + When managed datasets are read as Spark DataFrames, the `inputs` and `expectations` columns + are loaded as string columns of JSON strings. This function deserializes these columns into + dictionaries expected by mlflow.genai.evaluate(). + """ + target_columns = ["inputs", "expectations"] + for col in target_columns: + if col not in df.columns or not isinstance(df[col][0], str): + continue + + try: + df[col] = df[col].apply(json.loads) + except json.JSONDecodeError as e: + if col == "inputs": + msg = ( + "The `inputs` column must be a valid JSON string of field names and values. " + "For example, `{'question': 'What is the capital of France?'}`" + ) + else: + msg = ( + "The `expectations` column must be a valid JSON string of assessment names and " + "values. For example, `{'expected_facts': ['fact1', 'fact2']}`" + ) + raise MlflowException.invalid_parameter_value( + f"Failed to parse `{col}` column. Error: {e}\nHint: {msg}" + ) + + return df + + +def _deserialize_trace_column_if_needed(df: "pd.DataFrame") -> "pd.DataFrame": + """ + Deserialize the `trace` column from the dataframe if it is a string. + + Since MLflow 3.2.0, mlflow.search_traces() returns a pandas DataFrame with a `trace` + column that is a trace json representation rather than the Trace object itself. This + function deserializes the `trace` column into a Trace object. + """ + if "trace" in df.columns: + df["trace"] = df["trace"].apply(lambda t: Trace.from_json(t) if isinstance(t, str) else t) + return df + + +def _extract_request_response_from_trace(df: "pd.DataFrame") -> "pd.DataFrame": + """ + Add `request` and `response`columns from traces if it is not already present. + """ + if "trace" not in df.columns: + return df + + if "request" not in df.columns: + df["request"] = df["trace"].apply(lambda trace: json.loads(trace.data.request)) + if "response" not in df.columns: + df["response"] = df["trace"].apply(lambda trace: json.loads(trace.data.response)) + return df + + +def _extract_expectations_from_trace(df: "pd.DataFrame") -> "pd.DataFrame": + """ + Add `expectations` columns to the dataframe from assessments + stored in the traces, if the "expectations" column is not already present. + """ + if "trace" not in df.columns: + return df + + expectations_column = [] + for trace in df["trace"]: + expectations = {} + for assessment in trace.info.assessments or []: + if assessment.expectation is not None: + expectations[assessment.name] = assessment.expectation.value + expectations_column.append(expectations) + # If no trace has assessments, not add the column + if all(len(expectations) == 0 for expectations in expectations_column): + return df + + df["expectations"] = expectations_column + return df + + +def _convert_scorer_to_legacy_metric(scorer: Scorer) -> EvaluationMetric: + """ + Takes in a Scorer object and converts it into a legacy MLflow 2.x + Metric object. + """ + try: + from databricks.agents.evals import metric + except ImportError: + raise ImportError( + "The `databricks-agents` package is required to use mlflow.genai.evaluate() " + "Please install it with `pip install databricks-agents`." + ) + + from mlflow.genai.scorers.builtin_scorers import BuiltInScorer + from mlflow.types.llm import ChatCompletionRequest + + def eval_fn( + request_id: str, + request: ChatCompletionRequest | str, + response: Any | None, + expected_response: Any | None, + trace: Trace | None, + guidelines: list[str] | dict[str, list[str]] | None, + expected_facts: list[str] | None, + expected_retrieved_context: list[dict[str, str]] | None, + custom_expected: dict[str, Any] | None, + **kwargs, + ) -> int | float | bool | str | Assessment | list[Assessment]: + # Condense all expectations into a single dict + expectations = {} + if expected_response is not None: + expectations[AgentEvaluationReserverKey.EXPECTED_RESPONSE] = expected_response + if expected_facts is not None: + expectations[AgentEvaluationReserverKey.EXPECTED_FACTS] = expected_facts + if expected_retrieved_context is not None: + expectations[AgentEvaluationReserverKey.EXPECTED_RETRIEVED_CONTEXT] = ( + expected_retrieved_context + ) + if guidelines is not None: + expectations[AgentEvaluationReserverKey.GUIDELINES] = guidelines + if custom_expected is not None: + expectations.update(custom_expected) + + merged = { + "inputs": request, + "outputs": response, + "expectations": expectations, + "trace": trace, + } + return scorer.run(**merged) + + metric_instance = metric( + eval_fn=eval_fn, + name=scorer.name, + aggregations=scorer.aggregations, + ) + # Add attribute to indicate if this is a built-in scorer + metric_instance._is_builtin_scorer = isinstance(scorer, BuiltInScorer) + + return metric_instance + + +def standardize_scorer_value(scorer_name: str, value: Any) -> list[Feedback]: + """ + Convert the scorer return value to a list of MLflow Assessment (Feedback) objects. + + Scorer can return: + - A number, boolean, or string, a list of them. + - An Feedback object + - A list of Feedback objects + + All of the above will be converted to a list of Feedback objects. + """ + # None is a valid metric value, return an empty list + if value is None: + return [] + + # Primitives are valid metric values + if isinstance(value, (int, float, bool, str)): + return [ + Feedback( + name=scorer_name, + source=make_code_type_assessment_source(scorer_name), + value=value, + ) + ] + + if isinstance(value, Feedback): + value.name = _get_custom_assessment_name(value, scorer_name) + return [value] + + if isinstance(value, Collection): + assessments = [] + for item in value: + if isinstance(item, Feedback): + # Scorer returns multiple assessments as a list. + item.name = _get_custom_assessment_name(item, scorer_name) + assessments.append(item) + else: + # If the item is not assessment, the list represents a single assessment + # value of list type. Convert it to a Feedback object. + assessments.append( + Feedback( + name=scorer_name, + source=make_code_type_assessment_source(scorer_name), + value=item, + ) + ) + return assessments + + raise MlflowException.invalid_parameter_value( + f"Got unsupported result from scorer '{scorer_name}'. " + f"Expected the metric value to be a number, or a boolean, or a string, " + "or an Feedback, or a list of Feedbacks. " + f"Got {value}.", + ) + + +def _get_custom_assessment_name(assessment: Feedback, scorer_name: str) -> str: + """Get the name of the custom assessment. Use assessment name if present and not a builtin judge + name, otherwise use the scorer name. + + Args: + assessment: The assessment to get the name for. + scorer_name: The name of the scprer. + """ + # If the user didn't provide a name, use the scorer name + if assessment.name == DEFAULT_FEEDBACK_NAME or ( + assessment.metadata is not None + and assessment.metadata.get(USER_DEFINED_ASSESSMENT_NAME_KEY) == "false" + ): + return scorer_name + return assessment.name + + +def make_code_type_assessment_source(scorer_name: str) -> AssessmentSource: + return AssessmentSource(source_type=AssessmentSourceType.CODE, source_id=scorer_name) + + +def is_none_or_nan(value: Any) -> bool: + """ + Checks whether a value is None or NaN. + + NB: This function does not handle pandas.NA. + """ + # isinstance(value, float) check is needed to ensure that math.isnan is not called on an array. + return value is None or (isinstance(value, float) and math.isnan(value)) + + +def complete_eval_futures_with_progress_base(futures: list[Future]) -> list["EvalResult"]: + """Wraps the as_completed function with a progress bar.""" + futures_as_completed = as_completed(futures) + + try: + from tqdm.auto import tqdm + + futures_as_completed = tqdm( + futures_as_completed, + total=len(futures), + disable=False, + desc="Evaluating", + smoothing=0, # 0 means using average speed for remaining time estimates + bar_format=PGBAR_FORMAT, + ) + except ImportError: + # If tqdm is not installed, we don't show a progress bar + pass + + return [future.result() for future in futures_as_completed] diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/judges/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/judges/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..df0ef935f6af2d73bc6ec8392be102668bea5b3e --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/judges/__init__.py @@ -0,0 +1,21 @@ +from mlflow.genai.judges.builtin import ( + is_context_relevant, + is_context_sufficient, + is_correct, + is_grounded, + is_safe, + meets_guidelines, +) +from mlflow.genai.judges.custom_prompt_judge import custom_prompt_judge +from mlflow.genai.judges.utils import CategoricalRating + +__all__ = [ + "CategoricalRating", + "is_grounded", + "is_safe", + "is_correct", + "is_context_relevant", + "is_context_sufficient", + "meets_guidelines", + "custom_prompt_judge", +] diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/judges/__pycache__/__init__.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/judges/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..4c402dcf9a0f2d3c386b246566047e6dd5b1ea70 Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/judges/__pycache__/__init__.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/judges/__pycache__/builtin.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/judges/__pycache__/builtin.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..3f4975dcd5da8c51ec3874a5aef1fd4b502b6b06 Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/judges/__pycache__/builtin.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/judges/__pycache__/custom_prompt_judge.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/judges/__pycache__/custom_prompt_judge.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..70cd359fba1943804f76cb6988f2a1ce6072e945 Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/judges/__pycache__/custom_prompt_judge.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/judges/__pycache__/utils.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/judges/__pycache__/utils.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..029692e244650a49549ca3167a463561d0b8e7ce Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/judges/__pycache__/utils.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/judges/builtin.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/judges/builtin.py new file mode 100644 index 0000000000000000000000000000000000000000..02fccdc03416015ab372dd4517086fddbfe78b9e --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/judges/builtin.py @@ -0,0 +1,443 @@ +from functools import wraps +from typing import Any + +from mlflow.entities.assessment import Feedback +from mlflow.genai.judges.prompts.relevance_to_query import RELEVANCE_TO_QUERY_ASSESSMENT_NAME +from mlflow.genai.judges.utils import CategoricalRating, get_default_model, invoke_judge_model +from mlflow.utils.docstring_utils import format_docstring + +_MODEL_API_DOC = { + "model": """Judge model to use. Must be either `"databricks"` or a form of +`:/`, such as `"openai:/gpt-4.1-mini"`, +`"anthropic:/claude-3.5-sonnet-20240620"`. MLflow natively supports +`["openai", "anthropic", "bedrock", "mistral"]`, and more providers are supported +through `LiteLLM `_. +Default model depends on the tracking URI setup: + +* Databricks: `databricks` +* Otherwise: `openai:/gpt-4.1-mini`. +""", +} + + +def _sanitize_feedback(feedback: Feedback) -> Feedback: + """Sanitize the feedback object from the databricks judges. + + The judge returns a CategoricalRating class defined in the databricks-agents package. + This function converts it to our CategoricalRating definition above. + + Args: + feedback: The Feedback object to convert. + + Returns: + A new Feedback object with our CategoricalRating. + """ + feedback.value = CategoricalRating(feedback.value) if feedback.value else feedback.value + return feedback + + +def requires_databricks_agents(func): + """Decorator to check if the `databricks-agents` package is installed.""" + + @wraps(func) + def wrapper(*args, **kwargs): + try: + import databricks.agents.evals.judges # noqa: F401 + + except ImportError: + raise ImportError( + f"The `databricks-agents` package is required to use " + f"`mlflow.genai.judges.{func.__name__}`. " + "Please install it with `pip install databricks-agents`." + ) + + return func(*args, **kwargs) + + return wrapper + + +@format_docstring(_MODEL_API_DOC) +def is_context_relevant( + *, request: str, context: Any, name: str | None = None, model: str | None = None +) -> Feedback: + """ + LLM judge determines whether the given context is relevant to the input request. + + Args: + request: Input to the application to evaluate, user's question or query. + context: Context to evaluate the relevance to the request. + Supports any JSON-serializable object. + name: Optional name for overriding the default name of the returned feedback. + model: {{ model }} + + Returns: + A :py:class:`mlflow.entities.assessment.Feedback~` object with a "yes" or "no" value + indicating whether the context is relevant to the request. + + Example: + + The following example shows how to evaluate whether a document retrieved by a + retriever is relevant to the user's question. + + .. code-block:: python + + from mlflow.genai.judges import is_context_relevant + + feedback = is_context_relevant( + request="What is the capital of France?", + context="Paris is the capital of France.", + ) + print(feedback.value) # "yes" + + feedback = is_context_relevant( + request="What is the capital of France?", + context="Paris is known for its Eiffel Tower.", + ) + print(feedback.value) # "no" + + """ + from mlflow.genai.judges.prompts.relevance_to_query import get_prompt + + model = model or get_default_model() + + # NB: User-facing name for the is_context_relevant assessment. This is required + # since the existing databricks judge is called `relevance_to_query` + assessment_name = name or RELEVANCE_TO_QUERY_ASSESSMENT_NAME + + if model == "databricks": + from databricks.agents.evals.judges import relevance_to_query + + feedback = relevance_to_query( + request=request, + response=str(context), + assessment_name=assessment_name, + ) + else: + prompt = get_prompt(request, str(context)) + feedback = invoke_judge_model(model, prompt, assessment_name=assessment_name) + + return _sanitize_feedback(feedback) + + +@format_docstring(_MODEL_API_DOC) +def is_context_sufficient( + *, + request: str, + context: Any, + expected_facts: list[str], + expected_response: str | None = None, + name: str | None = None, + model: str | None = None, +) -> Feedback: + """ + LLM judge determines whether the given context is sufficient to answer the input request. + + Args: + request: Input to the application to evaluate, user's question or query. + context: Context to evaluate the sufficiency of. Supports any JSON-serializable object. + expected_facts: A list of expected facts that should be present in the context. Optional. + expected_response: The expected response from the application. Optional. + name: Optional name for overriding the default name of the returned feedback. + model: {{ model }} + + Returns: + A :py:class:`mlflow.entities.assessment.Feedback~` object with a "yes" or "no" + value indicating whether the context is sufficient to answer the request. + + Example: + + The following example shows how to evaluate whether the documents returned by a + retriever gives sufficient context to answer the user's question. + + .. code-block:: python + + from mlflow.genai.judges import is_context_sufficient + + feedback = is_context_sufficient( + request="What is the capital of France?", + context=[ + {"content": "Paris is the capital of France."}, + {"content": "Paris is known for its Eiffel Tower."}, + ], + expected_facts=["Paris is the capital of France."], + ) + print(feedback.value) # "yes" + + feedback = is_context_sufficient( + request="What is the capital of France?", + context={"content": "France is a country in Europe."}, + expected_response="Paris is the capital of France.", + ) + print(feedback.value) # "no" + """ + from mlflow.genai.judges.prompts.context_sufficiency import ( + CONTEXT_SUFFICIENCY_FEEDBACK_NAME, + get_prompt, + ) + + model = model or get_default_model() + assessment_name = name or CONTEXT_SUFFICIENCY_FEEDBACK_NAME + + if model == "databricks": + from databricks.agents.evals.judges import context_sufficiency + + feedback = context_sufficiency( + request=request, + retrieved_context=context, + expected_facts=expected_facts, + expected_response=expected_response, + assessment_name=assessment_name, + ) + else: + prompt = get_prompt( + request=request, + context=context, + expected_response=expected_response, + expected_facts=expected_facts, + ) + feedback = invoke_judge_model(model, prompt, assessment_name=assessment_name) + + return _sanitize_feedback(feedback) + + +@format_docstring(_MODEL_API_DOC) +def is_correct( + *, + request: str, + response: str, + expected_facts: list[str] | None = None, + expected_response: str | None = None, + name: str | None = None, + model: str | None = None, +) -> Feedback: + """ + LLM judge determines whether the given response is correct for the input request. + + Args: + request: Input to the application to evaluate, user's question or query. + response: The response from the application to evaluate. + expected_facts: A list of expected facts that should be present in the response. Optional. + expected_response: The expected response from the application. Optional. + name: Optional name for overriding the default name of the returned feedback. + model: {{ model }} + + Returns: + A :py:class:`mlflow.entities.assessment.Feedback~` object with a "yes" or "no" + value indicating whether the response is correct for the request. + + Example: + + The following example shows how to evaluate whether the response is correct. + + .. code-block:: python + + from mlflow.genai.judges import is_correct + + feedback = is_correct( + request="What is the capital of France?", + response="Paris is the capital of France.", + expected_response="Paris", + ) + print(feedback.value) # "yes" + + feedback = is_correct( + request="What is the capital of France?", + response="London is the capital of France.", + expected_facts=["Paris is the capital of France"], + ) + print(feedback.value) # "no" + """ + from mlflow.genai.judges.prompts.correctness import CORRECTNESS_FEEDBACK_NAME, get_prompt + + model = model or get_default_model() + assessment_name = name or CORRECTNESS_FEEDBACK_NAME + + if model == "databricks": + from databricks.agents.evals.judges import correctness + + feedback = correctness( + request=request, + response=response, + expected_facts=expected_facts, + expected_response=expected_response, + assessment_name=assessment_name, + ) + else: + prompt = get_prompt( + request=request, + response=response, + expected_response=expected_response, + expected_facts=expected_facts, + ) + feedback = invoke_judge_model(model, prompt, assessment_name=assessment_name) + + return _sanitize_feedback(feedback) + + +@format_docstring(_MODEL_API_DOC) +def is_grounded( + *, + request: str, + response: str, + context: Any, + name: str | None = None, + model: str | None = None, +) -> Feedback: + """ + LLM judge determines whether the given response is grounded in the given context. + + Args: + request: Input to the application to evaluate, user's question or query. + response: The response from the application to evaluate. + context: Context to evaluate the response against. Supports any JSON-serializable object. + name: Optional name for overriding the default name of the returned feedback. + model: {{ model }} + + Returns: + A :py:class:`mlflow.entities.assessment.Feedback~` object with a "yes" or "no" + value indicating whether the response is grounded in the context. + + Example: + + The following example shows how to evaluate whether the response is grounded in + the context. + + .. code-block:: python + + from mlflow.genai.judges import is_grounded + + feedback = is_grounded( + request="What is the capital of France?", + response="Paris", + context=[ + {"content": "Paris is the capital of France."}, + {"content": "Paris is known for its Eiffel Tower."}, + ], + ) + print(feedback.value) # "yes" + + feedback = is_grounded( + request="What is the capital of France?", + response="London is the capital of France.", + context=[ + {"content": "Paris is the capital of France."}, + {"content": "Paris is known for its Eiffel Tower."}, + ], + ) + print(feedback.value) # "no" + """ + from mlflow.genai.judges.prompts.groundedness import GROUNDEDNESS_FEEDBACK_NAME, get_prompt + + model = model or get_default_model() + assessment_name = name or GROUNDEDNESS_FEEDBACK_NAME + + if model == "databricks": + from databricks.agents.evals.judges import groundedness + + feedback = groundedness( + request=request, + response=response, + retrieved_context=context, + assessment_name=assessment_name, + ) + else: + prompt = get_prompt( + request=request, + response=response, + context=context, + ) + feedback = invoke_judge_model(model, prompt, assessment_name=assessment_name) + + return _sanitize_feedback(feedback) + + +@requires_databricks_agents +def is_safe(*, content: str, name: str | None = None) -> Feedback: + """ + LLM judge determines whether the given response is safe. + + Args: + content: Text content to evaluate for safety. + name: Optional name for overriding the default name of the returned feedback. + + Returns: + A :py:class:`mlflow.entities.assessment.Feedback~` object with a "yes" or "no" + value indicating whether the response is safe. + + Example: + + .. code-block:: python + + from mlflow.genai.judges import is_safe + + feedback = is_safe(content="I am a happy person.") + print(feedback.value) # "yes" + """ + from databricks.agents.evals.judges import safety + + return _sanitize_feedback(safety(response=content, assessment_name=name)) + + +@format_docstring(_MODEL_API_DOC) +def meets_guidelines( + *, + guidelines: str | list[str], + context: dict[str, Any], + name: str | None = None, + model: str | None = None, +) -> Feedback: + """ + LLM judge determines whether the given response meets the given guideline(s). + + Args: + guidelines: A single guideline or a list of guidelines. + context: Mapping of context to be evaluated against the guidelines. For example, + pass {"response": ""} to evaluate whether the response meets + the given guidelines. + name: Optional name for overriding the default name of the returned feedback. + model: {{ model }} + + Returns: + A :py:class:`mlflow.entities.assessment.Feedback~` object with a "yes" or "no" + value indicating whether the response meets the guideline(s). + + Example: + + The following example shows how to evaluate whether the response meets the given + guideline(s). + + .. code-block:: python + + from mlflow.genai.judges import meets_guidelines + + feedback = meets_guidelines( + guidelines="Be polite and respectful.", + context={"response": "Hello, how are you?"}, + ) + print(feedback.value) # "yes" + + feedback = meets_guidelines( + guidelines=["Be polite and respectful.", "Must be in English."], + context={"response": "Hola, ¿cómo estás?"}, + ) + print(feedback.value) # "no" + """ + from mlflow.genai.judges.prompts.guidelines import GUIDELINES_FEEDBACK_NAME, get_prompt + + model = model or get_default_model() + + if model == "databricks": + from databricks.agents.evals.judges import guidelines as guidelines_judge + + feedback = guidelines_judge( + guidelines=guidelines, + context=context, + assessment_name=name, + ) + else: + prompt = get_prompt(guidelines, context) + feedback = invoke_judge_model( + model, prompt, assessment_name=name or GUIDELINES_FEEDBACK_NAME + ) + + return _sanitize_feedback(feedback) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/judges/custom_prompt_judge.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/judges/custom_prompt_judge.py new file mode 100644 index 0000000000000000000000000000000000000000..10f13756206f2d15000161e75e86ce7f78efb595 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/judges/custom_prompt_judge.py @@ -0,0 +1,167 @@ +import re +from difflib import unified_diff +from typing import Callable + +from mlflow.entities.assessment import Feedback +from mlflow.entities.assessment_source import AssessmentSource, AssessmentSourceType +from mlflow.genai.judges.builtin import _MODEL_API_DOC +from mlflow.genai.judges.utils import ( + format_prompt, + get_default_model, + invoke_judge_model, +) +from mlflow.utils.annotations import experimental +from mlflow.utils.docstring_utils import format_docstring + +_CHOICE_PATTERN = re.compile(r"\[\[([\w ]+)\]\]") + + +@format_docstring(_MODEL_API_DOC) +@experimental(version="3.0.0") +def custom_prompt_judge( + *, + name: str, + prompt_template: str, + numeric_values: dict[str, float] | None = None, + model: str | None = None, +) -> Callable[..., Feedback]: + """ + Create a custom prompt judge that evaluates inputs using a template. + + Args: + name: Name of the judge, used as the name of returned + :py:class:`mlflow.entities.Feedback` object. + prompt_template: Template string with {{var_name}} placeholders for variable substitution. + Should be prompted with choices as outputs. + numeric_values: Optional mapping from categorical values to numeric scores. + Useful if you want to create a custom judge that returns continuous valued outputs. + Defaults to None. + model: {{ model }} + + Returns: + A callable that takes keyword arguments mapping to the template variables + and returns an mlflow :py:class:`mlflow.entities.Feedback`. + + Example prompt template: + + .. code-block:: + + You will look at the response and determine the formality of the response. + + {{request}} + {{response}} + + You must choose one of the following categories. + + [[formal]]: The response is very formal. + [[semi_formal]]: The response is somewhat formal. The response is somewhat formal if the + response mentions friendship, etc. + [[not_formal]]: The response is not formal. + + Variable names in the template should be enclosed in double curly + braces, e.g., `{{request}}`, `{{response}}`. They should be alphanumeric and can include + underscores, but should not contain spaces or special characters. + + It is required for the prompt template to request choices as outputs, with each choice + enclosed in square brackets. Choice names should be alphanumeric and can include + underscores and spaces. + """ + model = model or get_default_model() + + if model == "databricks": + try: + from databricks.agents.evals.judges import custom_prompt_judge as db_custom_prompt_judge + + return db_custom_prompt_judge( + name=name, + prompt_template=prompt_template, + numeric_values=numeric_values, + ) + except ImportError: + raise ImportError( + "The `databricks-agents` package is required to use " + "`mlflow.genai.judges.custom_prompt_judge` with model='databricks'. " + "Please install it with `pip install databricks-agents`." + ) + + # Extract choices from the prompt template + choices = _CHOICE_PATTERN.findall(prompt_template) + + if not choices: + raise ValueError( + "Prompt template must include choices denoted with [[CHOICE_NAME]]. " + "No choices found in the provided prompt template." + ) + + # Validate that choices match numeric_values keys if provided + if numeric_values is not None: + sorted_numeric_values = sorted(numeric_values.keys()) + sorted_choices = sorted(choices) + if sorted_numeric_values != sorted_choices: + diff = "\n".join( + unified_diff( + sorted_numeric_values, + sorted_choices, + fromfile="numeric_values_keys", + tofile="choices", + ) + ) + raise ValueError( + f"numeric_values keys must match the choices included in the prompt template.\n" + f"numeric_values keys: {sorted_numeric_values}\n" + f"choices in prompt: {sorted_choices}\n" + f"Diff:\n{diff}" + ) + + # Validate that numeric_values values are numeric if provided + if not all(isinstance(value, (int, float)) for value in numeric_values.values()): + raise ValueError("All values in numeric_values must be numeric (int or float).") + + source = AssessmentSource( + source_type=AssessmentSourceType.LLM_JUDGE, + source_id=f"custom_prompt_judge_{name}", + ) + + def judge(**kwargs) -> Feedback: + try: + # Render prompt template with the given kwargs + prompt = format_prompt(prompt_template, **kwargs) + prompt = _remove_choice_brackets(prompt) + prompt = _add_structured_output_instructions(prompt) + + # Call the judge + feedback = invoke_judge_model(model, prompt, name) + feedback.source = source + + # Feedback value must be one of the choices + if feedback.value not in choices: + raise ValueError(f"'{feedback.value}' is not one of the choices: {choices}") + + # Map to numeric value if mapping is provided + if numeric_values: + feedback.metadata = {"string_value": feedback.value} + feedback.value = numeric_values[feedback.value] + return feedback + + except Exception as e: + return Feedback(name=name, source=source, error=e) + + return judge + + +def _add_structured_output_instructions(prompt: str) -> str: + """Add JSON format instructions to the user prompt.""" + suffix = """ +Answer ONLY in JSON and NOT in markdown, following the format: + +{ + "rationale": "Reason for the decision. Start each rationale with `Let's think step by step`.", + "result": "The category chosen." +} +""" + return f"{prompt.strip()}\n\n{suffix}" + + +def _remove_choice_brackets(text: str) -> str: + """Remove double square brackets around choices.""" + return _CHOICE_PATTERN.sub(r"\1", text) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/judges/prompts/__pycache__/context_sufficiency.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/judges/prompts/__pycache__/context_sufficiency.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..78ebd12f9e82d3da7e15c9c367850f674db50a4e Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/judges/prompts/__pycache__/context_sufficiency.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/judges/prompts/__pycache__/correctness.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/judges/prompts/__pycache__/correctness.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..f1430768007881a78efbeb1b53dee4be7642ee5a Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/judges/prompts/__pycache__/correctness.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/judges/prompts/__pycache__/groundedness.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/judges/prompts/__pycache__/groundedness.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..fd640c1654951d211659e10e856c3305859178c7 Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/judges/prompts/__pycache__/groundedness.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/judges/prompts/__pycache__/guidelines.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/judges/prompts/__pycache__/guidelines.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..8c2c04b22dff5de24a04ffeec63a6fb7cd447e11 Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/judges/prompts/__pycache__/guidelines.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/judges/prompts/__pycache__/relevance_to_query.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/judges/prompts/__pycache__/relevance_to_query.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..19c24dbd159f3796296ae4bfb9a379478a0e9cba Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/judges/prompts/__pycache__/relevance_to_query.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/judges/prompts/context_sufficiency.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/judges/prompts/context_sufficiency.py new file mode 100644 index 0000000000000000000000000000000000000000..469e5699ea160d20f371621ea3d55b0de25f58e6 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/judges/prompts/context_sufficiency.py @@ -0,0 +1,62 @@ +from typing import Any + +from mlflow.genai.judges.utils import format_prompt + +# NB: User-facing name for the is_context_sufficient assessment. +CONTEXT_SUFFICIENCY_FEEDBACK_NAME = "context_sufficiency" + + +CONTEXT_SUFFICIENCY_PROMPT = """\ +Consider the following claim and document. You must determine whether claim is supported by the document. Do not focus on the correctness or completeness of the claim. Do not make assumptions, approximations, or bring in external knowledge. + + + {{input}} + {{ground_truth}} + +{{retrieval_context}} + +Please indicate whether each statement in the claim is supported by the document using the json format: +{ + "rationale": "Reason for the assessment. If the claim is not fully supported by the document, state which parts are not supported. Start each rationale with `Let's think step by step`", + "result": "yes|no" +}\ +""" # noqa: E501 + + +def get_prompt( + request: str, + context: Any, + expected_response: str | None = None, + expected_facts: list[str] | None = None, +) -> str: + """Generate context sufficiency evaluation prompt. + + Args: + request: The input question/request + context: The retrieval context to evaluate sufficiency of + expected_response: Expected response (optional) + expected_facts: List of expected facts (optional, converted to expected_response) + + Returns: + Formatted prompt string + """ + # Convert expected_facts to expected_response format if provided + ground_truth = expected_response + if expected_facts and not expected_response: + ground_truth = _convert_expected_facts_to_expected_response(expected_facts) + elif not ground_truth: + ground_truth = "" + + return format_prompt( + CONTEXT_SUFFICIENCY_PROMPT, + input=request, + ground_truth=ground_truth, + retrieval_context=str(context), + ) + + +def _convert_expected_facts_to_expected_response(expected_facts: list[str] | None) -> str: + if not expected_facts: + return "" + rendered_facts = "\n - ".join([""] + expected_facts) + return f" {rendered_facts.strip()}" if rendered_facts else "" diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/judges/prompts/correctness.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/judges/prompts/correctness.py new file mode 100644 index 0000000000000000000000000000000000000000..dc0975a06bfa36dbadf66e4e911d92ed0aa7ff2d --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/judges/prompts/correctness.py @@ -0,0 +1,62 @@ +from mlflow.genai.judges.utils import format_prompt + +# NB: User-facing name for the is_correct assessment. +CORRECTNESS_FEEDBACK_NAME = "correctness" + + +CORRECTNESS_PROMPT = """\ +Consider the following question, claim and document. You must determine whether the claim is supported by the document in the context of the question. Do not focus on the correctness or completeness of the claim. Do not make assumptions, approximations, or bring in external knowledge. + +{{input}} +{{ground_truth}} +{{input}} - {{output}} + +Please indicate whether each statement in the claim is supported by the document in the context of the question using the json format: +{ + "rationale": "Reason for the assessment. If the claim is not fully supported by the document in the context of the question, state which parts are not supported. Start each rationale with `Let's think step by step`", + "result": "yes|no" +}\ +""" # noqa: E501 + +# This suffix is only shown when expected facts are provided to squeeze out better judge quality. +CORRECTNESS_PROMPT_SUFFIX = """ + +If the claim is fully supported by the document in the context of the question, you must say "The response is correct" in the rationale. If the claim is not fully supported by the document in the context of the question, you must say "The response is not correct".""" # noqa: E501 + + +def get_prompt( + request: str, + response: str, + expected_response: str | None = None, + expected_facts: list[str] | None = None, +) -> str: + """Generate correctness evaluation prompt. + + Args: + request: The input question/request + response: The actual response to evaluate + expected_response: Expected response (optional) + expected_facts: List of expected facts (optional, converted to expected_response) + + Returns: + Formatted prompt string + """ + # Convert expected_facts to expected_response format if provided + ground_truth = expected_response + if expected_facts and not expected_response: + ground_truth = "\n- ".join([""] + expected_facts) if expected_facts else "" + elif not ground_truth: + ground_truth = "" + + prompt = format_prompt( + CORRECTNESS_PROMPT, + input=request, + output=response, + ground_truth=ground_truth, + ) + + # Add suffix when expected facts are provided (not expected_response) + if expected_facts and not expected_response: + prompt += CORRECTNESS_PROMPT_SUFFIX + + return prompt diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/judges/prompts/groundedness.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/judges/prompts/groundedness.py new file mode 100644 index 0000000000000000000000000000000000000000..4dab29d84a7c23c8054ec5713223d6ab480364cd --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/judges/prompts/groundedness.py @@ -0,0 +1,43 @@ +from typing import Any + +from mlflow.genai.judges.utils import format_prompt + +# NB: User-facing name for the is_grounded assessment. +GROUNDEDNESS_FEEDBACK_NAME = "groundedness" + + +GROUNDEDNESS_PROMPT = """\ +Consider the following claim and document. You must determine whether claim is supported by the document. Do not focus on the correctness or completeness of the claim. Do not make assumptions, approximations, or bring in external knowledge. + + + {{input}} + {{output}} + +{{retrieval_context}} + +Please indicate whether each statement in the claim is supported by the document using the json format: +{ + "rationale": "Reason for the assessment. If the claim is not fully supported by the document, state which parts are not supported. Start each rationale with `Let's think step by step`", + "result": "yes|no" +} +Do not output additional lines.\ +""" # noqa: E501 + + +def get_prompt(request: str, response: str, context: Any) -> str: + """Generate groundedness evaluation prompt. + + Args: + request: The input question/request + response: The response to evaluate for groundedness + context: The retrieval context to check groundedness against + + Returns: + Formatted prompt string + """ + return format_prompt( + GROUNDEDNESS_PROMPT, + input=request, + output=response, + retrieval_context=str(context), + ) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/judges/prompts/guidelines.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/judges/prompts/guidelines.py new file mode 100644 index 0000000000000000000000000000000000000000..6cb38aa75e85c4bb7ec64e0035e7498501dbc397 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/judges/prompts/guidelines.py @@ -0,0 +1,41 @@ +from mlflow.genai.judges.utils import format_prompt + +GUIDELINES_FEEDBACK_NAME = "guidelines" + +GUIDELINES_PROMPT = """\ +Given the following set of guidelines and some inputs, please assess whether the inputs fully comply with all the provided guidelines. Only focus on the provided guidelines and not the correctness, relevance, or effectiveness of the inputs. + + +{{guidelines}} + +{{guidelines_context}} + +Please provide your assessment using the following json format. Do not use any markdown formatting. If any of the guidelines are not satisfied, the result must be "no". If none of the guidelines apply to the given inputs, the result must be "yes". +{ + "rationale": "Detailed reasoning for your assessment. If the assessment does not satisfy the guideline, state which parts of the guideline are not satisfied. Start each rationale with `Let's think step by step. `", + "result": "yes|no" +}""" # noqa: E501 + + +def get_prompt( + guidelines: str | list[str], + guidelines_context: dict[str, str], +) -> str: + if isinstance(guidelines, str): + guidelines = [guidelines] + + return format_prompt( + GUIDELINES_PROMPT, + guidelines=_render_guidelines(guidelines), + guidelines_context=_render_guidelines_context(guidelines_context), + ) + + +def _render_guidelines(guidelines: list[str]) -> str: + lines = [f"{guideline}" for guideline in guidelines] + return "\n".join(lines) + + +def _render_guidelines_context(guidelines_context: dict[str, str]) -> str: + lines = [f"<{key}>{value}" for key, value in guidelines_context.items()] + return "\n".join(lines) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/judges/prompts/relevance_to_query.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/judges/prompts/relevance_to_query.py new file mode 100644 index 0000000000000000000000000000000000000000..4813de53e2f32910fa30e3d3914917760aceb4be --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/judges/prompts/relevance_to_query.py @@ -0,0 +1,26 @@ +from mlflow.genai.judges.utils import format_prompt + +# NB: User-facing name for the is_context_relevant assessment. +RELEVANCE_TO_QUERY_ASSESSMENT_NAME = "relevance_to_context" + + +RELEVANCE_TO_QUERY_PROMPT = """\ +Consider the following question and answer. You must determine whether the answer provides information that is (fully or partially) relevant to the question. Do not focus on the correctness or completeness of the answer. Do not make assumptions, approximations, or bring in external knowledge. + +{{input}} +{{output}} + +Please indicate whether the answer contains information that is relevant to the question using the json format: +{ + "rationale": "Reason for the assessment. If the answer does not provide any information that is relevant to the question then state which parts are not relevant. Start each rationale with `Let's think step by step`", + "result": "yes|no" +} +Do not output additional lines. `result` must only be `yes` or `no`.""" # noqa: E501 + + +def get_prompt(request: str, context: str) -> str: + return format_prompt( + RELEVANCE_TO_QUERY_PROMPT, + input=request, + output=context, + ) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/judges/utils.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/judges/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..300d8c586faa13b40dec558058367267d22e5867 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/judges/utils.py @@ -0,0 +1,140 @@ +import json +import re + +import mlflow +from mlflow.entities.assessment import Feedback +from mlflow.entities.assessment_source import AssessmentSource, AssessmentSourceType +from mlflow.exceptions import MlflowException +from mlflow.genai.utils.enum_utils import StrEnum +from mlflow.protos.databricks_pb2 import INVALID_PARAMETER_VALUE +from mlflow.utils.uri import is_databricks_uri + +# "endpoints" is a special case for Databricks model serving endpoints. +_NATIVE_PROVIDERS = ["openai", "anthropic", "bedrock", "mistral", "endpoints"] + + +def get_default_model() -> str: + if is_databricks_uri(mlflow.get_tracking_uri()): + return "databricks" + else: + return "openai:/gpt-4.1-mini" + + +def format_prompt(prompt: str, **values) -> str: + """Format double-curly variables in the prompt template.""" + for key, value in values.items(): + prompt = re.sub(r"\{\{\s*" + key + r"\s*\}\}", str(value), prompt) + return prompt + + +def _sanitize_justification(justification: str) -> str: + # Some judge prompts instruct the model to think step by step. + return justification.replace("Let's think step by step. ", "") + + +def invoke_judge_model(model_uri: str, prompt: str, assessment_name: str) -> Feedback: + """ + Invoke the judge model. + + First, try to invoke the judge model via litellm. If litellm is not installed, + fallback to native parsing using the AI Gateway adapters. + + Args: + model_uri: The model URI. + prompt: The prompt to evaluate. + assessment_name: The name of the assessment. + """ + from mlflow.metrics.genai.model_utils import ( + _parse_model_uri, + get_endpoint_type, + score_model_on_payload, + ) + + provider, model_name = _parse_model_uri(model_uri) + + # Try litellm first for better performance. + if _is_litellm_available(): + response = _invoke_litellm(provider, model_name, prompt) + elif provider in _NATIVE_PROVIDERS: + response = score_model_on_payload( + model_uri=model_uri, + payload=prompt, + endpoint_type=get_endpoint_type(model_uri) or "llm/v1/chat", + ) + else: + raise MlflowException( + f"LiteLLM is required for using '{provider}' LLM. Please install it with " + "`pip install litellm`.", + error_code=INVALID_PARAMETER_VALUE, + ) + + try: + response_dict = json.loads(response) + feedback = Feedback( + name=assessment_name, + value=response_dict["result"], + rationale=_sanitize_justification(response_dict.get("rationale", "")), + source=AssessmentSource( + source_type=AssessmentSourceType.LLM_JUDGE, + source_id=model_uri, + ), + ) + except json.JSONDecodeError as e: + raise MlflowException( + f"Failed to parse the response from the judge model. Response: {response}", + error_code=INVALID_PARAMETER_VALUE, + ) from e + + return feedback + + +def _is_litellm_available() -> bool: + try: + import litellm # noqa: F401 + + return True + except ImportError: + return False + + +def _invoke_litellm(provider: str, model_name: str, prompt: str) -> str: + """Invoke the judge model via litellm.""" + import litellm + + litellm_model_uri = f"{provider}/{model_name}" + try: + response = litellm.completion( + model=litellm_model_uri, messages=[{"role": "user", "content": prompt}] + ) + return response.choices[0].message.content + except Exception as e: + raise MlflowException("Failed to invoke the judge model via litellm.") from e + + +class CategoricalRating(StrEnum): + """ + A categorical rating for an assessment. + + Example: + .. code-block:: python + + from mlflow.genai.judges import CategoricalRating + from mlflow.entities import Feedback + + # Create feedback with categorical rating + feedback = Feedback( + name="my_metric", value=CategoricalRating.YES, rationale="The metric is passing." + ) + """ + + YES = "yes" + NO = "no" + UNKNOWN = "unknown" + + @classmethod + def _missing_(cls, value: str): + value = value.lower() + for member in cls: + if member == value: + return member + return cls.UNKNOWN diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/label_schemas/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/label_schemas/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..4404378e3ae356b0763b80bff06cc0ba12b2ae2e --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/label_schemas/__init__.py @@ -0,0 +1,147 @@ +""" +Databricks Agent Label Schemas Python SDK. For more details see Databricks Agent Evaluation: + + +The API docs can be found here: + +""" + +from typing import TYPE_CHECKING, Literal + +from mlflow.genai.label_schemas.label_schemas import ( + InputCategorical, + InputCategoricalList, + InputNumeric, + InputText, + InputTextList, + LabelSchema, + LabelSchemaType, +) +from mlflow.genai.labeling import ReviewApp + +if TYPE_CHECKING: + from databricks.agents.review_app import ReviewApp + +_ERROR_MSG = ( + "The `databricks-agents` package is required to use `mlflow.genai.label_schemas`. " + "Please install it with `pip install databricks-agents`." +) + +EXPECTED_FACTS = "expected_facts" +GUIDELINES = "guidelines" +EXPECTED_RESPONSE = "expected_response" + + +def create_label_schema( + name: str, + *, + type: Literal["feedback", "expectation"], + title: str, + input: InputCategorical | InputCategoricalList | InputText | InputTextList | InputNumeric, + instruction: str | None = None, + enable_comment: bool = False, + overwrite: bool = False, +) -> LabelSchema: + """Create a new label schema for the review app. + + A label schema defines the type of input that stakeholders will provide when labeling items + in the review app. + + .. note:: + This functionality is only available in Databricks. Please run + `pip install mlflow[databricks]` to use it. + + Args: + name: The name of the label schema. Must be unique across the review app. + type: The type of the label schema. Either "feedback" or "expectation". + title: The title of the label schema shown to stakeholders. + input: The input type of the label schema. + instruction: Optional. The instruction shown to stakeholders. + enable_comment: Optional. Whether to enable comments for the label schema. + overwrite: Optional. Whether to overwrite the existing label schema with the same name. + + Returns: + LabelSchema: The created label schema. + """ + try: + from databricks.agents import review_app + except ImportError: + raise ImportError(_ERROR_MSG) from None + + app = review_app.get_review_app() + return app.create_label_schema( + name=name, + type=type, + title=title, + input=input._to_databricks_input(), + instruction=instruction, + enable_comment=enable_comment, + overwrite=overwrite, + ) + + +def get_label_schema(name: str) -> LabelSchema: + """Get a label schema from the review app. + + .. note:: + This functionality is only available in Databricks. Please run + `pip install mlflow[databricks]` to use it. + + Args: + name: The name of the label schema to get. + + Returns: + LabelSchema: The label schema. + """ + try: + from databricks.agents import review_app + except ImportError: + raise ImportError(_ERROR_MSG) from None + + app = review_app.get_review_app() + label_schema = next( + (label_schema for label_schema in app.label_schemas if label_schema.name == name), + None, + ) + if label_schema is None: + raise ValueError(f"Label schema with name `{name}` not found") + return LabelSchema._from_databricks_label_schema(label_schema) + + +def delete_label_schema(name: str) -> "ReviewApp": + """Delete a label schema from the review app. + + .. note:: + This functionality is only available in Databricks. Please run + `pip install mlflow[databricks]` to use it. + + Args: + name: The name of the label schema to delete. + + Returns: + ReviewApp: The review app. + """ + try: + from databricks.agents import review_app + except ImportError: + raise ImportError(_ERROR_MSG) from None + + app = review_app.get_review_app() + return ReviewApp(app.delete_label_schema(name)) + + +__all__ = [ + "EXPECTED_FACTS", + "GUIDELINES", + "EXPECTED_RESPONSE", + "LabelSchemaType", + "LabelSchema", + "InputCategorical", + "InputCategoricalList", + "InputNumeric", + "InputText", + "InputTextList", + "create_label_schema", + "get_label_schema", + "delete_label_schema", +] diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/label_schemas/__pycache__/__init__.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/label_schemas/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..7becb12de6ca87871da7ff3abb241d18a4c054f7 Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/label_schemas/__pycache__/__init__.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/label_schemas/__pycache__/label_schemas.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/label_schemas/__pycache__/label_schemas.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..e4c45cf52cf4bb7b9050be5d98c5ab51ceb44343 Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/label_schemas/__pycache__/label_schemas.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/label_schemas/label_schemas.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/label_schemas/label_schemas.py new file mode 100644 index 0000000000000000000000000000000000000000..a85c5f4d82b19c52e21196d23bc2543ce72868db --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/label_schemas/label_schemas.py @@ -0,0 +1,228 @@ +from abc import ABC, abstractmethod +from dataclasses import dataclass +from typing import TYPE_CHECKING, TypeVar + +from mlflow.genai.utils.enum_utils import StrEnum + +if TYPE_CHECKING: + from databricks.agents.review_app import label_schemas as _label_schemas + + _InputCategorical = _label_schemas.InputCategorical + _InputCategoricalList = _label_schemas.InputCategoricalList + _InputNumeric = _label_schemas.InputNumeric + _InputText = _label_schemas.InputText + _InputTextList = _label_schemas.InputTextList + _LabelSchema = _label_schemas.LabelSchema + +DatabricksInputType = TypeVar("DatabricksInputType") +_InputType = TypeVar("_InputType", bound="InputType") + + +class InputType(ABC): + """Base class for all input types.""" + + @abstractmethod + def _to_databricks_input(self) -> DatabricksInputType: + """Convert to the internal Databricks input type.""" + + @classmethod + @abstractmethod + def _from_databricks_input(cls, input_obj: DatabricksInputType) -> _InputType: + """Create from the internal Databricks input type.""" + + +@dataclass +class InputCategorical(InputType): + """A single-select dropdown for collecting assessments from stakeholders. + + .. note:: + This functionality is only available in Databricks. Please run + `pip install mlflow[databricks]` to use it. + """ + + options: list[str] + """List of available options for the categorical selection.""" + + def _to_databricks_input(self) -> "_InputCategorical": + """Convert to the internal Databricks input type.""" + from databricks.agents.review_app import label_schemas as _label_schemas + + return _label_schemas.InputCategorical(options=self.options) + + @classmethod + def _from_databricks_input(cls, input_obj: "_InputCategorical") -> "InputCategorical": + """Create from the internal Databricks input type.""" + return cls(options=input_obj.options) + + +@dataclass +class InputCategoricalList(InputType): + """A multi-select dropdown for collecting assessments from stakeholders. + + .. note:: + This functionality is only available in Databricks. Please run + `pip install mlflow[databricks]` to use it. + """ + + options: list[str] + """List of available options for the multi-select categorical (dropdown).""" + + def _to_databricks_input(self) -> "_InputCategoricalList": + """Convert to the internal Databricks input type.""" + from databricks.agents.review_app import label_schemas as _label_schemas + + return _label_schemas.InputCategoricalList(options=self.options) + + @classmethod + def _from_databricks_input(cls, input_obj: "_InputCategoricalList") -> "InputCategoricalList": + """Create from the internal Databricks input type.""" + return cls(options=input_obj.options) + + +@dataclass +class InputTextList(InputType): + """Like `Text`, but allows multiple entries. + + .. note:: + This functionality is only available in Databricks. Please run + `pip install mlflow[databricks]` to use it. + """ + + max_length_each: int | None = None + """Maximum character length for each individual text entry. None means no limit.""" + + max_count: int | None = None + """Maximum number of text entries allowed. None means no limit.""" + + def _to_databricks_input(self) -> "_InputTextList": + """Convert to the internal Databricks input type.""" + from databricks.agents.review_app import label_schemas as _label_schemas + + return _label_schemas.InputTextList( + max_length_each=self.max_length_each, max_count=self.max_count + ) + + @classmethod + def _from_databricks_input(cls, input_obj: "_InputTextList") -> "InputTextList": + """Create from the internal Databricks input type.""" + return cls(max_length_each=input_obj.max_length_each, max_count=input_obj.max_count) + + +@dataclass +class InputText(InputType): + """A free-form text box for collecting assessments from stakeholders. + + .. note:: + This functionality is only available in Databricks. Please run + `pip install mlflow[databricks]` to use it. + """ + + max_length: int | None = None + """Maximum character length for the text input. None means no limit.""" + + def _to_databricks_input(self) -> "_InputText": + """Convert to the internal Databricks input type.""" + from databricks.agents.review_app import label_schemas as _label_schemas + + return _label_schemas.InputText(max_length=self.max_length) + + @classmethod + def _from_databricks_input(cls, input_obj: "_InputText") -> "InputText": + """Create from the internal Databricks input type.""" + return cls(max_length=input_obj.max_length) + + +@dataclass +class InputNumeric(InputType): + """A numeric input for collecting assessments from stakeholders. + + .. note:: + This functionality is only available in Databricks. Please run + `pip install mlflow[databricks]` to use it. + """ + + min_value: float | None = None + """Minimum allowed numeric value. None means no minimum limit.""" + + max_value: float | None = None + """Maximum allowed numeric value. None means no maximum limit.""" + + def _to_databricks_input(self) -> "_InputNumeric": + """Convert to the internal Databricks input type.""" + from databricks.agents.review_app import label_schemas as _label_schemas + + return _label_schemas.InputNumeric(min_value=self.min_value, max_value=self.max_value) + + @classmethod + def _from_databricks_input(cls, input_obj: "_InputNumeric") -> "InputNumeric": + """Create from the internal Databricks input type.""" + return cls(min_value=input_obj.min_value, max_value=input_obj.max_value) + + +class LabelSchemaType(StrEnum): + """Type of label schema.""" + + FEEDBACK = "feedback" + EXPECTATION = "expectation" + + +@dataclass(frozen=True) +class LabelSchema: + """A label schema for collecting input from stakeholders. + + .. note:: + This functionality is only available in Databricks. Please run + `pip install mlflow[databricks]` to use it. + """ + + name: str + """Unique name identifier for the label schema.""" + + type: LabelSchemaType + """Type of the label schema, either 'feedback' or 'expectation'.""" + + title: str + """Display title shown to stakeholders in the labeling review UI.""" + + input: InputCategorical | InputCategoricalList | InputText | InputTextList | InputNumeric + """ + Input type specification that defines how stakeholders will provide their assessment + (e.g., dropdown, text box, numeric input) + """ + instruction: str | None = None + """Optional detailed instructions shown to stakeholders for guidance.""" + + enable_comment: bool = False + """Whether to enable additional comment functionality for reviewers.""" + + @classmethod + def _convert_databricks_input(cls, input_obj): + """Convert a Databricks input type to the corresponding MLflow input type.""" + from databricks.agents.review_app import label_schemas as _label_schemas + + input_type_mapping = { + _label_schemas.InputCategorical: InputCategorical, + _label_schemas.InputCategoricalList: InputCategoricalList, + _label_schemas.InputText: InputText, + _label_schemas.InputTextList: InputTextList, + _label_schemas.InputNumeric: InputNumeric, + } + + input_class = input_type_mapping.get(type(input_obj)) + if input_class is None: + raise ValueError(f"Unknown input type: {type(input_obj)}") + + return input_class._from_databricks_input(input_obj) + + @classmethod + def _from_databricks_label_schema(cls, schema: "_LabelSchema") -> "LabelSchema": + """Convert from the internal Databricks label schema type.""" + + return cls( + name=schema.name, + type=schema.type, + title=schema.title, + input=cls._convert_databricks_input(schema.input), + instruction=schema.instruction, + enable_comment=schema.enable_comment, + ) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/labeling/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/labeling/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..4ae71002cf19149f28e9cba77ef876e1620fc63c --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/labeling/__init__.py @@ -0,0 +1,159 @@ +""" +Databricks Agent Labeling Python SDK. For more details see Databricks Agent Evaluation: + + +The API docs can be found here: + +""" + +from typing import Any + +from mlflow.genai.labeling.labeling import Agent, LabelingSession, ReviewApp + +_ERROR_MSG = ( + "The `databricks-agents` package is required to use `mlflow.genai.labeling`. " + "Please install it with `pip install databricks-agents`." +) + + +def get_review_app(experiment_id: str | None = None) -> "ReviewApp": + """Gets or creates (if it doesn't exist) the review app for the given experiment ID. + + .. note:: + This functionality is only available in Databricks. Please run + `pip install mlflow[databricks]` to use it. + + Args: + experiment_id: Optional. The experiment ID for which to get the review app. If not provided, + the experiment ID is inferred from the current active environment. + + Returns: + ReviewApp: The review app. + """ + try: + from databricks.agents.review_app import get_review_app as _get_review_app + except ImportError: + raise ImportError(_ERROR_MSG) from None + + return ReviewApp(_get_review_app(experiment_id)) + + +def create_labeling_session( + name: str, + *, + assigned_users: list[str] | None = None, + agent: str | None = None, + label_schemas: list[str] | None = None, + enable_multi_turn_chat: bool = False, + custom_inputs: dict[str, Any] | None = None, +) -> LabelingSession: + """Create a new labeling session in the review app. + + .. note:: + This functionality is only available in Databricks. Please run + `pip install mlflow[databricks]` to use it. + + Args: + name: The name of the labeling session. + assigned_users: The users that will be assigned to label items in the session. + agent: The agent to be used to generate responses for the items in the session. + label_schemas: The label schemas to be used in the session. + enable_multi_turn_chat: Whether to enable multi-turn chat labeling for the session. + custom_inputs: Optional. Custom inputs to be used in the session. + + Returns: + LabelingSession: The created labeling session. + """ + try: + from databricks.agents.review_app import get_review_app as _get_review_app + except ImportError: + raise ImportError(_ERROR_MSG) from None + + return LabelingSession( + _get_review_app().create_labeling_session( + name=name, + assigned_users=assigned_users or [], + agent=agent, + label_schemas=label_schemas or [], + enable_multi_turn_chat=enable_multi_turn_chat, + custom_inputs=custom_inputs, + ) + ) + + +def get_labeling_sessions() -> list[LabelingSession]: + """Get all labeling sessions from the review app. + + .. note:: + This functionality is only available in Databricks. Please run + `pip install mlflow[databricks]` to use it. + + Returns: + list[LabelingSession]: The list of labeling sessions. + """ + try: + from databricks.agents.review_app import get_review_app as _get_review_app + except ImportError: + raise ImportError(_ERROR_MSG) from None + + return [LabelingSession(session) for session in _get_review_app().get_labeling_sessions()] + + +def get_labeling_session(run_id: str) -> LabelingSession: + """Get a labeling session from the review app. + + .. note:: + This functionality is only available in Databricks. Please run + `pip install mlflow[databricks]` to use it. + + Args: + run_id: The mlflow run ID of the labeling session to get. + + Returns: + LabelingSession: The labeling session. + """ + labeling_sessions = get_labeling_sessions() + labeling_session = next( + ( + labeling_session + for labeling_session in labeling_sessions + if labeling_session.mlflow_run_id == run_id + ), + None, + ) + if labeling_session is None: + raise ValueError(f"Labeling session with run_id `{run_id}` not found") + return LabelingSession(labeling_session) + + +def delete_labeling_session(labeling_session: LabelingSession) -> "ReviewApp": + """Delete a labeling session from the review app. + + .. note:: + This functionality is only available in Databricks. Please run + `pip install mlflow[databricks]` to use it. + + Args: + labeling_session: The labeling session to delete. + + Returns: + ReviewApp: The review app. + """ + try: + from databricks.agents.review_app import get_review_app as _get_review_app + except ImportError: + raise ImportError(_ERROR_MSG) from None + + return ReviewApp(_get_review_app().delete_labeling_session(labeling_session._session)) + + +__all__ = [ + "Agent", + "LabelingSession", + "ReviewApp", + "get_review_app", + "create_labeling_session", + "get_labeling_sessions", + "get_labeling_session", + "delete_labeling_session", +] diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/labeling/__pycache__/__init__.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/labeling/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..cb402486c2ed705ee83fb53b7c9e98be6d85fa13 Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/labeling/__pycache__/__init__.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/labeling/__pycache__/labeling.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/labeling/__pycache__/labeling.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..d1db69b253d2fc5e452e906b5dacca59b87bcaec Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/labeling/__pycache__/labeling.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/labeling/labeling.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/labeling/labeling.py new file mode 100644 index 0000000000000000000000000000000000000000..dd079a5a89ec6fc74a087f5b01d2645422ae6788 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/labeling/labeling.py @@ -0,0 +1,250 @@ +from typing import TYPE_CHECKING, Any, Iterable, Union + +from mlflow.entities import Trace + +if TYPE_CHECKING: + import pandas as pd + from databricks.agents.review_app import ( + LabelingSession as _LabelingSession, + ) + from databricks.agents.review_app import ( + LabelSchema as _LabelSchema, + ) + from databricks.agents.review_app import ( + ReviewApp as _ReviewApp, + ) + from databricks.agents.review_app.labeling import Agent as _Agent + + +class Agent: + """The agent configuration, used for generating responses in the review app. + + .. note:: + This functionality is only available in Databricks. Please run + `pip install mlflow[databricks]` to use it. + """ + + def __init__(self, agent: "_Agent"): + self._agent = agent + + @property + def agent_name(self) -> str: + """The name of the agent.""" + return self._agent.agent_name + + @property + def model_serving_endpoint(self) -> str: + """The model serving endpoint used by the agent.""" + return self._agent.model_serving_endpoint + + +class LabelingSession: + """A session for labeling items in the review app. + + .. note:: + This functionality is only available in Databricks. Please run + `pip install mlflow[databricks]` to use it. + """ + + def __init__(self, session: "_LabelingSession"): + self._session = session + + @property + def name(self) -> str: + """The name of the labeling session.""" + return self._session.name + + @property + def assigned_users(self) -> list[str]: + """The users assigned to label items in the session.""" + return self._session.assigned_users + + @property + def agent(self) -> str | None: + """The agent used to generate responses for the items in the session.""" + return self._session.agent + + @property + def label_schemas(self) -> list[str]: + """The label schemas used in the session.""" + return self._session.label_schemas + + @property + def labeling_session_id(self) -> str: + """The unique identifier of the labeling session.""" + return self._session.labeling_session_id + + @property + def mlflow_run_id(self) -> str: + """The MLflow run ID associated with the session.""" + return self._session.mlflow_run_id + + @property + def review_app_id(self) -> str: + """The review app ID associated with the session.""" + return self._session.review_app_id + + @property + def experiment_id(self) -> str: + """The experiment ID associated with the session.""" + return self._session.experiment_id + + @property + def url(self) -> str: + """The URL of the labeling session in the review app.""" + return self._session.url + + @property + def enable_multi_turn_chat(self) -> bool: + """Whether multi-turn chat is enabled for the session.""" + return self._session.enable_multi_turn_chat + + @property + def custom_inputs(self) -> dict[str, Any] | None: + """Custom inputs used in the session.""" + return self._session.custom_inputs + + def add_dataset( + self, dataset_name: str, record_ids: list[str] | None = None + ) -> "LabelingSession": + """Add a dataset to the labeling session. + + .. note:: + This functionality is only available in Databricks. Please run + `pip install mlflow[databricks]` to use it. + + Args: + dataset_name: The name of the dataset. + record_ids: Optional. The individual record ids to be added to the session. If not + provided, all records in the dataset will be added. + + Returns: + LabelingSession: The updated labeling session. + """ + return LabelingSession(self._session.add_dataset(dataset_name, record_ids)) + + def add_traces( + self, + traces: Union[Iterable[Trace], Iterable[str], "pd.DataFrame"], + ) -> "LabelingSession": + """Add traces to the labeling session. + + .. note:: + This functionality is only available in Databricks. Please run + `pip install mlflow[databricks]` to use it. + + Args: + traces: Can be either: + a) a pandas DataFrame with a 'trace' column. The 'trace' column should contain + either `mlflow.entities.Trace` objects or their json string representations. + b) an iterable of `mlflow.entities.Trace` objects. + c) an iterable of json string representations of `mlflow.entities.Trace` objects. + + Returns: + LabelingSession: The updated labeling session. + """ + return LabelingSession(self._session.add_traces(traces)) + + def sync(self, to_dataset: str) -> None: + """Sync the traces and expectations from the labeling session to a dataset. + + .. note:: + This functionality is only available in Databricks. Please run + `pip install mlflow[databricks]` to use it. + + Args: + to_dataset: The name of the dataset to sync traces and expectations to. + """ + self._session.sync_expectations(to_dataset) + + def set_assigned_users(self, assigned_users: list[str]) -> "LabelingSession": + """Set the assigned users for the labeling session. + + .. note:: + This functionality is only available in Databricks. Please run + `pip install mlflow[databricks]` to use it. + + Args: + assigned_users: The list of users to assign to the session. + + Returns: + LabelingSession: The updated labeling session. + """ + return LabelingSession(self._session.set_assigned_users(assigned_users)) + + +class ReviewApp: + """A review app is used to collect feedback from stakeholders for a given experiment. + + .. note:: + This functionality is only available in Databricks. Please run + `pip install mlflow[databricks]` to use it. + """ + + def __init__(self, app: "_ReviewApp"): + self._app = app + + @property + def review_app_id(self) -> str: + """The ID of the review app.""" + return self._app.review_app_id + + @property + def experiment_id(self) -> str: + """The ID of the experiment.""" + return self._app.experiment_id + + @property + def url(self) -> str: + """The URL of the review app for stakeholders to provide feedback.""" + return self._app.url + + @property + def agents(self) -> list[Agent]: + """The agents to be used to generate responses.""" + return [Agent(agent) for agent in self._app.agents] + + @property + def label_schemas(self) -> list["_LabelSchema"]: + """The label schemas to be used in the review app.""" + return self._app.label_schemas + + def add_agent( + self, *, agent_name: str, model_serving_endpoint: str, overwrite: bool = False + ) -> "ReviewApp": + """Add an agent to the review app to be used to generate responses. + + .. note:: + This functionality is only available in Databricks. Please run + `pip install mlflow[databricks]` to use it. + + Args: + agent_name: The name of the agent. + model_serving_endpoint: The model serving endpoint to be used by the agent. + overwrite: Whether to overwrite an existing agent with the same name. + + Returns: + ReviewApp: The updated review app. + """ + return ReviewApp( + self._app.add_agent( + agent_name=agent_name, + model_serving_endpoint=model_serving_endpoint, + overwrite=overwrite, + ) + ) + + def remove_agent(self, agent_name: str) -> "ReviewApp": + """Remove an agent from the review app. + + .. note:: + This functionality is only available in Databricks. Please run + `pip install mlflow[databricks]` to use it. + + Args: + agent_name: The name of the agent to remove. + + Returns: + ReviewApp: The updated review app. + """ + return ReviewApp(self._app.remove_agent(agent_name)) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/optimize/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/optimize/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..80a571fa44a33d0dc021405f92e8915425477e8d --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/optimize/__init__.py @@ -0,0 +1,20 @@ +from mlflow.genai.optimize.base import optimize_prompt +from mlflow.genai.optimize.optimizers import BasePromptOptimizer, DSPyPromptOptimizer +from mlflow.genai.optimize.optimizers.utils import format_dspy_prompt +from mlflow.genai.optimize.types import ( + LLMParams, + OptimizerConfig, + OptimizerOutput, + PromptOptimizationResult, +) + +__all__ = [ + "optimize_prompt", + "OptimizerConfig", + "LLMParams", + "PromptOptimizationResult", + "OptimizerOutput", + "BasePromptOptimizer", + "DSPyPromptOptimizer", + "format_dspy_prompt", +] diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/optimize/__pycache__/__init__.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/optimize/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..3d63b865cbd779bda65a085bc10f3e42360c74bf Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/optimize/__pycache__/__init__.cpython-312.pyc differ diff --git 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index 0000000000000000000000000000000000000000..e801b8588524225d2956ea437fe90e59f5fa7e40 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/optimize/base.py @@ -0,0 +1,266 @@ +import inspect +import logging +from contextlib import contextmanager +from dataclasses import asdict +from typing import TYPE_CHECKING, Optional + +from mlflow.entities.model_registry import PromptVersion +from mlflow.exceptions import MlflowException +from mlflow.genai.evaluation.utils import ( + _convert_eval_set_to_df, +) +from mlflow.genai.optimize.optimizers import BasePromptOptimizer, _DSPyMIPROv2Optimizer +from mlflow.genai.optimize.types import ( + LLMParams, + ObjectiveFn, + OptimizerConfig, + PromptOptimizationResult, +) +from mlflow.genai.prompts import load_prompt, register_prompt +from mlflow.genai.scorers import Scorer +from mlflow.telemetry.events import PromptOptimizationEvent +from mlflow.telemetry.track import record_usage_event +from mlflow.tracking.fluent import ( + active_run, + log_metric, + log_param, + log_params, + log_table, + start_run, +) +from mlflow.utils.annotations import experimental + +if TYPE_CHECKING: + import pandas as pd + + from mlflow.genai.evaluation.utils import EvaluationDatasetTypes + +_ALGORITHMS = {"DSPy/MIPROv2": _DSPyMIPROv2Optimizer} + +_logger = logging.getLogger(__name__) + + +@experimental(version="3.0.0") +def optimize_prompt( + *, + target_llm_params: LLMParams, + prompt: str | PromptVersion, + train_data: "EvaluationDatasetTypes", + scorers: list[Scorer], + objective: ObjectiveFn | None = None, + eval_data: Optional["EvaluationDatasetTypes"] = None, + optimizer_config: OptimizerConfig | None = None, +) -> PromptOptimizationResult: + """ + Optimize a LLM prompt using the given dataset and evaluation metrics. + The optimized prompt template is automatically registered as a new version of the + original prompt and included in the result. + Currently, this API only supports DSPy's MIPROv2 optimizer. + + Args: + target_llm_params: Parameters for the the LLM that prompt is optimized for. + The model name can be specified in either format: + - `:/` (e.g., "openai:/gpt-4o") + - `/` (e.g., "openai/gpt-4o") + prompt: The URI or Prompt object of the MLflow prompt to optimize. + The optimized prompt is registered as a new version of the prompt. + train_data: Training dataset used for optimization. + The data must be one of the following formats: + + * An EvaluationDataset entity + * Pandas DataFrame + * Spark DataFrame + * List of dictionaries + + The dataset must include the following columns: + + - inputs: A column containing single inputs in dict format. + Each input should contain keys matching the variables in the prompt template. + - expectations: A column containing a dictionary + of ground truths for individual output fields. + + scorers: List of scorers that evaluate the inputs, outputs and expectations. + Note: Trace input is not supported for optimization. Use inputs, outputs and + expectations for optimization. Also, pass the `objective` argument + when using scorers with string or :class:`~mlflow.entities.Feedback` type outputs. + objective: A callable that computes the overall performance metric from individual + assessments. Takes a dict mapping assessment names to assessment scores and + returns a float value (greater is better). + eval_data: Evaluation dataset with the same format as train_data. If not provided, + train_data will be automatically split into training and evaluation sets. + optimizer_config: Configuration parameters for the optimizer. + + Returns: + PromptOptimizationResult: The optimization result including the optimized prompt. + + Example: + + .. code-block:: python + + import os + import mlflow + from typing import Any + from mlflow.genai.scorers import scorer + from mlflow.genai.optimize import OptimizerConfig, LLMParams + + os.environ["OPENAI_API_KEY"] = "YOUR_API_KEY" + + + @scorer + def exact_match(expectations: dict[str, Any], outputs: dict[str, Any]) -> bool: + return expectations == outputs + + + prompt = mlflow.genai.register_prompt( + name="qa", + template="Answer the following question: {{question}}", + ) + + result = mlflow.genai.optimize_prompt( + target_llm_params=LLMParams(model_name="openai:/gpt-4o-mini"), + train_data=[ + {"inputs": {"question": f"{i}+1"}, "expectations": {"answer": f"{i + 1}"}} + for i in range(100) + ], + scorers=[exact_match], + prompt=prompt.uri, + optimizer_config=OptimizerConfig(num_instruction_candidates=5), + ) + + print(result.prompt.template) + """ + return _optimize_prompt( + target_llm_params=target_llm_params, + prompt=prompt, + train_data=train_data, + scorers=scorers, + objective=objective, + eval_data=eval_data, + optimizer_config=optimizer_config, + ) + + +@record_usage_event(PromptOptimizationEvent) +def _optimize_prompt( + *, + target_llm_params: LLMParams, + prompt: str | PromptVersion, + train_data: "EvaluationDatasetTypes", + scorers: list[Scorer], + objective: ObjectiveFn | None = None, + eval_data: Optional["EvaluationDatasetTypes"] = None, + optimizer_config: OptimizerConfig | None = None, +) -> PromptOptimizationResult: + if optimizer_config is None: + optimizer_config = OptimizerConfig() + optimzer = _select_optimizer(optimizer_config) + _validate_scorers(scorers) + + train_data = _convert_eval_set_to_df(train_data) + if eval_data is not None: + eval_data = _convert_eval_set_to_df(eval_data) + + if isinstance(prompt, str): + prompt: PromptVersion = load_prompt(prompt) + + with _maybe_start_autolog(optimizer_config, train_data, eval_data, prompt, target_llm_params): + optimizer_output = optimzer.optimize( + prompt=prompt, + target_llm_params=target_llm_params, + train_data=train_data, + scorers=scorers, + objective=objective, + eval_data=eval_data, + ) + + optimized_prompt = register_prompt( + name=prompt.name, + template=optimizer_output.optimized_prompt, + tags={ + "overall_eval_score": str(optimizer_output.final_eval_score), + }, + ) + + if optimizer_config.autolog: + _log_optimization_result(optimizer_output.final_eval_score, optimized_prompt) + + return PromptOptimizationResult( + prompt=optimized_prompt, + initial_prompt=prompt, + optimizer_name=optimizer_output.optimizer_name, + final_eval_score=optimizer_output.final_eval_score, + initial_eval_score=optimizer_output.initial_eval_score, + ) + + +def _select_optimizer(optimizer_config: OptimizerConfig) -> BasePromptOptimizer: + if isinstance(optimizer_config.algorithm, type) and issubclass( + optimizer_config.algorithm, BasePromptOptimizer + ): + return optimizer_config.algorithm(optimizer_config) + + if optimizer_config.algorithm not in _ALGORITHMS: + raise ValueError( + f"Unsupported algorithm: '{optimizer_config.algorithm}'. " + f"Please use one of the following algorithms: {list(_ALGORITHMS.keys())}. " + "Or provide a custom optimizer class that inherits from BasePromptOptimizer." + ) + + return _ALGORITHMS[optimizer_config.algorithm](optimizer_config) + + +def _validate_scorers(scorers: list[Scorer]) -> None: + for scorer in scorers: + if not isinstance(scorer, Scorer): + raise MlflowException.invalid_parameter_value( + f"Invalid scorer: {scorer}. Please use @scorer decorator " + "on your function or inherit from Scorer class." + ) + + signature = inspect.signature(scorer) + if "trace" in signature.parameters: + raise MlflowException.invalid_parameter_value( + f"Invalid scorer parameter: {scorer} contains 'trace' parameter. " + "Optimization scorers can only use: inputs, outputs, or expectations. " + "Remove 'trace' parameter from your scorer function." + ) + + +@contextmanager +def _maybe_start_autolog( + optimizer_config: OptimizerConfig, + train_data: "pd.DataFrame", + eval_data: Optional["pd.DataFrame"], + prompt: PromptVersion, + target_llm_params: LLMParams, +): + if optimizer_config.autolog: + with start_run() as run: + _logger.info( + f"🚀 MLflow Run `{run.info.run_id}` started for prompt optimization! " + "Watch the run to track the optimization progress." + ) + log_table(train_data, "train_data.json") + if eval_data is not None: + log_table(eval_data, "eval_data.json") + params = { + "prompt_uri": prompt.uri, + **{f"target_llm_params.{k}": v for k, v in asdict(target_llm_params).items()}, + **{f"optimizer_config.{k}": v for k, v in asdict(optimizer_config).items()}, + } + log_params(params) + yield + else: + yield + + +def _log_optimization_result(final_score: float | None, optimized_prompt: PromptVersion): + if not active_run(): + return + + if final_score: + log_metric( + "final_eval_score", + final_score, + ) + log_param("optimized_prompt_uri", optimized_prompt.uri) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/optimize/optimizers/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/optimize/optimizers/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..57645277cd191226ae3d2869f308228ae583247d --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/optimize/optimizers/__init__.py @@ -0,0 +1,5 @@ +from mlflow.genai.optimize.optimizers.base_optimizer import BasePromptOptimizer +from mlflow.genai.optimize.optimizers.dspy_mipro_optimizer import _DSPyMIPROv2Optimizer +from mlflow.genai.optimize.optimizers.dspy_optimizer import DSPyPromptOptimizer + +__all__ = ["BasePromptOptimizer", "DSPyPromptOptimizer", "_DSPyMIPROv2Optimizer"] diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/optimize/optimizers/__pycache__/__init__.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/optimize/optimizers/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 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mlflow.genai.optimize.types import LLMParams, ObjectiveFn, OptimizerConfig, OptimizerOutput +from mlflow.genai.scorers import Scorer +from mlflow.utils.annotations import experimental + +if TYPE_CHECKING: + import pandas as pd + + +@experimental(version="3.3.0") +class BasePromptOptimizer(abc.ABC): + def __init__(self, optimizer_config: OptimizerConfig): + self._optimizer_config = optimizer_config + + @abc.abstractmethod + def optimize( + self, + prompt: PromptVersion, + target_llm_params: LLMParams, + train_data: "pd.DataFrame", + scorers: list[Scorer], + objective: ObjectiveFn | None = None, + eval_data: Optional["pd.DataFrame"] = None, + ) -> OptimizerOutput: + """Optimize the given prompt using the specified configuration. + + Args: + prompt: The prompt to optimize. + target_llm_params: Parameters for the agent LLM. + train_data: Training dataset for optimization. + scorers: List of scorers to evaluate the optimization. + objective: Optional function to compute overall performance metric. + eval_data: Optional evaluation dataset. + + Returns: + The optimized prompt version registered in the prompt registry as a new version. + """ + + @property + def optimizer_config(self) -> OptimizerConfig: + return self._optimizer_config diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/optimize/optimizers/dspy_mipro_optimizer.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/optimize/optimizers/dspy_mipro_optimizer.py new file mode 100644 index 0000000000000000000000000000000000000000..87b2789b0aaec99228e7d2ab43e2d9d6a3fe44d4 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/optimize/optimizers/dspy_mipro_optimizer.py @@ -0,0 +1,130 @@ +import logging +import math +from typing import TYPE_CHECKING, Callable, Optional + +from mlflow.entities.model_registry import PromptVersion +from mlflow.genai.optimize.optimizers.dspy_optimizer import DSPyPromptOptimizer +from mlflow.genai.optimize.types import OptimizerOutput + +if TYPE_CHECKING: + import dspy + import pandas as pd + + +_logger = logging.getLogger(__name__) + + +class _DSPyMIPROv2Optimizer(DSPyPromptOptimizer): + def run_optimization( + self, + prompt: PromptVersion, + program: "dspy.Module", + metric: Callable[["dspy.Example"], float], + train_data: list["dspy.Example"], + eval_data: list["dspy.Example"] | None, + ) -> OptimizerOutput: + import dspy + + from mlflow.genai.optimize.optimizers.utils.dspy_mipro_callback import _DSPyMIPROv2Callback + from mlflow.genai.optimize.optimizers.utils.dspy_optimizer_utils import ( + format_dspy_prompt, + ) + + teacher_settings = {} + + if self.optimizer_config.optimizer_llm: + teacher_lm = dspy.LM( + model=self._parse_model_name(self.optimizer_config.optimizer_llm.model_name), + temperature=self.optimizer_config.optimizer_llm.temperature, + api_base=self.optimizer_config.optimizer_llm.base_uri, + ) + teacher_settings["lm"] = teacher_lm + + num_candidates = self.optimizer_config.num_instruction_candidates + optimizer = dspy.MIPROv2( + metric=metric, + max_bootstrapped_demos=self.optimizer_config.max_few_show_examples, + num_candidates=num_candidates, + num_threads=self.optimizer_config.num_threads, + teacher_settings=teacher_settings, + auto=None, + ) + + callbacks = ( + [ + _DSPyMIPROv2Callback( + prompt_name=prompt.name, + input_fields=program.signature.input_fields, + convert_to_single_text=self.optimizer_config.convert_to_single_text, + ), + ] + if self.optimizer_config.autolog + else [] + ) + + with dspy.context(callbacks=callbacks): + with self._maybe_suppress_stdout_stderr(): + optimized_program = optimizer.compile( + program, + trainset=train_data, + valset=eval_data, + num_trials=self._get_num_trials(num_candidates), + minibatch_size=self._get_minibatch_size(train_data, eval_data), + requires_permission_to_run=False, + ) + + template = format_dspy_prompt( + program=optimized_program, + convert_to_single_text=self.optimizer_config.convert_to_single_text, + ) + + initial_score, final_score = self._extract_eval_scores(optimized_program) + self._display_optimization_result(initial_score, final_score) + + return OptimizerOutput( + final_eval_score=final_score, + initial_eval_score=initial_score, + optimized_prompt=template, + optimizer_name="DSPy/MIPROv2", + ) + + def _get_num_trials(self, num_candidates: int) -> int: + # MAX(2*log(num_candidates), 3/2*num_candidates) + return int(max(2 * math.log2(num_candidates), 1.5 * num_candidates)) + + def _get_minibatch_size( + self, + train_data: "pd.DataFrame", + eval_data: Optional["pd.DataFrame"] = None, + ) -> int: + # The default minibatch size is 35 in MIPROv2. + if eval_data is not None: + return min(35, len(eval_data) // 2) + return min(35, len(train_data) // 2) + + def _extract_eval_scores(self, program: "dspy.Predict") -> tuple[float | None, float | None]: + final_score = getattr(program, "score", None) + initial_score = None + # In DSPy < 2.6.17, trial_logs contains initial score in key=-1. + trial_logs = getattr(program, "trial_logs", {}) + if 1 in trial_logs: + initial_score = trial_logs[1].get("full_eval_score") + elif -1 in trial_logs: + initial_score = trial_logs[-1].get("full_eval_score") + return initial_score, final_score + + def _display_optimization_result(self, initial_score: float | None, final_score: float | None): + _logger.info(f"Optimization complete! Score remained stable at: {final_score}.") + if final_score is None: + return + + if initial_score is not None: + if initial_score == final_score: + _logger.info(f"Optimization complete! Score remained stable at: {final_score}.") + return + else: + _logger.info( + f"🎉 Optimization complete! " + f"Initial score: {initial_score}. " + f"Final score: {final_score}." + ) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/optimize/optimizers/dspy_optimizer.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/optimize/optimizers/dspy_optimizer.py new file mode 100644 index 0000000000000000000000000000000000000000..97d5406cd254dc9260af3b25b94d0eca4c929c88 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/optimize/optimizers/dspy_optimizer.py @@ -0,0 +1,282 @@ +import contextlib +import importlib.metadata +import importlib.util +import inspect +import io +import logging +import os +from typing import TYPE_CHECKING, Any, Callable, Optional + +from packaging.version import Version + +from mlflow.entities.model_registry import PromptVersion +from mlflow.exceptions import MlflowException +from mlflow.genai.optimize.optimizers import BasePromptOptimizer +from mlflow.genai.optimize.types import LLMParams, ObjectiveFn, OptimizerConfig, OptimizerOutput +from mlflow.genai.optimize.util import infer_type_from_value +from mlflow.genai.scorers import Scorer +from mlflow.utils.annotations import experimental + +if TYPE_CHECKING: + import dspy + import pandas as pd + +_logger = logging.getLogger(__name__) + + +@experimental(version="3.3.0") +class DSPyPromptOptimizer(BasePromptOptimizer): + def __init__(self, optimizer_config: OptimizerConfig): + super().__init__(optimizer_config) + + if not importlib.util.find_spec("dspy"): + raise ImportError("dspy is not installed. Please install it with `pip install dspy`.") + + dspy_version = importlib.metadata.version("dspy") + if Version(dspy_version) < Version("2.6.0"): + raise MlflowException( + f"Current dspy version {dspy_version} is unsupported. " + "Please upgrade to version >= 2.6.0" + ) + + def _parse_model_name(self, model_name: str) -> str: + """ + Parse model name from URI format to DSPy format. + + Accepts two formats: + - URI format: 'openai:/gpt-4o' -> converted to 'openai/gpt-4o' + - DSPy format: 'openai/gpt-4o' -> returned unchanged + + Raises MlflowException for invalid formats. + """ + from mlflow.metrics.genai.model_utils import _parse_model_uri + + if not model_name: + raise MlflowException.invalid_parameter_value( + "Model name cannot be empty. Please provide a model name in the format " + "':/' or '/'." + ) + + try: + scheme, path = _parse_model_uri(model_name) + return f"{scheme}/{path}" + except MlflowException: + if "/" in model_name and ":" not in model_name: + parts = model_name.split("/") + if len(parts) == 2 and parts[0] and parts[1]: + return model_name + + raise MlflowException.invalid_parameter_value( + f"Invalid model name format: '{model_name}'. " + "Model name must be in one of the following formats:\n" + " - '/' (e.g., 'openai/gpt-4')\n" + " - ':/' (e.g., 'openai:/gpt-4')" + ) + + def optimize( + self, + prompt: PromptVersion, + target_llm_params: LLMParams, + train_data: "pd.DataFrame", + scorers: list[Scorer], + objective: ObjectiveFn | None = None, + eval_data: Optional["pd.DataFrame"] = None, + ) -> OptimizerOutput: + import dspy + + _logger.info( + f"🎯 Starting prompt optimization for: {prompt.uri}\n" + f"⏱️ This may take several minutes or longer depending on dataset size...\n" + f"📊 Training with {len(train_data)} examples." + ) + + input_fields = self._get_input_fields(train_data) + self._validate_input_fields(input_fields, prompt) + output_fields = self._get_output_fields(train_data) + + lm = dspy.LM( + model=self._parse_model_name(target_llm_params.model_name), + temperature=target_llm_params.temperature, + api_base=target_llm_params.base_uri, + ) + + if self.optimizer_config.optimizer_llm: + teacher_lm = dspy.LM( + model=self._parse_model_name(self.optimizer_config.optimizer_llm.model_name), + temperature=self.optimizer_config.optimizer_llm.temperature, + api_base=self.optimizer_config.optimizer_llm.base_uri, + ) + else: + teacher_lm = lm + + if self.optimizer_config.extract_instructions: + instructions = self._extract_instructions(prompt.template, teacher_lm) + else: + instructions = prompt.template + + signature = dspy.make_signature( + {key: (type_, dspy.InputField()) for key, type_ in input_fields.items()} + | {key: (type_, dspy.OutputField()) for key, type_ in output_fields.items()}, + instructions, + ) + + # Define main student program + program = dspy.Predict(signature) + adapter = dspy.JSONAdapter() + + train_data = self._convert_to_dspy_dataset(train_data) + eval_data = self._convert_to_dspy_dataset(eval_data) if eval_data is not None else None + + with dspy.context(lm=lm, adapter=adapter): + return self.run_optimization( + prompt=prompt, + program=program, + metric=self._convert_to_dspy_metric( + input_fields, output_fields, scorers, objective + ), + train_data=train_data, + eval_data=eval_data, + ) + + def run_optimization( + self, + prompt: PromptVersion, + program: "dspy.Module", + metric: Callable[["dspy.Example"], float], + train_data: list["dspy.Example"], + eval_data: list["dspy.Example"], + ) -> OptimizerOutput: + """ + Run the optimization process for the given prompt and program. + + Parameters + ---------- + prompt : PromptVersion + The prompt version to optimize. + program : dspy.Module + The DSPy program/module to optimize. + metric : Callable[[dspy.Example], float] + A callable that computes a metric score for a given example. + train_data : list[dspy.Example] + List of training examples for optimization. + eval_data : list[dspy.Example] + List of evaluation examples for validation. + + Returns + ------- + OptimizerOutput + The result of the optimization, including the optimized prompt and metrics. + + Raises + ------ + NotImplementedError + This method must be implemented by subclasses. + """ + raise NotImplementedError( + "Subclasses of DSPyPromptOptimizer must implement `run_optimization`." + ) + + def _get_input_fields(self, train_data: "pd.DataFrame") -> dict[str, type]: + if "inputs" in train_data.columns: + sample_input = train_data["inputs"].values[0] + return {k: infer_type_from_value(v) for k, v in sample_input.items()} + return {} + + def _get_output_fields(self, train_data: "pd.DataFrame") -> dict[str, type]: + if "expectations" in train_data.columns: + sample_output = train_data["expectations"].values[0] + return {k: infer_type_from_value(v) for k, v in sample_output.items()} + return {} + + def _convert_to_dspy_dataset(self, data: "pd.DataFrame") -> list["dspy.Example"]: + import dspy + + examples = [] + for _, row in data.iterrows(): + expectations = row["expectations"] if "expectations" in row else {} + examples.append( + dspy.Example(**row["inputs"], **expectations).with_inputs(*row["inputs"].keys()) + ) + return examples + + def _convert_to_dspy_metric( + self, + input_fields: dict[str, type], + output_fields: dict[str, type], + scorers: list[Scorer], + objective: ObjectiveFn | None = None, + ) -> Callable[["dspy.Example"], float]: + def metric(example: "dspy.Example", pred: "dspy.Example", trace=None) -> float: + scores = {} + inputs = {key: example.get(key) for key in input_fields.keys()} + expectations = {key: example.get(key) for key in output_fields.keys()} + outputs = {key: pred.get(key) for key in output_fields.keys()} + + for scorer in scorers: + kwargs = {"inputs": inputs, "outputs": outputs, "expectations": expectations} + signature = inspect.signature(scorer) + kwargs = { + key: value for key, value in kwargs.items() if key in signature.parameters + } + scores[scorer.name] = scorer(**kwargs) + if objective is not None: + return objective(scores) + elif all(isinstance(score, (int, float, bool)) for score in scores.values()): + # Use total score by default if no objective is provided + return sum(scores.values()) + else: + non_numerical_scorers = [ + k for k, v in scores.items() if not isinstance(v, (int, float, bool)) + ] + raise MlflowException( + f"Scorer [{','.join(non_numerical_scorers)}] return a string, Assessment or a " + "list of Assessment. Please provide `objective` function to aggregate " + "non-numerical values into a single value for optimization." + ) + + return metric + + def _validate_input_fields(self, input_fields: dict[str, type], prompt: PromptVersion) -> None: + if missing_fields := set(prompt.variables) - set(input_fields.keys()): + raise MlflowException( + f"Validation failed. Missing prompt variables in dataset: {missing_fields}. " + "Please ensure your dataset contains columns for all prompt variables." + ) + + def _extract_instructions(self, template: str | dict[str, Any], lm: "dspy.LM") -> str: + import dspy + + extractor = dspy.Predict( + dspy.make_signature( + { + "prompt": (str, dspy.InputField()), + "instruction": (str, dspy.OutputField()), + }, + "Extract the core instructions from the prompt " + "to use as the system message for the LLM.", + ) + ) + + with dspy.context(lm=lm): + return extractor(prompt=template).instruction + + @contextlib.contextmanager + def _maybe_suppress_stdout_stderr(self): + """Context manager for redirecting stdout/stderr based on verbose setting. + If verbose is False, redirects output to devnull or StringIO. + If verbose is True, doesn't redirect output. + """ + if not self.optimizer_config.verbose: + try: + output_sink = open(os.devnull, "w") # noqa: SIM115 + except (OSError, IOError): + output_sink = io.StringIO() + + with output_sink: + with ( + contextlib.redirect_stdout(output_sink), + contextlib.redirect_stderr(output_sink), + ): + yield + else: + yield diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/optimize/optimizers/utils/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/optimize/optimizers/utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..1b6177e1facce63702a3dcfb3cbc620d27bea4e2 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/optimize/optimizers/utils/__init__.py @@ -0,0 +1,3 @@ +from mlflow.genai.optimize.optimizers.utils.dspy_optimizer_utils import format_dspy_prompt + +__all__ = ["format_dspy_prompt"] diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/optimize/optimizers/utils/__pycache__/__init__.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/optimize/optimizers/utils/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..0a4cf95a3361a290a418c3d9c5ad0efc365daf92 Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/optimize/optimizers/utils/__pycache__/__init__.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/optimize/optimizers/utils/__pycache__/dspy_mipro_callback.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/optimize/optimizers/utils/__pycache__/dspy_mipro_callback.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..e4b4238c4b158a491371d45e5598662a126ceff5 Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/optimize/optimizers/utils/__pycache__/dspy_mipro_callback.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/optimize/optimizers/utils/__pycache__/dspy_optimizer_utils.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/optimize/optimizers/utils/__pycache__/dspy_optimizer_utils.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..e4fead4c053cf56e0858030e6cdc195a1a18b2d9 Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/optimize/optimizers/utils/__pycache__/dspy_optimizer_utils.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/optimize/optimizers/utils/dspy_mipro_callback.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/optimize/optimizers/utils/dspy_mipro_callback.py new file mode 100644 index 0000000000000000000000000000000000000000..f4ef67e9c502fcb6d873fbce6ec189ab1590d170 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/optimize/optimizers/utils/dspy_mipro_callback.py @@ -0,0 +1,82 @@ +from collections import defaultdict +from typing import TYPE_CHECKING, Any + +from dspy import Prediction +from dspy.utils.callback import BaseCallback + +import mlflow +from mlflow.genai.optimize.optimizers.utils.dspy_optimizer_utils import format_dspy_prompt + +_FULL_EVAL_NAME = "eval_full" + +if TYPE_CHECKING: + import dspy + + +class _DSPyMIPROv2Callback(BaseCallback): + def __init__( + self, + prompt_name: str, + input_fields: dict[str, type], + convert_to_single_text: bool, + ): + self.prompt_name = prompt_name + self.input_fields = input_fields + # call_id: (key, step, program) + self._call_id_to_values: dict[str, tuple[str, int, "dspy.Predict"]] = {} + self._evaluation_counter = defaultdict(int) + self._best_score = None + self.convert_to_single_text = convert_to_single_text + + def on_evaluate_start(self, call_id: str, instance: Any, inputs: dict[str, Any]): + key = inputs.get("callback_metadata", {}).get("metric_key", "eval") + step = self._evaluation_counter[key] + self._evaluation_counter[key] += 1 + self._call_id_to_values[call_id] = (key, step, inputs.get("program")) + + def on_evaluate_end( + self, + call_id: str, + outputs: Any, + exception: Exception | None = None, + ): + if exception: + return + + if call_id not in self._call_id_to_values: + return + key, step, program = self._call_id_to_values.pop(call_id) + + # Only log the full evaluation result + if key != _FULL_EVAL_NAME: + return + + if isinstance(outputs, float): + score = outputs + elif isinstance(outputs, tuple): + score = outputs[0] + elif isinstance(outputs, Prediction): + score = float(outputs) + else: + return + + if self._best_score is None: + # This is the first evaluation with initial prompt, + # we don't register this prompt + self._best_score = score + elif score > self._best_score: + # When best score is updated, register the new prompt + self._best_score = score + template = format_dspy_prompt(program, self.convert_to_single_text) + mlflow.genai.register_prompt( + name=self.prompt_name, + template=template, + tags={"overall_eval_score": score, key: step}, + ) + + if mlflow.active_run() is not None: + mlflow.log_metric( + key, + score, + step=step, + ) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/optimize/optimizers/utils/dspy_optimizer_utils.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/optimize/optimizers/utils/dspy_optimizer_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..ed6685a973a0f2d0767e0ba6fb041a6ba5ed7b89 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/optimize/optimizers/utils/dspy_optimizer_utils.py @@ -0,0 +1,31 @@ +from typing import TYPE_CHECKING, Any + +from mlflow.utils.annotations import experimental + +if TYPE_CHECKING: + import dspy + + +@experimental(version="3.3.0") +def format_dspy_prompt( + program: "dspy.Predict", + convert_to_single_text: bool, +) -> dict[str, Any] | str: + import dspy + + signature = program.signature + messages = dspy.settings.adapter.format( + signature=signature, + demos=program.demos, + inputs={key: "{{" + key + "}}" for key in signature.input_fields.keys()}, + ) + + if convert_to_single_text: + messages = "\n\n".join( + [ + f"<{message['role']}>\n{message['content']}\n" + for message in messages + ] + ) + + return messages diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/optimize/types.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/optimize/types.py new file mode 100644 index 0000000000000000000000000000000000000000..c3b3335cb79f0e1e569b5167efd9f10fdca9618c --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/optimize/types.py @@ -0,0 +1,116 @@ +import multiprocessing +from dataclasses import dataclass, field +from typing import TYPE_CHECKING, Any, Callable + +from mlflow.entities import Feedback +from mlflow.entities.model_registry import PromptVersion +from mlflow.utils.annotations import experimental + +if TYPE_CHECKING: + from mlflow.genai.optimize.optimizers import BasePromptOptimizer + + +ObjectiveFn = Callable[[dict[str, bool | float | str | Feedback | list[Feedback]]], float] + + +@experimental(version="3.0.0") +@dataclass +class PromptOptimizationResult: + """ + Result of the :py:func:`mlflow.genai.optimize_prompt()` API. + + Args: + prompt: A prompt version entity containing the optimized template. + initial_prompt: A prompt version entity containing the initial template. + optimizer_name: The name of the optimizer. + final_eval_score: The final evaluation score of the optimized prompt. + initial_eval_score: The initial evaluation score of the optimized prompt. + """ + + prompt: PromptVersion + initial_prompt: PromptVersion + optimizer_name: str + final_eval_score: float | None + initial_eval_score: float | None + + +@experimental(version="3.0.0") +@dataclass +class LLMParams: + """ + Parameters for configuring a LLM Model. + + Args: + model_name: Name of the model in the format `:/` or + `/`. For example, "openai:/gpt-4o", "anthropic:/claude-4", + or "openai/gpt-4o". + base_uri: Optional base URI for the API endpoint. If not provided, + the default endpoint for the provider will be used. + temperature: Optional sampling temperature for the model's outputs. + Higher values (e.g., 0.8) make the output more random, + while lower values (e.g., 0.2) make it more deterministic. + """ + + model_name: str + base_uri: str | None = None + temperature: float | None = None + + +@experimental(version="3.0.0") +@dataclass +class OptimizerConfig: + """ + Configuration for prompt optimization. + + Args: + num_instruction_candidates: Number of candidate instructions to generate + during each optimization iteration. Higher values may lead to better + results but increase optimization time. Default: 6 + max_few_show_examples: Maximum number of examples to show in few-shot + demonstrations. Default: 6 + num_threads: Number of threads to use for parallel optimization. + Default: (number of CPU cores * 2 + 1) + optimizer_llm: Optional LLM parameters for the teacher model. If not provided, + the target LLM will be used as the teacher. + algorithm: The optimization algorithm to use. When a string is provided, + it must be one of the supported algorithms: "DSPy/MIPROv2". + When a BasePromptOptimizer is provided, it will be used as the optimizer. + Default: "DSPy/MIPROv2" + verbose: Whether to show optimizer logs during optimization. Default: False + autolog: Whether to log the optimization parameters, datasets and metrics. + If set to True, a MLflow run is automatically created to store them. + Default: False + convert_to_single_text: Whether to convert the optimized prompt to a single prompt. + Default: True + extract_instructions: Whether to extract instructions from the initial prompt. + Default: True + """ + + num_instruction_candidates: int = 6 + max_few_show_examples: int = 6 + num_threads: int = field(default_factory=lambda: (multiprocessing.cpu_count() or 1) * 2 + 1) + optimizer_llm: LLMParams | None = None + algorithm: str | type["BasePromptOptimizer"] = "DSPy/MIPROv2" + verbose: bool = False + autolog: bool = False + convert_to_single_text: bool = True + extract_instructions: bool = True + + +@experimental(version="3.3.0") +@dataclass(kw_only=True) +class OptimizerOutput: + """ + Output of the `optimize` method of :py:class:`mlflow.genai.optimize.BasePromptOptimizer`. + + Args: + optimized_prompt: The optimized prompt version entity. + optimizer_name: The name of the optimizer. + final_eval_score: The final evaluation score of the optimized prompt. + initial_eval_score: The initial evaluation score of the optimized prompt. + """ + + optimized_prompt: str | dict[str, Any] + optimizer_name: str + final_eval_score: float | None = None + initial_eval_score: float | None = None diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/optimize/util.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/optimize/util.py new file mode 100644 index 0000000000000000000000000000000000000000..bc7bb44d61efda1b8422b5136f5a5447758e8411 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/optimize/util.py @@ -0,0 +1,28 @@ +import functools +from typing import Any + +from pydantic import BaseModel, create_model + + +def infer_type_from_value(value: Any, model_name: str = "Output") -> type: + """ + Infer the type from the value. + Only supports primitive types, lists, and dict and Pydantic models. + """ + if value is None: + return type(None) + elif isinstance(value, (bool, int, float, str)): + return type(value) + elif isinstance(value, list): + if not value: + return list[Any] + element_types = set() + for item in value: + element_types.add(infer_type_from_value(item)) + return list[functools.reduce(lambda x, y: x | y, element_types)] + elif isinstance(value, dict): + fields = {k: (infer_type_from_value(v, model_name=k), ...) for k, v in value.items()} + return create_model(model_name, **fields) + elif isinstance(value, BaseModel): + return type(value) + return Any diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/prompts/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/prompts/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..4c56772eafb9959687eedc50c15acf51ce90fa44 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/prompts/__init__.py @@ -0,0 +1,228 @@ +import warnings +from contextlib import contextmanager +from typing import Any + +from pydantic import BaseModel + +import mlflow.tracking._model_registry.fluent as registry_api +from mlflow.entities.model_registry.prompt import Prompt +from mlflow.entities.model_registry.prompt_version import PromptVersion +from mlflow.prompt.registry_utils import require_prompt_registry +from mlflow.store.entities.paged_list import PagedList +from mlflow.utils.annotations import experimental + + +@contextmanager +def suppress_genai_migration_warning(): + """Suppress the deprecation warning when the api is called from `mlflow.genai` namespace.""" + with warnings.catch_warnings(): + warnings.filterwarnings( + action="ignore", + category=FutureWarning, + message="The `mlflow.*` API is moved to the `mlflow.genai` namespace.*", + ) + yield + + +@experimental(version="3.0.0") +@require_prompt_registry +def register_prompt( + name: str, + template: str | list[dict[str, Any]], + commit_message: str | None = None, + tags: dict[str, str] | None = None, + response_format: BaseModel | dict[str, Any] | None = None, +) -> PromptVersion: + """ + Register a new :py:class:`Prompt ` in the MLflow Prompt Registry. + + A :py:class:`Prompt ` is a pair of name and + template content at minimum. With MLflow Prompt Registry, you can create, manage, and + version control prompts with the MLflow's robust model tracking framework. + + If there is no registered prompt with the given name, a new prompt will be created. + Otherwise, a new version of the existing prompt will be created. + + Args: + name: The name of the prompt. + template: The template content of the prompt. Can be either: + + - A string containing text with variables enclosed in double curly braces, + e.g. {{variable}}, which will be replaced with actual values by the `format` method. + - A list of dictionaries representing chat messages, where each message has + 'role' and 'content' keys (e.g., [{"role": "user", "content": "Hello {{name}}"}]) + + + .. note:: + + If you want to use the prompt with a framework that uses single curly braces + e.g. LangChain, you can use the `to_single_brace_format` method to convert the + loaded prompt to a format that uses single curly braces. + + .. code-block:: python + + prompt = client.load_prompt("my_prompt") + langchain_format = prompt.to_single_brace_format() + + commit_message: A message describing the changes made to the prompt, similar to a + Git commit message. Optional. + tags: A dictionary of tags associated with the **prompt version**. + This is useful for storing version-specific information, such as the author of + the changes. Optional. + response_format: Optional Pydantic class or dictionary defining the expected response + structure. This can be used to specify the schema for structured outputs from LLM calls. + + Returns: + A :py:class:`Prompt ` object that was created. + + Example: + + .. code-block:: python + + import mlflow + + # Register a text prompt + mlflow.genai.register_prompt( + name="greeting_prompt", + template="Respond to the user's message as a {{style}} AI.", + ) + + # Register a chat prompt with multiple messages + mlflow.genai.register_prompt( + name="assistant_prompt", + template=[ + {"role": "system", "content": "You are a helpful {{style}} assistant."}, + {"role": "user", "content": "{{question}}"}, + ], + response_format={"type": "object", "properties": {"answer": {"type": "string"}}}, + ) + + # Load and use the prompt + prompt = mlflow.genai.load_prompt("greeting_prompt") + + # Use the prompt in your application + import openai + + openai_client = openai.OpenAI() + openai_client.chat.completion.create( + model="gpt-4o-mini", + messages=[ + {"role": "system", "content": prompt.format(style="friendly")}, + {"role": "user", "content": "Hello, how are you?"}, + ], + ) + + # Update the prompt with a new version + prompt = mlflow.genai.register_prompt( + name="greeting_prompt", + template="Respond to the user's message as a {{style}} AI. {{greeting}}", + commit_message="Add a greeting to the prompt.", + tags={"author": "Bob"}, + ) + + """ + with suppress_genai_migration_warning(): + return registry_api.register_prompt( + name=name, + template=template, + commit_message=commit_message, + tags=tags, + response_format=response_format, + ) + + +@experimental(version="3.0.0") +@require_prompt_registry +def search_prompts( + filter_string: str | None = None, + max_results: int | None = None, +) -> PagedList[Prompt]: + with suppress_genai_migration_warning(): + return registry_api.search_prompts(filter_string=filter_string, max_results=max_results) + + +@experimental(version="3.0.0") +@require_prompt_registry +def load_prompt( + name_or_uri: str, + version: str | int | None = None, + allow_missing: bool = False, +) -> PromptVersion: + """ + Load a :py:class:`Prompt ` from the MLflow Prompt Registry. + + The prompt can be specified by name and version, or by URI. + + Args: + name_or_uri: The name of the prompt, or the URI in the format "prompts:/name/version". + version: The version of the prompt (required when using name, not allowed when using URI). + allow_missing: If True, return None instead of raising Exception if the specified prompt + is not found. + + Example: + + .. code-block:: python + + import mlflow + + # Load a specific version of the prompt + prompt = mlflow.genai.load_prompt("my_prompt", version=1) + + # Load a specific version of the prompt by URI + prompt = mlflow.genai.load_prompt("prompts:/my_prompt/1") + + # Load a prompt version with an alias "production" + prompt = mlflow.genai.load_prompt("prompts:/my_prompt@production") + + """ + with suppress_genai_migration_warning(): + return registry_api.load_prompt( + name_or_uri=name_or_uri, version=version, allow_missing=allow_missing + ) + + +@experimental(version="3.0.0") +@require_prompt_registry +def set_prompt_alias(name: str, alias: str, version: int) -> None: + """ + Set an alias for a :py:class:`Prompt ` in the MLflow Prompt Registry. + + Args: + name: The name of the prompt. + alias: The alias to set for the prompt. + version: The version of the prompt. + + Example: + + .. code-block:: python + + import mlflow + + # Set an alias for the prompt + mlflow.genai.set_prompt_alias(name="my_prompt", version=1, alias="production") + + # Load the prompt by alias (use "@" to specify the alias) + prompt = mlflow.genai.load_prompt("prompts:/my_prompt@production") + + # Switch the alias to a new version of the prompt + mlflow.genai.set_prompt_alias(name="my_prompt", version=2, alias="production") + + # Delete the alias + mlflow.genai.delete_prompt_alias(name="my_prompt", alias="production") + """ + with suppress_genai_migration_warning(): + return registry_api.set_prompt_alias(name=name, version=version, alias=alias) + + +@experimental(version="3.0.0") +@require_prompt_registry +def delete_prompt_alias(name: str, alias: str) -> None: + """ + Delete an alias for a :py:class:`Prompt ` in the MLflow Prompt Registry. + + Args: + name: The name of the prompt. + alias: The alias to delete for the prompt. + """ + with suppress_genai_migration_warning(): + return registry_api.delete_prompt_alias(name=name, alias=alias) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/prompts/__pycache__/__init__.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/prompts/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..533abb0a06c957597fc428d7817faae4f869d32f Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/prompts/__pycache__/__init__.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/scheduled_scorers.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/scheduled_scorers.py new file mode 100644 index 0000000000000000000000000000000000000000..e70e011712d77d32f528a976c5389bee87f4713f --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/scheduled_scorers.py @@ -0,0 +1,84 @@ +from dataclasses import dataclass + +from mlflow.genai.scorers.base import Scorer +from mlflow.utils.annotations import experimental + +_ERROR_MSG = ( + "The `databricks-agents` package is required to use `mlflow.genai.scheduled_scorers`. " + "Please install it with `pip install databricks-agents`." +) + + +@experimental(version="3.0.0") +@dataclass() +class ScorerScheduleConfig: + """ + A scheduled scorer configuration for automated monitoring of generative AI applications. + + Scheduled scorers are used to automatically evaluate traces logged to MLflow experiments + by production applications. They are part of `Databricks Lakehouse Monitoring for GenAI + `_, + which helps track quality metrics like groundedness, safety, and guideline adherence + alongside operational metrics like volume, latency, and cost. + + When configured, scheduled scorers run automatically in the background to evaluate + a sample of traces based on the specified sampling rate and filter criteria. The + Assessments are displayed in the Traces tab of the MLflow experiment and can be used to + identify quality issues in production. + + Args: + scorer: The scorer function to run on sampled traces. Must be either a built-in + scorer (e.g., Safety, Correctness) or a function decorated with @scorer. + Subclasses of Scorer are not supported. + scheduled_scorer_name: The name for this scheduled scorer configuration + within the experiment. This name must be unique among all scheduled scorers + in the same experiment. + We recommend using the scorer's name (e.g., scorer.name) for consistency. + sample_rate: The fraction of traces to evaluate, between 0.0 and 1.0. For example, + 0.1 means 10% of traces will be randomly selected for evaluation. + filter_string: An optional MLflow search_traces compatible filter string to apply + before sampling traces. Only traces matching this filter will be considered + for evaluation. Uses the same syntax as mlflow.search_traces(). + + Example: + .. code-block:: python + + from mlflow.genai.scorers import Safety, scorer + from mlflow.genai.scheduled_scorers import ScorerScheduleConfig + + # Using a built-in scorer + safety_config = ScorerScheduleConfig( + scorer=Safety(), + scheduled_scorer_name="production_safety", + sample_rate=0.2, # Evaluate 20% of traces + filter_string="trace.status = 'OK'", + ) + + + # Using a custom scorer + @scorer + def response_length(outputs): + return len(str(outputs)) > 100 + + + length_config = ScorerScheduleConfig( + scorer=response_length, + scheduled_scorer_name="adequate_length", + sample_rate=0.1, # Evaluate 10% of traces + filter_string="trace.status = 'OK'", + ) + + Note: + Scheduled scorers are executed automatically by Databricks and do not need to be + manually triggered. The Assessments appear in the Traces tab of the MLflow + experiment. Only traces logged directly to the experiment are monitored; traces + logged to individual runs within the experiment are not evaluated. + + .. warning:: + This API is in Beta and may change or be removed in a future release without warning. + """ + + scorer: Scorer + scheduled_scorer_name: str + sample_rate: float + filter_string: str | None = None diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/scorers/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/scorers/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..f392b35dff4f62d0e0546a3f4e0eae5eb323743c --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/scorers/__init__.py @@ -0,0 +1,31 @@ +from mlflow.genai.scorers.base import Scorer, ScorerSamplingConfig, scorer +from mlflow.genai.scorers.builtin_scorers import ( + Correctness, + ExpectationsGuidelines, + Guidelines, + RelevanceToQuery, + RetrievalGroundedness, + RetrievalRelevance, + RetrievalSufficiency, + Safety, + get_all_scorers, +) +from mlflow.genai.scorers.registry import delete_scorer, get_scorer, list_scorers + +__all__ = [ + "Correctness", + "ExpectationsGuidelines", + "Guidelines", + "RelevanceToQuery", + "RetrievalGroundedness", + "RetrievalRelevance", + "RetrievalSufficiency", + "Safety", + "Scorer", + "scorer", + "ScorerSamplingConfig", + "get_all_scorers", + "get_scorer", + "list_scorers", + "delete_scorer", +] diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/scorers/__pycache__/__init__.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/scorers/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..5196a7a1a91aea17d874a9e376c51f489bcba32d Binary files 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AggregationFunc, Scorer + +_logger = logging.getLogger(__name__) + + +_AGGREGATE_FUNCTIONS = { + "min": np.min, + "max": np.max, + "mean": np.mean, + "median": np.median, + "variance": np.var, + "p90": lambda x: np.percentile(x, 90) if x else None, +} + + +def compute_aggregated_metrics( + eval_results: list[EvalResult], + scorers: list[Scorer], +) -> dict[str, float]: + """ + Generates the aggregated per-run metrics from the evaluation result for each row. + + Args: + eval_results: List of EvalResult objects + scorers: List of scorers + + Returns: + Dictionary of aggregated metrics (metric name -> aggregated value) + """ + # Collect assessment values in a flat dictionary + assessment_values: dict[str, list[float]] = collections.defaultdict(list) + for eval_result in eval_results: + for assessment in eval_result.assessments: + if isinstance(assessment, Feedback): + value = _cast_assessment_value_to_float(assessment) + if value is not None: # Exclude None from aggregation + assessment_values[assessment.name].append(value) + + # List all aggregations to compute for each scorer + scorer_aggregations = {} + for scorer in scorers: + if scorer.aggregations is not None: + scorer_aggregations[scorer.name] = scorer.aggregations + else: + scorer_aggregations[scorer.name] = ["mean"] # default to compute mean only + + # Compute aggregates + result = {} + for name, values in assessment_values.items(): + if not values: + continue + + # Get the function name from the returned assessment name. + scorer_function_name = name.split("/", 1)[-1] + + # Compute aggregations for the scorer, defaulting to just ["mean"] + aggregations_to_compute = scorer_aggregations.get(scorer_function_name, ["mean"]) + aggregation_results = _compute_aggregations(values, aggregations_to_compute) + + # Each aggregation should be logged as a separate metric + for agg_name, agg_value in aggregation_results.items(): + result[f"{name}/{agg_name}"] = agg_value + + return result + + +def _cast_assessment_value_to_float(assessment: Feedback) -> float | None: + """Cast the value of an assessment to a float.""" + if isinstance(assessment.value, (int, float, bool)): + return float(assessment.value) + elif ( + isinstance(assessment.value, str) + and CategoricalRating(assessment.value.lower()) != CategoricalRating.UNKNOWN + ): + return float(assessment.value.lower() == CategoricalRating.YES) + else: + _logger.error(f"Invalid assessment value for {assessment.name}: {assessment.value}") + + +def _compute_aggregations( + scores: list[float], aggregations: list[str | AggregationFunc] +) -> dict[str, float]: + """Compute aggregate statistics for a list of scores based on specified aggregations. + + Args: + scores: List of numeric scores to aggregate + aggregations: List of aggregation types to compute (e.g. ["min", "max", "mean"]) + + Returns: + Dictionary mapping aggregation names to computed values + """ + results = {} + for aggregation in aggregations: + # Aggregations specified as strings, e.g., "mean" + if isinstance(aggregation, str): + if aggregation not in _AGGREGATE_FUNCTIONS: + raise ValueError(f"Invalid aggregation: {aggregation}") + results[aggregation] = _AGGREGATE_FUNCTIONS[aggregation](scores) + # Aggregations specified as a custom function. + else: + try: + results[aggregation.__name__] = aggregation(scores) + except Exception as e: + _logger.error(f"Error computing aggregation {aggregation} due to: {e}") + continue + + return results diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/scorers/base.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/scorers/base.py new file mode 100644 index 0000000000000000000000000000000000000000..76d17d2a54c608a606aa64b37b85b7cbb6986be8 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/scorers/base.py @@ -0,0 +1,878 @@ +import functools +import inspect +import logging +from dataclasses import asdict, dataclass +from enum import Enum +from typing import Any, Callable, Literal, TypeAlias + +from pydantic import BaseModel, PrivateAttr + +import mlflow +from mlflow.entities import Assessment, Feedback +from mlflow.entities.assessment import DEFAULT_FEEDBACK_NAME +from mlflow.entities.trace import Trace +from mlflow.exceptions import MlflowException +from mlflow.utils.annotations import experimental + +_logger = logging.getLogger(__name__) + +# Serialization version for tracking changes to the serialization format +_SERIALIZATION_VERSION = 1 +_AggregationFunc: TypeAlias = Callable[[list[int | float]], float] +_AggregationType: TypeAlias = ( + Literal["min", "max", "mean", "median", "variance", "p90"] | _AggregationFunc +) + + +class ScorerKind(Enum): + CLASS = "class" + BUILTIN = "builtin" + DECORATOR = "decorator" + + +_ALLOWED_SCORERS_FOR_REGISTRATION = [ScorerKind.BUILTIN, ScorerKind.DECORATOR] + + +class ScorerStatus(Enum): + """Status of a scorer. + + Scorer status is determined by the sample rate due to the backend not having + a notion of whether a scorer is started or stopped. + """ + + UNREGISTERED = "UNREGISTERED" # sampling config not set + STARTED = "STARTED" # sample_rate > 0 + STOPPED = "STOPPED" # sample_rate == 0 + + +@dataclass +class ScorerSamplingConfig: + """Configuration for registered scorer sampling.""" + + sample_rate: float | None = None + filter_string: str | None = None + + +AggregationFunc = Callable[[list[float]], float] # List of per-row value -> aggregated value + + +@dataclass +class SerializedScorer: + """ + Dataclass defining the serialization schema for Scorer objects. + """ + + # Core scorer fields + name: str + aggregations: list[str] | None = None + + # Version metadata + mlflow_version: str = mlflow.__version__ + serialization_version: int = _SERIALIZATION_VERSION + + # Builtin scorer fields (for scorers from mlflow.genai.scorers.builtin_scorers) + builtin_scorer_class: str | None = None + builtin_scorer_pydantic_data: dict[str, Any] | None = None + + # Decorator scorer fields (for @scorer decorated functions) + call_source: str | None = None + call_signature: str | None = None + original_func_name: str | None = None + + def __post_init__(self): + """Validate that either builtin scorer fields or decorator scorer fields are present.""" + has_builtin_fields = self.builtin_scorer_class is not None + has_decorator_fields = self.call_source is not None + + if not has_builtin_fields and not has_decorator_fields: + raise ValueError( + "SerializedScorer must have either builtin scorer fields " + "(builtin_scorer_class) or decorator scorer fields (call_source) present" + ) + + if has_builtin_fields and has_decorator_fields: + raise ValueError( + "SerializedScorer cannot have both builtin scorer fields and " + "decorator scorer fields present simultaneously" + ) + + +@experimental(version="3.0.0") +class Scorer(BaseModel): + name: str + aggregations: list[_AggregationType] | None = None + + _cached_dump: dict[str, Any] | None = PrivateAttr(default=None) + _sampling_config: ScorerSamplingConfig | None = PrivateAttr(default=None) + + @property + @experimental(version="3.2.0") + def sample_rate(self) -> float | None: + """Get the sample rate for this scorer. Available when registered for monitoring.""" + return self._sampling_config.sample_rate if self._sampling_config else None + + @property + @experimental(version="3.2.0") + def filter_string(self) -> str | None: + """Get the filter string for this scorer.""" + return self._sampling_config.filter_string if self._sampling_config else None + + @property + @experimental(version="3.3.0") + def status(self) -> ScorerStatus: + """Get the status of this scorer, using only the local state.""" + + if self.sample_rate is None: + return ScorerStatus.UNREGISTERED + + return ScorerStatus.STARTED if self.sample_rate > 0 else ScorerStatus.STOPPED + + def __repr__(self) -> str: + # Get the standard representation from the parent class + base_repr = super().__repr__() + filter_string = self.filter_string + if filter_string is not None: + filter_string = f"'{filter_string}'" + + # Inject the property's value into the repr string + return f"{base_repr[:-1]}, sample_rate={self.sample_rate}, filter_string={filter_string})" + + def model_dump(self, **kwargs) -> dict[str, Any]: + """Override model_dump to include source code.""" + + # Return cached dump if available (prevents re-serialization issues with dynamic functions) + if self._cached_dump is not None: + return self._cached_dump + + # Check if this is a decorator scorer + if not getattr(self, "_original_func", None): + # BuiltInScorer overrides `model_dump`, so this is neither a builtin scorer nor a + # decorator scorer + raise MlflowException.invalid_parameter_value( + f"Unsupported scorer type: {self.__class__.__name__}. " + f"Scorer serialization only supports:\n" + f"1. Builtin scorers (from mlflow.genai.scorers.builtin_scorers)\n" + f"2. Decorator-created scorers (using @scorer decorator)\n" + f"Direct subclassing of Scorer is not supported for serialization. " + f"Please use the @scorer decorator instead." + ) + + # Decorator scorer - extract and store source code + source_info = self._extract_source_code_info() + + # Create serialized scorer with all fields at once + serialized = SerializedScorer( + name=self.name, + aggregations=self.aggregations, + mlflow_version=mlflow.__version__, + serialization_version=_SERIALIZATION_VERSION, + call_source=source_info.get("call_source"), + call_signature=source_info.get("call_signature"), + original_func_name=source_info.get("original_func_name"), + ) + return asdict(serialized) + + def _extract_source_code_info(self) -> dict[str, str | None]: + """Extract source code information for the original decorated function.""" + from mlflow.genai.scorers.scorer_utils import extract_function_body + + result = {"call_source": None, "call_signature": None, "original_func_name": None} + + # Extract original function source + call_body, _ = extract_function_body(self._original_func) + result["call_source"] = call_body + result["original_func_name"] = self._original_func.__name__ + + # Store the signature of the original function + result["call_signature"] = str(inspect.signature(self._original_func)) + + return result + + @classmethod + def model_validate(cls, obj: Any) -> "Scorer": + """Override model_validate to reconstruct scorer from source code.""" + if not isinstance(obj, dict): + raise MlflowException.invalid_parameter_value( + f"Invalid scorer data: expected a dictionary, got {type(obj).__name__}. " + f"Scorer data must be a dictionary containing serialized scorer information." + ) + + # Parse the serialized data using our dataclass + try: + serialized = SerializedScorer(**obj) + except Exception as e: + raise MlflowException.invalid_parameter_value( + f"Failed to parse serialized scorer data: {e}" + ) + + # Log version information for debugging + if serialized.mlflow_version: + _logger.debug( + f"Deserializing scorer created with MLflow version: {serialized.mlflow_version}" + ) + if serialized.serialization_version: + _logger.debug(f"Scorer serialization version: {serialized.serialization_version}") + + if serialized.builtin_scorer_class: + # Import here to avoid circular imports + from mlflow.genai.scorers.builtin_scorers import BuiltInScorer + + return BuiltInScorer.model_validate(obj) + + # Handle decorator scorers + elif serialized.call_source and serialized.call_signature and serialized.original_func_name: + return cls._reconstruct_decorator_scorer(serialized) + + # Invalid serialized data + else: + raise MlflowException.invalid_parameter_value( + f"Failed to load scorer '{serialized.name}'. The scorer is serialized in an " + f"unknown format that cannot be deserialized. Please make sure you are using " + f"a compatible MLflow version or recreate the scorer. " + f"Scorer was created with MLflow version: " + f"{serialized.mlflow_version or 'unknown'}, " + f"serialization version: {serialized.serialization_version or 'unknown'}, " + f"current MLflow version: {mlflow.__version__}." + ) + + @classmethod + def _reconstruct_decorator_scorer(cls, serialized: SerializedScorer) -> "Scorer": + """Reconstruct a decorator scorer from serialized data.""" + from mlflow.genai.scorers.scorer_utils import recreate_function + + # Recreate the original function from source code + recreated_func = recreate_function( + serialized.call_source, serialized.call_signature, serialized.original_func_name + ) + + if not recreated_func: + raise MlflowException.invalid_parameter_value( + f"Failed to recreate function from source code. " + f"Scorer was created with MLflow version: " + f"{serialized.mlflow_version or 'unknown'}, " + f"serialization version: {serialized.serialization_version or 'unknown'}. " + f"Current MLflow version: {mlflow.__version__}" + ) + + # Apply the scorer decorator to recreate the scorer + # Rather than serializing and deserializing the `run` method of `Scorer`, we recreate the + # Scorer using the original function and the `@scorer` decorator. This should be safe so + # long as `@scorer` is a stable API. + scorer_instance = scorer( + recreated_func, name=serialized.name, aggregations=serialized.aggregations + ) + # Cache the serialized data to prevent re-serialization issues with dynamic functions + original_serialized_data = asdict(serialized) + object.__setattr__(scorer_instance, "_cached_dump", original_serialized_data) + return scorer_instance + + def run(self, *, inputs=None, outputs=None, expectations=None, trace=None): + from mlflow.evaluation import Assessment as LegacyAssessment + + merged = { + "inputs": inputs, + "outputs": outputs, + "expectations": expectations, + "trace": trace, + } + # Filter to only the parameters the function actually expects + sig = inspect.signature(self.__call__) + filtered = {k: v for k, v in merged.items() if k in sig.parameters} + result = self(**filtered) + if not ( + # TODO: Replace 'Assessment' with 'Feedback' once we migrate from the agent eval harness + isinstance(result, (int, float, bool, str, Assessment, LegacyAssessment)) + or ( + isinstance(result, list) + and all(isinstance(item, (Assessment, LegacyAssessment)) for item in result) + ) + # Allow None to represent an empty assessment from the scorer. + or result is None + ): + if isinstance(result, list) and len(result) > 0: + result_type = "list[" + type(result[0]).__name__ + "]" + else: + result_type = type(result).__name__ + raise MlflowException.invalid_parameter_value( + f"{self.name} must return one of int, float, bool, str, " + f"Feedback, or list[Feedback]. Got {result_type}" + ) + + if isinstance(result, Feedback) and result.name == DEFAULT_FEEDBACK_NAME: + # NB: Overwrite the returned feedback name to the scorer name. This is important + # so we show a consistent name for the feedback regardless of whether the scorer + # succeeds or fails. For example, let's say we have a scorer like this: + # + # @scorer + # def my_scorer(): + # # do something + # ... + # return Feedback(value=True) + # + # If the scorer succeeds, the returned feedback name will be default "feedback". + # However, if the scorer fails, it doesn't return a Feedback object, and we + # only know the scorer name. To unify this behavior, we overwrite the feedback + # name to the scorer name in the happy path. + # This will not apply when the scorer returns a list of Feedback objects. + # or users explicitly specify the feedback name via Feedback constructor. + result.name = self.name + + return result + + def __call__( + self, + *, + inputs: Any = None, + outputs: Any = None, + expectations: dict[str, Any] | None = None, + trace: Trace | None = None, + ) -> int | float | bool | str | Feedback | list[Feedback]: + """ + Implement the custom scorer's logic here. + + + The scorer will be called for each row in the input evaluation dataset. + + Your scorer doesn't need to have all the parameters defined in the base + signature. You can define a custom scorer with only the parameters you need. + See the parameter details below for what values are passed for each parameter. + + .. list-table:: + :widths: 20 20 20 + :header-rows: 1 + + * - Parameter + - Description + - Source + + * - ``inputs`` + - A single input to the target model/app. + - Derived from either dataset or trace. + + * When the dataset contains ``inputs`` column, the value will be + passed as is. + * When traces are provided as evaluation dataset, this will be derived + from the ``inputs`` field of the trace (i.e. inputs captured as the + root span of the trace). + + * - ``outputs`` + - A single output from the target model/app. + - Derived from either dataset, trace, or output of ``predict_fn``. + + * When the dataset contains ``outputs`` column, the value will be + passed as is. + * When ``predict_fn`` is provided, MLflow will make a prediction using the + ``inputs`` and the ``predict_fn``, and pass the result as the ``outputs``. + * When traces are provided as evaluation dataset, this will be derived + from the ``response`` field of the trace (i.e. outputs captured as the + root span of the trace). + + * - ``expectations`` + - Ground truth or any expectation for each prediction, e.g. expected retrieved docs. + - Derived from either dataset or trace. + + * When the dataset contains ``expectations`` column, the value will be + passed as is. + * When traces are provided as evaluation dataset, this will be a dictionary + that contains a set of assessments in the format of + [assessment name]: [assessment value]. + + * - ``trace`` + - A trace object corresponding to the prediction for the row. + - Specified as a ``trace`` column in the dataset, or generated during the prediction. + + Example: + + .. code-block:: python + + class NotEmpty(BaseScorer): + name = "not_empty" + + def __call__(self, *, outputs) -> bool: + return outputs != "" + + + class ExactMatch(BaseScorer): + name = "exact_match" + + def __call__(self, *, outputs, expectations) -> bool: + return outputs == expectations["expected_response"] + + + class NumToolCalls(BaseScorer): + name = "num_tool_calls" + + def __call__(self, *, trace) -> int: + spans = trace.search_spans(name="tool_call") + return len(spans) + + + # Use the scorer in an evaluation + mlflow.genai.evaluate( + data=data, + scorers=[NotEmpty(), ExactMatch(), NumToolCalls()], + ) + """ + raise NotImplementedError("Implementation of __call__ is required for Scorer class") + + @property + def kind(self) -> ScorerKind: + return ScorerKind.CLASS + + @experimental(version="3.2.0") + def register(self, *, name: str | None = None, experiment_id: str | None = None) -> "Scorer": + """ + Register this scorer with the MLflow server. + + This method registers the scorer for use with automatic trace evaluation in the + specified experiment. Once registered, the scorer can be started to begin + evaluating traces automatically. + + Args: + name: Optional registered name for the scorer. If not provided, the current `name` + property value will be used as a registered name. + experiment_id: The ID of the MLflow experiment to register the scorer for. + If None, uses the currently active experiment. + + Returns: + A new Scorer instance with server registration information. + + Example: + + .. code-block:: python + + import mlflow + from mlflow.genai.scorers import RelevanceToQuery + + # Register a built-in scorer + mlflow.set_experiment("my_genai_app") + registered_scorer = RelevanceToQuery().register(name="relevance_scorer") + print(f"Registered scorer: {registered_scorer.name}") + + # Register a custom scorer + from mlflow.genai.scorers import scorer + + + @scorer + def custom_length_check(outputs) -> bool: + return len(outputs) > 100 + + + registered_custom = custom_length_check.register( + name="output_length_checker", experiment_id="12345" + ) + """ + from mlflow.genai.scorers.registry import add_registered_scorer + + self._check_can_be_registered() + + # Create a new scorer instance + new_scorer = self._create_copy() + + # If name is provided, update the copy's name + if name: + new_scorer.name = name + # Update cached dump to reflect the new name + if new_scorer._cached_dump is not None: + new_scorer._cached_dump["name"] = name + + # Add the scorer to the server with sample_rate=0 (not actively sampling) + add_registered_scorer( + name=new_scorer.name, + scorer=new_scorer, + sample_rate=0.0, + filter_string=None, + experiment_id=experiment_id, + ) + + # Set the sampling config on the new instance + new_scorer._sampling_config = ScorerSamplingConfig(sample_rate=0.0, filter_string=None) + + return new_scorer + + @experimental(version="3.2.0") + def start( + self, + *, + name: str | None = None, + experiment_id: str | None = None, + sampling_config: ScorerSamplingConfig, + ) -> "Scorer": + """ + Start registered scoring with the specified sampling configuration. + + This method activates automatic trace evaluation for the scorer. The scorer will + evaluate traces based on the provided sampling configuration, including the + sample rate and optional filter criteria. + + Args: + name: Optional scorer name. If not provided, uses the scorer's registered + name or default name. + experiment_id: The ID of the MLflow experiment containing the scorer. + If None, uses the currently active experiment. + sampling_config: Configuration object containing: + - sample_rate: Fraction of traces to evaluate (0.0 to 1.0). Required. + - filter_string: Optional MLflow search_traces compatible filter string. + + Returns: + A new Scorer instance with updated sampling configuration. + + Example: + .. code-block:: python + + import mlflow + from mlflow.genai.scorers import RelevanceToQuery, ScorerSamplingConfig + + # Start scorer with 50% sampling rate + mlflow.set_experiment("my_genai_app") + scorer = RelevanceToQuery().register() + active_scorer = scorer.start(sampling_config=ScorerSamplingConfig(sample_rate=0.5)) + print(f"Scorer is evaluating {active_scorer.sample_rate * 100}% of traces") + + # Start scorer with filter to only evaluate specific traces + filtered_scorer = scorer.start( + sampling_config=ScorerSamplingConfig( + sample_rate=1.0, filter_string="YOUR_FILTER_STRING" + ) + ) + """ + from mlflow.genai.scorers.registry import update_registered_scorer + + self._check_can_be_registered() + + if sampling_config.sample_rate is not None and sampling_config.sample_rate <= 0: + raise MlflowException.invalid_parameter_value( + "When starting a scorer, provided sample rate must be greater than 0" + ) + + scorer_name = name or self.name + + # Update the scorer on the server + return update_registered_scorer( + name=scorer_name, + scorer=self, + sample_rate=sampling_config.sample_rate, + filter_string=sampling_config.filter_string, + experiment_id=experiment_id, + ) + + @experimental(version="3.2.0") + def update( + self, + *, + name: str | None = None, + experiment_id: str | None = None, + sampling_config: ScorerSamplingConfig, + ) -> "Scorer": + """ + Update the sampling configuration for this scorer. + + This method modifies the sampling rate and/or filter criteria for an already + registered scorer. It can be used to dynamically adjust how many traces are + evaluated or change the filtering criteria without stopping and restarting + the scorer. + + Args: + name: Optional scorer name. If not provided, uses the scorer's registered name + or default name. + experiment_id: The ID of the MLflow experiment containing the scorer. + If None, uses the currently active experiment. + sampling_config: Configuration object containing: + - sample_rate: New fraction of traces to evaluate (0.0 to 1.0). Optional. + - filter_string: New MLflow search_traces compatible filter string. Optional. + + Returns: + A new Scorer instance with updated configuration. + + Example: + + .. code-block:: python + + import mlflow + from mlflow.genai.scorers import RelevanceToQuery, ScorerSamplingConfig + + # Start scorer with initial configuration + mlflow.set_experiment("my_genai_app") + scorer = RelevanceToQuery().register() + active_scorer = scorer.start(sampling_config=ScorerSamplingConfig(sample_rate=0.1)) + + # Update to increase sampling rate during high traffic + updated_scorer = active_scorer.update( + sampling_config=ScorerSamplingConfig(sample_rate=0.5) + ) + print(f"Updated sample rate: {updated_scorer.sample_rate}") + + # Update to add filtering criteria + filtered_scorer = updated_scorer.update( + sampling_config=ScorerSamplingConfig(filter_string="YOUR_FILTER_STRING") + ) + print(f"Added filter: {filtered_scorer.filter_string}") + """ + from mlflow.genai.scorers.registry import update_registered_scorer + + self._check_can_be_registered() + + scorer_name = name or self.name + + # Update the scorer on the server + return update_registered_scorer( + name=scorer_name, + scorer=self, + sample_rate=sampling_config.sample_rate, + filter_string=sampling_config.filter_string, + experiment_id=experiment_id, + ) + + @experimental(version="3.2.0") + def stop(self, *, name: str | None = None, experiment_id: str | None = None) -> "Scorer": + """ + Stop registered scoring by setting sample rate to 0. + + This method deactivates automatic trace evaluation for the scorer while keeping + the scorer registered. The scorer can be restarted later using the start() method. + + Args: + name: Optional scorer name. If not provided, uses the scorer's registered name + or default name. + experiment_id: The ID of the MLflow experiment containing the scorer. + If None, uses the currently active experiment. + + Returns: + A new Scorer instance with sample rate set to 0. + + Example: + + .. code-block:: python + + import mlflow + from mlflow.genai.scorers import RelevanceToQuery, ScorerSamplingConfig + + # Start and then stop a scorer + mlflow.set_experiment("my_genai_app") + scorer = RelevanceToQuery().register() + active_scorer = scorer.start(sampling_config=ScorerSamplingConfig(sample_rate=0.5)) + print(f"Scorer is active: {active_scorer.sample_rate > 0}") + + # Stop the scorer + stopped_scorer = active_scorer.stop() + print(f"Scorer is active: {stopped_scorer.sample_rate > 0}") + + # The scorer remains registered and can be restarted later + restarted_scorer = stopped_scorer.start( + sampling_config=ScorerSamplingConfig(sample_rate=0.3) + ) + """ + self._check_can_be_registered() + + scorer_name = name or self.name + return self.update( + name=scorer_name, + experiment_id=experiment_id, + sampling_config=ScorerSamplingConfig(sample_rate=0.0), + ) + + def _create_copy(self) -> "Scorer": + """ + Create a copy of this scorer instance. + """ + self._check_can_be_registered( + error_message="Scorer must be a builtin or decorator scorer to be copied." + ) + + copy = self.model_copy(deep=True) + # Duplicate the cached dump so modifications to the copy don't affect the original + if self._cached_dump is not None: + object.__setattr__(copy, "_cached_dump", dict(self._cached_dump)) + return copy + + def _check_can_be_registered(self, error_message: str | None = None) -> None: + if self.kind not in _ALLOWED_SCORERS_FOR_REGISTRATION: + if error_message is None: + error_message = ( + "Scorer must be a builtin or decorator scorer to be registered. " + f"Got {self.kind}." + ) + raise MlflowException.invalid_parameter_value(error_message) + + +@experimental(version="3.0.0") +def scorer( + func=None, + *, + name: str | None = None, + aggregations: list[_AggregationType] | None = None, +): + """ + A decorator to define a custom scorer that can be used in ``mlflow.genai.evaluate()``. + + The scorer function should take in a **subset** of the following parameters: + + .. list-table:: + :widths: 20 20 20 + :header-rows: 1 + + * - Parameter + - Description + - Source + + * - ``inputs`` + - A single input to the target model/app. + - Derived from either dataset or trace. + + * When the dataset contains ``inputs`` column, the value will be passed as is. + * When traces are provided as evaluation dataset, this will be derived + from the ``inputs`` field of the trace (i.e. inputs captured as the + root span of the trace). + + * - ``outputs`` + - A single output from the target model/app. + - Derived from either dataset, trace, or output of ``predict_fn``. + + * When the dataset contains ``outputs`` column, the value will be passed as is. + * When ``predict_fn`` is provided, MLflow will make a prediction using the + ``inputs`` and the ``predict_fn`` and pass the result as the ``outputs``. + * When traces are provided as evaluation dataset, this will be derived + from the ``response`` field of the trace (i.e. outputs captured as the + root span of the trace). + + * - ``expectations`` + - Ground truth or any expectation for each prediction e.g., expected retrieved docs. + - Derived from either dataset or trace. + + * When the dataset contains ``expectations`` column, the value will be passed as is. + * When traces are provided as evaluation dataset, this will be a dictionary + that contains a set of assessments in the format of + [assessment name]: [assessment value]. + + * - ``trace`` + - A trace object corresponding to the prediction for the row. + - Specified as a ``trace`` column in the dataset, or generated during the prediction. + + The scorer function should return one of the following: + + * A boolean value + * An integer value + * A float value + * A string value + * A single :class:`~mlflow.entities.Feedback` object + * A list of :class:`~mlflow.entities.Feedback` objects + + .. note:: + + The metric name will be determined by the scorer function's name or a custom name + specified in the `name` parameter for the scorer. + + Args: + func: The scorer function to be decorated. + name: The name of the scorer. + aggregations: A list of aggregation functions to apply to the scorer's output. + The aggregation functions can be either a string or a callable. + + * If a string, it must be one of `["min", "max", "mean", "median", "variance", "p90"]`. + * If a callable, it must take a list of values and return a single value. + + By default, "mean" is used as the aggregation function. + + Example: + + .. code-block:: python + + import json + from mlflow.genai.scorers import scorer + from mlflow.entities import AssessmentSource, Feedback + + + # Basic scorers that returns primitive values + @scorer + def not_empty(outputs) -> bool: + return outputs != "" + + + @scorer + def exact_match(outputs, expectations) -> bool: + return outputs == expectations["expected_response"] + + + @scorer + def num_tool_calls(trace) -> int: + spans = trace.search_spans(name="tool_call") + return len(spans) + + + # Use `Feedback` object to return additional information about the scorer's + # result, such as a rationale for the score. + @scorer + def harmfulness(outputs) -> Feedback: + import openai + + prompt = f''' + Judge if the following text is harmful or not. + + Text: + {outputs} + + Return the answer in a JSON object with the following format: + {{ + "harmful": true + "reason": "The text contains harmful content" + }} + + Do not output any other characters than the json object. + ''' + response = openai.OpenAI().chat.completions.create( + model="o4-mini", + messages=[{"role": "user", "content": prompt}], + ) + payload = json.loads(response.choices[0].message.content) + return Feedback( + value=payload["harmful"], + rationale=payload["reason"], + source=AssessmentSource( + source_type="LLM_JUDGE", + source_id="openai:/o4-mini", + ), + ) + + + # Use the scorer in an evaluation + mlflow.genai.evaluate( + data=data, + scorers=[not_empty, exact_match, num_tool_calls, harmfulness], + ) + """ + + if func is None: + return functools.partial(scorer, name=name, aggregations=aggregations) + + class CustomScorer(Scorer): + # Store reference to the original function + _original_func: Callable[..., Any] | None = PrivateAttr(default=None) + + def __init__(self, **data): + super().__init__(**data) + # Set the original function reference + # Use object.__setattr__ to bypass Pydantic's attribute handling for private attributes + # during model initialization, as direct assignment (self._original_func = func) may be + # ignored or fail in this context + object.__setattr__(self, "_original_func", func) + + def __call__(self, *args, **kwargs): + return func(*args, **kwargs) + + @property + def kind(self) -> ScorerKind: + return ScorerKind.DECORATOR + + # Update the __call__ method's signature to match the original function + # but add 'self' as the first parameter. This is required for MLflow to + # pass the correct set of parameters to the scorer. + signature = inspect.signature(func) + params = list(signature.parameters.values()) + new_params = [inspect.Parameter("self", inspect.Parameter.POSITIONAL_OR_KEYWORD)] + params + new_signature = signature.replace(parameters=new_params) + CustomScorer.__call__.__signature__ = new_signature + + return CustomScorer( + name=name or func.__name__, + aggregations=aggregations, + ) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/scorers/builtin_scorers.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/scorers/builtin_scorers.py new file mode 100644 index 0000000000000000000000000000000000000000..1b0a4462cc4f496bc1a4284c6bd053421760be2a --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/scorers/builtin_scorers.py @@ -0,0 +1,854 @@ +from dataclasses import asdict +from typing import Any + +import mlflow +from mlflow.entities.assessment import Feedback +from mlflow.entities.trace import Trace +from mlflow.exceptions import MlflowException +from mlflow.genai import judges +from mlflow.genai.judges.builtin import _MODEL_API_DOC, requires_databricks_agents +from mlflow.genai.scorers.base import _SERIALIZATION_VERSION, Scorer, ScorerKind, SerializedScorer +from mlflow.genai.utils.trace_utils import ( + extract_retrieval_context_from_trace, + parse_inputs_to_str, + parse_outputs_to_str, +) +from mlflow.utils.annotations import experimental +from mlflow.utils.docstring_utils import format_docstring +from mlflow.utils.uri import is_databricks_uri + +GENAI_CONFIG_NAME = "databricks-agent" + + +def _validate_tracking_uri_is_databricks(scorer_name: str) -> None: + """Validate that the current tracking URI is set to Databricks. + + Args: + scorer_name: The name of the scorer being validated (for error messages). + + Raises: + MlflowException: If the MLflow tracking URI is not set to Databricks. + """ + from mlflow.utils.uri import is_databricks_uri + + if not is_databricks_uri(mlflow.get_tracking_uri()): + raise MlflowException( + f"The {scorer_name} scorer is only available in Databricks managed " + "MLflow. If you have a Databricks workspace, please set MLflow tracking " + "URI to the workspace by calling `mlflow.set_tracking_uri('databricks')`." + ) + + +class BuiltInScorer(Scorer): + """ + Base class for built-in scorers that share a common implementation. All built-in scorers should + inherit from this class. + """ + + name: str + required_columns: set[str] = set() + + def model_dump(self, **kwargs) -> dict[str, Any]: + """Override model_dump to handle builtin scorer serialization.""" + # Use mode='json' to automatically convert sets to lists for JSON compatibility + from pydantic import BaseModel + + pydantic_model_data = BaseModel.model_dump(self, mode="json", **kwargs) + + # Create serialized scorer with core fields + serialized = SerializedScorer( + name=self.name, + aggregations=self.aggregations, + mlflow_version=mlflow.__version__, + serialization_version=_SERIALIZATION_VERSION, + builtin_scorer_class=self.__class__.__name__, + builtin_scorer_pydantic_data=pydantic_model_data, + ) + + return asdict(serialized) + + @classmethod + def model_validate(cls, obj) -> "BuiltInScorer": + """Override model_validate to handle builtin scorer deserialization.""" + if not isinstance(obj, dict) or "builtin_scorer_class" not in obj: + raise MlflowException.invalid_parameter_value( + f"Invalid builtin scorer data: expected a dictionary with 'builtin_scorer_class'" + f" field, got {type(obj).__name__}." + ) + + from mlflow.genai.scorers import builtin_scorers + from mlflow.genai.scorers.base import SerializedScorer + + # Parse the serialized data using our dataclass + try: + serialized = SerializedScorer(**obj) + except Exception as e: + raise MlflowException.invalid_parameter_value( + f"Failed to parse serialized scorer data: {e}" + ) + + try: + scorer_class = getattr(builtin_scorers, serialized.builtin_scorer_class) + except AttributeError: + raise MlflowException.invalid_parameter_value( + f"Unknown builtin scorer class: {serialized.builtin_scorer_class}" + ) + + # Use the builtin_scorer_pydantic_data directly to reconstruct the scorer + constructor_args = serialized.builtin_scorer_pydantic_data or {} + + return scorer_class(**constructor_args) + + def validate_columns(self, columns: set[str]) -> None: + missing_columns = self.required_columns - columns + if missing_columns: + raise MissingColumnsException(self.name, missing_columns) + + @property + def kind(self) -> ScorerKind: + return ScorerKind.BUILTIN + + +# === Builtin Scorers === +@experimental(version="3.0.0") +class RetrievalRelevance(BuiltInScorer): + """ + Retrieval relevance measures whether each chunk is relevant to the input request. + + .. warning:: + This scorer is currently only available in Databricks managed MLflow. It requires + the `databricks-agents` package and will only work when the MLflow tracking URI + is set to Databricks. + + You can invoke the scorer directly with a single input for testing, or pass it to + `mlflow.genai.evaluate` for running full evaluation on a dataset. + + Example (direct usage): + + .. code-block:: python + + import mlflow + from mlflow.genai.scorers import RetrievalRelevance + + trace = mlflow.get_trace("") + feedbacks = RetrievalRelevance(name="my_retrieval_relevance")(trace=trace) + print(feedbacks) + + Example (with evaluate): + + .. code-block:: python + + import mlflow + + data = mlflow.search_traces(...) + result = mlflow.genai.evaluate(data=data, scorers=[RetrievalRelevance()]) + """ + + name: str = "retrieval_relevance" + required_columns: set[str] = {"inputs", "trace"} + + def __init__(self, /, **kwargs): + _validate_tracking_uri_is_databricks("RetrievalRelevance") + super().__init__(**kwargs) + + def __call__(self, *, trace: Trace) -> Feedback: + """ + Evaluate chunk relevance for each context chunk. + + Args: + trace: The trace of the model's execution. Must contains at least one span with + type `RETRIEVER`. MLflow will extract the retrieved context from that span. + If multiple spans are found, MLflow will use the **last** one. + + Returns: + A list of assessments evaluating the relevance of each context chunk. + If the number of retrievers is N and each retriever has M chunks, the list will + contain N * (M + 1) assessments. Each retriever span will emit M assessments + for the relevance of its chunks and 1 assessment for the average relevance of all + chunks. + """ + request = parse_inputs_to_str(trace.data.spans[0].inputs) + span_id_to_context = extract_retrieval_context_from_trace(trace) + + feedbacks = [] + for span_id, context in span_id_to_context.items(): + feedbacks.extend(self._compute_span_relevance(span_id, request, context)) + return feedbacks + + @requires_databricks_agents + def _compute_span_relevance( + self, span_id: str, request: str, chunks: dict[str, str] + ) -> list[Feedback]: + """Compute the relevance of retrieved context for one retriever span.""" + from databricks.agents.evals.judges import chunk_relevance + + # Compute relevance for each chunk. Call `chunk_relevance` judge directly + # to get a list of feedbacks with ids. + chunk_feedbacks = chunk_relevance( + request=request, retrieved_context=chunks, assessment_name=self.name + ) + for feedback in chunk_feedbacks: + feedback.span_id = span_id + + if len(chunk_feedbacks) == 0: + return [] + + # Compute average relevance across all chunks. + # NB: Handling error feedback as 0.0 relevance for simplicity. + average = sum(f.value == "yes" for f in chunk_feedbacks) / len(chunk_feedbacks) + + span_level_feedback = Feedback( + # NB: Adding a special suffix for span-level aggregation so that UI can distinguish + # it from the chunk-level score and render it on span correctly. + name=self.name + "/precision", + value=average, + source=chunk_feedbacks[0].source, + span_id=span_id, + ) + return [span_level_feedback] + chunk_feedbacks + + +@format_docstring(_MODEL_API_DOC) +@experimental(version="3.0.0") +class RetrievalSufficiency(BuiltInScorer): + """ + Retrieval sufficiency evaluates whether the retrieved documents provide all necessary + information to generate the expected response. + + You can invoke the scorer directly with a single input for testing, or pass it to + `mlflow.genai.evaluate` for running full evaluation on a dataset. + + Args: + name: The name of the scorer. Defaults to "retrieval_sufficiency". + model: {{ model }} + + Example (direct usage): + + .. code-block:: python + + import mlflow + from mlflow.genai.scorers import RetrievalSufficiency + + trace = mlflow.get_trace("") + feedback = RetrievalSufficiency(name="my_retrieval_sufficiency")(trace=trace) + print(feedback) + + Example (with evaluate): + + .. code-block:: python + + import mlflow + + data = mlflow.search_traces(...) + result = mlflow.genai.evaluate(data=data, scorers=[RetrievalSufficiency()]) + """ + + name: str = "retrieval_sufficiency" + model: str | None = None + required_columns: set[str] = {"inputs", "trace"} + + def validate_columns(self, columns: set[str]) -> None: + super().validate_columns(columns) + if ( + "expectations/expected_response" not in columns + and "expectations/expected_facts" not in columns + ): + raise MissingColumnsException( + self.name, ["expectations/expected_response or expectations/expected_facts"] + ) + + def __call__( + self, *, trace: Trace, expectations: dict[str, Any] | None = None + ) -> list[Feedback]: + """ + Evaluate context sufficiency based on retrieved documents. + + Args: + trace: The trace of the model's execution. Must contains at least one span with + type `RETRIEVER`. MLflow will extract the retrieved context from that span. + If multiple spans are found, MLflow will use the **last** one. + expectations: A dictionary of expectations for the response. Either `expected_facts` or + `expected_response` key is required. Alternatively, you can pass a trace annotated + with `expected_facts` or `expected_response` label(s) and omit this argument. + """ + request = parse_inputs_to_str(trace.data.spans[0].inputs) + span_id_to_context = extract_retrieval_context_from_trace(trace) + + # If expectations are explicitly provided, use them. + expectations = expectations or {} + expected_facts = expectations.get("expected_facts") + expected_response = expectations.get("expected_response") + + # As a fallback, use the trace annotations as expectations. + if expected_facts is None or expected_response is None: + for assessment in trace.info.assessments: + if assessment.name == "expected_facts" and expected_facts is None: + expected_facts = assessment.value + if assessment.name == "expected_response" and expected_response is None: + expected_response = assessment.value + + # This scorer returns a list of feedbacks, one for retriever span in the trace. + feedbacks = [] + for span_id, context in span_id_to_context.items(): + feedback = judges.is_context_sufficient( + request=request, + context=context, + expected_response=expected_response, + expected_facts=expected_facts, + name=self.name, + model=self.model, + ) + feedback.span_id = span_id + feedbacks.append(feedback) + + return feedbacks + + +@format_docstring(_MODEL_API_DOC) +@experimental(version="3.0.0") +class RetrievalGroundedness(BuiltInScorer): + """ + RetrievalGroundedness assesses whether the agent's response is aligned with the information + provided in the retrieved context. + + You can invoke the scorer directly with a single input for testing, or pass it to + `mlflow.genai.evaluate` for running full evaluation on a dataset. + + Args: + name: The name of the scorer. Defaults to "retrieval_groundedness". + model: {{ model }} + + Example (direct usage): + + .. code-block:: python + + import mlflow + from mlflow.genai.scorers import RetrievalGroundedness + + trace = mlflow.get_trace("") + feedback = RetrievalGroundedness(name="my_retrieval_groundedness")(trace=trace) + print(feedback) + + Example (with evaluate): + + .. code-block:: python + + import mlflow + + data = mlflow.search_traces(...) + result = mlflow.genai.evaluate(data=data, scorers=[RetrievalGroundedness()]) + """ + + name: str = "retrieval_groundedness" + model: str | None = None + required_columns: set[str] = {"inputs", "trace"} + + def __call__(self, *, trace: Trace) -> list[Feedback]: + """ + Evaluate groundedness of response against retrieved context. + + Args: + trace: The trace of the model's execution. Must contains at least one span with + type `RETRIEVER`. MLflow will extract the retrieved context from that span. + If multiple spans are found, MLflow will use the **last** one. + + Returns: + An :py:class:`mlflow.entities.assessment.Feedback~` object with a boolean value + indicating the groundedness of the response. + """ + request = parse_inputs_to_str(trace.data.spans[0].inputs) + response = parse_outputs_to_str(trace.data.spans[0].outputs) + span_id_to_context = extract_retrieval_context_from_trace(trace) + feedbacks = [] + for span_id, context in span_id_to_context.items(): + feedback = judges.is_grounded( + request=request, + response=response, + context=context, + name=self.name, + model=self.model, + ) + feedback.span_id = span_id + feedbacks.append(feedback) + return feedbacks + + +@format_docstring(_MODEL_API_DOC) +@experimental(version="3.0.0") +class Guidelines(BuiltInScorer): + """ + Guideline adherence evaluates whether the agent's response follows specific constraints + or instructions provided in the guidelines. + + You can invoke the scorer directly with a single input for testing, or pass it to + `mlflow.genai.evaluate` for running full evaluation on a dataset. + + Args: + name: The name of the scorer. Defaults to "guidelines". + guidelines: A single guideline text or a list of guidelines. + model: {{ model }} + + Example (direct usage): + + .. code-block:: python + + import mlflow + from mlflow.genai.scorers import Guidelines + + # Create a global judge + english = Guidelines( + name="english_guidelines", + guidelines=["The response must be in English"], + ) + feedback = english( + inputs={"question": "What is the capital of France?"}, + outputs="The capital of France is Paris.", + ) + print(feedback) + + Example (with evaluate): + + In the following example, the guidelines specified in the `english` and `clarify` scorers + will be uniformly applied to all the examples in the dataset. The evaluation result will + contains two scores "english" and "clarify". + + .. code-block:: python + + import mlflow + from mlflow.genai.scorers import Guidelines + + english = Guidelines( + name="english", + guidelines=["The response must be in English"], + ) + clarify = Guidelines( + name="clarify", + guidelines=["The response must be clear, coherent, and concise"], + ) + + data = [ + { + "inputs": {"question": "What is the capital of France?"}, + "outputs": "The capital of France is Paris.", + }, + { + "inputs": {"question": "What is the capital of Germany?"}, + "outputs": "The capital of Germany is Berlin.", + }, + ] + mlflow.genai.evaluate(data=data, scorers=[english, clarify]) + """ + + name: str = "guidelines" + guidelines: str | list[str] + model: str | None = None + required_columns: set[str] = {"inputs", "outputs"} + + def __call__( + self, + *, + inputs: dict[str, Any], + outputs: Any, + ) -> Feedback: + """ + Evaluate adherence to specified guidelines. + + Args: + inputs: A dictionary of input data, e.g. {"question": "What is the capital of France?"}. + outputs: The response from the model, e.g. "The capital of France is Paris." + + Returns: + An :py:class:`mlflow.entities.assessment.Feedback~` object with a boolean value + indicating the adherence to the specified guidelines. + """ + return judges.meets_guidelines( + guidelines=self.guidelines, + context={ + "request": parse_inputs_to_str(inputs), + "response": parse_outputs_to_str(outputs), + }, + name=self.name, + model=self.model, + ) + + +@format_docstring(_MODEL_API_DOC) +@experimental(version="3.0.0") +class ExpectationsGuidelines(BuiltInScorer): + """ + This scorer evaluates whether the agent's response follows specific constraints + or instructions provided for each row in the input dataset. This scorer is useful when + you have a different set of guidelines for each example. + + To use this scorer, the input dataset should contain the `expectations` column with the + `guidelines` field. Then pass this scorer to `mlflow.genai.evaluate` for running full + evaluation on the input dataset. + + Args: + name: The name of the scorer. Defaults to "expectations_guidelines". + model: {{ model }} + + Example: + + In this example, the guidelines specified in the `guidelines` field of the `expectations` + column will be applied to each example individually. The evaluation result will contain a + single "expectations_guidelines" score. + + .. code-block:: python + + import mlflow + from mlflow.genai.scorers import ExpectationsGuidelines + + data = [ + { + "inputs": {"question": "What is the capital of France?"}, + "outputs": "The capital of France is Paris.", + "expectations": { + "guidelines": ["The response must be factual and concise"], + }, + }, + { + "inputs": {"question": "How to learn Python?"}, + "outputs": "You can read a book or take a course.", + "expectations": { + "guidelines": ["The response must be helpful and encouraging"], + }, + }, + ] + mlflow.genai.evaluate(data=data, scorers=[ExpectationsGuidelines()]) + """ + + name: str = "expectations_guidelines" + model: str | None = None + required_columns: set[str] = {"inputs", "outputs"} + + def validate_columns(self, columns: set[str]) -> None: + super().validate_columns(columns) + if "expectations/guidelines" not in columns: + raise MissingColumnsException(self.name, ["expectations/guidelines"]) + + def __call__( + self, + *, + inputs: dict[str, Any], + outputs: Any, + expectations: dict[str, Any] | None = None, + ) -> Feedback: + """ + Evaluate adherence to specified guidelines. + + Args: + inputs: A dictionary of input data, e.g. {"question": "What is the capital of France?"}. + outputs: The response from the model, e.g. "The capital of France is Paris." + expectations: A dictionary of expectations for the response. This must contain either + `guidelines` key, which is used to evaluate the response against the guidelines + specified in the `guidelines` field of the `expectations` column of the dataset. + E.g., {"guidelines": ["The response must be factual and concise"]} + + Returns: + An :py:class:`mlflow.entities.assessment.Feedback~` object with a boolean value + indicating the adherence to the specified guidelines. + """ + guidelines = (expectations or {}).get("guidelines") + if not guidelines: + raise MlflowException( + "Guidelines must be specified in the `expectations` parameter or " + "must be present in the trace." + ) + + return judges.meets_guidelines( + guidelines=guidelines, + context={ + "request": parse_inputs_to_str(inputs), + "response": parse_outputs_to_str(outputs), + }, + name=self.name, + model=self.model, + ) + + +@format_docstring(_MODEL_API_DOC) +@experimental(version="3.0.0") +class RelevanceToQuery(BuiltInScorer): + """ + Relevance ensures that the agent's response directly addresses the user's input without + deviating into unrelated topics. + + You can invoke the scorer directly with a single input for testing, or pass it to + `mlflow.genai.evaluate` for running full evaluation on a dataset. + + Args: + name: The name of the scorer. Defaults to "relevance_to_query". + model: {{ model }} + + Example (direct usage): + + .. code-block:: python + + import mlflow + from mlflow.genai.scorers import RelevanceToQuery + + assessment = RelevanceToQuery(name="my_relevance_to_query")( + inputs={"question": "What is the capital of France?"}, + outputs="The capital of France is Paris.", + ) + print(assessment) + + Example (with evaluate): + + .. code-block:: python + + import mlflow + from mlflow.genai.scorers import RelevanceToQuery + + data = [ + { + "inputs": {"question": "What is the capital of France?"}, + "outputs": "The capital of France is Paris.", + } + ] + result = mlflow.genai.evaluate(data=data, scorers=[RelevanceToQuery()]) + """ + + name: str = "relevance_to_query" + model: str | None = None + required_columns: set[str] = {"inputs", "outputs"} + + def __call__(self, *, inputs: dict[str, Any], outputs: Any) -> Feedback: + """ + Evaluate relevance to the user's query. + + Args: + inputs: A dictionary of input data, e.g. {"question": "What is the capital of France?"}. + outputs: The response from the model, e.g. "The capital of France is Paris." + + Returns: + An :py:class:`mlflow.entities.assessment.Feedback~` object with a boolean value + indicating the relevance of the response to the query. + """ + request = parse_inputs_to_str(inputs) + # NB: Reuse is_context_relevant judge to evaluate response + return judges.is_context_relevant( + request=request, context=outputs, name=self.name, model=self.model + ) + + +@experimental(version="3.0.0") +class Safety(BuiltInScorer): + """ + Safety ensures that the agent's responses do not contain harmful, offensive, or toxic content. + + .. warning:: + This scorer is currently only available in Databricks managed MLflow. It requires + the `databricks-agents` package and will only work when the MLflow tracking URI + is set to Databricks. + + You can invoke the scorer directly with a single input for testing, or pass it to + `mlflow.genai.evaluate` for running full evaluation on a dataset. + + Example (direct usage): + + .. code-block:: python + + import mlflow + from mlflow.genai.scorers import Safety + + assessment = Safety(name="my_safety")(outputs="The capital of France is Paris.") + print(assessment) + + Example (with evaluate): + + .. code-block:: python + + import mlflow + from mlflow.genai.scorers import Safety + + data = [ + { + "inputs": {"question": "What is the capital of France?"}, + "outputs": "The capital of France is Paris.", + } + ] + result = mlflow.genai.evaluate(data=data, scorers=[Safety()]) + """ + + name: str = "safety" + required_columns: set[str] = {"inputs", "outputs"} + + def __init__(self, /, **kwargs): + _validate_tracking_uri_is_databricks("Safety") + super().__init__(**kwargs) + + def __call__(self, *, outputs: Any) -> Feedback: + """ + Evaluate safety of the response. + + Args: + outputs: The response from the model, e.g. "The capital of France is Paris." + + Returns: + An :py:class:`mlflow.entities.assessment.Feedback~` object with a boolean value + indicating the safety of the response. + """ + return judges.is_safe(content=parse_outputs_to_str(outputs), name=self.name) + + +@format_docstring(_MODEL_API_DOC) +@experimental(version="3.0.0") +class Correctness(BuiltInScorer): + """ + Correctness ensures that the agent's responses are correct and accurate. + + You can invoke the scorer directly with a single input for testing, or pass it to + `mlflow.genai.evaluate` for running full evaluation on a dataset. + + Args: + name: The name of the scorer. Defaults to "correctness". + model: {{ model }} + + Example (direct usage): + + .. code-block:: python + + import mlflow + from mlflow.genai.scorers import Correctness + + assessment = Correctness(name="my_correctness")( + inputs={ + "question": "What is the difference between reduceByKey and groupByKey in Spark?" + }, + outputs=( + "reduceByKey aggregates data before shuffling, whereas groupByKey " + "shuffles all data, making reduceByKey more efficient." + ), + expectations=[ + {"expected_response": "reduceByKey aggregates data before shuffling"}, + {"expected_response": "groupByKey shuffles all data"}, + ], + ) + print(assessment) + + Example (with evaluate): + + .. code-block:: python + + import mlflow + from mlflow.genai.scorers import Correctness + + data = [ + { + "inputs": { + "question": ( + "What is the difference between reduceByKey and groupByKey in Spark?" + ) + }, + "outputs": ( + "reduceByKey aggregates data before shuffling, whereas groupByKey " + "shuffles all data, making reduceByKey more efficient." + ), + "expectations": { + "expected_response": ( + "reduceByKey aggregates data before shuffling. " + "groupByKey shuffles all data" + ), + }, + } + ] + result = mlflow.genai.evaluate(data=data, scorers=[Correctness()]) + """ + + name: str = "correctness" + model: str | None = None + required_columns: set[str] = {"inputs", "outputs"} + + def validate_columns(self, columns: set[str]) -> None: + super().validate_columns(columns) + if ( + "expectations/expected_response" not in columns + and "expectations/expected_facts" not in columns + ): + raise MissingColumnsException( + self.name, ["expectations/expected_response or expectations/expected_facts"] + ) + + def __call__( + self, *, inputs: dict[str, Any], outputs: Any, expectations: dict[str, Any] + ) -> Feedback: + """ + Evaluate correctness of the response against expectations. + + Args: + inputs: A dictionary of input data, e.g. {"question": "What is the capital of France?"}. + outputs: The response from the model, e.g. "The capital of France is Paris." + expectations: A dictionary of expectations for the response. This must contain either + `expected_response` or `expected_facts` key, which is used to evaluate the response + against the expected response or facts respectively. + E.g., {"expected_facts": ["Paris", "France", "Capital"]} + + Returns: + An :py:class:`mlflow.entities.assessment.Feedback~` object with a boolean value + indicating the correctness of the response. + """ + request = parse_inputs_to_str(inputs) + response = parse_outputs_to_str(outputs) + expected_facts = expectations.get("expected_facts") + expected_response = expectations.get("expected_response") + + if expected_response is None and expected_facts is None: + raise MlflowException( + "Correctness scorer requires either `expected_response` or `expected_facts` " + "in the `expectations` dictionary." + ) + + return judges.is_correct( + request=request, + response=response, + expected_response=expected_response, + expected_facts=expected_facts, + name=self.name, + model=self.model, + ) + + +# === Shorthand for getting preset of builtin scorers === +@experimental(version="3.0.0") +def get_all_scorers() -> list[BuiltInScorer]: + """ + Returns a list of all built-in scorers. + + Example: + + .. code-block:: python + + import mlflow + from mlflow.genai.scorers import get_all_scorers + + data = [ + { + "inputs": {"question": "What is the capital of France?"}, + "outputs": "The capital of France is Paris.", + "expectations": {"expected_response": "Paris is the capital city of France."}, + } + ] + result = mlflow.genai.evaluate(data=data, scorers=get_all_scorers()) + """ + scorers = [ + ExpectationsGuidelines(), + Correctness(), + RelevanceToQuery(), + RetrievalSufficiency(), + RetrievalGroundedness(), + ] + # TODO: Open-source these two scorers + if is_databricks_uri(mlflow.get_tracking_uri()): + scorers.extend([Safety(), RetrievalRelevance()]) + return scorers + + +class MissingColumnsException(MlflowException): + def __init__(self, scorer: str, missing_columns: set[str]): + self.scorer = scorer + self.missing_columns = list(missing_columns) + super().__init__( + f"The following columns are required for the scorer {scorer}: {missing_columns}" + ) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/scorers/registry.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/scorers/registry.py new file mode 100644 index 0000000000000000000000000000000000000000..23855cf9c00eda5f760ffdf2a1493cb7965eadc8 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/scorers/registry.py @@ -0,0 +1,224 @@ +""" +Registered scorer functionality for MLflow GenAI. + +This module provides functions to manage registered scorers that automatically +evaluate traces in MLflow experiments. +""" + +from mlflow.genai.scheduled_scorers import ScorerScheduleConfig +from mlflow.genai.scorers.base import Scorer, ScorerSamplingConfig +from mlflow.utils.annotations import experimental + +_ERROR_MSG = ( + "The `databricks-agents` package is required to register scorers. " + "Please install it with `pip install databricks-agents`." +) + + +def _scheduled_scorer_to_scorer(scheduled_scorer: ScorerScheduleConfig) -> Scorer: + scorer = scheduled_scorer.scorer + scorer._sampling_config = ScorerSamplingConfig( + sample_rate=scheduled_scorer.sample_rate, + filter_string=scheduled_scorer.filter_string, + ) + return scorer + + +@experimental(version="3.2.0") +def list_scorers(*, experiment_id: str | None = None) -> list[Scorer]: + """ + List all registered scorers for an experiment. + + This function returns all registered scorers configured for the specified experiment, + or for the current active experiment if no experiment ID is provided. + + Args: + experiment_id: The ID of the MLflow experiment to list scorers for. + If None, uses the currently active experiment. + + Returns: + A list of Scorer objects representing all registered scorers configured + for the specified experiment. + + Example: + .. code-block:: python + + import mlflow + from mlflow.genai.scorers import list_scorers + + # List scorers for a specific experiment + scorers = list_scorers(experiment_id="12345") + for scorer in scorers: + print(f"Scorer: {scorer.name}") + print(f"Sample rate: {scorer.sample_rate}") + print(f"Filter: {scorer.filter_string}") + + # List scorers for the current active experiment + mlflow.set_experiment("my_genai_app_monitoring") + current_scorers = list_scorers() + print(f"Found {len(current_scorers)} registered scorers") + """ + try: + from databricks.agents.scorers import list_scheduled_scorers + except ImportError as e: + raise ImportError(_ERROR_MSG) from e + + # Get scheduled scorers from the server + scheduled_scorers = list_scheduled_scorers(experiment_id=experiment_id) + + # Convert to Scorer instances with registration info + scorers = [] + for scheduled_scorer in scheduled_scorers: + scorers.append(_scheduled_scorer_to_scorer(scheduled_scorer)) + + return scorers + + +@experimental(version="3.2.0") +def get_scorer(*, name: str, experiment_id: str | None = None) -> Scorer: + """ + Retrieve a specific registered scorer by name. + + This function returns a Scorer instance with its current registration + configuration, including sampling rate and filter criteria. + + Args: + name: The name of the registered scorer to retrieve. + experiment_id: The ID of the MLflow experiment containing the scorer. + If None, uses the currently active experiment. + + Returns: + A Scorer object with its current registration configuration. + + Example: + .. code-block:: python + + from mlflow.genai.scorers import get_scorer + + # Get a specific scorer + my_scorer = get_scorer(name="my_safety_scorer") + + print(f"Sample rate: {my_scorer.sample_rate}") + print(f"Filter: {my_scorer.filter_string}") + + # Update the scorer + my_scorer = my_scorer.update(sample_rate=0.5) + """ + try: + from databricks.agents.scorers import get_scheduled_scorer + except ImportError as e: + raise ImportError(_ERROR_MSG) from e + + # Get the scheduled scorer from the server + scheduled_scorer = get_scheduled_scorer( + scheduled_scorer_name=name, + experiment_id=experiment_id, + ) + + # Extract the scorer and set registration fields + return _scheduled_scorer_to_scorer(scheduled_scorer) + + +@experimental(version="3.2.0") +def delete_scorer( + *, + name: str, + experiment_id: str | None = None, +) -> None: + """ + Delete scorer with given name from the server. + + This method permanently removes the scorer registration from the MLflow server. + After deletion, the scorer will no longer evaluate traces automatically and + must be registered again if needed. + + Args: + name: Name of the scorer to delete. + experiment_id: The ID of the MLflow experiment containing the scorer. + If None, uses the currently active experiment. + + Returns: + None + + Example: + .. code-block:: python + + import mlflow + from mlflow.genai.scorers import RelevanceToQuery, list_scorers, delete_scorer + + # Register and start a scorer + mlflow.set_experiment("my_genai_app") + scorer = RelevanceToQuery().register(name="relevance_checker") + + # List current scorers + scorers = list_scorers() + print(f"Active scorers: {[s.name for s in scorers]}") + + # Delete the scorer + delete_scorer(name="relevance_checker") + + # Verify deletion + scorers_after = list_scorers() + print(f"Active scorers after deletion: {[s.name for s in scorers_after]}") + + # To use the scorer again, it must be re-registered + new_scorer = RelevanceToQuery().register(name="relevance_checker_v2") + """ + try: + from databricks.agents.scorers import delete_scheduled_scorer + except ImportError as e: + raise ImportError(_ERROR_MSG) from e + + delete_scheduled_scorer( + experiment_id=experiment_id, + scheduled_scorer_name=name, + ) + + +# Private functions for internal use by Scorer methods +def add_registered_scorer( + *, + name: str, + scorer: Scorer, + sample_rate: float, + filter_string: str | None = None, + experiment_id: str | None = None, +) -> Scorer: + """Internal function to add a registered scorer.""" + try: + from databricks.agents.scorers import add_scheduled_scorer + except ImportError as e: + raise ImportError(_ERROR_MSG) from e + + scheduled_scorer = add_scheduled_scorer( + experiment_id=experiment_id, + scheduled_scorer_name=name, + scorer=scorer, + sample_rate=sample_rate, + filter_string=filter_string, + ) + return _scheduled_scorer_to_scorer(scheduled_scorer) + + +def update_registered_scorer( + *, + name: str, + scorer: Scorer | None = None, + sample_rate: float | None = None, + filter_string: str | None = None, + experiment_id: str | None = None, +) -> Scorer: + """Internal function to update a registered scorer.""" + try: + from databricks.agents.scorers import update_scheduled_scorer + except ImportError as e: + raise ImportError(_ERROR_MSG) from e + + scheduled_scorer = update_scheduled_scorer( + experiment_id=experiment_id, + scheduled_scorer_name=name, + scorer=scorer, + sample_rate=sample_rate, + filter_string=filter_string, + ) + return _scheduled_scorer_to_scorer(scheduled_scorer) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/scorers/scorer_utils.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/scorers/scorer_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..307526035689b27ae9fa64dad6b31ea409f729ce --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/scorers/scorer_utils.py @@ -0,0 +1,138 @@ +# This file contains utility functions for scorer functionality. + +import ast +import inspect +import logging +import re +from textwrap import dedent +from typing import Any, Callable + +_logger = logging.getLogger(__name__) + + +# FunctionBodyExtractor class is forked from https://github.com/unitycatalog/unitycatalog/blob/20dd3820be332ac04deec4e063099fb863eb3392/ai/core/src/unitycatalog/ai/core/utils/callable_utils.py +class FunctionBodyExtractor(ast.NodeVisitor): + """ + AST NodeVisitor class to extract the body of a function. + """ + + def __init__(self, func_name: str, source_code: str): + self.func_name = func_name + self.source_code = source_code + self.function_body = "" + self.indent_unit = 4 + self.found = False + + def visit_FunctionDef(self, node: ast.FunctionDef): + if not self.found and node.name == self.func_name: + self.found = True + self.extract_body(node) + + def extract_body(self, node: ast.FunctionDef): + body = node.body + # Skip the docstring + if ( + body + and isinstance(body[0], ast.Expr) + and isinstance(body[0].value, ast.Constant) + and isinstance(body[0].value.value, str) + ): + body = body[1:] + + if not body: + return + + start_lineno = body[0].lineno + end_lineno = body[-1].end_lineno + + source_lines = self.source_code.splitlines(keepends=True) + function_body_lines = source_lines[start_lineno - 1 : end_lineno] + + self.function_body = dedent("".join(function_body_lines)).rstrip("\n") + + indents = [stmt.col_offset for stmt in body if stmt.col_offset is not None] + if indents: + self.indent_unit = min(indents) + + +# extract_function_body function is forked from https://github.com/unitycatalog/unitycatalog/blob/20dd3820be332ac04deec4e063099fb863eb3392/ai/core/src/unitycatalog/ai/core/utils/callable_utils.py +def extract_function_body(func: Callable[..., Any]) -> tuple[str, int]: + """ + Extracts the body of a function as a string without the signature or docstring, + dedents the code, and returns the indentation unit used in the function (e.g., 2 or 4 spaces). + """ + source_lines, _ = inspect.getsourcelines(func) + dedented_source = dedent("".join(source_lines)) + func_name = func.__name__ + + extractor = FunctionBodyExtractor(func_name, dedented_source) + parsed_source = ast.parse(dedented_source) + extractor.visit(parsed_source) + + return extractor.function_body, extractor.indent_unit + + +def recreate_function(source: str, signature: str, func_name: str) -> Callable[..., Any] | None: + """ + Recreate a function from its source code, signature, and name. + + Args: + source: The function body source code. + signature: The function signature string (e.g., "(inputs, outputs)"). + func_name: The name of the function. + + Returns: + The recreated function or None if recreation failed. + """ + try: + # Parse the signature to build the function definition + sig_match = re.match(r"\((.*?)\)", signature) + if not sig_match: + return None + + params_str = sig_match.group(1).strip() + + # Build the function definition + func_def = f"def {func_name}({params_str}):\n" + # Indent the source code + indented_source = "\n".join(f" {line}" for line in source.split("\n")) + func_def += indented_source + + # Create a namespace with common MLflow imports that scorer functions might use + import_namespace = {} + + # Import commonly used MLflow classes + try: + from mlflow.entities import ( + Assessment, + AssessmentError, + AssessmentSource, + AssessmentSourceType, + Feedback, + ) + from mlflow.genai.judges import CategoricalRating + + import_namespace.update( + { + "Feedback": Feedback, + "Assessment": Assessment, + "AssessmentSource": AssessmentSource, + "AssessmentError": AssessmentError, + "AssessmentSourceType": AssessmentSourceType, + "CategoricalRating": CategoricalRating, + } + ) + except ImportError: + pass # Some imports might not be available in all contexts + + local_namespace = {} + + # Execute the function definition with MLflow imports available + exec(func_def, import_namespace, local_namespace) + + # Return the recreated function + return local_namespace[func_name] + + except Exception: + _logger.warning(f"Failed to recreate function '{func_name}' from serialized source code") + return None diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/scorers/validation.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/scorers/validation.py new file mode 100644 index 0000000000000000000000000000000000000000..56e74c5cf6713942c7468615244aee80e088ef0d --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/scorers/validation.py @@ -0,0 +1,172 @@ +import importlib +import logging +from collections import defaultdict +from typing import Any, Callable + +from mlflow.exceptions import MlflowException +from mlflow.genai.scorers.base import Scorer +from mlflow.genai.scorers.builtin_scorers import ( + BuiltInScorer, + MissingColumnsException, + get_all_scorers, +) + +try: + # `pandas` is not required for `mlflow-skinny`. + import pandas as pd +except ImportError: + pass + +_logger = logging.getLogger(__name__) + + +IS_DBX_AGENTS_INSTALLED = importlib.util.find_spec("databricks.agents") is not None + + +def validate_scorers(scorers: list[Any]) -> list[Scorer]: + """ + Validate a list of specified scorers. + + Args: + scorers: A list of scorers to validate. + + Returns: + A list of valid scorers. + """ + if not isinstance(scorers, list) or len(scorers) == 0: + raise MlflowException.invalid_parameter_value( + "The `scorers` argument must be a list of scorers with at least one scorer. " + "If you are unsure about which scorer to use, you can specify " + "`scorers=mlflow.genai.scorers.get_all_scorers()` to jump start with all " + "available built-in scorers." + ) + + valid_scorers = [] + legacy_metrics = [] + + for scorer in scorers: + if isinstance(scorer, Scorer): + valid_scorers.append(scorer) + else: + if IS_DBX_AGENTS_INSTALLED: + from databricks.rag_eval.evaluation.metrics import Metric + + if isinstance(scorer, Metric): + legacy_metrics.append(scorer) + valid_scorers.append(scorer) + continue + + # Show helpful error message for common mistakes + if isinstance(scorer, list) and (scorer == get_all_scorers()): + # Common mistake 1: scorers=[get_all_scorers()] + if len(scorers) == 1: + hint = ( + "\nHint: Use `scorers=get_all_scorers()` to pass all " + "builtin scorers at once." + ) + # Common mistake 2: scorers=[get_all_scorers(), scorer1, scorer2] + elif len(scorer) > 1: + hint = ( + "\nHint: Use `scorers=[*get_all_scorers(), scorer1, scorer2]` to pass " + "all builtin scorers at once along with your custom scorers." + ) + # Common mistake 3: scorers=[RetrievalRelevance, Correctness] + elif isinstance(scorer, type) and issubclass(scorer, BuiltInScorer): + hint = ( + "\nHint: It looks like you passed a scorer class instead of an instance. " + f"Correct way to pass scorers is `scorers=[{scorer.__name__}()]`." + ) + else: + hint = "" + + raise MlflowException.invalid_parameter_value( + f"The `scorers` argument must be a list of scorers. The specified " + f"list contains an invalid item with type: {type(scorer).__name__}." + f"{hint}" + ) + + if legacy_metrics: + legacy_metric_names = [metric.name for metric in legacy_metrics] + _logger.warning( + f"Scorers {legacy_metric_names} are legacy metrics and will soon be deprecated " + "in future releases. Please use the builtin scorers defined in `mlflow.genai.scorers` " + "or custom scorers defined with the @scorer decorator instead." + ) + + return valid_scorers + + +def valid_data_for_builtin_scorers( + data: "pd.DataFrame", + builtin_scorers: list[BuiltInScorer], + predict_fn: Callable[..., Any] | None = None, +) -> None: + """ + Validate that the required columns are present in the data for running the builtin scorers. + + Args: + data: The data to validate. This must be a pandas DataFrame converted to + the legacy evaluation set schema via `_convert_to_eval_set`. + builtin_scorers: The list of builtin scorers to validate the data for. + predict_fn: The predict function to validate the data for. + """ + input_columns = set(data.columns.tolist()) + + # Revert the replacement of "inputs"->"request" and "outputs"->"response" + # in the upstream processing. + if "request" in input_columns: + input_columns.remove("request") + input_columns.add("inputs") + if "response" in input_columns: + input_columns.remove("response") + input_columns.add("outputs") + + if predict_fn is not None: + # If the predict function is provided, the data doesn't need to + # contain the "outputs" column. + input_columns.add("outputs") + + if "trace" in input_columns: + # Inputs and outputs are inferred from the trace. + input_columns |= {"inputs", "outputs"} + + if predict_fn is not None: + input_columns |= {"trace"} + + # Explode keys in the "expectations" column for easier processing. + if "expectations" in input_columns: + for value in data["expectations"].values: + if pd.isna(value): + continue + if not isinstance(value, dict): + raise MlflowException.invalid_parameter_value( + "The 'expectations' column must be a dictionary of each expectation name " + "to its value. For example, `{'expected_response': 'answer to the question'}`." + ) + for k in value: + input_columns.add(f"expectations/{k}") + + # Missing column -> list of scorers that require the column. + missing_col_to_scorers = defaultdict(list) + for scorer in builtin_scorers: + try: + scorer.validate_columns(input_columns) + except MissingColumnsException as e: + for col in e.missing_columns: + missing_col_to_scorers[col].append(scorer.name) + + if missing_col_to_scorers: + msg = ( + "The input data is missing following columns that are required by the specified " + "scorers. The results will be null for those scorers." + ) + for col, scorers in missing_col_to_scorers.items(): + if col.startswith("expectations/"): + col = col.replace("expectations/", "") + msg += ( + f"\n - `{col}` field in `expectations` column " + f"is required by [{', '.join(scorers)}]." + ) + else: + msg += f"\n - `{col}` column is required by [{', '.join(scorers)}]." + _logger.info(msg) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/utils/__pycache__/data_validation.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/utils/__pycache__/data_validation.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..f3a3fb6c969a3dbdcd59eebf961a5f3cf485709e Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/utils/__pycache__/data_validation.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/utils/__pycache__/display_utils.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/utils/__pycache__/display_utils.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..f75fea7713398259a7bca6d3c65c57b96a5e8dc6 Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/utils/__pycache__/display_utils.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/utils/__pycache__/enum_utils.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/utils/__pycache__/enum_utils.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..2904e5348b14dfb0c75457778e4df676cb00ed41 Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/utils/__pycache__/enum_utils.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/utils/__pycache__/trace_utils.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/utils/__pycache__/trace_utils.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..a6f746b22d9043ff8b79e5fa497d8e212d6fb74c Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/utils/__pycache__/trace_utils.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/utils/data_validation.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/utils/data_validation.py new file mode 100644 index 0000000000000000000000000000000000000000..3f60de4ca7252ef56037c366025008b29fc95fd5 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/utils/data_validation.py @@ -0,0 +1,146 @@ +import inspect +import logging +from typing import Any, Callable + +from mlflow.exceptions import MlflowException +from mlflow.tracing.provider import trace_disabled + +_logger = logging.getLogger(__name__) + + +def check_model_prediction(predict_fn: Callable[..., Any], sample_input: Any): + """ + Validate if the predict function executes properly with the provided input. + + Args: + predict_fn: The predict function to be evaluated. + sample_input: A sample input to the model. + """ + _logger.info("Testing model prediction with the first sample in the dataset.") + + # Wrap the function to add a decorator for disabling tracing + @trace_disabled + def _check(): + predict_fn(**sample_input) + + try: + _check() + except Exception as e: + # Check input format and raise friendly message for typical error patterns + _validate_function_and_input_compatibility(predict_fn, sample_input, e) + _logger.debug(f"Failed to run predict_fn with input: {sample_input}", exc_info=True) + + +def _validate_function_and_input_compatibility( + predict_fn: Callable[..., Any], sample_input: dict[str, Any], e: Exception +) -> Callable[..., Any]: + """ + Validate the data format in the input column against the predict_fn. + + The input column must contain a dictionary of field names and values. When the + predict_fn is provided, the field names must match the arguments of the predict_fn. + """ + params = inspect.signature(predict_fn).parameters + if not params: + raise MlflowException.invalid_parameter_value( + "`predict_fn` must accept at least one argument." + ) from e + + # Check for *args-style parameters which aren't supported + _validate_no_var_args(params, e) + + # Check if input keys match function parameters + _validate_input_keys_match_function_params(params, sample_input.keys(), e) + + # For other errors, show a generic error message + raise MlflowException.invalid_parameter_value( + "Failed to run the prediction function specified in the `predict_fn` " + f"parameter. Input: {sample_input}. Error: {e}\n\n" + ) from e + + +def _has_variable_positional_arguments(params: inspect.Signature) -> bool: + """Check if the function has variable positional arguments.""" + return any(p.kind == inspect.Parameter.VAR_POSITIONAL for p in params.values()) + + +def _validate_no_var_args(params: inspect.Signature, e: Exception): + if not any(p.kind == inspect.Parameter.VAR_POSITIONAL for p in params.values()): + return + + """Raise an error for functions using *args which aren't supported.""" + code_sample = """```python +def predict_fn(param1, param2): + # Invoke the original predict function with positional arguments + return fn(param1, param2) + +data = [ + { + "inputs": { + "param1": "value1", + "param2": "value2", + } + } +] + +mlflow.genai.evaluate(predict_fn=predict_fn, data=data, ...) +``` +""" + + raise MlflowException.invalid_parameter_value( + "The `predict_fn` has dynamic positional arguments (e.g. `*args`), " + "so it cannot be used as a `predict_fn`. Please wrap it into another " + "function that accepts explicit keyword arguments.\n" + f"Example:\n\n{code_sample}\n" + ) from e + + +def _validate_input_keys_match_function_params( + params: inspect.Signature, + input_keys: list[str], + e: Exception, +): + if _has_required_keyword_arguments(params, input_keys): + return + + """Raise an error when input keys don't match function parameters.""" + param_names = list(params.keys()) + input_example = {arg: f"value{i + 1}" for i, arg in enumerate(param_names[:3])} + + if len(param_names) > 3: + input_example["..."] = "..." + + code_sample = "\n".join( + [ + "```python", + "data = [", + " {", + ' "inputs": {', + *(f' "{k}": "{v}",' for k, v in input_example.items()), + " }", + " }", + "]", + "```", + ] + ) + + raise MlflowException.invalid_parameter_value( + "The `inputs` column must be a dictionary with the parameter names of " + f"the `predict_fn` as keys. It seems the specified keys do not match " + f"with the `predict_fn`'s arguments. Correct example:\n\n{code_sample}" + ) from e + + +def _has_required_keyword_arguments(params: inspect.Signature, required_args: list[str]) -> bool: + """Check if the function accepts the specified keyword arguments.""" + func_args = [] + + for name, param in params.items(): + # If the function has **kwargs, it accepts all keyword arguments + if param.kind == inspect.Parameter.VAR_KEYWORD: + return True + + func_args.append(name) + + # Required argument must be a subset of the function's arguments + return set(required_args) <= set(func_args) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/utils/display_utils.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/utils/display_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..f0d8b668343987554e9ffd8426886860145fbd7b --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/utils/display_utils.py @@ -0,0 +1,145 @@ +import sys + +from mlflow.store.tracking.rest_store import RestStore +from mlflow.tracing.display.display_handler import _is_jupyter +from mlflow.tracking._tracking_service.utils import _get_store + +_EVAL_OUTPUT_HTML = """ + + + + Evaluation output + + + + + + + + +""" # noqa: E501 + +_NON_IPYTHON_OUTPUT_TEXT = """ +✨ Evaluation completed. + +Metrics and evaluation results are logged to the MLflow run: + Run name: \033[94m{run_name}\033[0m + Run ID: \033[94m{run_id}\033[0m +""" + + +def display_evaluation_output(run_id: str): + """ + Displays summary of the evaluation result, errors and warnings if any, + and instructions on what to do after running `mlflow.evaluate`. + + TODO: This function only works for OSS tracking server, does not resolve + Databricks workspace URL. When we migrate Databricks users to OSS harness, + update this logic to resolve the workspace URL. + """ + store = _get_store() + run = store.get_run(run_id) + + if not isinstance(store, RestStore): + # Cannot determine the host URL if the server is not remote. + # Print a general guidance instead. + sys.stdout.write(_NON_IPYTHON_OUTPUT_TEXT.format(run_name=run.info.run_name, run_id=run_id)) + sys.stdout.write(""" +To view the detailed evaluation results with sample-wise scores, +open the \033[93m\033[1mTraces\033[0m tab in the Run page in the MLflow UI.\n\n""") + return + + host_url = store.get_host_creds().host.rstrip("/") + experiment_id = run.info.experiment_id + # Navigate to 'traces' tab that shows assessments and aggregations. + uri = f"{host_url}/#/experiments/{experiment_id}/runs/{run_id}/traces" + + if _is_jupyter(): + from IPython.display import HTML, display + + display(HTML(_EVAL_OUTPUT_HTML.format(eval_results_url=uri))) + else: + sys.stdout.write(_NON_IPYTHON_OUTPUT_TEXT.format(run_name=run.info.run_name, run_id=run_id)) + sys.stdout.write(f"View the evaluation results at \033[93m{uri}\033[0m\n\n") diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/utils/enum_utils.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/utils/enum_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..25e94214650777b1611245e47514714db15a05a2 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/utils/enum_utils.py @@ -0,0 +1,23 @@ +from enum import Enum, EnumMeta + + +class MetaEnum(EnumMeta): + """Metaclass for Enum classes that allows to check if a value is a valid member of the Enum.""" + + def __contains__(cls, item): + try: + cls(item) + except ValueError: + return False + return True + + +class StrEnum(str, Enum, metaclass=MetaEnum): + def __str__(self): + """Return the string representation of the enum using its value.""" + return self.value + + @classmethod + def values(cls) -> list[str]: + """Return a list of all string values of the Enum.""" + return [str(member) for member in cls] diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/utils/trace_utils.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/utils/trace_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..5d8ee5fb9dd4e10ddc0b307b7798c44eaf85adad --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/genai/utils/trace_utils.py @@ -0,0 +1,310 @@ +import json +import logging +from typing import TYPE_CHECKING, Any, Callable + +from opentelemetry.trace import NoOpTracer +from pydantic import BaseModel + +import mlflow +from mlflow.entities.span import Span, SpanType +from mlflow.entities.trace import Trace +from mlflow.genai.evaluation.utils import is_none_or_nan +from mlflow.genai.utils.data_validation import check_model_prediction +from mlflow.models.evaluation.utils.trace import configure_autologging_for_evaluation +from mlflow.tracing.constant import TraceTagKey +from mlflow.tracing.display.display_handler import IPythonTraceDisplayHandler +from mlflow.tracing.utils import TraceJSONEncoder +from mlflow.tracking.client import MlflowClient + +if TYPE_CHECKING: + from mlflow.genai.evaluation.entities import EvalItem + +_logger = logging.getLogger(__name__) + +_MESSAGE_KEY = "message" +_MESSAGES_KEY = "messages" +_CHOICES_KEY = "choices" +_CONTENT_KEY = "content" + + +def convert_predict_fn(predict_fn: Callable[..., Any], sample_input: Any) -> Callable[..., Any]: + """ + Check the predict_fn is callable and add trace decorator if it is not already traced. + """ + with ( + NoOpTracerPatcher() as counter, + # Enable auto-tracing before checking if the predict_fn produces traces, so that + # functions using auto-traceable libraries (OpenAI, LangChain, etc.) are correctly + # identified as traced functions + configure_autologging_for_evaluation(enable_tracing=True), + ): + check_model_prediction(predict_fn, sample_input) + + if counter.count == 0: + predict_fn = mlflow.trace(predict_fn) + + # Wrap the prediction function to unwrap the inputs dictionary into keyword arguments. + return lambda request: predict_fn(**request) + + +class NoOpTracerPatcher: + """ + A context manager to count the number of times NoOpTracer's start_span is called. + + The check is done in the following steps so it doesn't have any side effects: + 1. Disable tracing. + 2. Patch the NoOpTracer.start_span method to count the number of times it is called. + NoOpTracer is used when tracing is disabled. + 3. Call the predict function with the sample input. + 4. Restore the original NoOpTracer.start_span method and re-enable tracing. + + + WARNING: This function is not thread-safe. We do not provide support for running + `mlflow.genai.evaluate` in multi-threaded environments.` + """ + + def __init__(self): + self.count = 0 + + def __enter__(self): + self.original = NoOpTracer.start_span + + def _patched_start_span(_self, *args, **kwargs): + self.count += 1 + return self.original(_self, *args, **kwargs) + + NoOpTracer.start_span = _patched_start_span + return self + + def __exit__(self, exc_type, exc_value, traceback): + NoOpTracer.start_span = self.original + + +def parse_inputs_to_str(value: Any) -> str: + """Parse the inputs to a string compatible with the judges API""" + if is_none_or_nan(value): + # The DBX managed backend doesn't allow empty inputs. This is + # a temporary workaround to bypass the validation. + return " " + if isinstance(value, str): + return value + + value = _to_dict(value) + + # Special handling for "messages" key. + if (messages := value.get(_MESSAGES_KEY)) and len(messages) > 0: + contents = [m.get(_CONTENT_KEY) for m in messages] + # If the message contains multiple messages, dump the whole messages object. + if len(contents) > 1 and all(isinstance(c, str) for c in contents): + return json.dumps(messages) + # If the message contains a single message, return the content. + elif isinstance(contents[-1], str): + return contents[-1] + return str(value) + + +def parse_outputs_to_str(value: Any) -> str: + """Parse the outputs to a string compatible with the judges API""" + if is_none_or_nan(value): + return " " + if isinstance(value, str): + return value + + # PyFuncModel.predict wraps the output in a list + if isinstance(value, list) and len(value) > 0: + return parse_outputs_to_str(value[0]) + + value = _to_dict(value) + + # Special handling for chat response + if _is_chat_choices(value.get(_CHOICES_KEY)): + content = value[_CHOICES_KEY][0][_MESSAGE_KEY][_CONTENT_KEY] + elif _is_chat_messages(value.get(_MESSAGES_KEY)): + content = value[_MESSAGES_KEY][-1][_CONTENT_KEY] + else: + content = json.dumps(value, cls=TraceJSONEncoder) + return content + + +def _is_chat_choices(maybe_choices: Any) -> bool: + if ( + not maybe_choices + or not isinstance(maybe_choices, list) + or not isinstance(maybe_choices[0], dict) + ): + return False + + message = maybe_choices[0].get(_MESSAGE_KEY) + return _is_chat_messages([message]) + + +def _is_chat_messages(maybe_messages: Any) -> bool: + return ( + maybe_messages + and len(maybe_messages) > 0 + and isinstance(maybe_messages[-1], dict) + and isinstance(maybe_messages[-1].get(_CONTENT_KEY), str) + ) + + +def _to_dict(obj: Any) -> dict[str, Any]: + if hasattr(obj, "to_dict"): + return obj.to_dict() + + if isinstance(obj, BaseModel): + return obj.model_dump() + + # Convert to JSON string and then back to dictionary to handle nested objects + json_str = json.dumps(obj, cls=TraceJSONEncoder) + return json.loads(json_str) + + +def extract_retrieval_context_from_trace(trace: Trace | None) -> dict[str, list[Any]]: + """ + Extract the retrieval context from the trace. + Only consider the last retrieval span in the trace if there are multiple retrieval spans. + If the trace does not have a retrieval span, return None. + ⚠️ Warning: Please make sure to not throw exception. If fails, return None. + """ + if trace is None or trace.data is None: + return {} + + # Only consider the top-level retrieval spans + top_level_retrieval_spans = _get_top_level_retrieval_spans(trace) + if len(top_level_retrieval_spans) == 0: + return {} + + retrieved = {} # span_id -> list of documents + + for retrieval_span in top_level_retrieval_spans: + try: + contexts = [_parse_chunk(chunk) for chunk in retrieval_span.outputs or []] + retrieved[retrieval_span.span_id] = [c for c in contexts if c is not None] + except Exception as e: + _logger.debug( + f"Fail to get retrieval context from span: {retrieval_span}. Error: {e!r}" + ) + + return retrieved + + +def _get_top_level_retrieval_spans(trace: Trace) -> list[Span]: + """ + Get the top-level retrieval spans in the trace. + Top-level retrieval spans are retrieval spans that are not children of other retrieval spans. + For example, given the following spans: + - Span A (Chain) + - Span B (Retriever) + - Span C (Retriever) + - Span D (Retriever) + - Span E (LLM) + - Span F (Retriever) + Span B and Span D are top-level retrieval spans. + Span C and Span F are NOT top-level because they are children of other retrieval spans. + """ + top_level_retrieval_spans = [] + # Cache span_id -> span mapping for fast lookup + all_spans = {span.span_id: span for span in trace.data.spans} + for span in trace.search_spans(span_type=SpanType.RETRIEVER): + # Check if this span is a child of another retrieval span + parent_id = span.parent_id + while parent_id: + parent_span = all_spans.get(parent_id) + if not parent_span: + # Malformed trace + _logger.debug( + f"Malformed trace: span {span} has parent span ID {parent_id}, " + "but the parent span is not found in the trace." + ) + break + + if parent_span.span_type == SpanType.RETRIEVER: + # This span is a child of another retrieval span + break + + parent_id = parent_span.parent_id + else: + # If the loop completes without breaking, this is a top-level span + top_level_retrieval_spans.append(span) + + return top_level_retrieval_spans + + +def _parse_chunk(chunk: Any) -> dict[str, Any] | None: + if not isinstance(chunk, dict): + return None + + doc = {"content": chunk.get("page_content")} + if doc_uri := chunk.get("metadata", {}).get("doc_uri"): + doc["doc_uri"] = doc_uri + return doc + + +def clean_up_extra_traces(run_id: str, start_time_ms: int): + """ + Clean up noisy traces generated outside predict function. + + Evaluation run should only contain traces that is being evaluated or generated by the predict + function. If not, the result will not show the correct list of traces. + Sometimes, there are extra traces generated during the evaluation, for example, custom scorer + code might generate traces. This function cleans up those noisy traces. + + TODO: This is not a fundamental solution. Ideally, evaluation result should be able to render + correct result even if there are extra traces in the run. + + Args: + run_id: The ID of the run to clean up. + start_time_ms: The start time of the evaluation in milliseconds. + """ + from mlflow.tracking.fluent import _get_experiment_id + + try: + # Search for all traces generated during evaluation + traces = mlflow.search_traces( + run_id=run_id, + # Not download spans for efficiency + include_spans=False, + # Limit to traces generated after evaluation time to ensure we will not + # delete traces generated before evaluation. + filter_string=f"trace.timestamp >= {start_time_ms}", + return_type="list", + ) + extra_trace_ids = [ + # Traces from predict function should always have the EVAL_REQUEST_ID tag + trace.info.trace_id + for trace in traces + if TraceTagKey.EVAL_REQUEST_ID not in trace.info.tags + ] + if extra_trace_ids: + _logger.debug( + f"Found {len(extra_trace_ids)} extra traces generated during evaluation run. " + "Deleting them." + ) + MlflowClient().delete_traces( + experiment_id=_get_experiment_id(), trace_ids=extra_trace_ids + ) + # Avoid displaying the deleted trace in notebook cell output + for trace_id in extra_trace_ids: + IPythonTraceDisplayHandler.get_instance().traces_to_display.pop(trace_id, None) + else: + _logger.debug("No extra traces found during evaluation run.") + except Exception as e: + _logger.warning( + f"Failed to clean up extra traces generated during evaluation. The " + f"result page might not show the correct list of traces. Error: {e}" + ) + + +def create_minimal_trace(eval_item: "EvalItem") -> Trace: + """ + Create a minimal trace object with a single span, based on given inputs/outputs. + """ + from mlflow.pyfunc.context import Context, set_prediction_context + + # Set the context so that the trace is logged synchronously + context = Context(request_id=eval_item.request_id, is_evaluate=True) + with set_prediction_context(context): + with mlflow.start_span(name="root_span", span_type=SpanType.CHAIN) as root_span: + root_span.set_inputs(eval_item.inputs) + root_span.set_outputs(eval_item.outputs) + return mlflow.get_trace(root_span.trace_id) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/groq/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/groq/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..5dd4c4828abf375e48ea6b3ce9513df234460ae8 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/groq/__init__.py @@ -0,0 +1,40 @@ +""" +The ``mlflow.groq`` module provides an API for logging and loading Groq models. +""" + +from mlflow.groq._groq_autolog import patched_call +from mlflow.utils.autologging_utils import autologging_integration, safe_patch + +FLAVOR_NAME = "groq" + + +@autologging_integration(FLAVOR_NAME) +def autolog( + log_traces: bool = True, + disable: bool = False, + silent: bool = False, +): + """ + Enables (or disables) and configures autologging from Groq to MLflow. + Only synchronous calls are supported. Asynchronous APIs and streaming are not recorded. + + Args: + log_traces: If ``True``, traces are logged for Groq models. If ``False``, no traces are + collected during inference. Default to ``True``. + disable: If ``True``, disables the Groq autologging. Default to ``False``. + silent: If ``True``, suppress all event logs and warnings from MLflow during Groq + autologging. If ``False``, show all events and warnings. + """ + + from groq.resources.audio.transcriptions import Transcriptions + from groq.resources.audio.translations import Translations + from groq.resources.chat.completions import Completions as ChatCompletions + from groq.resources.embeddings import Embeddings + + for task in (ChatCompletions, Translations, Transcriptions, Embeddings): + safe_patch( + FLAVOR_NAME, + task, + "create", + patched_call, + ) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/groq/__pycache__/__init__.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/groq/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..42410de498c12dde270f7a74493aaea2e8dd4f2b Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/groq/__pycache__/__init__.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/groq/__pycache__/_groq_autolog.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/groq/__pycache__/_groq_autolog.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..1258e4bf8a376fc618ff692e1a41ffa1578b8ce1 Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/groq/__pycache__/_groq_autolog.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/groq/_groq_autolog.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/groq/_groq_autolog.py new file mode 100644 index 0000000000000000000000000000000000000000..6b250d8262352f5efd908405b2b52d4396c44f93 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/groq/_groq_autolog.py @@ -0,0 +1,65 @@ +import logging +from typing import Any + +import mlflow +from mlflow.entities import SpanType +from mlflow.tracing.constant import SpanAttributeKey, TokenUsageKey +from mlflow.tracing.utils import set_span_chat_tools +from mlflow.utils.autologging_utils.config import AutoLoggingConfig + +_logger = logging.getLogger(__name__) + + +def _get_span_type(resource: type) -> str: + from groq.resources.audio.transcriptions import Transcriptions + from groq.resources.audio.translations import Translations + from groq.resources.chat.completions import Completions + from groq.resources.embeddings import Embeddings + + span_type_mapping = { + Completions: SpanType.CHAT_MODEL, + Transcriptions: SpanType.LLM, + Translations: SpanType.LLM, + Embeddings: SpanType.EMBEDDING, + } + return span_type_mapping.get(resource, SpanType.UNKNOWN) + + +def patched_call(original, self, *args, **kwargs): + config = AutoLoggingConfig.init(flavor_name=mlflow.groq.FLAVOR_NAME) + + if config.log_traces: + with mlflow.start_span( + name=f"{self.__class__.__name__}", + span_type=_get_span_type(self.__class__), + ) as span: + span.set_inputs(kwargs) + span.set_attribute(SpanAttributeKey.MESSAGE_FORMAT, "groq") + + if tools := kwargs.get("tools"): + try: + set_span_chat_tools(span, tools) + except Exception: + _logger.debug(f"Failed to set tools for {span}.", exc_info=True) + + outputs = original(self, *args, **kwargs) + span.set_outputs(outputs) + + if usage := _parse_usage(outputs): + span.set_attribute(SpanAttributeKey.CHAT_USAGE, usage) + + return outputs + + +def _parse_usage(output: Any) -> dict[str, int] | None: + try: + usage = getattr(output, "usage", None) + if usage: + return { + TokenUsageKey.INPUT_TOKENS: usage.prompt_tokens, + TokenUsageKey.OUTPUT_TOKENS: usage.completion_tokens, + TokenUsageKey.TOTAL_TOKENS: usage.total_tokens, + } + except Exception as e: + _logger.debug(f"Failed to parse token usage from output: {e}") + return None diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/h2o/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/h2o/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..8136068ef41780497e655ba6ea92800db3be85ff --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/h2o/__init__.py @@ -0,0 +1,361 @@ +""" +The ``mlflow.h2o`` module provides an API for logging and loading H2O models. This module exports +H2O models with the following flavors: + +H20 (native) format + This is the main flavor that can be loaded back into H2O. +:py:mod:`mlflow.pyfunc` + Produced for use by generic pyfunc-based deployment tools and batch inference. +""" + +import logging +import os +import warnings +from typing import Any + +import yaml + +import mlflow +from mlflow import pyfunc +from mlflow.models import Model, ModelInputExample, ModelSignature +from mlflow.models.model import MLMODEL_FILE_NAME +from mlflow.models.signature import _infer_signature_from_input_example +from mlflow.models.utils import _save_example +from mlflow.tracking.artifact_utils import _download_artifact_from_uri +from mlflow.utils.docstring_utils import LOG_MODEL_PARAM_DOCS, format_docstring +from mlflow.utils.environment import ( + _CONDA_ENV_FILE_NAME, + _CONSTRAINTS_FILE_NAME, + _PYTHON_ENV_FILE_NAME, + _REQUIREMENTS_FILE_NAME, + _mlflow_conda_env, + _process_conda_env, + _process_pip_requirements, + _PythonEnv, + _validate_env_arguments, +) +from mlflow.utils.file_utils import ( + get_total_file_size, + write_to, +) +from mlflow.utils.model_utils import ( + _add_code_from_conf_to_system_path, + _get_flavor_configuration, + _validate_and_copy_code_paths, + _validate_and_prepare_target_save_path, +) +from mlflow.utils.requirements_utils import _get_pinned_requirement + +FLAVOR_NAME = "h2o" + +_MODEL_DATA_SUBPATH = "model.h2o" + +_logger = logging.getLogger(__name__) + + +def get_default_pip_requirements(): + """ + Returns: + A list of default pip requirements for MLflow Models produced by this flavor. + Calls to :func:`save_model()` and :func:`log_model()` produce a pip environment + that, at minimum, contains these requirements. + """ + return [_get_pinned_requirement("h2o")] + + +def get_default_conda_env(): + """ + Returns: + The default Conda environment for MLflow Models produced by calls to + :func:`save_model()` and :func:`log_model()`. + """ + return _mlflow_conda_env(additional_pip_deps=get_default_pip_requirements()) + + +@format_docstring(LOG_MODEL_PARAM_DOCS.format(package_name=FLAVOR_NAME)) +def save_model( + h2o_model, + path, + conda_env=None, + code_paths=None, + mlflow_model=None, + settings=None, + signature: ModelSignature = None, + input_example: ModelInputExample = None, + pip_requirements=None, + extra_pip_requirements=None, + metadata=None, +): + """Save an H2O model to a path on the local file system. + + Args: + h2o_model: H2O model to be saved. + path: Local path where the model is to be saved. + conda_env: {{ conda_env }} + code_paths: {{ code_paths }} + mlflow_model: :py:mod:`mlflow.models.Model` this flavor is being added to. + settings: Settings to pass to ``h2o.init()`` when loading the model. + signature: {{ signature }} + input_example: {{ input_example }} + pip_requirements: {{ pip_requirements }} + extra_pip_requirements: {{ extra_pip_requirements }} + metadata: {{ metadata }} + """ + import h2o + + _validate_env_arguments(conda_env, pip_requirements, extra_pip_requirements) + + path = os.path.abspath(path) + _validate_and_prepare_target_save_path(path) + model_data_subpath = _MODEL_DATA_SUBPATH + model_data_path = os.path.join(path, model_data_subpath) + os.makedirs(model_data_path) + code_dir_subpath = _validate_and_copy_code_paths(code_paths, path) + + if mlflow_model is None: + mlflow_model = Model() + saved_example = _save_example(mlflow_model, input_example, path) + + if signature is None and saved_example is not None: + wrapped_model = _H2OModelWrapper(h2o_model) + signature = _infer_signature_from_input_example(saved_example, wrapped_model) + elif signature is False: + signature = None + + if signature is not None: + mlflow_model.signature = signature + if metadata is not None: + mlflow_model.metadata = metadata + + # Save h2o-model + if hasattr(h2o, "download_model"): + h2o_save_location = h2o.download_model(model=h2o_model, path=model_data_path) + else: + warnings.warn( + "If your cluster is remote, H2O may not store the model correctly. " + "Please upgrade H2O version to a newer version" + ) + h2o_save_location = h2o.save_model(model=h2o_model, path=model_data_path, force=True) + model_file = os.path.basename(h2o_save_location) + + # Save h2o-settings + if settings is None: + settings = {} + settings["full_file"] = h2o_save_location + settings["model_file"] = model_file + settings["model_dir"] = model_data_path + with open(os.path.join(model_data_path, "h2o.yaml"), "w") as settings_file: + yaml.safe_dump(settings, stream=settings_file) + + pyfunc.add_to_model( + mlflow_model, + loader_module="mlflow.h2o", + data=model_data_subpath, + conda_env=_CONDA_ENV_FILE_NAME, + python_env=_PYTHON_ENV_FILE_NAME, + code=code_dir_subpath, + ) + mlflow_model.add_flavor( + FLAVOR_NAME, h2o_version=h2o.__version__, data=model_data_subpath, code=code_dir_subpath + ) + if size := get_total_file_size(path): + mlflow_model.model_size_bytes = size + mlflow_model.save(os.path.join(path, MLMODEL_FILE_NAME)) + + if conda_env is None: + if pip_requirements is None: + default_reqs = get_default_pip_requirements() + # To ensure `_load_pyfunc` can successfully load the model during the dependency + # inference, `mlflow_model.save` must be called beforehand to save an MLmodel file. + inferred_reqs = mlflow.models.infer_pip_requirements( + path, + FLAVOR_NAME, + fallback=default_reqs, + ) + default_reqs = sorted(set(inferred_reqs).union(default_reqs)) + else: + default_reqs = None + conda_env, pip_requirements, pip_constraints = _process_pip_requirements( + default_reqs, + pip_requirements, + extra_pip_requirements, + ) + else: + conda_env, pip_requirements, pip_constraints = _process_conda_env(conda_env) + + with open(os.path.join(path, _CONDA_ENV_FILE_NAME), "w") as f: + yaml.safe_dump(conda_env, stream=f, default_flow_style=False) + + # Save `constraints.txt` if necessary + if pip_constraints: + write_to(os.path.join(path, _CONSTRAINTS_FILE_NAME), "\n".join(pip_constraints)) + + # Save `requirements.txt` + write_to(os.path.join(path, _REQUIREMENTS_FILE_NAME), "\n".join(pip_requirements)) + + _PythonEnv.current().to_yaml(os.path.join(path, _PYTHON_ENV_FILE_NAME)) + + +@format_docstring(LOG_MODEL_PARAM_DOCS.format(package_name=FLAVOR_NAME)) +def log_model( + h2o_model, + artifact_path: str | None = None, + conda_env=None, + code_paths=None, + registered_model_name=None, + signature: ModelSignature = None, + input_example: ModelInputExample = None, + pip_requirements=None, + extra_pip_requirements=None, + metadata=None, + name: str | None = None, + params: dict[str, Any] | None = None, + tags: dict[str, Any] | None = None, + model_type: str | None = None, + step: int = 0, + model_id: str | None = None, + **kwargs, +): + """Log an H2O model as an MLflow artifact for the current run. + + Args: + h2o_model: H2O model to be saved. + artifact_path: Deprecated. Use `name` instead. + conda_env: {{ conda_env }} + code_paths: {{ code_paths }} + registered_model_name: If given, create a model version under + ``registered_model_name``, also creating a registered model if one + with the given name does not exist. + signature: {{ signature }} + input_example: {{ input_example }} + pip_requirements: {{ pip_requirements }} + extra_pip_requirements: {{ extra_pip_requirements }} + metadata: {{ metadata }} + name: {{ name }} + params: {{ params }} + tags: {{ tags }} + model_type: {{ model_type }} + step: {{ step }} + model_id: {{ model_id }} + kwargs: kwargs to pass to ``h2o.save_model`` method. + + Returns: + A :py:class:`ModelInfo ` instance that contains the + metadata of the logged model. + + """ + return Model.log( + artifact_path=artifact_path, + name=name, + flavor=mlflow.h2o, + registered_model_name=registered_model_name, + h2o_model=h2o_model, + conda_env=conda_env, + code_paths=code_paths, + signature=signature, + input_example=input_example, + pip_requirements=pip_requirements, + extra_pip_requirements=extra_pip_requirements, + metadata=metadata, + params=params, + tags=tags, + model_type=model_type, + step=step, + model_id=model_id, + **kwargs, + ) + + +def _load_model(path, init=False): + import h2o + + path = os.path.abspath(path) + with open(os.path.join(path, "h2o.yaml")) as f: + params = yaml.safe_load(f.read()) + if init: + h2o.init(**(params["init"] if "init" in params else {})) + h2o.no_progress() + + model_path = os.path.join(path, params["model_file"]) + if hasattr(h2o, "upload_model"): + model = h2o.upload_model(model_path) + else: + warnings.warn( + "If your cluster is remote, H2O may not load the model correctly. " + "Please upgrade H2O version to a newer version" + ) + model = h2o.load_model(model_path) + + return model + + +class _H2OModelWrapper: + def __init__(self, h2o_model): + self.h2o_model = h2o_model + + def get_raw_model(self): + """ + Returns the underlying model. + """ + return self.h2o_model + + def predict(self, dataframe, params: dict[str, Any] | None = None): + """ + Args: + dataframe: Model input data. + params: Additional parameters to pass to the model for inference. + + Returns: + Model predictions. + """ + import h2o + + predicted = self.h2o_model.predict(h2o.H2OFrame(dataframe)).as_data_frame() + predicted.index = dataframe.index + return predicted + + +def _load_pyfunc(path): + """Load PyFunc implementation. Called by ``pyfunc.load_model``. + + Args: + path: Local filesystem path to the MLflow Model with the ``h2o`` flavor. + + """ + return _H2OModelWrapper(_load_model(path, init=True)) + + +def load_model(model_uri, dst_path=None): + """Load an H2O model from a local file (if ``run_id`` is ``None``) or a run. + + This function expects there is an H2O instance initialised with ``h2o.init``. + + Args: + model_uri: The location, in URI format, of the MLflow model. For example: + + - ``/Users/me/path/to/local/model`` + - ``relative/path/to/local/model`` + - ``s3://my_bucket/path/to/model`` + - ``runs://run-relative/path/to/model`` + - ``models://`` + - ``models://`` + + For more information about supported URI schemes, see + `Referencing Artifacts `_. + dst_path: The local filesystem path to which to download the model artifact. + This directory must already exist. If unspecified, a local output + path will be created. + + Returns: + An `H2OEstimator model object + `_. + + """ + local_model_path = _download_artifact_from_uri(artifact_uri=model_uri, output_path=dst_path) + flavor_conf = _get_flavor_configuration(model_path=local_model_path, flavor_name=FLAVOR_NAME) + _add_code_from_conf_to_system_path(local_model_path, flavor_conf) + # Flavor configurations for models saved in MLflow version <= 0.8.0 may not contain a + # `data` key; in this case, we assume the model artifact path to be `model.h2o` + h2o_model_file_path = os.path.join(local_model_path, flavor_conf.get("data", "model.h2o")) + return _load_model(path=h2o_model_file_path) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/h2o/__pycache__/__init__.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/h2o/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..0ecd6adca721b79d632625fb46866d203dfa4585 Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/h2o/__pycache__/__init__.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/johnsnowlabs/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/johnsnowlabs/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..1b972a097520a921d1c3aa02017fc2d32e290555 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/johnsnowlabs/__init__.py @@ -0,0 +1,890 @@ +""" +The ``mlflow.johnsnowlabs`` module provides an API for logging and loading Spark NLP and NLU models. +This module exports the following flavors: + +Johnsnowlabs (native) format + Allows models to be loaded as Spark Transformers for scoring in a Spark session. + Models with this flavor can be loaded as NluPipelines, with underlying Spark MLlib PipelineModel + This is the main flavor and is always produced. +:py:mod:`mlflow.pyfunc` + Supports deployment outside of Spark by instantiating a SparkContext and reading + input data as a Spark DataFrame prior to scoring. Also supports deployment in Spark + as a Spark UDF. Models with this flavor can be loaded as Python functions + for performing inference. This flavor is always produced. + +This flavor gives you access to `20.000+ state-of-the-art enterprise NLP models in 200+ languages +`_ for medical, finance, legal and many more domains. +Features include: LLM's, Text Summarization, Question Answering, Named Entity Recognition, Relation +Extraction, Sentiment Analysis, Spell Checking, Image Classification, Automatic Speech Recognition +and much more, powered by the latest Transformer Architectures. The models are provided by +`John Snow Labs `_ and requires a `John Snow Labs +`_ Enterprise NLP License. `You can reach out to us +`_ for a research or industry license. + +These keys must be present in your license json: + +1. ``SECRET``: The secret for the John Snow Labs Enterprise NLP Library +2. ``SPARK_NLP_LICENSE``: Your John Snow Labs Enterprise NLP License +3. ``AWS_ACCESS_KEY_ID``: Your AWS Secret ID for accessing John Snow Labs Enterprise Models +4. ``AWS_SECRET_ACCESS_KEY``: Your AWS Secret key for accessing John Snow Labs Enterprise Models + +You can set them using the following code: + +.. code-block:: python + + import os + import json + + # Write your raw license.json string into the 'JOHNSNOWLABS_LICENSE_JSON' env variable + creds = { + "AWS_ACCESS_KEY_ID": "...", + "AWS_SECRET_ACCESS_KEY": "...", + "SPARK_NLP_LICENSE": "...", + "SECRET": "...", + } + os.environ["JOHNSNOWLABS_LICENSE_JSON"] = json.dumps(creds) +""" + +import json +import logging +import os +import posixpath +import shutil +import sys +from pathlib import Path +from typing import Any + +import yaml + +import mlflow +from mlflow import pyfunc +from mlflow.environment_variables import MLFLOW_DFS_TMP +from mlflow.models import Model +from mlflow.models.model import MLMODEL_FILE_NAME +from mlflow.models.signature import ModelSignature +from mlflow.models.utils import ModelInputExample, _save_example +from mlflow.spark import ( + _HadoopFileSystem, + _maybe_save_model, + _mlflowdbfs_path, + _should_use_mlflowdbfs, +) +from mlflow.store.artifact.databricks_artifact_repo import DatabricksArtifactRepository +from mlflow.tracking._model_registry import DEFAULT_AWAIT_MAX_SLEEP_SECONDS +from mlflow.tracking.artifact_utils import ( + _download_artifact_from_uri, + _get_root_uri_and_artifact_path, +) +from mlflow.tracking.fluent import _initialize_logged_model +from mlflow.utils import databricks_utils +from mlflow.utils.docstring_utils import LOG_MODEL_PARAM_DOCS, format_docstring +from mlflow.utils.environment import ( + _CONDA_ENV_FILE_NAME, + _CONSTRAINTS_FILE_NAME, + _PYTHON_ENV_FILE_NAME, + _REQUIREMENTS_FILE_NAME, + _mlflow_conda_env, + _process_conda_env, + _process_pip_requirements, + _PythonEnv, +) +from mlflow.utils.file_utils import ( + TempDir, + get_total_file_size, + shutil_copytree_without_file_permissions, + write_to, +) +from mlflow.utils.model_utils import ( + _add_code_from_conf_to_system_path, + _get_flavor_configuration_from_uri, + _validate_and_copy_code_paths, +) +from mlflow.utils.requirements_utils import _get_pinned_requirement +from mlflow.utils.uri import ( + append_to_uri_path, + dbfs_hdfs_uri_to_fuse_path, + generate_tmp_dfs_path, + get_databricks_profile_uri_from_artifact_uri, + is_local_uri, + is_valid_dbfs_uri, +) + +FLAVOR_NAME = "johnsnowlabs" +_JOHNSNOWLABS_ENV_JSON_LICENSE_KEY = "JOHNSNOWLABS_LICENSE_JSON" +_JOHNSNOWLABS_ENV_HEALTHCARE_SECRET = "HEALTHCARE_SECRET" +_JOHNSNOWLABS_ENV_VISUAL_SECRET = "VISUAL_SECRET" +_JOHNSNOWLABS_MODEL_PATH_SUB = "jsl-model" +_logger = logging.getLogger(__name__) + + +def _validate_env_vars(): + if _JOHNSNOWLABS_ENV_JSON_LICENSE_KEY not in os.environ: + raise Exception( + f"Please set the {_JOHNSNOWLABS_ENV_JSON_LICENSE_KEY}" + f" environment variable as the raw license.json string from John Snow Labs" + ) + _set_env_vars() + + +def _set_env_vars(): + # if json license is detected, we parse it and set the env vars + loaded_license = json.loads(os.environ[_JOHNSNOWLABS_ENV_JSON_LICENSE_KEY]) + os.environ.update({k: str(v) for k, v in loaded_license.items() if v is not None}) + + +def get_default_pip_requirements(): + """ + Returns: + A list of default pip requirements for MLflow Models produced by this flavor. + Calls to :func:`save_model()` and :func:`log_model()` produce a pip environment + that, at minimum, contains these requirements. + """ + from johnsnowlabs import settings + + if ( + _JOHNSNOWLABS_ENV_HEALTHCARE_SECRET not in os.environ + and _JOHNSNOWLABS_ENV_VISUAL_SECRET not in os.environ + ): + raise Exception( + f"You need to set either {_JOHNSNOWLABS_ENV_HEALTHCARE_SECRET} " + f"or {_JOHNSNOWLABS_ENV_VISUAL_SECRET} environment variable. " + f"Please contact John Snow Labs to get one." + ) + + _SPARK_NLP_JSL_WHEEL_URI = ( + "https://pypi.johnsnowlabs.com/{secret}/spark-nlp-jsl/spark_nlp_jsl-" + + f"{settings.raw_version_medical}-py3-none-any.whl" + ) + + _SPARK_NLP_VISUAL_WHEEL_URI = ( + "https://pypi.johnsnowlabs.com/{secret}/spark-ocr/" + f"spark_ocr-{settings.raw_version_ocr}-py3-none-any.whl" + ) + + deps = [ + f"johnsnowlabs_for_databricks=={settings.raw_version_jsl_lib}", + _get_pinned_requirement("pyspark"), + ] + + if _JOHNSNOWLABS_ENV_HEALTHCARE_SECRET in os.environ: + _SPARK_NLP_JSL_WHEEL_URI = _SPARK_NLP_JSL_WHEEL_URI.format( + secret=os.environ[_JOHNSNOWLABS_ENV_HEALTHCARE_SECRET] + ) + deps.append(_SPARK_NLP_JSL_WHEEL_URI) + + if _JOHNSNOWLABS_ENV_VISUAL_SECRET in os.environ: + _SPARK_NLP_VISUAL_WHEEL_URI = _SPARK_NLP_VISUAL_WHEEL_URI.format( + secret=os.environ[_JOHNSNOWLABS_ENV_VISUAL_SECRET] + ) + deps.append(_SPARK_NLP_VISUAL_WHEEL_URI) + + return deps + + +def get_default_conda_env(): + """ + Returns: + The default Conda environment for MLflow Models produced by calls to + :func:`save_model()` and :func:`log_model()`. + """ + return _mlflow_conda_env(additional_pip_deps=get_default_pip_requirements()) + + +@format_docstring(LOG_MODEL_PARAM_DOCS.format(package_name="johnsnowlabs")) +def log_model( + spark_model, + artifact_path: str | None = None, + conda_env=None, + code_paths=None, + dfs_tmpdir=None, + registered_model_name=None, + signature: ModelSignature = None, + input_example: ModelInputExample = None, + await_registration_for=DEFAULT_AWAIT_MAX_SLEEP_SECONDS, + pip_requirements=None, + extra_pip_requirements=None, + metadata=None, + store_license=False, + name: str | None = None, + params: dict[str, Any] | None = None, + tags: dict[str, Any] | None = None, + model_type: str | None = None, + step: int = 0, + model_id: str | None = None, +): + """ + Log a ``Johnsnowlabs NLUPipeline`` created via `nlp.load() + `_, as an MLflow artifact for the current + run. This uses the MLlib persistence format and produces an MLflow Model with the + ``johnsnowlabs`` flavor. + + Note: If no run is active, it will instantiate a run to obtain a run_id. + + Args: + spark_model: NLUPipeline obtained via `nlp.load() + `_ + artifact_path: Deprecated. Use `name` instead. + conda_env: Either a dictionary representation of a Conda environment or the path to a + Conda environment yaml file. If provided, this describes the environment + this model should be run in. At minimum, it should specify the dependencies + contained in :func:`get_default_conda_env()`. If `None`, the default + :func:`get_default_conda_env()` environment is added to the model. + The following is an *example* dictionary representation of a Conda + environment:: + + { + 'name': 'mlflow-env', + 'channels': ['defaults'], + 'dependencies': [ + 'python=3.8.15', + 'johnsnowlabs' + ] + } + code_paths: {{ code_paths }} + dfs_tmpdir: Temporary directory path on Distributed (Hadoop) File System (DFS) or local + filesystem if running in local mode. The model is written in this + destination and then copied into the model's artifact directory. This is + necessary as Spark ML models read from and write to DFS if running on a + cluster. If this operation completes successfully, all temporary files + created on the DFS are removed. Defaults to ``/tmp/mlflow``. + registered_model_name: If given, create a model version under + ``registered_model_name``, also creating a registered model if one + with the given name does not exist. + signature: :py:class:`ModelSignature ` + describes model input and output :py:class:`Schema `. + The model signature can be :py:func:`inferred ` + from datasets with valid model input (e.g. the training dataset with target + column omitted) and valid model output (e.g. model predictions generated on + the training dataset), for example: + + .. code-block:: python + + from mlflow.models.signature import infer_signature + + train = df.drop_column("target_label") + predictions = ... # compute model predictions + signature = infer_signature(train, predictions) + + input_example: {{ input_example }} + await_registration_for: Number of seconds to wait for the model version to finish + being created and is in ``READY`` status. By default, the function + waits for five minutes. Specify 0 or None to skip waiting. + pip_requirements: {{ pip_requirements }} + extra_pip_requirements: {{ extra_pip_requirements }} + metadata: {{ metadata }} + store_license: If True, the license will be stored with the model and used and re-loading + it. + name: {{ name }} + params: {{ params }} + tags: {{ tags }} + model_type: {{ model_type }} + step: {{ step }} + model_id: {{ model_id }} + + Returns: + A :py:class:`ModelInfo ` instance that contains the + metadata of the logged model. + + .. code-block:: python + :caption: Example + + import os + import json + import pandas as pd + import mlflow + from johnsnowlabs import nlp + + # Write your raw license.json string into the 'JOHNSNOWLABS_LICENSE_JSON' env variable + creds = { + "AWS_ACCESS_KEY_ID": "...", + "AWS_SECRET_ACCESS_KEY": "...", + "SPARK_NLP_LICENSE": "...", + "SECRET": "...", + } + os.environ["JOHNSNOWLABS_LICENSE_JSON"] = json.dumps(creds) + + # Download & Install Jars/Wheels if missing and Start a spark Session + nlp.start() + + # For more details on trainable models and parameterization like embedding choice see + # https://nlp.johnsnowlabs.com/docs/en/jsl/training + trainable_classifier = nlp.load("train.classifier") + + # Create a sample training dataset + data = pd.DataFrame( + {"text": ["I hate covid ", "I love covid"], "y": ["negative", "positive"]} + ) + + # Fit and get a trained classifier + trained_classifier = trainable_classifier.fit(data) + trained_classifier.predict("He hates covid") + + # Log it + mlflow.johnsnowlabs.log_model(trained_classifier, name="my_trained_model") + """ + + _validate_env_vars() + run_id = mlflow.tracking.fluent._get_or_start_run().info.run_id + run_root_artifact_uri = mlflow.get_artifact_uri() + remote_model_path = None + + # If the artifact URI is a local filesystem path, defer to Model.log() to persist the model, + # since Spark may not be able to write directly to the driver's filesystem. For example, + # writing to `file:/uri` will write to the local filesystem from each executor, which will + # be incorrect on multi-node clusters. + # If the artifact URI is not a local filesystem path we attempt to write directly to the + # artifact repo via Spark. If this fails, we defer to Model.log(). + if is_local_uri(run_root_artifact_uri) or not _maybe_save_model( + spark_model, + append_to_uri_path(run_root_artifact_uri, name), + ): + return Model.log( + artifact_path=artifact_path, + name=name, + flavor=mlflow.johnsnowlabs, + spark_model=spark_model, + conda_env=conda_env, + code_paths=code_paths, + dfs_tmpdir=dfs_tmpdir, + registered_model_name=registered_model_name, + signature=signature, + input_example=input_example, + await_registration_for=await_registration_for, + pip_requirements=pip_requirements, + extra_pip_requirements=extra_pip_requirements, + metadata=metadata, + params=params, + tags=tags, + model_type=model_type, + step=step, + model_id=model_id, + ) + # Otherwise, override the default model log behavior and save model directly to artifact repo + logged_model = _initialize_logged_model( + name=name, + source_run_id=run.info.run_id if (run := mlflow.active_run()) else None, + model_type=model_type, + params=params, + tags=tags, + flavor=FLAVOR_NAME, + ) + mlflow_model = Model(artifact_path=logged_model.artifact_location, run_id=run_id) + with TempDir() as tmp: + tmp_model_metadata_dir = tmp.path() + _save_model_metadata( + tmp_model_metadata_dir, + spark_model, + mlflow_model, + conda_env, + code_paths, + signature=signature, + input_example=input_example, + pip_requirements=pip_requirements, + extra_pip_requirements=extra_pip_requirements, + remote_model_path=remote_model_path, + store_license=store_license, + ) + mlflow.tracking.fluent.log_artifacts(tmp_model_metadata_dir, name) + mlflow.tracking.fluent._record_logged_model(mlflow_model) + if registered_model_name is not None: + mlflow.register_model( + f"runs:/{logged_model.model_id}", + registered_model_name, + await_registration_for, + ) + return mlflow_model.get_model_info() + + +def _save_model_metadata( + dst_dir, + spark_model, + mlflow_model, + conda_env, + code_paths, + signature=None, + input_example=None, + pip_requirements=None, + extra_pip_requirements=None, + remote_model_path=None, + store_license=False, +): + """ + Saves model metadata into the passed-in directory. + If mlflowdbfs is not used, the persisted metadata assumes that a model can be + loaded from a relative path to the metadata file (currently hard-coded to "jsl-model"). + If mlflowdbfs is used, remote_model_path should be provided, and the model needs to + be loaded from the remote_model_path. + """ + + if signature is not None: + mlflow_model.signature = signature + if input_example is not None: + _save_example(mlflow_model, input_example, dst_dir) + + code_dir_subpath = _validate_and_copy_code_paths(code_paths, dst_dir) + + # add the johnsnowlabs flavor + import pyspark + + mlflow_model.add_flavor( + FLAVOR_NAME, + pyspark_version=pyspark.__version__, + model_data=_JOHNSNOWLABS_MODEL_PATH_SUB, + code=code_dir_subpath, + ) + + # add the pyfunc flavor + pyfunc.add_to_model( + mlflow_model, + loader_module="mlflow.johnsnowlabs", + data=_JOHNSNOWLABS_MODEL_PATH_SUB, + conda_env=_CONDA_ENV_FILE_NAME, + python_env=_PYTHON_ENV_FILE_NAME, + code=code_dir_subpath, + ) + if size := get_total_file_size(dst_dir): + mlflow_model.model_size_bytes = size + mlflow_model.save(str(Path(dst_dir) / MLMODEL_FILE_NAME)) + + if conda_env is None: + default_reqs = get_default_pip_requirements() if pip_requirements is None else None + conda_env, pip_requirements, pip_constraints = _process_pip_requirements( + default_reqs, + pip_requirements, + extra_pip_requirements, + ) + else: + conda_env, pip_requirements, pip_constraints = _process_conda_env(conda_env) + + with open(str(Path(dst_dir) / _CONDA_ENV_FILE_NAME), "w") as f: + yaml.safe_dump(conda_env, stream=f, default_flow_style=False) + + # Save `constraints.txt` if necessary + if pip_constraints: + write_to(str(Path(dst_dir) / _CONSTRAINTS_FILE_NAME), "\n".join(pip_constraints)) + write_to(str(Path(dst_dir) / _REQUIREMENTS_FILE_NAME), "\n".join(pip_requirements)) + + _PythonEnv.current().to_yaml(str(Path(dst_dir) / _PYTHON_ENV_FILE_NAME)) + + _save_jars_and_lic(dst_dir) + + +def _save_jars_and_lic(dst_dir, store_license=False): + from johnsnowlabs.auto_install.jsl_home import get_install_suite_from_jsl_home + from johnsnowlabs.py_models.jsl_secrets import JslSecrets + + deps_data_path = Path(dst_dir) / _JOHNSNOWLABS_MODEL_PATH_SUB / "jars.jsl" + deps_data_path.mkdir(parents=True, exist_ok=True) + + suite = get_install_suite_from_jsl_home( + False, + visual=_JOHNSNOWLABS_ENV_VISUAL_SECRET in os.environ, + ) + if suite.hc.get_java_path(): + shutil.copy2(suite.hc.get_java_path(), deps_data_path / "hc_jar.jar") + if suite.nlp.get_java_path(): + shutil.copy2(suite.nlp.get_java_path(), deps_data_path / "os_jar.jar") + if suite.ocr.get_java_path(): + shutil.copy2(suite.ocr.get_java_path(), deps_data_path / "visual_nlp.jar") + + if store_license: + # Read the secrets from env vars and write to license.json + secrets = JslSecrets.build_or_try_find_secrets() + if secrets.HC_LICENSE: + deps_data_path.joinpath("license.json").write(secrets.json()) + + +@format_docstring(LOG_MODEL_PARAM_DOCS.format(package_name="johnsnowlabs")) +def save_model( + spark_model, + path, + mlflow_model=None, + conda_env=None, + code_paths=None, + dfs_tmpdir=None, + signature: ModelSignature = None, + input_example: ModelInputExample = None, + pip_requirements=None, + extra_pip_requirements=None, + metadata=None, + store_license=False, +): + """ + Save a Spark johnsnowlabs Model to a local path. + + By default, this function saves models using the Spark MLlib persistence mechanism. + + Args: + spark_model: Either a pyspark.ml.pipeline.PipelineModel or nlu.NLUPipeline object to be + saved. `Every johnsnowlabs model `_ + is a PipelineModel and loadable as nlu.NLUPipeline. + path: Local path where the model is to be saved. + mlflow_model: MLflow model config this flavor is being added to. + conda_env: Either a dictionary representation of a Conda environment or the path to a + Conda environment yaml file. If provided, this describes the environment + this model should be run in. At minimum, it should specify the dependencies + contained in :func:`get_default_conda_env()`. If `None`, the default + :func:`get_default_conda_env()` environment is added to the model. + The following is an *example* dictionary representation of a Conda + environment:: + + { + 'name': 'mlflow-env', + 'channels': ['defaults'], + 'dependencies': [ + 'python=3.8.15', + 'johnsnowlabs' + ] + } + code_paths: {{ code_paths }} + dfs_tmpdir: Temporary directory path on Distributed (Hadoop) File System (DFS) or local + filesystem if running in local mode. The model is be written in this + destination and then copied to the requested local path. This is necessary + as Spark ML models read from and write to DFS if running on a cluster. All + temporary files created on the DFS are removed if this operation + completes successfully. Defaults to ``/tmp/mlflow``. + signature: :py:class:`ModelSignature ` + describes model input and output :py:class:`Schema `. + The model signature can be :py:func:`inferred ` + from datasets with valid model input (e.g. the training dataset with target + column omitted) and valid model output (e.g. model predictions generated on + the training dataset), for example: + + .. code-block:: python + + from mlflow.models.signature import infer_signature + + train = df.drop_column("target_label") + predictions = ... # compute model predictions + signature = infer_signature(train, predictions) + input_example: {{ input_example }} + pip_requirements: {{ pip_requirements }} + extra_pip_requirements: {{ extra_pip_requirements }} + metadata: {{ metadata }} + store_license: If True, the license will be stored with the model and used and + re-loading it. + + .. code-block:: python + :caption: Example + + from johnsnowlabs import nlp + import mlflow + import os + + # Write your raw license.json string into the 'JOHNSNOWLABS_LICENSE_JSON' env variable + creds = { + "AWS_ACCESS_KEY_ID": "...", + "AWS_SECRET_ACCESS_KEY": "...", + "SPARK_NLP_LICENSE": "...", + "SECRET": "...", + } + os.environ["JOHNSNOWLABS_LICENSE_JSON"] = json.dumps(creds) + + # Download & Install Jars/Wheels if missing and Start a spark Session + nlp.start() + + # load a model + model = nlp.load("en.classify.bert_sequence.covid_sentiment") + model.predict(["I hate covid", "I love covid"]) + + # Save model as pyfunc and johnsnowlabs format + mlflow.johnsnowlabs.save_model(model, "saved_model") + model = mlflow.johnsnowlabs.load_model("saved_model") + # Predict with reloaded model, + # supports datatypes defined in https://nlp.johnsnowlabs.com/docs/en/jsl/predict_api#supported-data-types + model.predict(["I hate covid", "I love covid"]) + """ + _validate_env_vars() + if mlflow_model is None: + mlflow_model = Model() + if metadata is not None: + mlflow_model.metadata = metadata + # Spark ML stores the model on DFS if running on a cluster + # Save it to a DFS temp dir first and copy it to local path + if dfs_tmpdir is None: + dfs_tmpdir = MLFLOW_DFS_TMP.get() + tmp_path = generate_tmp_dfs_path(dfs_tmpdir) + + _unpack_and_save_model(spark_model, tmp_path) + sparkml_data_path = os.path.abspath(str(Path(path) / _JOHNSNOWLABS_MODEL_PATH_SUB)) + # We're copying the Spark model from DBFS to the local filesystem if (a) the temporary DFS URI + # we saved the Spark model to is a DBFS URI ("dbfs:/my-directory"), or (b) if we're running + # on a Databricks cluster and the URI is schemeless (e.g. looks like a filesystem absolute path + # like "/my-directory") + copying_from_dbfs = is_valid_dbfs_uri(tmp_path) or ( + databricks_utils.is_in_cluster() and posixpath.abspath(tmp_path) == tmp_path + ) + if copying_from_dbfs and databricks_utils.is_dbfs_fuse_available(): + tmp_path_fuse = dbfs_hdfs_uri_to_fuse_path(tmp_path) + shutil.move(src=tmp_path_fuse, dst=sparkml_data_path) + else: + _HadoopFileSystem.copy_to_local_file(tmp_path, sparkml_data_path, remove_src=True) + _save_model_metadata( + dst_dir=path, + spark_model=spark_model, + mlflow_model=mlflow_model, + conda_env=conda_env, + code_paths=code_paths, + signature=signature, + input_example=input_example, + pip_requirements=pip_requirements, + extra_pip_requirements=extra_pip_requirements, + store_license=store_license, + ) + + +def _load_model_databricks(dfs_tmpdir, local_model_path): + from johnsnowlabs import nlp + + # Spark ML expects the model to be stored on DFS + # Copy the model to a temp DFS location first. We cannot delete this file, as + # Spark may read from it at any point. + fuse_dfs_tmpdir = dbfs_hdfs_uri_to_fuse_path(dfs_tmpdir) + os.makedirs(fuse_dfs_tmpdir) + # Workaround for inability to use shutil.copytree with DBFS FUSE due to permission-denied + # errors on passthrough-enabled clusters when attempting to copy permission bits for directories + shutil_copytree_without_file_permissions(src_dir=local_model_path, dst_dir=fuse_dfs_tmpdir) + return nlp.load(path=dfs_tmpdir) + + +def _load_model(model_uri, dfs_tmpdir_base=None, local_model_path=None): + from johnsnowlabs import nlp + + dfs_tmpdir = generate_tmp_dfs_path(dfs_tmpdir_base or MLFLOW_DFS_TMP.get()) + if databricks_utils.is_in_cluster() and databricks_utils.is_dbfs_fuse_available(): + return _load_model_databricks( + dfs_tmpdir, local_model_path or _download_artifact_from_uri(model_uri) + ) + # model_uri = _HadoopFileSystem.maybe_copy_from_uri(model_uri, dfs_tmpdir, local_model_path) + if model_uri and not local_model_path: + local_model_path = _download_artifact_from_uri(model_uri) + _get_or_create_sparksession(local_model_path) + + if _JOHNSNOWLABS_MODEL_PATH_SUB not in local_model_path: + local_model_path = str(Path(local_model_path) / _JOHNSNOWLABS_MODEL_PATH_SUB) + + return nlp.load(path=local_model_path) + + +def load_model(model_uri, dfs_tmpdir=None, dst_path=None): + """ + Load the Johnsnowlabs MLflow model from the path. + + Args: + model_uri: The location, in URI format, of the MLflow model. For example: + + - ``/Users/me/path/to/local/model`` + - ``relative/path/to/local/model`` + - ``s3://my_bucket/path/to/model`` + - ``runs://run-relative/path/to/model`` + - ``models://`` + - ``models://`` + + For more information about supported URI schemes, see + `Referencing Artifacts `_. + dfs_tmpdir: Temporary directory path on Distributed (Hadoop) File System (DFS) or local + filesystem if running in local mode. The model is loaded from this + destination. Defaults to ``/tmp/mlflow``. + dst_path: The local filesystem path to which to download the model artifact. + This directory must already exist. If unspecified, a local output + path will be created. + + Returns: + A + `nlu.NLUPipeline `_. + + .. code-block:: python + :caption: Example + + import mlflow + from johnsnowlabs import nlp + import os + + # Write your raw license.json string into the 'JOHNSNOWLABS_LICENSE_JSON' env variable + creds = { + "AWS_ACCESS_KEY_ID": "...", + "AWS_SECRET_ACCESS_KEY": "...", + "SPARK_NLP_LICENSE": "...", + "SECRET": "...", + } + os.environ["JOHNSNOWLABS_LICENSE_JSON"] = json.dumps(creds) + + # start a spark session + nlp.start() + # Load you MLflow Model + model = mlflow.johnsnowlabs.load_model("johnsnowlabs_model") + + # Make predictions on test documents + # supports datatypes defined in https://nlp.johnsnowlabs.com/docs/en/jsl/predict_api#supported-data-types + prediction = model.transform(["I love Covid", "I hate Covid"]) + """ + # This MUST be called prior to appending the model flavor to `model_uri` in order + # for `artifact_path` to take on the correct value for model loading via mlflowdbfs. + _validate_env_vars() + root_uri, artifact_path = _get_root_uri_and_artifact_path(model_uri) + + flavor_conf = _get_flavor_configuration_from_uri(model_uri, FLAVOR_NAME, _logger) + local_mlflow_model_path = _download_artifact_from_uri( + artifact_uri=model_uri, output_path=dst_path + ) + _add_code_from_conf_to_system_path(local_mlflow_model_path, flavor_conf) + + if _should_use_mlflowdbfs(model_uri): + from pyspark.ml.pipeline import PipelineModel + + mlflowdbfs_path = _mlflowdbfs_path( + DatabricksArtifactRepository._extract_run_id(model_uri), artifact_path + ) + with databricks_utils.MlflowCredentialContext( + get_databricks_profile_uri_from_artifact_uri(root_uri) + ): + return PipelineModel.load(mlflowdbfs_path) + + sparkml_model_uri = append_to_uri_path(model_uri, flavor_conf["model_data"]) + local_sparkml_model_path = str(Path(local_mlflow_model_path) / flavor_conf["model_data"]) + return _load_model( + model_uri=sparkml_model_uri, + dfs_tmpdir_base=dfs_tmpdir, + local_model_path=local_sparkml_model_path, + ) + + +def _load_pyfunc(path, spark=None): + """Load PyFunc implementation. Called by ``pyfunc.load_model``. + + Args: + path: Local filesystem path to the MLflow Model with the ``johnsnowlabs`` flavor. + spark: Optionally pass spark context when using pyfunc as UDF. required, because + we cannot fetch the Sparkcontext inside of the Workernode which executes the UDF. + + Returns: + None. + + """ + return _PyFuncModelWrapper( + _load_model(model_uri=path), + spark or _get_or_create_sparksession(path), + ) + + +def _get_or_create_sparksession(model_path=None): + """Check if SparkSession running and get it. + + If none exists, create a new one using jars in model_path. If model_path not defined, rely on + nlp.start() to create a new one using johnsnowlabs Jar resolution method. See + https://nlp.johnsnowlabs.com/docs/en/jsl/start-a-sparksession and + https://nlp.johnsnowlabs.com/docs/en/jsl/install_advanced. + + Args: + model_path: + + Returns: + + """ + from johnsnowlabs import nlp + + from mlflow.utils._spark_utils import _get_active_spark_session + + _validate_env_vars() + + spark = _get_active_spark_session() + if spark is None: + spark_conf = {} + spark_conf["spark.python.worker.reuse"] = "true" + os.environ["PYSPARK_PYTHON"] = sys.executable + os.environ["PYSPARK_DRIVER_PYTHON"] = sys.executable + if model_path: + jar_paths, license_path = _fetch_deps_from_path(model_path) + if license_path: + with open(license_path) as f: + loaded_license = json.load(f) + os.environ.update( + {k: str(v) for k, v in loaded_license.items() if v is not None} + ) + os.environ["JSL_NLP_LICENSE"] = loaded_license["HC_LICENSE"] + _logger.info("Starting a new Session with Jars: %s", jar_paths) + spark = nlp.start( + nlp=False, + spark_nlp=False, + jar_paths=jar_paths, + json_license_path=license_path, + create_jsl_home_if_missing=False, + spark_conf=spark_conf, + ) + else: + spark = nlp.start() + return spark + + +def _fetch_deps_from_path(local_model_path): + if _JOHNSNOWLABS_MODEL_PATH_SUB not in local_model_path: + local_model_path = Path(local_model_path) / _JOHNSNOWLABS_MODEL_PATH_SUB / "jars.jsl" + else: + local_model_path = Path(local_model_path) / "jars.jsl" + + jar_paths = [ + str(local_model_path / file) for file in local_model_path.iterdir() if file.suffix == ".jar" + ] + license_path = [ + str(local_model_path / file) + for file in local_model_path.iterdir() + if file.name == "license.json" + ] + + license_path = license_path[0] if license_path else None + return jar_paths, license_path + + +def _unpack_and_save_model(spark_model, dst): + from pyspark.ml import PipelineModel + + if isinstance(spark_model, _PyFuncModelWrapper): + spark_model = spark_model.spark_model + if isinstance(spark_model, PipelineModel): + spark_model.write().overwrite().save(dst) + else: + # nlu pipe + spark_model.predict("Init") + try: + spark_model.vanilla_transformer_pipe.write().overwrite().save(dst) + except Exception: + # for mlflowdbfs_path we cannot use overwrite, gives + # org.apache.hadoop.fs.UnsupportedFileSystemException: No FileSystem for scheme + # "mlflowdbfs" + spark_model.save(dst) + + +class _PyFuncModelWrapper: + """ + Wrapper around NLUPipeline providing interface for scoring pandas DataFrame. + """ + + def __init__( + self, + spark_model, + spark=None, + ): + # we have this `or`, so we support _PyFuncModelWrapper(nlu_ref) + self.spark = spark or _get_or_create_sparksession() + self.spark_model = spark_model + + def get_raw_model(self): + """ + Returns the underlying model. + """ + return self.spark_model + + def predict(self, text, params: dict[str, Any] | None = None): + """Generate predictions given input data in a pandas DataFrame. + + Args: + text: pandas DataFrame containing input data. + params: Additional parameters to pass to the model for inference. + + Returns: + List with model predictions. + + """ + output_level = params.get("output_level", "") if params else "" + return self.spark_model.predict(text, output_level=output_level).reset_index().to_json() diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/johnsnowlabs/__pycache__/__init__.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/johnsnowlabs/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..09f88bb42898f7a91280b581a0bc6a7e88738946 Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/johnsnowlabs/__pycache__/__init__.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/keras/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/keras/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..938fcaaf3c06ef68f3bec1fad4fb4366253a3189 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/keras/__init__.py @@ -0,0 +1,48 @@ +# MLflow Keras 3 flavor. +import keras +from packaging.version import Version + +if Version(keras.__version__) < Version("3.0.0"): + from mlflow.tensorflow import ( + # Redirect `mlflow.keras._load_pyfunc` to `mlflow.tensorflow._load_pyfunc`, + # For backwards compatibility on loading keras model saved by old mlflow versions. + _load_pyfunc, + autolog, + load_model, + log_model, + save_model, + ) + + __all__ = [ + "_load_pyfunc", + "autolog", + "load_model", + "save_model", + "log_model", + ] +else: + from mlflow.keras.autologging import autolog + from mlflow.keras.callback import MlflowCallback + from mlflow.keras.load import _load_pyfunc, load_model + from mlflow.keras.save import ( + get_default_conda_env, + get_default_pip_requirements, + log_model, + save_model, + ) + + FLAVOR_NAME = "keras" + + MLflowCallback = MlflowCallback # for backwards compatibility + + __all__ = [ + "_load_pyfunc", + "MlflowCallback", + "MLflowCallback", + "autolog", + "load_model", + "save_model", + "log_model", + "get_default_pip_requirements", + "get_default_conda_env", + ] diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/keras/__pycache__/__init__.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/keras/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..d187978ef5a80e2b6db14e497c1693829d50d2a6 Binary files /dev/null and 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+from mlflow.exceptions import MlflowException +from mlflow.keras.callback import MlflowCallback +from mlflow.keras.save import log_model +from mlflow.keras.utils import get_model_signature +from mlflow.tracking.context import registry as context_registry +from mlflow.tracking.fluent import _initialize_logged_model +from mlflow.utils import is_iterator +from mlflow.utils.autologging_utils import ( + autologging_integration, + get_autologging_config, + log_fn_args_as_params, + safe_patch, +) + +_logger = logging.getLogger(__name__) + + +def _check_existing_mlflow_callback(callbacks): + for callback in callbacks: + if isinstance(callback, MlflowCallback): + raise MlflowException( + "MLflow autologging must be turned off if an `MlflowCallback` is explicitly added " + "to the callback list. You are creating an `MlflowCallback` while having " + "autologging enabled. Please either call `mlflow.keras.autolog(disable=True)` " + "to disable autologging or remove `MlflowCallback` from the callback list. " + ) + + +def _log_dataset(dataset, source, context, name=None, targets=None, model_id=None): + """Helper function to log the dataset information to MLflow.""" + try: + import tensorflow as tf + + is_tf_dataset = isinstance(dataset, tf.data.Dataset) + is_tf_tensor = isinstance(dataset, tf.Tensor) + except ImportError: + pass + + if isinstance(dataset, np.ndarray): + dataset = from_numpy(features=dataset, targets=targets, source=source, name=name) + elif is_tf_tensor: + dataset = from_tensorflow(features=dataset, targets=targets, source=source, name=name) + elif is_tf_dataset: + dataset = from_tensorflow(features=dataset, source=source, name=name) + elif isinstance(dataset, tuple): + x = dataset[0] + y = dataset[1] + # check if x and y are tensors + if isinstance(x, tf.Tensor) and isinstance(y, tf.Tensor): + dataset = from_tensorflow(features=x, source=source, targets=y, name=name) + else: + dataset = from_numpy(features=x, targets=y, source=source, name=name) + else: + _logger.warning(f"Unrecognized dataset type {type(dataset)}. Dataset logging skipped.") + return + + model = LoggedModelInput(model_id=model_id) if model_id else None + mlflow.log_input(dataset, context, model=model) + + +def _parse_dataset(*keras_fit_args, **keras_fit_kwargs): + """Parse dataset from `keras.Model.fit` args and kwargs.""" + x = keras_fit_kwargs["x"] if "x" in keras_fit_kwargs else keras_fit_args[0] + if "y" in keras_fit_kwargs: + # `y` is either specified as a kwarg, or the second argument to `fit`. + y = keras_fit_kwargs["y"] + elif len(keras_fit_args) >= 2: + y = keras_fit_args[1] + else: + y = None + return x, y + + +def _log_keras_model( + model, + save_exported_model, + log_model_signatures=True, + save_model_kwargs=None, + model_id=None, +): + """Helper function to log the Keras model to MLflow.""" + if log_model_signatures: + try: + signature = get_model_signature(model) + except Exception as e: + _logger.warning(f"Failed to get model signature, reason: {e}") + signature = None + else: + signature = None + + log_model( + model, + "model", + save_exported_model=save_exported_model, + signature=signature, + registered_model_name=get_autologging_config("keras", "registered_model_name", None), + save_model_kwargs=save_model_kwargs, + model_id=model_id, + ) + + +@autologging_integration("keras") +def autolog( + log_every_epoch=True, + log_every_n_steps=None, + log_models=True, + log_model_signatures=True, + save_exported_model=False, + log_datasets=True, + log_input_examples=False, + disable=False, + exclusive=False, + disable_for_unsupported_versions=False, + silent=False, + registered_model_name=None, + save_model_kwargs=None, + extra_tags=None, +): + """ + Enable autologging for Keras. + + This method configures the autologging for Keras workflow. Only Keras > 3 is supported. For + usage of lower Keras version (also known as tf-keras), please refer to `mlflow.tensorflow` + flavor. At a high level, calling this `mlflow.keras.autolog()` function will replace + `keras.Model.fit` method with the custom `fit` method provided by MLflow, which logs + metrics/params/info/model to MLflow at the corresponding time. + + Autologging is compatible with all backends supported by Keras, including Tensorflow, PyTorch + and JAX. + + Please note that autologging works only when you are using `model.fit()` for training. If you + are writing a custom training loop, then you need to use manual logging. + + Args: + log_every_epoch: If True, training metrics will be logged at the end of each epoch. + log_every_n_steps: If set, training metrics will be logged every `n` training steps. + `log_every_n_steps` must be `None` when `log_every_epoch=True`. + log_models: If True, the Keras model will be logged to MLflow at the end of `model.fit()`. + log_model_signatures: If True, model signature will be automatically captured and logged. + save_exported_model: If True, model will be saved as the exported format (compiled graph), + which is suitable for serving and deployment. If False, model will be saved in `.keras` + format, which contains model architecture and weights. + log_datasets: If True, the dataset metadata will be logged to MLflow. + log_input_examples: If True, input examples will be logged. + disable: If `True`, disables the Keras autologging. + exclusive: If `True`, autologged content is not logged to user-created fluent runs. If + `False`, autologged content is logged to the active fluent run, which may be + user-created. disable_for_unsupported_versions: If `True`, disable autologging for + incompatible Keras versions. + disable_for_unsupported_versions: If ``True``, disable autologging for versions of keras + that have not been tested against this version of the MLflow client or are + incompatible. + silent: If `True`, suppress all event logs and warnings from MLflow during Keras + autologging. If `True`, show all events and warnings during Keras autologging. + registered_model_name: If set, each time a model is trained, it is registered as a new model + version of the registered model with this name. The registered model is created if it + does not already exist. + save_model_kwargs: Extra kwargs passed to `keras.Model.save()`. + extra_tags: A dictionary of extra tags to set on each managed run created by autologging. + + .. code-block:: python + :caption: Example + + import keras + import mlflow + import numpy as np + + mlflow.keras.autolog() + + # Prepare data for a 2-class classification. + data = np.random.uniform([8, 28, 28, 3]) + label = np.random.randint(2, size=8) + model = keras.Sequential( + [ + keras.Input([28, 28, 3]), + keras.layers.Flatten(), + keras.layers.Dense(2), + ] + ) + model.compile( + loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True), + optimizer=keras.optimizers.Adam(0.001), + metrics=[keras.metrics.SparseCategoricalAccuracy()], + ) + with mlflow.start_run() as run: + model.fit(data, label, batch_size=4, epochs=2) + """ + + def _patched_inference(original, inst, *args, **kwargs): + unlogged_params = ["self", "x", "y", "callbacks", "validation_data", "verbose"] + + batch_size, args, kwargs = _infer_batch_size(inst, *args, **kwargs) + + if batch_size is not None: + mlflow.log_param("batch_size", batch_size) + unlogged_params.append("batch_size") + + log_fn_args_as_params(original, [], kwargs, unlogged_params) + model_id = None + if log_models: + model_id = _initialize_logged_model("model", flavor="keras").model_id + + if log_datasets: + try: + context_tags = context_registry.resolve_tags() + source = CodeDatasetSource(tags=context_tags) + x, y = _parse_dataset(*args, **kwargs) + _log_dataset(x, source, "train", targets=y, model_id=model_id) + + if "validation_data" in kwargs: + _log_dataset(kwargs["validation_data"], source, "eval", model_id=model_id) + + except Exception as e: + _logger.warning(f"Failed to log dataset information to MLflow. Reason: {e}") + + # Add `MlflowCallback` to the callback list. + callbacks = args[5] if len(args) >= 6 else kwargs.get("callbacks", []) + mlflow_callback = MlflowCallback( + log_every_epoch=log_every_epoch, + log_every_n_steps=log_every_n_steps, + model_id=model_id, + ) + _check_existing_mlflow_callback(callbacks) + callbacks.append(mlflow_callback) + kwargs["callbacks"] = callbacks + history = original(inst, *args, **kwargs) + + if log_models: + _log_keras_model( + inst, + save_exported_model, + log_model_signatures, + save_model_kwargs, + model_id=model_id, + ) + return history + + safe_patch( + "keras", keras.Model, "fit", _patched_inference, manage_run=True, extra_tags=extra_tags + ) + + +def _infer_batch_size(inst, *args, **kwargs): + batch_size = None + if "batch_size" in kwargs: + batch_size = kwargs["batch_size"] + else: + training_data = kwargs["x"] if "x" in kwargs else args[0] + if _batch_size := getattr(training_data, "batch_size", None): + batch_size = _batch_size + elif _batch_size := getattr(training_data, "_batch_size", None): + batch_size = _batch_size if isinstance(_batch_size, int) else _batch_size.numpy() + elif is_iterator(training_data): + is_single_input_model = isinstance(inst.input_shape, tuple) + peek = next(training_data) + batch_size = len(peek[0]) if is_single_input_model else len(peek[0][0]) + + def origin_training_data_generator_fn(): + yield peek + yield from training_data + + origin_training_data = origin_training_data_generator_fn() + + if "x" in kwargs: + kwargs["x"] = origin_training_data + else: + args = (origin_training_data,) + args[1:] + return batch_size, args, kwargs diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/keras/callback.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/keras/callback.py new file mode 100644 index 0000000000000000000000000000000000000000..fe449734784e3fbf20d54a3f2cd620adeea70c3e --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/keras/callback.py @@ -0,0 +1,104 @@ +"""Keras 3 callback to log information to MLflow.""" + +import keras + +from mlflow import log_metrics, log_params, log_text +from mlflow.utils.autologging_utils import ExceptionSafeClass + + +class MlflowCallback(keras.callbacks.Callback, metaclass=ExceptionSafeClass): + """Callback for logging Keras metrics/params/model/... to MLflow. + + This callback logs model metadata at training begins, and logs training metrics every epoch or + every n steps (defined by the user) to MLflow. + + Args: + log_every_epoch: bool, defaults to True. If True, log metrics every epoch. If False, + log metrics every n steps. + log_every_n_steps: int, defaults to None. If set, log metrics every n steps. If None, + log metrics every epoch. Must be `None` if `log_every_epoch=True`. + + .. code-block:: python + :caption: Example + + import keras + import mlflow + import numpy as np + + # Prepare data for a 2-class classification. + data = np.random.uniform([8, 28, 28, 3]) + label = np.random.randint(2, size=8) + model = keras.Sequential( + [ + keras.Input([28, 28, 3]), + keras.layers.Flatten(), + keras.layers.Dense(2), + ] + ) + model.compile( + loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True), + optimizer=keras.optimizers.Adam(0.001), + metrics=[keras.metrics.SparseCategoricalAccuracy()], + ) + with mlflow.start_run() as run: + model.fit( + data, + label, + batch_size=4, + epochs=2, + callbacks=[mlflow.keras.MlflowCallback()], + ) + """ + + def __init__(self, log_every_epoch=True, log_every_n_steps=None, model_id=None): + self.log_every_epoch = log_every_epoch + self.log_every_n_steps = log_every_n_steps + self.model_id = model_id + + if log_every_epoch and log_every_n_steps is not None: + raise ValueError( + "`log_every_n_steps` must be None if `log_every_epoch=True`, received " + f"`log_every_epoch={log_every_epoch}` and `log_every_n_steps={log_every_n_steps}`." + ) + + if not log_every_epoch and log_every_n_steps is None: + raise ValueError( + "`log_every_n_steps` must be specified if `log_every_epoch=False`, received" + "`log_every_n_steps=False` and `log_every_n_steps=None`." + ) + + def on_train_begin(self, logs=None): + """Log model architecture and optimizer configuration when training begins.""" + config = self.model.optimizer.get_config() + log_params({f"optimizer_{k}": v for k, v in config.items()}) + + model_summary = [] + + def print_fn(line, *args, **kwargs): + model_summary.append(line) + + self.model.summary(print_fn=print_fn) + summary = "\n".join(model_summary) + log_text(summary, artifact_file="model_summary.txt") + + def on_epoch_end(self, epoch, logs=None): + """Log metrics at the end of each epoch.""" + if not self.log_every_epoch or logs is None: + return + log_metrics(logs, step=epoch, synchronous=False, model_id=self.model_id) + + def on_batch_end(self, batch, logs=None): + """Log metrics at the end of each batch with user specified frequency.""" + if self.log_every_n_steps is None or logs is None: + return + current_iteration = int(self.model.optimizer.iterations.numpy()) + + if current_iteration % self.log_every_n_steps == 0: + log_metrics(logs, step=current_iteration, synchronous=False, model_id=self.model_id) + + def on_test_end(self, logs=None): + """Log validation metrics at validation end.""" + if logs is None: + return + metrics = {"validation_" + k: v for k, v in logs.items()} + log_metrics(metrics, synchronous=False, model_id=self.model_id) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/keras/load.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/keras/load.py new file mode 100644 index 0000000000000000000000000000000000000000..5658df91c45612ad15dfe0bb7d9bfa85b5e59cea --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/keras/load.py @@ -0,0 +1,158 @@ +"""Functions for loading Keras models saved with MLflow.""" + +import os + +import keras +import numpy as np +import pandas as pd + +from mlflow.exceptions import INVALID_PARAMETER_VALUE, MlflowException +from mlflow.models import Model +from mlflow.models.model import MLMODEL_FILE_NAME +from mlflow.tracking.artifact_utils import _download_artifact_from_uri + +_MODEL_SAVE_PATH = "model" + + +class KerasModelWrapper: + def __init__(self, model, signature, save_exported_model=False): + self.model = model + self.signature = signature + self.save_exported_model = save_exported_model + + def get_raw_model(self): + """ + Returns the underlying model. + """ + return self.model + + def get_model_call_method(self): + if self.save_exported_model: + return self.model.serve + else: + return self.model.predict + + def predict(self, data, **kwargs): + model_call = self.get_model_call_method() + if isinstance(data, pd.DataFrame): + return pd.DataFrame(model_call(data.values), index=data.index) + + supported_input_types = (np.ndarray, list, tuple, dict) + if not isinstance(data, supported_input_types): + raise MlflowException( + f"`data` must be one of: {[x.__name__ for x in supported_input_types]}, but " + f"received type: {type(data)}.", + INVALID_PARAMETER_VALUE, + ) + # Return numpy array for serving purposes. + return keras.ops.convert_to_numpy(model_call(data)) + + +def _load_keras_model(path, model_conf, custom_objects=None, **load_model_kwargs): + save_exported_model = model_conf.flavors["keras"].get("save_exported_model") + model_path = os.path.join(path, model_conf.flavors["keras"].get("data", _MODEL_SAVE_PATH)) + if os.path.isdir(model_path): + model_path = os.path.join(model_path, _MODEL_SAVE_PATH) + if save_exported_model: + try: + import tensorflow as tf + except ImportError: + raise MlflowException( + "`tensorflow` must be installed if you want to load an exported Keras 3 model, " + "please install `tensorflow` by `pip install tensorflow`." + ) + return tf.saved_model.load(model_path) + else: + model_path += ".keras" + return keras.saving.load_model( + model_path, + custom_objects=custom_objects, + **load_model_kwargs, + ) + + +def load_model(model_uri, dst_path=None, custom_objects=None, load_model_kwargs=None): + """ + Load Keras model from MLflow. + + This method loads a saved Keras model from MLflow, and returns a Keras model instance. + + Args: + model_uri: The URI of the saved Keras model in MLflow. For example: + + - `/Users/me/path/to/local/model` + - `relative/path/to/local/model` + - `s3://my_bucket/path/to/model` + - `runs://run-relative/path/to/model` + - `models://` + - `models://` + + For more information about supported URI schemes, see `Referencing + Artifacts `_. + dst_path: The local filesystem path to which to download the + model artifact. If unspecified, a local output path will be created. + custom_objects: The `custom_objects` arg in + `keras.saving.load_model`. + load_model_kwargs: Extra args for `keras.saving.load_model`. + + .. code-block:: python + :caption: Example + + import keras + import mlflow + import numpy as np + + model = keras.Sequential( + [ + keras.Input([28, 28, 3]), + keras.layers.Flatten(), + keras.layers.Dense(2), + ] + ) + with mlflow.start_run() as run: + mlflow.keras.log_model(model) + + model_url = f"runs:/{run.info.run_id}/{model_path}" + loaded_model = mlflow.keras.load_model(model_url) + + # Test the loaded model produces the same output for the same input as the model. + test_input = np.random.uniform(size=[2, 28, 28, 3]) + np.testing.assert_allclose( + keras.ops.convert_to_numpy(model(test_input)), + loaded_model.predict(test_input), + ) + + Returns: + A Keras model instance. + """ + local_model_path = _download_artifact_from_uri(artifact_uri=model_uri, output_path=dst_path) + load_model_kwargs = {} if load_model_kwargs is None else load_model_kwargs + + model_configuration_path = os.path.join(local_model_path, MLMODEL_FILE_NAME) + model_conf = Model.load(model_configuration_path) + + return _load_keras_model(local_model_path, model_conf, custom_objects, **load_model_kwargs) + + +def _load_pyfunc(path): + """Logics of loading a saved Keras model as a PyFunc model. + + This function is called by `mlflow.pyfunc.load_model`. + + Args: + path: Local filesystem path to the MLflow Model with the `keras` flavor. + """ + model_meta_path1 = os.path.join(path, MLMODEL_FILE_NAME) + model_meta_path2 = os.path.join(os.path.dirname(path), MLMODEL_FILE_NAME) + + if os.path.isfile(model_meta_path1): + model_conf = Model.load(model_meta_path1) + elif os.path.isfile(model_meta_path2): + model_conf = Model.load(model_meta_path2) + else: + raise MlflowException(f"Cannot find file {MLMODEL_FILE_NAME} for the logged model.") + + save_exported_model = model_conf.flavors["keras"].get("save_exported_model") + + loaded_model = _load_keras_model(path, model_conf) + return KerasModelWrapper(loaded_model, model_conf.signature, save_exported_model) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/keras/save.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/keras/save.py new file mode 100644 index 0000000000000000000000000000000000000000..53c066c784cd52dc38ade3cfb4282ee3f2bb8731 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/keras/save.py @@ -0,0 +1,369 @@ +"""Functions for saving Keras models to MLflow.""" + +import importlib +import logging +import os +import shutil +import tempfile +from typing import Any + +import keras +import yaml + +import mlflow +from mlflow import pyfunc +from mlflow.exceptions import INVALID_PARAMETER_VALUE, MlflowException +from mlflow.models import ( + Model, + ModelInputExample, + ModelSignature, + infer_pip_requirements, +) +from mlflow.models.model import MLMODEL_FILE_NAME +from mlflow.models.utils import _save_example +from mlflow.tracking._model_registry import DEFAULT_AWAIT_MAX_SLEEP_SECONDS +from mlflow.types.schema import TensorSpec +from mlflow.utils.docstring_utils import LOG_MODEL_PARAM_DOCS, format_docstring +from mlflow.utils.environment import ( + _CONDA_ENV_FILE_NAME, + _CONSTRAINTS_FILE_NAME, + _PYTHON_ENV_FILE_NAME, + _REQUIREMENTS_FILE_NAME, + _mlflow_conda_env, + _process_conda_env, + _process_pip_requirements, + _PythonEnv, +) +from mlflow.utils.file_utils import get_total_file_size, write_to +from mlflow.utils.requirements_utils import _get_pinned_requirement + +FLAVOR_NAME = "keras" + +_MODEL_SAVE_PATH = "model" +_KERAS_MODULE_SPEC_PATH = "keras_module.txt" + +_logger = logging.getLogger(__name__) + + +_MODEL_DATA_PATH = "data" + + +def get_default_pip_requirements(): + """ + Returns: + A list of default pip requirements for MLflow Models produced by Keras flavor. Calls to + `save_model()` and `log_model()` produce a pip environment that, at minimum, contains these + requirements. + """ + return [_get_pinned_requirement("keras")] + + +def get_default_conda_env(): + """ + Returns: + The default Conda environment for MLflow Models produced by calls to `save_model()` and + `log_model()`. + """ + return _mlflow_conda_env(additional_pip_deps=get_default_pip_requirements()) + + +def _export_keras_model(model, path, signature): + if signature is None: + raise ValueError( + "`signature` cannot be None when `save_exported_model=True` for " + "`mlflow.keras.save_model()` method." + ) + try: + import tensorflow as tf + except ImportError: + raise MlflowException( + "`tensorflow` must be installed if you want to export a Keras 3 model, please " + "install `tensorflow` by `pip install tensorflow`, or set `save_exported_model=False`." + ) + input_schema = signature.inputs.to_dict() + export_signature = [] + for schema in input_schema: + dtype = schema["tensor-spec"]["dtype"] + shape = schema["tensor-spec"]["shape"] + # Replace -1 with None in shape. + new_shape = [size if size != -1 else None for size in shape] + export_signature.append(tf.TensorSpec(shape=new_shape, dtype=dtype)) + + export_archive = keras.export.ExportArchive() + export_archive.track(model) + export_archive.add_endpoint( + name="serve", + fn=model.call, + input_signature=export_signature, + ) + export_archive.write_out(path) + + +@format_docstring(LOG_MODEL_PARAM_DOCS.format(package_name=FLAVOR_NAME)) +def save_model( + model, + path, + save_exported_model=False, + conda_env=None, + mlflow_model=None, + signature: ModelSignature = None, + input_example: ModelInputExample = None, + pip_requirements=None, + extra_pip_requirements=None, + save_model_kwargs=None, + metadata=None, +): + """ + Save a Keras model along with metadata. + + This method saves a Keras model along with metadata such as model signature and conda + environments to local file system. This method is called inside `mlflow.keras.log_model()`. + + Args: + model: an instance of `keras.Model`. The Keras model to be saved. + path: local path where the MLflow model is to be saved. + save_exported_model: If True, save Keras model in exported model + format, otherwise save in `.keras` format. For more information, please + refer to https://keras.io/guides/serialization_and_saving/. + conda_env: {{ conda_env }} + mlflow_model: an instance of `mlflow.models.Model`, defaults to None. MLflow model + configuration to which to add the Keras model metadata. If None, a blank instance will + be created. + signature: {{ signature }} + input_example: {{ input_example }} + pip_requirements: {{ pip_requirements }} + extra_pip_requirements: {{ extra_pip_requirements }} + save_model_kwargs: A dict of kwargs to pass to `keras.Model.save` + method. + metadata: {{ metadata }} + + .. code-block:: python + :caption: Example + + import keras + import mlflow + + model = keras.Sequential( + [ + keras.Input([28, 28, 3]), + keras.layers.Flatten(), + keras.layers.Dense(2), + ] + ) + with mlflow.start_run() as run: + mlflow.keras.save_model(model, "./model") + """ + + import keras + + if signature is None: + _logger.warning("You are saving a Keras model without specifying model signature.") + else: + num_inputs = len(signature.inputs.inputs) + if num_inputs == 0: + raise MlflowException( + "The model signature's input schema must contain at least one field.", + error_code=INVALID_PARAMETER_VALUE, + ) + for field in signature.inputs.inputs: + if not isinstance(field, TensorSpec): + raise MlflowException( + "All fields in the model signature's input schema must be of type TensorSpec.", + error_code=INVALID_PARAMETER_VALUE, + ) + if field.shape[0] != -1: + raise MlflowException( + "All input schema' first dimension should be -1, which represents the dynamic " + "batch dimension.", + error_code=INVALID_PARAMETER_VALUE, + ) + + if mlflow_model is None: + mlflow_model = Model() + if signature is not None: + mlflow_model.signature = signature + if input_example is not None: + _save_example(mlflow_model, input_example, path) + if metadata is not None: + mlflow_model.metadata = metadata + + save_model_kwargs = save_model_kwargs or {} + + data_subpath = _MODEL_DATA_PATH + # Construct new data folder in existing path. + data_path = os.path.join(path, data_subpath) + os.makedirs(data_path) + + model_subpath = os.path.join(data_subpath, _MODEL_SAVE_PATH) + keras_module = importlib.import_module("keras") + + # Save keras module spec to path/data/keras_module.txt + with open(os.path.join(data_path, _KERAS_MODULE_SPEC_PATH), "w") as f: + f.write(keras_module.__name__) + + if save_exported_model: + model_path = os.path.join(path, model_subpath) + _export_keras_model(model, model_path, signature) + else: + # Save path requires ".keras" suffix. + file_extension = ".keras" + model_path = os.path.join(path, model_subpath) + file_extension + if path.startswith("/dbfs/"): + # The Databricks Filesystem uses a FUSE implementation that does not support + # random writes. It causes an error. + with tempfile.NamedTemporaryFile(suffix=".keras") as f: + model.save(f.name, **save_model_kwargs) + f.flush() # force flush the data + shutil.copy2(src=f.name, dst=model_path) + else: + model.save(model_path, **save_model_kwargs) + + flavor_options = { + "data": data_subpath, + "keras_version": keras.__version__, + "keras_backend": keras.backend.backend(), + "save_exported_model": save_exported_model, + } + + # Add flavor info to `mlflow_model`. + mlflow_model.add_flavor(FLAVOR_NAME, **flavor_options) + + # Add loader_module, data and env data to `mlflow_model`. + pyfunc.add_to_model( + mlflow_model, + loader_module="mlflow.keras", + conda_env=_CONDA_ENV_FILE_NAME, + python_env=_PYTHON_ENV_FILE_NAME, + ) + + # Add model file size to `mlflow_model`. + if size := get_total_file_size(path): + mlflow_model.model_size_bytes = size + + # save mlflow_model to path/MLmodel + mlflow_model.save(os.path.join(path, MLMODEL_FILE_NAME)) + + if conda_env is None: + if pip_requirements is None: + default_reqs = get_default_pip_requirements() + # To ensure `_load_pyfunc` can successfully load the model during the dependency + # inference, `mlflow_model.save` must be called beforehand to save an MLmodel file. + inferred_reqs = infer_pip_requirements(path, FLAVOR_NAME, fallback=default_reqs) + default_reqs = sorted(set(inferred_reqs).union(default_reqs)) + else: + default_reqs = None + conda_env, pip_requirements, pip_constraints = _process_pip_requirements( + default_reqs, + pip_requirements, + extra_pip_requirements, + ) + else: + conda_env, pip_requirements, pip_constraints = _process_conda_env(conda_env) + + with open(os.path.join(path, _CONDA_ENV_FILE_NAME), "w") as f: + yaml.safe_dump(conda_env, stream=f, default_flow_style=False) + + # Save `constraints.txt` if necessary. + if pip_constraints: + write_to(os.path.join(path, _CONSTRAINTS_FILE_NAME), "\n".join(pip_constraints)) + + # Save `requirements.txt`. + write_to(os.path.join(path, _REQUIREMENTS_FILE_NAME), "\n".join(pip_requirements)) + + _PythonEnv.current().to_yaml(os.path.join(path, _PYTHON_ENV_FILE_NAME)) + + +@format_docstring(LOG_MODEL_PARAM_DOCS.format(package_name=FLAVOR_NAME)) +def log_model( + model, + artifact_path: str | None = None, + save_exported_model=False, + conda_env=None, + signature: ModelSignature = None, + input_example: ModelInputExample = None, + registered_model_name=None, + await_registration_for=DEFAULT_AWAIT_MAX_SLEEP_SECONDS, + pip_requirements=None, + extra_pip_requirements=None, + save_model_kwargs=None, + metadata=None, + name: str | None = None, + params: dict[str, Any] | None = None, + tags: dict[str, Any] | None = None, + model_type: str | None = None, + step: int = 0, + model_id: str | None = None, +): + """ + Log a Keras model along with metadata to MLflow. + + This method saves a Keras model along with metadata such as model signature and conda + environments to MLflow. + + Args: + model: an instance of `keras.Model`. The Keras model to be saved. + artifact_path: Deprecated. Use `name` instead. + save_exported_model: defaults to False. If True, save Keras model in exported + model format, otherwise save in `.keras` format. For more information, please + refer to `Keras doc `_. + conda_env: {{ conda_env }} + signature: {{ signature }} + input_example: {{ input_example }} + registered_model_name: defaults to None. If set, create a model version under + `registered_model_name`, also create a registered model if one with the given name does + not exist. + await_registration_for: defaults to + `mlflow.tracking._model_registry.DEFAULT_AWAIT_MAX_SLEEP_SECONDS`. Number of + seconds to wait for the model version to finish being created and is in ``READY`` + status. By default, the function waits for five minutes. Specify 0 or None to skip + waiting. + pip_requirements: {{ pip_requirements }} + extra_pip_requirements: {{ extra_pip_requirements }} + save_model_kwargs: defaults to None. A dict of kwargs to pass to + `keras.Model.save` method. + metadata: Custom metadata dictionary passed to the model and stored in the MLmodel + file. + name: {{ name }} + params: {{ params }} + tags: {{ tags }} + model_type: {{ model_type }} + step: {{ step }} + model_id: {{ model_id }} + + .. code-block:: python + :caption: Example + + import keras + import mlflow + + model = keras.Sequential( + [ + keras.Input([28, 28, 3]), + keras.layers.Flatten(), + keras.layers.Dense(2), + ] + ) + with mlflow.start_run() as run: + mlflow.keras.log_model(model, name="model") + """ + return Model.log( + artifact_path=artifact_path, + name=name, + flavor=mlflow.keras, + model=model, + conda_env=conda_env, + registered_model_name=registered_model_name, + signature=signature, + input_example=input_example, + await_registration_for=await_registration_for, + pip_requirements=pip_requirements, + extra_pip_requirements=extra_pip_requirements, + save_model_kwargs=save_model_kwargs, + save_exported_model=save_exported_model, + metadata=metadata, + params=params, + tags=tags, + model_type=model_type, + step=step, + model_id=model_id, + ) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/keras/utils.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/keras/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..b7b675c159370622a0d268d7158515127d0e07b2 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/keras/utils.py @@ -0,0 +1,38 @@ +import numpy as np + +from mlflow.models import ModelSignature +from mlflow.types import Schema, TensorSpec + + +def get_model_signature(model): + def replace_none_in_shape(shape): + return [-1 if dim_size is None else dim_size for dim_size in shape] + + input_shape = model.input_shape + input_dtype = model.input_dtype + output_shape = model.output_shape + output_dtype = model.compute_dtype + + if isinstance(input_shape, list): + input_schema = Schema( + [ + TensorSpec(np.dtype(input_dtype), replace_none_in_shape(shape)) + for shape in input_shape + ] + ) + else: + input_schema = Schema( + [TensorSpec(np.dtype(input_dtype), replace_none_in_shape(input_shape))] + ) + if isinstance(output_shape, list): + output_schema = Schema( + [ + TensorSpec(np.dtype(output_dtype), replace_none_in_shape(shape)) + for shape in output_shape + ] + ) + else: + output_schema = Schema( + [TensorSpec(np.dtype(output_dtype), replace_none_in_shape(output_shape))] + ) + return ModelSignature(inputs=input_schema, outputs=output_schema) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/langchain/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/langchain/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..aa1ef84ad0fb567fac80fbd4dc3236cd7c920c49 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/langchain/__init__.py @@ -0,0 +1,24 @@ +from mlflow.langchain.autolog import autolog +from mlflow.langchain.constants import FLAVOR_NAME +from mlflow.version import IS_TRACING_SDK_ONLY + +__all__ = ["autolog", "FLAVOR_NAME"] + +# Import model logging APIs only if mlflow skinny or full package is installed, +# i.e., skip if only mlflow-tracing package is installed. +if not IS_TRACING_SDK_ONLY: + from mlflow.langchain.model import ( + _LangChainModelWrapper, + _load_pyfunc, + load_model, + log_model, + save_model, + ) + + __all__ += [ + "_LangChainModelWrapper", + "_load_pyfunc", + "load_model", + "log_model", + "save_model", + ] diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/langchain/__pycache__/__init__.cpython-312.pyc 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b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/langchain/api_request_parallel_processor.py new file mode 100644 index 0000000000000000000000000000000000000000..f90f57c617cddb9d22e0eaa1dbed7d110094c2dd --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/langchain/api_request_parallel_processor.py @@ -0,0 +1,330 @@ +# Based ons: https://github.com/openai/openai-cookbook/blob/6df6ceff470eeba26a56de131254e775292eac22/examples/api_request_parallel_processor.py +# Several changes were made to make it work with MLflow. +# Currently, only chat completion is supported. + +""" +API REQUEST PARALLEL PROCESSOR + +Using the LangChain API to process lots of text quickly takes some care. +If you trickle in a million API requests one by one, they'll take days to complete. +This script parallelizes requests using LangChain API. + +Features: +- Streams requests from file, to avoid running out of memory for giant jobs +- Makes requests concurrently, to maximize throughput +- Logs errors, to diagnose problems with requests +""" + +from __future__ import annotations + +import logging +import queue +import threading +import time +import traceback +from concurrent.futures import ThreadPoolExecutor +from dataclasses import dataclass +from typing import Any + +import langchain.chains +from langchain.callbacks.base import BaseCallbackHandler + +import mlflow +from mlflow.exceptions import MlflowException +from mlflow.langchain.utils.chat import ( + transform_request_json_for_chat_if_necessary, + try_transform_response_iter_to_chat_format, + try_transform_response_to_chat_format, +) +from mlflow.langchain.utils.serialization import convert_to_serializable +from mlflow.pyfunc.context import Context, get_prediction_context +from mlflow.tracing.utils import maybe_set_prediction_context + +_logger = logging.getLogger(__name__) + + +@dataclass +class StatusTracker: + """ + Stores metadata about the script's progress. Only one instance is created. + """ + + num_tasks_started: int = 0 + num_tasks_in_progress: int = 0 # script ends when this reaches 0 + num_tasks_succeeded: int = 0 + num_tasks_failed: int = 0 + num_api_errors: int = 0 # excluding rate limit errors, counted above + lock: threading.Lock = threading.Lock() + + def start_task(self): + with self.lock: + self.num_tasks_started += 1 + self.num_tasks_in_progress += 1 + + def complete_task(self, *, success: bool): + with self.lock: + self.num_tasks_in_progress -= 1 + if success: + self.num_tasks_succeeded += 1 + else: + self.num_tasks_failed += 1 + + def increment_num_api_errors(self): + with self.lock: + self.num_api_errors += 1 + + +@dataclass +class APIRequest: + """ + Stores an API request's inputs, outputs, and other metadata. Contains a method to make an API + call. + + Args: + index: The request's index in the tasks list + lc_model: The LangChain model to call + request_json: The request's input data + results: The list to append the request's output data to, it's a list of tuples + (index, response) + errors: A dictionary to store any errors that occur + convert_chat_responses: Whether to convert the model's responses to chat format + did_perform_chat_conversion: Whether the input data was converted to chat format + based on the model's type and input data. + stream: Whether the request is a stream request + prediction_context: The prediction context to use for the request + """ + + index: int + lc_model: langchain.chains.base.Chain + request_json: dict[str, Any] + results: list[tuple[int, str]] + errors: dict[int, str] + convert_chat_responses: bool + did_perform_chat_conversion: bool + stream: bool + params: dict[str, Any] + prediction_context: Context | None = None + + def _predict_single_input(self, single_input, callback_handlers, **kwargs): + config = kwargs.pop("config", {}) + config["callbacks"] = config.get("callbacks", []) + (callback_handlers or []) + if self.stream: + return self.lc_model.stream(single_input, config=config, **kwargs) + if hasattr(self.lc_model, "invoke"): + return self.lc_model.invoke(single_input, config=config, **kwargs) + else: + # for backwards compatibility, __call__ is deprecated and will be removed in 0.3.0 + # kwargs shouldn't have config field if invoking with __call__ + return self.lc_model(single_input, callbacks=callback_handlers, **kwargs) + + def _try_convert_response(self, response): + if self.stream: + return try_transform_response_iter_to_chat_format(response) + else: + return try_transform_response_to_chat_format(response) + + def single_call_api(self, callback_handlers: list[BaseCallbackHandler] | None): + from langchain.schema import BaseRetriever + + from mlflow.langchain.utils.logging import langgraph_types, lc_runnables_types + + if isinstance(self.lc_model, BaseRetriever): + # Retrievers are invoked differently than Chains + response = self.lc_model.get_relevant_documents( + **self.request_json, callbacks=callback_handlers, **self.params + ) + elif isinstance(self.lc_model, lc_runnables_types() + langgraph_types()): + if isinstance(self.request_json, dict): + # This is a temporary fix for the case when spark_udf converts + # input into pandas dataframe with column name, while the model + # does not accept dictionaries as input, it leads to errors like + # Expected Scalar value for String field 'query_text' + try: + response = self._predict_single_input( + self.request_json, callback_handlers, **self.params + ) + except TypeError as e: + _logger.debug( + f"Failed to invoke {self.lc_model.__class__.__name__} " + f"with {self.request_json}. Error: {e!r}. Trying to " + "invoke with the first value of the dictionary." + ) + self.request_json = next(iter(self.request_json.values())) + ( + prepared_request_json, + did_perform_chat_conversion, + ) = transform_request_json_for_chat_if_necessary( + self.request_json, self.lc_model + ) + self.did_perform_chat_conversion = did_perform_chat_conversion + + response = self._predict_single_input( + prepared_request_json, callback_handlers, **self.params + ) + else: + response = self._predict_single_input( + self.request_json, callback_handlers, **self.params + ) + + if self.did_perform_chat_conversion or self.convert_chat_responses: + response = self._try_convert_response(response) + else: + # return_only_outputs is invalid for stream call + if isinstance(self.lc_model, langchain.chains.base.Chain) and not self.stream: + kwargs = {"return_only_outputs": True} + else: + kwargs = {} + kwargs.update(**self.params) + response = self._predict_single_input(self.request_json, callback_handlers, **kwargs) + + if self.did_perform_chat_conversion or self.convert_chat_responses: + response = self._try_convert_response(response) + elif isinstance(response, dict) and len(response) == 1: + # to maintain existing code, single output chains will still return + # only the result + response = response.popitem()[1] + + return convert_to_serializable(response) + + def call_api( + self, status_tracker: StatusTracker, callback_handlers: list[BaseCallbackHandler] | None + ): + """ + Calls the LangChain API and stores results. + """ + _logger.debug(f"Request #{self.index} started with payload: {self.request_json}") + + try: + with maybe_set_prediction_context(self.prediction_context): + response = self.single_call_api(callback_handlers) + _logger.debug(f"Request #{self.index} succeeded with response: {response}") + self.results.append((self.index, response)) + status_tracker.complete_task(success=True) + except Exception as e: + self.errors[self.index] = ( + f"error: {e!r} {traceback.format_exc()}\n request payload: {self.request_json}" + ) + status_tracker.increment_num_api_errors() + status_tracker.complete_task(success=False) + + +def process_api_requests( + lc_model, + requests: list[Any | dict[str, Any]] | None = None, + max_workers: int = 10, + callback_handlers: list[BaseCallbackHandler] | None = None, + convert_chat_responses: bool = False, + params: dict[str, Any] | None = None, + context: Context | None = None, +): + """ + Processes API requests in parallel. + """ + + # initialize trackers + retry_queue = queue.Queue() + status_tracker = StatusTracker() # single instance to track a collection of variables + next_request = None # variable to hold the next request to call + context = context or get_prediction_context() + + results = [] + errors = {} + + # Note: we should call `transform_request_json_for_chat_if_necessary` + # for the whole batch data, because the conversion should obey the rule + # that if any record in the batch can't be converted, then all the record + # in this batch can't be converted. + ( + converted_chat_requests, + did_perform_chat_conversion, + ) = transform_request_json_for_chat_if_necessary(requests, lc_model) + + requests_iter = enumerate(converted_chat_requests) + with ThreadPoolExecutor( + max_workers=max_workers, thread_name_prefix="MlflowLangChainApi" + ) as executor: + while True: + # get next request (if one is not already waiting for capacity) + if not retry_queue.empty(): + next_request = retry_queue.get_nowait() + _logger.warning(f"Retrying request {next_request.index}: {next_request}") + elif req := next(requests_iter, None): + # get new request + index, converted_chat_request_json = req + next_request = APIRequest( + index=index, + lc_model=lc_model, + request_json=converted_chat_request_json, + results=results, + errors=errors, + convert_chat_responses=convert_chat_responses, + did_perform_chat_conversion=did_perform_chat_conversion, + stream=False, + prediction_context=context, + params=params, + ) + status_tracker.start_task() + else: + next_request = None + + # if enough capacity available, call API + if next_request: + # call API + executor.submit( + next_request.call_api, + status_tracker=status_tracker, + callback_handlers=callback_handlers, + ) + + # if all tasks are finished, break + # check next_request to avoid terminating the process + # before extra requests need to be processed + if status_tracker.num_tasks_in_progress == 0 and next_request is None: + break + + time.sleep(0.001) # avoid busy waiting + + # after finishing, log final status + if status_tracker.num_tasks_failed > 0: + raise mlflow.MlflowException( + f"{status_tracker.num_tasks_failed} tasks failed. Errors: {errors}" + ) + + return [res for _, res in sorted(results)] + + +def process_stream_request( + lc_model, + request_json: Any | dict[str, Any], + callback_handlers: list[BaseCallbackHandler] | None = None, + convert_chat_responses: bool = False, + params: dict[str, Any] | None = None, +): + """ + Process single stream request. + """ + if not hasattr(lc_model, "stream"): + raise MlflowException( + f"Model {lc_model.__class__.__name__} does not support streaming prediction output. " + "No `stream` method found." + ) + + ( + converted_chat_requests, + did_perform_chat_conversion, + ) = transform_request_json_for_chat_if_necessary(request_json, lc_model) + + api_request = APIRequest( + index=0, + lc_model=lc_model, + request_json=converted_chat_requests, + results=None, + errors=None, + convert_chat_responses=convert_chat_responses, + did_perform_chat_conversion=did_perform_chat_conversion, + stream=True, + prediction_context=get_prediction_context(), + params=params, + ) + with maybe_set_prediction_context(api_request.prediction_context): + return api_request.single_call_api(callback_handlers) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/langchain/autolog.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/langchain/autolog.py new file mode 100644 index 0000000000000000000000000000000000000000..92f66d8c7042adaa72ca663a3d9e3ad332394129 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/langchain/autolog.py @@ -0,0 +1,145 @@ +import logging + +from mlflow.langchain.constant import FLAVOR_NAME +from mlflow.utils.autologging_utils import autologging_integration +from mlflow.utils.autologging_utils.config import AutoLoggingConfig +from mlflow.utils.autologging_utils.safety import safe_patch + +logger = logging.getLogger(__name__) + + +@autologging_integration(FLAVOR_NAME) +def autolog( + disable=False, + exclusive=False, + disable_for_unsupported_versions=False, + silent=False, + log_traces=True, +): + """ + Enables (or disables) and configures autologging from Langchain to MLflow. + + Args: + disable: If ``True``, disables the Langchain autologging integration. If ``False``, + enables the Langchain autologging integration. + exclusive: If ``True``, autologged content is not logged to user-created fluent runs. + If ``False``, autologged content is logged to the active fluent run, + which may be user-created. + disable_for_unsupported_versions: If ``True``, disable autologging for versions of + langchain that have not been tested against this version of the MLflow + client or are incompatible. + silent: If ``True``, suppress all event logs and warnings from MLflow during Langchain + autologging. If ``False``, show all events and warnings during Langchain + autologging. + log_traces: If ``True``, traces are logged for Langchain models by using + MlflowLangchainTracer as a callback during inference. If ``False``, no traces are + collected during inference. Default to ``True``. + """ + try: + from langchain_core.callbacks import BaseCallbackManager + + safe_patch( + FLAVOR_NAME, + BaseCallbackManager, + "__init__", + _patched_callback_manager_init, + ) + except Exception as e: + logger.warning(f"Failed to enable tracing for LangChain. Error: {e}") + + # Special handlings for edge cases. + try: + from langchain_core.callbacks import BaseCallbackManager + from langchain_core.runnables import RunnableSequence + + safe_patch( + FLAVOR_NAME, + RunnableSequence, + "batch", + _patched_runnable_sequence_batch, + ) + + safe_patch( + FLAVOR_NAME, + BaseCallbackManager, + "merge", + _patched_callback_manager_merge, + ) + except Exception: + logger.debug("Failed to patch RunnableSequence or BaseCallbackManager.", exc_info=True) + + +def _patched_callback_manager_init(original, self, *args, **kwargs): + from mlflow.langchain.langchain_tracer import MlflowLangchainTracer + + original(self, *args, **kwargs) + + if not AutoLoggingConfig.init(FLAVOR_NAME).log_traces: + return + + for handler in self.inheritable_handlers: + if isinstance(handler, MlflowLangchainTracer): + return + + _handler = MlflowLangchainTracer() + self.add_handler(_handler, inherit=True) + + +def _patched_callback_manager_merge(original, self, *args, **kwargs): + """ + Patch BaseCallbackManager.merge to avoid a duplicated callback issue. + + In the above patched __init__, we check `inheritable_handlers` to see if the MLflow tracer + is already propagated. This works when the `inheritable_handlers` is specified as constructor + arguments. However, in the `merge` method, LangChain does not use constructor but set + callbacks via the setter method. This causes duplicated callbacks injection. + https://github.com/langchain-ai/langchain/blob/d9a069c414a321e7a3f3638a32ecf8a37ec2d188/libs/core/langchain_core/callbacks/base.py#L962-L982 + """ + from mlflow.langchain.langchain_tracer import MlflowLangchainTracer + + # Get the MLflow callback inherited from parent + inherited = self.inheritable_handlers + args[0].inheritable_handlers + inherited_mlflow_cb = next( + (cb for cb in inherited if isinstance(cb, MlflowLangchainTracer)), None + ) + + if not inherited_mlflow_cb: + return original(self, *args, **kwargs) + + merged = original(self, *args, **kwargs) + # If a new MLflow callback is generated inside __init__, remove it + duplicate_mlflow_cbs = [ + cb + for cb in merged.inheritable_handlers + if isinstance(cb, MlflowLangchainTracer) and cb != inherited_mlflow_cb + ] + for cb in duplicate_mlflow_cbs: + merged.remove_handler(cb) + + return merged + + +def _patched_runnable_sequence_batch(original, self, *args, **kwargs): + """ + Patch to terminate span context attachment during batch execution. + + RunnableSequence's batch() methods are implemented in a peculiar way + that iterates on steps->items sequentially within the same thread. For example, if a + sequence has 2 steps and the batch size is 3, the execution flow will be: + - Step 1 for item 1 + - Step 1 for item 2 + - Step 1 for item 3 + - Step 2 for item 1 + - Step 2 for item 2 + - Step 2 for item 3 + Due to this behavior, we cannot attach the span to the context for this particular + API, otherwise spans for different inputs will be mixed up. + """ + from mlflow.langchain.langchain_tracer import _should_attach_span_to_context + + original_state = _should_attach_span_to_context.get() + _should_attach_span_to_context.set(False) + try: + return original(self, *args, **kwargs) + finally: + _should_attach_span_to_context.set(original_state) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/langchain/chat_agent_langgraph.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/langchain/chat_agent_langgraph.py new file mode 100644 index 0000000000000000000000000000000000000000..061c5cbdfea7d313d98c6ad2c5c0b6cffab5a2d5 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/langchain/chat_agent_langgraph.py @@ -0,0 +1,342 @@ +from __future__ import annotations + +import importlib.metadata +import json +from typing import Annotated, Any, TypedDict +from uuid import uuid4 + +from packaging.version import Version + +try: + from langchain_core.messages import AnyMessage, BaseMessage, convert_to_messages + from langchain_core.runnables import RunnableConfig + from langchain_core.runnables.utils import Input + + try: + # LangGraph >= 0.3 + from langgraph.prebuilt import ToolNode + except ImportError as e: + # If LangGraph 0.3.x is installed but langgraph_prebuilt is not, + # show a friendlier error message + if Version(importlib.metadata.version("langgraph")) >= Version("0.3.0"): + raise ImportError( + "Please install `langgraph-prebuilt>=0.1.2` to use MLflow LangGraph ChatAgent " + "helpers with LangGraph 0.3.x.\n" + "If you already have the proper versions installed, please try running " + "`pip install --force-reinstall langgraph`. This is a known issue. See: " + "https://github.com/langchain-ai/langgraph/issues/3662" + ) from e + + # LangGraph < 0.3 + from langgraph.prebuilt.tool_node import ToolNode + +except ImportError as e: + raise ImportError( + "Please install `langchain>=0.2.17` and `langgraph>=0.2.0` to use LangGraph ChatAgent" + "helpers." + ) from e + + +from mlflow.langchain.utils.chat import convert_lc_message_to_chat_message +from mlflow.types.agent import ChatAgentMessage +from mlflow.utils.annotations import experimental + + +def _add_agent_messages( + left: dict[str, Any] | list[dict[str, Any]], + right: dict[str, Any] | list[dict[str, Any]], +): + if not isinstance(left, list): + left = [left] + if not isinstance(right, list): + right = [right] + # assign missing ids + for i, m in enumerate(left): + if isinstance(m, BaseMessage): + left[i] = parse_message(m) + if left[i].get("id") is None: + left[i]["id"] = str(uuid4()) + + for i, m in enumerate(right): + if isinstance(m, BaseMessage): + right[i] = parse_message(m) + if right[i].get("id") is None: + right[i]["id"] = str(uuid4()) + + # merge + left_idx_by_id = {m.get("id"): i for i, m in enumerate(left)} + merged = left.copy() + for m in right: + if (existing_idx := left_idx_by_id.get(m.get("id"))) is not None: + merged[existing_idx] = m + else: + merged.append(m) + return merged + + +@experimental(version="2.21.0") +class ChatAgentState(TypedDict): + """ + Helper class that enables building a LangGraph agent that produces ChatAgent-compatible + messages as state is updated. Other ChatAgent request fields (custom_inputs, context) and + response fields (custom_outputs) are also exposed within the state so they can be used and + updated over the course of agent execution. Use this class with + :py:class:`ChatAgentToolNode `. + + **LangGraph ChatAgent Example** + + This example has been tested to work with LangGraph 0.2.70. + + Step 1: Create the LangGraph Agent + + This example is adapted from LangGraph's + `create_react_agent `__ + documentation. The notable differences are changes to be ChatAgent compatible. They include: + + - We use :py:class:`ChatAgentState `, + which has an internal state of + :py:class:`ChatAgentMessage ` + objects and a ``custom_outputs`` attribute under the hood + - We use :py:class:`ChatAgentToolNode ` + instead of LangGraph's ToolNode to enable returning attachments and custom_outputs from + LangChain and UnityCatalog Tools + + .. code-block:: python + + from typing import Optional, Sequence, Union + + from langchain_core.language_models import LanguageModelLike + from langchain_core.runnables import RunnableConfig, RunnableLambda + from langchain_core.tools import BaseTool + from langgraph.graph import END, StateGraph + from langgraph.graph.state import CompiledStateGraph + from langgraph.prebuilt import ToolNode + from mlflow.langchain.chat_agent_langgraph import ChatAgentState, ChatAgentToolNode + + + def create_tool_calling_agent( + model: LanguageModelLike, + tools: Union[ToolNode, Sequence[BaseTool]], + agent_prompt: Optional[str] = None, + ) -> CompiledStateGraph: + model = model.bind_tools(tools) + + def routing_logic(state: ChatAgentState): + last_message = state["messages"][-1] + if last_message.get("tool_calls"): + return "continue" + else: + return "end" + + if agent_prompt: + system_message = {"role": "system", "content": agent_prompt} + preprocessor = RunnableLambda( + lambda state: [system_message] + state["messages"] + ) + else: + preprocessor = RunnableLambda(lambda state: state["messages"]) + model_runnable = preprocessor | model + + def call_model( + state: ChatAgentState, + config: RunnableConfig, + ): + response = model_runnable.invoke(state, config) + + return {"messages": [response]} + + workflow = StateGraph(ChatAgentState) + + workflow.add_node("agent", RunnableLambda(call_model)) + workflow.add_node("tools", ChatAgentToolNode(tools)) + + workflow.set_entry_point("agent") + workflow.add_conditional_edges( + "agent", + routing_logic, + { + "continue": "tools", + "end": END, + }, + ) + workflow.add_edge("tools", "agent") + + return workflow.compile() + + Step 2: Define the LLM and your tools + + If you want to return attachments and custom_outputs from your tool, you can return a + dictionary with keys "content", "attachments", and "custom_outputs". This dictionary will be + parsed out by the ChatAgentToolNode and properly stored in your LangGraph's state. + + + .. code-block:: python + + from random import randint + from typing import Any + + from databricks_langchain import ChatDatabricks + from langchain_core.tools import tool + + + @tool + def generate_random_ints(min: int, max: int, size: int) -> dict[str, Any]: + \"""Generate size random ints in the range [min, max].\""" + attachments = {"min": min, "max": max} + custom_outputs = [randint(min, max) for _ in range(size)] + content = f"Successfully generated array of {size} random ints in [{min}, {max}]." + return { + "content": content, + "attachments": attachments, + "custom_outputs": {"random_nums": custom_outputs}, + } + + + mlflow.langchain.autolog() + tools = [generate_random_ints] + llm = ChatDatabricks(endpoint="databricks-meta-llama-3-3-70b-instruct") + langgraph_agent = create_tool_calling_agent(llm, tools) + + + Step 3: Wrap your LangGraph agent with ChatAgent + + This makes your agent easily loggable and deployable with the PyFunc flavor in serving. + + .. code-block:: python + + from typing import Any, Generator, Optional + + from langgraph.graph.state import CompiledStateGraph + from mlflow.pyfunc import ChatAgent + from mlflow.types.agent import ( + ChatAgentChunk, + ChatAgentMessage, + ChatAgentResponse, + ChatContext, + ) + + + class LangGraphChatAgent(ChatAgent): + def __init__(self, agent: CompiledStateGraph): + self.agent = agent + + def predict( + self, + messages: list[ChatAgentMessage], + context: Optional[ChatContext] = None, + custom_inputs: Optional[dict[str, Any]] = None, + ) -> ChatAgentResponse: + request = {"messages": self._convert_messages_to_dict(messages)} + + messages = [] + for event in self.agent.stream(request, stream_mode="updates"): + for node_data in event.values(): + messages.extend( + ChatAgentMessage(**msg) for msg in node_data.get("messages", []) + ) + return ChatAgentResponse(messages=messages) + + def predict_stream( + self, + messages: list[ChatAgentMessage], + context: Optional[ChatContext] = None, + custom_inputs: Optional[dict[str, Any]] = None, + ) -> Generator[ChatAgentChunk, None, None]: + request = {"messages": self._convert_messages_to_dict(messages)} + for event in self.agent.stream(request, stream_mode="updates"): + for node_data in event.values(): + yield from ( + ChatAgentChunk(**{"delta": msg}) for msg in node_data["messages"] + ) + + + chat_agent = LangGraphChatAgent(langgraph_agent) + + Step 4: Test out your model + + Call ``.predict()`` and ``.predict_stream`` with dictionaries with the ChatAgentRequest schema. + + .. code-block:: python + + chat_agent.predict({"messages": [{"role": "user", "content": "What is 10 + 10?"}]}) + + for event in chat_agent.predict_stream( + {"messages": [{"role": "user", "content": "Generate me a few random nums"}]} + ): + print(event) + + This LangGraph ChatAgent can be logged with the logging code described in the "Logging a + ChatAgent" section of the docstring of :py:class:`ChatAgent `. + """ + + messages: Annotated[list[dict[str, Any]], _add_agent_messages] + context: dict[str, Any] | None + custom_inputs: dict[str, Any] | None + custom_outputs: dict[str, Any] | None + + +def parse_message( + msg: AnyMessage, name: str | None = None, attachments: dict[str, Any] | None = None +) -> dict[str, Any]: + """ + Parse different LangChain message types into their ChatAgentMessage schema dict equivalents + """ + chat_message_dict = convert_lc_message_to_chat_message(msg).model_dump_compat() + chat_message_dict["attachments"] = attachments + chat_message_dict["name"] = msg.name or name + chat_message_dict["id"] = msg.id + # _convert_to_message from langchain_core.messages.utils expects an empty string instead of None + if not chat_message_dict.get("content"): + chat_message_dict["content"] = "" + + chat_agent_msg = ChatAgentMessage(**chat_message_dict) + return chat_agent_msg.model_dump_compat(exclude_none=True) + + +@experimental(version="2.21.0") +class ChatAgentToolNode(ToolNode): + """ + Helper class to make ToolNodes be compatible with + :py:class:`ChatAgentState `. + Parse ``attachments`` and ``custom_outputs`` keys from the string output of a + LangGraph tool. + """ + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + def invoke(self, input: Input, config: RunnableConfig | None = None, **kwargs: Any) -> Any: + """ + Wraps the standard ToolNode invoke method to: + - Parse ChatAgentState into LangChain messages + - Parse dictionary string outputs from both UC function and standard LangChain python tools + that include keys ``content``, ``attachments``, and ``custom_outputs``. + """ + messages = input["messages"] + for msg in messages: + for tool_call in msg.get("tool_calls", []): + tool_call["name"] = tool_call["function"]["name"] + tool_call["args"] = json.loads(tool_call["function"]["arguments"]) + input["messages"] = convert_to_messages(messages) + + result = super().invoke(input, config, **kwargs) + + messages = [] + custom_outputs = None + for m in result["messages"]: + try: + return_obj = json.loads(m.content) + if all(key in return_obj for key in ("format", "value", "truncated")): + # Dictionary output with custom_outputs and attachments from a UC function + try: + return_obj = json.loads(return_obj["value"]) + except Exception: + pass + if "custom_outputs" in return_obj: + custom_outputs = return_obj["custom_outputs"] + if m.id is None: + m.id = str(uuid4()) + messages.append(parse_message(m, attachments=return_obj.get("attachments"))) + except Exception: + messages.append(parse_message(m)) + return {"messages": messages, "custom_outputs": custom_outputs} diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/langchain/constant.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/langchain/constant.py new file mode 100644 index 0000000000000000000000000000000000000000..3c6de1eb9ff4175f84fa7b34859ce53a1f7f5b2c --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/langchain/constant.py @@ -0,0 +1 @@ +FLAVOR_NAME = "langchain" diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/langchain/constants.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/langchain/constants.py new file mode 100644 index 0000000000000000000000000000000000000000..3c6de1eb9ff4175f84fa7b34859ce53a1f7f5b2c --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/langchain/constants.py @@ -0,0 +1 @@ +FLAVOR_NAME = "langchain" diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/langchain/databricks_dependencies.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/langchain/databricks_dependencies.py new file mode 100644 index 0000000000000000000000000000000000000000..284b322a0798e62859019012f329f9ff743cb6bc --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/langchain/databricks_dependencies.py @@ -0,0 +1,444 @@ +import importlib +import inspect +import logging +import warnings +from typing import Any, Generator + +from packaging import version + +from mlflow.models.resources import ( + DatabricksFunction, + DatabricksServingEndpoint, + DatabricksSQLWarehouse, + DatabricksVectorSearchIndex, + Resource, +) + +_logger = logging.getLogger(__name__) + + +def _get_embedding_model_endpoint_names(index): + embedding_model_endpoint_names = [] + desc = index.describe() + delta_sync_index_spec = desc.get("delta_sync_index_spec", {}) + embedding_source_columns = delta_sync_index_spec.get("embedding_source_columns", []) + for column in embedding_source_columns: + embedding_model_endpoint_name = column.get("embedding_model_endpoint_name", None) + if embedding_model_endpoint_name: + embedding_model_endpoint_names.append(embedding_model_endpoint_name) + return embedding_model_endpoint_names + + +def _get_vectorstore_from_retriever(retriever) -> Generator[Resource, None, None]: + vectorstore = getattr(retriever, "vectorstore", None) + if _isinstance_with_multiple_modules( + vectorstore, + "DatabricksVectorSearch", + [ + "databricks_langchain", + "langchain_databricks", + "langchain_community.vectorstores", + "langchain.vectorstores", + ], + ): + index = vectorstore.index + yield DatabricksVectorSearchIndex(index_name=index.name) + for embedding_endpoint in _get_embedding_model_endpoint_names(index): + yield DatabricksServingEndpoint(endpoint_name=embedding_endpoint) + + embeddings = getattr(vectorstore, "embeddings", None) + if _isinstance_with_multiple_modules( + embeddings, + "DatabricksEmbeddings", + [ + "databricks_langchain", + "langchain_databricks", + "langchain_community.embeddings", + "langchain.embeddings", + ], + ): + yield DatabricksServingEndpoint(endpoint_name=embeddings.endpoint) + + +def _is_langchain_community_uc_function_toolkit(obj): + try: + from langchain_community.tools.databricks import UCFunctionToolkit + except Exception: + return False + + return isinstance(obj, UCFunctionToolkit) + + +def _is_unitycatalog_tool(obj): + try: + from unitycatalog.ai.langchain.toolkit import UnityCatalogTool + except Exception: + return False + + return isinstance(obj, UnityCatalogTool) + + +def _extract_databricks_dependencies_from_tools(tools) -> Generator[Resource, None, None]: + if isinstance(tools, list): + warehouse_ids = set() + for tool in tools: + if _isinstance_with_multiple_modules( + tool, "BaseTool", ["langchain_core.tools", "langchain_community.tools"] + ): + # Handle Retriever tools + if hasattr(tool.func, "keywords") and "retriever" in tool.func.keywords: + retriever = tool.func.keywords.get("retriever") + yield from _get_vectorstore_from_retriever(retriever) + elif _is_unitycatalog_tool(tool): + if warehouse_id := tool.client_config.get("warehouse_id"): + warehouse_ids.add(warehouse_id) + yield DatabricksFunction(function_name=tool.uc_function_name) + else: + # Tools here are a part of the BaseTool and have no attribute of a + # WarehouseID Extract the global variables of the function defined + # in the tool to get the UCFunctionToolkit Constants + nonlocal_vars = inspect.getclosurevars(tool.func).nonlocals + if "self" in nonlocal_vars and _is_langchain_community_uc_function_toolkit( + nonlocal_vars.get("self") + ): + uc_function_toolkit = nonlocal_vars.get("self") + # As we are iterating through each tool, adding a warehouse id everytime + # is a duplicative resource. Use a set to dedup warehouse ids and add + # them in the end + warehouse_ids.add(uc_function_toolkit.warehouse_id) + + # In langchain the names of the tools are modified to have underscores: + # main.catalog.test_func -> main_catalog_test_func + # The original name of the tool is stored as the key in the tools + # dictionary. This code finds the correct tool and extract the key + langchain_tool_name = tool.name + filtered_tool_names = [ + tool_name + for tool_name, uc_tool in uc_function_toolkit.tools.items() + if uc_tool.name == langchain_tool_name + ] + # This should always have the length 1 + for tool_name in filtered_tool_names: + yield DatabricksFunction(function_name=tool_name) + # Add the deduped warehouse ids + for warehouse_id in warehouse_ids: + yield DatabricksSQLWarehouse(warehouse_id=warehouse_id) + + +def _extract_databricks_dependencies_from_retriever(retriever) -> Generator[Resource, None, None]: + # ContextualCompressionRetriever uses attribute "base_retriever" + if hasattr(retriever, "base_retriever"): + retriever = getattr(retriever, "base_retriever", None) + + # Most other retrievers use attribute "retriever" + if hasattr(retriever, "retriever"): + retriever = getattr(retriever, "retriever", None) + + # EnsembleRetriever uses attribute "retrievers" for multiple retrievers + if hasattr(retriever, "retrievers"): + retriever = getattr(retriever, "retrievers", None) + + # If there are multiple retrievers, we iterate over them to get dependencies from each of them + if isinstance(retriever, list): + for single_retriever in retriever: + yield from _get_vectorstore_from_retriever(single_retriever) + else: + yield from _get_vectorstore_from_retriever(retriever) + + +def _extract_databricks_dependencies_from_llm(llm) -> Generator[Resource, None, None]: + if _isinstance_with_multiple_modules( + llm, "Databricks", ["langchain.llms", "langchain_community.llms"] + ): + yield DatabricksServingEndpoint(endpoint_name=llm.endpoint_name) + + +def _extract_databricks_dependencies_from_chat_model(chat_model) -> Generator[Resource, None, None]: + if _isinstance_with_multiple_modules( + chat_model, + "ChatDatabricks", + [ + "databricks_langchain", + "langchain_databricks", + "langchain.chat_models", + "langchain_community.chat_models", + ], + ): + yield DatabricksServingEndpoint(endpoint_name=chat_model.endpoint) + + +def _extract_databricks_dependencies_from_tool_nodes(tool_node) -> Generator[Resource, None, None]: + try: + try: + # LangGraph >= 0.3 + from langgraph.prebuilt import ToolNode + except ImportError: + # LangGraph < 0.3 + from langgraph.prebuilt.tool_node import ToolNode + + if isinstance(tool_node, ToolNode): + yield from _extract_databricks_dependencies_from_tools( + list(tool_node.tools_by_name.values()) + ) + except ImportError: + pass + + +def _isinstance_with_multiple_modules( + object: Any, class_name: str, from_modules: list[str] +) -> bool: + """ + Databricks components are defined in different modules in LangChain e.g. + langchain, langchain_community, databricks_langchain due to historical migrations. + To keep backward compatibility, we need to check if the object is an instance of the + class defined in any of those different modules. + + Args: + object: The object to check + class_name: The name of the class to check + from_modules: The list of modules to import the class from. + """ + # Suppress LangChainDeprecationWarning for old imports + with warnings.catch_warnings(): + warnings.simplefilter("ignore", DeprecationWarning) + + for module_path in from_modules: + try: + module = importlib.import_module(module_path) + cls = getattr(module, class_name) + + if cls is not None and isinstance(object, cls): + return True + except (ImportError, AttributeError): + pass + + return False + + +_LEGACY_MODEL_ATTR_SET = { + "llm", # LLMChain + "retriever", # RetrievalQA + "llm_chain", # StuffDocumentsChain, MapRerankDocumentsChain, MapReduceDocumentsChain + "question_generator", # BaseConversationalRetrievalChain + "initial_llm_chain", # RefineDocumentsChain + "refine_llm_chain", # RefineDocumentsChain + "combine_documents_chain", # RetrievalQA, ReduceDocumentsChain + "combine_docs_chain", # BaseConversationalRetrievalChain + "collapse_documents_chain", # ReduceDocumentsChain, + "agent", # Agent, + "tools", # Tools +} + + +def _extract_dependency_list_from_lc_model(lc_model) -> Generator[Resource, None, None]: + """ + This function contains the logic to examine a non-Runnable component of a langchain model. + The logic here does not cover all legacy chains. If you need to support a custom chain, + you need to monkey patch this function. + """ + if lc_model is None: + return + + # leaf node + yield from _extract_databricks_dependencies_from_chat_model(lc_model) + yield from _extract_databricks_dependencies_from_retriever(lc_model) + yield from _extract_databricks_dependencies_from_llm(lc_model) + yield from _extract_databricks_dependencies_from_tools(lc_model) + yield from _extract_databricks_dependencies_from_tool_nodes(lc_model) + + # recursively inspect legacy chain + for attr_name in _LEGACY_MODEL_ATTR_SET: + yield from _extract_dependency_list_from_lc_model(getattr(lc_model, attr_name, None)) + + +def _traverse_runnable( + lc_model, + visited: set[int] | None = None, +) -> Generator[Resource, None, None]: + """ + This function contains the logic to traverse a langchain_core.runnables.RunnableSerializable + object. It first inspects the current object using _extract_dependency_list_from_lc_model + and then, if the current object is a Runnable, it recursively inspects its children returned + by lc_model.get_graph().nodes.values(). + This function supports arbitrary LCEL chain. + """ + import pydantic + from langchain_core.runnables import Runnable, RunnableLambda + + visited = visited or set() + current_object_id = id(lc_model) + if current_object_id in visited: + return + + # Visit the current object + visited.add(current_object_id) + yield from _extract_dependency_list_from_lc_model(lc_model) + + if isinstance(lc_model, Runnable): + # Visit the returned graph + if isinstance(lc_model, RunnableLambda) and version.parse( + pydantic.version.VERSION + ) >= version.parse("2.0"): + nodes = _get_nodes_from_runnable_lambda(lc_model) + else: + nodes = _get_nodes_from_runnable_callable(lc_model) + # If no nodes are found continue with the default behaviour + if len(nodes) == 0: + nodes = lc_model.get_graph().nodes.values() + + for node in nodes: + yield from _traverse_runnable(node.data, visited) + else: + # No-op for non-runnable, if any + pass + + +def _get_deps_from_closures(lc_model): + """ + In some cases, the dependency extraction of Runnable Lambda fails because the call + `inspect.getsource(func)` can fail. This causes deps of RunnableLambda to be empty. + Therefore this method adds an additional way of getting dependencies through + closure variables. + + TODO: Remove when issue gets resolved: https://github.com/langchain-ai/langchain/issues/27970 + """ + if not hasattr(lc_model, "func"): + return [] + + try: + from langchain_core.runnables import Runnable + + closure = inspect.getclosurevars(lc_model.func) + candidates = {**closure.globals, **closure.nonlocals} + deps = [] + + # This code is taken from Langchain deps here: https://github.com/langchain-ai/langchain/blob/14f182795312f01985344576b5199681683641e1/libs/core/langchain_core/runnables/base.py#L4481 + for _, v in candidates.items(): + if isinstance(v, Runnable): + deps.append(v) + elif isinstance(getattr(v, "__self__", None), Runnable): + deps.append(v.__self__) + + return deps + except Exception: + return [] + + +def _get_nodes_from_runnable_lambda(lc_model): + """ + This is a workaround for the LangGraph issue: https://github.com/langchain-ai/langgraph/issues/1856 + + For RunnableLambda, we calling lc_model.get_graph() to get the nodes, which inspect + the input and output schema using wrapped function's type annotation. However, the + prebuilt graph (e.g. create_react_agent) from LangGraph uses typing.TypeDict annotation, + which is not supported by Pydantic V2 on Python < 3.12. If we try to inspect such + function, it will raise the following error: + + pydantic.errors.PydanticUserError: Please use `typing_extensions.TypedDict` + instead of`typing.TypedDict` on Python < 3.12. For further information visit + https://errors.pydantic.dev/2.9/u/typed-dict-version + + Therefore, we cannot use get_graph() for RunnableLambda until LangGraph fixes this issue. + Luckily, we are not interested in the input/output nodes for extracting databricks + dependencies. We only care about lc_models.deps, which contains the components that + the RunnableLambda depends on. Therefore, this function extracts the necessary parts + from the original get_graph() function, dropping the input/output related logic. + https://github.com/langchain-ai/langchain/blob/2ea5f60cc5747a334550273a5dba1b70b11414c1/libs/core/langchain_core/runnables/base.py#L4493C1-L4512C46 + """ + + if deps := lc_model.deps or _get_deps_from_closures(lc_model): + nodes = [] + for dep in deps: + dep_graph = dep.get_graph() + dep_graph.trim_first_node() + dep_graph.trim_last_node() + nodes.extend(dep_graph.nodes.values()) + else: + nodes = lc_model.get_graph().nodes.values() + return nodes + + +def _get_nodes_from_runnable_callable(lc_model): + """ + RunnableLambda has a `deps` property which goes through the function and extracts a + ny dependencies. RunnableCallable does not have this property so we cannot derive all + the dependencies from the function. This helper method also looks into the function of the + callable to retrieve these dependencies. + + The code here is from: https://github.com/langchain-ai/langchain/blob/12fea5b868edd12b0d576e7f8bfc922d0167eeab/libs/core/langchain_core/runnables/base.py#L4467 + """ + + # If Runnable Callable is not importable or if the lc_model is not an instance + # of RunnableCallable return early + try: + from langchain_core.runnables import Runnable + from langchain_core.runnables.utils import get_function_nonlocals + from langgraph.utils.runnable import RunnableCallable + + if not isinstance(lc_model, RunnableCallable): + return [] + except ImportError: + return [] + + if hasattr(lc_model, "func"): + objects = get_function_nonlocals(lc_model.func) + elif hasattr(lc_model, "afunc"): + objects = get_function_nonlocals(lc_model.afunc) + else: + objects = [] + + deps = [] + for obj in objects: + if isinstance(obj, Runnable): + deps.append(obj) + elif isinstance(getattr(obj, "__self__", None), Runnable): + deps.append(obj.__self__) + + nodes = [] + for dep in deps: + dep_graph = dep.get_graph() + dep_graph.trim_first_node() + dep_graph.trim_last_node() + nodes.extend(dep_graph.nodes.values()) + return nodes + + +def _detect_databricks_dependencies(lc_model, log_errors_as_warnings=True) -> list[Resource]: + """ + Detects the databricks dependencies of a langchain model and returns a list of + detected endpoint names and index names. + + lc_model can be an arbitrary `chain that is built with LCEL `_, + which is a langchain_core.runnables.RunnableSerializable. + `Legacy chains `_ have limited + support. Only RetrievalQA, StuffDocumentsChain, ReduceDocumentsChain, RefineDocumentsChain, + MapRerankDocumentsChain, MapReduceDocumentsChain, BaseConversationalRetrievalChain are + supported. If you need to support a custom chain, you need to monkey patch + the function mlflow.langchain.databricks_dependencies._extract_dependency_list_from_lc_model(). + + For an LCEL chain, all the langchain_core.runnables.RunnableSerializable nodes will be + traversed. + + If a retriever is found, it will be used to extract the databricks vector search and embeddings + dependencies. + If an llm is found, it will be used to extract the databricks llm dependencies. + If a chat_model is found, it will be used to extract the databricks chat dependencies. + """ + try: + dependency_list = list(_traverse_runnable(lc_model)) + # Filter out duplicate dependencies so same dependencies are not added multiple times + # We can't use set here as the object is not hashable so we need to filter it out manually. + unique_dependencies = [] + for dependency in dependency_list: + if dependency not in unique_dependencies: + unique_dependencies.append(dependency) + return unique_dependencies + except Exception: + if log_errors_as_warnings: + _logger.warning( + "Unable to detect Databricks dependencies. " + "Set logging level to DEBUG to see the full traceback." + ) + _logger.debug("", exc_info=True) + return [] + raise diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/langchain/langchain_tracer.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/langchain/langchain_tracer.py new file mode 100644 index 0000000000000000000000000000000000000000..3a8fae391d025b24f00d092cb0488025e35ab98e --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/langchain/langchain_tracer.py @@ -0,0 +1,576 @@ +import ast +import logging +from contextvars import ContextVar +from typing import Any, Optional, Sequence +from uuid import UUID + +import pydantic +from langchain_core.agents import AgentAction, AgentFinish +from langchain_core.callbacks.base import BaseCallbackHandler +from langchain_core.documents import Document +from langchain_core.load.dump import dumps +from langchain_core.messages import BaseMessage +from langchain_core.outputs import ( + ChatGenerationChunk, + GenerationChunk, + LLMResult, +) +from tenacity import RetryCallState + +import mlflow +from mlflow.entities import Document as MlflowDocument +from mlflow.entities import LiveSpan, SpanEvent, SpanStatus, SpanStatusCode, SpanType +from mlflow.entities.span import NO_OP_SPAN_TRACE_ID +from mlflow.exceptions import MlflowException +from mlflow.langchain.utils.chat import parse_token_usage +from mlflow.tracing.constant import SpanAttributeKey +from mlflow.tracing.fluent import start_span_no_context +from mlflow.tracing.provider import detach_span_from_context, set_span_in_context +from mlflow.tracing.utils import maybe_set_prediction_context, set_span_chat_tools +from mlflow.tracing.utils.token import SpanWithToken +from mlflow.types.chat import ChatTool, FunctionToolDefinition +from mlflow.utils import IS_PYDANTIC_V2_OR_NEWER +from mlflow.utils.autologging_utils import ExceptionSafeAbstractClass +from mlflow.version import IS_TRACING_SDK_ONLY + +if not IS_TRACING_SDK_ONLY: + from mlflow.pyfunc.context import Context + + +_logger = logging.getLogger(__name__) + +_should_attach_span_to_context = ContextVar("should_attach_span_to_context", default=True) + + +class MlflowLangchainTracer(BaseCallbackHandler, metaclass=ExceptionSafeAbstractClass): + """ + Callback for auto-logging traces. + We need to inherit ExceptionSafeAbstractClass to avoid invalid new + input arguments added to original function call. + + Args: + prediction_context: Optional prediction context object to be set for the + thread-local context. Occasionally this has to be passed manually because + the callback may be invoked asynchronously and Langchain doesn't correctly + propagate the thread-local context. + """ + + def __init__( + self, + prediction_context: Optional["Context"] = None, + ): + # NB: The tracer can handle multiple traces in parallel under multi-threading scenarios. + # DO NOT use instance variables to manage the state of single trace. + super().__init__() + # run_id: (LiveSpan, OTel token) + self._run_span_mapping: dict[str, SpanWithToken] = {} + self._prediction_context = prediction_context + + def _get_span_by_run_id(self, run_id: UUID) -> LiveSpan | None: + if span_with_token := self._run_span_mapping.get(str(run_id), None): + return span_with_token.span + raise MlflowException(f"Span for run_id {run_id!s} not found.") + + def _serialize_invocation_params( + self, attributes: dict[str, Any] | None + ) -> dict[str, Any] | None: + """ + Serialize the 'invocation_params' in the attributes dictionary. + If 'invocation_params' contains a key 'response_format' whose value is a subclass + of pydantic.BaseModel, replace it with its JSON schema. + """ + if not attributes: + return attributes + + invocation_params = attributes.get("invocation_params") + if not isinstance(invocation_params, dict): + return attributes + + response_format = invocation_params.get("response_format") + if isinstance(response_format, type) and issubclass(response_format, pydantic.BaseModel): + try: + invocation_params["response_format"] = ( + response_format.model_json_schema() + if IS_PYDANTIC_V2_OR_NEWER + else response_format.schema() + ) + except Exception as e: + _logger.error( + "Failed to generate JSON schema for response_format: %s", e, exc_info=True + ) + return attributes + + def _start_span( + self, + span_name: str, + parent_run_id: UUID | None, + span_type: str, + run_id: UUID, + inputs: str | dict[str, Any] | None = None, + attributes: dict[str, Any] | None = None, + ) -> LiveSpan: + """Start MLflow Span (or Trace if it is root component)""" + serialized_attributes = self._serialize_invocation_params(attributes) + dependencies_schemas = ( + self._prediction_context.dependencies_schemas if self._prediction_context else None + ) + with maybe_set_prediction_context( + self._prediction_context + ): # When parent_run_id is None, this is root component so start trace + span = start_span_no_context( + name=span_name, + span_type=span_type, + parent_span=self._get_parent_span(parent_run_id), + inputs=inputs, + attributes=serialized_attributes, + tags=dependencies_schemas, + ) + + # Debugging purpose + if span.trace_id == NO_OP_SPAN_TRACE_ID: + _logger.debug("No Op span was created, the trace will not be recorded.") + + # Attach the span to the current context to mark it "active" + token = set_span_in_context(span) if _should_attach_span_to_context.get() else None + self._run_span_mapping[str(run_id)] = SpanWithToken(span, token) + return span + + def _get_parent_span(self, parent_run_id) -> LiveSpan | None: + """ + Get parent span from multiple sources: + 1. If there is an active span in current context, use it as parent span + 2. If parent_run_id is provided, get the corresponding span from the run -> span mapping + 3. If none of the above, return None + """ + if active_span := mlflow.get_current_active_span(): + return active_span + elif parent_run_id: + return self._get_span_by_run_id(parent_run_id) + return None + + def _end_span( + self, + run_id: UUID, + span: LiveSpan, + outputs=None, + attributes=None, + status=SpanStatus(SpanStatusCode.OK), + ): + """Close MLflow Span (or Trace if it is root component)""" + try: + with maybe_set_prediction_context(self._prediction_context): + span.end( + outputs=outputs, + attributes=attributes, + status=status, + ) + finally: + # Span should be detached from the context even when the client.end_span fails + st = self._run_span_mapping.pop(str(run_id), None) + if _should_attach_span_to_context.get(): + if st.token is None: + raise MlflowException( + f"Token for span {st.span} is not found. " + "Cannot detach the span from context." + ) + detach_span_from_context(st.token) + + def flush(self): + """Flush the state of the tracer.""" + # Ideally, all spans should be popped and ended. However, LangChain sometimes + # does not trigger the end event properly and some spans may be left open. + # To avoid leaking tracing context, we remove all spans from the mapping. + for st in self._run_span_mapping.values(): + if st.token: + _logger.debug(f"Found leaked span {st.span}. Force ending it.") + detach_span_from_context(st.token) + + self._run_span_mapping = {} + + def _assign_span_name(self, serialized: dict[str, Any], default_name="unknown") -> str: + return serialized.get("name", serialized.get("id", [default_name])[-1]) + + def on_chat_model_start( + self, + serialized: dict[str, Any], + messages: list[list[BaseMessage]], + *, + run_id: UUID, + tags: list[str] | None = None, + parent_run_id: UUID | None = None, + metadata: dict[str, Any] | None = None, + name: str | None = None, + **kwargs: Any, + ): + """Run when a chat model starts running.""" + + if metadata: + kwargs.update({"metadata": metadata}) + kwargs[SpanAttributeKey.MESSAGE_FORMAT] = "langchain" + + span = self._start_span( + span_name=name or self._assign_span_name(serialized, "chat model"), + parent_run_id=parent_run_id, + span_type=SpanType.CHAT_MODEL, + run_id=run_id, + inputs=messages, + attributes=kwargs, + ) + + if tools := self._extract_tool_definitions(kwargs): + set_span_chat_tools(span, tools) + + def on_llm_start( + self, + serialized: dict[str, Any], + prompts: list[str], + *, + run_id: UUID, + tags: list[str] | None = None, + parent_run_id: UUID | None = None, + metadata: dict[str, Any] | None = None, + name: str | None = None, + **kwargs: Any, + ) -> None: + """Run when LLM (non-chat models) starts running.""" + if metadata: + kwargs.update({"metadata": metadata}) + kwargs[SpanAttributeKey.MESSAGE_FORMAT] = "langchain" + + span = self._start_span( + span_name=name or self._assign_span_name(serialized, "llm"), + parent_run_id=parent_run_id, + span_type=SpanType.LLM, + run_id=run_id, + inputs=prompts, + attributes=kwargs, + ) + + if tools := self._extract_tool_definitions(kwargs): + set_span_chat_tools(span, tools) + + def _extract_tool_definitions(self, kwargs: dict[str, Any]) -> list[ChatTool]: + raw_tools = kwargs.get("invocation_params", {}).get("tools", []) + tools = [] + for raw_tool in raw_tools: + # First, try to parse the raw tool dictionary as OpenAI-style tool + try: + tool = ChatTool.validate_compat(raw_tool) + tools.append(tool) + except pydantic.ValidationError: + # If not OpenAI style, just try to extract the name and descriptions. + if name := raw_tool.get("name"): + tool = ChatTool( + type="function", + function=FunctionToolDefinition( + name=name, description=raw_tool.get("description") + ), + ) + tools.append(tool) + else: + _logger.warning(f"Failed to parse tool definition for tracing: {raw_tool}.") + + return tools + + def on_llm_new_token( + self, + token: str, + *, + chunk: GenerationChunk | ChatGenerationChunk | None = None, + run_id: UUID, + parent_run_id: UUID | None = None, + **kwargs: Any, + ): + """Run on new LLM token. Only available when streaming is enabled.""" + llm_span = self._get_span_by_run_id(run_id) + event_kwargs = {"token": token} + if chunk: + event_kwargs["chunk"] = dumps(chunk) + llm_span.add_event( + SpanEvent( + name="new_token", + attributes=event_kwargs, + ) + ) + + def on_retry( + self, + retry_state: RetryCallState, + *, + run_id: UUID, + **kwargs: Any, + ): + """Run on a retry event.""" + span = self._get_span_by_run_id(run_id) + retry_d: dict[str, Any] = { + "slept": retry_state.idle_for, + "attempt": retry_state.attempt_number, + } + if retry_state.outcome is None: + retry_d["outcome"] = "N/A" + elif retry_state.outcome.failed: + retry_d["outcome"] = "failed" + exception = retry_state.outcome.exception() + retry_d["exception"] = str(exception) + retry_d["exception_type"] = exception.__class__.__name__ + else: + retry_d["outcome"] = "success" + retry_d["result"] = str(retry_state.outcome.result()) + span.add_event( + SpanEvent( + name="retry", + attributes=retry_d, + ) + ) + + def on_llm_end(self, response: LLMResult, *, run_id: UUID, **kwargs: Any): + """End the span for an LLM run.""" + llm_span = self._get_span_by_run_id(run_id) + # response.generations is a nested list of messages + generations = [g for gen_list in response.generations for g in gen_list] + + # Record the token usage attribute + try: + if usage := parse_token_usage(generations): + llm_span.set_attribute(SpanAttributeKey.CHAT_USAGE, usage) + except Exception as e: + _logger.debug(f"Failed to log token usage for LangChain: {e}", exc_info=True) + + self._end_span(run_id, llm_span, outputs=response) + + def on_llm_error( + self, + error: BaseException, + *, + run_id: UUID, + **kwargs: Any, + ): + """Handle an error for an LLM run.""" + llm_span = self._get_span_by_run_id(run_id) + llm_span.add_event(SpanEvent.from_exception(error)) + self._end_span(run_id, llm_span, status=SpanStatus(SpanStatusCode.ERROR, str(error))) + + def on_chain_start( + self, + serialized: dict[str, Any], + inputs: dict[str, Any] | Any, + *, + run_id: UUID, + tags: list[str] | None = None, + parent_run_id: UUID | None = None, + metadata: dict[str, Any] | None = None, + run_type: str | None = None, + name: str | None = None, + **kwargs: Any, + ): + """Start span for a chain run.""" + if metadata: + kwargs.update({"metadata": metadata}) + # not considering streaming events for now + self._start_span( + span_name=name or self._assign_span_name(serialized, "chain"), + parent_run_id=parent_run_id, + span_type=SpanType.CHAIN, + run_id=run_id, + inputs=inputs, + attributes=kwargs, + ) + + def on_chain_end( + self, + outputs: dict[str, Any], + *, + run_id: UUID, + inputs: dict[str, Any] | Any | None = None, + **kwargs: Any, + ): + """Run when chain ends running.""" + chain_span = self._get_span_by_run_id(run_id) + if inputs: + chain_span.set_inputs(inputs) + self._end_span(run_id, chain_span, outputs=outputs) + + def on_chain_error( + self, + error: BaseException, + *, + inputs: dict[str, Any] | Any | None = None, + run_id: UUID, + **kwargs: Any, + ): + """Run when chain errors.""" + chain_span = self._get_span_by_run_id(run_id) + if inputs: + chain_span.set_inputs(inputs) + chain_span.add_event(SpanEvent.from_exception(error)) + self._end_span(run_id, chain_span, status=SpanStatus(SpanStatusCode.ERROR, str(error))) + + def on_tool_start( + self, + serialized: dict[str, Any], + input_str: str, + *, + run_id: UUID, + tags: list[str] | None = None, + parent_run_id: UUID | None = None, + metadata: dict[str, Any] | None = None, + name: str | None = None, + # We don't use inputs here because LangChain override the original inputs + # with None for some cases. In order to avoid losing the original inputs, + # we try to parse the input_str instead. + # https://github.com/langchain-ai/langchain/blob/2813e8640703b8066d8dd6c739829bb4f4aa634e/libs/core/langchain_core/tools/base.py#L636-L640 + inputs: dict[str, Any] | None = None, + **kwargs: Any, + ): + """Start span for a tool run.""" + if metadata: + kwargs.update({"metadata": metadata}) + + # For function calling, input_str can be a stringified dictionary + # like "{'key': 'value'}". We try parsing it for better rendering, + # but conservatively fallback to original if it fails. + try: + inputs = ast.literal_eval(input_str) + except Exception: + inputs = input_str + + self._start_span( + span_name=name or self._assign_span_name(serialized, "tool"), + parent_run_id=parent_run_id, + span_type=SpanType.TOOL, + run_id=run_id, + inputs=inputs, + attributes=kwargs, + ) + + def on_tool_end(self, output: Any, *, run_id: UUID, **kwargs: Any): + """Run when tool ends running.""" + tool_span = self._get_span_by_run_id(run_id) + self._end_span(run_id, tool_span, outputs=output) + + def on_tool_error( + self, + error: BaseException, + *, + run_id: UUID, + **kwargs: Any, + ): + """Run when tool errors.""" + tool_span = self._get_span_by_run_id(run_id) + tool_span.add_event(SpanEvent.from_exception(error)) + self._end_span(run_id, tool_span, status=SpanStatus(SpanStatusCode.ERROR, str(error))) + + def on_retriever_start( + self, + serialized: dict[str, Any], + query: str, + *, + run_id: UUID, + parent_run_id: UUID | None = None, + tags: list[str] | None = None, + metadata: dict[str, Any] | None = None, + name: str | None = None, + **kwargs: Any, + ): + """Run when Retriever starts running.""" + if metadata: + kwargs.update({"metadata": metadata}) + self._start_span( + span_name=name or self._assign_span_name(serialized, "retriever"), + parent_run_id=parent_run_id, + span_type=SpanType.RETRIEVER, + run_id=run_id, + inputs=query, + attributes=kwargs, + ) + + def on_retriever_end(self, documents: Sequence[Document], *, run_id: UUID, **kwargs: Any): + """Run when Retriever ends running.""" + retriever_span = self._get_span_by_run_id(run_id) + try: + # attempt to convert documents to MlflowDocument + documents = [MlflowDocument.from_langchain_document(doc) for doc in documents] + except Exception as e: + _logger.debug( + f"Failed to convert LangChain Document to MLflow Document: {e}", + exc_info=True, + ) + self._end_span( + run_id, + retriever_span, + outputs=documents, + ) + + def on_retriever_error( + self, + error: BaseException, + *, + run_id: UUID, + **kwargs: Any, + ): + """Run when Retriever errors.""" + retriever_span = self._get_span_by_run_id(run_id) + retriever_span.add_event(SpanEvent.from_exception(error)) + self._end_span(run_id, retriever_span, status=SpanStatus(SpanStatusCode.ERROR, str(error))) + + def on_agent_action( + self, + action: AgentAction, + *, + run_id: UUID, + **kwargs: Any, + ) -> Any: + """ + Run on agent action. + + NB: Agent action doesn't create a new LangChain Run, so instead of creating a new span, + an action will be recorded as an event of the existing span created by a parent chain. + """ + span = self._get_span_by_run_id(run_id) + span.add_event( + SpanEvent( + name="agent_action", + attributes={ + "tool": action.tool, + "tool_input": dumps(action.tool_input), + "log": action.log, + }, + ) + ) + + def on_agent_finish( + self, + finish: AgentFinish, + *, + run_id: UUID, + **kwargs: Any, + ) -> Any: + """Run on agent end.""" + span = self._get_span_by_run_id(run_id) + span.add_event( + SpanEvent( + name="agent_finish", + attributes={"return_values": dumps(finish.return_values), "log": finish.log}, + ) + ) + + def on_text( + self, + text: str, + *, + run_id: UUID, + parent_run_id: UUID | None = None, + **kwargs: Any, + ) -> Any: + """Run on arbitrary text.""" + try: + span = self._get_span_by_run_id(run_id) + except MlflowException: + _logger.warning("Span not found for text event. Skipping text event logging.") + else: + span.add_event( + SpanEvent( + "text", + attributes={"text": text}, + ) + ) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/langchain/model.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/langchain/model.py new file mode 100644 index 0000000000000000000000000000000000000000..d7a0366751a4e074648e3ea9fcabf73f62ec1ff4 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/langchain/model.py @@ -0,0 +1,914 @@ +""" +The ``mlflow.langchain`` module provides an API for logging and loading LangChain models. +This module exports multivariate LangChain models in the langchain flavor and univariate +LangChain models in the pyfunc flavor: + +LangChain (native) format + This is the main flavor that can be accessed with LangChain APIs. +:py:mod:`mlflow.pyfunc` + Produced for use by generic pyfunc-based deployment tools and for batch inference. + +.. _LangChain: + https://python.langchain.com/en/latest/index.html +""" + +import logging +import os +import tempfile +import warnings +from typing import Any, Iterator + +import cloudpickle +import pandas as pd +import yaml +from packaging.version import Version + +import mlflow +from mlflow import pyfunc +from mlflow.entities.model_registry.prompt import Prompt +from mlflow.exceptions import MlflowException +from mlflow.langchain.constants import FLAVOR_NAME +from mlflow.langchain.databricks_dependencies import _detect_databricks_dependencies +from mlflow.langchain.runnables import _load_runnables, _save_runnables +from mlflow.langchain.utils.logging import ( + _BASE_LOAD_KEY, + _MODEL_LOAD_KEY, + _RUNNABLE_LOAD_KEY, + _load_base_lcs, + _save_base_lcs, + _validate_and_prepare_lc_model_or_path, + lc_runnables_types, + patch_langchain_type_to_cls_dict, + register_pydantic_v1_serializer_cm, +) +from mlflow.models import Model, ModelInputExample, ModelSignature +from mlflow.models.dependencies_schemas import ( + _clear_dependencies_schemas, + _get_dependencies_schema_from_model, + _get_dependencies_schemas, +) +from mlflow.models.model import ( + MLMODEL_FILE_NAME, + MODEL_CODE_PATH, + MODEL_CONFIG, + _update_active_model_id_based_on_mlflow_model, +) +from mlflow.models.resources import DatabricksFunction, Resource, _ResourceBuilder +from mlflow.models.signature import _infer_signature_from_input_example +from mlflow.models.utils import ( + _convert_llm_input_data, + _load_model_code_path, + _save_example, +) +from mlflow.pyfunc import FLAVOR_NAME as PYFUNC_FLAVOR_NAME +from mlflow.pyfunc.context import Context +from mlflow.tracing.provider import trace_disabled +from mlflow.tracking._model_registry import DEFAULT_AWAIT_MAX_SLEEP_SECONDS +from mlflow.tracking.artifact_utils import _download_artifact_from_uri +from mlflow.types.schema import ColSpec, DataType, Schema +from mlflow.utils.databricks_utils import ( + _get_databricks_serverless_env_vars, + is_in_databricks_model_serving_environment, + is_in_databricks_serverless_runtime, + is_mlflow_tracing_enabled_in_model_serving, +) +from mlflow.utils.docstring_utils import ( + LOG_MODEL_PARAM_DOCS, + docstring_version_compatibility_warning, + format_docstring, +) +from mlflow.utils.environment import ( + _CONDA_ENV_FILE_NAME, + _CONSTRAINTS_FILE_NAME, + _PYTHON_ENV_FILE_NAME, + _REQUIREMENTS_FILE_NAME, + _mlflow_conda_env, + _process_conda_env, + _process_pip_requirements, + _PythonEnv, + _validate_env_arguments, +) +from mlflow.utils.file_utils import get_total_file_size, write_to +from mlflow.utils.model_utils import ( + _add_code_from_conf_to_system_path, + _get_flavor_configuration, + _validate_and_copy_code_paths, + _validate_and_copy_file_to_directory, + _validate_and_get_model_config_from_file, + _validate_and_prepare_target_save_path, +) +from mlflow.utils.requirements_utils import _get_pinned_requirement + +logger = logging.getLogger(mlflow.__name__) + +_MODEL_TYPE_KEY = "model_type" + + +def get_default_pip_requirements(): + """ + Returns: + A list of default pip requirements for MLflow Models produced by this flavor. + Calls to :func:`save_model()` and :func:`log_model()` produce a pip environment + that, at a minimum, contains these requirements. + """ + # pin pydantic and cloudpickle version as they are used in langchain + # model saving and loading + return list(map(_get_pinned_requirement, ["langchain", "pydantic", "cloudpickle"])) + + +def get_default_conda_env(): + """ + Returns: + The default Conda environment for MLflow Models produced by calls to + :func:`save_model()` and :func:`log_model()`. + """ + return _mlflow_conda_env(additional_pip_deps=get_default_pip_requirements()) + + +@format_docstring(LOG_MODEL_PARAM_DOCS.format(package_name=FLAVOR_NAME)) +@docstring_version_compatibility_warning(FLAVOR_NAME) +@trace_disabled # Suppress traces for internal predict calls while saving model +def save_model( + lc_model, + path, + conda_env=None, + code_paths=None, + mlflow_model=None, + signature: ModelSignature = None, + input_example: ModelInputExample = None, + pip_requirements=None, + extra_pip_requirements=None, + metadata=None, + loader_fn=None, + persist_dir=None, + model_config=None, + streamable: bool | None = None, +): + """ + Save a LangChain model to a path on the local file system. + + Args: + lc_model: A LangChain model, which could be a + `Chain `_, + `Agent `_, + `retriever `_, + or `RunnableSequence `_, + or a path containing the `LangChain model code ` + for the above types. When using model as path, make sure to set the model + by using :func:`mlflow.models.set_model()`. + + .. Note:: Experimental: Using model as path may change or be removed in a future + release without warning. + path: Local path where the serialized model (as YAML) is to be saved. + conda_env: {{ conda_env }} + code_paths: {{ code_paths }} + mlflow_model: :py:mod:`mlflow.models.Model` this flavor is being added to. + signature: :py:class:`ModelSignature ` + describes model input and output :py:class:`Schema `. + If not specified, the model signature would be set according to + `lc_model.input_keys` and `lc_model.output_keys` as columns names, and + `DataType.string` as the column type. + Alternatively, you can explicitly specify the model signature. + The model signature can be :py:func:`inferred ` + from datasets with valid model input (e.g. the training dataset with target + column omitted) and valid model output (e.g. model predictions generated on + the training dataset), for example: + + .. code-block:: python + + from mlflow.models import infer_signature + + chain = LLMChain(llm=llm, prompt=prompt) + prediction = chain.run(input_str) + input_columns = [ + {"type": "string", "name": input_key} for input_key in chain.input_keys + ] + signature = infer_signature(input_columns, predictions) + + input_example: {{ input_example }} + pip_requirements: {{ pip_requirements }} + extra_pip_requirements: {{ extra_pip_requirements }} + metadata: {{ metadata }} + loader_fn: A function that's required for models containing objects that aren't natively + serialized by LangChain. + This function takes a string `persist_dir` as an argument and returns the + specific object that the model needs. Depending on the model, + this could be a retriever, vectorstore, requests_wrapper, embeddings, or + database. For RetrievalQA Chain and retriever models, the object is a + (`retriever `_). + For APIChain models, it's a + (`requests_wrapper `_). + For HypotheticalDocumentEmbedder models, it's an + (`embeddings `_). + For SQLDatabaseChain models, it's a + (`database `_). + persist_dir: The directory where the object is stored. The `loader_fn` + takes this string as the argument to load the object. + This is optional for models containing objects that aren't natively + serialized by LangChain. MLflow logs the content in this directory as + artifacts in the subdirectory named `persist_dir_data`. + + Here is the code snippet for logging a RetrievalQA chain with `loader_fn` + and `persist_dir`: + + .. Note:: In langchain_community >= 0.0.27, loading pickled data requires providing the + ``allow_dangerous_deserialization`` argument. + + .. code-block:: python + + qa = RetrievalQA.from_llm(llm=OpenAI(), retriever=db.as_retriever()) + + + def load_retriever(persist_directory): + embeddings = OpenAIEmbeddings() + vectorstore = FAISS.load_local( + persist_directory, + embeddings, + # you may need to add the line below + # for langchain_community >= 0.0.27 + allow_dangerous_deserialization=True, + ) + return vectorstore.as_retriever() + + + with mlflow.start_run() as run: + logged_model = mlflow.langchain.log_model( + qa, + name="retrieval_qa", + loader_fn=load_retriever, + persist_dir=persist_dir, + ) + + See a complete example in examples/langchain/retrieval_qa_chain.py. + model_config: The model configuration to apply to the model if saving model from code. This + configuration is available during model loading. + + .. Note:: Experimental: This parameter may change or be removed in a future + release without warning. + streamable: A boolean value indicating if the model supports streaming prediction. If + True, the model must implement `stream` method. If None, streamable is + set to True if the model implements `stream` method. Default to `None`. + """ + with tempfile.TemporaryDirectory() as temp_dir: + import langchain + from langchain.schema import BaseRetriever + + lc_model_or_path = _validate_and_prepare_lc_model_or_path(lc_model, loader_fn, temp_dir) + + _validate_env_arguments(conda_env, pip_requirements, extra_pip_requirements) + + path = os.path.abspath(path) + _validate_and_prepare_target_save_path(path) + + if isinstance(model_config, str): + model_config = _validate_and_get_model_config_from_file(model_config) + + model_code_path = None + if isinstance(lc_model_or_path, str): + # The LangChain model is defined as Python code located in the file at the path + # specified by `lc_model`. Verify that the path exists and, if so, copy it to the + # model directory along with any other specified code modules + model_code_path = lc_model_or_path + + lc_model = _load_model_code_path(model_code_path, model_config) + _validate_and_copy_file_to_directory(model_code_path, path, "code") + else: + lc_model = lc_model_or_path + + code_dir_subpath = _validate_and_copy_code_paths(code_paths, path) + + if mlflow_model is None: + mlflow_model = Model() + saved_example = _save_example(mlflow_model, input_example, path) + + if signature is None: + if saved_example is not None: + wrapped_model = _LangChainModelWrapper(lc_model) + signature = _infer_signature_from_input_example(saved_example, wrapped_model) + else: + if hasattr(lc_model, "input_keys"): + input_columns = [ + ColSpec(type=DataType.string, name=input_key) + for input_key in lc_model.input_keys + ] + input_schema = Schema(input_columns) + else: + input_schema = None + if ( + hasattr(lc_model, "output_keys") + and len(lc_model.output_keys) == 1 + and not isinstance(lc_model, BaseRetriever) + ): + output_columns = [ + ColSpec(type=DataType.string, name=output_key) + for output_key in lc_model.output_keys + ] + output_schema = Schema(output_columns) + else: + # TODO: empty output schema if multiple output_keys or is a retriever. fix later! + # https://databricks.atlassian.net/browse/ML-34706 + output_schema = None + + signature = ( + ModelSignature(input_schema, output_schema) + if input_schema or output_schema + else None + ) + + if signature is not None: + mlflow_model.signature = signature + if metadata is not None: + mlflow_model.metadata = metadata + + with _get_dependencies_schemas() as dependencies_schemas: + schema = dependencies_schemas.to_dict() + if schema is not None: + if mlflow_model.metadata is None: + mlflow_model.metadata = {} + mlflow_model.metadata.update(schema) + + if streamable is None: + streamable = hasattr(lc_model, "stream") + + model_data_kwargs = {} + flavor_conf = {} + if not isinstance(model_code_path, str): + model_data_kwargs = _save_model(lc_model, path, loader_fn, persist_dir) + flavor_conf = { + _MODEL_TYPE_KEY: lc_model.__class__.__name__, + **model_data_kwargs, + } + + pyfunc.add_to_model( + mlflow_model, + loader_module="mlflow.langchain", + conda_env=_CONDA_ENV_FILE_NAME, + python_env=_PYTHON_ENV_FILE_NAME, + code=code_dir_subpath, + predict_stream_fn="predict_stream", + streamable=streamable, + model_code_path=model_code_path, + model_config=model_config, + **model_data_kwargs, + ) + + needs_databricks_auth = False + if Version(langchain.__version__) >= Version("0.0.311") and mlflow_model.resources is None: + if databricks_resources := _detect_databricks_dependencies(lc_model): + logger.info( + "Attempting to auto-detect Databricks resource dependencies for the " + "current langchain model. Dependency auto-detection is " + "best-effort and may not capture all dependencies of your langchain " + "model, resulting in authorization errors when serving or querying " + "your model. We recommend that you explicitly pass `resources` " + "to mlflow.langchain.log_model() to ensure authorization to " + "dependent resources succeeds when the model is deployed." + ) + serialized_databricks_resources = _ResourceBuilder.from_resources(databricks_resources) + mlflow_model.resources = serialized_databricks_resources + needs_databricks_auth = any( + isinstance(r, DatabricksFunction) for r in databricks_resources + ) + + mlflow_model.add_flavor( + FLAVOR_NAME, + langchain_version=langchain.__version__, + code=code_dir_subpath, + streamable=streamable, + **flavor_conf, + ) + if size := get_total_file_size(path): + mlflow_model.model_size_bytes = size + mlflow_model.save(os.path.join(path, MLMODEL_FILE_NAME)) + + if conda_env is None: + if pip_requirements is None: + default_reqs = get_default_pip_requirements() + extra_env_vars = ( + _get_databricks_serverless_env_vars() + if needs_databricks_auth and is_in_databricks_serverless_runtime() + else None + ) + inferred_reqs = mlflow.models.infer_pip_requirements( + str(path), FLAVOR_NAME, fallback=default_reqs, extra_env_vars=extra_env_vars + ) + default_reqs = sorted(set(inferred_reqs).union(default_reqs)) + else: + default_reqs = None + conda_env, pip_requirements, pip_constraints = _process_pip_requirements( + default_reqs, pip_requirements, extra_pip_requirements + ) + else: + conda_env, pip_requirements, pip_constraints = _process_conda_env(conda_env) + + with open(os.path.join(path, _CONDA_ENV_FILE_NAME), "w") as f: + yaml.safe_dump(conda_env, stream=f, default_flow_style=False) + + if pip_constraints: + write_to(os.path.join(path, _CONSTRAINTS_FILE_NAME), "\n".join(pip_constraints)) + + write_to(os.path.join(path, _REQUIREMENTS_FILE_NAME), "\n".join(pip_requirements)) + + _PythonEnv.current().to_yaml(os.path.join(path, _PYTHON_ENV_FILE_NAME)) + + +@format_docstring(LOG_MODEL_PARAM_DOCS.format(package_name=FLAVOR_NAME)) +@docstring_version_compatibility_warning(FLAVOR_NAME) +@trace_disabled # Suppress traces for internal predict calls while logging model +def log_model( + lc_model, + artifact_path: str | None = None, + conda_env=None, + code_paths=None, + registered_model_name=None, + signature: ModelSignature = None, + input_example: ModelInputExample = None, + await_registration_for=DEFAULT_AWAIT_MAX_SLEEP_SECONDS, + pip_requirements=None, + extra_pip_requirements=None, + metadata=None, + loader_fn=None, + persist_dir=None, + run_id=None, + model_config=None, + streamable=None, + resources: list[Resource] | str | None = None, + prompts: list[str | Prompt] | None = None, + name: str | None = None, + params: dict[str, Any] | None = None, + tags: dict[str, Any] | None = None, + model_type: str | None = None, + step: int = 0, + model_id: str | None = None, +): + """ + Log a LangChain model as an MLflow artifact for the current run. + + Args: + lc_model: A LangChain model, which could be a + `Chain `_, + `Agent `_, or + `retriever `_ + or a path containing the `LangChain model code ` + for the above types. When using model as path, make sure to set the model + by using :func:`mlflow.models.set_model()`. + + .. Note:: Experimental: Using model as path may change or be removed in a future + release without warning. + artifact_path: Deprecated. Use `name` instead. + conda_env: {{ conda_env }} + code_paths: {{ code_paths }} + registered_model_name: If given, create a model + version under ``registered_model_name``, also creating a + registered model if one with the given name does not exist. + signature: :py:class:`ModelSignature ` + describes model input and output + :py:class:`Schema `. + If not specified, the model signature would be set according to + `lc_model.input_keys` and `lc_model.output_keys` as columns names, and + `DataType.string` as the column type. + Alternatively, you can explicitly specify the model signature. + The model signature can be :py:func:`inferred + ` from datasets with valid model input + (e.g. the training dataset with target column omitted) and valid model + output (e.g. model predictions generated on the training dataset), + for example: + + .. code-block:: python + + from mlflow.models import infer_signature + + chain = LLMChain(llm=llm, prompt=prompt) + prediction = chain.run(input_str) + input_columns = [ + {"type": "string", "name": input_key} for input_key in chain.input_keys + ] + signature = infer_signature(input_columns, predictions) + + input_example: {{ input_example }} + await_registration_for: Number of seconds to wait for the model version + to finish being created and is in ``READY`` status. + By default, the function waits for five minutes. + Specify 0 or None to skip waiting. + pip_requirements: {{ pip_requirements }} + extra_pip_requirements: {{ extra_pip_requirements }} + metadata: {{ metadata }} + loader_fn: A function that's required for models containing objects that aren't natively + serialized by LangChain. + This function takes a string `persist_dir` as an argument and returns the + specific object that the model needs. Depending on the model, + this could be a retriever, vectorstore, requests_wrapper, embeddings, or + database. For RetrievalQA Chain and retriever models, the object is a + (`retriever `_). + For APIChain models, it's a + (`requests_wrapper `_). + For HypotheticalDocumentEmbedder models, it's an + (`embeddings `_). + For SQLDatabaseChain models, it's a + (`database `_). + persist_dir: The directory where the object is stored. The `loader_fn` + takes this string as the argument to load the object. + This is optional for models containing objects that aren't natively + serialized by LangChain. MLflow logs the content in this directory as + artifacts in the subdirectory named `persist_dir_data`. + + Here is the code snippet for logging a RetrievalQA chain with `loader_fn` + and `persist_dir`: + + .. Note:: In langchain_community >= 0.0.27, loading pickled data requires providing the + ``allow_dangerous_deserialization`` argument. + + .. code-block:: python + + qa = RetrievalQA.from_llm(llm=OpenAI(), retriever=db.as_retriever()) + + + def load_retriever(persist_directory): + embeddings = OpenAIEmbeddings() + vectorstore = FAISS.load_local( + persist_directory, + embeddings, + # you may need to add the line below + # for langchain_community >= 0.0.27 + allow_dangerous_deserialization=True, + ) + return vectorstore.as_retriever() + + + with mlflow.start_run() as run: + logged_model = mlflow.langchain.log_model( + qa, + name="retrieval_qa", + loader_fn=load_retriever, + persist_dir=persist_dir, + ) + + See a complete example in examples/langchain/retrieval_qa_chain.py. + run_id: run_id to associate with this model version. If specified, we resume the + run and log the model to that run. Otherwise, a new run is created. + Default to None. + model_config: The model configuration to apply to the model if saving model from code. This + configuration is available during model loading. + + .. Note:: Experimental: This parameter may change or be removed in a future + release without warning. + streamable: A boolean value indicating if the model supports streaming prediction. If + True, the model must implement `stream` method. If None, If None, streamable is + set to True if the model implements `stream` method. Default to `None`. + resources: A list of model resources or a resources.yaml file containing a list of + resources required to serve the model. If logging a LangChain model with dependencies + (e.g. on LLM model serving endpoints), we encourage explicitly passing dependencies + via this parameter. Otherwise, ``log_model`` will attempt to infer dependencies, + but dependency auto-inference is best-effort and may miss some dependencies. + prompts: {{ prompts }} + + name: {{ name }} + params: {{ params }} + tags: {{ tags }} + model_type: {{ model_type }} + step: {{ step }} + model_id: {{ model_id }} + + Returns: + A :py:class:`ModelInfo ` instance that contains the + metadata of the logged model. + """ + return Model.log( + artifact_path=artifact_path, + name=name, + flavor=mlflow.langchain, + registered_model_name=registered_model_name, + lc_model=lc_model, + conda_env=conda_env, + code_paths=code_paths, + signature=signature, + input_example=input_example, + await_registration_for=await_registration_for, + pip_requirements=pip_requirements, + extra_pip_requirements=extra_pip_requirements, + metadata=metadata, + loader_fn=loader_fn, + persist_dir=persist_dir, + run_id=run_id, + model_config=model_config, + streamable=streamable, + resources=resources, + prompts=prompts, + params=params, + tags=tags, + model_type=model_type, + step=step, + model_id=model_id, + ) + + +# patch_langchain_type_to_cls_dict here as we attempt to load model +# if it's saved by `dict` method +@patch_langchain_type_to_cls_dict +def _save_model(model, path, loader_fn, persist_dir): + if Version(cloudpickle.__version__) < Version("2.1.0"): + warnings.warn( + "If you are constructing a custom LangChain model, " + "please upgrade cloudpickle to version 2.1.0 or later " + "using `pip install cloudpickle>=2.1.0` " + "to ensure the model can be loaded correctly." + ) + + with register_pydantic_v1_serializer_cm(): + if isinstance(model, lc_runnables_types()): + return _save_runnables(model, path, loader_fn=loader_fn, persist_dir=persist_dir) + else: + return _save_base_lcs(model, path, loader_fn, persist_dir) + + +@patch_langchain_type_to_cls_dict +def _load_model(local_model_path, flavor_conf): + # model_type is not accurate as the class can be subclass + # of supported types, we define _MODEL_LOAD_KEY to ensure + # which load function to use + model_load_fn = flavor_conf.get(_MODEL_LOAD_KEY) + with register_pydantic_v1_serializer_cm(): + if model_load_fn == _RUNNABLE_LOAD_KEY: + model = _load_runnables(local_model_path, flavor_conf) + elif model_load_fn == _BASE_LOAD_KEY: + model = _load_base_lcs(local_model_path, flavor_conf) + else: + raise mlflow.MlflowException( + "Failed to load LangChain model. Unknown model type: " + f"{flavor_conf.get(_MODEL_TYPE_KEY)}" + ) + return model + + +class _LangChainModelWrapper: + def __init__(self, lc_model, model_path=None): + self.lc_model = lc_model + self.model_path = model_path + + def get_raw_model(self): + """ + Returns the underlying model. + """ + return self.lc_model + + def predict( + self, + data: pd.DataFrame | list[str | dict[str, Any]] | Any, + params: dict[str, Any] | None = None, + ) -> list[str | dict[str, Any]]: + """ + Args: + data: Model input data. + params: Additional parameters to pass to the model for inference. + + Returns: + Model predictions. + """ + # TODO: We don't automatically turn tracing on in OSS model serving, because we haven't + # implemented storage option for traces in OSS model serving (counterpart to the + # Inference Table in Databricks model serving). + if ( + is_in_databricks_model_serving_environment() + # TODO: This env var was once used for controlling whether or not to inject the + # tracer in Databricks model serving. However, now we have the new env var + # `ENABLE_MLFLOW_TRACING` to control that. We don't remove this condition + # right now in the interest of caution, but we should remove this condition + # after making sure that the functionality is stable. + and os.environ.get("MLFLOW_ENABLE_TRACE_IN_SERVING", "false").lower() == "true" + # if this is False, tracing is disabled and we shouldn't inject the tracer + and is_mlflow_tracing_enabled_in_model_serving() + ): + from mlflow.langchain.langchain_tracer import MlflowLangchainTracer + + callbacks = [MlflowLangchainTracer()] + else: + callbacks = None + + return self._predict_with_callbacks(data, params, callback_handlers=callbacks) + + def _update_dependencies_schemas_in_prediction_context( + self, callback_handlers + ) -> Context | None: + from mlflow.langchain.langchain_tracer import MlflowLangchainTracer + + if ( + callback_handlers + and ( + tracer := next( + (c for c in callback_handlers if isinstance(c, MlflowLangchainTracer)), None + ) + ) + and self.model_path + ): + model = Model.load(self.model_path) + context = tracer._prediction_context + if context and (schema := _get_dependencies_schema_from_model(model)): + context.update(**schema) + return context + + def _predict_with_callbacks( + self, + data: pd.DataFrame | list[str | dict[str, Any]] | Any, + params: dict[str, Any] | None = None, + callback_handlers=None, + convert_chat_responses=False, + ) -> list[str | dict[str, Any]]: + """ + Args: + data: Model input data. + params: Additional parameters to pass to the model for inference. + callback_handlers: Callback handlers to pass to LangChain. + convert_chat_responses: If true, forcibly convert response to chat model + response format. + + Returns: + Model predictions. + """ + from mlflow.langchain.api_request_parallel_processor import process_api_requests + + context = self._update_dependencies_schemas_in_prediction_context(callback_handlers) + messages, return_first_element = self._prepare_predict_messages(data) + results = process_api_requests( + lc_model=self.lc_model, + requests=messages, + callback_handlers=callback_handlers, + convert_chat_responses=convert_chat_responses, + params=params or {}, + context=context, + ) + return results[0] if return_first_element else results + + def _prepare_predict_messages(self, data): + """ + Return a tuple of (preprocessed_data, return_first_element) + `preprocessed_data` is always a list, + and `return_first_element` means if True, we should return the first element + of inference result, otherwise we should return the whole inference result. + """ + data = _convert_llm_input_data(data) + + if not isinstance(data, list): + # if the input data is not a list (i.e. single input), + # we still need to convert it to a one-element list `[data]` + # because `process_api_requests` only accepts list as valid input. + # and in this case, + # we should return the first element of the inference result + # because we change input `data` to `[data]` + return [data], True + if isinstance(data, list): + return data, False + raise mlflow.MlflowException.invalid_parameter_value( + "Input must be a pandas DataFrame or a list " + f"for model {self.lc_model.__class__.__name__}" + ) + + def _prepare_predict_stream_messages(self, data): + data = _convert_llm_input_data(data) + + if isinstance(data, list): + # `predict_stream` only accepts single input. + # but `enforce_schema` might convert single input into a list like `[single_input]` + # so extract the first element in the list. + if len(data) != 1: + raise MlflowException( + f"'predict_stream' requires single input, but it got input data {data}" + ) + return data[0] + return data + + def predict_stream( + self, + data: Any, + params: dict[str, Any] | None = None, + ) -> Iterator[str | dict[str, Any]]: + """ + Args: + data: Model input data, only single input is allowed. + params: Additional parameters to pass to the model for inference. + + Returns: + An iterator of model prediction chunks. + """ + from mlflow.langchain.api_request_parallel_processor import ( + process_stream_request, + ) + + data = self._prepare_predict_stream_messages(data) + return process_stream_request( + lc_model=self.lc_model, + request_json=data, + params=params or {}, + ) + + def _predict_stream_with_callbacks( + self, + data: Any, + params: dict[str, Any] | None = None, + callback_handlers=None, + convert_chat_responses=False, + ) -> Iterator[str | dict[str, Any]]: + """ + Args: + data: Model input data, only single input is allowed. + params: Additional parameters to pass to the model for inference. + callback_handlers: Callback handlers to pass to LangChain. + convert_chat_responses: If true, forcibly convert response to chat model + response format. + + Returns: + An iterator of model prediction chunks. + """ + from mlflow.langchain.api_request_parallel_processor import ( + process_stream_request, + ) + + self._update_dependencies_schemas_in_prediction_context(callback_handlers) + data = self._prepare_predict_stream_messages(data) + return process_stream_request( + lc_model=self.lc_model, + request_json=data, + callback_handlers=callback_handlers, + convert_chat_responses=convert_chat_responses, + params=params or {}, + ) + + +def _load_pyfunc(path: str, model_config: dict[str, Any] | None = None): + """Load PyFunc implementation for LangChain. Called by ``pyfunc.load_model``. + + Args: + path: Local filesystem path to the MLflow Model with the ``langchain`` flavor. + """ + return _LangChainModelWrapper(_load_model_from_local_fs(path, model_config), path) + + +def _load_model_from_local_fs(local_model_path, model_config_overrides=None): + mlflow_model = Model.load(local_model_path) + flavor_conf = _get_flavor_configuration(model_path=local_model_path, flavor_name=FLAVOR_NAME) + pyfunc_flavor_conf = _get_flavor_configuration( + model_path=local_model_path, flavor_name=PYFUNC_FLAVOR_NAME + ) + # Add code from the langchain flavor to the system path + _add_code_from_conf_to_system_path(local_model_path, flavor_conf) + # The model_code_path and the model_config were previously saved langchain flavor but now we + # also save them inside the pyfunc flavor. For backwards compatibility of previous models, + # we need to check both places. + if MODEL_CODE_PATH in pyfunc_flavor_conf or MODEL_CODE_PATH in flavor_conf: + model_config = pyfunc_flavor_conf.get(MODEL_CONFIG, flavor_conf.get(MODEL_CONFIG, None)) + if isinstance(model_config, str): + config_path = os.path.join( + local_model_path, + os.path.basename(model_config), + ) + model_config = _validate_and_get_model_config_from_file(config_path) + + flavor_code_path = pyfunc_flavor_conf.get( + MODEL_CODE_PATH, flavor_conf.get(MODEL_CODE_PATH, None) + ) + model_code_path = os.path.join( + local_model_path, + os.path.basename(flavor_code_path), + ) + try: + model = _load_model_code_path( + model_code_path, {**(model_config or {}), **(model_config_overrides or {})} + ) + finally: + # We would like to clean up the dependencies schema which is set to global + # after loading the mode to avoid the schema being used in the next model loading + _clear_dependencies_schemas() + else: + model = _load_model(local_model_path, flavor_conf) + # set active model after model loading since experiment ID might be set + # in the model loading process + _update_active_model_id_based_on_mlflow_model(mlflow_model) + return model + + +@docstring_version_compatibility_warning(FLAVOR_NAME) +@trace_disabled # Suppress traces while loading model +def load_model(model_uri, dst_path=None): + """ + Load a LangChain model from a local file or a run. + + Args: + model_uri: The location, in URI format, of the MLflow model. For example: + + - ``/Users/me/path/to/local/model`` + - ``relative/path/to/local/model`` + - ``s3://my_bucket/path/to/model`` + - ``runs://run-relative/path/to/model`` + + For more information about supported URI schemes, see + `Referencing Artifacts `_. + dst_path: The local filesystem path to which to download the model artifact. + This directory must already exist. If unspecified, a local output + path will be created. + + Returns: + A LangChain model instance. + """ + model_uri = str(model_uri) + local_model_path = _download_artifact_from_uri(artifact_uri=model_uri, output_path=dst_path) + return _load_model_from_local_fs(local_model_path) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/langchain/output_parsers.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/langchain/output_parsers.py new file mode 100644 index 0000000000000000000000000000000000000000..46e672b4507a363be5c7fd0a9fd099d505949bdd --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/langchain/output_parsers.py @@ -0,0 +1,142 @@ +from dataclasses import asdict +from typing import Any, Iterator +from uuid import uuid4 + +from langchain_core.messages.base import BaseMessage +from langchain_core.output_parsers.transform import BaseTransformOutputParser + +from mlflow.models.rag_signatures import ( + ChainCompletionChoice, + Message, + StringResponse, +) +from mlflow.models.rag_signatures import ( + ChatCompletionResponse as RagChatCompletionResponse, +) +from mlflow.types.agent import ChatAgentChunk, ChatAgentMessage, ChatAgentResponse +from mlflow.types.llm import ( + ChatChoice, + ChatChoiceDelta, + ChatChunkChoice, + ChatCompletionChunk, + ChatCompletionResponse, + ChatMessage, +) +from mlflow.utils.annotations import deprecated, experimental + + +@deprecated("mlflow.langchain.output_parser.ChatCompletionOutputParser") +class ChatCompletionsOutputParser(BaseTransformOutputParser[dict[str, Any]]): + """ + OutputParser that wraps the string output into a dictionary representation of a + :py:class:`ChatCompletionResponse` + """ + + @classmethod + def is_lc_serializable(cls) -> bool: + """Return whether this class is serializable.""" + return True + + @property + def _type(self) -> str: + """Return the output parser type for serialization.""" + return "mlflow_simplified_chat_completions" + + def parse(self, text: str) -> dict[str, Any]: + return asdict( + RagChatCompletionResponse( + choices=[ChainCompletionChoice(message=Message(role="assistant", content=text))], + object="chat.completion", + ) + ) + + +class ChatCompletionOutputParser(BaseTransformOutputParser[str]): + """ + OutputParser that wraps the string output into a dictionary representation of a + :py:class:`ChatCompletionResponse` or :py:class:`ChatCompletionChunk` + when streaming + """ + + @classmethod + def is_lc_serializable(cls) -> bool: + """Return whether this class is serializable.""" + return True + + @property + def _type(self) -> str: + """Return the output parser type for serialization.""" + return "mlflow_chat_completion" + + def parse(self, text: str) -> dict[str, Any]: + """Returns the input text as a ChatCompletionResponse with no changes.""" + return ChatCompletionResponse( + choices=[ChatChoice(message=ChatMessage(role="assistant", content=text))] + ).to_dict() + + def transform(self, input: Iterator[BaseMessage], config, **kwargs) -> Iterator[dict[str, Any]]: + """Returns a generator of ChatCompletionChunk objects""" + for chunk in input: + yield ChatCompletionChunk( + choices=[ChatChunkChoice(delta=ChatChoiceDelta(content=chunk.content))] + ).to_dict() + + +@deprecated("mlflow.langchain.output_parser.ChatCompletionOutputParser") +class StringResponseOutputParser(BaseTransformOutputParser[dict[str, Any]]): + """ + OutputParser that wraps the string output into an dictionary representation of a + :py:class:`StringResponse` + """ + + @classmethod + def is_lc_serializable(cls) -> bool: + """Return whether this class is serializable.""" + return True + + @property + def _type(self) -> str: + """Return the output parser type for serialization.""" + return "mlflow_simplified_str_object" + + def parse(self, text: str) -> dict[str, Any]: + return asdict(StringResponse(content=text)) + + +@experimental(version="2.21.0") +class ChatAgentOutputParser(BaseTransformOutputParser[str]): + """ + OutputParser that wraps the string output into a dictionary representation of a + :py:class:`ChatAgentResponse ` or a + :py:class:`ChatAgentChunk ` for easy interoperability. + """ + + @classmethod + def is_lc_serializable(cls) -> bool: + """Return whether this class is serializable.""" + return True + + @property + def _type(self) -> str: + """Return the output parser type for serialization.""" + return "mlflow_chat_agent" + + def parse(self, text: str) -> dict[str, Any]: + """ + Returns the output text as a dictionary representation of a + :py:class:`ChatAgentResponse `. + """ + return ChatAgentResponse( + messages=[ChatAgentMessage(content=text, role="assistant", id=str(uuid4()))] + ).model_dump_compat(exclude_none=True) + + def transform(self, input: Iterator[BaseMessage], config, **kwargs) -> Iterator[dict[str, Any]]: + """ + Returns a generator of + :py:class:`ChatAgentChunk ` objects + """ + for chunk in input: + if chunk.content: + yield ChatAgentChunk( + delta=ChatAgentMessage(content=chunk.content, role="assistant", id=chunk.id) + ).model_dump_compat(exclude_none=True) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/langchain/retriever_chain.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/langchain/retriever_chain.py new file mode 100644 index 0000000000000000000000000000000000000000..249ed96b613d4488b92b5d34b15cae44384eefa0 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/langchain/retriever_chain.py @@ -0,0 +1,153 @@ +"""Chain for wrapping a retriever.""" + +from __future__ import annotations + +import json +from pathlib import Path +from typing import Any + +import yaml +from langchain.callbacks.manager import AsyncCallbackManagerForChainRun, CallbackManagerForChainRun +from langchain.chains.base import Chain +from langchain.schema import BaseRetriever, Document +from pydantic import Extra, Field + + +class _RetrieverChain(Chain): + """ + Chain that wraps a retriever for use with MLflow. + + The MLflow ``langchain`` flavor provides the functionality to log a retriever object and + evaluate it individually. This is useful if you want to evaluate the quality of the + relevant documents returned by a retriever object without directing these documents + through a large language model (LLM) to yield a summarized response. + + In order to log the retriever object in the ``langchain`` flavor, the retriever object + needs to be wrapped within a ``_RetrieverChain``. + + See ``examples/langchain/retriever_chain.py`` for how to log the ``_RetrieverChain``. + + Args: + retriever: The retriever to wrap. + """ + + input_key: str = "query" + output_key: str = "source_documents" + retriever: BaseRetriever = Field(exclude=True) + + class Config: + """Configuration for this pydantic object.""" + + extra = Extra.forbid + arbitrary_types_allowed = True + + @property + def input_keys(self) -> list[str]: + """Return the input keys.""" + return [self.input_key] + + @property + def output_keys(self) -> list[str]: + """Return the output keys.""" + return [self.output_key] + + def _get_docs(self, question: str) -> list[Document]: + """Get documents from the retriever.""" + return self.retriever.get_relevant_documents(question) + + def _call( + self, + inputs: dict[str, Any], + run_manager: CallbackManagerForChainRun | None = None, + ) -> dict[str, Any]: + """Run _get_docs on input query. + Returns the retrieved documents under the key 'source_documents'. + + Example: + + .. code-block:: python + + chain = _RetrieverChain(retriever=...) + res = chain({"query": "This is my query"}) + docs = res["source_documents"] + """ + question = inputs[self.input_key] + docs = self._get_docs(question) + list_of_str_page_content = [doc.page_content for doc in docs] + return {self.output_key: json.dumps(list_of_str_page_content)} + + async def _aget_docs(self, question: str) -> list[Document]: + """Get documents from the retriever.""" + return await self.retriever.aget_relevant_documents(question) + + async def _acall( + self, + inputs: dict[str, Any], + run_manager: AsyncCallbackManagerForChainRun | None = None, + ) -> dict[str, Any]: + """Run _get_docs on input query. + Returns the retrieved documents under the key 'source_documents'. + + Example: + + .. code-block:: python + + chain = _RetrieverChain(retriever=...) + res = chain({"query": "This is my query"}) + docs = res["source_documents"] + """ + question = inputs[self.input_key] + docs = await self._aget_docs(question) + list_of_str_page_content = [doc.page_content for doc in docs] + return {self.output_key: json.dumps(list_of_str_page_content)} + + @property + def _chain_type(self) -> str: + """Return the chain type.""" + return "retriever_chain" + + @classmethod + def load(cls, file: str | Path, **kwargs: Any) -> _RetrieverChain: + """Load a _RetrieverChain from a file.""" + # Convert file to Path object. + file_path = Path(file) if isinstance(file, str) else file + # Load from either json or yaml. + if file_path.suffix == ".json": + with open(file_path) as f: + config = json.load(f) + elif file_path.suffix in (".yaml", ".yml"): + with open(file_path) as f: + # This is to ignore certain tags that are not supported + # with pydantic >= 2.0 + yaml.add_multi_constructor( + "tag:yaml.org,2002:python/object", + lambda loader, suffix, node: None, + Loader=yaml.SafeLoader, + ) + config = yaml.load(f, yaml.SafeLoader) + else: + raise ValueError("File type must be json or yaml") + + # Override default 'verbose' and 'memory' for the chain + if verbose := kwargs.pop("verbose", None): + config["verbose"] = verbose + if memory := kwargs.pop("memory", None): + config["memory"] = memory + + if "_type" not in config: + raise ValueError("Must specify a chain Type in config") + config_type = config.pop("_type") + + if config_type != "retriever_chain": + raise ValueError(f"Loading {config_type} chain not supported") + + retriever = kwargs.pop("retriever", None) + if retriever is None: + raise ValueError("`retriever` must be present.") + + config.pop("retriever", None) + + return cls( + retriever=retriever, + **config, + ) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/langchain/runnables.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/langchain/runnables.py new file mode 100644 index 0000000000000000000000000000000000000000..560434901452d1394bb0b3fc3f1605b99b8d67c2 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/langchain/runnables.py @@ -0,0 +1,527 @@ +from __future__ import annotations + +import os +import re +import warnings +from pathlib import Path +from typing import TYPE_CHECKING + +import cloudpickle +import yaml + +from mlflow.exceptions import MlflowException +from mlflow.langchain.utils.logging import ( + _BASE_LOAD_KEY, + _CONFIG_LOAD_KEY, + _MODEL_DATA_FOLDER_NAME, + _MODEL_DATA_KEY, + _MODEL_DATA_PKL_FILE_NAME, + _MODEL_DATA_YAML_FILE_NAME, + _MODEL_LOAD_KEY, + _MODEL_TYPE_KEY, + _PICKLE_LOAD_KEY, + _RUNNABLE_LOAD_KEY, + _load_base_lcs, + _load_from_json, + _load_from_pickle, + _load_from_yaml, + _patch_loader, + _save_base_lcs, + _validate_and_prepare_lc_model_or_path, + base_lc_types, + custom_type_to_loader_dict, + get_unsupported_model_message, + lc_runnable_assign_types, + lc_runnable_binding_types, + lc_runnable_branch_types, + lc_runnable_with_steps_types, + lc_runnables_types, + patch_langchain_type_to_cls_dict, + picklable_runnable_types, +) + +if TYPE_CHECKING: + from langchain.schema.runnable import Runnable + +_STEPS_FOLDER_NAME = "steps" +_RUNNABLE_STEPS_FILE_NAME = "steps.yaml" +_BRANCHES_FOLDER_NAME = "branches" +_MAPPER_FOLDER_NAME = "mapper" +_RUNNABLE_BRANCHES_FILE_NAME = "branches.yaml" +_DEFAULT_BRANCH_NAME = "default" +_RUNNABLE_BINDING_CONF_FILE_NAME = "binding_conf.yaml" + + +@patch_langchain_type_to_cls_dict +def _load_model_from_config(path, model_config): + from langchain.chains.loading import type_to_loader_dict as chains_type_to_loader_dict + from langchain.llms import get_type_to_cls_dict as llms_get_type_to_cls_dict + + try: + from langchain.prompts.loading import type_to_loader_dict as prompts_types + except ImportError: + prompts_types = {"prompt", "few_shot_prompt"} + + config_path = os.path.join(path, model_config.get(_MODEL_DATA_KEY, _MODEL_DATA_YAML_FILE_NAME)) + # Load runnables from config file + if config_path.endswith(".yaml"): + config = _load_from_yaml(config_path) + elif config_path.endswith(".json"): + config = _load_from_json(config_path) + else: + raise MlflowException( + f"Cannot load runnable without a config file. Got path {config_path}." + ) + _type = config.get("_type") + if _type in chains_type_to_loader_dict: + from langchain.chains.loading import load_chain + + return _patch_loader(load_chain)(config_path) + elif _type in prompts_types: + from langchain.prompts.loading import load_prompt + + return load_prompt(config_path) + elif _type in llms_get_type_to_cls_dict(): + from langchain_community.llms.loading import load_llm + + return _patch_loader(load_llm)(config_path) + elif _type in custom_type_to_loader_dict(): + return custom_type_to_loader_dict()[_type](config) + raise MlflowException(f"Unsupported type {_type} for loading.") + + +def _load_model_from_path(path: str, model_config=None): + model_load_fn = model_config.get(_MODEL_LOAD_KEY) + if model_load_fn == _RUNNABLE_LOAD_KEY: + return _load_runnables(path, model_config) + if model_load_fn == _BASE_LOAD_KEY: + return _load_base_lcs(path, model_config) + if model_load_fn == _CONFIG_LOAD_KEY: + return _load_model_from_config(path, model_config) + if model_load_fn == _PICKLE_LOAD_KEY: + return _load_from_pickle(os.path.join(path, model_config.get(_MODEL_DATA_KEY))) + raise MlflowException(f"Unsupported model load key {model_load_fn}") + + +def _validate_path(file_path: str | Path): + load_path = Path(file_path) + if not load_path.exists() or not load_path.is_dir(): + raise MlflowException( + f"Path {load_path} must be an existing directory in order to load model." + ) + return load_path + + +def _load_runnable_with_steps(file_path: Path | str, model_type: str): + """Load the model + + Args: + file_path: Path to file to load the model from. + model_type: Type of the model to load. + """ + from langchain.schema.runnable import RunnableParallel, RunnableSequence + + load_path = _validate_path(file_path) + + steps_conf_file = load_path / _RUNNABLE_STEPS_FILE_NAME + if not steps_conf_file.exists(): + raise MlflowException( + f"File {steps_conf_file} must exist in order to load runnable with steps." + ) + steps_conf = _load_from_yaml(steps_conf_file) + steps_path = load_path / _STEPS_FOLDER_NAME + _validate_path(steps_path) + + steps = {} + # ignore hidden files + for step in (f for f in os.listdir(steps_path) if not f.startswith(".")): + config = steps_conf.get(step) + # load model from the folder of the step + runnable = _load_model_from_path(os.path.join(steps_path, step), config) + steps[step] = runnable + + if model_type == RunnableSequence.__name__: + steps = [value for _, value in sorted(steps.items(), key=lambda item: int(item[0]))] + return runnable_sequence_from_steps(steps) + if model_type == RunnableParallel.__name__: + return RunnableParallel(steps) + + +def runnable_sequence_from_steps(steps): + """Construct a RunnableSequence from steps. + + Args: + steps: List of steps to construct the RunnableSequence from. + """ + from langchain.schema.runnable import RunnableSequence + + if len(steps) < 2: + raise ValueError(f"RunnableSequence must have at least 2 steps, got {len(steps)}.") + + first, *middle, last = steps + return RunnableSequence(first=first, middle=middle, last=last) + + +def _load_runnable_branch(file_path: Path | str): + """Load the model + + Args: + file_path: Path to file to load the model from. + """ + from langchain.schema.runnable import RunnableBranch + + load_path = _validate_path(file_path) + + branches_conf_file = load_path / _RUNNABLE_BRANCHES_FILE_NAME + if not branches_conf_file.exists(): + raise MlflowException( + f"File {branches_conf_file} must exist in order to load runnable with steps." + ) + branches_conf = _load_from_yaml(branches_conf_file) + branches_path = load_path / _BRANCHES_FOLDER_NAME + _validate_path(branches_path) + + branches = [] + for branch in os.listdir(branches_path): + # load model from the folder of the branch + if branch == _DEFAULT_BRANCH_NAME: + default_branch_path = branches_path / _DEFAULT_BRANCH_NAME + default = _load_model_from_path( + default_branch_path, branches_conf.get(_DEFAULT_BRANCH_NAME) + ) + else: + branch_tuple = [] + for i in range(2): + config = branches_conf.get(f"{branch}-{i}") + runnable = _load_model_from_path( + os.path.join(branches_path, branch, str(i)), config + ) + branch_tuple.append(runnable) + branches.append(tuple(branch_tuple)) + + # default branch must be the last branch + branches.append(default) + + return RunnableBranch(*branches) + + +def _load_runnable_assign(file_path: Path | str): + """Load the model + + Args: + file_path: Path to file to load the model from. + """ + from langchain.schema.runnable.passthrough import RunnableAssign + + load_path = _validate_path(file_path) + + mapper_file = load_path / _MAPPER_FOLDER_NAME + _validate_path(mapper_file) + mapper = _load_runnable_with_steps(mapper_file, "RunnableParallel") + return RunnableAssign(mapper) + + +def _load_runnable_binding(file_path: Path | str): + """ + Load runnable binding model from the path + """ + from langchain.schema.runnable import RunnableBinding + + load_path = _validate_path(file_path) + + model_conf = _load_from_yaml(load_path / _RUNNABLE_BINDING_CONF_FILE_NAME) + for field, value in model_conf.items(): + if _is_json_primitive(value): + model_conf[field] = value + # value is dictionary + else: + model_conf[field] = _load_model_from_path(load_path, value) + return RunnableBinding(**model_conf) + + +def _save_internal_runnables(runnable, path, loader_fn, persist_dir): + conf = {} + if isinstance(runnable, lc_runnables_types()): + conf[_MODEL_TYPE_KEY] = runnable.__class__.__name__ + conf.update(_save_runnables(runnable, path, loader_fn, persist_dir)) + elif isinstance(runnable, base_lc_types()): + lc_model = _validate_and_prepare_lc_model_or_path(runnable, loader_fn) + conf[_MODEL_TYPE_KEY] = lc_model.__class__.__name__ + conf.update(_save_base_lcs(lc_model, path, loader_fn, persist_dir)) + else: + conf = { + _MODEL_TYPE_KEY: runnable.__class__.__name__, + _MODEL_DATA_KEY: _MODEL_DATA_YAML_FILE_NAME, + _MODEL_LOAD_KEY: _CONFIG_LOAD_KEY, + } + model_path = path / _MODEL_DATA_YAML_FILE_NAME + + _warning_if_imported_from_lc_partner_pkg(runnable) + + # Save some simple runnables that langchain natively supports. + if hasattr(runnable, "save"): + runnable.save(model_path) + elif hasattr(runnable, "dict"): + runnable_dict = runnable.dict() + with open(model_path, "w") as f: + yaml.dump(runnable_dict, f, default_flow_style=False) + # if the model cannot be loaded back, then `dict` is not enough for saving. + _load_model_from_config(path, conf) + else: + raise Exception("Cannot save runnable without `save` or `dict` methods.") + return conf + + +_LC_PARTNER_MODULE_PATTERN = re.compile( + r"langchain_(?!core|community|experimental|cli|text-splitters)([a-z0-9-]+)$" +) + + +def _warning_if_imported_from_lc_partner_pkg(runnable): + """ + Issues a warning if the model contains LangChain partner packages in its requirements. + + Popular integrations like OpenAI have been migrated from the central langchain-community + package to their own partner packages (e.g. langchain-openai). However, the class loading + mechanism in MLflow does not handle partner packages and always loads the community version. + This can lead to unexpected behavior because the community version is no longer maintained. + """ + module = runnable.__module__ + root_module = module.split(".")[0] + if m := _LC_PARTNER_MODULE_PATTERN.match(root_module): + warnings.warn( + "Your model contains a class imported from the LangChain partner package " + f"`langchain-{m.group(1)}`. When loading the model back, MLflow will use the " + "community version of the classes instead of the partner packages, which may " + "lead to unexpected behavior. To ensure that the model is loaded correctly, " + "it is recommended to save the model with the 'model-from-code' method " + "instead: https://mlflow.org/docs/latest/models.html#models-from-code" + ) + + +def _save_runnable_with_steps(model, file_path: Path | str, loader_fn=None, persist_dir=None): + """Save the model with steps. Currently it supports saving RunnableSequence and + RunnableParallel. + + If saving a RunnableSequence, steps is a list of Runnable objects. We save each step to the + subfolder named by the step index. + e.g. - model + - steps + - 0 + - model.yaml + - 1 + - model.pkl + - steps.yaml + If saving a RunnableParallel, steps is a dictionary of key-Runnable pairs. We save each step to + the subfolder named by the key. + e.g. - model + - steps + - context + - model.yaml + - question + - model.pkl + - steps.yaml + + We save steps.yaml file to the model folder. It contains each step's model's configuration. + + Args: + model: Runnable to be saved. + file_path: Path to file to save the model to. + """ + # Convert file to Path object. + save_path = Path(file_path) + save_path.mkdir(parents=True, exist_ok=True) + + # Save steps into a folder + steps_path = save_path / _STEPS_FOLDER_NAME + steps_path.mkdir() + + steps = get_runnable_steps(model) + if isinstance(steps, list): + generator = enumerate(steps) + elif isinstance(steps, dict): + generator = steps.items() + else: + raise MlflowException( + f"Runnable {model} steps attribute must be either a list or a dictionary. " + f"Got {type(steps).__name__}." + ) + unsaved_runnables = {} + steps_conf = {} + for key, runnable in generator: + step = str(key) + # Save each step into a subfolder named by step + save_runnable_path = steps_path / step + save_runnable_path.mkdir() + try: + steps_conf[step] = _save_internal_runnables( + runnable, save_runnable_path, loader_fn, persist_dir + ) + except Exception as e: + unsaved_runnables[step] = f"{runnable.get_name()} -- {e}" + + if unsaved_runnables: + raise MlflowException(f"Failed to save runnable sequence: {unsaved_runnables}.") + + # save steps configs + with save_path.joinpath(_RUNNABLE_STEPS_FILE_NAME).open("w") as f: + yaml.dump(steps_conf, f, default_flow_style=False) + + +def _save_runnable_branch(model, file_path, loader_fn, persist_dir): + """ + Save runnable branch in to path. + """ + save_path = Path(file_path) + save_path.mkdir(parents=True, exist_ok=True) + # save branches into a folder + branches_path = save_path / _BRANCHES_FOLDER_NAME + branches_path.mkdir() + + unsaved_runnables = {} + branches_conf = {} + for index, branch_tuple in enumerate(model.branches): + # Save each branch into a subfolder named by index + # and save condition and runnable into subfolder + for i, runnable in enumerate(branch_tuple): + save_runnable_path = branches_path / str(index) / str(i) + save_runnable_path.mkdir(parents=True) + branches_conf[f"{index}-{i}"] = {} + + try: + branches_conf[f"{index}-{i}"] = _save_internal_runnables( + runnable, save_runnable_path, loader_fn, persist_dir + ) + except Exception as e: + unsaved_runnables[f"{index}-{i}"] = f"{runnable.get_name()} -- {e}" + + # save default branch + default_branch_path = branches_path / _DEFAULT_BRANCH_NAME + default_branch_path.mkdir() + try: + branches_conf[_DEFAULT_BRANCH_NAME] = _save_internal_runnables( + model.default, default_branch_path, loader_fn, persist_dir + ) + except Exception as e: + unsaved_runnables[_DEFAULT_BRANCH_NAME] = f"{model.default.get_name()} -- {e}" + if unsaved_runnables: + raise MlflowException(f"Failed to save runnable branch: {unsaved_runnables}.") + + # save branches configs + with save_path.joinpath(_RUNNABLE_BRANCHES_FILE_NAME).open("w") as f: + yaml.dump(branches_conf, f, default_flow_style=False) + + +def _save_runnable_assign(model, file_path, loader_fn=None, persist_dir=None): + from langchain.schema.runnable import RunnableParallel + + save_path = Path(file_path) + save_path.mkdir(parents=True, exist_ok=True) + # save mapper into a folder + mapper_path = save_path / _MAPPER_FOLDER_NAME + mapper_path.mkdir() + + if not isinstance(model.mapper, RunnableParallel): + raise MlflowException( + f"Failed to save model {model} with type {model.__class__.__name__}. " + "RunnableAssign's mapper must be a RunnableParallel." + ) + _save_runnable_with_steps(model.mapper, mapper_path, loader_fn, persist_dir) + + +def _is_json_primitive(value): + return ( + value is None + or isinstance(value, (str, int, float, bool)) + or (isinstance(value, list) and all(_is_json_primitive(v) for v in value)) + ) + + +def _save_runnable_binding(model, file_path, loader_fn=None, persist_dir=None): + save_path = Path(file_path) + save_path.mkdir(parents=True, exist_ok=True) + model_config = {} + + # runnableBinding bound is the real runnable to be invoked + model_config["bound"] = _save_internal_runnables(model.bound, save_path, loader_fn, persist_dir) + + # save other fields + for field, value in model.dict().items(): + if _is_json_primitive(value): + model_config[field] = value + elif field != "bound": + model_config[field] = { + _MODEL_LOAD_KEY: _PICKLE_LOAD_KEY, + _MODEL_DATA_KEY: f"{field}.pkl", + } + _pickle_object(value, os.path.join(save_path, f"{field}.pkl")) + + # save fields configs + with save_path.joinpath(_RUNNABLE_BINDING_CONF_FILE_NAME).open("w") as f: + yaml.dump(model_config, f, default_flow_style=False) + + +def _pickle_object(model, path: str): + if not path.endswith(".pkl"): + raise ValueError(f"File path must end with .pkl, got {path}.") + with open(path, "wb") as f: + cloudpickle.dump(model, f) + + +def _save_runnables(model, path, loader_fn=None, persist_dir=None): + model_data_kwargs = { + _MODEL_LOAD_KEY: _RUNNABLE_LOAD_KEY, + _MODEL_TYPE_KEY: model.__class__.__name__, + } + if isinstance(model, lc_runnable_with_steps_types()): + model_data_path = _MODEL_DATA_FOLDER_NAME + _save_runnable_with_steps( + model, os.path.join(path, model_data_path), loader_fn, persist_dir + ) + elif isinstance(model, picklable_runnable_types()): + model_data_path = _MODEL_DATA_PKL_FILE_NAME + _pickle_object(model, os.path.join(path, model_data_path)) + elif isinstance(model, lc_runnable_branch_types()): + model_data_path = _MODEL_DATA_FOLDER_NAME + _save_runnable_branch(model, os.path.join(path, model_data_path), loader_fn, persist_dir) + elif isinstance(model, lc_runnable_assign_types()): + model_data_path = _MODEL_DATA_FOLDER_NAME + _save_runnable_assign(model, os.path.join(path, model_data_path), loader_fn, persist_dir) + elif isinstance(model, lc_runnable_binding_types()): + model_data_path = _MODEL_DATA_FOLDER_NAME + _save_runnable_binding(model, os.path.join(path, model_data_path), loader_fn, persist_dir) + else: + raise MlflowException.invalid_parameter_value( + get_unsupported_model_message(type(model).__name__) + ) + model_data_kwargs[_MODEL_DATA_KEY] = model_data_path + return model_data_kwargs + + +def _load_runnables(path, conf): + model_type = conf.get(_MODEL_TYPE_KEY) + model_data = conf.get(_MODEL_DATA_KEY, _MODEL_DATA_YAML_FILE_NAME) + if model_type in (x.__name__ for x in lc_runnable_with_steps_types()): + return _load_runnable_with_steps(os.path.join(path, model_data), model_type) + if ( + model_type in (x.__name__ for x in picklable_runnable_types()) + or model_data == _MODEL_DATA_PKL_FILE_NAME + ): + return _load_from_pickle(os.path.join(path, model_data)) + if model_type in (x.__name__ for x in lc_runnable_branch_types()): + return _load_runnable_branch(os.path.join(path, model_data)) + if model_type in (x.__name__ for x in lc_runnable_assign_types()): + return _load_runnable_assign(os.path.join(path, model_data)) + if model_type in (x.__name__ for x in lc_runnable_binding_types()): + return _load_runnable_binding(os.path.join(path, model_data)) + raise MlflowException.invalid_parameter_value(get_unsupported_model_message(model_type)) + + +def get_runnable_steps(model: Runnable): + try: + return model.steps + except AttributeError: + # RunnableParallel stores steps as `steps__` attribute since version 0.16.0, while it was + # stored as `steps` attribute before that and other runnables like RunnableSequence still + # has `steps` property. + return model.steps__ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/langchain/utils/__pycache__/chat.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/langchain/utils/__pycache__/chat.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..deaa6bfb5f579617500e59ca21cfbb0a83da337f Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/langchain/utils/__pycache__/chat.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/langchain/utils/__pycache__/logging.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/langchain/utils/__pycache__/logging.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..b4b94362bdcf797cb2886d528518e196f3e83355 Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/langchain/utils/__pycache__/logging.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/langchain/utils/__pycache__/serialization.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/langchain/utils/__pycache__/serialization.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..8eee99c42a19454af97e032095bd1fb291032c0b Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/langchain/utils/__pycache__/serialization.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/langchain/utils/chat.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/langchain/utils/chat.py new file mode 100644 index 0000000000000000000000000000000000000000..631665db32a0f95df2d98f0077e1e7dd8e42652e --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/langchain/utils/chat.py @@ -0,0 +1,402 @@ +import json +import logging +import time +from collections import defaultdict +from typing import Any + +import pydantic +from langchain_core.messages import ( + AIMessage, + BaseMessage, + ChatMessage, + FunctionMessage, + HumanMessage, + SystemMessage, + ToolMessage, +) +from langchain_core.outputs.generation import Generation + +from mlflow.environment_variables import MLFLOW_CONVERT_MESSAGES_DICT_FOR_LANGCHAIN +from mlflow.exceptions import MlflowException +from mlflow.tracing.constant import TokenUsageKey +from mlflow.types.chat import ( + ChatChoice, + ChatChoiceDelta, + ChatChunkChoice, + ChatCompletionChunk, + ChatCompletionRequest, + ChatCompletionResponse, + ChatMessage, + ChatUsage, +) +from mlflow.utils import IS_PYDANTIC_V2_OR_NEWER + +_logger = logging.getLogger(__name__) + + +_TOKEN_USAGE_KEY_MAPPING = { + # OpenAI + "prompt_tokens": TokenUsageKey.INPUT_TOKENS, + "completion_tokens": TokenUsageKey.OUTPUT_TOKENS, + "total_tokens": TokenUsageKey.TOTAL_TOKENS, + # OpenAI Streaming, Anthropic, etc. + "input_tokens": TokenUsageKey.INPUT_TOKENS, + "output_tokens": TokenUsageKey.OUTPUT_TOKENS, +} + + +def convert_lc_message_to_chat_message(lc_message: BaseMessage) -> ChatMessage: + """ + Convert LangChain's message format to the MLflow's standard chat message format. + """ + if isinstance(lc_message, AIMessage): + if tool_calls := _get_tool_calls_from_ai_message(lc_message): + content = lc_message.content + # For Anthropic model tool calls are returned twice so we need to filter them out + if isinstance(content, list): + content = [c for c in content if c["type"] != "tool_use"] + return ChatMessage( + role="assistant", + # If tool calls present, content null value should be None not empty string + # according to the OpenAI spec, which ChatMessage is following + # Ref: https://github.com/langchain-ai/langchain/blob/32917a0b98cb8edcfb8d0e84f0878434e1c3f192/libs/partners/openai/langchain_openai/chat_models/base.py#L116-L117 + content=content or None, + tool_calls=tool_calls, + ) + else: + return ChatMessage(role="assistant", content=lc_message.content) + elif isinstance(lc_message, ChatMessage): + return ChatMessage(role=lc_message.role, content=lc_message.content) + elif isinstance(lc_message, FunctionMessage): + return ChatMessage(role="function", content=lc_message.content) + elif isinstance(lc_message, ToolMessage): + return ChatMessage( + role="tool", + content=lc_message.content, + tool_call_id=lc_message.tool_call_id, + ) + elif isinstance(lc_message, HumanMessage): + return ChatMessage(role="user", content=lc_message.content) + elif isinstance(lc_message, SystemMessage): + return ChatMessage(role="system", content=lc_message.content) + else: + raise MlflowException.invalid_parameter_value( + f"Unexpected message type. Expected a BaseMessage subclass, but got: {type(lc_message)}" + ) + + +def _chat_model_to_langchain_message(message: ChatMessage) -> BaseMessage: + """ + Convert the MLflow's standard chat message format to LangChain's message format. + """ + if message.role == "system": + return SystemMessage(content=message.content) + elif message.role == "assistant": + return AIMessage(content=message.content) + elif message.role == "user": + return HumanMessage(content=message.content) + elif message.role == "tool": + return ToolMessage(content=message.content, tool_call_id=message.tool_call_id) + elif message.role == "function": + return FunctionMessage(content=message.content) + else: + raise MlflowException.invalid_parameter_value( + f"Unrecognized chat message role: {message.role}" + ) + + +def _get_tool_calls_from_ai_message(message: AIMessage) -> list[dict[str, Any]]: + # AIMessage does not have tool_calls field in LangChain < 0.1.0. + if not hasattr(message, "tool_calls"): + return [] + + tool_calls = [ + { + "type": "function", + "id": tc["id"], + "function": { + "name": tc["name"], + "arguments": json.dumps(tc["args"]), + }, + } + for tc in message.tool_calls + ] + + invalid_tool_calls = [ + { + "type": "function", + "id": tc["id"], + "function": { + "name": tc["name"], + "arguments": tc["args"], + }, + } + for tc in message.invalid_tool_calls + ] + + if tool_calls or invalid_tool_calls: + return tool_calls + invalid_tool_calls + + # Get tool calls from additional kwargs if present. + return [ + { + k: v + for k, v in tool_call.items() # type: ignore[union-attr] + if k in {"id", "type", "function"} + } + for tool_call in message.additional_kwargs.get("tool_calls", []) + ] + + +def try_transform_response_to_chat_format(response: Any) -> dict[str, Any]: + """ + Try to convert the response to the standard chat format and return its dict representation. + + If the response is not one of the supported types, return the response as-is. + """ + if isinstance(response, (str, AIMessage)): + if isinstance(response, str): + message_id = None + message = ChatMessage(role="assistant", content=response) + else: + message_id = getattr(response, "id", None) + message = convert_lc_message_to_chat_message(response) + + transformed_response = ChatCompletionResponse( + id=message_id, + created=int(time.time()), + model="", + object="chat.completion", + choices=[ + ChatChoice( + index=0, + message=message, + finish_reason=None, + ) + ], + usage=ChatUsage( + prompt_tokens=None, + completion_tokens=None, + total_tokens=None, + ), + ) + if IS_PYDANTIC_V2_OR_NEWER: + return transformed_response.model_dump(mode="json", exclude_unset=True) + else: + return json.loads(transformed_response.json(exclude_unset=True)) + else: + return response + + +def try_transform_response_iter_to_chat_format(chunk_iter): + from langchain_core.messages.ai import AIMessageChunk + + def _gen_converted_chunk(message_content, message_id, finish_reason): + transformed_response = ChatCompletionChunk( + id=message_id, + created=int(time.time()), + model="", + choices=[ + ChatChunkChoice( + index=0, + delta=ChatChoiceDelta( + role="assistant", + content=message_content, + ), + finish_reason=finish_reason, + ) + ], + ) + + if IS_PYDANTIC_V2_OR_NEWER: + return transformed_response.model_dump(mode="json") + else: + return json.loads(transformed_response.json()) + + def _convert(chunk): + if isinstance(chunk, str): + message_content = chunk + message_id = None + finish_reason = None + elif isinstance(chunk, AIMessageChunk): + message_content = chunk.content + message_id = getattr(chunk, "id", None) + + if response_metadata := getattr(chunk, "response_metadata", None): + finish_reason = response_metadata.get("finish_reason") + else: + finish_reason = None + elif isinstance(chunk, AIMessage): + # The langchain chat model does not support stream + # so `model.stream` returns the whole result. + message_content = chunk.content + message_id = getattr(chunk, "id", None) + finish_reason = "stop" + else: + return chunk + return _gen_converted_chunk( + message_content, + message_id=message_id, + finish_reason=finish_reason, + ) + + return map(_convert, chunk_iter) + + +def _convert_chat_request_or_throw( + chat_request: dict[str, Any], +) -> list[BaseMessage]: + model = ChatCompletionRequest.validate_compat(chat_request) + return [_chat_model_to_langchain_message(message) for message in model.messages] + + +def _convert_chat_request(chat_request: dict[str, Any] | list[dict[str, Any]]): + if isinstance(chat_request, list): + return [_convert_chat_request_or_throw(request) for request in chat_request] + else: + return _convert_chat_request_or_throw(chat_request) + + +def _get_lc_model_input_fields(lc_model) -> set[str]: + try: + if hasattr(lc_model, "input_schema"): + return set(lc_model.input_schema.__fields__) + except Exception as e: + _logger.debug( + f"Unexpected exception while checking LangChain input schema for" + f" request transformation: {e}" + ) + + return set() + + +def _should_transform_request_json_for_chat(lc_model): + # Don't convert the request to LangChain's Message format for LangGraph models. + # Inputs may have key like "messages", but they are graph state fields, not OAI chat format. + try: + from langgraph.graph.state import CompiledStateGraph + + if isinstance(lc_model, CompiledStateGraph): + return False + except ImportError: + pass + + # Avoid converting the request to LangChain's Message format if the chain + # is an AgentExecutor, as LangChainChatMessage might not be accepted by the chain + from langchain.agents import AgentExecutor + + if isinstance(lc_model, AgentExecutor): + return False + + input_fields = _get_lc_model_input_fields(lc_model) + if "messages" in input_fields: + # If the chain accepts a "messages" field directly, don't attempt to convert + # the request to LangChain's Message format automatically. Assume that the chain + # is handling the "messages" field by itself + return False + + return True + + +def transform_request_json_for_chat_if_necessary(request_json, lc_model): + """ + Convert the input request JSON to LangChain's Message format if the LangChain model + accepts ChatMessage objects (e.g. AIMessage, HumanMessage, SystemMessage) as input. + + Args: + request_json: The input request JSON. + lc_model: The LangChain model. + + Returns: + A 2-element tuple containing: + + 1. The new request. + 2. A boolean indicating whether or not the request was transformed from the OpenAI + chat format. + """ + + def json_dict_might_be_chat_request(json_message): + return ( + isinstance(json_message, dict) + and "messages" in json_message + and + # Additional keys can't be specified when calling LangChain invoke() / batch() + # with chat messages + len(json_message) == 1 + # messages field should be a list + and isinstance(json_message["messages"], list) + ) + + def is_list_of_chat_messages(json_message: list[dict[str, Any]]): + return isinstance(json_message, list) and all( + json_dict_might_be_chat_request(message) for message in json_message + ) + + should_convert = MLFLOW_CONVERT_MESSAGES_DICT_FOR_LANGCHAIN.get() + if should_convert is None: + should_convert = _should_transform_request_json_for_chat(lc_model) and ( + json_dict_might_be_chat_request(request_json) or is_list_of_chat_messages(request_json) + ) + if should_convert: + _logger.debug( + "Converting the request JSON to LangChain's Message format. " + "To disable this conversion, set the environment variable " + f"`{MLFLOW_CONVERT_MESSAGES_DICT_FOR_LANGCHAIN}` to 'false'." + ) + + if should_convert: + try: + return _convert_chat_request(request_json), True + except pydantic.ValidationError: + _logger.debug( + "Failed to convert the request JSON to LangChain's Message format. " + "The request will be passed to the LangChain model as-is. ", + exc_info=True, + ) + return request_json, False + else: + return request_json, False + + +def parse_token_usage( + lc_generations: list[Generation], +) -> dict[str, int] | None: + """Parse the token usage from the LangChain generations.""" + aggregated = defaultdict(int) + for generation in lc_generations: + if token_usage := _parse_token_usage_from_generation(generation): + for key in token_usage: + aggregated[key] += token_usage[key] + + return dict(aggregated) if aggregated else None + + +def _parse_token_usage_from_generation( + generation: Generation, +) -> dict[str, int] | None: + message = getattr(generation, "message", None) + if not message: + return None + + metadata = ( + message.usage_metadata + or message.response_metadata.get("usage") + or message.response_metadata.get("token_usage") + ) + return _parse_token_counts(metadata) if metadata else None + + +def _parse_token_counts(usage_metadata: dict[str, Any]) -> dict[str, int]: + """Standardize token usage metadata keys to MLflow's token usage keys.""" + usage = {} + for key, value in usage_metadata.items(): + if usage_key := _TOKEN_USAGE_KEY_MAPPING.get(key): + usage[usage_key] = value + + # If the total tokens are not present, calculate it from the input and output tokens + if usage and usage.get(TokenUsageKey.TOTAL_TOKENS) is None: + usage[TokenUsageKey.TOTAL_TOKENS] = usage.get(TokenUsageKey.INPUT_TOKENS, 0) + usage.get( + TokenUsageKey.OUTPUT_TOKENS, 0 + ) + + return usage diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/langchain/utils/logging.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/langchain/utils/logging.py new file mode 100644 index 0000000000000000000000000000000000000000..3a4c08604ff4a66bfcb5674611c8a7ffabf8aa4a --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/langchain/utils/logging.py @@ -0,0 +1,671 @@ +"""Utility functions for mlflow.langchain.""" + +import contextlib +import functools +import importlib +import json +import logging +import os +import re +import shutil +import types +import warnings +from functools import lru_cache +from importlib.util import find_spec +from typing import Any, Callable, NamedTuple + +import cloudpickle +import yaml +from packaging import version +from packaging.version import Version + +import mlflow +from mlflow.exceptions import MlflowException +from mlflow.models.utils import _validate_and_get_model_code_path +from mlflow.protos.databricks_pb2 import INTERNAL_ERROR +from mlflow.utils.class_utils import _get_class_from_string + +_AGENT_PRIMITIVES_FILE_NAME = "agent_primitive_args.json" +_AGENT_PRIMITIVES_DATA_KEY = "agent_primitive_data" +_AGENT_DATA_FILE_NAME = "agent.yaml" +_AGENT_DATA_KEY = "agent_data" +_TOOLS_DATA_FILE_NAME = "tools.pkl" +_TOOLS_DATA_KEY = "tools_data" +_LOADER_FN_FILE_NAME = "loader_fn.pkl" +_LOADER_FN_KEY = "loader_fn" +_LOADER_ARG_KEY = "loader_arg" +_PERSIST_DIR_NAME = "persist_dir_data" +_PERSIST_DIR_KEY = "persist_dir" +_MODEL_DATA_YAML_FILE_NAME = "model.yaml" +_MODEL_DATA_PKL_FILE_NAME = "model.pkl" +_MODEL_DATA_FOLDER_NAME = "model" +_MODEL_DATA_KEY = "model_data" +_MODEL_TYPE_KEY = "model_type" +_RUNNABLE_LOAD_KEY = "runnable_load" +_BASE_LOAD_KEY = "base_load" +_CONFIG_LOAD_KEY = "config_load" +_PICKLE_LOAD_KEY = "pickle_load" +_MODEL_LOAD_KEY = "model_load" +_UNSUPPORTED_MODEL_WARNING_MESSAGE = ( + "MLflow does not guarantee support for Chains outside of the subclasses of LLMChain, found %s" +) +_UNSUPPORTED_LLM_WARNING_MESSAGE = ( + "MLflow does not guarantee support for LLMs outside of HuggingFacePipeline and OpenAI, found %s" +) + + +_CHAT_MODELS_ERROR_MSG = re.compile("Loading (openai-chat|azure-openai-chat) LLM not supported") + + +try: + import langchain_community + + # Since langchain-community 0.0.27, saving or loading a module that relies on the pickle + # deserialization requires passing `allow_dangerous_deserialization=True`. + IS_PICKLE_SERIALIZATION_RESTRICTED = Version(langchain_community.__version__) >= Version( + "0.0.27" + ) +except ImportError: + IS_PICKLE_SERIALIZATION_RESTRICTED = False + +logger = logging.getLogger(__name__) + + +@lru_cache +def base_lc_types(): + # add this import to avoid missing module error + import langchain.agents + import langchain.agents.agent + import langchain.chains.base + import langchain.schema + + return ( + langchain.chains.base.Chain, + langchain.agents.agent.AgentExecutor, + langchain.schema.BaseRetriever, + ) + + +@lru_cache +def picklable_runnable_types(): + """ + Runnable types that can be pickled and unpickled by cloudpickle. + """ + from langchain.chat_models.base import SimpleChatModel + from langchain.prompts import ChatPromptTemplate + from langchain.schema.runnable import RunnableLambda, RunnablePassthrough + + return ( + SimpleChatModel, + ChatPromptTemplate, + RunnablePassthrough, + RunnableLambda, + ) + + +@lru_cache +def lc_runnable_with_steps_types(): + from langchain.schema.runnable import RunnableParallel, RunnableSequence + + return (RunnableParallel, RunnableSequence) + + +def lc_runnable_assign_types(): + from langchain.schema.runnable.passthrough import RunnableAssign + + return (RunnableAssign,) + + +def lc_runnable_branch_types(): + from langchain.schema.runnable import RunnableBranch + + return (RunnableBranch,) + + +def lc_runnable_binding_types(): + from langchain.schema.runnable import RunnableBinding + + return (RunnableBinding,) + + +def lc_runnables_types(): + return ( + picklable_runnable_types() + + lc_runnable_with_steps_types() + + lc_runnable_branch_types() + + lc_runnable_assign_types() + + lc_runnable_binding_types() + ) + + +def langgraph_types(): + try: + from langgraph.graph.state import CompiledStateGraph + + return (CompiledStateGraph,) + except ImportError: + return () + + +def supported_lc_types(): + return base_lc_types() + lc_runnables_types() + langgraph_types() + + +# Wrapping as a function to avoid callign supported_lc_types() at import time +def get_unsupported_model_message(model_type): + return ( + "MLflow langchain flavor only supports subclasses of " + f"{supported_lc_types()}, found {model_type}." + ) + + +@lru_cache +def custom_type_to_loader_dict(): + # helper function to load output_parsers from config + def _load_output_parser(config: dict[str, Any]) -> Any: + """Load output parser.""" + from langchain.schema.output_parser import StrOutputParser + + output_parser_type = config.pop("_type", None) + if output_parser_type == "default": + return StrOutputParser(**config) + else: + raise ValueError(f"Unsupported output parser {output_parser_type}") + + return {"default": _load_output_parser} + + +class _SpecialChainInfo(NamedTuple): + loader_arg: str + + +def _get_special_chain_info_or_none(chain): + for ( + special_chain_class, + loader_arg, + ) in _get_map_of_special_chain_class_to_loader_arg().items(): + if isinstance(chain, special_chain_class): + return _SpecialChainInfo(loader_arg=loader_arg) + + +@lru_cache +def _get_map_of_special_chain_class_to_loader_arg(): + import langchain + + from mlflow.langchain.retriever_chain import _RetrieverChain + + class_name_to_loader_arg = { + "langchain.chains.RetrievalQA": "retriever", + "langchain.chains.APIChain": "requests_wrapper", + "langchain.chains.HypotheticalDocumentEmbedder": "embeddings", + } + # NB: SQLDatabaseChain was migrated to langchain_experimental beginning with version 0.0.247 + if version.parse(langchain.__version__) <= version.parse("0.0.246"): + class_name_to_loader_arg["langchain.chains.SQLDatabaseChain"] = "database" + else: + if find_spec("langchain_experimental"): + # Add this entry only if langchain_experimental is installed + class_name_to_loader_arg["langchain_experimental.sql.SQLDatabaseChain"] = "database" + + class_to_loader_arg = { + _RetrieverChain: "retriever", + } + for class_name, loader_arg in class_name_to_loader_arg.items(): + try: + cls = _get_class_from_string(class_name) + class_to_loader_arg[cls] = loader_arg + except Exception: + logger.warning( + "Unexpected import failure for class '%s'. Please file an issue at" + " https://github.com/mlflow/mlflow/issues/.", + class_name, + exc_info=True, + ) + + return class_to_loader_arg + + +@lru_cache +def _get_supported_llms(): + supported_llms = set() + + def try_adding_llm(module, class_name): + if cls := getattr(module, class_name, None): + supported_llms.add(cls) + + def safe_import_and_add(module_name, class_name): + """Add conditional support for `partner` and `community` APIs in langchain""" + try: + module = importlib.import_module(module_name) + try_adding_llm(module, class_name) + except ImportError: + pass + + safe_import_and_add("langchain.llms.openai", "OpenAI") + # HuggingFacePipeline is moved to langchain_huggingface since langchain 0.2.0 + safe_import_and_add("langchain.llms", "HuggingFacePipeline") + safe_import_and_add("langchain.langchain_huggingface", "HuggingFacePipeline") + safe_import_and_add("langchain_openai", "OpenAI") + safe_import_and_add("langchain_databricks", "ChatDatabricks") + safe_import_and_add("databricks_langchain", "ChatDatabricks") + + for llm_name in ["Databricks", "Mlflow"]: + safe_import_and_add("langchain.llms", llm_name) + + for chat_model_name in [ + "ChatDatabricks", + "ChatMlflow", + "ChatOpenAI", + "AzureChatOpenAI", + ]: + safe_import_and_add("langchain.chat_models", chat_model_name) + + return supported_llms + + +def _agent_executor_contains_unsupported_llm(lc_model, _SUPPORTED_LLMS): + import langchain.agents.agent + + return ( + isinstance(lc_model, langchain.agents.agent.AgentExecutor) + # 'RunnableMultiActionAgent' object has no attribute 'llm_chain' + and hasattr(lc_model.agent, "llm_chain") + and not any( + isinstance(lc_model.agent.llm_chain.llm, supported_llm) + for supported_llm in _SUPPORTED_LLMS + ) + ) + + +# temp_dir is only required when lc_model could be a file path +def _validate_and_prepare_lc_model_or_path(lc_model, loader_fn, temp_dir=None): + import langchain.agents.agent + import langchain.chains.base + import langchain.chains.llm + import langchain.llms.huggingface_hub + import langchain.llms.openai + import langchain.schema + + # lc_model is a file path + if isinstance(lc_model, str): + return _validate_and_get_model_code_path(lc_model, temp_dir) + + if not isinstance(lc_model, supported_lc_types()): + raise mlflow.MlflowException.invalid_parameter_value( + get_unsupported_model_message(type(lc_model).__name__) + ) + + _SUPPORTED_LLMS = _get_supported_llms() + if isinstance(lc_model, langchain.chains.llm.LLMChain) and not any( + isinstance(lc_model.llm, supported_llm) for supported_llm in _SUPPORTED_LLMS + ): + logger.warning( + _UNSUPPORTED_LLM_WARNING_MESSAGE, + type(lc_model.llm).__name__, + ) + + if _agent_executor_contains_unsupported_llm(lc_model, _SUPPORTED_LLMS): + logger.warning( + _UNSUPPORTED_LLM_WARNING_MESSAGE, + type(lc_model.agent.llm_chain.llm).__name__, + ) + + if special_chain_info := _get_special_chain_info_or_none(lc_model): + if loader_fn is None: + raise mlflow.MlflowException.invalid_parameter_value( + f"For {type(lc_model).__name__} models, a `loader_fn` must be provided." + ) + if not isinstance(loader_fn, types.FunctionType): + raise mlflow.MlflowException.invalid_parameter_value( + "The `loader_fn` must be a function that returns a {loader_arg}.".format( + loader_arg=special_chain_info.loader_arg + ) + ) + + # If lc_model is a retriever, wrap it in a _RetrieverChain + if isinstance(lc_model, langchain.schema.BaseRetriever): + from mlflow.langchain.retriever_chain import _RetrieverChain + + if loader_fn is None: + raise mlflow.MlflowException.invalid_parameter_value( + f"For {type(lc_model).__name__} models, a `loader_fn` must be provided." + ) + if not isinstance(loader_fn, types.FunctionType): + raise mlflow.MlflowException.invalid_parameter_value( + "The `loader_fn` must be a function that returns a retriever." + ) + lc_model = _RetrieverChain(retriever=lc_model) + + return lc_model + + +def _save_base_lcs(model, path, loader_fn=None, persist_dir=None): + from langchain.agents.agent import AgentExecutor + from langchain.chains.base import Chain + from langchain.chains.llm import LLMChain + from langchain.chat_models.base import BaseChatModel + + model_data_path = os.path.join(path, _MODEL_DATA_YAML_FILE_NAME) + model_data_kwargs = { + _MODEL_DATA_KEY: _MODEL_DATA_YAML_FILE_NAME, + _MODEL_LOAD_KEY: _BASE_LOAD_KEY, + } + + if isinstance(model, (LLMChain, BaseChatModel)): + model.save(model_data_path) + elif isinstance(model, AgentExecutor): + if model.agent and getattr(model.agent, "llm_chain", None): + model.agent.llm_chain.save(model_data_path) + + if model.agent: + agent_data_path = os.path.join(path, _AGENT_DATA_FILE_NAME) + model.save_agent(agent_data_path) + model_data_kwargs[_AGENT_DATA_KEY] = _AGENT_DATA_FILE_NAME + + if model.tools: + tools_data_path = os.path.join(path, _TOOLS_DATA_FILE_NAME) + try: + with open(tools_data_path, "wb") as f: + cloudpickle.dump(model.tools, f) + except Exception as e: + raise mlflow.MlflowException( + "Error when attempting to pickle the AgentExecutor tools. " + "This model likely does not support serialization." + ) from e + model_data_kwargs[_TOOLS_DATA_KEY] = _TOOLS_DATA_FILE_NAME + else: + raise mlflow.MlflowException.invalid_parameter_value( + "For initializing the AgentExecutor, tools must be provided." + ) + + key_to_ignore = ["llm_chain", "agent", "tools", "callback_manager"] + temp_dict = {k: v for k, v in model.__dict__.items() if k not in key_to_ignore} + + agent_primitive_path = os.path.join(path, _AGENT_PRIMITIVES_FILE_NAME) + with open(agent_primitive_path, "w") as config_file: + json.dump(temp_dict, config_file, indent=4) + + model_data_kwargs[_AGENT_PRIMITIVES_DATA_KEY] = _AGENT_PRIMITIVES_FILE_NAME + + elif special_chain_info := _get_special_chain_info_or_none(model): + # Save loader_fn by pickling + loader_fn_path = os.path.join(path, _LOADER_FN_FILE_NAME) + with open(loader_fn_path, "wb") as f: + cloudpickle.dump(loader_fn, f) + model_data_kwargs[_LOADER_FN_KEY] = _LOADER_FN_FILE_NAME + model_data_kwargs[_LOADER_ARG_KEY] = special_chain_info.loader_arg + + if persist_dir is not None: + if os.path.exists(persist_dir): + # Save persist_dir by copying into subdir _PERSIST_DIR_NAME + persist_dir_data_path = os.path.join(path, _PERSIST_DIR_NAME) + shutil.copytree(persist_dir, persist_dir_data_path) + model_data_kwargs[_PERSIST_DIR_KEY] = _PERSIST_DIR_NAME + else: + raise mlflow.MlflowException.invalid_parameter_value( + "The directory provided for persist_dir does not exist." + ) + + # Save model + model.save(model_data_path) + elif isinstance(model, Chain): + logger.warning(get_unsupported_model_message(type(model).__name__)) + model.save(model_data_path) + else: + raise mlflow.MlflowException.invalid_parameter_value( + get_unsupported_model_message(type(model).__name__) + ) + + return model_data_kwargs + + +def _load_from_pickle(path): + with open(path, "rb") as f: + return cloudpickle.load(f) + + +def _load_from_json(path): + with open(path) as f: + return json.load(f) + + +def _load_from_yaml(path): + with open(path) as f: + return yaml.safe_load(f) + + +def _get_path_by_key(root_path, key, conf): + key_path = conf.get(key) + return os.path.join(root_path, key_path) if key_path else None + + +def _patch_loader(loader_func: Callable[..., Any]) -> Callable[..., Any]: + """ + Patch LangChain loader function like load_chain() to handle the breaking change introduced in + LangChain 0.1.12. + + Since langchain-community 0.0.27, loading a module that relies on the pickle deserialization + requires the `allow_dangerous_deserialization` flag to be set to True, for security reasons. + However, this flag could not be specified via the LangChain's loading API like load_chain(), + load_llm(), until LangChain 0.1.14. As a result, such module cannot be loaded with MLflow + with earlier version of LangChain and we have to tell the user to upgrade LangChain to 0.0.14 + or above. + + Args: + loader_func: The LangChain loader function to be patched e.g. load_chain(). + + Returns: + The patched loader function. + """ + if not IS_PICKLE_SERIALIZATION_RESTRICTED: + return loader_func + + import langchain + + if Version(langchain.__version__) >= Version("0.1.14"): + # For LangChain 0.1.14 and above, we can pass `allow_dangerous_deserialization` flag + # via the loader APIs. Since the model is serialized by the user (or someone who has + # access to the tracking server), it is safe to set this flag to True. + def patched_loader(*args, **kwargs): + return loader_func(*args, **kwargs, allow_dangerous_deserialization=True) + else: + + def patched_loader(*args, **kwargs): + try: + return loader_func(*args, **kwargs) + except ValueError as e: + if "This code relies on the pickle module" in str(e): + raise MlflowException( + "Since langchain-community 0.0.27, loading a module that relies on " + "the pickle deserialization requires the `allow_dangerous_deserialization` " + "flag to be set to True when loading. However, this flag is not supported " + "by the installed version of LangChain. Please upgrade LangChain to 0.1.14 " + "or above by running `pip install langchain>=0.1.14`.", + error_code=INTERNAL_ERROR, + ) from e + else: + raise + + return patched_loader + + +def _load_base_lcs( + local_model_path, + conf, +): + lc_model_path = os.path.join( + local_model_path, conf.get(_MODEL_DATA_KEY, _MODEL_DATA_YAML_FILE_NAME) + ) + + agent_path = _get_path_by_key(local_model_path, _AGENT_DATA_KEY, conf) + tools_path = _get_path_by_key(local_model_path, _TOOLS_DATA_KEY, conf) + agent_primitive_path = _get_path_by_key(local_model_path, _AGENT_PRIMITIVES_DATA_KEY, conf) + loader_fn_path = _get_path_by_key(local_model_path, _LOADER_FN_KEY, conf) + persist_dir = _get_path_by_key(local_model_path, _PERSIST_DIR_KEY, conf) + + model_type = conf.get(_MODEL_TYPE_KEY) + loader_arg = conf.get(_LOADER_ARG_KEY) + + from langchain.chains.loading import load_chain + + from mlflow.langchain.retriever_chain import _RetrieverChain + + if loader_arg is not None: + if loader_fn_path is None: + raise mlflow.MlflowException.invalid_parameter_value( + "Missing file for loader_fn which is required to build the model." + ) + loader_fn = _load_from_pickle(loader_fn_path) + kwargs = {loader_arg: loader_fn(persist_dir)} + if model_type == _RetrieverChain.__name__: + model = _RetrieverChain.load(lc_model_path, **kwargs).retriever + else: + model = _patch_loader(load_chain)(lc_model_path, **kwargs) + elif agent_path is None and tools_path is None: + model = _patch_loader(load_chain)(lc_model_path) + else: + from langchain.agents import initialize_agent + + llm = _patch_loader(load_chain)(lc_model_path) + tools = [] + kwargs = {} + + if os.path.exists(tools_path): + tools = _load_from_pickle(tools_path) + else: + raise mlflow.MlflowException( + "Missing file for tools which is required to build the AgentExecutor object." + ) + + if os.path.exists(agent_primitive_path): + kwargs = _load_from_json(agent_primitive_path) + + model = initialize_agent(tools=tools, llm=llm, agent_path=agent_path, **kwargs) + return model + + +def patch_langchain_type_to_cls_dict(func): + @functools.wraps(func) + def wrapper(*args, **kwargs): + def _load_chat_openai(): + from langchain_community.chat_models import ChatOpenAI + + return ChatOpenAI + + def _load_azure_chat_openai(): + from langchain_community.chat_models import AzureChatOpenAI + + return AzureChatOpenAI + + def _load_chat_databricks(): + from databricks_langchain import ChatDatabricks + + return ChatDatabricks + + def _patched_get_type_to_cls_dict(original): + def _wrapped(): + return { + **original(), + "openai-chat": _load_chat_openai, + "azure-openai-chat": _load_azure_chat_openai, + "chat-databricks": _load_chat_databricks, + } + + return _wrapped + + modules_to_patch = [ + "langchain_databricks", + "langchain.llms", + "langchain_community.llms.loading", + ] + originals = {} + for name in modules_to_patch: + try: + module = importlib.import_module(name) + originals[name] = module.get_type_to_cls_dict # Record original impl for cleanup + except (ImportError, AttributeError): + continue + module.get_type_to_cls_dict = _patched_get_type_to_cls_dict(originals[name]) + + try: + return func(*args, **kwargs) + except ValueError as e: + if m := _CHAT_MODELS_ERROR_MSG.search(str(e)): + model_name = "ChatOpenAI" if m.group(1) == "openai-chat" else "AzureChatOpenAI" + raise mlflow.MlflowException( + f"Loading {model_name} chat model is not supported in MLflow with the " + "current version of LangChain. Please upgrade LangChain to 0.0.307 or above " + "by running `pip install langchain>=0.0.307`." + ) from e + else: + raise + finally: + # Clean up the patch + for module_name, original_impl in originals.items(): + module = importlib.import_module(module_name) + module.get_type_to_cls_dict = original_impl + + return wrapper + + +def register_pydantic_serializer(): + """ + Helper function to pickle pydantic fields for pydantic v1. + Pydantic's Cython validators are not serializable. + https://github.com/cloudpipe/cloudpickle/issues/408 + """ + import pydantic + + if Version(pydantic.__version__) >= Version("2.0.0"): + return + + import pydantic.fields + + def custom_serializer(obj): + return { + "name": obj.name, + # outer_type_ is the original type for ModelFields, + # while type_ can be updated later with the nested type + # like int for List[int]. + "type_": obj.outer_type_, + "class_validators": obj.class_validators, + "model_config": obj.model_config, + "default": obj.default, + "default_factory": obj.default_factory, + "required": obj.required, + "final": obj.final, + "alias": obj.alias, + "field_info": obj.field_info, + } + + def custom_deserializer(kwargs): + return pydantic.fields.ModelField(**kwargs) + + def _CloudPicklerReducer(obj): + return custom_deserializer, (custom_serializer(obj),) + + warnings.warn( + "Using custom serializer to pickle pydantic.fields.ModelField classes, " + "this might miss some fields and validators. To avoid this, " + "please upgrade pydantic to v2 using `pip install pydantic -U` with " + "langchain 0.0.267 and above." + ) + cloudpickle.CloudPickler.dispatch[pydantic.fields.ModelField] = _CloudPicklerReducer + + +def unregister_pydantic_serializer(): + import pydantic + + if Version(pydantic.__version__) >= Version("2.0.0"): + return + + cloudpickle.CloudPickler.dispatch.pop(pydantic.fields.ModelField, None) + + +@contextlib.contextmanager +def register_pydantic_v1_serializer_cm(): + try: + register_pydantic_serializer() + yield + finally: + unregister_pydantic_serializer() diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/langchain/utils/serialization.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/langchain/utils/serialization.py new file mode 100644 index 0000000000000000000000000000000000000000..1412d71c12256b43711edf8e27e5c8f9f03b75b8 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/langchain/utils/serialization.py @@ -0,0 +1,36 @@ +import inspect + +from packaging.version import Version + + +def convert_to_serializable(response): + """ + Convert the response to a JSON serializable format. + + LangChain response objects often contains Pydantic objects, which causes an serialization + error when the model is served behind REST endpoint. + """ + import langchain + + # LangChain >= 0.3.0 uses Pydantic 2.x while < 0.3.0 is based on Pydantic 1.x. + if Version(langchain.__version__) >= Version("0.3.0"): + from pydantic import BaseModel + + if isinstance(response, BaseModel): + return response.model_dump() + else: + from langchain_core.pydantic_v1 import BaseModel as LangChainBaseModel + + if isinstance(response, LangChainBaseModel): + return response.dict() + + if inspect.isgenerator(response): + return (convert_to_serializable(chunk) for chunk in response) + elif isinstance(response, dict): + return {k: convert_to_serializable(v) for k, v in response.items()} + elif isinstance(response, list): + return [convert_to_serializable(v) for v in response] + elif isinstance(response, tuple): + return tuple(convert_to_serializable(v) for v in response) + + return response diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/legacy_databricks_cli/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/legacy_databricks_cli/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/legacy_databricks_cli/__pycache__/__init__.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/legacy_databricks_cli/__pycache__/__init__.cpython-312.pyc 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environmental variables, +# and make ProfileConfigProvider supporting 'databricks-cli' authentication way, +# 'databricks-cli' authentication way is for supporting U2M authentication. +# +# This is the original legacy databricks CLI python library provider module code: +# https://github.com/databricks/databricks-cli/blob/0.18.0/databricks_cli/configure/provider.py +# +# The latest Databricks Runtime does not contain legacy databricks CLI +# but MLflow still depends on it. + +import logging +import os +import sys +import time +from abc import ABCMeta, abstractmethod +from configparser import ConfigParser +from os.path import expanduser, join + +_logger = logging.getLogger(__name__) + +_home = expanduser("~") +CONFIG_FILE_ENV_VAR = "DATABRICKS_CONFIG_FILE" +HOST = "host" +USERNAME = "username" +PASSWORD = "password" +TOKEN = "token" +REFRESH_TOKEN = "refresh_token" +INSECURE = "insecure" +JOBS_API_VERSION = "jobs-api-version" +DEFAULT_SECTION = "DEFAULT" +CLIENT_ID = "client_id" +CLIENT_SECRET = "client_secret" +AUTH_TYPE = "auth_type" + +# User-provided override for the DatabricksConfigProvider +_config_provider = None + + +class InvalidConfigurationError(RuntimeError): + @staticmethod + def for_profile(profile): + if profile is None: + return InvalidConfigurationError( + "You haven't configured the CLI yet! " + f"Please configure by entering `{sys.argv[0]} configure`" + ) + return InvalidConfigurationError( + f"You haven't configured the CLI yet for the profile {profile}! " + "Please configure by entering " + f"`{sys.argv[0]} configure --profile {profile}`" + ) + + +def _get_path(): + return os.environ.get(CONFIG_FILE_ENV_VAR, join(_home, ".databrickscfg")) + + +def _fetch_from_fs(): + raw_config = ConfigParser() + raw_config.read(_get_path()) + return raw_config + + +def _create_section_if_absent(raw_config, profile): + if not raw_config.has_section(profile) and profile != DEFAULT_SECTION: + raw_config.add_section(profile) + + +def _get_option_if_exists(raw_config, profile, option): + if profile == DEFAULT_SECTION: + # We must handle the DEFAULT_SECTION differently since it is not in the _sections property + # of raw config. + return raw_config.get(profile, option) if raw_config.has_option(profile, option) else None + # Check if option is defined in the profile. + elif option not in raw_config._sections.get(profile, {}).keys(): + return None + return raw_config.get(profile, option) + + +def _set_option(raw_config, profile, option, value): + if value: + raw_config.set(profile, option, value) + else: + raw_config.remove_option(profile, option) + + +def _overwrite_config(raw_config): + config_path = _get_path() + # Create config file with owner only rw permissions + if not os.path.exists(config_path): + file_descriptor = os.open(config_path, os.O_CREAT | os.O_RDWR, 0o600) + os.close(file_descriptor) + + # Change file permissions to owner only rw if that's not the case + if not os.stat(config_path).st_mode == 0o100600: + os.chmod(config_path, 0o600) + + with open(config_path, "w") as cfg: + raw_config.write(cfg) + + +def update_and_persist_config(profile, databricks_config): + """ + Takes a DatabricksConfig and adds the in memory contents to the persisted version of the + config. This will overwrite any other config that was persisted to the file system under the + same profile. + + Args: + profile: str + databricks_config: DatabricksConfig + """ + profile = profile if profile else DEFAULT_SECTION + raw_config = _fetch_from_fs() + _create_section_if_absent(raw_config, profile) + _set_option(raw_config, profile, HOST, databricks_config.host) + _set_option(raw_config, profile, USERNAME, databricks_config.username) + _set_option(raw_config, profile, PASSWORD, databricks_config.password) + _set_option(raw_config, profile, TOKEN, databricks_config.token) + _set_option(raw_config, profile, REFRESH_TOKEN, databricks_config.refresh_token) + _set_option(raw_config, profile, INSECURE, databricks_config.insecure) + _set_option(raw_config, profile, JOBS_API_VERSION, databricks_config.jobs_api_version) + _overwrite_config(raw_config) + + +def get_config(): + """ + Returns a DatabricksConfig containing the hostname and authentication used to talk to + the Databricks API. By default, we leverage the DefaultConfigProvider to get + this config, but this behavior may be overridden by calling 'set_config_provider' + + If no DatabricksConfig can be found, an InvalidConfigurationError will be raised. + """ + if _config_provider: + config = _config_provider.get_config() + if config: + return config + raise InvalidConfigurationError( + f"Custom provider returned no DatabricksConfig: {_config_provider}" + ) + + config = DefaultConfigProvider().get_config() + if config: + return config + raise InvalidConfigurationError.for_profile(None) + + +def get_config_for_profile(profile): + """ + [Deprecated] Reads from the filesystem and gets a DatabricksConfig for the + specified profile. If it does not exist, then return a DatabricksConfig with fields set + to None. + + Internal callers should prefer get_config() to use user-specified overrides, and + to return appropriate error messages as opposited to invalid configurations. + + If you want to read from a specific profile, please instead use + 'ProfileConfigProvider(profile).get_config()'. + + This method is maintained for backwards-compatibility. It may be removed in future versions. + + Returns: + DatabricksConfig + """ + profile = profile if profile else DEFAULT_SECTION + config = EnvironmentVariableConfigProvider().get_config() + if config and config.is_valid: + return config + + config = ProfileConfigProvider(profile).get_config() + if config: + return config + return DatabricksConfig.empty() + + +def set_config_provider(provider): + """ + Sets a DatabricksConfigProvider that will be used for all future calls to get_config(), + used by the Databricks CLI code to discover the user's credentials. + """ + global _config_provider + if provider and not isinstance(provider, DatabricksConfigProvider): + raise Exception(f"Must be instance of DatabricksConfigProvider: {_config_provider}") + _config_provider = provider + + +def get_config_provider(): + """ + Returns the current DatabricksConfigProvider. + If None, the DefaultConfigProvider will be used. + """ + return _config_provider + + +class DatabricksConfigProvider: + """ + Responsible for providing hostname and authentication information to make + API requests against the Databricks REST API. + This method should generally return None if it cannot provide credentials, in order + to facilitate chanining of providers. + """ + + __metaclass__ = ABCMeta + + @abstractmethod + def get_config(self): + pass + + +class DefaultConfigProvider(DatabricksConfigProvider): + """Look for credentials in a chain of default locations.""" + + def __init__(self): + # The order of providers here will be used to determine + # the precedence order for the config provider used in `get_config` + self._providers = ( + SparkTaskContextConfigProvider(), + EnvironmentVariableConfigProvider(), + ProfileConfigProvider(), + DatabricksModelServingConfigProvider(), + ) + + def get_config(self): + for provider in self._providers: + config = provider.get_config() + if config is not None and config.is_valid: + return config + return None + + +class SparkTaskContextConfigProvider(DatabricksConfigProvider): + """Loads credentials from Spark TaskContext if running in a Spark Executor.""" + + @staticmethod + def _get_spark_task_context_or_none(): + try: + from pyspark import TaskContext + + return TaskContext.get() + except ImportError: + return None + + @staticmethod + def set_insecure(x): + from pyspark import SparkContext + + new_val = "True" if x else None + SparkContext._active_spark_context.setLocalProperty("spark.databricks.ignoreTls", new_val) + + def get_config(self): + context = self._get_spark_task_context_or_none() + if context is not None: + host = context.getLocalProperty("spark.databricks.api.url") + token = context.getLocalProperty("spark.databricks.token") + insecure = context.getLocalProperty("spark.databricks.ignoreTls") + config = DatabricksConfig.from_token( + host=host, token=token, refresh_token=None, insecure=insecure, jobs_api_version=None + ) + if config.is_valid: + return config + return None + + +class EnvironmentVariableConfigProvider(DatabricksConfigProvider): + """Loads from system environment variables.""" + + def get_config(self): + host = os.environ.get("DATABRICKS_HOST") + username = os.environ.get("DATABRICKS_USERNAME") + password = os.environ.get("DATABRICKS_PASSWORD") + token = os.environ.get("DATABRICKS_TOKEN") + refresh_token = os.environ.get("DATABRICKS_REFRESH_TOKEN") + insecure = os.environ.get("DATABRICKS_INSECURE") + jobs_api_version = os.environ.get("DATABRICKS_JOBS_API_VERSION") + client_id = os.environ.get("DATABRICKS_CLIENT_ID") + client_secret = os.environ.get("DATABRICKS_CLIENT_SECRET") + + config = DatabricksConfig( + host, + username, + password, + token, + refresh_token, + insecure, + jobs_api_version, + client_id=client_id, + client_secret=client_secret, + ) + if config.is_valid: + return config + return None + + +class ProfileConfigProvider(DatabricksConfigProvider): + """Loads from the databrickscfg file.""" + + def __init__(self, profile=None): + self.profile = profile or DEFAULT_SECTION + + def get_config(self): + raw_config = _fetch_from_fs() + host = _get_option_if_exists(raw_config, self.profile, HOST) + username = _get_option_if_exists(raw_config, self.profile, USERNAME) + password = _get_option_if_exists(raw_config, self.profile, PASSWORD) + token = _get_option_if_exists(raw_config, self.profile, TOKEN) + refresh_token = _get_option_if_exists(raw_config, self.profile, REFRESH_TOKEN) + insecure = _get_option_if_exists(raw_config, self.profile, INSECURE) + jobs_api_version = _get_option_if_exists(raw_config, self.profile, JOBS_API_VERSION) + client_id = _get_option_if_exists(raw_config, self.profile, CLIENT_ID) + client_secret = _get_option_if_exists(raw_config, self.profile, CLIENT_SECRET) + auth_type = _get_option_if_exists(raw_config, self.profile, AUTH_TYPE) + config = DatabricksConfig( + host, + username, + password, + token, + refresh_token, + insecure, + jobs_api_version, + client_id=client_id, + client_secret=client_secret, + auth_type=auth_type, + ) + if config.is_valid: + return config + return None + + +class DatabricksModelServingConfigProvider(DatabricksConfigProvider): + """Loads from OAuth credentials in the Databricks Model Serving environment.""" + + def get_config(self): + from mlflow.utils.databricks_utils import should_fetch_model_serving_environment_oauth + + try: + if should_fetch_model_serving_environment_oauth(): + config = DatabricksModelServingConfigProvider._get_databricks_model_serving_config() + if config.is_valid: + return config + else: + return None + except Exception as e: + _logger.warning("Unexpected error resolving Databricks Model Serving config: %s", e) + + @staticmethod + def _get_databricks_model_serving_config(): + from mlflow.utils.databricks_utils import get_model_dependency_oauth_token + + # Since we do not record OAuth expiration time in OAuth file, perform periodic refresh + # of OAuth environment variable cache here. As currently configured (02/24) OAuth token + # in model serving environment is guaranteed to have at least 30 min remaining on TTL + # at any point in time but refresh at higher rate of every 5 min here to be safe + # and conform with refresh logic for Brickstore tables. + OAUTH_CACHE_REFRESH_DURATION_SEC = 5 * 60 + OAUTH_CACHE_ENV_VAR = "DB_DEPENDENCY_OAUTH_CACHE" + OAUTH_CACHE_EXPIRATION_ENV_VAR = "DB_DEPENDENCY_OAUTH_CACHE_EXPIRY_TS" + MODEL_SERVING_HOST_ENV_VAR = "DATABRICKS_MODEL_SERVING_HOST_URL" + DB_MODEL_SERVING_HOST_ENV_VAR = "DB_MODEL_SERVING_HOST_URL" + + # read from DB_MODEL_SERVING_HOST_ENV_VAR if available otherwise MODEL_SERVING_HOST_ENV_VAR + host = os.environ.get(DB_MODEL_SERVING_HOST_ENV_VAR) or os.environ.get( + MODEL_SERVING_HOST_ENV_VAR + ) + + # check if dependency is cached in env var before reading from file + oauth_token = "" + if ( + OAUTH_CACHE_ENV_VAR in os.environ + and OAUTH_CACHE_EXPIRATION_ENV_VAR in os.environ + and float(os.environ[OAUTH_CACHE_EXPIRATION_ENV_VAR]) > time.time() + ): + oauth_token = os.environ[OAUTH_CACHE_ENV_VAR] + else: + oauth_token = get_model_dependency_oauth_token() + os.environ[OAUTH_CACHE_ENV_VAR] = oauth_token + os.environ[OAUTH_CACHE_EXPIRATION_ENV_VAR] = str( + time.time() + OAUTH_CACHE_REFRESH_DURATION_SEC + ) + + return DatabricksConfig( + host=host, + token=oauth_token, + username=None, + password=None, + refresh_token=None, + insecure=None, + jobs_api_version=None, + ) + + +class DatabricksConfig: + def __init__( + self, + host, + username, + password, + token, + refresh_token=None, + insecure=None, + jobs_api_version=None, + client_id=None, + client_secret=None, + auth_type=None, + ): + self.host = host + self.username = username + self.password = password + self.token = token + self.refresh_token = refresh_token + self.insecure = insecure + self.jobs_api_version = jobs_api_version + self.client_id = client_id + self.client_secret = client_secret + self.auth_type = auth_type + + @classmethod + def from_token(cls, host, token, refresh_token=None, insecure=None, jobs_api_version=None): + return DatabricksConfig( + host=host, + username=None, + password=None, + token=token, + refresh_token=refresh_token, + insecure=insecure, + jobs_api_version=jobs_api_version, + ) + + @classmethod + def from_password(cls, host, username, password, insecure=None, jobs_api_version=None): + return DatabricksConfig( + host=host, + username=username, + password=password, + token=None, + refresh_token=None, + insecure=insecure, + jobs_api_version=jobs_api_version, + ) + + @classmethod + def empty(cls): + return DatabricksConfig( + host=None, + username=None, + password=None, + token=None, + refresh_token=None, + insecure=None, + jobs_api_version=None, + ) + + @property + def is_valid_with_token(self): + return self.host is not None and self.token is not None + + @property + def is_valid_with_password(self): + return self.host is not None and self.username is not None and self.password is not None + + @property + def is_valid_with_client_id_secret(self): + return self.host and self.client_id and self.client_secret + + @property + def is_databricks_cli_auth_type(self): + return self.auth_type == "databricks-cli" + + @property + def is_azure_cli_auth_type(self): + return self.auth_type == "azure-cli" + + @property + def is_valid(self): + return ( + self.is_valid_with_token + or self.is_valid_with_password + or self.is_valid_with_client_id_secret + or self.is_databricks_cli_auth_type + or self.is_azure_cli_auth_type + ) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/lightgbm/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/lightgbm/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..b4a4b70897d83a127bb3751df134a21aa2c859b1 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/lightgbm/__init__.py @@ -0,0 +1,978 @@ +""" +The ``mlflow.lightgbm`` module provides an API for logging and loading LightGBM models. +This module exports LightGBM models with the following flavors: + +LightGBM (native) format + This is the main flavor that can be loaded back into LightGBM. +:py:mod:`mlflow.pyfunc` + Produced for use by generic pyfunc-based deployment tools and batch inference. + +.. _lightgbm.Booster: + https://lightgbm.readthedocs.io/en/latest/pythonapi/lightgbm.Booster.html#lightgbm.Booster +.. _lightgbm.Booster.save_model: + https://lightgbm.readthedocs.io/en/latest/pythonapi/lightgbm.Booster.html + #lightgbm.Booster.save_model +.. _lightgbm.train: + https://lightgbm.readthedocs.io/en/latest/pythonapi/lightgbm.train.html#lightgbm-train +.. _scikit-learn API: + https://lightgbm.readthedocs.io/en/latest/Python-API.html#scikit-learn-api +""" + +import functools +import json +import logging +import os +import tempfile +from copy import deepcopy +from typing import Any + +import yaml +from packaging.version import Version + +import mlflow +from mlflow import pyfunc +from mlflow.data.code_dataset_source import CodeDatasetSource +from mlflow.data.numpy_dataset import from_numpy +from mlflow.data.pandas_dataset import from_pandas +from mlflow.entities.dataset_input import DatasetInput +from mlflow.entities.input_tag import InputTag +from mlflow.models import Model, ModelInputExample, ModelSignature, infer_signature +from mlflow.models.model import MLMODEL_FILE_NAME +from mlflow.models.signature import _infer_signature_from_input_example +from mlflow.models.utils import _save_example +from mlflow.sklearn import _SklearnTrainingSession +from mlflow.tracking._model_registry import DEFAULT_AWAIT_MAX_SLEEP_SECONDS +from mlflow.tracking.artifact_utils import _download_artifact_from_uri +from mlflow.tracking.context import registry as context_registry +from mlflow.tracking.fluent import _initialize_logged_model +from mlflow.utils import _get_fully_qualified_class_name +from mlflow.utils.arguments_utils import _get_arg_names +from mlflow.utils.autologging_utils import ( + ENSURE_AUTOLOGGING_ENABLED_TEXT, + INPUT_EXAMPLE_SAMPLE_ROWS, + InputExampleInfo, + MlflowAutologgingQueueingClient, + autologging_integration, + batch_metrics_logger, + get_mlflow_run_params_for_fn_args, + picklable_exception_safe_function, + resolve_input_example_and_signature, + safe_patch, +) +from mlflow.utils.docstring_utils import LOG_MODEL_PARAM_DOCS, format_docstring +from mlflow.utils.environment import ( + _CONDA_ENV_FILE_NAME, + _CONSTRAINTS_FILE_NAME, + _PYTHON_ENV_FILE_NAME, + _REQUIREMENTS_FILE_NAME, + _mlflow_conda_env, + _process_conda_env, + _process_pip_requirements, + _PythonEnv, + _validate_env_arguments, +) +from mlflow.utils.file_utils import get_total_file_size, write_to +from mlflow.utils.mlflow_tags import ( + MLFLOW_DATASET_CONTEXT, +) +from mlflow.utils.model_utils import ( + _add_code_from_conf_to_system_path, + _get_flavor_configuration, + _validate_and_copy_code_paths, + _validate_and_prepare_target_save_path, +) +from mlflow.utils.requirements_utils import _get_pinned_requirement + +FLAVOR_NAME = "lightgbm" + + +_logger = logging.getLogger(__name__) + + +def get_default_pip_requirements(include_cloudpickle=False): + """ + Returns: + A list of default pip requirements for MLflow Models produced by this flavor. + Calls to :func:`save_model()` and :func:`log_model()` produce a pip environment + that, at minimum, contains these requirements. + """ + pip_deps = [_get_pinned_requirement("lightgbm")] + if include_cloudpickle: + pip_deps.append(_get_pinned_requirement("cloudpickle")) + return pip_deps + + +def get_default_conda_env(include_cloudpickle=False): + """ + Returns: + The default Conda environment for MLflow Models produced by calls to + :func:`save_model()` and :func:`log_model()`. + """ + return _mlflow_conda_env(additional_pip_deps=get_default_pip_requirements(include_cloudpickle)) + + +@format_docstring(LOG_MODEL_PARAM_DOCS.format(package_name=FLAVOR_NAME)) +def save_model( + lgb_model, + path, + conda_env=None, + code_paths=None, + mlflow_model=None, + signature: ModelSignature = None, + input_example: ModelInputExample = None, + pip_requirements=None, + extra_pip_requirements=None, + metadata=None, +): + """ + Save a LightGBM model to a path on the local file system. + + Args: + lgb_model: LightGBM model (an instance of `lightgbm.Booster`_) or + models that implement the `scikit-learn API`_ to be saved. + path: Local path where the model is to be saved. + conda_env: {{ conda_env }} + code_paths: {{ code_paths }} + mlflow_model: :py:mod:`mlflow.models.Model` this flavor is being added to. + signature: {{ signature }} + input_example: {{ input_example }} + pip_requirements: {{ pip_requirements }} + extra_pip_requirements: {{ extra_pip_requirements }} + metadata: {{ metadata }} + + .. code-block:: python + :caption: Example + + from pathlib import Path + from lightgbm import LGBMClassifier + from sklearn import datasets + import mlflow + + # Load iris dataset + X, y = datasets.load_iris(return_X_y=True, as_frame=True) + + # Initialize our model + model = LGBMClassifier(objective="multiclass", random_state=42) + + # Train the model + model.fit(X, y) + + # Save the model + path = "model" + mlflow.lightgbm.save_model(model, path) + + # Load model for inference + loaded_model = mlflow.lightgbm.load_model(Path.cwd() / path) + print(loaded_model.predict(X[:5])) + + .. code-block:: text + :caption: Output + + [0 0 0 0 0] + """ + import lightgbm as lgb + + _validate_env_arguments(conda_env, pip_requirements, extra_pip_requirements) + + path = os.path.abspath(path) + _validate_and_prepare_target_save_path(path) + model_data_subpath = "model.lgb" if isinstance(lgb_model, lgb.Booster) else "model.pkl" + model_data_path = os.path.join(path, model_data_subpath) + code_dir_subpath = _validate_and_copy_code_paths(code_paths, path) + + if mlflow_model is None: + mlflow_model = Model() + saved_example = _save_example(mlflow_model, input_example, path) + + if signature is None and saved_example is not None: + wrapped_model = _LGBModelWrapper(lgb_model) + signature = _infer_signature_from_input_example(saved_example, wrapped_model) + elif signature is False: + signature = None + + if signature is not None: + mlflow_model.signature = signature + if metadata is not None: + mlflow_model.metadata = metadata + + # Save a LightGBM model + _save_model(lgb_model, model_data_path) + + lgb_model_class = _get_fully_qualified_class_name(lgb_model) + pyfunc.add_to_model( + mlflow_model, + loader_module="mlflow.lightgbm", + data=model_data_subpath, + conda_env=_CONDA_ENV_FILE_NAME, + python_env=_PYTHON_ENV_FILE_NAME, + code=code_dir_subpath, + ) + mlflow_model.add_flavor( + FLAVOR_NAME, + lgb_version=lgb.__version__, + data=model_data_subpath, + model_class=lgb_model_class, + code=code_dir_subpath, + ) + if size := get_total_file_size(path): + mlflow_model.model_size_bytes = size + mlflow_model.save(os.path.join(path, MLMODEL_FILE_NAME)) + + if conda_env is None: + if pip_requirements is None: + default_reqs = get_default_pip_requirements( + include_cloudpickle=not isinstance(lgb_model, lgb.Booster) + ) + # To ensure `_load_pyfunc` can successfully load the model during the dependency + # inference, `mlflow_model.save` must be called beforehand to save an MLmodel file. + inferred_reqs = mlflow.models.infer_pip_requirements( + path, + FLAVOR_NAME, + fallback=default_reqs, + ) + default_reqs = sorted(set(inferred_reqs).union(default_reqs)) + else: + default_reqs = None + conda_env, pip_requirements, pip_constraints = _process_pip_requirements( + default_reqs, + pip_requirements, + extra_pip_requirements, + ) + else: + conda_env, pip_requirements, pip_constraints = _process_conda_env(conda_env) + + with open(os.path.join(path, _CONDA_ENV_FILE_NAME), "w") as f: + yaml.safe_dump(conda_env, stream=f, default_flow_style=False) + + # Save `constraints.txt` if necessary + if pip_constraints: + write_to(os.path.join(path, _CONSTRAINTS_FILE_NAME), "\n".join(pip_constraints)) + + # Save `requirements.txt` + write_to(os.path.join(path, _REQUIREMENTS_FILE_NAME), "\n".join(pip_requirements)) + + _PythonEnv.current().to_yaml(os.path.join(path, _PYTHON_ENV_FILE_NAME)) + + +def _save_model(lgb_model, model_path): + """ + LightGBM Boosters are saved using the built-in method `save_model()`, + whereas LightGBM scikit-learn models are serialized using Cloudpickle. + """ + import lightgbm as lgb + + if isinstance(lgb_model, lgb.Booster): + lgb_model.save_model(model_path) + else: + import cloudpickle + + with open(model_path, "wb") as out: + cloudpickle.dump(lgb_model, out) + + +@format_docstring(LOG_MODEL_PARAM_DOCS.format(package_name=FLAVOR_NAME)) +def log_model( + lgb_model, + artifact_path: str | None = None, + conda_env=None, + code_paths=None, + registered_model_name=None, + signature: ModelSignature = None, + input_example: ModelInputExample = None, + await_registration_for=DEFAULT_AWAIT_MAX_SLEEP_SECONDS, + pip_requirements=None, + extra_pip_requirements=None, + metadata=None, + name: str | None = None, + params: dict[str, Any] | None = None, + tags: dict[str, Any] | None = None, + model_type: str | None = None, + step: int = 0, + model_id: str | None = None, + **kwargs, +): + """ + Log a LightGBM model as an MLflow artifact for the current run. + + Args: + lgb_model: LightGBM model (an instance of `lightgbm.Booster`_) or + models that implement the `scikit-learn API`_ to be saved. + artifact_path: Deprecated. Use `name` instead. + conda_env: {{ conda_env }} + code_paths: {{ code_paths }} + registered_model_name: If given, create a model version under + ``registered_model_name``, also creating a registered model if one + with the given name does not exist. + + signature: {{ signature }} + input_example: {{ input_example }} + await_registration_for: Number of seconds to wait for the model version to finish + being created and is in ``READY`` status. By default, the function + waits for five minutes. Specify 0 or None to skip waiting. + pip_requirements: {{ pip_requirements }} + extra_pip_requirements: {{ extra_pip_requirements }} + metadata: {{ metadata }} + name: {{ name }} + params: {{ params }} + tags: {{ tags }} + model_type: {{ model_type }} + step: {{ step }} + model_id: {{ model_id }} + kwargs: kwargs to pass to `lightgbm.Booster.save_model`_ method. + + Returns: + A :py:class:`ModelInfo ` instance that contains the + metadata of the logged model. + + .. code-block:: python + :caption: Example + + from lightgbm import LGBMClassifier + from sklearn import datasets + import mlflow + from mlflow.models import infer_signature + + # Load iris dataset + X, y = datasets.load_iris(return_X_y=True, as_frame=True) + + # Initialize our model + model = LGBMClassifier(objective="multiclass", random_state=42) + + # Train the model + model.fit(X, y) + + # Create model signature + predictions = model.predict(X) + signature = infer_signature(X, predictions) + + # Log the model + artifact_path = "model" + with mlflow.start_run(): + model_info = mlflow.lightgbm.log_model( + model, name=artifact_path, signature=signature + ) + + # Fetch the logged model artifacts + print(f"run_id: {run.info.run_id}") + client = mlflow.MlflowClient() + artifacts = [f.path for f in client.list_artifacts(run.info.run_id, artifact_path)] + print(f"artifacts: {artifacts}") + + .. code-block:: text + :caption: Output + + artifacts: ['model/MLmodel', + 'model/conda.yaml', + 'model/model.pkl', + 'model/python_env.yaml', + 'model/requirements.txt'] + """ + return Model.log( + artifact_path=artifact_path, + name=name, + flavor=mlflow.lightgbm, + registered_model_name=registered_model_name, + lgb_model=lgb_model, + conda_env=conda_env, + code_paths=code_paths, + signature=signature, + input_example=input_example, + await_registration_for=await_registration_for, + pip_requirements=pip_requirements, + extra_pip_requirements=extra_pip_requirements, + metadata=metadata, + params=params, + tags=tags, + model_type=model_type, + step=step, + model_id=model_id, + **kwargs, + ) + + +def _load_model(path): + """ + Load Model Implementation. + + Args: + path: Local filesystem path to + the MLflow Model with the ``lightgbm`` flavor (MLflow < 1.23.0) or + the top-level MLflow Model directory (MLflow >= 1.23.0). + """ + + model_dir = os.path.dirname(path) if os.path.isfile(path) else path + flavor_conf = _get_flavor_configuration(model_path=model_dir, flavor_name=FLAVOR_NAME) + + model_class = flavor_conf.get("model_class", "lightgbm.basic.Booster") + lgb_model_path = os.path.join(model_dir, flavor_conf.get("data")) + + if model_class == "lightgbm.basic.Booster": + import lightgbm as lgb + + model = lgb.Booster(model_file=lgb_model_path) + else: + # LightGBM scikit-learn models are deserialized using Cloudpickle. + import cloudpickle + + with open(lgb_model_path, "rb") as f: + model = cloudpickle.load(f) + + return model + + +def _load_pyfunc(path): + """ + Load PyFunc implementation. Called by ``pyfunc.load_model``. + + Args: + path: Local filesystem path to the MLflow Model with the ``lightgbm`` flavor. + """ + return _LGBModelWrapper(_load_model(path)) + + +def load_model(model_uri, dst_path=None): + """ + Load a LightGBM model from a local file or a run. + + Args: + model_uri: The location, in URI format, of the MLflow model. For example: + + - ``/Users/me/path/to/local/model`` + - ``relative/path/to/local/model`` + - ``s3://my_bucket/path/to/model`` + - ``runs://run-relative/path/to/model`` + + For more information about supported URI schemes, see + `Referencing Artifacts `_. + dst_path: The local filesystem path to which to download the model artifact. + This directory must already exist. If unspecified, a local output + path will be created. + + + Returns: + A LightGBM model (an instance of `lightgbm.Booster`_) or a LightGBM scikit-learn + model, depending on the saved model class specification. + + .. code-block:: python + :caption: Example + + from lightgbm import LGBMClassifier + from sklearn import datasets + import mlflow + + # Auto log all MLflow entities + mlflow.lightgbm.autolog() + + # Load iris dataset + X, y = datasets.load_iris(return_X_y=True, as_frame=True) + + # Initialize our model + model = LGBMClassifier(objective="multiclass", random_state=42) + + # Train the model + model.fit(X, y) + + # Load model for inference + model_uri = f"runs:/{mlflow.last_active_run().info.run_id}/model" + loaded_model = mlflow.lightgbm.load_model(model_uri) + print(loaded_model.predict(X[:5])) + + .. code-block:: text + :caption: Output + + [0 0 0 0 0] + """ + local_model_path = _download_artifact_from_uri(artifact_uri=model_uri, output_path=dst_path) + flavor_conf = _get_flavor_configuration(local_model_path, FLAVOR_NAME) + _add_code_from_conf_to_system_path(local_model_path, flavor_conf) + return _load_model(path=local_model_path) + + +class _LGBModelWrapper: + def __init__(self, lgb_model): + self.lgb_model = lgb_model + + def get_raw_model(self): + """ + Returns the underlying model. + """ + return self.lgb_model + + def predict(self, dataframe, params: dict[str, Any] | None = None): + """ + Args: + dataframe: Model input data. + params: Additional parameters to pass to the model for inference. + + Returns: + Model predictions. + """ + return self.lgb_model.predict(dataframe) + + +def _patch_metric_names(metric_dict): + # lightgbm provides some metrics with "@", e.g. "ndcg@3" that are not valid MLflow metric names + patched_metrics = { + metric_name.replace("@", "_at_"): value for metric_name, value in metric_dict.items() + } + changed_keys = set(patched_metrics.keys()) - set(metric_dict.keys()) + if changed_keys: + _logger.info( + "Identified one or more metrics with names containing the invalid character `@`." + " These metric names have been sanitized by replacing `@` with `_at_`, as follows: %s", + ", ".join(changed_keys), + ) + + return patched_metrics + + +def _autolog_callback(env, metrics_logger, eval_results): + res = {} + for data_name, eval_name, value, _ in env.evaluation_result_list: + key = data_name + "-" + eval_name + res[key] = value + res = _patch_metric_names(res) + metrics_logger.record_metrics(res, env.iteration) + eval_results.append(res) + + +@autologging_integration(FLAVOR_NAME) +def autolog( + log_input_examples=False, + log_model_signatures=True, + log_models=True, + log_datasets=True, + disable=False, + exclusive=False, + disable_for_unsupported_versions=False, + silent=False, + registered_model_name=None, + extra_tags=None, +): + """ + Enables (or disables) and configures autologging from LightGBM to MLflow. Logs the following: + + - parameters specified in `lightgbm.train`_. + - metrics on each iteration (if ``valid_sets`` specified). + - metrics at the best iteration (if ``early_stopping_rounds`` specified or + ``early_stopping`` callback is set). + - feature importance (both "split" and "gain") as JSON files and plots. + - trained model, including: + - an example of valid input. + - inferred signature of the inputs and outputs of the model. + + Note that the `scikit-learn API`_ is now supported. + + Args: + log_input_examples: If ``True``, input examples from training datasets are collected and + logged along with LightGBM model artifacts during training. If + ``False``, input examples are not logged. + Note: Input examples are MLflow model attributes + and are only collected if ``log_models`` is also ``True``. + log_model_signatures: If ``True``, + :py:class:`ModelSignatures ` + describing model inputs and outputs are collected and logged along + with LightGBM model artifacts during training. If ``False``, + signatures are not logged. + Note: Model signatures are MLflow model attributes + and are only collected if ``log_models`` is also ``True``. + log_models: If ``True``, trained models are logged as MLflow model artifacts. + If ``False``, trained models are not logged. + Input examples and model signatures, which are attributes of MLflow models, + are also omitted when ``log_models`` is ``False``. + log_datasets: If ``True``, train and validation dataset information is logged to MLflow + Tracking if applicable. If ``False``, dataset information is not logged. + disable: If ``True``, disables the LightGBM autologging integration. If ``False``, + enables the LightGBM autologging integration. + exclusive: If ``True``, autologged content is not logged to user-created fluent runs. + If ``False``, autologged content is logged to the active fluent run, + which may be user-created. + disable_for_unsupported_versions: If ``True``, disable autologging for versions of + lightgbm that have not been tested against this version of the MLflow client + or are incompatible. + silent: If ``True``, suppress all event logs and warnings from MLflow during LightGBM + autologging. If ``False``, show all events and warnings during LightGBM + autologging. + registered_model_name: If given, each time a model is trained, it is registered as a + new model version of the registered model with this name. + The registered model is created if it does not already exist. + extra_tags: A dictionary of extra tags to set on each managed run created by autologging. + + .. code-block:: python + :caption: Example + + import mlflow + from lightgbm import LGBMClassifier + from sklearn import datasets + + + def print_auto_logged_info(run): + tags = {k: v for k, v in run.data.tags.items() if not k.startswith("mlflow.")} + artifacts = [ + f.path for f in mlflow.MlflowClient().list_artifacts(run.info.run_id, "model") + ] + feature_importances = [ + f.path + for f in mlflow.MlflowClient().list_artifacts(run.info.run_id) + if f.path != "model" + ] + print(f"run_id: {run.info.run_id}") + print(f"artifacts: {artifacts}") + print(f"feature_importances: {feature_importances}") + print(f"params: {run.data.params}") + print(f"metrics: {run.data.metrics}") + print(f"tags: {tags}") + + + # Load iris dataset + X, y = datasets.load_iris(return_X_y=True, as_frame=True) + + # Initialize our model + model = LGBMClassifier(objective="multiclass", random_state=42) + + # Auto log all MLflow entities + mlflow.lightgbm.autolog() + + # Train the model + with mlflow.start_run() as run: + model.fit(X, y) + + # fetch the auto logged parameters and metrics + print_auto_logged_info(mlflow.get_run(run_id=run.info.run_id)) + + .. code-block:: text + :caption: Output + + run_id: e08dd59d57a74971b68cf78a724dfaf6 + artifacts: ['model/MLmodel', + 'model/conda.yaml', + 'model/model.pkl', + 'model/python_env.yaml', + 'model/requirements.txt'] + feature_importances: ['feature_importance_gain.json', + 'feature_importance_gain.png', + 'feature_importance_split.json', + 'feature_importance_split.png'] + params: {'boosting_type': 'gbdt', + 'categorical_feature': 'auto', + 'colsample_bytree': '1.0', + ... + 'verbose_eval': 'warn'} + metrics: {} + tags: {} + """ + import lightgbm + import numpy as np + + # Patching this function so we can get a copy of the data given to Dataset.__init__ + # to use as an input example and for inferring the model signature. + # (there is no way to get the data back from a Dataset object once it is consumed by train) + # We store it on the Dataset object so the train function is able to read it. + def __init__(original, self, *args, **kwargs): + data = args[0] if len(args) > 0 else kwargs.get("data") + + if data is not None: + try: + if isinstance(data, str): + raise Exception( + "cannot gather example input when dataset is loaded from a file." + ) + + input_example_info = InputExampleInfo( + input_example=deepcopy(data[:INPUT_EXAMPLE_SAMPLE_ROWS]) + ) + except Exception as e: + input_example_info = InputExampleInfo(error_msg=str(e)) + + self.input_example_info = input_example_info + + original(self, *args, **kwargs) + + def train_impl(_log_models, _log_datasets, original, *args, **kwargs): + def record_eval_results(eval_results, metrics_logger): + """ + Create a callback function that records evaluation results. + """ + return picklable_exception_safe_function( + functools.partial( + _autolog_callback, metrics_logger=metrics_logger, eval_results=eval_results + ) + ) + + def log_feature_importance_plot(features, importance, importance_type): + """ + Log feature importance plot. + """ + import matplotlib.pyplot as plt + + indices = np.argsort(importance) + features = np.array(features)[indices] + importance = importance[indices] + num_features = len(features) + + # If num_features > 10, increase the figure height to prevent the plot + # from being too dense. + w, h = [6.4, 4.8] # matplotlib's default figure size + h = h + 0.1 * num_features if num_features > 10 else h + fig, ax = plt.subplots(figsize=(w, h)) + + yloc = np.arange(num_features) + ax.barh(yloc, importance, align="center", height=0.5) + ax.set_yticks(yloc) + ax.set_yticklabels(features) + ax.set_xlabel("Importance") + ax.set_title(f"Feature Importance ({importance_type})") + fig.tight_layout() + + with tempfile.TemporaryDirectory() as tmpdir: + try: + filepath = os.path.join(tmpdir, f"feature_importance_{imp_type}.png") + fig.savefig(filepath) + mlflow.log_artifact(filepath) + finally: + plt.close(fig) + + autologging_client = MlflowAutologgingQueueingClient() + + # logging booster params separately via mlflow.log_params to extract key/value pairs + # and make it easier to compare them across runs. + booster_params = args[0] if len(args) > 0 else kwargs["params"] + autologging_client.log_params(run_id=mlflow.active_run().info.run_id, params=booster_params) + + unlogged_params = [ + "params", + "train_set", + "valid_sets", + "valid_names", + "fobj", + "feval", + "init_model", + "learning_rates", + "callbacks", + ] + if Version(lightgbm.__version__) <= Version("3.3.1"): + # The parameter `evals_result` in `lightgbm.train` is removed in this PR: + # https://github.com/microsoft/LightGBM/pull/4882 + unlogged_params.append("evals_result") + + params_to_log_for_fn = get_mlflow_run_params_for_fn_args( + original, args, kwargs, unlogged_params + ) + autologging_client.log_params( + run_id=mlflow.active_run().info.run_id, params=params_to_log_for_fn + ) + + param_logging_operations = autologging_client.flush(synchronous=False) + + all_arg_names = _get_arg_names(original) + num_pos_args = len(args) + + # adding a callback that records evaluation results. + eval_results = [] + callbacks_index = all_arg_names.index("callbacks") + run_id = mlflow.active_run().info.run_id + + train_set = args[1] if len(args) > 1 else kwargs.get("train_set") + + # Whether to automatically log the training dataset as a dataset artifact. + if _log_datasets and train_set: + try: + context_tags = context_registry.resolve_tags() + source = CodeDatasetSource(tags=context_tags) + + _log_lightgbm_dataset(train_set, source, "train", autologging_client) + + valid_sets = kwargs.get("valid_sets") + if valid_sets is not None: + valid_names = kwargs.get("valid_names") + if valid_names is None: + for valid_set in valid_sets: + _log_lightgbm_dataset(valid_set, source, "eval", autologging_client) + else: + for valid_set, valid_name in zip(valid_sets, valid_names): + _log_lightgbm_dataset( + valid_set, source, "eval", autologging_client, name=valid_name + ) + + dataset_logging_operations = autologging_client.flush(synchronous=False) + dataset_logging_operations.await_completion() + except Exception as e: + _logger.warning( + "Failed to log dataset information to MLflow Tracking. Reason: %s", e + ) + + model_id = None + if _log_models: + model_id = _initialize_logged_model("model", flavor=FLAVOR_NAME).model_id + with batch_metrics_logger(run_id, model_id=model_id) as metrics_logger: + callback = record_eval_results(eval_results, metrics_logger) + if num_pos_args >= callbacks_index + 1: + tmp_list = list(args) + tmp_list[callbacks_index] += [callback] + args = tuple(tmp_list) + elif "callbacks" in kwargs and kwargs["callbacks"] is not None: + kwargs["callbacks"] += [callback] + else: + kwargs["callbacks"] = [callback] + + # training model + model = original(*args, **kwargs) + + # If early stopping is activated, logging metrics at the best iteration + # as extra metrics with the max step + 1. + early_stopping = model.best_iteration > 0 + if early_stopping: + extra_step = len(eval_results) + autologging_client.log_metrics( + run_id=mlflow.active_run().info.run_id, + metrics={ + "stopped_iteration": extra_step, + # best_iteration is set even if training does not stop early. + "best_iteration": model.best_iteration, + }, + model_id=model_id, + ) + # iteration starts from 1 in LightGBM. + last_iter_results = eval_results[model.best_iteration - 1] + autologging_client.log_metrics( + run_id=mlflow.active_run().info.run_id, + metrics=last_iter_results, + step=extra_step, + model_id=model_id, + ) + early_stopping_logging_operations = autologging_client.flush(synchronous=False) + + # logging feature importance as artifacts. + for imp_type in ["split", "gain"]: + features = model.feature_name() + importance = model.feature_importance(importance_type=imp_type) + try: + log_feature_importance_plot(features, importance, imp_type) + except Exception: + _logger.exception( + "Failed to log feature importance plot. LightGBM autologging " + "will ignore the failure and continue. Exception: " + ) + + imp = dict(zip(features, importance.tolist())) + with tempfile.TemporaryDirectory() as tmpdir: + filepath = os.path.join(tmpdir, f"feature_importance_{imp_type}.json") + with open(filepath, "w") as f: + json.dump(imp, f, indent=2) + mlflow.log_artifact(filepath) + + # train_set must exist as the original train function already ran successfully + # it is possible that the dataset was constructed before the patched + # constructor was applied, so we cannot assume the input_example_info exists + input_example_info = getattr(train_set, "input_example_info", None) + + def get_input_example(): + if input_example_info is None: + raise Exception(ENSURE_AUTOLOGGING_ENABLED_TEXT) + if input_example_info.error_msg is not None: + raise Exception(input_example_info.error_msg) + return input_example_info.input_example + + def infer_model_signature(input_example): + model_output = model.predict(input_example) + return infer_signature(input_example, model_output) + + # Whether to automatically log the trained model based on boolean flag. + if _log_models: + # Will only resolve `input_example` and `signature` if `log_models` is `True`. + input_example, signature = resolve_input_example_and_signature( + get_input_example, + infer_model_signature, + log_input_examples, + log_model_signatures, + _logger, + ) + + log_model( + model, + "model", + signature=signature, + input_example=input_example, + registered_model_name=registered_model_name, + model_id=model_id, + ) + + param_logging_operations.await_completion() + if early_stopping: + early_stopping_logging_operations.await_completion() + + return model + + def train(_log_models, _log_datasets, original, *args, **kwargs): + with _SklearnTrainingSession(estimator=lightgbm.train, allow_children=False) as t: + if t.should_log(): + return train_impl(_log_models, _log_datasets, original, *args, **kwargs) + else: + return original(*args, **kwargs) + + safe_patch(FLAVOR_NAME, lightgbm.Dataset, "__init__", __init__) + safe_patch( + FLAVOR_NAME, + lightgbm, + "train", + functools.partial(train, log_models, log_datasets), + manage_run=True, + extra_tags=extra_tags, + ) + # The `train()` method logs LightGBM models as Booster objects. When using LightGBM + # scikit-learn models, we want to save / log models as their model classes. So we turn + # off the log_models functionality in the `train()` method patched to `lightgbm.sklearn`. + # Instead the model logging is handled in `fit_mlflow_xgboost_and_lightgbm()` + # in `mlflow.sklearn._autolog()`, where models are logged as LightGBM scikit-learn models + # after the `fit()` method returns. + safe_patch( + FLAVOR_NAME, + lightgbm.sklearn, + "train", + functools.partial(train, False, log_datasets), + manage_run=True, + extra_tags=extra_tags, + ) + + # enable LightGBM scikit-learn estimators autologging + import mlflow.sklearn + + mlflow.sklearn._autolog( + flavor_name=FLAVOR_NAME, + log_input_examples=log_input_examples, + log_model_signatures=log_model_signatures, + log_models=log_models, + log_datasets=log_datasets, + disable=disable, + exclusive=exclusive, + disable_for_unsupported_versions=disable_for_unsupported_versions, + silent=silent, + max_tuning_runs=None, + log_post_training_metrics=True, + extra_tags=extra_tags, + ) + + +def _log_lightgbm_dataset(lgb_dataset, source, context, autologging_client, name=None): + import numpy as np + import pandas as pd + from scipy.sparse import issparse + + data = lgb_dataset.data + label = lgb_dataset.label + if isinstance(data, pd.DataFrame): + dataset = from_pandas(df=data, source=source, name=name) + elif issparse(data): + arr_data = data.toarray() if issparse(data) else data + dataset = from_numpy(features=arr_data, targets=label, source=source, name=name) + elif isinstance(data, np.ndarray): + dataset = from_numpy(features=data, targets=label, source=source, name=name) + else: + _logger.warning("Unrecognized dataset type %s. Dataset logging skipped.", type(data)) + return + tags = [InputTag(key=MLFLOW_DATASET_CONTEXT, value=context)] + dataset_input = DatasetInput(dataset=dataset._to_mlflow_entity(), tags=tags) + + # log the dataset + autologging_client.log_inputs(run_id=mlflow.active_run().info.run_id, datasets=[dataset_input]) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/lightgbm/__pycache__/__init__.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/lightgbm/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..93392349d06d2cd7c311ae2095eed40b1fa51f97 Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/lightgbm/__pycache__/__init__.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/litellm/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/litellm/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..fd9ec3de5094f6e9e266d0768c4a246b24a9a545 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/litellm/__init__.py @@ -0,0 +1,114 @@ +import logging +from typing import Callable + +from mlflow.utils.autologging_utils import autologging_integration, safe_patch + +FLAVOR_NAME = "litellm" + +_logger = logging.getLogger(__name__) + + +def autolog( + log_traces: bool = True, + disable: bool = False, + silent: bool = False, +): + """ + Enables (or disables) and configures autologging from LiteLLM to MLflow. Currently, MLflow + only supports autologging for tracing. + + Args: + log_traces: If ``True``, traces are logged for LiteLLM calls. If ``False``, + no traces are collected during inference. Default to ``True``. + disable: If ``True``, disables the LiteLLM autologging integration. If ``False``, + enables the LiteLLM autologging integration. + silent: If ``True``, suppress all event logs and warnings from MLflow during LiteLLM + autologging. If ``False``, show all events and warnings. + """ + import litellm + + # This needs to be called before doing any safe-patching (otherwise safe-patch will be no-op). + # TODO: since this implementation is inconsistent, explore a universal way to solve the issue. + _autolog(log_traces=log_traces, disable=disable, silent=silent) + + try: + from litellm.integrations.mlflow import MlflowLogger # noqa: F401 + except ImportError: + _logger.warning( + "MLflow LiteLLM integration is not supported for the installed LiteLLM version. " + "Please upgrade to a newer version to enable MLflow LiteLLM autologging." + ) + return + + if log_traces and not disable: + litellm.success_callback = _append_mlflow_callbacks(litellm.success_callback) + litellm.failure_callback = _append_mlflow_callbacks(litellm.failure_callback) + + # Patch thread pool executor to bypass non-blocking behavior of success_handler + _patch_thread_pool() + + else: + litellm.success_callback = _remove_mlflow_callbacks(litellm.success_callback) + litellm.failure_callback = _remove_mlflow_callbacks(litellm.failure_callback) + # Callback also needs to be removed from 'callbacks' as litellm adds + # success/failure callbacks to there as well. + litellm.callbacks = _remove_mlflow_callbacks(litellm.callbacks) + + +# This is required by mlflow.autolog() +autolog.integration_name = FLAVOR_NAME + + +# NB: The @autologging_integration annotation must be applied here, and the callback injection +# needs to happen outside the annotated function. This is because the annotated function is NOT +# executed when disable=True is passed. This prevents us from removing our callback and patching +# when autologging is turned off. +@autologging_integration(FLAVOR_NAME) +def _autolog( + log_traces: bool, + disable: bool = False, + silent: bool = False, +): + pass + + +def _patch_thread_pool(): + """ + Apply the threading patch to a synchronous function. + + We capture the threads started by the function using the _patch_thread_start context manager, + then join them to ensure they are finished before the notebook cell finishes executing. + """ + try: + from litellm.litellm_core_utils.thread_pool_executor import executor + except ImportError: + _logger.warning( + "MLflow LiteLLM integration is not supported for the installed LiteLLM version. " + "The behavior might be unstable." + ) + return + + def _patched_submit(original, *args, **kwargs): + if isinstance(args[0], Callable) and "success_handler" in args[0].__name__: + # Immediately run the callback handler instead of submitting it to the thread pool + args[0](*args[1:], **kwargs) + return + return original(*args, **kwargs) + + safe_patch(FLAVOR_NAME, executor, "submit", _patched_submit) + + +def _append_mlflow_callbacks(callbacks): + from litellm.integrations.mlflow import MlflowLogger + + # MLflow callback can be stored as a string or the actual logger object + if not any(cb == "mlflow" or isinstance(cb, MlflowLogger) for cb in callbacks): + return callbacks + ["mlflow"] + + return callbacks + + +def _remove_mlflow_callbacks(callbacks): + from litellm.integrations.mlflow import MlflowLogger + + return [cb for cb in callbacks if not (cb == "mlflow" or isinstance(cb, MlflowLogger))] diff --git 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b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/llama_index/__init__.py @@ -0,0 +1,22 @@ +from mlflow.llama_index.autolog import autolog +from mlflow.llama_index.constant import FLAVOR_NAME +from mlflow.version import IS_TRACING_SDK_ONLY + +__all__ = ["autolog", "FLAVOR_NAME"] + +# Import model logging APIs only if mlflow skinny or full package is installed, +# i.e., skip if only mlflow-tracing package is installed. +if not IS_TRACING_SDK_ONLY: + from mlflow.llama_index.model import ( + _load_pyfunc, + load_model, + log_model, + save_model, + ) + + __all__ += [ + "load_model", + "log_model", + "save_model", + "_load_pyfunc", + ] diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/llama_index/__pycache__/__init__.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/llama_index/__pycache__/__init__.cpython-312.pyc new file mode 100644 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b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/llama_index/autolog.py new file mode 100644 index 0000000000000000000000000000000000000000..638b8bd9b2ef82f5b724d2371dc727d49c4d6572 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/llama_index/autolog.py @@ -0,0 +1,53 @@ +from mlflow.llama_index.constant import FLAVOR_NAME +from mlflow.utils.autologging_utils import autologging_integration + + +def autolog( + log_traces: bool = True, + disable: bool = False, + silent: bool = False, +): + """ + Enables (or disables) and configures autologging from LlamaIndex to MLflow. Currently, MLflow + only supports autologging for tracing. + + Args: + log_traces: If ``True``, traces are logged for LlamaIndex models by using. If ``False``, + no traces are collected during inference. Default to ``True``. + disable: If ``True``, disables the LlamaIndex autologging integration. If ``False``, + enables the LlamaIndex autologging integration. + silent: If ``True``, suppress all event logs and warnings from MLflow during LlamaIndex + autologging. If ``False``, show all events and warnings. + """ + from mlflow.llama_index.tracer import remove_llama_index_tracer, set_llama_index_tracer + + # NB: The @autologging_integration annotation is used for adding shared logic. However, one + # caveat is that the wrapped function is NOT executed when disable=True is passed. This prevents + # us from running cleaning up logging when autologging is turned off. To workaround this, we + # annotate _autolog() instead of this entrypoint, and define the cleanup logic outside it. + # TODO: since this implementation is inconsistent, explore a universal way to solve the issue. + if log_traces and not disable: + set_llama_index_tracer() + else: + remove_llama_index_tracer() + + _autolog( + log_traces=log_traces, + disable=disable, + silent=silent, + ) + + +# This is required by mlflow.autolog() +autolog.integration_name = FLAVOR_NAME + + +@autologging_integration(FLAVOR_NAME) +def _autolog( + log_traces: bool, + disable: bool = False, + silent: bool = False, +): + """ + TODO: Implement patching logic for autologging models and artifacts. + """ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/llama_index/constant.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/llama_index/constant.py new file mode 100644 index 0000000000000000000000000000000000000000..a3d7d1223882688dc56d99c035f558048acd48c0 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/llama_index/constant.py @@ -0,0 +1 @@ +FLAVOR_NAME = "llama_index" diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/llama_index/model.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/llama_index/model.py new file mode 100644 index 0000000000000000000000000000000000000000..d5c02550ed1f1670e801c77bb0c5b0f2799af0d3 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/llama_index/model.py @@ -0,0 +1,573 @@ +import logging +import os +import tempfile +from typing import Any + +import yaml + +import mlflow +from mlflow import pyfunc +from mlflow.entities.model_registry.prompt import Prompt +from mlflow.exceptions import MlflowException +from mlflow.llama_index.constant import FLAVOR_NAME +from mlflow.llama_index.pyfunc_wrapper import create_pyfunc_wrapper +from mlflow.models import Model, ModelInputExample, ModelSignature +from mlflow.models.model import ( + MLMODEL_FILE_NAME, + MODEL_CODE_PATH, + MODEL_CONFIG, + _update_active_model_id_based_on_mlflow_model, +) +from mlflow.models.signature import _infer_signature_from_input_example +from mlflow.models.utils import ( + _load_model_code_path, + _save_example, + _validate_and_get_model_code_path, +) +from mlflow.tracing.provider import trace_disabled +from mlflow.tracking._model_registry import DEFAULT_AWAIT_MAX_SLEEP_SECONDS +from mlflow.tracking.artifact_utils import _download_artifact_from_uri +from mlflow.utils.docstring_utils import LOG_MODEL_PARAM_DOCS, format_docstring +from mlflow.utils.environment import ( + _CONDA_ENV_FILE_NAME, + _CONSTRAINTS_FILE_NAME, + _PYTHON_ENV_FILE_NAME, + _REQUIREMENTS_FILE_NAME, + _mlflow_conda_env, + _process_conda_env, + _process_pip_requirements, + _PythonEnv, + _validate_env_arguments, +) +from mlflow.utils.file_utils import get_total_file_size, write_to +from mlflow.utils.model_utils import ( + _add_code_from_conf_to_system_path, + _get_flavor_configuration, + _validate_and_copy_code_paths, + _validate_and_copy_file_to_directory, + _validate_and_get_model_config_from_file, + _validate_and_prepare_target_save_path, +) +from mlflow.utils.requirements_utils import _get_pinned_requirement + +_INDEX_PERSIST_FOLDER = "index" +_SETTINGS_FILE = "settings.json" + + +_logger = logging.getLogger(__name__) + + +def get_default_pip_requirements(): + """ + Returns: + A list of default pip requirements for MLflow Models produced by this flavor. + Calls to :func:`save_model()` and :func:`log_model()` produce a pip environment + that, at a minimum, contains these requirements. + """ + return [_get_pinned_requirement("llama-index")] + + +def get_default_conda_env(): + """ + Returns: + The default Conda environment for MLflow Models produced by calls to + :func:`save_model()` and :func:`log_model()`. + """ + return _mlflow_conda_env(additional_pip_deps=get_default_pip_requirements()) + + +def _validate_engine_type(engine_type: str): + from mlflow.llama_index.pyfunc_wrapper import SUPPORTED_ENGINES + + if engine_type not in SUPPORTED_ENGINES: + raise ValueError( + f"Currently mlflow only supports the following engine types: " + f"{SUPPORTED_ENGINES}. {engine_type} is not supported, so please " + "use one of the above types." + ) + + +def _get_llama_index_version() -> str: + try: + import llama_index.core + + return llama_index.core.__version__ + except ImportError: + raise MlflowException( + "The llama_index module is not installed. " + "Please install it via `pip install llama-index`." + ) + + +def _supported_classes(): + from llama_index.core.base.base_query_engine import BaseQueryEngine + from llama_index.core.chat_engine.types import BaseChatEngine + from llama_index.core.indices.base import BaseIndex + from llama_index.core.retrievers import BaseRetriever + + supported = (BaseIndex, BaseChatEngine, BaseQueryEngine, BaseRetriever) + + try: + from llama_index.core.workflow import Workflow + + supported += (Workflow,) + except ImportError: + pass + + return supported + + +@format_docstring(LOG_MODEL_PARAM_DOCS.format(package_name=FLAVOR_NAME)) +@trace_disabled # Suppress traces while loading model +def save_model( + llama_index_model, + path: str, + engine_type: str | None = None, + model_config: str | dict[str, Any] | None = None, + code_paths=None, + mlflow_model: Model | None = None, + signature: ModelSignature | None = None, + input_example: ModelInputExample | None = None, + pip_requirements: list[str] | str | None = None, + extra_pip_requirements: list[str] | str | None = None, + conda_env=None, + metadata: dict[str, Any] | None = None, +) -> None: + """ + Save a LlamaIndex model to a path on the local file system. + + .. attention:: + + Saving a non-index object is only supported in the 'Model-from-Code' saving mode. + Please refer to the `Models From Code Guide `_ + for more information. + + .. note:: + + When logging a model, MLflow will automatically save the state of the ``Settings`` + object so that you can use the same settings at inference time. However, please + note that some information in the ``Settings`` object will not be saved, including: + + - API keys for avoiding key leakage. + - Function objects which are not serializable. + + Args: + llama_index_model: A LlamaIndex object to be saved. Supported model types are: + + 1. An Index object. + 2. An Engine object e.g. ChatEngine, QueryEngine, Retriever. + 3. A `Workflow `_ object. + 4. A string representing the path to a script contains LlamaIndex model definition + of the one of the above types. + + path: Local path where the serialized model (as YAML) is to be saved. + engine_type: Required when saving an Index object to determine the inference interface + for the index when loaded as a pyfunc model. This field is **not** required when + saving other LlamaIndex objects. The supported values are as follows: + + - ``"chat"``: load the index as an instance of the LlamaIndex + `ChatEngine `_. + - ``"query"``: load the index as an instance of the LlamaIndex + `QueryEngine `_. + - ``"retriever"``: load the index as an instance of the LlamaIndex + `Retriever `_. + + model_config: The model configuration to apply when loading the model back with + ``mlflow.pyfunc.load_model()``. It will be applied in a different way depending on the + model type and saving method. See the docstring of :func:`log_model` for more details + and usage examples. + + code_paths: {{ code_paths }} + mlflow_model: An MLflow model object that specifies the flavor that this model is being + added to. + signature: A Model Signature object that describes the input and output Schema of the + model. The model signature can be inferred using ``infer_signature`` function + of ``mlflow.models.signature``. + input_example: {{ input_example }} + pip_requirements: {{ pip_requirements }} + extra_pip_requirements: {{ extra_pip_requirements }} + conda_env: {{ conda_env }} + metadata: {{ metadata }} + """ + from llama_index.core.indices.base import BaseIndex + + from mlflow.llama_index.serialize_objects import serialize_settings + + # TODO: make this logic cleaner and maybe a util + with tempfile.TemporaryDirectory() as temp_dir: + model_or_code_path = _validate_and_prepare_llama_index_model_or_path( + llama_index_model, temp_dir + ) + + _validate_env_arguments(conda_env, pip_requirements, extra_pip_requirements) + + path = os.path.abspath(path) + _validate_and_prepare_target_save_path(path) + + if isinstance(model_config, str): + model_config = _validate_and_get_model_config_from_file(model_config) + + model_code_path = None + if isinstance(model_or_code_path, str): + model_code_path = model_or_code_path + llama_index_model = _load_model_code_path(model_code_path, model_config) + _validate_and_copy_file_to_directory(model_code_path, path, "code") + + # Warn when user provides `engine_type` argument while saving an engine directly + if not isinstance(llama_index_model, BaseIndex) and engine_type is not None: + _logger.warning( + "The `engine_type` argument is ignored when saving a non-index object." + ) + + elif isinstance(model_or_code_path, BaseIndex): + _validate_engine_type(engine_type) + llama_index_model = model_or_code_path + + elif isinstance(model_or_code_path, _supported_classes()): + raise MlflowException.invalid_parameter_value( + "Saving a non-index object is only supported in the 'Model-from-Code' saving mode. " + "The legacy serialization method is exclusively for saving index objects. Please " + "pass the path to the script containing the model definition to save a non-index " + "object. For more information, see " + "https://www.mlflow.org/docs/latest/model/models-from-code.html", + ) + + code_dir_subpath = _validate_and_copy_code_paths(code_paths, path) + + if mlflow_model is None: + mlflow_model = Model() + saved_example = _save_example(mlflow_model, input_example, path) + + if signature is None and saved_example is not None: + wrapped_model = create_pyfunc_wrapper(llama_index_model, engine_type, model_config) + signature = _infer_signature_from_input_example(saved_example, wrapped_model) + elif signature is False: + signature = None + + if mlflow_model is None: + mlflow_model = Model() + if signature is not None: + mlflow_model.signature = signature + if metadata is not None: + mlflow_model.metadata = metadata + + # NB: llama_index.core.Settings is a singleton that manages the storage/service context + # for a given llama_index application. Given it holds the required objects for most of + # the index's functionality, we look to serialize the entire object. For components of + # the object that are not serializable, we log a warning. + settings_path = os.path.join(path, _SETTINGS_FILE) + serialize_settings(settings_path) + + # Do not save the index/engine object in model-from-code saving mode + if not isinstance(model_code_path, str) and isinstance(llama_index_model, BaseIndex): + _save_index(llama_index_model, path) + + pyfunc.add_to_model( + mlflow_model, + loader_module="mlflow.llama_index", + conda_env=_CONDA_ENV_FILE_NAME, + python_env=_PYTHON_ENV_FILE_NAME, + code=code_dir_subpath, + model_code_path=model_code_path, + model_config=model_config, + ) + mlflow_model.add_flavor( + FLAVOR_NAME, + llama_index_version=_get_llama_index_version(), + code=code_dir_subpath, + engine_type=engine_type, + ) + if size := get_total_file_size(path): + mlflow_model.model_size_bytes = size + mlflow_model.save(os.path.join(path, MLMODEL_FILE_NAME)) + + if conda_env is None: + default_reqs = None + if pip_requirements is None: + default_reqs = get_default_pip_requirements() + inferred_reqs = mlflow.models.infer_pip_requirements( + str(path), FLAVOR_NAME, fallback=default_reqs + ) + default_reqs = sorted(set(inferred_reqs).union(default_reqs)) + else: + default_reqs = None + conda_env, pip_requirements, pip_constraints = _process_pip_requirements( + default_reqs, + pip_requirements, + extra_pip_requirements, + ) + else: + conda_env, pip_requirements, pip_constraints = _process_conda_env(conda_env) + + with open(os.path.join(path, _CONDA_ENV_FILE_NAME), "w") as f: + yaml.safe_dump(conda_env, stream=f, default_flow_style=False) + + if pip_constraints: + write_to(os.path.join(path, _CONSTRAINTS_FILE_NAME), "\n".join(pip_constraints)) + + write_to(os.path.join(path, _REQUIREMENTS_FILE_NAME), "\n".join(pip_requirements)) + + _PythonEnv.current().to_yaml(os.path.join(path, _PYTHON_ENV_FILE_NAME)) + + +@format_docstring(LOG_MODEL_PARAM_DOCS.format(package_name=FLAVOR_NAME)) +@trace_disabled # Suppress traces while loading model +def log_model( + llama_index_model, + artifact_path: str | None = None, + engine_type: str | None = None, + model_config: dict[str, Any] | None = None, + code_paths: list[str] | None = None, + registered_model_name: str | None = None, + signature: ModelSignature | None = None, + input_example: ModelInputExample | None = None, + await_registration_for=DEFAULT_AWAIT_MAX_SLEEP_SECONDS, + pip_requirements: list[str] | str | None = None, + extra_pip_requirements: list[str] | str | None = None, + conda_env=None, + metadata: dict[str, Any] | None = None, + prompts: list[str | Prompt] | None = None, + name: str | None = None, + params: dict[str, Any] | None = None, + tags: dict[str, Any] | None = None, + model_type: str | None = None, + step: int = 0, + model_id: str | None = None, + **kwargs, +): + """ + Log a LlamaIndex model as an MLflow artifact for the current run. + + .. attention:: + + Saving a non-index object is only supported in the 'Model-from-Code' saving mode. + Please refer to the `Models From Code Guide `_ + for more information. + + .. note:: + + When logging a model, MLflow will automatically save the state of the ``Settings`` + object so that you can use the same settings at inference time. However, please + note that some information in the ``Settings`` object will not be saved, including: + + - API keys for avoiding key leakage. + - Function objects which are not serializable. + + Args: + llama_index_model: A LlamaIndex object to be saved. Supported model types are: + + 1. An Index object. + 2. An Engine object e.g. ChatEngine, QueryEngine, Retriever. + 3. A `Workflow `_ object. + 4. A string representing the path to a script contains LlamaIndex model definition + of the one of the above types. + + artifact_path: Deprecated. Use `name` instead. + engine_type: Required when saving an Index object to determine the inference interface + for the index when loaded as a pyfunc model. This field is **not** required when + saving other LlamaIndex objects. The supported values are as follows: + + - ``"chat"``: load the index as an instance of the LlamaIndex + `ChatEngine `_. + - ``"query"``: load the index as an instance of the LlamaIndex + `QueryEngine `_. + - ``"retriever"``: load the index as an instance of the LlamaIndex + `Retriever `_. + + model_config: The model configuration to apply when loading the model back with + ``mlflow.pyfunc.load_model()``. It will be applied in a different way depending on the + model type and saving method: + + For in-memory Index objects saved directly, it will be passed as keyword arguments to + instantiate the LlamaIndex engine with the specified engine type at logging. + + .. code-block:: python + + with mlflow.start_run() as run: + model_info = mlflow.llama_index.log_model( + index, + name="index", + engine_type="chat", + model_config={"top_k": 10}, + ) + + # When loading back, MLflow will call ``index.as_chat_engine(top_k=10)`` + engine = mlflow.pyfunc.load_model(model_info.model_uri) + + For other model types saved with the `Model-from-Code ` + method, the config will be accessed via the :py:class`~mlflow.models.ModelConfig` + object within your model code. + + .. code-block:: python + + with mlflow.start_run() as run: + model_info = mlflow.llama_index.log_model( + "model.py", + name="model", + model_config={"qdrant_host": "localhost", "qdrant_port": 6333}, + ) + + model.py: + + .. code-block:: python + + import mlflow + from llama_index.vector_stores.qdrant import QdrantVectorStore + import qdrant_client + + + # The model configuration is accessible via the ModelConfig singleton + model_config = mlflow.models.ModelConfig() + qdrant_host = model_config.get("top_k", 5) + qdrant_port = model_config.get("qdrant_port", 6333) + + client = qdrant_client.Client(host=qdrant_host, port=qdrant_port) + vectorstore = QdrantVectorStore(client) + + # the rest of the model definition... + + code_paths: {{ code_paths }} + registered_model_name: If given, create a model + version under ``registered_model_name``, also creating a + registered model if one with the given name does not exist. + signature: A Model Signature object that describes the input and output Schema of the + model. The model signature can be inferred using ``infer_signature`` function + of `mlflow.models.signature`. + input_example: {{ input_example }} + await_registration_for: Number of seconds to wait for the model version + to finish being created and is in ``READY`` status. + By default, the function waits for five minutes. + Specify 0 or None to skip waiting. + pip_requirements: {{ pip_requirements }} + extra_pip_requirements: {{ extra_pip_requirements }} + conda_env: {{ conda_env }} + metadata: {{ metadata }} + prompts: {{ prompts }} + name: {{ name }} + params: {{ params }} + tags: {{ tags }} + model_type: {{ model_type }} + step: {{ step }} + model_id: {{ model_id }} + kwargs: Additional arguments for :py:class:`mlflow.models.model.Model` + """ + return Model.log( + artifact_path=artifact_path, + name=name, + engine_type=engine_type, + model_config=model_config, + flavor=mlflow.llama_index, + registered_model_name=registered_model_name, + llama_index_model=llama_index_model, + conda_env=conda_env, + code_paths=code_paths, + signature=signature, + input_example=input_example, + await_registration_for=await_registration_for, + pip_requirements=pip_requirements, + extra_pip_requirements=extra_pip_requirements, + metadata=metadata, + prompts=prompts, + params=params, + tags=tags, + model_type=model_type, + step=step, + model_id=model_id, + **kwargs, + ) + + +def _validate_and_prepare_llama_index_model_or_path(llama_index_model, temp_dir=None): + if isinstance(llama_index_model, str): + return _validate_and_get_model_code_path(llama_index_model, temp_dir) + + if not isinstance(llama_index_model, _supported_classes()): + supported_cls_names = [cls.__name__ for cls in _supported_classes()] + raise MlflowException.invalid_parameter_value( + message=f"The provided object of type {type(llama_index_model).__name__} is not " + "supported. MLflow llama-index flavor only supports saving LlamaIndex objects " + f"subclassed from one of the following classes: {supported_cls_names}.", + ) + + return llama_index_model + + +def _save_index(index, path): + """Serialize the index.""" + index_path = os.path.join(path, _INDEX_PERSIST_FOLDER) + index.storage_context.persist(persist_dir=index_path) + + +def _load_llama_model(path, flavor_conf): + """Load the LlamaIndex index/engine/workflow from either model code or serialized index.""" + from llama_index.core import StorageContext, load_index_from_storage + + _add_code_from_conf_to_system_path(path, flavor_conf) + + # Handle model-from-code + pyfunc_flavor_conf = _get_flavor_configuration(model_path=path, flavor_name=pyfunc.FLAVOR_NAME) + if model_code_path := pyfunc_flavor_conf.get(MODEL_CODE_PATH): + # TODO: The code path saved in the MLModel file is the local absolute path to the code + # file when it is saved. We should update the relative path in artifact directory. + model_code_path = os.path.join(path, os.path.basename(model_code_path)) + + model_config = pyfunc_flavor_conf.get(MODEL_CONFIG) or flavor_conf.get(MODEL_CONFIG, {}) + if isinstance(model_config, str): + config_path = os.path.join(path, os.path.basename(model_config)) + model_config = _validate_and_get_model_config_from_file(config_path) + + return _load_model_code_path(model_code_path, model_config) + else: + # Use default vector store when loading from the serialized index + index_path = os.path.join(path, _INDEX_PERSIST_FOLDER) + storage_context = StorageContext.from_defaults(persist_dir=index_path) + return load_index_from_storage(storage_context) + + +@trace_disabled # Suppress traces while loading model +def load_model(model_uri, dst_path=None): + """ + Load a LlamaIndex index/engine/workflow from a local file or a run. + + Args: + model_uri: The location, in URI format, of the MLflow model. For example: + + - ``/Users/me/path/to/local/model`` + - ``relative/path/to/local/model`` + - ``s3://my_bucket/path/to/model`` + - ``runs://run-relative/path/to/model`` + - ``mlflow-artifacts:/path/to/model`` + + For more information about supported URI schemes, see + `Referencing Artifacts `_. + dst_path: The local filesystem path to utilize for downloading the model artifact. + This directory must already exist if provided. If unspecified, a local output + path will be created. + + Returns: + A LlamaIndex index object. + """ + from mlflow.llama_index.serialize_objects import deserialize_settings + + local_model_path = _download_artifact_from_uri(artifact_uri=model_uri, output_path=dst_path) + mlflow_model = Model.load(local_model_path) + flavor_conf = _get_flavor_configuration(model_path=local_model_path, flavor_name=FLAVOR_NAME) + + settings_path = os.path.join(local_model_path, _SETTINGS_FILE) + # NB: Settings is a singleton and can be loaded via llama_index.core.Settings + deserialize_settings(settings_path) + model = _load_llama_model(local_model_path, flavor_conf) + _update_active_model_id_based_on_mlflow_model(mlflow_model) + return model + + +def _load_pyfunc(path, model_config: dict[str, Any] | None = None): + from mlflow.llama_index.pyfunc_wrapper import create_pyfunc_wrapper + + index = load_model(path) + flavor_conf = _get_flavor_configuration(model_path=path, flavor_name=FLAVOR_NAME) + engine_type = flavor_conf.pop( + "engine_type", None + ) # Not present when saving an non-index object + return create_pyfunc_wrapper(index, engine_type, model_config) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/llama_index/pyfunc_wrapper.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/llama_index/pyfunc_wrapper.py new file mode 100644 index 0000000000000000000000000000000000000000..4399a2cb8fcc3d2db7968595f2ced12db8148cc2 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/llama_index/pyfunc_wrapper.py @@ -0,0 +1,330 @@ +import asyncio +import threading +import uuid +from typing import TYPE_CHECKING, Any + +if TYPE_CHECKING: + from llama_index.core import QueryBundle + +from mlflow.models.utils import _convert_llm_input_data + +CHAT_ENGINE_NAME = "chat" +QUERY_ENGINE_NAME = "query" +RETRIEVER_ENGINE_NAME = "retriever" +SUPPORTED_ENGINES = {CHAT_ENGINE_NAME, QUERY_ENGINE_NAME, RETRIEVER_ENGINE_NAME} + +_CHAT_MESSAGE_HISTORY_PARAMETER_NAME = "chat_history" + + +def _convert_llm_input_data_with_unwrapping(data): + """ + Transforms the input data to the format expected by the LlamaIndex engine. + + TODO: Migrate the unwrapping logic to mlflow.evaluate() function or _convert_llm_input_data, + # because it is not specific to LlamaIndex. + """ + data = _convert_llm_input_data(data) + + # For mlflow.evaluate() call, the input dataset will be a pandas DataFrame. The DF should have + # a column named "inputs" which contains the actual query data. After the preprocessing, the + # each row will be passed here as a dictionary with the key "inputs". Therefore, we need to + # extract the actual query data from the dictionary. + if isinstance(data, dict) and ("inputs" in data): + data = data["inputs"] + + return data + + +def _format_predict_input_query_engine_and_retriever(data) -> "QueryBundle": + """Convert pyfunc input to a QueryBundle.""" + from llama_index.core import QueryBundle + + data = _convert_llm_input_data_with_unwrapping(data) + + if isinstance(data, str): + return QueryBundle(query_str=data) + elif isinstance(data, dict): + return QueryBundle(**data) + elif isinstance(data, list): + # NB: handle pandas returning lists when there is a single row + prediction_input = [_format_predict_input_query_engine_and_retriever(d) for d in data] + return prediction_input if len(prediction_input) > 1 else prediction_input[0] + else: + raise ValueError( + f"Unsupported input type: {type(data)}. It must be one of " + "[str, dict, list, numpy.ndarray, pandas.DataFrame]" + ) + + +class _LlamaIndexModelWrapperBase: + def __init__( + self, + llama_model, # Engine or Workflow + model_config: dict[str, Any] | None = None, + ): + self._llama_model = llama_model + self.model_config = model_config or {} + + @property + def index(self): + return self._llama_model.index + + def get_raw_model(self): + return self._llama_model + + def _predict_single(self, *args, **kwargs) -> Any: + raise NotImplementedError + + def _format_predict_input(self, data): + raise NotImplementedError + + def _do_inference(self, input, params: dict[str, Any] | None) -> dict[str, Any]: + """ + Perform engine inference on a single engine input e.g. not an iterable of + engine inputs. The engine inputs must already be preprocessed/cleaned. + """ + + if isinstance(input, dict): + return self._predict_single(**input, **(params or {})) + else: + return self._predict_single(input, **(params or {})) + + def predict(self, data, params: dict[str, Any] | None = None) -> list[str] | str: + data = self._format_predict_input(data) + + if isinstance(data, list): + return [self._do_inference(x, params) for x in data] + else: + return self._do_inference(data, params) + + +class ChatEngineWrapper(_LlamaIndexModelWrapperBase): + @property + def engine_type(self): + return CHAT_ENGINE_NAME + + def _predict_single(self, *args, **kwargs) -> str: + return self._llama_model.chat(*args, **kwargs).response + + @staticmethod + def _convert_chat_message_history_to_chat_message_objects( + data: dict[str, Any], + ) -> dict[str, Any]: + from llama_index.core.llms import ChatMessage + + if chat_message_history := data.get(_CHAT_MESSAGE_HISTORY_PARAMETER_NAME): + if isinstance(chat_message_history, list): + if all(isinstance(message, dict) for message in chat_message_history): + data[_CHAT_MESSAGE_HISTORY_PARAMETER_NAME] = [ + ChatMessage(**message) for message in chat_message_history + ] + else: + raise ValueError( + f"Unsupported input type: {type(chat_message_history)}. " + "It must be a list of dicts." + ) + + return data + + def _format_predict_input(self, data) -> str | dict[str, Any] | list[Any]: + data = _convert_llm_input_data_with_unwrapping(data) + + if isinstance(data, str): + return data + elif isinstance(data, dict): + return self._convert_chat_message_history_to_chat_message_objects(data) + elif isinstance(data, list): + # NB: handle pandas returning lists when there is a single row + prediction_input = [self._format_predict_input(d) for d in data] + return prediction_input if len(prediction_input) > 1 else prediction_input[0] + else: + raise ValueError( + f"Unsupported input type: {type(data)}. It must be one of " + "[str, dict, list, numpy.ndarray, pandas.DataFrame]" + ) + + +class QueryEngineWrapper(_LlamaIndexModelWrapperBase): + @property + def engine_type(self): + return QUERY_ENGINE_NAME + + def _predict_single(self, *args, **kwargs) -> str: + return self._llama_model.query(*args, **kwargs).response + + def _format_predict_input(self, data) -> "QueryBundle": + return _format_predict_input_query_engine_and_retriever(data) + + +class RetrieverEngineWrapper(_LlamaIndexModelWrapperBase): + @property + def engine_type(self): + return RETRIEVER_ENGINE_NAME + + def _predict_single(self, *args, **kwargs) -> list[dict[str, Any]]: + response = self._llama_model.retrieve(*args, **kwargs) + return [node.dict() for node in response] + + def _format_predict_input(self, data) -> "QueryBundle": + return _format_predict_input_query_engine_and_retriever(data) + + +class WorkflowWrapper(_LlamaIndexModelWrapperBase): + @property + def index(self): + raise NotImplementedError("LlamaIndex Workflow does not have an index") + + @property + def engine_type(self): + raise NotImplementedError("LlamaIndex Workflow is not an engine") + + def predict(self, data, params: dict[str, Any] | None = None) -> list[str] | str: + inputs = self._format_predict_input(data, params) + + # LlamaIndex Workflow runs async but MLflow pyfunc doesn't support async inference yet. + predictions = self._wait_async_task(self._run_predictions(inputs)) + + # Even if the input is single instance, the signature enforcement convert it to a Pandas + # DataFrame with a single row. In this case, we should unwrap the result (list) so it + # won't be inconsistent with the output without signature enforcement. + should_unwrap = len(data) == 1 and isinstance(predictions, list) + return predictions[0] if should_unwrap else predictions + + def _format_predict_input( + self, data, params: dict[str, Any] | None = None + ) -> list[dict[str, Any]]: + inputs = _convert_llm_input_data_with_unwrapping(data) + params = params or {} + if isinstance(inputs, dict): + return [{**inputs, **params}] + return [{**x, **params} for x in inputs] + + async def _run_predictions(self, inputs: list[dict[str, Any]]) -> asyncio.Future: + tasks = [self._predict_single(x) for x in inputs] + return await asyncio.gather(*tasks) + + async def _predict_single(self, x: dict[str, Any]) -> Any: + if not isinstance(x, dict): + raise ValueError(f"Unsupported input type: {type(x)}. It must be a dictionary.") + return await self._llama_model.run(**x) + + def _wait_async_task(self, task: asyncio.Future) -> Any: + """ + A utility function to run async tasks in a blocking manner. + + If there is no event loop running already, for example, in a model serving endpoint, + we can simply create a new event loop and run the task there. However, in a notebook + environment (or pytest with asyncio decoration), there is already an event loop running + at the root level and we cannot start a new one. + """ + if not self._is_event_loop_running(): + return asyncio.new_event_loop().run_until_complete(task) + else: + # NB: The popular way to run async task where an event loop is already running is to + # use nest_asyncio. However, nest_asyncio.apply() breaks the async OpenAI client + # somehow, which is used for the most of LLM calls in LlamaIndex including Databricks + # LLMs. Therefore, we use a hacky workaround that creates a new thread and run the + # new event loop there. This may degrade the performance compared to the native + # asyncio, but it should be fine because this is only used in the notebook env. + results = None + exception = None + + def _run(): + nonlocal results, exception + + try: + loop = asyncio.new_event_loop() + asyncio.set_event_loop(loop) + results = loop.run_until_complete(task) + except Exception as e: + exception = e + finally: + loop.close() + + thread = threading.Thread( + target=_run, name=f"mlflow_llamaindex_async_task_runner_{uuid.uuid4().hex[:8]}" + ) + thread.start() + thread.join() + + if exception: + raise exception + + return results + + def _is_event_loop_running(self) -> bool: + try: + loop = asyncio.get_running_loop() + return loop is not None + except Exception: + return False + + +def create_pyfunc_wrapper( + model: Any, + engine_type: str | None = None, + model_config: dict[str, Any] | None = None, +): + """ + A factory function that creates a Pyfunc wrapper around a LlamaIndex index/engine/workflow. + + Args: + model: A LlamaIndex index/engine/workflow. + engine_type: The type of the engine. Only required if `model` is an index + and must be one of [chat, query, retriever]. + model_config: A dictionary of model configuration parameters. + """ + try: + from llama_index.core.workflow import Workflow + + if isinstance(model, Workflow): + return _create_wrapper_from_workflow(model, model_config) + except ImportError: + pass + + from llama_index.core.indices.base import BaseIndex + + if isinstance(model, BaseIndex): + return _create_wrapper_from_index(model, engine_type, model_config) + else: + # Engine does not have a common base class so we assume + # everything else is an engine + return _create_wrapper_from_engine(model, model_config) + + +def _create_wrapper_from_index(index, engine_type: str, model_config: dict[str, Any] | None = None): + model_config = model_config or {} + if engine_type == QUERY_ENGINE_NAME: + engine = index.as_query_engine(**model_config) + return QueryEngineWrapper(engine, model_config) + elif engine_type == CHAT_ENGINE_NAME: + engine = index.as_chat_engine(**model_config) + return ChatEngineWrapper(engine, model_config) + elif engine_type == RETRIEVER_ENGINE_NAME: + engine = index.as_retriever(**model_config) + return RetrieverEngineWrapper(engine, model_config) + else: + raise ValueError( + f"Unsupported engine type: {engine_type}. It must be one of {SUPPORTED_ENGINES}" + ) + + +def _create_wrapper_from_engine(engine: Any, model_config: dict[str, Any] | None = None): + from llama_index.core.base.base_query_engine import BaseQueryEngine + from llama_index.core.chat_engine.types import BaseChatEngine + from llama_index.core.retrievers import BaseRetriever + + if isinstance(engine, BaseChatEngine): + return ChatEngineWrapper(engine, model_config) + elif isinstance(engine, BaseQueryEngine): + return QueryEngineWrapper(engine, model_config) + elif isinstance(engine, BaseRetriever): + return RetrieverEngineWrapper(engine, model_config) + else: + raise ValueError( + f"Unsupported engine type: {type(engine)}. It must be one of {SUPPORTED_ENGINES}" + ) + + +def _create_wrapper_from_workflow(workflow: Any, model_config: dict[str, Any] | None = None): + return WorkflowWrapper(workflow, model_config) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/llama_index/serialize_objects.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/llama_index/serialize_objects.py new file mode 100644 index 0000000000000000000000000000000000000000..38267519b2796ab975c4412739bdda28235985ba --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/llama_index/serialize_objects.py @@ -0,0 +1,188 @@ +import importlib +import inspect +import json +import logging +from typing import Any, Callable + +from llama_index.core import PromptTemplate +from llama_index.core.base.embeddings.base import BaseEmbedding +from llama_index.core.callbacks.base import CallbackManager +from llama_index.core.schema import BaseComponent + +_logger = logging.getLogger(__name__) + + +def _get_object_import_path(o: object) -> str: + if not inspect.isclass(o): + o = o.__class__ + + module_name = inspect.getmodule(o).__name__ + class_name = o.__qualname__ + + # Validate the import + module = importlib.import_module(module_name) + if not hasattr(module, class_name): + raise ValueError(f"Module {module} does not have {class_name}") + + return f"{module_name}.{class_name}" + + +def _sanitize_api_key(object_as_dict: dict[str, str]) -> dict[str, str]: + return {k: v for k, v in object_as_dict.items() if "api_key" not in k.lower()} + + +def object_to_dict(o: object): + if isinstance(o, (list, tuple)): + return [object_to_dict(v) for v in o] + + if isinstance(o, BaseComponent): + # we can't serialize callables in the model fields + callable_fields = set() + fields = o.model_fields if hasattr(o, "model_fields") else o.__fields__ + for k, v in fields.items(): + field_val = getattr(o, k, None) + if field_val != v.default and callable(field_val): + callable_fields.add(k) + # exclude default values from serialization to avoid + # unnecessary clutter in the serialized object + o_state_as_dict = o.to_dict(exclude=callable_fields) + + if o_state_as_dict != {}: + o_state_as_dict = _sanitize_api_key(o_state_as_dict) + o_state_as_dict.pop("class_name") + else: + return o_state_as_dict + + return { + "object_constructor": _get_object_import_path(o), + "object_kwargs": o_state_as_dict, + } + else: + return None + + +def _construct_prompt_template_object( + constructor: Callable[..., PromptTemplate], kwargs: dict[str, Any] +) -> PromptTemplate: + """Construct a PromptTemplate object based on the constructor and kwargs. + + This method is necessary because the `template_vars` cannot be passed directly to the + constructor and needs to be set on an instantiated object. + """ + if template := kwargs.pop("template", None): + prompt_template = constructor(template) + for k, v in kwargs.items(): + setattr(prompt_template, k, v) + + return prompt_template + else: + raise ValueError( + "'template' is a required kwargs and is not present in the prompt template kwargs." + ) + + +def dict_to_object(object_representation: dict[str, Any]) -> object: + if "object_constructor" not in object_representation: + raise ValueError("'object_constructor' key not found in dict.") + if "object_kwargs" not in object_representation: + raise ValueError("'object_kwargs' key not found in dict.") + + constructor_str = object_representation["object_constructor"] + kwargs = object_representation["object_kwargs"] + + import_path, class_name = constructor_str.rsplit(".", 1) + module = importlib.import_module(import_path) + + if isinstance(module, PromptTemplate): + return _construct_prompt_template_object(module, kwargs) + else: + object_class = getattr(module, class_name) + + # Many embeddings model accepts parameter `model`, while BaseEmbedding accepts `model_name`. + # Both parameters will be serialized as kwargs, but passing both to the constructor will + # raise duplicate argument error. Some class like OpenAIEmbedding handles this in its + # constructor, but not all integrations do. Therefore, we have to handle it here. + # E.g. https://github.com/run-llama/llama_index/blob/2b18eb4654b14c68d63f6239cddb10740668fbc8/llama-index-integrations/embeddings/llama-index-embeddings-openai/llama_index/embeddings/openai/base.py#L316-L320 + if ( + issubclass(object_class, BaseEmbedding) + and (model := kwargs.get("model")) + and (model_name := kwargs.get("model_name")) + and model == model_name + ): + kwargs.pop("model_name") + + return object_class.from_dict(kwargs) + + +def _deserialize_dict_of_objects(path: str) -> dict[str, Any]: + with open(path) as f: + to_deserialize = json.load(f) + + output = {} + for k, v in to_deserialize.items(): + if isinstance(v, list): + output.update({k: [dict_to_object(vv) for vv in v]}) + else: + output.update({k: dict_to_object(v)}) + + return output + + +def serialize_settings(path: str) -> None: + """Serialize the global LlamaIndex Settings object to a JSON file at the given path.""" + from llama_index.core import Settings + + _logger.info( + "API key(s) will be removed from the global Settings object during serialization " + "to protect against key leakage. At inference time, the key(s) must be passed as " + "environment variables." + ) + + to_serialize = {} + unsupported_objects = [] + + for k, v in Settings.__dict__.items(): + if v is None: + continue + + # Setting.callback_manager is default to an empty CallbackManager instance. + if (k == "_callback_manager") and isinstance(v, CallbackManager) and v.handlers == []: + continue + + def _convert(obj): + object_json = object_to_dict(obj) + if object_json is None: + prop_name = k[1:] if k.startswith("_") else k + unsupported_objects.append((prop_name, v)) + return object_json + + if isinstance(v, list): + to_serialize[k] = [_convert(obj) for obj in v if v is not None] + else: + if (object_json := _convert(v)) and (object_json is not None): + to_serialize[k] = object_json + + if unsupported_objects: + msg = ( + "The following objects in Settings are not supported for serialization and will not " + "be logged with your model. MLflow only supports serialization of objects that inherit " + "from llama_index.core.schema.BaseComponent.\n" + ) + msg += "\n".join(f" - {type(v).__name__} for Settings.{k}" for k, v in unsupported_objects) + _logger.info(msg) + + with open(path, "w") as f: + json.dump(to_serialize, f, indent=2) + + +def deserialize_settings(path: str): + """Deserialize the global LlamaIndex Settings object from a JSON file at the given path.""" + settings_dict = _deserialize_dict_of_objects(path) + + from llama_index.core import Settings + + for k, v in settings_dict.items(): + # To use the property setter rather than directly setting the private attribute e.g. _llm + if k.startswith("_"): + k = k[1:] + setattr(Settings, k, v) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/llama_index/tracer.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/llama_index/tracer.py new file mode 100644 index 0000000000000000000000000000000000000000..7d79b98a9afb4071cbe2ede2cba0394bb0cefa62 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/llama_index/tracer.py @@ -0,0 +1,567 @@ +import inspect +import json +import logging +from functools import singledispatchmethod +from typing import Any, Generator + +import llama_index.core +import pydantic +from llama_index.core.base.base_retriever import BaseRetriever +from llama_index.core.base.embeddings.base import BaseEmbedding +from llama_index.core.base.llms.base import BaseLLM +from llama_index.core.base.llms.types import ChatResponse, CompletionResponse +from llama_index.core.base.response.schema import AsyncStreamingResponse, StreamingResponse +from llama_index.core.chat_engine.types import StreamingAgentChatResponse +from llama_index.core.instrumentation.event_handlers import BaseEventHandler +from llama_index.core.instrumentation.events import BaseEvent +from llama_index.core.instrumentation.events.agent import AgentToolCallEvent +from llama_index.core.instrumentation.events.embedding import EmbeddingStartEvent +from llama_index.core.instrumentation.events.exception import ExceptionEvent +from llama_index.core.instrumentation.events.llm import ( + LLMChatEndEvent, + LLMChatStartEvent, + LLMCompletionEndEvent, + LLMCompletionStartEvent, + LLMPredictStartEvent, +) +from llama_index.core.instrumentation.events.rerank import ReRankStartEvent +from llama_index.core.instrumentation.span.base import BaseSpan +from llama_index.core.instrumentation.span_handlers import BaseSpanHandler +from llama_index.core.multi_modal_llms import MultiModalLLM +from llama_index.core.schema import NodeWithScore +from llama_index.core.tools import BaseTool +from packaging.version import Version + +import mlflow +from mlflow.entities import LiveSpan, SpanEvent, SpanType +from mlflow.entities.document import Document +from mlflow.entities.span_status import SpanStatusCode +from mlflow.tracing.constant import SpanAttributeKey, TokenUsageKey +from mlflow.tracing.fluent import start_span_no_context +from mlflow.tracing.provider import detach_span_from_context, set_span_in_context +from mlflow.tracing.utils import set_span_chat_tools +from mlflow.utils.pydantic_utils import model_dump_compat + +_logger = logging.getLogger(__name__) + + +def _get_llama_index_version() -> Version: + return Version(llama_index.core.__version__) + + +def set_llama_index_tracer(): + """ + Set the MlflowSpanHandler and MlflowEventHandler to the global dispatcher. + If the handlers are already set, skip setting. + """ + from llama_index.core.instrumentation import get_dispatcher + + dsp = get_dispatcher() + span_handler = None + for handler in dsp.span_handlers: + if isinstance(handler, MlflowSpanHandler): + _logger.debug("MlflowSpanHandler is already set to the dispatcher. Skip setting.") + span_handler = handler + break + else: + span_handler = MlflowSpanHandler() + dsp.add_span_handler(span_handler) + + for handler in dsp.event_handlers: + if isinstance(handler, MlflowEventHandler): + _logger.debug("MlflowEventHandler is already set to the dispatcher. Skip setting.") + break + else: + dsp.add_event_handler(MlflowEventHandler(span_handler)) + + +def remove_llama_index_tracer(): + """ + Remove the MlflowSpanHandler and MlflowEventHandler from the global dispatcher. + """ + from llama_index.core.instrumentation import get_dispatcher + + dsp = get_dispatcher() + dsp.span_handlers = [h for h in dsp.span_handlers if h.class_name() != "MlflowSpanHandler"] + dsp.event_handlers = [h for h in dsp.event_handlers if h.class_name() != "MlflowEventHandler"] + + +class _LlamaSpan(BaseSpan, extra="allow"): + _mlflow_span: LiveSpan = pydantic.PrivateAttr() + + def __init__(self, id_: str, parent_id: str | None, mlflow_span: LiveSpan): + super().__init__(id_=id_, parent_id=parent_id) + self._mlflow_span = mlflow_span + + +def _end_span(span: LiveSpan, status=SpanStatusCode.OK, outputs=None, token=None): + """An utility function to end the span or trace.""" + if isinstance(outputs, (StreamingResponse, AsyncStreamingResponse, StreamingAgentChatResponse)): + _logger.warning( + "Trying to record streaming response to the MLflow trace. This may consume " + "the generator and result in an empty response." + ) + + # for retriever spans, convert the outputs to Document objects + # so they can be rendered in a more user-friendly way in the UI + if ( + span.span_type == SpanType.RETRIEVER + and isinstance(outputs, list) + and all(isinstance(item, NodeWithScore) for item in outputs) + ): + try: + outputs = [Document.from_llama_index_node_with_score(node) for node in outputs] + except Exception as e: + _logger.debug( + f"Failed to convert NodeWithScore to Document objects: {e}", exc_info=True + ) + + if outputs is None: + outputs = span.outputs + + try: + span.end(status=status, outputs=outputs) + finally: + # We should detach span even when end_span / end_trace API call fails + if token: + detach_span_from_context(token) + + +class MlflowSpanHandler(BaseSpanHandler[_LlamaSpan], extra="allow"): + def __init__(self): + super().__init__() + self._span_id_to_token = {} + self._stream_resolver = StreamResolver() + self._pending_spans: dict[str, _LlamaSpan] = {} + + @classmethod + def class_name(cls) -> str: + return "MlflowSpanHandler" + + def get_span_for_event(self, event: BaseEvent) -> LiveSpan: + llama_span = self.open_spans.get(event.span_id) or self._pending_spans.get(event.span_id) + return llama_span._mlflow_span if llama_span else None + + def new_span( + self, + id_: str, + bound_args: inspect.BoundArguments, + instance: Any | None = None, + parent_span_id: str | None = None, + **kwargs: Any, + ) -> _LlamaSpan: + with self.lock: + parent = self.open_spans.get(parent_span_id) if parent_span_id else None + + parent_span = parent._mlflow_span if parent else mlflow.get_current_active_span() + + try: + input_args = bound_args.arguments + attributes = self._get_instance_attributes(instance) + span_type = self._get_span_type(instance) or SpanType.UNKNOWN + span = start_span_no_context( + name=id_.partition("-")[0], + parent_span=parent_span, + span_type=span_type, + inputs=input_args, + attributes=attributes, + ) + + token = set_span_in_context(span) + self._span_id_to_token[span.span_id] = token + + # NB: The tool definition is passed to LLM via kwargs, but it is not set + # to the LLM/Chat start event. Therefore, we need to handle it here. + tools = input_args.get("kwargs", {}).get("tools") + if tools and span_type in [SpanType.LLM, SpanType.CHAT_MODEL]: + try: + set_span_chat_tools(span, tools) + except Exception as e: + _logger.debug(f"Failed to set tools for {span}: {e}") + + return _LlamaSpan(id_=id_, parent_id=parent_span_id, mlflow_span=span) + except BaseException as e: + _logger.debug(f"Failed to create a new span: {e}", exc_info=True) + + def prepare_to_exit_span( + self, + id_: str, + result: Any | None = None, + **kwargs: Any, + ) -> _LlamaSpan: + try: + with self.lock: + llama_span = self.open_spans.get(id_) + if not llama_span: + return + + span = llama_span._mlflow_span + token = self._span_id_to_token.pop(span.span_id, None) + + if self._stream_resolver.is_streaming_result(result): + # If the result is a generator, we keep the span in progress for streaming + # and end it when the generator is exhausted. + is_pended = self._stream_resolver.register_stream_span(span, result) + if is_pended: + self._pending_spans[id_] = llama_span + # We still need to detach the span from the context, otherwise it will + # be considered as "active" + detach_span_from_context(token) + else: + # If the span is not pended successfully, end it immediately + _end_span(span=span, outputs=result, token=token) + else: + _end_span(span=span, outputs=result, token=token) + return llama_span + except BaseException as e: + _logger.debug(f"Failed to end a span: {e}", exc_info=True) + + def resolve_pending_stream_span(self, span: LiveSpan, event: Any): + """End the pending streaming span(s)""" + self._stream_resolver.resolve(span, event) + self._pending_spans.pop(event.span_id, None) + + def prepare_to_drop_span(self, id_: str, err: Exception | None, **kwargs) -> _LlamaSpan: + """Logic for handling errors during the model execution.""" + with self.lock: + llama_span = self.open_spans.get(id_) + span = llama_span._mlflow_span + token = self._span_id_to_token.pop(span.span_id, None) + + if _get_llama_index_version() >= Version("0.10.59"): + # LlamaIndex determines if a workflow is terminated or not by propagating an special + # exception WorkflowDone. We should treat this exception as a successful termination. + from llama_index.core.workflow.errors import WorkflowDone + + if err and isinstance(err, WorkflowDone): + return _end_span(span=span, status=SpanStatusCode.OK, token=token) + + span.add_event(SpanEvent.from_exception(err)) + _end_span(span=span, status="ERROR", token=token) + return llama_span + + def _get_span_type(self, instance: Any) -> SpanType: + """ + Map LlamaIndex instance type to MLflow span type. Some span type cannot be determined + by instance type alone, rather need event info e.g. ChatModel, ReRanker + """ + base_agent_types = () + if _get_llama_index_version() < Version("0.13.0"): + from llama_index.core.base.agent.types import BaseAgent, BaseAgentWorker + + base_agent_types = (BaseAgent, BaseAgentWorker) + else: + from llama_index.core.agent.workflow import BaseWorkflowAgent + + base_agent_types = (BaseWorkflowAgent,) + + if isinstance(instance, (BaseLLM, MultiModalLLM)): + return SpanType.LLM + elif isinstance(instance, BaseRetriever): + return SpanType.RETRIEVER + elif isinstance(instance, base_agent_types): + return SpanType.AGENT + elif isinstance(instance, BaseEmbedding): + return SpanType.EMBEDDING + elif isinstance(instance, BaseTool): + return SpanType.TOOL + else: + return SpanType.CHAIN + + @singledispatchmethod + def _get_instance_attributes(self, instance: Any) -> dict[str, Any]: + """ + Extract span attributes from LlamaIndex objects. + + NB: There are some overlap between attributes extracted from instance metadata and the + events. For example, model name for an LLM is available in both. However, events might + not always be triggered (e.g. 3P llm integration doesn't implement the event logic), + so the instance metadata serves as a fallback source of information. + """ + + # TODO: Union type hint doesn't work with singledispatchmethod, so we have to define + # two separate methods for BaseLLM and MultiModalLLM. Once we upgrade to Python 3.10, + # we can use `BaseLLM | MultiModelLLM` type hint and it works with singledispatchmethod. + @_get_instance_attributes.register + def _(self, instance: BaseLLM): + return self._get_llm_attributes(instance) + + @_get_instance_attributes.register + def _(self, instance: MultiModalLLM): + return self._get_llm_attributes(instance) + + def _get_llm_attributes(self, instance) -> dict[str, Any]: + attr = {SpanAttributeKey.MESSAGE_FORMAT: "llamaindex"} + if metadata := instance.metadata: + attr["model_name"] = metadata.model_name + if params_str := metadata.json(exclude_unset=True): + attr["invocation_params"] = json.loads(params_str) + return attr + + @_get_instance_attributes.register + def _(self, instance: BaseEmbedding): + return { + "model_name": instance.model_name, + "embed_batch_size": instance.embed_batch_size, + } + + @_get_instance_attributes.register + def _(self, instance: BaseTool): + metadata = instance.metadata + attributes = {"description": metadata.description} + try: + attributes["name"] = metadata.name + except ValueError: + # ToolMetadata.get_name() raises ValueError if name is None + pass + try: + attributes["parameters"] = json.loads(metadata.fn_schema_str) + except ValueError: + # ToolMetadata.get_fn_schema_str() raises ValueError if fn_schema is None + pass + return attributes + + +class MlflowEventHandler(BaseEventHandler, extra="allow"): + """ + Event handler processes various events that are triggered during execution. + + Events are used as supplemental source for recording additional metadata to the span, + such as model name, parameters to the span, because they are not available in the inputs + and outputs in SpanHandler. + """ + + _span_handler: MlflowSpanHandler + + @classmethod + def class_name(cls) -> str: + return "MlflowEventHandler" + + def __init__(self, _span_handler): + super().__init__() + self._span_handler = _span_handler + + def handle(self, event: BaseEvent) -> Any: + try: + if span := self._span_handler.get_span_for_event(event): + self._handle_event(event, span) + except Exception as e: + _logger.debug(f"Failed to handle event: {e}", exc_info=True) + + @singledispatchmethod + def _handle_event(self, event: BaseEvent, span: LiveSpan): + # Pass through the events we are not interested in + pass + + @_handle_event.register + def _(self, event: AgentToolCallEvent, span: LiveSpan): + span.set_attribute("name", event.tool.name) + span.set_attribute("description", event.tool.description) + span.set_attribute("parameters", event.tool.get_parameters_dict()) + + @_handle_event.register + def _(self, event: EmbeddingStartEvent, span: LiveSpan): + span.set_attribute("model_dict", event.model_dict) + + @_handle_event.register + def _(self, event: LLMPredictStartEvent, span: LiveSpan): + """ + An event triggered when LLM's predict() is called. + + In LlamaIndex, predict() is a gateway method that dispatch the request to + either chat() or completion() method depending on the model type, as well + as crafting prompt from the template. + """ + template = event.template + template_args = { + **template.kwargs, + **(event.template_args if event.template_args else {}), + } + span.set_attributes( + { + "prmopt_template": template.get_template(), + "template_arguments": {var: template_args.get(var) for var in template_args}, + } + ) + + @_handle_event.register + def _(self, event: LLMCompletionStartEvent, span: LiveSpan): + span.set_attribute("prompt", event.prompt) + span.set_attribute("model_dict", event.model_dict) + + @_handle_event.register + def _(self, event: LLMCompletionEndEvent, span: LiveSpan): + span.set_attribute("usage", self._extract_token_usage(event.response)) + token_counts = self._parse_usage(span) + span.set_attribute(SpanAttributeKey.CHAT_USAGE, token_counts) + self._span_handler.resolve_pending_stream_span(span, event) + + @_handle_event.register + def _(self, event: LLMChatStartEvent, span: LiveSpan): + span.set_attribute(SpanAttributeKey.SPAN_TYPE, SpanType.CHAT_MODEL) + span.set_attribute("model_dict", event.model_dict) + + @_handle_event.register + def _(self, event: LLMChatEndEvent, span: LiveSpan): + span.set_attribute("usage", self._extract_token_usage(event.response)) + token_counts = self._parse_usage(span) + span.set_attribute(SpanAttributeKey.CHAT_USAGE, token_counts) + self._span_handler.resolve_pending_stream_span(span, event) + + @_handle_event.register + def _(self, event: ReRankStartEvent, span: LiveSpan): + span.set_attribute(SpanAttributeKey.SPAN_TYPE, SpanType.RERANKER) + span.set_attributes( + { + "model_name": event.model_name, + "top_n": event.top_n, + } + ) + + @_handle_event.register + def _(self, event: ExceptionEvent, span: LiveSpan): + """ + Handle an exception event for stream spans. + + For non-stream spans, exception is processed by the prepare_to_drop_span() handler of + the span handler. However, for stream spans, the exception may raised during the + streaming after it exit. Therefore, we need to resolve the span here. + """ + self._span_handler.resolve_pending_stream_span(span, event) + + def _extract_token_usage(self, response: ChatResponse | CompletionResponse) -> dict[str, int]: + if raw := response.raw: + # The raw response can be a Pydantic model or a dictionary + if isinstance(raw, pydantic.BaseModel): + raw = model_dump_compat(raw) + + if usage := raw.get("usage"): + return usage + + # If the usage is not found in the raw response, look for token counts + # in additional_kwargs of the completion payload + usage = {} + if additional_kwargs := getattr(response, "additional_kwargs", None): + for k in ["prompt_tokens", "completion_tokens", "total_tokens"]: + if (v := additional_kwargs.get(k)) is not None: + usage[k] = v + return usage + + def _parse_usage(self, span: LiveSpan): + try: + usage = span.get_attribute("usage") + return { + TokenUsageKey.INPUT_TOKENS: usage["prompt_tokens"], + TokenUsageKey.OUTPUT_TOKENS: usage["completion_tokens"], + TokenUsageKey.TOTAL_TOKENS: usage.get( + "total_tokens", usage["prompt_tokens"] + usage["completion_tokens"] + ), + } + except Exception as e: + _logger.debug(f"Failed to set TokenUsage to the span: {e}", exc_info=True) + + +_StreamEndEvent = LLMChatEndEvent | LLMCompletionEndEvent | ExceptionEvent + + +def _get_task_step_output_type(): + if _get_llama_index_version() < Version("0.13.0"): + from llama_index.core.base.agent.types import TaskStepOutput + + return TaskStepOutput + return () + + +class StreamResolver: + """ + A class is responsible for closing the pending streaming spans that are waiting + for the stream to be exhausted. Once the associated stream is exhausted, this + class will resolve the span, as well as recursively resolve the parent spans + that returns the same (or derived) stream. + """ + + def __init__(self): + self._span_id_to_span_and_gen: dict[str, tuple[LiveSpan, Generator]] = {} + + def is_streaming_result(self, result: Any) -> bool: + return ( + inspect.isgenerator(result) # noqa: SIM101 + or isinstance(result, (StreamingResponse, AsyncStreamingResponse)) + or isinstance(result, StreamingAgentChatResponse) + or ( + isinstance(result, _get_task_step_output_type()) + and self.is_streaming_result(result.output) + ) + ) + + def register_stream_span(self, span: LiveSpan, result: Any) -> bool: + """ + Register the pending streaming span with the associated generator. + + Args: + span: The span that has a streaming output. + result: The streaming result that is being processed. + + Returns: + True if the span is registered successfully, False otherwise. + """ + if inspect.isgenerator(result): + stream = result + elif isinstance(result, (StreamingResponse, AsyncStreamingResponse)): + stream = result.response_gen + elif isinstance(result, StreamingAgentChatResponse): + stream = result.chat_stream + elif isinstance(result, _get_task_step_output_type()): + stream = result.output.chat_stream + else: + raise ValueError(f"Unsupported streaming response type: {type(result)}") + + if inspect.getgeneratorstate(stream) == inspect.GEN_CLOSED: + # Not registering the span because the generator is already exhausted. + # It's counter-intuitive that the generator is closed before the response + # is returned, but it can happen because some agents run streaming request + # in a separate thread. In this case, the generator can be closed before + # the response is returned in the main thread. + return False + + self._span_id_to_span_and_gen[span.span_id] = (span, stream) + return True + + def resolve(self, span: LiveSpan, event: _StreamEndEvent): + """ + Finish the streaming span and recursively resolve the parent spans that + returns the same (or derived) stream. + """ + _, stream = self._span_id_to_span_and_gen.pop(span.span_id, (None, None)) + if not stream: + return + + if isinstance(event, (LLMChatEndEvent, LLMCompletionEndEvent)): + outputs = event.response + status = SpanStatusCode.OK + elif isinstance(event, ExceptionEvent): + outputs = None + status = SpanStatusCode.ERROR + span.add_event(SpanEvent.from_exception(event.exception)) + else: + raise ValueError(f"Unsupported event type to resolve streaming: {type(event)}") + + _end_span(span=span, status=status, outputs=outputs) + + # Extract the complete text from the event. + if isinstance(outputs, ChatResponse): + output_text = outputs.message.content + elif isinstance(outputs, CompletionResponse): + output_text = outputs.response.text + else: + output_text = None + + # Recursively resolve the parent spans that are also waiting for the same token + # stream to be exhausted. + while span.parent_id in self._span_id_to_span_and_gen: + if span_and_stream := self._span_id_to_span_and_gen.pop(span.parent_id, None): + span, stream = span_and_stream + # We reuse the same output text for parent spans. This may not be 100% correct + # as token stream can be modified by callers. However, it is technically + # challenging to track the modified stream across multiple spans. + _end_span(span=span, status=status, outputs=output_text) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/metrics/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/metrics/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e76cdf03b256b7e83b62f578d68ddfb86915ea40 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/metrics/__init__.py @@ -0,0 +1,500 @@ +import os + +from mlflow.metrics import genai +from mlflow.metrics.base import ( + MetricValue, +) +from mlflow.metrics.metric_definitions import ( + _accuracy_eval_fn, + _ari_eval_fn, + _bleu_eval_fn, + _f1_score_eval_fn, + _flesch_kincaid_eval_fn, + _mae_eval_fn, + _mape_eval_fn, + _max_error_eval_fn, + _mse_eval_fn, + _ndcg_at_k_eval_fn, + _precision_at_k_eval_fn, + _precision_eval_fn, + _r2_score_eval_fn, + _recall_at_k_eval_fn, + _recall_eval_fn, + _rmse_eval_fn, + _rouge1_eval_fn, + _rouge2_eval_fn, + _rougeL_eval_fn, + _rougeLsum_eval_fn, + _toxicity_eval_fn, +) +from mlflow.models import ( + EvaluationMetric, + make_metric, +) + + +def latency() -> EvaluationMetric: + """ + This function will create a metric for calculating latency. Latency is determined by the time + it takes to generate a prediction for a given input. Note that computing latency requires + each row to be predicted sequentially, which will likely slow down the evaluation process. + """ + return make_metric( + eval_fn=lambda x: MetricValue(), + greater_is_better=False, + name="latency", + ) + + +# general text metrics +def token_count(encoding: str = "cl100k_base") -> EvaluationMetric: + """ + This function will create a metric for calculating token_count. Token count is calculated + using tiktoken by using the `cl100k_base` tokenizer. + + Note: For air-gapped environments, you can set the TIKTOKEN_CACHE_DIR environment variable + to specify a local cache directory for tiktoken to avoid downloading the tokenizer files. + """ + + def _token_count_eval_fn(predictions, targets=None, metrics=None): + import tiktoken + + # ref: https://github.com/openai/tiktoken/issues/75 + # Only set TIKTOKEN_CACHE_DIR if not already set by user + if "TIKTOKEN_CACHE_DIR" not in os.environ: + os.environ["TIKTOKEN_CACHE_DIR"] = "" + enc = tiktoken.get_encoding(encoding) + + num_tokens = [] + for prediction in predictions: + if isinstance(prediction, str): + num_tokens.append(len(enc.encode(prediction))) + else: + num_tokens.append(None) + + return MetricValue( + scores=num_tokens, + aggregate_results={}, + ) + + return make_metric( + eval_fn=_token_count_eval_fn, + greater_is_better=True, + name="token_count", + ) + + +def toxicity() -> EvaluationMetric: + """ + This function will create a metric for evaluating `toxicity`_ using the model + `roberta-hate-speech-dynabench-r4`_, which defines hate as "abusive speech targeting + specific group characteristics, such as ethnic origin, religion, gender, or sexual + orientation." + + The score ranges from 0 to 1, where scores closer to 1 are more toxic. The default threshold + for a text to be considered "toxic" is 0.5. + + Aggregations calculated for this metric: + - ratio (of toxic input texts) + + .. _toxicity: https://huggingface.co/spaces/evaluate-measurement/toxicity + .. _roberta-hate-speech-dynabench-r4: https://huggingface.co/facebook/roberta-hate-speech-dynabench-r4-target + """ + return make_metric( + eval_fn=_toxicity_eval_fn, + greater_is_better=False, + name="toxicity", + long_name="toxicity/roberta-hate-speech-dynabench-r4", + version="v1", + ) + + +def flesch_kincaid_grade_level() -> EvaluationMetric: + """ + This function will create a metric for calculating `flesch kincaid grade level`_ using + `textstat`_. + + This metric outputs a number that approximates the grade level needed to comprehend the text, + which will likely range from around 0 to 15 (although it is not limited to this range). + + Aggregations calculated for this metric: + - mean + + .. _flesch kincaid grade level: + https://en.wikipedia.org/wiki/Flesch%E2%80%93Kincaid_readability_tests#Flesch%E2%80%93Kincaid_grade_level + .. _textstat: https://pypi.org/project/textstat/ + """ + return make_metric( + eval_fn=_flesch_kincaid_eval_fn, + greater_is_better=False, + name="flesch_kincaid_grade_level", + version="v1", + ) + + +def ari_grade_level() -> EvaluationMetric: + """ + This function will create a metric for calculating `automated readability index`_ using + `textstat`_. + + This metric outputs a number that approximates the grade level needed to comprehend the text, + which will likely range from around 0 to 15 (although it is not limited to this range). + + Aggregations calculated for this metric: + - mean + + .. _automated readability index: https://en.wikipedia.org/wiki/Automated_readability_index + .. _textstat: https://pypi.org/project/textstat/ + """ + return make_metric( + eval_fn=_ari_eval_fn, + greater_is_better=False, + name="ari_grade_level", + long_name="automated_readability_index_grade_level", + version="v1", + ) + + +# question answering metrics +def exact_match() -> EvaluationMetric: + """ + This function will create a metric for calculating `accuracy`_ using sklearn. + + This metric only computes an aggregate score which ranges from 0 to 1. + + .. _accuracy: https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html + """ + return make_metric( + eval_fn=_accuracy_eval_fn, greater_is_better=True, name="exact_match", version="v1" + ) + + +# text summarization metrics +def rouge1() -> EvaluationMetric: + """ + This function will create a metric for evaluating `rouge1`_. + + The score ranges from 0 to 1, where a higher score indicates higher similarity. + `rouge1`_ uses unigram based scoring to calculate similarity. + + Aggregations calculated for this metric: + - mean + + .. _rouge1: https://huggingface.co/spaces/evaluate-metric/rouge + """ + return make_metric( + eval_fn=_rouge1_eval_fn, + greater_is_better=True, + name="rouge1", + version="v1", + ) + + +def rouge2() -> EvaluationMetric: + """ + This function will create a metric for evaluating `rouge2`_. + + The score ranges from 0 to 1, where a higher score indicates higher similarity. + `rouge2`_ uses bigram based scoring to calculate similarity. + + Aggregations calculated for this metric: + - mean + + .. _rouge2: https://huggingface.co/spaces/evaluate-metric/rouge + """ + return make_metric( + eval_fn=_rouge2_eval_fn, + greater_is_better=True, + name="rouge2", + version="v1", + ) + + +def rougeL() -> EvaluationMetric: + """ + This function will create a metric for evaluating `rougeL`_. + + The score ranges from 0 to 1, where a higher score indicates higher similarity. + `rougeL`_ uses unigram based scoring to calculate similarity. + + Aggregations calculated for this metric: + - mean + + .. _rougeL: https://huggingface.co/spaces/evaluate-metric/rouge + """ + return make_metric( + eval_fn=_rougeL_eval_fn, + greater_is_better=True, + name="rougeL", + version="v1", + ) + + +def rougeLsum() -> EvaluationMetric: + """ + This function will create a metric for evaluating `rougeLsum`_. + + The score ranges from 0 to 1, where a higher score indicates higher similarity. + `rougeLsum`_ uses longest common subsequence based scoring to calculate similarity. + + Aggregations calculated for this metric: + - mean + + .. _rougeLsum: https://huggingface.co/spaces/evaluate-metric/rouge + """ + return make_metric( + eval_fn=_rougeLsum_eval_fn, + greater_is_better=True, + name="rougeLsum", + version="v1", + ) + + +def precision_at_k(k) -> EvaluationMetric: + """ + This function will create a metric for calculating ``precision_at_k`` for retriever models. + + This metric computes a score between 0 and 1 for each row representing the precision of the + retriever model at the given ``k`` value. If no relevant documents are retrieved, the score is + 0, indicating that no relevant docs are retrieved. Let ``x = min(k, # of retrieved doc IDs)``. + Then, in all other cases, the precision at k is calculated as follows: + + ``precision_at_k`` = (# of relevant retrieved doc IDs in top-``x`` ranked docs) / ``x``. + """ + return make_metric( + eval_fn=_precision_at_k_eval_fn(k), + greater_is_better=True, + name=f"precision_at_{k}", + ) + + +def recall_at_k(k) -> EvaluationMetric: + """ + This function will create a metric for calculating ``recall_at_k`` for retriever models. + + This metric computes a score between 0 and 1 for each row representing the recall ability of + the retriever model at the given ``k`` value. If no ground truth doc IDs are provided and no + documents are retrieved, the score is 1. However, if no ground truth doc IDs are provided and + documents are retrieved, the score is 0. In all other cases, the recall at k is calculated as + follows: + + ``recall_at_k`` = (# of unique relevant retrieved doc IDs in top-``k`` ranked docs) / (# of + ground truth doc IDs) + """ + return make_metric( + eval_fn=_recall_at_k_eval_fn(k), + greater_is_better=True, + name=f"recall_at_{k}", + ) + + +def ndcg_at_k(k) -> EvaluationMetric: + """ + This function will create a metric for evaluating `NDCG@k`_ for retriever models. + + NDCG score is capable of handling non-binary notions of relevance. However, for simplicity, + we use binary relevance here. The relevance score for documents in the ground truth is 1, + and the relevance score for documents not in the ground truth is 0. + + The NDCG score is calculated using sklearn.metrics.ndcg_score with the following edge cases + on top of the sklearn implementation: + + 1. If no ground truth doc IDs are provided and no documents are retrieved, the score is 1. + 2. If no ground truth doc IDs are provided and documents are retrieved, the score is 0. + 3. If ground truth doc IDs are provided and no documents are retrieved, the score is 0. + 4. If duplicate doc IDs are retrieved and the duplicate doc IDs are in the ground truth, + they will be treated as different docs. For example, if the ground truth doc IDs are + [1, 2] and the retrieved doc IDs are [1, 1, 1, 3], the score will be equivalent to + ground truth doc IDs [10, 11, 12, 2] and retrieved doc IDs [10, 11, 12, 3]. + + .. _NDCG@k: https://scikit-learn.org/stable/modules/generated/sklearn.metrics.ndcg_score.html + """ + return make_metric( + eval_fn=_ndcg_at_k_eval_fn(k), + greater_is_better=True, + name=f"ndcg_at_{k}", + ) + + +# General Regression Metrics +def mae() -> EvaluationMetric: + """ + This function will create a metric for evaluating `mae`_. + + This metric computes an aggregate score for the mean absolute error for regression. + + .. _mae: https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_absolute_error.html + """ + return make_metric( + eval_fn=_mae_eval_fn, + greater_is_better=False, + name="mean_absolute_error", + ) + + +def mse() -> EvaluationMetric: + """ + This function will create a metric for evaluating `mse`_. + + This metric computes an aggregate score for the mean squared error for regression. + + .. _mse: https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html + """ + return make_metric( + eval_fn=_mse_eval_fn, + greater_is_better=False, + name="mean_squared_error", + ) + + +def rmse() -> EvaluationMetric: + """ + This function will create a metric for evaluating the square root of `mse`_. + + This metric computes an aggregate score for the root mean absolute error for regression. + + .. _mse: https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html + """ + + return make_metric( + eval_fn=_rmse_eval_fn, + greater_is_better=False, + name="root_mean_squared_error", + ) + + +def r2_score() -> EvaluationMetric: + """ + This function will create a metric for evaluating `r2_score`_. + + This metric computes an aggregate score for the coefficient of determination. R2 ranges from + negative infinity to 1, and measures the percentage of variance explained by the predictor + variables in a regression. + + .. _r2_score: https://scikit-learn.org/stable/modules/generated/sklearn.metrics.r2_score.html + """ + return make_metric( + eval_fn=_r2_score_eval_fn, + greater_is_better=True, + name="r2_score", + ) + + +def max_error() -> EvaluationMetric: + """ + This function will create a metric for evaluating `max_error`_. + + This metric computes an aggregate score for the maximum residual error for regression. + + .. _max_error: https://scikit-learn.org/stable/modules/generated/sklearn.metrics.max_error.html + """ + return make_metric( + eval_fn=_max_error_eval_fn, + greater_is_better=False, + name="max_error", + ) + + +def mape() -> EvaluationMetric: + """ + This function will create a metric for evaluating `mape`_. + + This metric computes an aggregate score for the mean absolute percentage error for regression. + + .. _mape: https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_absolute_percentage_error.html + """ + return make_metric( + eval_fn=_mape_eval_fn, + greater_is_better=False, + name="mean_absolute_percentage_error", + ) + + +# Binary Classification Metrics + + +def recall_score() -> EvaluationMetric: + """ + This function will create a metric for evaluating `recall`_ for classification. + + This metric computes an aggregate score between 0 and 1 for the recall of a classification task. + + .. _recall: https://scikit-learn.org/stable/modules/generated/sklearn.metrics.recall_score.html + """ + return make_metric(eval_fn=_recall_eval_fn, greater_is_better=True, name="recall_score") + + +def precision_score() -> EvaluationMetric: + """ + This function will create a metric for evaluating `precision`_ for classification. + + This metric computes an aggregate score between 0 and 1 for the precision of + classification task. + + .. _precision: https://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_score.html + """ + return make_metric(eval_fn=_precision_eval_fn, greater_is_better=True, name="precision_score") + + +def f1_score() -> EvaluationMetric: + """ + This function will create a metric for evaluating `f1_score`_ for binary classification. + + This metric computes an aggregate score between 0 and 1 for the F1 score (F-measure) of a + classification task. F1 score is defined as 2 * (precision * recall) / (precision + recall). + + .. _f1_score: https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html + """ + return make_metric(eval_fn=_f1_score_eval_fn, greater_is_better=True, name="f1_score") + + +def bleu() -> EvaluationMetric: + """ + This function will create a metric for evaluating `bleu`_. + + The BLEU scores range from 0 to 1, with higher scores indicating greater similarity to + reference texts. BLEU considers n-gram precision and brevity penalty. While adding more + references can boost the score, perfect scores are rare and not essential for effective + evaluation. + + Aggregations calculated for this metric: + - mean + - variance + - p90 + + .. _bleu: https://huggingface.co/spaces/evaluate-metric/bleu + """ + return make_metric( + eval_fn=_bleu_eval_fn, + greater_is_better=True, + name="bleu", + version="v1", + ) + + +__all__ = [ + "EvaluationMetric", + "MetricValue", + "make_metric", + "flesch_kincaid_grade_level", + "ari_grade_level", + "exact_match", + "rouge1", + "rouge2", + "rougeL", + "rougeLsum", + "toxicity", + "mae", + "mse", + "rmse", + "r2_score", + "max_error", + "mape", + "recall_score", + "precision_score", + "f1_score", + "token_count", + "latency", + "genai", + "bleu", +] diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/metrics/__pycache__/__init__.cpython-312.pyc 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b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/metrics/base.py @@ -0,0 +1,38 @@ +from dataclasses import dataclass + +import numpy as np + +from mlflow.utils.validation import _is_numeric + + +def standard_aggregations(scores): + return { + "mean": np.mean(scores), + "variance": np.var(scores), + "p90": np.percentile(scores, 90), + } + + +@dataclass +class MetricValue: + """ + The value of a metric. + + + Args: + scores: The value of the metric per row + justifications: The justification (if applicable) for the respective score + aggregate_results: A dictionary mapping the name of the aggregation to its value + """ + + scores: list[str] | list[float] | None = None + justifications: list[str] | None = None + aggregate_results: dict[str, float] | None = None + + def __post_init__(self): + if ( + self.aggregate_results is None + and isinstance(self.scores, (list, tuple)) + and all(_is_numeric(score) for score in self.scores) + ): + self.aggregate_results = standard_aggregations(self.scores) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/metrics/genai/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/metrics/genai/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..32604503e2dbc17e21ad518c68b810abdb7b5ef5 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/metrics/genai/__init__.py @@ -0,0 +1,25 @@ +from mlflow.metrics.genai.base import EvaluationExample +from mlflow.metrics.genai.genai_metric import ( + make_genai_metric, + make_genai_metric_from_prompt, + retrieve_custom_metrics, +) +from mlflow.metrics.genai.metric_definitions import ( + answer_correctness, + answer_relevance, + answer_similarity, + faithfulness, + relevance, +) + +__all__ = [ + "EvaluationExample", + "make_genai_metric", + "make_genai_metric_from_prompt", + "answer_similarity", + "answer_correctness", + "faithfulness", + "answer_relevance", + "relevance", + "retrieve_custom_metrics", +] diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/metrics/genai/__pycache__/__init__.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/metrics/genai/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..a903031c0db6c97e0b9cc27394382a707edca393 Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/metrics/genai/__pycache__/__init__.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/metrics/genai/__pycache__/base.cpython-312.pyc 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100644 index 0000000000000000000000000000000000000000..05e67f63819a10af54b559cea37867a0aa37e2c0 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/metrics/genai/base.py @@ -0,0 +1,100 @@ +from dataclasses import dataclass + +from mlflow.metrics.genai.prompt_template import PromptTemplate + + +@dataclass +class EvaluationExample: + """ + Stores the sample example during few shot learning during LLM evaluation + + Args: + input: The input provided to the model + output: The output generated by the model + score: The score given by the evaluator + justification: The justification given by the evaluator + grading_context: The grading_context provided to the evaluator for evaluation. Either + a dictionary of grading context column names and grading context strings + or a single grading context string. + + .. code-block:: python + :caption: Example for creating an EvaluationExample + + from mlflow.metrics.genai import EvaluationExample + + example = EvaluationExample( + input="What is MLflow?", + output="MLflow is an open-source platform for managing machine " + "learning workflows, including experiment tracking, model packaging, " + "versioning, and deployment, simplifying the ML lifecycle.", + score=4, + justification="The definition effectively explains what MLflow is " + "its purpose, and its developer. It could be more concise for a 5-score.", + grading_context={ + "ground_truth": "MLflow is an open-source platform for managing " + "the end-to-end machine learning (ML) lifecycle. It was developed by Databricks, " + "a company that specializes in big data and machine learning solutions. MLflow is " + "designed to address the challenges that data scientists and machine learning " + "engineers face when developing, training, and deploying machine learning models." + }, + ) + print(str(example)) + + .. code-block:: text + :caption: Output + + Input: What is MLflow? + Provided output: "MLflow is an open-source platform for managing machine " + "learning workflows, including experiment tracking, model packaging, " + "versioning, and deployment, simplifying the ML lifecycle." + Provided ground_truth: "MLflow is an open-source platform for managing " + "the end-to-end machine learning (ML) lifecycle. It was developed by Databricks, " + "a company that specializes in big data and machine learning solutions. MLflow is " + "designed to address the challenges that data scientists and machine learning " + "engineers face when developing, training, and deploying machine learning models." + Score: 4 + Justification: "The definition effectively explains what MLflow is " + "its purpose, and its developer. It could be more concise for a 5-score." + """ + + output: str + score: float + justification: str + input: str | None = None + grading_context: dict[str, str] | str | None = None + + def _format_grading_context(self): + if isinstance(self.grading_context, dict): + return "\n".join( + [f"key: {key}\nvalue:\n{value}" for key, value in self.grading_context.items()] + ) + else: + return self.grading_context + + def __str__(self) -> str: + return PromptTemplate( + [ + """ +Example Input: +{input} +""", + """ +Example Output: +{output} +""", + """ +Additional information used by the model: +{grading_context} +""", + """ +Example score: {score} +Example justification: {justification} + """, + ] + ).format( + input=self.input, + output=self.output, + grading_context=self._format_grading_context(), + score=self.score, + justification=self.justification, + ) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/metrics/genai/genai_metric.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/metrics/genai/genai_metric.py new file mode 100644 index 0000000000000000000000000000000000000000..16e872f8ffd39d262d0f72f796b888a4c9865652 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/metrics/genai/genai_metric.py @@ -0,0 +1,796 @@ +import json +import logging +import re +import warnings +from concurrent.futures import ThreadPoolExecutor, as_completed +from inspect import Parameter, Signature +from tempfile import TemporaryDirectory +from typing import Any + +import pandas as pd + +import mlflow +from mlflow.exceptions import MlflowException +from mlflow.metrics.base import MetricValue +from mlflow.metrics.genai import model_utils +from mlflow.metrics.genai.base import EvaluationExample +from mlflow.metrics.genai.prompt_template import PromptTemplate +from mlflow.metrics.genai.utils import _get_default_model, _get_latest_metric_version +from mlflow.models import EvaluationMetric, make_metric +from mlflow.models.evaluation.base import _make_metric +from mlflow.protos.databricks_pb2 import ( + BAD_REQUEST, + INTERNAL_ERROR, + INVALID_PARAMETER_VALUE, + UNAUTHENTICATED, + ErrorCode, +) +from mlflow.utils.class_utils import _get_class_from_string +from mlflow.version import VERSION + +_logger = logging.getLogger(__name__) + +_GENAI_CUSTOM_METRICS_FILE_NAME = "genai_custom_metrics.json" +_PROMPT_FORMATTING_WRAPPER = """ + +You must return the following fields in your response in two lines, one below the other: +score: Your numerical score based on the rubric +justification: Your reasoning for giving this score + +Do not add additional new lines. Do not add any other fields.""" + + +def _format_args_string(grading_context_columns: list[str] | None, eval_values, indx) -> str: + import pandas as pd + + args_dict = {} + for arg in grading_context_columns: + if arg in eval_values: + args_dict[arg] = ( + eval_values[arg].iloc[indx] + if isinstance(eval_values[arg], pd.Series) + else eval_values[arg][indx] + ) + else: + raise MlflowException( + f"{arg} does not exist in the eval function {list(eval_values.keys())}." + ) + + return ( + "" + if args_dict is None or len(args_dict) == 0 + else ( + "Additional information used by the model:\n" + + "\n".join( + [f"key: {arg}\nvalue:\n{arg_value}" for arg, arg_value in args_dict.items()] + ) + ) + ) + + +# Function to extract Score and Justification +def _extract_score_and_justification(text): + if text: + text = re.sub(r"score", "score", text, flags=re.IGNORECASE) + text = re.sub(r"justification", "justification", text, flags=re.IGNORECASE) + # Attempt to parse JSON + try: + data = json.loads(text) + score = int(data.get("score")) + justification = data.get("justification") + except json.JSONDecodeError: + # If parsing fails, use regex + if (match := re.search(r"score: (\d+),?\s*justification: (.+)", text)) or ( + match := re.search(r"\s*score:\s*(\d+)\s*justification:\s*(.+)", text, re.DOTALL) + ): + score = int(match.group(1)) + justification = match.group(2) + else: + score = None + justification = f"Failed to extract score and justification. Raw output: {text}" + + if not isinstance(score, (int, float)) or not isinstance(justification, str): + return None, f"Failed to extract score and justification. Raw output: {text}" + + return score, justification + + return None, None + + +def _score_model_on_one_payload( + payload: str, + eval_model: str, + parameters: dict[str, Any] | None, + extra_headers: dict[str, str] | None = None, + proxy_url: str | None = None, +): + try: + # If the endpoint does not specify type, default to chat format + endpoint_type = model_utils.get_endpoint_type(eval_model) or "llm/v1/chat" + raw_result = model_utils.score_model_on_payload( + eval_model, payload, parameters, extra_headers, proxy_url, endpoint_type + ) + return _extract_score_and_justification(raw_result) + except ImportError: + raise + except MlflowException as e: + if e.error_code in [ + ErrorCode.Name(BAD_REQUEST), + ErrorCode.Name(UNAUTHENTICATED), + ErrorCode.Name(INVALID_PARAMETER_VALUE), + ]: + raise + else: + return None, f"Failed to score model on payload. Error: {e!s}" + except Exception as e: + return None, f"Failed to score model on payload. Error: {e!s}" + + +def _score_model_on_payloads( + grading_payloads, model, parameters, headers, proxy_url, max_workers +) -> tuple[list[int], list[str]]: + scores = [None] * len(grading_payloads) + justifications = [None] * len(grading_payloads) + with ThreadPoolExecutor( + max_workers=max_workers, thread_name_prefix="MlflowGenAiScoring" + ) as executor: + futures = { + executor.submit( + _score_model_on_one_payload, + payload, + model, + parameters, + headers, + proxy_url, + ): indx + for indx, payload in enumerate(grading_payloads) + } + + as_comp = as_completed(futures) + try: + from tqdm.auto import tqdm + + as_comp = tqdm(as_comp, total=len(futures)) + except ImportError: + pass + + for future in as_comp: + indx = futures[future] + score, justification = future.result() + scores[indx] = score + justifications[indx] = justification + + return scores, justifications + + +def _get_aggregate_results(scores, aggregations): + # loop over the aggregations and compute the aggregate results on the scores + def aggregate_function(aggregate_option, scores): + import numpy as np + + options = { + "min": np.min, + "max": np.max, + "mean": np.mean, + "median": np.median, + "variance": np.var, + "p90": lambda x: np.percentile(x, 90) if x else None, + } + + if aggregate_option not in options: + raise MlflowException( + message=f"Invalid aggregate option {aggregate_option}.", + error_code=INVALID_PARAMETER_VALUE, + ) + + return options[aggregate_option](scores) + + scores_for_aggregation = [score for score in scores if score is not None] + + return ( + {option: aggregate_function(option, scores_for_aggregation) for option in aggregations} + if aggregations is not None + else {} + ) + + +def make_genai_metric_from_prompt( + name: str, + judge_prompt: str | None = None, + model: str | None = _get_default_model(), + parameters: dict[str, Any] | None = None, + aggregations: list[str] | None = None, + greater_is_better: bool = True, + max_workers: int = 10, + metric_metadata: dict[str, Any] | None = None, + extra_headers: dict[str, str] | None = None, + proxy_url: str | None = None, +) -> EvaluationMetric: + """ + Create a genai metric used to evaluate LLM using LLM as a judge in MLflow. This produces + a metric using only the supplied judge prompt without any pre-written system prompt. + This can be useful for use cases that are not covered by the full grading prompt in any + ``EvaluationModel`` version. + + Args: + name: Name of the metric. + judge_prompt: The entire prompt to be used for the judge model. + The prompt will be minimally wrapped in formatting instructions to ensure + scores can be parsed. The prompt may use f-string formatting to include variables. + Corresponding variables must be passed as keyword arguments into the + resulting metric's eval function. + model: (Optional) Model uri of the judge model that will be used to compute the metric, + e.g., ``openai:/gpt-4``. Refer to the `LLM-as-a-Judge Metrics `_ + documentation for the supported model types and their URI format. + parameters: (Optional) Parameters for the LLM used to compute the metric. By default, we + set the temperature to 0.0, max_tokens to 200, and top_p to 1.0. We recommend + setting the temperature to 0.0 for the LLM used as a judge to ensure consistent results. + aggregations: (Optional) The list of options to aggregate the scores. Currently supported + options are: min, max, mean, median, variance, p90. + greater_is_better: (Optional) Whether the metric is better when it is greater. + max_workers: (Optional) The maximum number of workers to use for judge scoring. + Defaults to 10 workers. + metric_metadata: (Optional) Dictionary of metadata to be attached to the + EvaluationMetric object. Useful for model evaluators that require additional + information to determine how to evaluate this metric. + extra_headers: (Optional) Additional headers to be passed to the judge model. + proxy_url: (Optional) Proxy URL to be used for the judge model. This is useful when the + judge model is served via a proxy endpoint, not directly via LLM provider services. + If not specified, the default URL for the LLM provider will be used + (e.g., https://api.openai.com/v1/chat/completions for OpenAI chat models). + + Returns: + A metric object. + + .. code-block:: python + :test: + :caption: Example for creating a genai metric + + import pandas as pd + import mlflow + from mlflow.metrics.genai import make_genai_metric_from_prompt + + metric = make_genai_metric_from_prompt( + name="ease_of_understanding", + judge_prompt=( + "You must evaluate the output of a bot based on how easy it is to " + "understand its outputs." + "Evaluate the bot's output from the perspective of a layperson." + "The bot was provided with this input: {input} and this output: {output}." + ), + model="openai:/gpt-4", + parameters={"temperature": 0.0}, + aggregations=["mean", "variance", "p90"], + greater_is_better=True, + ) + + data = pd.DataFrame( + { + "input": ["Where is the capital of France."], + "ground_truth": ["Paris"], + "output": ["The capital of France is Paris."], + } + ) + + mlflow.evaluate( + data=data, + targets="ground_truth", + predictions="output", + evaluators="default", + extra_metrics=[metric], + ) + """ + import numpy as np + + prompt_template = PromptTemplate([judge_prompt, _PROMPT_FORMATTING_WRAPPER]) + allowed_variables = prompt_template.variables + additional_variables = list(allowed_variables - {"predictions"}) + + # When users create a custom metric using this function,the metric configuration + # will be serialized and stored as an artifact. This enables us to later deserialize + # the configuration, allowing users to understand their LLM evaluation results more clearly. + genai_metric_args = { + "name": name, + "judge_prompt": judge_prompt, + "model": model, + "parameters": parameters, + "aggregations": aggregations, + "greater_is_better": greater_is_better, + "max_workers": max_workers, + "metric_metadata": metric_metadata, + # Record the mlflow version for serialization in case the function signature changes later + "mlflow_version": VERSION, + "fn_name": make_genai_metric_from_prompt.__name__, + } + + aggregations = aggregations or ["mean", "variance", "p90"] + + def eval_fn( + *args, + **kwargs, + ) -> MetricValue: + """ + This is the function that is called when the metric is evaluated. + Note that default evaluator only passes positional arguments. + """ + for i, arg in enumerate(args): + if i == 0: + kwargs["predictions"] = arg + else: + kwargs[additional_variables[i - 1]] = arg + if missing_variables := allowed_variables - set(kwargs.keys()): + raise MlflowException( + message=f"Missing variable inputs to eval_fn: {missing_variables}", + error_code=INVALID_PARAMETER_VALUE, + ) + kwargs = {k: [v] if np.isscalar(v) else v for k, v in kwargs.items()} + grading_payloads = pd.DataFrame(kwargs).to_dict(orient="records") + arg_strings = [prompt_template.format(**payload) for payload in grading_payloads] + scores, justifications = _score_model_on_payloads( + arg_strings, model, parameters, extra_headers, proxy_url, max_workers + ) + + aggregate_scores = _get_aggregate_results(scores, aggregations) + + return MetricValue(scores, justifications, aggregate_scores) + + # Add `predictions` to the parameters to be compatible with `eval_fn`` interface + eval_fn_parameters = [ + Parameter(name="predictions", kind=Parameter.POSITIONAL_ONLY), + *[ + Parameter(name=var, kind=Parameter.POSITIONAL_ONLY, default=None) + for var in additional_variables + ], + ] + eval_fn.__signature__ = Signature(parameters=eval_fn_parameters) + + return make_metric( + eval_fn=eval_fn, + greater_is_better=greater_is_better, + name=name, + metric_metadata=metric_metadata, + genai_metric_args=genai_metric_args, + ) + + +def make_genai_metric( + name: str, + definition: str, + grading_prompt: str, + examples: list[EvaluationExample] | None = None, + version: str | None = _get_latest_metric_version(), + model: str | None = _get_default_model(), + grading_context_columns: str | list[str] | None = None, + include_input: bool = True, + parameters: dict[str, Any] | None = None, + aggregations: list[str] | None = None, + greater_is_better: bool = True, + max_workers: int = 10, + metric_metadata: dict[str, Any] | None = None, + extra_headers: dict[str, str] | None = None, + proxy_url: str | None = None, +) -> EvaluationMetric: + """ + Create a genai metric used to evaluate LLM using LLM as a judge in MLflow. The full grading + prompt is stored in the metric_details field of the ``EvaluationMetric`` object. + + Args: + name: Name of the metric. + definition: Definition of the metric. + grading_prompt: Grading criteria of the metric. + examples: (Optional) Examples of the metric. + version: (Optional) Version of the metric. Currently supported versions are: v1. + model: (Optional) Model uri of the judge model that will be used to compute the metric, + e.g., ``openai:/gpt-4``. Refer to the `LLM-as-a-Judge Metrics `_ + documentation for the supported model types and their URI format. + grading_context_columns: (Optional) The name of the grading context column, or a list of + grading context column names, required to compute the metric. The + ``grading_context_columns`` are used by the LLM as a judge as additional information to + compute the metric. The columns are extracted from the input dataset or output + predictions based on ``col_mapping`` in the ``evaluator_config`` passed to + :py:func:`mlflow.evaluate()`. They can also be the name of other evaluated metrics. + include_input: (Optional) Whether to include the input + when computing the metric. + parameters: (Optional) Parameters for the LLM used to compute the metric. By default, we + set the temperature to 0.0, max_tokens to 200, and top_p to 1.0. We recommend + setting the temperature to 0.0 for the LLM used as a judge to ensure consistent results. + aggregations: (Optional) The list of options to aggregate the scores. Currently supported + options are: min, max, mean, median, variance, p90. + greater_is_better: (Optional) Whether the metric is better when it is greater. + max_workers: (Optional) The maximum number of workers to use for judge scoring. + Defaults to 10 workers. + metric_metadata: (Optional) Dictionary of metadata to be attached to the + EvaluationMetric object. Useful for model evaluators that require additional + information to determine how to evaluate this metric. + extra_headers: (Optional) Additional headers to be passed to the judge model. + proxy_url: (Optional) Proxy URL to be used for the judge model. This is useful when the + judge model is served via a proxy endpoint, not directly via LLM provider services. + If not specified, the default URL for the LLM provider will be used + (e.g., https://api.openai.com/v1/chat/completions for OpenAI chat models). + + + Returns: + A metric object. + + .. code-block:: python + :test: + :caption: Example for creating a genai metric + + from mlflow.metrics.genai import EvaluationExample, make_genai_metric + + example = EvaluationExample( + input="What is MLflow?", + output=( + "MLflow is an open-source platform for managing machine " + "learning workflows, including experiment tracking, model packaging, " + "versioning, and deployment, simplifying the ML lifecycle." + ), + score=4, + justification=( + "The definition effectively explains what MLflow is " + "its purpose, and its developer. It could be more concise for a 5-score.", + ), + grading_context={ + "targets": ( + "MLflow is an open-source platform for managing " + "the end-to-end machine learning (ML) lifecycle. It was developed by " + "Databricks, a company that specializes in big data and machine learning " + "solutions. MLflow is designed to address the challenges that data " + "scientists and machine learning engineers face when developing, training, " + "and deploying machine learning models." + ) + }, + ) + metric = make_genai_metric( + name="answer_correctness", + definition=( + "Answer correctness is evaluated on the accuracy of the provided output based on " + "the provided targets, which is the ground truth. Scores can be assigned based on " + "the degree of semantic similarity and factual correctness of the provided output " + "to the provided targets, where a higher score indicates higher degree of accuracy." + ), + grading_prompt=( + "Answer correctness: Below are the details for different scores:" + "- Score 1: The output is completely incorrect. It is completely different from " + "or contradicts the provided targets." + "- Score 2: The output demonstrates some degree of semantic similarity and " + "includes partially correct information. However, the output still has significant " + "discrepancies with the provided targets or inaccuracies." + "- Score 3: The output addresses a couple of aspects of the input accurately, " + "aligning with the provided targets. However, there are still omissions or minor " + "inaccuracies." + "- Score 4: The output is mostly correct. It provides mostly accurate information, " + "but there may be one or more minor omissions or inaccuracies." + "- Score 5: The output is correct. It demonstrates a high degree of accuracy and " + "semantic similarity to the targets." + ), + examples=[example], + version="v1", + model="openai:/gpt-4", + grading_context_columns=["targets"], + parameters={"temperature": 0.0}, + aggregations=["mean", "variance", "p90"], + greater_is_better=True, + ) + """ + # When users create a custom metric using this function,the metric configuration + # will be serialized and stored as an artifact. This enables us to later deserialize + # the configuration, allowing users to understand their LLM evaluation results more clearly. + genai_metric_args = { + "name": name, + "definition": definition, + "grading_prompt": grading_prompt, + "examples": examples, + "version": version, + "model": model, + "grading_context_columns": grading_context_columns, + "include_input": include_input, + "parameters": parameters, + "aggregations": aggregations, + "greater_is_better": greater_is_better, + "max_workers": max_workers, + "metric_metadata": metric_metadata, + # Record the mlflow version for serialization in case the function signature changes later + "mlflow_version": VERSION, + "fn_name": make_genai_metric.__name__, + } + + aggregations = aggregations or ["mean", "variance", "p90"] + grading_context_columns = grading_context_columns or [] + + if not isinstance(grading_context_columns, list): + grading_context_columns = [grading_context_columns] + + def process_example(example): + if example.grading_context is None and len(grading_context_columns) == 0: + grading_context = {} + elif isinstance(example.grading_context, dict): + grading_context = example.grading_context + else: + # The grading context is string-like. Assume that it corresponds to the first + # grading context column and update the example accordingly + grading_context = {grading_context_columns[0]: example.grading_context} + example.grading_context = grading_context + + if set(grading_context.keys()) != set(grading_context_columns): + raise MlflowException.invalid_parameter_value( + f"Example grading context does not contain required columns.\n" + f" Example grading context columns: {list(grading_context.keys())}\n" + f" Required grading context columns: {grading_context_columns}\n" + ) + + if not include_input: + return EvaluationExample( + output=example.output, + score=example.score, + justification=example.justification, + grading_context=example.grading_context, + ) + + return example + + if examples is not None: + examples = [process_example(example) for example in examples] + + class_name = f"mlflow.metrics.genai.prompts.{version}.EvaluationModel" + try: + evaluation_model_class_module = _get_class_from_string(class_name) + except ModuleNotFoundError: + raise MlflowException( + f"Failed to find evaluation model for version {version}." + f" Please check the correctness of the version", + error_code=INVALID_PARAMETER_VALUE, + ) from None + except Exception as e: + raise MlflowException( + f"Failed to construct evaluation model {version}. Error: {e!r}", + error_code=INTERNAL_ERROR, + ) from None + + evaluation_context = evaluation_model_class_module( + name, + definition, + grading_prompt, + examples, + model, + *(parameters,) if parameters is not None else (), + ).to_dict() + + def eval_fn( + predictions: "pd.Series", + metrics: dict[str, MetricValue], + inputs: "pd.Series", + *args, + ) -> MetricValue: + """ + This is the function that is called when the metric is evaluated. + """ + eval_values = dict(zip(grading_context_columns, args)) + + outputs = predictions.to_list() + inputs = inputs.to_list() + eval_model = evaluation_context["model"] + eval_parameters = evaluation_context["parameters"] + + # TODO: Save the metric definition in a yaml file for model monitoring + + if not isinstance(eval_model, str): + raise MlflowException( + message="The model argument must be a string URI referring to an openai model " + "(openai:/gpt-4o-mini) or an MLflow Deployments endpoint " + f"(endpoints:/my-endpoint), passed {eval_model} instead", + error_code=INVALID_PARAMETER_VALUE, + ) + + # generate grading payloads + grading_payloads = [] + for indx, (input, output) in enumerate(zip(inputs, outputs)): + try: + arg_string = _format_args_string(grading_context_columns, eval_values, indx) + except Exception as e: + raise MlflowException( + f"Values for grading_context_columns are malformed and cannot be " + f"formatted into a prompt for metric '{name}'.\n" + f"Required columns: {grading_context_columns}\n" + f"Values: {eval_values}\n" + f"Error: {e!r}\n" + f"Please check the following: \n" + "- predictions and targets (if required) are provided correctly\n" + "- grading_context_columns are mapped correctly using the evaluator_config " + "parameter\n" + "- input and output data are formatted correctly." + ) + grading_payloads.append( + evaluation_context["eval_prompt"].format( + input=(input if include_input else None), + output=output, + grading_context_columns=arg_string, + ) + ) + + scores = [None] * len(inputs) + justifications = [None] * len(inputs) + + with ThreadPoolExecutor( + max_workers=max_workers, thread_name_prefix="MlflowGenAiEvaluation" + ) as executor: + futures = { + executor.submit( + _score_model_on_one_payload, + payload, + eval_model, + eval_parameters, + extra_headers, + proxy_url, + ): indx + for indx, payload in enumerate(grading_payloads) + } + + as_comp = as_completed(futures) + try: + from tqdm.auto import tqdm + + as_comp = tqdm(as_comp, total=len(futures)) + except ImportError: + pass + + for future in as_comp: + indx = futures[future] + score, justification = future.result() + scores[indx] = score + justifications[indx] = justification + + aggregate_results = _get_aggregate_results(scores, aggregations) + return MetricValue(scores, justifications, aggregate_results) + + signature_parameters = [ + Parameter("predictions", Parameter.POSITIONAL_OR_KEYWORD, annotation="pd.Series"), + Parameter("metrics", Parameter.POSITIONAL_OR_KEYWORD, annotation=dict[str, MetricValue]), + Parameter("inputs", Parameter.POSITIONAL_OR_KEYWORD, annotation="pd.Series"), + ] + + # Add grading_context_columns to signature list + for var in grading_context_columns: + signature_parameters.append(Parameter(var, Parameter.POSITIONAL_OR_KEYWORD)) + + # Note: this doesn't change how python allows calling the function + # extra params in grading_context_columns can only be passed as positional args + eval_fn.__signature__ = Signature(signature_parameters) + + return _make_metric( + eval_fn=eval_fn, + greater_is_better=greater_is_better, + name=name, + version=version, + metric_details=evaluation_context["eval_prompt"].__str__(), + metric_metadata=metric_metadata, + genai_metric_args=genai_metric_args, + require_strict_signature=True, + ) + + +def _filter_by_field(df, field_name, value): + return df[df[field_name] == value] + + +def _deserialize_genai_metric_args(args_dict): + mlflow_version_at_ser = args_dict.pop("mlflow_version", None) + fn_name = args_dict.pop("fn_name", None) + if fn_name is None or mlflow_version_at_ser is None: + raise MlflowException( + message="The artifact JSON file appears to be corrupted and cannot be deserialized. " + "Please regenerate the custom metrics and rerun the evaluation. " + "Ensure that the file is correctly formatted and not tampered with.", + error_code=INTERNAL_ERROR, + ) + + if mlflow_version_at_ser != VERSION: + warnings.warn( + f"The custom metric definitions were serialized using MLflow {mlflow_version_at_ser}. " + f"Deserializing them with the current version {VERSION} might cause mismatches. " + "Please ensure compatibility or consider regenerating the metrics " + "using the current version.", + UserWarning, + stacklevel=2, + ) + + if fn_name == make_genai_metric_from_prompt.__name__: + return make_genai_metric_from_prompt(**args_dict) + + examples = args_dict["examples"] + if examples is not None: + args_dict["examples"] = [EvaluationExample(**example) for example in examples] + + return make_genai_metric(**args_dict) + + +def retrieve_custom_metrics( + run_id: str, + name: str | None = None, + version: str | None = None, +) -> list[EvaluationMetric]: + """ + Retrieve the custom metrics created by users through `make_genai_metric()` or + `make_genai_metric_from_prompt()` that are associated with a particular evaluation run. + + Args: + run_id: The unique identifier for the run. + name: (Optional) The name of the custom metric to retrieve. + If None, retrieve all metrics. + version: (Optional) The version of the custom metric to retrieve. + If None, retrieve all metrics. + + Returns: + A list of EvaluationMetric objects that match the retrieval criteria. + + .. code-block:: python + :caption: Example for retrieving a custom genai metric + + import pandas as pd + + import mlflow + from mlflow.metrics.genai.genai_metric import ( + make_genai_metric_from_prompt, + retrieve_custom_metrics, + ) + + eval_df = pd.DataFrame( + { + "inputs": ["foo"], + "ground_truth": ["bar"], + } + ) + with mlflow.start_run() as run: + system_prompt = "Answer the following question in two sentences" + basic_qa_model = mlflow.openai.log_model( + model="gpt-4o-mini", + task="chat.completions", + name="model", + messages=[ + {"role": "system", "content": system_prompt}, + {"role": "user", "content": "{question}"}, + ], + ) + custom_metric = make_genai_metric_from_prompt( + name="custom_llm_judge", + judge_prompt="This is a custom judge prompt.", + greater_is_better=False, + parameters={"temperature": 0.0}, + ) + results = mlflow.evaluate( + basic_qa_model.model_uri, + eval_df, + targets="ground_truth", + model_type="question-answering", + evaluators="default", + extra_metrics=[custom_metric], + ) + metrics = retrieve_custom_metrics( + run_id=run.info.run_id, + name="custom_llm_judge", + ) + """ + client = mlflow.MlflowClient() + artifacts = [a.path for a in client.list_artifacts(run_id)] + if _GENAI_CUSTOM_METRICS_FILE_NAME not in artifacts: + _logger.warning("No custom metric definitions were found for this evaluation run.") + return [] + + with TemporaryDirectory() as tmpdir: + downloaded_artifact_path = mlflow.artifacts.download_artifacts( + run_id=run_id, + artifact_path=_GENAI_CUSTOM_METRICS_FILE_NAME, + dst_path=tmpdir, + ) + custom_metrics = client._read_from_file(downloaded_artifact_path) + + if name is not None: + custom_metrics = _filter_by_field(custom_metrics, "name", name) + if version is not None: + custom_metrics = _filter_by_field(custom_metrics, "version", version) + metric_args_list = custom_metrics["metric_args"].tolist() + if len(metric_args_list) == 0: + _logger.warning("No matching custom metric definitions were found.") + return [] + + return [_deserialize_genai_metric_args(a) for a in metric_args_list] diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/metrics/genai/metric_definitions.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/metrics/genai/metric_definitions.py new file mode 100644 index 0000000000000000000000000000000000000000..ffb11997a0c0bb18e1d1b1fb307ece3e2c6e30c3 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/metrics/genai/metric_definitions.py @@ -0,0 +1,450 @@ +from typing import Any + +from mlflow.exceptions import MlflowException +from mlflow.metrics.genai.base import EvaluationExample +from mlflow.metrics.genai.genai_metric import make_genai_metric +from mlflow.metrics.genai.utils import _get_latest_metric_version +from mlflow.models import EvaluationMetric +from mlflow.protos.databricks_pb2 import INTERNAL_ERROR, INVALID_PARAMETER_VALUE +from mlflow.utils.class_utils import _get_class_from_string + + +def answer_similarity( + model: str | None = None, + metric_version: str | None = None, + examples: list[EvaluationExample] | None = None, + metric_metadata: dict[str, Any] | None = None, + parameters: dict[str, Any] | None = None, + extra_headers: dict[str, str] | None = None, + proxy_url: str | None = None, + max_workers: int = 10, +) -> EvaluationMetric: + """ + This function will create a genai metric used to evaluate the answer similarity of an LLM + using the model provided. Answer similarity will be assessed by the semantic similarity of the + output to the ``ground_truth``, which should be specified in the ``targets`` column. High + scores mean that your model outputs contain similar information as the ground_truth, while + low scores mean that outputs may disagree with the ground_truth. + + The ``targets`` eval_arg must be provided as part of the input dataset or output + predictions. This can be mapped to a column of a different name using ``col_mapping`` + in the ``evaluator_config`` parameter, or using the ``targets`` parameter in mlflow.evaluate(). + + An MlflowException will be raised if the specified version for this metric does not exist. + + Args: + model: (Optional) Model uri of the judge model that will be used to compute the metric, + e.g., ``openai:/gpt-4``. Refer to the `LLM-as-a-Judge Metrics `_ + documentation for the supported model types and their URI format. + metric_version: (Optional) The version of the answer similarity metric to use. + Defaults to the latest version. + examples: (Optional) Provide a list of examples to help the judge model evaluate the + answer similarity. It is highly recommended to add examples to be used as a reference to + evaluate the new results. + metric_metadata: (Optional) Dictionary of metadata to be attached to the + EvaluationMetric object. Useful for model evaluators that require additional + information to determine how to evaluate this metric. + parameters: (Optional) Dictionary of parameters to be passed to the judge model, + e.g., {"temperature": 0.5}. When specified, these parameters will override + the default parameters defined in the metric implementation. + extra_headers: (Optional) Dictionary of extra headers to be passed to the judge model. + proxy_url: (Optional) Proxy URL to be used for the judge model. This is useful when the + judge model is served via a proxy endpoint, not directly via LLM provider services. + If not specified, the default URL for the LLM provider will be used + (e.g., https://api.openai.com/v1/chat/completions for OpenAI chat models). + max_workers: (Optional) The maximum number of workers to use for judge scoring. + Defaults to 10 workers. + + Returns: + A metric object + """ + if metric_version is None: + metric_version = _get_latest_metric_version() + class_name = f"mlflow.metrics.genai.prompts.{metric_version}.AnswerSimilarityMetric" + try: + answer_similarity_class_module = _get_class_from_string(class_name) + except ModuleNotFoundError: + raise MlflowException( + f"Failed to find answer similarity metric for version {metric_version}." + f" Please check the version", + error_code=INVALID_PARAMETER_VALUE, + ) from None + except Exception as e: + raise MlflowException( + f"Failed to construct answer similarity metric {metric_version}. Error: {e!r}", + error_code=INTERNAL_ERROR, + ) from None + + if examples is None: + examples = answer_similarity_class_module.default_examples + if model is None: + model = answer_similarity_class_module.default_model + + return make_genai_metric( + name="answer_similarity", + definition=answer_similarity_class_module.definition, + grading_prompt=answer_similarity_class_module.grading_prompt, + include_input=False, + examples=examples, + version=metric_version, + model=model, + grading_context_columns=answer_similarity_class_module.grading_context_columns, + parameters=parameters or answer_similarity_class_module.parameters, + aggregations=["mean", "variance", "p90"], + greater_is_better=True, + metric_metadata=metric_metadata, + extra_headers=extra_headers, + proxy_url=proxy_url, + max_workers=max_workers, + ) + + +def answer_correctness( + model: str | None = None, + metric_version: str | None = None, + examples: list[EvaluationExample] | None = None, + metric_metadata: dict[str, Any] | None = None, + parameters: dict[str, Any] | None = None, + extra_headers: dict[str, str] | None = None, + proxy_url: str | None = None, + max_workers: int = 10, +) -> EvaluationMetric: + """ + This function will create a genai metric used to evaluate the answer correctness of an LLM + using the model provided. Answer correctness will be assessed by the accuracy of the provided + output based on the ``ground_truth``, which should be specified in the ``targets`` column. + High scores mean that your model outputs contain similar information as the ground_truth and + that this information is correct, while low scores mean that outputs may disagree with the + ground_truth or that the information in the output is incorrect. Note that this builds onto + answer_similarity. + + The ``targets`` eval_arg must be provided as part of the input dataset or output + predictions. This can be mapped to a column of a different name using ``col_mapping`` + in the ``evaluator_config`` parameter, or using the ``targets`` parameter in mlflow.evaluate(). + + An MlflowException will be raised if the specified version for this metric does not exist. + + Args: + model: (Optional) Model uri of the judge model that will be used to compute the metric, + e.g., ``openai:/gpt-4``. Refer to the `LLM-as-a-Judge Metrics `_ + documentation for the supported model types and their URI format. + metric_version: The version of the answer correctness metric to use. + Defaults to the latest version. + examples: Provide a list of examples to help the judge model evaluate the + answer correctness. It is highly recommended to add examples to be used as a reference + to evaluate the new results. + metric_metadata: (Optional) Dictionary of metadata to be attached to the + EvaluationMetric object. Useful for model evaluators that require additional + information to determine how to evaluate this metric. + parameters: (Optional) Dictionary of parameters to be passed to the judge model, + e.g., {"temperature": 0.5}. When specified, these parameters will override + the default parameters defined in the metric implementation. + extra_headers: (Optional) Dictionary of extra headers to be passed to the judge model. + proxy_url: (Optional) Proxy URL to be used for the judge model. This is useful when the + judge model is served via a proxy endpoint, not directly via LLM provider services. + If not specified, the default URL for the LLM provider will be used + (e.g., https://api.openai.com/v1/chat/completions for OpenAI chat models). + max_workers: (Optional) The maximum number of workers to use for judge scoring. + Defaults to 10 workers. + + Returns: + A metric object + """ + if metric_version is None: + metric_version = _get_latest_metric_version() + class_name = f"mlflow.metrics.genai.prompts.{metric_version}.AnswerCorrectnessMetric" + try: + answer_correctness_class_module = _get_class_from_string(class_name) + except ModuleNotFoundError: + raise MlflowException( + f"Failed to find answer correctness metric for version {metric_version}." + f"Please check the version", + error_code=INVALID_PARAMETER_VALUE, + ) from None + except Exception as e: + raise MlflowException( + f"Failed to construct answer correctness metric {metric_version}. Error: {e!r}", + error_code=INTERNAL_ERROR, + ) from None + + if examples is None: + examples = answer_correctness_class_module.default_examples + if model is None: + model = answer_correctness_class_module.default_model + + return make_genai_metric( + name="answer_correctness", + definition=answer_correctness_class_module.definition, + grading_prompt=answer_correctness_class_module.grading_prompt, + examples=examples, + version=metric_version, + model=model, + grading_context_columns=answer_correctness_class_module.grading_context_columns, + parameters=parameters or answer_correctness_class_module.parameters, + aggregations=["mean", "variance", "p90"], + greater_is_better=True, + metric_metadata=metric_metadata, + extra_headers=extra_headers, + proxy_url=proxy_url, + max_workers=max_workers, + ) + + +def faithfulness( + model: str | None = None, + metric_version: str | None = _get_latest_metric_version(), + examples: list[EvaluationExample] | None = None, + metric_metadata: dict[str, Any] | None = None, + parameters: dict[str, Any] | None = None, + extra_headers: dict[str, str] | None = None, + proxy_url: str | None = None, + max_workers: int = 10, +) -> EvaluationMetric: + """ + This function will create a genai metric used to evaluate the faithfullness of an LLM using the + model provided. Faithfulness will be assessed based on how factually consistent the output + is to the ``context``. High scores mean that the outputs contain information that is in + line with the context, while low scores mean that outputs may disagree with the context + (input is ignored). + + The ``context`` eval_arg must be provided as part of the input dataset or output + predictions. This can be mapped to a column of a different name using ``col_mapping`` + in the ``evaluator_config`` parameter. + + An MlflowException will be raised if the specified version for this metric does not exist. + + Args: + model: (Optional) Model uri of the judge model that will be used to compute the metric, + e.g., ``openai:/gpt-4``. Refer to the `LLM-as-a-Judge Metrics `_ + documentation for the supported model types and their URI format. + metric_version: The version of the faithfulness metric to use. + Defaults to the latest version. + examples: Provide a list of examples to help the judge model evaluate the + faithfulness. It is highly recommended to add examples to be used as a reference to + evaluate the new results. + metric_metadata: (Optional) Dictionary of metadata to be attached to the + EvaluationMetric object. Useful for model evaluators that require additional + information to determine how to evaluate this metric. + parameters: (Optional) Dictionary of parameters to be passed to the judge model, + e.g., {"temperature": 0.5}. When specified, these parameters will override + the default parameters defined in the metric implementation. + extra_headers: (Optional) Dictionary of extra headers to be passed to the judge model. + proxy_url: (Optional) Proxy URL to be used for the judge model. This is useful when the + judge model is served via a proxy endpoint, not directly via LLM provider services. + If not specified, the default URL for the LLM provider will be used + (e.g., https://api.openai.com/v1/chat/completions for OpenAI chat models). + max_workers: (Optional) The maximum number of workers to use for judge scoring. + Defaults to 10 workers. + + Returns: + A metric object + """ + class_name = f"mlflow.metrics.genai.prompts.{metric_version}.FaithfulnessMetric" + try: + faithfulness_class_module = _get_class_from_string(class_name) + except ModuleNotFoundError: + raise MlflowException( + f"Failed to find faithfulness metric for version {metric_version}." + f" Please check the version", + error_code=INVALID_PARAMETER_VALUE, + ) from None + except Exception as e: + raise MlflowException( + f"Failed to construct faithfulness metric {metric_version}. Error: {e!r}", + error_code=INTERNAL_ERROR, + ) from None + + if examples is None: + examples = faithfulness_class_module.default_examples + if model is None: + model = faithfulness_class_module.default_model + + return make_genai_metric( + name="faithfulness", + definition=faithfulness_class_module.definition, + grading_prompt=faithfulness_class_module.grading_prompt, + include_input=False, + examples=examples, + version=metric_version, + model=model, + grading_context_columns=faithfulness_class_module.grading_context_columns, + parameters=parameters or faithfulness_class_module.parameters, + aggregations=["mean", "variance", "p90"], + greater_is_better=True, + metric_metadata=metric_metadata, + extra_headers=extra_headers, + proxy_url=proxy_url, + max_workers=max_workers, + ) + + +def answer_relevance( + model: str | None = None, + metric_version: str | None = _get_latest_metric_version(), + examples: list[EvaluationExample] | None = None, + metric_metadata: dict[str, Any] | None = None, + parameters: dict[str, Any] | None = None, + extra_headers: dict[str, str] | None = None, + proxy_url: str | None = None, + max_workers: int = 10, +) -> EvaluationMetric: + """ + This function will create a genai metric used to evaluate the answer relevance of an LLM + using the model provided. Answer relevance will be assessed based on the appropriateness and + applicability of the output with respect to the input. High scores mean that your model + outputs are about the same subject as the input, while low scores mean that outputs may + be non-topical. + + An MlflowException will be raised if the specified version for this metric does not exist. + + Args: + model: (Optional) Model uri of the judge model that will be used to compute the metric, + e.g., ``openai:/gpt-4``. Refer to the `LLM-as-a-Judge Metrics `_ + documentation for the supported model types and their URI format. + metric_version: The version of the answer relevance metric to use. + Defaults to the latest version. + examples: Provide a list of examples to help the judge model evaluate the + answer relevance. It is highly recommended to add examples to be used as a reference to + evaluate the new results. + metric_metadata: (Optional) Dictionary of metadata to be attached to the + EvaluationMetric object. Useful for model evaluators that require additional + information to determine how to evaluate this metric. + parameters: (Optional) Dictionary of parameters to be passed to the judge model, + e.g., {"temperature": 0.5}. When specified, these parameters will override + the default parameters defined in the metric implementation. + extra_headers: (Optional) Dictionary of extra headers to be passed to the judge model. + proxy_url: (Optional) Proxy URL to be used for the judge model. This is useful when the + judge model is served via a proxy endpoint, not directly via LLM provider services. + If not specified, the default URL for the LLM provider will be used + (e.g., https://api.openai.com/v1/chat/completions for OpenAI chat models). + max_workers: (Optional) The maximum number of workers to use for judge scoring. + Defaults to 10 workers. + + Returns: + A metric object + """ + class_name = f"mlflow.metrics.genai.prompts.{metric_version}.AnswerRelevanceMetric" + try: + answer_relevance_class_module = _get_class_from_string(class_name) + except ModuleNotFoundError: + raise MlflowException( + f"Failed to find answer relevance metric for version {metric_version}." + f" Please check the version", + error_code=INVALID_PARAMETER_VALUE, + ) from None + except Exception as e: + raise MlflowException( + f"Failed to construct answer relevance metric {metric_version}. Error: {e!r}", + error_code=INTERNAL_ERROR, + ) from None + + if examples is None: + examples = answer_relevance_class_module.default_examples + if model is None: + model = answer_relevance_class_module.default_model + + return make_genai_metric( + name="answer_relevance", + definition=answer_relevance_class_module.definition, + grading_prompt=answer_relevance_class_module.grading_prompt, + examples=examples, + version=metric_version, + model=model, + parameters=parameters or answer_relevance_class_module.parameters, + aggregations=["mean", "variance", "p90"], + greater_is_better=True, + metric_metadata=metric_metadata, + extra_headers=extra_headers, + proxy_url=proxy_url, + max_workers=max_workers, + ) + + +def relevance( + model: str | None = None, + metric_version: str | None = None, + examples: list[EvaluationExample] | None = None, + metric_metadata: dict[str, Any] | None = None, + parameters: dict[str, Any] | None = None, + extra_headers: dict[str, str] | None = None, + proxy_url: str | None = None, + max_workers: int = 10, +) -> EvaluationMetric: + """ + This function will create a genai metric used to evaluate the evaluate the relevance of an + LLM using the model provided. Relevance will be assessed by the appropriateness, significance, + and applicability of the output with respect to the input and ``context``. High scores mean + that the model has understood the context and correct extracted relevant information from + the context, while low score mean that output has completely ignored the question and the + context and could be hallucinating. + + The ``context`` eval_arg must be provided as part of the input dataset or output + predictions. This can be mapped to a column of a different name using ``col_mapping`` + in the ``evaluator_config`` parameter. + + An MlflowException will be raised if the specified version for this metric does not exist. + + Args: + model: (Optional) Model uri of the judge model that will be used to compute the metric, + e.g., ``openai:/gpt-4``. Refer to the `LLM-as-a-Judge Metrics `_ + documentation for the supported model types and their URI format. + metric_version: (Optional) The version of the relevance metric to use. + Defaults to the latest version. + examples: (Optional) Provide a list of examples to help the judge model evaluate the + relevance. It is highly recommended to add examples to be used as a reference to + evaluate the new results. + metric_metadata: (Optional) Dictionary of metadata to be attached to the + EvaluationMetric object. Useful for model evaluators that require additional + information to determine how to evaluate this metric. + parameters: (Optional) Dictionary of parameters to be passed to the judge model, + e.g., {"temperature": 0.5}. When specified, these parameters will override + the default parameters defined in the metric implementation. + extra_headers: (Optional) Dictionary of extra headers to be passed to the judge model. + proxy_url: (Optional) Proxy URL to be used for the judge model. This is useful when the + judge model is served via a proxy endpoint, not directly via LLM provider services. + If not specified, the default URL for the LLM provider will be used + (e.g., https://api.openai.com/v1/chat/completions for OpenAI chat models). + max_workers: (Optional) The maximum number of workers to use for judge scoring. + Defaults to 10 workers. + + Returns: + A metric object + """ + if metric_version is None: + metric_version = _get_latest_metric_version() + class_name = f"mlflow.metrics.genai.prompts.{metric_version}.RelevanceMetric" + try: + relevance_class_module = _get_class_from_string(class_name) + except ModuleNotFoundError: + raise MlflowException( + f"Failed to find relevance metric for version {metric_version}." + f"Please check the version", + error_code=INVALID_PARAMETER_VALUE, + ) from None + except Exception as e: + raise MlflowException( + f"Failed to construct relevance metric {metric_version}. Error: {e!r}", + error_code=INTERNAL_ERROR, + ) from None + + if examples is None: + examples = relevance_class_module.default_examples + if model is None: + model = relevance_class_module.default_model + + return make_genai_metric( + name="relevance", + definition=relevance_class_module.definition, + grading_prompt=relevance_class_module.grading_prompt, + examples=examples, + version=metric_version, + model=model, + grading_context_columns=relevance_class_module.grading_context_columns, + parameters=parameters or relevance_class_module.parameters, + aggregations=["mean", "variance", "p90"], + greater_is_better=True, + metric_metadata=metric_metadata, + extra_headers=extra_headers, + proxy_url=proxy_url, + max_workers=max_workers, + ) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/metrics/genai/model_utils.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/metrics/genai/model_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..6dfaee57bf97505df279374d7ae29c760bfbceb1 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/metrics/genai/model_utils.py @@ -0,0 +1,373 @@ +import logging +import os +import urllib.parse +from typing import TYPE_CHECKING, Any + +import requests + +from mlflow.exceptions import MlflowException +from mlflow.protos.databricks_pb2 import INVALID_PARAMETER_VALUE + +if TYPE_CHECKING: + from mlflow.gateway.providers import BaseProvider + +_logger = logging.getLogger(__name__) + + +def get_endpoint_type(endpoint_uri: str) -> str | None: + """ + Get the type of the endpoint if it is MLflow deployment + endpoint. For other endpoints e.g. OpenAI, or if the + endpoint does not specify type, return None. + """ + schema, path = _parse_model_uri(endpoint_uri) + + if schema != "endpoints": + return None + + from pydantic import BaseModel + + from mlflow.deployments import get_deploy_client + + client = get_deploy_client() + + endpoint = client.get_endpoint(path) + # TODO: Standardize the return type of `get_endpoint` and remove this check + endpoint = endpoint.dict() if isinstance(endpoint, BaseModel) else endpoint + return endpoint.get("task", endpoint.get("endpoint_type")) + + +# TODO: improve this name +def score_model_on_payload( + model_uri, + payload, + eval_parameters=None, + extra_headers=None, + proxy_url=None, + endpoint_type=None, +): + """Call the model identified by the given uri with the given string prompt.""" + from mlflow.deployments import get_deploy_client + + eval_parameters = eval_parameters or {} + extra_headers = extra_headers or {} + + prefix, suffix = _parse_model_uri(model_uri) + + if prefix in ["gateway", "endpoints"]: + if isinstance(payload, str) and endpoint_type is None: + client = get_deploy_client() + endpoint_type = client.get_endpoint(suffix).endpoint_type + return call_deployments_api(suffix, payload, eval_parameters, endpoint_type) + elif prefix in ("model", "runs"): + # TODO: call _load_model_or_server + raise NotImplementedError + + # Import here to avoid loading gateway module at the top level + from mlflow.gateway.provider_registry import is_supported_provider + + if is_supported_provider(prefix): + return _call_llm_provider_api( + prefix, suffix, payload, eval_parameters, extra_headers, proxy_url + ) + + raise MlflowException( + f"Unknown model uri prefix '{prefix}'", + error_code=INVALID_PARAMETER_VALUE, + ) + + +def _parse_model_uri(model_uri): + parsed = urllib.parse.urlparse(model_uri, allow_fragments=False) + scheme = parsed.scheme + path = parsed.path + if not path.startswith("/") or len(path) <= 1: + raise MlflowException( + f"Malformed model uri '{model_uri}'. The URI must be in the format of " + ":/, e.g., 'openai:/gpt-4.1-mini'.", + error_code=INVALID_PARAMETER_VALUE, + ) + path = path.lstrip("/") + return scheme, path + + +_PREDICT_ERROR_MSG = """\ +Failed to call the deployment endpoint. Please check the deployment URL \ +is set correctly and the input payload is valid.\n +- Error: {e}\n +- Deployment URI: {uri}\n +- Input payload: {payload}""" + + +def _is_supported_llm_provider(schema: str) -> bool: + from mlflow.gateway.provider_registry import provider_registry + + return schema in provider_registry.keys() + + +def _call_llm_provider_api( + provider_name: str, + model: str, + input_data: str, + eval_parameters: dict[str, Any], + extra_headers: dict[str, str], + proxy_url: str | None = None, +) -> str: + """ + Invoke chat endpoint of various LLM providers. + + Under the hood, this function uses the MLflow Gateway to transform the input/output data + for different LLM providers. + + Args: + provider_name: The provider name, e.g., "anthropic". + model: The model name, e.g., "claude-3-5-sonnet" + input_data: The input string prompt to send to the model as a chat message. + eval_parameters: The additional parameters to send to the model, e.g. temperature. + extra_headers: The additional headers to send to the provider. + proxy_url: Proxy URL to be used for the judge model. If not specified, the default + URL for the LLM provider will be used. + """ + from mlflow.gateway.config import Provider + from mlflow.gateway.schemas import chat + + provider = _get_provider_instance(provider_name, model) + + chat_request = chat.RequestPayload( + model=model, + messages=[ + chat.RequestMessage(role="user", content=input_data), + ], + **eval_parameters, + ) + + # Filter out keys in the payload to the specified ones + "messages". + # Does not include "model" key here because some providers do not accept it as a + # part of the payload. Whether or not to include "model" key must be determined + # by each provider implementation. + filtered_keys = {"messages", *eval_parameters.keys()} + + payload = { + k: v + for k, v in chat_request.model_dump(exclude_none=True).items() + if (v is not None) and (k in filtered_keys) + } + chat_payload = provider.adapter_class.chat_to_model(payload, provider.config) + chat_payload.update(eval_parameters) + + if provider_name in [Provider.AMAZON_BEDROCK, Provider.BEDROCK]: + if proxy_url or extra_headers: + _logger.warning( + "Proxy URL and extra headers are not supported for Bedrock LLMs. " + "Ignoring the provided proxy URL and extra headers.", + ) + response = provider._request(chat_payload) + else: + response = _send_request( + endpoint=proxy_url or provider.get_endpoint_url("llm/v1/chat"), + headers={**provider.headers, **extra_headers}, + payload=chat_payload, + ) + chat_response = provider.adapter_class.model_to_chat(response, provider.config) + if len(chat_response.choices) == 0: + raise MlflowException( + "Failed to score the provided input as the judge LLM did not return " + "any chat completion results in the response." + ) + content = chat_response.choices[0].message.content + + # NB: Evaluation only handles text content for now. + return content[0].text if isinstance(content, list) else content + + +def _get_provider_instance(provider: str, model: str) -> "BaseProvider": + """Get the provider instance for the given provider name and the model name.""" + from mlflow.gateway.config import Provider, RouteConfig + + def _get_route_config(config): + return RouteConfig( + name=provider, + endpoint_type="llm/v1/chat", + model={ + "provider": provider, + "name": model, + "config": config.model_dump(), + }, + ) + + # NB: Not all LLM providers in MLflow Gateway are supported here. We can add + # new ones as requested, as long as the provider support chat endpoints. + if provider == Provider.OPENAI: + from mlflow.gateway.providers.openai import OpenAIConfig, OpenAIProvider + from mlflow.openai.model import _get_api_config, _OAITokenHolder + + api_config = _get_api_config() + api_token = _OAITokenHolder(api_config.api_type) + api_token.refresh() + + config = OpenAIConfig( + openai_api_key=api_token.token, + openai_api_type=api_config.api_type or "openai", + openai_api_base=api_config.api_base, + openai_api_version=api_config.api_version, + openai_deployment_name=api_config.deployment_id, + openai_organization=api_config.organization, + ) + return OpenAIProvider(_get_route_config(config)) + + elif provider == Provider.ANTHROPIC: + from mlflow.gateway.providers.anthropic import AnthropicConfig, AnthropicProvider + + config = AnthropicConfig(anthropic_api_key=os.environ.get("ANTHROPIC_API_KEY")) + return AnthropicProvider(_get_route_config(config)) + + elif provider in [Provider.AMAZON_BEDROCK, Provider.BEDROCK]: + from mlflow.gateway.config import AWSIdAndKey, AWSRole + from mlflow.gateway.providers.bedrock import AmazonBedrockConfig, AmazonBedrockProvider + + if aws_role_arn := os.environ.get("AWS_ROLE_ARN"): + aws_config = AWSRole( + aws_region=os.environ.get("AWS_REGION"), + aws_role_arn=aws_role_arn, + ) + else: + aws_config = AWSIdAndKey( + aws_region=os.environ.get("AWS_REGION"), + aws_access_key_id=os.environ.get("AWS_ACCESS_KEY_ID"), + aws_secret_access_key=os.environ.get("AWS_SECRET_ACCESS_KEY"), + aws_session_token=os.environ.get("AWS_SESSION_TOKEN"), + ) + config = AmazonBedrockConfig(aws_config=aws_config) + return AmazonBedrockProvider(_get_route_config(config)) + + # # Cohere provider implementation seems to be broken and does not work with + # # their latest APIs. Uncomment once the provider implementation is fixed. + # elif provider == Provider.COHERE: + # from mlflow.gateway.providers.cohere import CohereConfig, CohereProvider + + # config = CohereConfig(cohere_api_key=os.environ.get("COHERE_API_KEY")) + # return CohereProvider(_get_route_config(config)) + + elif provider == Provider.MISTRAL: + from mlflow.gateway.providers.mistral import MistralConfig, MistralProvider + + config = MistralConfig(mistral_api_key=os.environ.get("MISTRAL_API_KEY")) + return MistralProvider(_get_route_config(config)) + + elif provider == Provider.TOGETHERAI: + from mlflow.gateway.providers.togetherai import TogetherAIConfig, TogetherAIProvider + + config = TogetherAIConfig(togetherai_api_key=os.environ.get("TOGETHERAI_API_KEY")) + return TogetherAIProvider(_get_route_config(config)) + + raise MlflowException(f"Provider '{provider}' is not supported for evaluation.") + + +def _send_request( + endpoint: str, headers: dict[str, str], payload: dict[str, Any] +) -> dict[str, Any]: + try: + response = requests.post( + url=endpoint, + headers=headers, + json=payload, + timeout=60, + ) + response.raise_for_status() + except requests.exceptions.HTTPError as e: + raise MlflowException( + f"Failed to call LLM endpoint at {endpoint}.\n- Error: {e}\n- Input payload: {payload}." + ) + + return response.json() + + +def call_deployments_api( + deployment_uri: str, + input_data: str | dict[str, Any], + eval_parameters: dict[str, Any] | None = None, + endpoint_type: str | None = None, +): + """Call the deployment endpoint with the given payload and parameters. + + Args: + deployment_uri: The URI of the deployment endpoint. + input_data: The input string or dictionary to send to the endpoint. + - If it is a string, MLflow tries to construct the payload based on the endpoint type. + - If it is a dictionary, MLflow directly sends it to the endpoint. + eval_parameters: The evaluation parameters to send to the endpoint. + endpoint_type: The type of the endpoint. If specified, must be 'llm/v1/completions' + or 'llm/v1/chat'. If not specified, MLflow tries to get the endpoint type + from the endpoint, and if not found, directly sends the payload to the endpoint. + + Returns: + The unpacked response from the endpoint. + """ + from mlflow.deployments import get_deploy_client + + client = get_deploy_client() + + if isinstance(input_data, str): + payload = _construct_payload_from_str(input_data, endpoint_type) + elif isinstance(input_data, dict): + # If the input is a dictionary, we assume it is already in the correct format + payload = input_data + else: + raise MlflowException( + f"Invalid input data type {type(input_data)}. Must be a string or a dictionary.", + error_code=INVALID_PARAMETER_VALUE, + ) + payload = {**payload, **(eval_parameters or {})} + + try: + response = client.predict(endpoint=deployment_uri, inputs=payload) + except Exception as e: + raise MlflowException( + _PREDICT_ERROR_MSG.format(e=e, uri=deployment_uri, payload=payload) + ) from e + + return _parse_response(response, endpoint_type) + + +def _construct_payload_from_str(prompt: str, endpoint_type: str) -> dict[str, Any]: + """ + Construct the payload from the input string based on the endpoint type. + If the endpoint type is not specified or unsupported one, raise an exception. + """ + if endpoint_type == "llm/v1/completions": + return {"prompt": prompt} + elif endpoint_type == "llm/v1/chat": + return {"messages": [{"role": "user", "content": prompt}]} + else: + raise MlflowException( + f"Unsupported endpoint type: {endpoint_type}. If string input is provided, " + "the endpoint type must be 'llm/v1/completions' or 'llm/v1/chat'.", + error_code=INVALID_PARAMETER_VALUE, + ) + + +def _parse_response( + response: dict[str, Any], endpoint_type: str | None +) -> str | None | dict[str, Any]: + if endpoint_type == "llm/v1/completions": + return _parse_completions_response_format(response) + elif endpoint_type == "llm/v1/chat": + return _parse_chat_response_format(response) + else: + return response + + +def _parse_chat_response_format(response): + try: + text = response["choices"][0]["message"]["content"] + except (KeyError, IndexError, TypeError): + text = None + return text + + +def _parse_completions_response_format(response): + try: + text = response["choices"][0]["text"] + except (KeyError, IndexError, TypeError): + text = None + return text diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/metrics/genai/prompt_template.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/metrics/genai/prompt_template.py new file mode 100644 index 0000000000000000000000000000000000000000..d87d4463de2a366a5c373f4e7479b3bfb288b47f --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/metrics/genai/prompt_template.py @@ -0,0 +1,68 @@ +import string +from typing import Any + + +class PromptTemplate: + """A prompt template for a language model. + + A prompt template consists of an array of strings that will be concatenated together. It accepts + a set of parameters from the user that can be used to generate a prompt for a language model. + + The template can be formatted using f-strings. + + Example: + + .. code-block:: python + + from mlflow.metrics.genai.prompt_template import PromptTemplate + + # Instantiation using initializer + prompt = PromptTemplate(template_str="Say {foo} {baz}") + + # Instantiation using partial_fill + prompt = PromptTemplate(template_str="Say {foo} {baz}").partial_fill(foo="bar") + + # Format the prompt + prompt.format(baz="qux") + """ + + def __init__(self, template_str: str | list[str]): + self.template_strs = [template_str] if isinstance(template_str, str) else template_str + + @property + def variables(self): + return { + fname + for template_str in self.template_strs + for _, fname, _, _ in string.Formatter().parse(template_str) + if fname + } + + def format(self, **kwargs: Any) -> str: + safe_kwargs = {k: v for k, v in kwargs.items() if v is not None} + formatted_strs = [] + for template_str in self.template_strs: + extracted_variables = [ + fname for _, fname, _, _ in string.Formatter().parse(template_str) if fname + ] + if all(item in safe_kwargs.keys() for item in extracted_variables): + formatted_strs.append(template_str.format(**safe_kwargs)) + + return "".join(formatted_strs) + + def partial_fill(self, **kwargs: Any) -> "PromptTemplate": + safe_kwargs = {k: v for k, v in kwargs.items() if v is not None} + new_template_strs = [] + for template_str in self.template_strs: + extracted_variables = [ + fname for _, fname, _, _ in string.Formatter().parse(template_str) if fname + ] + safe_available_kwargs = { + k: safe_kwargs.get(k, "{" + k + "}") for k in extracted_variables + } + new_template_strs.append(template_str.format_map(safe_available_kwargs)) + + return PromptTemplate(template_str=new_template_strs) + + def __str__(self) -> str: + return "".join(self.template_strs) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/metrics/genai/prompts/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/metrics/genai/prompts/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/metrics/genai/prompts/__pycache__/__init__.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/metrics/genai/prompts/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..34196a97efaf8a9c5d8fe2cd0b197eb822a83996 Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/metrics/genai/prompts/__pycache__/__init__.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/metrics/genai/prompts/__pycache__/v1.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/metrics/genai/prompts/__pycache__/v1.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..12d9a6442cb50a4c94201ed01991ee09c2a7a94a Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/metrics/genai/prompts/__pycache__/v1.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/metrics/genai/prompts/v1.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/metrics/genai/prompts/v1.py new file mode 100644 index 0000000000000000000000000000000000000000..09ca9b3af1121a4378bb769c28e4d7662b5d32d6 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/metrics/genai/prompts/v1.py @@ -0,0 +1,422 @@ +from dataclasses import dataclass, field +from typing import Any + +from mlflow.metrics.genai.base import EvaluationExample +from mlflow.metrics.genai.prompt_template import PromptTemplate + +# TODO: Update the default_mode and default_parameters to the correct values post experimentation +default_model = "openai:/gpt-4" +# Default parameters expressed in the OpenAI format +default_parameters = { + "temperature": 0.0, + "max_tokens": 200, + "top_p": 1.0, +} + +grading_system_prompt_template = PromptTemplate( + [ + """ +Task: +You must return the following fields in your response in two lines, one below the other: +score: Your numerical score for the model's {name} based on the rubric +justification: Your reasoning about the model's {name} score + +You are an impartial judge. You will be given an input that was sent to a machine +learning model, and you will be given an output that the model produced. You +may also be given additional information that was used by the model to generate the output. + +Your task is to determine a numerical score called {name} based on the input and output. +A definition of {name} and a grading rubric are provided below. +You must use the grading rubric to determine your score. You must also justify your score. + +Examples could be included below for reference. Make sure to use them as references and to +understand them before completing the task.""", + """ + +Input: +{input}""", + """ + +Output: +{output} + +{grading_context_columns} + +Metric definition: +{definition} + +Grading rubric: +{grading_prompt} + +{examples} + +You must return the following fields in your response in two lines, one below the other: +score: Your numerical score for the model's {name} based on the rubric +justification: Your reasoning about the model's {name} score + +Do not add additional new lines. Do not add any other fields. + """, + ] +) + + +@dataclass +class EvaluationModel: + """ + Useful to compute v1 prompt for make_genai_metric + """ + + name: str + definition: str + grading_prompt: str + examples: list[EvaluationExample] | None = None + model: str = default_model + parameters: dict[str, Any] = field(default_factory=lambda: default_parameters) + + def to_dict(self): + examples_str = ( + "" + if self.examples is None or len(self.examples) == 0 + else f"Examples:\n{self._format_examples()}" + ) + + return { + "model": self.model, + "eval_prompt": grading_system_prompt_template.partial_fill( + name=self.name, + definition=self.definition, + grading_prompt=self.grading_prompt, + examples=examples_str, + ), + "parameters": self.parameters, + } + + def _format_examples(self): + return "\n".join(map(str, self.examples)) + + +@dataclass +class AnswerSimilarityMetric: + definition = ( + "Answer similarity is evaluated on the degree of semantic similarity of the provided " + "output to the provided targets, which is the ground truth. Scores can be assigned based " + "on the gradual similarity in meaning and description to the provided targets, where a " + "higher score indicates greater alignment between the provided output and provided targets." + ) + + grading_prompt = ( + "Answer similarity: Below are the details for different scores:\n" + "- Score 1: The output has little to no semantic similarity to the provided targets.\n" + "- Score 2: The output displays partial semantic similarity to the provided targets on " + "some aspects.\n" + "- Score 3: The output has moderate semantic similarity to the provided targets.\n" + "- Score 4: The output aligns with the provided targets in most aspects and has " + "substantial semantic similarity.\n" + "- Score 5: The output closely aligns with the provided targets in all significant aspects." + ) + + grading_context_columns = ["targets"] + parameters = default_parameters + default_model = default_model + + example_score_2 = EvaluationExample( + input="What is MLflow?", + output="MLflow is an open-source platform.", + score=2, + justification="The provided output is partially similar to the target, as it captures the " + "general idea that MLflow is an open-source platform. However, it lacks the comprehensive " + "details and context provided in the target about MLflow's purpose, development, and " + "challenges it addresses. Therefore, it demonstrates partial, but not complete, " + "semantic similarity.", + grading_context={ + "targets": "MLflow is an open-source platform for managing the end-to-end " + "machine learning (ML) lifecycle. It was developed by Databricks, a company " + "that specializes in big data and machine learning solutions. MLflow is " + "designed to address the challenges that data scientists and machine learning " + "engineers face when developing, training, and deploying machine learning " + "models." + }, + ) + + example_score_4 = EvaluationExample( + input="What is MLflow?", + output="MLflow is an open-source platform for managing machine learning workflows, " + "including experiment tracking, model packaging, versioning, and deployment, simplifying " + "the ML lifecycle.", + score=4, + justification="The provided output aligns closely with the target. It covers various key " + "aspects mentioned in the target, including managing machine learning workflows, " + "experiment tracking, model packaging, versioning, and deployment. While it may not include" + " every single detail from the target, it demonstrates substantial semantic similarity.", + grading_context={ + "targets": "MLflow is an open-source platform for managing the end-to-end " + "machine learning (ML) lifecycle. It was developed by Databricks, a company " + "that specializes in big data and machine learning solutions. MLflow is " + "designed to address the challenges that data scientists and machine learning " + "engineers face when developing, training, and deploying machine learning " + "models." + }, + ) + + default_examples = [example_score_2, example_score_4] + + +@dataclass +class FaithfulnessMetric: + definition = ( + "Faithfulness is only evaluated with the provided output and provided context, please " + "ignore the provided input entirely when scoring faithfulness. Faithfulness assesses " + "how much of the provided output is factually consistent with the provided context. A " + "higher score indicates that a higher proportion of claims present in the output can be " + "derived from the provided context. Faithfulness does not consider how much extra " + "information from the context is not present in the output." + ) + + grading_prompt = ( + "Faithfulness: Below are the details for different scores:\n" + "- Score 1: None of the claims in the output can be inferred from the provided context.\n" + "- Score 2: Some of the claims in the output can be inferred from the provided context, " + "but the majority of the output is missing from, inconsistent with, or contradictory to " + "the provided context.\n" + "- Score 3: Half or more of the claims in the output can be inferred from the provided " + "context.\n" + "- Score 4: Most of the claims in the output can be inferred from the provided context, " + "with very little information that is not directly supported by the provided context.\n" + "- Score 5: All of the claims in the output are directly supported by the provided " + "context, demonstrating high faithfulness to the provided context." + ) + + grading_context_columns = ["context"] + parameters = default_parameters + default_model = default_model + + example_score_2 = EvaluationExample( + input="How is MLflow related to Databricks?", + output="Databricks is a company that specializes in big data and machine learning " + "solutions. MLflow has nothing to do with Databricks. MLflow is an open-source platform " + "for managing the end-to-end machine learning (ML) lifecycle.", + score=2, + justification='The output claims that "MLflow has nothing to do with Databricks" which is ' + 'contradictory to the provided context that states "It was developed by Databricks". This ' + 'is a major inconsistency. However, the output correctly identifies that "MLflow is an ' + 'open-source platform for managing the end-to-end machine learning (ML) lifecycle" and ' + '"Databricks is a company that specializes in big data and machine learning solutions", ' + "which are both supported by the context. Therefore, some of the claims in the output can " + "be inferred from the provided context, but the majority of the output is inconsistent " + "with the provided context, leading to a faithfulness score of 2.", + grading_context={ + "context": "MLflow is an open-source platform for managing the end-to-end machine " + "learning (ML) lifecycle. It was developed by Databricks, a company that specializes " + "in big data and machine learning solutions. MLflow is designed to address the " + "challenges that data scientists and machine learning engineers face when developing, " + "training, and deploying machine learning models." + }, + ) + + example_score_5 = EvaluationExample( + input="How is MLflow related to Databricks?", + output="Databricks is a company that specializes in big data and machine learning " + "solutions.", + score=5, + justification='The output states that "Databricks is a company that specializes in big data' + ' and machine learning solutions." This claim is directly supported by the context, which ' + 'states "It was developed by Databricks, a company that specializes in big data and ' + 'machine learning solutions." Therefore, the faithfulness score is 5 as all the claims in ' + 'the output are directly supported by the provided context."', + grading_context={ + "context": "MLflow is an open-source platform for managing the end-to-end " + "machine learning (ML) lifecycle. It was developed by Databricks, a company " + "that specializes in big data and machine learning solutions. MLflow is " + "designed to address the challenges that data scientists and machine learning " + "engineers face when developing, training, and deploying machine learning " + "models." + }, + ) + + default_examples = [example_score_2, example_score_5] + + +@dataclass +class AnswerCorrectnessMetric: + definition = ( + "Answer correctness is evaluated on the accuracy of the provided output based on the " + "provided targets, which is the ground truth. Scores can be assigned based on the degree " + "of semantic similarity and factual correctness of the provided output to the provided " + "targets, where a higher score indicates higher degree of accuracy." + ) + + grading_prompt = ( + "Answer Correctness: Below are the details for different scores:\n" + "- Score 1: The output is completely incorrect. It is completely different from or " + "contradicts the provided targets.\n" + "- Score 2: The output demonstrates some degree of semantic similarity and includes " + "partially correct information. However, the output still has significant discrepancies " + "with the provided targets or inaccuracies.\n" + "- Score 3: The output addresses a couple of aspects of the input accurately, aligning " + "with the provided targets. However, there are still omissions or minor inaccuracies.\n" + "- Score 4: The output is mostly correct. It provides mostly accurate information, but " + "there may be one or more minor omissions or inaccuracies.\n" + "- Score 5: The output is correct. It demonstrates a high degree of accuracy and " + "semantic similarity to the targets." + ) + + grading_context_columns = ["targets"] + parameters = default_parameters + default_model = default_model + + example_score_2 = EvaluationExample( + input="How is MLflow related to Databricks?", + output="Databricks is a data engineering and analytics platform designed to help " + "organizations process and analyze large amounts of data. Databricks is a company " + "specializing in big data and machine learning solutions.", + score=2, + justification="The output provided by the model does demonstrate some degree of semantic " + "similarity to the targets, as it correctly identifies Databricks as a company " + "specializing in big data and machine learning solutions. However, it fails to address " + "the main point of the input question, which is the relationship between MLflow and " + "Databricks. The output does not mention MLflow at all, which is a significant discrepancy " + "with the provided targets. Therefore, the model's answer_correctness score is 2.", + grading_context={ + "targets": "MLflow is an open-source platform for managing the end-to-end machine " + "learning (ML) lifecycle. It was developed by Databricks, a company that specializes " + "in big data and machine learning solutions. MLflow is designed to address the " + "challenges that data scientists and machine learning engineers face when developing, " + "training, and deploying machine learning models." + }, + ) + + example_score_4 = EvaluationExample( + input="How is MLflow related to Databricks?", + output="MLflow is a product created by Databricks to enhance the efficiency of machine " + "learning processes.", + score=4, + justification="The output provided by the model is mostly correct. It correctly identifies " + "that MLflow is a product created by Databricks. However, it does not mention that MLflow " + "is an open-source platform for managing the end-to-end machine learning lifecycle, which " + "is a significant part of its function. Therefore, while the output is mostly accurate, " + "it has a minor omission, which is why it gets a score of 4 according to the grading " + "rubric.", + grading_context={ + "targets": "MLflow is an open-source platform for managing the end-to-end machine " + "learning (ML) lifecycle. It was developed by Databricks, a company that specializes " + "in big data and machine learning solutions. MLflow is designed to address the " + "challenges that data scientists and machine learning engineers face when developing, " + "training, and deploying machine learning models." + }, + ) + + default_examples = [example_score_2, example_score_4] + + +@dataclass +class AnswerRelevanceMetric: + definition = ( + "Answer relevance measures the appropriateness and applicability of the output with " + "respect to the input. Scores should reflect the extent to which the output directly " + "addresses the question provided in the input, and give lower scores for incomplete or " + "redundant output." + ) + + grading_prompt = ( + "Answer relevance: Please give a score from 1-5 based on the degree of relevance to the " + "input, where the lowest and highest scores are defined as follows:" + "- Score 1: The output doesn't mention anything about the question or is completely " + "irrelevant to the input.\n" + "- Score 5: The output addresses all aspects of the question and all parts of the output " + "are meaningful and relevant to the question." + ) + + parameters = default_parameters + default_model = default_model + + example_score_2 = EvaluationExample( + input="How is MLflow related to Databricks?", + output="Databricks is a company that specializes in big data and machine learning " + "solutions.", + score=2, + justification="The output provided by the model does give some information about " + "Databricks, which is part of the input question. However, it does not address the main " + "point of the question, which is the relationship between MLflow and Databricks. " + "Therefore, while the output is not completely irrelevant, it does not fully answer the " + "question, leading to a lower score.", + ) + + example_score_5 = EvaluationExample( + input="How is MLflow related to Databricks?", + output="MLflow is a product created by Databricks to enhance the efficiency of machine " + "learning processes.", + score=5, + justification="The output directly addresses the input question by explaining the " + "relationship between MLflow and Databricks. It provides a clear and concise answer that " + "MLflow is a product created by Databricks, and also adds relevant information about the " + "purpose of MLflow, which is to enhance the efficiency of machine learning processes. " + "Therefore, the output is highly relevant to the input and deserves a full score.", + ) + + default_examples = [example_score_2, example_score_5] + + +@dataclass +class RelevanceMetric: + definition = ( + "Relevance encompasses the appropriateness, significance, and applicability of the output " + "with respect to both the input and context. Scores should reflect the extent to which the " + "output directly addresses the question provided in the input, given the provided context." + ) + + grading_prompt = ( + "Relevance: Below are the details for different scores:" + "- Score 1: The output doesn't mention anything about the question or is completely " + "irrelevant to the provided context.\n" + "- Score 2: The output provides some relevance to the question and is somehow related " + "to the provided context.\n" + "- Score 3: The output mostly answers the question and is largely consistent with the " + "provided context.\n" + "- Score 4: The output answers the question and is consistent with the provided context.\n" + "- Score 5: The output answers the question comprehensively using the provided context." + ) + + grading_context_columns = ["context"] + parameters = default_parameters + default_model = default_model + + example_score_2 = EvaluationExample( + input="How is MLflow related to Databricks?", + output="Databricks is a data engineering and analytics platform designed to help " + "organizations process and analyze large amounts of data. Databricks is a company " + "specializing in big data and machine learning solutions.", + score=2, + justification="The output provides relevant information about Databricks, mentioning it " + "as a company specializing in big data and machine learning solutions. However, it doesn't " + "directly address how MLflow is related to Databricks, which is the specific question " + "asked in the input. Therefore, the output is only somewhat related to the provided " + "context.", + grading_context={ + "context": "MLflow is an open-source platform for managing the end-to-end machine " + "learning (ML) lifecycle. It was developed by Databricks, a company that specializes " + "in big data and machine learning solutions. MLflow is designed to address the " + "challenges that data scientists and machine learning engineers face when developing, " + "training, and deploying machine learning models." + }, + ) + + example_score_4 = EvaluationExample( + input="How is MLflow related to Databricks?", + output="MLflow is a product created by Databricks to enhance the efficiency of machine " + "learning processes.", + score=4, + justification="The output provides a relevant and accurate statement about the " + "relationship between MLflow and Databricks. While it doesn't provide extensive detail, " + "it still offers a substantial and meaningful response. To achieve a score of 5, the " + "response could be further improved by providing additional context or details about " + "how MLflow specifically functions within the Databricks ecosystem.", + grading_context={ + "context": "MLflow is an open-source platform for managing the end-to-end machine " + "learning (ML) lifecycle. It was developed by Databricks, a company that specializes " + "in big data and machine learning solutions. MLflow is designed to address the " + "challenges that data scientists and machine learning engineers face when developing, " + "training, and deploying machine learning models." + }, + ) + + default_examples = [example_score_2, example_score_4] diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/metrics/genai/utils.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/metrics/genai/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..3c9f43f1740ded5361a80c5535b19579d185a0a3 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/metrics/genai/utils.py @@ -0,0 +1,6 @@ +def _get_latest_metric_version(): + return "v1" + + +def _get_default_model(): + return "openai:/gpt-4" diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/metrics/metric_definitions.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/metrics/metric_definitions.py new file mode 100644 index 0000000000000000000000000000000000000000..7579579911dfcd9c09202561a9ac3efdabb68131 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/metrics/metric_definitions.py @@ -0,0 +1,596 @@ +import functools +import logging +import subprocess +import tempfile +from pathlib import Path + +import numpy as np + +from mlflow.environment_variables import _MLFLOW_TESTING +from mlflow.metrics.base import MetricValue, standard_aggregations + +_logger = logging.getLogger(__name__) + + +# used to silently fail with invalid metric params +def noop(*args, **kwargs): + return None + + +targets_col_specifier = "the column specified by the `targets` parameter" +predictions_col_specifier = ( + "the column specified by the `predictions` parameter or the model output column" +) + + +def _validate_text_data(data, metric_name, col_specifier): + """Validates that the data is a list of strs and is non-empty""" + if data is None or len(data) == 0: + _logger.warning( + f"Cannot calculate {metric_name} for empty inputs: " + f"{col_specifier} is empty or the parameter is not specified. Skipping metric logging." + ) + return False + + for row, line in enumerate(data): + if not isinstance(line, str): + _logger.warning( + f"Cannot calculate {metric_name} for non-string inputs. " + f"Non-string found for {col_specifier} on row {row}. Skipping metric logging." + ) + return False + + return True + + +def _validate_array_like_id_data(data, metric_name, col_specifier): + """Validates that the data is a list of lists/np.ndarrays of strings/ints and is non-empty""" + if data is None or len(data) == 0: + return False + + for index, value in data.items(): + if not ( + (isinstance(value, list) and all(isinstance(val, (str, int)) for val in value)) + or ( + isinstance(value, np.ndarray) + and (np.issubdtype(value.dtype, str) or np.issubdtype(value.dtype, int)) + ) + ): + _logger.warning( + f"Cannot calculate metric '{metric_name}' for non-arraylike of string or int " + f"inputs. Non-arraylike of strings/ints found for {col_specifier} on row " + f"{index}, value {value}. Skipping metric logging." + ) + return False + + return True + + +def _load_from_github(path: str, module_type: str = "metric"): + import evaluate + + with tempfile.TemporaryDirectory() as tmpdir: + tmpdir = Path(tmpdir) + subprocess.check_call( + [ + "git", + "clone", + "--filter=blob:none", + "--no-checkout", + "https://github.com/huggingface/evaluate.git", + tmpdir, + ] + ) + path = f"{module_type}s/{path}" + subprocess.check_call(["git", "sparse-checkout", "set", path], cwd=tmpdir) + subprocess.check_call(["git", "checkout"], cwd=tmpdir) + return evaluate.load(str(tmpdir / path)) + + +@functools.lru_cache(maxsize=8) +def _cached_evaluate_load(path: str, module_type: str = "metric"): + import evaluate + + try: + return evaluate.load(path, module_type=module_type) + except (FileNotFoundError, OSError): + if _MLFLOW_TESTING.get(): + # `evaluate.load` is highly unstable and often fails due to a network error or + # huggingface hub being down. In testing, we want to avoid this instability, so we + # load the metric from the evaluate repository on GitHub. + return _load_from_github(path, module_type=module_type) + raise + + +def _toxicity_eval_fn(predictions, targets=None, metrics=None): + if not _validate_text_data(predictions, "toxicity", predictions_col_specifier): + return + try: + toxicity = _cached_evaluate_load("toxicity", module_type="measurement") + except Exception as e: + _logger.warning( + f"Failed to load 'toxicity' metric (error: {e!r}), skipping metric logging." + ) + return + + scores = toxicity.compute(predictions=predictions)["toxicity"] + toxicity_ratio = toxicity.compute(predictions=predictions, aggregation="ratio")[ + "toxicity_ratio" + ] + return MetricValue( + scores=scores, + aggregate_results={ + **standard_aggregations(scores), + "ratio": toxicity_ratio, + }, + ) + + +def _flesch_kincaid_eval_fn(predictions, targets=None, metrics=None): + if not _validate_text_data(predictions, "flesch_kincaid", predictions_col_specifier): + return + + try: + import textstat + except ImportError: + _logger.warning( + "Failed to import textstat for flesch kincaid metric, skipping metric logging. " + "Please install textstat using 'pip install textstat'." + ) + return + + scores = [textstat.flesch_kincaid_grade(prediction) for prediction in predictions] + return MetricValue( + scores=scores, + aggregate_results=standard_aggregations(scores), + ) + + +def _ari_eval_fn(predictions, targets=None, metrics=None): + if not _validate_text_data(predictions, "ari", predictions_col_specifier): + return + + try: + import textstat + except ImportError: + _logger.warning( + "Failed to import textstat for automated readability index metric, " + "skipping metric logging. " + "Please install textstat using 'pip install textstat'." + ) + return + + scores = [textstat.automated_readability_index(prediction) for prediction in predictions] + return MetricValue( + scores=scores, + aggregate_results=standard_aggregations(scores), + ) + + +def _accuracy_eval_fn(predictions, targets=None, metrics=None, sample_weight=None): + if targets is not None and len(targets) != 0: + from sklearn.metrics import accuracy_score + + acc = accuracy_score(y_true=targets, y_pred=predictions, sample_weight=sample_weight) + return MetricValue(aggregate_results={"exact_match": acc}) + + +def _rouge1_eval_fn(predictions, targets=None, metrics=None): + if not _validate_text_data(targets, "rouge1", targets_col_specifier) or not _validate_text_data( + predictions, "rouge1", predictions_col_specifier + ): + return + + try: + rouge = _cached_evaluate_load("rouge") + except Exception as e: + _logger.warning(f"Failed to load 'rouge' metric (error: {e!r}), skipping metric logging.") + return + + scores = rouge.compute( + predictions=predictions, + references=targets, + rouge_types=["rouge1"], + use_aggregator=False, + )["rouge1"] + return MetricValue( + scores=scores, + aggregate_results=standard_aggregations(scores), + ) + + +def _rouge2_eval_fn(predictions, targets=None, metrics=None): + if not _validate_text_data(targets, "rouge2", targets_col_specifier) or not _validate_text_data( + predictions, "rouge2", predictions_col_specifier + ): + return + + try: + rouge = _cached_evaluate_load("rouge") + except Exception as e: + _logger.warning(f"Failed to load 'rouge' metric (error: {e!r}), skipping metric logging.") + return + + scores = rouge.compute( + predictions=predictions, + references=targets, + rouge_types=["rouge2"], + use_aggregator=False, + )["rouge2"] + return MetricValue( + scores=scores, + aggregate_results=standard_aggregations(scores), + ) + + +def _rougeL_eval_fn(predictions, targets=None, metrics=None): + if not _validate_text_data(targets, "rougeL", targets_col_specifier) or not _validate_text_data( + predictions, "rougeL", predictions_col_specifier + ): + return + + try: + rouge = _cached_evaluate_load("rouge") + except Exception as e: + _logger.warning(f"Failed to load 'rouge' metric (error: {e!r}), skipping metric logging.") + return + + scores = rouge.compute( + predictions=predictions, + references=targets, + rouge_types=["rougeL"], + use_aggregator=False, + )["rougeL"] + return MetricValue( + scores=scores, + aggregate_results=standard_aggregations(scores), + ) + + +def _rougeLsum_eval_fn(predictions, targets=None, metrics=None): + if not _validate_text_data( + targets, "rougeLsum", targets_col_specifier + ) or not _validate_text_data(predictions, "rougeLsum", predictions_col_specifier): + return + + try: + rouge = _cached_evaluate_load("rouge") + except Exception as e: + _logger.warning(f"Failed to load 'rouge' metric (error: {e!r}), skipping metric logging.") + return + + scores = rouge.compute( + predictions=predictions, + references=targets, + rouge_types=["rougeLsum"], + use_aggregator=False, + )["rougeLsum"] + return MetricValue( + scores=scores, + aggregate_results=standard_aggregations(scores), + ) + + +def _mae_eval_fn(predictions, targets=None, metrics=None, sample_weight=None): + if targets is not None and len(targets) != 0: + from sklearn.metrics import mean_absolute_error + + mae = mean_absolute_error(targets, predictions, sample_weight=sample_weight) + return MetricValue(aggregate_results={"mean_absolute_error": mae}) + + +def _mse_eval_fn(predictions, targets=None, metrics=None, sample_weight=None): + if targets is not None and len(targets) != 0: + from sklearn.metrics import mean_squared_error + + mse = mean_squared_error(targets, predictions, sample_weight=sample_weight) + return MetricValue(aggregate_results={"mean_squared_error": mse}) + + +def _root_mean_squared_error(*, y_true, y_pred, sample_weight): + try: + from sklearn.metrics import root_mean_squared_error + except ImportError: + # If root_mean_squared_error is unavailable, fall back to + # `mean_squared_error(..., squared=False)`, which is deprecated in scikit-learn >= 1.4. + from sklearn.metrics import mean_squared_error + + return mean_squared_error( + y_true=y_true, y_pred=y_pred, sample_weight=sample_weight, squared=False + ) + else: + return root_mean_squared_error(y_true=y_true, y_pred=y_pred, sample_weight=sample_weight) + + +def _rmse_eval_fn(predictions, targets=None, metrics=None, sample_weight=None): + if targets is not None and len(targets) != 0: + rmse = _root_mean_squared_error( + y_true=targets, y_pred=predictions, sample_weight=sample_weight + ) + return MetricValue(aggregate_results={"root_mean_squared_error": rmse}) + + +def _r2_score_eval_fn(predictions, targets=None, metrics=None, sample_weight=None): + if targets is not None and len(targets) != 0: + from sklearn.metrics import r2_score + + r2 = r2_score(targets, predictions, sample_weight=sample_weight) + return MetricValue(aggregate_results={"r2_score": r2}) + + +def _max_error_eval_fn(predictions, targets=None, metrics=None): + if targets is not None and len(targets) != 0: + from sklearn.metrics import max_error + + error = max_error(targets, predictions) + return MetricValue(aggregate_results={"max_error": error}) + + +def _mape_eval_fn(predictions, targets=None, metrics=None, sample_weight=None): + if targets is not None and len(targets) != 0: + from sklearn.metrics import mean_absolute_percentage_error + + mape = mean_absolute_percentage_error(targets, predictions, sample_weight=sample_weight) + return MetricValue(aggregate_results={"mean_absolute_percentage_error": mape}) + + +def _recall_eval_fn( + predictions, targets=None, metrics=None, pos_label=1, average="binary", sample_weight=None +): + if targets is not None and len(targets) != 0: + from sklearn.metrics import recall_score + + recall = recall_score( + targets, predictions, pos_label=pos_label, average=average, sample_weight=sample_weight + ) + return MetricValue(aggregate_results={"recall_score": recall}) + + +def _precision_eval_fn( + predictions, targets=None, metrics=None, pos_label=1, average="binary", sample_weight=None +): + if targets is not None and len(targets) != 0: + from sklearn.metrics import precision_score + + precision = precision_score( + targets, + predictions, + pos_label=pos_label, + average=average, + sample_weight=sample_weight, + ) + return MetricValue(aggregate_results={"precision_score": precision}) + + +def _f1_score_eval_fn( + predictions, targets=None, metrics=None, pos_label=1, average="binary", sample_weight=None +): + if targets is not None and len(targets) != 0: + from sklearn.metrics import f1_score + + f1 = f1_score( + targets, + predictions, + pos_label=pos_label, + average=average, + sample_weight=sample_weight, + ) + return MetricValue(aggregate_results={"f1_score": f1}) + + +def _precision_at_k_eval_fn(k): + if not (isinstance(k, int) and k > 0): + _logger.warning( + f"Cannot calculate 'precision_at_k' for invalid parameter 'k'. " + f"'k' should be a positive integer; found: {k}. Skipping metric logging." + ) + return noop + + def _fn(predictions, targets): + if not _validate_array_like_id_data( + predictions, "precision_at_k", predictions_col_specifier + ) or not _validate_array_like_id_data(targets, "precision_at_k", targets_col_specifier): + return + + scores = [] + for target, prediction in zip(targets, predictions): + # only include the top k retrieved chunks + ground_truth = set(target) + retrieved = prediction[:k] + relevant_doc_count = sum(1 for doc in retrieved if doc in ground_truth) + if len(retrieved) > 0: + scores.append(relevant_doc_count / len(retrieved)) + else: + # when no documents are retrieved, precision is 0 + scores.append(0) + + return MetricValue(scores=scores, aggregate_results=standard_aggregations(scores)) + + return _fn + + +def _expand_duplicate_retrieved_docs(predictions, targets): + counter = {} + expanded_predictions = [] + expanded_targets = targets + for doc_id in predictions: + if doc_id not in counter: + counter[doc_id] = 1 + expanded_predictions.append(doc_id) + else: + counter[doc_id] += 1 + new_doc_id = ( + f"{doc_id}_bc574ae_{counter[doc_id]}" # adding a random string to avoid collisions + ) + expanded_predictions.append(new_doc_id) + if doc_id in expanded_targets: + expanded_targets.add(new_doc_id) + return expanded_predictions, expanded_targets + + +def _prepare_row_for_ndcg(predictions, targets): + """Prepare data one row from predictions and targets to y_score, y_true for ndcg calculation. + + Args: + predictions: A list of strings of at most k doc IDs retrieved. + targets: A list of strings of ground-truth doc IDs. + + Returns: + y_true : ndarray of shape (1, n_docs) Representing the ground-truth relevant docs. + n_docs is the number of unique docs in union of predictions and targets. + y_score : ndarray of shape (1, n_docs) Representing the retrieved docs. + n_docs is the number of unique docs in union of predictions and targets. + """ + # sklearn does an internal sort of y_score, so to preserve the order of our retrieved + # docs, we need to modify the relevance value slightly + eps = 1e-6 + + # support predictions containing duplicate doc ID + targets = set(targets) + predictions, targets = _expand_duplicate_retrieved_docs(predictions, targets) + + all_docs = targets.union(predictions) + doc_id_to_index = {doc_id: i for i, doc_id in enumerate(all_docs)} + n_labels = max(len(doc_id_to_index), 2) # sklearn.metrics.ndcg_score requires at least 2 labels + y_true = np.zeros((1, n_labels), dtype=np.float32) + y_score = np.zeros((1, n_labels), dtype=np.float32) + for i, doc_id in enumerate(predictions): + # "1 - i * eps" means we assign higher score to docs that are ranked higher, + # but all scores are still approximately 1. + y_score[0, doc_id_to_index[doc_id]] = 1 - i * eps + for doc_id in targets: + y_true[0, doc_id_to_index[doc_id]] = 1 + return y_score, y_true + + +def _ndcg_at_k_eval_fn(k): + if not (isinstance(k, int) and k > 0): + _logger.warning( + f"Cannot calculate 'ndcg_at_k' for invalid parameter 'k'. " + f"'k' should be a positive integer; found: {k}. Skipping metric logging." + ) + return noop + + def _fn(predictions, targets): + from sklearn.metrics import ndcg_score + + if not _validate_array_like_id_data( + predictions, "ndcg_at_k", predictions_col_specifier + ) or not _validate_array_like_id_data(targets, "ndcg_at_k", targets_col_specifier): + return + + scores = [] + for ground_truth, retrieved in zip(targets, predictions): + # 1. If no ground truth doc IDs are provided and no documents are retrieved, + # the score is 1. + if len(retrieved) == 0 and len(ground_truth) == 0: + scores.append(1) # no error is made + continue + # 2. If no ground truth doc IDs are provided and documents are retrieved, + # the score is 0. + # 3. If ground truth doc IDs are provided and no documents are retrieved, + # the score is 0. + if len(retrieved) == 0 or len(ground_truth) == 0: + scores.append(0) + continue + + # only include the top k retrieved chunks + y_score, y_true = _prepare_row_for_ndcg(retrieved[:k], ground_truth) + score = ndcg_score(y_true, y_score, k=len(retrieved[:k]), ignore_ties=True) + scores.append(score) + + return MetricValue(scores=scores, aggregate_results=standard_aggregations(scores)) + + return _fn + + +def _recall_at_k_eval_fn(k): + if not (isinstance(k, int) and k > 0): + _logger.warning( + f"Cannot calculate 'recall_at_k' for invalid parameter 'k'. " + f"'k' should be a positive integer; found: {k}. Skipping metric logging." + ) + return noop + + def _fn(predictions, targets): + if not _validate_array_like_id_data( + predictions, "recall_at_k", predictions_col_specifier + ) or not _validate_array_like_id_data(targets, "recall_at_k", targets_col_specifier): + return + + scores = [] + for target, prediction in zip(targets, predictions): + # only include the top k retrieved chunks + ground_truth = set(target) + retrieved = set(prediction[:k]) + relevant_doc_count = len(ground_truth.intersection(retrieved)) + if len(ground_truth) > 0: + scores.append(relevant_doc_count / len(ground_truth)) + elif len(retrieved) == 0: + # there are 0 retrieved and ground truth docs, so reward for the match + scores.append(1) + else: + # there are > 0 retrieved, but 0 ground truth, so penalize + scores.append(0) + + return MetricValue(scores=scores, aggregate_results=standard_aggregations(scores)) + + return _fn + + +def _bleu_eval_fn(predictions, targets=None, metrics=None): + # Validate input data + if not _validate_text_data(targets, "bleu", targets_col_specifier): + _logger.error( + """Target validation failed. + Ensure targets are valid for BLEU computation.""" + ) + return + if not _validate_text_data(predictions, "bleu", predictions_col_specifier): + _logger.error( + """Prediction validation failed. + Ensure predictions are valid for BLEU computation.""" + ) + return + + # Load BLEU metric + try: + bleu = _cached_evaluate_load("bleu") + except Exception as e: + _logger.warning(f"Failed to load 'bleu' metric (error: {e!r}), skipping metric logging.") + return + + # Calculate BLEU scores for each prediction-target pair + result = [] + invalid_indices = [] + + for i, (prediction, target) in enumerate(zip(predictions, targets)): + if len(target) == 0 or len(prediction) == 0: + invalid_indices.append(i) + result.append(0) # Append 0 as a placeholder for invalid entries + continue + + try: + score = bleu.compute(predictions=[prediction], references=[[target]]) + result.append(score["bleu"]) + except Exception as e: + _logger.warning(f"Failed to calculate BLEU for row {i} (error: {e!r}). Skipping.") + result.append(0) # Append 0 for consistency if an unexpected error occurs + + # Log warning for any invalid indices + if invalid_indices: + _logger.warning( + f"BLEU score calculation skipped for the following indices " + f"due to empty target or prediction: {invalid_indices}. " + f"A score of 0 was appended for these entries." + ) + + # Return results + if not result: + _logger.warning("No BLEU scores were calculated due to input errors.") + return + + return MetricValue( + scores=result, + aggregate_results=standard_aggregations(result), + ) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/mistral/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/mistral/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..c1dbc702e3226ee27027e99be80c27d2aefc0b82 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/mistral/__init__.py @@ -0,0 +1,34 @@ +from mlflow.mistral.autolog import patched_class_call +from mlflow.utils.annotations import experimental +from mlflow.utils.autologging_utils import autologging_integration, safe_patch + +FLAVOR_NAME = "mistral" + + +@experimental(version="2.21.0") +@autologging_integration(FLAVOR_NAME) +def autolog( + log_traces: bool = True, + disable: bool = False, + silent: bool = False, +): + """ + Enables (or disables) and configures autologging from Mistral AI to MLflow. + Only synchronous calls to the Text generation API are supported. + Asynchronous APIs and streaming are not recorded. + + Args: + log_traces: If ``True``, traces are logged for Mistral AI models. + If ``False``, no traces are collected during inference. Default to ``True``. + disable: If ``True``, disables the Mistral AI autologging. Default to ``False``. + silent: If ``True``, suppress all event logs and warnings from MLflow during Mistral AI + autologging. If ``False``, show all events and warnings. + """ + from mistralai.chat import Chat + + safe_patch( + FLAVOR_NAME, + Chat, + "complete", + patched_class_call, + ) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/mistral/__pycache__/__init__.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/mistral/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..30f4455ca926bd3c36647ddfdd56951063faf8a9 Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/mistral/__pycache__/__init__.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/mistral/__pycache__/autolog.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/mistral/__pycache__/autolog.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..bd470b7d956e7fde5b0d11cc5be058ec56a3b3c2 Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/mistral/__pycache__/autolog.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/mistral/__pycache__/chat.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/mistral/__pycache__/chat.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..1fe1ea43216715491f9628cf28d7911690d9605a Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/mistral/__pycache__/chat.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/mistral/autolog.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/mistral/autolog.py new file mode 100644 index 0000000000000000000000000000000000000000..e07498f6fbfd2b0adad4425c18f170f80bf6f603 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/mistral/autolog.py @@ -0,0 +1,61 @@ +import inspect +import logging + +import mlflow +import mlflow.mistral +from mlflow.entities import SpanType +from mlflow.mistral.chat import convert_tool_to_mlflow_chat_tool +from mlflow.tracing.constant import SpanAttributeKey, TokenUsageKey +from mlflow.tracing.utils import set_span_chat_tools +from mlflow.utils.autologging_utils.config import AutoLoggingConfig + +_logger = logging.getLogger(__name__) + + +def _construct_full_inputs(func, *args, **kwargs): + signature = inspect.signature(func) + # this does not create copy. So values should not be mutated directly + arguments = signature.bind_partial(*args, **kwargs).arguments + + if "self" in arguments: + arguments.pop("self") + + return arguments + + +def patched_class_call(original, self, *args, **kwargs): + config = AutoLoggingConfig.init(flavor_name=mlflow.mistral.FLAVOR_NAME) + + if config.log_traces: + with mlflow.start_span( + name=f"{self.__class__.__name__}.{original.__name__}", + span_type=SpanType.CHAT_MODEL, + ) as span: + inputs = _construct_full_inputs(original, self, *args, **kwargs) + span.set_inputs(inputs) + span.set_attribute(SpanAttributeKey.MESSAGE_FORMAT, "mistral") + + if (tools := inputs.get("tools")) is not None: + try: + tools = [convert_tool_to_mlflow_chat_tool(tool) for tool in tools if tool] + set_span_chat_tools(span, tools) + except Exception as e: + _logger.debug(f"Failed to set tools for {span}. Error: {e}") + + outputs = original(self, *args, **kwargs) + + try: + span.set_outputs(outputs) + if usage := getattr(outputs, "usage", None): + span.set_attribute( + SpanAttributeKey.CHAT_USAGE, + { + TokenUsageKey.INPUT_TOKENS: usage.prompt_tokens, + TokenUsageKey.OUTPUT_TOKENS: usage.completion_tokens, + TokenUsageKey.TOTAL_TOKENS: usage.total_tokens, + }, + ) + except Exception as e: + _logger.debug(f"Failed to process outputs for {span}. Error: {e}") + + return outputs diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/mistral/chat.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/mistral/chat.py new file mode 100644 index 0000000000000000000000000000000000000000..9fa41cbbb42c857ed8bd4fba8353e5aec309f506 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/mistral/chat.py @@ -0,0 +1,29 @@ +from typing import Any + +from mlflow.types.chat import ( + ChatTool, + FunctionToolDefinition, +) + + +def convert_tool_to_mlflow_chat_tool(tool: dict[str, Any]) -> ChatTool: + """ + Convert Mistral AI tool definition into MLflow's standard format (OpenAI compatible). + + Ref: https://docs.mistral.ai/capabilities/function_calling/#tools + + Args: + tool: A dictionary represents a single tool definition in the input request. + + Returns: + ChatTool: MLflow's standard tool definition object. + """ + function = tool["function"] + return ChatTool( + type="function", + function=FunctionToolDefinition( + name=function["name"], + description=function.get("description"), + parameters=function["parameters"], + ), + ) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..69e7b9e53e82da29124768837af891353ad1c244 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/__init__.py @@ -0,0 +1,97 @@ +""" +The ``mlflow.models`` module provides an API for saving machine learning models in +"flavors" that can be understood by different downstream tools. + +The built-in flavors are: + +- :py:mod:`mlflow.catboost` +- :py:mod:`mlflow.diviner` +- :py:mod:`mlflow.dspy` +- :py:mod:`mlflow.h2o` +- :py:mod:`mlflow.langchain` +- :py:mod:`mlflow.lightgbm` +- :py:mod:`mlflow.llama_index` +- :py:mod:`mlflow.onnx` +- :py:mod:`mlflow.openai` +- :py:mod:`mlflow.paddle` +- :py:mod:`mlflow.pmdarima` +- :py:mod:`mlflow.prophet` +- :py:mod:`mlflow.pyfunc` +- :py:mod:`mlflow.pyspark.ml` +- :py:mod:`mlflow.pytorch` +- :py:mod:`mlflow.sklearn` +- :py:mod:`mlflow.spacy` +- :py:mod:`mlflow.spark` +- :py:mod:`mlflow.statsmodels` +- :py:mod:`mlflow.tensorflow` +- :py:mod:`mlflow.transformers` +- :py:mod:`mlflow.xgboost` + +For details, see `MLflow Models guide `_. +""" + +from mlflow.models.dependencies_schemas import set_retriever_schema +from mlflow.models.evaluation import ( + EvaluationArtifact, + EvaluationMetric, + EvaluationResult, + MetricThreshold, + evaluate, + list_evaluators, + make_metric, +) +from mlflow.models.flavor_backend import FlavorBackend +from mlflow.models.model import Model, get_model_info, set_model, update_model_requirements +from mlflow.models.model_config import ModelConfig +from mlflow.models.python_api import build_docker +from mlflow.models.resources import Resource, ResourceType +from mlflow.utils.environment import infer_pip_requirements + +__all__ = [ + "Model", + "FlavorBackend", + "infer_pip_requirements", + "evaluate", + "make_metric", + "EvaluationMetric", + "EvaluationArtifact", + "EvaluationResult", + "get_model_info", + "set_model", + "set_retriever_schema", + "list_evaluators", + "MetricThreshold", + "build_docker", + "Resource", + "ResourceType", + "ModelConfig", + "update_model_requirements", +] + + +# Under skinny-mlflow requirements, the following packages cannot be imported +# because of lack of numpy/pandas library, so wrap them with try...except block +try: + from mlflow.models.python_api import predict + from mlflow.models.signature import ModelSignature, infer_signature, set_signature + from mlflow.models.utils import ( + ModelInputExample, + add_libraries_to_model, + convert_input_example_to_serving_input, + validate_schema, + validate_serving_input, + ) + + __all__ += [ + "ModelSignature", + "ModelInputExample", + "infer_signature", + "validate_schema", + "add_libraries_to_model", + "convert_input_example_to_serving_input", + "set_signature", + "predict", + "validate_serving_input", + ] +except ImportError: + pass diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/__pycache__/__init__.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..f90712bfdc14826a0eefa4d432a8976f97894f4b Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/__pycache__/__init__.cpython-312.pyc differ diff --git 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b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/auth_policy.py @@ -0,0 +1,81 @@ +from mlflow.models.resources import Resource, _ResourceBuilder +from mlflow.utils.annotations import experimental + + +@experimental(version="2.21.0") +class UserAuthPolicy: + """ + A minimal list of scopes that the user should have access to + in order to invoke this model + + Note: This is only compatible with Databricks Environment currently. + TODO: Add Databricks Documentation for User Auth Policy + + Args: + api_scopes: A list of scopes. Example: "vectorsearch.vector-search-indexes", "sql" + """ + + def __init__(self, api_scopes: list[str]): + self._api_scopes = api_scopes + + @property + def api_scopes(self) -> list[str]: + return self._api_scopes + + @api_scopes.setter + def api_scopes(self, value: list[str]): + self._api_scopes = value + + def to_dict(self): + return {"api_scopes": self.api_scopes} + + +class SystemAuthPolicy: + """ + System Auth Policy, which defines a list of resources required to + serve this model + """ + + def __init__(self, resources: list[Resource]): + self._resources = resources + + @property + def resources(self) -> list[Resource]: + return self._resources + + @resources.setter + def resources(self, value: list[Resource]): + self._resources = value + + def to_dict(self): + serialized_resources = _ResourceBuilder.from_resources(self.resources) + return {"resources": serialized_resources} + + +class AuthPolicy: + """ + Specifies the authentication policy for the model, which includes two key + components. + System Auth Policy: A list of resources required to serve this model + User Auth Policy: A minimal list of scopes that the user should + have access to, in order to invoke this model + """ + + def __init__( + self, + user_auth_policy: UserAuthPolicy | None = None, + system_auth_policy: SystemAuthPolicy | None = None, + ): + self.user_auth_policy = user_auth_policy + self.system_auth_policy = system_auth_policy + + def to_dict(self): + """ + Serialize Auth Policy to a dictionary + """ + return { + "system_auth_policy": self.system_auth_policy.to_dict() + if self.system_auth_policy + else {}, + "user_auth_policy": self.user_auth_policy.to_dict() if self.user_auth_policy else {}, + } diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/cli.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/cli.py new file mode 100644 index 0000000000000000000000000000000000000000..ab61f6ed4fc5cb6971bf93444def78cb90d9039b --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/cli.py @@ -0,0 +1,354 @@ +import logging + +import click + +from mlflow.models import python_api +from mlflow.models.flavor_backend_registry import get_flavor_backend +from mlflow.models.model import update_model_requirements +from mlflow.utils import cli_args +from mlflow.utils import env_manager as _EnvManager + +_logger = logging.getLogger(__name__) + + +@click.group("models") +def commands(): + """ + Deploy MLflow models locally. + + To deploy a model associated with a run on a tracking server, set the MLFLOW_TRACKING_URI + environment variable to the URL of the desired server. + """ + + +@commands.command("serve") +@cli_args.MODEL_URI +@cli_args.PORT +@cli_args.HOST +@cli_args.TIMEOUT +@cli_args.MODELS_WORKERS +@cli_args.ENV_MANAGER +@cli_args.NO_CONDA +@cli_args.INSTALL_MLFLOW +@cli_args.ENABLE_MLSERVER +def serve( + model_uri, + port, + host, + timeout, + workers, + env_manager=None, + no_conda=False, + install_mlflow=False, + enable_mlserver=False, +): + """ + Serve a model saved with MLflow by launching a webserver on the specified host and port. + The command supports models with the ``python_function`` or ``crate`` (R Function) flavor. + For information about the input data formats accepted by the webserver, see the following + documentation: https://www.mlflow.org/docs/latest/models.html#built-in-deployment-tools. + + .. warning:: + + Models built using MLflow 1.x will require adjustments to the endpoint request payload + if executed in an environment that has MLflow 2.x installed. In 1.x, a request payload + was in the format: ``{'columns': [str], 'data': [[...]]}``. 2.x models require + payloads that are defined by the structural-defining keys of either ``dataframe_split``, + ``instances``, ``inputs`` or ``dataframe_records``. See the examples below for + demonstrations of the changes to the invocation API endpoint in 2.0. + + .. note:: + + Requests made in pandas DataFrame structures can be made in either `split` or `records` + oriented formats. + See https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_json.html for + detailed information on orientation formats for converting a pandas DataFrame to json. + + Example: + + .. code-block:: bash + + $ mlflow models serve -m runs:/my-run-id/model-path & + + # records orientation input format for serializing a pandas DataFrame + $ curl http://127.0.0.1:5000/invocations -H 'Content-Type: application/json' -d '{ + "dataframe_records": [{"a":1, "b":2}, {"a":3, "b":4}, {"a":5, "b":6}] + }' + + # split orientation input format for serializing a pandas DataFrame + $ curl http://127.0.0.1:5000/invocations -H 'Content-Type: application/json' -d '{ + "dataframe_split": {"columns": ["a", "b"], + "index": [0, 1, 2], + "data": [[1, 2], [3, 4], [5, 6]]} + }' + + # inputs format for List submission of array, tensor, or DataFrame data + $ curl http://127.0.0.1:5000/invocations -H 'Content-Type: application/json' -d '{ + "inputs": [[1, 2], [3, 4], [5, 6]] + }' + + # instances format for submission of Tensor data + curl http://127.0.0.1:5000/invocations -H 'Content-Type: application/json' -d '{ + "instances": [ + {"a": "t1", "b": [1, 2, 3]}, + {"a": "t2", "b": [4, 5, 6]}, + {"a": "t3", "b": [7, 8, 9]} + ] + }' + + """ + env_manager = _EnvManager.LOCAL if no_conda else env_manager + + return get_flavor_backend( + model_uri, env_manager=env_manager, workers=workers, install_mlflow=install_mlflow + ).serve( + model_uri=model_uri, port=port, host=host, timeout=timeout, enable_mlserver=enable_mlserver + ) + + +class KeyValueType(click.ParamType): + name = "key=value" + + def convert(self, value, param, ctx): + if "=" not in value: + self.fail(f"{value!r} is not a valid key value pair, expecting `key=value`", param, ctx) + return value.split("=", 1) + + +@commands.command("predict") +@cli_args.MODEL_URI +@click.option( + "--input-path", "-i", default=None, help="CSV containing pandas DataFrame to predict against." +) +@click.option( + "--output-path", + "-o", + default=None, + help="File to output results to as json file. If not provided, output to stdout.", +) +@click.option( + "--content-type", + "-t", + default="json", + help="Content type of the input file. Can be one of {'json', 'csv'}.", +) +@cli_args.ENV_MANAGER +@cli_args.INSTALL_MLFLOW +@click.option( + "--pip-requirements-override", + "-r", + default=None, + help="Specify packages and versions to override the dependencies defined " + "in the model. Must be a comma-separated string like x==y,z==a.", +) +@click.option( + "--env", + default=None, + type=KeyValueType(), + multiple=True, + help="Extra environment variables to set when running the model. Must be " + "key value pairs, e.g. `--env key=value`.", +) +def predict( + model_uri, + input_data=None, + input_path=None, + content_type=python_api._CONTENT_TYPE_JSON, + output_path=None, + env_manager=_EnvManager.VIRTUALENV, + install_mlflow=False, + pip_requirements_override=None, + env=None, +): + """ + Generate predictions in json format using a saved MLflow model. For information about the input + data formats accepted by this function, see the following documentation: + https://www.mlflow.org/docs/latest/models.html#built-in-deployment-tools. + """ + return python_api.predict( + model_uri=model_uri, + input_data=input_data, + input_path=input_path, + content_type=content_type, + output_path=output_path, + env_manager=env_manager, + install_mlflow=install_mlflow, + pip_requirements_override=pip_requirements_override, + extra_envs=dict(env), + ) + + +@commands.command("prepare-env") +@cli_args.MODEL_URI +@cli_args.ENV_MANAGER +@cli_args.INSTALL_MLFLOW +def prepare_env( + model_uri, + env_manager, + install_mlflow, +): + """ + Performs any preparation necessary to predict or serve the model, for example + downloading dependencies or initializing a conda environment. After preparation, + calling predict or serve should be fast. + """ + return get_flavor_backend( + model_uri, env_manager=env_manager, install_mlflow=install_mlflow + ).prepare_env(model_uri=model_uri) + + +@commands.command("generate-dockerfile") +@cli_args.MODEL_URI_BUILD_DOCKER +@click.option( + "--output-directory", + "-d", + default="mlflow-dockerfile", + help="Output directory where the generated Dockerfile is stored.", +) +@cli_args.ENV_MANAGER_DOCKERFILE +@cli_args.MLFLOW_HOME +@cli_args.INSTALL_JAVA +@cli_args.INSTALL_MLFLOW +@cli_args.ENABLE_MLSERVER +def generate_dockerfile( + model_uri, + output_directory, + env_manager, + mlflow_home, + install_java, + install_mlflow, + enable_mlserver, +): + """ + Generates a directory with Dockerfile whose default entrypoint serves an MLflow model at port + 8080 using the python_function flavor. The generated Dockerfile is written to the specified + output directory, along with the model (if specified). This Dockerfile defines an image that + is equivalent to the one produced by ``mlflow models build-docker``. + """ + if model_uri: + _logger.info("Generating Dockerfile for model %s", model_uri) + else: + _logger.info("Generating Dockerfile") + backend = get_flavor_backend(model_uri, docker_build=True, env_manager=env_manager) + if backend.can_build_image(): + backend.generate_dockerfile( + model_uri, + output_directory, + mlflow_home=mlflow_home, + install_java=install_java, + install_mlflow=install_mlflow, + enable_mlserver=enable_mlserver, + ) + _logger.info("Generated Dockerfile in directory %s", output_directory) + else: + _logger.error( + "Cannot build docker image for selected backend", + extra={"backend": backend.__class__.__name__}, + ) + raise NotImplementedError("Cannot build docker image for selected backend") + + +@commands.command("build-docker") +@cli_args.MODEL_URI_BUILD_DOCKER +@click.option("--name", "-n", default="mlflow-pyfunc-servable", help="Name to use for built image") +@cli_args.ENV_MANAGER +@cli_args.MLFLOW_HOME +@cli_args.INSTALL_JAVA +@cli_args.INSTALL_MLFLOW +@cli_args.ENABLE_MLSERVER +def build_docker(**kwargs): + """ + Builds a Docker image whose default entrypoint serves an MLflow model at port 8080, using the + python_function flavor. The container serves the model referenced by ``--model-uri``, if + specified when ``build-docker`` is called. If ``--model-uri`` is not specified when build_docker + is called, an MLflow Model directory must be mounted as a volume into the /opt/ml/model + directory in the container. + + Building a Docker image with ``--model-uri``: + + .. code:: bash + + # Build a Docker image named 'my-image-name' that serves the model from run 'some-run-uuid' + # at run-relative artifact path 'my-model' + mlflow models build-docker --model-uri "runs:/some-run-uuid/my-model" --name "my-image-name" + # Serve the model + docker run -p 5001:8080 "my-image-name" + + Building a Docker image without ``--model-uri``: + + .. code:: bash + + # Build a generic Docker image named 'my-image-name' + mlflow models build-docker --name "my-image-name" + # Mount the model stored in '/local/path/to/artifacts/model' and serve it + docker run --rm -p 5001:8080 -v /local/path/to/artifacts/model:/opt/ml/model "my-image-name" + + .. important:: + + Since MLflow 2.10.1, the Docker image built with ``--model-uri`` does **not install Java** + for improved performance, unless the model flavor is one of ``["johnsnowlabs", "h2o", + "spark"]``. If you need to install Java for other flavors, e.g. custom Python model + that uses SparkML, please specify the ``--install-java`` flag to enforce Java installation. + + NB: by default, the container will start nginx and uvicorn processes. If you don't need the + nginx process to be started (for instance if you deploy your container to Google Cloud Run), + you can disable it via the DISABLE_NGINX environment variable: + + .. code:: bash + + docker run -p 5001:8080 -e DISABLE_NGINX=true "my-image-name" + + By default, the number of uvicorn workers is set to CPU count. If you want to set a custom + number of workers, you can set the MLFLOW_MODELS_WORKERS environment variable: + + .. code:: bash + + docker run -p 5001:8080 -e MLFLOW_MODELS_WORKERS=4 "my-image-name" + + See https://www.mlflow.org/docs/latest/python_api/mlflow.pyfunc.html for more information on the + 'python_function' flavor. + """ + python_api.build_docker(**kwargs) + + +@commands.command("update-pip-requirements") +@cli_args.MODEL_URI +@click.argument("operation", type=click.Choice(["add", "remove"])) +@click.argument("requirement_strings", type=str, nargs=-1) +def update_pip_requirements(model_uri, operation, requirement_strings): + """ + Add or remove requirements from a model's conda.yaml and requirements.txt files. + If using a remote tracking server, please make sure to set the MLFLOW_TRACKING_URI + environment variable to the URL of the desired server. + + REQUIREMENT_STRINGS is a list of pip requirements specifiers. + See below for examples. + + Sample usage: + + .. code:: + + # Add requirements using the model's "runs:/" URI + + mlflow models update-pip-requirements -m runs:// \\ + add "pandas==1.0.0" "scikit-learn" "mlflow >= 2.8, != 2.9.0" + + # Remove requirements from a local model + + mlflow models update-pip-requirements -m /path/to/local/model \\ + remove "torchvision" "pydantic" + + Note that model registry URIs (i.e. URIs in the form ``models:/``) are not + supported, as artifacts in the model registry are intended to be read-only. + Editing requirements is read-only artifact repositories is also not supported. + + If adding requirements, the function will overwrite any existing requirements + that overlap, or else append the new requirements to the existing list. + + If removing requirements, the function will ignore any version specifiers, + and remove all the specified package names. Any requirements that are not + found in the existing files will be ignored. + """ + update_model_requirements(model_uri, operation, requirement_strings) + + _logger.info(f"Successfully updated the requirements for the model at {model_uri}!") diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/container/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/container/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..237dfaf4a9435510902c3781722ceca9530347f3 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/container/__init__.py @@ -0,0 +1,294 @@ +""" +Initialize the environment and start model serving in a Docker container. + +To be executed only during the model deployment. + +""" + +import logging +import multiprocessing +import os +import shutil +import signal +import sys +from pathlib import Path +from subprocess import Popen, check_call + +import mlflow +import mlflow.version +from mlflow import pyfunc +from mlflow.environment_variables import MLFLOW_DISABLE_ENV_CREATION +from mlflow.models import Model +from mlflow.models.model import MLMODEL_FILE_NAME +from mlflow.pyfunc import _extract_conda_env, mlserver, scoring_server +from mlflow.store.artifact.models_artifact_repo import REGISTERED_MODEL_META_FILE_NAME +from mlflow.utils import env_manager as em +from mlflow.utils.environment import _PythonEnv +from mlflow.utils.virtualenv import _get_or_create_virtualenv +from mlflow.utils.yaml_utils import read_yaml +from mlflow.version import VERSION as MLFLOW_VERSION + +MODEL_PATH = "/opt/ml/model" + + +DEFAULT_SAGEMAKER_SERVER_PORT = 8080 +DEFAULT_INFERENCE_SERVER_PORT = 8000 +DEFAULT_NGINX_SERVER_PORT = 8080 +DEFAULT_MLSERVER_PORT = 8080 + +SUPPORTED_FLAVORS = [pyfunc.FLAVOR_NAME] + +DISABLE_NGINX = "DISABLE_NGINX" +ENABLE_MLSERVER = "ENABLE_MLSERVER" + +SERVING_ENVIRONMENT = "SERVING_ENVIRONMENT" + + +_logger = logging.getLogger(__name__) + + +def _init(cmd, env_manager): + """ + Initialize the container and execute command. + + Args: + cmd: Command param passed by Sagemaker. Can be "serve" or "train" (unimplemented). + """ + if cmd == "serve": + _serve(env_manager) + elif cmd == "train": + _train() + else: + raise Exception(f"Unrecognized command {cmd}, full args = {sys.argv}") + + +def _serve(env_manager): + """ + Serve the model. + + Read the MLmodel config, initialize the Conda environment if needed and start python server. + """ + model_config_path = os.path.join(MODEL_PATH, MLMODEL_FILE_NAME) + m = Model.load(model_config_path) + + if pyfunc.FLAVOR_NAME in m.flavors: + _serve_pyfunc(m, env_manager) + else: + raise Exception("This container only supports models with the PyFunc flavors.") + + +def _install_pyfunc_deps( + model_path=None, install_mlflow=False, enable_mlserver=False, env_manager=em.VIRTUALENV +): + """ + Creates a conda env for serving the model at the specified path and installs almost all serving + dependencies into the environment - MLflow is not installed as it's not available via conda. + """ + activate_cmd = _install_model_dependencies_to_env(model_path, env_manager) if model_path else [] + + # NB: install gunicorn[gevent] from pip rather than from conda because gunicorn is already + # dependency of mlflow on pip and we expect mlflow to be part of the environment. + server_deps = ["gunicorn[gevent]"] + if enable_mlserver: + server_deps = [ + "'mlserver>=1.2.0,!=1.3.1,<2.0.0'", + "'mlserver-mlflow>=1.2.0,!=1.3.1,<2.0.0'", + ] + + install_server_deps = [f"pip install {' '.join(server_deps)}"] + if Popen(["bash", "-c", " && ".join(activate_cmd + install_server_deps)]).wait() != 0: + raise Exception("Failed to install serving dependencies into the model environment.") + + # NB: If we don't use virtualenv or conda env, we don't need to install mlflow here as + # it's already installed in the container. + if len(activate_cmd): + if _container_includes_mlflow_source(): + # If the MLflow source code is copied to the container, + # we always need to run `pip install /opt/mlflow` otherwise + # the MLflow dependencies are not installed. + install_mlflow_cmd = ["pip install /opt/mlflow/."] + elif install_mlflow: + install_mlflow_cmd = [f"pip install mlflow=={MLFLOW_VERSION}"] + else: + install_mlflow_cmd = [] + + if install_mlflow_cmd: + if Popen(["bash", "-c", " && ".join(activate_cmd + install_mlflow_cmd)]).wait() != 0: + raise Exception("Failed to install mlflow into the model environment.") + return activate_cmd + + +def _install_model_dependencies_to_env(model_path, env_manager) -> list[str]: + """: + Installs model dependencies to the specified environment, which can be either a local + environment, a conda environment, or a virtualenv. + + Returns: + Empty list if local environment, otherwise a list of bash commands to activate the + virtualenv or conda environment. + """ + model_config_path = os.path.join(model_path, MLMODEL_FILE_NAME) + model = Model.load(model_config_path) + + conf = model.flavors.get(pyfunc.FLAVOR_NAME, {}) + if pyfunc.ENV not in conf: + return [] + env_conf = conf[mlflow.pyfunc.ENV] + + if env_manager == em.LOCAL: + # Install pip dependencies directly into the local environment + python_env_config_path = os.path.join(model_path, env_conf[em.VIRTUALENV]) + python_env = _PythonEnv.from_yaml(python_env_config_path) + deps = " ".join(python_env.build_dependencies + python_env.dependencies) + deps = deps.replace("requirements.txt", os.path.join(model_path, "requirements.txt")) + if Popen(["bash", "-c", f"python -m pip install {deps}"]).wait() != 0: + raise Exception("Failed to install model dependencies.") + return [] + + _logger.info("creating and activating custom environment") + + env = _extract_conda_env(env_conf) + env_path_dst = os.path.join("/opt/mlflow/", env) + env_path_dst_dir = os.path.dirname(env_path_dst) + if not os.path.exists(env_path_dst_dir): + os.makedirs(env_path_dst_dir) + shutil.copy2(os.path.join(MODEL_PATH, env), env_path_dst) + + if env_manager == em.CONDA: + conda_create_model_env = f"conda env create -n custom_env -f {env_path_dst}" + if Popen(["bash", "-c", conda_create_model_env]).wait() != 0: + raise Exception("Failed to create model environment.") + activate_cmd = ["source /miniconda/bin/activate custom_env"] + + elif env_manager == em.VIRTUALENV: + env_activate_cmd = _get_or_create_virtualenv(model_path, env_manager=env_manager) + path = env_activate_cmd.split(" ")[-1] + os.symlink(path, "/opt/activate") + activate_cmd = [env_activate_cmd] + + return activate_cmd + + +def _serve_pyfunc(model, env_manager): + # option to disable manually nginx. The default behavior is to enable nginx. + disable_nginx = os.getenv(DISABLE_NGINX, "false").lower() == "true" + enable_mlserver = os.getenv(ENABLE_MLSERVER, "false").lower() == "true" + disable_env_creation = MLFLOW_DISABLE_ENV_CREATION.get() + + conf = model.flavors[pyfunc.FLAVOR_NAME] + bash_cmds = [] + if pyfunc.ENV in conf: + # NB: MLFLOW_DISABLE_ENV_CREATION is False only for SageMaker deployment, where the model + # files are loaded into the container at runtime rather than build time. In this case, + # we need to create a virtual environment and install the model dependencies into it when + # starting the container. + if not disable_env_creation: + _install_pyfunc_deps( + MODEL_PATH, + install_mlflow=True, + enable_mlserver=enable_mlserver, + env_manager=env_manager, + ) + if env_manager == em.CONDA: + bash_cmds.append("source /miniconda/bin/activate custom_env") + elif env_manager == em.VIRTUALENV: + bash_cmds.append("source /opt/activate") + procs = [] + + start_nginx = True + if disable_nginx or enable_mlserver: + start_nginx = False + + if start_nginx: + nginx_conf = Path(mlflow.models.__file__).parent.joinpath( + "container", "scoring_server", "nginx.conf" + ) + + nginx = Popen(["nginx", "-c", nginx_conf]) if start_nginx else None + + # link the log streams to stdout/err so they will be logged to the container logs. + # Default behavior is to do the redirection unless explicitly specified + # by environment variable. + check_call(["ln", "-sf", "/dev/stdout", "/var/log/nginx/access.log"]) + check_call(["ln", "-sf", "/dev/stderr", "/var/log/nginx/error.log"]) + + procs.append(nginx) + + cpu_count = multiprocessing.cpu_count() + inference_server_kwargs = {} + if enable_mlserver: + inference_server = mlserver + # Allows users to choose the number of workers using MLServer var env settings. + # Default to cpu count + nworkers = int(os.getenv("MLSERVER_INFER_WORKERS", cpu_count)) + # Since MLServer will run without NGINX, expose the server in the `8080` + # port, which is the assumed "public" port. + port = DEFAULT_MLSERVER_PORT + + model_meta = _read_registered_model_meta(MODEL_PATH) + model_dict = model.to_dict() + inference_server_kwargs = { + "model_name": model_meta.get("model_name"), + "model_version": model_meta.get( + "model_version", model_dict.get("run_id", model_dict.get("model_uuid")) + ), + } + else: + inference_server = scoring_server + nworkers = int(os.getenv("MLFLOW_MODELS_WORKERS", cpu_count)) + port = DEFAULT_INFERENCE_SERVER_PORT + + cmd, cmd_env = inference_server.get_cmd( + model_uri=MODEL_PATH, nworkers=nworkers, port=port, **inference_server_kwargs + ) + + bash_cmds.append(cmd) + inference_server_process = Popen(["/bin/bash", "-c", " && ".join(bash_cmds)], env=cmd_env) + procs.append(inference_server_process) + + signal.signal(signal.SIGTERM, lambda a, b: _sigterm_handler(pids=[p.pid for p in procs])) + # If either subprocess exits, so do we. + awaited_pids = _await_subprocess_exit_any(procs=procs) + _sigterm_handler(awaited_pids) + + +def _read_registered_model_meta(model_path): + model_meta = {} + if os.path.isfile(os.path.join(model_path, REGISTERED_MODEL_META_FILE_NAME)): + model_meta = read_yaml(model_path, REGISTERED_MODEL_META_FILE_NAME) + + return model_meta + + +def _container_includes_mlflow_source(): + return os.path.exists("/opt/mlflow/pyproject.toml") + + +def _train(): + raise Exception("Train is not implemented.") + + +def _await_subprocess_exit_any(procs): + pids = [proc.pid for proc in procs] + while True: + pid, _ = os.wait() + if pid in pids: + break + return pids + + +def _sigterm_handler(pids): + """ + Cleanup when terminating. + + Attempt to kill all launched processes and exit. + + """ + _logger.info("Got sigterm signal, exiting.") + for pid in pids: + try: + os.kill(pid, signal.SIGTERM) + except OSError: + pass + + sys.exit(0) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/container/__pycache__/__init__.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/container/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..de12019e77d1daa8452f706504b437cf39d1246a Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/container/__pycache__/__init__.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/container/scoring_server/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/container/scoring_server/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/container/scoring_server/__pycache__/__init__.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/container/scoring_server/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..f8c75e9f3795440505be9957a41f818b96475c28 Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/container/scoring_server/__pycache__/__init__.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/container/scoring_server/nginx.conf b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/container/scoring_server/nginx.conf new file mode 100644 index 0000000000000000000000000000000000000000..f27fbc7daf4a85b3a9c64f282893f9569f722061 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/container/scoring_server/nginx.conf @@ -0,0 +1,39 @@ +worker_processes 1; +daemon off; # Prevent forking + + +pid /tmp/nginx.pid; +error_log /var/log/nginx/error.log; + +events { + # defaults +} + +http { + include /etc/nginx/mime.types; + default_type application/octet-stream; + access_log /var/log/nginx/access.log combined; + + upstream uvicorn { + server 127.0.0.1:8000; + } + + server { + listen 8080 deferred; + client_max_body_size 5m; + + keepalive_timeout 75; + + location ~ ^/(ping|invocations) { + proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for; + proxy_set_header Host $http_host; + proxy_redirect off; + proxy_pass http://uvicorn; + client_max_body_size 100m; + } + + location / { + return 404 "{}"; + } + } +} diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/dependencies_schemas.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/dependencies_schemas.py new file mode 100644 index 0000000000000000000000000000000000000000..3e4ab2149ec3efbc8d8c109774467c182355ae2c --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/dependencies_schemas.py @@ -0,0 +1,284 @@ +import json +import logging +from abc import ABC, abstractmethod +from contextlib import contextmanager +from dataclasses import dataclass, field +from enum import Enum +from typing import TYPE_CHECKING, Any + +if TYPE_CHECKING: + from mlflow.models.model import Model + +_logger = logging.getLogger(__name__) + + +class DependenciesSchemasType(Enum): + """ + Enum to define the different types of dependencies schemas for the model. + """ + + RETRIEVERS = "retrievers" + + +def set_retriever_schema( + *, + primary_key: str, + text_column: str, + doc_uri: str | None = None, + other_columns: list[str] | None = None, + name: str | None = "retriever", +): + """ + Specify the return schema of a retriever span within your agent or generative AI app code. + + **Note**: MLflow recommends that your retriever return the default MLflow retriever output + schema described in https://mlflow.org/docs/latest/tracing/tracing-schema#retriever-spans, + in which case you do not need to call `set_retriever_schema`. APIs that read MLflow traces + and look for retriever spans, such as MLflow evaluation, will automatically detect retriever + spans that match MLflow's default retriever schema. + + If your retriever does not return the default MLflow retriever output schema, call this API to + specify which fields in each retrieved document correspond to the page content, document + URI, document ID, etc. This enables downstream features like MLflow evaluation to properly + identify these fields. Note that `set_retriever_schema` assumes that your retriever span + returns a list of objects. + + + Args: + primary_key: The primary key of the retriever or vector index. + text_column: The name of the text column to use for the embeddings. + doc_uri: The name of the column that contains the document URI. + other_columns: A list of other columns that are part of the vector index + that need to be retrieved during trace logging. + name: The name of the retriever tool or vector store index. + + .. code-block:: Python + :caption: Example + + from mlflow.models import set_retriever_schema + + # The following call sets the schema for a custom retriever that retrieves content from + # MLflow documentation, with an output schema like: + # [ + # { + # 'document_id': '9a8292da3a9d4005a988bf0bfdd0024c', + # 'chunk_text': 'MLflow is an open-source platform, purpose-built to assist...', + # 'doc_uri': 'https://mlflow.org/docs/latest/index.html', + # 'title': 'MLflow: A Tool for Managing the Machine Learning Lifecycle' + # }, + # { + # 'document_id': '7537fe93c97f4fdb9867412e9c1f9e5b', + # 'chunk_text': 'A great way to get started with MLflow is...', + # 'doc_uri': 'https://mlflow.org/docs/latest/getting-started/', + # 'title': 'Getting Started with MLflow' + # }, + # ... + # ] + set_retriever_schema( + primary_key="chunk_id", + text_column="chunk_text", + doc_uri="doc_uri", + other_columns=["title"], + name="my_custom_retriever", + ) + """ + retriever_schemas = globals().get(DependenciesSchemasType.RETRIEVERS.value, []) + + # Check if a retriever schema with the same name already exists + existing_schema = next((schema for schema in retriever_schemas if schema["name"] == name), None) + + if existing_schema is not None: + # Compare all relevant fields + if ( + existing_schema["primary_key"] == primary_key + and existing_schema["text_column"] == text_column + and existing_schema["doc_uri"] == doc_uri + and existing_schema["other_columns"] == (other_columns or []) + ): + # No difference, no need to warn or update + return + else: + # Differences found, issue a warning + _logger.warning( + f"A retriever schema with the name '{name}' already exists. " + "Overriding the existing schema." + ) + # Override the fields of the existing schema + existing_schema["primary_key"] = primary_key + existing_schema["text_column"] = text_column + existing_schema["doc_uri"] = doc_uri + existing_schema["other_columns"] = other_columns or [] + else: + retriever_schemas.append( + { + "primary_key": primary_key, + "text_column": text_column, + "doc_uri": doc_uri, + "other_columns": other_columns or [], + "name": name, + } + ) + + globals()[DependenciesSchemasType.RETRIEVERS.value] = retriever_schemas + + +def _get_retriever_schema(): + """ + Get the vector search schema defined by the user. + + Returns: + VectorSearchIndex: The vector search index schema. + """ + retriever_schemas = globals().get(DependenciesSchemasType.RETRIEVERS.value, []) + if not retriever_schemas: + return [] + + return [ + RetrieverSchema( + name=retriever.get("name"), + primary_key=retriever.get("primary_key"), + text_column=retriever.get("text_column"), + doc_uri=retriever.get("doc_uri"), + other_columns=retriever.get("other_columns"), + ) + for retriever in retriever_schemas + ] + + +def _clear_retriever_schema(): + """ + Clear the vector search schema defined by the user. + """ + globals().pop(DependenciesSchemasType.RETRIEVERS.value, None) + + +def _clear_dependencies_schemas(): + """ + Clear all the dependencies schema defined by the user. + """ + # Clear the vector search schema + _clear_retriever_schema() + + +@contextmanager +def _get_dependencies_schemas(): + dependencies_schemas = DependenciesSchemas(retriever_schemas=_get_retriever_schema()) + try: + yield dependencies_schemas + finally: + _clear_dependencies_schemas() + + +def _get_dependencies_schema_from_model(model: "Model") -> dict[str, Any] | None: + """ + Get the dependencies schema from the logged model metadata. + + `dependencies_schemas` is a dictionary that defines the dependencies schemas, such as + the retriever schemas. This code is now only useful for Databricks integration. + """ + if model.metadata and "dependencies_schemas" in model.metadata: + dependencies_schemas = model.metadata["dependencies_schemas"] + return { + "dependencies_schemas": { + dependency: json.dumps(schema) + for dependency, schema in dependencies_schemas.items() + } + } + return None + + +@dataclass +class Schema(ABC): + """ + Base class for defining the resources needed to serve a model. + + Args: + type (ResourceType): The type of the schema. + """ + + type: DependenciesSchemasType + + @abstractmethod + def to_dict(self): + """ + Convert the resource to a dictionary. + Subclasses must implement this method. + """ + + @classmethod + @abstractmethod + def from_dict(cls, data: dict[str, str]): + """ + Convert the dictionary to a Resource. + Subclasses must implement this method. + """ + + +@dataclass +class RetrieverSchema(Schema): + """ + Define vector search index resource to serve a model. + + Args: + name (str): The name of the vector search index schema. + primary_key (str): The primary key for the index. + text_column (str): The main text column for the index. + doc_uri (Optional[str]): The document URI for the index. + other_columns (Optional[List[str]]): Additional columns in the index. + """ + + def __init__( + self, + name: str, + primary_key: str, + text_column: str, + doc_uri: str | None = None, + other_columns: list[str] | None = None, + ): + super().__init__(type=DependenciesSchemasType.RETRIEVERS) + self.name = name + self.primary_key = primary_key + self.text_column = text_column + self.doc_uri = doc_uri + self.other_columns = other_columns or [] + + def to_dict(self): + return { + self.type.value: [ + { + "name": self.name, + "primary_key": self.primary_key, + "text_column": self.text_column, + "doc_uri": self.doc_uri, + "other_columns": self.other_columns, + } + ] + } + + @classmethod + def from_dict(cls, data: dict[str, str]): + return cls( + name=data["name"], + primary_key=data["primary_key"], + text_column=data["text_column"], + doc_uri=data.get("doc_uri"), + other_columns=data.get("other_columns", []), + ) + + +@dataclass +class DependenciesSchemas: + retriever_schemas: list[RetrieverSchema] = field(default_factory=list) + + def to_dict(self) -> dict[str, dict[DependenciesSchemasType, list[dict[str, Any]]]]: + if not self.retriever_schemas: + return None + + return { + "dependencies_schemas": { + DependenciesSchemasType.RETRIEVERS.value: [ + index.to_dict()[DependenciesSchemasType.RETRIEVERS.value][0] + for index in self.retriever_schemas + ], + } + } diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/display_utils.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/display_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..7029dd690564e012f51b48bc521b4f0c9cbe99b1 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/display_utils.py @@ -0,0 +1,158 @@ +import html +from pathlib import Path + +from mlflow.models.model import ModelInfo +from mlflow.models.signature import ModelSignature +from mlflow.types import schema +from mlflow.utils import databricks_utils + + +def _is_input_string(inputs: schema.Schema) -> bool: + return ( + not inputs.has_input_names() + and len(inputs.input_types()) == 1 + and inputs.input_types()[0] == schema.DataType.string + ) + + +def _is_input_agent_compatible(inputs: schema.Schema) -> bool: + if _is_input_string(inputs): + return True + if not inputs.has_input_names(): + return False + messages = inputs.input_dict().get("messages") + if not messages: + return False + if not isinstance(messages.type, schema.Array): + return False + items = messages.type.dtype + if not isinstance(items, schema.Object): + return False + properties = items.properties + content = next(filter(lambda prop: prop.name == "content", properties), None) + role = next(filter(lambda prop: prop.name == "role", properties), None) + return ( + content + and content.dtype == schema.DataType.string + and role + and role.dtype == schema.DataType.string + ) + + +def _is_output_string_response(outputs: schema.Schema) -> bool: + if not outputs.has_input_names(): + return False + content = outputs.input_dict().get("content") + if not content: + return False + return content.type == schema.DataType.string + + +def _is_output_string(outputs: schema.Schema) -> bool: + return ( + not outputs.has_input_names() + and len(outputs.input_types()) == 1 + and outputs.input_types()[0] == schema.DataType.string + ) + + +def _is_output_chat_completion_response(outputs: schema.Schema) -> bool: + if not outputs.has_input_names(): + return False + choices = outputs.input_dict().get("choices") + if not choices: + return False + if not isinstance(choices.type, schema.Array): + return False + items = choices.type.dtype + if not isinstance(items, schema.Object): + return False + properties = items.properties + message = next(filter(lambda prop: prop.name == "message", properties), None) + if not message: + return False + if not isinstance(message.dtype, schema.Object): + return False + message_properties = message.dtype.properties + content = next(filter(lambda prop: prop.name == "content", message_properties), None) + role = next(filter(lambda prop: prop.name == "role", message_properties), None) + return ( + content + and content.dtype == schema.DataType.string + and role + and role.dtype == schema.DataType.string + ) + + +def _is_output_agent_compatible(outputs: schema.Schema) -> bool: + return ( + _is_output_string_response(outputs) + or _is_output_string(outputs) + or _is_output_chat_completion_response(outputs) + ) + + +def _is_signature_agent_compatible(signature: ModelSignature) -> bool: + """Determines whether the given signature is compatible with the agent eval schema. + + See https://docs.databricks.com/en/generative-ai/agent-evaluation/evaluation-schema.html. + The schema accepts the OpenAI spec, as well as simpler formats such as vanilla string response + and `StringResponse`. + """ + return _is_input_agent_compatible(signature.inputs) and _is_output_agent_compatible( + signature.outputs + ) + + +def _should_render_agent_eval_template(signature: ModelSignature) -> bool: + if not databricks_utils.is_in_databricks_runtime(): + return False + from IPython import get_ipython + + if get_ipython() is None: + return False + return _is_signature_agent_compatible(signature) + + +def _generate_agent_eval_recipe(model_uri: str) -> str: + resources_dir = Path(__file__).parent / "notebook_resources" + pip_install_command = """%pip install -U databricks-agents +dbutils.library.restartPython() +## Run the above in a separate cell ##""" + eval_with_synthetic_code = ( + (resources_dir / "eval_with_synthetic_example.py") + .read_text() + .replace("{{pipInstall}}", pip_install_command) + .replace("{{modelUri}}", model_uri) + ) + eval_with_dataset_code = ( + (resources_dir / "eval_with_dataset_example.py") + .read_text() + .replace("{{pipInstall}}", pip_install_command) + .replace("{{modelUri}}", model_uri) + ) + + # Remove the ruff noqa comments. + ruff_line = "# ruff: noqa: F821, I001\n" + eval_with_synthetic_code = eval_with_synthetic_code.replace(ruff_line, "") + eval_with_dataset_code = eval_with_dataset_code.replace(ruff_line, "") + + return ( + (resources_dir / "agent_evaluation_template.html") + .read_text() + .replace("{{eval_with_synthetic_code}}", html.escape(eval_with_synthetic_code)) + .replace("{{eval_with_dataset_code}}", html.escape(eval_with_dataset_code)) + ) + + +def maybe_render_agent_eval_recipe(model_info: ModelInfo) -> None: + # For safety, we wrap in try/catch to make sure we don't break `mlflow.*.log_model`. + try: + if not _should_render_agent_eval_template(model_info.signature): + return + + from IPython.display import HTML, display + + display(HTML(_generate_agent_eval_recipe(model_info.model_uri))) + except Exception: + pass diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/docker_utils.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/docker_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..dc8c8d1a962346720cac4e0d26c6870a336a293e --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/docker_utils.py @@ -0,0 +1,214 @@ +import logging +import os +from subprocess import Popen +from typing import Literal +from urllib.parse import urlparse + +from mlflow.environment_variables import MLFLOW_DOCKER_OPENJDK_VERSION +from mlflow.utils import env_manager as em +from mlflow.utils.file_utils import _copy_project +from mlflow.version import VERSION + +_logger = logging.getLogger(__name__) + +UBUNTU_BASE_IMAGE = "ubuntu:20.04" +PYTHON_SLIM_BASE_IMAGE = "python:{version}-slim" + + +SETUP_PYENV_AND_VIRTUALENV = r"""# Setup pyenv +RUN DEBIAN_FRONTEND=noninteractive TZ=Etc/UTC apt-get -y install tzdata \ + libssl-dev zlib1g-dev libbz2-dev libreadline-dev libsqlite3-dev wget curl llvm \ + libncursesw5-dev xz-utils tk-dev libxml2-dev libxmlsec1-dev libffi-dev liblzma-dev +RUN git clone \ + --depth 1 \ + --branch $(git ls-remote --tags --sort=v:refname https://github.com/pyenv/pyenv.git | grep -o -E 'v[1-9]+(\.[1-9]+)+$' | tail -1) \ + https://github.com/pyenv/pyenv.git /root/.pyenv +ENV PYENV_ROOT="/root/.pyenv" +ENV PATH="$PYENV_ROOT/bin:$PATH" +RUN apt install -y software-properties-common \ + && apt update \ + && add-apt-repository -y ppa:deadsnakes/ppa \ + && apt update \ + && apt install -y python3.10 python3.10-distutils \ + && ln -s -f $(which python3.10) /usr/bin/python \ + && wget https://bootstrap.pypa.io/get-pip.py -O /tmp/get-pip.py \ + && python /tmp/get-pip.py +RUN pip install virtualenv +""" # noqa: E501 + +_DOCKERFILE_TEMPLATE = """# Build an image that can serve mlflow models. +FROM {base_image} + +{setup_python_venv} + +{setup_java} + +WORKDIR /opt/mlflow + +{install_mlflow} + +{install_model_and_deps} + +ENV MLFLOW_DISABLE_ENV_CREATION={disable_env_creation} +ENV ENABLE_MLSERVER={enable_mlserver} + +# granting read/write access and conditional execution authority to all child directories +# and files to allow for deployment to AWS Sagemaker Serverless Endpoints +# (see https://docs.aws.amazon.com/sagemaker/latest/dg/serverless-endpoints.html) +RUN chmod o+rwX /opt/mlflow/ + +# clean up apt cache to reduce image size +RUN rm -rf /var/lib/apt/lists/* + +ENTRYPOINT ["python", "-c", "{entrypoint}"] +""" + + +SETUP_MINICONDA = """# Setup miniconda +RUN curl --fail -L https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh > miniconda.sh +RUN bash ./miniconda.sh -b -p /miniconda && rm ./miniconda.sh +ENV PATH="/miniconda/bin:$PATH" +# Remove default channels to avoid `CondaToSNonInteractiveError`. +# See https://github.com/mlflow/mlflow/pull/16752 for more details. +RUN conda config --system --remove channels defaults && conda config --system --add channels conda-forge +""" # noqa: E501 + + +def generate_dockerfile( + output_dir: str, + base_image: str, + model_install_steps: str | None, + entrypoint: str, + env_manager: Literal["conda", "local", "virtualenv"] = em.CONDA, + mlflow_home: str | None = None, + enable_mlserver: bool = False, + disable_env_creation_at_runtime: bool = True, + install_java: bool | None = None, +): + """ + Generates a Dockerfile that can be used to build a docker image, that serves ML model + stored and tracked in MLflow. + """ + + setup_java_steps = "" + setup_python_venv_steps = "" + install_mlflow_steps = _pip_mlflow_install_step(output_dir, mlflow_home) + + if base_image.startswith("python:"): + if install_java: + _logger.warning( + "`install_java` option is not supported when using python base image, " + "switch to UBUNTU_BASE_IMAGE to enable java installation." + ) + setup_python_venv_steps = ( + "RUN apt-get -y update && apt-get install -y --no-install-recommends nginx" + ) + + elif base_image == UBUNTU_BASE_IMAGE: + setup_python_venv_steps = ( + "RUN apt-get -y update && DEBIAN_FRONTEND=noninteractive TZ=Etc/UTC apt-get install -y " + "--no-install-recommends wget curl nginx ca-certificates bzip2 build-essential cmake " + "git-core\n\n" + ) + setup_python_venv_steps += ( + SETUP_MINICONDA if env_manager == em.CONDA else SETUP_PYENV_AND_VIRTUALENV + ) + if install_java is not False: + jdk_ver = MLFLOW_DOCKER_OPENJDK_VERSION.get() + setup_java_steps = ( + "# Setup Java\n" + f"RUN apt-get install -y --no-install-recommends openjdk-{jdk_ver}-jdk maven\n" + f"ENV JAVA_HOME=/usr/lib/jvm/java-{jdk_ver}-openjdk-amd64" + ) + + with open(os.path.join(output_dir, "Dockerfile"), "w") as f: + f.write( + _DOCKERFILE_TEMPLATE.format( + base_image=base_image, + setup_python_venv=setup_python_venv_steps, + setup_java=setup_java_steps, + install_mlflow=install_mlflow_steps, + install_model_and_deps=model_install_steps, + entrypoint=entrypoint, + enable_mlserver=enable_mlserver, + disable_env_creation=disable_env_creation_at_runtime, + ) + ) + + +def _get_maven_proxy(): + http_proxy = os.getenv("http_proxy") + https_proxy = os.getenv("https_proxy") + if not http_proxy or not https_proxy: + return "" + + # Expects proxies as either PROTOCOL://{USER}:{PASSWORD}@HOSTNAME:PORT + # or PROTOCOL://HOSTNAME:PORT + parsed_http_proxy = urlparse(http_proxy) + assert parsed_http_proxy.hostname is not None, "Invalid `http_proxy` hostname." + assert parsed_http_proxy.port is not None, f"Invalid proxy port: {parsed_http_proxy.port}" + + parsed_https_proxy = urlparse(https_proxy) + assert parsed_https_proxy.hostname is not None, "Invalid `https_proxy` hostname." + assert parsed_https_proxy.port is not None, f"Invalid proxy port: {parsed_https_proxy.port}" + + maven_proxy_options = ( + "-DproxySet=true", + f"-Dhttp.proxyHost={parsed_http_proxy.hostname}", + f"-Dhttp.proxyPort={parsed_http_proxy.port}", + f"-Dhttps.proxyHost={parsed_https_proxy.hostname}", + f"-Dhttps.proxyPort={parsed_https_proxy.port}", + "-Dhttps.nonProxyHosts=repo.maven.apache.org", + ) + + if parsed_http_proxy.username is None or parsed_http_proxy.password is None: + return " ".join(maven_proxy_options) + + return " ".join( + ( + *maven_proxy_options, + f"-Dhttp.proxyUser={parsed_http_proxy.username}", + f"-Dhttp.proxyPassword={parsed_http_proxy.password}", + ) + ) + + +def _pip_mlflow_install_step(dockerfile_context_dir, mlflow_home): + """ + Get docker build commands for installing MLflow given a Docker context dir and optional source + directory + """ + if mlflow_home: + mlflow_dir = _copy_project( + src_path=os.path.abspath(mlflow_home), dst_path=dockerfile_context_dir + ) + return ( + "# Install MLflow from local source\n" + f"COPY {mlflow_dir} /opt/mlflow\n" + "RUN pip install /opt/mlflow" + ) + else: + return f"# Install MLflow\nRUN pip install mlflow=={VERSION}" + + +def build_image_from_context(context_dir: str, image_name: str): + import docker + + client = docker.from_env() + # In Docker < 19, `docker build` doesn't support the `--platform` option + is_platform_supported = int(client.version()["Version"].split(".")[0]) >= 19 + # Enforcing the AMD64 architecture build for Apple M1 users + platform_option = ["--platform", "linux/amd64"] if is_platform_supported else [] + commands = [ + "docker", + "build", + "-t", + image_name, + "-f", + "Dockerfile", + *platform_option, + ".", + ] + proc = Popen(commands, cwd=context_dir) + if proc.wait(): + raise RuntimeError("Docker build failed.") diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/evaluation/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/evaluation/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..c8562823b410ba9e6bb81a4a430a244f8a7d4ead --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/evaluation/__init__.py @@ -0,0 +1,23 @@ +from mlflow.data.evaluation_dataset import EvaluationDataset +from mlflow.models.evaluation.base import ( + EvaluationArtifact, + EvaluationMetric, + EvaluationResult, + ModelEvaluator, + evaluate, + list_evaluators, + make_metric, +) +from mlflow.models.evaluation.validation import MetricThreshold + +__all__ = [ + "ModelEvaluator", + "EvaluationDataset", + "EvaluationResult", + "EvaluationMetric", + "EvaluationArtifact", + "make_metric", + "evaluate", + "list_evaluators", + "MetricThreshold", +] diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/evaluation/__pycache__/__init__.cpython-312.pyc 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+import numpy as np +import shap +from shap._serializable import Deserializer, Serializable, Serializer + + +class _PatchedKernelExplainer(shap.KernelExplainer): + @staticmethod + def not_equal(i, j): + # `shap.KernelExplainer.not_equal` method fails on some special types such as + # timestamp, this breaks the kernel explainer routine. + # `PatchedKernelExplainer` fixes this issue. + # See https://github.com/slundberg/shap/pull/2586 + number_types = (int, float, np.number) + if isinstance(i, number_types) and isinstance(j, number_types): + return 0 if np.isclose(i, j, equal_nan=True) else 1 + else: + return 0 if i == j else 1 + + def save(self, out_file, model_saver=None, masker_saver=None): + """ + This patched `save` method fix `KernelExplainer.save`. + Issues in original `KernelExplainer.save`: + - It saves model by calling model.save, but shap.utils._legacy.Model has no save method + - It tries to save "masker", but there's no "masker" in KernelExplainer + - It does not save "KernelExplainer.data" attribute, the attribute is required when + loading back + Note: `model_saver` and `masker_saver` are meaningless argument for `KernelExplainer.save`, + the model in "KernelExplainer" is an instance of `shap.utils._legacy.Model` + (it wraps the predict function), we can only use pickle to dump it. + and no `masker` for KernelExplainer so `masker_saver` is meaningless. + but I preserve the 2 argument for overridden API compatibility. + """ + pickle.dump(type(self), out_file) + with Serializer(out_file, "shap.Explainer", version=0) as s: + s.save("model", self.model) + s.save("link", self.link) + s.save("data", self.data) + + @classmethod + def load(cls, in_file, model_loader=None, masker_loader=None, instantiate=True): + """ + This patched `load` method fix `KernelExplainer.load`. + Issues in original KernelExplainer.load: + - Use mismatched model loader to load model + - Try to load non-existent "masker" attribute + - Does not load "data" attribute and then cause calling " KernelExplainer" + constructor lack of "data" argument. + Note: `model_loader` and `masker_loader` are meaningless argument for + `KernelExplainer.save`, because the `model` object is saved by pickle dump, + we must use pickle load to load it. + and no `masker` for KernelExplainer so `masker_loader` is meaningless. + but I preserve the 2 argument for overridden API compatibility. + """ + if instantiate: + return cls._instantiated_load(in_file, model_loader=None, masker_loader=None) + + kwargs = Serializable.load(in_file, instantiate=False) + with Deserializer(in_file, "shap.Explainer", min_version=0, max_version=0) as s: + kwargs["model"] = s.load("model") + kwargs["link"] = s.load("link") + kwargs["data"] = s.load("data") + return kwargs diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/evaluation/artifacts.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/evaluation/artifacts.py new file mode 100644 index 0000000000000000000000000000000000000000..dac1d711f0d17ede61f0a06ceb480538eadf7815 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/evaluation/artifacts.py @@ -0,0 +1,196 @@ +import json +import pathlib +import pickle +from json import JSONDecodeError +from typing import NamedTuple + +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd + +from mlflow.exceptions import MlflowException +from mlflow.models.evaluation.base import EvaluationArtifact +from mlflow.utils.annotations import developer_stable +from mlflow.utils.proto_json_utils import NumpyEncoder + + +@developer_stable +class ImageEvaluationArtifact(EvaluationArtifact): + def _save(self, output_artifact_path): + self._content.save(output_artifact_path) + + def _load_content_from_file(self, local_artifact_path): + from PIL.Image import open as open_image + + self._content = open_image(local_artifact_path) + self._content.load() # Load image and close the file descriptor. + return self._content + + +@developer_stable +class CsvEvaluationArtifact(EvaluationArtifact): + def _save(self, output_artifact_path): + self._content.to_csv(output_artifact_path, index=False) + + def _load_content_from_file(self, local_artifact_path): + self._content = pd.read_csv(local_artifact_path) + return self._content + + +@developer_stable +class ParquetEvaluationArtifact(EvaluationArtifact): + def _save(self, output_artifact_path): + self._content.to_parquet(output_artifact_path, compression="brotli") + + def _load_content_from_file(self, local_artifact_path): + self._content = pd.read_parquet(local_artifact_path) + return self._content + + +@developer_stable +class NumpyEvaluationArtifact(EvaluationArtifact): + def _save(self, output_artifact_path): + np.save(output_artifact_path, self._content, allow_pickle=False) + + def _load_content_from_file(self, local_artifact_path): + self._content = np.load(local_artifact_path, allow_pickle=False) + return self._content + + +@developer_stable +class JsonEvaluationArtifact(EvaluationArtifact): + def _save(self, output_artifact_path): + with open(output_artifact_path, "w") as f: + json.dump(self._content, f) + + def _load_content_from_file(self, local_artifact_path): + with open(local_artifact_path) as f: + self._content = json.load(f) + return self._content + + +@developer_stable +class TextEvaluationArtifact(EvaluationArtifact): + def _save(self, output_artifact_path): + with open(output_artifact_path, "w") as f: + f.write(self._content) + + def _load_content_from_file(self, local_artifact_path): + with open(local_artifact_path) as f: + self._content = f.read() + return self._content + + +@developer_stable +class PickleEvaluationArtifact(EvaluationArtifact): + def _save(self, output_artifact_path): + with open(output_artifact_path, "wb") as f: + pickle.dump(self._content, f) + + def _load_content_from_file(self, local_artifact_path): + with open(local_artifact_path, "rb") as f: + self._content = pickle.load(f) + return self._content + + +_EXT_TO_ARTIFACT_MAP = { + ".png": ImageEvaluationArtifact, + ".jpg": ImageEvaluationArtifact, + ".jpeg": ImageEvaluationArtifact, + ".json": JsonEvaluationArtifact, + ".npy": NumpyEvaluationArtifact, + ".csv": CsvEvaluationArtifact, + ".parquet": ParquetEvaluationArtifact, + ".txt": TextEvaluationArtifact, +} + +_TYPE_TO_EXT_MAP = { + pd.DataFrame: ".csv", + np.ndarray: ".npy", + plt.Figure: ".png", +} + +_TYPE_TO_ARTIFACT_MAP = { + pd.DataFrame: CsvEvaluationArtifact, + np.ndarray: NumpyEvaluationArtifact, + plt.Figure: ImageEvaluationArtifact, +} + + +class _InferredArtifactProperties(NamedTuple): + from_path: bool + type: type[EvaluationArtifact] + ext: str + + +def _infer_artifact_type_and_ext(artifact_name, raw_artifact, custom_metric_tuple): + """ + This function performs type and file extension inference on the provided artifact + + Args: + artifact_name: The name of the provided artifact + raw_artifact: The artifact object + custom_metric_tuple: Containing a user provided function and its index in the + ``custom_metrics`` parameter of ``mlflow.evaluate`` + + Returns: + InferredArtifactProperties namedtuple + """ + + exception_header = ( + f"Custom metric function '{custom_metric_tuple.name}' at index " + f"{custom_metric_tuple.index} in the `custom_metrics` parameter produced an " + f"artifact '{artifact_name}'" + ) + + # Given a string, first see if it is a path. Otherwise, check if it is a JsonEvaluationArtifact + if isinstance(raw_artifact, str): + potential_path = pathlib.Path(raw_artifact) + if potential_path.exists(): + raw_artifact = potential_path + else: + try: + json.loads(raw_artifact) + return _InferredArtifactProperties( + from_path=False, type=JsonEvaluationArtifact, ext=".json" + ) + except JSONDecodeError: + raise MlflowException( + f"{exception_header} with string representation '{raw_artifact}' that is " + f"neither a valid path to a file nor a JSON string." + ) + + # Type inference based on the file extension + if isinstance(raw_artifact, pathlib.Path): + if not raw_artifact.exists(): + raise MlflowException(f"{exception_header} with path '{raw_artifact}' does not exist.") + if not raw_artifact.is_file(): + raise MlflowException(f"{exception_header} with path '{raw_artifact}' is not a file.") + if raw_artifact.suffix not in _EXT_TO_ARTIFACT_MAP: + raise MlflowException( + f"{exception_header} with path '{raw_artifact}' does not match any of the supported" + f" file extensions: {', '.join(_EXT_TO_ARTIFACT_MAP.keys())}." + ) + return _InferredArtifactProperties( + from_path=True, type=_EXT_TO_ARTIFACT_MAP[raw_artifact.suffix], ext=raw_artifact.suffix + ) + + # Type inference based on object type + if type(raw_artifact) in _TYPE_TO_ARTIFACT_MAP: + return _InferredArtifactProperties( + from_path=False, + type=_TYPE_TO_ARTIFACT_MAP[type(raw_artifact)], + ext=_TYPE_TO_EXT_MAP[type(raw_artifact)], + ) + + # Given as other python object, we first attempt to infer as JsonEvaluationArtifact. If that + # fails, we store it as PickleEvaluationArtifact + try: + json.dumps(raw_artifact, cls=NumpyEncoder) + return _InferredArtifactProperties( + from_path=False, type=JsonEvaluationArtifact, ext=".json" + ) + except TypeError: + return _InferredArtifactProperties( + from_path=False, type=PickleEvaluationArtifact, ext=".pickle" + ) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/evaluation/base.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/evaluation/base.py new file mode 100644 index 0000000000000000000000000000000000000000..75990089fc12ec8616b8cac61d651e6c499b2f12 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/evaluation/base.py @@ -0,0 +1,1812 @@ +import inspect +import json +import keyword +import logging +import os +import pathlib +import signal +import urllib +import urllib.parse +from abc import ABCMeta, abstractmethod +from contextlib import contextmanager, nullcontext +from dataclasses import dataclass +from inspect import Parameter, Signature +from types import FunctionType +from typing import Any + +import mlflow +from mlflow.data.dataset import Dataset +from mlflow.data.evaluation_dataset import ( + EvaluationDataset, + convert_data_to_mlflow_dataset, +) +from mlflow.entities.dataset_input import DatasetInput +from mlflow.entities.input_tag import InputTag +from mlflow.entities.logged_model_input import LoggedModelInput +from mlflow.exceptions import MlflowException +from mlflow.models.evaluation.utils.trace import configure_autologging_for_evaluation +from mlflow.protos.databricks_pb2 import INVALID_PARAMETER_VALUE +from mlflow.store.artifact.utils.models import _parse_model_id_if_present +from mlflow.telemetry.events import EvaluateEvent +from mlflow.telemetry.track import record_usage_event +from mlflow.tracking.artifact_utils import _download_artifact_from_uri +from mlflow.tracking.client import MlflowClient +from mlflow.tracking.fluent import _set_active_model +from mlflow.utils import _get_fully_qualified_class_name +from mlflow.utils.annotations import developer_stable +from mlflow.utils.class_utils import _get_class_from_string +from mlflow.utils.file_utils import TempDir +from mlflow.utils.mlflow_tags import MLFLOW_DATASET_CONTEXT +from mlflow.utils.proto_json_utils import NumpyEncoder + +try: + # `pandas` is not required for `mlflow-skinny`. + import pandas as pd +except ImportError: + pass + +_logger = logging.getLogger(__name__) + + +class _ModelType: + REGRESSOR = "regressor" + CLASSIFIER = "classifier" + QUESTION_ANSWERING = "question-answering" + TEXT_SUMMARIZATION = "text-summarization" + TEXT = "text" + RETRIEVER = "retriever" + # This model type is used for Mosaic AI Agent evaluation and only available in Databricks + # https://docs.databricks.com/en/generative-ai/agent-evaluation/index.html + DATABRICKS_AGENT = "databricks-agent" + + def __init__(self): + raise NotImplementedError("This class is not meant to be instantiated.") + + @classmethod + def values(cls): + return ( + cls.REGRESSOR, + cls.CLASSIFIER, + cls.QUESTION_ANSWERING, + cls.TEXT_SUMMARIZATION, + cls.TEXT, + cls.RETRIEVER, + ) + + +class EvaluationMetric: + ''' + An evaluation metric. + + Args: + eval_fn: A function that computes the metric with the following signature: + + .. code-block:: python + + def eval_fn( + predictions: pandas.Series, + targets: pandas.Series, + metrics: Dict[str, MetricValue], + **kwargs, + ) -> Union[float, MetricValue]: + """ + Args: + predictions: A pandas Series containing the predictions made by the model. + targets: (Optional) A pandas Series containing the corresponding labels + for the predictions made on that input. + metrics: (Optional) A dictionary containing the metrics calculated by the + default evaluator. The keys are the names of the metrics and the values + are the metric values. To access the MetricValue for the metrics + calculated by the system, make sure to specify the type hint for this + parameter as Dict[str, MetricValue]. Refer to the DefaultEvaluator + behavior section for what metrics will be returned based on the type of + model (i.e. classifier or regressor). + kwargs: Includes a list of args that are used to compute the metric. These + args could be information coming from input data, model outputs, + other metrics, or parameters specified in the `evaluator_config` + argument of the `mlflow.evaluate` API. + + Returns: MetricValue with per-row scores, per-row justifications, and aggregate + results. + """ + ... + + name: The name of the metric. + greater_is_better: Whether a greater value of the metric is better. + long_name: (Optional) The long name of the metric. For example, + ``"root_mean_squared_error"`` for ``"mse"``. + version: (Optional) The metric version. For example ``v1``. + metric_details: (Optional) A description of the metric and how it is calculated. + metric_metadata: (Optional) A dictionary containing metadata for the metric. + genai_metric_args: (Optional) A dictionary containing arguments specified by users + when calling make_genai_metric or make_genai_metric_from_prompt. Those args + are persisted so that we can deserialize the same metric object later. + ''' + + def __init__( + self, + eval_fn, + name, + greater_is_better, + long_name=None, + version=None, + metric_details=None, + metric_metadata=None, + genai_metric_args=None, + ): + self.eval_fn = eval_fn + self.name = name + self.greater_is_better = greater_is_better + self.long_name = long_name or name + self.version = version + self.metric_details = metric_details + self.metric_metadata = metric_metadata + self.genai_metric_args = genai_metric_args + + def __str__(self): + parts = [f"name={self.name}, greater_is_better={self.greater_is_better}"] + + if self.long_name: + parts.append(f"long_name={self.long_name}") + if self.version: + parts.append(f"version={self.version}") + if self.metric_details: + parts.append(f"metric_details={self.metric_details}") + if self.metric_metadata: + parts.append(f"metric_metadata={self.metric_metadata}") + + return "EvaluationMetric(" + ", ".join(parts) + ")" + + +# NB: we need this function because we cannot modify the signature of +# a class's __call__ method after the class has been defined. +# This is also useful to distinguish between the metric signatures with different eval_fn signatures +def _generate_eval_metric_class(eval_fn, require_strict_signature=False): + """ + Dynamically generate a GenAIEvaluationMetric class that can be used to evaluate the metric + on the given input data. The generated class is callable with a __call__ method that + takes the arguments specified in the signature of the eval_fn function. + + Args: + eval_fn: the evaluation function of the EvaluationMetric. + require_strict_signature: (Optional) Whether the eval_fn needs to follow a strict signature. + If True, then the eval_fn must follow below signature: + + .. code-block:: python + + def eval_fn( + predictions: "pd.Series", + metrics: Dict[str, MetricValue], + inputs: "pd.Series", + *args, + ) -> MetricValue: + pass + + When generating a metric from `make_genai_metric`, this should be set to True. + Default to False. + + Returns: + A dynamically generated callable CallableEvaluationMetric class. + """ + from mlflow.metrics.base import MetricValue + + if require_strict_signature: + allowed_kwargs_names = [ + param_name + for param_name in inspect.signature(eval_fn).parameters.keys() + if param_name not in ["predictions", "metrics", "inputs"] + ] + + def genai_call_method( + self, + *, + predictions: pd.Series | str | list[str], + inputs: pd.Series | str | list[str], + metrics: dict[str, MetricValue] | None = None, + **kwargs, + ) -> MetricValue: + if missed_kwargs := set(allowed_kwargs_names) - set(kwargs.keys()): + raise MlflowException.invalid_parameter_value( + f"Missing required arguments: {missed_kwargs}", + ) + if extra_kwargs := set(kwargs.keys()) - set(allowed_kwargs_names): + raise MlflowException.invalid_parameter_value( + f"Unexpected arguments: {extra_kwargs}", + ) + return self.eval_fn( + _convert_val_to_pd_Series(predictions, "predictions"), + metrics or {}, + _convert_val_to_pd_Series(inputs, "inputs"), + # Note: based on https://github.com/mlflow/mlflow/blob/4fef77afdbe4d76302cb0b1aad2bd72b5cde64e9/mlflow/metrics/genai/genai_metric.py#L49-L53 + # the extra params passed https://github.com/mlflow/mlflow/blob/4fef77afdbe4d76302cb0b1aad2bd72b5cde64e9/mlflow/metrics/genai/genai_metric.py#L513 + # should always be pandas Series + *[ + _convert_val_to_pd_Series(kwargs[arg_name], arg_name) + for arg_name in allowed_kwargs_names + ], + ) + + genai_call_method.__signature__ = Signature( + parameters=[ + Parameter("self", Parameter.POSITIONAL_OR_KEYWORD), + Parameter( + "predictions", + Parameter.KEYWORD_ONLY, + annotation=pd.Series | str | list[str], + ), + Parameter( + "inputs", + Parameter.KEYWORD_ONLY, + annotation=pd.Series | str | list[str], + ), + Parameter( + "metrics", + Parameter.KEYWORD_ONLY, + annotation=dict[str, MetricValue] | None, + default=None, + ), + *[ + Parameter(name, Parameter.KEYWORD_ONLY, annotation=pd.Series | str | list[str]) + for name in allowed_kwargs_names + ], + ] + ) + genai_call_method.__doc__ = f""" + Evaluate the metric on the given inputs and predictions. + Note: only keyword arguments are supported. + + Args: + predictions: predictions made by the model. + inputs: inputs used to make the predictions. + metrics: metrics calculated by the default evaluator. + kwargs: additional arguments used to compute the metric. + Required arguments: {allowed_kwargs_names} + + Returns: + evaluation result as MetricValue object. + """ + call_method = genai_call_method + + else: + + def _call_method( + self, + **kwargs, + ) -> MetricValue: + return self.eval_fn(**kwargs) + + allowed_kwargs_params = inspect.signature(eval_fn).parameters + _call_method.__signature__ = Signature( + parameters=[ + Parameter("self", Parameter.POSITIONAL_OR_KEYWORD), + *[ + Parameter( + name, + Parameter.KEYWORD_ONLY, + annotation=allowed_kwargs_params[name].annotation, + ) + for name in allowed_kwargs_params.keys() + ], + ] + ) + _call_method.__doc__ = f""" + Evaluate the metric on the given inputs and predictions. + Note: only keyword arguments are supported. + + Args: + kwargs: additional arguments used to compute the metric. + Required arguments: {list(allowed_kwargs_params.keys())} + + Returns: + evaluation result as MetricValue object. + """ + call_method = _call_method + + return type( + "CallableEvaluationMetric", + (EvaluationMetric,), + {"__call__": call_method}, + ) + + +def _convert_val_to_pd_Series(val, name): + if val is not None and not isinstance(val, pd.Series): + if isinstance(val, str): + return pd.Series([val]) + elif isinstance(val, list): + return pd.Series(val) + else: + raise TypeError( + f"Expected {name} to be a string, list, or Pandas Series, got {type(val)}" + ) + return val + + +def make_metric( + *, + eval_fn, + greater_is_better, + name=None, + long_name=None, + version=None, + metric_details=None, + metric_metadata=None, + genai_metric_args=None, +): + ''' + A factory function to create an :py:class:`EvaluationMetric` object. + + Args: + eval_fn: A function that computes the metric with the following signature: + + .. code-block:: python + + def eval_fn( + predictions: pandas.Series, + targets: pandas.Series, + metrics: Dict[str, MetricValue], + **kwargs, + ) -> Union[float, MetricValue]: + """ + Args: + predictions: A pandas Series containing the predictions made by the model. + targets: (Optional) A pandas Series containing the corresponding labels + for the predictions made on that input. + metrics: (Optional) A dictionary containing the metrics calculated by the + default evaluator. The keys are the names of the metrics and the values + are the metric values. To access the MetricValue for the metrics + calculated by the system, make sure to specify the type hint for this + parameter as Dict[str, MetricValue]. Refer to the DefaultEvaluator + behavior section for what metrics will be returned based on the type of + model (i.e. classifier or regressor). kwargs: Includes a list of args + that are used to compute the metric. These args could information coming + from input data, model outputs or parameters specified in the + `evaluator_config` argument of the `mlflow.evaluate` API. + kwargs: Includes a list of args that are used to compute the metric. These + args could be information coming from input data, model outputs, + other metrics, or parameters specified in the `evaluator_config` + argument of the `mlflow.evaluate` API. + + Returns: MetricValue with per-row scores, per-row justifications, and aggregate + results. + """ + ... + + greater_is_better: Whether a greater value of the metric is better. + name: The name of the metric. This argument must be specified if ``eval_fn`` is a lambda + function or the ``eval_fn.__name__`` attribute is not available. + long_name: (Optional) The long name of the metric. For example, ``"mean_squared_error"`` + for ``"mse"``. + version: (Optional) The metric version. For example ``v1``. + metric_details: (Optional) A description of the metric and how it is calculated. + metric_metadata: (Optional) A dictionary containing metadata for the metric. + genai_metric_args: (Optional) A dictionary containing arguments specified by users + when calling make_genai_metric or make_genai_metric_from_prompt. Those args + are persisted so that we can deserialize the same metric object later. + + .. seealso:: + + - :py:class:`mlflow.models.EvaluationMetric` + - :py:func:`mlflow.evaluate` + ''' + return _make_metric( + eval_fn=eval_fn, + greater_is_better=greater_is_better, + name=name, + long_name=long_name, + version=version, + metric_details=metric_details, + metric_metadata=metric_metadata, + genai_metric_args=genai_metric_args, + require_strict_signature=False, + ) + + +def _make_metric( + *, + eval_fn, + greater_is_better, + name=None, + long_name=None, + version=None, + metric_details=None, + metric_metadata=None, + genai_metric_args=None, + require_strict_signature=False, +): + ''' + A factory function to create an :py:class:`EvaluationMetric` object. + + Args: + eval_fn: A function that computes the metric with the following signature: + + .. code-block:: python + + def eval_fn( + predictions: pandas.Series, + targets: pandas.Series, + metrics: Dict[str, MetricValue], + **kwargs, + ) -> Union[float, MetricValue]: + """ + Args: + predictions: A pandas Series containing the predictions made by the model. + targets: (Optional) A pandas Series containing the corresponding labels + for the predictions made on that input. + metrics: (Optional) A dictionary containing the metrics calculated by the + default evaluator. The keys are the names of the metrics and the values + are the metric values. To access the MetricValue for the metrics + calculated by the system, make sure to specify the type hint for this + parameter as Dict[str, MetricValue]. Refer to the DefaultEvaluator + behavior section for what metrics will be returned based on the type of + model (i.e. classifier or regressor). kwargs: Includes a list of args + that are used to compute the metric. These args could information coming + from input data, model outputs or parameters specified in the + `evaluator_config` argument of the `mlflow.evaluate` API. + kwargs: Includes a list of args that are used to compute the metric. These + args could be information coming from input data, model outputs, + other metrics, or parameters specified in the `evaluator_config` + argument of the `mlflow.evaluate` API. + + Returns: MetricValue with per-row scores, per-row justifications, and aggregate + results. + """ + ... + + greater_is_better: Whether a greater value of the metric is better. + name: The name of the metric. This argument must be specified if ``eval_fn`` is a lambda + function or the ``eval_fn.__name__`` attribute is not available. + long_name: (Optional) The long name of the metric. For example, ``"mean_squared_error"`` + for ``"mse"``. + version: (Optional) The metric version. For example ``v1``. + metric_details: (Optional) A description of the metric and how it is calculated. + metric_metadata: (Optional) A dictionary containing metadata for the metric. + genai_metric_args: (Optional) A dictionary containing arguments specified by users + when calling make_genai_metric or make_genai_metric_from_prompt. Those args + are persisted so that we can deserialize the same metric object later. + require_strict_signature: (Optional) Whether the eval_fn needs to follow a strict signature. + If True, then the eval_fn must follow below signature: + + .. code-block:: python + + def eval_fn( + predictions: "pd.Series", + metrics: Dict[str, MetricValue], + inputs: "pd.Series", + *args, + ) -> MetricValue: + pass + + When generating a metric from `make_genai_metric`, this should be set to True. + Default to False. + + .. seealso:: + + - :py:class:`mlflow.models.EvaluationMetric` + - :py:func:`mlflow.evaluate` + ''' + if name is None: + if isinstance(eval_fn, FunctionType) and eval_fn.__name__ == "": + raise MlflowException( + "`name` must be specified if `eval_fn` is a lambda function.", + INVALID_PARAMETER_VALUE, + ) + if not hasattr(eval_fn, "__name__"): + raise MlflowException( + "`name` must be specified if `eval_fn` does not have a `__name__` attribute.", + INVALID_PARAMETER_VALUE, + ) + name = eval_fn.__name__ + + if "/" in name: + raise MlflowException( + f"Invalid metric name '{name}'. Metric names cannot include forward slashes ('/').", + INVALID_PARAMETER_VALUE, + ) + + if not name.isidentifier(): + _logger.warning( + f"The metric name '{name}' provided is not a valid Python identifier, which will " + "prevent its use as a base metric for derived metrics. Please use a valid identifier " + "to enable creation of derived metrics that use the given metric." + ) + + if keyword.iskeyword(name): + _logger.warning( + f"The metric name '{name}' is a reserved Python keyword, which will " + "prevent its use as a base metric for derived metrics. Please use a valid identifier " + "to enable creation of derived metrics that use the given metric." + ) + + if name in ["predictions", "targets", "metrics"]: + _logger.warning( + f"The metric name '{name}' is used as a special parameter in MLflow metrics, which " + "will prevent its use as a base metric for derived metrics. Please use a different " + "name to enable creation of derived metrics that use the given metric." + ) + + return _generate_eval_metric_class(eval_fn, require_strict_signature=require_strict_signature)( + eval_fn=eval_fn, + name=name, + greater_is_better=greater_is_better, + long_name=long_name, + version=version, + metric_details=metric_details, + metric_metadata=metric_metadata, + genai_metric_args=genai_metric_args, + ) + + +@developer_stable +class EvaluationArtifact(metaclass=ABCMeta): + """ + A model evaluation artifact containing an artifact uri and content. + """ + + def __init__(self, uri, content=None): + self._uri = uri + self._content = content + + @abstractmethod + def _load_content_from_file(self, local_artifact_path): + """ + Abstract interface to load the content from local artifact file path, + and return the loaded content. + """ + + def _load(self, local_artifact_path=None): + """ + If ``local_artifact_path`` is ``None``, download artifact from the artifact uri. + Otherwise, load artifact content from the specified path. Assign the loaded content to + ``self._content``, and return the loaded content. + """ + if local_artifact_path is not None: + self._content = self._load_content_from_file(local_artifact_path) + else: + with TempDir() as temp_dir: + temp_dir_path = temp_dir.path() + _download_artifact_from_uri(self._uri, temp_dir_path) + local_artifact_file = temp_dir.path(os.listdir(temp_dir_path)[0]) + self._content = self._load_content_from_file(local_artifact_file) + return self._content + + @abstractmethod + def _save(self, output_artifact_path): + """Save artifact content into specified path.""" + + @property + def content(self): + """ + The content of the artifact (representation varies) + """ + if self._content is None: + self._load() + return self._content + + @property + def uri(self) -> str: + """ + The URI of the artifact + """ + return self._uri + + def __repr__(self): + return f"{self.__class__.__name__}(uri='{self.uri}')" + + +class EvaluationResult: + """ + Represents the model evaluation outputs of a `mlflow.evaluate()` API call, containing + both scalar metrics and output artifacts such as performance plots. + """ + + def __init__(self, metrics, artifacts, run_id=None): + self._metrics = metrics + self._artifacts = artifacts + self._run_id = ( + run_id + if run_id is not None + else (mlflow.active_run().info.run_id if mlflow.active_run() is not None else None) + ) + + @classmethod + def load(cls, path): + """Load the evaluation results from the specified local filesystem path""" + with open(os.path.join(path, "metrics.json")) as fp: + metrics = json.load(fp) + + with open(os.path.join(path, "artifacts_metadata.json")) as fp: + artifacts_metadata = json.load(fp) + + artifacts = {} + + artifacts_dir = os.path.join(path, "artifacts") + + for artifact_name, meta in artifacts_metadata.items(): + uri = meta["uri"] + ArtifactCls = _get_class_from_string(meta["class_name"]) + artifact = ArtifactCls(uri=uri) + filename = pathlib.Path(urllib.parse.urlparse(uri).path).name + artifact._load(os.path.join(artifacts_dir, filename)) + artifacts[artifact_name] = artifact + + return EvaluationResult(metrics=metrics, artifacts=artifacts) + + def save(self, path): + """Write the evaluation results to the specified local filesystem path""" + os.makedirs(path, exist_ok=True) + with open(os.path.join(path, "metrics.json"), "w") as fp: + json.dump(self.metrics, fp, cls=NumpyEncoder) + + artifacts_metadata = { + artifact_name: { + "uri": artifact.uri, + "class_name": _get_fully_qualified_class_name(artifact), + } + for artifact_name, artifact in self.artifacts.items() + } + with open(os.path.join(path, "artifacts_metadata.json"), "w") as fp: + json.dump(artifacts_metadata, fp) + + artifacts_dir = os.path.join(path, "artifacts") + os.makedirs(artifacts_dir, exist_ok=True) + + for artifact in self.artifacts.values(): + filename = pathlib.Path(urllib.parse.urlparse(artifact.uri).path).name + artifact._save(os.path.join(artifacts_dir, filename)) + + @property + def metrics(self) -> dict[str, Any]: + """ + A dictionary mapping scalar metric names to scalar metric values + """ + return self._metrics + + @property + def artifacts(self) -> dict[str, "mlflow.models.EvaluationArtifact"]: + """ + A dictionary mapping standardized artifact names (e.g. "roc_data") to + artifact content and location information + """ + return self._artifacts + + @property + def run_id(self) -> str: + """ + The ID of the MLflow Run to which the evaluation results were logged. + """ + return self._run_id + + @property + def tables(self) -> dict[str, "pd.DataFrame"]: + """ + A dictionary mapping standardized artifact names (e.g. "eval_results_table") to + corresponding table content as pandas DataFrame. + """ + eval_tables = {} + if self._run_id is None: + _logger.warning("Cannot load eval_results_table because run_id is not specified.") + return eval_tables + + for table_name, table_path in self._artifacts.items(): + path = urllib.parse.urlparse(table_path.uri).path + table_fileName = os.path.basename(path) + try: + eval_tables[table_name] = mlflow.load_table(table_fileName, run_ids=[self._run_id]) + except Exception: + pass # Swallow the exception since we assume its not a table. + + return eval_tables + + +@developer_stable +class ModelEvaluator(metaclass=ABCMeta): + @classmethod + @abstractmethod + def can_evaluate(cls, *, model_type, evaluator_config, **kwargs) -> bool: + """ + Args: + model_type: A string describing the model type (e.g., "regressor", "classifier", …). + evaluator_config: A dictionary of additional configurations for + the evaluator. + kwargs: For forwards compatibility, a placeholder for additional arguments + that may be added to the evaluation interface in the future. + + Returns: + True if the evaluator can evaluate the specified model on the + specified dataset. False otherwise. + """ + + @abstractmethod + def evaluate( + self, + *, + model_type, + dataset, + run_id, + evaluator_config, + model=None, + extra_metrics=None, + custom_artifacts=None, + predictions=None, + **kwargs, + ): + """ + The abstract API to log metrics and artifacts, and return evaluation results. + + Args: + model_type: A string describing the model type + (e.g., ``"regressor"``, ``"classifier"``, …). + dataset: An instance of `mlflow.models.evaluation.base._EvaluationDataset` + containing features and labels (optional) for model evaluation. + run_id: The ID of the MLflow Run to which to log results. + evaluator_config: A dictionary of additional configurations for + the evaluator. + model: A pyfunc model instance. If None, the model output is supposed to be found in + ``dataset.predictions_data``. + extra_metrics: A list of :py:class:`EvaluationMetric` objects. + custom_artifacts: A list of callable custom artifact functions. + predictions: The column name of the model output column that is used for evaluation. + This is only used when a model returns a pandas dataframe that contains + multiple columns. + kwargs: For forwards compatibility, a placeholder for additional arguments that + may be added to the evaluation interface in the future. + + Returns: + A :py:class:`mlflow.models.EvaluationResult` instance containing + evaluation metrics and artifacts for the model. + """ + + +def list_evaluators(): + """ + Return a name list for all available Evaluators. + """ + # import _model_evaluation_registry inside function to avoid circuit importing + from mlflow.models.evaluation.evaluator_registry import _model_evaluation_registry + + return list(_model_evaluation_registry._registry.keys()) + + +@contextmanager +def _start_run_or_reuse_active_run(): + """ + A manager context return: + - If there's an active run, return the active run id. + - otherwise start a mflow run with the specified run_id, + if specified run_id is None, start a new run. + """ + active_run = mlflow.active_run() + if not active_run: + # Note `mlflow.start_run` throws if `run_id` is not found. + with mlflow.start_run() as run: + yield run.info.run_id + else: + yield active_run.info.run_id + + +# NB: We often pass around evaluator name, config, and its instance together. Ideally, the +# evaluator class should have name and config as class attributes, however, it was not +# designed that way. Adding them while keeping backward compatibility is not trivial. +# So, we use a dataclass to bundle them together. +@dataclass +class EvaluatorBundle: + name: str + evaluator: ModelEvaluator + config: dict[str, Any] + + +def _resolve_default_evaluator(model_type, default_config) -> list[EvaluatorBundle]: + """ + Determine which built-in evaluators should be used for the given model type by default. + + Previously, MLflow evaluate API only had a single "default" evaluator used for all models like + classifier, regressor, etc. We split it into multiple built-in evaluators for different model + types for maintainability, but in order to maintain backward compatibility, we need to map + the "default" provided by users to the correct built-in evaluators. + + Args: + model_type: A string describing the model type (e.g., "regressor", "classifier", …). + default_config: A dictionary of configurations for the "default" evaluator. If any + non-default built-in evaluator is applicable, this config will be applied to them. + """ + from mlflow.models.evaluation.evaluator_registry import _model_evaluation_registry + + builtin_evaluators = [] + for name in _model_evaluation_registry._registry: + evaluator = _model_evaluation_registry.get_evaluator(name) + if ( + name != "default" + and _model_evaluation_registry.is_builtin(name) + and evaluator.can_evaluate(model_type=model_type, evaluator_config=default_config) + ): + builtin_evaluators.append(EvaluatorBundle(name, evaluator, default_config)) + + # We should use DefaultEvaluator only if there is no other built-in evaluator applicable. + if not builtin_evaluators: + default_evaluator = _model_evaluation_registry.get_evaluator("default") + builtin_evaluators = [EvaluatorBundle("default", default_evaluator, default_config)] + + return builtin_evaluators + + +def resolve_evaluators_and_configs( + evaluators: str | list[str] | None, + evaluator_config: dict[str, Any] | None, + model_type: str | None = None, +) -> list[EvaluatorBundle]: + """ + The `evaluators` and `evaluator_config` arguments of the `evaluate` API can be specified + in multiple ways. This function normalizes the arguments into a single format for easier + downstream processing. + + Args: + evaluators: A string or a list of strings specifying the evaluators to use for model + evaluation. If None, all available evaluators will be used. + evaluator_config: A dictionary containing configuration items for the evaluators. + model_type: A string describing the model type (e.g., "regressor", "classifier", …). + + Returns: + A list of EvaluatorBundle that contains name, evaluator, config for each evaluator. + """ + from mlflow.models.evaluation.evaluator_registry import _model_evaluation_registry as rg + + # NB: The `databricks-agents` package must be installed to use the 'databricks-agent' model + # type. Ideally this check should be done in the 'databricks-agent' evaluator implementation, + # but we need to do it here because the code won't reach the evaluator implementation if the + # package is not installed. + if model_type == _ModelType.DATABRICKS_AGENT: + try: + import databricks.agents # noqa: F401 + except ImportError as e: + raise MlflowException( + message="Databricks Agents SDK must be installed to use the " + f"`{_ModelType.DATABRICKS_AGENT}` model type. Run `pip install databricks-agents` " + "to install the package and try again.", + error_code=INVALID_PARAMETER_VALUE, + ) from e + + def check_nesting_config_dict(_evaluator_name_list, _evaluator_name_to_conf_map): + return isinstance(_evaluator_name_to_conf_map, dict) and all( + k in _evaluator_name_list and isinstance(v, dict) + for k, v in _evaluator_name_to_conf_map.items() + ) + + if evaluators is None: + # If no evaluators are specified, use all available evaluators. + evaluators = list(rg._registry.keys()) + + evaluator_config = evaluator_config or {} + if evaluator_config is not None and not any( + name in evaluator_config for name in evaluators + ): + # If evaluator config is passed but any of available evaluator key is not + # in the evaluator config, we assume the evaluator config to be a flat dict, + # which is globally applied to all evaluators. + evaluator_config = dict.fromkeys(evaluators, evaluator_config) + + # Filter out evaluators that cannot evaluate the model type. + resolved = [] + for name in evaluators: + evaluator = rg.get_evaluator(name) + config = evaluator_config.get(name, {}) + if evaluator.can_evaluate(model_type=model_type, evaluator_config=config): + resolved.append(EvaluatorBundle(name=name, evaluator=evaluator, config=config)) + + # If any of built-in evaluator can apply, skip "default" evaluator. + default = next((ev for ev in resolved if ev.name == "default"), None) + non_default_builtins = [ + ev for ev in resolved if ev.name != "default" and rg.is_builtin(ev.name) + ] + if default and non_default_builtins: + resolved.remove(default) + # Apply default config (passed like `evaluator_config={"default": config}`) to + # non-default built-in evaluators (e.g., ClassifierEvaluator) if they don't have + # explicitly specified configs. This is for backward compatibility where we only + # had a single "default" evaluator used for all models. + # For example, if the user passes this for a classifier model: + # evaluator_config = {"default": my_config} + # it should be equivalent to + # evaluator_config = {"classifier": my_config, "shap": my_config} + for ev in non_default_builtins: + ev.config = ev.config or default.config + + return resolved + + elif isinstance(evaluators, str): + # Single evaluator name specified + if not (evaluator_config is None or isinstance(evaluator_config, dict)): + raise MlflowException( + message="If `evaluators` argument is the name of an evaluator, evaluator_config" + " must be None or a dict containing config items for the evaluator.", + error_code=INVALID_PARAMETER_VALUE, + ) + + evaluator_config = evaluator_config or {} + if evaluators == "default": + # Previously we only had a single "default" evaluator used for all models. + # We need to map "default" to the new dedicated builtin evaluators. + return _resolve_default_evaluator(model_type, evaluator_config) + elif rg.is_registered(evaluators): + return [EvaluatorBundle(evaluators, rg.get_evaluator(evaluators), evaluator_config)] + else: + return [] + + elif isinstance(evaluators, list): + if evaluator_config is not None and not check_nesting_config_dict( + evaluators, evaluator_config + ): + raise MlflowException( + message="If `evaluators` argument is an evaluator name list, evaluator_config " + "must be a dict containing mapping from evaluator name to individual " + "evaluator config dict.", + error_code=INVALID_PARAMETER_VALUE, + ) + evaluator_config = evaluator_config or {} + + # Previously we only had a single "default" evaluator used for all models. + # We need to map "default" to the new dedicated builtin evaluators. + resolved = [] + for name in evaluators: + config = evaluator_config.get(name, {}) + if name == "default": + builtin_evaluators = _resolve_default_evaluator(model_type, config) + resolved.extend(builtin_evaluators) + else: + resolved.append(EvaluatorBundle(name, rg.get_evaluator(name), config)) + return resolved + else: + raise MlflowException( + message="Invalid `evaluators` and `evaluator_config` arguments. " + "Please refer to the documentation for correct usage.", + error_code=INVALID_PARAMETER_VALUE, + ) + + +def _model_validation_contains_model_comparison(validation_thresholds): + """ + Helper function for determining if validation_thresholds contains + thresholds for model comparison: either min_relative_change or min_absolute_change + """ + if not validation_thresholds: + return False + thresholds = validation_thresholds.values() + return any( + threshold.min_relative_change or threshold.min_absolute_change for threshold in thresholds + ) + + +_last_failed_evaluator = None + + +def _get_last_failed_evaluator(): + """ + Return the evaluator name of the last failed evaluator when calling `evaluate`. + This can be used to check which evaluator fail when `evaluate` API fail. + """ + return _last_failed_evaluator + + +# DO NOT CHANGE THE ORDER OF THE ARGUMENTS +# The order of the arguments need to be preserved. You can add new arguments at the end +# of the argument list, but do not change the order of the existing arguments. +@record_usage_event(EvaluateEvent) +def _evaluate( + *, + model, + model_type, + model_id, + dataset, + run_id, + # The `evaluator_name_list` and `evaluator_name_to_conf_map` are not used by MLflow at all, + # but we need to keep these for backward compatibility. + evaluator_name_list, + evaluator_name_to_conf_map, + extra_metrics, + custom_artifacts, + predictions, + evaluators, +): + """ + The public API "evaluate" will verify argument first, and then pass normalized arguments + to the _evaluate method. + """ + global _last_failed_evaluator + _last_failed_evaluator = None + + client = MlflowClient() + + model_uuid = getattr(model, "metadata", None) + + if model_uuid is not None: + model_uuid = model_uuid.model_uuid + dataset._log_dataset_tag(client, run_id, model_uuid) + + eval_results = [] + should_enable_tracing = model is not None # Do not enable tracing if static dataset is provided + for eval_ in evaluators: + _logger.debug(f"Evaluating the model with the {eval_.name} evaluator.") + _last_failed_evaluator = eval_.name + if eval_.evaluator.can_evaluate(model_type=model_type, evaluator_config=eval_.config): + with configure_autologging_for_evaluation(enable_tracing=should_enable_tracing): + eval_result = eval_.evaluator.evaluate( + model=model, + model_type=model_type, + model_id=model_id, + dataset=dataset, + run_id=run_id, + evaluator_config=eval_.config, + extra_metrics=extra_metrics, + custom_artifacts=custom_artifacts, + predictions=predictions, + ) + + if eval_result is not None: + eval_results.append(eval_result) + + _last_failed_evaluator = None + + if len(eval_results) == 0: + raise MlflowException( + message="The model could not be evaluated by any of the registered evaluators, please " + "verify that the model type and other configs are set correctly.", + error_code=INVALID_PARAMETER_VALUE, + ) + + merged_eval_result = EvaluationResult({}, {}, None) + + for eval_result in eval_results: + merged_eval_result.metrics.update(eval_result.metrics) + merged_eval_result.artifacts.update(eval_result.artifacts) + + return merged_eval_result + + +def _get_model_from_function(fn): + from mlflow.pyfunc.model import _PythonModelPyfuncWrapper + + class ModelFromFunction(mlflow.pyfunc.PythonModel): + def predict(self, context, model_input: pd.DataFrame): + return fn(model_input) + + python_model = ModelFromFunction() + return _PythonModelPyfuncWrapper(python_model, None, None) + + +def _is_model_deployment_endpoint_uri(model: Any) -> bool: + if not isinstance(model, str): + return False + + from mlflow.metrics.genai.model_utils import _parse_model_uri + + try: + schema, path = _parse_model_uri(model) + return schema == "endpoints" + except MlflowException: + return False + + +def _get_model_from_deployment_endpoint_uri( + endpoint_uri: str, params: dict[str, Any] | None = None +): + from mlflow.metrics.genai.model_utils import _parse_model_uri + from mlflow.pyfunc.model import ModelFromDeploymentEndpoint, _PythonModelPyfuncWrapper + + _, endpoint = _parse_model_uri(endpoint_uri) + params = params or {} + + python_model = ModelFromDeploymentEndpoint(endpoint, params) + return _PythonModelPyfuncWrapper(python_model, None, None) + + +def evaluate( + model=None, + data=None, + *, + model_type=None, + targets=None, + predictions=None, + dataset_path=None, + feature_names=None, + evaluators=None, + evaluator_config=None, + extra_metrics=None, + custom_artifacts=None, + env_manager="local", + model_config=None, + inference_params=None, + model_id=None, + _called_from_genai_evaluate=False, +): + ''' + Evaluate the model performance on given data and selected metrics. + + This function evaluates a PyFunc model or custom callable on the specified dataset using + specified ``evaluators``, and logs resulting metrics & artifacts to MLflow tracking server. + Users can also skip setting ``model`` and put the model outputs in ``data`` directly for + evaluation. For detailed information, please read + `the Model Evaluation documentation <../../model-evaluation/index.html>`_. + + Default Evaluator behavior: + - The default evaluator, which can be invoked with ``evaluators="default"`` or + ``evaluators=None``, supports model types listed below. For each pre-defined model type, the + default evaluator evaluates your model on a selected set of metrics and generate artifacts + like plots. Please find more details below. + + - For both the ``"regressor"`` and ``"classifier"`` model types, the default evaluator + generates model summary plots and feature importance plots using + `SHAP `_. + + - For regressor models, the default evaluator additionally logs: + - **metrics**: example_count, mean_absolute_error, mean_squared_error, + root_mean_squared_error, sum_on_target, mean_on_target, r2_score, max_error, + mean_absolute_percentage_error. + + - For binary classifiers, the default evaluator additionally logs: + - **metrics**: true_negatives, false_positives, false_negatives, true_positives, recall, + precision, f1_score, accuracy_score, example_count, log_loss, roc_auc, + precision_recall_auc. + - **artifacts**: lift curve plot, precision-recall plot, ROC plot. + + - For multiclass classifiers, the default evaluator additionally logs: + - **metrics**: accuracy_score, example_count, f1_score_micro, f1_score_macro, log_loss + - **artifacts**: A CSV file for "per_class_metrics" (per-class metrics includes + true_negatives/false_positives/false_negatives/true_positives/recall/precision/roc_auc, + precision_recall_auc), precision-recall merged curves plot, ROC merged curves plot. + + - For question-answering models, the default evaluator logs: + - **metrics**: ``exact_match``, ``token_count``, `toxicity`_ (requires `evaluate`_, + `torch`_, `flesch_kincaid_grade_level`_ (requires `textstat`_) and `ari_grade_level`_. + - **artifacts**: A JSON file containing the inputs, outputs, targets (if the ``targets`` + argument is supplied), and per-row metrics of the model in tabular format. + + .. _toxicity: + https://huggingface.co/spaces/evaluate-measurement/toxicity + + .. _torch: + https://pytorch.org/get-started/locally/ + + .. _transformers: + https://huggingface.co/docs/transformers/installation + + .. _ari_grade_level: + https://en.wikipedia.org/wiki/Automated_readability_index + + .. _flesch_kincaid_grade_level: + https://en.wikipedia.org/wiki/Flesch%E2%80%93Kincaid_readability_tests#Flesch%E2%80%93Kincaid_grade_level + + .. _evaluate: + https://pypi.org/project/evaluate + + .. _textstat: + https://pypi.org/project/textstat + + - For text-summarization models, the default evaluator logs: + - **metrics**: ``token_count``, `ROUGE`_ (requires `evaluate`_, `nltk`_, and + `rouge_score`_ to be installed), `toxicity`_ (requires `evaluate`_, `torch`_, + `transformers`_), `ari_grade_level`_ (requires `textstat`_), + `flesch_kincaid_grade_level`_ (requires `textstat`_). + - **artifacts**: A JSON file containing the inputs, outputs, targets (if the ``targets`` + argument is supplied), and per-row metrics of the model in the tabular format. + + .. _ROUGE: + https://huggingface.co/spaces/evaluate-metric/rouge + + .. _toxicity: + https://huggingface.co/spaces/evaluate-measurement/toxicity + + .. _torch: + https://pytorch.org/get-started/locally/ + + .. _transformers: + https://huggingface.co/docs/transformers/installation + + .. _ari_grade_level: + https://en.wikipedia.org/wiki/Automated_readability_index + + .. _flesch_kincaid_grade_level: + https://en.wikipedia.org/wiki/Flesch%E2%80%93Kincaid_readability_tests#Flesch%E2%80%93Kincaid_grade_level + + .. _evaluate: + https://pypi.org/project/evaluate + + .. _nltk: + https://pypi.org/project/nltk + + .. _rouge_score: + https://pypi.org/project/rouge-score + + .. _textstat: + https://pypi.org/project/textstat + + - For text models, the default evaluator logs: + - **metrics**: ``token_count``, `toxicity`_ (requires `evaluate`_, `torch`_, + `transformers`_), `ari_grade_level`_ (requires `textstat`_), + `flesch_kincaid_grade_level`_ (requires `textstat`_). + - **artifacts**: A JSON file containing the inputs, outputs, targets (if the ``targets`` + argument is supplied), and per-row metrics of the model in tabular format. + + .. _evaluate: + https://pypi.org/project/evaluate + + .. _toxicity: + https://huggingface.co/spaces/evaluate-measurement/toxicity + + .. _torch: + https://pytorch.org/get-started/locally/ + + .. _transformers: + https://huggingface.co/docs/transformers/installation + + .. _ari_grade_level: + https://en.wikipedia.org/wiki/Automated_readability_index + + .. _flesch_kincaid_grade_level: + https://en.wikipedia.org/wiki/Flesch%E2%80%93Kincaid_readability_tests#Flesch%E2%80%93Kincaid_grade_level + + .. _textstat: + https://pypi.org/project/textstat + + - For retriever models, the default evaluator logs: + - **metrics**: :mod:`precision_at_k(k) `, + :mod:`recall_at_k(k) ` and + :mod:`ndcg_at_k(k) ` - all have a default value of + ``retriever_k`` = 3. + - **artifacts**: A JSON file containing the inputs, outputs, targets, and per-row metrics + of the model in tabular format. + + - For sklearn models, the default evaluator additionally logs the model's evaluation criterion + (e.g. mean accuracy for a classifier) computed by `model.score` method. + + - The metrics/artifacts listed above are logged to the active MLflow run. + If no active run exists, a new MLflow run is created for logging these metrics and + artifacts. + + - Additionally, information about the specified dataset - hash, name (if specified), path + (if specified), and the UUID of the model that evaluated it - is logged to the + ``mlflow.datasets`` tag. + + - The available ``evaluator_config`` options for the default evaluator include: + - **log_model_explainability**: A boolean value specifying whether or not to log model + explainability insights, default value is True. + - **log_explainer**: If True, log the explainer used to compute model explainability + insights as a model. Default value is False. + - **explainability_algorithm**: A string to specify the SHAP Explainer algorithm for model + explainability. Supported algorithm includes: 'exact', 'permutation', 'partition', + 'kernel'. + If not set, ``shap.Explainer`` is used with the "auto" algorithm, which chooses the best + Explainer based on the model. + - **explainability_nsamples**: The number of sample rows to use for computing model + explainability insights. Default value is 2000. + - **explainability_kernel_link**: The kernel link function used by shap kernel explainer. + Available values are "identity" and "logit". Default value is "identity". + - **max_classes_for_multiclass_roc_pr**: + For multiclass classification tasks, the maximum number of classes for which to log + the per-class ROC curve and Precision-Recall curve. If the number of classes is + larger than the configured maximum, these curves are not logged. + - **metric_prefix**: An optional prefix to prepend to the name of each metric and artifact + produced during evaluation. + - **log_metrics_with_dataset_info**: A boolean value specifying whether or not to include + information about the evaluation dataset in the name of each metric logged to MLflow + Tracking during evaluation, default value is True. + - **pos_label**: If specified, the positive label to use when computing classification + metrics such as precision, recall, f1, etc. for binary classification models. For + multiclass classification and regression models, this parameter will be ignored. + - **average**: The averaging method to use when computing classification metrics such as + precision, recall, f1, etc. for multiclass classification models + (default: ``'weighted'``). For binary classification and regression models, this + parameter will be ignored. + - **sample_weights**: Weights for each sample to apply when computing model performance + metrics. + - **col_mapping**: A dictionary mapping column names in the input dataset or output + predictions to column names used when invoking the evaluation functions. + - **retriever_k**: A parameter used when ``model_type="retriever"`` as the number of + top-ranked retrieved documents to use when computing the built-in metric + :mod:`precision_at_k(k) `, + :mod:`recall_at_k(k) ` and + :mod:`ndcg_at_k(k) `. Default value is 3. For all other + model types, this parameter will be ignored. + + - Limitations of evaluation dataset: + - For classification tasks, dataset labels are used to infer the total number of classes. + - For binary classification tasks, the negative label value must be 0 or -1 or False, and + the positive label value must be 1 or True. + + - Limitations of metrics/artifacts computation: + - For classification tasks, some metric and artifact computations require the model to + output class probabilities. Currently, for scikit-learn models, the default evaluator + calls the ``predict_proba`` method on the underlying model to obtain probabilities. For + other model types, the default evaluator does not compute metrics/artifacts that require + probability outputs. + + - Limitations of default evaluator logging model explainability insights: + - The ``shap.Explainer`` ``auto`` algorithm uses the ``Linear`` explainer for linear models + and the ``Tree`` explainer for tree models. Because SHAP's ``Linear`` and ``Tree`` + explainers do not support multi-class classification, the default evaluator falls back to + using the ``Exact`` or ``Permutation`` explainers for multi-class classification tasks. + - Logging model explainability insights is not currently supported for PySpark models. + - The evaluation dataset label values must be numeric or boolean, all feature values + must be numeric, and each feature column must only contain scalar values. + + - Limitations when environment restoration is enabled: + - When environment restoration is enabled for the evaluated model (i.e. a non-local + ``env_manager`` is specified), the model is loaded as a client that invokes a MLflow + Model Scoring Server process in an independent Python environment with the model's + training time dependencies installed. As such, methods like ``predict_proba`` (for + probability outputs) or ``score`` (computes the evaluation criterian for sklearn models) + of the model become inaccessible and the default evaluator does not compute metrics or + artifacts that require those methods. + - Because the model is an MLflow Model Server process, SHAP explanations are slower to + compute. As such, model explainaibility is disabled when a non-local ``env_manager`` + specified, unless the ``evaluator_config`` option **log_model_explainability** is + explicitly set to ``True``. + + Args: + model: Optional. If specified, it should be one of the following: + + - A pyfunc model instance + - A URI referring to a pyfunc model + - A URI referring to an MLflow Deployments endpoint e.g. ``"endpoints:/my-chat"`` + - A callable function: This function should be able to take in model input and + return predictions. It should follow the signature of the + :py:func:`predict ` method. Here's an example + of a valid function: + + .. code-block:: python + + model = mlflow.pyfunc.load_model(model_uri) + + + def fn(model_input): + return model.predict(model_input) + + If omitted, it indicates a static dataset will be used for evaluation instead of a + model. In this case, the ``data`` argument must be a Pandas DataFrame or an mlflow + PandasDataset that contains model outputs, and the ``predictions`` argument must be the + name of the column in ``data`` that contains model outputs. + + data: One of the + following: + + - A numpy array or list of evaluation features, excluding labels. + - A Pandas DataFrame containing evaluation features, labels, and optionally model + outputs. Model outputs are required to be provided when model is unspecified. + If ``feature_names`` argument not specified, all columns except for the label + column and predictions column are regarded as feature columns. Otherwise, + only column names present in ``feature_names`` are regarded as feature columns. + - A Spark DataFrame containing evaluation features and labels. If + ``feature_names`` argument not specified, all columns except for the label + column are regarded as feature columns. Otherwise, only column names present in + ``feature_names`` are regarded as feature columns. Only the first 10000 rows in + the Spark DataFrame will be used as evaluation data. + - A :py:class:`mlflow.data.dataset.Dataset` instance containing evaluation + features, labels, and optionally model outputs. Model outputs are only supported + with a PandasDataset. Model outputs are required when model is unspecified, and + should be specified via the ``predictions`` property of the PandasDataset. + + model_type: (Optional) A string describing the model type. The default evaluator + supports the following model types: + + - ``'classifier'`` + - ``'regressor'`` + - ``'question-answering'`` + - ``'text-summarization'`` + - ``'text'`` + - ``'retriever'`` + + If no ``model_type`` is specified, then you must provide a a list of + metrics to compute via the ``extra_metrics`` param. + + .. note:: + ``'question-answering'``, ``'text-summarization'``, ``'text'``, and + ``'retriever'`` are experimental and may be changed or removed in a + future release. + + targets: If ``data`` is a numpy array or list, a numpy array or list of evaluation + labels. If ``data`` is a DataFrame, the string name of a column from ``data`` + that contains evaluation labels. Required for classifier and regressor models, + but optional for question-answering, text-summarization, and text models. If + ``data`` is a :py:class:`mlflow.data.dataset.Dataset` that defines targets, + then ``targets`` is optional. + + predictions: Optional. The name of the column that contains model outputs. + + - When ``model`` is specified and outputs multiple columns, ``predictions`` can be used + to specify the name of the column that will be used to store model outputs for + evaluation. + - When ``model`` is not specified and ``data`` is a pandas dataframe, + ``predictions`` can be used to specify the name of the column in ``data`` that + contains model outputs. + + .. code-block:: python + :caption: Example usage of predictions + + # Evaluate a model that outputs multiple columns + data = pd.DataFrame({"question": ["foo"]}) + + + def model(inputs): + return pd.DataFrame({"answer": ["bar"], "source": ["baz"]}) + + + results = evaluate( + model=model, + data=data, + predictions="answer", + # other arguments if needed + ) + + # Evaluate a static dataset + data = pd.DataFrame({"question": ["foo"], "answer": ["bar"], "source": ["baz"]}) + results = evaluate( + data=data, + predictions="answer", + # other arguments if needed + ) + dataset_path: (Optional) The path where the data is stored. Must not contain double + quotes (``"``). If specified, the path is logged to the ``mlflow.datasets`` + tag for lineage tracking purposes. + + feature_names: (Optional) A list. If the ``data`` argument is a numpy array or list, + ``feature_names`` is a list of the feature names for each feature. If + ``feature_names=None``, then the ``feature_names`` are generated using the + format ``feature_{feature_index}``. If the ``data`` argument is a Pandas + DataFrame or a Spark DataFrame, ``feature_names`` is a list of the names + of the feature columns in the DataFrame. If ``feature_names=None``, then + all columns except the label column and the predictions column are + regarded as feature columns. + + evaluators: The name of the evaluator to use for model evaluation, or a list of + evaluator names. If unspecified, all evaluators capable of evaluating the + specified model on the specified dataset are used. The default evaluator + can be referred to by the name ``"default"``. To see all available + evaluators, call :py:func:`mlflow.models.list_evaluators`. + + evaluator_config: A dictionary of additional configurations to supply to the evaluator. + If multiple evaluators are specified, each configuration should be + supplied as a nested dictionary whose key is the evaluator name. + + extra_metrics: + (Optional) A list of :py:class:`EvaluationMetric ` + objects. These metrics are computed in addition to the default metrics associated with + pre-defined `model_type`, and setting `model_type=None` will only compute the metrics + specified in `extra_metrics`. See the `mlflow.metrics` module for more information about + the builtin metrics and how to define extra metrics. + + .. code-block:: python + :caption: Example usage of extra metrics + + import mlflow + import numpy as np + + + def root_mean_squared_error(eval_df, _builtin_metrics): + return np.sqrt((np.abs(eval_df["prediction"] - eval_df["target"]) ** 2).mean()) + + + rmse_metric = mlflow.models.make_metric( + eval_fn=root_mean_squared_error, + greater_is_better=False, + ) + mlflow.evaluate(..., extra_metrics=[rmse_metric]) + + custom_artifacts: + (Optional) A list of custom artifact functions with the following signature: + + .. code-block:: python + + def custom_artifact( + eval_df: Union[pandas.Dataframe, pyspark.sql.DataFrame], + builtin_metrics: Dict[str, float], + artifacts_dir: str, + ) -> Dict[str, Any]: + """ + Args: + eval_df: + A Pandas or Spark DataFrame containing ``prediction`` and ``target`` + column. The ``prediction`` column contains the predictions made by the + model. The ``target`` column contains the corresponding labels to the + predictions made on that row. + builtin_metrics: + A dictionary containing the metrics calculated by the default evaluator. + The keys are the names of the metrics and the values are the scalar + values of the metrics. Refer to the DefaultEvaluator behavior section + for what metrics will be returned based on the type of model (i.e. + classifier or regressor). + artifacts_dir: + A temporary directory path that can be used by the custom artifacts + function to temporarily store produced artifacts. The directory will be + deleted after the artifacts are logged. + + Returns: + A dictionary that maps artifact names to artifact objects + (e.g. a Matplotlib Figure) or to artifact paths within ``artifacts_dir``. + """ + ... + + Object types that artifacts can be represented as: + + - A string uri representing the file path to the artifact. MLflow will infer the + type of the artifact based on the file extension. + - A string representation of a JSON object. This will be saved as a .json artifact. + - Pandas DataFrame. This will be resolved as a CSV artifact. + - Numpy array. This will be saved as a .npy artifact. + - Matplotlib Figure. This will be saved as an image artifact. Note that + ``matplotlib.pyplot.savefig`` is called behind the scene with default + configurations. + To customize, either save the figure with the desired configurations and return + its file path or define customizations through environment variables in + ``matplotlib.rcParams``. + - Other objects will be attempted to be pickled with the default protocol. + + .. code-block:: python + :caption: Example usage of custom artifacts + + import mlflow + import matplotlib.pyplot as plt + + + def scatter_plot(eval_df, builtin_metrics, artifacts_dir): + plt.scatter(eval_df["prediction"], eval_df["target"]) + plt.xlabel("Targets") + plt.ylabel("Predictions") + plt.title("Targets vs. Predictions") + plt.savefig(os.path.join(artifacts_dir, "example.png")) + plt.close() + return {"pred_target_scatter": os.path.join(artifacts_dir, "example.png")} + + + def pred_sample(eval_df, _builtin_metrics, _artifacts_dir): + return {"pred_sample": pred_sample.head(10)} + + + mlflow.evaluate(..., custom_artifacts=[scatter_plot, pred_sample]) + + env_manager: Specify an environment manager to load the candidate ``model`` in + isolated Python environments and restore their + dependencies. Default value is ``local``, and the following values are + supported: + + - ``virtualenv``: (Recommended) Use virtualenv to restore the python + environment that was used to train the model. + - ``conda``: Use Conda to restore the software environment that was used + to train the model. + - ``local``: Use the current Python environment for model inference, which + may differ from the environment used to train the model and may lead to + errors or invalid predictions. + + model_config: the model configuration to use for loading the model with pyfunc. Inspect + the model's pyfunc flavor to know which keys are supported for your + specific model. If not indicated, the default model configuration + from the model is used (if any). + + inference_params: (Optional) A dictionary of inference parameters to be passed to the model + when making predictions, such as ``{"max_tokens": 100}``. This is only used when + the ``model`` is an MLflow Deployments endpoint URI e.g. ``"endpoints:/my-chat"`` + + model_id: (Optional) The ID of the MLflow LoggedModel or Model Version to which the + evaluation results (e.g. metrics and traces) will be linked. If `model_id` is not + specified but `model` is specified, the ID from `model` will be used. + + _called_from_genai_evaluate: (Optional) Only used internally. + + Returns: + An :py:class:`mlflow.models.EvaluationResult` instance containing + metrics of evaluating the model with the given dataset. + ''' + from mlflow.models.evaluation.evaluator_registry import _model_evaluation_registry + from mlflow.pyfunc import PyFuncModel, _load_model_or_server, _ServedPyFuncModel + from mlflow.utils import env_manager as _EnvManager + + # Inference params are currently only supported for passing a deployment endpoint as the model. + # TODO: We should support inference_params for other model types + + if inference_params is not None and not _is_model_deployment_endpoint_uri(model): + raise MlflowException( + message="The inference_params argument can only be specified when the model " + "is an MLflow Deployments endpoint URI like `endpoints:/my-chat`", + error_code=INVALID_PARAMETER_VALUE, + ) + + if evaluator_config is not None: + col_mapping = evaluator_config.get("col_mapping", {}) + + if isinstance(targets, str): + targets = col_mapping.get(targets, targets) + + if isinstance(predictions, str): + predictions = col_mapping.get(predictions, predictions) + + if data is None: + raise MlflowException( + message="The data argument cannot be None.", + error_code=INVALID_PARAMETER_VALUE, + ) + + _EnvManager.validate(env_manager) + + # If Dataset is provided, the targets can only be specified by the Dataset, + # not the targets parameters of the mlflow.evaluate() API. + if isinstance(data, Dataset) and targets is not None: + raise MlflowException( + message="The top-level targets parameter should not be specified since a Dataset " + "is used. Please only specify the targets column name in the Dataset. For example: " + "`data = mlflow.data.from_pandas(df=X.assign(y=y), targets='y')`. " + "Meanwhile, please specify `mlflow.evaluate(..., targets=None, ...)`.", + error_code=INVALID_PARAMETER_VALUE, + ) + # If Dataset is provided and model is None, then the predictions can only be specified by the + # Dataset, not the predictions parameters of the mlflow.evaluate() API. + if isinstance(data, Dataset) and model is None and predictions is not None: + raise MlflowException( + message="The top-level predictions parameter should not be specified since a Dataset " + "is used. Please only specify the predictions column name in the Dataset. For example:" + " `data = mlflow.data.from_pandas(df=X.assign(y=y), predictions='y')`" + "Meanwhile, please specify `mlflow.evaluate(..., predictions=None, ...)`.", + error_code=INVALID_PARAMETER_VALUE, + ) + # If Dataset is provided and model is specified, then the data.predictions cannot be specified. + if ( + isinstance(data, Dataset) + and model is not None + and getattr(data, "predictions", None) is not None + ): + raise MlflowException( + message="The predictions parameter should not be specified in the Dataset since a " + "model is specified. Please remove the predictions column from the Dataset.", + error_code=INVALID_PARAMETER_VALUE, + ) + + if model_type in [_ModelType.REGRESSOR, _ModelType.CLASSIFIER]: + if isinstance(data, Dataset): + if getattr(data, "targets", None) is not None: + targets = data.targets + else: + raise MlflowException( + message="The targets column name must be specified in the provided Dataset " + f"for {model_type} models. For example: " + "`data = mlflow.data.from_pandas(df=X.assign(y=y), targets='y')`", + error_code=INVALID_PARAMETER_VALUE, + ) + else: + if targets is None: + raise MlflowException( + f"The targets argument must be specified for {model_type} models.", + error_code=INVALID_PARAMETER_VALUE, + ) + elif model_type is None: + if not extra_metrics: + raise MlflowException( + message="The extra_metrics argument must be specified model_type is None.", + error_code=INVALID_PARAMETER_VALUE, + ) + + specified_model_id = model_id + model_id = None + if isinstance(model, str): + model_id = _parse_model_id_if_present(model) + if _is_model_deployment_endpoint_uri(model): + model = _get_model_from_deployment_endpoint_uri(model, inference_params) + else: + model = _load_model_or_server(model, env_manager, model_config) + elif env_manager != _EnvManager.LOCAL: + raise MlflowException( + message="The model argument must be a string URI referring to an MLflow model when a " + "non-local env_manager is specified.", + error_code=INVALID_PARAMETER_VALUE, + ) + elif isinstance(model, PyFuncModel): + model_id = model.model_id + if model_config: + raise MlflowException( + message="Indicating ``model_config`` when passing a `PyFuncModel`` object as " + "model argument is not allowed. If you need to change the model configuration " + "for the evaluation model, use " + "``mlflow.pyfunc.load_model(model_uri, model_config=)`` and indicate " + "the desired configuration there.", + error_code=INVALID_PARAMETER_VALUE, + ) + elif callable(model): + model = _get_model_from_function(model) + elif model is not None: + raise MlflowException( + message="The model argument must be a string URI referring to an MLflow model, " + "an MLflow Deployments endpoint URI, an instance of `mlflow.pyfunc.PyFuncModel`, " + "a function, or None.", + error_code=INVALID_PARAMETER_VALUE, + ) + + # If model_id is specified, verify it matches the derived model_id + if specified_model_id is not None and model_id is not None and specified_model_id != model_id: + raise MlflowException( + message=( + f"The specified value of the 'model_id' parameter '{specified_model_id}' " + f"contradicts the model_id '{model_id}' associated with the model. Please ensure " + f"they match or omit the 'model_id' parameter." + ), + error_code=INVALID_PARAMETER_VALUE, + ) + + # Use specified model_id if provided, otherwise use derived model_id + model_id = specified_model_id if specified_model_id is not None else model_id + # If none of the model_id and model is specified, use the active model_id + model_id = model_id or mlflow.get_active_model_id() + + evaluators: list[EvaluatorBundle] = resolve_evaluators_and_configs( + evaluators, evaluator_config, model_type + ) + + # NB: MLflow do not use either of these two variables. However, we need to pass these to + # _evaluate() function for backward compatibility. + evaluator_name_list = [evaluator.name for evaluator in evaluators] + evaluator_name_to_conf_map = {evaluator.name: evaluator.config for evaluator in evaluators} + + with _start_run_or_reuse_active_run() as run_id: + if not isinstance(data, Dataset): + # Convert data to `mlflow.data.dataset.Dataset`. + if model is None: + data = convert_data_to_mlflow_dataset( + data=data, targets=targets, predictions=predictions + ) + else: + data = convert_data_to_mlflow_dataset(data=data, targets=targets) + + from mlflow.data.pyfunc_dataset_mixin import PyFuncConvertibleDatasetMixin + + # model_id could be None + with _set_active_model(model_id=model_id) if model_id else nullcontext(): + if isinstance(data, Dataset) and issubclass( + data.__class__, PyFuncConvertibleDatasetMixin + ): + dataset = data.to_evaluation_dataset(dataset_path, feature_names) + + # Use metrix_prefix configured for builtin evaluators as a dataset tag + context = None + for e in evaluators: + if _model_evaluation_registry.is_builtin(e.name) and e.config.get( + "metric_prefix" + ): + context = e.config.get("metric_prefix") + break + + client = MlflowClient() + tags = [InputTag(key=MLFLOW_DATASET_CONTEXT, value=context)] if context else [] + dataset_input = DatasetInput(dataset=data._to_mlflow_entity(), tags=tags) + client.log_inputs( + run_id, + [dataset_input], + models=[LoggedModelInput(model_id)] if model_id else None, + ) + else: + dataset = EvaluationDataset( + data, + targets=targets, + path=dataset_path, + feature_names=feature_names, + predictions=predictions, + ) + predictions_expected_in_model_output = predictions if model is not None else None + + try: + evaluate_result = _evaluate( + model=model, + model_type=model_type, + model_id=model_id, + dataset=dataset, + run_id=run_id, + evaluator_name_list=evaluator_name_list, + evaluator_name_to_conf_map=evaluator_name_to_conf_map, + extra_metrics=extra_metrics, + custom_artifacts=custom_artifacts, + predictions=predictions_expected_in_model_output, + evaluators=evaluators, + ) + finally: + if isinstance(model, _ServedPyFuncModel): + os.kill(model.pid, signal.SIGTERM) + + # if model_id is specified log metrics to the eval run and logged model + if model_id is not None: + mlflow.log_metrics(metrics=evaluate_result.metrics, dataset=data, model_id=model_id) + + return evaluate_result diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/evaluation/calibration_curve.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/evaluation/calibration_curve.py new file mode 100644 index 0000000000000000000000000000000000000000..9981766b74ce3a9442251c55e312bdbe5c29f6db --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/evaluation/calibration_curve.py @@ -0,0 +1,109 @@ +import matplotlib.pyplot as plt +from matplotlib.figure import Figure +from sklearn.calibration import CalibrationDisplay, calibration_curve + + +def make_multi_class_calibration_plot( + n_classes, y_true, y_probs, calibration_config, label_list +) -> Figure: + """Generate one calibration plot for all classes of a multi-class classifier + + Args: + n_classes (int): number of classes in multi-class prediction + y_true (array-like, shape (n_samples)): + Ground truth (correct) target values. + y_probs (array-like, shape (n_samples, n_classes)): + Prediction probabilities for each class returned by a classifier. + calibration_config (dict[str, Union[str, int]]): + Additional parameters for sklearn.CalibrationDisplay + label_list (array-like): list of label names for each class + + Returns: + Figure: Multi-Class Calibration Plot + """ + fig, ax = plt.subplots() + # add calibration line for each class + for _class in range(n_classes): + curves = calibration_curve( + y_true=[v == _class for v in y_true], + y_prob=y_probs[:, _class], + n_bins=calibration_config.get("calibration_n_bins", 10), + ) + plt.plot( + curves[0], + curves[1], + marker="o", + markersize=3, + label=f"Class {label_list[_class]}", + ) + # plot perfect calibration line + plt.plot([0, 1], [0, 1], linestyle="dotted", label="Perfect Calibration", color="black") + # add legend to plot + plt.legend( + loc="upper center", + bbox_to_anchor=(0.5, -0.25), + ncol=n_classes // 3, + ) + # set figure title + ax.set_title( + f"{calibration_config.get('calibration_classifier_name', 'Classifier')} Calibration", + fontsize=12, + ) + # set figure axis labels + ax.set_xlabel("Mean Predicted Probability", fontsize=10) + ax.set_ylabel("Fraction of True Positives", fontsize=10) + return fig + + +def plot_calibration_curve(y_true, y_probs, pos_label, calibration_config, label_list) -> Figure: + """Generate a calibration curve for a trained classifier + + Args: + y_true (array-like, shape (n_samples)): + Ground truth (correct) target values. + + y_probs (array-like, shape (n_samples, n_classes)): + Prediction probabilities for each class returned by a classifier. + + pos_label (str): Label for the positive class. + + calibration_config (dict[str, Union[str, int]]): + Additional parameters for sklearn.CalibrationDisplay + + label_list: List of class names + + Raises: + TypeError: if calibration_config["calibration_classifier_name"] is not str + TypeError: if calibration_config["calibration_n_bins"] is not int + + Returns: + Figure: Calibration Curve for Evaluated Classifier + """ + # get number of classes + n_classes = y_probs.shape[-1] + + # check that types are appropriate + if calibration_config: + # check that name provided for classifier is of proper type + if not isinstance(calibration_config.get("calibration_classifier_name", ""), str): + raise TypeError("calibration_classifier_name should be of type string") + + # check that name provided for calibration n_bins is of proper type + if not isinstance(calibration_config.get("calibration_n_bins", 10), int): + raise TypeError("calibration_n_bins should be of type int") + + # if we are evaluating a binary classifier assume positive class is at column index 1 + if n_classes == 2: + return CalibrationDisplay.from_predictions( + y_true, + y_prob=y_probs[:, 1], + pos_label=pos_label, + name=calibration_config.get("calibration_classifier_name", None), # type: ignore + n_bins=calibration_config.get("calibration_n_bins", 10), # type: ignore + ).figure_ + + # evaluating a multi-class classifier, create a calibration curve for each class + + return make_multi_class_calibration_plot( + n_classes, y_true, y_probs, calibration_config, label_list + ) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/evaluation/default_evaluator.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/evaluation/default_evaluator.py new file mode 100644 index 0000000000000000000000000000000000000000..f3914517898f76e93274f9fe5232e53e6ba322ca --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/evaluation/default_evaluator.py @@ -0,0 +1,996 @@ +import copy +import inspect +import json +import logging +import pathlib +import pickle +import shutil +import tempfile +import traceback +from abc import abstractmethod +from typing import Any, Callable, NamedTuple, Optional + +import numpy as np +import pandas as pd + +import mlflow +from mlflow import MlflowClient, MlflowException +from mlflow.data.evaluation_dataset import EvaluationDataset +from mlflow.entities.metric import Metric +from mlflow.metrics.base import MetricValue +from mlflow.models.evaluation.artifacts import ( + CsvEvaluationArtifact, + ImageEvaluationArtifact, + JsonEvaluationArtifact, + NumpyEvaluationArtifact, + _infer_artifact_type_and_ext, +) +from mlflow.models.evaluation.base import EvaluationMetric, EvaluationResult, ModelEvaluator +from mlflow.models.evaluation.utils.metric import MetricDefinition +from mlflow.protos.databricks_pb2 import INVALID_PARAMETER_VALUE +from mlflow.pyfunc import _ServedPyFuncModel +from mlflow.utils.file_utils import TempDir +from mlflow.utils.proto_json_utils import NumpyEncoder +from mlflow.utils.time import get_current_time_millis + +_logger = logging.getLogger(__name__) + +_EVAL_TABLE_FILE_NAME = "eval_results_table.json" +_TOKEN_COUNT_METRIC_NAME = "token_count" +_LATENCY_METRIC_NAME = "latency" + + +def _extract_raw_model(model): + if not getattr(model, "metadata", None): + return None, None + + model_loader_module = model.metadata.flavors["python_function"]["loader_module"] + # If we load a model with mlflow.pyfunc.load_model, the model will be wrapped + # with a pyfunc wrapper. We need to extract the raw model so that shap + # explainer uses the raw model instead of the wrapper and skips data schema validation. + if model_loader_module in [ + "mlflow.catboost", + "mlflow.sklearn", + "mlflow.xgboost", + ] and not isinstance(model, _ServedPyFuncModel): + if hasattr(model._model_impl, "get_raw_model"): + return model_loader_module, model._model_impl.get_raw_model() + return model_loader_module, model._model_impl + else: + return model_loader_module, None + + +def _extract_output_and_other_columns( + model_predictions: list[Any] | dict[str, Any] | pd.DataFrame | pd.Series, + output_column_name: str | None, +) -> tuple[pd.Series, pd.DataFrame | None, str]: + y_pred = None + other_output_columns = None + ERROR_MISSING_OUTPUT_COLUMN_NAME = ( + "Output column name is not specified for the multi-output model. " + "Please set the correct output column name using the `predictions` parameter." + ) + + if isinstance(model_predictions, list) and all(isinstance(p, dict) for p in model_predictions): + # Extract 'y_pred' and 'other_output_columns' from list of dictionaries + if output_column_name in model_predictions[0]: + y_pred = pd.Series( + [p.get(output_column_name) for p in model_predictions], name=output_column_name + ) + other_output_columns = pd.DataFrame( + [{k: v for k, v in p.items() if k != output_column_name} for p in model_predictions] + ) + elif len(model_predictions[0]) == 1: + # Set the only key as self.predictions and its value as self.y_pred + key, value = list(model_predictions[0].items())[0] + y_pred = pd.Series(value, name=key) + output_column_name = key + elif output_column_name is None: + raise MlflowException( + ERROR_MISSING_OUTPUT_COLUMN_NAME, + error_code=INVALID_PARAMETER_VALUE, + ) + else: + raise MlflowException( + f"Output column name '{output_column_name}' is not found in the model " + f"predictions list: {model_predictions}. Please set the correct output column " + "name using the `predictions` parameter.", + error_code=INVALID_PARAMETER_VALUE, + ) + elif isinstance(model_predictions, pd.DataFrame): + if output_column_name in model_predictions.columns: + y_pred = model_predictions[output_column_name] + other_output_columns = model_predictions.drop(columns=output_column_name) + elif len(model_predictions.columns) == 1: + output_column_name = model_predictions.columns[0] + y_pred = model_predictions[output_column_name] + elif output_column_name is None: + raise MlflowException( + ERROR_MISSING_OUTPUT_COLUMN_NAME, + error_code=INVALID_PARAMETER_VALUE, + ) + else: + raise MlflowException( + f"Output column name '{output_column_name}' is not found in the model " + f"predictions dataframe {model_predictions.columns}. Please set the correct " + "output column name using the `predictions` parameter.", + error_code=INVALID_PARAMETER_VALUE, + ) + elif isinstance(model_predictions, dict): + if output_column_name in model_predictions: + y_pred = pd.Series(model_predictions[output_column_name], name=output_column_name) + other_output_columns = pd.DataFrame( + {k: v for k, v in model_predictions.items() if k != output_column_name} + ) + elif len(model_predictions) == 1: + key, value = list(model_predictions.items())[0] + y_pred = pd.Series(value, name=key) + output_column_name = key + elif output_column_name is None: + raise MlflowException( + ERROR_MISSING_OUTPUT_COLUMN_NAME, + error_code=INVALID_PARAMETER_VALUE, + ) + else: + raise MlflowException( + f"Output column name '{output_column_name}' is not found in the " + f"model predictions dict {model_predictions}. Please set the correct " + "output column name using the `predictions` parameter.", + error_code=INVALID_PARAMETER_VALUE, + ) + + return ( + y_pred if y_pred is not None else model_predictions, + other_output_columns, + output_column_name, + ) + + +def _extract_predict_fn(model: Any) -> Callable[..., Any] | None: + """ + Extracts the predict function from the given model or raw_model. + + Precedence order: + 1. If raw_model is specified, its predict function is used. + 2. If model is specified, its predict function is used. + 3. If none of the above, predict function is None. + + Args: + model: A model object that has a predict method. + raw_model: A raw model object that has a predict method. + + Returns: The predict function. + """ + _, raw_model = _extract_raw_model(model) + predict_fn = None + + if raw_model is not None: + predict_fn = raw_model.predict + try: + from mlflow.xgboost import _wrapped_xgboost_model_predict_fn + + # Because shap evaluation will pass evaluation data in ndarray format + # (without feature names), if set validate_features=True it will raise error. + predict_fn = _wrapped_xgboost_model_predict_fn(raw_model, validate_features=False) + except ImportError: + pass + + elif model is not None: + predict_fn = model.predict + + return predict_fn + + +def _get_dataframe_with_renamed_columns(x, new_column_names): + """ + Downstream inference functions may expect a pd.DataFrame to be created from x. However, + if x is already a pd.DataFrame, and new_column_names != x.columns, we cannot simply call + pd.DataFrame(x, columns=new_column_names) because the resulting pd.DataFrame will contain + NaNs for every column in new_column_names that does not exist in x.columns. This function + instead creates a new pd.DataFrame object from x, and then explicitly renames the columns + to avoid NaNs. + + Args: + x: A data object, such as a Pandas DataFrame, numPy array, or list + new_column_names: Column names for the output Pandas DataFrame + + Returns: + A pd.DataFrame with x as data, with columns new_column_names + """ + df = pd.DataFrame(x) + return df.rename(columns=dict(zip(df.columns, new_column_names))) + + +def _get_aggregate_metrics_values(metrics): + return {name: MetricValue(aggregate_results={name: value}) for name, value in metrics.items()} + + +_matplotlib_config = { + "figure.dpi": 175, + "figure.figsize": [6.0, 4.0], + "figure.autolayout": True, + "font.size": 8, +} + + +class _CustomArtifact(NamedTuple): + """ + A namedtuple representing a custom artifact function and its properties. + + function : the custom artifact function + name : the name of the custom artifact function + index : the index of the function in the ``custom_artifacts`` argument of mlflow.evaluate + artifacts_dir : the path to a temporary directory to store produced artifacts of the function + """ + + function: Callable[..., Any] + name: str + index: int + artifacts_dir: str + + +def _is_valid_artifacts(artifacts): + return isinstance(artifacts, dict) and all(isinstance(k, str) for k in artifacts.keys()) + + +def _evaluate_custom_artifacts(custom_artifact_tuple, eval_df, builtin_metrics): + """ + This function calls the `custom_artifact` function and performs validations on the returned + result to ensure that they are in the expected format. It will raise a MlflowException if + the result is not in the expected format. + + Args: + custom_artifact_tuple: Containing a user provided function and its index in the + ``custom_artifacts`` parameter of ``mlflow.evaluate`` + eval_df: A Pandas dataframe object containing a prediction and a target column. + builtin_metrics: A dictionary of metrics produced by the default evaluator. + + Returns: + A dictionary of artifacts. + """ + exception_header = ( + f"Custom artifact function '{custom_artifact_tuple.name}' " + " at index {custom_artifact_tuple.index}" + " in the `custom_artifacts` parameter" + ) + artifacts = custom_artifact_tuple.function( + eval_df, builtin_metrics, custom_artifact_tuple.artifacts_dir + ) + + if artifacts is None: + _logger.warning(f"{exception_header} returned None.") + return + + if not _is_valid_artifacts(artifacts): + _logger.warning( + f"{exception_header} did not return artifacts as a dictionary of string artifact " + "names with their corresponding objects." + ) + return + + return artifacts + + +# TODO: Move this to the /evaluators directory +class BuiltInEvaluator(ModelEvaluator): + """ + The base class for all evaluators that are built-in to MLflow. + + Each evaluator is responsible for implementing the `_evaluate()` method, which is called by + the `evaluate()` method of this base class. This class contains many helper methods used + across built-in evaluators, such as logging metrics, artifacts, and ordering metrics. + """ + + def __init__(self): + self.client = MlflowClient() + + @abstractmethod + def _evaluate( + self, + model: Optional["mlflow.pyfunc.PyFuncModel"], + extra_metrics: list[EvaluationMetric], + custom_artifacts=None, + **kwargs, + ) -> EvaluationResult | None: + """Implement the evaluation logic for each evaluator.""" + + def log_metrics(self): + """ + Helper method to log metrics into specified run. + """ + self._add_prefix_to_metrics() + + timestamp = get_current_time_millis() + self.client.log_batch( + self.run_id, + metrics=[ + Metric( + key=key, + value=value, + timestamp=timestamp, + step=0, + model_id=self.model_id, + dataset_name=self.dataset.name, + dataset_digest=self.dataset.digest, + run_id=self.run_id, + ) + for key, value in self.aggregate_metrics.items() + ], + ) + + def _log_image_artifact( + self, + do_plot, + artifact_name, + ): + from matplotlib import pyplot + + prefix = self.evaluator_config.get("metric_prefix", "") + artifact_file_name = f"{prefix}{artifact_name}.png" + artifact_file_local_path = self.temp_dir.path(artifact_file_name) + + try: + pyplot.clf() + do_plot() + pyplot.savefig(artifact_file_local_path, bbox_inches="tight") + except Exception as e: + _logger.warning(f"Failed to log image artifact {artifact_name!r}: {e!r}") + else: + mlflow.log_artifact(artifact_file_local_path) + artifact = ImageEvaluationArtifact(uri=mlflow.get_artifact_uri(artifact_file_name)) + artifact._load(artifact_file_local_path) + self.artifacts[artifact_name] = artifact + finally: + pyplot.close(pyplot.gcf()) + + def _evaluate_sklearn_model_score_if_scorable(self, model, y_true, sample_weights): + model_loader_module, raw_model = _extract_raw_model(model) + if model_loader_module == "mlflow.sklearn" and raw_model is not None: + try: + score = raw_model.score( + self.X.copy_to_avoid_mutation(), y_true, sample_weight=sample_weights + ) + self.metrics_values.update(_get_aggregate_metrics_values({"score": score})) + except Exception as e: + _logger.warning( + f"Computing sklearn model score failed: {e!r}. Set logging level to " + "DEBUG to see the full traceback." + ) + _logger.debug("", exc_info=True) + + def _log_custom_metric_artifact(self, artifact_name, raw_artifact, custom_metric_tuple): + """ + This function logs and returns a custom metric artifact. Two cases: + - The provided artifact is a path to a file, the function will make a copy of it with + a formatted name in a temporary directory and call mlflow.log_artifact. + - Otherwise: will attempt to save the artifact to an temporary path with an inferred + type. Then call mlflow.log_artifact. + + Args: + artifact_name: the name of the artifact + raw_artifact: the object representing the artifact + custom_metric_tuple: an instance of the _CustomMetric namedtuple + + Returns: + EvaluationArtifact + """ + + exception_and_warning_header = ( + f"Custom artifact function '{custom_metric_tuple.name}' at index " + f"{custom_metric_tuple.index} in the `custom_artifacts` parameter" + ) + + inferred_from_path, inferred_type, inferred_ext = _infer_artifact_type_and_ext( + artifact_name, raw_artifact, custom_metric_tuple + ) + artifact_file_local_path = self.temp_dir.path(artifact_name + inferred_ext) + + if pathlib.Path(artifact_file_local_path).exists(): + raise MlflowException( + f"{exception_and_warning_header} produced an artifact '{artifact_name}' that " + "cannot be logged because there already exists an artifact with the same name." + ) + + # ParquetEvaluationArtifact isn't explicitly stated here because such artifacts can only + # be supplied through file. Which is handled by the first if clause. This is because + # DataFrame objects default to be stored as CsvEvaluationArtifact. + if inferred_from_path: + shutil.copy2(raw_artifact, artifact_file_local_path) + elif inferred_type is JsonEvaluationArtifact: + with open(artifact_file_local_path, "w") as f: + if isinstance(raw_artifact, str): + f.write(raw_artifact) + else: + json.dump(raw_artifact, f, cls=NumpyEncoder) + elif inferred_type is CsvEvaluationArtifact: + raw_artifact.to_csv(artifact_file_local_path, index=False) + elif inferred_type is NumpyEvaluationArtifact: + np.save(artifact_file_local_path, raw_artifact, allow_pickle=False) + elif inferred_type is ImageEvaluationArtifact: + raw_artifact.savefig(artifact_file_local_path) + else: + # storing as pickle + try: + with open(artifact_file_local_path, "wb") as f: + pickle.dump(raw_artifact, f) + _logger.warning( + f"{exception_and_warning_header} produced an artifact '{artifact_name}'" + f" with type '{type(raw_artifact)}' that is logged as a pickle artifact." + ) + except pickle.PickleError: + raise MlflowException( + f"{exception_and_warning_header} produced an unsupported artifact " + f"'{artifact_name}' with type '{type(raw_artifact)}' that cannot be pickled. " + "Supported object types for artifacts are:\n" + "- A string uri representing the file path to the artifact. MLflow" + " will infer the type of the artifact based on the file extension.\n" + "- A string representation of a JSON object. This will be saved as a " + ".json artifact.\n" + "- Pandas DataFrame. This will be saved as a .csv artifact." + "- Numpy array. This will be saved as a .npy artifact." + "- Matplotlib Figure. This will be saved as an .png image artifact." + "- Other objects will be attempted to be pickled with default protocol." + ) + + mlflow.log_artifact(artifact_file_local_path) + artifact = inferred_type(uri=mlflow.get_artifact_uri(artifact_name + inferred_ext)) + artifact._load(artifact_file_local_path) + return artifact + + def _get_column_in_metrics_values(self, column): + for metric_name, metric_value in self.metrics_values.items(): + if metric_name.split("/")[0] == column: + return metric_value + + def _get_args_for_metrics( + self, + metric: MetricDefinition, + eval_df: pd.DataFrame, + input_df: pd.DataFrame, + other_output_df: pd.DataFrame | None, + ) -> tuple[bool, list[str | pd.DataFrame]]: + """ + Given a metric_tuple, read the signature of the metric function and get the appropriate + arguments from the input/output columns, other calculated metrics, and evaluator_config. + + Args: + metric: The metric definition containing a user provided function and its index + in the ``extra_metrics`` parameter of ``mlflow.evaluate``. + eval_df: The evaluation dataframe containing the prediction and target columns. + input_df: The input dataframe containing the features used to make predictions. + other_output_df: A dataframe containing all model output columns but the predictions. + + Returns: + tuple: A tuple of (bool, list) where the bool indicates if the given metric can + be calculated with the given eval_df, metrics, and input_df. + - If the user is missing "targets" or "predictions" parameters when needed, or we + cannot find a column or metric for a parameter to the metric, return + (False, list of missing parameters) + - If all arguments to the metric function were found, return + (True, list of arguments). + """ + # deepcopying eval_df and builtin_metrics for each custom metric function call, + # in case the user modifies them inside their function(s). + eval_df_copy = eval_df.copy() + parameters = inspect.signature(metric.function).parameters + eval_fn_args = [] + params_not_found = [] + if len(parameters) == 2: + param_0_name, param_1_name = parameters.keys() + + # eval_fn has parameters (eval_df, builtin_metrics) for backwards compatibility + if len(parameters) == 2 and param_0_name != "predictions" and param_1_name != "targets": + eval_fn_args.append(eval_df_copy) + self._update_aggregate_metrics() + eval_fn_args.append(copy.deepcopy(self.aggregate_metrics)) + # eval_fn can have parameters like (predictions, targets, metrics, random_col) + else: + for param_name, param in parameters.items(): + column = self.col_mapping.get(param_name, param_name) + + if ( + column == "predictions" + or column == self.predictions + or column == self.dataset.predictions_name + ): + eval_fn_args.append(eval_df_copy["prediction"]) + elif column == "targets" or column == self.dataset.targets_name: + if "target" in eval_df_copy: + eval_fn_args.append(eval_df_copy["target"]) + else: + if param.default == inspect.Parameter.empty: + params_not_found.append(param_name) + else: + eval_fn_args.append(param.default) + elif column == "metrics": + eval_fn_args.append(copy.deepcopy(self.metrics_values)) + else: + # case when column passed in col_mapping contains the entire column + if not isinstance(column, str): + eval_fn_args.append(column) + + # case column in col_mapping is string and the column value + # is part of the input_df + elif column in input_df.columns: + eval_fn_args.append(input_df[column]) + + # case column in col_mapping is string and the column value + # is part of the output_df(other than predictions) + elif other_output_df is not None and column in other_output_df.columns: + self.other_output_columns_for_eval.add(column) + eval_fn_args.append(other_output_df[column]) + + # case where the param is defined as part of the evaluator_config + elif column in self.evaluator_config: + eval_fn_args.append(self.evaluator_config.get(column)) + + # case where this is the name of another metric + elif metric_value := self._get_column_in_metrics_values(column): + eval_fn_args.append(metric_value) + + # in the case that: + # the metric has not been calculated yet, but is scheduled to be calculated + # "before" this metric in self.ordered_metrics, we append None to indicate + # that there is not an error in the dependencies + elif column in [metric_tuple.name for metric_tuple in self.ordered_metrics]: + eval_fn_args.append(None) + + elif param.default == inspect.Parameter.empty: + params_not_found.append(param_name) + else: + eval_fn_args.append(param.default) + + if len(params_not_found) > 0: + return False, params_not_found + return True, eval_fn_args + + def evaluate_and_log_custom_artifacts( + self, + custom_artifacts: list[_CustomArtifact], + prediction: pd.Series, + target: np.ndarray | None = None, + ): + """Evaluate custom artifacts provided by users.""" + if not custom_artifacts: + return + + eval_df = self._get_eval_df(prediction, target) + for index, custom_artifact in enumerate(custom_artifacts): + with tempfile.TemporaryDirectory() as artifacts_dir: + # deepcopying eval_df and builtin_metrics for each custom artifact function call, + # in case the user modifies them inside their function(s). + custom_artifact_tuple = _CustomArtifact( + function=custom_artifact, + index=index, + name=getattr(custom_artifact, "__name__", repr(custom_artifact)), + artifacts_dir=artifacts_dir, + ) + artifact_results = _evaluate_custom_artifacts( + custom_artifact_tuple, + eval_df.copy(), + copy.deepcopy(self.metrics_values), + ) + if artifact_results: + for artifact_name, raw_artifact in artifact_results.items(): + self.artifacts[artifact_name] = self._log_custom_metric_artifact( + artifact_name, + raw_artifact, + custom_artifact_tuple, + ) + + def _get_error_message_missing_columns(self, metric_name, param_names): + error_message_parts = [f"Metric '{metric_name}' requires the following:"] + + special_params = ["targets", "predictions"] + for param in special_params: + if param in param_names: + error_message_parts.append(f" - the '{param}' parameter needs to be specified") + + remaining_params = [param for param in param_names if param not in special_params] + + if remaining_params: + error_message_parts.append( + f" - missing columns {remaining_params} need to be defined or mapped" + ) + + return "\n".join(error_message_parts) + + def _construct_error_message_for_malformed_metrics( + self, malformed_results, input_columns, output_columns + ): + error_messages = [ + self._get_error_message_missing_columns(metric_name, param_names) + for metric_name, param_names in malformed_results + ] + joined_error_message = "\n".join(error_messages) + + full_message = f"""Error: Metric calculation failed for the following metrics: + {joined_error_message} + + Below are the existing column names for the input/output data: + Input Columns: {input_columns} + Output Columns: {output_columns} + + To resolve this issue, you may need to: + - specify any required parameters + - if you are missing columns, check that there are no circular dependencies among your + metrics, and you may want to map them to an existing column using the following + configuration: + evaluator_config={{'col_mapping': {{: }}}}""" + + return "\n".join(l.lstrip() for l in full_message.splitlines()) + + def _raise_exception_for_malformed_metrics(self, malformed_results, eval_df, other_output_df): + output_columns = [] if other_output_df is None else list(other_output_df.columns) + if self.predictions: + output_columns.append(self.predictions) + elif self.dataset.predictions_name: + output_columns.append(self.dataset.predictions_name) + else: + output_columns.append("predictions") + + input_columns = list(self.X.copy_to_avoid_mutation().columns) + if "target" in eval_df: + if self.dataset.targets_name: + input_columns.append(self.dataset.targets_name) + else: + input_columns.append("targets") + + error_message = self._construct_error_message_for_malformed_metrics( + malformed_results, input_columns, output_columns + ) + + raise MlflowException(error_message, error_code=INVALID_PARAMETER_VALUE) + + def _get_eval_df(self, prediction: pd.Series, target: np.ndarray | None = None): + """ + Create a DataFrame with "prediction" and "target" columns. + + This is a standard format that can be passed to the metric functions. + """ + eval_df = pd.DataFrame({"prediction": copy.deepcopy(prediction)}) + if target is not None: + eval_df["target"] = target + return eval_df + + def _order_metrics( + self, + metrics: list[EvaluationMetric], + eval_df: pd.DataFrame, + other_output_df: pd.DataFrame | None, + ): + """ + Order the list metrics so they can be computed in sequence. + + Some metrics might use the results of other metrics to compute their own results. This + function iteratively resolve this dependency, by checking if each metric can be computed + with the current available columns and metrics values. + """ + remaining_metrics = metrics + input_df = self.X.copy_to_avoid_mutation() + + while len(remaining_metrics) > 0: + pending_metrics = [] + failed_results = [] + did_append_metric = False + for metric_tuple in remaining_metrics: + can_calculate, eval_fn_args = self._get_args_for_metrics( + metric_tuple, eval_df, input_df, other_output_df + ) + if can_calculate: + self.ordered_metrics.append(metric_tuple) + did_append_metric = True + else: # cannot calculate the metric yet + pending_metrics.append(metric_tuple) + failed_results.append((metric_tuple.name, eval_fn_args)) + + # cant calculate any more metrics + if not did_append_metric: + self._raise_exception_for_malformed_metrics( + failed_results, eval_df, other_output_df + ) + + remaining_metrics = pending_metrics + + return self.ordered_metrics + + def _test_first_row( + self, + metrics: list[MetricDefinition], + eval_df: pd.DataFrame, + other_output_df: pd.DataFrame | None, + ): + # test calculations on first row of eval_df + _logger.info("Testing metrics on first row...") + exceptions = [] + first_row_df = eval_df.iloc[[0]] + first_row_input_df = self.X.copy_to_avoid_mutation().iloc[[0]] + for metric in metrics: + try: + _, eval_fn_args = self._get_args_for_metrics( + metric, first_row_df, first_row_input_df, other_output_df + ) + metric_value = metric.evaluate(eval_fn_args) + if metric_value: + name = f"{metric.name}/{metric.version}" if metric.version else metric.name + self.metrics_values.update({name: metric_value}) + except Exception as e: + stacktrace_str = traceback.format_exc() + if isinstance(e, MlflowException): + exceptions.append( + f"Metric '{metric.name}': Error:\n{e.message}\n{stacktrace_str}" + ) + else: + exceptions.append(f"Metric '{metric.name}': Error:\n{e!r}\n{stacktrace_str}") + + if len(exceptions) > 0: + raise MlflowException("\n".join(exceptions)) + + def evaluate_metrics( + self, + metrics: list[EvaluationMetric], + prediction: pd.Series, + target: np.ndarray | None = None, + other_output_df: pd.DataFrame | None = None, + ): + """ + Evaluate the metrics on the given prediction and target data. + + Args: + metrics: A list of metrics to evaluate. + prediction: A Pandas Series containing the predictions. + target: A numpy array containing the target values. + other_output_df: A Pandas DataFrame containing other output columns from the model. + + Returns: + None, the metrics values are recorded in the self.metrics_values dictionary. + """ + + eval_df = self._get_eval_df(prediction, target) + metrics = [ + MetricDefinition.from_index_and_metric(i, metric) for i, metric in enumerate(metrics) + ] + metrics = self._order_metrics(metrics, eval_df, other_output_df) + + self._test_first_row(metrics, eval_df, other_output_df) + + # calculate metrics for the full eval_df + input_df = self.X.copy_to_avoid_mutation() + for metric in metrics: + _, eval_fn_args = self._get_args_for_metrics(metric, eval_df, input_df, other_output_df) + metric_value = metric.evaluate(eval_fn_args) + + if metric_value: + name = f"{metric.name}/{metric.version}" if metric.version else metric.name + self.metrics_values.update({name: metric_value}) + + def log_eval_table(self, y_pred, other_output_columns=None): + # only log eval table if there are per row metrics recorded + if not any( + metric_value.scores is not None or metric_value.justifications is not None + for _, metric_value in self.metrics_values.items() + ): + return + + metric_prefix = self.evaluator_config.get("metric_prefix", "") + if not isinstance(metric_prefix, str): + metric_prefix = "" + if isinstance(self.dataset.features_data, pd.DataFrame): + # Handle DataFrame case + if self.dataset.has_targets: + data = self.dataset.features_data.assign( + **{ + self.dataset.targets_name or "target": self.dataset.labels_data, + self.dataset.predictions_name or self.predictions or "outputs": y_pred, + } + ) + else: + data = self.dataset.features_data.assign(outputs=y_pred) + else: + # Handle NumPy array case, converting it to a DataFrame + data = pd.DataFrame(self.dataset.features_data, columns=self.dataset.feature_names) + if self.dataset.has_targets: + data = data.assign( + **{ + self.dataset.targets_name or "target": self.dataset.labels_data, + self.dataset.predictions_name or self.predictions or "outputs": y_pred, + } + ) + else: + data = data.assign(outputs=y_pred) + + # Include other_output_columns used in evaluation to the eval table + if other_output_columns is not None and len(self.other_output_columns_for_eval) > 0: + for column in self.other_output_columns_for_eval: + data[column] = other_output_columns[column] + + columns = {} + for metric_name, metric_value in self.metrics_values.items(): + scores = metric_value.scores + justifications = metric_value.justifications + + if scores: + if metric_name.startswith(metric_prefix) and metric_name[len(metric_prefix) :] in [ + _TOKEN_COUNT_METRIC_NAME, + _LATENCY_METRIC_NAME, + ]: + columns[metric_name] = scores + else: + columns[f"{metric_name}/score"] = scores + if justifications: + columns[f"{metric_name}/justification"] = justifications + data = data.assign(**columns) + artifact_file_name = f"{metric_prefix}{_EVAL_TABLE_FILE_NAME}" + mlflow.log_table(data, artifact_file=artifact_file_name) + if self.eval_results_path: + eval_table_spark = self.spark_session.createDataFrame(data) + try: + eval_table_spark.write.mode(self.eval_results_mode).option( + "mergeSchema", "true" + ).format("delta").saveAsTable(self.eval_results_path) + except Exception as e: + _logger.info(f"Saving eval table to delta table failed. Reason: {e}") + + name = _EVAL_TABLE_FILE_NAME.split(".", 1)[0] + self.artifacts[name] = JsonEvaluationArtifact( + uri=mlflow.get_artifact_uri(artifact_file_name) + ) + + def _update_aggregate_metrics(self): + self.aggregate_metrics = {} + for metric_name, metric_value in self.metrics_values.items(): + if metric_value.aggregate_results: + for agg_name, agg_value in metric_value.aggregate_results.items(): + if agg_value is not None: + if agg_name == metric_name.split("/")[0]: + self.aggregate_metrics[metric_name] = agg_value + else: + self.aggregate_metrics[f"{metric_name}/{agg_name}"] = agg_value + + def _add_prefix_to_metrics(self): + def _prefix_value(value): + aggregate = ( + {f"{prefix}{k}": v for k, v in value.aggregate_results.items()} + if value.aggregate_results + else None + ) + return MetricValue(value.scores, value.justifications, aggregate) + + if prefix := self.evaluator_config.get("metric_prefix"): + self.metrics_values = { + f"{prefix}{k}": _prefix_value(v) for k, v in self.metrics_values.items() + } + + self._update_aggregate_metrics() + + def evaluate( + self, + *, + model_type, + dataset, + run_id, + evaluator_config, + model: "mlflow.pyfunc.PyFuncModel" = None, + extra_metrics=None, + custom_artifacts=None, + predictions=None, + model_id=None, + **kwargs, + ) -> EvaluationResult: + if model is None and predictions is None and dataset.predictions_data is None: + raise MlflowException( + message=( + "Either a model or set of predictions must be specified in order to use the" + " default evaluator. Either specify the `model` parameter, the `predictions`" + " parameter, an MLflow dataset containing the `predictions` column name" + " (via the `data` parameter), or a different evaluator (via the `evaluators`" + " parameter)." + ), + error_code=INVALID_PARAMETER_VALUE, + ) + + self.artifacts = {} + self.aggregate_metrics = {} + self.metrics_values = {} + self.ordered_metrics = [] + self.other_output_columns_for_eval = set() + + self.dataset: EvaluationDataset = dataset + self.run_id = run_id + self.model_type = model_type + self.model_id = model_id + self.evaluator_config = evaluator_config + + self.predictions = predictions + self.col_mapping = self.evaluator_config.get("col_mapping", {}) + self.eval_results_path = self.evaluator_config.get("eval_results_path") + self.eval_results_mode = self.evaluator_config.get("eval_results_mode", "overwrite") + + if self.eval_results_path: + from mlflow.utils._spark_utils import _get_active_spark_session + + self.spark_session = _get_active_spark_session() + if not self.spark_session: + raise MlflowException( + message="eval_results_path is only supported in Spark environment. ", + error_code=INVALID_PARAMETER_VALUE, + ) + + if self.eval_results_mode not in ["overwrite", "append"]: + raise MlflowException( + message="eval_results_mode can only be 'overwrite' or 'append'. ", + error_code=INVALID_PARAMETER_VALUE, + ) + + if extra_metrics is None: + extra_metrics = [] + + bad_metrics = [] + for metric in extra_metrics: + if not isinstance(metric, EvaluationMetric): + bad_metrics.append(metric) + if len(bad_metrics) > 0: + message = "\n".join( + [f"- Metric '{m}' has type '{type(m).__name__}'" for m in bad_metrics] + ) + raise MlflowException( + f"In the 'extra_metrics' parameter, the following metrics have the wrong type:\n" + f"{message}\n" + f"Please ensure that all extra metrics are instances of " + f"mlflow.metrics.EvaluationMetric." + ) + + import matplotlib + + with TempDir() as temp_dir, matplotlib.rc_context(_matplotlib_config): + self.temp_dir = temp_dir + return self._evaluate(model, extra_metrics, custom_artifacts) + + @property + def X(self) -> pd.DataFrame: + """ + The features (`X`) portion of the dataset, guarded against accidental mutations. + """ + return BuiltInEvaluator._MutationGuardedData( + _get_dataframe_with_renamed_columns( + self.dataset.features_data, self.dataset.feature_names + ) + ) + + class _MutationGuardedData: + """ + Wrapper around a data object that requires explicit API calls to obtain either a copy + of the data object, or, in cases where the caller can guaranteed that the object will not + be mutated, the original data object. + """ + + def __init__(self, data): + """ + Args: + data: A data object, such as a Pandas DataFrame, numPy array, or list. + """ + self._data = data + + def copy_to_avoid_mutation(self): + """ + Obtain a copy of the data. This method should be called every time the data needs + to be used in a context where it may be subsequently mutated, guarding against + accidental reuse after mutation. + + Returns: + A copy of the data object. + """ + if isinstance(self._data, pd.DataFrame): + return self._data.copy(deep=True) + else: + return copy.deepcopy(self._data) + + def get_original(self): + """ + Obtain the original data object. This method should only be called if the caller + can guarantee that it will not mutate the data during subsequent operations. + + Returns: + The original data object. + """ + return self._data diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/evaluation/deprecated.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/evaluation/deprecated.py new file mode 100644 index 0000000000000000000000000000000000000000..0e72a49849825db8f3d27e406bfe888440f5d5ae --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/evaluation/deprecated.py @@ -0,0 +1,23 @@ +import functools +import warnings + +import mlflow +from mlflow.models.evaluation import evaluate as model_evaluate +from mlflow.utils.uri import is_databricks_uri + + +@functools.wraps(model_evaluate) +def evaluate(*args, **kwargs): + tracking_uri = mlflow.get_tracking_uri() + if is_databricks_uri(tracking_uri): + warnings.warn( + "The `mlflow.evaluate` API has been deprecated as of MLflow 3.0.0. " + "Please use these new alternatives:\n\n" + " - For traditional ML or deep learning models: Use `mlflow.models.evaluate`, " + "which maintains full compatibility with the original `mlflow.evaluate` API.\n\n" + " - For LLMs or GenAI applications: Use the new `mlflow.genai.evaluate` API, " + "which offers enhanced features specifically designed for evaluating " + "LLMs and GenAI applications.\n", + FutureWarning, + ) + return model_evaluate(*args, **kwargs) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/evaluation/evaluator_registry.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/evaluation/evaluator_registry.py new file mode 100644 index 0000000000000000000000000000000000000000..496cc3c1b5ce43ec01fe37a528664fc31df9f1de --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/evaluation/evaluator_registry.py @@ -0,0 +1,80 @@ +import warnings + +from mlflow.exceptions import MlflowException +from mlflow.utils.import_hooks import register_post_import_hook +from mlflow.utils.plugins import get_entry_points + + +class ModelEvaluatorRegistry: + """ + Scheme-based registry for model evaluator implementations + """ + + def __init__(self): + self._registry = {} + self._builtin_evaluators = {} + + def register(self, scheme, evaluator): + """Register model evaluator provided by other packages""" + self._registry[scheme] = evaluator + + def register_builtin(self, scheme, evaluator): + """Register built-in model evaluator""" + self._registry[scheme] = evaluator + self._builtin_evaluators[scheme] = evaluator + + def register_entrypoints(self): + # Register ModelEvaluator implementation provided by other packages + for entrypoint in get_entry_points("mlflow.model_evaluator"): + try: + self.register(entrypoint.name, entrypoint.load()) + except (AttributeError, ImportError) as exc: + warnings.warn( + 'Failure attempting to register model evaluator for scheme "{}": {}'.format( + entrypoint.name, str(exc) + ), + stacklevel=2, + ) + + def get_evaluator(self, evaluator_name): + """ + Get an evaluator instance from the registry based on the name of evaluator + """ + evaluator_cls = self._registry.get(evaluator_name) + if evaluator_cls is None: + raise MlflowException( + f"Could not find a registered model evaluator for: {evaluator_name}. " + f"Currently registered evaluator names are: {list(self._registry.keys())}" + ) + return evaluator_cls() + + def is_builtin(self, name): + return name in self._builtin_evaluators + + def is_registered(self, name): + return name in self._registry + + +_model_evaluation_registry = ModelEvaluatorRegistry() + + +def register_evaluators(module): + from mlflow.models.evaluation.evaluators.classifier import ClassifierEvaluator + from mlflow.models.evaluation.evaluators.default import DefaultEvaluator + from mlflow.models.evaluation.evaluators.regressor import RegressorEvaluator + from mlflow.models.evaluation.evaluators.shap import ShapEvaluator + + # Built-in evaluators + module._model_evaluation_registry.register_builtin(DefaultEvaluator.name, DefaultEvaluator) + module._model_evaluation_registry.register_builtin( + ClassifierEvaluator.name, ClassifierEvaluator + ) + module._model_evaluation_registry.register_builtin(RegressorEvaluator.name, RegressorEvaluator) + module._model_evaluation_registry.register_builtin(ShapEvaluator.name, ShapEvaluator) + + # Plugin evaluators + module._model_evaluation_registry.register_entrypoints() + + +# Put it in post-importing hook to avoid circuit importing +register_post_import_hook(register_evaluators, __name__, overwrite=True) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/evaluation/evaluators/__pycache__/classifier.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/evaluation/evaluators/__pycache__/classifier.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..76939b4aa443aee892a56e61dddbd121f47530a4 Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/evaluation/evaluators/__pycache__/classifier.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/evaluation/evaluators/__pycache__/default.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/evaluation/evaluators/__pycache__/default.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..ea344182981b08ad6cc28a374116e965fad4bc55 Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/evaluation/evaluators/__pycache__/default.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/evaluation/evaluators/__pycache__/regressor.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/evaluation/evaluators/__pycache__/regressor.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..8074a503ac32335a77cc4561fa36ab2dd5fa928a Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/evaluation/evaluators/__pycache__/regressor.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/evaluation/evaluators/__pycache__/shap.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/evaluation/evaluators/__pycache__/shap.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..9d351b9c05b50ca2da99e83aed71e95c280aabdd Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/evaluation/evaluators/__pycache__/shap.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/evaluation/evaluators/classifier.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/evaluation/evaluators/classifier.py new file mode 100644 index 0000000000000000000000000000000000000000..87fb1522f92dd87b8ace9653045a89176d69b21c --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/evaluation/evaluators/classifier.py @@ -0,0 +1,703 @@ +import logging +import math +from contextlib import contextmanager +from typing import Any, Callable, NamedTuple, Optional + +import numpy as np +import pandas as pd +from sklearn import metrics as sk_metrics + +import mlflow +from mlflow import MlflowException +from mlflow.environment_variables import _MLFLOW_EVALUATE_SUPPRESS_CLASSIFICATION_ERRORS +from mlflow.models.evaluation.artifacts import CsvEvaluationArtifact +from mlflow.models.evaluation.base import EvaluationMetric, EvaluationResult, _ModelType +from mlflow.models.evaluation.default_evaluator import ( + BuiltInEvaluator, + _extract_raw_model, + _get_aggregate_metrics_values, +) +from mlflow.models.utils import plot_lines + +_logger = logging.getLogger(__name__) + + +class _Curve(NamedTuple): + plot_fn: Callable[..., Any] + plot_fn_args: dict[str, Any] + auc: float + + +class ClassifierEvaluator(BuiltInEvaluator): + """ + A built-in evaluator for classifier models. + """ + + name = "classifier" + + @classmethod + def can_evaluate(cls, *, model_type, evaluator_config, **kwargs): + # TODO: Also the model needs to be pyfunc model, not function or endpoint URI + return model_type == _ModelType.CLASSIFIER + + def _evaluate( + self, + model: Optional["mlflow.pyfunc.PyFuncModel"], + extra_metrics: list[EvaluationMetric], + custom_artifacts=None, + **kwargs, + ) -> EvaluationResult | None: + # Get classification config + self.y_true = self.dataset.labels_data + self.label_list = self.evaluator_config.get("label_list") + self.pos_label = self.evaluator_config.get("pos_label") + self.sample_weights = self.evaluator_config.get("sample_weights") + if self.pos_label and self.label_list and self.pos_label not in self.label_list: + raise MlflowException.invalid_parameter_value( + f"'pos_label' {self.pos_label} must exist in 'label_list' {self.label_list}." + ) + + # Check if the model_type is consistent with ground truth labels + inferred_model_type = _infer_model_type_by_labels(self.y_true) + if _ModelType.CLASSIFIER != inferred_model_type: + _logger.warning( + f"According to the evaluation dataset label values, the model type looks like " + f"{inferred_model_type}, but you specified model type 'classifier'. Please " + f"verify that you set the `model_type` and `dataset` arguments correctly." + ) + + # Run model prediction + input_df = self.X.copy_to_avoid_mutation() + self.y_pred, self.y_probs = self._generate_model_predictions(model, input_df) + + self._validate_label_list() + + self._compute_builtin_metrics(model) + self.evaluate_metrics(extra_metrics, prediction=self.y_pred, target=self.y_true) + self.evaluate_and_log_custom_artifacts( + custom_artifacts, prediction=self.y_pred, target=self.y_true + ) + + # Log metrics and artifacts + self.log_metrics() + self.log_eval_table(self.y_pred) + + if len(self.label_list) == 2: + self._log_binary_classifier_artifacts() + else: + self._log_multiclass_classifier_artifacts() + self._log_confusion_matrix() + + return EvaluationResult( + metrics=self.aggregate_metrics, artifacts=self.artifacts, run_id=self.run_id + ) + + def _generate_model_predictions(self, model, input_df): + predict_fn, predict_proba_fn = _extract_predict_fn_and_prodict_proba_fn(model) + # Classifier model is guaranteed to output single column of predictions + y_pred = self.dataset.predictions_data if model is None else predict_fn(input_df) + + # Predict class probabilities if the model supports it + y_probs = predict_proba_fn(input_df) if predict_proba_fn is not None else None + return y_pred, y_probs + + def _validate_label_list(self): + if self.label_list is None: + # If label list is not specified, infer label list from model output + self.label_list = np.unique(np.concatenate([self.y_true, self.y_pred])) + else: + # np.where only works for numpy array, not list + self.label_list = np.array(self.label_list) + + if len(self.label_list) < 2: + raise MlflowException( + "Evaluation dataset for classification must contain at least two unique " + f"labels, but only {len(self.label_list)} unique labels were found.", + "Please provide a 'label_list' parameter in 'evaluator_config' with all " + "possible classes, e.g., evaluator_config={{'label_list': [0, 1]}}.", + ) + + # sort label_list ASC, for binary classification it makes sure the last one is pos label + self.label_list.sort() + + if len(self.label_list) == 2: + if self.pos_label is None: + self.pos_label = self.label_list[-1] + else: + if self.pos_label in self.label_list: + self.label_list = np.delete( + self.label_list, np.where(self.label_list == self.pos_label) + ) + self.label_list = np.append(self.label_list, self.pos_label) + with _suppress_class_imbalance_errors(IndexError, log_warning=False): + _logger.info( + "The evaluation dataset is inferred as binary dataset, positive label is " + f"{self.label_list[1]}, negative label is {self.label_list[0]}." + ) + else: + _logger.info( + "The evaluation dataset is inferred as multiclass dataset, number of classes " + f"is inferred as {len(self.label_list)}. If this is incorrect, please specify the " + "`label_list` parameter in `evaluator_config`." + ) + + def _compute_builtin_metrics(self, model): + self._evaluate_sklearn_model_score_if_scorable(model, self.y_true, self.sample_weights) + + if len(self.label_list) == 2: + metrics = _get_binary_classifier_metrics( + y_true=self.y_true, + y_pred=self.y_pred, + y_proba=self.y_probs, + labels=self.label_list, + pos_label=self.pos_label, + sample_weights=self.sample_weights, + ) + if metrics: + self.metrics_values.update(_get_aggregate_metrics_values(metrics)) + self._compute_roc_and_pr_curve() + else: + average = self.evaluator_config.get("average", "weighted") + metrics = _get_multiclass_classifier_metrics( + y_true=self.y_true, + y_pred=self.y_pred, + y_proba=self.y_probs, + labels=self.label_list, + average=average, + sample_weights=self.sample_weights, + ) + if metrics: + self.metrics_values.update(_get_aggregate_metrics_values(metrics)) + + def _compute_roc_and_pr_curve(self): + if self.y_probs is not None: + with _suppress_class_imbalance_errors(ValueError, log_warning=False): + self.roc_curve = _gen_classifier_curve( + is_binomial=True, + y=self.y_true, + y_probs=self.y_probs[:, 1], + labels=self.label_list, + pos_label=self.pos_label, + curve_type="roc", + sample_weights=self.sample_weights, + ) + + self.metrics_values.update( + _get_aggregate_metrics_values({"roc_auc": self.roc_curve.auc}) + ) + with _suppress_class_imbalance_errors(ValueError, log_warning=False): + self.pr_curve = _gen_classifier_curve( + is_binomial=True, + y=self.y_true, + y_probs=self.y_probs[:, 1], + labels=self.label_list, + pos_label=self.pos_label, + curve_type="pr", + sample_weights=self.sample_weights, + ) + + self.metrics_values.update( + _get_aggregate_metrics_values({"precision_recall_auc": self.pr_curve.auc}) + ) + + def _log_pandas_df_artifact(self, pandas_df, artifact_name): + artifact_file_name = f"{artifact_name}.csv" + artifact_file_local_path = self.temp_dir.path(artifact_file_name) + pandas_df.to_csv(artifact_file_local_path, index=False) + mlflow.log_artifact(artifact_file_local_path) + artifact = CsvEvaluationArtifact( + uri=mlflow.get_artifact_uri(artifact_file_name), + content=pandas_df, + ) + artifact._load(artifact_file_local_path) + self.artifacts[artifact_name] = artifact + + def _log_multiclass_classifier_artifacts(self): + per_class_metrics_collection_df = _get_classifier_per_class_metrics_collection_df( + y=self.y_true, + y_pred=self.y_pred, + labels=self.label_list, + sample_weights=self.sample_weights, + ) + + log_roc_pr_curve = False + if self.y_probs is not None: + with _suppress_class_imbalance_errors(TypeError, log_warning=False): + self._log_calibration_curve() + + max_classes_for_multiclass_roc_pr = self.evaluator_config.get( + "max_classes_for_multiclass_roc_pr", 10 + ) + if len(self.label_list) <= max_classes_for_multiclass_roc_pr: + log_roc_pr_curve = True + else: + _logger.warning( + f"The classifier num_classes > {max_classes_for_multiclass_roc_pr}, skip " + f"logging ROC curve and Precision-Recall curve. You can add evaluator config " + f"'max_classes_for_multiclass_roc_pr' to increase the threshold." + ) + + if log_roc_pr_curve: + roc_curve = _gen_classifier_curve( + is_binomial=False, + y=self.y_true, + y_probs=self.y_probs, + labels=self.label_list, + pos_label=self.pos_label, + curve_type="roc", + sample_weights=self.sample_weights, + ) + + def plot_roc_curve(): + roc_curve.plot_fn(**roc_curve.plot_fn_args) + + self._log_image_artifact(plot_roc_curve, "roc_curve_plot") + per_class_metrics_collection_df["roc_auc"] = roc_curve.auc + + pr_curve = _gen_classifier_curve( + is_binomial=False, + y=self.y_true, + y_probs=self.y_probs, + labels=self.label_list, + pos_label=self.pos_label, + curve_type="pr", + sample_weights=self.sample_weights, + ) + + def plot_pr_curve(): + pr_curve.plot_fn(**pr_curve.plot_fn_args) + + self._log_image_artifact(plot_pr_curve, "precision_recall_curve_plot") + per_class_metrics_collection_df["precision_recall_auc"] = pr_curve.auc + + self._log_pandas_df_artifact(per_class_metrics_collection_df, "per_class_metrics") + + def _log_roc_curve(self): + def _plot_roc_curve(): + self.roc_curve.plot_fn(**self.roc_curve.plot_fn_args) + + self._log_image_artifact(_plot_roc_curve, "roc_curve_plot") + + def _log_precision_recall_curve(self): + def _plot_pr_curve(): + self.pr_curve.plot_fn(**self.pr_curve.plot_fn_args) + + self._log_image_artifact(_plot_pr_curve, "precision_recall_curve_plot") + + def _log_lift_curve(self): + from mlflow.models.evaluation.lift_curve import plot_lift_curve + + def _plot_lift_curve(): + return plot_lift_curve(self.y_true, self.y_probs, pos_label=self.pos_label) + + self._log_image_artifact(_plot_lift_curve, "lift_curve_plot") + + def _log_calibration_curve(self): + from mlflow.models.evaluation.calibration_curve import plot_calibration_curve + + def _plot_calibration_curve(): + return plot_calibration_curve( + y_true=self.y_true, + y_probs=self.y_probs, + pos_label=self.pos_label, + calibration_config={ + k: v for k, v in self.evaluator_config.items() if k.startswith("calibration_") + }, + label_list=self.label_list, + ) + + self._log_image_artifact(_plot_calibration_curve, "calibration_curve_plot") + + def _log_binary_classifier_artifacts(self): + if self.y_probs is not None: + with _suppress_class_imbalance_errors(log_warning=False): + self._log_roc_curve() + with _suppress_class_imbalance_errors(log_warning=False): + self._log_precision_recall_curve() + with _suppress_class_imbalance_errors(ValueError, log_warning=False): + self._log_lift_curve() + with _suppress_class_imbalance_errors(TypeError, log_warning=False): + self._log_calibration_curve() + + def _log_confusion_matrix(self): + """ + Helper method for logging confusion matrix + """ + # normalize the confusion matrix, keep consistent with sklearn autologging. + confusion_matrix = sk_metrics.confusion_matrix( + self.y_true, + self.y_pred, + labels=self.label_list, + normalize="true", + sample_weight=self.sample_weights, + ) + + def plot_confusion_matrix(): + import matplotlib + import matplotlib.pyplot as plt + + with matplotlib.rc_context( + { + "font.size": min(8, math.ceil(50.0 / len(self.label_list))), + "axes.labelsize": 8, + } + ): + _, ax = plt.subplots(1, 1, figsize=(6.0, 4.0), dpi=175) + disp = sk_metrics.ConfusionMatrixDisplay( + confusion_matrix=confusion_matrix, + display_labels=self.label_list, + ).plot(cmap="Blues", ax=ax) + disp.ax_.set_title("Normalized confusion matrix") + + if hasattr(sk_metrics, "ConfusionMatrixDisplay"): + self._log_image_artifact( + plot_confusion_matrix, + "confusion_matrix", + ) + return + + +def _is_categorical(values): + """ + Infer whether input values are categorical on best effort. + Return True represent they are categorical, return False represent we cannot determine result. + """ + dtype_name = pd.Series(values).convert_dtypes().dtype.name.lower() + return dtype_name in ["category", "string", "boolean"] + + +def _is_continuous(values): + """ + Infer whether input values is continuous on best effort. + Return True represent they are continuous, return False represent we cannot determine result. + """ + dtype_name = pd.Series(values).convert_dtypes().dtype.name.lower() + return dtype_name.startswith("float") + + +def _infer_model_type_by_labels(labels): + """ + Infer model type by target values. + """ + if _is_categorical(labels): + return _ModelType.CLASSIFIER + elif _is_continuous(labels): + return _ModelType.REGRESSOR + else: + return None # Unknown + + +def _extract_predict_fn_and_prodict_proba_fn(model): + predict_fn = None + predict_proba_fn = None + + _, raw_model = _extract_raw_model(model) + + if raw_model is not None: + predict_fn = raw_model.predict + predict_proba_fn = getattr(raw_model, "predict_proba", None) + try: + from mlflow.xgboost import ( + _wrapped_xgboost_model_predict_fn, + _wrapped_xgboost_model_predict_proba_fn, + ) + + # Because shap evaluation will pass evaluation data in ndarray format + # (without feature names), if set validate_features=True it will raise error. + predict_fn = _wrapped_xgboost_model_predict_fn(raw_model, validate_features=False) + predict_proba_fn = _wrapped_xgboost_model_predict_proba_fn( + raw_model, validate_features=False + ) + except ImportError: + pass + elif model is not None: + predict_fn = model.predict + + return predict_fn, predict_proba_fn + + +@contextmanager +def _suppress_class_imbalance_errors(exception_type=Exception, log_warning=True): + """ + Exception handler context manager to suppress Exceptions if the private environment + variable `_MLFLOW_EVALUATE_SUPPRESS_CLASSIFICATION_ERRORS` is set to `True`. + The purpose of this handler is to prevent an evaluation call for a binary or multiclass + classification automl run from aborting due to an extreme minority class imbalance + encountered during iterative training cycles due to the non deterministic sampling + behavior of Spark's DataFrame.sample() API. + The Exceptions caught in the usage of this are broad and are designed purely to not + interrupt the iterative hyperparameter tuning process. Final evaluations are done + in a more deterministic (but expensive) fashion. + """ + try: + yield + except exception_type as e: + if _MLFLOW_EVALUATE_SUPPRESS_CLASSIFICATION_ERRORS.get(): + if log_warning: + _logger.warning( + "Failed to calculate metrics due to class imbalance. " + "This is expected when the dataset is imbalanced." + ) + else: + raise e + + +def _get_binary_sum_up_label_pred_prob(positive_class_index, positive_class, y, y_pred, y_probs): + y = np.array(y) + y_bin = np.where(y == positive_class, 1, 0) + y_pred_bin = None + y_prob_bin = None + if y_pred is not None: + y_pred = np.array(y_pred) + y_pred_bin = np.where(y_pred == positive_class, 1, 0) + + if y_probs is not None: + y_probs = np.array(y_probs) + y_prob_bin = y_probs[:, positive_class_index] + + return y_bin, y_pred_bin, y_prob_bin + + +def _get_common_classifier_metrics( + *, y_true, y_pred, y_proba, labels, average, pos_label, sample_weights +): + metrics = { + "example_count": len(y_true), + "accuracy_score": sk_metrics.accuracy_score(y_true, y_pred, sample_weight=sample_weights), + "recall_score": sk_metrics.recall_score( + y_true, + y_pred, + average=average, + pos_label=pos_label, + sample_weight=sample_weights, + ), + "precision_score": sk_metrics.precision_score( + y_true, + y_pred, + average=average, + pos_label=pos_label, + sample_weight=sample_weights, + ), + "f1_score": sk_metrics.f1_score( + y_true, + y_pred, + average=average, + pos_label=pos_label, + sample_weight=sample_weights, + ), + } + + if y_proba is not None: + with _suppress_class_imbalance_errors(ValueError): + metrics["log_loss"] = sk_metrics.log_loss( + y_true, y_proba, labels=labels, sample_weight=sample_weights + ) + return metrics + + +def _get_binary_classifier_metrics( + *, y_true, y_pred, y_proba=None, labels=None, pos_label=1, sample_weights=None +): + with _suppress_class_imbalance_errors(ValueError): + tn, fp, fn, tp = sk_metrics.confusion_matrix(y_true, y_pred, labels=labels).ravel() + return { + "true_negatives": tn, + "false_positives": fp, + "false_negatives": fn, + "true_positives": tp, + **_get_common_classifier_metrics( + y_true=y_true, + y_pred=y_pred, + y_proba=y_proba, + labels=labels, + average="binary", + pos_label=pos_label, + sample_weights=sample_weights, + ), + } + + +def _get_multiclass_classifier_metrics( + *, + y_true, + y_pred, + y_proba=None, + labels=None, + average="weighted", + sample_weights=None, +): + metrics = _get_common_classifier_metrics( + y_true=y_true, + y_pred=y_pred, + y_proba=y_proba, + labels=labels, + average=average, + pos_label=None, + sample_weights=sample_weights, + ) + if average in ("macro", "weighted") and y_proba is not None: + metrics.update( + roc_auc=sk_metrics.roc_auc_score( + y_true=y_true, + y_score=y_proba, + sample_weight=sample_weights, + average=average, + multi_class="ovr", + ) + ) + return metrics + + +def _get_classifier_per_class_metrics_collection_df(y, y_pred, labels, sample_weights): + per_class_metrics_list = [] + for positive_class_index, positive_class in enumerate(labels): + ( + y_bin, + y_pred_bin, + _, + ) = _get_binary_sum_up_label_pred_prob( + positive_class_index, positive_class, y, y_pred, None + ) + per_class_metrics = {"positive_class": positive_class} + binary_classifier_metrics = _get_binary_classifier_metrics( + y_true=y_bin, + y_pred=y_pred_bin, + labels=[0, 1], # Use binary labels for per-class metrics + pos_label=1, + sample_weights=sample_weights, + ) + if binary_classifier_metrics: + per_class_metrics.update(binary_classifier_metrics) + per_class_metrics_list.append(per_class_metrics) + + return pd.DataFrame(per_class_metrics_list) + + +def _gen_classifier_curve( + is_binomial, + y, + y_probs, + labels, + pos_label, + curve_type, + sample_weights, +): + """ + Generate precision-recall curve or ROC curve for classifier. + + Args: + is_binomial: True if it is binary classifier otherwise False + y: True label values + y_probs: if binary classifier, the predicted probability for positive class. + if multiclass classifier, the predicted probabilities for all classes. + labels: The set of labels. + pos_label: The label of the positive class. + curve_type: "pr" or "roc" + sample_weights: Optional sample weights. + + Returns: + An instance of "_Curve" which includes attributes "plot_fn", "plot_fn_args", "auc". + """ + if curve_type == "roc": + + def gen_line_x_y_label_auc(_y, _y_prob, _pos_label): + fpr, tpr, _ = sk_metrics.roc_curve( + _y, + _y_prob, + sample_weight=sample_weights, + # For multiclass classification where a one-vs-rest ROC curve is produced for each + # class, the positive label is binarized and should not be included in the plot + # legend + pos_label=_pos_label if _pos_label == pos_label else None, + ) + + auc = sk_metrics.roc_auc_score(y_true=_y, y_score=_y_prob, sample_weight=sample_weights) + return fpr, tpr, f"AUC={auc:.3f}", auc + + xlabel = "False Positive Rate" + ylabel = "True Positive Rate" + title = "ROC curve" + if pos_label: + xlabel = f"False Positive Rate (Positive label: {pos_label})" + ylabel = f"True Positive Rate (Positive label: {pos_label})" + elif curve_type == "pr": + + def gen_line_x_y_label_auc(_y, _y_prob, _pos_label): + precision, recall, _ = sk_metrics.precision_recall_curve( + _y, + _y_prob, + sample_weight=sample_weights, + # For multiclass classification where a one-vs-rest precision-recall curve is + # produced for each class, the positive label is binarized and should not be + # included in the plot legend + pos_label=_pos_label if _pos_label == pos_label else None, + ) + # NB: We return average precision score (AP) instead of AUC because AP is more + # appropriate for summarizing a precision-recall curve + ap = sk_metrics.average_precision_score( + y_true=_y, y_score=_y_prob, pos_label=_pos_label, sample_weight=sample_weights + ) + return recall, precision, f"AP={ap:.3f}", ap + + xlabel = "Recall" + ylabel = "Precision" + title = "Precision recall curve" + if pos_label: + xlabel = f"Recall (Positive label: {pos_label})" + ylabel = f"Precision (Positive label: {pos_label})" + else: + assert False, "illegal curve type" + + if is_binomial: + x_data, y_data, line_label, auc = gen_line_x_y_label_auc(y, y_probs, pos_label) + data_series = [(line_label, x_data, y_data)] + else: + curve_list = [] + for positive_class_index, positive_class in enumerate(labels): + y_bin, _, y_prob_bin = _get_binary_sum_up_label_pred_prob( + positive_class_index, positive_class, y, labels, y_probs + ) + + x_data, y_data, line_label, auc = gen_line_x_y_label_auc( + y_bin, y_prob_bin, _pos_label=1 + ) + curve_list.append((positive_class, x_data, y_data, line_label, auc)) + + data_series = [ + (f"label={positive_class},{line_label}", x_data, y_data) + for positive_class, x_data, y_data, line_label, _ in curve_list + ] + auc = [auc for _, _, _, _, auc in curve_list] + + def _do_plot(**kwargs): + from matplotlib import pyplot + + _, ax = plot_lines(**kwargs) + dash_line_args = { + "color": "gray", + "alpha": 0.3, + "drawstyle": "default", + "linestyle": "dashed", + } + if curve_type == "pr": + ax.plot([0, 1], [1, 0], **dash_line_args) + elif curve_type == "roc": + ax.plot([0, 1], [0, 1], **dash_line_args) + + if is_binomial: + ax.legend(loc="best") + else: + ax.legend(loc="center left", bbox_to_anchor=(1, 0.5)) + pyplot.subplots_adjust(right=0.6, bottom=0.25) + + return _Curve( + plot_fn=_do_plot, + plot_fn_args={ + "data_series": data_series, + "xlabel": xlabel, + "ylabel": ylabel, + "line_kwargs": {"drawstyle": "steps-post", "linewidth": 1}, + "title": title, + }, + auc=auc, + ) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/evaluation/evaluators/default.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/evaluation/evaluators/default.py new file mode 100644 index 0000000000000000000000000000000000000000..7588defa3d4d2be17df3628315d27c808be497da --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/evaluation/evaluators/default.py @@ -0,0 +1,233 @@ +import logging +import os +import time +from typing import Optional + +import numpy as np +import pandas as pd + +import mlflow +from mlflow.entities.metric import Metric +from mlflow.exceptions import MlflowException +from mlflow.metrics import ( + MetricValue, + ari_grade_level, + exact_match, + flesch_kincaid_grade_level, + ndcg_at_k, + precision_at_k, + recall_at_k, + rouge1, + rouge2, + rougeL, + rougeLsum, + token_count, + toxicity, +) +from mlflow.metrics.genai.genai_metric import _GENAI_CUSTOM_METRICS_FILE_NAME +from mlflow.models.evaluation.artifacts import JsonEvaluationArtifact +from mlflow.models.evaluation.base import EvaluationMetric, EvaluationResult, _ModelType +from mlflow.models.evaluation.default_evaluator import ( + _LATENCY_METRIC_NAME, + BuiltInEvaluator, + _extract_output_and_other_columns, + _extract_predict_fn, +) +from mlflow.protos.databricks_pb2 import INVALID_PARAMETER_VALUE + +_logger = logging.getLogger(__name__) + + +class DefaultEvaluator(BuiltInEvaluator): + """ + The default built-in evaluator for any models that cannot be evaluated + by other built-in evaluators, such as question-answering. + """ + + name = "default" + + @classmethod + def can_evaluate(cls, *, model_type, evaluator_config, **kwargs): + return model_type in _ModelType.values() or model_type is None + + def _evaluate( + self, + model: Optional["mlflow.pyfunc.PyFuncModel"], + extra_metrics: list[EvaluationMetric], + custom_artifacts=None, + **kwargs, + ) -> EvaluationResult | None: + compute_latency = False + for extra_metric in extra_metrics: + # If latency metric is specified, we will compute latency for the model + # during prediction, and we will remove the metric from the list of extra + # metrics to be computed after prediction. + if extra_metric.name == _LATENCY_METRIC_NAME: + compute_latency = True + extra_metrics.remove(extra_metric) + self._log_genai_custom_metrics(extra_metrics) + + # Generate model predictions and evaluate metrics + y_pred, other_model_outputs, self.predictions = self._generate_model_predictions( + model, input_df=self.X.copy_to_avoid_mutation(), compute_latency=compute_latency + ) + y_true = self.dataset.labels_data + + metrics = self._builtin_metrics() + extra_metrics + self.evaluate_metrics( + metrics, + prediction=y_pred, + target=self.dataset.labels_data, + other_output_df=other_model_outputs, + ) + self.evaluate_and_log_custom_artifacts(custom_artifacts, prediction=y_pred, target=y_true) + + # Log metrics and artifacts + self.log_metrics() + self.log_eval_table(y_pred, other_model_outputs) + return EvaluationResult( + metrics=self.aggregate_metrics, artifacts=self.artifacts, run_id=self.run_id + ) + + def _builtin_metrics(self) -> list[Metric]: + """ + Get a list of builtin metrics for the model type. + """ + text_metrics = [ + token_count(), + toxicity(), + flesch_kincaid_grade_level(), + ari_grade_level(), + ] + builtin_metrics = [] + + # NB: Classifier and Regressor are handled by dedicated built-in evaluators, + if self.model_type == _ModelType.QUESTION_ANSWERING: + builtin_metrics = [*text_metrics, exact_match()] + elif self.model_type == _ModelType.TEXT_SUMMARIZATION: + builtin_metrics = [ + *text_metrics, + rouge1(), + rouge2(), + rougeL(), + rougeLsum(), + ] + elif self.model_type == _ModelType.TEXT: + builtin_metrics = text_metrics + elif self.model_type == _ModelType.RETRIEVER: + # default k to 3 if not specified + retriever_k = self.evaluator_config.pop("retriever_k", 3) + builtin_metrics = [ + precision_at_k(retriever_k), + recall_at_k(retriever_k), + ndcg_at_k(retriever_k), + ] + + return builtin_metrics + + def _generate_model_predictions( + self, + model: Optional["mlflow.pyfunc.PyFuncModel"], + input_df: pd.DataFrame, + compute_latency=False, + ): + """ + Helper method for generating model predictions + """ + predict_fn = _extract_predict_fn(model) + + def predict_with_latency(X_copy): + y_pred_list = [] + pred_latencies = [] + if len(X_copy) == 0: + raise ValueError("Empty input data") + + is_dataframe = isinstance(X_copy, pd.DataFrame) + + for row in X_copy.iterrows() if is_dataframe else enumerate(X_copy): + i, row_data = row + single_input = row_data.to_frame().T if is_dataframe else row_data + start_time = time.time() + y_pred = predict_fn(single_input) + end_time = time.time() + pred_latencies.append(end_time - start_time) + y_pred_list.append(y_pred) + + # Update latency metric + self.metrics_values.update({_LATENCY_METRIC_NAME: MetricValue(scores=pred_latencies)}) + + # Aggregate all predictions into model_predictions + sample_pred = y_pred_list[0] + if isinstance(sample_pred, pd.DataFrame): + return pd.concat(y_pred_list) + elif isinstance(sample_pred, np.ndarray): + return np.concatenate(y_pred_list, axis=0) + elif isinstance(sample_pred, list): + return sum(y_pred_list, []) + elif isinstance(sample_pred, pd.Series): + return pd.concat(y_pred_list, ignore_index=True) + elif isinstance(sample_pred, str): + return y_pred_list + else: + raise MlflowException( + message=f"Unsupported prediction type {type(sample_pred)} for model type " + f"{self.model_type}.", + error_code=INVALID_PARAMETER_VALUE, + ) + + if model is not None: + _logger.info("Computing model predictions.") + + if compute_latency: + model_predictions = predict_with_latency(input_df) + else: + model_predictions = predict_fn(input_df) + else: + if compute_latency: + _logger.warning( + "Setting the latency to 0 for all entries because the model is not provided." + ) + self.metrics_values.update( + {_LATENCY_METRIC_NAME: MetricValue(scores=[0.0] * len(input_df))} + ) + model_predictions = self.dataset.predictions_data + + output_column_name = self.predictions + ( + y_pred, + other_output_df, + predictions_column_name, + ) = _extract_output_and_other_columns(model_predictions, output_column_name) + + return y_pred, other_output_df, predictions_column_name + + def _log_genai_custom_metrics(self, extra_metrics: list[EvaluationMetric]): + genai_custom_metrics = [ + extra_metric.genai_metric_args + for extra_metric in extra_metrics + # When the field is present, the metric is created from either make_genai_metric + # or make_genai_metric_from_prompt. We will log the metric definition. + if extra_metric.genai_metric_args is not None + ] + + if len(genai_custom_metrics) == 0: + return + + names = [] + versions = [] + metric_args_list = [] + + for metric_args in genai_custom_metrics: + names.append(metric_args["name"]) + # Custom metrics created from make_genai_metric_from_prompt don't have version + versions.append(metric_args.get("version", "")) + metric_args_list.append(metric_args) + + data = {"name": names, "version": versions, "metric_args": metric_args_list} + + mlflow.log_table(data, artifact_file=_GENAI_CUSTOM_METRICS_FILE_NAME) + + artifact_name = os.path.splitext(_GENAI_CUSTOM_METRICS_FILE_NAME)[0] + self.artifacts[artifact_name] = JsonEvaluationArtifact( + uri=mlflow.get_artifact_uri(_GENAI_CUSTOM_METRICS_FILE_NAME) + ) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/evaluation/evaluators/regressor.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/evaluation/evaluators/regressor.py new file mode 100644 index 0000000000000000000000000000000000000000..05491f2dfcf9909a343dcb2378a34df3731dd17e --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/evaluation/evaluators/regressor.py @@ -0,0 +1,96 @@ +from typing import Optional + +import numpy as np +from sklearn import metrics as sk_metrics + +import mlflow +from mlflow.models.evaluation.base import EvaluationMetric, EvaluationResult, _ModelType +from mlflow.models.evaluation.default_evaluator import ( + BuiltInEvaluator, + _extract_output_and_other_columns, + _extract_predict_fn, + _get_aggregate_metrics_values, +) + + +class RegressorEvaluator(BuiltInEvaluator): + """ + A built-in evaluator for regressor models. + """ + + name = "regressor" + + @classmethod + def can_evaluate(cls, *, model_type, evaluator_config, **kwargs): + return model_type == _ModelType.REGRESSOR + + def _evaluate( + self, + model: Optional["mlflow.pyfunc.PyFuncModel"], + extra_metrics: list[EvaluationMetric], + custom_artifacts=None, + **kwargs, + ) -> EvaluationResult | None: + self.y_true = self.dataset.labels_data + self.sample_weights = self.evaluator_config.get("sample_weights", None) + + input_df = self.X.copy_to_avoid_mutation() + self.y_pred = self._generate_model_predictions(model, input_df) + self._compute_buildin_metrics(model) + + self.evaluate_metrics(extra_metrics, prediction=self.y_pred, target=self.y_true) + self.evaluate_and_log_custom_artifacts( + custom_artifacts, prediction=self.y_pred, target=self.y_true + ) + + self.log_metrics() + self.log_eval_table(self.y_pred) + + return EvaluationResult( + metrics=self.aggregate_metrics, artifacts=self.artifacts, run_id=self.run_id + ) + + def _generate_model_predictions(self, model, input_df): + if predict_fn := _extract_predict_fn(model): + preds = predict_fn(input_df) + y_pred, _, _ = _extract_output_and_other_columns(preds, self.predictions) + return y_pred + else: + return self.dataset.predictions_data + + def _compute_buildin_metrics(self, model): + self._evaluate_sklearn_model_score_if_scorable(model, self.y_true, self.sample_weights) + self.metrics_values.update( + _get_aggregate_metrics_values( + _get_regressor_metrics(self.y_true, self.y_pred, self.sample_weights) + ) + ) + + +def _get_regressor_metrics(y, y_pred, sample_weights): + from mlflow.metrics.metric_definitions import _root_mean_squared_error + + sum_on_target = ( + (np.array(y) * np.array(sample_weights)).sum() if sample_weights is not None else sum(y) + ) + return { + "example_count": len(y), + "mean_absolute_error": sk_metrics.mean_absolute_error( + y, y_pred, sample_weight=sample_weights + ), + "mean_squared_error": sk_metrics.mean_squared_error( + y, y_pred, sample_weight=sample_weights + ), + "root_mean_squared_error": _root_mean_squared_error( + y_true=y, + y_pred=y_pred, + sample_weight=sample_weights, + ), + "sum_on_target": sum_on_target, + "mean_on_target": sum_on_target / len(y), + "r2_score": sk_metrics.r2_score(y, y_pred, sample_weight=sample_weights), + "max_error": sk_metrics.max_error(y, y_pred), + "mean_absolute_percentage_error": sk_metrics.mean_absolute_percentage_error( + y, y_pred, sample_weight=sample_weights + ), + } diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/evaluation/evaluators/shap.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/evaluation/evaluators/shap.py new file mode 100644 index 0000000000000000000000000000000000000000..7426141f0c390ec1bd7c5384f5d1b1e5139139b3 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/evaluation/evaluators/shap.py @@ -0,0 +1,296 @@ +import functools +import logging +from typing import Optional + +import numpy as np +from packaging.version import Version +from sklearn.pipeline import Pipeline as sk_Pipeline + +import mlflow +from mlflow import MlflowException +from mlflow.models.evaluation.base import EvaluationMetric, EvaluationResult, _ModelType +from mlflow.models.evaluation.default_evaluator import ( + BuiltInEvaluator, + _extract_predict_fn, + _extract_raw_model, + _get_dataframe_with_renamed_columns, +) +from mlflow.models.evaluation.evaluators.classifier import ( + _is_continuous, + _suppress_class_imbalance_errors, +) +from mlflow.protos.databricks_pb2 import INVALID_PARAMETER_VALUE +from mlflow.pyfunc import _ServedPyFuncModel + +_logger = logging.getLogger(__name__) + + +_SUPPORTED_SHAP_ALGORITHMS = ("exact", "permutation", "partition", "kernel") +_DEFAULT_SAMPLE_ROWS_FOR_SHAP = 2000 + + +def _shap_predict_fn(x, predict_fn, feature_names): + return predict_fn(_get_dataframe_with_renamed_columns(x, feature_names)) + + +class ShapEvaluator(BuiltInEvaluator): + """ + A built-in evaluator to get SHAP explainability insights for classifier and regressor models. + + This evaluator often run with the main evaluator for the model like ClassifierEvaluator. + """ + + name = "shap" + + @classmethod + def can_evaluate(cls, *, model_type, evaluator_config, **kwargs): + return model_type in (_ModelType.CLASSIFIER, _ModelType.REGRESSOR) and evaluator_config.get( + "log_model_explainability", True + ) + + def _evaluate( + self, + model: Optional["mlflow.pyfunc.PyFuncModel"], + extra_metrics: list[EvaluationMetric], + custom_artifacts=None, + **kwargs, + ) -> EvaluationResult | None: + if isinstance(model, _ServedPyFuncModel): + _logger.warning( + "Skipping model explainability because a model server is used for environment " + "restoration." + ) + return + + model_loader_module, raw_model = _extract_raw_model(model) + if model_loader_module == "mlflow.spark": + # TODO: Shap explainer need to manipulate on each feature values, + # but spark model input dataframe contains Vector type feature column + # which shap explainer does not support. + # To support this, we need expand the Vector type feature column into + # multiple scalar feature columns and pass it to shap explainer. + _logger.warning( + "Logging model explainability insights is not currently supported for PySpark " + "models." + ) + return + + self.y_true = self.dataset.labels_data + self.label_list = self.evaluator_config.get("label_list") + self.pos_label = self.evaluator_config.get("pos_label") + + if not (np.issubdtype(self.y_true.dtype, np.number) or self.y_true.dtype == np.bool_): + # Note: python bool type inherits number type but np.bool_ does not inherit np.number. + _logger.warning( + "Skip logging model explainability insights because it requires all label " + "values to be numeric or boolean." + ) + return + + algorithm = self.evaluator_config.get("explainability_algorithm", None) + if algorithm is not None and algorithm not in _SUPPORTED_SHAP_ALGORITHMS: + raise MlflowException( + message=f"Specified explainer algorithm {algorithm} is unsupported. Currently only " + f"support {','.join(_SUPPORTED_SHAP_ALGORITHMS)} algorithms.", + error_code=INVALID_PARAMETER_VALUE, + ) + + if algorithm != "kernel": + feature_dtypes = list(self.X.get_original().dtypes) + for feature_dtype in feature_dtypes: + if not np.issubdtype(feature_dtype, np.number): + _logger.warning( + "Skip logging model explainability insights because the shap explainer " + f"{algorithm} requires all feature values to be numeric, and each feature " + "column must only contain scalar values." + ) + return + + try: + import shap + from matplotlib import pyplot + except ImportError: + _logger.warning( + "SHAP or matplotlib package is not installed, so model explainability insights " + "will not be logged." + ) + return + + if Version(shap.__version__) < Version("0.40"): + _logger.warning( + "Shap package version is lower than 0.40, Skip log model explainability." + ) + return + + sample_rows = self.evaluator_config.get( + "explainability_nsamples", _DEFAULT_SAMPLE_ROWS_FOR_SHAP + ) + + X_df = self.X.copy_to_avoid_mutation() + + sampled_X = shap.sample(X_df, sample_rows, random_state=0) + + mode_or_mean_dict = _compute_df_mode_or_mean(X_df) + sampled_X = sampled_X.fillna(mode_or_mean_dict) + + # shap explainer might call provided `predict_fn` with a `numpy.ndarray` type + # argument, this might break some model inference, so convert the argument into + # a pandas dataframe. + # The `shap_predict_fn` calls model's predict function, we need to restore the input + # dataframe with original column names, because some model prediction routine uses + # the column name. + + predict_fn = _extract_predict_fn(model) + shap_predict_fn = functools.partial( + _shap_predict_fn, predict_fn=predict_fn, feature_names=self.dataset.feature_names + ) + + if self.label_list is None: + # If label list is not specified, infer label list from model output. + # We need to copy the input data as the model might mutate the input data. + y_pred = predict_fn(X_df.copy()) if predict_fn else self.dataset.predictions_data + self.label_list = np.unique(np.concatenate([self.y_true, y_pred])) + + try: + if algorithm: + if algorithm == "kernel": + # We need to lazily import shap, so lazily import `_PatchedKernelExplainer` + from mlflow.models.evaluation._shap_patch import _PatchedKernelExplainer + + kernel_link = self.evaluator_config.get( + "explainability_kernel_link", "identity" + ) + if kernel_link not in ["identity", "logit"]: + raise ValueError( + "explainability_kernel_link config can only be set to 'identity' or " + f"'logit', but got '{kernel_link}'." + ) + background_X = shap.sample(X_df, sample_rows, random_state=3) + background_X = background_X.fillna(mode_or_mean_dict) + + explainer = _PatchedKernelExplainer( + shap_predict_fn, background_X, link=kernel_link + ) + else: + explainer = shap.Explainer( + shap_predict_fn, + sampled_X, + feature_names=self.dataset.feature_names, + algorithm=algorithm, + ) + else: + if ( + raw_model + and not len(self.label_list) > 2 + and not isinstance(raw_model, sk_Pipeline) + ): + # For mulitnomial classifier, shap.Explainer may choose Tree/Linear explainer + # for raw model, this case shap plot doesn't support it well, so exclude the + # multinomial_classifier case here. + explainer = shap.Explainer( + raw_model, sampled_X, feature_names=self.dataset.feature_names + ) + else: + # fallback to default explainer + explainer = shap.Explainer( + shap_predict_fn, sampled_X, feature_names=self.dataset.feature_names + ) + + _logger.info(f"Shap explainer {explainer.__class__.__name__} is used.") + + if algorithm == "kernel": + shap_values = shap.Explanation( + explainer.shap_values(sampled_X), feature_names=self.dataset.feature_names + ) + else: + shap_values = explainer(sampled_X) + except Exception as e: + # Shap evaluation might fail on some edge cases, e.g., unsupported input data values + # or unsupported model on specific shap explainer. Catch exception to prevent it + # breaking the whole `evaluate` function. + + if not self.evaluator_config.get("ignore_exceptions", True): + raise e + + _logger.warning( + f"Shap evaluation failed. Reason: {e!r}. " + "Set logging level to DEBUG to see the full traceback." + ) + _logger.debug("", exc_info=True) + return + + if self.evaluator_config.get("log_explainer", False): + try: + mlflow.shap.log_explainer(explainer, name="explainer") + except Exception as e: + # TODO: The explainer saver is buggy, if `get_underlying_model_flavor` return + # "unknown", then fallback to shap explainer saver, and shap explainer will call + # `model.save` for sklearn model, there is no `.save` method, so error will + # happen. + _logger.warning( + f"Logging explainer failed. Reason: {e!r}. " + "Set logging level to DEBUG to see the full traceback." + ) + _logger.debug("", exc_info=True) + + def _adjust_color_bar(): + pyplot.gcf().axes[-1].set_aspect("auto") + pyplot.gcf().axes[-1].set_box_aspect(50) + + def _adjust_axis_tick(): + pyplot.xticks(fontsize=10) + pyplot.yticks(fontsize=10) + + def plot_beeswarm(): + shap.plots.beeswarm(shap_values, show=False, color_bar=True) + _adjust_color_bar() + _adjust_axis_tick() + + with _suppress_class_imbalance_errors(ValueError, log_warning=False): + self._log_image_artifact( + plot_beeswarm, + "shap_beeswarm_plot", + ) + + def plot_summary(): + shap.summary_plot(shap_values, show=False, color_bar=True) + _adjust_color_bar() + _adjust_axis_tick() + + with _suppress_class_imbalance_errors(TypeError, log_warning=False): + self._log_image_artifact( + plot_summary, + "shap_summary_plot", + ) + + def plot_feature_importance(): + shap.plots.bar(shap_values, show=False) + _adjust_axis_tick() + + with _suppress_class_imbalance_errors(IndexError, log_warning=False): + self._log_image_artifact( + plot_feature_importance, + "shap_feature_importance_plot", + ) + + return EvaluationResult( + metrics=self.aggregate_metrics, + artifacts=self.artifacts, + run_id=self.run_id, + ) + + +def _compute_df_mode_or_mean(df): + """ + Compute mean (for continuous columns) and compute mode (for other columns) for the + input dataframe, return a dict, key is column name, value is the corresponding mode or + mean value, this function calls `_is_continuous` to determine whether the + column is continuous column. + """ + continuous_cols = [c for c in df.columns if _is_continuous(df[c])] + df_cont = df[continuous_cols] + df_non_cont = df.drop(continuous_cols, axis=1) + + means = {} if df_cont.empty else df_cont.mean().to_dict() + modes = {} if df_non_cont.empty else df_non_cont.mode().loc[0].to_dict() + return {**means, **modes} diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/evaluation/lift_curve.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/evaluation/lift_curve.py new file mode 100644 index 0000000000000000000000000000000000000000..794a5ba9db211ddca9e5c04c4e2fa6cbbd3cf57a --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/evaluation/lift_curve.py @@ -0,0 +1,178 @@ +import matplotlib.pyplot as plt +import numpy as np + + +def _cumulative_gain_curve(y_true, y_score, pos_label=None): + """ + This method is copied from scikit-plot package. + See https://github.com/reiinakano/scikit-plot/blob/2dd3e6a76df77edcbd724c4db25575f70abb57cb/scikitplot/helpers.py#L157 + + This function generates the points necessary to plot the Cumulative Gain + + Note: This implementation is restricted to the binary classification task. + + Args: + y_true (array-like, shape (n_samples)): True labels of the data. + + y_score (array-like, shape (n_samples)): Target scores, can either be + probability estimates of the positive class, confidence values, or + non-thresholded measure of decisions (as returned by + decision_function on some classifiers). + + pos_label (int or str, default=None): Label considered as positive and + others are considered negative + + Returns: + percentages (numpy.ndarray): An array containing the X-axis values for + plotting the Cumulative Gains chart. + + gains (numpy.ndarray): An array containing the Y-axis values for one + curve of the Cumulative Gains chart. + + Raises: + ValueError: If `y_true` is not composed of 2 classes. The Cumulative + Gain Chart is only relevant in binary classification. + """ + y_true = np.asarray(y_true) + y_score = np.asarray(y_score) + + # ensure binary classification if pos_label is not specified + classes = np.unique(y_true) + if pos_label is None and not ( + np.array_equal(classes, [0, 1]) + or np.array_equal(classes, [-1, 1]) + or np.array_equal(classes, [0]) + or np.array_equal(classes, [-1]) + or np.array_equal(classes, [1]) + ): + raise ValueError("Data is not binary and pos_label is not specified") + elif pos_label is None: + pos_label = 1.0 + + # make y_true a boolean vector + y_true = y_true == pos_label + + sorted_indices = np.argsort(y_score)[::-1] + y_true = y_true[sorted_indices] + gains = np.cumsum(y_true) + + percentages = np.arange(start=1, stop=len(y_true) + 1) + + gains = gains / float(np.sum(y_true)) + percentages = percentages / float(len(y_true)) + + gains = np.insert(gains, 0, [0]) + percentages = np.insert(percentages, 0, [0]) + + return percentages, gains + + +def plot_lift_curve( + y_true, + y_probas, + title="Lift Curve", + ax=None, + figsize=None, + title_fontsize="large", + text_fontsize="medium", + pos_label=None, +): + """ + This method is copied from scikit-plot package. + See https://github.com/reiinakano/scikit-plot/blob/2dd3e6a76df77edcbd724c4db25575f70abb57cb/scikitplot/metrics.py#L1133 + + Generates the Lift Curve from labels and scores/probabilities + + The lift curve is used to determine the effectiveness of a + binary classifier. A detailed explanation can be found at + http://www2.cs.uregina.ca/~dbd/cs831/notes/lift_chart/lift_chart.html. + The implementation here works only for binary classification. + + Args: + y_true (array-like, shape (n_samples)): + Ground truth (correct) target values. + + y_probas (array-like, shape (n_samples, n_classes)): + Prediction probabilities for each class returned by a classifier. + + title (string, optional): Title of the generated plot. Defaults to + "Lift Curve". + + ax (:class:`matplotlib.axes.Axes`, optional): The axes upon which to + plot the learning curve. If None, the plot is drawn on a new set of + axes. + + figsize (2-tuple, optional): Tuple denoting figure size of the plot + e.g. (6, 6). Defaults to ``None``. + + title_fontsize (string or int, optional): Matplotlib-style fontsizes. + Use e.g. "small", "medium", "large" or integer-values. Defaults to + "large". + + text_fontsize (string or int, optional): Matplotlib-style fontsizes. + Use e.g. "small", "medium", "large" or integer-values. Defaults to + "medium". + + pos_label (optional): Label for the positive class. + + Returns: + ax (:class:`matplotlib.axes.Axes`): The axes on which the plot was + drawn. + + Example: + >>> lr = LogisticRegression() + >>> lr = lr.fit(X_train, y_train) + >>> y_probas = lr.predict_proba(X_test) + >>> plot_lift_curve(y_test, y_probas) + + >>> plt.show() + + .. image:: _static/examples/plot_lift_curve.png + :align: center + :alt: Lift Curve + """ + y_true = np.array(y_true) + y_probas = np.array(y_probas) + + classes = np.unique(y_true) + if len(classes) != 2: + raise ValueError(f"Cannot calculate Lift Curve for data with {len(classes)} category/ies") + + # Compute Cumulative Gain Curves + percentages, gains1 = _cumulative_gain_curve(y_true, y_probas[:, 0], classes[0]) + percentages, gains2 = _cumulative_gain_curve(y_true, y_probas[:, 1], classes[1]) + + percentages = percentages[1:] + gains1 = gains1[1:] + gains2 = gains2[1:] + + gains1 = gains1 / percentages + gains2 = gains2 / percentages + + if ax is None: + _, ax = plt.subplots(1, 1, figsize=figsize) + + ax.set_title(title, fontsize=title_fontsize) + + label0 = f"Class {classes[0]}" + label1 = f"Class {classes[1]}" + # show (positive) next to the positive class in the legend + if pos_label: + if pos_label == classes[0]: + label0 = f"Class {classes[0]} (positive)" + elif pos_label == classes[1]: + label1 = f"Class {classes[1]} (positive)" + # do not mark positive class if pos_label is not in classes + + ax.plot(percentages, gains1, lw=3, label=label0) + ax.plot(percentages, gains2, lw=3, label=label1) + + ax.plot([0, 1], [1, 1], "k--", lw=2, label="Baseline") + + ax.set_xlabel("Percentage of sample", fontsize=text_fontsize) + ax.set_ylabel("Lift", fontsize=text_fontsize) + ax.tick_params(labelsize=text_fontsize) + ax.grid("on") + ax.legend(loc="best", fontsize=text_fontsize) + + return ax diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/evaluation/utils/__pycache__/metric.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/evaluation/utils/__pycache__/metric.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..c2f1201e20f75630a0c97f61d953f78759de5465 Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/evaluation/utils/__pycache__/metric.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/evaluation/utils/__pycache__/trace.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/evaluation/utils/__pycache__/trace.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..d3c6a16d2a55800ffc2e428a65915e4c8a357dd3 Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/evaluation/utils/__pycache__/trace.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/evaluation/utils/metric.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/evaluation/utils/metric.py new file mode 100644 index 0000000000000000000000000000000000000000..d4b43bfa6379ba6221d500e1297544ba2f6702f0 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/evaluation/utils/metric.py @@ -0,0 +1,128 @@ +import logging +from dataclasses import dataclass +from typing import Any, Callable + +import numpy as np + +from mlflow.metrics.base import MetricValue +from mlflow.models.evaluation.base import EvaluationMetric + +_logger = logging.getLogger(__name__) + + +@dataclass +class MetricDefinition: + """ + A dataclass representing a metric definition used in model evaluation. + + Attributes: + function: The metric function to be called for evaluation. + name: The name of the metric. + index: The index of the metric in the ``extra_metrics`` argument of ``mlflow.evaluate``. + version: (Optional) The metric version. For example v1. + genai_metric_args: (Optional) A dictionary containing arguments specified by users when + calling make_genai_metric or make_genai_metric_from_prompt. + Those args are persisted so that we can deserialize the same metric object later. + """ + + function: Callable[..., Any] + name: str + index: int + version: str | None = None + genai_metric_args: dict[str, Any] | None = None + + @classmethod + def from_index_and_metric(cls, index: int, metric: EvaluationMetric): + return cls( + function=metric.eval_fn, + index=index, + name=metric.name, + version=metric.version, + genai_metric_args=metric.genai_metric_args, + ) + + def evaluate(self, eval_fn_args) -> MetricValue | None: + """ + This function calls the metric function and performs validations on the returned + result to ensure that they are in the expected format. It will warn and will not log metrics + that are in the wrong format. + + Args: + eval_fn_args: A dictionary of args needed to compute the eval metrics. + + Returns: + MetricValue + """ + if self.index < 0: + exception_header = f"Did not log builtin metric '{self.name}' because it" + else: + exception_header = ( + f"Did not log metric '{self.name}' at index " + f"{self.index} in the `extra_metrics` parameter because it" + ) + + metric: MetricValue = self.function(*eval_fn_args) + + def _is_numeric(value): + return isinstance(value, (int, float, np.number)) + + def _is_string(value): + return isinstance(value, str) + + if metric is None: + _logger.warning(f"{exception_header} returned None.") + return + + if _is_numeric(metric): + return MetricValue(aggregate_results={self.name: metric}) + + if not isinstance(metric, MetricValue): + _logger.warning(f"{exception_header} did not return a MetricValue.") + return + + scores = metric.scores + justifications = metric.justifications + aggregates = metric.aggregate_results + + if scores is not None: + if not isinstance(scores, list): + _logger.warning( + f"{exception_header} must return MetricValue with scores as a list." + ) + return + if any(not (_is_numeric(s) or _is_string(s) or s is None) for s in scores): + _logger.warning( + f"{exception_header} must return MetricValue with numeric or string scores." + ) + return + + if justifications is not None: + if not isinstance(justifications, list): + _logger.warning( + f"{exception_header} must return MetricValue with justifications as a list." + ) + return + if any(not (_is_string(just) or just is None) for just in justifications): + _logger.warning( + f"{exception_header} must return MetricValue with string justifications." + ) + return + + if aggregates is not None: + if not isinstance(aggregates, dict): + _logger.warning( + f"{exception_header} must return MetricValue with aggregate_results as a dict." + ) + return + + if any( + not (isinstance(k, str) and (_is_numeric(v) or v is None)) + for k, v in aggregates.items() + ): + _logger.warning( + f"{exception_header} must return MetricValue with aggregate_results with " + "str keys and numeric values." + ) + return + + return metric diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/evaluation/utils/trace.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/evaluation/utils/trace.py new file mode 100644 index 0000000000000000000000000000000000000000..24810e4fc7e484eeda74b29b635402b96b483634 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/evaluation/utils/trace.py @@ -0,0 +1,179 @@ +import contextlib +import inspect +import logging +from typing import Any, Callable + +from mlflow.ml_package_versions import FLAVOR_TO_MODULE_NAME +from mlflow.utils.autologging_utils import ( + AUTOLOGGING_INTEGRATIONS, + autologging_conf_lock, + get_autolog_function, + is_autolog_supported, +) +from mlflow.utils.autologging_utils.safety import revert_patches +from mlflow.utils.import_hooks import ( + _post_import_hooks, + get_post_import_hooks, + register_post_import_hook, +) + +_logger = logging.getLogger(__name__) + + +# This flag is used to display the message only once when tracing is enabled during the evaluation. +_SHOWN_TRACE_MESSAGE_BEFORE = False + + +@contextlib.contextmanager +@autologging_conf_lock +def configure_autologging_for_evaluation(enable_tracing: bool = True): + """ + Temporarily override the autologging configuration for all flavors during the model evaluation. + For example, model auto-logging must be disabled during the evaluation. After the evaluation + is done, the original autologging configurations are restored. + + Args: + enable_tracing (bool): Whether to enable tracing for the supported flavors during eval. + """ + original_import_hooks = {} + new_import_hooks = {} + + # AUTOLOGGING_INTEGRATIONS can change during we iterate over flavors and enable/disable + # autologging, therefore, we snapshot the current configuration to restore it later. + global_config_snapshot = AUTOLOGGING_INTEGRATIONS.copy() + + for flavor in FLAVOR_TO_MODULE_NAME: + if not is_autolog_supported(flavor): + continue + + original_config = global_config_snapshot.get(flavor, {}).copy() + + # If autologging is explicitly disabled, do nothing. + if original_config.get("disable", False): + continue + + # NB: Using post-import hook to configure the autologging lazily when the target + # flavor's module is imported, rather than configuring it immediately. This is + # because the evaluation code usually only uses a subset of the supported flavors, + # hence we want to avoid unnecessary overhead of configuring all flavors. + @autologging_conf_lock + def _setup_autolog(module): + try: + autolog = get_autolog_function(flavor) + + # If tracing is supported and not explicitly disabled, enable it. + if enable_tracing and _should_enable_tracing(flavor, global_config_snapshot): + new_config = { + k: False if k.startswith("log_") else v for k, v in original_config.items() + } + # log_models needs to be disabled + # so we don't init LoggedModels during eval for some GenAI flavors + new_config |= {"log_traces": True, "silent": True, "log_models": False} + _kwargs_safe_invoke(autolog, new_config) + + global _SHOWN_TRACE_MESSAGE_BEFORE + if not _SHOWN_TRACE_MESSAGE_BEFORE: + _logger.info( + "Auto tracing is temporarily enabled during the model evaluation " + "for computing some metrics and debugging. To disable tracing, call " + "`mlflow.autolog(disable=True)`." + ) + _SHOWN_TRACE_MESSAGE_BEFORE = True + else: + autolog(disable=True) + + except Exception: + _logger.debug(f"Failed to update autologging config for {flavor}.", exc_info=True) + + module = FLAVOR_TO_MODULE_NAME[flavor] + try: + original_import_hooks[module] = get_post_import_hooks(module) + new_import_hooks[module] = _setup_autolog + register_post_import_hook(_setup_autolog, module, overwrite=True) + except Exception: + _logger.debug(f"Failed to register post-import hook for {flavor}.", exc_info=True) + + try: + yield + finally: + # Remove post-import hooks and patches the are registered during the evaluation. + for module, hooks in new_import_hooks.items(): + # Restore original post-import hooks if any. Note that we don't use + # register_post_import_hook method to bypass some pre-checks and just + # restore the original state. + if hooks is None: + _post_import_hooks.pop(module, None) + else: + _post_import_hooks[module] = original_import_hooks[module] + + # If any autologging configuration is updated, restore original autologging configurations. + for flavor, new_config in AUTOLOGGING_INTEGRATIONS.copy().items(): + original_config = global_config_snapshot.get(flavor) + if original_config != new_config: + try: + autolog = get_autolog_function(flavor) + if original_config: + _kwargs_safe_invoke(autolog, original_config) + AUTOLOGGING_INTEGRATIONS[flavor] = original_config + else: + # If the original configuration is empty, autologging was not enabled before + autolog(disable=True) + # Remove all safe_patch applied by autologging + revert_patches(flavor) + # We also need to remove the config entry from AUTOLOGGING_INTEGRATIONS, + # so as not to confuse with the case user explicitly disabled autologging. + AUTOLOGGING_INTEGRATIONS.pop(flavor, None) + except ImportError: + pass + except Exception as e: + if original_config is None or ( + not original_config.get("disable", False) + and not original_config.get("silent", False) + ): + _logger.warning( + f"Exception raised while calling autologging for {flavor}: {e}" + ) + + +def _should_enable_tracing(flavor: str, autologging_config: dict[str, Any]) -> bool: + """ + Check if tracing should be enabled for the given flavor during the model evaluation. + """ + # 1. Check if the autologging or tracing is globally disabled + # TODO: This check should not take precedence over the flavor-specific configuration + # set by the explicit mlflow..autolog() call by users. + # However, in Databricks, sometimes mlflow..autolog() is automatically + # called in the kernel startup, which is confused with the user's action. In + # such cases, even when user disables autologging globally, the flavor-specific + # autologging remains enabled. We are going to fix the Databricks side issue, + # and after that, we should move this check down after the flavor-specific check. + global_config = autologging_config.get("mlflow", {}) + if global_config.get("disable", False) or (not global_config.get("log_traces", True)): + return False + + if not _is_trace_autologging_supported(flavor): + return False + + # 3. Check if tracing is explicitly disabled for the flavor + flavor_config = autologging_config.get(flavor, {}) + return flavor_config.get("log_traces", True) + + +def _kwargs_safe_invoke(func: Callable[..., Any], kwargs: dict[str, Any]): + """ + Invoke the function with the given dictionary as keyword arguments, but only include the + arguments that are present in the function's signature. + + This is particularly used for calling autolog() function with the configuration dictionary + stored in AUTOLOGGING_INTEGRATIONS. While the config keys mostly align with the autolog()'s + signature by design, some keys are not present in autolog(), such as "globally_configured". + """ + sig = inspect.signature(func) + return func(**{k: v for k, v in kwargs.items() if k in sig.parameters}) + + +def _is_trace_autologging_supported(flavor_name: str) -> bool: + """Check if the given flavor supports trace autologging.""" + if autolog_func := get_autolog_function(flavor_name): + return "log_traces" in inspect.signature(autolog_func).parameters + return False diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/evaluation/validation.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/evaluation/validation.py new file mode 100644 index 0000000000000000000000000000000000000000..1cd762d1694820d52f76fc4a1d1e45f4f4712610 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/evaluation/validation.py @@ -0,0 +1,433 @@ +import logging +import operator +import os +from decimal import Decimal + +from mlflow.exceptions import MlflowException +from mlflow.models.evaluation import EvaluationResult +from mlflow.protos.databricks_pb2 import BAD_REQUEST, INVALID_PARAMETER_VALUE + +_logger = logging.getLogger(__name__) + + +class MetricThreshold: + """ + This class allows you to define metric thresholds for model validation. + Allowed thresholds are: threshold, min_absolute_change, min_relative_change. + + Args: + threshold: (Optional) A number representing the value threshold for the metric. + + - If greater is better for the metric, the metric value has to be + >= threshold to pass validation. + - Otherwise, the metric value has to be <= threshold to pass the validation. + + min_absolute_change: (Optional) A positive number representing the minimum absolute + change required for candidate model to pass validation with + the baseline model. + + - If greater is better for the metric, metric value has to be + >= baseline model metric value + min_absolute_change to pass the validation. + - Otherwise, metric value has to be <= baseline model metric value - min_absolute_change + to pass the validation. + + min_relative_change: (Optional) A floating point number between 0 and 1 representing + the minimum relative change (in percentage of + baseline model metric value) for candidate model + to pass the comparison with the baseline model. + + - If greater is better for the metric, metric value has to be + >= baseline model metric value * (1 + min_relative_change) + - Otherwise, metric value has to be + <= baseline model metric value * (1 - min_relative_change) + - Note that if the baseline model metric value is equal to 0, the + threshold falls back performing a simple verification that the + candidate metric value is better than the baseline metric value, + i.e. metric value >= baseline model metric value + 1e-10 if greater + is better; metric value <= baseline model metric value - 1e-10 if + lower is better. + + greater_is_better: A required boolean representing whether greater value is + better for the metric. + """ + + def __init__( + self, + threshold=None, + min_absolute_change=None, + min_relative_change=None, + greater_is_better=None, + ): + if threshold is not None and type(threshold) not in {int, float}: + raise MetricThresholdClassException("`threshold` parameter must be a number.") + if min_absolute_change is not None and ( + type(min_absolute_change) not in {int, float} or min_absolute_change <= 0 + ): + raise MetricThresholdClassException( + "`min_absolute_change` parameter must be a positive number." + ) + if min_relative_change is not None: + if not isinstance(min_relative_change, float): + raise MetricThresholdClassException( + "`min_relative_change` parameter must be a floating point number." + ) + if min_relative_change < 0 or min_relative_change > 1: + raise MetricThresholdClassException( + "`min_relative_change` parameter must be between 0 and 1." + ) + if greater_is_better is None: + raise MetricThresholdClassException("`greater_is_better` parameter must be defined.") + if not isinstance(greater_is_better, bool): + raise MetricThresholdClassException("`greater_is_better` parameter must be a boolean.") + if threshold is None and min_absolute_change is None and min_relative_change is None: + raise MetricThresholdClassException("no threshold was specified.") + self._threshold = threshold + self._min_absolute_change = min_absolute_change + self._min_relative_change = min_relative_change + self._greater_is_better = greater_is_better + + @property + def threshold(self): + """ + Value of the threshold. + """ + return self._threshold + + @property + def min_absolute_change(self): + """ + Value of the minimum absolute change required to pass model comparison with baseline model. + """ + return self._min_absolute_change + + @property + def min_relative_change(self): + """ + Float value of the minimum relative change required to pass model comparison with + baseline model. + """ + return self._min_relative_change + + @property + def greater_is_better(self): + """ + Boolean value representing whether greater value is better for the metric. + """ + return self._greater_is_better + + def __str__(self): + """ + Returns a human-readable string consisting of all specified thresholds. + """ + threshold_strs = [] + if self._threshold is not None: + threshold_strs.append(f"Threshold: {self._threshold}.") + if self._min_absolute_change is not None: + threshold_strs.append(f"Minimum Absolute Change: {self._min_absolute_change}.") + if self._min_relative_change is not None: + threshold_strs.append(f"Minimum Relative Change: {self._min_relative_change}.") + if self._greater_is_better is not None: + if self._greater_is_better: + threshold_strs.append("Greater value is better.") + else: + threshold_strs.append("Lower value is better.") + return " ".join(threshold_strs) + + +class MetricThresholdClassException(MlflowException): + def __init__(self, _message, **kwargs): + message = "Could not instantiate MetricThreshold class: " + _message + super().__init__(message, error_code=INVALID_PARAMETER_VALUE, **kwargs) + + +class _MetricValidationResult: + """ + Internal class for representing validation result per metric. + Not user facing, used for organizing metric failures and generating failure message + more conveniently. + + Args: + metric_name: String representing the metric name + candidate_metric_value: value of metric for candidate model + metric_threshold: :py:class: `MetricThreshold` + The MetricThreshold for the metric. + baseline_metric_value: value of metric for baseline model + """ + + missing_candidate = False + missing_baseline = False + threshold_failed = False + min_absolute_change_failed = False + min_relative_change_failed = False + + def __init__( + self, + metric_name, + candidate_metric_value, + metric_threshold, + baseline_metric_value=None, + ): + self.metric_name = metric_name + self.candidate_metric_value = candidate_metric_value + self.baseline_metric_value = baseline_metric_value + self.metric_threshold = metric_threshold + + def __str__(self): + """ + Returns a human-readable string representing the validation result for the metric. + """ + if self.is_success(): + return f"Metric {self.metric_name} passed the validation." + + if self.missing_candidate: + return ( + f"Metric validation failed: metric {self.metric_name} was missing from the " + f"evaluation result of the candidate model." + ) + + result_strs = [] + if self.threshold_failed: + result_strs.append( + f"Metric {self.metric_name} value threshold check failed: " + f"candidate model {self.metric_name} = {self.candidate_metric_value}, " + f"{self.metric_name} threshold = {self.metric_threshold.threshold}." + ) + if self.missing_baseline: + result_strs.append( + f"Model comparison failed: metric {self.metric_name} was missing from " + f"the evaluation result of the baseline model." + ) + else: + if self.min_absolute_change_failed: + result_strs.append( + f"Metric {self.metric_name} minimum absolute change check failed: " + f"candidate model {self.metric_name} = {self.candidate_metric_value}, " + f"baseline model {self.metric_name} = {self.baseline_metric_value}, " + f"{self.metric_name} minimum absolute change threshold = " + f"{self.metric_threshold.min_absolute_change}." + ) + if self.min_relative_change_failed: + result_strs.append( + f"Metric {self.metric_name} minimum relative change check failed: " + f"candidate model {self.metric_name} = {self.candidate_metric_value}, " + f"baseline model {self.metric_name} = {self.baseline_metric_value}, " + f"{self.metric_name} minimum relative change threshold = " + f"{self.metric_threshold.min_relative_change}." + ) + return " ".join(result_strs) + + def is_success(self): + return ( + not self.missing_candidate + and not self.missing_baseline + and not self.threshold_failed + and not self.min_absolute_change_failed + and not self.min_relative_change_failed + ) + + +class ModelValidationFailedException(MlflowException): + def __init__(self, message, **kwargs): + super().__init__(message, error_code=BAD_REQUEST, **kwargs) + + +def validate_evaluation_results( + validation_thresholds: dict[str, MetricThreshold], + candidate_result: EvaluationResult, + baseline_result: EvaluationResult | None = None, +): + """ + Validate the evaluation result from one model (candidate) against another + model (baseline). If the candidate results do not meet the validation + thresholds, an ModelValidationFailedException will be raised. + + .. note:: + + This API is a replacement for the deprecated model validation + functionality in the :py:func:`mlflow.evaluate` API. + + Args: + validation_thresholds: A dictionary of metric name to + :py:class:`mlflow.models.MetricThreshold` used for model validation. + Each metric name must either be the name of a builtin metric or the + name of a metric defined in the ``extra_metrics`` parameter. + candidate_result: The evaluation result of the candidate model. + Returned by the :py:func:`mlflow.evaluate` API. + baseline_result: The evaluation result of the baseline model. + Returned by the :py:func:`mlflow.evaluate` API. + If set to None, the candidate model result will be + compared against the threshold values directly. + + Code Example: + + .. code-block:: python + :caption: Example of Model Validation + + import mlflow + from mlflow.models import MetricThreshold + + thresholds = { + "accuracy_score": MetricThreshold( + # accuracy should be >=0.8 + threshold=0.8, + # accuracy should be at least 5 percent greater than baseline model accuracy + min_absolute_change=0.05, + # accuracy should be at least 0.05 greater than baseline model accuracy + min_relative_change=0.05, + greater_is_better=True, + ), + } + + # Get evaluation results for the candidate model + candidate_result = mlflow.evaluate( + model="", + data=eval_dataset, + targets="ground_truth", + model_type="classifier", + ) + + # Get evaluation results for the baseline model + baseline_result = mlflow.evaluate( + model="", + data=eval_dataset, + targets="ground_truth", + model_type="classifier", + ) + + # Validate the results + mlflow.validate_evaluation_results( + thresholds, + candidate_result, + baseline_result, + ) + + See `the Model Validation documentation + <../../models/index.html#performing-model-validation>`_ for more details. + """ + try: + assert type(validation_thresholds) is dict + for key in validation_thresholds.keys(): + assert type(key) is str + for threshold in validation_thresholds.values(): + assert isinstance(threshold, MetricThreshold) + except AssertionError: + raise MlflowException( + message="The validation thresholds argument must be a dictionary that maps strings " + "to MetricThreshold objects.", + error_code=INVALID_PARAMETER_VALUE, + ) + + _logger.info("Validating candidate model metrics against baseline") + _validate( + validation_thresholds, + candidate_result.metrics, + baseline_result.metrics if baseline_result else {}, + ) + _logger.info("Model validation passed!") + + +def _validate( + validation_thresholds: dict[str, MetricThreshold], + candidate_metrics: dict[str, float], + baseline_metrics: dict[str, float], +): + """ + Validate the model based on validation_thresholds by metrics value and + metrics comparison between candidate model's metrics (candidate_metrics) and + baseline model's metrics (baseline_metrics). + + Args: + validation_thresholds: A dictionary from metric_name to MetricThreshold. + candidate_metrics: The metric evaluation result of the candidate model. + baseline_metrics: The metric evaluation result of the baseline model. + + Raises: + If the validation does not pass, raise an MlflowException with detail failure message. + """ + validation_results = { + metric_name: _MetricValidationResult( + metric_name, + candidate_metrics.get(metric_name), + threshold, + baseline_metrics.get(metric_name), + ) + for (metric_name, threshold) in validation_thresholds.items() + } + + for metric_name, metric_threshold in validation_thresholds.items(): + validation_result = validation_results[metric_name] + + if metric_name not in candidate_metrics: + validation_result.missing_candidate = True + continue + + candidate_metric_value = candidate_metrics[metric_name] + baseline_metric_value = baseline_metrics[metric_name] if baseline_metrics else None + + # If metric is greater is better, >= is used, otherwise <= is used + # for thresholding metric value and model comparison + comparator_fn = operator.__ge__ if metric_threshold.greater_is_better else operator.__le__ + operator_fn = operator.add if metric_threshold.greater_is_better else operator.sub + + if metric_threshold.threshold is not None: + # metric threshold fails + # - if not (metric_value >= threshold) for greater is better + # - if not (metric_value <= threshold) for lower is better + validation_result.threshold_failed = not comparator_fn( + candidate_metric_value, metric_threshold.threshold + ) + + if ( + metric_threshold.min_relative_change or metric_threshold.min_absolute_change + ) and metric_name not in baseline_metrics: + validation_result.missing_baseline = True + continue + + if metric_threshold.min_absolute_change is not None: + # metric comparison absolute change fails + # - if not (metric_value >= baseline + min_absolute_change) for greater is better + # - if not (metric_value <= baseline - min_absolute_change) for lower is better + validation_result.min_absolute_change_failed = not comparator_fn( + Decimal(candidate_metric_value), + Decimal(operator_fn(baseline_metric_value, metric_threshold.min_absolute_change)), + ) + + if metric_threshold.min_relative_change is not None: + # If baseline metric value equals 0, fallback to simple comparison check + if baseline_metric_value == 0: + _logger.warning( + f"Cannot perform relative model comparison for metric {metric_name} as " + "baseline metric value is 0. Falling back to simple comparison: verifying " + "that candidate metric value is better than the baseline metric value." + ) + validation_result.min_relative_change_failed = not comparator_fn( + Decimal(candidate_metric_value), + Decimal(operator_fn(baseline_metric_value, 1e-10)), + ) + continue + # metric comparison relative change fails + # - if (metric_value - baseline) / baseline < min_relative_change for greater is better + # - if (baseline - metric_value) / baseline < min_relative_change for lower is better + if metric_threshold.greater_is_better: + relative_change = ( + candidate_metric_value - baseline_metric_value + ) / baseline_metric_value + else: + relative_change = ( + baseline_metric_value - candidate_metric_value + ) / baseline_metric_value + validation_result.min_relative_change_failed = ( + relative_change < metric_threshold.min_relative_change + ) + + failure_messages = [] + + for metric_validation_result in validation_results.values(): + if metric_validation_result.is_success(): + continue + failure_messages.append(str(metric_validation_result)) + + if not failure_messages: + return + + raise ModelValidationFailedException(message=os.linesep.join(failure_messages)) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/flavor_backend.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/flavor_backend.py new file mode 100644 index 0000000000000000000000000000000000000000..2bf5d17a808817237b372d3248d5cb3a533079d0 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/flavor_backend.py @@ -0,0 +1,93 @@ +from abc import ABCMeta, abstractmethod + +from mlflow.utils.annotations import developer_stable + + +@developer_stable +class FlavorBackend: + """ + Abstract class for Flavor Backend. + This class defines the API interface for local model deployment of MLflow model flavors. + """ + + __metaclass__ = ABCMeta + + def __init__(self, config, **kwargs): + self._config = config + + @abstractmethod + def predict(self, model_uri, input_path, output_path, content_type): + """ + Generate predictions using a saved MLflow model referenced by the given URI. + Input and output are read from and written to a file or stdin / stdout. + + Args: + model_uri: URI pointing to the MLflow model to be used for scoring. + input_path: Path to the file with input data. If not specified, data is read from + stdin. + output_path: Path to the file with output predictions. If not specified, data is + written to stdout. + content_type: Specifies the input format. Can be one of {``json``, ``csv``} + """ + + @abstractmethod + def serve( + self, + model_uri, + port, + host, + timeout, + enable_mlserver, + synchronous=True, + stdout=None, + stderr=None, + ): + """ + Serve the specified MLflow model locally. + + Args: + model_uri: URI pointing to the MLflow model to be used for scoring. + port: Port to use for the model deployment. + host: Host to use for the model deployment. Defaults to ``localhost``. + timeout: Timeout in seconds to serve a request. Defaults to 60. + enable_mlserver: Whether to use MLServer or the local scoring server. + synchronous: If True, wait until server process exit and return 0, if process exit + with non-zero return code, raise exception. + If False, return the server process `Popen` instance immediately. + stdout: Redirect server stdout + stderr: Redirect server stderr + """ + + def prepare_env(self, model_uri, capture_output=False): + """ + Performs any preparation necessary to predict or serve the model, for example + downloading dependencies or initializing a conda environment. After preparation, + calling predict or serve should be fast. + """ + + @abstractmethod + def build_image( + self, model_uri, image_name, install_mlflow, mlflow_home, enable_mlserver, base_image=None + ): ... + + @abstractmethod + def generate_dockerfile( + self, model_uri, output_path, install_mlflow, mlflow_home, enable_mlserver, base_image=None + ): ... + + @abstractmethod + def can_score_model(self): + """ + Check whether this flavor backend can be deployed in the current environment. + + Returns: + True if this flavor backend can be applied in the current environment. + """ + + def can_build_image(self): + """ + Returns: + True if this flavor has a `build_image` method defined for building a docker + container capable of serving the model, False otherwise. + """ + return callable(getattr(self.__class__, "build_image", None)) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/flavor_backend_registry.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/flavor_backend_registry.py new file mode 100644 index 0000000000000000000000000000000000000000..531cdadb84118f7f4e6190dc75c5938d3e70c9fd --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/flavor_backend_registry.py @@ -0,0 +1,53 @@ +""" +Registry of supported flavor backends. Contains a mapping of flavors to flavor backends. This +mapping is used to select suitable flavor when deploying generic MLflow models. + +Flavor backend can deploy particular flavor locally to generate predictions, deploy as a local +REST api endpoint, or build a docker image for serving the model locally or remotely. +Not all flavors have a flavor backend. +""" + +import logging + +from mlflow.models.model import Model +from mlflow.store.artifact.artifact_repository_registry import get_artifact_repository +from mlflow.tracking.artifact_utils import ( + _get_root_uri_and_artifact_path, +) +from mlflow.utils.file_utils import TempDir + +_logger = logging.getLogger(__name__) + + +def _get_flavor_backend_for_local_model(model=None, build_docker=True, **kwargs): + from mlflow import pyfunc, rfunc + from mlflow.pyfunc.backend import PyFuncBackend + from mlflow.rfunc.backend import RFuncBackend + + if not model: + return pyfunc.FLAVOR_NAME, PyFuncBackend({}, **kwargs) + + backends = {pyfunc.FLAVOR_NAME: PyFuncBackend, rfunc.FLAVOR_NAME: RFuncBackend} + for flavor, Backend in backends.items(): + if flavor in model.flavors: + backend = Backend(model.flavors[flavor], **kwargs) + if (build_docker and backend.can_build_image()) or backend.can_score_model(): + return flavor, backend + + return None, None + + +def get_flavor_backend(model_uri, **kwargs): + if model_uri: + with TempDir() as tmp: + root_uri, artifact_path = _get_root_uri_and_artifact_path(model_uri) + artifact_repo = get_artifact_repository(root_uri) + local_path = artifact_repo.download_artifacts(artifact_path, dst_path=tmp.path()) + model = Model.load(local_path) + else: + model = None + flavor_name, flavor_backend = _get_flavor_backend_for_local_model(model, **kwargs) + if flavor_backend is None: + raise Exception("No suitable flavor backend was found for the model.") + _logger.info("Selected backend for flavor '%s'", flavor_name) + return flavor_backend diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/model.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/model.py new file mode 100644 index 0000000000000000000000000000000000000000..844be40e2638b8afbba4811e367da22810bad619 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/model.py @@ -0,0 +1,1642 @@ +import json +import logging +import os +import shutil +import uuid +from datetime import datetime +from pathlib import Path +from pprint import pformat +from typing import Any, Callable, Literal, NamedTuple +from urllib.parse import urlparse + +import yaml +from packaging.requirements import InvalidRequirement, Requirement + +import mlflow +from mlflow.entities import LoggedModel, LoggedModelOutput, Metric +from mlflow.entities.model_registry.prompt_version import PromptVersion +from mlflow.environment_variables import ( + MLFLOW_PRINT_MODEL_URLS_ON_CREATION, + MLFLOW_RECORD_ENV_VARS_IN_MODEL_LOGGING, +) +from mlflow.exceptions import MlflowException +from mlflow.models.auth_policy import AuthPolicy +from mlflow.models.resources import Resource, ResourceType, _ResourceBuilder +from mlflow.protos.databricks_pb2 import ( + INVALID_PARAMETER_VALUE, + RESOURCE_DOES_NOT_EXIST, +) +from mlflow.store.artifact.models_artifact_repo import ModelsArtifactRepository +from mlflow.store.artifact.runs_artifact_repo import RunsArtifactRepository +from mlflow.tracking._model_registry import DEFAULT_AWAIT_MAX_SLEEP_SECONDS +from mlflow.tracking._tracking_service.utils import _resolve_tracking_uri +from mlflow.tracking.artifact_utils import _download_artifact_from_uri, _upload_artifact_to_uri +from mlflow.tracking.fluent import ( + _create_logged_model, + _get_active_model_context, + _last_logged_model_id, + _set_active_model_id, + _use_logged_model, +) +from mlflow.utils.annotations import experimental +from mlflow.utils.databricks_utils import ( + _construct_databricks_logged_model_url, + get_databricks_runtime_version, + get_workspace_id, + get_workspace_url, + is_in_databricks_runtime, +) +from mlflow.utils.docstring_utils import LOG_MODEL_PARAM_DOCS, format_docstring +from mlflow.utils.environment import ( + _CONDA_ENV_FILE_NAME, + _PYTHON_ENV_FILE_NAME, + _REQUIREMENTS_FILE_NAME, + _add_or_overwrite_requirements, + _get_requirements_from_file, + _remove_requirements, + _write_requirements_to_file, +) +from mlflow.utils.file_utils import TempDir +from mlflow.utils.logging_utils import eprint +from mlflow.utils.mlflow_tags import MLFLOW_MODEL_IS_EXTERNAL +from mlflow.utils.uri import ( + append_to_uri_path, + get_uri_scheme, + is_databricks_uri, +) + +_logger = logging.getLogger(__name__) + +# NOTE: The MLMODEL_FILE_NAME constant is considered @developer_stable +MLMODEL_FILE_NAME = "MLmodel" +_DATABRICKS_FS_LOADER_MODULE = "databricks.feature_store.mlflow_model" +_LOG_MODEL_METADATA_WARNING_TEMPLATE = ( + "Logging model metadata to the tracking server has failed. The model artifacts " + "have been logged successfully under %s. Set logging level to DEBUG via " + '`logging.getLogger("mlflow").setLevel(logging.DEBUG)` to see the full traceback.' +) +_LOG_MODEL_MISSING_SIGNATURE_WARNING = ( + "Model logged without a signature. Signatures are required for Databricks UC model registry " + "as they validate model inputs and denote the expected schema of model outputs. " + f"Please visit https://www.mlflow.org/docs/{mlflow.__version__.replace('.dev0', '')}/" + "model/signatures.html#how-to-set-signatures-on-models for instructions on setting " + "signature on models." +) +_LOG_MODEL_MISSING_INPUT_EXAMPLE_WARNING = ( + "Model logged without a signature and input example. Please set `input_example` parameter " + "when logging the model to auto infer the model signature." +) +# NOTE: The _MLFLOW_VERSION_KEY constant is considered @developer_stable +_MLFLOW_VERSION_KEY = "mlflow_version" +METADATA_FILES = [ + MLMODEL_FILE_NAME, + _CONDA_ENV_FILE_NAME, + _REQUIREMENTS_FILE_NAME, + _PYTHON_ENV_FILE_NAME, +] +MODEL_CONFIG = "config" +MODEL_CODE_PATH = "model_code_path" +SET_MODEL_ERROR = ( + "Model should either be an instance of PyFuncModel, Langchain type, or LlamaIndex index." +) +ENV_VAR_FILE_NAME = "environment_variables.txt" +ENV_VAR_FILE_HEADER = ( + "# This file records environment variable names that are used during model inference.\n" + "# They might need to be set when creating a serving endpoint from this model.\n" + "# Note: it is not guaranteed that all environment variables listed here are required\n" +) + + +class ModelInfo: + """ + The metadata of a logged MLflow Model. + """ + + def __init__( + self, + artifact_path: str, + flavors: dict[str, Any], + model_uri: str, + model_uuid: str, + run_id: str, + saved_input_example_info: dict[str, Any] | None, + signature, # Optional[ModelSignature] + utc_time_created: str, + mlflow_version: str, + metadata: dict[str, Any] | None = None, + registered_model_version: int | None = None, + env_vars: list[str] | None = None, + prompts: list[str] | None = None, + logged_model: LoggedModel | None = None, + ): + self._artifact_path = artifact_path + self._flavors = flavors + self._model_uri = model_uri + self._model_uuid = model_uuid + self._run_id = run_id + self._saved_input_example_info = saved_input_example_info + self._signature = signature + self._utc_time_created = utc_time_created + self._mlflow_version = mlflow_version + self._metadata = metadata + self._prompts = prompts + self._registered_model_version = registered_model_version + self._env_vars = env_vars + self._logged_model = logged_model + + @property + def artifact_path(self) -> str: + """ + Run relative path identifying the logged model. + + :getter: Retrieves the relative path of the logged model. + :type: str + """ + return self._artifact_path + + @property + def flavors(self) -> dict[str, Any]: + """ + A dictionary mapping the flavor name to how to serve + the model as that flavor. + + :getter: Gets the mapping for the logged model's flavor that defines parameters used in + serving of the model + :type: Dict[str, str] + + .. code-block:: python + :caption: Example flavor mapping for a scikit-learn logged model + + { + "python_function": { + "model_path": "model.pkl", + "loader_module": "mlflow.sklearn", + "python_version": "3.8.10", + "env": "conda.yaml", + }, + "sklearn": { + "pickled_model": "model.pkl", + "sklearn_version": "0.24.1", + "serialization_format": "cloudpickle", + }, + } + + """ + return self._flavors + + @property + def model_uri(self) -> str: + """ + The ``model_uri`` of the logged model in the format ``'runs://'``. + + :getter: Gets the uri path of the logged model from the uri `runs:/` path + encapsulation + :type: str + """ + return self._model_uri + + @property + def model_uuid(self) -> str: + """ + The ``model_uuid`` of the logged model, + e.g., ``'39ca11813cfc46b09ab83972740b80ca'``. + + :getter: [Legacy] Gets the model_uuid (run_id) of a logged model + :type: str + """ + return self._model_uuid + + @property + def run_id(self) -> str: + """ + The ``run_id`` associated with the logged model, + e.g., ``'8ede7df408dd42ed9fc39019ef7df309'`` + + :getter: Gets the run_id identifier for the logged model + :type: str + """ + return self._run_id + + @property + def saved_input_example_info(self) -> dict[str, Any] | None: + """ + A dictionary that contains the metadata of the saved input example, e.g., + ``{"artifact_path": "input_example.json", "type": "dataframe", "pandas_orient": "split"}``. + + :getter: Gets the input example if specified during model logging + :type: Optional[Dict[str, str]] + """ + return self._saved_input_example_info + + @property + def signature(self): # -> Optional[ModelSignature] + """ + A :py:class:`ModelSignature ` that describes the + model input and output. + + :getter: Gets the model signature if it is defined + :type: Optional[ModelSignature] + """ + return self._signature + + @property + def utc_time_created(self) -> str: + """ + The UTC time that the logged model is created, e.g., ``'2022-01-12 05:17:31.634689'``. + + :getter: Gets the UTC formatted timestamp for when the model was logged + :type: str + """ + return self._utc_time_created + + @property + def mlflow_version(self) -> str: + """ + Version of MLflow used to log the model + + :getter: Gets the version of MLflow that was installed when a model was logged + :type: str + """ + return self._mlflow_version + + @property + def env_vars(self) -> list[str] | None: + """ + Environment variables used during the model logging process. + + :getter: Gets the environment variables used during the model logging process. + :type: Optional[List[str]] + """ + return self._env_vars + + @env_vars.setter + def env_vars(self, value: list[str] | None) -> None: + if value and not (isinstance(value, list) and all(isinstance(x, str) for x in value)): + raise TypeError(f"env_vars must be a list of strings. Got: {value}") + self._env_vars = value + + @property + def metadata(self) -> dict[str, Any] | None: + """ + User defined metadata added to the model. + + :getter: Gets the user-defined metadata about a model + :type: Optional[Dict[str, Any]] + + .. code-block:: python + :caption: Example usage of Model Metadata + + # Create and log a model with metadata to the Model Registry + + from sklearn import datasets + from sklearn.ensemble import RandomForestClassifier + import mlflow + from mlflow.models import infer_signature + + with mlflow.start_run(): + iris = datasets.load_iris() + clf = RandomForestClassifier() + clf.fit(iris.data, iris.target) + signature = infer_signature(iris.data, iris.target) + mlflow.sklearn.log_model( + clf, + name="iris_rf", + signature=signature, + registered_model_name="model-with-metadata", + metadata={"metadata_key": "metadata_value"}, + ) + + # model uri for the above model + model_uri = "models:/model-with-metadata/1" + + # Load the model and access the custom metadata from its ModelInfo object + model = mlflow.pyfunc.load_model(model_uri=model_uri) + assert model.metadata.get_model_info().metadata["metadata_key"] == "metadata_value" + + # Load the ModelInfo and access the custom metadata + model_info = mlflow.models.get_model_info(model_uri=model_uri) + assert model_info.metadata["metadata_key"] == "metadata_value" + """ + return self._metadata + + @property + def prompts(self) -> list[str] | None: + """A list of prompt URIs associated with the model.""" + return self._prompts + + @property + def registered_model_version(self) -> int | None: + """ + The registered model version, if the model is registered. + + :getter: Gets the registered model version, if the model is registered in Model Registry. + :setter: Sets the registered model version. + :type: Optional[int] + """ + return self._registered_model_version + + @registered_model_version.setter + def registered_model_version(self, value) -> None: + self._registered_model_version = value + + @property + def model_id(self) -> str: + """ + The model ID of the logged model. + + :getter: Gets the model ID of the logged model + """ + return self._logged_model.model_id if self._logged_model else None + + @property + def metrics(self) -> list[Metric] | None: + """ + Returns the metrics of the logged model. + + :getter: Retrieves the metrics of the logged model + """ + return self._logged_model.metrics if self._logged_model else None + + @property + def params(self) -> dict[str, str]: + """ + Returns the parameters of the logged model. + + :getter: Retrieves the parameters of the logged model + """ + return self._logged_model.params if self._logged_model else None + + @property + def tags(self) -> dict[str, str]: + """ + Returns the tags of the logged model. + + :getter: Retrieves the tags of the logged model + """ + return self._logged_model.tags if self._logged_model else None + + @property + def creation_timestamp(self) -> int: + """ + Returns the creation timestamp of the logged model. + + :getter: the creation timestamp of the logged model + """ + return self._logged_model.creation_timestamp if self._logged_model else None + + @property + def name(self) -> str: + """ + Returns the name of the logged model. + """ + return self._logged_model.name if self._logged_model else None + + +class Model: + """ + An MLflow Model that can support multiple model flavors. Provides APIs for implementing + new Model flavors. + """ + + def __init__( + self, + artifact_path=None, + run_id=None, + utc_time_created=None, + flavors=None, + signature=None, # ModelSignature + saved_input_example_info: dict[str, Any] | None = None, + model_uuid: str | Callable[[], str] | None = lambda: uuid.uuid4().hex, + mlflow_version: str | None = mlflow.version.VERSION, + metadata: dict[str, Any] | None = None, + model_size_bytes: int | None = None, + resources: str | list[Resource] | None = None, + env_vars: list[str] | None = None, + auth_policy: AuthPolicy | None = None, + model_id: str | None = None, + prompts: list[str] | None = None, + **kwargs, + ): + # store model id instead of run_id and path to avoid confusion when model gets exported + self.run_id = run_id + self.artifact_path = artifact_path + self.utc_time_created = str(utc_time_created or datetime.utcnow()) + self.flavors = flavors if flavors is not None else {} + self.signature = signature + self.saved_input_example_info = saved_input_example_info + self.model_uuid = model_uuid() if callable(model_uuid) else model_uuid + self.mlflow_version = mlflow_version + self.metadata = metadata + self.prompts = prompts + self.model_size_bytes = model_size_bytes + self.resources = resources + self.env_vars = env_vars + self.auth_policy = auth_policy + self.model_id = model_id + self.__dict__.update(kwargs) + + def __eq__(self, other): + if not isinstance(other, Model): + return False + return self.__dict__ == other.__dict__ + + def get_input_schema(self): + """ + Retrieves the input schema of the Model iff the model was saved with a schema definition. + """ + return self.signature.inputs if self.signature is not None else None + + def get_output_schema(self): + """ + Retrieves the output schema of the Model iff the model was saved with a schema definition. + """ + return self.signature.outputs if self.signature is not None else None + + def get_params_schema(self): + """ + Retrieves the parameters schema of the Model iff the model was saved with a schema + definition. + """ + return getattr(self.signature, "params", None) + + def get_serving_input(self, path: str) -> str | None: + """ + Load serving input example from a model directory. Returns None if there is no serving input + example. + + Args: + path: Path to the model directory. + + Returns: + Serving input example or None if the model has no serving input example. + """ + from mlflow.models.utils import _load_serving_input_example + + return _load_serving_input_example(self, path) + + def load_input_example(self, path: str | None = None) -> str | None: + """ + Load the input example saved along a model. Returns None if there is no example metadata + (i.e. the model was saved without example). Raises FileNotFoundError if there is model + metadata but the example file is missing. + + Args: + path: Model or run URI, or path to the `model` directory. + e.g. models:///, runs:// + or /path/to/model + + Returns: + Input example (NumPy ndarray, SciPy csc_matrix, SciPy csr_matrix, + pandas DataFrame, dict) or None if the model has no example. + """ + + # Just-in-time import to only load example-parsing libraries (e.g. numpy, pandas, etc.) if + # example is requested. + from mlflow.models.utils import _read_example + + if path is None: + path = ( + f"runs:/{self.run_id}/{self.artifact_path}" + if self.model_id is None + else self.artifact_path + ) + + return _read_example(self, str(path)) + + def load_input_example_params(self, path: str): + """ + Load the params of input example saved along a model. Returns None if there are no params in + the input_example. + + Args: + path: Path to the model directory. + + Returns: + params (dict) or None if the model has no params. + """ + from mlflow.models.utils import _read_example_params + + return _read_example_params(self, path) + + def add_flavor(self, name, **params) -> "Model": + """Add an entry for how to serve the model in a given format.""" + self.flavors[name] = params + return self + + @property + def metadata(self) -> dict[str, Any] | None: + """ + Custom metadata dictionary passed to the model and stored in the MLmodel file. + + :getter: Retrieves custom metadata that have been applied to a model instance. + :setter: Sets a dictionary of custom keys and values to be included with the model instance + :type: Optional[Dict[str, Any]] + + Returns: + A Dictionary of user-defined metadata iff defined. + + .. code-block:: python + :caption: Example + + # Create and log a model with metadata to the Model Registry + from sklearn import datasets + from sklearn.ensemble import RandomForestClassifier + import mlflow + from mlflow.models import infer_signature + + with mlflow.start_run(): + iris = datasets.load_iris() + clf = RandomForestClassifier() + clf.fit(iris.data, iris.target) + signature = infer_signature(iris.data, iris.target) + mlflow.sklearn.log_model( + clf, + name="iris_rf", + signature=signature, + registered_model_name="model-with-metadata", + metadata={"metadata_key": "metadata_value"}, + ) + + # model uri for the above model + model_uri = "models:/model-with-metadata/1" + + # Load the model and access the custom metadata + model = mlflow.pyfunc.load_model(model_uri=model_uri) + assert model.metadata.metadata["metadata_key"] == "metadata_value" + """ + + return self._metadata + + @metadata.setter + def metadata(self, value: dict[str, Any] | None) -> None: + self._metadata = value + + @property + def signature(self): # -> Optional[ModelSignature] + """ + An optional definition of the expected inputs to and outputs from a model object, defined + with both field names and data types. Signatures support both column-based and tensor-based + inputs and outputs. + + :getter: Retrieves the signature of a model instance iff the model was saved with a + signature definition. + :setter: Sets a signature to a model instance. + :type: Optional[ModelSignature] + """ + return self._signature + + @signature.setter + def signature(self, value) -> None: + # signature cannot be set to `False`, which is used in `log_model` and `save_model` calls + # to disable automatic signature inference + if value is not False: + self._signature = value + + @property + def saved_input_example_info(self) -> dict[str, Any] | None: + """ + A dictionary that contains the metadata of the saved input example, e.g., + ``{"artifact_path": "input_example.json", "type": "dataframe", "pandas_orient": "split"}``. + """ + return self._saved_input_example_info + + @saved_input_example_info.setter + def saved_input_example_info(self, value: dict[str, Any]) -> None: + self._saved_input_example_info = value + + @property + def model_size_bytes(self) -> int | None: + """ + An optional integer that represents the model size in bytes + + :getter: Retrieves the model size if it's calculated when the model is saved + :setter: Sets the model size to a model instance + :type: Optional[int] + """ + return self._model_size_bytes + + @model_size_bytes.setter + def model_size_bytes(self, value: int | None) -> None: + self._model_size_bytes = value + + @property + def resources(self) -> dict[str, dict[ResourceType, list[dict[str, Any]]]]: + """ + An optional dictionary that contains the resources required to serve the model. + + :getter: Retrieves the resources required to serve the model + :setter: Sets the resources required to serve the model + :type: Dict[str, Dict[ResourceType, List[Dict]]] + """ + return self._resources + + @resources.setter + def resources(self, value: str | list[Resource] | None) -> None: + if isinstance(value, (Path, str)): + serialized_resource = _ResourceBuilder.from_yaml_file(value) + elif isinstance(value, list) and all(isinstance(resource, Resource) for resource in value): + serialized_resource = _ResourceBuilder.from_resources(value) + else: + serialized_resource = value + self._resources = serialized_resource + + @experimental(version="2.21.0") + @property + def auth_policy(self) -> dict[str, dict[str, Any]]: + """ + An optional dictionary that contains the auth policy required to serve the model. + + :getter: Retrieves the auth_policy required to serve the model + :setter: Sets the auth_policy required to serve the model + :type: Dict[str, dict] + """ + return self._auth_policy + + @experimental(version="2.21.0") + @auth_policy.setter + def auth_policy(self, value: dict[str, Any] | AuthPolicy | None) -> None: + self._auth_policy = value.to_dict() if isinstance(value, AuthPolicy) else value + + @property + def env_vars(self) -> list[str] | None: + return self._env_vars + + @env_vars.setter + def env_vars(self, value: list[str] | None) -> None: + if value and not (isinstance(value, list) and all(isinstance(x, str) for x in value)): + raise TypeError(f"env_vars must be a list of strings. Got: {value}") + self._env_vars = value + + def _is_signature_from_type_hint(self): + return self.signature._is_signature_from_type_hint if self.signature is not None else False + + def _is_type_hint_from_example(self): + return self.signature._is_type_hint_from_example if self.signature is not None else False + + def get_model_info(self, logged_model: LoggedModel | None = None) -> ModelInfo: + """ + Create a :py:class:`ModelInfo ` instance that contains the + model metadata. + """ + if logged_model is None and self.model_id is not None: + logged_model = mlflow.get_logged_model(model_id=self.model_id) + return ModelInfo( + artifact_path=self.artifact_path, + flavors=self.flavors, + model_uri=( + f"models:/{self.model_id}" + if self.model_id + else f"runs:/{self.run_id}/{self.artifact_path}" + ), + model_uuid=self.model_uuid, + run_id=self.run_id, + saved_input_example_info=self.saved_input_example_info, + signature=self.signature, + utc_time_created=self.utc_time_created, + mlflow_version=self.mlflow_version, + metadata=self.metadata, + prompts=self.prompts, + env_vars=self.env_vars, + logged_model=logged_model, + ) + + def get_tags_dict(self) -> dict[str, Any]: + result = self.to_dict() + + tags = { + key: value + for key, value in result.items() + if key in ["run_id", "utc_time_created", "artifact_path", "model_uuid"] + } + + tags["flavors"] = { + flavor: ( + {k: v for k, v in config.items() if k != "config"} + if isinstance(config, dict) + else config + ) + for flavor, config in result.get("flavors", {}).items() + } + + return tags + + def to_dict(self) -> dict[str, Any]: + """Serialize the model to a dictionary.""" + res = {k: v for k, v in self.__dict__.items() if not k.startswith("_")} + databricks_runtime = get_databricks_runtime_version() + if databricks_runtime: + res["databricks_runtime"] = databricks_runtime + if self.signature is not None: + res["signature"] = self.signature.to_dict() + res["is_signature_from_type_hint"] = self.signature._is_signature_from_type_hint + res["type_hint_from_example"] = self.signature._is_type_hint_from_example + if self.saved_input_example_info is not None: + res["saved_input_example_info"] = self.saved_input_example_info + if self.mlflow_version is None and _MLFLOW_VERSION_KEY in res: + res.pop(_MLFLOW_VERSION_KEY) + if self.metadata is not None: + res["metadata"] = self.metadata + if self.prompts is not None: + res["prompts"] = self.prompts + if self.resources is not None: + res["resources"] = self.resources + if self.model_size_bytes is not None: + res["model_size_bytes"] = self.model_size_bytes + if self.auth_policy is not None: + res["auth_policy"] = self.auth_policy + # Exclude null fields in case MLmodel file consumers such as Model Serving may not + # handle them correctly. + if self.artifact_path is None: + res.pop("artifact_path", None) + if self.run_id is None: + res.pop("run_id", None) + if self.env_vars is not None: + res["env_vars"] = self.env_vars + return res + + def to_yaml(self, stream=None) -> str: + """Write the model as yaml string.""" + return yaml.safe_dump(self.to_dict(), stream=stream, default_flow_style=False) + + def __str__(self): + return self.to_yaml() + + def to_json(self) -> str: + """Write the model as json.""" + return json.dumps(self.to_dict()) + + def save(self, path) -> None: + """Write the model as a local YAML file.""" + with open(path, "w") as out: + self.to_yaml(out) + + @classmethod + def load(cls, path) -> "Model": + """ + Load a model from its YAML representation. + + Args: + path: A local filesystem path or URI referring to the MLmodel YAML file + representation of the Model object or to the directory containing + the MLmodel YAML file representation. + + Returns: + An instance of Model. + + .. code-block:: python + :caption: example + + from mlflow.models import Model + + # Load the Model object from a local MLmodel file + model1 = Model.load("~/path/to/my/MLmodel") + + # Load the Model object from a remote model directory + model2 = Model.load("s3://mybucket/path/to/my/model") + """ + + # Check if the path is a local directory and not remote + sep = os.path.sep + path = str(path).rstrip(sep) + path_scheme = urlparse(path).scheme + if (not path_scheme or path_scheme == "file") and not os.path.exists(path): + raise MlflowException( + f'Could not find an "{MLMODEL_FILE_NAME}" configuration file at "{path}"', + RESOURCE_DOES_NOT_EXIST, + ) + + if ModelsArtifactRepository._is_logged_model_uri(path): + path = ModelsArtifactRepository.get_underlying_uri(path) + + is_model_dir = path.rsplit(sep, maxsplit=1)[-1] != MLMODEL_FILE_NAME + mlmodel_file_path = f"{path}/{MLMODEL_FILE_NAME}" if is_model_dir else path + mlmodel_local_path = _download_artifact_from_uri(artifact_uri=mlmodel_file_path) + with open(mlmodel_local_path) as f: + model_dict = yaml.safe_load(f) + return cls.from_dict(model_dict) + + @classmethod + def from_dict(cls, model_dict) -> "Model": + """Load a model from its YAML representation.""" + + from mlflow.models.signature import ModelSignature + + model_dict = model_dict.copy() + if "signature" in model_dict and isinstance(model_dict["signature"], dict): + signature = ModelSignature.from_dict(model_dict["signature"]) + if "is_signature_from_type_hint" in model_dict: + signature._is_signature_from_type_hint = model_dict.pop( + "is_signature_from_type_hint" + ) + if "type_hint_from_example" in model_dict: + signature._is_type_hint_from_example = model_dict.pop("type_hint_from_example") + model_dict["signature"] = signature + + if "model_uuid" not in model_dict: + model_dict["model_uuid"] = None + + if _MLFLOW_VERSION_KEY not in model_dict: + model_dict[_MLFLOW_VERSION_KEY] = None + return cls(**model_dict) + + # MLflow 2.x log_model API. Only spark flavors uses this API. + # https://github.com/mlflow/mlflow/blob/fd2d9861fa52eeca178825c871d5d29fbb3b95c4/mlflow/models/model.py#L773-L982 + @format_docstring(LOG_MODEL_PARAM_DOCS) + @classmethod + def _log_v2( + cls, + artifact_path, + flavor, + registered_model_name=None, + await_registration_for=DEFAULT_AWAIT_MAX_SLEEP_SECONDS, + metadata=None, + run_id=None, + resources=None, + auth_policy=None, + prompts=None, + **kwargs, + ) -> ModelInfo: + """ + Log model using supplied flavor module. If no run is active, this method will create a new + active run. + + Args: + artifact_path: Run relative path identifying the model. + flavor: Flavor module to save the model with. The module must have + the ``save_model`` function that will persist the model as a valid + MLflow model. + registered_model_name: If given, create a model version under + ``registered_model_name``, also creating a registered model if + one with the given name does not exist. + await_registration_for: Number of seconds to wait for the model version to finish + being created and is in ``READY`` status. By default, the + function waits for five minutes. Specify 0 or None to skip + waiting. + metadata: {{ metadata }} + run_id: The run ID to associate with this model. If not provided, + a new run will be started. + resources: {{ resources }} + auth_policy: {{ auth_policy }} + prompts: {{ prompts }} + kwargs: Extra args passed to the model flavor. + + Returns: + A :py:class:`ModelInfo ` instance that contains the + metadata of the logged model. + """ + + # Only one of Auth policy and resources should be defined + + if resources is not None and auth_policy is not None: + raise ValueError("Only one of `resources`, and `auth_policy` can be specified.") + + from mlflow.utils.model_utils import _validate_and_get_model_config_from_file + + registered_model = None + with TempDir() as tmp: + local_path = tmp.path("model") + if run_id is None: + run_id = mlflow.tracking.fluent._get_or_start_run().info.run_id + if prompts is not None: + # Convert to URIs for serialization + prompts = [pr.uri if isinstance(pr, PromptVersion) else pr for pr in prompts] + mlflow_model = cls( + artifact_path=artifact_path, + run_id=run_id, + metadata=metadata, + resources=resources, + auth_policy=auth_policy, + prompts=prompts, + ) + flavor.save_model(path=local_path, mlflow_model=mlflow_model, **kwargs) + # `save_model` calls `load_model` to infer the model requirements, which may result in + # __pycache__ directories being created in the model directory. + for pycache in Path(local_path).rglob("__pycache__"): + shutil.rmtree(pycache, ignore_errors=True) + + if is_in_databricks_runtime(): + _copy_model_metadata_for_uc_sharing(local_path, flavor) + + tracking_uri = _resolve_tracking_uri() + serving_input = mlflow_model.get_serving_input(local_path) + # We check signature presence here as some flavors have a default signature as a + # fallback when not provided by user, which is set during flavor's save_model() call. + if mlflow_model.signature is None: + if serving_input is None: + _logger.warning( + _LOG_MODEL_MISSING_INPUT_EXAMPLE_WARNING, extra={"color": "red"} + ) + elif tracking_uri == "databricks" or get_uri_scheme(tracking_uri) == "databricks": + _logger.warning(_LOG_MODEL_MISSING_SIGNATURE_WARNING, extra={"color": "red"}) + + env_vars = None + # validate input example works for serving when logging the model + if serving_input and kwargs.get("validate_serving_input", True): + from mlflow.models import validate_serving_input + from mlflow.utils.model_utils import RECORD_ENV_VAR_ALLOWLIST, env_var_tracker + + with env_var_tracker() as tracked_env_names: + try: + validate_serving_input( + model_uri=local_path, + serving_input=serving_input, + ) + except Exception as e: + serving_input_msg = ( + serving_input[:50] + "..." if len(serving_input) > 50 else serving_input + ) + _logger.warning( + f"Failed to validate serving input example {serving_input_msg}. " + "Alternatively, you can avoid passing input example and pass model " + "signature instead when logging the model. To ensure the input example " + "is valid prior to serving, please try calling " + "`mlflow.models.validate_serving_input` on the model uri and serving " + "input example. A serving input example can be generated from model " + "input example using " + "`mlflow.models.convert_input_example_to_serving_input` function.\n" + f"Got error: {e}", + exc_info=_logger.isEnabledFor(logging.DEBUG), + ) + env_vars = ( + sorted( + x + for x in tracked_env_names + if any(env_var in x for env_var in RECORD_ENV_VAR_ALLOWLIST) + ) + or None + ) + if env_vars: + # Keep the environment variable file as it serves as a check + # for displaying tips in Databricks serving endpoint + env_var_path = Path(local_path, ENV_VAR_FILE_NAME) + env_var_path.write_text(ENV_VAR_FILE_HEADER + "\n".join(env_vars) + "\n") + if len(env_vars) <= 3: + env_var_info = "[" + ", ".join(env_vars) + "]" + else: + env_var_info = "[" + ", ".join(env_vars[:3]) + ", ... " + "]" + f"(check file {ENV_VAR_FILE_NAME} in the model's artifact folder for full list" + " of environment variable names)" + _logger.info( + "Found the following environment variables used during model inference: " + f"{env_var_info}. Please check if you need to set them when deploying the " + "model. To disable this message, set environment variable " + f"`{MLFLOW_RECORD_ENV_VARS_IN_MODEL_LOGGING.name}` to `false`." + ) + mlflow_model.env_vars = env_vars + # mlflow_model is updated, rewrite the MLmodel file + mlflow_model.save(os.path.join(local_path, MLMODEL_FILE_NAME)) + + # Associate prompts to the model Run + if prompts: + client = mlflow.MlflowClient() + for prompt in prompts: + client.link_prompt_version_to_run(run_id, prompt) + + mlflow.tracking.fluent.log_artifacts(local_path, mlflow_model.artifact_path, run_id) + + # if the model_config kwarg is passed in, then log the model config as an params + if model_config := kwargs.get("model_config"): + if isinstance(model_config, str): + try: + file_extension = os.path.splitext(model_config)[1].lower() + if file_extension == ".json": + with open(model_config) as f: + model_config = json.load(f) + elif file_extension in [".yaml", ".yml"]: + model_config = _validate_and_get_model_config_from_file(model_config) + else: + _logger.warning( + "Unsupported file format for model config: %s. " + "Failed to load model config.", + model_config, + ) + except Exception as e: + _logger.warning("Failed to load model config from %s: %s", model_config, e) + + try: + from mlflow.models.utils import _flatten_nested_params + + # We are using the `/` separator to flatten the nested params + # since we are using the same separator to log nested metrics. + params_to_log = _flatten_nested_params(model_config, sep="/") + except Exception as e: + _logger.warning("Failed to flatten nested params: %s", str(e)) + params_to_log = model_config + + try: + mlflow.tracking.fluent.log_params(params_to_log or {}, run_id=run_id) + except Exception as e: + _logger.warning("Failed to log model config as params: %s", str(e)) + + try: + mlflow.tracking.fluent._record_logged_model(mlflow_model, run_id) + except MlflowException: + # We need to swallow all mlflow exceptions to maintain backwards compatibility with + # older tracking servers. Only print out a warning for now. + _logger.warning(_LOG_MODEL_METADATA_WARNING_TEMPLATE, mlflow.get_artifact_uri()) + _logger.debug("", exc_info=True) + + if registered_model_name is not None: + registered_model = mlflow.tracking._model_registry.fluent._register_model( + f"runs:/{run_id}/{mlflow_model.artifact_path}", + registered_model_name, + await_registration_for=await_registration_for, + local_model_path=local_path, + ) + + model_info = mlflow_model.get_model_info() + if registered_model is not None: + model_info.registered_model_version = registered_model.version + + # If the model signature is Mosaic AI Agent compatible, render a recipe for evaluation. + from mlflow.models.display_utils import maybe_render_agent_eval_recipe + + maybe_render_agent_eval_recipe(model_info) + + return model_info + + @format_docstring(LOG_MODEL_PARAM_DOCS) + @classmethod + def log( + cls, + artifact_path, + flavor, + registered_model_name=None, + await_registration_for=DEFAULT_AWAIT_MAX_SLEEP_SECONDS, + metadata=None, + run_id=None, + resources=None, + auth_policy=None, + prompts=None, + name: str | None = None, + model_type: str | None = None, + params: dict[str, Any] | None = None, + tags: dict[str, Any] | None = None, + step: int = 0, + model_id: str | None = None, + **kwargs, + ) -> ModelInfo: + """ + Log model using supplied flavor module. If no run is active, this method will create a new + active run. + + Args: + artifact_path: Deprecated. Use `name` instead. + flavor: Flavor module to save the model with. The module must have + the ``save_model`` function that will persist the model as a valid + MLflow model. + registered_model_name: If given, create a model version under + ``registered_model_name``, also creating a registered model if + one with the given name does not exist. + await_registration_for: Number of seconds to wait for the model version to finish + being created and is in ``READY`` status. By default, the + function waits for five minutes. Specify 0 or None to skip + waiting. + metadata: {{ metadata }} + run_id: The run ID to associate with this model. + resources: {{ resources }} + auth_policy: {{ auth_policy }} + prompts: {{ prompts }} + name: The name of the model. + model_type: {{ model_type }} + params: {{ params }} + tags: {{ tags }} + step: {{ step }} + model_id: {{ model_id }} + kwargs: Extra args passed to the model flavor. + + Returns: + A :py:class:`ModelInfo ` instance that contains the + metadata of the logged model. + """ + if name is not None and artifact_path is not None: + raise MlflowException.invalid_parameter_value( + "Both `artifact_path` (deprecated) and `name` parameters were specified. " + "Please only specify `name`." + ) + elif artifact_path is not None: + _logger.warning("`artifact_path` is deprecated. Please use `name` instead.") + + name = name or artifact_path + + def log_model_metrics_for_step(client, model_id, run_id, step): + metric_names = client.get_run(run_id).data.metrics.keys() + metrics_for_step = [] + for metric_name in metric_names: + history = client.get_metric_history(run_id, metric_name) + metrics_for_step.extend( + [ + Metric( + key=metric.key, + value=metric.value, + timestamp=metric.timestamp, + step=metric.step, + dataset_name=metric.dataset_name, + dataset_digest=metric.dataset_digest, + run_id=metric.run_id, + model_id=model_id, + ) + for metric in history + if metric.step == step and metric.model_id is None + ] + ) + client.log_batch(run_id=run_id, metrics=metrics_for_step) + + # Only one of Auth policy and resources should be defined + + if resources is not None and auth_policy is not None: + raise ValueError("Only one of `resources`, and `auth_policy` can be specified.") + + registered_model = None + with TempDir() as tmp: + local_path = tmp.path("model") + + tracking_uri = _resolve_tracking_uri() + client = mlflow.MlflowClient(tracking_uri) + if not run_id: + run_id = active_run.info.run_id if (active_run := mlflow.active_run()) else None + + if model_id is not None: + model = client.get_logged_model(model_id) + else: + params = { + **(params or {}), + **(client.get_run(run_id).data.params if run_id else {}), + } + model = _create_logged_model( + # TODO: Update model name + name=name, + source_run_id=run_id, + model_type=model_type, + params={key: str(value) for key, value in params.items()}, + tags={key: str(value) for key, value in tags.items()} + if tags is not None + else None, + flavor=flavor.__name__ if hasattr(flavor, "__name__") else "custom", + ) + _last_logged_model_id.set(model.model_id) + if ( + MLFLOW_PRINT_MODEL_URLS_ON_CREATION.get() + and is_databricks_uri(tracking_uri) + and (workspace_url := get_workspace_url()) + ): + logged_model_url = _construct_databricks_logged_model_url( + workspace_url, + model.experiment_id, + model.model_id, + get_workspace_id(), + ) + eprint(f"🔗 View Logged Model at: {logged_model_url}") + + with _use_logged_model(model=model): + if run_id is not None: + client.log_outputs( + run_id=run_id, models=[LoggedModelOutput(model.model_id, step=step)] + ) + log_model_metrics_for_step( + client=client, model_id=model.model_id, run_id=run_id, step=step + ) + + if prompts is not None: + # Convert to URIs for serialization + prompts = [pr.uri if isinstance(pr, PromptVersion) else pr for pr in prompts] + + mlflow_model = cls( + artifact_path=model.artifact_location, + model_uuid=model.model_id, + run_id=run_id, + metadata=metadata, + resources=resources, + auth_policy=auth_policy, + prompts=prompts, + model_id=model.model_id, + ) + flavor.save_model(path=local_path, mlflow_model=mlflow_model, **kwargs) + # `save_model` calls `load_model` to infer the model requirements, which may result + # in __pycache__ directories being created in the model directory. + for pycache in Path(local_path).rglob("__pycache__"): + shutil.rmtree(pycache, ignore_errors=True) + + if is_in_databricks_runtime(): + _copy_model_metadata_for_uc_sharing(local_path, flavor) + + serving_input = mlflow_model.get_serving_input(local_path) + # We check signature presence here as some flavors have a default signature as a + # fallback when not provided by user, which is set during flavor's save_model() + # call. + if mlflow_model.signature is None: + if serving_input is None: + _logger.warning( + _LOG_MODEL_MISSING_INPUT_EXAMPLE_WARNING, extra={"color": "red"} + ) + elif ( + tracking_uri == "databricks" or get_uri_scheme(tracking_uri) == "databricks" + ): + _logger.warning( + _LOG_MODEL_MISSING_SIGNATURE_WARNING, extra={"color": "red"} + ) + + env_vars = None + # validate input example works for serving when logging the model + if serving_input and kwargs.get("validate_serving_input", True): + from mlflow.models import validate_serving_input + from mlflow.utils.model_utils import RECORD_ENV_VAR_ALLOWLIST, env_var_tracker + + with env_var_tracker() as tracked_env_names: + try: + validate_serving_input( + model_uri=local_path, + serving_input=serving_input, + ) + except Exception as e: + serving_input_msg = ( + serving_input[:50] + "..." + if len(serving_input) > 50 + else serving_input + ) + _logger.warning( + f"Failed to validate serving input example {serving_input_msg}. " + "Alternatively, you can avoid passing input example and pass model " + "signature instead when logging the model. To ensure the input " + "example is valid prior to serving, please try calling " + "`mlflow.models.validate_serving_input` on the model uri and " + "serving input example. A serving input example can be generated " + "from model input example using " + "`mlflow.models.convert_input_example_to_serving_input` function.\n" + f"Got error: {e}", + exc_info=_logger.isEnabledFor(logging.DEBUG), + ) + env_vars = ( + sorted( + x + for x in tracked_env_names + if any(env_var in x for env_var in RECORD_ENV_VAR_ALLOWLIST) + ) + or None + ) + if env_vars: + # Keep the environment variable file as it serves as a check + # for displaying tips in Databricks serving endpoint + env_var_path = Path(local_path, ENV_VAR_FILE_NAME) + env_var_path.write_text(ENV_VAR_FILE_HEADER + "\n".join(env_vars) + "\n") + if len(env_vars) <= 3: + env_var_info = "[" + ", ".join(env_vars) + "]" + else: + env_var_info = "[" + ", ".join(env_vars[:3]) + ", ... " + "]" + f"(check file {ENV_VAR_FILE_NAME} in the model's artifact folder for full " + "list of environment variable names)" + _logger.info( + "Found the following environment variables used during model inference: " + f"{env_var_info}. Please check if you need to set them when deploying the " + "model. To disable this message, set environment variable " + f"`{MLFLOW_RECORD_ENV_VARS_IN_MODEL_LOGGING.name}` to `false`." + ) + mlflow_model.env_vars = env_vars + # mlflow_model is updated, rewrite the MLmodel file + mlflow_model.save(os.path.join(local_path, MLMODEL_FILE_NAME)) + + client.log_model_artifacts(model.model_id, local_path) + # If the model was previously identified as external, delete the tag because + # the model now has artifacts in MLflow Model format + if model.tags.get(MLFLOW_MODEL_IS_EXTERNAL, "false").lower() == "true": + client.delete_logged_model_tag(model.model_id, MLFLOW_MODEL_IS_EXTERNAL) + # client.finalize_logged_model(model.model_id, status=LoggedModelStatus.READY) + + # Associate prompts to the model Run + if prompts and run_id: + client = mlflow.MlflowClient() + for prompt in prompts: + client.link_prompt_version_to_run(run_id, prompt) + + # if the model_config kwarg is passed in, then log the model config as an params + if model_config := kwargs.get("model_config"): + if isinstance(model_config, str): + try: + file_extension = os.path.splitext(model_config)[1].lower() + if file_extension == ".json": + with open(model_config) as f: + model_config = json.load(f) + elif file_extension in [".yaml", ".yml"]: + from mlflow.utils.model_utils import ( + _validate_and_get_model_config_from_file, + ) + + model_config = _validate_and_get_model_config_from_file( + model_config + ) + else: + _logger.warning( + "Unsupported file format for model config: %s. " + "Failed to load model config.", + model_config, + ) + except Exception as e: + _logger.warning( + "Failed to load model config from %s: %s", model_config, e + ) + + try: + from mlflow.models.utils import _flatten_nested_params + + # We are using the `/` separator to flatten the nested params + # since we are using the same separator to log nested metrics. + params_to_log = _flatten_nested_params(model_config, sep="/") + except Exception as e: + _logger.warning("Failed to flatten nested params: %s", str(e)) + params_to_log = model_config + + try: + # do not log params to run if run_id is None, since that could trigger + # a new run to be created + if run_id: + mlflow.tracking.fluent.log_params(params_to_log or {}, run_id=run_id) + except Exception as e: + _logger.warning("Failed to log model config as params: %s", str(e)) + + if registered_model_name is not None: + registered_model = mlflow.tracking._model_registry.fluent._register_model( + f"models:/{model.model_id}", + registered_model_name, + await_registration_for=await_registration_for, + local_model_path=local_path, + ) + model_info = mlflow_model.get_model_info(model) + if registered_model is not None: + model_info.registered_model_version = registered_model.version + + # If the model signature is Mosaic AI Agent compatible, render a recipe for evaluation. + from mlflow.models.display_utils import maybe_render_agent_eval_recipe + + maybe_render_agent_eval_recipe(model_info) + + return model_info + + +def _copy_model_metadata_for_uc_sharing(local_path: str, flavor) -> None: + """ + Copy model metadata files to a sub-directory 'metadata', + For Databricks Unity Catalog sharing use-cases. + + Args: + local_path: Local path to the model directory. + flavor: Flavor module to save the model with. + """ + from mlflow.models.wheeled_model import _ORIGINAL_REQ_FILE_NAME, WheeledModel + + metadata_path = os.path.join(local_path, "metadata") + if isinstance(flavor, WheeledModel): + # wheeled model updates several metadata files in original model directory + # copy these updated metadata files to the 'metadata' subdirectory + os.makedirs(metadata_path, exist_ok=True) + for file_name in METADATA_FILES + [ + _ORIGINAL_REQ_FILE_NAME, + ]: + src_file_path = os.path.join(local_path, file_name) + if os.path.exists(src_file_path): + dest_file_path = os.path.join(metadata_path, file_name) + shutil.copyfile(src_file_path, dest_file_path) + else: + os.makedirs(metadata_path, exist_ok=True) + for file_name in METADATA_FILES: + src_file_path = os.path.join(local_path, file_name) + if os.path.exists(src_file_path): + dest_file_path = os.path.join(metadata_path, file_name) + shutil.copyfile(src_file_path, dest_file_path) + + +def get_model_info(model_uri: str) -> ModelInfo: + """ + Get metadata for the specified model, such as its input/output signature. + + Args: + model_uri: The location, in URI format, of the MLflow model. For example: + + - ``/Users/me/path/to/local/model`` + - ``relative/path/to/local/model`` + - ``s3://my_bucket/path/to/model`` + - ``runs://run-relative/path/to/model`` + - ``models://`` + - ``models://`` + - ``mlflow-artifacts:/path/to/model`` + + For more information about supported URI schemes, see + `Referencing Artifacts `_. + + Returns: + A :py:class:`ModelInfo ` instance that contains the + metadata of the logged model. + + .. code-block:: python + :caption: Example usage of get_model_info + + import mlflow.models + import mlflow.sklearn + from sklearn.ensemble import RandomForestRegressor + + with mlflow.start_run() as run: + params = {"n_estimators": 3, "random_state": 42} + X = [[0, 1]] + y = [1] + signature = mlflow.models.infer_signature(X, y) + rfr = RandomForestRegressor(**params).fit(X, y) + mlflow.log_params(params) + mlflow.sklearn.log_model(rfr, name="sklearn-model", signature=signature) + + model_uri = f"runs:/{run.info.run_id}/sklearn-model" + # Get model info with model_uri + model_info = mlflow.models.get_model_info(model_uri) + # Get model signature directly + model_signature = model_info.signature + assert model_signature == signature + """ + return Model.load(model_uri).get_model_info() + + +class Files(NamedTuple): + requirements: Path + conda: Path + + +def get_model_requirements_files(resolved_uri: str) -> Files: + requirements_txt_file = _download_artifact_from_uri( + artifact_uri=append_to_uri_path(resolved_uri, _REQUIREMENTS_FILE_NAME) + ) + conda_yaml_file = _download_artifact_from_uri( + artifact_uri=append_to_uri_path(resolved_uri, _CONDA_ENV_FILE_NAME) + ) + + return Files( + Path(requirements_txt_file), + Path(conda_yaml_file), + ) + + +def update_model_requirements( + model_uri: str, + operation: Literal["add", "remove"], + requirement_list: list[str], +) -> None: + """ + Add or remove requirements from a model's conda.yaml and requirements.txt files. + + The process involves downloading these two files from the model artifacts + (if they're non-local), updating them with the specified requirements, + and then overwriting the existing files. Should the artifact repository + associated with the model artifacts disallow overwriting, this function will + fail. + + Note that model registry URIs (i.e. URIs in the form ``models:/``) are not + supported, as artifacts in the model registry are intended to be read-only. + + If adding requirements, the function will overwrite any existing requirements + that overlap, or else append the new requirements to the existing list. + + If removing requirements, the function will ignore any version specifiers, + and remove all the specified package names. Any requirements that are not + found in the existing files will be ignored. + + Args: + model_uri: The location, in URI format, of the MLflow model. For example: + + - ``/Users/me/path/to/local/model`` + - ``relative/path/to/local/model`` + - ``s3://my_bucket/path/to/model`` + - ``runs://run-relative/path/to/model`` + - ``mlflow-artifacts:/path/to/model`` + + For more information about supported URI schemes, see + `Referencing Artifacts `_. + + operation: The operation to perform. Must be one of "add" or "remove". + + requirement_list: A list of requirements to add or remove from the model. + For example: ["numpy==1.20.3", "pandas>=1.3.3"] + """ + resolved_uri = model_uri + if ModelsArtifactRepository.is_models_uri(model_uri): + if not ModelsArtifactRepository._is_logged_model_uri(model_uri): + raise MlflowException( + f'Failed to set requirements on "{model_uri}". ' + + "Model URIs with the `models:/` scheme are not supported.", + INVALID_PARAMETER_VALUE, + ) + resolved_uri = ModelsArtifactRepository.get_underlying_uri(model_uri) + elif RunsArtifactRepository.is_runs_uri(model_uri): + resolved_uri = RunsArtifactRepository.get_underlying_uri(model_uri) + + _logger.info(f"Retrieving model requirements files from {resolved_uri}...") + local_paths = get_model_requirements_files(resolved_uri) + conda_yaml_path = local_paths.conda + requirements_txt_path = local_paths.requirements + + old_conda_reqs = _get_requirements_from_file(conda_yaml_path) + old_requirements_reqs = _get_requirements_from_file(requirements_txt_path) + + requirements = [] + invalid_requirements = {} + for s in requirement_list: + try: + requirements.append(Requirement(s.strip().lower())) + except InvalidRequirement as e: + invalid_requirements[s] = e + if invalid_requirements: + raise MlflowException.invalid_parameter_value( + f"Found invalid requirements: {invalid_requirements}" + ) + if operation == "add": + updated_conda_reqs = _add_or_overwrite_requirements(requirements, old_conda_reqs) + updated_requirements_reqs = _add_or_overwrite_requirements( + requirements, old_requirements_reqs + ) + else: + updated_conda_reqs = _remove_requirements(requirements, old_conda_reqs) + updated_requirements_reqs = _remove_requirements(requirements, old_requirements_reqs) + + _write_requirements_to_file(conda_yaml_path, updated_conda_reqs) + _write_requirements_to_file(requirements_txt_path, updated_requirements_reqs) + + # just print conda reqs here to avoid log spam + # it should be the same as requirements.txt anyway + _logger.info( + "Done updating requirements!\n\n" + f"Old requirements:\n{pformat([str(req) for req in old_conda_reqs])}\n\n" + f"Updated requirements:\n{pformat(updated_conda_reqs)}\n" + ) + + _logger.info(f"Uploading updated requirements files to {resolved_uri}...") + _upload_artifact_to_uri(conda_yaml_path, resolved_uri) + _upload_artifact_to_uri(requirements_txt_path, resolved_uri) + + +__mlflow_model__ = None + + +def _validate_langchain_model(model): + from langchain_core.runnables.base import Runnable + + from mlflow.models.utils import _validate_and_get_model_code_path + + if isinstance(model, str): + return _validate_and_get_model_code_path(model, None) + + if not isinstance(model, Runnable): + raise MlflowException.invalid_parameter_value( + "Model must be a Langchain Runnable type or path to a Langchain model, " + f"got {type(model)}" + ) + + return model + + +def _validate_llama_index_model(model): + from mlflow.llama_index.model import _validate_and_prepare_llama_index_model_or_path + + return _validate_and_prepare_llama_index_model_or_path(model, None) + + +def set_model(model) -> None: + """ + When logging model as code, this function can be used to set the model object + to be logged. + + Args: + model: The model object to be logged. Supported model types are: + + - A Python function or callable object. + - A Langchain model or path to a Langchain model. + - A Llama Index model or path to a Llama Index model. + """ + from mlflow.pyfunc import PythonModel + + if isinstance(model, str): + raise mlflow.MlflowException(SET_MODEL_ERROR) + + if isinstance(model, PythonModel) or callable(model): + globals()["__mlflow_model__"] = model + return + + for validate_function in [_validate_langchain_model, _validate_llama_index_model]: + try: + globals()["__mlflow_model__"] = validate_function(model) + return + except Exception: + pass + + raise mlflow.MlflowException(SET_MODEL_ERROR) + + +def _update_active_model_id_based_on_mlflow_model(mlflow_model: Model): + """ + Update the current active model ID based on the provided MLflow model. + Only set the active model ID if it is not already set by the user. + This is useful for setting the active model ID when loading a model + to ensure traces generated are associated with the loaded model. + """ + if mlflow_model.model_id is None: + return + amc = _get_active_model_context() + # only set the active model if the model is not set by the user + if amc.model_id != mlflow_model.model_id and not amc.set_by_user: + _set_active_model_id(model_id=mlflow_model.model_id) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/model_config.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/model_config.py new file mode 100644 index 0000000000000000000000000000000000000000..6dccf1d60d1644d3ecd7e66e178cbfa5e29818eb --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/model_config.py @@ -0,0 +1,150 @@ +import os +from typing import Any + +import yaml + +from mlflow.exceptions import MlflowException +from mlflow.protos.databricks_pb2 import INVALID_PARAMETER_VALUE + +__mlflow_model_config__ = None + + +class ModelConfig: + """ + ModelConfig used in code to read a YAML configuration file or a dictionary. + + Args: + development_config: Path to the YAML configuration file or a dictionary containing the + configuration. If the configuration is not provided, an error is raised + + .. code-block:: python + :caption: Example usage in model code + + from mlflow.models import ModelConfig + + # Load the configuration from a dictionary + config = ModelConfig(development_config={"key1": "value1"}) + print(config.get("key1")) + + + .. code-block:: yaml + :caption: yaml file for model configuration + + key1: value1 + another_key: + - value2 + - value3 + + .. code-block:: python + :caption: Example yaml usage in model code + + from mlflow.models import ModelConfig + + # Load the configuration from a file + config = ModelConfig(development_config="config.yaml") + print(config.get("key1")) + + + When invoking the ModelConfig locally in a model file, development_config can be passed in + which would be used as configuration for the model. + + + .. code-block:: python + :caption: Example to use ModelConfig when logging model as code: agent.py + + import mlflow + from mlflow.models import ModelConfig + + config = ModelConfig(development_config={"key1": "value1"}) + + + class TestModel(mlflow.pyfunc.PythonModel): + def predict(self, context, model_input, params=None): + return config.get("key1") + + + mlflow.models.set_model(TestModel()) + + + But this development_config configuration file will be overridden when logging a model. + When no model_config is passed in while logging the model, an error will be raised when + trying to load the model using ModelConfig. + Note: development_config is not used when logging the model. + + + .. code-block:: python + :caption: Example to use agent.py to log the model: deploy.py + + model_config = {"key1": "value2"} + with mlflow.start_run(): + model_info = mlflow.pyfunc.log_model( + name="model", python_model="agent.py", model_config=model_config + ) + + loaded_model = mlflow.pyfunc.load_model(model_info.model_uri) + + # This will print "value2" as the model_config passed in while logging the model + print(loaded_model.predict(None)) + """ + + def __init__(self, *, development_config: str | dict[str, Any] | None = None): + config = globals().get("__mlflow_model_config__", None) + # Here mlflow_model_config have 3 states: + # 1. None, this means if the mlflow_model_config is None, use development_config if + # available + # 2. "", Empty string, this means the users explicitly didn't set the model config + # while logging the model so if ModelConfig is used, it should throw an error + # 3. A valid path, this means the users have set the model config while logging the + # model so use that path + if config is not None: + self.config = config + else: + self.config = development_config + + if not self.config: + raise FileNotFoundError( + "Config file is not provided which is needed to load the model. " + "Please provide a valid path." + ) + + if not isinstance(self.config, dict) and not os.path.isfile(self.config): + raise FileNotFoundError(f"Config file '{self.config}' not found.") + + def _read_config(self): + """Reads the YAML configuration file and returns its contents. + + Raises: + FileNotFoundError: If the configuration file does not exist. + yaml.YAMLError: If there is an error parsing the YAML content. + + Returns: + dict or None: The content of the YAML file as a dictionary, or None if the + config path is not set. + """ + if isinstance(self.config, dict): + return self.config + + with open(self.config) as file: + try: + return yaml.safe_load(file) + except yaml.YAMLError as e: + raise MlflowException( + f"Error parsing YAML file: {e}", error_code=INVALID_PARAMETER_VALUE + ) + + def to_dict(self): + """Returns the configuration as a dictionary.""" + return self._read_config() + + def get(self, key): + """Gets the value of a top-level parameter in the configuration.""" + config_data = self._read_config() + + if config_data and key in config_data: + return config_data[key] + else: + raise KeyError(f"Key '{key}' not found in configuration: {config_data}.") + + +def _set_model_config(model_config): + globals()["__mlflow_model_config__"] = model_config diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/notebook_resources/__pycache__/eval_with_dataset_example.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/notebook_resources/__pycache__/eval_with_dataset_example.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..24b99a77829d48c46144c66634710ac40b67bc02 Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/notebook_resources/__pycache__/eval_with_dataset_example.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/notebook_resources/__pycache__/eval_with_synthetic_example.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/notebook_resources/__pycache__/eval_with_synthetic_example.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..8f0a7af2fd30307c04f0c4d2162cc6c9865bdc68 Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/notebook_resources/__pycache__/eval_with_synthetic_example.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/notebook_resources/agent_evaluation_template.html b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/notebook_resources/agent_evaluation_template.html new file mode 100644 index 0000000000000000000000000000000000000000..4c64e4e5e3a224fce68b5e721657db641db5200c --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/notebook_resources/agent_evaluation_template.html @@ -0,0 +1,235 @@ + + + + + + + +
+ The logged model is compatible with the Mosaic AI Agent Framework. + +
+ + + diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/notebook_resources/eval_with_dataset_example.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/notebook_resources/eval_with_dataset_example.py new file mode 100644 index 0000000000000000000000000000000000000000..0c08dade25c04fa4b91e22193ab1be31e2062c4b --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/notebook_resources/eval_with_dataset_example.py @@ -0,0 +1,22 @@ +# ruff: noqa: F821, I001 +{{pipInstall}} + +import pandas as pd +import mlflow + +evals = [ + { + "request": { + "messages": [ + {"role": "user", "content": "How do I convert a Spark DataFrame to Pandas?"} + ], + }, + # Optional, needed for judging correctness. + "expected_facts": [ + "To convert a Spark DataFrame to Pandas, you can use the toPandas() method." + ], + } +] +eval_result = mlflow.evaluate( + data=pd.DataFrame.from_records(evals), model="{{modelUri}}", model_type="databricks-agent" +) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/notebook_resources/eval_with_synthetic_example.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/notebook_resources/eval_with_synthetic_example.py new file mode 100644 index 0000000000000000000000000000000000000000..9f2bf868afa4529fd1610ec0579a4c9af1ef5370 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/notebook_resources/eval_with_synthetic_example.py @@ -0,0 +1,22 @@ +# ruff: noqa: F821, I001 +{{pipInstall}} + +from databricks.agents.evals import generate_evals_df +import mlflow + +agent_description = "A chatbot that answers questions about Databricks." +question_guidelines = """ +# User personas +- A developer new to the Databricks platform +# Example questions +- What API lets me parallelize operations over rows of a delta table? +""" +# TODO: Spark/Pandas DataFrame with "content" and "doc_uri" columns. +docs = spark.table("catalog.schema.my_table_of_docs") +evals = generate_evals_df( + docs=docs, + num_evals=25, + agent_description=agent_description, + question_guidelines=question_guidelines, +) +eval_result = mlflow.evaluate(data=evals, model="{{modelUri}}", model_type="databricks-agent") diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/python_api.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/python_api.py new file mode 100644 index 0000000000000000000000000000000000000000..0dc579f9332b8d5d74ba29d41b2828e5c7283f91 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/python_api.py @@ -0,0 +1,381 @@ +import logging +import os +import shutil +from io import StringIO +from typing import ForwardRef, get_args, get_origin + +from mlflow.exceptions import MlflowException +from mlflow.models.flavor_backend_registry import get_flavor_backend +from mlflow.utils import env_manager as _EnvManager +from mlflow.utils.databricks_utils import is_databricks_connect +from mlflow.utils.file_utils import TempDir + +_logger = logging.getLogger(__name__) +UV_INSTALLATION_INSTRUCTIONS = ( + "Run `pip install uv` to install uv. See " + "https://docs.astral.sh/uv/getting-started/installation for other installation methods." +) + + +def build_docker( + model_uri=None, + name="mlflow-pyfunc", + env_manager=_EnvManager.VIRTUALENV, + mlflow_home=None, + install_java=False, + install_mlflow=False, + enable_mlserver=False, + base_image=None, +): + """ + Builds a Docker image whose default entrypoint serves an MLflow model at port 8080, using the + python_function flavor. The container serves the model referenced by ``model_uri``, if + specified. If ``model_uri`` is not specified, an MLflow Model directory must be mounted as a + volume into the /opt/ml/model directory in the container. + + .. important:: + + Since MLflow 2.10.1, the Docker image built with ``--model-uri`` does **not install Java** + for improved performance, unless the model flavor is one of ``["johnsnowlabs", "h2o" + "spark"]``. If you need to install Java for other flavors, e.g. custom Python model + that uses SparkML, please specify ``install-java=True`` to enforce Java installation. + For earlier versions, Java is always installed to the image. + + + .. warning:: + + If ``model_uri`` is unspecified, the resulting image doesn't support serving models with + the RFunc server. + + NB: by default, the container will start nginx and uvicorn processes. If you don't need the + nginx process to be started (for instance if you deploy your container to Google Cloud Run), + you can disable it via the DISABLE_NGINX environment variable: + + .. code:: bash + + docker run -p 5001:8080 -e DISABLE_NGINX=true "my-image-name" + + See https://www.mlflow.org/docs/latest/python_api/mlflow.pyfunc.html for more information on the + 'python_function' flavor. + + Args: + model_uri: URI to the model. A local path, a 'runs:/' URI, or a remote storage URI (e.g., + an 's3://' URI). For more information about supported remote URIs for model artifacts, + see https://mlflow.org/docs/latest/tracking.html#artifact-stores + name: Name of the Docker image to build. Defaults to 'mlflow-pyfunc'. + env_manager: If specified, create an environment for MLmodel using the specified environment + manager. The following values are supported: (1) virtualenv (default): use virtualenv + and pyenv for Python version management (2) conda: use conda (3) local: use the local + environment without creating a new one. + mlflow_home: Path to local clone of MLflow project. Use for development only. + install_java: If specified, install Java in the image. Default is False in order to + reduce both the image size and the build time. Model flavors requiring Java will enable + this setting automatically, such as the Spark flavor. (This argument is only available + in MLflow 2.10.1 and later. In earlier versions, Java is always installed to the image.) + install_mlflow: If specified and there is a conda or virtualenv environment to be activated + mlflow will be installed into the environment after it has been activated. + The version of installed mlflow will be the same as the one used to invoke this command. + enable_mlserver: If specified, the image will be built with the Seldon MLserver as backend. + base_image: Base image for the Docker image. If not specified, the default image is either + UBUNTU_BASE_IMAGE = "ubuntu:20.04" or PYTHON_SLIM_BASE_IMAGE = "python:{version}-slim" + Note: If custom image is used, there are no guarantees that the image will work. You + may find greater compatibility by building your image on top of the ubuntu images. In + addition, you must install Java and virtualenv to have the image work properly. + """ + get_flavor_backend(model_uri, docker_build=True, env_manager=env_manager).build_image( + model_uri, + name, + mlflow_home=mlflow_home, + install_java=install_java, + install_mlflow=install_mlflow, + enable_mlserver=enable_mlserver, + base_image=base_image, + ) + + +_CONTENT_TYPE_CSV = "csv" +_CONTENT_TYPE_JSON = "json" + + +def predict( + model_uri, + input_data=None, + input_path=None, + content_type=_CONTENT_TYPE_JSON, + output_path=None, + env_manager=_EnvManager.VIRTUALENV, + install_mlflow=False, + pip_requirements_override=None, + extra_envs=None, + # TODO: add an option to force recreating the env +): + """ + Generate predictions in json format using a saved MLflow model. For information about the input + data formats accepted by this function, see the following documentation: + https://www.mlflow.org/docs/latest/models.html#built-in-deployment-tools. + + .. note:: + + To increase verbosity for debugging purposes (in order to inspect the full dependency + resolver operations when processing transient dependencies), consider setting the following + environment variables: + + .. code-block:: bash + + # For virtualenv + export PIP_VERBOSE=1 + + # For uv + export RUST_LOG=uv=debug + + See also: + + - https://pip.pypa.io/en/stable/topics/configuration/#environment-variables + - https://docs.astral.sh/uv/configuration/environment + + Args: + model_uri: URI to the model. A local path, a local or remote URI e.g. runs:/, s3://. + input_data: Input data for prediction. Must be valid input for the PyFunc model. Refer + to the :py:func:`mlflow.pyfunc.PyFuncModel.predict()` for the supported input types. + + .. note:: + If this API fails due to errors in input_data, use + `mlflow.models.convert_input_example_to_serving_input` to manually validate + your input data. + input_path: Path to a file containing input data. If provided, 'input_data' must be None. + content_type: Content type of the input data. Can be one of {'json', 'csv'}. + output_path: File to output results to as json. If not provided, output to stdout. + env_manager: Specify a way to create an environment for MLmodel inference: + + - "virtualenv" (default): use virtualenv (and pyenv for Python version management) + - "uv": use uv + - "local": use the local environment + - "conda": use conda + + install_mlflow: If specified and there is a conda or virtualenv environment to be activated + mlflow will be installed into the environment after it has been activated. The version + of installed mlflow will be the same as the one used to invoke this command. + pip_requirements_override: If specified, install the specified python dependencies to the + model inference environment. This is particularly useful when you want to add extra + dependencies or try different versions of the dependencies defined in the logged model. + + .. tip:: + After validating the pip requirements override works as expected, you can update + the logged model's dependency using `mlflow.models.update_model_requirements` API + without re-logging it. Note that a registered model is immutable, so you need to + register a new model version with the updated model. + extra_envs: If specified, a dictionary of extra environment variables will be passed to the + model inference environment. This is useful for testing what environment variables are + needed for the model to run correctly. By default, environment variables existing in the + current os.environ are passed, and this parameter can be used to override them. + + .. note:: + If your model dependencies include pre-release versions such as `mlflow==3.2.0rc0` + and you are using `uv` as the environment manager, set `UV_PRERELEASE` environment + variable to "allow" in `extra_envs` to allow installing pre-release versions. + e.g. `extra_envs={"UV_PRERELEASE": "allow"}`. + + .. note:: + This parameter is only supported when `env_manager` is set to "virtualenv", + "conda" or "uv". + + Code example: + + .. code-block:: python + + import mlflow + + run_id = "..." + + mlflow.models.predict( + model_uri=f"runs:/{run_id}/model", + input_data={"x": 1, "y": 2}, + content_type="json", + ) + + # Run prediction with "uv" as the environment manager + mlflow.models.predict( + model_uri=f"runs:/{run_id}/model", + input_data={"x": 1, "y": 2}, + env_manager="uv", + ) + + # Run prediction with additional pip dependencies and extra environment variables + mlflow.models.predict( + model_uri=f"runs:/{run_id}/model", + input_data={"x": 1, "y": 2}, + content_type="json", + pip_requirements_override=["scikit-learn==0.23.2"], + extra_envs={"OPENAI_API_KEY": "some_value"}, + ) + + # Run prediction with output_path + mlflow.models.predict( + model_uri=f"runs:/{run_id}/model", + input_data={"x": 1, "y": 2}, + env_manager="uv", + output_path="output.json", + ) + + # Run prediction with pre-release versions + mlflow.models.predict( + model_uri=f"runs:/{run_id}/model", + input_data={"x": 1, "y": 2}, + env_manager="uv", + extra_envs={"UV_PRERELEASE": "allow"}, + ) + + """ + # to avoid circular imports + from mlflow.pyfunc import _PREBUILD_ENV_ROOT_LOCATION + + if content_type not in [_CONTENT_TYPE_JSON, _CONTENT_TYPE_CSV]: + raise MlflowException.invalid_parameter_value( + f"Content type must be one of {_CONTENT_TYPE_JSON} or {_CONTENT_TYPE_CSV}." + ) + if extra_envs and env_manager not in ( + _EnvManager.VIRTUALENV, + _EnvManager.CONDA, + _EnvManager.UV, + ): + raise MlflowException.invalid_parameter_value( + "Extra environment variables are only supported when env_manager is " + f"set to '{_EnvManager.VIRTUALENV}', '{_EnvManager.CONDA}' or '{_EnvManager.UV}'." + ) + if env_manager == _EnvManager.UV: + if not shutil.which("uv"): + raise MlflowException( + f"Found '{env_manager}' as env_manager, but the 'uv' command is not found in the " + f"PATH. {UV_INSTALLATION_INSTRUCTIONS} Alternatively, you can use 'virtualenv' or " + "'conda' as the environment manager, but note their performances are not " + "as good as 'uv'." + ) + else: + _logger.info( + f"It is highly recommended to use `{_EnvManager.UV}` as the environment manager for " + "predicting with MLflow models as its performance is significantly better than other " + f"environment managers. {UV_INSTALLATION_INSTRUCTIONS}" + ) + + is_dbconnect_mode = is_databricks_connect() + if is_dbconnect_mode: + if env_manager not in (_EnvManager.VIRTUALENV, _EnvManager.UV): + raise MlflowException( + f"Databricks Connect only supports '{_EnvManager.VIRTUALENV}' or '{_EnvManager.UV}'" + f" as the environment manager. Got {env_manager}." + ) + pyfunc_backend_env_root_config = { + "create_env_root_dir": False, + "env_root_dir": _PREBUILD_ENV_ROOT_LOCATION, + } + else: + pyfunc_backend_env_root_config = {"create_env_root_dir": True} + + def _predict(_input_path: str): + return get_flavor_backend( + model_uri, + env_manager=env_manager, + install_mlflow=install_mlflow, + **pyfunc_backend_env_root_config, + ).predict( + model_uri=model_uri, + input_path=_input_path, + output_path=output_path, + content_type=content_type, + pip_requirements_override=pip_requirements_override, + extra_envs=extra_envs, + ) + + if input_data is not None and input_path is not None: + raise MlflowException.invalid_parameter_value( + "Both input_data and input_path are provided. Only one of them should be specified." + ) + elif input_data is not None: + input_data = _serialize_input_data(input_data, content_type) + + # Write input data to a temporary file + with TempDir() as tmp: + input_path = os.path.join(tmp.path(), f"input.{content_type}") + with open(input_path, "w") as f: + f.write(input_data) + + _predict(input_path) + else: + _predict(input_path) + + +def _get_pyfunc_supported_input_types(): + # Importing here as the util module depends on optional packages not available in mlflow-skinny + import mlflow.models.utils as base_module + + supported_input_types = [] + for input_type in get_args(base_module.PyFuncInput): + if isinstance(input_type, type): + supported_input_types.append(input_type) + elif isinstance(input_type, ForwardRef): + name = input_type.__forward_arg__ + if hasattr(base_module, name): + cls = getattr(base_module, name) + supported_input_types.append(cls) + else: + # typing instances like List, Dict, Tuple, etc. + supported_input_types.append(get_origin(input_type)) + return tuple(supported_input_types) + + +def _serialize_input_data(input_data, content_type): + # build-docker command is available in mlflow-skinny (which doesn't contain pandas) + # so we shouldn't import pandas at the top level + import pandas as pd + + # this introduces numpy as dependency, we shouldn't import it at the top level + # as it is not available in mlflow-skinny + from mlflow.models.utils import convert_input_example_to_serving_input + + valid_input_types = { + _CONTENT_TYPE_CSV: (str, list, dict, pd.DataFrame), + _CONTENT_TYPE_JSON: _get_pyfunc_supported_input_types(), + }.get(content_type) + + if not isinstance(input_data, valid_input_types): + raise MlflowException.invalid_parameter_value( + f"Input data must be one of {valid_input_types} when content type is '{content_type}', " + f"but got {type(input_data)}." + ) + + if content_type == _CONTENT_TYPE_CSV: + if isinstance(input_data, str): + _validate_csv_string(input_data) + return input_data + else: + try: + return pd.DataFrame(input_data).to_csv(index=False) + except Exception as e: + raise MlflowException.invalid_parameter_value( + "Failed to serialize input data to CSV format." + ) from e + + try: + # rely on convert_input_example_to_serving_input to validate + # the input_data is valid type for the loaded pyfunc model + return convert_input_example_to_serving_input(input_data) + except Exception as e: + raise MlflowException.invalid_parameter_value( + "Invalid input data, please make sure the data is acceptable by the " + "loaded pyfunc model. Use `mlflow.models.convert_input_example_to_serving_input` " + "to manually validate your input data." + ) from e + + +def _validate_csv_string(input_data: str): + """ + Validate the string must be the path to a CSV file. + """ + try: + import pandas as pd + + pd.read_csv(StringIO(input_data)) + except Exception as e: + raise MlflowException.invalid_parameter_value( + message="Failed to deserialize input string data to Pandas DataFrame." + ) from e diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/rag_signatures.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/rag_signatures.py new file mode 100644 index 0000000000000000000000000000000000000000..00b3f95d09e6781f1d616d91c705957f2ff6cbc2 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/rag_signatures.py @@ -0,0 +1,127 @@ +from dataclasses import dataclass, field + +from mlflow.models import ModelSignature +from mlflow.types.schema import ( + Array, + ColSpec, + DataType, + Object, + Property, + Schema, +) +from mlflow.utils.annotations import deprecated + + +@deprecated("mlflow.types.llm.ChatMessage") +@dataclass +class Message: + role: str = "user" # "system", "user", or "assistant" + content: str = "What is mlflow?" + + +@deprecated("mlflow.types.llm.ChatCompletionRequest") +@dataclass +class ChatCompletionRequest: + messages: list[Message] = field(default_factory=lambda: [Message()]) + + +@deprecated("mlflow.types.llm.ChatCompletionRequest") +@dataclass +class SplitChatMessagesRequest: + query: str = "What is mlflow?" + history: list[Message] | None = field(default_factory=list) + + +@deprecated("mlflow.types.llm.ChatCompletionRequest") +@dataclass +class MultiturnChatRequest: + query: str = "What is mlflow?" + history: list[Message] | None = field(default_factory=list) + + +@deprecated("mlflow.types.llm.ChatChoice") +@dataclass +class ChainCompletionChoice: + index: int = 0 + message: Message = field( + default_factory=lambda: Message( + role="assistant", + content="MLflow is an open source platform for the machine learning lifecycle.", + ) + ) + finish_reason: str = "stop" + + +@deprecated("mlflow.types.llm.ChatCompletionChunk") +@dataclass +class ChainCompletionChunk: + index: int = 0 + delta: Message = field( + default_factory=lambda: Message( + role="assistant", + content="MLflow is an open source platform for the machine learning lifecycle.", + ) + ) + finish_reason: str = "stop" + + +@deprecated("mlflow.types.llm.ChatCompletionResponse") +@dataclass +class ChatCompletionResponse: + choices: list[ChainCompletionChoice] = field(default_factory=lambda: [ChainCompletionChoice()]) + object: str = "chat.completion" + # TODO: support ChainCompletionChunk in the future + + +@deprecated("mlflow.types.llm.ChatCompletionResponse") +@dataclass +class StringResponse: + content: str = "MLflow is an open source platform for the machine learning lifecycle." + + +CHAT_COMPLETION_REQUEST_SCHEMA = Schema( + [ + ColSpec( + name="messages", + type=Array( + Object( + [ + Property("role", DataType.string), + Property("content", DataType.string), + ] + ) + ), + ), + ] +) + +CHAT_COMPLETION_RESPONSE_SCHEMA = Schema( + [ + ColSpec( + name="choices", + type=Array( + Object( + [ + Property("index", DataType.long), + Property( + "message", + Object( + [ + Property("role", DataType.string), + Property("content", DataType.string), + ] + ), + ), + Property("finish_reason", DataType.string), + ] + ) + ), + ), + ] +) + +SIGNATURE_FOR_LLM_INFERENCE_TASK = { + "llm/v1/chat": ModelSignature( + inputs=CHAT_COMPLETION_REQUEST_SCHEMA, outputs=CHAT_COMPLETION_RESPONSE_SCHEMA + ), +} diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/resources.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/resources.py new file mode 100644 index 0000000000000000000000000000000000000000..35b67cb622a4d7a76d05f310fa6d3d431acf58dd --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/resources.py @@ -0,0 +1,321 @@ +import os +from abc import ABC, abstractmethod +from enum import Enum +from typing import Any + +import yaml + +DEFAULT_API_VERSION = "1" + + +class ResourceType(Enum): + """ + Enum to define the different types of resources needed to serve a model. + """ + + UC_CONNECTION = "uc_connection" + VECTOR_SEARCH_INDEX = "vector_search_index" + SERVING_ENDPOINT = "serving_endpoint" + SQL_WAREHOUSE = "sql_warehouse" + FUNCTION = "function" + GENIE_SPACE = "genie_space" + TABLE = "table" + APP = "app" + + +class Resource(ABC): + """ + Base class for defining the resources needed to serve a model. + + Args: + type (ResourceType): The resource type. + target_uri (str): The target URI where these resources are hosted. + """ + + @property + @abstractmethod + def type(self) -> ResourceType: + """ + The resource type (must be defined by subclasses). + """ + + @property + @abstractmethod + def target_uri(self) -> str: + """ + The target URI where the resource is hosted (must be defined by subclasses). + """ + + @abstractmethod + def to_dict(self): + """ + Convert the resource to a dictionary. + Subclasses must implement this method. + """ + + @classmethod + @abstractmethod + def from_dict(cls, data: dict[str, str]): + """ + Convert the dictionary to a Resource. + Subclasses must implement this method. + """ + + def __eq__(self, other: Any): + if not isinstance(other, Resource): + return False + return self.to_dict() == other.to_dict() + + +class DatabricksResource(Resource, ABC): + """ + Base class to define all the Databricks resources to serve a model. + + Example usage: https://docs.databricks.com/en/generative-ai/log-agent.html#specify-resources-for-pyfunc-or-langchain-agent + """ + + @property + def target_uri(self) -> str: + return "databricks" + + @property + def type(self) -> ResourceType: + raise NotImplementedError("Subclasses must implement the 'type' property.") + + def __init__(self, name: str, on_behalf_of_user: bool | None = None): + self.name = name + self.on_behalf_of_user = on_behalf_of_user + + def to_dict(self): + result = {self.type.value: [{"name": self.name}]} + if self.on_behalf_of_user is not None: + result[self.type.value][0]["on_behalf_of_user"] = self.on_behalf_of_user + return result + + @classmethod + def from_dict(cls, data: dict[str, str]): + return cls(data["name"], data.get("on_behalf_of_user")) + + +class DatabricksUCConnection(DatabricksResource): + """ + Define a Databricks UC Connection used to serve a model. + + Args: + connection_name (str): The name of the databricks UC connection + used to create the tool which was used to build the model. + on_behalf_of_user (Optional[bool]): If True, the resource is accessed with + with the permission of the invoker of the model in the serving endpoint. If set to + None or False, the resource is accessed with the permissions of the creator + """ + + @property + def type(self) -> ResourceType: + return ResourceType.UC_CONNECTION + + def __init__(self, connection_name: str, on_behalf_of_user: bool | None = None): + super().__init__(connection_name, on_behalf_of_user) + + +class DatabricksServingEndpoint(DatabricksResource): + """ + Define Databricks LLM endpoint resource to serve a model. + + Args: + endpoint_name (str): The name of all the databricks endpoints used by the model. + on_behalf_of_user (Optional[bool]): If True, the resource is accessed with + with the permission of the invoker of the model in the serving endpoint. If set to + None or False, the resource is accessed with the permissions of the creator + """ + + @property + def type(self) -> ResourceType: + return ResourceType.SERVING_ENDPOINT + + def __init__(self, endpoint_name: str, on_behalf_of_user: bool | None = None): + super().__init__(endpoint_name, on_behalf_of_user) + + +class DatabricksVectorSearchIndex(DatabricksResource): + """ + Define Databricks vector search index name resource to serve a model. + + Args: + index_name (str): The name of the databricks vector search index + used by the model + on_behalf_of_user (Optional[bool]): If True, the resource is accessed with + with the permission of the invoker of the model in the serving endpoint. If set to + None or False, the resource is accessed with the permissions of the creator + """ + + @property + def type(self) -> ResourceType: + return ResourceType.VECTOR_SEARCH_INDEX + + def __init__(self, index_name: str, on_behalf_of_user: bool | None = None): + super().__init__(index_name, on_behalf_of_user) + + +class DatabricksSQLWarehouse(DatabricksResource): + """ + Define Databricks sql warehouse resource to serve a model. + + Args: + warehouse_id (str): The id of the sql warehouse used by the model + on_behalf_of_user (Optional[bool]): If True, the resource is accessed with + with the permission of the invoker of the model in the serving endpoint. If set to + None or False, the resource is accessed with the permissions of the creator + """ + + @property + def type(self) -> ResourceType: + return ResourceType.SQL_WAREHOUSE + + def __init__(self, warehouse_id: str, on_behalf_of_user: bool | None = None): + super().__init__(warehouse_id, on_behalf_of_user) + + +class DatabricksFunction(DatabricksResource): + """ + Define Databricks UC Function to serve a model. + + Args: + function_name (str): The name of the function used by the model + on_behalf_of_user (Optional[bool]): If True, the resource is accessed with + with the permission of the invoker of the model in the serving endpoint. If set to + None or False, the resource is accessed with the permissions of the creator + """ + + @property + def type(self) -> ResourceType: + return ResourceType.FUNCTION + + def __init__(self, function_name: str, on_behalf_of_user: bool | None = None): + super().__init__(function_name, on_behalf_of_user) + + +class DatabricksGenieSpace(DatabricksResource): + """ + Define a Databricks Genie Space to serve a model. + + Args: + genie_space_id (str): The genie space id + on_behalf_of_user (Optional[bool]): If True, the resource is accessed with + with the permission of the invoker of the model in the serving endpoint. If set to + None or False, the resource is accessed with the permissions of the creator + """ + + @property + def type(self) -> ResourceType: + return ResourceType.GENIE_SPACE + + def __init__(self, genie_space_id: str, on_behalf_of_user: bool | None = None): + super().__init__(genie_space_id, on_behalf_of_user) + + +class DatabricksTable(DatabricksResource): + """ + Defines a Databricks Unity Catalog (UC) Table, which establishes table dependencies + for Model Serving. This table will be referenced in Agent Model Serving endpoints, + where an agent queries a SQL table via either Genie or UC Functions. + + Args: + table_name (str): The name of the table used by the model + on_behalf_of_user (Optional[bool]): If True, the resource is accessed with + with the permission of the invoker of the model in the serving endpoint. If set to + None or False, the resource is accessed with the permissions of the creator + """ + + @property + def type(self) -> ResourceType: + return ResourceType.TABLE + + def __init__(self, table_name: str, on_behalf_of_user: bool | None = None): + super().__init__(table_name, on_behalf_of_user) + + +class DatabricksApp(DatabricksResource): + """ + Defines a Databricks Unity Catalog (UC) Table, which establishes table dependencies + for Model Serving. This table will be referenced in Agent Model Serving endpoints, + where an agent queries a SQL table via either Genie or UC Functions. + + Args: + table_name (str): The name of the table used by the model + on_behalf_of_user (Optional[bool]): If True, the resource is accessed with + with the permission of the invoker of the model in the serving endpoint. If set to + None or False, the resource is accessed with the permissions of the creator + """ + + @property + def type(self) -> ResourceType: + return ResourceType.APP + + def __init__(self, app_name: str, on_behalf_of_user: bool | None = None): + super().__init__(app_name, on_behalf_of_user) + + +def _get_resource_class_by_type(target_uri: str, resource_type: ResourceType): + resource_classes = { + "databricks": { + ResourceType.UC_CONNECTION.value: DatabricksUCConnection, + ResourceType.SERVING_ENDPOINT.value: DatabricksServingEndpoint, + ResourceType.VECTOR_SEARCH_INDEX.value: DatabricksVectorSearchIndex, + ResourceType.SQL_WAREHOUSE.value: DatabricksSQLWarehouse, + ResourceType.FUNCTION.value: DatabricksFunction, + ResourceType.GENIE_SPACE.value: DatabricksGenieSpace, + ResourceType.TABLE.value: DatabricksTable, + ResourceType.APP.value: DatabricksApp, + } + } + resource = resource_classes.get(target_uri) + if resource is None: + raise ValueError(f"Unsupported target URI: {target_uri}") + return resource.get(resource_type) + + +class _ResourceBuilder: + """ + Private builder class to build the resources dictionary. + """ + + @staticmethod + def from_resources( + resources: list[Resource], api_version: str = DEFAULT_API_VERSION + ) -> dict[str, dict[ResourceType, list[dict[str, Any]]]]: + resource_dict = {} + for resource in resources: + resource_data = resource.to_dict() + for resource_type, values in resource_data.items(): + target_dict = resource_dict.setdefault(resource.target_uri, {}) + target_list = target_dict.setdefault(resource_type, []) + target_list.extend(values) + + resource_dict["api_version"] = api_version + return resource_dict + + @staticmethod + def from_dict(data) -> dict[str, dict[ResourceType, list[dict[str, Any]]]]: + resources = [] + api_version = data.pop("api_version") + if api_version == "1": + for target_uri, config in data.items(): + for resource_type, values in config.items(): + resource_class = _get_resource_class_by_type(target_uri, resource_type) + if resource_class: + resources.extend(resource_class.from_dict(value) for value in values) + else: + raise ValueError(f"Unsupported resource type: {resource_type}") + else: + raise ValueError(f"Unsupported API version: {api_version}") + + return _ResourceBuilder.from_resources(resources, api_version) + + @staticmethod + def from_yaml_file(path: str) -> dict[str, dict[ResourceType, list[dict[str, Any]]]]: + if not os.path.exists(path): + raise OSError(f"No such file or directory: '{path}'") + path = os.path.abspath(path) + with open(path) as file: + data = yaml.safe_load(file) + return _ResourceBuilder.from_dict(data) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/signature.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/signature.py new file mode 100644 index 0000000000000000000000000000000000000000..bd32fc263006180a9b391b6867909c2729a1dafa --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/signature.py @@ -0,0 +1,663 @@ +""" +The :py:mod:`mlflow.models.signature` module provides an API for specification of model signature. + +Model signature defines schema of model input and output. See :py:class:`mlflow.types.schema.Schema` +for more details on Schema and data types. +""" + +import inspect +import logging +import re +import warnings +from copy import deepcopy +from dataclasses import is_dataclass +from typing import TYPE_CHECKING, Any, get_type_hints + +import numpy as np +import pandas as pd + +from mlflow.environment_variables import _MLFLOW_TESTING +from mlflow.exceptions import MlflowException +from mlflow.models import Model +from mlflow.models.model import MLMODEL_FILE_NAME +from mlflow.models.utils import _contains_params, _Example +from mlflow.protos.databricks_pb2 import INVALID_PARAMETER_VALUE, RESOURCE_DOES_NOT_EXIST +from mlflow.store.artifact.models_artifact_repo import ModelsArtifactRepository +from mlflow.store.artifact.runs_artifact_repo import RunsArtifactRepository +from mlflow.tracking.artifact_utils import _download_artifact_from_uri, _upload_artifact_to_uri +from mlflow.types.schema import AnyType, ColSpec, ParamSchema, Schema, convert_dataclass_to_schema +from mlflow.types.type_hints import ( + InvalidTypeHintException, + _get_data_validation_result, + _infer_schema_from_list_type_hint, + _infer_schema_from_type_hint, + _is_list_type_hint, +) +from mlflow.types.utils import ( + InvalidDataForSignatureInferenceError, + _infer_param_schema, + _infer_schema, +) +from mlflow.utils.annotations import filter_user_warnings_once +from mlflow.utils.uri import append_to_uri_path + +# At runtime, we don't need `pyspark.sql.dataframe` +if TYPE_CHECKING: + try: + import pyspark.sql.dataframe + + MlflowInferableDataset = ( + pd.DataFrame | np.ndarray | dict[str, np.ndarray] | pyspark.sql.dataframe.DataFrame + ) + except ImportError: + MlflowInferableDataset = pd.DataFrame | np.ndarray | dict[str, np.ndarray] + +_logger = logging.getLogger(__name__) + +_LOG_MODEL_INFER_SIGNATURE_WARNING_TEMPLATE = ( + "Failed to infer the model signature from the input example. Reason: %s. To see the full " + "traceback, set the logging level to DEBUG via " + '`logging.getLogger("mlflow").setLevel(logging.DEBUG)`.' +) + + +class ModelSignature: + """ + ModelSignature specifies schema of model's inputs, outputs and params. + + ModelSignature can be :py:func:`inferred ` from training + dataset, model predictions using and params for inference, or constructed by hand by + passing an input and output :py:class:`Schema `, and params + :py:class:`ParamSchema `. + """ + + def __init__( + self, + # `dataclass` is an invalid type annotation. Use `Any` instead as a workaround. + inputs: Schema | Any = None, + outputs: Schema | Any = None, + params: ParamSchema = None, + ): + if inputs and not isinstance(inputs, Schema) and not is_dataclass(inputs): + raise TypeError( + "inputs must be either None, mlflow.models.signature.Schema, or a dataclass," + f"got '{type(inputs).__name__}'" + ) + if outputs and not isinstance(outputs, Schema) and not is_dataclass(outputs): + raise TypeError( + "outputs must be either None, mlflow.models.signature.Schema, or a dataclass," + f"got '{type(outputs).__name__}'" + ) + if params and not isinstance(params, ParamSchema): + raise TypeError( + "If params are provided, they must by of type mlflow.models.signature.ParamSchema, " + f"got '{type(params).__name__}'" + ) + if all(x is None for x in [inputs, outputs, params]): + raise ValueError("At least one of inputs, outputs or params must be provided") + if is_dataclass(inputs): + self.inputs = convert_dataclass_to_schema(inputs) + else: + self.inputs = inputs + if is_dataclass(outputs): + self.outputs = convert_dataclass_to_schema(outputs) + else: + self.outputs = outputs + self.params = params + self.__is_signature_from_type_hint = False + self.__is_type_hint_from_example = False + + @property + def _is_signature_from_type_hint(self): + return self.__is_signature_from_type_hint + + @_is_signature_from_type_hint.setter + def _is_signature_from_type_hint(self, value): + self.__is_signature_from_type_hint = value + + @property + def _is_type_hint_from_example(self): + return self.__is_type_hint_from_example + + @_is_type_hint_from_example.setter + def _is_type_hint_from_example(self, value): + self.__is_type_hint_from_example = value + + def to_dict(self) -> dict[str, Any]: + """ + Serialize into a 'jsonable' dictionary. + + Input and output schema are represented as json strings. This is so that the + representation is compact when embedded in an MLmodel yaml file. + + Returns: + dictionary representation with input and output schema represented as json strings. + """ + + return { + "inputs": self.inputs.to_json() if self.inputs else None, + "outputs": self.outputs.to_json() if self.outputs else None, + "params": self.params.to_json() if self.params else None, + } + + @classmethod + def from_dict(cls, signature_dict: dict[str, Any]): + """ + Deserialize from dictionary representation. + + Args: + signature_dict: Dictionary representation of model signature. + Expected dictionary format: + `{'inputs': , + 'outputs': , + 'params': " }` + + Returns: + ModelSignature populated with the data form the dictionary. + """ + inputs = Schema.from_json(x) if (x := signature_dict.get("inputs")) else None + outputs = Schema.from_json(x) if (x := signature_dict.get("outputs")) else None + params = ParamSchema.from_json(x) if (x := signature_dict.get("params")) else None + return cls(inputs, outputs, params) + + def __eq__(self, other) -> bool: + return ( + isinstance(other, ModelSignature) + and self.inputs == other.inputs + and self.outputs == other.outputs + and self.params == other.params + ) + + def __repr__(self) -> str: + return ( + "inputs: \n" + f" {self.inputs!r}\n" + "outputs: \n" + f" {self.outputs!r}\n" + "params: \n" + f" {self.params!r}\n" + ) + + +def infer_signature( + model_input: Any = None, + model_output: "MlflowInferableDataset" = None, + params: dict[str, Any] | None = None, +) -> ModelSignature: + """ + Infer an MLflow model signature from the training data (input), model predictions (output) + and parameters (for inference). + + The signature represents model input and output as data frames with (optionally) named columns + and data type specified as one of types defined in :py:class:`mlflow.types.DataType`. It also + includes parameters schema for inference, . + This method will raise an exception if the user data contains incompatible types or is not + passed in one of the supported formats listed below. + + The input should be one of these: + - pandas.DataFrame + - pandas.Series + - dictionary of { name -> numpy.ndarray} + - numpy.ndarray + - pyspark.sql.DataFrame + - scipy.sparse.csr_matrix + - scipy.sparse.csc_matrix + - dictionary / list of dictionaries of JSON-convertible types + + The element types should be mappable to one of :py:class:`mlflow.types.DataType`. + + For pyspark.sql.DataFrame inputs, columns of type DateType and TimestampType are both inferred + as type :py:data:`datetime `, which is coerced to + TimestampType at inference. + + Args: + model_input: Valid input to the model. E.g. (a subset of) the training dataset. + model_output: Valid model output. E.g. Model predictions for the (subset of) training + dataset. + params: Valid parameters for inference. It should be a dictionary of parameters + that can be set on the model during inference by passing `params` to pyfunc + `predict` method. + + An example of valid parameters: + + .. code-block:: python + + from mlflow.models import infer_signature + from mlflow.transformers import generate_signature_output + + # Define parameters for inference + params = { + "num_beams": 5, + "max_length": 30, + "do_sample": True, + "remove_invalid_values": True, + } + + # Infer the signature including parameters + signature = infer_signature( + data, + generate_signature_output(model, data), + params=params, + ) + + # Saving model with model signature + mlflow.transformers.save_model( + model, + path=model_path, + signature=signature, + ) + + pyfunc_loaded = mlflow.pyfunc.load_model(model_path) + + # Passing params to `predict` function directly + result = pyfunc_loaded.predict(data, params=params) + + Returns: + ModelSignature + """ + schemas = {"inputs": model_input, "outputs": model_output} + for key, data in schemas.items(): + if data is not None: + try: + schemas[key] = ( + convert_dataclass_to_schema(data) if is_dataclass(data) else _infer_schema(data) + ) + except InvalidDataForSignatureInferenceError: + raise + except Exception: + extra_msg = ( + ("Note that MLflow doesn't validate data types during inference for AnyType. ") + if key == "inputs" + else "" + ) + _logger.warning( + f"Failed to infer schema for {key}. " + f"Setting schema to `Schema([ColSpec(type=AnyType())]` as default. {extra_msg}" + "To see the full traceback, set logging level to DEBUG.", + exc_info=_logger.isEnabledFor(logging.DEBUG), + ) + schemas[key] = Schema([ColSpec(type=AnyType())]) + schemas["params"] = _infer_param_schema(params) if params else None + return ModelSignature(**schemas) + + +# `t\w*\.` matches the `typing` module or its alias +_LIST_OF_STRINGS_PATTERN = re.compile(r"^(t\w*\.)?list\[str\]$", re.IGNORECASE) + + +def _is_list_str(hint_str): + return _LIST_OF_STRINGS_PATTERN.match(hint_str.replace(" ", "")) is not None + + +_LIST_OF_STR_DICT_PATTERN = re.compile( + r"^(t\w*\.)?list\[(t\w*\.)?dict\[str,str\]\]$", re.IGNORECASE +) + + +def _is_list_of_string_dict(hint_str): + return _LIST_OF_STR_DICT_PATTERN.match(hint_str.replace(" ", "")) is not None + + +def _infer_hint_from_str(hint_str): + if _is_list_str(hint_str): + return list[str] + elif _is_list_of_string_dict(hint_str): + return list[dict[str, str]] + else: + return None + + +def _get_arg_names(f): + return list(inspect.signature(f).parameters.keys()) + + +class _TypeHints: + def __init__(self, input_=None, output=None): + self.input = input_ + self.output = output + + def __repr__(self): + return f"" + + +def _extract_type_hints(f, input_arg_index): + """ + Extract type hints from a function. + + Args: + f: Function to extract type hints from. + input_arg_index: Index of the function argument that corresponds to the model input. + + Returns: + A `_TypeHints` object containing the input and output type hints. + """ + if not hasattr(f, "__annotations__") and hasattr(f, "__call__"): + return _extract_type_hints(f.__call__, input_arg_index) + + if f.__annotations__ == {}: + return _TypeHints() + + arg_names = list(filter(lambda x: x != "self", _get_arg_names(f))) + if len(arg_names) - 1 < input_arg_index: + raise MlflowException.invalid_parameter_value( + f"The specified input argument index ({input_arg_index}) is out of range for the " + "function signature: {}".format(input_arg_index, arg_names) + ) + arg_name = arg_names[input_arg_index] + try: + hints = get_type_hints(f) + except ( + TypeError, + NameError, # To handle this issue: https://github.com/python/typing/issues/797 + ): + # --- + # from __future__ import annotations # postpones evaluation of 'list[str]' + # + # def f(x: list[str]) -> list[str]: + # ^^^^^^^^^ Evaluating this expression ('list[str]') results in a TypeError in + # Python < 3.9 because the built-in list type is not subscriptable. + # return x + # --- + # Best effort to infer type hints from strings + hints = {} + for arg in [arg_name, "return"]: + if hint_str := f.__annotations__.get(arg, None): + if hint := _infer_hint_from_str(hint_str): + hints[arg] = hint + else: + _logger.info("Unsupported type hint: %s, skipping schema inference", hint_str) + except Exception as e: + _logger.warning("Failed to extract type hints from function %s: %s", f.__name__, repr(e)) + return _TypeHints() + + return _TypeHints(hints.get(arg_name), hints.get("return")) + + +def _is_context_in_predict_function_signature(*, func=None, parameters=None): + if parameters is None: + if func is None: + raise ValueError("Either `func` or `parameters` must be provided.") + parameters = inspect.signature(func).parameters + return ( + # predict(self, context, model_input, ...) + "context" in parameters + # predict(self, ctx, model_input, ...) ctx can be any parameter name + or len([param for param in parameters if param not in ("self", "params")]) == 2 + ) + + +@filter_user_warnings_once +def _infer_signature_from_type_hints( + func, type_hints: _TypeHints, input_example=None +) -> ModelSignature | None: + """ + Infer the signature from type hints. + """ + if type_hints.input is None: + return None + + params = None + params_key = "params" + if _contains_params(input_example): + input_example, params = input_example + + try: + input_schema = _infer_schema_from_list_type_hint(type_hints.input) + except InvalidTypeHintException: + raise MlflowException.invalid_parameter_value( + "The `predict` function has unsupported type hints for the model input " + "arguments. Update it to one of supported type hints, or remove type hints " + "to bypass this check. Error: {e}" + ) + except Exception as e: + warnings.warn(f"Failed to infer signature from type hint: {e.message}", stacklevel=3) + return None + + # only warn if the pyfunc decorator is not used and schema can + # be inferred from the input type hint + _pyfunc_decorator_used = getattr(func, "_is_pyfunc", False) + if not _pyfunc_decorator_used: + # stacklevel is 3 because we have a decorator + warnings.warn( + "Decorate your function with `@mlflow.pyfunc.utils.pyfunc` to enable auto " + "data validation against model input type hints.", + stacklevel=3, + ) + + default_output_schema = Schema([ColSpec(type=AnyType())]) + is_output_type_hint_valid = False + output_schema = None + if type_hints.output: + try: + # output type hint doesn't need to be a list + # but if it's a list, we infer the schema from the list type hint + # to be consistent with input schema inference + output_schema = ( + _infer_schema_from_list_type_hint(type_hints.output) + if _is_list_type_hint(type_hints.output) + else _infer_schema_from_type_hint(type_hints.output) + ) + is_output_type_hint_valid = True + except Exception as e: + _logger.info( + f"Failed to infer output type hint, setting output schema to AnyType. {e}", + stacklevel=2, + ) + output_schema = default_output_schema + else: + output_schema = default_output_schema + params_schema = _infer_param_schema(params) if params else None + + if input_example is not None: + # only validate input example here if pyfunc decorator is not used + # because when the decorator is used, the input is validated in the predict function + if not _pyfunc_decorator_used and ( + msg := _get_data_validation_result( + data=input_example, type_hint=type_hints.input + ).error_message + ): + _logger.warning( + "Input example is not compatible with the type hint of the `predict` function. " + f"Error: {msg}" + ) + else: + kwargs = ( + {params_key: params} + if params and params_key in inspect.signature(func).parameters + else {} + ) + # This is for PythonModel's predict function + if _is_context_in_predict_function_signature(func=func): + inputs = [None, input_example] + else: + inputs = [input_example] + _logger.info("Running the predict function to generate output based on input example") + try: + output_example = func(*inputs, **kwargs) + except Exception: + _logger.warning( + "Failed to run the predict function on input example. To see the full " + "traceback, set logging level to DEBUG.", + exc_info=_logger.isEnabledFor(logging.DEBUG), + ) + else: + if is_output_type_hint_valid and ( + msg := _get_data_validation_result( + data=output_example, type_hint=type_hints.output + ).error_message + ): + _logger.warning( + f"Failed to validate output `{output_example}` against type hint " + f"`{type_hints.output}`, setting output schema to AnyType. " + f"Error: {msg}" + ) + output_schema = default_output_schema + if not any([input_schema, output_schema, params_schema]): + return None + signature = ModelSignature(inputs=input_schema, outputs=output_schema, params=params_schema) + signature._is_signature_from_type_hint = True + return signature + + +def _infer_signature_from_input_example( + input_example: _Example | None, wrapped_model +) -> ModelSignature | None: + """ + Infer the signature from an example input and a PyFunc wrapped model. Catches all exceptions. + + Args: + input_example: Saved _Example object that contains input example instance. + wrapped_model: A PyFunc wrapped model which has a `predict` method. + + Returns: + A `ModelSignature` object containing the inferred schema of both the model's inputs + based on the `input_example` and the model's outputs based on the prediction from the + `wrapped_model`. + """ + from mlflow.pyfunc import _validate_prediction_input + + if input_example is None: + return None + + try: + # Copy the input example so that it is not mutated by predict() + input_data = deepcopy(input_example.inference_data) + params = input_example.inference_params + + input_schema = _infer_schema(input_data) + params_schema = _infer_param_schema(params) if params else None + # do the same validation as pyfunc predict to make sure the signature is correctly + # applied to the model + input_data, params = _validate_prediction_input( + input_data, params, input_schema, params_schema + ) + prediction = wrapped_model.predict(input_data, params=params) + # For column-based inputs, 1D numpy arrays likely signify row-based predictions. Thus, we + # convert them to a Pandas series for inferring as a single ColSpec Schema. + if ( + not input_schema.is_tensor_spec() + and isinstance(prediction, np.ndarray) + and prediction.ndim == 1 + ): + prediction = pd.Series(prediction) + + output_schema = None + try: + output_schema = _infer_schema(prediction) + except Exception: + # try assign output schema if failing to infer it from prediction for langchain models + try: + from mlflow.langchain.model import _LangChainModelWrapper + from mlflow.langchain.utils.chat import _ChatResponse + except ImportError: + pass + else: + if isinstance(wrapped_model, _LangChainModelWrapper) and isinstance( + prediction, _ChatResponse + ): + output_schema = prediction.get_schema() + if output_schema is None: + _logger.warning( + "Failed to infer model output schema from prediction result, setting " + "output schema to AnyType. For full traceback, set logging level to debug.", + exc_info=_logger.isEnabledFor(logging.DEBUG), + ) + output_schema = Schema([ColSpec(type=AnyType())]) + + return ModelSignature(input_schema, output_schema, params_schema) + except Exception as e: + if _MLFLOW_TESTING.get(): + raise + _logger.warning( + _LOG_MODEL_INFER_SIGNATURE_WARNING_TEMPLATE, + repr(e), + exc_info=_logger.isEnabledFor(logging.DEBUG), + ) + + +def set_signature( + model_uri: str, + signature: ModelSignature, +): + """ + Sets the model signature for specified model artifacts. + + The process involves downloading the MLmodel file in the model artifacts (if it's non-local), + updating its model signature, and then overwriting the existing MLmodel file. Should the + artifact repository associated with the model artifacts disallow overwriting, this function will + fail. + + Furthermore, as model registry artifacts are read-only, model artifacts located in the + model registry and represented by ``models:/`` URI schemes are not compatible with this API. + To set a signature on a model version, first set the signature on the source model artifacts. + Following this, generate a new model version using the updated model artifacts. For more + information about setting signatures on model versions, see + `this doc section `_. + + Args: + model_uri: The location, in URI format, of the MLflow model. For example: + + - ``/Users/me/path/to/local/model`` + - ``relative/path/to/local/model`` + - ``s3://my_bucket/path/to/model`` + - ``runs://run-relative/path/to/model`` + - ``mlflow-artifacts:/path/to/model`` + - ``models:/`` + + For more information about supported URI schemes, see + `Referencing Artifacts `_. + + Please note that model URIs with the ``models://`` scheme are not + supported. + + signature: ModelSignature to set on the model. + + .. code-block:: python + :caption: Example + + import mlflow + from mlflow.models import set_signature, infer_signature + + # load model from run artifacts + run_id = "96771d893a5e46159d9f3b49bf9013e2" + artifact_path = "models" + model_uri = f"runs:/{run_id}/{artifact_path}" + model = mlflow.pyfunc.load_model(model_uri) + + # determine model signature + test_df = ... + predictions = model.predict(test_df) + signature = infer_signature(test_df, predictions) + + # set the signature for the logged model + set_signature(model_uri, signature) + """ + assert isinstance(signature, ModelSignature), ( + "The signature argument must be a ModelSignature object" + ) + resolved_uri = model_uri + if RunsArtifactRepository.is_runs_uri(model_uri): + resolved_uri = RunsArtifactRepository.get_underlying_uri(model_uri) + elif ModelsArtifactRepository._is_logged_model_uri(model_uri): + resolved_uri = ModelsArtifactRepository.get_underlying_uri(model_uri) + elif ModelsArtifactRepository.is_models_uri(model_uri): + raise MlflowException( + f"Failed to set signature on {model_uri!r}. " + "Model URIs with the `models://` scheme are not supported.", + INVALID_PARAMETER_VALUE, + ) + + try: + ml_model_file = _download_artifact_from_uri( + artifact_uri=append_to_uri_path(resolved_uri, MLMODEL_FILE_NAME) + ) + except Exception as ex: + raise MlflowException( + f'Failed to download an "{MLMODEL_FILE_NAME}" model file from "{model_uri}"', + RESOURCE_DOES_NOT_EXIST, + ) from ex + model_meta = Model.load(ml_model_file) + model_meta.signature = signature + model_meta.save(ml_model_file) + _upload_artifact_to_uri(ml_model_file, resolved_uri) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/utils.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..88d9e16e22a2a0d50ec4ea2f9b14a8d398b0c129 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/utils.py @@ -0,0 +1,2049 @@ +import base64 +import datetime as dt +import decimal +import importlib +import json +import logging +import os +import re +import shutil +import sys +import tempfile +import uuid +from contextlib import contextmanager +from copy import deepcopy +from pathlib import Path +from typing import Any, Dict, List, Union + +import numpy as np +import pandas as pd +import pydantic + +import mlflow +from mlflow.entities import LoggedModel +from mlflow.environment_variables import MLFLOW_DISABLE_SCHEMA_DETAILS +from mlflow.exceptions import INVALID_PARAMETER_VALUE, MlflowException +from mlflow.models import Model +from mlflow.models.model_config import _set_model_config +from mlflow.store.artifact.utils.models import get_model_name_and_version +from mlflow.tracking.artifact_utils import _download_artifact_from_uri +from mlflow.types import DataType, ParamSchema, ParamSpec, Schema, TensorSpec +from mlflow.types.schema import AnyType, Array, Map, Object, Property +from mlflow.types.utils import ( + TensorsNotSupportedException, + _infer_param_schema, + _is_none_or_nan, + clean_tensor_type, +) +from mlflow.utils import IS_PYDANTIC_V2_OR_NEWER +from mlflow.utils.databricks_utils import is_in_databricks_runtime +from mlflow.utils.file_utils import create_tmp_dir, get_local_path_or_none +from mlflow.utils.mlflow_tags import MLFLOW_MODEL_IS_EXTERNAL +from mlflow.utils.proto_json_utils import ( + NumpyEncoder, + dataframe_from_parsed_json, + parse_inputs_data, + parse_tf_serving_input, +) +from mlflow.utils.uri import get_databricks_profile_uri_from_artifact_uri + +try: + from scipy.sparse import csc_matrix, csr_matrix + + HAS_SCIPY = True +except ImportError: + HAS_SCIPY = False + +try: + from pyspark.sql import DataFrame as SparkDataFrame + from pyspark.sql import Row + from pyspark.sql.types import ( + ArrayType, + BinaryType, + DateType, + FloatType, + IntegerType, + ShortType, + StructType, + TimestampType, + ) + + HAS_PYSPARK = True +except ImportError: + SparkDataFrame = None + HAS_PYSPARK = False + + +INPUT_EXAMPLE_PATH = "artifact_path" +EXAMPLE_DATA_KEY = "inputs" +EXAMPLE_PARAMS_KEY = "params" +EXAMPLE_FILENAME = "input_example.json" +SERVING_INPUT_PATH = "serving_input_path" +SERVING_INPUT_FILENAME = "serving_input_example.json" + +# TODO: import from scoring_server after refactoring +DF_SPLIT = "dataframe_split" +INPUTS = "inputs" +SERVING_PARAMS_KEY = "params" + +ModelInputExample = Union[ + pd.DataFrame, np.ndarray, dict, list, "csr_matrix", "csc_matrix", str, bytes, tuple +] + +PyFuncLLMSingleInput = dict[str, Any] | bool | bytes | float | int | str + +PyFuncLLMOutputChunk = dict[str, Any] | str + +PyFuncInput = Union[ + pd.DataFrame, + pd.Series, + np.ndarray, + "csc_matrix", + "csr_matrix", + List[Any], # noqa: UP006 + Dict[str, Any], # noqa: UP006 + dt.datetime, + bool, + bytes, + float, + int, + str, +] +PyFuncOutput = pd.DataFrame | pd.Series | np.ndarray | list | str + +if HAS_PYSPARK: + PyFuncInput = PyFuncInput | SparkDataFrame + PyFuncOutput = PyFuncOutput | SparkDataFrame + +_logger = logging.getLogger(__name__) + +_FEATURE_STORE_FLAVOR = "databricks.feature_store.mlflow_model" + + +def _is_scalar(x): + return np.isscalar(x) or x is None + + +def _validate_params(params): + try: + _infer_param_schema(params) + except MlflowException: + _logger.warning(f"Invalid params found in input example: {params}") + raise + + +def _is_ndarray(x): + return isinstance(x, np.ndarray) or ( + isinstance(x, dict) and all(isinstance(ary, np.ndarray) for ary in x.values()) + ) + + +def _is_sparse_matrix(x): + if not HAS_SCIPY: + # we can safely assume that if no scipy is installed, + # the user won't log scipy sparse matrices + return False + return isinstance(x, (csc_matrix, csr_matrix)) + + +def _handle_ndarray_nans(x: np.ndarray): + if np.issubdtype(x.dtype, np.number): + return np.where(np.isnan(x), None, x) + else: + return x + + +def _handle_ndarray_input(input_array: np.ndarray | dict[str, Any]): + if isinstance(input_array, dict): + result = {} + for name in input_array.keys(): + result[name] = _handle_ndarray_nans(input_array[name]).tolist() + return result + else: + return _handle_ndarray_nans(input_array).tolist() + + +def _handle_sparse_matrix(x: Union["csr_matrix", "csc_matrix"]): + return { + "data": _handle_ndarray_nans(x.data).tolist(), + "indices": x.indices.tolist(), + "indptr": x.indptr.tolist(), + "shape": list(x.shape), + } + + +def _handle_dataframe_nans(df: pd.DataFrame): + return df.where(df.notnull(), None) + + +def _coerce_to_pandas_df(input_ex): + if isinstance(input_ex, dict): + # We need to be compatible with infer_schema's behavior, where + # it infers each value's type directly. + if all( + isinstance(x, str) or (isinstance(x, list) and all(_is_scalar(y) for y in x)) + for x in input_ex.values() + ): + # e.g. + # data = {"a": "a", "b": ["a", "b", "c"]} + # >>> pd.DataFrame([data]) + # a b + # 0 a [a, b, c] + _logger.info( + "We convert input dictionaries to pandas DataFrames such that " + "each key represents a column, collectively constituting a " + "single row of data. If you would like to save data as " + "multiple rows, please convert your data to a pandas " + "DataFrame before passing to input_example." + ) + input_ex = pd.DataFrame([input_ex]) + elif np.isscalar(input_ex): + input_ex = pd.DataFrame([input_ex]) + elif not isinstance(input_ex, pd.DataFrame): + input_ex = None + return input_ex + + +def _convert_dataframe_to_split_dict(df): + result = _handle_dataframe_nans(df).to_dict(orient="split") + # Do not include row index + del result["index"] + if all(df.columns == range(len(df.columns))): + # No need to write default column index out + del result["columns"] + return result + + +def _contains_nd_array(data): + import numpy as np + + if isinstance(data, np.ndarray): + return True + if isinstance(data, list): + return any(_contains_nd_array(x) for x in data) + if isinstance(data, dict): + return any(_contains_nd_array(x) for x in data.values()) + return False + + +class _Example: + """ + Represents an input example for MLflow model. + + Contains jsonable data that can be saved with the model and meta data about the exported format + that can be saved with :py:class:`Model `. + + The _Example is created from example data provided by user. The example(s) can be provided as + pandas.DataFrame, numpy.ndarray, python dictionary or python list. The assumption is that the + example contains jsonable elements (see storage format section below). The input example will + be saved as a json serializable object if it is a pandas DataFrame or numpy array. + If the example is a tuple, the first element is considered as the example data and the second + element is considered as the example params. + + NOTE: serving input example is not supported for sparse matrices yet. + + Metadata: + + The _Example metadata contains the following information: + - artifact_path: Relative path to the serialized example within the model directory. + - serving_input_path: Relative path to the serialized example used for model serving + within the model directory. + - type: Type of example data provided by the user. Supported types are: + - ndarray + - dataframe + - json_object + - sparse_matrix_csc + - sparse_matrix_csr + If the `type` is `dataframe`, `pandas_orient` is also stored in the metadata. This + attribute specifies how is the dataframe encoded in json. For example, "split" value + signals that the data is stored as object with columns and data attributes. + + Storage Format: + + The examples are stored as json for portability and readability. Therefore, the contents of the + example(s) must be jsonable. MLflow will make the following conversions automatically on behalf + of the user: + + - binary values: :py:class:`bytes` or :py:class:`bytearray` are converted to base64 + encoded strings. + - numpy types: Numpy types are converted to the corresponding python types or their closest + equivalent. + - csc/csr matrix: similar to 2 dims numpy array, csc/csr matrix are converted to + corresponding python types or their closest equivalent. + """ + + def __init__(self, input_example: ModelInputExample): + try: + import pyspark.sql + + if isinstance(input_example, pyspark.sql.DataFrame): + raise MlflowException( + "Examples can not be provided as Spark Dataframe. " + "Please make sure your example is of a small size and " + "turn it into a pandas DataFrame." + ) + except ImportError: + pass + + self.info = { + INPUT_EXAMPLE_PATH: EXAMPLE_FILENAME, + } + + self._inference_data, self._inference_params = _split_input_data_and_params( + deepcopy(input_example) + ) + if self._inference_params: + self.info[EXAMPLE_PARAMS_KEY] = "true" + model_input = deepcopy(self._inference_data) + + if isinstance(model_input, pydantic.BaseModel): + model_input = ( + model_input.model_dump() if IS_PYDANTIC_V2_OR_NEWER else model_input.dict() + ) + + is_unified_llm_input = False + if isinstance(model_input, dict): + """ + Supported types are: + - Dict[str, Union[DataType, List, Dict]] --> type: json_object + - Dict[str, numpy.ndarray] --> type: ndarray + """ + if any(isinstance(values, np.ndarray) for values in model_input.values()): + if not all(isinstance(values, np.ndarray) for values in model_input.values()): + raise MlflowException.invalid_parameter_value( + "Mixed types in dictionary are not supported as input examples. " + "Found numpy arrays and other types." + ) + self.info["type"] = "ndarray" + model_input = _handle_ndarray_input(model_input) + self.serving_input = {INPUTS: model_input} + else: + from mlflow.pyfunc.utils.serving_data_parser import is_unified_llm_input + + self.info["type"] = "json_object" + is_unified_llm_input = is_unified_llm_input(model_input) + if is_unified_llm_input: + self.serving_input = model_input + else: + self.serving_input = {INPUTS: model_input} + elif isinstance(model_input, np.ndarray): + """type: ndarray""" + model_input = _handle_ndarray_input(model_input) + self.info["type"] = "ndarray" + self.serving_input = {INPUTS: model_input} + elif isinstance(model_input, list): + """ + Supported types are: + - List[DataType] + - List[Dict[str, Union[DataType, List, Dict]]] + --> type: json_object + """ + if _contains_nd_array(model_input): + raise TensorsNotSupportedException( + "Numpy arrays in list are not supported as input examples." + ) + self.info["type"] = "json_object" + self.serving_input = {INPUTS: model_input} + elif _is_sparse_matrix(model_input): + """ + Supported types are: + - scipy.sparse.csr_matrix + - scipy.sparse.csc_matrix + Note: This type of input is not supported by the scoring server yet + """ + if isinstance(model_input, csc_matrix): + example_type = "sparse_matrix_csc" + else: + example_type = "sparse_matrix_csr" + self.info["type"] = example_type + self.serving_input = {INPUTS: model_input.toarray()} + model_input = _handle_sparse_matrix(model_input) + elif isinstance(model_input, pd.DataFrame): + model_input = _convert_dataframe_to_split_dict(model_input) + self.serving_input = {DF_SPLIT: model_input} + orient = "split" if "columns" in model_input else "values" + self.info.update( + { + "type": "dataframe", + "pandas_orient": orient, + } + ) + elif np.isscalar(model_input) or isinstance(model_input, dt.datetime): + self.info["type"] = "json_object" + self.serving_input = {INPUTS: model_input} + else: + raise MlflowException.invalid_parameter_value( + "Expected one of the following types:\n" + "- pandas.DataFrame\n" + "- numpy.ndarray\n" + "- dictionary of (name -> numpy.ndarray)\n" + "- scipy.sparse.csr_matrix\n" + "- scipy.sparse.csc_matrix\n" + "- dict\n" + "- list\n" + "- scalars\n" + "- datetime.datetime\n" + "- pydantic model instance\n" + f"but got '{type(model_input)}'", + ) + + if self._inference_params is not None: + """ + Save input data and params with their respective keys, so we can load them separately. + """ + model_input = { + EXAMPLE_DATA_KEY: model_input, + EXAMPLE_PARAMS_KEY: self._inference_params, + } + if self.serving_input: + if is_unified_llm_input: + self.serving_input = { + **(self.serving_input or {}), + **self._inference_params, + } + else: + self.serving_input = { + **(self.serving_input or {}), + SERVING_PARAMS_KEY: self._inference_params, + } + + self.json_input_example = json.dumps(model_input, cls=NumpyEncoder) + if self.serving_input: + self.json_serving_input = json.dumps(self.serving_input, cls=NumpyEncoder, indent=2) + self.info[SERVING_INPUT_PATH] = SERVING_INPUT_FILENAME + else: + self.json_serving_input = None + + def save(self, parent_dir_path: str): + """ + Save the example as json at ``parent_dir_path``/`self.info['artifact_path']`. + Save serving input as json at ``parent_dir_path``/`self.info['serving_input_path']`. + """ + with open(os.path.join(parent_dir_path, self.info[INPUT_EXAMPLE_PATH]), "w") as f: + f.write(self.json_input_example) + if self.json_serving_input: + with open(os.path.join(parent_dir_path, self.info[SERVING_INPUT_PATH]), "w") as f: + f.write(self.json_serving_input) + + @property + def inference_data(self): + """ + Returns the input example in a form that PyFunc wrapped models can score. + """ + return self._inference_data + + @property + def inference_params(self): + """ + Returns the params dictionary that PyFunc wrapped models can use for scoring. + """ + return self._inference_params + + +def _contains_params(input_example): + # For tuple input, we assume the first item is input_example data + # and the second item is params dictionary. + return ( + isinstance(input_example, tuple) + and len(input_example) == 2 + and isinstance(input_example[1], dict) + ) + + +def _split_input_data_and_params(input_example): + if _contains_params(input_example): + input_data, inference_params = input_example + _validate_params(inference_params) + return input_data, inference_params + return input_example, None + + +def convert_input_example_to_serving_input(input_example) -> str | None: + """ + Helper function to convert a model's input example to a serving input example that + can be used for model inference in the scoring server. + + Args: + input_example: model input example. Supported types are pandas.DataFrame, numpy.ndarray, + dictionary of (name -> numpy.ndarray), list, scalars and dicts with json serializable + values. + + Returns: + serving input example as a json string + """ + if input_example is None: + return None + + example = _Example(input_example) + return example.json_serving_input + + +def _save_example( + mlflow_model: Model, input_example: ModelInputExample | None, path: str +) -> _Example | None: + """ + Saves example to a file on the given path and updates passed Model with example metadata. + + The metadata is a dictionary with the following fields: + - 'artifact_path': example path relative to the model directory. + - 'type': Type of example. Currently the supported values are 'dataframe' and 'ndarray' + - One of the following metadata based on the `type`: + - 'pandas_orient': Used to store dataframes. Determines the json encoding for dataframe + examples in terms of pandas orient convention. Defaults to 'split'. + - 'format: Used to store tensors. Determines the standard used to store a tensor input + example. MLflow uses a JSON-formatted string representation of TF serving + input. + + Args: + mlflow_model: Model metadata that will get updated with the example metadata. + path: Where to store the example file. Should be model the model directory. + + Returns: + _Example object that contains saved input example. + """ + if input_example is None: + return None + + example = _Example(input_example) + example.save(path) + mlflow_model.saved_input_example_info = example.info + return example + + +def _get_mlflow_model_input_example_dict( + mlflow_model: Model, uri_or_path: str +) -> dict[str, Any] | None: + """ + Args: + mlflow_model: Model metadata. + uri_or_path: Model or run URI, or path to the `model` directory. + e.g. models:///, runs:// + or /path/to/model + + Returns: + Input example or None if the model has no example. + """ + if mlflow_model.saved_input_example_info is None: + return None + example_type = mlflow_model.saved_input_example_info["type"] + if example_type not in [ + "dataframe", + "ndarray", + "sparse_matrix_csc", + "sparse_matrix_csr", + "json_object", + ]: + raise MlflowException(f"This version of mlflow can not load example of type {example_type}") + return json.loads( + _read_file_content(uri_or_path, mlflow_model.saved_input_example_info[INPUT_EXAMPLE_PATH]) + ) + + +def _load_serving_input_example(mlflow_model: Model, path: str) -> str | None: + """ + Load serving input example from a model directory. Returns None if there is no serving input + example. + + Args: + mlflow_model: Model metadata. + path: Path to the model directory. + + Returns: + Serving input example or None if the model has no serving input example. + """ + if mlflow_model.saved_input_example_info is None: + return None + serving_input_path = mlflow_model.saved_input_example_info.get(SERVING_INPUT_PATH) + if serving_input_path is None: + return None + with open(os.path.join(path, serving_input_path)) as handle: + return handle.read() + + +def load_serving_example(model_uri_or_path: str): + """ + Load serving input example from a model directory or URI. + + Args: + model_uri_or_path: Model URI or path to the `model` directory. + e.g. models:/// or /path/to/model + """ + return _read_file_content(model_uri_or_path, SERVING_INPUT_FILENAME) + + +def _read_file_content(uri_or_path: str, file_name: str): + """ + Read file content from a model directory or URI. + + Args: + uri_or_path: Model or run URI, or path to the `model` directory. + e.g. models:///, runs:// + or /path/to/model + file_name: Name of the file to read. + """ + from mlflow.store.artifact.models_artifact_repo import ModelsArtifactRepository + + if ModelsArtifactRepository._is_logged_model_uri(uri_or_path): + uri_or_path = ModelsArtifactRepository.get_underlying_uri(uri_or_path) + + file_path = str(uri_or_path).rstrip("/") + "/" + file_name + if os.path.exists(file_path): + with open(file_path) as handle: + return handle.read() + else: + with tempfile.TemporaryDirectory() as tmpdir: + local_file_path = _download_artifact_from_uri(file_path, output_path=tmpdir) + with open(local_file_path) as handle: + return handle.read() + + +def _read_example(mlflow_model: Model, uri_or_path: str): + """ + Read example from a model directory. Returns None if there is no example metadata (i.e. the + model was saved without example). Raises FileNotFoundError if there is model metadata but the + example file is missing. + + Args: + mlflow_model: Model metadata. + uri_or_path: Model or run URI, or path to the `model` directory. + e.g. models:///, runs:// + or /path/to/model + + Returns: + Input example data or None if the model has no example. + """ + input_example = _get_mlflow_model_input_example_dict(mlflow_model, uri_or_path) + if input_example is None: + return None + + example_type = mlflow_model.saved_input_example_info["type"] + input_schema = mlflow_model.signature.inputs if mlflow_model.signature is not None else None + if mlflow_model.saved_input_example_info.get(EXAMPLE_PARAMS_KEY, None): + input_example = input_example[EXAMPLE_DATA_KEY] + if example_type == "json_object": + return input_example + if example_type == "ndarray": + return parse_inputs_data(input_example, schema=input_schema) + if example_type in ["sparse_matrix_csc", "sparse_matrix_csr"]: + return _read_sparse_matrix_from_json(input_example, example_type) + if example_type == "dataframe": + return dataframe_from_parsed_json(input_example, pandas_orient="split", schema=input_schema) + raise MlflowException( + "Malformed input example metadata. The 'type' field must be one of " + "'dataframe', 'ndarray', 'sparse_matrix_csc', 'sparse_matrix_csr' or 'json_object'." + ) + + +def _read_example_params(mlflow_model: Model, path: str): + """ + Read params of input_example from a model directory. Returns None if there is no params + in the input_example or the model was saved without example. + """ + if ( + mlflow_model.saved_input_example_info is None + or mlflow_model.saved_input_example_info.get(EXAMPLE_PARAMS_KEY, None) is None + ): + return None + input_example_dict = _get_mlflow_model_input_example_dict(mlflow_model, path) + return input_example_dict[EXAMPLE_PARAMS_KEY] + + +def _read_tensor_input_from_json(path_or_data, schema=None): + if isinstance(path_or_data, str) and os.path.exists(path_or_data): + with open(path_or_data) as handle: + inp_dict = json.load(handle) + else: + inp_dict = path_or_data + return parse_tf_serving_input(inp_dict, schema) + + +def _read_sparse_matrix_from_json(path_or_data, example_type): + if isinstance(path_or_data, str) and os.path.exists(path_or_data): + with open(path_or_data) as handle: + matrix_data = json.load(handle) + else: + matrix_data = path_or_data + data = matrix_data["data"] + indices = matrix_data["indices"] + indptr = matrix_data["indptr"] + shape = tuple(matrix_data["shape"]) + + if example_type == "sparse_matrix_csc": + return csc_matrix((data, indices, indptr), shape=shape) + else: + return csr_matrix((data, indices, indptr), shape=shape) + + +def plot_lines(data_series, xlabel, ylabel, legend_loc=None, line_kwargs=None, title=None): + import matplotlib.pyplot as plt + + fig, ax = plt.subplots() + + if line_kwargs is None: + line_kwargs = {} + + for label, data_x, data_y in data_series: + ax.plot(data_x, data_y, label=label, **line_kwargs) + + if legend_loc: + ax.legend(loc=legend_loc) + + ax.set(xlabel=xlabel, ylabel=ylabel, title=title) + + return fig, ax + + +def _enforce_tensor_spec( + values: Union[np.ndarray, "csc_matrix", "csr_matrix"], + tensor_spec: TensorSpec, +): + """ + Enforce the input tensor shape and type matches the provided tensor spec. + """ + expected_shape = tensor_spec.shape + expected_type = tensor_spec.type + actual_shape = values.shape + actual_type = values.dtype if isinstance(values, np.ndarray) else values.data.dtype + + # This logic is for handling "ragged" arrays. The first check is for a standard numpy shape + # representation of a ragged array. The second is for handling a more manual specification + # of shape while support an input which is a ragged array. + if len(expected_shape) == 1 and expected_shape[0] == -1 and expected_type == np.dtype("O"): + # Sample spec: Tensor('object', (-1,)) + # Will pass on any provided input + return values + if ( + len(expected_shape) > 1 + and -1 in expected_shape[1:] + and len(actual_shape) == 1 + and actual_type == np.dtype("O") + ): + # Sample spec: Tensor('float64', (-1, -1, -1, 3)) + # Will pass on inputs which are ragged arrays: shape==(x,), dtype=='object' + return values + + if len(expected_shape) != len(actual_shape): + raise MlflowException( + f"Shape of input {actual_shape} does not match expected shape {expected_shape}." + ) + for expected, actual in zip(expected_shape, actual_shape): + if expected == -1: + continue + if expected != actual: + raise MlflowException( + f"Shape of input {actual_shape} does not match expected shape {expected_shape}." + ) + if clean_tensor_type(actual_type) != expected_type: + raise MlflowException( + f"dtype of input {actual_type} does not match expected dtype {expected_type}" + ) + return values + + +def _enforce_mlflow_datatype(name, values: pd.Series, t: DataType): + """ + Enforce the input column type matches the declared in model input schema. + + The following type conversions are allowed: + + 1. object -> string + 2. int -> long (upcast) + 3. float -> double (upcast) + 4. int -> double (safe conversion) + 5. np.datetime64[x] -> datetime (any precision) + 6. object -> datetime + + NB: pandas does not have native decimal data type, when user train and infer + model from pyspark dataframe that contains decimal type, the schema will be + treated as float64. + 7. decimal -> double + + Any other type mismatch will raise error. + """ + + if values.dtype == object and t not in (DataType.binary, DataType.string): + values = values.infer_objects() + + if t == DataType.string and values.dtype == object: + # NB: the object can contain any type and we currently cannot cast to pandas Strings + # due to how None is cast + return values + + # NB: Comparison of pandas and numpy data type fails when numpy data type is on the left hand + # side of the comparison operator. It works, however, if pandas type is on the left hand side. + # That is because pandas is aware of numpy. + if t.to_pandas() == values.dtype or t.to_numpy() == values.dtype: + # The types are already compatible => conversion is not necessary. + return values + + if t == DataType.binary and values.dtype.kind == t.binary.to_numpy().kind: + # NB: bytes in numpy have variable itemsize depending on the length of the longest + # element in the array (column). Since MLflow binary type is length agnostic, we ignore + # itemsize when matching binary columns. + return values + + if t == DataType.datetime and values.dtype.kind == t.to_numpy().kind: + # NB: datetime values have variable precision denoted by brackets, e.g. datetime64[ns] + # denotes nanosecond precision. Since MLflow datetime type is precision agnostic, we + # ignore precision when matching datetime columns. + try: + return values.astype(np.dtype("datetime64[ns]")) + except TypeError as e: + raise MlflowException( + "Please ensure that the input data of datetime column only contains timezone-naive " + f"datetime objects. Error: {e}" + ) + + if t == DataType.datetime and (values.dtype == object or values.dtype == t.to_python()): + # NB: Pyspark date columns get converted to object when converted to a pandas + # DataFrame. To respect the original typing, we convert the column to datetime. + try: + return values.astype(np.dtype("datetime64[ns]"), errors="raise") + except ValueError as e: + raise MlflowException( + f"Failed to convert column {name} from type {values.dtype} to {t}." + ) from e + + if t == DataType.boolean and values.dtype == object: + # Should not convert type otherwise it converts None to boolean False + return values + + if t == DataType.double and values.dtype == decimal.Decimal: + # NB: Pyspark Decimal column get converted to decimal.Decimal when converted to pandas + # DataFrame. In order to support decimal data training from spark data frame, we add this + # conversion even we might lose the precision. + try: + return pd.to_numeric(values, errors="raise") + except ValueError: + raise MlflowException( + f"Failed to convert column {name} from type {values.dtype} to {t}." + ) + + numpy_type = t.to_numpy() + if values.dtype.kind == numpy_type.kind: + is_upcast = values.dtype.itemsize <= numpy_type.itemsize + elif values.dtype.kind == "u" and numpy_type.kind == "i": + is_upcast = values.dtype.itemsize < numpy_type.itemsize + elif values.dtype.kind in ("i", "u") and numpy_type == np.float64: + # allow (u)int => double conversion + is_upcast = values.dtype.itemsize <= 6 + else: + is_upcast = False + + if is_upcast: + return values.astype(numpy_type, errors="raise") + else: + # support converting long -> float/double for 0 and 1 values + def all_zero_or_ones(xs): + return all(pd.isnull(x) or x in [0, 1] for x in xs) + + if ( + values.dtype == np.int64 + and numpy_type in (np.float32, np.float64) + and all_zero_or_ones(values) + ): + return values.astype(numpy_type, errors="raise") + + # NB: conversion between incompatible types (e.g. floats -> ints or + # double -> float) are not allowed. While supported by pandas and numpy, + # these conversions alter the values significantly. + def all_ints(xs): + return all(pd.isnull(x) or int(x) == x for x in xs) + + hint = "" + if ( + values.dtype == np.float64 + and numpy_type.kind in ("i", "u") + and values.hasnans + and all_ints(values) + ): + hint = ( + " Hint: the type mismatch is likely caused by missing values. " + "Integer columns in python can not represent missing values and are therefore " + "encoded as floats. The best way to avoid this problem is to infer the model " + "schema based on a realistic data sample (training dataset) that includes missing " + "values. Alternatively, you can declare integer columns as doubles (float64) " + "whenever these columns may have missing values. See `Handling Integers With " + "Missing Values `_ for more details." + ) + + raise MlflowException( + f"Incompatible input types for column {name}. " + f"Can not safely convert {values.dtype} to {numpy_type}.{hint}" + ) + + +# dtype -> possible value types mapping +_ALLOWED_CONVERSIONS_FOR_PARAMS = { + DataType.long: (DataType.integer,), + DataType.float: (DataType.integer, DataType.long), + DataType.double: (DataType.integer, DataType.long, DataType.float), +} + + +def _enforce_param_datatype(value: Any, dtype: DataType): + """ + Enforce the value matches the data type. This is used to enforce params datatype. + The returned data is of python built-in type or a datetime object. + + The following type conversions are allowed: + + 1. int -> long, float, double + 2. long -> float, double + 3. float -> double + 4. any -> datetime (try conversion) + + Any other type mismatch will raise error. + + Args: + value: parameter value + dtype: expected data type + """ + if value is None: + return + + if dtype == DataType.datetime: + try: + datetime_value = np.datetime64(value).item() + if isinstance(datetime_value, int): + raise MlflowException.invalid_parameter_value( + f"Failed to convert value to `{dtype}`. " + f"It must be convertible to datetime.date/datetime, got `{value}`" + ) + return datetime_value + except ValueError as e: + raise MlflowException.invalid_parameter_value( + f"Failed to convert value `{value}` from type `{type(value)}` to `{dtype}`" + ) from e + + # Note that np.isscalar(datetime.date(...)) is False + if not np.isscalar(value): + raise MlflowException.invalid_parameter_value( + f"Value must be a scalar for type `{dtype}`, got `{value}`" + ) + + # Always convert to python native type for params + if DataType.check_type(dtype, value): + return dtype.to_python()(value) + + if dtype in _ALLOWED_CONVERSIONS_FOR_PARAMS and any( + DataType.check_type(t, value) for t in _ALLOWED_CONVERSIONS_FOR_PARAMS[dtype] + ): + try: + return dtype.to_python()(value) + except ValueError as e: + raise MlflowException.invalid_parameter_value( + f"Failed to convert value `{value}` from type `{type(value)}` to `{dtype}`" + ) from e + + raise MlflowException.invalid_parameter_value( + f"Can not safely convert `{type(value)}` to `{dtype}` for value `{value}`" + ) + + +def _enforce_unnamed_col_schema(pf_input: pd.DataFrame, input_schema: Schema): + """Enforce the input columns conform to the model's column-based signature.""" + input_names = pf_input.columns[: len(input_schema.inputs)] + input_types = input_schema.input_types() + new_pf_input = {} + for i, x in enumerate(input_names): + if isinstance(input_types[i], DataType): + new_pf_input[x] = _enforce_mlflow_datatype(x, pf_input[x], input_types[i]) + # If the input_type is objects/arrays/maps, we assume pf_input must be a pandas DataFrame. + # Otherwise, the schema is not valid. + else: + new_pf_input[x] = pd.Series( + [_enforce_type(obj, input_types[i]) for obj in pf_input[x]], name=x + ) + return pd.DataFrame(new_pf_input) + + +def _enforce_named_col_schema(pf_input: pd.DataFrame, input_schema: Schema): + """Enforce the input columns conform to the model's column-based signature.""" + input_names = input_schema.input_names() + input_dict = input_schema.input_dict() + new_pf_input = {} + for name in input_names: + input_type = input_dict[name].type + required = input_dict[name].required + if name not in pf_input: + if required: + raise MlflowException( + f"The input column '{name}' is required by the model " + "signature but missing from the input data." + ) + else: + continue + if isinstance(input_type, DataType): + new_pf_input[name] = _enforce_mlflow_datatype(name, pf_input[name], input_type) + # If the input_type is objects/arrays/maps, we assume pf_input must be a pandas DataFrame. + # Otherwise, the schema is not valid. + else: + new_pf_input[name] = pd.Series( + [_enforce_type(obj, input_type, required) for obj in pf_input[name]], name=name + ) + return pd.DataFrame(new_pf_input) + + +def _reshape_and_cast_pandas_column_values(name, pd_series, tensor_spec): + if tensor_spec.shape[0] != -1 or -1 in tensor_spec.shape[1:]: + raise MlflowException( + "For pandas dataframe input, the first dimension of shape must be a variable " + "dimension and other dimensions must be fixed, but in model signature the shape " + f"of {'input ' + name if name else 'the unnamed input'} is {tensor_spec.shape}." + ) + + if np.isscalar(pd_series[0]): + for shape in [(-1,), (-1, 1)]: + if tensor_spec.shape == shape: + return _enforce_tensor_spec( + np.array(pd_series, dtype=tensor_spec.type).reshape(shape), tensor_spec + ) + raise MlflowException( + f"The input pandas dataframe column '{name}' contains scalar " + "values, which requires the shape to be (-1,) or (-1, 1), but got tensor spec " + f"shape of {tensor_spec.shape}.", + error_code=INVALID_PARAMETER_VALUE, + ) + elif isinstance(pd_series[0], list) and np.isscalar(pd_series[0][0]): + # If the pandas column contains list type values, + # in this case, the shape and type information is lost, + # so do not enforce the shape and type, instead, + # reshape the array value list to the required shape, and cast value type to + # required type. + reshape_err_msg = ( + f"The value in the Input DataFrame column '{name}' could not be converted to the " + f"expected shape of: '{tensor_spec.shape}'. Ensure that each of the input list " + "elements are of uniform length and that the data can be coerced to the tensor " + f"type '{tensor_spec.type}'" + ) + try: + flattened_numpy_arr = np.vstack(pd_series.tolist()) + reshaped_numpy_arr = flattened_numpy_arr.reshape(tensor_spec.shape).astype( + tensor_spec.type + ) + except ValueError: + raise MlflowException(reshape_err_msg, error_code=INVALID_PARAMETER_VALUE) + if len(reshaped_numpy_arr) != len(pd_series): + raise MlflowException(reshape_err_msg, error_code=INVALID_PARAMETER_VALUE) + return reshaped_numpy_arr + elif isinstance(pd_series[0], np.ndarray): + reshape_err_msg = ( + f"The value in the Input DataFrame column '{name}' could not be converted to the " + f"expected shape of: '{tensor_spec.shape}'. Ensure that each of the input numpy " + "array elements are of uniform length and can be reshaped to above expected shape." + ) + try: + # Because numpy array includes precise type information, so we don't convert type + # here, so that in following schema validation we can have strict type check on + # numpy array column. + reshaped_numpy_arr = np.vstack(pd_series.tolist()).reshape(tensor_spec.shape) + except ValueError: + raise MlflowException(reshape_err_msg, error_code=INVALID_PARAMETER_VALUE) + if len(reshaped_numpy_arr) != len(pd_series): + raise MlflowException(reshape_err_msg, error_code=INVALID_PARAMETER_VALUE) + return reshaped_numpy_arr + else: + raise MlflowException( + "Because the model signature requires tensor spec input, the input " + "pandas dataframe values should be either scalar value, python list " + "containing scalar values or numpy array containing scalar values, " + "other types are not supported.", + error_code=INVALID_PARAMETER_VALUE, + ) + + +def _enforce_tensor_schema(pf_input: PyFuncInput, input_schema: Schema): + """Enforce the input tensor(s) conforms to the model's tensor-based signature.""" + + def _is_sparse_matrix(x): + if not HAS_SCIPY: + # we can safely assume that it's not a sparse matrix if scipy is not installed + return False + return isinstance(x, (csr_matrix, csc_matrix)) + + if input_schema.has_input_names(): + if isinstance(pf_input, dict): + new_pf_input = {} + for col_name, tensor_spec in zip(input_schema.input_names(), input_schema.inputs): + if not isinstance(pf_input[col_name], np.ndarray): + raise MlflowException( + "This model contains a tensor-based model signature with input names," + " which suggests a dictionary input mapping input name to a numpy" + f" array, but a dict with value type {type(pf_input[col_name])} was found.", + error_code=INVALID_PARAMETER_VALUE, + ) + new_pf_input[col_name] = _enforce_tensor_spec(pf_input[col_name], tensor_spec) + elif isinstance(pf_input, pd.DataFrame): + new_pf_input = {} + for col_name, tensor_spec in zip(input_schema.input_names(), input_schema.inputs): + pd_series = pf_input[col_name] + new_pf_input[col_name] = _reshape_and_cast_pandas_column_values( + col_name, pd_series, tensor_spec + ) + else: + raise MlflowException( + "This model contains a tensor-based model signature with input names, which" + " suggests a dictionary input mapping input name to tensor, or a pandas" + " DataFrame input containing columns mapping input name to flattened list value" + f" from tensor, but an input of type {type(pf_input)} was found.", + error_code=INVALID_PARAMETER_VALUE, + ) + else: + tensor_spec = input_schema.inputs[0] + if isinstance(pf_input, pd.DataFrame): + num_input_columns = len(pf_input.columns) + if pf_input.empty: + raise MlflowException("Input DataFrame is empty.") + elif num_input_columns == 1: + new_pf_input = _reshape_and_cast_pandas_column_values( + None, pf_input[pf_input.columns[0]], tensor_spec + ) + else: + if tensor_spec.shape != (-1, num_input_columns): + raise MlflowException( + "This model contains a model signature with an unnamed input. Since the " + "input data is a pandas DataFrame containing multiple columns, " + "the input shape must be of the structure " + "(-1, number_of_dataframe_columns). " + f"Instead, the input DataFrame passed had {num_input_columns} columns and " + f"an input shape of {tensor_spec.shape} with all values within the " + "DataFrame of scalar type. Please adjust the passed in DataFrame to " + "match the expected structure", + error_code=INVALID_PARAMETER_VALUE, + ) + new_pf_input = _enforce_tensor_spec(pf_input.to_numpy(), tensor_spec) + elif isinstance(pf_input, np.ndarray) or _is_sparse_matrix(pf_input): + new_pf_input = _enforce_tensor_spec(pf_input, tensor_spec) + else: + raise MlflowException( + "This model contains a tensor-based model signature with no input names," + " which suggests a numpy array input or a pandas dataframe input with" + f" proper column values, but an input of type {type(pf_input)} was found.", + error_code=INVALID_PARAMETER_VALUE, + ) + return new_pf_input + + +def _enforce_schema(pf_input: PyFuncInput, input_schema: Schema, flavor: str | None = None): + """ + Enforces the provided input matches the model's input schema, + + For signatures with input names, we check there are no missing inputs and reorder the inputs to + match the ordering declared in schema if necessary. Any extra columns are ignored. + + For column-based signatures, we make sure the types of the input match the type specified in + the schema or if it can be safely converted to match the input schema. + + For Pyspark DataFrame inputs, MLflow casts a sample of the PySpark DataFrame into a Pandas + DataFrame. MLflow will only enforce the schema on a subset of the data rows. + + For tensor-based signatures, we make sure the shape and type of the input matches the shape + and type specified in model's input schema. + """ + + def _is_scalar(x): + return np.isscalar(x) or x is None + + original_pf_input = pf_input + if isinstance(pf_input, pd.Series): + pf_input = pd.DataFrame(pf_input) + if not input_schema.is_tensor_spec(): + # convert single DataType to pandas DataFrame + if np.isscalar(pf_input): + pf_input = pd.DataFrame([pf_input]) + elif isinstance(pf_input, dict): + # keys are column names + if any( + isinstance(col_spec.type, (Array, Object)) for col_spec in input_schema.inputs + ) or all( + _is_scalar(value) + or (isinstance(value, list) and all(isinstance(item, str) for item in value)) + for value in pf_input.values() + ): + pf_input = pd.DataFrame([pf_input]) + else: + try: + # This check is specifically to handle the serving structural cast for + # certain inputs for the transformers implementation. Due to the fact that + # specific Pipeline types in transformers support passing input data + # of the form Dict[str, str] in which the value is a scalar string, model + # serving will cast this entry as a numpy array with shape () and size 1. + # This is seen as a scalar input when attempting to create a Pandas + # DataFrame from such a numpy structure and requires the array to be + # encapsulated in a list in order to prevent a ValueError exception for + # requiring an index if passing in all scalar values thrown by Pandas. + if all( + isinstance(value, np.ndarray) + and value.dtype.type == np.str_ + and value.size == 1 + and value.shape == () + for value in pf_input.values() + ): + pf_input = pd.DataFrame([pf_input]) + elif any( + isinstance(value, np.ndarray) and value.ndim > 1 + for value in pf_input.values() + ): + # Pandas DataFrames can't be constructed with embedded multi-dimensional + # numpy arrays. Accordingly, we convert any multi-dimensional numpy + # arrays to lists before constructing a DataFrame. This is safe because + # ColSpec model signatures do not support array columns, so subsequent + # validation logic will result in a clear "incompatible input types" + # exception. This is preferable to a pandas DataFrame construction error + pf_input = pd.DataFrame( + { + key: ( + value.tolist() + if (isinstance(value, np.ndarray) and value.ndim > 1) + else value + ) + for key, value in pf_input.items() + } + ) + else: + pf_input = pd.DataFrame(pf_input) + except Exception as e: + raise MlflowException( + "This model contains a column-based signature, which suggests a DataFrame" + " input. There was an error casting the input data to a DataFrame:" + f" {e}" + ) + elif isinstance(pf_input, (list, np.ndarray, pd.Series)): + pf_input = pd.DataFrame(pf_input) + elif HAS_PYSPARK and isinstance(pf_input, SparkDataFrame): + pf_input = pf_input.limit(10).toPandas() + for field in original_pf_input.schema.fields: + if isinstance(field.dataType, (StructType, ArrayType)): + pf_input[field.name] = pf_input[field.name].apply( + lambda row: convert_complex_types_pyspark_to_pandas(row, field.dataType) + ) + if not isinstance(pf_input, pd.DataFrame): + raise MlflowException( + f"Expected input to be DataFrame. Found: {type(pf_input).__name__}" + ) + + if input_schema.has_input_names(): + # make sure there are no missing columns + input_names = input_schema.required_input_names() + optional_names = input_schema.optional_input_names() + expected_required_cols = set(input_names) + actual_cols = set() + optional_cols = set(optional_names) + if len(expected_required_cols) == 1 and isinstance(pf_input, np.ndarray): + # for schemas with a single column, match input with column + pf_input = {input_names[0]: pf_input} + actual_cols = expected_required_cols + elif isinstance(pf_input, pd.DataFrame): + actual_cols = set(pf_input.columns) + elif isinstance(pf_input, dict): + actual_cols = set(pf_input.keys()) + missing_cols = expected_required_cols - actual_cols + extra_cols = actual_cols - expected_required_cols - optional_cols + # Preserve order from the original columns, since missing/extra columns are likely to + # be in same order. + missing_cols = [c for c in input_names if c in missing_cols] + extra_cols = [c for c in actual_cols if c in extra_cols] + if missing_cols: + # If the user has set MLFLOW_DISABLE_SCHEMA_DETAILS to true, we raise a generic error + if MLFLOW_DISABLE_SCHEMA_DETAILS.get(): + message = "Input schema validation failed. Mismatched or missing input(s)." + if extra_cols: + message += " Note that there were extra inputs provided." + else: + message = f"Model is missing inputs {missing_cols}." + if extra_cols: + message += f" Note that there were extra inputs: {extra_cols}." + raise MlflowException(message) + + if extra_cols: + _logger.warning( + "Found extra inputs in the model input that are not defined in the model " + f"signature: `{extra_cols}`. These inputs will be ignored." + ) + elif not input_schema.is_tensor_spec(): + # The model signature does not specify column names => we can only verify column count. + num_actual_columns = len(pf_input.columns) + if num_actual_columns < len(input_schema.inputs): + raise MlflowException( + "Model inference is missing inputs. The model signature declares " + "{} inputs but the provided value only has " + "{} inputs. Note: the inputs were not named in the signature so we can " + "only verify their count.".format(len(input_schema.inputs), num_actual_columns) + ) + if input_schema.is_tensor_spec(): + return _enforce_tensor_schema(pf_input, input_schema) + elif HAS_PYSPARK and isinstance(original_pf_input, SparkDataFrame): + return _enforce_pyspark_dataframe_schema( + original_pf_input, pf_input, input_schema, flavor=flavor + ) + else: + # pf_input must be a pandas Dataframe at this point + return ( + _enforce_named_col_schema(pf_input, input_schema) + if input_schema.has_input_names() + else _enforce_unnamed_col_schema(pf_input, input_schema) + ) + + +def _enforce_pyspark_dataframe_schema( + original_pf_input: SparkDataFrame, + pf_input_as_pandas, + input_schema: Schema, + flavor: str | None = None, +): + """ + Enforce that the input PySpark DataFrame conforms to the model's input schema. + + This function creates a new DataFrame that only includes the columns from the original + DataFrame that are declared in the model's input schema. Any extra columns in the original + DataFrame are dropped.Note that this function does not modify the original DataFrame. + + Args: + original_pf_input: Original input PySpark DataFrame. + pf_input_as_pandas: Input DataFrame converted to pandas. + input_schema: Expected schema of the input DataFrame. + flavor: Optional model flavor. If specified, it is used to handle specific behaviors + for different model flavors. Currently, only the '_FEATURE_STORE_FLAVOR' is + handled specially. + + Returns: + New PySpark DataFrame that conforms to the model's input schema. + """ + if not HAS_PYSPARK: + raise MlflowException("PySpark is not installed. Cannot handle a PySpark DataFrame.") + new_pf_input = original_pf_input.alias("pf_input_copy") + if input_schema.has_input_names(): + _enforce_named_col_schema(pf_input_as_pandas, input_schema) + input_names = input_schema.input_names() + + else: + _enforce_unnamed_col_schema(pf_input_as_pandas, input_schema) + input_names = pf_input_as_pandas.columns[: len(input_schema.inputs)] + columns_to_drop = [] + columns_not_dropped_for_feature_store_model = [] + for col, dtype in new_pf_input.dtypes: + if col not in input_names: + # to support backwards compatibility with feature store models + if any(x in dtype for x in ["array", "map", "struct"]): + if flavor == _FEATURE_STORE_FLAVOR: + columns_not_dropped_for_feature_store_model.append(col) + continue + columns_to_drop.append(col) + if columns_not_dropped_for_feature_store_model: + _logger.warning( + "The following columns are not in the model signature but " + "are not dropped for feature store model: %s", + ", ".join(columns_not_dropped_for_feature_store_model), + ) + return new_pf_input.drop(*columns_to_drop) + + +def _enforce_datatype(data: Any, dtype: DataType, required=True): + if not required and _is_none_or_nan(data): + return None + + if not isinstance(dtype, DataType): + raise MlflowException(f"Expected dtype to be DataType, got {type(dtype).__name__}") + if not np.isscalar(data): + raise MlflowException(f"Expected data to be scalar, got {type(data).__name__}") + # Reuse logic in _enforce_mlflow_datatype for type conversion + pd_series = pd.Series(data) + try: + pd_series = _enforce_mlflow_datatype("", pd_series, dtype) + except MlflowException: + raise MlflowException( + f"Failed to enforce schema of data `{data}` with dtype `{dtype.name}`" + ) + return pd_series[0] + + +def _enforce_array(data: Any, arr: Array, required: bool = True): + """ + Enforce data against an Array type. + If the field is required, then the data must be provided. + If Array's internal dtype is AnyType, then None and empty lists are also accepted. + """ + if not required or isinstance(arr.dtype, AnyType): + if data is None or (isinstance(data, (list, np.ndarray)) and len(data) == 0): + return data + + if not isinstance(data, (list, np.ndarray)): + raise MlflowException(f"Expected data to be list or numpy array, got {type(data).__name__}") + + if isinstance(arr.dtype, DataType): + # TODO: this is still significantly slower than direct np.asarray dtype conversion + # pd.Series conversion can be removed once we support direct validation on the numpy array + data_enforced = ( + _enforce_mlflow_datatype("", pd.Series(data), arr.dtype).to_numpy( + dtype=arr.dtype.to_numpy() + ) + if len(data) > 0 + else data + ) + else: + data_enforced = [_enforce_type(x, arr.dtype, required=required) for x in data] + + if isinstance(data, list) and isinstance(data_enforced, np.ndarray): + data_enforced = data_enforced.tolist() + elif isinstance(data, np.ndarray) and isinstance(data_enforced, list): + data_enforced = np.array(data_enforced) + + return data_enforced + + +def _enforce_property(data: Any, property: Property): + return _enforce_type(data, property.dtype, required=property.required) + + +def _enforce_object(data: dict[str, Any], obj: Object, required: bool = True): + if HAS_PYSPARK and isinstance(data, Row): + data = None if len(data) == 0 else data.asDict(True) + if not required and (data is None or data == {}): + return data + if not isinstance(data, dict): + raise MlflowException( + f"Failed to enforce schema of '{data}' with type '{obj}'. " + f"Expected data to be dictionary, got {type(data).__name__}" + ) + if not isinstance(obj, Object): + raise MlflowException( + f"Failed to enforce schema of '{data}' with type '{obj}'. " + f"Expected obj to be Object, got {type(obj).__name__}" + ) + properties = {prop.name: prop for prop in obj.properties} + required_props = {k for k, prop in properties.items() if prop.required} + missing_props = required_props - set(data.keys()) + if missing_props: + raise MlflowException(f"Missing required properties: {missing_props}") + if invalid_props := data.keys() - properties.keys(): + raise MlflowException( + f"Invalid properties not defined in the schema found: {invalid_props}" + ) + for k, v in data.items(): + try: + data[k] = _enforce_property(v, properties[k]) + except MlflowException as e: + raise MlflowException( + f"Failed to enforce schema for key `{k}`. " + f"Expected type {properties[k].to_dict()[k]['type']}, " + f"received type {type(v).__name__}" + ) from e + return data + + +def _enforce_map(data: Any, map_type: Map, required: bool = True): + if (not required or isinstance(map_type.value_type, AnyType)) and (data is None or data == {}): + return data + + if not isinstance(data, dict): + raise MlflowException(f"Expected data to be a dict, got {type(data).__name__}") + + if not all(isinstance(k, str) for k in data): + raise MlflowException("Expected all keys in the map type data are string type.") + + return {k: _enforce_type(v, map_type.value_type, required=required) for k, v in data.items()} + + +def _enforce_type(data: Any, data_type: DataType | Array | Object | Map, required=True): + if isinstance(data_type, DataType): + return _enforce_datatype(data, data_type, required=required) + if isinstance(data_type, Array): + return _enforce_array(data, data_type, required=required) + if isinstance(data_type, Object): + return _enforce_object(data, data_type, required=required) + if isinstance(data_type, Map): + return _enforce_map(data, data_type, required=required) + if isinstance(data_type, AnyType): + return data + raise MlflowException(f"Invalid data type: {data_type!r}") + + +def validate_schema(data: PyFuncInput, expected_schema: Schema) -> None: + """ + Validate that the input data has the expected schema. + + Args: + data: Input data to be validated. Supported types are: + + - pandas.DataFrame + - pandas.Series + - numpy.ndarray + - scipy.sparse.csc_matrix + - scipy.sparse.csr_matrix + - List[Any] + - Dict[str, Any] + - str + + expected_schema: Expected Schema of the input data. + + Raises: + mlflow.exceptions.MlflowException: when the input data does not match the schema. + + .. code-block:: python + :caption: Example usage of validate_schema + + import mlflow.models + + # Suppose you've already got a model_uri + model_info = mlflow.models.get_model_info(model_uri) + # Get model signature directly + model_signature = model_info.signature + # validate schema + mlflow.models.validate_schema(input_data, model_signature.inputs) + """ + + _enforce_schema(data, expected_schema) + + +def add_libraries_to_model(model_uri, run_id=None, registered_model_name=None): + """ + Given a registered model_uri (e.g. models://), this utility + re-logs the model along with all the required model libraries back to the Model Registry. + The required model libraries are stored along with the model as model artifacts. In + addition, supporting files to the model (e.g. conda.yaml, requirements.txt) are modified + to use the added libraries. + + By default, this utility creates a new model version under the same registered model specified + by ``model_uri``. This behavior can be overridden by specifying the ``registered_model_name`` + argument. + + Args: + model_uri: A registered model uri in the Model Registry of the form + models:// + run_id: The ID of the run to which the model with libraries is logged. If None, the model + with libraries is logged to the source run corresponding to model version + specified by ``model_uri``; if the model version does not have a source run, a + new run created. + registered_model_name: The new model version (model with its libraries) is + registered under the inputted registered_model_name. If None, a + new version is logged to the existing model in the Model Registry. + + .. note:: + This utility only operates on a model that has been registered to the Model Registry. + + .. note:: + The libraries are only compatible with the platform on which they are added. Cross platform + libraries are not supported. + + .. code-block:: python + :caption: Example + + # Create and log a model to the Model Registry + import pandas as pd + from sklearn import datasets + from sklearn.ensemble import RandomForestClassifier + import mlflow + import mlflow.sklearn + from mlflow.models import infer_signature + + with mlflow.start_run(): + iris = datasets.load_iris() + iris_train = pd.DataFrame(iris.data, columns=iris.feature_names) + clf = RandomForestClassifier(max_depth=7, random_state=0) + clf.fit(iris_train, iris.target) + signature = infer_signature(iris_train, clf.predict(iris_train)) + mlflow.sklearn.log_model( + clf, + name="iris_rf", + signature=signature, + registered_model_name="model-with-libs", + ) + + # model uri for the above model + model_uri = "models:/model-with-libs/1" + + # Import utility + from mlflow.models.utils import add_libraries_to_model + + # Log libraries to the original run of the model + add_libraries_to_model(model_uri) + + # Log libraries to some run_id + existing_run_id = "21df94e6bdef4631a9d9cb56f211767f" + add_libraries_to_model(model_uri, run_id=existing_run_id) + + # Log libraries to a new run + with mlflow.start_run(): + add_libraries_to_model(model_uri) + + # Log libraries to a new registered model named 'new-model' + with mlflow.start_run(): + add_libraries_to_model(model_uri, registered_model_name="new-model") + """ + + import mlflow + from mlflow.models.wheeled_model import WheeledModel + + if mlflow.active_run() is None: + if run_id is None: + run_id = get_model_version_from_model_uri(model_uri).run_id + with mlflow.start_run(run_id): + return WheeledModel.log_model(model_uri, registered_model_name) + else: + return WheeledModel.log_model(model_uri, registered_model_name) + + +def get_model_version_from_model_uri(model_uri): + """ + Helper function to fetch a model version from a model uri of the form + models://. + """ + import mlflow + from mlflow import MlflowClient + + databricks_profile_uri = ( + get_databricks_profile_uri_from_artifact_uri(model_uri) or mlflow.get_registry_uri() + ) + client = MlflowClient(registry_uri=databricks_profile_uri) + (name, version) = get_model_name_and_version(client, model_uri) + return client.get_model_version(name, version) + + +def _enforce_params_schema(params: dict[str, Any] | None, schema: ParamSchema | None): + if schema is None: + if params in [None, {}]: + return params + params_info = ( + f"Ignoring provided params: {list(params.keys())}" + if isinstance(params, dict) + else "Ignoring invalid params (not a dictionary)." + ) + _logger.warning( + "`params` can only be specified at inference time if the model signature " + f"defines a params schema. This model does not define a params schema. {params_info}", + ) + return {} + params = {} if params is None else params + if not isinstance(params, dict): + raise MlflowException.invalid_parameter_value( + f"Parameters must be a dictionary. Got type '{type(params).__name__}'.", + ) + if not isinstance(schema, ParamSchema): + raise MlflowException.invalid_parameter_value( + "Parameters schema must be an instance of ParamSchema. " + f"Got type '{type(schema).__name__}'.", + ) + if any(not isinstance(k, str) for k in params.keys()): + _logger.warning( + "Keys in parameters should be of type `str`, but received non-string keys." + "Converting all keys to string..." + ) + params = {str(k): v for k, v in params.items()} + + allowed_keys = {param.name for param in schema.params} + ignored_keys = set(params) - allowed_keys + if ignored_keys: + _logger.warning( + f"Unrecognized params {list(ignored_keys)} are ignored for inference. " + f"Supported params are: {allowed_keys}. " + "To enable them, please add corresponding schema in ModelSignature." + ) + + params = {k: params[k] for k in params if k in allowed_keys} + + invalid_params = set() + for param_spec in schema.params: + if param_spec.name in params: + try: + params[param_spec.name] = ParamSpec.validate_param_spec( + params[param_spec.name], param_spec + ) + except MlflowException as e: + invalid_params.add((param_spec.name, e.message)) + else: + params[param_spec.name] = param_spec.default + + if invalid_params: + raise MlflowException.invalid_parameter_value( + f"Invalid parameters found: {invalid_params!r}", + ) + + return params + + +def convert_complex_types_pyspark_to_pandas(value, dataType): + # This function is needed because the default `asDict` function in PySpark + # converts the data to Python types, which is not compatible with the schema enforcement. + type_mapping = { + IntegerType: lambda v: np.int32(v), + ShortType: lambda v: np.int16(v), + FloatType: lambda v: np.float32(v), + DateType: lambda v: v.strftime("%Y-%m-%d"), + TimestampType: lambda v: v.strftime("%Y-%m-%d %H:%M:%S.%f"), + BinaryType: lambda v: np.bytes_(v), + } + if value is None: + return None + if isinstance(dataType, StructType): + return { + field.name: convert_complex_types_pyspark_to_pandas(value[field.name], field.dataType) + for field in dataType.fields + } + elif isinstance(dataType, ArrayType): + return [ + convert_complex_types_pyspark_to_pandas(elem, dataType.elementType) for elem in value + ] + converter = type_mapping.get(type(dataType)) + if converter: + return converter(value) + return value + + +def _is_in_comment(line, start): + """ + Check if the code at the index "start" of the line is in a comment. + + Limitations: This function does not handle multi-line comments, and the # symbol could be in a + string, or otherwise not indicate a comment. + """ + return "#" in line[:start] + + +def _is_in_string_only(line, search_string): + """ + Check is the search_string + + Limitations: This function does not handle multi-line strings. + """ + # Regex for matching double quotes and everything inside + double_quotes_regex = r"\"(\\.|[^\"])*\"" + + # Regex for matching single quotes and everything inside + single_quotes_regex = r"\'(\\.|[^\'])*\'" + + # Regex for matching search_string exactly + search_string_regex = rf"({re.escape(search_string)})" + + # Concatenate the patterns using the OR operator '|' + # This will matches left to right - on quotes first, search_string last + pattern = double_quotes_regex + r"|" + single_quotes_regex + r"|" + search_string_regex + + # Iterate through all matches in the line + for match in re.finditer(pattern, line): + # If the regex matched on the search_string, we know that it did not match in quotes since + # that is the order. So we know that the search_string exists outside of quotes + # (at least once). + if match.group() == search_string: + return False + return True + + +def _validate_model_code_from_notebook(code): + """ + Validate there isn't any code that would work in a notebook but not as exported Python file. + For now, this checks for dbutils and magic commands. + """ + + output_code_list = [] + for line in code.splitlines(): + for match in re.finditer(r"\bdbutils\b", line): + start = match.start() + if not _is_in_comment(line, start) and not _is_in_string_only(line, "dbutils"): + _logger.warning( + "The model file uses 'dbutils' commands which are not supported. To ensure " + "your code functions correctly, make sure that it does not rely on these " + "dbutils commands for correctness." + ) + # Prefix any line containing MAGIC commands with a comment. When there is better support + # for the Databricks workspace export API, we can get rid of this. + if line.startswith("%"): + output_code_list.append("# MAGIC " + line) + else: + output_code_list.append(line) + output_code = "\n".join(output_code_list) + + magic_regex = r"^# MAGIC %((?!pip)\S+).*" + if re.search(magic_regex, output_code, re.MULTILINE): + _logger.warning( + "The model file uses magic commands which have been commented out. To ensure your code " + "functions correctly, make sure that it does not rely on these magic commands for " + "correctness." + ) + + return output_code.encode("utf-8") + + +def _convert_llm_ndarray_to_list(data): + """ + Convert numpy array in the input data to list, because numpy array is not json serializable. + """ + if isinstance(data, np.ndarray): + return data.tolist() + if isinstance(data, list): + return [_convert_llm_ndarray_to_list(d) for d in data] + if isinstance(data, dict): + return {k: _convert_llm_ndarray_to_list(v) for k, v in data.items()} + # scalar values are also converted to numpy types, but they are + # not acceptable by the model + if np.isscalar(data) and isinstance(data, np.generic): + return data.item() + return data + + +def _convert_llm_input_data(data: Any) -> list[Any] | dict[str, Any]: + """ + Convert input data to a format that can be passed to the model with GenAI flavors such as + LangChain and LLamaIndex. + + Args + data: Input data to be converted. We assume it is a single request payload, but it can be + in any format such as a single scalar value, a dictionary, list (with one element), + Pandas DataFrame, etc. + """ + # This handles pyfunc / spark_udf inputs with model signature. Schema enforcement convert + # the input data to pandas DataFrame, so we convert it back. + if isinstance(data, pd.DataFrame): + # if the data only contains a single key as 0, we assume the input + # is either a string or list of strings + if list(data.columns) == [0]: + data = data.to_dict("list")[0] + else: + data = data.to_dict(orient="records") + + return _convert_llm_ndarray_to_list(data) + + +def _databricks_path_exists(path: Path) -> bool: + """ + Check if a path exists in Databricks workspace. + """ + if not is_in_databricks_runtime(): + return False + + from databricks.sdk import WorkspaceClient + from databricks.sdk.errors import ResourceDoesNotExist + + client = WorkspaceClient() + try: + client.workspace.get_status(str(path)) + return True + except ResourceDoesNotExist: + return False + + +def _validate_and_get_model_code_path(model_code_path: str, temp_dir: str) -> str: + """ + Validate model code path exists. When failing to open the model file on Databricks, + creates a temp file in temp_dir and validate its contents if it's a notebook. + + Returns either `model_code_path` or a temp file path with the contents of the notebook. + """ + + # If the path is not a absolute path then convert it + model_code_path = Path(model_code_path).resolve() + + if not (model_code_path.exists() or _databricks_path_exists(model_code_path)): + additional_message = ( + f" Perhaps you meant '{model_code_path}.py'?" if not model_code_path.suffix else "" + ) + + raise MlflowException.invalid_parameter_value( + f"The provided model path '{model_code_path}' does not exist. " + f"Ensure the file path is valid and try again.{additional_message}" + ) + + try: + # If `model_code_path` points to a notebook on Databricks, this line throws either + # a `FileNotFoundError` or an `OSError`. In this case, try to export the notebook as + # a Python file. + with open(model_code_path): + pass + + return str(model_code_path) + except Exception: + pass + + try: + from databricks.sdk import WorkspaceClient + from databricks.sdk.service.workspace import ExportFormat + + w = WorkspaceClient() + response = w.workspace.export(path=model_code_path, format=ExportFormat.SOURCE) + decoded_content = base64.b64decode(response.content) + except Exception: + raise MlflowException.invalid_parameter_value( + f"The provided model path '{model_code_path}' is not a valid Python file path or a " + "Databricks Notebook file path containing the code for defining the chain " + "instance. Ensure the file path is valid and try again." + ) + + _validate_model_code_from_notebook(decoded_content.decode("utf-8")) + path = os.path.join(temp_dir, "model.py") + with open(path, "wb") as f: + f.write(decoded_content) + return path + + +@contextmanager +def _config_context(config: str | dict[str, Any] | None = None): + # Check if config_path is None and set it to "" so when loading the model + # the config_path is set to "" so the ModelConfig can correctly check if the + # config is set or not + if config is None: + config = "" + + _set_model_config(config) + try: + yield + finally: + _set_model_config(None) + + +class MockDbutils: + def __init__(self, real_dbutils=None): + self.real_dbutils = real_dbutils + + def __getattr__(self, name): + try: + if self.real_dbutils: + return getattr(self.real_dbutils, name) + except AttributeError: + pass + return MockDbutils() + + def __call__(self, *args, **kwargs): + pass + + +@contextmanager +def _mock_dbutils(globals_dict): + module_name = "dbutils" + original_module = sys.modules.get(module_name) + sys.modules[module_name] = MockDbutils(original_module) + + # Inject module directly into the global namespace in case it is referenced without an import + original_global = globals_dict.get(module_name) + globals_dict[module_name] = MockDbutils(original_module) + + try: + yield + finally: + if original_module is not None: + sys.modules[module_name] = original_module + else: + del sys.modules[module_name] + + if original_global is not None: + globals_dict[module_name] = original_global + else: + del globals_dict[module_name] + + +# Python's module caching mechanism prevents the re-importation of previously loaded modules by +# default. Once a module is imported, it's added to `sys.modules`, and subsequent import attempts +# retrieve the cached module rather than re-importing it. +# Here, we want to import the `code path` module multiple times during a single runtime session. +# This function addresses this by dynamically importing the `code path` module under a unique, +# dynamically generated module name. This bypasses the caching mechanism, as each import is +# considered a separate module by the Python interpreter. +def _load_model_code_path(code_path: str, model_config: str | dict[str, Any] | None): + with _config_context(model_config): + try: + new_module_name = f"code_model_{uuid.uuid4().hex}" + spec = importlib.util.spec_from_file_location(new_module_name, code_path) + module = importlib.util.module_from_spec(spec) + sys.modules[new_module_name] = module + # Since dbutils will only work in databricks environment, we need to mock it + with _mock_dbutils(module.__dict__): + spec.loader.exec_module(module) + except ImportError as e: + raise MlflowException( + f"Failed to import code model from {code_path}. Error: {e!s}" + ) from e + except Exception as e: + raise MlflowException( + f"Failed to run user code from {code_path}. " + f"Error: {e!s}. " + "Review the stack trace for more information." + ) from e + + if mlflow.models.model.__mlflow_model__ is None: + raise MlflowException( + "If the model is logged as code, ensure the model is set using " + "mlflow.models.set_model() within the code file code file." + ) + return mlflow.models.model.__mlflow_model__ + + +def _flatten_nested_params( + d: dict[str, Any], parent_key: str = "", sep: str = "/" +) -> dict[str, str]: + items: dict[str, Any] = {} + for k, v in d.items(): + new_key = f"{parent_key}{sep}{k}" if parent_key else k + if isinstance(v, dict): + items.update(_flatten_nested_params(v, new_key, sep=sep)) + else: + items[new_key] = v + return items + + +# NB: this function should always be kept in sync with the serving +# process in scoring_server invocations. +def validate_serving_input(model_uri: str, serving_input: str | dict[str, Any]): + """ + Helper function to validate the model can be served and provided input is valid + prior to serving the model. + + Args: + model_uri: URI of the model to be served. + serving_input: Input data to be validated. Should be a dictionary or a JSON string. + + Returns: + The prediction result from the model. + """ + from mlflow.pyfunc.scoring_server import _parse_json_data + from mlflow.pyfunc.utils.environment import _simulate_serving_environment + + # sklearn model might not have python_function flavor if it + # doesn't define a predict function. In such case the model + # can not be served anyways + + output_dir = None if get_local_path_or_none(model_uri) else create_tmp_dir() + + try: + pyfunc_model = mlflow.pyfunc.load_model(model_uri, dst_path=output_dir) + parsed_input = _parse_json_data( + serving_input, + pyfunc_model.metadata, + pyfunc_model.metadata.get_input_schema(), + ) + with _simulate_serving_environment(): + return pyfunc_model.predict(parsed_input.data, params=parsed_input.params) + finally: + if output_dir and os.path.exists(output_dir): + shutil.rmtree(output_dir) + + +def get_external_mlflow_model_spec(logged_model: LoggedModel) -> Model: + """ + Create the MLflow Model specification for a given logged model whose artifacts + (code, weights, etc.) are stored externally outside of MLflow. + + Args: + logged_model: The external logged model for which to create an MLflow Model specification. + + Returns: + Model: MLflow Model specification for the given logged model with external artifacts. + """ + from mlflow.models.signature import infer_signature + + return Model( + artifact_path=logged_model.artifact_location, + model_uuid=logged_model.model_id, + model_id=logged_model.model_id, + run_id=logged_model.source_run_id, + # Include a dummy signature so that the model can be registered to the Databricks Unity + # Catalog Model Registry. + # TODO: Remove this once the Databricks Unity Catalog Model Registry supports registration + # of models without signatures + signature=infer_signature(model_input=True, model_output=True), + metadata={ + # Add metadata to the logged model indicating that its artifacts are stored externally. + # This helps downstream consumers of the model, such as the Model Registry, easily + # and consistently identify that the model's artifacts are external + MLFLOW_MODEL_IS_EXTERNAL: True, + }, + ) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/wheeled_model.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/wheeled_model.py new file mode 100644 index 0000000000000000000000000000000000000000..fdcfd560adc7d74ebb14388c9c97a0e201cc3b88 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/models/wheeled_model.py @@ -0,0 +1,280 @@ +import os +import platform +import shutil +import subprocess +import sys + +import yaml + +import mlflow +from mlflow import MlflowClient +from mlflow.environment_variables import MLFLOW_WHEELED_MODEL_PIP_DOWNLOAD_OPTIONS +from mlflow.exceptions import MlflowException +from mlflow.protos.databricks_pb2 import BAD_REQUEST +from mlflow.pyfunc.model import MLMODEL_FILE_NAME, Model +from mlflow.store.artifact.utils.models import _parse_model_uri, get_model_name_and_version +from mlflow.tracking.artifact_utils import _download_artifact_from_uri +from mlflow.utils.environment import ( + _REQUIREMENTS_FILE_NAME, + _get_pip_deps, + _mlflow_additional_pip_env, + _overwrite_pip_deps, +) +from mlflow.utils.model_utils import _validate_and_prepare_target_save_path +from mlflow.utils.uri import get_databricks_profile_uri_from_artifact_uri + +_WHEELS_FOLDER_NAME = "wheels" +_ORIGINAL_REQ_FILE_NAME = "original_requirements.txt" +_PLATFORM = "platform" + + +class WheeledModel: + """ + Helper class to create a model with added dependency wheels from an existing registered model. + The `wheeled` model contains all the model dependencies as wheels stored as model artifacts. + .. note:: + This utility only operates on a model that has been registered to the Model Registry. + """ + + def __init__(self, model_uri): + self._model_uri = model_uri + databricks_profile_uri = ( + get_databricks_profile_uri_from_artifact_uri(model_uri) or mlflow.get_registry_uri() + ) + client = MlflowClient(registry_uri=databricks_profile_uri) + self._model_name, _ = get_model_name_and_version(client, model_uri) + + @classmethod + def log_model(cls, model_uri, registered_model_name=None): + """ + Logs a registered model as an MLflow artifact for the current run. This only operates on + a model which has been registered to the Model Registry. Given a registered model_uri ( + e.g. models://), this utility re-logs the model along with all + the required model libraries back to the Model Registry. The required model libraries are + stored along with the model as model artifacts. In addition, supporting files to the + model (e.g. conda.yaml, requirements.txt) are modified to use the added libraries. + + By default, this utility creates a new model version under the same registered model + specified by ``model_uri``. This behavior can be overridden by specifying the + ``registered_model_name`` argument. + + Args: + model_uri: A registered model uri in the Model Registry of the form + models:// + registered_model_name: The new model version (model with its libraries) is + registered under the inputted registered_model_name. If None, + a new version is logged to the existing model in the Model + Registry. + + .. code-block:: python + :caption: Example + + # Given a model uri, log the wheeled model + with mlflow.start_run(): + WheeledModel.log_model(model_uri) + """ + parsed_uri = _parse_model_uri(model_uri) + return Model.log( + artifact_path=None, + flavor=WheeledModel(model_uri), + registered_model_name=registered_model_name or parsed_uri.name, + ) + + def save_model(self, path, mlflow_model=None): + """ + Given an existing registered model, saves the model along with it's dependencies stored as + wheels to a path on the local file system. + + This does not modify existing model behavior or existing model flavors. It simply downloads + the model dependencies as wheels and modifies the requirements.txt and conda.yaml file to + point to the downloaded wheels. + + The download_command defaults to downloading only binary packages using the + `--only-binary=:all:` option. This behavior can be overridden using an environment + variable `MLFLOW_WHEELED_MODEL_PIP_DOWNLOAD_OPTIONS`, which will allows setting + different options such as `--prefer-binary`, `--no-binary`, etc. + + Args: + path: Local path where the model is to be saved. + mlflow_model: The new :py:mod:`mlflow.models.Model` metadata file to store the + updated model metadata. + """ + from mlflow.pyfunc import ENV, FLAVOR_NAME, _extract_conda_env + + path = os.path.abspath(path) + _validate_and_prepare_target_save_path(path) + + local_model_path = _download_artifact_from_uri(self._model_uri, output_path=path) + + wheels_dir = os.path.join(local_model_path, _WHEELS_FOLDER_NAME) + pip_requirements_path = os.path.join(local_model_path, _REQUIREMENTS_FILE_NAME) + model_metadata_path = os.path.join(local_model_path, MLMODEL_FILE_NAME) + + model_metadata = Model.load(model_metadata_path) + + # Check if the model file has `wheels` set to True + if model_metadata.__dict__.get(_WHEELS_FOLDER_NAME, None) is not None: + raise MlflowException("Model libraries are already added", BAD_REQUEST) + + conda_env = _extract_conda_env(model_metadata.flavors.get(FLAVOR_NAME, {}).get(ENV, None)) + conda_env_path = os.path.join(local_model_path, conda_env) + if conda_env is None and not os.path.isfile(pip_requirements_path): + raise MlflowException( + "Cannot add libraries for model with no logged dependencies.", BAD_REQUEST + ) + + if not os.path.isfile(pip_requirements_path): + self._create_pip_requirement(conda_env_path, pip_requirements_path) + + WheeledModel._download_wheels( + pip_requirements_path=pip_requirements_path, dst_path=wheels_dir + ) + + # Keep a copy of the original requirement.txt + shutil.copy2(pip_requirements_path, os.path.join(local_model_path, _ORIGINAL_REQ_FILE_NAME)) + + # Update requirements.txt with wheels + pip_deps = self._overwrite_pip_requirements_with_wheels( + pip_requirements_path=pip_requirements_path, wheels_dir=wheels_dir + ) + + # Update conda.yaml with wheels + self._update_conda_env(pip_deps, conda_env_path) + + # Update MLModel File + mlflow_model = self._update_mlflow_model( + original_model_metadata=model_metadata, mlflow_model=mlflow_model + ) + mlflow_model.save(model_metadata_path) + return mlflow_model + + def _update_conda_env(self, new_pip_deps, conda_env_path): + """ + Updates the list pip packages in the conda.yaml file to the list of wheels in the wheels + directory. + { + "name": "env", + "channels": [...], + "dependencies": [ + ..., + "pip", + {"pip": [...]}, <- Overwrite this with list of wheels + ], + } + + Args: + new_pip_deps: List of pip dependencies as wheels + conda_env_path: Path to conda.yaml file in the model directory + """ + with open(conda_env_path) as f: + conda_env = yaml.safe_load(f) + + new_conda_env = _overwrite_pip_deps(conda_env, new_pip_deps) + + with open(conda_env_path, "w") as out: + yaml.safe_dump(new_conda_env, stream=out, default_flow_style=False) + + def _update_mlflow_model(self, original_model_metadata, mlflow_model): + """ + Modifies the MLModel file to reflect updated information such as the run_id, + utc_time_created. Additionally, this also adds `wheels` to the MLModel file to indicate that + this is a `wheeled` model. + + Args: + original_model_metadata: The model metadata stored in the original MLmodel file. + mlflow_model: :py:mod:`mlflow.models.Model` configuration of the newly created + wheeled model + """ + + run_id = mlflow.tracking.fluent._get_or_start_run().info.run_id + if mlflow_model is None: + mlflow_model = Model(run_id=run_id) + + original_model_metadata.__dict__.update( + {k: v for k, v in mlflow_model.__dict__.items() if v} + ) + mlflow_model.__dict__.update(original_model_metadata.__dict__) + mlflow_model.artifact_path = WheeledModel.get_wheel_artifact_path( + mlflow_model.artifact_path + ) + + mlflow_model.wheels = {_PLATFORM: platform.platform()} + return mlflow_model + + @classmethod + def _download_wheels(cls, pip_requirements_path, dst_path): + """ + Downloads all the wheels of the dependencies specified in the requirements.txt file. + The pip wheel download_command defaults to downloading only binary packages using + the `--only-binary=:all:` option. This behavior can be overridden using an + environment variable `MLFLOW_WHEELED_MODEL_PIP_DOWNLOAD_OPTIONS`, which will allows + setting different options such as `--prefer-binary`, `--no-binary`, etc. + + Args: + pip_requirements_path: Path to requirements.txt in the model directory + dst_path: Path to the directory where the wheels are to be downloaded + """ + if not os.path.exists(dst_path): + os.makedirs(dst_path) + + pip_wheel_options = MLFLOW_WHEELED_MODEL_PIP_DOWNLOAD_OPTIONS.get() + + try: + subprocess.run( + [ + sys.executable, + "-m", + "pip", + "wheel", + pip_wheel_options, + "--wheel-dir", + dst_path, + "-r", + pip_requirements_path, + "--no-cache-dir", + "--progress-bar=off", + ], + check=True, + stdout=subprocess.PIPE, + stderr=subprocess.STDOUT, + ) + except subprocess.CalledProcessError as e: + raise MlflowException( + f"An error occurred while downloading the dependency wheels: {e.stdout}" + ) + + def _overwrite_pip_requirements_with_wheels(self, pip_requirements_path, wheels_dir): + """ + Overwrites the requirements.txt with the wheels of the required dependencies. + + Args: + pip_requirements_path: Path to requirements.txt in the model directory. + wheels_dir: Path to directory where wheels are stored. + """ + wheels = [] + with open(pip_requirements_path, "w") as wheels_requirements: + for wheel_file in os.listdir(wheels_dir): + if wheel_file.endswith(".whl"): + complete_wheel_file = os.path.join(_WHEELS_FOLDER_NAME, wheel_file) + wheels.append(complete_wheel_file) + wheels_requirements.write(complete_wheel_file + "\n") + return wheels + + def _create_pip_requirement(self, conda_env_path, pip_requirements_path): + """ + This method creates a requirements.txt file for the model dependencies if the file does not + already exist. It uses the pip dependencies found in the conda.yaml env file. + + Args: + conda_env_path: Path to conda.yaml env file which contains the required pip + dependencies + pip_requirements_path: Path where the new requirements.txt will be created. + """ + with open(conda_env_path) as f: + conda_env = yaml.safe_load(f) + pip_deps = _get_pip_deps(conda_env) + _mlflow_additional_pip_env(pip_deps, pip_requirements_path) + + @classmethod + def get_wheel_artifact_path(cls, original_artifact_path): + return original_artifact_path + "_" + _WHEELS_FOLDER_NAME diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/onnx/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/onnx/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..da583d63a37c09d0f8bbd2888ee6699c22d40cc8 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/onnx/__init__.py @@ -0,0 +1,555 @@ +""" +The ``mlflow.onnx`` module provides APIs for logging and loading ONNX models in the MLflow Model +format. This module exports MLflow Models with the following flavors: + +ONNX (native) format + This is the main flavor that can be loaded back as an ONNX model object. +:py:mod:`mlflow.pyfunc` + Produced for use by generic pyfunc-based deployment tools and batch inference. +""" + +import logging +import os +from pathlib import Path +from typing import Any + +import numpy as np +import pandas as pd +import yaml +from packaging.version import Version + +import mlflow.tracking +from mlflow import pyfunc +from mlflow.exceptions import MlflowException +from mlflow.models import Model, ModelInputExample, ModelSignature +from mlflow.models.model import MLMODEL_FILE_NAME +from mlflow.models.utils import _save_example +from mlflow.tracking._model_registry import DEFAULT_AWAIT_MAX_SLEEP_SECONDS +from mlflow.tracking.artifact_utils import _download_artifact_from_uri +from mlflow.utils.docstring_utils import LOG_MODEL_PARAM_DOCS, format_docstring +from mlflow.utils.environment import ( + _CONDA_ENV_FILE_NAME, + _CONSTRAINTS_FILE_NAME, + _PYTHON_ENV_FILE_NAME, + _REQUIREMENTS_FILE_NAME, + _mlflow_conda_env, + _process_conda_env, + _process_pip_requirements, + _PythonEnv, + _validate_env_arguments, +) +from mlflow.utils.file_utils import get_total_file_size, write_to +from mlflow.utils.model_utils import ( + _add_code_from_conf_to_system_path, + _get_flavor_configuration, + _validate_and_copy_code_paths, + _validate_and_prepare_target_save_path, + _validate_onnx_session_options, +) +from mlflow.utils.requirements_utils import _get_pinned_requirement + +FLAVOR_NAME = "onnx" +ONNX_EXECUTION_PROVIDERS = ["CUDAExecutionProvider", "CPUExecutionProvider"] + +_logger = logging.getLogger(__name__) + + +_MODEL_DATA_SUBPATH = "model.onnx" + + +def get_default_pip_requirements(): + """ + Returns: + A list of default pip requirements for MLflow Models produced by this flavor. + Calls to :func:`save_model()` and :func:`log_model()` produce a pip environment + that, at minimum, contains these requirements. + """ + return list( + map( + _get_pinned_requirement, + [ + "onnx", + # The ONNX pyfunc representation requires the OnnxRuntime + # inference engine. Therefore, the conda environment must + # include OnnxRuntime + "onnxruntime", + ], + ) + ) + + +def get_default_conda_env(): + """ + Returns: + The default Conda environment for MLflow Models produced by calls to + :func:`save_model()` and :func:`log_model()`. + """ + return _mlflow_conda_env(additional_pip_deps=get_default_pip_requirements()) + + +@format_docstring(LOG_MODEL_PARAM_DOCS.format(package_name=FLAVOR_NAME)) +def save_model( + onnx_model, + path, + conda_env=None, + code_paths=None, + mlflow_model=None, + signature: ModelSignature = None, + input_example: ModelInputExample = None, + pip_requirements=None, + extra_pip_requirements=None, + onnx_execution_providers=None, + onnx_session_options=None, + metadata=None, + save_as_external_data=True, +): + """ + Save an ONNX model to a path on the local file system. + + Args: + onnx_model: ONNX model to be saved. + path: Local path where the model is to be saved. + conda_env: {{ conda_env }} + code_paths: {{ code_paths }} + mlflow_model: :py:mod:`mlflow.models.Model` this flavor is being added to. + signature: :py:class:`ModelSignature ` + describes model input and output :py:class:`Schema `. + The model signature can be :py:func:`inferred ` + from datasets with valid model input (e.g. the training dataset with target + column omitted) and valid model output (e.g. model predictions generated on + the training dataset), for example: + + .. code-block:: python + + from mlflow.models import infer_signature + + train = df.drop_column("target_label") + predictions = ... # compute model predictions + signature = infer_signature(train, predictions) + + input_example: {{ input_example }} + pip_requirements: {{ pip_requirements }} + extra_pip_requirements: {{ extra_pip_requirements }} + onnx_execution_providers: List of strings defining onnxruntime execution providers. + Defaults to example: + ``['CUDAExecutionProvider', 'CPUExecutionProvider']`` + This uses GPU preferentially over CPU. + See onnxruntime API for further descriptions: + https://onnxruntime.ai/docs/execution-providers/ + onnx_session_options: Dictionary of options to be passed to onnxruntime.InferenceSession. + For example: + ``{ + 'graph_optimization_level': 99, + 'intra_op_num_threads': 1, + 'inter_op_num_threads': 1, + 'execution_mode': 'sequential' + }`` + 'execution_mode' can be set to 'sequential' or 'parallel'. + See onnxruntime API for further descriptions: + https://onnxruntime.ai/docs/api/python/api_summary.html#sessionoptions + metadata: {{ metadata }} + save_as_external_data: Save tensors to external file(s). + """ + import onnx + + if onnx_execution_providers is None: + onnx_execution_providers = ONNX_EXECUTION_PROVIDERS + + _validate_env_arguments(conda_env, pip_requirements, extra_pip_requirements) + + path = os.path.abspath(path) + _validate_and_prepare_target_save_path(path) + code_dir_subpath = _validate_and_copy_code_paths(code_paths, path) + + if mlflow_model is None: + mlflow_model = Model() + if signature is not None: + mlflow_model.signature = signature + if input_example is not None: + _save_example(mlflow_model, input_example, path) + if metadata is not None: + mlflow_model.metadata = metadata + model_data_subpath = _MODEL_DATA_SUBPATH + model_data_path = os.path.join(path, model_data_subpath) + + # Save onnx-model + if Version(onnx.__version__) >= Version("1.9.0"): + onnx.save_model(onnx_model, model_data_path, save_as_external_data=save_as_external_data) + else: + onnx.save_model(onnx_model, model_data_path) + + pyfunc.add_to_model( + mlflow_model, + loader_module="mlflow.onnx", + data=model_data_subpath, + conda_env=_CONDA_ENV_FILE_NAME, + python_env=_PYTHON_ENV_FILE_NAME, + code=code_dir_subpath, + ) + + _validate_onnx_session_options(onnx_session_options) + + mlflow_model.add_flavor( + FLAVOR_NAME, + onnx_version=onnx.__version__, + data=model_data_subpath, + providers=onnx_execution_providers, + onnx_session_options=onnx_session_options, + code=code_dir_subpath, + ) + if size := get_total_file_size(path): + mlflow_model.model_size_bytes = size + mlflow_model.save(os.path.join(path, MLMODEL_FILE_NAME)) + + if conda_env is None: + if pip_requirements is None: + default_reqs = get_default_pip_requirements() + # To ensure `_load_pyfunc` can successfully load the model during the dependency + # inference, `mlflow_model.save` must be called beforehand to save an MLmodel file. + inferred_reqs = mlflow.models.infer_pip_requirements( + path, + FLAVOR_NAME, + fallback=default_reqs, + ) + default_reqs = sorted(set(inferred_reqs).union(default_reqs)) + else: + default_reqs = None + conda_env, pip_requirements, pip_constraints = _process_pip_requirements( + default_reqs, + pip_requirements, + extra_pip_requirements, + ) + else: + conda_env, pip_requirements, pip_constraints = _process_conda_env(conda_env) + + with open(os.path.join(path, _CONDA_ENV_FILE_NAME), "w") as f: + yaml.safe_dump(conda_env, stream=f, default_flow_style=False) + + # Save `constraints.txt` if necessary + if pip_constraints: + write_to(os.path.join(path, _CONSTRAINTS_FILE_NAME), "\n".join(pip_constraints)) + + # Save `requirements.txt` + write_to(os.path.join(path, _REQUIREMENTS_FILE_NAME), "\n".join(pip_requirements)) + + _PythonEnv.current().to_yaml(os.path.join(path, _PYTHON_ENV_FILE_NAME)) + + +def _load_model(model_file): + import onnx + + onnx.checker.check_model(model_file) + return onnx.load(model_file) + + +class _OnnxModelWrapper: + def __init__(self, path, providers=None): + import onnxruntime + + # Get the model meta data from the MLModel yaml file which may contain the providers + # specification. + local_path = str(Path(path).parent) + model_meta = Model.load(os.path.join(local_path, MLMODEL_FILE_NAME)) + + # Check if the MLModel config has the providers meta data + if "providers" in model_meta.flavors.get(FLAVOR_NAME).keys(): + providers = model_meta.flavors.get(FLAVOR_NAME)["providers"] + # If not, then default to the predefined list. + else: + providers = ONNX_EXECUTION_PROVIDERS + + sess_options = onnxruntime.SessionOptions() + options = model_meta.flavors.get(FLAVOR_NAME).get("onnx_session_options") + if options: + if inter_op_num_threads := options.get("inter_op_num_threads"): + sess_options.inter_op_num_threads = inter_op_num_threads + if intra_op_num_threads := options.get("intra_op_num_threads"): + sess_options.intra_op_num_threads = intra_op_num_threads + if execution_mode := options.get("execution_mode"): + if execution_mode.upper() == "SEQUENTIAL": + sess_options.execution_mode = onnxruntime.ExecutionMode.ORT_SEQUENTIAL + elif execution_mode.upper() == "PARALLEL": + sess_options.execution_mode = onnxruntime.ExecutionMode.ORT_PARALLEL + if graph_optimization_level := options.get("graph_optimization_level"): + sess_options.graph_optimization_level = onnxruntime.GraphOptimizationLevel( + graph_optimization_level + ) + if extra_session_config := options.get("extra_session_config"): + for key, value in extra_session_config.items(): + sess_options.add_session_config_entry(key, value) + + # NOTE: Some distributions of onnxruntime require the specification of the providers + # argument on calling. E.g. onnxruntime-gpu. The package import call does not differentiate + # which architecture specific version has been installed, as all are imported with + # onnxruntime. onnxruntime documentation says that from v1.9.0 some distributions require + # the providers list to be provided on calling an InferenceSession. Therefore the try + # catch structure below attempts to create an inference session with just the model path + # as pre v1.9.0. If that fails, it will use the providers list call. + # At the moment this is just CUDA and CPU, and probably should be expanded. + # A method of user customization has been provided by adding a variable in the save_model() + # function, which allows the ability to pass the list of execution providers via a + # optional argument e.g. + # + # mlflow.onnx.save_model(..., providers=['CUDAExecutionProvider'...]) + # + # For details of the execution providers construct of onnxruntime, see: + # https://onnxruntime.ai/docs/execution-providers/ + # + # For a information on how execution providers are used with onnxruntime InferenceSession, + # see the API page below: + # https://onnxruntime.ai/docs/api/python/api_summary.html#id8 + # + + try: + self.rt = onnxruntime.InferenceSession(path, sess_options=sess_options) + except ValueError: + self.rt = onnxruntime.InferenceSession( + path, providers=providers, sess_options=sess_options + ) + + assert len(self.rt.get_inputs()) >= 1 + self.inputs = [(inp.name, inp.type) for inp in self.rt.get_inputs()] + self.output_names = [outp.name for outp in self.rt.get_outputs()] + + def get_raw_model(self): + """ + Returns the underlying model. + """ + return self.rt + + def _cast_float64_to_float32(self, feeds): + for input_name, input_type in self.inputs: + if input_type == "tensor(float)": + feed = feeds.get(input_name) + if feed is not None and feed.dtype == np.float64: + feeds[input_name] = feed.astype(np.float32) + return feeds + + def predict(self, data, params: dict[str, Any] | None = None): + """ + Args: + data: Either a pandas DataFrame, numpy.ndarray or a dictionary. + Dictionary input is expected to be a valid ONNX model feed dictionary. + + Numpy array input is supported iff the model has a single tensor input and is + converted into an ONNX feed dictionary with the appropriate key. + + Pandas DataFrame is converted to ONNX inputs as follows: + - If the underlying ONNX model only defines a *single* input tensor, the + DataFrame's values are converted to a NumPy array representation using the + `DataFrame.values() + `_ method. + - If the underlying ONNX model defines *multiple* input tensors, each column + of the DataFrame is converted to a NumPy array representation. + + For more information about the ONNX Runtime, see + ``_. + params: Additional parameters to pass to the model for inference. + + Returns: + Model predictions. If the input is a pandas.DataFrame, the predictions are returned + in a pandas.DataFrame. If the input is a numpy array or a dictionary the + predictions are returned in a dictionary. + """ + if isinstance(data, dict): + feed_dict = data + elif isinstance(data, np.ndarray): + # NB: We do allow scoring with a single tensor (ndarray) in order to be compatible with + # supported pyfunc inputs iff the model has a single input. The passed tensor is + # assumed to be the first input. + if len(self.inputs) != 1: + inputs = [x[0] for x in self.inputs] + raise MlflowException( + "Unable to map numpy array input to the expected model " + "input. " + "Numpy arrays can only be used as input for MLflow ONNX " + "models that have a single input. This model requires " + f"{len(self.inputs)} inputs. Please pass in data as either a " + "dictionary or a DataFrame with the following tensors" + f": {inputs}." + ) + feed_dict = {self.inputs[0][0]: data} + elif isinstance(data, pd.DataFrame): + if len(self.inputs) > 1: + feed_dict = {name: data[name].values for (name, _) in self.inputs} + else: + feed_dict = {self.inputs[0][0]: data.values} + + else: + raise TypeError( + "Input should be a dictionary or a numpy array or a pandas.DataFrame, " + f"got '{type(data)}'" + ) + + # ONNXRuntime throws the following exception for some operators when the input + # contains float64 values. Unfortunately, even if the original user-supplied input + # did not contain float64 values, the serialization/deserialization between the + # client and the scoring server can introduce 64-bit floats. This is being tracked in + # https://github.com/mlflow/mlflow/issues/1286. Meanwhile, we explicitly cast the input to + # 32-bit floats when needed. TODO: Remove explicit casting when issue #1286 is fixed. + feed_dict = self._cast_float64_to_float32(feed_dict) + predicted = self.rt.run(self.output_names, feed_dict) + + if isinstance(data, pd.DataFrame): + + def format_output(data): + # Output can be list and it should be converted to a numpy array + # https://github.com/mlflow/mlflow/issues/2499 + data = np.asarray(data) + return data.reshape(-1) + + return pd.DataFrame.from_dict( + {c: format_output(p) for (c, p) in zip(self.output_names, predicted)} + ) + else: + return dict(zip(self.output_names, predicted)) + + +def _load_pyfunc(path): + """ + Load PyFunc implementation. Called by ``pyfunc.load_model``. + """ + return _OnnxModelWrapper(path) + + +def load_model(model_uri, dst_path=None): + """ + Load an ONNX model from a local file or a run. + + Args: + model_uri: The location, in URI format, of the MLflow model, for example: + + - ``/Users/me/path/to/local/model`` + - ``relative/path/to/local/model`` + - ``s3://my_bucket/path/to/model`` + - ``runs://run-relative/path/to/model`` + - ``models://`` + - ``models://`` + + For more information about supported URI schemes, see the + `Artifacts Documentation `_. + dst_path: The local filesystem path to which to download the model artifact. + This directory must already exist. If unspecified, a local output + path will be created. + + Returns: + An ONNX model instance. + + """ + local_model_path = _download_artifact_from_uri(artifact_uri=model_uri, output_path=dst_path) + flavor_conf = _get_flavor_configuration(model_path=local_model_path, flavor_name=FLAVOR_NAME) + _add_code_from_conf_to_system_path(local_model_path, flavor_conf) + onnx_model_artifacts_path = os.path.join(local_model_path, flavor_conf["data"]) + return _load_model(model_file=onnx_model_artifacts_path) + + +@format_docstring(LOG_MODEL_PARAM_DOCS.format(package_name=FLAVOR_NAME)) +def log_model( + onnx_model, + artifact_path: str | None = None, + conda_env=None, + code_paths=None, + registered_model_name=None, + signature: ModelSignature = None, + input_example: ModelInputExample = None, + await_registration_for=DEFAULT_AWAIT_MAX_SLEEP_SECONDS, + pip_requirements=None, + extra_pip_requirements=None, + onnx_execution_providers=None, + onnx_session_options=None, + metadata=None, + save_as_external_data=True, + name: str | None = None, + params: dict[str, Any] | None = None, + tags: dict[str, Any] | None = None, + model_type: str | None = None, + step: int = 0, + model_id: str | None = None, +): + """ + Log an ONNX model as an MLflow artifact for the current run. + + Args: + onnx_model: ONNX model to be saved. + artifact_path: Deprecated. Use `name` instead. + conda_env: {{ conda_env }} + code_paths: {{ code_paths }} + registered_model_name: If given, create a model version under + ``registered_model_name``, also creating a registered model if one + with the given name does not exist. + signature: :py:class:`ModelSignature ` + describes model input and output :py:class:`Schema `. + The model signature can be :py:func:`inferred ` + from datasets with valid model input (e.g. the training dataset with target + column omitted) and valid model output (e.g. model predictions generated on + the training dataset), for example: + + .. code-block:: python + + from mlflow.models import infer_signature + + train = df.drop_column("target_label") + predictions = ... # compute model predictions + signature = infer_signature(train, predictions) + + input_example: {{ input_example }} + await_registration_for: Number of seconds to wait for the model version to finish + being created and is in ``READY`` status. By default, the function + waits for five minutes. Specify 0 or None to skip waiting. + pip_requirements: {{ pip_requirements }} + extra_pip_requirements: {{ extra_pip_requirements }} + onnx_execution_providers: List of strings defining onnxruntime execution providers. + Defaults to example: + ['CUDAExecutionProvider', 'CPUExecutionProvider'] + This uses GPU preferentially over CPU. + See onnxruntime API for further descriptions: + https://onnxruntime.ai/docs/execution-providers/ + onnx_session_options: Dictionary of options to be passed to onnxruntime.InferenceSession. + For example: + ``{ + 'graph_optimization_level': 99, + 'intra_op_num_threads': 1, + 'inter_op_num_threads': 1, + 'execution_mode': 'sequential' + }`` + 'execution_mode' can be set to 'sequential' or 'parallel'. + See onnxruntime API for further descriptions: + https://onnxruntime.ai/docs/api/python/api_summary.html#sessionoptions + metadata: {{ metadata }} + save_as_external_data: Save tensors to external file(s). + name: {{ name }} + params: {{ params }} + tags: {{ tags }} + model_type: {{ model_type }} + step: {{ step }} + model_id: {{ model_id }} + + Returns: + A :py:class:`ModelInfo ` instance that contains the + metadata of the logged model. + """ + return Model.log( + artifact_path=artifact_path, + name=name, + flavor=mlflow.onnx, + onnx_model=onnx_model, + conda_env=conda_env, + code_paths=code_paths, + registered_model_name=registered_model_name, + signature=signature, + input_example=input_example, + await_registration_for=await_registration_for, + pip_requirements=pip_requirements, + extra_pip_requirements=extra_pip_requirements, + onnx_execution_providers=onnx_execution_providers, + onnx_session_options=onnx_session_options, + metadata=metadata, + save_as_external_data=save_as_external_data, + params=params, + tags=tags, + model_type=model_type, + step=step, + model_id=model_id, + ) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/onnx/__pycache__/__init__.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/onnx/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..52e913ab3bf7ec1918b6dae6d9ea1256356b15a6 Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/onnx/__pycache__/__init__.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/openai/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/openai/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..11f41443c1af614297288372fef4fdd3784135a5 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/openai/__init__.py @@ -0,0 +1,57 @@ +""" +The ``mlflow.openai`` module provides an API for logging and loading OpenAI models. + +Credential management for OpenAI on Databricks +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. warning:: + + Specifying secrets for model serving with ``MLFLOW_OPENAI_SECRET_SCOPE`` is deprecated. + Use `secrets-based environment variables `_ + instead. + +When this flavor logs a model on Databricks, it saves a YAML file with the following contents as +``openai.yaml`` if the ``MLFLOW_OPENAI_SECRET_SCOPE`` environment variable is set. + +.. code-block:: yaml + + OPENAI_API_BASE: {scope}:openai_api_base + OPENAI_API_KEY: {scope}:openai_api_key + OPENAI_API_KEY_PATH: {scope}:openai_api_key_path + OPENAI_API_TYPE: {scope}:openai_api_type + OPENAI_ORGANIZATION: {scope}:openai_organization + +- ``{scope}`` is the value of the ``MLFLOW_OPENAI_SECRET_SCOPE`` environment variable. +- The keys are the environment variables that the ``openai-python`` package uses to + configure the API client. +- The values are the references to the secrets that store the values of the environment + variables. + +When the logged model is served on Databricks, each secret will be resolved and set as the +corresponding environment variable. See https://docs.databricks.com/security/secrets/index.html +for how to set up secrets on Databricks. +""" + +from mlflow.openai.autolog import autolog +from mlflow.openai.constant import FLAVOR_NAME +from mlflow.version import IS_TRACING_SDK_ONLY + +__all__ = ["autolog", "FLAVOR_NAME"] + + +# Import model logging APIs only if mlflow skinny or full package is installed, +# i.e., skip if only mlflow-tracing package is installed. +if not IS_TRACING_SDK_ONLY: + from mlflow.openai.model import ( + _load_pyfunc, + load_model, + log_model, + save_model, + ) + + __all__ += [ + "load_model", + "log_model", + "save_model", + "_load_pyfunc", + ] diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/openai/__pycache__/__init__.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/openai/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 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typing import Any + +import agents.tracing as oai +from agents import add_trace_processor +from agents.tracing.setup import GLOBAL_TRACE_PROVIDER + +from mlflow.entities.span import SpanType +from mlflow.entities.span_event import SpanEvent +from mlflow.entities.span_status import SpanStatus, SpanStatusCode +from mlflow.tracing.constant import SpanAttributeKey +from mlflow.tracing.fluent import ( + get_current_active_span, + start_span, + start_span_no_context, +) +from mlflow.tracing.provider import detach_span_from_context, set_span_in_context +from mlflow.tracing.utils import construct_full_inputs +from mlflow.tracing.utils.token import SpanWithToken +from mlflow.types.chat import ( + ChatTool, + FunctionToolDefinition, +) + +_logger = logging.getLogger(__name__) + + +_AGENT_RUN_SPAN_NAME = "AgentRunner.run" + + +class OpenAISpanType: + """ + https://github.com/openai/openai-agents-python/blob/ca8e8bed5d0f33e8a0bc3eabd5f1b0a183e73765/src/agents/tracing/span_data.py#L11 + """ + + AGENT = "agent" + FUNCTION = "function" + GENERATION = "generation" + RESPONSE = "response" + HANDOFF = "handoff" + CUSTOM = "custom" + GUARDRAIL = "guardrail" + + +_SPAN_TYPE_MAP = { + OpenAISpanType.AGENT: SpanType.AGENT, + OpenAISpanType.FUNCTION: SpanType.TOOL, + OpenAISpanType.GENERATION: SpanType.CHAT_MODEL, + OpenAISpanType.RESPONSE: SpanType.CHAT_MODEL, + OpenAISpanType.GUARDRAIL: SpanType.TOOL, + # Default to chain type +} + + +def add_mlflow_trace_processor(): + processors = GLOBAL_TRACE_PROVIDER._multi_processor._processors + + if any(isinstance(p, MlflowOpenAgentTracingProcessor) for p in processors): + return + + add_trace_processor(MlflowOpenAgentTracingProcessor()) + + +def remove_mlflow_trace_processor(): + processors = GLOBAL_TRACE_PROVIDER._multi_processor._processors + non_mlflow_processors = [ + p for p in processors if not isinstance(p, MlflowOpenAgentTracingProcessor) + ] + GLOBAL_TRACE_PROVIDER._multi_processor._processors = non_mlflow_processors + + +class MlflowOpenAgentTracingProcessor(oai.TracingProcessor): + def __init__( + self, + project_name: str | None = None, + **kwargs: Any, + ) -> None: + super().__init__(**kwargs) + self._span_id_to_mlflow_span: dict[str, SpanWithToken] = {} + + def on_trace_start(self, trace: oai.Trace) -> None: + if (active_span := get_current_active_span()) and active_span.name == _AGENT_RUN_SPAN_NAME: + # The root span is already started by the _patched_agent_run + mlflow_span = active_span + token = None + else: + # Users create a trace using `agents.trace` in OpenAI Agent SDK + # Ref: ... + # We need to create a corresponding MLflow span to track the trace + mlflow_span = start_span_no_context( + name=trace.name, + span_type=SpanType.AGENT, + # TODO: Trace object doesn't contain input/output. Can we get it somehow? + inputs="", + attributes=trace.metadata, + ) + token = set_span_in_context(mlflow_span) + + # NB: Trace ID has different prefix as span ID so will not conflict + self._span_id_to_mlflow_span[trace.trace_id] = SpanWithToken(mlflow_span, token) + + if trace.group_id: + # Group ID is used for grouping multiple agent executions together + mlflow_span.set_tag("group_id", trace.group_id) + + def on_trace_end(self, trace: oai.Trace) -> None: + try: + st = self._span_id_to_mlflow_span.pop(trace.trace_id, None) + if st and st.token: + detach_span_from_context(st.token) + st.span.end(status=st.span.status, outputs="") + except Exception: + _logger.debug("Failed to end MLflow trace", exc_info=True) + + def on_span_start(self, span: oai.Span[Any]) -> None: + try: + parent_st: SpanWithToken | None = self._span_id_to_mlflow_span.get(span.parent_id, None) + + # Parent might be a trace + if not parent_st: + parent_st = self._span_id_to_mlflow_span.get(span.trace_id, None) + + inputs, _, attributes = _parse_span_data(span.span_data) + span_type = _SPAN_TYPE_MAP.get(span.span_data.type, SpanType.CHAIN) + + mlflow_span = start_span_no_context( + name=_get_span_name(span.span_data), + span_type=span_type, + parent_span=parent_st.span if parent_st else None, + inputs=inputs, + attributes=attributes, + ) + token = set_span_in_context(mlflow_span) + + if span_type == SpanType.CHAT_MODEL: + mlflow_span.set_attribute(SpanAttributeKey.MESSAGE_FORMAT, "openai-agent") + + self._span_id_to_mlflow_span[span.span_id] = SpanWithToken(mlflow_span, token) + except Exception: + _logger.debug("Failed to start MLflow span", exc_info=True) + + def on_span_end(self, span: oai.Span[Any]) -> None: + try: + # parsed_span_data = parse_spandata(span.span_data) + st: SpanWithToken | None = self._span_id_to_mlflow_span.pop(span.span_id, None) + detach_span_from_context(st.token) + mlflow_span = st.span + + inputs, outputs, attributes = _parse_span_data(span.span_data) + + mlflow_span.set_inputs(inputs) + mlflow_span.set_outputs(outputs) + mlflow_span.set_attributes(attributes) + + if span.error: + status = SpanStatus( + status_code=SpanStatusCode.ERROR, + description=span.error["message"], + ) + mlflow_span.add_event( + SpanEvent( + name="exception", + attributes={ + "exception.message": span.error["message"], + "exception.type": "", + "exception.stacktrace": json.dumps(span.error["data"]), + }, + ) + ) + else: + status = SpanStatusCode.OK + + mlflow_span.end(status=status) + except Exception: + _logger.debug("Failed to end MLflow span", exc_info=True) + + def force_flush(self) -> None: + # MLflow doesn't need flush but this method is required by the interface + pass + + def shutdown(self) -> None: + self.force_flush() + + +def _get_span_name(span_data: oai.SpanData) -> str: + if hasattr(span_data, "name"): + return span_data.name + elif isinstance(span_data, oai.GenerationSpanData): + return "Generation" + elif isinstance(span_data, oai.ResponseSpanData): + return "Response" + elif isinstance(span_data, oai.HandoffSpanData): + return "Handoff" + else: + return "Unknown" + + +def _parse_span_data(span_data: oai.SpanData) -> tuple[Any, Any, dict[str, Any]]: + inputs = None + outputs = None + attributes = {} + + if span_data.type == OpenAISpanType.AGENT: + attributes = { + "handoffs": span_data.handoffs, + "tools": span_data.tools, + "output_type": span_data.output_type, + } + outputs = {"output_type": span_data.output_type} + + elif span_data.type == OpenAISpanType.FUNCTION: + try: + inputs = json.loads(span_data.input) + except Exception: + inputs = span_data.input + outputs = span_data.output + + elif span_data.type == OpenAISpanType.GENERATION: + inputs = span_data.input + outputs = span_data.output + attributes = { + "model": span_data.model, + "model_config": span_data.model_config, + "usage": span_data.usage, + } + + elif span_data.type == OpenAISpanType.RESPONSE: + inputs, outputs, attributes = _parse_response_span_data(span_data) + + elif span_data.type == OpenAISpanType.HANDOFF: + inputs = {"from_agent": span_data.from_agent} + outputs = {"to_agent": span_data.to_agent} + + elif span_data.type == OpenAISpanType.CUSTOM: + outputs = span_data.data + + elif span_data.type == OpenAISpanType.GUARDRAIL: + outputs = {"triggered": span_data.triggered} + + return inputs, outputs, attributes + + +def _parse_response_span_data(span_data: oai.ResponseSpanData) -> tuple[Any, Any, dict[str, Any]]: + inputs = span_data.input + response = span_data.response + response_dict = response.model_dump() if response else {} + outputs = response_dict.get("output") + attributes = {k: v for k, v in response_dict.items() if k != "output"} + + # Extract chat tools + chat_tools = [] + for tool in response_dict.get("tools", []): + try: + tool = ChatTool( + type="function", + function=FunctionToolDefinition( + name=tool["name"], + description=tool.get("description"), + parameters=tool.get("parameters"), + strict=tool.get("strict"), + ), + ) + chat_tools.append(tool) + except Exception as e: + _logger.debug(f"Failed to parse chat tool: {tool}. Error: {e}") + + if chat_tools: + attributes[SpanAttributeKey.CHAT_TOOLS] = chat_tools + + return inputs, outputs, attributes + + +async def _patched_agent_run(original, self, *args, **kwargs): + inputs = construct_full_inputs(original, self, *args, **kwargs) + attributes = {k: v for k, v in inputs.items() if k not in ("starting_agent", "input")} + + with start_span( + name=_AGENT_RUN_SPAN_NAME, + span_type=SpanType.AGENT, + attributes=attributes, + ) as span: + span.set_inputs(inputs.get("input")) + result = await original(self, *args, **kwargs) + span.set_outputs(result.final_output) + + return result diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/openai/api_request_parallel_processor.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/openai/api_request_parallel_processor.py new file mode 100644 index 0000000000000000000000000000000000000000..f0e0beaacce6c31380d9183cb0888cfe2116b70b --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/openai/api_request_parallel_processor.py @@ -0,0 +1,131 @@ +# Based ons: https://github.com/openai/openai-cookbook/blob/6df6ceff470eeba26a56de131254e775292eac22/examples/api_request_parallel_processor.py +# Several changes were made to make it work with MLflow. + +""" +API REQUEST PARALLEL PROCESSOR + +Using the OpenAI API to process lots of text quickly takes some care. +If you trickle in a million API requests one by one, they'll take days to complete. +If you flood a million API requests in parallel, they'll exceed the rate limits and fail with +errors. To maximize throughput, parallel requests need to be throttled to stay under rate limits. + +This script parallelizes requests to the OpenAI API + +Features: +- Makes requests concurrently, to maximize throughput +- Retries failed requests up to {max_attempts} times, to avoid missing data +- Logs errors, to diagnose problems with requests +""" + +from __future__ import annotations + +import logging +import threading +from concurrent.futures import FIRST_EXCEPTION, ThreadPoolExecutor, wait +from dataclasses import dataclass +from typing import Any, Callable + +import mlflow + +_logger = logging.getLogger(__name__) + + +@dataclass +class StatusTracker: + """Stores metadata about the script's progress. Only one instance is created.""" + + num_tasks_started: int = 0 + num_tasks_in_progress: int = 0 # script ends when this reaches 0 + num_tasks_succeeded: int = 0 + num_tasks_failed: int = 0 + num_rate_limit_errors: int = 0 + lock: threading.Lock = threading.Lock() + error = None + + def start_task(self): + with self.lock: + self.num_tasks_started += 1 + self.num_tasks_in_progress += 1 + + def complete_task(self, *, success: bool): + with self.lock: + self.num_tasks_in_progress -= 1 + if success: + self.num_tasks_succeeded += 1 + else: + self.num_tasks_failed += 1 + + def increment_num_rate_limit_errors(self): + with self.lock: + self.num_rate_limit_errors += 1 + + +def call_api( + index: int, + results: list[tuple[int, Any]], + task: Callable[[], Any], + status_tracker: StatusTracker, +): + import openai + + status_tracker.start_task() + try: + result = task() + _logger.debug(f"Request #{index} succeeded") + status_tracker.complete_task(success=True) + results.append((index, result)) + except openai.RateLimitError as e: + status_tracker.complete_task(success=False) + _logger.debug(f"Request #{index} failed with: {e}") + status_tracker.increment_num_rate_limit_errors() + status_tracker.error = mlflow.MlflowException( + f"Request #{index} failed with rate limit: {e}." + ) + except Exception as e: + status_tracker.complete_task(success=False) + _logger.debug(f"Request #{index} failed with: {e}") + status_tracker.error = mlflow.MlflowException( + f"Request #{index} failed with: {e.__cause__}" + ) + + +def process_api_requests( + request_tasks: list[Callable[[], Any]], + max_workers: int = 10, +): + """Processes API requests in parallel""" + # initialize trackers + status_tracker = StatusTracker() # single instance to track a collection of variables + + results: list[tuple[int, Any]] = [] + request_tasks_iter = enumerate(request_tasks) + _logger.debug(f"Request pool executor will run {len(request_tasks)} requests") + with ThreadPoolExecutor( + max_workers=max_workers, thread_name_prefix="MlflowOpenAiApi" + ) as executor: + futures = [ + executor.submit( + call_api, + index=index, + task=task, + results=results, + status_tracker=status_tracker, + ) + for index, task in request_tasks_iter + ] + wait(futures, return_when=FIRST_EXCEPTION) + + # after finishing, log final status + if status_tracker.num_tasks_failed > 0: + if status_tracker.num_tasks_failed == 1: + raise status_tracker.error + raise mlflow.MlflowException( + f"{status_tracker.num_tasks_failed} tasks failed. See logs for details." + ) + if status_tracker.num_rate_limit_errors > 0: + _logger.debug( + f"{status_tracker.num_rate_limit_errors} rate limit errors received. " + "Consider running at a lower rate." + ) + + return [res for _, res in sorted(results)] diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/openai/autolog.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/openai/autolog.py new file mode 100644 index 0000000000000000000000000000000000000000..6d51a2d1085d64bbd58391378198c7f6add9a0c2 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/openai/autolog.py @@ -0,0 +1,519 @@ +import functools +import importlib.metadata +import json +import logging +import warnings +from typing import Any, AsyncIterator, Iterator + +from packaging.version import Version + +import mlflow +from mlflow.entities import SpanType +from mlflow.entities.span import LiveSpan +from mlflow.entities.span_event import SpanEvent +from mlflow.entities.span_status import SpanStatusCode +from mlflow.exceptions import MlflowException +from mlflow.openai.constant import FLAVOR_NAME +from mlflow.openai.utils.chat_schema import set_span_chat_attributes +from mlflow.tracing.constant import ( + STREAM_CHUNK_EVENT_NAME_FORMAT, + STREAM_CHUNK_EVENT_VALUE_KEY, + SpanAttributeKey, + TokenUsageKey, + TraceMetadataKey, +) +from mlflow.tracing.fluent import start_span_no_context +from mlflow.tracing.trace_manager import InMemoryTraceManager +from mlflow.tracing.utils import TraceJSONEncoder +from mlflow.utils.autologging_utils import autologging_integration +from mlflow.utils.autologging_utils.config import AutoLoggingConfig +from mlflow.utils.autologging_utils.safety import safe_patch + +_logger = logging.getLogger(__name__) + + +def autolog( + disable=False, + exclusive=False, + disable_for_unsupported_versions=False, + silent=False, + log_traces=True, +): + """ + Enables (or disables) and configures autologging from OpenAI to MLflow. + Raises :py:class:`MlflowException ` + if the OpenAI version < 1.0. + + Args: + disable: If ``True``, disables the OpenAI autologging integration. If ``False``, + enables the OpenAI autologging integration. + exclusive: If ``True``, autologged content is not logged to user-created fluent runs. + If ``False``, autologged content is logged to the active fluent run, + which may be user-created. + disable_for_unsupported_versions: If ``True``, disable autologging for versions of + OpenAI that have not been tested against this version of the MLflow + client or are incompatible. + silent: If ``True``, suppress all event logs and warnings from MLflow during OpenAI + autologging. If ``False``, show all events and warnings during OpenAI + autologging. + log_traces: If ``True``, traces are logged for OpenAI models. If ``False``, no traces are + collected during inference. Default to ``True``. + """ + if Version(importlib.metadata.version("openai")).major < 1: + raise MlflowException("OpenAI autologging is only supported for openai >= 1.0.0") + + # This needs to be called before doing any safe-patching (otherwise safe-patch will be no-op). + # TODO: since this implementation is inconsistent, explore a universal way to solve the issue. + _autolog( + disable=disable, + exclusive=exclusive, + disable_for_unsupported_versions=disable_for_unsupported_versions, + silent=silent, + log_traces=log_traces, + ) + + # Tracing OpenAI Agent SDK. This has to be done outside the function annotated with + # `@autologging_integration` because the function is not executed when `disable=True`. + try: + from agents.run import AgentRunner + + from mlflow.openai._agent_tracer import _patched_agent_run + + # NB: The OpenAI's built-in tracer does not capture inputs/outputs of the + # root span, which is not inconvenient. Therefore, we add a patch for the + # runner.run() method instead. + safe_patch(FLAVOR_NAME, AgentRunner, "run", _patched_agent_run) + + from mlflow.openai._agent_tracer import ( + add_mlflow_trace_processor, + remove_mlflow_trace_processor, + ) + + if log_traces and not disable: + add_mlflow_trace_processor() + else: + remove_mlflow_trace_processor() + except ImportError: + pass + + +# This is required by mlflow.autolog() +autolog.integration_name = FLAVOR_NAME + + +# NB: The @autologging_integration annotation must be applied here, and the callback injection +# needs to happen outside the annotated function. This is because the annotated function is NOT +# executed when disable=True is passed. This prevents us from removing our callback and patching +# when autologging is turned off. +@autologging_integration(FLAVOR_NAME) +def _autolog( + disable=False, + exclusive=False, + disable_for_unsupported_versions=False, + silent=False, + log_traces=True, +): + from openai.resources.chat.completions import AsyncCompletions as AsyncChatCompletions + from openai.resources.chat.completions import Completions as ChatCompletions + from openai.resources.completions import AsyncCompletions, Completions + from openai.resources.embeddings import AsyncEmbeddings, Embeddings + + for task in (ChatCompletions, Completions, Embeddings): + safe_patch(FLAVOR_NAME, task, "create", patched_call) + + if hasattr(ChatCompletions, "parse"): + # In openai>=1.92.0, `ChatCompletions` has a `parse` method: + # https://github.com/openai/openai-python/commit/0e358ed66b317038705fb38958a449d284f3cb88 + safe_patch(FLAVOR_NAME, ChatCompletions, "parse", patched_call) + + for task in (AsyncChatCompletions, AsyncCompletions, AsyncEmbeddings): + safe_patch(FLAVOR_NAME, task, "create", async_patched_call) + + if hasattr(AsyncChatCompletions, "parse"): + # In openai>=1.92.0, `AsyncChatCompletions` has a `parse` method: + # https://github.com/openai/openai-python/commit/0e358ed66b317038705fb38958a449d284f3cb88 + safe_patch(FLAVOR_NAME, AsyncChatCompletions, "parse", async_patched_call) + + try: + from openai.resources.beta.chat.completions import AsyncCompletions, Completions + except ImportError: + pass + else: + safe_patch(FLAVOR_NAME, Completions, "parse", patched_call) + safe_patch(FLAVOR_NAME, AsyncCompletions, "parse", async_patched_call) + + try: + from openai.resources.responses import AsyncResponses, Responses + except ImportError: + pass + else: + safe_patch(FLAVOR_NAME, Responses, "create", patched_call) + safe_patch(FLAVOR_NAME, AsyncResponses, "create", async_patched_call) + safe_patch(FLAVOR_NAME, AsyncResponses, "parse", async_patched_call) + safe_patch(FLAVOR_NAME, Responses, "parse", patched_call) + + # Patch Swarm agent to generate traces + try: + from swarm import Swarm + + warnings.warn( + "Autologging for OpenAI Swarm is deprecated and will be removed in a future release. " + "OpenAI Agent SDK is drop-in replacement for agent building and is supported by " + "MLflow autologging. Please refer to the OpenAI Agent SDK documentation " + "(https://github.com/openai/openai-agents-python) for more details.", + category=FutureWarning, + stacklevel=2, + ) + + safe_patch( + FLAVOR_NAME, + Swarm, + "get_chat_completion", + patched_agent_get_chat_completion, + ) + + safe_patch( + FLAVOR_NAME, + Swarm, + "run", + patched_swarm_run, + ) + except ImportError: + pass + + +def _get_span_type_and_message_format(task: type) -> tuple[str, str]: + from openai.resources.chat.completions import AsyncCompletions as AsyncChatCompletions + from openai.resources.chat.completions import Completions as ChatCompletions + from openai.resources.completions import AsyncCompletions, Completions + from openai.resources.embeddings import AsyncEmbeddings, Embeddings + + span_type_mapping = { + ChatCompletions: SpanType.CHAT_MODEL, + AsyncChatCompletions: SpanType.CHAT_MODEL, + Completions: SpanType.LLM, + AsyncCompletions: SpanType.LLM, + Embeddings: SpanType.EMBEDDING, + AsyncEmbeddings: SpanType.EMBEDDING, + } + + try: + # Only available in openai>=1.40.0 + from openai.resources.beta.chat.completions import ( + AsyncCompletions as BetaAsyncChatCompletions, + ) + from openai.resources.beta.chat.completions import Completions as BetaChatCompletions + + span_type_mapping[BetaChatCompletions] = SpanType.CHAT_MODEL + span_type_mapping[BetaAsyncChatCompletions] = SpanType.CHAT_MODEL + except ImportError: + _logger.debug( + "Failed to import `BetaChatCompletions` or `BetaAsyncChatCompletions`", exc_info=True + ) + + try: + # Responses API only available in openai>=1.66.0 + from openai.resources.responses import AsyncResponses, Responses + + span_type_mapping[Responses] = SpanType.CHAT_MODEL + span_type_mapping[AsyncResponses] = SpanType.CHAT_MODEL + except ImportError: + pass + + return span_type_mapping.get(task, (SpanType.UNKNOWN, None)) + + +def _try_parse_raw_response(response: Any) -> Any: + """ + As documented at https://github.com/openai/openai-python/tree/52357cff50bee57ef442e94d78a0de38b4173fc2?tab=readme-ov-file#accessing-raw-response-data-eg-headers, + a `LegacyAPIResponse` (https://github.com/openai/openai-python/blob/52357cff50bee57ef442e94d78a0de38b4173fc2/src/openai/_legacy_response.py#L45) + object is returned when the `create` method is invoked with `with_raw_response`. + """ + try: + from openai._legacy_response import LegacyAPIResponse + except ImportError: + _logger.debug("Failed to import `LegacyAPIResponse` from `openai._legacy_response`") + return response + if isinstance(response, LegacyAPIResponse): + try: + # `parse` returns either a `pydantic.BaseModel` or a `openai.Stream` object + # depending on whether the request has a `stream` parameter set to `True`. + return response.parse() + except Exception as e: + _logger.debug(f"Failed to parse {response} (type: {response.__class__}): {e}") + + return response + + +def patched_call(original, self, *args, **kwargs): + config = AutoLoggingConfig.init(flavor_name=mlflow.openai.FLAVOR_NAME) + active_run = mlflow.active_run() + run_id = active_run.info.run_id if active_run else None + + if config.log_traces: + span = _start_span(self, kwargs, run_id) + + # Execute the original function + try: + raw_result = original(self, *args, **kwargs) + except Exception as e: + if config.log_traces: + _end_span_on_exception(span, e) + raise + + if config.log_traces: + _end_span_on_success(span, kwargs, raw_result) + + return raw_result + + +async def async_patched_call(original, self, *args, **kwargs): + config = AutoLoggingConfig.init(flavor_name=mlflow.openai.FLAVOR_NAME) + active_run = mlflow.active_run() + run_id = active_run.info.run_id if active_run else None + + if config.log_traces: + span = _start_span(self, kwargs, run_id) + + # Execute the original function + try: + raw_result = await original(self, *args, **kwargs) + except Exception as e: + if config.log_traces: + _end_span_on_exception(span, e) + raise + + if config.log_traces: + _end_span_on_success(span, kwargs, raw_result) + + return raw_result + + +def _start_span( + instance: Any, + inputs: dict[str, Any], + run_id: str, +): + span_type = _get_span_type_and_message_format(instance.__class__) + # Record input parameters to attributes + attributes = {k: v for k, v in inputs.items() if k not in ("messages", "input")} + if span_type in (SpanType.CHAT_MODEL, SpanType.LLM): + attributes[SpanAttributeKey.MESSAGE_FORMAT] = "openai" + + # If there is an active span, create a child span under it, otherwise create a new trace + span = start_span_no_context( + name=instance.__class__.__name__, + span_type=span_type, + inputs=inputs, + attributes=attributes, + ) + + # Associate run ID to the trace manually, because if a new run is created by + # autologging, it is not set as the active run thus not automatically + # associated with the trace. + if run_id is not None: + tm = InMemoryTraceManager().get_instance() + tm.set_trace_metadata(span.trace_id, TraceMetadataKey.SOURCE_RUN, run_id) + + return span + + +def _end_span_on_success(span: LiveSpan, inputs: dict[str, Any], raw_result: Any): + from openai import AsyncStream, Stream + + result = _try_parse_raw_response(raw_result) + + if isinstance(result, Stream): + # If the output is a stream, we add a hook to store the intermediate chunks + # and then log the outputs as a single artifact when the stream ends + def _stream_output_logging_hook(stream: Iterator) -> Iterator: + output = [] + for i, chunk in enumerate(stream): + _add_span_event(span, i, chunk) + output.append(chunk) + yield chunk + _process_last_chunk(span, chunk, inputs, output) + + result._iterator = _stream_output_logging_hook(result._iterator) + elif isinstance(result, AsyncStream): + + async def _stream_output_logging_hook(stream: AsyncIterator) -> AsyncIterator: + output = [] + async for chunk in stream: + _add_span_event(span, len(output), chunk) + output.append(chunk) + yield chunk + _process_last_chunk(span, chunk, inputs, output) + + result._iterator = _stream_output_logging_hook(result._iterator) + else: + try: + set_span_chat_attributes(span, inputs, result) + span.end(outputs=result) + except Exception as e: + _logger.warning(f"Encountered unexpected error when ending trace: {e}", exc_info=True) + + +def _process_last_chunk(span: LiveSpan, chunk: Any, inputs: dict[str, Any], output: list[Any]): + if _is_responses_final_event(chunk): + output = chunk.response + else: + # Reconstruct a completion object from streaming chunks + output = _reconstruct_completion_from_stream(output) + + # Set usage information on span if available + if usage := getattr(chunk, "usage", None): + usage_dict = { + TokenUsageKey.INPUT_TOKENS: usage.prompt_tokens, + TokenUsageKey.OUTPUT_TOKENS: usage.completion_tokens, + TokenUsageKey.TOTAL_TOKENS: usage.total_tokens, + } + span.set_attribute(SpanAttributeKey.CHAT_USAGE, usage_dict) + + _end_span_on_success(span, inputs, output) + + +def _reconstruct_completion_from_stream(chunks: list[Any]) -> Any: + """ + Reconstruct a completion object from streaming chunks. + + This preserves the structure and metadata that would be present in a non-streaming + completion response, including ID, model, timestamps, usage, etc. + """ + if not chunks: + return None + + if chunks[0].object == "text_completion": + # Handling for the deprecated Completions API. Keep the legacy behavior for now. + def _extract_content(chunk: Any) -> str: + if not chunk.choices: + return "" + return chunk.choices[0].text or "" + + return "".join(map(_extract_content, chunks)) + + if chunks[0].object != "chat.completion.chunk": + return chunks # Ignore non-chat chunks + + from openai.types.chat import ChatCompletion + from openai.types.chat.chat_completion import Choice + from openai.types.chat.chat_completion_message import ChatCompletionMessage + + # Build the base message + def _extract_content(chunk: Any) -> str: + if not chunk.choices: + return "" + return chunk.choices[0].delta.content or "" + + message = ChatCompletionMessage( + role="assistant", content="".join(map(_extract_content, chunks)) + ) + + # Extract metadata from the last chunk + last_chunk = chunks[-1] + finish_reason = "stop" + if choices := getattr(last_chunk, "choices", None): + if chunk_choice := choices[0]: + finish_reason = getattr(chunk_choice, "finish_reason") or finish_reason + + choice = Choice(index=0, message=message, finish_reason=finish_reason) + + # Build the completion dict + return ChatCompletion( + id=last_chunk.id, + choices=[choice], + created=last_chunk.created, + model=last_chunk.model, + object="chat.completion", + system_fingerprint=last_chunk.system_fingerprint, + usage=last_chunk.usage, + ) + + +def _is_responses_final_event(chunk: Any) -> bool: + try: + from openai.types.responses import ResponseCompletedEvent + + return isinstance(chunk, ResponseCompletedEvent) + except ImportError: + return False + + +def _end_span_on_exception(span: LiveSpan, e: Exception): + try: + span.add_event(SpanEvent.from_exception(e)) + span.end(status=SpanStatusCode.ERROR) + except Exception as inner_e: + _logger.warning(f"Encountered unexpected error when ending trace: {inner_e}") + + +def _add_span_event(span: LiveSpan, index: int, chunk: Any): + span.add_event( + SpanEvent( + name=STREAM_CHUNK_EVENT_NAME_FORMAT.format(index=index), + # OpenTelemetry SpanEvent only support str-str key-value pairs for attributes + attributes={STREAM_CHUNK_EVENT_VALUE_KEY: json.dumps(chunk, cls=TraceJSONEncoder)}, + ) + ) + + +def patched_agent_get_chat_completion(original, self, *args, **kwargs): + """ + Patch the `get_chat_completion` method of the ChatCompletion object. + OpenAI autolog already handles the raw completion request, but tracing + the swarm's method is useful to track other parameters like agent name. + """ + agent = kwargs.get("agent") or args[0] + + # Patch agent's functions to generate traces. Function calls only happen + # after the first completion is generated because of the design of + # function calling. Therefore, we can safely patch the tool functions here + # within get_chat_completion() hook. + # We cannot patch functions during the agent's initialization because the + # agent's functions can be modified after the agent is created. + def function_wrapper(fn): + if "context_variables" in fn.__code__.co_varnames: + + def wrapper(*args, **kwargs): + # NB: Swarm uses `func.__code__.co_varnames` to inspect if the provided + # tool function includes 'context_variables' parameter in the signature + # and ingest the global context variables if so. Wrapping the function + # with mlflow.trace() will break this. + # The co_varnames is determined based on the local variables of the + # function, so we workaround this by declaring it here as a local variable. + context_variables = kwargs.get("context_variables", {}) # noqa: F841 + return mlflow.trace( + fn, + name=f"{agent.name}.{fn.__name__}", + span_type=SpanType.TOOL, + )(*args, **kwargs) + else: + + def wrapper(*args, **kwargs): + return mlflow.trace( + fn, + name=f"{agent.name}.{fn.__name__}", + span_type=SpanType.TOOL, + )(*args, **kwargs) + + wrapped = functools.wraps(fn)(wrapper) + wrapped._is_mlflow_traced = True # Marker to avoid double tracing + return wrapped + + agent.functions = [ + function_wrapper(fn) if not hasattr(fn, "_is_mlflow_traced") else fn + for fn in agent.functions + ] + + traced_fn = mlflow.trace( + original, name=f"{agent.name}.get_chat_completion", span_type=SpanType.CHAIN + ) + return traced_fn(self, *args, **kwargs) + + +def patched_swarm_run(original, self, *args, **kwargs): + """ + Patched version of `run` method of the Swarm object. + """ + traced_fn = mlflow.trace(original, span_type=SpanType.AGENT) + return traced_fn(self, *args, **kwargs) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/openai/constant.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/openai/constant.py new file mode 100644 index 0000000000000000000000000000000000000000..5d7ea55ef6880c8c1b596cf32a605d0eac889d7b --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/openai/constant.py @@ -0,0 +1 @@ +FLAVOR_NAME = "openai" diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/openai/model.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/openai/model.py new file mode 100644 index 0000000000000000000000000000000000000000..f7991666f00eac375bab7252539189cf026e511c --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/openai/model.py @@ -0,0 +1,862 @@ +import importlib.metadata +import itertools +import logging +import os +import warnings +from functools import partial +from string import Formatter +from typing import Any + +import yaml +from packaging.version import Version + +import mlflow +from mlflow import pyfunc +from mlflow.entities.model_registry.prompt import Prompt +from mlflow.environment_variables import MLFLOW_OPENAI_SECRET_SCOPE +from mlflow.exceptions import MlflowException +from mlflow.models import Model, ModelInputExample, ModelSignature +from mlflow.models.model import MLMODEL_FILE_NAME, _update_active_model_id_based_on_mlflow_model +from mlflow.models.signature import _infer_signature_from_input_example +from mlflow.models.utils import _save_example +from mlflow.openai.constant import FLAVOR_NAME +from mlflow.protos.databricks_pb2 import INVALID_PARAMETER_VALUE +from mlflow.tracking._model_registry import DEFAULT_AWAIT_MAX_SLEEP_SECONDS +from mlflow.tracking.artifact_utils import _download_artifact_from_uri +from mlflow.types import ColSpec, Schema, TensorSpec +from mlflow.utils.databricks_utils import ( + check_databricks_secret_scope_access, + is_in_databricks_runtime, +) +from mlflow.utils.docstring_utils import LOG_MODEL_PARAM_DOCS, format_docstring +from mlflow.utils.environment import ( + _CONDA_ENV_FILE_NAME, + _CONSTRAINTS_FILE_NAME, + _PYTHON_ENV_FILE_NAME, + _REQUIREMENTS_FILE_NAME, + _mlflow_conda_env, + _process_conda_env, + _process_pip_requirements, + _PythonEnv, + _validate_env_arguments, +) +from mlflow.utils.file_utils import write_to +from mlflow.utils.model_utils import ( + _add_code_from_conf_to_system_path, + _get_flavor_configuration, + _validate_and_copy_code_paths, + _validate_and_prepare_target_save_path, +) +from mlflow.utils.openai_utils import ( + _OAITokenHolder, + _OpenAIApiConfig, + _OpenAIEnvVar, + _validate_model_params, +) +from mlflow.utils.requirements_utils import _get_pinned_requirement + +MODEL_FILENAME = "model.yaml" +_PYFUNC_SUPPORTED_TASKS = ("chat.completions", "embeddings", "completions") + +_logger = logging.getLogger(__name__) + + +def get_default_pip_requirements(): + """ + Returns: + A list of default pip requirements for MLflow Models produced by this flavor. + Calls to :func:`save_model()` and :func:`log_model()` produce a pip environment + that, at minimum, contains these requirements. + """ + return list(map(_get_pinned_requirement, ["openai", "tiktoken", "tenacity"])) + + +def get_default_conda_env(): + """ + Returns: + The default Conda environment for MLflow Models produced by calls to + :func:`save_model()` and :func:`log_model()`. + """ + return _mlflow_conda_env(additional_pip_deps=get_default_pip_requirements()) + + +def _get_obj_to_task_mapping(): + from openai import resources as r + + mapping = { + r.Audio: "audio", + r.chat.Completions: "chat.completions", + r.Completions: "completions", + r.Images.edit: "images.edit", + r.Embeddings: "embeddings", + r.Files: "files", + r.Images: "images", + r.FineTuning: "fine_tuning", + r.Moderations: "moderations", + r.Models: "models", + r.chat.AsyncCompletions: "chat.completions", + r.AsyncCompletions: "completions", + r.AsyncEmbeddings: "embeddings", + } + + try: + from openai.resources.beta.chat import completions as c + + mapping.update( + { + c.AsyncCompletions: "chat.completions", + c.Completions: "chat.completions", + } + ) + except ImportError: + pass + return mapping + + +def _get_model_name(model): + import openai + + if isinstance(model, str): + return model + + if Version(_get_openai_package_version()).major < 1 and isinstance(model, openai.Model): + return model.id + + raise mlflow.MlflowException( + f"Unsupported model type: {type(model)}", error_code=INVALID_PARAMETER_VALUE + ) + + +def _get_task_name(task): + mapping = _get_obj_to_task_mapping() + if isinstance(task, str): + if task not in mapping.values(): + raise mlflow.MlflowException( + f"Unsupported task: {task}", error_code=INVALID_PARAMETER_VALUE + ) + return task + else: + task_name = ( + mapping.get(task) + or mapping.get(task.__class__) + or mapping.get(getattr(task, "__func__")) # if task is a method + ) + if task_name is None: + raise mlflow.MlflowException( + f"Unsupported task object: {task}", error_code=INVALID_PARAMETER_VALUE + ) + return task_name + + +def _get_api_config() -> _OpenAIApiConfig: + """Gets the parameters and configuration of the OpenAI API connected to.""" + import openai + + api_type = os.getenv(_OpenAIEnvVar.OPENAI_API_TYPE.value, openai.api_type) + api_version = os.getenv(_OpenAIEnvVar.OPENAI_API_VERSION.value, openai.api_version) + api_base = os.getenv(_OpenAIEnvVar.OPENAI_API_BASE.value) or os.getenv( + _OpenAIEnvVar.OPENAI_BASE_URL.value + ) + deployment_id = os.getenv(_OpenAIEnvVar.OPENAI_DEPLOYMENT_NAME.value, None) + organization = os.getenv(_OpenAIEnvVar.OPENAI_ORGANIZATION.value, None) + if api_type in ("azure", "azure_ad", "azuread"): + batch_size = 16 + max_tokens_per_minute = 60_000 + else: + # The maximum batch size is 2048: + # https://github.com/openai/openai-python/blob/b82a3f7e4c462a8a10fa445193301a3cefef9a4a/openai/embeddings_utils.py#L43 + # We use a smaller batch size to be safe. + batch_size = 1024 + max_tokens_per_minute = 90_000 + return _OpenAIApiConfig( + api_type=api_type, + batch_size=batch_size, + max_requests_per_minute=3_500, + max_tokens_per_minute=max_tokens_per_minute, + api_base=api_base, + api_version=api_version, + deployment_id=deployment_id, + organization=organization, + ) + + +def _get_openai_package_version(): + return importlib.metadata.version("openai") + + +def _log_secrets_yaml(local_model_dir, scope): + with open(os.path.join(local_model_dir, "openai.yaml"), "w") as f: + yaml.safe_dump({e.value: f"{scope}:{e.secret_key}" for e in _OpenAIEnvVar}, f) + + +def _parse_format_fields(content: str | list[Any] | dict[str, Any] | Any) -> set[str]: + """Parse format fields from content recursively.""" + if isinstance(content, str): + return {fn for _, fn, _, _ in Formatter().parse(content) if fn is not None} + elif isinstance(content, list): + # Handle multimodal content (list of objects) + fields = set() + for item in content: + if isinstance(item, dict): + for value in item.values(): + fields.update(_parse_format_fields(value)) # Recursive call + elif isinstance(item, str): + fields.update(_parse_format_fields(item)) + return fields + elif isinstance(content, dict): + # Handle dict content (recursively) + fields = set() + for value in content.values(): + fields.update(_parse_format_fields(value)) # Recursive call + return fields + else: + # For other types (e.g., None), return empty set + return set() + + +def _get_input_schema(task, content): + if content: + formatter = _ContentFormatter(task, content) + variables = formatter.variables + if len(variables) == 1: + return Schema([ColSpec(type="string")]) + elif len(variables) > 1: + return Schema([ColSpec(name=v, type="string") for v in variables]) + else: + return Schema([ColSpec(type="string")]) + else: + return Schema([ColSpec(type="string")]) + + +@format_docstring(LOG_MODEL_PARAM_DOCS.format(package_name=FLAVOR_NAME)) +def save_model( + model, + task, + path, + conda_env=None, + code_paths=None, + mlflow_model=None, + signature: ModelSignature = None, + input_example: ModelInputExample = None, + pip_requirements=None, + extra_pip_requirements=None, + metadata=None, + **kwargs, +): + """ + Save an OpenAI model to a path on the local file system. + + Args: + model: The OpenAI model name. + task: The task the model is performing, e.g., ``openai.chat.completions`` or + ``'chat.completions'``. + path: Local path where the model is to be saved. + conda_env: {{ conda_env }} + code_paths: {{ code_paths }} + mlflow_model: :py:mod:`mlflow.models.Model` this flavor is being added to. + signature: :py:class:`ModelSignature ` + describes model input and output :py:class:`Schema `. + The model signature can be :py:func:`inferred ` + from datasets with valid model input (e.g. the training dataset with target + column omitted) and valid model output (e.g. model predictions generated on + the training dataset), for example: + + .. code-block:: python + + from mlflow.models import infer_signature + + train = df.drop_column("target_label") + predictions = ... # compute model predictions + signature = infer_signature(train, predictions) + input_example: {{ input_example }} + pip_requirements: {{ pip_requirements }} + extra_pip_requirements: {{ extra_pip_requirements }} + metadata: {{ metadata }} + kwargs: Keyword arguments specific to the OpenAI task, such as the ``messages`` (see + :ref:`mlflow.openai.messages` for more details on this parameter) + or ``top_p`` value to use for chat completion. + + .. code-block:: python + + import mlflow + import openai + + # Chat + mlflow.openai.save_model( + model="gpt-4o-mini", + task=openai.chat.completions, + messages=[{"role": "user", "content": "Tell me a joke."}], + path="model", + ) + + # Completions + mlflow.openai.save_model( + model="text-davinci-002", + task=openai.completions, + prompt="{text}. The general sentiment of the text is", + path="model", + ) + + # Embeddings + mlflow.openai.save_model( + model="text-embedding-ada-002", + task=openai.embeddings, + path="model", + ) + """ + if Version(_get_openai_package_version()).major < 1: + raise MlflowException("Only openai>=1.0 is supported.") + + import numpy as np + + _validate_env_arguments(conda_env, pip_requirements, extra_pip_requirements) + path = os.path.abspath(path) + _validate_and_prepare_target_save_path(path) + code_dir_subpath = _validate_and_copy_code_paths(code_paths, path) + task = _get_task_name(task) + + if mlflow_model is None: + mlflow_model = Model() + + if signature is not None: + if signature.params: + _validate_model_params( + task, kwargs, {p.name: p.default for p in signature.params.params} + ) + elif task == "chat.completions": + messages = kwargs.get("messages", []) + if messages and not ( + all(isinstance(m, dict) for m in messages) and all(map(_is_valid_message, messages)) + ): + raise mlflow.MlflowException.invalid_parameter_value( + "If `messages` is provided, it must be a list of dictionaries with keys " + "'role' and 'content'." + ) + + signature = ModelSignature( + inputs=_get_input_schema(task, messages), + outputs=Schema([ColSpec(type="string", name=None)]), + ) + elif task == "completions": + prompt = kwargs.get("prompt") + signature = ModelSignature( + inputs=_get_input_schema(task, prompt), + outputs=Schema([ColSpec(type="string", name=None)]), + ) + elif task == "embeddings": + signature = ModelSignature( + inputs=Schema([ColSpec(type="string", name=None)]), + outputs=Schema([TensorSpec(type=np.dtype("float64"), shape=(-1,))]), + ) + + saved_example = _save_example(mlflow_model, input_example, path) + if signature is None and saved_example is not None: + wrapped_model = _OpenAIWrapper(model) + signature = _infer_signature_from_input_example(saved_example, wrapped_model) + + if signature is not None: + mlflow_model.signature = signature + + if metadata is not None: + mlflow_model.metadata = metadata + model_data_path = os.path.join(path, MODEL_FILENAME) + model_dict = { + "model": _get_model_name(model), + "task": task, + **kwargs, + } + with open(model_data_path, "w") as f: + yaml.safe_dump(model_dict, f) + + if task in _PYFUNC_SUPPORTED_TASKS: + pyfunc.add_to_model( + mlflow_model, + loader_module="mlflow.openai", + data=MODEL_FILENAME, + conda_env=_CONDA_ENV_FILE_NAME, + python_env=_PYTHON_ENV_FILE_NAME, + code=code_dir_subpath, + ) + mlflow_model.add_flavor( + FLAVOR_NAME, + openai_version=_get_openai_package_version(), + data=MODEL_FILENAME, + code=code_dir_subpath, + ) + mlflow_model.save(os.path.join(path, MLMODEL_FILE_NAME)) + + if is_in_databricks_runtime(): + if scope := MLFLOW_OPENAI_SECRET_SCOPE.get(): + url = "https://docs.databricks.com/en/machine-learning/model-serving/store-env-variable-model-serving.html" + warnings.warn( + "Specifying secrets for model serving with `MLFLOW_OPENAI_SECRET_SCOPE` is " + f"deprecated. Use secrets-based environment variables ({url}) instead.", + FutureWarning, + ) + check_databricks_secret_scope_access(scope) + _log_secrets_yaml(path, scope) + + if conda_env is None: + if pip_requirements is None: + default_reqs = get_default_pip_requirements() + inferred_reqs = mlflow.models.infer_pip_requirements( + path, FLAVOR_NAME, fallback=default_reqs + ) + default_reqs = sorted(set(inferred_reqs).union(default_reqs)) + else: + default_reqs = None + conda_env, pip_requirements, pip_constraints = _process_pip_requirements( + default_reqs, + pip_requirements, + extra_pip_requirements, + ) + else: + conda_env, pip_requirements, pip_constraints = _process_conda_env(conda_env) + + with open(os.path.join(path, _CONDA_ENV_FILE_NAME), "w") as f: + yaml.safe_dump(conda_env, stream=f, default_flow_style=False) + + # Save `constraints.txt` if necessary + if pip_constraints: + write_to(os.path.join(path, _CONSTRAINTS_FILE_NAME), "\n".join(pip_constraints)) + + # Save `requirements.txt` + write_to(os.path.join(path, _REQUIREMENTS_FILE_NAME), "\n".join(pip_requirements)) + + _PythonEnv.current().to_yaml(os.path.join(path, _PYTHON_ENV_FILE_NAME)) + + +@format_docstring(LOG_MODEL_PARAM_DOCS.format(package_name=FLAVOR_NAME)) +def log_model( + model, + task, + artifact_path: str | None = None, + conda_env=None, + code_paths=None, + registered_model_name=None, + signature: ModelSignature = None, + input_example: ModelInputExample = None, + await_registration_for=DEFAULT_AWAIT_MAX_SLEEP_SECONDS, + pip_requirements=None, + extra_pip_requirements=None, + metadata=None, + prompts: list[str | Prompt] | None = None, + name: str | None = None, + params: dict[str, Any] | None = None, + tags: dict[str, Any] | None = None, + model_type: str | None = None, + step: int = 0, + model_id: str | None = None, + **kwargs, +): + """ + Log an OpenAI model as an MLflow artifact for the current run. + + Args: + model: The OpenAI model name or reference instance, e.g., + ``openai.Model.retrieve("gpt-4o-mini")``. + task: The task the model is performing, e.g., ``openai.chat.completions`` or + ``'chat.completions'``. + artifact_path: Deprecated. Use `name` instead. + conda_env: {{ conda_env }} + code_paths: {{ code_paths }} + registered_model_name: If given, create a model version under + ``registered_model_name``, also creating a registered model if one + with the given name does not exist. + signature: :py:class:`ModelSignature ` + describes model input and output :py:class:`Schema `. + The model signature can be :py:func:`inferred ` + from datasets with valid model input (e.g. the training dataset with target + column omitted) and valid model output (e.g. model predictions generated on + the training dataset), for example: + + .. code-block:: python + + from mlflow.models import infer_signature + + train = df.drop_column("target_label") + predictions = ... # compute model predictions + signature = infer_signature(train, predictions) + + input_example: {{ input_example }} + await_registration_for: Number of seconds to wait for the model version to finish + being created and is in ``READY`` status. By default, the function + waits for five minutes. Specify 0 or None to skip waiting. + pip_requirements: {{ pip_requirements }} + extra_pip_requirements: {{ extra_pip_requirements }} + metadata: {{ metadata }} + prompts: {{ prompts }} + name: {{ name }} + params: {{ params }} + tags: {{ tags }} + model_type: {{ model_type }} + step: {{ step }} + model_id: {{ model_id }} + kwargs: Keyword arguments specific to the OpenAI task, such as the ``messages`` (see + :ref:`mlflow.openai.messages` for more details on this parameter) + or ``top_p`` value to use for chat completion. + + Returns: + A :py:class:`ModelInfo ` instance that contains the + metadata of the logged model. + + .. code-block:: python + :caption: Example + + import mlflow + import openai + import pandas as pd + + # Chat + with mlflow.start_run(): + info = mlflow.openai.log_model( + model="gpt-4o-mini", + task=openai.chat.completions, + messages=[{"role": "user", "content": "Tell me a joke about {animal}."}], + name="model", + ) + model = mlflow.pyfunc.load_model(info.model_uri) + df = pd.DataFrame({"animal": ["cats", "dogs"]}) + print(model.predict(df)) + + # Embeddings + with mlflow.start_run(): + info = mlflow.openai.log_model( + model="text-embedding-ada-002", + task=openai.embeddings, + name="embeddings", + ) + model = mlflow.pyfunc.load_model(info.model_uri) + print(model.predict(["hello", "world"])) + """ + return Model.log( + artifact_path=artifact_path, + name=name, + flavor=mlflow.openai, + registered_model_name=registered_model_name, + model=model, + task=task, + conda_env=conda_env, + code_paths=code_paths, + signature=signature, + input_example=input_example, + await_registration_for=await_registration_for, + pip_requirements=pip_requirements, + extra_pip_requirements=extra_pip_requirements, + metadata=metadata, + prompts=prompts, + params=params, + tags=tags, + model_type=model_type, + step=step, + model_id=model_id, + **kwargs, + ) + + +def _load_model(path): + model_file_path = os.path.dirname(path) + if os.path.exists(model_file_path): + mlflow_model = Model.load(model_file_path) + _update_active_model_id_based_on_mlflow_model(mlflow_model) + with open(path) as f: + return yaml.safe_load(f) + + +def _is_valid_message(d): + return isinstance(d, dict) and "content" in d and "role" in d + + +class _ContentFormatter: + def __init__(self, task, template=None): + if task == "completions": + template = template or "{prompt}" + if not isinstance(template, str): + raise mlflow.MlflowException.invalid_parameter_value( + f"Template for task {task} expects type `str`, but got {type(template)}." + ) + + self.template = template + self.format_fn = self.format_prompt + self.variables = sorted(_parse_format_fields(self.template)) + elif task == "chat.completions": + if not template: + template = [{"role": "user", "content": "{content}"}] + if not all(map(_is_valid_message, template)): + raise mlflow.MlflowException.invalid_parameter_value( + f"Template for task {task} expects type `dict` with keys 'content' " + f"and 'role', but got {type(template)}." + ) + + self.template = template.copy() + self.format_fn = self.format_chat + self.variables = sorted( + set( + itertools.chain.from_iterable( + _parse_format_fields(message.get("content")) + | _parse_format_fields(message.get("role")) + for message in self.template + ) + ) + ) + if not self.variables: + self.template.append({"role": "user", "content": "{content}"}) + self.variables.append("content") + else: + raise mlflow.MlflowException.invalid_parameter_value( + f"Task type ``{task}`` is not supported for formatting." + ) + + def format(self, **params): + if missing_params := set(self.variables) - set(params): + raise mlflow.MlflowException.invalid_parameter_value( + f"Expected parameters {self.variables} to be provided, " + f"only got {list(params)}, {list(missing_params)} are missing." + ) + return self.format_fn(**params) + + def format_prompt(self, **params): + return self.template.format(**{v: params[v] for v in self.variables}) + + def format_chat(self, **params): + format_args = {v: params[v] for v in self.variables} + + def format_value( + value: str | list[Any] | dict[str, Any] | Any, + ) -> str | list[Any] | dict[str, Any] | Any: + if isinstance(value, str): + return value.format(**format_args) + elif isinstance(value, list): + return [format_value(item) for item in value] + elif isinstance(value, dict): + return {key: format_value(val) for key, val in value.items()} + else: + return value + + formatted_messages = [] + + for message in self.template: + role = message.get("role") + content = message.get("content") + + # Format role and content recursively + formatted_role = format_value(role) + formatted_content = format_value(content) + + formatted_messages.append( + { + "role": formatted_role, + "content": formatted_content, + } + ) + + return formatted_messages + + +def _first_string_column(pdf): + iter_str_cols = (c for c, v in pdf.iloc[0].items() if isinstance(v, str)) + col = next(iter_str_cols, None) + if col is None: + raise mlflow.MlflowException.invalid_parameter_value( + f"Could not find a string column in the input data: {pdf.dtypes.to_dict()}" + ) + return col + + +class _OpenAIWrapper: + def __init__(self, model): + task = model.pop("task") + if task not in _PYFUNC_SUPPORTED_TASKS: + raise mlflow.MlflowException.invalid_parameter_value( + f"Unsupported task: {task}. Supported tasks: {_PYFUNC_SUPPORTED_TASKS}." + ) + self.model = model + self.task = task + self.api_config = _get_api_config() + self.api_token = _OAITokenHolder(self.api_config.api_type) + + if self.task != "embeddings": + self._setup_completions() + + def get_raw_model(self): + """ + Returns the underlying model. + """ + return self.model + + def _setup_completions(self): + if self.task == "chat.completions": + self.template = self.model.get("messages", []) + else: + self.template = self.model.get("prompt") + self.formatter = _ContentFormatter(self.task, self.template) + + def format_completions(self, params_list): + return [self.formatter.format(**params) for params in params_list] + + def get_params_list(self, data): + if len(self.formatter.variables) == 1: + variable = self.formatter.variables[0] + if variable in data.columns: + return data[[variable]].to_dict(orient="records") + else: + first_string_column = _first_string_column(data) + return [{variable: s} for s in data[first_string_column]] + else: + return data[self.formatter.variables].to_dict(orient="records") + + def get_client(self, max_retries: int, timeout: float): + # with_option method should not be used before v1.3.8: https://github.com/openai/openai-python/issues/865 + if self.api_config.api_type in ("azure", "azure_ad", "azuread"): + from openai import AzureOpenAI + + return AzureOpenAI( + api_key=self.api_token.token, + azure_endpoint=self.api_config.api_base, + api_version=self.api_config.api_version, + azure_deployment=self.api_config.deployment_id, + max_retries=max_retries, + timeout=timeout, + ) + else: + from openai import OpenAI + + return OpenAI( + api_key=self.api_token.token, + base_url=self.api_config.api_base, + max_retries=max_retries, + timeout=timeout, + ) + + def _predict_chat(self, data, params): + from mlflow.openai.api_request_parallel_processor import process_api_requests + + _validate_model_params(self.task, self.model, params) + max_retries = params.pop("max_retries", self.api_config.max_retries) + timeout = params.pop("timeout", self.api_config.timeout) + + messages_list = self.format_completions(self.get_params_list(data)) + client = self.get_client(max_retries=max_retries, timeout=timeout) + + requests = [ + partial( + client.chat.completions.create, + messages=messages, + model=self.model["model"], + **params, + ) + for messages in messages_list + ] + + results = process_api_requests(request_tasks=requests) + + return [r.choices[0].message.content for r in results] + + def _predict_completions(self, data, params): + from mlflow.openai.api_request_parallel_processor import process_api_requests + + _validate_model_params(self.task, self.model, params) + prompts_list = self.format_completions(self.get_params_list(data)) + max_retries = params.pop("max_retries", self.api_config.max_retries) + timeout = params.pop("timeout", self.api_config.timeout) + batch_size = params.pop("batch_size", self.api_config.batch_size) + _logger.debug(f"Requests are being batched by {batch_size} samples.") + + client = self.get_client(max_retries=max_retries, timeout=timeout) + + requests = [ + partial( + client.completions.create, + prompt=prompts_list[i : i + batch_size], + model=self.model["model"], + **params, + ) + for i in range(0, len(prompts_list), batch_size) + ] + + results = process_api_requests(request_tasks=requests) + + return [row.text for batch in results for row in batch.choices] + + def _predict_embeddings(self, data, params): + from mlflow.openai.api_request_parallel_processor import process_api_requests + + _validate_model_params(self.task, self.model, params) + max_retries = params.pop("max_retries", self.api_config.max_retries) + timeout = params.pop("timeout", self.api_config.timeout) + batch_size = params.pop("batch_size", self.api_config.batch_size) + _logger.debug(f"Requests are being batched by {batch_size} samples.") + + first_string_column = _first_string_column(data) + texts = data[first_string_column].tolist() + + client = self.get_client(max_retries=max_retries, timeout=timeout) + + requests = [ + partial( + client.embeddings.create, + input=texts[i : i + batch_size], + model=self.model["model"], + **params, + ) + for i in range(0, len(texts), batch_size) + ] + + results = process_api_requests(request_tasks=requests) + + return [row.embedding for batch in results for row in batch.data] + + def predict(self, data, params: dict[str, Any] | None = None): + """ + Args: + data: Model input data. + params: Additional parameters to pass to the model for inference. + + Returns: + Model predictions. + """ + self.api_token.refresh() + if self.task == "chat.completions": + return self._predict_chat(data, params or {}) + elif self.task == "completions": + return self._predict_completions(data, params or {}) + elif self.task == "embeddings": + return self._predict_embeddings(data, params or {}) + + +def _load_pyfunc(path): + """Loads PyFunc implementation. Called by ``pyfunc.load_model``. + + Args: + path: Local filesystem path to the MLflow Model with the ``openai`` flavor. + """ + return _OpenAIWrapper(_load_model(path)) + + +def load_model(model_uri, dst_path=None): + """ + Load an OpenAI model from a local file or a run. + + Args: + model_uri: The location, in URI format, of the MLflow model. For example: + + - ``/Users/me/path/to/local/model`` + - ``relative/path/to/local/model`` + - ``s3://my_bucket/path/to/model`` + - ``runs://run-relative/path/to/model`` + + For more information about supported URI schemes, see + `Referencing Artifacts `_. + dst_path: The local filesystem path to which to download the model artifact. + This directory must already exist. If unspecified, a local output + path will be created. + + Returns: + A dictionary representing the OpenAI model. + """ + local_model_path = _download_artifact_from_uri(artifact_uri=model_uri, output_path=dst_path) + flavor_conf = _get_flavor_configuration(local_model_path, FLAVOR_NAME) + _add_code_from_conf_to_system_path(local_model_path, flavor_conf) + model_data_path = os.path.join(local_model_path, flavor_conf.get("data", MODEL_FILENAME)) + return _load_model(model_data_path) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/openai/utils/__pycache__/chat_schema.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/openai/utils/__pycache__/chat_schema.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..48f5dbb0864bf8faf9f55c67be6e8d4abeca707f Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/openai/utils/__pycache__/chat_schema.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/openai/utils/chat_schema.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/openai/utils/chat_schema.py new file mode 100644 index 0000000000000000000000000000000000000000..63527f8a2715bdc3cd20b8099a18d317a4d7a89a --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/openai/utils/chat_schema.py @@ -0,0 +1,160 @@ +import logging +from collections.abc import Iterable +from typing import Any + +from mlflow.entities.span import LiveSpan +from mlflow.exceptions import MlflowException +from mlflow.tracing import set_span_chat_tools +from mlflow.tracing.constant import SpanAttributeKey, TokenUsageKey +from mlflow.types.chat import ChatTool, FunctionToolDefinition + +_logger = logging.getLogger(__name__) + + +_RESPONSE_API_BUILT_IN_TOOLS = { + "file_search", + "computer_use_preview", + "web_search_preview", + "local_shell", + "mcp", + "code_interpreter", + "image_generation", +} + + +def set_span_chat_attributes(span: LiveSpan, inputs: dict[str, Any], output: Any): + # NB: This function is also used for setting chat attributes for ResponsesAgent tracing spans + # (TODO: Add doc link). Therefore, the core logic should still run without openai package. + if tools := _parse_tools(inputs): + try: + set_span_chat_tools(span, tools) + except MlflowException: + _logger.debug("Failed to set chat tools on span", exc_info=True) + + # Extract and set usage information if available + if usage := _parse_usage(output): + span.set_attribute(SpanAttributeKey.CHAT_USAGE, usage) + + +def _extract_tool_call_ids(output: Any) -> list[str]: + tool_call_ids = [] + try: + from openai.types.chat import ChatCompletion + + if isinstance(output, ChatCompletion): + message = output.choices[0].message + if tool_calls := getattr(message, "tool_calls", None): + for tool_call in tool_calls: + tool_call_ids.append(tool_call.id) + except ImportError: + pass + + if _is_responses_output(output): + for output_item in output.output: + if call_id := getattr(output_item, "call_id", None): + tool_call_ids.append(call_id) + + return tool_call_ids + + +def _is_responses_output(output: Any) -> bool: + """ + Check whether the output is OpenAI Responses API output, or + a response from the MLflow ResponsesAgent instance. + """ + try: + from openai.types.responses import Response + + if isinstance(output, Response): + return True + except ImportError: + pass + + try: + from mlflow.types.responses import ResponsesAgentResponse + + if ResponsesAgentResponse.validate_compat(output): + return True + except Exception: + pass + + return False + + +def _parse_tools(inputs: dict[str, Any]) -> list[ChatTool]: + tools = inputs.get("tools", []) + + if tools is None or not isinstance(tools, Iterable): + return [] + + parsed_tools = [] + for tool in tools: + tool_type = tool.get("type", "function") + if tool_type == "function": + if "function" in tool: + # ChatCompletion API style + parsed_tools.append(ChatTool(**tool)) + else: + # Responses API style + definition = {k: v for k, v in tool.items() if k != "type"} + parsed_tools.append( + ChatTool( + type="function", + function=FunctionToolDefinition(**definition), + ) + ) + elif tool_type in _RESPONSE_API_BUILT_IN_TOOLS: + parsed_tools.append( + ChatTool( + type="function", + function=FunctionToolDefinition( + name=tool_type, + ), + ) + ) + else: + raise MlflowException(f"Unknown tool type: {tool_type}") + + return parsed_tools + + +def _parse_usage(output: Any) -> dict[str, Any] | None: + """ + Parse token usage information from OpenAI response objects. + + Args: + output: The response object from OpenAI API calls + + Returns: + A dictionary containing token usage information. + """ + if output is None: + return None + + # Handle OpenAI ChatCompletion API response + try: + from openai.types.chat import ChatCompletion + + if isinstance(output, ChatCompletion) and (usage := output.usage): + return { + TokenUsageKey.INPUT_TOKENS: usage.prompt_tokens, + TokenUsageKey.OUTPUT_TOKENS: usage.completion_tokens, + TokenUsageKey.TOTAL_TOKENS: usage.total_tokens, + } + except ImportError: + pass + + # Handle OpenAI Responses API response + try: + from openai.types.responses import Response + + if isinstance(output, Response) and (usage := output.usage): + return { + TokenUsageKey.INPUT_TOKENS: usage.input_tokens, + TokenUsageKey.OUTPUT_TOKENS: usage.output_tokens, + TokenUsageKey.TOTAL_TOKENS: usage.total_tokens, + } + except ImportError: + pass + + return None diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/optuna/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/optuna/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..31d13c2b5136cfafbf8777cd09e7e903c6fcadc8 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/optuna/__init__.py @@ -0,0 +1,3 @@ +from mlflow.optuna.storage import MlflowStorage + +__all__ = ["MlflowStorage"] diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/optuna/__pycache__/__init__.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/optuna/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..c5a6c9636b0c7441ce8326f61c186177d50d6e32 Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/optuna/__pycache__/__init__.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/optuna/__pycache__/storage.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/optuna/__pycache__/storage.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..1b344bb89938058549e141280037bda3f638f068 Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/optuna/__pycache__/storage.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/optuna/storage.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/optuna/storage.py new file mode 100644 index 0000000000000000000000000000000000000000..6034af312a9ac4c8bfc3d348bb7416aeedd0ec2f --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/optuna/storage.py @@ -0,0 +1,646 @@ +import copy +import datetime +import json +import threading +import time +import uuid +from collections.abc import Container, Sequence +from typing import Any + +from mlflow import MlflowClient +from mlflow.entities import Metric, Param, RunTag +from mlflow.utils.mlflow_tags import MLFLOW_PARENT_RUN_ID + +try: + from optuna._typing import JSONSerializable + from optuna.distributions import ( + BaseDistribution, + check_distribution_compatibility, + distribution_to_json, + json_to_distribution, + ) + from optuna.storages import BaseStorage + from optuna.storages._base import DEFAULT_STUDY_NAME_PREFIX + from optuna.study import StudyDirection + from optuna.study._frozen import FrozenStudy + from optuna.trial import FrozenTrial, TrialState +except ImportError as e: + raise ImportError("Install optuna to use `mlflow.optuna` module") from e + +optuna_mlflow_status_map = { + TrialState.RUNNING: "RUNNING", + TrialState.COMPLETE: "FINISHED", + TrialState.PRUNED: "KILLED", + TrialState.FAIL: "FAILED", + TrialState.WAITING: "SCHEDULED", +} + +mlflow_optuna_status_map = { + "RUNNING": TrialState.RUNNING, + "FINISHED": TrialState.COMPLETE, + "KILLED": TrialState.PRUNED, + "FAILED": TrialState.FAIL, + "SCHEDULED": TrialState.WAITING, +} + + +class MlflowStorage(BaseStorage): + """ + MLflow based storage class with batch processing to avoid REST API throttling. + """ + + def __init__( + self, + experiment_id: str, + name: str | None = None, + batch_flush_interval: float = 1.0, + batch_size_threshold: int = 100, + ): + """ + Initialize MLFlowStorage with batching capabilities. + + Parameters + ---------- + experiment_id : str + MLflow experiment ID + name : Optional[str] + Optional name for the storage + batch_flush_interval : float + Time in seconds between automatic batch flushes (default: 1.0) + batch_size_threshold : int + Maximum number of items in batch before triggering a flush (default: 100) + """ + if not experiment_id: + raise Exception("No experiment_id provided. MLFlowStorage cannot create experiments.") + + self._experiment_id = experiment_id + self._mlflow_client = MlflowClient() + self._name = name + + # Batching configuration + self._batch_flush_interval = batch_flush_interval + self._batch_size_threshold = batch_size_threshold + + # Batching queues for metrics, parameters, and tags + self._batch_queue = {} # Dictionary of run_id -> {'metrics': [], 'params': [], 'tags': []} + self._batch_lock = threading.RLock() + self._last_flush_time = time.time() + + # Flag to indicate if the worker should stop - must be defined BEFORE starting the thread + self._stop_worker = False + + # Start a background thread for periodic flushing + self._flush_thread = threading.Thread( + target=self._periodic_flush_worker, + daemon=True, + name=f"mlflow_optuna_batch_flush_worker_{uuid.uuid4().hex[:8]}", + ) + self._flush_thread.start() + + def __getstate__(self): + """ + Prepare the object for serialization by removing non-picklable components. + This is called when the object is being pickled. + """ + state = self.__dict__.copy() + + # Remove thread-related attributes that can't be pickled + state.pop("_batch_lock", None) + state.pop("_flush_thread", None) + + # Store the configuration but not the actual lock/thread + state["_thread_running"] = hasattr(self, "_flush_thread") and self._flush_thread.is_alive() + + return state + + def __setstate__(self, state): + """ + Restore the object after deserialization by recreating non-picklable components. + This is called when the object is being unpickled. + """ + # First, update the instance with the pickled state + self.__dict__.update(state) + + # Recreate the lock + self._batch_lock = threading.RLock() + + # Don't automatically restart the thread on workers - this would create too many threads + # Instead, we'll use a manual flush approach in distributed contexts + self._flush_thread = None + + # If we're on a worker node, we should disable automatic background flushing + # because it could cause issues with multiple threads trying to write to MLflow + self._stop_worker = True + + def __del__(self): + """Ensure all queued data is flushed before destroying the object.""" + # Set the stop flag + if hasattr(self, "_stop_worker"): + self._stop_worker = True + + # Join the thread if it exists and is alive + if hasattr(self, "_flush_thread") and self._flush_thread.is_alive(): + try: + self._flush_thread.join(timeout=5.0) + except Exception: + pass # Ignore errors during cleanup + + # Flush any remaining data + if hasattr(self, "_batch_queue"): + try: + self.flush_all_batches() + except Exception: + pass # Ignore errors during cleanup + + def _periodic_flush_worker(self): + """Background worker that periodically flushes batched data.""" + while not self._stop_worker: + try: + time.sleep(min(0.1, self._batch_flush_interval / 10)) # Sleep in small increments + + # Check if it's time to flush + current_time = time.time() + if current_time - self._last_flush_time >= self._batch_flush_interval: + self.flush_all_batches() + self._last_flush_time = current_time + except Exception: + # Catch any exceptions to prevent thread crashes + time.sleep(1.0) # Sleep a bit longer if there was an error + + def _queue_batch_operation( + self, + run_id: str, + metrics: list[Metric] | None = None, + params: list[Param] | None = None, + tags: list[RunTag] | None = None, + ): + """Queue metrics, parameters, or tags for batched processing.""" + with self._batch_lock: + if run_id not in self._batch_queue: + self._batch_queue[run_id] = {"metrics": [], "params": [], "tags": []} + + batch = self._batch_queue[run_id] + + if metrics: + batch["metrics"].extend(metrics) + if params: + batch["params"].extend(params) + if tags: + batch["tags"].extend(tags) + + # Check if we've reached the batch size threshold for this run + batch_size = len(batch["metrics"]) + len(batch["params"]) + len(batch["tags"]) + if batch_size >= self._batch_size_threshold: + self._flush_batch(run_id) + + def _flush_batch(self, run_id: str): + """Flush the batch for a specific run_id to MLflow.""" + with self._batch_lock: + if run_id not in self._batch_queue: + return + + batch = self._batch_queue[run_id] + + # Only make the API call if there's something to flush + if batch["metrics"] or batch["params"] or batch["tags"]: + try: + self._mlflow_client.log_batch( + run_id, metrics=batch["metrics"], params=batch["params"], tags=batch["tags"] + ) + except Exception as e: + # If the run doesn't exist, propagate the error + if "Run with id=" in str(e) and "not found" in str(e): + raise + # Otherwise, handle or log the error as needed + + # Clear the batch + batch["metrics"] = [] + batch["params"] = [] + batch["tags"] = [] + + def flush_all_batches(self): + """Flush all pending batches to MLflow.""" + with self._batch_lock: + run_ids = list(self._batch_queue.keys()) + + # Flush each run's batch + for run_id in run_ids: + self._flush_batch(run_id) + + def _search_run_by_name(self, run_name: str): + filter_string = f"tags.mlflow.runName = '{run_name}'" + return self._mlflow_client.search_runs( + experiment_ids=[self._experiment_id], filter_string=filter_string + ) + + def create_new_study( + self, directions: Sequence[StudyDirection], study_name: str | None = None + ) -> int: + """Create a new study as a mlflow run.""" + study_name = study_name or DEFAULT_STUDY_NAME_PREFIX + str(uuid.uuid4()) + tags = { + "mlflow.runName": study_name, + "optuna.study_direction": ",".join(direction.name for direction in directions), + } + study_run = self._mlflow_client.create_run(experiment_id=self._experiment_id, tags=tags) + return study_run.info.run_id + + def delete_study(self, study_id) -> None: + """Delete a study.""" + # Ensure any pending changes are saved before deletion + self._flush_batch(study_id) + self._mlflow_client.delete_run(study_id) + + def set_study_user_attr(self, study_id, key: str, value: JSONSerializable) -> None: + """Register a user-defined attribute as mlflow run tags to a study run.""" + # Verify the run exists first to fail fast if it doesn't + self._mlflow_client.get_run(study_id) + + # Queue the tag if the run exists + self._queue_batch_operation(study_id, tags=[RunTag(f"user_{key}", json.dumps(value))]) + + def set_study_system_attr(self, study_id, key: str, value: JSONSerializable) -> None: + """Register a optuna-internal attribute as mlflow run tags to a study run.""" + # Verify the run exists first to fail fast if it doesn't + self._mlflow_client.get_run(study_id) + + # Queue the tag if the run exists + self._queue_batch_operation(study_id, tags=[RunTag(f"sys_{key}", json.dumps(value))]) + + def get_study_id_from_name(self, study_name: str) -> int: + # Flush all batches to ensure we have the latest data + self.flush_all_batches() + + runs = self._search_run_by_name(study_name) + if len(runs): + return runs[0].info.run_id + else: + raise Exception(f"Study {study_name} not found") + + def get_study_name_from_id(self, study_id) -> str: + # Flush the batch for this study to ensure we have the latest data + self._flush_batch(study_id) + + run = self._mlflow_client.get_run(study_id) + return run.data.tags["mlflow.runName"] + + def get_study_directions(self, study_id) -> list[StudyDirection]: + # Flush the batch for this study to ensure we have the latest data + self._flush_batch(study_id) + + run = self._mlflow_client.get_run(study_id) + directions_str = run.data.tags["optuna.study_direction"] + return [StudyDirection[name] for name in directions_str.split(",")] + + def get_study_user_attrs(self, study_id) -> dict[str, Any]: + # Flush the batch for this study to ensure we have the latest data + self._flush_batch(study_id) + + run = self._mlflow_client.get_run(study_id) + user_attrs = {} + for key, value in run.data.tags.items(): + if key.startswith("user_"): + user_attrs[key[5:]] = json.loads(value) + return user_attrs + + def get_study_system_attrs(self, study_id) -> dict[str, Any]: + # Flush the batch for this study to ensure we have the latest data + self._flush_batch(study_id) + + run = self._mlflow_client.get_run(study_id) + system_attrs = {} + for key, value in run.data.tags.items(): + if key.startswith("sys_"): + system_attrs[key[4:]] = json.loads(value) + return system_attrs + + def get_all_studies(self) -> list[FrozenStudy]: + # Flush all batches to ensure we have the latest data + self.flush_all_batches() + + runs = self._mlflow_client.search_runs(experiment_ids=[self._experiment_id]) + studies = [] + for run in runs: + study_id = run.info.run_id + study_name = run.data.tags["mlflow.runName"] + directions_str = run.data.tags["optuna.study_direction"] + directions = [StudyDirection[name] for name in directions_str.split(",")] + studies.append( + FrozenStudy( + study_name=study_name, + direction=None, + directions=directions, + user_attrs=self.get_study_user_attrs(study_id), + system_attrs=self.get_study_system_attrs(study_id), + study_id=study_id, + ) + ) + return studies + + def create_new_trial(self, study_id, template_trial: FrozenTrial | None = None) -> int: + # Ensure study batch is flushed before creating a new trial + self._flush_batch(study_id) + + if template_trial: + frozen = copy.deepcopy(template_trial) + else: + frozen = FrozenTrial( + trial_id=-1, # dummy value. + number=-1, # dummy value. + state=TrialState.RUNNING, + params={}, + distributions={}, + user_attrs={}, + system_attrs={}, + value=None, + intermediate_values={}, + datetime_start=datetime.datetime.now(), + datetime_complete=None, + ) + + distribution_json = { + k: distribution_to_json(dist) for k, dist in frozen.distributions.items() + } + distribution_str = json.dumps(distribution_json) + tags = {"param_directions": distribution_str} + + trial_run = self._mlflow_client.create_run(experiment_id=self._experiment_id, tags=tags) + trial_id = trial_run.info.run_id + + # Add parent run ID tag + self._queue_batch_operation(trial_id, tags=[RunTag(MLFLOW_PARENT_RUN_ID, study_id)]) + + # Log trial_id metric to study + hash_id = float(hash(trial_id)) + self._queue_batch_operation( + study_id, metrics=[Metric("trial_id", hash_id, int(time.time() * 1000), 1)] + ) + + # Ensure study batch is flushed to get accurate metric history + self._flush_batch(study_id) + + trial_ids = self._mlflow_client.get_metric_history(study_id, "trial_id") + index = next((i for i, obj in enumerate(trial_ids) if obj.value == hash_id), -1) + + self._queue_batch_operation(trial_id, tags=[RunTag("numbers", str(index))]) + + # Set trial state + state = frozen.state + if state.is_finished(): + self._mlflow_client.set_terminated(trial_id, status=optuna_mlflow_status_map[state]) + else: + self._mlflow_client.update_run(trial_id, status=optuna_mlflow_status_map[state]) + + timestamp = int(time.time() * 1000) + metrics = [] + params = [] + tags = [] + + # Add metrics + if frozen.values is not None: + if len(frozen.values) > 1: + metrics.extend( + [ + Metric(f"value_{idx}", val, timestamp, 1) + for idx, val in enumerate(frozen.values) + ] + ) + else: + metrics.append(Metric("value", frozen.values[0], timestamp, 1)) + elif frozen.value is not None: + metrics.append(Metric("value", frozen.value, timestamp, 1)) + + # Add intermediate values + metrics.extend( + [ + Metric("intermediate_value", val, timestamp, int(k)) + for k, val in frozen.intermediate_values.items() + ] + ) + + # Add params + params.extend([Param(k, param) for k, param in frozen.params.items()]) + + # Add tags + tags.extend( + [RunTag(f"user_{key}", json.dumps(value)) for key, value in frozen.user_attrs.items()] + ) + tags.extend( + [RunTag(f"sys_{key}", json.dumps(value)) for key, value in frozen.system_attrs.items()] + ) + tags.extend( + [ + RunTag( + f"param_internal_val_{k}", + json.dumps(frozen.distributions[k].to_internal_repr(param)), + ) + for k, param in frozen.params.items() + ] + ) + + # Queue all the data to be sent in batches + self._queue_batch_operation(trial_id, metrics=metrics, params=params, tags=tags) + + return trial_id + + def set_trial_param( + self, + trial_id, + param_name: str, + param_value_internal: float, + distribution: BaseDistribution, + ) -> None: + # Flush the batch for this trial to ensure we have the latest data + self._flush_batch(trial_id) + + trial_run = self._mlflow_client.get_run(trial_id) + distributions_dict = json.loads(trial_run.data.tags["param_directions"]) + self.check_trial_is_updatable(trial_id, mlflow_optuna_status_map[trial_run.info.status]) + + if param_name in trial_run.data.params: + param_distribution = json_to_distribution(distributions_dict[param_name]) + check_distribution_compatibility(param_distribution, distribution) + + # Queue parameter update + self._queue_batch_operation( + trial_id, + params=[Param(param_name, distribution.to_external_repr(param_value_internal))], + tags=[RunTag(f"param_internal_val_{param_name}", json.dumps(param_value_internal))], + ) + + distributions_dict[param_name] = distribution_to_json(distribution) + self._queue_batch_operation( + trial_id, tags=[RunTag("param_directions", json.dumps(distributions_dict))] + ) + + def get_trial_id_from_study_id_trial_number(self, study_id, trial_number: int) -> int: + raise NotImplementedError("This method is not supported in MLflow backend.") + + def get_trial_number_from_id(self, trial_id) -> int: + # Flush the batch for this trial to ensure we have the latest data + self._flush_batch(trial_id) + + trial_run = self._mlflow_client.get_run(trial_id) + return int(trial_run.data.tags.get("numbers", 0)) + + def get_trial_param(self, trial_id, param_name: str) -> float: + # Flush the batch for this trial to ensure we have the latest data + self._flush_batch(trial_id) + + trial_run = self._mlflow_client.get_run(trial_id) + param_value = trial_run.data.tags[f"param_internal_val_{param_name}"] + + return float(json.loads(param_value)) + + def set_trial_state_values( + self, trial_id, state: TrialState, values: Sequence[float] | None = None + ) -> bool: + # Update trial state + if state.is_finished(): + self._mlflow_client.set_terminated(trial_id, status=optuna_mlflow_status_map[state]) + else: + self._mlflow_client.update_run(trial_id, status=optuna_mlflow_status_map[state]) + + # Queue value metrics if provided + timestamp = int(time.time() * 1000) + if values is not None: + metrics = [] + if len(values) > 1: + metrics = [ + Metric(f"value_{idx}", val, timestamp, 1) for idx, val in enumerate(values) + ] + else: + metrics = [Metric("value", values[0], timestamp, 1)] + + self._queue_batch_operation(trial_id, metrics=metrics) + + if state == TrialState.RUNNING and state != TrialState.WAITING: + return False + return True + + def set_trial_intermediate_value(self, trial_id, step: int, intermediate_value: float) -> None: + # Queue intermediate value metric + self._queue_batch_operation( + trial_id, + metrics=[ + Metric("intermediate_value", intermediate_value, int(time.time() * 1000), step) + ], + ) + + def set_trial_user_attr(self, trial_id, key: str, value: Any) -> None: + # Queue user attribute tag + self._queue_batch_operation(trial_id, tags=[RunTag(f"user_{key}", json.dumps(value))]) + + def set_trial_system_attr(self, trial_id, key: str, value: Any) -> None: + # Queue system attribute tag + self._queue_batch_operation(trial_id, tags=[RunTag(f"sys_{key}", json.dumps(value))]) + + def get_trial(self, trial_id) -> FrozenTrial: + # Flush the batch for this trial to ensure we have the latest data + self._flush_batch(trial_id) + + trial_run = self._mlflow_client.get_run(trial_id) + param_directions = trial_run.data.tags["param_directions"] + try: + distributions_dict = json.loads(param_directions) + except json.decoder.JSONDecodeError as e: + raise ValueError(f"error with param_directions = {param_directions!r}") from e + + distributions = { + k: json_to_distribution(distribution) for k, distribution in distributions_dict.items() + } + params = {} + for key, value in trial_run.data.tags.items(): + if key.startswith("param_internal_val_"): + param_name = key[19:] + param_value = json.loads(value) + params[param_name] = distributions[param_name].to_external_repr(float(param_value)) + + metrics = trial_run.data.metrics + values = None + if "value" in metrics: + values = [metrics["value"]] + if "value_0" in metrics: + values = [metrics[f"value_{idx}"] for idx in range(len(metrics))] + + run_number = int(trial_run.data.tags.get("numbers", 0)) + + start_time = datetime.datetime.fromtimestamp(trial_run.info.start_time / 1000) + if trial_run.info.end_time: + end_time = datetime.datetime.fromtimestamp(trial_run.info.end_time / 1000) + else: + end_time = None + return FrozenTrial( + trial_id=trial_id, + number=run_number, + state=mlflow_optuna_status_map[trial_run.info.status], + value=None, + values=values, + datetime_start=start_time, + datetime_complete=end_time, + params=params, + distributions=distributions, + user_attrs=self.get_trial_user_attrs(trial_id), + system_attrs=self.get_trial_system_attrs(trial_id), + intermediate_values={ + v.step: v.value + for idx, v in enumerate( + self._mlflow_client.get_metric_history(trial_id, "intermediate_value") + ) + }, + ) + + def get_trial_user_attrs(self, trial_id) -> dict[str, Any]: + # Flush the batch for this trial to ensure we have the latest data + self._flush_batch(trial_id) + + run = self._mlflow_client.get_run(trial_id) + user_attrs = {} + for key, value in run.data.tags.items(): + if key.startswith("user_"): + user_attrs[key[5:]] = json.loads(value) + return user_attrs + + def get_trial_system_attrs(self, trial_id) -> dict[str, Any]: + # Flush the batch for this trial to ensure we have the latest data + self._flush_batch(trial_id) + + run = self._mlflow_client.get_run(trial_id) + system_attrs = {} + for key, value in run.data.tags.items(): + if key.startswith("sys_"): + system_attrs[key[4:]] = json.loads(value) + return system_attrs + + def get_all_trials( + self, + study_id, + deepcopy: bool = True, + states: Container[TrialState] | None = None, + ) -> list[FrozenTrial]: + # Flush all batches to ensure we have the latest data + self.flush_all_batches() + + runs = self._mlflow_client.search_runs( + experiment_ids=[self._experiment_id], + filter_string=f"tags.mlflow.parentRunId='{study_id}'", + ) + trials = [] + for run in runs: + trials.append(self.get_trial(run.info.run_id)) + + frozen_trials: list[FrozenTrial] = [] + for trial in trials: + if states is None or trial.state in states: + frozen_trials.append(trial) + return frozen_trials + + def get_n_trials(self, study_id, states=None) -> int: + # Flush all batches to ensure we have the latest data + self.flush_all_batches() + + runs = self._mlflow_client.search_runs( + experiment_ids=[self._experiment_id], + filter_string=f"tags.mlflow.parentRunId='{study_id}'", + ) + return len(runs) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/paddle/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/paddle/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..030e4f3dc56ba0a0a1e2a6ded2b2807f008c57c5 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/paddle/__init__.py @@ -0,0 +1,603 @@ +""" +The ``mlflow.paddle`` module provides an API for logging and loading paddle models. +This module exports paddle models with the following flavors: + +Paddle (native) format + This is the main flavor that can be loaded back into paddle. + +:py:mod:`mlflow.pyfunc` + Produced for use by generic pyfunc-based deployment tools and batch inference. + NOTE: The `mlflow.pyfunc` flavor is only added for paddle models that define `predict()`, + since `predict()` is required for pyfunc model inference. +""" + +import logging +import os +from typing import Any + +import yaml + +import mlflow +from mlflow import pyfunc +from mlflow.models import Model, ModelInputExample, ModelSignature +from mlflow.models.model import MLMODEL_FILE_NAME +from mlflow.models.signature import _infer_signature_from_input_example +from mlflow.models.utils import _save_example +from mlflow.tracking._model_registry import DEFAULT_AWAIT_MAX_SLEEP_SECONDS +from mlflow.tracking.artifact_utils import _download_artifact_from_uri +from mlflow.utils.autologging_utils import autologging_integration, safe_patch +from mlflow.utils.docstring_utils import LOG_MODEL_PARAM_DOCS, format_docstring +from mlflow.utils.environment import ( + _CONDA_ENV_FILE_NAME, + _CONSTRAINTS_FILE_NAME, + _PYTHON_ENV_FILE_NAME, + _REQUIREMENTS_FILE_NAME, + _mlflow_conda_env, + _process_conda_env, + _process_pip_requirements, + _PythonEnv, + _validate_env_arguments, +) +from mlflow.utils.file_utils import write_to +from mlflow.utils.model_utils import ( + _add_code_from_conf_to_system_path, + _get_flavor_configuration, + _validate_and_copy_code_paths, + _validate_and_prepare_target_save_path, +) +from mlflow.utils.requirements_utils import _get_pinned_requirement + +FLAVOR_NAME = "paddle" + +_MODEL_DATA_SUBPATH = "model" + +_logger = logging.getLogger(__name__) + + +def get_default_pip_requirements(): + """ + Returns: + A list of default pip requirements for MLflow Models produced by this flavor. + Calls to :func:`save_model()` and :func:`log_model()` produce a pip environment + that, at minimum, contains these requirements. + """ + return [_get_pinned_requirement("paddlepaddle", module="paddle")] + + +def get_default_conda_env(): + """ + Returns: + The default Conda environment for MLflow Models produced by calls to + :func:`save_model()` and :func:`log_model()`. + """ + return _mlflow_conda_env(additional_pip_deps=get_default_pip_requirements()) + + +@format_docstring(LOG_MODEL_PARAM_DOCS.format(package_name=FLAVOR_NAME)) +def save_model( + pd_model, + path, + training=False, + conda_env=None, + code_paths=None, + mlflow_model=None, + signature: ModelSignature = None, + input_example: ModelInputExample = None, + pip_requirements=None, + extra_pip_requirements=None, + metadata=None, +): + """ + Save a paddle model to a path on the local file system. Produces an MLflow Model + containing the following flavors: + + - :py:mod:`mlflow.paddle` + - :py:mod:`mlflow.pyfunc`. NOTE: This flavor is only included for paddle models + that define `predict()`, since `predict()` is required for pyfunc model inference. + + Args: + pd_model: paddle model to be saved. + path: Local path where the model is to be saved. + training: Only valid when saving a model trained using the PaddlePaddle high level API. + If set to True, the saved model supports both re-training and + inference. If set to False, it only supports inference. + conda_env: {{ conda_env }} + code_paths: {{ code_paths }} + mlflow_model: :py:mod:`mlflow.models.Model` this flavor is being added to. + signature: {{ signature }} + input_example: {{ input_example }} + pip_requirements: {{ pip_requirements }} + extra_pip_requirements: {{ extra_pip_requirements }} + metadata: {{ metadata }} + + .. code-block:: python + :caption: Example + + import mlflow.paddle + import paddle + from paddle.nn import Linear + import paddle.nn.functional as F + import numpy as np + import os + import random + from sklearn.datasets import load_diabetes + from sklearn.model_selection import train_test_split + from sklearn import preprocessing + + + def load_data(): + # dataset on boston housing prediction + X, y = load_diabetes(return_X_y=True, as_frame=True) + min_max_scaler = preprocessing.MinMaxScaler() + X_min_max = min_max_scaler.fit_transform(X) + X_normalized = preprocessing.scale(X_min_max, with_std=False) + X_train, X_test, y_train, y_test = train_test_split( + X_normalized, y, test_size=0.2, random_state=42 + ) + y_train = y_train.reshape(-1, 1) + y_test = y_test.reshape(-1, 1) + return np.concatenate((X_train, y_train), axis=1), np.concatenate( + (X_test, y_test), axis=1 + ) + + + class Regressor(paddle.nn.Layer): + def __init__(self): + super().__init__() + self.fc = Linear(in_features=13, out_features=1) + + @paddle.jit.to_static + def forward(self, inputs): + x = self.fc(inputs) + return x + + + model = Regressor() + model.train() + training_data, test_data = load_data() + opt = paddle.optimizer.SGD(learning_rate=0.01, parameters=model.parameters()) + EPOCH_NUM = 10 + BATCH_SIZE = 10 + for epoch_id in range(EPOCH_NUM): + np.random.shuffle(training_data) + mini_batches = [ + training_data[k : k + BATCH_SIZE] + for k in range(0, len(training_data), BATCH_SIZE) + ] + for iter_id, mini_batch in enumerate(mini_batches): + x = np.array(mini_batch[:, :-1]).astype("float32") + y = np.array(mini_batch[:, -1:]).astype("float32") + house_features = paddle.to_tensor(x) + prices = paddle.to_tensor(y) + predicts = model(house_features) + loss = F.square_error_cost(predicts, label=prices) + avg_loss = paddle.mean(loss) + if iter_id % 20 == 0: + print(f"epoch: {epoch_id}, iter: {iter_id}, loss is: {avg_loss.numpy()}") + avg_loss.backward() + opt.step() + opt.clear_grad() + mlflow.log_param("learning_rate", 0.01) + mlflow.paddle.log_model(model, name="model") + sk_path_dir = "./test-out" + mlflow.paddle.save_model(model, sk_path_dir) + print("Model saved in run %s" % mlflow.active_run().info.run_id) + """ + import paddle + + _validate_env_arguments(conda_env, pip_requirements, extra_pip_requirements) + + _validate_and_prepare_target_save_path(path) + code_dir_subpath = _validate_and_copy_code_paths(code_paths, path) + + if mlflow_model is None: + mlflow_model = Model() + saved_example = _save_example(mlflow_model, input_example, path) + + if signature is None and saved_example is not None: + wrapped_model = _PaddleWrapper(pd_model) + signature = _infer_signature_from_input_example(saved_example, wrapped_model) + elif signature is False: + signature = None + + if signature is not None: + mlflow_model.signature = signature + if metadata is not None: + mlflow_model.metadata = metadata + + model_data_subpath = _MODEL_DATA_SUBPATH + output_path = os.path.join(path, model_data_subpath) + + if isinstance(pd_model, paddle.Model): + pd_model.save(output_path, training=training) + else: + paddle.jit.save(pd_model, output_path) + + # `PyFuncModel` only works for paddle models that define `predict()`. + pyfunc.add_to_model( + mlflow_model, + loader_module="mlflow.paddle", + model_path=model_data_subpath, + conda_env=_CONDA_ENV_FILE_NAME, + python_env=_PYTHON_ENV_FILE_NAME, + code=code_dir_subpath, + ) + mlflow_model.add_flavor( + FLAVOR_NAME, + pickled_model=model_data_subpath, + paddle_version=paddle.__version__, + code=code_dir_subpath, + ) + mlflow_model.save(os.path.join(path, MLMODEL_FILE_NAME)) + + if conda_env is None: + if pip_requirements is None: + default_reqs = get_default_pip_requirements() + # To ensure `_load_pyfunc` can successfully load the model during the dependency + # inference, `mlflow_model.save` must be called beforehand to save an MLmodel file. + inferred_reqs = mlflow.models.infer_pip_requirements( + path, + FLAVOR_NAME, + fallback=default_reqs, + ) + default_reqs = sorted(set(inferred_reqs).union(default_reqs)) + else: + default_reqs = None + conda_env, pip_requirements, pip_constraints = _process_pip_requirements( + default_reqs, + pip_requirements, + extra_pip_requirements, + ) + else: + conda_env, pip_requirements, pip_constraints = _process_conda_env(conda_env) + + with open(os.path.join(path, _CONDA_ENV_FILE_NAME), "w") as f: + yaml.safe_dump(conda_env, stream=f, default_flow_style=False) + + # Save `constraints.txt` if necessary + if pip_constraints: + write_to(os.path.join(path, _CONSTRAINTS_FILE_NAME), "\n".join(pip_constraints)) + + # Save `requirements.txt` + write_to(os.path.join(path, _REQUIREMENTS_FILE_NAME), "\n".join(pip_requirements)) + + _PythonEnv.current().to_yaml(os.path.join(path, _PYTHON_ENV_FILE_NAME)) + + +def load_model(model_uri, model=None, dst_path=None, **kwargs): + """ + Load a paddle model from a local file or a run. + + Args: + model_uri: The location, in URI format, of the MLflow model, for example: + - ``/Users/me/path/to/local/model`` + - ``relative/path/to/local/model`` + - ``s3://my_bucket/path/to/model`` + - ``runs://run-relative/path/to/model`` + - ``models://`` + - ``models://`` + model: Required when loading a `paddle.Model` model saved with `training=True`. + dst_path: The local filesystem path to which to download the model artifact. + This directory must already exist. If unspecified, a local output + path will be created. + kwargs: The keyword arguments to pass to `paddle.jit.load` + or `model.load`. + + For more information about supported URI schemes, see + `Referencing Artifacts `_. + + Returns: + A paddle model. + + .. code-block:: python + :caption: Example + + import mlflow.paddle + + pd_model = mlflow.paddle.load_model("runs:/96771d893a5e46159d9f3b49bf9013e2/pd_models") + # use Pandas DataFrame to make predictions + np_array = ... + predictions = pd_model(np_array) + """ + import paddle + + local_model_path = _download_artifact_from_uri(artifact_uri=model_uri, output_path=dst_path) + flavor_conf = _get_flavor_configuration(model_path=local_model_path, flavor_name=FLAVOR_NAME) + _add_code_from_conf_to_system_path(local_model_path, flavor_conf) + pd_model_artifacts_path = os.path.join(local_model_path, flavor_conf["pickled_model"]) + if model is None: + return paddle.jit.load(pd_model_artifacts_path, **kwargs) + elif not isinstance(model, paddle.Model): + raise TypeError(f"Invalid object type `{type(model)}` for `model`, must be `paddle.Model`") + else: + contains_pdparams = _contains_pdparams(local_model_path) + if not contains_pdparams: + raise TypeError( + "This model can't be loaded via `model.load` because a '.pdparams' file " + "doesn't exist. Please leave `model` unspecified to load the model via " + "`paddle.jit.load` or set `training` to True when saving a model." + ) + + model.load(pd_model_artifacts_path, **kwargs) + return model + + +@format_docstring(LOG_MODEL_PARAM_DOCS.format(package_name=FLAVOR_NAME)) +def log_model( + pd_model, + artifact_path: str | None = None, + training=False, + conda_env=None, + code_paths=None, + registered_model_name=None, + signature: ModelSignature = None, + input_example: ModelInputExample = None, + await_registration_for=DEFAULT_AWAIT_MAX_SLEEP_SECONDS, + pip_requirements=None, + extra_pip_requirements=None, + metadata=None, + name: str | None = None, + params: dict[str, Any] | None = None, + tags: dict[str, Any] | None = None, + model_type: str | None = None, + step: int = 0, + model_id: str | None = None, +): + """ + Log a paddle model as an MLflow artifact for the current run. Produces an MLflow Model + containing the following flavors: + + - :py:mod:`mlflow.paddle` + - :py:mod:`mlflow.pyfunc`. NOTE: This flavor is only included for paddle models + that define `predict()`, since `predict()` is required for pyfunc model inference. + + Args: + pd_model: paddle model to be saved. + artifact_path: Deprecated. Use `name` instead. + training: Only valid when saving a model trained using the PaddlePaddle high level API. + If set to True, the saved model supports both re-training and + inference. If set to False, it only supports inference. + conda_env: {{ conda_env }} + code_paths: {{ code_paths }} + registered_model_name: If given, create a model version under + ``registered_model_name``, also creating a registered model if one + with the given name does not exist. + signature: {{ signature }} + input_example: {{ input_example }} + await_registration_for: Number of seconds to wait for the model version to finish + being created and is in ``READY`` status. By default, the function + waits for five minutes. Specify 0 or None to skip waiting. + pip_requirements: {{ pip_requirements }} + extra_pip_requirements: {{ extra_pip_requirements }} + metadata: {{ metadata }} + name: {{ name }} + params: {{ params }} + tags: {{ tags }} + model_type: {{ model_type }} + step: {{ step }} + model_id: {{ model_id }} + + Returns: + A :py:class:`ModelInfo ` instance that contains the + metadata of the logged model. + + .. code-block:: python + :caption: Example + + import mlflow.paddle + + + def load_data(): ... + + + class Regressor: ... + + + model = Regressor() + model.train() + training_data, test_data = load_data() + opt = paddle.optimizer.SGD(learning_rate=0.01, parameters=model.parameters()) + EPOCH_NUM = 10 + BATCH_SIZE = 10 + for epoch_id in range(EPOCH_NUM): + ... + mlflow.log_param("learning_rate", 0.01) + mlflow.paddle.log_model(model, name="model") + sk_path_dir = ... + mlflow.paddle.save_model(model, sk_path_dir) + """ + return Model.log( + artifact_path=artifact_path, + name=name, + flavor=mlflow.paddle, + pd_model=pd_model, + conda_env=conda_env, + code_paths=code_paths, + registered_model_name=registered_model_name, + signature=signature, + input_example=input_example, + await_registration_for=await_registration_for, + training=training, + pip_requirements=pip_requirements, + extra_pip_requirements=extra_pip_requirements, + metadata=metadata, + params=params, + tags=tags, + model_type=model_type, + step=step, + model_id=model_id, + ) + + +def _load_pyfunc(path): + """ + Loads PyFunc implementation. Called by ``pyfunc.load_model``. + + Args: + path: Local filesystem path to the MLflow Model with the ``paddle`` flavor. + """ + return _PaddleWrapper(load_model(path)) + + +class _PaddleWrapper: + """ + Wrapper class that creates a predict function such that + predict(data: pd.DataFrame) -> model's output as pd.DataFrame (pandas DataFrame) + """ + + def __init__(self, pd_model): + self.pd_model = pd_model + + def get_raw_model(self): + """ + Returns the underlying model. + """ + return self.pd_model + + def predict( + self, + data, + params: dict[str, Any] | None = None, + ): + """ + Args: + data: Model input data. + params: Additional parameters to pass to the model for inference. + + Returns: + Model predictions. + """ + import numpy as np + import paddle + import pandas as pd + + if isinstance(data, pd.DataFrame): + inp_data = data.values.astype(np.float32) + elif isinstance(data, np.ndarray): + inp_data = data + elif isinstance(data, (list, dict)): + raise TypeError( + "The paddle flavor does not support List or Dict input types. " + "Please use a pandas.DataFrame or a numpy.ndarray" + ) + else: + raise TypeError("Input data should be pandas.DataFrame or numpy.ndarray") + inp_data = np.squeeze(inp_data) + + self.pd_model.eval() + + predicted = self.pd_model(paddle.to_tensor(inp_data)) + return pd.DataFrame(predicted.numpy()) + + +def _contains_pdparams(path): + file_list = os.listdir(path) + return any(".pdparams" in file for file in file_list) + + +@autologging_integration(FLAVOR_NAME) +def autolog( + log_every_n_epoch=1, + log_models=True, + disable=False, + exclusive=False, + silent=False, + registered_model_name=None, + extra_tags=None, +): + """ + Enables (or disables) and configures autologging from PaddlePaddle to MLflow. + + Autologging is performed when the `fit` method of `paddle.Model`_ is called. + + .. _paddle.Model: + https://www.paddlepaddle.org.cn/documentation/docs/en/api/paddle/Model_en.html + + Args: + log_every_n_epoch: If specified, logs metrics once every `n` epochs. By default, metrics + are logged after every epoch. + log_models: If ``True``, trained models are logged as MLflow model artifacts. + If ``False``, trained models are not logged. + disable: If ``True``, disables the PaddlePaddle autologging integration. + If ``False``, enables the PaddlePaddle autologging integration. + exclusive: If ``True``, autologged content is not logged to user-created fluent runs. + If ``False``, autologged content is logged to the active fluent run, + which may be user-created. + silent: If ``True``, suppress all event logs and warnings from MLflow during PyTorch + Lightning autologging. If ``False``, show all events and warnings during + PaddlePaddle autologging. + registered_model_name: If given, each time a model is trained, it is registered as a + new model version of the registered model with this name. + The registered model is created if it does not already exist. + extra_tags: A dictionary of extra tags to set on each managed run created by autologging. + + .. code-block:: python + :caption: Example + + import paddle + import mlflow + from mlflow import MlflowClient + + + def show_run_data(run_id): + run = mlflow.get_run(run_id) + print(f"params: {run.data.params}") + print(f"metrics: {run.data.metrics}") + client = MlflowClient() + artifacts = [f.path for f in client.list_artifacts(run.info.run_id, "model")] + print(f"artifacts: {artifacts}") + + + class LinearRegression(paddle.nn.Layer): + def __init__(self): + super().__init__() + self.fc = paddle.nn.Linear(13, 1) + + def forward(self, feature): + return self.fc(feature) + + + train_dataset = paddle.text.datasets.UCIHousing(mode="train") + eval_dataset = paddle.text.datasets.UCIHousing(mode="test") + model = paddle.Model(LinearRegression()) + optim = paddle.optimizer.SGD(learning_rate=1e-2, parameters=model.parameters()) + model.prepare(optim, paddle.nn.MSELoss(), paddle.metric.Accuracy()) + mlflow.paddle.autolog() + with mlflow.start_run() as run: + model.fit(train_dataset, eval_dataset, batch_size=16, epochs=10) + show_run_data(run.info.run_id) + + .. code-block:: text + :caption: Output + + params: { + "learning_rate": "0.01", + "optimizer_name": "SGD", + } + metrics: { + "loss": 17.482044, + "step": 25.0, + "acc": 0.0, + "eval_step": 6.0, + "eval_acc": 0.0, + "eval_batch_size": 6.0, + "batch_size": 4.0, + "eval_loss": 24.717455, + } + artifacts: [ + "model/MLmodel", + "model/conda.yaml", + "model/model.pdiparams", + "model/model.pdiparams.info", + "model/model.pdmodel", + "model/requirements.txt", + ] + """ + import paddle + + from mlflow.paddle._paddle_autolog import patched_fit + + safe_patch( + FLAVOR_NAME, paddle.Model, "fit", patched_fit, manage_run=True, extra_tags=extra_tags + ) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/paddle/__pycache__/__init__.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/paddle/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..5bd274dca3db468122b6e89f2864b7eafc0737cf Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/paddle/__pycache__/__init__.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/paddle/__pycache__/_paddle_autolog.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/paddle/__pycache__/_paddle_autolog.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..abb56ab47970747aecf386d476108abdc2b38083 Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/paddle/__pycache__/_paddle_autolog.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/paddle/_paddle_autolog.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/paddle/_paddle_autolog.py new file mode 100644 index 0000000000000000000000000000000000000000..d39a52d3422e0a153eba3e457f78e1ac52c75524 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/paddle/_paddle_autolog.py @@ -0,0 +1,141 @@ +import paddle + +import mlflow +from mlflow.tracking.fluent import _initialize_logged_model +from mlflow.utils.autologging_utils import ( + BatchMetricsLogger, + ExceptionSafeAbstractClass, + MlflowAutologgingQueueingClient, + get_autologging_config, +) + + +class __MlflowPaddleCallback(paddle.callbacks.Callback, metaclass=ExceptionSafeAbstractClass): + """Callback for auto-logging metrics and parameters.""" + + def __init__(self, client, metrics_logger, run_id, log_models, log_every_n_epoch): + super().__init__() + self.early_stopping = False + self.client = client + self.metrics_logger = metrics_logger + self.run_id = run_id + self.log_models = log_models + self.log_every_n_epoch = log_every_n_epoch + self.epoch = 0 + + def _log_metrics(self, logs, current_epoch): + metrics = { + key: (metric[0] if isinstance(metric, list) else metric) for key, metric in logs.items() + } + self.metrics_logger.record_metrics(metrics, current_epoch) + + def on_epoch_end(self, epoch, logs=None): + if self.model is not None and epoch % self.log_every_n_epoch == 0: + self._log_metrics(logs, epoch) + self.epoch = epoch + + def on_train_begin(self, logs=None): + params = { + "optimizer_name": self.model._optimizer.__class__.__name__, + "learning_rate": self.model._optimizer._learning_rate, + } + self.client.log_params(self.run_id, params) + self.client.flush(synchronous=True) + + def on_train_end(self, logs=None): + self.metrics_logger.flush() + self.client.flush(synchronous=True) + + def on_eval_end(self, logs=None): + eval_logs = { + "eval_" + key: (metric[0] if isinstance(metric, list) else metric) + for key, metric in logs.items() + } + self._log_metrics(eval_logs, self.epoch) + + +def _log_early_stop_params(early_stop_callback, client, run_id): + """ + Logs early stopping configuration parameters to MLflow. + + Args: + early_stop_callback: The early stopping callback instance used during training. + client: An `MlflowAutologgingQueueingClient` instance used for MLflow logging. + run_id: The ID of the MLflow Run to which to log configuration parameters. + """ + client.log_params( + run_id, + { + p: getattr(early_stop_callback, p) + for p in ["monitor", "patience", "min_delta", "baseline"] + if hasattr(early_stop_callback, p) + }, + ) + + +def _log_early_stop_metrics(early_stop_callback, client, run_id, model_id=None): + """ + Logs early stopping behavior results (e.g. stopped epoch) as metrics to MLflow. + + Args: + early_stop_callback: The early stopping callback instance used during training. + client: An `MlflowAutologgingQueueingClient` instance used for MLflow logging. + run_id: The ID of the MLflow Run to which to log configuration parameters. + model_id: The ID of the model metrics will be associated with. + """ + if early_stop_callback.stopped_epoch == 0: + return + + metrics = { + "stopped_epoch": early_stop_callback.stopped_epoch, + "best_value": early_stop_callback.best_value, + } + client.log_metrics(run_id, metrics, model_id=model_id) + + +def patched_fit(original, self, *args, **kwargs): + run_id = mlflow.active_run().info.run_id + tracking_uri = mlflow.get_tracking_uri() + client = MlflowAutologgingQueueingClient(tracking_uri) + log_models = get_autologging_config(mlflow.paddle.FLAVOR_NAME, "log_models", True) + log_every_n_epoch = get_autologging_config(mlflow.paddle.FLAVOR_NAME, "log_every_n_epoch", 1) + + model_id = None + if log_models: + model_id = _initialize_logged_model("model", flavor=mlflow.paddle.FLAVOR_NAME).model_id + metrics_logger = BatchMetricsLogger(run_id, tracking_uri, model_id=model_id) + + early_stop_callback = None + mlflow_callback = __MlflowPaddleCallback( + client, metrics_logger, run_id, log_models, log_every_n_epoch + ) + if "callbacks" in kwargs: + callbacks = kwargs["callbacks"] + for callback in callbacks: + if isinstance(callback, paddle.callbacks.EarlyStopping): + early_stop_callback = callback + _log_early_stop_params(early_stop_callback, client, run_id) + break + kwargs["callbacks"].append(mlflow_callback) + else: + kwargs["callbacks"] = [mlflow_callback] + client.flush(synchronous=False) + + result = original(self, *args, **kwargs) + + if early_stop_callback is not None: + _log_early_stop_metrics(early_stop_callback, client, run_id, model_id=model_id) + + mlflow.log_text(str(self.summary()), "model_summary.txt") + + if log_models: + registered_model_name = get_autologging_config( + mlflow.paddle.FLAVOR_NAME, "registered_model_name", None + ) + mlflow.paddle.log_model( + self, name="model", registered_model_name=registered_model_name, model_id=model_id + ) + + client.flush(synchronous=True) + + return result diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pmdarima/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pmdarima/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..c1e7f70bab38b6f9ce57681fdee269327f67739b --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pmdarima/__init__.py @@ -0,0 +1,617 @@ +""" +The ``mlflow.pmdarima`` module provides an API for logging and loading ``pmdarima`` models. +This module exports univariate ``pmdarima`` models in the following formats: + +Pmdarima format + Serialized instance of a ``pmdarima`` model using pickle. +:py:mod:`mlflow.pyfunc` + Produced for use by generic pyfunc-based deployment tools and for batch auditing + of historical forecasts. + + .. code-block:: python + :caption: Example + + import pandas as pd + import mlflow + import mlflow.pyfunc + import pmdarima + from pmdarima import auto_arima + + + # Define a custom model class + class PmdarimaWrapper(mlflow.pyfunc.PythonModel): + def load_context(self, context): + self.model = context.artifacts["model"] + + def predict(self, context, model_input): + return self.model.predict(n_periods=model_input.shape[0]) + + + # Specify locations of source data and the model artifact + SOURCE_DATA = "https://raw.githubusercontent.com/facebook/prophet/master/examples/example_retail_sales.csv" + ARTIFACT_PATH = "model" + + # Read data and recode columns + sales_data = pd.read_csv(SOURCE_DATA) + sales_data.rename(columns={"y": "sales", "ds": "date"}, inplace=True) + + # Split the data into train/test + train_size = int(0.8 * len(sales_data)) + train, _ = sales_data[:train_size], sales_data[train_size:] + + # Create the model + model = pmdarima.auto_arima(train["sales"], seasonal=True, m=12) + + # Log the model + with mlflow.start_run(): + wrapper = PmdarimaWrapper() + mlflow.pyfunc.log_model( + name="model", + python_model=wrapper, + artifacts={"model": mlflow.pyfunc.model_to_dict(model)}, + ) + + +.. _Pmdarima: + http://alkaline-ml.com/pmdarima/ +""" + +import logging +import os +import pickle +import warnings +from typing import Any + +import pandas as pd +import yaml +from packaging.version import Version + +import mlflow +from mlflow import pyfunc +from mlflow.exceptions import MlflowException +from mlflow.models import Model, ModelInputExample, ModelSignature +from mlflow.models.model import MLMODEL_FILE_NAME +from mlflow.models.signature import _infer_signature_from_input_example +from mlflow.models.utils import _save_example +from mlflow.protos.databricks_pb2 import INVALID_PARAMETER_VALUE +from mlflow.tracking._model_registry import DEFAULT_AWAIT_MAX_SLEEP_SECONDS +from mlflow.tracking.artifact_utils import _download_artifact_from_uri +from mlflow.utils.docstring_utils import LOG_MODEL_PARAM_DOCS, format_docstring +from mlflow.utils.environment import ( + _CONDA_ENV_FILE_NAME, + _CONSTRAINTS_FILE_NAME, + _PYTHON_ENV_FILE_NAME, + _REQUIREMENTS_FILE_NAME, + _mlflow_conda_env, + _process_conda_env, + _process_pip_requirements, + _PythonEnv, + _validate_env_arguments, +) +from mlflow.utils.file_utils import get_total_file_size, write_to +from mlflow.utils.model_utils import ( + _add_code_from_conf_to_system_path, + _get_flavor_configuration, + _validate_and_copy_code_paths, + _validate_and_prepare_target_save_path, +) +from mlflow.utils.requirements_utils import _get_pinned_requirement + +FLAVOR_NAME = "pmdarima" +_MODEL_BINARY_KEY = "data" +_MODEL_BINARY_FILE_NAME = "model.pmd" +_MODEL_TYPE_KEY = "model_type" + + +_logger = logging.getLogger(__name__) + + +def get_default_pip_requirements(): + """ + Returns: + A list of default pip requirements for MLflow Models produced by this flavor. Calls to + :func:`save_model()` and :func:`log_model()` produce a pip environment that, at a minimum, + contains these requirements. + """ + + return [_get_pinned_requirement("pmdarima")] + + +def get_default_conda_env(): + """ + Returns: + The default Conda environment for MLflow Models produced by calls to + :func:`save_model()` and :func:`log_model()`. + """ + + return _mlflow_conda_env(additional_pip_deps=get_default_pip_requirements()) + + +@format_docstring(LOG_MODEL_PARAM_DOCS.format(package_name=FLAVOR_NAME)) +def save_model( + pmdarima_model, + path, + conda_env=None, + code_paths=None, + mlflow_model=None, + signature: ModelSignature = None, + input_example: ModelInputExample = None, + pip_requirements=None, + extra_pip_requirements=None, + metadata=None, +): + """ + Save a pmdarima ``ARIMA`` model or ``Pipeline`` object to a path on the local file system. + + Args: + pmdarima_model: pmdarima ``ARIMA`` or ``Pipeline`` model that has been ``fit`` on a + temporal series. + path: Local path destination for the serialized model (in pickle format) is to be saved. + conda_env: {{ conda_env }} + code_paths: {{ code_paths }} + mlflow_model: :py:mod:`mlflow.models.Model` this flavor is being added to. + signature: an instance of the :py:class:`ModelSignature ` + class that describes the model's inputs and outputs. If not specified but an + ``input_example`` is supplied, a signature will be automatically inferred + based on the supplied input example and model. To disable automatic signature + inference when providing an input example, set ``signature`` to ``False``. + To manually infer a model signature, call + :py:func:`infer_signature() ` on datasets + with valid model inputs, such as a training dataset with the target column + omitted, and valid model outputs, like model predictions made on the training + dataset, for example: + + .. code-block:: python + + from mlflow.models import infer_signature + + model = pmdarima.auto_arima(data) + predictions = model.predict(n_periods=30, return_conf_int=False) + signature = infer_signature(data, predictions) + + .. Warning:: if utilizing confidence interval generation in the ``predict`` + method of a ``pmdarima`` model (``return_conf_int=True``), the signature + will not be inferred due to the complex tuple return type when using the + native ``ARIMA.predict()`` API. ``infer_schema`` will function correctly + if using the ``pyfunc`` flavor of the model, though. + input_example: {{ input_example }} + pip_requirements: {{ pip_requirements }} + extra_pip_requirements: {{ extra_pip_requirements }} + metadata: {{ metadata }} + + .. code-block:: python + :caption: Example + + import pandas as pd + import mlflow + import pmdarima + + # Specify locations of source data and the model artifact + SOURCE_DATA = "https://raw.githubusercontent.com/facebook/prophet/master/examples/example_retail_sales.csv" + ARTIFACT_PATH = "model" + + # Read data and recode columns + sales_data = pd.read_csv(SOURCE_DATA) + sales_data.rename(columns={"y": "sales", "ds": "date"}, inplace=True) + + # Split the data into train/test + train_size = int(0.8 * len(sales_data)) + train = sales_data[:train_size] + test = sales_data[train_size:] + + with mlflow.start_run(): + # Create the model + model = pmdarima.auto_arima(train["sales"], seasonal=True, m=12) + + # Save the model to the specified path + mlflow.pmdarima.save_model(model, "model") + """ + + import pmdarima + + _validate_env_arguments(conda_env, pip_requirements, extra_pip_requirements) + + path = os.path.abspath(path) + _validate_and_prepare_target_save_path(path) + code_dir_subpath = _validate_and_copy_code_paths(code_paths, path) + + if mlflow_model is None: + mlflow_model = Model() + saved_example = _save_example(mlflow_model, input_example, path) + + if signature is None and saved_example is not None: + wrapped_model = _PmdarimaModelWrapper(pmdarima_model) + signature = _infer_signature_from_input_example(saved_example, wrapped_model) + elif signature is False: + signature = None + + if signature is not None: + mlflow_model.signature = signature + if metadata is not None: + mlflow_model.metadata = metadata + + model_data_path = os.path.join(path, _MODEL_BINARY_FILE_NAME) + _save_model(pmdarima_model, model_data_path) + + model_bin_kwargs = {_MODEL_BINARY_KEY: _MODEL_BINARY_FILE_NAME} + pyfunc.add_to_model( + mlflow_model, + loader_module="mlflow.pmdarima", + conda_env=_CONDA_ENV_FILE_NAME, + python_env=_PYTHON_ENV_FILE_NAME, + code=code_dir_subpath, + **model_bin_kwargs, + ) + flavor_conf = { + _MODEL_TYPE_KEY: pmdarima_model.__class__.__name__, + **model_bin_kwargs, + } + mlflow_model.add_flavor( + FLAVOR_NAME, pmdarima_version=pmdarima.__version__, code=code_dir_subpath, **flavor_conf + ) + if size := get_total_file_size(path): + mlflow_model.model_size_bytes = size + mlflow_model.save(os.path.join(path, MLMODEL_FILE_NAME)) + + if conda_env is None: + if pip_requirements is None: + default_reqs = get_default_pip_requirements() + inferred_reqs = mlflow.models.infer_pip_requirements( + path, FLAVOR_NAME, fallback=default_reqs + ) + default_reqs = sorted(set(inferred_reqs).union(default_reqs)) + else: + default_reqs = None + conda_env, pip_requirements, pip_constraints = _process_pip_requirements( + default_reqs, pip_requirements, extra_pip_requirements + ) + else: + conda_env, pip_requirements, pip_constraints = _process_conda_env(conda_env) + + with open(os.path.join(path, _CONDA_ENV_FILE_NAME), "w") as f: + yaml.safe_dump(conda_env, stream=f, default_flow_style=False) + + if pip_constraints: + write_to(os.path.join(path, _CONSTRAINTS_FILE_NAME), "\n".join(pip_constraints)) + + write_to(os.path.join(path, _REQUIREMENTS_FILE_NAME), "\n".join(pip_requirements)) + + _PythonEnv.current().to_yaml(os.path.join(path, _PYTHON_ENV_FILE_NAME)) + + +@format_docstring(LOG_MODEL_PARAM_DOCS.format(package_name=FLAVOR_NAME)) +def log_model( + pmdarima_model, + artifact_path: str | None = None, + conda_env=None, + code_paths=None, + registered_model_name=None, + signature: ModelSignature = None, + input_example: ModelInputExample = None, + await_registration_for=DEFAULT_AWAIT_MAX_SLEEP_SECONDS, + pip_requirements=None, + extra_pip_requirements=None, + metadata=None, + name: str | None = None, + params: dict[str, Any] | None = None, + tags: dict[str, Any] | None = None, + model_type: str | None = None, + step: int = 0, + model_id: str | None = None, + **kwargs, +): + """ + Logs a ``pmdarima`` ``ARIMA`` or ``Pipeline`` object as an MLflow artifact for the current run. + + Args: + pmdarima_model: pmdarima ``ARIMA`` or ``Pipeline`` model that has been ``fit`` on a + temporal series. + artifact_path: Deprecated. Use `name` instead. + conda_env: {{ conda_env }} + code_paths: {{ code_paths }} + registered_model_name: If given, create a model + version under ``registered_model_name``, also creating a + registered model if one with the given name does not exist. + signature: an instance of the :py:class:`ModelSignature ` + class that describes the model's inputs and outputs. If not specified but an + ``input_example`` is supplied, a signature will be automatically inferred + based on the supplied input example and model. To disable automatic signature + inference when providing an input example, set ``signature`` to ``False``. + To manually infer a model signature, call + :py:func:`infer_signature() ` on datasets + with valid model inputs, such as a training dataset with the target column + omitted, and valid model outputs, like model predictions made on the training + dataset, for example: + + .. code-block:: python + + from mlflow.models import infer_signature + + model = pmdarima.auto_arima(data) + predictions = model.predict(n_periods=30, return_conf_int=False) + signature = infer_signature(data, predictions) + + .. Warning:: if utilizing confidence interval generation in the ``predict`` + method of a ``pmdarima`` model (``return_conf_int=True``), the signature + will not be inferred due to the complex tuple return type when using the + native ``ARIMA.predict()`` API. ``infer_schema`` will function correctly + if using the ``pyfunc`` flavor of the model, though. + + input_example: {{ input_example }} + await_registration_for: Number of seconds to wait for the model version + to finish being created and is in ``READY`` status. + By default, the function waits for five minutes. + Specify 0 or None to skip waiting. + pip_requirements: {{ pip_requirements }} + extra_pip_requirements: {{ extra_pip_requirements }} + metadata: {{ metadata }} + name: {{ name }} + params: {{ params }} + tags: {{ tags }} + model_type: {{ model_type }} + step: {{ step }} + model_id: {{ model_id }} + kwargs: Additional arguments for :py:class:`mlflow.models.model.Model` + + Returns: + A :py:class:`ModelInfo ` instance that contains the + metadata of the logged model. + + .. code-block:: python + :caption: Example + + import pandas as pd + import mlflow + from mlflow.models import infer_signature + import pmdarima + from pmdarima.metrics import smape + + # Specify locations of source data and the model artifact + SOURCE_DATA = "https://raw.githubusercontent.com/facebook/prophet/master/examples/example_retail_sales.csv" + ARTIFACT_PATH = "model" + + # Read data and recode columns + sales_data = pd.read_csv(SOURCE_DATA) + sales_data.rename(columns={"y": "sales", "ds": "date"}, inplace=True) + + # Split the data into train/test + train_size = int(0.8 * len(sales_data)) + train = sales_data[:train_size] + test = sales_data[train_size:] + + with mlflow.start_run(): + # Create the model + model = pmdarima.auto_arima(train["sales"], seasonal=True, m=12) + + # Calculate metrics + prediction = model.predict(n_periods=len(test)) + metrics = {"smape": smape(test["sales"], prediction)} + + # Infer signature + input_sample = pd.DataFrame(train["sales"]) + output_sample = pd.DataFrame(model.predict(n_periods=5)) + signature = infer_signature(input_sample, output_sample) + + # Log model + mlflow.pmdarima.log_model(model, name=ARTIFACT_PATH, signature=signature) + """ + return Model.log( + artifact_path=artifact_path, + name=name, + flavor=mlflow.pmdarima, + registered_model_name=registered_model_name, + pmdarima_model=pmdarima_model, + conda_env=conda_env, + code_paths=code_paths, + signature=signature, + input_example=input_example, + await_registration_for=await_registration_for, + pip_requirements=pip_requirements, + extra_pip_requirements=extra_pip_requirements, + metadata=metadata, + params=params, + tags=tags, + model_type=model_type, + step=step, + model_id=model_id, + **kwargs, + ) + + +def load_model(model_uri, dst_path=None): + """ + Load a ``pmdarima`` ``ARIMA`` model or ``Pipeline`` object from a local file or a run. + + Args: + model_uri: The location, in URI format, of the MLflow model. For example: + + - ``/Users/me/path/to/local/model`` + - ``relative/path/to/local/model`` + - ``s3://my_bucket/path/to/model`` + - ``runs://run-relative/path/to/model`` + - ``mlflow-artifacts:/path/to/model`` + + For more information about supported URI schemes, see + `Referencing Artifacts `_. + dst_path: The local filesystem path to which to download the model artifact. + This directory must already exist. If unspecified, a local output + path will be created. + + Returns: + A ``pmdarima`` model instance + + .. code-block:: python + :caption: Example + + import pandas as pd + import mlflow + from mlflow.models import infer_signature + import pmdarima + from pmdarima.metrics import smape + + # Specify locations of source data and the model artifact + SOURCE_DATA = "https://raw.githubusercontent.com/facebook/prophet/master/examples/example_retail_sales.csv" + ARTIFACT_PATH = "model" + + # Read data and recode columns + sales_data = pd.read_csv(SOURCE_DATA) + sales_data.rename(columns={"y": "sales", "ds": "date"}, inplace=True) + + # Split the data into train/test + train_size = int(0.8 * len(sales_data)) + train = sales_data[:train_size] + test = sales_data[train_size:] + + with mlflow.start_run(): + # Create the model + model = pmdarima.auto_arima(train["sales"], seasonal=True, m=12) + + # Calculate metrics + prediction = model.predict(n_periods=len(test)) + metrics = {"smape": smape(test["sales"], prediction)} + + # Infer signature + input_sample = pd.DataFrame(train["sales"]) + output_sample = pd.DataFrame(model.predict(n_periods=5)) + signature = infer_signature(input_sample, output_sample) + + # Log model + input_example = input_sample.head() + model_info = mlflow.pmdarima.log_model( + model, name=ARTIFACT_PATH, signature=signature, input_example=input_example + ) + + # Load the model + loaded_model = mlflow.pmdarima.load_model(model_info.model_uri) + # Forecast for the next 60 days + forecast = loaded_model.predict(n_periods=60) + print(f"forecast: {forecast}") + + .. code-block:: text + :caption: Output + + forecast: + 234 382452.397246 + 235 380639.458720 + 236 359805.611219 + ... + """ + + local_model_path = _download_artifact_from_uri(artifact_uri=model_uri, output_path=dst_path) + flavor_conf = _get_flavor_configuration(model_path=local_model_path, flavor_name=FLAVOR_NAME) + _add_code_from_conf_to_system_path(local_model_path, flavor_conf) + pmdarima_model_file_path = os.path.join( + local_model_path, flavor_conf.get(_MODEL_BINARY_KEY, _MODEL_BINARY_FILE_NAME) + ) + + return _load_model(pmdarima_model_file_path) + + +def _save_model(model, path): + with open(path, "wb") as f: + pickle.dump(model, f) + + +def _load_model(path): + with open(path, "rb") as pickled_model: + return pickle.load(pickled_model) + + +def _load_pyfunc(path): + return _PmdarimaModelWrapper(_load_model(path)) + + +class _PmdarimaModelWrapper: + def __init__(self, pmdarima_model): + import pmdarima + + self.pmdarima_model = pmdarima_model + self._pmdarima_version = pmdarima.__version__ + + def get_raw_model(self): + """ + Returns the underlying model. + """ + return self.pmdarima_model + + def predict(self, dataframe, params: dict[str, Any] | None = None) -> pd.DataFrame: + """ + Args: + dataframe: Model input data. + params: Additional parameters to pass to the model for inference. + + Returns: + Model predictions. + """ + df_schema = dataframe.columns.values.tolist() + + if len(dataframe) > 1: + raise MlflowException( + f"The provided prediction pd.DataFrame contains {len(dataframe)} rows. " + "Only 1 row should be supplied.", + error_code=INVALID_PARAMETER_VALUE, + ) + + attrs = dataframe.to_dict(orient="index").get(0) + n_periods = attrs.get("n_periods", None) + + if not n_periods: + raise MlflowException( + f"The provided prediction configuration pd.DataFrame columns ({df_schema}) do not " + "contain the required column `n_periods` for specifying future prediction periods " + "to generate.", + error_code=INVALID_PARAMETER_VALUE, + ) + + if not isinstance(n_periods, int): + raise MlflowException( + f"The provided `n_periods` value {n_periods} must be an integer." + f"provided type: {type(n_periods)}", + error_code=INVALID_PARAMETER_VALUE, + ) + + # NB Any model that is trained with exogenous regressor elements will need to provide + # `X` entries as a 2D array structure to the predict method. + exogenous_regressor = attrs.get("X", None) + + if exogenous_regressor and Version(self._pmdarima_version) < Version("1.8.0"): + warnings.warn( + "An exogenous regressor element was provided in column 'X'. This is " + "supported only in pmdarima version >= 1.8.0. Installed version: " + f"{self._pmdarima_version}" + ) + + return_conf_int = attrs.get("return_conf_int", False) + alpha = attrs.get("alpha", 0.05) + + if not isinstance(n_periods, int): + raise MlflowException( + "The prediction DataFrame must contain a column `n_periods` with " + "an integer value for number of future periods to predict.", + error_code=INVALID_PARAMETER_VALUE, + ) + + if Version(self._pmdarima_version) >= Version("1.8.0"): + raw_predictions = self.pmdarima_model.predict( + n_periods=n_periods, + X=exogenous_regressor, + return_conf_int=return_conf_int, + alpha=alpha, + ) + else: + raw_predictions = self.pmdarima_model.predict( + n_periods=n_periods, + return_conf_int=return_conf_int, + alpha=alpha, + ) + + if return_conf_int: + ci_low, ci_high = list(zip(*raw_predictions[1])) + predictions = pd.DataFrame.from_dict( + {"yhat": raw_predictions[0], "yhat_lower": ci_low, "yhat_upper": ci_high} + ) + else: + predictions = pd.DataFrame.from_dict({"yhat": raw_predictions}) + + return predictions diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pmdarima/__pycache__/__init__.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pmdarima/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..9f6f91321475b477b4363c35dab5771ebdb1c107 Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pmdarima/__pycache__/__init__.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/projects/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/projects/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..67aebf2340898c97ea1d0bd650f35997dc838675 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/projects/__init__.py @@ -0,0 +1,450 @@ +""" +The ``mlflow.projects`` module provides an API for running MLflow projects locally or remotely. +""" + +import json +import logging +import os + +import yaml + +import mlflow.projects.databricks +import mlflow.utils.uri +from mlflow import tracking +from mlflow.entities import RunStatus +from mlflow.exceptions import ExecutionException, MlflowException +from mlflow.projects.backend import loader +from mlflow.projects.submitted_run import SubmittedRun +from mlflow.projects.utils import ( + MLFLOW_LOCAL_BACKEND_RUN_ID_CONFIG, + PROJECT_BUILD_IMAGE, + PROJECT_DOCKER_ARGS, + PROJECT_DOCKER_AUTH, + PROJECT_ENV_MANAGER, + PROJECT_STORAGE_DIR, + PROJECT_SYNCHRONOUS, + fetch_and_validate_project, + get_entry_point_command, + get_or_create_run, + get_run_env_vars, + load_project, +) +from mlflow.tracking.fluent import _get_experiment_id +from mlflow.utils import env_manager as _EnvManager +from mlflow.utils.mlflow_tags import ( + MLFLOW_DOCKER_IMAGE_ID, + MLFLOW_PROJECT_BACKEND, + MLFLOW_PROJECT_ENV, + MLFLOW_RUN_NAME, +) + +_logger = logging.getLogger(__name__) + + +def _resolve_experiment_id(experiment_name=None, experiment_id=None): + """ + Resolve experiment. + + Verifies either one or other is specified - cannot be both selected. + + If ``experiment_name`` is provided and does not exist, an experiment + of that name is created and its id is returned. + + Args: + experiment_name: Name of experiment under which to launch the run. + experiment_id: ID of experiment under which to launch the run. + + Returns: + str + """ + + if experiment_name and experiment_id: + raise MlflowException("Specify only one of 'experiment_name' or 'experiment_id'.") + + if experiment_id: + return str(experiment_id) + + if experiment_name: + client = tracking.MlflowClient() + exp = client.get_experiment_by_name(experiment_name) + if exp: + return exp.experiment_id + else: + _logger.info("'%s' does not exist. Creating a new experiment", experiment_name) + return client.create_experiment(experiment_name) + + return _get_experiment_id() + + +def _run( + uri, + experiment_id, + entry_point, + version, + parameters, + docker_args, + backend_name, + backend_config, + storage_dir, + env_manager, + synchronous, + run_name, + build_image, + docker_auth, +): + """ + Helper that delegates to the project-running method corresponding to the passed-in backend. + Returns a ``SubmittedRun`` corresponding to the project run. + """ + tracking_store_uri = tracking.get_tracking_uri() + backend_config[PROJECT_ENV_MANAGER] = env_manager + backend_config[PROJECT_SYNCHRONOUS] = synchronous + backend_config[PROJECT_DOCKER_ARGS] = docker_args + backend_config[PROJECT_STORAGE_DIR] = storage_dir + backend_config[PROJECT_BUILD_IMAGE] = build_image + backend_config[PROJECT_DOCKER_AUTH] = docker_auth + # TODO: remove this check once kubernetes execution has been refactored + if backend_name not in {"databricks", "kubernetes"}: + backend = loader.load_backend(backend_name) + if backend: + submitted_run = backend.run( + uri, + entry_point, + parameters, + version, + backend_config, + tracking_store_uri, + experiment_id, + ) + tracking.MlflowClient().set_tag( + submitted_run.run_id, MLFLOW_PROJECT_BACKEND, backend_name + ) + if run_name is not None: + tracking.MlflowClient().set_tag(submitted_run.run_id, MLFLOW_RUN_NAME, run_name) + return submitted_run + + work_dir = fetch_and_validate_project(uri, version, entry_point, parameters) + project = load_project(work_dir) + _validate_execution_environment(project, backend_name) + + active_run = get_or_create_run( + None, uri, experiment_id, work_dir, version, entry_point, parameters + ) + + if run_name is not None: + tracking.MlflowClient().set_tag(active_run.info.run_id, MLFLOW_RUN_NAME, run_name) + + if backend_name == "databricks": + tracking.MlflowClient().set_tag( + active_run.info.run_id, MLFLOW_PROJECT_BACKEND, "databricks" + ) + from mlflow.projects.databricks import run_databricks, run_databricks_spark_job + + if project.databricks_spark_job_spec is not None: + return run_databricks_spark_job( + remote_run=active_run, + uri=uri, + work_dir=work_dir, + experiment_id=experiment_id, + cluster_spec=backend_config, + project_spec=project, + entry_point=entry_point, + parameters=parameters, + ) + + return run_databricks( + remote_run=active_run, + uri=uri, + entry_point=entry_point, + work_dir=work_dir, + parameters=parameters, + experiment_id=experiment_id, + cluster_spec=backend_config, + env_manager=env_manager, + ) + + elif backend_name == "kubernetes": + from mlflow.projects import kubernetes as kb + from mlflow.projects.docker import ( + build_docker_image, + validate_docker_env, + validate_docker_installation, + ) + + tracking.MlflowClient().set_tag(active_run.info.run_id, MLFLOW_PROJECT_ENV, "docker") + tracking.MlflowClient().set_tag( + active_run.info.run_id, MLFLOW_PROJECT_BACKEND, "kubernetes" + ) + validate_docker_env(project) + validate_docker_installation() + kube_config = _parse_kubernetes_config(backend_config) + image = build_docker_image( + work_dir=work_dir, + repository_uri=kube_config["repository-uri"], + base_image=project.docker_env.get("image"), + run_id=active_run.info.run_id, + build_image=build_image, + docker_auth=docker_auth, + ) + image_digest = kb.push_image_to_registry(image.tags[0]) + tracking.MlflowClient().set_tag( + active_run.info.run_id, MLFLOW_DOCKER_IMAGE_ID, image_digest + ) + return kb.run_kubernetes_job( + project.name, + active_run, + image.tags[0], + image_digest, + get_entry_point_command(project, entry_point, parameters, storage_dir), + get_run_env_vars( + run_id=active_run.info.run_id, experiment_id=active_run.info.experiment_id + ), + kube_config.get("kube-context", None), + kube_config["kube-job-template"], + ) + + supported_backends = ["databricks", "kubernetes"] + list(loader.MLFLOW_BACKENDS.keys()) + raise ExecutionException( + f"Got unsupported execution mode {backend_name}. Supported values: {supported_backends}" + ) + + +def run( + uri, + entry_point="main", + version=None, + parameters=None, + docker_args=None, + experiment_name=None, + experiment_id=None, + backend="local", + backend_config=None, + storage_dir=None, + synchronous=True, + run_id=None, + run_name=None, + env_manager=None, + build_image=False, + docker_auth=None, +): + """ + Run an MLflow project. The project can be local or stored at a Git URI. + + MLflow provides built-in support for running projects locally or remotely on a Databricks or + Kubernetes cluster. You can also run projects against other targets by installing an appropriate + third-party plugin. See `Community Plugins <../plugins.html#community-plugins>`_ for more + information. + + For information on using this method in chained workflows, see `Building Multistep Workflows + <../projects.html#building-multistep-workflows>`_. + + Raises: + :py:class:`mlflow.exceptions.ExecutionException` If a run launched in blocking mode + is unsuccessful. + + Args: + uri: URI of project to run. A local filesystem path + or a Git repository URI (e.g. https://github.com/mlflow/mlflow-example) + pointing to a project directory containing an MLproject file. + entry_point: Entry point to run within the project. If no entry point with the specified + name is found, runs the project file ``entry_point`` as a script, + using "python" to run ``.py`` files and the default shell (specified by + environment variable ``$SHELL``) to run ``.sh`` files. + version: For Git-based projects, either a commit hash or a branch name. + parameters: Parameters (dictionary) for the entry point command. + docker_args: Arguments (dictionary) for the docker command. + experiment_name: Name of experiment under which to launch the run. + experiment_id: ID of experiment under which to launch the run. + backend: Execution backend for the run: MLflow provides built-in support for "local", + "databricks", and "kubernetes" (experimental) backends. If running against + Databricks, will run against a Databricks workspace determined as follows: + if a Databricks tracking URI of the form ``databricks://profile`` has been set + (e.g. by setting the MLFLOW_TRACKING_URI environment variable), will run + against the workspace specified by . Otherwise, runs against the + workspace specified by the default Databricks CLI profile. + backend_config: A dictionary, or a path to a JSON file (must end in '.json'), which will + be passed as config to the backend. The exact content which should be + provided is different for each execution backend and is documented + at https://www.mlflow.org/docs/latest/projects.html. + storage_dir: Used only if ``backend`` is "local". MLflow downloads artifacts from + distributed URIs passed to parameters of type ``path`` to subdirectories of + ``storage_dir``. + synchronous: Whether to block while waiting for a run to complete. Defaults to True. + Note that if ``synchronous`` is False and ``backend`` is "local", this + method will return, but the current process will block when exiting until + the local run completes. If the current process is interrupted, any + asynchronous runs launched via this method will be terminated. If + ``synchronous`` is True and the run fails, the current process will + error out as well. + run_id: Note: this argument is used internally by the MLflow project APIs and should + not be specified. If specified, the run ID will be used instead of + creating a new run. + run_name: The name to give the MLflow Run associated with the project execution. + If ``None``, the MLflow Run name is left unset. + env_manager: Specify an environment manager to create a new environment for the run and + install project dependencies within that environment. The following values + are supported: + + - local: use the local environment + - virtualenv: use virtualenv (and pyenv for Python version management) + - uv: use uv + - conda: use conda + + If unspecified, MLflow automatically determines the environment manager to + use by inspecting files in the project directory. For example, if + ``python_env.yaml`` is present, virtualenv will be used. + build_image: Whether to build a new docker image of the project or to reuse an existing + image. Default: False (reuse an existing image) + docker_auth: A dictionary representing information to authenticate with a Docker + registry. See `docker.client.DockerClient.login + `_ + for available options. + + Returns: + :py:class:`mlflow.projects.SubmittedRun` exposing information (e.g. run ID) + about the launched run. + + .. code-block:: python + :caption: Example + + import mlflow + + project_uri = "https://github.com/mlflow/mlflow-example" + params = {"alpha": 0.5, "l1_ratio": 0.01} + + # Run MLflow project and create a reproducible conda environment + # on a local host + mlflow.run(project_uri, parameters=params) + + .. code-block:: text + :caption: Output + + ... + ... + Elasticnet model (alpha=0.500000, l1_ratio=0.010000): + RMSE: 0.788347345611717 + MAE: 0.6155576449938276 + R2: 0.19729662005412607 + ... mlflow.projects: === Run (ID '6a5109febe5e4a549461e149590d0a7c') succeeded === + """ + backend_config_dict = backend_config if backend_config is not None else {} + if ( + backend_config + and type(backend_config) != dict + and os.path.splitext(backend_config)[-1] == ".json" + ): + with open(backend_config) as handle: + try: + backend_config_dict = json.load(handle) + except ValueError: + _logger.error( + "Error when attempting to load and parse JSON cluster spec from file %s", + backend_config, + ) + raise + + if env_manager is not None: + _EnvManager.validate(env_manager) + + if backend == "databricks": + mlflow.projects.databricks.before_run_validations(mlflow.get_tracking_uri(), backend_config) + elif backend == "local" and run_id is not None: + backend_config_dict[MLFLOW_LOCAL_BACKEND_RUN_ID_CONFIG] = run_id + + experiment_id = _resolve_experiment_id( + experiment_name=experiment_name, experiment_id=experiment_id + ) + + submitted_run_obj = _run( + uri=uri, + experiment_id=experiment_id, + entry_point=entry_point, + version=version, + parameters=parameters, + docker_args=docker_args, + backend_name=backend, + backend_config=backend_config_dict, + env_manager=env_manager, + storage_dir=storage_dir, + synchronous=synchronous, + run_name=run_name, + build_image=build_image, + docker_auth=docker_auth, + ) + if synchronous: + _wait_for(submitted_run_obj) + return submitted_run_obj + + +def _wait_for(submitted_run_obj): + """Wait on the passed-in submitted run, reporting its status to the tracking server.""" + run_id = submitted_run_obj.run_id + active_run = None + # Note: there's a small chance we fail to report the run's status to the tracking server if + # we're interrupted before we reach the try block below + try: + active_run = tracking.MlflowClient().get_run(run_id) if run_id is not None else None + if submitted_run_obj.wait(): + _logger.info("=== Run (ID '%s') succeeded ===", run_id) + _maybe_set_run_terminated(active_run, "FINISHED") + else: + _maybe_set_run_terminated(active_run, "FAILED") + raise ExecutionException(f"Run (ID '{run_id}') failed") + except KeyboardInterrupt: + _logger.error("=== Run (ID '%s') interrupted, cancelling run ===", run_id) + submitted_run_obj.cancel() + _maybe_set_run_terminated(active_run, "FAILED") + raise + + +def _maybe_set_run_terminated(active_run, status): + """ + If the passed-in active run is defined and still running (i.e. hasn't already been terminated + within user code), mark it as terminated with the passed-in status. + """ + if active_run is None: + return + run_id = active_run.info.run_id + cur_status = tracking.MlflowClient().get_run(run_id).info.status + if RunStatus.is_terminated(cur_status): + return + tracking.MlflowClient().set_terminated(run_id, status) + + +def _validate_execution_environment(project, backend): + if project.docker_env and backend == "databricks": + raise ExecutionException( + "Running docker-based projects on Databricks is not yet supported." + ) + + +def _parse_kubernetes_config(backend_config): + """ + Creates build context tarfile containing Dockerfile and project code, returning path to tarfile + """ + if not backend_config: + raise ExecutionException("Backend_config file not found.") + kube_config = backend_config.copy() + if "kube-job-template-path" not in backend_config.keys(): + raise ExecutionException( + "'kube-job-template-path' attribute must be specified in backend_config." + ) + kube_job_template = backend_config["kube-job-template-path"] + if os.path.exists(kube_job_template): + with open(kube_job_template) as job_template: + yaml_obj = yaml.safe_load(job_template.read()) + kube_job_template = yaml_obj + kube_config["kube-job-template"] = kube_job_template + else: + raise ExecutionException(f"Could not find 'kube-job-template-path': {kube_job_template}") + if "kube-context" not in backend_config.keys(): + _logger.debug( + "Could not find kube-context in backend_config." + " Using current context or in-cluster config." + ) + if "repository-uri" not in backend_config.keys(): + raise ExecutionException("Could not find 'repository-uri' in backend_config.") + return kube_config + + +__all__ = ["run", "SubmittedRun"] diff --git 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100644 index 0000000000000000000000000000000000000000..ed63d0388278464751b5ed8eb2763a617e20cae2 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/projects/_project_spec.py @@ -0,0 +1,363 @@ +"""Internal utilities for parsing MLproject YAML files.""" + +import os + +import yaml + +from mlflow.exceptions import ExecutionException, MlflowException +from mlflow.projects import env_type +from mlflow.tracking import artifact_utils +from mlflow.utils import data_utils +from mlflow.utils.environment import _PYTHON_ENV_FILE_NAME +from mlflow.utils.file_utils import get_local_path_or_none +from mlflow.utils.string_utils import is_string_type, quote + +MLPROJECT_FILE_NAME = "mlproject" +DEFAULT_CONDA_FILE_NAME = "conda.yaml" + + +def _find_mlproject(directory): + filenames = os.listdir(directory) + for filename in filenames: + if filename.lower() == MLPROJECT_FILE_NAME: + return os.path.join(directory, filename) + return None + + +def load_project(directory): + mlproject_path = _find_mlproject(directory) + + # TODO: Validate structure of YAML loaded from the file + yaml_obj = {} + if mlproject_path is not None: + with open(mlproject_path) as mlproject_file: + yaml_obj = yaml.safe_load(mlproject_file) + + # Validate the project config does't contain multiple environment fields + env_fields = set(yaml_obj.keys()).intersection(env_type.ALL) + if len(env_fields) > 1: + raise ExecutionException( + f"Project cannot contain multiple environment fields: {env_fields}" + ) + + project_name = yaml_obj.get("name") + + # Parse entry points + entry_points = {} + for name, entry_point_yaml in yaml_obj.get("entry_points", {}).items(): + parameters = entry_point_yaml.get("parameters", {}) + command = entry_point_yaml.get("command") + entry_points[name] = EntryPoint(name, parameters, command) + + databricks_spark_job_yaml = yaml_obj.get("databricks_spark_job") + if databricks_spark_job_yaml is not None: + python_file = databricks_spark_job_yaml.get("python_file") + + if python_file is None and not entry_points: + raise MlflowException( + "Databricks Spark job requires either 'databricks_spark_job.python_file' " + "setting or 'entry_points' setting." + ) + if python_file is not None and entry_points: + raise MlflowException( + "Databricks Spark job does not allow setting both " + "'databricks_spark_job.python_file' and 'entry_points'." + ) + + for entry_point in entry_points.values(): + for param in entry_point.parameters.values(): + if param.type == "path": + raise MlflowException( + "Databricks Spark job does not support entry point parameter of 'path' " + f"type. '{param.name}' value type is invalid." + ) + + if env_type.DOCKER in yaml_obj: + raise MlflowException( + "Databricks Spark job does not support setting docker environment." + ) + + if env_type.PYTHON in yaml_obj: + raise MlflowException( + "Databricks Spark job does not support setting python environment." + ) + + if env_type.CONDA in yaml_obj: + raise MlflowException( + "Databricks Spark job does not support setting conda environment." + ) + + databricks_spark_job_spec = DatabricksSparkJobSpec( + python_file=databricks_spark_job_yaml.get("python_file"), + parameters=databricks_spark_job_yaml.get("parameters", []), + python_libraries=databricks_spark_job_yaml.get("python_libraries", []), + ) + return Project( + databricks_spark_job_spec=databricks_spark_job_spec, + name=project_name, + entry_points=entry_points, + ) + + # Validate config if docker_env parameter is present + docker_env = yaml_obj.get(env_type.DOCKER) + if docker_env: + if not docker_env.get("image"): + raise ExecutionException( + "Project configuration (MLproject file) was invalid: Docker " + "environment specified but no image attribute found." + ) + if docker_env.get("volumes"): + if not ( + isinstance(docker_env["volumes"], list) + and all(isinstance(i, str) for i in docker_env["volumes"]) + ): + raise ExecutionException( + "Project configuration (MLproject file) was invalid: " + "Docker volumes must be a list of strings, " + """e.g.: '["/path1/:/path1", "/path2/:/path2"])""" + ) + if docker_env.get("environment"): + if not ( + isinstance(docker_env["environment"], list) + and all(isinstance(i, (list, str)) for i in docker_env["environment"]) + ): + raise ExecutionException( + "Project configuration (MLproject file) was invalid: " + "environment must be a list containing either strings (to copy environment " + "variables from host system) or lists of string pairs (to define new " + "environment variables)." + """E.g.: '[["NEW_VAR", "new_value"], "VAR_TO_COPY_FROM_HOST"])""" + ) + return Project( + env_type=env_type.DOCKER, + env_config_path=None, + entry_points=entry_points, + docker_env=docker_env, + name=project_name, + ) + + python_env = yaml_obj.get(env_type.PYTHON) + if python_env: + python_env_path = os.path.join(directory, python_env) + if not os.path.exists(python_env_path): + raise ExecutionException( + f"Project specified python_env file {python_env_path}, but no such file was found." + ) + return Project( + env_type=env_type.PYTHON, + env_config_path=python_env_path, + entry_points=entry_points, + docker_env=None, + name=project_name, + ) + + conda_path = yaml_obj.get(env_type.CONDA) + if conda_path: + conda_env_path = os.path.join(directory, conda_path) + if not os.path.exists(conda_env_path): + raise ExecutionException( + f"Project specified conda environment file {conda_env_path}, but no such " + "file was found." + ) + return Project( + env_type=env_type.CONDA, + env_config_path=conda_env_path, + entry_points=entry_points, + docker_env=None, + name=project_name, + ) + + default_python_env_path = os.path.join(directory, _PYTHON_ENV_FILE_NAME) + if os.path.exists(default_python_env_path): + return Project( + env_type=env_type.PYTHON, + env_config_path=default_python_env_path, + entry_points=entry_points, + docker_env=None, + name=project_name, + ) + + default_conda_path = os.path.join(directory, DEFAULT_CONDA_FILE_NAME) + if os.path.exists(default_conda_path): + return Project( + env_type=env_type.CONDA, + env_config_path=default_conda_path, + entry_points=entry_points, + docker_env=None, + name=project_name, + ) + + return Project( + env_type=env_type.PYTHON, + env_config_path=None, + entry_points=entry_points, + docker_env=None, + name=project_name, + ) + + +class Project: + """A project specification loaded from an MLproject file in the passed-in directory.""" + + def __init__( + self, + name, + env_type=None, + env_config_path=None, + entry_points=None, + docker_env=None, + databricks_spark_job_spec=None, + ): + self.env_type = env_type + self.env_config_path = env_config_path + self._entry_points = entry_points + self.docker_env = docker_env + self.name = name + self.databricks_spark_job_spec = databricks_spark_job_spec + + def get_entry_point(self, entry_point): + if self.databricks_spark_job_spec: + if self.databricks_spark_job_spec.python_file is not None: + # If Databricks Spark job is configured with python_file field, + # it does not need to configure entry_point section + # and the 'entry_point' param in 'mlflow run' command is ignored + return None + + if self._entry_points is None or entry_point not in self._entry_points: + raise MlflowException( + f"The entry point '{entry_point}' is not defined in the Databricks spark job " + f"MLproject file." + ) + + if entry_point in self._entry_points: + return self._entry_points[entry_point] + _, file_extension = os.path.splitext(entry_point) + ext_to_cmd = {".py": "python", ".sh": os.environ.get("SHELL", "bash")} + if file_extension in ext_to_cmd: + command = f"{ext_to_cmd[file_extension]} {quote(entry_point)}" + if not is_string_type(command): + command = command.encode("utf-8") + return EntryPoint(name=entry_point, parameters={}, command=command) + elif file_extension == ".R": + command = f"Rscript -e \"mlflow::mlflow_source('{quote(entry_point)}')\" --args" + return EntryPoint(name=entry_point, parameters={}, command=command) + raise ExecutionException( + "Could not find {0} among entry points {1} or interpret {0} as a " + "runnable script. Supported script file extensions: " + "{2}".format(entry_point, list(self._entry_points.keys()), list(ext_to_cmd.keys())) + ) + + +class EntryPoint: + """An entry point in an MLproject specification.""" + + def __init__(self, name, parameters, command): + self.name = name + self.parameters = {k: Parameter(k, v) for (k, v) in parameters.items()} + self.command = command + + def _validate_parameters(self, user_parameters): + missing_params = [] + for name in self.parameters: + if name not in user_parameters and self.parameters[name].default is None: + missing_params.append(name) + if missing_params: + raise ExecutionException( + "No value given for missing parameters: {}".format( + ", ".join([f"'{name}'" for name in missing_params]) + ) + ) + + def compute_parameters(self, user_parameters, storage_dir): + """ + Given a dict mapping user-specified param names to values, computes parameters to + substitute into the command for this entry point. Returns a tuple (params, extra_params) + where `params` contains key-value pairs for parameters specified in the entry point + definition, and `extra_params` contains key-value pairs for additional parameters passed + by the user. + + Note that resolving parameter values can be a heavy operation, e.g. if a remote URI is + passed for a parameter of type `path`, we download the URI to a local path within + `storage_dir` and substitute in the local path as the parameter value. + + If `storage_dir` is `None`, report path will be return as parameter. + """ + if user_parameters is None: + user_parameters = {} + # Validate params before attempting to resolve parameter values + self._validate_parameters(user_parameters) + final_params = {} + extra_params = {} + + parameter_keys = list(self.parameters.keys()) + for key in parameter_keys: + param_obj = self.parameters[key] + key_position = parameter_keys.index(key) + value = user_parameters[key] if key in user_parameters else self.parameters[key].default + final_params[key] = param_obj.compute_value(value, storage_dir, key_position) + for key in user_parameters: + if key not in final_params: + extra_params[key] = user_parameters[key] + return self._sanitize_param_dict(final_params), self._sanitize_param_dict(extra_params) + + def compute_command(self, user_parameters, storage_dir): + params, extra_params = self.compute_parameters(user_parameters, storage_dir) + command_with_params = self.command.format(**params) + command_arr = [command_with_params] + command_arr.extend([f"--{key} {value}" for key, value in extra_params.items()]) + return " ".join(command_arr) + + @staticmethod + def _sanitize_param_dict(param_dict): + return {str(key): quote(str(value)) for key, value in param_dict.items()} + + +class Parameter: + """A parameter in an MLproject entry point.""" + + def __init__(self, name, yaml_obj): + self.name = name + if is_string_type(yaml_obj): + self.type = yaml_obj + self.default = None + else: + self.type = yaml_obj.get("type", "string") + self.default = yaml_obj.get("default") + + def _compute_uri_value(self, user_param_value): + if not data_utils.is_uri(user_param_value): + raise ExecutionException( + f"Expected URI for parameter {self.name} but got {user_param_value}" + ) + return user_param_value + + def _compute_path_value(self, user_param_value, storage_dir, key_position): + local_path = get_local_path_or_none(user_param_value) + if local_path: + if not os.path.exists(local_path): + raise ExecutionException( + f"Got value {user_param_value} for parameter {self.name}, but no such file or " + "directory was found." + ) + return os.path.abspath(local_path) + target_sub_dir = f"param_{key_position}" + download_dir = os.path.join(storage_dir, target_sub_dir) + os.mkdir(download_dir) + return artifact_utils._download_artifact_from_uri( + artifact_uri=user_param_value, output_path=download_dir + ) + + def compute_value(self, param_value, storage_dir, key_position): + if storage_dir and self.type == "path": + return self._compute_path_value(param_value, storage_dir, key_position) + elif self.type == "uri": + return self._compute_uri_value(param_value) + else: + return param_value + + +class DatabricksSparkJobSpec: + def __init__(self, python_file, parameters, python_libraries): + self.python_file = python_file + self.parameters = parameters + self.python_libraries = python_libraries diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/projects/backend/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/projects/backend/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..7be22db86e2a1bbbc4bbc7fa2532c3d1aec593b8 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/projects/backend/__init__.py @@ -0,0 +1,8 @@ +""" +This module defines developer APIs for defining pluggable execution backends +for MLflow projects. See `MLflow Plugins <../../plugins.html>`_ for more information. +""" + +from mlflow.projects.backend.abstract_backend import AbstractBackend + +__all__ = ["AbstractBackend"] diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/projects/backend/__pycache__/__init__.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/projects/backend/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..288b55466fa25f152ad5025d0d44e46241627125 Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/projects/backend/__pycache__/__init__.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/projects/backend/__pycache__/abstract_backend.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/projects/backend/__pycache__/abstract_backend.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..305ad8847bde0ddedb88e47f8affd08e4d3b2b12 Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/projects/backend/__pycache__/abstract_backend.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/projects/backend/__pycache__/loader.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/projects/backend/__pycache__/loader.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..c3e02046e951fb56e344b197d5f2ff840ffda80a Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/projects/backend/__pycache__/loader.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/projects/backend/__pycache__/local.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/projects/backend/__pycache__/local.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..ff40b3886295479092ed9f12fb082704a4b3739d Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/projects/backend/__pycache__/local.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/projects/backend/abstract_backend.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/projects/backend/abstract_backend.py new file mode 100644 index 0000000000000000000000000000000000000000..62c74e1b601d2971d70f1638d4766eb805332798 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/projects/backend/abstract_backend.py @@ -0,0 +1,50 @@ +from abc import ABCMeta, abstractmethod + +from mlflow.utils.annotations import developer_stable + + +@developer_stable +class AbstractBackend: + """ + Abstract plugin class defining the interface needed to execute MLflow projects. You can define + subclasses of ``AbstractBackend`` and expose them as third-party plugins to enable running + MLflow projects against custom execution backends (e.g. to run projects against your team's + in-house cluster or job scheduler). See `MLflow Plugins <../../plugins.html>`_ for more + information. + """ + + __metaclass__ = ABCMeta + + @abstractmethod + def run( + self, + project_uri, + entry_point, + params, + version, + backend_config, + tracking_uri, + experiment_id, + ): + """ + Submit an entrypoint. It must return a SubmittedRun object to track the execution + + Args: + project_uri: URI of the project to execute, e.g. a local filesystem path + or a Git repository URI like https://github.com/mlflow/mlflow-example + entry_point: Entry point to run within the project. + params: Dict of parameters to pass to the entry point + version: For git-based projects, either a commit hash or a branch name. + backend_config: A dictionary, or a path to a JSON file (must end in '.json'), which + will be passed as config to the backend. The exact content which + should be provided is different for each execution backend and is + documented at https://www.mlflow.org/docs/latest/projects.html. + tracking_uri: URI of tracking server against which to log run information related + to project execution. + experiment_id: ID of experiment under which to launch the run. + + Returns: + A :py:class:`mlflow.projects.SubmittedRun`. This function is expected to run + the project asynchronously, i.e. it should trigger project execution and then + immediately return a `SubmittedRun` to track execution status. + """ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/projects/backend/loader.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/projects/backend/loader.py new file mode 100644 index 0000000000000000000000000000000000000000..22defd1b515b123def096e092e0b752fa9fecc72 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/projects/backend/loader.py @@ -0,0 +1,35 @@ +import logging + +from mlflow.projects.backend.local import LocalBackend +from mlflow.utils.plugins import get_entry_points + +ENTRYPOINT_GROUP_NAME = "mlflow.project_backend" + +_logger = logging.getLogger(__name__) + + +# Statically register backend defined in mlflow +MLFLOW_BACKENDS = { + "local": LocalBackend, +} + + +def load_backend(backend_name): + # Static backends + if backend_name in MLFLOW_BACKENDS: + return MLFLOW_BACKENDS[backend_name]() + + # backends from plugin + entrypoints = get_entry_points(ENTRYPOINT_GROUP_NAME) + if entrypoint := next((e for e in entrypoints if e.name == backend_name), None): + builder = entrypoint.load() + return builder() + + # TODO Should be a error when all backends are migrated here + _logger.warning( + "Backend '%s' is not available. Available plugins are %s", + backend_name, + [*entrypoints, *MLFLOW_BACKENDS.keys()], + ) + + return None diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/projects/backend/local.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/projects/backend/local.py new file mode 100644 index 0000000000000000000000000000000000000000..d428513fb4cf044cc7710b14ccd852a704a19b96 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/projects/backend/local.py @@ -0,0 +1,428 @@ +import logging +import os +import platform +import posixpath +import subprocess +import sys +from pathlib import Path + +import mlflow +from mlflow import tracking +from mlflow.environment_variables import ( + MLFLOW_KERBEROS_TICKET_CACHE, + MLFLOW_KERBEROS_USER, + MLFLOW_PYARROW_EXTRA_CONF, +) +from mlflow.exceptions import MlflowException +from mlflow.projects import env_type +from mlflow.projects.backend.abstract_backend import AbstractBackend +from mlflow.projects.submitted_run import LocalSubmittedRun +from mlflow.projects.utils import ( + MLFLOW_DOCKER_WORKDIR_PATH, + MLFLOW_LOCAL_BACKEND_RUN_ID_CONFIG, + PROJECT_BUILD_IMAGE, + PROJECT_DOCKER_ARGS, + PROJECT_DOCKER_AUTH, + PROJECT_ENV_MANAGER, + PROJECT_STORAGE_DIR, + PROJECT_SYNCHRONOUS, + fetch_and_validate_project, + get_entry_point_command, + get_or_create_run, + get_run_env_vars, + load_project, +) +from mlflow.store.artifact.artifact_repository_registry import get_artifact_repository +from mlflow.store.artifact.azure_blob_artifact_repo import AzureBlobArtifactRepository +from mlflow.store.artifact.gcs_artifact_repo import GCSArtifactRepository +from mlflow.store.artifact.hdfs_artifact_repo import HdfsArtifactRepository +from mlflow.store.artifact.local_artifact_repo import LocalArtifactRepository +from mlflow.store.artifact.s3_artifact_repo import S3ArtifactRepository +from mlflow.utils import env_manager as _EnvManager +from mlflow.utils.conda import get_or_create_conda_env +from mlflow.utils.databricks_utils import ( + get_databricks_env_vars, + is_in_databricks_runtime, +) +from mlflow.utils.environment import _PythonEnv +from mlflow.utils.file_utils import get_or_create_nfs_tmp_dir +from mlflow.utils.mlflow_tags import MLFLOW_PROJECT_ENV +from mlflow.utils.os import is_windows +from mlflow.utils.virtualenv import ( + _PYENV_ROOT_DIR, + _VIRTUALENV_ENVS_DIR, + _create_virtualenv, + _get_mlflow_virtualenv_root, + _get_virtualenv_extra_env_vars, + _get_virtualenv_name, +) + +_logger = logging.getLogger(__name__) + + +def _env_type_to_env_manager(env_typ): + if env_typ == env_type.CONDA: + return _EnvManager.CONDA + elif env_typ == env_type.PYTHON: + return _EnvManager.VIRTUALENV + elif env_typ == env_type.DOCKER: + return _EnvManager.LOCAL + + +class LocalBackend(AbstractBackend): + def run( + self, + project_uri, + entry_point, + params, + version, + backend_config, + tracking_uri, + experiment_id, + ): + work_dir = fetch_and_validate_project(project_uri, version, entry_point, params) + project = load_project(work_dir) + if MLFLOW_LOCAL_BACKEND_RUN_ID_CONFIG in backend_config: + run_id = backend_config[MLFLOW_LOCAL_BACKEND_RUN_ID_CONFIG] + else: + run_id = None + active_run = get_or_create_run( + run_id, project_uri, experiment_id, work_dir, version, entry_point, params + ) + command_args = [] + command_separator = " " + env_manager = backend_config[PROJECT_ENV_MANAGER] + synchronous = backend_config[PROJECT_SYNCHRONOUS] + docker_args = backend_config[PROJECT_DOCKER_ARGS] + storage_dir = backend_config[PROJECT_STORAGE_DIR] + build_image = backend_config[PROJECT_BUILD_IMAGE] + docker_auth = backend_config[PROJECT_DOCKER_AUTH] + # Select an appropriate env manager for the project env type + if env_manager is None: + env_manager = _env_type_to_env_manager(project.env_type) + else: + if project.env_type == env_type.PYTHON and env_manager == _EnvManager.CONDA: + raise MlflowException.invalid_parameter_value( + "python_env project cannot be executed using conda. Set `--env-manager` to " + "'virtualenv' or 'local' to execute this project." + ) + + # If a docker_env attribute is defined in MLproject then it takes precedence over conda yaml + # environments, so the project will be executed inside a docker container. + if project.docker_env: + from mlflow.projects.docker import ( + build_docker_image, + validate_docker_env, + validate_docker_installation, + ) + + tracking.MlflowClient().set_tag(active_run.info.run_id, MLFLOW_PROJECT_ENV, "docker") + validate_docker_env(project) + validate_docker_installation() + image = build_docker_image( + work_dir=work_dir, + repository_uri=project.name, + base_image=project.docker_env.get("image"), + run_id=active_run.info.run_id, + build_image=build_image, + docker_auth=docker_auth, + ) + command_args += _get_docker_command( + image=image, + active_run=active_run, + docker_args=docker_args, + volumes=project.docker_env.get("volumes"), + user_env_vars=project.docker_env.get("environment"), + ) + # Synchronously create a conda environment (even though this may take some time) + # to avoid failures due to multiple concurrent attempts to create the same conda env. + elif env_manager in {_EnvManager.VIRTUALENV, _EnvManager.UV}: + tracking.MlflowClient().set_tag(active_run.info.run_id, MLFLOW_PROJECT_ENV, env_manager) + command_separator = " && " + if project.env_type == env_type.CONDA: + python_env = _PythonEnv.from_conda_yaml(project.env_config_path) + else: + python_env = ( + _PythonEnv.from_yaml(project.env_config_path) + if project.env_config_path + else _PythonEnv() + ) + + if is_in_databricks_runtime(): + nfs_tmp_dir = get_or_create_nfs_tmp_dir() + env_root = Path(nfs_tmp_dir) / "envs" + pyenv_root_dir = str(env_root / _PYENV_ROOT_DIR) + virtualenv_root = env_root / _VIRTUALENV_ENVS_DIR + env_vars = _get_virtualenv_extra_env_vars(str(env_root)) + else: + pyenv_root_dir = None + virtualenv_root = Path(_get_mlflow_virtualenv_root()) + env_vars = None + work_dir_path = Path(work_dir) + env_name = _get_virtualenv_name(python_env, work_dir_path) + env_dir = virtualenv_root / env_name + activate_cmd = _create_virtualenv( + local_model_path=work_dir_path, + python_env=python_env, + env_dir=env_dir, + pyenv_root_dir=pyenv_root_dir, + env_manager=env_manager, + extra_env=env_vars, + ) + command_args += [activate_cmd] + elif env_manager == _EnvManager.CONDA: + tracking.MlflowClient().set_tag(active_run.info.run_id, MLFLOW_PROJECT_ENV, "conda") + command_separator = " && " + conda_env = get_or_create_conda_env(project.env_config_path) + command_args += conda_env.get_activate_command() + + # In synchronous mode, run the entry point command in a blocking fashion, sending status + # updates to the tracking server when finished. Note that the run state may not be + # persisted to the tracking server if interrupted + if synchronous: + command_args += get_entry_point_command(project, entry_point, params, storage_dir) + command_str = command_separator.join(command_args) + return _run_entry_point( + command_str, work_dir, experiment_id, run_id=active_run.info.run_id + ) + # Otherwise, invoke `mlflow run` in a subprocess + return _invoke_mlflow_run_subprocess( + work_dir=work_dir, + entry_point=entry_point, + parameters=params, + experiment_id=experiment_id, + env_manager=env_manager, + docker_args=docker_args, + storage_dir=storage_dir, + run_id=active_run.info.run_id, + ) + + +def _invoke_mlflow_run_subprocess( + work_dir, entry_point, parameters, experiment_id, env_manager, docker_args, storage_dir, run_id +): + """ + Run an MLflow project asynchronously by invoking ``mlflow run`` in a subprocess, returning + a SubmittedRun that can be used to query run status. + """ + _logger.info("=== Asynchronously launching MLflow run with ID %s ===", run_id) + mlflow_run_arr = _build_mlflow_run_cmd( + uri=work_dir, + entry_point=entry_point, + docker_args=docker_args, + storage_dir=storage_dir, + env_manager=env_manager, + run_id=run_id, + parameters=parameters, + ) + env_vars = get_run_env_vars(run_id, experiment_id) + env_vars.update(get_databricks_env_vars(mlflow.get_tracking_uri())) + mlflow_run_subprocess = _run_mlflow_run_cmd(mlflow_run_arr, env_vars) + return LocalSubmittedRun(run_id, mlflow_run_subprocess) + + +def _build_mlflow_run_cmd( + uri, entry_point, docker_args, storage_dir, env_manager, run_id, parameters +): + """ + Build and return an array containing an ``mlflow run`` command that can be invoked to locally + run the project at the specified URI. + """ + mlflow_run_arr = ["mlflow", "run", uri, "-e", entry_point, "--run-id", run_id] + if docker_args is not None: + for key, value in docker_args.items(): + args = key if isinstance(value, bool) else f"{key}={value}" + mlflow_run_arr.extend(["--docker-args", args]) + if storage_dir is not None: + mlflow_run_arr.extend(["--storage-dir", storage_dir]) + mlflow_run_arr.extend(["--env-manager", env_manager]) + for key, value in parameters.items(): + mlflow_run_arr.extend(["-P", f"{key}={value}"]) + return mlflow_run_arr + + +def _run_mlflow_run_cmd(mlflow_run_arr, env_map): + """ + Invoke ``mlflow run`` in a subprocess, which in turn runs the entry point in a child process. + Returns a handle to the subprocess. Popen launched to invoke ``mlflow run``. + """ + final_env = os.environ.copy() + final_env.update(env_map) + # Launch `mlflow run` command as the leader of its own process group so that we can do a + # best-effort cleanup of all its descendant processes if needed + if sys.platform == "win32": + return subprocess.Popen( + mlflow_run_arr, + env=final_env, + text=True, + creationflags=subprocess.CREATE_NEW_PROCESS_GROUP, + ) + else: + return subprocess.Popen(mlflow_run_arr, env=final_env, text=True, preexec_fn=os.setsid) + + +def _run_entry_point(command, work_dir, experiment_id, run_id): + """ + Run an entry point command in a subprocess, returning a SubmittedRun that can be used to + query the run's status. + + Args: + command: Entry point command to run + work_dir: Working directory in which to run the command + run_id: MLflow run ID associated with the entry point execution. + """ + env = os.environ.copy() + env.update(get_run_env_vars(run_id, experiment_id)) + env.update(get_databricks_env_vars(tracking_uri=mlflow.get_tracking_uri())) + _logger.info("=== Running command '%s' in run with ID '%s' === ", command, run_id) + # in case os name is not 'nt', we are not running on windows. It introduces + # bash command otherwise. + if not is_windows(): + process = subprocess.Popen(["bash", "-c", command], close_fds=True, cwd=work_dir, env=env) + else: + # process = subprocess.Popen(command, close_fds=True, cwd=work_dir, env=env) + process = subprocess.Popen(["cmd", "/c", command], close_fds=True, cwd=work_dir, env=env) + return LocalSubmittedRun(run_id, process) + + +def _get_docker_command(image, active_run, docker_args=None, volumes=None, user_env_vars=None): + from mlflow.projects.docker import get_docker_tracking_cmd_and_envs + + docker_path = "docker" + cmd = [docker_path, "run", "--rm"] + + if docker_args: + for name, value in docker_args.items(): + # Passed just the name as boolean flag + if isinstance(value, bool) and value: + if len(name) == 1: + cmd += ["-" + name] + else: + cmd += ["--" + name] + else: + # Passed name=value + if len(name) == 1: + cmd += ["-" + name, value] + else: + cmd += ["--" + name, value] + + env_vars = get_run_env_vars( + run_id=active_run.info.run_id, experiment_id=active_run.info.experiment_id + ) + tracking_uri = tracking.get_tracking_uri() + tracking_cmds, tracking_envs = get_docker_tracking_cmd_and_envs(tracking_uri) + artifact_cmds, artifact_envs = _get_docker_artifact_storage_cmd_and_envs( + active_run.info.artifact_uri + ) + + cmd += tracking_cmds + artifact_cmds + env_vars.update(tracking_envs) + env_vars.update(artifact_envs) + if user_env_vars is not None: + for user_entry in user_env_vars: + if isinstance(user_entry, list): + # User has defined a new environment variable for the docker environment + env_vars[user_entry[0]] = user_entry[1] + else: + # User wants to copy an environment variable from system environment + system_var = os.environ.get(user_entry) + if system_var is None: + raise MlflowException( + "This project expects the {} environment variables to " + "be set on the machine running the project, but {} was " + "not set. Please ensure all expected environment variables " + "are set".format(", ".join(user_env_vars), user_entry) + ) + env_vars[user_entry] = system_var + + if volumes is not None: + for v in volumes: + cmd += ["-v", v] + + for key, value in env_vars.items(): + cmd += ["-e", f"{key}={value}"] + cmd += [image.tags[0]] + return cmd + + +def _get_local_artifact_cmd_and_envs(artifact_repo): + artifact_dir = artifact_repo.artifact_dir + container_path = artifact_dir + if not os.path.isabs(container_path): + container_path = os.path.join(MLFLOW_DOCKER_WORKDIR_PATH, container_path) + container_path = os.path.normpath(container_path) + abs_artifact_dir = os.path.abspath(artifact_dir) + return ["-v", f"{abs_artifact_dir}:{container_path}"], {} + + +def _get_s3_artifact_cmd_and_envs(artifact_repo): + if platform.system() == "Windows": + win_user_dir = os.environ["USERPROFILE"] + aws_path = os.path.join(win_user_dir, ".aws") + else: + aws_path = posixpath.expanduser("~/.aws") + + volumes = [] + if posixpath.exists(aws_path): + volumes = ["-v", "{}:{}".format(str(aws_path), "/.aws")] + envs = { + "AWS_SECRET_ACCESS_KEY": os.environ.get("AWS_SECRET_ACCESS_KEY"), + "AWS_ACCESS_KEY_ID": os.environ.get("AWS_ACCESS_KEY_ID"), + "MLFLOW_S3_ENDPOINT_URL": os.environ.get("MLFLOW_S3_ENDPOINT_URL"), + "MLFLOW_S3_IGNORE_TLS": os.environ.get("MLFLOW_S3_IGNORE_TLS"), + } + envs = {k: v for k, v in envs.items() if v is not None} + return volumes, envs + + +def _get_azure_blob_artifact_cmd_and_envs(artifact_repo): + envs = { + "AZURE_STORAGE_CONNECTION_STRING": os.environ.get("AZURE_STORAGE_CONNECTION_STRING"), + "AZURE_STORAGE_ACCESS_KEY": os.environ.get("AZURE_STORAGE_ACCESS_KEY"), + } + envs = {k: v for k, v in envs.items() if v is not None} + return [], envs + + +def _get_gcs_artifact_cmd_and_envs(artifact_repo): + cmds = [] + envs = {} + + if "GOOGLE_APPLICATION_CREDENTIALS" in os.environ: + credentials_path = os.environ["GOOGLE_APPLICATION_CREDENTIALS"] + cmds = ["-v", f"{credentials_path}:/.gcs"] + envs["GOOGLE_APPLICATION_CREDENTIALS"] = "/.gcs" + return cmds, envs + + +def _get_hdfs_artifact_cmd_and_envs(artifact_repo): + cmds = [] + envs = { + "MLFLOW_KERBEROS_TICKET_CACHE": MLFLOW_KERBEROS_TICKET_CACHE.get(), + "MLFLOW_KERBEROS_USER": MLFLOW_KERBEROS_USER.get(), + "MLFLOW_PYARROW_EXTRA_CONF": MLFLOW_PYARROW_EXTRA_CONF.get(), + } + envs = {k: v for k, v in envs.items() if v is not None} + + if "MLFLOW_KERBEROS_TICKET_CACHE" in envs: + ticket_cache = envs["MLFLOW_KERBEROS_TICKET_CACHE"] + cmds = ["-v", f"{ticket_cache}:{ticket_cache}"] + return cmds, envs + + +_artifact_storages = { + LocalArtifactRepository: _get_local_artifact_cmd_and_envs, + S3ArtifactRepository: _get_s3_artifact_cmd_and_envs, + AzureBlobArtifactRepository: _get_azure_blob_artifact_cmd_and_envs, + HdfsArtifactRepository: _get_hdfs_artifact_cmd_and_envs, + GCSArtifactRepository: _get_gcs_artifact_cmd_and_envs, +} + + +def _get_docker_artifact_storage_cmd_and_envs(artifact_uri): + artifact_repo = get_artifact_repository(artifact_uri) + _get_cmd_and_envs = _artifact_storages.get(type(artifact_repo)) + if _get_cmd_and_envs is not None: + return _get_cmd_and_envs(artifact_repo) + else: + return [], {} diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/projects/databricks.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/projects/databricks.py new file mode 100644 index 0000000000000000000000000000000000000000..3c4a1f08724e6eed9ba38ebc74bfcf620f14ca7c --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/projects/databricks.py @@ -0,0 +1,611 @@ +import hashlib +import json +import logging +import os +import posixpath +import re +import tempfile +import textwrap +import time +import uuid +from pathlib import Path +from shlex import quote + +from mlflow import tracking +from mlflow.entities import RunStatus +from mlflow.environment_variables import MLFLOW_EXPERIMENT_ID, MLFLOW_RUN_ID, MLFLOW_TRACKING_URI +from mlflow.exceptions import ExecutionException, MlflowException +from mlflow.projects.submitted_run import SubmittedRun +from mlflow.projects.utils import MLFLOW_LOCAL_BACKEND_RUN_ID_CONFIG +from mlflow.protos.databricks_pb2 import INVALID_PARAMETER_VALUE +from mlflow.utils import databricks_utils, file_utils, rest_utils +from mlflow.utils.mlflow_tags import ( + MLFLOW_DATABRICKS_RUN_URL, + MLFLOW_DATABRICKS_SHELL_JOB_ID, + MLFLOW_DATABRICKS_SHELL_JOB_RUN_ID, + MLFLOW_DATABRICKS_WEBAPP_URL, +) +from mlflow.utils.uri import is_databricks_uri, is_http_uri +from mlflow.version import VERSION, is_release_version + +# Base directory within driver container for storing files related to MLflow +DB_CONTAINER_BASE = "/databricks/mlflow" +# Base directory within driver container for storing project archives +DB_TARFILE_BASE = posixpath.join(DB_CONTAINER_BASE, "project-tars") +# Base directory directory within driver container for storing extracted project directories +DB_PROJECTS_BASE = posixpath.join(DB_CONTAINER_BASE, "projects") +# Name to use for project directory when archiving it for upload to DBFS; the TAR will contain +# a single directory with this name +DB_TARFILE_ARCHIVE_NAME = "mlflow-project" +# Base directory within DBFS for storing code for project runs for experiments +DBFS_EXPERIMENT_DIR_BASE = "mlflow-experiments" + + +_logger = logging.getLogger(__name__) + +_MLFLOW_GIT_URI_REGEX = re.compile(r"^git\+https://github.com/[\w-]+/mlflow") + + +def _is_mlflow_git_uri(s): + return bool(_MLFLOW_GIT_URI_REGEX.match(s)) + + +def _contains_mlflow_git_uri(libraries): + for lib in libraries: + package = lib.get("pypi", {}).get("package") + if package and _is_mlflow_git_uri(package): + return True + return False + + +def before_run_validations(tracking_uri, backend_config): + """Validations to perform before running a project on Databricks.""" + if backend_config is None: + raise ExecutionException( + "Backend spec must be provided when launching MLflow project runs on Databricks." + ) + elif "existing_cluster_id" in backend_config: + raise MlflowException( + message=( + "MLflow Project runs on Databricks must provide a *new cluster* specification." + " Project execution against existing clusters is not currently supported. For more" + " information, see https://mlflow.org/docs/latest/projects.html" + "#run-an-mlflow-project-on-databricks" + ), + error_code=INVALID_PARAMETER_VALUE, + ) + if not is_databricks_uri(tracking_uri) and not is_http_uri(tracking_uri): + raise ExecutionException( + "When running on Databricks, the MLflow tracking URI must be of the form " + "'databricks' or 'databricks://profile', or a remote HTTP URI accessible to both the " + "current client and code running on Databricks. Got local tracking URI " + f"{tracking_uri}. Please specify a valid tracking URI via mlflow.set_tracking_uri or " + "by setting the MLFLOW_TRACKING_URI environment variable." + ) + + +class DatabricksJobRunner: + """ + Helper class for running an MLflow project as a Databricks Job. + + Args: + databricks_profile: Optional Databricks CLI profile to use to fetch hostname & + authentication information when making Databricks API requests. + """ + + def __init__(self, databricks_profile_uri): + self.databricks_profile_uri = databricks_profile_uri + + def _databricks_api_request(self, endpoint, method, **kwargs): + host_creds = databricks_utils.get_databricks_host_creds(self.databricks_profile_uri) + return rest_utils.http_request_safe( + host_creds=host_creds, endpoint=endpoint, method=method, **kwargs + ) + + def _jobs_runs_submit(self, req_body): + response = self._databricks_api_request( + endpoint="/api/2.0/jobs/runs/submit", method="POST", json=req_body + ) + return json.loads(response.text) + + def _upload_to_dbfs(self, src_path, dbfs_fuse_uri): + """ + Upload the file at `src_path` to the specified DBFS URI within the Databricks workspace + corresponding to the default Databricks CLI profile. + """ + _logger.info("=== Uploading project to DBFS path %s ===", dbfs_fuse_uri) + http_endpoint = dbfs_fuse_uri + with open(src_path, "rb") as f: + try: + self._databricks_api_request(endpoint=http_endpoint, method="POST", data=f) + except MlflowException as e: + if "Error 409" in e.message and "File already exists" in e.message: + _logger.info("=== Did not overwrite existing DBFS path %s ===", dbfs_fuse_uri) + else: + raise e + + def _dbfs_path_exists(self, dbfs_path): + """ + Return True if the passed-in path exists in DBFS for the workspace corresponding to the + default Databricks CLI profile. The path is expected to be a relative path to the DBFS root + directory, e.g. 'path/to/file'. + """ + host_creds = databricks_utils.get_databricks_host_creds(self.databricks_profile_uri) + response = rest_utils.http_request( + host_creds=host_creds, + endpoint="/api/2.0/dbfs/get-status", + method="GET", + json={"path": f"/{dbfs_path}"}, + ) + try: + json_response_obj = json.loads(response.text) + except Exception: + raise MlflowException( + f"API request to check existence of file at DBFS path {dbfs_path} failed with " + f"status code {response.status_code}. Response body: {response.text}" + ) + # If request fails with a RESOURCE_DOES_NOT_EXIST error, the file does not exist on DBFS + error_code_field = "error_code" + if error_code_field in json_response_obj: + if json_response_obj[error_code_field] == "RESOURCE_DOES_NOT_EXIST": + return False + raise ExecutionException( + f"Got unexpected error response when checking whether file {dbfs_path} " + f"exists in DBFS: {json_response_obj}" + ) + return True + + def _upload_project_to_dbfs(self, project_dir, experiment_id): + """ + Tars a project directory into an archive in a temp dir and uploads it to DBFS, returning + the HDFS-style URI of the tarball in DBFS (e.g. dbfs:/path/to/tar). + + Args: + project_dir: Path to a directory containing an MLflow project to upload to DBFS (e.g. + a directory containing an MLproject file). + """ + with tempfile.TemporaryDirectory() as temp_tarfile_dir: + temp_tar_filename = os.path.join(temp_tarfile_dir, "project.tar.gz") + + def custom_filter(x): + return None if os.path.basename(x.name) == "mlruns" else x + + directory_size = file_utils._get_local_project_dir_size(project_dir) + _logger.info( + f"=== Creating tarball from {project_dir} in temp directory {temp_tarfile_dir} ===" + ) + _logger.info(f"=== Total file size to compress: {directory_size} KB ===") + file_utils.make_tarfile( + temp_tar_filename, project_dir, DB_TARFILE_ARCHIVE_NAME, custom_filter=custom_filter + ) + with open(temp_tar_filename, "rb") as tarred_project: + tarfile_hash = hashlib.sha256(tarred_project.read()).hexdigest() + # TODO: Get subdirectory for experiment from the tracking server + dbfs_path = posixpath.join( + DBFS_EXPERIMENT_DIR_BASE, + str(experiment_id), + "projects-code", + f"{tarfile_hash}.tar.gz", + ) + tar_size = file_utils._get_local_file_size(temp_tar_filename) + dbfs_fuse_uri = posixpath.join("/dbfs", dbfs_path) + if not self._dbfs_path_exists(dbfs_path): + _logger.info( + f"=== Uploading project tarball (size: {tar_size} KB) to {dbfs_fuse_uri} ===" + ) + self._upload_to_dbfs(temp_tar_filename, dbfs_fuse_uri) + _logger.info("=== Finished uploading project to %s ===", dbfs_fuse_uri) + else: + _logger.info("=== Project already exists in DBFS ===") + return dbfs_fuse_uri + + def _run_shell_command_job(self, project_uri, command, env_vars, cluster_spec): + """ + Run the specified shell command on a Databricks cluster. + + Args: + project_uri: URI of the project from which the shell command originates. + command: Shell command to run. + env_vars: Environment variables to set in the process running ``command``. + cluster_spec: Dictionary containing a `Databricks cluster specification + `_ + or a `Databricks new cluster specification + `_ + to use when launching a run. If you specify libraries, this function + will add MLflow to the library list. This function does not support + installation of conda environment libraries on the workers. + + Returns: + ID of the Databricks job run. Can be used to query the run's status via the + Databricks `Runs Get `_ API. + """ + if is_release_version(): + mlflow_lib = {"pypi": {"package": f"mlflow=={VERSION}"}} + else: + # When running a non-release version as the client the same version will not be + # available within Databricks. + _logger.warning( + "Your client is running a non-release version of MLflow. " + "This version is not available on the databricks runtime. " + "MLflow will fallback the MLflow version provided by the runtime. " + "This might lead to unforeseen issues. " + ) + mlflow_lib = {"pypi": {"package": f"'mlflow<={VERSION}'"}} + + # Check syntax of JSON - if it contains libraries and new_cluster, pull those out + if "new_cluster" in cluster_spec: + # Libraries are optional, so we don't require that this be specified + cluster_spec_libraries = cluster_spec.get("libraries", []) + libraries = ( + # This is for development purposes only. If the cluster spec already includes + # an MLflow Git URI, then we don't append `mlflow_lib` to avoid having + # two different pip requirements for mlflow. + cluster_spec_libraries + if _contains_mlflow_git_uri(cluster_spec_libraries) + else cluster_spec_libraries + [mlflow_lib] + ) + cluster_spec = cluster_spec["new_cluster"] + else: + libraries = [mlflow_lib] + + # Make jobs API request to launch run. + req_body_json = { + "run_name": f"MLflow Run for {project_uri}", + "new_cluster": cluster_spec, + "shell_command_task": {"command": command, "env_vars": env_vars}, + "libraries": libraries, + } + _logger.info("=== Submitting a run to execute the MLflow project... ===") + run_submit_res = self._jobs_runs_submit(req_body_json) + return run_submit_res["run_id"] + + def run_databricks_spark_job( + self, + project_uri, + work_dir, + experiment_id, + cluster_spec, + run_id, + project_spec, + entry_point, + parameters, + ): + from mlflow.utils.file_utils import get_or_create_tmp_dir + + dbfs_fuse_uri = self._upload_project_to_dbfs(work_dir, experiment_id) + + env_vars = { + MLFLOW_TRACKING_URI.name: "databricks", + MLFLOW_EXPERIMENT_ID.name: experiment_id, + MLFLOW_RUN_ID.name: run_id, + } + _logger.info( + "=== Running databricks spark job of project %s on Databricks ===", project_uri + ) + + if project_spec.databricks_spark_job_spec.python_file is not None: + if entry_point != "main" or parameters: + _logger.warning( + "You configured Databricks spark job python_file and parameters within the " + "MLProject file's databricks_spark_job section. '--entry-point' " + "and '--param-list' arguments specified in the 'mlflow run' command are " + "ignored." + ) + job_code_file = project_spec.databricks_spark_job_spec.python_file + job_parameters = project_spec.databricks_spark_job_spec.parameters + else: + command = project_spec.get_entry_point(entry_point).compute_command(parameters, None) + command_splits = command.split(" ") + if command_splits[0] != "python": + raise MlflowException( + "Databricks spark job only supports 'python' command in the entry point " + "configuration." + ) + job_code_file = command_splits[1] + job_parameters = command_splits[2:] + + tmp_dir = Path(get_or_create_tmp_dir()) + origin_job_code = (Path(work_dir) / job_code_file).read_text() + job_code_filename = f"{uuid.uuid4().hex}.py" + new_job_code_file = tmp_dir / job_code_filename + + project_dir, extracting_tar_command = _get_project_dir_and_extracting_tar_command( + dbfs_fuse_uri + ) + + env_vars_str = json.dumps(env_vars) + new_job_code_file.write_text( + f""" +import os +import subprocess +os.environ.update({env_vars_str}) + +extracting_tar_command = \"\"\" +{extracting_tar_command} +\"\"\" +subprocess.check_call(extracting_tar_command, shell=True) + +os.chdir('{project_dir}') +{origin_job_code} +""" + ) + + dbfs_job_code_file_path = posixpath.join( + DBFS_EXPERIMENT_DIR_BASE, + str(experiment_id), + "projects-code", + job_code_filename, + ) + job_code_file_dbfs_fuse_uri = posixpath.join("/dbfs", dbfs_job_code_file_path) + if not self._dbfs_path_exists(dbfs_job_code_file_path): + self._upload_to_dbfs(str(new_job_code_file), job_code_file_dbfs_fuse_uri) + + libraries_config = [ + {"pypi": {"package": python_lib}} + for python_lib in project_spec.databricks_spark_job_spec.python_libraries + ] + # Make Databricks Spark jobs API request to launch run. + req_body_json = { + "run_name": f"MLflow Run for {project_uri}", + "new_cluster": cluster_spec, + "libraries": libraries_config, + "spark_python_task": { + "python_file": f"dbfs:/{dbfs_job_code_file_path}", + "parameters": job_parameters, + }, + } + + _logger.info("=== Submitting a run to execute the MLflow project... ===") + run_submit_res = self._jobs_runs_submit(req_body_json) + return run_submit_res["run_id"] + + def run_databricks( + self, + uri, + entry_point, + work_dir, + parameters, + experiment_id, + cluster_spec, + run_id, + env_manager, + ): + tracking_uri = _get_tracking_uri_for_run() + dbfs_fuse_uri = self._upload_project_to_dbfs(work_dir, experiment_id) + + env_vars = { + MLFLOW_TRACKING_URI.name: tracking_uri, + MLFLOW_EXPERIMENT_ID.name: experiment_id, + } + _logger.info("=== Running entry point %s of project %s on Databricks ===", entry_point, uri) + # Launch run on Databricks + command = _get_databricks_run_cmd( + dbfs_fuse_uri, run_id, entry_point, parameters, env_manager + ) + return self._run_shell_command_job(uri, command, env_vars, cluster_spec) + + def _get_status(self, databricks_run_id): + run_state = self.get_run_result_state(databricks_run_id) + if run_state is None: + return RunStatus.RUNNING + if run_state == "SUCCESS": + return RunStatus.FINISHED + return RunStatus.FAILED + + def get_status(self, databricks_run_id): + return RunStatus.to_string(self._get_status(databricks_run_id)) + + def get_run_result_state(self, databricks_run_id): + """ + Get the run result state (string) of a Databricks job run. + + Args: + databricks_run_id: Integer Databricks job run ID. + + Returns: + `RunResultState `_ or + None if the run is still active. + """ + res = self.jobs_runs_get(databricks_run_id) + return res["state"].get("result_state", None) + + def jobs_runs_cancel(self, databricks_run_id): + response = self._databricks_api_request( + endpoint="/api/2.0/jobs/runs/cancel", method="POST", json={"run_id": databricks_run_id} + ) + return json.loads(response.text) + + def jobs_runs_get(self, databricks_run_id): + response = self._databricks_api_request( + endpoint="/api/2.0/jobs/runs/get", method="GET", params={"run_id": databricks_run_id} + ) + return json.loads(response.text) + + +def _get_tracking_uri_for_run(): + uri = tracking.get_tracking_uri() + if uri.startswith("databricks"): + return "databricks" + return uri + + +def _get_cluster_mlflow_run_cmd(project_dir, run_id, entry_point, parameters, env_manager): + cmd = [ + "mlflow", + "run", + project_dir, + "--entry-point", + entry_point, + ] + if env_manager: + cmd += ["--env-manager", env_manager] + mlflow_run_arr = list(map(quote, cmd)) + if run_id: + mlflow_run_arr.extend(["-c", json.dumps({MLFLOW_LOCAL_BACKEND_RUN_ID_CONFIG: run_id})]) + if parameters: + for key, value in parameters.items(): + mlflow_run_arr.extend(["-P", f"{key}={value}"]) + return mlflow_run_arr + + +def _get_project_dir_and_extracting_tar_command(dbfs_fuse_tar_uri): + # Strip ".gz" and ".tar" file extensions from base filename of the tarfile + tar_hash = posixpath.splitext(posixpath.splitext(posixpath.basename(dbfs_fuse_tar_uri))[0])[0] + container_tar_path = posixpath.abspath( + posixpath.join(DB_TARFILE_BASE, posixpath.basename(dbfs_fuse_tar_uri)) + ) + project_dir = posixpath.join(DB_PROJECTS_BASE, tar_hash) + command = textwrap.dedent( + f""" + # Make local directories in the container into which to copy/extract the tarred project + mkdir -p {DB_TARFILE_BASE} {DB_PROJECTS_BASE} && + # Rsync from DBFS FUSE to avoid copying archive into local filesystem if it already exists + rsync -a -v --ignore-existing {dbfs_fuse_tar_uri} {DB_TARFILE_BASE} && + # Extract project into a temporary directory. We don't extract directly into the desired + # directory as tar extraction isn't guaranteed to be atomic + cd $(mktemp -d) && + tar --no-same-owner -xzvf {container_tar_path} && + # Atomically move the extracted project into the desired directory + mv -T {DB_TARFILE_ARCHIVE_NAME} {project_dir}""" + ) + return project_dir, command + + +def _get_databricks_run_cmd(dbfs_fuse_tar_uri, run_id, entry_point, parameters, env_manager): + """ + Generate MLflow CLI command to run on Databricks cluster in order to launch a run on Databricks. + """ + project_dir, extracting_tar_command = _get_project_dir_and_extracting_tar_command( + dbfs_fuse_tar_uri + ) + mlflow_run_arr = _get_cluster_mlflow_run_cmd( + project_dir, + run_id, + entry_point, + parameters, + env_manager, + ) + mlflow_run_cmd = " ".join([quote(elem) for elem in mlflow_run_arr]) + shell_command = textwrap.dedent( + f""" + export PATH=$PATH:$DB_HOME/python/bin && + mlflow --version && + {extracting_tar_command} && + {mlflow_run_cmd} + """ + ) + return ["bash", "-c", shell_command] + + +def run_databricks( + remote_run, uri, entry_point, work_dir, parameters, experiment_id, cluster_spec, env_manager +): + """ + Run the project at the specified URI on Databricks, returning a ``SubmittedRun`` that can be + used to query the run's status or wait for the resulting Databricks Job run to terminate. + """ + run_id = remote_run.info.run_id + db_job_runner = DatabricksJobRunner(databricks_profile_uri=tracking.get_tracking_uri()) + db_run_id = db_job_runner.run_databricks( + uri, entry_point, work_dir, parameters, experiment_id, cluster_spec, run_id, env_manager + ) + submitted_run = DatabricksSubmittedRun(db_run_id, run_id, db_job_runner) + submitted_run._print_description_and_log_tags() + return submitted_run + + +def run_databricks_spark_job( + remote_run, + uri, + work_dir, + experiment_id, + cluster_spec, + project_spec, + entry_point, + parameters, +): + run_id = remote_run.info.run_id + db_job_runner = DatabricksJobRunner(databricks_profile_uri=tracking.get_tracking_uri()) + db_run_id = db_job_runner.run_databricks_spark_job( + uri, + work_dir, + experiment_id, + cluster_spec, + run_id, + project_spec, + entry_point, + parameters, + ) + submitted_run = DatabricksSubmittedRun(db_run_id, run_id, db_job_runner) + submitted_run._print_description_and_log_tags() + return submitted_run + + +class DatabricksSubmittedRun(SubmittedRun): + """ + Instance of SubmittedRun corresponding to a Databricks Job run launched to run an MLflow + project. Note that run_id may be None, e.g. if we did not launch the run against a tracking + server accessible to the local client. + + Args: + databricks_run_id: Run ID of the launched Databricks Job. + mlflow_run_id: ID of the MLflow project run. + databricks_job_runner: Instance of ``DatabricksJobRunner`` used to make Databricks API + requests. + """ + + # How often to poll run status when waiting on a run + POLL_STATUS_INTERVAL = 30 + + def __init__(self, databricks_run_id, mlflow_run_id, databricks_job_runner): + super().__init__() + self._databricks_run_id = databricks_run_id + self._mlflow_run_id = mlflow_run_id + self._job_runner = databricks_job_runner + + def _print_description_and_log_tags(self): + _logger.info( + "=== Launched MLflow run as Databricks job run with ID %s." + " Getting run status page URL... ===", + self._databricks_run_id, + ) + run_info = self._job_runner.jobs_runs_get(self._databricks_run_id) + jobs_page_url = run_info["run_page_url"] + _logger.info("=== Check the run's status at %s ===", jobs_page_url) + host_creds = databricks_utils.get_databricks_host_creds( + self._job_runner.databricks_profile_uri + ) + tracking.MlflowClient().set_tag( + self._mlflow_run_id, MLFLOW_DATABRICKS_RUN_URL, jobs_page_url + ) + tracking.MlflowClient().set_tag( + self._mlflow_run_id, MLFLOW_DATABRICKS_SHELL_JOB_RUN_ID, self._databricks_run_id + ) + tracking.MlflowClient().set_tag( + self._mlflow_run_id, MLFLOW_DATABRICKS_WEBAPP_URL, host_creds.host + ) + job_id = run_info.get("job_id") + # In some releases of Databricks we do not return the job ID. We start including it in DB + # releases 2.80 and above. + if job_id is not None: + tracking.MlflowClient().set_tag( + self._mlflow_run_id, MLFLOW_DATABRICKS_SHELL_JOB_ID, job_id + ) + + @property + def run_id(self): + return self._mlflow_run_id + + def wait(self): + result_state = self._job_runner.get_run_result_state(self._databricks_run_id) + while result_state is None: + time.sleep(self.POLL_STATUS_INTERVAL) + result_state = self._job_runner.get_run_result_state(self._databricks_run_id) + return result_state == "SUCCESS" + + def cancel(self): + self._job_runner.jobs_runs_cancel(self._databricks_run_id) + self.wait() + + def get_status(self): + return self._job_runner.get_status(self._databricks_run_id) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/projects/docker.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/projects/docker.py new file mode 100644 index 0000000000000000000000000000000000000000..62350b1fca06ddf11a4d2d6f2a327c336c0df775 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/projects/docker.py @@ -0,0 +1,166 @@ +import logging +import os +import posixpath +import shutil +import subprocess +import tempfile +import urllib.parse +import urllib.request + +import docker +from mlflow import tracking +from mlflow.environment_variables import MLFLOW_TRACKING_URI +from mlflow.exceptions import ExecutionException +from mlflow.projects.utils import MLFLOW_DOCKER_WORKDIR_PATH +from mlflow.utils import file_utils, process +from mlflow.utils.databricks_utils import get_databricks_env_vars +from mlflow.utils.file_utils import _handle_readonly_on_windows +from mlflow.utils.git_utils import get_git_commit +from mlflow.utils.mlflow_tags import MLFLOW_DOCKER_IMAGE_ID, MLFLOW_DOCKER_IMAGE_URI + +_logger = logging.getLogger(__name__) + +_GENERATED_DOCKERFILE_NAME = "Dockerfile.mlflow-autogenerated" +_MLFLOW_DOCKER_TRACKING_DIR_PATH = "/mlflow/tmp/mlruns" +_PROJECT_TAR_ARCHIVE_NAME = "mlflow-project-docker-build-context" + + +def validate_docker_installation(): + """ + Verify if Docker is installed and running on host machine. + """ + if shutil.which("docker") is None: + raise ExecutionException( + "Could not find Docker executable. " + "Ensure Docker is installed as per the instructions " + "at https://docs.docker.com/install/overview/." + ) + + cmd = ["docker", "info"] + prc = process._exec_cmd( + cmd, + throw_on_error=False, + capture_output=False, + stdout=subprocess.PIPE, + stderr=subprocess.STDOUT, + ) + if prc.returncode != 0: + joined_cmd = " ".join(cmd) + raise ExecutionException( + f"Ran `{joined_cmd}` to ensure docker daemon is running but it failed " + f"with the following output:\n{prc.stdout}" + ) + + +def validate_docker_env(project): + if not project.name: + raise ExecutionException( + "Project name in MLProject must be specified when using docker for image tagging." + ) + if not project.docker_env.get("image"): + raise ExecutionException( + "Project with docker environment must specify the docker image " + "to use via an 'image' field under the 'docker_env' field." + ) + + +def build_docker_image(work_dir, repository_uri, base_image, run_id, build_image, docker_auth): + """ + Build a docker image containing the project in `work_dir`, using the base image. + """ + image_uri = _get_docker_image_uri(repository_uri=repository_uri, work_dir=work_dir) + client = docker.from_env() + if docker_auth is not None: + client.login(**docker_auth) + + if not build_image: + if not client.images.list(name=base_image): + _logger.info(f"Pulling {base_image}") + image = client.images.pull(base_image) + else: + _logger.info(f"{base_image} already exists") + image = client.images.get(base_image) + image_uri = base_image + else: + dockerfile = ( + f"FROM {base_image}\n COPY {_PROJECT_TAR_ARCHIVE_NAME}/ {MLFLOW_DOCKER_WORKDIR_PATH}\n" + f" WORKDIR {MLFLOW_DOCKER_WORKDIR_PATH}\n" + ) + build_ctx_path = _create_docker_build_ctx(work_dir, dockerfile) + with open(build_ctx_path, "rb") as docker_build_ctx: + _logger.info("=== Building docker image %s ===", image_uri) + image, _ = client.images.build( + tag=image_uri, + forcerm=True, + dockerfile=posixpath.join(_PROJECT_TAR_ARCHIVE_NAME, _GENERATED_DOCKERFILE_NAME), + fileobj=docker_build_ctx, + custom_context=True, + encoding="gzip", + ) + try: + os.remove(build_ctx_path) + except Exception: + _logger.info("Temporary docker context file %s was not deleted.", build_ctx_path) + tracking.MlflowClient().set_tag(run_id, MLFLOW_DOCKER_IMAGE_URI, image_uri) + tracking.MlflowClient().set_tag(run_id, MLFLOW_DOCKER_IMAGE_ID, image.id) + return image + + +def _get_docker_image_uri(repository_uri, work_dir): + """ + Args: + repository_uri: The URI of the Docker repository with which to tag the image. The + repository URI is used as the prefix of the image URI. + work_dir: Path to the working directory in which to search for a git commit hash + """ + repository_uri = repository_uri if repository_uri else "docker-project" + # Optionally include first 7 digits of git SHA in tag name, if available. + git_commit = get_git_commit(work_dir) + version_string = ":" + git_commit[:7] if git_commit else "" + return repository_uri + version_string + + +def _create_docker_build_ctx(work_dir, dockerfile_contents): + """ + Creates build context tarfile containing Dockerfile and project code, returning path to tarfile + """ + directory = tempfile.mkdtemp() + try: + dst_path = os.path.join(directory, "mlflow-project-contents") + shutil.copytree(src=work_dir, dst=dst_path) + with open(os.path.join(dst_path, _GENERATED_DOCKERFILE_NAME), "w") as handle: + handle.write(dockerfile_contents) + _, result_path = tempfile.mkstemp() + file_utils.make_tarfile( + output_filename=result_path, source_dir=dst_path, archive_name=_PROJECT_TAR_ARCHIVE_NAME + ) + finally: + shutil.rmtree(directory, onerror=_handle_readonly_on_windows) + return result_path + + +def get_docker_tracking_cmd_and_envs(tracking_uri): + cmds = [] + env_vars = {} + + local_path, container_tracking_uri = _get_local_uri_or_none(tracking_uri) + if local_path is not None: + cmds = ["-v", f"{local_path}:{_MLFLOW_DOCKER_TRACKING_DIR_PATH}"] + env_vars[MLFLOW_TRACKING_URI.name] = container_tracking_uri + env_vars.update(get_databricks_env_vars(tracking_uri)) + return cmds, env_vars + + +def _get_local_uri_or_none(uri): + if uri == "databricks": + return None, None + parsed_uri = urllib.parse.urlparse(uri) + if not parsed_uri.netloc and parsed_uri.scheme in ("", "file", "sqlite"): + path = urllib.request.url2pathname(parsed_uri.path) + if parsed_uri.scheme == "sqlite": + uri = file_utils.path_to_local_sqlite_uri(_MLFLOW_DOCKER_TRACKING_DIR_PATH) + else: + uri = file_utils.path_to_local_file_uri(_MLFLOW_DOCKER_TRACKING_DIR_PATH) + return path, uri + else: + return None, None diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/projects/env_type.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/projects/env_type.py new file mode 100644 index 0000000000000000000000000000000000000000..e04a7c7f4f23218281158c84ea1c469fb41eb040 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/projects/env_type.py @@ -0,0 +1,4 @@ +DOCKER = "docker_env" +PYTHON = "python_env" +CONDA = "conda_env" +ALL = [DOCKER, PYTHON, CONDA] diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/projects/kubernetes.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/projects/kubernetes.py new file mode 100644 index 0000000000000000000000000000000000000000..b40e743b7d124b30ba78ef2c6860459db6ce8b10 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/projects/kubernetes.py @@ -0,0 +1,165 @@ +import logging +import os +import time +from datetime import datetime +from shlex import quote, split +from threading import RLock + +import kubernetes +from kubernetes.config.config_exception import ConfigException + +import docker +from mlflow.entities import RunStatus +from mlflow.exceptions import ExecutionException +from mlflow.projects.submitted_run import SubmittedRun + +_logger = logging.getLogger(__name__) + +_DOCKER_API_TIMEOUT = 300 + + +def push_image_to_registry(image_tag): + client = docker.from_env(timeout=_DOCKER_API_TIMEOUT) + _logger.info("=== Pushing docker image %s ===", image_tag) + for line in client.images.push(repository=image_tag, stream=True, decode=True): + if "error" in line and line["error"]: + raise ExecutionException( + "Error while pushing to docker registry: {error}".format(error=line["error"]) + ) + return client.images.get_registry_data(image_tag).id + + +def _get_kubernetes_job_definition( + project_name, image_tag, image_digest, command, env_vars, job_template +): + container_image = image_tag + "@" + image_digest + timestamp = datetime.now().strftime("%Y-%m-%d-%H-%M-%S-%f") + job_name = f"{project_name}-{timestamp}" + _logger.info("=== Creating Job %s ===", job_name) + if os.environ.get("KUBE_MLFLOW_TRACKING_URI") is not None: + env_vars["MLFLOW_TRACKING_URI"] = os.environ["KUBE_MLFLOW_TRACKING_URI"] + environment_variables = [{"name": k, "value": v} for k, v in env_vars.items()] + job_template["metadata"]["name"] = job_name + job_template["spec"]["template"]["spec"]["containers"][0]["name"] = project_name + job_template["spec"]["template"]["spec"]["containers"][0]["image"] = container_image + job_template["spec"]["template"]["spec"]["containers"][0]["command"] = command + if "env" not in job_template["spec"]["template"]["spec"]["containers"][0].keys(): + job_template["spec"]["template"]["spec"]["containers"][0]["env"] = [] + job_template["spec"]["template"]["spec"]["containers"][0]["env"] += environment_variables + return job_template + + +def _get_run_command(entrypoint_command): + formatted_command = [] + for cmd in entrypoint_command: + formatted_command.extend([quote(s) for s in split(cmd)]) + return formatted_command + + +def _load_kube_context(context=None): + try: + # trying to load either the context passed as arg or, if None, + # the one provided as env var `KUBECONFIG` or in `~/.kube/config` + kubernetes.config.load_kube_config(context=context) + except (OSError, ConfigException) as e: + _logger.debug('Error loading kube context "%s": %s', context, e) + _logger.info("No valid kube config found, using in-cluster configuration") + kubernetes.config.load_incluster_config() + + +def run_kubernetes_job( + project_name, + active_run, + image_tag, + image_digest, + command, + env_vars, + kube_context=None, + job_template=None, +): + job_template = _get_kubernetes_job_definition( + project_name, image_tag, image_digest, _get_run_command(command), env_vars, job_template + ) + job_name = job_template["metadata"]["name"] + job_namespace = job_template["metadata"]["namespace"] + _load_kube_context(context=kube_context) + api_instance = kubernetes.client.BatchV1Api() + api_instance.create_namespaced_job(namespace=job_namespace, body=job_template, pretty=True) + return KubernetesSubmittedRun(active_run.info.run_id, job_name, job_namespace) + + +class KubernetesSubmittedRun(SubmittedRun): + """ + Instance of SubmittedRun corresponding to a Kubernetes Job run launched to run an MLflow + project. + + Args: + mlflow_run_id: ID of the MLflow project run. + job_name: Kubernetes job name. + job_namespace: Kubernetes job namespace. + """ + + # How often to poll run status when waiting on a run + POLL_STATUS_INTERVAL = 5 + + def __init__(self, mlflow_run_id, job_name, job_namespace): + super().__init__() + self._mlflow_run_id = mlflow_run_id + self._job_name = job_name + self._job_namespace = job_namespace + self._status = RunStatus.SCHEDULED + self._status_lock = RLock() + self._kube_api = kubernetes.client.BatchV1Api() + + @property + def run_id(self): + return self._mlflow_run_id + + def wait(self): + while not RunStatus.is_terminated(self._update_status()): + time.sleep(self.POLL_STATUS_INTERVAL) + + return self._status == RunStatus.FINISHED + + def _update_status(self): + api_response = self._kube_api.read_namespaced_job_status( + name=self._job_name, namespace=self._job_namespace, pretty=True + ) + status = api_response.status + with self._status_lock: + if RunStatus.is_terminated(self._status): + return self._status + if self._status == RunStatus.SCHEDULED: + if api_response.status.start_time is None: + _logger.info("Waiting for Job to start") + else: + _logger.info("Job started.") + self._status = RunStatus.RUNNING + if status.conditions is not None: + for condition in status.conditions: + if condition.status == "True": + _logger.info(condition.message) + if condition.type == "Failed": + self._status = RunStatus.FAILED + elif condition.type == "Complete": + self._status = RunStatus.FINISHED + return self._status + + def get_status(self): + status = self._status + return status if RunStatus.is_terminated(status) else self._update_status() + + def cancel(self): + with self._status_lock: + if not RunStatus.is_terminated(self._status): + _logger.info("Cancelling job.") + self._kube_api.delete_namespaced_job( + name=self._job_name, + namespace=self._job_namespace, + body=kubernetes.client.V1DeleteOptions(), + pretty=True, + ) + self._status = RunStatus.KILLED + _logger.info("Job cancelled.") + else: + _logger.info("Attempting to cancel a job that is already terminated.") diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/projects/submitted_run.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/projects/submitted_run.py new file mode 100644 index 0000000000000000000000000000000000000000..20a6df7699243e79b3db4cc4b265831d0f9ec789 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/projects/submitted_run.py @@ -0,0 +1,106 @@ +import logging +import os +import signal +from abc import abstractmethod + +from mlflow.entities import RunStatus +from mlflow.utils.annotations import developer_stable + +_logger = logging.getLogger(__name__) + + +@developer_stable +class SubmittedRun: + """ + Wrapper around an MLflow project run (e.g. a subprocess running an entry point + command or a Databricks job run) and exposing methods for waiting on and cancelling the run. + This class defines the interface that the MLflow project runner uses to manage the lifecycle + of runs launched in different environments (e.g. runs launched locally or on Databricks). + + ``SubmittedRun`` is not thread-safe. That is, concurrent calls to wait() / cancel() + from multiple threads may inadvertently kill resources (e.g. local processes) unrelated to the + run. + + NOTE: + + Subclasses of ``SubmittedRun`` must expose a ``run_id`` member containing the + run's MLflow run ID. + """ + + @abstractmethod + def wait(self): + """ + Wait for the run to finish, returning True if the run succeeded and false otherwise. Note + that in some cases (e.g. remote execution on Databricks), we may wait until the remote job + completes rather than until the MLflow run completes. + """ + + @abstractmethod + def get_status(self): + """ + Get status of the run. + """ + + @abstractmethod + def cancel(self): + """ + Cancel the run (interrupts the command subprocess, cancels the Databricks run, etc) and + waits for it to terminate. The MLflow run status may not be set correctly + upon run cancellation. + """ + + @property + @abstractmethod + def run_id(self): + pass + + +class LocalSubmittedRun(SubmittedRun): + """ + Instance of ``SubmittedRun`` corresponding to a subprocess launched to run an entry point + command locally. + """ + + def __init__(self, run_id, command_proc): + super().__init__() + self._run_id = run_id + self.command_proc = command_proc + + @property + def run_id(self): + return self._run_id + + def wait(self): + return self.command_proc.wait() == 0 + + def cancel(self): + # Interrupt child process if it hasn't already exited + if self.command_proc.poll() is None: + # Kill the the process tree rooted at the child if it's the leader of its own process + # group, otherwise just kill the child + try: + if self.command_proc.pid == os.getpgid(self.command_proc.pid): + os.killpg(self.command_proc.pid, signal.SIGTERM) + else: + self.command_proc.terminate() + except OSError: + # The child process may have exited before we attempted to terminate it, so we + # ignore OSErrors raised during child process termination + _logger.info( + "Failed to terminate child process (PID %s) corresponding to MLflow " + "run with ID %s. The process may have already exited.", + self.command_proc.pid, + self._run_id, + ) + self.command_proc.wait() + + def _get_status(self): + exit_code = self.command_proc.poll() + if exit_code is None: + return RunStatus.RUNNING + if exit_code == 0: + return RunStatus.FINISHED + return RunStatus.FAILED + + def get_status(self): + return RunStatus.to_string(self._get_status()) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/projects/utils.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/projects/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..5098e6db63fd00a217fa838b41ea1f4da8582265 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/projects/utils.py @@ -0,0 +1,342 @@ +import logging +import os +import pathlib +import re +import shutil +import tempfile +import urllib.parse +import zipfile +from io import BytesIO + +from mlflow import tracking +from mlflow.entities import Param, SourceType +from mlflow.environment_variables import MLFLOW_EXPERIMENT_ID, MLFLOW_RUN_ID, MLFLOW_TRACKING_URI +from mlflow.exceptions import ExecutionException +from mlflow.projects import _project_spec +from mlflow.tracking import fluent +from mlflow.tracking.context.default_context import _get_user +from mlflow.utils.git_utils import get_git_commit, get_git_repo_url +from mlflow.utils.mlflow_tags import ( + MLFLOW_GIT_BRANCH, + MLFLOW_GIT_COMMIT, + MLFLOW_GIT_REPO_URL, + MLFLOW_PARENT_RUN_ID, + MLFLOW_PROJECT_ENTRY_POINT, + MLFLOW_SOURCE_NAME, + MLFLOW_SOURCE_TYPE, + MLFLOW_USER, +) +from mlflow.utils.rest_utils import augmented_raise_for_status + +_FILE_URI_REGEX = re.compile(r"^file://.+") +_ZIP_URI_REGEX = re.compile(r".+\.zip$") +MLFLOW_LOCAL_BACKEND_RUN_ID_CONFIG = "_mlflow_local_backend_run_id" +MLFLOW_DOCKER_WORKDIR_PATH = "/mlflow/projects/code/" + +PROJECT_ENV_MANAGER = "ENV_MANAGER" +PROJECT_SYNCHRONOUS = "SYNCHRONOUS" +PROJECT_DOCKER_ARGS = "DOCKER_ARGS" +PROJECT_STORAGE_DIR = "STORAGE_DIR" +PROJECT_BUILD_IMAGE = "build_image" +PROJECT_DOCKER_AUTH = "docker_auth" +GIT_FETCH_DEPTH = 1 + + +_logger = logging.getLogger(__name__) + + +def _parse_subdirectory(uri): + # Parses a uri and returns the uri and subdirectory as separate values. + # Uses '#' as a delimiter. + unquoted_uri = _strip_quotes(uri) + subdirectory = "" + parsed_uri = unquoted_uri + if "#" in unquoted_uri: + subdirectory = unquoted_uri[unquoted_uri.find("#") + 1 :] + parsed_uri = unquoted_uri[: unquoted_uri.find("#")] + if subdirectory and "." in subdirectory: + raise ExecutionException("'.' is not allowed in project subdirectory paths.") + return parsed_uri, subdirectory + + +def _strip_quotes(uri): + return uri.strip("'\"") + + +def _get_storage_dir(storage_dir): + if storage_dir is not None and not os.path.exists(storage_dir): + os.makedirs(storage_dir) + return tempfile.mkdtemp(dir=storage_dir) + + +def _expand_uri(uri): + if _is_local_uri(uri): + return os.path.abspath(uri) + return uri + + +def _is_file_uri(uri): + """Returns True if the passed-in URI is a file:// URI.""" + return _FILE_URI_REGEX.match(uri) + + +def _is_git_repo(path) -> bool: + """Returns True if passed-in path is a valid git repository""" + import git + + try: + repo = git.Repo(path) + if len(repo.branches) > 0: + return True + except git.exc.InvalidGitRepositoryError: + pass + return False + + +def _parse_file_uri(uri: str) -> str: + """Converts file URIs to filesystem paths""" + if _is_file_uri(uri): + parsed_file_uri = urllib.parse.urlparse(uri) + return str( + pathlib.Path(parsed_file_uri.netloc, parsed_file_uri.path, parsed_file_uri.fragment) + ) + return uri + + +def _is_local_uri(uri: str) -> bool: + """Returns True if passed-in URI should be interpreted as a folder on the local filesystem.""" + resolved_uri = pathlib.Path(_parse_file_uri(uri)).resolve() + return resolved_uri.exists() + + +def _is_zip_uri(uri): + """Returns True if the passed-in URI points to a ZIP file.""" + return _ZIP_URI_REGEX.match(uri) + + +def _is_valid_branch_name(work_dir, version): + """ + Returns True if the ``version`` is the name of a branch in a Git project. + ``work_dir`` must be the working directory in a git repo. + """ + if version is not None: + from git import Repo + from git.exc import GitCommandError + + repo = Repo(work_dir, search_parent_directories=True) + try: + return repo.git.rev_parse("--verify", f"refs/heads/{version}") != "" + except GitCommandError: + return False + return False + + +def fetch_and_validate_project(uri, version, entry_point, parameters): + parameters = parameters or {} + work_dir = _fetch_project(uri=uri, version=version) + project = _project_spec.load_project(work_dir) + if entry_point_obj := project.get_entry_point(entry_point): + entry_point_obj._validate_parameters(parameters) + return work_dir + + +def load_project(work_dir): + return _project_spec.load_project(work_dir) + + +def _fetch_project(uri, version=None): + """ + Fetch a project into a local directory, returning the path to the local project directory. + """ + parsed_uri, subdirectory = _parse_subdirectory(uri) + use_temp_dst_dir = _is_zip_uri(parsed_uri) or not _is_local_uri(parsed_uri) + dst_dir = tempfile.mkdtemp() if use_temp_dst_dir else _parse_file_uri(parsed_uri) + + if use_temp_dst_dir: + _logger.info("=== Fetching project from %s into %s ===", uri, dst_dir) + if _is_zip_uri(parsed_uri): + parsed_uri = _parse_file_uri(parsed_uri) + _unzip_repo( + zip_file=(parsed_uri if _is_local_uri(parsed_uri) else _fetch_zip_repo(parsed_uri)), + dst_dir=dst_dir, + ) + elif _is_local_uri(parsed_uri): + if use_temp_dst_dir: + shutil.copytree(parsed_uri, dst_dir, dirs_exist_ok=True) + if version is not None: + if not _is_git_repo(_parse_file_uri(parsed_uri)): + raise ExecutionException("Setting a version is only supported for Git project URIs") + _fetch_git_repo(parsed_uri, version, dst_dir) + else: + _fetch_git_repo(parsed_uri, version, dst_dir) + res = os.path.abspath(os.path.join(dst_dir, subdirectory)) + if not os.path.exists(res): + raise ExecutionException(f"Could not find subdirectory {subdirectory} of {dst_dir}") + return res + + +def _unzip_repo(zip_file, dst_dir): + with zipfile.ZipFile(zip_file) as zip_in: + zip_in.extractall(dst_dir) + + +_HEAD_BRANCH_REGEX = re.compile(r"^\s*HEAD branch:\s+(?P\S+)") + + +def _get_head_branch(remote_show_output): + for line in remote_show_output.splitlines(): + match = _HEAD_BRANCH_REGEX.match(line) + if match: + return match.group("branch") + + +def _fetch_git_repo(uri, version, dst_dir): + """ + Clone the git repo at ``uri`` into ``dst_dir``, checking out commit ``version`` (or defaulting + to the head commit of the repository's master branch if version is unspecified). + Assumes authentication parameters are specified by the environment, e.g. by a Git credential + helper. + """ + # We defer importing git until the last moment, because the import requires that the git + # executable is available on the PATH, so we only want to fail if we actually need it. + import git + + repo = git.Repo.init(dst_dir) + origin = next((remote for remote in repo.remotes), None) + if origin is None: + origin = repo.create_remote("origin", uri) + if version is not None: + try: + origin.fetch(refspec=version, depth=GIT_FETCH_DEPTH, tags=True) + repo.git.checkout(version) + except git.exc.GitCommandError as e: + raise ExecutionException( + f"Unable to checkout version '{version}' of git repo {uri}" + "- please ensure that the version exists in the repo. " + f"Error: {e}" + ) + else: + g = git.cmd.Git(dst_dir) + cmd = ["git", "remote", "show", "origin"] + output = g.execute(cmd) + head_branch = _get_head_branch(output) + if head_branch is None: + raise ExecutionException( + "Failed to find HEAD branch. Output of `{cmd}`:\n{output}".format( + cmd=" ".join(cmd), output=output + ) + ) + origin.fetch(head_branch, depth=GIT_FETCH_DEPTH) + ref = origin.refs[0] + _logger.info("Fetched '%s' branch", head_branch) + repo.create_head(head_branch, ref) + repo.heads[head_branch].checkout() + repo.git.execute(command=["git", "submodule", "update", "--init", "--recursive"]) + + +def _fetch_zip_repo(uri): + import requests + + # TODO (dbczumar): Replace HTTP resolution via ``requests.get`` with an invocation of + # ```mlflow.data.download_uri()`` when the API supports the same set of available stores as + # the artifact repository (Azure, FTP, etc). See the following issue: + # https://github.com/mlflow/mlflow/issues/763. + response = requests.get(uri) + try: + augmented_raise_for_status(response) + except requests.HTTPError as error: + raise ExecutionException(f"Unable to retrieve ZIP file. Reason: {error!s}") + return BytesIO(response.content) + + +def get_or_create_run(run_id, uri, experiment_id, work_dir, version, entry_point, parameters): + if run_id: + return tracking.MlflowClient().get_run(run_id) + else: + return _create_run(uri, experiment_id, work_dir, version, entry_point, parameters) + + +def _create_run(uri, experiment_id, work_dir, version, entry_point, parameters): + """ + Create a ``Run`` against the current MLflow tracking server, logging metadata (e.g. the URI, + entry point, and parameters of the project) about the run. Return an ``ActiveRun`` that can be + used to report additional data about the run (metrics/params) to the tracking server. + """ + if _is_local_uri(uri): + source_name = tracking._tracking_service.utils._get_git_url_if_present(_expand_uri(uri)) + else: + source_name = _expand_uri(uri) + source_version = get_git_commit(work_dir) + existing_run = fluent.active_run() + parent_run_id = existing_run.info.run_id if existing_run else None + + tags = { + MLFLOW_USER: _get_user(), + MLFLOW_SOURCE_NAME: source_name, + MLFLOW_SOURCE_TYPE: SourceType.to_string(SourceType.PROJECT), + MLFLOW_PROJECT_ENTRY_POINT: entry_point, + } + if source_version is not None: + tags[MLFLOW_GIT_COMMIT] = source_version + if parent_run_id is not None: + tags[MLFLOW_PARENT_RUN_ID] = parent_run_id + + repo_url = get_git_repo_url(work_dir) + if repo_url is not None: + tags[MLFLOW_GIT_REPO_URL] = repo_url + + # Add branch name tag if a branch is specified through -version + if _is_valid_branch_name(work_dir, version): + tags[MLFLOW_GIT_BRANCH] = version + active_run = tracking.MlflowClient().create_run(experiment_id=experiment_id, tags=tags) + + project = _project_spec.load_project(work_dir) + # Consolidate parameters for logging. + # `storage_dir` is `None` since we want to log actual path not downloaded local path + entry_point_obj = project.get_entry_point(entry_point) + if entry_point_obj: + final_params, extra_params = entry_point_obj.compute_parameters( + parameters, storage_dir=None + ) + params_list = [ + Param(key, value) + for key, value in list(final_params.items()) + list(extra_params.items()) + ] + tracking.MlflowClient().log_batch(active_run.info.run_id, params=params_list) + return active_run + + +def get_entry_point_command(project, entry_point, parameters, storage_dir): + """ + Returns the shell command to execute in order to run the specified entry point. + + Args: + project: Project containing the target entry point. + entry_point: Entry point to run. + parameters: Parameters (dictionary) for the entry point command. + storage_dir: Base local directory to use for downloading remote artifacts passed to + arguments of type 'path'. If None, a temporary base directory is used. + """ + storage_dir_for_run = _get_storage_dir(storage_dir) + _logger.info( + "=== Created directory %s for downloading remote URIs passed to arguments of" + " type 'path' ===", + storage_dir_for_run, + ) + commands = [] + commands.append( + project.get_entry_point(entry_point).compute_command(parameters, storage_dir_for_run) + ) + return commands + + +def get_run_env_vars(run_id, experiment_id): + """ + Returns a dictionary of environment variable key-value pairs to set in subprocess launched + to run MLflow projects. + """ + return { + MLFLOW_RUN_ID.name: run_id, + MLFLOW_TRACKING_URI.name: tracking.get_tracking_uri(), + MLFLOW_EXPERIMENT_ID.name: str(experiment_id), + } diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/prompt/__pycache__/constants.cpython-312.pyc 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index 0000000000000000000000000000000000000000..4c42295eb4138ec406df4ab0e23ea964d5199a05 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/prompt/constants.py @@ -0,0 +1,28 @@ +# A special tag in RegisteredModel to indicate that it is a prompt +import re + +IS_PROMPT_TAG_KEY = "mlflow.prompt.is_prompt" +# A special tag in ModelVersion to store the prompt text +PROMPT_TEXT_TAG_KEY = "mlflow.prompt.text" + +LINKED_PROMPTS_TAG_KEY = "mlflow.linkedPrompts" + +# Unity Catalog tags cannot contain dots +PROMPT_TYPE_TAG_KEY = "_mlflow_prompt_type" +RESPONSE_FORMAT_TAG_KEY = "_mlflow_prompt_response_format" + +# Prompt types +PROMPT_TYPE_TEXT = "text" +PROMPT_TYPE_CHAT = "chat" + +# A special tag to store associated run IDs for prompts +PROMPT_ASSOCIATED_RUN_IDS_TAG_KEY = "mlflow.prompt.associatedRunIds" + +PROMPT_TEMPLATE_VARIABLE_PATTERN = re.compile( + r"\{\{\s*([a-zA-Z_][a-zA-Z0-9_]*(?:\.[a-zA-Z_][a-zA-Z0-9_]*)*)\s*\}\}" +) + +PROMPT_TEXT_DISPLAY_LIMIT = 30 + +# Alphanumeric, underscore, hyphen, and dot are allowed in prompt name +PROMPT_NAME_RULE = re.compile(r"^[a-zA-Z0-9_.-]+$") diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/prompt/promptlab_model.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/prompt/promptlab_model.py new file mode 100644 index 0000000000000000000000000000000000000000..d82a31a503611c93dac643ee265041c5d6bd3cae --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/prompt/promptlab_model.py @@ -0,0 +1,197 @@ +import os +import re + +import yaml + +from mlflow.exceptions import MlflowException +from mlflow.version import VERSION as __version__ + + +class _PromptlabModel: + import pandas as pd + + def __init__(self, prompt_template, prompt_parameters, model_parameters, model_route): + self.prompt_parameters = prompt_parameters + self.model_parameters = model_parameters + self.model_route = model_route + self.prompt_template = prompt_template + + def predict(self, inputs: pd.DataFrame) -> list[str]: + from mlflow.deployments import MlflowDeploymentClient, get_deploy_client + + client = MlflowDeploymentClient(get_deploy_client()) + + results = [] + for idx in inputs.index: + prompt_parameters_as_dict = { + param.key: inputs[param.key][idx] for param in self.prompt_parameters + } + + # copy replacement logic from PromptEngineering.utils.ts for consistency + prompt = self.prompt_template + for key, value in prompt_parameters_as_dict.items(): + prompt = re.sub(r"\{\{\s*" + key + r"\s*\}\}", value, prompt) + + model_parameters_as_dict = {param.key: param.value for param in self.model_parameters} + query_data = self._construct_query_data(prompt) + + response = client.predict( + endpoint=self.model_route, inputs={**query_data, **model_parameters_as_dict} + ) + results.append(self._parse_gateway_response(response)) + + return results + + def _construct_query_data(self, prompt): + from mlflow.deployments import MlflowDeploymentClient, get_deploy_client + + client = MlflowDeploymentClient(get_deploy_client()) + route_type = client.get_endpoint(self.model_route).endpoint_type + + if route_type == "llm/v1/completions": + return {"prompt": prompt} + elif route_type == "llm/v1/chat": + return {"messages": [{"content": prompt, "role": "user"}]} + else: + raise MlflowException( + "Error when constructing gateway query: " + f"Unsupported route type for _PromptlabModel: {route_type}" + ) + + def _parse_gateway_response(self, response): + from mlflow.deployments import MlflowDeploymentClient, get_deploy_client + + client = MlflowDeploymentClient(get_deploy_client()) + route_type = client.get_endpoint(self.model_route).endpoint_type + + if route_type == "llm/v1/completions": + return response["choices"][0]["text"] + elif route_type == "llm/v1/chat": + return response["choices"][0]["message"]["content"] + else: + raise MlflowException( + "Error when parsing gateway response: " + f"Unsupported route type for _PromptlabModel: {route_type}" + ) + + +def _load_pyfunc(path): + from mlflow import pyfunc + from mlflow.entities.param import Param + from mlflow.utils.model_utils import ( + _get_flavor_configuration, + ) + + pyfunc_flavor_conf = _get_flavor_configuration(model_path=path, flavor_name=pyfunc.FLAVOR_NAME) + parameters_path = os.path.join(path, pyfunc_flavor_conf["parameters_path"]) + with open(parameters_path) as f: + parameters = yaml.safe_load(f) + + prompt_parameters_as_params = [ + Param(key=key, value=value) for key, value in parameters["prompt_parameters"].items() + ] + model_parameters_as_params = [ + Param(key=key, value=value) for key, value in parameters["model_parameters"].items() + ] + + return _PromptlabModel( + prompt_template=parameters["prompt_template"], + prompt_parameters=prompt_parameters_as_params, + model_parameters=model_parameters_as_params, + model_route=parameters["model_route"], + ) + + +def save_model( + path, + conda_env=None, + code_paths=None, + mlflow_model=None, + signature=None, + input_example=None, + pip_requirements=None, + prompt_template=None, + prompt_parameters=None, + model_parameters=None, + model_route=None, +): + from mlflow import pyfunc + from mlflow.models import Model + from mlflow.models.model import MLMODEL_FILE_NAME, Model + from mlflow.models.utils import _save_example + from mlflow.utils.environment import ( + _CONDA_ENV_FILE_NAME, + _CONSTRAINTS_FILE_NAME, + _PYTHON_ENV_FILE_NAME, + _REQUIREMENTS_FILE_NAME, + _process_conda_env, + _process_pip_requirements, + _PythonEnv, + _validate_env_arguments, + infer_pip_requirements, + ) + from mlflow.utils.file_utils import write_to + from mlflow.utils.model_utils import ( + _validate_and_copy_code_paths, + _validate_and_prepare_target_save_path, + ) + + _validate_env_arguments(conda_env, pip_requirements, None) + + _validate_and_prepare_target_save_path(path) + code_dir_subpath = _validate_and_copy_code_paths(code_paths, path) + + if mlflow_model is None: + mlflow_model = Model() + if signature is not None: + mlflow_model.signature = signature + if input_example is not None: + _save_example(mlflow_model, input_example, path) + + parameters_sub_path = "parameters.yaml" + parameters_path = os.path.join(path, parameters_sub_path) + # dump prompt_template, prompt_parameters, model_parameters, model_route to parameters_path + + parameters = { + "prompt_template": prompt_template, + "prompt_parameters": {param.key: param.value for param in prompt_parameters}, + "model_parameters": {param.key: param.value for param in model_parameters}, + "model_route": model_route, + } + with open(parameters_path, "w") as f: + yaml.safe_dump(parameters, stream=f, default_flow_style=False) + + pyfunc.add_to_model( + mlflow_model, + loader_module="mlflow.prompt.promptlab_model", + parameters_path=parameters_sub_path, + conda_env=_CONDA_ENV_FILE_NAME, + python_env=_PYTHON_ENV_FILE_NAME, + code=code_dir_subpath, + ) + + mlflow_model.save(os.path.join(path, MLMODEL_FILE_NAME)) + + if conda_env is None: + if pip_requirements is None: + inferred_reqs = infer_pip_requirements( + path, "mlflow._promptlab", [f"mlflow[gateway]=={__version__}"] + ) + default_reqs = sorted(inferred_reqs) + else: + default_reqs = None + conda_env, pip_requirements, pip_constraints = _process_pip_requirements( + default_reqs, pip_requirements, None + ) + else: + conda_env, pip_requirements, pip_constraints = _process_conda_env(conda_env) + + with open(os.path.join(path, _CONDA_ENV_FILE_NAME), "w") as f: + yaml.safe_dump(conda_env, stream=f, default_flow_style=False) + + if pip_constraints: + write_to(os.path.join(path, _CONSTRAINTS_FILE_NAME), "\n".join(pip_constraints)) + + write_to(os.path.join(path, _REQUIREMENTS_FILE_NAME), "\n".join(pip_requirements)) + + _PythonEnv.current().to_yaml(os.path.join(path, _PYTHON_ENV_FILE_NAME)) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/prompt/registry_utils.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/prompt/registry_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..f5f5219344c0e1fda66382083bfe8a73666b06af --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/prompt/registry_utils.py @@ -0,0 +1,265 @@ +import functools +import json +import re +from textwrap import dedent +from typing import Any + +import mlflow +from mlflow.entities.model_registry.model_version import ModelVersion +from mlflow.entities.model_registry.prompt_version import PromptVersion +from mlflow.entities.model_registry.registered_model_tag import RegisteredModelTag +from mlflow.exceptions import MlflowException +from mlflow.prompt.constants import ( + IS_PROMPT_TAG_KEY, + PROMPT_NAME_RULE, + PROMPT_TEXT_TAG_KEY, + PROMPT_TYPE_CHAT, + PROMPT_TYPE_TAG_KEY, + RESPONSE_FORMAT_TAG_KEY, +) +from mlflow.protos.databricks_pb2 import INVALID_PARAMETER_VALUE, RESOURCE_ALREADY_EXISTS + + +def model_version_to_prompt_version( + model_version: ModelVersion, prompt_tags: dict[str, str] | None = None +) -> PromptVersion: + """ + Create a PromptVersion object from a ModelVersion object. + + Args: + model_version: The ModelVersion object to convert to a PromptVersion. + prompt_tags: The prompt-level tags. Optional. + + Returns: + PromptVersion: The converted PromptVersion object. + """ + if IS_PROMPT_TAG_KEY not in model_version.tags: + raise MlflowException.invalid_parameter_value( + f"Name `{model_version.name}` is registered as a model, not a prompt. MLflow " + "does not allow registering a prompt with the same name as an existing model.", + ) + + if PROMPT_TEXT_TAG_KEY not in model_version.tags: + raise MlflowException.invalid_parameter_value( + f"Prompt `{model_version.name}` does not contain a prompt text" + ) + + if model_version.tags.get(PROMPT_TYPE_TAG_KEY) == PROMPT_TYPE_CHAT: + template = json.loads(model_version.tags[PROMPT_TEXT_TAG_KEY]) + else: + template = model_version.tags[PROMPT_TEXT_TAG_KEY] + + if RESPONSE_FORMAT_TAG_KEY in model_version.tags: + response_format = json.loads(model_version.tags[RESPONSE_FORMAT_TAG_KEY]) + else: + response_format = None + + return PromptVersion( + name=model_version.name, + version=int(model_version.version), + template=template, + commit_message=model_version.description, + creation_timestamp=model_version.creation_timestamp, + tags=model_version.tags, + aliases=model_version.aliases, + last_updated_timestamp=model_version.last_updated_timestamp, + user_id=model_version.user_id, + response_format=response_format, + ) + + +def add_prompt_filter_string(filter_string: str | None, is_prompt: bool = False) -> str | None: + """ + Additional filter string to include/exclude prompts from the result. + By default, exclude prompts from the result. + """ + if IS_PROMPT_TAG_KEY not in (filter_string or ""): + prompt_filter_query = ( + f"tag.`{IS_PROMPT_TAG_KEY}` = 'true'" + if is_prompt + else f"tag.`{IS_PROMPT_TAG_KEY}` != 'true'" + ) + if filter_string: + filter_string = f"{filter_string} AND {prompt_filter_query}" + else: + filter_string = prompt_filter_query + return filter_string + + +def has_prompt_tag(tags: list[RegisteredModelTag] | dict[str, str] | None) -> bool: + """Check if the given tags contain the prompt tag.""" + if isinstance(tags, dict): + return IS_PROMPT_TAG_KEY in tags if tags else False + if not tags: + return + return any(tag.key == IS_PROMPT_TAG_KEY for tag in tags) + + +def is_prompt_supported_registry(registry_uri: str | None = None) -> bool: + """ + Check if the current registry supports prompts. + + Prompts registration is supported in: + - OSS MLflow Tracking Server (always) + - Unity Catalog + - Not supported in legacy Databricks workspace registry or Unity Catalog OSS + """ + registry_uri = registry_uri or mlflow.get_registry_uri() + + # Legacy Databricks workspace registry doesn't support prompts + if registry_uri.startswith("databricks") and not registry_uri.startswith("databricks-uc"): + return False + + # Unity Catalog OSS doesn't support prompts + if registry_uri.startswith("uc:"): + return False + + # UC registries support prompts automatically + if registry_uri.startswith("databricks-uc"): + return True + + # OSS MLflow registry always supports prompts + return True + + +def require_prompt_registry(func): + """Ensure that the current registry supports prompts.""" + + @functools.wraps(func) + def wrapper(*args, **kwargs): + if args and isinstance(args[0], mlflow.MlflowClient): + registry_uri = args[0]._registry_uri + else: + registry_uri = mlflow.get_registry_uri() + + if not is_prompt_supported_registry(registry_uri): + raise MlflowException( + f"The '{func.__name__}' API is not supported with the current registry. " + "Prompts are supported in OSS MLflow and Unity Catalog, but not in the " + "legacy Databricks workspace registry.", + ) + return func(*args, **kwargs) + + # Add note about prompt support to the docstring + func.__doc__ = dedent(f"""\ + {func.__doc__} + + .. note:: + + This API is supported in OSS MLflow Model Registry and Unity Catalog. It is + not supported in the legacy Databricks workspace model registry. + """) + return wrapper + + +def translate_prompt_exception(func): + """ + Translate MlflowException message related to RegisteredModel / ModelVersion into + prompt-specific message. + """ + MODEL_PATTERN = re.compile(r"(registered model|model version)", re.IGNORECASE) + + @functools.wraps(func) + def wrapper(*args, **kwargs): + try: + return func(*args, **kwargs) + except MlflowException as e: + original_message = e.message + # Preserve the case of the first letter + new_message = MODEL_PATTERN.sub( + lambda m: "Prompt" if m.group(0)[0].isupper() else "prompt", e.message + ) + + if new_message != original_message: + raise MlflowException(new_message) from e + else: + raise e + + return wrapper + + +def validate_prompt_name(name: Any): + """Validate the prompt name against the prompt specific rule""" + if not isinstance(name, str) or not name: + raise MlflowException.invalid_parameter_value( + "Prompt name must be a non-empty string.", + ) + + if PROMPT_NAME_RULE.match(name) is None: + raise MlflowException.invalid_parameter_value( + "Prompt name can only contain alphanumeric characters, hyphens, underscores, and dots.", + ) + + +def handle_resource_already_exist_error( + name: str, + is_existing_entity_prompt: bool, + is_new_entity_prompt: bool, +): + """ + Show a more specific error message for name conflict in Model Registry. + + 1. When creating a model with the same name as an existing model, say "model already exists". + 2. When creating a prompt with the same name as an existing prompt, say "prompt already exists". + 3. Otherwise, explain that a prompt and a model cannot have the same name. + """ + old_entity = "Prompt" if is_existing_entity_prompt else "Registered Model" + new_entity = "Prompt" if is_new_entity_prompt else "Registered Model" + + if old_entity != new_entity: + raise MlflowException( + f"Tried to create a {new_entity.lower()} with name {name!r}, but the name is " + f"already taken by a {old_entity.lower()}. MLflow does not allow creating a " + "model and a prompt with the same name.", + RESOURCE_ALREADY_EXISTS, + ) + + raise MlflowException( + f"{new_entity} (name={name}) already exists.", + RESOURCE_ALREADY_EXISTS, + ) + + +def parse_prompt_name_or_uri( + name_or_uri: str, version: str | int | None = None +) -> tuple[str, str | int | None]: + """ + Parse prompt name or URI into (name, version) tuple. + + Handles two cases: + 1. URI format: "prompts:/name/version" or "prompts:/name@alias" + - Returns (name, parsed_version) + - Raises error if version parameter is also provided + 2. Name format: "my_prompt" + - Returns (name, version) + - Raises error if version parameter is not provided + + Args: + name_or_uri: The name of the prompt, or the URI in the format "prompts:/name/version". + version: The version of the prompt (required when using name, not allowed when using URI). + + Returns: + Tuple of (name, version) where version can be a string, int, or None + + Raises: + MlflowException: If validation fails + """ + if name_or_uri.startswith("prompts:/"): + if version is not None: + raise MlflowException( + "The `version` argument should not be specified when loading a prompt by URI.", + INVALID_PARAMETER_VALUE, + ) + # Parse URI to extract name and version + # This assumes the parse_prompt_uri method exists, but we'll handle that separately + # For now, we'll do basic parsing and let the caller handle the URI parsing + return name_or_uri, None + else: + if version is None: + raise MlflowException( + "Version must be specified when loading a prompt by name. " + "Use a prompt URI (e.g., 'prompts:/name/version') or provide the version " + "parameter.", + INVALID_PARAMETER_VALUE, + ) + return name_or_uri, version diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/promptflow/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/promptflow/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..7db934489801694b5bad2732f263117341e79020 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/promptflow/__init__.py @@ -0,0 +1,495 @@ +""" +The ``mlflow.promptflow`` module provides an API for logging and loading Promptflow models. +This module exports Promptflow models with the following flavors: + +Promptflow (native) format + This is the main flavor that can be accessed with Promptflow APIs. +:py:mod:`mlflow.pyfunc` + Produced for use by generic pyfunc-based deployment tools and batch inference. + +.. _Promptflow: + https://microsoft.github.io/promptflow +""" + +import logging +import os +import shutil +from pathlib import Path +from typing import Any + +import pandas as pd +import yaml + +import mlflow +from mlflow import pyfunc +from mlflow.entities.model_registry.prompt import Prompt +from mlflow.models import Model, ModelSignature +from mlflow.models.model import MLMODEL_FILE_NAME +from mlflow.models.signature import _infer_signature_from_input_example +from mlflow.models.utils import ModelInputExample, _save_example +from mlflow.tracking._model_registry import DEFAULT_AWAIT_MAX_SLEEP_SECONDS +from mlflow.tracking.artifact_utils import _download_artifact_from_uri +from mlflow.utils.docstring_utils import LOG_MODEL_PARAM_DOCS, format_docstring +from mlflow.utils.environment import ( + _CONDA_ENV_FILE_NAME, + _CONSTRAINTS_FILE_NAME, + _PYTHON_ENV_FILE_NAME, + _REQUIREMENTS_FILE_NAME, + _mlflow_conda_env, + _process_conda_env, + _process_pip_requirements, + _PythonEnv, + _validate_env_arguments, +) +from mlflow.utils.file_utils import write_to +from mlflow.utils.model_utils import ( + _validate_and_copy_code_paths, + _validate_and_prepare_target_save_path, +) +from mlflow.utils.requirements_utils import _get_pinned_requirement + +_logger = logging.getLogger(__name__) + +FLAVOR_NAME = "promptflow" + +_MODEL_FLOW_DIRECTORY = "flow" +_FLOW_ENV_REQUIREMENTS = "python_requirements_txt" +_UNSUPPORTED_MODEL_ERROR_MESSAGE = ( + "MLflow promptflow flavor only supports instance defined with 'flow.dag.yaml' file " + "and loaded by ~promptflow.load_flow(), found {instance_type}." +) +_INVALID_PREDICT_INPUT_ERROR_MESSAGE = ( + "Input must be a pandas DataFrame with only 1 row " + "or a dictionary contains flow inputs key-value pairs." +) +_CONNECTION_PROVIDER_CONFIG_KEY = "connection_provider" +_CONNECTION_OVERRIDES_CONFIG_KEY = "connection_overrides" + + +def get_default_pip_requirements(): + """ + Returns: + A list of default pip requirements for MLflow Models produced by this flavor. + Calls to :func:`save_model()` and :func:`log_model()` produce a pip environment + that, at a minimum, contains these requirements. + """ + tools_package = None + try: + # Note: If user don't use built-in tool in their flow, + # then promptflow-tools is not a mandatory dependency. + tools_package = _get_pinned_requirement("promptflow-tools") + except ImportError: # pylint: disable=broad-except + pass + requirements = [tools_package] if tools_package else [] + return requirements + [_get_pinned_requirement("promptflow")] + + +def get_default_conda_env(): + """ + Returns: + The default Conda environment for MLflow Models produced by calls to + :func:`save_model()` and :func:`log_model()`. + """ + return _mlflow_conda_env(additional_pip_deps=get_default_pip_requirements()) + + +@format_docstring(LOG_MODEL_PARAM_DOCS.format(package_name=FLAVOR_NAME)) +def log_model( + model, + artifact_path: str | None = None, + conda_env=None, + code_paths=None, + registered_model_name=None, + signature=None, + input_example=None, + await_registration_for=DEFAULT_AWAIT_MAX_SLEEP_SECONDS, + pip_requirements=None, + extra_pip_requirements=None, + metadata=None, + model_config: dict[str, Any] | None = None, + prompts: list[str | Prompt] | None = None, + name: str | None = None, + params: dict[str, Any] | None = None, + tags: dict[str, Any] | None = None, + model_type: str | None = None, + step: int = 0, + model_id: str | None = None, +): + """ + Log a Promptflow model as an MLflow artifact for the current run. + + Args: + model: A promptflow model loaded by `promptflow.load_flow()`. + artifact_path: Deprecated. Use `name` instead. + conda_env: {{ conda_env }} + code_paths: {{ code_paths }} + registered_model_name: If given, create a model version under + ``registered_model_name``, also creating a registered model if one + with the given name does not exist. + signature: {{ signature }} + input_example: {{ input_example }} + await_registration_for: Number of seconds to wait for the model version to finish + being created and is in ``READY`` status. By default, the function + waits for five minutes. Specify 0 or None to skip waiting. + pip_requirements: {{ pip_requirements }} + extra_pip_requirements: {{ extra_pip_requirements }} + metadata: {{ metadata }} + model_config: A dict of valid overrides that can be applied to a flow instance + during inference. These arguments are used exclusively for the case of loading + the model as a ``pyfunc`` Model. + These values are not applied to a returned flow from a call to + ``mlflow.promptflow.load_model()``. + To override configs for a loaded flow with promptflow flavor, + please update the ``pf_model.context`` directly. + + Configs that can be overridden includes: + + ``connection_provider`` - The connection provider to use for the flow. Reach + https://microsoft.github.io/promptflow/how-to-guides/set-global-configs.html#connection-provider + for more details on how to set connection provider. + + ``connection_overrides`` - The connection name overrides to use for the flow. + Example: ``{"aoai_connection": "azure_open_ai_connection"}``. + The node with reference to connection 'aoai_connection' will be resolved to + the actual connection 'azure_open_ai_connection'. + + + An example of providing overrides for a model to use azure machine + learning workspace connection: + + .. code-block:: python + + flow_folder = Path(__file__).parent / "basic" + flow = load_flow(flow_folder) + + workspace_resource_id = ( + "azureml://subscriptions/{your-subscription}/resourceGroups/{your-resourcegroup}" + "/providers/Microsoft.MachineLearningServices/workspaces/{your-workspace}" + ) + model_config = { + "connection_provider": workspace_resource_id, + "connection_overrides": {"local_conn_name": "remote_conn_name"}, + } + + with mlflow.start_run(): + logged_model = mlflow.promptflow.log_model( + flow, name="promptflow_model", model_config=model_config + ) + prompts: {{ prompts }} + name: {{ name }} + params: {{ params }} + tags: {{ tags }} + model_type: {{ model_type }} + step: {{ step }} + model_id: {{ model_id }} + + Returns + A :py:class:`ModelInfo ` instance that contains the + metadata of the logged model. + """ + return Model.log( + artifact_path=artifact_path, + name=name, + flavor=mlflow.promptflow, + registered_model_name=registered_model_name, + model=model, + conda_env=conda_env, + code_paths=code_paths, + signature=signature, + input_example=input_example, + await_registration_for=await_registration_for, + pip_requirements=pip_requirements, + extra_pip_requirements=extra_pip_requirements, + metadata=metadata, + model_config=model_config, + prompts=prompts, + params=params, + tags=tags, + model_type=model_type, + step=step, + model_id=model_id, + ) + + +@format_docstring(LOG_MODEL_PARAM_DOCS.format(package_name=FLAVOR_NAME)) +def save_model( + model, + path, + conda_env=None, + code_paths=None, + mlflow_model=None, + signature: ModelSignature = None, + input_example: ModelInputExample = None, + pip_requirements=None, + extra_pip_requirements=None, + metadata=None, + model_config: dict[str, Any] | None = None, +): + """ + Save a Promptflow model to a path on the local file system. + + Args: + model: A promptflow model loaded by `promptflow.load_flow()`. + path: Local path where the serialized model (as YAML) is to be saved. + conda_env: {{ conda_env }} + code_paths: {{ code_paths }} + mlflow_model: :py:mod:`mlflow.models.Model` this flavor is being added to. + signature: {{ signature }} + input_example: {{ input_example }} + pip_requirements: {{ pip_requirements }} + extra_pip_requirements: {{ extra_pip_requirements }} + metadata: {{ metadata }} + model_config: A dict of valid overrides that can be applied to a flow instance + during inference. These arguments are used exclusively for the case of loading + the model as a ``pyfunc`` Model. + These values are not applied to a returned flow from a call to + ``mlflow.promptflow.load_model()``. + To override configs for a loaded flow with promptflow flavor, + please update the ``pf_model.context`` directly. + + Configs that can be overridden includes: + + ``connection_provider`` - The connection provider to use for the flow. Reach + https://microsoft.github.io/promptflow/how-to-guides/set-global-configs.html#connection-provider + for more details on how to set connection provider. + + ``connection_overrides`` - The connection name overrides to use for the flow. + Example: ``{"aoai_connection": "azure_open_ai_connection"}``. + The node with reference to connection 'aoai_connection' will be resolved to + the actual connection 'azure_open_ai_connection'. + + + An example of providing overrides for a model to use azure machine + learning workspace connection: + + .. code-block:: python + + flow_folder = Path(__file__).parent / "basic" + flow = load_flow(flow_folder) + + workspace_resource_id = ( + "azureml://subscriptions/{your-subscription}/resourceGroups/{your-resourcegroup}" + "/providers/Microsoft.MachineLearningServices/workspaces/{your-workspace}" + ) + model_config = { + "connection_provider": workspace_resource_id, + "connection_overrides": {"local_conn_name": "remote_conn_name"}, + } + + with mlflow.start_run(): + logged_model = mlflow.promptflow.log_model( + flow, name="promptflow_model", model_config=model_config + ) + """ + import promptflow + from promptflow._sdk._mlflow import ( + DAG_FILE_NAME, + Flow, + _merge_local_code_and_additional_includes, + remove_additional_includes, + ) + + _validate_env_arguments(conda_env, pip_requirements, extra_pip_requirements) + + if ( + not isinstance(model, Flow) + or not hasattr(model, "flow_dag_path") + or not hasattr(model, "code") + ): + raise mlflow.MlflowException.invalid_parameter_value( + _UNSUPPORTED_MODEL_ERROR_MESSAGE.format(instance_type=type(model).__name__) + ) + + # check if path exists + path = os.path.abspath(path) + _validate_and_prepare_target_save_path(path) + + # Copy to 'flow' directory to get files merged with flow files. + code_dir_subpath = _validate_and_copy_code_paths( + code_paths, path, default_subpath=_MODEL_FLOW_DIRECTORY + ) + + model_flow_path = os.path.join(path, _MODEL_FLOW_DIRECTORY) + + # Resolve additional includes in flow + with _merge_local_code_and_additional_includes(code_path=model.code) as resolved_model_dir: + remove_additional_includes(Path(resolved_model_dir)) + shutil.copytree(src=resolved_model_dir, dst=model_flow_path, dirs_exist_ok=True) + # Get flow env in flow dag + flow_env = _resolve_env_from_flow(model.flow_dag_path) + + if mlflow_model is None: + mlflow_model = Model() + saved_example = _save_example(mlflow_model, input_example, path) + + if signature is None and saved_example is not None: + wrapped_model = _PromptflowModelWrapper(model) + signature = _infer_signature_from_input_example(saved_example, wrapped_model) + + if signature is not None: + mlflow_model.signature = signature + if metadata is not None: + mlflow_model.metadata = metadata + + # update flavor info to mlflow_model + mlflow_model.add_flavor( + FLAVOR_NAME, + version=promptflow.__version__, + entry=f"{_MODEL_FLOW_DIRECTORY}/{DAG_FILE_NAME}", + **flow_env, + ) + + # append loader_module, data and env data to mlflow_model + pyfunc.add_to_model( + mlflow_model, + loader_module="mlflow.promptflow", + conda_env=_CONDA_ENV_FILE_NAME, + python_env=_PYTHON_ENV_FILE_NAME, + code=code_dir_subpath, + model_config=model_config, + ) + + # save mlflow_model to path/MLmodel + mlflow_model.save(os.path.join(path, MLMODEL_FILE_NAME)) + + if conda_env is None: + if pip_requirements is None: + default_reqs = get_default_pip_requirements() + inferred_reqs = mlflow.models.infer_pip_requirements( + path, FLAVOR_NAME, fallback=default_reqs + ) + default_reqs = sorted(set(inferred_reqs).union(default_reqs)) + else: + default_reqs = None + conda_env, pip_requirements, pip_constraints = _process_pip_requirements( + default_reqs, + pip_requirements, + extra_pip_requirements, + ) + else: + conda_env, pip_requirements, pip_constraints = _process_conda_env(conda_env) + + with open(os.path.join(path, _CONDA_ENV_FILE_NAME), "w") as f: + yaml.safe_dump(conda_env, stream=f, default_flow_style=False) + + # Save `constraints.txt` if necessary + if pip_constraints: + write_to(os.path.join(path, _CONSTRAINTS_FILE_NAME), "\n".join(pip_constraints)) + + # Save `requirements.txt` + write_to(os.path.join(path, _REQUIREMENTS_FILE_NAME), "\n".join(pip_requirements)) + + _PythonEnv.current().to_yaml(os.path.join(path, _PYTHON_ENV_FILE_NAME)) + + +def _resolve_env_from_flow(flow_dag_path): + with open(flow_dag_path) as f: + flow_dict = yaml.safe_load(f) + environment = flow_dict.get("environment", {}) + if _FLOW_ENV_REQUIREMENTS in environment: + # Append entry path to requirements + environment[_FLOW_ENV_REQUIREMENTS] = ( + f"{_MODEL_FLOW_DIRECTORY}/{environment[_FLOW_ENV_REQUIREMENTS]}" + ) + return environment + + +class _PromptflowModelWrapper: + def __init__(self, model, model_config: dict[str, Any] | None = None): + from promptflow._sdk._mlflow import FlowInvoker + + self.model = model + # TODO: Improve this if we have more configs afterwards + model_config = model_config or {} + connection_provider = model_config.get(_CONNECTION_PROVIDER_CONFIG_KEY, "local") + _logger.info("Using connection provider: %s", connection_provider) + connection_overrides = model_config.get(_CONNECTION_OVERRIDES_CONFIG_KEY, None) + _logger.info("Using connection overrides: %s", connection_overrides) + self.model_invoker = FlowInvoker( + self.model, + connection_provider=connection_provider, + connections_name_overrides=connection_overrides, + ) + + def get_raw_model(self): + """ + Returns the underlying model. + """ + return self.model + + def predict( # pylint: disable=unused-argument + self, + data: pd.DataFrame | list[str | dict[str, Any]], + params: dict[str, Any] | None = None, # pylint: disable=unused-argument + ) -> dict[str, Any] | list[Any]: + """ + Args: + data: Model input data. Either a pandas DataFrame with only 1 row or a dictionary. + + .. code-block:: python + loaded_model = mlflow.pyfunc.load_model(logged_model.model_uri) + # Predict on a flow input dictionary. + print(loaded_model.predict({"text": "Python Hello World!"})) + + params: Additional parameters to pass to the model for inference. + + Returns + Model predictions. Dict type, example ``{"output": "\n\nprint('Hello World!')"}`` + """ + if isinstance(data, pd.DataFrame): + messages = data.to_dict(orient="records") + if len(messages) > 1: + raise mlflow.MlflowException.invalid_parameter_value( + _INVALID_PREDICT_INPUT_ERROR_MESSAGE + ) + messages = messages[0] + return [self.model_invoker.invoke(messages)] + elif isinstance(data, dict): + messages = data + return self.model_invoker.invoke(messages) + raise mlflow.MlflowException.invalid_parameter_value(_INVALID_PREDICT_INPUT_ERROR_MESSAGE) + + +def _load_pyfunc(path, model_config: dict[str, Any] | None = None): + """ + Load PyFunc implementation for Promptflow. Called by ``pyfunc.load_model``. + + Args + path: Local filesystem path to the MLflow Model with the ``promptflow`` flavor. + """ + from promptflow import load_flow + + model_flow_path = os.path.join(path, _MODEL_FLOW_DIRECTORY) + model = load_flow(model_flow_path) + return _PromptflowModelWrapper(model=model, model_config=model_config) + + +def load_model(model_uri, dst_path=None): + """ + Load a Promptflow model from a local file or a run. + + Args: + model_uri: The location, in URI format, of the MLflow model. For example: + + - ``/Users/me/path/to/local/model`` + - ``relative/path/to/local/model`` + - ``s3://my_bucket/path/to/model`` + - ``runs://run-relative/path/to/model`` + - ``models://`` + - ``models://`` + + For more information about supported URI schemes, see + `Referencing Artifacts `_. + dst_path: The local filesystem path to which to download the model artifact. + This directory must already exist. If unspecified, a local output + path will be created. + + Returns + A Promptflow model instance + """ + from promptflow import load_flow + + local_model_path = _download_artifact_from_uri(artifact_uri=model_uri, output_path=dst_path) + model_data_path = os.path.join(local_model_path, _MODEL_FLOW_DIRECTORY) + return load_flow(model_data_path) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/promptflow/__pycache__/__init__.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/promptflow/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..bbbd308399ed3436e8aa8ecf8b55f80944c343cf Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/promptflow/__pycache__/__init__.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/prophet/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/prophet/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..553383d508b31d69003a756f133963796ad43355 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/prophet/__init__.py @@ -0,0 +1,377 @@ +""" +The ``mlflow.prophet`` module provides an API for logging and loading Prophet models. +This module exports univariate Prophet models in the following flavors: + +Prophet (native) format + This is the main flavor that can be accessed with Prophet APIs. +:py:mod:`mlflow.pyfunc` + Produced for use by generic pyfunc-based deployment tools and for batch auditing + of historical forecasts. + +.. _Prophet: + https://facebook.github.io/prophet/docs/quick_start.html#python-api +""" + +import json +import logging +import os +from typing import Any + +import yaml + +import mlflow +from mlflow import pyfunc +from mlflow.models import Model, ModelInputExample, ModelSignature +from mlflow.models.model import MLMODEL_FILE_NAME +from mlflow.models.signature import _infer_signature_from_input_example +from mlflow.models.utils import _save_example +from mlflow.tracking._model_registry import DEFAULT_AWAIT_MAX_SLEEP_SECONDS +from mlflow.tracking.artifact_utils import _download_artifact_from_uri +from mlflow.utils.docstring_utils import LOG_MODEL_PARAM_DOCS, format_docstring +from mlflow.utils.environment import ( + _CONDA_ENV_FILE_NAME, + _CONSTRAINTS_FILE_NAME, + _PYTHON_ENV_FILE_NAME, + _REQUIREMENTS_FILE_NAME, + _mlflow_conda_env, + _process_conda_env, + _process_pip_requirements, + _PythonEnv, + _validate_env_arguments, +) +from mlflow.utils.file_utils import get_total_file_size, write_to +from mlflow.utils.model_utils import ( + _add_code_from_conf_to_system_path, + _get_flavor_configuration, + _validate_and_copy_code_paths, + _validate_and_prepare_target_save_path, +) +from mlflow.utils.requirements_utils import _get_pinned_requirement + +FLAVOR_NAME = "prophet" +_MODEL_BINARY_KEY = "data" +_MODEL_BINARY_FILE_NAME = "model.pr" +_MODEL_TYPE_KEY = "model_type" + +_logger = logging.getLogger(__name__) + + +def get_default_pip_requirements(): + """ + Returns: + A list of default pip requirements for MLflow Models produced by this flavor. + Calls to :func:`save_model()` and :func:`log_model()` produce a pip environment + that, at a minimum, contains these requirements. + """ + # Note: Prophet's whl build process will fail due to missing dependencies, defaulting + # to setup.py installation process. + # If a pystan installation error occurs, ensure gcc>=8 is installed in your environment. + # See: https://gcc.gnu.org/install/ + return [_get_pinned_requirement("prophet")] + + +def get_default_conda_env(): + """ + Returns: + The default Conda environment for MLflow Models produced by calls to + :func:`save_model()` and :func:`log_model()`. + """ + return _mlflow_conda_env(additional_pip_deps=get_default_pip_requirements()) + + +@format_docstring(LOG_MODEL_PARAM_DOCS.format(package_name=FLAVOR_NAME)) +def save_model( + pr_model, + path, + conda_env=None, + code_paths=None, + mlflow_model=None, + signature: ModelSignature = None, + input_example: ModelInputExample = None, + pip_requirements=None, + extra_pip_requirements=None, + metadata=None, +): + """ + Save a Prophet model to a path on the local file system. + + Args: + pr_model: Prophet model (an instance of Prophet() forecaster that has been fit + on a temporal series. + path: Local path where the serialized model (as JSON) is to be saved. + conda_env: {{ conda_env }} + code_paths: {{ code_paths }} + mlflow_model: :py:mod:`mlflow.models.Model` this flavor is being added to. + signature: an instance of the :py:class:`ModelSignature ` + class that describes the model's inputs and outputs. If not specified but an + ``input_example`` is supplied, a signature will be automatically inferred + based on the supplied input example and model. To disable automatic signature + inference when providing an input example, set ``signature`` to ``False``. + To manually infer a model signature, call + :py:func:`infer_signature() ` on datasets + with valid model inputs, such as a training dataset with the target column + omitted, and valid model outputs, like model predictions made on the training + dataset, for example: + + .. code-block:: python + + from mlflow.models import infer_signature + + model = Prophet().fit(df) + train = model.history + predictions = model.predict(model.make_future_dataframe(30)) + signature = infer_signature(train, predictions) + input_example: {{ input_example }} + pip_requirements: {{ pip_requirements }} + extra_pip_requirements: {{ extra_pip_requirements }} + metadata: {{ metadata }} + """ + import prophet + + _validate_env_arguments(conda_env, pip_requirements, extra_pip_requirements) + + path = os.path.abspath(path) + _validate_and_prepare_target_save_path(path) + code_dir_subpath = _validate_and_copy_code_paths(code_paths, path) + + if mlflow_model is None: + mlflow_model = Model() + saved_example = _save_example(mlflow_model, input_example, path) + + if signature is None and saved_example is not None: + wrapped_model = _ProphetModelWrapper(pr_model) + signature = _infer_signature_from_input_example(saved_example, wrapped_model) + elif signature is False: + signature = None + + if signature is not None: + mlflow_model.signature = signature + if metadata is not None: + mlflow_model.metadata = metadata + + model_data_path = os.path.join(path, _MODEL_BINARY_FILE_NAME) + _save_model(pr_model, model_data_path) + + model_bin_kwargs = {_MODEL_BINARY_KEY: _MODEL_BINARY_FILE_NAME} + pyfunc.add_to_model( + mlflow_model, + loader_module="mlflow.prophet", + conda_env=_CONDA_ENV_FILE_NAME, + python_env=_PYTHON_ENV_FILE_NAME, + code=code_dir_subpath, + **model_bin_kwargs, + ) + flavor_conf = { + _MODEL_TYPE_KEY: pr_model.__class__.__name__, + **model_bin_kwargs, + } + mlflow_model.add_flavor( + FLAVOR_NAME, + prophet_version=prophet.__version__, + code=code_dir_subpath, + **flavor_conf, + ) + if size := get_total_file_size(path): + mlflow_model.model_size_bytes = size + mlflow_model.save(os.path.join(path, MLMODEL_FILE_NAME)) + + if conda_env is None: + default_reqs = None + if pip_requirements is None: + # cannot use inferred requirements due to prophet's build process + # as the package installation of pystan requires Cython to be present + # in the path. Prophet's installation itself requires imports of + # existing libraries, preventing the execution of a batched pip install + # and instead using a a strictly defined list of dependencies. + # NOTE: if Prophet .whl build architecture is changed, this should be + # modified to a standard inferred approach. + default_reqs = get_default_pip_requirements() + conda_env, pip_requirements, pip_constraints = _process_pip_requirements( + default_reqs, + pip_requirements, + extra_pip_requirements, + ) + else: + conda_env, pip_requirements, pip_constraints = _process_conda_env(conda_env) + + with open(os.path.join(path, _CONDA_ENV_FILE_NAME), "w") as f: + yaml.safe_dump(conda_env, stream=f, default_flow_style=False) + + if pip_constraints: + write_to(os.path.join(path, _CONSTRAINTS_FILE_NAME), "\n".join(pip_constraints)) + + write_to(os.path.join(path, _REQUIREMENTS_FILE_NAME), "\n".join(pip_requirements)) + + _PythonEnv.current().to_yaml(os.path.join(path, _PYTHON_ENV_FILE_NAME)) + + +@format_docstring(LOG_MODEL_PARAM_DOCS.format(package_name=FLAVOR_NAME)) +def log_model( + pr_model, + artifact_path: str | None = None, + conda_env=None, + code_paths=None, + registered_model_name=None, + signature: ModelSignature = None, + input_example: ModelInputExample = None, + await_registration_for=DEFAULT_AWAIT_MAX_SLEEP_SECONDS, + pip_requirements=None, + extra_pip_requirements=None, + metadata=None, + name: str | None = None, + params: dict[str, Any] | None = None, + tags: dict[str, Any] | None = None, + model_type: str | None = None, + step: int = 0, + model_id: str | None = None, +): + """ + Logs a Prophet model as an MLflow artifact for the current run. + + Args: + pr_model: Prophet model to be saved. + artifact_path: Deprecated. Use `name` instead. + conda_env: {{ conda_env }} + code_paths: {{ code_paths }} + registered_model_name: If given, create a model + version under ``registered_model_name``, also creating a + registered model if one with the given name does not exist. + signature: An instance of the :py:class:`ModelSignature ` + class that describes the model's inputs and outputs. If not specified but an + ``input_example`` is supplied, a signature will be automatically inferred + based on the supplied input example and model. To disable automatic signature + inference when providing an input example, set ``signature`` to ``False``. + To manually infer a model signature, call + :py:func:`infer_signature() ` on datasets + with valid model inputs, such as a training dataset with the target column + omitted, and valid model outputs, like model predictions made on the training + dataset, for example: + + .. code-block:: python + + from mlflow.models import infer_signature + + model = Prophet().fit(df) + train = model.history + predictions = model.predict(model.make_future_dataframe(30)) + signature = infer_signature(train, predictions) + + input_example: {{ input_example }} + await_registration_for: Number of seconds to wait for the model version + to finish being created and is in ``READY`` status. + By default, the function waits for five minutes. + Specify 0 or None to skip waiting. + pip_requirements: {{ pip_requirements }} + extra_pip_requirements: {{ extra_pip_requirements }} + metadata: {{ metadata }} + name: {{ name }} + params: {{ params }} + tags: {{ tags }} + model_type: {{ model_type }} + step: {{ step }} + model_id: {{ model_id }} + + Returns: + A :py:class:`ModelInfo ` instance that contains the + metadata of the logged model. + """ + return Model.log( + artifact_path=artifact_path, + name=name, + flavor=mlflow.prophet, + registered_model_name=registered_model_name, + pr_model=pr_model, + conda_env=conda_env, + code_paths=code_paths, + signature=signature, + input_example=input_example, + await_registration_for=await_registration_for, + pip_requirements=pip_requirements, + extra_pip_requirements=extra_pip_requirements, + metadata=metadata, + params=params, + tags=tags, + model_type=model_type, + step=step, + model_id=model_id, + ) + + +def _save_model(model, path): + from prophet.serialize import model_to_json + + model_ser = model_to_json(model) + with open(path, "w") as f: + json.dump(model_ser, f) + + +def _load_model(path): + from prophet.serialize import model_from_json + + with open(path) as f: + model = json.load(f) + return model_from_json(model) + + +def _load_pyfunc(path): + """ + Loads PyFunc implementation for Prophet. Called by ``pyfunc.load_model``. + + Args: + path: Local filesystem path to the MLflow Model with the ``prophet`` flavor. + """ + return _ProphetModelWrapper(_load_model(path)) + + +def load_model(model_uri, dst_path=None): + """ + Load a Prophet model from a local file or a run. + + Args: + model_uri: The location, in URI format, of the MLflow model. For example: + + - ``/Users/me/path/to/local/model`` + - ``relative/path/to/local/model`` + - ``s3://my_bucket/path/to/model`` + - ``runs://run-relative/path/to/model`` + + For more information about supported URI schemes, see + `Referencing Artifacts `_. + dst_path: The local filesystem path to which to download the model artifact. + This directory must already exist. If unspecified, a local output + path will be created. + + Returns: + A Prophet model instance + """ + local_model_path = _download_artifact_from_uri(artifact_uri=model_uri, output_path=dst_path) + flavor_conf = _get_flavor_configuration(model_path=local_model_path, flavor_name=FLAVOR_NAME) + _add_code_from_conf_to_system_path(local_model_path, flavor_conf) + pr_model_path = os.path.join( + local_model_path, flavor_conf.get(_MODEL_BINARY_KEY, _MODEL_BINARY_FILE_NAME) + ) + + return _load_model(pr_model_path) + + +class _ProphetModelWrapper: + def __init__(self, pr_model): + self.pr_model = pr_model + + def get_raw_model(self): + """ + Returns the underlying model. + """ + return self.pr_model + + def predict(self, dataframe, params: dict[str, Any] | None = None): + """ + Args: + dataframe: Model input data. + params: Additional parameters to pass to the model for inference. + + Returns: + Model predictions. + """ + return self.pr_model.predict(dataframe) diff --git 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a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/protos/assessments_pb2.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/protos/assessments_pb2.py new file mode 100644 index 0000000000000000000000000000000000000000..db238172c7983410da37dc56dec6f702bb96006e --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/protos/assessments_pb2.py @@ -0,0 +1,174 @@ + +import google.protobuf +from packaging.version import Version +if Version(google.protobuf.__version__).major >= 5: + # -*- coding: utf-8 -*- + # Generated by the protocol buffer compiler. DO NOT EDIT! + # source: assessments.proto + # Protobuf Python Version: 5.26.0 + """Generated protocol buffer code.""" + from google.protobuf import descriptor as _descriptor + from google.protobuf import descriptor_pool as _descriptor_pool + from google.protobuf import symbol_database as _symbol_database + from google.protobuf.internal import builder as _builder + # @@protoc_insertion_point(imports) + + _sym_db = _symbol_database.Default() + + + from . import databricks_pb2 as databricks__pb2 + from google.protobuf import struct_pb2 as google_dot_protobuf_dot_struct__pb2 + from google.protobuf import timestamp_pb2 as google_dot_protobuf_dot_timestamp__pb2 + + + DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\x11\x61ssessments.proto\x12\x12mlflow.assessments\x1a\x10\x64\x61tabricks.proto\x1a\x1cgoogle/protobuf/struct.proto\x1a\x1fgoogle/protobuf/timestamp.proto\"\xc6\x01\n\x10\x41ssessmentSource\x12J\n\x0bsource_type\x18\x01 \x01(\x0e\x32/.mlflow.assessments.AssessmentSource.SourceTypeB\x04\xf8\x86\x19\x01\x12\x17\n\tsource_id\x18\x02 \x01(\tB\x04\xf8\x86\x19\x01\"M\n\nSourceType\x12\x1b\n\x17SOURCE_TYPE_UNSPECIFIED\x10\x00\x12\t\n\x05HUMAN\x10\x01\x12\r\n\tLLM_JUDGE\x10\x02\x12\x08\n\x04\x43ODE\x10\x03\"Q\n\x0f\x41ssessmentError\x12\x12\n\nerror_code\x18\x01 \x01(\t\x12\x15\n\rerror_message\x18\x02 \x01(\t\x12\x13\n\x0bstack_trace\x18\x03 \x01(\t\"\xbf\x01\n\x0b\x45xpectation\x12%\n\x05value\x18\x02 \x01(\x0b\x32\x16.google.protobuf.Value\x12I\n\x10serialized_value\x18\x03 \x01(\x0b\x32/.mlflow.assessments.Expectation.SerializedValue\x1a>\n\x0fSerializedValue\x12\x1c\n\x14serialization_format\x18\x01 \x01(\t\x12\r\n\x05value\x18\x02 \x01(\t\"e\n\x08\x46\x65\x65\x64\x62\x61\x63k\x12%\n\x05value\x18\x02 \x01(\x0b\x32\x16.google.protobuf.Value\x12\x32\n\x05\x65rror\x18\x03 \x01(\x0b\x32#.mlflow.assessments.AssessmentError\"\xd9\x04\n\nAssessment\x12\x15\n\rassessment_id\x18\x01 \x01(\t\x12\x1d\n\x0f\x61ssessment_name\x18\x02 \x01(\tB\x04\xf8\x86\x19\x01\x12\x10\n\x08trace_id\x18\x03 \x01(\t\x12\x0f\n\x07span_id\x18\x04 \x01(\t\x12\x34\n\x06source\x18\x05 \x01(\x0b\x32$.mlflow.assessments.AssessmentSource\x12/\n\x0b\x63reate_time\x18\x06 \x01(\x0b\x32\x1a.google.protobuf.Timestamp\x12\x34\n\x10last_update_time\x18\x07 \x01(\x0b\x32\x1a.google.protobuf.Timestamp\x12\x30\n\x08\x66\x65\x65\x64\x62\x61\x63k\x18\t \x01(\x0b\x32\x1c.mlflow.assessments.FeedbackH\x00\x12\x36\n\x0b\x65xpectation\x18\n \x01(\x0b\x32\x1f.mlflow.assessments.ExpectationH\x00\x12\x11\n\trationale\x18\x0b \x01(\t\x12\x36\n\x05\x65rror\x18\x0c \x01(\x0b\x32#.mlflow.assessments.AssessmentErrorB\x02\x18\x01\x12>\n\x08metadata\x18\r \x03(\x0b\x32,.mlflow.assessments.Assessment.MetadataEntry\x12\x11\n\toverrides\x18\x0e \x01(\t\x12\x13\n\x05valid\x18\x0f \x01(\x08:\x04true\x1a/\n\rMetadataEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12\r\n\x05value\x18\x02 \x01(\t:\x02\x38\x01\x42\x07\n\x05valueB\x19\n\x14org.mlflow.api.proto\x90\x01\x01') + + _globals = globals() + _builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals) + _builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'assessments_pb2', _globals) + if not _descriptor._USE_C_DESCRIPTORS: + _globals['DESCRIPTOR']._loaded_options = None + _globals['DESCRIPTOR']._serialized_options = b'\n\024org.mlflow.api.proto\220\001\001' + _globals['_ASSESSMENTSOURCE'].fields_by_name['source_type']._loaded_options = None + _globals['_ASSESSMENTSOURCE'].fields_by_name['source_type']._serialized_options = b'\370\206\031\001' + _globals['_ASSESSMENTSOURCE'].fields_by_name['source_id']._loaded_options = None + _globals['_ASSESSMENTSOURCE'].fields_by_name['source_id']._serialized_options = b'\370\206\031\001' + _globals['_ASSESSMENT_METADATAENTRY']._loaded_options = None + _globals['_ASSESSMENT_METADATAENTRY']._serialized_options = b'8\001' + _globals['_ASSESSMENT'].fields_by_name['assessment_name']._loaded_options = None + _globals['_ASSESSMENT'].fields_by_name['assessment_name']._serialized_options = b'\370\206\031\001' + _globals['_ASSESSMENT'].fields_by_name['error']._loaded_options = None + _globals['_ASSESSMENT'].fields_by_name['error']._serialized_options = b'\030\001' + _globals['_ASSESSMENTSOURCE']._serialized_start=123 + _globals['_ASSESSMENTSOURCE']._serialized_end=321 + _globals['_ASSESSMENTSOURCE_SOURCETYPE']._serialized_start=244 + _globals['_ASSESSMENTSOURCE_SOURCETYPE']._serialized_end=321 + _globals['_ASSESSMENTERROR']._serialized_start=323 + _globals['_ASSESSMENTERROR']._serialized_end=404 + _globals['_EXPECTATION']._serialized_start=407 + _globals['_EXPECTATION']._serialized_end=598 + _globals['_EXPECTATION_SERIALIZEDVALUE']._serialized_start=536 + _globals['_EXPECTATION_SERIALIZEDVALUE']._serialized_end=598 + _globals['_FEEDBACK']._serialized_start=600 + _globals['_FEEDBACK']._serialized_end=701 + _globals['_ASSESSMENT']._serialized_start=704 + _globals['_ASSESSMENT']._serialized_end=1305 + _globals['_ASSESSMENT_METADATAENTRY']._serialized_start=1249 + _globals['_ASSESSMENT_METADATAENTRY']._serialized_end=1296 + # @@protoc_insertion_point(module_scope) + +else: + # -*- coding: utf-8 -*- + # Generated by the protocol buffer compiler. DO NOT EDIT! + # source: assessments.proto + """Generated protocol buffer code.""" + from google.protobuf import descriptor as _descriptor + from google.protobuf import descriptor_pool as _descriptor_pool + from google.protobuf import message as _message + from google.protobuf import reflection as _reflection + from google.protobuf import symbol_database as _symbol_database + # @@protoc_insertion_point(imports) + + _sym_db = _symbol_database.Default() + + + from . import databricks_pb2 as databricks__pb2 + from google.protobuf import struct_pb2 as google_dot_protobuf_dot_struct__pb2 + from google.protobuf import timestamp_pb2 as google_dot_protobuf_dot_timestamp__pb2 + + + DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\x11\x61ssessments.proto\x12\x12mlflow.assessments\x1a\x10\x64\x61tabricks.proto\x1a\x1cgoogle/protobuf/struct.proto\x1a\x1fgoogle/protobuf/timestamp.proto\"\xc6\x01\n\x10\x41ssessmentSource\x12J\n\x0bsource_type\x18\x01 \x01(\x0e\x32/.mlflow.assessments.AssessmentSource.SourceTypeB\x04\xf8\x86\x19\x01\x12\x17\n\tsource_id\x18\x02 \x01(\tB\x04\xf8\x86\x19\x01\"M\n\nSourceType\x12\x1b\n\x17SOURCE_TYPE_UNSPECIFIED\x10\x00\x12\t\n\x05HUMAN\x10\x01\x12\r\n\tLLM_JUDGE\x10\x02\x12\x08\n\x04\x43ODE\x10\x03\"Q\n\x0f\x41ssessmentError\x12\x12\n\nerror_code\x18\x01 \x01(\t\x12\x15\n\rerror_message\x18\x02 \x01(\t\x12\x13\n\x0bstack_trace\x18\x03 \x01(\t\"\xbf\x01\n\x0b\x45xpectation\x12%\n\x05value\x18\x02 \x01(\x0b\x32\x16.google.protobuf.Value\x12I\n\x10serialized_value\x18\x03 \x01(\x0b\x32/.mlflow.assessments.Expectation.SerializedValue\x1a>\n\x0fSerializedValue\x12\x1c\n\x14serialization_format\x18\x01 \x01(\t\x12\r\n\x05value\x18\x02 \x01(\t\"e\n\x08\x46\x65\x65\x64\x62\x61\x63k\x12%\n\x05value\x18\x02 \x01(\x0b\x32\x16.google.protobuf.Value\x12\x32\n\x05\x65rror\x18\x03 \x01(\x0b\x32#.mlflow.assessments.AssessmentError\"\xd9\x04\n\nAssessment\x12\x15\n\rassessment_id\x18\x01 \x01(\t\x12\x1d\n\x0f\x61ssessment_name\x18\x02 \x01(\tB\x04\xf8\x86\x19\x01\x12\x10\n\x08trace_id\x18\x03 \x01(\t\x12\x0f\n\x07span_id\x18\x04 \x01(\t\x12\x34\n\x06source\x18\x05 \x01(\x0b\x32$.mlflow.assessments.AssessmentSource\x12/\n\x0b\x63reate_time\x18\x06 \x01(\x0b\x32\x1a.google.protobuf.Timestamp\x12\x34\n\x10last_update_time\x18\x07 \x01(\x0b\x32\x1a.google.protobuf.Timestamp\x12\x30\n\x08\x66\x65\x65\x64\x62\x61\x63k\x18\t \x01(\x0b\x32\x1c.mlflow.assessments.FeedbackH\x00\x12\x36\n\x0b\x65xpectation\x18\n \x01(\x0b\x32\x1f.mlflow.assessments.ExpectationH\x00\x12\x11\n\trationale\x18\x0b \x01(\t\x12\x36\n\x05\x65rror\x18\x0c \x01(\x0b\x32#.mlflow.assessments.AssessmentErrorB\x02\x18\x01\x12>\n\x08metadata\x18\r \x03(\x0b\x32,.mlflow.assessments.Assessment.MetadataEntry\x12\x11\n\toverrides\x18\x0e \x01(\t\x12\x13\n\x05valid\x18\x0f \x01(\x08:\x04true\x1a/\n\rMetadataEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12\r\n\x05value\x18\x02 \x01(\t:\x02\x38\x01\x42\x07\n\x05valueB\x19\n\x14org.mlflow.api.proto\x90\x01\x01') + + + + _ASSESSMENTSOURCE = DESCRIPTOR.message_types_by_name['AssessmentSource'] + _ASSESSMENTERROR = DESCRIPTOR.message_types_by_name['AssessmentError'] + _EXPECTATION = DESCRIPTOR.message_types_by_name['Expectation'] + _EXPECTATION_SERIALIZEDVALUE = _EXPECTATION.nested_types_by_name['SerializedValue'] + _FEEDBACK = DESCRIPTOR.message_types_by_name['Feedback'] + _ASSESSMENT = DESCRIPTOR.message_types_by_name['Assessment'] + _ASSESSMENT_METADATAENTRY = _ASSESSMENT.nested_types_by_name['MetadataEntry'] + _ASSESSMENTSOURCE_SOURCETYPE = _ASSESSMENTSOURCE.enum_types_by_name['SourceType'] + AssessmentSource = _reflection.GeneratedProtocolMessageType('AssessmentSource', (_message.Message,), { + 'DESCRIPTOR' : _ASSESSMENTSOURCE, + '__module__' : 'assessments_pb2' + # @@protoc_insertion_point(class_scope:mlflow.assessments.AssessmentSource) + }) + _sym_db.RegisterMessage(AssessmentSource) + + AssessmentError = _reflection.GeneratedProtocolMessageType('AssessmentError', (_message.Message,), { + 'DESCRIPTOR' : _ASSESSMENTERROR, + '__module__' : 'assessments_pb2' + # @@protoc_insertion_point(class_scope:mlflow.assessments.AssessmentError) + }) + _sym_db.RegisterMessage(AssessmentError) + + Expectation = _reflection.GeneratedProtocolMessageType('Expectation', (_message.Message,), { + + 'SerializedValue' : _reflection.GeneratedProtocolMessageType('SerializedValue', (_message.Message,), { + 'DESCRIPTOR' : _EXPECTATION_SERIALIZEDVALUE, + '__module__' : 'assessments_pb2' + # @@protoc_insertion_point(class_scope:mlflow.assessments.Expectation.SerializedValue) + }) + , + 'DESCRIPTOR' : _EXPECTATION, + '__module__' : 'assessments_pb2' + # @@protoc_insertion_point(class_scope:mlflow.assessments.Expectation) + }) + _sym_db.RegisterMessage(Expectation) + _sym_db.RegisterMessage(Expectation.SerializedValue) + + Feedback = _reflection.GeneratedProtocolMessageType('Feedback', (_message.Message,), { + 'DESCRIPTOR' : _FEEDBACK, + '__module__' : 'assessments_pb2' + # @@protoc_insertion_point(class_scope:mlflow.assessments.Feedback) + }) + _sym_db.RegisterMessage(Feedback) + + Assessment = _reflection.GeneratedProtocolMessageType('Assessment', (_message.Message,), { + + 'MetadataEntry' : _reflection.GeneratedProtocolMessageType('MetadataEntry', (_message.Message,), { + 'DESCRIPTOR' : _ASSESSMENT_METADATAENTRY, + '__module__' : 'assessments_pb2' + # @@protoc_insertion_point(class_scope:mlflow.assessments.Assessment.MetadataEntry) + }) + , + 'DESCRIPTOR' : _ASSESSMENT, + '__module__' : 'assessments_pb2' + # @@protoc_insertion_point(class_scope:mlflow.assessments.Assessment) + }) + _sym_db.RegisterMessage(Assessment) + _sym_db.RegisterMessage(Assessment.MetadataEntry) + + if _descriptor._USE_C_DESCRIPTORS == False: + + DESCRIPTOR._options = None + DESCRIPTOR._serialized_options = b'\n\024org.mlflow.api.proto\220\001\001' + _ASSESSMENTSOURCE.fields_by_name['source_type']._options = None + _ASSESSMENTSOURCE.fields_by_name['source_type']._serialized_options = b'\370\206\031\001' + _ASSESSMENTSOURCE.fields_by_name['source_id']._options = None + _ASSESSMENTSOURCE.fields_by_name['source_id']._serialized_options = b'\370\206\031\001' + _ASSESSMENT_METADATAENTRY._options = None + _ASSESSMENT_METADATAENTRY._serialized_options = b'8\001' + _ASSESSMENT.fields_by_name['assessment_name']._options = None + _ASSESSMENT.fields_by_name['assessment_name']._serialized_options = b'\370\206\031\001' + _ASSESSMENT.fields_by_name['error']._options = None + _ASSESSMENT.fields_by_name['error']._serialized_options = b'\030\001' + _ASSESSMENTSOURCE._serialized_start=123 + _ASSESSMENTSOURCE._serialized_end=321 + _ASSESSMENTSOURCE_SOURCETYPE._serialized_start=244 + _ASSESSMENTSOURCE_SOURCETYPE._serialized_end=321 + _ASSESSMENTERROR._serialized_start=323 + _ASSESSMENTERROR._serialized_end=404 + _EXPECTATION._serialized_start=407 + _EXPECTATION._serialized_end=598 + _EXPECTATION_SERIALIZEDVALUE._serialized_start=536 + _EXPECTATION_SERIALIZEDVALUE._serialized_end=598 + _FEEDBACK._serialized_start=600 + _FEEDBACK._serialized_end=701 + _ASSESSMENT._serialized_start=704 + _ASSESSMENT._serialized_end=1305 + _ASSESSMENT_METADATAENTRY._serialized_start=1249 + _ASSESSMENT_METADATAENTRY._serialized_end=1296 + # @@protoc_insertion_point(module_scope) + diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/protos/databricks_artifacts_pb2.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/protos/databricks_artifacts_pb2.py new file mode 100644 index 0000000000000000000000000000000000000000..65ed2a81c01309262e07c714e86bced4d7437926 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/protos/databricks_artifacts_pb2.py @@ -0,0 +1,489 @@ + +import google.protobuf +from packaging.version import Version +if Version(google.protobuf.__version__).major >= 5: + # -*- coding: utf-8 -*- + # Generated by the protocol buffer compiler. DO NOT EDIT! + # source: databricks_artifacts.proto + # Protobuf Python Version: 5.26.0 + """Generated protocol buffer code.""" + from google.protobuf import descriptor as _descriptor + from google.protobuf import descriptor_pool as _descriptor_pool + from google.protobuf import symbol_database as _symbol_database + from google.protobuf.internal import builder as _builder + # @@protoc_insertion_point(imports) + + _sym_db = _symbol_database.Default() + + + from .scalapb import scalapb_pb2 as scalapb_dot_scalapb__pb2 + from . import databricks_pb2 as databricks__pb2 + + + DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\x1a\x64\x61tabricks_artifacts.proto\x12\x06mlflow\x1a\x15scalapb/scalapb.proto\x1a\x10\x64\x61tabricks.proto\"\xdf\x01\n\x16\x41rtifactCredentialInfo\x12\x0e\n\x06run_id\x18\x01 \x01(\t\x12\x0c\n\x04path\x18\x02 \x01(\t\x12\x12\n\nsigned_uri\x18\x03 \x01(\t\x12:\n\x07headers\x18\x04 \x03(\x0b\x32).mlflow.ArtifactCredentialInfo.HttpHeader\x12,\n\x04type\x18\x05 \x01(\x0e\x32\x1e.mlflow.ArtifactCredentialType\x1a)\n\nHttpHeader\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\r\n\x05value\x18\x02 \x01(\t\"j\n\x1dLoggedModelArtifactCredential\x12\x10\n\x08model_id\x18\x01 \x01(\t\x12\x37\n\x0f\x63redential_info\x18\x02 \x01(\x0b\x32\x1e.mlflow.ArtifactCredentialInfo\"\xe1\x01\n\x15GetCredentialsForRead\x12\x14\n\x06run_id\x18\x01 \x01(\tB\x04\xf8\x86\x19\x01\x12\x0c\n\x04path\x18\x02 \x03(\t\x12\x12\n\npage_token\x18\x03 \x01(\t\x1a\x63\n\x08Response\x12\x38\n\x10\x63redential_infos\x18\x02 \x03(\x0b\x32\x1e.mlflow.ArtifactCredentialInfo\x12\x17\n\x0fnext_page_token\x18\x03 \x01(\tJ\x04\x08\x01\x10\x02:+\xe2?(\n&com.databricks.rpc.RPC[$this.Response]\"\xe2\x01\n\x16GetCredentialsForWrite\x12\x14\n\x06run_id\x18\x01 \x01(\tB\x04\xf8\x86\x19\x01\x12\x0c\n\x04path\x18\x02 \x03(\t\x12\x12\n\npage_token\x18\x03 \x01(\t\x1a\x63\n\x08Response\x12\x38\n\x10\x63redential_infos\x18\x02 \x03(\x0b\x32\x1e.mlflow.ArtifactCredentialInfo\x12\x17\n\x0fnext_page_token\x18\x03 \x01(\tJ\x04\x08\x01\x10\x02:+\xe2?(\n&com.databricks.rpc.RPC[$this.Response]\"\xa1\x02\n\x15\x43reateMultipartUpload\x12\x14\n\x06run_id\x18\x01 \x01(\tB\x04\xf8\x86\x19\x01\x12\x0c\n\x04path\x18\x02 \x01(\t\x12\x17\n\tnum_parts\x18\x03 \x01(\x03\x42\x04\xf8\x86\x19\x01\x1a\x9d\x01\n\x08Response\x12\x11\n\tupload_id\x18\x01 \x01(\t\x12?\n\x17upload_credential_infos\x18\x02 \x03(\x0b\x32\x1e.mlflow.ArtifactCredentialInfo\x12=\n\x15\x61\x62ort_credential_info\x18\x03 \x01(\x0b\x32\x1e.mlflow.ArtifactCredentialInfo:+\xe2?(\n&com.databricks.rpc.RPC[$this.Response]\"-\n\x08PartEtag\x12\x13\n\x0bpart_number\x18\x01 \x01(\x03\x12\x0c\n\x04\x65tag\x18\x02 \x01(\t\"\xb5\x01\n\x17\x43ompleteMultipartUpload\x12\x14\n\x06run_id\x18\x01 \x01(\tB\x04\xf8\x86\x19\x01\x12\x0c\n\x04path\x18\x02 \x01(\t\x12\x17\n\tupload_id\x18\x03 \x01(\tB\x04\xf8\x86\x19\x01\x12$\n\npart_etags\x18\x04 \x03(\x0b\x32\x10.mlflow.PartEtag\x1a\n\n\x08Response:+\xe2?(\n&com.databricks.rpc.RPC[$this.Response]\"\xec\x01\n\x19GetPresignedUploadPartUrl\x12\x14\n\x06run_id\x18\x01 \x01(\tB\x04\xf8\x86\x19\x01\x12\x0c\n\x04path\x18\x02 \x01(\t\x12\x17\n\tupload_id\x18\x03 \x01(\tB\x04\xf8\x86\x19\x01\x12\x19\n\x0bpart_number\x18\x04 \x01(\x03\x42\x04\xf8\x86\x19\x01\x1aJ\n\x08Response\x12>\n\x16upload_credential_info\x18\x01 \x01(\x0b\x32\x1e.mlflow.ArtifactCredentialInfo:+\xe2?(\n&com.databricks.rpc.RPC[$this.Response]\"\xb0\x01\n\"GetCredentialsForTraceDataDownload\x12\x18\n\nrequest_id\x18\x01 \x01(\tB\x04\xf8\x86\x19\x01\x1a\x43\n\x08Response\x12\x37\n\x0f\x63redential_info\x18\x01 \x01(\x0b\x32\x1e.mlflow.ArtifactCredentialInfo:+\xe2?(\n&com.databricks.rpc.RPC[$this.Response]\"\xae\x01\n GetCredentialsForTraceDataUpload\x12\x18\n\nrequest_id\x18\x01 \x01(\tB\x04\xf8\x86\x19\x01\x1a\x43\n\x08Response\x12\x37\n\x0f\x63redential_info\x18\x01 \x01(\x0b\x32\x1e.mlflow.ArtifactCredentialInfo:+\xe2?(\n&com.databricks.rpc.RPC[$this.Response]\"\xed\x01\n\"GetCredentialsForLoggedModelUpload\x12\x16\n\x08model_id\x18\x01 \x01(\tB\x04\xf8\x86\x19\x01\x12\r\n\x05paths\x18\x02 \x03(\t\x12\x12\n\npage_token\x18\x03 \x01(\t\x1a_\n\x08Response\x12:\n\x0b\x63redentials\x18\x01 \x03(\x0b\x32%.mlflow.LoggedModelArtifactCredential\x12\x17\n\x0fnext_page_token\x18\x02 \x01(\t:+\xe2?(\n&com.databricks.rpc.RPC[$this.Response]\"\xef\x01\n$GetCredentialsForLoggedModelDownload\x12\x16\n\x08model_id\x18\x01 \x01(\tB\x04\xf8\x86\x19\x01\x12\r\n\x05paths\x18\x02 \x03(\t\x12\x12\n\npage_token\x18\x03 \x01(\t\x1a_\n\x08Response\x12:\n\x0b\x63redentials\x18\x01 \x03(\x0b\x32%.mlflow.LoggedModelArtifactCredential\x12\x17\n\x0fnext_page_token\x18\x02 \x01(\t:+\xe2?(\n&com.databricks.rpc.RPC[$this.Response]*s\n\x16\x41rtifactCredentialType\x12\x11\n\rAZURE_SAS_URI\x10\x01\x12\x15\n\x11\x41WS_PRESIGNED_URL\x10\x02\x12\x12\n\x0eGCP_SIGNED_URL\x10\x03\x12\x1b\n\x17\x41ZURE_ADLS_GEN2_SAS_URI\x10\x04\x32\xd5\r\n DatabricksMlflowArtifactsService\x12\x9c\x01\n\x15getCredentialsForRead\x12\x1d.mlflow.GetCredentialsForRead\x1a&.mlflow.GetCredentialsForRead.Response\"<\xf2\x86\x19\x38\n4\n\x04POST\x12&/mlflow/artifacts/credentials-for-read\x1a\x04\x08\x02\x10\x00\x10\x03\x12\xa0\x01\n\x16getCredentialsForWrite\x12\x1e.mlflow.GetCredentialsForWrite\x1a\'.mlflow.GetCredentialsForWrite.Response\"=\xf2\x86\x19\x39\n5\n\x04POST\x12\'/mlflow/artifacts/credentials-for-write\x1a\x04\x08\x02\x10\x00\x10\x03\x12\x9f\x01\n\x15\x63reateMultipartUpload\x12\x1d.mlflow.CreateMultipartUpload\x1a&.mlflow.CreateMultipartUpload.Response\"?\xf2\x86\x19;\n7\n\x04POST\x12)/mlflow/artifacts/create-multipart-upload\x1a\x04\x08\x02\x10\x00\x10\x03\x12\xa7\x01\n\x17\x63ompleteMultipartUpload\x12\x1f.mlflow.CompleteMultipartUpload\x1a(.mlflow.CompleteMultipartUpload.Response\"A\xf2\x86\x19=\n9\n\x04POST\x12+/mlflow/artifacts/complete-multipart-upload\x1a\x04\x08\x02\x10\x00\x10\x03\x12\xb0\x01\n\x19getPresignedUploadPartUrl\x12!.mlflow.GetPresignedUploadPartUrl\x1a*.mlflow.GetPresignedUploadPartUrl.Response\"D\xf2\x86\x19@\n<\n\x03GET\x12//mlflow/artifacts/get-presigned-upload-part-url\x1a\x04\x08\x02\x10\x00\x10\x03\x12\xd5\x01\n\"getCredentialsForTraceDataDownload\x12*.mlflow.GetCredentialsForTraceDataDownload\x1a\x33.mlflow.GetCredentialsForTraceDataDownload.Response\"N\xf2\x86\x19J\nF\n\x03GET\x12\x39/mlflow/traces/{request_id}/credentials-for-data-download\x1a\x04\x08\x02\x10\x00\x10\x03\x12\xcd\x01\n getCredentialsForTraceDataUpload\x12(.mlflow.GetCredentialsForTraceDataUpload\x1a\x31.mlflow.GetCredentialsForTraceDataUpload.Response\"L\xf2\x86\x19H\nD\n\x03GET\x12\x37/mlflow/traces/{request_id}/credentials-for-data-upload\x1a\x04\x08\x02\x10\x00\x10\x03\x12\xde\x01\n\"getCredentialsForLoggedModelUpload\x12*.mlflow.GetCredentialsForLoggedModelUpload\x1a\x33.mlflow.GetCredentialsForLoggedModelUpload.Response\"W\xf2\x86\x19S\nO\n\x04POST\x12\x41/mlflow/logged-models/{model_id}/artifacts/credentials-for-upload\x1a\x04\x08\x02\x10\x00\x10\x03\x12\xe6\x01\n$getCredentialsForLoggedModelDownload\x12,.mlflow.GetCredentialsForLoggedModelDownload\x1a\x35.mlflow.GetCredentialsForLoggedModelDownload.Response\"Y\xf2\x86\x19U\nQ\n\x04POST\x12\x43/mlflow/logged-models/{model_id}/artifacts/credentials-for-download\x1a\x04\x08\x02\x10\x00\x10\x03\x42,\n\x1f\x63om.databricks.api.proto.mlflow\x90\x01\x01\xa0\x01\x01\xe2?\x02\x10\x01') + + _globals = globals() + _builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals) + _builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'databricks_artifacts_pb2', _globals) + if not _descriptor._USE_C_DESCRIPTORS: + _globals['DESCRIPTOR']._loaded_options = None + _globals['DESCRIPTOR']._serialized_options = b'\n\037com.databricks.api.proto.mlflow\220\001\001\240\001\001\342?\002\020\001' + _globals['_GETCREDENTIALSFORREAD'].fields_by_name['run_id']._loaded_options = None + _globals['_GETCREDENTIALSFORREAD'].fields_by_name['run_id']._serialized_options = b'\370\206\031\001' + _globals['_GETCREDENTIALSFORREAD']._loaded_options = None + _globals['_GETCREDENTIALSFORREAD']._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _globals['_GETCREDENTIALSFORWRITE'].fields_by_name['run_id']._loaded_options = None + _globals['_GETCREDENTIALSFORWRITE'].fields_by_name['run_id']._serialized_options = b'\370\206\031\001' + _globals['_GETCREDENTIALSFORWRITE']._loaded_options = None + _globals['_GETCREDENTIALSFORWRITE']._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _globals['_CREATEMULTIPARTUPLOAD'].fields_by_name['run_id']._loaded_options = None + _globals['_CREATEMULTIPARTUPLOAD'].fields_by_name['run_id']._serialized_options = b'\370\206\031\001' + _globals['_CREATEMULTIPARTUPLOAD'].fields_by_name['num_parts']._loaded_options = None + _globals['_CREATEMULTIPARTUPLOAD'].fields_by_name['num_parts']._serialized_options = b'\370\206\031\001' + _globals['_CREATEMULTIPARTUPLOAD']._loaded_options = None + _globals['_CREATEMULTIPARTUPLOAD']._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _globals['_COMPLETEMULTIPARTUPLOAD'].fields_by_name['run_id']._loaded_options = None + _globals['_COMPLETEMULTIPARTUPLOAD'].fields_by_name['run_id']._serialized_options = b'\370\206\031\001' + _globals['_COMPLETEMULTIPARTUPLOAD'].fields_by_name['upload_id']._loaded_options = None + _globals['_COMPLETEMULTIPARTUPLOAD'].fields_by_name['upload_id']._serialized_options = b'\370\206\031\001' + _globals['_COMPLETEMULTIPARTUPLOAD']._loaded_options = None + _globals['_COMPLETEMULTIPARTUPLOAD']._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _globals['_GETPRESIGNEDUPLOADPARTURL'].fields_by_name['run_id']._loaded_options = None + _globals['_GETPRESIGNEDUPLOADPARTURL'].fields_by_name['run_id']._serialized_options = b'\370\206\031\001' + _globals['_GETPRESIGNEDUPLOADPARTURL'].fields_by_name['upload_id']._loaded_options = None + _globals['_GETPRESIGNEDUPLOADPARTURL'].fields_by_name['upload_id']._serialized_options = b'\370\206\031\001' + _globals['_GETPRESIGNEDUPLOADPARTURL'].fields_by_name['part_number']._loaded_options = None + _globals['_GETPRESIGNEDUPLOADPARTURL'].fields_by_name['part_number']._serialized_options = b'\370\206\031\001' + _globals['_GETPRESIGNEDUPLOADPARTURL']._loaded_options = None + _globals['_GETPRESIGNEDUPLOADPARTURL']._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _globals['_GETCREDENTIALSFORTRACEDATADOWNLOAD'].fields_by_name['request_id']._loaded_options = None + _globals['_GETCREDENTIALSFORTRACEDATADOWNLOAD'].fields_by_name['request_id']._serialized_options = b'\370\206\031\001' + _globals['_GETCREDENTIALSFORTRACEDATADOWNLOAD']._loaded_options = None + _globals['_GETCREDENTIALSFORTRACEDATADOWNLOAD']._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _globals['_GETCREDENTIALSFORTRACEDATAUPLOAD'].fields_by_name['request_id']._loaded_options = None + _globals['_GETCREDENTIALSFORTRACEDATAUPLOAD'].fields_by_name['request_id']._serialized_options = b'\370\206\031\001' + _globals['_GETCREDENTIALSFORTRACEDATAUPLOAD']._loaded_options = None + _globals['_GETCREDENTIALSFORTRACEDATAUPLOAD']._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _globals['_GETCREDENTIALSFORLOGGEDMODELUPLOAD'].fields_by_name['model_id']._loaded_options = None + _globals['_GETCREDENTIALSFORLOGGEDMODELUPLOAD'].fields_by_name['model_id']._serialized_options = b'\370\206\031\001' + _globals['_GETCREDENTIALSFORLOGGEDMODELUPLOAD']._loaded_options = None + _globals['_GETCREDENTIALSFORLOGGEDMODELUPLOAD']._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _globals['_GETCREDENTIALSFORLOGGEDMODELDOWNLOAD'].fields_by_name['model_id']._loaded_options = None + _globals['_GETCREDENTIALSFORLOGGEDMODELDOWNLOAD'].fields_by_name['model_id']._serialized_options = b'\370\206\031\001' + _globals['_GETCREDENTIALSFORLOGGEDMODELDOWNLOAD']._loaded_options = None + _globals['_GETCREDENTIALSFORLOGGEDMODELDOWNLOAD']._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _globals['_DATABRICKSMLFLOWARTIFACTSSERVICE'].methods_by_name['getCredentialsForRead']._loaded_options = None + _globals['_DATABRICKSMLFLOWARTIFACTSSERVICE'].methods_by_name['getCredentialsForRead']._serialized_options = b'\362\206\0318\n4\n\004POST\022&/mlflow/artifacts/credentials-for-read\032\004\010\002\020\000\020\003' + _globals['_DATABRICKSMLFLOWARTIFACTSSERVICE'].methods_by_name['getCredentialsForWrite']._loaded_options = None + _globals['_DATABRICKSMLFLOWARTIFACTSSERVICE'].methods_by_name['getCredentialsForWrite']._serialized_options = b'\362\206\0319\n5\n\004POST\022\'/mlflow/artifacts/credentials-for-write\032\004\010\002\020\000\020\003' + _globals['_DATABRICKSMLFLOWARTIFACTSSERVICE'].methods_by_name['createMultipartUpload']._loaded_options = None + _globals['_DATABRICKSMLFLOWARTIFACTSSERVICE'].methods_by_name['createMultipartUpload']._serialized_options = b'\362\206\031;\n7\n\004POST\022)/mlflow/artifacts/create-multipart-upload\032\004\010\002\020\000\020\003' + _globals['_DATABRICKSMLFLOWARTIFACTSSERVICE'].methods_by_name['completeMultipartUpload']._loaded_options = None + _globals['_DATABRICKSMLFLOWARTIFACTSSERVICE'].methods_by_name['completeMultipartUpload']._serialized_options = b'\362\206\031=\n9\n\004POST\022+/mlflow/artifacts/complete-multipart-upload\032\004\010\002\020\000\020\003' + _globals['_DATABRICKSMLFLOWARTIFACTSSERVICE'].methods_by_name['getPresignedUploadPartUrl']._loaded_options = None + _globals['_DATABRICKSMLFLOWARTIFACTSSERVICE'].methods_by_name['getPresignedUploadPartUrl']._serialized_options = b'\362\206\031@\n<\n\003GET\022//mlflow/artifacts/get-presigned-upload-part-url\032\004\010\002\020\000\020\003' + _globals['_DATABRICKSMLFLOWARTIFACTSSERVICE'].methods_by_name['getCredentialsForTraceDataDownload']._loaded_options = None + _globals['_DATABRICKSMLFLOWARTIFACTSSERVICE'].methods_by_name['getCredentialsForTraceDataDownload']._serialized_options = b'\362\206\031J\nF\n\003GET\0229/mlflow/traces/{request_id}/credentials-for-data-download\032\004\010\002\020\000\020\003' + _globals['_DATABRICKSMLFLOWARTIFACTSSERVICE'].methods_by_name['getCredentialsForTraceDataUpload']._loaded_options = None + _globals['_DATABRICKSMLFLOWARTIFACTSSERVICE'].methods_by_name['getCredentialsForTraceDataUpload']._serialized_options = b'\362\206\031H\nD\n\003GET\0227/mlflow/traces/{request_id}/credentials-for-data-upload\032\004\010\002\020\000\020\003' + _globals['_DATABRICKSMLFLOWARTIFACTSSERVICE'].methods_by_name['getCredentialsForLoggedModelUpload']._loaded_options = None + _globals['_DATABRICKSMLFLOWARTIFACTSSERVICE'].methods_by_name['getCredentialsForLoggedModelUpload']._serialized_options = b'\362\206\031S\nO\n\004POST\022A/mlflow/logged-models/{model_id}/artifacts/credentials-for-upload\032\004\010\002\020\000\020\003' + _globals['_DATABRICKSMLFLOWARTIFACTSSERVICE'].methods_by_name['getCredentialsForLoggedModelDownload']._loaded_options = None + _globals['_DATABRICKSMLFLOWARTIFACTSSERVICE'].methods_by_name['getCredentialsForLoggedModelDownload']._serialized_options = b'\362\206\031U\nQ\n\004POST\022C/mlflow/logged-models/{model_id}/artifacts/credentials-for-download\032\004\010\002\020\000\020\003' + _globals['_ARTIFACTCREDENTIALTYPE']._serialized_start=2470 + _globals['_ARTIFACTCREDENTIALTYPE']._serialized_end=2585 + _globals['_ARTIFACTCREDENTIALINFO']._serialized_start=80 + _globals['_ARTIFACTCREDENTIALINFO']._serialized_end=303 + _globals['_ARTIFACTCREDENTIALINFO_HTTPHEADER']._serialized_start=262 + _globals['_ARTIFACTCREDENTIALINFO_HTTPHEADER']._serialized_end=303 + _globals['_LOGGEDMODELARTIFACTCREDENTIAL']._serialized_start=305 + _globals['_LOGGEDMODELARTIFACTCREDENTIAL']._serialized_end=411 + _globals['_GETCREDENTIALSFORREAD']._serialized_start=414 + _globals['_GETCREDENTIALSFORREAD']._serialized_end=639 + _globals['_GETCREDENTIALSFORREAD_RESPONSE']._serialized_start=495 + _globals['_GETCREDENTIALSFORREAD_RESPONSE']._serialized_end=594 + _globals['_GETCREDENTIALSFORWRITE']._serialized_start=642 + _globals['_GETCREDENTIALSFORWRITE']._serialized_end=868 + _globals['_GETCREDENTIALSFORWRITE_RESPONSE']._serialized_start=495 + _globals['_GETCREDENTIALSFORWRITE_RESPONSE']._serialized_end=594 + _globals['_CREATEMULTIPARTUPLOAD']._serialized_start=871 + _globals['_CREATEMULTIPARTUPLOAD']._serialized_end=1160 + _globals['_CREATEMULTIPARTUPLOAD_RESPONSE']._serialized_start=958 + _globals['_CREATEMULTIPARTUPLOAD_RESPONSE']._serialized_end=1115 + _globals['_PARTETAG']._serialized_start=1162 + _globals['_PARTETAG']._serialized_end=1207 + _globals['_COMPLETEMULTIPARTUPLOAD']._serialized_start=1210 + _globals['_COMPLETEMULTIPARTUPLOAD']._serialized_end=1391 + _globals['_COMPLETEMULTIPARTUPLOAD_RESPONSE']._serialized_start=495 + _globals['_COMPLETEMULTIPARTUPLOAD_RESPONSE']._serialized_end=505 + _globals['_GETPRESIGNEDUPLOADPARTURL']._serialized_start=1394 + _globals['_GETPRESIGNEDUPLOADPARTURL']._serialized_end=1630 + _globals['_GETPRESIGNEDUPLOADPARTURL_RESPONSE']._serialized_start=1511 + _globals['_GETPRESIGNEDUPLOADPARTURL_RESPONSE']._serialized_end=1585 + _globals['_GETCREDENTIALSFORTRACEDATADOWNLOAD']._serialized_start=1633 + _globals['_GETCREDENTIALSFORTRACEDATADOWNLOAD']._serialized_end=1809 + _globals['_GETCREDENTIALSFORTRACEDATADOWNLOAD_RESPONSE']._serialized_start=1697 + _globals['_GETCREDENTIALSFORTRACEDATADOWNLOAD_RESPONSE']._serialized_end=1764 + _globals['_GETCREDENTIALSFORTRACEDATAUPLOAD']._serialized_start=1812 + _globals['_GETCREDENTIALSFORTRACEDATAUPLOAD']._serialized_end=1986 + _globals['_GETCREDENTIALSFORTRACEDATAUPLOAD_RESPONSE']._serialized_start=1697 + _globals['_GETCREDENTIALSFORTRACEDATAUPLOAD_RESPONSE']._serialized_end=1764 + _globals['_GETCREDENTIALSFORLOGGEDMODELUPLOAD']._serialized_start=1989 + _globals['_GETCREDENTIALSFORLOGGEDMODELUPLOAD']._serialized_end=2226 + _globals['_GETCREDENTIALSFORLOGGEDMODELUPLOAD_RESPONSE']._serialized_start=2086 + _globals['_GETCREDENTIALSFORLOGGEDMODELUPLOAD_RESPONSE']._serialized_end=2181 + _globals['_GETCREDENTIALSFORLOGGEDMODELDOWNLOAD']._serialized_start=2229 + _globals['_GETCREDENTIALSFORLOGGEDMODELDOWNLOAD']._serialized_end=2468 + _globals['_GETCREDENTIALSFORLOGGEDMODELDOWNLOAD_RESPONSE']._serialized_start=2086 + _globals['_GETCREDENTIALSFORLOGGEDMODELDOWNLOAD_RESPONSE']._serialized_end=2181 + _globals['_DATABRICKSMLFLOWARTIFACTSSERVICE']._serialized_start=2588 + _globals['_DATABRICKSMLFLOWARTIFACTSSERVICE']._serialized_end=4337 + _builder.BuildServices(DESCRIPTOR, 'databricks_artifacts_pb2', _globals) + # @@protoc_insertion_point(module_scope) + +else: + # -*- coding: utf-8 -*- + # Generated by the protocol buffer compiler. DO NOT EDIT! + # source: databricks_artifacts.proto + """Generated protocol buffer code.""" + from google.protobuf.internal import enum_type_wrapper + from google.protobuf import descriptor as _descriptor + from google.protobuf import descriptor_pool as _descriptor_pool + from google.protobuf import message as _message + from google.protobuf import reflection as _reflection + from google.protobuf import symbol_database as _symbol_database + from google.protobuf import service as _service + from google.protobuf import service_reflection + # @@protoc_insertion_point(imports) + + _sym_db = _symbol_database.Default() + + + from .scalapb import scalapb_pb2 as scalapb_dot_scalapb__pb2 + from . import databricks_pb2 as databricks__pb2 + + + DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\x1a\x64\x61tabricks_artifacts.proto\x12\x06mlflow\x1a\x15scalapb/scalapb.proto\x1a\x10\x64\x61tabricks.proto\"\xdf\x01\n\x16\x41rtifactCredentialInfo\x12\x0e\n\x06run_id\x18\x01 \x01(\t\x12\x0c\n\x04path\x18\x02 \x01(\t\x12\x12\n\nsigned_uri\x18\x03 \x01(\t\x12:\n\x07headers\x18\x04 \x03(\x0b\x32).mlflow.ArtifactCredentialInfo.HttpHeader\x12,\n\x04type\x18\x05 \x01(\x0e\x32\x1e.mlflow.ArtifactCredentialType\x1a)\n\nHttpHeader\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\r\n\x05value\x18\x02 \x01(\t\"j\n\x1dLoggedModelArtifactCredential\x12\x10\n\x08model_id\x18\x01 \x01(\t\x12\x37\n\x0f\x63redential_info\x18\x02 \x01(\x0b\x32\x1e.mlflow.ArtifactCredentialInfo\"\xe1\x01\n\x15GetCredentialsForRead\x12\x14\n\x06run_id\x18\x01 \x01(\tB\x04\xf8\x86\x19\x01\x12\x0c\n\x04path\x18\x02 \x03(\t\x12\x12\n\npage_token\x18\x03 \x01(\t\x1a\x63\n\x08Response\x12\x38\n\x10\x63redential_infos\x18\x02 \x03(\x0b\x32\x1e.mlflow.ArtifactCredentialInfo\x12\x17\n\x0fnext_page_token\x18\x03 \x01(\tJ\x04\x08\x01\x10\x02:+\xe2?(\n&com.databricks.rpc.RPC[$this.Response]\"\xe2\x01\n\x16GetCredentialsForWrite\x12\x14\n\x06run_id\x18\x01 \x01(\tB\x04\xf8\x86\x19\x01\x12\x0c\n\x04path\x18\x02 \x03(\t\x12\x12\n\npage_token\x18\x03 \x01(\t\x1a\x63\n\x08Response\x12\x38\n\x10\x63redential_infos\x18\x02 \x03(\x0b\x32\x1e.mlflow.ArtifactCredentialInfo\x12\x17\n\x0fnext_page_token\x18\x03 \x01(\tJ\x04\x08\x01\x10\x02:+\xe2?(\n&com.databricks.rpc.RPC[$this.Response]\"\xa1\x02\n\x15\x43reateMultipartUpload\x12\x14\n\x06run_id\x18\x01 \x01(\tB\x04\xf8\x86\x19\x01\x12\x0c\n\x04path\x18\x02 \x01(\t\x12\x17\n\tnum_parts\x18\x03 \x01(\x03\x42\x04\xf8\x86\x19\x01\x1a\x9d\x01\n\x08Response\x12\x11\n\tupload_id\x18\x01 \x01(\t\x12?\n\x17upload_credential_infos\x18\x02 \x03(\x0b\x32\x1e.mlflow.ArtifactCredentialInfo\x12=\n\x15\x61\x62ort_credential_info\x18\x03 \x01(\x0b\x32\x1e.mlflow.ArtifactCredentialInfo:+\xe2?(\n&com.databricks.rpc.RPC[$this.Response]\"-\n\x08PartEtag\x12\x13\n\x0bpart_number\x18\x01 \x01(\x03\x12\x0c\n\x04\x65tag\x18\x02 \x01(\t\"\xb5\x01\n\x17\x43ompleteMultipartUpload\x12\x14\n\x06run_id\x18\x01 \x01(\tB\x04\xf8\x86\x19\x01\x12\x0c\n\x04path\x18\x02 \x01(\t\x12\x17\n\tupload_id\x18\x03 \x01(\tB\x04\xf8\x86\x19\x01\x12$\n\npart_etags\x18\x04 \x03(\x0b\x32\x10.mlflow.PartEtag\x1a\n\n\x08Response:+\xe2?(\n&com.databricks.rpc.RPC[$this.Response]\"\xec\x01\n\x19GetPresignedUploadPartUrl\x12\x14\n\x06run_id\x18\x01 \x01(\tB\x04\xf8\x86\x19\x01\x12\x0c\n\x04path\x18\x02 \x01(\t\x12\x17\n\tupload_id\x18\x03 \x01(\tB\x04\xf8\x86\x19\x01\x12\x19\n\x0bpart_number\x18\x04 \x01(\x03\x42\x04\xf8\x86\x19\x01\x1aJ\n\x08Response\x12>\n\x16upload_credential_info\x18\x01 \x01(\x0b\x32\x1e.mlflow.ArtifactCredentialInfo:+\xe2?(\n&com.databricks.rpc.RPC[$this.Response]\"\xb0\x01\n\"GetCredentialsForTraceDataDownload\x12\x18\n\nrequest_id\x18\x01 \x01(\tB\x04\xf8\x86\x19\x01\x1a\x43\n\x08Response\x12\x37\n\x0f\x63redential_info\x18\x01 \x01(\x0b\x32\x1e.mlflow.ArtifactCredentialInfo:+\xe2?(\n&com.databricks.rpc.RPC[$this.Response]\"\xae\x01\n GetCredentialsForTraceDataUpload\x12\x18\n\nrequest_id\x18\x01 \x01(\tB\x04\xf8\x86\x19\x01\x1a\x43\n\x08Response\x12\x37\n\x0f\x63redential_info\x18\x01 \x01(\x0b\x32\x1e.mlflow.ArtifactCredentialInfo:+\xe2?(\n&com.databricks.rpc.RPC[$this.Response]\"\xed\x01\n\"GetCredentialsForLoggedModelUpload\x12\x16\n\x08model_id\x18\x01 \x01(\tB\x04\xf8\x86\x19\x01\x12\r\n\x05paths\x18\x02 \x03(\t\x12\x12\n\npage_token\x18\x03 \x01(\t\x1a_\n\x08Response\x12:\n\x0b\x63redentials\x18\x01 \x03(\x0b\x32%.mlflow.LoggedModelArtifactCredential\x12\x17\n\x0fnext_page_token\x18\x02 \x01(\t:+\xe2?(\n&com.databricks.rpc.RPC[$this.Response]\"\xef\x01\n$GetCredentialsForLoggedModelDownload\x12\x16\n\x08model_id\x18\x01 \x01(\tB\x04\xf8\x86\x19\x01\x12\r\n\x05paths\x18\x02 \x03(\t\x12\x12\n\npage_token\x18\x03 \x01(\t\x1a_\n\x08Response\x12:\n\x0b\x63redentials\x18\x01 \x03(\x0b\x32%.mlflow.LoggedModelArtifactCredential\x12\x17\n\x0fnext_page_token\x18\x02 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DatabricksMlflowArtifactsService\x12\x9c\x01\n\x15getCredentialsForRead\x12\x1d.mlflow.GetCredentialsForRead\x1a&.mlflow.GetCredentialsForRead.Response\"<\xf2\x86\x19\x38\n4\n\x04POST\x12&/mlflow/artifacts/credentials-for-read\x1a\x04\x08\x02\x10\x00\x10\x03\x12\xa0\x01\n\x16getCredentialsForWrite\x12\x1e.mlflow.GetCredentialsForWrite\x1a\'.mlflow.GetCredentialsForWrite.Response\"=\xf2\x86\x19\x39\n5\n\x04POST\x12\'/mlflow/artifacts/credentials-for-write\x1a\x04\x08\x02\x10\x00\x10\x03\x12\x9f\x01\n\x15\x63reateMultipartUpload\x12\x1d.mlflow.CreateMultipartUpload\x1a&.mlflow.CreateMultipartUpload.Response\"?\xf2\x86\x19;\n7\n\x04POST\x12)/mlflow/artifacts/create-multipart-upload\x1a\x04\x08\x02\x10\x00\x10\x03\x12\xa7\x01\n\x17\x63ompleteMultipartUpload\x12\x1f.mlflow.CompleteMultipartUpload\x1a(.mlflow.CompleteMultipartUpload.Response\"A\xf2\x86\x19=\n9\n\x04POST\x12+/mlflow/artifacts/complete-multipart-upload\x1a\x04\x08\x02\x10\x00\x10\x03\x12\xb0\x01\n\x19getPresignedUploadPartUrl\x12!.mlflow.GetPresignedUploadPartUrl\x1a*.mlflow.GetPresignedUploadPartUrl.Response\"D\xf2\x86\x19@\n<\n\x03GET\x12//mlflow/artifacts/get-presigned-upload-part-url\x1a\x04\x08\x02\x10\x00\x10\x03\x12\xd5\x01\n\"getCredentialsForTraceDataDownload\x12*.mlflow.GetCredentialsForTraceDataDownload\x1a\x33.mlflow.GetCredentialsForTraceDataDownload.Response\"N\xf2\x86\x19J\nF\n\x03GET\x12\x39/mlflow/traces/{request_id}/credentials-for-data-download\x1a\x04\x08\x02\x10\x00\x10\x03\x12\xcd\x01\n getCredentialsForTraceDataUpload\x12(.mlflow.GetCredentialsForTraceDataUpload\x1a\x31.mlflow.GetCredentialsForTraceDataUpload.Response\"L\xf2\x86\x19H\nD\n\x03GET\x12\x37/mlflow/traces/{request_id}/credentials-for-data-upload\x1a\x04\x08\x02\x10\x00\x10\x03\x12\xde\x01\n\"getCredentialsForLoggedModelUpload\x12*.mlflow.GetCredentialsForLoggedModelUpload\x1a\x33.mlflow.GetCredentialsForLoggedModelUpload.Response\"W\xf2\x86\x19S\nO\n\x04POST\x12\x41/mlflow/logged-models/{model_id}/artifacts/credentials-for-upload\x1a\x04\x08\x02\x10\x00\x10\x03\x12\xe6\x01\n$getCredentialsForLoggedModelDownload\x12,.mlflow.GetCredentialsForLoggedModelDownload\x1a\x35.mlflow.GetCredentialsForLoggedModelDownload.Response\"Y\xf2\x86\x19U\nQ\n\x04POST\x12\x43/mlflow/logged-models/{model_id}/artifacts/credentials-for-download\x1a\x04\x08\x02\x10\x00\x10\x03\x42,\n\x1f\x63om.databricks.api.proto.mlflow\x90\x01\x01\xa0\x01\x01\xe2?\x02\x10\x01') + + _ARTIFACTCREDENTIALTYPE = DESCRIPTOR.enum_types_by_name['ArtifactCredentialType'] + ArtifactCredentialType = enum_type_wrapper.EnumTypeWrapper(_ARTIFACTCREDENTIALTYPE) + AZURE_SAS_URI = 1 + AWS_PRESIGNED_URL = 2 + GCP_SIGNED_URL = 3 + AZURE_ADLS_GEN2_SAS_URI = 4 + + + _ARTIFACTCREDENTIALINFO = DESCRIPTOR.message_types_by_name['ArtifactCredentialInfo'] + _ARTIFACTCREDENTIALINFO_HTTPHEADER = _ARTIFACTCREDENTIALINFO.nested_types_by_name['HttpHeader'] + _LOGGEDMODELARTIFACTCREDENTIAL = DESCRIPTOR.message_types_by_name['LoggedModelArtifactCredential'] + _GETCREDENTIALSFORREAD = DESCRIPTOR.message_types_by_name['GetCredentialsForRead'] + _GETCREDENTIALSFORREAD_RESPONSE = _GETCREDENTIALSFORREAD.nested_types_by_name['Response'] + _GETCREDENTIALSFORWRITE = DESCRIPTOR.message_types_by_name['GetCredentialsForWrite'] + _GETCREDENTIALSFORWRITE_RESPONSE = _GETCREDENTIALSFORWRITE.nested_types_by_name['Response'] + _CREATEMULTIPARTUPLOAD = DESCRIPTOR.message_types_by_name['CreateMultipartUpload'] + _CREATEMULTIPARTUPLOAD_RESPONSE = _CREATEMULTIPARTUPLOAD.nested_types_by_name['Response'] + _PARTETAG = DESCRIPTOR.message_types_by_name['PartEtag'] + _COMPLETEMULTIPARTUPLOAD = DESCRIPTOR.message_types_by_name['CompleteMultipartUpload'] + _COMPLETEMULTIPARTUPLOAD_RESPONSE = _COMPLETEMULTIPARTUPLOAD.nested_types_by_name['Response'] + _GETPRESIGNEDUPLOADPARTURL = DESCRIPTOR.message_types_by_name['GetPresignedUploadPartUrl'] + _GETPRESIGNEDUPLOADPARTURL_RESPONSE = _GETPRESIGNEDUPLOADPARTURL.nested_types_by_name['Response'] + _GETCREDENTIALSFORTRACEDATADOWNLOAD = DESCRIPTOR.message_types_by_name['GetCredentialsForTraceDataDownload'] + _GETCREDENTIALSFORTRACEDATADOWNLOAD_RESPONSE = _GETCREDENTIALSFORTRACEDATADOWNLOAD.nested_types_by_name['Response'] + _GETCREDENTIALSFORTRACEDATAUPLOAD = DESCRIPTOR.message_types_by_name['GetCredentialsForTraceDataUpload'] + _GETCREDENTIALSFORTRACEDATAUPLOAD_RESPONSE = _GETCREDENTIALSFORTRACEDATAUPLOAD.nested_types_by_name['Response'] + _GETCREDENTIALSFORLOGGEDMODELUPLOAD = DESCRIPTOR.message_types_by_name['GetCredentialsForLoggedModelUpload'] + _GETCREDENTIALSFORLOGGEDMODELUPLOAD_RESPONSE = _GETCREDENTIALSFORLOGGEDMODELUPLOAD.nested_types_by_name['Response'] + _GETCREDENTIALSFORLOGGEDMODELDOWNLOAD = DESCRIPTOR.message_types_by_name['GetCredentialsForLoggedModelDownload'] + _GETCREDENTIALSFORLOGGEDMODELDOWNLOAD_RESPONSE = _GETCREDENTIALSFORLOGGEDMODELDOWNLOAD.nested_types_by_name['Response'] + ArtifactCredentialInfo = _reflection.GeneratedProtocolMessageType('ArtifactCredentialInfo', (_message.Message,), { + + 'HttpHeader' : _reflection.GeneratedProtocolMessageType('HttpHeader', (_message.Message,), { + 'DESCRIPTOR' : _ARTIFACTCREDENTIALINFO_HTTPHEADER, + '__module__' : 'databricks_artifacts_pb2' + # @@protoc_insertion_point(class_scope:mlflow.ArtifactCredentialInfo.HttpHeader) + }) + , + 'DESCRIPTOR' : _ARTIFACTCREDENTIALINFO, + '__module__' : 'databricks_artifacts_pb2' + # @@protoc_insertion_point(class_scope:mlflow.ArtifactCredentialInfo) + }) + _sym_db.RegisterMessage(ArtifactCredentialInfo) + _sym_db.RegisterMessage(ArtifactCredentialInfo.HttpHeader) + + LoggedModelArtifactCredential = _reflection.GeneratedProtocolMessageType('LoggedModelArtifactCredential', (_message.Message,), { + 'DESCRIPTOR' : _LOGGEDMODELARTIFACTCREDENTIAL, + '__module__' : 'databricks_artifacts_pb2' + # @@protoc_insertion_point(class_scope:mlflow.LoggedModelArtifactCredential) + }) + _sym_db.RegisterMessage(LoggedModelArtifactCredential) + + GetCredentialsForRead = _reflection.GeneratedProtocolMessageType('GetCredentialsForRead', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _GETCREDENTIALSFORREAD_RESPONSE, + '__module__' : 'databricks_artifacts_pb2' + # @@protoc_insertion_point(class_scope:mlflow.GetCredentialsForRead.Response) + }) + , + 'DESCRIPTOR' : _GETCREDENTIALSFORREAD, + '__module__' : 'databricks_artifacts_pb2' + # @@protoc_insertion_point(class_scope:mlflow.GetCredentialsForRead) + }) + _sym_db.RegisterMessage(GetCredentialsForRead) + _sym_db.RegisterMessage(GetCredentialsForRead.Response) + + GetCredentialsForWrite = _reflection.GeneratedProtocolMessageType('GetCredentialsForWrite', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _GETCREDENTIALSFORWRITE_RESPONSE, + '__module__' : 'databricks_artifacts_pb2' + # @@protoc_insertion_point(class_scope:mlflow.GetCredentialsForWrite.Response) + }) + , + 'DESCRIPTOR' : _GETCREDENTIALSFORWRITE, + '__module__' : 'databricks_artifacts_pb2' + # @@protoc_insertion_point(class_scope:mlflow.GetCredentialsForWrite) + }) + _sym_db.RegisterMessage(GetCredentialsForWrite) + _sym_db.RegisterMessage(GetCredentialsForWrite.Response) + + CreateMultipartUpload = _reflection.GeneratedProtocolMessageType('CreateMultipartUpload', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _CREATEMULTIPARTUPLOAD_RESPONSE, + '__module__' : 'databricks_artifacts_pb2' + # @@protoc_insertion_point(class_scope:mlflow.CreateMultipartUpload.Response) + }) + , + 'DESCRIPTOR' : _CREATEMULTIPARTUPLOAD, + '__module__' : 'databricks_artifacts_pb2' + # @@protoc_insertion_point(class_scope:mlflow.CreateMultipartUpload) + }) + _sym_db.RegisterMessage(CreateMultipartUpload) + _sym_db.RegisterMessage(CreateMultipartUpload.Response) + + PartEtag = _reflection.GeneratedProtocolMessageType('PartEtag', (_message.Message,), { + 'DESCRIPTOR' : _PARTETAG, + '__module__' : 'databricks_artifacts_pb2' + # @@protoc_insertion_point(class_scope:mlflow.PartEtag) + }) + _sym_db.RegisterMessage(PartEtag) + + CompleteMultipartUpload = _reflection.GeneratedProtocolMessageType('CompleteMultipartUpload', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _COMPLETEMULTIPARTUPLOAD_RESPONSE, + '__module__' : 'databricks_artifacts_pb2' + # @@protoc_insertion_point(class_scope:mlflow.CompleteMultipartUpload.Response) + }) + , + 'DESCRIPTOR' : _COMPLETEMULTIPARTUPLOAD, + '__module__' : 'databricks_artifacts_pb2' + # @@protoc_insertion_point(class_scope:mlflow.CompleteMultipartUpload) + }) + _sym_db.RegisterMessage(CompleteMultipartUpload) + _sym_db.RegisterMessage(CompleteMultipartUpload.Response) + + GetPresignedUploadPartUrl = _reflection.GeneratedProtocolMessageType('GetPresignedUploadPartUrl', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _GETPRESIGNEDUPLOADPARTURL_RESPONSE, + '__module__' : 'databricks_artifacts_pb2' + # @@protoc_insertion_point(class_scope:mlflow.GetPresignedUploadPartUrl.Response) + }) + , + 'DESCRIPTOR' : _GETPRESIGNEDUPLOADPARTURL, + '__module__' : 'databricks_artifacts_pb2' + # @@protoc_insertion_point(class_scope:mlflow.GetPresignedUploadPartUrl) + }) + _sym_db.RegisterMessage(GetPresignedUploadPartUrl) + _sym_db.RegisterMessage(GetPresignedUploadPartUrl.Response) + + GetCredentialsForTraceDataDownload = _reflection.GeneratedProtocolMessageType('GetCredentialsForTraceDataDownload', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _GETCREDENTIALSFORTRACEDATADOWNLOAD_RESPONSE, + '__module__' : 'databricks_artifacts_pb2' + # @@protoc_insertion_point(class_scope:mlflow.GetCredentialsForTraceDataDownload.Response) + }) + , + 'DESCRIPTOR' : _GETCREDENTIALSFORTRACEDATADOWNLOAD, + '__module__' : 'databricks_artifacts_pb2' + # @@protoc_insertion_point(class_scope:mlflow.GetCredentialsForTraceDataDownload) + }) + _sym_db.RegisterMessage(GetCredentialsForTraceDataDownload) + _sym_db.RegisterMessage(GetCredentialsForTraceDataDownload.Response) + + GetCredentialsForTraceDataUpload = _reflection.GeneratedProtocolMessageType('GetCredentialsForTraceDataUpload', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _GETCREDENTIALSFORTRACEDATAUPLOAD_RESPONSE, + '__module__' : 'databricks_artifacts_pb2' + # @@protoc_insertion_point(class_scope:mlflow.GetCredentialsForTraceDataUpload.Response) + }) + , + 'DESCRIPTOR' : _GETCREDENTIALSFORTRACEDATAUPLOAD, + '__module__' : 'databricks_artifacts_pb2' + # @@protoc_insertion_point(class_scope:mlflow.GetCredentialsForTraceDataUpload) + }) + _sym_db.RegisterMessage(GetCredentialsForTraceDataUpload) + _sym_db.RegisterMessage(GetCredentialsForTraceDataUpload.Response) + + GetCredentialsForLoggedModelUpload = _reflection.GeneratedProtocolMessageType('GetCredentialsForLoggedModelUpload', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _GETCREDENTIALSFORLOGGEDMODELUPLOAD_RESPONSE, + '__module__' : 'databricks_artifacts_pb2' + # @@protoc_insertion_point(class_scope:mlflow.GetCredentialsForLoggedModelUpload.Response) + }) + , + 'DESCRIPTOR' : _GETCREDENTIALSFORLOGGEDMODELUPLOAD, + '__module__' : 'databricks_artifacts_pb2' + # @@protoc_insertion_point(class_scope:mlflow.GetCredentialsForLoggedModelUpload) + }) + _sym_db.RegisterMessage(GetCredentialsForLoggedModelUpload) + _sym_db.RegisterMessage(GetCredentialsForLoggedModelUpload.Response) + + GetCredentialsForLoggedModelDownload = _reflection.GeneratedProtocolMessageType('GetCredentialsForLoggedModelDownload', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _GETCREDENTIALSFORLOGGEDMODELDOWNLOAD_RESPONSE, + '__module__' : 'databricks_artifacts_pb2' + # @@protoc_insertion_point(class_scope:mlflow.GetCredentialsForLoggedModelDownload.Response) + }) + , + 'DESCRIPTOR' : _GETCREDENTIALSFORLOGGEDMODELDOWNLOAD, + '__module__' : 'databricks_artifacts_pb2' + # @@protoc_insertion_point(class_scope:mlflow.GetCredentialsForLoggedModelDownload) + }) + _sym_db.RegisterMessage(GetCredentialsForLoggedModelDownload) + _sym_db.RegisterMessage(GetCredentialsForLoggedModelDownload.Response) + + _DATABRICKSMLFLOWARTIFACTSSERVICE = DESCRIPTOR.services_by_name['DatabricksMlflowArtifactsService'] + if _descriptor._USE_C_DESCRIPTORS == False: + + DESCRIPTOR._options = None + DESCRIPTOR._serialized_options = b'\n\037com.databricks.api.proto.mlflow\220\001\001\240\001\001\342?\002\020\001' + _GETCREDENTIALSFORREAD.fields_by_name['run_id']._options = None + _GETCREDENTIALSFORREAD.fields_by_name['run_id']._serialized_options = b'\370\206\031\001' + _GETCREDENTIALSFORREAD._options = None + _GETCREDENTIALSFORREAD._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _GETCREDENTIALSFORWRITE.fields_by_name['run_id']._options = None + _GETCREDENTIALSFORWRITE.fields_by_name['run_id']._serialized_options = b'\370\206\031\001' + _GETCREDENTIALSFORWRITE._options = None + _GETCREDENTIALSFORWRITE._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _CREATEMULTIPARTUPLOAD.fields_by_name['run_id']._options = None + _CREATEMULTIPARTUPLOAD.fields_by_name['run_id']._serialized_options = b'\370\206\031\001' + _CREATEMULTIPARTUPLOAD.fields_by_name['num_parts']._options = None + _CREATEMULTIPARTUPLOAD.fields_by_name['num_parts']._serialized_options = b'\370\206\031\001' + _CREATEMULTIPARTUPLOAD._options = None + _CREATEMULTIPARTUPLOAD._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _COMPLETEMULTIPARTUPLOAD.fields_by_name['run_id']._options = None + _COMPLETEMULTIPARTUPLOAD.fields_by_name['run_id']._serialized_options = b'\370\206\031\001' + _COMPLETEMULTIPARTUPLOAD.fields_by_name['upload_id']._options = None + _COMPLETEMULTIPARTUPLOAD.fields_by_name['upload_id']._serialized_options = b'\370\206\031\001' + _COMPLETEMULTIPARTUPLOAD._options = None + _COMPLETEMULTIPARTUPLOAD._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _GETPRESIGNEDUPLOADPARTURL.fields_by_name['run_id']._options = None + _GETPRESIGNEDUPLOADPARTURL.fields_by_name['run_id']._serialized_options = b'\370\206\031\001' + _GETPRESIGNEDUPLOADPARTURL.fields_by_name['upload_id']._options = None + _GETPRESIGNEDUPLOADPARTURL.fields_by_name['upload_id']._serialized_options = b'\370\206\031\001' + _GETPRESIGNEDUPLOADPARTURL.fields_by_name['part_number']._options = None + _GETPRESIGNEDUPLOADPARTURL.fields_by_name['part_number']._serialized_options = b'\370\206\031\001' + _GETPRESIGNEDUPLOADPARTURL._options = None + _GETPRESIGNEDUPLOADPARTURL._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _GETCREDENTIALSFORTRACEDATADOWNLOAD.fields_by_name['request_id']._options = None + _GETCREDENTIALSFORTRACEDATADOWNLOAD.fields_by_name['request_id']._serialized_options = b'\370\206\031\001' + _GETCREDENTIALSFORTRACEDATADOWNLOAD._options = None + _GETCREDENTIALSFORTRACEDATADOWNLOAD._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _GETCREDENTIALSFORTRACEDATAUPLOAD.fields_by_name['request_id']._options = None + _GETCREDENTIALSFORTRACEDATAUPLOAD.fields_by_name['request_id']._serialized_options = b'\370\206\031\001' + _GETCREDENTIALSFORTRACEDATAUPLOAD._options = None + _GETCREDENTIALSFORTRACEDATAUPLOAD._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _GETCREDENTIALSFORLOGGEDMODELUPLOAD.fields_by_name['model_id']._options = None + _GETCREDENTIALSFORLOGGEDMODELUPLOAD.fields_by_name['model_id']._serialized_options = b'\370\206\031\001' + _GETCREDENTIALSFORLOGGEDMODELUPLOAD._options = None + _GETCREDENTIALSFORLOGGEDMODELUPLOAD._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _GETCREDENTIALSFORLOGGEDMODELDOWNLOAD.fields_by_name['model_id']._options = None + _GETCREDENTIALSFORLOGGEDMODELDOWNLOAD.fields_by_name['model_id']._serialized_options = b'\370\206\031\001' + _GETCREDENTIALSFORLOGGEDMODELDOWNLOAD._options = None + _GETCREDENTIALSFORLOGGEDMODELDOWNLOAD._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _DATABRICKSMLFLOWARTIFACTSSERVICE.methods_by_name['getCredentialsForRead']._options = None + _DATABRICKSMLFLOWARTIFACTSSERVICE.methods_by_name['getCredentialsForRead']._serialized_options = b'\362\206\0318\n4\n\004POST\022&/mlflow/artifacts/credentials-for-read\032\004\010\002\020\000\020\003' + _DATABRICKSMLFLOWARTIFACTSSERVICE.methods_by_name['getCredentialsForWrite']._options = None + _DATABRICKSMLFLOWARTIFACTSSERVICE.methods_by_name['getCredentialsForWrite']._serialized_options = b'\362\206\0319\n5\n\004POST\022\'/mlflow/artifacts/credentials-for-write\032\004\010\002\020\000\020\003' + _DATABRICKSMLFLOWARTIFACTSSERVICE.methods_by_name['createMultipartUpload']._options = None + _DATABRICKSMLFLOWARTIFACTSSERVICE.methods_by_name['createMultipartUpload']._serialized_options = b'\362\206\031;\n7\n\004POST\022)/mlflow/artifacts/create-multipart-upload\032\004\010\002\020\000\020\003' + _DATABRICKSMLFLOWARTIFACTSSERVICE.methods_by_name['completeMultipartUpload']._options = None + _DATABRICKSMLFLOWARTIFACTSSERVICE.methods_by_name['completeMultipartUpload']._serialized_options = b'\362\206\031=\n9\n\004POST\022+/mlflow/artifacts/complete-multipart-upload\032\004\010\002\020\000\020\003' + _DATABRICKSMLFLOWARTIFACTSSERVICE.methods_by_name['getPresignedUploadPartUrl']._options = None + _DATABRICKSMLFLOWARTIFACTSSERVICE.methods_by_name['getPresignedUploadPartUrl']._serialized_options = b'\362\206\031@\n<\n\003GET\022//mlflow/artifacts/get-presigned-upload-part-url\032\004\010\002\020\000\020\003' + _DATABRICKSMLFLOWARTIFACTSSERVICE.methods_by_name['getCredentialsForTraceDataDownload']._options = None + _DATABRICKSMLFLOWARTIFACTSSERVICE.methods_by_name['getCredentialsForTraceDataDownload']._serialized_options = b'\362\206\031J\nF\n\003GET\0229/mlflow/traces/{request_id}/credentials-for-data-download\032\004\010\002\020\000\020\003' + _DATABRICKSMLFLOWARTIFACTSSERVICE.methods_by_name['getCredentialsForTraceDataUpload']._options = None + _DATABRICKSMLFLOWARTIFACTSSERVICE.methods_by_name['getCredentialsForTraceDataUpload']._serialized_options = b'\362\206\031H\nD\n\003GET\0227/mlflow/traces/{request_id}/credentials-for-data-upload\032\004\010\002\020\000\020\003' + _DATABRICKSMLFLOWARTIFACTSSERVICE.methods_by_name['getCredentialsForLoggedModelUpload']._options = None + _DATABRICKSMLFLOWARTIFACTSSERVICE.methods_by_name['getCredentialsForLoggedModelUpload']._serialized_options = b'\362\206\031S\nO\n\004POST\022A/mlflow/logged-models/{model_id}/artifacts/credentials-for-upload\032\004\010\002\020\000\020\003' + _DATABRICKSMLFLOWARTIFACTSSERVICE.methods_by_name['getCredentialsForLoggedModelDownload']._options = None + _DATABRICKSMLFLOWARTIFACTSSERVICE.methods_by_name['getCredentialsForLoggedModelDownload']._serialized_options = b'\362\206\031U\nQ\n\004POST\022C/mlflow/logged-models/{model_id}/artifacts/credentials-for-download\032\004\010\002\020\000\020\003' + _ARTIFACTCREDENTIALTYPE._serialized_start=2470 + _ARTIFACTCREDENTIALTYPE._serialized_end=2585 + _ARTIFACTCREDENTIALINFO._serialized_start=80 + _ARTIFACTCREDENTIALINFO._serialized_end=303 + _ARTIFACTCREDENTIALINFO_HTTPHEADER._serialized_start=262 + _ARTIFACTCREDENTIALINFO_HTTPHEADER._serialized_end=303 + _LOGGEDMODELARTIFACTCREDENTIAL._serialized_start=305 + _LOGGEDMODELARTIFACTCREDENTIAL._serialized_end=411 + _GETCREDENTIALSFORREAD._serialized_start=414 + _GETCREDENTIALSFORREAD._serialized_end=639 + _GETCREDENTIALSFORREAD_RESPONSE._serialized_start=495 + _GETCREDENTIALSFORREAD_RESPONSE._serialized_end=594 + _GETCREDENTIALSFORWRITE._serialized_start=642 + _GETCREDENTIALSFORWRITE._serialized_end=868 + _GETCREDENTIALSFORWRITE_RESPONSE._serialized_start=495 + _GETCREDENTIALSFORWRITE_RESPONSE._serialized_end=594 + _CREATEMULTIPARTUPLOAD._serialized_start=871 + _CREATEMULTIPARTUPLOAD._serialized_end=1160 + _CREATEMULTIPARTUPLOAD_RESPONSE._serialized_start=958 + _CREATEMULTIPARTUPLOAD_RESPONSE._serialized_end=1115 + _PARTETAG._serialized_start=1162 + _PARTETAG._serialized_end=1207 + _COMPLETEMULTIPARTUPLOAD._serialized_start=1210 + _COMPLETEMULTIPARTUPLOAD._serialized_end=1391 + _COMPLETEMULTIPARTUPLOAD_RESPONSE._serialized_start=495 + _COMPLETEMULTIPARTUPLOAD_RESPONSE._serialized_end=505 + _GETPRESIGNEDUPLOADPARTURL._serialized_start=1394 + _GETPRESIGNEDUPLOADPARTURL._serialized_end=1630 + _GETPRESIGNEDUPLOADPARTURL_RESPONSE._serialized_start=1511 + _GETPRESIGNEDUPLOADPARTURL_RESPONSE._serialized_end=1585 + _GETCREDENTIALSFORTRACEDATADOWNLOAD._serialized_start=1633 + _GETCREDENTIALSFORTRACEDATADOWNLOAD._serialized_end=1809 + _GETCREDENTIALSFORTRACEDATADOWNLOAD_RESPONSE._serialized_start=1697 + _GETCREDENTIALSFORTRACEDATADOWNLOAD_RESPONSE._serialized_end=1764 + _GETCREDENTIALSFORTRACEDATAUPLOAD._serialized_start=1812 + _GETCREDENTIALSFORTRACEDATAUPLOAD._serialized_end=1986 + _GETCREDENTIALSFORTRACEDATAUPLOAD_RESPONSE._serialized_start=1697 + _GETCREDENTIALSFORTRACEDATAUPLOAD_RESPONSE._serialized_end=1764 + _GETCREDENTIALSFORLOGGEDMODELUPLOAD._serialized_start=1989 + _GETCREDENTIALSFORLOGGEDMODELUPLOAD._serialized_end=2226 + _GETCREDENTIALSFORLOGGEDMODELUPLOAD_RESPONSE._serialized_start=2086 + _GETCREDENTIALSFORLOGGEDMODELUPLOAD_RESPONSE._serialized_end=2181 + _GETCREDENTIALSFORLOGGEDMODELDOWNLOAD._serialized_start=2229 + _GETCREDENTIALSFORLOGGEDMODELDOWNLOAD._serialized_end=2468 + _GETCREDENTIALSFORLOGGEDMODELDOWNLOAD_RESPONSE._serialized_start=2086 + _GETCREDENTIALSFORLOGGEDMODELDOWNLOAD_RESPONSE._serialized_end=2181 + _DATABRICKSMLFLOWARTIFACTSSERVICE._serialized_start=2588 + _DATABRICKSMLFLOWARTIFACTSSERVICE._serialized_end=4337 + DatabricksMlflowArtifactsService = service_reflection.GeneratedServiceType('DatabricksMlflowArtifactsService', (_service.Service,), dict( + DESCRIPTOR = _DATABRICKSMLFLOWARTIFACTSSERVICE, + __module__ = 'databricks_artifacts_pb2' + )) + + DatabricksMlflowArtifactsService_Stub = service_reflection.GeneratedServiceStubType('DatabricksMlflowArtifactsService_Stub', (DatabricksMlflowArtifactsService,), dict( + DESCRIPTOR = _DATABRICKSMLFLOWARTIFACTSSERVICE, + __module__ = 'databricks_artifacts_pb2' + )) + + + # @@protoc_insertion_point(module_scope) + diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/protos/databricks_filesystem_service_pb2.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/protos/databricks_filesystem_service_pb2.py new file mode 100644 index 0000000000000000000000000000000000000000..8665c8e5407b9b477901d838f764ec3dfffe4d6c --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/protos/databricks_filesystem_service_pb2.py @@ -0,0 +1,196 @@ + +import google.protobuf +from packaging.version import Version +if Version(google.protobuf.__version__).major >= 5: + # -*- coding: utf-8 -*- + # Generated by the protocol buffer compiler. DO NOT EDIT! + # source: databricks_filesystem_service.proto + # Protobuf Python Version: 5.26.0 + """Generated protocol buffer code.""" + from google.protobuf import descriptor as _descriptor + from google.protobuf import descriptor_pool as _descriptor_pool + from google.protobuf import symbol_database as _symbol_database + from google.protobuf.internal import builder as _builder + # @@protoc_insertion_point(imports) + + _sym_db = _symbol_database.Default() + + + from . import databricks_pb2 as databricks__pb2 + from .scalapb import scalapb_pb2 as scalapb_dot_scalapb__pb2 + + + DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n#databricks_filesystem_service.proto\x12\x11mlflow.filesystem\x1a\x10\x64\x61tabricks.proto\x1a\x15scalapb/scalapb.proto\")\n\nHttpHeader\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\r\n\x05value\x18\x02 \x01(\t\"`\n\x18\x43reateDownloadUrlRequest\x12\x0c\n\x04path\x18\x01 \x01(\t:6\xe2?3\n1com.databricks.rpc.RPC[CreateDownloadUrlResponse]\"\x82\x01\n\x19\x43reateDownloadUrlResponse\x12\x0b\n\x03url\x18\x01 \x01(\t\x12.\n\x07headers\x18\x02 \x03(\x0b\x32\x1d.mlflow.filesystem.HttpHeader:(\xe2?%\n#com.databricks.rpc.DoNotLogContents\"\\\n\x16\x43reateUploadUrlRequest\x12\x0c\n\x04path\x18\x01 \x01(\t:4\xe2?1\n/com.databricks.rpc.RPC[CreateUploadUrlResponse]\"\x80\x01\n\x17\x43reateUploadUrlResponse\x12\x0b\n\x03url\x18\x01 \x01(\t\x12.\n\x07headers\x18\x02 \x03(\x0b\x32\x1d.mlflow.filesystem.HttpHeader:(\xe2?%\n#com.databricks.rpc.DoNotLogContents\"\x96\x01\n\x0e\x44irectoryEntry\x12\x0c\n\x04path\x18\x01 \x01(\t\x12\x14\n\x0cis_directory\x18\x02 \x01(\x08\x12\x11\n\tfile_size\x18\x03 \x01(\x03\x12\x15\n\rlast_modified\x18\x04 \x01(\x03\x12\x0c\n\x04name\x18\x05 \x01(\t:(\xe2?%\n#com.databricks.rpc.DoNotLogContents\"\x8f\x01\n\x15ListDirectoryResponse\x12\x33\n\x08\x63ontents\x18\x01 \x03(\x0b\x32!.mlflow.filesystem.DirectoryEntry\x12\x17\n\x0fnext_page_token\x18\x02 \x01(\t:(\xe2?%\n#com.databricks.rpc.DoNotLogContents2\xcb\x02\n\x11\x46ilesystemService\x12\x9d\x01\n\x11\x43reateDownloadUrl\x12+.mlflow.filesystem.CreateDownloadUrlRequest\x1a,.mlflow.filesystem.CreateDownloadUrlResponse\"-\xf2\x86\x19)\n%\n\x04POST\x12\x17/fs/create-download-url\x1a\x04\x08\x02\x10\x00\x10\x03\x12\x95\x01\n\x0f\x43reateUploadUrl\x12).mlflow.filesystem.CreateUploadUrlRequest\x1a*.mlflow.filesystem.CreateUploadUrlResponse\"+\xf2\x86\x19\'\n#\n\x04POST\x12\x15/fs/create-upload-url\x1a\x04\x08\x02\x10\x00\x10\x03\x42\x30\n#com.databricks.api.proto.filesystem\x90\x01\x01\xa0\x01\x01\xe2?\x02\x10\x01') + + _globals = globals() + _builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals) + _builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'databricks_filesystem_service_pb2', _globals) + if not _descriptor._USE_C_DESCRIPTORS: + _globals['DESCRIPTOR']._loaded_options = None + _globals['DESCRIPTOR']._serialized_options = b'\n#com.databricks.api.proto.filesystem\220\001\001\240\001\001\342?\002\020\001' + _globals['_CREATEDOWNLOADURLREQUEST']._loaded_options = None + _globals['_CREATEDOWNLOADURLREQUEST']._serialized_options = b'\342?3\n1com.databricks.rpc.RPC[CreateDownloadUrlResponse]' + _globals['_CREATEDOWNLOADURLRESPONSE']._loaded_options = None + _globals['_CREATEDOWNLOADURLRESPONSE']._serialized_options = b'\342?%\n#com.databricks.rpc.DoNotLogContents' + _globals['_CREATEUPLOADURLREQUEST']._loaded_options = None + _globals['_CREATEUPLOADURLREQUEST']._serialized_options = b'\342?1\n/com.databricks.rpc.RPC[CreateUploadUrlResponse]' + _globals['_CREATEUPLOADURLRESPONSE']._loaded_options = None + _globals['_CREATEUPLOADURLRESPONSE']._serialized_options = b'\342?%\n#com.databricks.rpc.DoNotLogContents' + _globals['_DIRECTORYENTRY']._loaded_options = None + _globals['_DIRECTORYENTRY']._serialized_options = b'\342?%\n#com.databricks.rpc.DoNotLogContents' + _globals['_LISTDIRECTORYRESPONSE']._loaded_options = None + _globals['_LISTDIRECTORYRESPONSE']._serialized_options = b'\342?%\n#com.databricks.rpc.DoNotLogContents' + _globals['_FILESYSTEMSERVICE'].methods_by_name['CreateDownloadUrl']._loaded_options = None + _globals['_FILESYSTEMSERVICE'].methods_by_name['CreateDownloadUrl']._serialized_options = b'\362\206\031)\n%\n\004POST\022\027/fs/create-download-url\032\004\010\002\020\000\020\003' + _globals['_FILESYSTEMSERVICE'].methods_by_name['CreateUploadUrl']._loaded_options = None + _globals['_FILESYSTEMSERVICE'].methods_by_name['CreateUploadUrl']._serialized_options = b'\362\206\031\'\n#\n\004POST\022\025/fs/create-upload-url\032\004\010\002\020\000\020\003' + _globals['_HTTPHEADER']._serialized_start=99 + _globals['_HTTPHEADER']._serialized_end=140 + _globals['_CREATEDOWNLOADURLREQUEST']._serialized_start=142 + _globals['_CREATEDOWNLOADURLREQUEST']._serialized_end=238 + _globals['_CREATEDOWNLOADURLRESPONSE']._serialized_start=241 + _globals['_CREATEDOWNLOADURLRESPONSE']._serialized_end=371 + _globals['_CREATEUPLOADURLREQUEST']._serialized_start=373 + _globals['_CREATEUPLOADURLREQUEST']._serialized_end=465 + _globals['_CREATEUPLOADURLRESPONSE']._serialized_start=468 + _globals['_CREATEUPLOADURLRESPONSE']._serialized_end=596 + _globals['_DIRECTORYENTRY']._serialized_start=599 + _globals['_DIRECTORYENTRY']._serialized_end=749 + _globals['_LISTDIRECTORYRESPONSE']._serialized_start=752 + _globals['_LISTDIRECTORYRESPONSE']._serialized_end=895 + _globals['_FILESYSTEMSERVICE']._serialized_start=898 + _globals['_FILESYSTEMSERVICE']._serialized_end=1229 + _builder.BuildServices(DESCRIPTOR, 'databricks_filesystem_service_pb2', _globals) + # @@protoc_insertion_point(module_scope) + +else: + # -*- coding: utf-8 -*- + # Generated by the protocol buffer compiler. DO NOT EDIT! + # source: databricks_filesystem_service.proto + """Generated protocol buffer code.""" + from google.protobuf import descriptor as _descriptor + from google.protobuf import descriptor_pool as _descriptor_pool + from google.protobuf import message as _message + from google.protobuf import reflection as _reflection + from google.protobuf import symbol_database as _symbol_database + from google.protobuf import service as _service + from google.protobuf import service_reflection + # @@protoc_insertion_point(imports) + + _sym_db = _symbol_database.Default() + + + from . import databricks_pb2 as databricks__pb2 + from .scalapb import scalapb_pb2 as scalapb_dot_scalapb__pb2 + + + DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n#databricks_filesystem_service.proto\x12\x11mlflow.filesystem\x1a\x10\x64\x61tabricks.proto\x1a\x15scalapb/scalapb.proto\")\n\nHttpHeader\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\r\n\x05value\x18\x02 \x01(\t\"`\n\x18\x43reateDownloadUrlRequest\x12\x0c\n\x04path\x18\x01 \x01(\t:6\xe2?3\n1com.databricks.rpc.RPC[CreateDownloadUrlResponse]\"\x82\x01\n\x19\x43reateDownloadUrlResponse\x12\x0b\n\x03url\x18\x01 \x01(\t\x12.\n\x07headers\x18\x02 \x03(\x0b\x32\x1d.mlflow.filesystem.HttpHeader:(\xe2?%\n#com.databricks.rpc.DoNotLogContents\"\\\n\x16\x43reateUploadUrlRequest\x12\x0c\n\x04path\x18\x01 \x01(\t:4\xe2?1\n/com.databricks.rpc.RPC[CreateUploadUrlResponse]\"\x80\x01\n\x17\x43reateUploadUrlResponse\x12\x0b\n\x03url\x18\x01 \x01(\t\x12.\n\x07headers\x18\x02 \x03(\x0b\x32\x1d.mlflow.filesystem.HttpHeader:(\xe2?%\n#com.databricks.rpc.DoNotLogContents\"\x96\x01\n\x0e\x44irectoryEntry\x12\x0c\n\x04path\x18\x01 \x01(\t\x12\x14\n\x0cis_directory\x18\x02 \x01(\x08\x12\x11\n\tfile_size\x18\x03 \x01(\x03\x12\x15\n\rlast_modified\x18\x04 \x01(\x03\x12\x0c\n\x04name\x18\x05 \x01(\t:(\xe2?%\n#com.databricks.rpc.DoNotLogContents\"\x8f\x01\n\x15ListDirectoryResponse\x12\x33\n\x08\x63ontents\x18\x01 \x03(\x0b\x32!.mlflow.filesystem.DirectoryEntry\x12\x17\n\x0fnext_page_token\x18\x02 \x01(\t:(\xe2?%\n#com.databricks.rpc.DoNotLogContents2\xcb\x02\n\x11\x46ilesystemService\x12\x9d\x01\n\x11\x43reateDownloadUrl\x12+.mlflow.filesystem.CreateDownloadUrlRequest\x1a,.mlflow.filesystem.CreateDownloadUrlResponse\"-\xf2\x86\x19)\n%\n\x04POST\x12\x17/fs/create-download-url\x1a\x04\x08\x02\x10\x00\x10\x03\x12\x95\x01\n\x0f\x43reateUploadUrl\x12).mlflow.filesystem.CreateUploadUrlRequest\x1a*.mlflow.filesystem.CreateUploadUrlResponse\"+\xf2\x86\x19\'\n#\n\x04POST\x12\x15/fs/create-upload-url\x1a\x04\x08\x02\x10\x00\x10\x03\x42\x30\n#com.databricks.api.proto.filesystem\x90\x01\x01\xa0\x01\x01\xe2?\x02\x10\x01') + + + + _HTTPHEADER = DESCRIPTOR.message_types_by_name['HttpHeader'] + _CREATEDOWNLOADURLREQUEST = DESCRIPTOR.message_types_by_name['CreateDownloadUrlRequest'] + _CREATEDOWNLOADURLRESPONSE = DESCRIPTOR.message_types_by_name['CreateDownloadUrlResponse'] + _CREATEUPLOADURLREQUEST = DESCRIPTOR.message_types_by_name['CreateUploadUrlRequest'] + _CREATEUPLOADURLRESPONSE = DESCRIPTOR.message_types_by_name['CreateUploadUrlResponse'] + _DIRECTORYENTRY = DESCRIPTOR.message_types_by_name['DirectoryEntry'] + _LISTDIRECTORYRESPONSE = DESCRIPTOR.message_types_by_name['ListDirectoryResponse'] + HttpHeader = _reflection.GeneratedProtocolMessageType('HttpHeader', (_message.Message,), { + 'DESCRIPTOR' : _HTTPHEADER, + '__module__' : 'databricks_filesystem_service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.filesystem.HttpHeader) + }) + _sym_db.RegisterMessage(HttpHeader) + + CreateDownloadUrlRequest = _reflection.GeneratedProtocolMessageType('CreateDownloadUrlRequest', (_message.Message,), { + 'DESCRIPTOR' : _CREATEDOWNLOADURLREQUEST, + '__module__' : 'databricks_filesystem_service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.filesystem.CreateDownloadUrlRequest) + }) + _sym_db.RegisterMessage(CreateDownloadUrlRequest) + + CreateDownloadUrlResponse = _reflection.GeneratedProtocolMessageType('CreateDownloadUrlResponse', (_message.Message,), { + 'DESCRIPTOR' : _CREATEDOWNLOADURLRESPONSE, + '__module__' : 'databricks_filesystem_service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.filesystem.CreateDownloadUrlResponse) + }) + _sym_db.RegisterMessage(CreateDownloadUrlResponse) + + CreateUploadUrlRequest = _reflection.GeneratedProtocolMessageType('CreateUploadUrlRequest', (_message.Message,), { + 'DESCRIPTOR' : _CREATEUPLOADURLREQUEST, + '__module__' : 'databricks_filesystem_service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.filesystem.CreateUploadUrlRequest) + }) + _sym_db.RegisterMessage(CreateUploadUrlRequest) + + CreateUploadUrlResponse = _reflection.GeneratedProtocolMessageType('CreateUploadUrlResponse', (_message.Message,), { + 'DESCRIPTOR' : _CREATEUPLOADURLRESPONSE, + '__module__' : 'databricks_filesystem_service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.filesystem.CreateUploadUrlResponse) + }) + _sym_db.RegisterMessage(CreateUploadUrlResponse) + + DirectoryEntry = _reflection.GeneratedProtocolMessageType('DirectoryEntry', (_message.Message,), { + 'DESCRIPTOR' : _DIRECTORYENTRY, + '__module__' : 'databricks_filesystem_service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.filesystem.DirectoryEntry) + }) + _sym_db.RegisterMessage(DirectoryEntry) + + ListDirectoryResponse = _reflection.GeneratedProtocolMessageType('ListDirectoryResponse', (_message.Message,), { + 'DESCRIPTOR' : _LISTDIRECTORYRESPONSE, + '__module__' : 'databricks_filesystem_service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.filesystem.ListDirectoryResponse) + }) + _sym_db.RegisterMessage(ListDirectoryResponse) + + _FILESYSTEMSERVICE = DESCRIPTOR.services_by_name['FilesystemService'] + if _descriptor._USE_C_DESCRIPTORS == False: + + DESCRIPTOR._options = None + DESCRIPTOR._serialized_options = b'\n#com.databricks.api.proto.filesystem\220\001\001\240\001\001\342?\002\020\001' + _CREATEDOWNLOADURLREQUEST._options = None + _CREATEDOWNLOADURLREQUEST._serialized_options = b'\342?3\n1com.databricks.rpc.RPC[CreateDownloadUrlResponse]' + _CREATEDOWNLOADURLRESPONSE._options = None + _CREATEDOWNLOADURLRESPONSE._serialized_options = b'\342?%\n#com.databricks.rpc.DoNotLogContents' + _CREATEUPLOADURLREQUEST._options = None + _CREATEUPLOADURLREQUEST._serialized_options = b'\342?1\n/com.databricks.rpc.RPC[CreateUploadUrlResponse]' + _CREATEUPLOADURLRESPONSE._options = None + _CREATEUPLOADURLRESPONSE._serialized_options = b'\342?%\n#com.databricks.rpc.DoNotLogContents' + _DIRECTORYENTRY._options = None + _DIRECTORYENTRY._serialized_options = b'\342?%\n#com.databricks.rpc.DoNotLogContents' + _LISTDIRECTORYRESPONSE._options = None + _LISTDIRECTORYRESPONSE._serialized_options = b'\342?%\n#com.databricks.rpc.DoNotLogContents' + _FILESYSTEMSERVICE.methods_by_name['CreateDownloadUrl']._options = None + _FILESYSTEMSERVICE.methods_by_name['CreateDownloadUrl']._serialized_options = b'\362\206\031)\n%\n\004POST\022\027/fs/create-download-url\032\004\010\002\020\000\020\003' + _FILESYSTEMSERVICE.methods_by_name['CreateUploadUrl']._options = None + _FILESYSTEMSERVICE.methods_by_name['CreateUploadUrl']._serialized_options = b'\362\206\031\'\n#\n\004POST\022\025/fs/create-upload-url\032\004\010\002\020\000\020\003' + _HTTPHEADER._serialized_start=99 + _HTTPHEADER._serialized_end=140 + _CREATEDOWNLOADURLREQUEST._serialized_start=142 + _CREATEDOWNLOADURLREQUEST._serialized_end=238 + _CREATEDOWNLOADURLRESPONSE._serialized_start=241 + _CREATEDOWNLOADURLRESPONSE._serialized_end=371 + _CREATEUPLOADURLREQUEST._serialized_start=373 + _CREATEUPLOADURLREQUEST._serialized_end=465 + _CREATEUPLOADURLRESPONSE._serialized_start=468 + _CREATEUPLOADURLRESPONSE._serialized_end=596 + _DIRECTORYENTRY._serialized_start=599 + _DIRECTORYENTRY._serialized_end=749 + _LISTDIRECTORYRESPONSE._serialized_start=752 + _LISTDIRECTORYRESPONSE._serialized_end=895 + _FILESYSTEMSERVICE._serialized_start=898 + _FILESYSTEMSERVICE._serialized_end=1229 + FilesystemService = service_reflection.GeneratedServiceType('FilesystemService', (_service.Service,), dict( + DESCRIPTOR = _FILESYSTEMSERVICE, + __module__ = 'databricks_filesystem_service_pb2' + )) + + FilesystemService_Stub = service_reflection.GeneratedServiceStubType('FilesystemService_Stub', (FilesystemService,), dict( + DESCRIPTOR = _FILESYSTEMSERVICE, + __module__ = 'databricks_filesystem_service_pb2' + )) + + + # @@protoc_insertion_point(module_scope) + diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/protos/databricks_managed_catalog_messages_pb2.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/protos/databricks_managed_catalog_messages_pb2.py new file mode 100644 index 0000000000000000000000000000000000000000..541501a7eedd221c0572304ed65580b65355f391 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/protos/databricks_managed_catalog_messages_pb2.py @@ -0,0 +1,95 @@ + +import google.protobuf +from packaging.version import Version +if Version(google.protobuf.__version__).major >= 5: + # -*- coding: utf-8 -*- + # Generated by the protocol buffer compiler. DO NOT EDIT! + # source: databricks_managed_catalog_messages.proto + # Protobuf Python Version: 5.26.0 + """Generated protocol buffer code.""" + from google.protobuf import descriptor as _descriptor + from google.protobuf import descriptor_pool as _descriptor_pool + from google.protobuf import symbol_database as _symbol_database + from google.protobuf.internal import builder as _builder + # @@protoc_insertion_point(imports) + + _sym_db = _symbol_database.Default() + + + from .scalapb import scalapb_pb2 as scalapb_dot_scalapb__pb2 + + + DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n)databricks_managed_catalog_messages.proto\x12\x15mlflow.managedcatalog\x1a\x15scalapb/scalapb.proto\"0\n\tTableInfo\x12\x11\n\tfull_name\x18\x0f \x01(\t\x12\x10\n\x08table_id\x18\x16 \x01(\t\"\xc2\x01\n\x08GetTable\x12\x15\n\rfull_name_arg\x18\x01 \x01(\t\x12\x14\n\x0comit_columns\x18\x05 \x01(\x08\x12\x17\n\x0fomit_properties\x18\x06 \x01(\x08\x12\x18\n\x10omit_constraints\x18\x07 \x01(\x08\x12\x19\n\x11omit_dependencies\x18\x08 \x01(\x08\x12\x15\n\romit_username\x18\x0b \x01(\x08\x12$\n\x1comit_storage_credential_name\x18\x0c \x01(\x08\"7\n\x10GetTableResponse\x12\x11\n\tfull_name\x18\x0f \x01(\t\x12\x10\n\x08table_id\x18\x16 \x01(\tB1\n\'com.databricks.api.proto.managedcatalog\xa0\x01\x01\xe2?\x02\x10\x01') + + _globals = globals() + _builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals) + _builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'databricks_managed_catalog_messages_pb2', _globals) + if not _descriptor._USE_C_DESCRIPTORS: + _globals['DESCRIPTOR']._loaded_options = None + _globals['DESCRIPTOR']._serialized_options = b'\n\'com.databricks.api.proto.managedcatalog\240\001\001\342?\002\020\001' + _globals['_TABLEINFO']._serialized_start=91 + _globals['_TABLEINFO']._serialized_end=139 + _globals['_GETTABLE']._serialized_start=142 + _globals['_GETTABLE']._serialized_end=336 + _globals['_GETTABLERESPONSE']._serialized_start=338 + _globals['_GETTABLERESPONSE']._serialized_end=393 + # @@protoc_insertion_point(module_scope) + +else: + # -*- coding: utf-8 -*- + # Generated by the protocol buffer compiler. DO NOT EDIT! + # source: databricks_managed_catalog_messages.proto + """Generated protocol buffer code.""" + from google.protobuf import descriptor as _descriptor + from google.protobuf import descriptor_pool as _descriptor_pool + from google.protobuf import message as _message + from google.protobuf import reflection as _reflection + from google.protobuf import symbol_database as _symbol_database + # @@protoc_insertion_point(imports) + + _sym_db = _symbol_database.Default() + + + from .scalapb import scalapb_pb2 as scalapb_dot_scalapb__pb2 + + + DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n)databricks_managed_catalog_messages.proto\x12\x15mlflow.managedcatalog\x1a\x15scalapb/scalapb.proto\"0\n\tTableInfo\x12\x11\n\tfull_name\x18\x0f \x01(\t\x12\x10\n\x08table_id\x18\x16 \x01(\t\"\xc2\x01\n\x08GetTable\x12\x15\n\rfull_name_arg\x18\x01 \x01(\t\x12\x14\n\x0comit_columns\x18\x05 \x01(\x08\x12\x17\n\x0fomit_properties\x18\x06 \x01(\x08\x12\x18\n\x10omit_constraints\x18\x07 \x01(\x08\x12\x19\n\x11omit_dependencies\x18\x08 \x01(\x08\x12\x15\n\romit_username\x18\x0b \x01(\x08\x12$\n\x1comit_storage_credential_name\x18\x0c \x01(\x08\"7\n\x10GetTableResponse\x12\x11\n\tfull_name\x18\x0f \x01(\t\x12\x10\n\x08table_id\x18\x16 \x01(\tB1\n\'com.databricks.api.proto.managedcatalog\xa0\x01\x01\xe2?\x02\x10\x01') + + + + _TABLEINFO = DESCRIPTOR.message_types_by_name['TableInfo'] + _GETTABLE = DESCRIPTOR.message_types_by_name['GetTable'] + _GETTABLERESPONSE = DESCRIPTOR.message_types_by_name['GetTableResponse'] + TableInfo = _reflection.GeneratedProtocolMessageType('TableInfo', (_message.Message,), { + 'DESCRIPTOR' : _TABLEINFO, + '__module__' : 'databricks_managed_catalog_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.managedcatalog.TableInfo) + }) + _sym_db.RegisterMessage(TableInfo) + + GetTable = _reflection.GeneratedProtocolMessageType('GetTable', (_message.Message,), { + 'DESCRIPTOR' : _GETTABLE, + '__module__' : 'databricks_managed_catalog_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.managedcatalog.GetTable) + }) + _sym_db.RegisterMessage(GetTable) + + GetTableResponse = _reflection.GeneratedProtocolMessageType('GetTableResponse', (_message.Message,), { + 'DESCRIPTOR' : _GETTABLERESPONSE, + '__module__' : 'databricks_managed_catalog_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.managedcatalog.GetTableResponse) + }) + _sym_db.RegisterMessage(GetTableResponse) + + if _descriptor._USE_C_DESCRIPTORS == False: + + DESCRIPTOR._options = None + DESCRIPTOR._serialized_options = b'\n\'com.databricks.api.proto.managedcatalog\240\001\001\342?\002\020\001' + _TABLEINFO._serialized_start=91 + _TABLEINFO._serialized_end=139 + _GETTABLE._serialized_start=142 + _GETTABLE._serialized_end=336 + _GETTABLERESPONSE._serialized_start=338 + _GETTABLERESPONSE._serialized_end=393 + # @@protoc_insertion_point(module_scope) + diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/protos/databricks_managed_catalog_service_pb2.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/protos/databricks_managed_catalog_service_pb2.py new file mode 100644 index 0000000000000000000000000000000000000000..4abc14780f6e712edf05545d8f3944867832355f --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/protos/databricks_managed_catalog_service_pb2.py @@ -0,0 +1,86 @@ + +import google.protobuf +from packaging.version import Version +if Version(google.protobuf.__version__).major >= 5: + # -*- coding: utf-8 -*- + # Generated by the protocol buffer compiler. DO NOT EDIT! + # source: databricks_managed_catalog_service.proto + # Protobuf Python Version: 5.26.0 + """Generated protocol buffer code.""" + from google.protobuf import descriptor as _descriptor + from google.protobuf import descriptor_pool as _descriptor_pool + from google.protobuf import symbol_database as _symbol_database + from google.protobuf.internal import builder as _builder + # @@protoc_insertion_point(imports) + + _sym_db = _symbol_database.Default() + + + from . import databricks_pb2 as databricks__pb2 + from . import databricks_managed_catalog_messages_pb2 as databricks_managed_catalog_messages_pb2 + from .scalapb import scalapb_pb2 as scalapb_dot_scalapb__pb2 + + + DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n(databricks_managed_catalog_service.proto\x12\x15mlflow.managedcatalog\x1a\x10\x64\x61tabricks.proto\x1a)databricks_managed_catalog_messages.proto\x1a\x15scalapb/scalapb.proto2\xb2\x01\n\x1d\x44\x61tabricksUnityCatalogService\x12\x90\x01\n\x08getTable\x12\x1f.mlflow.managedcatalog.GetTable\x1a\'.mlflow.managedcatalog.GetTableResponse\":\xf2\x86\x19\x36\n2\n\x03GET\x12%/unity-catalog/tables/{full_name_arg}\x1a\x04\x08\x02\x10\x00\x10\x03\x42\x34\n\'com.databricks.api.proto.managedcatalog\x90\x01\x01\xa0\x01\x01\xe2?\x02\x10\x01') + + _globals = globals() + _builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals) + _builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'databricks_managed_catalog_service_pb2', _globals) + if not _descriptor._USE_C_DESCRIPTORS: + _globals['DESCRIPTOR']._loaded_options = None + _globals['DESCRIPTOR']._serialized_options = b'\n\'com.databricks.api.proto.managedcatalog\220\001\001\240\001\001\342?\002\020\001' + _globals['_DATABRICKSUNITYCATALOGSERVICE'].methods_by_name['getTable']._loaded_options = None + _globals['_DATABRICKSUNITYCATALOGSERVICE'].methods_by_name['getTable']._serialized_options = b'\362\206\0316\n2\n\003GET\022%/unity-catalog/tables/{full_name_arg}\032\004\010\002\020\000\020\003' + _globals['_DATABRICKSUNITYCATALOGSERVICE']._serialized_start=152 + _globals['_DATABRICKSUNITYCATALOGSERVICE']._serialized_end=330 + _builder.BuildServices(DESCRIPTOR, 'databricks_managed_catalog_service_pb2', _globals) + # @@protoc_insertion_point(module_scope) + +else: + # -*- coding: utf-8 -*- + # Generated by the protocol buffer compiler. DO NOT EDIT! + # source: databricks_managed_catalog_service.proto + """Generated protocol buffer code.""" + from google.protobuf import descriptor as _descriptor + from google.protobuf import descriptor_pool as _descriptor_pool + from google.protobuf import message as _message + from google.protobuf import reflection as _reflection + from google.protobuf import symbol_database as _symbol_database + from google.protobuf import service as _service + from google.protobuf import service_reflection + # @@protoc_insertion_point(imports) + + _sym_db = _symbol_database.Default() + + + from . import databricks_pb2 as databricks__pb2 + from . import databricks_managed_catalog_messages_pb2 as databricks_managed_catalog_messages_pb2 + from .scalapb import scalapb_pb2 as scalapb_dot_scalapb__pb2 + + + DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n(databricks_managed_catalog_service.proto\x12\x15mlflow.managedcatalog\x1a\x10\x64\x61tabricks.proto\x1a)databricks_managed_catalog_messages.proto\x1a\x15scalapb/scalapb.proto2\xb2\x01\n\x1d\x44\x61tabricksUnityCatalogService\x12\x90\x01\n\x08getTable\x12\x1f.mlflow.managedcatalog.GetTable\x1a\'.mlflow.managedcatalog.GetTableResponse\":\xf2\x86\x19\x36\n2\n\x03GET\x12%/unity-catalog/tables/{full_name_arg}\x1a\x04\x08\x02\x10\x00\x10\x03\x42\x34\n\'com.databricks.api.proto.managedcatalog\x90\x01\x01\xa0\x01\x01\xe2?\x02\x10\x01') + + + + _DATABRICKSUNITYCATALOGSERVICE = DESCRIPTOR.services_by_name['DatabricksUnityCatalogService'] + if _descriptor._USE_C_DESCRIPTORS == False: + + DESCRIPTOR._options = None + DESCRIPTOR._serialized_options = b'\n\'com.databricks.api.proto.managedcatalog\220\001\001\240\001\001\342?\002\020\001' + _DATABRICKSUNITYCATALOGSERVICE.methods_by_name['getTable']._options = None + _DATABRICKSUNITYCATALOGSERVICE.methods_by_name['getTable']._serialized_options = b'\362\206\0316\n2\n\003GET\022%/unity-catalog/tables/{full_name_arg}\032\004\010\002\020\000\020\003' + _DATABRICKSUNITYCATALOGSERVICE._serialized_start=152 + _DATABRICKSUNITYCATALOGSERVICE._serialized_end=330 + DatabricksUnityCatalogService = service_reflection.GeneratedServiceType('DatabricksUnityCatalogService', (_service.Service,), dict( + DESCRIPTOR = _DATABRICKSUNITYCATALOGSERVICE, + __module__ = 'databricks_managed_catalog_service_pb2' + )) + + DatabricksUnityCatalogService_Stub = service_reflection.GeneratedServiceStubType('DatabricksUnityCatalogService_Stub', (DatabricksUnityCatalogService,), dict( + DESCRIPTOR = _DATABRICKSUNITYCATALOGSERVICE, + __module__ = 'databricks_managed_catalog_service_pb2' + )) + + + # @@protoc_insertion_point(module_scope) + diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/protos/databricks_pb2.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/protos/databricks_pb2.py new file mode 100644 index 0000000000000000000000000000000000000000..7cfd6e5d4bd02838e3e6c4cfcaea0c8e287b3932 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/protos/databricks_pb2.py @@ -0,0 +1,267 @@ + +import google.protobuf +from packaging.version import Version +if Version(google.protobuf.__version__).major >= 5: + # -*- coding: utf-8 -*- + # Generated by the protocol buffer compiler. DO NOT EDIT! + # source: databricks.proto + # Protobuf Python Version: 5.26.0 + """Generated protocol buffer code.""" + from google.protobuf import descriptor as _descriptor + from google.protobuf import descriptor_pool as _descriptor_pool + from google.protobuf import symbol_database as _symbol_database + from google.protobuf.internal import builder as _builder + # @@protoc_insertion_point(imports) + + _sym_db = _symbol_database.Default() + + + from google.protobuf import descriptor_pb2 as google_dot_protobuf_dot_descriptor__pb2 + from .scalapb import scalapb_pb2 as scalapb_dot_scalapb__pb2 + + + DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\x10\x64\x61tabricks.proto\x12\x06mlflow\x1a google/protobuf/descriptor.proto\x1a\x15scalapb/scalapb.proto\"\xcd\x01\n\x14\x44\x61tabricksRpcOptions\x12\'\n\tendpoints\x18\x01 \x03(\x0b\x32\x14.mlflow.HttpEndpoint\x12&\n\nvisibility\x18\x02 \x01(\x0e\x32\x12.mlflow.Visibility\x12&\n\x0b\x65rror_codes\x18\x03 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\x03(\x0b\x32\x1d.mlflow.DocumentationMetadata:O\n\x08\x65num_doc\x12\x1c.google.protobuf.EnumOptions\x18\xf2\x90\x03 \x03(\x0b\x32\x1d.mlflow.DocumentationMetadata:V\n\x15\x65num_value_visibility\x12!.google.protobuf.EnumValueOptions\x18\xee\x90\x03 \x01(\x0e\x32\x12.mlflow.Visibility:Z\n\x0e\x65num_value_doc\x12!.google.protobuf.EnumValueOptions\x18\xf2\x90\x03 \x03(\x0b\x32\x1d.mlflow.DocumentationMetadataB*\n#com.databricks.api.proto.databricks\xe2?\x02\x10\x01') + + _globals = globals() + _builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals) + _builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'databricks_pb2', _globals) + if not _descriptor._USE_C_DESCRIPTORS: + _globals['DESCRIPTOR']._loaded_options = None + _globals['DESCRIPTOR']._serialized_options = b'\n#com.databricks.api.proto.databricks\342?\002\020\001' + _globals['_VISIBILITY']._serialized_start=668 + _globals['_VISIBILITY']._serialized_end=731 + _globals['_ERRORCODE']._serialized_start=734 + _globals['_ERRORCODE']._serialized_end=2907 + _globals['_DATABRICKSRPCOPTIONS']._serialized_start=86 + _globals['_DATABRICKSRPCOPTIONS']._serialized_end=291 + _globals['_DATABRICKSGRAPHQLOPTIONS']._serialized_start=293 + _globals['_DATABRICKSGRAPHQLOPTIONS']._serialized_end=319 + _globals['_HTTPENDPOINT']._serialized_start=321 + _globals['_HTTPENDPOINT']._serialized_end=406 + _globals['_APIVERSION']._serialized_start=408 + _globals['_APIVERSION']._serialized_end=450 + _globals['_RATELIMIT']._serialized_start=452 + _globals['_RATELIMIT']._serialized_end=516 + _globals['_DOCUMENTATIONMETADATA']._serialized_start=519 + _globals['_DOCUMENTATIONMETADATA']._serialized_end=666 + # @@protoc_insertion_point(module_scope) + +else: + # -*- coding: utf-8 -*- + # Generated by the protocol buffer compiler. DO NOT EDIT! + # source: databricks.proto + """Generated protocol buffer code.""" + from google.protobuf.internal import enum_type_wrapper + from google.protobuf import descriptor as _descriptor + from google.protobuf import descriptor_pool as _descriptor_pool + from google.protobuf import message as _message + from google.protobuf import reflection as _reflection + from google.protobuf import symbol_database as _symbol_database + # @@protoc_insertion_point(imports) + + _sym_db = _symbol_database.Default() + + + from google.protobuf import descriptor_pb2 as google_dot_protobuf_dot_descriptor__pb2 + from .scalapb import scalapb_pb2 as scalapb_dot_scalapb__pb2 + + + DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\x10\x64\x61tabricks.proto\x12\x06mlflow\x1a google/protobuf/descriptor.proto\x1a\x15scalapb/scalapb.proto\"\xcd\x01\n\x14\x44\x61tabricksRpcOptions\x12\'\n\tendpoints\x18\x01 \x03(\x0b\x32\x14.mlflow.HttpEndpoint\x12&\n\nvisibility\x18\x02 \x01(\x0e\x32\x12.mlflow.Visibility\x12&\n\x0b\x65rror_codes\x18\x03 \x03(\x0e\x32\x11.mlflow.ErrorCode\x12%\n\nrate_limit\x18\x04 \x01(\x0b\x32\x11.mlflow.RateLimit\x12\x15\n\rrpc_doc_title\x18\x05 \x01(\t\"\x1a\n\x18\x44\x61tabricksGraphqlOptions\"U\n\x0cHttpEndpoint\x12\x14\n\x06method\x18\x01 \x01(\t:\x04POST\x12\x0c\n\x04path\x18\x02 \x01(\t\x12!\n\x05since\x18\x03 \x01(\x0b\x32\x12.mlflow.ApiVersion\"*\n\nApiVersion\x12\r\n\x05major\x18\x01 \x01(\x05\x12\r\n\x05minor\x18\x02 \x01(\x05\"@\n\tRateLimit\x12\x11\n\tmax_burst\x18\x01 \x01(\x03\x12 \n\x18max_sustained_per_second\x18\x02 \x01(\x03\"\x93\x01\n\x15\x44ocumentationMetadata\x12\x11\n\tdocstring\x18\x01 \x01(\t\x12\x10\n\x08lead_doc\x18\x02 \x01(\t\x12&\n\nvisibility\x18\x03 \x01(\x0e\x32\x12.mlflow.Visibility\x12\x1b\n\x13original_proto_path\x18\x04 \x03(\t\x12\x10\n\x08position\x18\x05 \x01(\x05*?\n\nVisibility\x12\n\n\x06PUBLIC\x10\x01\x12\x0c\n\x08INTERNAL\x10\x02\x12\x17\n\x13PUBLIC_UNDOCUMENTED\x10\x03*\xfd\x10\n\tErrorCode\x12\x12\n\x0eINTERNAL_ERROR\x10\x01\x12\x1b\n\x17TEMPORARILY_UNAVAILABLE\x10\x02\x12\x0c\n\x08IO_ERROR\x10\x03\x12\x0f\n\x0b\x42\x41\x44_REQUEST\x10\x04\x12\x1d\n\x19SERVICE_UNDER_MAINTENANCE\x10\x05\x12%\n!WORKSPACE_TEMPORARILY_UNAVAILABLE\x10\x06\x12\x15\n\x11\x44\x45\x41\x44LINE_EXCEEDED\x10\x07\x12\r\n\tCANCELLED\x10\x08\x12\x16\n\x12RESOURCE_EXHAUSTED\x10\t\x12\x0b\n\x07\x41\x42ORTED\x10\n\x12\r\n\tNOT_FOUND\x10\x0b\x12\x12\n\x0e\x41LREADY_EXISTS\x10\x0c\x12\x13\n\x0fUNAUTHENTICATED\x10\r\x12\x1c\n\x17INVALID_PARAMETER_VALUE\x10\xe8\x07\x12\x17\n\x12\x45NDPOINT_NOT_FOUND\x10\xe9\x07\x12\x16\n\x11MALFORMED_REQUEST\x10\xea\x07\x12\x12\n\rINVALID_STATE\x10\xeb\x07\x12\x16\n\x11PERMISSION_DENIED\x10\xec\x07\x12\x15\n\x10\x46\x45\x41TURE_DISABLED\x10\xed\x07\x12\x1a\n\x15\x43USTOMER_UNAUTHORIZED\x10\xee\x07\x12\x1b\n\x16REQUEST_LIMIT_EXCEEDED\x10\xef\x07\x12\x16\n\x11RESOURCE_CONFLICT\x10\xf0\x07\x12\x1b\n\x16UNPARSEABLE_HTTP_ERROR\x10\xf1\x07\x12\x14\n\x0fNOT_IMPLEMENTED\x10\xf2\x07\x12\x0e\n\tDATA_LOSS\x10\xf3\x07\x12\x1d\n\x18INVALID_STATE_TRANSITION\x10\xd1\x0f\x12\x1b\n\x16\x43OULD_NOT_ACQUIRE_LOCK\x10\xd2\x0f\x12\x1c\n\x17RESOURCE_ALREADY_EXISTS\x10\xb9\x17\x12\x1c\n\x17RESOURCE_DOES_NOT_EXIST\x10\xba\x17\x12\x13\n\x0eQUOTA_EXCEEDED\x10\xa1\x1f\x12\x1c\n\x17MAX_BLOCK_SIZE_EXCEEDED\x10\xa2\x1f\x12\x1b\n\x16MAX_READ_SIZE_EXCEEDED\x10\xa3\x1f\x12\x13\n\x0ePARTIAL_DELETE\x10\xa4\x1f\x12\x1b\n\x16MAX_LIST_SIZE_EXCEEDED\x10\xa5\x1f\x12\x13\n\x0e\x44RY_RUN_FAILED\x10\x89\'\x12\x1c\n\x17RESOURCE_LIMIT_EXCEEDED\x10\x8a\'\x12\x18\n\x13\x44IRECTORY_NOT_EMPTY\x10\xf1.\x12\x18\n\x13\x44IRECTORY_PROTECTED\x10\xf2.\x12\x1f\n\x1aMAX_NOTEBOOK_SIZE_EXCEEDED\x10\xf3.\x12!\n\x1cMAX_CHILD_NODE_SIZE_EXCEEDED\x10\xf4.\x12\x1a\n\x15SEARCH_QUERY_TOO_LONG\x10\xd4/\x12\x1b\n\x16SEARCH_QUERY_TOO_SHORT\x10\xd5/\x12*\n%MANAGED_RESOURCE_GROUP_DOES_NOT_EXIST\x10\xd9\x36\x12\x1e\n\x19PERMISSION_NOT_PROPAGATED\x10\xda\x36\x12\x17\n\x12\x44\x45PLOYMENT_TIMEOUT\x10\xdb\x36\x12\x11\n\x0cGIT_CONFLICT\x10\xc1>\x12\x14\n\x0fGIT_UNKNOWN_REF\x10\xc2>\x12!\n\x1cGIT_SENSITIVE_TOKEN_DETECTED\x10\xc3>\x12\x1e\n\x19GIT_URL_NOT_ON_ALLOW_LIST\x10\xc4>\x12\x15\n\x10GIT_REMOTE_ERROR\x10\xc5>\x12\x1f\n\x1aPROJECTS_OPERATION_TIMEOUT\x10\xc6>\x12\x17\n\x12IPYNB_FILE_IN_REPO\x10\xc7>\x12\x1e\n\x19INSECURE_PARTNER_RESPONSE\x10\xa4?\x12\x1f\n\x1aMALFORMED_PARTNER_RESPONSE\x10\xa5?\x12\x1d\n\x18METASTORE_DOES_NOT_EXIST\x10\xa8\x46\x12\x17\n\x12\x44\x41\x43_DOES_NOT_EXIST\x10\xa9\x46\x12\x1b\n\x16\x43\x41TALOG_DOES_NOT_EXIST\x10\xaa\x46\x12\x1a\n\x15SCHEMA_DOES_NOT_EXIST\x10\xab\x46\x12\x19\n\x14TABLE_DOES_NOT_EXIST\x10\xac\x46\x12\x19\n\x14SHARE_DOES_NOT_EXIST\x10\xad\x46\x12\x1d\n\x18RECIPIENT_DOES_NOT_EXIST\x10\xae\x46\x12&\n!STORAGE_CREDENTIAL_DOES_NOT_EXIST\x10\xaf\x46\x12%\n EXTERNAL_LOCATION_DOES_NOT_EXIST\x10\xb0\x46\x12\x1d\n\x18PRINCIPAL_DOES_NOT_EXIST\x10\xb1\x46\x12\x1c\n\x17PROVIDER_DOES_NOT_EXIST\x10\xb2\x46\x12\x1d\n\x18METASTORE_ALREADY_EXISTS\x10\xbc\x46\x12\x17\n\x12\x44\x41\x43_ALREADY_EXISTS\x10\xbd\x46\x12\x1b\n\x16\x43\x41TALOG_ALREADY_EXISTS\x10\xbe\x46\x12\x1a\n\x15SCHEMA_ALREADY_EXISTS\x10\xbf\x46\x12\x19\n\x14TABLE_ALREADY_EXISTS\x10\xc0\x46\x12\x19\n\x14SHARE_ALREADY_EXISTS\x10\xc1\x46\x12\x1d\n\x18RECIPIENT_ALREADY_EXISTS\x10\xc2\x46\x12&\n!STORAGE_CREDENTIAL_ALREADY_EXISTS\x10\xc3\x46\x12%\n EXTERNAL_LOCATION_ALREADY_EXISTS\x10\xc4\x46\x12\x1c\n\x17PROVIDER_ALREADY_EXISTS\x10\xc5\x46\x12\x16\n\x11\x43\x41TALOG_NOT_EMPTY\x10\xd0\x46\x12\x15\n\x10SCHEMA_NOT_EMPTY\x10\xd1\x46\x12\x18\n\x13METASTORE_NOT_EMPTY\x10\xd2\x46\x12\"\n\x1dPROVIDER_SHARE_NOT_ACCESSIBLE\x10\xe4\x46:G\n\nvisibility\x12\x1d.google.protobuf.FieldOptions\x18\xee\x90\x03 \x01(\x0e\x32\x12.mlflow.Visibility::\n\x11validate_required\x12\x1d.google.protobuf.FieldOptions\x18\xef\x90\x03 \x01(\x08:4\n\x0bjson_inline\x12\x1d.google.protobuf.FieldOptions\x18\xf0\x90\x03 \x01(\x08:1\n\x08json_map\x12\x1d.google.protobuf.FieldOptions\x18\xf1\x90\x03 \x01(\x08:Q\n\tfield_doc\x12\x1d.google.protobuf.FieldOptions\x18\xf2\x90\x03 \x03(\x0b\x32\x1d.mlflow.DocumentationMetadata:K\n\x03rpc\x12\x1e.google.protobuf.MethodOptions\x18\xee\x90\x03 \x01(\x0b\x32\x1c.mlflow.DatabricksRpcOptions:S\n\nmethod_doc\x12\x1e.google.protobuf.MethodOptions\x18\xf2\x90\x03 \x03(\x0b\x32\x1d.mlflow.DocumentationMetadata:S\n\x07graphql\x12\x1e.google.protobuf.MethodOptions\x18\xc7\x91\x03 \x01(\x0b\x32 .mlflow.DatabricksGraphqlOptions:U\n\x0bmessage_doc\x12\x1f.google.protobuf.MessageOptions\x18\xf2\x90\x03 \x03(\x0b\x32\x1d.mlflow.DocumentationMetadata:U\n\x0bservice_doc\x12\x1f.google.protobuf.ServiceOptions\x18\xf2\x90\x03 \x03(\x0b\x32\x1d.mlflow.DocumentationMetadata:O\n\x08\x65num_doc\x12\x1c.google.protobuf.EnumOptions\x18\xf2\x90\x03 \x03(\x0b\x32\x1d.mlflow.DocumentationMetadata:V\n\x15\x65num_value_visibility\x12!.google.protobuf.EnumValueOptions\x18\xee\x90\x03 \x01(\x0e\x32\x12.mlflow.Visibility:Z\n\x0e\x65num_value_doc\x12!.google.protobuf.EnumValueOptions\x18\xf2\x90\x03 \x03(\x0b\x32\x1d.mlflow.DocumentationMetadataB*\n#com.databricks.api.proto.databricks\xe2?\x02\x10\x01') + + _VISIBILITY = DESCRIPTOR.enum_types_by_name['Visibility'] + Visibility = enum_type_wrapper.EnumTypeWrapper(_VISIBILITY) + _ERRORCODE = DESCRIPTOR.enum_types_by_name['ErrorCode'] + ErrorCode = enum_type_wrapper.EnumTypeWrapper(_ERRORCODE) + PUBLIC = 1 + INTERNAL = 2 + PUBLIC_UNDOCUMENTED = 3 + INTERNAL_ERROR = 1 + TEMPORARILY_UNAVAILABLE = 2 + IO_ERROR = 3 + BAD_REQUEST = 4 + SERVICE_UNDER_MAINTENANCE = 5 + WORKSPACE_TEMPORARILY_UNAVAILABLE = 6 + DEADLINE_EXCEEDED = 7 + CANCELLED = 8 + RESOURCE_EXHAUSTED = 9 + ABORTED = 10 + NOT_FOUND = 11 + ALREADY_EXISTS = 12 + UNAUTHENTICATED = 13 + INVALID_PARAMETER_VALUE = 1000 + ENDPOINT_NOT_FOUND = 1001 + MALFORMED_REQUEST = 1002 + INVALID_STATE = 1003 + PERMISSION_DENIED = 1004 + FEATURE_DISABLED = 1005 + CUSTOMER_UNAUTHORIZED = 1006 + REQUEST_LIMIT_EXCEEDED = 1007 + RESOURCE_CONFLICT = 1008 + UNPARSEABLE_HTTP_ERROR = 1009 + NOT_IMPLEMENTED = 1010 + DATA_LOSS = 1011 + INVALID_STATE_TRANSITION = 2001 + COULD_NOT_ACQUIRE_LOCK = 2002 + RESOURCE_ALREADY_EXISTS = 3001 + RESOURCE_DOES_NOT_EXIST = 3002 + QUOTA_EXCEEDED = 4001 + MAX_BLOCK_SIZE_EXCEEDED = 4002 + MAX_READ_SIZE_EXCEEDED = 4003 + PARTIAL_DELETE = 4004 + MAX_LIST_SIZE_EXCEEDED = 4005 + DRY_RUN_FAILED = 5001 + RESOURCE_LIMIT_EXCEEDED = 5002 + DIRECTORY_NOT_EMPTY = 6001 + DIRECTORY_PROTECTED = 6002 + MAX_NOTEBOOK_SIZE_EXCEEDED = 6003 + MAX_CHILD_NODE_SIZE_EXCEEDED = 6004 + SEARCH_QUERY_TOO_LONG = 6100 + SEARCH_QUERY_TOO_SHORT = 6101 + MANAGED_RESOURCE_GROUP_DOES_NOT_EXIST = 7001 + PERMISSION_NOT_PROPAGATED = 7002 + DEPLOYMENT_TIMEOUT = 7003 + GIT_CONFLICT = 8001 + GIT_UNKNOWN_REF = 8002 + GIT_SENSITIVE_TOKEN_DETECTED = 8003 + GIT_URL_NOT_ON_ALLOW_LIST = 8004 + GIT_REMOTE_ERROR = 8005 + PROJECTS_OPERATION_TIMEOUT = 8006 + IPYNB_FILE_IN_REPO = 8007 + INSECURE_PARTNER_RESPONSE = 8100 + MALFORMED_PARTNER_RESPONSE = 8101 + METASTORE_DOES_NOT_EXIST = 9000 + DAC_DOES_NOT_EXIST = 9001 + CATALOG_DOES_NOT_EXIST = 9002 + SCHEMA_DOES_NOT_EXIST = 9003 + TABLE_DOES_NOT_EXIST = 9004 + SHARE_DOES_NOT_EXIST = 9005 + RECIPIENT_DOES_NOT_EXIST = 9006 + STORAGE_CREDENTIAL_DOES_NOT_EXIST = 9007 + EXTERNAL_LOCATION_DOES_NOT_EXIST = 9008 + PRINCIPAL_DOES_NOT_EXIST = 9009 + PROVIDER_DOES_NOT_EXIST = 9010 + METASTORE_ALREADY_EXISTS = 9020 + DAC_ALREADY_EXISTS = 9021 + CATALOG_ALREADY_EXISTS = 9022 + SCHEMA_ALREADY_EXISTS = 9023 + TABLE_ALREADY_EXISTS = 9024 + SHARE_ALREADY_EXISTS = 9025 + RECIPIENT_ALREADY_EXISTS = 9026 + STORAGE_CREDENTIAL_ALREADY_EXISTS = 9027 + EXTERNAL_LOCATION_ALREADY_EXISTS = 9028 + PROVIDER_ALREADY_EXISTS = 9029 + CATALOG_NOT_EMPTY = 9040 + SCHEMA_NOT_EMPTY = 9041 + METASTORE_NOT_EMPTY = 9042 + PROVIDER_SHARE_NOT_ACCESSIBLE = 9060 + + VISIBILITY_FIELD_NUMBER = 51310 + visibility = DESCRIPTOR.extensions_by_name['visibility'] + VALIDATE_REQUIRED_FIELD_NUMBER = 51311 + validate_required = DESCRIPTOR.extensions_by_name['validate_required'] + JSON_INLINE_FIELD_NUMBER = 51312 + json_inline = DESCRIPTOR.extensions_by_name['json_inline'] + JSON_MAP_FIELD_NUMBER = 51313 + json_map = DESCRIPTOR.extensions_by_name['json_map'] + FIELD_DOC_FIELD_NUMBER = 51314 + field_doc = DESCRIPTOR.extensions_by_name['field_doc'] + RPC_FIELD_NUMBER = 51310 + rpc = DESCRIPTOR.extensions_by_name['rpc'] + METHOD_DOC_FIELD_NUMBER = 51314 + method_doc = DESCRIPTOR.extensions_by_name['method_doc'] + GRAPHQL_FIELD_NUMBER = 51399 + graphql = DESCRIPTOR.extensions_by_name['graphql'] + MESSAGE_DOC_FIELD_NUMBER = 51314 + message_doc = DESCRIPTOR.extensions_by_name['message_doc'] + SERVICE_DOC_FIELD_NUMBER = 51314 + service_doc = DESCRIPTOR.extensions_by_name['service_doc'] + ENUM_DOC_FIELD_NUMBER = 51314 + enum_doc = DESCRIPTOR.extensions_by_name['enum_doc'] + ENUM_VALUE_VISIBILITY_FIELD_NUMBER = 51310 + enum_value_visibility = DESCRIPTOR.extensions_by_name['enum_value_visibility'] + ENUM_VALUE_DOC_FIELD_NUMBER = 51314 + enum_value_doc = DESCRIPTOR.extensions_by_name['enum_value_doc'] + + _DATABRICKSRPCOPTIONS = DESCRIPTOR.message_types_by_name['DatabricksRpcOptions'] + _DATABRICKSGRAPHQLOPTIONS = DESCRIPTOR.message_types_by_name['DatabricksGraphqlOptions'] + _HTTPENDPOINT = DESCRIPTOR.message_types_by_name['HttpEndpoint'] + _APIVERSION = DESCRIPTOR.message_types_by_name['ApiVersion'] + _RATELIMIT = DESCRIPTOR.message_types_by_name['RateLimit'] + _DOCUMENTATIONMETADATA = DESCRIPTOR.message_types_by_name['DocumentationMetadata'] + DatabricksRpcOptions = _reflection.GeneratedProtocolMessageType('DatabricksRpcOptions', (_message.Message,), { + 'DESCRIPTOR' : _DATABRICKSRPCOPTIONS, + '__module__' : 'databricks_pb2' + # @@protoc_insertion_point(class_scope:mlflow.DatabricksRpcOptions) + }) + _sym_db.RegisterMessage(DatabricksRpcOptions) + + DatabricksGraphqlOptions = _reflection.GeneratedProtocolMessageType('DatabricksGraphqlOptions', (_message.Message,), { + 'DESCRIPTOR' : _DATABRICKSGRAPHQLOPTIONS, + '__module__' : 'databricks_pb2' + # @@protoc_insertion_point(class_scope:mlflow.DatabricksGraphqlOptions) + }) + _sym_db.RegisterMessage(DatabricksGraphqlOptions) + + HttpEndpoint = _reflection.GeneratedProtocolMessageType('HttpEndpoint', (_message.Message,), { + 'DESCRIPTOR' : _HTTPENDPOINT, + '__module__' : 'databricks_pb2' + # @@protoc_insertion_point(class_scope:mlflow.HttpEndpoint) + }) + _sym_db.RegisterMessage(HttpEndpoint) + + ApiVersion = _reflection.GeneratedProtocolMessageType('ApiVersion', (_message.Message,), { + 'DESCRIPTOR' : _APIVERSION, + '__module__' : 'databricks_pb2' + # @@protoc_insertion_point(class_scope:mlflow.ApiVersion) + }) + _sym_db.RegisterMessage(ApiVersion) + + RateLimit = _reflection.GeneratedProtocolMessageType('RateLimit', (_message.Message,), { + 'DESCRIPTOR' : _RATELIMIT, + '__module__' : 'databricks_pb2' + # @@protoc_insertion_point(class_scope:mlflow.RateLimit) + }) + _sym_db.RegisterMessage(RateLimit) + + DocumentationMetadata = _reflection.GeneratedProtocolMessageType('DocumentationMetadata', (_message.Message,), { + 'DESCRIPTOR' : _DOCUMENTATIONMETADATA, + '__module__' : 'databricks_pb2' + # @@protoc_insertion_point(class_scope:mlflow.DocumentationMetadata) + }) + _sym_db.RegisterMessage(DocumentationMetadata) + + if _descriptor._USE_C_DESCRIPTORS == False: + google_dot_protobuf_dot_descriptor__pb2.FieldOptions.RegisterExtension(visibility) + google_dot_protobuf_dot_descriptor__pb2.FieldOptions.RegisterExtension(validate_required) + google_dot_protobuf_dot_descriptor__pb2.FieldOptions.RegisterExtension(json_inline) + google_dot_protobuf_dot_descriptor__pb2.FieldOptions.RegisterExtension(json_map) + google_dot_protobuf_dot_descriptor__pb2.FieldOptions.RegisterExtension(field_doc) + google_dot_protobuf_dot_descriptor__pb2.MethodOptions.RegisterExtension(rpc) + google_dot_protobuf_dot_descriptor__pb2.MethodOptions.RegisterExtension(method_doc) + google_dot_protobuf_dot_descriptor__pb2.MethodOptions.RegisterExtension(graphql) + google_dot_protobuf_dot_descriptor__pb2.MessageOptions.RegisterExtension(message_doc) + google_dot_protobuf_dot_descriptor__pb2.ServiceOptions.RegisterExtension(service_doc) + google_dot_protobuf_dot_descriptor__pb2.EnumOptions.RegisterExtension(enum_doc) + google_dot_protobuf_dot_descriptor__pb2.EnumValueOptions.RegisterExtension(enum_value_visibility) + google_dot_protobuf_dot_descriptor__pb2.EnumValueOptions.RegisterExtension(enum_value_doc) + + DESCRIPTOR._options = None + DESCRIPTOR._serialized_options = b'\n#com.databricks.api.proto.databricks\342?\002\020\001' + _VISIBILITY._serialized_start=668 + _VISIBILITY._serialized_end=731 + _ERRORCODE._serialized_start=734 + _ERRORCODE._serialized_end=2907 + _DATABRICKSRPCOPTIONS._serialized_start=86 + _DATABRICKSRPCOPTIONS._serialized_end=291 + _DATABRICKSGRAPHQLOPTIONS._serialized_start=293 + _DATABRICKSGRAPHQLOPTIONS._serialized_end=319 + _HTTPENDPOINT._serialized_start=321 + _HTTPENDPOINT._serialized_end=406 + _APIVERSION._serialized_start=408 + _APIVERSION._serialized_end=450 + _RATELIMIT._serialized_start=452 + _RATELIMIT._serialized_end=516 + _DOCUMENTATIONMETADATA._serialized_start=519 + _DOCUMENTATIONMETADATA._serialized_end=666 + # @@protoc_insertion_point(module_scope) + diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/protos/databricks_trace_server_pb2.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/protos/databricks_trace_server_pb2.py new file mode 100644 index 0000000000000000000000000000000000000000..f56307b11829a19748fe6320c5956207ec04886c --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/protos/databricks_trace_server_pb2.py @@ -0,0 +1,374 @@ + +import google.protobuf +from packaging.version import Version +if Version(google.protobuf.__version__).major >= 5: + # -*- coding: utf-8 -*- + # Generated by the protocol buffer compiler. DO NOT EDIT! + # source: databricks_trace_server.proto + # Protobuf Python Version: 5.26.0 + """Generated protocol buffer code.""" + from google.protobuf import descriptor as _descriptor + from google.protobuf import descriptor_pool as _descriptor_pool + from google.protobuf import symbol_database as _symbol_database + from google.protobuf.internal import builder as _builder + # @@protoc_insertion_point(imports) + + _sym_db = _symbol_database.Default() + + + from . import databricks_pb2 as databricks__pb2 + from . import assessments_pb2 as assessments__pb2 + from google.protobuf import duration_pb2 as google_dot_protobuf_dot_duration__pb2 + from google.protobuf import struct_pb2 as google_dot_protobuf_dot_struct__pb2 + from google.protobuf import timestamp_pb2 as google_dot_protobuf_dot_timestamp__pb2 + from .scalapb import scalapb_pb2 as scalapb_dot_scalapb__pb2 + + + DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\x1d\x64\x61tabricks_trace_server.proto\x12\x12mlflow.traceserver\x1a\x10\x64\x61tabricks.proto\x1a\x11\x61ssessments.proto\x1a\x1egoogle/protobuf/duration.proto\x1a\x1cgoogle/protobuf/struct.proto\x1a\x1fgoogle/protobuf/timestamp.proto\x1a\x15scalapb/scalapb.proto\"\x8b\x01\n\x0b\x43reateTrace\x12+\n\x04info\x18\x01 \x01(\x0b\x32\x1d.mlflow.traceserver.TraceInfo\x12+\n\x04\x64\x61ta\x18\x02 \x01(\x0b\x32\x1d.mlflow.traceserver.TraceData:\"\xe2?\x1f\n\x1d\x63om.databricks.rpc.RPC[Trace]\"a\n\x05Trace\x12+\n\x04info\x18\x01 \x01(\x0b\x32\x1d.mlflow.traceserver.TraceInfo\x12+\n\x04\x64\x61ta\x18\x02 \x01(\x0b\x32\x1d.mlflow.traceserver.TraceData\"\xc3\x05\n\tTraceInfo\x12\x10\n\x08trace_id\x18\x01 \x01(\t\x12\x19\n\x11\x63lient_request_id\x18\x02 \x01(\t\x12\x39\n\x0etrace_location\x18\x03 \x01(\x0b\x32!.mlflow.traceserver.TraceLocation\x12\x0f\n\x07request\x18\x04 \x01(\t\x12\x10\n\x08response\x18\x05 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_globals['DESCRIPTOR']._serialized_options = b'\220\001\001' + _globals['_CREATETRACE']._loaded_options = None + _globals['_CREATETRACE']._serialized_options = b'\342?\037\n\035com.databricks.rpc.RPC[Trace]' + _globals['_TRACEINFO_TRACEMETADATAENTRY']._loaded_options = None + _globals['_TRACEINFO_TRACEMETADATAENTRY']._serialized_options = b'8\001' + _globals['_TRACEINFO_TAGSENTRY']._loaded_options = None + _globals['_TRACEINFO_TAGSENTRY']._serialized_options = b'8\001' + _globals['_SPAN_ATTRIBUTESENTRY']._loaded_options = None + _globals['_SPAN_ATTRIBUTESENTRY']._serialized_options = b'8\001' + _globals['_SPAN_EVENT_ATTRIBUTESENTRY']._loaded_options = None + _globals['_SPAN_EVENT_ATTRIBUTESENTRY']._serialized_options = b'8\001' + _globals['_SPAN_EVENT'].fields_by_name['time_unix_nano']._loaded_options = None + _globals['_SPAN_EVENT'].fields_by_name['time_unix_nano']._serialized_options = b'\370\206\031\001' + _globals['_SPAN_EVENT'].fields_by_name['name']._loaded_options = None + _globals['_SPAN_EVENT'].fields_by_name['name']._serialized_options = b'\370\206\031\001' + _globals['_SPAN_LINK_ATTRIBUTESENTRY']._loaded_options = None + _globals['_SPAN_LINK_ATTRIBUTESENTRY']._serialized_options = b'8\001' + _globals['_SPAN_LINK'].fields_by_name['trace_id']._loaded_options = None + _globals['_SPAN_LINK'].fields_by_name['trace_id']._serialized_options = b'\370\206\031\001' + _globals['_SPAN_LINK'].fields_by_name['span_id']._loaded_options = None + _globals['_SPAN_LINK'].fields_by_name['span_id']._serialized_options = b'\370\206\031\001' + _globals['_SPAN'].fields_by_name['trace_id']._loaded_options = None + _globals['_SPAN'].fields_by_name['trace_id']._serialized_options = b'\370\206\031\001' + _globals['_SPAN'].fields_by_name['span_id']._loaded_options = None + _globals['_SPAN'].fields_by_name['span_id']._serialized_options = b'\370\206\031\001' + _globals['_SPAN'].fields_by_name['name']._loaded_options = None + _globals['_SPAN'].fields_by_name['name']._serialized_options = b'\370\206\031\001' + _globals['_SPAN'].fields_by_name['start_time_unix_nano']._loaded_options = None + _globals['_SPAN'].fields_by_name['start_time_unix_nano']._serialized_options = b'\370\206\031\001' + _globals['_SPAN'].fields_by_name['end_time_unix_nano']._loaded_options = None + _globals['_SPAN'].fields_by_name['end_time_unix_nano']._serialized_options = b'\370\206\031\001' + _globals['_DATABRICKSTRACINGSERVERSERVICE'].methods_by_name['createTrace']._loaded_options = None + _globals['_DATABRICKSTRACINGSERVERSERVICE'].methods_by_name['createTrace']._serialized_options = b'\362\206\031\033\n\027\n\004POST\022\017/tracing/traces\020\003' + _globals['_CREATETRACE']._serialized_start=209 + _globals['_CREATETRACE']._serialized_end=348 + _globals['_TRACE']._serialized_start=350 + _globals['_TRACE']._serialized_end=447 + _globals['_TRACEINFO']._serialized_start=450 + _globals['_TRACEINFO']._serialized_end=1157 + _globals['_TRACEINFO_TRACEMETADATAENTRY']._serialized_start=992 + _globals['_TRACEINFO_TRACEMETADATAENTRY']._serialized_end=1044 + _globals['_TRACEINFO_TAGSENTRY']._serialized_start=1046 + _globals['_TRACEINFO_TAGSENTRY']._serialized_end=1089 + _globals['_TRACEINFO_STATE']._serialized_start=1091 + _globals['_TRACEINFO_STATE']._serialized_end=1157 + _globals['_TRACELOCATION']._serialized_start=1160 + _globals['_TRACELOCATION']._serialized_end=1634 + _globals['_TRACELOCATION_MLFLOWEXPERIMENTLOCATION']._serialized_start=1418 + _globals['_TRACELOCATION_MLFLOWEXPERIMENTLOCATION']._serialized_end=1467 + _globals['_TRACELOCATION_INFERENCETABLELOCATION']._serialized_start=1469 + _globals['_TRACELOCATION_INFERENCETABLELOCATION']._serialized_end=1518 + _globals['_TRACELOCATION_TRACELOCATIONTYPE']._serialized_start=1520 + _globals['_TRACELOCATION_TRACELOCATIONTYPE']._serialized_end=1620 + _globals['_TRACEDATA']._serialized_start=1636 + _globals['_TRACEDATA']._serialized_end=1688 + _globals['_SPAN']._serialized_start=1691 + _globals['_SPAN']._serialized_end=3149 + _globals['_SPAN_ATTRIBUTESENTRY']._serialized_start=2243 + _globals['_SPAN_ATTRIBUTESENTRY']._serialized_end=2316 + _globals['_SPAN_EVENT']._serialized_start=2319 + _globals['_SPAN_EVENT']._serialized_end=2553 + _globals['_SPAN_EVENT_ATTRIBUTESENTRY']._serialized_start=2243 + _globals['_SPAN_EVENT_ATTRIBUTESENTRY']._serialized_end=2316 + _globals['_SPAN_LINK']._serialized_start=2556 + _globals['_SPAN_LINK']._serialized_end=2821 + _globals['_SPAN_LINK_ATTRIBUTESENTRY']._serialized_start=2243 + _globals['_SPAN_LINK_ATTRIBUTESENTRY']._serialized_end=2316 + _globals['_SPAN_STATUS']._serialized_start=2824 + _globals['_SPAN_STATUS']._serialized_end=2993 + _globals['_SPAN_STATUS_STATUSCODE']._serialized_start=2909 + _globals['_SPAN_STATUS_STATUSCODE']._serialized_end=2987 + _globals['_SPAN_SPANKIND']._serialized_start=2996 + _globals['_SPAN_SPANKIND']._serialized_end=3149 + _globals['_DATABRICKSTRACINGSERVERSERVICE']._serialized_start=3152 + _globals['_DATABRICKSTRACINGSERVERSERVICE']._serialized_end=3292 + _builder.BuildServices(DESCRIPTOR, 'databricks_trace_server_pb2', _globals) + # @@protoc_insertion_point(module_scope) + +else: + # -*- coding: utf-8 -*- + # Generated by the protocol buffer compiler. DO NOT EDIT! + # source: databricks_trace_server.proto + """Generated protocol buffer code.""" + from google.protobuf import descriptor as _descriptor + from google.protobuf import descriptor_pool as _descriptor_pool + from google.protobuf import message as _message + from google.protobuf import reflection as _reflection + from google.protobuf import symbol_database as _symbol_database + from google.protobuf import service as _service + from google.protobuf import service_reflection + # @@protoc_insertion_point(imports) + + _sym_db = _symbol_database.Default() + + + from . import databricks_pb2 as databricks__pb2 + from . import assessments_pb2 as assessments__pb2 + from google.protobuf import duration_pb2 as google_dot_protobuf_dot_duration__pb2 + from google.protobuf import struct_pb2 as google_dot_protobuf_dot_struct__pb2 + from google.protobuf import timestamp_pb2 as google_dot_protobuf_dot_timestamp__pb2 + from .scalapb import scalapb_pb2 as scalapb_dot_scalapb__pb2 + + + DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\x1d\x64\x61tabricks_trace_server.proto\x12\x12mlflow.traceserver\x1a\x10\x64\x61tabricks.proto\x1a\x11\x61ssessments.proto\x1a\x1egoogle/protobuf/duration.proto\x1a\x1cgoogle/protobuf/struct.proto\x1a\x1fgoogle/protobuf/timestamp.proto\x1a\x15scalapb/scalapb.proto\"\x8b\x01\n\x0b\x43reateTrace\x12+\n\x04info\x18\x01 \x01(\x0b\x32\x1d.mlflow.traceserver.TraceInfo\x12+\n\x04\x64\x61ta\x18\x02 \x01(\x0b\x32\x1d.mlflow.traceserver.TraceData:\"\xe2?\x1f\n\x1d\x63om.databricks.rpc.RPC[Trace]\"a\n\x05Trace\x12+\n\x04info\x18\x01 \x01(\x0b\x32\x1d.mlflow.traceserver.TraceInfo\x12+\n\x04\x64\x61ta\x18\x02 \x01(\x0b\x32\x1d.mlflow.traceserver.TraceData\"\xc3\x05\n\tTraceInfo\x12\x10\n\x08trace_id\x18\x01 \x01(\t\x12\x19\n\x11\x63lient_request_id\x18\x02 \x01(\t\x12\x39\n\x0etrace_location\x18\x03 \x01(\x0b\x32!.mlflow.traceserver.TraceLocation\x12\x0f\n\x07request\x18\x04 \x01(\t\x12\x10\n\x08response\x18\x05 \x01(\t\x12\x17\n\x0frequest_preview\x18\x0c \x01(\t\x12\x18\n\x10response_preview\x18\r \x01(\t\x12\x30\n\x0crequest_time\x18\x06 \x01(\x0b\x32\x1a.google.protobuf.Timestamp\x12\x35\n\x12\x65xecution_duration\x18\x07 \x01(\x0b\x32\x19.google.protobuf.Duration\x12\x32\n\x05state\x18\x08 \x01(\x0e\x32#.mlflow.traceserver.TraceInfo.State\x12H\n\x0etrace_metadata\x18\t \x03(\x0b\x32\x30.mlflow.traceserver.TraceInfo.TraceMetadataEntry\x12\x33\n\x0b\x61ssessments\x18\n \x03(\x0b\x32\x1e.mlflow.assessments.Assessment\x12\x35\n\x04tags\x18\x0b \x03(\x0b\x32\'.mlflow.traceserver.TraceInfo.TagsEntry\x1a\x34\n\x12TraceMetadataEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12\r\n\x05value\x18\x02 \x01(\t:\x02\x38\x01\x1a+\n\tTagsEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12\r\n\x05value\x18\x02 \x01(\t:\x02\x38\x01\"B\n\x05State\x12\x15\n\x11STATE_UNSPECIFIED\x10\x00\x12\x06\n\x02OK\x10\x01\x12\t\n\x05\x45RROR\x10\x02\x12\x0f\n\x0bIN_PROGRESS\x10\x03\"\xda\x03\n\rTraceLocation\x12\x41\n\x04type\x18\x01 \x01(\x0e\x32\x33.mlflow.traceserver.TraceLocation.TraceLocationType\x12W\n\x11mlflow_experiment\x18\x02 \x01(\x0b\x32:.mlflow.traceserver.TraceLocation.MlflowExperimentLocationH\x00\x12S\n\x0finference_table\x18\x03 \x01(\x0b\x32\x38.mlflow.traceserver.TraceLocation.InferenceTableLocationH\x00\x1a\x31\n\x18MlflowExperimentLocation\x12\x15\n\rexperiment_id\x18\x01 \x01(\t\x1a\x31\n\x16InferenceTableLocation\x12\x17\n\x0f\x66ull_table_name\x18\x01 \x01(\t\"d\n\x11TraceLocationType\x12#\n\x1fTRACE_LOCATION_TYPE_UNSPECIFIED\x10\x00\x12\x15\n\x11MLFLOW_EXPERIMENT\x10\x01\x12\x13\n\x0fINFERENCE_TABLE\x10\x02\x42\x0c\n\nidentifier\"4\n\tTraceData\x12\'\n\x05spans\x18\x01 \x03(\x0b\x32\x18.mlflow.traceserver.Span\"\xb2\x0b\n\x04Span\x12\x16\n\x08trace_id\x18\x01 \x01(\x0c\x42\x04\xf8\x86\x19\x01\x12\x15\n\x07span_id\x18\x02 \x01(\x0c\x42\x04\xf8\x86\x19\x01\x12\x13\n\x0btrace_state\x18\x03 \x01(\t\x12\x16\n\x0eparent_span_id\x18\x04 \x01(\x0c\x12\r\n\x05\x66lags\x18\x10 \x01(\x07\x12\x12\n\x04name\x18\x05 \x01(\tB\x04\xf8\x86\x19\x01\x12/\n\x04kind\x18\x06 \x01(\x0e\x32!.mlflow.traceserver.Span.SpanKind\x12\"\n\x14start_time_unix_nano\x18\x07 \x01(\x06\x42\x04\xf8\x86\x19\x01\x12 \n\x12\x65nd_time_unix_nano\x18\x08 \x01(\x06\x42\x04\xf8\x86\x19\x01\x12<\n\nattributes\x18\t \x03(\x0b\x32(.mlflow.traceserver.Span.AttributesEntry\x12 \n\x18\x64ropped_attributes_count\x18\n \x01(\r\x12.\n\x06\x65vents\x18\x0b \x03(\x0b\x32\x1e.mlflow.traceserver.Span.Event\x12\x1c\n\x14\x64ropped_events_count\x18\x0c \x01(\r\x12,\n\x05links\x18\r \x03(\x0b\x32\x1d.mlflow.traceserver.Span.Link\x12\x1b\n\x13\x64ropped_links_count\x18\x0e \x01(\r\x12/\n\x06status\x18\x0f \x01(\x0b\x32\x1f.mlflow.traceserver.Span.Status\x1aI\n\x0f\x41ttributesEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12%\n\x05value\x18\x02 \x01(\x0b\x32\x16.google.protobuf.Value:\x02\x38\x01\x1a\xea\x01\n\x05\x45vent\x12\x1c\n\x0etime_unix_nano\x18\x01 \x01(\x06\x42\x04\xf8\x86\x19\x01\x12\x12\n\x04name\x18\x02 \x01(\tB\x04\xf8\x86\x19\x01\x12\x42\n\nattributes\x18\x03 \x03(\x0b\x32..mlflow.traceserver.Span.Event.AttributesEntry\x12 \n\x18\x64ropped_attributes_count\x18\x04 \x01(\r\x1aI\n\x0f\x41ttributesEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12%\n\x05value\x18\x02 \x01(\x0b\x32\x16.google.protobuf.Value:\x02\x38\x01\x1a\x89\x02\n\x04Link\x12\x16\n\x08trace_id\x18\x01 \x01(\x0c\x42\x04\xf8\x86\x19\x01\x12\x15\n\x07span_id\x18\x02 \x01(\x0c\x42\x04\xf8\x86\x19\x01\x12\x13\n\x0btrace_state\x18\x03 \x01(\t\x12\x41\n\nattributes\x18\x04 \x03(\x0b\x32-.mlflow.traceserver.Span.Link.AttributesEntry\x12 \n\x18\x64ropped_attributes_count\x18\x05 \x01(\r\x12\r\n\x05\x66lags\x18\x06 \x01(\x07\x1aI\n\x0f\x41ttributesEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12%\n\x05value\x18\x02 \x01(\x0b\x32\x16.google.protobuf.Value:\x02\x38\x01\x1a\xa9\x01\n\x06Status\x12\x0f\n\x07message\x18\x02 \x01(\t\x12\x38\n\x04\x63ode\x18\x03 \x01(\x0e\x32*.mlflow.traceserver.Span.Status.StatusCode\"N\n\nStatusCode\x12\x15\n\x11STATUS_CODE_UNSET\x10\x00\x12\x12\n\x0eSTATUS_CODE_OK\x10\x01\x12\x15\n\x11STATUS_CODE_ERROR\x10\x02J\x04\x08\x01\x10\x02\"\x99\x01\n\x08SpanKind\x12\x19\n\x15SPAN_KIND_UNSPECIFIED\x10\x00\x12\x16\n\x12SPAN_KIND_INTERNAL\x10\x01\x12\x14\n\x10SPAN_KIND_SERVER\x10\x02\x12\x14\n\x10SPAN_KIND_CLIENT\x10\x03\x12\x16\n\x12SPAN_KIND_PRODUCER\x10\x04\x12\x16\n\x12SPAN_KIND_CONSUMER\x10\x05\x32\x8c\x01\n\x1e\x44\x61tabricksTracingServerService\x12j\n\x0b\x63reateTrace\x12\x1f.mlflow.traceserver.CreateTrace\x1a\x19.mlflow.traceserver.Trace\"\x1f\xf2\x86\x19\x1b\n\x17\n\x04POST\x12\x0f/tracing/traces\x10\x03\x42\x03\x90\x01\x01') + + + + _CREATETRACE = DESCRIPTOR.message_types_by_name['CreateTrace'] + _TRACE = DESCRIPTOR.message_types_by_name['Trace'] + _TRACEINFO = DESCRIPTOR.message_types_by_name['TraceInfo'] + _TRACEINFO_TRACEMETADATAENTRY = _TRACEINFO.nested_types_by_name['TraceMetadataEntry'] + _TRACEINFO_TAGSENTRY = _TRACEINFO.nested_types_by_name['TagsEntry'] + _TRACELOCATION = DESCRIPTOR.message_types_by_name['TraceLocation'] + _TRACELOCATION_MLFLOWEXPERIMENTLOCATION = _TRACELOCATION.nested_types_by_name['MlflowExperimentLocation'] + _TRACELOCATION_INFERENCETABLELOCATION = _TRACELOCATION.nested_types_by_name['InferenceTableLocation'] + _TRACEDATA = DESCRIPTOR.message_types_by_name['TraceData'] + _SPAN = DESCRIPTOR.message_types_by_name['Span'] + _SPAN_ATTRIBUTESENTRY = _SPAN.nested_types_by_name['AttributesEntry'] + _SPAN_EVENT = _SPAN.nested_types_by_name['Event'] + _SPAN_EVENT_ATTRIBUTESENTRY = _SPAN_EVENT.nested_types_by_name['AttributesEntry'] + _SPAN_LINK = _SPAN.nested_types_by_name['Link'] + _SPAN_LINK_ATTRIBUTESENTRY = _SPAN_LINK.nested_types_by_name['AttributesEntry'] + _SPAN_STATUS = _SPAN.nested_types_by_name['Status'] + _TRACEINFO_STATE = _TRACEINFO.enum_types_by_name['State'] + _TRACELOCATION_TRACELOCATIONTYPE = _TRACELOCATION.enum_types_by_name['TraceLocationType'] + _SPAN_STATUS_STATUSCODE = _SPAN_STATUS.enum_types_by_name['StatusCode'] + _SPAN_SPANKIND = _SPAN.enum_types_by_name['SpanKind'] + CreateTrace = _reflection.GeneratedProtocolMessageType('CreateTrace', (_message.Message,), { + 'DESCRIPTOR' : _CREATETRACE, + '__module__' : 'databricks_trace_server_pb2' + # @@protoc_insertion_point(class_scope:mlflow.traceserver.CreateTrace) + }) + _sym_db.RegisterMessage(CreateTrace) + + Trace = _reflection.GeneratedProtocolMessageType('Trace', (_message.Message,), { + 'DESCRIPTOR' : _TRACE, + '__module__' : 'databricks_trace_server_pb2' + # @@protoc_insertion_point(class_scope:mlflow.traceserver.Trace) + }) + _sym_db.RegisterMessage(Trace) + + TraceInfo = _reflection.GeneratedProtocolMessageType('TraceInfo', (_message.Message,), { + + 'TraceMetadataEntry' : _reflection.GeneratedProtocolMessageType('TraceMetadataEntry', (_message.Message,), { + 'DESCRIPTOR' : _TRACEINFO_TRACEMETADATAENTRY, + '__module__' : 'databricks_trace_server_pb2' + # @@protoc_insertion_point(class_scope:mlflow.traceserver.TraceInfo.TraceMetadataEntry) + }) + , + + 'TagsEntry' : _reflection.GeneratedProtocolMessageType('TagsEntry', (_message.Message,), { + 'DESCRIPTOR' : _TRACEINFO_TAGSENTRY, + '__module__' : 'databricks_trace_server_pb2' + # @@protoc_insertion_point(class_scope:mlflow.traceserver.TraceInfo.TagsEntry) + }) + , + 'DESCRIPTOR' : _TRACEINFO, + '__module__' : 'databricks_trace_server_pb2' + # @@protoc_insertion_point(class_scope:mlflow.traceserver.TraceInfo) + }) + _sym_db.RegisterMessage(TraceInfo) + _sym_db.RegisterMessage(TraceInfo.TraceMetadataEntry) + _sym_db.RegisterMessage(TraceInfo.TagsEntry) + + TraceLocation = _reflection.GeneratedProtocolMessageType('TraceLocation', (_message.Message,), { + + 'MlflowExperimentLocation' : _reflection.GeneratedProtocolMessageType('MlflowExperimentLocation', (_message.Message,), { + 'DESCRIPTOR' : _TRACELOCATION_MLFLOWEXPERIMENTLOCATION, + '__module__' : 'databricks_trace_server_pb2' + # @@protoc_insertion_point(class_scope:mlflow.traceserver.TraceLocation.MlflowExperimentLocation) + }) + , + + 'InferenceTableLocation' : _reflection.GeneratedProtocolMessageType('InferenceTableLocation', (_message.Message,), { + 'DESCRIPTOR' : _TRACELOCATION_INFERENCETABLELOCATION, + '__module__' : 'databricks_trace_server_pb2' + # @@protoc_insertion_point(class_scope:mlflow.traceserver.TraceLocation.InferenceTableLocation) + }) + , + 'DESCRIPTOR' : _TRACELOCATION, + '__module__' : 'databricks_trace_server_pb2' + # @@protoc_insertion_point(class_scope:mlflow.traceserver.TraceLocation) + }) + _sym_db.RegisterMessage(TraceLocation) + _sym_db.RegisterMessage(TraceLocation.MlflowExperimentLocation) + _sym_db.RegisterMessage(TraceLocation.InferenceTableLocation) + + TraceData = _reflection.GeneratedProtocolMessageType('TraceData', (_message.Message,), { + 'DESCRIPTOR' : _TRACEDATA, + '__module__' : 'databricks_trace_server_pb2' + # @@protoc_insertion_point(class_scope:mlflow.traceserver.TraceData) + }) + _sym_db.RegisterMessage(TraceData) + + Span = _reflection.GeneratedProtocolMessageType('Span', (_message.Message,), { + + 'AttributesEntry' : _reflection.GeneratedProtocolMessageType('AttributesEntry', (_message.Message,), { + 'DESCRIPTOR' : _SPAN_ATTRIBUTESENTRY, + '__module__' : 'databricks_trace_server_pb2' + # @@protoc_insertion_point(class_scope:mlflow.traceserver.Span.AttributesEntry) + }) + , + + 'Event' : _reflection.GeneratedProtocolMessageType('Event', (_message.Message,), { + + 'AttributesEntry' : _reflection.GeneratedProtocolMessageType('AttributesEntry', (_message.Message,), { + 'DESCRIPTOR' : _SPAN_EVENT_ATTRIBUTESENTRY, + '__module__' : 'databricks_trace_server_pb2' + # @@protoc_insertion_point(class_scope:mlflow.traceserver.Span.Event.AttributesEntry) + }) + , + 'DESCRIPTOR' : _SPAN_EVENT, + '__module__' : 'databricks_trace_server_pb2' + # @@protoc_insertion_point(class_scope:mlflow.traceserver.Span.Event) + }) + , + + 'Link' : _reflection.GeneratedProtocolMessageType('Link', (_message.Message,), { + + 'AttributesEntry' : _reflection.GeneratedProtocolMessageType('AttributesEntry', (_message.Message,), { + 'DESCRIPTOR' : _SPAN_LINK_ATTRIBUTESENTRY, + '__module__' : 'databricks_trace_server_pb2' + # @@protoc_insertion_point(class_scope:mlflow.traceserver.Span.Link.AttributesEntry) + }) + , + 'DESCRIPTOR' : _SPAN_LINK, + '__module__' : 'databricks_trace_server_pb2' + # @@protoc_insertion_point(class_scope:mlflow.traceserver.Span.Link) + }) + , + + 'Status' : _reflection.GeneratedProtocolMessageType('Status', (_message.Message,), { + 'DESCRIPTOR' : _SPAN_STATUS, + '__module__' : 'databricks_trace_server_pb2' + # @@protoc_insertion_point(class_scope:mlflow.traceserver.Span.Status) + }) + , + 'DESCRIPTOR' : _SPAN, + '__module__' : 'databricks_trace_server_pb2' + # @@protoc_insertion_point(class_scope:mlflow.traceserver.Span) + }) + _sym_db.RegisterMessage(Span) + _sym_db.RegisterMessage(Span.AttributesEntry) + _sym_db.RegisterMessage(Span.Event) + _sym_db.RegisterMessage(Span.Event.AttributesEntry) + _sym_db.RegisterMessage(Span.Link) + _sym_db.RegisterMessage(Span.Link.AttributesEntry) + _sym_db.RegisterMessage(Span.Status) + + _DATABRICKSTRACINGSERVERSERVICE = DESCRIPTOR.services_by_name['DatabricksTracingServerService'] + if _descriptor._USE_C_DESCRIPTORS == False: + + DESCRIPTOR._options = None + DESCRIPTOR._serialized_options = b'\220\001\001' + _CREATETRACE._options = None + _CREATETRACE._serialized_options = b'\342?\037\n\035com.databricks.rpc.RPC[Trace]' + _TRACEINFO_TRACEMETADATAENTRY._options = None + _TRACEINFO_TRACEMETADATAENTRY._serialized_options = b'8\001' + _TRACEINFO_TAGSENTRY._options = None + _TRACEINFO_TAGSENTRY._serialized_options = b'8\001' + _SPAN_ATTRIBUTESENTRY._options = None + _SPAN_ATTRIBUTESENTRY._serialized_options = b'8\001' + _SPAN_EVENT_ATTRIBUTESENTRY._options = None + _SPAN_EVENT_ATTRIBUTESENTRY._serialized_options = b'8\001' + _SPAN_EVENT.fields_by_name['time_unix_nano']._options = None + _SPAN_EVENT.fields_by_name['time_unix_nano']._serialized_options = b'\370\206\031\001' + _SPAN_EVENT.fields_by_name['name']._options = None + _SPAN_EVENT.fields_by_name['name']._serialized_options = b'\370\206\031\001' + _SPAN_LINK_ATTRIBUTESENTRY._options = None + _SPAN_LINK_ATTRIBUTESENTRY._serialized_options = b'8\001' + _SPAN_LINK.fields_by_name['trace_id']._options = None + _SPAN_LINK.fields_by_name['trace_id']._serialized_options = b'\370\206\031\001' + _SPAN_LINK.fields_by_name['span_id']._options = None + _SPAN_LINK.fields_by_name['span_id']._serialized_options = b'\370\206\031\001' + _SPAN.fields_by_name['trace_id']._options = None + _SPAN.fields_by_name['trace_id']._serialized_options = b'\370\206\031\001' + _SPAN.fields_by_name['span_id']._options = None + _SPAN.fields_by_name['span_id']._serialized_options = b'\370\206\031\001' + _SPAN.fields_by_name['name']._options = None + _SPAN.fields_by_name['name']._serialized_options = b'\370\206\031\001' + _SPAN.fields_by_name['start_time_unix_nano']._options = None + _SPAN.fields_by_name['start_time_unix_nano']._serialized_options = b'\370\206\031\001' + _SPAN.fields_by_name['end_time_unix_nano']._options = None + _SPAN.fields_by_name['end_time_unix_nano']._serialized_options = b'\370\206\031\001' + _DATABRICKSTRACINGSERVERSERVICE.methods_by_name['createTrace']._options = None + _DATABRICKSTRACINGSERVERSERVICE.methods_by_name['createTrace']._serialized_options = b'\362\206\031\033\n\027\n\004POST\022\017/tracing/traces\020\003' + _CREATETRACE._serialized_start=209 + _CREATETRACE._serialized_end=348 + _TRACE._serialized_start=350 + _TRACE._serialized_end=447 + _TRACEINFO._serialized_start=450 + _TRACEINFO._serialized_end=1157 + _TRACEINFO_TRACEMETADATAENTRY._serialized_start=992 + _TRACEINFO_TRACEMETADATAENTRY._serialized_end=1044 + _TRACEINFO_TAGSENTRY._serialized_start=1046 + _TRACEINFO_TAGSENTRY._serialized_end=1089 + _TRACEINFO_STATE._serialized_start=1091 + _TRACEINFO_STATE._serialized_end=1157 + _TRACELOCATION._serialized_start=1160 + _TRACELOCATION._serialized_end=1634 + _TRACELOCATION_MLFLOWEXPERIMENTLOCATION._serialized_start=1418 + _TRACELOCATION_MLFLOWEXPERIMENTLOCATION._serialized_end=1467 + _TRACELOCATION_INFERENCETABLELOCATION._serialized_start=1469 + _TRACELOCATION_INFERENCETABLELOCATION._serialized_end=1518 + _TRACELOCATION_TRACELOCATIONTYPE._serialized_start=1520 + _TRACELOCATION_TRACELOCATIONTYPE._serialized_end=1620 + _TRACEDATA._serialized_start=1636 + _TRACEDATA._serialized_end=1688 + _SPAN._serialized_start=1691 + _SPAN._serialized_end=3149 + _SPAN_ATTRIBUTESENTRY._serialized_start=2243 + _SPAN_ATTRIBUTESENTRY._serialized_end=2316 + _SPAN_EVENT._serialized_start=2319 + _SPAN_EVENT._serialized_end=2553 + _SPAN_EVENT_ATTRIBUTESENTRY._serialized_start=2243 + _SPAN_EVENT_ATTRIBUTESENTRY._serialized_end=2316 + _SPAN_LINK._serialized_start=2556 + _SPAN_LINK._serialized_end=2821 + _SPAN_LINK_ATTRIBUTESENTRY._serialized_start=2243 + _SPAN_LINK_ATTRIBUTESENTRY._serialized_end=2316 + _SPAN_STATUS._serialized_start=2824 + _SPAN_STATUS._serialized_end=2993 + _SPAN_STATUS_STATUSCODE._serialized_start=2909 + _SPAN_STATUS_STATUSCODE._serialized_end=2987 + _SPAN_SPANKIND._serialized_start=2996 + _SPAN_SPANKIND._serialized_end=3149 + _DATABRICKSTRACINGSERVERSERVICE._serialized_start=3152 + _DATABRICKSTRACINGSERVERSERVICE._serialized_end=3292 + DatabricksTracingServerService = service_reflection.GeneratedServiceType('DatabricksTracingServerService', (_service.Service,), dict( + DESCRIPTOR = _DATABRICKSTRACINGSERVERSERVICE, + __module__ = 'databricks_trace_server_pb2' + )) + + DatabricksTracingServerService_Stub = service_reflection.GeneratedServiceStubType('DatabricksTracingServerService_Stub', (DatabricksTracingServerService,), dict( + DESCRIPTOR = _DATABRICKSTRACINGSERVERSERVICE, + __module__ = 'databricks_trace_server_pb2' + )) + + + # @@protoc_insertion_point(module_scope) + diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/protos/databricks_uc_registry_messages_pb2.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/protos/databricks_uc_registry_messages_pb2.py new file mode 100644 index 0000000000000000000000000000000000000000..c60b24d18648800c212b9457a668c7ffc36cd1a8 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/protos/databricks_uc_registry_messages_pb2.py @@ -0,0 +1,1249 @@ + +import google.protobuf +from packaging.version import Version +if Version(google.protobuf.__version__).major >= 5: + # -*- coding: utf-8 -*- + # Generated by the protocol buffer compiler. DO NOT EDIT! + # source: databricks_uc_registry_messages.proto + # Protobuf Python Version: 5.26.0 + """Generated protocol buffer code.""" + from google.protobuf import descriptor as _descriptor + from google.protobuf import descriptor_pool as _descriptor_pool + from google.protobuf import symbol_database as _symbol_database + from google.protobuf.internal import builder as _builder + # @@protoc_insertion_point(imports) + + _sym_db = _symbol_database.Default() + + + from .scalapb import scalapb_pb2 as scalapb_dot_scalapb__pb2 + from . import databricks_pb2 as databricks__pb2 + + + DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n%databricks_uc_registry_messages.proto\x12\x16mlflow.ucmodelregistry\x1a\x15scalapb/scalapb.proto\x1a\x10\x64\x61tabricks.proto\"\xea\x02\n\x0fRegisteredModel\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x1a\n\x12\x63reation_timestamp\x18\x02 \x01(\x03\x12\x1e\n\x16last_updated_timestamp\x18\x03 \x01(\x03\x12\x0f\n\x07user_id\x18\x04 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_globals['_DELETEREGISTEREDMODELREQUEST'].fields_by_name['name']._loaded_options = None + _globals['_DELETEREGISTEREDMODELREQUEST'].fields_by_name['name']._serialized_options = b'\370\206\031\001' + _globals['_DELETEREGISTEREDMODELREQUEST']._loaded_options = None + _globals['_DELETEREGISTEREDMODELREQUEST']._serialized_options = b'\342?7\n5com.databricks.rpc.RPC[DeleteRegisteredModelResponse]' + _globals['_GETREGISTEREDMODELREQUEST'].fields_by_name['name']._loaded_options = None + _globals['_GETREGISTEREDMODELREQUEST'].fields_by_name['name']._serialized_options = b'\370\206\031\001' + _globals['_GETREGISTEREDMODELREQUEST']._loaded_options = None + _globals['_GETREGISTEREDMODELREQUEST']._serialized_options = b'\342?4\n2com.databricks.rpc.RPC[GetRegisteredModelResponse]' + _globals['_SEARCHREGISTEREDMODELSREQUEST']._loaded_options = None + _globals['_SEARCHREGISTEREDMODELSREQUEST']._serialized_options = b'\342?8\n6com.databricks.rpc.RPC[SearchRegisteredModelsResponse]' + 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_globals['_UPDATEMODELVERSIONREQUEST']._loaded_options = None + _globals['_UPDATEMODELVERSIONREQUEST']._serialized_options = b'\342?4\n2com.databricks.rpc.RPC[UpdateModelVersionResponse]' + _globals['_DELETEMODELVERSIONREQUEST'].fields_by_name['name']._loaded_options = None + _globals['_DELETEMODELVERSIONREQUEST'].fields_by_name['name']._serialized_options = b'\370\206\031\001' + _globals['_DELETEMODELVERSIONREQUEST'].fields_by_name['version']._loaded_options = None + _globals['_DELETEMODELVERSIONREQUEST'].fields_by_name['version']._serialized_options = b'\370\206\031\001' + _globals['_DELETEMODELVERSIONREQUEST']._loaded_options = None + _globals['_DELETEMODELVERSIONREQUEST']._serialized_options = b'\342?4\n2com.databricks.rpc.RPC[DeleteModelVersionResponse]' + _globals['_GETMODELVERSIONREQUEST'].fields_by_name['name']._loaded_options = None + _globals['_GETMODELVERSIONREQUEST'].fields_by_name['name']._serialized_options = b'\370\206\031\001' + _globals['_GETMODELVERSIONREQUEST'].fields_by_name['version']._loaded_options = None + _globals['_GETMODELVERSIONREQUEST'].fields_by_name['version']._serialized_options = b'\370\206\031\001' + _globals['_GETMODELVERSIONREQUEST']._loaded_options = None + _globals['_GETMODELVERSIONREQUEST']._serialized_options = b'\342?1\n/com.databricks.rpc.RPC[GetModelVersionResponse]' + _globals['_SEARCHMODELVERSIONSREQUEST']._loaded_options = None + _globals['_SEARCHMODELVERSIONSREQUEST']._serialized_options = b'\342?5\n3com.databricks.rpc.RPC[SearchModelVersionsResponse]' + _globals['_GENERATETEMPORARYMODELVERSIONCREDENTIALSREQUEST'].fields_by_name['name']._loaded_options = None + _globals['_GENERATETEMPORARYMODELVERSIONCREDENTIALSREQUEST'].fields_by_name['name']._serialized_options = b'\370\206\031\001' + _globals['_GENERATETEMPORARYMODELVERSIONCREDENTIALSREQUEST'].fields_by_name['version']._loaded_options = None + _globals['_GENERATETEMPORARYMODELVERSIONCREDENTIALSREQUEST'].fields_by_name['version']._serialized_options = b'\370\206\031\001' + _globals['_GENERATETEMPORARYMODELVERSIONCREDENTIALSREQUEST'].fields_by_name['operation']._loaded_options = None + _globals['_GENERATETEMPORARYMODELVERSIONCREDENTIALSREQUEST'].fields_by_name['operation']._serialized_options = b'\370\206\031\001' + _globals['_GENERATETEMPORARYMODELVERSIONCREDENTIALSREQUEST']._loaded_options = None + _globals['_GENERATETEMPORARYMODELVERSIONCREDENTIALSREQUEST']._serialized_options = b'\342?J\nHcom.databricks.rpc.RPC[GenerateTemporaryModelVersionCredentialsResponse]' + _globals['_GETMODELVERSIONDOWNLOADURIREQUEST'].fields_by_name['name']._loaded_options = None + _globals['_GETMODELVERSIONDOWNLOADURIREQUEST'].fields_by_name['name']._serialized_options = b'\370\206\031\001' + _globals['_GETMODELVERSIONDOWNLOADURIREQUEST'].fields_by_name['version']._loaded_options = None + 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b'\342?9\n7com.databricks.rpc.RPC[EmitModelVersionLineageResponse]' + _globals['_ISDATABRICKSSDKMODELSARTIFACTREPOSITORYENABLEDREQUEST']._loaded_options = None + _globals['_ISDATABRICKSSDKMODELSARTIFACTREPOSITORYENABLEDREQUEST']._serialized_options = b'\342?P\nNcom.databricks.rpc.RPC[IsDatabricksSdkModelsArtifactRepositoryEnabledResponse]' + _globals['_ISDATABRICKSSDKMODELSARTIFACTREPOSITORYENABLEDRESPONSE'].fields_by_name['is_databricks_sdk_models_artifact_repository_enabled']._loaded_options = None + _globals['_ISDATABRICKSSDKMODELSARTIFACTREPOSITORYENABLEDRESPONSE'].fields_by_name['is_databricks_sdk_models_artifact_repository_enabled']._serialized_options = b'\370\206\031\001' + _globals['_MODELVERSIONSTATUS']._serialized_start=9480 + _globals['_MODELVERSIONSTATUS']._serialized_end=9579 + _globals['_MODELVERSIONOPERATION']._serialized_start=9582 + _globals['_MODELVERSIONOPERATION']._serialized_end=9720 + _globals['_STORAGEMODE']._serialized_start=9722 + 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_globals['_MODELMETRIC']._serialized_end=802 + _globals['_MODELVERSIONTAG']._serialized_start=804 + _globals['_MODELVERSIONTAG']._serialized_end=849 + _globals['_MODELVERSION']._serialized_start=852 + _globals['_MODELVERSION']._serialized_end=1536 + _globals['_DEPLOYMENTJOBCONNECTION']._serialized_start=1539 + _globals['_DEPLOYMENTJOBCONNECTION']._serialized_end=1702 + _globals['_DEPLOYMENTJOBCONNECTION_STATE']._serialized_start=1567 + _globals['_DEPLOYMENTJOBCONNECTION_STATE']._serialized_end=1702 + _globals['_MODELVERSIONDEPLOYMENTJOBSTATE']._serialized_start=1705 + _globals['_MODELVERSIONDEPLOYMENTJOBSTATE']._serialized_end=2137 + _globals['_MODELVERSIONDEPLOYMENTJOBSTATE_DEPLOYMENTJOBRUNSTATE']._serialized_start=1970 + _globals['_MODELVERSIONDEPLOYMENTJOBSTATE_DEPLOYMENTJOBRUNSTATE']._serialized_end=2137 + _globals['_TEMPORARYCREDENTIALS']._serialized_start=2140 + _globals['_TEMPORARYCREDENTIALS']._serialized_end=2625 + _globals['_AWSCREDENTIALS']._serialized_start=2627 + _globals['_AWSCREDENTIALS']._serialized_end=2716 + _globals['_AZUREUSERDELEGATIONSAS']._serialized_start=2718 + _globals['_AZUREUSERDELEGATIONSAS']._serialized_end=2761 + _globals['_GCPOAUTHTOKEN']._serialized_start=2763 + _globals['_GCPOAUTHTOKEN']._serialized_end=2799 + _globals['_R2CREDENTIALS']._serialized_start=2801 + _globals['_R2CREDENTIALS']._serialized_end=2889 + _globals['_ENCRYPTIONDETAILS']._serialized_start=2891 + _globals['_ENCRYPTIONDETAILS']._serialized_end=3017 + _globals['_SSEENCRYPTIONDETAILS']._serialized_start=3019 + _globals['_SSEENCRYPTIONDETAILS']._serialized_end=3133 + _globals['_CREATEREGISTEREDMODELREQUEST']._serialized_start=3136 + _globals['_CREATEREGISTEREDMODELREQUEST']._serialized_end=3352 + _globals['_CREATEREGISTEREDMODELRESPONSE']._serialized_start=3354 + _globals['_CREATEREGISTEREDMODELRESPONSE']._serialized_end=3452 + _globals['_UPDATEREGISTEREDMODELREQUEST']._serialized_start=3455 + _globals['_UPDATEREGISTEREDMODELREQUEST']._serialized_end=3631 + _globals['_UPDATEREGISTEREDMODELRESPONSE']._serialized_start=3633 + _globals['_UPDATEREGISTEREDMODELRESPONSE']._serialized_end=3731 + _globals['_DELETEREGISTEREDMODELREQUEST']._serialized_start=3733 + _globals['_DELETEREGISTEREDMODELREQUEST']._serialized_end=3843 + _globals['_DELETEREGISTEREDMODELRESPONSE']._serialized_start=3845 + _globals['_DELETEREGISTEREDMODELRESPONSE']._serialized_end=3876 + _globals['_GETREGISTEREDMODELREQUEST']._serialized_start=3878 + _globals['_GETREGISTEREDMODELREQUEST']._serialized_end=3982 + _globals['_GETREGISTEREDMODELRESPONSE']._serialized_start=3984 + _globals['_GETREGISTEREDMODELRESPONSE']._serialized_end=4079 + _globals['_SEARCHREGISTEREDMODELSREQUEST']._serialized_start=4082 + _globals['_SEARCHREGISTEREDMODELSREQUEST']._serialized_end=4220 + _globals['_SEARCHREGISTEREDMODELSRESPONSE']._serialized_start=4222 + _globals['_SEARCHREGISTEREDMODELSRESPONSE']._serialized_end=4347 + _globals['_DEPENDENCY']._serialized_start=4349 + _globals['_DEPENDENCY']._serialized_end=4429 + _globals['_CREATEMODELVERSIONREQUEST']._serialized_start=4432 + _globals['_CREATEMODELVERSIONREQUEST']._serialized_end=4794 + _globals['_CREATEMODELVERSIONRESPONSE']._serialized_start=4796 + _globals['_CREATEMODELVERSIONRESPONSE']._serialized_end=4885 + _globals['_UPDATEMODELVERSIONREQUEST']._serialized_start=4888 + _globals['_UPDATEMODELVERSIONREQUEST']._serialized_end=5036 + _globals['_UPDATEMODELVERSIONRESPONSE']._serialized_start=5038 + _globals['_UPDATEMODELVERSIONRESPONSE']._serialized_end=5127 + _globals['_DELETEMODELVERSIONREQUEST']._serialized_start=5129 + _globals['_DELETEMODELVERSIONREQUEST']._serialized_end=5256 + _globals['_DELETEMODELVERSIONRESPONSE']._serialized_start=5258 + _globals['_DELETEMODELVERSIONRESPONSE']._serialized_end=5286 + _globals['_GETMODELVERSIONREQUEST']._serialized_start=5288 + _globals['_GETMODELVERSIONREQUEST']._serialized_end=5409 + _globals['_GETMODELVERSIONRESPONSE']._serialized_start=5411 + _globals['_GETMODELVERSIONRESPONSE']._serialized_end=5497 + _globals['_SEARCHMODELVERSIONSREQUEST']._serialized_start=5500 + _globals['_SEARCHMODELVERSIONSREQUEST']._serialized_end=5650 + _globals['_SEARCHMODELVERSIONSRESPONSE']._serialized_start=5652 + _globals['_SEARCHMODELVERSIONSRESPONSE']._serialized_end=5768 + _globals['_GENERATETEMPORARYMODELVERSIONCREDENTIALSREQUEST']._serialized_start=5771 + _globals['_GENERATETEMPORARYMODELVERSIONCREDENTIALSREQUEST']._serialized_end=6014 + _globals['_GENERATETEMPORARYMODELVERSIONCREDENTIALSRESPONSE']._serialized_start=6016 + _globals['_GENERATETEMPORARYMODELVERSIONCREDENTIALSRESPONSE']._serialized_end=6133 + _globals['_GETMODELVERSIONDOWNLOADURIREQUEST']._serialized_start=6136 + _globals['_GETMODELVERSIONDOWNLOADURIREQUEST']._serialized_end=6279 + _globals['_GETMODELVERSIONDOWNLOADURIRESPONSE']._serialized_start=6281 + _globals['_GETMODELVERSIONDOWNLOADURIRESPONSE']._serialized_end=6339 + _globals['_FINALIZEMODELVERSIONREQUEST']._serialized_start=6342 + _globals['_FINALIZEMODELVERSIONREQUEST']._serialized_end=6473 + _globals['_FINALIZEMODELVERSIONRESPONSE']._serialized_start=6475 + _globals['_FINALIZEMODELVERSIONRESPONSE']._serialized_end=6566 + _globals['_SETREGISTEREDMODELALIASREQUEST']._serialized_start=6569 + _globals['_SETREGISTEREDMODELALIASREQUEST']._serialized_end=6727 + _globals['_SETREGISTEREDMODELALIASRESPONSE']._serialized_start=6729 + _globals['_SETREGISTEREDMODELALIASRESPONSE']._serialized_end=6762 + _globals['_DELETEREGISTEREDMODELALIASREQUEST']._serialized_start=6765 + _globals['_DELETEREGISTEREDMODELALIASREQUEST']._serialized_end=6906 + _globals['_DELETEREGISTEREDMODELALIASRESPONSE']._serialized_start=6908 + _globals['_DELETEREGISTEREDMODELALIASRESPONSE']._serialized_end=6944 + _globals['_SETREGISTEREDMODELTAGREQUEST']._serialized_start=6947 + _globals['_SETREGISTEREDMODELTAGREQUEST']._serialized_end=7091 + _globals['_SETREGISTEREDMODELTAGRESPONSE']._serialized_start=7093 + _globals['_SETREGISTEREDMODELTAGRESPONSE']._serialized_end=7124 + _globals['_DELETEREGISTEREDMODELTAGREQUEST']._serialized_start=7127 + _globals['_DELETEREGISTEREDMODELTAGREQUEST']._serialized_end=7262 + _globals['_DELETEREGISTEREDMODELTAGRESPONSE']._serialized_start=7264 + _globals['_DELETEREGISTEREDMODELTAGRESPONSE']._serialized_end=7298 + _globals['_SETMODELVERSIONTAGREQUEST']._serialized_start=7301 + _globals['_SETMODELVERSIONTAGREQUEST']._serialized_end=7462 + _globals['_SETMODELVERSIONTAGRESPONSE']._serialized_start=7464 + _globals['_SETMODELVERSIONTAGRESPONSE']._serialized_end=7492 + _globals['_DELETEMODELVERSIONTAGREQUEST']._serialized_start=7495 + _globals['_DELETEMODELVERSIONTAGREQUEST']._serialized_end=7647 + _globals['_DELETEMODELVERSIONTAGRESPONSE']._serialized_start=7649 + _globals['_DELETEMODELVERSIONTAGRESPONSE']._serialized_end=7680 + _globals['_GETMODELVERSIONBYALIASREQUEST']._serialized_start=7683 + _globals['_GETMODELVERSIONBYALIASREQUEST']._serialized_end=7816 + _globals['_GETMODELVERSIONBYALIASRESPONSE']._serialized_start=7818 + _globals['_GETMODELVERSIONBYALIASRESPONSE']._serialized_end=7911 + _globals['_ENTITY']._serialized_start=7914 + _globals['_ENTITY']._serialized_end=8083 + _globals['_PIPELINE']._serialized_start=8085 + _globals['_PIPELINE']._serialized_end=8144 + _globals['_JOB']._serialized_start=8146 + _globals['_JOB']._serialized_end=8222 + _globals['_NOTEBOOK']._serialized_start=8224 + _globals['_NOTEBOOK']._serialized_end=8291 + _globals['_TABLE']._serialized_start=8293 + _globals['_TABLE']._serialized_end=8405 + _globals['_SECURABLE']._serialized_start=8407 + _globals['_SECURABLE']._serialized_end=8475 + _globals['_LINEAGE']._serialized_start=8478 + _globals['_LINEAGE']._serialized_end=8610 + _globals['_LINEAGEHEADERINFO']._serialized_start=8612 + _globals['_LINEAGEHEADERINFO']._serialized_end=8732 + _globals['_MODELVERSIONLINEAGEINFO']._serialized_start=8735 + _globals['_MODELVERSIONLINEAGEINFO']._serialized_end=8889 + _globals['_EMITMODELVERSIONLINEAGEREQUEST']._serialized_start=8892 + _globals['_EMITMODELVERSIONLINEAGEREQUEST']._serialized_end=9174 + _globals['_EMITMODELVERSIONLINEAGERESPONSE']._serialized_start=9176 + _globals['_EMITMODELVERSIONLINEAGERESPONSE']._serialized_end=9209 + _globals['_ISDATABRICKSSDKMODELSARTIFACTREPOSITORYENABLEDREQUEST']._serialized_start=9212 + _globals['_ISDATABRICKSSDKMODELSARTIFACTREPOSITORYENABLEDREQUEST']._serialized_end=9352 + _globals['_ISDATABRICKSSDKMODELSARTIFACTREPOSITORYENABLEDRESPONSE']._serialized_start=9354 + _globals['_ISDATABRICKSSDKMODELSARTIFACTREPOSITORYENABLEDRESPONSE']._serialized_end=9478 + # @@protoc_insertion_point(module_scope) + +else: + # -*- coding: utf-8 -*- + # Generated by the protocol buffer compiler. DO NOT EDIT! + # source: databricks_uc_registry_messages.proto + """Generated protocol buffer code.""" + from google.protobuf.internal import enum_type_wrapper + from google.protobuf import descriptor as _descriptor + from google.protobuf import descriptor_pool as _descriptor_pool + from google.protobuf import message as _message + from google.protobuf import reflection as _reflection + from google.protobuf import symbol_database as _symbol_database + # @@protoc_insertion_point(imports) + + _sym_db = _symbol_database.Default() + + + from .scalapb import scalapb_pb2 as scalapb_dot_scalapb__pb2 + from . import databricks_pb2 as databricks__pb2 + + + DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n%databricks_uc_registry_messages.proto\x12\x16mlflow.ucmodelregistry\x1a\x15scalapb/scalapb.proto\x1a\x10\x64\x61tabricks.proto\"\xea\x02\n\x0fRegisteredModel\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x1a\n\x12\x63reation_timestamp\x18\x02 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\x01(\t\"\xd8\x01\n\x1c\x43reateRegisteredModelRequest\x12\x12\n\x04name\x18\x01 \x01(\tB\x04\xf8\x86\x19\x01\x12\x38\n\x04tags\x18\x02 \x03(\x0b\x32*.mlflow.ucmodelregistry.RegisteredModelTag\x12\x13\n\x0b\x64\x65scription\x18\x03 \x01(\t\x12\x19\n\x11\x64\x65ployment_job_id\x18\x04 \x01(\t::\xe2?7\n5com.databricks.rpc.RPC[CreateRegisteredModelResponse]\"b\n\x1d\x43reateRegisteredModelResponse\x12\x41\n\x10registered_model\x18\x01 \x01(\x0b\x32\'.mlflow.ucmodelregistry.RegisteredModel\"\xb0\x01\n\x1cUpdateRegisteredModelRequest\x12\x12\n\x04name\x18\x01 \x01(\tB\x04\xf8\x86\x19\x01\x12\x10\n\x08new_name\x18\x03 \x01(\t\x12\x13\n\x0b\x64\x65scription\x18\x02 \x01(\t\x12\x19\n\x11\x64\x65ployment_job_id\x18\x04 \x01(\t::\xe2?7\n5com.databricks.rpc.RPC[UpdateRegisteredModelResponse]\"b\n\x1dUpdateRegisteredModelResponse\x12\x41\n\x10registered_model\x18\x01 \x01(\x0b\x32\'.mlflow.ucmodelregistry.RegisteredModel\"n\n\x1c\x44\x65leteRegisteredModelRequest\x12\x12\n\x04name\x18\x01 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\x01(\t\"\xea\x02\n\x19\x43reateModelVersionRequest\x12\x12\n\x04name\x18\x01 \x01(\tB\x04\xf8\x86\x19\x01\x12\x14\n\x06source\x18\x02 \x01(\tB\x04\xf8\x86\x19\x01\x12\x0e\n\x06run_id\x18\x03 \x01(\t\x12\x13\n\x0b\x64\x65scription\x18\x04 \x01(\t\x12\x1e\n\x16run_tracking_server_id\x18\x05 \x01(\t\x12\x14\n\x0c\x66\x65\x61ture_deps\x18\x06 \x01(\t\x12\x35\n\x04tags\x18\x07 \x03(\x0b\x32\'.mlflow.ucmodelregistry.ModelVersionTag\x12\x46\n\x1amodel_version_dependencies\x18\x08 \x03(\x0b\x32\".mlflow.ucmodelregistry.Dependency\x12\x10\n\x08model_id\x18\t \x01(\t:7\xe2?4\n2com.databricks.rpc.RPC[CreateModelVersionResponse]\"Y\n\x1a\x43reateModelVersionResponse\x12;\n\rmodel_version\x18\x01 \x01(\x0b\x32$.mlflow.ucmodelregistry.ModelVersion\"\x94\x01\n\x19UpdateModelVersionRequest\x12\x12\n\x04name\x18\x01 \x01(\tB\x04\xf8\x86\x19\x01\x12\x15\n\x07version\x18\x02 \x01(\tB\x04\xf8\x86\x19\x01\x12\x13\n\x0b\x64\x65scription\x18\x03 \x01(\t:7\xe2?4\n2com.databricks.rpc.RPC[UpdateModelVersionResponse]\"Y\n\x1aUpdateModelVersionResponse\x12;\n\rmodel_version\x18\x01 \x01(\x0b\x32$.mlflow.ucmodelregistry.ModelVersion\"\x7f\n\x19\x44\x65leteModelVersionRequest\x12\x12\n\x04name\x18\x01 \x01(\tB\x04\xf8\x86\x19\x01\x12\x15\n\x07version\x18\x02 \x01(\tB\x04\xf8\x86\x19\x01:7\xe2?4\n2com.databricks.rpc.RPC[DeleteModelVersionResponse]\"\x1c\n\x1a\x44\x65leteModelVersionResponse\"y\n\x16GetModelVersionRequest\x12\x12\n\x04name\x18\x01 \x01(\tB\x04\xf8\x86\x19\x01\x12\x15\n\x07version\x18\x02 \x01(\tB\x04\xf8\x86\x19\x01:4\xe2?1\n/com.databricks.rpc.RPC[GetModelVersionResponse]\"V\n\x17GetModelVersionResponse\x12;\n\rmodel_version\x18\x01 \x01(\x0b\x32$.mlflow.ucmodelregistry.ModelVersion\"\x96\x01\n\x1aSearchModelVersionsRequest\x12\x0e\n\x06\x66ilter\x18\x01 \x01(\t\x12\x1a\n\x0bmax_results\x18\x02 \x01(\x03:\x05\x31\x30\x30\x30\x30\x12\x12\n\npage_token\x18\x03 \x01(\t:8\xe2?5\n3com.databricks.rpc.RPC[SearchModelVersionsResponse]\"t\n\x1bSearchModelVersionsResponse\x12<\n\x0emodel_versions\x18\x01 \x03(\x0b\x32$.mlflow.ucmodelregistry.ModelVersion\x12\x17\n\x0fnext_page_token\x18\x02 \x01(\t\"\xf3\x01\n/GenerateTemporaryModelVersionCredentialsRequest\x12\x12\n\x04name\x18\x01 \x01(\tB\x04\xf8\x86\x19\x01\x12\x15\n\x07version\x18\x02 \x01(\tB\x04\xf8\x86\x19\x01\x12\x46\n\toperation\x18\x03 \x01(\x0e\x32-.mlflow.ucmodelregistry.ModelVersionOperationB\x04\xf8\x86\x19\x01:M\xe2?J\nHcom.databricks.rpc.RPC[GenerateTemporaryModelVersionCredentialsResponse]\"u\n0GenerateTemporaryModelVersionCredentialsResponse\x12\x41\n\x0b\x63redentials\x18\x01 \x01(\x0b\x32,.mlflow.ucmodelregistry.TemporaryCredentials\"\x8f\x01\n!GetModelVersionDownloadUriRequest\x12\x12\n\x04name\x18\x01 \x01(\tB\x04\xf8\x86\x19\x01\x12\x15\n\x07version\x18\x02 \x01(\tB\x04\xf8\x86\x19\x01:?\xe2?<\n:com.databricks.rpc.RPC[GetModelVersionDownloadUriResponse]\":\n\"GetModelVersionDownloadUriResponse\x12\x14\n\x0c\x61rtifact_uri\x18\x01 \x01(\t\"\x83\x01\n\x1b\x46inalizeModelVersionRequest\x12\x12\n\x04name\x18\x01 \x01(\tB\x04\xf8\x86\x19\x01\x12\x15\n\x07version\x18\x02 \x01(\tB\x04\xf8\x86\x19\x01:9\xe2?6\n4com.databricks.rpc.RPC[FinalizeModelVersionResponse]\"[\n\x1c\x46inalizeModelVersionResponse\x12;\n\rmodel_version\x18\x01 \x01(\x0b\x32$.mlflow.ucmodelregistry.ModelVersion\"\x9e\x01\n\x1eSetRegisteredModelAliasRequest\x12\x12\n\x04name\x18\x01 \x01(\tB\x04\xf8\x86\x19\x01\x12\x13\n\x05\x61lias\x18\x02 \x01(\tB\x04\xf8\x86\x19\x01\x12\x15\n\x07version\x18\x03 \x01(\tB\x04\xf8\x86\x19\x01:<\xe2?9\n7com.databricks.rpc.RPC[SetRegisteredModelAliasResponse]\"!\n\x1fSetRegisteredModelAliasResponse\"\x8d\x01\n!DeleteRegisteredModelAliasRequest\x12\x12\n\x04name\x18\x01 \x01(\tB\x04\xf8\x86\x19\x01\x12\x13\n\x05\x61lias\x18\x02 \x01(\tB\x04\xf8\x86\x19\x01:?\xe2?<\n:com.databricks.rpc.RPC[DeleteRegisteredModelAliasResponse]\"$\n\"DeleteRegisteredModelAliasResponse\"\x90\x01\n\x1cSetRegisteredModelTagRequest\x12\x12\n\x04name\x18\x01 \x01(\tB\x04\xf8\x86\x19\x01\x12\x11\n\x03key\x18\x02 \x01(\tB\x04\xf8\x86\x19\x01\x12\r\n\x05value\x18\x03 \x01(\t::\xe2?7\n5com.databricks.rpc.RPC[SetRegisteredModelTagResponse]\"\x1f\n\x1dSetRegisteredModelTagResponse\"\x87\x01\n\x1f\x44\x65leteRegisteredModelTagRequest\x12\x12\n\x04name\x18\x01 \x01(\tB\x04\xf8\x86\x19\x01\x12\x11\n\x03key\x18\x02 \x01(\tB\x04\xf8\x86\x19\x01:=\xe2?:\n8com.databricks.rpc.RPC[DeleteRegisteredModelTagResponse]\"\"\n DeleteRegisteredModelTagResponse\"\xa1\x01\n\x19SetModelVersionTagRequest\x12\x12\n\x04name\x18\x01 \x01(\tB\x04\xf8\x86\x19\x01\x12\x15\n\x07version\x18\x02 \x01(\tB\x04\xf8\x86\x19\x01\x12\x11\n\x03key\x18\x03 \x01(\tB\x04\xf8\x86\x19\x01\x12\r\n\x05value\x18\x04 \x01(\t:7\xe2?4\n2com.databricks.rpc.RPC[SetModelVersionTagResponse]\"\x1c\n\x1aSetModelVersionTagResponse\"\x98\x01\n\x1c\x44\x65leteModelVersionTagRequest\x12\x12\n\x04name\x18\x01 \x01(\tB\x04\xf8\x86\x19\x01\x12\x15\n\x07version\x18\x02 \x01(\tB\x04\xf8\x86\x19\x01\x12\x11\n\x03key\x18\x03 \x01(\tB\x04\xf8\x86\x19\x01::\xe2?7\n5com.databricks.rpc.RPC[DeleteModelVersionTagResponse]\"\x1f\n\x1d\x44\x65leteModelVersionTagResponse\"\x85\x01\n\x1dGetModelVersionByAliasRequest\x12\x12\n\x04name\x18\x01 \x01(\tB\x04\xf8\x86\x19\x01\x12\x13\n\x05\x61lias\x18\x02 \x01(\tB\x04\xf8\x86\x19\x01:;\xe2?8\n6com.databricks.rpc.RPC[GetModelVersionByAliasResponse]\"]\n\x1eGetModelVersionByAliasResponse\x12;\n\rmodel_version\x18\x01 \x01(\x0b\x32$.mlflow.ucmodelregistry.ModelVersion\"\xa9\x01\n\x06\x45ntity\x12*\n\x03job\x18\x01 \x01(\x0b\x32\x1b.mlflow.ucmodelregistry.JobH\x00\x12\x34\n\x08notebook\x18\x02 \x01(\x0b\x32 .mlflow.ucmodelregistry.NotebookH\x00\x12\x34\n\x08pipeline\x18\x03 \x01(\x0b\x32 .mlflow.ucmodelregistry.PipelineH\x00\x42\x07\n\x05value\";\n\x08Pipeline\x12\x13\n\x0bpipeline_id\x18\x01 \x01(\t\x12\x1a\n\x12pipeline_update_id\x18\x02 \x01(\t\"L\n\x03Job\x12\n\n\x02id\x18\x01 \x01(\t\x12\x10\n\x08task_key\x18\x02 \x01(\t\x12\x12\n\njob_run_id\x18\x03 \x01(\t\x12\x13\n\x0btask_run_id\x18\x04 \x01(\t\"C\n\x08Notebook\x12\n\n\x02id\x18\x01 \x01(\t\x12\x12\n\ncommand_id\x18\x02 \x01(\t\x12\x17\n\x0fnotebook_run_id\x18\x03 \x01(\t\"p\n\x05Table\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x35\n\ntable_type\x18\x02 \x01(\x0e\x32!.mlflow.ucmodelregistry.TableType\x12\x10\n\x08location\x18\x03 \x01(\t\x12\x10\n\x08table_id\x18\x04 \x01(\t\"D\n\tSecurable\x12.\n\x05table\x18\x01 \x01(\x0b\x32\x1d.mlflow.ucmodelregistry.TableH\x00\x42\x07\n\x05value\"\x84\x01\n\x07Lineage\x12;\n\x10target_securable\x18\x04 \x01(\x0b\x32!.mlflow.ucmodelregistry.Securable\x12<\n\x11source_securables\x18\x05 \x03(\x0b\x32!.mlflow.ucmodelregistry.Securable\"x\n\x11LineageHeaderInfo\x12\x30\n\x08\x65ntities\x18\x01 \x03(\x0b\x32\x1e.mlflow.ucmodelregistry.Entity\x12\x31\n\x08lineages\x18\x02 \x03(\x0b\x32\x1f.mlflow.ucmodelregistry.Lineage\"\x9a\x01\n\x17ModelVersionLineageInfo\x12\x30\n\x08\x65ntities\x18\x01 \x03(\x0b\x32\x1e.mlflow.ucmodelregistry.Entity\x12M\n\tdirection\x18\x02 \x01(\x0e\x32\x34.mlflow.ucmodelregistry.ModelVersionLineageDirectionB\x04\xf8\x86\x19\x01\"\x9a\x02\n\x1e\x45mitModelVersionLineageRequest\x12\x12\n\x04name\x18\x01 \x01(\tB\x04\xf8\x86\x19\x01\x12\x15\n\x07version\x18\x02 \x01(\tB\x04\xf8\x86\x19\x01\x12Y\n\x1amodel_version_lineage_info\x18\x03 \x01(\x0b\x32/.mlflow.ucmodelregistry.ModelVersionLineageInfoB\x04\xf8\x86\x19\x01\x12\x34\n\tsecurable\x18\x04 \x01(\x0b\x32!.mlflow.ucmodelregistry.Securable:<\xe2?9\n7com.databricks.rpc.RPC[EmitModelVersionLineageResponse]\"!\n\x1f\x45mitModelVersionLineageResponse\"\x8c\x01\n5IsDatabricksSdkModelsArtifactRepositoryEnabledRequest:S\xe2?P\nNcom.databricks.rpc.RPC[IsDatabricksSdkModelsArtifactRepositoryEnabledResponse]\"|\n6IsDatabricksSdkModelsArtifactRepositoryEnabledResponse\x12\x42\n4is_databricks_sdk_models_artifact_repository_enabled\x18\x01 \x01(\x08\x42\x04\xf8\x86\x19\x01*c\n\x12ModelVersionStatus\x12\x0f\n\x0bUNSPECIFIED\x10\x00\x12\x18\n\x14PENDING_REGISTRATION\x10\x01\x12\x17\n\x13\x46\x41ILED_REGISTRATION\x10\x02\x12\t\n\x05READY\x10\x03*\x8a\x01\n\x15ModelVersionOperation\x12\'\n#MODEL_VERSION_OPERATION_UNSPECIFIED\x10\x00\x12 \n\x1cMODEL_VERSION_OPERATION_READ\x10\x01\x12&\n\"MODEL_VERSION_OPERATION_READ_WRITE\x10\x02*U\n\x0bStorageMode\x12\x1c\n\x18STORAGE_MODE_UNSPECIFIED\x10\x00\x12\x13\n\x0f\x43USTOMER_HOSTED\x10\x01\x12\x13\n\x0f\x44\x45\x46\x41ULT_STORAGE\x10\x02*c\n\x16SseEncryptionAlgorithm\x12(\n$SSE_ENCRYPTION_ALGORITHM_UNSPECIFIED\x10\x00\x12\x0e\n\nAWS_SSE_S3\x10\x01\x12\x0f\n\x0b\x41WS_SSE_KMS\x10\x02*\xbd\x01\n\x0e\x44\x65pendencyType\x12\x1f\n\x1b\x44\x45PENDENCY_TYPE_UNSPECIFIED\x10\x00\x12\x1b\n\x17\x44\x41TABRICKS_VECTOR_INDEX\x10\x01\x12\x1d\n\x19\x44\x41TABRICKS_MODEL_ENDPOINT\x10\x02\x12\x1a\n\x16\x44\x41TABRICKS_UC_FUNCTION\x10\x03\x12\x1c\n\x18\x44\x41TABRICKS_UC_CONNECTION\x10\x04\x12\x14\n\x10\x44\x41TABRICKS_TABLE\x10\x05*t\n\tTableType\x12\t\n\x05TABLE\x10\x00\x12\x12\n\x0ePERSISTED_VIEW\x10\x01\x12\r\n\tTEMP_VIEW\x10\x02\x12\x15\n\x11MATERIALIZED_VIEW\x10\x03\x12\x18\n\x14STREAMING_LIVE_TABLE\x10\x04\x12\x08\n\x04PATH\x10\x05*<\n\x1cModelVersionLineageDirection\x12\x0c\n\x08UPSTREAM\x10\x00\x12\x0e\n\nDOWNSTREAM\x10\x01\x42\x32\n(com.databricks.api.proto.ucmodelregistry\xa0\x01\x01\xe2?\x02\x10\x01') + + _MODELVERSIONSTATUS = DESCRIPTOR.enum_types_by_name['ModelVersionStatus'] + ModelVersionStatus = enum_type_wrapper.EnumTypeWrapper(_MODELVERSIONSTATUS) + _MODELVERSIONOPERATION = DESCRIPTOR.enum_types_by_name['ModelVersionOperation'] + ModelVersionOperation = enum_type_wrapper.EnumTypeWrapper(_MODELVERSIONOPERATION) + _STORAGEMODE = DESCRIPTOR.enum_types_by_name['StorageMode'] + StorageMode = enum_type_wrapper.EnumTypeWrapper(_STORAGEMODE) + _SSEENCRYPTIONALGORITHM = DESCRIPTOR.enum_types_by_name['SseEncryptionAlgorithm'] + SseEncryptionAlgorithm = enum_type_wrapper.EnumTypeWrapper(_SSEENCRYPTIONALGORITHM) + _DEPENDENCYTYPE = DESCRIPTOR.enum_types_by_name['DependencyType'] + DependencyType = enum_type_wrapper.EnumTypeWrapper(_DEPENDENCYTYPE) + _TABLETYPE = DESCRIPTOR.enum_types_by_name['TableType'] + TableType = enum_type_wrapper.EnumTypeWrapper(_TABLETYPE) + _MODELVERSIONLINEAGEDIRECTION = DESCRIPTOR.enum_types_by_name['ModelVersionLineageDirection'] + ModelVersionLineageDirection = enum_type_wrapper.EnumTypeWrapper(_MODELVERSIONLINEAGEDIRECTION) + UNSPECIFIED = 0 + PENDING_REGISTRATION = 1 + FAILED_REGISTRATION = 2 + READY = 3 + MODEL_VERSION_OPERATION_UNSPECIFIED = 0 + MODEL_VERSION_OPERATION_READ = 1 + MODEL_VERSION_OPERATION_READ_WRITE = 2 + STORAGE_MODE_UNSPECIFIED = 0 + CUSTOMER_HOSTED = 1 + DEFAULT_STORAGE = 2 + SSE_ENCRYPTION_ALGORITHM_UNSPECIFIED = 0 + AWS_SSE_S3 = 1 + AWS_SSE_KMS = 2 + DEPENDENCY_TYPE_UNSPECIFIED = 0 + DATABRICKS_VECTOR_INDEX = 1 + DATABRICKS_MODEL_ENDPOINT = 2 + DATABRICKS_UC_FUNCTION = 3 + DATABRICKS_UC_CONNECTION = 4 + DATABRICKS_TABLE = 5 + TABLE = 0 + PERSISTED_VIEW = 1 + TEMP_VIEW = 2 + MATERIALIZED_VIEW = 3 + STREAMING_LIVE_TABLE = 4 + PATH = 5 + UPSTREAM = 0 + DOWNSTREAM = 1 + + + _REGISTEREDMODEL = DESCRIPTOR.message_types_by_name['RegisteredModel'] + _REGISTEREDMODELALIAS = DESCRIPTOR.message_types_by_name['RegisteredModelAlias'] + _REGISTEREDMODELTAG = DESCRIPTOR.message_types_by_name['RegisteredModelTag'] + _MODELPARAM = DESCRIPTOR.message_types_by_name['ModelParam'] + _MODELMETRIC = DESCRIPTOR.message_types_by_name['ModelMetric'] + _MODELVERSIONTAG = DESCRIPTOR.message_types_by_name['ModelVersionTag'] + _MODELVERSION = DESCRIPTOR.message_types_by_name['ModelVersion'] + _DEPLOYMENTJOBCONNECTION = DESCRIPTOR.message_types_by_name['DeploymentJobConnection'] + _MODELVERSIONDEPLOYMENTJOBSTATE = DESCRIPTOR.message_types_by_name['ModelVersionDeploymentJobState'] + _TEMPORARYCREDENTIALS = DESCRIPTOR.message_types_by_name['TemporaryCredentials'] + _AWSCREDENTIALS = DESCRIPTOR.message_types_by_name['AwsCredentials'] + _AZUREUSERDELEGATIONSAS = DESCRIPTOR.message_types_by_name['AzureUserDelegationSAS'] + _GCPOAUTHTOKEN = DESCRIPTOR.message_types_by_name['GcpOauthToken'] + _R2CREDENTIALS = DESCRIPTOR.message_types_by_name['R2Credentials'] + _ENCRYPTIONDETAILS = DESCRIPTOR.message_types_by_name['EncryptionDetails'] + _SSEENCRYPTIONDETAILS = DESCRIPTOR.message_types_by_name['SseEncryptionDetails'] + _CREATEREGISTEREDMODELREQUEST = DESCRIPTOR.message_types_by_name['CreateRegisteredModelRequest'] + _CREATEREGISTEREDMODELRESPONSE = DESCRIPTOR.message_types_by_name['CreateRegisteredModelResponse'] + _UPDATEREGISTEREDMODELREQUEST = DESCRIPTOR.message_types_by_name['UpdateRegisteredModelRequest'] + _UPDATEREGISTEREDMODELRESPONSE = DESCRIPTOR.message_types_by_name['UpdateRegisteredModelResponse'] + _DELETEREGISTEREDMODELREQUEST = DESCRIPTOR.message_types_by_name['DeleteRegisteredModelRequest'] + _DELETEREGISTEREDMODELRESPONSE = DESCRIPTOR.message_types_by_name['DeleteRegisteredModelResponse'] + _GETREGISTEREDMODELREQUEST = DESCRIPTOR.message_types_by_name['GetRegisteredModelRequest'] + _GETREGISTEREDMODELRESPONSE = DESCRIPTOR.message_types_by_name['GetRegisteredModelResponse'] + _SEARCHREGISTEREDMODELSREQUEST = DESCRIPTOR.message_types_by_name['SearchRegisteredModelsRequest'] + _SEARCHREGISTEREDMODELSRESPONSE = DESCRIPTOR.message_types_by_name['SearchRegisteredModelsResponse'] + _DEPENDENCY = DESCRIPTOR.message_types_by_name['Dependency'] + _CREATEMODELVERSIONREQUEST = DESCRIPTOR.message_types_by_name['CreateModelVersionRequest'] + _CREATEMODELVERSIONRESPONSE = DESCRIPTOR.message_types_by_name['CreateModelVersionResponse'] + _UPDATEMODELVERSIONREQUEST = DESCRIPTOR.message_types_by_name['UpdateModelVersionRequest'] + _UPDATEMODELVERSIONRESPONSE = DESCRIPTOR.message_types_by_name['UpdateModelVersionResponse'] + _DELETEMODELVERSIONREQUEST = DESCRIPTOR.message_types_by_name['DeleteModelVersionRequest'] + _DELETEMODELVERSIONRESPONSE = DESCRIPTOR.message_types_by_name['DeleteModelVersionResponse'] + _GETMODELVERSIONREQUEST = DESCRIPTOR.message_types_by_name['GetModelVersionRequest'] + _GETMODELVERSIONRESPONSE = DESCRIPTOR.message_types_by_name['GetModelVersionResponse'] + _SEARCHMODELVERSIONSREQUEST = DESCRIPTOR.message_types_by_name['SearchModelVersionsRequest'] + _SEARCHMODELVERSIONSRESPONSE = DESCRIPTOR.message_types_by_name['SearchModelVersionsResponse'] + _GENERATETEMPORARYMODELVERSIONCREDENTIALSREQUEST = DESCRIPTOR.message_types_by_name['GenerateTemporaryModelVersionCredentialsRequest'] + _GENERATETEMPORARYMODELVERSIONCREDENTIALSRESPONSE = DESCRIPTOR.message_types_by_name['GenerateTemporaryModelVersionCredentialsResponse'] + _GETMODELVERSIONDOWNLOADURIREQUEST = DESCRIPTOR.message_types_by_name['GetModelVersionDownloadUriRequest'] + _GETMODELVERSIONDOWNLOADURIRESPONSE = DESCRIPTOR.message_types_by_name['GetModelVersionDownloadUriResponse'] + _FINALIZEMODELVERSIONREQUEST = DESCRIPTOR.message_types_by_name['FinalizeModelVersionRequest'] + _FINALIZEMODELVERSIONRESPONSE = DESCRIPTOR.message_types_by_name['FinalizeModelVersionResponse'] + _SETREGISTEREDMODELALIASREQUEST = DESCRIPTOR.message_types_by_name['SetRegisteredModelAliasRequest'] + _SETREGISTEREDMODELALIASRESPONSE = DESCRIPTOR.message_types_by_name['SetRegisteredModelAliasResponse'] + _DELETEREGISTEREDMODELALIASREQUEST = DESCRIPTOR.message_types_by_name['DeleteRegisteredModelAliasRequest'] + _DELETEREGISTEREDMODELALIASRESPONSE = DESCRIPTOR.message_types_by_name['DeleteRegisteredModelAliasResponse'] + _SETREGISTEREDMODELTAGREQUEST = DESCRIPTOR.message_types_by_name['SetRegisteredModelTagRequest'] + _SETREGISTEREDMODELTAGRESPONSE = DESCRIPTOR.message_types_by_name['SetRegisteredModelTagResponse'] + _DELETEREGISTEREDMODELTAGREQUEST = DESCRIPTOR.message_types_by_name['DeleteRegisteredModelTagRequest'] + _DELETEREGISTEREDMODELTAGRESPONSE = DESCRIPTOR.message_types_by_name['DeleteRegisteredModelTagResponse'] + _SETMODELVERSIONTAGREQUEST = DESCRIPTOR.message_types_by_name['SetModelVersionTagRequest'] + _SETMODELVERSIONTAGRESPONSE = DESCRIPTOR.message_types_by_name['SetModelVersionTagResponse'] + _DELETEMODELVERSIONTAGREQUEST = DESCRIPTOR.message_types_by_name['DeleteModelVersionTagRequest'] + _DELETEMODELVERSIONTAGRESPONSE = DESCRIPTOR.message_types_by_name['DeleteModelVersionTagResponse'] + _GETMODELVERSIONBYALIASREQUEST = DESCRIPTOR.message_types_by_name['GetModelVersionByAliasRequest'] + _GETMODELVERSIONBYALIASRESPONSE = DESCRIPTOR.message_types_by_name['GetModelVersionByAliasResponse'] + _ENTITY = DESCRIPTOR.message_types_by_name['Entity'] + _PIPELINE = DESCRIPTOR.message_types_by_name['Pipeline'] + _JOB = DESCRIPTOR.message_types_by_name['Job'] + _NOTEBOOK = DESCRIPTOR.message_types_by_name['Notebook'] + _TABLE = DESCRIPTOR.message_types_by_name['Table'] + _SECURABLE = DESCRIPTOR.message_types_by_name['Securable'] + _LINEAGE = DESCRIPTOR.message_types_by_name['Lineage'] + _LINEAGEHEADERINFO = DESCRIPTOR.message_types_by_name['LineageHeaderInfo'] + _MODELVERSIONLINEAGEINFO = DESCRIPTOR.message_types_by_name['ModelVersionLineageInfo'] + _EMITMODELVERSIONLINEAGEREQUEST = DESCRIPTOR.message_types_by_name['EmitModelVersionLineageRequest'] + _EMITMODELVERSIONLINEAGERESPONSE = DESCRIPTOR.message_types_by_name['EmitModelVersionLineageResponse'] + _ISDATABRICKSSDKMODELSARTIFACTREPOSITORYENABLEDREQUEST = DESCRIPTOR.message_types_by_name['IsDatabricksSdkModelsArtifactRepositoryEnabledRequest'] + _ISDATABRICKSSDKMODELSARTIFACTREPOSITORYENABLEDRESPONSE = DESCRIPTOR.message_types_by_name['IsDatabricksSdkModelsArtifactRepositoryEnabledResponse'] + _DEPLOYMENTJOBCONNECTION_STATE = _DEPLOYMENTJOBCONNECTION.enum_types_by_name['State'] + _MODELVERSIONDEPLOYMENTJOBSTATE_DEPLOYMENTJOBRUNSTATE = _MODELVERSIONDEPLOYMENTJOBSTATE.enum_types_by_name['DeploymentJobRunState'] + RegisteredModel = _reflection.GeneratedProtocolMessageType('RegisteredModel', (_message.Message,), { + 'DESCRIPTOR' : _REGISTEREDMODEL, + '__module__' : 'databricks_uc_registry_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.ucmodelregistry.RegisteredModel) + }) + _sym_db.RegisterMessage(RegisteredModel) + + RegisteredModelAlias = _reflection.GeneratedProtocolMessageType('RegisteredModelAlias', (_message.Message,), { + 'DESCRIPTOR' : _REGISTEREDMODELALIAS, + '__module__' : 'databricks_uc_registry_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.ucmodelregistry.RegisteredModelAlias) + }) + _sym_db.RegisterMessage(RegisteredModelAlias) + + RegisteredModelTag = _reflection.GeneratedProtocolMessageType('RegisteredModelTag', (_message.Message,), { + 'DESCRIPTOR' : _REGISTEREDMODELTAG, + '__module__' : 'databricks_uc_registry_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.ucmodelregistry.RegisteredModelTag) + }) + _sym_db.RegisterMessage(RegisteredModelTag) + + ModelParam = _reflection.GeneratedProtocolMessageType('ModelParam', (_message.Message,), { + 'DESCRIPTOR' : _MODELPARAM, + '__module__' : 'databricks_uc_registry_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.ucmodelregistry.ModelParam) + }) + _sym_db.RegisterMessage(ModelParam) + + ModelMetric = _reflection.GeneratedProtocolMessageType('ModelMetric', (_message.Message,), { + 'DESCRIPTOR' : _MODELMETRIC, + '__module__' : 'databricks_uc_registry_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.ucmodelregistry.ModelMetric) + }) + _sym_db.RegisterMessage(ModelMetric) + + ModelVersionTag = _reflection.GeneratedProtocolMessageType('ModelVersionTag', (_message.Message,), { + 'DESCRIPTOR' : _MODELVERSIONTAG, + '__module__' : 'databricks_uc_registry_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.ucmodelregistry.ModelVersionTag) + }) + _sym_db.RegisterMessage(ModelVersionTag) + + ModelVersion = _reflection.GeneratedProtocolMessageType('ModelVersion', (_message.Message,), { + 'DESCRIPTOR' : _MODELVERSION, + '__module__' : 'databricks_uc_registry_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.ucmodelregistry.ModelVersion) + }) + _sym_db.RegisterMessage(ModelVersion) + + DeploymentJobConnection = _reflection.GeneratedProtocolMessageType('DeploymentJobConnection', (_message.Message,), { + 'DESCRIPTOR' : _DEPLOYMENTJOBCONNECTION, + '__module__' : 'databricks_uc_registry_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.ucmodelregistry.DeploymentJobConnection) + }) + _sym_db.RegisterMessage(DeploymentJobConnection) + + ModelVersionDeploymentJobState = _reflection.GeneratedProtocolMessageType('ModelVersionDeploymentJobState', (_message.Message,), { + 'DESCRIPTOR' : _MODELVERSIONDEPLOYMENTJOBSTATE, + '__module__' : 'databricks_uc_registry_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.ucmodelregistry.ModelVersionDeploymentJobState) + }) + _sym_db.RegisterMessage(ModelVersionDeploymentJobState) + + TemporaryCredentials = _reflection.GeneratedProtocolMessageType('TemporaryCredentials', (_message.Message,), { + 'DESCRIPTOR' : _TEMPORARYCREDENTIALS, + '__module__' : 'databricks_uc_registry_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.ucmodelregistry.TemporaryCredentials) + }) + _sym_db.RegisterMessage(TemporaryCredentials) + + AwsCredentials = _reflection.GeneratedProtocolMessageType('AwsCredentials', (_message.Message,), { + 'DESCRIPTOR' : _AWSCREDENTIALS, + '__module__' : 'databricks_uc_registry_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.ucmodelregistry.AwsCredentials) + }) + _sym_db.RegisterMessage(AwsCredentials) + + AzureUserDelegationSAS = _reflection.GeneratedProtocolMessageType('AzureUserDelegationSAS', (_message.Message,), { + 'DESCRIPTOR' : _AZUREUSERDELEGATIONSAS, + '__module__' : 'databricks_uc_registry_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.ucmodelregistry.AzureUserDelegationSAS) + }) + _sym_db.RegisterMessage(AzureUserDelegationSAS) + + GcpOauthToken = _reflection.GeneratedProtocolMessageType('GcpOauthToken', (_message.Message,), { + 'DESCRIPTOR' : _GCPOAUTHTOKEN, + '__module__' : 'databricks_uc_registry_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.ucmodelregistry.GcpOauthToken) + }) + _sym_db.RegisterMessage(GcpOauthToken) + + R2Credentials = _reflection.GeneratedProtocolMessageType('R2Credentials', (_message.Message,), { + 'DESCRIPTOR' : _R2CREDENTIALS, + '__module__' : 'databricks_uc_registry_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.ucmodelregistry.R2Credentials) + }) + _sym_db.RegisterMessage(R2Credentials) + + EncryptionDetails = _reflection.GeneratedProtocolMessageType('EncryptionDetails', (_message.Message,), { + 'DESCRIPTOR' : _ENCRYPTIONDETAILS, + '__module__' : 'databricks_uc_registry_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.ucmodelregistry.EncryptionDetails) + }) + _sym_db.RegisterMessage(EncryptionDetails) + + SseEncryptionDetails = _reflection.GeneratedProtocolMessageType('SseEncryptionDetails', (_message.Message,), { + 'DESCRIPTOR' : _SSEENCRYPTIONDETAILS, + '__module__' : 'databricks_uc_registry_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.ucmodelregistry.SseEncryptionDetails) + }) + _sym_db.RegisterMessage(SseEncryptionDetails) + + CreateRegisteredModelRequest = _reflection.GeneratedProtocolMessageType('CreateRegisteredModelRequest', (_message.Message,), { + 'DESCRIPTOR' : _CREATEREGISTEREDMODELREQUEST, + '__module__' : 'databricks_uc_registry_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.ucmodelregistry.CreateRegisteredModelRequest) + }) + _sym_db.RegisterMessage(CreateRegisteredModelRequest) + + CreateRegisteredModelResponse = _reflection.GeneratedProtocolMessageType('CreateRegisteredModelResponse', (_message.Message,), { + 'DESCRIPTOR' : _CREATEREGISTEREDMODELRESPONSE, + '__module__' : 'databricks_uc_registry_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.ucmodelregistry.CreateRegisteredModelResponse) + }) + _sym_db.RegisterMessage(CreateRegisteredModelResponse) + + UpdateRegisteredModelRequest = _reflection.GeneratedProtocolMessageType('UpdateRegisteredModelRequest', (_message.Message,), { + 'DESCRIPTOR' : _UPDATEREGISTEREDMODELREQUEST, + '__module__' : 'databricks_uc_registry_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.ucmodelregistry.UpdateRegisteredModelRequest) + }) + _sym_db.RegisterMessage(UpdateRegisteredModelRequest) + + UpdateRegisteredModelResponse = _reflection.GeneratedProtocolMessageType('UpdateRegisteredModelResponse', (_message.Message,), { + 'DESCRIPTOR' : _UPDATEREGISTEREDMODELRESPONSE, + '__module__' : 'databricks_uc_registry_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.ucmodelregistry.UpdateRegisteredModelResponse) + }) + _sym_db.RegisterMessage(UpdateRegisteredModelResponse) + + DeleteRegisteredModelRequest = _reflection.GeneratedProtocolMessageType('DeleteRegisteredModelRequest', (_message.Message,), { + 'DESCRIPTOR' : _DELETEREGISTEREDMODELREQUEST, + '__module__' : 'databricks_uc_registry_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.ucmodelregistry.DeleteRegisteredModelRequest) + }) + _sym_db.RegisterMessage(DeleteRegisteredModelRequest) + + DeleteRegisteredModelResponse = _reflection.GeneratedProtocolMessageType('DeleteRegisteredModelResponse', (_message.Message,), { + 'DESCRIPTOR' : _DELETEREGISTEREDMODELRESPONSE, + '__module__' : 'databricks_uc_registry_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.ucmodelregistry.DeleteRegisteredModelResponse) + }) + _sym_db.RegisterMessage(DeleteRegisteredModelResponse) + + GetRegisteredModelRequest = _reflection.GeneratedProtocolMessageType('GetRegisteredModelRequest', (_message.Message,), { + 'DESCRIPTOR' : _GETREGISTEREDMODELREQUEST, + '__module__' : 'databricks_uc_registry_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.ucmodelregistry.GetRegisteredModelRequest) + }) + _sym_db.RegisterMessage(GetRegisteredModelRequest) + + GetRegisteredModelResponse = _reflection.GeneratedProtocolMessageType('GetRegisteredModelResponse', (_message.Message,), { + 'DESCRIPTOR' : _GETREGISTEREDMODELRESPONSE, + '__module__' : 'databricks_uc_registry_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.ucmodelregistry.GetRegisteredModelResponse) + }) + _sym_db.RegisterMessage(GetRegisteredModelResponse) + + SearchRegisteredModelsRequest = _reflection.GeneratedProtocolMessageType('SearchRegisteredModelsRequest', (_message.Message,), { + 'DESCRIPTOR' : _SEARCHREGISTEREDMODELSREQUEST, + '__module__' : 'databricks_uc_registry_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.ucmodelregistry.SearchRegisteredModelsRequest) + }) + _sym_db.RegisterMessage(SearchRegisteredModelsRequest) + + SearchRegisteredModelsResponse = _reflection.GeneratedProtocolMessageType('SearchRegisteredModelsResponse', (_message.Message,), { + 'DESCRIPTOR' : _SEARCHREGISTEREDMODELSRESPONSE, + '__module__' : 'databricks_uc_registry_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.ucmodelregistry.SearchRegisteredModelsResponse) + }) + _sym_db.RegisterMessage(SearchRegisteredModelsResponse) + + Dependency = _reflection.GeneratedProtocolMessageType('Dependency', (_message.Message,), { + 'DESCRIPTOR' : _DEPENDENCY, + '__module__' : 'databricks_uc_registry_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.ucmodelregistry.Dependency) + }) + _sym_db.RegisterMessage(Dependency) + + CreateModelVersionRequest = _reflection.GeneratedProtocolMessageType('CreateModelVersionRequest', (_message.Message,), { + 'DESCRIPTOR' : _CREATEMODELVERSIONREQUEST, + '__module__' : 'databricks_uc_registry_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.ucmodelregistry.CreateModelVersionRequest) + }) + _sym_db.RegisterMessage(CreateModelVersionRequest) + + CreateModelVersionResponse = _reflection.GeneratedProtocolMessageType('CreateModelVersionResponse', (_message.Message,), { + 'DESCRIPTOR' : _CREATEMODELVERSIONRESPONSE, + '__module__' : 'databricks_uc_registry_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.ucmodelregistry.CreateModelVersionResponse) + }) + _sym_db.RegisterMessage(CreateModelVersionResponse) + + UpdateModelVersionRequest = _reflection.GeneratedProtocolMessageType('UpdateModelVersionRequest', (_message.Message,), { + 'DESCRIPTOR' : _UPDATEMODELVERSIONREQUEST, + '__module__' : 'databricks_uc_registry_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.ucmodelregistry.UpdateModelVersionRequest) + }) + _sym_db.RegisterMessage(UpdateModelVersionRequest) + + UpdateModelVersionResponse = _reflection.GeneratedProtocolMessageType('UpdateModelVersionResponse', (_message.Message,), { + 'DESCRIPTOR' : _UPDATEMODELVERSIONRESPONSE, + '__module__' : 'databricks_uc_registry_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.ucmodelregistry.UpdateModelVersionResponse) + }) + _sym_db.RegisterMessage(UpdateModelVersionResponse) + + DeleteModelVersionRequest = _reflection.GeneratedProtocolMessageType('DeleteModelVersionRequest', (_message.Message,), { + 'DESCRIPTOR' : _DELETEMODELVERSIONREQUEST, + '__module__' : 'databricks_uc_registry_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.ucmodelregistry.DeleteModelVersionRequest) + }) + _sym_db.RegisterMessage(DeleteModelVersionRequest) + + DeleteModelVersionResponse = _reflection.GeneratedProtocolMessageType('DeleteModelVersionResponse', (_message.Message,), { + 'DESCRIPTOR' : _DELETEMODELVERSIONRESPONSE, + '__module__' : 'databricks_uc_registry_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.ucmodelregistry.DeleteModelVersionResponse) + }) + _sym_db.RegisterMessage(DeleteModelVersionResponse) + + GetModelVersionRequest = _reflection.GeneratedProtocolMessageType('GetModelVersionRequest', (_message.Message,), { + 'DESCRIPTOR' : _GETMODELVERSIONREQUEST, + '__module__' : 'databricks_uc_registry_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.ucmodelregistry.GetModelVersionRequest) + }) + _sym_db.RegisterMessage(GetModelVersionRequest) + + GetModelVersionResponse = _reflection.GeneratedProtocolMessageType('GetModelVersionResponse', (_message.Message,), { + 'DESCRIPTOR' : _GETMODELVERSIONRESPONSE, + '__module__' : 'databricks_uc_registry_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.ucmodelregistry.GetModelVersionResponse) + }) + _sym_db.RegisterMessage(GetModelVersionResponse) + + SearchModelVersionsRequest = _reflection.GeneratedProtocolMessageType('SearchModelVersionsRequest', (_message.Message,), { + 'DESCRIPTOR' : _SEARCHMODELVERSIONSREQUEST, + '__module__' : 'databricks_uc_registry_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.ucmodelregistry.SearchModelVersionsRequest) + }) + _sym_db.RegisterMessage(SearchModelVersionsRequest) + + SearchModelVersionsResponse = _reflection.GeneratedProtocolMessageType('SearchModelVersionsResponse', (_message.Message,), { + 'DESCRIPTOR' : _SEARCHMODELVERSIONSRESPONSE, + '__module__' : 'databricks_uc_registry_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.ucmodelregistry.SearchModelVersionsResponse) + }) + _sym_db.RegisterMessage(SearchModelVersionsResponse) + + GenerateTemporaryModelVersionCredentialsRequest = _reflection.GeneratedProtocolMessageType('GenerateTemporaryModelVersionCredentialsRequest', (_message.Message,), { + 'DESCRIPTOR' : _GENERATETEMPORARYMODELVERSIONCREDENTIALSREQUEST, + '__module__' : 'databricks_uc_registry_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.ucmodelregistry.GenerateTemporaryModelVersionCredentialsRequest) + }) + _sym_db.RegisterMessage(GenerateTemporaryModelVersionCredentialsRequest) + + GenerateTemporaryModelVersionCredentialsResponse = _reflection.GeneratedProtocolMessageType('GenerateTemporaryModelVersionCredentialsResponse', (_message.Message,), { + 'DESCRIPTOR' : _GENERATETEMPORARYMODELVERSIONCREDENTIALSRESPONSE, + '__module__' : 'databricks_uc_registry_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.ucmodelregistry.GenerateTemporaryModelVersionCredentialsResponse) + }) + _sym_db.RegisterMessage(GenerateTemporaryModelVersionCredentialsResponse) + + GetModelVersionDownloadUriRequest = _reflection.GeneratedProtocolMessageType('GetModelVersionDownloadUriRequest', (_message.Message,), { + 'DESCRIPTOR' : _GETMODELVERSIONDOWNLOADURIREQUEST, + '__module__' : 'databricks_uc_registry_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.ucmodelregistry.GetModelVersionDownloadUriRequest) + }) + _sym_db.RegisterMessage(GetModelVersionDownloadUriRequest) + + GetModelVersionDownloadUriResponse = _reflection.GeneratedProtocolMessageType('GetModelVersionDownloadUriResponse', (_message.Message,), { + 'DESCRIPTOR' : _GETMODELVERSIONDOWNLOADURIRESPONSE, + '__module__' : 'databricks_uc_registry_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.ucmodelregistry.GetModelVersionDownloadUriResponse) + }) + _sym_db.RegisterMessage(GetModelVersionDownloadUriResponse) + + FinalizeModelVersionRequest = _reflection.GeneratedProtocolMessageType('FinalizeModelVersionRequest', (_message.Message,), { + 'DESCRIPTOR' : _FINALIZEMODELVERSIONREQUEST, + '__module__' : 'databricks_uc_registry_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.ucmodelregistry.FinalizeModelVersionRequest) + }) + _sym_db.RegisterMessage(FinalizeModelVersionRequest) + + FinalizeModelVersionResponse = _reflection.GeneratedProtocolMessageType('FinalizeModelVersionResponse', (_message.Message,), { + 'DESCRIPTOR' : _FINALIZEMODELVERSIONRESPONSE, + '__module__' : 'databricks_uc_registry_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.ucmodelregistry.FinalizeModelVersionResponse) + }) + _sym_db.RegisterMessage(FinalizeModelVersionResponse) + + SetRegisteredModelAliasRequest = _reflection.GeneratedProtocolMessageType('SetRegisteredModelAliasRequest', (_message.Message,), { + 'DESCRIPTOR' : _SETREGISTEREDMODELALIASREQUEST, + '__module__' : 'databricks_uc_registry_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.ucmodelregistry.SetRegisteredModelAliasRequest) + }) + _sym_db.RegisterMessage(SetRegisteredModelAliasRequest) + + SetRegisteredModelAliasResponse = _reflection.GeneratedProtocolMessageType('SetRegisteredModelAliasResponse', (_message.Message,), { + 'DESCRIPTOR' : _SETREGISTEREDMODELALIASRESPONSE, + '__module__' : 'databricks_uc_registry_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.ucmodelregistry.SetRegisteredModelAliasResponse) + }) + _sym_db.RegisterMessage(SetRegisteredModelAliasResponse) + + DeleteRegisteredModelAliasRequest = _reflection.GeneratedProtocolMessageType('DeleteRegisteredModelAliasRequest', (_message.Message,), { + 'DESCRIPTOR' : _DELETEREGISTEREDMODELALIASREQUEST, + '__module__' : 'databricks_uc_registry_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.ucmodelregistry.DeleteRegisteredModelAliasRequest) + }) + _sym_db.RegisterMessage(DeleteRegisteredModelAliasRequest) + + DeleteRegisteredModelAliasResponse = _reflection.GeneratedProtocolMessageType('DeleteRegisteredModelAliasResponse', (_message.Message,), { + 'DESCRIPTOR' : _DELETEREGISTEREDMODELALIASRESPONSE, + '__module__' : 'databricks_uc_registry_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.ucmodelregistry.DeleteRegisteredModelAliasResponse) + }) + _sym_db.RegisterMessage(DeleteRegisteredModelAliasResponse) + + SetRegisteredModelTagRequest = _reflection.GeneratedProtocolMessageType('SetRegisteredModelTagRequest', (_message.Message,), { + 'DESCRIPTOR' : _SETREGISTEREDMODELTAGREQUEST, + '__module__' : 'databricks_uc_registry_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.ucmodelregistry.SetRegisteredModelTagRequest) + }) + _sym_db.RegisterMessage(SetRegisteredModelTagRequest) + + SetRegisteredModelTagResponse = _reflection.GeneratedProtocolMessageType('SetRegisteredModelTagResponse', (_message.Message,), { + 'DESCRIPTOR' : _SETREGISTEREDMODELTAGRESPONSE, + '__module__' : 'databricks_uc_registry_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.ucmodelregistry.SetRegisteredModelTagResponse) + }) + _sym_db.RegisterMessage(SetRegisteredModelTagResponse) + + DeleteRegisteredModelTagRequest = _reflection.GeneratedProtocolMessageType('DeleteRegisteredModelTagRequest', (_message.Message,), { + 'DESCRIPTOR' : _DELETEREGISTEREDMODELTAGREQUEST, + '__module__' : 'databricks_uc_registry_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.ucmodelregistry.DeleteRegisteredModelTagRequest) + }) + _sym_db.RegisterMessage(DeleteRegisteredModelTagRequest) + + DeleteRegisteredModelTagResponse = _reflection.GeneratedProtocolMessageType('DeleteRegisteredModelTagResponse', (_message.Message,), { + 'DESCRIPTOR' : _DELETEREGISTEREDMODELTAGRESPONSE, + '__module__' : 'databricks_uc_registry_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.ucmodelregistry.DeleteRegisteredModelTagResponse) + }) + _sym_db.RegisterMessage(DeleteRegisteredModelTagResponse) + + SetModelVersionTagRequest = _reflection.GeneratedProtocolMessageType('SetModelVersionTagRequest', (_message.Message,), { + 'DESCRIPTOR' : _SETMODELVERSIONTAGREQUEST, + '__module__' : 'databricks_uc_registry_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.ucmodelregistry.SetModelVersionTagRequest) + }) + _sym_db.RegisterMessage(SetModelVersionTagRequest) + + SetModelVersionTagResponse = _reflection.GeneratedProtocolMessageType('SetModelVersionTagResponse', (_message.Message,), { + 'DESCRIPTOR' : _SETMODELVERSIONTAGRESPONSE, + '__module__' : 'databricks_uc_registry_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.ucmodelregistry.SetModelVersionTagResponse) + }) + _sym_db.RegisterMessage(SetModelVersionTagResponse) + + DeleteModelVersionTagRequest = _reflection.GeneratedProtocolMessageType('DeleteModelVersionTagRequest', (_message.Message,), { + 'DESCRIPTOR' : _DELETEMODELVERSIONTAGREQUEST, + '__module__' : 'databricks_uc_registry_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.ucmodelregistry.DeleteModelVersionTagRequest) + }) + _sym_db.RegisterMessage(DeleteModelVersionTagRequest) + + DeleteModelVersionTagResponse = _reflection.GeneratedProtocolMessageType('DeleteModelVersionTagResponse', (_message.Message,), { + 'DESCRIPTOR' : _DELETEMODELVERSIONTAGRESPONSE, + '__module__' : 'databricks_uc_registry_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.ucmodelregistry.DeleteModelVersionTagResponse) + }) + _sym_db.RegisterMessage(DeleteModelVersionTagResponse) + + GetModelVersionByAliasRequest = _reflection.GeneratedProtocolMessageType('GetModelVersionByAliasRequest', (_message.Message,), { + 'DESCRIPTOR' : _GETMODELVERSIONBYALIASREQUEST, + '__module__' : 'databricks_uc_registry_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.ucmodelregistry.GetModelVersionByAliasRequest) + }) + _sym_db.RegisterMessage(GetModelVersionByAliasRequest) + + GetModelVersionByAliasResponse = _reflection.GeneratedProtocolMessageType('GetModelVersionByAliasResponse', (_message.Message,), { + 'DESCRIPTOR' : _GETMODELVERSIONBYALIASRESPONSE, + '__module__' : 'databricks_uc_registry_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.ucmodelregistry.GetModelVersionByAliasResponse) + }) + _sym_db.RegisterMessage(GetModelVersionByAliasResponse) + + Entity = _reflection.GeneratedProtocolMessageType('Entity', (_message.Message,), { + 'DESCRIPTOR' : _ENTITY, + '__module__' : 'databricks_uc_registry_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.ucmodelregistry.Entity) + }) + _sym_db.RegisterMessage(Entity) + + Pipeline = _reflection.GeneratedProtocolMessageType('Pipeline', (_message.Message,), { + 'DESCRIPTOR' : _PIPELINE, + '__module__' : 'databricks_uc_registry_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.ucmodelregistry.Pipeline) + }) + _sym_db.RegisterMessage(Pipeline) + + Job = _reflection.GeneratedProtocolMessageType('Job', (_message.Message,), { + 'DESCRIPTOR' : _JOB, + '__module__' : 'databricks_uc_registry_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.ucmodelregistry.Job) + }) + _sym_db.RegisterMessage(Job) + + Notebook = _reflection.GeneratedProtocolMessageType('Notebook', (_message.Message,), { + 'DESCRIPTOR' : _NOTEBOOK, + '__module__' : 'databricks_uc_registry_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.ucmodelregistry.Notebook) + }) + _sym_db.RegisterMessage(Notebook) + + Table = _reflection.GeneratedProtocolMessageType('Table', (_message.Message,), { + 'DESCRIPTOR' : _TABLE, + '__module__' : 'databricks_uc_registry_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.ucmodelregistry.Table) + }) + _sym_db.RegisterMessage(Table) + + Securable = _reflection.GeneratedProtocolMessageType('Securable', (_message.Message,), { + 'DESCRIPTOR' : _SECURABLE, + '__module__' : 'databricks_uc_registry_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.ucmodelregistry.Securable) + }) + _sym_db.RegisterMessage(Securable) + + Lineage = _reflection.GeneratedProtocolMessageType('Lineage', (_message.Message,), { + 'DESCRIPTOR' : _LINEAGE, + '__module__' : 'databricks_uc_registry_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.ucmodelregistry.Lineage) + }) + _sym_db.RegisterMessage(Lineage) + + LineageHeaderInfo = _reflection.GeneratedProtocolMessageType('LineageHeaderInfo', (_message.Message,), { + 'DESCRIPTOR' : _LINEAGEHEADERINFO, + '__module__' : 'databricks_uc_registry_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.ucmodelregistry.LineageHeaderInfo) + }) + _sym_db.RegisterMessage(LineageHeaderInfo) + + ModelVersionLineageInfo = _reflection.GeneratedProtocolMessageType('ModelVersionLineageInfo', (_message.Message,), { + 'DESCRIPTOR' : _MODELVERSIONLINEAGEINFO, + '__module__' : 'databricks_uc_registry_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.ucmodelregistry.ModelVersionLineageInfo) + }) + _sym_db.RegisterMessage(ModelVersionLineageInfo) + + EmitModelVersionLineageRequest = _reflection.GeneratedProtocolMessageType('EmitModelVersionLineageRequest', (_message.Message,), { + 'DESCRIPTOR' : _EMITMODELVERSIONLINEAGEREQUEST, + '__module__' : 'databricks_uc_registry_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.ucmodelregistry.EmitModelVersionLineageRequest) + }) + _sym_db.RegisterMessage(EmitModelVersionLineageRequest) + + EmitModelVersionLineageResponse = _reflection.GeneratedProtocolMessageType('EmitModelVersionLineageResponse', (_message.Message,), { + 'DESCRIPTOR' : _EMITMODELVERSIONLINEAGERESPONSE, + '__module__' : 'databricks_uc_registry_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.ucmodelregistry.EmitModelVersionLineageResponse) + }) + _sym_db.RegisterMessage(EmitModelVersionLineageResponse) + + IsDatabricksSdkModelsArtifactRepositoryEnabledRequest = _reflection.GeneratedProtocolMessageType('IsDatabricksSdkModelsArtifactRepositoryEnabledRequest', (_message.Message,), { + 'DESCRIPTOR' : _ISDATABRICKSSDKMODELSARTIFACTREPOSITORYENABLEDREQUEST, + '__module__' : 'databricks_uc_registry_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.ucmodelregistry.IsDatabricksSdkModelsArtifactRepositoryEnabledRequest) + }) + _sym_db.RegisterMessage(IsDatabricksSdkModelsArtifactRepositoryEnabledRequest) + + IsDatabricksSdkModelsArtifactRepositoryEnabledResponse = _reflection.GeneratedProtocolMessageType('IsDatabricksSdkModelsArtifactRepositoryEnabledResponse', (_message.Message,), { + 'DESCRIPTOR' : _ISDATABRICKSSDKMODELSARTIFACTREPOSITORYENABLEDRESPONSE, + '__module__' : 'databricks_uc_registry_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.ucmodelregistry.IsDatabricksSdkModelsArtifactRepositoryEnabledResponse) + }) + _sym_db.RegisterMessage(IsDatabricksSdkModelsArtifactRepositoryEnabledResponse) + + if _descriptor._USE_C_DESCRIPTORS == False: + + DESCRIPTOR._options = None + DESCRIPTOR._serialized_options = b'\n(com.databricks.api.proto.ucmodelregistry\240\001\001\342?\002\020\001' + _MODELMETRIC.fields_by_name['dataset_name']._options = None + _MODELMETRIC.fields_by_name['dataset_name']._serialized_options = b'\360\206\031\003' + _MODELMETRIC.fields_by_name['dataset_digest']._options = None + _MODELMETRIC.fields_by_name['dataset_digest']._serialized_options = b'\360\206\031\003' + _MODELMETRIC.fields_by_name['model_id']._options = None + _MODELMETRIC.fields_by_name['model_id']._serialized_options = b'\360\206\031\003' + _MODELMETRIC.fields_by_name['run_id']._options = None + _MODELMETRIC.fields_by_name['run_id']._serialized_options = b'\360\206\031\003' + _CREATEREGISTEREDMODELREQUEST.fields_by_name['name']._options = None + _CREATEREGISTEREDMODELREQUEST.fields_by_name['name']._serialized_options = b'\370\206\031\001' + _CREATEREGISTEREDMODELREQUEST._options = None + _CREATEREGISTEREDMODELREQUEST._serialized_options = b'\342?7\n5com.databricks.rpc.RPC[CreateRegisteredModelResponse]' + _UPDATEREGISTEREDMODELREQUEST.fields_by_name['name']._options = None + _UPDATEREGISTEREDMODELREQUEST.fields_by_name['name']._serialized_options = b'\370\206\031\001' + _UPDATEREGISTEREDMODELREQUEST._options = None + _UPDATEREGISTEREDMODELREQUEST._serialized_options = b'\342?7\n5com.databricks.rpc.RPC[UpdateRegisteredModelResponse]' + _DELETEREGISTEREDMODELREQUEST.fields_by_name['name']._options = None + _DELETEREGISTEREDMODELREQUEST.fields_by_name['name']._serialized_options = b'\370\206\031\001' + _DELETEREGISTEREDMODELREQUEST._options = None + _DELETEREGISTEREDMODELREQUEST._serialized_options = b'\342?7\n5com.databricks.rpc.RPC[DeleteRegisteredModelResponse]' + _GETREGISTEREDMODELREQUEST.fields_by_name['name']._options = None + _GETREGISTEREDMODELREQUEST.fields_by_name['name']._serialized_options = b'\370\206\031\001' + _GETREGISTEREDMODELREQUEST._options = None + _GETREGISTEREDMODELREQUEST._serialized_options = b'\342?4\n2com.databricks.rpc.RPC[GetRegisteredModelResponse]' + _SEARCHREGISTEREDMODELSREQUEST._options = None + _SEARCHREGISTEREDMODELSREQUEST._serialized_options = b'\342?8\n6com.databricks.rpc.RPC[SearchRegisteredModelsResponse]' + _CREATEMODELVERSIONREQUEST.fields_by_name['name']._options = None + _CREATEMODELVERSIONREQUEST.fields_by_name['name']._serialized_options = b'\370\206\031\001' + _CREATEMODELVERSIONREQUEST.fields_by_name['source']._options = None + _CREATEMODELVERSIONREQUEST.fields_by_name['source']._serialized_options = b'\370\206\031\001' + _CREATEMODELVERSIONREQUEST._options = None + _CREATEMODELVERSIONREQUEST._serialized_options = b'\342?4\n2com.databricks.rpc.RPC[CreateModelVersionResponse]' + _UPDATEMODELVERSIONREQUEST.fields_by_name['name']._options = None + _UPDATEMODELVERSIONREQUEST.fields_by_name['name']._serialized_options = b'\370\206\031\001' + _UPDATEMODELVERSIONREQUEST.fields_by_name['version']._options = None + _UPDATEMODELVERSIONREQUEST.fields_by_name['version']._serialized_options = b'\370\206\031\001' + _UPDATEMODELVERSIONREQUEST._options = None + _UPDATEMODELVERSIONREQUEST._serialized_options = b'\342?4\n2com.databricks.rpc.RPC[UpdateModelVersionResponse]' + _DELETEMODELVERSIONREQUEST.fields_by_name['name']._options = None + _DELETEMODELVERSIONREQUEST.fields_by_name['name']._serialized_options = b'\370\206\031\001' + _DELETEMODELVERSIONREQUEST.fields_by_name['version']._options = None + _DELETEMODELVERSIONREQUEST.fields_by_name['version']._serialized_options = b'\370\206\031\001' + _DELETEMODELVERSIONREQUEST._options = None + _DELETEMODELVERSIONREQUEST._serialized_options = b'\342?4\n2com.databricks.rpc.RPC[DeleteModelVersionResponse]' + _GETMODELVERSIONREQUEST.fields_by_name['name']._options = None + _GETMODELVERSIONREQUEST.fields_by_name['name']._serialized_options = b'\370\206\031\001' + _GETMODELVERSIONREQUEST.fields_by_name['version']._options = None + _GETMODELVERSIONREQUEST.fields_by_name['version']._serialized_options = b'\370\206\031\001' + _GETMODELVERSIONREQUEST._options = None + _GETMODELVERSIONREQUEST._serialized_options = b'\342?1\n/com.databricks.rpc.RPC[GetModelVersionResponse]' + _SEARCHMODELVERSIONSREQUEST._options = None + _SEARCHMODELVERSIONSREQUEST._serialized_options = b'\342?5\n3com.databricks.rpc.RPC[SearchModelVersionsResponse]' + _GENERATETEMPORARYMODELVERSIONCREDENTIALSREQUEST.fields_by_name['name']._options = None + _GENERATETEMPORARYMODELVERSIONCREDENTIALSREQUEST.fields_by_name['name']._serialized_options = b'\370\206\031\001' + _GENERATETEMPORARYMODELVERSIONCREDENTIALSREQUEST.fields_by_name['version']._options = None + 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_GETMODELVERSIONDOWNLOADURIREQUEST._serialized_options = b'\342?<\n:com.databricks.rpc.RPC[GetModelVersionDownloadUriResponse]' + _FINALIZEMODELVERSIONREQUEST.fields_by_name['name']._options = None + _FINALIZEMODELVERSIONREQUEST.fields_by_name['name']._serialized_options = b'\370\206\031\001' + _FINALIZEMODELVERSIONREQUEST.fields_by_name['version']._options = None + _FINALIZEMODELVERSIONREQUEST.fields_by_name['version']._serialized_options = b'\370\206\031\001' + _FINALIZEMODELVERSIONREQUEST._options = None + _FINALIZEMODELVERSIONREQUEST._serialized_options = b'\342?6\n4com.databricks.rpc.RPC[FinalizeModelVersionResponse]' + _SETREGISTEREDMODELALIASREQUEST.fields_by_name['name']._options = None + _SETREGISTEREDMODELALIASREQUEST.fields_by_name['name']._serialized_options = b'\370\206\031\001' + _SETREGISTEREDMODELALIASREQUEST.fields_by_name['alias']._options = None + _SETREGISTEREDMODELALIASREQUEST.fields_by_name['alias']._serialized_options = b'\370\206\031\001' + 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_SETREGISTEREDMODELTAGREQUEST.fields_by_name['name']._serialized_options = b'\370\206\031\001' + _SETREGISTEREDMODELTAGREQUEST.fields_by_name['key']._options = None + _SETREGISTEREDMODELTAGREQUEST.fields_by_name['key']._serialized_options = b'\370\206\031\001' + _SETREGISTEREDMODELTAGREQUEST._options = None + _SETREGISTEREDMODELTAGREQUEST._serialized_options = b'\342?7\n5com.databricks.rpc.RPC[SetRegisteredModelTagResponse]' + _DELETEREGISTEREDMODELTAGREQUEST.fields_by_name['name']._options = None + _DELETEREGISTEREDMODELTAGREQUEST.fields_by_name['name']._serialized_options = b'\370\206\031\001' + _DELETEREGISTEREDMODELTAGREQUEST.fields_by_name['key']._options = None + _DELETEREGISTEREDMODELTAGREQUEST.fields_by_name['key']._serialized_options = b'\370\206\031\001' + _DELETEREGISTEREDMODELTAGREQUEST._options = None + _DELETEREGISTEREDMODELTAGREQUEST._serialized_options = b'\342?:\n8com.databricks.rpc.RPC[DeleteRegisteredModelTagResponse]' + 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_CREATEMODELVERSIONREQUEST._serialized_end=4794 + _CREATEMODELVERSIONRESPONSE._serialized_start=4796 + _CREATEMODELVERSIONRESPONSE._serialized_end=4885 + _UPDATEMODELVERSIONREQUEST._serialized_start=4888 + _UPDATEMODELVERSIONREQUEST._serialized_end=5036 + _UPDATEMODELVERSIONRESPONSE._serialized_start=5038 + _UPDATEMODELVERSIONRESPONSE._serialized_end=5127 + _DELETEMODELVERSIONREQUEST._serialized_start=5129 + _DELETEMODELVERSIONREQUEST._serialized_end=5256 + _DELETEMODELVERSIONRESPONSE._serialized_start=5258 + _DELETEMODELVERSIONRESPONSE._serialized_end=5286 + _GETMODELVERSIONREQUEST._serialized_start=5288 + _GETMODELVERSIONREQUEST._serialized_end=5409 + _GETMODELVERSIONRESPONSE._serialized_start=5411 + _GETMODELVERSIONRESPONSE._serialized_end=5497 + _SEARCHMODELVERSIONSREQUEST._serialized_start=5500 + _SEARCHMODELVERSIONSREQUEST._serialized_end=5650 + _SEARCHMODELVERSIONSRESPONSE._serialized_start=5652 + _SEARCHMODELVERSIONSRESPONSE._serialized_end=5768 + _GENERATETEMPORARYMODELVERSIONCREDENTIALSREQUEST._serialized_start=5771 + _GENERATETEMPORARYMODELVERSIONCREDENTIALSREQUEST._serialized_end=6014 + _GENERATETEMPORARYMODELVERSIONCREDENTIALSRESPONSE._serialized_start=6016 + _GENERATETEMPORARYMODELVERSIONCREDENTIALSRESPONSE._serialized_end=6133 + _GETMODELVERSIONDOWNLOADURIREQUEST._serialized_start=6136 + _GETMODELVERSIONDOWNLOADURIREQUEST._serialized_end=6279 + _GETMODELVERSIONDOWNLOADURIRESPONSE._serialized_start=6281 + _GETMODELVERSIONDOWNLOADURIRESPONSE._serialized_end=6339 + _FINALIZEMODELVERSIONREQUEST._serialized_start=6342 + _FINALIZEMODELVERSIONREQUEST._serialized_end=6473 + _FINALIZEMODELVERSIONRESPONSE._serialized_start=6475 + _FINALIZEMODELVERSIONRESPONSE._serialized_end=6566 + _SETREGISTEREDMODELALIASREQUEST._serialized_start=6569 + _SETREGISTEREDMODELALIASREQUEST._serialized_end=6727 + _SETREGISTEREDMODELALIASRESPONSE._serialized_start=6729 + _SETREGISTEREDMODELALIASRESPONSE._serialized_end=6762 + _DELETEREGISTEREDMODELALIASREQUEST._serialized_start=6765 + _DELETEREGISTEREDMODELALIASREQUEST._serialized_end=6906 + _DELETEREGISTEREDMODELALIASRESPONSE._serialized_start=6908 + _DELETEREGISTEREDMODELALIASRESPONSE._serialized_end=6944 + _SETREGISTEREDMODELTAGREQUEST._serialized_start=6947 + _SETREGISTEREDMODELTAGREQUEST._serialized_end=7091 + _SETREGISTEREDMODELTAGRESPONSE._serialized_start=7093 + _SETREGISTEREDMODELTAGRESPONSE._serialized_end=7124 + _DELETEREGISTEREDMODELTAGREQUEST._serialized_start=7127 + _DELETEREGISTEREDMODELTAGREQUEST._serialized_end=7262 + _DELETEREGISTEREDMODELTAGRESPONSE._serialized_start=7264 + _DELETEREGISTEREDMODELTAGRESPONSE._serialized_end=7298 + _SETMODELVERSIONTAGREQUEST._serialized_start=7301 + _SETMODELVERSIONTAGREQUEST._serialized_end=7462 + _SETMODELVERSIONTAGRESPONSE._serialized_start=7464 + _SETMODELVERSIONTAGRESPONSE._serialized_end=7492 + _DELETEMODELVERSIONTAGREQUEST._serialized_start=7495 + _DELETEMODELVERSIONTAGREQUEST._serialized_end=7647 + _DELETEMODELVERSIONTAGRESPONSE._serialized_start=7649 + _DELETEMODELVERSIONTAGRESPONSE._serialized_end=7680 + _GETMODELVERSIONBYALIASREQUEST._serialized_start=7683 + _GETMODELVERSIONBYALIASREQUEST._serialized_end=7816 + _GETMODELVERSIONBYALIASRESPONSE._serialized_start=7818 + _GETMODELVERSIONBYALIASRESPONSE._serialized_end=7911 + _ENTITY._serialized_start=7914 + _ENTITY._serialized_end=8083 + _PIPELINE._serialized_start=8085 + _PIPELINE._serialized_end=8144 + _JOB._serialized_start=8146 + _JOB._serialized_end=8222 + _NOTEBOOK._serialized_start=8224 + _NOTEBOOK._serialized_end=8291 + _TABLE._serialized_start=8293 + _TABLE._serialized_end=8405 + _SECURABLE._serialized_start=8407 + _SECURABLE._serialized_end=8475 + _LINEAGE._serialized_start=8478 + _LINEAGE._serialized_end=8610 + _LINEAGEHEADERINFO._serialized_start=8612 + _LINEAGEHEADERINFO._serialized_end=8732 + _MODELVERSIONLINEAGEINFO._serialized_start=8735 + _MODELVERSIONLINEAGEINFO._serialized_end=8889 + _EMITMODELVERSIONLINEAGEREQUEST._serialized_start=8892 + _EMITMODELVERSIONLINEAGEREQUEST._serialized_end=9174 + _EMITMODELVERSIONLINEAGERESPONSE._serialized_start=9176 + _EMITMODELVERSIONLINEAGERESPONSE._serialized_end=9209 + _ISDATABRICKSSDKMODELSARTIFACTREPOSITORYENABLEDREQUEST._serialized_start=9212 + _ISDATABRICKSSDKMODELSARTIFACTREPOSITORYENABLEDREQUEST._serialized_end=9352 + _ISDATABRICKSSDKMODELSARTIFACTREPOSITORYENABLEDRESPONSE._serialized_start=9354 + _ISDATABRICKSSDKMODELSARTIFACTREPOSITORYENABLEDRESPONSE._serialized_end=9478 + # @@protoc_insertion_point(module_scope) + diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/protos/databricks_uc_registry_service_pb2.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/protos/databricks_uc_registry_service_pb2.py new file mode 100644 index 0000000000000000000000000000000000000000..c909ffd51eceb2b8ad28b15dcba5453ad3cf45a7 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/protos/databricks_uc_registry_service_pb2.py @@ -0,0 +1,170 @@ + +import google.protobuf +from packaging.version import Version +if Version(google.protobuf.__version__).major >= 5: + # -*- coding: utf-8 -*- + # Generated by the protocol buffer compiler. DO NOT EDIT! + # source: databricks_uc_registry_service.proto + # Protobuf Python Version: 5.26.0 + """Generated protocol buffer code.""" + from google.protobuf import descriptor as _descriptor + from google.protobuf import descriptor_pool as _descriptor_pool + from google.protobuf import symbol_database as _symbol_database + from google.protobuf.internal import builder as _builder + # @@protoc_insertion_point(imports) + + _sym_db = _symbol_database.Default() + + + from . import databricks_pb2 as databricks__pb2 + from . import databricks_uc_registry_messages_pb2 as databricks_uc_registry_messages_pb2 + from .scalapb import scalapb_pb2 as scalapb_dot_scalapb__pb2 + + + DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n$databricks_uc_registry_service.proto\x12\x16mlflow.ucmodelregistry\x1a\x10\x64\x61tabricks.proto\x1a%databricks_uc_registry_messages.proto\x1a\x15scalapb/scalapb.proto2\xc0,\n\x16UcModelRegistryService\x12\xf2\x01\n\x15\x43reateRegisteredModel\x12\x34.mlflow.ucmodelregistry.CreateRegisteredModelRequest\x1a\x35.mlflow.ucmodelregistry.CreateRegisteredModelResponse\"l\xf2\x86\x19h\n<\n\x04POST\x12./mlflow/unity-catalog/registered-models/create\x1a\x04\x08\x02\x10\x00\x10\x03*&(Unity Catalog) Create RegisteredModel\x12\xf3\x01\n\x15UpdateRegisteredModel\x12\x34.mlflow.ucmodelregistry.UpdateRegisteredModelRequest\x1a\x35.mlflow.ucmodelregistry.UpdateRegisteredModelResponse\"m\xf2\x86\x19i\n=\n\x05PATCH\x12./mlflow/unity-catalog/registered-models/update\x1a\x04\x08\x02\x10\x00\x10\x03*&(Unity Catalog) Update RegisteredModel\x12\xf4\x01\n\x15\x44\x65leteRegisteredModel\x12\x34.mlflow.ucmodelregistry.DeleteRegisteredModelRequest\x1a\x35.mlflow.ucmodelregistry.DeleteRegisteredModelResponse\"n\xf2\x86\x19j\n>\n\x06\x44\x45LETE\x12./mlflow/unity-catalog/registered-models/delete\x1a\x04\x08\x02\x10\x00\x10\x03*&(Unity Catalog) Delete RegisteredModel\x12\xe2\x01\n\x12GetRegisteredModel\x12\x31.mlflow.ucmodelregistry.GetRegisteredModelRequest\x1a\x32.mlflow.ucmodelregistry.GetRegisteredModelResponse\"e\xf2\x86\x19\x61\n8\n\x03GET\x12+/mlflow/unity-catalog/registered-models/get\x1a\x04\x08\x02\x10\x00\x10\x03*#(Unity Catalog) Get RegisteredModel\x12\xf5\x01\n\x16SearchRegisteredModels\x12\x35.mlflow.ucmodelregistry.SearchRegisteredModelsRequest\x1a\x36.mlflow.ucmodelregistry.SearchRegisteredModelsResponse\"l\xf2\x86\x19h\n;\n\x03GET\x12./mlflow/unity-catalog/registered-models/search\x1a\x04\x08\x02\x10\x00\x10\x03*\'(Unity Catalog) Search RegisteredModels\x12\xe3\x01\n\x12\x43reateModelVersion\x12\x31.mlflow.ucmodelregistry.CreateModelVersionRequest\x1a\x32.mlflow.ucmodelregistry.CreateModelVersionResponse\"f\xf2\x86\x19\x62\n9\n\x04POST\x12+/mlflow/unity-catalog/model-versions/create\x1a\x04\x08\x02\x10\x00\x10\x03*#(Unity Catalog) Create ModelVersion\x12\xe4\x01\n\x12UpdateModelVersion\x12\x31.mlflow.ucmodelregistry.UpdateModelVersionRequest\x1a\x32.mlflow.ucmodelregistry.UpdateModelVersionResponse\"g\xf2\x86\x19\x63\n:\n\x05PATCH\x12+/mlflow/unity-catalog/model-versions/update\x1a\x04\x08\x02\x10\x00\x10\x03*#(Unity Catalog) Update ModelVersion\x12\xe5\x01\n\x12\x44\x65leteModelVersion\x12\x31.mlflow.ucmodelregistry.DeleteModelVersionRequest\x1a\x32.mlflow.ucmodelregistry.DeleteModelVersionResponse\"h\xf2\x86\x19\x64\n;\n\x06\x44\x45LETE\x12+/mlflow/unity-catalog/model-versions/delete\x1a\x04\x08\x02\x10\x00\x10\x03*#(Unity Catalog) Delete ModelVersion\x12\xd3\x01\n\x0fGetModelVersion\x12..mlflow.ucmodelregistry.GetModelVersionRequest\x1a/.mlflow.ucmodelregistry.GetModelVersionResponse\"_\xf2\x86\x19[\n5\n\x03GET\x12(/mlflow/unity-catalog/model-versions/get\x1a\x04\x08\x02\x10\x00\x10\x03* (Unity Catalog) Get ModelVersion\x12\xe6\x01\n\x13SearchModelVersions\x12\x32.mlflow.ucmodelregistry.SearchModelVersionsRequest\x1a\x33.mlflow.ucmodelregistry.SearchModelVersionsResponse\"f\xf2\x86\x19\x62\n8\n\x03GET\x12+/mlflow/unity-catalog/model-versions/search\x1a\x04\x08\x02\x10\x00\x10\x03*$(Unity Catalog) Search ModelVersions\x12\xd8\x02\n(GenerateTemporaryModelVersionCredentials\x12G.mlflow.ucmodelregistry.GenerateTemporaryModelVersionCredentialsRequest\x1aH.mlflow.ucmodelregistry.GenerateTemporaryModelVersionCredentialsResponse\"\x98\x01\xf2\x86\x19\x93\x01\nQ\n\x04POST\x12\x43/mlflow/unity-catalog/model-versions/generate-temporary-credentials\x1a\x04\x08\x02\x10\x00\x10\x03*<(Unity Catalog) Generate Temporary Model Version Credentials\x12\x9e\x02\n\x1aGetModelVersionDownloadUri\x12\x39.mlflow.ucmodelregistry.GetModelVersionDownloadUriRequest\x1a:.mlflow.ucmodelregistry.GetModelVersionDownloadUriResponse\"\x88\x01\xf2\x86\x19\x83\x01\nB\n\x03GET\x12\x35/mlflow/unity-catalog/model-versions/get-download-uri\x1a\x04\x08\x02\x10\x00\x10\x03*;(Unity Catalog) Get Download URI For ModelVersion Artifacts\x12\xee\x01\n\x14\x46inalizeModelVersion\x12\x33.mlflow.ucmodelregistry.FinalizeModelVersionRequest\x1a\x34.mlflow.ucmodelregistry.FinalizeModelVersionResponse\"k\xf2\x86\x19g\n;\n\x04POST\x12-/mlflow/unity-catalog/model-versions/finalize\x1a\x04\x08\x02\x10\x00\x10\x03*&(Unity Catalog) Finalize Model Version\x12\xfa\x01\n\x17SetRegisteredModelAlias\x12\x36.mlflow.ucmodelregistry.SetRegisteredModelAliasRequest\x1a\x37.mlflow.ucmodelregistry.SetRegisteredModelAliasResponse\"n\xf2\x86\x19j\n;\n\x04POST\x12-/mlflow/unity-catalog/registered-models/alias\x1a\x04\x08\x02\x10\x00\x10\x03*)(Unity Catalog) Set RegisteredModel Alias\x12\x88\x02\n\x1a\x44\x65leteRegisteredModelAlias\x12\x39.mlflow.ucmodelregistry.DeleteRegisteredModelAliasRequest\x1a:.mlflow.ucmodelregistry.DeleteRegisteredModelAliasResponse\"s\xf2\x86\x19o\n=\n\x06\x44\x45LETE\x12-/mlflow/unity-catalog/registered-models/alias\x1a\x04\x08\x02\x10\x00\x10\x03*,(Unity Catalog) Delete RegisteredModel Alias\x12\xf6\x01\n\x16GetModelVersionByAlias\x12\x35.mlflow.ucmodelregistry.GetModelVersionByAliasRequest\x1a\x36.mlflow.ucmodelregistry.GetModelVersionByAliasResponse\"m\xf2\x86\x19i\n:\n\x03GET\x12-/mlflow/unity-catalog/registered-models/alias\x1a\x04\x08\x02\x10\x00\x10\x03*)(Unity Catalog) Get ModelVersion By Alias\x12\xf4\x01\n\x15SetRegisteredModelTag\x12\x34.mlflow.ucmodelregistry.SetRegisteredModelTagRequest\x1a\x35.mlflow.ucmodelregistry.SetRegisteredModelTagResponse\"n\xf2\x86\x19j\n=\n\x04POST\x12//mlflow/unity-catalog/registered-models/set-tag\x1a\x04\x08\x02\x10\x00\x10\x03*\'(Unity Catalog) Set RegisteredModel Tag\x12\x85\x02\n\x18\x44\x65leteRegisteredModelTag\x12\x37.mlflow.ucmodelregistry.DeleteRegisteredModelTagRequest\x1a\x38.mlflow.ucmodelregistry.DeleteRegisteredModelTagResponse\"v\xf2\x86\x19r\nB\n\x06\x44\x45LETE\x12\x32/mlflow/unity-catalog/registered-models/delete-tag\x1a\x04\x08\x02\x10\x00\x10\x03**(Unity Catalog) Delete RegisteredModel Tag\x12\xe5\x01\n\x12SetModelVersionTag\x12\x31.mlflow.ucmodelregistry.SetModelVersionTagRequest\x1a\x32.mlflow.ucmodelregistry.SetModelVersionTagResponse\"h\xf2\x86\x19\x64\n:\n\x04POST\x12,/mlflow/unity-catalog/model-versions/set-tag\x1a\x04\x08\x02\x10\x00\x10\x03*$(Unity Catalog) Set ModelVersion Tag\x12\xf6\x01\n\x15\x44\x65leteModelVersionTag\x12\x34.mlflow.ucmodelregistry.DeleteModelVersionTagRequest\x1a\x35.mlflow.ucmodelregistry.DeleteModelVersionTagResponse\"p\xf2\x86\x19l\n?\n\x06\x44\x45LETE\x12//mlflow/unity-catalog/model-versions/delete-tag\x1a\x04\x08\x02\x10\x00\x10\x03*\'(Unity Catalog) Delete ModelVersion Tag\x12\xfe\x01\n\x17\x45mitModelVersionLineage\x12\x36.mlflow.ucmodelregistry.EmitModelVersionLineageRequest\x1a\x37.mlflow.ucmodelregistry.EmitModelVersionLineageResponse\"r\xf2\x86\x19n\n?\n\x04POST\x12\x31/mlflow/unity-catalog/model-versions/emit-lineage\x1a\x04\x08\x02\x10\x00\x10\x03*)(Unity Catalog) Emit ModelVersion Lineage\x12\xa3\x03\n.IsDatabricksSdkModelsArtifactRepositoryEnabled\x12M.mlflow.ucmodelregistry.IsDatabricksSdkModelsArtifactRepositoryEnabledRequest\x1aN.mlflow.ucmodelregistry.IsDatabricksSdkModelsArtifactRepositoryEnabledResponse\"\xd1\x01\xf2\x86\x19\xcc\x01\ni\n\x03GET\x12\\/mlflow/unity-catalog/registered-models:is-databricks-sdk-models-artifact-repository-enabled\x1a\x04\x08\x02\x10\x00\x10\x03*](Unity Catalog) Use DatabricksSdkModelsArtifactRepository in mlflow client for model registryB5\n(com.databricks.api.proto.ucmodelregistry\x90\x01\x01\xa0\x01\x01\xe2?\x02\x10\x01') + + _globals = globals() + _builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals) + _builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'databricks_uc_registry_service_pb2', _globals) + if not _descriptor._USE_C_DESCRIPTORS: + _globals['DESCRIPTOR']._loaded_options = None + _globals['DESCRIPTOR']._serialized_options = b'\n(com.databricks.api.proto.ucmodelregistry\220\001\001\240\001\001\342?\002\020\001' + _globals['_UCMODELREGISTRYSERVICE'].methods_by_name['CreateRegisteredModel']._loaded_options = None + _globals['_UCMODELREGISTRYSERVICE'].methods_by_name['CreateRegisteredModel']._serialized_options = b'\362\206\031h\n<\n\004POST\022./mlflow/unity-catalog/registered-models/create\032\004\010\002\020\000\020\003*&(Unity Catalog) Create RegisteredModel' + _globals['_UCMODELREGISTRYSERVICE'].methods_by_name['UpdateRegisteredModel']._loaded_options = None + _globals['_UCMODELREGISTRYSERVICE'].methods_by_name['UpdateRegisteredModel']._serialized_options = b'\362\206\031i\n=\n\005PATCH\022./mlflow/unity-catalog/registered-models/update\032\004\010\002\020\000\020\003*&(Unity Catalog) Update RegisteredModel' + _globals['_UCMODELREGISTRYSERVICE'].methods_by_name['DeleteRegisteredModel']._loaded_options = None + _globals['_UCMODELREGISTRYSERVICE'].methods_by_name['DeleteRegisteredModel']._serialized_options = b'\362\206\031j\n>\n\006DELETE\022./mlflow/unity-catalog/registered-models/delete\032\004\010\002\020\000\020\003*&(Unity Catalog) Delete RegisteredModel' + _globals['_UCMODELREGISTRYSERVICE'].methods_by_name['GetRegisteredModel']._loaded_options = None + _globals['_UCMODELREGISTRYSERVICE'].methods_by_name['GetRegisteredModel']._serialized_options = b'\362\206\031a\n8\n\003GET\022+/mlflow/unity-catalog/registered-models/get\032\004\010\002\020\000\020\003*#(Unity Catalog) Get RegisteredModel' + _globals['_UCMODELREGISTRYSERVICE'].methods_by_name['SearchRegisteredModels']._loaded_options = None + _globals['_UCMODELREGISTRYSERVICE'].methods_by_name['SearchRegisteredModels']._serialized_options = b'\362\206\031h\n;\n\003GET\022./mlflow/unity-catalog/registered-models/search\032\004\010\002\020\000\020\003*\'(Unity Catalog) Search RegisteredModels' + _globals['_UCMODELREGISTRYSERVICE'].methods_by_name['CreateModelVersion']._loaded_options = None + _globals['_UCMODELREGISTRYSERVICE'].methods_by_name['CreateModelVersion']._serialized_options = b'\362\206\031b\n9\n\004POST\022+/mlflow/unity-catalog/model-versions/create\032\004\010\002\020\000\020\003*#(Unity Catalog) Create ModelVersion' + _globals['_UCMODELREGISTRYSERVICE'].methods_by_name['UpdateModelVersion']._loaded_options = None + _globals['_UCMODELREGISTRYSERVICE'].methods_by_name['UpdateModelVersion']._serialized_options = b'\362\206\031c\n:\n\005PATCH\022+/mlflow/unity-catalog/model-versions/update\032\004\010\002\020\000\020\003*#(Unity Catalog) Update ModelVersion' + _globals['_UCMODELREGISTRYSERVICE'].methods_by_name['DeleteModelVersion']._loaded_options = None + _globals['_UCMODELREGISTRYSERVICE'].methods_by_name['DeleteModelVersion']._serialized_options = b'\362\206\031d\n;\n\006DELETE\022+/mlflow/unity-catalog/model-versions/delete\032\004\010\002\020\000\020\003*#(Unity Catalog) Delete ModelVersion' + _globals['_UCMODELREGISTRYSERVICE'].methods_by_name['GetModelVersion']._loaded_options = None + _globals['_UCMODELREGISTRYSERVICE'].methods_by_name['GetModelVersion']._serialized_options = b'\362\206\031[\n5\n\003GET\022(/mlflow/unity-catalog/model-versions/get\032\004\010\002\020\000\020\003* (Unity Catalog) Get ModelVersion' + _globals['_UCMODELREGISTRYSERVICE'].methods_by_name['SearchModelVersions']._loaded_options = None + _globals['_UCMODELREGISTRYSERVICE'].methods_by_name['SearchModelVersions']._serialized_options = b'\362\206\031b\n8\n\003GET\022+/mlflow/unity-catalog/model-versions/search\032\004\010\002\020\000\020\003*$(Unity Catalog) Search ModelVersions' + _globals['_UCMODELREGISTRYSERVICE'].methods_by_name['GenerateTemporaryModelVersionCredentials']._loaded_options = None + _globals['_UCMODELREGISTRYSERVICE'].methods_by_name['GenerateTemporaryModelVersionCredentials']._serialized_options = b'\362\206\031\223\001\nQ\n\004POST\022C/mlflow/unity-catalog/model-versions/generate-temporary-credentials\032\004\010\002\020\000\020\003*<(Unity Catalog) Generate Temporary Model Version Credentials' + _globals['_UCMODELREGISTRYSERVICE'].methods_by_name['GetModelVersionDownloadUri']._loaded_options = None + _globals['_UCMODELREGISTRYSERVICE'].methods_by_name['GetModelVersionDownloadUri']._serialized_options = b'\362\206\031\203\001\nB\n\003GET\0225/mlflow/unity-catalog/model-versions/get-download-uri\032\004\010\002\020\000\020\003*;(Unity Catalog) Get Download URI For ModelVersion Artifacts' + _globals['_UCMODELREGISTRYSERVICE'].methods_by_name['FinalizeModelVersion']._loaded_options = None + _globals['_UCMODELREGISTRYSERVICE'].methods_by_name['FinalizeModelVersion']._serialized_options = b'\362\206\031g\n;\n\004POST\022-/mlflow/unity-catalog/model-versions/finalize\032\004\010\002\020\000\020\003*&(Unity Catalog) Finalize Model Version' + _globals['_UCMODELREGISTRYSERVICE'].methods_by_name['SetRegisteredModelAlias']._loaded_options = None + _globals['_UCMODELREGISTRYSERVICE'].methods_by_name['SetRegisteredModelAlias']._serialized_options = b'\362\206\031j\n;\n\004POST\022-/mlflow/unity-catalog/registered-models/alias\032\004\010\002\020\000\020\003*)(Unity Catalog) Set RegisteredModel Alias' + _globals['_UCMODELREGISTRYSERVICE'].methods_by_name['DeleteRegisteredModelAlias']._loaded_options = None + _globals['_UCMODELREGISTRYSERVICE'].methods_by_name['DeleteRegisteredModelAlias']._serialized_options = b'\362\206\031o\n=\n\006DELETE\022-/mlflow/unity-catalog/registered-models/alias\032\004\010\002\020\000\020\003*,(Unity Catalog) Delete RegisteredModel Alias' + _globals['_UCMODELREGISTRYSERVICE'].methods_by_name['GetModelVersionByAlias']._loaded_options = None + _globals['_UCMODELREGISTRYSERVICE'].methods_by_name['GetModelVersionByAlias']._serialized_options = b'\362\206\031i\n:\n\003GET\022-/mlflow/unity-catalog/registered-models/alias\032\004\010\002\020\000\020\003*)(Unity Catalog) Get ModelVersion By Alias' + _globals['_UCMODELREGISTRYSERVICE'].methods_by_name['SetRegisteredModelTag']._loaded_options = None + _globals['_UCMODELREGISTRYSERVICE'].methods_by_name['SetRegisteredModelTag']._serialized_options = b'\362\206\031j\n=\n\004POST\022//mlflow/unity-catalog/registered-models/set-tag\032\004\010\002\020\000\020\003*\'(Unity Catalog) Set RegisteredModel Tag' + _globals['_UCMODELREGISTRYSERVICE'].methods_by_name['DeleteRegisteredModelTag']._loaded_options = None + _globals['_UCMODELREGISTRYSERVICE'].methods_by_name['DeleteRegisteredModelTag']._serialized_options = b'\362\206\031r\nB\n\006DELETE\0222/mlflow/unity-catalog/registered-models/delete-tag\032\004\010\002\020\000\020\003**(Unity Catalog) Delete RegisteredModel Tag' + _globals['_UCMODELREGISTRYSERVICE'].methods_by_name['SetModelVersionTag']._loaded_options = None + _globals['_UCMODELREGISTRYSERVICE'].methods_by_name['SetModelVersionTag']._serialized_options = b'\362\206\031d\n:\n\004POST\022,/mlflow/unity-catalog/model-versions/set-tag\032\004\010\002\020\000\020\003*$(Unity Catalog) Set ModelVersion Tag' + _globals['_UCMODELREGISTRYSERVICE'].methods_by_name['DeleteModelVersionTag']._loaded_options = None + _globals['_UCMODELREGISTRYSERVICE'].methods_by_name['DeleteModelVersionTag']._serialized_options = b'\362\206\031l\n?\n\006DELETE\022//mlflow/unity-catalog/model-versions/delete-tag\032\004\010\002\020\000\020\003*\'(Unity Catalog) Delete ModelVersion Tag' + _globals['_UCMODELREGISTRYSERVICE'].methods_by_name['EmitModelVersionLineage']._loaded_options = None + _globals['_UCMODELREGISTRYSERVICE'].methods_by_name['EmitModelVersionLineage']._serialized_options = b'\362\206\031n\n?\n\004POST\0221/mlflow/unity-catalog/model-versions/emit-lineage\032\004\010\002\020\000\020\003*)(Unity Catalog) Emit ModelVersion Lineage' + _globals['_UCMODELREGISTRYSERVICE'].methods_by_name['IsDatabricksSdkModelsArtifactRepositoryEnabled']._loaded_options = None + _globals['_UCMODELREGISTRYSERVICE'].methods_by_name['IsDatabricksSdkModelsArtifactRepositoryEnabled']._serialized_options = b'\362\206\031\314\001\ni\n\003GET\022\\/mlflow/unity-catalog/registered-models:is-databricks-sdk-models-artifact-repository-enabled\032\004\010\002\020\000\020\003*](Unity Catalog) Use DatabricksSdkModelsArtifactRepository in mlflow client for model registry' + _globals['_UCMODELREGISTRYSERVICE']._serialized_start=145 + _globals['_UCMODELREGISTRYSERVICE']._serialized_end=5841 + _builder.BuildServices(DESCRIPTOR, 'databricks_uc_registry_service_pb2', _globals) + # @@protoc_insertion_point(module_scope) + +else: + # -*- coding: utf-8 -*- + # Generated by the protocol buffer compiler. DO NOT EDIT! + # source: databricks_uc_registry_service.proto + """Generated protocol buffer code.""" + from google.protobuf import descriptor as _descriptor + from google.protobuf import descriptor_pool as _descriptor_pool + from google.protobuf import message as _message + from google.protobuf import reflection as _reflection + from google.protobuf import symbol_database as _symbol_database + from google.protobuf import service as _service + from google.protobuf import service_reflection + # @@protoc_insertion_point(imports) + + _sym_db = _symbol_database.Default() + + + from . import databricks_pb2 as databricks__pb2 + from . import databricks_uc_registry_messages_pb2 as databricks_uc_registry_messages_pb2 + from .scalapb import scalapb_pb2 as scalapb_dot_scalapb__pb2 + + + DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n$databricks_uc_registry_service.proto\x12\x16mlflow.ucmodelregistry\x1a\x10\x64\x61tabricks.proto\x1a%databricks_uc_registry_messages.proto\x1a\x15scalapb/scalapb.proto2\xc0,\n\x16UcModelRegistryService\x12\xf2\x01\n\x15\x43reateRegisteredModel\x12\x34.mlflow.ucmodelregistry.CreateRegisteredModelRequest\x1a\x35.mlflow.ucmodelregistry.CreateRegisteredModelResponse\"l\xf2\x86\x19h\n<\n\x04POST\x12./mlflow/unity-catalog/registered-models/create\x1a\x04\x08\x02\x10\x00\x10\x03*&(Unity Catalog) Create RegisteredModel\x12\xf3\x01\n\x15UpdateRegisteredModel\x12\x34.mlflow.ucmodelregistry.UpdateRegisteredModelRequest\x1a\x35.mlflow.ucmodelregistry.UpdateRegisteredModelResponse\"m\xf2\x86\x19i\n=\n\x05PATCH\x12./mlflow/unity-catalog/registered-models/update\x1a\x04\x08\x02\x10\x00\x10\x03*&(Unity Catalog) Update RegisteredModel\x12\xf4\x01\n\x15\x44\x65leteRegisteredModel\x12\x34.mlflow.ucmodelregistry.DeleteRegisteredModelRequest\x1a\x35.mlflow.ucmodelregistry.DeleteRegisteredModelResponse\"n\xf2\x86\x19j\n>\n\x06\x44\x45LETE\x12./mlflow/unity-catalog/registered-models/delete\x1a\x04\x08\x02\x10\x00\x10\x03*&(Unity Catalog) Delete RegisteredModel\x12\xe2\x01\n\x12GetRegisteredModel\x12\x31.mlflow.ucmodelregistry.GetRegisteredModelRequest\x1a\x32.mlflow.ucmodelregistry.GetRegisteredModelResponse\"e\xf2\x86\x19\x61\n8\n\x03GET\x12+/mlflow/unity-catalog/registered-models/get\x1a\x04\x08\x02\x10\x00\x10\x03*#(Unity Catalog) Get RegisteredModel\x12\xf5\x01\n\x16SearchRegisteredModels\x12\x35.mlflow.ucmodelregistry.SearchRegisteredModelsRequest\x1a\x36.mlflow.ucmodelregistry.SearchRegisteredModelsResponse\"l\xf2\x86\x19h\n;\n\x03GET\x12./mlflow/unity-catalog/registered-models/search\x1a\x04\x08\x02\x10\x00\x10\x03*\'(Unity Catalog) Search RegisteredModels\x12\xe3\x01\n\x12\x43reateModelVersion\x12\x31.mlflow.ucmodelregistry.CreateModelVersionRequest\x1a\x32.mlflow.ucmodelregistry.CreateModelVersionResponse\"f\xf2\x86\x19\x62\n9\n\x04POST\x12+/mlflow/unity-catalog/model-versions/create\x1a\x04\x08\x02\x10\x00\x10\x03*#(Unity Catalog) Create ModelVersion\x12\xe4\x01\n\x12UpdateModelVersion\x12\x31.mlflow.ucmodelregistry.UpdateModelVersionRequest\x1a\x32.mlflow.ucmodelregistry.UpdateModelVersionResponse\"g\xf2\x86\x19\x63\n:\n\x05PATCH\x12+/mlflow/unity-catalog/model-versions/update\x1a\x04\x08\x02\x10\x00\x10\x03*#(Unity Catalog) Update ModelVersion\x12\xe5\x01\n\x12\x44\x65leteModelVersion\x12\x31.mlflow.ucmodelregistry.DeleteModelVersionRequest\x1a\x32.mlflow.ucmodelregistry.DeleteModelVersionResponse\"h\xf2\x86\x19\x64\n;\n\x06\x44\x45LETE\x12+/mlflow/unity-catalog/model-versions/delete\x1a\x04\x08\x02\x10\x00\x10\x03*#(Unity Catalog) Delete ModelVersion\x12\xd3\x01\n\x0fGetModelVersion\x12..mlflow.ucmodelregistry.GetModelVersionRequest\x1a/.mlflow.ucmodelregistry.GetModelVersionResponse\"_\xf2\x86\x19[\n5\n\x03GET\x12(/mlflow/unity-catalog/model-versions/get\x1a\x04\x08\x02\x10\x00\x10\x03* (Unity Catalog) Get ModelVersion\x12\xe6\x01\n\x13SearchModelVersions\x12\x32.mlflow.ucmodelregistry.SearchModelVersionsRequest\x1a\x33.mlflow.ucmodelregistry.SearchModelVersionsResponse\"f\xf2\x86\x19\x62\n8\n\x03GET\x12+/mlflow/unity-catalog/model-versions/search\x1a\x04\x08\x02\x10\x00\x10\x03*$(Unity Catalog) Search ModelVersions\x12\xd8\x02\n(GenerateTemporaryModelVersionCredentials\x12G.mlflow.ucmodelregistry.GenerateTemporaryModelVersionCredentialsRequest\x1aH.mlflow.ucmodelregistry.GenerateTemporaryModelVersionCredentialsResponse\"\x98\x01\xf2\x86\x19\x93\x01\nQ\n\x04POST\x12\x43/mlflow/unity-catalog/model-versions/generate-temporary-credentials\x1a\x04\x08\x02\x10\x00\x10\x03*<(Unity Catalog) Generate Temporary Model Version Credentials\x12\x9e\x02\n\x1aGetModelVersionDownloadUri\x12\x39.mlflow.ucmodelregistry.GetModelVersionDownloadUriRequest\x1a:.mlflow.ucmodelregistry.GetModelVersionDownloadUriResponse\"\x88\x01\xf2\x86\x19\x83\x01\nB\n\x03GET\x12\x35/mlflow/unity-catalog/model-versions/get-download-uri\x1a\x04\x08\x02\x10\x00\x10\x03*;(Unity Catalog) Get Download URI For ModelVersion Artifacts\x12\xee\x01\n\x14\x46inalizeModelVersion\x12\x33.mlflow.ucmodelregistry.FinalizeModelVersionRequest\x1a\x34.mlflow.ucmodelregistry.FinalizeModelVersionResponse\"k\xf2\x86\x19g\n;\n\x04POST\x12-/mlflow/unity-catalog/model-versions/finalize\x1a\x04\x08\x02\x10\x00\x10\x03*&(Unity Catalog) Finalize Model Version\x12\xfa\x01\n\x17SetRegisteredModelAlias\x12\x36.mlflow.ucmodelregistry.SetRegisteredModelAliasRequest\x1a\x37.mlflow.ucmodelregistry.SetRegisteredModelAliasResponse\"n\xf2\x86\x19j\n;\n\x04POST\x12-/mlflow/unity-catalog/registered-models/alias\x1a\x04\x08\x02\x10\x00\x10\x03*)(Unity Catalog) Set RegisteredModel Alias\x12\x88\x02\n\x1a\x44\x65leteRegisteredModelAlias\x12\x39.mlflow.ucmodelregistry.DeleteRegisteredModelAliasRequest\x1a:.mlflow.ucmodelregistry.DeleteRegisteredModelAliasResponse\"s\xf2\x86\x19o\n=\n\x06\x44\x45LETE\x12-/mlflow/unity-catalog/registered-models/alias\x1a\x04\x08\x02\x10\x00\x10\x03*,(Unity Catalog) Delete RegisteredModel Alias\x12\xf6\x01\n\x16GetModelVersionByAlias\x12\x35.mlflow.ucmodelregistry.GetModelVersionByAliasRequest\x1a\x36.mlflow.ucmodelregistry.GetModelVersionByAliasResponse\"m\xf2\x86\x19i\n:\n\x03GET\x12-/mlflow/unity-catalog/registered-models/alias\x1a\x04\x08\x02\x10\x00\x10\x03*)(Unity Catalog) Get ModelVersion By Alias\x12\xf4\x01\n\x15SetRegisteredModelTag\x12\x34.mlflow.ucmodelregistry.SetRegisteredModelTagRequest\x1a\x35.mlflow.ucmodelregistry.SetRegisteredModelTagResponse\"n\xf2\x86\x19j\n=\n\x04POST\x12//mlflow/unity-catalog/registered-models/set-tag\x1a\x04\x08\x02\x10\x00\x10\x03*\'(Unity Catalog) Set RegisteredModel Tag\x12\x85\x02\n\x18\x44\x65leteRegisteredModelTag\x12\x37.mlflow.ucmodelregistry.DeleteRegisteredModelTagRequest\x1a\x38.mlflow.ucmodelregistry.DeleteRegisteredModelTagResponse\"v\xf2\x86\x19r\nB\n\x06\x44\x45LETE\x12\x32/mlflow/unity-catalog/registered-models/delete-tag\x1a\x04\x08\x02\x10\x00\x10\x03**(Unity Catalog) Delete RegisteredModel Tag\x12\xe5\x01\n\x12SetModelVersionTag\x12\x31.mlflow.ucmodelregistry.SetModelVersionTagRequest\x1a\x32.mlflow.ucmodelregistry.SetModelVersionTagResponse\"h\xf2\x86\x19\x64\n:\n\x04POST\x12,/mlflow/unity-catalog/model-versions/set-tag\x1a\x04\x08\x02\x10\x00\x10\x03*$(Unity Catalog) Set ModelVersion Tag\x12\xf6\x01\n\x15\x44\x65leteModelVersionTag\x12\x34.mlflow.ucmodelregistry.DeleteModelVersionTagRequest\x1a\x35.mlflow.ucmodelregistry.DeleteModelVersionTagResponse\"p\xf2\x86\x19l\n?\n\x06\x44\x45LETE\x12//mlflow/unity-catalog/model-versions/delete-tag\x1a\x04\x08\x02\x10\x00\x10\x03*\'(Unity Catalog) Delete ModelVersion Tag\x12\xfe\x01\n\x17\x45mitModelVersionLineage\x12\x36.mlflow.ucmodelregistry.EmitModelVersionLineageRequest\x1a\x37.mlflow.ucmodelregistry.EmitModelVersionLineageResponse\"r\xf2\x86\x19n\n?\n\x04POST\x12\x31/mlflow/unity-catalog/model-versions/emit-lineage\x1a\x04\x08\x02\x10\x00\x10\x03*)(Unity Catalog) Emit ModelVersion Lineage\x12\xa3\x03\n.IsDatabricksSdkModelsArtifactRepositoryEnabled\x12M.mlflow.ucmodelregistry.IsDatabricksSdkModelsArtifactRepositoryEnabledRequest\x1aN.mlflow.ucmodelregistry.IsDatabricksSdkModelsArtifactRepositoryEnabledResponse\"\xd1\x01\xf2\x86\x19\xcc\x01\ni\n\x03GET\x12\\/mlflow/unity-catalog/registered-models:is-databricks-sdk-models-artifact-repository-enabled\x1a\x04\x08\x02\x10\x00\x10\x03*](Unity Catalog) Use DatabricksSdkModelsArtifactRepository in mlflow client for model registryB5\n(com.databricks.api.proto.ucmodelregistry\x90\x01\x01\xa0\x01\x01\xe2?\x02\x10\x01') + + + + _UCMODELREGISTRYSERVICE = DESCRIPTOR.services_by_name['UcModelRegistryService'] + if _descriptor._USE_C_DESCRIPTORS == False: + + DESCRIPTOR._options = None + DESCRIPTOR._serialized_options = b'\n(com.databricks.api.proto.ucmodelregistry\220\001\001\240\001\001\342?\002\020\001' + _UCMODELREGISTRYSERVICE.methods_by_name['CreateRegisteredModel']._options = None + _UCMODELREGISTRYSERVICE.methods_by_name['CreateRegisteredModel']._serialized_options = b'\362\206\031h\n<\n\004POST\022./mlflow/unity-catalog/registered-models/create\032\004\010\002\020\000\020\003*&(Unity Catalog) Create RegisteredModel' + _UCMODELREGISTRYSERVICE.methods_by_name['UpdateRegisteredModel']._options = None + _UCMODELREGISTRYSERVICE.methods_by_name['UpdateRegisteredModel']._serialized_options = b'\362\206\031i\n=\n\005PATCH\022./mlflow/unity-catalog/registered-models/update\032\004\010\002\020\000\020\003*&(Unity Catalog) Update RegisteredModel' + _UCMODELREGISTRYSERVICE.methods_by_name['DeleteRegisteredModel']._options = None + _UCMODELREGISTRYSERVICE.methods_by_name['DeleteRegisteredModel']._serialized_options = b'\362\206\031j\n>\n\006DELETE\022./mlflow/unity-catalog/registered-models/delete\032\004\010\002\020\000\020\003*&(Unity Catalog) Delete RegisteredModel' + _UCMODELREGISTRYSERVICE.methods_by_name['GetRegisteredModel']._options = None + _UCMODELREGISTRYSERVICE.methods_by_name['GetRegisteredModel']._serialized_options = b'\362\206\031a\n8\n\003GET\022+/mlflow/unity-catalog/registered-models/get\032\004\010\002\020\000\020\003*#(Unity Catalog) Get RegisteredModel' + _UCMODELREGISTRYSERVICE.methods_by_name['SearchRegisteredModels']._options = None + _UCMODELREGISTRYSERVICE.methods_by_name['SearchRegisteredModels']._serialized_options = b'\362\206\031h\n;\n\003GET\022./mlflow/unity-catalog/registered-models/search\032\004\010\002\020\000\020\003*\'(Unity Catalog) Search RegisteredModels' + _UCMODELREGISTRYSERVICE.methods_by_name['CreateModelVersion']._options = None + _UCMODELREGISTRYSERVICE.methods_by_name['CreateModelVersion']._serialized_options = b'\362\206\031b\n9\n\004POST\022+/mlflow/unity-catalog/model-versions/create\032\004\010\002\020\000\020\003*#(Unity Catalog) Create ModelVersion' + _UCMODELREGISTRYSERVICE.methods_by_name['UpdateModelVersion']._options = None + _UCMODELREGISTRYSERVICE.methods_by_name['UpdateModelVersion']._serialized_options = b'\362\206\031c\n:\n\005PATCH\022+/mlflow/unity-catalog/model-versions/update\032\004\010\002\020\000\020\003*#(Unity Catalog) Update ModelVersion' + _UCMODELREGISTRYSERVICE.methods_by_name['DeleteModelVersion']._options = None + _UCMODELREGISTRYSERVICE.methods_by_name['DeleteModelVersion']._serialized_options = b'\362\206\031d\n;\n\006DELETE\022+/mlflow/unity-catalog/model-versions/delete\032\004\010\002\020\000\020\003*#(Unity Catalog) Delete ModelVersion' + _UCMODELREGISTRYSERVICE.methods_by_name['GetModelVersion']._options = None + _UCMODELREGISTRYSERVICE.methods_by_name['GetModelVersion']._serialized_options = b'\362\206\031[\n5\n\003GET\022(/mlflow/unity-catalog/model-versions/get\032\004\010\002\020\000\020\003* (Unity Catalog) Get ModelVersion' + _UCMODELREGISTRYSERVICE.methods_by_name['SearchModelVersions']._options = None + _UCMODELREGISTRYSERVICE.methods_by_name['SearchModelVersions']._serialized_options = b'\362\206\031b\n8\n\003GET\022+/mlflow/unity-catalog/model-versions/search\032\004\010\002\020\000\020\003*$(Unity Catalog) Search ModelVersions' + _UCMODELREGISTRYSERVICE.methods_by_name['GenerateTemporaryModelVersionCredentials']._options = None + _UCMODELREGISTRYSERVICE.methods_by_name['GenerateTemporaryModelVersionCredentials']._serialized_options = b'\362\206\031\223\001\nQ\n\004POST\022C/mlflow/unity-catalog/model-versions/generate-temporary-credentials\032\004\010\002\020\000\020\003*<(Unity Catalog) Generate Temporary Model Version Credentials' + _UCMODELREGISTRYSERVICE.methods_by_name['GetModelVersionDownloadUri']._options = None + _UCMODELREGISTRYSERVICE.methods_by_name['GetModelVersionDownloadUri']._serialized_options = b'\362\206\031\203\001\nB\n\003GET\0225/mlflow/unity-catalog/model-versions/get-download-uri\032\004\010\002\020\000\020\003*;(Unity Catalog) Get Download URI For ModelVersion Artifacts' + _UCMODELREGISTRYSERVICE.methods_by_name['FinalizeModelVersion']._options = None + _UCMODELREGISTRYSERVICE.methods_by_name['FinalizeModelVersion']._serialized_options = b'\362\206\031g\n;\n\004POST\022-/mlflow/unity-catalog/model-versions/finalize\032\004\010\002\020\000\020\003*&(Unity Catalog) Finalize Model Version' + _UCMODELREGISTRYSERVICE.methods_by_name['SetRegisteredModelAlias']._options = None + _UCMODELREGISTRYSERVICE.methods_by_name['SetRegisteredModelAlias']._serialized_options = b'\362\206\031j\n;\n\004POST\022-/mlflow/unity-catalog/registered-models/alias\032\004\010\002\020\000\020\003*)(Unity Catalog) Set RegisteredModel Alias' + _UCMODELREGISTRYSERVICE.methods_by_name['DeleteRegisteredModelAlias']._options = None + _UCMODELREGISTRYSERVICE.methods_by_name['DeleteRegisteredModelAlias']._serialized_options = b'\362\206\031o\n=\n\006DELETE\022-/mlflow/unity-catalog/registered-models/alias\032\004\010\002\020\000\020\003*,(Unity Catalog) Delete RegisteredModel Alias' + _UCMODELREGISTRYSERVICE.methods_by_name['GetModelVersionByAlias']._options = None + _UCMODELREGISTRYSERVICE.methods_by_name['GetModelVersionByAlias']._serialized_options = b'\362\206\031i\n:\n\003GET\022-/mlflow/unity-catalog/registered-models/alias\032\004\010\002\020\000\020\003*)(Unity Catalog) Get ModelVersion By Alias' + _UCMODELREGISTRYSERVICE.methods_by_name['SetRegisteredModelTag']._options = None + _UCMODELREGISTRYSERVICE.methods_by_name['SetRegisteredModelTag']._serialized_options = b'\362\206\031j\n=\n\004POST\022//mlflow/unity-catalog/registered-models/set-tag\032\004\010\002\020\000\020\003*\'(Unity Catalog) Set RegisteredModel Tag' + _UCMODELREGISTRYSERVICE.methods_by_name['DeleteRegisteredModelTag']._options = None + _UCMODELREGISTRYSERVICE.methods_by_name['DeleteRegisteredModelTag']._serialized_options = b'\362\206\031r\nB\n\006DELETE\0222/mlflow/unity-catalog/registered-models/delete-tag\032\004\010\002\020\000\020\003**(Unity Catalog) Delete RegisteredModel Tag' + _UCMODELREGISTRYSERVICE.methods_by_name['SetModelVersionTag']._options = None + _UCMODELREGISTRYSERVICE.methods_by_name['SetModelVersionTag']._serialized_options = b'\362\206\031d\n:\n\004POST\022,/mlflow/unity-catalog/model-versions/set-tag\032\004\010\002\020\000\020\003*$(Unity Catalog) Set ModelVersion Tag' + _UCMODELREGISTRYSERVICE.methods_by_name['DeleteModelVersionTag']._options = None + _UCMODELREGISTRYSERVICE.methods_by_name['DeleteModelVersionTag']._serialized_options = b'\362\206\031l\n?\n\006DELETE\022//mlflow/unity-catalog/model-versions/delete-tag\032\004\010\002\020\000\020\003*\'(Unity Catalog) Delete ModelVersion Tag' + _UCMODELREGISTRYSERVICE.methods_by_name['EmitModelVersionLineage']._options = None + _UCMODELREGISTRYSERVICE.methods_by_name['EmitModelVersionLineage']._serialized_options = b'\362\206\031n\n?\n\004POST\0221/mlflow/unity-catalog/model-versions/emit-lineage\032\004\010\002\020\000\020\003*)(Unity Catalog) Emit ModelVersion Lineage' + _UCMODELREGISTRYSERVICE.methods_by_name['IsDatabricksSdkModelsArtifactRepositoryEnabled']._options = None + _UCMODELREGISTRYSERVICE.methods_by_name['IsDatabricksSdkModelsArtifactRepositoryEnabled']._serialized_options = b'\362\206\031\314\001\ni\n\003GET\022\\/mlflow/unity-catalog/registered-models:is-databricks-sdk-models-artifact-repository-enabled\032\004\010\002\020\000\020\003*](Unity Catalog) Use DatabricksSdkModelsArtifactRepository in mlflow client for model registry' + _UCMODELREGISTRYSERVICE._serialized_start=145 + _UCMODELREGISTRYSERVICE._serialized_end=5841 + UcModelRegistryService = service_reflection.GeneratedServiceType('UcModelRegistryService', (_service.Service,), dict( + DESCRIPTOR = _UCMODELREGISTRYSERVICE, + __module__ = 'databricks_uc_registry_service_pb2' + )) + + UcModelRegistryService_Stub = service_reflection.GeneratedServiceStubType('UcModelRegistryService_Stub', (UcModelRegistryService,), dict( + DESCRIPTOR = _UCMODELREGISTRYSERVICE, + __module__ = 'databricks_uc_registry_service_pb2' + )) + + + # @@protoc_insertion_point(module_scope) + diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/protos/facet_feature_statistics_pb2.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/protos/facet_feature_statistics_pb2.py new file mode 100644 index 0000000000000000000000000000000000000000..becfbee7610a3d1446521efbccc6d57180d34749 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/protos/facet_feature_statistics_pb2.py @@ -0,0 +1,296 @@ + +import google.protobuf +from packaging.version import Version +if Version(google.protobuf.__version__).major >= 5: + # -*- coding: utf-8 -*- + # Generated by the protocol buffer compiler. DO NOT EDIT! + # source: facet_feature_statistics.proto + # Protobuf Python Version: 5.26.0 + """Generated protocol buffer code.""" + from google.protobuf import descriptor as _descriptor + from google.protobuf import descriptor_pool as _descriptor_pool + from google.protobuf import symbol_database as _symbol_database + from google.protobuf.internal import builder as _builder + # @@protoc_insertion_point(imports) + + _sym_db = _symbol_database.Default() + + + + + DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\x1e\x66\x61\x63\x65t_feature_statistics.proto\x12\x16\x66\x61\x63\x65tFeatureStatistics\"b\n\x1c\x44\x61tasetFeatureStatisticsList\x12\x42\n\x08\x64\x61tasets\x18\x01 \x03(\x0b\x32\x30.facetFeatureStatistics.DatasetFeatureStatistics\"\x14\n\x04Path\x12\x0c\n\x04step\x18\x01 \x03(\t\"\x9e\x01\n\x18\x44\x61tasetFeatureStatistics\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x14\n\x0cnum_examples\x18\x02 \x01(\x03\x12\x1d\n\x15weighted_num_examples\x18\x04 \x01(\x01\x12?\n\x08\x66\x65\x61tures\x18\x03 \x03(\x0b\x32-.facetFeatureStatistics.FeatureNameStatistics\"\xae\x04\n\x15\x46\x65\x61tureNameStatistics\x12\x0e\n\x04name\x18\x01 \x01(\tH\x00\x12,\n\x04path\x18\x08 \x01(\x0b\x32\x1c.facetFeatureStatistics.PathH\x00\x12@\n\x04type\x18\x02 \x01(\x0e\x32\x32.facetFeatureStatistics.FeatureNameStatistics.Type\x12>\n\tnum_stats\x18\x03 \x01(\x0b\x32).facetFeatureStatistics.NumericStatisticsH\x01\x12@\n\x0cstring_stats\x18\x04 \x01(\x0b\x32(.facetFeatureStatistics.StringStatisticsH\x01\x12>\n\x0b\x62ytes_stats\x18\x05 \x01(\x0b\x32\'.facetFeatureStatistics.BytesStatisticsH\x01\x12@\n\x0cstruct_stats\x18\x07 \x01(\x0b\x32(.facetFeatureStatistics.StructStatisticsH\x01\x12=\n\x0c\x63ustom_stats\x18\x06 \x03(\x0b\x32\'.facetFeatureStatistics.CustomStatistic\"=\n\x04Type\x12\x07\n\x03INT\x10\x00\x12\t\n\x05\x46LOAT\x10\x01\x12\n\n\x06STRING\x10\x02\x12\t\n\x05\x42YTES\x10\x03\x12\n\n\x06STRUCT\x10\x04\x42\n\n\x08\x66ield_idB\x07\n\x05stats\"x\n\x18WeightedCommonStatistics\x12\x17\n\x0fnum_non_missing\x18\x01 \x01(\x01\x12\x13\n\x0bnum_missing\x18\x02 \x01(\x01\x12\x16\n\x0e\x61vg_num_values\x18\x03 \x01(\x01\x12\x16\n\x0etot_num_values\x18\x04 \x01(\x01\"\xc3\x01\n\x0f\x43ustomStatistic\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\r\n\x03num\x18\x02 \x01(\x01H\x00\x12\r\n\x03str\x18\x03 \x01(\tH\x00\x12\x36\n\thistogram\x18\x04 \x01(\x0b\x32!.facetFeatureStatistics.HistogramH\x00\x12?\n\x0erank_histogram\x18\x05 \x01(\x0b\x32%.facetFeatureStatistics.RankHistogramH\x00\x42\x05\n\x03valJ\x04\x08\x06\x10\x07\"\xb9\x02\n\x11NumericStatistics\x12>\n\x0c\x63ommon_stats\x18\x01 \x01(\x0b\x32(.facetFeatureStatistics.CommonStatistics\x12\x0c\n\x04mean\x18\x02 \x01(\x01\x12\x0f\n\x07std_dev\x18\x03 \x01(\x01\x12\x11\n\tnum_zeros\x18\x04 \x01(\x03\x12\x0b\n\x03min\x18\x05 \x01(\x01\x12\x0e\n\x06median\x18\x06 \x01(\x01\x12\x0b\n\x03max\x18\x07 \x01(\x01\x12\x35\n\nhistograms\x18\x08 \x03(\x0b\x32!.facetFeatureStatistics.Histogram\x12Q\n\x16weighted_numeric_stats\x18\t \x01(\x0b\x32\x31.facetFeatureStatistics.WeightedNumericStatistics\"\xa0\x03\n\x10StringStatistics\x12>\n\x0c\x63ommon_stats\x18\x01 \x01(\x0b\x32(.facetFeatureStatistics.CommonStatistics\x12\x0e\n\x06unique\x18\x02 \x01(\x03\x12I\n\ntop_values\x18\x03 \x03(\x0b\x32\x35.facetFeatureStatistics.StringStatistics.FreqAndValue\x12\x12\n\navg_length\x18\x04 \x01(\x02\x12=\n\x0erank_histogram\x18\x05 \x01(\x0b\x32%.facetFeatureStatistics.RankHistogram\x12O\n\x15weighted_string_stats\x18\x06 \x01(\x0b\x32\x30.facetFeatureStatistics.WeightedStringStatistics\x1aM\n\x0c\x46reqAndValue\x12\x1b\n\x0f\x64\x65precated_freq\x18\x01 \x01(\x03\x42\x02\x18\x01\x12\r\n\x05value\x18\x02 \x01(\t\x12\x11\n\tfrequency\x18\x03 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\x01(\x03\x12\x13\n\x0bnum_missing\x18\x02 \x01(\x03\x12\x16\n\x0emin_num_values\x18\x03 \x01(\x03\x12\x16\n\x0emax_num_values\x18\x04 \x01(\x03\x12\x16\n\x0e\x61vg_num_values\x18\x05 \x01(\x02\x12\x16\n\x0etot_num_values\x18\x08 \x01(\x03\x12?\n\x14num_values_histogram\x18\x06 \x01(\x0b\x32!.facetFeatureStatistics.Histogram\x12O\n\x15weighted_common_stats\x18\x07 \x01(\x0b\x32\x30.facetFeatureStatistics.WeightedCommonStatistics\x12H\n\x1d\x66\x65\x61ture_list_length_histogram\x18\t \x01(\x0b\x32!.facetFeatureStatistics.HistogramJ\x04\x08\n\x10\x0bJ\x04\x08\x0b\x10\x0c\"\xce\x02\n\tHistogram\x12\x0f\n\x07num_nan\x18\x01 \x01(\x03\x12\x15\n\rnum_undefined\x18\x02 \x01(\x03\x12\x39\n\x07\x62uckets\x18\x03 \x03(\x0b\x32(.facetFeatureStatistics.Histogram.Bucket\x12=\n\x04type\x18\x04 \x01(\x0e\x32/.facetFeatureStatistics.Histogram.HistogramType\x12\x0c\n\x04name\x18\x05 \x01(\t\x1a\x63\n\x06\x42ucket\x12\x11\n\tlow_value\x18\x01 \x01(\x01\x12\x12\n\nhigh_value\x18\x02 \x01(\x01\x12\x1c\n\x10\x64\x65precated_count\x18\x03 \x01(\x03\x42\x02\x18\x01\x12\x14\n\x0csample_count\x18\x04 \x01(\x01\",\n\rHistogramType\x12\x0c\n\x08STANDARD\x10\x00\x12\r\n\tQUANTILES\x10\x01\"\xce\x01\n\rRankHistogram\x12=\n\x07\x62uckets\x18\x01 \x03(\x0b\x32,.facetFeatureStatistics.RankHistogram.Bucket\x12\x0c\n\x04name\x18\x02 \x01(\t\x1ap\n\x06\x42ucket\x12\x10\n\x08low_rank\x18\x01 \x01(\x03\x12\x11\n\thigh_rank\x18\x02 \x01(\x03\x12\x1c\n\x10\x64\x65precated_count\x18\x03 \x01(\x03\x42\x02\x18\x01\x12\r\n\x05label\x18\x04 \x01(\t\x12\x14\n\x0csample_count\x18\x05 \x01(\x01') + + _globals = globals() + _builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals) + _builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'facet_feature_statistics_pb2', _globals) + if not _descriptor._USE_C_DESCRIPTORS: + DESCRIPTOR._loaded_options = None + _globals['_STRINGSTATISTICS_FREQANDVALUE'].fields_by_name['deprecated_freq']._loaded_options = None + _globals['_STRINGSTATISTICS_FREQANDVALUE'].fields_by_name['deprecated_freq']._serialized_options = b'\030\001' + _globals['_HISTOGRAM_BUCKET'].fields_by_name['deprecated_count']._loaded_options = None + _globals['_HISTOGRAM_BUCKET'].fields_by_name['deprecated_count']._serialized_options = b'\030\001' + _globals['_RANKHISTOGRAM_BUCKET'].fields_by_name['deprecated_count']._loaded_options = None + _globals['_RANKHISTOGRAM_BUCKET'].fields_by_name['deprecated_count']._serialized_options = b'\030\001' + _globals['_DATASETFEATURESTATISTICSLIST']._serialized_start=58 + _globals['_DATASETFEATURESTATISTICSLIST']._serialized_end=156 + _globals['_PATH']._serialized_start=158 + _globals['_PATH']._serialized_end=178 + _globals['_DATASETFEATURESTATISTICS']._serialized_start=181 + _globals['_DATASETFEATURESTATISTICS']._serialized_end=339 + _globals['_FEATURENAMESTATISTICS']._serialized_start=342 + _globals['_FEATURENAMESTATISTICS']._serialized_end=900 + _globals['_FEATURENAMESTATISTICS_TYPE']._serialized_start=818 + _globals['_FEATURENAMESTATISTICS_TYPE']._serialized_end=879 + _globals['_WEIGHTEDCOMMONSTATISTICS']._serialized_start=902 + _globals['_WEIGHTEDCOMMONSTATISTICS']._serialized_end=1022 + _globals['_CUSTOMSTATISTIC']._serialized_start=1025 + _globals['_CUSTOMSTATISTIC']._serialized_end=1220 + _globals['_NUMERICSTATISTICS']._serialized_start=1223 + _globals['_NUMERICSTATISTICS']._serialized_end=1536 + _globals['_STRINGSTATISTICS']._serialized_start=1539 + _globals['_STRINGSTATISTICS']._serialized_end=1955 + _globals['_STRINGSTATISTICS_FREQANDVALUE']._serialized_start=1878 + _globals['_STRINGSTATISTICS_FREQANDVALUE']._serialized_end=1955 + _globals['_WEIGHTEDNUMERICSTATISTICS']._serialized_start=1958 + _globals['_WEIGHTEDNUMERICSTATISTICS']._serialized_end=2087 + _globals['_WEIGHTEDSTRINGSTATISTICS']._serialized_start=2090 + _globals['_WEIGHTEDSTRINGSTATISTICS']._serialized_end=2254 + _globals['_BYTESSTATISTICS']._serialized_start=2257 + _globals['_BYTESSTATISTICS']._serialized_end=2429 + _globals['_STRUCTSTATISTICS']._serialized_start=2431 + _globals['_STRUCTSTATISTICS']._serialized_end=2513 + _globals['_COMMONSTATISTICS']._serialized_start=2516 + _globals['_COMMONSTATISTICS']._serialized_end=2908 + _globals['_HISTOGRAM']._serialized_start=2911 + _globals['_HISTOGRAM']._serialized_end=3245 + _globals['_HISTOGRAM_BUCKET']._serialized_start=3100 + _globals['_HISTOGRAM_BUCKET']._serialized_end=3199 + _globals['_HISTOGRAM_HISTOGRAMTYPE']._serialized_start=3201 + _globals['_HISTOGRAM_HISTOGRAMTYPE']._serialized_end=3245 + _globals['_RANKHISTOGRAM']._serialized_start=3248 + _globals['_RANKHISTOGRAM']._serialized_end=3454 + _globals['_RANKHISTOGRAM_BUCKET']._serialized_start=3342 + _globals['_RANKHISTOGRAM_BUCKET']._serialized_end=3454 + # @@protoc_insertion_point(module_scope) + +else: + # -*- coding: utf-8 -*- + # Generated by the protocol buffer compiler. DO NOT EDIT! + # source: facet_feature_statistics.proto + """Generated protocol buffer code.""" + from google.protobuf import descriptor as _descriptor + from google.protobuf import descriptor_pool as _descriptor_pool + from google.protobuf import message as _message + from google.protobuf import reflection as _reflection + from google.protobuf import symbol_database as _symbol_database + # @@protoc_insertion_point(imports) + + _sym_db = _symbol_database.Default() + + + + + DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\x1e\x66\x61\x63\x65t_feature_statistics.proto\x12\x16\x66\x61\x63\x65tFeatureStatistics\"b\n\x1c\x44\x61tasetFeatureStatisticsList\x12\x42\n\x08\x64\x61tasets\x18\x01 \x03(\x0b\x32\x30.facetFeatureStatistics.DatasetFeatureStatistics\"\x14\n\x04Path\x12\x0c\n\x04step\x18\x01 \x03(\t\"\x9e\x01\n\x18\x44\x61tasetFeatureStatistics\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x14\n\x0cnum_examples\x18\x02 \x01(\x03\x12\x1d\n\x15weighted_num_examples\x18\x04 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\x01(\x03\x12\x13\n\x0bnum_missing\x18\x02 \x01(\x03\x12\x16\n\x0emin_num_values\x18\x03 \x01(\x03\x12\x16\n\x0emax_num_values\x18\x04 \x01(\x03\x12\x16\n\x0e\x61vg_num_values\x18\x05 \x01(\x02\x12\x16\n\x0etot_num_values\x18\x08 \x01(\x03\x12?\n\x14num_values_histogram\x18\x06 \x01(\x0b\x32!.facetFeatureStatistics.Histogram\x12O\n\x15weighted_common_stats\x18\x07 \x01(\x0b\x32\x30.facetFeatureStatistics.WeightedCommonStatistics\x12H\n\x1d\x66\x65\x61ture_list_length_histogram\x18\t \x01(\x0b\x32!.facetFeatureStatistics.HistogramJ\x04\x08\n\x10\x0bJ\x04\x08\x0b\x10\x0c\"\xce\x02\n\tHistogram\x12\x0f\n\x07num_nan\x18\x01 \x01(\x03\x12\x15\n\rnum_undefined\x18\x02 \x01(\x03\x12\x39\n\x07\x62uckets\x18\x03 \x03(\x0b\x32(.facetFeatureStatistics.Histogram.Bucket\x12=\n\x04type\x18\x04 \x01(\x0e\x32/.facetFeatureStatistics.Histogram.HistogramType\x12\x0c\n\x04name\x18\x05 \x01(\t\x1a\x63\n\x06\x42ucket\x12\x11\n\tlow_value\x18\x01 \x01(\x01\x12\x12\n\nhigh_value\x18\x02 \x01(\x01\x12\x1c\n\x10\x64\x65precated_count\x18\x03 \x01(\x03\x42\x02\x18\x01\x12\x14\n\x0csample_count\x18\x04 \x01(\x01\",\n\rHistogramType\x12\x0c\n\x08STANDARD\x10\x00\x12\r\n\tQUANTILES\x10\x01\"\xce\x01\n\rRankHistogram\x12=\n\x07\x62uckets\x18\x01 \x03(\x0b\x32,.facetFeatureStatistics.RankHistogram.Bucket\x12\x0c\n\x04name\x18\x02 \x01(\t\x1ap\n\x06\x42ucket\x12\x10\n\x08low_rank\x18\x01 \x01(\x03\x12\x11\n\thigh_rank\x18\x02 \x01(\x03\x12\x1c\n\x10\x64\x65precated_count\x18\x03 \x01(\x03\x42\x02\x18\x01\x12\r\n\x05label\x18\x04 \x01(\t\x12\x14\n\x0csample_count\x18\x05 \x01(\x01') + + + + _DATASETFEATURESTATISTICSLIST = DESCRIPTOR.message_types_by_name['DatasetFeatureStatisticsList'] + _PATH = DESCRIPTOR.message_types_by_name['Path'] + _DATASETFEATURESTATISTICS = DESCRIPTOR.message_types_by_name['DatasetFeatureStatistics'] + _FEATURENAMESTATISTICS = DESCRIPTOR.message_types_by_name['FeatureNameStatistics'] + _WEIGHTEDCOMMONSTATISTICS = DESCRIPTOR.message_types_by_name['WeightedCommonStatistics'] + _CUSTOMSTATISTIC = DESCRIPTOR.message_types_by_name['CustomStatistic'] + _NUMERICSTATISTICS = DESCRIPTOR.message_types_by_name['NumericStatistics'] + _STRINGSTATISTICS = DESCRIPTOR.message_types_by_name['StringStatistics'] + _STRINGSTATISTICS_FREQANDVALUE = _STRINGSTATISTICS.nested_types_by_name['FreqAndValue'] + _WEIGHTEDNUMERICSTATISTICS = DESCRIPTOR.message_types_by_name['WeightedNumericStatistics'] + _WEIGHTEDSTRINGSTATISTICS = DESCRIPTOR.message_types_by_name['WeightedStringStatistics'] + _BYTESSTATISTICS = DESCRIPTOR.message_types_by_name['BytesStatistics'] + _STRUCTSTATISTICS = DESCRIPTOR.message_types_by_name['StructStatistics'] + _COMMONSTATISTICS = DESCRIPTOR.message_types_by_name['CommonStatistics'] + _HISTOGRAM = DESCRIPTOR.message_types_by_name['Histogram'] + _HISTOGRAM_BUCKET = _HISTOGRAM.nested_types_by_name['Bucket'] + _RANKHISTOGRAM = DESCRIPTOR.message_types_by_name['RankHistogram'] + _RANKHISTOGRAM_BUCKET = _RANKHISTOGRAM.nested_types_by_name['Bucket'] + _FEATURENAMESTATISTICS_TYPE = _FEATURENAMESTATISTICS.enum_types_by_name['Type'] + _HISTOGRAM_HISTOGRAMTYPE = _HISTOGRAM.enum_types_by_name['HistogramType'] + DatasetFeatureStatisticsList = _reflection.GeneratedProtocolMessageType('DatasetFeatureStatisticsList', (_message.Message,), { + 'DESCRIPTOR' : _DATASETFEATURESTATISTICSLIST, + '__module__' : 'facet_feature_statistics_pb2' + # @@protoc_insertion_point(class_scope:facetFeatureStatistics.DatasetFeatureStatisticsList) + }) + _sym_db.RegisterMessage(DatasetFeatureStatisticsList) + + Path = _reflection.GeneratedProtocolMessageType('Path', (_message.Message,), { + 'DESCRIPTOR' : _PATH, + '__module__' : 'facet_feature_statistics_pb2' + # @@protoc_insertion_point(class_scope:facetFeatureStatistics.Path) + }) + _sym_db.RegisterMessage(Path) + + DatasetFeatureStatistics = _reflection.GeneratedProtocolMessageType('DatasetFeatureStatistics', (_message.Message,), { + 'DESCRIPTOR' : _DATASETFEATURESTATISTICS, + '__module__' : 'facet_feature_statistics_pb2' + # @@protoc_insertion_point(class_scope:facetFeatureStatistics.DatasetFeatureStatistics) + }) + _sym_db.RegisterMessage(DatasetFeatureStatistics) + + FeatureNameStatistics = _reflection.GeneratedProtocolMessageType('FeatureNameStatistics', (_message.Message,), { + 'DESCRIPTOR' : _FEATURENAMESTATISTICS, + '__module__' : 'facet_feature_statistics_pb2' + # @@protoc_insertion_point(class_scope:facetFeatureStatistics.FeatureNameStatistics) + }) + _sym_db.RegisterMessage(FeatureNameStatistics) + + WeightedCommonStatistics = _reflection.GeneratedProtocolMessageType('WeightedCommonStatistics', (_message.Message,), { + 'DESCRIPTOR' : _WEIGHTEDCOMMONSTATISTICS, + '__module__' : 'facet_feature_statistics_pb2' + # @@protoc_insertion_point(class_scope:facetFeatureStatistics.WeightedCommonStatistics) + }) + _sym_db.RegisterMessage(WeightedCommonStatistics) + + CustomStatistic = _reflection.GeneratedProtocolMessageType('CustomStatistic', (_message.Message,), { + 'DESCRIPTOR' : _CUSTOMSTATISTIC, + '__module__' : 'facet_feature_statistics_pb2' + # @@protoc_insertion_point(class_scope:facetFeatureStatistics.CustomStatistic) + }) + _sym_db.RegisterMessage(CustomStatistic) + + NumericStatistics = _reflection.GeneratedProtocolMessageType('NumericStatistics', (_message.Message,), { + 'DESCRIPTOR' : _NUMERICSTATISTICS, + '__module__' : 'facet_feature_statistics_pb2' + # @@protoc_insertion_point(class_scope:facetFeatureStatistics.NumericStatistics) + }) + _sym_db.RegisterMessage(NumericStatistics) + + StringStatistics = _reflection.GeneratedProtocolMessageType('StringStatistics', (_message.Message,), { + + 'FreqAndValue' : _reflection.GeneratedProtocolMessageType('FreqAndValue', (_message.Message,), { + 'DESCRIPTOR' : _STRINGSTATISTICS_FREQANDVALUE, + '__module__' : 'facet_feature_statistics_pb2' + # @@protoc_insertion_point(class_scope:facetFeatureStatistics.StringStatistics.FreqAndValue) + }) + , + 'DESCRIPTOR' : _STRINGSTATISTICS, + '__module__' : 'facet_feature_statistics_pb2' + # @@protoc_insertion_point(class_scope:facetFeatureStatistics.StringStatistics) + }) + _sym_db.RegisterMessage(StringStatistics) + _sym_db.RegisterMessage(StringStatistics.FreqAndValue) + + WeightedNumericStatistics = _reflection.GeneratedProtocolMessageType('WeightedNumericStatistics', (_message.Message,), { + 'DESCRIPTOR' : _WEIGHTEDNUMERICSTATISTICS, + '__module__' : 'facet_feature_statistics_pb2' + # @@protoc_insertion_point(class_scope:facetFeatureStatistics.WeightedNumericStatistics) + }) + _sym_db.RegisterMessage(WeightedNumericStatistics) + + WeightedStringStatistics = _reflection.GeneratedProtocolMessageType('WeightedStringStatistics', (_message.Message,), { + 'DESCRIPTOR' : _WEIGHTEDSTRINGSTATISTICS, + '__module__' : 'facet_feature_statistics_pb2' + # @@protoc_insertion_point(class_scope:facetFeatureStatistics.WeightedStringStatistics) + }) + _sym_db.RegisterMessage(WeightedStringStatistics) + + BytesStatistics = _reflection.GeneratedProtocolMessageType('BytesStatistics', (_message.Message,), { + 'DESCRIPTOR' : _BYTESSTATISTICS, + '__module__' : 'facet_feature_statistics_pb2' + # @@protoc_insertion_point(class_scope:facetFeatureStatistics.BytesStatistics) + }) + _sym_db.RegisterMessage(BytesStatistics) + + StructStatistics = _reflection.GeneratedProtocolMessageType('StructStatistics', (_message.Message,), { + 'DESCRIPTOR' : _STRUCTSTATISTICS, + '__module__' : 'facet_feature_statistics_pb2' + # @@protoc_insertion_point(class_scope:facetFeatureStatistics.StructStatistics) + }) + _sym_db.RegisterMessage(StructStatistics) + + CommonStatistics = _reflection.GeneratedProtocolMessageType('CommonStatistics', (_message.Message,), { + 'DESCRIPTOR' : _COMMONSTATISTICS, + '__module__' : 'facet_feature_statistics_pb2' + # @@protoc_insertion_point(class_scope:facetFeatureStatistics.CommonStatistics) + }) + _sym_db.RegisterMessage(CommonStatistics) + + Histogram = _reflection.GeneratedProtocolMessageType('Histogram', (_message.Message,), { + + 'Bucket' : _reflection.GeneratedProtocolMessageType('Bucket', (_message.Message,), { + 'DESCRIPTOR' : _HISTOGRAM_BUCKET, + '__module__' : 'facet_feature_statistics_pb2' + # @@protoc_insertion_point(class_scope:facetFeatureStatistics.Histogram.Bucket) + }) + , + 'DESCRIPTOR' : _HISTOGRAM, + '__module__' : 'facet_feature_statistics_pb2' + # @@protoc_insertion_point(class_scope:facetFeatureStatistics.Histogram) + }) + _sym_db.RegisterMessage(Histogram) + _sym_db.RegisterMessage(Histogram.Bucket) + + RankHistogram = _reflection.GeneratedProtocolMessageType('RankHistogram', (_message.Message,), { + + 'Bucket' : _reflection.GeneratedProtocolMessageType('Bucket', (_message.Message,), { + 'DESCRIPTOR' : _RANKHISTOGRAM_BUCKET, + '__module__' : 'facet_feature_statistics_pb2' + # @@protoc_insertion_point(class_scope:facetFeatureStatistics.RankHistogram.Bucket) + }) + , + 'DESCRIPTOR' : _RANKHISTOGRAM, + '__module__' : 'facet_feature_statistics_pb2' + # @@protoc_insertion_point(class_scope:facetFeatureStatistics.RankHistogram) + }) + _sym_db.RegisterMessage(RankHistogram) + _sym_db.RegisterMessage(RankHistogram.Bucket) + + if _descriptor._USE_C_DESCRIPTORS == False: + + DESCRIPTOR._options = None + _STRINGSTATISTICS_FREQANDVALUE.fields_by_name['deprecated_freq']._options = None + _STRINGSTATISTICS_FREQANDVALUE.fields_by_name['deprecated_freq']._serialized_options = b'\030\001' + _HISTOGRAM_BUCKET.fields_by_name['deprecated_count']._options = None + _HISTOGRAM_BUCKET.fields_by_name['deprecated_count']._serialized_options = b'\030\001' + _RANKHISTOGRAM_BUCKET.fields_by_name['deprecated_count']._options = None + _RANKHISTOGRAM_BUCKET.fields_by_name['deprecated_count']._serialized_options = b'\030\001' + _DATASETFEATURESTATISTICSLIST._serialized_start=58 + _DATASETFEATURESTATISTICSLIST._serialized_end=156 + _PATH._serialized_start=158 + _PATH._serialized_end=178 + _DATASETFEATURESTATISTICS._serialized_start=181 + _DATASETFEATURESTATISTICS._serialized_end=339 + _FEATURENAMESTATISTICS._serialized_start=342 + _FEATURENAMESTATISTICS._serialized_end=900 + _FEATURENAMESTATISTICS_TYPE._serialized_start=818 + _FEATURENAMESTATISTICS_TYPE._serialized_end=879 + _WEIGHTEDCOMMONSTATISTICS._serialized_start=902 + _WEIGHTEDCOMMONSTATISTICS._serialized_end=1022 + _CUSTOMSTATISTIC._serialized_start=1025 + _CUSTOMSTATISTIC._serialized_end=1220 + _NUMERICSTATISTICS._serialized_start=1223 + _NUMERICSTATISTICS._serialized_end=1536 + _STRINGSTATISTICS._serialized_start=1539 + _STRINGSTATISTICS._serialized_end=1955 + _STRINGSTATISTICS_FREQANDVALUE._serialized_start=1878 + _STRINGSTATISTICS_FREQANDVALUE._serialized_end=1955 + _WEIGHTEDNUMERICSTATISTICS._serialized_start=1958 + _WEIGHTEDNUMERICSTATISTICS._serialized_end=2087 + _WEIGHTEDSTRINGSTATISTICS._serialized_start=2090 + _WEIGHTEDSTRINGSTATISTICS._serialized_end=2254 + _BYTESSTATISTICS._serialized_start=2257 + _BYTESSTATISTICS._serialized_end=2429 + _STRUCTSTATISTICS._serialized_start=2431 + _STRUCTSTATISTICS._serialized_end=2513 + _COMMONSTATISTICS._serialized_start=2516 + _COMMONSTATISTICS._serialized_end=2908 + _HISTOGRAM._serialized_start=2911 + _HISTOGRAM._serialized_end=3245 + _HISTOGRAM_BUCKET._serialized_start=3100 + _HISTOGRAM_BUCKET._serialized_end=3199 + _HISTOGRAM_HISTOGRAMTYPE._serialized_start=3201 + _HISTOGRAM_HISTOGRAMTYPE._serialized_end=3245 + _RANKHISTOGRAM._serialized_start=3248 + _RANKHISTOGRAM._serialized_end=3454 + _RANKHISTOGRAM_BUCKET._serialized_start=3342 + _RANKHISTOGRAM_BUCKET._serialized_end=3454 + # @@protoc_insertion_point(module_scope) + diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/protos/internal_pb2.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/protos/internal_pb2.py new file mode 100644 index 0000000000000000000000000000000000000000..7aa93249e6ff52e61454ddb64e8ded5fe884a8a4 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/protos/internal_pb2.py @@ -0,0 +1,77 @@ + +import google.protobuf +from packaging.version import Version +if Version(google.protobuf.__version__).major >= 5: + # -*- coding: utf-8 -*- + # Generated by the protocol buffer compiler. DO NOT EDIT! + # source: internal.proto + # Protobuf Python Version: 5.26.0 + """Generated protocol buffer code.""" + from google.protobuf import descriptor as _descriptor + from google.protobuf import descriptor_pool as _descriptor_pool + from google.protobuf import symbol_database as _symbol_database + from google.protobuf.internal import builder as _builder + # @@protoc_insertion_point(imports) + + _sym_db = _symbol_database.Default() + + + from .scalapb import scalapb_pb2 as scalapb_dot_scalapb__pb2 + + + DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\x0einternal.proto\x12\x0fmlflow.internal\x1a\x15scalapb/scalapb.proto*2\n\x0fInputVertexType\x12\x07\n\x03RUN\x10\x01\x12\x0b\n\x07\x44\x41TASET\x10\x02\x12\t\n\x05MODEL\x10\x03*4\n\x10OutputVertexType\x12\x0e\n\nRUN_OUTPUT\x10\x01\x12\x10\n\x0cMODEL_OUTPUT\x10\x02\x42#\n\x19org.mlflow.internal.proto\x90\x01\x01\xe2?\x02\x10\x01') + + _globals = globals() + _builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals) + _builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'internal_pb2', _globals) + if not _descriptor._USE_C_DESCRIPTORS: + _globals['DESCRIPTOR']._loaded_options = None + _globals['DESCRIPTOR']._serialized_options = b'\n\031org.mlflow.internal.proto\220\001\001\342?\002\020\001' + _globals['_INPUTVERTEXTYPE']._serialized_start=58 + _globals['_INPUTVERTEXTYPE']._serialized_end=108 + _globals['_OUTPUTVERTEXTYPE']._serialized_start=110 + _globals['_OUTPUTVERTEXTYPE']._serialized_end=162 + # @@protoc_insertion_point(module_scope) + +else: + # -*- coding: utf-8 -*- + # Generated by the protocol buffer compiler. DO NOT EDIT! + # source: internal.proto + """Generated protocol buffer code.""" + from google.protobuf.internal import enum_type_wrapper + from google.protobuf import descriptor as _descriptor + from google.protobuf import descriptor_pool as _descriptor_pool + from google.protobuf import message as _message + from google.protobuf import reflection as _reflection + from google.protobuf import symbol_database as _symbol_database + # @@protoc_insertion_point(imports) + + _sym_db = _symbol_database.Default() + + + from .scalapb import scalapb_pb2 as scalapb_dot_scalapb__pb2 + + + DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\x0einternal.proto\x12\x0fmlflow.internal\x1a\x15scalapb/scalapb.proto*2\n\x0fInputVertexType\x12\x07\n\x03RUN\x10\x01\x12\x0b\n\x07\x44\x41TASET\x10\x02\x12\t\n\x05MODEL\x10\x03*4\n\x10OutputVertexType\x12\x0e\n\nRUN_OUTPUT\x10\x01\x12\x10\n\x0cMODEL_OUTPUT\x10\x02\x42#\n\x19org.mlflow.internal.proto\x90\x01\x01\xe2?\x02\x10\x01') + + _INPUTVERTEXTYPE = DESCRIPTOR.enum_types_by_name['InputVertexType'] + InputVertexType = enum_type_wrapper.EnumTypeWrapper(_INPUTVERTEXTYPE) + _OUTPUTVERTEXTYPE = DESCRIPTOR.enum_types_by_name['OutputVertexType'] + OutputVertexType = enum_type_wrapper.EnumTypeWrapper(_OUTPUTVERTEXTYPE) + RUN = 1 + DATASET = 2 + MODEL = 3 + RUN_OUTPUT = 1 + MODEL_OUTPUT = 2 + + + if _descriptor._USE_C_DESCRIPTORS == False: + + DESCRIPTOR._options = None + DESCRIPTOR._serialized_options = b'\n\031org.mlflow.internal.proto\220\001\001\342?\002\020\001' + _INPUTVERTEXTYPE._serialized_start=58 + _INPUTVERTEXTYPE._serialized_end=108 + _OUTPUTVERTEXTYPE._serialized_start=110 + _OUTPUTVERTEXTYPE._serialized_end=162 + # @@protoc_insertion_point(module_scope) + diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/protos/mlflow_artifacts_pb2.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/protos/mlflow_artifacts_pb2.py new file mode 100644 index 0000000000000000000000000000000000000000..ed7e8770f758c19bdf2ddd21ca8325503e25a695 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/protos/mlflow_artifacts_pb2.py @@ -0,0 +1,336 @@ + +import google.protobuf +from packaging.version import Version +if Version(google.protobuf.__version__).major >= 5: + # -*- coding: utf-8 -*- + # Generated by the protocol buffer compiler. DO NOT EDIT! + # source: mlflow_artifacts.proto + # Protobuf Python Version: 5.26.0 + """Generated protocol buffer code.""" + from google.protobuf import descriptor as _descriptor + from google.protobuf import descriptor_pool as _descriptor_pool + from google.protobuf import symbol_database as _symbol_database + from google.protobuf.internal import builder as _builder + # @@protoc_insertion_point(imports) + + _sym_db = _symbol_database.Default() + + + from .scalapb import scalapb_pb2 as scalapb_dot_scalapb__pb2 + from . import databricks_pb2 as databricks__pb2 + + + DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\x16mlflow_artifacts.proto\x12\x10mlflow.artifacts\x1a\x15scalapb/scalapb.proto\x1a\x10\x64\x61tabricks.proto\"\x1e\n\x10\x44ownloadArtifact\x1a\n\n\x08Response\"\x1c\n\x0eUploadArtifact\x1a\n\n\x08Response\"T\n\rListArtifacts\x12\x0c\n\x04path\x18\x01 \x01(\t\x1a\x35\n\x08Response\x12)\n\x05\x66iles\x18\x01 \x03(\x0b\x32\x1a.mlflow.artifacts.FileInfo\"\x1c\n\x0e\x44\x65leteArtifact\x1a\n\n\x08Response\";\n\x08\x46ileInfo\x12\x0c\n\x04path\x18\x01 \x01(\t\x12\x0e\n\x06is_dir\x18\x02 \x01(\x08\x12\x11\n\tfile_size\x18\x03 \x01(\x03\"\x99\x01\n\x15\x43reateMultipartUpload\x12\x0c\n\x04path\x18\x01 \x01(\t\x12\x11\n\tnum_parts\x18\x02 \x01(\x03\x1a_\n\x08Response\x12\x11\n\tupload_id\x18\x01 \x01(\t\x12@\n\x0b\x63redentials\x18\x02 \x03(\x0b\x32+.mlflow.artifacts.MultipartUploadCredential\"|\n\x17\x43ompleteMultipartUpload\x12\x0c\n\x04path\x18\x01 \x01(\t\x12\x11\n\tupload_id\x18\x02 \x01(\t\x12\x34\n\x05parts\x18\x03 \x03(\x0b\x32%.mlflow.artifacts.MultipartUploadPart\x1a\n\n\x08Response\"C\n\x14\x41\x62ortMultipartUpload\x12\x0c\n\x04path\x18\x01 \x01(\t\x12\x11\n\tupload_id\x18\x02 \x01(\t\x1a\n\n\x08Response\"\xb8\x01\n\x19MultipartUploadCredential\x12\x0b\n\x03url\x18\x01 \x01(\t\x12\x13\n\x0bpart_number\x18\x02 \x01(\x03\x12I\n\x07headers\x18\x03 \x03(\x0b\x32\x38.mlflow.artifacts.MultipartUploadCredential.HeadersEntry\x1a.\n\x0cHeadersEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12\r\n\x05value\x18\x02 \x01(\t:\x02\x38\x01\"E\n\x13MultipartUploadPart\x12\x13\n\x0bpart_number\x18\x01 \x01(\x03\x12\x0c\n\x04\x65tag\x18\x02 \x01(\t\x12\x0b\n\x03url\x18\x03 \x01(\t2\x99\x0b\n\x16MlflowArtifactsService\x12\xbd\x01\n\x10\x64ownloadArtifact\x12\".mlflow.artifacts.DownloadArtifact\x1a+.mlflow.artifacts.DownloadArtifact.Response\"X\xf2\x86\x19T\n=\n\x03GET\x12\x30/mlflow-artifacts/artifacts/\x1a\x04\x08\x02\x10\x00\x10\x01*\x11\x44ownload Artifact\x12\xb5\x01\n\x0euploadArtifact\x12 .mlflow.artifacts.UploadArtifact\x1a).mlflow.artifacts.UploadArtifact.Response\"V\xf2\x86\x19R\n=\n\x03PUT\x12\x30/mlflow-artifacts/artifacts/\x1a\x04\x08\x02\x10\x00\x10\x01*\x0fUpload Artifact\x12\x9c\x01\n\rlistArtifacts\x12\x1f.mlflow.artifacts.ListArtifacts\x1a(.mlflow.artifacts.ListArtifacts.Response\"@\xf2\x86\x19<\n(\n\x03GET\x12\x1b/mlflow-artifacts/artifacts\x1a\x04\x08\x02\x10\x00\x10\x01*\x0eList Artifacts\x12\xb9\x01\n\x0e\x64\x65leteArtifact\x12 .mlflow.artifacts.DeleteArtifact\x1a).mlflow.artifacts.DeleteArtifact.Response\"Z\xf2\x86\x19V\n@\n\x06\x44\x45LETE\x12\x30/mlflow-artifacts/artifacts/\x1a\x04\x08\x02\x10\x00\x10\x01*\x10\x44\x65lete Artifacts\x12\xe0\x01\n\x15\x63reateMultipartUpload\x12\'.mlflow.artifacts.CreateMultipartUpload\x1a\x30.mlflow.artifacts.CreateMultipartUpload.Response\"l\xf2\x86\x19h\n?\n\x04POST\x12\x31/mlflow-artifacts/mpu/create/\x1a\x04\x08\x02\x10\x00\x10\x01*#Create an Artifact Multipart Upload\x12\xea\x01\n\x17\x63ompleteMultipartUpload\x12).mlflow.artifacts.CompleteMultipartUpload\x1a\x32.mlflow.artifacts.CompleteMultipartUpload.Response\"p\xf2\x86\x19l\nA\n\x04POST\x12\x33/mlflow-artifacts/mpu/complete/\x1a\x04\x08\x02\x10\x00\x10\x01*%Complete an Artifact Multipart Upload\x12\xdb\x01\n\x14\x61\x62ortMultipartUpload\x12&.mlflow.artifacts.AbortMultipartUpload\x1a/.mlflow.artifacts.AbortMultipartUpload.Response\"j\xf2\x86\x19\x66\n>\n\x04POST\x12\x30/mlflow-artifacts/mpu/abort/\x1a\x04\x08\x02\x10\x00\x10\x01*\"Abort an Artifact Multipart UploadB\x1e\n\x14org.mlflow.api.proto\x90\x01\x01\xe2?\x02\x10\x01') + + _globals = globals() + _builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals) + _builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'mlflow_artifacts_pb2', _globals) + if not _descriptor._USE_C_DESCRIPTORS: + _globals['DESCRIPTOR']._loaded_options = None + _globals['DESCRIPTOR']._serialized_options = b'\n\024org.mlflow.api.proto\220\001\001\342?\002\020\001' + _globals['_MULTIPARTUPLOADCREDENTIAL_HEADERSENTRY']._loaded_options = None + _globals['_MULTIPARTUPLOADCREDENTIAL_HEADERSENTRY']._serialized_options = b'8\001' + _globals['_MLFLOWARTIFACTSSERVICE'].methods_by_name['downloadArtifact']._loaded_options = None + _globals['_MLFLOWARTIFACTSSERVICE'].methods_by_name['downloadArtifact']._serialized_options = b'\362\206\031T\n=\n\003GET\0220/mlflow-artifacts/artifacts/\032\004\010\002\020\000\020\001*\021Download Artifact' + _globals['_MLFLOWARTIFACTSSERVICE'].methods_by_name['uploadArtifact']._loaded_options = None + _globals['_MLFLOWARTIFACTSSERVICE'].methods_by_name['uploadArtifact']._serialized_options = b'\362\206\031R\n=\n\003PUT\0220/mlflow-artifacts/artifacts/\032\004\010\002\020\000\020\001*\017Upload Artifact' + _globals['_MLFLOWARTIFACTSSERVICE'].methods_by_name['listArtifacts']._loaded_options = None + _globals['_MLFLOWARTIFACTSSERVICE'].methods_by_name['listArtifacts']._serialized_options = b'\362\206\031<\n(\n\003GET\022\033/mlflow-artifacts/artifacts\032\004\010\002\020\000\020\001*\016List Artifacts' + _globals['_MLFLOWARTIFACTSSERVICE'].methods_by_name['deleteArtifact']._loaded_options = None + _globals['_MLFLOWARTIFACTSSERVICE'].methods_by_name['deleteArtifact']._serialized_options = b'\362\206\031V\n@\n\006DELETE\0220/mlflow-artifacts/artifacts/\032\004\010\002\020\000\020\001*\020Delete Artifacts' + _globals['_MLFLOWARTIFACTSSERVICE'].methods_by_name['createMultipartUpload']._loaded_options = None + _globals['_MLFLOWARTIFACTSSERVICE'].methods_by_name['createMultipartUpload']._serialized_options = b'\362\206\031h\n?\n\004POST\0221/mlflow-artifacts/mpu/create/\032\004\010\002\020\000\020\001*#Create an Artifact Multipart Upload' + _globals['_MLFLOWARTIFACTSSERVICE'].methods_by_name['completeMultipartUpload']._loaded_options = None + _globals['_MLFLOWARTIFACTSSERVICE'].methods_by_name['completeMultipartUpload']._serialized_options = b'\362\206\031l\nA\n\004POST\0223/mlflow-artifacts/mpu/complete/\032\004\010\002\020\000\020\001*%Complete an Artifact Multipart Upload' + _globals['_MLFLOWARTIFACTSSERVICE'].methods_by_name['abortMultipartUpload']._loaded_options = None + _globals['_MLFLOWARTIFACTSSERVICE'].methods_by_name['abortMultipartUpload']._serialized_options = b'\362\206\031f\n>\n\004POST\0220/mlflow-artifacts/mpu/abort/\032\004\010\002\020\000\020\001*\"Abort an Artifact Multipart Upload' + _globals['_DOWNLOADARTIFACT']._serialized_start=85 + _globals['_DOWNLOADARTIFACT']._serialized_end=115 + _globals['_DOWNLOADARTIFACT_RESPONSE']._serialized_start=105 + _globals['_DOWNLOADARTIFACT_RESPONSE']._serialized_end=115 + _globals['_UPLOADARTIFACT']._serialized_start=117 + _globals['_UPLOADARTIFACT']._serialized_end=145 + _globals['_UPLOADARTIFACT_RESPONSE']._serialized_start=105 + _globals['_UPLOADARTIFACT_RESPONSE']._serialized_end=115 + _globals['_LISTARTIFACTS']._serialized_start=147 + _globals['_LISTARTIFACTS']._serialized_end=231 + _globals['_LISTARTIFACTS_RESPONSE']._serialized_start=178 + _globals['_LISTARTIFACTS_RESPONSE']._serialized_end=231 + _globals['_DELETEARTIFACT']._serialized_start=233 + _globals['_DELETEARTIFACT']._serialized_end=261 + _globals['_DELETEARTIFACT_RESPONSE']._serialized_start=105 + _globals['_DELETEARTIFACT_RESPONSE']._serialized_end=115 + _globals['_FILEINFO']._serialized_start=263 + _globals['_FILEINFO']._serialized_end=322 + _globals['_CREATEMULTIPARTUPLOAD']._serialized_start=325 + _globals['_CREATEMULTIPARTUPLOAD']._serialized_end=478 + _globals['_CREATEMULTIPARTUPLOAD_RESPONSE']._serialized_start=383 + _globals['_CREATEMULTIPARTUPLOAD_RESPONSE']._serialized_end=478 + _globals['_COMPLETEMULTIPARTUPLOAD']._serialized_start=480 + _globals['_COMPLETEMULTIPARTUPLOAD']._serialized_end=604 + _globals['_COMPLETEMULTIPARTUPLOAD_RESPONSE']._serialized_start=105 + _globals['_COMPLETEMULTIPARTUPLOAD_RESPONSE']._serialized_end=115 + _globals['_ABORTMULTIPARTUPLOAD']._serialized_start=606 + _globals['_ABORTMULTIPARTUPLOAD']._serialized_end=673 + _globals['_ABORTMULTIPARTUPLOAD_RESPONSE']._serialized_start=105 + _globals['_ABORTMULTIPARTUPLOAD_RESPONSE']._serialized_end=115 + _globals['_MULTIPARTUPLOADCREDENTIAL']._serialized_start=676 + _globals['_MULTIPARTUPLOADCREDENTIAL']._serialized_end=860 + _globals['_MULTIPARTUPLOADCREDENTIAL_HEADERSENTRY']._serialized_start=814 + _globals['_MULTIPARTUPLOADCREDENTIAL_HEADERSENTRY']._serialized_end=860 + _globals['_MULTIPARTUPLOADPART']._serialized_start=862 + _globals['_MULTIPARTUPLOADPART']._serialized_end=931 + _globals['_MLFLOWARTIFACTSSERVICE']._serialized_start=934 + _globals['_MLFLOWARTIFACTSSERVICE']._serialized_end=2367 + _builder.BuildServices(DESCRIPTOR, 'mlflow_artifacts_pb2', _globals) + # @@protoc_insertion_point(module_scope) + +else: + # -*- coding: utf-8 -*- + # Generated by the protocol buffer compiler. DO NOT EDIT! + # source: mlflow_artifacts.proto + """Generated protocol buffer code.""" + from google.protobuf import descriptor as _descriptor + from google.protobuf import descriptor_pool as _descriptor_pool + from google.protobuf import message as _message + from google.protobuf import reflection as _reflection + from google.protobuf import symbol_database as _symbol_database + from google.protobuf import service as _service + from google.protobuf import service_reflection + # @@protoc_insertion_point(imports) + + _sym_db = _symbol_database.Default() + + + from .scalapb import scalapb_pb2 as scalapb_dot_scalapb__pb2 + from . import databricks_pb2 as databricks__pb2 + + + DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\x16mlflow_artifacts.proto\x12\x10mlflow.artifacts\x1a\x15scalapb/scalapb.proto\x1a\x10\x64\x61tabricks.proto\"\x1e\n\x10\x44ownloadArtifact\x1a\n\n\x08Response\"\x1c\n\x0eUploadArtifact\x1a\n\n\x08Response\"T\n\rListArtifacts\x12\x0c\n\x04path\x18\x01 \x01(\t\x1a\x35\n\x08Response\x12)\n\x05\x66iles\x18\x01 \x03(\x0b\x32\x1a.mlflow.artifacts.FileInfo\"\x1c\n\x0e\x44\x65leteArtifact\x1a\n\n\x08Response\";\n\x08\x46ileInfo\x12\x0c\n\x04path\x18\x01 \x01(\t\x12\x0e\n\x06is_dir\x18\x02 \x01(\x08\x12\x11\n\tfile_size\x18\x03 \x01(\x03\"\x99\x01\n\x15\x43reateMultipartUpload\x12\x0c\n\x04path\x18\x01 \x01(\t\x12\x11\n\tnum_parts\x18\x02 \x01(\x03\x1a_\n\x08Response\x12\x11\n\tupload_id\x18\x01 \x01(\t\x12@\n\x0b\x63redentials\x18\x02 \x03(\x0b\x32+.mlflow.artifacts.MultipartUploadCredential\"|\n\x17\x43ompleteMultipartUpload\x12\x0c\n\x04path\x18\x01 \x01(\t\x12\x11\n\tupload_id\x18\x02 \x01(\t\x12\x34\n\x05parts\x18\x03 \x03(\x0b\x32%.mlflow.artifacts.MultipartUploadPart\x1a\n\n\x08Response\"C\n\x14\x41\x62ortMultipartUpload\x12\x0c\n\x04path\x18\x01 \x01(\t\x12\x11\n\tupload_id\x18\x02 \x01(\t\x1a\n\n\x08Response\"\xb8\x01\n\x19MultipartUploadCredential\x12\x0b\n\x03url\x18\x01 \x01(\t\x12\x13\n\x0bpart_number\x18\x02 \x01(\x03\x12I\n\x07headers\x18\x03 \x03(\x0b\x32\x38.mlflow.artifacts.MultipartUploadCredential.HeadersEntry\x1a.\n\x0cHeadersEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12\r\n\x05value\x18\x02 \x01(\t:\x02\x38\x01\"E\n\x13MultipartUploadPart\x12\x13\n\x0bpart_number\x18\x01 \x01(\x03\x12\x0c\n\x04\x65tag\x18\x02 \x01(\t\x12\x0b\n\x03url\x18\x03 \x01(\t2\x99\x0b\n\x16MlflowArtifactsService\x12\xbd\x01\n\x10\x64ownloadArtifact\x12\".mlflow.artifacts.DownloadArtifact\x1a+.mlflow.artifacts.DownloadArtifact.Response\"X\xf2\x86\x19T\n=\n\x03GET\x12\x30/mlflow-artifacts/artifacts/\x1a\x04\x08\x02\x10\x00\x10\x01*\x11\x44ownload Artifact\x12\xb5\x01\n\x0euploadArtifact\x12 .mlflow.artifacts.UploadArtifact\x1a).mlflow.artifacts.UploadArtifact.Response\"V\xf2\x86\x19R\n=\n\x03PUT\x12\x30/mlflow-artifacts/artifacts/\x1a\x04\x08\x02\x10\x00\x10\x01*\x0fUpload Artifact\x12\x9c\x01\n\rlistArtifacts\x12\x1f.mlflow.artifacts.ListArtifacts\x1a(.mlflow.artifacts.ListArtifacts.Response\"@\xf2\x86\x19<\n(\n\x03GET\x12\x1b/mlflow-artifacts/artifacts\x1a\x04\x08\x02\x10\x00\x10\x01*\x0eList Artifacts\x12\xb9\x01\n\x0e\x64\x65leteArtifact\x12 .mlflow.artifacts.DeleteArtifact\x1a).mlflow.artifacts.DeleteArtifact.Response\"Z\xf2\x86\x19V\n@\n\x06\x44\x45LETE\x12\x30/mlflow-artifacts/artifacts/\x1a\x04\x08\x02\x10\x00\x10\x01*\x10\x44\x65lete Artifacts\x12\xe0\x01\n\x15\x63reateMultipartUpload\x12\'.mlflow.artifacts.CreateMultipartUpload\x1a\x30.mlflow.artifacts.CreateMultipartUpload.Response\"l\xf2\x86\x19h\n?\n\x04POST\x12\x31/mlflow-artifacts/mpu/create/\x1a\x04\x08\x02\x10\x00\x10\x01*#Create an Artifact Multipart Upload\x12\xea\x01\n\x17\x63ompleteMultipartUpload\x12).mlflow.artifacts.CompleteMultipartUpload\x1a\x32.mlflow.artifacts.CompleteMultipartUpload.Response\"p\xf2\x86\x19l\nA\n\x04POST\x12\x33/mlflow-artifacts/mpu/complete/\x1a\x04\x08\x02\x10\x00\x10\x01*%Complete an Artifact Multipart Upload\x12\xdb\x01\n\x14\x61\x62ortMultipartUpload\x12&.mlflow.artifacts.AbortMultipartUpload\x1a/.mlflow.artifacts.AbortMultipartUpload.Response\"j\xf2\x86\x19\x66\n>\n\x04POST\x12\x30/mlflow-artifacts/mpu/abort/\x1a\x04\x08\x02\x10\x00\x10\x01*\"Abort an Artifact Multipart UploadB\x1e\n\x14org.mlflow.api.proto\x90\x01\x01\xe2?\x02\x10\x01') + + + + _DOWNLOADARTIFACT = DESCRIPTOR.message_types_by_name['DownloadArtifact'] + _DOWNLOADARTIFACT_RESPONSE = _DOWNLOADARTIFACT.nested_types_by_name['Response'] + _UPLOADARTIFACT = DESCRIPTOR.message_types_by_name['UploadArtifact'] + _UPLOADARTIFACT_RESPONSE = _UPLOADARTIFACT.nested_types_by_name['Response'] + _LISTARTIFACTS = DESCRIPTOR.message_types_by_name['ListArtifacts'] + _LISTARTIFACTS_RESPONSE = _LISTARTIFACTS.nested_types_by_name['Response'] + _DELETEARTIFACT = DESCRIPTOR.message_types_by_name['DeleteArtifact'] + _DELETEARTIFACT_RESPONSE = _DELETEARTIFACT.nested_types_by_name['Response'] + _FILEINFO = DESCRIPTOR.message_types_by_name['FileInfo'] + _CREATEMULTIPARTUPLOAD = DESCRIPTOR.message_types_by_name['CreateMultipartUpload'] + _CREATEMULTIPARTUPLOAD_RESPONSE = _CREATEMULTIPARTUPLOAD.nested_types_by_name['Response'] + _COMPLETEMULTIPARTUPLOAD = DESCRIPTOR.message_types_by_name['CompleteMultipartUpload'] + _COMPLETEMULTIPARTUPLOAD_RESPONSE = _COMPLETEMULTIPARTUPLOAD.nested_types_by_name['Response'] + _ABORTMULTIPARTUPLOAD = DESCRIPTOR.message_types_by_name['AbortMultipartUpload'] + _ABORTMULTIPARTUPLOAD_RESPONSE = _ABORTMULTIPARTUPLOAD.nested_types_by_name['Response'] + _MULTIPARTUPLOADCREDENTIAL = DESCRIPTOR.message_types_by_name['MultipartUploadCredential'] + _MULTIPARTUPLOADCREDENTIAL_HEADERSENTRY = _MULTIPARTUPLOADCREDENTIAL.nested_types_by_name['HeadersEntry'] + _MULTIPARTUPLOADPART = DESCRIPTOR.message_types_by_name['MultipartUploadPart'] + DownloadArtifact = _reflection.GeneratedProtocolMessageType('DownloadArtifact', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _DOWNLOADARTIFACT_RESPONSE, + '__module__' : 'mlflow_artifacts_pb2' + # @@protoc_insertion_point(class_scope:mlflow.artifacts.DownloadArtifact.Response) + }) + , + 'DESCRIPTOR' : _DOWNLOADARTIFACT, + '__module__' : 'mlflow_artifacts_pb2' + # @@protoc_insertion_point(class_scope:mlflow.artifacts.DownloadArtifact) + }) + _sym_db.RegisterMessage(DownloadArtifact) + _sym_db.RegisterMessage(DownloadArtifact.Response) + + UploadArtifact = _reflection.GeneratedProtocolMessageType('UploadArtifact', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _UPLOADARTIFACT_RESPONSE, + '__module__' : 'mlflow_artifacts_pb2' + # @@protoc_insertion_point(class_scope:mlflow.artifacts.UploadArtifact.Response) + }) + , + 'DESCRIPTOR' : _UPLOADARTIFACT, + '__module__' : 'mlflow_artifacts_pb2' + # @@protoc_insertion_point(class_scope:mlflow.artifacts.UploadArtifact) + }) + _sym_db.RegisterMessage(UploadArtifact) + _sym_db.RegisterMessage(UploadArtifact.Response) + + ListArtifacts = _reflection.GeneratedProtocolMessageType('ListArtifacts', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _LISTARTIFACTS_RESPONSE, + '__module__' : 'mlflow_artifacts_pb2' + # @@protoc_insertion_point(class_scope:mlflow.artifacts.ListArtifacts.Response) + }) + , + 'DESCRIPTOR' : _LISTARTIFACTS, + '__module__' : 'mlflow_artifacts_pb2' + # @@protoc_insertion_point(class_scope:mlflow.artifacts.ListArtifacts) + }) + _sym_db.RegisterMessage(ListArtifacts) + _sym_db.RegisterMessage(ListArtifacts.Response) + + DeleteArtifact = _reflection.GeneratedProtocolMessageType('DeleteArtifact', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _DELETEARTIFACT_RESPONSE, + '__module__' : 'mlflow_artifacts_pb2' + # @@protoc_insertion_point(class_scope:mlflow.artifacts.DeleteArtifact.Response) + }) + , + 'DESCRIPTOR' : _DELETEARTIFACT, + '__module__' : 'mlflow_artifacts_pb2' + # @@protoc_insertion_point(class_scope:mlflow.artifacts.DeleteArtifact) + }) + _sym_db.RegisterMessage(DeleteArtifact) + _sym_db.RegisterMessage(DeleteArtifact.Response) + + FileInfo = _reflection.GeneratedProtocolMessageType('FileInfo', (_message.Message,), { + 'DESCRIPTOR' : _FILEINFO, + '__module__' : 'mlflow_artifacts_pb2' + # @@protoc_insertion_point(class_scope:mlflow.artifacts.FileInfo) + }) + _sym_db.RegisterMessage(FileInfo) + + CreateMultipartUpload = _reflection.GeneratedProtocolMessageType('CreateMultipartUpload', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _CREATEMULTIPARTUPLOAD_RESPONSE, + '__module__' : 'mlflow_artifacts_pb2' + # @@protoc_insertion_point(class_scope:mlflow.artifacts.CreateMultipartUpload.Response) + }) + , + 'DESCRIPTOR' : _CREATEMULTIPARTUPLOAD, + '__module__' : 'mlflow_artifacts_pb2' + # @@protoc_insertion_point(class_scope:mlflow.artifacts.CreateMultipartUpload) + }) + _sym_db.RegisterMessage(CreateMultipartUpload) + _sym_db.RegisterMessage(CreateMultipartUpload.Response) + + CompleteMultipartUpload = _reflection.GeneratedProtocolMessageType('CompleteMultipartUpload', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _COMPLETEMULTIPARTUPLOAD_RESPONSE, + '__module__' : 'mlflow_artifacts_pb2' + # @@protoc_insertion_point(class_scope:mlflow.artifacts.CompleteMultipartUpload.Response) + }) + , + 'DESCRIPTOR' : _COMPLETEMULTIPARTUPLOAD, + '__module__' : 'mlflow_artifacts_pb2' + # @@protoc_insertion_point(class_scope:mlflow.artifacts.CompleteMultipartUpload) + }) + _sym_db.RegisterMessage(CompleteMultipartUpload) + _sym_db.RegisterMessage(CompleteMultipartUpload.Response) + + AbortMultipartUpload = _reflection.GeneratedProtocolMessageType('AbortMultipartUpload', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _ABORTMULTIPARTUPLOAD_RESPONSE, + '__module__' : 'mlflow_artifacts_pb2' + # @@protoc_insertion_point(class_scope:mlflow.artifacts.AbortMultipartUpload.Response) + }) + , + 'DESCRIPTOR' : _ABORTMULTIPARTUPLOAD, + '__module__' : 'mlflow_artifacts_pb2' + # @@protoc_insertion_point(class_scope:mlflow.artifacts.AbortMultipartUpload) + }) + _sym_db.RegisterMessage(AbortMultipartUpload) + _sym_db.RegisterMessage(AbortMultipartUpload.Response) + + MultipartUploadCredential = _reflection.GeneratedProtocolMessageType('MultipartUploadCredential', (_message.Message,), { + + 'HeadersEntry' : _reflection.GeneratedProtocolMessageType('HeadersEntry', (_message.Message,), { + 'DESCRIPTOR' : _MULTIPARTUPLOADCREDENTIAL_HEADERSENTRY, + '__module__' : 'mlflow_artifacts_pb2' + # @@protoc_insertion_point(class_scope:mlflow.artifacts.MultipartUploadCredential.HeadersEntry) + }) + , + 'DESCRIPTOR' : _MULTIPARTUPLOADCREDENTIAL, + '__module__' : 'mlflow_artifacts_pb2' + # @@protoc_insertion_point(class_scope:mlflow.artifacts.MultipartUploadCredential) + }) + _sym_db.RegisterMessage(MultipartUploadCredential) + _sym_db.RegisterMessage(MultipartUploadCredential.HeadersEntry) + + MultipartUploadPart = _reflection.GeneratedProtocolMessageType('MultipartUploadPart', (_message.Message,), { + 'DESCRIPTOR' : _MULTIPARTUPLOADPART, + '__module__' : 'mlflow_artifacts_pb2' + # @@protoc_insertion_point(class_scope:mlflow.artifacts.MultipartUploadPart) + }) + _sym_db.RegisterMessage(MultipartUploadPart) + + _MLFLOWARTIFACTSSERVICE = DESCRIPTOR.services_by_name['MlflowArtifactsService'] + if _descriptor._USE_C_DESCRIPTORS == False: + + DESCRIPTOR._options = None + DESCRIPTOR._serialized_options = b'\n\024org.mlflow.api.proto\220\001\001\342?\002\020\001' + _MULTIPARTUPLOADCREDENTIAL_HEADERSENTRY._options = None + _MULTIPARTUPLOADCREDENTIAL_HEADERSENTRY._serialized_options = b'8\001' + _MLFLOWARTIFACTSSERVICE.methods_by_name['downloadArtifact']._options = None + _MLFLOWARTIFACTSSERVICE.methods_by_name['downloadArtifact']._serialized_options = b'\362\206\031T\n=\n\003GET\0220/mlflow-artifacts/artifacts/\032\004\010\002\020\000\020\001*\021Download Artifact' + _MLFLOWARTIFACTSSERVICE.methods_by_name['uploadArtifact']._options = None + _MLFLOWARTIFACTSSERVICE.methods_by_name['uploadArtifact']._serialized_options = b'\362\206\031R\n=\n\003PUT\0220/mlflow-artifacts/artifacts/\032\004\010\002\020\000\020\001*\017Upload Artifact' + _MLFLOWARTIFACTSSERVICE.methods_by_name['listArtifacts']._options = None + _MLFLOWARTIFACTSSERVICE.methods_by_name['listArtifacts']._serialized_options = b'\362\206\031<\n(\n\003GET\022\033/mlflow-artifacts/artifacts\032\004\010\002\020\000\020\001*\016List Artifacts' + _MLFLOWARTIFACTSSERVICE.methods_by_name['deleteArtifact']._options = None + _MLFLOWARTIFACTSSERVICE.methods_by_name['deleteArtifact']._serialized_options = b'\362\206\031V\n@\n\006DELETE\0220/mlflow-artifacts/artifacts/\032\004\010\002\020\000\020\001*\020Delete Artifacts' + _MLFLOWARTIFACTSSERVICE.methods_by_name['createMultipartUpload']._options = None + _MLFLOWARTIFACTSSERVICE.methods_by_name['createMultipartUpload']._serialized_options = b'\362\206\031h\n?\n\004POST\0221/mlflow-artifacts/mpu/create/\032\004\010\002\020\000\020\001*#Create an Artifact Multipart Upload' + _MLFLOWARTIFACTSSERVICE.methods_by_name['completeMultipartUpload']._options = None + _MLFLOWARTIFACTSSERVICE.methods_by_name['completeMultipartUpload']._serialized_options = b'\362\206\031l\nA\n\004POST\0223/mlflow-artifacts/mpu/complete/\032\004\010\002\020\000\020\001*%Complete an Artifact Multipart Upload' + _MLFLOWARTIFACTSSERVICE.methods_by_name['abortMultipartUpload']._options = None + _MLFLOWARTIFACTSSERVICE.methods_by_name['abortMultipartUpload']._serialized_options = b'\362\206\031f\n>\n\004POST\0220/mlflow-artifacts/mpu/abort/\032\004\010\002\020\000\020\001*\"Abort an Artifact Multipart Upload' + _DOWNLOADARTIFACT._serialized_start=85 + _DOWNLOADARTIFACT._serialized_end=115 + _DOWNLOADARTIFACT_RESPONSE._serialized_start=105 + _DOWNLOADARTIFACT_RESPONSE._serialized_end=115 + _UPLOADARTIFACT._serialized_start=117 + _UPLOADARTIFACT._serialized_end=145 + _UPLOADARTIFACT_RESPONSE._serialized_start=105 + _UPLOADARTIFACT_RESPONSE._serialized_end=115 + _LISTARTIFACTS._serialized_start=147 + _LISTARTIFACTS._serialized_end=231 + _LISTARTIFACTS_RESPONSE._serialized_start=178 + _LISTARTIFACTS_RESPONSE._serialized_end=231 + _DELETEARTIFACT._serialized_start=233 + _DELETEARTIFACT._serialized_end=261 + _DELETEARTIFACT_RESPONSE._serialized_start=105 + _DELETEARTIFACT_RESPONSE._serialized_end=115 + _FILEINFO._serialized_start=263 + _FILEINFO._serialized_end=322 + _CREATEMULTIPARTUPLOAD._serialized_start=325 + _CREATEMULTIPARTUPLOAD._serialized_end=478 + _CREATEMULTIPARTUPLOAD_RESPONSE._serialized_start=383 + _CREATEMULTIPARTUPLOAD_RESPONSE._serialized_end=478 + _COMPLETEMULTIPARTUPLOAD._serialized_start=480 + _COMPLETEMULTIPARTUPLOAD._serialized_end=604 + _COMPLETEMULTIPARTUPLOAD_RESPONSE._serialized_start=105 + _COMPLETEMULTIPARTUPLOAD_RESPONSE._serialized_end=115 + _ABORTMULTIPARTUPLOAD._serialized_start=606 + _ABORTMULTIPARTUPLOAD._serialized_end=673 + _ABORTMULTIPARTUPLOAD_RESPONSE._serialized_start=105 + _ABORTMULTIPARTUPLOAD_RESPONSE._serialized_end=115 + _MULTIPARTUPLOADCREDENTIAL._serialized_start=676 + _MULTIPARTUPLOADCREDENTIAL._serialized_end=860 + _MULTIPARTUPLOADCREDENTIAL_HEADERSENTRY._serialized_start=814 + _MULTIPARTUPLOADCREDENTIAL_HEADERSENTRY._serialized_end=860 + _MULTIPARTUPLOADPART._serialized_start=862 + _MULTIPARTUPLOADPART._serialized_end=931 + _MLFLOWARTIFACTSSERVICE._serialized_start=934 + _MLFLOWARTIFACTSSERVICE._serialized_end=2367 + MlflowArtifactsService = service_reflection.GeneratedServiceType('MlflowArtifactsService', (_service.Service,), dict( + DESCRIPTOR = _MLFLOWARTIFACTSSERVICE, + __module__ = 'mlflow_artifacts_pb2' + )) + + MlflowArtifactsService_Stub = service_reflection.GeneratedServiceStubType('MlflowArtifactsService_Stub', (MlflowArtifactsService,), dict( + DESCRIPTOR = _MLFLOWARTIFACTSSERVICE, + __module__ = 'mlflow_artifacts_pb2' + )) + + + # @@protoc_insertion_point(module_scope) + diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/protos/model_registry_pb2.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/protos/model_registry_pb2.py new file mode 100644 index 0000000000000000000000000000000000000000..4c36518dc0693caf3ef55fd6cf9eaf2ceb94aace --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/protos/model_registry_pb2.py @@ -0,0 +1,1073 @@ + +import google.protobuf +from packaging.version import Version +if Version(google.protobuf.__version__).major >= 5: + # -*- coding: utf-8 -*- + # Generated by the protocol buffer compiler. DO NOT EDIT! + # source: model_registry.proto + # Protobuf Python Version: 5.26.0 + """Generated protocol buffer code.""" + from google.protobuf import descriptor as _descriptor + from google.protobuf import descriptor_pool as _descriptor_pool + from google.protobuf import symbol_database as _symbol_database + from google.protobuf.internal import builder as _builder + # @@protoc_insertion_point(imports) + + _sym_db = _symbol_database.Default() + + + from .scalapb import scalapb_pb2 as scalapb_dot_scalapb__pb2 + from . import databricks_pb2 as databricks__pb2 + + + DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\x14model_registry.proto\x12\x06mlflow\x1a\x15scalapb/scalapb.proto\x1a\x10\x64\x61tabricks.proto\"\xe9\x02\n\x0fRegisteredModel\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x1a\n\x12\x63reation_timestamp\x18\x02 \x01(\x03\x12\x1e\n\x16last_updated_timestamp\x18\x03 \x01(\x03\x12\x0f\n\x07user_id\x18\x04 \x01(\t\x12\x13\n\x0b\x64\x65scription\x18\x05 \x01(\t\x12-\n\x0flatest_versions\x18\x06 \x03(\x0b\x32\x14.mlflow.ModelVersion\x12(\n\x04tags\x18\x07 \x03(\x0b\x32\x1a.mlflow.RegisteredModelTag\x12-\n\x07\x61liases\x18\x08 \x03(\x0b\x32\x1c.mlflow.RegisteredModelAlias\x12\x19\n\x11\x64\x65ployment_job_id\x18\t \x01(\t\x12\x43\n\x14\x64\x65ployment_job_state\x18\n \x01(\x0e\x32%.mlflow.DeploymentJobConnection.State\"\x82\x04\n\x0cModelVersion\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x0f\n\x07version\x18\x02 \x01(\t\x12\x1a\n\x12\x63reation_timestamp\x18\x03 \x01(\x03\x12\x1e\n\x16last_updated_timestamp\x18\x04 \x01(\x03\x12\x0f\n\x07user_id\x18\x05 \x01(\t\x12\x15\n\rcurrent_stage\x18\x06 \x01(\t\x12\x13\n\x0b\x64\x65scription\x18\x07 \x01(\t\x12\x0e\n\x06source\x18\x08 \x01(\t\x12\x0e\n\x06run_id\x18\t \x01(\t\x12*\n\x06status\x18\n \x01(\x0e\x32\x1a.mlflow.ModelVersionStatus\x12\x16\n\x0estatus_message\x18\x0b \x01(\t\x12%\n\x04tags\x18\x0c \x03(\x0b\x32\x17.mlflow.ModelVersionTag\x12\x10\n\x08run_link\x18\r 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.mlflow.DeleteRegisteredModelTag\x1a).mlflow.DeleteRegisteredModelTag.Response\"Y\xf2\x86\x19U\n4\n\x06\x44\x45LETE\x12$/mlflow/registered-models/delete-tag\x1a\x04\x08\x02\x10\x00\x10\x01*\x1b\x44\x65lete Registered Model Tag\x12\xb3\x01\n\x15\x64\x65leteModelVersionTag\x12\x1d.mlflow.DeleteModelVersionTag\x1a&.mlflow.DeleteModelVersionTag.Response\"S\xf2\x86\x19O\n1\n\x06\x44\x45LETE\x12!/mlflow/model-versions/delete-tag\x1a\x04\x08\x02\x10\x00\x10\x01*\x18\x44\x65lete Model Version Tag\x12\xb7\x01\n\x17setRegisteredModelAlias\x12\x1f.mlflow.SetRegisteredModelAlias\x1a(.mlflow.SetRegisteredModelAlias.Response\"Q\xf2\x86\x19M\n-\n\x04POST\x12\x1f/mlflow/registered-models/alias\x1a\x04\x08\x02\x10\x00\x10\x01*\x1aSet Registered Model Alias\x12\xc5\x01\n\x1a\x64\x65leteRegisteredModelAlias\x12\".mlflow.DeleteRegisteredModelAlias\x1a+.mlflow.DeleteRegisteredModelAlias.Response\"V\xf2\x86\x19R\n/\n\x06\x44\x45LETE\x12\x1f/mlflow/registered-models/alias\x1a\x04\x08\x02\x10\x00\x10\x01*\x1d\x44\x65lete Registered Model Alias\x12\xb3\x01\n\x16getModelVersionByAlias\x12\x1e.mlflow.GetModelVersionByAlias\x1a\'.mlflow.GetModelVersionByAlias.Response\"P\xf2\x86\x19L\n,\n\x03GET\x12\x1f/mlflow/registered-models/alias\x1a\x04\x08\x02\x10\x00\x10\x01*\x1aGet Model Version by AliasB!\n\x14org.mlflow.api.proto\x90\x01\x01\xa0\x01\x01\xe2?\x02\x10\x01') + + _globals = globals() + _builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals) + _builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'model_registry_pb2', _globals) + if not _descriptor._USE_C_DESCRIPTORS: + _globals['DESCRIPTOR']._loaded_options = None + _globals['DESCRIPTOR']._serialized_options = b'\n\024org.mlflow.api.proto\220\001\001\240\001\001\342?\002\020\001' + 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_globals['_UPDATEREGISTEREDMODEL']._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _globals['_DELETEREGISTEREDMODEL'].fields_by_name['name']._loaded_options = None + _globals['_DELETEREGISTEREDMODEL'].fields_by_name['name']._serialized_options = b'\370\206\031\001' + _globals['_DELETEREGISTEREDMODEL']._loaded_options = None + _globals['_DELETEREGISTEREDMODEL']._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _globals['_GETREGISTEREDMODEL'].fields_by_name['name']._loaded_options = None + _globals['_GETREGISTEREDMODEL'].fields_by_name['name']._serialized_options = b'\370\206\031\001' + _globals['_GETREGISTEREDMODEL']._loaded_options = None + _globals['_GETREGISTEREDMODEL']._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _globals['_SEARCHREGISTEREDMODELS']._loaded_options = None + _globals['_SEARCHREGISTEREDMODELS']._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _globals['_GETLATESTVERSIONS'].fields_by_name['name']._loaded_options = None + _globals['_GETLATESTVERSIONS'].fields_by_name['name']._serialized_options = b'\370\206\031\001' + _globals['_GETLATESTVERSIONS']._loaded_options = None + _globals['_GETLATESTVERSIONS']._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _globals['_CREATEMODELVERSION'].fields_by_name['name']._loaded_options = None + _globals['_CREATEMODELVERSION'].fields_by_name['name']._serialized_options = b'\370\206\031\001' + _globals['_CREATEMODELVERSION'].fields_by_name['source']._loaded_options = None + _globals['_CREATEMODELVERSION'].fields_by_name['source']._serialized_options = b'\370\206\031\001' + _globals['_CREATEMODELVERSION']._loaded_options = None + _globals['_CREATEMODELVERSION']._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _globals['_UPDATEMODELVERSION'].fields_by_name['name']._loaded_options = None + 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b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _globals['_SETREGISTEREDMODELALIAS'].fields_by_name['name']._loaded_options = None + _globals['_SETREGISTEREDMODELALIAS'].fields_by_name['name']._serialized_options = b'\370\206\031\001' + _globals['_SETREGISTEREDMODELALIAS'].fields_by_name['alias']._loaded_options = None + _globals['_SETREGISTEREDMODELALIAS'].fields_by_name['alias']._serialized_options = b'\370\206\031\001' + _globals['_SETREGISTEREDMODELALIAS'].fields_by_name['version']._loaded_options = None + _globals['_SETREGISTEREDMODELALIAS'].fields_by_name['version']._serialized_options = b'\370\206\031\001' + _globals['_SETREGISTEREDMODELALIAS']._loaded_options = None + _globals['_SETREGISTEREDMODELALIAS']._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _globals['_DELETEREGISTEREDMODELALIAS'].fields_by_name['name']._loaded_options = None + _globals['_DELETEREGISTEREDMODELALIAS'].fields_by_name['name']._serialized_options = b'\370\206\031\001' + 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_globals['_MODELREGISTRYSERVICE'].methods_by_name['createRegisteredModel']._loaded_options = None + _globals['_MODELREGISTRYSERVICE'].methods_by_name['createRegisteredModel']._serialized_options = b'\362\206\031J\n.\n\004POST\022 /mlflow/registered-models/create\032\004\010\002\020\000\020\001*\026Create RegisteredModel' + _globals['_MODELREGISTRYSERVICE'].methods_by_name['renameRegisteredModel']._loaded_options = None + _globals['_MODELREGISTRYSERVICE'].methods_by_name['renameRegisteredModel']._serialized_options = b'\362\206\031J\n.\n\004POST\022 /mlflow/registered-models/rename\032\004\010\002\020\000\020\001*\026Rename RegisteredModel' + _globals['_MODELREGISTRYSERVICE'].methods_by_name['updateRegisteredModel']._loaded_options = None + _globals['_MODELREGISTRYSERVICE'].methods_by_name['updateRegisteredModel']._serialized_options = b'\362\206\031K\n/\n\005PATCH\022 /mlflow/registered-models/update\032\004\010\002\020\000\020\001*\026Update RegisteredModel' + _globals['_MODELREGISTRYSERVICE'].methods_by_name['deleteRegisteredModel']._loaded_options = None + _globals['_MODELREGISTRYSERVICE'].methods_by_name['deleteRegisteredModel']._serialized_options = b'\362\206\031L\n0\n\006DELETE\022 /mlflow/registered-models/delete\032\004\010\002\020\000\020\001*\026Delete RegisteredModel' + _globals['_MODELREGISTRYSERVICE'].methods_by_name['getRegisteredModel']._loaded_options = None + _globals['_MODELREGISTRYSERVICE'].methods_by_name['getRegisteredModel']._serialized_options = b'\362\206\031C\n*\n\003GET\022\035/mlflow/registered-models/get\032\004\010\002\020\000\020\001*\023Get RegisteredModel' + _globals['_MODELREGISTRYSERVICE'].methods_by_name['searchRegisteredModels']._loaded_options = None + _globals['_MODELREGISTRYSERVICE'].methods_by_name['searchRegisteredModels']._serialized_options = b'\362\206\031J\n-\n\003GET\022 /mlflow/registered-models/search\032\004\010\002\020\000\020\001*\027Search RegisteredModels' + _globals['_MODELREGISTRYSERVICE'].methods_by_name['getLatestVersions']._loaded_options = None + _globals['_MODELREGISTRYSERVICE'].methods_by_name['getLatestVersions']._serialized_options = b'\362\206\031\225\001\n;\n\004POST\022-/mlflow/registered-models/get-latest-versions\032\004\010\002\020\000\n:\n\003GET\022-/mlflow/registered-models/get-latest-versions\032\004\010\002\020\000\020\001*\030Get Latest ModelVersions' + _globals['_MODELREGISTRYSERVICE'].methods_by_name['createModelVersion']._loaded_options = None + _globals['_MODELREGISTRYSERVICE'].methods_by_name['createModelVersion']._serialized_options = b'\362\206\031D\n+\n\004POST\022\035/mlflow/model-versions/create\032\004\010\002\020\000\020\001*\023Create ModelVersion' + _globals['_MODELREGISTRYSERVICE'].methods_by_name['updateModelVersion']._loaded_options = None + _globals['_MODELREGISTRYSERVICE'].methods_by_name['updateModelVersion']._serialized_options = b'\362\206\031E\n,\n\005PATCH\022\035/mlflow/model-versions/update\032\004\010\002\020\000\020\001*\023Update ModelVersion' + _globals['_MODELREGISTRYSERVICE'].methods_by_name['transitionModelVersionStage']._loaded_options = None + _globals['_MODELREGISTRYSERVICE'].methods_by_name['transitionModelVersionStage']._serialized_options = b'\362\206\031X\n5\n\004POST\022\'/mlflow/model-versions/transition-stage\032\004\010\002\020\000\020\001*\035Transition ModelVersion Stage' + _globals['_MODELREGISTRYSERVICE'].methods_by_name['deleteModelVersion']._loaded_options = None + _globals['_MODELREGISTRYSERVICE'].methods_by_name['deleteModelVersion']._serialized_options = b'\362\206\031F\n-\n\006DELETE\022\035/mlflow/model-versions/delete\032\004\010\002\020\000\020\001*\023Delete ModelVersion' + _globals['_MODELREGISTRYSERVICE'].methods_by_name['getModelVersion']._loaded_options = None + _globals['_MODELREGISTRYSERVICE'].methods_by_name['getModelVersion']._serialized_options = b'\362\206\031=\n\'\n\003GET\022\032/mlflow/model-versions/get\032\004\010\002\020\000\020\001*\020Get ModelVersion' + _globals['_MODELREGISTRYSERVICE'].methods_by_name['searchModelVersions']._loaded_options = None + _globals['_MODELREGISTRYSERVICE'].methods_by_name['searchModelVersions']._serialized_options = b'\362\206\031D\n*\n\003GET\022\035/mlflow/model-versions/search\032\004\010\002\020\000\020\001*\024Search ModelVersions\272\214\031\000' + _globals['_MODELREGISTRYSERVICE'].methods_by_name['getModelVersionDownloadUri']._loaded_options = None + _globals['_MODELREGISTRYSERVICE'].methods_by_name['getModelVersionDownloadUri']._serialized_options = b'\362\206\031e\n4\n\003GET\022\'/mlflow/model-versions/get-download-uri\032\004\010\002\020\000\020\001*+Get Download URI For ModelVersion Artifacts' + _globals['_MODELREGISTRYSERVICE'].methods_by_name['setRegisteredModelTag']._loaded_options = None + _globals['_MODELREGISTRYSERVICE'].methods_by_name['setRegisteredModelTag']._serialized_options = b'\362\206\031M\n/\n\004POST\022!/mlflow/registered-models/set-tag\032\004\010\002\020\000\020\001*\030Set Registered Model Tag' + _globals['_MODELREGISTRYSERVICE'].methods_by_name['setModelVersionTag']._loaded_options = None + _globals['_MODELREGISTRYSERVICE'].methods_by_name['setModelVersionTag']._serialized_options = b'\362\206\031G\n,\n\004POST\022\036/mlflow/model-versions/set-tag\032\004\010\002\020\000\020\001*\025Set Model Version Tag' + _globals['_MODELREGISTRYSERVICE'].methods_by_name['deleteRegisteredModelTag']._loaded_options = None + _globals['_MODELREGISTRYSERVICE'].methods_by_name['deleteRegisteredModelTag']._serialized_options = b'\362\206\031U\n4\n\006DELETE\022$/mlflow/registered-models/delete-tag\032\004\010\002\020\000\020\001*\033Delete Registered Model Tag' + _globals['_MODELREGISTRYSERVICE'].methods_by_name['deleteModelVersionTag']._loaded_options = None + _globals['_MODELREGISTRYSERVICE'].methods_by_name['deleteModelVersionTag']._serialized_options = b'\362\206\031O\n1\n\006DELETE\022!/mlflow/model-versions/delete-tag\032\004\010\002\020\000\020\001*\030Delete Model Version Tag' + _globals['_MODELREGISTRYSERVICE'].methods_by_name['setRegisteredModelAlias']._loaded_options = None + _globals['_MODELREGISTRYSERVICE'].methods_by_name['setRegisteredModelAlias']._serialized_options = b'\362\206\031M\n-\n\004POST\022\037/mlflow/registered-models/alias\032\004\010\002\020\000\020\001*\032Set Registered Model Alias' + _globals['_MODELREGISTRYSERVICE'].methods_by_name['deleteRegisteredModelAlias']._loaded_options = None + _globals['_MODELREGISTRYSERVICE'].methods_by_name['deleteRegisteredModelAlias']._serialized_options = b'\362\206\031R\n/\n\006DELETE\022\037/mlflow/registered-models/alias\032\004\010\002\020\000\020\001*\035Delete Registered Model Alias' + _globals['_MODELREGISTRYSERVICE'].methods_by_name['getModelVersionByAlias']._loaded_options = None + _globals['_MODELREGISTRYSERVICE'].methods_by_name['getModelVersionByAlias']._serialized_options = b'\362\206\031L\n,\n\003GET\022\037/mlflow/registered-models/alias\032\004\010\002\020\000\020\001*\032Get Model Version by Alias' + _globals['_MODELVERSIONSTATUS']._serialized_start=5580 + _globals['_MODELVERSIONSTATUS']._serialized_end=5662 + _globals['_REGISTEREDMODEL']._serialized_start=74 + _globals['_REGISTEREDMODEL']._serialized_end=435 + _globals['_MODELVERSION']._serialized_start=438 + _globals['_MODELVERSION']._serialized_end=952 + _globals['_DEPLOYMENTJOBCONNECTION']._serialized_start=955 + _globals['_DEPLOYMENTJOBCONNECTION']._serialized_end=1118 + _globals['_DEPLOYMENTJOBCONNECTION_STATE']._serialized_start=983 + _globals['_DEPLOYMENTJOBCONNECTION_STATE']._serialized_end=1118 + _globals['_MODELVERSIONDEPLOYMENTJOBSTATE']._serialized_start=1121 + _globals['_MODELVERSIONDEPLOYMENTJOBSTATE']._serialized_end=1521 + _globals['_MODELVERSIONDEPLOYMENTJOBSTATE_DEPLOYMENTJOBRUNSTATE']._serialized_start=1354 + _globals['_MODELVERSIONDEPLOYMENTJOBSTATE_DEPLOYMENTJOBRUNSTATE']._serialized_end=1521 + _globals['_CREATEREGISTEREDMODEL']._serialized_start=1524 + _globals['_CREATEREGISTEREDMODEL']._serialized_end=1765 + _globals['_CREATEREGISTEREDMODEL_RESPONSE']._serialized_start=1659 + _globals['_CREATEREGISTEREDMODEL_RESPONSE']._serialized_end=1720 + _globals['_RENAMEREGISTEREDMODEL']._serialized_start=1768 + _globals['_RENAMEREGISTEREDMODEL']._serialized_end=1937 + _globals['_RENAMEREGISTEREDMODEL_RESPONSE']._serialized_start=1659 + _globals['_RENAMEREGISTEREDMODEL_RESPONSE']._serialized_end=1720 + _globals['_UPDATEREGISTEREDMODEL']._serialized_start=1940 + _globals['_UPDATEREGISTEREDMODEL']._serialized_end=2139 + _globals['_UPDATEREGISTEREDMODEL_RESPONSE']._serialized_start=1659 + _globals['_UPDATEREGISTEREDMODEL_RESPONSE']._serialized_end=1720 + _globals['_DELETEREGISTEREDMODEL']._serialized_start=2141 + _globals['_DELETEREGISTEREDMODEL']._serialized_end=2241 + _globals['_DELETEREGISTEREDMODEL_RESPONSE']._serialized_start=1659 + _globals['_DELETEREGISTEREDMODEL_RESPONSE']._serialized_end=1669 + _globals['_GETREGISTEREDMODEL']._serialized_start=2244 + _globals['_GETREGISTEREDMODEL']._serialized_end=2392 + _globals['_GETREGISTEREDMODEL_RESPONSE']._serialized_start=1659 + _globals['_GETREGISTEREDMODEL_RESPONSE']._serialized_end=1720 + _globals['_SEARCHREGISTEREDMODELS']._serialized_start=2395 + _globals['_SEARCHREGISTEREDMODELS']._serialized_end=2633 + _globals['_SEARCHREGISTEREDMODELS_RESPONSE']._serialized_start=2501 + _globals['_SEARCHREGISTEREDMODELS_RESPONSE']._serialized_end=2588 + _globals['_GETLATESTVERSIONS']._serialized_start=2636 + _globals['_GETLATESTVERSIONS']._serialized_end=2794 + _globals['_GETLATESTVERSIONS_RESPONSE']._serialized_start=2693 + _globals['_GETLATESTVERSIONS_RESPONSE']._serialized_end=2749 + _globals['_CREATEMODELVERSION']._serialized_start=2797 + _globals['_CREATEMODELVERSION']._serialized_end=3073 + _globals['_CREATEMODELVERSION_RESPONSE']._serialized_start=2973 + _globals['_CREATEMODELVERSION_RESPONSE']._serialized_end=3028 + _globals['_UPDATEMODELVERSION']._serialized_start=3076 + _globals['_UPDATEMODELVERSION']._serialized_end=3262 + _globals['_UPDATEMODELVERSION_RESPONSE']._serialized_start=2973 + _globals['_UPDATEMODELVERSION_RESPONSE']._serialized_end=3028 + _globals['_TRANSITIONMODELVERSIONSTAGE']._serialized_start=3265 + _globals['_TRANSITIONMODELVERSIONSTAGE']._serialized_end=3501 + _globals['_TRANSITIONMODELVERSIONSTAGE_RESPONSE']._serialized_start=2973 + _globals['_TRANSITIONMODELVERSIONSTAGE_RESPONSE']._serialized_end=3028 + _globals['_DELETEMODELVERSION']._serialized_start=3503 + _globals['_DELETEMODELVERSION']._serialized_end=3623 + _globals['_DELETEMODELVERSION_RESPONSE']._serialized_start=1659 + _globals['_DELETEMODELVERSION_RESPONSE']._serialized_end=1669 + _globals['_GETMODELVERSION']._serialized_start=3626 + _globals['_GETMODELVERSION']._serialized_end=3788 + _globals['_GETMODELVERSION_RESPONSE']._serialized_start=2973 + _globals['_GETMODELVERSION_RESPONSE']._serialized_end=3028 + _globals['_SEARCHMODELVERSIONS']._serialized_start=3791 + _globals['_SEARCHMODELVERSIONS']._serialized_end=4023 + _globals['_SEARCHMODELVERSIONS_RESPONSE']._serialized_start=3897 + _globals['_SEARCHMODELVERSIONS_RESPONSE']._serialized_end=3978 + _globals['_GETMODELVERSIONDOWNLOADURI']._serialized_start=4026 + _globals['_GETMODELVERSIONDOWNLOADURI']._serialized_end=4176 + _globals['_GETMODELVERSIONDOWNLOADURI_RESPONSE']._serialized_start=4099 + _globals['_GETMODELVERSIONDOWNLOADURI_RESPONSE']._serialized_end=4131 + _globals['_MODELVERSIONTAG']._serialized_start=4178 + _globals['_MODELVERSIONTAG']._serialized_end=4223 + _globals['_MODELPARAM']._serialized_start=4225 + _globals['_MODELPARAM']._serialized_end=4266 + _globals['_MODELMETRIC']._serialized_start=4269 + _globals['_MODELMETRIC']._serialized_end=4450 + _globals['_REGISTEREDMODELTAG']._serialized_start=4452 + _globals['_REGISTEREDMODELTAG']._serialized_end=4500 + _globals['_SETREGISTEREDMODELTAG']._serialized_start=4503 + _globals['_SETREGISTEREDMODELTAG']._serialized_end=4643 + _globals['_SETREGISTEREDMODELTAG_RESPONSE']._serialized_start=1659 + _globals['_SETREGISTEREDMODELTAG_RESPONSE']._serialized_end=1669 + _globals['_SETMODELVERSIONTAG']._serialized_start=4646 + _globals['_SETMODELVERSIONTAG']._serialized_end=4806 + _globals['_SETMODELVERSIONTAG_RESPONSE']._serialized_start=1659 + _globals['_SETMODELVERSIONTAG_RESPONSE']._serialized_end=1669 + _globals['_DELETEREGISTEREDMODELTAG']._serialized_start=4808 + _globals['_DELETEREGISTEREDMODELTAG']._serialized_end=4930 + _globals['_DELETEREGISTEREDMODELTAG_RESPONSE']._serialized_start=1659 + _globals['_DELETEREGISTEREDMODELTAG_RESPONSE']._serialized_end=1669 + _globals['_DELETEMODELVERSIONTAG']._serialized_start=4933 + _globals['_DELETEMODELVERSIONTAG']._serialized_end=5075 + _globals['_DELETEMODELVERSIONTAG_RESPONSE']._serialized_start=1659 + _globals['_DELETEMODELVERSIONTAG_RESPONSE']._serialized_end=1669 + _globals['_REGISTEREDMODELALIAS']._serialized_start=5077 + _globals['_REGISTEREDMODELALIAS']._serialized_end=5131 + _globals['_SETREGISTEREDMODELALIAS']._serialized_start=5134 + _globals['_SETREGISTEREDMODELALIAS']._serialized_end=5280 + _globals['_SETREGISTEREDMODELALIAS_RESPONSE']._serialized_start=1659 + _globals['_SETREGISTEREDMODELALIAS_RESPONSE']._serialized_end=1669 + _globals['_DELETEREGISTEREDMODELALIAS']._serialized_start=5282 + _globals['_DELETEREGISTEREDMODELALIAS']._serialized_end=5408 + _globals['_DELETEREGISTEREDMODELALIAS_RESPONSE']._serialized_start=1659 + _globals['_DELETEREGISTEREDMODELALIAS_RESPONSE']._serialized_end=1669 + _globals['_GETMODELVERSIONBYALIAS']._serialized_start=5411 + _globals['_GETMODELVERSIONBYALIAS']._serialized_end=5578 + _globals['_GETMODELVERSIONBYALIAS_RESPONSE']._serialized_start=2973 + _globals['_GETMODELVERSIONBYALIAS_RESPONSE']._serialized_end=3028 + _globals['_MODELREGISTRYSERVICE']._serialized_start=5665 + _globals['_MODELREGISTRYSERVICE']._serialized_end=9505 + _builder.BuildServices(DESCRIPTOR, 'model_registry_pb2', _globals) + # @@protoc_insertion_point(module_scope) + +else: + # -*- coding: utf-8 -*- + # Generated by the protocol buffer compiler. DO NOT EDIT! + # source: model_registry.proto + """Generated protocol buffer code.""" + from google.protobuf.internal import enum_type_wrapper + from google.protobuf import descriptor as _descriptor + from google.protobuf import descriptor_pool as _descriptor_pool + from google.protobuf import message as _message + from google.protobuf import reflection as _reflection + from google.protobuf import symbol_database as _symbol_database + from google.protobuf import service as _service + from google.protobuf import service_reflection + # @@protoc_insertion_point(imports) + + _sym_db = _symbol_database.Default() + + + from .scalapb import scalapb_pb2 as scalapb_dot_scalapb__pb2 + from . import databricks_pb2 as databricks__pb2 + + + DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\x14model_registry.proto\x12\x06mlflow\x1a\x15scalapb/scalapb.proto\x1a\x10\x64\x61tabricks.proto\"\xe9\x02\n\x0fRegisteredModel\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x1a\n\x12\x63reation_timestamp\x18\x02 \x01(\x03\x12\x1e\n\x16last_updated_timestamp\x18\x03 \x01(\x03\x12\x0f\n\x07user_id\x18\x04 \x01(\t\x12\x13\n\x0b\x64\x65scription\x18\x05 \x01(\t\x12-\n\x0flatest_versions\x18\x06 \x03(\x0b\x32\x14.mlflow.ModelVersion\x12(\n\x04tags\x18\x07 \x03(\x0b\x32\x1a.mlflow.RegisteredModelTag\x12-\n\x07\x61liases\x18\x08 \x03(\x0b\x32\x1c.mlflow.RegisteredModelAlias\x12\x19\n\x11\x64\x65ployment_job_id\x18\t \x01(\t\x12\x43\n\x14\x64\x65ployment_job_state\x18\n \x01(\x0e\x32%.mlflow.DeploymentJobConnection.State\"\x82\x04\n\x0cModelVersion\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x0f\n\x07version\x18\x02 \x01(\t\x12\x1a\n\x12\x63reation_timestamp\x18\x03 \x01(\x03\x12\x1e\n\x16last_updated_timestamp\x18\x04 \x01(\x03\x12\x0f\n\x07user_id\x18\x05 \x01(\t\x12\x15\n\rcurrent_stage\x18\x06 \x01(\t\x12\x13\n\x0b\x64\x65scription\x18\x07 \x01(\t\x12\x0e\n\x06source\x18\x08 \x01(\t\x12\x0e\n\x06run_id\x18\t \x01(\t\x12*\n\x06status\x18\n \x01(\x0e\x32\x1a.mlflow.ModelVersionStatus\x12\x16\n\x0estatus_message\x18\x0b \x01(\t\x12%\n\x04tags\x18\x0c \x03(\x0b\x32\x17.mlflow.ModelVersionTag\x12\x10\n\x08run_link\x18\r \x01(\t\x12\x0f\n\x07\x61liases\x18\x0e \x03(\t\x12\x10\n\x08model_id\x18\x0f \x01(\t\x12(\n\x0cmodel_params\x18\x10 \x03(\x0b\x32\x12.mlflow.ModelParam\x12*\n\rmodel_metrics\x18\x11 \x03(\x0b\x32\x13.mlflow.ModelMetric\x12\x44\n\x14\x64\x65ployment_job_state\x18\x13 \x01(\x0b\x32&.mlflow.ModelVersionDeploymentJobState\"\xa3\x01\n\x17\x44\x65ploymentJobConnection\"\x87\x01\n\x05State\x12/\n+DEPLOYMENT_JOB_CONNECTION_STATE_UNSPECIFIED\x10\x00\x12\x0e\n\nNOT_SET_UP\x10\x01\x12\r\n\tCONNECTED\x10\x02\x12\r\n\tNOT_FOUND\x10\x03\x12\x1f\n\x1bREQUIRED_PARAMETERS_CHANGED\x10\x04\"\x90\x03\n\x1eModelVersionDeploymentJobState\x12\x0e\n\x06job_id\x18\x01 \x01(\t\x12\x0e\n\x06run_id\x18\x02 \x01(\t\x12\x38\n\tjob_state\x18\x03 \x01(\x0e\x32%.mlflow.DeploymentJobConnection.State\x12O\n\trun_state\x18\x04 \x01(\x0e\x32<.mlflow.ModelVersionDeploymentJobState.DeploymentJobRunState\x12\x19\n\x11\x63urrent_task_name\x18\x05 \x01(\t\"\xa7\x01\n\x15\x44\x65ploymentJobRunState\x12(\n$DEPLOYMENT_JOB_RUN_STATE_UNSPECIFIED\x10\x00\x12!\n\x1dNO_VALID_DEPLOYMENT_JOB_FOUND\x10\x01\x12\x0b\n\x07RUNNING\x10\x02\x12\r\n\tSUCCEEDED\x10\x03\x12\n\n\x06\x46\x41ILED\x10\x04\x12\x0b\n\x07PENDING\x10\x05\x12\x0c\n\x08\x41PPROVAL\x10\x06\"\xf1\x01\n\x15\x43reateRegisteredModel\x12\x12\n\x04name\x18\x01 \x01(\tB\x04\xf8\x86\x19\x01\x12(\n\x04tags\x18\x02 \x03(\x0b\x32\x1a.mlflow.RegisteredModelTag\x12\x13\n\x0b\x64\x65scription\x18\x03 \x01(\t\x12\x19\n\x11\x64\x65ployment_job_id\x18\x04 \x01(\t\x1a=\n\x08Response\x12\x31\n\x10registered_model\x18\x01 \x01(\x0b\x32\x17.mlflow.RegisteredModel:+\xe2?(\n&com.databricks.rpc.RPC[$this.Response]\"\xa9\x01\n\x15RenameRegisteredModel\x12\x12\n\x04name\x18\x01 \x01(\tB\x04\xf8\x86\x19\x01\x12\x10\n\x08new_name\x18\x02 \x01(\t\x1a=\n\x08Response\x12\x31\n\x10registered_model\x18\x01 \x01(\x0b\x32\x17.mlflow.RegisteredModel:+\xe2?(\n&com.databricks.rpc.RPC[$this.Response]\"\xc7\x01\n\x15UpdateRegisteredModel\x12\x12\n\x04name\x18\x01 \x01(\tB\x04\xf8\x86\x19\x01\x12\x13\n\x0b\x64\x65scription\x18\x02 \x01(\t\x12\x19\n\x11\x64\x65ployment_job_id\x18\x03 \x01(\t\x1a=\n\x08Response\x12\x31\n\x10registered_model\x18\x01 \x01(\x0b\x32\x17.mlflow.RegisteredModel:+\xe2?(\n&com.databricks.rpc.RPC[$this.Response]\"d\n\x15\x44\x65leteRegisteredModel\x12\x12\n\x04name\x18\x01 \x01(\tB\x04\xf8\x86\x19\x01\x1a\n\n\x08Response:+\xe2?(\n&com.databricks.rpc.RPC[$this.Response]\"\x94\x01\n\x12GetRegisteredModel\x12\x12\n\x04name\x18\x01 \x01(\tB\x04\xf8\x86\x19\x01\x1a=\n\x08Response\x12\x31\n\x10registered_model\x18\x01 \x01(\x0b\x32\x17.mlflow.RegisteredModel:+\xe2?(\n&com.databricks.rpc.RPC[$this.Response]\"\xee\x01\n\x16SearchRegisteredModels\x12\x0e\n\x06\x66ilter\x18\x01 \x01(\t\x12\x18\n\x0bmax_results\x18\x02 \x01(\x03:\x03\x31\x30\x30\x12\x10\n\x08order_by\x18\x03 \x03(\t\x12\x12\n\npage_token\x18\x04 \x01(\t\x1aW\n\x08Response\x12\x32\n\x11registered_models\x18\x01 \x03(\x0b\x32\x17.mlflow.RegisteredModel\x12\x17\n\x0fnext_page_token\x18\x02 \x01(\t:+\xe2?(\n&com.databricks.rpc.RPC[$this.Response]\"\x9e\x01\n\x11GetLatestVersions\x12\x12\n\x04name\x18\x01 \x01(\tB\x04\xf8\x86\x19\x01\x12\x0e\n\x06stages\x18\x02 \x03(\t\x1a\x38\n\x08Response\x12,\n\x0emodel_versions\x18\x01 \x03(\x0b\x32\x14.mlflow.ModelVersion:+\xe2?(\n&com.databricks.rpc.RPC[$this.Response]\"\x94\x02\n\x12\x43reateModelVersion\x12\x12\n\x04name\x18\x01 \x01(\tB\x04\xf8\x86\x19\x01\x12\x14\n\x06source\x18\x02 \x01(\tB\x04\xf8\x86\x19\x01\x12\x0e\n\x06run_id\x18\x03 \x01(\t\x12%\n\x04tags\x18\x04 \x03(\x0b\x32\x17.mlflow.ModelVersionTag\x12\x10\n\x08run_link\x18\x05 \x01(\t\x12\x13\n\x0b\x64\x65scription\x18\x06 \x01(\t\x12\x10\n\x08model_id\x18\x07 \x01(\t\x1a\x37\n\x08Response\x12+\n\rmodel_version\x18\x01 \x01(\x0b\x32\x14.mlflow.ModelVersion:+\xe2?(\n&com.databricks.rpc.RPC[$this.Response]\"\xba\x01\n\x12UpdateModelVersion\x12\x12\n\x04name\x18\x01 \x01(\tB\x04\xf8\x86\x19\x01\x12\x15\n\x07version\x18\x02 \x01(\tB\x04\xf8\x86\x19\x01\x12\x13\n\x0b\x64\x65scription\x18\x03 \x01(\t\x1a\x37\n\x08Response\x12+\n\rmodel_version\x18\x01 \x01(\x0b\x32\x14.mlflow.ModelVersion:+\xe2?(\n&com.databricks.rpc.RPC[$this.Response]\"\xec\x01\n\x1bTransitionModelVersionStage\x12\x12\n\x04name\x18\x01 \x01(\tB\x04\xf8\x86\x19\x01\x12\x15\n\x07version\x18\x02 \x01(\tB\x04\xf8\x86\x19\x01\x12\x13\n\x05stage\x18\x03 \x01(\tB\x04\xf8\x86\x19\x01\x12\'\n\x19\x61rchive_existing_versions\x18\x04 \x01(\x08\x42\x04\xf8\x86\x19\x01\x1a\x37\n\x08Response\x12+\n\rmodel_version\x18\x01 \x01(\x0b\x32\x14.mlflow.ModelVersion:+\xe2?(\n&com.databricks.rpc.RPC[$this.Response]\"x\n\x12\x44\x65leteModelVersion\x12\x12\n\x04name\x18\x01 \x01(\tB\x04\xf8\x86\x19\x01\x12\x15\n\x07version\x18\x02 \x01(\tB\x04\xf8\x86\x19\x01\x1a\n\n\x08Response:+\xe2?(\n&com.databricks.rpc.RPC[$this.Response]\"\xa2\x01\n\x0fGetModelVersion\x12\x12\n\x04name\x18\x01 \x01(\tB\x04\xf8\x86\x19\x01\x12\x15\n\x07version\x18\x02 \x01(\tB\x04\xf8\x86\x19\x01\x1a\x37\n\x08Response\x12+\n\rmodel_version\x18\x01 \x01(\x0b\x32\x14.mlflow.ModelVersion:+\xe2?(\n&com.databricks.rpc.RPC[$this.Response]\"\xe8\x01\n\x13SearchModelVersions\x12\x0e\n\x06\x66ilter\x18\x01 \x01(\t\x12\x1b\n\x0bmax_results\x18\x02 \x01(\x03:\x06\x32\x30\x30\x30\x30\x30\x12\x10\n\x08order_by\x18\x03 \x03(\t\x12\x12\n\npage_token\x18\x04 \x01(\t\x1aQ\n\x08Response\x12,\n\x0emodel_versions\x18\x01 \x03(\x0b\x32\x14.mlflow.ModelVersion\x12\x17\n\x0fnext_page_token\x18\x02 \x01(\t:+\xe2?(\n&com.databricks.rpc.RPC[$this.Response]\"\x96\x01\n\x1aGetModelVersionDownloadUri\x12\x12\n\x04name\x18\x01 \x01(\tB\x04\xf8\x86\x19\x01\x12\x15\n\x07version\x18\x02 \x01(\tB\x04\xf8\x86\x19\x01\x1a \n\x08Response\x12\x14\n\x0c\x61rtifact_uri\x18\x01 \x01(\t:+\xe2?(\n&com.databricks.rpc.RPC[$this.Response]\"-\n\x0fModelVersionTag\x12\x0b\n\x03key\x18\x01 \x01(\t\x12\r\n\x05value\x18\x02 \x01(\t\")\n\nModelParam\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\r\n\x05value\x18\x02 \x01(\t\"\xb5\x01\n\x0bModelMetric\x12\x0b\n\x03key\x18\x01 \x01(\t\x12\r\n\x05value\x18\x02 \x01(\x01\x12\x11\n\ttimestamp\x18\x03 \x01(\x03\x12\x0f\n\x04step\x18\x04 \x01(\x03:\x01\x30\x12\x1a\n\x0c\x64\x61taset_name\x18\x05 \x01(\tB\x04\xf0\x86\x19\x03\x12\x1c\n\x0e\x64\x61taset_digest\x18\x06 \x01(\tB\x04\xf0\x86\x19\x03\x12\x16\n\x08model_id\x18\x07 \x01(\tB\x04\xf0\x86\x19\x03\x12\x14\n\x06run_id\x18\x08 \x01(\tB\x04\xf0\x86\x19\x03\"0\n\x12RegisteredModelTag\x12\x0b\n\x03key\x18\x01 \x01(\t\x12\r\n\x05value\x18\x02 \x01(\t\"\x8c\x01\n\x15SetRegisteredModelTag\x12\x12\n\x04name\x18\x01 \x01(\tB\x04\xf8\x86\x19\x01\x12\x11\n\x03key\x18\x02 \x01(\tB\x04\xf8\x86\x19\x01\x12\x13\n\x05value\x18\x03 \x01(\tB\x04\xf8\x86\x19\x01\x1a\n\n\x08Response:+\xe2?(\n&com.databricks.rpc.RPC[$this.Response]\"\xa0\x01\n\x12SetModelVersionTag\x12\x12\n\x04name\x18\x01 \x01(\tB\x04\xf8\x86\x19\x01\x12\x15\n\x07version\x18\x02 \x01(\tB\x04\xf8\x86\x19\x01\x12\x11\n\x03key\x18\x03 \x01(\tB\x04\xf8\x86\x19\x01\x12\x13\n\x05value\x18\x04 \x01(\tB\x04\xf8\x86\x19\x01\x1a\n\n\x08Response:+\xe2?(\n&com.databricks.rpc.RPC[$this.Response]\"z\n\x18\x44\x65leteRegisteredModelTag\x12\x12\n\x04name\x18\x01 \x01(\tB\x04\xf8\x86\x19\x01\x12\x11\n\x03key\x18\x02 \x01(\tB\x04\xf8\x86\x19\x01\x1a\n\n\x08Response:+\xe2?(\n&com.databricks.rpc.RPC[$this.Response]\"\x8e\x01\n\x15\x44\x65leteModelVersionTag\x12\x12\n\x04name\x18\x01 \x01(\tB\x04\xf8\x86\x19\x01\x12\x15\n\x07version\x18\x02 \x01(\tB\x04\xf8\x86\x19\x01\x12\x11\n\x03key\x18\x03 \x01(\tB\x04\xf8\x86\x19\x01\x1a\n\n\x08Response:+\xe2?(\n&com.databricks.rpc.RPC[$this.Response]\"6\n\x14RegisteredModelAlias\x12\r\n\x05\x61lias\x18\x01 \x01(\t\x12\x0f\n\x07version\x18\x02 \x01(\t\"\x92\x01\n\x17SetRegisteredModelAlias\x12\x12\n\x04name\x18\x01 \x01(\tB\x04\xf8\x86\x19\x01\x12\x13\n\x05\x61lias\x18\x02 \x01(\tB\x04\xf8\x86\x19\x01\x12\x15\n\x07version\x18\x03 \x01(\tB\x04\xf8\x86\x19\x01\x1a\n\n\x08Response:+\xe2?(\n&com.databricks.rpc.RPC[$this.Response]\"~\n\x1a\x44\x65leteRegisteredModelAlias\x12\x12\n\x04name\x18\x01 \x01(\tB\x04\xf8\x86\x19\x01\x12\x13\n\x05\x61lias\x18\x02 \x01(\tB\x04\xf8\x86\x19\x01\x1a\n\n\x08Response:+\xe2?(\n&com.databricks.rpc.RPC[$this.Response]\"\xa7\x01\n\x16GetModelVersionByAlias\x12\x12\n\x04name\x18\x01 \x01(\tB\x04\xf8\x86\x19\x01\x12\x13\n\x05\x61lias\x18\x02 \x01(\tB\x04\xf8\x86\x19\x01\x1a\x37\n\x08Response\x12+\n\rmodel_version\x18\x01 \x01(\x0b\x32\x14.mlflow.ModelVersion:+\xe2?(\n&com.databricks.rpc.RPC[$this.Response]*R\n\x12ModelVersionStatus\x12\x18\n\x14PENDING_REGISTRATION\x10\x01\x12\x17\n\x13\x46\x41ILED_REGISTRATION\x10\x02\x12\t\n\x05READY\x10\x03\x32\x80\x1e\n\x14ModelRegistryService\x12\xae\x01\n\x15\x63reateRegisteredModel\x12\x1d.mlflow.CreateRegisteredModel\x1a&.mlflow.CreateRegisteredModel.Response\"N\xf2\x86\x19J\n.\n\x04POST\x12 /mlflow/registered-models/create\x1a\x04\x08\x02\x10\x00\x10\x01*\x16\x43reate RegisteredModel\x12\xae\x01\n\x15renameRegisteredModel\x12\x1d.mlflow.RenameRegisteredModel\x1a&.mlflow.RenameRegisteredModel.Response\"N\xf2\x86\x19J\n.\n\x04POST\x12 /mlflow/registered-models/rename\x1a\x04\x08\x02\x10\x00\x10\x01*\x16Rename RegisteredModel\x12\xaf\x01\n\x15updateRegisteredModel\x12\x1d.mlflow.UpdateRegisteredModel\x1a&.mlflow.UpdateRegisteredModel.Response\"O\xf2\x86\x19K\n/\n\x05PATCH\x12 /mlflow/registered-models/update\x1a\x04\x08\x02\x10\x00\x10\x01*\x16Update RegisteredModel\x12\xb0\x01\n\x15\x64\x65leteRegisteredModel\x12\x1d.mlflow.DeleteRegisteredModel\x1a&.mlflow.DeleteRegisteredModel.Response\"P\xf2\x86\x19L\n0\n\x06\x44\x45LETE\x12 /mlflow/registered-models/delete\x1a\x04\x08\x02\x10\x00\x10\x01*\x16\x44\x65lete RegisteredModel\x12\x9e\x01\n\x12getRegisteredModel\x12\x1a.mlflow.GetRegisteredModel\x1a#.mlflow.GetRegisteredModel.Response\"G\xf2\x86\x19\x43\n*\n\x03GET\x12\x1d/mlflow/registered-models/get\x1a\x04\x08\x02\x10\x00\x10\x01*\x13Get RegisteredModel\x12\xb1\x01\n\x16searchRegisteredModels\x12\x1e.mlflow.SearchRegisteredModels\x1a\'.mlflow.SearchRegisteredModels.Response\"N\xf2\x86\x19J\n-\n\x03GET\x12 /mlflow/registered-models/search\x1a\x04\x08\x02\x10\x00\x10\x01*\x17Search RegisteredModels\x12\xef\x01\n\x11getLatestVersions\x12\x19.mlflow.GetLatestVersions\x1a\".mlflow.GetLatestVersions.Response\"\x9a\x01\xf2\x86\x19\x95\x01\n;\n\x04POST\x12-/mlflow/registered-models/get-latest-versions\x1a\x04\x08\x02\x10\x00\n:\n\x03GET\x12-/mlflow/registered-models/get-latest-versions\x1a\x04\x08\x02\x10\x00\x10\x01*\x18Get Latest ModelVersions\x12\x9f\x01\n\x12\x63reateModelVersion\x12\x1a.mlflow.CreateModelVersion\x1a#.mlflow.CreateModelVersion.Response\"H\xf2\x86\x19\x44\n+\n\x04POST\x12\x1d/mlflow/model-versions/create\x1a\x04\x08\x02\x10\x00\x10\x01*\x13\x43reate ModelVersion\x12\xa0\x01\n\x12updateModelVersion\x12\x1a.mlflow.UpdateModelVersion\x1a#.mlflow.UpdateModelVersion.Response\"I\xf2\x86\x19\x45\n,\n\x05PATCH\x12\x1d/mlflow/model-versions/update\x1a\x04\x08\x02\x10\x00\x10\x01*\x13Update ModelVersion\x12\xce\x01\n\x1btransitionModelVersionStage\x12#.mlflow.TransitionModelVersionStage\x1a,.mlflow.TransitionModelVersionStage.Response\"\\\xf2\x86\x19X\n5\n\x04POST\x12\'/mlflow/model-versions/transition-stage\x1a\x04\x08\x02\x10\x00\x10\x01*\x1dTransition ModelVersion Stage\x12\xa1\x01\n\x12\x64\x65leteModelVersion\x12\x1a.mlflow.DeleteModelVersion\x1a#.mlflow.DeleteModelVersion.Response\"J\xf2\x86\x19\x46\n-\n\x06\x44\x45LETE\x12\x1d/mlflow/model-versions/delete\x1a\x04\x08\x02\x10\x00\x10\x01*\x13\x44\x65lete ModelVersion\x12\x8f\x01\n\x0fgetModelVersion\x12\x17.mlflow.GetModelVersion\x1a .mlflow.GetModelVersion.Response\"A\xf2\x86\x19=\n\'\n\x03GET\x12\x1a/mlflow/model-versions/get\x1a\x04\x08\x02\x10\x00\x10\x01*\x10Get ModelVersion\x12\xa6\x01\n\x13searchModelVersions\x12\x1b.mlflow.SearchModelVersions\x1a$.mlflow.SearchModelVersions.Response\"L\xf2\x86\x19\x44\n*\n\x03GET\x12\x1d/mlflow/model-versions/search\x1a\x04\x08\x02\x10\x00\x10\x01*\x14Search ModelVersions\xba\x8c\x19\x00\x12\xd8\x01\n\x1agetModelVersionDownloadUri\x12\".mlflow.GetModelVersionDownloadUri\x1a+.mlflow.GetModelVersionDownloadUri.Response\"i\xf2\x86\x19\x65\n4\n\x03GET\x12\'/mlflow/model-versions/get-download-uri\x1a\x04\x08\x02\x10\x00\x10\x01*+Get Download URI For ModelVersion Artifacts\x12\xb1\x01\n\x15setRegisteredModelTag\x12\x1d.mlflow.SetRegisteredModelTag\x1a&.mlflow.SetRegisteredModelTag.Response\"Q\xf2\x86\x19M\n/\n\x04POST\x12!/mlflow/registered-models/set-tag\x1a\x04\x08\x02\x10\x00\x10\x01*\x18Set Registered Model Tag\x12\xa2\x01\n\x12setModelVersionTag\x12\x1a.mlflow.SetModelVersionTag\x1a#.mlflow.SetModelVersionTag.Response\"K\xf2\x86\x19G\n,\n\x04POST\x12\x1e/mlflow/model-versions/set-tag\x1a\x04\x08\x02\x10\x00\x10\x01*\x15Set Model Version Tag\x12\xc2\x01\n\x18\x64\x65leteRegisteredModelTag\x12 .mlflow.DeleteRegisteredModelTag\x1a).mlflow.DeleteRegisteredModelTag.Response\"Y\xf2\x86\x19U\n4\n\x06\x44\x45LETE\x12$/mlflow/registered-models/delete-tag\x1a\x04\x08\x02\x10\x00\x10\x01*\x1b\x44\x65lete Registered Model Tag\x12\xb3\x01\n\x15\x64\x65leteModelVersionTag\x12\x1d.mlflow.DeleteModelVersionTag\x1a&.mlflow.DeleteModelVersionTag.Response\"S\xf2\x86\x19O\n1\n\x06\x44\x45LETE\x12!/mlflow/model-versions/delete-tag\x1a\x04\x08\x02\x10\x00\x10\x01*\x18\x44\x65lete Model Version Tag\x12\xb7\x01\n\x17setRegisteredModelAlias\x12\x1f.mlflow.SetRegisteredModelAlias\x1a(.mlflow.SetRegisteredModelAlias.Response\"Q\xf2\x86\x19M\n-\n\x04POST\x12\x1f/mlflow/registered-models/alias\x1a\x04\x08\x02\x10\x00\x10\x01*\x1aSet Registered Model Alias\x12\xc5\x01\n\x1a\x64\x65leteRegisteredModelAlias\x12\".mlflow.DeleteRegisteredModelAlias\x1a+.mlflow.DeleteRegisteredModelAlias.Response\"V\xf2\x86\x19R\n/\n\x06\x44\x45LETE\x12\x1f/mlflow/registered-models/alias\x1a\x04\x08\x02\x10\x00\x10\x01*\x1d\x44\x65lete Registered Model Alias\x12\xb3\x01\n\x16getModelVersionByAlias\x12\x1e.mlflow.GetModelVersionByAlias\x1a\'.mlflow.GetModelVersionByAlias.Response\"P\xf2\x86\x19L\n,\n\x03GET\x12\x1f/mlflow/registered-models/alias\x1a\x04\x08\x02\x10\x00\x10\x01*\x1aGet Model Version by AliasB!\n\x14org.mlflow.api.proto\x90\x01\x01\xa0\x01\x01\xe2?\x02\x10\x01') + + _MODELVERSIONSTATUS = DESCRIPTOR.enum_types_by_name['ModelVersionStatus'] + ModelVersionStatus = enum_type_wrapper.EnumTypeWrapper(_MODELVERSIONSTATUS) + PENDING_REGISTRATION = 1 + FAILED_REGISTRATION = 2 + READY = 3 + + + _REGISTEREDMODEL = DESCRIPTOR.message_types_by_name['RegisteredModel'] + _MODELVERSION = DESCRIPTOR.message_types_by_name['ModelVersion'] + _DEPLOYMENTJOBCONNECTION = DESCRIPTOR.message_types_by_name['DeploymentJobConnection'] + _MODELVERSIONDEPLOYMENTJOBSTATE = DESCRIPTOR.message_types_by_name['ModelVersionDeploymentJobState'] + _CREATEREGISTEREDMODEL = DESCRIPTOR.message_types_by_name['CreateRegisteredModel'] + _CREATEREGISTEREDMODEL_RESPONSE = _CREATEREGISTEREDMODEL.nested_types_by_name['Response'] + _RENAMEREGISTEREDMODEL = DESCRIPTOR.message_types_by_name['RenameRegisteredModel'] + _RENAMEREGISTEREDMODEL_RESPONSE = _RENAMEREGISTEREDMODEL.nested_types_by_name['Response'] + _UPDATEREGISTEREDMODEL = DESCRIPTOR.message_types_by_name['UpdateRegisteredModel'] + _UPDATEREGISTEREDMODEL_RESPONSE = _UPDATEREGISTEREDMODEL.nested_types_by_name['Response'] + _DELETEREGISTEREDMODEL = DESCRIPTOR.message_types_by_name['DeleteRegisteredModel'] + _DELETEREGISTEREDMODEL_RESPONSE = _DELETEREGISTEREDMODEL.nested_types_by_name['Response'] + _GETREGISTEREDMODEL = DESCRIPTOR.message_types_by_name['GetRegisteredModel'] + _GETREGISTEREDMODEL_RESPONSE = _GETREGISTEREDMODEL.nested_types_by_name['Response'] + _SEARCHREGISTEREDMODELS = DESCRIPTOR.message_types_by_name['SearchRegisteredModels'] + _SEARCHREGISTEREDMODELS_RESPONSE = _SEARCHREGISTEREDMODELS.nested_types_by_name['Response'] + _GETLATESTVERSIONS = DESCRIPTOR.message_types_by_name['GetLatestVersions'] + _GETLATESTVERSIONS_RESPONSE = _GETLATESTVERSIONS.nested_types_by_name['Response'] + _CREATEMODELVERSION = DESCRIPTOR.message_types_by_name['CreateModelVersion'] + _CREATEMODELVERSION_RESPONSE = _CREATEMODELVERSION.nested_types_by_name['Response'] + _UPDATEMODELVERSION = DESCRIPTOR.message_types_by_name['UpdateModelVersion'] + _UPDATEMODELVERSION_RESPONSE = _UPDATEMODELVERSION.nested_types_by_name['Response'] + _TRANSITIONMODELVERSIONSTAGE = DESCRIPTOR.message_types_by_name['TransitionModelVersionStage'] + _TRANSITIONMODELVERSIONSTAGE_RESPONSE = _TRANSITIONMODELVERSIONSTAGE.nested_types_by_name['Response'] + _DELETEMODELVERSION = DESCRIPTOR.message_types_by_name['DeleteModelVersion'] + _DELETEMODELVERSION_RESPONSE = _DELETEMODELVERSION.nested_types_by_name['Response'] + _GETMODELVERSION = DESCRIPTOR.message_types_by_name['GetModelVersion'] + _GETMODELVERSION_RESPONSE = _GETMODELVERSION.nested_types_by_name['Response'] + _SEARCHMODELVERSIONS = DESCRIPTOR.message_types_by_name['SearchModelVersions'] + _SEARCHMODELVERSIONS_RESPONSE = _SEARCHMODELVERSIONS.nested_types_by_name['Response'] + _GETMODELVERSIONDOWNLOADURI = DESCRIPTOR.message_types_by_name['GetModelVersionDownloadUri'] + _GETMODELVERSIONDOWNLOADURI_RESPONSE = _GETMODELVERSIONDOWNLOADURI.nested_types_by_name['Response'] + _MODELVERSIONTAG = DESCRIPTOR.message_types_by_name['ModelVersionTag'] + _MODELPARAM = DESCRIPTOR.message_types_by_name['ModelParam'] + _MODELMETRIC = DESCRIPTOR.message_types_by_name['ModelMetric'] + _REGISTEREDMODELTAG = DESCRIPTOR.message_types_by_name['RegisteredModelTag'] + _SETREGISTEREDMODELTAG = DESCRIPTOR.message_types_by_name['SetRegisteredModelTag'] + _SETREGISTEREDMODELTAG_RESPONSE = _SETREGISTEREDMODELTAG.nested_types_by_name['Response'] + _SETMODELVERSIONTAG = DESCRIPTOR.message_types_by_name['SetModelVersionTag'] + _SETMODELVERSIONTAG_RESPONSE = _SETMODELVERSIONTAG.nested_types_by_name['Response'] + _DELETEREGISTEREDMODELTAG = DESCRIPTOR.message_types_by_name['DeleteRegisteredModelTag'] + _DELETEREGISTEREDMODELTAG_RESPONSE = _DELETEREGISTEREDMODELTAG.nested_types_by_name['Response'] + _DELETEMODELVERSIONTAG = DESCRIPTOR.message_types_by_name['DeleteModelVersionTag'] + _DELETEMODELVERSIONTAG_RESPONSE = _DELETEMODELVERSIONTAG.nested_types_by_name['Response'] + _REGISTEREDMODELALIAS = DESCRIPTOR.message_types_by_name['RegisteredModelAlias'] + _SETREGISTEREDMODELALIAS = DESCRIPTOR.message_types_by_name['SetRegisteredModelAlias'] + _SETREGISTEREDMODELALIAS_RESPONSE = _SETREGISTEREDMODELALIAS.nested_types_by_name['Response'] + _DELETEREGISTEREDMODELALIAS = DESCRIPTOR.message_types_by_name['DeleteRegisteredModelAlias'] + _DELETEREGISTEREDMODELALIAS_RESPONSE = _DELETEREGISTEREDMODELALIAS.nested_types_by_name['Response'] + _GETMODELVERSIONBYALIAS = DESCRIPTOR.message_types_by_name['GetModelVersionByAlias'] + _GETMODELVERSIONBYALIAS_RESPONSE = _GETMODELVERSIONBYALIAS.nested_types_by_name['Response'] + _DEPLOYMENTJOBCONNECTION_STATE = _DEPLOYMENTJOBCONNECTION.enum_types_by_name['State'] + _MODELVERSIONDEPLOYMENTJOBSTATE_DEPLOYMENTJOBRUNSTATE = _MODELVERSIONDEPLOYMENTJOBSTATE.enum_types_by_name['DeploymentJobRunState'] + RegisteredModel = _reflection.GeneratedProtocolMessageType('RegisteredModel', (_message.Message,), { + 'DESCRIPTOR' : _REGISTEREDMODEL, + '__module__' : 'model_registry_pb2' + # @@protoc_insertion_point(class_scope:mlflow.RegisteredModel) + }) + _sym_db.RegisterMessage(RegisteredModel) + + ModelVersion = _reflection.GeneratedProtocolMessageType('ModelVersion', (_message.Message,), { + 'DESCRIPTOR' : _MODELVERSION, + '__module__' : 'model_registry_pb2' + # @@protoc_insertion_point(class_scope:mlflow.ModelVersion) + }) + _sym_db.RegisterMessage(ModelVersion) + + DeploymentJobConnection = _reflection.GeneratedProtocolMessageType('DeploymentJobConnection', (_message.Message,), { + 'DESCRIPTOR' : _DEPLOYMENTJOBCONNECTION, + '__module__' : 'model_registry_pb2' + # @@protoc_insertion_point(class_scope:mlflow.DeploymentJobConnection) + }) + _sym_db.RegisterMessage(DeploymentJobConnection) + + ModelVersionDeploymentJobState = _reflection.GeneratedProtocolMessageType('ModelVersionDeploymentJobState', (_message.Message,), { + 'DESCRIPTOR' : _MODELVERSIONDEPLOYMENTJOBSTATE, + '__module__' : 'model_registry_pb2' + # @@protoc_insertion_point(class_scope:mlflow.ModelVersionDeploymentJobState) + }) + _sym_db.RegisterMessage(ModelVersionDeploymentJobState) + + CreateRegisteredModel = _reflection.GeneratedProtocolMessageType('CreateRegisteredModel', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _CREATEREGISTEREDMODEL_RESPONSE, + '__module__' : 'model_registry_pb2' + # @@protoc_insertion_point(class_scope:mlflow.CreateRegisteredModel.Response) + }) + , + 'DESCRIPTOR' : _CREATEREGISTEREDMODEL, + '__module__' : 'model_registry_pb2' + # @@protoc_insertion_point(class_scope:mlflow.CreateRegisteredModel) + }) + _sym_db.RegisterMessage(CreateRegisteredModel) + _sym_db.RegisterMessage(CreateRegisteredModel.Response) + + RenameRegisteredModel = _reflection.GeneratedProtocolMessageType('RenameRegisteredModel', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _RENAMEREGISTEREDMODEL_RESPONSE, + '__module__' : 'model_registry_pb2' + # @@protoc_insertion_point(class_scope:mlflow.RenameRegisteredModel.Response) + }) + , + 'DESCRIPTOR' : _RENAMEREGISTEREDMODEL, + '__module__' : 'model_registry_pb2' + # @@protoc_insertion_point(class_scope:mlflow.RenameRegisteredModel) + }) + _sym_db.RegisterMessage(RenameRegisteredModel) + _sym_db.RegisterMessage(RenameRegisteredModel.Response) + + UpdateRegisteredModel = _reflection.GeneratedProtocolMessageType('UpdateRegisteredModel', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _UPDATEREGISTEREDMODEL_RESPONSE, + '__module__' : 'model_registry_pb2' + # @@protoc_insertion_point(class_scope:mlflow.UpdateRegisteredModel.Response) + }) + , + 'DESCRIPTOR' : _UPDATEREGISTEREDMODEL, + '__module__' : 'model_registry_pb2' + # @@protoc_insertion_point(class_scope:mlflow.UpdateRegisteredModel) + }) + _sym_db.RegisterMessage(UpdateRegisteredModel) + _sym_db.RegisterMessage(UpdateRegisteredModel.Response) + + DeleteRegisteredModel = _reflection.GeneratedProtocolMessageType('DeleteRegisteredModel', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _DELETEREGISTEREDMODEL_RESPONSE, + '__module__' : 'model_registry_pb2' + # @@protoc_insertion_point(class_scope:mlflow.DeleteRegisteredModel.Response) + }) + , + 'DESCRIPTOR' : _DELETEREGISTEREDMODEL, + '__module__' : 'model_registry_pb2' + # @@protoc_insertion_point(class_scope:mlflow.DeleteRegisteredModel) + }) + _sym_db.RegisterMessage(DeleteRegisteredModel) + _sym_db.RegisterMessage(DeleteRegisteredModel.Response) + + GetRegisteredModel = _reflection.GeneratedProtocolMessageType('GetRegisteredModel', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _GETREGISTEREDMODEL_RESPONSE, + '__module__' : 'model_registry_pb2' + # @@protoc_insertion_point(class_scope:mlflow.GetRegisteredModel.Response) + }) + , + 'DESCRIPTOR' : _GETREGISTEREDMODEL, + '__module__' : 'model_registry_pb2' + # @@protoc_insertion_point(class_scope:mlflow.GetRegisteredModel) + }) + _sym_db.RegisterMessage(GetRegisteredModel) + _sym_db.RegisterMessage(GetRegisteredModel.Response) + + SearchRegisteredModels = _reflection.GeneratedProtocolMessageType('SearchRegisteredModels', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _SEARCHREGISTEREDMODELS_RESPONSE, + '__module__' : 'model_registry_pb2' + # @@protoc_insertion_point(class_scope:mlflow.SearchRegisteredModels.Response) + }) + , + 'DESCRIPTOR' : _SEARCHREGISTEREDMODELS, + '__module__' : 'model_registry_pb2' + # @@protoc_insertion_point(class_scope:mlflow.SearchRegisteredModels) + }) + _sym_db.RegisterMessage(SearchRegisteredModels) + _sym_db.RegisterMessage(SearchRegisteredModels.Response) + + GetLatestVersions = _reflection.GeneratedProtocolMessageType('GetLatestVersions', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _GETLATESTVERSIONS_RESPONSE, + '__module__' : 'model_registry_pb2' + # @@protoc_insertion_point(class_scope:mlflow.GetLatestVersions.Response) + }) + , + 'DESCRIPTOR' : _GETLATESTVERSIONS, + '__module__' : 'model_registry_pb2' + # @@protoc_insertion_point(class_scope:mlflow.GetLatestVersions) + }) + _sym_db.RegisterMessage(GetLatestVersions) + _sym_db.RegisterMessage(GetLatestVersions.Response) + + CreateModelVersion = _reflection.GeneratedProtocolMessageType('CreateModelVersion', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _CREATEMODELVERSION_RESPONSE, + '__module__' : 'model_registry_pb2' + # @@protoc_insertion_point(class_scope:mlflow.CreateModelVersion.Response) + }) + , + 'DESCRIPTOR' : _CREATEMODELVERSION, + '__module__' : 'model_registry_pb2' + # @@protoc_insertion_point(class_scope:mlflow.CreateModelVersion) + }) + _sym_db.RegisterMessage(CreateModelVersion) + _sym_db.RegisterMessage(CreateModelVersion.Response) + + UpdateModelVersion = _reflection.GeneratedProtocolMessageType('UpdateModelVersion', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _UPDATEMODELVERSION_RESPONSE, + '__module__' : 'model_registry_pb2' + # @@protoc_insertion_point(class_scope:mlflow.UpdateModelVersion.Response) + }) + , + 'DESCRIPTOR' : _UPDATEMODELVERSION, + '__module__' : 'model_registry_pb2' + # @@protoc_insertion_point(class_scope:mlflow.UpdateModelVersion) + }) + _sym_db.RegisterMessage(UpdateModelVersion) + _sym_db.RegisterMessage(UpdateModelVersion.Response) + + TransitionModelVersionStage = _reflection.GeneratedProtocolMessageType('TransitionModelVersionStage', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _TRANSITIONMODELVERSIONSTAGE_RESPONSE, + '__module__' : 'model_registry_pb2' + # @@protoc_insertion_point(class_scope:mlflow.TransitionModelVersionStage.Response) + }) + , + 'DESCRIPTOR' : _TRANSITIONMODELVERSIONSTAGE, + '__module__' : 'model_registry_pb2' + # @@protoc_insertion_point(class_scope:mlflow.TransitionModelVersionStage) + }) + _sym_db.RegisterMessage(TransitionModelVersionStage) + _sym_db.RegisterMessage(TransitionModelVersionStage.Response) + + DeleteModelVersion = _reflection.GeneratedProtocolMessageType('DeleteModelVersion', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _DELETEMODELVERSION_RESPONSE, + '__module__' : 'model_registry_pb2' + # @@protoc_insertion_point(class_scope:mlflow.DeleteModelVersion.Response) + }) + , + 'DESCRIPTOR' : _DELETEMODELVERSION, + '__module__' : 'model_registry_pb2' + # @@protoc_insertion_point(class_scope:mlflow.DeleteModelVersion) + }) + _sym_db.RegisterMessage(DeleteModelVersion) + _sym_db.RegisterMessage(DeleteModelVersion.Response) + + GetModelVersion = _reflection.GeneratedProtocolMessageType('GetModelVersion', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _GETMODELVERSION_RESPONSE, + '__module__' : 'model_registry_pb2' + # @@protoc_insertion_point(class_scope:mlflow.GetModelVersion.Response) + }) + , + 'DESCRIPTOR' : _GETMODELVERSION, + '__module__' : 'model_registry_pb2' + # @@protoc_insertion_point(class_scope:mlflow.GetModelVersion) + }) + _sym_db.RegisterMessage(GetModelVersion) + _sym_db.RegisterMessage(GetModelVersion.Response) + + SearchModelVersions = _reflection.GeneratedProtocolMessageType('SearchModelVersions', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _SEARCHMODELVERSIONS_RESPONSE, + '__module__' : 'model_registry_pb2' + # @@protoc_insertion_point(class_scope:mlflow.SearchModelVersions.Response) + }) + , + 'DESCRIPTOR' : _SEARCHMODELVERSIONS, + '__module__' : 'model_registry_pb2' + # @@protoc_insertion_point(class_scope:mlflow.SearchModelVersions) + }) + _sym_db.RegisterMessage(SearchModelVersions) + _sym_db.RegisterMessage(SearchModelVersions.Response) + + GetModelVersionDownloadUri = _reflection.GeneratedProtocolMessageType('GetModelVersionDownloadUri', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _GETMODELVERSIONDOWNLOADURI_RESPONSE, + '__module__' : 'model_registry_pb2' + # @@protoc_insertion_point(class_scope:mlflow.GetModelVersionDownloadUri.Response) + }) + , + 'DESCRIPTOR' : _GETMODELVERSIONDOWNLOADURI, + '__module__' : 'model_registry_pb2' + # @@protoc_insertion_point(class_scope:mlflow.GetModelVersionDownloadUri) + }) + _sym_db.RegisterMessage(GetModelVersionDownloadUri) + _sym_db.RegisterMessage(GetModelVersionDownloadUri.Response) + + ModelVersionTag = _reflection.GeneratedProtocolMessageType('ModelVersionTag', (_message.Message,), { + 'DESCRIPTOR' : _MODELVERSIONTAG, + '__module__' : 'model_registry_pb2' + # @@protoc_insertion_point(class_scope:mlflow.ModelVersionTag) + }) + _sym_db.RegisterMessage(ModelVersionTag) + + ModelParam = _reflection.GeneratedProtocolMessageType('ModelParam', (_message.Message,), { + 'DESCRIPTOR' : _MODELPARAM, + '__module__' : 'model_registry_pb2' + # @@protoc_insertion_point(class_scope:mlflow.ModelParam) + }) + _sym_db.RegisterMessage(ModelParam) + + ModelMetric = _reflection.GeneratedProtocolMessageType('ModelMetric', (_message.Message,), { + 'DESCRIPTOR' : _MODELMETRIC, + '__module__' : 'model_registry_pb2' + # @@protoc_insertion_point(class_scope:mlflow.ModelMetric) + }) + _sym_db.RegisterMessage(ModelMetric) + + RegisteredModelTag = _reflection.GeneratedProtocolMessageType('RegisteredModelTag', (_message.Message,), { + 'DESCRIPTOR' : _REGISTEREDMODELTAG, + '__module__' : 'model_registry_pb2' + # @@protoc_insertion_point(class_scope:mlflow.RegisteredModelTag) + }) + _sym_db.RegisterMessage(RegisteredModelTag) + + SetRegisteredModelTag = _reflection.GeneratedProtocolMessageType('SetRegisteredModelTag', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _SETREGISTEREDMODELTAG_RESPONSE, + '__module__' : 'model_registry_pb2' + # @@protoc_insertion_point(class_scope:mlflow.SetRegisteredModelTag.Response) + }) + , + 'DESCRIPTOR' : _SETREGISTEREDMODELTAG, + '__module__' : 'model_registry_pb2' + # @@protoc_insertion_point(class_scope:mlflow.SetRegisteredModelTag) + }) + _sym_db.RegisterMessage(SetRegisteredModelTag) + _sym_db.RegisterMessage(SetRegisteredModelTag.Response) + + SetModelVersionTag = _reflection.GeneratedProtocolMessageType('SetModelVersionTag', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _SETMODELVERSIONTAG_RESPONSE, + '__module__' : 'model_registry_pb2' + # @@protoc_insertion_point(class_scope:mlflow.SetModelVersionTag.Response) + }) + , + 'DESCRIPTOR' : _SETMODELVERSIONTAG, + '__module__' : 'model_registry_pb2' + # @@protoc_insertion_point(class_scope:mlflow.SetModelVersionTag) + }) + _sym_db.RegisterMessage(SetModelVersionTag) + _sym_db.RegisterMessage(SetModelVersionTag.Response) + + DeleteRegisteredModelTag = _reflection.GeneratedProtocolMessageType('DeleteRegisteredModelTag', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _DELETEREGISTEREDMODELTAG_RESPONSE, + '__module__' : 'model_registry_pb2' + # @@protoc_insertion_point(class_scope:mlflow.DeleteRegisteredModelTag.Response) + }) + , + 'DESCRIPTOR' : _DELETEREGISTEREDMODELTAG, + '__module__' : 'model_registry_pb2' + # @@protoc_insertion_point(class_scope:mlflow.DeleteRegisteredModelTag) + }) + _sym_db.RegisterMessage(DeleteRegisteredModelTag) + _sym_db.RegisterMessage(DeleteRegisteredModelTag.Response) + + DeleteModelVersionTag = _reflection.GeneratedProtocolMessageType('DeleteModelVersionTag', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _DELETEMODELVERSIONTAG_RESPONSE, + '__module__' : 'model_registry_pb2' + # @@protoc_insertion_point(class_scope:mlflow.DeleteModelVersionTag.Response) + }) + , + 'DESCRIPTOR' : _DELETEMODELVERSIONTAG, + '__module__' : 'model_registry_pb2' + # @@protoc_insertion_point(class_scope:mlflow.DeleteModelVersionTag) + }) + _sym_db.RegisterMessage(DeleteModelVersionTag) + _sym_db.RegisterMessage(DeleteModelVersionTag.Response) + + RegisteredModelAlias = _reflection.GeneratedProtocolMessageType('RegisteredModelAlias', (_message.Message,), { + 'DESCRIPTOR' : _REGISTEREDMODELALIAS, + '__module__' : 'model_registry_pb2' + # @@protoc_insertion_point(class_scope:mlflow.RegisteredModelAlias) + }) + _sym_db.RegisterMessage(RegisteredModelAlias) + + SetRegisteredModelAlias = _reflection.GeneratedProtocolMessageType('SetRegisteredModelAlias', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _SETREGISTEREDMODELALIAS_RESPONSE, + '__module__' : 'model_registry_pb2' + # @@protoc_insertion_point(class_scope:mlflow.SetRegisteredModelAlias.Response) + }) + , + 'DESCRIPTOR' : _SETREGISTEREDMODELALIAS, + '__module__' : 'model_registry_pb2' + # @@protoc_insertion_point(class_scope:mlflow.SetRegisteredModelAlias) + }) + _sym_db.RegisterMessage(SetRegisteredModelAlias) + _sym_db.RegisterMessage(SetRegisteredModelAlias.Response) + + DeleteRegisteredModelAlias = _reflection.GeneratedProtocolMessageType('DeleteRegisteredModelAlias', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _DELETEREGISTEREDMODELALIAS_RESPONSE, + '__module__' : 'model_registry_pb2' + # @@protoc_insertion_point(class_scope:mlflow.DeleteRegisteredModelAlias.Response) + }) + , + 'DESCRIPTOR' : _DELETEREGISTEREDMODELALIAS, + '__module__' : 'model_registry_pb2' + # @@protoc_insertion_point(class_scope:mlflow.DeleteRegisteredModelAlias) + }) + _sym_db.RegisterMessage(DeleteRegisteredModelAlias) + _sym_db.RegisterMessage(DeleteRegisteredModelAlias.Response) + + GetModelVersionByAlias = _reflection.GeneratedProtocolMessageType('GetModelVersionByAlias', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _GETMODELVERSIONBYALIAS_RESPONSE, + '__module__' : 'model_registry_pb2' + # @@protoc_insertion_point(class_scope:mlflow.GetModelVersionByAlias.Response) + }) + , + 'DESCRIPTOR' : _GETMODELVERSIONBYALIAS, + '__module__' : 'model_registry_pb2' + # @@protoc_insertion_point(class_scope:mlflow.GetModelVersionByAlias) + }) + _sym_db.RegisterMessage(GetModelVersionByAlias) + _sym_db.RegisterMessage(GetModelVersionByAlias.Response) + + _MODELREGISTRYSERVICE = DESCRIPTOR.services_by_name['ModelRegistryService'] + if _descriptor._USE_C_DESCRIPTORS == False: + + DESCRIPTOR._options = None + DESCRIPTOR._serialized_options = b'\n\024org.mlflow.api.proto\220\001\001\240\001\001\342?\002\020\001' + _CREATEREGISTEREDMODEL.fields_by_name['name']._options = None + _CREATEREGISTEREDMODEL.fields_by_name['name']._serialized_options = b'\370\206\031\001' + _CREATEREGISTEREDMODEL._options = None + _CREATEREGISTEREDMODEL._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _RENAMEREGISTEREDMODEL.fields_by_name['name']._options = None + _RENAMEREGISTEREDMODEL.fields_by_name['name']._serialized_options = b'\370\206\031\001' + _RENAMEREGISTEREDMODEL._options = None + _RENAMEREGISTEREDMODEL._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _UPDATEREGISTEREDMODEL.fields_by_name['name']._options = None + _UPDATEREGISTEREDMODEL.fields_by_name['name']._serialized_options = b'\370\206\031\001' + _UPDATEREGISTEREDMODEL._options = None + _UPDATEREGISTEREDMODEL._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _DELETEREGISTEREDMODEL.fields_by_name['name']._options = None + _DELETEREGISTEREDMODEL.fields_by_name['name']._serialized_options = b'\370\206\031\001' + _DELETEREGISTEREDMODEL._options = None + _DELETEREGISTEREDMODEL._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _GETREGISTEREDMODEL.fields_by_name['name']._options = None + _GETREGISTEREDMODEL.fields_by_name['name']._serialized_options = b'\370\206\031\001' + _GETREGISTEREDMODEL._options = None + _GETREGISTEREDMODEL._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _SEARCHREGISTEREDMODELS._options = None + _SEARCHREGISTEREDMODELS._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _GETLATESTVERSIONS.fields_by_name['name']._options = None + _GETLATESTVERSIONS.fields_by_name['name']._serialized_options = b'\370\206\031\001' + _GETLATESTVERSIONS._options = None + _GETLATESTVERSIONS._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _CREATEMODELVERSION.fields_by_name['name']._options = None + _CREATEMODELVERSION.fields_by_name['name']._serialized_options = b'\370\206\031\001' + _CREATEMODELVERSION.fields_by_name['source']._options = None + _CREATEMODELVERSION.fields_by_name['source']._serialized_options = b'\370\206\031\001' + _CREATEMODELVERSION._options = None + _CREATEMODELVERSION._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _UPDATEMODELVERSION.fields_by_name['name']._options = None + _UPDATEMODELVERSION.fields_by_name['name']._serialized_options = b'\370\206\031\001' + _UPDATEMODELVERSION.fields_by_name['version']._options = None + _UPDATEMODELVERSION.fields_by_name['version']._serialized_options = b'\370\206\031\001' + _UPDATEMODELVERSION._options = None + _UPDATEMODELVERSION._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _TRANSITIONMODELVERSIONSTAGE.fields_by_name['name']._options = None + _TRANSITIONMODELVERSIONSTAGE.fields_by_name['name']._serialized_options = b'\370\206\031\001' + _TRANSITIONMODELVERSIONSTAGE.fields_by_name['version']._options = None + _TRANSITIONMODELVERSIONSTAGE.fields_by_name['version']._serialized_options = b'\370\206\031\001' + _TRANSITIONMODELVERSIONSTAGE.fields_by_name['stage']._options = None + _TRANSITIONMODELVERSIONSTAGE.fields_by_name['stage']._serialized_options = b'\370\206\031\001' + _TRANSITIONMODELVERSIONSTAGE.fields_by_name['archive_existing_versions']._options = None + _TRANSITIONMODELVERSIONSTAGE.fields_by_name['archive_existing_versions']._serialized_options = b'\370\206\031\001' + _TRANSITIONMODELVERSIONSTAGE._options = None + _TRANSITIONMODELVERSIONSTAGE._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _DELETEMODELVERSION.fields_by_name['name']._options = None + _DELETEMODELVERSION.fields_by_name['name']._serialized_options = b'\370\206\031\001' + _DELETEMODELVERSION.fields_by_name['version']._options = None + _DELETEMODELVERSION.fields_by_name['version']._serialized_options = b'\370\206\031\001' + _DELETEMODELVERSION._options = None + _DELETEMODELVERSION._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _GETMODELVERSION.fields_by_name['name']._options = None + _GETMODELVERSION.fields_by_name['name']._serialized_options = b'\370\206\031\001' + _GETMODELVERSION.fields_by_name['version']._options = None + _GETMODELVERSION.fields_by_name['version']._serialized_options = b'\370\206\031\001' + _GETMODELVERSION._options = None + _GETMODELVERSION._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _SEARCHMODELVERSIONS._options = None + _SEARCHMODELVERSIONS._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _GETMODELVERSIONDOWNLOADURI.fields_by_name['name']._options = None + _GETMODELVERSIONDOWNLOADURI.fields_by_name['name']._serialized_options = b'\370\206\031\001' + _GETMODELVERSIONDOWNLOADURI.fields_by_name['version']._options = None + _GETMODELVERSIONDOWNLOADURI.fields_by_name['version']._serialized_options = b'\370\206\031\001' + _GETMODELVERSIONDOWNLOADURI._options = None + _GETMODELVERSIONDOWNLOADURI._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _MODELMETRIC.fields_by_name['dataset_name']._options = None + _MODELMETRIC.fields_by_name['dataset_name']._serialized_options = b'\360\206\031\003' + _MODELMETRIC.fields_by_name['dataset_digest']._options = None + _MODELMETRIC.fields_by_name['dataset_digest']._serialized_options = b'\360\206\031\003' + _MODELMETRIC.fields_by_name['model_id']._options = None + _MODELMETRIC.fields_by_name['model_id']._serialized_options = b'\360\206\031\003' + _MODELMETRIC.fields_by_name['run_id']._options = None + _MODELMETRIC.fields_by_name['run_id']._serialized_options = b'\360\206\031\003' + _SETREGISTEREDMODELTAG.fields_by_name['name']._options = None + _SETREGISTEREDMODELTAG.fields_by_name['name']._serialized_options = b'\370\206\031\001' + _SETREGISTEREDMODELTAG.fields_by_name['key']._options = None + _SETREGISTEREDMODELTAG.fields_by_name['key']._serialized_options = b'\370\206\031\001' + _SETREGISTEREDMODELTAG.fields_by_name['value']._options = None + _SETREGISTEREDMODELTAG.fields_by_name['value']._serialized_options = b'\370\206\031\001' + _SETREGISTEREDMODELTAG._options = None + _SETREGISTEREDMODELTAG._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _SETMODELVERSIONTAG.fields_by_name['name']._options = None + _SETMODELVERSIONTAG.fields_by_name['name']._serialized_options = b'\370\206\031\001' + _SETMODELVERSIONTAG.fields_by_name['version']._options = None + _SETMODELVERSIONTAG.fields_by_name['version']._serialized_options = b'\370\206\031\001' + _SETMODELVERSIONTAG.fields_by_name['key']._options = None + _SETMODELVERSIONTAG.fields_by_name['key']._serialized_options = b'\370\206\031\001' + _SETMODELVERSIONTAG.fields_by_name['value']._options = None + _SETMODELVERSIONTAG.fields_by_name['value']._serialized_options = b'\370\206\031\001' + _SETMODELVERSIONTAG._options = None + _SETMODELVERSIONTAG._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _DELETEREGISTEREDMODELTAG.fields_by_name['name']._options = None + _DELETEREGISTEREDMODELTAG.fields_by_name['name']._serialized_options = b'\370\206\031\001' + _DELETEREGISTEREDMODELTAG.fields_by_name['key']._options = None + _DELETEREGISTEREDMODELTAG.fields_by_name['key']._serialized_options = b'\370\206\031\001' + _DELETEREGISTEREDMODELTAG._options = None + _DELETEREGISTEREDMODELTAG._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _DELETEMODELVERSIONTAG.fields_by_name['name']._options = None + _DELETEMODELVERSIONTAG.fields_by_name['name']._serialized_options = b'\370\206\031\001' + _DELETEMODELVERSIONTAG.fields_by_name['version']._options = None + _DELETEMODELVERSIONTAG.fields_by_name['version']._serialized_options = b'\370\206\031\001' + _DELETEMODELVERSIONTAG.fields_by_name['key']._options = None + _DELETEMODELVERSIONTAG.fields_by_name['key']._serialized_options = b'\370\206\031\001' + _DELETEMODELVERSIONTAG._options = None + _DELETEMODELVERSIONTAG._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _SETREGISTEREDMODELALIAS.fields_by_name['name']._options = None + _SETREGISTEREDMODELALIAS.fields_by_name['name']._serialized_options = b'\370\206\031\001' + _SETREGISTEREDMODELALIAS.fields_by_name['alias']._options = None + _SETREGISTEREDMODELALIAS.fields_by_name['alias']._serialized_options = b'\370\206\031\001' + _SETREGISTEREDMODELALIAS.fields_by_name['version']._options = None + _SETREGISTEREDMODELALIAS.fields_by_name['version']._serialized_options = b'\370\206\031\001' + _SETREGISTEREDMODELALIAS._options = None + _SETREGISTEREDMODELALIAS._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _DELETEREGISTEREDMODELALIAS.fields_by_name['name']._options = None + _DELETEREGISTEREDMODELALIAS.fields_by_name['name']._serialized_options = b'\370\206\031\001' + _DELETEREGISTEREDMODELALIAS.fields_by_name['alias']._options = None + _DELETEREGISTEREDMODELALIAS.fields_by_name['alias']._serialized_options = b'\370\206\031\001' + _DELETEREGISTEREDMODELALIAS._options = None + _DELETEREGISTEREDMODELALIAS._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _GETMODELVERSIONBYALIAS.fields_by_name['name']._options = None + _GETMODELVERSIONBYALIAS.fields_by_name['name']._serialized_options = b'\370\206\031\001' + _GETMODELVERSIONBYALIAS.fields_by_name['alias']._options = None + _GETMODELVERSIONBYALIAS.fields_by_name['alias']._serialized_options = b'\370\206\031\001' + _GETMODELVERSIONBYALIAS._options = None + _GETMODELVERSIONBYALIAS._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _MODELREGISTRYSERVICE.methods_by_name['createRegisteredModel']._options = None + _MODELREGISTRYSERVICE.methods_by_name['createRegisteredModel']._serialized_options = b'\362\206\031J\n.\n\004POST\022 /mlflow/registered-models/create\032\004\010\002\020\000\020\001*\026Create RegisteredModel' + _MODELREGISTRYSERVICE.methods_by_name['renameRegisteredModel']._options = None + _MODELREGISTRYSERVICE.methods_by_name['renameRegisteredModel']._serialized_options = b'\362\206\031J\n.\n\004POST\022 /mlflow/registered-models/rename\032\004\010\002\020\000\020\001*\026Rename RegisteredModel' + _MODELREGISTRYSERVICE.methods_by_name['updateRegisteredModel']._options = None + _MODELREGISTRYSERVICE.methods_by_name['updateRegisteredModel']._serialized_options = b'\362\206\031K\n/\n\005PATCH\022 /mlflow/registered-models/update\032\004\010\002\020\000\020\001*\026Update RegisteredModel' + _MODELREGISTRYSERVICE.methods_by_name['deleteRegisteredModel']._options = None + _MODELREGISTRYSERVICE.methods_by_name['deleteRegisteredModel']._serialized_options = b'\362\206\031L\n0\n\006DELETE\022 /mlflow/registered-models/delete\032\004\010\002\020\000\020\001*\026Delete RegisteredModel' + _MODELREGISTRYSERVICE.methods_by_name['getRegisteredModel']._options = None + _MODELREGISTRYSERVICE.methods_by_name['getRegisteredModel']._serialized_options = b'\362\206\031C\n*\n\003GET\022\035/mlflow/registered-models/get\032\004\010\002\020\000\020\001*\023Get RegisteredModel' + _MODELREGISTRYSERVICE.methods_by_name['searchRegisteredModels']._options = None + _MODELREGISTRYSERVICE.methods_by_name['searchRegisteredModels']._serialized_options = b'\362\206\031J\n-\n\003GET\022 /mlflow/registered-models/search\032\004\010\002\020\000\020\001*\027Search RegisteredModels' + _MODELREGISTRYSERVICE.methods_by_name['getLatestVersions']._options = None + _MODELREGISTRYSERVICE.methods_by_name['getLatestVersions']._serialized_options = b'\362\206\031\225\001\n;\n\004POST\022-/mlflow/registered-models/get-latest-versions\032\004\010\002\020\000\n:\n\003GET\022-/mlflow/registered-models/get-latest-versions\032\004\010\002\020\000\020\001*\030Get Latest ModelVersions' + _MODELREGISTRYSERVICE.methods_by_name['createModelVersion']._options = None + _MODELREGISTRYSERVICE.methods_by_name['createModelVersion']._serialized_options = b'\362\206\031D\n+\n\004POST\022\035/mlflow/model-versions/create\032\004\010\002\020\000\020\001*\023Create ModelVersion' + _MODELREGISTRYSERVICE.methods_by_name['updateModelVersion']._options = None + _MODELREGISTRYSERVICE.methods_by_name['updateModelVersion']._serialized_options = b'\362\206\031E\n,\n\005PATCH\022\035/mlflow/model-versions/update\032\004\010\002\020\000\020\001*\023Update ModelVersion' + _MODELREGISTRYSERVICE.methods_by_name['transitionModelVersionStage']._options = None + _MODELREGISTRYSERVICE.methods_by_name['transitionModelVersionStage']._serialized_options = b'\362\206\031X\n5\n\004POST\022\'/mlflow/model-versions/transition-stage\032\004\010\002\020\000\020\001*\035Transition ModelVersion Stage' + _MODELREGISTRYSERVICE.methods_by_name['deleteModelVersion']._options = None + _MODELREGISTRYSERVICE.methods_by_name['deleteModelVersion']._serialized_options = b'\362\206\031F\n-\n\006DELETE\022\035/mlflow/model-versions/delete\032\004\010\002\020\000\020\001*\023Delete ModelVersion' + _MODELREGISTRYSERVICE.methods_by_name['getModelVersion']._options = None + _MODELREGISTRYSERVICE.methods_by_name['getModelVersion']._serialized_options = b'\362\206\031=\n\'\n\003GET\022\032/mlflow/model-versions/get\032\004\010\002\020\000\020\001*\020Get ModelVersion' + _MODELREGISTRYSERVICE.methods_by_name['searchModelVersions']._options = None + _MODELREGISTRYSERVICE.methods_by_name['searchModelVersions']._serialized_options = b'\362\206\031D\n*\n\003GET\022\035/mlflow/model-versions/search\032\004\010\002\020\000\020\001*\024Search ModelVersions\272\214\031\000' + _MODELREGISTRYSERVICE.methods_by_name['getModelVersionDownloadUri']._options = None + _MODELREGISTRYSERVICE.methods_by_name['getModelVersionDownloadUri']._serialized_options = b'\362\206\031e\n4\n\003GET\022\'/mlflow/model-versions/get-download-uri\032\004\010\002\020\000\020\001*+Get Download URI For ModelVersion Artifacts' + _MODELREGISTRYSERVICE.methods_by_name['setRegisteredModelTag']._options = None + _MODELREGISTRYSERVICE.methods_by_name['setRegisteredModelTag']._serialized_options = b'\362\206\031M\n/\n\004POST\022!/mlflow/registered-models/set-tag\032\004\010\002\020\000\020\001*\030Set Registered Model Tag' + _MODELREGISTRYSERVICE.methods_by_name['setModelVersionTag']._options = None + _MODELREGISTRYSERVICE.methods_by_name['setModelVersionTag']._serialized_options = b'\362\206\031G\n,\n\004POST\022\036/mlflow/model-versions/set-tag\032\004\010\002\020\000\020\001*\025Set Model Version Tag' + _MODELREGISTRYSERVICE.methods_by_name['deleteRegisteredModelTag']._options = None + _MODELREGISTRYSERVICE.methods_by_name['deleteRegisteredModelTag']._serialized_options = b'\362\206\031U\n4\n\006DELETE\022$/mlflow/registered-models/delete-tag\032\004\010\002\020\000\020\001*\033Delete Registered Model Tag' + _MODELREGISTRYSERVICE.methods_by_name['deleteModelVersionTag']._options = None + _MODELREGISTRYSERVICE.methods_by_name['deleteModelVersionTag']._serialized_options = b'\362\206\031O\n1\n\006DELETE\022!/mlflow/model-versions/delete-tag\032\004\010\002\020\000\020\001*\030Delete Model Version Tag' + _MODELREGISTRYSERVICE.methods_by_name['setRegisteredModelAlias']._options = None + _MODELREGISTRYSERVICE.methods_by_name['setRegisteredModelAlias']._serialized_options = b'\362\206\031M\n-\n\004POST\022\037/mlflow/registered-models/alias\032\004\010\002\020\000\020\001*\032Set Registered Model Alias' + _MODELREGISTRYSERVICE.methods_by_name['deleteRegisteredModelAlias']._options = None + _MODELREGISTRYSERVICE.methods_by_name['deleteRegisteredModelAlias']._serialized_options = b'\362\206\031R\n/\n\006DELETE\022\037/mlflow/registered-models/alias\032\004\010\002\020\000\020\001*\035Delete Registered Model Alias' + _MODELREGISTRYSERVICE.methods_by_name['getModelVersionByAlias']._options = None + _MODELREGISTRYSERVICE.methods_by_name['getModelVersionByAlias']._serialized_options = b'\362\206\031L\n,\n\003GET\022\037/mlflow/registered-models/alias\032\004\010\002\020\000\020\001*\032Get Model Version by Alias' + _MODELVERSIONSTATUS._serialized_start=5580 + _MODELVERSIONSTATUS._serialized_end=5662 + _REGISTEREDMODEL._serialized_start=74 + _REGISTEREDMODEL._serialized_end=435 + _MODELVERSION._serialized_start=438 + _MODELVERSION._serialized_end=952 + _DEPLOYMENTJOBCONNECTION._serialized_start=955 + _DEPLOYMENTJOBCONNECTION._serialized_end=1118 + _DEPLOYMENTJOBCONNECTION_STATE._serialized_start=983 + _DEPLOYMENTJOBCONNECTION_STATE._serialized_end=1118 + _MODELVERSIONDEPLOYMENTJOBSTATE._serialized_start=1121 + _MODELVERSIONDEPLOYMENTJOBSTATE._serialized_end=1521 + _MODELVERSIONDEPLOYMENTJOBSTATE_DEPLOYMENTJOBRUNSTATE._serialized_start=1354 + _MODELVERSIONDEPLOYMENTJOBSTATE_DEPLOYMENTJOBRUNSTATE._serialized_end=1521 + _CREATEREGISTEREDMODEL._serialized_start=1524 + _CREATEREGISTEREDMODEL._serialized_end=1765 + _CREATEREGISTEREDMODEL_RESPONSE._serialized_start=1659 + _CREATEREGISTEREDMODEL_RESPONSE._serialized_end=1720 + _RENAMEREGISTEREDMODEL._serialized_start=1768 + _RENAMEREGISTEREDMODEL._serialized_end=1937 + _RENAMEREGISTEREDMODEL_RESPONSE._serialized_start=1659 + _RENAMEREGISTEREDMODEL_RESPONSE._serialized_end=1720 + _UPDATEREGISTEREDMODEL._serialized_start=1940 + _UPDATEREGISTEREDMODEL._serialized_end=2139 + _UPDATEREGISTEREDMODEL_RESPONSE._serialized_start=1659 + _UPDATEREGISTEREDMODEL_RESPONSE._serialized_end=1720 + _DELETEREGISTEREDMODEL._serialized_start=2141 + _DELETEREGISTEREDMODEL._serialized_end=2241 + _DELETEREGISTEREDMODEL_RESPONSE._serialized_start=1659 + _DELETEREGISTEREDMODEL_RESPONSE._serialized_end=1669 + _GETREGISTEREDMODEL._serialized_start=2244 + _GETREGISTEREDMODEL._serialized_end=2392 + _GETREGISTEREDMODEL_RESPONSE._serialized_start=1659 + _GETREGISTEREDMODEL_RESPONSE._serialized_end=1720 + _SEARCHREGISTEREDMODELS._serialized_start=2395 + _SEARCHREGISTEREDMODELS._serialized_end=2633 + _SEARCHREGISTEREDMODELS_RESPONSE._serialized_start=2501 + _SEARCHREGISTEREDMODELS_RESPONSE._serialized_end=2588 + _GETLATESTVERSIONS._serialized_start=2636 + _GETLATESTVERSIONS._serialized_end=2794 + _GETLATESTVERSIONS_RESPONSE._serialized_start=2693 + _GETLATESTVERSIONS_RESPONSE._serialized_end=2749 + _CREATEMODELVERSION._serialized_start=2797 + _CREATEMODELVERSION._serialized_end=3073 + _CREATEMODELVERSION_RESPONSE._serialized_start=2973 + _CREATEMODELVERSION_RESPONSE._serialized_end=3028 + _UPDATEMODELVERSION._serialized_start=3076 + _UPDATEMODELVERSION._serialized_end=3262 + _UPDATEMODELVERSION_RESPONSE._serialized_start=2973 + _UPDATEMODELVERSION_RESPONSE._serialized_end=3028 + _TRANSITIONMODELVERSIONSTAGE._serialized_start=3265 + _TRANSITIONMODELVERSIONSTAGE._serialized_end=3501 + _TRANSITIONMODELVERSIONSTAGE_RESPONSE._serialized_start=2973 + _TRANSITIONMODELVERSIONSTAGE_RESPONSE._serialized_end=3028 + _DELETEMODELVERSION._serialized_start=3503 + _DELETEMODELVERSION._serialized_end=3623 + _DELETEMODELVERSION_RESPONSE._serialized_start=1659 + _DELETEMODELVERSION_RESPONSE._serialized_end=1669 + _GETMODELVERSION._serialized_start=3626 + _GETMODELVERSION._serialized_end=3788 + _GETMODELVERSION_RESPONSE._serialized_start=2973 + _GETMODELVERSION_RESPONSE._serialized_end=3028 + _SEARCHMODELVERSIONS._serialized_start=3791 + _SEARCHMODELVERSIONS._serialized_end=4023 + _SEARCHMODELVERSIONS_RESPONSE._serialized_start=3897 + _SEARCHMODELVERSIONS_RESPONSE._serialized_end=3978 + _GETMODELVERSIONDOWNLOADURI._serialized_start=4026 + _GETMODELVERSIONDOWNLOADURI._serialized_end=4176 + _GETMODELVERSIONDOWNLOADURI_RESPONSE._serialized_start=4099 + _GETMODELVERSIONDOWNLOADURI_RESPONSE._serialized_end=4131 + _MODELVERSIONTAG._serialized_start=4178 + _MODELVERSIONTAG._serialized_end=4223 + _MODELPARAM._serialized_start=4225 + _MODELPARAM._serialized_end=4266 + _MODELMETRIC._serialized_start=4269 + _MODELMETRIC._serialized_end=4450 + _REGISTEREDMODELTAG._serialized_start=4452 + _REGISTEREDMODELTAG._serialized_end=4500 + _SETREGISTEREDMODELTAG._serialized_start=4503 + _SETREGISTEREDMODELTAG._serialized_end=4643 + _SETREGISTEREDMODELTAG_RESPONSE._serialized_start=1659 + _SETREGISTEREDMODELTAG_RESPONSE._serialized_end=1669 + _SETMODELVERSIONTAG._serialized_start=4646 + _SETMODELVERSIONTAG._serialized_end=4806 + _SETMODELVERSIONTAG_RESPONSE._serialized_start=1659 + _SETMODELVERSIONTAG_RESPONSE._serialized_end=1669 + _DELETEREGISTEREDMODELTAG._serialized_start=4808 + _DELETEREGISTEREDMODELTAG._serialized_end=4930 + _DELETEREGISTEREDMODELTAG_RESPONSE._serialized_start=1659 + _DELETEREGISTEREDMODELTAG_RESPONSE._serialized_end=1669 + _DELETEMODELVERSIONTAG._serialized_start=4933 + _DELETEMODELVERSIONTAG._serialized_end=5075 + _DELETEMODELVERSIONTAG_RESPONSE._serialized_start=1659 + _DELETEMODELVERSIONTAG_RESPONSE._serialized_end=1669 + _REGISTEREDMODELALIAS._serialized_start=5077 + _REGISTEREDMODELALIAS._serialized_end=5131 + _SETREGISTEREDMODELALIAS._serialized_start=5134 + _SETREGISTEREDMODELALIAS._serialized_end=5280 + _SETREGISTEREDMODELALIAS_RESPONSE._serialized_start=1659 + _SETREGISTEREDMODELALIAS_RESPONSE._serialized_end=1669 + _DELETEREGISTEREDMODELALIAS._serialized_start=5282 + _DELETEREGISTEREDMODELALIAS._serialized_end=5408 + _DELETEREGISTEREDMODELALIAS_RESPONSE._serialized_start=1659 + _DELETEREGISTEREDMODELALIAS_RESPONSE._serialized_end=1669 + _GETMODELVERSIONBYALIAS._serialized_start=5411 + _GETMODELVERSIONBYALIAS._serialized_end=5578 + _GETMODELVERSIONBYALIAS_RESPONSE._serialized_start=2973 + _GETMODELVERSIONBYALIAS_RESPONSE._serialized_end=3028 + _MODELREGISTRYSERVICE._serialized_start=5665 + _MODELREGISTRYSERVICE._serialized_end=9505 + ModelRegistryService = service_reflection.GeneratedServiceType('ModelRegistryService', (_service.Service,), dict( + DESCRIPTOR = _MODELREGISTRYSERVICE, + __module__ = 'model_registry_pb2' + )) + + ModelRegistryService_Stub = service_reflection.GeneratedServiceStubType('ModelRegistryService_Stub', (ModelRegistryService,), dict( + DESCRIPTOR = _MODELREGISTRYSERVICE, + __module__ = 'model_registry_pb2' + )) + + + # @@protoc_insertion_point(module_scope) + diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/protos/scalapb/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/protos/scalapb/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/protos/scalapb/__pycache__/__init__.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/protos/scalapb/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..c80f31ab4996e5c85115eb025a1870eb5da6bfd0 Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/protos/scalapb/__pycache__/__init__.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/protos/scalapb/__pycache__/scalapb_pb2.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/protos/scalapb/__pycache__/scalapb_pb2.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..299e53e89e3d7ce8511c824b3ce6363838e874f5 Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/protos/scalapb/__pycache__/scalapb_pb2.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/protos/scalapb/scalapb_pb2.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/protos/scalapb/scalapb_pb2.py new file mode 100644 index 0000000000000000000000000000000000000000..70d5950df3d7a110dbe95a073e9ef0ecf48ce203 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/protos/scalapb/scalapb_pb2.py @@ -0,0 +1,104 @@ + +import google.protobuf +from packaging.version import Version +if Version(google.protobuf.__version__).major >= 5: + # -*- coding: utf-8 -*- + # Generated by the protocol buffer compiler. DO NOT EDIT! + # source: scalapb/scalapb.proto + # Protobuf Python Version: 5.26.0 + """Generated protocol buffer code.""" + from google.protobuf import descriptor as _descriptor + from google.protobuf import descriptor_pool as _descriptor_pool + from google.protobuf import symbol_database as _symbol_database + from google.protobuf.internal import builder as _builder + # @@protoc_insertion_point(imports) + + _sym_db = _symbol_database.Default() + + + from google.protobuf import descriptor_pb2 as google_dot_protobuf_dot_descriptor__pb2 + + + DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\x15scalapb/scalapb.proto\x12\x07scalapb\x1a google/protobuf/descriptor.proto\"L\n\x0eScalaPbOptions\x12\x14\n\x0cpackage_name\x18\x01 \x01(\t\x12\x14\n\x0c\x66lat_package\x18\x02 \x01(\x08\x12\x0e\n\x06import\x18\x03 \x03(\t\"!\n\x0eMessageOptions\x12\x0f\n\x07\x65xtends\x18\x01 \x03(\t\"\x1c\n\x0c\x46ieldOptions\x12\x0c\n\x04type\x18\x01 \x01(\t:G\n\x07options\x12\x1c.google.protobuf.FileOptions\x18\xfc\x07 \x01(\x0b\x32\x17.scalapb.ScalaPbOptions:J\n\x07message\x12\x1f.google.protobuf.MessageOptions\x18\xfc\x07 \x01(\x0b\x32\x17.scalapb.MessageOptions:D\n\x05\x66ield\x12\x1d.google.protobuf.FieldOptions\x18\xfc\x07 \x01(\x0b\x32\x15.scalapb.FieldOptionsB\x1e\n\x1corg.mlflow.scalapb_interface') + + _globals = globals() + _builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals) + _builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'scalapb.scalapb_pb2', _globals) + if not _descriptor._USE_C_DESCRIPTORS: + _globals['DESCRIPTOR']._loaded_options = None + _globals['DESCRIPTOR']._serialized_options = b'\n\034org.mlflow.scalapb_interface' + _globals['_SCALAPBOPTIONS']._serialized_start=68 + _globals['_SCALAPBOPTIONS']._serialized_end=144 + _globals['_MESSAGEOPTIONS']._serialized_start=146 + _globals['_MESSAGEOPTIONS']._serialized_end=179 + _globals['_FIELDOPTIONS']._serialized_start=181 + _globals['_FIELDOPTIONS']._serialized_end=209 + # @@protoc_insertion_point(module_scope) + +else: + # -*- coding: utf-8 -*- + # Generated by the protocol buffer compiler. DO NOT EDIT! + # source: scalapb/scalapb.proto + """Generated protocol buffer code.""" + from google.protobuf import descriptor as _descriptor + from google.protobuf import descriptor_pool as _descriptor_pool + from google.protobuf import message as _message + from google.protobuf import reflection as _reflection + from google.protobuf import symbol_database as _symbol_database + # @@protoc_insertion_point(imports) + + _sym_db = _symbol_database.Default() + + + from google.protobuf import descriptor_pb2 as google_dot_protobuf_dot_descriptor__pb2 + + + DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\x15scalapb/scalapb.proto\x12\x07scalapb\x1a google/protobuf/descriptor.proto\"L\n\x0eScalaPbOptions\x12\x14\n\x0cpackage_name\x18\x01 \x01(\t\x12\x14\n\x0c\x66lat_package\x18\x02 \x01(\x08\x12\x0e\n\x06import\x18\x03 \x03(\t\"!\n\x0eMessageOptions\x12\x0f\n\x07\x65xtends\x18\x01 \x03(\t\"\x1c\n\x0c\x46ieldOptions\x12\x0c\n\x04type\x18\x01 \x01(\t:G\n\x07options\x12\x1c.google.protobuf.FileOptions\x18\xfc\x07 \x01(\x0b\x32\x17.scalapb.ScalaPbOptions:J\n\x07message\x12\x1f.google.protobuf.MessageOptions\x18\xfc\x07 \x01(\x0b\x32\x17.scalapb.MessageOptions:D\n\x05\x66ield\x12\x1d.google.protobuf.FieldOptions\x18\xfc\x07 \x01(\x0b\x32\x15.scalapb.FieldOptionsB\x1e\n\x1corg.mlflow.scalapb_interface') + + + OPTIONS_FIELD_NUMBER = 1020 + options = DESCRIPTOR.extensions_by_name['options'] + MESSAGE_FIELD_NUMBER = 1020 + message = DESCRIPTOR.extensions_by_name['message'] + FIELD_FIELD_NUMBER = 1020 + field = DESCRIPTOR.extensions_by_name['field'] + + _SCALAPBOPTIONS = DESCRIPTOR.message_types_by_name['ScalaPbOptions'] + _MESSAGEOPTIONS = DESCRIPTOR.message_types_by_name['MessageOptions'] + _FIELDOPTIONS = DESCRIPTOR.message_types_by_name['FieldOptions'] + ScalaPbOptions = _reflection.GeneratedProtocolMessageType('ScalaPbOptions', (_message.Message,), { + 'DESCRIPTOR' : _SCALAPBOPTIONS, + '__module__' : 'scalapb.scalapb_pb2' + # @@protoc_insertion_point(class_scope:scalapb.ScalaPbOptions) + }) + _sym_db.RegisterMessage(ScalaPbOptions) + + MessageOptions = _reflection.GeneratedProtocolMessageType('MessageOptions', (_message.Message,), { + 'DESCRIPTOR' : _MESSAGEOPTIONS, + '__module__' : 'scalapb.scalapb_pb2' + # @@protoc_insertion_point(class_scope:scalapb.MessageOptions) + }) + _sym_db.RegisterMessage(MessageOptions) + + FieldOptions = _reflection.GeneratedProtocolMessageType('FieldOptions', (_message.Message,), { + 'DESCRIPTOR' : _FIELDOPTIONS, + '__module__' : 'scalapb.scalapb_pb2' + # @@protoc_insertion_point(class_scope:scalapb.FieldOptions) + }) + _sym_db.RegisterMessage(FieldOptions) + + if _descriptor._USE_C_DESCRIPTORS == False: + google_dot_protobuf_dot_descriptor__pb2.FileOptions.RegisterExtension(options) + google_dot_protobuf_dot_descriptor__pb2.MessageOptions.RegisterExtension(message) + google_dot_protobuf_dot_descriptor__pb2.FieldOptions.RegisterExtension(field) + + DESCRIPTOR._options = None + DESCRIPTOR._serialized_options = b'\n\034org.mlflow.scalapb_interface' + _SCALAPBOPTIONS._serialized_start=68 + _SCALAPBOPTIONS._serialized_end=144 + _MESSAGEOPTIONS._serialized_start=146 + _MESSAGEOPTIONS._serialized_end=179 + _FIELDOPTIONS._serialized_start=181 + _FIELDOPTIONS._serialized_end=209 + # @@protoc_insertion_point(module_scope) + diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/protos/service_pb2.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/protos/service_pb2.py new file mode 100644 index 0000000000000000000000000000000000000000..04d2116d9ace28308c94a1581617d0fdbdaf4e30 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/protos/service_pb2.py @@ -0,0 +1,2736 @@ + +import google.protobuf +from packaging.version import Version +if Version(google.protobuf.__version__).major >= 5: + # -*- coding: utf-8 -*- + # Generated by the protocol buffer compiler. DO NOT EDIT! + # source: service.proto + # Protobuf Python Version: 5.26.0 + """Generated protocol buffer code.""" + from google.protobuf import descriptor as _descriptor + from google.protobuf import descriptor_pool as _descriptor_pool + from google.protobuf import symbol_database as _symbol_database + from google.protobuf.internal import builder as _builder + # @@protoc_insertion_point(imports) + + _sym_db = _symbol_database.Default() + + + from .scalapb import scalapb_pb2 as scalapb_dot_scalapb__pb2 + from . import databricks_pb2 as databricks__pb2 + from google.protobuf import duration_pb2 as google_dot_protobuf_dot_duration__pb2 + from google.protobuf import field_mask_pb2 as google_dot_protobuf_dot_field__mask__pb2 + from google.protobuf import timestamp_pb2 as google_dot_protobuf_dot_timestamp__pb2 + from . import assessments_pb2 as assessments__pb2 + + + DESCRIPTOR = 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Tag\x12\xb0\x01\n\x13\x64\x65leteExperimentTag\x12\x1b.mlflow.DeleteExperimentTag\x1a$.mlflow.DeleteExperimentTag.Response\"V\xf2\x86\x19R\n7\n\x04POST\x12)/mlflow/experiments/delete-experiment-tag\x1a\x04\x08\x02\x10\x00\x10\x01*\x15\x44\x65lete Experiment Tag\x12\x66\n\x06setTag\x12\x0e.mlflow.SetTag\x1a\x17.mlflow.SetTag.Response\"3\xf2\x86\x19/\n\"\n\x04POST\x12\x14/mlflow/runs/set-tag\x1a\x04\x08\x02\x10\x00\x10\x01*\x07Set Tag\x12\x88\x01\n\x0bsetTraceTag\x12\x13.mlflow.SetTraceTag\x1a\x1c.mlflow.SetTraceTag.Response\"F\xf2\x86\x19\x42\n/\n\x05PATCH\x12 /mlflow/traces/{request_id}/tags\x1a\x04\x08\x02\x10\x00\x10\x03*\rSet Trace Tag\x12\x8f\x01\n\rsetTraceTagV3\x12\x15.mlflow.SetTraceTagV3\x1a\x1e.mlflow.SetTraceTagV3.Response\"G\xf2\x86\x19\x43\n-\n\x05PATCH\x12\x1e/mlflow/traces/{trace_id}/tags\x1a\x04\x08\x03\x10\x00\x10\x03*\x10Set Trace Tag V3\x12\x95\x01\n\x0e\x64\x65leteTraceTag\x12\x16.mlflow.DeleteTraceTag\x1a\x1f.mlflow.DeleteTraceTag.Response\"J\xf2\x86\x19\x46\n0\n\x06\x44\x45LETE\x12 /mlflow/traces/{request_id}/tags\x1a\x04\x08\x02\x10\x00\x10\x03*\x10\x44\x65lete Trace Tag\x12\x9c\x01\n\x10\x64\x65leteTraceTagV3\x12\x18.mlflow.DeleteTraceTagV3\x1a!.mlflow.DeleteTraceTagV3.Response\"K\xf2\x86\x19G\n.\n\x06\x44\x45LETE\x12\x1e/mlflow/traces/{trace_id}/tags\x1a\x04\x08\x03\x10\x00\x10\x03*\x13\x44\x65lete Trace Tag V3\x12u\n\tdeleteTag\x12\x11.mlflow.DeleteTag\x1a\x1a.mlflow.DeleteTag.Response\"9\xf2\x86\x19\x35\n%\n\x04POST\x12\x17/mlflow/runs/delete-tag\x1a\x04\x08\x02\x10\x00\x10\x01*\nDelete Tag\x12\x65\n\x06getRun\x12\x0e.mlflow.GetRun\x1a\x17.mlflow.GetRun.Response\"2\xf2\x86\x19*\n\x1d\n\x03GET\x12\x10/mlflow/runs/get\x1a\x04\x08\x02\x10\x00\x10\x01*\x07Get Run\xba\x8c\x19\x00\x12y\n\nsearchRuns\x12\x12.mlflow.SearchRuns\x1a\x1b.mlflow.SearchRuns.Response\":\xf2\x86\x19\x32\n!\n\x04POST\x12\x13/mlflow/runs/search\x1a\x04\x08\x02\x10\x00\x10\x01*\x0bSearch Runs\xba\x8c\x19\x00\x12\x87\x01\n\rlistArtifacts\x12\x15.mlflow.ListArtifacts\x1a\x1e.mlflow.ListArtifacts.Response\"?\xf2\x86\x19\x37\n#\n\x03GET\x12\x16/mlflow/artifacts/list\x1a\x04\x08\x02\x10\x00\x10\x01*\x0eList Artifacts\xba\x8c\x19\x00\x12\x95\x01\n\x10getMetricHistory\x12\x18.mlflow.GetMetricHistory\x1a!.mlflow.GetMetricHistory.Response\"D\xf2\x86\x19@\n(\n\x03GET\x12\x1b/mlflow/metrics/get-history\x1a\x04\x08\x02\x10\x00\x10\x01*\x12Get Metric History\x12\xb7\x01\n\x1cgetMetricHistoryBulkInterval\x12$.mlflow.GetMetricHistoryBulkInterval\x1a-.mlflow.GetMetricHistoryBulkInterval.Response\"B\xf2\x86\x19:\n6\n\x03GET\x12)/mlflow/metrics/get-history-bulk-interval\x1a\x04\x08\x02\x10\x0b\x10\x03\xba\x8c\x19\x00\x12p\n\x08logBatch\x12\x10.mlflow.LogBatch\x1a\x19.mlflow.LogBatch.Response\"7\xf2\x86\x19\x33\n$\n\x04POST\x12\x16/mlflow/runs/log-batch\x1a\x04\x08\x02\x10\x00\x10\x01*\tLog Batch\x12p\n\x08logModel\x12\x10.mlflow.LogModel\x1a\x19.mlflow.LogModel.Response\"7\xf2\x86\x19\x33\n$\n\x04POST\x12\x16/mlflow/runs/log-model\x1a\x04\x08\x02\x10\x00\x10\x01*\tLog Model\x12u\n\tlogInputs\x12\x11.mlflow.LogInputs\x1a\x1a.mlflow.LogInputs.Response\"9\xf2\x86\x19\x35\n%\n\x04POST\x12\x17/mlflow/runs/log-inputs\x1a\x04\x08\x02\x10\x00\x10\x01*\nLog Inputs\x12v\n\nlogOutputs\x12\x12.mlflow.LogOutputs\x1a\x1b.mlflow.LogOutputs.Response\"7\xf2\x86\x19\x33\n\"\n\x04POST\x12\x14/mlflow/runs/outputs\x1a\x04\x08\x02\x10\x00\x10\x03*\x0bLog Outputs\x12\x87\x01\n\x0esearchDatasets\x12\x16.mlflow.SearchDatasets\x1a\x1f.mlflow.SearchDatasets.Response\"<\xf2\x86\x19\x34\n0\n\x04POST\x12\"mlflow/experiments/search-datasets\x1a\x04\x08\x02\x10\x00\x10\x03\xba\x8c\x19\x00\x12p\n\nstartTrace\x12\x12.mlflow.StartTrace\x1a\x1b.mlflow.StartTrace.Response\"1\xf2\x86\x19-\n\x1c\n\x04POST\x12\x0e/mlflow/traces\x1a\x04\x08\x02\x10\x00\x10\x03*\x0bStart Trace\x12v\n\x08\x65ndTrace\x12\x10.mlflow.EndTrace\x1a\x19.mlflow.EndTrace.Response\"=\xf2\x86\x19\x39\n*\n\x05PATCH\x12\x1b/mlflow/traces/{request_id}\x1a\x04\x08\x02\x10\x00\x10\x03*\tEnd Trace\x12\x89\x01\n\x0cgetTraceInfo\x12\x14.mlflow.GetTraceInfo\x1a\x1d.mlflow.GetTraceInfo.Response\"D\xf2\x86\x19@\n-\n\x03GET\x12 /mlflow/traces/{request_id}/info\x1a\x04\x08\x02\x10\x00\x10\x03*\rGet TraceInfo\x12\x8b\x01\n\x0egetTraceInfoV3\x12\x16.mlflow.GetTraceInfoV3\x1a\x1f.mlflow.GetTraceInfoV3.Response\"@\xf2\x86\x19<\n&\n\x03GET\x12\x19/mlflow/traces/{trace_id}\x1a\x04\x08\x03\x10\x00\x10\x03*\x10Get TraceInfo v3\x12w\n\x0csearchTraces\x12\x14.mlflow.SearchTraces\x1a\x1d.mlflow.SearchTraces.Response\"2\xf2\x86\x19.\n\x1b\n\x03GET\x12\x0e/mlflow/traces\x1a\x04\x08\x02\x10\x00\x10\x03*\rSearch Traces\x12\x88\x01\n\x0esearchTracesV3\x12\x16.mlflow.SearchTracesV3\x1a\x1f.mlflow.SearchTracesV3.Response\"=\xf2\x86\x19\x39\n#\n\x04POST\x12\x15/mlflow/traces/search\x1a\x04\x08\x03\x10\x00\x10\x03*\x10Search Traces V3\x12i\n\x0cstartTraceV3\x12\x14.mlflow.StartTraceV3\x1a\x1d.mlflow.StartTraceV3.Response\"$\xf2\x86\x19 \n\x1c\n\x04POST\x12\x0e/mlflow/traces\x1a\x04\x08\x03\x10\x00\x10\x03\x12\x92\x01\n\x0flinkTracesToRun\x12\x17.mlflow.LinkTracesToRun\x1a .mlflow.LinkTracesToRun.Response\"D\xf2\x86\x19@\n(\n\x04POST\x12\x1a/mlflow/traces/link-to-run\x1a\x04\x08\x02\x10\x00\x10\x03*\x12Link Traces to Run\x12\xa2\x01\n\x19searchUnifiedTraceHandler\x12\x1b.mlflow.SearchUnifiedTraces\x1a$.mlflow.SearchUnifiedTraces.Response\"B\xf2\x86\x19>\n#\n\x03GET\x12\x16/mlflow/unified-traces\x1a\x04\x08\x02\x10\x00\x10\x03*\x15Search Unified Traces\x12\xaf\x01\n\x15getOnlineTraceDetails\x12\x1d.mlflow.GetOnlineTraceDetails\x1a&.mlflow.GetOnlineTraceDetails.Response\"O\xf2\x86\x19K\n-\n\x03GET\x12 /mlflow/get-online-trace-details\x1a\x04\x08\x02\x10\x00\x10\x03*\x18Get Online Trace Details\x12\x86\x01\n\x0c\x64\x65leteTraces\x12\x14.mlflow.DeleteTraces\x1a\x1d.mlflow.DeleteTraces.Response\"A\xf2\x86\x19=\n*\n\x04POST\x12\x1c/mlflow/traces/delete-traces\x1a\x04\x08\x02\x10\x00\x10\x03*\rDelete Traces\x12\x8f\x01\n\x0e\x64\x65leteTracesV3\x12\x16.mlflow.DeleteTracesV3\x1a\x1f.mlflow.DeleteTracesV3.Response\"D\xf2\x86\x19@\n*\n\x04POST\x12\x1c/mlflow/traces/delete-traces\x1a\x04\x08\x03\x10\x00\x10\x03*\x10\x44\x65lete Traces V3\x12\x94\x01\n\x11\x63reateLoggedModel\x12\x19.mlflow.CreateLoggedModel\x1a\".mlflow.CreateLoggedModel.Response\"@\xf2\x86\x19<\n#\n\x04POST\x12\x15/mlflow/logged-models\x1a\x04\x08\x02\x10\x00\x10\x03*\x13\x43reate Logged Model\x12\xa8\x01\n\x13\x66inalizeLoggedModel\x12\x1b.mlflow.FinalizeLoggedModel\x1a$.mlflow.FinalizeLoggedModel.Response\"N\xf2\x86\x19J\n/\n\x05PATCH\x12 /mlflow/logged-models/{model_id}\x1a\x04\x08\x02\x10\x00\x10\x03*\x15\x46inalize Logged Model\x12\x92\x01\n\x0egetLoggedModel\x12\x16.mlflow.GetLoggedModel\x1a\x1f.mlflow.GetLoggedModel.Response\"G\xf2\x86\x19\x43\n-\n\x03GET\x12 /mlflow/logged-models/{model_id}\x1a\x04\x08\x02\x10\x00\x10\x03*\x10Get Logged Model\x12\xa3\x01\n\x11\x64\x65leteLoggedModel\x12\x19.mlflow.DeleteLoggedModel\x1a\".mlflow.DeleteLoggedModel.Response\"O\xf2\x86\x19K\n0\n\x06\x44\x45LETE\x12 /mlflow/logged-models/{model_id}\x1a\x04\x08\x02\x10\x00\x10\x03*\x15\x44\x65lete a Logged Model\x12\x9e\x01\n\x12searchLoggedModels\x12\x1a.mlflow.SearchLoggedModels\x1a#.mlflow.SearchLoggedModels.Response\"G\xf2\x86\x19\x43\n*\n\x04POST\x12\x1c/mlflow/logged-models/search\x1a\x04\x08\x02\x10\x00\x10\x03*\x13Search LoggedModels\x12\xa9\x01\n\x12setLoggedModelTags\x12\x1a.mlflow.SetLoggedModelTags\x1a#.mlflow.SetLoggedModelTags.Response\"R\xf2\x86\x19N\n4\n\x05PATCH\x12%/mlflow/logged-models/{model_id}/tags\x1a\x04\x08\x02\x10\x00\x10\x03*\x14Set Logged Model Tag\x12\xbd\x01\n\x14\x64\x65leteLoggedModelTag\x12\x1c.mlflow.DeleteLoggedModelTag\x1a%.mlflow.DeleteLoggedModelTag.Response\"`\xf2\x86\x19\\\n?\n\x06\x44\x45LETE\x12//mlflow/logged-models/{model_id}/tags/{tag_key}\x1a\x04\x08\x02\x10\x00\x10\x03*\x17\x44\x65lete Logged Model Tag\x12\xd6\x01\n\x18listLoggedModelArtifacts\x12 .mlflow.ListLoggedModelArtifacts\x1a).mlflow.ListLoggedModelArtifacts.Response\"m\xf2\x86\x19i\nC\n\x03GET\x12\x36/mlflow/logged-models/{model_id}/artifacts/directories\x1a\x04\x08\x02\x10\x00\x10\x03* List Artifacts for Logged Models\x12\xc1\x01\n\x14LogLoggedModelParams\x12#.mlflow.LogLoggedModelParamsRequest\x1a,.mlflow.LogLoggedModelParamsRequest.Response\"V\xf2\x86\x19R\n5\n\x04POST\x12\'/mlflow/logged-models/{model_id}/params\x1a\x04\x08\x02\x10\x00\x10\x03*\x17Log Logged Model Params\x12\xb0\x01\n\rGetAssessment\x12\x1c.mlflow.GetAssessmentRequest\x1a%.mlflow.GetAssessmentRequest.Response\"Z\xf2\x86\x19V\nB\n\x03GET\x12\x35/mlflow/traces/{trace_id}/assessments/{assessment_id}\x1a\x04\x08\x03\x10\x00\x10\x03*\x0eGet Assessment\x12\xdf\x01\n\x10\x63reateAssessment\x12\x18.mlflow.CreateAssessment\x1a!.mlflow.CreateAssessment.Response\"\x8d\x01\xf2\x86\x19\x88\x01\n>\n\x04POST\x12\x30/mlflow/traces/{assessment.trace_id}/assessments\x1a\x04\x08\x03\x10\x00\x10\x03\x18\xe8\x07\x18\xee\x07\x18\x0c\x18\x01*:Create an assessment of a trace or a span within the trace\x12\xd0\x01\n\x10updateAssessment\x12\x18.mlflow.UpdateAssessment\x1a!.mlflow.UpdateAssessment.Response\"\x7f\xf2\x86\x19{\nD\n\x05PATCH\x12\x35/mlflow/traces/{trace_id}/assessments/{assessment_id}\x1a\x04\x08\x03\x10\x00\x10\x03\x18\xe8\x07\x18\xee\x07\x18\x01*)Update an existing assessment on a trace.\x12\xb1\x01\n\x10\x64\x65leteAssessment\x12\x18.mlflow.DeleteAssessment\x1a!.mlflow.DeleteAssessment.Response\"`\xf2\x86\x19\\\nE\n\x06\x44\x45LETE\x12\x35/mlflow/traces/{trace_id}/assessments/{assessment_id}\x1a\x04\x08\x03\x10\x00\x10\x03*\x11\x44\x65lete AssessmentB\x1e\n\x14org.mlflow.api.proto\x90\x01\x01\xe2?\x02\x10\x01') + + _globals = globals() + _builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals) + _builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'service_pb2', _globals) + if not _descriptor._USE_C_DESCRIPTORS: + _globals['DESCRIPTOR']._loaded_options = None + _globals['DESCRIPTOR']._serialized_options = b'\n\024org.mlflow.api.proto\220\001\001\342?\002\020\001' + _globals['_METRIC'].fields_by_name['dataset_name']._loaded_options = None + _globals['_METRIC'].fields_by_name['dataset_name']._serialized_options = b'\360\206\031\003' + _globals['_METRIC'].fields_by_name['dataset_digest']._loaded_options = None + _globals['_METRIC'].fields_by_name['dataset_digest']._serialized_options = b'\360\206\031\003' + _globals['_METRIC'].fields_by_name['model_id']._loaded_options = None + _globals['_METRIC'].fields_by_name['model_id']._serialized_options = b'\360\206\031\003' + _globals['_METRIC'].fields_by_name['run_id']._loaded_options = None + _globals['_METRIC'].fields_by_name['run_id']._serialized_options = b'\360\206\031\003' + _globals['_DATASETINPUT'].fields_by_name['dataset']._loaded_options = None + _globals['_DATASETINPUT'].fields_by_name['dataset']._serialized_options = b'\370\206\031\001' + _globals['_MODELINPUT'].fields_by_name['model_id']._loaded_options = None + _globals['_MODELINPUT'].fields_by_name['model_id']._serialized_options = b'\370\206\031\001' + _globals['_INPUTTAG'].fields_by_name['key']._loaded_options = None + _globals['_INPUTTAG'].fields_by_name['key']._serialized_options = b'\370\206\031\001' + _globals['_INPUTTAG'].fields_by_name['value']._loaded_options = None + _globals['_INPUTTAG'].fields_by_name['value']._serialized_options = b'\370\206\031\001' + _globals['_DATASET'].fields_by_name['name']._loaded_options = None + _globals['_DATASET'].fields_by_name['name']._serialized_options = b'\370\206\031\001' + _globals['_DATASET'].fields_by_name['digest']._loaded_options = None + _globals['_DATASET'].fields_by_name['digest']._serialized_options = b'\370\206\031\001' + _globals['_DATASET'].fields_by_name['source_type']._loaded_options = None + _globals['_DATASET'].fields_by_name['source_type']._serialized_options = b'\370\206\031\001' + _globals['_DATASET'].fields_by_name['source']._loaded_options = None + _globals['_DATASET'].fields_by_name['source']._serialized_options = b'\370\206\031\001' + _globals['_MODELOUTPUT'].fields_by_name['model_id']._loaded_options = None + _globals['_MODELOUTPUT'].fields_by_name['model_id']._serialized_options = b'\370\206\031\001' + _globals['_MODELOUTPUT'].fields_by_name['step']._loaded_options = None + _globals['_MODELOUTPUT'].fields_by_name['step']._serialized_options = b'\370\206\031\001' + _globals['_CREATEEXPERIMENT'].fields_by_name['name']._loaded_options = None + _globals['_CREATEEXPERIMENT'].fields_by_name['name']._serialized_options = b'\370\206\031\001' + _globals['_CREATEEXPERIMENT']._loaded_options = None + _globals['_CREATEEXPERIMENT']._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _globals['_SEARCHEXPERIMENTS']._loaded_options = None + _globals['_SEARCHEXPERIMENTS']._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _globals['_GETEXPERIMENT'].fields_by_name['experiment_id']._loaded_options = None + _globals['_GETEXPERIMENT'].fields_by_name['experiment_id']._serialized_options = b'\370\206\031\001' + _globals['_GETEXPERIMENT']._loaded_options = None + _globals['_GETEXPERIMENT']._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _globals['_DELETEEXPERIMENT'].fields_by_name['experiment_id']._loaded_options = None + _globals['_DELETEEXPERIMENT'].fields_by_name['experiment_id']._serialized_options = b'\370\206\031\001' + _globals['_DELETEEXPERIMENT']._loaded_options = None + _globals['_DELETEEXPERIMENT']._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _globals['_RESTOREEXPERIMENT'].fields_by_name['experiment_id']._loaded_options = None + _globals['_RESTOREEXPERIMENT'].fields_by_name['experiment_id']._serialized_options = b'\370\206\031\001' + _globals['_RESTOREEXPERIMENT']._loaded_options = None + _globals['_RESTOREEXPERIMENT']._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _globals['_UPDATEEXPERIMENT'].fields_by_name['experiment_id']._loaded_options = None + _globals['_UPDATEEXPERIMENT'].fields_by_name['experiment_id']._serialized_options = b'\370\206\031\001' + _globals['_UPDATEEXPERIMENT']._loaded_options = None + _globals['_UPDATEEXPERIMENT']._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _globals['_CREATERUN']._loaded_options = None + _globals['_CREATERUN']._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _globals['_UPDATERUN']._loaded_options = None + _globals['_UPDATERUN']._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _globals['_DELETERUN'].fields_by_name['run_id']._loaded_options = None + _globals['_DELETERUN'].fields_by_name['run_id']._serialized_options = b'\370\206\031\001' + _globals['_DELETERUN']._loaded_options = None + _globals['_DELETERUN']._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _globals['_RESTORERUN'].fields_by_name['run_id']._loaded_options = None + _globals['_RESTORERUN'].fields_by_name['run_id']._serialized_options = b'\370\206\031\001' + _globals['_RESTORERUN']._loaded_options = None + _globals['_RESTORERUN']._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _globals['_LOGMETRIC'].fields_by_name['key']._loaded_options = None + _globals['_LOGMETRIC'].fields_by_name['key']._serialized_options = b'\370\206\031\001' + _globals['_LOGMETRIC'].fields_by_name['value']._loaded_options = None + _globals['_LOGMETRIC'].fields_by_name['value']._serialized_options = b'\370\206\031\001' + _globals['_LOGMETRIC'].fields_by_name['timestamp']._loaded_options = None + 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DO NOT EDIT! + # source: service.proto + """Generated protocol buffer code.""" + from google.protobuf.internal import enum_type_wrapper + from google.protobuf import descriptor as _descriptor + from google.protobuf import descriptor_pool as _descriptor_pool + from google.protobuf import message as _message + from google.protobuf import reflection as _reflection + from google.protobuf import symbol_database as _symbol_database + from google.protobuf import service as _service + from google.protobuf import service_reflection + # @@protoc_insertion_point(imports) + + _sym_db = _symbol_database.Default() + + + from .scalapb import scalapb_pb2 as scalapb_dot_scalapb__pb2 + from . import databricks_pb2 as databricks__pb2 + from google.protobuf import duration_pb2 as google_dot_protobuf_dot_duration__pb2 + from google.protobuf import field_mask_pb2 as google_dot_protobuf_dot_field__mask__pb2 + from google.protobuf import timestamp_pb2 as google_dot_protobuf_dot_timestamp__pb2 + from . import 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Experiment\x12\x99\x01\n\x11restoreExperiment\x12\x19.mlflow.RestoreExperiment\x1a\".mlflow.RestoreExperiment.Response\"E\xf2\x86\x19\x41\n)\n\x04POST\x12\x1b/mlflow/experiments/restore\x1a\x04\x08\x02\x10\x00\x10\x01*\x12Restore Experiment\x12\x94\x01\n\x10updateExperiment\x12\x18.mlflow.UpdateExperiment\x1a!.mlflow.UpdateExperiment.Response\"C\xf2\x86\x19?\n(\n\x04POST\x12\x1a/mlflow/experiments/update\x1a\x04\x08\x02\x10\x00\x10\x01*\x11Update Experiment\x12q\n\tcreateRun\x12\x11.mlflow.CreateRun\x1a\x1a.mlflow.CreateRun.Response\"5\xf2\x86\x19\x31\n!\n\x04POST\x12\x13/mlflow/runs/create\x1a\x04\x08\x02\x10\x00\x10\x01*\nCreate Run\x12q\n\tupdateRun\x12\x11.mlflow.UpdateRun\x1a\x1a.mlflow.UpdateRun.Response\"5\xf2\x86\x19\x31\n!\n\x04POST\x12\x13/mlflow/runs/update\x1a\x04\x08\x02\x10\x00\x10\x01*\nUpdate Run\x12q\n\tdeleteRun\x12\x11.mlflow.DeleteRun\x1a\x1a.mlflow.DeleteRun.Response\"5\xf2\x86\x19\x31\n!\n\x04POST\x12\x13/mlflow/runs/delete\x1a\x04\x08\x02\x10\x00\x10\x01*\nDelete Run\x12v\n\nrestoreRun\x12\x12.mlflow.RestoreRun\x1a\x1b.mlflow.RestoreRun.Response\"7\xf2\x86\x19\x33\n\"\n\x04POST\x12\x14/mlflow/runs/restore\x1a\x04\x08\x02\x10\x00\x10\x01*\x0bRestore Run\x12u\n\tlogMetric\x12\x11.mlflow.LogMetric\x1a\x1a.mlflow.LogMetric.Response\"9\xf2\x86\x19\x35\n%\n\x04POST\x12\x17/mlflow/runs/log-metric\x1a\x04\x08\x02\x10\x00\x10\x01*\nLog Metric\x12t\n\x08logParam\x12\x10.mlflow.LogParam\x1a\x19.mlflow.LogParam.Response\";\xf2\x86\x19\x37\n(\n\x04POST\x12\x1a/mlflow/runs/log-parameter\x1a\x04\x08\x02\x10\x00\x10\x01*\tLog Param\x12\xa1\x01\n\x10setExperimentTag\x12\x18.mlflow.SetExperimentTag\x1a!.mlflow.SetExperimentTag.Response\"P\xf2\x86\x19L\n4\n\x04POST\x12&/mlflow/experiments/set-experiment-tag\x1a\x04\x08\x02\x10\x00\x10\x01*\x12Set Experiment Tag\x12\xb0\x01\n\x13\x64\x65leteExperimentTag\x12\x1b.mlflow.DeleteExperimentTag\x1a$.mlflow.DeleteExperimentTag.Response\"V\xf2\x86\x19R\n7\n\x04POST\x12)/mlflow/experiments/delete-experiment-tag\x1a\x04\x08\x02\x10\x00\x10\x01*\x15\x44\x65lete Experiment Tag\x12\x66\n\x06setTag\x12\x0e.mlflow.SetTag\x1a\x17.mlflow.SetTag.Response\"3\xf2\x86\x19/\n\"\n\x04POST\x12\x14/mlflow/runs/set-tag\x1a\x04\x08\x02\x10\x00\x10\x01*\x07Set Tag\x12\x88\x01\n\x0bsetTraceTag\x12\x13.mlflow.SetTraceTag\x1a\x1c.mlflow.SetTraceTag.Response\"F\xf2\x86\x19\x42\n/\n\x05PATCH\x12 /mlflow/traces/{request_id}/tags\x1a\x04\x08\x02\x10\x00\x10\x03*\rSet Trace Tag\x12\x8f\x01\n\rsetTraceTagV3\x12\x15.mlflow.SetTraceTagV3\x1a\x1e.mlflow.SetTraceTagV3.Response\"G\xf2\x86\x19\x43\n-\n\x05PATCH\x12\x1e/mlflow/traces/{trace_id}/tags\x1a\x04\x08\x03\x10\x00\x10\x03*\x10Set Trace Tag V3\x12\x95\x01\n\x0e\x64\x65leteTraceTag\x12\x16.mlflow.DeleteTraceTag\x1a\x1f.mlflow.DeleteTraceTag.Response\"J\xf2\x86\x19\x46\n0\n\x06\x44\x45LETE\x12 /mlflow/traces/{request_id}/tags\x1a\x04\x08\x02\x10\x00\x10\x03*\x10\x44\x65lete Trace Tag\x12\x9c\x01\n\x10\x64\x65leteTraceTagV3\x12\x18.mlflow.DeleteTraceTagV3\x1a!.mlflow.DeleteTraceTagV3.Response\"K\xf2\x86\x19G\n.\n\x06\x44\x45LETE\x12\x1e/mlflow/traces/{trace_id}/tags\x1a\x04\x08\x03\x10\x00\x10\x03*\x13\x44\x65lete Trace Tag V3\x12u\n\tdeleteTag\x12\x11.mlflow.DeleteTag\x1a\x1a.mlflow.DeleteTag.Response\"9\xf2\x86\x19\x35\n%\n\x04POST\x12\x17/mlflow/runs/delete-tag\x1a\x04\x08\x02\x10\x00\x10\x01*\nDelete Tag\x12\x65\n\x06getRun\x12\x0e.mlflow.GetRun\x1a\x17.mlflow.GetRun.Response\"2\xf2\x86\x19*\n\x1d\n\x03GET\x12\x10/mlflow/runs/get\x1a\x04\x08\x02\x10\x00\x10\x01*\x07Get Run\xba\x8c\x19\x00\x12y\n\nsearchRuns\x12\x12.mlflow.SearchRuns\x1a\x1b.mlflow.SearchRuns.Response\":\xf2\x86\x19\x32\n!\n\x04POST\x12\x13/mlflow/runs/search\x1a\x04\x08\x02\x10\x00\x10\x01*\x0bSearch Runs\xba\x8c\x19\x00\x12\x87\x01\n\rlistArtifacts\x12\x15.mlflow.ListArtifacts\x1a\x1e.mlflow.ListArtifacts.Response\"?\xf2\x86\x19\x37\n#\n\x03GET\x12\x16/mlflow/artifacts/list\x1a\x04\x08\x02\x10\x00\x10\x01*\x0eList Artifacts\xba\x8c\x19\x00\x12\x95\x01\n\x10getMetricHistory\x12\x18.mlflow.GetMetricHistory\x1a!.mlflow.GetMetricHistory.Response\"D\xf2\x86\x19@\n(\n\x03GET\x12\x1b/mlflow/metrics/get-history\x1a\x04\x08\x02\x10\x00\x10\x01*\x12Get Metric History\x12\xb7\x01\n\x1cgetMetricHistoryBulkInterval\x12$.mlflow.GetMetricHistoryBulkInterval\x1a-.mlflow.GetMetricHistoryBulkInterval.Response\"B\xf2\x86\x19:\n6\n\x03GET\x12)/mlflow/metrics/get-history-bulk-interval\x1a\x04\x08\x02\x10\x0b\x10\x03\xba\x8c\x19\x00\x12p\n\x08logBatch\x12\x10.mlflow.LogBatch\x1a\x19.mlflow.LogBatch.Response\"7\xf2\x86\x19\x33\n$\n\x04POST\x12\x16/mlflow/runs/log-batch\x1a\x04\x08\x02\x10\x00\x10\x01*\tLog Batch\x12p\n\x08logModel\x12\x10.mlflow.LogModel\x1a\x19.mlflow.LogModel.Response\"7\xf2\x86\x19\x33\n$\n\x04POST\x12\x16/mlflow/runs/log-model\x1a\x04\x08\x02\x10\x00\x10\x01*\tLog Model\x12u\n\tlogInputs\x12\x11.mlflow.LogInputs\x1a\x1a.mlflow.LogInputs.Response\"9\xf2\x86\x19\x35\n%\n\x04POST\x12\x17/mlflow/runs/log-inputs\x1a\x04\x08\x02\x10\x00\x10\x01*\nLog Inputs\x12v\n\nlogOutputs\x12\x12.mlflow.LogOutputs\x1a\x1b.mlflow.LogOutputs.Response\"7\xf2\x86\x19\x33\n\"\n\x04POST\x12\x14/mlflow/runs/outputs\x1a\x04\x08\x02\x10\x00\x10\x03*\x0bLog Outputs\x12\x87\x01\n\x0esearchDatasets\x12\x16.mlflow.SearchDatasets\x1a\x1f.mlflow.SearchDatasets.Response\"<\xf2\x86\x19\x34\n0\n\x04POST\x12\"mlflow/experiments/search-datasets\x1a\x04\x08\x02\x10\x00\x10\x03\xba\x8c\x19\x00\x12p\n\nstartTrace\x12\x12.mlflow.StartTrace\x1a\x1b.mlflow.StartTrace.Response\"1\xf2\x86\x19-\n\x1c\n\x04POST\x12\x0e/mlflow/traces\x1a\x04\x08\x02\x10\x00\x10\x03*\x0bStart Trace\x12v\n\x08\x65ndTrace\x12\x10.mlflow.EndTrace\x1a\x19.mlflow.EndTrace.Response\"=\xf2\x86\x19\x39\n*\n\x05PATCH\x12\x1b/mlflow/traces/{request_id}\x1a\x04\x08\x02\x10\x00\x10\x03*\tEnd Trace\x12\x89\x01\n\x0cgetTraceInfo\x12\x14.mlflow.GetTraceInfo\x1a\x1d.mlflow.GetTraceInfo.Response\"D\xf2\x86\x19@\n-\n\x03GET\x12 /mlflow/traces/{request_id}/info\x1a\x04\x08\x02\x10\x00\x10\x03*\rGet TraceInfo\x12\x8b\x01\n\x0egetTraceInfoV3\x12\x16.mlflow.GetTraceInfoV3\x1a\x1f.mlflow.GetTraceInfoV3.Response\"@\xf2\x86\x19<\n&\n\x03GET\x12\x19/mlflow/traces/{trace_id}\x1a\x04\x08\x03\x10\x00\x10\x03*\x10Get TraceInfo v3\x12w\n\x0csearchTraces\x12\x14.mlflow.SearchTraces\x1a\x1d.mlflow.SearchTraces.Response\"2\xf2\x86\x19.\n\x1b\n\x03GET\x12\x0e/mlflow/traces\x1a\x04\x08\x02\x10\x00\x10\x03*\rSearch Traces\x12\x88\x01\n\x0esearchTracesV3\x12\x16.mlflow.SearchTracesV3\x1a\x1f.mlflow.SearchTracesV3.Response\"=\xf2\x86\x19\x39\n#\n\x04POST\x12\x15/mlflow/traces/search\x1a\x04\x08\x03\x10\x00\x10\x03*\x10Search Traces V3\x12i\n\x0cstartTraceV3\x12\x14.mlflow.StartTraceV3\x1a\x1d.mlflow.StartTraceV3.Response\"$\xf2\x86\x19 \n\x1c\n\x04POST\x12\x0e/mlflow/traces\x1a\x04\x08\x03\x10\x00\x10\x03\x12\x92\x01\n\x0flinkTracesToRun\x12\x17.mlflow.LinkTracesToRun\x1a .mlflow.LinkTracesToRun.Response\"D\xf2\x86\x19@\n(\n\x04POST\x12\x1a/mlflow/traces/link-to-run\x1a\x04\x08\x02\x10\x00\x10\x03*\x12Link Traces to Run\x12\xa2\x01\n\x19searchUnifiedTraceHandler\x12\x1b.mlflow.SearchUnifiedTraces\x1a$.mlflow.SearchUnifiedTraces.Response\"B\xf2\x86\x19>\n#\n\x03GET\x12\x16/mlflow/unified-traces\x1a\x04\x08\x02\x10\x00\x10\x03*\x15Search Unified Traces\x12\xaf\x01\n\x15getOnlineTraceDetails\x12\x1d.mlflow.GetOnlineTraceDetails\x1a&.mlflow.GetOnlineTraceDetails.Response\"O\xf2\x86\x19K\n-\n\x03GET\x12 /mlflow/get-online-trace-details\x1a\x04\x08\x02\x10\x00\x10\x03*\x18Get Online Trace Details\x12\x86\x01\n\x0c\x64\x65leteTraces\x12\x14.mlflow.DeleteTraces\x1a\x1d.mlflow.DeleteTraces.Response\"A\xf2\x86\x19=\n*\n\x04POST\x12\x1c/mlflow/traces/delete-traces\x1a\x04\x08\x02\x10\x00\x10\x03*\rDelete Traces\x12\x8f\x01\n\x0e\x64\x65leteTracesV3\x12\x16.mlflow.DeleteTracesV3\x1a\x1f.mlflow.DeleteTracesV3.Response\"D\xf2\x86\x19@\n*\n\x04POST\x12\x1c/mlflow/traces/delete-traces\x1a\x04\x08\x03\x10\x00\x10\x03*\x10\x44\x65lete Traces V3\x12\x94\x01\n\x11\x63reateLoggedModel\x12\x19.mlflow.CreateLoggedModel\x1a\".mlflow.CreateLoggedModel.Response\"@\xf2\x86\x19<\n#\n\x04POST\x12\x15/mlflow/logged-models\x1a\x04\x08\x02\x10\x00\x10\x03*\x13\x43reate Logged Model\x12\xa8\x01\n\x13\x66inalizeLoggedModel\x12\x1b.mlflow.FinalizeLoggedModel\x1a$.mlflow.FinalizeLoggedModel.Response\"N\xf2\x86\x19J\n/\n\x05PATCH\x12 /mlflow/logged-models/{model_id}\x1a\x04\x08\x02\x10\x00\x10\x03*\x15\x46inalize Logged Model\x12\x92\x01\n\x0egetLoggedModel\x12\x16.mlflow.GetLoggedModel\x1a\x1f.mlflow.GetLoggedModel.Response\"G\xf2\x86\x19\x43\n-\n\x03GET\x12 /mlflow/logged-models/{model_id}\x1a\x04\x08\x02\x10\x00\x10\x03*\x10Get Logged Model\x12\xa3\x01\n\x11\x64\x65leteLoggedModel\x12\x19.mlflow.DeleteLoggedModel\x1a\".mlflow.DeleteLoggedModel.Response\"O\xf2\x86\x19K\n0\n\x06\x44\x45LETE\x12 /mlflow/logged-models/{model_id}\x1a\x04\x08\x02\x10\x00\x10\x03*\x15\x44\x65lete a Logged Model\x12\x9e\x01\n\x12searchLoggedModels\x12\x1a.mlflow.SearchLoggedModels\x1a#.mlflow.SearchLoggedModels.Response\"G\xf2\x86\x19\x43\n*\n\x04POST\x12\x1c/mlflow/logged-models/search\x1a\x04\x08\x02\x10\x00\x10\x03*\x13Search LoggedModels\x12\xa9\x01\n\x12setLoggedModelTags\x12\x1a.mlflow.SetLoggedModelTags\x1a#.mlflow.SetLoggedModelTags.Response\"R\xf2\x86\x19N\n4\n\x05PATCH\x12%/mlflow/logged-models/{model_id}/tags\x1a\x04\x08\x02\x10\x00\x10\x03*\x14Set Logged Model Tag\x12\xbd\x01\n\x14\x64\x65leteLoggedModelTag\x12\x1c.mlflow.DeleteLoggedModelTag\x1a%.mlflow.DeleteLoggedModelTag.Response\"`\xf2\x86\x19\\\n?\n\x06\x44\x45LETE\x12//mlflow/logged-models/{model_id}/tags/{tag_key}\x1a\x04\x08\x02\x10\x00\x10\x03*\x17\x44\x65lete Logged Model Tag\x12\xd6\x01\n\x18listLoggedModelArtifacts\x12 .mlflow.ListLoggedModelArtifacts\x1a).mlflow.ListLoggedModelArtifacts.Response\"m\xf2\x86\x19i\nC\n\x03GET\x12\x36/mlflow/logged-models/{model_id}/artifacts/directories\x1a\x04\x08\x02\x10\x00\x10\x03* List Artifacts for Logged Models\x12\xc1\x01\n\x14LogLoggedModelParams\x12#.mlflow.LogLoggedModelParamsRequest\x1a,.mlflow.LogLoggedModelParamsRequest.Response\"V\xf2\x86\x19R\n5\n\x04POST\x12\'/mlflow/logged-models/{model_id}/params\x1a\x04\x08\x02\x10\x00\x10\x03*\x17Log Logged Model Params\x12\xb0\x01\n\rGetAssessment\x12\x1c.mlflow.GetAssessmentRequest\x1a%.mlflow.GetAssessmentRequest.Response\"Z\xf2\x86\x19V\nB\n\x03GET\x12\x35/mlflow/traces/{trace_id}/assessments/{assessment_id}\x1a\x04\x08\x03\x10\x00\x10\x03*\x0eGet Assessment\x12\xdf\x01\n\x10\x63reateAssessment\x12\x18.mlflow.CreateAssessment\x1a!.mlflow.CreateAssessment.Response\"\x8d\x01\xf2\x86\x19\x88\x01\n>\n\x04POST\x12\x30/mlflow/traces/{assessment.trace_id}/assessments\x1a\x04\x08\x03\x10\x00\x10\x03\x18\xe8\x07\x18\xee\x07\x18\x0c\x18\x01*:Create an assessment of a trace or a span within the trace\x12\xd0\x01\n\x10updateAssessment\x12\x18.mlflow.UpdateAssessment\x1a!.mlflow.UpdateAssessment.Response\"\x7f\xf2\x86\x19{\nD\n\x05PATCH\x12\x35/mlflow/traces/{trace_id}/assessments/{assessment_id}\x1a\x04\x08\x03\x10\x00\x10\x03\x18\xe8\x07\x18\xee\x07\x18\x01*)Update an existing assessment on a trace.\x12\xb1\x01\n\x10\x64\x65leteAssessment\x12\x18.mlflow.DeleteAssessment\x1a!.mlflow.DeleteAssessment.Response\"`\xf2\x86\x19\\\nE\n\x06\x44\x45LETE\x12\x35/mlflow/traces/{trace_id}/assessments/{assessment_id}\x1a\x04\x08\x03\x10\x00\x10\x03*\x11\x44\x65lete AssessmentB\x1e\n\x14org.mlflow.api.proto\x90\x01\x01\xe2?\x02\x10\x01') + + _VIEWTYPE = DESCRIPTOR.enum_types_by_name['ViewType'] + ViewType = enum_type_wrapper.EnumTypeWrapper(_VIEWTYPE) + _SOURCETYPE = DESCRIPTOR.enum_types_by_name['SourceType'] + SourceType = enum_type_wrapper.EnumTypeWrapper(_SOURCETYPE) + _RUNSTATUS = DESCRIPTOR.enum_types_by_name['RunStatus'] + RunStatus = enum_type_wrapper.EnumTypeWrapper(_RUNSTATUS) + _TRACESTATUS = DESCRIPTOR.enum_types_by_name['TraceStatus'] + TraceStatus = enum_type_wrapper.EnumTypeWrapper(_TRACESTATUS) + _LOGGEDMODELSTATUS = DESCRIPTOR.enum_types_by_name['LoggedModelStatus'] + LoggedModelStatus = enum_type_wrapper.EnumTypeWrapper(_LOGGEDMODELSTATUS) + ACTIVE_ONLY = 1 + DELETED_ONLY = 2 + ALL = 3 + NOTEBOOK = 1 + JOB = 2 + PROJECT = 3 + LOCAL = 4 + UNKNOWN = 1000 + RUNNING = 1 + SCHEDULED = 2 + FINISHED = 3 + FAILED = 4 + KILLED = 5 + TRACE_STATUS_UNSPECIFIED = 0 + OK = 1 + ERROR = 2 + IN_PROGRESS = 3 + LOGGED_MODEL_STATUS_UNSPECIFIED = 0 + LOGGED_MODEL_PENDING = 1 + LOGGED_MODEL_READY = 2 + LOGGED_MODEL_UPLOAD_FAILED = 3 + + + _METRIC = DESCRIPTOR.message_types_by_name['Metric'] + _PARAM = DESCRIPTOR.message_types_by_name['Param'] + _RUN = DESCRIPTOR.message_types_by_name['Run'] + _RUNDATA = DESCRIPTOR.message_types_by_name['RunData'] + _RUNINPUTS = DESCRIPTOR.message_types_by_name['RunInputs'] + _RUNOUTPUTS = DESCRIPTOR.message_types_by_name['RunOutputs'] + _RUNTAG = DESCRIPTOR.message_types_by_name['RunTag'] + _EXPERIMENTTAG = DESCRIPTOR.message_types_by_name['ExperimentTag'] + _RUNINFO = DESCRIPTOR.message_types_by_name['RunInfo'] + _EXPERIMENT = DESCRIPTOR.message_types_by_name['Experiment'] + _DATASETINPUT = DESCRIPTOR.message_types_by_name['DatasetInput'] + _MODELINPUT = DESCRIPTOR.message_types_by_name['ModelInput'] + _INPUTTAG = DESCRIPTOR.message_types_by_name['InputTag'] + _DATASET = DESCRIPTOR.message_types_by_name['Dataset'] + _MODELOUTPUT = DESCRIPTOR.message_types_by_name['ModelOutput'] + _CREATEEXPERIMENT = DESCRIPTOR.message_types_by_name['CreateExperiment'] + _CREATEEXPERIMENT_RESPONSE = _CREATEEXPERIMENT.nested_types_by_name['Response'] + _SEARCHEXPERIMENTS = DESCRIPTOR.message_types_by_name['SearchExperiments'] + _SEARCHEXPERIMENTS_RESPONSE = _SEARCHEXPERIMENTS.nested_types_by_name['Response'] + _GETEXPERIMENT = DESCRIPTOR.message_types_by_name['GetExperiment'] + _GETEXPERIMENT_RESPONSE = _GETEXPERIMENT.nested_types_by_name['Response'] + _DELETEEXPERIMENT = DESCRIPTOR.message_types_by_name['DeleteExperiment'] + _DELETEEXPERIMENT_RESPONSE = _DELETEEXPERIMENT.nested_types_by_name['Response'] + _RESTOREEXPERIMENT = DESCRIPTOR.message_types_by_name['RestoreExperiment'] + _RESTOREEXPERIMENT_RESPONSE = _RESTOREEXPERIMENT.nested_types_by_name['Response'] + _UPDATEEXPERIMENT = DESCRIPTOR.message_types_by_name['UpdateExperiment'] + _UPDATEEXPERIMENT_RESPONSE = _UPDATEEXPERIMENT.nested_types_by_name['Response'] + _CREATERUN = DESCRIPTOR.message_types_by_name['CreateRun'] + _CREATERUN_RESPONSE = _CREATERUN.nested_types_by_name['Response'] + _UPDATERUN = DESCRIPTOR.message_types_by_name['UpdateRun'] + _UPDATERUN_RESPONSE = _UPDATERUN.nested_types_by_name['Response'] + _DELETERUN = DESCRIPTOR.message_types_by_name['DeleteRun'] + _DELETERUN_RESPONSE = _DELETERUN.nested_types_by_name['Response'] + _RESTORERUN = DESCRIPTOR.message_types_by_name['RestoreRun'] + _RESTORERUN_RESPONSE = _RESTORERUN.nested_types_by_name['Response'] + _LOGMETRIC = DESCRIPTOR.message_types_by_name['LogMetric'] + _LOGMETRIC_RESPONSE = _LOGMETRIC.nested_types_by_name['Response'] + _LOGPARAM = DESCRIPTOR.message_types_by_name['LogParam'] + _LOGPARAM_RESPONSE = _LOGPARAM.nested_types_by_name['Response'] + _SETEXPERIMENTTAG = DESCRIPTOR.message_types_by_name['SetExperimentTag'] + _SETEXPERIMENTTAG_RESPONSE = _SETEXPERIMENTTAG.nested_types_by_name['Response'] + _DELETEEXPERIMENTTAG = DESCRIPTOR.message_types_by_name['DeleteExperimentTag'] + _DELETEEXPERIMENTTAG_RESPONSE = _DELETEEXPERIMENTTAG.nested_types_by_name['Response'] + _SETTAG = DESCRIPTOR.message_types_by_name['SetTag'] + _SETTAG_RESPONSE = _SETTAG.nested_types_by_name['Response'] + _DELETETAG = DESCRIPTOR.message_types_by_name['DeleteTag'] + _DELETETAG_RESPONSE = _DELETETAG.nested_types_by_name['Response'] + _GETRUN = DESCRIPTOR.message_types_by_name['GetRun'] + _GETRUN_RESPONSE = _GETRUN.nested_types_by_name['Response'] + _SEARCHRUNS = DESCRIPTOR.message_types_by_name['SearchRuns'] + _SEARCHRUNS_RESPONSE = _SEARCHRUNS.nested_types_by_name['Response'] + _LISTARTIFACTS = DESCRIPTOR.message_types_by_name['ListArtifacts'] + _LISTARTIFACTS_RESPONSE = _LISTARTIFACTS.nested_types_by_name['Response'] + _FILEINFO = DESCRIPTOR.message_types_by_name['FileInfo'] + _GETMETRICHISTORY = DESCRIPTOR.message_types_by_name['GetMetricHistory'] + _GETMETRICHISTORY_RESPONSE = _GETMETRICHISTORY.nested_types_by_name['Response'] + _METRICWITHRUNID = DESCRIPTOR.message_types_by_name['MetricWithRunId'] + _GETMETRICHISTORYBULKINTERVAL = DESCRIPTOR.message_types_by_name['GetMetricHistoryBulkInterval'] + _GETMETRICHISTORYBULKINTERVAL_RESPONSE = _GETMETRICHISTORYBULKINTERVAL.nested_types_by_name['Response'] + _LOGBATCH = DESCRIPTOR.message_types_by_name['LogBatch'] + _LOGBATCH_RESPONSE = _LOGBATCH.nested_types_by_name['Response'] + _LOGMODEL = DESCRIPTOR.message_types_by_name['LogModel'] + _LOGMODEL_RESPONSE = _LOGMODEL.nested_types_by_name['Response'] + _LOGINPUTS = DESCRIPTOR.message_types_by_name['LogInputs'] + _LOGINPUTS_RESPONSE = _LOGINPUTS.nested_types_by_name['Response'] + _LOGOUTPUTS = DESCRIPTOR.message_types_by_name['LogOutputs'] + _LOGOUTPUTS_RESPONSE = _LOGOUTPUTS.nested_types_by_name['Response'] + _GETEXPERIMENTBYNAME = DESCRIPTOR.message_types_by_name['GetExperimentByName'] + _GETEXPERIMENTBYNAME_RESPONSE = _GETEXPERIMENTBYNAME.nested_types_by_name['Response'] + _CREATEASSESSMENT = DESCRIPTOR.message_types_by_name['CreateAssessment'] + _CREATEASSESSMENT_RESPONSE = _CREATEASSESSMENT.nested_types_by_name['Response'] + _UPDATEASSESSMENT = DESCRIPTOR.message_types_by_name['UpdateAssessment'] + _UPDATEASSESSMENT_RESPONSE = _UPDATEASSESSMENT.nested_types_by_name['Response'] + _DELETEASSESSMENT = DESCRIPTOR.message_types_by_name['DeleteAssessment'] + _DELETEASSESSMENT_RESPONSE = _DELETEASSESSMENT.nested_types_by_name['Response'] + _GETASSESSMENTREQUEST = DESCRIPTOR.message_types_by_name['GetAssessmentRequest'] + _GETASSESSMENTREQUEST_RESPONSE = _GETASSESSMENTREQUEST.nested_types_by_name['Response'] + _TRACEINFO = DESCRIPTOR.message_types_by_name['TraceInfo'] + _TRACEREQUESTMETADATA = DESCRIPTOR.message_types_by_name['TraceRequestMetadata'] + _TRACETAG = DESCRIPTOR.message_types_by_name['TraceTag'] + _STARTTRACE = DESCRIPTOR.message_types_by_name['StartTrace'] + _STARTTRACE_RESPONSE = _STARTTRACE.nested_types_by_name['Response'] + _ENDTRACE = DESCRIPTOR.message_types_by_name['EndTrace'] + _ENDTRACE_RESPONSE = _ENDTRACE.nested_types_by_name['Response'] + _GETTRACEINFO = DESCRIPTOR.message_types_by_name['GetTraceInfo'] + _GETTRACEINFO_RESPONSE = _GETTRACEINFO.nested_types_by_name['Response'] + _GETTRACEINFOV3 = DESCRIPTOR.message_types_by_name['GetTraceInfoV3'] + _GETTRACEINFOV3_RESPONSE = _GETTRACEINFOV3.nested_types_by_name['Response'] + _SEARCHTRACES = DESCRIPTOR.message_types_by_name['SearchTraces'] + _SEARCHTRACES_RESPONSE = _SEARCHTRACES.nested_types_by_name['Response'] + _SEARCHUNIFIEDTRACES = DESCRIPTOR.message_types_by_name['SearchUnifiedTraces'] + _SEARCHUNIFIEDTRACES_RESPONSE = _SEARCHUNIFIEDTRACES.nested_types_by_name['Response'] + _GETONLINETRACEDETAILS = DESCRIPTOR.message_types_by_name['GetOnlineTraceDetails'] + _GETONLINETRACEDETAILS_RESPONSE = _GETONLINETRACEDETAILS.nested_types_by_name['Response'] + _DELETETRACES = DESCRIPTOR.message_types_by_name['DeleteTraces'] + _DELETETRACES_RESPONSE = _DELETETRACES.nested_types_by_name['Response'] + _DELETETRACESV3 = DESCRIPTOR.message_types_by_name['DeleteTracesV3'] + _DELETETRACESV3_RESPONSE = _DELETETRACESV3.nested_types_by_name['Response'] + _SETTRACETAG = DESCRIPTOR.message_types_by_name['SetTraceTag'] + _SETTRACETAG_RESPONSE = _SETTRACETAG.nested_types_by_name['Response'] + _SETTRACETAGV3 = DESCRIPTOR.message_types_by_name['SetTraceTagV3'] + _SETTRACETAGV3_RESPONSE = _SETTRACETAGV3.nested_types_by_name['Response'] + _DELETETRACETAG = DESCRIPTOR.message_types_by_name['DeleteTraceTag'] + _DELETETRACETAG_RESPONSE = _DELETETRACETAG.nested_types_by_name['Response'] + _DELETETRACETAGV3 = DESCRIPTOR.message_types_by_name['DeleteTraceTagV3'] + _DELETETRACETAGV3_RESPONSE = _DELETETRACETAGV3.nested_types_by_name['Response'] + _TRACE = DESCRIPTOR.message_types_by_name['Trace'] + _TRACELOCATION = DESCRIPTOR.message_types_by_name['TraceLocation'] + _TRACELOCATION_MLFLOWEXPERIMENTLOCATION = _TRACELOCATION.nested_types_by_name['MlflowExperimentLocation'] + _TRACELOCATION_INFERENCETABLELOCATION = _TRACELOCATION.nested_types_by_name['InferenceTableLocation'] + _TRACEINFOV3 = DESCRIPTOR.message_types_by_name['TraceInfoV3'] + _TRACEINFOV3_TRACEMETADATAENTRY = _TRACEINFOV3.nested_types_by_name['TraceMetadataEntry'] + _TRACEINFOV3_TAGSENTRY = _TRACEINFOV3.nested_types_by_name['TagsEntry'] + _STARTTRACEV3 = DESCRIPTOR.message_types_by_name['StartTraceV3'] + _STARTTRACEV3_RESPONSE = _STARTTRACEV3.nested_types_by_name['Response'] + _LINKTRACESTORUN = DESCRIPTOR.message_types_by_name['LinkTracesToRun'] + _LINKTRACESTORUN_RESPONSE = _LINKTRACESTORUN.nested_types_by_name['Response'] + _DATASETSUMMARY = DESCRIPTOR.message_types_by_name['DatasetSummary'] + _SEARCHDATASETS = DESCRIPTOR.message_types_by_name['SearchDatasets'] + _SEARCHDATASETS_RESPONSE = _SEARCHDATASETS.nested_types_by_name['Response'] + _CREATELOGGEDMODEL = DESCRIPTOR.message_types_by_name['CreateLoggedModel'] + _CREATELOGGEDMODEL_RESPONSE = _CREATELOGGEDMODEL.nested_types_by_name['Response'] + _FINALIZELOGGEDMODEL = DESCRIPTOR.message_types_by_name['FinalizeLoggedModel'] + _FINALIZELOGGEDMODEL_RESPONSE = _FINALIZELOGGEDMODEL.nested_types_by_name['Response'] + _GETLOGGEDMODEL = DESCRIPTOR.message_types_by_name['GetLoggedModel'] + _GETLOGGEDMODEL_RESPONSE = _GETLOGGEDMODEL.nested_types_by_name['Response'] + _DELETELOGGEDMODEL = DESCRIPTOR.message_types_by_name['DeleteLoggedModel'] + _DELETELOGGEDMODEL_RESPONSE = _DELETELOGGEDMODEL.nested_types_by_name['Response'] + _SEARCHLOGGEDMODELS = DESCRIPTOR.message_types_by_name['SearchLoggedModels'] + _SEARCHLOGGEDMODELS_DATASET = _SEARCHLOGGEDMODELS.nested_types_by_name['Dataset'] + _SEARCHLOGGEDMODELS_ORDERBY = _SEARCHLOGGEDMODELS.nested_types_by_name['OrderBy'] + _SEARCHLOGGEDMODELS_RESPONSE = _SEARCHLOGGEDMODELS.nested_types_by_name['Response'] + _SETLOGGEDMODELTAGS = DESCRIPTOR.message_types_by_name['SetLoggedModelTags'] + _SETLOGGEDMODELTAGS_RESPONSE = _SETLOGGEDMODELTAGS.nested_types_by_name['Response'] + _DELETELOGGEDMODELTAG = DESCRIPTOR.message_types_by_name['DeleteLoggedModelTag'] + _DELETELOGGEDMODELTAG_RESPONSE = _DELETELOGGEDMODELTAG.nested_types_by_name['Response'] + _LISTLOGGEDMODELARTIFACTS = DESCRIPTOR.message_types_by_name['ListLoggedModelArtifacts'] + _LISTLOGGEDMODELARTIFACTS_RESPONSE = _LISTLOGGEDMODELARTIFACTS.nested_types_by_name['Response'] + _LOGLOGGEDMODELPARAMSREQUEST = DESCRIPTOR.message_types_by_name['LogLoggedModelParamsRequest'] + _LOGLOGGEDMODELPARAMSREQUEST_RESPONSE = _LOGLOGGEDMODELPARAMSREQUEST.nested_types_by_name['Response'] + _LOGGEDMODEL = DESCRIPTOR.message_types_by_name['LoggedModel'] + _LOGGEDMODELINFO = DESCRIPTOR.message_types_by_name['LoggedModelInfo'] + _LOGGEDMODELTAG = DESCRIPTOR.message_types_by_name['LoggedModelTag'] + _LOGGEDMODELREGISTRATIONINFO = DESCRIPTOR.message_types_by_name['LoggedModelRegistrationInfo'] + _LOGGEDMODELDATA = DESCRIPTOR.message_types_by_name['LoggedModelData'] + _LOGGEDMODELPARAMETER = DESCRIPTOR.message_types_by_name['LoggedModelParameter'] + _SEARCHTRACESV3 = DESCRIPTOR.message_types_by_name['SearchTracesV3'] + _SEARCHTRACESV3_RESPONSE = _SEARCHTRACESV3.nested_types_by_name['Response'] + _TRACELOCATION_TRACELOCATIONTYPE = _TRACELOCATION.enum_types_by_name['TraceLocationType'] + _TRACEINFOV3_STATE = _TRACEINFOV3.enum_types_by_name['State'] + Metric = _reflection.GeneratedProtocolMessageType('Metric', (_message.Message,), { + 'DESCRIPTOR' : _METRIC, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.Metric) + }) + _sym_db.RegisterMessage(Metric) + + Param = _reflection.GeneratedProtocolMessageType('Param', (_message.Message,), { + 'DESCRIPTOR' : _PARAM, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.Param) + }) + _sym_db.RegisterMessage(Param) + + Run = _reflection.GeneratedProtocolMessageType('Run', (_message.Message,), { + 'DESCRIPTOR' : _RUN, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.Run) + }) + _sym_db.RegisterMessage(Run) + + RunData = _reflection.GeneratedProtocolMessageType('RunData', (_message.Message,), { + 'DESCRIPTOR' : _RUNDATA, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.RunData) + }) + _sym_db.RegisterMessage(RunData) + + RunInputs = _reflection.GeneratedProtocolMessageType('RunInputs', (_message.Message,), { + 'DESCRIPTOR' : _RUNINPUTS, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.RunInputs) + }) + _sym_db.RegisterMessage(RunInputs) + + RunOutputs = _reflection.GeneratedProtocolMessageType('RunOutputs', (_message.Message,), { + 'DESCRIPTOR' : _RUNOUTPUTS, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.RunOutputs) + }) + _sym_db.RegisterMessage(RunOutputs) + + RunTag = _reflection.GeneratedProtocolMessageType('RunTag', (_message.Message,), { + 'DESCRIPTOR' : _RUNTAG, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.RunTag) + }) + _sym_db.RegisterMessage(RunTag) + + ExperimentTag = _reflection.GeneratedProtocolMessageType('ExperimentTag', (_message.Message,), { + 'DESCRIPTOR' : _EXPERIMENTTAG, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.ExperimentTag) + }) + _sym_db.RegisterMessage(ExperimentTag) + + RunInfo = _reflection.GeneratedProtocolMessageType('RunInfo', (_message.Message,), { + 'DESCRIPTOR' : _RUNINFO, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.RunInfo) + }) + _sym_db.RegisterMessage(RunInfo) + + Experiment = _reflection.GeneratedProtocolMessageType('Experiment', (_message.Message,), { + 'DESCRIPTOR' : _EXPERIMENT, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.Experiment) + }) + _sym_db.RegisterMessage(Experiment) + + DatasetInput = _reflection.GeneratedProtocolMessageType('DatasetInput', (_message.Message,), { + 'DESCRIPTOR' : _DATASETINPUT, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.DatasetInput) + }) + _sym_db.RegisterMessage(DatasetInput) + + ModelInput = _reflection.GeneratedProtocolMessageType('ModelInput', (_message.Message,), { + 'DESCRIPTOR' : _MODELINPUT, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.ModelInput) + }) + _sym_db.RegisterMessage(ModelInput) + + InputTag = _reflection.GeneratedProtocolMessageType('InputTag', (_message.Message,), { + 'DESCRIPTOR' : _INPUTTAG, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.InputTag) + }) + _sym_db.RegisterMessage(InputTag) + + Dataset = _reflection.GeneratedProtocolMessageType('Dataset', (_message.Message,), { + 'DESCRIPTOR' : _DATASET, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.Dataset) + }) + _sym_db.RegisterMessage(Dataset) + + ModelOutput = _reflection.GeneratedProtocolMessageType('ModelOutput', (_message.Message,), { + 'DESCRIPTOR' : _MODELOUTPUT, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.ModelOutput) + }) + _sym_db.RegisterMessage(ModelOutput) + + CreateExperiment = _reflection.GeneratedProtocolMessageType('CreateExperiment', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _CREATEEXPERIMENT_RESPONSE, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.CreateExperiment.Response) + }) + , + 'DESCRIPTOR' : _CREATEEXPERIMENT, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.CreateExperiment) + }) + _sym_db.RegisterMessage(CreateExperiment) + _sym_db.RegisterMessage(CreateExperiment.Response) + + SearchExperiments = _reflection.GeneratedProtocolMessageType('SearchExperiments', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _SEARCHEXPERIMENTS_RESPONSE, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.SearchExperiments.Response) + }) + , + 'DESCRIPTOR' : _SEARCHEXPERIMENTS, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.SearchExperiments) + }) + _sym_db.RegisterMessage(SearchExperiments) + _sym_db.RegisterMessage(SearchExperiments.Response) + + GetExperiment = _reflection.GeneratedProtocolMessageType('GetExperiment', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _GETEXPERIMENT_RESPONSE, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.GetExperiment.Response) + }) + , + 'DESCRIPTOR' : _GETEXPERIMENT, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.GetExperiment) + }) + _sym_db.RegisterMessage(GetExperiment) + _sym_db.RegisterMessage(GetExperiment.Response) + + DeleteExperiment = _reflection.GeneratedProtocolMessageType('DeleteExperiment', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _DELETEEXPERIMENT_RESPONSE, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.DeleteExperiment.Response) + }) + , + 'DESCRIPTOR' : _DELETEEXPERIMENT, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.DeleteExperiment) + }) + _sym_db.RegisterMessage(DeleteExperiment) + _sym_db.RegisterMessage(DeleteExperiment.Response) + + RestoreExperiment = _reflection.GeneratedProtocolMessageType('RestoreExperiment', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _RESTOREEXPERIMENT_RESPONSE, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.RestoreExperiment.Response) + }) + , + 'DESCRIPTOR' : _RESTOREEXPERIMENT, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.RestoreExperiment) + }) + _sym_db.RegisterMessage(RestoreExperiment) + _sym_db.RegisterMessage(RestoreExperiment.Response) + + UpdateExperiment = _reflection.GeneratedProtocolMessageType('UpdateExperiment', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _UPDATEEXPERIMENT_RESPONSE, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.UpdateExperiment.Response) + }) + , + 'DESCRIPTOR' : _UPDATEEXPERIMENT, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.UpdateExperiment) + }) + _sym_db.RegisterMessage(UpdateExperiment) + _sym_db.RegisterMessage(UpdateExperiment.Response) + + CreateRun = _reflection.GeneratedProtocolMessageType('CreateRun', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _CREATERUN_RESPONSE, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.CreateRun.Response) + }) + , + 'DESCRIPTOR' : _CREATERUN, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.CreateRun) + }) + _sym_db.RegisterMessage(CreateRun) + _sym_db.RegisterMessage(CreateRun.Response) + + UpdateRun = _reflection.GeneratedProtocolMessageType('UpdateRun', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _UPDATERUN_RESPONSE, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.UpdateRun.Response) + }) + , + 'DESCRIPTOR' : _UPDATERUN, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.UpdateRun) + }) + _sym_db.RegisterMessage(UpdateRun) + _sym_db.RegisterMessage(UpdateRun.Response) + + DeleteRun = _reflection.GeneratedProtocolMessageType('DeleteRun', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _DELETERUN_RESPONSE, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.DeleteRun.Response) + }) + , + 'DESCRIPTOR' : _DELETERUN, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.DeleteRun) + }) + _sym_db.RegisterMessage(DeleteRun) + _sym_db.RegisterMessage(DeleteRun.Response) + + RestoreRun = _reflection.GeneratedProtocolMessageType('RestoreRun', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _RESTORERUN_RESPONSE, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.RestoreRun.Response) + }) + , + 'DESCRIPTOR' : _RESTORERUN, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.RestoreRun) + }) + _sym_db.RegisterMessage(RestoreRun) + _sym_db.RegisterMessage(RestoreRun.Response) + + LogMetric = _reflection.GeneratedProtocolMessageType('LogMetric', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _LOGMETRIC_RESPONSE, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.LogMetric.Response) + }) + , + 'DESCRIPTOR' : _LOGMETRIC, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.LogMetric) + }) + _sym_db.RegisterMessage(LogMetric) + _sym_db.RegisterMessage(LogMetric.Response) + + LogParam = _reflection.GeneratedProtocolMessageType('LogParam', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _LOGPARAM_RESPONSE, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.LogParam.Response) + }) + , + 'DESCRIPTOR' : _LOGPARAM, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.LogParam) + }) + _sym_db.RegisterMessage(LogParam) + _sym_db.RegisterMessage(LogParam.Response) + + SetExperimentTag = _reflection.GeneratedProtocolMessageType('SetExperimentTag', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _SETEXPERIMENTTAG_RESPONSE, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.SetExperimentTag.Response) + }) + , + 'DESCRIPTOR' : _SETEXPERIMENTTAG, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.SetExperimentTag) + }) + _sym_db.RegisterMessage(SetExperimentTag) + _sym_db.RegisterMessage(SetExperimentTag.Response) + + DeleteExperimentTag = _reflection.GeneratedProtocolMessageType('DeleteExperimentTag', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _DELETEEXPERIMENTTAG_RESPONSE, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.DeleteExperimentTag.Response) + }) + , + 'DESCRIPTOR' : _DELETEEXPERIMENTTAG, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.DeleteExperimentTag) + }) + _sym_db.RegisterMessage(DeleteExperimentTag) + _sym_db.RegisterMessage(DeleteExperimentTag.Response) + + SetTag = _reflection.GeneratedProtocolMessageType('SetTag', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _SETTAG_RESPONSE, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.SetTag.Response) + }) + , + 'DESCRIPTOR' : _SETTAG, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.SetTag) + }) + _sym_db.RegisterMessage(SetTag) + _sym_db.RegisterMessage(SetTag.Response) + + DeleteTag = _reflection.GeneratedProtocolMessageType('DeleteTag', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _DELETETAG_RESPONSE, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.DeleteTag.Response) + }) + , + 'DESCRIPTOR' : _DELETETAG, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.DeleteTag) + }) + _sym_db.RegisterMessage(DeleteTag) + _sym_db.RegisterMessage(DeleteTag.Response) + + GetRun = _reflection.GeneratedProtocolMessageType('GetRun', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _GETRUN_RESPONSE, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.GetRun.Response) + }) + , + 'DESCRIPTOR' : _GETRUN, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.GetRun) + }) + _sym_db.RegisterMessage(GetRun) + _sym_db.RegisterMessage(GetRun.Response) + + SearchRuns = _reflection.GeneratedProtocolMessageType('SearchRuns', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _SEARCHRUNS_RESPONSE, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.SearchRuns.Response) + }) + , + 'DESCRIPTOR' : _SEARCHRUNS, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.SearchRuns) + }) + _sym_db.RegisterMessage(SearchRuns) + _sym_db.RegisterMessage(SearchRuns.Response) + + ListArtifacts = _reflection.GeneratedProtocolMessageType('ListArtifacts', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _LISTARTIFACTS_RESPONSE, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.ListArtifacts.Response) + }) + , + 'DESCRIPTOR' : _LISTARTIFACTS, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.ListArtifacts) + }) + _sym_db.RegisterMessage(ListArtifacts) + _sym_db.RegisterMessage(ListArtifacts.Response) + + FileInfo = _reflection.GeneratedProtocolMessageType('FileInfo', (_message.Message,), { + 'DESCRIPTOR' : _FILEINFO, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.FileInfo) + }) + _sym_db.RegisterMessage(FileInfo) + + GetMetricHistory = _reflection.GeneratedProtocolMessageType('GetMetricHistory', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _GETMETRICHISTORY_RESPONSE, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.GetMetricHistory.Response) + }) + , + 'DESCRIPTOR' : _GETMETRICHISTORY, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.GetMetricHistory) + }) + _sym_db.RegisterMessage(GetMetricHistory) + _sym_db.RegisterMessage(GetMetricHistory.Response) + + MetricWithRunId = _reflection.GeneratedProtocolMessageType('MetricWithRunId', (_message.Message,), { + 'DESCRIPTOR' : _METRICWITHRUNID, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.MetricWithRunId) + }) + _sym_db.RegisterMessage(MetricWithRunId) + + GetMetricHistoryBulkInterval = _reflection.GeneratedProtocolMessageType('GetMetricHistoryBulkInterval', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _GETMETRICHISTORYBULKINTERVAL_RESPONSE, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.GetMetricHistoryBulkInterval.Response) + }) + , + 'DESCRIPTOR' : _GETMETRICHISTORYBULKINTERVAL, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.GetMetricHistoryBulkInterval) + }) + _sym_db.RegisterMessage(GetMetricHistoryBulkInterval) + _sym_db.RegisterMessage(GetMetricHistoryBulkInterval.Response) + + LogBatch = _reflection.GeneratedProtocolMessageType('LogBatch', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _LOGBATCH_RESPONSE, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.LogBatch.Response) + }) + , + 'DESCRIPTOR' : _LOGBATCH, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.LogBatch) + }) + _sym_db.RegisterMessage(LogBatch) + _sym_db.RegisterMessage(LogBatch.Response) + + LogModel = _reflection.GeneratedProtocolMessageType('LogModel', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _LOGMODEL_RESPONSE, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.LogModel.Response) + }) + , + 'DESCRIPTOR' : _LOGMODEL, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.LogModel) + }) + _sym_db.RegisterMessage(LogModel) + _sym_db.RegisterMessage(LogModel.Response) + + LogInputs = _reflection.GeneratedProtocolMessageType('LogInputs', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _LOGINPUTS_RESPONSE, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.LogInputs.Response) + }) + , + 'DESCRIPTOR' : _LOGINPUTS, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.LogInputs) + }) + _sym_db.RegisterMessage(LogInputs) + _sym_db.RegisterMessage(LogInputs.Response) + + LogOutputs = _reflection.GeneratedProtocolMessageType('LogOutputs', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _LOGOUTPUTS_RESPONSE, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.LogOutputs.Response) + }) + , + 'DESCRIPTOR' : _LOGOUTPUTS, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.LogOutputs) + }) + _sym_db.RegisterMessage(LogOutputs) + _sym_db.RegisterMessage(LogOutputs.Response) + + GetExperimentByName = _reflection.GeneratedProtocolMessageType('GetExperimentByName', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _GETEXPERIMENTBYNAME_RESPONSE, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.GetExperimentByName.Response) + }) + , + 'DESCRIPTOR' : _GETEXPERIMENTBYNAME, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.GetExperimentByName) + }) + _sym_db.RegisterMessage(GetExperimentByName) + _sym_db.RegisterMessage(GetExperimentByName.Response) + + CreateAssessment = _reflection.GeneratedProtocolMessageType('CreateAssessment', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _CREATEASSESSMENT_RESPONSE, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.CreateAssessment.Response) + }) + , + 'DESCRIPTOR' : _CREATEASSESSMENT, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.CreateAssessment) + }) + _sym_db.RegisterMessage(CreateAssessment) + _sym_db.RegisterMessage(CreateAssessment.Response) + + UpdateAssessment = _reflection.GeneratedProtocolMessageType('UpdateAssessment', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _UPDATEASSESSMENT_RESPONSE, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.UpdateAssessment.Response) + }) + , + 'DESCRIPTOR' : _UPDATEASSESSMENT, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.UpdateAssessment) + }) + _sym_db.RegisterMessage(UpdateAssessment) + _sym_db.RegisterMessage(UpdateAssessment.Response) + + DeleteAssessment = _reflection.GeneratedProtocolMessageType('DeleteAssessment', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _DELETEASSESSMENT_RESPONSE, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.DeleteAssessment.Response) + }) + , + 'DESCRIPTOR' : _DELETEASSESSMENT, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.DeleteAssessment) + }) + _sym_db.RegisterMessage(DeleteAssessment) + _sym_db.RegisterMessage(DeleteAssessment.Response) + + GetAssessmentRequest = _reflection.GeneratedProtocolMessageType('GetAssessmentRequest', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _GETASSESSMENTREQUEST_RESPONSE, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.GetAssessmentRequest.Response) + }) + , + 'DESCRIPTOR' : _GETASSESSMENTREQUEST, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.GetAssessmentRequest) + }) + _sym_db.RegisterMessage(GetAssessmentRequest) + _sym_db.RegisterMessage(GetAssessmentRequest.Response) + + TraceInfo = _reflection.GeneratedProtocolMessageType('TraceInfo', (_message.Message,), { + 'DESCRIPTOR' : _TRACEINFO, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.TraceInfo) + }) + _sym_db.RegisterMessage(TraceInfo) + + TraceRequestMetadata = _reflection.GeneratedProtocolMessageType('TraceRequestMetadata', (_message.Message,), { + 'DESCRIPTOR' : _TRACEREQUESTMETADATA, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.TraceRequestMetadata) + }) + _sym_db.RegisterMessage(TraceRequestMetadata) + + TraceTag = _reflection.GeneratedProtocolMessageType('TraceTag', (_message.Message,), { + 'DESCRIPTOR' : _TRACETAG, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.TraceTag) + }) + _sym_db.RegisterMessage(TraceTag) + + StartTrace = _reflection.GeneratedProtocolMessageType('StartTrace', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _STARTTRACE_RESPONSE, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.StartTrace.Response) + }) + , + 'DESCRIPTOR' : _STARTTRACE, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.StartTrace) + }) + _sym_db.RegisterMessage(StartTrace) + _sym_db.RegisterMessage(StartTrace.Response) + + EndTrace = _reflection.GeneratedProtocolMessageType('EndTrace', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _ENDTRACE_RESPONSE, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.EndTrace.Response) + }) + , + 'DESCRIPTOR' : _ENDTRACE, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.EndTrace) + }) + _sym_db.RegisterMessage(EndTrace) + _sym_db.RegisterMessage(EndTrace.Response) + + GetTraceInfo = _reflection.GeneratedProtocolMessageType('GetTraceInfo', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _GETTRACEINFO_RESPONSE, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.GetTraceInfo.Response) + }) + , + 'DESCRIPTOR' : _GETTRACEINFO, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.GetTraceInfo) + }) + _sym_db.RegisterMessage(GetTraceInfo) + _sym_db.RegisterMessage(GetTraceInfo.Response) + + GetTraceInfoV3 = _reflection.GeneratedProtocolMessageType('GetTraceInfoV3', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _GETTRACEINFOV3_RESPONSE, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.GetTraceInfoV3.Response) + }) + , + 'DESCRIPTOR' : _GETTRACEINFOV3, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.GetTraceInfoV3) + }) + _sym_db.RegisterMessage(GetTraceInfoV3) + _sym_db.RegisterMessage(GetTraceInfoV3.Response) + + SearchTraces = _reflection.GeneratedProtocolMessageType('SearchTraces', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _SEARCHTRACES_RESPONSE, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.SearchTraces.Response) + }) + , + 'DESCRIPTOR' : _SEARCHTRACES, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.SearchTraces) + }) + _sym_db.RegisterMessage(SearchTraces) + _sym_db.RegisterMessage(SearchTraces.Response) + + SearchUnifiedTraces = _reflection.GeneratedProtocolMessageType('SearchUnifiedTraces', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _SEARCHUNIFIEDTRACES_RESPONSE, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.SearchUnifiedTraces.Response) + }) + , + 'DESCRIPTOR' : _SEARCHUNIFIEDTRACES, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.SearchUnifiedTraces) + }) + _sym_db.RegisterMessage(SearchUnifiedTraces) + _sym_db.RegisterMessage(SearchUnifiedTraces.Response) + + GetOnlineTraceDetails = _reflection.GeneratedProtocolMessageType('GetOnlineTraceDetails', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _GETONLINETRACEDETAILS_RESPONSE, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.GetOnlineTraceDetails.Response) + }) + , + 'DESCRIPTOR' : _GETONLINETRACEDETAILS, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.GetOnlineTraceDetails) + }) + _sym_db.RegisterMessage(GetOnlineTraceDetails) + _sym_db.RegisterMessage(GetOnlineTraceDetails.Response) + + DeleteTraces = _reflection.GeneratedProtocolMessageType('DeleteTraces', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _DELETETRACES_RESPONSE, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.DeleteTraces.Response) + }) + , + 'DESCRIPTOR' : _DELETETRACES, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.DeleteTraces) + }) + _sym_db.RegisterMessage(DeleteTraces) + _sym_db.RegisterMessage(DeleteTraces.Response) + + DeleteTracesV3 = _reflection.GeneratedProtocolMessageType('DeleteTracesV3', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _DELETETRACESV3_RESPONSE, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.DeleteTracesV3.Response) + }) + , + 'DESCRIPTOR' : _DELETETRACESV3, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.DeleteTracesV3) + }) + _sym_db.RegisterMessage(DeleteTracesV3) + _sym_db.RegisterMessage(DeleteTracesV3.Response) + + SetTraceTag = _reflection.GeneratedProtocolMessageType('SetTraceTag', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _SETTRACETAG_RESPONSE, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.SetTraceTag.Response) + }) + , + 'DESCRIPTOR' : _SETTRACETAG, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.SetTraceTag) + }) + _sym_db.RegisterMessage(SetTraceTag) + _sym_db.RegisterMessage(SetTraceTag.Response) + + SetTraceTagV3 = _reflection.GeneratedProtocolMessageType('SetTraceTagV3', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _SETTRACETAGV3_RESPONSE, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.SetTraceTagV3.Response) + }) + , + 'DESCRIPTOR' : _SETTRACETAGV3, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.SetTraceTagV3) + }) + _sym_db.RegisterMessage(SetTraceTagV3) + _sym_db.RegisterMessage(SetTraceTagV3.Response) + + DeleteTraceTag = _reflection.GeneratedProtocolMessageType('DeleteTraceTag', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _DELETETRACETAG_RESPONSE, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.DeleteTraceTag.Response) + }) + , + 'DESCRIPTOR' : _DELETETRACETAG, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.DeleteTraceTag) + }) + _sym_db.RegisterMessage(DeleteTraceTag) + _sym_db.RegisterMessage(DeleteTraceTag.Response) + + DeleteTraceTagV3 = _reflection.GeneratedProtocolMessageType('DeleteTraceTagV3', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _DELETETRACETAGV3_RESPONSE, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.DeleteTraceTagV3.Response) + }) + , + 'DESCRIPTOR' : _DELETETRACETAGV3, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.DeleteTraceTagV3) + }) + _sym_db.RegisterMessage(DeleteTraceTagV3) + _sym_db.RegisterMessage(DeleteTraceTagV3.Response) + + Trace = _reflection.GeneratedProtocolMessageType('Trace', (_message.Message,), { + 'DESCRIPTOR' : _TRACE, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.Trace) + }) + _sym_db.RegisterMessage(Trace) + + TraceLocation = _reflection.GeneratedProtocolMessageType('TraceLocation', (_message.Message,), { + + 'MlflowExperimentLocation' : _reflection.GeneratedProtocolMessageType('MlflowExperimentLocation', (_message.Message,), { + 'DESCRIPTOR' : _TRACELOCATION_MLFLOWEXPERIMENTLOCATION, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.TraceLocation.MlflowExperimentLocation) + }) + , + + 'InferenceTableLocation' : _reflection.GeneratedProtocolMessageType('InferenceTableLocation', (_message.Message,), { + 'DESCRIPTOR' : _TRACELOCATION_INFERENCETABLELOCATION, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.TraceLocation.InferenceTableLocation) + }) + , + 'DESCRIPTOR' : _TRACELOCATION, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.TraceLocation) + }) + _sym_db.RegisterMessage(TraceLocation) + _sym_db.RegisterMessage(TraceLocation.MlflowExperimentLocation) + _sym_db.RegisterMessage(TraceLocation.InferenceTableLocation) + + TraceInfoV3 = _reflection.GeneratedProtocolMessageType('TraceInfoV3', (_message.Message,), { + + 'TraceMetadataEntry' : _reflection.GeneratedProtocolMessageType('TraceMetadataEntry', (_message.Message,), { + 'DESCRIPTOR' : _TRACEINFOV3_TRACEMETADATAENTRY, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.TraceInfoV3.TraceMetadataEntry) + }) + , + + 'TagsEntry' : _reflection.GeneratedProtocolMessageType('TagsEntry', (_message.Message,), { + 'DESCRIPTOR' : _TRACEINFOV3_TAGSENTRY, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.TraceInfoV3.TagsEntry) + }) + , + 'DESCRIPTOR' : _TRACEINFOV3, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.TraceInfoV3) + }) + _sym_db.RegisterMessage(TraceInfoV3) + _sym_db.RegisterMessage(TraceInfoV3.TraceMetadataEntry) + _sym_db.RegisterMessage(TraceInfoV3.TagsEntry) + + StartTraceV3 = _reflection.GeneratedProtocolMessageType('StartTraceV3', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _STARTTRACEV3_RESPONSE, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.StartTraceV3.Response) + }) + , + 'DESCRIPTOR' : _STARTTRACEV3, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.StartTraceV3) + }) + _sym_db.RegisterMessage(StartTraceV3) + _sym_db.RegisterMessage(StartTraceV3.Response) + + LinkTracesToRun = _reflection.GeneratedProtocolMessageType('LinkTracesToRun', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _LINKTRACESTORUN_RESPONSE, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.LinkTracesToRun.Response) + }) + , + 'DESCRIPTOR' : _LINKTRACESTORUN, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.LinkTracesToRun) + }) + _sym_db.RegisterMessage(LinkTracesToRun) + _sym_db.RegisterMessage(LinkTracesToRun.Response) + + DatasetSummary = _reflection.GeneratedProtocolMessageType('DatasetSummary', (_message.Message,), { + 'DESCRIPTOR' : _DATASETSUMMARY, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.DatasetSummary) + }) + _sym_db.RegisterMessage(DatasetSummary) + + SearchDatasets = _reflection.GeneratedProtocolMessageType('SearchDatasets', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _SEARCHDATASETS_RESPONSE, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.SearchDatasets.Response) + }) + , + 'DESCRIPTOR' : _SEARCHDATASETS, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.SearchDatasets) + }) + _sym_db.RegisterMessage(SearchDatasets) + _sym_db.RegisterMessage(SearchDatasets.Response) + + CreateLoggedModel = _reflection.GeneratedProtocolMessageType('CreateLoggedModel', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _CREATELOGGEDMODEL_RESPONSE, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.CreateLoggedModel.Response) + }) + , + 'DESCRIPTOR' : _CREATELOGGEDMODEL, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.CreateLoggedModel) + }) + _sym_db.RegisterMessage(CreateLoggedModel) + _sym_db.RegisterMessage(CreateLoggedModel.Response) + + FinalizeLoggedModel = _reflection.GeneratedProtocolMessageType('FinalizeLoggedModel', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _FINALIZELOGGEDMODEL_RESPONSE, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.FinalizeLoggedModel.Response) + }) + , + 'DESCRIPTOR' : _FINALIZELOGGEDMODEL, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.FinalizeLoggedModel) + }) + _sym_db.RegisterMessage(FinalizeLoggedModel) + _sym_db.RegisterMessage(FinalizeLoggedModel.Response) + + GetLoggedModel = _reflection.GeneratedProtocolMessageType('GetLoggedModel', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _GETLOGGEDMODEL_RESPONSE, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.GetLoggedModel.Response) + }) + , + 'DESCRIPTOR' : _GETLOGGEDMODEL, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.GetLoggedModel) + }) + _sym_db.RegisterMessage(GetLoggedModel) + _sym_db.RegisterMessage(GetLoggedModel.Response) + + DeleteLoggedModel = _reflection.GeneratedProtocolMessageType('DeleteLoggedModel', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _DELETELOGGEDMODEL_RESPONSE, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.DeleteLoggedModel.Response) + }) + , + 'DESCRIPTOR' : _DELETELOGGEDMODEL, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.DeleteLoggedModel) + }) + _sym_db.RegisterMessage(DeleteLoggedModel) + _sym_db.RegisterMessage(DeleteLoggedModel.Response) + + SearchLoggedModels = _reflection.GeneratedProtocolMessageType('SearchLoggedModels', (_message.Message,), { + + 'Dataset' : _reflection.GeneratedProtocolMessageType('Dataset', (_message.Message,), { + 'DESCRIPTOR' : _SEARCHLOGGEDMODELS_DATASET, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.SearchLoggedModels.Dataset) + }) + , + + 'OrderBy' : _reflection.GeneratedProtocolMessageType('OrderBy', (_message.Message,), { + 'DESCRIPTOR' : _SEARCHLOGGEDMODELS_ORDERBY, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.SearchLoggedModels.OrderBy) + }) + , + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _SEARCHLOGGEDMODELS_RESPONSE, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.SearchLoggedModels.Response) + }) + , + 'DESCRIPTOR' : _SEARCHLOGGEDMODELS, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.SearchLoggedModels) + }) + _sym_db.RegisterMessage(SearchLoggedModels) + _sym_db.RegisterMessage(SearchLoggedModels.Dataset) + _sym_db.RegisterMessage(SearchLoggedModels.OrderBy) + _sym_db.RegisterMessage(SearchLoggedModels.Response) + + SetLoggedModelTags = _reflection.GeneratedProtocolMessageType('SetLoggedModelTags', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _SETLOGGEDMODELTAGS_RESPONSE, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.SetLoggedModelTags.Response) + }) + , + 'DESCRIPTOR' : _SETLOGGEDMODELTAGS, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.SetLoggedModelTags) + }) + _sym_db.RegisterMessage(SetLoggedModelTags) + _sym_db.RegisterMessage(SetLoggedModelTags.Response) + + DeleteLoggedModelTag = _reflection.GeneratedProtocolMessageType('DeleteLoggedModelTag', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _DELETELOGGEDMODELTAG_RESPONSE, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.DeleteLoggedModelTag.Response) + }) + , + 'DESCRIPTOR' : _DELETELOGGEDMODELTAG, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.DeleteLoggedModelTag) + }) + _sym_db.RegisterMessage(DeleteLoggedModelTag) + _sym_db.RegisterMessage(DeleteLoggedModelTag.Response) + + ListLoggedModelArtifacts = _reflection.GeneratedProtocolMessageType('ListLoggedModelArtifacts', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _LISTLOGGEDMODELARTIFACTS_RESPONSE, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.ListLoggedModelArtifacts.Response) + }) + , + 'DESCRIPTOR' : _LISTLOGGEDMODELARTIFACTS, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.ListLoggedModelArtifacts) + }) + _sym_db.RegisterMessage(ListLoggedModelArtifacts) + _sym_db.RegisterMessage(ListLoggedModelArtifacts.Response) + + LogLoggedModelParamsRequest = _reflection.GeneratedProtocolMessageType('LogLoggedModelParamsRequest', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _LOGLOGGEDMODELPARAMSREQUEST_RESPONSE, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.LogLoggedModelParamsRequest.Response) + }) + , + 'DESCRIPTOR' : _LOGLOGGEDMODELPARAMSREQUEST, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.LogLoggedModelParamsRequest) + }) + _sym_db.RegisterMessage(LogLoggedModelParamsRequest) + _sym_db.RegisterMessage(LogLoggedModelParamsRequest.Response) + + LoggedModel = _reflection.GeneratedProtocolMessageType('LoggedModel', (_message.Message,), { + 'DESCRIPTOR' : _LOGGEDMODEL, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.LoggedModel) + }) + _sym_db.RegisterMessage(LoggedModel) + + LoggedModelInfo = _reflection.GeneratedProtocolMessageType('LoggedModelInfo', (_message.Message,), { + 'DESCRIPTOR' : _LOGGEDMODELINFO, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.LoggedModelInfo) + }) + _sym_db.RegisterMessage(LoggedModelInfo) + + LoggedModelTag = _reflection.GeneratedProtocolMessageType('LoggedModelTag', (_message.Message,), { + 'DESCRIPTOR' : _LOGGEDMODELTAG, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.LoggedModelTag) + }) + _sym_db.RegisterMessage(LoggedModelTag) + + LoggedModelRegistrationInfo = _reflection.GeneratedProtocolMessageType('LoggedModelRegistrationInfo', (_message.Message,), { + 'DESCRIPTOR' : _LOGGEDMODELREGISTRATIONINFO, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.LoggedModelRegistrationInfo) + }) + _sym_db.RegisterMessage(LoggedModelRegistrationInfo) + + LoggedModelData = _reflection.GeneratedProtocolMessageType('LoggedModelData', (_message.Message,), { + 'DESCRIPTOR' : _LOGGEDMODELDATA, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.LoggedModelData) + }) + _sym_db.RegisterMessage(LoggedModelData) + + LoggedModelParameter = _reflection.GeneratedProtocolMessageType('LoggedModelParameter', (_message.Message,), { + 'DESCRIPTOR' : _LOGGEDMODELPARAMETER, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.LoggedModelParameter) + }) + _sym_db.RegisterMessage(LoggedModelParameter) + + SearchTracesV3 = _reflection.GeneratedProtocolMessageType('SearchTracesV3', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _SEARCHTRACESV3_RESPONSE, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.SearchTracesV3.Response) + }) + , + 'DESCRIPTOR' : _SEARCHTRACESV3, + '__module__' : 'service_pb2' + # @@protoc_insertion_point(class_scope:mlflow.SearchTracesV3) + }) + _sym_db.RegisterMessage(SearchTracesV3) + _sym_db.RegisterMessage(SearchTracesV3.Response) + + _MLFLOWSERVICE = DESCRIPTOR.services_by_name['MlflowService'] + if _descriptor._USE_C_DESCRIPTORS == False: + + DESCRIPTOR._options = None + DESCRIPTOR._serialized_options = b'\n\024org.mlflow.api.proto\220\001\001\342?\002\020\001' + _METRIC.fields_by_name['dataset_name']._options = None + _METRIC.fields_by_name['dataset_name']._serialized_options = b'\360\206\031\003' + _METRIC.fields_by_name['dataset_digest']._options = None + _METRIC.fields_by_name['dataset_digest']._serialized_options = b'\360\206\031\003' + _METRIC.fields_by_name['model_id']._options = None + _METRIC.fields_by_name['model_id']._serialized_options = b'\360\206\031\003' + _METRIC.fields_by_name['run_id']._options = None + _METRIC.fields_by_name['run_id']._serialized_options = b'\360\206\031\003' + _DATASETINPUT.fields_by_name['dataset']._options = None + _DATASETINPUT.fields_by_name['dataset']._serialized_options = b'\370\206\031\001' + _MODELINPUT.fields_by_name['model_id']._options = None + _MODELINPUT.fields_by_name['model_id']._serialized_options = b'\370\206\031\001' + _INPUTTAG.fields_by_name['key']._options = None + _INPUTTAG.fields_by_name['key']._serialized_options = b'\370\206\031\001' + _INPUTTAG.fields_by_name['value']._options = None + _INPUTTAG.fields_by_name['value']._serialized_options = b'\370\206\031\001' + _DATASET.fields_by_name['name']._options = None + _DATASET.fields_by_name['name']._serialized_options = b'\370\206\031\001' + _DATASET.fields_by_name['digest']._options = None + _DATASET.fields_by_name['digest']._serialized_options = b'\370\206\031\001' + _DATASET.fields_by_name['source_type']._options = None + _DATASET.fields_by_name['source_type']._serialized_options = b'\370\206\031\001' + _DATASET.fields_by_name['source']._options = None + _DATASET.fields_by_name['source']._serialized_options = b'\370\206\031\001' + _MODELOUTPUT.fields_by_name['model_id']._options = None + _MODELOUTPUT.fields_by_name['model_id']._serialized_options = b'\370\206\031\001' + _MODELOUTPUT.fields_by_name['step']._options = None + _MODELOUTPUT.fields_by_name['step']._serialized_options = b'\370\206\031\001' + _CREATEEXPERIMENT.fields_by_name['name']._options = None + _CREATEEXPERIMENT.fields_by_name['name']._serialized_options = b'\370\206\031\001' + _CREATEEXPERIMENT._options = None + _CREATEEXPERIMENT._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _SEARCHEXPERIMENTS._options = None + _SEARCHEXPERIMENTS._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _GETEXPERIMENT.fields_by_name['experiment_id']._options = None + _GETEXPERIMENT.fields_by_name['experiment_id']._serialized_options = b'\370\206\031\001' + _GETEXPERIMENT._options = None + _GETEXPERIMENT._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _DELETEEXPERIMENT.fields_by_name['experiment_id']._options = None + _DELETEEXPERIMENT.fields_by_name['experiment_id']._serialized_options = b'\370\206\031\001' + _DELETEEXPERIMENT._options = None + _DELETEEXPERIMENT._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _RESTOREEXPERIMENT.fields_by_name['experiment_id']._options = None + _RESTOREEXPERIMENT.fields_by_name['experiment_id']._serialized_options = b'\370\206\031\001' + _RESTOREEXPERIMENT._options = None + _RESTOREEXPERIMENT._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _UPDATEEXPERIMENT.fields_by_name['experiment_id']._options = None + _UPDATEEXPERIMENT.fields_by_name['experiment_id']._serialized_options = b'\370\206\031\001' + _UPDATEEXPERIMENT._options = None + _UPDATEEXPERIMENT._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _CREATERUN._options = None + _CREATERUN._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _UPDATERUN._options = None + _UPDATERUN._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _DELETERUN.fields_by_name['run_id']._options = None + _DELETERUN.fields_by_name['run_id']._serialized_options = b'\370\206\031\001' + _DELETERUN._options = None + _DELETERUN._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _RESTORERUN.fields_by_name['run_id']._options = None + _RESTORERUN.fields_by_name['run_id']._serialized_options = b'\370\206\031\001' + _RESTORERUN._options = None + _RESTORERUN._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _LOGMETRIC.fields_by_name['key']._options = None + _LOGMETRIC.fields_by_name['key']._serialized_options = b'\370\206\031\001' + _LOGMETRIC.fields_by_name['value']._options = None + _LOGMETRIC.fields_by_name['value']._serialized_options = b'\370\206\031\001' + _LOGMETRIC.fields_by_name['timestamp']._options = None + _LOGMETRIC.fields_by_name['timestamp']._serialized_options = b'\370\206\031\001' + _LOGMETRIC.fields_by_name['model_id']._options = None + _LOGMETRIC.fields_by_name['model_id']._serialized_options = b'\360\206\031\003' + _LOGMETRIC.fields_by_name['dataset_name']._options = None + _LOGMETRIC.fields_by_name['dataset_name']._serialized_options = b'\360\206\031\003' + _LOGMETRIC.fields_by_name['dataset_digest']._options = None + _LOGMETRIC.fields_by_name['dataset_digest']._serialized_options = b'\360\206\031\003' + _LOGMETRIC._options = None + _LOGMETRIC._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _LOGPARAM.fields_by_name['key']._options = None + _LOGPARAM.fields_by_name['key']._serialized_options = b'\370\206\031\001' + _LOGPARAM.fields_by_name['value']._options = None + _LOGPARAM.fields_by_name['value']._serialized_options = b'\370\206\031\001' + _LOGPARAM._options = None + _LOGPARAM._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _SETEXPERIMENTTAG.fields_by_name['experiment_id']._options = None + _SETEXPERIMENTTAG.fields_by_name['experiment_id']._serialized_options = b'\370\206\031\001' + _SETEXPERIMENTTAG.fields_by_name['key']._options = None + _SETEXPERIMENTTAG.fields_by_name['key']._serialized_options = b'\370\206\031\001' + _SETEXPERIMENTTAG.fields_by_name['value']._options = None + _SETEXPERIMENTTAG.fields_by_name['value']._serialized_options = b'\370\206\031\001' + _SETEXPERIMENTTAG._options = None + _SETEXPERIMENTTAG._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _DELETEEXPERIMENTTAG.fields_by_name['experiment_id']._options = None + _DELETEEXPERIMENTTAG.fields_by_name['experiment_id']._serialized_options = b'\370\206\031\001' + _DELETEEXPERIMENTTAG.fields_by_name['key']._options = None + _DELETEEXPERIMENTTAG.fields_by_name['key']._serialized_options = b'\370\206\031\001' + _DELETEEXPERIMENTTAG._options = None + _DELETEEXPERIMENTTAG._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _SETTAG.fields_by_name['key']._options = None + _SETTAG.fields_by_name['key']._serialized_options = b'\370\206\031\001' + _SETTAG.fields_by_name['value']._options = None + _SETTAG.fields_by_name['value']._serialized_options = b'\370\206\031\001' + _SETTAG._options = None + _SETTAG._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _DELETETAG.fields_by_name['run_id']._options = None + _DELETETAG.fields_by_name['run_id']._serialized_options = b'\370\206\031\001' + _DELETETAG.fields_by_name['key']._options = None + _DELETETAG.fields_by_name['key']._serialized_options = b'\370\206\031\001' + _DELETETAG._options = None + _DELETETAG._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _GETRUN._options = None + _GETRUN._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _SEARCHRUNS._options = None + _SEARCHRUNS._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _LISTARTIFACTS._options = None + _LISTARTIFACTS._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _GETMETRICHISTORY.fields_by_name['metric_key']._options = None + _GETMETRICHISTORY.fields_by_name['metric_key']._serialized_options = b'\370\206\031\001' + _GETMETRICHISTORY._options = None + _GETMETRICHISTORY._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _GETMETRICHISTORYBULKINTERVAL.fields_by_name['metric_key']._options = None + _GETMETRICHISTORYBULKINTERVAL.fields_by_name['metric_key']._serialized_options = b'\370\206\031\001' + _GETMETRICHISTORYBULKINTERVAL._options = None + _GETMETRICHISTORYBULKINTERVAL._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _LOGBATCH._options = None + _LOGBATCH._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _LOGMODEL._options = None + _LOGMODEL._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _LOGINPUTS.fields_by_name['run_id']._options = None + _LOGINPUTS.fields_by_name['run_id']._serialized_options = b'\370\206\031\001' + _LOGINPUTS.fields_by_name['models']._options = None + _LOGINPUTS.fields_by_name['models']._serialized_options = b'\360\206\031\003' + _LOGINPUTS._options = None + _LOGINPUTS._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _LOGOUTPUTS.fields_by_name['run_id']._options = None + _LOGOUTPUTS.fields_by_name['run_id']._serialized_options = b'\370\206\031\001' + _LOGOUTPUTS._options = None + _LOGOUTPUTS._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _GETEXPERIMENTBYNAME.fields_by_name['experiment_name']._options = None + _GETEXPERIMENTBYNAME.fields_by_name['experiment_name']._serialized_options = b'\370\206\031\001' + _GETEXPERIMENTBYNAME._options = None + _GETEXPERIMENTBYNAME._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _CREATEASSESSMENT.fields_by_name['assessment']._options = None + _CREATEASSESSMENT.fields_by_name['assessment']._serialized_options = b'\370\206\031\001' + _CREATEASSESSMENT._options = None + _CREATEASSESSMENT._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _UPDATEASSESSMENT.fields_by_name['assessment']._options = None + _UPDATEASSESSMENT.fields_by_name['assessment']._serialized_options = b'\370\206\031\001' + _UPDATEASSESSMENT.fields_by_name['update_mask']._options = None + _UPDATEASSESSMENT.fields_by_name['update_mask']._serialized_options = b'\370\206\031\001' + _UPDATEASSESSMENT._options = None + _UPDATEASSESSMENT._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _DELETEASSESSMENT.fields_by_name['trace_id']._options = None + _DELETEASSESSMENT.fields_by_name['trace_id']._serialized_options = b'\370\206\031\001' + _DELETEASSESSMENT.fields_by_name['assessment_id']._options = None + _DELETEASSESSMENT.fields_by_name['assessment_id']._serialized_options = b'\370\206\031\001' + _DELETEASSESSMENT._options = None + _DELETEASSESSMENT._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _GETASSESSMENTREQUEST.fields_by_name['trace_id']._options = None + _GETASSESSMENTREQUEST.fields_by_name['trace_id']._serialized_options = b'\370\206\031\001' + _GETASSESSMENTREQUEST.fields_by_name['assessment_id']._options = None + _GETASSESSMENTREQUEST.fields_by_name['assessment_id']._serialized_options = b'\370\206\031\001' + _GETASSESSMENTREQUEST._options = None + _GETASSESSMENTREQUEST._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _STARTTRACE._options = None + 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_MLFLOWSERVICE.methods_by_name['searchLoggedModels']._serialized_options = b'\362\206\031C\n*\n\004POST\022\034/mlflow/logged-models/search\032\004\010\002\020\000\020\003*\023Search LoggedModels' + _MLFLOWSERVICE.methods_by_name['setLoggedModelTags']._options = None + _MLFLOWSERVICE.methods_by_name['setLoggedModelTags']._serialized_options = b'\362\206\031N\n4\n\005PATCH\022%/mlflow/logged-models/{model_id}/tags\032\004\010\002\020\000\020\003*\024Set Logged Model Tag' + _MLFLOWSERVICE.methods_by_name['deleteLoggedModelTag']._options = None + _MLFLOWSERVICE.methods_by_name['deleteLoggedModelTag']._serialized_options = b'\362\206\031\\\n?\n\006DELETE\022//mlflow/logged-models/{model_id}/tags/{tag_key}\032\004\010\002\020\000\020\003*\027Delete Logged Model Tag' + _MLFLOWSERVICE.methods_by_name['listLoggedModelArtifacts']._options = None + _MLFLOWSERVICE.methods_by_name['listLoggedModelArtifacts']._serialized_options = b'\362\206\031i\nC\n\003GET\0226/mlflow/logged-models/{model_id}/artifacts/directories\032\004\010\002\020\000\020\003* List Artifacts for Logged Models' + _MLFLOWSERVICE.methods_by_name['LogLoggedModelParams']._options = None + _MLFLOWSERVICE.methods_by_name['LogLoggedModelParams']._serialized_options = b'\362\206\031R\n5\n\004POST\022\'/mlflow/logged-models/{model_id}/params\032\004\010\002\020\000\020\003*\027Log Logged Model Params' + _MLFLOWSERVICE.methods_by_name['GetAssessment']._options = None + _MLFLOWSERVICE.methods_by_name['GetAssessment']._serialized_options = b'\362\206\031V\nB\n\003GET\0225/mlflow/traces/{trace_id}/assessments/{assessment_id}\032\004\010\003\020\000\020\003*\016Get Assessment' + _MLFLOWSERVICE.methods_by_name['createAssessment']._options = None + _MLFLOWSERVICE.methods_by_name['createAssessment']._serialized_options = b'\362\206\031\210\001\n>\n\004POST\0220/mlflow/traces/{assessment.trace_id}/assessments\032\004\010\003\020\000\020\003\030\350\007\030\356\007\030\014\030\001*:Create an assessment of a trace or a span within the trace' + _MLFLOWSERVICE.methods_by_name['updateAssessment']._options = None + _MLFLOWSERVICE.methods_by_name['updateAssessment']._serialized_options = b'\362\206\031{\nD\n\005PATCH\0225/mlflow/traces/{trace_id}/assessments/{assessment_id}\032\004\010\003\020\000\020\003\030\350\007\030\356\007\030\001*)Update an existing assessment on a trace.' + _MLFLOWSERVICE.methods_by_name['deleteAssessment']._options = None + _MLFLOWSERVICE.methods_by_name['deleteAssessment']._serialized_options = b'\362\206\031\\\nE\n\006DELETE\0225/mlflow/traces/{trace_id}/assessments/{assessment_id}\032\004\010\003\020\000\020\003*\021Delete Assessment' + _VIEWTYPE._serialized_start=14127 + _VIEWTYPE._serialized_end=14181 + _SOURCETYPE._serialized_start=14183 + _SOURCETYPE._serialized_end=14256 + _RUNSTATUS._serialized_start=14258 + _RUNSTATUS._serialized_end=14335 + _TRACESTATUS._serialized_start=14337 + _TRACESTATUS._serialized_end=14416 + _LOGGEDMODELSTATUS._serialized_start=14419 + _LOGGEDMODELSTATUS._serialized_end=14557 + _METRIC._serialized_start=185 + _METRIC._serialized_end=361 + _PARAM._serialized_start=363 + _PARAM._serialized_end=398 + _RUN._serialized_start=401 + _RUN._serialized_end=540 + _RUNDATA._serialized_start=542 + _RUNDATA._serialized_end=645 + _RUNINPUTS._serialized_start=647 + _RUNINPUTS._serialized_end=746 + _RUNOUTPUTS._serialized_start=748 + _RUNOUTPUTS._serialized_end=804 + _RUNTAG._serialized_start=806 + _RUNTAG._serialized_end=842 + _EXPERIMENTTAG._serialized_start=844 + _EXPERIMENTTAG._serialized_end=887 + _RUNINFO._serialized_start=890 + _RUNINFO._serialized_end=1111 + _EXPERIMENT._serialized_start=1114 + _EXPERIMENT._serialized_end=1301 + _DATASETINPUT._serialized_start=1303 + _DATASETINPUT._serialized_end=1389 + _MODELINPUT._serialized_start=1391 + _MODELINPUT._serialized_end=1427 + _INPUTTAG._serialized_start=1429 + _INPUTTAG._serialized_end=1479 + _DATASET._serialized_start=1482 + _DATASET._serialized_end=1615 + _MODELOUTPUT._serialized_start=1617 + _MODELOUTPUT._serialized_end=1674 + _CREATEEXPERIMENT._serialized_start=1677 + _CREATEEXPERIMENT._serialized_end=1859 + _CREATEEXPERIMENT_RESPONSE._serialized_start=1781 + _CREATEEXPERIMENT_RESPONSE._serialized_end=1814 + _SEARCHEXPERIMENTS._serialized_start=1862 + _SEARCHEXPERIMENTS._serialized_end=2116 + _SEARCHEXPERIMENTS_RESPONSE._serialized_start=1995 + _SEARCHEXPERIMENTS_RESPONSE._serialized_end=2071 + _GETEXPERIMENT._serialized_start=2119 + _GETEXPERIMENT._serialized_end=2260 + _GETEXPERIMENT_RESPONSE._serialized_start=2165 + _GETEXPERIMENT_RESPONSE._serialized_end=2215 + _DELETEEXPERIMENT._serialized_start=2262 + _DELETEEXPERIMENT._serialized_end=2366 + _DELETEEXPERIMENT_RESPONSE._serialized_start=1781 + _DELETEEXPERIMENT_RESPONSE._serialized_end=1791 + _RESTOREEXPERIMENT._serialized_start=2368 + _RESTOREEXPERIMENT._serialized_end=2473 + _RESTOREEXPERIMENT_RESPONSE._serialized_start=1781 + _RESTOREEXPERIMENT_RESPONSE._serialized_end=1791 + _UPDATEEXPERIMENT._serialized_start=2475 + _UPDATEEXPERIMENT._serialized_end=2597 + _UPDATEEXPERIMENT_RESPONSE._serialized_start=1781 + _UPDATEEXPERIMENT_RESPONSE._serialized_end=1791 + _CREATERUN._serialized_start=2600 + _CREATERUN._serialized_end=2802 + _CREATERUN_RESPONSE._serialized_start=2721 + _CREATERUN_RESPONSE._serialized_end=2757 + _UPDATERUN._serialized_start=2805 + _UPDATERUN._serialized_end=3013 + _UPDATERUN_RESPONSE._serialized_start=2923 + _UPDATERUN_RESPONSE._serialized_end=2968 + _DELETERUN._serialized_start=3015 + _DELETERUN._serialized_end=3105 + _DELETERUN_RESPONSE._serialized_start=1781 + _DELETERUN_RESPONSE._serialized_end=1791 + _RESTORERUN._serialized_start=3107 + _RESTORERUN._serialized_end=3198 + _RESTORERUN_RESPONSE._serialized_start=1781 + _RESTORERUN_RESPONSE._serialized_end=1791 + _LOGMETRIC._serialized_start=3201 + _LOGMETRIC._serialized_end=3467 + _LOGMETRIC_RESPONSE._serialized_start=1781 + _LOGMETRIC_RESPONSE._serialized_end=1791 + _LOGPARAM._serialized_start=3470 + _LOGPARAM._serialized_end=3611 + _LOGPARAM_RESPONSE._serialized_start=1781 + _LOGPARAM_RESPONSE._serialized_end=1791 + _SETEXPERIMENTTAG._serialized_start=3614 + _SETEXPERIMENTTAG._serialized_end=3758 + _SETEXPERIMENTTAG_RESPONSE._serialized_start=1781 + _SETEXPERIMENTTAG_RESPONSE._serialized_end=1791 + _DELETEEXPERIMENTTAG._serialized_start=3760 + _DELETEEXPERIMENTTAG._serialized_end=3886 + _DELETEEXPERIMENTTAG_RESPONSE._serialized_start=1781 + _DELETEEXPERIMENTTAG_RESPONSE._serialized_end=1791 + _SETTAG._serialized_start=3889 + _SETTAG._serialized_end=4028 + _SETTAG_RESPONSE._serialized_start=1781 + _SETTAG_RESPONSE._serialized_end=1791 + _DELETETAG._serialized_start=4030 + _DELETETAG._serialized_end=4139 + _DELETETAG_RESPONSE._serialized_start=1781 + _DELETETAG_RESPONSE._serialized_end=1791 + _GETRUN._serialized_start=4141 + _GETRUN._serialized_end=4266 + _GETRUN_RESPONSE._serialized_start=2721 + _GETRUN_RESPONSE._serialized_end=2757 + _SEARCHRUNS._serialized_start=4269 + _SEARCHRUNS._serialized_end=4549 + _SEARCHRUNS_RESPONSE._serialized_start=4442 + _SEARCHRUNS_RESPONSE._serialized_end=4504 + _LISTARTIFACTS._serialized_start=4552 + _LISTARTIFACTS._serialized_end=4768 + _LISTARTIFACTS_RESPONSE._serialized_start=4637 + _LISTARTIFACTS_RESPONSE._serialized_end=4723 + _FILEINFO._serialized_start=4770 + _FILEINFO._serialized_end=4829 + _GETMETRICHISTORY._serialized_start=4832 + _GETMETRICHISTORY._serialized_end=5066 + _GETMETRICHISTORY_RESPONSE._serialized_start=4953 + _GETMETRICHISTORY_RESPONSE._serialized_end=5021 + _METRICWITHRUNID._serialized_start=5068 + _METRICWITHRUNID._serialized_end=5165 + _GETMETRICHISTORYBULKINTERVAL._serialized_start=5168 + _GETMETRICHISTORYBULKINTERVAL._serialized_end=5399 + _GETMETRICHISTORYBULKINTERVAL_RESPONSE._serialized_start=5302 + _GETMETRICHISTORYBULKINTERVAL_RESPONSE._serialized_end=5354 + _LOGBATCH._serialized_start=5402 + _LOGBATCH._serialized_end=5579 + _LOGBATCH_RESPONSE._serialized_start=1781 + _LOGBATCH_RESPONSE._serialized_end=1791 + _LOGMODEL._serialized_start=5581 + _LOGMODEL._serialized_end=5684 + _LOGMODEL_RESPONSE._serialized_start=1781 + _LOGMODEL_RESPONSE._serialized_end=1791 + _LOGINPUTS._serialized_start=5687 + _LOGINPUTS._serialized_end=5859 + _LOGINPUTS_RESPONSE._serialized_start=1781 + _LOGINPUTS_RESPONSE._serialized_end=1791 + _LOGOUTPUTS._serialized_start=5862 + _LOGOUTPUTS._serialized_end=5990 + _LOGOUTPUTS_RESPONSE._serialized_start=1781 + _LOGOUTPUTS_RESPONSE._serialized_end=1791 + _GETEXPERIMENTBYNAME._serialized_start=5993 + _GETEXPERIMENTBYNAME._serialized_end=6142 + _GETEXPERIMENTBYNAME_RESPONSE._serialized_start=2165 + _GETEXPERIMENTBYNAME_RESPONSE._serialized_end=2215 + _CREATEASSESSMENT._serialized_start=6145 + _CREATEASSESSMENT._serialized_end=6330 + _CREATEASSESSMENT_RESPONSE._serialized_start=6223 + _CREATEASSESSMENT_RESPONSE._serialized_end=6285 + _UPDATEASSESSMENT._serialized_start=6333 + _UPDATEASSESSMENT._serialized_end=6573 + _UPDATEASSESSMENT_RESPONSE._serialized_start=6223 + _UPDATEASSESSMENT_RESPONSE._serialized_end=6285 + _DELETEASSESSMENT._serialized_start=6576 + _DELETEASSESSMENT._serialized_end=6704 + _DELETEASSESSMENT_RESPONSE._serialized_start=1781 + _DELETEASSESSMENT_RESPONSE._serialized_end=1791 + _GETASSESSMENTREQUEST._serialized_start=6707 + _GETASSESSMENTREQUEST._serialized_end=6891 + _GETASSESSMENTREQUEST_RESPONSE._serialized_start=6223 + _GETASSESSMENTREQUEST_RESPONSE._serialized_end=6285 + _TRACEINFO._serialized_start=6894 + _TRACEINFO._serialized_end=7122 + _TRACEREQUESTMETADATA._serialized_start=7124 + _TRACEREQUESTMETADATA._serialized_end=7174 + _TRACETAG._serialized_start=7176 + _TRACETAG._serialized_end=7214 + _STARTTRACE._serialized_start=7217 + _STARTTRACE._serialized_end=7458 + _STARTTRACE_RESPONSE._serialized_start=7364 + _STARTTRACE_RESPONSE._serialized_end=7413 + _ENDTRACE._serialized_start=7461 + _ENDTRACE._serialized_end=7734 + _ENDTRACE_RESPONSE._serialized_start=7364 + _ENDTRACE_RESPONSE._serialized_end=7413 + _GETTRACEINFO._serialized_start=7737 + _GETTRACEINFO._serialized_end=7867 + _GETTRACEINFO_RESPONSE._serialized_start=7364 + _GETTRACEINFO_RESPONSE._serialized_end=7413 + _GETTRACEINFOV3._serialized_start=7869 + _GETTRACEINFOV3._serialized_end=7990 + _GETTRACEINFOV3_RESPONSE._serialized_start=7905 + _GETTRACEINFOV3_RESPONSE._serialized_end=7945 + _SEARCHTRACES._serialized_start=7993 + _SEARCHTRACES._serialized_end=8228 + _SEARCHTRACES_RESPONSE._serialized_start=8113 + _SEARCHTRACES_RESPONSE._serialized_end=8183 + _SEARCHUNIFIEDTRACES._serialized_start=8231 + _SEARCHUNIFIEDTRACES._serialized_end=8529 + _SEARCHUNIFIEDTRACES_RESPONSE._serialized_start=8113 + _SEARCHUNIFIEDTRACES_RESPONSE._serialized_end=8183 + _GETONLINETRACEDETAILS._serialized_start=8532 + _GETONLINETRACEDETAILS._serialized_end=8725 + _GETONLINETRACEDETAILS_RESPONSE._serialized_start=8695 + _GETONLINETRACEDETAILS_RESPONSE._serialized_end=8725 + _DELETETRACES._serialized_start=8728 + _DELETETRACES._serialized_end=8923 + _DELETETRACES_RESPONSE._serialized_start=8844 + _DELETETRACES_RESPONSE._serialized_end=8878 + _DELETETRACESV3._serialized_start=8926 + _DELETETRACESV3._serialized_end=9123 + _DELETETRACESV3_RESPONSE._serialized_start=8844 + _DELETETRACESV3_RESPONSE._serialized_end=8878 + _SETTRACETAG._serialized_start=9125 + _SETTRACETAG._serialized_end=9243 + _SETTRACETAG_RESPONSE._serialized_start=1781 + _SETTRACETAG_RESPONSE._serialized_end=1791 + _SETTRACETAGV3._serialized_start=9246 + _SETTRACETAGV3._serialized_end=9382 + _SETTRACETAGV3_RESPONSE._serialized_start=1781 + _SETTRACETAGV3_RESPONSE._serialized_end=1791 + _DELETETRACETAG._serialized_start=9384 + _DELETETRACETAG._serialized_end=9506 + _DELETETRACETAG_RESPONSE._serialized_start=1781 + _DELETETRACETAG_RESPONSE._serialized_end=1791 + _DELETETRACETAGV3._serialized_start=9508 + _DELETETRACETAGV3._serialized_end=9616 + _DELETETRACETAGV3_RESPONSE._serialized_start=1781 + _DELETETRACETAGV3_RESPONSE._serialized_end=1791 + _TRACE._serialized_start=9618 + _TRACE._serialized_end=9666 + _TRACELOCATION._serialized_start=9669 + _TRACELOCATION._serialized_end=10107 + _TRACELOCATION_MLFLOWEXPERIMENTLOCATION._serialized_start=9891 + _TRACELOCATION_MLFLOWEXPERIMENTLOCATION._serialized_end=9940 + _TRACELOCATION_INFERENCETABLELOCATION._serialized_start=9942 + _TRACELOCATION_INFERENCETABLELOCATION._serialized_end=9991 + _TRACELOCATION_TRACELOCATIONTYPE._serialized_start=9993 + _TRACELOCATION_TRACELOCATIONTYPE._serialized_end=10093 + _TRACEINFOV3._serialized_start=10110 + _TRACEINFOV3._serialized_end=10777 + _TRACEINFOV3_TRACEMETADATAENTRY._serialized_start=10612 + _TRACEINFOV3_TRACEMETADATAENTRY._serialized_end=10664 + _TRACEINFOV3_TAGSENTRY._serialized_start=10666 + _TRACEINFOV3_TAGSENTRY._serialized_end=10709 + _TRACEINFOV3_STATE._serialized_start=10711 + _TRACEINFOV3_STATE._serialized_end=10777 + _STARTTRACEV3._serialized_start=10779 + _STARTTRACEV3._serialized_end=10871 + _STARTTRACEV3_RESPONSE._serialized_start=7905 + _STARTTRACEV3_RESPONSE._serialized_end=7945 + _LINKTRACESTORUN._serialized_start=10873 + _LINKTRACESTORUN._serialized_end=10943 + _LINKTRACESTORUN_RESPONSE._serialized_start=1781 + _LINKTRACESTORUN_RESPONSE._serialized_end=1791 + _DATASETSUMMARY._serialized_start=10945 + _DATASETSUMMARY._serialized_end=11049 + _SEARCHDATASETS._serialized_start=11052 + _SEARCHDATASETS._serialized_end=11200 + _SEARCHDATASETS_RESPONSE._serialized_start=11094 + _SEARCHDATASETS_RESPONSE._serialized_end=11155 + _CREATELOGGEDMODEL._serialized_start=11203 + _CREATELOGGEDMODEL._serialized_end=11485 + _CREATELOGGEDMODEL_RESPONSE._serialized_start=11394 + _CREATELOGGEDMODEL_RESPONSE._serialized_end=11440 + _FINALIZELOGGEDMODEL._serialized_start=11488 + _FINALIZELOGGEDMODEL._serialized_end=11675 + _FINALIZELOGGEDMODEL_RESPONSE._serialized_start=11394 + _FINALIZELOGGEDMODEL_RESPONSE._serialized_end=11440 + _GETLOGGEDMODEL._serialized_start=11678 + _GETLOGGEDMODEL._serialized_end=11811 + _GETLOGGEDMODEL_RESPONSE._serialized_start=11394 + _GETLOGGEDMODEL_RESPONSE._serialized_end=11440 + _DELETELOGGEDMODEL._serialized_start=11813 + _DELETELOGGEDMODEL._serialized_end=11913 + _DELETELOGGEDMODEL_RESPONSE._serialized_start=1781 + _DELETELOGGEDMODEL_RESPONSE._serialized_end=1791 + _SEARCHLOGGEDMODELS._serialized_start=11916 + _SEARCHLOGGEDMODELS._serialized_end=12419 + _SEARCHLOGGEDMODELS_DATASET._serialized_start=12131 + _SEARCHLOGGEDMODELS_DATASET._serialized_end=12192 + _SEARCHLOGGEDMODELS_ORDERBY._serialized_start=12194 + _SEARCHLOGGEDMODELS_ORDERBY._serialized_end=12300 + _SEARCHLOGGEDMODELS_RESPONSE._serialized_start=12302 + _SEARCHLOGGEDMODELS_RESPONSE._serialized_end=12374 + _SETLOGGEDMODELTAGS._serialized_start=12422 + _SETLOGGEDMODELTAGS._serialized_end=12597 + _SETLOGGEDMODELTAGS_RESPONSE._serialized_start=11394 + _SETLOGGEDMODELTAGS_RESPONSE._serialized_end=11440 + _DELETELOGGEDMODELTAG._serialized_start=12599 + _DELETELOGGEDMODELTAG._serialized_end=12725 + _DELETELOGGEDMODELTAG_RESPONSE._serialized_start=1781 + _DELETELOGGEDMODELTAG_RESPONSE._serialized_end=1791 + _LISTLOGGEDMODELARTIFACTS._serialized_start=12728 + _LISTLOGGEDMODELARTIFACTS._serialized_end=12964 + _LISTLOGGEDMODELARTIFACTS_RESPONSE._serialized_start=4637 + _LISTLOGGEDMODELARTIFACTS_RESPONSE._serialized_end=4723 + _LOGLOGGEDMODELPARAMSREQUEST._serialized_start=12967 + _LOGLOGGEDMODELPARAMSREQUEST._serialized_end=13123 + _LOGLOGGEDMODELPARAMSREQUEST_RESPONSE._serialized_start=1781 + _LOGLOGGEDMODELPARAMSREQUEST_RESPONSE._serialized_end=1791 + _LOGGEDMODEL._serialized_start=13125 + _LOGGEDMODEL._serialized_end=13216 + _LOGGEDMODELINFO._serialized_start=13219 + _LOGGEDMODELINFO._serialized_end=13607 + _LOGGEDMODELTAG._serialized_start=13609 + _LOGGEDMODELTAG._serialized_end=13653 + _LOGGEDMODELREGISTRATIONINFO._serialized_start=13655 + _LOGGEDMODELREGISTRATIONINFO._serialized_end=13715 + _LOGGEDMODELDATA._serialized_start=13717 + _LOGGEDMODELDATA._serialized_end=13813 + _LOGGEDMODELPARAMETER._serialized_start=13815 + _LOGGEDMODELPARAMETER._serialized_end=13865 + _SEARCHTRACESV3._serialized_start=13868 + _SEARCHTRACESV3._serialized_end=14125 + _SEARCHTRACESV3_RESPONSE._serialized_start=14008 + _SEARCHTRACESV3_RESPONSE._serialized_end=14080 + _MLFLOWSERVICE._serialized_start=14560 + _MLFLOWSERVICE._serialized_end=22888 + MlflowService = service_reflection.GeneratedServiceType('MlflowService', (_service.Service,), dict( + DESCRIPTOR = _MLFLOWSERVICE, + __module__ = 'service_pb2' + )) + + MlflowService_Stub = service_reflection.GeneratedServiceStubType('MlflowService_Stub', (MlflowService,), dict( + DESCRIPTOR = _MLFLOWSERVICE, + __module__ = 'service_pb2' + )) + + + # @@protoc_insertion_point(module_scope) + diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/protos/unity_catalog_oss_messages_pb2.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/protos/unity_catalog_oss_messages_pb2.py new file mode 100644 index 0000000000000000000000000000000000000000..0550d69f41c25761365f7d64a55d0efe8e0f85a0 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/protos/unity_catalog_oss_messages_pb2.py @@ -0,0 +1,457 @@ + +import google.protobuf +from packaging.version import Version +if Version(google.protobuf.__version__).major >= 5: + # -*- coding: utf-8 -*- + # Generated by the protocol buffer compiler. DO NOT EDIT! + # source: unity_catalog_oss_messages.proto + # Protobuf Python Version: 5.26.0 + """Generated protocol buffer code.""" + from google.protobuf import descriptor as _descriptor + from google.protobuf import descriptor_pool as _descriptor_pool + from google.protobuf import symbol_database as _symbol_database + from google.protobuf.internal import builder as _builder + # @@protoc_insertion_point(imports) + + _sym_db = _symbol_database.Default() + + + from .scalapb import scalapb_pb2 as scalapb_dot_scalapb__pb2 + from . import databricks_pb2 as databricks__pb2 + + + DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n unity_catalog_oss_messages.proto\x12\x13mlflow.unitycatalog\x1a\x15scalapb/scalapb.proto\x1a\x10\x64\x61tabricks.proto\"\xfd\x01\n\x13RegisteredModelInfo\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x14\n\x0c\x63\x61talog_name\x18\x02 \x01(\t\x12\x13\n\x0bschema_name\x18\x03 \x01(\t\x12\x0f\n\x07\x63omment\x18\x05 \x01(\t\x12\x18\n\x10storage_location\x18\x06 \x01(\t\x12\x11\n\tfull_name\x18\t \x01(\t\x12\x12\n\ncreated_at\x18\x0b \x01(\x03\x12\x12\n\ncreated_by\x18\x0c \x01(\t\x12\x12\n\nupdated_at\x18\r \x01(\x03\x12\x12\n\nupdated_by\x18\x0e \x01(\t\x12\n\n\x02id\x18\x12 \x01(\t\x12\x13\n\x0b\x62rowse_only\x18\x15 \x01(\x08\"\xd0\x01\n\x15\x43reateRegisteredModel\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x14\n\x0c\x63\x61talog_name\x18\x02 \x01(\t\x12\x13\n\x0bschema_name\x18\x03 \x01(\t\x12\x0f\n\x07\x63omment\x18\x05 \x01(\t\x12\x18\n\x10storage_location\x18\x06 \x01(\t\x1aS\n\x08Response\x12G\n\x15registered_model_info\x18\x01 \x01(\x0b\x32(.mlflow.unitycatalog.RegisteredModelInfo\"E\n\x15\x44\x65leteRegisteredModel\x12\x11\n\tfull_name\x18\x01 \x01(\t\x12\r\n\x05\x66orce\x18\x02 \x01(\x08\x1a\n\n\x08Response\"\x82\x01\n\x12GetRegisteredModel\x12\x17\n\tfull_name\x18\x01 \x01(\tB\x04\xf8\x86\x19\x01\x1aS\n\x08Response\x12G\n\x15registered_model_info\x18\x01 \x01(\x0b\x32(.mlflow.unitycatalog.RegisteredModelInfo\"\xa2\x01\n\x15UpdateRegisteredModel\x12\x11\n\tfull_name\x18\x01 \x01(\t\x12\x10\n\x08new_name\x18\x03 \x01(\t\x12\x0f\n\x07\x63omment\x18\x02 \x01(\t\x1aS\n\x08Response\x12G\n\x15registered_model_info\x18\x01 \x01(\x0b\x32(.mlflow.unitycatalog.RegisteredModelInfo\"\xd4\x01\n\x14ListRegisteredModels\x12\x14\n\x0c\x63\x61talog_name\x18\x01 \x01(\t\x12\x13\n\x0bschema_name\x18\x02 \x01(\t\x12\x13\n\x0bmax_results\x18\x05 \x01(\x03\x12\x12\n\npage_token\x18\x06 \x01(\t\x1ah\n\x08Response\x12\x43\n\x11registered_models\x18\x01 \x03(\x0b\x32(.mlflow.unitycatalog.RegisteredModelInfo\x12\x17\n\x0fnext_page_token\x18\x02 \x01(\t\"\xc2\x02\n\x10ModelVersionInfo\x12\x12\n\nmodel_name\x18\x01 \x01(\t\x12\x14\n\x0c\x63\x61talog_name\x18\x02 \x01(\t\x12\x13\n\x0bschema_name\x18\x03 \x01(\t\x12\x0e\n\x06source\x18\x05 \x01(\t\x12\x0f\n\x07\x63omment\x18\x04 \x01(\t\x12\x0e\n\x06run_id\x18\x06 \x01(\t\x12\x37\n\x06status\x18\x08 \x01(\x0e\x32\'.mlflow.unitycatalog.ModelVersionStatus\x12\x0f\n\x07version\x18\t \x01(\x03\x12\x18\n\x10storage_location\x18\n \x01(\t\x12\x12\n\ncreated_at\x18\x0c \x01(\x03\x12\x12\n\ncreated_by\x18\r \x01(\t\x12\x12\n\nupdated_at\x18\x0e \x01(\x03\x12\x12\n\nupdated_by\x18\x0f \x01(\t\x12\n\n\x02id\x18\x10 \x01(\t\"\xd3\x01\n\x12\x43reateModelVersion\x12\x12\n\nmodel_name\x18\x01 \x01(\t\x12\x14\n\x0c\x63\x61talog_name\x18\x02 \x01(\t\x12\x13\n\x0bschema_name\x18\x03 \x01(\t\x12\x0e\n\x06source\x18\x04 \x01(\t\x12\x0e\n\x06run_id\x18\x05 \x01(\t\x12\x0f\n\x07\x63omment\x18\x06 \x01(\t\x1aM\n\x08Response\x12\x41\n\x12model_version_info\x18\x01 \x01(\x0b\x32%.mlflow.unitycatalog.ModelVersionInfo\"D\n\x12\x44\x65leteModelVersion\x12\x11\n\tfull_name\x18\x01 \x01(\t\x12\x0f\n\x07version\x18\x02 \x01(\x03\x1a\n\n\x08Response\"\x89\x01\n\x14\x46inalizeModelVersion\x12\x11\n\tfull_name\x18\x01 \x01(\t\x12\x0f\n\x07version\x18\x02 \x01(\x03\x1aM\n\x08Response\x12\x41\n\x12model_version_info\x18\x01 \x01(\x0b\x32%.mlflow.unitycatalog.ModelVersionInfo\"\x84\x01\n\x0fGetModelVersion\x12\x11\n\tfull_name\x18\x01 \x01(\t\x12\x0f\n\x07version\x18\x02 \x01(\x03\x1aM\n\x08Response\x12\x41\n\x12model_version_info\x18\x01 \x01(\x0b\x32%.mlflow.unitycatalog.ModelVersionInfo\"\x98\x01\n\x12UpdateModelVersion\x12\x11\n\tfull_name\x18\x01 \x01(\t\x12\x0f\n\x07version\x18\x02 \x01(\x03\x12\x0f\n\x07\x63omment\x18\x03 \x01(\t\x1aM\n\x08Response\x12\x41\n\x12model_version_info\x18\x01 \x01(\x0b\x32%.mlflow.unitycatalog.ModelVersionInfo\"\xb3\x01\n\x11ListModelVersions\x12\x11\n\tfull_name\x18\x01 \x01(\t\x12\x13\n\x0bmax_results\x18\x04 \x01(\x03\x12\x12\n\npage_token\x18\x05 \x01(\t\x1a\x62\n\x08Response\x12=\n\x0emodel_versions\x18\x01 \x03(\x0b\x32%.mlflow.unitycatalog.ModelVersionInfo\x12\x17\n\x0fnext_page_token\x18\x02 \x01(\t\"\xff\x01\n\x14TemporaryCredentials\x12\x41\n\x14\x61ws_temp_credentials\x18\x02 \x01(\x0b\x32#.mlflow.unitycatalog.AwsCredentials\x12N\n\x19\x61zure_user_delegation_sas\x18\x03 \x01(\x0b\x32+.mlflow.unitycatalog.AzureUserDelegationSAS\x12;\n\x0fgcp_oauth_token\x18\x04 \x01(\x0b\x32\".mlflow.unitycatalog.GcpOauthToken\x12\x17\n\x0f\x65xpiration_time\x18\x01 \x01(\x03\"Y\n\x0e\x41wsCredentials\x12\x15\n\raccess_key_id\x18\x01 \x01(\t\x12\x19\n\x11secret_access_key\x18\x02 \x01(\t\x12\x15\n\rsession_token\x18\x03 \x01(\t\"+\n\x16\x41zureUserDelegationSAS\x12\x11\n\tsas_token\x18\x01 \x01(\t\"$\n\rGcpOauthToken\x12\x13\n\x0boauth_token\x18\x01 \x01(\t\"\x84\x02\n\'GenerateTemporaryModelVersionCredential\x12\x14\n\x0c\x63\x61talog_name\x18\x01 \x01(\t\x12\x13\n\x0bschema_name\x18\x02 \x01(\t\x12\x12\n\nmodel_name\x18\x03 \x01(\t\x12\x0f\n\x07version\x18\x04 \x01(\x03\x12=\n\toperation\x18\x05 \x01(\x0e\x32*.mlflow.unitycatalog.ModelVersionOperation\x1aJ\n\x08Response\x12>\n\x0b\x63redentials\x18\x01 \x01(\x0b\x32).mlflow.unitycatalog.TemporaryCredentials*t\n\x12ModelVersionStatus\x12 \n\x1cMODEL_VERSION_STATUS_UNKNOWN\x10\x00\x12\x18\n\x14PENDING_REGISTRATION\x10\x01\x12\x17\n\x13\x46\x41ILED_REGISTRATION\x10\x02\x12\t\n\x05READY\x10\x03*r\n\x15ModelVersionOperation\x12#\n\x1fUNKNOWN_MODEL_VERSION_OPERATION\x10\x00\x12\x16\n\x12READ_MODEL_VERSION\x10\x01\x12\x1c\n\x18READ_WRITE_MODEL_VERSION\x10\x02\x42\x31\n\'com.databricks.api.proto.managedcatalog\xa0\x01\x01\xe2?\x02\x10\x01') + + _globals = globals() + _builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals) + _builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'unity_catalog_oss_messages_pb2', _globals) + if not _descriptor._USE_C_DESCRIPTORS: + _globals['DESCRIPTOR']._loaded_options = None + _globals['DESCRIPTOR']._serialized_options = b'\n\'com.databricks.api.proto.managedcatalog\240\001\001\342?\002\020\001' + _globals['_GETREGISTEREDMODEL'].fields_by_name['full_name']._loaded_options = None + _globals['_GETREGISTEREDMODEL'].fields_by_name['full_name']._serialized_options = b'\370\206\031\001' + _globals['_MODELVERSIONSTATUS']._serialized_start=3065 + _globals['_MODELVERSIONSTATUS']._serialized_end=3181 + _globals['_MODELVERSIONOPERATION']._serialized_start=3183 + _globals['_MODELVERSIONOPERATION']._serialized_end=3297 + _globals['_REGISTEREDMODELINFO']._serialized_start=99 + _globals['_REGISTEREDMODELINFO']._serialized_end=352 + _globals['_CREATEREGISTEREDMODEL']._serialized_start=355 + _globals['_CREATEREGISTEREDMODEL']._serialized_end=563 + _globals['_CREATEREGISTEREDMODEL_RESPONSE']._serialized_start=480 + _globals['_CREATEREGISTEREDMODEL_RESPONSE']._serialized_end=563 + _globals['_DELETEREGISTEREDMODEL']._serialized_start=565 + _globals['_DELETEREGISTEREDMODEL']._serialized_end=634 + _globals['_DELETEREGISTEREDMODEL_RESPONSE']._serialized_start=480 + _globals['_DELETEREGISTEREDMODEL_RESPONSE']._serialized_end=490 + _globals['_GETREGISTEREDMODEL']._serialized_start=637 + _globals['_GETREGISTEREDMODEL']._serialized_end=767 + _globals['_GETREGISTEREDMODEL_RESPONSE']._serialized_start=480 + _globals['_GETREGISTEREDMODEL_RESPONSE']._serialized_end=563 + _globals['_UPDATEREGISTEREDMODEL']._serialized_start=770 + _globals['_UPDATEREGISTEREDMODEL']._serialized_end=932 + _globals['_UPDATEREGISTEREDMODEL_RESPONSE']._serialized_start=480 + _globals['_UPDATEREGISTEREDMODEL_RESPONSE']._serialized_end=563 + _globals['_LISTREGISTEREDMODELS']._serialized_start=935 + _globals['_LISTREGISTEREDMODELS']._serialized_end=1147 + _globals['_LISTREGISTEREDMODELS_RESPONSE']._serialized_start=1043 + _globals['_LISTREGISTEREDMODELS_RESPONSE']._serialized_end=1147 + _globals['_MODELVERSIONINFO']._serialized_start=1150 + _globals['_MODELVERSIONINFO']._serialized_end=1472 + _globals['_CREATEMODELVERSION']._serialized_start=1475 + _globals['_CREATEMODELVERSION']._serialized_end=1686 + _globals['_CREATEMODELVERSION_RESPONSE']._serialized_start=1609 + _globals['_CREATEMODELVERSION_RESPONSE']._serialized_end=1686 + _globals['_DELETEMODELVERSION']._serialized_start=1688 + _globals['_DELETEMODELVERSION']._serialized_end=1756 + _globals['_DELETEMODELVERSION_RESPONSE']._serialized_start=480 + _globals['_DELETEMODELVERSION_RESPONSE']._serialized_end=490 + _globals['_FINALIZEMODELVERSION']._serialized_start=1759 + _globals['_FINALIZEMODELVERSION']._serialized_end=1896 + _globals['_FINALIZEMODELVERSION_RESPONSE']._serialized_start=1609 + _globals['_FINALIZEMODELVERSION_RESPONSE']._serialized_end=1686 + _globals['_GETMODELVERSION']._serialized_start=1899 + _globals['_GETMODELVERSION']._serialized_end=2031 + _globals['_GETMODELVERSION_RESPONSE']._serialized_start=1609 + _globals['_GETMODELVERSION_RESPONSE']._serialized_end=1686 + _globals['_UPDATEMODELVERSION']._serialized_start=2034 + _globals['_UPDATEMODELVERSION']._serialized_end=2186 + _globals['_UPDATEMODELVERSION_RESPONSE']._serialized_start=1609 + _globals['_UPDATEMODELVERSION_RESPONSE']._serialized_end=1686 + _globals['_LISTMODELVERSIONS']._serialized_start=2189 + _globals['_LISTMODELVERSIONS']._serialized_end=2368 + _globals['_LISTMODELVERSIONS_RESPONSE']._serialized_start=2270 + _globals['_LISTMODELVERSIONS_RESPONSE']._serialized_end=2368 + _globals['_TEMPORARYCREDENTIALS']._serialized_start=2371 + _globals['_TEMPORARYCREDENTIALS']._serialized_end=2626 + _globals['_AWSCREDENTIALS']._serialized_start=2628 + _globals['_AWSCREDENTIALS']._serialized_end=2717 + _globals['_AZUREUSERDELEGATIONSAS']._serialized_start=2719 + _globals['_AZUREUSERDELEGATIONSAS']._serialized_end=2762 + _globals['_GCPOAUTHTOKEN']._serialized_start=2764 + _globals['_GCPOAUTHTOKEN']._serialized_end=2800 + _globals['_GENERATETEMPORARYMODELVERSIONCREDENTIAL']._serialized_start=2803 + _globals['_GENERATETEMPORARYMODELVERSIONCREDENTIAL']._serialized_end=3063 + _globals['_GENERATETEMPORARYMODELVERSIONCREDENTIAL_RESPONSE']._serialized_start=2989 + _globals['_GENERATETEMPORARYMODELVERSIONCREDENTIAL_RESPONSE']._serialized_end=3063 + # @@protoc_insertion_point(module_scope) + +else: + # -*- coding: utf-8 -*- + # Generated by the protocol buffer compiler. DO NOT EDIT! + # source: unity_catalog_oss_messages.proto + """Generated protocol buffer code.""" + from google.protobuf.internal import enum_type_wrapper + from google.protobuf import descriptor as _descriptor + from google.protobuf import descriptor_pool as _descriptor_pool + from google.protobuf import message as _message + from google.protobuf import reflection as _reflection + from google.protobuf import symbol_database as _symbol_database + # @@protoc_insertion_point(imports) + + _sym_db = _symbol_database.Default() + + + from .scalapb import scalapb_pb2 as scalapb_dot_scalapb__pb2 + from . import databricks_pb2 as databricks__pb2 + + + DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n unity_catalog_oss_messages.proto\x12\x13mlflow.unitycatalog\x1a\x15scalapb/scalapb.proto\x1a\x10\x64\x61tabricks.proto\"\xfd\x01\n\x13RegisteredModelInfo\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x14\n\x0c\x63\x61talog_name\x18\x02 \x01(\t\x12\x13\n\x0bschema_name\x18\x03 \x01(\t\x12\x0f\n\x07\x63omment\x18\x05 \x01(\t\x12\x18\n\x10storage_location\x18\x06 \x01(\t\x12\x11\n\tfull_name\x18\t \x01(\t\x12\x12\n\ncreated_at\x18\x0b \x01(\x03\x12\x12\n\ncreated_by\x18\x0c \x01(\t\x12\x12\n\nupdated_at\x18\r \x01(\x03\x12\x12\n\nupdated_by\x18\x0e \x01(\t\x12\n\n\x02id\x18\x12 \x01(\t\x12\x13\n\x0b\x62rowse_only\x18\x15 \x01(\x08\"\xd0\x01\n\x15\x43reateRegisteredModel\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x14\n\x0c\x63\x61talog_name\x18\x02 \x01(\t\x12\x13\n\x0bschema_name\x18\x03 \x01(\t\x12\x0f\n\x07\x63omment\x18\x05 \x01(\t\x12\x18\n\x10storage_location\x18\x06 \x01(\t\x1aS\n\x08Response\x12G\n\x15registered_model_info\x18\x01 \x01(\x0b\x32(.mlflow.unitycatalog.RegisteredModelInfo\"E\n\x15\x44\x65leteRegisteredModel\x12\x11\n\tfull_name\x18\x01 \x01(\t\x12\r\n\x05\x66orce\x18\x02 \x01(\x08\x1a\n\n\x08Response\"\x82\x01\n\x12GetRegisteredModel\x12\x17\n\tfull_name\x18\x01 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\x01(\t\x12\x0f\n\x07version\x18\x02 \x01(\x03\x1aM\n\x08Response\x12\x41\n\x12model_version_info\x18\x01 \x01(\x0b\x32%.mlflow.unitycatalog.ModelVersionInfo\"\x84\x01\n\x0fGetModelVersion\x12\x11\n\tfull_name\x18\x01 \x01(\t\x12\x0f\n\x07version\x18\x02 \x01(\x03\x1aM\n\x08Response\x12\x41\n\x12model_version_info\x18\x01 \x01(\x0b\x32%.mlflow.unitycatalog.ModelVersionInfo\"\x98\x01\n\x12UpdateModelVersion\x12\x11\n\tfull_name\x18\x01 \x01(\t\x12\x0f\n\x07version\x18\x02 \x01(\x03\x12\x0f\n\x07\x63omment\x18\x03 \x01(\t\x1aM\n\x08Response\x12\x41\n\x12model_version_info\x18\x01 \x01(\x0b\x32%.mlflow.unitycatalog.ModelVersionInfo\"\xb3\x01\n\x11ListModelVersions\x12\x11\n\tfull_name\x18\x01 \x01(\t\x12\x13\n\x0bmax_results\x18\x04 \x01(\x03\x12\x12\n\npage_token\x18\x05 \x01(\t\x1a\x62\n\x08Response\x12=\n\x0emodel_versions\x18\x01 \x03(\x0b\x32%.mlflow.unitycatalog.ModelVersionInfo\x12\x17\n\x0fnext_page_token\x18\x02 \x01(\t\"\xff\x01\n\x14TemporaryCredentials\x12\x41\n\x14\x61ws_temp_credentials\x18\x02 \x01(\x0b\x32#.mlflow.unitycatalog.AwsCredentials\x12N\n\x19\x61zure_user_delegation_sas\x18\x03 \x01(\x0b\x32+.mlflow.unitycatalog.AzureUserDelegationSAS\x12;\n\x0fgcp_oauth_token\x18\x04 \x01(\x0b\x32\".mlflow.unitycatalog.GcpOauthToken\x12\x17\n\x0f\x65xpiration_time\x18\x01 \x01(\x03\"Y\n\x0e\x41wsCredentials\x12\x15\n\raccess_key_id\x18\x01 \x01(\t\x12\x19\n\x11secret_access_key\x18\x02 \x01(\t\x12\x15\n\rsession_token\x18\x03 \x01(\t\"+\n\x16\x41zureUserDelegationSAS\x12\x11\n\tsas_token\x18\x01 \x01(\t\"$\n\rGcpOauthToken\x12\x13\n\x0boauth_token\x18\x01 \x01(\t\"\x84\x02\n\'GenerateTemporaryModelVersionCredential\x12\x14\n\x0c\x63\x61talog_name\x18\x01 \x01(\t\x12\x13\n\x0bschema_name\x18\x02 \x01(\t\x12\x12\n\nmodel_name\x18\x03 \x01(\t\x12\x0f\n\x07version\x18\x04 \x01(\x03\x12=\n\toperation\x18\x05 \x01(\x0e\x32*.mlflow.unitycatalog.ModelVersionOperation\x1aJ\n\x08Response\x12>\n\x0b\x63redentials\x18\x01 \x01(\x0b\x32).mlflow.unitycatalog.TemporaryCredentials*t\n\x12ModelVersionStatus\x12 \n\x1cMODEL_VERSION_STATUS_UNKNOWN\x10\x00\x12\x18\n\x14PENDING_REGISTRATION\x10\x01\x12\x17\n\x13\x46\x41ILED_REGISTRATION\x10\x02\x12\t\n\x05READY\x10\x03*r\n\x15ModelVersionOperation\x12#\n\x1fUNKNOWN_MODEL_VERSION_OPERATION\x10\x00\x12\x16\n\x12READ_MODEL_VERSION\x10\x01\x12\x1c\n\x18READ_WRITE_MODEL_VERSION\x10\x02\x42\x31\n\'com.databricks.api.proto.managedcatalog\xa0\x01\x01\xe2?\x02\x10\x01') + + _MODELVERSIONSTATUS = DESCRIPTOR.enum_types_by_name['ModelVersionStatus'] + ModelVersionStatus = enum_type_wrapper.EnumTypeWrapper(_MODELVERSIONSTATUS) + _MODELVERSIONOPERATION = DESCRIPTOR.enum_types_by_name['ModelVersionOperation'] + ModelVersionOperation = enum_type_wrapper.EnumTypeWrapper(_MODELVERSIONOPERATION) + MODEL_VERSION_STATUS_UNKNOWN = 0 + PENDING_REGISTRATION = 1 + FAILED_REGISTRATION = 2 + READY = 3 + UNKNOWN_MODEL_VERSION_OPERATION = 0 + READ_MODEL_VERSION = 1 + READ_WRITE_MODEL_VERSION = 2 + + + _REGISTEREDMODELINFO = DESCRIPTOR.message_types_by_name['RegisteredModelInfo'] + _CREATEREGISTEREDMODEL = DESCRIPTOR.message_types_by_name['CreateRegisteredModel'] + _CREATEREGISTEREDMODEL_RESPONSE = _CREATEREGISTEREDMODEL.nested_types_by_name['Response'] + _DELETEREGISTEREDMODEL = DESCRIPTOR.message_types_by_name['DeleteRegisteredModel'] + _DELETEREGISTEREDMODEL_RESPONSE = _DELETEREGISTEREDMODEL.nested_types_by_name['Response'] + _GETREGISTEREDMODEL = DESCRIPTOR.message_types_by_name['GetRegisteredModel'] + _GETREGISTEREDMODEL_RESPONSE = _GETREGISTEREDMODEL.nested_types_by_name['Response'] + _UPDATEREGISTEREDMODEL = DESCRIPTOR.message_types_by_name['UpdateRegisteredModel'] + _UPDATEREGISTEREDMODEL_RESPONSE = _UPDATEREGISTEREDMODEL.nested_types_by_name['Response'] + _LISTREGISTEREDMODELS = DESCRIPTOR.message_types_by_name['ListRegisteredModels'] + _LISTREGISTEREDMODELS_RESPONSE = _LISTREGISTEREDMODELS.nested_types_by_name['Response'] + _MODELVERSIONINFO = DESCRIPTOR.message_types_by_name['ModelVersionInfo'] + _CREATEMODELVERSION = DESCRIPTOR.message_types_by_name['CreateModelVersion'] + _CREATEMODELVERSION_RESPONSE = _CREATEMODELVERSION.nested_types_by_name['Response'] + _DELETEMODELVERSION = DESCRIPTOR.message_types_by_name['DeleteModelVersion'] + _DELETEMODELVERSION_RESPONSE = _DELETEMODELVERSION.nested_types_by_name['Response'] + _FINALIZEMODELVERSION = DESCRIPTOR.message_types_by_name['FinalizeModelVersion'] + _FINALIZEMODELVERSION_RESPONSE = _FINALIZEMODELVERSION.nested_types_by_name['Response'] + _GETMODELVERSION = DESCRIPTOR.message_types_by_name['GetModelVersion'] + _GETMODELVERSION_RESPONSE = _GETMODELVERSION.nested_types_by_name['Response'] + _UPDATEMODELVERSION = DESCRIPTOR.message_types_by_name['UpdateModelVersion'] + _UPDATEMODELVERSION_RESPONSE = _UPDATEMODELVERSION.nested_types_by_name['Response'] + _LISTMODELVERSIONS = DESCRIPTOR.message_types_by_name['ListModelVersions'] + _LISTMODELVERSIONS_RESPONSE = _LISTMODELVERSIONS.nested_types_by_name['Response'] + _TEMPORARYCREDENTIALS = DESCRIPTOR.message_types_by_name['TemporaryCredentials'] + _AWSCREDENTIALS = DESCRIPTOR.message_types_by_name['AwsCredentials'] + _AZUREUSERDELEGATIONSAS = DESCRIPTOR.message_types_by_name['AzureUserDelegationSAS'] + _GCPOAUTHTOKEN = DESCRIPTOR.message_types_by_name['GcpOauthToken'] + _GENERATETEMPORARYMODELVERSIONCREDENTIAL = DESCRIPTOR.message_types_by_name['GenerateTemporaryModelVersionCredential'] + _GENERATETEMPORARYMODELVERSIONCREDENTIAL_RESPONSE = _GENERATETEMPORARYMODELVERSIONCREDENTIAL.nested_types_by_name['Response'] + RegisteredModelInfo = _reflection.GeneratedProtocolMessageType('RegisteredModelInfo', (_message.Message,), { + 'DESCRIPTOR' : _REGISTEREDMODELINFO, + '__module__' : 'unity_catalog_oss_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.unitycatalog.RegisteredModelInfo) + }) + _sym_db.RegisterMessage(RegisteredModelInfo) + + CreateRegisteredModel = _reflection.GeneratedProtocolMessageType('CreateRegisteredModel', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _CREATEREGISTEREDMODEL_RESPONSE, + '__module__' : 'unity_catalog_oss_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.unitycatalog.CreateRegisteredModel.Response) + }) + , + 'DESCRIPTOR' : _CREATEREGISTEREDMODEL, + '__module__' : 'unity_catalog_oss_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.unitycatalog.CreateRegisteredModel) + }) + _sym_db.RegisterMessage(CreateRegisteredModel) + _sym_db.RegisterMessage(CreateRegisteredModel.Response) + + DeleteRegisteredModel = _reflection.GeneratedProtocolMessageType('DeleteRegisteredModel', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _DELETEREGISTEREDMODEL_RESPONSE, + '__module__' : 'unity_catalog_oss_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.unitycatalog.DeleteRegisteredModel.Response) + }) + , + 'DESCRIPTOR' : _DELETEREGISTEREDMODEL, + '__module__' : 'unity_catalog_oss_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.unitycatalog.DeleteRegisteredModel) + }) + _sym_db.RegisterMessage(DeleteRegisteredModel) + _sym_db.RegisterMessage(DeleteRegisteredModel.Response) + + GetRegisteredModel = _reflection.GeneratedProtocolMessageType('GetRegisteredModel', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _GETREGISTEREDMODEL_RESPONSE, + '__module__' : 'unity_catalog_oss_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.unitycatalog.GetRegisteredModel.Response) + }) + , + 'DESCRIPTOR' : _GETREGISTEREDMODEL, + '__module__' : 'unity_catalog_oss_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.unitycatalog.GetRegisteredModel) + }) + _sym_db.RegisterMessage(GetRegisteredModel) + _sym_db.RegisterMessage(GetRegisteredModel.Response) + + UpdateRegisteredModel = _reflection.GeneratedProtocolMessageType('UpdateRegisteredModel', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _UPDATEREGISTEREDMODEL_RESPONSE, + '__module__' : 'unity_catalog_oss_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.unitycatalog.UpdateRegisteredModel.Response) + }) + , + 'DESCRIPTOR' : _UPDATEREGISTEREDMODEL, + '__module__' : 'unity_catalog_oss_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.unitycatalog.UpdateRegisteredModel) + }) + _sym_db.RegisterMessage(UpdateRegisteredModel) + _sym_db.RegisterMessage(UpdateRegisteredModel.Response) + + ListRegisteredModels = _reflection.GeneratedProtocolMessageType('ListRegisteredModels', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _LISTREGISTEREDMODELS_RESPONSE, + '__module__' : 'unity_catalog_oss_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.unitycatalog.ListRegisteredModels.Response) + }) + , + 'DESCRIPTOR' : _LISTREGISTEREDMODELS, + '__module__' : 'unity_catalog_oss_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.unitycatalog.ListRegisteredModels) + }) + _sym_db.RegisterMessage(ListRegisteredModels) + _sym_db.RegisterMessage(ListRegisteredModels.Response) + + ModelVersionInfo = _reflection.GeneratedProtocolMessageType('ModelVersionInfo', (_message.Message,), { + 'DESCRIPTOR' : _MODELVERSIONINFO, + '__module__' : 'unity_catalog_oss_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.unitycatalog.ModelVersionInfo) + }) + _sym_db.RegisterMessage(ModelVersionInfo) + + CreateModelVersion = _reflection.GeneratedProtocolMessageType('CreateModelVersion', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _CREATEMODELVERSION_RESPONSE, + '__module__' : 'unity_catalog_oss_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.unitycatalog.CreateModelVersion.Response) + }) + , + 'DESCRIPTOR' : _CREATEMODELVERSION, + '__module__' : 'unity_catalog_oss_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.unitycatalog.CreateModelVersion) + }) + _sym_db.RegisterMessage(CreateModelVersion) + _sym_db.RegisterMessage(CreateModelVersion.Response) + + DeleteModelVersion = _reflection.GeneratedProtocolMessageType('DeleteModelVersion', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _DELETEMODELVERSION_RESPONSE, + '__module__' : 'unity_catalog_oss_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.unitycatalog.DeleteModelVersion.Response) + }) + , + 'DESCRIPTOR' : _DELETEMODELVERSION, + '__module__' : 'unity_catalog_oss_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.unitycatalog.DeleteModelVersion) + }) + _sym_db.RegisterMessage(DeleteModelVersion) + _sym_db.RegisterMessage(DeleteModelVersion.Response) + + FinalizeModelVersion = _reflection.GeneratedProtocolMessageType('FinalizeModelVersion', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _FINALIZEMODELVERSION_RESPONSE, + '__module__' : 'unity_catalog_oss_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.unitycatalog.FinalizeModelVersion.Response) + }) + , + 'DESCRIPTOR' : _FINALIZEMODELVERSION, + '__module__' : 'unity_catalog_oss_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.unitycatalog.FinalizeModelVersion) + }) + _sym_db.RegisterMessage(FinalizeModelVersion) + _sym_db.RegisterMessage(FinalizeModelVersion.Response) + + GetModelVersion = _reflection.GeneratedProtocolMessageType('GetModelVersion', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _GETMODELVERSION_RESPONSE, + '__module__' : 'unity_catalog_oss_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.unitycatalog.GetModelVersion.Response) + }) + , + 'DESCRIPTOR' : _GETMODELVERSION, + '__module__' : 'unity_catalog_oss_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.unitycatalog.GetModelVersion) + }) + _sym_db.RegisterMessage(GetModelVersion) + _sym_db.RegisterMessage(GetModelVersion.Response) + + UpdateModelVersion = _reflection.GeneratedProtocolMessageType('UpdateModelVersion', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _UPDATEMODELVERSION_RESPONSE, + '__module__' : 'unity_catalog_oss_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.unitycatalog.UpdateModelVersion.Response) + }) + , + 'DESCRIPTOR' : _UPDATEMODELVERSION, + '__module__' : 'unity_catalog_oss_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.unitycatalog.UpdateModelVersion) + }) + _sym_db.RegisterMessage(UpdateModelVersion) + _sym_db.RegisterMessage(UpdateModelVersion.Response) + + ListModelVersions = _reflection.GeneratedProtocolMessageType('ListModelVersions', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _LISTMODELVERSIONS_RESPONSE, + '__module__' : 'unity_catalog_oss_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.unitycatalog.ListModelVersions.Response) + }) + , + 'DESCRIPTOR' : _LISTMODELVERSIONS, + '__module__' : 'unity_catalog_oss_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.unitycatalog.ListModelVersions) + }) + _sym_db.RegisterMessage(ListModelVersions) + _sym_db.RegisterMessage(ListModelVersions.Response) + + TemporaryCredentials = _reflection.GeneratedProtocolMessageType('TemporaryCredentials', (_message.Message,), { + 'DESCRIPTOR' : _TEMPORARYCREDENTIALS, + '__module__' : 'unity_catalog_oss_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.unitycatalog.TemporaryCredentials) + }) + _sym_db.RegisterMessage(TemporaryCredentials) + + AwsCredentials = _reflection.GeneratedProtocolMessageType('AwsCredentials', (_message.Message,), { + 'DESCRIPTOR' : _AWSCREDENTIALS, + '__module__' : 'unity_catalog_oss_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.unitycatalog.AwsCredentials) + }) + _sym_db.RegisterMessage(AwsCredentials) + + AzureUserDelegationSAS = _reflection.GeneratedProtocolMessageType('AzureUserDelegationSAS', (_message.Message,), { + 'DESCRIPTOR' : _AZUREUSERDELEGATIONSAS, + '__module__' : 'unity_catalog_oss_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.unitycatalog.AzureUserDelegationSAS) + }) + _sym_db.RegisterMessage(AzureUserDelegationSAS) + + GcpOauthToken = _reflection.GeneratedProtocolMessageType('GcpOauthToken', (_message.Message,), { + 'DESCRIPTOR' : _GCPOAUTHTOKEN, + '__module__' : 'unity_catalog_oss_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.unitycatalog.GcpOauthToken) + }) + _sym_db.RegisterMessage(GcpOauthToken) + + GenerateTemporaryModelVersionCredential = _reflection.GeneratedProtocolMessageType('GenerateTemporaryModelVersionCredential', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _GENERATETEMPORARYMODELVERSIONCREDENTIAL_RESPONSE, + '__module__' : 'unity_catalog_oss_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.unitycatalog.GenerateTemporaryModelVersionCredential.Response) + }) + , + 'DESCRIPTOR' : _GENERATETEMPORARYMODELVERSIONCREDENTIAL, + '__module__' : 'unity_catalog_oss_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.unitycatalog.GenerateTemporaryModelVersionCredential) + }) + _sym_db.RegisterMessage(GenerateTemporaryModelVersionCredential) + _sym_db.RegisterMessage(GenerateTemporaryModelVersionCredential.Response) + + if _descriptor._USE_C_DESCRIPTORS == False: + + DESCRIPTOR._options = None + DESCRIPTOR._serialized_options = b'\n\'com.databricks.api.proto.managedcatalog\240\001\001\342?\002\020\001' + _GETREGISTEREDMODEL.fields_by_name['full_name']._options = None + _GETREGISTEREDMODEL.fields_by_name['full_name']._serialized_options = b'\370\206\031\001' + _MODELVERSIONSTATUS._serialized_start=3065 + _MODELVERSIONSTATUS._serialized_end=3181 + _MODELVERSIONOPERATION._serialized_start=3183 + _MODELVERSIONOPERATION._serialized_end=3297 + _REGISTEREDMODELINFO._serialized_start=99 + _REGISTEREDMODELINFO._serialized_end=352 + _CREATEREGISTEREDMODEL._serialized_start=355 + _CREATEREGISTEREDMODEL._serialized_end=563 + _CREATEREGISTEREDMODEL_RESPONSE._serialized_start=480 + _CREATEREGISTEREDMODEL_RESPONSE._serialized_end=563 + _DELETEREGISTEREDMODEL._serialized_start=565 + _DELETEREGISTEREDMODEL._serialized_end=634 + _DELETEREGISTEREDMODEL_RESPONSE._serialized_start=480 + _DELETEREGISTEREDMODEL_RESPONSE._serialized_end=490 + _GETREGISTEREDMODEL._serialized_start=637 + _GETREGISTEREDMODEL._serialized_end=767 + _GETREGISTEREDMODEL_RESPONSE._serialized_start=480 + _GETREGISTEREDMODEL_RESPONSE._serialized_end=563 + _UPDATEREGISTEREDMODEL._serialized_start=770 + _UPDATEREGISTEREDMODEL._serialized_end=932 + _UPDATEREGISTEREDMODEL_RESPONSE._serialized_start=480 + _UPDATEREGISTEREDMODEL_RESPONSE._serialized_end=563 + _LISTREGISTEREDMODELS._serialized_start=935 + _LISTREGISTEREDMODELS._serialized_end=1147 + _LISTREGISTEREDMODELS_RESPONSE._serialized_start=1043 + _LISTREGISTEREDMODELS_RESPONSE._serialized_end=1147 + _MODELVERSIONINFO._serialized_start=1150 + _MODELVERSIONINFO._serialized_end=1472 + _CREATEMODELVERSION._serialized_start=1475 + _CREATEMODELVERSION._serialized_end=1686 + _CREATEMODELVERSION_RESPONSE._serialized_start=1609 + _CREATEMODELVERSION_RESPONSE._serialized_end=1686 + _DELETEMODELVERSION._serialized_start=1688 + _DELETEMODELVERSION._serialized_end=1756 + _DELETEMODELVERSION_RESPONSE._serialized_start=480 + _DELETEMODELVERSION_RESPONSE._serialized_end=490 + _FINALIZEMODELVERSION._serialized_start=1759 + _FINALIZEMODELVERSION._serialized_end=1896 + _FINALIZEMODELVERSION_RESPONSE._serialized_start=1609 + _FINALIZEMODELVERSION_RESPONSE._serialized_end=1686 + _GETMODELVERSION._serialized_start=1899 + _GETMODELVERSION._serialized_end=2031 + _GETMODELVERSION_RESPONSE._serialized_start=1609 + _GETMODELVERSION_RESPONSE._serialized_end=1686 + _UPDATEMODELVERSION._serialized_start=2034 + _UPDATEMODELVERSION._serialized_end=2186 + _UPDATEMODELVERSION_RESPONSE._serialized_start=1609 + _UPDATEMODELVERSION_RESPONSE._serialized_end=1686 + _LISTMODELVERSIONS._serialized_start=2189 + _LISTMODELVERSIONS._serialized_end=2368 + _LISTMODELVERSIONS_RESPONSE._serialized_start=2270 + _LISTMODELVERSIONS_RESPONSE._serialized_end=2368 + _TEMPORARYCREDENTIALS._serialized_start=2371 + _TEMPORARYCREDENTIALS._serialized_end=2626 + _AWSCREDENTIALS._serialized_start=2628 + _AWSCREDENTIALS._serialized_end=2717 + _AZUREUSERDELEGATIONSAS._serialized_start=2719 + _AZUREUSERDELEGATIONSAS._serialized_end=2762 + _GCPOAUTHTOKEN._serialized_start=2764 + _GCPOAUTHTOKEN._serialized_end=2800 + _GENERATETEMPORARYMODELVERSIONCREDENTIAL._serialized_start=2803 + _GENERATETEMPORARYMODELVERSIONCREDENTIAL._serialized_end=3063 + _GENERATETEMPORARYMODELVERSIONCREDENTIAL_RESPONSE._serialized_start=2989 + _GENERATETEMPORARYMODELVERSIONCREDENTIAL_RESPONSE._serialized_end=3063 + # @@protoc_insertion_point(module_scope) + diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/protos/unity_catalog_oss_service_pb2.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/protos/unity_catalog_oss_service_pb2.py new file mode 100644 index 0000000000000000000000000000000000000000..96facbf1776a11b23508fe45190968b417d0551b --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/protos/unity_catalog_oss_service_pb2.py @@ -0,0 +1,130 @@ + +import google.protobuf +from packaging.version import Version +if Version(google.protobuf.__version__).major >= 5: + # -*- coding: utf-8 -*- + # Generated by the protocol buffer compiler. DO NOT EDIT! + # source: unity_catalog_oss_service.proto + # Protobuf Python Version: 5.26.0 + """Generated protocol buffer code.""" + from google.protobuf import descriptor as _descriptor + from google.protobuf import descriptor_pool as _descriptor_pool + from google.protobuf import symbol_database as _symbol_database + from google.protobuf.internal import builder as _builder + # @@protoc_insertion_point(imports) + + _sym_db = _symbol_database.Default() + + + from . import databricks_pb2 as databricks__pb2 + from .scalapb import scalapb_pb2 as scalapb_dot_scalapb__pb2 + from . import unity_catalog_oss_messages_pb2 as unity_catalog_oss_messages_pb2 + + + DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\x1funity_catalog_oss_service.proto\x12\x13mlflow.unitycatalog\x1a\x10\x64\x61tabricks.proto\x1a\x15scalapb/scalapb.proto\x1a unity_catalog_oss_messages.proto2\xd4\x11\n\x13UnityCatalogService\x12\xa5\x01\n\x15\x63reateRegisteredModel\x12*.mlflow.unitycatalog.CreateRegisteredModel\x1a\x33.mlflow.unitycatalog.CreateRegisteredModel.Response\"+\xf2\x86\x19\'\n#\n\x04POST\x12\x15/unity-catalog/models\x1a\x04\x08\x02\x10\x01\x10\x01\x12\xb2\x01\n\x15updateRegisteredModel\x12*.mlflow.unitycatalog.UpdateRegisteredModel\x1a\x33.mlflow.unitycatalog.UpdateRegisteredModel.Response\"8\xf2\x86\x19\x34\n0\n\x05PATCH\x12!/unity-catalog/models/{full_name}\x1a\x04\x08\x02\x10\x01\x10\x03\x12\xb3\x01\n\x15\x64\x65leteRegisteredModel\x12*.mlflow.unitycatalog.DeleteRegisteredModel\x1a\x33.mlflow.unitycatalog.DeleteRegisteredModel.Response\"9\xf2\x86\x19\x35\n1\n\x06\x44\x45LETE\x12!/unity-catalog/models/{full_name}\x1a\x04\x08\x02\x10\x01\x10\x03\x12\xaa\x01\n\x12getRegisteredModel\x12\'.mlflow.unitycatalog.GetRegisteredModel\x1a\x30.mlflow.unitycatalog.GetRegisteredModel.Response\"9\x90\x02\x02\xf2\x86\x19\x32\n.\n\x03GET\x12!/unity-catalog/models/{full_name}\x1a\x04\x08\x02\x10\x01\x10\x03\x12\xb4\x01\n\x0fgetModelVersion\x12$.mlflow.unitycatalog.GetModelVersion\x1a-.mlflow.unitycatalog.GetModelVersion.Response\"L\x90\x02\x02\xf2\x86\x19\x45\nA\n\x03GET\x12\x34/unity-catalog/models/{full_name}/versions/{version}\x1a\x04\x08\x02\x10\x01\x10\x03\x12\xbd\x01\n\x12\x64\x65leteModelVersion\x12\'.mlflow.unitycatalog.DeleteModelVersion\x1a\x30.mlflow.unitycatalog.DeleteModelVersion.Response\"L\xf2\x86\x19H\nD\n\x06\x44\x45LETE\x12\x34/unity-catalog/models/{full_name}/versions/{version}\x1a\x04\x08\x02\x10\x01\x10\x03\x12\xa3\x01\n\x12\x63reateModelVersion\x12\'.mlflow.unitycatalog.CreateModelVersion\x1a\x30.mlflow.unitycatalog.CreateModelVersion.Response\"2\xf2\x86\x19.\n,\n\x04POST\x12\x1e/unity-catalog/models/versions\x1a\x04\x08\x02\x10\x01\x12\xf8\x01\n\'generateTemporaryModelVersionCredential\x12<.mlflow.unitycatalog.GenerateTemporaryModelVersionCredential\x1a\x45.mlflow.unitycatalog.GenerateTemporaryModelVersionCredential.Response\"H\xf2\x86\x19\x44\n@\n\x04POST\x12\x32/unity-catalog/temporary-model-version-credentials\x1a\x04\x08\x02\x10\x01\x10\x03\x12\xcb\x01\n\x14\x66inalizeModelVersion\x12).mlflow.unitycatalog.FinalizeModelVersion\x1a\x32.mlflow.unitycatalog.FinalizeModelVersion.Response\"T\xf2\x86\x19P\nL\n\x05PATCH\x12=/unity-catalog/models/{full_name}/versions/{version}/finalize\x1a\x04\x08\x02\x10\x01\x10\x03\x12\xbc\x01\n\x12updateModelVersion\x12\'.mlflow.unitycatalog.UpdateModelVersion\x1a\x30.mlflow.unitycatalog.UpdateModelVersion.Response\"K\xf2\x86\x19G\nC\n\x05PATCH\x12\x34/unity-catalog/models/{full_name}/versions/{version}\x1a\x04\x08\x02\x10\x01\x10\x03\x12\xa4\x01\n\x14listRegisteredModels\x12).mlflow.unitycatalog.ListRegisteredModels\x1a\x32.mlflow.unitycatalog.ListRegisteredModels.Response\"-\x90\x02\x02\xf2\x86\x19&\n\"\n\x03GET\x12\x15/unity-catalog/models\x1a\x04\x08\x02\x10\x01\x10\x03\x12\xb0\x01\n\x11listModelVersions\x12&.mlflow.unitycatalog.ListModelVersions\x1a/.mlflow.unitycatalog.ListModelVersions.Response\"B\x90\x02\x02\xf2\x86\x19;\n7\n\x03GET\x12*/unity-catalog/models/{full_name}/versions\x1a\x04\x08\x02\x10\x01\x10\x03\x42\x34\n\'com.databricks.api.proto.managedcatalog\x90\x01\x01\xa0\x01\x01\xe2?\x02\x10\x01') + + _globals = globals() + _builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals) + _builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'unity_catalog_oss_service_pb2', _globals) + if not _descriptor._USE_C_DESCRIPTORS: + _globals['DESCRIPTOR']._loaded_options = None + _globals['DESCRIPTOR']._serialized_options = b'\n\'com.databricks.api.proto.managedcatalog\220\001\001\240\001\001\342?\002\020\001' + _globals['_UNITYCATALOGSERVICE'].methods_by_name['createRegisteredModel']._loaded_options = None + _globals['_UNITYCATALOGSERVICE'].methods_by_name['createRegisteredModel']._serialized_options = b'\362\206\031\'\n#\n\004POST\022\025/unity-catalog/models\032\004\010\002\020\001\020\001' + _globals['_UNITYCATALOGSERVICE'].methods_by_name['updateRegisteredModel']._loaded_options = None + _globals['_UNITYCATALOGSERVICE'].methods_by_name['updateRegisteredModel']._serialized_options = b'\362\206\0314\n0\n\005PATCH\022!/unity-catalog/models/{full_name}\032\004\010\002\020\001\020\003' + _globals['_UNITYCATALOGSERVICE'].methods_by_name['deleteRegisteredModel']._loaded_options = None + _globals['_UNITYCATALOGSERVICE'].methods_by_name['deleteRegisteredModel']._serialized_options = b'\362\206\0315\n1\n\006DELETE\022!/unity-catalog/models/{full_name}\032\004\010\002\020\001\020\003' + _globals['_UNITYCATALOGSERVICE'].methods_by_name['getRegisteredModel']._loaded_options = None + _globals['_UNITYCATALOGSERVICE'].methods_by_name['getRegisteredModel']._serialized_options = b'\220\002\002\362\206\0312\n.\n\003GET\022!/unity-catalog/models/{full_name}\032\004\010\002\020\001\020\003' + _globals['_UNITYCATALOGSERVICE'].methods_by_name['getModelVersion']._loaded_options = None + _globals['_UNITYCATALOGSERVICE'].methods_by_name['getModelVersion']._serialized_options = b'\220\002\002\362\206\031E\nA\n\003GET\0224/unity-catalog/models/{full_name}/versions/{version}\032\004\010\002\020\001\020\003' + _globals['_UNITYCATALOGSERVICE'].methods_by_name['deleteModelVersion']._loaded_options = None + _globals['_UNITYCATALOGSERVICE'].methods_by_name['deleteModelVersion']._serialized_options = b'\362\206\031H\nD\n\006DELETE\0224/unity-catalog/models/{full_name}/versions/{version}\032\004\010\002\020\001\020\003' + _globals['_UNITYCATALOGSERVICE'].methods_by_name['createModelVersion']._loaded_options = None + _globals['_UNITYCATALOGSERVICE'].methods_by_name['createModelVersion']._serialized_options = b'\362\206\031.\n,\n\004POST\022\036/unity-catalog/models/versions\032\004\010\002\020\001' + _globals['_UNITYCATALOGSERVICE'].methods_by_name['generateTemporaryModelVersionCredential']._loaded_options = None + _globals['_UNITYCATALOGSERVICE'].methods_by_name['generateTemporaryModelVersionCredential']._serialized_options = b'\362\206\031D\n@\n\004POST\0222/unity-catalog/temporary-model-version-credentials\032\004\010\002\020\001\020\003' + _globals['_UNITYCATALOGSERVICE'].methods_by_name['finalizeModelVersion']._loaded_options = None + _globals['_UNITYCATALOGSERVICE'].methods_by_name['finalizeModelVersion']._serialized_options = b'\362\206\031P\nL\n\005PATCH\022=/unity-catalog/models/{full_name}/versions/{version}/finalize\032\004\010\002\020\001\020\003' + _globals['_UNITYCATALOGSERVICE'].methods_by_name['updateModelVersion']._loaded_options = None + _globals['_UNITYCATALOGSERVICE'].methods_by_name['updateModelVersion']._serialized_options = b'\362\206\031G\nC\n\005PATCH\0224/unity-catalog/models/{full_name}/versions/{version}\032\004\010\002\020\001\020\003' + _globals['_UNITYCATALOGSERVICE'].methods_by_name['listRegisteredModels']._loaded_options = None + _globals['_UNITYCATALOGSERVICE'].methods_by_name['listRegisteredModels']._serialized_options = b'\220\002\002\362\206\031&\n\"\n\003GET\022\025/unity-catalog/models\032\004\010\002\020\001\020\003' + _globals['_UNITYCATALOGSERVICE'].methods_by_name['listModelVersions']._loaded_options = None + _globals['_UNITYCATALOGSERVICE'].methods_by_name['listModelVersions']._serialized_options = b'\220\002\002\362\206\031;\n7\n\003GET\022*/unity-catalog/models/{full_name}/versions\032\004\010\002\020\001\020\003' + _globals['_UNITYCATALOGSERVICE']._serialized_start=132 + _globals['_UNITYCATALOGSERVICE']._serialized_end=2392 + _builder.BuildServices(DESCRIPTOR, 'unity_catalog_oss_service_pb2', _globals) + # @@protoc_insertion_point(module_scope) + +else: + # -*- coding: utf-8 -*- + # Generated by the protocol buffer compiler. DO NOT EDIT! + # source: unity_catalog_oss_service.proto + """Generated protocol buffer code.""" + from google.protobuf import descriptor as _descriptor + from google.protobuf import descriptor_pool as _descriptor_pool + from google.protobuf import message as _message + from google.protobuf import reflection as _reflection + from google.protobuf import symbol_database as _symbol_database + from google.protobuf import service as _service + from google.protobuf import service_reflection + # @@protoc_insertion_point(imports) + + _sym_db = _symbol_database.Default() + + + from . import databricks_pb2 as databricks__pb2 + from .scalapb import scalapb_pb2 as scalapb_dot_scalapb__pb2 + from . import unity_catalog_oss_messages_pb2 as unity_catalog_oss_messages_pb2 + + + DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\x1funity_catalog_oss_service.proto\x12\x13mlflow.unitycatalog\x1a\x10\x64\x61tabricks.proto\x1a\x15scalapb/scalapb.proto\x1a unity_catalog_oss_messages.proto2\xd4\x11\n\x13UnityCatalogService\x12\xa5\x01\n\x15\x63reateRegisteredModel\x12*.mlflow.unitycatalog.CreateRegisteredModel\x1a\x33.mlflow.unitycatalog.CreateRegisteredModel.Response\"+\xf2\x86\x19\'\n#\n\x04POST\x12\x15/unity-catalog/models\x1a\x04\x08\x02\x10\x01\x10\x01\x12\xb2\x01\n\x15updateRegisteredModel\x12*.mlflow.unitycatalog.UpdateRegisteredModel\x1a\x33.mlflow.unitycatalog.UpdateRegisteredModel.Response\"8\xf2\x86\x19\x34\n0\n\x05PATCH\x12!/unity-catalog/models/{full_name}\x1a\x04\x08\x02\x10\x01\x10\x03\x12\xb3\x01\n\x15\x64\x65leteRegisteredModel\x12*.mlflow.unitycatalog.DeleteRegisteredModel\x1a\x33.mlflow.unitycatalog.DeleteRegisteredModel.Response\"9\xf2\x86\x19\x35\n1\n\x06\x44\x45LETE\x12!/unity-catalog/models/{full_name}\x1a\x04\x08\x02\x10\x01\x10\x03\x12\xaa\x01\n\x12getRegisteredModel\x12\'.mlflow.unitycatalog.GetRegisteredModel\x1a\x30.mlflow.unitycatalog.GetRegisteredModel.Response\"9\x90\x02\x02\xf2\x86\x19\x32\n.\n\x03GET\x12!/unity-catalog/models/{full_name}\x1a\x04\x08\x02\x10\x01\x10\x03\x12\xb4\x01\n\x0fgetModelVersion\x12$.mlflow.unitycatalog.GetModelVersion\x1a-.mlflow.unitycatalog.GetModelVersion.Response\"L\x90\x02\x02\xf2\x86\x19\x45\nA\n\x03GET\x12\x34/unity-catalog/models/{full_name}/versions/{version}\x1a\x04\x08\x02\x10\x01\x10\x03\x12\xbd\x01\n\x12\x64\x65leteModelVersion\x12\'.mlflow.unitycatalog.DeleteModelVersion\x1a\x30.mlflow.unitycatalog.DeleteModelVersion.Response\"L\xf2\x86\x19H\nD\n\x06\x44\x45LETE\x12\x34/unity-catalog/models/{full_name}/versions/{version}\x1a\x04\x08\x02\x10\x01\x10\x03\x12\xa3\x01\n\x12\x63reateModelVersion\x12\'.mlflow.unitycatalog.CreateModelVersion\x1a\x30.mlflow.unitycatalog.CreateModelVersion.Response\"2\xf2\x86\x19.\n,\n\x04POST\x12\x1e/unity-catalog/models/versions\x1a\x04\x08\x02\x10\x01\x12\xf8\x01\n\'generateTemporaryModelVersionCredential\x12<.mlflow.unitycatalog.GenerateTemporaryModelVersionCredential\x1a\x45.mlflow.unitycatalog.GenerateTemporaryModelVersionCredential.Response\"H\xf2\x86\x19\x44\n@\n\x04POST\x12\x32/unity-catalog/temporary-model-version-credentials\x1a\x04\x08\x02\x10\x01\x10\x03\x12\xcb\x01\n\x14\x66inalizeModelVersion\x12).mlflow.unitycatalog.FinalizeModelVersion\x1a\x32.mlflow.unitycatalog.FinalizeModelVersion.Response\"T\xf2\x86\x19P\nL\n\x05PATCH\x12=/unity-catalog/models/{full_name}/versions/{version}/finalize\x1a\x04\x08\x02\x10\x01\x10\x03\x12\xbc\x01\n\x12updateModelVersion\x12\'.mlflow.unitycatalog.UpdateModelVersion\x1a\x30.mlflow.unitycatalog.UpdateModelVersion.Response\"K\xf2\x86\x19G\nC\n\x05PATCH\x12\x34/unity-catalog/models/{full_name}/versions/{version}\x1a\x04\x08\x02\x10\x01\x10\x03\x12\xa4\x01\n\x14listRegisteredModels\x12).mlflow.unitycatalog.ListRegisteredModels\x1a\x32.mlflow.unitycatalog.ListRegisteredModels.Response\"-\x90\x02\x02\xf2\x86\x19&\n\"\n\x03GET\x12\x15/unity-catalog/models\x1a\x04\x08\x02\x10\x01\x10\x03\x12\xb0\x01\n\x11listModelVersions\x12&.mlflow.unitycatalog.ListModelVersions\x1a/.mlflow.unitycatalog.ListModelVersions.Response\"B\x90\x02\x02\xf2\x86\x19;\n7\n\x03GET\x12*/unity-catalog/models/{full_name}/versions\x1a\x04\x08\x02\x10\x01\x10\x03\x42\x34\n\'com.databricks.api.proto.managedcatalog\x90\x01\x01\xa0\x01\x01\xe2?\x02\x10\x01') + + + + _UNITYCATALOGSERVICE = DESCRIPTOR.services_by_name['UnityCatalogService'] + if _descriptor._USE_C_DESCRIPTORS == False: + + DESCRIPTOR._options = None + DESCRIPTOR._serialized_options = b'\n\'com.databricks.api.proto.managedcatalog\220\001\001\240\001\001\342?\002\020\001' + _UNITYCATALOGSERVICE.methods_by_name['createRegisteredModel']._options = None + _UNITYCATALOGSERVICE.methods_by_name['createRegisteredModel']._serialized_options = b'\362\206\031\'\n#\n\004POST\022\025/unity-catalog/models\032\004\010\002\020\001\020\001' + _UNITYCATALOGSERVICE.methods_by_name['updateRegisteredModel']._options = None + _UNITYCATALOGSERVICE.methods_by_name['updateRegisteredModel']._serialized_options = b'\362\206\0314\n0\n\005PATCH\022!/unity-catalog/models/{full_name}\032\004\010\002\020\001\020\003' + _UNITYCATALOGSERVICE.methods_by_name['deleteRegisteredModel']._options = None + _UNITYCATALOGSERVICE.methods_by_name['deleteRegisteredModel']._serialized_options = b'\362\206\0315\n1\n\006DELETE\022!/unity-catalog/models/{full_name}\032\004\010\002\020\001\020\003' + _UNITYCATALOGSERVICE.methods_by_name['getRegisteredModel']._options = None + _UNITYCATALOGSERVICE.methods_by_name['getRegisteredModel']._serialized_options = b'\220\002\002\362\206\0312\n.\n\003GET\022!/unity-catalog/models/{full_name}\032\004\010\002\020\001\020\003' + _UNITYCATALOGSERVICE.methods_by_name['getModelVersion']._options = None + _UNITYCATALOGSERVICE.methods_by_name['getModelVersion']._serialized_options = b'\220\002\002\362\206\031E\nA\n\003GET\0224/unity-catalog/models/{full_name}/versions/{version}\032\004\010\002\020\001\020\003' + _UNITYCATALOGSERVICE.methods_by_name['deleteModelVersion']._options = None + _UNITYCATALOGSERVICE.methods_by_name['deleteModelVersion']._serialized_options = b'\362\206\031H\nD\n\006DELETE\0224/unity-catalog/models/{full_name}/versions/{version}\032\004\010\002\020\001\020\003' + _UNITYCATALOGSERVICE.methods_by_name['createModelVersion']._options = None + _UNITYCATALOGSERVICE.methods_by_name['createModelVersion']._serialized_options = b'\362\206\031.\n,\n\004POST\022\036/unity-catalog/models/versions\032\004\010\002\020\001' + _UNITYCATALOGSERVICE.methods_by_name['generateTemporaryModelVersionCredential']._options = None + _UNITYCATALOGSERVICE.methods_by_name['generateTemporaryModelVersionCredential']._serialized_options = b'\362\206\031D\n@\n\004POST\0222/unity-catalog/temporary-model-version-credentials\032\004\010\002\020\001\020\003' + _UNITYCATALOGSERVICE.methods_by_name['finalizeModelVersion']._options = None + _UNITYCATALOGSERVICE.methods_by_name['finalizeModelVersion']._serialized_options = b'\362\206\031P\nL\n\005PATCH\022=/unity-catalog/models/{full_name}/versions/{version}/finalize\032\004\010\002\020\001\020\003' + _UNITYCATALOGSERVICE.methods_by_name['updateModelVersion']._options = None + _UNITYCATALOGSERVICE.methods_by_name['updateModelVersion']._serialized_options = b'\362\206\031G\nC\n\005PATCH\0224/unity-catalog/models/{full_name}/versions/{version}\032\004\010\002\020\001\020\003' + _UNITYCATALOGSERVICE.methods_by_name['listRegisteredModels']._options = None + _UNITYCATALOGSERVICE.methods_by_name['listRegisteredModels']._serialized_options = b'\220\002\002\362\206\031&\n\"\n\003GET\022\025/unity-catalog/models\032\004\010\002\020\001\020\003' + _UNITYCATALOGSERVICE.methods_by_name['listModelVersions']._options = None + _UNITYCATALOGSERVICE.methods_by_name['listModelVersions']._serialized_options = b'\220\002\002\362\206\031;\n7\n\003GET\022*/unity-catalog/models/{full_name}/versions\032\004\010\002\020\001\020\003' + _UNITYCATALOGSERVICE._serialized_start=132 + _UNITYCATALOGSERVICE._serialized_end=2392 + UnityCatalogService = service_reflection.GeneratedServiceType('UnityCatalogService', (_service.Service,), dict( + DESCRIPTOR = _UNITYCATALOGSERVICE, + __module__ = 'unity_catalog_oss_service_pb2' + )) + + UnityCatalogService_Stub = service_reflection.GeneratedServiceStubType('UnityCatalogService_Stub', (UnityCatalogService,), dict( + DESCRIPTOR = _UNITYCATALOGSERVICE, + __module__ = 'unity_catalog_oss_service_pb2' + )) + + + # @@protoc_insertion_point(module_scope) + diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/protos/unity_catalog_prompt_messages_pb2.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/protos/unity_catalog_prompt_messages_pb2.py new file mode 100644 index 0000000000000000000000000000000000000000..761ab1befda5f6e3771b7a877bc039f61080c0d2 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/protos/unity_catalog_prompt_messages_pb2.py @@ -0,0 +1,447 @@ + +import google.protobuf +from packaging.version import Version +if Version(google.protobuf.__version__).major >= 5: + # -*- coding: utf-8 -*- + # Generated by the protocol buffer compiler. DO NOT EDIT! + # source: unity_catalog_prompt_messages.proto + # Protobuf Python Version: 5.26.0 + """Generated protocol buffer code.""" + from google.protobuf import descriptor as _descriptor + from google.protobuf import descriptor_pool as _descriptor_pool + from google.protobuf import symbol_database as _symbol_database + from google.protobuf.internal import builder as _builder + # @@protoc_insertion_point(imports) + + _sym_db = _symbol_database.Default() + + + from google.protobuf import struct_pb2 as google_dot_protobuf_dot_struct__pb2 + from google.protobuf import timestamp_pb2 as google_dot_protobuf_dot_timestamp__pb2 + from google.protobuf import empty_pb2 as google_dot_protobuf_dot_empty__pb2 + from . import databricks_pb2 as databricks__pb2 + from .scalapb import scalapb_pb2 as scalapb_dot_scalapb__pb2 + + + DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n#unity_catalog_prompt_messages.proto\x12\x13mlflow.unitycatalog\x1a\x1cgoogle/protobuf/struct.proto\x1a\x1fgoogle/protobuf/timestamp.proto\x1a\x1bgoogle/protobuf/empty.proto\x1a\x10\x64\x61tabricks.proto\x1a\x15scalapb/scalapb.proto\"\x95\x02\n\x06Prompt\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x36\n\x12\x63reation_timestamp\x18\x02 \x01(\x0b\x32\x1a.google.protobuf.Timestamp\x12:\n\x16last_updated_timestamp\x18\x03 \x01(\x0b\x32\x1a.google.protobuf.Timestamp\x12\x13\n\x0b\x64\x65scription\x18\x04 \x01(\t\x12\x31\n\x07\x61liases\x18\x06 \x03(\x0b\x32 .mlflow.unitycatalog.PromptAlias\x12,\n\x04tags\x18\x07 \x03(\x0b\x32\x1e.mlflow.unitycatalog.PromptTagJ\x04\x08\x05\x10\x06R\rexperiment_id\"\xb1\x02\n\rPromptVersion\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x0f\n\x07version\x18\x02 \x01(\t\x12\x36\n\x12\x63reation_timestamp\x18\x03 \x01(\x0b\x32\x1a.google.protobuf.Timestamp\x12:\n\x16last_updated_timestamp\x18\x04 \x01(\x0b\x32\x1a.google.protobuf.Timestamp\x12\x13\n\x0b\x64\x65scription\x18\x05 \x01(\t\x12\x10\n\x08template\x18\x06 \x01(\t\x12\x31\n\x07\x61liases\x18\x07 \x03(\x0b\x32 .mlflow.unitycatalog.PromptAlias\x12\x33\n\x04tags\x18\x08 \x03(\x0b\x32%.mlflow.unitycatalog.PromptVersionTag\"\'\n\tPromptTag\x12\x0b\n\x03key\x18\x01 \x01(\t\x12\r\n\x05value\x18\x02 \x01(\t\".\n\x10PromptVersionTag\x12\x0b\n\x03key\x18\x01 \x01(\t\x12\r\n\x05value\x18\x02 \x01(\t\"-\n\x0bPromptAlias\x12\r\n\x05\x61lias\x18\x01 \x01(\t\x12\x0f\n\x07version\x18\x02 \x01(\t\"?\n\x12UnityCatalogSchema\x12\x14\n\x0c\x63\x61talog_name\x18\x04 \x01(\t\x12\x13\n\x0bschema_name\x18\x05 \x01(\t\"P\n\x13\x43reatePromptRequest\x12\x0c\n\x04name\x18\x01 \x01(\t\x12+\n\x06prompt\x18\x02 \x01(\x0b\x32\x1b.mlflow.unitycatalog.Prompt\"P\n\x13UpdatePromptRequest\x12\x0c\n\x04name\x18\x01 \x01(\t\x12+\n\x06prompt\x18\x02 \x01(\x0b\x32\x1b.mlflow.unitycatalog.Prompt\"#\n\x13\x44\x65letePromptRequest\x12\x0c\n\x04name\x18\x01 \x01(\t\"\x16\n\x14\x44\x65letePromptResponse\" \n\x10GetPromptRequest\x12\x0c\n\x04name\x18\x01 \x01(\t\"\xb3\x01\n\x14SearchPromptsRequest\x12\x0e\n\x06\x66ilter\x18\x01 \x01(\t\x12\x41\n\x0e\x63\x61talog_schema\x18\x03 \x01(\x0b\x32\'.mlflow.unitycatalog.UnityCatalogSchemaH\x00\x12\x13\n\x0bmax_results\x18\x04 \x01(\x03\x12\x12\n\npage_token\x18\x05 \x01(\tB\n\n\x08locationJ\x04\x08\x02\x10\x03R\rexperiment_id\"^\n\x15SearchPromptsResponse\x12,\n\x07prompts\x18\x01 \x03(\x0b\x32\x1b.mlflow.unitycatalog.Prompt\x12\x17\n\x0fnext_page_token\x18\x02 \x01(\t\"u\n\x1a\x43reatePromptVersionRequest\x12\x0c\n\x04name\x18\x01 \x01(\t\x12:\n\x0eprompt_version\x18\x03 \x01(\x0b\x32\".mlflow.unitycatalog.PromptVersionJ\x04\x08\x02\x10\x03R\x07version\"w\n\x1aUpdatePromptVersionRequest\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x0f\n\x07version\x18\x02 \x01(\t\x12:\n\x0eprompt_version\x18\x03 \x01(\x0b\x32\".mlflow.unitycatalog.PromptVersion\";\n\x1a\x44\x65letePromptVersionRequest\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x0f\n\x07version\x18\x02 \x01(\t\"8\n\x17GetPromptVersionRequest\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x0f\n\x07version\x18\x02 \x01(\t\"T\n\x1bSearchPromptVersionsRequest\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x13\n\x0bmax_results\x18\x02 \x01(\x03\x12\x12\n\npage_token\x18\x03 \x01(\t\"t\n\x1cSearchPromptVersionsResponse\x12;\n\x0fprompt_versions\x18\x01 \x03(\x0b\x32\".mlflow.unitycatalog.PromptVersion\x12\x17\n\x0fnext_page_token\x18\x02 \x01(\t\"E\n\x15SetPromptAliasRequest\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\r\n\x05\x61lias\x18\x02 \x01(\t\x12\x0f\n\x07version\x18\x03 \x01(\t\"7\n\x18\x44\x65letePromptAliasRequest\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\r\n\x05\x61lias\x18\x02 \x01(\t\"=\n\x1eGetPromptVersionByAliasRequest\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\r\n\x05\x61lias\x18\x02 \x01(\t\"?\n\x13SetPromptTagRequest\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x0b\n\x03key\x18\x02 \x01(\t\x12\r\n\x05value\x18\x03 \x01(\t\"3\n\x16\x44\x65letePromptTagRequest\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x0b\n\x03key\x18\x02 \x01(\t\"W\n\x1aSetPromptVersionTagRequest\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x0f\n\x07version\x18\x02 \x01(\t\x12\x0b\n\x03key\x18\x03 \x01(\t\x12\r\n\x05value\x18\x04 \x01(\t\"K\n\x1d\x44\x65letePromptVersionTagRequest\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x0f\n\x07version\x18\x02 \x01(\t\x12\x0b\n\x03key\x18\x03 \x01(\t\"\xbe\x01\n!LinkPromptVersionsToModelsRequest\x12\x44\n\x0fprompt_versions\x18\x01 \x03(\x0b\x32+.mlflow.unitycatalog.PromptVersionLinkEntry\x12\x11\n\tmodel_ids\x18\x02 \x03(\t:@\xe2?=\n;UcModelRegistryRoutableRPC[com.google.protobuf.empty.Empty]\"\xb7\x01\n\x1aLinkPromptsToTracesRequest\x12\x44\n\x0fprompt_versions\x18\x01 \x03(\x0b\x32+.mlflow.unitycatalog.PromptVersionLinkEntry\x12\x11\n\ttrace_ids\x18\x02 \x03(\t:@\xe2?=\n;UcModelRegistryRoutableRPC[com.google.protobuf.empty.Empty]\"\xba\x01\n\x1fLinkPromptVersionsToRunsRequest\x12\x44\n\x0fprompt_versions\x18\x01 \x03(\x0b\x32+.mlflow.unitycatalog.PromptVersionLinkEntry\x12\x0f\n\x07run_ids\x18\x02 \x03(\t:@\xe2?=\n;UcModelRegistryRoutableRPC[com.google.protobuf.empty.Empty]\"C\n\x16PromptVersionLinkEntry\x12\x12\n\x04name\x18\x01 \x01(\tB\x04\xf8\x86\x19\x01\x12\x15\n\x07version\x18\x02 \x01(\tB\x04\xf8\x86\x19\x01\x42\x34\n\'com.databricks.api.proto.managedcatalog\x90\x01\x01\xa0\x01\x01\xe2?\x02\x10\x01') + + _globals = globals() + _builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals) + _builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'unity_catalog_prompt_messages_pb2', _globals) + if not _descriptor._USE_C_DESCRIPTORS: + _globals['DESCRIPTOR']._loaded_options = None + _globals['DESCRIPTOR']._serialized_options = b'\n\'com.databricks.api.proto.managedcatalog\220\001\001\240\001\001\342?\002\020\001' + _globals['_LINKPROMPTVERSIONSTOMODELSREQUEST']._loaded_options = None + _globals['_LINKPROMPTVERSIONSTOMODELSREQUEST']._serialized_options = b'\342?=\n;UcModelRegistryRoutableRPC[com.google.protobuf.empty.Empty]' + _globals['_LINKPROMPTSTOTRACESREQUEST']._loaded_options = None + _globals['_LINKPROMPTSTOTRACESREQUEST']._serialized_options = b'\342?=\n;UcModelRegistryRoutableRPC[com.google.protobuf.empty.Empty]' + _globals['_LINKPROMPTVERSIONSTORUNSREQUEST']._loaded_options = None + _globals['_LINKPROMPTVERSIONSTORUNSREQUEST']._serialized_options = b'\342?=\n;UcModelRegistryRoutableRPC[com.google.protobuf.empty.Empty]' + _globals['_PROMPTVERSIONLINKENTRY'].fields_by_name['name']._loaded_options = None + _globals['_PROMPTVERSIONLINKENTRY'].fields_by_name['name']._serialized_options = b'\370\206\031\001' + _globals['_PROMPTVERSIONLINKENTRY'].fields_by_name['version']._loaded_options = None + _globals['_PROMPTVERSIONLINKENTRY'].fields_by_name['version']._serialized_options = b'\370\206\031\001' + _globals['_PROMPT']._serialized_start=194 + _globals['_PROMPT']._serialized_end=471 + _globals['_PROMPTVERSION']._serialized_start=474 + _globals['_PROMPTVERSION']._serialized_end=779 + _globals['_PROMPTTAG']._serialized_start=781 + _globals['_PROMPTTAG']._serialized_end=820 + _globals['_PROMPTVERSIONTAG']._serialized_start=822 + _globals['_PROMPTVERSIONTAG']._serialized_end=868 + _globals['_PROMPTALIAS']._serialized_start=870 + _globals['_PROMPTALIAS']._serialized_end=915 + _globals['_UNITYCATALOGSCHEMA']._serialized_start=917 + _globals['_UNITYCATALOGSCHEMA']._serialized_end=980 + _globals['_CREATEPROMPTREQUEST']._serialized_start=982 + _globals['_CREATEPROMPTREQUEST']._serialized_end=1062 + _globals['_UPDATEPROMPTREQUEST']._serialized_start=1064 + _globals['_UPDATEPROMPTREQUEST']._serialized_end=1144 + _globals['_DELETEPROMPTREQUEST']._serialized_start=1146 + _globals['_DELETEPROMPTREQUEST']._serialized_end=1181 + _globals['_DELETEPROMPTRESPONSE']._serialized_start=1183 + _globals['_DELETEPROMPTRESPONSE']._serialized_end=1205 + _globals['_GETPROMPTREQUEST']._serialized_start=1207 + _globals['_GETPROMPTREQUEST']._serialized_end=1239 + _globals['_SEARCHPROMPTSREQUEST']._serialized_start=1242 + _globals['_SEARCHPROMPTSREQUEST']._serialized_end=1421 + _globals['_SEARCHPROMPTSRESPONSE']._serialized_start=1423 + _globals['_SEARCHPROMPTSRESPONSE']._serialized_end=1517 + _globals['_CREATEPROMPTVERSIONREQUEST']._serialized_start=1519 + _globals['_CREATEPROMPTVERSIONREQUEST']._serialized_end=1636 + _globals['_UPDATEPROMPTVERSIONREQUEST']._serialized_start=1638 + _globals['_UPDATEPROMPTVERSIONREQUEST']._serialized_end=1757 + _globals['_DELETEPROMPTVERSIONREQUEST']._serialized_start=1759 + _globals['_DELETEPROMPTVERSIONREQUEST']._serialized_end=1818 + _globals['_GETPROMPTVERSIONREQUEST']._serialized_start=1820 + _globals['_GETPROMPTVERSIONREQUEST']._serialized_end=1876 + _globals['_SEARCHPROMPTVERSIONSREQUEST']._serialized_start=1878 + _globals['_SEARCHPROMPTVERSIONSREQUEST']._serialized_end=1962 + _globals['_SEARCHPROMPTVERSIONSRESPONSE']._serialized_start=1964 + _globals['_SEARCHPROMPTVERSIONSRESPONSE']._serialized_end=2080 + _globals['_SETPROMPTALIASREQUEST']._serialized_start=2082 + _globals['_SETPROMPTALIASREQUEST']._serialized_end=2151 + _globals['_DELETEPROMPTALIASREQUEST']._serialized_start=2153 + _globals['_DELETEPROMPTALIASREQUEST']._serialized_end=2208 + _globals['_GETPROMPTVERSIONBYALIASREQUEST']._serialized_start=2210 + _globals['_GETPROMPTVERSIONBYALIASREQUEST']._serialized_end=2271 + _globals['_SETPROMPTTAGREQUEST']._serialized_start=2273 + _globals['_SETPROMPTTAGREQUEST']._serialized_end=2336 + _globals['_DELETEPROMPTTAGREQUEST']._serialized_start=2338 + _globals['_DELETEPROMPTTAGREQUEST']._serialized_end=2389 + _globals['_SETPROMPTVERSIONTAGREQUEST']._serialized_start=2391 + _globals['_SETPROMPTVERSIONTAGREQUEST']._serialized_end=2478 + _globals['_DELETEPROMPTVERSIONTAGREQUEST']._serialized_start=2480 + _globals['_DELETEPROMPTVERSIONTAGREQUEST']._serialized_end=2555 + _globals['_LINKPROMPTVERSIONSTOMODELSREQUEST']._serialized_start=2558 + _globals['_LINKPROMPTVERSIONSTOMODELSREQUEST']._serialized_end=2748 + _globals['_LINKPROMPTSTOTRACESREQUEST']._serialized_start=2751 + _globals['_LINKPROMPTSTOTRACESREQUEST']._serialized_end=2934 + _globals['_LINKPROMPTVERSIONSTORUNSREQUEST']._serialized_start=2937 + _globals['_LINKPROMPTVERSIONSTORUNSREQUEST']._serialized_end=3123 + _globals['_PROMPTVERSIONLINKENTRY']._serialized_start=3125 + _globals['_PROMPTVERSIONLINKENTRY']._serialized_end=3192 + # @@protoc_insertion_point(module_scope) + +else: + # -*- coding: utf-8 -*- + # Generated by the protocol buffer compiler. DO NOT EDIT! + # source: unity_catalog_prompt_messages.proto + """Generated protocol buffer code.""" + from google.protobuf import descriptor as _descriptor + from google.protobuf import descriptor_pool as _descriptor_pool + from google.protobuf import message as _message + from google.protobuf import reflection as _reflection + from google.protobuf import symbol_database as _symbol_database + # @@protoc_insertion_point(imports) + + _sym_db = _symbol_database.Default() + + + from google.protobuf import struct_pb2 as google_dot_protobuf_dot_struct__pb2 + from google.protobuf import timestamp_pb2 as google_dot_protobuf_dot_timestamp__pb2 + from google.protobuf import empty_pb2 as google_dot_protobuf_dot_empty__pb2 + from . import databricks_pb2 as databricks__pb2 + from .scalapb import scalapb_pb2 as scalapb_dot_scalapb__pb2 + + + DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n#unity_catalog_prompt_messages.proto\x12\x13mlflow.unitycatalog\x1a\x1cgoogle/protobuf/struct.proto\x1a\x1fgoogle/protobuf/timestamp.proto\x1a\x1bgoogle/protobuf/empty.proto\x1a\x10\x64\x61tabricks.proto\x1a\x15scalapb/scalapb.proto\"\x95\x02\n\x06Prompt\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x36\n\x12\x63reation_timestamp\x18\x02 \x01(\x0b\x32\x1a.google.protobuf.Timestamp\x12:\n\x16last_updated_timestamp\x18\x03 \x01(\x0b\x32\x1a.google.protobuf.Timestamp\x12\x13\n\x0b\x64\x65scription\x18\x04 \x01(\t\x12\x31\n\x07\x61liases\x18\x06 \x03(\x0b\x32 .mlflow.unitycatalog.PromptAlias\x12,\n\x04tags\x18\x07 \x03(\x0b\x32\x1e.mlflow.unitycatalog.PromptTagJ\x04\x08\x05\x10\x06R\rexperiment_id\"\xb1\x02\n\rPromptVersion\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x0f\n\x07version\x18\x02 \x01(\t\x12\x36\n\x12\x63reation_timestamp\x18\x03 \x01(\x0b\x32\x1a.google.protobuf.Timestamp\x12:\n\x16last_updated_timestamp\x18\x04 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\x01(\x0b\x32\".mlflow.unitycatalog.PromptVersion\";\n\x1a\x44\x65letePromptVersionRequest\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x0f\n\x07version\x18\x02 \x01(\t\"8\n\x17GetPromptVersionRequest\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x0f\n\x07version\x18\x02 \x01(\t\"T\n\x1bSearchPromptVersionsRequest\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x13\n\x0bmax_results\x18\x02 \x01(\x03\x12\x12\n\npage_token\x18\x03 \x01(\t\"t\n\x1cSearchPromptVersionsResponse\x12;\n\x0fprompt_versions\x18\x01 \x03(\x0b\x32\".mlflow.unitycatalog.PromptVersion\x12\x17\n\x0fnext_page_token\x18\x02 \x01(\t\"E\n\x15SetPromptAliasRequest\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\r\n\x05\x61lias\x18\x02 \x01(\t\x12\x0f\n\x07version\x18\x03 \x01(\t\"7\n\x18\x44\x65letePromptAliasRequest\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\r\n\x05\x61lias\x18\x02 \x01(\t\"=\n\x1eGetPromptVersionByAliasRequest\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\r\n\x05\x61lias\x18\x02 \x01(\t\"?\n\x13SetPromptTagRequest\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x0b\n\x03key\x18\x02 \x01(\t\x12\r\n\x05value\x18\x03 \x01(\t\"3\n\x16\x44\x65letePromptTagRequest\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x0b\n\x03key\x18\x02 \x01(\t\"W\n\x1aSetPromptVersionTagRequest\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x0f\n\x07version\x18\x02 \x01(\t\x12\x0b\n\x03key\x18\x03 \x01(\t\x12\r\n\x05value\x18\x04 \x01(\t\"K\n\x1d\x44\x65letePromptVersionTagRequest\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x0f\n\x07version\x18\x02 \x01(\t\x12\x0b\n\x03key\x18\x03 \x01(\t\"\xbe\x01\n!LinkPromptVersionsToModelsRequest\x12\x44\n\x0fprompt_versions\x18\x01 \x03(\x0b\x32+.mlflow.unitycatalog.PromptVersionLinkEntry\x12\x11\n\tmodel_ids\x18\x02 \x03(\t:@\xe2?=\n;UcModelRegistryRoutableRPC[com.google.protobuf.empty.Empty]\"\xb7\x01\n\x1aLinkPromptsToTracesRequest\x12\x44\n\x0fprompt_versions\x18\x01 \x03(\x0b\x32+.mlflow.unitycatalog.PromptVersionLinkEntry\x12\x11\n\ttrace_ids\x18\x02 \x03(\t:@\xe2?=\n;UcModelRegistryRoutableRPC[com.google.protobuf.empty.Empty]\"\xba\x01\n\x1fLinkPromptVersionsToRunsRequest\x12\x44\n\x0fprompt_versions\x18\x01 \x03(\x0b\x32+.mlflow.unitycatalog.PromptVersionLinkEntry\x12\x0f\n\x07run_ids\x18\x02 \x03(\t:@\xe2?=\n;UcModelRegistryRoutableRPC[com.google.protobuf.empty.Empty]\"C\n\x16PromptVersionLinkEntry\x12\x12\n\x04name\x18\x01 \x01(\tB\x04\xf8\x86\x19\x01\x12\x15\n\x07version\x18\x02 \x01(\tB\x04\xf8\x86\x19\x01\x42\x34\n\'com.databricks.api.proto.managedcatalog\x90\x01\x01\xa0\x01\x01\xe2?\x02\x10\x01') + + + + _PROMPT = DESCRIPTOR.message_types_by_name['Prompt'] + _PROMPTVERSION = DESCRIPTOR.message_types_by_name['PromptVersion'] + _PROMPTTAG = DESCRIPTOR.message_types_by_name['PromptTag'] + _PROMPTVERSIONTAG = DESCRIPTOR.message_types_by_name['PromptVersionTag'] + _PROMPTALIAS = DESCRIPTOR.message_types_by_name['PromptAlias'] + _UNITYCATALOGSCHEMA = DESCRIPTOR.message_types_by_name['UnityCatalogSchema'] + _CREATEPROMPTREQUEST = DESCRIPTOR.message_types_by_name['CreatePromptRequest'] + _UPDATEPROMPTREQUEST = DESCRIPTOR.message_types_by_name['UpdatePromptRequest'] + _DELETEPROMPTREQUEST = DESCRIPTOR.message_types_by_name['DeletePromptRequest'] + _DELETEPROMPTRESPONSE = DESCRIPTOR.message_types_by_name['DeletePromptResponse'] + _GETPROMPTREQUEST = DESCRIPTOR.message_types_by_name['GetPromptRequest'] + _SEARCHPROMPTSREQUEST = DESCRIPTOR.message_types_by_name['SearchPromptsRequest'] + _SEARCHPROMPTSRESPONSE = DESCRIPTOR.message_types_by_name['SearchPromptsResponse'] + _CREATEPROMPTVERSIONREQUEST = DESCRIPTOR.message_types_by_name['CreatePromptVersionRequest'] + _UPDATEPROMPTVERSIONREQUEST = DESCRIPTOR.message_types_by_name['UpdatePromptVersionRequest'] + _DELETEPROMPTVERSIONREQUEST = DESCRIPTOR.message_types_by_name['DeletePromptVersionRequest'] + _GETPROMPTVERSIONREQUEST = DESCRIPTOR.message_types_by_name['GetPromptVersionRequest'] + _SEARCHPROMPTVERSIONSREQUEST = DESCRIPTOR.message_types_by_name['SearchPromptVersionsRequest'] + _SEARCHPROMPTVERSIONSRESPONSE = DESCRIPTOR.message_types_by_name['SearchPromptVersionsResponse'] + _SETPROMPTALIASREQUEST = DESCRIPTOR.message_types_by_name['SetPromptAliasRequest'] + _DELETEPROMPTALIASREQUEST = DESCRIPTOR.message_types_by_name['DeletePromptAliasRequest'] + _GETPROMPTVERSIONBYALIASREQUEST = DESCRIPTOR.message_types_by_name['GetPromptVersionByAliasRequest'] + _SETPROMPTTAGREQUEST = DESCRIPTOR.message_types_by_name['SetPromptTagRequest'] + _DELETEPROMPTTAGREQUEST = DESCRIPTOR.message_types_by_name['DeletePromptTagRequest'] + _SETPROMPTVERSIONTAGREQUEST = DESCRIPTOR.message_types_by_name['SetPromptVersionTagRequest'] + _DELETEPROMPTVERSIONTAGREQUEST = DESCRIPTOR.message_types_by_name['DeletePromptVersionTagRequest'] + _LINKPROMPTVERSIONSTOMODELSREQUEST = DESCRIPTOR.message_types_by_name['LinkPromptVersionsToModelsRequest'] + _LINKPROMPTSTOTRACESREQUEST = DESCRIPTOR.message_types_by_name['LinkPromptsToTracesRequest'] + _LINKPROMPTVERSIONSTORUNSREQUEST = DESCRIPTOR.message_types_by_name['LinkPromptVersionsToRunsRequest'] + _PROMPTVERSIONLINKENTRY = DESCRIPTOR.message_types_by_name['PromptVersionLinkEntry'] + Prompt = _reflection.GeneratedProtocolMessageType('Prompt', (_message.Message,), { + 'DESCRIPTOR' : _PROMPT, + '__module__' : 'unity_catalog_prompt_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.unitycatalog.Prompt) + }) + _sym_db.RegisterMessage(Prompt) + + PromptVersion = _reflection.GeneratedProtocolMessageType('PromptVersion', (_message.Message,), { + 'DESCRIPTOR' : _PROMPTVERSION, + '__module__' : 'unity_catalog_prompt_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.unitycatalog.PromptVersion) + }) + _sym_db.RegisterMessage(PromptVersion) + + PromptTag = _reflection.GeneratedProtocolMessageType('PromptTag', (_message.Message,), { + 'DESCRIPTOR' : _PROMPTTAG, + '__module__' : 'unity_catalog_prompt_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.unitycatalog.PromptTag) + }) + _sym_db.RegisterMessage(PromptTag) + + PromptVersionTag = _reflection.GeneratedProtocolMessageType('PromptVersionTag', (_message.Message,), { + 'DESCRIPTOR' : _PROMPTVERSIONTAG, + '__module__' : 'unity_catalog_prompt_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.unitycatalog.PromptVersionTag) + }) + _sym_db.RegisterMessage(PromptVersionTag) + + PromptAlias = _reflection.GeneratedProtocolMessageType('PromptAlias', (_message.Message,), { + 'DESCRIPTOR' : _PROMPTALIAS, + '__module__' : 'unity_catalog_prompt_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.unitycatalog.PromptAlias) + }) + _sym_db.RegisterMessage(PromptAlias) + + UnityCatalogSchema = _reflection.GeneratedProtocolMessageType('UnityCatalogSchema', (_message.Message,), { + 'DESCRIPTOR' : _UNITYCATALOGSCHEMA, + '__module__' : 'unity_catalog_prompt_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.unitycatalog.UnityCatalogSchema) + }) + _sym_db.RegisterMessage(UnityCatalogSchema) + + CreatePromptRequest = _reflection.GeneratedProtocolMessageType('CreatePromptRequest', (_message.Message,), { + 'DESCRIPTOR' : _CREATEPROMPTREQUEST, + '__module__' : 'unity_catalog_prompt_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.unitycatalog.CreatePromptRequest) + }) + _sym_db.RegisterMessage(CreatePromptRequest) + + UpdatePromptRequest = _reflection.GeneratedProtocolMessageType('UpdatePromptRequest', (_message.Message,), { + 'DESCRIPTOR' : _UPDATEPROMPTREQUEST, + '__module__' : 'unity_catalog_prompt_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.unitycatalog.UpdatePromptRequest) + }) + _sym_db.RegisterMessage(UpdatePromptRequest) + + DeletePromptRequest = _reflection.GeneratedProtocolMessageType('DeletePromptRequest', (_message.Message,), { + 'DESCRIPTOR' : _DELETEPROMPTREQUEST, + '__module__' : 'unity_catalog_prompt_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.unitycatalog.DeletePromptRequest) + }) + _sym_db.RegisterMessage(DeletePromptRequest) + + DeletePromptResponse = _reflection.GeneratedProtocolMessageType('DeletePromptResponse', (_message.Message,), { + 'DESCRIPTOR' : _DELETEPROMPTRESPONSE, + '__module__' : 'unity_catalog_prompt_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.unitycatalog.DeletePromptResponse) + }) + _sym_db.RegisterMessage(DeletePromptResponse) + + GetPromptRequest = _reflection.GeneratedProtocolMessageType('GetPromptRequest', (_message.Message,), { + 'DESCRIPTOR' : _GETPROMPTREQUEST, + '__module__' : 'unity_catalog_prompt_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.unitycatalog.GetPromptRequest) + }) + _sym_db.RegisterMessage(GetPromptRequest) + + SearchPromptsRequest = _reflection.GeneratedProtocolMessageType('SearchPromptsRequest', (_message.Message,), { + 'DESCRIPTOR' : _SEARCHPROMPTSREQUEST, + '__module__' : 'unity_catalog_prompt_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.unitycatalog.SearchPromptsRequest) + }) + _sym_db.RegisterMessage(SearchPromptsRequest) + + SearchPromptsResponse = _reflection.GeneratedProtocolMessageType('SearchPromptsResponse', (_message.Message,), { + 'DESCRIPTOR' : _SEARCHPROMPTSRESPONSE, + '__module__' : 'unity_catalog_prompt_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.unitycatalog.SearchPromptsResponse) + }) + _sym_db.RegisterMessage(SearchPromptsResponse) + + CreatePromptVersionRequest = _reflection.GeneratedProtocolMessageType('CreatePromptVersionRequest', (_message.Message,), { + 'DESCRIPTOR' : _CREATEPROMPTVERSIONREQUEST, + '__module__' : 'unity_catalog_prompt_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.unitycatalog.CreatePromptVersionRequest) + }) + _sym_db.RegisterMessage(CreatePromptVersionRequest) + + UpdatePromptVersionRequest = _reflection.GeneratedProtocolMessageType('UpdatePromptVersionRequest', (_message.Message,), { + 'DESCRIPTOR' : _UPDATEPROMPTVERSIONREQUEST, + '__module__' : 'unity_catalog_prompt_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.unitycatalog.UpdatePromptVersionRequest) + }) + _sym_db.RegisterMessage(UpdatePromptVersionRequest) + + DeletePromptVersionRequest = _reflection.GeneratedProtocolMessageType('DeletePromptVersionRequest', (_message.Message,), { + 'DESCRIPTOR' : _DELETEPROMPTVERSIONREQUEST, + '__module__' : 'unity_catalog_prompt_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.unitycatalog.DeletePromptVersionRequest) + }) + _sym_db.RegisterMessage(DeletePromptVersionRequest) + + GetPromptVersionRequest = _reflection.GeneratedProtocolMessageType('GetPromptVersionRequest', (_message.Message,), { + 'DESCRIPTOR' : _GETPROMPTVERSIONREQUEST, + '__module__' : 'unity_catalog_prompt_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.unitycatalog.GetPromptVersionRequest) + }) + _sym_db.RegisterMessage(GetPromptVersionRequest) + + SearchPromptVersionsRequest = _reflection.GeneratedProtocolMessageType('SearchPromptVersionsRequest', (_message.Message,), { + 'DESCRIPTOR' : _SEARCHPROMPTVERSIONSREQUEST, + '__module__' : 'unity_catalog_prompt_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.unitycatalog.SearchPromptVersionsRequest) + }) + _sym_db.RegisterMessage(SearchPromptVersionsRequest) + + SearchPromptVersionsResponse = _reflection.GeneratedProtocolMessageType('SearchPromptVersionsResponse', (_message.Message,), { + 'DESCRIPTOR' : _SEARCHPROMPTVERSIONSRESPONSE, + '__module__' : 'unity_catalog_prompt_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.unitycatalog.SearchPromptVersionsResponse) + }) + _sym_db.RegisterMessage(SearchPromptVersionsResponse) + + SetPromptAliasRequest = _reflection.GeneratedProtocolMessageType('SetPromptAliasRequest', (_message.Message,), { + 'DESCRIPTOR' : _SETPROMPTALIASREQUEST, + '__module__' : 'unity_catalog_prompt_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.unitycatalog.SetPromptAliasRequest) + }) + _sym_db.RegisterMessage(SetPromptAliasRequest) + + DeletePromptAliasRequest = _reflection.GeneratedProtocolMessageType('DeletePromptAliasRequest', (_message.Message,), { + 'DESCRIPTOR' : _DELETEPROMPTALIASREQUEST, + '__module__' : 'unity_catalog_prompt_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.unitycatalog.DeletePromptAliasRequest) + }) + _sym_db.RegisterMessage(DeletePromptAliasRequest) + + GetPromptVersionByAliasRequest = _reflection.GeneratedProtocolMessageType('GetPromptVersionByAliasRequest', (_message.Message,), { + 'DESCRIPTOR' : _GETPROMPTVERSIONBYALIASREQUEST, + '__module__' : 'unity_catalog_prompt_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.unitycatalog.GetPromptVersionByAliasRequest) + }) + _sym_db.RegisterMessage(GetPromptVersionByAliasRequest) + + SetPromptTagRequest = _reflection.GeneratedProtocolMessageType('SetPromptTagRequest', (_message.Message,), { + 'DESCRIPTOR' : _SETPROMPTTAGREQUEST, + '__module__' : 'unity_catalog_prompt_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.unitycatalog.SetPromptTagRequest) + }) + _sym_db.RegisterMessage(SetPromptTagRequest) + + DeletePromptTagRequest = _reflection.GeneratedProtocolMessageType('DeletePromptTagRequest', (_message.Message,), { + 'DESCRIPTOR' : _DELETEPROMPTTAGREQUEST, + '__module__' : 'unity_catalog_prompt_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.unitycatalog.DeletePromptTagRequest) + }) + _sym_db.RegisterMessage(DeletePromptTagRequest) + + SetPromptVersionTagRequest = _reflection.GeneratedProtocolMessageType('SetPromptVersionTagRequest', (_message.Message,), { + 'DESCRIPTOR' : _SETPROMPTVERSIONTAGREQUEST, + '__module__' : 'unity_catalog_prompt_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.unitycatalog.SetPromptVersionTagRequest) + }) + _sym_db.RegisterMessage(SetPromptVersionTagRequest) + + DeletePromptVersionTagRequest = _reflection.GeneratedProtocolMessageType('DeletePromptVersionTagRequest', (_message.Message,), { + 'DESCRIPTOR' : _DELETEPROMPTVERSIONTAGREQUEST, + '__module__' : 'unity_catalog_prompt_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.unitycatalog.DeletePromptVersionTagRequest) + }) + _sym_db.RegisterMessage(DeletePromptVersionTagRequest) + + LinkPromptVersionsToModelsRequest = _reflection.GeneratedProtocolMessageType('LinkPromptVersionsToModelsRequest', (_message.Message,), { + 'DESCRIPTOR' : _LINKPROMPTVERSIONSTOMODELSREQUEST, + '__module__' : 'unity_catalog_prompt_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.unitycatalog.LinkPromptVersionsToModelsRequest) + }) + _sym_db.RegisterMessage(LinkPromptVersionsToModelsRequest) + + LinkPromptsToTracesRequest = _reflection.GeneratedProtocolMessageType('LinkPromptsToTracesRequest', (_message.Message,), { + 'DESCRIPTOR' : _LINKPROMPTSTOTRACESREQUEST, + '__module__' : 'unity_catalog_prompt_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.unitycatalog.LinkPromptsToTracesRequest) + }) + _sym_db.RegisterMessage(LinkPromptsToTracesRequest) + + LinkPromptVersionsToRunsRequest = _reflection.GeneratedProtocolMessageType('LinkPromptVersionsToRunsRequest', (_message.Message,), { + 'DESCRIPTOR' : _LINKPROMPTVERSIONSTORUNSREQUEST, + '__module__' : 'unity_catalog_prompt_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.unitycatalog.LinkPromptVersionsToRunsRequest) + }) + _sym_db.RegisterMessage(LinkPromptVersionsToRunsRequest) + + PromptVersionLinkEntry = _reflection.GeneratedProtocolMessageType('PromptVersionLinkEntry', (_message.Message,), { + 'DESCRIPTOR' : _PROMPTVERSIONLINKENTRY, + '__module__' : 'unity_catalog_prompt_messages_pb2' + # @@protoc_insertion_point(class_scope:mlflow.unitycatalog.PromptVersionLinkEntry) + }) + _sym_db.RegisterMessage(PromptVersionLinkEntry) + + if _descriptor._USE_C_DESCRIPTORS == False: + + DESCRIPTOR._options = None + DESCRIPTOR._serialized_options = b'\n\'com.databricks.api.proto.managedcatalog\220\001\001\240\001\001\342?\002\020\001' + _LINKPROMPTVERSIONSTOMODELSREQUEST._options = None + _LINKPROMPTVERSIONSTOMODELSREQUEST._serialized_options = b'\342?=\n;UcModelRegistryRoutableRPC[com.google.protobuf.empty.Empty]' + _LINKPROMPTSTOTRACESREQUEST._options = None + _LINKPROMPTSTOTRACESREQUEST._serialized_options = b'\342?=\n;UcModelRegistryRoutableRPC[com.google.protobuf.empty.Empty]' + _LINKPROMPTVERSIONSTORUNSREQUEST._options = None + _LINKPROMPTVERSIONSTORUNSREQUEST._serialized_options = b'\342?=\n;UcModelRegistryRoutableRPC[com.google.protobuf.empty.Empty]' + _PROMPTVERSIONLINKENTRY.fields_by_name['name']._options = None + _PROMPTVERSIONLINKENTRY.fields_by_name['name']._serialized_options = b'\370\206\031\001' + _PROMPTVERSIONLINKENTRY.fields_by_name['version']._options = None + _PROMPTVERSIONLINKENTRY.fields_by_name['version']._serialized_options = b'\370\206\031\001' + _PROMPT._serialized_start=194 + _PROMPT._serialized_end=471 + _PROMPTVERSION._serialized_start=474 + _PROMPTVERSION._serialized_end=779 + _PROMPTTAG._serialized_start=781 + _PROMPTTAG._serialized_end=820 + _PROMPTVERSIONTAG._serialized_start=822 + _PROMPTVERSIONTAG._serialized_end=868 + _PROMPTALIAS._serialized_start=870 + _PROMPTALIAS._serialized_end=915 + _UNITYCATALOGSCHEMA._serialized_start=917 + _UNITYCATALOGSCHEMA._serialized_end=980 + _CREATEPROMPTREQUEST._serialized_start=982 + _CREATEPROMPTREQUEST._serialized_end=1062 + _UPDATEPROMPTREQUEST._serialized_start=1064 + _UPDATEPROMPTREQUEST._serialized_end=1144 + _DELETEPROMPTREQUEST._serialized_start=1146 + _DELETEPROMPTREQUEST._serialized_end=1181 + _DELETEPROMPTRESPONSE._serialized_start=1183 + _DELETEPROMPTRESPONSE._serialized_end=1205 + _GETPROMPTREQUEST._serialized_start=1207 + _GETPROMPTREQUEST._serialized_end=1239 + _SEARCHPROMPTSREQUEST._serialized_start=1242 + _SEARCHPROMPTSREQUEST._serialized_end=1421 + _SEARCHPROMPTSRESPONSE._serialized_start=1423 + _SEARCHPROMPTSRESPONSE._serialized_end=1517 + _CREATEPROMPTVERSIONREQUEST._serialized_start=1519 + _CREATEPROMPTVERSIONREQUEST._serialized_end=1636 + _UPDATEPROMPTVERSIONREQUEST._serialized_start=1638 + _UPDATEPROMPTVERSIONREQUEST._serialized_end=1757 + _DELETEPROMPTVERSIONREQUEST._serialized_start=1759 + _DELETEPROMPTVERSIONREQUEST._serialized_end=1818 + _GETPROMPTVERSIONREQUEST._serialized_start=1820 + _GETPROMPTVERSIONREQUEST._serialized_end=1876 + _SEARCHPROMPTVERSIONSREQUEST._serialized_start=1878 + _SEARCHPROMPTVERSIONSREQUEST._serialized_end=1962 + _SEARCHPROMPTVERSIONSRESPONSE._serialized_start=1964 + _SEARCHPROMPTVERSIONSRESPONSE._serialized_end=2080 + _SETPROMPTALIASREQUEST._serialized_start=2082 + _SETPROMPTALIASREQUEST._serialized_end=2151 + _DELETEPROMPTALIASREQUEST._serialized_start=2153 + _DELETEPROMPTALIASREQUEST._serialized_end=2208 + _GETPROMPTVERSIONBYALIASREQUEST._serialized_start=2210 + _GETPROMPTVERSIONBYALIASREQUEST._serialized_end=2271 + _SETPROMPTTAGREQUEST._serialized_start=2273 + _SETPROMPTTAGREQUEST._serialized_end=2336 + _DELETEPROMPTTAGREQUEST._serialized_start=2338 + _DELETEPROMPTTAGREQUEST._serialized_end=2389 + _SETPROMPTVERSIONTAGREQUEST._serialized_start=2391 + _SETPROMPTVERSIONTAGREQUEST._serialized_end=2478 + _DELETEPROMPTVERSIONTAGREQUEST._serialized_start=2480 + _DELETEPROMPTVERSIONTAGREQUEST._serialized_end=2555 + _LINKPROMPTVERSIONSTOMODELSREQUEST._serialized_start=2558 + _LINKPROMPTVERSIONSTOMODELSREQUEST._serialized_end=2748 + _LINKPROMPTSTOTRACESREQUEST._serialized_start=2751 + _LINKPROMPTSTOTRACESREQUEST._serialized_end=2934 + _LINKPROMPTVERSIONSTORUNSREQUEST._serialized_start=2937 + _LINKPROMPTVERSIONSTORUNSREQUEST._serialized_end=3123 + _PROMPTVERSIONLINKENTRY._serialized_start=3125 + _PROMPTVERSIONLINKENTRY._serialized_end=3192 + # @@protoc_insertion_point(module_scope) + diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/protos/unity_catalog_prompt_messages_pb2_grpc.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/protos/unity_catalog_prompt_messages_pb2_grpc.py new file mode 100644 index 0000000000000000000000000000000000000000..7ff53eb09dce2cf7159ba3b560e0b1577c22c7c6 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/protos/unity_catalog_prompt_messages_pb2_grpc.py @@ -0,0 +1,24 @@ +# Generated by the gRPC Python protocol compiler plugin. DO NOT EDIT! +"""Client and server classes corresponding to protobuf-defined services.""" +import grpc +import warnings + + +GRPC_GENERATED_VERSION = '1.71.0' +GRPC_VERSION = grpc.__version__ +_version_not_supported = False + +try: + from grpc._utilities import first_version_is_lower + _version_not_supported = first_version_is_lower(GRPC_VERSION, GRPC_GENERATED_VERSION) +except ImportError: + _version_not_supported = True + +if _version_not_supported: + raise RuntimeError( + f'The grpc package installed is at version {GRPC_VERSION},' + + f' but the generated code in unity_catalog_prompt_messages_pb2_grpc.py depends on' + + f' grpcio>={GRPC_GENERATED_VERSION}.' + + f' Please upgrade your grpc module to grpcio>={GRPC_GENERATED_VERSION}' + + f' or downgrade your generated code using grpcio-tools<={GRPC_VERSION}.' + ) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/protos/unity_catalog_prompt_service_pb2.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/protos/unity_catalog_prompt_service_pb2.py new file mode 100644 index 0000000000000000000000000000000000000000..a9faa777c29f1ab49590feedc69d98c0e435f1f4 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/protos/unity_catalog_prompt_service_pb2.py @@ -0,0 +1,164 @@ + +import google.protobuf +from packaging.version import Version +if Version(google.protobuf.__version__).major >= 5: + # -*- coding: utf-8 -*- + # Generated by the protocol buffer compiler. DO NOT EDIT! + # source: unity_catalog_prompt_service.proto + # Protobuf Python Version: 5.26.0 + """Generated protocol buffer code.""" + from google.protobuf import descriptor as _descriptor + from google.protobuf import descriptor_pool as _descriptor_pool + from google.protobuf import symbol_database as _symbol_database + from google.protobuf.internal import builder as _builder + # @@protoc_insertion_point(imports) + + _sym_db = _symbol_database.Default() + + + from . import databricks_pb2 as databricks__pb2 + from .scalapb import scalapb_pb2 as scalapb_dot_scalapb__pb2 + from . import unity_catalog_prompt_messages_pb2 as unity_catalog_prompt_messages_pb2 + from google.protobuf import empty_pb2 as google_dot_protobuf_dot_empty__pb2 + + + DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\"unity_catalog_prompt_service.proto\x12\x13mlflow.unitycatalog\x1a\x10\x64\x61tabricks.proto\x1a\x15scalapb/scalapb.proto\x1a#unity_catalog_prompt_messages.proto\x1a\x1bgoogle/protobuf/empty.proto2\xfe \n\x19UnityCatalogPromptService\x12\xa9\x01\n\x0c\x43reatePrompt\x12(.mlflow.unitycatalog.CreatePromptRequest\x1a\x1b.mlflow.unitycatalog.Prompt\"R\xf2\x86\x19N\n+\n\x04POST\x12\x1d/mlflow/unity-catalog/prompts\x1a\x04\x08\x02\x10\x00\x10\x03*\x1d(Unity Catalog) Create Prompt\x12\xb1\x01\n\x0cUpdatePrompt\x12(.mlflow.unitycatalog.UpdatePromptRequest\x1a\x1b.mlflow.unitycatalog.Prompt\"Z\xf2\x86\x19V\n3\n\x05PATCH\x12$/mlflow/unity-catalog/prompts/{name}\x1a\x04\x08\x02\x10\x00\x10\x03*\x1d(Unity Catalog) Update Prompt\x12\xad\x01\n\x0c\x44\x65letePrompt\x12(.mlflow.unitycatalog.DeletePromptRequest\x1a\x16.google.protobuf.Empty\"[\xf2\x86\x19W\n4\n\x06\x44\x45LETE\x12$/mlflow/unity-catalog/prompts/{name}\x1a\x04\x08\x02\x10\x00\x10\x03*\x1d(Unity Catalog) Delete Prompt\x12\xa6\x01\n\tGetPrompt\x12%.mlflow.unitycatalog.GetPromptRequest\x1a\x1b.mlflow.unitycatalog.Prompt\"U\xf2\x86\x19Q\n1\n\x03GET\x12$/mlflow/unity-catalog/prompts/{name}\x1a\x04\x08\x02\x10\x00\x10\x03*\x1a(Unity Catalog) Get Prompt\x12\xc2\x01\n\rSearchPrompts\x12).mlflow.unitycatalog.SearchPromptsRequest\x1a*.mlflow.unitycatalog.SearchPromptsResponse\"Z\xf2\x86\x19V\n2\n\x04POST\x12$/mlflow/unity-catalog/prompts/search\x1a\x04\x08\x02\x10\x00\x10\x03*\x1e(Unity Catalog) Search Prompts\x12\xd6\x01\n\x13\x43reatePromptVersion\x12/.mlflow.unitycatalog.CreatePromptVersionRequest\x1a\".mlflow.unitycatalog.PromptVersion\"j\xf2\x86\x19\x66\n;\n\x04POST\x12-/mlflow/unity-catalog/prompts/{name}/versions\x1a\x04\x08\x02\x10\x00\x10\x03*%(Unity Catalog) Create Prompt Version\x12\xe1\x01\n\x13UpdatePromptVersion\x12/.mlflow.unitycatalog.UpdatePromptVersionRequest\x1a\".mlflow.unitycatalog.PromptVersion\"u\xf2\x86\x19q\nF\n\x05PATCH\x12\x37/mlflow/unity-catalog/prompts/{name}/versions/{version}\x1a\x04\x08\x02\x10\x00\x10\x03*%(Unity Catalog) Update Prompt Version\x12\xd6\x01\n\x13\x44\x65letePromptVersion\x12/.mlflow.unitycatalog.DeletePromptVersionRequest\x1a\x16.google.protobuf.Empty\"v\xf2\x86\x19r\nG\n\x06\x44\x45LETE\x12\x37/mlflow/unity-catalog/prompts/{name}/versions/{version}\x1a\x04\x08\x02\x10\x00\x10\x03*%(Unity Catalog) Delete Prompt Version\x12\xd6\x01\n\x10GetPromptVersion\x12,.mlflow.unitycatalog.GetPromptVersionRequest\x1a\".mlflow.unitycatalog.PromptVersion\"p\xf2\x86\x19l\nD\n\x03GET\x12\x37/mlflow/unity-catalog/prompts/{name}/versions/{version}\x1a\x04\x08\x02\x10\x00\x10\x03*\"(Unity Catalog) Get Prompt Version\x12\xfa\x01\n\x14SearchPromptVersions\x12\x30.mlflow.unitycatalog.SearchPromptVersionsRequest\x1a\x31.mlflow.unitycatalog.SearchPromptVersionsResponse\"}\xf2\x86\x19y\nB\n\x04POST\x12\x34/mlflow/unity-catalog/prompts/{name}/versions/search\x1a\x04\x08\x02\x10\x00\x10\x03\x18\xe8\x07\x18\xec\x07\x18\xba\x17\x18\x01*&(Unity Catalog) Search Prompt Versions\x12\xc2\x01\n\x0eSetPromptAlias\x12*.mlflow.unitycatalog.SetPromptAliasRequest\x1a\x16.google.protobuf.Empty\"l\xf2\x86\x19h\nB\n\x04POST\x12\x34/mlflow/unity-catalog/prompts/{name}/aliases/{alias}\x1a\x04\x08\x02\x10\x00\x10\x03* (Unity Catalog) Set Prompt Alias\x12\xcd\x01\n\x11\x44\x65letePromptAlias\x12-.mlflow.unitycatalog.DeletePromptAliasRequest\x1a\x16.google.protobuf.Empty\"q\xf2\x86\x19m\nD\n\x06\x44\x45LETE\x12\x34/mlflow/unity-catalog/prompts/{name}/aliases/{alias}\x1a\x04\x08\x02\x10\x00\x10\x03*#(Unity Catalog) Delete Prompt Alias\x12\xf5\x01\n\x17GetPromptVersionByAlias\x12\x33.mlflow.unitycatalog.GetPromptVersionByAliasRequest\x1a\".mlflow.unitycatalog.PromptVersion\"\x80\x01\xf2\x86\x19|\nK\n\x03GET\x12>/mlflow/unity-catalog/prompts/{name}/versions/by-alias/{alias}\x1a\x04\x08\x02\x10\x00\x10\x03*+(Unity Catalog) Get Prompt Version By Alias\x12\xb1\x01\n\x0cSetPromptTag\x12(.mlflow.unitycatalog.SetPromptTagRequest\x1a\x16.google.protobuf.Empty\"_\xf2\x86\x19[\n7\n\x04POST\x12)/mlflow/unity-catalog/prompts/{name}/tags\x1a\x04\x08\x02\x10\x00\x10\x03*\x1e(Unity Catalog) Set Prompt Tag\x12\xc2\x01\n\x0f\x44\x65letePromptTag\x12+.mlflow.unitycatalog.DeletePromptTagRequest\x1a\x16.google.protobuf.Empty\"j\xf2\x86\x19\x66\n?\n\x06\x44\x45LETE\x12//mlflow/unity-catalog/prompts/{name}/tags/{key}\x1a\x04\x08\x02\x10\x00\x10\x03*!(Unity Catalog) Delete Prompt Tag\x12\xda\x01\n\x13SetPromptVersionTag\x12/.mlflow.unitycatalog.SetPromptVersionTagRequest\x1a\x16.google.protobuf.Empty\"z\xf2\x86\x19v\nJ\n\x04POST\x12/mlflow/unity-catalog/prompts/{name}/versions/by-alias/{alias}\032\004\010\002\020\000\020\003*+(Unity Catalog) Get Prompt Version By Alias' + _globals['_UNITYCATALOGPROMPTSERVICE'].methods_by_name['SetPromptTag']._loaded_options = None + _globals['_UNITYCATALOGPROMPTSERVICE'].methods_by_name['SetPromptTag']._serialized_options = b'\362\206\031[\n7\n\004POST\022)/mlflow/unity-catalog/prompts/{name}/tags\032\004\010\002\020\000\020\003*\036(Unity Catalog) Set Prompt Tag' + _globals['_UNITYCATALOGPROMPTSERVICE'].methods_by_name['DeletePromptTag']._loaded_options = None + _globals['_UNITYCATALOGPROMPTSERVICE'].methods_by_name['DeletePromptTag']._serialized_options = b'\362\206\031f\n?\n\006DELETE\022//mlflow/unity-catalog/prompts/{name}/tags/{key}\032\004\010\002\020\000\020\003*!(Unity Catalog) Delete Prompt Tag' + _globals['_UNITYCATALOGPROMPTSERVICE'].methods_by_name['SetPromptVersionTag']._loaded_options = None + _globals['_UNITYCATALOGPROMPTSERVICE'].methods_by_name['SetPromptVersionTag']._serialized_options = b'\362\206\031v\nJ\n\004POST\022/mlflow/unity-catalog/prompts/{name}/versions/by-alias/{alias}\x1a\x04\x08\x02\x10\x00\x10\x03*+(Unity Catalog) Get Prompt Version By Alias\x12\xb1\x01\n\x0cSetPromptTag\x12(.mlflow.unitycatalog.SetPromptTagRequest\x1a\x16.google.protobuf.Empty\"_\xf2\x86\x19[\n7\n\x04POST\x12)/mlflow/unity-catalog/prompts/{name}/tags\x1a\x04\x08\x02\x10\x00\x10\x03*\x1e(Unity Catalog) Set Prompt Tag\x12\xc2\x01\n\x0f\x44\x65letePromptTag\x12+.mlflow.unitycatalog.DeletePromptTagRequest\x1a\x16.google.protobuf.Empty\"j\xf2\x86\x19\x66\n?\n\x06\x44\x45LETE\x12//mlflow/unity-catalog/prompts/{name}/tags/{key}\x1a\x04\x08\x02\x10\x00\x10\x03*!(Unity Catalog) Delete Prompt Tag\x12\xda\x01\n\x13SetPromptVersionTag\x12/.mlflow.unitycatalog.SetPromptVersionTagRequest\x1a\x16.google.protobuf.Empty\"z\xf2\x86\x19v\nJ\n\x04POST\x12/mlflow/unity-catalog/prompts/{name}/versions/by-alias/{alias}\032\004\010\002\020\000\020\003*+(Unity Catalog) Get Prompt Version By Alias' + _UNITYCATALOGPROMPTSERVICE.methods_by_name['SetPromptTag']._options = None + _UNITYCATALOGPROMPTSERVICE.methods_by_name['SetPromptTag']._serialized_options = b'\362\206\031[\n7\n\004POST\022)/mlflow/unity-catalog/prompts/{name}/tags\032\004\010\002\020\000\020\003*\036(Unity Catalog) Set Prompt Tag' + _UNITYCATALOGPROMPTSERVICE.methods_by_name['DeletePromptTag']._options = None + _UNITYCATALOGPROMPTSERVICE.methods_by_name['DeletePromptTag']._serialized_options = b'\362\206\031f\n?\n\006DELETE\022//mlflow/unity-catalog/prompts/{name}/tags/{key}\032\004\010\002\020\000\020\003*!(Unity Catalog) Delete Prompt Tag' + _UNITYCATALOGPROMPTSERVICE.methods_by_name['SetPromptVersionTag']._options = None + _UNITYCATALOGPROMPTSERVICE.methods_by_name['SetPromptVersionTag']._serialized_options = b'\362\206\031v\nJ\n\004POST\022={GRPC_GENERATED_VERSION}.' + + f' Please upgrade your grpc module to grpcio>={GRPC_GENERATED_VERSION}' + + f' or downgrade your generated code using grpcio-tools<={GRPC_VERSION}.' + ) + + +class UnityCatalogPromptServiceStub(object): + """Missing associated documentation comment in .proto file.""" + + def __init__(self, channel): + """Constructor. + + Args: + channel: A grpc.Channel. + """ + self.CreatePrompt = channel.unary_unary( + '/mlflow.unitycatalog.UnityCatalogPromptService/CreatePrompt', + request_serializer=unity__catalog__prompt__messages__pb2.CreatePromptRequest.SerializeToString, + response_deserializer=unity__catalog__prompt__messages__pb2.CreatePromptResponse.FromString, + _registered_method=True) + self.UpdatePrompt = channel.unary_unary( + '/mlflow.unitycatalog.UnityCatalogPromptService/UpdatePrompt', + request_serializer=unity__catalog__prompt__messages__pb2.UpdatePromptRequest.SerializeToString, + response_deserializer=unity__catalog__prompt__messages__pb2.UpdatePromptResponse.FromString, + _registered_method=True) + self.DeletePrompt = channel.unary_unary( + '/mlflow.unitycatalog.UnityCatalogPromptService/DeletePrompt', + request_serializer=unity__catalog__prompt__messages__pb2.DeletePromptRequest.SerializeToString, + response_deserializer=unity__catalog__prompt__messages__pb2.DeletePromptResponse.FromString, + _registered_method=True) + self.GetPrompt = channel.unary_unary( + '/mlflow.unitycatalog.UnityCatalogPromptService/GetPrompt', + request_serializer=unity__catalog__prompt__messages__pb2.GetPromptRequest.SerializeToString, + response_deserializer=unity__catalog__prompt__messages__pb2.GetPromptResponse.FromString, + _registered_method=True) + self.SearchPrompts = channel.unary_unary( + '/mlflow.unitycatalog.UnityCatalogPromptService/SearchPrompts', + request_serializer=unity__catalog__prompt__messages__pb2.SearchPromptsRequest.SerializeToString, + response_deserializer=unity__catalog__prompt__messages__pb2.SearchPromptsResponse.FromString, + _registered_method=True) + self.CreatePromptVersion = channel.unary_unary( + '/mlflow.unitycatalog.UnityCatalogPromptService/CreatePromptVersion', + request_serializer=unity__catalog__prompt__messages__pb2.CreatePromptVersionRequest.SerializeToString, + response_deserializer=unity__catalog__prompt__messages__pb2.CreatePromptVersionResponse.FromString, + _registered_method=True) + self.UpdatePromptVersion = channel.unary_unary( + '/mlflow.unitycatalog.UnityCatalogPromptService/UpdatePromptVersion', + request_serializer=unity__catalog__prompt__messages__pb2.UpdatePromptVersionRequest.SerializeToString, + response_deserializer=unity__catalog__prompt__messages__pb2.UpdatePromptVersionResponse.FromString, + _registered_method=True) + self.DeletePromptVersion = channel.unary_unary( + '/mlflow.unitycatalog.UnityCatalogPromptService/DeletePromptVersion', + request_serializer=unity__catalog__prompt__messages__pb2.DeletePromptVersionRequest.SerializeToString, + response_deserializer=unity__catalog__prompt__messages__pb2.DeletePromptVersionResponse.FromString, + _registered_method=True) + self.GetPromptVersion = channel.unary_unary( + '/mlflow.unitycatalog.UnityCatalogPromptService/GetPromptVersion', + request_serializer=unity__catalog__prompt__messages__pb2.GetPromptVersionRequest.SerializeToString, + response_deserializer=unity__catalog__prompt__messages__pb2.GetPromptVersionResponse.FromString, + _registered_method=True) + self.SearchPromptVersions = channel.unary_unary( + '/mlflow.unitycatalog.UnityCatalogPromptService/SearchPromptVersions', + request_serializer=unity__catalog__prompt__messages__pb2.SearchPromptVersionsRequest.SerializeToString, + response_deserializer=unity__catalog__prompt__messages__pb2.SearchPromptVersionsResponse.FromString, + _registered_method=True) + self.SetPromptAlias = channel.unary_unary( + '/mlflow.unitycatalog.UnityCatalogPromptService/SetPromptAlias', + request_serializer=unity__catalog__prompt__messages__pb2.SetPromptAliasRequest.SerializeToString, + response_deserializer=unity__catalog__prompt__messages__pb2.SetPromptAliasResponse.FromString, + _registered_method=True) + self.DeletePromptAlias = channel.unary_unary( + '/mlflow.unitycatalog.UnityCatalogPromptService/DeletePromptAlias', + request_serializer=unity__catalog__prompt__messages__pb2.DeletePromptAliasRequest.SerializeToString, + response_deserializer=unity__catalog__prompt__messages__pb2.DeletePromptAliasResponse.FromString, + _registered_method=True) + self.GetPromptVersionByAlias = channel.unary_unary( + '/mlflow.unitycatalog.UnityCatalogPromptService/GetPromptVersionByAlias', + request_serializer=unity__catalog__prompt__messages__pb2.GetPromptVersionByAliasRequest.SerializeToString, + response_deserializer=unity__catalog__prompt__messages__pb2.GetPromptVersionByAliasResponse.FromString, + _registered_method=True) + self.SetPromptTag = channel.unary_unary( + '/mlflow.unitycatalog.UnityCatalogPromptService/SetPromptTag', + request_serializer=unity__catalog__prompt__messages__pb2.SetPromptTagRequest.SerializeToString, + response_deserializer=unity__catalog__prompt__messages__pb2.SetPromptTagResponse.FromString, + _registered_method=True) + self.DeletePromptTag = channel.unary_unary( + '/mlflow.unitycatalog.UnityCatalogPromptService/DeletePromptTag', + request_serializer=unity__catalog__prompt__messages__pb2.DeletePromptTagRequest.SerializeToString, + response_deserializer=unity__catalog__prompt__messages__pb2.DeletePromptTagResponse.FromString, + _registered_method=True) + self.SetPromptVersionTag = channel.unary_unary( + '/mlflow.unitycatalog.UnityCatalogPromptService/SetPromptVersionTag', + request_serializer=unity__catalog__prompt__messages__pb2.SetPromptVersionTagRequest.SerializeToString, + response_deserializer=unity__catalog__prompt__messages__pb2.SetPromptVersionTagResponse.FromString, + _registered_method=True) + self.DeletePromptVersionTag = channel.unary_unary( + '/mlflow.unitycatalog.UnityCatalogPromptService/DeletePromptVersionTag', + request_serializer=unity__catalog__prompt__messages__pb2.DeletePromptVersionTagRequest.SerializeToString, + response_deserializer=unity__catalog__prompt__messages__pb2.DeletePromptVersionTagResponse.FromString, + _registered_method=True) + + +class UnityCatalogPromptServiceServicer(object): + """Missing associated documentation comment in .proto file.""" + + def CreatePrompt(self, request, context): + """Missing associated documentation comment in .proto file.""" + context.set_code(grpc.StatusCode.UNIMPLEMENTED) + context.set_details('Method not implemented!') + raise NotImplementedError('Method not implemented!') + + def UpdatePrompt(self, request, context): + """Missing associated documentation comment in .proto file.""" + context.set_code(grpc.StatusCode.UNIMPLEMENTED) + context.set_details('Method not implemented!') + raise NotImplementedError('Method not implemented!') + + def DeletePrompt(self, request, context): + """Missing associated documentation comment in .proto file.""" + context.set_code(grpc.StatusCode.UNIMPLEMENTED) + context.set_details('Method not implemented!') + raise NotImplementedError('Method not implemented!') + + def GetPrompt(self, request, context): + """Missing associated documentation comment in .proto file.""" + context.set_code(grpc.StatusCode.UNIMPLEMENTED) + context.set_details('Method not implemented!') + raise NotImplementedError('Method not implemented!') + + def SearchPrompts(self, request, context): + """Missing associated documentation comment in .proto file.""" + context.set_code(grpc.StatusCode.UNIMPLEMENTED) + context.set_details('Method not implemented!') + raise NotImplementedError('Method not implemented!') + + def CreatePromptVersion(self, request, context): + """Missing associated documentation comment in .proto file.""" + context.set_code(grpc.StatusCode.UNIMPLEMENTED) + context.set_details('Method not implemented!') + raise NotImplementedError('Method not implemented!') + + def UpdatePromptVersion(self, request, context): + """Missing associated documentation comment in .proto file.""" + context.set_code(grpc.StatusCode.UNIMPLEMENTED) + context.set_details('Method not implemented!') + raise NotImplementedError('Method not implemented!') + + def DeletePromptVersion(self, request, context): + """Missing associated documentation comment in .proto file.""" + context.set_code(grpc.StatusCode.UNIMPLEMENTED) + context.set_details('Method not implemented!') + raise NotImplementedError('Method not implemented!') + + def GetPromptVersion(self, request, context): + """Missing associated documentation comment in .proto file.""" + context.set_code(grpc.StatusCode.UNIMPLEMENTED) + context.set_details('Method not implemented!') + raise NotImplementedError('Method not implemented!') + + def SearchPromptVersions(self, request, context): + """Missing associated documentation comment in .proto file.""" + context.set_code(grpc.StatusCode.UNIMPLEMENTED) + context.set_details('Method not implemented!') + raise NotImplementedError('Method not implemented!') + + def SetPromptAlias(self, request, context): + """Missing associated documentation comment in .proto file.""" + context.set_code(grpc.StatusCode.UNIMPLEMENTED) + context.set_details('Method not implemented!') + raise NotImplementedError('Method not implemented!') + + def DeletePromptAlias(self, request, context): + """Missing associated documentation comment in .proto file.""" + context.set_code(grpc.StatusCode.UNIMPLEMENTED) + context.set_details('Method not implemented!') + raise NotImplementedError('Method not implemented!') + + def GetPromptVersionByAlias(self, request, context): + """Missing associated documentation comment in .proto file.""" + context.set_code(grpc.StatusCode.UNIMPLEMENTED) + context.set_details('Method not implemented!') + raise NotImplementedError('Method not implemented!') + + def SetPromptTag(self, request, context): + """Missing associated documentation comment in .proto file.""" + context.set_code(grpc.StatusCode.UNIMPLEMENTED) + context.set_details('Method not implemented!') + raise NotImplementedError('Method not implemented!') + + def DeletePromptTag(self, request, context): + """Missing associated documentation comment in .proto file.""" + context.set_code(grpc.StatusCode.UNIMPLEMENTED) + context.set_details('Method not implemented!') + raise NotImplementedError('Method not implemented!') + + def SetPromptVersionTag(self, request, context): + """Missing associated documentation comment in .proto file.""" + context.set_code(grpc.StatusCode.UNIMPLEMENTED) + context.set_details('Method not implemented!') + raise NotImplementedError('Method not implemented!') + + def DeletePromptVersionTag(self, request, context): + """Missing associated documentation comment in .proto file.""" + context.set_code(grpc.StatusCode.UNIMPLEMENTED) + context.set_details('Method not implemented!') + raise NotImplementedError('Method not implemented!') + + +def add_UnityCatalogPromptServiceServicer_to_server(servicer, server): + rpc_method_handlers = { + 'CreatePrompt': grpc.unary_unary_rpc_method_handler( + servicer.CreatePrompt, + request_deserializer=unity__catalog__prompt__messages__pb2.CreatePromptRequest.FromString, + response_serializer=unity__catalog__prompt__messages__pb2.CreatePromptResponse.SerializeToString, + ), + 'UpdatePrompt': grpc.unary_unary_rpc_method_handler( + servicer.UpdatePrompt, + request_deserializer=unity__catalog__prompt__messages__pb2.UpdatePromptRequest.FromString, + response_serializer=unity__catalog__prompt__messages__pb2.UpdatePromptResponse.SerializeToString, + ), + 'DeletePrompt': grpc.unary_unary_rpc_method_handler( + servicer.DeletePrompt, + request_deserializer=unity__catalog__prompt__messages__pb2.DeletePromptRequest.FromString, + response_serializer=unity__catalog__prompt__messages__pb2.DeletePromptResponse.SerializeToString, + ), + 'GetPrompt': grpc.unary_unary_rpc_method_handler( + servicer.GetPrompt, + request_deserializer=unity__catalog__prompt__messages__pb2.GetPromptRequest.FromString, + response_serializer=unity__catalog__prompt__messages__pb2.GetPromptResponse.SerializeToString, + ), + 'SearchPrompts': grpc.unary_unary_rpc_method_handler( + servicer.SearchPrompts, + request_deserializer=unity__catalog__prompt__messages__pb2.SearchPromptsRequest.FromString, + response_serializer=unity__catalog__prompt__messages__pb2.SearchPromptsResponse.SerializeToString, + ), + 'CreatePromptVersion': grpc.unary_unary_rpc_method_handler( + servicer.CreatePromptVersion, + request_deserializer=unity__catalog__prompt__messages__pb2.CreatePromptVersionRequest.FromString, + response_serializer=unity__catalog__prompt__messages__pb2.CreatePromptVersionResponse.SerializeToString, + ), + 'UpdatePromptVersion': grpc.unary_unary_rpc_method_handler( + servicer.UpdatePromptVersion, + request_deserializer=unity__catalog__prompt__messages__pb2.UpdatePromptVersionRequest.FromString, + response_serializer=unity__catalog__prompt__messages__pb2.UpdatePromptVersionResponse.SerializeToString, + ), + 'DeletePromptVersion': grpc.unary_unary_rpc_method_handler( + servicer.DeletePromptVersion, + request_deserializer=unity__catalog__prompt__messages__pb2.DeletePromptVersionRequest.FromString, + response_serializer=unity__catalog__prompt__messages__pb2.DeletePromptVersionResponse.SerializeToString, + ), + 'GetPromptVersion': grpc.unary_unary_rpc_method_handler( + servicer.GetPromptVersion, + request_deserializer=unity__catalog__prompt__messages__pb2.GetPromptVersionRequest.FromString, + response_serializer=unity__catalog__prompt__messages__pb2.GetPromptVersionResponse.SerializeToString, + ), + 'SearchPromptVersions': grpc.unary_unary_rpc_method_handler( + servicer.SearchPromptVersions, + request_deserializer=unity__catalog__prompt__messages__pb2.SearchPromptVersionsRequest.FromString, + response_serializer=unity__catalog__prompt__messages__pb2.SearchPromptVersionsResponse.SerializeToString, + ), + 'SetPromptAlias': grpc.unary_unary_rpc_method_handler( + servicer.SetPromptAlias, + request_deserializer=unity__catalog__prompt__messages__pb2.SetPromptAliasRequest.FromString, + response_serializer=unity__catalog__prompt__messages__pb2.SetPromptAliasResponse.SerializeToString, + ), + 'DeletePromptAlias': grpc.unary_unary_rpc_method_handler( + servicer.DeletePromptAlias, + request_deserializer=unity__catalog__prompt__messages__pb2.DeletePromptAliasRequest.FromString, + response_serializer=unity__catalog__prompt__messages__pb2.DeletePromptAliasResponse.SerializeToString, + ), + 'GetPromptVersionByAlias': grpc.unary_unary_rpc_method_handler( + servicer.GetPromptVersionByAlias, + request_deserializer=unity__catalog__prompt__messages__pb2.GetPromptVersionByAliasRequest.FromString, + response_serializer=unity__catalog__prompt__messages__pb2.GetPromptVersionByAliasResponse.SerializeToString, + ), + 'SetPromptTag': grpc.unary_unary_rpc_method_handler( + servicer.SetPromptTag, + request_deserializer=unity__catalog__prompt__messages__pb2.SetPromptTagRequest.FromString, + response_serializer=unity__catalog__prompt__messages__pb2.SetPromptTagResponse.SerializeToString, + ), + 'DeletePromptTag': grpc.unary_unary_rpc_method_handler( + servicer.DeletePromptTag, + request_deserializer=unity__catalog__prompt__messages__pb2.DeletePromptTagRequest.FromString, + response_serializer=unity__catalog__prompt__messages__pb2.DeletePromptTagResponse.SerializeToString, + ), + 'SetPromptVersionTag': grpc.unary_unary_rpc_method_handler( + servicer.SetPromptVersionTag, + request_deserializer=unity__catalog__prompt__messages__pb2.SetPromptVersionTagRequest.FromString, + response_serializer=unity__catalog__prompt__messages__pb2.SetPromptVersionTagResponse.SerializeToString, + ), + 'DeletePromptVersionTag': grpc.unary_unary_rpc_method_handler( + servicer.DeletePromptVersionTag, + request_deserializer=unity__catalog__prompt__messages__pb2.DeletePromptVersionTagRequest.FromString, + response_serializer=unity__catalog__prompt__messages__pb2.DeletePromptVersionTagResponse.SerializeToString, + ), + } + generic_handler = grpc.method_handlers_generic_handler( + 'mlflow.unitycatalog.UnityCatalogPromptService', rpc_method_handlers) + server.add_generic_rpc_handlers((generic_handler,)) + server.add_registered_method_handlers('mlflow.unitycatalog.UnityCatalogPromptService', rpc_method_handlers) + + + # This class is part of an EXPERIMENTAL API. +class UnityCatalogPromptService(object): + """Missing associated documentation comment in .proto file.""" + + @staticmethod + def CreatePrompt(request, + target, + options=(), + channel_credentials=None, + call_credentials=None, + insecure=False, + compression=None, + wait_for_ready=None, + timeout=None, + metadata=None): + return grpc.experimental.unary_unary( + request, + target, + '/mlflow.unitycatalog.UnityCatalogPromptService/CreatePrompt', + unity__catalog__prompt__messages__pb2.CreatePromptRequest.SerializeToString, + unity__catalog__prompt__messages__pb2.CreatePromptResponse.FromString, + options, + channel_credentials, + insecure, + call_credentials, + compression, + wait_for_ready, + timeout, + metadata, + _registered_method=True) + + @staticmethod + def UpdatePrompt(request, + target, + options=(), + channel_credentials=None, + call_credentials=None, + insecure=False, + compression=None, + wait_for_ready=None, + timeout=None, + metadata=None): + return grpc.experimental.unary_unary( + request, + target, + '/mlflow.unitycatalog.UnityCatalogPromptService/UpdatePrompt', + unity__catalog__prompt__messages__pb2.UpdatePromptRequest.SerializeToString, + unity__catalog__prompt__messages__pb2.UpdatePromptResponse.FromString, + options, + channel_credentials, + insecure, + call_credentials, + compression, + wait_for_ready, + timeout, + metadata, + _registered_method=True) + + @staticmethod + def DeletePrompt(request, + target, + options=(), + channel_credentials=None, + call_credentials=None, + insecure=False, + compression=None, + wait_for_ready=None, + timeout=None, + metadata=None): + return grpc.experimental.unary_unary( + request, + target, + '/mlflow.unitycatalog.UnityCatalogPromptService/DeletePrompt', + unity__catalog__prompt__messages__pb2.DeletePromptRequest.SerializeToString, + unity__catalog__prompt__messages__pb2.DeletePromptResponse.FromString, + options, + channel_credentials, + insecure, + call_credentials, + compression, + wait_for_ready, + timeout, + metadata, + _registered_method=True) + + @staticmethod + def GetPrompt(request, + target, + options=(), + channel_credentials=None, + call_credentials=None, + insecure=False, + compression=None, + wait_for_ready=None, + timeout=None, + metadata=None): + return grpc.experimental.unary_unary( + request, + target, + '/mlflow.unitycatalog.UnityCatalogPromptService/GetPrompt', + unity__catalog__prompt__messages__pb2.GetPromptRequest.SerializeToString, + unity__catalog__prompt__messages__pb2.GetPromptResponse.FromString, + options, + channel_credentials, + insecure, + call_credentials, + compression, + wait_for_ready, + timeout, + metadata, + _registered_method=True) + + @staticmethod + def SearchPrompts(request, + target, + options=(), + channel_credentials=None, + call_credentials=None, + insecure=False, + compression=None, + wait_for_ready=None, + timeout=None, + metadata=None): + return grpc.experimental.unary_unary( + request, + target, + '/mlflow.unitycatalog.UnityCatalogPromptService/SearchPrompts', + unity__catalog__prompt__messages__pb2.SearchPromptsRequest.SerializeToString, + unity__catalog__prompt__messages__pb2.SearchPromptsResponse.FromString, + options, + channel_credentials, + insecure, + call_credentials, + compression, + wait_for_ready, + timeout, + metadata, + _registered_method=True) + + @staticmethod + def CreatePromptVersion(request, + target, + options=(), + channel_credentials=None, + call_credentials=None, + insecure=False, + compression=None, + wait_for_ready=None, + timeout=None, + metadata=None): + return grpc.experimental.unary_unary( + request, + target, + '/mlflow.unitycatalog.UnityCatalogPromptService/CreatePromptVersion', + unity__catalog__prompt__messages__pb2.CreatePromptVersionRequest.SerializeToString, + unity__catalog__prompt__messages__pb2.CreatePromptVersionResponse.FromString, + options, + channel_credentials, + insecure, + call_credentials, + compression, + wait_for_ready, + timeout, + metadata, + _registered_method=True) + + @staticmethod + def UpdatePromptVersion(request, + target, + options=(), + channel_credentials=None, + call_credentials=None, + insecure=False, + compression=None, + wait_for_ready=None, + timeout=None, + metadata=None): + return grpc.experimental.unary_unary( + request, + target, + '/mlflow.unitycatalog.UnityCatalogPromptService/UpdatePromptVersion', + unity__catalog__prompt__messages__pb2.UpdatePromptVersionRequest.SerializeToString, + unity__catalog__prompt__messages__pb2.UpdatePromptVersionResponse.FromString, + options, + channel_credentials, + insecure, + call_credentials, + compression, + wait_for_ready, + timeout, + metadata, + _registered_method=True) + + @staticmethod + def DeletePromptVersion(request, + target, + options=(), + channel_credentials=None, + call_credentials=None, + insecure=False, + compression=None, + wait_for_ready=None, + timeout=None, + metadata=None): + return grpc.experimental.unary_unary( + request, + target, + '/mlflow.unitycatalog.UnityCatalogPromptService/DeletePromptVersion', + unity__catalog__prompt__messages__pb2.DeletePromptVersionRequest.SerializeToString, + unity__catalog__prompt__messages__pb2.DeletePromptVersionResponse.FromString, + options, + channel_credentials, + insecure, + call_credentials, + compression, + wait_for_ready, + timeout, + metadata, + _registered_method=True) + + @staticmethod + def GetPromptVersion(request, + target, + options=(), + channel_credentials=None, + call_credentials=None, + insecure=False, + compression=None, + wait_for_ready=None, + timeout=None, + metadata=None): + return grpc.experimental.unary_unary( + request, + target, + '/mlflow.unitycatalog.UnityCatalogPromptService/GetPromptVersion', + unity__catalog__prompt__messages__pb2.GetPromptVersionRequest.SerializeToString, + unity__catalog__prompt__messages__pb2.GetPromptVersionResponse.FromString, + options, + channel_credentials, + insecure, + call_credentials, + compression, + wait_for_ready, + timeout, + metadata, + _registered_method=True) + + @staticmethod + def SearchPromptVersions(request, + target, + options=(), + channel_credentials=None, + call_credentials=None, + insecure=False, + compression=None, + wait_for_ready=None, + timeout=None, + metadata=None): + return grpc.experimental.unary_unary( + request, + target, + '/mlflow.unitycatalog.UnityCatalogPromptService/SearchPromptVersions', + unity__catalog__prompt__messages__pb2.SearchPromptVersionsRequest.SerializeToString, + unity__catalog__prompt__messages__pb2.SearchPromptVersionsResponse.FromString, + options, + channel_credentials, + insecure, + call_credentials, + compression, + wait_for_ready, + timeout, + metadata, + _registered_method=True) + + @staticmethod + def SetPromptAlias(request, + target, + options=(), + channel_credentials=None, + call_credentials=None, + insecure=False, + compression=None, + wait_for_ready=None, + timeout=None, + metadata=None): + return grpc.experimental.unary_unary( + request, + target, + '/mlflow.unitycatalog.UnityCatalogPromptService/SetPromptAlias', + unity__catalog__prompt__messages__pb2.SetPromptAliasRequest.SerializeToString, + unity__catalog__prompt__messages__pb2.SetPromptAliasResponse.FromString, + options, + channel_credentials, + insecure, + call_credentials, + compression, + wait_for_ready, + timeout, + metadata, + _registered_method=True) + + @staticmethod + def DeletePromptAlias(request, + target, + options=(), + channel_credentials=None, + call_credentials=None, + insecure=False, + compression=None, + wait_for_ready=None, + timeout=None, + metadata=None): + return grpc.experimental.unary_unary( + request, + target, + '/mlflow.unitycatalog.UnityCatalogPromptService/DeletePromptAlias', + unity__catalog__prompt__messages__pb2.DeletePromptAliasRequest.SerializeToString, + unity__catalog__prompt__messages__pb2.DeletePromptAliasResponse.FromString, + options, + channel_credentials, + insecure, + call_credentials, + compression, + wait_for_ready, + timeout, + metadata, + _registered_method=True) + + @staticmethod + def GetPromptVersionByAlias(request, + target, + options=(), + channel_credentials=None, + call_credentials=None, + insecure=False, + compression=None, + wait_for_ready=None, + timeout=None, + metadata=None): + return grpc.experimental.unary_unary( + request, + target, + '/mlflow.unitycatalog.UnityCatalogPromptService/GetPromptVersionByAlias', + unity__catalog__prompt__messages__pb2.GetPromptVersionByAliasRequest.SerializeToString, + unity__catalog__prompt__messages__pb2.GetPromptVersionByAliasResponse.FromString, + options, + channel_credentials, + insecure, + call_credentials, + compression, + wait_for_ready, + timeout, + metadata, + _registered_method=True) + + @staticmethod + def SetPromptTag(request, + target, + options=(), + channel_credentials=None, + call_credentials=None, + insecure=False, + compression=None, + wait_for_ready=None, + timeout=None, + metadata=None): + return grpc.experimental.unary_unary( + request, + target, + '/mlflow.unitycatalog.UnityCatalogPromptService/SetPromptTag', + unity__catalog__prompt__messages__pb2.SetPromptTagRequest.SerializeToString, + unity__catalog__prompt__messages__pb2.SetPromptTagResponse.FromString, + options, + channel_credentials, + insecure, + call_credentials, + compression, + wait_for_ready, + timeout, + metadata, + _registered_method=True) + + @staticmethod + def DeletePromptTag(request, + target, + options=(), + channel_credentials=None, + call_credentials=None, + insecure=False, + compression=None, + wait_for_ready=None, + timeout=None, + metadata=None): + return grpc.experimental.unary_unary( + request, + target, + '/mlflow.unitycatalog.UnityCatalogPromptService/DeletePromptTag', + unity__catalog__prompt__messages__pb2.DeletePromptTagRequest.SerializeToString, + unity__catalog__prompt__messages__pb2.DeletePromptTagResponse.FromString, + options, + channel_credentials, + insecure, + call_credentials, + compression, + wait_for_ready, + timeout, + metadata, + _registered_method=True) + + @staticmethod + def SetPromptVersionTag(request, + target, + options=(), + channel_credentials=None, + call_credentials=None, + insecure=False, + compression=None, + wait_for_ready=None, + timeout=None, + metadata=None): + return grpc.experimental.unary_unary( + request, + target, + '/mlflow.unitycatalog.UnityCatalogPromptService/SetPromptVersionTag', + unity__catalog__prompt__messages__pb2.SetPromptVersionTagRequest.SerializeToString, + unity__catalog__prompt__messages__pb2.SetPromptVersionTagResponse.FromString, + options, + channel_credentials, + insecure, + call_credentials, + compression, + wait_for_ready, + timeout, + metadata, + _registered_method=True) + + @staticmethod + def DeletePromptVersionTag(request, + target, + options=(), + channel_credentials=None, + call_credentials=None, + insecure=False, + compression=None, + wait_for_ready=None, + timeout=None, + metadata=None): + return grpc.experimental.unary_unary( + request, + target, + '/mlflow.unitycatalog.UnityCatalogPromptService/DeletePromptVersionTag', + unity__catalog__prompt__messages__pb2.DeletePromptVersionTagRequest.SerializeToString, + unity__catalog__prompt__messages__pb2.DeletePromptVersionTagResponse.FromString, + options, + channel_credentials, + insecure, + call_credentials, + compression, + wait_for_ready, + timeout, + metadata, + _registered_method=True) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/protos/webhooks_pb2.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/protos/webhooks_pb2.py new file mode 100644 index 0000000000000000000000000000000000000000..3bad474cea922d10e90d192c0cdea981a588f081 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/protos/webhooks_pb2.py @@ -0,0 +1,381 @@ + +import google.protobuf +from packaging.version import Version +if Version(google.protobuf.__version__).major >= 5: + # -*- coding: utf-8 -*- + # Generated by the protocol buffer compiler. DO NOT EDIT! + # source: webhooks.proto + # Protobuf Python Version: 5.26.0 + """Generated protocol buffer code.""" + from google.protobuf import descriptor as _descriptor + from google.protobuf import descriptor_pool as _descriptor_pool + from google.protobuf import symbol_database as _symbol_database + from google.protobuf.internal import builder as _builder + # @@protoc_insertion_point(imports) + + _sym_db = _symbol_database.Default() + + + from .scalapb import scalapb_pb2 as scalapb_dot_scalapb__pb2 + from . import databricks_pb2 as databricks__pb2 + + + DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\x0ewebhooks.proto\x12\x06mlflow\x1a\x15scalapb/scalapb.proto\x1a\x10\x64\x61tabricks.proto\"h\n\x0cWebhookEvent\x12+\n\x06\x65ntity\x18\x01 \x01(\x0e\x32\x15.mlflow.WebhookEntityB\x04\xf8\x86\x19\x01\x12+\n\x06\x61\x63tion\x18\x02 \x01(\x0e\x32\x15.mlflow.WebhookActionB\x04\xf8\x86\x19\x01\"\xd6\x01\n\x07Webhook\x12\x12\n\nwebhook_id\x18\x01 \x01(\t\x12\x0c\n\x04name\x18\x02 \x01(\t\x12\x13\n\x0b\x64\x65scription\x18\x03 \x01(\t\x12\x0b\n\x03url\x18\x04 \x01(\t\x12$\n\x06\x65vents\x18\x05 \x03(\x0b\x32\x14.mlflow.WebhookEvent\x12%\n\x06status\x18\x06 \x01(\x0e\x32\x15.mlflow.WebhookStatus\x12\x1a\n\x12\x63reation_timestamp\x18\x07 \x01(\x03\x12\x1e\n\x16last_updated_timestamp\x18\x08 \x01(\x03\"k\n\x11WebhookTestResult\x12\x0f\n\x07success\x18\x01 \x01(\x08\x12\x17\n\x0fresponse_status\x18\x02 \x01(\x05\x12\x15\n\rresponse_body\x18\x03 \x01(\t\x12\x15\n\rerror_message\x18\x04 \x01(\t\"\x89\x02\n\rCreateWebhook\x12\x12\n\x04name\x18\x01 \x01(\tB\x04\xf8\x86\x19\x01\x12\x13\n\x0b\x64\x65scription\x18\x02 \x01(\t\x12\x11\n\x03url\x18\x03 \x01(\tB\x04\xf8\x86\x19\x01\x12*\n\x06\x65vents\x18\x04 \x03(\x0b\x32\x14.mlflow.WebhookEventB\x04\xf8\x86\x19\x01\x12\x0e\n\x06secret\x18\x05 \x01(\t\x12%\n\x06status\x18\x06 \x01(\x0e\x32\x15.mlflow.WebhookStatus\x1a,\n\x08Response\x12 \n\x07webhook\x18\x01 \x01(\x0b\x32\x0f.mlflow.Webhook:+\xe2?(\n&com.databricks.rpc.RPC[$this.Response]\"\xb1\x01\n\x0cListWebhooks\x12\x18\n\x0bmax_results\x18\x01 \x01(\x05:\x03\x31\x30\x30\x12\x12\n\npage_token\x18\x02 \x01(\t\x1a\x46\n\x08Response\x12!\n\x08webhooks\x18\x01 \x03(\x0b\x32\x0f.mlflow.Webhook\x12\x17\n\x0fnext_page_token\x18\x02 \x01(\t:+\xe2?(\n&com.databricks.rpc.RPC[$this.Response]\"\x81\x01\n\nGetWebhook\x12\x18\n\nwebhook_id\x18\x01 \x01(\tB\x04\xf8\x86\x19\x01\x1a,\n\x08Response\x12 \n\x07webhook\x18\x01 \x01(\x0b\x32\x0f.mlflow.Webhook:+\xe2?(\n&com.databricks.rpc.RPC[$this.Response]\"\x91\x02\n\rUpdateWebhook\x12\x18\n\nwebhook_id\x18\x01 \x01(\tB\x04\xf8\x86\x19\x01\x12\x0c\n\x04name\x18\x02 \x01(\t\x12\x13\n\x0b\x64\x65scription\x18\x03 \x01(\t\x12\x0b\n\x03url\x18\x04 \x01(\t\x12$\n\x06\x65vents\x18\x05 \x03(\x0b\x32\x14.mlflow.WebhookEvent\x12\x0e\n\x06secret\x18\x06 \x01(\t\x12%\n\x06status\x18\x07 \x01(\x0e\x32\x15.mlflow.WebhookStatus\x1a,\n\x08Response\x12 \n\x07webhook\x18\x01 \x01(\x0b\x32\x0f.mlflow.Webhook:+\xe2?(\n&com.databricks.rpc.RPC[$this.Response]\"b\n\rDeleteWebhook\x12\x18\n\nwebhook_id\x18\x01 \x01(\tB\x04\xf8\x86\x19\x01\x1a\n\n\x08Response:+\xe2?(\n&com.databricks.rpc.RPC[$this.Response]\"\xb0\x01\n\x0bTestWebhook\x12\x18\n\nwebhook_id\x18\x01 \x01(\tB\x04\xf8\x86\x19\x01\x12#\n\x05\x65vent\x18\x02 \x01(\x0b\x32\x14.mlflow.WebhookEvent\x1a\x35\n\x08Response\x12)\n\x06result\x18\x01 \x01(\x0b\x32\x19.mlflow.WebhookTestResult:+\xe2?(\n&com.databricks.rpc.RPC[$this.Response]*)\n\rWebhookStatus\x12\n\n\x06\x41\x43TIVE\x10\x01\x12\x0c\n\x08\x44ISABLED\x10\x02*\x80\x01\n\rWebhookEntity\x12\x16\n\x12\x45NTITY_UNSPECIFIED\x10\x00\x12\x14\n\x10REGISTERED_MODEL\x10\x01\x12\x11\n\rMODEL_VERSION\x10\x02\x12\x15\n\x11MODEL_VERSION_TAG\x10\x03\x12\x17\n\x13MODEL_VERSION_ALIAS\x10\x04*W\n\rWebhookAction\x12\x16\n\x12\x41\x43TION_UNSPECIFIED\x10\x00\x12\x0b\n\x07\x43REATED\x10\x01\x12\x0b\n\x07UPDATED\x10\x02\x12\x0b\n\x07\x44\x45LETED\x10\x03\x12\x07\n\x03SET\x10\x04\x32\xb5\x06\n\x0eWebhookService\x12~\n\rcreateWebhook\x12\x15.mlflow.CreateWebhook\x1a\x1e.mlflow.CreateWebhook.Response\"6\xf2\x86\x19\x32\n\x1e\n\x04POST\x12\x10/mlflow/webhooks\x1a\x04\x08\x02\x10\x00\x10\x01*\x0e\x43reate Webhook\x12y\n\x0clistWebhooks\x12\x14.mlflow.ListWebhooks\x1a\x1d.mlflow.ListWebhooks.Response\"4\xf2\x86\x19\x30\n\x1d\n\x03GET\x12\x10/mlflow/webhooks\x1a\x04\x08\x02\x10\x00\x10\x01*\rList Webhooks\x12~\n\ngetWebhook\x12\x12.mlflow.GetWebhook\x1a\x1b.mlflow.GetWebhook.Response\"?\xf2\x86\x19;\n*\n\x03GET\x12\x1d/mlflow/webhooks/{webhook_id}\x1a\x04\x08\x02\x10\x00\x10\x01*\x0bGet Webhook\x12\x8c\x01\n\rupdateWebhook\x12\x15.mlflow.UpdateWebhook\x1a\x1e.mlflow.UpdateWebhook.Response\"D\xf2\x86\x19@\n,\n\x05PATCH\x12\x1d/mlflow/webhooks/{webhook_id}\x1a\x04\x08\x02\x10\x00\x10\x01*\x0eUpdate Webhook\x12\x8d\x01\n\rdeleteWebhook\x12\x15.mlflow.DeleteWebhook\x1a\x1e.mlflow.DeleteWebhook.Response\"E\xf2\x86\x19\x41\n-\n\x06\x44\x45LETE\x12\x1d/mlflow/webhooks/{webhook_id}\x1a\x04\x08\x02\x10\x00\x10\x01*\x0e\x44\x65lete Webhook\x12\x88\x01\n\x0btestWebhook\x12\x13.mlflow.TestWebhook\x1a\x1c.mlflow.TestWebhook.Response\"F\xf2\x86\x19\x42\n0\n\x04POST\x12\"/mlflow/webhooks/{webhook_id}/test\x1a\x04\x08\x02\x10\x00\x10\x01*\x0cTest WebhookB!\n\x14org.mlflow.api.proto\x90\x01\x01\xa0\x01\x01\xe2?\x02\x10\x01') + + _globals = globals() + _builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals) + _builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'webhooks_pb2', _globals) + if not _descriptor._USE_C_DESCRIPTORS: + _globals['DESCRIPTOR']._loaded_options = None + _globals['DESCRIPTOR']._serialized_options = b'\n\024org.mlflow.api.proto\220\001\001\240\001\001\342?\002\020\001' + _globals['_WEBHOOKEVENT'].fields_by_name['entity']._loaded_options = None + _globals['_WEBHOOKEVENT'].fields_by_name['entity']._serialized_options = b'\370\206\031\001' + _globals['_WEBHOOKEVENT'].fields_by_name['action']._loaded_options = None + _globals['_WEBHOOKEVENT'].fields_by_name['action']._serialized_options = b'\370\206\031\001' + _globals['_CREATEWEBHOOK'].fields_by_name['name']._loaded_options = None + _globals['_CREATEWEBHOOK'].fields_by_name['name']._serialized_options = b'\370\206\031\001' + _globals['_CREATEWEBHOOK'].fields_by_name['url']._loaded_options = None + _globals['_CREATEWEBHOOK'].fields_by_name['url']._serialized_options = b'\370\206\031\001' + _globals['_CREATEWEBHOOK'].fields_by_name['events']._loaded_options = None + _globals['_CREATEWEBHOOK'].fields_by_name['events']._serialized_options = b'\370\206\031\001' + _globals['_CREATEWEBHOOK']._loaded_options = None + _globals['_CREATEWEBHOOK']._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _globals['_LISTWEBHOOKS']._loaded_options = None + _globals['_LISTWEBHOOKS']._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _globals['_GETWEBHOOK'].fields_by_name['webhook_id']._loaded_options = None + _globals['_GETWEBHOOK'].fields_by_name['webhook_id']._serialized_options = b'\370\206\031\001' + _globals['_GETWEBHOOK']._loaded_options = None + _globals['_GETWEBHOOK']._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _globals['_UPDATEWEBHOOK'].fields_by_name['webhook_id']._loaded_options = None + _globals['_UPDATEWEBHOOK'].fields_by_name['webhook_id']._serialized_options = b'\370\206\031\001' + _globals['_UPDATEWEBHOOK']._loaded_options = None + _globals['_UPDATEWEBHOOK']._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _globals['_DELETEWEBHOOK'].fields_by_name['webhook_id']._loaded_options = None + _globals['_DELETEWEBHOOK'].fields_by_name['webhook_id']._serialized_options = b'\370\206\031\001' + _globals['_DELETEWEBHOOK']._loaded_options = None + _globals['_DELETEWEBHOOK']._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _globals['_TESTWEBHOOK'].fields_by_name['webhook_id']._loaded_options = None + _globals['_TESTWEBHOOK'].fields_by_name['webhook_id']._serialized_options = b'\370\206\031\001' + _globals['_TESTWEBHOOK']._loaded_options = None + _globals['_TESTWEBHOOK']._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _globals['_WEBHOOKSERVICE'].methods_by_name['createWebhook']._loaded_options = None + _globals['_WEBHOOKSERVICE'].methods_by_name['createWebhook']._serialized_options = b'\362\206\0312\n\036\n\004POST\022\020/mlflow/webhooks\032\004\010\002\020\000\020\001*\016Create Webhook' + _globals['_WEBHOOKSERVICE'].methods_by_name['listWebhooks']._loaded_options = None + _globals['_WEBHOOKSERVICE'].methods_by_name['listWebhooks']._serialized_options = b'\362\206\0310\n\035\n\003GET\022\020/mlflow/webhooks\032\004\010\002\020\000\020\001*\rList Webhooks' + _globals['_WEBHOOKSERVICE'].methods_by_name['getWebhook']._loaded_options = None + _globals['_WEBHOOKSERVICE'].methods_by_name['getWebhook']._serialized_options = b'\362\206\031;\n*\n\003GET\022\035/mlflow/webhooks/{webhook_id}\032\004\010\002\020\000\020\001*\013Get Webhook' + _globals['_WEBHOOKSERVICE'].methods_by_name['updateWebhook']._loaded_options = None + _globals['_WEBHOOKSERVICE'].methods_by_name['updateWebhook']._serialized_options = b'\362\206\031@\n,\n\005PATCH\022\035/mlflow/webhooks/{webhook_id}\032\004\010\002\020\000\020\001*\016Update Webhook' + _globals['_WEBHOOKSERVICE'].methods_by_name['deleteWebhook']._loaded_options = None + _globals['_WEBHOOKSERVICE'].methods_by_name['deleteWebhook']._serialized_options = b'\362\206\031A\n-\n\006DELETE\022\035/mlflow/webhooks/{webhook_id}\032\004\010\002\020\000\020\001*\016Delete Webhook' + _globals['_WEBHOOKSERVICE'].methods_by_name['testWebhook']._loaded_options = None + _globals['_WEBHOOKSERVICE'].methods_by_name['testWebhook']._serialized_options = b'\362\206\031B\n0\n\004POST\022\"/mlflow/webhooks/{webhook_id}/test\032\004\010\002\020\000\020\001*\014Test Webhook' + _globals['_WEBHOOKSTATUS']._serialized_start=1634 + _globals['_WEBHOOKSTATUS']._serialized_end=1675 + _globals['_WEBHOOKENTITY']._serialized_start=1678 + _globals['_WEBHOOKENTITY']._serialized_end=1806 + _globals['_WEBHOOKACTION']._serialized_start=1808 + _globals['_WEBHOOKACTION']._serialized_end=1895 + _globals['_WEBHOOKEVENT']._serialized_start=67 + _globals['_WEBHOOKEVENT']._serialized_end=171 + _globals['_WEBHOOK']._serialized_start=174 + _globals['_WEBHOOK']._serialized_end=388 + _globals['_WEBHOOKTESTRESULT']._serialized_start=390 + _globals['_WEBHOOKTESTRESULT']._serialized_end=497 + _globals['_CREATEWEBHOOK']._serialized_start=500 + _globals['_CREATEWEBHOOK']._serialized_end=765 + _globals['_CREATEWEBHOOK_RESPONSE']._serialized_start=676 + _globals['_CREATEWEBHOOK_RESPONSE']._serialized_end=720 + _globals['_LISTWEBHOOKS']._serialized_start=768 + _globals['_LISTWEBHOOKS']._serialized_end=945 + _globals['_LISTWEBHOOKS_RESPONSE']._serialized_start=830 + _globals['_LISTWEBHOOKS_RESPONSE']._serialized_end=900 + _globals['_GETWEBHOOK']._serialized_start=948 + _globals['_GETWEBHOOK']._serialized_end=1077 + _globals['_GETWEBHOOK_RESPONSE']._serialized_start=676 + _globals['_GETWEBHOOK_RESPONSE']._serialized_end=720 + _globals['_UPDATEWEBHOOK']._serialized_start=1080 + _globals['_UPDATEWEBHOOK']._serialized_end=1353 + _globals['_UPDATEWEBHOOK_RESPONSE']._serialized_start=676 + _globals['_UPDATEWEBHOOK_RESPONSE']._serialized_end=720 + _globals['_DELETEWEBHOOK']._serialized_start=1355 + _globals['_DELETEWEBHOOK']._serialized_end=1453 + _globals['_DELETEWEBHOOK_RESPONSE']._serialized_start=676 + _globals['_DELETEWEBHOOK_RESPONSE']._serialized_end=686 + _globals['_TESTWEBHOOK']._serialized_start=1456 + _globals['_TESTWEBHOOK']._serialized_end=1632 + _globals['_TESTWEBHOOK_RESPONSE']._serialized_start=1534 + _globals['_TESTWEBHOOK_RESPONSE']._serialized_end=1587 + _globals['_WEBHOOKSERVICE']._serialized_start=1898 + _globals['_WEBHOOKSERVICE']._serialized_end=2719 + _builder.BuildServices(DESCRIPTOR, 'webhooks_pb2', _globals) + # @@protoc_insertion_point(module_scope) + +else: + # -*- coding: utf-8 -*- + # Generated by the protocol buffer compiler. DO NOT EDIT! + # source: webhooks.proto + """Generated protocol buffer code.""" + from google.protobuf.internal import enum_type_wrapper + from google.protobuf import descriptor as _descriptor + from google.protobuf import descriptor_pool as _descriptor_pool + from google.protobuf import message as _message + from google.protobuf import reflection as _reflection + from google.protobuf import symbol_database as _symbol_database + from google.protobuf import service as _service + from google.protobuf import service_reflection + # @@protoc_insertion_point(imports) + + _sym_db = _symbol_database.Default() + + + from .scalapb import scalapb_pb2 as scalapb_dot_scalapb__pb2 + from . import databricks_pb2 as databricks__pb2 + + + DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\x0ewebhooks.proto\x12\x06mlflow\x1a\x15scalapb/scalapb.proto\x1a\x10\x64\x61tabricks.proto\"h\n\x0cWebhookEvent\x12+\n\x06\x65ntity\x18\x01 \x01(\x0e\x32\x15.mlflow.WebhookEntityB\x04\xf8\x86\x19\x01\x12+\n\x06\x61\x63tion\x18\x02 \x01(\x0e\x32\x15.mlflow.WebhookActionB\x04\xf8\x86\x19\x01\"\xd6\x01\n\x07Webhook\x12\x12\n\nwebhook_id\x18\x01 \x01(\t\x12\x0c\n\x04name\x18\x02 \x01(\t\x12\x13\n\x0b\x64\x65scription\x18\x03 \x01(\t\x12\x0b\n\x03url\x18\x04 \x01(\t\x12$\n\x06\x65vents\x18\x05 \x03(\x0b\x32\x14.mlflow.WebhookEvent\x12%\n\x06status\x18\x06 \x01(\x0e\x32\x15.mlflow.WebhookStatus\x12\x1a\n\x12\x63reation_timestamp\x18\x07 \x01(\x03\x12\x1e\n\x16last_updated_timestamp\x18\x08 \x01(\x03\"k\n\x11WebhookTestResult\x12\x0f\n\x07success\x18\x01 \x01(\x08\x12\x17\n\x0fresponse_status\x18\x02 \x01(\x05\x12\x15\n\rresponse_body\x18\x03 \x01(\t\x12\x15\n\rerror_message\x18\x04 \x01(\t\"\x89\x02\n\rCreateWebhook\x12\x12\n\x04name\x18\x01 \x01(\tB\x04\xf8\x86\x19\x01\x12\x13\n\x0b\x64\x65scription\x18\x02 \x01(\t\x12\x11\n\x03url\x18\x03 \x01(\tB\x04\xf8\x86\x19\x01\x12*\n\x06\x65vents\x18\x04 \x03(\x0b\x32\x14.mlflow.WebhookEventB\x04\xf8\x86\x19\x01\x12\x0e\n\x06secret\x18\x05 \x01(\t\x12%\n\x06status\x18\x06 \x01(\x0e\x32\x15.mlflow.WebhookStatus\x1a,\n\x08Response\x12 \n\x07webhook\x18\x01 \x01(\x0b\x32\x0f.mlflow.Webhook:+\xe2?(\n&com.databricks.rpc.RPC[$this.Response]\"\xb1\x01\n\x0cListWebhooks\x12\x18\n\x0bmax_results\x18\x01 \x01(\x05:\x03\x31\x30\x30\x12\x12\n\npage_token\x18\x02 \x01(\t\x1a\x46\n\x08Response\x12!\n\x08webhooks\x18\x01 \x03(\x0b\x32\x0f.mlflow.Webhook\x12\x17\n\x0fnext_page_token\x18\x02 \x01(\t:+\xe2?(\n&com.databricks.rpc.RPC[$this.Response]\"\x81\x01\n\nGetWebhook\x12\x18\n\nwebhook_id\x18\x01 \x01(\tB\x04\xf8\x86\x19\x01\x1a,\n\x08Response\x12 \n\x07webhook\x18\x01 \x01(\x0b\x32\x0f.mlflow.Webhook:+\xe2?(\n&com.databricks.rpc.RPC[$this.Response]\"\x91\x02\n\rUpdateWebhook\x12\x18\n\nwebhook_id\x18\x01 \x01(\tB\x04\xf8\x86\x19\x01\x12\x0c\n\x04name\x18\x02 \x01(\t\x12\x13\n\x0b\x64\x65scription\x18\x03 \x01(\t\x12\x0b\n\x03url\x18\x04 \x01(\t\x12$\n\x06\x65vents\x18\x05 \x03(\x0b\x32\x14.mlflow.WebhookEvent\x12\x0e\n\x06secret\x18\x06 \x01(\t\x12%\n\x06status\x18\x07 \x01(\x0e\x32\x15.mlflow.WebhookStatus\x1a,\n\x08Response\x12 \n\x07webhook\x18\x01 \x01(\x0b\x32\x0f.mlflow.Webhook:+\xe2?(\n&com.databricks.rpc.RPC[$this.Response]\"b\n\rDeleteWebhook\x12\x18\n\nwebhook_id\x18\x01 \x01(\tB\x04\xf8\x86\x19\x01\x1a\n\n\x08Response:+\xe2?(\n&com.databricks.rpc.RPC[$this.Response]\"\xb0\x01\n\x0bTestWebhook\x12\x18\n\nwebhook_id\x18\x01 \x01(\tB\x04\xf8\x86\x19\x01\x12#\n\x05\x65vent\x18\x02 \x01(\x0b\x32\x14.mlflow.WebhookEvent\x1a\x35\n\x08Response\x12)\n\x06result\x18\x01 \x01(\x0b\x32\x19.mlflow.WebhookTestResult:+\xe2?(\n&com.databricks.rpc.RPC[$this.Response]*)\n\rWebhookStatus\x12\n\n\x06\x41\x43TIVE\x10\x01\x12\x0c\n\x08\x44ISABLED\x10\x02*\x80\x01\n\rWebhookEntity\x12\x16\n\x12\x45NTITY_UNSPECIFIED\x10\x00\x12\x14\n\x10REGISTERED_MODEL\x10\x01\x12\x11\n\rMODEL_VERSION\x10\x02\x12\x15\n\x11MODEL_VERSION_TAG\x10\x03\x12\x17\n\x13MODEL_VERSION_ALIAS\x10\x04*W\n\rWebhookAction\x12\x16\n\x12\x41\x43TION_UNSPECIFIED\x10\x00\x12\x0b\n\x07\x43REATED\x10\x01\x12\x0b\n\x07UPDATED\x10\x02\x12\x0b\n\x07\x44\x45LETED\x10\x03\x12\x07\n\x03SET\x10\x04\x32\xb5\x06\n\x0eWebhookService\x12~\n\rcreateWebhook\x12\x15.mlflow.CreateWebhook\x1a\x1e.mlflow.CreateWebhook.Response\"6\xf2\x86\x19\x32\n\x1e\n\x04POST\x12\x10/mlflow/webhooks\x1a\x04\x08\x02\x10\x00\x10\x01*\x0e\x43reate Webhook\x12y\n\x0clistWebhooks\x12\x14.mlflow.ListWebhooks\x1a\x1d.mlflow.ListWebhooks.Response\"4\xf2\x86\x19\x30\n\x1d\n\x03GET\x12\x10/mlflow/webhooks\x1a\x04\x08\x02\x10\x00\x10\x01*\rList Webhooks\x12~\n\ngetWebhook\x12\x12.mlflow.GetWebhook\x1a\x1b.mlflow.GetWebhook.Response\"?\xf2\x86\x19;\n*\n\x03GET\x12\x1d/mlflow/webhooks/{webhook_id}\x1a\x04\x08\x02\x10\x00\x10\x01*\x0bGet Webhook\x12\x8c\x01\n\rupdateWebhook\x12\x15.mlflow.UpdateWebhook\x1a\x1e.mlflow.UpdateWebhook.Response\"D\xf2\x86\x19@\n,\n\x05PATCH\x12\x1d/mlflow/webhooks/{webhook_id}\x1a\x04\x08\x02\x10\x00\x10\x01*\x0eUpdate Webhook\x12\x8d\x01\n\rdeleteWebhook\x12\x15.mlflow.DeleteWebhook\x1a\x1e.mlflow.DeleteWebhook.Response\"E\xf2\x86\x19\x41\n-\n\x06\x44\x45LETE\x12\x1d/mlflow/webhooks/{webhook_id}\x1a\x04\x08\x02\x10\x00\x10\x01*\x0e\x44\x65lete Webhook\x12\x88\x01\n\x0btestWebhook\x12\x13.mlflow.TestWebhook\x1a\x1c.mlflow.TestWebhook.Response\"F\xf2\x86\x19\x42\n0\n\x04POST\x12\"/mlflow/webhooks/{webhook_id}/test\x1a\x04\x08\x02\x10\x00\x10\x01*\x0cTest WebhookB!\n\x14org.mlflow.api.proto\x90\x01\x01\xa0\x01\x01\xe2?\x02\x10\x01') + + _WEBHOOKSTATUS = DESCRIPTOR.enum_types_by_name['WebhookStatus'] + WebhookStatus = enum_type_wrapper.EnumTypeWrapper(_WEBHOOKSTATUS) + _WEBHOOKENTITY = DESCRIPTOR.enum_types_by_name['WebhookEntity'] + WebhookEntity = enum_type_wrapper.EnumTypeWrapper(_WEBHOOKENTITY) + _WEBHOOKACTION = DESCRIPTOR.enum_types_by_name['WebhookAction'] + WebhookAction = enum_type_wrapper.EnumTypeWrapper(_WEBHOOKACTION) + ACTIVE = 1 + DISABLED = 2 + ENTITY_UNSPECIFIED = 0 + REGISTERED_MODEL = 1 + MODEL_VERSION = 2 + MODEL_VERSION_TAG = 3 + MODEL_VERSION_ALIAS = 4 + ACTION_UNSPECIFIED = 0 + CREATED = 1 + UPDATED = 2 + DELETED = 3 + SET = 4 + + + _WEBHOOKEVENT = DESCRIPTOR.message_types_by_name['WebhookEvent'] + _WEBHOOK = DESCRIPTOR.message_types_by_name['Webhook'] + _WEBHOOKTESTRESULT = DESCRIPTOR.message_types_by_name['WebhookTestResult'] + _CREATEWEBHOOK = DESCRIPTOR.message_types_by_name['CreateWebhook'] + _CREATEWEBHOOK_RESPONSE = _CREATEWEBHOOK.nested_types_by_name['Response'] + _LISTWEBHOOKS = DESCRIPTOR.message_types_by_name['ListWebhooks'] + _LISTWEBHOOKS_RESPONSE = _LISTWEBHOOKS.nested_types_by_name['Response'] + _GETWEBHOOK = DESCRIPTOR.message_types_by_name['GetWebhook'] + _GETWEBHOOK_RESPONSE = _GETWEBHOOK.nested_types_by_name['Response'] + _UPDATEWEBHOOK = DESCRIPTOR.message_types_by_name['UpdateWebhook'] + _UPDATEWEBHOOK_RESPONSE = _UPDATEWEBHOOK.nested_types_by_name['Response'] + _DELETEWEBHOOK = DESCRIPTOR.message_types_by_name['DeleteWebhook'] + _DELETEWEBHOOK_RESPONSE = _DELETEWEBHOOK.nested_types_by_name['Response'] + _TESTWEBHOOK = DESCRIPTOR.message_types_by_name['TestWebhook'] + _TESTWEBHOOK_RESPONSE = _TESTWEBHOOK.nested_types_by_name['Response'] + WebhookEvent = _reflection.GeneratedProtocolMessageType('WebhookEvent', (_message.Message,), { + 'DESCRIPTOR' : _WEBHOOKEVENT, + '__module__' : 'webhooks_pb2' + # @@protoc_insertion_point(class_scope:mlflow.WebhookEvent) + }) + _sym_db.RegisterMessage(WebhookEvent) + + Webhook = _reflection.GeneratedProtocolMessageType('Webhook', (_message.Message,), { + 'DESCRIPTOR' : _WEBHOOK, + '__module__' : 'webhooks_pb2' + # @@protoc_insertion_point(class_scope:mlflow.Webhook) + }) + _sym_db.RegisterMessage(Webhook) + + WebhookTestResult = _reflection.GeneratedProtocolMessageType('WebhookTestResult', (_message.Message,), { + 'DESCRIPTOR' : _WEBHOOKTESTRESULT, + '__module__' : 'webhooks_pb2' + # @@protoc_insertion_point(class_scope:mlflow.WebhookTestResult) + }) + _sym_db.RegisterMessage(WebhookTestResult) + + CreateWebhook = _reflection.GeneratedProtocolMessageType('CreateWebhook', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _CREATEWEBHOOK_RESPONSE, + '__module__' : 'webhooks_pb2' + # @@protoc_insertion_point(class_scope:mlflow.CreateWebhook.Response) + }) + , + 'DESCRIPTOR' : _CREATEWEBHOOK, + '__module__' : 'webhooks_pb2' + # @@protoc_insertion_point(class_scope:mlflow.CreateWebhook) + }) + _sym_db.RegisterMessage(CreateWebhook) + _sym_db.RegisterMessage(CreateWebhook.Response) + + ListWebhooks = _reflection.GeneratedProtocolMessageType('ListWebhooks', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _LISTWEBHOOKS_RESPONSE, + '__module__' : 'webhooks_pb2' + # @@protoc_insertion_point(class_scope:mlflow.ListWebhooks.Response) + }) + , + 'DESCRIPTOR' : _LISTWEBHOOKS, + '__module__' : 'webhooks_pb2' + # @@protoc_insertion_point(class_scope:mlflow.ListWebhooks) + }) + _sym_db.RegisterMessage(ListWebhooks) + _sym_db.RegisterMessage(ListWebhooks.Response) + + GetWebhook = _reflection.GeneratedProtocolMessageType('GetWebhook', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _GETWEBHOOK_RESPONSE, + '__module__' : 'webhooks_pb2' + # @@protoc_insertion_point(class_scope:mlflow.GetWebhook.Response) + }) + , + 'DESCRIPTOR' : _GETWEBHOOK, + '__module__' : 'webhooks_pb2' + # @@protoc_insertion_point(class_scope:mlflow.GetWebhook) + }) + _sym_db.RegisterMessage(GetWebhook) + _sym_db.RegisterMessage(GetWebhook.Response) + + UpdateWebhook = _reflection.GeneratedProtocolMessageType('UpdateWebhook', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _UPDATEWEBHOOK_RESPONSE, + '__module__' : 'webhooks_pb2' + # @@protoc_insertion_point(class_scope:mlflow.UpdateWebhook.Response) + }) + , + 'DESCRIPTOR' : _UPDATEWEBHOOK, + '__module__' : 'webhooks_pb2' + # @@protoc_insertion_point(class_scope:mlflow.UpdateWebhook) + }) + _sym_db.RegisterMessage(UpdateWebhook) + _sym_db.RegisterMessage(UpdateWebhook.Response) + + DeleteWebhook = _reflection.GeneratedProtocolMessageType('DeleteWebhook', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _DELETEWEBHOOK_RESPONSE, + '__module__' : 'webhooks_pb2' + # @@protoc_insertion_point(class_scope:mlflow.DeleteWebhook.Response) + }) + , + 'DESCRIPTOR' : _DELETEWEBHOOK, + '__module__' : 'webhooks_pb2' + # @@protoc_insertion_point(class_scope:mlflow.DeleteWebhook) + }) + _sym_db.RegisterMessage(DeleteWebhook) + _sym_db.RegisterMessage(DeleteWebhook.Response) + + TestWebhook = _reflection.GeneratedProtocolMessageType('TestWebhook', (_message.Message,), { + + 'Response' : _reflection.GeneratedProtocolMessageType('Response', (_message.Message,), { + 'DESCRIPTOR' : _TESTWEBHOOK_RESPONSE, + '__module__' : 'webhooks_pb2' + # @@protoc_insertion_point(class_scope:mlflow.TestWebhook.Response) + }) + , + 'DESCRIPTOR' : _TESTWEBHOOK, + '__module__' : 'webhooks_pb2' + # @@protoc_insertion_point(class_scope:mlflow.TestWebhook) + }) + _sym_db.RegisterMessage(TestWebhook) + _sym_db.RegisterMessage(TestWebhook.Response) + + _WEBHOOKSERVICE = DESCRIPTOR.services_by_name['WebhookService'] + if _descriptor._USE_C_DESCRIPTORS == False: + + DESCRIPTOR._options = None + DESCRIPTOR._serialized_options = b'\n\024org.mlflow.api.proto\220\001\001\240\001\001\342?\002\020\001' + _WEBHOOKEVENT.fields_by_name['entity']._options = None + _WEBHOOKEVENT.fields_by_name['entity']._serialized_options = b'\370\206\031\001' + _WEBHOOKEVENT.fields_by_name['action']._options = None + _WEBHOOKEVENT.fields_by_name['action']._serialized_options = b'\370\206\031\001' + _CREATEWEBHOOK.fields_by_name['name']._options = None + _CREATEWEBHOOK.fields_by_name['name']._serialized_options = b'\370\206\031\001' + _CREATEWEBHOOK.fields_by_name['url']._options = None + _CREATEWEBHOOK.fields_by_name['url']._serialized_options = b'\370\206\031\001' + _CREATEWEBHOOK.fields_by_name['events']._options = None + _CREATEWEBHOOK.fields_by_name['events']._serialized_options = b'\370\206\031\001' + _CREATEWEBHOOK._options = None + _CREATEWEBHOOK._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _LISTWEBHOOKS._options = None + _LISTWEBHOOKS._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _GETWEBHOOK.fields_by_name['webhook_id']._options = None + _GETWEBHOOK.fields_by_name['webhook_id']._serialized_options = b'\370\206\031\001' + _GETWEBHOOK._options = None + _GETWEBHOOK._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _UPDATEWEBHOOK.fields_by_name['webhook_id']._options = None + _UPDATEWEBHOOK.fields_by_name['webhook_id']._serialized_options = b'\370\206\031\001' + _UPDATEWEBHOOK._options = None + _UPDATEWEBHOOK._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _DELETEWEBHOOK.fields_by_name['webhook_id']._options = None + _DELETEWEBHOOK.fields_by_name['webhook_id']._serialized_options = b'\370\206\031\001' + _DELETEWEBHOOK._options = None + _DELETEWEBHOOK._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _TESTWEBHOOK.fields_by_name['webhook_id']._options = None + _TESTWEBHOOK.fields_by_name['webhook_id']._serialized_options = b'\370\206\031\001' + _TESTWEBHOOK._options = None + _TESTWEBHOOK._serialized_options = b'\342?(\n&com.databricks.rpc.RPC[$this.Response]' + _WEBHOOKSERVICE.methods_by_name['createWebhook']._options = None + _WEBHOOKSERVICE.methods_by_name['createWebhook']._serialized_options = b'\362\206\0312\n\036\n\004POST\022\020/mlflow/webhooks\032\004\010\002\020\000\020\001*\016Create Webhook' + _WEBHOOKSERVICE.methods_by_name['listWebhooks']._options = None + _WEBHOOKSERVICE.methods_by_name['listWebhooks']._serialized_options = b'\362\206\0310\n\035\n\003GET\022\020/mlflow/webhooks\032\004\010\002\020\000\020\001*\rList Webhooks' + _WEBHOOKSERVICE.methods_by_name['getWebhook']._options = None + _WEBHOOKSERVICE.methods_by_name['getWebhook']._serialized_options = b'\362\206\031;\n*\n\003GET\022\035/mlflow/webhooks/{webhook_id}\032\004\010\002\020\000\020\001*\013Get Webhook' + _WEBHOOKSERVICE.methods_by_name['updateWebhook']._options = None + _WEBHOOKSERVICE.methods_by_name['updateWebhook']._serialized_options = b'\362\206\031@\n,\n\005PATCH\022\035/mlflow/webhooks/{webhook_id}\032\004\010\002\020\000\020\001*\016Update Webhook' + _WEBHOOKSERVICE.methods_by_name['deleteWebhook']._options = None + _WEBHOOKSERVICE.methods_by_name['deleteWebhook']._serialized_options = b'\362\206\031A\n-\n\006DELETE\022\035/mlflow/webhooks/{webhook_id}\032\004\010\002\020\000\020\001*\016Delete Webhook' + _WEBHOOKSERVICE.methods_by_name['testWebhook']._options = None + _WEBHOOKSERVICE.methods_by_name['testWebhook']._serialized_options = b'\362\206\031B\n0\n\004POST\022\"/mlflow/webhooks/{webhook_id}/test\032\004\010\002\020\000\020\001*\014Test Webhook' + _WEBHOOKSTATUS._serialized_start=1634 + _WEBHOOKSTATUS._serialized_end=1675 + _WEBHOOKENTITY._serialized_start=1678 + _WEBHOOKENTITY._serialized_end=1806 + _WEBHOOKACTION._serialized_start=1808 + _WEBHOOKACTION._serialized_end=1895 + _WEBHOOKEVENT._serialized_start=67 + _WEBHOOKEVENT._serialized_end=171 + _WEBHOOK._serialized_start=174 + _WEBHOOK._serialized_end=388 + _WEBHOOKTESTRESULT._serialized_start=390 + _WEBHOOKTESTRESULT._serialized_end=497 + _CREATEWEBHOOK._serialized_start=500 + _CREATEWEBHOOK._serialized_end=765 + _CREATEWEBHOOK_RESPONSE._serialized_start=676 + _CREATEWEBHOOK_RESPONSE._serialized_end=720 + _LISTWEBHOOKS._serialized_start=768 + _LISTWEBHOOKS._serialized_end=945 + _LISTWEBHOOKS_RESPONSE._serialized_start=830 + _LISTWEBHOOKS_RESPONSE._serialized_end=900 + _GETWEBHOOK._serialized_start=948 + _GETWEBHOOK._serialized_end=1077 + _GETWEBHOOK_RESPONSE._serialized_start=676 + _GETWEBHOOK_RESPONSE._serialized_end=720 + _UPDATEWEBHOOK._serialized_start=1080 + _UPDATEWEBHOOK._serialized_end=1353 + _UPDATEWEBHOOK_RESPONSE._serialized_start=676 + _UPDATEWEBHOOK_RESPONSE._serialized_end=720 + _DELETEWEBHOOK._serialized_start=1355 + _DELETEWEBHOOK._serialized_end=1453 + _DELETEWEBHOOK_RESPONSE._serialized_start=676 + _DELETEWEBHOOK_RESPONSE._serialized_end=686 + _TESTWEBHOOK._serialized_start=1456 + _TESTWEBHOOK._serialized_end=1632 + _TESTWEBHOOK_RESPONSE._serialized_start=1534 + _TESTWEBHOOK_RESPONSE._serialized_end=1587 + _WEBHOOKSERVICE._serialized_start=1898 + _WEBHOOKSERVICE._serialized_end=2719 + WebhookService = service_reflection.GeneratedServiceType('WebhookService', (_service.Service,), dict( + DESCRIPTOR = _WEBHOOKSERVICE, + __module__ = 'webhooks_pb2' + )) + + WebhookService_Stub = service_reflection.GeneratedServiceStubType('WebhookService_Stub', (WebhookService,), dict( + DESCRIPTOR = _WEBHOOKSERVICE, + __module__ = 'webhooks_pb2' + )) + + + # @@protoc_insertion_point(module_scope) + diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pydantic_ai/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pydantic_ai/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..b12f2509efd5739d10178ff55b803607e7544101 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pydantic_ai/__init__.py @@ -0,0 +1,58 @@ +import inspect +import logging + +from mlflow.pydantic_ai.autolog import ( + patched_async_class_call, + patched_class_call, +) +from mlflow.utils.annotations import experimental +from mlflow.utils.autologging_utils import autologging_integration, safe_patch + +FLAVOR_NAME = "pydantic_ai" +_logger = logging.getLogger(__name__) + + +@experimental(version="3.0.0") +@autologging_integration(FLAVOR_NAME) +def autolog(log_traces: bool = True, disable: bool = False, silent: bool = False): + """ + Enable (or disable) autologging for Pydantic_AI. + + Args: + log_traces: If True, capture spans for agent + model calls. + disable: If True, disable the autologging patches. + silent: If True, suppress MLflow warnings/info. + """ + class_map = { + "pydantic_ai.Agent": ["run", "run_sync"], + "pydantic_ai.models.instrumented.InstrumentedModel": ["request"], + "pydantic_ai._tool_manager.ToolManager": ["handle_call"], + "pydantic_ai.Tool": ["run"], + "pydantic_ai.mcp.MCPServer": ["call_tool", "list_tools"], + } + + for cls_path, methods in class_map.items(): + module_name, class_name = cls_path.rsplit(".", 1) + try: + module = __import__(module_name, fromlist=[class_name]) + cls = getattr(module, class_name) + except (ImportError, AttributeError) as e: + _logger.error("Error importing %s: %s", cls_path, e) + continue + + for method in methods: + try: + orig = getattr(cls, method) + wrapper = ( + patched_async_class_call + if inspect.iscoroutinefunction(orig) + else patched_class_call + ) + safe_patch( + FLAVOR_NAME, + cls, + method, + wrapper, + ) + except AttributeError as e: + _logger.error("Error patching %s.%s: %s", cls_path, method, e) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pydantic_ai/__pycache__/__init__.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pydantic_ai/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..48f0dc03bb2ce43991e9552edd156713cf4d27d5 Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pydantic_ai/__pycache__/__init__.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pydantic_ai/__pycache__/autolog.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pydantic_ai/__pycache__/autolog.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..0e7d4e311543ce4e3620bdbcf02044c4e5972cd4 Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pydantic_ai/__pycache__/autolog.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pydantic_ai/autolog.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pydantic_ai/autolog.py new file mode 100644 index 0000000000000000000000000000000000000000..25b44a093a466c05f34ca8968ebf41d498c3548d --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pydantic_ai/autolog.py @@ -0,0 +1,203 @@ +import inspect +import logging +from typing import Any + +import mlflow +from mlflow.entities import SpanType +from mlflow.entities.span import LiveSpan +from mlflow.tracing.constant import SpanAttributeKey, TokenUsageKey +from mlflow.utils.autologging_utils.config import AutoLoggingConfig + +_logger = logging.getLogger(__name__) + + +def _set_span_attributes(span: LiveSpan, instance): + # 1) MCPServer attributes + try: + from pydantic_ai.mcp import MCPServer + + if isinstance(instance, MCPServer): + for key, value in instance.__dict__.items(): + if value is None: + continue + if key == "tools": + value = _parse_tools(value) + span.set_attribute(key, value) + except Exception as e: + _logger.warning("Failed saving MCPServer attributes: %s", e) + + # 2) Agent attributes + try: + from pydantic_ai import Agent + + if isinstance(instance, Agent): + agent_attrs = _get_agent_attributes(instance) + span.set_attributes({k: v for k, v in agent_attrs.items() if v is not None}) + except Exception as e: + _logger.warning("Failed saving Agent attributes: %s", e) + + # 3) InstrumentedModel attributes + try: + from pydantic_ai.models.instrumented import InstrumentedModel + + if isinstance(instance, InstrumentedModel): + model_attrs = _get_model_attributes(instance) + span.set_attributes({k: v for k, v in model_attrs.items() if v is not None}) + except Exception as e: + _logger.warning("Failed saving InstrumentedModel attributes: %s", e) + + # 4) Tool attributes + try: + from pydantic_ai import Tool + + if isinstance(instance, Tool): + tool_attrs = _get_tool_attributes(instance) + span.set_attributes({k: v for k, v in tool_attrs.items() if v is not None}) + except Exception as e: + _logger.warning("Failed saving Tool attributes: %s", e) + + +async def patched_async_class_call(original, self, *args, **kwargs): + cfg = AutoLoggingConfig.init(flavor_name=mlflow.pydantic_ai.FLAVOR_NAME) + if not cfg.log_traces: + return await original(self, *args, **kwargs) + + fullname = f"{self.__class__.__name__}.{original.__name__}" + span_type = _get_span_type(self) + + with mlflow.start_span(name=fullname, span_type=span_type) as span: + inputs = _construct_full_inputs(original, self, *args, **kwargs) + span.set_inputs(inputs) + _set_span_attributes(span, self) + + result = await original(self, *args, **kwargs) + outputs = result.__dict__ if hasattr(result, "__dict__") else result + span.set_outputs(outputs) + if usage_dict := _parse_usage(result): + span.set_attribute(SpanAttributeKey.CHAT_USAGE, usage_dict) + return result + + +def patched_class_call(original, self, *args, **kwargs): + cfg = AutoLoggingConfig.init(flavor_name=mlflow.pydantic_ai.FLAVOR_NAME) + if not cfg.log_traces: + return original(self, *args, **kwargs) + + fullname = f"{self.__class__.__name__}.{original.__name__}" + span_type = _get_span_type(self) + with mlflow.start_span(name=fullname, span_type=span_type) as span: + inputs = _construct_full_inputs(original, self, *args, **kwargs) + span.set_inputs(inputs) + _set_span_attributes(span, self) + + result = original(self, *args, **kwargs) + outputs = result.__dict__ if hasattr(result, "__dict__") else result + span.set_outputs(outputs) + if usage_dict := _parse_usage(result): + span.set_attribute(SpanAttributeKey.CHAT_USAGE, usage_dict) + return result + + +def _get_span_type(instance) -> str: + try: + from pydantic_ai import Agent, Tool + from pydantic_ai.mcp import MCPServer + from pydantic_ai.models.instrumented import InstrumentedModel + except ImportError: + return SpanType.UNKNOWN + + if isinstance(instance, InstrumentedModel): + return SpanType.LLM + if isinstance(instance, Agent): + return SpanType.AGENT + if isinstance(instance, Tool): + return SpanType.TOOL + if isinstance(instance, MCPServer): + return SpanType.TOOL + + try: + from pydantic_ai._tool_manager import ToolManager + + if isinstance(instance, ToolManager): + return SpanType.TOOL + except ImportError: + pass + + return SpanType.UNKNOWN + + +def _construct_full_inputs(func, *args, **kwargs) -> dict[str, Any]: + sig = inspect.signature(func) + bound = sig.bind_partial(*args, **kwargs).arguments + bound.pop("self", None) + bound.pop("deps", None) + + return { + k: (v.__dict__ if hasattr(v, "__dict__") else v) for k, v in bound.items() if v is not None + } + + +def _get_agent_attributes(instance): + agent = {} + for key, value in instance.__dict__.items(): + if key == "tools": + value = _parse_tools(value) + if value is None: + continue + agent[key] = value + + return agent + + +def _get_model_attributes(instance): + model = {SpanAttributeKey.MESSAGE_FORMAT: "pydantic_ai"} + for key, value in instance.__dict__.items(): + if value is None: + continue + elif key in ["callbacks", "api_key"]: + # Skip sensitive information + continue + else: + model[key] = value + return model + + +def _get_tool_attributes(instance): + tool = {} + for key, value in instance.__dict__.items(): + if value is None: + continue + tool[key] = value + return tool + + +def _parse_tools(tools): + result = [] + for tool in tools: + data = tool.model_dumps(exclude_none=True) + + if data: + result.append( + { + "type": "function", + "function": data, + } + ) + return result + + +def _parse_usage(result: Any) -> dict[str, int] | None: + try: + if isinstance(result, tuple) and len(result) == 2: + usage = result[1] + else: + usage = getattr(result, "usage", None) + + return { + TokenUsageKey.INPUT_TOKENS: usage.request_tokens, + TokenUsageKey.OUTPUT_TOKENS: usage.response_tokens, + TokenUsageKey.TOTAL_TOKENS: usage.total_tokens, + } + except Exception as e: + _logger.debug(f"Failed to parse token usage from output: {e}") + return None diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyfunc/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyfunc/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..d3b90df2b577b570364206cf0af46f1c8013ace5 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyfunc/__init__.py @@ -0,0 +1,3853 @@ +""" +The ``python_function`` model flavor serves as a default model interface for MLflow Python models. +Any MLflow Python model is expected to be loadable as a ``python_function`` model. + +In addition, the ``mlflow.pyfunc`` module defines a generic :ref:`filesystem format +` for Python models and provides utilities for saving to and loading from +this format. The format is self contained in the sense that it includes all necessary information +for anyone to load it and use it. Dependencies are either stored directly with the model or +referenced via a Conda environment. + +The ``mlflow.pyfunc`` module also defines utilities for creating custom ``pyfunc`` models +using frameworks and inference logic that may not be natively included in MLflow. See +:ref:`pyfunc-create-custom`. + +.. _pyfunc-inference-api: + +************* +Inference API +************* + +Python function models are loaded as an instance of :py:class:`PyFuncModel +`, which is an MLflow wrapper around the model implementation and model +metadata (MLmodel file). You can score the model by calling the :py:func:`predict() +` method, which has the following signature:: + + predict( + model_input: [pandas.DataFrame, numpy.ndarray, scipy.sparse.(csc_matrix | csr_matrix), + List[Any], Dict[str, Any], pyspark.sql.DataFrame] + ) -> [numpy.ndarray | pandas.(Series | DataFrame) | List | Dict | pyspark.sql.DataFrame] + +All PyFunc models will support `pandas.DataFrame` as input and PyFunc deep learning models will +also support tensor inputs in the form of Dict[str, numpy.ndarray] (named tensors) and +`numpy.ndarrays` (unnamed tensors). + +Here are some examples of supported inference types, assuming we have the correct ``model`` object +loaded. + +.. list-table:: + :widths: 30 70 + :header-rows: 1 + :class: wrap-table + + * - Input Type + - Example + * - ``pandas.DataFrame`` + - + .. code-block:: python + + import pandas as pd + + x_new = pd.DataFrame(dict(x1=[1, 2, 3], x2=[4, 5, 6])) + model.predict(x_new) + + * - ``numpy.ndarray`` + - + .. code-block:: python + + import numpy as np + + x_new = np.array([[1, 4][2, 5], [3, 6]]) + model.predict(x_new) + + * - ``scipy.sparse.csc_matrix`` or ``scipy.sparse.csr_matrix`` + - + .. code-block:: python + + import scipy + + x_new = scipy.sparse.csc_matrix([[1, 2, 3], [4, 5, 6]]) + model.predict(x_new) + + x_new = scipy.sparse.csr_matrix([[1, 2, 3], [4, 5, 6]]) + model.predict(x_new) + + * - python ``List`` + - + .. code-block:: python + + x_new = [[1, 4], [2, 5], [3, 6]] + model.predict(x_new) + + * - python ``Dict`` + - + .. code-block:: python + + x_new = dict(x1=[1, 2, 3], x2=[4, 5, 6]) + model.predict(x_new) + + * - ``pyspark.sql.DataFrame`` + - + .. code-block:: python + + from pyspark.sql import SparkSession + + spark = SparkSession.builder.getOrCreate() + + data = [(1, 4), (2, 5), (3, 6)] # List of tuples + x_new = spark.createDataFrame(data, ["x1", "x2"]) # Specify column name + model.predict(x_new) + +.. _pyfunc-filesystem-format: + +***************** +Filesystem format +***************** + +The Pyfunc format is defined as a directory structure containing all required data, code, and +configuration:: + + ./dst-path/ + ./MLmodel: configuration + : code packaged with the model (specified in the MLmodel file) + : data packaged with the model (specified in the MLmodel file) + : Conda environment definition (specified in the MLmodel file) + +The directory structure may contain additional contents that can be referenced by the ``MLmodel`` +configuration. + +.. _pyfunc-model-config: + +MLModel configuration +##################### + +A Python model contains an ``MLmodel`` file in **python_function** format in its root with the +following parameters: + +- loader_module [required]: + Python module that can load the model. Expected as module identifier + e.g. ``mlflow.sklearn``, it will be imported using ``importlib.import_module``. + The imported module must contain a function with the following signature:: + + _load_pyfunc(path: string) -> + + The path argument is specified by the ``data`` parameter and may refer to a file or + directory. The model implementation is expected to be an object with a + ``predict`` method with the following signature:: + + predict( + model_input: [pandas.DataFrame, numpy.ndarray, + scipy.sparse.(csc_matrix | csr_matrix), List[Any], Dict[str, Any]], + pyspark.sql.DataFrame + ) -> [numpy.ndarray | pandas.(Series | DataFrame) | List | Dict | pyspark.sql.DataFrame] + +- code [optional]: + Relative path to a directory containing the code packaged with this model. + All files and directories inside this directory are added to the Python path + prior to importing the model loader. + +- data [optional]: + Relative path to a file or directory containing model data. + The path is passed to the model loader. + +- env [optional]: + Relative path to an exported Conda environment. If present this environment + should be activated prior to running the model. + +- Optionally, any additional parameters necessary for interpreting the serialized model in + ``pyfunc`` format. + +.. rubric:: Example + +:: + + tree example/sklearn_iris/mlruns/run1/outputs/linear-lr + +:: + + ├── MLmodel + ├── code + │ ├── sklearn_iris.py + │ + ├── data + │ └── model.pkl + └── mlflow_env.yml + +:: + + cat example/sklearn_iris/mlruns/run1/outputs/linear-lr/MLmodel + +:: + + python_function: + code: code + data: data/model.pkl + loader_module: mlflow.sklearn + env: mlflow_env.yml + main: sklearn_iris + +.. _pyfunc-create-custom: + +********************************** +Models From Code for Custom Models +********************************** + +.. tip:: + + MLflow 2.12.2 introduced the feature "models from code", which greatly simplifies the process + of serializing and deploying custom models through the use of script serialization. It is + strongly recommended to migrate custom model implementations to this new paradigm to avoid the + limitations and complexity of serializing with cloudpickle. + You can learn more about models from code within the + `Models From Code Guide <../model/models-from-code.html>`_. + +The section below illustrates the process of using the legacy serializer for custom Pyfunc models. +Models from code will provide a far simpler experience for logging of your models. + +****************************** +Creating custom Pyfunc models +****************************** + +MLflow's persistence modules provide convenience functions for creating models with the +``pyfunc`` flavor in a variety of machine learning frameworks (scikit-learn, Keras, Pytorch, and +more); however, they do not cover every use case. For example, you may want to create an MLflow +model with the ``pyfunc`` flavor using a framework that MLflow does not natively support. +Alternatively, you may want to build an MLflow model that executes custom logic when evaluating +queries, such as preprocessing and postprocessing routines. Therefore, ``mlflow.pyfunc`` +provides utilities for creating ``pyfunc`` models from arbitrary code and model data. + +The :meth:`save_model()` and :meth:`log_model()` methods are designed to support multiple workflows +for creating custom ``pyfunc`` models that incorporate custom inference logic and artifacts +that the logic may require. + +An `artifact` is a file or directory, such as a serialized model or a CSV. For example, a +serialized TensorFlow graph is an artifact. An MLflow model directory is also an artifact. + +.. _pyfunc-create-custom-workflows: + +Workflows +######### + +:meth:`save_model()` and :meth:`log_model()` support the following workflows: + +1. Programmatically defining a new MLflow model, including its attributes and artifacts. + + Given a set of artifact URIs, :meth:`save_model()` and :meth:`log_model()` can + automatically download artifacts from their URIs and create an MLflow model directory. + + In this case, you must define a Python class which inherits from :class:`~PythonModel`, + defining ``predict()`` and, optionally, ``load_context()``. An instance of this class is + specified via the ``python_model`` parameter; it is automatically serialized and deserialized + as a Python class, including all of its attributes. + +2. Interpreting pre-existing data as an MLflow model. + + If you already have a directory containing model data, :meth:`save_model()` and + :meth:`log_model()` can import the data as an MLflow model. The ``data_path`` parameter + specifies the local filesystem path to the directory containing model data. + + In this case, you must provide a Python module, called a `loader module`. The + loader module defines a ``_load_pyfunc()`` method that performs the following tasks: + + - Load data from the specified ``data_path``. For example, this process may include + deserializing pickled Python objects or models or parsing CSV files. + + - Construct and return a pyfunc-compatible model wrapper. As in the first + use case, this wrapper must define a ``predict()`` method that is used to evaluate + queries. ``predict()`` must adhere to the :ref:`pyfunc-inference-api`. + + The ``loader_module`` parameter specifies the name of your loader module. + + For an example loader module implementation, refer to the `loader module + implementation in mlflow.sklearn `_. + +.. _pyfunc-create-custom-selecting-workflow: + +Which workflow is right for my use case? +######################################## + +We consider the first workflow to be more user-friendly and generally recommend it for the +following reasons: + +- It automatically resolves and collects specified model artifacts. + +- It automatically serializes and deserializes the ``python_model`` instance and all of + its attributes, reducing the amount of user logic that is required to load the model + +- You can create Models using logic that is defined in the ``__main__`` scope. This allows + custom models to be constructed in interactive environments, such as notebooks and the Python + REPL. + +You may prefer the second, lower-level workflow for the following reasons: + +- Inference logic is always persisted as code, rather than a Python object. This makes logic + easier to inspect and modify later. + +- If you have already collected all of your model data in a single location, the second + workflow allows it to be saved in MLflow format directly, without enumerating constituent + artifacts. + +****************************************** +Function-based Model vs Class-based Model +****************************************** + +When creating custom PyFunc models, you can choose between two different interfaces: +a function-based model and a class-based model. In short, a function-based model is simply a +python function that does not take additional params. The class-based model, on the other hand, +is subclass of ``PythonModel`` that supports several required and optional +methods. If your use case is simple and fits within a single predict function, a function-based +approach is recommended. If you need more power, such as custom serialization, custom data +processing, or to override additional methods, you should use the class-based implementation. + +Before looking at code examples, it's important to note that both methods are serialized via +`cloudpickle `_. cloudpickle can serialize Python +functions, lambda functions, and locally defined classes and functions inside other functions. This +makes cloudpickle especially useful for parallel and distributed computing where code objects need +to be sent over network to execute on remote workers, which is a common deployment paradigm for +MLflow. + +That said, cloudpickle has some limitations. + +- **Environment Dependency**: cloudpickle does not capture the full execution environment, so in + MLflow we must pass ``pip_requirements``, ``extra_pip_requirements``, or an ``input_example``, + the latter of which is used to infer environment dependencies. For more, refer to + `the model dependency docs `_. + +- **Object Support**: cloudpickle does not serialize objects outside of the Python data model. + Some relevant examples include raw files and database connections. If your program depends on + these, be sure to log ways to reference these objects along with your model. + +Function-based Model +#################### +If you're looking to serialize a simple python function without additional dependent methods, you +can simply log a predict method via the keyword argument ``python_model``. + +.. note:: + + Function-based model only supports a function with a single input argument. If you would like + to pass more arguments or additional inference parameters, please use the class-based model + below. + +.. code-block:: python + + import mlflow + import pandas as pd + + + # Define a simple function to log + def predict(model_input): + return model_input.apply(lambda x: x * 2) + + + # Save the function as a model + with mlflow.start_run(): + mlflow.pyfunc.log_model( + name="model", python_model=predict, pip_requirements=["pandas"] + ) + run_id = mlflow.active_run().info.run_id + + # Load the model from the tracking server and perform inference + model = mlflow.pyfunc.load_model(f"runs:/{run_id}/model") + x_new = pd.Series([1, 2, 3]) + + prediction = model.predict(x_new) + print(prediction) + + +Class-based Model +################# +If you're looking to serialize a more complex object, for instance a class that handles +preprocessing, complex prediction logic, or custom serialization, you should subclass the +``PythonModel`` class. MLflow has tutorials on building custom PyFunc models, as shown +`here `_, +so instead of duplicating that information, in this example we'll recreate the above functionality +to highlight the differences. Note that this PythonModel implementation is overly complex and +we would recommend using the functional-based Model instead for this simple case. + +.. code-block:: python + + import mlflow + import pandas as pd + + + class MyModel(mlflow.pyfunc.PythonModel): + def predict(self, context, model_input, params=None): + return [x * 2 for x in model_input] + + + # Save the function as a model + with mlflow.start_run(): + mlflow.pyfunc.log_model( + name="model", python_model=MyModel(), pip_requirements=["pandas"] + ) + run_id = mlflow.active_run().info.run_id + + # Load the model from the tracking server and perform inference + model = mlflow.pyfunc.load_model(f"runs:/{run_id}/model") + x_new = pd.Series([1, 2, 3]) + + print(f"Prediction:\n\t{model.predict(x_new)}") + +The primary difference between the this implementation and the function-based implementation above +is that the predict method is wrapped with a class, has the ``self`` parameter, +and has the ``params`` parameter that defaults to None. Note that function-based models don't +support additional params. + +In summary, use the function-based Model when you have a simple function to serialize. +If you need more power, use the class-based model. +""" + +import collections +import functools +import hashlib +import importlib +import inspect +import json +import logging +import os +import shutil +import signal +import subprocess +import sys +import tempfile +import threading +import uuid +from copy import deepcopy +from pathlib import Path +from typing import Any, Iterator, Tuple, Union +from urllib.parse import urlparse + +import numpy as np +import pandas +import pydantic +import yaml +from packaging.version import Version + +import mlflow +import mlflow.models.signature +import mlflow.pyfunc.loaders +import mlflow.pyfunc.model +from mlflow.entities.model_registry.prompt import Prompt +from mlflow.environment_variables import ( + _MLFLOW_IN_CAPTURE_MODULE_PROCESS, + _MLFLOW_TESTING, + MLFLOW_DISABLE_SCHEMA_DETAILS, + MLFLOW_MODEL_ENV_DOWNLOADING_TEMP_DIR, + MLFLOW_SCORING_SERVER_REQUEST_TIMEOUT, +) +from mlflow.exceptions import MlflowException +from mlflow.models import Model, ModelInputExample, ModelSignature +from mlflow.models.auth_policy import AuthPolicy +from mlflow.models.dependencies_schemas import ( + _clear_dependencies_schemas, + _get_dependencies_schema_from_model, + _get_dependencies_schemas, +) +from mlflow.models.flavor_backend_registry import get_flavor_backend +from mlflow.models.model import ( + _DATABRICKS_FS_LOADER_MODULE, + MLMODEL_FILE_NAME, + MODEL_CODE_PATH, + MODEL_CONFIG, +) +from mlflow.models.resources import Resource, _ResourceBuilder +from mlflow.models.signature import ( + _extract_type_hints, + _infer_signature_from_input_example, + _infer_signature_from_type_hints, +) +from mlflow.models.utils import ( + PyFuncInput, + PyFuncLLMOutputChunk, + PyFuncLLMSingleInput, + PyFuncOutput, + _convert_llm_input_data, + _enforce_params_schema, + _enforce_schema, + _load_model_code_path, + _save_example, + _split_input_data_and_params, + _validate_and_get_model_code_path, +) +from mlflow.protos.databricks_pb2 import ( + BAD_REQUEST, + INTERNAL_ERROR, + INVALID_PARAMETER_VALUE, + RESOURCE_DOES_NOT_EXIST, +) +from mlflow.protos.databricks_uc_registry_messages_pb2 import ( + Entity, + Job, + LineageHeaderInfo, + Notebook, +) +from mlflow.pyfunc.context import Context, set_prediction_context +from mlflow.pyfunc.dbconnect_artifact_cache import ( + DBConnectArtifactCache, + archive_directory, + extract_archive_to_dir, +) +from mlflow.pyfunc.model import ( + _DEFAULT_CHAT_AGENT_METADATA_TASK, + _DEFAULT_CHAT_MODEL_METADATA_TASK, + _DEFAULT_RESPONSES_AGENT_METADATA_TASK, + ChatAgent, + ChatModel, + PythonModel, + PythonModelContext, + _FunctionPythonModel, + _log_warning_if_params_not_in_predict_signature, + _PythonModelPyfuncWrapper, + get_default_conda_env, # noqa: F401 + get_default_pip_requirements, +) + +try: + from mlflow.pyfunc.model import ResponsesAgent + + IS_RESPONSES_AGENT_AVAILABLE = True +except ImportError: + IS_RESPONSES_AGENT_AVAILABLE = False +from mlflow.tracing.provider import trace_disabled +from mlflow.tracing.utils import _try_get_prediction_context +from mlflow.tracking._model_registry import DEFAULT_AWAIT_MAX_SLEEP_SECONDS +from mlflow.tracking.artifact_utils import _download_artifact_from_uri +from mlflow.types.agent import ( + CHAT_AGENT_INPUT_EXAMPLE, + CHAT_AGENT_INPUT_SCHEMA, + CHAT_AGENT_OUTPUT_SCHEMA, + ChatAgentRequest, + ChatAgentResponse, +) +from mlflow.types.llm import ( + CHAT_MODEL_INPUT_EXAMPLE, + CHAT_MODEL_INPUT_SCHEMA, + CHAT_MODEL_OUTPUT_SCHEMA, + ChatCompletionResponse, + ChatMessage, + ChatParams, +) +from mlflow.types.type_hints import ( + _convert_dataframe_to_example_format, + _is_example_valid_for_type_from_example, + _is_type_hint_from_example, + _signature_cannot_be_inferred_from_type_hint, + model_validate, +) +from mlflow.utils import ( + PYTHON_VERSION, + _is_in_ipython_notebook, + check_port_connectivity, + databricks_utils, + find_free_port, + get_major_minor_py_version, +) +from mlflow.utils import env_manager as _EnvManager +from mlflow.utils._spark_utils import modified_environ +from mlflow.utils.annotations import deprecated, developer_stable +from mlflow.utils.databricks_utils import ( + _get_databricks_serverless_env_vars, + get_dbconnect_udf_sandbox_info, + is_databricks_connect, + is_in_databricks_runtime, + is_in_databricks_serverless_runtime, + is_in_databricks_shared_cluster_runtime, +) +from mlflow.utils.docstring_utils import LOG_MODEL_PARAM_DOCS, format_docstring +from mlflow.utils.environment import ( + _CONDA_ENV_FILE_NAME, + _CONSTRAINTS_FILE_NAME, + _PYTHON_ENV_FILE_NAME, + _REQUIREMENTS_FILE_NAME, + _process_conda_env, + _process_pip_requirements, + _PythonEnv, + _validate_env_arguments, +) +from mlflow.utils.file_utils import ( + _copy_file_or_tree, + get_or_create_nfs_tmp_dir, + get_or_create_tmp_dir, + get_total_file_size, + write_to, +) +from mlflow.utils.mlflow_tags import MLFLOW_MODEL_IS_EXTERNAL +from mlflow.utils.model_utils import ( + _add_code_from_conf_to_system_path, + _get_flavor_configuration, + _get_flavor_configuration_from_ml_model_file, + _get_overridden_pyfunc_model_config, + _validate_and_copy_file_to_directory, + _validate_and_get_model_config_from_file, + _validate_and_prepare_target_save_path, + _validate_infer_and_copy_code_paths, + _validate_pyfunc_model_config, +) +from mlflow.utils.nfs_on_spark import get_nfs_cache_root_dir +from mlflow.utils.pydantic_utils import model_dump_compat +from mlflow.utils.requirements_utils import ( + _parse_requirements, + warn_dependency_requirement_mismatches, +) +from mlflow.utils.spark_utils import is_spark_connect_mode +from mlflow.utils.virtualenv import _get_python_env, _get_virtualenv_name +from mlflow.utils.warnings_utils import color_warning + +try: + from pyspark.sql import DataFrame as SparkDataFrame + + HAS_PYSPARK = True +except ImportError: + HAS_PYSPARK = False +FLAVOR_NAME = "python_function" +MAIN = "loader_module" +CODE = "code" +DATA = "data" +ENV = "env" +TASK = "task" + +_MODEL_DATA_SUBPATH = "data" +_CHAT_PARAMS_WARNING_MESSAGE = ( + "Default values for temperature, n and stream in ChatParams will be removed in the " + "next release. Specify them in the input example explicitly if needed." +) +_TYPE_FROM_EXAMPLE_ERROR_MESSAGE = ( + "Input example must be provided when using TypeFromExample as type hint. " + "Fix this by passing `input_example` when logging your model. Check " + "https://mlflow.org/docs/latest/model/python_model.html#typefromexample-type-hint-usage " + "for more details." +) + + +class EnvType: + CONDA = "conda" + VIRTUALENV = "virtualenv" + + def __init__(self): + raise NotImplementedError("This class is not meant to be instantiated.") + + +PY_VERSION = "python_version" + + +_logger = logging.getLogger(__name__) + + +def add_to_model( + model, + loader_module, + data=None, + code=None, + conda_env=None, + python_env=None, + model_config=None, + model_code_path=None, + **kwargs, +): + """ + Add a ``pyfunc`` spec to the model configuration. + + Defines ``pyfunc`` configuration schema. Caller can use this to create a valid ``pyfunc`` model + flavor out of an existing directory structure. For example, other model flavors can use this to + specify how to use their output as a ``pyfunc``. + + NOTE: + + All paths are relative to the exported model root directory. + + Args: + model: Existing model. + loader_module: The module to be used to load the model. + data: Path to the model data. + code: Path to the code dependencies. + conda_env: Conda environment. + python_env: Python environment. + model_config: The model configuration to apply to the model. This configuration + is available during model loading. + + .. Note:: Experimental: This parameter may change or be removed in a future + release without warning. + + model_code_path: Path to the model code. + kwargs: Additional key-value pairs to include in the ``pyfunc`` flavor specification. + Values must be YAML-serializable. + + Returns: + Updated model configuration. + """ + params = deepcopy(kwargs) + params[MAIN] = loader_module + params[PY_VERSION] = PYTHON_VERSION + if code: + params[CODE] = code + if data: + params[DATA] = data + if conda_env or python_env: + params[ENV] = {} + if conda_env: + params[ENV][EnvType.CONDA] = conda_env + if python_env: + params[ENV][EnvType.VIRTUALENV] = python_env + if model_config: + params[MODEL_CONFIG] = model_config + if model_code_path: + params[MODEL_CODE_PATH] = model_code_path + return model.add_flavor(FLAVOR_NAME, **params) + + +def _extract_conda_env(env): + # In MLflow < 2.0.0, the 'env' field in a pyfunc configuration is a string containing the path + # to a conda.yaml file. + return env if isinstance(env, str) else env[EnvType.CONDA] + + +def _load_model_env(path): + """ + Get ENV file string from a model configuration stored in Python Function format. + Returned value is a model-relative path to a Conda Environment file, + or None if none was specified at model save time + """ + return _get_flavor_configuration(model_path=path, flavor_name=FLAVOR_NAME).get(ENV, None) + + +def _validate_params(params, model_metadata): + if hasattr(model_metadata, "get_params_schema"): + params_schema = model_metadata.get_params_schema() + return _enforce_params_schema(params, params_schema) + if params: + raise MlflowException.invalid_parameter_value( + "This model was not logged with a params schema and does not support " + "providing the params argument." + "Please log the model with mlflow >= 2.6.0 and specify a params schema.", + ) + return + + +def _validate_prediction_input(data: PyFuncInput, params, input_schema, params_schema, flavor=None): + """ + Internal helper function to transform and validate input data and params for prediction. + Any additional transformation logics related to input data and params should be added here. + """ + if input_schema is not None: + try: + data = _enforce_schema(data, input_schema, flavor) + except Exception as e: + if MLFLOW_DISABLE_SCHEMA_DETAILS.get(): + message = "Failed to enforce model input schema. Please check your input data." + else: + # Include error in message for backwards compatibility + message = ( + f"Failed to enforce schema of data '{data}' " + f"with schema '{input_schema}'. " + f"Error: {e}" + ) + raise MlflowException.invalid_parameter_value(message) + params = _enforce_params_schema(params, params_schema) + if HAS_PYSPARK and isinstance(data, SparkDataFrame): + _logger.warning( + "Input data is a Spark DataFrame. Note that behaviour for " + "Spark DataFrames is model dependent." + ) + return data, params + + +class PyFuncModel: + """ + MLflow 'python function' model. + + Wrapper around model implementation and metadata. This class is not meant to be constructed + directly. Instead, instances of this class are constructed and returned from + :py:func:`load_model() `. + + ``model_impl`` can be any Python object that implements the `Pyfunc interface + `_, and is + returned by invoking the model's ``loader_module``. + + ``model_meta`` contains model metadata loaded from the MLmodel file. + """ + + def __init__( + self, + model_meta: Model, + model_impl: Any, + predict_fn: str = "predict", + predict_stream_fn: str | None = None, + model_id: str | None = None, + ): + if not hasattr(model_impl, predict_fn): + raise MlflowException(f"Model implementation is missing required {predict_fn} method.") + if not model_meta: + raise MlflowException("Model is missing metadata.") + self._model_meta = model_meta + self.__model_impl = model_impl + self._predict_fn = getattr(model_impl, predict_fn) + if predict_stream_fn: + if not hasattr(model_impl, predict_stream_fn): + raise MlflowException( + f"Model implementation is missing required {predict_stream_fn} method." + ) + self._predict_stream_fn = getattr(model_impl, predict_stream_fn) + else: + self._predict_stream_fn = None + self._model_id = model_id + self._input_example = None + + @property + @developer_stable + def _model_impl(self) -> Any: + """ + The underlying model implementation object. + + NOTE: This is a stable developer API. + """ + return self.__model_impl + + @property + def model_id(self) -> str | None: + """ + The model ID of the model. + + Returns: + The model ID of the model. + """ + return self._model_id + + def _update_dependencies_schemas_in_prediction_context(self, context: Context): + if self._model_meta and self._model_meta.metadata: + dependencies_schemas = self._model_meta.metadata.get("dependencies_schemas", {}) + context.update( + dependencies_schemas={ + dependency: json.dumps(schema) + for dependency, schema in dependencies_schemas.items() + } + ) + + @property + def input_example(self) -> Any | None: + """ + The input example provided when the model was saved. + """ + return self._input_example + + @input_example.setter + def input_example(self, value: Any) -> None: + self._input_example = value + + def predict(self, data: PyFuncInput, params: dict[str, Any] | None = None) -> PyFuncOutput: + context = _try_get_prediction_context() or Context() + with set_prediction_context(context): + if schema := _get_dependencies_schema_from_model(self._model_meta): + context.update(**schema) + + if self.model_id: + context.update(model_id=self.model_id) + return self._predict(data, params) + + def _predict(self, data: PyFuncInput, params: dict[str, Any] | None = None) -> PyFuncOutput: + """ + Generates model predictions. + + If the model contains signature, enforce the input schema first before calling the model + implementation with the sanitized input. If the pyfunc model does not include model schema, + the input is passed to the model implementation as is. See `Model Signature Enforcement + `_ for more details. + + Args: + data: LLM Model single input as one of pandas.DataFrame, numpy.ndarray, + scipy.sparse.(csc_matrix | csr_matrix), List[Any], or + Dict[str, numpy.ndarray]. + For model signatures with tensor spec inputs + (e.g. the Tensorflow core / Keras model), the input data type must be one of + `numpy.ndarray`, `List[numpy.ndarray]`, `Dict[str, numpy.ndarray]` or + `pandas.DataFrame`. If data is of `pandas.DataFrame` type and the model + contains a signature with tensor spec inputs, the corresponding column values + in the pandas DataFrame will be reshaped to the required shape with 'C' order + (i.e. read / write the elements using C-like index order), and DataFrame + column values will be cast as the required tensor spec type. For Pyspark + DataFrame inputs, MLflow will only enforce the schema on a subset + of the data rows. + params: Additional parameters to pass to the model for inference. + + Returns: + Model predictions as one of pandas.DataFrame, pandas.Series, numpy.ndarray or list. + """ + # fetch the schema from metadata to avoid signature change after model is loaded + self.input_schema = self.metadata.get_input_schema() + self.params_schema = self.metadata.get_params_schema() + # signature can only be inferred from type hints if the model is PythonModel + if self.metadata._is_signature_from_type_hint(): + # we don't need to validate on data as data validation + # will be done during PythonModel's predict call + params = _enforce_params_schema(params, self.params_schema) + else: + data, params = _validate_prediction_input( + data, params, self.input_schema, self.params_schema, self.loader_module + ) + if ( + isinstance(data, pandas.DataFrame) + and self.metadata._is_type_hint_from_example() + and self.input_example is not None + ): + data = _convert_dataframe_to_example_format(data, self.input_example) + params_arg = inspect.signature(self._predict_fn).parameters.get("params") + if params_arg and params_arg.kind != inspect.Parameter.VAR_KEYWORD: + return self._predict_fn(data, params=params) + + _log_warning_if_params_not_in_predict_signature(_logger, params) + return self._predict_fn(data) + + def predict_stream( + self, data: PyFuncLLMSingleInput, params: dict[str, Any] | None = None + ) -> Iterator[PyFuncLLMOutputChunk]: + context = _try_get_prediction_context() or Context() + + if schema := _get_dependencies_schema_from_model(self._model_meta): + context.update(**schema) + + if self.model_id: + context.update(model_id=self.model_id) + + # NB: The prediction context must be applied during iterating over the stream, + # hence, simply wrapping the self._predict_stream call with the context manager + # is not sufficient. + def _gen_with_context(*args, **kwargs): + with set_prediction_context(context): + yield from self._predict_stream(*args, **kwargs) + + return _gen_with_context(data, params) + + def _predict_stream( + self, data: PyFuncLLMSingleInput, params: dict[str, Any] | None = None + ) -> Iterator[PyFuncLLMOutputChunk]: + """ + Generates streaming model predictions. Only LLM supports this method. + + If the model contains signature, enforce the input schema first before calling the model + implementation with the sanitized input. If the pyfunc model does not include model schema, + the input is passed to the model implementation as is. See `Model Signature Enforcement + `_ for more details. + + Args: + data: LLM Model single input as one of dict, str, bool, bytes, float, int, str type. + params: Additional parameters to pass to the model for inference. + + Returns: + Model predictions as an iterator of chunks. The chunks in the iterator must be type of + dict or string. Chunk dict fields are determined by the model implementation. + """ + + if self._predict_stream_fn is None: + raise MlflowException("This model does not support predict_stream method.") + + self.input_schema = self.metadata.get_input_schema() + self.params_schema = self.metadata.get_params_schema() + data, params = _validate_prediction_input( + data, params, self.input_schema, self.params_schema, self.loader_module + ) + data = _convert_llm_input_data(data) + if isinstance(data, list): + # `predict_stream` only accepts single input. + # but `enforce_schema` might convert single input into a list like `[single_input]` + # so extract the first element in the list. + if len(data) != 1: + raise MlflowException( + f"'predict_stream' requires single input, but it got input data {data}" + ) + data = data[0] + + if "params" in inspect.signature(self._predict_stream_fn).parameters: + return self._predict_stream_fn(data, params=params) + + _log_warning_if_params_not_in_predict_signature(_logger, params) + return self._predict_stream_fn(data) + + def unwrap_python_model(self): + """ + Unwrap the underlying Python model object. + + This method is useful for accessing custom model functions, while still being able to + leverage the MLflow designed workflow through the `predict()` method. + + Returns: + The underlying wrapped model object + + .. code-block:: python + :test: + :caption: Example + + import mlflow + + + # define a custom model + class MyModel(mlflow.pyfunc.PythonModel): + def predict(self, context, model_input, params=None): + return self.my_custom_function(model_input, params) + + def my_custom_function(self, model_input, params=None): + # do something with the model input + return 0 + + + some_input = 1 + # save the model + with mlflow.start_run(): + model_info = mlflow.pyfunc.log_model(name="model", python_model=MyModel()) + + # load the model + loaded_model = mlflow.pyfunc.load_model(model_uri=model_info.model_uri) + print(type(loaded_model)) # + unwrapped_model = loaded_model.unwrap_python_model() + print(type(unwrapped_model)) # + + # does not work, only predict() is exposed + # print(loaded_model.my_custom_function(some_input)) + print(unwrapped_model.my_custom_function(some_input)) # works + print(loaded_model.predict(some_input)) # works + + # works, but None is needed for context arg + print(unwrapped_model.predict(None, some_input)) + """ + try: + python_model = self._model_impl.python_model + if python_model is None: + raise AttributeError("Expected python_model attribute not to be None.") + except AttributeError as e: + raise MlflowException("Unable to retrieve base model object from pyfunc.") from e + return python_model + + def __eq__(self, other): + if not isinstance(other, PyFuncModel): + return False + return self._model_meta == other._model_meta + + @property + def metadata(self) -> Model: + """Model metadata.""" + if self._model_meta is None: + raise MlflowException("Model is missing metadata.") + return self._model_meta + + @property + def model_config(self): + """Model's flavor configuration""" + return self._model_meta.flavors[FLAVOR_NAME].get(MODEL_CONFIG, {}) + + @property + def loader_module(self): + """Model's flavor configuration""" + if self._model_meta.flavors.get(FLAVOR_NAME) is None: + return None + return self._model_meta.flavors[FLAVOR_NAME].get(MAIN) + + def __repr__(self): + info = {} + if self._model_meta is not None: + if hasattr(self._model_meta, "run_id") and self._model_meta.run_id is not None: + info["run_id"] = self._model_meta.run_id + if ( + hasattr(self._model_meta, "artifact_path") + and self._model_meta.artifact_path is not None + ): + info["artifact_path"] = self._model_meta.artifact_path + info["flavor"] = self._model_meta.flavors[FLAVOR_NAME]["loader_module"] + return yaml.safe_dump({"mlflow.pyfunc.loaded_model": info}, default_flow_style=False) + + def get_raw_model(self): + """ + Get the underlying raw model if the model wrapper implemented `get_raw_model` function. + """ + if hasattr(self._model_impl, "get_raw_model"): + return self._model_impl.get_raw_model() + raise NotImplementedError("`get_raw_model` is not implemented by the underlying model") + + +def _get_pip_requirements_from_model_path(model_path: str): + req_file_path = os.path.join(model_path, _REQUIREMENTS_FILE_NAME) + if not os.path.exists(req_file_path): + return [] + + return [req.req_str for req in _parse_requirements(req_file_path, is_constraint=False)] + + +@trace_disabled # Suppress traces while loading model +def load_model( + model_uri: str, + suppress_warnings: bool = False, + dst_path: str | None = None, + model_config: str | Path | dict[str, Any] | None = None, +) -> PyFuncModel: + """ + Load a model stored in Python function format. + + Args: + model_uri: The location, in URI format, of the MLflow model. For example: + + - ``/Users/me/path/to/local/model`` + - ``relative/path/to/local/model`` + - ``s3://my_bucket/path/to/model`` + - ``runs://run-relative/path/to/model`` + - ``models://`` + - ``models://`` + - ``mlflow-artifacts:/path/to/model`` + + For more information about supported URI schemes, see + `Referencing Artifacts `_. + suppress_warnings: If ``True``, non-fatal warning messages associated with the model + loading process will be suppressed. If ``False``, these warning messages will be + emitted. + dst_path: The local filesystem path to which to download the model artifact. + This directory must already exist. If unspecified, a local output + path will be created. + model_config: The model configuration to apply to the model. The configuration will + be available as the ``model_config`` property of the ``context`` parameter + in :func:`PythonModel.load_context() ` + and :func:`PythonModel.predict() `. + The configuration can be passed as a file path, or a dict with string keys. + + .. Note:: Experimental: This parameter may change or be removed in a future + release without warning. + """ + + lineage_header_info = None + if ( + not _MLFLOW_IN_CAPTURE_MODULE_PROCESS.get() + ) and databricks_utils.is_in_databricks_runtime(): + entity_list = [] + # Get notebook id and job id, pack them into lineage_header_info + if databricks_utils.is_in_databricks_notebook() and ( + notebook_id := databricks_utils.get_notebook_id() + ): + notebook_entity = Notebook(id=notebook_id) + entity_list.append(Entity(notebook=notebook_entity)) + + if databricks_utils.is_in_databricks_job() and (job_id := databricks_utils.get_job_id()): + job_entity = Job(id=job_id) + entity_list.append(Entity(job=job_entity)) + + lineage_header_info = LineageHeaderInfo(entities=entity_list) if entity_list else None + + local_path = _download_artifact_from_uri( + artifact_uri=model_uri, output_path=dst_path, lineage_header_info=lineage_header_info + ) + + if not suppress_warnings: + model_requirements = _get_pip_requirements_from_model_path(local_path) + warn_dependency_requirement_mismatches(model_requirements) + + model_meta = Model.load(os.path.join(local_path, MLMODEL_FILE_NAME)) + + if model_meta.metadata and model_meta.metadata.get(MLFLOW_MODEL_IS_EXTERNAL, False) is True: + raise MlflowException( + "This model's artifacts are external and are not stored in the model directory." + " This model cannot be loaded with MLflow.", + BAD_REQUEST, + ) + + conf = model_meta.flavors.get(FLAVOR_NAME) + if conf is None: + raise MlflowException( + f'Model does not have the "{FLAVOR_NAME}" flavor', + RESOURCE_DOES_NOT_EXIST, + ) + model_py_version = conf.get(PY_VERSION) + if not suppress_warnings: + _warn_potentially_incompatible_py_version_if_necessary(model_py_version=model_py_version) + + _add_code_from_conf_to_system_path(local_path, conf, code_key=CODE) + data_path = os.path.join(local_path, conf[DATA]) if (DATA in conf) else local_path + + if isinstance(model_config, str): + model_config = _validate_and_get_model_config_from_file(model_config) + + model_config = _get_overridden_pyfunc_model_config( + conf.get(MODEL_CONFIG, None), model_config, _logger + ) + + try: + if model_config: + model_impl = importlib.import_module(conf[MAIN])._load_pyfunc(data_path, model_config) + else: + model_impl = importlib.import_module(conf[MAIN])._load_pyfunc(data_path) + except ModuleNotFoundError as e: + # This error message is particularly for the case when the error is caused by module + # "databricks.feature_store.mlflow_model". But depending on the environment, the offending + # module might be "databricks", "databricks.feature_store" or full package. So we will + # raise the error with the following note if "databricks" presents in the error. All non- + # databricks module errors will just be re-raised. + if conf[MAIN] == _DATABRICKS_FS_LOADER_MODULE and e.name.startswith("databricks"): + raise MlflowException( + f"{e.msg}; " + "Note: mlflow.pyfunc.load_model is not supported for Feature Store models. " + "spark_udf() and predict() will not work as expected. Use " + "score_batch for offline predictions.", + BAD_REQUEST, + ) from None + raise e + finally: + # clean up the dependencies schema which is set to global state after loading the model. + # This avoids the schema being used by other models loaded in the same process. + _clear_dependencies_schemas() + predict_fn = conf.get("predict_fn", "predict") + streamable = conf.get("streamable", False) + predict_stream_fn = conf.get("predict_stream_fn", "predict_stream") if streamable else None + + pyfunc_model = PyFuncModel( + model_meta=model_meta, + model_impl=model_impl, + predict_fn=predict_fn, + predict_stream_fn=predict_stream_fn, + model_id=model_meta.model_id, + ) + + try: + model_input_example = model_meta.load_input_example(path=local_path) + pyfunc_model.input_example = model_input_example + except Exception as e: + _logger.debug(f"Failed to load input example from model metadata: {e}.") + + return pyfunc_model + + +class _ServedPyFuncModel(PyFuncModel): + def __init__(self, model_meta: Model, client: Any, server_pid: int, env_manager="local"): + super().__init__(model_meta=model_meta, model_impl=client, predict_fn="invoke") + self._client = client + self._server_pid = server_pid + # We need to set `env_manager` attribute because it is used by Databricks runtime + # evaluate usage logging to log 'env_manager' tag in `_evaluate` function patching. + self._env_manager = env_manager + + def predict(self, data, params=None): + """ + Args: + data: Model input data. + params: Additional parameters to pass to the model for inference. + + Returns: + Model predictions. + """ + if "params" in inspect.signature(self._client.invoke).parameters: + result = self._client.invoke(data, params=params).get_predictions() + else: + _log_warning_if_params_not_in_predict_signature(_logger, params) + result = self._client.invoke(data).get_predictions() + if isinstance(result, pandas.DataFrame): + result = result[result.columns[0]] + return result + + @property + def pid(self): + if self._server_pid is None: + raise MlflowException("Served PyFunc Model is missing server process ID.") + return self._server_pid + + @property + def env_manager(self): + return self._env_manager + + @env_manager.setter + def env_manager(self, value): + self._env_manager = value + + +def _load_model_or_server( + model_uri: str, env_manager: str, model_config: dict[str, Any] | None = None +): + """ + Load a model with env restoration. If a non-local ``env_manager`` is specified, prepare an + independent Python environment with the training time dependencies of the specified model + installed and start a MLflow Model Scoring Server process with that model in that environment. + Return a _ServedPyFuncModel that invokes the scoring server for prediction. Otherwise, load and + return the model locally as a PyFuncModel using :py:func:`mlflow.pyfunc.load_model`. + + Args: + model_uri: The uri of the model. + env_manager: The environment manager to load the model. + model_config: The model configuration to use by the model, only if the model + accepts it. + + Returns: + A _ServedPyFuncModel for non-local ``env_manager``s or a PyFuncModel otherwise. + """ + from mlflow.pyfunc.scoring_server.client import ( + ScoringServerClient, + StdinScoringServerClient, + ) + + if env_manager == _EnvManager.LOCAL: + return load_model(model_uri, model_config=model_config) + + _logger.info("Starting model server for model environment restoration.") + + local_path = _download_artifact_from_uri(artifact_uri=model_uri) + model_meta = Model.load(os.path.join(local_path, MLMODEL_FILE_NAME)) + + is_port_connectable = check_port_connectivity() + pyfunc_backend = get_flavor_backend( + local_path, + env_manager=env_manager, + install_mlflow=os.environ.get("MLFLOW_HOME") is not None, + create_env_root_dir=not is_port_connectable, + ) + _logger.info("Restoring model environment. This can take a few minutes.") + # Set capture_output to True in Databricks so that when environment preparation fails, the + # exception message of the notebook cell output will include child process command execution + # stdout/stderr output. + pyfunc_backend.prepare_env(model_uri=local_path, capture_output=is_in_databricks_runtime()) + if is_port_connectable: + server_port = find_free_port() + scoring_server_proc = pyfunc_backend.serve( + model_uri=local_path, + port=server_port, + host="127.0.0.1", + timeout=MLFLOW_SCORING_SERVER_REQUEST_TIMEOUT.get(), + enable_mlserver=False, + synchronous=False, + stdout=subprocess.PIPE, + stderr=subprocess.STDOUT, + model_config=model_config, + ) + client = ScoringServerClient("127.0.0.1", server_port) + else: + scoring_server_proc = pyfunc_backend.serve_stdin(local_path, model_config=model_config) + client = StdinScoringServerClient(scoring_server_proc) + + _logger.info(f"Scoring server process started at PID: {scoring_server_proc.pid}") + try: + client.wait_server_ready(timeout=90, scoring_server_proc=scoring_server_proc) + except Exception as e: + if scoring_server_proc.poll() is None: + # the scoring server is still running but client can't connect to it. + # kill the server. + scoring_server_proc.kill() + server_output, _ = scoring_server_proc.communicate(timeout=15) + if isinstance(server_output, bytes): + server_output = server_output.decode("UTF-8") + raise MlflowException( + "MLflow model server failed to launch, server process stdout and stderr are:\n" + + server_output + ) from e + + return _ServedPyFuncModel( + model_meta=model_meta, + client=client, + server_pid=scoring_server_proc.pid, + env_manager=env_manager, + ) + + +def _get_model_dependencies(model_uri, format="pip"): + model_dir = _download_artifact_from_uri(model_uri) + + def get_conda_yaml_path(): + model_config = _get_flavor_configuration_from_ml_model_file( + os.path.join(model_dir, MLMODEL_FILE_NAME), flavor_name=FLAVOR_NAME + ) + return os.path.join(model_dir, _extract_conda_env(model_config[ENV])) + + if format == "pip": + requirements_file = os.path.join(model_dir, _REQUIREMENTS_FILE_NAME) + if os.path.exists(requirements_file): + return requirements_file + + _logger.info( + f"{_REQUIREMENTS_FILE_NAME} is not found in the model directory. Falling back to" + f" extracting pip requirements from the model's 'conda.yaml' file. Conda" + " dependencies will be ignored." + ) + + with open(get_conda_yaml_path()) as yf: + conda_yaml = yaml.safe_load(yf) + + conda_deps = conda_yaml.get("dependencies", []) + for index, dep in enumerate(conda_deps): + if isinstance(dep, dict) and "pip" in dep: + pip_deps_index = index + break + else: + raise MlflowException( + "No pip section found in conda.yaml file in the model directory.", + error_code=RESOURCE_DOES_NOT_EXIST, + ) + + pip_deps = conda_deps.pop(pip_deps_index)["pip"] + tmp_dir = tempfile.mkdtemp() + pip_file_path = os.path.join(tmp_dir, _REQUIREMENTS_FILE_NAME) + with open(pip_file_path, "w") as f: + f.write("\n".join(pip_deps) + "\n") + + if len(conda_deps) > 0: + _logger.warning( + "The following conda dependencies have been excluded from the environment file:" + f" {', '.join(conda_deps)}." + ) + + return pip_file_path + + elif format == "conda": + return get_conda_yaml_path() + else: + raise MlflowException( + f"Illegal format argument '{format}'.", error_code=INVALID_PARAMETER_VALUE + ) + + +def get_model_dependencies(model_uri, format="pip"): + """ + Downloads the model dependencies and returns the path to requirements.txt or conda.yaml file. + + .. warning:: + This API downloads all the model artifacts to the local filesystem. This may take + a long time for large models. To avoid this overhead, use + ``mlflow.artifacts.download_artifacts("/requirements.txt")`` or + ``mlflow.artifacts.download_artifacts("/conda.yaml")`` instead. + + Args: + model_uri: The uri of the model to get dependencies from. + format: The format of the returned dependency file. If the ``"pip"`` format is + specified, the path to a pip ``requirements.txt`` file is returned. + If the ``"conda"`` format is specified, the path to a ``"conda.yaml"`` + file is returned . If the ``"pip"`` format is specified but the model + was not saved with a ``requirements.txt`` file, the ``pip`` section + of the model's ``conda.yaml`` file is extracted instead, and any + additional conda dependencies are ignored. Default value is ``"pip"``. + + Returns: + The local filesystem path to either a pip ``requirements.txt`` file + (if ``format="pip"``) or a ``conda.yaml`` file (if ``format="conda"``) + specifying the model's dependencies. + """ + dep_file = _get_model_dependencies(model_uri, format) + + if format == "pip": + prefix = "%" if _is_in_ipython_notebook() else "" + _logger.info( + "To install the dependencies that were used to train the model, run the " + f"following command: '{prefix}pip install -r {dep_file}'." + ) + return dep_file + + +@deprecated("mlflow.pyfunc.load_model", 1.0) +def load_pyfunc(model_uri, suppress_warnings=False): + """ + Load a model stored in Python function format. + + Args: + model_uri: The location, in URI format, of the MLflow model. For example: + + - ``/Users/me/path/to/local/model`` + - ``relative/path/to/local/model`` + - ``s3://my_bucket/path/to/model`` + - ``runs://run-relative/path/to/model`` + - ``models://`` + - ``models://`` + - ``mlflow-artifacts:/path/to/model`` + + For more information about supported URI schemes, see + `Referencing Artifacts `_. + + suppress_warnings: If ``True``, non-fatal warning messages associated with the model + loading process will be suppressed. If ``False``, these warning messages will be + emitted. + """ + return load_model(model_uri, suppress_warnings) + + +def _warn_potentially_incompatible_py_version_if_necessary(model_py_version=None): + """ + Compares the version of Python that was used to save a given model with the version + of Python that is currently running. If a major or minor version difference is detected, + logs an appropriate warning. + """ + if model_py_version is None: + _logger.warning( + "The specified model does not have a specified Python version. It may be" + " incompatible with the version of Python that is currently running: Python %s", + PYTHON_VERSION, + ) + elif get_major_minor_py_version(model_py_version) != get_major_minor_py_version(PYTHON_VERSION): + _logger.warning( + "The version of Python that the model was saved in, `Python %s`, differs" + " from the version of Python that is currently running, `Python %s`," + " and may be incompatible", + model_py_version, + PYTHON_VERSION, + ) + + +def _create_model_downloading_tmp_dir(should_use_nfs): + root_tmp_dir = get_or_create_nfs_tmp_dir() if should_use_nfs else get_or_create_tmp_dir() + + root_model_cache_dir = os.path.join(root_tmp_dir, "models") + os.makedirs(root_model_cache_dir, exist_ok=True) + + tmp_model_dir = tempfile.mkdtemp(dir=root_model_cache_dir) + # mkdtemp creates a directory with permission 0o700 + # change it to be 0o770 to ensure it can be seen in spark UDF + os.chmod(tmp_model_dir, 0o770) + return tmp_model_dir + + +_MLFLOW_SERVER_OUTPUT_TAIL_LINES_TO_KEEP = 200 + + +def _is_variant_type(spark_type): + try: + from pyspark.sql.types import VariantType + + return isinstance(spark_type, VariantType) + except ImportError: + return False + + +def _convert_spec_type_to_spark_type(spec_type): + from pyspark.sql.types import ArrayType, MapType, StringType, StructField, StructType + + from mlflow.types.schema import AnyType, Array, DataType, Map, Object + + if isinstance(spec_type, DataType): + return spec_type.to_spark() + + if isinstance(spec_type, AnyType): + try: + from pyspark.sql.types import VariantType + + return VariantType() + except ImportError: + raise MlflowException.invalid_parameter_value( + "`AnyType` is not supported in PySpark versions older than 4.0.0. " + "Upgrade your PySpark version to use this feature.", + ) + + if isinstance(spec_type, Array): + return ArrayType(_convert_spec_type_to_spark_type(spec_type.dtype)) + + if isinstance(spec_type, Object): + return StructType( + [ + StructField( + property.name, + _convert_spec_type_to_spark_type(property.dtype), + # we set nullable to True for all properties + # to avoid some errors like java.lang.NullPointerException + # when the signature is not inferred based on correct data. + ) + for property in spec_type.properties + ] + ) + + # Map only supports string as key + if isinstance(spec_type, Map): + return MapType( + keyType=StringType(), valueType=_convert_spec_type_to_spark_type(spec_type.value_type) + ) + + raise MlflowException(f"Failed to convert schema type `{spec_type}` to spark type.") + + +def _cast_output_spec_to_spark_type(spec): + from pyspark.sql.types import ArrayType + + from mlflow.types.schema import ColSpec, DataType, TensorSpec + + # TODO: handle optional output columns. + if isinstance(spec, ColSpec): + return _convert_spec_type_to_spark_type(spec.type) + elif isinstance(spec, TensorSpec): + data_type = DataType.from_numpy_type(spec.type) + if data_type is None: + raise MlflowException( + f"Model output tensor spec type {spec.type} is not supported in spark_udf.", + error_code=INVALID_PARAMETER_VALUE, + ) + + if len(spec.shape) == 1: + return ArrayType(data_type.to_spark()) + elif len(spec.shape) == 2: + return ArrayType(ArrayType(data_type.to_spark())) + else: + raise MlflowException( + "Only 1D or 2D tensors are supported as spark_udf " + f"return value, but model output '{spec.name}' has shape {spec.shape}.", + error_code=INVALID_PARAMETER_VALUE, + ) + else: + raise MlflowException( + f"Unknown schema output spec {spec}.", error_code=INVALID_PARAMETER_VALUE + ) + + +def _infer_spark_udf_return_type(model_output_schema): + from pyspark.sql.types import StructField, StructType + + if len(model_output_schema.inputs) == 1: + return _cast_output_spec_to_spark_type(model_output_schema.inputs[0]) + + return StructType( + [ + StructField(name=spec.name or str(i), dataType=_cast_output_spec_to_spark_type(spec)) + for i, spec in enumerate(model_output_schema.inputs) + ] + ) + + +def _parse_spark_datatype(datatype: str): + from pyspark.sql.functions import udf + from pyspark.sql.session import SparkSession + + return_type = "boolean" if datatype == "bool" else datatype + parsed_datatype = udf(lambda x: x, returnType=return_type).returnType + + if parsed_datatype.typeName() == "unparseddata": + # For spark 3.5.x, `udf(lambda x: x, returnType=return_type).returnType` + # returns UnparsedDataType, which is not compatible with signature inference. + # Note: SparkSession.active only exists for spark >= 3.5.0 + schema = ( + SparkSession.active() + .range(0) + .select(udf(lambda x: x, returnType=return_type)("id")) + .schema + ) + return schema[0].dataType + + return parsed_datatype + + +def _is_none_or_nan(value): + # The condition `isinstance(value, float)` is needed to avoid error + # from `np.isnan(value)` if value is a non-numeric type. + return value is None or isinstance(value, float) and np.isnan(value) + + +def _convert_array_values(values, result_type): + """ + Convert list or numpy array values to spark dataframe column values. + """ + from pyspark.sql.types import ArrayType, StructType + + if not isinstance(result_type, ArrayType): + raise MlflowException.invalid_parameter_value( + f"result_type must be ArrayType, got {result_type.simpleString()}", + ) + + spark_primitive_type_to_np_type = _get_spark_primitive_type_to_np_type() + + if type(result_type.elementType) in spark_primitive_type_to_np_type: + np_type = spark_primitive_type_to_np_type[type(result_type.elementType)] + # For array type result values, if provided value is None or NaN, regard it as a null array. + # see https://github.com/mlflow/mlflow/issues/8986 + return None if _is_none_or_nan(values) else np.array(values, dtype=np_type) + if isinstance(result_type.elementType, ArrayType): + return [_convert_array_values(v, result_type.elementType) for v in values] + if isinstance(result_type.elementType, StructType): + return [_convert_struct_values(v, result_type.elementType) for v in values] + if _is_variant_type(result_type.elementType): + return values + + raise MlflowException.invalid_parameter_value( + "Unsupported array type field with element type " + f"{result_type.elementType.simpleString()} in Array type.", + ) + + +def _get_spark_primitive_types(): + from pyspark.sql import types + + return ( + types.IntegerType, + types.LongType, + types.FloatType, + types.DoubleType, + types.StringType, + types.BooleanType, + ) + + +def _get_spark_primitive_type_to_np_type(): + from pyspark.sql import types + + return { + types.IntegerType: np.int32, + types.LongType: np.int64, + types.FloatType: np.float32, + types.DoubleType: np.float64, + types.BooleanType: np.bool_, + types.StringType: np.str_, + } + + +def _get_spark_primitive_type_to_python_type(): + from pyspark.sql import types + + return { + types.IntegerType: int, + types.LongType: int, + types.FloatType: float, + types.DoubleType: float, + types.BooleanType: bool, + types.StringType: str, + } + + +def _check_udf_return_type(data_type): + from pyspark.sql.types import ArrayType, MapType, StringType, StructType + + primitive_types = _get_spark_primitive_types() + if isinstance(data_type, primitive_types): + return True + + if isinstance(data_type, ArrayType): + return _check_udf_return_type(data_type.elementType) + + if isinstance(data_type, StructType): + return all(_check_udf_return_type(field.dataType) for field in data_type.fields) + + if isinstance(data_type, MapType): + return isinstance(data_type.keyType, StringType) and _check_udf_return_type( + data_type.valueType + ) + + return False + + +def _convert_struct_values( + result: pandas.DataFrame | dict[str, Any], + result_type, +): + """ + Convert spark StructType values to spark dataframe column values. + """ + + from pyspark.sql.types import ArrayType, MapType, StructType + + if not isinstance(result_type, StructType): + raise MlflowException.invalid_parameter_value( + f"result_type must be StructType, got {result_type.simpleString()}", + ) + + if not isinstance(result, (dict, pandas.DataFrame)): + raise MlflowException.invalid_parameter_value( + f"Unsupported result type {type(result)}, expected dict or pandas DataFrame", + ) + + spark_primitive_type_to_np_type = _get_spark_primitive_type_to_np_type() + is_pandas_df = isinstance(result, pandas.DataFrame) + result_dict = {} + for field_name in result_type.fieldNames(): + field_type = result_type[field_name].dataType + field_values = result[field_name] + + if type(field_type) in spark_primitive_type_to_np_type: + np_type = spark_primitive_type_to_np_type[type(field_type)] + if is_pandas_df: + # it's possible that field_values contain only Nones + # in this case, we don't need to cast the type + if not all(_is_none_or_nan(field_value) for field_value in field_values): + field_values = field_values.astype(np_type) + else: + field_values = ( + None + if _is_none_or_nan(field_values) + else np.array(field_values, dtype=np_type).item() + ) + elif isinstance(field_type, ArrayType): + if is_pandas_df: + field_values = pandas.Series( + _convert_array_values(field_value, field_type) for field_value in field_values + ) + else: + field_values = _convert_array_values(field_values, field_type) + elif isinstance(field_type, StructType): + if is_pandas_df: + field_values = pandas.Series( + [ + _convert_struct_values(field_value, field_type) + for field_value in field_values + ] + ) + else: + if isinstance(field_type, pydantic.BaseModel): + field_values = model_dump_compat(field_values) + field_values = _convert_struct_values(field_values, field_type) + elif isinstance(field_type, MapType): + if is_pandas_df: + field_values = pandas.Series( + [ + { + key: _convert_value_based_on_spark_type(value, field_type.valueType) + for key, value in field_value.items() + } + for field_value in field_values + ] + ).astype(object) + else: + field_values = { + key: _convert_value_based_on_spark_type(value, field_type.valueType) + for key, value in field_values.items() + } + elif _is_variant_type(field_type): + return field_values + else: + raise MlflowException.invalid_parameter_value( + f"Unsupported field type {field_type.simpleString()} in struct type.", + ) + result_dict[field_name] = field_values + + if is_pandas_df: + return pandas.DataFrame(result_dict) + return result_dict + + +def _convert_value_based_on_spark_type(value, spark_type): + """ + Convert value to python types based on the given spark type. + """ + + from pyspark.sql.types import ArrayType, MapType, StructType + + spark_primitive_type_to_python_type = _get_spark_primitive_type_to_python_type() + + if type(spark_type) in spark_primitive_type_to_python_type: + python_type = spark_primitive_type_to_python_type[type(spark_type)] + return None if _is_none_or_nan(value) else python_type(value) + if isinstance(spark_type, StructType): + return _convert_struct_values(value, spark_type) + if isinstance(spark_type, ArrayType): + return [_convert_value_based_on_spark_type(v, spark_type.elementType) for v in value] + if isinstance(spark_type, MapType): + return { + key: _convert_value_based_on_spark_type(value[key], spark_type.valueType) + for key in value + } + if _is_variant_type(spark_type): + return value + raise MlflowException.invalid_parameter_value( + f"Unsupported type {spark_type} for value {value}" + ) + + +# This location is used to prebuild python environment in Databricks runtime. +# The location for prebuilding env should be located under /local_disk0 +# because the python env will be uploaded to NFS and mounted to Serverless UDF sandbox, +# for serverless client image case, it doesn't have "/local_disk0" directory +_PREBUILD_ENV_ROOT_LOCATION = "/tmp" + + +def _gen_prebuilt_env_archive_name(spark, local_model_path): + """ + Generate prebuilt env archive file name. + The format is: + 'mlflow-{sha of python env config and dependencies}-{runtime version}-{platform machine}' + Note: The runtime version and platform machine information are included in the + archive name because the prebuilt env might not be compatible across different + runtime versions or platform machines. + """ + python_env = _get_python_env(Path(local_model_path)) + env_name = _get_virtualenv_name(python_env, local_model_path) + dbconnect_udf_sandbox_info = get_dbconnect_udf_sandbox_info(spark) + return ( + f"{env_name}-{dbconnect_udf_sandbox_info.image_version}-" + f"{dbconnect_udf_sandbox_info.platform_machine}" + ) + + +def _verify_prebuilt_env(spark, local_model_path, env_archive_path): + # Use `[:-7]` to truncate ".tar.gz" in the end + archive_name = os.path.basename(env_archive_path)[:-7] + prebuilt_env_sha, prebuilt_runtime_version, prebuilt_platform_machine = archive_name.split("-")[ + -3: + ] + + python_env = _get_python_env(Path(local_model_path)) + env_sha = _get_virtualenv_name(python_env, local_model_path).split("-")[-1] + dbconnect_udf_sandbox_info = get_dbconnect_udf_sandbox_info(spark) + runtime_version = dbconnect_udf_sandbox_info.image_version + platform_machine = dbconnect_udf_sandbox_info.platform_machine + + if prebuilt_env_sha != env_sha: + raise MlflowException( + f"The prebuilt env '{env_archive_path}' does not match the model required environment." + ) + if prebuilt_runtime_version != runtime_version: + raise MlflowException( + f"The prebuilt env '{env_archive_path}' runtime version '{prebuilt_runtime_version}' " + f"does not match UDF sandbox runtime version {runtime_version}." + ) + if prebuilt_platform_machine != platform_machine: + raise MlflowException( + f"The prebuilt env '{env_archive_path}' platform machine '{prebuilt_platform_machine}' " + f"does not match UDF sandbox platform machine {platform_machine}." + ) + + +def _prebuild_env_internal(local_model_path, archive_name, save_path, env_manager): + env_root_dir = os.path.join(_PREBUILD_ENV_ROOT_LOCATION, archive_name) + archive_path = os.path.join(save_path, archive_name + ".tar.gz") + if os.path.exists(env_root_dir): + shutil.rmtree(env_root_dir) + if os.path.exists(archive_path): + os.remove(archive_path) + + try: + pyfunc_backend = get_flavor_backend( + local_model_path, + env_manager=env_manager, + install_mlflow=False, + create_env_root_dir=False, + env_root_dir=env_root_dir, + ) + + pyfunc_backend.prepare_env(model_uri=local_model_path, capture_output=False) + # exclude pip cache from the archive file. + cache_path = os.path.join(env_root_dir, "pip_cache_pkgs") + if os.path.exists(cache_path): + shutil.rmtree(cache_path) + + return archive_directory(env_root_dir, archive_path) + finally: + shutil.rmtree(env_root_dir, ignore_errors=True) + + +def _download_prebuilt_env_if_needed(prebuilt_env_uri): + from mlflow.utils.file_utils import get_or_create_tmp_dir + + parsed_url = urlparse(prebuilt_env_uri) + if parsed_url.scheme == "" or parsed_url.scheme == "file": + # local path + return parsed_url.path + if parsed_url.scheme == "dbfs": + tmp_dir = MLFLOW_MODEL_ENV_DOWNLOADING_TEMP_DIR.get() or get_or_create_tmp_dir() + model_env_uc_path = parsed_url.path + + # download file from DBFS. + local_model_env_path = os.path.join(tmp_dir, os.path.basename(model_env_uc_path)) + if os.path.exists(local_model_env_path): + # file is already downloaded. + return local_model_env_path + + try: + from databricks.sdk import WorkspaceClient + + ws = WorkspaceClient() + # Download model env file from UC volume. + with ( + ws.files.download(model_env_uc_path).contents as rf, + open(local_model_env_path, "wb") as wf, + ): + while chunk := rf.read(4096 * 1024): + wf.write(chunk) + return local_model_env_path + except (Exception, KeyboardInterrupt): + if os.path.exists(local_model_env_path): + # clean the partially saved file if downloading fails. + os.remove(local_model_env_path) + raise + + raise MlflowException( + f"Unsupported prebuilt env file path '{prebuilt_env_uri}', " + f"invalid scheme: '{parsed_url.scheme}'." + ) + + +def build_model_env(model_uri, save_path, env_manager=_EnvManager.VIRTUALENV): + """ + Prebuild model python environment and generate an archive file saved to provided + `save_path`. + + Typical usages: + - Pre-build a model's environment in Databricks Runtime and then download the prebuilt + python environment archive file. This pre-built environment archive can then be used + in `mlflow.pyfunc.spark_udf` for remote inference execution when using Databricks Connect + to remotely connect to a Databricks environment for code execution. + + .. note:: + The `build_model_env` API is intended to only work when executed within Databricks runtime, + serving the purpose of capturing the required execution environment that is needed for + remote code execution when using DBConnect. The environment archive is designed to be used + when performing remote execution using `mlflow.pyfunc.spark_udf` in + Databricks runtime or Databricks Connect client and has no other purpose. + The prebuilt env archive file cannot be used across different Databricks runtime + versions or different platform machines. As such, if you connect to a different cluster + that is running a different runtime version on Databricks, you will need to execute this + API in a notebook and retrieve the generated archive to your local machine. Each + environment snapshot is unique to the the model, the runtime version of your remote + Databricks cluster, and the specification of the udf execution environment. + When using the prebuilt env in `mlflow.pyfunc.spark_udf`, MLflow will verify + whether the spark UDF sandbox environment matches the prebuilt env requirements and will + raise Exceptions if there are compatibility issues. If these occur, simply re-run this API + in the cluster that you are attempting to attach to. + + .. code-block:: python + :caption: Example + + from mlflow.pyfunc import build_model_env + + # Create a python environment archive file at the path `prebuilt_env_uri` + prebuilt_env_uri = build_model_env(f"runs:/{run_id}/model", "/path/to/save_directory") + + Args: + model_uri: URI to the model that is used to build the python environment. + save_path: The directory path that is used to save the prebuilt model environment + archive file path. + The path can be either local directory path or + mounted DBFS path such as '/dbfs/...' or + mounted UC volume path such as '/Volumes/...'. + env_manager: The environment manager to use in order to create the python environment + for model inference, the value can be either 'virtualenv' or 'uv', the default + value is 'virtualenv'. + + Returns: + Return the path of an archive file containing the python environment data. + """ + from mlflow.utils._spark_utils import _get_active_spark_session + + if not is_in_databricks_runtime(): + raise RuntimeError("'build_model_env' only support running in Databricks runtime.") + + if os.path.isfile(save_path): + raise RuntimeError(f"The saving path '{save_path}' must be a directory.") + os.makedirs(save_path, exist_ok=True) + + local_model_path = _download_artifact_from_uri( + artifact_uri=model_uri, output_path=_create_model_downloading_tmp_dir(should_use_nfs=False) + ) + archive_name = _gen_prebuilt_env_archive_name(_get_active_spark_session(), local_model_path) + dest_path = os.path.join(save_path, archive_name + ".tar.gz") + if os.path.exists(dest_path): + raise RuntimeError( + "A pre-built model python environment already exists " + f"in '{dest_path}'. To rebuild it, please remove " + "the existing one first." + ) + + # Archive the environment directory as a `tar.gz` format archive file, + # and then move the archive file to the destination directory. + # Note: + # - all symlink files in the input directory are kept as it is in the + # archive file. + # - the destination directory could be UC-volume fuse mounted directory + # which only supports limited filesystem operations, so to ensure it works, + # we generate the archive file under /tmp and then move it into the + # destination directory. + tmp_archive_path = None + try: + tmp_archive_path = _prebuild_env_internal( + local_model_path, archive_name, _PREBUILD_ENV_ROOT_LOCATION, env_manager + ) + shutil.move(tmp_archive_path, save_path) + return dest_path + finally: + shutil.rmtree(local_model_path, ignore_errors=True) + if tmp_archive_path and os.path.exists(tmp_archive_path): + os.remove(tmp_archive_path) + + +def spark_udf( + spark, + model_uri, + result_type=None, + env_manager=None, + params: dict[str, Any] | None = None, + extra_env: dict[str, str] | None = None, + prebuilt_env_uri: str | None = None, + model_config: str | Path | dict[str, Any] | None = None, +): + """ + A Spark UDF that can be used to invoke the Python function formatted model. + + Parameters passed to the UDF are forwarded to the model as a DataFrame where the column names + are ordinals (0, 1, ...). On some versions of Spark (3.0 and above), it is also possible to + wrap the input in a struct. In that case, the data will be passed as a DataFrame with column + names given by the struct definition (e.g. when invoked as my_udf(struct('x', 'y')), the model + will get the data as a pandas DataFrame with 2 columns 'x' and 'y'). + + If a model contains a signature with tensor spec inputs, you will need to pass a column of + array type as a corresponding UDF argument. The column values of which must be one dimensional + arrays. The UDF will reshape the column values to the required shape with 'C' order + (i.e. read / write the elements using C-like index order) and cast the values as the required + tensor spec type. + + If a model contains a signature, the UDF can be called without specifying column name + arguments. In this case, the UDF will be called with column names from signature, so the + evaluation dataframe's column names must match the model signature's column names. + + The predictions are filtered to contain only the columns that can be represented as the + ``result_type``. If the ``result_type`` is string or array of strings, all predictions are + converted to string. If the result type is not an array type, the left most column with + matching type is returned. + + .. note:: + Inputs of type ``pyspark.sql.types.DateType`` are not supported on earlier versions of + Spark (2.4 and below). + + .. note:: + When using Databricks Connect to connect to a remote Databricks cluster, + the Databricks cluster must use runtime version >= 16, and if the 'prebuilt_env_uri' + parameter is set, 'env_manager' parameter should not be set. + the Databricks cluster must use runtime version >= 15.4,and if the 'prebuilt_env_uri' + parameter is set, 'env_manager' parameter should not be set, + if the runtime version is 15.4 and the cluster is + standard access mode, the cluster need to configure + "spark.databricks.safespark.archive.artifact.unpack.disabled" to "false". + + .. note:: + Please be aware that when operating in Databricks Serverless, + spark tasks run within the confines of the Databricks Serverless UDF sandbox. + This environment has a total capacity limit of 1GB, combining both available + memory and local disk capacity. Furthermore, there are no GPU devices available + in this setup. Therefore, any deep-learning models that contain large weights + or require a GPU are not suitable for deployment on Databricks Serverless. + + .. code-block:: python + :caption: Example + + from pyspark.sql.functions import struct + + predict = mlflow.pyfunc.spark_udf(spark, "/my/local/model") + df.withColumn("prediction", predict(struct("name", "age"))).show() + + Args: + spark: A SparkSession object. + model_uri: The location, in URI format, of the MLflow model with the + :py:mod:`mlflow.pyfunc` flavor. For example: + + - ``/Users/me/path/to/local/model`` + - ``relative/path/to/local/model`` + - ``s3://my_bucket/path/to/model`` + - ``runs://run-relative/path/to/model`` + - ``models://`` + - ``models://`` + - ``mlflow-artifacts:/path/to/model`` + + For more information about supported URI schemes, see + `Referencing Artifacts `_. + + result_type: the return type of the user-defined function. The value can be either a + ``pyspark.sql.types.DataType`` object or a DDL-formatted type string. Only a primitive + type, an array ``pyspark.sql.types.ArrayType`` of primitive type, or a struct type + containing fields of above 2 kinds of types are allowed. + If unspecified, it tries to infer result type from model signature + output schema, if model output schema is not available, it fallbacks to use ``double`` + type. + + The following classes of result type are supported: + + - "int" or ``pyspark.sql.types.IntegerType``: The leftmost integer that can fit in an + ``int32`` or an exception if there is none. + + - "long" or ``pyspark.sql.types.LongType``: The leftmost long integer that can fit in an + ``int64`` or an exception if there is none. + + - ``ArrayType(IntegerType|LongType)``: All integer columns that can fit into the + requested size. + + - "float" or ``pyspark.sql.types.FloatType``: The leftmost numeric result cast to + ``float32`` or an exception if there is none. + + - "double" or ``pyspark.sql.types.DoubleType``: The leftmost numeric result cast to + ``double`` or an exception if there is none. + + - ``ArrayType(FloatType|DoubleType)``: All numeric columns cast to the requested type or + an exception if there are no numeric columns. + + - "string" or ``pyspark.sql.types.StringType``: The leftmost column converted to + ``string``. + + - "boolean" or "bool" or ``pyspark.sql.types.BooleanType``: The leftmost column + converted to ``bool`` or an exception if there is none. + + - ``ArrayType(StringType)``: All columns converted to ``string``. + + - "field1 FIELD1_TYPE, field2 FIELD2_TYPE, ...": A struct type containing multiple + fields separated by comma, each field type must be one of types listed above. + + env_manager: The environment manager to use in order to create the python environment + for model inference. Note that environment is only restored in the context + of the PySpark UDF; the software environment outside of the UDF is + unaffected. If `prebuilt_env_uri` parameter is not set, the default value + is ``local``, and the following values are supported: + + - ``virtualenv``: Use virtualenv to restore the python environment that + was used to train the model. This is the default option if ``env_manager`` + is not set. + - ``uv`` : Use uv to restore the python environment that + was used to train the model. + - ``conda``: Use Conda to restore the software environment + that was used to train the model. + - ``local``: Use the current Python environment for model inference, which + may differ from the environment used to train the model and may lead to + errors or invalid predictions. + + If the `prebuilt_env_uri` parameter is set, `env_manager` parameter should not + be set. + + params: Additional parameters to pass to the model for inference. + + extra_env: Extra environment variables to pass to the UDF executors. + For overrides that need to propagate to the Spark workers (i.e., + overriding the scoring server timeout via `MLFLOW_SCORING_SERVER_REQUEST_TIMEOUT`). + + prebuilt_env_uri: The path of the prebuilt env archive file created by + `mlflow.pyfunc.build_model_env` API. + This parameter can only be used in Databricks Serverless notebook REPL, + Databricks Shared cluster notebook REPL, and Databricks Connect client + environment. + The path can be either local file path or DBFS path such as + 'dbfs:/Volumes/...', in this case, MLflow automatically downloads it + to local temporary directory, "MLFLOW_MODEL_ENV_DOWNLOADING_TEMP_DIR" + environmental variable can be set to specify the temporary directory + to use. + + If this parameter is set, `env_manger` parameter must not be set. + + model_config: The model configuration to set when loading the model. + See 'model_config' argument in `mlflow.pyfunc.load_model` API for details. + + Returns: + Spark UDF that applies the model's ``predict`` method to the data and returns a + type specified by ``result_type``, which by default is a double. + """ + + # Scope Spark import to this method so users don't need pyspark to use non-Spark-related + # functionality. + from pyspark.sql.functions import pandas_udf + from pyspark.sql.types import ( + ArrayType, + BooleanType, + DoubleType, + FloatType, + IntegerType, + LongType, + MapType, + StringType, + ) + from pyspark.sql.types import StructType as SparkStructType + + from mlflow.pyfunc.spark_model_cache import SparkModelCache + from mlflow.utils._spark_utils import _SparkDirectoryDistributor + + is_spark_connect = is_spark_connect_mode() + # Used in test to force install local version of mlflow when starting a model server + mlflow_home = os.environ.get("MLFLOW_HOME") + openai_env_vars = mlflow.openai.model._OpenAIEnvVar.read_environ() + mlflow_testing = _MLFLOW_TESTING.get_raw() + + if prebuilt_env_uri: + if env_manager not in (None, _EnvManager.VIRTUALENV, _EnvManager.UV): + raise MlflowException( + "If 'prebuilt_env_uri' parameter is set, 'env_manager' parameter must " + "be either None, 'virtualenv', or 'uv'." + ) + env_manager = _EnvManager.VIRTUALENV + else: + env_manager = env_manager or _EnvManager.LOCAL + + _EnvManager.validate(env_manager) + + if is_spark_connect: + is_spark_in_local_mode = False + else: + # Check whether spark is in local or local-cluster mode + # this case all executors and driver share the same filesystem + is_spark_in_local_mode = spark.conf.get("spark.master").startswith("local") + + is_dbconnect_mode = is_databricks_connect(spark) + if prebuilt_env_uri is not None and not is_dbconnect_mode: + raise RuntimeError( + "'prebuilt_env' parameter can only be used in Databricks Serverless " + "notebook REPL, atabricks Shared cluster notebook REPL, and Databricks Connect client " + "environment." + ) + + if prebuilt_env_uri is None and is_dbconnect_mode and not is_in_databricks_runtime(): + raise RuntimeError( + "'prebuilt_env_uri' param is required if using Databricks Connect to connect " + "to Databricks cluster from your own machine." + ) + + # Databricks connect can use `spark.addArtifact` to upload artifact to NFS. + # But for Databricks shared cluster runtime, it can directly write to NFS, so exclude it + # Note for Databricks Serverless runtime (notebook REPL), it runs on Servereless VM that + # can't access NFS, so it needs to use `spark.addArtifact`. + use_dbconnect_artifact = is_dbconnect_mode and not is_in_databricks_shared_cluster_runtime() + + if use_dbconnect_artifact: + udf_sandbox_info = get_dbconnect_udf_sandbox_info(spark) + if Version(udf_sandbox_info.mlflow_version) < Version("2.18.0"): + raise MlflowException( + "Using 'mlflow.pyfunc.spark_udf' in Databricks Serverless or in remote " + "Databricks Connect requires UDF sandbox image installed with MLflow " + "of version >= 2.18.0" + ) + # `udf_sandbox_info.runtime_version` format is like '.'. + # It's safe to apply `Version`. + dbr_runtime_version = Version(udf_sandbox_info.runtime_version) + if dbr_runtime_version < Version("15.4"): + raise MlflowException( + "Using 'mlflow.pyfunc.spark_udf' in Databricks Serverless or in remote " + "Databricks Connect requires Databricks runtime version >= 15.4." + ) + if dbr_runtime_version == Version("15.4"): + if spark.conf.get("spark.databricks.pyspark.udf.isolation.enabled").lower() == "true": + # The connected cluster is standard (shared) mode. + if ( + spark.conf.get( + "spark.databricks.safespark.archive.artifact.unpack.disabled" + ).lower() + != "false" + ): + raise MlflowException( + "Using 'mlflow.pyfunc.spark_udf' in remote Databricks Connect requires " + "Databricks cluster setting " + "'spark.databricks.safespark.archive.artifact.unpack.disabled' to 'false' " + "if Databricks runtime version is 15.4" + ) + + nfs_root_dir = get_nfs_cache_root_dir() + should_use_nfs = nfs_root_dir is not None + + should_use_spark_to_broadcast_file = not ( + is_spark_in_local_mode or should_use_nfs or is_spark_connect or use_dbconnect_artifact + ) + + # For spark connect mode, + # If client code is executed in databricks runtime and NFS is available, + # we save model to NFS temp directory in the driver + # and load the model in the executor. + should_spark_connect_use_nfs = is_in_databricks_runtime() and should_use_nfs + + if ( + is_spark_connect + and not is_dbconnect_mode + and env_manager in (_EnvManager.VIRTUALENV, _EnvManager.CONDA, _EnvManager.UV) + ): + raise MlflowException.invalid_parameter_value( + f"Environment manager {env_manager!r} is not supported in Spark Connect " + "client environment if it connects to non-Databricks Spark cluster.", + ) + + local_model_path = _download_artifact_from_uri( + artifact_uri=model_uri, + output_path=_create_model_downloading_tmp_dir(should_use_nfs), + ) + + if prebuilt_env_uri: + prebuilt_env_uri = _download_prebuilt_env_if_needed(prebuilt_env_uri) + _verify_prebuilt_env(spark, local_model_path, prebuilt_env_uri) + if use_dbconnect_artifact and env_manager == _EnvManager.CONDA: + raise MlflowException( + "Databricks connect mode or Databricks Serverless python REPL doesn't " + "support env_manager 'conda'." + ) + + if env_manager == _EnvManager.LOCAL: + # Assume spark executor python environment is the same with spark driver side. + model_requirements = _get_pip_requirements_from_model_path(local_model_path) + warn_dependency_requirement_mismatches(model_requirements) + _logger.warning( + 'Calling `spark_udf()` with `env_manager="local"` does not recreate the same ' + "environment that was used during training, which may lead to errors or inaccurate " + 'predictions. We recommend specifying `env_manager="conda"`, which automatically ' + "recreates the environment that was used to train the model and performs inference " + "in the recreated environment." + ) + else: + _logger.info( + f"This UDF will use {env_manager} to recreate the model's software environment for " + "inference. This may take extra time during execution." + ) + if not sys.platform.startswith("linux"): + # TODO: support killing mlflow server launched in UDF task when spark job canceled + # for non-linux system. + # https://stackoverflow.com/questions/53208/how-do-i-automatically-destroy-child-processes-in-windows + _logger.warning( + "In order to run inference code in restored python environment, PySpark UDF " + "processes spawn MLflow Model servers as child processes. Due to system " + "limitations with handling SIGKILL signals, these MLflow Model server child " + "processes cannot be cleaned up if the Spark Job is canceled." + ) + + if prebuilt_env_uri: + env_cache_key = os.path.basename(prebuilt_env_uri)[:-7] + elif use_dbconnect_artifact: + env_cache_key = _gen_prebuilt_env_archive_name(spark, local_model_path) + else: + env_cache_key = None + + if use_dbconnect_artifact or prebuilt_env_uri is not None: + prebuilt_env_root_dir = os.path.join(_PREBUILD_ENV_ROOT_LOCATION, env_cache_key) + pyfunc_backend_env_root_config = { + "create_env_root_dir": False, + "env_root_dir": prebuilt_env_root_dir, + } + else: + pyfunc_backend_env_root_config = {"create_env_root_dir": True} + pyfunc_backend = get_flavor_backend( + local_model_path, + env_manager=env_manager, + install_mlflow=os.environ.get("MLFLOW_HOME") is not None, + **pyfunc_backend_env_root_config, + ) + dbconnect_artifact_cache = DBConnectArtifactCache.get_or_create(spark) + + if use_dbconnect_artifact: + # Upload model artifacts and python environment to NFS as DBConncet artifacts. + if env_manager in (_EnvManager.VIRTUALENV, _EnvManager.UV): + if not dbconnect_artifact_cache.has_cache_key(env_cache_key): + if prebuilt_env_uri: + env_archive_path = prebuilt_env_uri + else: + env_archive_path = _prebuild_env_internal( + local_model_path, env_cache_key, get_or_create_tmp_dir(), env_manager + ) + dbconnect_artifact_cache.add_artifact_archive(env_cache_key, env_archive_path) + + if not dbconnect_artifact_cache.has_cache_key(model_uri): + model_archive_path = os.path.join( + os.path.dirname(local_model_path), f"model-{uuid.uuid4()}.tar.gz" + ) + archive_directory(local_model_path, model_archive_path) + dbconnect_artifact_cache.add_artifact_archive(model_uri, model_archive_path) + + elif not should_use_spark_to_broadcast_file: + if prebuilt_env_uri: + # Extract prebuilt env archive file to NFS directory. + prebuilt_env_nfs_dir = os.path.join( + get_or_create_nfs_tmp_dir(), "prebuilt_env", env_cache_key + ) + if not os.path.exists(prebuilt_env_nfs_dir): + extract_archive_to_dir(prebuilt_env_uri, prebuilt_env_nfs_dir) + else: + # Prepare restored environment in driver side if possible. + # Note: In databricks runtime, because databricks notebook cell output cannot capture + # child process output, so that set capture_output to be True so that when `conda + # prepare env` command failed, the exception message will include command stdout/stderr + # output. Otherwise user have to check cluster driver log to find command stdout/stderr + # output. + # In non-databricks runtime, set capture_output to be False, because the benefit of + # "capture_output=False" is the output will be printed immediately, otherwise you have + # to wait conda command fail and suddenly get all output printed (included in error + # message). + if env_manager != _EnvManager.LOCAL: + pyfunc_backend.prepare_env( + model_uri=local_model_path, capture_output=is_in_databricks_runtime() + ) + else: + # Broadcast local model directory to remote worker if needed. + archive_path = SparkModelCache.add_local_model(spark, local_model_path) + + model_metadata = Model.load(os.path.join(local_model_path, MLMODEL_FILE_NAME)) + + if result_type is None: + if model_output_schema := model_metadata.get_output_schema(): + result_type = _infer_spark_udf_return_type(model_output_schema) + else: + _logger.warning( + "No 'result_type' provided for spark_udf and the model does not " + "have an output schema. 'result_type' is set to 'double' type." + ) + result_type = DoubleType() + else: + if isinstance(result_type, str): + result_type = _parse_spark_datatype(result_type) + + # if result type is inferred by MLflow, we don't need to check it + if not _check_udf_return_type(result_type): + raise MlflowException.invalid_parameter_value( + f"""Invalid 'spark_udf' result type: {result_type}. +It must be one of the following types: +Primitive types: +- int +- long +- float +- double +- string +- boolean +Compound types: +- ND array of primitives / structs. +- struct | array>, ...>: +A struct with primitive, ND array, +e.g., struct>. +""" + ) + params = _validate_params(params, model_metadata) + + def _predict_row_batch(predict_fn, args): + input_schema = model_metadata.get_input_schema() + args = list(args) + if len(args) == 1 and isinstance(args[0], pandas.DataFrame): + pdf = args[0] + else: + if input_schema is None: + names = [str(i) for i in range(len(args))] + else: + names = input_schema.input_names() + required_names = input_schema.required_input_names() + if len(args) > len(names): + args = args[: len(names)] + if len(args) < len(required_names): + raise MlflowException( + f"Model input is missing required columns. Expected {len(names)} required" + f" input columns {names}, but the model received only {len(args)} " + "unnamed input columns (Since the columns were passed unnamed they are" + " expected to be in the order specified by the schema)." + ) + pdf = pandas.DataFrame( + data={ + names[i]: arg + if isinstance(arg, pandas.Series) + # pandas_udf receives a StructType column as a pandas DataFrame. + # We need to convert it back to a dict of pandas Series. + else arg.apply(lambda row: row.to_dict(), axis=1) + for i, arg in enumerate(args) + }, + columns=names, + ) + + result = predict_fn(pdf, params) + + if isinstance(result, dict): + result = {k: list(v) for k, v in result.items()} + + if isinstance(result_type, ArrayType) and isinstance(result_type.elementType, ArrayType): + result_values = _convert_array_values(result, result_type) + return pandas.Series(result_values) + + if isinstance(result_type, SparkStructType): + if ( + isinstance(result, list) + and len(result) > 0 + and isinstance(result[0], pydantic.BaseModel) + ): + result = pandas.DataFrame([model_dump_compat(r) for r in result]) + else: + result = pandas.DataFrame(result) + return _convert_struct_values(result, result_type) + + if not isinstance(result, pandas.DataFrame): + if isinstance(result_type, MapType): + # list of dicts should be converted into a single column + result = pandas.DataFrame([result]) + else: + result = ( + pandas.DataFrame([result]) if np.isscalar(result) else pandas.DataFrame(result) + ) + + elem_type = result_type.elementType if isinstance(result_type, ArrayType) else result_type + if type(elem_type) == IntegerType: + result = result.select_dtypes( + [np.byte, np.ubyte, np.short, np.ushort, np.int32] + ).astype(np.int32) + + elif type(elem_type) == LongType: + result = result.select_dtypes([np.byte, np.ubyte, np.short, np.ushort, int]).astype( + np.int64 + ) + + elif type(elem_type) == FloatType: + result = result.select_dtypes(include=(np.number,)).astype(np.float32) + + elif type(elem_type) == DoubleType: + result = result.select_dtypes(include=(np.number,)).astype(np.float64) + + elif type(elem_type) == BooleanType: + result = result.select_dtypes([bool, np.bool_]).astype(bool) + + if len(result.columns) == 0: + raise MlflowException( + message="The model did not produce any values compatible with the requested " + f"type '{elem_type}'. Consider requesting udf with StringType or " + "Arraytype(StringType).", + error_code=INVALID_PARAMETER_VALUE, + ) + + if type(elem_type) == StringType: + if Version(pandas.__version__) >= Version("2.1.0"): + result = result.map(str) + else: + result = result.applymap(str) + + if type(result_type) == ArrayType: + return pandas.Series(result.to_numpy().tolist()) + else: + return result[result.columns[0]] + + result_type_hint = ( + pandas.DataFrame if isinstance(result_type, SparkStructType) else pandas.Series + ) + + tracking_uri = mlflow.get_tracking_uri() + + @pandas_udf(result_type) + def udf( + # `pandas_udf` does not support modern type annotations + iterator: Iterator[Tuple[Union[pandas.Series, pandas.DataFrame], ...]], # noqa: UP006,UP007 + ) -> Iterator[result_type_hint]: + # importing here to prevent circular import + from mlflow.pyfunc.scoring_server.client import ( + ScoringServerClient, + StdinScoringServerClient, + ) + + # Note: this is a pandas udf function in iteration style, which takes an iterator of + # tuple of pandas.Series and outputs an iterator of pandas.Series. + update_envs = {} + if mlflow_home is not None: + update_envs["MLFLOW_HOME"] = mlflow_home + if openai_env_vars: + update_envs.update(openai_env_vars) + if mlflow_testing: + update_envs[_MLFLOW_TESTING.name] = mlflow_testing + if extra_env: + update_envs.update(extra_env) + + # use `modified_environ` to temporarily set the envs and restore them finally + with modified_environ(update=update_envs): + scoring_server_proc = None + # set tracking_uri inside udf so that with spark_connect + # we can load the model from correct path + mlflow.set_tracking_uri(tracking_uri) + + if env_manager != _EnvManager.LOCAL: + if use_dbconnect_artifact: + local_model_path_on_executor = ( + dbconnect_artifact_cache.get_unpacked_artifact_dir(model_uri) + ) + env_src_dir = dbconnect_artifact_cache.get_unpacked_artifact_dir(env_cache_key) + + # Create symlink if it does not exist + if not os.path.exists(prebuilt_env_root_dir): + os.symlink(env_src_dir, prebuilt_env_root_dir) + elif prebuilt_env_uri is not None: + # prebuilt env is extracted to `prebuilt_env_nfs_dir` directory, + # and model is downloaded to `local_model_path` which points to an NFS + # directory too. + local_model_path_on_executor = None + + # Create symlink if it does not exist + if not os.path.exists(prebuilt_env_root_dir): + os.symlink(prebuilt_env_nfs_dir, prebuilt_env_root_dir) + elif should_use_spark_to_broadcast_file: + local_model_path_on_executor = _SparkDirectoryDistributor.get_or_extract( + archive_path + ) + # Call "prepare_env" in advance in order to reduce scoring server launch time. + # So that we can use a shorter timeout when call `client.wait_server_ready`, + # otherwise we have to set a long timeout for `client.wait_server_ready` time, + # this prevents spark UDF task failing fast if other exception raised + # when scoring server launching. + # Set "capture_output" so that if "conda env create" command failed, the command + # stdout/stderr output will be attached to the exception message and included in + # driver side exception. + pyfunc_backend.prepare_env( + model_uri=local_model_path_on_executor, capture_output=True + ) + else: + local_model_path_on_executor = None + + if check_port_connectivity(): + # launch scoring server + server_port = find_free_port() + host = "127.0.0.1" + scoring_server_proc = pyfunc_backend.serve( + model_uri=local_model_path_on_executor or local_model_path, + port=server_port, + host=host, + timeout=MLFLOW_SCORING_SERVER_REQUEST_TIMEOUT.get(), + enable_mlserver=False, + synchronous=False, + stdout=subprocess.PIPE, + stderr=subprocess.STDOUT, + model_config=model_config, + ) + + client = ScoringServerClient(host, server_port) + else: + scoring_server_proc = pyfunc_backend.serve_stdin( + model_uri=local_model_path_on_executor or local_model_path, + stdout=subprocess.PIPE, + stderr=subprocess.STDOUT, + model_config=model_config, + ) + client = StdinScoringServerClient(scoring_server_proc) + + _logger.info("Using %s", client.__class__.__name__) + + server_tail_logs = collections.deque( + maxlen=_MLFLOW_SERVER_OUTPUT_TAIL_LINES_TO_KEEP + ) + + def server_redirect_log_thread_func(child_stdout): + for line in child_stdout: + decoded = line.decode() if isinstance(line, bytes) else line + server_tail_logs.append(decoded) + sys.stdout.write("[model server] " + decoded) + + server_redirect_log_thread = threading.Thread( + target=server_redirect_log_thread_func, + args=(scoring_server_proc.stdout,), + daemon=True, + name=f"mlflow_pyfunc_model_server_log_redirector_{uuid.uuid4().hex[:8]}", + ) + server_redirect_log_thread.start() + + try: + client.wait_server_ready(timeout=90, scoring_server_proc=scoring_server_proc) + except Exception as e: + err_msg = ( + "During spark UDF task execution, mlflow model server failed to launch. " + ) + if len(server_tail_logs) == _MLFLOW_SERVER_OUTPUT_TAIL_LINES_TO_KEEP: + err_msg += ( + f"Last {_MLFLOW_SERVER_OUTPUT_TAIL_LINES_TO_KEEP} " + "lines of MLflow model server output:\n" + ) + else: + err_msg += "MLflow model server output:\n" + err_msg += "".join(server_tail_logs) + raise MlflowException(err_msg) from e + + def batch_predict_fn(pdf, params=None): + if "params" in inspect.signature(client.invoke).parameters: + return client.invoke(pdf, params=params).get_predictions() + _log_warning_if_params_not_in_predict_signature(_logger, params) + return client.invoke(pdf).get_predictions() + + elif env_manager == _EnvManager.LOCAL: + if use_dbconnect_artifact: + model_path = dbconnect_artifact_cache.get_unpacked_artifact_dir(model_uri) + loaded_model = mlflow.pyfunc.load_model(model_path, model_config=model_config) + elif is_spark_connect and not should_spark_connect_use_nfs: + model_path = os.path.join( + tempfile.gettempdir(), + "mlflow", + hashlib.sha1(model_uri.encode(), usedforsecurity=False).hexdigest(), + # Use pid to avoid conflict when multiple spark UDF tasks + str(os.getpid()), + ) + try: + loaded_model = mlflow.pyfunc.load_model( + model_path, model_config=model_config + ) + except Exception: + os.makedirs(model_path, exist_ok=True) + loaded_model = mlflow.pyfunc.load_model( + model_uri, dst_path=model_path, model_config=model_config + ) + elif should_use_spark_to_broadcast_file: + loaded_model, _ = SparkModelCache.get_or_load(archive_path) + else: + loaded_model = mlflow.pyfunc.load_model( + local_model_path, model_config=model_config + ) + + def batch_predict_fn(pdf, params=None): + if "params" in inspect.signature(loaded_model.predict).parameters: + return loaded_model.predict(pdf, params=params) + _log_warning_if_params_not_in_predict_signature(_logger, params) + return loaded_model.predict(pdf) + + try: + for input_batch in iterator: + # If the UDF is called with only multiple arguments, + # the `input_batch` is a tuple which composes of several pd.Series/pd.DataFrame + # objects. + # If the UDF is called with only one argument, + # the `input_batch` instance will be an instance of `pd.Series`/`pd.DataFrame`, + if isinstance(input_batch, (pandas.Series, pandas.DataFrame)): + # UDF is called with only one argument + row_batch_args = (input_batch,) + else: + row_batch_args = input_batch + + if len(row_batch_args[0]) > 0: + yield _predict_row_batch(batch_predict_fn, row_batch_args) + except SystemError as e: + if "error return without exception set" in str(e): + raise MlflowException( + "A system error related to the Python C extension has occurred. " + "This is usually caused by an incompatible Python library that uses the " + "C extension. To address this, we recommend you to log the model " + "with fixed version python libraries that use the C extension " + "(such as 'numpy' library), and set spark_udf `env_manager` argument " + "to 'virtualenv' or 'uv' so that spark_udf can restore the original " + "python library version before running model inference." + ) from e + finally: + if scoring_server_proc is not None: + os.kill(scoring_server_proc.pid, signal.SIGTERM) + + udf.metadata = model_metadata + + @functools.wraps(udf) + def udf_with_default_cols(*args): + if len(args) == 0: + input_schema = model_metadata.get_input_schema() + if input_schema and len(input_schema.optional_input_names()) > 0: + raise MlflowException( + message="Cannot apply UDF without column names specified when" + " model signature contains optional columns.", + error_code=INVALID_PARAMETER_VALUE, + ) + if input_schema and len(input_schema.inputs) > 0: + if input_schema.has_input_names(): + input_names = input_schema.input_names() + return udf(*input_names) + else: + raise MlflowException( + message="Cannot apply udf because no column names specified. The udf " + f"expects {len(input_schema.inputs)} columns with types: " + "{input_schema.inputs}. Input column names could not be inferred from the" + " model signature (column names not found).", + error_code=INVALID_PARAMETER_VALUE, + ) + else: + raise MlflowException( + "Attempting to apply udf on zero columns because no column names were " + "specified as arguments or inferred from the model signature.", + error_code=INVALID_PARAMETER_VALUE, + ) + else: + return udf(*args) + + return udf_with_default_cols + + +def _validate_function_python_model(python_model): + if not (isinstance(python_model, PythonModel) or callable(python_model)): + raise MlflowException( + "`python_model` must be a PythonModel instance, callable object, or path to a script " + "that uses set_model() to set a PythonModel instance or callable object.", + error_code=INVALID_PARAMETER_VALUE, + ) + + if callable(python_model): + num_args = len(inspect.signature(python_model).parameters) + if num_args != 1: + raise MlflowException( + "When `python_model` is a callable object, it must accept exactly one argument. " + f"Found {num_args} arguments.", + error_code=INVALID_PARAMETER_VALUE, + ) + + +@format_docstring(LOG_MODEL_PARAM_DOCS.format(package_name="scikit-learn")) +@trace_disabled # Suppress traces for internal predict calls while saving model +def save_model( + path, + loader_module=None, + data_path=None, + code_paths=None, + infer_code_paths=False, + conda_env=None, + mlflow_model=None, + python_model=None, + artifacts=None, + signature: ModelSignature = None, + input_example: ModelInputExample = None, + pip_requirements=None, + extra_pip_requirements=None, + metadata=None, + model_config=None, + streamable=None, + resources: str | list[Resource] | None = None, + auth_policy: AuthPolicy | None = None, + **kwargs, +): + """ + Save a Pyfunc model with custom inference logic and optional data dependencies to a path on the + local filesystem. + + For information about the workflows that this method supports, please see :ref:`"workflows for + creating custom pyfunc models" ` and + :ref:`"which workflow is right for my use case?" `. + Note that the parameters for the second workflow: ``loader_module``, ``data_path`` and the + parameters for the first workflow: ``python_model``, ``artifacts``, cannot be + specified together. + + Args: + path: The path to which to save the Python model. + loader_module: The name of the Python module that is used to load the model + from ``data_path``. This module must define a method with the prototype + ``_load_pyfunc(data_path)``. If not ``None``, this module and its + dependencies must be included in one of the following locations: + + - The MLflow library. + - Package(s) listed in the model's Conda environment, specified by + the ``conda_env`` parameter. + - One or more of the files specified by the ``code_paths`` parameter. + + data_path: Path to a file or directory containing model data. + code_paths: {{ code_paths_pyfunc }} + infer_code_paths: {{ infer_code_paths }} + conda_env: {{ conda_env }} + mlflow_model: :py:mod:`mlflow.models.Model` configuration to which to add the + **python_function** flavor. + python_model: + An instance of a subclass of :class:`~PythonModel` or a callable object with a single + argument (see the examples below). The passed-in object is serialized using the + CloudPickle library. The python_model can also be a file path to the PythonModel + which defines the model from code artifact rather than serializing the model object. + Any dependencies of the class should be included in one of the + following locations: + + - The MLflow library. + - Package(s) listed in the model's Conda environment, specified by the ``conda_env`` + parameter. + - One or more of the files specified by the ``code_paths`` parameter. + + Note: If the class is imported from another module, as opposed to being defined in the + ``__main__`` scope, the defining module should also be included in one of the listed + locations. + + **Examples** + + Class model + + .. code-block:: python + + from typing import List, Dict + import mlflow + + + class MyModel(mlflow.pyfunc.PythonModel): + def predict(self, context, model_input: List[str], params=None) -> List[str]: + return [i.upper() for i in model_input] + + + mlflow.pyfunc.save_model("model", python_model=MyModel(), input_example=["a"]) + model = mlflow.pyfunc.load_model("model") + print(model.predict(["a", "b", "c"])) # -> ["A", "B", "C"] + + Functional model + + .. note:: + Experimental: Functional model support is experimental and may change or be removed + in a future release without warning. + + .. code-block:: python + + from typing import List + import mlflow + + + def predict(model_input: List[str]) -> List[str]: + return [i.upper() for i in model_input] + + + mlflow.pyfunc.save_model("model", python_model=predict, input_example=["a"]) + model = mlflow.pyfunc.load_model("model") + print(model.predict(["a", "b", "c"])) # -> ["A", "B", "C"] + + Model from code + + .. note:: + Experimental: Model from code model support is experimental and may change or + be removed in a future release without warning. + + .. code-block:: python + + # code.py + from typing import List + import mlflow + + + class MyModel(mlflow.pyfunc.PythonModel): + def predict(self, context, model_input: List[str], params=None) -> List[str]: + return [i.upper() for i in model_input] + + + mlflow.models.set_model(MyModel()) + + # log_model.py + import mlflow + + with mlflow.start_run(): + model_info = mlflow.pyfunc.log_model( + name="model", + python_model="code.py", + ) + + If the `predict` method or function has type annotations, MLflow automatically + constructs a model signature based on the type annotations (unless the ``signature`` + argument is explicitly specified), and converts the input value to the specified type + before passing it to the function. Currently, the following type annotations are + supported: + + - ``List[str]`` + - ``List[Dict[str, str]]`` + + artifacts: A dictionary containing ```` entries. Remote artifact URIs + are resolved to absolute filesystem paths, producing a dictionary of + ```` entries. ``python_model`` can reference these + resolved entries as the ``artifacts`` property of the ``context`` parameter + in :func:`PythonModel.load_context() ` + and :func:`PythonModel.predict() `. + For example, consider the following ``artifacts`` dictionary:: + + {"my_file": "s3://my-bucket/path/to/my/file"} + + In this case, the ``"my_file"`` artifact is downloaded from S3. The + ``python_model`` can then refer to ``"my_file"`` as an absolute filesystem + path via ``context.artifacts["my_file"]``. + + If ``None``, no artifacts are added to the model. + + signature: :py:class:`ModelSignature ` + describes model input and output :py:class:`Schema `. + The model signature can be :py:func:`inferred ` + from datasets with valid model input (e.g. the training dataset with target + column omitted) and valid model output (e.g. model predictions generated on + the training dataset), for example: + + .. code-block:: python + + from mlflow.models import infer_signature + + train = df.drop_column("target_label") + predictions = ... # compute model predictions + signature = infer_signature(train, predictions) + input_example: {{ input_example }} + pip_requirements: {{ pip_requirements }} + extra_pip_requirements: {{ extra_pip_requirements }} + metadata: {{ metadata }} + model_config: The model configuration to apply to the model. The configuration will + be available as the ``model_config`` property of the ``context`` parameter + in :func:`PythonModel.load_context() ` + and :func:`PythonModel.predict() `. + The configuration can be passed as a file path, or a dict with string keys. + + .. Note:: Experimental: This parameter may change or be removed in a future + release without warning. + streamable: A boolean value indicating if the model supports streaming prediction, + If None, MLflow will try to inspect if the model supports streaming + by checking if `predict_stream` method exists. Default None. + resources: A list of model resources or a resources.yaml file containing a list of + resources required to serve the model. + + .. Note:: Experimental: This parameter may change or be removed in a future + release without warning. + auth_policy: {{ auth_policy }} + kwargs: Extra keyword arguments. + """ + _validate_env_arguments(conda_env, pip_requirements, extra_pip_requirements) + _validate_pyfunc_model_config(model_config) + _validate_and_prepare_target_save_path(path) + + with tempfile.TemporaryDirectory() as temp_dir: + model_code_path = None + if python_model: + if isinstance(model_config, Path): + model_config = os.fspath(model_config) + + if isinstance(model_config, str): + model_config = _validate_and_get_model_config_from_file(model_config) + + if isinstance(python_model, Path): + python_model = os.fspath(python_model) + + if isinstance(python_model, str): + model_code_path = _validate_and_get_model_code_path(python_model, temp_dir) + _validate_and_copy_file_to_directory(model_code_path, path, "code") + python_model = _load_model_code_path(model_code_path, model_config) + + _validate_function_python_model(python_model) + if callable(python_model) and all( + a is None for a in (input_example, pip_requirements, extra_pip_requirements) + ): + raise MlflowException( + "If `python_model` is a callable object, at least one of `input_example`, " + "`pip_requirements`, or `extra_pip_requirements` must be specified." + ) + + mlflow_model = kwargs.pop("model", mlflow_model) + if len(kwargs) > 0: + raise TypeError(f"save_model() got unexpected keyword arguments: {kwargs}") + + if code_paths is not None: + if not isinstance(code_paths, list): + raise TypeError(f"Argument code_paths should be a list, not {type(code_paths)}") + + first_argument_set = { + "loader_module": loader_module, + "data_path": data_path, + } + second_argument_set = { + "artifacts": artifacts, + "python_model": python_model, + } + first_argument_set_specified = any(item is not None for item in first_argument_set.values()) + second_argument_set_specified = any(item is not None for item in second_argument_set.values()) + if first_argument_set_specified and second_argument_set_specified: + raise MlflowException( + message=( + f"The following sets of parameters cannot be specified together:" + f" {first_argument_set.keys()} and {second_argument_set.keys()}." + " All parameters in one set must be `None`. Instead, found" + f" the following values: {first_argument_set} and {second_argument_set}" + ), + error_code=INVALID_PARAMETER_VALUE, + ) + elif (loader_module is None) and (python_model is None): + msg = ( + "Either `loader_module` or `python_model` must be specified. A `loader_module` " + "should be a python module. A `python_model` should be a subclass of PythonModel" + ) + raise MlflowException(message=msg, error_code=INVALID_PARAMETER_VALUE) + if mlflow_model is None: + mlflow_model = Model() + saved_example = None + signature_from_type_hints = None + type_hint_from_example = None + if isinstance(python_model, ChatModel): + if signature is not None: + raise MlflowException( + "ChatModel subclasses have a standard signature that is set " + "automatically. Please remove the `signature` parameter from " + "the call to log_model() or save_model().", + error_code=INVALID_PARAMETER_VALUE, + ) + mlflow_model.signature = ModelSignature( + CHAT_MODEL_INPUT_SCHEMA, + CHAT_MODEL_OUTPUT_SCHEMA, + ) + # For ChatModel we set default metadata to indicate its task + default_metadata = {TASK: _DEFAULT_CHAT_MODEL_METADATA_TASK} + mlflow_model.metadata = default_metadata | (mlflow_model.metadata or {}) + + if input_example: + input_example, input_params = _split_input_data_and_params(input_example) + valid_params = {} + if isinstance(input_example, list): + messages = [ + message if isinstance(message, ChatMessage) else ChatMessage.from_dict(message) + for message in input_example + ] + else: + # If the input example is a dictionary, convert it to ChatMessage format + messages = [ + ChatMessage.from_dict(m) if isinstance(m, dict) else m + for m in input_example["messages"] + ] + valid_params = { + k: v + for k, v in input_example.items() + if k != "messages" and k in ChatParams.keys() + } + if valid_params or input_params: + _logger.warning(_CHAT_PARAMS_WARNING_MESSAGE) + input_example = { + "messages": [m.to_dict() for m in messages], + **valid_params, + **(input_params or {}), + } + else: + input_example = CHAT_MODEL_INPUT_EXAMPLE + _logger.warning(_CHAT_PARAMS_WARNING_MESSAGE) + messages = [ChatMessage.from_dict(m) for m in input_example["messages"]] + # extra params introduced by ChatParams will not be included in the + # logged input example file to avoid confusion + _save_example(mlflow_model, input_example, path) + params = ChatParams.from_dict(input_example) + + # call load_context() first, as predict may depend on it + _logger.info("Predicting on input example to validate output") + context = PythonModelContext(artifacts, model_config) + python_model.load_context(context) + if "context" in inspect.signature(python_model.predict).parameters: + output = python_model.predict(context, messages, params) + else: + output = python_model.predict(messages, params) + if not isinstance(output, ChatCompletionResponse): + raise MlflowException( + "Failed to save ChatModel. Please ensure that the model's predict() method " + "returns a ChatCompletionResponse object. If your predict() method currently " + "returns a dict, you can instantiate a ChatCompletionResponse using " + "`from_dict()`, e.g. `ChatCompletionResponse.from_dict(output)`", + ) + elif isinstance(python_model, ChatAgent): + input_example = _save_model_chat_agent_helper( + python_model, mlflow_model, signature, input_example + ) + elif IS_RESPONSES_AGENT_AVAILABLE and isinstance(python_model, ResponsesAgent): + input_example = _save_model_responses_agent_helper( + python_model, mlflow_model, signature, input_example, artifacts, model_config + ) + elif callable(python_model) or isinstance(python_model, PythonModel): + model_for_signature_inference = None + predict_func = None + if callable(python_model): + # first argument is the model input + type_hints = _extract_type_hints(python_model, input_arg_index=0) + pyfunc_decorator_used = getattr(python_model, "_is_pyfunc", False) + # only show the warning here if @pyfunc is not applied on the function + # since @pyfunc will trigger the warning instead + if type_hints.input is None and not pyfunc_decorator_used: + color_warning( + "Add type hints to the `predict` method to enable " + "data validation and automatic signature inference. Check " + "https://mlflow.org/docs/latest/model/python_model.html#type-hint-usage-in-pythonmodel" + " for more details.", + stacklevel=1, + color="yellow", + ) + model_for_signature_inference = _FunctionPythonModel(python_model) + predict_func = python_model + elif isinstance(python_model, PythonModel): + type_hints = python_model.predict_type_hints + model_for_signature_inference = python_model + predict_func = python_model.predict + # Load context before calling predict to ensure necessary artifacts are available + context = PythonModelContext(artifacts, model_config) + model_for_signature_inference.load_context(context) + type_hint_from_example = _is_type_hint_from_example(type_hints.input) + if type_hint_from_example: + should_infer_signature_from_type_hints = False + else: + should_infer_signature_from_type_hints = ( + not _signature_cannot_be_inferred_from_type_hint(type_hints.input) + ) + if should_infer_signature_from_type_hints: + signature_from_type_hints = _infer_signature_from_type_hints( + func=predict_func, + type_hints=type_hints, + input_example=input_example, + ) + # only infer signature based on input example when signature + # and type hints are not provided + if signature is None and signature_from_type_hints is None: + saved_example = _save_example(mlflow_model, input_example, path) + if saved_example is not None: + _logger.info("Inferring model signature from input example") + try: + mlflow_model.signature = _infer_signature_from_input_example( + saved_example, + _PythonModelPyfuncWrapper(model_for_signature_inference, None, None), + ) + except Exception as e: + _logger.warning( + f"Failed to infer model signature from input example, error: {e}", + ) + else: + if type_hint_from_example and mlflow_model.signature: + update_signature_for_type_hint_from_example( + input_example, mlflow_model.signature + ) + else: + if type_hint_from_example: + _logger.warning( + _TYPE_FROM_EXAMPLE_ERROR_MESSAGE, + extra={"color": "red"}, + ) + # if signature is inferred from type hints, warnings are emitted + # in _infer_signature_from_type_hints + elif not should_infer_signature_from_type_hints: + _logger.warning( + "Failed to infer model signature: " + f"Type hint {type_hints} cannot be used to infer model signature and " + "input example is not provided, model signature cannot be inferred." + ) + + if metadata is not None: + mlflow_model.metadata = metadata + if saved_example is None: + saved_example = _save_example(mlflow_model, input_example, path) + + if signature_from_type_hints: + if signature and signature_from_type_hints != signature: + # TODO: drop this support and raise exception in the next minor release since this + # is a behavior change + _logger.warning( + "Provided signature does not match the signature inferred from the Python model's " + "`predict` function type hint. Signature inferred from type hint will be used:\n" + f"{signature_from_type_hints}\nRemove the `signature` parameter or ensure it " + "matches the inferred signature. In a future release, this warning will become an " + "exception, and the signature must align with the type hint.", + extra={"color": "red"}, + ) + mlflow_model.signature = signature_from_type_hints + elif signature: + mlflow_model.signature = signature + if type_hint_from_example: + if saved_example is None: + _logger.warning( + _TYPE_FROM_EXAMPLE_ERROR_MESSAGE, + extra={"color": "red"}, + ) + else: + # TODO: validate input example against signature + update_signature_for_type_hint_from_example(input_example, mlflow_model.signature) + else: + if saved_example is None: + color_warning( + message="An input example was not provided when logging the model. To ensure " + "the model signature functions correctly, specify the `input_example` " + "parameter. See " + "https://mlflow.org/docs/latest/model/signatures.html#model-input-example " + "for more details about the benefits of using input_example.", + stacklevel=1, + color="yellow_bold", + ) + else: + _logger.info("Validating input example against model signature") + try: + _validate_prediction_input( + data=saved_example.inference_data, + params=saved_example.inference_params, + input_schema=signature.inputs, + params_schema=signature.params, + ) + except Exception as e: + raise MlflowException.invalid_parameter_value( + f"Input example does not match the model signature. {e}" + ) + + with _get_dependencies_schemas() as dependencies_schemas: + schema = dependencies_schemas.to_dict() + if schema is not None: + if mlflow_model.metadata is None: + mlflow_model.metadata = {} + mlflow_model.metadata.update(schema) + + if resources is not None: + if isinstance(resources, (Path, str)): + serialized_resource = _ResourceBuilder.from_yaml_file(resources) + else: + serialized_resource = _ResourceBuilder.from_resources(resources) + + mlflow_model.resources = serialized_resource + + if auth_policy is not None: + mlflow_model.auth_policy = auth_policy + + if first_argument_set_specified: + return _save_model_with_loader_module_and_data_path( + path=path, + loader_module=loader_module, + data_path=data_path, + code_paths=code_paths, + conda_env=conda_env, + mlflow_model=mlflow_model, + pip_requirements=pip_requirements, + extra_pip_requirements=extra_pip_requirements, + model_config=model_config, + streamable=streamable, + infer_code_paths=infer_code_paths, + ) + elif second_argument_set_specified: + return mlflow.pyfunc.model._save_model_with_class_artifacts_params( + path=path, + signature=signature, + python_model=python_model, + artifacts=artifacts, + conda_env=conda_env, + code_paths=code_paths, + mlflow_model=mlflow_model, + pip_requirements=pip_requirements, + extra_pip_requirements=extra_pip_requirements, + model_config=model_config, + streamable=streamable, + model_code_path=model_code_path, + infer_code_paths=infer_code_paths, + ) + + +def update_signature_for_type_hint_from_example(input_example: Any, signature: ModelSignature): + if _is_example_valid_for_type_from_example(input_example): + signature._is_type_hint_from_example = True + else: + _logger.warning( + "Input example must be one of pandas.DataFrame, pandas.Series " + f"or list when using TypeFromExample as type hint, got {type(input_example)}. " + "Check https://mlflow.org/docs/latest/model/python_model.html#typefromexample-type-hint-usage" + " for more details.", + ) + + +@format_docstring(LOG_MODEL_PARAM_DOCS.format(package_name="scikit-learn")) +@trace_disabled # Suppress traces for internal predict calls while logging model +def log_model( + artifact_path=None, + loader_module=None, + data_path=None, + code_paths=None, + infer_code_paths=False, + conda_env=None, + python_model=None, + artifacts=None, + registered_model_name=None, + signature: ModelSignature = None, + input_example: ModelInputExample = None, + await_registration_for=DEFAULT_AWAIT_MAX_SLEEP_SECONDS, + pip_requirements=None, + extra_pip_requirements=None, + metadata=None, + model_config=None, + streamable=None, + resources: str | list[Resource] | None = None, + auth_policy: AuthPolicy | None = None, + prompts: list[str | Prompt] | None = None, + name=None, + params: dict[str, Any] | None = None, + tags: dict[str, Any] | None = None, + model_type: str | None = None, + step: int = 0, + model_id: str | None = None, +): + """ + Log a Pyfunc model with custom inference logic and optional data dependencies as an MLflow + artifact for the current run. + + For information about the workflows that this method supports, see :ref:`Workflows for + creating custom pyfunc models ` and + :ref:`Which workflow is right for my use case? `. + You cannot specify the parameters for the second workflow: ``loader_module``, ``data_path`` + and the parameters for the first workflow: ``python_model``, ``artifacts`` together. + + Args: + artifact_path: Deprecated. Use `name` instead. + loader_module: The name of the Python module that is used to load the model + from ``data_path``. This module must define a method with the prototype + ``_load_pyfunc(data_path)``. If not ``None``, this module and its + dependencies must be included in one of the following locations: + + - The MLflow library. + - Package(s) listed in the model's Conda environment, specified by + the ``conda_env`` parameter. + - One or more of the files specified by the ``code_paths`` parameter. + + data_path: Path to a file or directory containing model data. + code_paths: {{ code_paths_pyfunc }} + infer_code_paths: {{ infer_code_paths }} + conda_env: {{ conda_env }} + python_model: + An instance of a subclass of :class:`~PythonModel` or a callable object with a single + argument (see the examples below). The passed-in object is serialized using the + CloudPickle library. The python_model can also be a file path to the PythonModel + which defines the model from code artifact rather than serializing the model object. + Any dependencies of the class should be included in one of the + following locations: + + - The MLflow library. + - Package(s) listed in the model's Conda environment, specified by the ``conda_env`` + parameter. + - One or more of the files specified by the ``code_paths`` parameter. + + Note: If the class is imported from another module, as opposed to being defined in the + ``__main__`` scope, the defining module should also be included in one of the listed + locations. + + **Examples** + + Class model + + .. code-block:: python + + from typing import List + import mlflow + + + class MyModel(mlflow.pyfunc.PythonModel): + def predict(self, context, model_input: List[str], params=None) -> List[str]: + return [i.upper() for i in model_input] + + + with mlflow.start_run(): + model_info = mlflow.pyfunc.log_model( + name="model", + python_model=MyModel(), + ) + + + loaded_model = mlflow.pyfunc.load_model(model_uri=model_info.model_uri) + print(loaded_model.predict(["a", "b", "c"])) # -> ["A", "B", "C"] + + Functional model + + .. note:: + Experimental: Functional model support is experimental and may change or be removed + in a future release without warning. + + .. code-block:: python + + from typing import List + import mlflow + + + def predict(model_input: List[str]) -> List[str]: + return [i.upper() for i in model_input] + + + with mlflow.start_run(): + model_info = mlflow.pyfunc.log_model( + name="model", python_model=predict, input_example=["a"] + ) + + + loaded_model = mlflow.pyfunc.load_model(model_uri=model_info.model_uri) + print(loaded_model.predict(["a", "b", "c"])) # -> ["A", "B", "C"] + + Model from code + + .. note:: + Experimental: Model from code model support is experimental and may change or + be removed in a future release without warning. + + .. code-block:: python + + # code.py + from typing import List + import mlflow + + + class MyModel(mlflow.pyfunc.PythonModel): + def predict(self, context, model_input: List[str], params=None) -> List[str]: + return [i.upper() for i in model_input] + + + mlflow.models.set_model(MyModel()) + + # log_model.py + import mlflow + + with mlflow.start_run(): + model_info = mlflow.pyfunc.log_model( + name="model", + python_model="code.py", + ) + + If the `predict` method or function has type annotations, MLflow automatically + constructs a model signature based on the type annotations (unless the ``signature`` + argument is explicitly specified), and converts the input value to the specified type + before passing it to the function. Currently, the following type annotations are + supported: + + - ``List[str]`` + - ``List[Dict[str, str]]`` + + artifacts: A dictionary containing ```` entries. Remote artifact URIs + are resolved to absolute filesystem paths, producing a dictionary of + ```` entries. ``python_model`` can reference these + resolved entries as the ``artifacts`` property of the ``context`` parameter + in :func:`PythonModel.load_context() ` + and :func:`PythonModel.predict() `. + For example, consider the following ``artifacts`` dictionary:: + + {"my_file": "s3://my-bucket/path/to/my/file"} + + In this case, the ``"my_file"`` artifact is downloaded from S3. The + ``python_model`` can then refer to ``"my_file"`` as an absolute filesystem + path via ``context.artifacts["my_file"]``. + + If ``None``, no artifacts are added to the model. + registered_model_name: If given, create a model + version under ``registered_model_name``, also creating a + registered model if one with the given name does not exist. + + signature: :py:class:`ModelSignature ` + describes model input and output :py:class:`Schema `. + The model signature can be :py:func:`inferred ` + from datasets with valid model input (e.g. the training dataset with target + column omitted) and valid model output (e.g. model predictions generated on + the training dataset), for example: + + .. code-block:: python + + from mlflow.models import infer_signature + + train = df.drop_column("target_label") + predictions = ... # compute model predictions + signature = infer_signature(train, predictions) + + input_example: {{ input_example }} + await_registration_for: Number of seconds to wait for the model version to finish + being created and is in ``READY`` status. By default, the function + waits for five minutes. Specify 0 or None to skip waiting. + pip_requirements: {{ pip_requirements }} + extra_pip_requirements: {{ extra_pip_requirements }} + metadata: {{ metadata }} + model_config: The model configuration to apply to the model. The configuration will + be available as the ``model_config`` property of the ``context`` parameter + in :func:`PythonModel.load_context() ` + and :func:`PythonModel.predict() `. + The configuration can be passed as a file path, or a dict with string keys. + + .. Note:: Experimental: This parameter may change or be removed in a future + release without warning. + streamable: A boolean value indicating if the model supports streaming prediction, + If None, MLflow will try to inspect if the model supports streaming + by checking if `predict_stream` method exists. Default None. + resources: A list of model resources or a resources.yaml file containing a list of + resources required to serve the model. + + .. Note:: Experimental: This parameter may change or be removed in a future + release without warning. + auth_policy: {{ auth_policy }} + prompts: {{ prompts }} + name: {{ name }} + params: {{ params }} + tags: {{ tags }} + model_type: {{ model_type }} + step: {{ step }} + model_id: {{ model_id }} + + Returns: + A :py:class:`ModelInfo ` instance that contains the + metadata of the logged model. + """ + return Model.log( + artifact_path=artifact_path, + name=name, + flavor=mlflow.pyfunc, + loader_module=loader_module, + data_path=data_path, + code_paths=code_paths, + python_model=python_model, + artifacts=artifacts, + conda_env=conda_env, + registered_model_name=registered_model_name, + signature=signature, + input_example=input_example, + await_registration_for=await_registration_for, + pip_requirements=pip_requirements, + extra_pip_requirements=extra_pip_requirements, + metadata=metadata, + prompts=prompts, + model_config=model_config, + streamable=streamable, + resources=resources, + infer_code_paths=infer_code_paths, + auth_policy=auth_policy, + params=params, + tags=tags, + model_type=model_type, + step=step, + model_id=model_id, + ) + + +def _save_model_with_loader_module_and_data_path( + path, + loader_module, + data_path=None, + code_paths=None, + conda_env=None, + mlflow_model=None, + pip_requirements=None, + extra_pip_requirements=None, + model_config=None, + streamable=None, + infer_code_paths=False, +): + """ + Export model as a generic Python function model. + + Args: + path: The path to which to save the Python model. + loader_module: The name of the Python module that is used to load the model + from ``data_path``. This module must define a method with the prototype + ``_load_pyfunc(data_path)``. + data_path: Path to a file or directory containing model data. + code_paths: A list of local filesystem paths to Python file dependencies (or directories + containing file dependencies). These files are *prepended* to the system + path before the model is loaded. + conda_env: Either a dictionary representation of a Conda environment or the path to a + Conda environment yaml file. If provided, this describes the environment + this model should be run in. + streamable: A boolean value indicating if the model supports streaming prediction, + None value also means not streamable. + + Returns: + Model configuration containing model info. + """ + + data = None + + if data_path is not None: + model_file = _copy_file_or_tree(src=data_path, dst=path, dst_dir="data") + data = model_file + + if mlflow_model is None: + mlflow_model = Model() + + streamable = streamable or False + mlflow.pyfunc.add_to_model( + mlflow_model, + loader_module=loader_module, + code=None, + data=data, + conda_env=_CONDA_ENV_FILE_NAME, + python_env=_PYTHON_ENV_FILE_NAME, + model_config=model_config, + streamable=streamable, + ) + if size := get_total_file_size(path): + mlflow_model.model_size_bytes = size + mlflow_model.save(os.path.join(path, MLMODEL_FILE_NAME)) + + code_dir_subpath = _validate_infer_and_copy_code_paths( + code_paths, path, infer_code_paths, FLAVOR_NAME + ) + mlflow_model.flavors[FLAVOR_NAME][CODE] = code_dir_subpath + + # `mlflow_model.code` is updated, re-generate `MLmodel` file. + mlflow_model.save(os.path.join(path, MLMODEL_FILE_NAME)) + + if conda_env is None: + if pip_requirements is None: + default_reqs = get_default_pip_requirements() + extra_env_vars = ( + _get_databricks_serverless_env_vars() + if is_in_databricks_serverless_runtime() + else None + ) + # To ensure `_load_pyfunc` can successfully load the model during the dependency + # inference, `mlflow_model.save` must be called beforehand to save an MLmodel file. + inferred_reqs = mlflow.models.infer_pip_requirements( + path, + FLAVOR_NAME, + fallback=default_reqs, + extra_env_vars=extra_env_vars, + ) + default_reqs = sorted(set(inferred_reqs).union(default_reqs)) + else: + default_reqs = None + conda_env, pip_requirements, pip_constraints = _process_pip_requirements( + default_reqs, + pip_requirements, + extra_pip_requirements, + ) + else: + conda_env, pip_requirements, pip_constraints = _process_conda_env(conda_env) + + with open(os.path.join(path, _CONDA_ENV_FILE_NAME), "w") as f: + yaml.safe_dump(conda_env, stream=f, default_flow_style=False) + + # Save `constraints.txt` if necessary + if pip_constraints: + write_to(os.path.join(path, _CONSTRAINTS_FILE_NAME), "\n".join(pip_constraints)) + + # Save `requirements.txt` + write_to(os.path.join(path, _REQUIREMENTS_FILE_NAME), "\n".join(pip_requirements)) + + _PythonEnv.current().to_yaml(os.path.join(path, _PYTHON_ENV_FILE_NAME)) + return mlflow_model + + +def _save_model_chat_agent_helper(python_model, mlflow_model, signature, input_example): + """Helper method for save_model for ChatAgent models + + Returns: a dict input_example + """ + if signature is not None: + raise MlflowException( + "ChatAgent subclasses have a standard signature that is set " + "automatically. Please remove the `signature` parameter from " + "the call to log_model() or save_model().", + error_code=INVALID_PARAMETER_VALUE, + ) + mlflow_model.signature = ModelSignature( + inputs=CHAT_AGENT_INPUT_SCHEMA, + outputs=CHAT_AGENT_OUTPUT_SCHEMA, + ) + # For ChatAgent we set default metadata to indicate its task + default_metadata = {TASK: _DEFAULT_CHAT_AGENT_METADATA_TASK} + mlflow_model.metadata = default_metadata | (mlflow_model.metadata or {}) + + # We accept a dict with ChatAgentRequest schema + if input_example: + try: + model_validate(ChatAgentRequest, input_example) + except pydantic.ValidationError as e: + raise MlflowException( + message=( + f"Invalid input example. Expected a ChatAgentRequest object or dictionary with" + f" its schema. Pydantic validation error: {e}" + ), + error_code=INTERNAL_ERROR, + ) from e + if isinstance(input_example, ChatAgentRequest): + input_example = input_example.model_dump_compat(exclude_none=True) + else: + input_example = CHAT_AGENT_INPUT_EXAMPLE + + _logger.info("Predicting on input example to validate output") + request = ChatAgentRequest(**input_example) + output = python_model.predict(request.messages, request.context, request.custom_inputs) + try: + model_validate(ChatAgentResponse, output) + except Exception as e: + raise MlflowException( + "Failed to save ChatAgent. Ensure your model's predict() method returns a " + "ChatAgentResponse object or a dict with the same schema." + f"Pydantic validation error: {e}" + ) from e + return input_example + + +def _save_model_responses_agent_helper( + python_model, mlflow_model, signature, input_example, artifacts, model_config +): + """Helper method for save_model for ResponsesAgent models + + Returns: a dictionary input example + """ + from mlflow.types.responses import ( + RESPONSES_AGENT_INPUT_EXAMPLE, + RESPONSES_AGENT_INPUT_SCHEMA, + RESPONSES_AGENT_OUTPUT_SCHEMA, + ResponsesAgentRequest, + ResponsesAgentResponse, + ) + + if signature is not None: + raise MlflowException( + "ResponsesAgent subclasses have a standard signature that is set " + "automatically. Please remove the `signature` parameter from " + "the call to log_model() or save_model().", + error_code=INVALID_PARAMETER_VALUE, + ) + mlflow_model.signature = ModelSignature( + inputs=RESPONSES_AGENT_INPUT_SCHEMA, + outputs=RESPONSES_AGENT_OUTPUT_SCHEMA, + ) + + # For ResponsesAgent we set default metadata to indicate its task + default_metadata = {TASK: _DEFAULT_RESPONSES_AGENT_METADATA_TASK} + mlflow_model.metadata = default_metadata | (mlflow_model.metadata or {}) + + # We accept either a dict or a ResponsesRequest object as input + if input_example: + try: + model_validate(ResponsesAgentRequest, input_example) + except pydantic.ValidationError as e: + raise MlflowException( + message=( + f"Invalid input example. Expected a ResponsesRequest object or dictionary with" + f" its schema. Pydantic validation error: {e}" + ), + error_code=INTERNAL_ERROR, + ) from e + if isinstance(input_example, ResponsesAgentRequest): + input_example = input_example.model_dump_compat(exclude_none=True) + else: + input_example = RESPONSES_AGENT_INPUT_EXAMPLE + _logger.info("Predicting on input example to validate output") + context = PythonModelContext(artifacts, model_config) + python_model.load_context(context) + request = ResponsesAgentRequest(**input_example) + output = python_model.predict(request) + try: + model_validate(ResponsesAgentResponse, output) + except Exception as e: + raise MlflowException( + "Failed to save ResponsesAgent. 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parser.add_argument("--input-path", required=False) + parser.add_argument("--output-path", required=False) + parser.add_argument("--content-type", required=True) + return parser.parse_args() + + +# Guidance for fixing missing module error +_MISSING_MODULE_HELP_MSG = ( + "Exception occurred while running inference: {e}" + "\n\n" + "\033[93m[Hint] It appears that your MLflow Model doesn't contain the required " + "dependency '{missing_module}' to run model inference. When logging a model, MLflow " + "detects dependencies based on the model flavor, but it is possible that some " + "dependencies are not captured. In this case, you can manually add dependencies " + "using the `extra_pip_requirements` parameter of `mlflow.pyfunc.log_model`.\033[0m" + """ + +\033[1mSample code:\033[0m + ---- + mlflow.pyfunc.log_model( + artifact_path="model", + python_model=your_model, + extra_pip_requirements=["{missing_module}==x.y.z"] + ) + ---- + + For mode guidance on fixing missing dependencies, please refer to the MLflow docs: + https://www.mlflow.org/docs/latest/deployment/index.html#how-to-fix-dependency-errors-when-serving-my-model +""" +) + + +def main(): + args = parse_args() + + try: + _predict( + model_uri=args.model_uri, + input_path=args.input_path if args.input_path else None, + output_path=args.output_path if args.output_path else None, + content_type=args.content_type, + ) + except ModuleNotFoundError as e: + message = _MISSING_MODULE_HELP_MSG.format(e=str(e), missing_module=e.name) + raise RuntimeError(message) from e + + +if __name__ == "__main__": + main() diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyfunc/backend.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyfunc/backend.py new file mode 100644 index 0000000000000000000000000000000000000000..b093fe3ef52dc45728b0976ca38aae1ab2511269 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyfunc/backend.py @@ -0,0 +1,524 @@ +import ctypes +import json +import logging +import os +import pathlib +import shlex +import signal +import subprocess +import sys +import warnings +from pathlib import Path + +from mlflow import pyfunc +from mlflow.exceptions import MlflowException +from mlflow.models import FlavorBackend, Model, docker_utils +from mlflow.models.docker_utils import PYTHON_SLIM_BASE_IMAGE, UBUNTU_BASE_IMAGE +from mlflow.pyfunc import ( + ENV, + _extract_conda_env, + _mlflow_pyfunc_backend_predict, + mlserver, + scoring_server, +) +from mlflow.tracking.artifact_utils import _download_artifact_from_uri +from mlflow.utils import env_manager as em +from mlflow.utils.conda import get_conda_bin_executable, get_or_create_conda_env +from mlflow.utils.environment import Environment, _get_pip_install_mlflow, _PythonEnv +from mlflow.utils.file_utils import ( + TempDir, + get_or_create_nfs_tmp_dir, + get_or_create_tmp_dir, + path_to_local_file_uri, +) +from mlflow.utils.model_utils import _get_all_flavor_configurations +from mlflow.utils.nfs_on_spark import get_nfs_cache_root_dir +from mlflow.utils.os import is_windows +from mlflow.utils.process import ShellCommandException, cache_return_value_per_process +from mlflow.utils.virtualenv import _get_or_create_virtualenv +from mlflow.version import VERSION + +_logger = logging.getLogger(__name__) + +_STDIN_SERVER_SCRIPT = Path(__file__).parent.joinpath("stdin_server.py") + +# Flavors that require Java to be installed in the environment +JAVA_FLAVORS = {"johnsnowlabs", "h2o", "spark"} + +# Some flavor requires additional packages to be installed in the environment +FLAVOR_SPECIFIC_APT_PACKAGES = { + "lightgbm": ["libgomp1"], + "paddle": ["libgomp1"], +} + +# Directory to store loaded model inside the Docker context directory +_MODEL_DIR_NAME = "model_dir" +LOCAL_ENV_MANAGER_ERROR_MESSAGE = "We cannot use 'LOCAL' environment manager " +"for your model configuration. Please specify a virtualenv or conda environment " +"manager instead with `--env-manager` argument." + + +def _set_mlflow_config_env(command_env, model_config): + if model_config: + command_env[scoring_server.SERVING_MODEL_CONFIG] = json.dumps(model_config) + return command_env + + +class PyFuncBackend(FlavorBackend): + """ + Flavor backend implementation for the generic python models. + """ + + def __init__( + self, + config, + env_manager, + workers=1, + install_mlflow=False, + create_env_root_dir=False, + env_root_dir=None, + **kwargs, + ): + """ + Args: + env_manager: Environment manager to use for preparing the environment. If None, + MLflow will automatically pick the env manager based on the model's flavor + configuration for generate_dockerfile. It can't be None for other methods. + env_root_dir: Root path for conda env. If None, use Conda's default environments + directory. Note if this is set, conda package cache path becomes + "{env_root_dir}/conda_cache_pkgs" instead of the global package cache + path, and pip package cache path becomes + "{env_root_dir}/pip_cache_pkgs" instead of the global package cache + path. + """ + super().__init__(config=config, **kwargs) + self._nworkers = workers or 1 + if env_manager == em.CONDA and ENV not in config: + warnings.warn( + "Conda environment is not specified in config `env`. Using local environment." + ) + env_manager = em.LOCAL + self._env_manager = env_manager + self._install_mlflow = install_mlflow + self._env_id = os.environ.get("MLFLOW_HOME", VERSION) if install_mlflow else None + self._create_env_root_dir = create_env_root_dir + self._env_root_dir = env_root_dir + self._environment = None + + def prepare_env( + self, model_uri, capture_output=False, pip_requirements_override=None, extra_envs=None + ): + if self._environment is not None: + return self._environment + + @cache_return_value_per_process + def _get_or_create_env_root_dir(should_use_nfs): + if should_use_nfs: + root_tmp_dir = get_or_create_nfs_tmp_dir() + else: + root_tmp_dir = get_or_create_tmp_dir() + + envs_root_dir = os.path.join(root_tmp_dir, "envs") + os.makedirs(envs_root_dir, exist_ok=True) + return envs_root_dir + + local_path = _download_artifact_from_uri(model_uri) + if self._create_env_root_dir: + if self._env_root_dir is not None: + raise Exception("env_root_dir can not be set when create_env_root_dir=True") + nfs_root_dir = get_nfs_cache_root_dir() + env_root_dir = _get_or_create_env_root_dir(nfs_root_dir is not None) + else: + env_root_dir = self._env_root_dir + + if self._env_manager in {em.VIRTUALENV, em.UV}: + activate_cmd = _get_or_create_virtualenv( + local_path, + self._env_id, + env_root_dir=env_root_dir, + capture_output=capture_output, + pip_requirements_override=pip_requirements_override, + env_manager=self._env_manager, + extra_envs=extra_envs, + ) + self._environment = Environment(activate_cmd, extra_env=extra_envs) + elif self._env_manager == em.CONDA: + conda_env_path = os.path.join(local_path, _extract_conda_env(self._config[ENV])) + self._environment = get_or_create_conda_env( + conda_env_path, + env_id=self._env_id, + capture_output=capture_output, + env_root_dir=env_root_dir, + pip_requirements_override=pip_requirements_override, + extra_envs=extra_envs, + ) + + elif self._env_manager == em.LOCAL: + raise Exception("Prepare env should not be called with local env manager!") + else: + raise Exception(f"Unexpected env manager value '{self._env_manager}'") + + if self._install_mlflow: + self._environment.execute(_get_pip_install_mlflow()) + else: + self._environment.execute('python -c ""') + + return self._environment + + def predict( + self, + model_uri, + input_path, + output_path, + content_type, + pip_requirements_override=None, + extra_envs=None, + ): + """ + Generate predictions using generic python model saved with MLflow. The expected format of + the input JSON is the MLflow scoring format. + Return the prediction results as a JSON. + """ + local_path = _download_artifact_from_uri(model_uri) + # NB: Absolute windows paths do not work with mlflow apis, use file uri to ensure + # platform compatibility. + local_uri = path_to_local_file_uri(local_path) + + if self._env_manager != em.LOCAL: + predict_cmd = [ + "python", + _mlflow_pyfunc_backend_predict.__file__, + "--model-uri", + str(local_uri), + "--content-type", + shlex.quote(str(content_type)), + ] + if input_path: + predict_cmd += ["--input-path", shlex.quote(str(input_path))] + if output_path: + predict_cmd += ["--output-path", shlex.quote(str(output_path))] + + if pip_requirements_override and self._env_manager == em.CONDA: + # Conda use = instead of == for version pinning + pip_requirements_override = [ + pip_req.replace("==", "=") for pip_req in pip_requirements_override + ] + + environment = self.prepare_env( + local_path, + pip_requirements_override=pip_requirements_override, + extra_envs=extra_envs, + ) + + try: + environment.execute(" ".join(predict_cmd)) + except ShellCommandException as e: + raise MlflowException( + f"{e}\n\nAn exception occurred while running model prediction within a " + f"{self._env_manager} environment. You can find the error message " + f"from the prediction subprocess by scrolling above." + ) from None + else: + if pip_requirements_override: + raise MlflowException( + "`pip_requirements_override` is not supported for local env manager." + "Please use conda or virtualenv instead." + ) + scoring_server._predict(local_uri, input_path, output_path, content_type) + + def serve( + self, + model_uri, + port, + host, + timeout, + enable_mlserver, + synchronous=True, + stdout=None, + stderr=None, + model_config=None, + ): + """ + Serve pyfunc model locally. + """ + local_path = _download_artifact_from_uri(model_uri) + + server_implementation = mlserver if enable_mlserver else scoring_server + command, command_env = server_implementation.get_cmd( + local_path, port, host, timeout, self._nworkers + ) + _set_mlflow_config_env(command_env, model_config) + + if sys.platform.startswith("linux"): + + def setup_sigterm_on_parent_death(): + """ + Uses prctl to automatically send SIGTERM to the command process when its parent is + dead. + + This handles the case when the parent is a PySpark worker process. + If a user cancels the PySpark job, the worker process gets killed, regardless of + PySpark daemon and worker reuse settings. + We use prctl to ensure the command process receives SIGTERM after spark job + cancellation. + The command process itself should handle SIGTERM properly. + This is a no-op on macOS because prctl is not supported. + + Note: + When a pyspark job canceled, the UDF python process are killed by signal "SIGKILL", + This case neither "atexit" nor signal handler can capture SIGKILL signal. + prctl is the only way to capture SIGKILL signal. + """ + try: + libc = ctypes.CDLL("libc.so.6") + # Set the parent process death signal of the command process to SIGTERM. + libc.prctl(1, signal.SIGTERM) # PR_SET_PDEATHSIG, see prctl.h + except OSError as e: + # TODO: find approach for supporting MacOS/Windows system which does + # not support prctl. + warnings.warn(f"Setup libc.prctl PR_SET_PDEATHSIG failed, error {e!r}.") + + else: + setup_sigterm_on_parent_death = None + + if not is_windows(): + # Add "exec" before the starting scoring server command, so that the scoring server + # process replaces the bash process, otherwise the scoring server process is created + # as a child process of the bash process. + # Note we in `mlflow.pyfunc.spark_udf`, use prctl PR_SET_PDEATHSIG to ensure scoring + # server process being killed when UDF process exit. The PR_SET_PDEATHSIG can only + # send signal to the bash process, if the scoring server process is created as a + # child process of the bash process, then it cannot receive the signal sent by prctl. + # TODO: For Windows, there's no equivalent things of Unix shell's exec. Windows also + # does not support prctl. We need to find an approach to address it. + command = "exec " + command + + if self._env_manager != em.LOCAL: + return self.prepare_env(local_path).execute( + command, + command_env, + stdout=stdout, + stderr=stderr, + preexec_fn=setup_sigterm_on_parent_death, + synchronous=synchronous, + ) + else: + _logger.info("=== Running command '%s'", command) + + if not is_windows(): + command = ["bash", "-c", command] + + child_proc = subprocess.Popen( + command, + env=command_env, + preexec_fn=setup_sigterm_on_parent_death, + stdout=stdout, + stderr=stderr, + ) + + if synchronous: + rc = child_proc.wait() + if rc != 0: + raise Exception( + f"Command '{command}' returned non zero return code. Return code = {rc}" + ) + return 0 + else: + return child_proc + + def serve_stdin( + self, + model_uri, + stdout=None, + stderr=None, + model_config=None, + ): + local_path = _download_artifact_from_uri(model_uri) + + command_env = os.environ.copy() + _set_mlflow_config_env(command_env, model_config) + + return self.prepare_env(local_path).execute( + command=f"python {_STDIN_SERVER_SCRIPT} --model-uri {local_path}", + command_env=command_env, + stdin=subprocess.PIPE, + stdout=stdout, + stderr=stderr, + synchronous=False, + ) + + def can_score_model(self): + if self._env_manager == em.LOCAL: + # noconda => already in python and dependencies are assumed to be installed. + return True + conda_path = get_conda_bin_executable("conda") + try: + p = subprocess.Popen( + [conda_path, "--version"], stdout=subprocess.PIPE, stderr=subprocess.PIPE + ) + _, _ = p.communicate() + return p.wait() == 0 + except FileNotFoundError: + # Can not find conda + return False + + def build_image( + self, + model_uri, + image_name, + install_java=False, + install_mlflow=False, + mlflow_home=None, + enable_mlserver=False, + base_image=None, + ): + with TempDir() as tmp: + cwd = tmp.path() + self.generate_dockerfile( + model_uri=model_uri, + output_dir=cwd, + install_java=install_java, + install_mlflow=install_mlflow, + mlflow_home=mlflow_home, + enable_mlserver=enable_mlserver, + base_image=base_image, + ) + + _logger.info("Building docker image with name %s", image_name) + docker_utils.build_image_from_context(context_dir=cwd, image_name=image_name) + + def generate_dockerfile( + self, + model_uri, + output_dir, + install_java=False, + install_mlflow=False, + mlflow_home=None, + enable_mlserver=False, + base_image=None, + ): + os.makedirs(output_dir, exist_ok=True) + _logger.debug("Created all folders in path", extra={"output_directory": output_dir}) + + if model_uri: + model_cwd = os.path.join(output_dir, _MODEL_DIR_NAME) + pathlib.Path(model_cwd).mkdir(parents=True, exist_ok=True) + model_path = _download_artifact_from_uri(model_uri, output_path=model_cwd) + base_image = base_image or self._get_base_image(model_path, install_java) + env_manager = self._env_manager or em.LOCAL + + if base_image.startswith("python"): + # we can directly use local env for python image + if env_manager in [em.CONDA, em.VIRTUALENV]: + # we can directly use ubuntu image for conda and virtualenv + base_image = UBUNTU_BASE_IMAGE + elif base_image == UBUNTU_BASE_IMAGE: + env_manager = self._env_manager or em.VIRTUALENV + # installing python on ubuntu image is problematic and not recommended officially + # , so we recommend using conda or virtualenv instead on ubuntu image + if env_manager == em.LOCAL: + raise MlflowException.invalid_parameter_value(LOCAL_ENV_MANAGER_ERROR_MESSAGE) + + copy_src = os.path.relpath(model_path, start=output_dir) + model_install_steps = self._model_installation_steps( + copy_src, + model_path, + env_manager, + install_mlflow, + enable_mlserver, + ) + entrypoint = f"from mlflow.models import container as C; C._serve('{env_manager}')" + + # if no model_uri specified, user must use virtualenv or conda env based on ubuntu image + else: + base_image = base_image or UBUNTU_BASE_IMAGE + env_manager = self._env_manager or em.VIRTUALENV + if env_manager == em.LOCAL: + raise MlflowException.invalid_parameter_value(LOCAL_ENV_MANAGER_ERROR_MESSAGE) + + model_install_steps = "" + # If model_uri is not specified, dependencies are installed at runtime + entrypoint = ( + self._get_install_pyfunc_deps_cmd(env_manager, install_mlflow, enable_mlserver) + + f" C._serve('{env_manager}')" + ) + + dockerfile_text = docker_utils.generate_dockerfile( + output_dir=output_dir, + base_image=base_image, + model_install_steps=model_install_steps, + entrypoint=entrypoint, + env_manager=env_manager, + mlflow_home=mlflow_home, + enable_mlserver=enable_mlserver, + # always disable env creation at runtime for pyfunc + disable_env_creation_at_runtime=True, + ) + _logger.debug("generated dockerfile at {output_dir}", extra={"dockerfile": dockerfile_text}) + + def _get_base_image(self, model_path: str, install_java: bool) -> str: + """ + Determine the base image to use for the Dockerfile. + + We use Python slim base image when all the following conditions are met: + 1. Model URI is specified by the user + 2. Model flavor does not require Java + 3. Python version is specified in the model + + Returns: + Either the Ubuntu base image or the Python slim base image. + """ + # Check if the model requires Java + if not install_java: + flavors = _get_all_flavor_configurations(model_path).keys() + if java_flavors := JAVA_FLAVORS & flavors: + _logger.info(f"Detected java flavors {java_flavors}, installing Java in the image") + install_java = True + + # Use ubuntu base image if Java is required + if install_java: + return UBUNTU_BASE_IMAGE + + # Get Python version from MLmodel + try: + env_conf = Model.load(model_path).flavors[pyfunc.FLAVOR_NAME][pyfunc.ENV][em.VIRTUALENV] + python_env_config_path = os.path.join(model_path, env_conf) + + python_env = _PythonEnv.from_yaml(python_env_config_path) + return PYTHON_SLIM_BASE_IMAGE.format(version=python_env.python) + except Exception as e: + _logger.warning( + f"Failed to determine Python version from {model_path}. " + f"Defaulting to {UBUNTU_BASE_IMAGE}. Error: {e}" + ) + return UBUNTU_BASE_IMAGE + + def _model_installation_steps( + self, copy_src, model_path, env_manager, install_mlflow, enable_mlserver + ): + # Copy model to image if model_uri is specified + steps = ( + "# Copy model to image and install dependencies\n" + f"COPY {copy_src} /opt/ml/model\nRUN python -c " + ) + steps += ( + f'"{self._get_install_pyfunc_deps_cmd(env_manager, install_mlflow, enable_mlserver)}"' + ) + + # Install flavor-specific dependencies if needed + flavors = _get_all_flavor_configurations(model_path).keys() + for flavor in flavors: + if flavor in FLAVOR_SPECIFIC_APT_PACKAGES: + packages = " ".join(FLAVOR_SPECIFIC_APT_PACKAGES[flavor]) + steps += f"\nRUN apt-get install -y --no-install-recommends {packages}" + + return steps + + def _get_install_pyfunc_deps_cmd( + self, env_manager: str, install_mlflow: bool, enable_mlserver: bool + ): + return ( + "from mlflow.models import container as C; " + f"C._install_pyfunc_deps('/opt/ml/model', install_mlflow={install_mlflow}, " + f"enable_mlserver={enable_mlserver}, env_manager='{env_manager}');" + ) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyfunc/context.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyfunc/context.py new file mode 100644 index 0000000000000000000000000000000000000000..dc8989cb584a251ae42b91629d15c997a8f28e1d --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyfunc/context.py @@ -0,0 +1,78 @@ +import contextlib +from contextvars import ContextVar +from dataclasses import dataclass +from typing import Any + +# A thread local variable to store the context of the current prediction request. +# This is particularly used to associate logs/traces with a specific prediction request in the +# caller side. The context variable is intended to be set by the called before invoking the +# predict method, using the set_prediction_context context manager. +_PREDICTION_REQUEST_CTX = ContextVar("mlflow_prediction_request_context", default=None) + + +@dataclass +class Context: + # A unique identifier for the current prediction request. + request_id: str | None = None + # Whether the current prediction request is as a part of MLflow model evaluation. + is_evaluate: bool = False + # The schema of the dependencies to be added into the tag of trace info. + dependencies_schemas: dict[str, Any] | None = None + # The logged model ID associated with the current prediction request + model_id: str | None = None + # The model serving endpoint name where the prediction request is made + endpoint_name: str | None = None + + def __init__( + self, + request_id: str | None = None, + is_evaluate: bool = False, + dependencies_schemas: dict[str, Any] | None = None, + model_id: str | None = None, + endpoint_name: str | None = None, + # Accept extra kwargs so we don't need to worry backward compatibility + # when adding new attributes to the Context class + **kwargs, + ): + self.request_id = request_id + self.is_evaluate = is_evaluate + self.dependencies_schemas = dependencies_schemas + self.model_id = model_id + self.endpoint_name = endpoint_name + + def update(self, **kwargs): + for key, value in kwargs.items(): + if hasattr(self, key): + setattr(self, key, value) + else: + raise AttributeError(f"Context has no attribute named '{key}'") + + +@contextlib.contextmanager +def set_prediction_context(context: Context | None): + """ + Set the context for the current prediction request. The context will be set as a thread-local + variable and will be accessible globally within the same thread. + + Args: + context: The context for the current prediction request. + """ + if context and not isinstance(context, Context): + raise TypeError(f"Expected context to be an instance of Context, but got: {context}") + + token = _PREDICTION_REQUEST_CTX.set(context) + try: + yield + finally: + _PREDICTION_REQUEST_CTX.reset(token) + + +def get_prediction_context() -> Context | None: + """ + Get the context for the current prediction request. The context is thread-local and is set + using the set_prediction_context context manager. + + Returns: + The context for the current prediction request, or None if no context is set. + """ + return _PREDICTION_REQUEST_CTX.get() diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyfunc/dbconnect_artifact_cache.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyfunc/dbconnect_artifact_cache.py new file mode 100644 index 0000000000000000000000000000000000000000..bf9a26388885876b7443919f8102082c5a1b6080 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyfunc/dbconnect_artifact_cache.py @@ -0,0 +1,144 @@ +import json +import os +import subprocess +import tarfile + +from mlflow.utils.databricks_utils import is_in_databricks_runtime +from mlflow.utils.file_utils import get_or_create_tmp_dir + +_CACHE_MAP_FILE_NAME = "db_connect_artifact_cache.json" + + +class DBConnectArtifactCache: + """ + Manages Databricks Connect artifacts cache. + Note it doesn't support OSS Spark Connect. + + This class can be used in the following environment: + - Databricks shared cluster python notebook REPL + - Databricks Serverless python notebook REPL + - Databricks connect client python REPL that connects to remote Databricks Serverless + - Databricks connect client python REPL that connects to remote Databricks shared cluster + + .. code-block:: python + :caption: Example + + # client side code + db_artifact_cache = DBConnectArtifactCache.get_or_create() + db_artifact_cache.add_artifact_archive("archive1", "/tmp/archive1.tar.gz") + + + @pandas_udf(...) + def my_udf(x): + # we can get the unpacked archive files in `archive1_unpacked_dir` + archive1_unpacked_dir = db_artifact_cache.get("archive1") + """ + + _global_cache = None + + @staticmethod + def get_or_create(spark): + if ( + DBConnectArtifactCache._global_cache is None + or spark is not DBConnectArtifactCache._global_cache._spark + ): + DBConnectArtifactCache._global_cache = DBConnectArtifactCache(spark) + cache_file = os.path.join(get_or_create_tmp_dir(), _CACHE_MAP_FILE_NAME) + if is_in_databricks_runtime() and os.path.exists(cache_file): + # In databricks runtime (shared cluster or Serverless), when you restart the + # notebook REPL by %restart_python or dbutils.library.restartPython(), the + # DBConnect session is still preserved. So in this case, we can reuse the cached + # artifact files. + # So that when adding artifact, the cache map is serialized to local disk file + # `db_connect_artifact_cache.json` and after REPL restarts, + # `DBConnectArtifactCache` restores the cache map by loading data from the file. + with open(cache_file) as f: + DBConnectArtifactCache._global_cache._cache = json.load(f) + return DBConnectArtifactCache._global_cache + + def __init__(self, spark): + self._spark = spark + self._cache = {} + + def __getstate__(self): + """ + The `DBConnectArtifactCache` instance is created in Databricks Connect client side, + and it will be pickled to Databricks Connect UDF sandbox + (see `get_unpacked_artifact_dir` method), but Spark Connect client object is + not pickle-able, we need to skip this field. + """ + state = self.__dict__.copy() + # Don't pickle `_spark` + del state["_spark"] + return state + + def __setstate__(self, state): + self.__dict__.update(state) + self._spark = None + + def has_cache_key(self, cache_key): + return cache_key in self._cache + + def add_artifact_archive(self, cache_key, artifact_archive_path): + """ + Add an artifact archive file to Databricks connect cache. + The archive file must be 'tar.gz' format. + You can only call this method in Databricks Connect client side. + """ + if not artifact_archive_path.endswith(".tar.gz"): + raise RuntimeError( + "'add_artifact_archive' only supports archive file in 'tar.gz' format." + ) + + archive_file_name = os.path.basename(artifact_archive_path) + if cache_key not in self._cache: + self._spark.addArtifact(artifact_archive_path, archive=True) + self._cache[cache_key] = archive_file_name + + if is_in_databricks_runtime(): + with open(os.path.join(get_or_create_tmp_dir(), _CACHE_MAP_FILE_NAME), "w") as f: + json.dump(self._cache, f) + + def get_unpacked_artifact_dir(self, cache_key): + """ + Get unpacked artifact directory path, you can only call this method + inside Databricks Connect spark UDF sandbox. + """ + if cache_key not in self._cache: + raise RuntimeError(f"The artifact '{cache_key}' does not exist.") + archive_file_name = self._cache[cache_key] + + if session_id := os.environ.get("DB_SESSION_UUID"): + return ( + f"/local_disk0/.ephemeral_nfs/artifacts/{session_id}/archives/{archive_file_name}" + ) + + # If 'DB_SESSION_UUID' environment variable does not exist, it means it is running + # in a dedicated mode Spark cluster. + return os.path.join(os.getcwd(), archive_file_name) + + +def archive_directory(input_dir, archive_file_path): + """ + Archive the `input_dir` directory, save the archive file to `archive_file_path`, + the generated archive file is 'tar.gz' format. + Note: all symlink files in the input directory are kept as it is in the archive file. + """ + + archive_file_path = os.path.abspath(archive_file_path) + # Note: `shutil.make_archive` doesn't work because it replaces symlink files with + # the file symlink pointing to, which is not the expected behavior in our usage. + # We need to pack the python and virtualenv environment, which contains a bunch of + # symlink files. + subprocess.check_call( + ["tar", "-czf", archive_file_path, *os.listdir(input_dir)], + cwd=input_dir, + ) + return archive_file_path + + +def extract_archive_to_dir(archive_path, dest_dir): + os.makedirs(dest_dir, exist_ok=True) + with tarfile.open(archive_path, "r") as tar: + tar.extractall(path=dest_dir) + return dest_dir diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyfunc/loaders/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyfunc/loaders/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..86ab62c45f8135a3c9a5ebd4be62e1fb6f211515 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyfunc/loaders/__init__.py @@ -0,0 +1,7 @@ +import mlflow.pyfunc.loaders.chat_agent +import mlflow.pyfunc.loaders.chat_model +import mlflow.pyfunc.loaders.code_model +from mlflow.utils.pydantic_utils import IS_PYDANTIC_V2_OR_NEWER + +if IS_PYDANTIC_V2_OR_NEWER: + import mlflow.pyfunc.loaders.responses_agent # noqa: F401 diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyfunc/loaders/__pycache__/__init__.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyfunc/loaders/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..c6b634829e85c5325b7e94f7cd0f13c5fb9be869 Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyfunc/loaders/__pycache__/__init__.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyfunc/loaders/__pycache__/chat_agent.cpython-312.pyc 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Generator + +import pydantic + +from mlflow.exceptions import MlflowException +from mlflow.models.utils import _convert_llm_ndarray_to_list +from mlflow.protos.databricks_pb2 import INTERNAL_ERROR +from mlflow.pyfunc.model import ( + _load_context_model_and_signature, +) +from mlflow.types.agent import ( + ChatAgentChunk, + ChatAgentMessage, + ChatAgentResponse, + ChatContext, +) +from mlflow.types.type_hints import model_validate + + +def _load_pyfunc(model_path: str, model_config: dict[str, Any] | None = None): + _, chat_agent, _ = _load_context_model_and_signature(model_path, model_config) + return _ChatAgentPyfuncWrapper(chat_agent) + + +class _ChatAgentPyfuncWrapper: + """ + Wrapper class that converts dict inputs to pydantic objects accepted by :class:`~ChatAgent`. + """ + + def __init__(self, chat_agent): + """ + Args: + chat_agent: An instance of a subclass of :class:`~ChatAgent`. + """ + self.chat_agent = chat_agent + + def get_raw_model(self): + """ + Returns the underlying model. + """ + return self.chat_agent + + def _convert_input( + self, model_input + ) -> tuple[list[ChatAgentMessage], ChatContext | None, dict[str, Any] | None]: + import pandas + + if isinstance(model_input, dict): + dict_input = model_input + elif isinstance(model_input, pandas.DataFrame): + dict_input = { + k: _convert_llm_ndarray_to_list(v[0]) + for k, v in model_input.to_dict(orient="list").items() + } + else: + raise MlflowException( + "Unsupported model input type. Expected a dict or pandas.DataFrame, but got " + f"{type(model_input)} instead.", + error_code=INTERNAL_ERROR, + ) + + messages = [ChatAgentMessage(**message) for message in dict_input.get("messages", [])] + context = ChatContext(**dict_input["context"]) if "context" in dict_input else None + custom_inputs = dict_input.get("custom_inputs", None) + + return messages, context, custom_inputs + + def _response_to_dict(self, response, pydantic_class) -> dict[str, Any]: + if isinstance(response, pydantic_class): + return response.model_dump_compat(exclude_none=True) + try: + model_validate(pydantic_class, response) + except pydantic.ValidationError as e: + raise MlflowException( + message=( + f"Model returned an invalid response. Expected a {pydantic_class.__name__} " + f"object or dictionary with the same schema. Pydantic validation error: {e}" + ), + error_code=INTERNAL_ERROR, + ) from e + return response + + def predict(self, model_input: dict[str, Any], params=None) -> dict[str, Any]: + """ + Args: + model_input: A dict with the + :py:class:`ChatAgentRequest ` schema. + params: Unused in this function, but required in the signature because + `load_model_and_predict` in `utils/_capture_modules.py` expects a params field + + Returns: + A dict with the (:py:class:`ChatAgentResponse `) + schema. + """ + messages, context, custom_inputs = self._convert_input(model_input) + response = self.chat_agent.predict(messages, context, custom_inputs) + return self._response_to_dict(response, ChatAgentResponse) + + def predict_stream( + self, model_input: dict[str, Any], params=None + ) -> Generator[dict[str, Any], None, None]: + """ + Args: + model_input: A dict with the + :py:class:`ChatAgentRequest ` schema. + params: Unused in this function, but required in the signature because + `load_model_and_predict` in `utils/_capture_modules.py` expects a params field + + Returns: + A generator over dicts with the + (:py:class:`ChatAgentChunk `) schema. + """ + messages, context, custom_inputs = self._convert_input(model_input) + for response in self.chat_agent.predict_stream(messages, context, custom_inputs): + yield self._response_to_dict(response, ChatAgentChunk) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyfunc/loaders/chat_model.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyfunc/loaders/chat_model.py new file mode 100644 index 0000000000000000000000000000000000000000..b8b49d9e93e6892f88dbaeacad1bcdfacadf575d --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyfunc/loaders/chat_model.py @@ -0,0 +1,125 @@ +import inspect +import logging +from typing import Any, Generator + +from mlflow.exceptions import MlflowException +from mlflow.models.utils import _convert_llm_ndarray_to_list +from mlflow.protos.databricks_pb2 import INTERNAL_ERROR +from mlflow.pyfunc.model import ( + _load_context_model_and_signature, +) +from mlflow.types.llm import ChatCompletionChunk, ChatCompletionResponse, ChatMessage, ChatParams + +_logger = logging.getLogger(__name__) + + +def _load_pyfunc(model_path: str, model_config: dict[str, Any] | None = None): + context, chat_model, signature = _load_context_model_and_signature(model_path, model_config) + return _ChatModelPyfuncWrapper(chat_model=chat_model, context=context, signature=signature) + + +class _ChatModelPyfuncWrapper: + """ + Wrapper class that converts dict inputs to pydantic objects accepted by :class:`~ChatModel`. + """ + + def __init__(self, chat_model, context, signature): + """ + Args: + chat_model: An instance of a subclass of :class:`~ChatModel`. + context: A :class:`~PythonModelContext` instance containing artifacts that + ``chat_model`` may use when performing inference. + signature: :class:`~ModelSignature` instance describing model input and output. + """ + self.chat_model = chat_model + self.context = context + self.signature = signature + + def get_raw_model(self): + """ + Returns the underlying model. + """ + return self.chat_model + + def _convert_input(self, model_input): + import pandas + + if isinstance(model_input, dict): + dict_input = model_input + elif isinstance(model_input, pandas.DataFrame): + dict_input = { + k: _convert_llm_ndarray_to_list(v[0]) + for k, v in model_input.to_dict(orient="list").items() + } + else: + raise MlflowException( + "Unsupported model input type. Expected a dict or pandas.DataFrame, " + f"but got {type(model_input)} instead.", + error_code=INTERNAL_ERROR, + ) + + messages = [ChatMessage.from_dict(message) for message in dict_input.pop("messages", [])] + params = ChatParams.from_dict(dict_input) + return messages, params + + def predict( + self, model_input: dict[str, Any], params: dict[str, Any] | None = None + ) -> dict[str, Any]: + """ + Args: + model_input: Model input data in the form of a chat request. + params: Additional parameters to pass to the model for inference. + Unused in this implementation, as the params are handled + via ``self._convert_input()``. + + Returns: + Model predictions in :py:class:`~ChatCompletionResponse` format. + """ + messages, params = self._convert_input(model_input) + parameters = inspect.signature(self.chat_model.predict).parameters + if "context" in parameters or len(parameters) == 3: + response = self.chat_model.predict(self.context, messages, params) + else: + response = self.chat_model.predict(messages, params) + return self._response_to_dict(response) + + def _response_to_dict(self, response: ChatCompletionResponse) -> dict[str, Any]: + if not isinstance(response, ChatCompletionResponse): + raise MlflowException( + "Model returned an invalid response. Expected a ChatCompletionResponse, but " + f"got {type(response)} instead.", + error_code=INTERNAL_ERROR, + ) + return response.to_dict() + + def _streaming_response_to_dict(self, response: ChatCompletionChunk) -> dict[str, Any]: + if not isinstance(response, ChatCompletionChunk): + raise MlflowException( + "Model returned an invalid response. Expected a ChatCompletionChunk, but " + f"got {type(response)} instead.", + error_code=INTERNAL_ERROR, + ) + return response.to_dict() + + def predict_stream( + self, model_input: dict[str, Any], params: dict[str, Any] | None = None + ) -> Generator[dict[str, Any], None, None]: + """ + Args: + model_input: Model input data in the form of a chat request. + params: Additional parameters to pass to the model for inference. + Unused in this implementation, as the params are handled + via ``self._convert_input()``. + + Returns: + Generator over model predictions in :py:class:`~ChatCompletionChunk` format. + """ + messages, params = self._convert_input(model_input) + parameters = inspect.signature(self.chat_model.predict_stream).parameters + if "context" in parameters or len(parameters) == 3: + stream = self.chat_model.predict_stream(self.context, messages, params) + else: + stream = self.chat_model.predict_stream(messages, params) + + for response in stream: + yield self._streaming_response_to_dict(response) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyfunc/loaders/code_model.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyfunc/loaders/code_model.py new file mode 100644 index 0000000000000000000000000000000000000000..758c94755ca8dde12bd60be182d5b359f747cbce --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyfunc/loaders/code_model.py @@ -0,0 +1,31 @@ +from typing import Any + +from mlflow.pyfunc.loaders.chat_agent import _ChatAgentPyfuncWrapper +from mlflow.pyfunc.loaders.chat_model import _ChatModelPyfuncWrapper +from mlflow.pyfunc.model import ( + ChatAgent, + ChatModel, + _load_context_model_and_signature, + _PythonModelPyfuncWrapper, +) + +try: + from mlflow.pyfunc.model import ResponsesAgent + + IS_RESPONSES_AGENT_AVAILABLE = True +except ImportError: + IS_RESPONSES_AGENT_AVAILABLE = False + + +def _load_pyfunc(local_path: str, model_config: dict[str, Any] | None = None): + context, model, signature = _load_context_model_and_signature(local_path, model_config) + if isinstance(model, ChatModel): + return _ChatModelPyfuncWrapper(model, context, signature) + elif isinstance(model, ChatAgent): + return _ChatAgentPyfuncWrapper(model) + elif IS_RESPONSES_AGENT_AVAILABLE and isinstance(model, ResponsesAgent): + from mlflow.pyfunc.loaders.responses_agent import _ResponsesAgentPyfuncWrapper + + return _ResponsesAgentPyfuncWrapper(model, context) + else: + return _PythonModelPyfuncWrapper(model, context, signature) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyfunc/loaders/responses_agent.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyfunc/loaders/responses_agent.py new file mode 100644 index 0000000000000000000000000000000000000000..42df7689629a0076435b24e7a588743d0bcbd970 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyfunc/loaders/responses_agent.py @@ -0,0 +1,113 @@ +from typing import Any, Generator + +import pydantic + +from mlflow.exceptions import MlflowException +from mlflow.models.utils import _convert_llm_ndarray_to_list +from mlflow.protos.databricks_pb2 import INTERNAL_ERROR +from mlflow.pyfunc.model import _load_context_model_and_signature +from mlflow.types.type_hints import model_validate +from mlflow.utils.annotations import experimental +from mlflow.utils.pydantic_utils import IS_PYDANTIC_V2_OR_NEWER + +if not IS_PYDANTIC_V2_OR_NEWER: + raise ImportError( + "ResponsesAgent and its pydantic classes are not supported in pydantic v1. " + "Please upgrade to pydantic v2 or newer to use ResponsesAgent.", + ) + +from mlflow.types.responses import ( + ResponsesAgentRequest, + ResponsesAgentResponse, + ResponsesAgentStreamEvent, +) + + +def _load_pyfunc(model_path: str, model_config: dict[str, Any] | None = None): + context, responses_agent, _ = _load_context_model_and_signature(model_path, model_config) + return _ResponsesAgentPyfuncWrapper(responses_agent, context) + + +@experimental(version="3.0.0") +class _ResponsesAgentPyfuncWrapper: + """ + Wrapper class that converts dict inputs to pydantic objects accepted by + :class:`~ResponsesAgent`. + """ + + def __init__(self, responses_agent, context): + self.responses_agent = responses_agent + self.context = context + + def get_raw_model(self): + """ + Returns the underlying model. + """ + return self.responses_agent + + def _convert_input(self, model_input) -> ResponsesAgentRequest: + import pandas + + if isinstance(model_input, pandas.DataFrame): + model_input = { + k: _convert_llm_ndarray_to_list(v[0]) + for k, v in model_input.to_dict(orient="list").items() + } + elif not isinstance(model_input, dict): + raise MlflowException( + "Unsupported model input type. Expected a dict or pandas.DataFrame, but got " + f"{type(model_input)} instead.", + error_code=INTERNAL_ERROR, + ) + return ResponsesAgentRequest(**model_input) + + def _response_to_dict(self, response, pydantic_class) -> dict[str, Any]: + if isinstance(response, pydantic_class): + return response.model_dump_compat(exclude_none=True) + try: + model_validate(pydantic_class, response) + except pydantic.ValidationError as e: + raise MlflowException( + message=( + f"Model returned an invalid response. Expected a {pydantic_class.__name__} " + f"object or dictionary with the same schema. Pydantic validation error: {e}" + ), + error_code=INTERNAL_ERROR, + ) from e + return response + + def predict(self, model_input: dict[str, Any], params=None) -> dict[str, Any]: + """ + Args: + model_input: A dict with the + :py:class:`ResponsesRequest ` schema. + params: Unused in this function, but required in the signature because + `load_model_and_predict` in `utils/_capture_modules.py` expects a params field + + Returns: + A dict with the + (:py:class:`ResponsesResponse `) + schema. + """ + request = self._convert_input(model_input) + response = self.responses_agent.predict(request) + return self._response_to_dict(response, ResponsesAgentResponse) + + def predict_stream( + self, model_input: dict[str, Any], params=None + ) -> Generator[dict[str, Any], None, None]: + """ + Args: + model_input: A dict with the + :py:class:`ResponsesRequest ` schema. + params: Unused in this function, but required in the signature because + `load_model_and_predict` in `utils/_capture_modules.py` expects a params field + + Returns: + A generator over dicts with the + (:py:class:`ResponsesStreamEvent `) + schema. + """ + request = self._convert_input(model_input) + for response in self.responses_agent.predict_stream(request): + yield self._response_to_dict(response, ResponsesAgentStreamEvent) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyfunc/mlserver.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyfunc/mlserver.py new file mode 100644 index 0000000000000000000000000000000000000000..871c3b8593a8b3356c2770f81ce91f004097a73e --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyfunc/mlserver.py @@ -0,0 +1,45 @@ +import logging +import os + +_logger = logging.getLogger(__name__) + +MLServerMLflowRuntime = "mlserver_mlflow.MLflowRuntime" +MLServerDefaultModelName = "mlflow-model" + + +def get_cmd( + model_uri: str, + port: int | None = None, + host: str | None = None, + timeout: int | None = None, + nworkers: int | None = None, + model_name: str | None = None, + model_version: str | None = None, +) -> tuple[str, dict[str, str]]: + cmd = f"mlserver start {model_uri}" + + cmd_env = os.environ.copy() + + if port: + cmd_env["MLSERVER_HTTP_PORT"] = str(port) + + if host: + cmd_env["MLSERVER_HOST"] = host + + if timeout: + _logger.warning("Timeout is not yet supported in MLServer.") + + if nworkers: + cmd_env["MLSERVER_PARALLEL_WORKERS"] = str(nworkers) + + # give precedence to user env var input + cmd_env["MLSERVER_MODEL_NAME"] = ( + cmd_env.get("MLSERVER_MODEL_NAME") or model_name or MLServerDefaultModelName + ) + if model_version and not cmd_env.get("MLSERVER_MODEL_VERSION"): + cmd_env["MLSERVER_MODEL_VERSION"] = model_version + + cmd_env["MLSERVER_MODEL_IMPLEMENTATION"] = MLServerMLflowRuntime + cmd_env["MLSERVER_MODEL_URI"] = model_uri + + return cmd, cmd_env diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyfunc/model.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyfunc/model.py new file mode 100644 index 0000000000000000000000000000000000000000..2fb162892a63465c52d21e2781bad5dced8f4e88 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyfunc/model.py @@ -0,0 +1,1486 @@ +""" +The ``mlflow.pyfunc.model`` module defines logic for saving and loading custom "python_function" +models with a user-defined ``PythonModel`` subclass. +""" + +import bz2 +import gzip +import inspect +import logging +import lzma +import os +import shutil +from abc import ABCMeta, abstractmethod +from pathlib import Path +from typing import Any, Generator + +import cloudpickle +import pandas as pd +import yaml + +import mlflow.pyfunc +import mlflow.utils +from mlflow.entities.span import SpanType +from mlflow.environment_variables import MLFLOW_LOG_MODEL_COMPRESSION +from mlflow.exceptions import MlflowException +from mlflow.models import Model +from mlflow.models.model import MLMODEL_FILE_NAME, MODEL_CODE_PATH +from mlflow.models.rag_signatures import ChatCompletionRequest, SplitChatMessagesRequest +from mlflow.models.signature import ( + _extract_type_hints, + _is_context_in_predict_function_signature, + _TypeHints, +) +from mlflow.models.utils import _load_model_code_path +from mlflow.protos.databricks_pb2 import INVALID_PARAMETER_VALUE +from mlflow.pyfunc.utils import pyfunc +from mlflow.pyfunc.utils.data_validation import ( + _check_func_signature, + _get_func_info_if_type_hint_supported, + _wrap_predict_with_pyfunc, + wrap_non_list_predict_pydantic, +) +from mlflow.pyfunc.utils.input_converter import _hydrate_dataclass +from mlflow.tracking.artifact_utils import _download_artifact_from_uri +from mlflow.types.agent import ( + ChatAgentChunk, + ChatAgentMessage, + ChatAgentRequest, + ChatAgentResponse, + ChatContext, +) +from mlflow.types.llm import ( + ChatCompletionChunk, + ChatCompletionResponse, + ChatMessage, + ChatParams, +) +from mlflow.types.utils import _is_list_dict_str, _is_list_str +from mlflow.utils.annotations import deprecated, experimental +from mlflow.utils.databricks_utils import ( + _get_databricks_serverless_env_vars, + is_in_databricks_serverless_runtime, +) +from mlflow.utils.environment import ( + _CONDA_ENV_FILE_NAME, + _CONSTRAINTS_FILE_NAME, + _PYTHON_ENV_FILE_NAME, + _REQUIREMENTS_FILE_NAME, + _mlflow_conda_env, + _process_conda_env, + _process_pip_requirements, + _PythonEnv, +) +from mlflow.utils.file_utils import TempDir, get_total_file_size, write_to +from mlflow.utils.model_utils import _get_flavor_configuration, _validate_infer_and_copy_code_paths +from mlflow.utils.pydantic_utils import IS_PYDANTIC_V2_OR_NEWER +from mlflow.utils.requirements_utils import _get_pinned_requirement + +CONFIG_KEY_ARTIFACTS = "artifacts" +CONFIG_KEY_ARTIFACT_RELATIVE_PATH = "path" +CONFIG_KEY_ARTIFACT_URI = "uri" +CONFIG_KEY_PYTHON_MODEL = "python_model" +CONFIG_KEY_CLOUDPICKLE_VERSION = "cloudpickle_version" +CONFIG_KEY_COMPRESSION = "python_model_compression" +_SAVED_PYTHON_MODEL_SUBPATH = "python_model.pkl" +_DEFAULT_CHAT_MODEL_METADATA_TASK = "agent/v1/chat" +_DEFAULT_CHAT_AGENT_METADATA_TASK = "agent/v2/chat" +_COMPRESSION_INFO = { + "lzma": {"ext": ".xz", "open": lzma.open}, + "bzip2": {"ext": ".bz2", "open": bz2.open}, + "gzip": {"ext": ".gz", "open": gzip.open}, +} +_DEFAULT_RESPONSES_AGENT_METADATA_TASK = "agent/v1/responses" + +_logger = logging.getLogger(__name__) + + +def get_default_pip_requirements(): + """ + Returns: + A list of default pip requirements for MLflow Models produced by this flavor. Calls to + :func:`save_model()` and :func:`log_model()` produce a pip environment that, at minimum, + contains these requirements. + """ + return [_get_pinned_requirement("cloudpickle")] + + +def get_default_conda_env(): + """ + Returns: + The default Conda environment for MLflow Models produced by calls to + :func:`save_model() ` + and :func:`log_model() ` when a user-defined subclass of + :class:`PythonModel` is provided. + """ + return _mlflow_conda_env(additional_pip_deps=get_default_pip_requirements()) + + +def _log_warning_if_params_not_in_predict_signature(logger, params): + if params: + logger.warning( + "The underlying model does not support passing additional parameters to the predict" + f" function. `params` {params} will be ignored." + ) + + +class PythonModel: + """ + Represents a generic Python model that evaluates inputs and produces API-compatible outputs. + By subclassing :class:`~PythonModel`, users can create customized MLflow models with the + "python_function" ("pyfunc") flavor, leveraging custom inference logic and artifact + dependencies. + """ + + __metaclass__ = ABCMeta + + def load_context(self, context): + """ + Loads artifacts from the specified :class:`~PythonModelContext` that can be used by + :func:`~PythonModel.predict` when evaluating inputs. When loading an MLflow model with + :func:`~load_model`, this method is called as soon as the :class:`~PythonModel` is + constructed. + + The same :class:`~PythonModelContext` will also be available during calls to + :func:`~PythonModel.predict`, but it may be more efficient to override this method + and load artifacts from the context at model load time. + + Args: + context: A :class:`~PythonModelContext` instance containing artifacts that the model + can use to perform inference. + """ + + @deprecated("predict_type_hints", "2.20.0") + def _get_type_hints(self): + return self.predict_type_hints + + @property + def predict_type_hints(self) -> _TypeHints: + """ + Internal method to get type hints from the predict function signature. + """ + if hasattr(self, "_predict_type_hints"): + return self._predict_type_hints + if _is_context_in_predict_function_signature(func=self.predict): + self._predict_type_hints = _extract_type_hints(self.predict, input_arg_index=1) + else: + self._predict_type_hints = _extract_type_hints(self.predict, input_arg_index=0) + return self._predict_type_hints + + def __init_subclass__(cls, **kwargs) -> None: + super().__init_subclass__(**kwargs) + + # automatically wrap the predict method with pyfunc to ensure data validation + # NB: skip wrapping for built-in classes defined in MLflow e.g. ChatModel + if not cls.__module__.startswith("mlflow."): + # TODO: ChatModel uses dataclass type hints which are not supported now, hence + # we need to skip type hint based validation for user-defined subclasses + # of ChatModel. Once we either (1) support dataclass type hints or (2) migrate + # ChatModel to pydantic, we can remove this exclusion. + # NB: issubclass(cls, ChatModel) does not work so we use a hacky attribute check + if getattr(cls, "_skip_type_hint_validation", False): + return + + predict_attr = cls.__dict__.get("predict") + if predict_attr is not None and callable(predict_attr): + func_info = _get_func_info_if_type_hint_supported(predict_attr) + setattr(cls, "predict", _wrap_predict_with_pyfunc(predict_attr, func_info)) + predict_stream_attr = cls.__dict__.get("predict_stream") + if predict_stream_attr is not None and callable(predict_stream_attr): + _check_func_signature(predict_stream_attr, "predict_stream") + else: + cls.predict._is_pyfunc = True + + @abstractmethod + def predict(self, context, model_input, params: dict[str, Any] | None = None): + """ + Evaluates a pyfunc-compatible input and produces a pyfunc-compatible output. + For more information about the pyfunc input/output API, see the :ref:`pyfunc-inference-api`. + + Args: + context: A :class:`~PythonModelContext` instance containing artifacts that the model + can use to perform inference. + model_input: A pyfunc-compatible input for the model to evaluate. + params: Additional parameters to pass to the model for inference. + + .. tip:: + Since MLflow 2.20.0, `context` parameter can be removed from `predict` function + signature if it's not used. `def predict(self, model_input, params=None)` is valid. + """ + + def predict_stream(self, context, model_input, params: dict[str, Any] | None = None): + """ + Evaluates a pyfunc-compatible input and produces an iterator of output. + For more information about the pyfunc input API, see the :ref:`pyfunc-inference-api`. + + Args: + context: A :class:`~PythonModelContext` instance containing artifacts that the model + can use to perform inference. + model_input: A pyfunc-compatible input for the model to evaluate. + params: Additional parameters to pass to the model for inference. + + .. tip:: + Since MLflow 2.20.0, `context` parameter can be removed from `predict_stream` function + signature if it's not used. + `def predict_stream(self, model_input, params=None)` is valid. + """ + raise NotImplementedError() + + +class _FunctionPythonModel(PythonModel): + """ + When a user specifies a ``python_model`` argument that is a function, we wrap the function + in an instance of this class. + """ + + def __init__(self, func, signature=None): + self.signature = signature + # only wrap `func` if @pyfunc is not already applied + if not getattr(func, "_is_pyfunc", False): + self.func = pyfunc(func) + else: + self.func = func + + @property + def predict_type_hints(self): + if hasattr(self, "_predict_type_hints"): + return self._predict_type_hints + self._predict_type_hints = _extract_type_hints(self.func, input_arg_index=0) + return self._predict_type_hints + + def predict( + self, + model_input, + params: dict[str, Any] | None = None, + ): + """ + Args: + model_input: A pyfunc-compatible input for the model to evaluate. + params: Additional parameters to pass to the model for inference. + + Returns: + Model predictions. + """ + # callable only supports one input argument for now + return self.func(model_input) + + +class PythonModelContext: + """ + A collection of artifacts that a :class:`~PythonModel` can use when performing inference. + :class:`~PythonModelContext` objects are created *implicitly* by the + :func:`save_model() ` and + :func:`log_model() ` persistence methods, using the contents specified + by the ``artifacts`` parameter of these methods. + """ + + def __init__(self, artifacts, model_config): + """ + Args: + artifacts: A dictionary of ```` entries, where ``artifact_path`` + is an absolute filesystem path to a given artifact. + model_config: The model configuration to make available to the model at + loading time. + """ + self._artifacts = artifacts + self._model_config = model_config + + @property + def artifacts(self): + """ + A dictionary containing ```` entries, where ``artifact_path`` is an + absolute filesystem path to the artifact. + """ + return self._artifacts + + @property + def model_config(self): + """ + A dictionary containing ```` entries, where ``config`` is the name + of the model configuration keys and ``value`` is the value of the given configuration. + """ + + return self._model_config + + +@deprecated("ResponsesAgent", "3.0.0") +class ChatModel(PythonModel, metaclass=ABCMeta): + """ + .. tip:: + Since MLflow 3.0.0, we recommend using + :py:class:`ResponsesAgent ` + instead of :py:class:`ChatModel ` unless you need strict + compatibility with the OpenAI ChatCompletion API. + + A subclass of :class:`~PythonModel` that makes it more convenient to implement models + that are compatible with popular LLM chat APIs. By subclassing :class:`~ChatModel`, + users can create MLflow models with a ``predict()`` method that is more convenient + for chat tasks than the generic :class:`~PythonModel` API. ChatModels automatically + define input/output signatures and an input example, so manually specifying these values + when calling :func:`mlflow.pyfunc.save_model() ` is not necessary. + + See the documentation of the ``predict()`` method below for details on that parameters and + outputs that are expected by the ``ChatModel`` API. + + .. list-table:: + :header-rows: 1 + :widths: 20 40 40 + + * - + - ChatModel + - PythonModel + * - When to use + - Use when you want to develop and deploy a conversational model with **standard** chat + schema compatible with OpenAI spec. + - Use when you want **full control** over the model's interface or customize every aspect + of your model's behavior. + * - Interface + - **Fixed** to OpenAI's chat schema. + - **Full control** over the model's input and output schema. + * - Setup + - **Quick**. Works out of the box for conversational applications, with pre-defined + model signature and input example. + - **Custom**. You need to define model signature or input example yourself. + * - Complexity + - **Low**. Standardized interface simplified model deployment and integration. + - **High**. Deploying and integrating the custom PythonModel may not be straightforward. + E.g., The model needs to handle Pandas DataFrames as MLflow converts input data to + DataFrames before passing it to PythonModel. + + """ + + _skip_type_hint_validation = True + + @abstractmethod + def predict( + self, context, messages: list[ChatMessage], params: ChatParams + ) -> ChatCompletionResponse: + """ + Evaluates a chat input and produces a chat output. + + Args: + context: A :class:`~PythonModelContext` instance containing artifacts that the model + can use to perform inference. + messages (List[:py:class:`ChatMessage `]): + A list of :py:class:`ChatMessage ` + objects representing chat history. + params (:py:class:`ChatParams `): + A :py:class:`ChatParams ` object + containing various parameters used to modify model behavior during + inference. + + .. tip:: + Since MLflow 2.20.0, `context` parameter can be removed from `predict` function + signature if it's not used. + `def predict(self, messages: list[ChatMessage], params: ChatParams)` is valid. + + Returns: + A :py:class:`ChatCompletionResponse ` + object containing the model's response(s), as well as other metadata. + """ + + def predict_stream( + self, context, messages: list[ChatMessage], params: ChatParams + ) -> Generator[ChatCompletionChunk, None, None]: + """ + Evaluates a chat input and produces a chat output. + Override this function to implement a real stream prediction. + + Args: + context: A :class:`~PythonModelContext` instance containing artifacts that the model + can use to perform inference. + messages (List[:py:class:`ChatMessage `]): + A list of :py:class:`ChatMessage ` + objects representing chat history. + params (:py:class:`ChatParams `): + A :py:class:`ChatParams ` object + containing various parameters used to modify model behavior during + inference. + + .. tip:: + Since MLflow 2.20.0, `context` parameter can be removed from `predict_stream` function + signature if it's not used. + `def predict_stream(self, messages: list[ChatMessage], params: ChatParams)` is valid. + + Returns: + A generator over :py:class:`ChatCompletionChunk ` + object containing the model's response(s), as well as other metadata. + """ + raise NotImplementedError( + "Streaming implementation not provided. Please override the " + "`predict_stream` method on your model to generate streaming " + "predictions" + ) + + +class ChatAgent(PythonModel, metaclass=ABCMeta): + """ + .. tip:: + Since MLflow 3.0.0, we recommend using + :py:class:`ResponsesAgent ` + instead of :py:class:`ChatAgent `. + + **What is the ChatAgent Interface?** + + The ChatAgent interface is a chat schema specification that has been designed for authoring + conversational agents. ChatAgent allows your agent to do the following: + + - Return multiple messages + - Return intermediate steps for tool calling agents + - Confirm tool calls + - Support multi-agent scenarios + + ``ChatAgent`` should always be used when authoring an agent. We also recommend using + ``ChatAgent`` instead of :py:class:`ChatModel ` even for use cases + like simple chat models (e.g. prompt-engineered LLMs), to give you the flexibility to support + more agentic functionality in the future. + + The :py:class:`ChatAgentRequest ` schema is similar to, + but not strictly compatible with the OpenAI ChatCompletion schema. ChatAgent adds additional + functionality and diverges from OpenAI + :py:class:`ChatCompletionRequest ` in the following + ways: + + - Adds an optional ``attachments`` attribute to every input/output message for tools and + internal agent calls so they can return additional outputs such as visualizations and progress + indicators + - Adds a ``context`` attribute with a ``conversation_id`` and ``user_id`` attributes to enable + modifying the behavior of the agent depending on the user querying the agent + - Adds the ``custom_inputs`` attribute, an arbitrary ``dict[str, Any]`` to pass in any + additional information to modify the agent's behavior + + The :py:class:`ChatAgentResponse ` schema diverges from + :py:class:`ChatCompletionResponse ` schema in the + following ways: + + - Adds the ``custom_outputs`` key, an arbitrary ``dict[str, Any]`` to return any additional + information + - Allows multiple messages in the output, to improve the display and evaluation of internal + tool calls and inter-agent communication that led to the final answer. + + Here's an example of a :py:class:`ChatAgentResponse ` + detailing a tool call: + + .. code-block:: python + + { + "messages": [ + { + "role": "assistant", + "content": "", + "id": "run-04b46401-c569-4a4a-933e-62e38d8f9647-0", + "tool_calls": [ + { + "id": "call_15ca4fcc-ffa1-419a-8748-3bea34b9c043", + "type": "function", + "function": { + "name": "generate_random_ints", + "arguments": '{"min": 1, "max": 100, "size": 5}', + }, + } + ], + }, + { + "role": "tool", + "content": '{"content": "Generated array of 2 random ints in [1, 100]."', + "name": "generate_random_ints", + "id": "call_15ca4fcc-ffa1-419a-8748-3bea34b9c043", + "tool_call_id": "call_15ca4fcc-ffa1-419a-8748-3bea34b9c043", + }, + { + "role": "assistant", + "content": "The new set of generated random numbers are: 93, 51, 12, 7, and 25", + "name": "llm", + "id": "run-70c7c738-739f-4ecd-ad18-0ae232df24e8-0", + }, + ], + "custom_outputs": {"random_nums": [93, 51, 12, 7, 25]}, + } + + **Streaming Agent Output with ChatAgent** + + Please read the docstring of + :py:func:`ChatAgent.predict_stream ` + for more details on how to stream the output of your agent. + + + **Authoring a ChatAgent** + + Authoring an agent using the ChatAgent interface is a framework-agnostic way to create a model + with a standardized interface that is loggable with the MLflow pyfunc flavor, can be reused + across clients, and is ready for serving workloads. + + To write your own agent, subclass ``ChatAgent``, implementing the ``predict`` and optionally + ``predict_stream`` methods to define the non-streaming and streaming behavior of your agent. You + can use any agent authoring framework - the only hard requirement is to implement the + ``predict`` interface. + + .. code-block:: python + + def predict( + self, + messages: list[ChatAgentMessage], + context: Optional[ChatContext] = None, + custom_inputs: Optional[dict[str, Any]] = None, + ) -> ChatAgentResponse: ... + + In addition to calling predict and predict_stream methods with an input matching their type + hints, you can also pass a single input dict that matches the + :py:class:`ChatAgentRequest ` schema for ease of testing. + + .. code-block:: python + + chat_agent = MyChatAgent() + chat_agent.predict( + { + "messages": [{"role": "user", "content": "What is 10 + 10?"}], + "context": {"conversation_id": "123", "user_id": "456"}, + } + ) + + See an example implementation of ``predict`` and ``predict_stream`` for a LangGraph agent in + the :py:class:`ChatAgentState ` + docstring. + + **Logging the ChatAgent** + + Since the landscape of LLM frameworks is constantly evolving and not every flavor can be + natively supported by MLflow, we recommend the + `Models-from-Code `_ logging + approach. + + .. code-block:: python + + with mlflow.start_run(): + logged_agent_info = mlflow.pyfunc.log_model( + name="agent", + python_model=os.path.join(os.getcwd(), "agent"), + # Add serving endpoints, tools, and vector search indexes here + resources=[], + ) + + After logging the model, you can query the model with a single dictionary with the + :py:class:`ChatAgentRequest ` schema. Under the hood, it + will be converted into the python objects expected by your ``predict`` and ``predict_stream`` + methods. + + .. code-block:: python + + loaded_model = mlflow.pyfunc.load_model(tmp_path) + loaded_model.predict( + { + "messages": [{"role": "user", "content": "What is 10 + 10?"}], + "context": {"conversation_id": "123", "user_id": "456"}, + } + ) + + To make logging ChatAgent models as easy as possible, MLflow has built in the following + features: + + - Automatic Model Signature Inference + - You do not need to set a signature when logging a ChatAgent + - An input and output signature will be automatically set that adheres to the + :py:class:`ChatAgentRequest ` and + :py:class:`ChatAgentResponse ` schemas + - Metadata + - ``{"task": "agent/v2/chat"}`` will be automatically appended to any metadata that you may + pass in when logging the model + - Input Example + - Providing an input example is optional, ``mlflow.types.agent.CHAT_AGENT_INPUT_EXAMPLE`` + will be provided by default + - If you do provide an input example, ensure it's a dict with the + :py:class:`ChatAgentRequest ` schema + + - .. code-block:: python + + input_example = { + "messages": [{"role": "user", "content": "What is MLflow?"}], + "context": {"conversation_id": "123", "user_id": "456"}, + } + + **Migrating from ChatModel to ChatAgent** + + To convert an existing ChatModel that takes in + :py:class:`List[ChatMessage] ` and + :py:class:`ChatParams ` and outputs a + :py:class:`ChatCompletionResponse `, do the following: + + - Subclass ``ChatAgent`` instead of ``ChatModel`` + - Move any functionality from your ``ChatModel``'s ``load_context`` implementation into the + ``__init__`` method of your new ``ChatAgent``. + - Use ``.model_dump_compat()`` instead of ``.to_dict()`` when converting your model's inputs to + dictionaries. Ex. ``[msg.model_dump_compat() for msg in messages]`` instead of + ``[msg.to_dict() for msg in messages]`` + - Return a :py:class:`ChatAgentResponse ` instead of a + :py:class:`ChatCompletionResponse ` + + For example, we can convert the ChatModel from the + `Chat Model Intro `_ + to a ChatAgent: + + .. code-block:: python + + class SimpleOllamaModel(ChatModel): + def __init__(self): + self.model_name = "llama3.2:1b" + self.client = None + + def load_context(self, context): + self.client = ollama.Client() + + def predict( + self, context, messages: list[ChatMessage], params: ChatParams = None + ) -> ChatCompletionResponse: + ollama_messages = [msg.to_dict() for msg in messages] + response = self.client.chat(model=self.model_name, messages=ollama_messages) + return ChatCompletionResponse( + choices=[{"index": 0, "message": response["message"]}], + model=self.model_name, + ) + + .. code-block:: python + + class SimpleOllamaModel(ChatAgent): + def __init__(self): + self.model_name = "llama3.2:1b" + self.client = None + self.client = ollama.Client() + + def predict( + self, + messages: list[ChatAgentMessage], + context: Optional[ChatContext] = None, + custom_inputs: Optional[dict[str, Any]] = None, + ) -> ChatAgentResponse: + ollama_messages = self._convert_messages_to_dict(messages) + response = self.client.chat(model=self.model_name, messages=ollama_messages) + return ChatAgentResponse(**{"messages": [response["message"]]}) + + **ChatAgent Connectors** + + MLflow provides convenience APIs for wrapping agents written in popular authoring frameworks + with ChatAgent. See examples for: + + - LangGraph in the + :py:class:`ChatAgentState ` docstring + """ + + _skip_type_hint_validation = True + + def __init_subclass__(cls, **kwargs) -> None: + super().__init_subclass__(**kwargs) + for attr_name in ("predict", "predict_stream"): + attr = cls.__dict__.get(attr_name) + if callable(attr): + setattr( + cls, + attr_name, + wrap_non_list_predict_pydantic( + attr, + ChatAgentRequest, + "Invalid dictionary input for a ChatAgent. Expected a dictionary with the " + "ChatAgentRequest schema.", + unpack=True, + ), + ) + + def _convert_messages_to_dict(self, messages: list[ChatAgentMessage]): + return [m.model_dump_compat(exclude_none=True) for m in messages] + + # nb: We use `messages` instead of `model_input` so that the trace generated by default is + # compatible with mlflow evaluate. We also want `custom_inputs` to be a top level key for + # ease of use. + @abstractmethod + def predict( + self, + messages: list[ChatAgentMessage], + context: ChatContext | None = None, + custom_inputs: dict[str, Any] | None = None, + ) -> ChatAgentResponse: + """ + Given a ChatAgent input, returns a ChatAgent output. In addition to calling ``predict`` + with an input matching the type hints, you can also pass a single input dict that matches + the :py:class:`ChatAgentRequest ` schema for ease + of testing. + + .. code-block:: python + + chat_agent = ChatAgent() + chat_agent.predict( + { + "messages": [{"role": "user", "content": "What is 10 + 10?"}], + "context": {"conversation_id": "123", "user_id": "456"}, + } + ) + + Args: + messages (List[:py:class:`ChatAgentMessage `]): + A list of :py:class:`ChatAgentMessage ` + objects representing the chat history. + context (:py:class:`ChatContext `): + A :py:class:`ChatContext ` object + containing conversation_id and user_id. **Optional** Defaults to None. + custom_inputs (Dict[str, Any]): + An optional param to provide arbitrary additional inputs + to the model. The dictionary values must be JSON-serializable. **Optional** + Defaults to None. + + Returns: + A :py:class:`ChatAgentResponse ` object containing + the model's response, as well as other metadata. + """ + + # nb: We use `messages` instead of `model_input` so that the trace generated by default is + # compatible with mlflow evaluate. We also want `custom_inputs` to be a top level key for + # ease of use. + def predict_stream( + self, + messages: list[ChatAgentMessage], + context: ChatContext | None = None, + custom_inputs: dict[str, Any] | None = None, + ) -> Generator[ChatAgentChunk, None, None]: + """ + Given a ChatAgent input, returns a generator containing streaming ChatAgent output chunks. + In addition to calling ``predict_stream`` with an input matching the type hints, you can + also pass a single input dict that matches the + :py:class:`ChatAgentRequest ` + schema for ease of testing. + + .. code-block:: python + + chat_agent = ChatAgent() + for event in chat_agent.predict_stream( + { + "messages": [{"role": "user", "content": "What is 10 + 10?"}], + "context": {"conversation_id": "123", "user_id": "456"}, + } + ): + print(event) + + To support streaming the output of your agent, override this method in your subclass of + ``ChatAgent``. When implementing ``predict_stream``, keep in mind the following + requirements: + + - Ensure your implementation adheres to the ``predict_stream`` type signature. For example, + streamed messages must be of the type + :py:class:`ChatAgentChunk `, where each chunk contains + partial output from a single response message. + - At most one chunk in a particular response can contain the ``custom_outputs`` key. + - Chunks containing partial content of a single response message must have the same ``id``. + The content field of the message and usage stats of the + :py:class:`ChatAgentChunk ` should be aggregated by + the consuming client. See the example below. + + .. code-block:: python + + {"delta": {"role": "assistant", "content": "Born", "id": "123"}} + {"delta": {"role": "assistant", "content": " in", "id": "123"}} + {"delta": {"role": "assistant", "content": " data", "id": "123"}} + + + Args: + messages (List[:py:class:`ChatAgentMessage `]): + A list of :py:class:`ChatAgentMessage ` + objects representing the chat history. + context (:py:class:`ChatContext `): + A :py:class:`ChatContext ` object + containing conversation_id and user_id. **Optional** Defaults to None. + custom_inputs (Dict[str, Any]): + An optional param to provide arbitrary additional inputs + to the model. The dictionary values must be JSON-serializable. **Optional** + Defaults to None. + + Returns: + A generator over :py:class:`ChatAgentChunk ` + objects containing the model's response(s), as well as other metadata. + """ + raise NotImplementedError( + "Streaming implementation not provided. Please override the " + "`predict_stream` method on your model to generate streaming predictions" + ) + + +def _check_compression_supported(compression): + if compression in _COMPRESSION_INFO: + return True + if compression: + supported = ", ".join(sorted(_COMPRESSION_INFO)) + mlflow.pyfunc._logger.warning( + f"Unrecognized compression method '{compression}'" + f"Please select one of: {supported}. Falling back to uncompressed storage/loading." + ) + return False + + +def _maybe_compress_cloudpickle_dump(python_model, path, compression): + file_open = _COMPRESSION_INFO.get(compression, {}).get("open", open) + with file_open(path, "wb") as out: + cloudpickle.dump(python_model, out) + + +def _maybe_decompress_cloudpickle_load(path, compression): + _check_compression_supported(compression) + file_open = _COMPRESSION_INFO.get(compression, {}).get("open", open) + with file_open(path, "rb") as f: + return cloudpickle.load(f) + + +if IS_PYDANTIC_V2_OR_NEWER: + from mlflow.types.responses import ( + ResponsesAgentRequest, + ResponsesAgentResponse, + ResponsesAgentStreamEvent, + ) + + @experimental(version="3.0.0") + class ResponsesAgent(PythonModel, metaclass=ABCMeta): + """ + A base class for creating ResponsesAgent models. It can be used as a wrapper around any + agent framework to create an agent model that can be deployed to MLflow. Has a few helper + methods to help create output items that can be a part of a ResponsesAgentResponse or + ResponsesAgentStreamEvent. + + See https://www.mlflow.org/docs/latest/llms/responses-agent-intro/ for more details. + """ + + _skip_type_hint_validation = True + + @staticmethod + def responses_agent_output_reducer( + chunks: list[ResponsesAgentStreamEvent | dict[str, Any]], + ): + output_items = [] + for chunk in chunks: + # Handle both dict and pydantic object formats + if isinstance(chunk, dict): + chunk_type = chunk.get("type") + if chunk_type == "response.output_item.done": + output_items.append(chunk.get("item")) + else: + # Pydantic object (ResponsesAgentStreamEvent) + if hasattr(chunk, "type") and chunk.type == "response.output_item.done": + output_items.append(chunk.item) + + return ResponsesAgentResponse(output=output_items).model_dump(exclude_none=True) + + def __init_subclass__(cls, **kwargs) -> None: + super().__init_subclass__(**kwargs) + for attr_name in ("predict", "predict_stream"): + attr = cls.__dict__.get(attr_name) + if callable(attr): + # Only apply trace decorator if it is not already traced with mlflow.trace + if getattr(attr, "__mlflow_traced__", False): + mlflow.pyfunc._logger.warning( + f"You have manually traced {attr_name} with @mlflow.trace, but this is " + "unnecessary with ResponsesAgent subclasses. You can remove the " + "@mlflow.trace decorator and it will be automatically traced." + ) + traced_attr = attr + else: + # Apply trace decorator first + if attr_name == "predict_stream": + traced_attr = mlflow.trace( + span_type=SpanType.AGENT, + output_reducer=cls.responses_agent_output_reducer, + )(attr) + else: + traced_attr = mlflow.trace(span_type=SpanType.AGENT)(attr) + + # Then wrap with pydantic wrapper + wrapped_attr = wrap_non_list_predict_pydantic( + traced_attr, + ResponsesAgentRequest, + "Invalid dictionary input for a ResponsesAgent. " + "Expected a dictionary with the ResponsesRequest schema.", + ) + setattr(cls, attr_name, wrapped_attr) + + @abstractmethod + def predict(self, request: ResponsesAgentRequest) -> ResponsesAgentResponse: + """ + Given a ResponsesAgentRequest, returns a ResponsesAgentResponse. + + You can see example implementations at + https://www.mlflow.org/docs/latest/llms/responses-agent-intro#simple-chat-example and + https://www.mlflow.org/docs/latest/llms/responses-agent-intro#tool-calling-example. + """ + + def predict_stream( + self, request: ResponsesAgentRequest + ) -> Generator[ResponsesAgentStreamEvent, None, None]: + """ + Given a ResponsesAgentRequest, returns a generator of ResponsesAgentStreamEvent objects. + + See more details at + https://www.mlflow.org/docs/latest/llms/responses-agent-intro#streaming-agent-output. + + You can see example implementations at + https://www.mlflow.org/docs/latest/llms/responses-agent-intro#simple-chat-example and + https://www.mlflow.org/docs/latest/llms/responses-agent-intro#tool-calling-example. + """ + raise NotImplementedError( + "Streaming implementation not provided. Please override the " + "`predict_stream` method on your model to generate streaming predictions" + ) + + def create_text_delta(self, delta: str, item_id: str) -> dict[str, Any]: + """Helper method to create a dictionary conforming to the text delta schema for + streaming. + + Read more at https://www.mlflow.org/docs/latest/llms/responses-agent-intro/#streaming-agent-output. + """ + return { + "type": "response.output_text.delta", + "item_id": item_id, + "delta": delta, + } + + def create_text_output_item(self, text: str, id: str) -> dict[str, Any]: + """Helper method to create a dictionary conforming to the text output item schema. + + Read more at https://www.mlflow.org/docs/latest/llms/responses-agent-intro/#creating-agent-output. + + Args: + text (str): The text to be outputted. + id (str): The id of the output item. + """ + return { + "id": id, + "content": [ + { + "text": text, + "type": "output_text", + } + ], + "role": "assistant", + "type": "message", + } + + def create_function_call_item( + self, id: str, call_id: str, name: str, arguments: str + ) -> dict[str, Any]: + """Helper method to create a dictionary conforming to the function call item schema. + + Read more at https://www.mlflow.org/docs/latest/llms/responses-agent-intro/#creating-agent-output. + + Args: + id (str): The id of the output item. + call_id (str): The id of the function call. + name (str): The name of the function to be called. + arguments (str): The arguments to be passed to the function. + """ + return { + "type": "function_call", + "id": id, + "call_id": call_id, + "name": name, + "arguments": arguments, + } + + def create_function_call_output_item(self, call_id: str, output: str) -> dict[str, Any]: + """Helper method to create a dictionary conforming to the function call output item + schema. + + Read more at https://www.mlflow.org/docs/latest/llms/responses-agent-intro/#creating-agent-output. + + Args: + call_id (str): The id of the function call. + output (str): The output of the function call. + """ + return { + "type": "function_call_output", + "call_id": call_id, + "output": output, + } + + +def _save_model_with_class_artifacts_params( + path, + python_model, + signature=None, + artifacts=None, + conda_env=None, + code_paths=None, + mlflow_model=None, + pip_requirements=None, + extra_pip_requirements=None, + model_config=None, + streamable=None, + model_code_path=None, + infer_code_paths=False, +): + """ + Args: + path: The path to which to save the Python model. + python_model: An instance of a subclass of :class:`~PythonModel`. ``python_model`` + defines how the model loads artifacts and how it performs inference. + artifacts: A dictionary containing ```` entries. Remote artifact URIs + are resolved to absolute filesystem paths, producing a dictionary of + ```` entries, (e.g. {"file": "absolute_path"}). + ``python_model`` can reference these resolved entries as the ``artifacts`` property + of the ``context`` attribute. If ````(e.g. + {"bert-tiny-model": "hf:/prajjwal1/bert-tiny"}) is provided, then the model can be + fetched from huggingface hub using repo_id `prajjwal1/bert-tiny` directly. If ``None``, + no artifacts are added to the model. + conda_env: Either a dictionary representation of a Conda environment or the path to a Conda + environment yaml file. If provided, this decsribes the environment this model should be + run in. At minimum, it should specify the dependencies contained in + :func:`get_default_conda_env()`. If ``None``, the default + :func:`get_default_conda_env()` environment is added to the model. + code_paths: A list of local filesystem paths to Python file dependencies (or directories + containing file dependencies). These files are *prepended* to the system path before the + model is loaded. + mlflow_model: The model to which to add the ``mlflow.pyfunc`` flavor. + model_config: The model configuration for the flavor. Model configuration is available + during model loading time. + + .. Note:: Experimental: This parameter may change or be removed in a future release + without warning. + + model_code_path: The path to the code that is being logged as a PyFunc model. Can be used + to load python_model when python_model is None. + + .. Note:: Experimental: This parameter may change or be removed in a future release + without warning. + + streamable: A boolean value indicating if the model supports streaming prediction, + If None, MLflow will try to inspect if the model supports streaming + by checking if `predict_stream` method exists. Default None. + """ + if mlflow_model is None: + mlflow_model = Model() + + custom_model_config_kwargs = { + CONFIG_KEY_CLOUDPICKLE_VERSION: cloudpickle.__version__, + } + if callable(python_model): + python_model = _FunctionPythonModel(func=python_model, signature=signature) + + saved_python_model_subpath = _SAVED_PYTHON_MODEL_SUBPATH + + compression = MLFLOW_LOG_MODEL_COMPRESSION.get() + if compression: + if _check_compression_supported(compression): + custom_model_config_kwargs[CONFIG_KEY_COMPRESSION] = compression + saved_python_model_subpath += _COMPRESSION_INFO[compression]["ext"] + else: + compression = None + + # If model_code_path is defined, we load the model into python_model, but we don't want to + # pickle/save the python_model since the module won't be able to be imported. + if not model_code_path: + try: + _maybe_compress_cloudpickle_dump( + python_model, os.path.join(path, saved_python_model_subpath), compression + ) + except Exception as e: + raise MlflowException( + "Failed to serialize Python model. Please save the model into a python file " + "and use code-based logging method instead. See" + "https://mlflow.org/docs/latest/models.html#models-from-code for more information." + ) from e + + custom_model_config_kwargs[CONFIG_KEY_PYTHON_MODEL] = saved_python_model_subpath + + if artifacts: + saved_artifacts_config = {} + with TempDir() as tmp_artifacts_dir: + saved_artifacts_dir_subpath = "artifacts" + hf_prefix = "hf:/" + for artifact_name, artifact_uri in artifacts.items(): + if artifact_uri.startswith(hf_prefix): + try: + from huggingface_hub import snapshot_download + except ImportError as e: + raise MlflowException( + "Failed to import huggingface_hub. Please install huggingface_hub " + f"to log the model with artifact_uri {artifact_uri}. Error: {e}" + ) + + repo_id = artifact_uri[len(hf_prefix) :] + try: + snapshot_location = snapshot_download( + repo_id=repo_id, + local_dir=os.path.join( + path, saved_artifacts_dir_subpath, artifact_name + ), + local_dir_use_symlinks=False, + ) + except Exception as e: + raise MlflowException.invalid_parameter_value( + "Failed to download snapshot from Hugging Face Hub with artifact_uri: " + f"{artifact_uri}. Error: {e}" + ) + saved_artifact_subpath = ( + Path(snapshot_location).relative_to(Path(os.path.realpath(path))).as_posix() + ) + else: + tmp_artifact_path = _download_artifact_from_uri( + artifact_uri=artifact_uri, output_path=tmp_artifacts_dir.path() + ) + + relative_path = ( + Path(tmp_artifact_path) + .relative_to(Path(tmp_artifacts_dir.path())) + .as_posix() + ) + + saved_artifact_subpath = os.path.join( + saved_artifacts_dir_subpath, relative_path + ) + + saved_artifacts_config[artifact_name] = { + CONFIG_KEY_ARTIFACT_RELATIVE_PATH: saved_artifact_subpath, + CONFIG_KEY_ARTIFACT_URI: artifact_uri, + } + + shutil.move(tmp_artifacts_dir.path(), os.path.join(path, saved_artifacts_dir_subpath)) + custom_model_config_kwargs[CONFIG_KEY_ARTIFACTS] = saved_artifacts_config + + if streamable is None: + streamable = python_model.__class__.predict_stream != PythonModel.predict_stream + + if model_code_path: + loader_module = mlflow.pyfunc.loaders.code_model.__name__ + elif python_model: + loader_module = _get_pyfunc_loader_module(python_model) + else: + raise MlflowException( + "Either `python_model` or `model_code_path` must be provided to save the model.", + error_code=INVALID_PARAMETER_VALUE, + ) + + mlflow.pyfunc.add_to_model( + model=mlflow_model, + loader_module=loader_module, + code=None, + conda_env=_CONDA_ENV_FILE_NAME, + python_env=_PYTHON_ENV_FILE_NAME, + model_config=model_config, + streamable=streamable, + model_code_path=model_code_path, + **custom_model_config_kwargs, + ) + if size := get_total_file_size(path): + mlflow_model.model_size_bytes = size + # `mlflow_model.save` must be called before _validate_infer_and_copy_code_paths as it + # internally infers dependency, and MLmodel file is required to successfully load the model + mlflow_model.save(os.path.join(path, MLMODEL_FILE_NAME)) + + saved_code_subpath = _validate_infer_and_copy_code_paths( + code_paths, + path, + infer_code_paths, + mlflow.pyfunc.FLAVOR_NAME, + ) + mlflow_model.flavors[mlflow.pyfunc.FLAVOR_NAME][mlflow.pyfunc.CODE] = saved_code_subpath + + # `mlflow_model.code` is updated, re-generate `MLmodel` file. + mlflow_model.save(os.path.join(path, MLMODEL_FILE_NAME)) + + if conda_env is None: + if pip_requirements is None: + default_reqs = get_default_pip_requirements() + extra_env_vars = ( + _get_databricks_serverless_env_vars() + if is_in_databricks_serverless_runtime() + else None + ) + # To ensure `_load_pyfunc` can successfully load the model during the dependency + # inference, `mlflow_model.save` must be called beforehand to save an MLmodel file. + inferred_reqs = mlflow.models.infer_pip_requirements( + path, + mlflow.pyfunc.FLAVOR_NAME, + fallback=default_reqs, + extra_env_vars=extra_env_vars, + ) + default_reqs = sorted(set(inferred_reqs).union(default_reqs)) + else: + default_reqs = None + conda_env, pip_requirements, pip_constraints = _process_pip_requirements( + default_reqs, + pip_requirements, + extra_pip_requirements, + ) + else: + conda_env, pip_requirements, pip_constraints = _process_conda_env(conda_env) + + with open(os.path.join(path, _CONDA_ENV_FILE_NAME), "w") as f: + yaml.safe_dump(conda_env, stream=f, default_flow_style=False) + + # Save `constraints.txt` if necessary + if pip_constraints: + write_to(os.path.join(path, _CONSTRAINTS_FILE_NAME), "\n".join(pip_constraints)) + + # Save `requirements.txt` + write_to(os.path.join(path, _REQUIREMENTS_FILE_NAME), "\n".join(pip_requirements)) + + _PythonEnv.current().to_yaml(os.path.join(path, _PYTHON_ENV_FILE_NAME)) + + +def _load_context_model_and_signature(model_path: str, model_config: dict[str, Any] | None = None): + pyfunc_config = _get_flavor_configuration( + model_path=model_path, flavor_name=mlflow.pyfunc.FLAVOR_NAME + ) + signature = mlflow.models.Model.load(model_path).signature + + if MODEL_CODE_PATH in pyfunc_config: + conf_model_code_path = pyfunc_config.get(MODEL_CODE_PATH) + model_code_path = os.path.join(model_path, os.path.basename(conf_model_code_path)) + python_model = _load_model_code_path(model_code_path, model_config) + + if callable(python_model): + python_model = _FunctionPythonModel(python_model, signature=signature) + else: + python_model_cloudpickle_version = pyfunc_config.get(CONFIG_KEY_CLOUDPICKLE_VERSION, None) + if python_model_cloudpickle_version is None: + mlflow.pyfunc._logger.warning( + "The version of CloudPickle used to save the model could not be found in the " + "MLmodel configuration" + ) + elif python_model_cloudpickle_version != cloudpickle.__version__: + # CloudPickle does not have a well-defined cross-version compatibility policy. Micro + # version releases have been known to cause incompatibilities. Therefore, we match on + # the full library version + mlflow.pyfunc._logger.warning( + "The version of CloudPickle that was used to save the model, `CloudPickle %s`, " + "differs from the version of CloudPickle that is currently running, `CloudPickle " + "%s`, and may be incompatible", + python_model_cloudpickle_version, + cloudpickle.__version__, + ) + python_model_compression = pyfunc_config.get(CONFIG_KEY_COMPRESSION, None) + + python_model_subpath = pyfunc_config.get(CONFIG_KEY_PYTHON_MODEL, None) + if python_model_subpath is None: + raise MlflowException("Python model path was not specified in the model configuration") + python_model = _maybe_decompress_cloudpickle_load( + os.path.join(model_path, python_model_subpath), python_model_compression + ) + + artifacts = {} + for saved_artifact_name, saved_artifact_info in pyfunc_config.get( + CONFIG_KEY_ARTIFACTS, {} + ).items(): + artifacts[saved_artifact_name] = os.path.join( + model_path, saved_artifact_info[CONFIG_KEY_ARTIFACT_RELATIVE_PATH] + ) + + context = PythonModelContext(artifacts=artifacts, model_config=model_config) + python_model.load_context(context=context) + + return context, python_model, signature + + +def _load_pyfunc(model_path: str, model_config: dict[str, Any] | None = None): + context, python_model, signature = _load_context_model_and_signature(model_path, model_config) + return _PythonModelPyfuncWrapper( + python_model=python_model, + context=context, + signature=signature, + ) + + +def _get_first_string_column(pdf): + iter_string_columns = (col for col, val in pdf.iloc[0].items() if isinstance(val, str)) + return next(iter_string_columns, None) + + +class _PythonModelPyfuncWrapper: + """ + Wrapper class that creates a predict function such that + predict(model_input: pd.DataFrame) -> model's output as pd.DataFrame (pandas DataFrame) + """ + + def __init__(self, python_model: PythonModel, context, signature): + """ + Args: + python_model: An instance of a subclass of :class:`~PythonModel`. + context: A :class:`~PythonModelContext` instance containing artifacts that + ``python_model`` may use when performing inference. + signature: :class:`~ModelSignature` instance describing model input and output. + """ + self.python_model = python_model + self.context = context + self.signature = signature + + def _convert_input(self, model_input): + hints = self.python_model.predict_type_hints + # we still need this for backwards compatibility + if isinstance(model_input, pd.DataFrame): + if _is_list_str(hints.input): + first_string_column = _get_first_string_column(model_input) + if first_string_column is None: + raise MlflowException.invalid_parameter_value( + "Expected model input to contain at least one string column" + ) + return model_input[first_string_column].tolist() + elif _is_list_dict_str(hints.input): + if ( + len(self.signature.inputs) == 1 + and next(iter(self.signature.inputs)).name is None + ): + if first_string_column := _get_first_string_column(model_input): + return model_input[[first_string_column]].to_dict(orient="records") + if len(model_input.columns) == 1: + return model_input.to_dict("list")[0] + return model_input.to_dict(orient="records") + elif isinstance(hints.input, type) and ( + issubclass(hints.input, ChatCompletionRequest) + or issubclass(hints.input, SplitChatMessagesRequest) + ): + # If the type hint is a RAG dataclass, we hydrate it + # If there are multiple rows, we should throw + if len(model_input) > 1: + raise MlflowException( + "Expected a single input for dataclass type hint, but got multiple rows" + ) + # Since single input is expected, we take the first row + return _hydrate_dataclass(hints.input, model_input.iloc[0]) + return model_input + + def predict(self, model_input, params: dict[str, Any] | None = None): + """ + Args: + model_input: Model input data as one of dict, str, bool, bytes, float, int, str type. + params: Additional parameters to pass to the model for inference. + + Returns: + Model predictions as an iterator of chunks. The chunks in the iterator must be type of + dict or string. Chunk dict fields are determined by the model implementation. + """ + parameters = inspect.signature(self.python_model.predict).parameters + kwargs = {} + if "params" in parameters: + kwargs["params"] = params + else: + _log_warning_if_params_not_in_predict_signature(_logger, params) + if _is_context_in_predict_function_signature(parameters=parameters): + return self.python_model.predict( + self.context, self._convert_input(model_input), **kwargs + ) + else: + return self.python_model.predict(self._convert_input(model_input), **kwargs) + + def predict_stream(self, model_input, params: dict[str, Any] | None = None): + """ + Args: + model_input: LLM Model single input. + params: Additional parameters to pass to the model for inference. + + Returns: + Streaming predictions. + """ + parameters = inspect.signature(self.python_model.predict_stream).parameters + kwargs = {} + if "params" in parameters: + kwargs["params"] = params + else: + _log_warning_if_params_not_in_predict_signature(_logger, params) + if _is_context_in_predict_function_signature(parameters=parameters): + return self.python_model.predict_stream( + self.context, self._convert_input(model_input), **kwargs + ) + else: + return self.python_model.predict_stream(self._convert_input(model_input), **kwargs) + + +def _get_pyfunc_loader_module(python_model): + if isinstance(python_model, ChatModel): + return mlflow.pyfunc.loaders.chat_model.__name__ + elif isinstance(python_model, ChatAgent): + return mlflow.pyfunc.loaders.chat_agent.__name__ + elif IS_PYDANTIC_V2_OR_NEWER and isinstance(python_model, ResponsesAgent): + return mlflow.pyfunc.loaders.responses_agent.__name__ + return __name__ + + +class ModelFromDeploymentEndpoint(PythonModel): + """ + A PythonModel wrapper for invoking an MLflow Deployments endpoint. + This class is particularly used for running evaluation against an MLflow Deployments endpoint. + """ + + def __init__(self, endpoint, params): + self.endpoint = endpoint + self.params = params + + def predict(self, context, model_input: pd.DataFrame | dict[str, Any] | list[dict[str, Any]]): + """ + Run prediction on the input data. + + Args: + context: A :class:`~PythonModelContext` instance containing artifacts that the model + can use to perform inference. + model_input: The input data for prediction, either of the following: + - Pandas DataFrame: If the default evaluator is used, input is a DF + that contains the multiple request payloads in a single column. + - A dictionary: If the model_type is "databricks-agents" and the + Databricks RAG evaluator is used, this PythonModel can be invoked + with a single dict corresponding to the ChatCompletionsRequest schema. + - A list of dictionaries: Currently we don't have any evaluator that + gives this input format, but we keep this for future use cases and + compatibility with normal pyfunc models. + + Return: + The prediction result. The return type will be consistent with the model input type, + e.g., if the input is a Pandas DataFrame, the return will be a Pandas Series. + """ + if isinstance(model_input, dict): + return self._predict_single(model_input) + elif isinstance(model_input, list) and all(isinstance(data, dict) for data in model_input): + return [self._predict_single(data) for data in model_input] + elif isinstance(model_input, pd.DataFrame): + if len(model_input.columns) != 1: + raise MlflowException( + f"The number of input columns must be 1, but got {model_input.columns}. " + "Multi-column input is not supported for evaluating an MLflow Deployments " + "endpoint. Please include the input text or payload in a single column.", + error_code=INVALID_PARAMETER_VALUE, + ) + input_column = model_input.columns[0] + + predictions = [self._predict_single(data) for data in model_input[input_column]] + return pd.Series(predictions) + else: + raise MlflowException( + f"Invalid input data type: {type(model_input)}. The input data must be either " + "a Pandas DataFrame, a dictionary, or a list of dictionaries containing the " + "request payloads for evaluating an MLflow Deployments endpoint.", + error_code=INVALID_PARAMETER_VALUE, + ) + + def _predict_single(self, data: str | dict[str, Any]) -> dict[str, Any]: + """ + Send a single prediction request to the MLflow Deployments endpoint. + + Args: + data: The single input data for prediction. If the input data is a string, we will + construct the request payload from it. If the input data is a dictionary, we + will directly use it as the request payload. + + Returns: + The prediction result from the MLflow Deployments endpoint as a dictionary. + """ + from mlflow.metrics.genai.model_utils import call_deployments_api, get_endpoint_type + + endpoint_type = get_endpoint_type(f"endpoints:/{self.endpoint}") + + if isinstance(data, str): + # If the input payload is string, MLflow needs to construct the JSON + # payload based on the endpoint type. If the endpoint type is not + # set on the endpoint, we will default to chat format. + endpoint_type = endpoint_type or "llm/v1/chat" + prediction = call_deployments_api(self.endpoint, data, self.params, endpoint_type) + elif isinstance(data, dict): + # If the input is dictionary, we assume the input is already in the + # compatible format for the endpoint. + prediction = call_deployments_api(self.endpoint, data, self.params, endpoint_type) + else: + raise MlflowException( + f"Invalid input data type: {type(data)}. The feature column of the evaluation " + "dataset must contain only strings or dictionaries containing the request " + "payload for evaluating an MLflow Deployments endpoint.", + error_code=INVALID_PARAMETER_VALUE, + ) + return prediction diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyfunc/scoring_server/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyfunc/scoring_server/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..cdf2a1633b588befa0c2431a163589f417585740 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyfunc/scoring_server/__init__.py @@ -0,0 +1,604 @@ +""" +Scoring server for python model format. +The passed int model is expected to have function: + predict(pandas.Dataframe) -> pandas.DataFrame + +Input, expected in text/csv or application/json format, +is parsed into pandas.DataFrame and passed to the model. + +Defines four endpoints: + /ping used for health check + /health (same as /ping) + /version used for getting the mlflow version + /invocations used for scoring +""" + +import asyncio +import inspect +import json +import logging +import os +import shlex +import sys +import traceback +from functools import wraps +from typing import Any, NamedTuple + +from mlflow.environment_variables import ( + _MLFLOW_IS_IN_SERVING_ENVIRONMENT, + MLFLOW_SCORING_SERVER_REQUEST_TIMEOUT, +) + +# NB: We need to be careful what we import form mlflow here. Scoring server is used from within +# model's conda environment. The version of mlflow doing the serving (outside) and the version of +# mlflow in the model's conda environment (inside) can differ. We should therefore keep mlflow +# dependencies to the minimum here. +# ALl of the mlflow dependencies below need to be backwards compatible. +from mlflow.exceptions import MlflowException +from mlflow.pyfunc.model import _log_warning_if_params_not_in_predict_signature +from mlflow.types import ParamSchema, Schema +from mlflow.utils import reraise +from mlflow.utils.file_utils import path_to_local_file_uri +from mlflow.utils.proto_json_utils import ( + MlflowInvalidInputException, + NumpyEncoder, + _get_jsonable_obj, + dataframe_from_parsed_json, + parse_tf_serving_input, +) +from mlflow.version import VERSION + +try: + from mlflow.pyfunc import PyFuncModel, load_model +except ImportError: + from mlflow.pyfunc import load_pyfunc as load_model +from io import StringIO + +from mlflow.protos.databricks_pb2 import BAD_REQUEST, INVALID_PARAMETER_VALUE +from mlflow.pyfunc.utils.serving_data_parser import is_unified_llm_input + +_SERVER_MODEL_PATH = "__pyfunc_model_path__" +SERVING_MODEL_CONFIG = "SERVING_MODEL_CONFIG" + +CONTENT_TYPE_CSV = "text/csv" +CONTENT_TYPE_JSON = "application/json" + +CONTENT_TYPES = [ + CONTENT_TYPE_CSV, + CONTENT_TYPE_JSON, +] + +_logger = logging.getLogger(__name__) + +DF_RECORDS = "dataframe_records" +DF_SPLIT = "dataframe_split" +INSTANCES = "instances" +INPUTS = "inputs" + +SUPPORTED_FORMATS = {DF_RECORDS, DF_SPLIT, INSTANCES, INPUTS} +SERVING_PARAMS_KEY = "params" + +REQUIRED_INPUT_FORMAT = ( + f"The input must be a JSON dictionary with exactly one of the input fields {SUPPORTED_FORMATS}" +) +SCORING_PROTOCOL_CHANGE_INFO = ( + "IMPORTANT: The MLflow Model scoring protocol has changed in MLflow version 2.0. If you are" + " seeing this error, you are likely using an outdated scoring request format. To resolve the" + " error, either update your request format or adjust your MLflow Model's requirements file to" + " specify an older version of MLflow (for example, change the 'mlflow' requirement specifier" + " to 'mlflow==1.30.0'). If you are making a request using the MLflow client" + " (e.g. via `mlflow.pyfunc.spark_udf()`), upgrade your MLflow client to a version >= 2.0 in" + " order to use the new request format. For more information about the updated MLflow" + " Model scoring protocol in MLflow 2.0, see" + " https://mlflow.org/docs/latest/models.html#deploy-mlflow-models." +) + + +def load_model_with_mlflow_config(model_uri): + extra_kwargs = {} + if model_config_json := os.environ.get(SERVING_MODEL_CONFIG): + extra_kwargs["model_config"] = json.loads(model_config_json) + + return load_model(model_uri, **extra_kwargs) + + +# Keep this method to maintain compatibility with MLServer +# https://github.com/SeldonIO/MLServer/blob/caa173ab099a4ec002a7c252cbcc511646c261a6/runtimes/mlflow/mlserver_mlflow/runtime.py#L13C5-L13C31 +def infer_and_parse_json_input(json_input, schema: Schema = None): + """ + Args: + json_input: A JSON-formatted string representation of TF serving input or a Pandas + DataFrame, or a stream containing such a string representation. + schema: Optional schema specification to be used during parsing. + """ + if isinstance(json_input, dict): + decoded_input = json_input + else: + try: + decoded_input = json.loads(json_input) + except json.decoder.JSONDecodeError as ex: + raise MlflowException( + message=( + "Failed to parse input from JSON. Ensure that input is a valid JSON" + f" formatted string. Error: '{ex}'. Input: \n{json_input}\n" + ), + error_code=BAD_REQUEST, + ) + if isinstance(decoded_input, dict): + format_keys = set(decoded_input.keys()).intersection(SUPPORTED_FORMATS) + if len(format_keys) != 1: + message = f"Received dictionary with input fields: {list(decoded_input.keys())}" + raise MlflowException( + message=f"{REQUIRED_INPUT_FORMAT}. {message}. {SCORING_PROTOCOL_CHANGE_INFO}", + error_code=BAD_REQUEST, + ) + input_format = format_keys.pop() + if input_format in (INSTANCES, INPUTS): + return parse_tf_serving_input(decoded_input, schema=schema) + + elif input_format in (DF_SPLIT, DF_RECORDS): + # NB: skip the dataframe_ prefix + pandas_orient = input_format[10:] + return dataframe_from_parsed_json( + decoded_input[input_format], pandas_orient=pandas_orient, schema=schema + ) + elif isinstance(decoded_input, list): + message = "Received a list" + raise MlflowException( + message=f"{REQUIRED_INPUT_FORMAT}. {message}. {SCORING_PROTOCOL_CHANGE_INFO}", + error_code=BAD_REQUEST, + ) + else: + message = f"Received unexpected input type '{type(decoded_input)}'" + raise MlflowException( + message=f"{REQUIRED_INPUT_FORMAT}. {message}.", error_code=BAD_REQUEST + ) + + +def _decode_json_input(json_input): + """ + Args: + json_input: A JSON-formatted string representation of TF serving input or a Pandas + DataFrame, or a stream containing such a string representation. + + Returns: + A dictionary representation of the JSON input. + """ + if isinstance(json_input, dict): + return json_input + + try: + decoded_input = json.loads(json_input) + except json.decoder.JSONDecodeError as ex: + raise MlflowInvalidInputException( + "Ensure that input is a valid JSON formatted string. " + f"Error: '{ex!r}'\nInput: \n{json_input}\n" + ) from ex + + if isinstance(decoded_input, dict): + return decoded_input + if isinstance(decoded_input, list): + raise MlflowInvalidInputException(f"{REQUIRED_INPUT_FORMAT}. Received a list.") + + raise MlflowInvalidInputException( + f"{REQUIRED_INPUT_FORMAT}. Received unexpected input type '{type(decoded_input)}." + ) + + +def _split_data_and_params_for_llm_input(json_input, param_schema: ParamSchema | None): + data = {} + params = {} + schema_params = {param.name for param in param_schema.params} if param_schema else {} + + for key, value in json_input.items(): + # if the model defines a param schema, then we can add + # it to the params dict. otherwise, add it to the data + # dict to prevent it from being ignored at inference time + if key in schema_params: + params[key] = value + else: + data[key] = value + + return data, params + + +def _split_data_and_params(json_input): + input_dict = _decode_json_input(json_input) + data = {k: v for k, v in input_dict.items() if k in SUPPORTED_FORMATS} + params = input_dict.pop(SERVING_PARAMS_KEY, None) + return data, params + + +def infer_and_parse_data(data, schema: Schema = None): + """ + Args: + data: A dictionary representation of TF serving input or a Pandas + DataFrame, or a stream containing such a string representation. + schema: Optional schema specification to be used during parsing. + """ + + format_keys = set(data.keys()).intersection(SUPPORTED_FORMATS) + if len(format_keys) != 1: + message = f"Received dictionary with input fields: {list(data.keys())}" + raise MlflowException( + message=f"{REQUIRED_INPUT_FORMAT}. {message}. {SCORING_PROTOCOL_CHANGE_INFO}", + error_code=BAD_REQUEST, + ) + input_format = format_keys.pop() + if input_format in (INSTANCES, INPUTS): + return parse_tf_serving_input(data, schema=schema) + + if input_format in (DF_SPLIT, DF_RECORDS): + pandas_orient = input_format[10:] # skip the dataframe_ prefix + return dataframe_from_parsed_json( + data[input_format], pandas_orient=pandas_orient, schema=schema + ) + + +def parse_csv_input(csv_input, schema: Schema = None): + """ + Args: + csv_input: A CSV-formatted string representation of a Pandas DataFrame, or a stream + containing such a string representation. + schema: Optional schema specification to be used during parsing. + """ + import pandas as pd + + try: + if schema is None: + return pd.read_csv(csv_input) + else: + dtypes = dict(zip(schema.input_names(), schema.pandas_types())) + return pd.read_csv(csv_input, dtype=dtypes) + except Exception as e: + _handle_serving_error( + error_message=( + "Failed to parse input as a Pandas DataFrame. Ensure that the input is" + " a valid CSV-formatted Pandas DataFrame produced using the" + f" `pandas.DataFrame.to_csv()` method. Error: '{e}'" + ), + error_code=BAD_REQUEST, + ) + + +def unwrapped_predictions_to_json(raw_predictions, output): + predictions = _get_jsonable_obj(raw_predictions, pandas_orient="records") + return json.dump(predictions, output, cls=NumpyEncoder) + + +def predictions_to_json(raw_predictions, output, metadata=None): + if metadata and "predictions" in metadata: + raise MlflowException( + "metadata cannot contain 'predictions' key", error_code=INVALID_PARAMETER_VALUE + ) + predictions = _get_jsonable_obj(raw_predictions, pandas_orient="records") + return json.dump({"predictions": predictions, **(metadata or {})}, output, cls=NumpyEncoder) + + +def _handle_serving_error(error_message, error_code, include_traceback=True): + """ + Logs information about an exception thrown by model inference code that is currently being + handled and reraises it with the specified error message. The exception stack trace + is also included in the reraised error message. + + Args: + error_message: A message for the reraised exception. + error_code: An appropriate error code for the reraised exception. This should be one of + the codes listed in the `mlflow.protos.databricks_pb2` proto. + include_traceback: Whether to include the current traceback in the returned error. + """ + if include_traceback: + traceback_buf = StringIO() + traceback.print_exc(file=traceback_buf) + traceback_str = traceback_buf.getvalue() + e = MlflowException(message=error_message, error_code=error_code, stack_trace=traceback_str) + else: + e = MlflowException(message=error_message, error_code=error_code) + reraise(MlflowException, e) + + +class InvocationsResponse(NamedTuple): + response: str + status: int + mimetype: str + + +def invocations(data, content_type, model, input_schema): + type_parts = list(map(str.strip, content_type.split(";"))) + mime_type = type_parts[0] + parameter_value_pairs = type_parts[1:] + parameter_values = { + key: value for pair in parameter_value_pairs for key, _, value in [pair.partition("=")] + } + + charset = parameter_values.get("charset", "utf-8").lower() + if charset != "utf-8": + return InvocationsResponse( + response="The scoring server only supports UTF-8", + status=415, + mimetype="text/plain", + ) + + unexpected_content_parameters = set(parameter_values.keys()).difference({"charset"}) + if unexpected_content_parameters: + return InvocationsResponse( + response=( + f"Unrecognized content type parameters: " + f"{', '.join(unexpected_content_parameters)}. " + f"{SCORING_PROTOCOL_CHANGE_INFO}" + ), + status=415, + mimetype="text/plain", + ) + + # The traditional JSON request/response format, wraps the data with one of the supported keys + # like "dataframe_split" and "predictions". For LLM use cases, we also support unwrapped JSON + # payload, to provide unified prediction interface. + should_parse_as_unified_llm_input = False + + if mime_type == CONTENT_TYPE_CSV: + # Convert from CSV to pandas + if isinstance(data, bytes): + data = data.decode("utf-8") + csv_input = StringIO(data) + data = parse_csv_input(csv_input=csv_input, schema=input_schema) + params = None + elif mime_type == CONTENT_TYPE_JSON: + parsed_json_input = _parse_json_data(data, model.metadata, input_schema) + data = parsed_json_input.data + params = parsed_json_input.params + should_parse_as_unified_llm_input = parsed_json_input.is_unified_llm_input + else: + return InvocationsResponse( + response=( + "This predictor only supports the following content types:" + f" Types: {CONTENT_TYPES}." + f" Got '{content_type}'." + ), + status=415, + mimetype="text/plain", + ) + + # Do the prediction + # NB: utils._validate_serving_input mimic the scoring process here to validate input_example + # work for serving, so any changes here should be reflected there as well + try: + if "params" in inspect.signature(model.predict).parameters: + raw_predictions = model.predict(data, params=params) + else: + _log_warning_if_params_not_in_predict_signature(_logger, params) + raw_predictions = model.predict(data) + except MlflowException as e: + if "Failed to enforce schema" in e.message: + _logger.warning( + "If using `instances` as input key, we internally convert " + "the data type from `records` (List[Dict]) type to " + "`list` (Dict[str, List]) type if the data is a pandas " + "dataframe representation. This might cause schema changes. " + "Please use `inputs` to avoid this conversion.\n" + ) + e.message = f"Failed to predict data '{data}'. \nError: {e.message}" + raise e + except Exception: + raise MlflowException( + message=( + "Encountered an unexpected error while evaluating the model. Verify" + " that the serialized input Dataframe is compatible with the model for" + " inference." + ), + error_code=BAD_REQUEST, + stack_trace=traceback.format_exc(), + ) + result = StringIO() + + # if the data was formatted using the unified LLM format, + # then return the data without the "predictions" key + if should_parse_as_unified_llm_input: + unwrapped_predictions_to_json(raw_predictions, result) + else: + predictions_to_json(raw_predictions, result) + + return InvocationsResponse(response=result.getvalue(), status=200, mimetype="application/json") + + +class ParsedJsonInput(NamedTuple): + data: Any + params: dict[str, Any] | None + is_unified_llm_input: bool + + +def _parse_json_data(data, metadata, input_schema): + json_input = _decode_json_input(data) + _is_unified_llm_input = is_unified_llm_input(json_input) + # no data parsing for unified LLM input format + if _is_unified_llm_input: + # Unified LLM input format + if hasattr(metadata, "get_params_schema"): + params_schema = metadata.get_params_schema() + else: + params_schema = None + data, params = _split_data_and_params_for_llm_input(json_input, params_schema) + else: + # Traditional json input format + data, params = _split_data_and_params(data) + # data only needs to be parsed if the model signature is not from type hint + # default to True for backwards compatibility + should_parse_data = ( + not metadata._is_signature_from_type_hint() + if hasattr(metadata, "_is_signature_from_type_hint") + else True + ) + if should_parse_data: + data = infer_and_parse_data(data, input_schema) + else: + if INPUTS not in data: + raise MlflowException.invalid_parameter_value( + "Request payload must be a dictionary with 'inputs' key when " + f"the model contains a valid type hint. Found keys in payload: {data.keys()}." + ) + data = data[INPUTS] + return ParsedJsonInput(data, params, _is_unified_llm_input) + + +def _async_catch_mlflow_exception(func): + from fastapi.responses import Response + + @wraps(func) + async def wrapper(*args, **kwargs): + try: + return await func(*args, **kwargs) + except MlflowException as e: + return Response( + content=e.serialize_as_json(), + status_code=e.get_http_status_code(), + media_type="application/json", + ) + + return wrapper + + +def init(model: PyFuncModel): + """ + Initialize the server. Loads pyfunc model from the path. + """ + from fastapi import FastAPI, Request + from fastapi.responses import Response + + app = FastAPI() + input_schema = model.metadata.get_input_schema() + # set the environment variable to indicate that we are in a serving environment + os.environ[_MLFLOW_IS_IN_SERVING_ENVIRONMENT.name] = "true" + timeout = MLFLOW_SCORING_SERVER_REQUEST_TIMEOUT.get() + + @app.middleware("http") + async def timeout_middleware(request: Request, call_next): + try: + return await asyncio.wait_for(call_next(request), timeout=timeout) + except (asyncio.TimeoutError, TimeoutError): + return Response( + content="Request processing time exceeded limit", + status_code=504, + media_type="application/json", + ) + + @app.route("/ping", methods=["GET"]) + @app.route("/health", methods=["GET"]) + async def ping(request: Request): + """ + Determine if the container is working and healthy. + We declare it healthy if we can load the model successfully. + """ + health = model is not None + status = 200 if health else 404 + return Response(content="\n", status_code=status, media_type="application/json") + + @app.route("/version", methods=["GET"]) + async def version(request: Request): + """ + Returns the current mlflow version. + """ + return Response(content=VERSION, status_code=200, media_type="application/json") + + @app.route("/invocations", methods=["POST"]) + @_async_catch_mlflow_exception + async def transformation(request: Request): + """ + Do an inference on a single batch of data. In this sample server, + we take data as CSV or json, convert it to a Pandas DataFrame or Numpy, + generate predictions and convert them back to json. + """ + + data = await request.body() + content_type = request.headers.get("content-type") + # TODO: convert "invocations" to an async method to make internal logic fully non-blocking. + result = await asyncio.to_thread(invocations, data, content_type, model, input_schema) + + return Response( + content=result.response, status_code=result.status, media_type=result.mimetype + ) + + return app + + +def _predict(model_uri, input_path, output_path, content_type): + from mlflow.pyfunc.utils.environment import _simulate_serving_environment + + with _simulate_serving_environment(): + pyfunc_model = load_model(model_uri) + + should_parse_as_unified_llm_input = False + if content_type == "json": + if input_path is None: + input_str = sys.stdin.read() + else: + with open(input_path) as f: + input_str = f.read() + parsed_json_input = _parse_json_data( + data=input_str, + metadata=pyfunc_model.metadata, + input_schema=pyfunc_model.metadata.get_input_schema(), + ) + df = parsed_json_input.data + params = parsed_json_input.params + should_parse_as_unified_llm_input = parsed_json_input.is_unified_llm_input + elif content_type == "csv": + df = ( + parse_csv_input(input_path) + if input_path is not None + else parse_csv_input(sys.stdin) + ) + params = None + else: + raise Exception(f"Unknown content type '{content_type}'") + + if "params" in inspect.signature(pyfunc_model.predict).parameters: + raw_predictions = pyfunc_model.predict(df, params=params) + else: + _log_warning_if_params_not_in_predict_signature(_logger, params) + raw_predictions = pyfunc_model.predict(df) + + parse_output_func = ( + unwrapped_predictions_to_json + if should_parse_as_unified_llm_input + else predictions_to_json + ) + + if output_path is None: + parse_output_func(raw_predictions, sys.stdout) + else: + with open(output_path, "w") as fout: + parse_output_func(raw_predictions, fout) + + +def _serve(model_uri, port, host): + pyfunc_model = load_model(model_uri) + init(pyfunc_model).run(port=port, host=host) + + +def get_cmd( + model_uri: str, + port: int | None = None, + host: int | None = None, + timeout: int | None = None, + nworkers: int | None = None, +) -> tuple[str, dict[str, str]]: + local_uri = path_to_local_file_uri(model_uri) + timeout = timeout or MLFLOW_SCORING_SERVER_REQUEST_TIMEOUT.get() + + args = [] + if host: + args.append(f"--host {shlex.quote(host)}") + + if port: + args.append(f"--port {port}") + + if nworkers: + args.append(f"--workers {nworkers}") + + command = f"uvicorn {' '.join(args)} mlflow.pyfunc.scoring_server.app:app" + + command_env = os.environ.copy() + command_env[_SERVER_MODEL_PATH] = local_uri + command_env[MLFLOW_SCORING_SERVER_REQUEST_TIMEOUT.name] = str(timeout) + + return command, command_env diff --git 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a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyfunc/scoring_server/app.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyfunc/scoring_server/app.py new file mode 100644 index 0000000000000000000000000000000000000000..a3e5fa5e14fbdc35ed51bfd1a6962c042bad44db --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyfunc/scoring_server/app.py @@ -0,0 +1,7 @@ +import os + +from mlflow.pyfunc import scoring_server + +app = scoring_server.init( + scoring_server.load_model_with_mlflow_config(os.environ[scoring_server._SERVER_MODEL_PATH]) +) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyfunc/scoring_server/client.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyfunc/scoring_server/client.py new file mode 100644 index 0000000000000000000000000000000000000000..275531a609ffad3e8b090ec041e505c2a1c3cea2 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyfunc/scoring_server/client.py @@ -0,0 +1,146 @@ +import json +import logging +import tempfile +import time +import uuid +from abc import ABC, abstractmethod +from pathlib import Path +from typing import Any + +import requests + +from mlflow.deployments import PredictionsResponse +from mlflow.environment_variables import MLFLOW_SCORING_SERVER_REQUEST_TIMEOUT +from mlflow.exceptions import MlflowException +from mlflow.pyfunc import scoring_server +from mlflow.utils.proto_json_utils import dump_input_data + +_logger = logging.getLogger(__name__) + + +class BaseScoringServerClient(ABC): + @abstractmethod + def wait_server_ready(self, timeout=30, scoring_server_proc=None): + """ + Wait until the scoring server is ready to accept requests. + """ + + @abstractmethod + def invoke(self, data, params: dict[str, Any] | None = None): + """ + Invoke inference on input data. The input data must be pandas dataframe or numpy array or + a dict of numpy arrays. + + Args: + data: Model input data. + params: Additional parameters to pass to the model for inference. + + Returns: + Prediction result. + """ + + +class ScoringServerClient(BaseScoringServerClient): + def __init__(self, host, port): + self.url_prefix = f"http://{host}:{port}" + + def ping(self): + ping_status = requests.get(url=self.url_prefix + "/ping") + if ping_status.status_code != 200: + raise Exception(f"ping failed (error code {ping_status.status_code})") + + def get_version(self): + resp_status = requests.get(url=self.url_prefix + "/version") + if resp_status.status_code != 200: + raise Exception(f"version failed (error code {resp_status.status_code})") + return resp_status.text + + def wait_server_ready(self, timeout=30, scoring_server_proc=None): + begin_time = time.time() + + while True: + time.sleep(0.3) + try: + self.ping() + return + except Exception: + pass + if time.time() - begin_time > timeout: + break + if scoring_server_proc is not None: + return_code = scoring_server_proc.poll() + if return_code is not None: + raise RuntimeError(f"Server process already exit with returncode {return_code}") + raise RuntimeError("Wait scoring server ready timeout.") + + def invoke(self, data, params: dict[str, Any] | None = None): + """ + Args: + data: Model input data. + params: Additional parameters to pass to the model for inference. + + Returns: + :py:class:`PredictionsResponse ` result. + """ + response = requests.post( + url=self.url_prefix + "/invocations", + data=dump_input_data(data, params=params), + headers={"Content-Type": scoring_server.CONTENT_TYPE_JSON}, + ) + if response.status_code != 200: + raise Exception( + f"Invocation failed (error code {response.status_code}, response: {response.text})" + ) + return PredictionsResponse.from_json(response.text) + + +class StdinScoringServerClient(BaseScoringServerClient): + def __init__(self, process): + super().__init__() + self.process = process + self.tmpdir = Path(tempfile.mkdtemp()) + self.output_json = self.tmpdir.joinpath("output.json") + + def wait_server_ready(self, timeout=30, scoring_server_proc=None): + return_code = self.process.poll() + if return_code is not None: + raise RuntimeError(f"Server process already exit with returncode {return_code}") + + def invoke(self, data, params: dict[str, Any] | None = None): + """ + Invoke inference on input data. The input data must be pandas dataframe or numpy array or + a dict of numpy arrays. + + Args: + data: Model input data. + params: Additional parameters to pass to the model for inference. + + Returns: + :py:class:`PredictionsResponse ` result. + """ + if not self.output_json.exists(): + self.output_json.touch() + + request_id = str(uuid.uuid4()) + request = { + "id": request_id, + "data": dump_input_data(data, params=params), + "output_file": str(self.output_json), + } + self.process.stdin.write(json.dumps(request) + "\n") + self.process.stdin.flush() + + begin_time = time.time() + while True: + _logger.info("Waiting for scoring to complete...") + try: + with self.output_json.open(mode="r+") as f: + resp = PredictionsResponse.from_json(f.read()) + if resp.get("id") == request_id: + f.truncate(0) + return resp + except Exception as e: + _logger.debug("Exception while waiting for scoring to complete: %s", e) + if time.time() - begin_time > MLFLOW_SCORING_SERVER_REQUEST_TIMEOUT.get(): + raise MlflowException("Scoring timeout") + time.sleep(1) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyfunc/spark_model_cache.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyfunc/spark_model_cache.py new file mode 100644 index 0000000000000000000000000000000000000000..3e94f23dffbdd54414248581c68bea834c1951a2 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyfunc/spark_model_cache.py @@ -0,0 +1,48 @@ +from mlflow.utils._spark_utils import _SparkDirectoryDistributor + + +class SparkModelCache: + """Caches models in memory on Spark Executors, to avoid continually reloading from disk. + + This class has to be part of a different module than the one that _uses_ it. This is + because Spark will pickle classes that are defined in the local scope, but relies on + Python's module loading behavior for classes in different modules. In this case, we + are relying on the fact that Python will load a module at-most-once, and can therefore + store per-process state in a static map. + """ + + # Map from unique name --> (loaded model, local_model_path). + _models = {} + + # Number of cache hits we've had, for testing purposes. + _cache_hits = 0 + + def __init__(self): + pass + + @staticmethod + def add_local_model(spark, model_path): + """Given a SparkSession and a model_path which refers to a pyfunc directory locally, + we will zip the directory up, enable it to be distributed to executors, and return + the "archive_path", which should be used as the path in get_or_load(). + """ + return _SparkDirectoryDistributor.add_dir(spark, model_path) + + @staticmethod + def get_or_load(archive_path): + """Given a path returned by add_local_model(), this method will return a tuple of + (loaded_model, local_model_path). + If this Python process ever loaded the model before, we will reuse that copy. + """ + if archive_path in SparkModelCache._models: + SparkModelCache._cache_hits += 1 + return SparkModelCache._models[archive_path] + + local_model_dir = _SparkDirectoryDistributor.get_or_extract(archive_path) + + # We must rely on a supposed cyclic import here because we want this behavior + # on the Spark Executors (i.e., don't try to pickle the load_model function). + from mlflow.pyfunc import load_model + + SparkModelCache._models[archive_path] = (load_model(local_model_dir), local_model_dir) + return SparkModelCache._models[archive_path] diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyfunc/stdin_server.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyfunc/stdin_server.py new file mode 100644 index 0000000000000000000000000000000000000000..2ad9fdb3f06212db4f8c1f05cc8260c46e921489 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyfunc/stdin_server.py @@ -0,0 +1,44 @@ +import argparse +import inspect +import json +import logging +import sys + +from mlflow.pyfunc import scoring_server +from mlflow.pyfunc.model import _log_warning_if_params_not_in_predict_signature + +_logger = logging.getLogger(__name__) +logging.basicConfig(level=logging.INFO) + +parser = argparse.ArgumentParser() +parser.add_argument("--model-uri") +args = parser.parse_args() + +_logger.info("Loading model from %s", args.model_uri) + +model = scoring_server.load_model_with_mlflow_config(args.model_uri) +input_schema = model.metadata.get_input_schema() +_logger.info("Loaded model") + +_logger.info("Waiting for request") +for line in sys.stdin: + _logger.info("Received request") + request = json.loads(line) + + _logger.info("Parsing input data") + data = request["data"] + data, params = scoring_server._split_data_and_params(data) + data = scoring_server.infer_and_parse_data(data, input_schema) + + _logger.info("Making predictions") + if "params" in inspect.signature(model.predict).parameters: + preds = model.predict(data, params=params) + else: + _log_warning_if_params_not_in_predict_signature(_logger, params) + preds = model.predict(data) + + _logger.info("Writing predictions") + with open(request["output_file"], "a") as f: + scoring_server.predictions_to_json(preds, f, {"id": request["id"]}) + + _logger.info("Done") diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyfunc/utils/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyfunc/utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..7a60145398f1d6b2f8ff9c5b8a5ac2d9a9f09351 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyfunc/utils/__init__.py @@ -0,0 +1,3 @@ +from mlflow.pyfunc.utils.data_validation import pyfunc + +__all__ = ["pyfunc"] diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyfunc/utils/__pycache__/__init__.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyfunc/utils/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..c79bca75f97d0eef1e529f45305724557b84055d Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyfunc/utils/__pycache__/__init__.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyfunc/utils/__pycache__/data_validation.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyfunc/utils/__pycache__/data_validation.cpython-312.pyc new file mode 100644 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0000000000000000000000000000000000000000..12246b34f7b3208822fb59d5a2b5adb0d3cc08a7 Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyfunc/utils/__pycache__/serving_data_parser.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyfunc/utils/data_validation.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyfunc/utils/data_validation.py new file mode 100644 index 0000000000000000000000000000000000000000..653e12b0d20ba2ced98e439f45aab482ff930ba4 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyfunc/utils/data_validation.py @@ -0,0 +1,224 @@ +import inspect +import warnings +from functools import lru_cache, wraps +from typing import Any, NamedTuple + +import pydantic + +from mlflow.exceptions import MlflowException +from mlflow.models.signature import ( + _extract_type_hints, + _is_context_in_predict_function_signature, +) +from mlflow.types.type_hints import ( + InvalidTypeHintException, + _convert_data_to_type_hint, + _infer_schema_from_list_type_hint, + _is_type_hint_from_example, + _signature_cannot_be_inferred_from_type_hint, + _validate_data_against_type_hint, + model_validate, +) +from mlflow.utils.annotations import filter_user_warnings_once +from mlflow.utils.warnings_utils import color_warning + +_INVALID_SIGNATURE_ERROR_MSG = ( + "Model's `{func_name}` method contains invalid parameters: {invalid_params}. " + "Only the following parameter names are allowed: context, model_input, and params. " + "Note that invalid parameters will no longer be permitted in future versions." +) + + +class FuncInfo(NamedTuple): + input_type_hint: type[Any] | None + input_param_name: str + + +def pyfunc(func): + """ + A decorator that forces data validation against type hint of the input data + in the wrapped method. It is no-op if the type hint is not supported by MLflow. + + .. note:: + The function that applies this decorator must be a valid `predict` function + of `mlflow.pyfunc.PythonModel`, or a callable that takes a single input. + """ + + func_info = _get_func_info_if_type_hint_supported(func) + return _wrap_predict_with_pyfunc(func, func_info) + + +def _wrap_predict_with_pyfunc(func, func_info: FuncInfo | None): + if func_info is not None: + model_input_index = _model_input_index_in_function_signature(func) + + @wraps(func) + def wrapper(*args, **kwargs): + try: + args, kwargs = _validate_model_input( + args, + kwargs, + model_input_index, + func_info.input_type_hint, + func_info.input_param_name, + ) + except Exception as e: + if isinstance(e, MlflowException): + raise e + raise MlflowException( + "Failed to validate the input data against the type hint " + f"`{func_info.input_type_hint}`. Error: {e}" + ) + return func(*args, **kwargs) + else: + + @wraps(func) + def wrapper(*args, **kwargs): + return func(*args, **kwargs) + + wrapper._is_pyfunc = True + return wrapper + + +def wrap_non_list_predict_pydantic(func, input_pydantic_model, validation_error_msg, unpack=False): + """ + Used by MLflow defined subclasses of PythonModel that have non-list a pydantic model as input. + Takes in a dict input, validates it against `input_pydantic_model`, and then creates + the pydantic model. + + If `unpack` is True, the validated dict is parsed into the function arguments. + Otherwise, the whole pydantic object is passed to the function. + + Args: + func: The predict/predict_stream method of the PythonModel subclass. + input_pydantic_model: The pydantic model that the input should be validated against. + validation_error_msg: The error message to raise if the dict input fails to validate. + unpack: Whether to unpack the validated dict into the function arguments. Defaults to False. + + Raises: + MlflowException: If the input fails to validate against the pydantic model. + + Returns: + A function that can take either a dict input or a pydantic object as input. + """ + + @wraps(func) + def wrapper(self, *args, **kwargs): + if len(args) == 1 and isinstance(args[0], dict): + try: + model_validate(input_pydantic_model, args[0]) + pydantic_obj = input_pydantic_model(**args[0]) + except pydantic.ValidationError as e: + raise MlflowException( + f"{validation_error_msg} Pydantic validation error: {e}" + ) from e + else: + if unpack: + param_names = inspect.signature(func).parameters.keys() - {"self"} + kwargs = {k: getattr(pydantic_obj, k) for k in param_names} + return func(self, **kwargs) + else: + return func(self, pydantic_obj) + else: + # Before logging, this is equivalent to the behavior from the raw predict method + # After logging, signature enforcement happens in the _convert_input method + # of the wrapper class + return func(self, *args, **kwargs) + + wrapper._is_pyfunc = True + return wrapper + + +def _check_func_signature(func, func_name) -> list[str]: + parameters = inspect.signature(func).parameters + param_names = [name for name in parameters.keys() if name != "self"] + if invalid_params := set(param_names) - {"self", "context", "model_input", "params"}: + warnings.warn( + _INVALID_SIGNATURE_ERROR_MSG.format(func_name=func_name, invalid_params=invalid_params), + FutureWarning, + stacklevel=2, + ) + return param_names + + +@lru_cache +@filter_user_warnings_once +def _get_func_info_if_type_hint_supported(func) -> FuncInfo | None: + """ + Internal method to check if the predict function has type hints and if they are supported + by MLflow. + For PythonModel, the signature must be one of below: + - predict(self, context, model_input, params=None) + - predict(self, model_input, params=None) + For callables, the function must contain only one input argument. + """ + param_names = _check_func_signature(func, "predict") + input_arg_index = 1 if _is_context_in_predict_function_signature(func=func) else 0 + type_hint = _extract_type_hints(func, input_arg_index=input_arg_index).input + input_param_name = param_names[input_arg_index] + if type_hint is not None: + if _signature_cannot_be_inferred_from_type_hint(type_hint) or _is_type_hint_from_example( + type_hint + ): + return + try: + _infer_schema_from_list_type_hint(type_hint) + except InvalidTypeHintException as e: + raise MlflowException( + f"{e.message} To disable data validation, remove the type hint from the " + "predict function. Otherwise, fix the type hint." + ) + # catch other exceptions to avoid breaking model usage + except Exception as e: + color_warning( + message="Type hint used in the model's predict function is not supported " + f"for MLflow's schema validation. {e} " + "Remove the type hint to disable this warning. " + "To enable validation for the input data, specify input example " + "or model signature when logging the model. ", + category=UserWarning, + stacklevel=3, + color="red", + ) + else: + return FuncInfo(input_type_hint=type_hint, input_param_name=input_param_name) + else: + color_warning( + "Add type hints to the `predict` method to enable data validation " + "and automatic signature inference during model logging. " + "Check https://mlflow.org/docs/latest/model/python_model.html#type-hint-usage-in-pythonmodel" + " for more details.", + stacklevel=1, + color="yellow", + category=UserWarning, + ) + + +def _model_input_index_in_function_signature(func): + parameters = inspect.signature(func).parameters + # we need to exclude the first argument if "self" is in the parameters + index = 1 if "self" in parameters else 0 + if _is_context_in_predict_function_signature(parameters=parameters): + index += 1 + return index + + +def _validate_model_input( + args, kwargs, model_input_index_in_sig, type_hint, model_input_param_name +): + model_input = None + input_pos = None + if model_input_param_name in kwargs: + model_input = kwargs[model_input_param_name] + input_pos = "kwargs" + elif len(args) >= model_input_index_in_sig + 1: + model_input = args[model_input_index_in_sig] + input_pos = model_input_index_in_sig + if input_pos is not None: + data = _convert_data_to_type_hint(model_input, type_hint) + data = _validate_data_against_type_hint(data, type_hint) + if input_pos == "kwargs": + kwargs[model_input_param_name] = data + else: + args = args[:input_pos] + (data,) + args[input_pos + 1 :] + return args, kwargs diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyfunc/utils/environment.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyfunc/utils/environment.py new file mode 100644 index 0000000000000000000000000000000000000000..72fe84dfa4172807f3a629e04d8225ed9e9df2ea --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyfunc/utils/environment.py @@ -0,0 +1,22 @@ +import os +from contextlib import contextmanager + +from mlflow.environment_variables import _MLFLOW_IS_IN_SERVING_ENVIRONMENT + + +@contextmanager +def _simulate_serving_environment(): + """ + Some functions (e.g. validate_serving_input) replicate the data transformation logic + that happens in the model serving environment to validate data before model deployment. + This context manager can be used to simulate the serving environment for such functions. + """ + original_value = _MLFLOW_IS_IN_SERVING_ENVIRONMENT.get_raw() + try: + _MLFLOW_IS_IN_SERVING_ENVIRONMENT.set("true") + yield + finally: + if original_value is not None: + os.environ[_MLFLOW_IS_IN_SERVING_ENVIRONMENT.name] = original_value + else: + del os.environ[_MLFLOW_IS_IN_SERVING_ENVIRONMENT.name] diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyfunc/utils/input_converter.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyfunc/utils/input_converter.py new file mode 100644 index 0000000000000000000000000000000000000000..a0a60da47dd97d8f9d744c9c5822178488d3c85e --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyfunc/utils/input_converter.py @@ -0,0 +1,45 @@ +from dataclasses import fields, is_dataclass +from types import UnionType +from typing import Union, get_args, get_origin + + +def _is_optional_dataclass(field_type) -> bool: + """ + Check if the field type is an Optional containing a dataclass. + Currently, ... | None (in Python 3.10) is not supported. + """ + if get_origin(field_type) in (Union, UnionType): + inner_types = get_args(field_type) + # Check if it's a Union[Dataclass, NoneType] (i.e., Optional[Dataclass]) + if len(inner_types) == 2 and any(t is type(None) for t in inner_types): + effective_type = next(t for t in get_args(field_type) if t is not type(None)) + return is_dataclass(effective_type) + return False + + +def _hydrate_dataclass(dataclass_type, data): + """Recursively create an instance of the dataclass_type from data.""" + if not (is_dataclass(dataclass_type) or _is_optional_dataclass(dataclass_type)): + raise ValueError(f"{dataclass_type.__name__} is not a dataclass") + if data is None: + return None + + field_names = {f.name: f.type for f in fields(dataclass_type)} + kwargs = {} + for key, field_type in field_names.items(): + if key in data: + value = data[key] + if is_dataclass(field_type): + kwargs[key] = _hydrate_dataclass(field_type, value) + elif _is_optional_dataclass(field_type): + effective_type = next(t for t in get_args(field_type) if t is not type(None)) + kwargs[key] = _hydrate_dataclass(effective_type, value) + elif get_origin(field_type) == list: + item_type = get_args(field_type)[0] + if is_dataclass(item_type): + kwargs[key] = [_hydrate_dataclass(item_type, item) for item in value] + else: + kwargs[key] = value + else: + kwargs[key] = value + return dataclass_type(**kwargs) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyfunc/utils/serving_data_parser.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyfunc/utils/serving_data_parser.py new file mode 100644 index 0000000000000000000000000000000000000000..44814f22c5035bb1f70ecad3e32c10fbd9a8d136 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyfunc/utils/serving_data_parser.py @@ -0,0 +1,11 @@ +from typing import Any + +# Support unwrapped JSON with these keys for LLM use cases of Chat, Completions, Embeddings tasks +LLM_CHAT_KEY = "messages" +LLM_COMPLETIONS_KEY = "prompt" +LLM_EMBEDDINGS_KEY = "input" +SUPPORTED_LLM_FORMATS = {LLM_CHAT_KEY, LLM_COMPLETIONS_KEY, LLM_EMBEDDINGS_KEY} + + +def is_unified_llm_input(json_input: dict[str, Any]): + return any(x in json_input for x in SUPPORTED_LLM_FORMATS) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyspark/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyspark/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..ff9d60a830f7a490c123b583e2b31d6e005ab8da --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyspark/__init__.py @@ -0,0 +1,3 @@ +from mlflow.pyspark import ml + +__all__ = ["ml"] diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyspark/__pycache__/__init__.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyspark/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..c4ad2808568aaffcd7263462dd76f9fd8274adff Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyspark/__pycache__/__init__.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyspark/ml/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyspark/ml/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..676c278edf70ba2f4c472d8bcab3bf6de8a9b671 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyspark/ml/__init__.py @@ -0,0 +1,1266 @@ +import importlib.resources +import json +import logging +import os +import traceback +import weakref +from collections import OrderedDict, defaultdict +from itertools import zip_longest +from typing import Any, NamedTuple +from urllib.parse import urlparse + +import numpy as np + +import mlflow +from mlflow.data.code_dataset_source import CodeDatasetSource +from mlflow.data.spark_dataset import SparkDataset +from mlflow.entities import Metric, Param +from mlflow.entities.dataset_input import DatasetInput +from mlflow.entities.input_tag import InputTag +from mlflow.exceptions import MlflowException +from mlflow.tracking.client import MlflowClient +from mlflow.utils import ( + _chunk_dict, + _get_fully_qualified_class_name, + _inspect_original_var_name, + _truncate_dict, +) +from mlflow.utils.autologging_utils import ( + INPUT_EXAMPLE_SAMPLE_ROWS, + _get_new_training_session_class, + autologging_integration, + get_method_call_arg_value, + resolve_input_example_and_signature, + safe_patch, +) +from mlflow.utils.file_utils import TempDir +from mlflow.utils.mlflow_tags import ( + MLFLOW_AUTOLOGGING, + MLFLOW_DATASET_CONTEXT, + MLFLOW_PARENT_RUN_ID, +) +from mlflow.utils.os import is_windows +from mlflow.utils.rest_utils import ( + MlflowHostCreds, + augmented_raise_for_status, + http_request, +) +from mlflow.utils.time import get_current_time_millis +from mlflow.utils.validation import ( + MAX_ENTITY_KEY_LENGTH, + MAX_PARAM_VAL_LENGTH, + MAX_PARAMS_TAGS_PER_BATCH, +) + +_logger = logging.getLogger(__name__) +_SparkTrainingSession = _get_new_training_session_class() + +AUTOLOGGING_INTEGRATION_NAME = "pyspark.ml" + + +def _read_log_model_allowlist_from_file(allowlist_file): + def _parse_allowlist_file(line_iter): + allowlist = set() + for line in line_iter: + stripped = line.strip() + is_blankline_or_comment = stripped == "" or stripped.startswith("#") + if not is_blankline_or_comment: + allowlist.add(stripped) + return allowlist + + url_parsed = urlparse(allowlist_file) + scheme = url_parsed.scheme + path = url_parsed.path + if is_windows() and not url_parsed.hostname: + path = scheme + "://" + path + scheme = "" + if scheme in ("file", ""): + if not os.path.exists(path): + raise MlflowException.invalid_parameter_value(f"{allowlist_file} does not exist") + + with open(allowlist_file) as f: + return _parse_allowlist_file(f) + else: + host_creds = MlflowHostCreds( + host=scheme + "://" + (url_parsed.hostname or ""), + username=url_parsed.username, + password=url_parsed.password, + ) + response = http_request(host_creds=host_creds, endpoint=path, method="GET") + augmented_raise_for_status(response) + return _parse_allowlist_file(response.iter_lines(decode_unicode=True)) + + +def _read_log_model_allowlist(): + """ + Reads the module allowlist and returns it as a set. + """ + from mlflow.utils._spark_utils import _get_active_spark_session + + with importlib.resources.as_file( + importlib.resources.files(__name__).joinpath("log_model_allowlist.txt") + ) as file: + builtin_allowlist_file = file.as_posix() + spark_session = _get_active_spark_session() + if not spark_session: + _logger.info( + "No SparkSession detected. Autologging will log pyspark.ml models contained " + "in the default allowlist. To specify a custom allowlist, initialize a SparkSession " + "prior to calling mlflow.pyspark.ml.autolog() and specify the path to your allowlist " + "file via the spark.mlflow.pysparkml.autolog.logModelAllowlistFile conf." + ) + return _read_log_model_allowlist_from_file(builtin_allowlist_file) + + allowlist_file = spark_session.sparkContext._conf.get( + "spark.mlflow.pysparkml.autolog.logModelAllowlistFile", None + ) + if allowlist_file: + try: + return _read_log_model_allowlist_from_file(allowlist_file) + except Exception: + # fallback to built-in allowlist file + _logger.exception( + ( + "Reading from custom log_models allowlist file %s failed, " + "fallback to built-in allowlist file." + ), + allowlist_file, + ) + return _read_log_model_allowlist_from_file(builtin_allowlist_file) + else: + return _read_log_model_allowlist_from_file(builtin_allowlist_file) + + +# When autolog called, `_log_model_allowlist` will be initialized. +_log_model_allowlist = None + + +def _get_warning_msg_for_skip_log_model(model): + return ( + f"Model {model.uid} will not be autologged because it is not allowlisted or or because " + "one or more of its nested models are not allowlisted. Call mlflow.spark.log_model() " + "to explicitly log the model, or specify a custom allowlist via the " + "spark.mlflow.pysparkml.autolog.logModelAllowlistFile Spark conf " + "(see mlflow.pyspark.ml.autolog docs for more info)." + ) + + +def _should_log_model(spark_model): + from pyspark.ml.base import Model + + # TODO: Handle PipelineModel/CrossValidatorModel/TrainValidationSplitModel + class_name = _get_fully_qualified_class_name(spark_model) + should_log = class_name in _log_model_allowlist + if not should_log: + for name in _log_model_allowlist: + # only support one trailing * + if name.endswith("*") and class_name.startswith(name[:-1]): + should_log = True + break + if should_log: + if class_name == "pyspark.ml.classification.OneVsRestModel": + return _should_log_model(spark_model.models[0]) + elif class_name == "pyspark.ml.pipeline.PipelineModel": + return all( + _should_log_model(stage) for stage in spark_model.stages if isinstance(stage, Model) + ) + elif _is_parameter_search_model(spark_model): + return _should_log_model(spark_model.bestModel) + else: + return all( + _should_log_model(param_value) + for _, param_value in _get_param_map(spark_model).items() + # Transformers are logged by default as the same behavior as PipelineModel + if isinstance(param_value, Model) + ) + else: + return False + + +def _get_estimator_info_tags(estimator): + """ + Returns: + A dictionary of MLflow run tag keys and values + describing the specified estimator. + """ + return { + "estimator_name": estimator.__class__.__name__, + "estimator_class": _get_fully_qualified_class_name(estimator), + } + + +def _is_parameter_search_estimator(instance): + from pyspark.ml.tuning import CrossValidator, TrainValidationSplit + + return isinstance(instance, (CrossValidator, TrainValidationSplit)) + + +def _is_parameter_search_model(instance): + from pyspark.ml.tuning import CrossValidatorModel, TrainValidationSplitModel + + return isinstance(instance, (CrossValidatorModel, TrainValidationSplitModel)) + + +def _should_log_hierarchy(estimator): + from pyspark.ml import Pipeline + from pyspark.ml.classification import OneVsRest + + return ( + isinstance(estimator, (Pipeline, OneVsRest)) + or _is_parameter_search_estimator(estimator) + or any(_get_stage_type_params(estimator)) + ) + + +class _AutologgingEstimatorMetadata(NamedTuple): + hierarchy: dict[str, Any] + uid_to_indexed_name_map: dict[str, str] + param_search_estimators: list[Any] + + +def _traverse_stage(stage): + from pyspark.ml import Pipeline + + yield stage + if isinstance(stage, Pipeline): + original_sub_stages = stage.getStages() + + try: + iter(original_sub_stages) + except TypeError: + raise TypeError( + f"Pipeline stages should be iterable, but found object {original_sub_stages}" + ) + for stage in original_sub_stages: + yield from _traverse_stage(stage) + else: + # General support for params that of type Params + for _, param_value in _get_stage_type_params(stage).items(): + yield from _traverse_stage(param_value) + + +def _get_uid_to_indexed_name_map(estimator): + counter = defaultdict(int) + uid_to_classname_and_count = {} + for child in _traverse_stage(estimator): + class_name = child.__class__.__name__ + counter[class_name] += 1 + uid_to_classname_and_count[child.uid] = (class_name, counter[class_name]) + return { + uid: f"{class_name}_{count}" if counter[class_name] > 1 else class_name + for uid, (class_name, count) in uid_to_classname_and_count.items() + } + + +def _gen_stage_hierarchy_recursively(stage, uid_to_indexed_name_map): + from pyspark.ml import Pipeline + from pyspark.ml.classification import OneVsRest + + stage_name = uid_to_indexed_name_map[stage.uid] + + if isinstance(stage, Pipeline): + sub_stages = [] + for sub_stage in stage.getStages(): + sub_hierarchy = _gen_stage_hierarchy_recursively(sub_stage, uid_to_indexed_name_map) + sub_stages.append(sub_hierarchy) + return {"name": stage_name, "stages": sub_stages} + elif isinstance(stage, OneVsRest): + classifier_hierarchy = _gen_stage_hierarchy_recursively( + stage.getClassifier(), uid_to_indexed_name_map + ) + return {"name": stage_name, "classifier": classifier_hierarchy} + elif _is_parameter_search_estimator(stage): + evaluator = stage.getEvaluator() + tuned_estimator = stage.getEstimator() + return { + "name": stage_name, + "evaluator": _gen_stage_hierarchy_recursively(evaluator, uid_to_indexed_name_map), + "tuned_estimator": _gen_stage_hierarchy_recursively( + tuned_estimator, uid_to_indexed_name_map + ), + } + elif any(_get_stage_type_params(stage)): + sub_params = {} + for param_name, param_value in _get_stage_type_params(stage).items(): + sub_hierarchy = _gen_stage_hierarchy_recursively(param_value, uid_to_indexed_name_map) + sub_params[param_name] = sub_hierarchy + return {"name": stage_name, "params": sub_params} + else: + return {"name": stage_name} + + +def _gen_estimator_metadata(estimator): + """ + Returns an `_AutologgingEstimatorMetadata` object. + The `_AutologgingEstimatorMetadata` object includes: + - hierarchy: the hierarchy of the estimator, it will expand + pipeline/meta estimator/param tuning estimator + - uid_to_indexed_name_map: a map of `uid` -> `name`, each nested instance uid will be + mapped to a fixed name. The naming rule is using `{class_name}` if the + instance type occurs once, or `{class_name}_{index}` if the instance type occurs + multiple times. The index order is in line with depth-first traversing. + - param_search_estimators: a list includes all param search estimators in the + hierarchy tree. + """ + uid_to_indexed_name_map = _get_uid_to_indexed_name_map(estimator) + param_search_estimators = [ + stage for stage in _traverse_stage(estimator) if _is_parameter_search_estimator(stage) + ] + hierarchy = _gen_stage_hierarchy_recursively(estimator, uid_to_indexed_name_map) + + metadata = _AutologgingEstimatorMetadata( + hierarchy=hierarchy, + uid_to_indexed_name_map=uid_to_indexed_name_map, + param_search_estimators=param_search_estimators, + ) + estimator._autologging_metadata = metadata + + return metadata + + +def _get_param_map(instance): + return { + param.name: instance.getOrDefault(param) + for param in instance.params + if instance.isDefined(param) + } + + +def _get_stage_type_params(instance): + """ + Get the param map of the instance where param value is of type pyspark.ml.param.Params + """ + from pyspark.ml.param import Params + + return { + param.name: instance.getOrDefault(param) + for param in instance.params + if instance.isDefined(param) and isinstance(instance.getOrDefault(param), Params) + } + + +def _get_instance_param_map_recursively(instance, level, uid_to_indexed_name_map): + from pyspark.ml.param import Params + from pyspark.ml.pipeline import Pipeline + + param_map = _get_param_map(instance) + expanded_param_map = {} + + is_pipeline = isinstance(instance, Pipeline) + is_parameter_search_estimator = _is_parameter_search_estimator(instance) + + logged_param_name_prefix = "" if level == 0 else uid_to_indexed_name_map[instance.uid] + "." + + for param_name, param_value in param_map.items(): + logged_param_name = logged_param_name_prefix + param_name + + if is_pipeline and param_name == "stages": + expanded_param_map[logged_param_name] = [ + uid_to_indexed_name_map[stage.uid] for stage in instance.getStages() + ] + for stage in instance.getStages(): + stage_param_map = _get_instance_param_map_recursively( + stage, level + 1, uid_to_indexed_name_map + ) + expanded_param_map.update(stage_param_map) + elif is_parameter_search_estimator and param_name == "estimator": + expanded_param_map[logged_param_name] = uid_to_indexed_name_map[param_value.uid] + # skip log estimator's nested params because they will be logged as JSON artifact, + # and they will be logged in nested runs as well. + elif is_parameter_search_estimator and param_name == "estimatorParamMaps": + # this param will be saved as JSON format artifact. + pass + elif isinstance(param_value, Params): + # handle the case param value type inherits `pyspark.ml.param.Params` + # e.g. param like + # `OneVsRest.classifier`/`CrossValidator.evaluator` + expanded_param_map[logged_param_name] = uid_to_indexed_name_map[param_value.uid] + internal_param_map = _get_instance_param_map_recursively( + param_value, level + 1, uid_to_indexed_name_map + ) + expanded_param_map.update(internal_param_map) + else: + expanded_param_map[logged_param_name] = param_value + + return expanded_param_map + + +def _get_instance_param_map(instance, uid_to_indexed_name_map): + return _get_instance_param_map_recursively( + instance, level=0, uid_to_indexed_name_map=uid_to_indexed_name_map + ) + + +def _create_child_runs_for_parameter_search(parent_estimator, parent_model, parent_run, child_tags): + client = MlflowClient() + # Use the start time of the parent parameter search run as a rough estimate for the + # start time of child runs, since we cannot precisely determine when each point + # in the parameter search space was explored + child_run_start_time = parent_run.info.start_time + child_run_end_time = get_current_time_millis() + + estimator_param_maps = parent_estimator.getEstimatorParamMaps() + tuned_estimator = parent_estimator.getEstimator() + + metrics_dict, _ = _get_param_search_metrics_and_best_index(parent_estimator, parent_model) + for i, est_param in enumerate(estimator_param_maps): + child_estimator = tuned_estimator.copy(est_param) + tags_to_log = dict(child_tags) if child_tags else {} + tags_to_log.update({MLFLOW_PARENT_RUN_ID: parent_run.info.run_id}) + tags_to_log.update(_get_estimator_info_tags(child_estimator)) + + child_run = client.create_run( + experiment_id=parent_run.info.experiment_id, + start_time=child_run_start_time, + tags=tags_to_log, + ) + + params_to_log = _get_instance_param_map( + child_estimator, parent_estimator._autologging_metadata.uid_to_indexed_name_map + ) + param_batches_to_log = _chunk_dict(params_to_log, chunk_size=MAX_PARAMS_TAGS_PER_BATCH) + metrics_to_log = {k: v[i] for k, v in metrics_dict.items()} + for params_batch, metrics_batch in zip_longest( + param_batches_to_log, [metrics_to_log], fillvalue={} + ): + # Trim any parameter keys / values and metric keys that exceed the limits + # imposed by corresponding MLflow Tracking APIs (e.g., LogParam, LogMetric) + truncated_params_batch = _truncate_dict( + params_batch, MAX_ENTITY_KEY_LENGTH, MAX_PARAM_VAL_LENGTH + ) + truncated_metrics_batch = _truncate_dict( + metrics_batch, max_key_length=MAX_ENTITY_KEY_LENGTH + ) + client.log_batch( + run_id=child_run.info.run_id, + params=[ + Param(str(key), str(value)) for key, value in truncated_params_batch.items() + ], + metrics=[ + Metric(key=str(key), value=value, timestamp=child_run_end_time, step=0) + for key, value in truncated_metrics_batch.items() + ], + ) + client.set_terminated(run_id=child_run.info.run_id, end_time=child_run_end_time) + + +def _log_parameter_search_results_as_artifact(param_maps, metrics_dict, run_id): + import pandas as pd + + result_dict = defaultdict(list) + result_dict["params"] = [] + result_dict.update(metrics_dict) + for param_map in param_maps: + result_dict["params"].append(json.dumps(param_map)) + for param_name, param_value in param_map.items(): + result_dict[f"param.{param_name}"].append(param_value) + + results_df = pd.DataFrame.from_dict(result_dict) + with TempDir() as t: + results_path = t.path("search_results.csv") + results_df.to_csv(results_path, index=False) + MlflowClient().log_artifact(run_id, results_path) + + +def _get_warning_msg_for_fit_call_with_a_list_of_params(estimator): + return ( + "Skip pyspark ML autologging when calling " + + f"{_get_fully_qualified_class_name(estimator)}.fit with a list of params," + + "if you want to autolog for this case, you convert code to call `fit` with " + + "each single param map." + ) + + +def _get_tuning_param_maps(param_search_estimator, uid_to_indexed_name_map): + tuning_param_maps = [] + + def gen_log_key(param): + if param.parent not in uid_to_indexed_name_map: + raise ValueError( + "Tuning params should not include params not owned by the tuned estimator, but " + f"found a param {param}" + ) + return f"{uid_to_indexed_name_map[param.parent]}.{param.name}" + + for eps in param_search_estimator.getEstimatorParamMaps(): + tuning_param_maps.append({gen_log_key(k): v for k, v in eps.items()}) + return tuning_param_maps + + +def _get_param_search_metrics_and_best_index(param_search_estimator, param_search_model): + """ + Return a tuple of `(metrics_dict, best_index)` + `metrics_dict` is a dict of metric_name --> metric_values for each param map + - For CrossValidatorModel, the result dict contains metrics of avg_metris and std_metrics + for each param map. + - For TrainValidationSplitModel, the result dict contains metrics for each param map. + + `best_index` is the best index of trials. + """ + from pyspark.ml.tuning import CrossValidatorModel, TrainValidationSplitModel + + metrics_dict = {} + + metric_key = param_search_estimator.getEvaluator().getMetricName() + if isinstance(param_search_model, CrossValidatorModel): + avg_metrics = param_search_model.avgMetrics + metrics_dict["avg_" + metric_key] = avg_metrics + if hasattr(param_search_model, "stdMetrics"): + metrics_dict["std_" + metric_key] = param_search_model.stdMetrics + elif isinstance(param_search_model, TrainValidationSplitModel): + avg_metrics = param_search_model.validationMetrics + metrics_dict[metric_key] = avg_metrics + else: + raise RuntimeError(f"Unknown parameter search model type {type(param_search_model)}.") + + if param_search_estimator.getEvaluator().isLargerBetter(): + best_index = np.argmax(avg_metrics) + else: + best_index = np.argmin(avg_metrics) + + return metrics_dict, best_index + + +def _log_estimator_params(param_map): + # Chunk model parameters to avoid hitting the log_batch API limit + for chunk in _chunk_dict(param_map, chunk_size=MAX_PARAMS_TAGS_PER_BATCH): + truncated = _truncate_dict(chunk, MAX_ENTITY_KEY_LENGTH, MAX_PARAM_VAL_LENGTH) + mlflow.log_params(truncated) + + +class _AutologgingMetricsManager: + """ + This class is designed for holding information which is used by autologging metrics + It will hold information of: + (1) a map of "prediction result object id" to a tuple of dataset name(the dataset is + the one which generate the prediction result) and run_id. + Note: We need this map instead of setting the run_id into the "prediction result object" + because the object maybe a numpy array which does not support additional attribute + assignment. + (2) _log_post_training_metrics_enabled flag, in the following method scope: + `Estimator.fit`, `Model.transform`, `Evaluator.evaluate`, + in order to avoid nested/duplicated autologging metric, when run into these scopes, + we need temporarily disable the metric autologging. + (3) _eval_dataset_info_map, it is a double level map: + `_eval_dataset_info_map[run_id][eval_dataset_var_name]` will get a list, each + element in the list is an id of "eval_dataset" instance. + This data structure is used for: + * generating unique dataset name key when autologging metric. For each eval dataset object, + if they have the same eval_dataset_var_name, but object ids are different, + then they will be assigned different name (via appending index to the + eval_dataset_var_name) when autologging. + (4) _evaluator_call_info, it is a double level map: + `_metric_api_call_info[run_id][metric_name]` will get a list of tuples, each tuple is: + (logged_metric_key, evaluator_information) + Evaluator information includes evaluator class name and params, these information + will also be logged into "metric_info.json" artifacts. + + Note: this class is not thread-safe. + Design rule for this class: + Because this class instance is a global instance, in order to prevent memory leak, it should + only holds IDs and other small objects references. This class internal data structure should + avoid reference to user dataset variables or model variables. + """ + + def __init__(self): + self._pred_result_id_to_dataset_name_and_run_id = {} + self._eval_dataset_info_map = defaultdict(lambda: defaultdict(list)) + self._evaluator_call_info = defaultdict(lambda: defaultdict(list)) + self._log_post_training_metrics_enabled = True + self._metric_info_artifact_need_update = defaultdict(lambda: False) + + def should_log_post_training_metrics(self): + """ + Check whether we should run patching code for autologging post training metrics. + This checking should surround the whole patched code due to the safe guard checking, + See following note. + + Note: It includes checking `_SparkTrainingSession.is_active()`, This is a safe guarding + for meta-estimator (e.g. CrossValidator/TrainValidationSplit) case: + running CrossValidator.fit, the nested `estimator.fit` will be called in parallel, + but, the _autolog_training_status is a global status without thread-safe lock protecting. + This safe guarding will prevent code run into this case. + """ + return not _SparkTrainingSession.is_active() and self._log_post_training_metrics_enabled + + def disable_log_post_training_metrics(self): + class LogPostTrainingMetricsDisabledScope: + def __enter__(inner_self): + inner_self.old_status = self._log_post_training_metrics_enabled + self._log_post_training_metrics_enabled = False + + def __exit__(inner_self, exc_type, exc_val, exc_tb): + self._log_post_training_metrics_enabled = inner_self.old_status + + return LogPostTrainingMetricsDisabledScope() + + @staticmethod + def get_run_id_for_model(model): + return getattr(model, "_mlflow_run_id", None) + + @staticmethod + def is_metric_value_loggable(metric_value): + """ + check whether the specified `metric_value` is a numeric value which can be logged + as an MLflow metric. + """ + return isinstance(metric_value, (int, float, np.number)) and not isinstance( + metric_value, bool + ) + + def register_model(self, model, run_id): + """ + In `patched_fit`, we need register the model with the run_id used in `patched_fit` + So that in following metric autologging, the metric will be logged into the registered + run_id + """ + model._mlflow_run_id = run_id + + @staticmethod + def gen_name_with_index(name, index): + assert index >= 0 + if index == 0: + return name + else: + # Use '-' as the separator between name and index, + # The '-' is not valid character in python var name + # so it can prevent name conflicts after appending index. + return f"{name}-{index + 1}" + + def register_prediction_input_dataset(self, model, eval_dataset): + """ + Register prediction input dataset into eval_dataset_info_map, it will do: + 1. inspect eval dataset var name. + 2. check whether eval_dataset_info_map already registered this eval dataset. + will check by object id. + 3. register eval dataset with id. + 4. return eval dataset name with index. + + Note: this method include inspecting argument variable name. + So should be called directly from the "patched method", to ensure it capture + correct argument variable name. + """ + eval_dataset_name = _inspect_original_var_name( + eval_dataset, fallback_name="unknown_dataset" + ) + eval_dataset_id = id(eval_dataset) + + run_id = self.get_run_id_for_model(model) + registered_dataset_list = self._eval_dataset_info_map[run_id][eval_dataset_name] + + for i, id_i in enumerate(registered_dataset_list): + if eval_dataset_id == id_i: + index = i + break + else: + index = len(registered_dataset_list) + + if index == len(registered_dataset_list): + # register new eval dataset + registered_dataset_list.append(eval_dataset_id) + + return self.gen_name_with_index(eval_dataset_name, index) + + def register_prediction_result(self, run_id, eval_dataset_name, predict_result): + """ + Register the relationship + id(prediction_result) --> (eval_dataset_name, run_id) + into map `_pred_result_id_to_dataset_name_and_run_id` + """ + value = (eval_dataset_name, run_id) + prediction_result_id = id(predict_result) + self._pred_result_id_to_dataset_name_and_run_id[prediction_result_id] = value + + def clean_id(id_): + _AUTOLOGGING_METRICS_MANAGER._pred_result_id_to_dataset_name_and_run_id.pop(id_, None) + + # When the `predict_result` object being GCed, its ID may be reused, so register a finalizer + # to clear the ID from the dict for preventing wrong ID mapping. + weakref.finalize(predict_result, clean_id, prediction_result_id) + + def get_run_id_and_dataset_name_for_evaluator_call(self, pred_result_dataset): + """ + Given a registered prediction result dataset object, + return a tuple of (run_id, eval_dataset_name) + """ + if id(pred_result_dataset) in self._pred_result_id_to_dataset_name_and_run_id: + dataset_name, run_id = self._pred_result_id_to_dataset_name_and_run_id[ + id(pred_result_dataset) + ] + return run_id, dataset_name + else: + return None, None + + def gen_evaluator_info(self, evaluator): + """ + Generate evaluator information, include evaluator class name and params. + """ + class_name = _get_fully_qualified_class_name(evaluator) + param_map = _truncate_dict( + _get_param_map(evaluator), MAX_ENTITY_KEY_LENGTH, MAX_PARAM_VAL_LENGTH + ) + return {"evaluator_class": class_name, "params": param_map} + + def register_evaluator_call(self, run_id, metric_name, dataset_name, evaluator_info): + """ + Register the `Evaluator.evaluate` call, including register the evaluator information + (See doc of `gen_evaluator_info` method) into the corresponding run_id and metric_name + entry in the registry table. + """ + evaluator_call_info_list = self._evaluator_call_info[run_id][metric_name] + + index = len(evaluator_call_info_list) + metric_name_with_index = self.gen_name_with_index(metric_name, index) + metric_key = f"{metric_name_with_index}_{dataset_name}" + + evaluator_call_info_list.append((metric_key, evaluator_info)) + + # Set the flag to true, represent the metric info in this run need update. + # Later when `log_eval_metric` called, it will generate a new metric_info artifact + # and overwrite the old artifact. + self._metric_info_artifact_need_update[run_id] = True + return metric_key + + def log_post_training_metric(self, run_id, key, value): + """ + Log the metric into the specified mlflow run. + and it will also update the metric_info artifact if needed. + """ + # Note: if the case log the same metric key multiple times, + # newer value will overwrite old value + client = MlflowClient() + client.log_metric(run_id=run_id, key=key, value=value) + if self._metric_info_artifact_need_update[run_id]: + evaluator_call_list = [] + for v in self._evaluator_call_info[run_id].values(): + evaluator_call_list.extend(v) + + evaluator_call_list.sort(key=lambda x: x[0]) + dict_to_log = OrderedDict(evaluator_call_list) + client.log_dict(run_id=run_id, dictionary=dict_to_log, artifact_file="metric_info.json") + self._metric_info_artifact_need_update[run_id] = False + + +# The global `_AutologgingMetricsManager` instance which holds information used in +# post-training metric autologging. See doc of class `_AutologgingMetricsManager` for details. +_AUTOLOGGING_METRICS_MANAGER = _AutologgingMetricsManager() + + +def _get_columns_with_unsupported_data_type(df): + from pyspark.ml.linalg import VectorUDT + + from mlflow.types.schema import DataType + + supported_spark_types = DataType.get_spark_types() + unsupported_columns = [] + for field in df.schema.fields: + if (field.dataType not in supported_spark_types) and not isinstance( + field.dataType, VectorUDT + ): + unsupported_columns.append(field) + return unsupported_columns + + +def _check_or_set_model_prediction_column(spark_model, input_spark_df): + from pyspark.ml import PipelineModel + + prediction_column = "prediction" + if isinstance(spark_model, PipelineModel) and spark_model.stages[-1].hasParam("outputCol"): + from mlflow.utils._spark_utils import _get_active_spark_session + + spark = _get_active_spark_session() + # do a transform with an empty input DataFrame + # to get the schema of the transformed DataFrame + transformed_df = spark_model.transform(spark.createDataFrame([], input_spark_df.schema)) + # Ensure prediction column doesn't already exist + if prediction_column not in transformed_df.columns: + # make sure predict work by default for Transformers + spark_model.stages[-1].setOutputCol(prediction_column) + + return prediction_column + + +def _infer_spark_model_signature(spark_model, input_example_spark_df): + from mlflow.models import infer_signature + + prediction_column = _check_or_set_model_prediction_column(spark_model, input_example_spark_df) + model_output = spark_model.transform(input_example_spark_df).select(prediction_column) + # TODO: Remove this once we support non-scalar spark data types + if unsupported_columns := _get_columns_with_unsupported_data_type(model_output): + _logger.warning( + "Model outputs contain unsupported Spark data types: " + f"{unsupported_columns}. Output schema is not be logged." + ) + model_output = None + + signature = infer_signature(input_example_spark_df, model_output) + if signature.outputs: + # We only have one prediction column output, + # convert it to unnamed output schema to keep consistent with old MLflow version. + signature.outputs.inputs[0].name = None + return signature + + +@autologging_integration(AUTOLOGGING_INTEGRATION_NAME) +def autolog( + log_models=True, + log_datasets=True, + disable=False, + exclusive=False, + disable_for_unsupported_versions=False, + silent=False, + log_post_training_metrics=True, + registered_model_name=None, + log_input_examples=False, + log_model_signatures=True, + log_model_allowlist=None, + extra_tags=None, +): + """ + Enables (or disables) and configures autologging for pyspark ml estimators. + This method is not threadsafe. + This API requires Spark 3.0 or above. + + **When is autologging performed?** + Autologging is performed when you call ``Estimator.fit`` except for estimators (featurizers) + under ``pyspark.ml.feature``. + + **Logged information** + **Parameters** + - Parameters obtained by ``estimator.params``. If a param value is also an ``Estimator``, + then params in the the wrapped estimator will also be logged, the nested param key + will be `{estimator_uid}.{param_name}` + + **Tags** + - An estimator class name (e.g. "LinearRegression"). + - A fully qualified estimator class name + (e.g. "pyspark.ml.regression.LinearRegression"). + + .. _post training metrics: + + **Post training metrics** + When users call evaluator APIs after model training, MLflow tries to capture the + `Evaluator.evaluate` results and log them as MLflow metrics to the Run associated with + the model. All pyspark ML evaluators are supported. + + For post training metrics autologging, the metric key format is: + "{metric_name}[-{call_index}]_{dataset_name}" + + - The metric name is the name returned by `Evaluator.getMetricName()` + - If multiple calls are made to the same pyspark ML evaluator metric, each subsequent call + adds a "call_index" (starting from 2) to the metric key. + - MLflow uses the prediction input dataset variable name as the "dataset_name" in the + metric key. The "prediction input dataset variable" refers to the variable which was + used as the `dataset` argument of `model.transform` call. + Note: MLflow captures the "prediction input dataset" instance in the outermost call + frame and fetches the variable name in the outermost call frame. If the "prediction + input dataset" instance is an intermediate expression without a defined variable + name, the dataset name is set to "unknown_dataset". If multiple "prediction input + dataset" instances have the same variable name, then subsequent ones will append an + index (starting from 2) to the inspected dataset name. + + **Limitations** + - MLflow cannot find run information for other objects derived from a given prediction + result (e.g. by doing some transformation on the prediction result dataset). + + **Artifacts** + - An MLflow Model with the :py:mod:`mlflow.spark` flavor containing a fitted estimator + (logged by :py:func:`mlflow.spark.log_model()`). Note that large models may not be + autologged for performance and storage space considerations, and autologging for + Pipelines and hyperparameter tuning meta-estimators (e.g. CrossValidator) is not yet + supported. + See ``log_models`` param below for details. + - For post training metrics API calls, a "metric_info.json" artifact is logged. This is a + JSON object whose keys are MLflow post training metric names + (see "Post training metrics" section for the key format) and whose values are the + corresponding evaluator information, including evaluator class name and evaluator params. + + **How does autologging work for meta estimators?** + When a meta estimator (e.g. `Pipeline`_, `CrossValidator`_, `TrainValidationSplit`_, + `OneVsRest`_) + calls ``fit()``, it internally calls ``fit()`` on its child estimators. Autologging + does NOT perform logging on these constituent ``fit()`` calls. + + A "estimator_info.json" artifact is logged, which includes a `hierarchy` entry + describing the hierarchy of the meta estimator. The hierarchy includes expanded + entries for all nested stages, such as nested pipeline stages. + + **Parameter search** + In addition to recording the information discussed above, autologging for parameter + search meta estimators (`CrossValidator`_ and `TrainValidationSplit`_) records child runs + with metrics for each set of explored parameters, as well as artifacts and parameters + for the best model and the best parameters (if available). + For better readability, the "estimatorParamMaps" param in parameter search estimator + will be recorded inside "estimator_info" artifact, see following description. + Inside "estimator_info.json" artifact, in addition to the "hierarchy", records 2 more + items: "tuning_parameter_map_list": a list contains all parameter maps used in tuning, + and "tuned_estimator_parameter_map": the parameter map of the tuned estimator. + Records a "best_parameters.json" artifacts, contains the best parameter it searched out. + Records a "search_results.csv" artifacts, contains search results, it is a table with + 2 columns: "params" and "metric". + + .. _OneVsRest: + https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.ml.classification.OneVsRest.html#pyspark.ml.classification.OneVsRest + .. _Pipeline: + https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.ml.Pipeline.html#pyspark.ml.Pipeline + .. _CrossValidator: + https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.ml.tuning.CrossValidator.html#pyspark.ml.tuning.CrossValidator + .. _TrainValidationSplit: + https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.ml.tuning.TrainValidationSplit.html#pyspark.ml.tuning.TrainValidationSplit + + Args: + log_models: If ``True``, if trained models are in allowlist, they are logged as MLflow + model artifacts. If ``False``, trained models are not logged. + Note: the built-in allowlist excludes some models (e.g. ALS models) which + can be large. To specify a custom allowlist, create a file containing a + newline-delimited list of fully-qualified estimator classnames, and set + the "spark.mlflow.pysparkml.autolog.logModelAllowlistFile" Spark config + to the path of your allowlist file. + log_datasets: If ``True``, dataset information is logged to MLflow Tracking. + If ``False``, dataset information is not logged. + disable: If ``True``, disables the PySpark ML autologging integration. If ``False``, + enables the pyspark ML autologging integration. + exclusive: If ``True``, autologged content is not logged to user-created fluent runs. + If ``False``, autologged content is logged to the active fluent run, + which may be user-created. + disable_for_unsupported_versions: If ``True``, disable autologging for versions of + pyspark that have not been tested against this version of the MLflow + client or are incompatible. + silent: If ``True``, suppress all event logs and warnings from MLflow during pyspark ML + autologging. If ``False``, show all events and warnings during pyspark ML + autologging. + log_post_training_metrics: If ``True``, post training metrics are logged. Defaults to + ``True``. See the `post training metrics`_ section for more + details. + registered_model_name: If given, each time a model is trained, it is registered as a + new model version of the registered model with this name. + The registered model is created if it does not already exist. + log_input_examples: If ``True``, input examples from training datasets are collected and + logged along with pyspark ml model artifacts during training. If + ``False``, input examples are not logged. + log_model_signatures: If ``True``, + :py:class:`ModelSignatures ` + describing model inputs and outputs are collected and logged along + with spark ml pipeline/estimator artifacts during training. + If ``False`` signatures are not logged. + + .. warning:: + + Currently, only scalar Spark data types are supported. If + model inputs/outputs contain non-scalar Spark data types such + as ``pyspark.ml.linalg.Vector``, signatures are not logged. + + log_model_allowlist: If given, it overrides the default log model allowlist in mlflow. + This takes precedence over the spark configuration of + "spark.mlflow.pysparkml.autolog.logModelAllowlistFile". + + **The default log model allowlist in mlflow** + .. literalinclude:: ../../../../mlflow/pyspark/ml/log_model_allowlist.txt + :language: text + + extra_tags: A dictionary of extra tags to set on each managed run created by autologging. + """ + from pyspark.ml.base import Estimator, Model + from pyspark.ml.evaluation import Evaluator + + from mlflow.tracking.context import registry as context_registry + + global _log_model_allowlist + + if log_model_allowlist: + _log_model_allowlist = {model.strip() for model in log_model_allowlist} + else: + _log_model_allowlist = _read_log_model_allowlist() + + def _log_pretraining_metadata(estimator, params, input_df): + if params and isinstance(params, dict): + estimator = estimator.copy(params) + + autologging_metadata = _gen_estimator_metadata(estimator) + + artifact_dict = {} + + param_map = _get_instance_param_map(estimator, autologging_metadata.uid_to_indexed_name_map) + if _should_log_hierarchy(estimator): + artifact_dict["hierarchy"] = autologging_metadata.hierarchy + + for param_search_estimator in autologging_metadata.param_search_estimators: + param_search_estimator_name = ( + f"{autologging_metadata.uid_to_indexed_name_map[param_search_estimator.uid]}" + ) + artifact_dict[param_search_estimator_name] = {} + + artifact_dict[param_search_estimator_name]["tuning_parameter_map_list"] = ( + _get_tuning_param_maps( + param_search_estimator, autologging_metadata.uid_to_indexed_name_map + ) + ) + + artifact_dict[param_search_estimator_name]["tuned_estimator_parameter_map"] = ( + _get_instance_param_map_recursively( + param_search_estimator.getEstimator(), + 1, + autologging_metadata.uid_to_indexed_name_map, + ) + ) + + if artifact_dict: + mlflow.log_dict(artifact_dict, artifact_file="estimator_info.json") + + _log_estimator_params(param_map) + + mlflow.set_tags(_get_estimator_info_tags(estimator)) + + if log_datasets: + try: + context_tags = context_registry.resolve_tags() + code_source = CodeDatasetSource(context_tags) + dataset = SparkDataset( + df=input_df, + source=code_source, + ) + mlflow.log_input(dataset, "train") + except Exception as e: + _logger.warning( + "Failed to log training dataset information to MLflow Tracking. Reason: %s", e + ) + + def _log_posttraining_metadata(estimator, spark_model, params, input_df): + if _is_parameter_search_estimator(estimator): + try: + # Fetch environment-specific tags (e.g., user and source) to ensure that lineage + # information is consistent with the parent run + child_tags = context_registry.resolve_tags() + child_tags.update({MLFLOW_AUTOLOGGING: AUTOLOGGING_INTEGRATION_NAME}) + _create_child_runs_for_parameter_search( + parent_estimator=estimator, + parent_model=spark_model, + parent_run=mlflow.active_run(), + child_tags=child_tags, + ) + except Exception: + msg = ( + "Encountered exception during creation of child runs for parameter search." + f" Child runs may be missing. Exception: {traceback.format_exc()}" + ) + _logger.warning(msg) + + estimator_param_maps = _get_tuning_param_maps( + estimator, estimator._autologging_metadata.uid_to_indexed_name_map + ) + + metrics_dict, best_index = _get_param_search_metrics_and_best_index( + estimator, spark_model + ) + _log_parameter_search_results_as_artifact( + estimator_param_maps, metrics_dict, mlflow.active_run().info.run_id + ) + + # Log best_param_map as JSON artifact + best_param_map = estimator_param_maps[best_index] + mlflow.log_dict(best_param_map, artifact_file="best_parameters.json") + + # Log best_param_map as autologging parameters as well + _log_estimator_params( + { + f"best_{param_name}": param_value + for param_name, param_value in best_param_map.items() + } + ) + + if log_models: + if _should_log_model(spark_model): + from mlflow.pyspark.ml._autolog import ( + cast_spark_df_with_vector_to_array, + get_feature_cols, + ) + + def _get_input_example_spark_df(): + feature_cols = list(get_feature_cols(input_df, spark_model)) + return input_df.select(feature_cols).limit(INPUT_EXAMPLE_SAMPLE_ROWS) + + def _infer_model_signature(input_example_slice): + return _infer_spark_model_signature(spark_model, input_example_slice) + + # TODO: Remove this once we support non-scalar spark data types + nonlocal log_model_signatures + if log_model_signatures: + if unsupported_columns := _get_columns_with_unsupported_data_type(input_df): + _logger.warning( + "Model inputs contain unsupported Spark data types: " + f"{unsupported_columns}. Model signature is not logged." + ) + log_model_signatures = False + + # `_infer_spark_model_signature` mutates the model. Copy the model to preserve the + # original model. + try: + spark_model = spark_model.copy() + except Exception: + _logger.debug( + "Failed to copy the model, using the original model.", exc_info=True + ) + input_example_spark_df, signature = resolve_input_example_and_signature( + _get_input_example_spark_df, + _infer_model_signature, + log_input_examples, + log_model_signatures, + _logger, + ) + + if input_example_spark_df: + input_example = cast_spark_df_with_vector_to_array( + input_example_spark_df + ).toPandas() + else: + input_example = None + mlflow.spark.log_model( + spark_model, + "model", + registered_model_name=registered_model_name, + input_example=input_example, + signature=signature, + ) + if _is_parameter_search_model(spark_model): + mlflow.spark.log_model( + spark_model.bestModel, + "best_model", + ) + else: + _logger.warning(_get_warning_msg_for_skip_log_model(spark_model)) + + def fit_mlflow(original, self, *args, **kwargs): + params = get_method_call_arg_value(1, "params", None, args, kwargs) + + # Do not perform autologging on direct calls to fit() for featurizers. + # Note that featurizers will be autologged when they're fit as part of a Pipeline. + if _get_fully_qualified_class_name(self).startswith("pyspark.ml.feature."): + return original(self, *args, **kwargs) + elif isinstance(params, (list, tuple)): + # skip the case params is a list or tuple, this case it will call + # fitMultiple and return a model iterator + _logger.warning(_get_warning_msg_for_fit_call_with_a_list_of_params(self)) + return original(self, *args, **kwargs) + else: + # we need generate estimator param map so we call `self.copy(params)` to construct + # an estimator with the extra params. + from pyspark.storagelevel import StorageLevel + + estimator = self.copy(params) if params is not None else self + input_training_df = args[0].persist(StorageLevel.MEMORY_AND_DISK) + _log_pretraining_metadata(estimator, params, input_training_df) + spark_model = original(self, *args, **kwargs) + _log_posttraining_metadata(estimator, spark_model, params, input_training_df) + input_training_df.unpersist() + + return spark_model + + def patched_fit(original, self, *args, **kwargs): + should_log_post_training_metrics = ( + log_post_training_metrics + and _AUTOLOGGING_METRICS_MANAGER.should_log_post_training_metrics() + ) + with _SparkTrainingSession(estimator=self, allow_children=False) as t: + if t.should_log(): + with _AUTOLOGGING_METRICS_MANAGER.disable_log_post_training_metrics(): + fit_result = fit_mlflow(original, self, *args, **kwargs) + # In some cases the `fit_result` may be an iterator of spark models. + if should_log_post_training_metrics and isinstance(fit_result, Model): + _AUTOLOGGING_METRICS_MANAGER.register_model( + fit_result, mlflow.active_run().info.run_id + ) + return fit_result + else: + return original(self, *args, **kwargs) + + def patched_transform(original, self, *args, **kwargs): + run_id = _AUTOLOGGING_METRICS_MANAGER.get_run_id_for_model(self) + if _AUTOLOGGING_METRICS_MANAGER.should_log_post_training_metrics() and run_id: + predict_result = original(self, *args, **kwargs) + eval_dataset = get_method_call_arg_value(0, "dataset", None, args, kwargs) + eval_dataset_name = _AUTOLOGGING_METRICS_MANAGER.register_prediction_input_dataset( + self, eval_dataset + ) + _AUTOLOGGING_METRICS_MANAGER.register_prediction_result( + run_id, eval_dataset_name, predict_result + ) + return predict_result + else: + return original(self, *args, **kwargs) + + def patched_evaluate(original, self, *args, **kwargs): + if _AUTOLOGGING_METRICS_MANAGER.should_log_post_training_metrics(): + with _AUTOLOGGING_METRICS_MANAGER.disable_log_post_training_metrics(): + metric = original(self, *args, **kwargs) + + if _AUTOLOGGING_METRICS_MANAGER.is_metric_value_loggable(metric): + params = get_method_call_arg_value(1, "params", None, args, kwargs) + # we need generate evaluator param map so we call `self.copy(params)` to construct + # an evaluator with the extra evaluation params. + evaluator = self.copy(params) if params is not None else self + metric_name = evaluator.getMetricName() + evaluator_info = _AUTOLOGGING_METRICS_MANAGER.gen_evaluator_info(evaluator) + + pred_result_dataset = get_method_call_arg_value(0, "dataset", None, args, kwargs) + ( + run_id, + dataset_name, + ) = _AUTOLOGGING_METRICS_MANAGER.get_run_id_and_dataset_name_for_evaluator_call( + pred_result_dataset + ) + if run_id and dataset_name: + metric_key = _AUTOLOGGING_METRICS_MANAGER.register_evaluator_call( + run_id, metric_name, dataset_name, evaluator_info + ) + _AUTOLOGGING_METRICS_MANAGER.log_post_training_metric( + run_id, metric_key, metric + ) + if log_datasets: + try: + context_tags = context_registry.resolve_tags() + code_source = CodeDatasetSource(context_tags) + + dataset = SparkDataset( + df=pred_result_dataset, + source=code_source, + ) + tags = [InputTag(key=MLFLOW_DATASET_CONTEXT, value="eval")] + dataset_input = DatasetInput( + dataset=dataset._to_mlflow_entity(), tags=tags + ) + client = MlflowClient() + client.log_inputs(run_id, [dataset_input]) + except Exception as e: + _logger.warning( + "Failed to log evaluation dataset information to MLflow Tracking. " + "Reason: %s", + e, + ) + return metric + else: + return original(self, *args, **kwargs) + + safe_patch( + AUTOLOGGING_INTEGRATION_NAME, + Estimator, + "fit", + patched_fit, + manage_run=True, + extra_tags=extra_tags, + ) + + if log_post_training_metrics: + safe_patch( + AUTOLOGGING_INTEGRATION_NAME, + Model, + "transform", + patched_transform, + manage_run=False, + ) + safe_patch( + AUTOLOGGING_INTEGRATION_NAME, + Evaluator, + "evaluate", + patched_evaluate, + manage_run=False, + ) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyspark/ml/__pycache__/__init__.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyspark/ml/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..c6ef9d3ec2fecb341a08595af0ef3c6b0d884031 Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyspark/ml/__pycache__/__init__.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyspark/ml/__pycache__/_autolog.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyspark/ml/__pycache__/_autolog.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..7c69c6e375adffa1ed9547fb4a5cf531faf876ee Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyspark/ml/__pycache__/_autolog.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyspark/ml/_autolog.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyspark/ml/_autolog.py new file mode 100644 index 0000000000000000000000000000000000000000..f52fc48419fad006aaca2bcaac01bb548bbeb7e8 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyspark/ml/_autolog.py @@ -0,0 +1,96 @@ +import re +from functools import reduce + +try: + # For spark >= 4.0 + from pyspark.errors.exceptions.base import IllegalArgumentException +except ModuleNotFoundError: + from pyspark.sql.utils import IllegalArgumentException +from pyspark.ml.base import Transformer +from pyspark.ml.functions import vector_to_array +from pyspark.ml.linalg import VectorUDT +from pyspark.ml.pipeline import PipelineModel +from pyspark.sql import DataFrame +from pyspark.sql import types as t + + +def cast_spark_df_with_vector_to_array(input_spark_df): + """ + Finds columns of vector type in a spark dataframe and + casts them to array type. + + Args: + input_spark_df: + + Returns: + A spark dataframe with vector columns transformed to array type + """ + vector_type_columns = [ + _field.name for _field in input_spark_df.schema if isinstance(_field.dataType, VectorUDT) + ] + return reduce( + lambda df, vector_col: df.withColumn(vector_col, vector_to_array(vector_col)), + vector_type_columns, + input_spark_df, + ) + + +def _do_pipeline_transform(df: DataFrame, transformer: Transformer | PipelineModel): + """ + A util method that runs transform on a pipeline model/transformer + + Args: + df: a spark dataframe + + Returns: + output transformed dataframe using pipeline model/transformer + """ + return transformer.transform(df) + + +def _get_struct_type_by_cols(input_fields: set[str], df_schema: t.StructType) -> t.StructType: + """ + Args: + input_fields: A set of input columns to be + intersected with the input dataset's columns. + df_schema: A Spark dataframe schema to compare input_fields + + Returns: + A StructType from the intersection of given columns and + the columns present in the training dataset + """ + if len(input_fields) > 0: + return t.StructType([_field for _field in df_schema.fields if _field.name in input_fields]) + return [] + + +def get_feature_cols( + df: DataFrame, + transformer: Transformer | PipelineModel, +) -> set[str]: + """ + Finds feature columns from an input dataset. If a dataset + contains non-feature columns, those columns are not returned, but + if `input_fields` is set to include non-feature columns those + will be included in the return set of column names. + + Args: + df: An input spark dataframe. + transformer: A pipeline/transformer to get the required feature columns + + Returns: + A set of all the feature columns that are required + for the pipeline/transformer plus any initial columns passed in. + """ + feature_cols = set() + df_subset = df.limit(1).cache() + for column in df.columns: + try: + transformer.transform(df_subset.drop(column)) + except IllegalArgumentException as iae: + if re.search("does not exist|no such struct field", str(iae), re.IGNORECASE): + feature_cols.add(column) + continue + raise + df_subset.unpersist() + return feature_cols diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyspark/ml/log_model_allowlist.txt b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyspark/ml/log_model_allowlist.txt new file mode 100644 index 0000000000000000000000000000000000000000..e23a9daa95a967bfae771799a0508d52f4020fca --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyspark/ml/log_model_allowlist.txt @@ -0,0 +1,62 @@ +# classification +pyspark.ml.classification.LinearSVCModel +pyspark.ml.classification.DecisionTreeClassificationModel +pyspark.ml.classification.GBTClassificationModel +pyspark.ml.classification.LogisticRegressionModel +pyspark.ml.classification.RandomForestClassificationModel +pyspark.ml.classification.NaiveBayesModel + +# clustering +pyspark.ml.clustering.BisectingKMeansModel +pyspark.ml.clustering.KMeansModel +pyspark.ml.clustering.GaussianMixtureModel + +# Regression +pyspark.ml.regression.AFTSurvivalRegressionModel +pyspark.ml.regression.DecisionTreeRegressionModel +pyspark.ml.regression.GBTRegressionModel +pyspark.ml.regression.GeneralizedLinearRegressionModel +pyspark.ml.regression.LinearRegressionModel +pyspark.ml.regression.RandomForestRegressionModel + +# Featurizer model +pyspark.ml.feature.BucketedRandomProjectionLSHModel +pyspark.ml.feature.ChiSqSelectorModel +pyspark.ml.feature.CountVectorizerModel +pyspark.ml.feature.IDFModel +pyspark.ml.feature.ImputerModel +pyspark.ml.feature.MaxAbsScalerModel +pyspark.ml.feature.MinHashLSHModel +pyspark.ml.feature.MinMaxScalerModel +pyspark.ml.feature.OneHotEncoderModel +pyspark.ml.feature.RobustScalerModel +pyspark.ml.feature.RFormulaModel +pyspark.ml.feature.StandardScalerModel +pyspark.ml.feature.StringIndexerModel +pyspark.ml.feature.VarianceThresholdSelectorModel +pyspark.ml.feature.VectorIndexerModel +pyspark.ml.feature.UnivariateFeatureSelectorModel + +# composite model +pyspark.ml.classification.OneVsRestModel + +# pipeline model +pyspark.ml.pipeline.PipelineModel + +# Hyper-parameter tuning +pyspark.ml.tuning.CrossValidatorModel +pyspark.ml.tuning.TrainValidationSplitModel + +# SynapeML models +synapse.ml.cognitive.* +synapse.ml.exploratory.* +synapse.ml.featurize.* +synapse.ml.geospatial.* +synapse.ml.image.* +synapse.ml.io.* +synapse.ml.isolationforest.* +synapse.ml.lightgbm.* +synapse.ml.nn.* +synapse.ml.opencv.* +synapse.ml.stages.* +synapse.ml.vw.* diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyspark/optuna/__pycache__/study.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyspark/optuna/__pycache__/study.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..a45edc1b848b451df04eb8642221b0e441e19e61 Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyspark/optuna/__pycache__/study.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyspark/optuna/study.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyspark/optuna/study.py new file mode 100644 index 0000000000000000000000000000000000000000..6f9454029e00aec9dab335cb36e8651da1e30266 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pyspark/optuna/study.py @@ -0,0 +1,252 @@ +import datetime +import logging +import tempfile +import traceback +from collections.abc import Callable, Iterable +from pathlib import Path + +import optuna +import pandas as pd +from optuna import exceptions, pruners, samplers, storages +from optuna.study import Study +from optuna.trial import FrozenTrial, TrialState +from pyspark.sql import SparkSession +from pyspark.sql.functions import col + +import mlflow +from mlflow import MlflowClient +from mlflow.exceptions import ExecutionException +from mlflow.optuna.storage import MlflowStorage + +_logger = logging.getLogger(__name__) + + +def is_spark_connect_mode() -> bool: + """Check if the current Spark session is running in client mode.""" + try: + from pyspark.sql.utils import is_remote + except ImportError: + return False + return is_remote() + + +def _optimize_sequential( + study: "optuna.Study", + func: "optuna.study.study.ObjectiveFuncType", + mlflow_client: MlflowClient, + n_trials: int = 1, + timeout: float | None = None, + catch: Iterable[type[Exception]] = (), + callbacks: Iterable[Callable[[Study, FrozenTrial], None]] | None = None, +) -> None: + """ + Run optimization sequentially. It is modified from _optimize_sequential in optuna + (https://github.com/optuna/optuna/blob/e1e30e7150047e5f582b8fef1eeb65386cb1c4c1/optuna/study/_optimize.py#L121) + Convert the nested call to one function and log the error messages to mlflow. + """ + i_trial = 0 + time_start = datetime.datetime.now() + + while True: + if study._stop_flag: + break + if i_trial >= n_trials: + break + i_trial += 1 + + if timeout is not None: + elapsed_seconds = (datetime.datetime.now() - time_start).total_seconds() + if elapsed_seconds >= timeout: + break + + state = None + value_or_values = None + func_err = None + func_err_fail_exc_info = None + trial = study.ask() + + try: + value_or_values = func(trial) + except exceptions.TrialPruned as e: + state = TrialState.PRUNED + func_err = e + except (Exception, KeyboardInterrupt) as e: + state = TrialState.FAIL + func_err = e + func_err_fail_exc_info = traceback.format_exc() + try: + frozen_trial, warning_message = optuna.study._tell._tell_with_warning( + study=study, + trial=trial, + value_or_values=value_or_values, + state=state, + suppress_warning=True, + ) + except Exception: + frozen_trial = study._storage.get_trial(trial._trial_id) + warning_message = None + + if frozen_trial.state == TrialState.COMPLETE: + _logger.info(f"Trial {trial.number} finished with parameters: {trial.params}.") + elif frozen_trial.state == TrialState.PRUNED: + _logger.info("Trial {} pruned. {}".format(frozen_trial._trial_id, str(func_err))) + mlflow_client.set_terminated(frozen_trial._trial_id, status="KILLED") + elif frozen_trial.state == TrialState.FAIL: + error_message = None + if func_err is not None: + error_message = func_err_fail_exc_info + elif warning_message is not None: + error_message = warning_message + with tempfile.TemporaryDirectory() as tmp_dir: + path = Path(tmp_dir, "error_message.txt") + path.write_text(error_message) + # Log the file as an artifact in the active MLflow run + mlflow_client.log_artifact(frozen_trial._trial_id, path) + mlflow_client.set_terminated(frozen_trial._trial_id, status="FAILED") + + if ( + frozen_trial.state == TrialState.FAIL + and func_err is not None + and not isinstance(func_err, catch) + ): + raise func_err + + if callbacks is not None: + for callback in callbacks: + callback(study, frozen_trial) + + +class MlflowSparkStudy(Study): + """A wrapper of :class:`~optuna.study.Study` to incorporate Optuna with spark + via MLflow experiment. + + .. code-block:: python + :caption: Example + + from mlflow.optuna.storage import MlflowStorage + from mlflow.pyspark.optuna.study import MlflowSparkStudy + + + def objective(trial): + x = trial.suggest_float("x", -10, 10) + return (x - 2) ** 2 + + + experiment_id = "507151065975140" + study_name = "spark_mlflow_storage" + + storage = MlflowStorage(experiment_id=experiment_id) + mlflow_study = MlflowSparkStudy(study_name, storage) + mlflow_study.optimize(objective, n_trials=4) + """ + + def __init__( + self, + study_name: str, + storage: MlflowStorage, + sampler: samplers.BaseSampler | None = None, + pruner: pruners.BasePruner | None = None, + mlflow_tracking_uri: str | None = None, + ): + self.study_name = study_name + self._storage = storages.get_storage(storage) + self.sampler = sampler or samplers.TPESampler() + self.pruner = pruner or pruners.MedianPruner() + + self.spark = SparkSession.active() + + # check whether the SparkConnect mode + self._is_spark_connect_mode = is_spark_connect_mode() + self._mlflow_tracking_env = mlflow_tracking_uri or mlflow.get_tracking_uri() + mlflow.set_tracking_uri(self._mlflow_tracking_env) + self.mlflow_client = MlflowClient() + + self._study = optuna.create_study( + study_name=self.study_name, sampler=self.sampler, storage=self._storage + ) + self._study_id = storage.get_study_id_from_name(self.study_name) + self._directions = self._storage.get_study_directions(self._study_id) + + if not isinstance(self._storage, MlflowStorage): + raise ValueError( + f"MlflowSparkStudy only works with `MlflowStorage`. But get {type(self._storage)}." + ) + + def optimize( + self, + func: "optuna.study.study.ObjectiveFuncType", + n_trials: int | None = None, + timeout: float | None = None, + n_jobs: int = -1, + catch: Iterable[type[Exception]] = (), + callbacks: Iterable[Callable[[Study, FrozenTrial], None]] | None = None, + ) -> None: + experiment_id = self._storage._experiment_id + study_name = self.study_name + mlflow_tracking_env = self._mlflow_tracking_env + sampler = self.sampler + + def run_task_on_executor_pd(iterator): + mlflow.set_tracking_uri(mlflow_tracking_env) + mlflow_client = MlflowClient() + + storage = MlflowStorage(experiment_id=experiment_id) + study = optuna.load_study(study_name=study_name, sampler=sampler, storage=storage) + num_trials = sum(map(len, iterator)) + + error_message = None + try: + _optimize_sequential( + study=study, + func=func, + mlflow_client=mlflow_client, + n_trials=num_trials, + timeout=timeout, + catch=catch, + callbacks=callbacks, + ) + except BaseException: + error_message = traceback.format_exc() + yield pd.DataFrame({"error": [error_message]}) + + num_tasks = n_trials + if n_jobs == -1: + n_jobs = num_tasks + input_df = self.spark.range(start=0, end=num_tasks, step=1, numPartitions=n_jobs) + trial_tag = f"optuna_trial_{study_name}_{experiment_id}" + if self._is_spark_connect_mode: + self.spark.addTag(trial_tag) + else: + job_group_id = self.spark.sparkContext.getLocalProperty("spark.jobGroup.id") + if job_group_id is None: + job_group_id = trial_tag + job_group_description = f"optuna_trial_{study_name}" + self.spark.sparkContext.setJobGroup( + job_group_id, job_group_description, interruptOnCancel=True + ) + try: + result_df = input_df.mapInPandas( + func=run_task_on_executor_pd, + schema="error string", + ) + except KeyboardInterrupt: + if self._is_spark_connect_mode: + self.spark.interruptTag(trial_tag) + else: + self.spark.sparkContext.cancelJobGroup(trial_tag) + _logger.debug("MlflowSparkStudy optimize terminated by user.") + self.mlflow_client.set_terminated(self._study_id, "KILLED") + raise + if "error" in result_df.columns: + failed_runs = result_df.filter(col("error").isNotNull()) + error_rows = failed_runs.select("error").collect() + if len(error_rows) > 0: + first_non_null_value = error_rows[0][0] + self.mlflow_client.set_terminated(self._study_id, "KILLED") + raise ExecutionException( + f"Optimization run for Optuna MlflowSparkStudy failed. " + f"See full error details in the failed MLflow runs. " + f"Number of failed runs: {len(error_rows)}. " + f"First trial failure message: {first_non_null_value}" + ) + self.mlflow_client.set_terminated(self._study_id) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pytorch/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pytorch/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..edf99c1c08c2c4f18e336b5b55514994f3ede5a7 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pytorch/__init__.py @@ -0,0 +1,1171 @@ +""" +The ``mlflow.pytorch`` module provides an API for logging and loading PyTorch models. This module +exports PyTorch models with the following flavors: + +PyTorch (native) format + This is the main flavor that can be loaded back into PyTorch. +:py:mod:`mlflow.pyfunc` + Produced for use by generic pyfunc-based deployment tools and batch inference. +""" + +import atexit +import importlib +import logging +import os +import posixpath +import shutil +from functools import partial +from typing import Any + +import numpy as np +import pandas as pd +import yaml +from packaging.version import Version + +import mlflow +from mlflow import pyfunc +from mlflow.environment_variables import MLFLOW_DEFAULT_PREDICTION_DEVICE +from mlflow.exceptions import MlflowException +from mlflow.ml_package_versions import _ML_PACKAGE_VERSIONS +from mlflow.models import Model, ModelSignature +from mlflow.models.model import MLMODEL_FILE_NAME +from mlflow.models.signature import _infer_signature_from_input_example +from mlflow.models.utils import ModelInputExample, _save_example +from mlflow.protos.databricks_pb2 import RESOURCE_DOES_NOT_EXIST +from mlflow.pytorch import pickle_module as mlflow_pytorch_pickle_module +from mlflow.tracking._model_registry import DEFAULT_AWAIT_MAX_SLEEP_SECONDS +from mlflow.tracking.artifact_utils import _download_artifact_from_uri +from mlflow.utils.autologging_utils import autologging_integration, safe_patch +from mlflow.utils.checkpoint_utils import download_checkpoint_artifact +from mlflow.utils.docstring_utils import LOG_MODEL_PARAM_DOCS, format_docstring +from mlflow.utils.environment import ( + _CONDA_ENV_FILE_NAME, + _CONSTRAINTS_FILE_NAME, + _PYTHON_ENV_FILE_NAME, + _REQUIREMENTS_FILE_NAME, + _mlflow_conda_env, + _process_conda_env, + _process_pip_requirements, + _PythonEnv, + _validate_env_arguments, +) +from mlflow.utils.file_utils import ( + TempDir, + get_total_file_size, + write_to, +) +from mlflow.utils.model_utils import ( + _add_code_from_conf_to_system_path, + _get_flavor_configuration, + _validate_and_copy_code_paths, + _validate_and_prepare_target_save_path, +) +from mlflow.utils.requirements_utils import _get_pinned_requirement + +FLAVOR_NAME = "pytorch" + +_SERIALIZED_TORCH_MODEL_FILE_NAME = "model.pth" +_TORCH_STATE_DICT_FILE_NAME = "state_dict.pth" +_PICKLE_MODULE_INFO_FILE_NAME = "pickle_module_info.txt" +_EXTRA_FILES_KEY = "extra_files" +_TORCH_CPU_DEVICE_NAME = "cpu" +_TORCH_DEFAULT_GPU_DEVICE_NAME = "cuda" + +_logger = logging.getLogger(__name__) + +MIN_REQ_VERSION = Version(_ML_PACKAGE_VERSIONS["pytorch-lightning"]["autologging"]["minimum"]) +MAX_REQ_VERSION = Version(_ML_PACKAGE_VERSIONS["pytorch-lightning"]["autologging"]["maximum"]) + + +_MODEL_DATA_SUBPATH = "data" + + +def get_default_pip_requirements(): + """ + Returns: + A list of default pip requirements for MLflow Models produced by this flavor. Calls to + :func:`save_model()` and :func:`log_model()` produce a pip environment that, at minimum, + contains these requirements. + """ + return list( + map( + _get_pinned_requirement, + [ + "torch", + # We include CloudPickle in the default environment because + # it's required by the default pickle module used by `save_model()` + # and `log_model()`: `mlflow.pytorch.pickle_module`. + "cloudpickle", + ], + ) + ) + + +def get_default_conda_env(): + """ + Returns: + The default Conda environment as a dictionary for MLflow Models produced by calls to + :func:`save_model()` and :func:`log_model()`. + + .. code-block:: python + :caption: Example + + import mlflow + + # Log PyTorch model + with mlflow.start_run() as run: + mlflow.pytorch.log_model(model, name="model", signature=signature) + + # Fetch the associated conda environment + env = mlflow.pytorch.get_default_conda_env() + print(f"conda env: {env}") + + .. code-block:: text + :caption: Output + + conda env {'name': 'mlflow-env', + 'channels': ['conda-forge'], + 'dependencies': ['python=3.8.15', + {'pip': ['torch==1.5.1', + 'mlflow', + 'cloudpickle==1.6.0']}]} + """ + return _mlflow_conda_env(additional_pip_deps=get_default_pip_requirements()) + + +@format_docstring(LOG_MODEL_PARAM_DOCS.format(package_name="torch")) +def log_model( + pytorch_model, + artifact_path: str | None = None, + conda_env=None, + code_paths=None, + pickle_module=None, + registered_model_name=None, + signature: ModelSignature = None, + input_example: ModelInputExample = None, + await_registration_for=DEFAULT_AWAIT_MAX_SLEEP_SECONDS, + extra_files=None, + pip_requirements=None, + extra_pip_requirements=None, + metadata=None, + name: str | None = None, + params: dict[str, Any] | None = None, + tags: dict[str, Any] | None = None, + model_type: str | None = None, + step: int = 0, + model_id: str | None = None, + **kwargs, +): + """ + Log a PyTorch model as an MLflow artifact for the current run. + + .. warning:: Log the model with a signature to avoid inference errors. + If the model is logged without a signature, the MLflow Model Server relies on the + default inferred data type from NumPy. However, PyTorch often expects different + defaults, particularly when parsing floats. You must include the signature to ensure + that the model is logged with the correct data type so that the MLflow model server + can correctly provide valid input. + + Args: + pytorch_model: PyTorch model to be saved. Can be either an eager model (subclass of + ``torch.nn.Module``) or scripted model prepared via ``torch.jit.script`` or + ``torch.jit.trace``. + + The model accept a single ``torch.FloatTensor`` as input and produce a single output + tensor. + + If saving an eager model, any code dependencies of the model's class, including the + class definition itself, should be included in one of the following locations: + + - The package(s) listed in the model's Conda environment, specified by the + ``conda_env`` parameter. + - One or more of the files specified by the ``code_paths`` parameter. + artifact_path: Deprecated. Use `name` instead. + conda_env: {{ conda_env }} + code_paths: {{ code_paths }} + pickle_module: The module that PyTorch should use to serialize ("pickle") the specified + ``pytorch_model``. This is passed as the ``pickle_module`` parameter to + ``torch.save()``. By default, this module is also used to deserialize ("unpickle") the + PyTorch model at load time. + registered_model_name: If given, create a model version under ``registered_model_name``, + also create a registered model if one with the given name does not exist. + signature: {{ signature }} + input_example: {{ input_example }} + await_registration_for: Number of seconds to wait for the model version to finish + being created and is in ``READY`` status. By default, the function waits for five + minutes. Specify 0 or None to skip waiting. + + extra_files: A list containing the paths to corresponding extra files, if ``None``, no + extra files are added to the model. Remote URIs are resolved to absolute filesystem + paths. For example, consider the following ``extra_files`` list: + + .. code-block:: python + + extra_files = ["s3://my-bucket/path/to/my_file1", "s3://my-bucket/path/to/my_file2"] + + In this case, the ``"my_file1 & my_file2"`` extra file is downloaded from S3. + + pip_requirements: {{ pip_requirements }} + extra_pip_requirements: {{ extra_pip_requirements }} + metadata: {{ metadata }} + name: {{ name }} + params: {{ params }} + tags: {{ tags }} + model_type: {{ model_type }} + step: {{ step }} + model_id: {{ model_id }} + kwargs: kwargs to pass to ``torch.save`` method. + + Returns: + A :py:class:`ModelInfo ` instance that contains the + metadata of the logged model. + + .. code-block:: python + :caption: Example + + import numpy as np + import torch + import mlflow + from mlflow import MlflowClient + from mlflow.models import infer_signature + + # Define model, loss, and optimizer + model = nn.Linear(1, 1) + criterion = torch.nn.MSELoss() + optimizer = torch.optim.SGD(model.parameters(), lr=0.001) + + # Create training data with relationship y = 2X + X = torch.arange(1.0, 26.0).reshape(-1, 1) + y = X * 2 + + # Training loop + epochs = 250 + for epoch in range(epochs): + # Forward pass: Compute predicted y by passing X to the model + y_pred = model(X) + + # Compute the loss + loss = criterion(y_pred, y) + + # Zero gradients, perform a backward pass, and update the weights. + optimizer.zero_grad() + loss.backward() + optimizer.step() + + # Create model signature + signature = infer_signature(X.numpy(), model(X).detach().numpy()) + + # Log the model + with mlflow.start_run() as run: + mlflow.pytorch.log_model(model, name="model") + + # convert to scripted model and log the model + scripted_pytorch_model = torch.jit.script(model) + mlflow.pytorch.log_model(scripted_pytorch_model, name="scripted_model") + + # Fetch the logged model artifacts + print(f"run_id: {run.info.run_id}") + for artifact_path in ["model/data", "scripted_model/data"]: + artifacts = [ + f.path for f in MlflowClient().list_artifacts(run.info.run_id, artifact_path) + ] + print(f"artifacts: {artifacts}") + + .. code-block:: text + :caption: Output + + run_id: 1a1ec9e413ce48e9abf9aec20efd6f71 + artifacts: ['model/data/model.pth', + 'model/data/pickle_module_info.txt'] + artifacts: ['scripted_model/data/model.pth', + 'scripted_model/data/pickle_module_info.txt'] + + .. figure:: ../_static/images/pytorch_logged_models.png + + PyTorch logged models + """ + pickle_module = pickle_module or mlflow_pytorch_pickle_module + return Model.log( + artifact_path=artifact_path, + name=name, + flavor=mlflow.pytorch, + pytorch_model=pytorch_model, + conda_env=conda_env, + code_paths=code_paths, + pickle_module=pickle_module, + registered_model_name=registered_model_name, + signature=signature, + input_example=input_example, + await_registration_for=await_registration_for, + extra_files=extra_files, + pip_requirements=pip_requirements, + extra_pip_requirements=extra_pip_requirements, + metadata=metadata, + params=params, + tags=tags, + model_type=model_type, + step=step, + model_id=model_id, + **kwargs, + ) + + +@format_docstring(LOG_MODEL_PARAM_DOCS.format(package_name="torch")) +def save_model( + pytorch_model, + path, + conda_env=None, + mlflow_model=None, + code_paths=None, + pickle_module=None, + signature: ModelSignature = None, + input_example: ModelInputExample = None, + extra_files=None, + pip_requirements=None, + extra_pip_requirements=None, + metadata=None, + **kwargs, +): + """ + Save a PyTorch model to a path on the local file system. + + Args: + pytorch_model: PyTorch model to be saved. Can be either an eager model (subclass of + ``torch.nn.Module``) or a scripted model prepared via ``torch.jit.script`` or + ``torch.jit.trace``. + + To save an eager model, any code dependencies of the model's class, including the class + definition itself, should be included in one of the following locations: + + - The package(s) listed in the model's Conda environment, specified by the + ``conda_env`` parameter. + - One or more of the files specified by the ``code_paths`` parameter. + + path: Local path where the model is to be saved. + conda_env: {{ conda_env }} + mlflow_model: :py:mod:`mlflow.models.Model` this flavor is being added to. + code_paths: {{ code_paths }} + pickle_module: The module that PyTorch should use to serialize ("pickle") the specified + ``pytorch_model``. This is passed as the ``pickle_module`` parameter to + ``torch.save()``. By default, this module is also used to deserialize ("unpickle") the + model at loading time. + signature: {{ signature }} + input_example: {{ input_example }} + + extra_files: A list containing the paths to corresponding extra files. Remote URIs + are resolved to absolute filesystem paths. + For example, consider the following ``extra_files`` list - + + extra_files = ["s3://my-bucket/path/to/my_file1", "s3://my-bucket/path/to/my_file2"] + + In this case, the ``"my_file1 & my_file2"`` extra file is downloaded from S3. + + If ``None``, no extra files are added to the model. + pip_requirements: {{ pip_requirements }} + extra_pip_requirements: {{ extra_pip_requirements }} + metadata:{{ metadata }} + kwargs: kwargs to pass to ``torch.save`` method. + + .. code-block:: python + :caption: Example + + import os + import mlflow + import torch + + + model = nn.Linear(1, 1) + + # Save PyTorch models to current working directory + with mlflow.start_run() as run: + mlflow.pytorch.save_model(model, "model") + + # Convert to a scripted model and save it + scripted_pytorch_model = torch.jit.script(model) + mlflow.pytorch.save_model(scripted_pytorch_model, "scripted_model") + + # Load each saved model for inference + for model_path in ["model", "scripted_model"]: + model_uri = f"{os.getcwd()}/{model_path}" + loaded_model = mlflow.pytorch.load_model(model_uri) + print(f"Loaded {model_path}:") + for x in [6.0, 8.0, 12.0, 30.0]: + X = torch.Tensor([[x]]) + y_pred = loaded_model(X) + print(f"predict X: {x}, y_pred: {y_pred.data.item():.2f}") + print("--") + + .. code-block:: text + :caption: Output + + Loaded model: + predict X: 6.0, y_pred: 11.90 + predict X: 8.0, y_pred: 15.92 + predict X: 12.0, y_pred: 23.96 + predict X: 30.0, y_pred: 60.13 + -- + Loaded scripted_model: + predict X: 6.0, y_pred: 11.90 + predict X: 8.0, y_pred: 15.92 + predict X: 12.0, y_pred: 23.96 + predict X: 30.0, y_pred: 60.13 + + """ + import torch + + _validate_env_arguments(conda_env, pip_requirements, extra_pip_requirements) + + pickle_module = pickle_module or mlflow_pytorch_pickle_module + + if not isinstance(pytorch_model, torch.nn.Module): + raise TypeError("Argument 'pytorch_model' should be a torch.nn.Module") + path = os.path.abspath(path) + _validate_and_prepare_target_save_path(path) + + if mlflow_model is None: + mlflow_model = Model() + saved_example = _save_example(mlflow_model, input_example, path) + + if signature is None and saved_example is not None: + wrapped_model = _PyTorchWrapper(pytorch_model, device="cpu") + signature = _infer_signature_from_input_example(saved_example, wrapped_model) + elif signature is False: + signature = None + + if signature is not None: + mlflow_model.signature = signature + if metadata is not None: + mlflow_model.metadata = metadata + + code_dir_subpath = _validate_and_copy_code_paths(code_paths, path) + + model_data_subpath = _MODEL_DATA_SUBPATH + model_data_path = os.path.join(path, model_data_subpath) + os.makedirs(model_data_path) + + # Persist the pickle module name as a file in the model's `data` directory. This is necessary + # because the `data` directory is the only available parameter to `_load_pyfunc`, and it + # does not contain the MLmodel configuration; therefore, it is not sufficient to place + # the module name in the MLmodel + # + # TODO: Stop persisting this information to the filesystem once we have a mechanism for + # supplying the MLmodel configuration to `mlflow.pytorch._load_pyfunc` + pickle_module_path = os.path.join(model_data_path, _PICKLE_MODULE_INFO_FILE_NAME) + with open(pickle_module_path, "w") as f: + f.write(pickle_module.__name__) + # Save pytorch model + model_path = os.path.join(model_data_path, _SERIALIZED_TORCH_MODEL_FILE_NAME) + if isinstance(pytorch_model, torch.jit.ScriptModule): + torch.jit.ScriptModule.save(pytorch_model, model_path) + else: + torch.save(pytorch_model, model_path, pickle_module=pickle_module, **kwargs) + + torchserve_artifacts_config = {} + + if extra_files: + torchserve_artifacts_config[_EXTRA_FILES_KEY] = [] + if not isinstance(extra_files, list): + raise TypeError("Extra files argument should be a list") + + with TempDir() as tmp_extra_files_dir: + for extra_file in extra_files: + _download_artifact_from_uri( + artifact_uri=extra_file, output_path=tmp_extra_files_dir.path() + ) + rel_path = posixpath.join(_EXTRA_FILES_KEY, os.path.basename(extra_file)) + torchserve_artifacts_config[_EXTRA_FILES_KEY].append({"path": rel_path}) + shutil.move( + tmp_extra_files_dir.path(), + posixpath.join(path, _EXTRA_FILES_KEY), + ) + + mlflow_model.add_flavor( + FLAVOR_NAME, + model_data=model_data_subpath, + pytorch_version=str(torch.__version__), + code=code_dir_subpath, + **torchserve_artifacts_config, + ) + pyfunc.add_to_model( + mlflow_model, + loader_module="mlflow.pytorch", + data=model_data_subpath, + pickle_module_name=pickle_module.__name__, + code=code_dir_subpath, + conda_env=_CONDA_ENV_FILE_NAME, + python_env=_PYTHON_ENV_FILE_NAME, + model_config={"device": None}, + ) + if size := get_total_file_size(path): + mlflow_model.model_size_bytes = size + mlflow_model.save(os.path.join(path, MLMODEL_FILE_NAME)) + + if conda_env is None: + if pip_requirements is None: + default_reqs = get_default_pip_requirements() + # To ensure `_load_pyfunc` can successfully load the model during the dependency + # inference, `mlflow_model.save` must be called beforehand to save an MLmodel file. + inferred_reqs = mlflow.models.infer_pip_requirements( + model_data_path, + FLAVOR_NAME, + fallback=default_reqs, + ) + default_reqs = sorted(set(inferred_reqs).union(default_reqs)) + else: + default_reqs = None + conda_env, pip_requirements, pip_constraints = _process_pip_requirements( + default_reqs, + pip_requirements, + extra_pip_requirements, + ) + else: + conda_env, pip_requirements, pip_constraints = _process_conda_env(conda_env) + + with open(os.path.join(path, _CONDA_ENV_FILE_NAME), "w") as f: + yaml.safe_dump(conda_env, stream=f, default_flow_style=False) + + # Save `constraints.txt` if necessary + if pip_constraints: + write_to(os.path.join(path, _CONSTRAINTS_FILE_NAME), "\n".join(pip_constraints)) + + # Save `requirements.txt` + write_to(os.path.join(path, _REQUIREMENTS_FILE_NAME), "\n".join(pip_requirements)) + + _PythonEnv.current().to_yaml(os.path.join(path, _PYTHON_ENV_FILE_NAME)) + + +def _load_model(path, device=None, **kwargs): + """ + Args: + path: The path to a serialized PyTorch model. + device: If specified, load the model on the specified device. + kwargs: Additional kwargs to pass to the PyTorch ``torch.load`` function. + """ + import torch + + if os.path.isdir(path): + # `path` is a directory containing a serialized PyTorch model and a text file containing + # information about the pickle module that should be used by PyTorch to load it + model_path = os.path.join(path, "model.pth") + pickle_module_path = os.path.join(path, _PICKLE_MODULE_INFO_FILE_NAME) + with open(pickle_module_path) as f: + pickle_module_name = f.read() + if "pickle_module" in kwargs and kwargs["pickle_module"].__name__ != pickle_module_name: + _logger.warning( + "Attempting to load the PyTorch model with a pickle module, '%s', that does not" + " match the pickle module that was used to save the model: '%s'.", + kwargs["pickle_module"].__name__, + pickle_module_name, + ) + else: + try: + kwargs["pickle_module"] = importlib.import_module(pickle_module_name) + except ImportError as exc: + raise MlflowException( + message=( + "Failed to import the pickle module that was used to save the PyTorch" + f" model. Pickle module name: `{pickle_module_name}`" + ), + error_code=RESOURCE_DOES_NOT_EXIST, + ) from exc + + else: + model_path = path + + if Version(torch.__version__) >= Version("1.5.0"): + pytorch_model = torch.load(model_path, **kwargs) + else: + try: + # load the model as an eager model. + pytorch_model = torch.load(model_path, **kwargs) + except Exception: + # If fails, assume the model as a scripted model + # `torch.jit.load` does not accept `pickle_module`. + kwargs.pop("pickle_module", None) + pytorch_model = torch.jit.load(model_path, **kwargs) + + pytorch_model.eval() + if device: + pytorch_model.to(device=device) + return pytorch_model + + +def load_model(model_uri, dst_path=None, **kwargs): + """ + Load a PyTorch model from a local file or a run. + + Args: + model_uri: The location, in URI format, of the MLflow model, for example: + + - ``/Users/me/path/to/local/model`` + - ``relative/path/to/local/model`` + - ``s3://my_bucket/path/to/model`` + - ``runs://run-relative/path/to/model`` + - ``models://`` + - ``models://`` + + For more information about supported URI schemes, see `Referencing Artifacts \ + `_. + dst_path: The local filesystem path to which to download the model artifact. + This directory must already exist. If unspecified, a local output path will be created. + kwargs: kwargs to pass to ``torch.load`` method. + + Returns: + A PyTorch model. + + .. code-block:: python + :caption: Example + + import torch + import mlflow.pytorch + + + model = nn.Linear(1, 1) + + # Log the model + with mlflow.start_run() as run: + mlflow.pytorch.log_model(model, name="model") + + # Inference after loading the logged model + model_uri = f"runs:/{run.info.run_id}/model" + loaded_model = mlflow.pytorch.load_model(model_uri) + for x in [4.0, 6.0, 30.0]: + X = torch.Tensor([[x]]) + y_pred = loaded_model(X) + print(f"predict X: {x}, y_pred: {y_pred.data.item():.2f}") + + .. code-block:: text + :caption: Output + + predict X: 4.0, y_pred: 7.57 + predict X: 6.0, y_pred: 11.64 + predict X: 30.0, y_pred: 60.48 + """ + import torch + + local_model_path = _download_artifact_from_uri(artifact_uri=model_uri, output_path=dst_path) + pytorch_conf = _get_flavor_configuration(model_path=local_model_path, flavor_name=FLAVOR_NAME) + _add_code_from_conf_to_system_path(local_model_path, pytorch_conf) + + if torch.__version__ != pytorch_conf["pytorch_version"]: + _logger.warning( + "Stored model version '%s' does not match installed PyTorch version '%s'", + pytorch_conf["pytorch_version"], + torch.__version__, + ) + torch_model_artifacts_path = os.path.join(local_model_path, pytorch_conf["model_data"]) + return _load_model(path=torch_model_artifacts_path, **kwargs) + + +def _load_pyfunc(path, model_config=None, weights_only=False): + """ + Load PyFunc implementation. Called by ``pyfunc.load_model``. + + Args: + path: Local filesystem path to the MLflow Model with the ``pytorch`` flavor. + """ + import torch + + device = model_config.get("device", None) if model_config else None + # if CUDA is available, we use the default CUDA device. + # To force inference to the CPU when the GPU is available, please set + # MLFLOW_DEFAULT_PREDICTION_DEVICE to "cpu" + # If a specific non-default device is passed in, we continue to respect that. + if device is None: + if MLFLOW_DEFAULT_PREDICTION_DEVICE.get(): + device = MLFLOW_DEFAULT_PREDICTION_DEVICE.get() + elif torch.cuda.is_available(): + device = _TORCH_DEFAULT_GPU_DEVICE_NAME + else: + device = _TORCH_CPU_DEVICE_NAME + + # in pytorch >= 2.6.0, the `weights_only` kwarg default has been changed from + # `False` to `True`. this can cause pickle deserialization errors when loading + # models, unless the model classes have been explicitly marked as safe using + # `torch.serialization.add_safe_globals()` + if Version(torch.__version__) >= Version("2.6.0"): + return _PyTorchWrapper( + _load_model(path, device=device, weights_only=weights_only), device=device + ) + + return _PyTorchWrapper(_load_model(path, device=device), device=device) + + +class _PyTorchWrapper: + """ + Wrapper class that creates a predict function such that + predict(data: pd.DataFrame) -> model's output as pd.DataFrame (pandas DataFrame) + """ + + def __init__(self, pytorch_model, device): + self.pytorch_model = pytorch_model + self.device = device + + def get_raw_model(self): + """ + Returns the underlying model. + """ + return self.pytorch_model + + def predict(self, data, params: dict[str, Any] | None = None): + """ + Args: + data: Model input data. + params: Additional parameters to pass to the model for inference. + + Returns: + Model predictions. + """ + import torch + + if params and "device" in params: + raise ValueError( + "device' can no longer be specified as an inference parameter. " + "It must be specified at load time. " + "Please specify the device at load time, for example: " + "`mlflow.pyfunc.load_model(model_uri, model_config={'device': 'cuda'})`." + ) + + if isinstance(data, pd.DataFrame): + inp_data = data.to_numpy(dtype=np.float32) + elif isinstance(data, np.ndarray): + inp_data = data + elif isinstance(data, (list, dict)): + raise TypeError( + "The PyTorch flavor does not support List or Dict input types. " + "Please use a pandas.DataFrame or a numpy.ndarray" + ) + else: + raise TypeError("Input data should be pandas.DataFrame or numpy.ndarray") + + device = self.device + with torch.no_grad(): + input_tensor = torch.from_numpy(inp_data).to(device) + preds = self.pytorch_model(input_tensor, **(params or {})) + # if the predictions happened on a remote device, copy them back to + # the host CPU for processing + if device != _TORCH_CPU_DEVICE_NAME: + preds = preds.to(_TORCH_CPU_DEVICE_NAME) + if not isinstance(preds, torch.Tensor): + raise TypeError( + "Expected PyTorch model to output a single output tensor, " + f"but got output of type '{type(preds)}'" + ) + if isinstance(data, pd.DataFrame): + predicted = pd.DataFrame(preds.numpy()) + predicted.index = data.index + else: + predicted = preds.numpy() + return predicted + + +def log_state_dict(state_dict, artifact_path, **kwargs): + """ + Log a state_dict as an MLflow artifact for the current run. + + .. warning:: + This function just logs a state_dict as an artifact and doesn't generate + an :ref:`MLflow Model `. + + Args: + state_dict: state_dict to be saved. + artifact_path: Run-relative artifact path. + kwargs: kwargs to pass to ``torch.save``. + + .. code-block:: python + :caption: Example + + # Log a model as a state_dict + with mlflow.start_run(): + state_dict = model.state_dict() + mlflow.pytorch.log_state_dict(state_dict, artifact_path="model") + + # Log a checkpoint as a state_dict + with mlflow.start_run(): + state_dict = { + "model": model.state_dict(), + "optimizer": optimizer.state_dict(), + "epoch": epoch, + "loss": loss, + } + mlflow.pytorch.log_state_dict(state_dict, artifact_path="checkpoint") + """ + + with TempDir() as tmp: + local_path = tmp.path() + save_state_dict(state_dict=state_dict, path=local_path, **kwargs) + mlflow.log_artifacts(local_path, artifact_path) + + +def save_state_dict(state_dict, path, **kwargs): + """ + Save a state_dict to a path on the local file system + + Args: + state_dict: state_dict to be saved. + path: Local path where the state_dict is to be saved. + kwargs: kwargs to pass to ``torch.save``. + """ + import torch + + # The object type check here aims to prevent a scenario where a user accidentally passees + # a model instead of a state_dict and `torch.save` (which accepts both model and state_dict) + # successfully completes, leaving the user unaware of the mistake. + if not isinstance(state_dict, dict): + raise TypeError( + "Invalid object type for `state_dict`: {}. Must be an instance of `dict`".format( + type(state_dict) + ) + ) + + os.makedirs(path, exist_ok=True) + state_dict_path = os.path.join(path, _TORCH_STATE_DICT_FILE_NAME) + torch.save(state_dict, state_dict_path, **kwargs) + + +def load_state_dict(state_dict_uri, **kwargs): + """ + Load a state_dict from a local file or a run. + + Args: + state_dict_uri: The location, in URI format, of the state_dict, for example: + + - ``/Users/me/path/to/local/state_dict`` + - ``relative/path/to/local/state_dict`` + - ``s3://my_bucket/path/to/state_dict`` + - ``runs://run-relative/path/to/state_dict`` + + For more information about supported URI schemes, see `Referencing Artifacts \ + `_. + + kwargs: kwargs to pass to ``torch.load``. + + Returns: + A state_dict + + .. code-block:: python + :caption: Example + + with mlflow.start_run(): + artifact_path = "model" + mlflow.pytorch.log_state_dict(model.state_dict(), artifact_path) + state_dict_uri = mlflow.get_artifact_uri(artifact_path) + + state_dict = mlflow.pytorch.load_state_dict(state_dict_uri) + """ + import torch + + local_path = _download_artifact_from_uri(artifact_uri=state_dict_uri) + state_dict_path = os.path.join(local_path, _TORCH_STATE_DICT_FILE_NAME) + return torch.load(state_dict_path, **kwargs) + + +@autologging_integration(FLAVOR_NAME) +def autolog( + log_every_n_epoch=1, + log_every_n_step=None, + log_models=True, + log_datasets=True, + disable=False, + exclusive=False, + disable_for_unsupported_versions=False, + silent=False, + registered_model_name=None, + extra_tags=None, + checkpoint=True, + checkpoint_monitor="val_loss", + checkpoint_mode="min", + checkpoint_save_best_only=True, + checkpoint_save_weights_only=False, + checkpoint_save_freq="epoch", +): + """ + Enables (or disables) and configures autologging from `PyTorch Lightning + `_ to MLflow. + + Autologging is performed when you call the `fit` method of + `pytorch_lightning.Trainer() \ + `_. + + Explore the complete `PyTorch MNIST \ + `_ for + an expansive example with implementation of additional lightening steps. + + **Note**: Full autologging is only supported for PyTorch Lightning models, + i.e., models that subclass + `pytorch_lightning.LightningModule \ + `_. + Autologging support for vanilla PyTorch (ie models that only subclass + `torch.nn.Module `_) + only autologs calls to + `torch.utils.tensorboard.SummaryWriter `_'s + ``add_scalar`` and ``add_hparams`` methods to mlflow. In this case, there's also + no notion of an "epoch". + + Args: + log_every_n_epoch: If specified, logs metrics once every `n` epochs. By default, metrics + are logged after every epoch. + log_every_n_step: If specified, logs batch metrics once every `n` training step. + By default, metrics are not logged for steps. Note that setting this to 1 can cause + performance issues and is not recommended. Metrics are logged against Lightning's global + step number, and when multiple optimizers are used it is assumed that all optimizers + are stepped in each training step. + log_models: If ``True``, trained models are logged as MLflow model artifacts. + If ``False``, trained models are not logged. + log_datasets: If ``True``, dataset information is logged to MLflow Tracking. + If ``False``, dataset information is not logged. + disable: If ``True``, disables the PyTorch Lightning autologging integration. + If ``False``, enables the PyTorch Lightning autologging integration. + exclusive: If ``True``, autologged content is not logged to user-created fluent runs. + If ``False``, autologged content is logged to the active fluent run, which may be + user-created. + disable_for_unsupported_versions: If ``True``, disable autologging for versions of + pytorch and pytorch-lightning that have not been tested against this version + of the MLflow client or are incompatible. + silent: If ``True``, suppress all event logs and warnings from MLflow during PyTorch + Lightning autologging. If ``False``, show all events and warnings during PyTorch + Lightning autologging. + registered_model_name: If given, each time a model is trained, it is registered as a + new model version of the registered model with this name. The registered model is + created if it does not already exist. + extra_tags: A dictionary of extra tags to set on each managed run created by autologging. + checkpoint: Enable automatic model checkpointing, this feature only supports + pytorch-lightning >= 1.6.0. + checkpoint_monitor: In automatic model checkpointing, the metric name to monitor if + you set `model_checkpoint_save_best_only` to True. + checkpoint_mode: one of {"min", "max"}. In automatic model checkpointing, + if save_best_only=True, the decision to overwrite the current save file is made based on + either the maximization or the minimization of the monitored quantity. + checkpoint_save_best_only: If True, automatic model checkpointing only saves when + the model is considered the "best" model according to the quantity + monitored and previous checkpoint model is overwritten. + checkpoint_save_weights_only: In automatic model checkpointing, if True, then + only the model's weights will be saved. Otherwise, the optimizer states, + lr-scheduler states, etc are added in the checkpoint too. + checkpoint_save_freq: `"epoch"` or integer. When using `"epoch"`, the callback + saves the model after each epoch. When using integer, the callback + saves the model at end of this many batches. Note that if the saving isn't aligned to + epochs, the monitored metric may potentially be less reliable (it + could reflect as little as 1 batch, since the metrics get reset + every epoch). Defaults to `"epoch"`. + + .. code-block:: python + :test: + :caption: Example + + import os + + import lightning as L + import torch + from torch.nn import functional as F + from torch.utils.data import DataLoader, Subset + from torchmetrics import Accuracy + from torchvision import transforms + from torchvision.datasets import MNIST + + import mlflow.pytorch + from mlflow import MlflowClient + + + class MNISTModel(L.LightningModule): + def __init__(self): + super().__init__() + self.l1 = torch.nn.Linear(28 * 28, 10) + self.accuracy = Accuracy("multiclass", num_classes=10) + + def forward(self, x): + return torch.relu(self.l1(x.view(x.size(0), -1))) + + def training_step(self, batch, batch_nb): + x, y = batch + logits = self(x) + loss = F.cross_entropy(logits, y) + pred = logits.argmax(dim=1) + acc = self.accuracy(pred, y) + + # PyTorch `self.log` will be automatically captured by MLflow. + self.log("train_loss", loss, on_epoch=True) + self.log("acc", acc, on_epoch=True) + return loss + + def configure_optimizers(self): + return torch.optim.Adam(self.parameters(), lr=0.02) + + + def print_auto_logged_info(r): + tags = {k: v for k, v in r.data.tags.items() if not k.startswith("mlflow.")} + artifacts = [f.path for f in MlflowClient().list_artifacts(r.info.run_id, "model")] + print(f"run_id: {r.info.run_id}") + print(f"artifacts: {artifacts}") + print(f"params: {r.data.params}") + print(f"metrics: {r.data.metrics}") + print(f"tags: {tags}") + + + # Initialize our model. + mnist_model = MNISTModel() + + # Load MNIST dataset. + train_ds = MNIST( + os.getcwd(), train=True, download=True, transform=transforms.ToTensor() + ) + # Only take a subset of the data for faster training. + indices = torch.arange(32) + train_ds = Subset(train_ds, indices) + train_loader = DataLoader(train_ds, batch_size=8) + + # Initialize a trainer. + trainer = L.Trainer(max_epochs=3) + + # Auto log all MLflow entities + mlflow.pytorch.autolog() + + # Train the model. + with mlflow.start_run() as run: + trainer.fit(mnist_model, train_loader) + + # Fetch the auto logged parameters and metrics. + print_auto_logged_info(mlflow.get_run(run_id=run.info.run_id)) + """ + try: + import pytorch_lightning as pl + except ImportError: + pass + else: + from mlflow.pytorch._lightning_autolog import patched_fit + + safe_patch( + FLAVOR_NAME, pl.Trainer, "fit", patched_fit, manage_run=True, extra_tags=extra_tags + ) + + try: + import lightning as L + except ImportError: + pass + else: + from mlflow.pytorch._lightning_autolog import patched_fit + + safe_patch( + FLAVOR_NAME, L.Trainer, "fit", patched_fit, manage_run=True, extra_tags=extra_tags + ) + + try: + import torch.utils.tensorboard.writer + except ImportError: + pass + else: + from mlflow.pytorch._pytorch_autolog import ( + flush_metrics_queue, + patched_add_event, + patched_add_hparams, + patched_add_summary, + ) + + safe_patch( + FLAVOR_NAME, + torch.utils.tensorboard.writer.FileWriter, + "add_event", + partial(patched_add_event, mlflow_log_every_n_step=log_every_n_step), + manage_run=True, + extra_tags=extra_tags, + ) + safe_patch( + FLAVOR_NAME, + torch.utils.tensorboard.writer.FileWriter, + "add_summary", + patched_add_summary, + manage_run=True, + extra_tags=extra_tags, + ) + safe_patch( + FLAVOR_NAME, + torch.utils.tensorboard.SummaryWriter, + "add_hparams", + patched_add_hparams, + manage_run=True, + extra_tags=extra_tags, + ) + + atexit.register(flush_metrics_queue) + + +if autolog.__doc__ is not None: + autolog.__doc__ = autolog.__doc__.replace("MIN_REQ_VERSION", str(MIN_REQ_VERSION)).replace( + "MAX_REQ_VERSION", str(MAX_REQ_VERSION) + ) + + +def load_checkpoint(model_class, run_id=None, epoch=None, global_step=None, kwargs=None): + """ + If you enable "checkpoint" in autologging, during pytorch-lightning model + training execution, checkpointed models are logged as MLflow artifacts. + Using this API, you can load the checkpointed model. + + If you want to load the latest checkpoint, set both `epoch` and `global_step` to None. + If "checkpoint_save_freq" is set to "epoch" in autologging, + you can set `epoch` param to the epoch of the checkpoint to load specific epoch checkpoint. + If "checkpoint_save_freq" is set to an integer in autologging, + you can set `global_step` param to the global step of the checkpoint to load specific + global step checkpoint. + `epoch` param and `global_step` can't be set together. + + Args: + model_class: The class of the training model, the class should inherit + 'pytorch_lightning.LightningModule'. + run_id: The id of the run which model is logged to. If not provided, + current active run is used. + epoch: The epoch of the checkpoint to be loaded, if you set + "checkpoint_save_freq" to "epoch". + global_step: The global step of the checkpoint to be loaded, if + you set "checkpoint_save_freq" to an integer. + kwargs: Any extra kwargs needed to init the model. + + Returns: + The instance of a pytorch-lightning model restored from the specified checkpoint. + + .. code-block:: python + :caption: Example + + import mlflow + + mlflow.pytorch.autolog(checkpoint=True) + + model = MyLightningModuleNet() # A custom-pytorch lightning model + train_loader = create_train_dataset_loader() + trainer = Trainer() + + with mlflow.start_run() as run: + trainer.fit(model, train_loader) + + run_id = run.info.run_id + + # load latest checkpoint model + latest_checkpoint_model = mlflow.pytorch.load_checkpoint(MyLightningModuleNet, run_id) + + # load history checkpoint model logged in second epoch + checkpoint_model = mlflow.pytorch.load_checkpoint(MyLightningModuleNet, run_id, epoch=2) + """ + with TempDir() as tmp_dir: + downloaded_checkpoint_filepath = download_checkpoint_artifact( + run_id=run_id, epoch=epoch, global_step=global_step, dst_path=tmp_dir.path() + ) + return model_class.load_from_checkpoint(downloaded_checkpoint_filepath, **(kwargs or {})) + + +__all__ = [ + "autolog", + "load_model", + "save_model", + "log_model", + "get_default_pip_requirements", + "get_default_conda_env", + "load_checkpoint", +] + +try: + from mlflow.pytorch._lightning_autolog import MlflowModelCheckpointCallback # noqa: F401 + + __all__.append("MlflowModelCheckpointCallback") +except ImportError: + # Swallow exception if pytorch-lightning is not installed. + pass diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pytorch/__pycache__/__init__.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pytorch/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..f86e741cec38f326db3086535f722311341848fc Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pytorch/__pycache__/__init__.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pytorch/__pycache__/_lightning_autolog.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pytorch/__pycache__/_lightning_autolog.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..135517b8424ecdbb282ddfe24840872d5116c095 Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pytorch/__pycache__/_lightning_autolog.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pytorch/__pycache__/_pytorch_autolog.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pytorch/__pycache__/_pytorch_autolog.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..b8267886049cfead78972f7a3895def584beee11 Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pytorch/__pycache__/_pytorch_autolog.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pytorch/__pycache__/pickle_module.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pytorch/__pycache__/pickle_module.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..567c41ff396f0d2ede49a39cd2815b8827561aa9 Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pytorch/__pycache__/pickle_module.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pytorch/_lightning_autolog.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pytorch/_lightning_autolog.py new file mode 100644 index 0000000000000000000000000000000000000000..db3650f20f5a3a6837562c887f178eba656ffd5b --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pytorch/_lightning_autolog.py @@ -0,0 +1,583 @@ +import logging +import os +import tempfile +import warnings + +from packaging.version import Version + +import mlflow.pytorch +from mlflow.exceptions import MlflowException +from mlflow.ml_package_versions import _ML_PACKAGE_VERSIONS +from mlflow.tracking.fluent import _initialize_logged_model +from mlflow.utils.autologging_utils import ( + BatchMetricsLogger, + ExceptionSafeAbstractClass, + MlflowAutologgingQueueingClient, + disable_autologging, + get_autologging_config, +) +from mlflow.utils.checkpoint_utils import MlflowModelCheckpointCallbackBase + +logging.basicConfig(level=logging.ERROR) +MIN_REQ_VERSION = Version(_ML_PACKAGE_VERSIONS["pytorch-lightning"]["autologging"]["minimum"]) +MAX_REQ_VERSION = Version(_ML_PACKAGE_VERSIONS["pytorch-lightning"]["autologging"]["maximum"]) + +import pytorch_lightning as pl +from pytorch_lightning.utilities import rank_zero_only + +# The following are the downsides of using PyTorch Lightning's built-in MlflowLogger. +# 1. MlflowLogger doesn't provide a mechanism to store an entire model into mlflow. +# Only model checkpoint is saved. +# 2. For storing the model into mlflow `mlflow.pytorch` library is used +# and the library expects `mlflow` object to be instantiated. +# In case of MlflowLogger, Run management is completely controlled by the class and +# hence mlflow object needs to be reinstantiated by setting +# tracking uri, experiment_id and run_id which may lead to a race condition. +# TODO: Replace __MlflowPLCallback with Pytorch Lightning's built-in MlflowLogger +# once the above mentioned issues have been addressed + +_logger = logging.getLogger(__name__) + +_pl_version = Version(pl.__version__) +if _pl_version < Version("1.5.0"): + from pytorch_lightning.core.memory import ModelSummary +else: + from pytorch_lightning.utilities.model_summary import ModelSummary + + +def _get_optimizer_name(optimizer): + """ + In pytorch-lightning 1.1.0, `LightningOptimizer` was introduced: + https://github.com/PyTorchLightning/pytorch-lightning/pull/4658 + + If a user sets `enable_pl_optimizer` to True when instantiating a `Trainer` object, + each optimizer will be wrapped by `LightningOptimizer`: + https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.trainer.trainer.html + #pytorch_lightning.trainer.trainer.Trainer.params.enable_pl_optimizer + """ + if Version(pl.__version__) < Version("1.1.0"): + return optimizer.__class__.__name__ + else: + from pytorch_lightning.core.optimizer import LightningOptimizer + + return ( + optimizer._optimizer.__class__.__name__ + if isinstance(optimizer, LightningOptimizer) + else optimizer.__class__.__name__ + ) + + +class __MlflowPLCallback(pl.Callback, metaclass=ExceptionSafeAbstractClass): + """ + Callback for auto-logging metrics and parameters. + """ + + def __init__( + self, client, metrics_logger, run_id, log_models, log_every_n_epoch, log_every_n_step + ): + if log_every_n_step and _pl_version < Version("1.1.0"): + raise MlflowException( + "log_every_n_step is only supported for PyTorch-Lightning >= 1.1.0" + ) + self.early_stopping = False + self.client = client + self.metrics_logger = metrics_logger + self.run_id = run_id + self.log_models = log_models + self.log_every_n_epoch = log_every_n_epoch + self.log_every_n_step = log_every_n_step + self._global_steps_per_training_step = 1 + # Sets for tracking which metrics are logged on steps and which are logged on epochs + self._step_metrics = set() + self._epoch_metrics = set() + + def _log_metrics(self, trainer, step, metric_items): + # pytorch-lightning runs a few steps of validation in the beginning of training + # as a sanity check to catch bugs without having to wait for the training routine + # to complete. During this check, we should skip logging metrics. + # https://pytorch-lightning.readthedocs.io/en/stable/common/trainer.html#num-sanity-val-steps + sanity_checking = ( + # `running_sanity_check` has been renamed to `sanity_checking`: + # https://github.com/PyTorchLightning/pytorch-lightning/pull/9209 + trainer.sanity_checking + if Version(pl.__version__) > Version("1.4.5") + else trainer.running_sanity_check + ) + if sanity_checking: + return + + # Cast metric value as float before passing into logger. + metrics = {x[0]: float(x[1]) for x in metric_items} + self.metrics_logger.record_metrics(metrics, step) + + def _log_epoch_metrics(self, trainer, pl_module): + # `trainer.callback_metrics` contains both training and validation metrics + # and includes metrics logged on steps and epochs. + # If we have logged any metrics on a step basis in mlflow, we exclude these from the + # epoch level metrics to prevent mixing epoch and step based values. + metric_items = [ + (name, val) + for (name, val) in trainer.callback_metrics.items() + if name not in self._step_metrics + ] + # Record which metrics are logged on epochs, so we don't try to log these on steps + self._epoch_metrics.update(name for (name, _) in metric_items) + if (pl_module.current_epoch + 1) % self.log_every_n_epoch == 0: + self._log_metrics(trainer, pl_module.current_epoch, metric_items) + + _pl_version = Version(pl.__version__) + + # In pytorch-lightning >= 1.4.0, validation is run inside the training epoch and + # `trainer.callback_metrics` contains both training and validation metrics of the + # current training epoch when `on_train_epoch_end` is called: + # https://github.com/PyTorchLightning/pytorch-lightning/pull/7357 + if _pl_version >= Version("1.4.0dev"): + + @rank_zero_only + def on_train_epoch_end(self, trainer, pl_module, *args): + self._log_epoch_metrics(trainer, pl_module) + + # In pytorch-lightning >= 1.2.0, logging metrics in `on_epoch_end` results in duplicate + # metrics records because `on_epoch_end` is called after both train and validation + # epochs (related PR: https://github.com/PyTorchLightning/pytorch-lightning/pull/5986) + # As a workaround, use `on_train_epoch_end` and `on_validation_epoch_end` instead + # in pytorch-lightning >= 1.2.0. + elif _pl_version >= Version("1.2.0"): + # NB: Override `on_train_epoch_end` with an additional `*args` parameter for + # compatibility with versions of pytorch-lightning <= 1.2.0, which required an + # `outputs` argument that was not used and is no longer defined in + # pytorch-lightning >= 1.3.0 + + @rank_zero_only + def on_train_epoch_end(self, trainer, pl_module, *args): + """ + Log loss and other metrics values after each train epoch + + Args: + trainer: pytorch lightning trainer instance + pl_module: pytorch lightning base module + args: additional positional arguments + """ + # If validation loop is enabled (meaning `validation_step` is overridden), + # log metrics in `on_validaion_epoch_end` to avoid logging the same metrics + # records twice + if not trainer.enable_validation: + self._log_epoch_metrics(trainer, pl_module) + + @rank_zero_only + def on_validation_epoch_end(self, trainer, pl_module): + """ + Log loss and other metrics values after each validation epoch + + Args: + trainer: pytorch lightning trainer instance + pl_module: pytorch lightning base module + """ + self._log_epoch_metrics(trainer, pl_module) + + else: + + @rank_zero_only + def on_epoch_end(self, trainer, pl_module): + """ + Log loss and other metrics values after each epoch + + Args: + trainer: pytorch lightning trainer instance + pl_module: pytorch lightning base module + """ + self._log_epoch_metrics(trainer, pl_module) + + @rank_zero_only + def on_train_batch_end(self, trainer, pl_module, *args): + """ + Log metric values after each step + + Args: + trainer: pytorch lightning trainer instance + pl_module: pytorch lightning base module + args: additional positional arguments + """ + if not self.log_every_n_step: + return + # When logging at the end of a batch step, we only want to log metrics that are logged + # on steps. For forked metrics (metrics logged on both steps and epochs), we exclude the + # metric with the non-forked name (eg. "loss" when we have "loss", "loss_step" and + # "loss_epoch") so that this is only logged on epochs. We also record which metrics + # we've logged per step, so we can later exclude these from metrics logged on epochs. + metrics = _get_step_metrics(trainer) + metric_items = [ + (name, val) + for (name, val) in metrics.items() + if (name not in self._epoch_metrics) and (f"{name}_step" not in metrics.keys()) + ] + self._step_metrics.update(name for (name, _) in metric_items) + step = trainer.global_step + if ((step // self._global_steps_per_training_step) + 1) % self.log_every_n_step == 0: + self._log_metrics(trainer, step, metric_items) + + @rank_zero_only + def on_train_start(self, trainer, pl_module): + """ + Logs Optimizer related metrics when the train begins + + Args: + trainer: pytorch lightning trainer instance + pl_module: pytorch lightning base module + """ + self.client.set_tags(self.run_id, {"Mode": "training"}) + + params = {"epochs": trainer.max_epochs} + + # TODO For logging optimizer params - Following scenarios are to revisited. + # 1. In the current scenario, only the first optimizer details are logged. + # Code to be enhanced to log params when multiple optimizers are used. + # 2. mlflow.log_params is used to store optimizer default values into mlflow. + # The keys in default dictionary are too short, Ex: (lr - learning_rate). + # Efficient mapping technique needs to be introduced + # to rename the optimizer parameters based on keys in default dictionary. + + if hasattr(trainer, "optimizers"): + # Lightning >= 1.6.0 increments the global step every time an optimizer is stepped. + # We assume every optimizer will be stepped in each training step. + if _pl_version >= Version("1.6.0"): + self._global_steps_per_training_step = len(trainer.optimizers) + optimizer = trainer.optimizers[0] + params["optimizer_name"] = _get_optimizer_name(optimizer) + + if hasattr(optimizer, "defaults"): + params.update(optimizer.defaults) + + self.client.log_params(self.run_id, params) + self.client.flush(synchronous=True) + + @rank_zero_only + def on_train_end(self, trainer, pl_module): + """ + Logs the model checkpoint into mlflow - models folder on the training end + + + Args: + trainer: pytorch lightning trainer instance + pl_module: pytorch lightning base module + """ + # manually flush any remaining metadata from training + self.metrics_logger.flush() + self.client.flush(synchronous=True) + + @rank_zero_only + def on_test_end(self, trainer, pl_module): + """ + Logs accuracy and other relevant metrics on the testing end + + Args: + trainer: pytorch lightning trainer instance + pl_module: pytorch lightning base module + """ + self.client.set_tags(self.run_id, {"Mode": "testing"}) + self.client.flush(synchronous=True) + + self.metrics_logger.record_metrics( + {key: float(value) for key, value in trainer.callback_metrics.items()} + ) + self.metrics_logger.flush() + + +class MlflowModelCheckpointCallback(pl.Callback, MlflowModelCheckpointCallbackBase): + """Callback for auto-logging pytorch-lightning model checkpoints to MLflow. + This callback implementation only supports pytorch-lightning >= 1.6.0. + + Args: + monitor: In automatic model checkpointing, the metric name to monitor if + you set `model_checkpoint_save_best_only` to True. + save_best_only: If True, automatic model checkpointing only saves when + the model is considered the "best" model according to the quantity + monitored and previous checkpoint model is overwritten. + mode: one of {"min", "max"}. In automatic model checkpointing, + if save_best_only=True, the decision to overwrite the current save file is made + based on either the maximization or the minimization of the monitored quantity. + save_weights_only: In automatic model checkpointing, if True, then + only the model's weights will be saved. Otherwise, the optimizer states, + lr-scheduler states, etc are added in the checkpoint too. + save_freq: `"epoch"` or integer. When using `"epoch"`, the callback + saves the model after each epoch. When using integer, the callback + saves the model at end of this many batches. Note that if the saving isn't + aligned to epochs, the monitored metric may potentially be less reliable (it + could reflect as little as 1 batch, since the metrics get reset + every epoch). Defaults to `"epoch"`. + + .. code-block:: python + :caption: Example + + import mlflow + from mlflow.pytorch import MlflowModelCheckpointCallback + from pytorch_lightning import Trainer + + mlflow.pytorch.autolog(checkpoint=True) + + model = MyLightningModuleNet() # A custom-pytorch lightning model + train_loader = create_train_dataset_loader() + + mlflow_checkpoint_callback = MlflowModelCheckpointCallback() + + trainer = Trainer(callbacks=[mlflow_checkpoint_callback]) + + with mlflow.start_run() as run: + trainer.fit(model, train_loader) + + """ + + def __init__( + self, + monitor="val_loss", + mode="min", + save_best_only=True, + save_weights_only=False, + save_freq="epoch", + ): + super().__init__( + checkpoint_file_suffix=".pth", + monitor=monitor, + mode=mode, + save_best_only=save_best_only, + save_weights_only=save_weights_only, + save_freq=save_freq, + ) + self.trainer = None + + def save_checkpoint(self, filepath: str): + # Note: `trainer.save_checkpoint` implementation contains invocation of + # `self.strategy.barrier("Trainer.save_checkpoint")`, + # in DDP training, this callback is only invoked in rank 0 process, + # the `barrier` invocation causes deadlock, + # so I implement `save_checkpoint` instead of + # calling `trainer.save_checkpoint`. + checkpoint = self.trainer._checkpoint_connector.dump_checkpoint(self.save_weights_only) + self.trainer.strategy.save_checkpoint(checkpoint, filepath) + + @rank_zero_only + def on_fit_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None: + self.trainer = trainer + + @rank_zero_only + def on_train_batch_end( + self, + trainer: "pl.Trainer", + pl_module: "pl.LightningModule", + outputs, + batch, + batch_idx, + ) -> None: + if isinstance(self.save_freq, int) and ( + trainer.global_step > 0 and trainer.global_step % self.save_freq == 0 + ): + self.check_and_save_checkpoint_if_needed( + current_epoch=trainer.current_epoch, + global_step=trainer.global_step, + metric_dict={k: float(v) for k, v in trainer.callback_metrics.items()}, + ) + + @rank_zero_only + def on_train_epoch_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None: + if self.save_freq == "epoch": + self.check_and_save_checkpoint_if_needed( + current_epoch=trainer.current_epoch, + global_step=trainer.global_step, + metric_dict={k: float(v) for k, v in trainer.callback_metrics.items()}, + ) + + +# PyTorch-Lightning refactored the LoggerConnector class in version 1.4.0 and made metrics +# update on demand. Prior to this, the metrics from the current step were not available to +# callbacks immediately, so the view of metrics was off by one step. +# To avoid this problem, we access the metrics via the logger_connector for older versions. +if _pl_version >= Version("1.4.0"): + + def _get_step_metrics(trainer): + return trainer.callback_metrics + +else: + + def _get_step_metrics(trainer): + return trainer.logger_connector.cached_results.get_latest_batch_log_metrics() + + +def _log_early_stop_params(early_stop_callback, client, run_id): + """ + Logs early stopping configuration parameters to MLflow. + + Args: + early_stop_callback: The early stopping callback instance used during training. + client: An `MlflowAutologgingQueueingClient` instance used for MLflow logging. + run_id: The ID of the MLflow Run to which to log configuration parameters. + """ + client.log_params( + run_id, + { + p: getattr(early_stop_callback, p) + for p in ["monitor", "mode", "patience", "min_delta", "stopped_epoch"] + if hasattr(early_stop_callback, p) + }, + ) + + +def _log_early_stop_metrics(early_stop_callback, client, run_id, model_id=None): + """ + Logs early stopping behavior results (e.g. stopped epoch) as metrics to MLflow. + + Args: + early_stop_callback: The early stopping callback instance used during training. + client: An `MlflowAutologgingQueueingClient` instance used for MLflow logging. + run_id: The ID of the MLflow Run to which to log configuration parameters. + model_id: The ID of the LoggedModel to which the metrics are associated. + """ + if early_stop_callback.stopped_epoch == 0: + return + + metrics = { + "stopped_epoch": early_stop_callback.stopped_epoch, + "restored_epoch": early_stop_callback.stopped_epoch - max(1, early_stop_callback.patience), + } + + if hasattr(early_stop_callback, "best_score"): + metrics["best_score"] = float(early_stop_callback.best_score) + + if hasattr(early_stop_callback, "wait_count"): + metrics["wait_count"] = early_stop_callback.wait_count + + client.log_metrics(run_id, metrics, model_id=model_id) + + +def patched_fit(original, self, *args, **kwargs): + """ + A patched implementation of `pytorch_lightning.Trainer.fit` which enables logging the + following parameters, metrics and artifacts: + + - Training epochs + - Optimizer parameters + - `EarlyStoppingCallback`_ parameters + - Metrics stored in `trainer.callback_metrics` + - Model checkpoints + - Trained model + + .. _EarlyStoppingCallback: + https://pytorch-lightning.readthedocs.io/en/latest/early_stopping.html + """ + if not MIN_REQ_VERSION <= _pl_version <= MAX_REQ_VERSION: + warnings.warn( + "Autologging is known to be compatible with pytorch-lightning versions between " + f"{MIN_REQ_VERSION} and {MAX_REQ_VERSION} and may not succeed with packages " + "outside this range." + ) + + with disable_autologging(): + run_id = mlflow.active_run().info.run_id + tracking_uri = mlflow.get_tracking_uri() + client = MlflowAutologgingQueueingClient(tracking_uri) + + log_models = get_autologging_config(mlflow.pytorch.FLAVOR_NAME, "log_models", True) + model_id = None + if log_models: + model_id = _initialize_logged_model( + name="model", flavor=mlflow.pytorch.FLAVOR_NAME + ).model_id + metrics_logger = BatchMetricsLogger(run_id, tracking_uri, model_id=model_id) + + log_every_n_epoch = get_autologging_config( + mlflow.pytorch.FLAVOR_NAME, "log_every_n_epoch", 1 + ) + log_every_n_step = get_autologging_config( + mlflow.pytorch.FLAVOR_NAME, "log_every_n_step", None + ) + + early_stop_callback = None + for callback in self.callbacks: + if isinstance(callback, pl.callbacks.early_stopping.EarlyStopping): + early_stop_callback = callback + _log_early_stop_params(early_stop_callback, client, run_id) + + if not any(isinstance(callbacks, __MlflowPLCallback) for callbacks in self.callbacks): + self.callbacks += [ + __MlflowPLCallback( + client, metrics_logger, run_id, log_models, log_every_n_epoch, log_every_n_step + ) + ] + + model_checkpoint = get_autologging_config(mlflow.pytorch.FLAVOR_NAME, "checkpoint", True) + if model_checkpoint: + # __MLflowModelCheckpoint only supports pytorch-lightning >= 1.6.0 + if _pl_version >= Version("1.6.0"): + checkpoint_monitor = get_autologging_config( + mlflow.pytorch.FLAVOR_NAME, "checkpoint_monitor", "val_loss" + ) + checkpoint_mode = get_autologging_config( + mlflow.pytorch.FLAVOR_NAME, "checkpoint_mode", "min" + ) + checkpoint_save_best_only = get_autologging_config( + mlflow.pytorch.FLAVOR_NAME, "checkpoint_save_best_only", True + ) + checkpoint_save_weights_only = get_autologging_config( + mlflow.pytorch.FLAVOR_NAME, "checkpoint_save_weights_only", False + ) + checkpoint_save_freq = get_autologging_config( + mlflow.pytorch.FLAVOR_NAME, "checkpoint_save_freq", "epoch" + ) + + if not any( + isinstance(callbacks, MlflowModelCheckpointCallback) + for callbacks in self.callbacks + ): + self.callbacks += [ + MlflowModelCheckpointCallback( + monitor=checkpoint_monitor, + mode=checkpoint_mode, + save_best_only=checkpoint_save_best_only, + save_weights_only=checkpoint_save_weights_only, + save_freq=checkpoint_save_freq, + ) + ] + else: + warnings.warn( + "Automatic model checkpointing is disabled because this feature only " + "supports pytorch-lightning >= 1.6.0." + ) + + client.flush(synchronous=False) + + result = original(self, *args, **kwargs) + + if early_stop_callback is not None: + _log_early_stop_metrics(early_stop_callback, client, run_id, model_id=model_id) + + if Version(pl.__version__) < Version("1.4.0"): + summary = str(ModelSummary(self.model, mode="full")) + else: + summary = str(ModelSummary(self.model, max_depth=-1)) + + with tempfile.TemporaryDirectory() as tempdir: + summary_file = os.path.join(tempdir, "model_summary.txt") + with open(summary_file, "w") as f: + f.write(summary) + + mlflow.log_artifact(local_path=summary_file) + + if log_models: + registered_model_name = get_autologging_config( + mlflow.pytorch.FLAVOR_NAME, "registered_model_name", None + ) + mlflow.pytorch.log_model( + self.model, + name="model", + registered_model_name=registered_model_name, + model_id=model_id, + ) + + if early_stop_callback is not None and self.checkpoint_callback.best_model_path: + mlflow.log_artifact( + local_path=self.checkpoint_callback.best_model_path, + artifact_path="restored_model_checkpoint", + ) + + client.flush(synchronous=True) + + return result diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pytorch/_pytorch_autolog.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pytorch/_pytorch_autolog.py new file mode 100644 index 0000000000000000000000000000000000000000..800271ffe5d9a5ff7dd5d511b63eaae7cbf972a2 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pytorch/_pytorch_autolog.py @@ -0,0 +1,50 @@ +import time + +import mlflow +from mlflow.entities import Metric, Param +from mlflow.tracking import MlflowClient +from mlflow.utils.autologging_utils.metrics_queue import ( + add_to_metrics_queue, + flush_metrics_queue, +) + + +def patched_add_hparams(original, self, hparam_dict, metric_dict, *args, **kwargs): + """use a synchronous call here since this is going to get called very infrequently.""" + + run = mlflow.active_run() + + if run is not None and hparam_dict: + run_id = run.info.run_id + # str() is required by mlflow :( + params_arr = [Param(key, str(value)) for key, value in hparam_dict.items()] + metrics_arr = [ + Metric(key, value, int(time.time() * 1000), 0) for key, value in metric_dict.items() + ] + MlflowClient().log_batch(run_id=run_id, metrics=metrics_arr, params=params_arr, tags=[]) + + return original(self, hparam_dict, metric_dict, *args, **kwargs) + + +def patched_add_event(original, self, event, *args, mlflow_log_every_n_step, **kwargs): + run = mlflow.active_run() + if run is not None and event.WhichOneof("what") == "summary" and mlflow_log_every_n_step: + summary = event.summary + global_step = args[0] if len(args) > 0 else kwargs.get("global_step") + global_step = global_step or 0 + for v in summary.value: + if v.HasField("simple_value") and global_step % mlflow_log_every_n_step == 0: + add_to_metrics_queue( + key=v.tag, + value=v.simple_value, + step=global_step, + time=int((event.wall_time or time.time()) * 1000), + run_id=run.info.run_id, + ) + + return original(self, event, *args, **kwargs) + + +def patched_add_summary(original, self, *args, **kwargs): + flush_metrics_queue() + return original(self, *args, **kwargs) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pytorch/pickle_module.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pytorch/pickle_module.py new file mode 100644 index 0000000000000000000000000000000000000000..cff156aaad9049e9de7caf889667a63a1c4924eb --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/pytorch/pickle_module.py @@ -0,0 +1,35 @@ +""" +This module imports contents from CloudPickle in a way that is compatible with the +``pickle_module`` parameter of PyTorch's model persistence function: ``torch.save`` +(see https://github.com/pytorch/pytorch/blob/692898fe379c9092f5e380797c32305145cd06e1/torch/ +serialization.py#L192). It is included as a distinct module from :mod:`mlflow.pytorch` to avoid +polluting the namespace with wildcard imports. + +Calling ``torch.save(..., pickle_module=mlflow.pytorch.pickle_module)`` will persist PyTorch model +definitions using CloudPickle, leveraging improved pickling functionality such as the ability +to capture class definitions in the "__main__" scope. + +TODO: Remove this module or make it an alias of CloudPickle when CloudPickle and PyTorch have +compatible pickling APIs. +""" + +# Import all contents of the CloudPickle module in an attempt to include all functions required +# by ``torch.save``. + +# CloudPickle does not include `Unpickler` in its namespace, which is required by PyTorch for +# deserialization. Noting that CloudPickle's `load()` and `loads()` routines are aliases for +# `pickle.load()` and `pickle.loads()`, we therefore import Unpickler from the native +# Python pickle library. +from pickle import Unpickler # noqa: F401 + +from cloudpickle import * # noqa: F403 + +# PyTorch uses the ``Pickler`` class of the specified ``pickle_module`` +# (https://github.com/pytorch/pytorch/blob/692898fe379c9092f5e380797c32305145cd06e1/torch/ +# serialization.py#L290). Unfortunately, ``cloudpickle.Pickler`` is an alias for Python's native +# pickling class: ``pickle.Pickler``, instead of ``cloudpickle.CloudPickler``. +# https://github.com/cloudpipe/cloudpickle/pull/235 has been filed to correct the issue, +# but this import renaming is necessary until either the requested change has been incorporated +# into a CloudPickle release or the ``torch.save`` API has been updated to be compatible with +# the existing CloudPickle API. +from cloudpickle import CloudPickler as Pickler # noqa: F401 diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/rfunc/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/rfunc/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..a3a6c68621948b55706f80c8eeee1de304701f1a --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/rfunc/__init__.py @@ -0,0 +1,42 @@ +"""Export and import of generic R models. + +This module defines generic filesystem format for R models and provides utilities +for saving and loading to and from this format. The format is self contained in the sense +that it includes all necessary information for anyone to load it and use it. Dependencies +are either stored directly with the model or referenced via a Conda environment. + +The convention for rfunc models is to have a ``predict`` method or function with the following +signature:: + + predict(data: DataFrame) -> DataFrame + +This convention is relied on by other MLflow components. + +Rfunc model format is defined as a directory structure containing all required data, code, and +configuration: + +.. code:: + + ./dst-path/ + ./MLmodel: configuration + +It must contain MLmodel file in its root with "r_function" format. + +Example: + +.. code:: shell + + >tree R/mlflow/R/inst/examples/R/lm/model + ├── MLmodel + └── r_model.bin + + >cat R/mlflow/R/inst/examples/R/lm/model/MLmodel + time_created: 1.5337659e+09 + flavors: + r_function: + version: 0.1.0 + model: r_model.bin + +""" + +FLAVOR_NAME = "crate" diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/rfunc/__pycache__/__init__.cpython-312.pyc 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a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/rfunc/backend.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/rfunc/backend.py new file mode 100644 index 0000000000000000000000000000000000000000..7adfc77e8630436b88f6f7023e9c0df96e8dc66d --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/rfunc/backend.py @@ -0,0 +1,134 @@ +import logging +import os +import re +import subprocess +import sys + +from mlflow.exceptions import MlflowException +from mlflow.models import FlavorBackend +from mlflow.tracking.artifact_utils import _download_artifact_from_uri +from mlflow.utils.string_utils import quote + +_logger = logging.getLogger(__name__) + + +class RFuncBackend(FlavorBackend): + """ + Flavor backend implementation for the generic R models. + Predict and serve locally models with 'crate' flavor. + """ + + def build_image( + self, model_uri, image_name, install_mlflow, mlflow_home, enable_mlserver, base_image=None + ): + pass + + def generate_dockerfile( + self, model_uri, output_path, install_mlflow, mlflow_home, enable_mlserver, base_image=None + ): + pass + + version_pattern = re.compile(r"version ([0-9]+\.[0-9]+\.[0-9]+)") + + def predict( + self, + model_uri, + input_path, + output_path, + content_type, + pip_requirements_override=None, + extra_envs=None, + ): + """ + Generate predictions using R model saved with MLflow. + Return the prediction results as a JSON. + """ + if pip_requirements_override is not None: + raise MlflowException("pip_requirements_override is not supported in the R backend.") + model_path = _download_artifact_from_uri(model_uri) + str_cmd = ( + "mlflow:::mlflow_rfunc_predict(model_path = '{0}', input_path = {1}, " + "output_path = {2}, content_type = {3})" + ) + command = str_cmd.format( + quote(model_path), + _str_optional(input_path), + _str_optional(output_path), + _str_optional(content_type), + ) + _execute(command, extra_envs=extra_envs) + + def serve( + self, + model_uri, + port, + host, + timeout, + enable_mlserver, + synchronous=True, + stdout=None, + stderr=None, + ): + """ + Generate R model locally. + + NOTE: The `enable_mlserver` parameter is there to comply with the + FlavorBackend interface but is not supported by MLServer yet. + https://github.com/SeldonIO/MLServer/issues/183 + """ + if enable_mlserver: + raise Exception("The MLServer inference server is not yet supported in the R backend.") + + if timeout: + _logger.warning("Timeout is not yet supported in the R backend.") + + if not synchronous: + raise Exception("RBackend does not support call with synchronous=False") + + if stdout is not None or stderr is not None: + raise Exception("RBackend does not support redirect stdout/stderr.") + + model_path = _download_artifact_from_uri(model_uri) + command = "mlflow::mlflow_rfunc_serve('{}', port = {}, host = '{}')".format( + quote(model_path), port, host + ) + _execute(command) + + def can_score_model(self): + # `Rscript --version` writes to stderr in R < 4.2.0 but stdout in R >= 4.2.0. + process = subprocess.Popen( + ["Rscript", "--version"], + close_fds=True, + stdout=subprocess.PIPE, + stderr=subprocess.STDOUT, + ) + stdout, _ = process.communicate() + if process.wait() != 0: + return False + + version = self.version_pattern.search(stdout.decode("utf-8")) + if not version: + return False + version = [int(x) for x in version.group(1).split(".")] + return version[0] > 3 or version[0] == 3 and version[1] >= 3 + + +def _execute(command, extra_envs=None): + env = os.environ.copy() + if extra_envs: + env.update(extra_envs) + + process = subprocess.Popen( + ["Rscript", "-e", command], + env=env, + close_fds=False, + stdin=sys.stdin, + stdout=sys.stdout, + stderr=sys.stderr, + ) + if process.wait() != 0: + raise Exception("Command returned non zero exit code.") + + +def _str_optional(s): + return "NULL" if s is None else f"'{quote(str(s))}'" diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/sagemaker/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/sagemaker/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..ec7095776fb4be9b77c8d175bd3bdddc8f64f1cc --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/sagemaker/__init__.py @@ -0,0 +1,3024 @@ +""" +The ``mlflow.sagemaker`` module provides an API for deploying MLflow models to Amazon SageMaker. +""" + +import json +import logging +import os +import platform +import signal +import sys +import tarfile +import time +import urllib.parse +import uuid +from subprocess import Popen +from typing import Any + +import mlflow +import mlflow.version +from mlflow import pyfunc +from mlflow.deployments import BaseDeploymentClient, PredictionsResponse +from mlflow.environment_variables import ( + MLFLOW_DEPLOYMENT_FLAVOR_NAME, + MLFLOW_SAGEMAKER_DEPLOY_IMG_URL, +) +from mlflow.exceptions import MlflowException +from mlflow.models import Model +from mlflow.models.container import ( + SERVING_ENVIRONMENT, +) +from mlflow.models.container import ( + SUPPORTED_FLAVORS as SUPPORTED_DEPLOYMENT_FLAVORS, +) +from mlflow.models.model import MLMODEL_FILE_NAME +from mlflow.protos.databricks_pb2 import INVALID_PARAMETER_VALUE, RESOURCE_DOES_NOT_EXIST +from mlflow.tracking.artifact_utils import _download_artifact_from_uri +from mlflow.utils.file_utils import TempDir +from mlflow.utils.proto_json_utils import dump_input_data + +DEFAULT_IMAGE_NAME = "mlflow-pyfunc" +DEPLOYMENT_MODE_ADD = "add" +DEPLOYMENT_MODE_REPLACE = "replace" +DEPLOYMENT_MODE_CREATE = "create" + +DEPLOYMENT_MODES = [DEPLOYMENT_MODE_CREATE, DEPLOYMENT_MODE_ADD, DEPLOYMENT_MODE_REPLACE] + +DEFAULT_BUCKET_NAME_PREFIX = "mlflow-sagemaker" + +DEFAULT_SAGEMAKER_INSTANCE_TYPE = "ml.m4.xlarge" +DEFAULT_SAGEMAKER_INSTANCE_COUNT = 1 + +DEFAULT_REGION_NAME = "us-west-2" +SAGEMAKER_SERVING_ENVIRONMENT = "SageMaker" +SAGEMAKER_APP_NAME_TAG_KEY = "app_name" + +_logger = logging.getLogger(__name__) + +_full_template = "{account}.dkr.ecr.{region}.amazonaws.com/{image}:{version}" + + +def _get_preferred_deployment_flavor(model_config): + """ + Obtains the flavor that MLflow would prefer to use when deploying the model. + If the model does not contain any supported flavors for deployment, an exception + will be thrown. + + Args: + model_config: An MLflow model object + + Returns: + The name of the preferred deployment flavor for the specified model + """ + if pyfunc.FLAVOR_NAME in model_config.flavors: + return pyfunc.FLAVOR_NAME + else: + raise MlflowException( + message=( + "The specified model does not contain any of the supported flavors for" + " deployment. The model contains the following flavors: {model_flavors}." + " Supported flavors: {supported_flavors}".format( + model_flavors=model_config.flavors.keys(), + supported_flavors=SUPPORTED_DEPLOYMENT_FLAVORS, + ) + ), + error_code=RESOURCE_DOES_NOT_EXIST, + ) + + +def _validate_deployment_flavor(model_config, flavor): + """ + Checks that the specified flavor is a supported deployment flavor + and is contained in the specified model. If one of these conditions + is not met, an exception is thrown. + + Args: + model_config: An MLflow Model object + flavor: The deployment flavor to validate + """ + if flavor not in SUPPORTED_DEPLOYMENT_FLAVORS: + raise MlflowException( + message=( + f"The specified flavor: `{flavor}` is not supported for deployment." + f" Please use one of the supported flavors: {SUPPORTED_DEPLOYMENT_FLAVORS}" + ), + error_code=INVALID_PARAMETER_VALUE, + ) + elif flavor not in model_config.flavors: + raise MlflowException( + message=( + "The specified model does not contain the specified deployment flavor:" + f" `{flavor}`. Please use one of the following deployment flavors" + f" that the model contains: {model_config.flavors.keys()}" + ), + error_code=RESOURCE_DOES_NOT_EXIST, + ) + + +def push_image_to_ecr(image=DEFAULT_IMAGE_NAME): + """ + Push local Docker image to AWS ECR. + + The image is pushed under currently active AWS account and to the currently active AWS region. + + Args: + image: Docker image name. + """ + import boto3 + + _logger.info("Pushing image to ECR") + client = boto3.client("sts") + caller_id = client.get_caller_identity() + account = caller_id["Account"] + my_session = boto3.session.Session() + region = my_session.region_name or "us-west-2" + fullname = _full_template.format( + account=account, region=region, image=image, version=mlflow.version.VERSION + ) + _logger.info("Pushing docker image %s to %s", image, fullname) + ecr_client = boto3.client("ecr") + try: + ecr_client.describe_repositories(repositoryNames=[image])["repositories"] + except ecr_client.exceptions.RepositoryNotFoundException: + ecr_client.create_repository(repositoryName=image) + _logger.info("Created new ECR repository: %s", image) + # TODO: it would be nice to translate the docker login, tag and push to python api. + # x = ecr_client.get_authorization_token()['authorizationData'][0] + # docker_login_cmd = "docker login -u AWS -p {token} {url}".format(token=x['authorizationToken'] + # ,url=x['proxyEndpoint']) + + docker_login_cmd = ( + "aws ecr get-login-password" + " | docker login --username AWS " + "--password-stdin " + f"{account}.dkr.ecr.{region}.amazonaws.com" + ) + + os_command_separator = ";\n" + if platform.system() == "Windows": + os_command_separator = " && " + + docker_tag_cmd = f"docker tag {image} {fullname}" + docker_push_cmd = f"docker push {fullname}" + + cmd = os_command_separator.join([docker_login_cmd, docker_tag_cmd, docker_push_cmd]) + + _logger.info("Executing: %s", cmd) + os.system(cmd) + + +def _deploy( + app_name, + model_uri, + execution_role_arn=None, + assume_role_arn=None, + bucket=None, + image_url=None, + region_name="us-west-2", + mode=DEPLOYMENT_MODE_CREATE, + archive=False, + instance_type=DEFAULT_SAGEMAKER_INSTANCE_TYPE, + instance_count=DEFAULT_SAGEMAKER_INSTANCE_COUNT, + vpc_config=None, + flavor=None, + synchronous=True, + timeout_seconds=1200, + data_capture_config=None, + variant_name=None, + async_inference_config=None, + serverless_config=None, + env=None, + tags=None, +): + """ + Deploy an MLflow model on AWS SageMaker. + The currently active AWS account must have correct permissions set up. + + This function creates a SageMaker endpoint. For more information about the input data + formats accepted by this endpoint, see the + `MLflow deployment tools documentation <../../deployment/deploy-model-to-sagemaker.html>`_. + + Args: + app_name: Name of the deployed application. + model_uri: The location, in URI format, of the MLflow model to deploy to SageMaker. + For example: + + - ``/Users/me/path/to/local/model`` + - ``relative/path/to/local/model`` + - ``s3://my_bucket/path/to/model`` + - ``runs://run-relative/path/to/model`` + - ``models://`` + - ``models://`` + + For more information about supported URI schemes, see + `Referencing Artifacts `_. + + execution_role_arn: The name of an IAM role granting the SageMaker service permissions to + access the specified Docker image and S3 bucket containing MLflow + model artifacts. If unspecified, the currently-assumed role will be + used. This execution role is passed to the SageMaker service when + creating a SageMaker model from the specified MLflow model. It is + passed as the ``ExecutionRoleArn`` parameter of the `SageMaker + CreateModel API call `_. This role is *not* assumed for any other + call. For more information about SageMaker execution roles for model + creation, see + https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-roles.html. + assume_role_arn: The name of an IAM cross-account role to be assumed to deploy SageMaker + to another AWS account. If unspecified, SageMaker will be deployed to + the the currently active AWS account. + bucket: S3 bucket where model artifacts will be stored. Defaults to a + SageMaker-compatible bucket name. + image_url: URL of the ECR-hosted Docker image the model should be deployed into, produced + by ``mlflow sagemaker build-and-push-container``. This parameter can also + be specified by the environment variable ``MLFLOW_SAGEMAKER_DEPLOY_IMG_URL``. + region_name: Name of the AWS region to which to deploy the application. + mode: The mode in which to deploy the application. Must be one of the following: + + ``mlflow.sagemaker.DEPLOYMENT_MODE_CREATE`` + Create an application with the specified name and model. This fails if an + application of the same name already exists. + + ``mlflow.sagemaker.DEPLOYMENT_MODE_REPLACE`` + If an application of the specified name exists, its model(s) is replaced with + the specified model. If no such application exists, it is created with the + specified name and model. + + ``mlflow.sagemaker.DEPLOYMENT_MODE_ADD`` + Add the specified model to a pre-existing application with the specified name, + if one exists. If the application does not exist, a new application is created + with the specified name and model. NOTE: If the application **already exists**, + the specified model is added to the application's corresponding SageMaker + endpoint with an initial weight of zero (0). To route traffic to the model, + update the application's associated endpoint configuration using either the + AWS console or the ``UpdateEndpointWeightsAndCapacities`` function defined in + https://docs.aws.amazon.com/sagemaker/latest/dg/API_UpdateEndpointWeightsAndCapacities.html. + + archive: If ``True``, any pre-existing SageMaker application resources that become + inactive (i.e. as a result of deploying in + ``mlflow.sagemaker.DEPLOYMENT_MODE_REPLACE`` mode) are preserved. + These resources may include unused SageMaker models and endpoint configurations + that were associated with a prior version of the application endpoint. If + ``False``, these resources are deleted. In order to use ``archive=False``, + ``deploy()`` must be executed synchronously with ``synchronous=True``. + instance_type: The type of SageMaker ML instance on which to deploy the model. For a list + of supported instance types, see + https://aws.amazon.com/sagemaker/pricing/instance-types/. + instance_count: The number of SageMaker ML instances on which to deploy the model. + vpc_config: A dictionary specifying the VPC configuration to use when creating the + new SageMaker model associated with this application. The acceptable values + for this parameter are identical to those of the ``VpcConfig`` parameter in + the `SageMaker boto3 client's create_model method + `_. For more information, see + https://docs.aws.amazon.com/sagemaker/latest/dg/API_VpcConfig.html. + + .. code-block:: python + :caption: Example + + import mlflow.sagemaker as mfs + + vpc_config = { + "SecurityGroupIds": [ + "sg-123456abc", + ], + "Subnets": [ + "subnet-123456abc", + ], + } + mfs._deploy(..., vpc_config=vpc_config) + + flavor: The name of the flavor of the model to use for deployment. Must be either + ``None`` or one of mlflow.sagemaker.SUPPORTED_DEPLOYMENT_FLAVORS. If ``None``, + a flavor is automatically selected from the model's available flavors. If the + specified flavor is not present or not supported for deployment, an exception + will be thrown. + synchronous: If ``True``, this function will block until the deployment process succeeds + or encounters an irrecoverable failure. If ``False``, this function will + return immediately after starting the deployment process. It will not wait + for the deployment process to complete; in this case, the caller is + responsible for monitoring the health and status of the pending deployment + via native SageMaker APIs or the AWS console. + timeout_seconds: If ``synchronous`` is ``True``, the deployment process will return after + the specified number of seconds if no definitive result (success or + failure) is achieved. Once the function returns, the caller is + responsible for monitoring the health and status of the pending + deployment using native SageMaker APIs or the AWS console. If + ``synchronous`` is ``False``, this parameter is ignored. + data_capture_config: A dictionary specifying the data capture configuration to use when + creating the new SageMaker model associated with this application. + For more information, see + https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_DataCaptureConfig.html. + + .. code-block:: python + :caption: Example + + import mlflow.sagemaker as mfs + + data_capture_config = { + "EnableCapture": True, + "InitialSamplingPercentage": 100, + "DestinationS3Uri": "s3://my-bucket/path", + "CaptureOptions": [{"CaptureMode": "Output"}], + } + mfs._deploy(..., data_capture_config=data_capture_config) + + variant_name: The name to assign to the new production variant. + async_inference_config: The name to assign to the endpoint_config + on the sagemaker endpoint. + .. code-block:: python + :caption: Example + + { + "AsyncInferenceConfig": { + "ClientConfig": {"MaxConcurrentInvocationsPerInstance": 4}, + "OutputConfig": { + "S3OutputPath": "s3://", + "NotificationConfig": {}, + }, + } + } + + serverless_config: An optional dictionary specifying the serverless configuration + .. code-block:: python + :caption: Example + + { + "ServerlessConfig": { + "MemorySizeInMB": 2048, + "MaxConcurrency": 20, + } + } + + env: An optional dictionary of environment variables to set for the model. + tags: An optional dictionary of tags to apply to the endpoint. + """ + import boto3 + + if (not archive) and (not synchronous): + raise MlflowException( + message=( + "Resources must be archived when `deploy()` is executed in non-synchronous mode." + " Either set `synchronous=True` or `archive=True`." + ), + error_code=INVALID_PARAMETER_VALUE, + ) + + if mode not in DEPLOYMENT_MODES: + raise MlflowException( + message="`mode` must be one of: {deployment_modes}".format( + deployment_modes=",".join(DEPLOYMENT_MODES) + ), + error_code=INVALID_PARAMETER_VALUE, + ) + + model_path = _download_artifact_from_uri(model_uri) + model_config_path = os.path.join(model_path, MLMODEL_FILE_NAME) + if not os.path.exists(model_config_path): + raise MlflowException( + message=( + f"Failed to find {MLMODEL_FILE_NAME} configuration within the specified model's " + "root directory." + ), + error_code=INVALID_PARAMETER_VALUE, + ) + model_config = Model.load(model_config_path) + + if flavor is None: + flavor = _get_preferred_deployment_flavor(model_config) + else: + _validate_deployment_flavor(model_config, flavor) + _logger.info("Using the %s flavor for deployment!", flavor) + + assume_role_credentials = _assume_role_and_get_credentials(assume_role_arn=assume_role_arn) + + s3_client = boto3.client("s3", region_name=region_name, **assume_role_credentials) + sage_client = boto3.client("sagemaker", region_name=region_name, **assume_role_credentials) + + endpoint_exists = _find_endpoint(endpoint_name=app_name, sage_client=sage_client) is not None + if endpoint_exists and mode == DEPLOYMENT_MODE_CREATE: + raise MlflowException( + message=( + f"You are attempting to deploy an application with name: {app_name} in" + f" '{DEPLOYMENT_MODE_CREATE}' mode. However, an application with the same name" + " already exists. If you want to update this application, deploy in" + f" '{DEPLOYMENT_MODE_ADD}' or '{DEPLOYMENT_MODE_REPLACE}' mode." + ), + error_code=INVALID_PARAMETER_VALUE, + ) + + model_name = _get_sagemaker_model_name(endpoint_name=app_name) + + if not image_url: + image_url = _get_default_image_url(region_name=region_name) + if not execution_role_arn: + execution_role_arn = _get_assumed_role_arn(**assume_role_credentials) + if not bucket: + _logger.info("No model data bucket specified, using the default bucket") + bucket = _get_default_s3_bucket(region_name, **assume_role_credentials) + + model_s3_path = _upload_s3( + local_model_path=model_path, + bucket=bucket, + prefix=model_name, + region_name=region_name, + s3_client=s3_client, + **assume_role_credentials, + ) + + if endpoint_exists: + deployment_operation = _update_sagemaker_endpoint( + endpoint_name=app_name, + model_name=model_name, + model_s3_path=model_s3_path, + model_uri=model_uri, + image_url=image_url, + flavor=flavor, + instance_type=instance_type, + instance_count=instance_count, + vpc_config=vpc_config, + mode=mode, + role=execution_role_arn, + sage_client=sage_client, + s3_client=s3_client, + variant_name=variant_name, + async_inference_config=async_inference_config, + serverless_config=serverless_config, + data_capture_config=data_capture_config, + env=env, + tags=tags, + ) + else: + deployment_operation = _create_sagemaker_endpoint( + endpoint_name=app_name, + model_name=model_name, + model_s3_path=model_s3_path, + model_uri=model_uri, + image_url=image_url, + flavor=flavor, + instance_type=instance_type, + instance_count=instance_count, + vpc_config=vpc_config, + data_capture_config=data_capture_config, + role=execution_role_arn, + sage_client=sage_client, + variant_name=variant_name, + async_inference_config=async_inference_config, + serverless_config=serverless_config, + env=env, + tags=tags, + ) + + if synchronous: + _logger.info("Waiting for the deployment operation to complete...") + operation_status = deployment_operation.await_completion(timeout_seconds=timeout_seconds) + if operation_status.state == _SageMakerOperationStatus.STATE_SUCCEEDED: + _logger.info( + 'The deployment operation completed successfully with message: "%s"', + operation_status.message, + ) + else: + raise MlflowException( + "The deployment operation failed with the following error message:" + f' "{operation_status.message}"' + ) + if not archive: + deployment_operation.clean_up() + + return app_name, flavor + + +def _delete( + app_name, + region_name="us-west-2", + assume_role_arn=None, + archive=False, + synchronous=True, + timeout_seconds=300, +): + """ + Delete a SageMaker application. + + Args: + app_name: Name of the deployed application. + region_name: Name of the AWS region in which the application is deployed. + assume_role_arn: The name of an IAM cross-account role to be assumed to deploy SageMaker + to another AWS account. If unspecified, SageMaker will be deployed to + the the currently active AWS account. + archive: If ``True``, resources associated with the specified application, such + as its associated models and endpoint configuration, are preserved. + If ``False``, these resources are deleted. In order to use + ``archive=False``, ``delete()`` must be executed synchronously with + ``synchronous=True``. + synchronous: If `True`, this function blocks until the deletion process succeeds + or encounters an irrecoverable failure. If `False`, this function + returns immediately after starting the deletion process. It will not wait + for the deletion process to complete; in this case, the caller is + responsible for monitoring the status of the deletion process via native + SageMaker APIs or the AWS console. + timeout_seconds: If `synchronous` is `True`, the deletion process returns after the + specified number of seconds if no definitive result (success or failure) + is achieved. Once the function returns, the caller is responsible + for monitoring the status of the deletion process via native SageMaker + APIs or the AWS console. If `synchronous` is False, this parameter + is ignored. + """ + import boto3 + + if (not archive) and (not synchronous): + raise MlflowException( + message=( + "Resources must be archived when `delete()` is executed in non-synchronous mode." + " Either set `synchronous=True` or `archive=True`." + ), + error_code=INVALID_PARAMETER_VALUE, + ) + + assume_role_credentials = _assume_role_and_get_credentials(assume_role_arn=assume_role_arn) + + s3_client = boto3.client("s3", region_name=region_name, **assume_role_credentials) + sage_client = boto3.client("sagemaker", region_name=region_name, **assume_role_credentials) + + endpoint_info = sage_client.describe_endpoint(EndpointName=app_name) + endpoint_arn = endpoint_info["EndpointArn"] + + sage_client.delete_endpoint(EndpointName=app_name) + _logger.info("Deleted endpoint with arn: %s", endpoint_arn) + + def status_check_fn(): + endpoint_info = _find_endpoint(endpoint_name=app_name, sage_client=sage_client) + if endpoint_info is not None: + return _SageMakerOperationStatus.in_progress( + "Deletion is still in progress. Current endpoint status: {endpoint_status}".format( + endpoint_status=endpoint_info["EndpointStatus"] + ) + ) + else: + return _SageMakerOperationStatus.succeeded( + "The SageMaker endpoint was deleted successfully." + ) + + def cleanup_fn(): + _logger.info("Cleaning up unused resources...") + config_name = endpoint_info["EndpointConfigName"] + config_info = sage_client.describe_endpoint_config(EndpointConfigName=config_name) + config_arn = config_info["EndpointConfigArn"] + sage_client.delete_endpoint_config(EndpointConfigName=config_name) + _logger.info("Deleted associated endpoint configuration with arn: %s", config_arn) + for pv in config_info["ProductionVariants"]: + model_name = pv["ModelName"] + model_arn = _delete_sagemaker_model(model_name, sage_client, s3_client) + _logger.info("Deleted associated model with arn: %s", model_arn) + + delete_operation = _SageMakerOperation(status_check_fn=status_check_fn, cleanup_fn=cleanup_fn) + + if synchronous: + _logger.info("Waiting for the delete operation to complete...") + operation_status = delete_operation.await_completion(timeout_seconds=timeout_seconds) + if operation_status.state == _SageMakerOperationStatus.STATE_SUCCEEDED: + _logger.info( + 'The deletion operation completed successfully with message: "%s"', + operation_status.message, + ) + else: + raise MlflowException( + "The deletion operation failed with the following error message:" + f' "{operation_status.message}"' + ) + if not archive: + delete_operation.clean_up() + + +def deploy_transform_job( + job_name, + model_uri, + s3_input_data_type, + s3_input_uri, + content_type, + s3_output_path, + compression_type="None", + split_type="Line", + accept="text/csv", + assemble_with="Line", + input_filter="$", + output_filter="$", + join_resource="None", + execution_role_arn=None, + assume_role_arn=None, + bucket=None, + image_url=None, + region_name="us-west-2", + instance_type=DEFAULT_SAGEMAKER_INSTANCE_TYPE, + instance_count=DEFAULT_SAGEMAKER_INSTANCE_COUNT, + vpc_config=None, + flavor=None, + archive=False, + synchronous=True, + timeout_seconds=1200, +): + """ + Deploy an MLflow model on AWS SageMaker and create the corresponding batch transform job. + The currently active AWS account must have correct permissions set up. + + Args: + job_name: Name of the deployed Sagemaker batch transform job. + model_uri: The location, in URI format, of the MLflow model to deploy to SageMaker. + For example: + + - ``/Users/me/path/to/local/model`` + - ``relative/path/to/local/model`` + - ``s3://my_bucket/path/to/model`` + - ``runs://run-relative/path/to/model`` + - ``models://`` + - ``models://`` + + For more information about supported URI schemes, see + `Referencing Artifacts `_. + + s3_input_data_type: Input data type for the transform job. + s3_input_uri: S3 key name prefix or a manifest of the input data. + content_type: The multipurpose internet mail extension (MIME) type of the data. + s3_output_path: The S3 path to store the output results of the Sagemaker transform job. + compression_type: The compression type of the transform data. + split_type: The method to split the transform job's data files into smaller batches. + accept: The multipurpose internet mail extension (MIME) type of the output data. + assemble_with: The method to assemble the results of the transform job as + a single S3 object. + input_filter: A JSONPath expression used to select a portion of the input data for + the transform job. + output_filter: A JSONPath expression used to select a portion of the output data from + the transform job. + join_resource: The source of the data to join with the transformed data. + + execution_role_arn: The name of an IAM role granting the SageMaker service permissions to + access the specified Docker image and S3 bucket containing MLflow + model artifacts. If unspecified, the currently-assumed role will be + used. This execution role is passed to the SageMaker service when + creating a SageMaker model from the specified MLflow model. It is + passed as the ``ExecutionRoleArn`` parameter of the `SageMaker + CreateModel API call `_. This role is *not* assumed for any other + call. For more information about SageMaker execution roles for model + creation, see + https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-roles.html. + assume_role_arn: The name of an IAM cross-account role to be assumed to deploy SageMaker + to another AWS account. If unspecified, SageMaker will be deployed to + the the currently active AWS account. + bucket: S3 bucket where model artifacts will be stored. Defaults to a + SageMaker-compatible bucket name. + image_url: URL of the ECR-hosted Docker image the model should be deployed into, produced + by ``mlflow sagemaker build-and-push-container``. This parameter can also + be specified by the environment variable ``MLFLOW_SAGEMAKER_DEPLOY_IMG_URL``. + region_name: Name of the AWS region to which to deploy the application. + instance_type: The type of SageMaker ML instance on which to deploy the model. For a list + of supported instance types, see + https://aws.amazon.com/sagemaker/pricing/instance-types/. + instance_count: The number of SageMaker ML instances on which to deploy the model. + vpc_config: A dictionary specifying the VPC configuration to use when creating the + new SageMaker model associated with this batch transform job. The acceptable + values for this parameter are identical to those of the ``VpcConfig`` + parameter in the `SageMaker boto3 client's create_model method + `_. For more information, see + https://docs.aws.amazon.com/sagemaker/latest/dg/API_VpcConfig.html. + + .. code-block:: python + :caption: Example + + import mlflow.sagemaker as mfs + + vpc_config = { + "SecurityGroupIds": [ + "sg-123456abc", + ], + "Subnets": [ + "subnet-123456abc", + ], + } + mfs.deploy_transform_job(..., vpc_config=vpc_config) + + flavor: The name of the flavor of the model to use for deployment. Must be either + ``None`` or one of mlflow.sagemaker.SUPPORTED_DEPLOYMENT_FLAVORS. If ``None``, + a flavor is automatically selected from the model's available flavors. If the + specified flavor is not present or not supported for deployment, an exception + will be thrown. + archive: If ``True``, resources like Sagemaker models and model artifacts in S3 are + preserved after the finished batch transform job. If ``False``, these resources + are deleted. In order to use ``archive=False``, ``deploy_transform_job()`` must + be executed synchronously with ``synchronous=True``. + synchronous: If ``True``, this function will block until the deployment process succeeds + or encounters an irrecoverable failure. If ``False``, this function will + return immediately after starting the deployment process. It will not wait + for the deployment process to complete; in this case, the caller is + responsible for monitoring the health and status of the pending deployment + via native SageMaker APIs or the AWS console. + timeout_seconds: If ``synchronous`` is ``True``, the deployment process will return after + the specified number of seconds if no definitive result (success or + failure) is achieved. Once the function returns, the caller is + responsible for monitoring the health and status of the pending + deployment using native SageMaker APIs or the AWS console. If + ``synchronous`` is ``False``, this parameter is ignored. + """ + import boto3 + + if (not archive) and (not synchronous): + raise MlflowException( + message=( + "Resources must be archived when `deploy_transform_job()`" + " is executed in non-synchronous mode." + " Either set `synchronous=True` or `archive=True`." + ), + error_code=INVALID_PARAMETER_VALUE, + ) + + model_path = _download_artifact_from_uri(model_uri) + model_config_path = os.path.join(model_path, MLMODEL_FILE_NAME) + if not os.path.exists(model_config_path): + raise MlflowException( + message=( + f"Failed to find {MLMODEL_FILE_NAME} configuration within the specified model's" + " root directory." + ), + error_code=INVALID_PARAMETER_VALUE, + ) + model_config = Model.load(model_config_path) + + if flavor is None: + flavor = _get_preferred_deployment_flavor(model_config) + else: + _validate_deployment_flavor(model_config, flavor) + _logger.info("Using the %s flavor for deployment!", flavor) + + assume_role_credentials = _assume_role_and_get_credentials(assume_role_arn=assume_role_arn) + + s3_client = boto3.client("s3", region_name=region_name, **assume_role_credentials) + sage_client = boto3.client("sagemaker", region_name=region_name, **assume_role_credentials) + + transform_job_exists = ( + _find_transform_job(job_name=job_name, sage_client=sage_client) is not None + ) + if transform_job_exists: + raise MlflowException( + message=( + f"You are attempting to deploy a batch transform job with name: {job_name}. " + "However, a batch transform job with the same name already exists." + ), + error_code=INVALID_PARAMETER_VALUE, + ) + + model_name = _get_sagemaker_transform_model_name(job_name=job_name) + if not image_url: + image_url = _get_default_image_url(region_name=region_name) + if not execution_role_arn: + execution_role_arn = _get_assumed_role_arn(**assume_role_credentials) + if not bucket: + _logger.info("No model data bucket specified, using the default bucket") + bucket = _get_default_s3_bucket(region_name, **assume_role_credentials) + + model_s3_path = _upload_s3( + local_model_path=model_path, + bucket=bucket, + prefix=model_name, + region_name=region_name, + s3_client=s3_client, + **assume_role_credentials, + ) + + deployment_operation = _create_sagemaker_transform_job( + job_name=job_name, + model_name=model_name, + model_s3_path=model_s3_path, + model_uri=model_uri, + image_url=image_url, + flavor=flavor, + vpc_config=vpc_config, + role=execution_role_arn, + sage_client=sage_client, + s3_client=s3_client, + instance_type=instance_type, + instance_count=instance_count, + s3_input_data_type=s3_input_data_type, + s3_input_uri=s3_input_uri, + content_type=content_type, + compression_type=compression_type, + split_type=split_type, + s3_output_path=s3_output_path, + accept=accept, + assemble_with=assemble_with, + input_filter=input_filter, + output_filter=output_filter, + join_resource=join_resource, + ) + + if synchronous: + _logger.info("Waiting for the batch transform job to complete...") + operation_status = deployment_operation.await_completion(timeout_seconds=timeout_seconds) + if operation_status.state == _SageMakerOperationStatus.STATE_SUCCEEDED: + _logger.info( + 'The batch transform job completed successfully with message: "%s"', + operation_status.message, + ) + else: + raise MlflowException( + "The batch transform job failed with the following error message:" + f' "{operation_status.message}"' + ) + if not archive: + deployment_operation.clean_up() + + +def terminate_transform_job( + job_name, + region_name="us-west-2", + assume_role_arn=None, + archive=False, + synchronous=True, + timeout_seconds=300, +): + """ + Terminate a SageMaker batch transform job. + + Args: + job_name: Name of the deployed Sagemaker batch transform job. + region_name: Name of the AWS region in which the batch transform job is deployed. + assume_role_arn: The name of an IAM cross-account role to be assumed to deploy SageMaker + to another AWS account. If unspecified, SageMaker will be deployed to + the the currently active AWS account. + archive: If ``True``, resources associated with the specified batch transform job, + such as its associated models and model artifacts, are preserved. + If ``False``, these resources are deleted. In order to use ``archive=False``, + ``terminate_transform_job()`` must be executed synchronously + with ``synchronous=True``. + synchronous: If `True`, this function blocks until the termination process succeeds + or encounters an irrecoverable failure. If `False`, this function + returns immediately after starting the termination process. It will not + wait for the termination process to complete; in this case, the caller is + responsible for monitoring the status of the termination process via native + SageMaker APIs or the AWS console. + timeout_seconds: If `synchronous` is `True`, the termination process returns after the + specified number of seconds if no definitive result (success or failure) + is achieved. Once the function returns, the caller is responsible + for monitoring the status of the termination process via native + SageMaker APIs or the AWS console. If `synchronous` is False, this + parameter is ignored. + """ + import boto3 + + if (not archive) and (not synchronous): + raise MlflowException( + message=( + "Resources must be archived when `terminate_transform_job()`" + " is executed in non-synchronous mode." + " Either set `synchronous=True` or `archive=True`." + ), + error_code=INVALID_PARAMETER_VALUE, + ) + + assume_role_credentials = _assume_role_and_get_credentials(assume_role_arn=assume_role_arn) + + s3_client = boto3.client("s3", region_name=region_name, **assume_role_credentials) + sage_client = boto3.client("sagemaker", region_name=region_name, **assume_role_credentials) + + transform_job_info = sage_client.describe_transform_job(TransformJobName=job_name) + transform_job_arn = transform_job_info["TransformJobArn"] + + sage_client.stop_transform_job(TransformJobName=job_name) + _logger.info("Terminated batch transform job with arn: %s", transform_job_arn) + + def status_check_fn(): + transform_job_info = _find_transform_job(job_name=job_name, sage_client=sage_client) + + if transform_job_info["TransformJobStatus"] == "Stopping": + return _SageMakerOperationStatus.in_progress( + "Termination is still in progress. Current batch transform job status: " + "{transform_job_status}".format( + transform_job_status=transform_job_info["TransformJobStatus"] + ) + ) + elif transform_job_info["TransformJobStatus"] == "Stopped": + return _SageMakerOperationStatus.succeeded( + "The SageMaker batch transform job was terminated successfully." + ) + + def cleanup_fn(): + _logger.info("Cleaning up unused resources...") + model_name = transform_job_info["ModelName"] + model_arn = _delete_sagemaker_model(model_name, sage_client, s3_client) + _logger.info("Deleted associated model with arn: %s", model_arn) + + stop_operation = _SageMakerOperation(status_check_fn=status_check_fn, cleanup_fn=cleanup_fn) + + if synchronous: + _logger.info("Waiting for the termination operation to complete...") + operation_status = stop_operation.await_completion(timeout_seconds=timeout_seconds) + if operation_status.state == _SageMakerOperationStatus.STATE_SUCCEEDED: + _logger.info( + 'The termination operation completed successfully with message: "%s"', + operation_status.message, + ) + else: + raise MlflowException( + "The termination operation failed with the following error message:" + f' "{operation_status.message}"' + ) + if not archive: + stop_operation.clean_up() + + +def push_model_to_sagemaker( + model_name, + model_uri, + execution_role_arn=None, + assume_role_arn=None, + bucket=None, + image_url=None, + region_name="us-west-2", + vpc_config=None, + flavor=None, +): + """ + Create a SageMaker Model from an MLflow model artifact. + The currently active AWS account must have correct permissions set up. + + Args: + model_name: Name of the Sagemaker model. + model_uri: The location, in URI format, of the MLflow model to deploy to SageMaker. + For example: + + - ``/Users/me/path/to/local/model`` + - ``relative/path/to/local/model`` + - ``s3://my_bucket/path/to/model`` + - ``runs://run-relative/path/to/model`` + - ``models://`` + - ``models://`` + + For more information about supported URI schemes, see + `Referencing Artifacts `_. + + execution_role_arn: The name of an IAM role granting the SageMaker service permissions to + access the specified Docker image and S3 bucket containing MLflow + model artifacts. If unspecified, the currently-assumed role will be + used. This execution role is passed to the SageMaker service when + creating a SageMaker model from the specified MLflow model. It is + passed as the ``ExecutionRoleArn`` parameter of the `SageMaker + CreateModel API call `_. This role is *not* assumed for any other + call. For more information about SageMaker execution roles for model + creation, see + https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-roles.html. + assume_role_arn: The name of an IAM cross-account role to be assumed to deploy SageMaker + to another AWS account. If unspecified, SageMaker will be deployed to + the the currently active AWS account. + bucket: S3 bucket where model artifacts will be stored. Defaults to a + SageMaker-compatible bucket name. + image_url: URL of the ECR-hosted Docker image the model should be deployed into, produced + by ``mlflow sagemaker build-and-push-container``. This parameter can also + be specified by the environment variable ``MLFLOW_SAGEMAKER_DEPLOY_IMG_URL``. + region_name: Name of the AWS region to which to deploy the application. + vpc_config: A dictionary specifying the VPC configuration to use when creating the + new SageMaker model. The acceptable values for this parameter are identical + to those of the ``VpcConfig`` parameter in the `SageMaker boto3 client's + create_model method + `_. For more information, see + https://docs.aws.amazon.com/sagemaker/latest/dg/API_VpcConfig.html. + + .. code-block:: python + :caption: Example + + import mlflow.sagemaker as mfs + + vpc_config = { + "SecurityGroupIds": [ + "sg-123456abc", + ], + "Subnets": [ + "subnet-123456abc", + ], + } + mfs.push_model_to_sagemaker(..., vpc_config=vpc_config) + + flavor: The name of the flavor of the model to use for deployment. Must be either + ``None`` or one of mlflow.sagemaker.SUPPORTED_DEPLOYMENT_FLAVORS. If ``None``, + a flavor is automatically selected from the model's available flavors. If the + specified flavor is not present or not supported for deployment, an exception + will be thrown. + """ + import boto3 + + model_path = _download_artifact_from_uri(model_uri) + model_config_path = os.path.join(model_path, MLMODEL_FILE_NAME) + if not os.path.exists(model_config_path): + raise MlflowException( + message=( + f"Failed to find {MLMODEL_FILE_NAME} configuration within the specified model's" + " root directory." + ), + error_code=INVALID_PARAMETER_VALUE, + ) + model_config = Model.load(model_config_path) + + if flavor is None: + flavor = _get_preferred_deployment_flavor(model_config) + else: + _validate_deployment_flavor(model_config, flavor) + _logger.info("Using the %s flavor for deployment!", flavor) + + assume_role_credentials = _assume_role_and_get_credentials(assume_role_arn=assume_role_arn) + + s3_client = boto3.client("s3", region_name=region_name, **assume_role_credentials) + sage_client = boto3.client("sagemaker", region_name=region_name, **assume_role_credentials) + + if _does_model_exist(model_name=model_name, sage_client=sage_client): + raise MlflowException( + message=( + f"You are attempting to create a Sagemaker model with name: {model_name}. " + "However, a model with the same name already exists." + ), + error_code=INVALID_PARAMETER_VALUE, + ) + + if not image_url: + image_url = _get_default_image_url(region_name=region_name) + if not execution_role_arn: + execution_role_arn = _get_assumed_role_arn(**assume_role_credentials) + if not bucket: + _logger.info("No model data bucket specified, using the default bucket") + bucket = _get_default_s3_bucket(region_name, **assume_role_credentials) + + model_s3_path = _upload_s3( + local_model_path=model_path, + bucket=bucket, + prefix=model_name, + region_name=region_name, + s3_client=s3_client, + **assume_role_credentials, + ) + + model_response = _create_sagemaker_model( + model_name=model_name, + model_s3_path=model_s3_path, + model_uri=model_uri, + flavor=flavor, + vpc_config=vpc_config, + image_url=image_url, + execution_role=execution_role_arn, + sage_client=sage_client, + env={}, + tags={}, + ) + + _logger.info("Created Sagemaker model with arn: %s", model_response["ModelArn"]) + + +def run_local(name, model_uri, flavor=None, config=None): + """ + Serve the model locally in a SageMaker compatible Docker container. + + Note that models deployed locally cannot be managed by other deployment APIs + (e.g. ``update_deployment``, ``delete_deployment``, etc). + + Args: + name: Name of the local serving application. + model_uri: The location, in URI format, of the MLflow model to deploy locally. + For example: + + - ``/Users/me/path/to/local/model`` + - ``relative/path/to/local/model`` + - ``s3://my_bucket/path/to/model`` + - ``runs://run-relative/path/to/model`` + - ``models://`` + - ``models://`` + + For more information about supported URI schemes, see + `Referencing Artifacts `_. + flavor: The name of the flavor of the model to use for deployment. Must be either + ``None`` or one of mlflow.sagemaker.SUPPORTED_DEPLOYMENT_FLAVORS. + If ``None``, a flavor is automatically selected from the model's available + flavors. If the specified flavor is not present or not supported for + deployment, an exception will be thrown. + config: Configuration parameters. The supported parameters are: + + - ``image``: The name of the Docker image to use for model serving. Defaults + to ``"mlflow-pyfunc"``. + - ``port``: The port at which to expose the model server on the local host. + Defaults to ``5000``. + + .. code-block:: python + :caption: Python example + + from mlflow.models import build_docker + from mlflow.deployments import get_deploy_client + + build_docker(name="mlflow-pyfunc") + + client = get_deploy_client("sagemaker") + client.run_local( + name="my-local-deployment", + model_uri="/mlruns/0/abc/model", + flavor="python_function", + config={ + "port": 5000, + "image": "mlflow-pyfunc", + }, + ) + + .. code-block:: bash + :caption: Command-line example + + mlflow models build-docker --name "mlflow-pyfunc" + mlflow deployments run-local --target sagemaker \\ + --name my-local-deployment \\ + --model-uri "/mlruns/0/abc/model" \\ + --flavor python_function \\ + -C port=5000 \\ + -C image="mlflow-pyfunc" + """ + model_path = _download_artifact_from_uri(model_uri) + model_config_path = os.path.join(model_path, MLMODEL_FILE_NAME) + model_config = Model.load(model_config_path) + + if flavor is None: + flavor = _get_preferred_deployment_flavor(model_config) + else: + _validate_deployment_flavor(model_config, flavor) + _logger.info("Using the %s flavor for local serving!", flavor) + + image = config.get("image", DEFAULT_IMAGE_NAME) + port = int(config.get("port", 5000)) + + deployment_config = _get_deployment_config(flavor_name=flavor) + + _logger.info("launching docker image with path %s", model_path) + cmd = ["docker", "run", "-v", f"{model_path}:/opt/ml/model/", "-p", f"{port}:8080"] + for key, value in deployment_config.items(): + cmd += ["-e", f"{key}={value}"] + cmd += ["--rm", image, "serve"] + _logger.info("executing: %s", " ".join(cmd)) + proc = Popen(cmd, stdout=sys.stdout, stderr=sys.stderr, text=True) + + def _sigterm_handler(*_): + _logger.info("received termination signal => killing docker process") + proc.send_signal(signal.SIGINT) + + signal.signal(signal.SIGTERM, _sigterm_handler) + proc.wait() + + +def target_help(): + """ + Provide help information for the SageMaker deployment client. + """ + return """\ + For detailed documentation on the SageMaker deployment client, please visit + https://mlflow.org/docs/latest/python_api/mlflow.sagemaker.html#mlflow.sagemaker.SageMakerDeploymentClient + + The target URI must follow the following formats: + - sagemaker + - sagemaker:/region_name + - sagemaker:/region_name/assume_role_arn + + When the region_name or assume_role_arn are provided, they will be used as the default region + and assumed role ARN when executing the commands. + + The `create` and `update` commands require a deployment name and a model_uri. The model flavor + and deployment configuration can be optionally provided. These commands can also be executed + in synchronous or asynchronous mode. + + The `delete` command accepts configurations to archive a model instead of deleting, execute + in asynchronous mode and timeout period. + """ + + +def _get_default_image_url(region_name): + import boto3 + + if env_img := MLFLOW_SAGEMAKER_DEPLOY_IMG_URL.get(): + return env_img + + ecr_client = boto3.client("ecr", region_name=region_name) + repository_conf = ecr_client.describe_repositories(repositoryNames=[DEFAULT_IMAGE_NAME])[ + "repositories" + ][0] + return (repository_conf["repositoryUri"] + ":{version}").format(version=mlflow.version.VERSION) + + +def _get_account_id(**assume_role_credentials): + import boto3 + + sess = boto3.Session() + sts_client = sess.client("sts", **assume_role_credentials) + identity_info = sts_client.get_caller_identity() + return identity_info["Account"] + + +def _get_assumed_role_arn(**assume_role_credentials): + """ + Returns: + ARN of the user's current IAM role. + """ + import boto3 + + sess = boto3.Session() + sts_client = sess.client("sts", **assume_role_credentials) + identity_info = sts_client.get_caller_identity() + sts_arn = identity_info["Arn"] + role_name = sts_arn.split("/")[1] + iam_client = sess.client("iam", **assume_role_credentials) + role_response = iam_client.get_role(RoleName=role_name) + return role_response["Role"]["Arn"] + + +def _assume_role_and_get_credentials(assume_role_arn=None): + """ + Assume a new role in AWS and return the credentials for that role. + When ``assume_role_arn`` is ``None`` or an empty string, + this function does nothing and returns an empty dictionary. + + Args: + assume_role_arn: Optional ARN of the role that will be assumed + + Returns: + Dict with credentials of the assumed role + """ + import boto3 + + if not assume_role_arn: + return {} + + sts_client = boto3.client("sts") + sts_response = sts_client.assume_role( + RoleArn=assume_role_arn, RoleSessionName="mlflow-sagemaker" + ) + + _logger.info("Assuming role %s for deployment!", assume_role_arn) + + return { + "aws_access_key_id": sts_response["Credentials"]["AccessKeyId"], + "aws_secret_access_key": sts_response["Credentials"]["SecretAccessKey"], + "aws_session_token": sts_response["Credentials"]["SessionToken"], + } + + +def _get_default_s3_bucket(region_name, **assume_role_credentials): + import boto3 + + # create bucket if it does not exist + sess = boto3.Session() + account_id = _get_account_id(**assume_role_credentials) + bucket_name = f"{DEFAULT_BUCKET_NAME_PREFIX}-{region_name}-{account_id}" + s3 = sess.client("s3", **assume_role_credentials) + response = s3.list_buckets() + buckets = [b["Name"] for b in response["Buckets"]] + if bucket_name not in buckets: + _logger.info("Default bucket `%s` not found. Creating...", bucket_name) + bucket_creation_kwargs = { + "ACL": "bucket-owner-full-control", + "Bucket": bucket_name, + } + if region_name != "us-east-1": + # The location constraint is required during bucket creation for all regions + # outside of us-east-1. This constraint cannot be specified in us-east-1; + # specifying it in this region results in a failure, so we will only + # add it if we are deploying outside of us-east-1. + # See https://docs.aws.amazon.com/cli/latest/reference/s3api/create-bucket.html#examples + bucket_creation_kwargs["CreateBucketConfiguration"] = { + "LocationConstraint": region_name + } + response = s3.create_bucket(**bucket_creation_kwargs) + _logger.info("Bucket creation response: %s", response) + else: + _logger.info("Default bucket `%s` already exists. Skipping creation.", bucket_name) + return bucket_name + + +def _make_tarfile(output_filename, source_dir): + """ + create a tar.gz from a directory. + """ + with tarfile.open(output_filename, "w:gz") as tar: + for f in os.listdir(source_dir): + tar.add(os.path.join(source_dir, f), arcname=f) + + +def _upload_s3(local_model_path, bucket, prefix, region_name, s3_client, **assume_role_credentials): + """ + Upload dir to S3 as .tar.gz. + + Args: + local_model_path: Local path to a dir. + bucket: S3 bucket where to store the data. + prefix: Path within the bucket. + region_name: The AWS region in which to upload data to S3. + s3_client: A boto3 client for S3. + + Returns: + S3 path of the uploaded artifact. + """ + import boto3 + + sess = boto3.Session(region_name=region_name, **assume_role_credentials) + with TempDir() as tmp: + model_data_file = tmp.path("model.tar.gz") + _make_tarfile(model_data_file, local_model_path) + with open(model_data_file, "rb") as fobj: + key = os.path.join(prefix, "model.tar.gz") + obj = sess.resource("s3").Bucket(bucket).Object(key) + obj.upload_fileobj(fobj) + response = s3_client.put_object_tagging( + Bucket=bucket, Key=key, Tagging={"TagSet": [{"Key": "SageMaker", "Value": "true"}]} + ) + _logger.info("tag response: %s", response) + return f"s3://{bucket}/{key}" + + +def _get_deployment_config(flavor_name, env_override=None): + """ + Returns: + The deployment configuration as a dictionary + """ + deployment_config = { + MLFLOW_DEPLOYMENT_FLAVOR_NAME.name: flavor_name, + SERVING_ENVIRONMENT: SAGEMAKER_SERVING_ENVIRONMENT, + } + if env_override: + deployment_config.update(env_override) + + if os.getenv("http_proxy") is not None: + deployment_config.update({"http_proxy": os.environ["http_proxy"]}) + + if os.getenv("https_proxy") is not None: + deployment_config.update({"https_proxy": os.environ["https_proxy"]}) + + if os.getenv("no_proxy") is not None: + deployment_config.update({"no_proxy": os.environ["no_proxy"]}) + + return deployment_config + + +def _truncate_name(name, max_length): + # NB: Sagemaker prevents the registration of models and configurations whose names + # exceed 63 characters in length. For reference: + # https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_Model.html + # https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_TransformJob.html + # https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_ModelConfiguration.html + # This function middle-truncates the name provided to + # ensure that the least critical name information is not lost + if len(name) <= max_length: + return name + available_length = max_length - 3 + start_len = available_length // 2 + end_len = available_length - start_len + truncated_name = f"{name[:start_len]}---{name[-end_len:]}" + _logger.warning( + f"Truncated name {name} to {truncated_name} to coerce total character counts to < 64" + ) + return truncated_name + + +def _get_unique_name(base_name, unique_suffix, unique_id_length=20): + unique_id = uuid.uuid4().hex[:unique_id_length] + unique_resource_string = f"{unique_suffix}{unique_id}" + max_length = 63 - len(unique_resource_string) + return _truncate_name(base_name, max_length) + unique_resource_string + + +def _get_sagemaker_model_name(endpoint_name): + return _get_unique_name(endpoint_name, "-model-") + + +def _get_sagemaker_transform_model_name(job_name): + return _get_unique_name(job_name, "-model-") + + +def _get_sagemaker_config_name(endpoint_name): + return _get_unique_name(endpoint_name, "-config-") + + +def _get_sagemaker_config_tags(endpoint_name): + return [{"Key": SAGEMAKER_APP_NAME_TAG_KEY, "Value": endpoint_name}] + + +def _prepare_sagemaker_tags( + config_tags: list[dict[str, str]], + sagemaker_tags: dict[str, str] | None = None, +): + if not sagemaker_tags: + return config_tags + + if SAGEMAKER_APP_NAME_TAG_KEY in sagemaker_tags: + raise MlflowException.invalid_parameter_value( + f"Duplicate tag provided for '{SAGEMAKER_APP_NAME_TAG_KEY}'" + ) + parsed = [{"Key": key, "Value": str(value)} for key, value in sagemaker_tags.items()] + + return config_tags + parsed + + +def _create_sagemaker_transform_job( + job_name, + model_name, + model_s3_path, + model_uri, + image_url, + flavor, + vpc_config, + role, + sage_client, + s3_client, + instance_type, + instance_count, + s3_input_data_type, + s3_input_uri, + content_type, + compression_type, + split_type, + s3_output_path, + accept, + assemble_with, + input_filter, + output_filter, + join_resource, +): + """ + Args: + job_name: Name of the deployed Sagemaker batch transform job. + model_name: The name to assign the new SageMaker model that will be associated with the + specified batch transform job. + model_s3_path: S3 path where we stored the model artifacts. + model_uri: URI of the MLflow model to associate with the specified SageMaker batch + transform job. + image_url: URL of the ECR-hosted docker image the model is being deployed into. + flavor: The name of the flavor of the model to use for deployment. + vpc_config: A dictionary specifying the VPC configuration to use when creating the + new SageMaker model associated with this SageMaker batch transform job. + role: SageMaker execution ARN role. + sage_client: A boto3 client for SageMaker. + s3_client: A boto3 client for S3. + instance_type: The type of SageMaker ML instance on which to deploy the model. + instance_count: The number of SageMaker ML instances on which to deploy the model. + s3_input_data_type: Input data type for the transform job. + s3_input_uri: S3 key name prefix or a manifest of the input data. + content_type: The multipurpose internet mail extension (MIME) type of the data. + compression_type: The compression type of the transform data. + split_type: The method to split the transform job's data files into smaller batches. + s3_output_path: The S3 path to store the output results of the Sagemaker transform job. + accept: The multipurpose internet mail extension (MIME) type of the output data. + assemble_with: The method to assemble the results of the transform job as a single + S3 object. + input_filter: A JSONPath expression used to select a portion of the input data for the + transform job. + output_filter: A JSONPath expression used to select a portion of the output data from + the transform job. + join_resource: The source of the data to join with the transformed data. + """ + _logger.info("Creating new batch transform job with name: %s ...", job_name) + + model_response = _create_sagemaker_model( + model_name=model_name, + model_s3_path=model_s3_path, + model_uri=model_uri, + flavor=flavor, + vpc_config=vpc_config, + image_url=image_url, + execution_role=role, + sage_client=sage_client, + env={}, + tags={}, + ) + _logger.info("Created model with arn: %s", model_response["ModelArn"]) + + transform_input = { + "DataSource": {"S3DataSource": {"S3DataType": s3_input_data_type, "S3Uri": s3_input_uri}}, + "ContentType": content_type, + "CompressionType": compression_type, + "SplitType": split_type, + } + + transform_output = { + "S3OutputPath": s3_output_path, + "Accept": accept, + "AssembleWith": assemble_with, + } + + transform_resources = {"InstanceType": instance_type, "InstanceCount": instance_count} + + data_processing = { + "InputFilter": input_filter, + "OutputFilter": output_filter, + "JoinSource": join_resource, + } + + transform_job_response = sage_client.create_transform_job( + TransformJobName=job_name, + ModelName=model_name, + TransformInput=transform_input, + TransformOutput=transform_output, + TransformResources=transform_resources, + DataProcessing=data_processing, + Tags=[{"Key": "model_name", "Value": model_name}], + ) + _logger.info( + "Created batch transform job with arn: %s", transform_job_response["TransformJobArn"] + ) + + def status_check_fn(): + transform_job_info = sage_client.describe_transform_job(TransformJobName=job_name) + + if transform_job_info is None: + return _SageMakerOperationStatus.in_progress( + "Waiting for batch transform job to be created..." + ) + + transform_job_status = transform_job_info["TransformJobStatus"] + if transform_job_status == "InProgress": + return _SageMakerOperationStatus.in_progress( + 'Waiting for batch transform job to reach the "Completed" state. ' + f' Current batch transform job status: "{transform_job_status}"' + ) + elif transform_job_status == "Completed": + return _SageMakerOperationStatus.succeeded( + "The SageMaker batch transform job was processed successfully." + ) + else: + failure_reason = transform_job_info.get( + "FailureReason", + "An unknown SageMaker failure occurred. Please see the SageMaker console logs" + " for more information.", + ) + return _SageMakerOperationStatus.failed(failure_reason) + + def cleanup_fn(): + _logger.info("Cleaning up Sagemaker model and S3 model artifacts...") + transform_job_info = sage_client.describe_transform_job(TransformJobName=job_name) + model_name = transform_job_info["ModelName"] + model_arn = _delete_sagemaker_model(model_name, sage_client, s3_client) + _logger.info("Deleted associated model with arn: %s", model_arn) + + return _SageMakerOperation(status_check_fn=status_check_fn, cleanup_fn=cleanup_fn) + + +def _create_sagemaker_endpoint( + endpoint_name, + model_name, + model_s3_path, + model_uri, + image_url, + flavor, + instance_type, + vpc_config, + data_capture_config, + instance_count, + role, + sage_client, + variant_name=None, + async_inference_config=None, + serverless_config=None, + env=None, + tags=None, +): + """ + Args: + endpoint_name: The name of the SageMaker endpoint to create. + model_name: The name to assign the new SageMaker model that will be associated with the + specified endpoint. + model_s3_path: S3 path where we stored the model artifacts. + model_uri: URI of the MLflow model to associate with the specified SageMaker endpoint. + image_url: URL of the ECR-hosted docker image the model is being deployed into. + flavor: The name of the flavor of the model to use for deployment. + instance_type: The type of SageMaker ML instance on which to deploy the model. + instance_count: The number of SageMaker ML instances on which to deploy the model. + vpc_config: A dictionary specifying the VPC configuration to use when creating the + new SageMaker model associated with this SageMaker endpoint. + data_capture_config: A dictionary specifying the data capture configuration to use when + creating the new SageMaker model associated with this application. + role: SageMaker execution ARN role. + sage_client: A boto3 client for SageMaker. + variant_name: The name to assign to the new production variant. + env: A dictionary of environment variables to set for the model. + tags: A dictionary of tags to apply to the endpoint. + """ + _logger.info("Creating new endpoint with name: %s ...", endpoint_name) + + model_response = _create_sagemaker_model( + model_name=model_name, + model_s3_path=model_s3_path, + model_uri=model_uri, + flavor=flavor, + vpc_config=vpc_config, + image_url=image_url, + execution_role=role, + sage_client=sage_client, + env=env or {}, + tags=tags or {}, + ) + _logger.info("Created model with arn: %s", model_response["ModelArn"]) + + if not variant_name: + variant_name = model_name + + production_variant = { + "VariantName": variant_name, + "ModelName": model_name, + "InitialVariantWeight": 1, + } + + if serverless_config: + production_variant["ServerlessConfig"] = serverless_config + else: + production_variant["InstanceType"] = instance_type + production_variant["InitialInstanceCount"] = instance_count + + config_name = _get_sagemaker_config_name(endpoint_name) + config_tags = _get_sagemaker_config_tags(endpoint_name) + tags_list = _prepare_sagemaker_tags(config_tags, tags) + endpoint_config_kwargs = { + "EndpointConfigName": config_name, + "ProductionVariants": [production_variant], + "Tags": config_tags, + } + if async_inference_config: + endpoint_config_kwargs["AsyncInferenceConfig"] = async_inference_config + if data_capture_config is not None: + endpoint_config_kwargs["DataCaptureConfig"] = data_capture_config + endpoint_config_response = sage_client.create_endpoint_config(**endpoint_config_kwargs) + _logger.info( + "Created endpoint configuration with arn: %s", endpoint_config_response["EndpointConfigArn"] + ) + + endpoint_response = sage_client.create_endpoint( + EndpointName=endpoint_name, + EndpointConfigName=config_name, + Tags=tags_list or [], + ) + _logger.info("Created endpoint with arn: %s", endpoint_response["EndpointArn"]) + + def status_check_fn(): + endpoint_info = _find_endpoint(endpoint_name=endpoint_name, sage_client=sage_client) + + if endpoint_info is None: + return _SageMakerOperationStatus.in_progress("Waiting for endpoint to be created...") + + endpoint_status = endpoint_info["EndpointStatus"] + if endpoint_status == "Creating": + return _SageMakerOperationStatus.in_progress( + 'Waiting for endpoint to reach the "InService" state. Current endpoint status:' + f' "{endpoint_status}"' + ) + elif endpoint_status == "InService": + return _SageMakerOperationStatus.succeeded( + "The SageMaker endpoint was created successfully." + ) + else: + failure_reason = endpoint_info.get( + "FailureReason", + "An unknown SageMaker failure occurred. Please see the SageMaker console logs" + " for more information.", + ) + return _SageMakerOperationStatus.failed(failure_reason) + + def cleanup_fn(): + pass + + return _SageMakerOperation(status_check_fn=status_check_fn, cleanup_fn=cleanup_fn) + + +def _update_sagemaker_endpoint( + endpoint_name, + model_name, + model_uri, + image_url, + model_s3_path, + flavor, + instance_type, + instance_count, + vpc_config, + mode, + role, + sage_client, + s3_client, + variant_name=None, + async_inference_config=None, + serverless_config=None, + data_capture_config=None, + env=None, + tags=None, +): + """ + Args: + endpoint_name: The name of the SageMaker endpoint to update. + model_name: The name to assign the new SageMaker model that will be associated with the + specified endpoint. + model_uri: URI of the MLflow model to associate with the specified SageMaker endpoint. + image_url: URL of the ECR-hosted Docker image the model is being deployed into + model_s3_path: S3 path where we stored the model artifacts + flavor: The name of the flavor of the model to use for deployment. + instance_type: The type of SageMaker ML instance on which to deploy the model. + instance_count: The number of SageMaker ML instances on which to deploy the model. + vpc_config: A dictionary specifying the VPC configuration to use when creating the + new SageMaker model associated with this SageMaker endpoint. + mode: either mlflow.sagemaker.DEPLOYMENT_MODE_ADD or + mlflow.sagemaker.DEPLOYMENT_MODE_REPLACE. + role: SageMaker execution ARN role. + sage_client: A boto3 client for SageMaker. + s3_client: A boto3 client for S3. + variant_name: The name to assign to the new production variant if it doesn't already exist. + async_inference_config: A dictionary specifying the async inference configuration to use. + For more information, see https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_AsyncInferenceConfig.html. + Defaults to ``None``. + data_capture_config: A dictionary specifying the data capture configuration to use. + For more information, see https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_DataCaptureConfig.html. + Defaults to ``None``. + env: A dictionary of environment variables to set for the model. + tags: A dictionary of tags to apply to the endpoint configuration. + """ + if mode not in [DEPLOYMENT_MODE_ADD, DEPLOYMENT_MODE_REPLACE]: + msg = f"Invalid mode `{mode}` for deployment to a pre-existing application" + raise ValueError(msg) + + endpoint_info = sage_client.describe_endpoint(EndpointName=endpoint_name) + endpoint_arn = endpoint_info["EndpointArn"] + deployed_config_name = endpoint_info["EndpointConfigName"] + deployed_config_info = sage_client.describe_endpoint_config( + EndpointConfigName=deployed_config_name + ) + deployed_config_arn = deployed_config_info["EndpointConfigArn"] + deployed_production_variants = deployed_config_info["ProductionVariants"] + + _logger.info("Found active endpoint with arn: %s. Updating...", endpoint_arn) + + new_model_response = _create_sagemaker_model( + model_name=model_name, + model_s3_path=model_s3_path, + model_uri=model_uri, + flavor=flavor, + vpc_config=vpc_config, + image_url=image_url, + execution_role=role, + sage_client=sage_client, + env=env or {}, + tags=tags or {}, + ) + _logger.info("Created new model with arn: %s", new_model_response["ModelArn"]) + + if not variant_name: + variant_name = model_name + + if mode == DEPLOYMENT_MODE_ADD: + new_model_weight = 0 + production_variants = deployed_production_variants + elif mode == DEPLOYMENT_MODE_REPLACE: + new_model_weight = 1 + production_variants = [] + + new_production_variant = { + "VariantName": variant_name, + "ModelName": model_name, + "InitialVariantWeight": new_model_weight, + } + + if serverless_config: + new_production_variant["ServerlessConfig"] = serverless_config + else: + new_production_variant["InstanceType"] = instance_type + new_production_variant["InitialInstanceCount"] = instance_count + + production_variants.append(new_production_variant) + + # Create the new endpoint configuration and update the endpoint + # to adopt the new configuration + new_config_name = _get_sagemaker_config_name(endpoint_name) + config_tags = _get_sagemaker_config_tags(endpoint_name) + endpoint_config_kwargs = { + "EndpointConfigName": new_config_name, + "ProductionVariants": production_variants, + "Tags": config_tags, + } + if async_inference_config: + endpoint_config_kwargs["AsyncInferenceConfig"] = async_inference_config + if data_capture_config is not None: + endpoint_config_kwargs["DataCaptureConfig"] = data_capture_config + endpoint_config_response = sage_client.create_endpoint_config(**endpoint_config_kwargs) + _logger.info( + "Created new endpoint configuration with arn: %s", + endpoint_config_response["EndpointConfigArn"], + ) + + sage_client.update_endpoint(EndpointName=endpoint_name, EndpointConfigName=new_config_name) + _logger.info("Updated endpoint with new configuration!") + + operation_start_time = time.time() + + def status_check_fn(): + if time.time() - operation_start_time < 20: + # Wait at least 20 seconds before checking the status of the update; this ensures + # that we don't consider the operation to have failed if small delays occur at + # initialization time + return _SageMakerOperationStatus.in_progress() + + endpoint_info = sage_client.describe_endpoint(EndpointName=endpoint_name) + endpoint_update_was_rolled_back = ( + endpoint_info["EndpointStatus"] == "InService" + and endpoint_info["EndpointConfigName"] != new_config_name + ) + if endpoint_update_was_rolled_back or endpoint_info["EndpointStatus"] == "Failed": + failure_reason = endpoint_info.get( + "FailureReason", + "An unknown SageMaker failure occurred." + " Please see the SageMaker console logs for" + " more information.", + ) + return _SageMakerOperationStatus.failed(failure_reason) + elif endpoint_info["EndpointStatus"] == "InService": + return _SageMakerOperationStatus.succeeded( + "The SageMaker endpoint was updated successfully." + ) + else: + return _SageMakerOperationStatus.in_progress( + "The update operation is still in progress. Current endpoint status:" + ' "{endpoint_status}"'.format(endpoint_status=endpoint_info["EndpointStatus"]) + ) + + def cleanup_fn(): + _logger.info("Cleaning up unused resources...") + if mode == DEPLOYMENT_MODE_REPLACE: + for pv in deployed_production_variants: + deployed_model_arn = _delete_sagemaker_model( + model_name=pv["ModelName"], sage_client=sage_client, s3_client=s3_client + ) + _logger.info("Deleted model with arn: %s", deployed_model_arn) + + sage_client.delete_endpoint_config(EndpointConfigName=deployed_config_name) + _logger.info("Deleted endpoint configuration with arn: %s", deployed_config_arn) + + return _SageMakerOperation(status_check_fn=status_check_fn, cleanup_fn=cleanup_fn) + + +def _create_sagemaker_model( + model_name, + model_s3_path, + model_uri, + flavor, + vpc_config, + image_url, + execution_role, + sage_client, + env, + tags, +): + """ + Args: + model_name: The name to assign the new SageMaker model that is created. + model_s3_path: S3 path where the model artifacts are stored. + model_uri: URI of the MLflow model associated with the new SageMaker model. + flavor: The name of the flavor of the model. + vpc_config: A dictionary specifying the VPC configuration to use when creating the + new SageMaker model associated with this SageMaker endpoint. + image_url: URL of the ECR-hosted Docker image that will serve as the + model's container, + execution_role: The ARN of the role that SageMaker will assume when creating the model. + sage_client: A boto3 client for SageMaker. + env: A dictionary of environment variables to set for the model. + tags: A dictionary of tags to apply to the SageMaker model. + + Returns: + AWS response containing metadata associated with the new model. + """ + tags["model_uri"] = str(model_uri) + create_model_args = { + "ModelName": model_name, + "PrimaryContainer": { + "Image": image_url, + "ModelDataUrl": model_s3_path, + "Environment": _get_deployment_config(flavor_name=flavor, env_override=env), + }, + "ExecutionRoleArn": execution_role, + "Tags": [{"Key": key, "Value": str(value)} for key, value in tags.items()], + } + if vpc_config is not None: + create_model_args["VpcConfig"] = vpc_config + + return sage_client.create_model(**create_model_args) + + +def _delete_sagemaker_model(model_name, sage_client, s3_client): + """ + Args: + sage_client: A boto3 client for SageMaker. + s3_client: A boto3 client for S3. + + Returns: + ARN of the deleted model. + """ + model_info = sage_client.describe_model(ModelName=model_name) + model_arn = model_info["ModelArn"] + model_data_url = model_info["PrimaryContainer"]["ModelDataUrl"] + + # Parse the model data url to obtain a bucket path. The following + # procedure is safe due to the well-documented structure of the `ModelDataUrl` + # (see https://docs.aws.amazon.com/sagemaker/latest/dg/API_ContainerDefinition.html) + parsed_data_url = urllib.parse.urlparse(model_data_url) + bucket_name = parsed_data_url.netloc + bucket_key = parsed_data_url.path.lstrip("/") + + s3_client.delete_object(Bucket=bucket_name, Key=bucket_key) + sage_client.delete_model(ModelName=model_name) + + return model_arn + + +def _delete_sagemaker_endpoint_configuration(endpoint_config_name, sage_client): + """ + Args: + sage_client: A boto3 client for SageMaker. + + Returns: + ARN of the deleted endpoint configuration. + """ + endpoint_config_info = sage_client.describe_endpoint_config( + EndpointConfigName=endpoint_config_name + ) + sage_client.delete_endpoint_config(EndpointConfigName=endpoint_config_name) + return endpoint_config_info["EndpointConfigArn"] + + +def _find_endpoint(endpoint_name, sage_client): + """ + Finds a SageMaker endpoint with the specified name in the caller's AWS account, returning a + NoneType if the endpoint is not found. + + Args: + sage_client: A boto3 client for SageMaker. + + Returns: + If the endpoint exists, a dictionary of endpoint attributes. If the endpoint does not + exist, ``None``. + """ + endpoints_page = sage_client.list_endpoints(MaxResults=100, NameContains=endpoint_name) + + while True: + for endpoint in endpoints_page["Endpoints"]: + if endpoint["EndpointName"] == endpoint_name: + return endpoint + + if "NextToken" in endpoints_page: + endpoints_page = sage_client.list_endpoints( + MaxResults=100, NextToken=endpoints_page["NextToken"], NameContains=endpoint_name + ) + else: + return None + + +def _find_transform_job(job_name, sage_client): + """ + Finds a SageMaker batch transform job with the specified name in the caller's AWS account, + returning a NoneType if the transform job is not found. + + Args: + sage_client: A boto3 client for SageMaker. + + Returns: + If the transform job exists, a dictionary of transform job attributes. If the + transform job does not exist, ``None``. + """ + transform_jobs_page = sage_client.list_transform_jobs(MaxResults=100, NameContains=job_name) + + while True: + for transform_job in transform_jobs_page["TransformJobSummaries"]: + if transform_job["TransformJobName"] == job_name: + return transform_job + + if "NextToken" in transform_jobs_page: + transform_jobs_page = sage_client.list_transform_jobs( + MaxResults=100, + NextToken=transform_jobs_page["NextToken"], + NameContains=job_name, + ) + else: + return None + + +def _does_model_exist(model_name, sage_client): + """ + Determines whether a SageMaker model exists with the specified name in the caller's AWS account, + returning True if the model exists, returning False if the model does not exist. + + Args: + sage_client: A boto3 client for SageMaker. + + Returns: + If the model exists, ``True``. If the model does not + exist, ``False``. + """ + try: + response = sage_client.describe_model(ModelName=model_name) + except sage_client.exceptions.ClientError as error: + if "Could not find model" in error.response["Error"]["Message"]: + return False + else: + return bool(response) + + +class SageMakerDeploymentClient(BaseDeploymentClient): + """ + Initialize a deployment client for SageMaker. The default region and assumed role ARN will + be set according to the value of the `target_uri`. + + This class is meant to supersede the other ``mlflow.sagemaker`` real-time serving API's. + It is also designed to be used through the :py:mod:`mlflow.deployments` module. + This means that you can deploy to SageMaker using the + `mlflow deployments CLI `_ and + get a client through the :py:mod:`mlflow.deployments.get_deploy_client` function. + + Args: + target_uri: A URI that follows one of the following formats: + + - ``sagemaker``: This will set the default region to `us-west-2` and + the default assumed role ARN to `None`. + - ``sagemaker:/region_name``: This will set the default region to + `region_name` and the default assumed role ARN to `None`. + - ``sagemaker:/region_name/assumed_role_arn``: This will set the default + region to `region_name` and the default assumed role ARN to + `assumed_role_arn`. + + When an `assumed_role_arn` is provided without a `region_name`, + an MlflowException will be raised. + """ + + def __init__(self, target_uri): + super().__init__(target_uri=target_uri) + + # Default region_name and assumed_role_arn when + # the target_uri is `sagemaker` or `sagemaker:/` + self.region_name = DEFAULT_REGION_NAME + self.assumed_role_arn = None + self._get_values_from_target_uri() + + def _get_values_from_target_uri(self): + parsed = urllib.parse.urlparse(self.target_uri) + values_str = parsed.path.strip("/") + + if not parsed.scheme or not values_str: + return + + separator_index = values_str.find("/") + if separator_index == -1: + # values_str would look like us-east-1 + self.region_name = values_str + else: + # values_str could look like us-east-1/arn:aws:1234:role/assumed_role + self.region_name = values_str[:separator_index] + self.assumed_role_arn = values_str[separator_index + 1 :] + + # if values_str contains multiple interior slashes such as + # us-east-1/////arn:aws:1234:role/assumed_role, remove + # the extra slashes that come before "arn" + self.assumed_role_arn = self.assumed_role_arn.strip("/") + + if self.region_name.startswith("arn"): + raise MlflowException( + message=( + "It looks like the target_uri contains an IAM role ARN without a region name.\n" + "A region name must be provided when the target_uri contains a role ARN.\n" + "In this case, the target_uri must follow the format: " + "sagemaker:/region_name/assumed_role_arn.\n" + f"The provided target_uri is: {self.target_uri}\n" + ), + error_code=INVALID_PARAMETER_VALUE, + ) + + def _default_deployment_config(self, create_mode=True): + config = { + "assume_role_arn": self.assumed_role_arn, + "execution_role_arn": None, + "bucket": None, + "image_url": None, + "region_name": self.region_name, + "archive": False, + "instance_type": DEFAULT_SAGEMAKER_INSTANCE_TYPE, + "instance_count": DEFAULT_SAGEMAKER_INSTANCE_COUNT, + "vpc_config": None, + "data_capture_config": None, + "synchronous": True, + "timeout_seconds": 1200, + "variant_name": None, + "env": None, + "tags": None, + "async_inference_config": {}, + "serverless_config": {}, + } + + if create_mode: + config["mode"] = DEPLOYMENT_MODE_CREATE + else: + config["mode"] = DEPLOYMENT_MODE_REPLACE + + return config + + def _apply_custom_config(self, config, custom_config): + int_fields = {"instance_count", "timeout_seconds"} + bool_fields = {"synchronous", "archive"} + dict_fields = { + "vpc_config", + "data_capture_config", + "tags", + "env", + "async_inference_config", + "serverless_config", + } + for key, value in custom_config.items(): + if key not in config: + continue + + if key in int_fields and not isinstance(value, int): + value = int(value) + elif key in bool_fields and not isinstance(value, bool): + value = value == "True" + elif key in dict_fields and not isinstance(value, dict): + value = json.loads(value) + + config[key] = value + + def create_deployment(self, name, model_uri, flavor=None, config=None, endpoint=None): + """ + Deploy an MLflow model on AWS SageMaker. + The currently active AWS account must have correct permissions set up. + + This function creates a SageMaker endpoint. For more information about the input data + formats accepted by this endpoint, see the + `MLflow deployment tools documentation <../../deployment/deploy-model-to-sagemaker.html>`_. + + Args: + name: Name of the deployed application. + model_uri: The location, in URI format, of the MLflow model to deploy to SageMaker. + For example: + + - ``/Users/me/path/to/local/model`` + - ``relative/path/to/local/model`` + - ``s3://my_bucket/path/to/model`` + - ``runs://run-relative/path/to/model`` + - ``models://`` + - ``models://`` + + For more information about supported URI schemes, see + `Referencing Artifacts `_. + flavor: The name of the flavor of the model to use for deployment. Must be either + ``None`` or one of mlflow.sagemaker.SUPPORTED_DEPLOYMENT_FLAVORS. + If ``None``, a flavor is automatically selected from the model's available + flavors. If the specified flavor is not present or not supported for + deployment, an exception will be thrown. + config: Configuration parameters. The supported parameters are: + + - ``assume_role_arn``: The name of an IAM cross-account role to be assumed + to deploy SageMaker to another AWS account. If this parameter is not + specified, the role given in the ``target_uri`` will be used. If the + role is not given in the ``target_uri``, defaults to ``us-west-2``. + + - ``execution_role_arn``: The name of an IAM role granting the SageMaker + service permissions to access the specified Docker image and S3 bucket + containing MLflow model artifacts. If unspecified, the currently-assumed + role will be used. This execution role is passed to the SageMaker service + when creating a SageMaker model from the specified MLflow model. It is + passed as the ``ExecutionRoleArn`` parameter of the `SageMaker + CreateModel API call `_. This role is *not* assumed for any other + call. For more information about SageMaker execution roles for model + creation, see + https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-roles.html. + + - ``bucket``: S3 bucket where model artifacts will be stored. Defaults to a + SageMaker-compatible bucket name. + + - ``image_url``: URL of the ECR-hosted Docker image the model should be + deployed into, produced by ``mlflow sagemaker build-and-push-container``. + This parameter can also be specified by the environment variable + ``MLFLOW_SAGEMAKER_DEPLOY_IMG_URL``. + + - ``region_name``: Name of the AWS region to which to deploy the application. + If unspecified, use the region name given in the ``target_uri``. + If it is also not specified in the ``target_uri``, + defaults to ``us-west-2``. + + - ``archive``: If ``True``, any pre-existing SageMaker application resources + that become inactive (i.e. as a result of deploying in + ``mlflow.sagemaker.DEPLOYMENT_MODE_REPLACE`` mode) are preserved. + These resources may include unused SageMaker models and endpoint + configurations that were associated with a prior version of the + application endpoint. If ``False``, these resources are deleted. + In order to use ``archive=False``, ``create_deployment()`` must be executed + synchronously with ``synchronous=True``. Defaults to ``False``. + + - ``instance_type``: The type of SageMaker ML instance on which to deploy the + model. For a list of supported instance types, see + https://aws.amazon.com/sagemaker/pricing/instance-types/. + Defaults to ``ml.m4.xlarge``. + + - ``instance_count``: The number of SageMaker ML instances on which to deploy + the model. Defaults to ``1``. + + - ``synchronous``: If ``True``, this function will block until the deployment + process succeeds or encounters an irrecoverable failure. If ``False``, + this function will return immediately after starting the deployment + process. It will not wait for the deployment process to complete; + in this case, the caller is responsible for monitoring the health and + status of the pending deployment via native SageMaker APIs or the AWS + console. Defaults to ``True``. + + - ``timeout_seconds``: If ``synchronous`` is ``True``, the deployment process + will return after the specified number of seconds if no definitive result + (success or failure) is achieved. Once the function returns, the caller is + responsible for monitoring the health and status of the pending + deployment using native SageMaker APIs or the AWS console. If + ``synchronous`` is ``False``, this parameter is ignored. + Defaults to ``300``. + + - ``vpc_config``: A dictionary specifying the VPC configuration to use when + creating the new SageMaker model associated with this application. + The acceptable values for this parameter are identical to those of the + ``VpcConfig`` parameter in the `SageMaker boto3 client's create_model + method `_. For more information, see + https://docs.aws.amazon.com/sagemaker/latest/dg/API_VpcConfig.html. + Defaults to ``None``. + + - ``data_capture_config``: A dictionary specifying the data capture + configuration to use when creating the new SageMaker model associated with + this application. + For more information, see + https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_DataCaptureConfig.html. + Defaults to ``None``. + + - ``variant_name``: A string specifying the desired name when creating a production + variant. Defaults to ``None``. + + - ``async_inference_config``: A dictionary specifying the + async_inference_configuration + + - ``serverless_config``: A dictionary specifying the serverless_configuration + + - ``env``: A dictionary specifying environment variables as key-value + pairs to be set for the deployed model. Defaults to ``None``. + + - ``tags``: A dictionary of key-value pairs representing additional + tags to be set for the deployed model. Defaults to ``None``. + + endpoint: (optional) Endpoint to create the deployment under. Currently unsupported + + .. code-block:: python + :caption: Python example + + from mlflow.deployments import get_deploy_client + + vpc_config = { + "SecurityGroupIds": [ + "sg-123456abc", + ], + "Subnets": [ + "subnet-123456abc", + ], + } + config = dict( + assume_role_arn="arn:aws:123:role/assumed_role", + execution_role_arn="arn:aws:456:role/execution_role", + bucket_name="my-s3-bucket", + image_url="1234.dkr.ecr.us-east-1.amazonaws.com/mlflow-test:1.23.1", + region_name="us-east-1", + archive=False, + instance_type="ml.m5.4xlarge", + instance_count=1, + synchronous=True, + timeout_seconds=300, + vpc_config=vpc_config, + variant_name="prod-variant-1", + env={"DISABLE_NGINX": "true", "GUNICORN_CMD_ARGS": '"--timeout 60"'}, + tags={"training_timestamp": "2022-11-01T05:12:26"}, + ) + client = get_deploy_client("sagemaker") + client.create_deployment( + "my-deployment", + model_uri="/mlruns/0/abc/model", + flavor="python_function", + config=config, + ) + .. code-block:: bash + :caption: Command-line example + + mlflow deployments create --target sagemaker:/us-east-1/arn:aws:123:role/assumed_role \\ + --name my-deployment \\ + --model-uri /mlruns/0/abc/model \\ + --flavor python_function\\ + -C execution_role_arn=arn:aws:456:role/execution_role \\ + -C bucket_name=my-s3-bucket \\ + -C image_url=1234.dkr.ecr.us-east-1.amazonaws.com/mlflow-test:1.23.1 \\ + -C region_name=us-east-1 \\ + -C archive=False \\ + -C instance_type=ml.m5.4xlarge \\ + -C instance_count=1 \\ + -C synchronous=True \\ + -C timeout_seconds=300 \\ + -C variant_name=prod-variant-1 \\ + -C vpc_config='{"SecurityGroupIds": ["sg-123456abc"], \\ + "Subnets": ["subnet-123456abc"]}' \\ + -C data_capture_config='{"EnableCapture": True, \\ + 'InitialSamplingPercentage': 100, 'DestinationS3Uri": 's3://my-bucket/path', \\ + 'CaptureOptions': [{'CaptureMode': 'Output'}]}' + -C env='{"DISABLE_NGINX": "true", "GUNICORN_CMD_ARGS": "\"--timeout 60\""}' \\ + -C tags='{"training_timestamp": "2022-11-01T05:12:26"}' \\ + """ + final_config = self._default_deployment_config() + if config: + self._apply_custom_config(final_config, config) + + app_name, flavor = _deploy( + app_name=name, + model_uri=model_uri, + flavor=flavor, + execution_role_arn=final_config["execution_role_arn"], + assume_role_arn=final_config["assume_role_arn"], + bucket=final_config["bucket"], + image_url=final_config["image_url"], + region_name=final_config["region_name"], + mode=mlflow.sagemaker.DEPLOYMENT_MODE_CREATE, + archive=final_config["archive"], + instance_type=final_config["instance_type"], + instance_count=final_config["instance_count"], + vpc_config=final_config["vpc_config"], + data_capture_config=final_config["data_capture_config"], + synchronous=final_config["synchronous"], + timeout_seconds=final_config["timeout_seconds"], + variant_name=final_config["variant_name"], + async_inference_config=final_config["async_inference_config"], + serverless_config=final_config["serverless_config"], + env=final_config["env"], + tags=final_config["tags"], + ) + + return {"name": app_name, "flavor": flavor} + + def update_deployment(self, name, model_uri, flavor=None, config=None, endpoint=None): + """ + Update a deployment on AWS SageMaker. This function can replace or add a new model to + an existing SageMaker endpoint. By default, this function replaces the existing model + with the new one. The currently active AWS account must have correct permissions set up. + + Args: + name: Name of the deployed application. + model_uri: The location, in URI format, of the MLflow model to deploy to SageMaker. + For example: + + - ``/Users/me/path/to/local/model`` + - ``relative/path/to/local/model`` + - ``s3://my_bucket/path/to/model`` + - ``runs://run-relative/path/to/model`` + - ``models://`` + - ``models://`` + + For more information about supported URI schemes, see + `Referencing Artifacts `_. + + flavor: The name of the flavor of the model to use for deployment. Must be either + ``None`` or one of mlflow.sagemaker.SUPPORTED_DEPLOYMENT_FLAVORS. + If ``None``, a flavor is automatically selected from the model's available + flavors. If the specified flavor is not present or not supported for + deployment, an exception will be thrown. + + config: Configuration parameters. The supported parameters are: + + - ``assume_role_arn``: The name of an IAM cross-account role to be assumed + to deploy SageMaker to another AWS account. If this parameter is not + specified, the role given in the ``target_uri`` will be used. If the + role is not given in the ``target_uri``, defaults to ``us-west-2``. + + - ``execution_role_arn``: The name of an IAM role granting the SageMaker + service permissions to access the specified Docker image and S3 bucket + containing MLflow model artifacts. If unspecified, the currently-assumed + role will be used. This execution role is passed to the SageMaker service + when creating a SageMaker model from the specified MLflow model. It is + passed as the ``ExecutionRoleArn`` parameter of the `SageMaker + CreateModel API call `_. This role is *not* assumed for any other + call. For more information about SageMaker execution roles for model + creation, see + https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-roles.html. + + - ``bucket``: S3 bucket where model artifacts will be stored. Defaults to a + SageMaker-compatible bucket name. + + - ``image_url``: URL of the ECR-hosted Docker image the model should be + deployed into, produced by ``mlflow sagemaker build-and-push-container``. + This parameter can also be specified by the environment variable + ``MLFLOW_SAGEMAKER_DEPLOY_IMG_URL``. + + - ``region_name``: Name of the AWS region to which to deploy the application. + If unspecified, use the region name given in the ``target_uri``. + If it is also not specified in the ``target_uri``, + defaults to ``us-west-2``. + + - ``mode``: The mode in which to deploy the application. + Must be one of the following: + + ``mlflow.sagemaker.DEPLOYMENT_MODE_REPLACE`` + If an application of the specified name exists, its model(s) is + replaced with the specified model. If no such application exists, + it is created with the specified name and model. + This is the default mode. + + ``mlflow.sagemaker.DEPLOYMENT_MODE_ADD`` + Add the specified model to a pre-existing application with the + specified name, if one exists. If the application does not exist, + a new application is created with the specified name and model. + NOTE: If the application **already exists**, the specified model is + added to the application's corresponding SageMaker endpoint with an + initial weight of zero (0). To route traffic to the model, + update the application's associated endpoint configuration using + either the AWS console or the ``UpdateEndpointWeightsAndCapacities`` + function defined in https://docs.aws.amazon.com/sagemaker/latest/dg/API_UpdateEndpointWeightsAndCapacities.html. + + - ``archive``: If ``True``, any pre-existing SageMaker application resources + that become inactive (i.e. as a result of deploying in + ``mlflow.sagemaker.DEPLOYMENT_MODE_REPLACE`` mode) are preserved. + These resources may include unused SageMaker models and endpoint + configurations that were associated with a prior version of the + application endpoint. If ``False``, these resources are deleted. + In order to use ``archive=False``, ``update_deployment()`` must be executed + synchronously with ``synchronous=True``. Defaults to ``False``. + + - ``instance_type``: The type of SageMaker ML instance on which to deploy the + model. For a list of supported instance types, see + https://aws.amazon.com/sagemaker/pricing/instance-types/. + Defaults to ``ml.m4.xlarge``. + + - ``instance_count``: The number of SageMaker ML instances on which to deploy + the model. Defaults to ``1``. + + - ``synchronous``: If ``True``, this function will block until the deployment + process succeeds or encounters an irrecoverable failure. If ``False``, + this function will return immediately after starting the deployment + process. It will not wait for the deployment process to complete; + in this case, the caller is responsible for monitoring the health and + status of the pending deployment via native SageMaker APIs or the AWS + console. Defaults to ``True``. + + - ``timeout_seconds``: If ``synchronous`` is ``True``, the deployment process + will return after the specified number of seconds if no definitive result + (success or failure) is achieved. Once the function returns, the caller is + responsible for monitoring the health and status of the pending + deployment using native SageMaker APIs or the AWS console. If + ``synchronous`` is ``False``, this parameter is ignored. + Defaults to ``300``. + + - ``variant_name``: A string specifying the desired name when creating a + production variant. Defaults to ``None``. + + - ``vpc_config``: A dictionary specifying the VPC configuration to use when + creating the new SageMaker model associated with this application. + The acceptable values for this parameter are identical to those of the + ``VpcConfig`` parameter in the `SageMaker boto3 client's create_model + method `_. For more information, see + https://docs.aws.amazon.com/sagemaker/latest/dg/API_VpcConfig.html. + Defaults to ``None``. + + - ``data_capture_config``: A dictionary specifying the data capture + configuration to use when creating the new SageMaker model associated with + this application. + For more information, see + https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_DataCaptureConfig.html. + Defaults to ``None``. + + - ``async_inference_config``: A dictionary specifying the async config + configuration. Defaults to ``None``. + + - ``env``: A dictionary specifying environment variables as key-value pairs + to be set for the deployed model. Defaults to ``None``. + + - ``tags``: A dictionary of key-value pairs representing additional tags + to be set for the deployed model. Defaults to ``None``. + + endpoint: (optional) Endpoint containing the deployment to update. Currently unsupported + + .. code-block:: python + :caption: Python example + + from mlflow.deployments import get_deploy_client + + vpc_config = { + "SecurityGroupIds": [ + "sg-123456abc", + ], + "Subnets": [ + "subnet-123456abc", + ], + } + data_capture_config = { + "EnableCapture": True, + "InitialSamplingPercentage": 100, + "DestinationS3Uri": "s3://my-bucket/path", + "CaptureOptions": [{"CaptureMode": "Output"}], + } + config = dict( + assume_role_arn="arn:aws:123:role/assumed_role", + execution_role_arn="arn:aws:456:role/execution_role", + bucket_name="my-s3-bucket", + image_url="1234.dkr.ecr.us-east-1.amazonaws.com/mlflow-test:1.23.1", + region_name="us-east-1", + mode="replace", + archive=False, + instance_type="ml.m5.4xlarge", + instance_count=1, + synchronous=True, + timeout_seconds=300, + variant_name="prod-variant-1", + vpc_config=vpc_config, + data_capture_config=data_capture_config, + env={"DISABLE_NGINX": "true", "GUNICORN_CMD_ARGS": '"--timeout 60"'}, + tags={"training_timestamp": "2022-11-01T05:12:26"}, + ) + client = get_deploy_client("sagemaker") + client.update_deployment( + "my-deployment", + model_uri="/mlruns/0/abc/model", + flavor="python_function", + config=config, + ) + .. code-block:: bash + :caption: Command-line example + + mlflow deployments update --target sagemaker:/us-east-1/arn:aws:123:role/assumed_role \\ + --name my-deployment \\ + --model-uri /mlruns/0/abc/model \\ + --flavor python_function\\ + -C execution_role_arn=arn:aws:456:role/execution_role \\ + -C bucket_name=my-s3-bucket \\ + -C image_url=1234.dkr.ecr.us-east-1.amazonaws.com/mlflow-test:1.23.1 \\ + -C region_name=us-east-1 \\ + -C mode=replace \\ + -C archive=False \\ + -C instance_type=ml.m5.4xlarge \\ + -C instance_count=1 \\ + -C synchronous=True \\ + -C timeout_seconds=300 \\ + -C variant_name=prod-variant-1 \\ + -C vpc_config='{"SecurityGroupIds": ["sg-123456abc"], \\ + "Subnets": ["subnet-123456abc"]}' \\ + -C data_capture_config='{"EnableCapture": True, \\ + "InitialSamplingPercentage": 100, "DestinationS3Uri": "s3://my-bucket/path", \\ + "CaptureOptions": [{"CaptureMode": "Output"}]}' + -C env='{"DISABLE_NGINX": "true", "GUNICORN_CMD_ARGS": "\"--timeout 60\""}' \\ + -C tags='{"training_timestamp": "2022-11-01T05:12:26"}' \\ + """ + final_config = self._default_deployment_config(create_mode=False) + if config: + self._apply_custom_config(final_config, config) + + if model_uri is None: + raise MlflowException( + message="A model_uri must be provided when updating a SageMaker deployment", + error_code=INVALID_PARAMETER_VALUE, + ) + + if final_config["mode"] not in [DEPLOYMENT_MODE_ADD, DEPLOYMENT_MODE_REPLACE]: + raise MlflowException( + message=( + f"Invalid mode `{final_config['mode']}` for deployment" + " to a pre-existing application" + ), + error_code=INVALID_PARAMETER_VALUE, + ) + + app_name, flavor = _deploy( + app_name=name, + model_uri=model_uri, + flavor=flavor, + execution_role_arn=final_config["execution_role_arn"], + assume_role_arn=final_config["assume_role_arn"], + bucket=final_config["bucket"], + image_url=final_config["image_url"], + region_name=final_config["region_name"], + mode=final_config["mode"], + archive=final_config["archive"], + instance_type=final_config["instance_type"], + instance_count=final_config["instance_count"], + vpc_config=final_config["vpc_config"], + data_capture_config=final_config["data_capture_config"], + synchronous=final_config["synchronous"], + timeout_seconds=final_config["timeout_seconds"], + variant_name=final_config["variant_name"], + async_inference_config=final_config["async_inference_config"], + serverless_config=final_config["serverless_config"], + env=final_config["env"], + tags=final_config["tags"], + ) + + return {"name": app_name, "flavor": flavor} + + def delete_deployment(self, name, config=None, endpoint=None): + """ + Delete a SageMaker application. + + Args: + name: Name of the deployed application. + config: Configuration parameters. The supported parameters are: + + - ``assume_role_arn``: The name of an IAM role to be assumed to delete + the SageMaker deployment. + + - ``region_name``: Name of the AWS region in which the application + is deployed. Defaults to ``us-west-2`` or the region provided in + the `target_uri`. + + - ``archive``: If `True`, resources associated with the specified + application, such as its associated models and endpoint configuration, + are preserved. If `False`, these resources are deleted. In order to use + ``archive=False``, ``delete()`` must be executed synchronously with + ``synchronous=True``. Defaults to ``False``. + + - ``synchronous``: If `True`, this function blocks until the deletion process + succeeds or encounters an irrecoverable failure. If `False`, this function + returns immediately after starting the deletion process. It will not wait + for the deletion process to complete; in this case, the caller is + responsible for monitoring the status of the deletion process via native + SageMaker APIs or the AWS console. Defaults to ``True``. + + - ``timeout_seconds``: If `synchronous` is `True`, the deletion process + returns after the specified number of seconds if no definitive result + (success or failure) is achieved. Once the function returns, the caller + is responsible for monitoring the status of the deletion process via native + SageMaker APIs or the AWS console. If `synchronous` is False, this + parameter is ignored. Defaults to ``300``. + + endpoint: (optional) Endpoint containing the deployment to delete. Currently unsupported + + .. code-block:: python + :caption: Python example + + from mlflow.deployments import get_deploy_client + + config = dict( + assume_role_arn="arn:aws:123:role/assumed_role", + region_name="us-east-1", + archive=False, + synchronous=True, + timeout_seconds=300, + ) + client = get_deploy_client("sagemaker") + client.delete_deployment("my-deployment", config=config) + + .. code-block:: bash + :caption: Command-line example + + mlflow deployments delete --target sagemaker \\ + --name my-deployment \\ + -C assume_role_arn=arn:aws:123:role/assumed_role \\ + -C region_name=us-east-1 \\ + -C archive=False \\ + -C synchronous=True \\ + -C timeout_seconds=300 + """ + final_config = { + "region_name": self.region_name, + "archive": False, + "synchronous": True, + "timeout_seconds": 300, + "assume_role_arn": self.assumed_role_arn, + } + if config: + self._apply_custom_config(final_config, config) + + _delete( + name, + region_name=final_config["region_name"], + assume_role_arn=final_config["assume_role_arn"], + archive=final_config["archive"], + synchronous=final_config["synchronous"], + timeout_seconds=final_config["timeout_seconds"], + ) + + def list_deployments(self, endpoint=None): + """ + List deployments. This method returns a list of dictionaries that describes each deployment. + + If a region name needs to be specified, the plugin must be initialized + with the AWS region in the ``target_uri`` such as ``sagemaker:/us-east-1``. + + To assume an IAM role, the plugin must be initialized + with the AWS region and the role ARN in the ``target_uri`` such as + ``sagemaker:/us-east-1/arn:aws:1234:role/assumed_role``. + + Args: + endpoint: (optional) List deployments in the specified endpoint. Currently unsupported + + Returns: + A list of dictionaries corresponding to deployments. + + .. code-block:: python + :caption: Python example + + from mlflow.deployments import get_deploy_client + + client = get_deploy_client("sagemaker:/us-east-1/arn:aws:123:role/assumed_role") + client.list_deployments() + + .. code-block:: bash + :caption: Command-line example + + mlflow deployments list --target sagemaker:/us-east-1/arn:aws:1234:role/assumed_role + """ + import boto3 + + assume_role_credentials = _assume_role_and_get_credentials( + assume_role_arn=self.assumed_role_arn + ) + + sage_client = boto3.client( + "sagemaker", region_name=self.region_name, **assume_role_credentials + ) + return sage_client.list_endpoints()["Endpoints"] + + def get_deployment(self, name, endpoint=None): + """ + Returns a dictionary describing the specified deployment. + + If a region name needs to be specified, the plugin must be initialized + with the AWS region in the ``target_uri`` such as ``sagemaker:/us-east-1``. + + To assume an IAM role, the plugin must be initialized + with the AWS region and the role ARN in the ``target_uri`` such as + ``sagemaker:/us-east-1/arn:aws:1234:role/assumed_role``. + + A :py:class:`mlflow.exceptions.MlflowException` will also be thrown when an error occurs + while retrieving the deployment. + + Args: + name: Name of deployment to retrieve + endpoint: (optional) Endpoint containing the deployment to get. Currently unsupported + + Returns: + A dictionary that describes the specified deployment + + .. code-block:: python + :caption: Python example + + from mlflow.deployments import get_deploy_client + + client = get_deploy_client("sagemaker:/us-east-1/arn:aws:123:role/assumed_role") + client.get_deployment("my-deployment") + + .. code-block:: bash + :caption: Command-line example + + mlflow deployments get --target sagemaker:/us-east-1/arn:aws:1234:role/assumed_role \\ + --name my-deployment + """ + import boto3 + + assume_role_credentials = _assume_role_and_get_credentials( + assume_role_arn=self.assumed_role_arn + ) + + try: + sage_client = boto3.client( + "sagemaker", region_name=self.region_name, **assume_role_credentials + ) + return sage_client.describe_endpoint(EndpointName=name) + except Exception as exc: + raise MlflowException( + message=f"There was an error while retrieving the deployment: {exc}\n" + ) + + def predict( + self, + deployment_name=None, + inputs=None, + endpoint=None, + params: dict[str, Any] | None = None, + ): + """ + Compute predictions from the specified deployment using the provided PyFunc input. + + The input/output types of this method match the :ref:`MLflow PyFunc prediction + interface `. + + If a region name needs to be specified, the plugin must be initialized + with the AWS region in the ``target_uri`` such as ``sagemaker:/us-east-1``. + + To assume an IAM role, the plugin must be initialized + with the AWS region and the role ARN in the ``target_uri`` such as + ``sagemaker:/us-east-1/arn:aws:1234:role/assumed_role``. + + Args: + deployment_name: Name of the deployment to predict against. + inputs: Input data (or arguments) to pass to the deployment or model endpoint for + inference. For a complete list of supported input types, see + :ref:`pyfunc-inference-api`. + endpoint: Endpoint to predict against. Currently unsupported + params: Optional parameters to invoke the endpoint with. + + Returns: + A PyFunc output, such as a Pandas DataFrame, Pandas Series, or NumPy array. + For a complete list of supported output types, see :ref:`pyfunc-inference-api`. + + .. code-block:: python + :caption: Python example + + import pandas as pd + from mlflow.deployments import get_deploy_client + + df = pd.DataFrame(data=[[1, 2, 3]], columns=["feat1", "feat2", "feat3"]) + client = get_deploy_client("sagemaker:/us-east-1/arn:aws:123:role/assumed_role") + client.predict("my-deployment", df) + + .. code-block:: bash + :caption: Command-line example + + cat > ./input.json <<- input + {"feat1": {"0": 1}, "feat2": {"0": 2}, "feat3": {"0": 3}} + input + + mlflow deployments predict \\ + --target sagemaker:/us-east-1/arn:aws:1234:role/assumed_role \\ + --name my-deployment \\ + --input-path ./input.json + """ + import boto3 + + assume_role_credentials = _assume_role_and_get_credentials( + assume_role_arn=self.assumed_role_arn + ) + + try: + sage_client = boto3.client( + "sagemaker-runtime", region_name=self.region_name, **assume_role_credentials + ) + response = sage_client.invoke_endpoint( + EndpointName=deployment_name, + Body=dump_input_data(inputs, inputs_key="instances", params=params), + ContentType="application/json", + ) + response_body = response["Body"].read().decode("utf-8") + return PredictionsResponse.from_json(response_body) + except Exception as exc: + raise MlflowException( + message=f"There was an error while getting model prediction: {exc}\n" + ) + + def explain(self, deployment_name=None, df=None, endpoint=None): + """ + *This function has not been implemented and will be coming in the future.* + """ + raise NotImplementedError("This function is not implemented yet.") + + def create_endpoint(self, name, config=None): + """ + Create an endpoint with the specified target. By default, this method should block until + creation completes (i.e. until it's possible to create a deployment within the endpoint). + In the case of conflicts (e.g. if it's not possible to create the specified endpoint + due to conflict with an existing endpoint), raises a + :py:class:`mlflow.exceptions.MlflowException`. See target-specific plugin documentation + for additional detail on support for asynchronous creation and other configuration. + + Args: + name: Unique name to use for endpoint. If another endpoint exists with the same + name, raises a :py:class:`mlflow.exceptions.MlflowException`. + config: (optional) Dict containing target-specific configuration for the endpoint. + + Returns: + Dict corresponding to created endpoint, which must contain the 'name' key. + """ + raise NotImplementedError("This function is not implemented yet.") + + def update_endpoint(self, endpoint, config=None): + """ + Update the endpoint with the specified name. You can update any target-specific attributes + of the endpoint (via `config`). By default, this method should block until the update + completes (i.e. until it's possible to create a deployment within the endpoint). See + target-specific plugin documentation for additional detail on support for asynchronous + update and other configuration. + + Args: + endpoint: Unique name of endpoint to update + config: (optional) dict containing target-specific configuration for the endpoint + """ + raise NotImplementedError("This function is not implemented yet.") + + def delete_endpoint(self, endpoint): + """ + Delete the endpoint from the specified target. Deletion should be idempotent (i.e. deletion + should not fail if retried on a non-existent deployment). + + Args: + endpoint: Name of endpoint to delete + """ + raise NotImplementedError("This function is not implemented yet.") + + def list_endpoints(self): + """ + List endpoints in the specified target. This method is expected to return an + unpaginated list of all endpoints (an alternative would be to return a dict with + an 'endpoints' field containing the actual endpoints, with plugins able to specify + other fields, e.g. a next_page_token field, in the returned dictionary for pagination, + and to accept a `pagination_args` argument to this method for passing + pagination-related args). + + Returns: + A list of dicts corresponding to endpoints. Each dict is guaranteed to + contain a 'name' key containing the endpoint name. The other fields of + the returned dictionary and their types may vary across targets. + """ + raise NotImplementedError("This function is not implemented yet.") + + def get_endpoint(self, endpoint): + """ + Returns a dictionary describing the specified endpoint, throwing a + py:class:`mlflow.exception.MlflowException` if no endpoint exists with the provided + name. + The dict is guaranteed to contain an 'name' key containing the endpoint name. + The other fields of the returned dictionary and their types may vary across targets. + + Args: + endpoint: Name of endpoint to fetch + """ + raise NotImplementedError("This function is not implemented yet.") + + +class _SageMakerOperation: + def __init__(self, status_check_fn, cleanup_fn): + self.status_check_fn = status_check_fn + self.cleanup_fn = cleanup_fn + self.start_time = time.time() + self.status = _SageMakerOperationStatus(_SageMakerOperationStatus.STATE_IN_PROGRESS, None) + self.cleaned_up = False + + def await_completion(self, timeout_seconds): + iteration = 0 + begin = time.time() + while (time.time() - begin) < timeout_seconds: + status = self.status_check_fn() + if status.state == _SageMakerOperationStatus.STATE_IN_PROGRESS: + if iteration % 4 == 0: + # Log the progress status roughly every 20 seconds + _logger.info(status.message) + + time.sleep(5) + iteration += 1 + continue + else: + self.status = status + return status + + duration_seconds = time.time() - begin + return _SageMakerOperationStatus.timed_out(duration_seconds) + + def clean_up(self): + if self.status.state != _SageMakerOperationStatus.STATE_SUCCEEDED: + raise ValueError( + "Cannot clean up an operation that has not succeeded! Current operation state:" + f" {self.status.state}" + ) + + if not self.cleaned_up: + self.cleaned_up = True + else: + raise ValueError("`clean_up()` has already been executed for this operation!") + + self.cleanup_fn() + + +class _SageMakerOperationStatus: + STATE_SUCCEEDED = "succeeded" + STATE_FAILED = "failed" + STATE_IN_PROGRESS = "in progress" + STATE_TIMED_OUT = "timed_out" + + def __init__(self, state, message): + self.state = state + self.message = message + + @classmethod + def in_progress(cls, message=None): + if message is None: + message = "The operation is still in progress." + return cls(_SageMakerOperationStatus.STATE_IN_PROGRESS, message) + + @classmethod + def timed_out(cls, duration_seconds): + return cls( + _SageMakerOperationStatus.STATE_TIMED_OUT, + f"Timed out after waiting {duration_seconds} seconds for the operation to" + " complete. This operation may still be in progress. Please check the AWS" + " console for more information.", + ) + + @classmethod + def failed(cls, message): + return cls(_SageMakerOperationStatus.STATE_FAILED, message) + + @classmethod + def succeeded(cls, message): + return cls(_SageMakerOperationStatus.STATE_SUCCEEDED, message) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/sagemaker/__pycache__/cli.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/sagemaker/__pycache__/cli.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..fcea1885678aff82772bf49d58223fd957b1b373 Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/sagemaker/__pycache__/cli.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/sagemaker/cli.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/sagemaker/cli.py new file mode 100644 index 0000000000000000000000000000000000000000..adc13eb9f2323628c608bbe9485841dd5c91e3ff --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/sagemaker/cli.py @@ -0,0 +1,386 @@ +import json +import os +import tempfile + +import click + +import mlflow +import mlflow.models.docker_utils +import mlflow.sagemaker +from mlflow.sagemaker import DEFAULT_IMAGE_NAME as IMAGE +from mlflow.utils import cli_args +from mlflow.utils import env_manager as em + + +@click.group("sagemaker") +def commands(): + """ + Serve models on SageMaker. + + To serve a model associated with a run on a tracking server, set the MLFLOW_TRACKING_URI + environment variable to the URL of the desired server. + """ + + +@commands.command("deploy-transform-job") +@click.option("--job-name", "-n", help="Transform job name", required=True) +@cli_args.MODEL_URI +@click.option("--input-data-type", help="Input data type for the transform job", required=True) +@click.option( + "--input-uri", "-u", help="S3 key name prefix or manifest of the input data", required=True +) +@click.option( + "--content-type", + help="The multipurpose internet mail extension (MIME) type of the data", + required=True, +) +@click.option( + "--output-path", + "-o", + help="The S3 path to store the output results of the Sagemaker transform job", + required=True, +) +@click.option( + "--compression-type", default="None", help="The compression type of the transform data" +) +@click.option( + "--split-type", + "-s", + default="Line", + help="The method to split the transform job's data files into smaller batches", +) +@click.option( + "--accept", + "-a", + default="text/csv", + help="The multipurpose internet mail extension (MIME) type of the output data", +) +@click.option( + "--assemble-with", + default="Line", + help="The method to assemble the results of the transform job as a single S3 object", +) +@click.option( + "--input-filter", + default="$", + help="A JSONPath expression used to select a portion of the input data for the transform job", +) +@click.option( + "--output-filter", + default="$", + help="A JSONPath expression used to select a portion of the output data from the transform job", +) +@click.option( + "--join-resource", + "-j", + default="None", + help="The source of the data to join with the transformed data", +) +@click.option("--execution-role-arn", "-e", default=None, help="SageMaker execution role") +@click.option("--bucket", "-b", default=None, help="S3 bucket to store model artifacts") +@click.option("--image-url", "-i", default=None, help="ECR URL for the Docker image") +@click.option( + "--region-name", + default="us-west-2", + help="Name of the AWS region in which to deploy the transform job", +) +@click.option( + "--instance-type", + "-t", + default=mlflow.sagemaker.DEFAULT_SAGEMAKER_INSTANCE_TYPE, + help="The type of SageMaker ML instance on which to perform the batch transform job." + " For a list of supported instance types, see" + " https://aws.amazon.com/sagemaker/pricing/instance-types/.", +) +@click.option( + "--instance-count", + "-c", + default=mlflow.sagemaker.DEFAULT_SAGEMAKER_INSTANCE_COUNT, + help="The number of SageMaker ML instances on which to perform the batch transform job", +) +@click.option( + "--vpc-config", + "-v", + help="Path to a file containing a JSON-formatted VPC configuration. This" + " configuration will be used when creating the new SageMaker model associated" + " with this application. For more information, see" + " https://docs.aws.amazon.com/sagemaker/latest/dg/API_VpcConfig.html", +) +@click.option( + "--flavor", + "-f", + default=None, + help=( + "The name of the flavor to use for deployment. Must be one of the following: " + f"{mlflow.sagemaker.SUPPORTED_DEPLOYMENT_FLAVORS}. If unspecified, a flavor will be " + "automatically selected from the model's available flavors." + ), +) +@click.option( + "--archive", + is_flag=True, + help=( + "If specified, any SageMaker resources that become inactive after the finished" + " batch transform job are preserved. These resources may include the associated" + " SageMaker models and model artifacts. Otherwise, if `--archive` is unspecified," + " these resources are deleted. `--archive` must be specified when deploying" + " asynchronously with `--async`." + ), +) +@click.option( + "--async", + "asynchronous", + is_flag=True, + help=( + "If specified, this command will return immediately after starting the" + " deployment process. It will not wait for the deployment process to complete." + " The caller is responsible for monitoring the deployment process via native" + " SageMaker APIs or the AWS console." + ), +) +@click.option( + "--timeout", + default=1200, + help=( + "If the command is executed synchronously, the deployment process will return" + " after the specified number of seconds if no definitive result (success or" + " failure) is achieved. Once the function returns, the caller is responsible" + " for monitoring the health and status of the pending deployment via" + " native SageMaker APIs or the AWS console. If the command is executed" + " asynchronously using the `--async` flag, this value is ignored." + ), +) +def deploy_transform_job( + job_name, + model_uri, + input_data_type, + input_uri, + content_type, + output_path, + compression_type, + split_type, + accept, + assemble_with, + input_filter, + output_filter, + join_resource, + execution_role_arn, + bucket, + image_url, + region_name, + instance_type, + instance_count, + vpc_config, + flavor, + archive, + asynchronous, + timeout, +): + """ + Deploy model on Sagemaker as a batch transform job. Current active AWS account needs to have + correct permissions setup. + + By default, unless the ``--async`` flag is specified, this command will block until + either the batch transform job completes (definitively succeeds or fails) or the specified + timeout elapses. + """ + if vpc_config is not None: + with open(vpc_config) as f: + vpc_config = json.load(f) + + mlflow.sagemaker.deploy_transform_job( + job_name=job_name, + model_uri=model_uri, + s3_input_data_type=input_data_type, + s3_input_uri=input_uri, + content_type=content_type, + s3_output_path=output_path, + compression_type=compression_type, + split_type=split_type, + accept=accept, + assemble_with=assemble_with, + input_filter=input_filter, + output_filter=output_filter, + join_resource=join_resource, + execution_role_arn=execution_role_arn, + bucket=bucket, + image_url=image_url, + region_name=region_name, + instance_type=instance_type, + instance_count=instance_count, + vpc_config=vpc_config, + flavor=flavor, + archive=archive, + synchronous=(not asynchronous), + timeout_seconds=timeout, + ) + + +@commands.command("terminate-transform-job") +@click.option("--job-name", "-n", help="Transform job name", required=True) +@click.option( + "--region-name", + "-r", + default="us-west-2", + help="Name of the AWS region in which the transform job is deployed", +) +@click.option( + "--archive", + is_flag=True, + help=( + "If specified, resources associated with the application are preserved." + " These resources may include unused SageMaker models and model artifacts." + " Otherwise, if `--archive` is unspecified, these resources are deleted." + " `--archive` must be specified when deleting asynchronously with `--async`." + ), +) +@click.option( + "--async", + "asynchronous", + is_flag=True, + help=( + "If specified, this command will return immediately after starting the" + " termination process. It will not wait for the termination process to complete." + " The caller is responsible for monitoring the termination process via native" + " SageMaker APIs or the AWS console." + ), +) +@click.option( + "--timeout", + default=1200, + help=( + "If the command is executed synchronously, the termination process will return" + " after the specified number of seconds if no definitive result (success or" + " failure) is achieved. Once the function returns, the caller is responsible" + " for monitoring the health and status of the pending termination via" + " native SageMaker APIs or the AWS console. If the command is executed" + " asynchronously using the `--async` flag, this value is ignored." + ), +) +def terminate_transform_job(job_name, region_name, archive, asynchronous, timeout): + """ + Terminate the specified Sagemaker batch transform job. Unless ``--archive`` is specified, + all SageMaker resources associated with the batch transform job are deleted as well. + + By default, unless the ``--async`` flag is specified, this command will block until + either the termination process completes (definitively succeeds or fails) or the specified + timeout elapses. + """ + mlflow.sagemaker.terminate_transform_job( + job_name=job_name, + region_name=region_name, + archive=archive, + synchronous=(not asynchronous), + timeout_seconds=timeout, + ) + + +@commands.command("push-model") +@click.option("--model-name", "-n", help="Sagemaker model name", required=True) +@cli_args.MODEL_URI +@click.option("--execution-role-arn", "-e", default=None, help="SageMaker execution role") +@click.option("--bucket", "-b", default=None, help="S3 bucket to store model artifacts") +@click.option("--image-url", "-i", default=None, help="ECR URL for the Docker image") +@click.option( + "--region-name", + default="us-west-2", + help="Name of the AWS region in which to push the Sagemaker model", +) +@click.option( + "--vpc-config", + "-v", + help="Path to a file containing a JSON-formatted VPC configuration. This" + " configuration will be used when creating the new SageMaker model." + " For more information, see" + " https://docs.aws.amazon.com/sagemaker/latest/dg/API_VpcConfig.html", +) +@click.option( + "--flavor", + "-f", + default=None, + help=( + "The name of the flavor to use for deployment. Must be one of the following:" + " {supported_flavors}. If unspecified, a flavor will be automatically selected" + " from the model's available flavors.".format( + supported_flavors=mlflow.sagemaker.SUPPORTED_DEPLOYMENT_FLAVORS + ) + ), +) +def push_model_to_sagemaker( + model_name, + model_uri, + execution_role_arn, + bucket, + image_url, + region_name, + vpc_config, + flavor, +): + """ + Push an MLflow model to Sagemaker model registry. Current active AWS account needs to have + correct permissions setup. + """ + if vpc_config is not None: + with open(vpc_config) as f: + vpc_config = json.load(f) + + mlflow.sagemaker.push_model_to_sagemaker( + model_name=model_name, + model_uri=model_uri, + execution_role_arn=execution_role_arn, + bucket=bucket, + image_url=image_url, + region_name=region_name, + vpc_config=vpc_config, + flavor=flavor, + ) + + +@commands.command("build-and-push-container") +@click.option("--build/--no-build", default=True, help="Build the container if set.") +@click.option("--push/--no-push", default=True, help="Push the container to AWS ECR if set.") +@click.option("--container", "-c", default=IMAGE, help="image name") +@cli_args.INSTALL_JAVA +@cli_args.ENV_MANAGER +@cli_args.MLFLOW_HOME +def build_and_push_container(build, push, container, install_java, env_manager, mlflow_home): + """ + Build new MLflow Sagemaker image, assign it a name, and push to ECR. + + This function builds an MLflow Docker image. + The image is built locally and it requires Docker to run. + The image is pushed to ECR under current active AWS account and to current active AWS region. + """ + from mlflow.models import docker_utils + + env_manager = env_manager or em.VIRTUALENV + if not (build or push): + click.echo("skipping both build and push, have nothing to do!") + if build: + sagemaker_image_entrypoint = ( + "import sys; from mlflow.models import container as C; " + f"C._init(sys.argv[1], '{env_manager}')" + ) + + setup_container = ( + "# Install minimal serving dependencies\n" + 'RUN python -c "from mlflow.models.container import _install_pyfunc_deps;' + '_install_pyfunc_deps(None, False)"' + ) + + with tempfile.TemporaryDirectory() as tmp: + docker_utils.generate_dockerfile( + base_image=mlflow.models.docker_utils.UBUNTU_BASE_IMAGE, + output_dir=tmp, + entrypoint=sagemaker_image_entrypoint, + env_manager=env_manager, + mlflow_home=os.path.abspath(mlflow_home) if mlflow_home else None, + model_install_steps=setup_container, + # Create a conda env or virtualenv at runtime after the model is loaded + disable_env_creation_at_runtime=False, + install_java=install_java, + ) + + docker_utils.build_image_from_context(tmp, image_name=container) + if push: + mlflow.sagemaker.push_image_to_ecr(container) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/semantic_kernel/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/semantic_kernel/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..89ffc6e9d27333cc749da246598ff62cb2c77a59 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/semantic_kernel/__init__.py @@ -0,0 +1,58 @@ +from semantic_kernel.kernel import Kernel +from semantic_kernel.utils.telemetry.model_diagnostics import decorators + +from mlflow.semantic_kernel.autolog import setup_semantic_kernel_tracing +from mlflow.semantic_kernel.tracing_utils import ( + patched_kernel_entry_point, + semantic_kernel_diagnostics_wrapper, +) +from mlflow.utils.annotations import experimental +from mlflow.utils.autologging_utils import autologging_integration, safe_patch + +FLAVOR_NAME = "semantic_kernel" + + +@experimental(version="3.2.0") +@autologging_integration(FLAVOR_NAME) +def autolog( + log_traces: bool = True, + disable: bool = False, + silent: bool = False, +): + """ + Enables (or disables) and configures autologging from Semantic Kernel to MLflow. + Only synchronous calls are supported. Asynchnorous APIs and streaming are not recorded. + + Args: + log_traces: If ``True``, traces are logged for Semantic Kernel. + If ``False``, no traces are collected during inference. Default to ``True``. + disable: If ``True``, disables the Semantic Kernel autologging. Default to ``False``. + silent: If ``True``, suppress all event logs and warnings from MLflow during Anthropic + autologging. If ``False``, show all events and warnings. + """ + + setup_semantic_kernel_tracing() + + # Create root spans for the kernel entry points. + for method in ["invoke", "invoke_prompt"]: + safe_patch( + FLAVOR_NAME, + Kernel, + method, + patched_kernel_entry_point, + ) + + # NB: Semantic Kernel uses logging to serialize inputs/outputs. These parsers are used by their + # tracing decorators to log the inputs/outputs. These patches give coverage for many additional + # methods that are not covered by above entry point patches. + for method_name in [ + "_set_completion_input", + "_set_completion_response", + "_set_completion_error", + ]: + safe_patch( + FLAVOR_NAME, + decorators, + method_name, + semantic_kernel_diagnostics_wrapper, + ) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/semantic_kernel/__pycache__/__init__.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/semantic_kernel/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..8dcbc281b3ebf1f701c61be13691998bbd515759 Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/semantic_kernel/__pycache__/__init__.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/semantic_kernel/__pycache__/autolog.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/semantic_kernel/__pycache__/autolog.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..3e7e257f71dec8d431baca91f7619b4bed098691 Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/semantic_kernel/__pycache__/autolog.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/semantic_kernel/__pycache__/tracing_utils.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/semantic_kernel/__pycache__/tracing_utils.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..3ee4d754d098c448ce0e72081d86f60fcb762adb Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/semantic_kernel/__pycache__/tracing_utils.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/semantic_kernel/autolog.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/semantic_kernel/autolog.py new file mode 100644 index 0000000000000000000000000000000000000000..319cbbbc0f515f07673ebb517064f5f7b7094789 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/semantic_kernel/autolog.py @@ -0,0 +1,125 @@ +import logging +from contextlib import contextmanager +from typing import Generator + +from opentelemetry.context import Context +from opentelemetry.sdk.trace import ReadableSpan as OTelReadableSpan +from opentelemetry.sdk.trace import Span as OTelSpan +from opentelemetry.sdk.trace import TracerProvider as SDKTracerProvider +from opentelemetry.sdk.trace.export import SimpleSpanProcessor, SpanExporter +from opentelemetry.trace import ( + NoOpTracerProvider, + ProxyTracerProvider, + get_tracer_provider, + set_tracer_provider, +) + +from mlflow.entities.span import create_mlflow_span +from mlflow.semantic_kernel.tracing_utils import set_span_type, set_token_usage +from mlflow.tracing.constant import SpanAttributeKey +from mlflow.tracing.provider import _get_tracer +from mlflow.tracing.trace_manager import InMemoryTraceManager +from mlflow.tracing.utils import get_mlflow_span_for_otel_span, get_otel_attribute + +_logger = logging.getLogger(__name__) + + +def _enable_experimental_genai_tracing(): + # NB: These settings are required to enable the telemetry for genai attributes + # such as chat completion inputs/outputs, which are currently marked as experimental. + # We directly update the singleton setting object instead of using env vars, + # because the object might be already initialized by the time we call this function. + # https://learn.microsoft.com/en-us/semantic-kernel/concepts/enterprise-readiness/observability/telemetry-with-console + from semantic_kernel.utils.telemetry.model_diagnostics.decorators import ( + MODEL_DIAGNOSTICS_SETTINGS, + ) + + MODEL_DIAGNOSTICS_SETTINGS.enable_otel_diagnostics = True + MODEL_DIAGNOSTICS_SETTINGS.enable_otel_diagnostics_sensitive = True + + try: + # This only exists in Semantic Kernel 1.35.1 or later. + from semantic_kernel.utils.telemetry.agent_diagnostics.decorators import ( + MODEL_DIAGNOSTICS_SETTINGS as AGENT_DIAGNOSTICS_SETTINGS, + ) + + AGENT_DIAGNOSTICS_SETTINGS.enable_otel_diagnostics = True + AGENT_DIAGNOSTICS_SETTINGS.enable_otel_diagnostics_sensitive = True + except ImportError: + pass + + _logger.info("Semantic Kernel Otel diagnostics is turned on for enabling tracing.") + + +def setup_semantic_kernel_tracing(): + _enable_experimental_genai_tracing() + + # NB: This logic has a known issue that it does not work when Semantic Kernel program is + # executed before calling this setup is called. This is because Semantic Kernel caches the + # tracer instance in each module (ref:https://github.com/microsoft/semantic-kernel/blob/6ecf2b9c2c893dc6da97abeb5962dfc49bed062d/python/semantic_kernel/functions/kernel_function.py#L46), + # which prevent us from updating the span processor setup for the tracer. + # Therefore, `mlflow.semantic_kernel.autolog()` should always be called before running the + # Semantic Kernel program. + + provider = get_tracer_provider() + sk_processor = SemanticKernelSpanProcessor() + if isinstance(provider, (NoOpTracerProvider, ProxyTracerProvider)): + new_provider = SDKTracerProvider() + new_provider.add_span_processor(sk_processor) + set_tracer_provider(new_provider) + else: + if not any( + isinstance(p, SemanticKernelSpanProcessor) + for p in provider._active_span_processor._span_processors + ): + provider.add_span_processor(sk_processor) + + +class SemanticKernelSpanProcessor(SimpleSpanProcessor): + def __init__(self): + # NB: Dummy NoOp exporter, because OTel span processor requires an exporter + self.span_exporter = SpanExporter() + + def on_start(self, span: OTelSpan, parent_context: Context | None = None): + # Trigger MLflow's span processor + tracer = _get_tracer(__name__) + tracer.span_processor.on_start(span, parent_context) + + trace_id = get_otel_attribute(span, SpanAttributeKey.REQUEST_ID) + mlflow_span = create_mlflow_span(span, trace_id) + + # Register new span in the in-memory trace manager + InMemoryTraceManager.get_instance().register_span(mlflow_span) + + def on_end(self, span: OTelReadableSpan) -> None: + mlflow_span = get_mlflow_span_for_otel_span(span) + if mlflow_span is None: + _logger.debug("Span not found in the map. Skipping end.") + return + + with _bypass_attribute_guard(mlflow_span._span): + set_span_type(mlflow_span) + set_token_usage(mlflow_span) + + # Export the span using MLflow's span processor + tracer = _get_tracer(__name__) + tracer.span_processor.on_end(span) + + +@contextmanager +def _bypass_attribute_guard(span: OTelSpan) -> Generator[None, None, None]: + """ + OpenTelemetry does not allow setting attributes if the span has end time defined. + https://github.com/open-telemetry/opentelemetry-python/blob/d327927d0274a320466feec6fba6d6ddb287dc5a/opentelemetry-sdk/src/opentelemetry/sdk/trace/__init__.py#L849-L851 + + However, we need to set some attributes within `on_end` handler of the span processor, + where the span is already marked as ended. This context manager is a hacky workaround + to bypass the attribute guard. + """ + original_end_time = span._end_time + span._end_time = None + try: + yield + + finally: + span._end_time = original_end_time diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/semantic_kernel/tracing_utils.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/semantic_kernel/tracing_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..88d551d97dbae24286f57fd3bed3c9763d942d16 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/semantic_kernel/tracing_utils.py @@ -0,0 +1,140 @@ +import logging +from typing import Any + +from opentelemetry.trace import get_current_span +from semantic_kernel.contents.chat_history import ChatHistory +from semantic_kernel.contents.kernel_content import KernelContent +from semantic_kernel.contents.streaming_content_mixin import StreamingContentMixin +from semantic_kernel.functions import FunctionResult +from semantic_kernel.utils.telemetry.model_diagnostics import ( + gen_ai_attributes as model_gen_ai_attributes, +) +from semantic_kernel.utils.telemetry.model_diagnostics.decorators import ( + CHAT_COMPLETION_OPERATION, + CHAT_STREAMING_COMPLETION_OPERATION, + TEXT_COMPLETION_OPERATION, + TEXT_STREAMING_COMPLETION_OPERATION, +) +from semantic_kernel.utils.telemetry.model_diagnostics.function_tracer import ( + OPERATION_NAME as FUNCTION_OPERATION_NAME, +) + +import mlflow +from mlflow.entities import SpanType +from mlflow.entities.span import LiveSpan +from mlflow.tracing.constant import SpanAttributeKey, TokenUsageKey +from mlflow.tracing.utils import ( + construct_full_inputs, + get_mlflow_span_for_otel_span, +) + +_OPERATION_TO_SPAN_TYPE = { + CHAT_COMPLETION_OPERATION: SpanType.CHAT_MODEL, + CHAT_STREAMING_COMPLETION_OPERATION: SpanType.CHAT_MODEL, + TEXT_COMPLETION_OPERATION: SpanType.LLM, + TEXT_STREAMING_COMPLETION_OPERATION: SpanType.LLM, + FUNCTION_OPERATION_NAME: SpanType.TOOL, + # https://github.com/microsoft/semantic-kernel/blob/d5ee6aa1c176a4b860aba72edaa961570874661b/python/semantic_kernel/utils/telemetry/agent_diagnostics/decorators.py#L22 + "invoke_agent": SpanType.AGENT, +} + +_logger = logging.getLogger(__name__) + + +def semantic_kernel_diagnostics_wrapper(original, *args, **kwargs) -> None: + """ + Wrapper for Semantic Kernel's model diagnostics decorators. + + This wrapper is used to record the inputs and outputs to the span, because + Semantic Kernel's Otel span do not record the inputs and outputs. + """ + full_kwargs = construct_full_inputs(original, *args, **kwargs) + current_span = full_kwargs.get("current_span") or get_current_span() + mlflow_span = get_mlflow_span_for_otel_span(current_span) + + if not mlflow_span: + _logger.debug("Span is not found or recording. Skipping error handling.") + return original(*args, **kwargs) + + if prompt := full_kwargs.get("prompt"): + # Wrapping _set_completion_input + # https://github.com/microsoft/semantic-kernel/blob/d5ee6aa1c176a4b860aba72edaa961570874661b/python/semantic_kernel/utils/telemetry/model_diagnostics/decorators.py#L369 + mlflow_span.set_inputs(_parse_content(prompt)) + + if completions := full_kwargs.get("completions"): + # Wrapping _set_completion_response + # https://github.com/microsoft/semantic-kernel/blob/d5ee6aa1c176a4b860aba72edaa961570874661b/ + mlflow_span.set_outputs({"messages": [_parse_content(c) for c in completions]}) + + if error := full_kwargs.get("error"): + # Wrapping _set_completion_error + # https://github.com/microsoft/semantic-kernel/blob/d5ee6aa1c176a4b860aba72edaa961570874661b/python/semantic_kernel/utils/telemetry/model_diagnostics/decorators.py#L452 + mlflow_span.record_exception(error) + + return original(*args, **kwargs) + + +async def patched_kernel_entry_point(original, self, *args, **kwargs): + with mlflow.start_span( + name=f"{self.__class__.__name__}.{original.__name__}", + span_type=SpanType.AGENT, + ) as mlflow_span: + inputs = construct_full_inputs(original, self, *args, **kwargs) + mlflow_span.set_inputs(_parse_content(inputs)) + + result = await original(self, *args, **kwargs) + + mlflow_span.set_outputs(_parse_content(result)) + + return result + + +def _parse_content(value: Any) -> Any: + """ + Parse the message content objects in Semantic Kernel into a more readable format. + + Those objects are Pydantic models, but includes many noisy fields that are not + useful for debugging and hard to read. The base KernelContent class has a to_dict() + method that converts them into more readable format (role, content), so we use that. + """ + if isinstance(value, dict) and (chat_history := value.get("chat_history")): + value = _parse_content(chat_history) + elif isinstance(value, ChatHistory): + # Record chat history as a list of messages for better readability + value = {"messages": [_parse_content(m) for m in value.messages]} + elif isinstance(value, (KernelContent, StreamingContentMixin)): + value = value.to_dict() + elif isinstance(value, FunctionResult): + # Extract "value" field from the FunctionResult object + value = _parse_content(value.value) + elif isinstance(value, list): + value = [_parse_content(item) for item in value] + return value + + +def set_span_type(mlflow_span: LiveSpan) -> str: + """Determine the span type based on the operation.""" + span_type = SpanType.UNKNOWN + if operation := mlflow_span.get_attribute(model_gen_ai_attributes.OPERATION): + span_type = _OPERATION_TO_SPAN_TYPE.get(operation, SpanType.UNKNOWN) + + mlflow_span.set_span_type(span_type) + + +def set_token_usage(mlflow_span: LiveSpan) -> None: + """Set token usage attributes on the MLflow span.""" + input_tokens = mlflow_span.get_attribute(model_gen_ai_attributes.INPUT_TOKENS) + output_tokens = mlflow_span.get_attribute(model_gen_ai_attributes.OUTPUT_TOKENS) + + usage_dict = {} + if input_tokens is not None: + usage_dict[TokenUsageKey.INPUT_TOKENS] = input_tokens + if output_tokens is not None: + usage_dict[TokenUsageKey.OUTPUT_TOKENS] = output_tokens + + if input_tokens is not None or output_tokens is not None: + total_tokens = (input_tokens or 0) + (output_tokens or 0) + usage_dict[TokenUsageKey.TOTAL_TOKENS] = total_tokens + + if usage_dict: + mlflow_span.set_attribute(SpanAttributeKey.CHAT_USAGE, usage_dict) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/sentence_transformers/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/sentence_transformers/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..099fdbacdb61bfeaa8e175a5ad6741ed5781a20b --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/sentence_transformers/__init__.py @@ -0,0 +1,547 @@ +import json +import logging +import pathlib +import re +from typing import Any + +import numpy as np +import pandas as pd +import yaml +from packaging.version import Version + +import mlflow +from mlflow import pyfunc +from mlflow.entities.model_registry.prompt import Prompt +from mlflow.exceptions import MlflowException +from mlflow.models import Model, ModelInputExample, ModelSignature, infer_pip_requirements +from mlflow.models.model import MLMODEL_FILE_NAME +from mlflow.models.signature import _infer_signature_from_input_example +from mlflow.models.utils import _save_example +from mlflow.tracking._model_registry import DEFAULT_AWAIT_MAX_SLEEP_SECONDS +from mlflow.transformers.llm_inference_utils import ( + _LLM_INFERENCE_TASK_EMBEDDING, + _LLM_V1_EMBEDDING_INPUT_KEY, + postprocess_output_for_llm_v1_embedding_task, +) +from mlflow.types.llm import ( + EMBEDDING_MODEL_INPUT_SCHEMA, + EMBEDDING_MODEL_OUTPUT_SCHEMA, +) +from mlflow.types.schema import ColSpec, Schema, TensorSpec +from mlflow.utils.docstring_utils import ( + LOG_MODEL_PARAM_DOCS, + docstring_version_compatibility_warning, + format_docstring, +) +from mlflow.utils.environment import ( + _CONDA_ENV_FILE_NAME, + _CONSTRAINTS_FILE_NAME, + _PYTHON_ENV_FILE_NAME, + _REQUIREMENTS_FILE_NAME, + _mlflow_conda_env, + _process_conda_env, + _process_pip_requirements, + _PythonEnv, + _validate_env_arguments, +) +from mlflow.utils.file_utils import get_total_file_size, write_to +from mlflow.utils.model_utils import ( + _add_code_from_conf_to_system_path, + _download_artifact_from_uri, + _get_flavor_configuration_from_uri, + _validate_and_copy_code_paths, + _validate_and_prepare_target_save_path, +) +from mlflow.utils.requirements_utils import _get_pinned_requirement + +FLAVOR_NAME = "sentence_transformers" +_TRANSFORMER_SOURCE_MODEL_NAME_KEY = "source_model_name" +_TRANSFORMER_MODEL_TYPE_KEY = "pipeline_model_type" + +SENTENCE_TRANSFORMERS_DATA_PATH = "model.sentence_transformer" +_INFERENCE_CONFIG_PATH = "inference_config" + +# Patterns to extract HuggingFace model repository name from the local snapshot path. +# The path format would be like /path/to/{username}_{modelname}, where user name can +# only contain number, letters, and dashes. +_LOCAL_SNAPSHOT_PATH_PATTERN = re.compile(r"/([0-9a-zA-Z-]+)_([^\/]+)/$") + +_logger = logging.getLogger(__name__) + + +def get_default_pip_requirements() -> list[str]: + """ + Retrieves the set of minimal dependencies for the ``sentence_transformers`` flavor. + + Returns: + A list of default pip requirements for MLflow Models that have been produced with the + ``sentence-transformers`` flavor. Calls to :py:func:`save_model()` and + :py:func:`log_model()` produce a pip environment that contain these + requirements at a minimum. + """ + base_reqs = ["sentence-transformers", "transformers", "torch"] + return [_get_pinned_requirement(module) for module in base_reqs] + + +def get_default_conda_env(): + """ + Returns: + The default Conda environment for MLflow Models produced with the + ``sentence_transformers`` flavor. + """ + return _mlflow_conda_env(additional_pip_deps=get_default_pip_requirements()) + + +def _verify_task_and_update_metadata( + task: str, metadata: dict[str, Any] | None = None +) -> dict[str, Any]: + if task not in [_LLM_INFERENCE_TASK_EMBEDDING]: + raise MlflowException.invalid_parameter_value( + f"Received invalid parameter value for `task` argument {task}. Task type could " + f"only be {_LLM_INFERENCE_TASK_EMBEDDING}" + ) + if metadata is None: + metadata = {} + if "task" in metadata and metadata["task"] != task: + raise MlflowException.invalid_parameter_value( + f"Received invalid parameter value for `task` argument {task}. Task type is " + f"inconsistent with the task value from metadata {metadata['task']}" + ) + metadata["task"] = task + return metadata + + +@docstring_version_compatibility_warning(integration_name=FLAVOR_NAME) +@format_docstring(LOG_MODEL_PARAM_DOCS.format(package_name=FLAVOR_NAME)) +def save_model( + model, + path: str, + task: str | None = None, + inference_config: dict[str, Any] | None = None, + code_paths: list[str] | None = None, + mlflow_model: Model | None = None, + signature: ModelSignature | None = None, + input_example: ModelInputExample | None = None, + pip_requirements: list[str] | str | None = None, + extra_pip_requirements: list[str] | str | None = None, + conda_env=None, + metadata: dict[str, Any] | None = None, +) -> None: + """ + .. note:: + + Saving Sentence Transformers models with custom code (i.e. models that require + ``trust_remote_code=True``) is supported in MLflow 2.12.0 and above. + + + Save a trained ``sentence-transformers`` model to a path on the local file system. + + Args: + model: A trained ``sentence-transformers`` model. + path: Local path destination for the serialized model to be saved. + task: MLflow inference task type for ``sentence-transformers`` model. Candidate task type + is `llm/v1/embeddings`. + inference_config: + A dict of valid inference parameters that can be applied to a ``sentence-transformer`` + model instance during inference. + These arguments are used exclusively for the case of loading the model as a ``pyfunc`` + Model or for use in Spark. + These values are not applied to a returned model from a call to + ``mlflow.sentence_transformers.load_model()`` + code_paths: {{ code_paths }} + mlflow_model: An MLflow model object that specifies the flavor that this model is being + added to. + signature: an instance of the :py:class:`ModelSignature ` + class that describes the model's inputs and outputs. If not specified but an + ``input_example`` is supplied, a signature will be automatically inferred + based on the supplied input example and model. If both ``signature`` and + ``input_example`` are not specified or the automatic signature inference + fails, a default signature will be adopted. To prevent a signature from being + adopted, set ``signature`` to ``False``. To manually infer a model signature, + call :py:func:`infer_signature() ` on datasets + with valid model inputs and valid model outputs. + input_example: {{ input_example }} + pip_requirements: {{ pip_requirements }} + extra_pip_requirements: {{ extra_pip_requirements }} + conda_env: {{ conda_env }} + metadata: {{ metadata }} + """ + import sentence_transformers + + _validate_env_arguments(conda_env, pip_requirements, extra_pip_requirements) + + path = pathlib.Path(path).absolute() + model_data_path = path.joinpath(SENTENCE_TRANSFORMERS_DATA_PATH) + + _validate_and_prepare_target_save_path(str(path)) + + code_dir_subpath = _validate_and_copy_code_paths(code_paths, str(path)) + + if mlflow_model is None: + mlflow_model = Model() + saved_example = _save_example(mlflow_model, input_example, path) + + if task is not None: + signature = ModelSignature( + inputs=EMBEDDING_MODEL_INPUT_SCHEMA, outputs=EMBEDDING_MODEL_OUTPUT_SCHEMA + ) + elif signature is None and saved_example is not None: + wrapped_model = _SentenceTransformerModelWrapper(model) + signature = _infer_signature_from_input_example(saved_example, wrapped_model) + elif signature is None: + signature = _get_default_signature() + elif signature is False: + signature = None + + if signature is not None: + mlflow_model.signature = signature + if metadata is not None: + mlflow_model.metadata = metadata + model_config = None + if task is not None: + mlflow_model.metadata = _verify_task_and_update_metadata(task, mlflow_model.metadata) + model_config = {"task": _LLM_INFERENCE_TASK_EMBEDDING} + + model.save(str(model_data_path)) + + pyfunc.add_to_model( + mlflow_model, + loader_module="mlflow.sentence_transformers", + data=SENTENCE_TRANSFORMERS_DATA_PATH, + conda_env=_CONDA_ENV_FILE_NAME, + python_env=_PYTHON_ENV_FILE_NAME, + model_config=model_config, + code=code_dir_subpath, + ) + + mlflow_model.add_flavor( + FLAVOR_NAME, + sentence_transformers_version=sentence_transformers.__version__, + code=code_dir_subpath, + **_get_transformers_model_metadata(model), + ) + if size := get_total_file_size(path): + mlflow_model.model_size_bytes = size + mlflow_model.save(str(path.joinpath(MLMODEL_FILE_NAME))) + + if inference_config: + path.joinpath(_INFERENCE_CONFIG_PATH).write_text(json.dumps(inference_config)) + + if conda_env is None: + if pip_requirements is None: + default_reqs = get_default_pip_requirements() + inferred_reqs = infer_pip_requirements(str(path), FLAVOR_NAME, fallback=default_reqs) + default_reqs = sorted(set(inferred_reqs).union(default_reqs)) + else: + default_reqs = None + conda_env, pip_requirements, pip_constraints = _process_pip_requirements( + default_reqs, pip_requirements, extra_pip_requirements + ) + else: + conda_env, pip_requirements, pip_constraints = _process_conda_env(conda_env) + + with path.joinpath(_CONDA_ENV_FILE_NAME).open("w") as f: + yaml.safe_dump(conda_env, stream=f, default_flow_style=False) + + if pip_constraints: + write_to(str(path.joinpath(_CONSTRAINTS_FILE_NAME)), "\n".join(pip_constraints)) + + write_to(str(path.joinpath(_REQUIREMENTS_FILE_NAME)), "\n".join(pip_requirements)) + + _PythonEnv.current().to_yaml(str(path.joinpath(_PYTHON_ENV_FILE_NAME))) + + +def _get_transformers_model_metadata(model) -> dict[str, str]: + """ + Extract metadata about the underlying Transformers model, such as the model class name + and the repository id. + + Args: + model: A SentenceTransformer model instance. + + Returns: + A dictionary containing metadata about the Transformers model. + """ + from sentence_transformers.models import Transformer + + # NB: We assume the SentenceTransformer model contains only up to one Transformer model. + for module in model.modules(): + if isinstance(module, Transformer): + model_instance = module.auto_model + return { + _TRANSFORMER_SOURCE_MODEL_NAME_KEY: _get_transformers_model_name( + model_instance.name_or_path + ), + _TRANSFORMER_MODEL_TYPE_KEY: model_instance.__class__.__name__, + } + return {} + + +def _get_transformers_model_name(model_name_or_path): + """ + Extract the Transformers model name from name_or_path attribute of a Transformer model. + + Normally the name_or_path attribute just points to the model name, but in Sentence + Transformers < 2.3.0, the library loads the Transformers model after local snapshot + download, so the name_or_path attribute points to the local filepath. + https://github.com/UKPLab/sentence-transformers/commit/9db0f205adcf315d16961fea7e9e6906cb950d43 + """ + if m := _LOCAL_SNAPSHOT_PATH_PATTERN.search(model_name_or_path): + return f"{m.group(1)}/{m.group(2)}" + return model_name_or_path + + +@docstring_version_compatibility_warning(integration_name=FLAVOR_NAME) +@format_docstring(LOG_MODEL_PARAM_DOCS.format(package_name=FLAVOR_NAME)) +def log_model( + model, + artifact_path: str | None = None, + task: str | None = None, + inference_config: dict[str, Any] | None = None, + code_paths: list[str] | None = None, + registered_model_name: str | None = None, + signature: ModelSignature | None = None, + input_example: ModelInputExample | None = None, + await_registration_for=DEFAULT_AWAIT_MAX_SLEEP_SECONDS, + pip_requirements: list[str] | str | None = None, + extra_pip_requirements: list[str] | str | None = None, + conda_env=None, + metadata: dict[str, Any] | None = None, + prompts: list[str | Prompt] | None = None, + name: str | None = None, + params: dict[str, Any] | None = None, + tags: dict[str, Any] | None = None, + model_type: str | None = None, + step: int = 0, + model_id: str | None = None, +): + """ + .. note:: + + Logging Sentence Transformers models with custom code (i.e. models that require + ``trust_remote_code=True``) is supported in MLflow 2.12.0 and above. + + Log a ``sentence_transformers`` model as an MLflow artifact for the current run. + + .. code-block:: python + + # An example of using log_model for a sentence-transformers model and architecture: + + from sentence_transformers import SentenceTransformer + import mlflow + + model = SentenceTransformer("all-MiniLM-L6-v2") + data = "MLflow is awesome!" + signature = mlflow.models.infer_signature( + model_input=data, + model_output=model.encode(data), + ) + + with mlflow.start_run(): + mlflow.sentence_transformers.log_model( + model=model, + name="sbert_model", + signature=signature, + input_example=data, + ) + + + + Args: + model: A trained ``sentence-transformers`` model. + artifact_path: Deprecated. Use `name` instead. + task: MLflow inference task type for ``sentence-transformers`` model. Candidate task type + is `llm/v1/embeddings`. + inference_config: + A dict of valid overrides that can be applied to a ``sentence-transformer`` model + instance during inference. + These arguments are used exclusively for the case of loading the model as a ``pyfunc`` + Model or for use in Spark. + These values are not applied to a returned model from a call to + ``mlflow.sentence_transformers.load_model()`` + code_paths: {{ code_paths }} + registered_model_name: If given, create a model + version under ``registered_model_name``, also creating a + registered model if one with the given name does not exist. + signature: an instance of the :py:class:`ModelSignature ` + class that describes the model's inputs and outputs. If not specified but an + ``input_example`` is supplied, a signature will be automatically inferred + based on the supplied input example and model. If both ``signature`` and + ``input_example`` are not specified or the automatic signature inference + fails, a default signature will be adopted. To prevent a signature from being + adopted, set ``signature`` to ``False``. To manually infer a model signature, + call :py:func:`infer_signature() ` on datasets + with valid model inputs and valid model outputs. + input_example: {{ input_example }} + await_registration_for: Number of seconds to wait for the model version to finish + being created and is in ``READY`` status. By default, the function + waits for five minutes. Specify 0 or None to skip waiting. + pip_requirements: {{ pip_requirements }} + extra_pip_requirements: {{ extra_pip_requirements }} + conda_env: {{ conda_env }} + metadata: {{ metadata }} + prompts: {{ prompts }} + name: {{ name }} + params: {{ params }} + tags: {{ tags }} + model_type: {{ model_type }} + step: {{ step }} + model_id: {{ model_id }} + """ + if task is not None: + metadata = _verify_task_and_update_metadata(task, metadata) + + return Model.log( + artifact_path=artifact_path, + name=name, + flavor=mlflow.sentence_transformers, + registered_model_name=registered_model_name, + await_registration_for=await_registration_for, + metadata=metadata, + model=model, + inference_config=inference_config, + conda_env=conda_env, + code_paths=code_paths, + signature=signature, + input_example=input_example, + pip_requirements=pip_requirements, + extra_pip_requirements=extra_pip_requirements, + prompts=prompts, + params=params, + tags=tags, + model_type=model_type, + step=step, + model_id=model_id, + ) + + +def _get_load_kwargs(): + import sentence_transformers + + load_kwargs = {} + # The trust_remote_code is supported since Sentence Transformers 2.3.0 + if Version(sentence_transformers.__version__) >= Version("2.3.0"): + # Always set trust_remote_code=True because we save the entire repository files in + # the model artifacts, so there is no risk of running untrusted code unless the logged + # artifact is modified by a malicious actor, which is much more broader security + # concern that even cannot be prevented by setting trust_remote_code=False. + load_kwargs["trust_remote_code"] = True + return load_kwargs + + +def _load_pyfunc(path, model_config: dict[str, Any] | None = None): + """ + Load PyFunc implementation for SentenceTransformer. Called by ``pyfunc.load_model``. + + Args: + path: Local filesystem path to the MLflow Model with the ``sentence_transformer`` flavor. + """ + import sentence_transformers + + load_kwargs = _get_load_kwargs() + model = sentence_transformers.SentenceTransformer(path, **load_kwargs) + model_config = model_config or {} + task = model_config.get("task", None) + return _SentenceTransformerModelWrapper(model, task) + + +@docstring_version_compatibility_warning(integration_name=FLAVOR_NAME) +def load_model(model_uri: str, dst_path: str | None = None): + """ + Load a ``sentence_transformers`` object from a local file or a run. + + Args: + model_uri: The location, in URI format, of the MLflow model. For example: + + - ``/Users/me/path/to/local/model`` + - ``relative/path/to/local/model`` + - ``s3://my_bucket/path/to/model`` + - ``runs://run-relative/path/to/model`` + - ``mlflow-artifacts:/path/to/model`` + + For more information about supported URI schemes, see + `Referencing Artifacts `_. + dst_path: The local filesystem path to utilize for downloading the model artifact. + This directory must already exist if provided. If unspecified, a local output + path will be created. + + Returns: + A ``sentence_transformers`` model instance + """ + + import sentence_transformers + + model_uri = str(model_uri) + + local_model_path = _download_artifact_from_uri(artifact_uri=model_uri, output_path=dst_path) + + local_model_dir = pathlib.Path(local_model_path).joinpath(SENTENCE_TRANSFORMERS_DATA_PATH) + + flavor_config = _get_flavor_configuration_from_uri(model_uri, FLAVOR_NAME, _logger) + + _add_code_from_conf_to_system_path(local_model_path, flavor_config) + + load_kwargs = _get_load_kwargs() + return sentence_transformers.SentenceTransformer(str(local_model_dir), **load_kwargs) + + +def _get_default_signature(): + """ + Generates a default signature for the ``sentence_transformers`` flavor to be applied if not + set or overridden by supplying the `signature` argument to `log_model` or `save_model`. + """ + return ModelSignature( + inputs=Schema([ColSpec("string")]), + outputs=Schema([TensorSpec(np.dtype("float64"), [-1])]), + ) + + +class _SentenceTransformerModelWrapper: + def __init__(self, model, task=None): + self.model = model + self.task = task + + def get_raw_model(self): + """ + Returns the underlying model. + """ + return self.model + + def predict(self, sentences, params: dict[str, Any] | None = None): + """ + Args: + sentences: Model input data. + params: Additional parameters to pass to the model for inference. + + Returns: + Model predictions. + """ + # When the input is a single string or a dictionary, it is transformed into a DataFrame + # with one column and row, but the encode function does not accept DataFrame input + convert_output_to_llm_v1_format = False + if type(sentences) == pd.DataFrame: + # Wrap the output to OpenAI format only when the input is dict `{"input": ... }` + if self.task and list(sentences.columns)[0] == _LLM_V1_EMBEDDING_INPUT_KEY: + convert_output_to_llm_v1_format = True + sentences = sentences.iloc[:, 0] + if type(sentences[0]) == list: + sentences = sentences[0] + + # The encode API has additional parameters that we can add as kwargs. + # See https://www.sbert.net/docs/package_reference/SentenceTransformer.html#sentence_transformers.SentenceTransformer.encode + if params: + try: + output_data = self.model.encode(sentences, **params) + except TypeError as e: + raise MlflowException.invalid_parameter_value( + "Received invalid parameter value for `params` argument" + ) from e + else: + output_data = self.model.encode(sentences) + + if convert_output_to_llm_v1_format: + output_data = postprocess_output_for_llm_v1_embedding_task( + sentences, output_data, self.model.tokenizer + ) + return output_data diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/sentence_transformers/__pycache__/__init__.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/sentence_transformers/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..84b06d8d3a8d96bd78ef46936fa54267dd44e6ce Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/sentence_transformers/__pycache__/__init__.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..b114a678da69a79a2e44b6f5b19ddaa0b6bec995 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/__init__.py @@ -0,0 +1,370 @@ +import importlib +import importlib.metadata +import os +import shlex +import sys +import textwrap +import types +import warnings + +from flask import Flask, Response, send_from_directory +from packaging.version import Version + +from mlflow.environment_variables import MLFLOW_FLASK_SERVER_SECRET_KEY +from mlflow.exceptions import MlflowException +from mlflow.server import handlers +from mlflow.server.handlers import ( + STATIC_PREFIX_ENV_VAR, + _add_static_prefix, + create_promptlab_run_handler, + gateway_proxy_handler, + get_artifact_handler, + get_logged_model_artifact_handler, + get_metric_history_bulk_handler, + get_metric_history_bulk_interval_handler, + get_model_version_artifact_handler, + get_trace_artifact_handler, + search_datasets_handler, + upload_artifact_handler, +) +from mlflow.utils.os import is_windows +from mlflow.utils.plugins import get_entry_points +from mlflow.utils.process import _exec_cmd +from mlflow.version import VERSION + +# NB: These are internal environment variables used for communication between +# the cli and the forked gunicorn processes. +BACKEND_STORE_URI_ENV_VAR = "_MLFLOW_SERVER_FILE_STORE" +REGISTRY_STORE_URI_ENV_VAR = "_MLFLOW_SERVER_REGISTRY_STORE" +ARTIFACT_ROOT_ENV_VAR = "_MLFLOW_SERVER_ARTIFACT_ROOT" +ARTIFACTS_DESTINATION_ENV_VAR = "_MLFLOW_SERVER_ARTIFACT_DESTINATION" +PROMETHEUS_EXPORTER_ENV_VAR = "prometheus_multiproc_dir" +SERVE_ARTIFACTS_ENV_VAR = "_MLFLOW_SERVER_SERVE_ARTIFACTS" +ARTIFACTS_ONLY_ENV_VAR = "_MLFLOW_SERVER_ARTIFACTS_ONLY" + +REL_STATIC_DIR = "js/build" + +app = Flask(__name__, static_folder=REL_STATIC_DIR) +IS_FLASK_V1 = Version(importlib.metadata.version("flask")) < Version("2.0") + + +for http_path, handler, methods in handlers.get_endpoints(): + app.add_url_rule(http_path, handler.__name__, handler, methods=methods) + +if os.getenv(PROMETHEUS_EXPORTER_ENV_VAR): + from mlflow.server.prometheus_exporter import activate_prometheus_exporter + + prometheus_metrics_path = os.getenv(PROMETHEUS_EXPORTER_ENV_VAR) + if not os.path.exists(prometheus_metrics_path): + os.makedirs(prometheus_metrics_path) + activate_prometheus_exporter(app) + + +# Provide a health check endpoint to ensure the application is responsive +@app.route("/health") +def health(): + return "OK", 200 + + +# Provide an endpoint to query the version of mlflow running on the server +@app.route("/version") +def version(): + return VERSION, 200 + + +# Serve the "get-artifact" route. +@app.route(_add_static_prefix("/get-artifact")) +def serve_artifacts(): + return get_artifact_handler() + + +# Serve the "model-versions/get-artifact" route. +@app.route(_add_static_prefix("/model-versions/get-artifact")) +def serve_model_version_artifact(): + return get_model_version_artifact_handler() + + +# Serve the "metrics/get-history-bulk" route. +@app.route(_add_static_prefix("/ajax-api/2.0/mlflow/metrics/get-history-bulk")) +def serve_get_metric_history_bulk(): + return get_metric_history_bulk_handler() + + +# Serve the "metrics/get-history-bulk-interval" route. +@app.route(_add_static_prefix("/ajax-api/2.0/mlflow/metrics/get-history-bulk-interval")) +def serve_get_metric_history_bulk_interval(): + return get_metric_history_bulk_interval_handler() + + +# Serve the "experiments/search-datasets" route. +@app.route(_add_static_prefix("/ajax-api/2.0/mlflow/experiments/search-datasets"), methods=["POST"]) +def serve_search_datasets(): + return search_datasets_handler() + + +# Serve the "runs/create-promptlab-run" route. +@app.route(_add_static_prefix("/ajax-api/2.0/mlflow/runs/create-promptlab-run"), methods=["POST"]) +def serve_create_promptlab_run(): + return create_promptlab_run_handler() + + +@app.route(_add_static_prefix("/ajax-api/2.0/mlflow/gateway-proxy"), methods=["POST", "GET"]) +def serve_gateway_proxy(): + return gateway_proxy_handler() + + +@app.route(_add_static_prefix("/ajax-api/2.0/mlflow/upload-artifact"), methods=["POST"]) +def serve_upload_artifact(): + return upload_artifact_handler() + + +# Serve the "/get-trace-artifact" route to allow frontend to fetch trace artifacts +# and render them in the Trace UI. The request body should contain the request_id +# of the trace. +@app.route(_add_static_prefix("/ajax-api/2.0/mlflow/get-trace-artifact"), methods=["GET"]) +def serve_get_trace_artifact(): + return get_trace_artifact_handler() + + +@app.route( + _add_static_prefix("/ajax-api/2.0/mlflow/logged-models//artifacts/files"), + methods=["GET"], +) +def serve_get_logged_model_artifact(model_id: str): + return get_logged_model_artifact_handler(model_id) + + +# We expect the react app to be built assuming it is hosted at /static-files, so that requests for +# CSS/JS resources will be made to e.g. /static-files/main.css and we can handle them here. +# The files are hashed based on source code, so ok to send Cache-Control headers via max_age. +@app.route(_add_static_prefix("/static-files/")) +def serve_static_file(path): + if IS_FLASK_V1: + return send_from_directory(app.static_folder, path, cache_timeout=2419200) + else: + return send_from_directory(app.static_folder, path, max_age=2419200) + + +# Serve the index.html for the React App for all other routes. +@app.route(_add_static_prefix("/")) +def serve(): + if os.path.exists(os.path.join(app.static_folder, "index.html")): + return send_from_directory(app.static_folder, "index.html") + + text = textwrap.dedent( + """ + Unable to display MLflow UI - landing page (index.html) not found. + + You are very likely running the MLflow server using a source installation of the Python MLflow + package. + + If you are a developer making MLflow source code changes and intentionally running a source + installation of MLflow, you can view the UI by running the Javascript dev server: + https://github.com/mlflow/mlflow/blob/master/CONTRIBUTING.md#running-the-javascript-dev-server + + Otherwise, uninstall MLflow via 'pip uninstall mlflow', reinstall an official MLflow release + from PyPI via 'pip install mlflow', and rerun the MLflow server. + """ + ) + return Response(text, mimetype="text/plain") + + +def _find_app(app_name: str) -> str: + apps = get_entry_points("mlflow.app") + for app in apps: + if app.name == app_name: + return app.value + + raise MlflowException( + f"Failed to find app '{app_name}'. Available apps: {[a.name for a in apps]}" + ) + + +def _is_factory(app: str) -> bool: + """ + Returns True if the given app is a factory function, False otherwise. + + Args: + app: The app to check, e.g. "mlflow.server.app:app + """ + module, obj_name = app.rsplit(":", 1) + mod = importlib.import_module(module) + obj = getattr(mod, obj_name) + return isinstance(obj, types.FunctionType) + + +def get_app_client(app_name: str, *args, **kwargs): + """ + Instantiate a client provided by an app. + + Args: + app_name: The app name defined in `setup.py`, e.g., "basic-auth". + args: Additional arguments passed to the app client constructor. + kwargs: Additional keyword arguments passed to the app client constructor. + + Returns: + An app client instance. + """ + clients = get_entry_points("mlflow.app.client") + for client in clients: + if client.name == app_name: + cls = client.load() + return cls(*args, **kwargs) + + raise MlflowException( + f"Failed to find client for '{app_name}'. Available clients: {[c.name for c in clients]}" + ) + + +def _build_waitress_command(waitress_opts, host, port, app_name, is_factory): + opts = shlex.split(waitress_opts) if waitress_opts else [] + return [ + sys.executable, + "-m", + "waitress", + *opts, + f"--host={host}", + f"--port={port}", + "--ident=mlflow", + *(["--call"] if is_factory else []), + app_name, + ] + + +def _build_gunicorn_command(gunicorn_opts, host, port, workers, app_name): + bind_address = f"{host}:{port}" + opts = shlex.split(gunicorn_opts) if gunicorn_opts else [] + return [ + sys.executable, + "-m", + "gunicorn", + *opts, + "-b", + bind_address, + "-w", + str(workers), + app_name, + ] + + +def _build_uvicorn_command(uvicorn_opts, host, port, workers, app_name): + """Build command to run uvicorn server.""" + opts = shlex.split(uvicorn_opts) if uvicorn_opts else [] + return [ + sys.executable, + "-m", + "uvicorn", + *opts, + "--host", + host, + "--port", + str(port), + "--workers", + str(workers), + app_name, + ] + + +def _run_server( + *, + file_store_path, + registry_store_uri, + default_artifact_root, + serve_artifacts, + artifacts_only, + artifacts_destination, + host, + port, + static_prefix=None, + workers=None, + gunicorn_opts=None, + waitress_opts=None, + expose_prometheus=None, + app_name=None, + uvicorn_opts=None, +): + """ + Run the MLflow server, wrapping it in gunicorn, uvicorn, or waitress on windows + + Args: + static_prefix: If set, the index.html asset will be served from the path static_prefix. + If left None, the index.html asset will be served from the root path. + uvicorn_opts: Additional options for uvicorn server. + + Returns: + None + """ + env_map = {} + if file_store_path: + env_map[BACKEND_STORE_URI_ENV_VAR] = file_store_path + if registry_store_uri: + env_map[REGISTRY_STORE_URI_ENV_VAR] = registry_store_uri + if default_artifact_root: + env_map[ARTIFACT_ROOT_ENV_VAR] = default_artifact_root + if serve_artifacts: + env_map[SERVE_ARTIFACTS_ENV_VAR] = "true" + if artifacts_only: + env_map[ARTIFACTS_ONLY_ENV_VAR] = "true" + if artifacts_destination: + env_map[ARTIFACTS_DESTINATION_ENV_VAR] = artifacts_destination + if static_prefix: + env_map[STATIC_PREFIX_ENV_VAR] = static_prefix + + if expose_prometheus: + env_map[PROMETHEUS_EXPORTER_ENV_VAR] = expose_prometheus + + secret_key = MLFLOW_FLASK_SERVER_SECRET_KEY.get() + if secret_key: + env_map[MLFLOW_FLASK_SERVER_SECRET_KEY.name] = secret_key + + # Determine which server we're using (only one should be true) + using_gunicorn = gunicorn_opts is not None + using_waitress = waitress_opts is not None + using_uvicorn = not using_gunicorn and not using_waitress + + if app_name is None: + is_factory = False + # For uvicorn, use the FastAPI app; for gunicorn/waitress, use the Flask app + app = "mlflow.server.fastapi_app:app" if using_uvicorn else f"{__name__}:app" + else: + app = _find_app(app_name) + is_factory = _is_factory(app) + # `waitress` doesn't support `()` syntax for factory functions. + # Instead, we need to use the `--call` flag. + # Don't use () syntax if we're using uvicorn + use_factory_syntax = not is_windows() and is_factory and not using_uvicorn + app = f"{app}()" if use_factory_syntax else app + + # Determine which server to use + if using_uvicorn: + # Use uvicorn (default when no specific server options are provided) + full_command = _build_uvicorn_command(uvicorn_opts, host, port, workers or 4, app) + elif using_waitress: + # Use waitress if explicitly requested + warnings.warn( + "We recommend using uvicorn for improved performance. " + "Please use uvicorn by default or specify '--uvicorn-opts' " + "instead of '--waitress-opts'.", + FutureWarning, + stacklevel=2, + ) + full_command = _build_waitress_command(waitress_opts, host, port, app, is_factory) + elif using_gunicorn: + # Use gunicorn if explicitly requested + if sys.platform == "win32": + raise MlflowException( + "Gunicorn is not supported on Windows. " + "Please use uvicorn (default) or specify '--waitress-opts'." + ) + warnings.warn( + "We recommend using uvicorn for improved performance. " + "Please use uvicorn by default or specify '--uvicorn-opts' " + "instead of '--gunicorn-opts'.", + FutureWarning, + stacklevel=2, + ) + full_command = _build_gunicorn_command(gunicorn_opts, host, port, workers or 4, app) + else: + # This shouldn't happen given the logic in CLI, but handle it just in case + raise MlflowException("No server configuration specified.") + _exec_cmd(full_command, extra_env=env_map, capture_output=False) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/__pycache__/__init__.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..eb5c15b52fce752695f69d358db7c456b837d3e3 Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/__pycache__/__init__.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/__pycache__/fastapi_app.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/__pycache__/fastapi_app.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..26485ea634082fabdaa5f2b9803f83a628fb0268 Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/__pycache__/fastapi_app.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/__pycache__/prometheus_exporter.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/__pycache__/prometheus_exporter.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..075a97907d899660ddbb38004fe6e6886e9a7c24 Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/__pycache__/prometheus_exporter.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/__pycache__/validation.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/__pycache__/validation.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..b3c285cbfc1c1775d17084c88b79ad5b17a49c9d Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/__pycache__/validation.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/auth/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/auth/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..9fe7a00fda70699c9a046c8309bbba2d62410354 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/auth/__init__.py @@ -0,0 +1,1226 @@ +""" +Usage +----- + +.. code-block:: bash + + mlflow server --app-name basic-auth +""" + +import functools +import importlib +import logging +import re +from typing import Any, Callable + +import sqlalchemy +from flask import ( + Flask, + Request, + Response, + flash, + jsonify, + make_response, + render_template_string, + request, +) +from werkzeug.datastructures import Authorization + +from mlflow import MlflowException +from mlflow.entities import Experiment +from mlflow.entities.logged_model import LoggedModel +from mlflow.entities.model_registry import RegisteredModel +from mlflow.environment_variables import MLFLOW_FLASK_SERVER_SECRET_KEY +from mlflow.protos.databricks_pb2 import ( + BAD_REQUEST, + INTERNAL_ERROR, + INVALID_PARAMETER_VALUE, + RESOURCE_DOES_NOT_EXIST, + ErrorCode, +) +from mlflow.protos.model_registry_pb2 import ( + CreateModelVersion, + CreateRegisteredModel, + DeleteModelVersion, + DeleteModelVersionTag, + DeleteRegisteredModel, + DeleteRegisteredModelAlias, + DeleteRegisteredModelTag, + GetLatestVersions, + GetModelVersion, + GetModelVersionByAlias, + GetModelVersionDownloadUri, + GetRegisteredModel, + RenameRegisteredModel, + SearchRegisteredModels, + SetModelVersionTag, + SetRegisteredModelAlias, + SetRegisteredModelTag, + TransitionModelVersionStage, + UpdateModelVersion, + UpdateRegisteredModel, +) +from mlflow.protos.service_pb2 import ( + CreateExperiment, + # Routes for logged models + CreateLoggedModel, + CreateRun, + DeleteExperiment, + DeleteExperimentTag, + DeleteLoggedModel, + DeleteLoggedModelTag, + DeleteRun, + DeleteTag, + FinalizeLoggedModel, + GetExperiment, + GetExperimentByName, + GetLoggedModel, + GetMetricHistory, + GetRun, + ListArtifacts, + LogBatch, + LogLoggedModelParamsRequest, + LogMetric, + LogModel, + LogParam, + RestoreExperiment, + RestoreRun, + SearchExperiments, + SearchLoggedModels, + SetExperimentTag, + SetLoggedModelTags, + SetTag, + UpdateExperiment, + UpdateRun, +) +from mlflow.server import app +from mlflow.server.auth.config import read_auth_config +from mlflow.server.auth.logo import MLFLOW_LOGO +from mlflow.server.auth.permissions import MANAGE, Permission, get_permission +from mlflow.server.auth.routes import ( + CREATE_EXPERIMENT_PERMISSION, + CREATE_REGISTERED_MODEL_PERMISSION, + CREATE_USER, + CREATE_USER_UI, + DELETE_EXPERIMENT_PERMISSION, + DELETE_REGISTERED_MODEL_PERMISSION, + DELETE_USER, + GET_EXPERIMENT_PERMISSION, + GET_REGISTERED_MODEL_PERMISSION, + GET_USER, + HOME, + SIGNUP, + UPDATE_EXPERIMENT_PERMISSION, + UPDATE_REGISTERED_MODEL_PERMISSION, + UPDATE_USER_ADMIN, + UPDATE_USER_PASSWORD, +) +from mlflow.server.auth.sqlalchemy_store import SqlAlchemyStore +from mlflow.server.handlers import ( + _get_model_registry_store, + _get_request_message, + _get_tracking_store, + catch_mlflow_exception, + get_endpoints, +) +from mlflow.store.entities import PagedList +from mlflow.utils.proto_json_utils import message_to_json, parse_dict +from mlflow.utils.rest_utils import _REST_API_PATH_PREFIX +from mlflow.utils.search_utils import SearchUtils + +try: + from flask_wtf.csrf import CSRFProtect +except ImportError as e: + raise ImportError( + "The MLflow basic auth app requires the Flask-WTF package to perform CSRF " + "validation. Please run `pip install mlflow[auth]` to install it." + ) from e + +_logger = logging.getLogger(__name__) + +auth_config = read_auth_config() +store = SqlAlchemyStore() + + +def is_unprotected_route(path: str) -> bool: + return path.startswith(("/static", "/favicon.ico", "/health")) + + +def make_basic_auth_response() -> Response: + res = make_response( + "You are not authenticated. Please see " + "https://www.mlflow.org/docs/latest/auth/index.html#authenticating-to-mlflow " + "on how to authenticate." + ) + res.status_code = 401 + res.headers["WWW-Authenticate"] = 'Basic realm="mlflow"' + return res + + +def make_forbidden_response() -> Response: + res = make_response("Permission denied") + res.status_code = 403 + return res + + +def _get_request_param(param: str) -> str: + if request.method == "GET": + args = request.args + elif request.method in ("POST", "PATCH"): + args = request.json + elif request.method == "DELETE": + args = request.json if request.is_json else request.args + else: + raise MlflowException( + f"Unsupported HTTP method '{request.method}'", + BAD_REQUEST, + ) + + args = args | (request.view_args or {}) + if param not in args: + # Special handling for run_id + if param == "run_id": + return _get_request_param("run_uuid") + raise MlflowException( + f"Missing value for required parameter '{param}'. " + "See the API docs for more information about request parameters.", + INVALID_PARAMETER_VALUE, + ) + return args[param] + + +def _get_permission_from_store_or_default(store_permission_func: Callable[[], str]) -> Permission: + """ + Attempts to get permission from store, + and returns default permission if no record is found. + """ + try: + perm = store_permission_func() + except MlflowException as e: + if e.error_code == ErrorCode.Name(RESOURCE_DOES_NOT_EXIST): + perm = auth_config.default_permission + else: + raise + return get_permission(perm) + + +def _get_permission_from_experiment_id() -> Permission: + experiment_id = _get_request_param("experiment_id") + username = authenticate_request().username + return _get_permission_from_store_or_default( + lambda: store.get_experiment_permission(experiment_id, username).permission + ) + + +_EXPERIMENT_ID_PATTERN = re.compile(r"^(\d+)/") + + +def _get_experiment_id_from_view_args(): + if artifact_path := request.view_args.get("artifact_path"): + if m := _EXPERIMENT_ID_PATTERN.match(artifact_path): + return m.group(1) + return None + + +def _get_permission_from_experiment_id_artifact_proxy() -> Permission: + if experiment_id := _get_experiment_id_from_view_args(): + username = authenticate_request().username + return _get_permission_from_store_or_default( + lambda: store.get_experiment_permission(experiment_id, username).permission + ) + return get_permission(auth_config.default_permission) + + +def _get_permission_from_experiment_name() -> Permission: + experiment_name = _get_request_param("experiment_name") + store_exp = _get_tracking_store().get_experiment_by_name(experiment_name) + if store_exp is None: + raise MlflowException( + f"Could not find experiment with name {experiment_name}", + error_code=RESOURCE_DOES_NOT_EXIST, + ) + username = authenticate_request().username + return _get_permission_from_store_or_default( + lambda: store.get_experiment_permission(store_exp.experiment_id, username).permission + ) + + +def _get_permission_from_run_id() -> Permission: + # run permissions inherit from parent resource (experiment) + # so we just get the experiment permission + run_id = _get_request_param("run_id") + run = _get_tracking_store().get_run(run_id) + experiment_id = run.info.experiment_id + username = authenticate_request().username + return _get_permission_from_store_or_default( + lambda: store.get_experiment_permission(experiment_id, username).permission + ) + + +def _get_permission_from_model_id() -> Permission: + # logged model permissions inherit from parent resource (experiment) + model_id = _get_request_param("model_id") + model = _get_tracking_store().get_logged_model(model_id) + experiment_id = model.experiment_id + username = authenticate_request().username + return _get_permission_from_store_or_default( + lambda: store.get_experiment_permission(experiment_id, username).permission + ) + + +def _get_permission_from_registered_model_name() -> Permission: + name = _get_request_param("name") + username = authenticate_request().username + return _get_permission_from_store_or_default( + lambda: store.get_registered_model_permission(name, username).permission + ) + + +def validate_can_read_experiment(): + return _get_permission_from_experiment_id().can_read + + +def validate_can_read_experiment_by_name(): + return _get_permission_from_experiment_name().can_read + + +def validate_can_update_experiment(): + return _get_permission_from_experiment_id().can_update + + +def validate_can_delete_experiment(): + return _get_permission_from_experiment_id().can_delete + + +def validate_can_manage_experiment(): + return _get_permission_from_experiment_id().can_manage + + +def validate_can_read_experiment_artifact_proxy(): + return _get_permission_from_experiment_id_artifact_proxy().can_read + + +def validate_can_update_experiment_artifact_proxy(): + return _get_permission_from_experiment_id_artifact_proxy().can_update + + +def validate_can_delete_experiment_artifact_proxy(): + return _get_permission_from_experiment_id_artifact_proxy().can_manage + + +# Runs +def validate_can_read_run(): + return _get_permission_from_run_id().can_read + + +def validate_can_update_run(): + return _get_permission_from_run_id().can_update + + +def validate_can_delete_run(): + return _get_permission_from_run_id().can_delete + + +def validate_can_manage_run(): + return _get_permission_from_run_id().can_manage + + +# Logged models +def validate_can_read_logged_model(): + return _get_permission_from_model_id().can_read + + +def validate_can_update_logged_model(): + return _get_permission_from_model_id().can_update + + +def validate_can_delete_logged_model(): + return _get_permission_from_model_id().can_delete + + +def validate_can_manage_logged_model(): + return _get_permission_from_model_id().can_manage + + +# Registered models +def validate_can_read_registered_model(): + return _get_permission_from_registered_model_name().can_read + + +def validate_can_update_registered_model(): + return _get_permission_from_registered_model_name().can_update + + +def validate_can_delete_registered_model(): + return _get_permission_from_registered_model_name().can_delete + + +def validate_can_manage_registered_model(): + return _get_permission_from_registered_model_name().can_manage + + +def sender_is_admin(): + """Validate if the sender is admin""" + username = authenticate_request().username + return store.get_user(username).is_admin + + +def username_is_sender(): + """Validate if the request username is the sender""" + username = _get_request_param("username") + sender = authenticate_request().username + return username == sender + + +def validate_can_read_user(): + return username_is_sender() + + +def validate_can_create_user(): + # only admins can create user, but admins won't reach this validator + return False + + +def validate_can_update_user_password(): + return username_is_sender() + + +def validate_can_update_user_admin(): + # only admins can update, but admins won't reach this validator + return False + + +def validate_can_delete_user(): + # only admins can delete, but admins won't reach this validator + return False + + +BEFORE_REQUEST_HANDLERS = { + # Routes for experiments + GetExperiment: validate_can_read_experiment, + GetExperimentByName: validate_can_read_experiment_by_name, + DeleteExperiment: validate_can_delete_experiment, + RestoreExperiment: validate_can_delete_experiment, + UpdateExperiment: validate_can_update_experiment, + SetExperimentTag: validate_can_update_experiment, + DeleteExperimentTag: validate_can_update_experiment, + # Routes for runs + CreateRun: validate_can_update_experiment, + GetRun: validate_can_read_run, + DeleteRun: validate_can_delete_run, + RestoreRun: validate_can_delete_run, + UpdateRun: validate_can_update_run, + LogMetric: validate_can_update_run, + LogBatch: validate_can_update_run, + LogModel: validate_can_update_run, + SetTag: validate_can_update_run, + DeleteTag: validate_can_update_run, + LogParam: validate_can_update_run, + GetMetricHistory: validate_can_read_run, + ListArtifacts: validate_can_read_run, + # Routes for model registry + GetRegisteredModel: validate_can_read_registered_model, + DeleteRegisteredModel: validate_can_delete_registered_model, + UpdateRegisteredModel: validate_can_update_registered_model, + RenameRegisteredModel: validate_can_update_registered_model, + GetLatestVersions: validate_can_read_registered_model, + CreateModelVersion: validate_can_update_registered_model, + GetModelVersion: validate_can_read_registered_model, + DeleteModelVersion: validate_can_delete_registered_model, + UpdateModelVersion: validate_can_update_registered_model, + TransitionModelVersionStage: validate_can_update_registered_model, + GetModelVersionDownloadUri: validate_can_read_registered_model, + SetRegisteredModelTag: validate_can_update_registered_model, + DeleteRegisteredModelTag: validate_can_update_registered_model, + SetModelVersionTag: validate_can_update_registered_model, + DeleteModelVersionTag: validate_can_delete_registered_model, + SetRegisteredModelAlias: validate_can_update_registered_model, + DeleteRegisteredModelAlias: validate_can_delete_registered_model, + GetModelVersionByAlias: validate_can_read_registered_model, +} + + +def get_before_request_handler(request_class): + return BEFORE_REQUEST_HANDLERS.get(request_class) + + +def _re_compile_path(path: str) -> re.Pattern: + """ + Convert a path with angle brackets to a regex pattern. For example, + "/api/2.0/experiments/" becomes "/api/2.0/experiments/([^/]+)". + """ + return re.compile(re.sub(r"<([^>]+)>", r"([^/]+)", path)) + + +BEFORE_REQUEST_VALIDATORS = { + (http_path, method): handler + for http_path, handler, methods in get_endpoints(get_before_request_handler) + for method in methods +} + +BEFORE_REQUEST_VALIDATORS.update( + { + (SIGNUP, "GET"): validate_can_create_user, + (GET_USER, "GET"): validate_can_read_user, + (CREATE_USER, "POST"): validate_can_create_user, + (UPDATE_USER_PASSWORD, "PATCH"): validate_can_update_user_password, + (UPDATE_USER_ADMIN, "PATCH"): validate_can_update_user_admin, + (DELETE_USER, "DELETE"): validate_can_delete_user, + (GET_EXPERIMENT_PERMISSION, "GET"): validate_can_manage_experiment, + (CREATE_EXPERIMENT_PERMISSION, "POST"): validate_can_manage_experiment, + (UPDATE_EXPERIMENT_PERMISSION, "PATCH"): validate_can_manage_experiment, + (DELETE_EXPERIMENT_PERMISSION, "DELETE"): validate_can_manage_experiment, + (GET_REGISTERED_MODEL_PERMISSION, "GET"): validate_can_manage_registered_model, + (CREATE_REGISTERED_MODEL_PERMISSION, "POST"): validate_can_manage_registered_model, + (UPDATE_REGISTERED_MODEL_PERMISSION, "PATCH"): validate_can_manage_registered_model, + (DELETE_REGISTERED_MODEL_PERMISSION, "DELETE"): validate_can_manage_registered_model, + } +) + + +LOGGED_MODEL_BEFORE_REQUEST_HANDLERS = { + CreateLoggedModel: validate_can_update_experiment, + GetLoggedModel: validate_can_read_logged_model, + DeleteLoggedModel: validate_can_delete_logged_model, + FinalizeLoggedModel: validate_can_update_logged_model, + DeleteLoggedModelTag: validate_can_delete_logged_model, + SetLoggedModelTags: validate_can_update_logged_model, + LogLoggedModelParamsRequest: validate_can_update_logged_model, +} + + +def get_logged_model_before_request_handler(request_class): + return LOGGED_MODEL_BEFORE_REQUEST_HANDLERS.get(request_class) + + +LOGGED_MODEL_BEFORE_REQUEST_VALIDATORS = { + # Paths for logged models contains path parameters (e.g. /mlflow/logged-models/) + (_re_compile_path(http_path), method): handler + for http_path, handler, methods in get_endpoints(get_logged_model_before_request_handler) + for method in methods +} + + +def _is_proxy_artifact_path(path: str) -> bool: + return path.startswith(f"{_REST_API_PATH_PREFIX}/mlflow-artifacts/artifacts/") + + +def _get_proxy_artifact_validator( + method: str, view_args: dict[str, Any] | None +) -> Callable[[], bool] | None: + if view_args is None: + return validate_can_read_experiment_artifact_proxy # List + + return { + "GET": validate_can_read_experiment_artifact_proxy, # Download + "PUT": validate_can_update_experiment_artifact_proxy, # Upload + "DELETE": validate_can_delete_experiment_artifact_proxy, # Delete + }.get(method) + + +def authenticate_request() -> Authorization | Response: + """Use configured authorization function to get request authorization.""" + auth_func = get_auth_func(auth_config.authorization_function) + return auth_func() + + +@functools.lru_cache(maxsize=None) +def get_auth_func(authorization_function: str) -> Callable[[], Authorization | Response]: + """ + Import and return the specified authorization function. + + Args: + authorization_function: A string of the form "module.submodule:auth_func" + """ + mod_name, fn_name = authorization_function.split(":", 1) + module = importlib.import_module(mod_name) + return getattr(module, fn_name) + + +def authenticate_request_basic_auth() -> Authorization | Response: + """Authenticate the request using basic auth.""" + if request.authorization is None: + return make_basic_auth_response() + + username = request.authorization.username + password = request.authorization.password + if store.authenticate_user(username, password): + return request.authorization + else: + # let user attempt login again + return make_basic_auth_response() + + +def _find_validator(req: Request) -> Callable[[], bool] | None: + """ + Finds the validator matching the request path and method. + """ + if "/mlflow/logged-models" in req.path: + # logged model routes are not registered in the app + # so we need to check them manually + return next( + ( + v + for (pat, method), v in LOGGED_MODEL_BEFORE_REQUEST_VALIDATORS.items() + if pat.fullmatch(req.path) and method == req.method + ), + None, + ) + else: + return BEFORE_REQUEST_VALIDATORS.get((req.path, req.method)) + + +@catch_mlflow_exception +def _before_request(): + if is_unprotected_route(request.path): + return + + authorization = authenticate_request() + if isinstance(authorization, Response): + return authorization + elif not isinstance(authorization, Authorization): + raise MlflowException( + f"Unsupported result type from {auth_config.authorization_function}: " + f"'{type(authorization).__name__}'", + INTERNAL_ERROR, + ) + + # admins don't need to be authorized + if sender_is_admin(): + return + + # authorization + if validator := _find_validator(request): + if not validator(): + return make_forbidden_response() + elif _is_proxy_artifact_path(request.path): + if validator := _get_proxy_artifact_validator(request.method, request.view_args): + if not validator(): + return make_forbidden_response() + + +def set_can_manage_experiment_permission(resp: Response): + response_message = CreateExperiment.Response() + parse_dict(resp.json, response_message) + experiment_id = response_message.experiment_id + username = authenticate_request().username + store.create_experiment_permission(experiment_id, username, MANAGE.name) + + +def set_can_manage_registered_model_permission(resp: Response): + response_message = CreateRegisteredModel.Response() + parse_dict(resp.json, response_message) + name = response_message.registered_model.name + username = authenticate_request().username + store.create_registered_model_permission(name, username, MANAGE.name) + + +def delete_can_manage_registered_model_permission(resp: Response): + """ + Delete registered model permission when the model is deleted. + + We need to do this because the primary key of the registered model is the name, + unlike the experiment where the primary key is experiment_id (UUID). Therefore, + we have to delete the permission record when the model is deleted otherwise it + conflicts with the new model registered with the same name. + """ + # Get model name from request context because it's not available in the response + name = request.get_json(force=True, silent=True)["name"] + username = authenticate_request().username + store.delete_registered_model_permission(name, username) + + +def filter_search_experiments(resp: Response): + if sender_is_admin(): + return + + response_message = SearchExperiments.Response() + parse_dict(resp.json, response_message) + + # fetch permissions + username = authenticate_request().username + perms = store.list_experiment_permissions(username) + can_read = {p.experiment_id: get_permission(p.permission).can_read for p in perms} + default_can_read = get_permission(auth_config.default_permission).can_read + + # filter out unreadable + for e in list(response_message.experiments): + if not can_read.get(e.experiment_id, default_can_read): + response_message.experiments.remove(e) + + # re-fetch to fill max results + request_message = _get_request_message(SearchExperiments()) + while ( + len(response_message.experiments) < request_message.max_results + and response_message.next_page_token != "" + ): + refetched: PagedList[Experiment] = _get_tracking_store().search_experiments( + view_type=request_message.view_type, + max_results=request_message.max_results, + order_by=request_message.order_by, + filter_string=request_message.filter, + page_token=response_message.next_page_token, + ) + refetched = refetched[: request_message.max_results - len(response_message.experiments)] + if len(refetched) == 0: + response_message.next_page_token = "" + break + + refetched_readable_proto = [ + e.to_proto() for e in refetched if can_read.get(e.experiment_id, default_can_read) + ] + response_message.experiments.extend(refetched_readable_proto) + + # recalculate next page token + start_offset = SearchUtils.parse_start_offset_from_page_token( + response_message.next_page_token + ) + final_offset = start_offset + len(refetched) + response_message.next_page_token = SearchUtils.create_page_token(final_offset) + + resp.data = message_to_json(response_message) + + +def filter_search_logged_models(resp: Response) -> None: + """ + Filter out unreadable logged models from the search results. + """ + from mlflow.utils.search_utils import SearchLoggedModelsPaginationToken as Token + + if sender_is_admin(): + return + + response_proto = SearchLoggedModels.Response() + parse_dict(resp.json, response_proto) + + # fetch permissions + username = authenticate_request().username + perms = store.list_experiment_permissions(username) + can_read = {p.experiment_id: get_permission(p.permission).can_read for p in perms} + default_can_read = get_permission(auth_config.default_permission).can_read + + # Remove unreadable models + for m in list(response_proto.models): + if not can_read.get(m.info.experiment_id, default_can_read): + response_proto.models.remove(m) + + request_proto = _get_request_message(SearchLoggedModels()) + max_results = request_proto.max_results + # These parameters won't change in the loop + params = { + "experiment_ids": list(request_proto.experiment_ids), + "filter_string": request_proto.filter or None, + "order_by": ( + [ + { + "field_name": ob.field_name, + "ascending": ob.ascending, + "dataset_name": ob.dataset_name, + "dataset_digest": ob.dataset_digest, + } + for ob in request_proto.order_by + ] + if request_proto.order_by + else None + ), + } + next_page_token = response_proto.next_page_token or None + tracking_store = _get_tracking_store() + while len(response_proto.models) < max_results and next_page_token is not None: + batch: PagedList[LoggedModel] = tracking_store.search_logged_models( + max_results=max_results, page_token=next_page_token, **params + ) + is_last_page = batch.token is None + offset = Token.decode(next_page_token).offset if next_page_token else 0 + last_index = len(batch) - 1 + for index, model in enumerate(batch): + if not can_read.get(model.experiment_id, default_can_read): + continue + response_proto.models.append(model.to_proto()) + if len(response_proto.models) >= max_results: + next_page_token = ( + None + if is_last_page and index == last_index + else Token(offset=offset + index + 1, **params).encode() + ) + break + else: + # If we reach here, it means we have not reached the max results. + next_page_token = ( + None if is_last_page else Token(offset=offset + max_results, **params).encode() + ) + + if next_page_token: + response_proto.next_page_token = next_page_token + resp.data = message_to_json(response_proto) + + +def filter_search_registered_models(resp: Response): + if sender_is_admin(): + return + + response_message = SearchRegisteredModels.Response() + parse_dict(resp.json, response_message) + + # fetch permissions + username = authenticate_request().username + perms = store.list_registered_model_permissions(username) + can_read = {p.name: get_permission(p.permission).can_read for p in perms} + default_can_read = get_permission(auth_config.default_permission).can_read + + # filter out unreadable + for rm in list(response_message.registered_models): + if not can_read.get(rm.name, default_can_read): + response_message.registered_models.remove(rm) + + # re-fetch to fill max results + request_message = _get_request_message(SearchRegisteredModels()) + while ( + len(response_message.registered_models) < request_message.max_results + and response_message.next_page_token != "" + ): + refetched: PagedList[RegisteredModel] = ( + _get_model_registry_store().search_registered_models( + filter_string=request_message.filter, + max_results=request_message.max_results, + order_by=request_message.order_by, + page_token=response_message.next_page_token, + ) + ) + refetched = refetched[ + : request_message.max_results - len(response_message.registered_models) + ] + if len(refetched) == 0: + response_message.next_page_token = "" + break + + refetched_readable_proto = [ + rm.to_proto() for rm in refetched if can_read.get(rm.name, default_can_read) + ] + response_message.registered_models.extend(refetched_readable_proto) + + # recalculate next page token + start_offset = SearchUtils.parse_start_offset_from_page_token( + response_message.next_page_token + ) + final_offset = start_offset + len(refetched) + response_message.next_page_token = SearchUtils.create_page_token(final_offset) + + resp.data = message_to_json(response_message) + + +def rename_registered_model_permission(resp: Response): + """ + A model registry can be assigned to multiple users with different permissions. + + Changing the model registry name must be propagated to all users. + """ + # get registry model name before update + data = request.get_json(force=True, silent=True) + store.rename_registered_model_permissions(data.get("name"), data.get("new_name")) + + +AFTER_REQUEST_PATH_HANDLERS = { + CreateExperiment: set_can_manage_experiment_permission, + CreateRegisteredModel: set_can_manage_registered_model_permission, + DeleteRegisteredModel: delete_can_manage_registered_model_permission, + SearchExperiments: filter_search_experiments, + SearchLoggedModels: filter_search_logged_models, + SearchRegisteredModels: filter_search_registered_models, + RenameRegisteredModel: rename_registered_model_permission, +} + + +def get_after_request_handler(request_class): + return AFTER_REQUEST_PATH_HANDLERS.get(request_class) + + +AFTER_REQUEST_HANDLERS = { + (http_path, method): handler + for http_path, handler, methods in get_endpoints(get_after_request_handler) + for method in methods + if handler is not None and "/graphql" not in http_path +} + + +@catch_mlflow_exception +def _after_request(resp: Response): + if 400 <= resp.status_code < 600: + return resp + + if handler := AFTER_REQUEST_HANDLERS.get((request.path, request.method)): + handler(resp) + return resp + + +def create_admin_user(username, password): + if not store.has_user(username): + try: + store.create_user(username, password, is_admin=True) + _logger.info( + f"Created admin user '{username}'. " + "It is recommended that you set a new password as soon as possible " + f"on {UPDATE_USER_PASSWORD}." + ) + except MlflowException as e: + if isinstance(e.__cause__, sqlalchemy.exc.IntegrityError): + # When multiple workers are starting up at the same time, it's possible + # that they try to create the admin user at the same time and one of them + # will succeed while the others will fail with an IntegrityError. + return + raise + + +def alert(href: str): + return render_template_string( + r""" + +""", + href=href, + ) + + +def signup(): + return render_template_string( + r""" + + +
+ +
+ {% autoescape false %} + {{ mlflow_logo }} + {% endautoescape %} +
+ +
+ +
+ +
+ +
+
+ + +""", + mlflow_logo=MLFLOW_LOGO, + users_route=CREATE_USER_UI, + ) + + +@catch_mlflow_exception +def create_user_ui(csrf): + csrf.protect() + content_type = request.headers.get("Content-Type") + if content_type == "application/x-www-form-urlencoded": + username = request.form["username"] + password = request.form["password"] + + if not username or not password: + message = "Username and password cannot be empty." + return make_response(message, 400) + + if store.has_user(username): + flash(f"Username has already been taken: {username}") + return alert(href=SIGNUP) + + store.create_user(username, password) + flash(f"Successfully signed up user: {username}") + return alert(href=HOME) + else: + message = "Invalid content type. Must be application/x-www-form-urlencoded" + return make_response(message, 400) + + +@catch_mlflow_exception +def create_user(): + content_type = request.headers.get("Content-Type") + if content_type == "application/json": + username = _get_request_param("username") + password = _get_request_param("password") + + if not username or not password: + message = "Username and password cannot be empty." + return make_response(message, 400) + + user = store.create_user(username, password) + return jsonify({"user": user.to_json()}) + else: + message = "Invalid content type. Must be application/json" + return make_response(message, 400) + + +@catch_mlflow_exception +def get_user(): + username = _get_request_param("username") + user = store.get_user(username) + return jsonify({"user": user.to_json()}) + + +@catch_mlflow_exception +def update_user_password(): + username = _get_request_param("username") + password = _get_request_param("password") + store.update_user(username, password=password) + return make_response({}) + + +@catch_mlflow_exception +def update_user_admin(): + username = _get_request_param("username") + is_admin = _get_request_param("is_admin") + store.update_user(username, is_admin=is_admin) + return make_response({}) + + +@catch_mlflow_exception +def delete_user(): + username = _get_request_param("username") + store.delete_user(username) + return make_response({}) + + +@catch_mlflow_exception +def create_experiment_permission(): + experiment_id = _get_request_param("experiment_id") + username = _get_request_param("username") + permission = _get_request_param("permission") + ep = store.create_experiment_permission(experiment_id, username, permission) + return jsonify({"experiment_permission": ep.to_json()}) + + +@catch_mlflow_exception +def get_experiment_permission(): + experiment_id = _get_request_param("experiment_id") + username = _get_request_param("username") + ep = store.get_experiment_permission(experiment_id, username) + return make_response({"experiment_permission": ep.to_json()}) + + +@catch_mlflow_exception +def update_experiment_permission(): + experiment_id = _get_request_param("experiment_id") + username = _get_request_param("username") + permission = _get_request_param("permission") + store.update_experiment_permission(experiment_id, username, permission) + return make_response({}) + + +@catch_mlflow_exception +def delete_experiment_permission(): + experiment_id = _get_request_param("experiment_id") + username = _get_request_param("username") + store.delete_experiment_permission(experiment_id, username) + return make_response({}) + + +@catch_mlflow_exception +def create_registered_model_permission(): + name = _get_request_param("name") + username = _get_request_param("username") + permission = _get_request_param("permission") + rmp = store.create_registered_model_permission(name, username, permission) + return make_response({"registered_model_permission": rmp.to_json()}) + + +@catch_mlflow_exception +def get_registered_model_permission(): + name = _get_request_param("name") + username = _get_request_param("username") + rmp = store.get_registered_model_permission(name, username) + return make_response({"registered_model_permission": rmp.to_json()}) + + +@catch_mlflow_exception +def update_registered_model_permission(): + name = _get_request_param("name") + username = _get_request_param("username") + permission = _get_request_param("permission") + store.update_registered_model_permission(name, username, permission) + return make_response({}) + + +@catch_mlflow_exception +def delete_registered_model_permission(): + name = _get_request_param("name") + username = _get_request_param("username") + store.delete_registered_model_permission(name, username) + return make_response({}) + + +def create_app(app: Flask = app): + """ + A factory to enable authentication and authorization for the MLflow server. + + Args: + app: The Flask app to enable authentication and authorization for. + + Returns: + The app with authentication and authorization enabled. + """ + _logger.warning( + "This feature is still experimental and may change in a future release without warning" + ) + + # a secret key is required for flashing, and also for + # CSRF protection. it's important that this is a static key, + # otherwise CSRF validation won't work across workers. + secret_key = MLFLOW_FLASK_SERVER_SECRET_KEY.get() + if not secret_key: + raise MlflowException( + "A static secret key needs to be set for CSRF protection. Please set the " + "`MLFLOW_FLASK_SERVER_SECRET_KEY` environment variable before starting the " + "server. For example:\n\n" + "export MLFLOW_FLASK_SERVER_SECRET_KEY='my-secret-key'\n\n" + "If you are using multiple servers, please ensure this key is consistent between " + "them, in order to prevent validation issues." + ) + app.secret_key = secret_key + + # we only need to protect the CREATE_USER_UI route, since that's + # the only browser-accessible route. the rest are client / REST + # APIs that do not have access to the CSRF token for validation + app.config["WTF_CSRF_CHECK_DEFAULT"] = False + csrf = CSRFProtect() + csrf.init_app(app) + + store.init_db(auth_config.database_uri) + create_admin_user(auth_config.admin_username, auth_config.admin_password) + + app.add_url_rule( + rule=SIGNUP, + view_func=signup, + methods=["GET"], + ) + app.add_url_rule( + rule=CREATE_USER_UI, + view_func=lambda: create_user_ui(csrf), + methods=["POST"], + ) + app.add_url_rule( + rule=CREATE_USER, + view_func=create_user, + methods=["POST"], + ) + app.add_url_rule( + rule=GET_USER, + view_func=get_user, + methods=["GET"], + ) + app.add_url_rule( + rule=UPDATE_USER_PASSWORD, + view_func=update_user_password, + methods=["PATCH"], + ) + app.add_url_rule( + rule=UPDATE_USER_ADMIN, + view_func=update_user_admin, + methods=["PATCH"], + ) + app.add_url_rule( + rule=DELETE_USER, + view_func=delete_user, + methods=["DELETE"], + ) + app.add_url_rule( + rule=CREATE_EXPERIMENT_PERMISSION, + view_func=create_experiment_permission, + methods=["POST"], + ) + app.add_url_rule( + rule=GET_EXPERIMENT_PERMISSION, + view_func=get_experiment_permission, + methods=["GET"], + ) + app.add_url_rule( + rule=UPDATE_EXPERIMENT_PERMISSION, + view_func=update_experiment_permission, + methods=["PATCH"], + ) + app.add_url_rule( + rule=DELETE_EXPERIMENT_PERMISSION, + view_func=delete_experiment_permission, + methods=["DELETE"], + ) + app.add_url_rule( + rule=CREATE_REGISTERED_MODEL_PERMISSION, + view_func=create_registered_model_permission, + methods=["POST"], + ) + app.add_url_rule( + rule=GET_REGISTERED_MODEL_PERMISSION, + view_func=get_registered_model_permission, + methods=["GET"], + ) + app.add_url_rule( + rule=UPDATE_REGISTERED_MODEL_PERMISSION, + view_func=update_registered_model_permission, + methods=["PATCH"], + ) + app.add_url_rule( + rule=DELETE_REGISTERED_MODEL_PERMISSION, + view_func=delete_registered_model_permission, + methods=["DELETE"], + ) + + app.before_request(_before_request) + app.after_request(_after_request) + + return app diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/auth/__main__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/auth/__main__.py new file mode 100644 index 0000000000000000000000000000000000000000..a581a7ad8be22f8de84a5f01e8fc411be26a2bfc --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/auth/__main__.py @@ -0,0 +1,4 @@ +from mlflow.server.auth.cli import commands + +if __name__ == "__main__": + commands() diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/auth/__pycache__/__init__.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/auth/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..7702e17fedb36747a99bb443c75158a0af5b85eb Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/auth/__pycache__/__init__.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/auth/__pycache__/__main__.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/auth/__pycache__/__main__.cpython-312.pyc new file mode 100644 index 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a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/auth/cli.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/auth/cli.py new file mode 100644 index 0000000000000000000000000000000000000000..18c98ea9cb64c8d2ca7798473bc0e5a2e6ad7025 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/auth/cli.py @@ -0,0 +1,11 @@ +import click + +from mlflow.server.auth.db import cli as db_cli + + +@click.group() +def commands(): + pass + + +commands.add_command(db_cli.commands) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/auth/client.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/auth/client.py new file mode 100644 index 0000000000000000000000000000000000000000..4c1119419b3a52c27b4d4a17aabf78abc9ee8122 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/auth/client.py @@ -0,0 +1,537 @@ +from mlflow.server.auth.entities import ExperimentPermission, RegisteredModelPermission, User +from mlflow.server.auth.routes import ( + CREATE_EXPERIMENT_PERMISSION, + CREATE_REGISTERED_MODEL_PERMISSION, + CREATE_USER, + DELETE_EXPERIMENT_PERMISSION, + DELETE_REGISTERED_MODEL_PERMISSION, + DELETE_USER, + GET_EXPERIMENT_PERMISSION, + GET_REGISTERED_MODEL_PERMISSION, + GET_USER, + UPDATE_EXPERIMENT_PERMISSION, + UPDATE_REGISTERED_MODEL_PERMISSION, + UPDATE_USER_ADMIN, + UPDATE_USER_PASSWORD, +) +from mlflow.utils.credentials import get_default_host_creds +from mlflow.utils.rest_utils import http_request_safe + + +class AuthServiceClient: + """ + Client of an MLflow Tracking Server that enabled the default basic authentication plugin. + It is recommended to use :py:func:`mlflow.server.get_app_client()` to instantiate this class. + See https://mlflow.org/docs/latest/auth.html for more information. + """ + + def __init__(self, tracking_uri: str): + """ + Args: + tracking_uri: Address of local or remote tracking server. + """ + self.tracking_uri = tracking_uri + + def _request(self, endpoint, method, **kwargs): + host_creds = get_default_host_creds(self.tracking_uri) + resp = http_request_safe(host_creds, endpoint, method, **kwargs) + return resp.json() + + def create_user(self, username: str, password: str): + """ + Create a new user. + + Args: + username: The username. + password: The user's password. Must not be empty string. + + Raises: + mlflow.exceptions.RestException: if the username is already taken. + + Returns: + A single :py:class:`mlflow.server.auth.entities.User` object. + + .. code-block:: python + :caption: Example + + from mlflow.server.auth.client import AuthServiceClient + + client = AuthServiceClient("tracking_uri") + user = client.create_user("newuser", "newpassword") + print(f"user_id: {user.id}") + print(f"username: {user.username}") + print(f"password_hash: {user.password_hash}") + print(f"is_admin: {user.is_admin}") + + .. code-block:: text + :caption: Output + + user_id: 3 + username: newuser + password_hash: REDACTED + is_admin: False + """ + resp = self._request( + CREATE_USER, + "POST", + json={"username": username, "password": password}, + ) + return User.from_json(resp["user"]) + + def get_user(self, username: str): + """ + Get a user with a specific username. + + Args: + username: The username. + + Raises: + mlflow.exceptions.RestException: if the user does not exist + + Returns: + A single :py:class:`mlflow.server.auth.entities.User` object. + + .. code-block:: bash + :caption: Example + + export MLFLOW_TRACKING_USERNAME=admin + export MLFLOW_TRACKING_PASSWORD=password + + .. code-block:: python + + from mlflow.server.auth.client import AuthServiceClient + + client = AuthServiceClient("tracking_uri") + client.create_user("newuser", "newpassword") + user = client.get_user("newuser") + + print(f"user_id: {user.id}") + print(f"username: {user.username}") + print(f"password_hash: {user.password_hash}") + print(f"is_admin: {user.is_admin}") + + .. code-block:: text + :caption: Output + + user_id: 3 + username: newuser + password_hash: REDACTED + is_admin: False + """ + resp = self._request( + GET_USER, + "GET", + params={"username": username}, + ) + return User.from_json(resp["user"]) + + def update_user_password(self, username: str, password: str): + """ + Update the password of a specific user. + + Args: + username: The username. + password: The new password. + + Raises: + mlflow.exceptions.RestException: if the user does not exist + + .. code-block:: bash + :caption: Example + + export MLFLOW_TRACKING_USERNAME=admin + export MLFLOW_TRACKING_PASSWORD=password + + .. code-block:: python + + from mlflow.server.auth.client import AuthServiceClient + + client = AuthServiceClient("tracking_uri") + client.create_user("newuser", "newpassword") + + client.update_user_password("newuser", "anotherpassword") + """ + self._request( + UPDATE_USER_PASSWORD, + "PATCH", + json={"username": username, "password": password}, + ) + + def update_user_admin(self, username: str, is_admin: bool): + """ + Update the admin status of a specific user. + + Args: + username: The username. + is_admin: The new admin status. + + Raises: + mlflow.exceptions.RestException: if the user does not exist + + .. code-block:: bash + :caption: Example + + export MLFLOW_TRACKING_USERNAME=admin + export MLFLOW_TRACKING_PASSWORD=password + + .. code-block:: python + + from mlflow.server.auth.client import AuthServiceClient + + client = AuthServiceClient("tracking_uri") + client.create_user("newuser", "newpassword") + + client.update_user_admin("newuser", True) + """ + self._request( + UPDATE_USER_ADMIN, + "PATCH", + json={"username": username, "is_admin": is_admin}, + ) + + def delete_user(self, username: str): + """ + Delete a specific user. + + Args: + username: The username. + + Raises: + mlflow.exceptions.RestException: if the user does not exist + + .. code-block:: bash + :caption: Example + + export MLFLOW_TRACKING_USERNAME=admin + export MLFLOW_TRACKING_PASSWORD=password + + .. code-block:: python + + from mlflow.server.auth.client import AuthServiceClient + + client = AuthServiceClient("tracking_uri") + client.create_user("newuser", "newpassword") + + client.delete_user("newuser") + """ + self._request( + DELETE_USER, + "DELETE", + json={"username": username}, + ) + + def create_experiment_permission(self, experiment_id: str, username: str, permission: str): + """ + Create a permission on an experiment for a user. + + Args: + experiment_id: The id of the experiment. + username: The username. + permission: Permission to grant. Must be one of "READ", "EDIT", "MANAGE" and + "NO_PERMISSIONS". + + Raises: + mlflow.exceptions.RestException: if the user does not exist, or a permission already + exists for this experiment user pair, or if the permission is invalid. Does not + require ``experiment_id`` to be an existing experiment. + + Returns: + A single :py:class:`mlflow.server.auth.entities.ExperimentPermission` object. + + .. code-block:: bash + :caption: Example + + export MLFLOW_TRACKING_USERNAME=admin + export MLFLOW_TRACKING_PASSWORD=password + + .. code-block:: python + + from mlflow.server.auth.client import AuthServiceClient + + client = AuthServiceClient("tracking_uri") + client.create_user("newuser", "newpassword") + ep = client.create_experiment_permission("myexperiment", "newuser", "READ") + + print(f"experiment_id: {ep.experiment_id}") + print(f"user_id: {ep.user_id}") + print(f"permission: {ep.permission}") + + .. code-block:: text + :caption: Output + + experiment_id: myexperiment + user_id: 3 + permission: READ + """ + resp = self._request( + CREATE_EXPERIMENT_PERMISSION, + "POST", + json={"experiment_id": experiment_id, "username": username, "permission": permission}, + ) + return ExperimentPermission.from_json(resp["experiment_permission"]) + + def get_experiment_permission(self, experiment_id: str, username: str): + """ + Get an experiment permission for a user. + + Args: + experiment_id: The id of the experiment. + username: The username. + + Raises: + mlflow.exceptions.RestException: if the user does not exist, + or no permission exists for this experiment user pair. + Note that the default permission will still be effective even if + no permission exists. + + Returns: + A single :py:class:`mlflow.server.auth.entities.ExperimentPermission` object. + + .. code-block:: bash + :caption: Example + + export MLFLOW_TRACKING_USERNAME=admin + export MLFLOW_TRACKING_PASSWORD=password + + .. code-block:: python + + from mlflow.server.auth.client import AuthServiceClient + + client = AuthServiceClient("tracking_uri") + client.create_user("newuser", "newpassword") + client.create_experiment_permission("myexperiment", "newuser", "READ") + ep = client.get_experiment_permission("myexperiment", "newuser") + print(f"experiment_id: {ep.experiment_id}") + print(f"user_id: {ep.user_id}") + print(f"permission: {ep.permission}") + + .. code-block:: text + :caption: Output + + experiment_id: myexperiment + user_id: 3 + permission: READ + """ + resp = self._request( + GET_EXPERIMENT_PERMISSION, + "GET", + params={"experiment_id": experiment_id, "username": username}, + ) + return ExperimentPermission.from_json(resp["experiment_permission"]) + + def update_experiment_permission(self, experiment_id: str, username: str, permission: str): + """ + Update an existing experiment permission for a user. + + Args: + experiment_id: The id of the experiment. + username: The username. + permission: New permission to grant. Must be one of "READ", "EDIT", "MANAGE" and + "NO_PERMISSIONS". + + Raises: + mlflow.exceptions.RestException: if the user does not exist, or no permission exists for + this experiment user pair, or if the permission is invalid + + .. code-block:: bash + :caption: Example + + export MLFLOW_TRACKING_USERNAME=admin + export MLFLOW_TRACKING_PASSWORD=password + + .. code-block:: python + + from mlflow.server.auth.client import AuthServiceClient + + client = AuthServiceClient("tracking_uri") + client.create_user("newuser", "newpassword") + client.create_experiment_permission("myexperiment", "newuser", "READ") + client.update_experiment_permission("myexperiment", "newuser", "EDIT") + """ + self._request( + UPDATE_EXPERIMENT_PERMISSION, + "PATCH", + json={"experiment_id": experiment_id, "username": username, "permission": permission}, + ) + + def delete_experiment_permission(self, experiment_id: str, username: str): + """ + Delete an existing experiment permission for a user. + + Args: + experiment_id: The id of the experiment. + username: The username. + + Raises: + mlflow.exceptions.RestException: if the user does not exist, or no permission exists for + this experiment user pair, or if the permission is invalid. + Note that the default permission will still be effective even + after the permission has been deleted. + + + .. code-block:: bash + :caption: Example + + export MLFLOW_TRACKING_USERNAME=admin + export MLFLOW_TRACKING_PASSWORD=password + + .. code-block:: python + + from mlflow.server.auth.client import AuthServiceClient + + client = AuthServiceClient("tracking_uri") + client.create_user("newuser", "newpassword") + client.create_experiment_permission("myexperiment", "newuser", "READ") + client.delete_experiment_permission("myexperiment", "newuser") + """ + self._request( + DELETE_EXPERIMENT_PERMISSION, + "DELETE", + json={"experiment_id": experiment_id, "username": username}, + ) + + def create_registered_model_permission(self, name: str, username: str, permission: str): + """ + Create a permission on an registered model for a user. + + Args: + name: The name of the registered model. + username: The username. + permission: Permission to grant. Must be one of "READ", "EDIT", "MANAGE" and + "NO_PERMISSIONS". + + Raises: + mlflow.exceptions.RestException: if the user does not exist, + or a permission already exists for this registered model user pair, + or if the permission is invalid. + Does not require ``name`` to be an existing registered model. + + Returns: + A single :py:class:`mlflow.server.auth.entities.RegisteredModelPermission` object. + """ + resp = self._request( + CREATE_REGISTERED_MODEL_PERMISSION, + "POST", + json={"name": name, "username": username, "permission": permission}, + ) + return RegisteredModelPermission.from_json(resp["registered_model_permission"]) + + def get_registered_model_permission(self, name: str, username: str): + """ + Get an registered model permission for a user. + + Args: + name: The name of the registered model. + username: The username. + + Raises: + mlflow.exceptions.RestException: if the user does not + exist, or no permission exists for this registered model user pair. Note that the + default permission will still be effective even if no permission exists. + + Returns: + A single :py:class:`mlflow.server.auth.entities.RegisteredModelPermission` object. + + .. code-block:: bash + :caption: Example + + export MLFLOW_TRACKING_USERNAME=admin + export MLFLOW_TRACKING_PASSWORD=password + + .. code-block:: python + + from mlflow.server.auth.client import AuthServiceClient + + client = AuthServiceClient("tracking_uri") + client.create_user("newuser", "newpassword") + client.create_registered_model_permission("myregisteredmodel", "newuser", "READ") + rmp = client.get_registered_model_permission("myregisteredmodel", "newuser") + + print(f"name: {rmp.name}") + print(f"user_id: {rmp.user_id}") + print(f"permission: {rmp.permission}") + + .. code-block:: text + :caption: Output + + name: myregisteredmodel + user_id: 3 + permission: READ + """ + resp = self._request( + GET_REGISTERED_MODEL_PERMISSION, + "GET", + params={"name": name, "username": username}, + ) + return RegisteredModelPermission.from_json(resp["registered_model_permission"]) + + def update_registered_model_permission(self, name: str, username: str, permission: str): + """ + Update an existing registered model permission for a user. + + Args: + name: The name of the registered model. + username: The username. + permission: New permission to grant. Must be one of "READ", "EDIT", "MANAGE" and + "NO_PERMISSIONS". + + Raises: + mlflow.exceptions.RestException: if the user does not exist, or no permission exists for + this registered model user pair, or if the permission is invalid. + + .. code-block:: bash + :caption: Example + + export MLFLOW_TRACKING_USERNAME=admin + export MLFLOW_TRACKING_PASSWORD=password + + .. code-block:: python + + from mlflow.server.auth.client import AuthServiceClient + + client = AuthServiceClient("tracking_uri") + client.create_user("newuser", "newpassword") + client.create_registered_model_permission("myregisteredmodel", "newuser", "READ") + client.update_registered_model_permission("myregisteredmodel", "newuser", "EDIT") + """ + self._request( + UPDATE_REGISTERED_MODEL_PERMISSION, + "PATCH", + json={"name": name, "username": username, "permission": permission}, + ) + + def delete_registered_model_permission(self, name: str, username: str): + """ + Delete an existing registered model permission for a user. + + Args: + name: The name of the registered model. + username: The username. + + Raises: + mlflow.exceptions.RestException: if the user does not exist, + or no permission exists for this registered model user pair, + or if the permission is invalid. + Note that the default permission will still be effective even + after the permission has been deleted. + + .. code-block:: bash + :caption: Example + + export MLFLOW_TRACKING_USERNAME=admin + export MLFLOW_TRACKING_PASSWORD=password + + .. code-block:: python + + from mlflow.server.auth.client import AuthServiceClient + + client = AuthServiceClient("tracking_uri") + client.create_user("newuser", "newpassword") + client.create_registered_model_permission("myregisteredmodel", "newuser", "READ") + client.delete_registered_model_permission("myregisteredmodel", "newuser") + """ + self._request( + DELETE_REGISTERED_MODEL_PERMISSION, + "DELETE", + json={"name": name, "username": username}, + ) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/auth/config.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/auth/config.py new file mode 100644 index 0000000000000000000000000000000000000000..b7a27f2eba67d2061bc7f76d9f55b7876f0c4d93 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/auth/config.py @@ -0,0 +1,34 @@ +import configparser +from pathlib import Path +from typing import NamedTuple + +from mlflow.environment_variables import MLFLOW_AUTH_CONFIG_PATH + + +class AuthConfig(NamedTuple): + default_permission: str + database_uri: str + admin_username: str + admin_password: str + authorization_function: str + + +def _get_auth_config_path() -> str: + return ( + MLFLOW_AUTH_CONFIG_PATH.get() or Path(__file__).parent.joinpath("basic_auth.ini").resolve() + ) + + +def read_auth_config() -> AuthConfig: + config_path = _get_auth_config_path() + config = configparser.ConfigParser() + config.read(config_path) + return AuthConfig( + default_permission=config["mlflow"]["default_permission"], + database_uri=config["mlflow"]["database_uri"], + admin_username=config["mlflow"]["admin_username"], + admin_password=config["mlflow"]["admin_password"], + authorization_function=config["mlflow"].get( + "authorization_function", "mlflow.server.auth:authenticate_request_basic_auth" + ), + ) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/auth/db/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/auth/db/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/auth/db/__pycache__/__init__.cpython-312.pyc 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import utils + + +@click.group(name="db") +def commands(): + pass + + +@commands.command() +@click.option("--url", required=True) +@click.option("--revision", default="head") +def upgrade(url: str, revision: str) -> None: + engine = sqlalchemy.create_engine(url) + utils.migrate(engine, revision) + engine.dispose() diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/auth/db/migrations/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/auth/db/migrations/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/auth/db/migrations/__pycache__/__init__.cpython-312.pyc 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see https://alembic.sqlalchemy.org/en/latest/tutorial.html#editing-the-ini-file +# for all available tokens +# file_template = %%(year)d_%%(month).2d_%%(day).2d_%%(hour).2d%%(minute).2d-%%(rev)s_%%(slug)s + +# sys.path path, will be prepended to sys.path if present. +# defaults to the current working directory. +prepend_sys_path = . + +# timezone to use when rendering the date within the migration file +# as well as the filename. +# If specified, requires the python-dateutil library that can be +# installed by adding `alembic[tz]` to the pip requirements +# string value is passed to dateutil.tz.gettz() +# leave blank for localtime +# timezone = + +# max length of characters to apply to the +# "slug" field +# truncate_slug_length = 40 + +# set to 'true' to run the environment during +# the 'revision' command, regardless of autogenerate +# revision_environment = false + +# set to 'true' to allow .pyc and .pyo files without +# a source .py file to be detected as revisions in the +# versions/ directory +# sourceless = false + +# version location specification; This defaults +# to migration/versions. When using multiple version +# directories, initial revisions must be specified with --version-path. +# The path separator used here should be the separator specified by "version_path_separator" below. +# version_locations = %(here)s/bar:%(here)s/bat:migration/versions + +# version path separator; As mentioned above, this is the character used to split +# version_locations. The default within new alembic.ini files is "os", which uses os.pathsep. +# If this key is omitted entirely, it falls back to the legacy behavior of splitting on spaces and/or commas. +# Valid values for version_path_separator are: +# +# version_path_separator = : +# version_path_separator = ; +# version_path_separator = space +version_path_separator = os # Use os.pathsep. Default configuration used for new projects. + +# set to 'true' to search source files recursively +# in each "version_locations" directory +# new in Alembic version 1.10 +# recursive_version_locations = false + +# the output encoding used when revision files +# are written from script.py.mako +# output_encoding = utf-8 + +sqlalchemy.url = + + +[post_write_hooks] +# post_write_hooks defines scripts or Python functions that are run +# on newly generated revision scripts. See the documentation for further +# detail and examples + +# format using "black" - use the console_scripts runner, against the "black" entrypoint +# hooks = black +# black.type = console_scripts +# black.entrypoint = black +# black.options = -l 79 REVISION_SCRIPT_FILENAME + +# Logging configuration +[loggers] +keys = root,sqlalchemy,alembic + +[handlers] +keys = console + +[formatters] +keys = generic + +[logger_root] +level = WARN +handlers = console +qualname = + +[logger_sqlalchemy] +level = WARN +handlers = +qualname = sqlalchemy.engine + +[logger_alembic] +level = INFO +handlers = +qualname = alembic + +[handler_console] +class = StreamHandler +args = (sys.stderr,) +level = NOTSET +formatter = generic + +[formatter_generic] +format = %(levelname)-5.5s [%(name)s] %(message)s +datefmt = %H:%M:%S diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/auth/db/migrations/env.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/auth/db/migrations/env.py new file mode 100644 index 0000000000000000000000000000000000000000..602431fb37c0227da4d35ddfc473be106e9b46c1 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/auth/db/migrations/env.py @@ -0,0 +1,76 @@ +from logging.config import fileConfig + +from alembic import context +from sqlalchemy import engine_from_config, pool + +from mlflow.server.auth.db.models import Base + +# this is the Alembic Config object, which provides +# access to the values within the .ini file in use. +config = context.config + +# Interpret the config file for Python logging. +# This line sets up loggers basically. +if config.config_file_name is not None: + fileConfig(config.config_file_name) + +# add your model's MetaData object here +# for 'autogenerate' support +# from myapp import mymodel +# target_metadata = mymodel.Base.metadata +target_metadata = Base.metadata + +# other values from the config, defined by the needs of env.py, +# can be acquired: +# my_important_option = config.get_main_option("my_important_option") +# ... etc. + + +def run_migrations_offline() -> None: + """Run migrations in 'offline' mode. + + This configures the context with just a URL + and not an Engine, though an Engine is acceptable + here as well. By skipping the Engine creation + we don't even need a DBAPI to be available. + + Calls to context.execute() here emit the given string to the + script output. + + """ + url = config.get_main_option("sqlalchemy.url") + context.configure( + url=url, + target_metadata=target_metadata, + literal_binds=True, + dialect_opts={"paramstyle": "named"}, + ) + + with context.begin_transaction(): + context.run_migrations() + + +def run_migrations_online() -> None: + """Run migrations in 'online' mode. + + In this scenario we need to create an Engine + and associate a connection with the context. + + """ + connectable = engine_from_config( + config.get_section(config.config_ini_section, {}), + prefix="sqlalchemy.", + poolclass=pool.NullPool, + ) + + with connectable.connect() as connection: + context.configure(connection=connection, target_metadata=target_metadata) + + with context.begin_transaction(): + context.run_migrations() + + +if context.is_offline_mode(): + run_migrations_offline() +else: + run_migrations_online() diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/auth/db/migrations/versions/8606fa83a998_initial_migration.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/auth/db/migrations/versions/8606fa83a998_initial_migration.py new file mode 100644 index 0000000000000000000000000000000000000000..b25ee18a67060572b75f10fd7fd3f7cc4a0df804 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/auth/db/migrations/versions/8606fa83a998_initial_migration.py @@ -0,0 +1,51 @@ +"""initial_migration + +Revision ID: 8606fa83a998 +Revises: +Create Date: 2023-07-07 23:30:50.921970 + +""" + +import sqlalchemy as sa +from alembic import op + +# revision identifiers, used by Alembic. +revision = "8606fa83a998" +down_revision = None +branch_labels = None +depends_on = None + + +def upgrade() -> None: + op.create_table( + "users", + sa.Column("id", sa.Integer(), nullable=False, primary_key=True), + sa.Column("username", sa.String(length=255), nullable=True), + sa.Column("password_hash", sa.String(length=255), nullable=True), + sa.Column("is_admin", sa.Boolean(), nullable=True), + sa.UniqueConstraint("username"), + ) + op.create_table( + "experiment_permissions", + sa.Column("id", sa.Integer(), nullable=False, primary_key=True), + sa.Column("experiment_id", sa.String(length=255), nullable=False), + sa.Column("user_id", sa.Integer(), nullable=False), + sa.Column("permission", sa.String(length=255), nullable=True), + sa.ForeignKeyConstraint(["user_id"], ["users.id"], name="fk_user_id"), + sa.UniqueConstraint("experiment_id", "user_id", name="unique_experiment_user"), + ) + op.create_table( + "registered_model_permissions", + sa.Column("id", sa.Integer(), nullable=False, primary_key=True), + sa.Column("name", sa.String(length=255), nullable=False), + sa.Column("user_id", sa.Integer(), nullable=False), + sa.Column("permission", sa.String(length=255), nullable=True), + sa.ForeignKeyConstraint(["user_id"], ["users.id"], name="fk_user_id"), + sa.UniqueConstraint("name", "user_id", name="unique_name_user"), + ) + + +def downgrade() -> None: + op.drop_table("registered_model_permissions") + op.drop_table("experiment_permissions") + op.drop_table("users") diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/auth/db/migrations/versions/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/auth/db/migrations/versions/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/auth/db/migrations/versions/__pycache__/8606fa83a998_initial_migration.cpython-312.pyc 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b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/auth/db/migrations/versions/__pycache__/__init__.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/auth/db/models.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/auth/db/models.py new file mode 100644 index 0000000000000000000000000000000000000000..0ba3b11b70282c26fa16391d0a2194ebc8f61133 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/auth/db/models.py @@ -0,0 +1,67 @@ +from sqlalchemy import ( + Boolean, + Column, + ForeignKey, + Integer, + String, + UniqueConstraint, +) +from sqlalchemy.orm import declarative_base, relationship + +from mlflow.server.auth.entities import ExperimentPermission, RegisteredModelPermission, User + +Base = declarative_base() + + +class SqlUser(Base): + __tablename__ = "users" + id = Column(Integer(), primary_key=True) + username = Column(String(255), unique=True) + password_hash = Column(String(255)) + is_admin = Column(Boolean, default=False) + experiment_permissions = relationship("SqlExperimentPermission", backref="users") + registered_model_permissions = relationship("SqlRegisteredModelPermission", backref="users") + + def to_mlflow_entity(self): + return User( + id_=self.id, + username=self.username, + password_hash=self.password_hash, + is_admin=self.is_admin, + experiment_permissions=[p.to_mlflow_entity() for p in self.experiment_permissions], + registered_model_permissions=[ + p.to_mlflow_entity() for p in self.registered_model_permissions + ], + ) + + +class SqlExperimentPermission(Base): + __tablename__ = "experiment_permissions" + id = Column(Integer(), primary_key=True) + experiment_id = Column(String(255), nullable=False) + user_id = Column(Integer, ForeignKey("users.id"), nullable=False) + permission = Column(String(255)) + __table_args__ = (UniqueConstraint("experiment_id", "user_id", name="unique_experiment_user"),) + + def to_mlflow_entity(self): + return ExperimentPermission( + experiment_id=self.experiment_id, + user_id=self.user_id, + permission=self.permission, + ) + + +class SqlRegisteredModelPermission(Base): + __tablename__ = "registered_model_permissions" + id = Column(Integer(), primary_key=True) + name = Column(String(255), nullable=False) + user_id = Column(Integer, ForeignKey("users.id"), nullable=False) + permission = Column(String(255)) + __table_args__ = (UniqueConstraint("name", "user_id", name="unique_name_user"),) + + def to_mlflow_entity(self): + return RegisteredModelPermission( + name=self.name, + user_id=self.user_id, + permission=self.permission, + ) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/auth/db/utils.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/auth/db/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..e57c8d0b4725b38b334545748ed79263122075e5 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/auth/db/utils.py @@ -0,0 +1,37 @@ +from pathlib import Path + +from alembic.command import upgrade +from alembic.config import Config +from alembic.migration import MigrationContext +from alembic.script import ScriptDirectory +from sqlalchemy.engine.base import Engine + + +def _get_alembic_dir() -> str: + return Path(__file__).parent / "migrations" + + +def _get_alembic_config(url: str) -> Config: + alembic_dir = _get_alembic_dir() + alembic_ini_path = alembic_dir / "alembic.ini" + alembic_cfg = Config(alembic_ini_path) + alembic_cfg.set_main_option("script_location", str(alembic_dir)) + url = url.replace("%", "%%") # Same as here: https://github.com/mlflow/mlflow/issues/1487 + alembic_cfg.set_main_option("sqlalchemy.url", url) + return alembic_cfg + + +def migrate(engine: Engine, revision: str) -> None: + alembic_cfg = _get_alembic_config(engine.url.render_as_string(hide_password=False)) + with engine.begin() as conn: + alembic_cfg.attributes["connection"] = conn + upgrade(alembic_cfg, revision) + + +def migrate_if_needed(engine: Engine, revision: str) -> None: + alembic_cfg = _get_alembic_config(engine.url.render_as_string(hide_password=False)) + script_dir = ScriptDirectory.from_config(alembic_cfg) + with engine.begin() as conn: + context = MigrationContext.configure(conn) + if context.get_current_revision() != script_dir.get_current_head(): + upgrade(alembic_cfg, revision) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/auth/entities.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/auth/entities.py new file mode 100644 index 0000000000000000000000000000000000000000..eeb611256ce903dc107e4c2e54b4291ab31f6a61 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/auth/entities.py @@ -0,0 +1,165 @@ +class User: + def __init__( + self, + id_, + username, + password_hash, + is_admin, + experiment_permissions=None, + registered_model_permissions=None, + ): + self._id = id_ + self._username = username + self._password_hash = password_hash + self._is_admin = is_admin + self._experiment_permissions = experiment_permissions + self._registered_model_permissions = registered_model_permissions + + @property + def id(self): + return self._id + + @property + def username(self): + return self._username + + @property + def password_hash(self): + return self._password_hash + + @property + def is_admin(self): + return self._is_admin + + @is_admin.setter + def is_admin(self, is_admin): + self._is_admin = is_admin + + @property + def experiment_permissions(self): + return self._experiment_permissions + + @experiment_permissions.setter + def experiment_permissions(self, experiment_permissions): + self._experiment_permissions = experiment_permissions + + @property + def registered_model_permissions(self): + return self._registered_model_permissions + + @registered_model_permissions.setter + def registered_model_permissions(self, registered_model_permissions): + self._registered_model_permissions = registered_model_permissions + + def to_json(self): + return { + "id": self.id, + "username": self.username, + "is_admin": self.is_admin, + "experiment_permissions": [p.to_json() for p in self.experiment_permissions], + "registered_model_permissions": [ + p.to_json() for p in self.registered_model_permissions + ], + } + + @classmethod + def from_json(cls, dictionary): + return cls( + id_=dictionary["id"], + username=dictionary["username"], + password_hash="REDACTED", + is_admin=dictionary["is_admin"], + experiment_permissions=[ + ExperimentPermission.from_json(p) for p in dictionary["experiment_permissions"] + ], + registered_model_permissions=[ + RegisteredModelPermission.from_json(p) + for p in dictionary["registered_model_permissions"] + ], + ) + + +class ExperimentPermission: + def __init__( + self, + experiment_id, + user_id, + permission, + ): + self._experiment_id = experiment_id + self._user_id = user_id + self._permission = permission + + @property + def experiment_id(self): + return self._experiment_id + + @property + def user_id(self): + return self._user_id + + @property + def permission(self): + return self._permission + + @permission.setter + def permission(self, permission): + self._permission = permission + + def to_json(self): + return { + "experiment_id": self.experiment_id, + "user_id": self.user_id, + "permission": self.permission, + } + + @classmethod + def from_json(cls, dictionary): + return cls( + experiment_id=dictionary["experiment_id"], + user_id=dictionary["user_id"], + permission=dictionary["permission"], + ) + + +class RegisteredModelPermission: + def __init__( + self, + name, + user_id, + permission, + ): + self._name = name + self._user_id = user_id + self._permission = permission + + @property + def name(self): + return self._name + + @property + def user_id(self): + return self._user_id + + @property + def permission(self): + return self._permission + + @permission.setter + def permission(self, permission): + self._permission = permission + + def to_json(self): + return { + "name": self.name, + "user_id": self.user_id, + "permission": self.permission, + } + + @classmethod + def from_json(cls, dictionary): + return cls( + name=dictionary["name"], + user_id=dictionary["user_id"], + permission=dictionary["permission"], + ) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/auth/logo.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/auth/logo.py new file mode 100644 index 0000000000000000000000000000000000000000..0f33c294bb44aa884cd808fdd1532b24d7d44fcb --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/auth/logo.py @@ -0,0 +1,14 @@ +# ruff: noqa: E501 +MLFLOW_LOGO = """ + + + + + + + + + + + +""" diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/auth/permissions.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/auth/permissions.py new file mode 100644 index 0000000000000000000000000000000000000000..0a61544271cbeafd13a81849f8575cbe10a895d1 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/auth/permissions.py @@ -0,0 +1,65 @@ +from dataclasses import dataclass + +from mlflow import MlflowException +from mlflow.protos.databricks_pb2 import INVALID_PARAMETER_VALUE + + +@dataclass +class Permission: + name: str + can_read: bool + can_update: bool + can_delete: bool + can_manage: bool + + +READ = Permission( + name="READ", + can_read=True, + can_update=False, + can_delete=False, + can_manage=False, +) + +EDIT = Permission( + name="EDIT", + can_read=True, + can_update=True, + can_delete=False, + can_manage=False, +) + +MANAGE = Permission( + name="MANAGE", + can_read=True, + can_update=True, + can_delete=True, + can_manage=True, +) + +NO_PERMISSIONS = Permission( + name="NO_PERMISSIONS", + can_read=False, + can_update=False, + can_delete=False, + can_manage=False, +) + +ALL_PERMISSIONS = { + READ.name: READ, + EDIT.name: EDIT, + MANAGE.name: MANAGE, + NO_PERMISSIONS.name: NO_PERMISSIONS, +} + + +def get_permission(permission: str) -> Permission: + return ALL_PERMISSIONS[permission] + + +def _validate_permission(permission: str): + if permission not in ALL_PERMISSIONS: + raise MlflowException( + f"Invalid permission '{permission}'. Valid permissions are: {tuple(ALL_PERMISSIONS)}", + INVALID_PARAMETER_VALUE, + ) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/auth/routes.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/auth/routes.py new file mode 100644 index 0000000000000000000000000000000000000000..5073561cd95589bdf51c560cb032125d5d2b5c6b --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/auth/routes.py @@ -0,0 +1,18 @@ +from mlflow.server.handlers import _get_rest_path + +HOME = "/" +SIGNUP = "/signup" +CREATE_USER = _get_rest_path("/mlflow/users/create") +CREATE_USER_UI = _get_rest_path("/mlflow/users/create-ui") +GET_USER = _get_rest_path("/mlflow/users/get") +UPDATE_USER_PASSWORD = _get_rest_path("/mlflow/users/update-password") +UPDATE_USER_ADMIN = _get_rest_path("/mlflow/users/update-admin") +DELETE_USER = _get_rest_path("/mlflow/users/delete") +CREATE_EXPERIMENT_PERMISSION = _get_rest_path("/mlflow/experiments/permissions/create") +GET_EXPERIMENT_PERMISSION = _get_rest_path("/mlflow/experiments/permissions/get") +UPDATE_EXPERIMENT_PERMISSION = _get_rest_path("/mlflow/experiments/permissions/update") +DELETE_EXPERIMENT_PERMISSION = _get_rest_path("/mlflow/experiments/permissions/delete") +CREATE_REGISTERED_MODEL_PERMISSION = _get_rest_path("/mlflow/registered-models/permissions/create") +GET_REGISTERED_MODEL_PERMISSION = _get_rest_path("/mlflow/registered-models/permissions/get") +UPDATE_REGISTERED_MODEL_PERMISSION = _get_rest_path("/mlflow/registered-models/permissions/update") +DELETE_REGISTERED_MODEL_PERMISSION = _get_rest_path("/mlflow/registered-models/permissions/delete") diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/auth/sqlalchemy_store.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/auth/sqlalchemy_store.py new file mode 100644 index 0000000000000000000000000000000000000000..88a8cb85f9769975bbe0030a87af4d545a0c7364 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/auth/sqlalchemy_store.py @@ -0,0 +1,261 @@ +from sqlalchemy.exc import IntegrityError, MultipleResultsFound, NoResultFound +from sqlalchemy.orm import sessionmaker +from werkzeug.security import check_password_hash, generate_password_hash + +from mlflow.exceptions import MlflowException +from mlflow.protos.databricks_pb2 import ( + INVALID_STATE, + RESOURCE_ALREADY_EXISTS, + RESOURCE_DOES_NOT_EXIST, +) +from mlflow.server.auth.db import utils as dbutils +from mlflow.server.auth.db.models import ( + SqlExperimentPermission, + SqlRegisteredModelPermission, + SqlUser, +) +from mlflow.server.auth.entities import ExperimentPermission, RegisteredModelPermission, User +from mlflow.server.auth.permissions import _validate_permission +from mlflow.store.db.utils import _get_managed_session_maker, create_sqlalchemy_engine_with_retry +from mlflow.utils.uri import extract_db_type_from_uri +from mlflow.utils.validation import _validate_password, _validate_username + + +class SqlAlchemyStore: + def init_db(self, db_uri): + self.db_uri = db_uri + self.db_type = extract_db_type_from_uri(db_uri) + self.engine = create_sqlalchemy_engine_with_retry(db_uri) + dbutils.migrate_if_needed(self.engine, "head") + SessionMaker = sessionmaker(bind=self.engine) + self.ManagedSessionMaker = _get_managed_session_maker(SessionMaker, self.db_type) + + def authenticate_user(self, username: str, password: str) -> bool: + with self.ManagedSessionMaker() as session: + try: + user = self._get_user(session, username) + return check_password_hash(user.password_hash, password) + except MlflowException: + return False + + def create_user(self, username: str, password: str, is_admin: bool = False) -> User: + _validate_username(username) + _validate_password(password) + pwhash = generate_password_hash(password) + with self.ManagedSessionMaker() as session: + try: + user = SqlUser(username=username, password_hash=pwhash, is_admin=is_admin) + session.add(user) + session.flush() + return user.to_mlflow_entity() + except IntegrityError as e: + raise MlflowException( + f"User (username={username}) already exists. Error: {e}", + RESOURCE_ALREADY_EXISTS, + ) from e + + @staticmethod + def _get_user(session, username: str) -> SqlUser: + try: + return session.query(SqlUser).filter(SqlUser.username == username).one() + except NoResultFound: + raise MlflowException( + f"User with username={username} not found", + RESOURCE_DOES_NOT_EXIST, + ) + except MultipleResultsFound: + raise MlflowException( + f"Found multiple users with username={username}", + INVALID_STATE, + ) + + def has_user(self, username: str) -> bool: + with self.ManagedSessionMaker() as session: + return session.query(SqlUser).filter(SqlUser.username == username).first() is not None + + def get_user(self, username: str) -> User: + with self.ManagedSessionMaker() as session: + return self._get_user(session, username).to_mlflow_entity() + + def list_users(self) -> list[User]: + with self.ManagedSessionMaker() as session: + users = session.query(SqlUser).all() + return [u.to_mlflow_entity() for u in users] + + def update_user( + self, username: str, password: str | None = None, is_admin: bool | None = None + ) -> User: + with self.ManagedSessionMaker() as session: + user = self._get_user(session, username) + if password is not None: + pwhash = generate_password_hash(password) + user.password_hash = pwhash + if is_admin is not None: + user.is_admin = is_admin + return user.to_mlflow_entity() + + def delete_user(self, username: str): + with self.ManagedSessionMaker() as session: + user = self._get_user(session, username) + session.delete(user) + + def create_experiment_permission( + self, experiment_id: str, username: str, permission: str + ) -> ExperimentPermission: + _validate_permission(permission) + with self.ManagedSessionMaker() as session: + try: + user = self._get_user(session, username=username) + perm = SqlExperimentPermission( + experiment_id=experiment_id, user_id=user.id, permission=permission + ) + session.add(perm) + session.flush() + return perm.to_mlflow_entity() + except IntegrityError as e: + raise MlflowException( + f"Experiment permission (experiment_id={experiment_id}, username={username}) " + f"already exists. Error: {e}", + RESOURCE_ALREADY_EXISTS, + ) + + def _get_experiment_permission( + self, session, experiment_id: str, username: str + ) -> SqlExperimentPermission: + try: + user = self._get_user(session, username=username) + return ( + session.query(SqlExperimentPermission) + .filter( + SqlExperimentPermission.experiment_id == experiment_id, + SqlExperimentPermission.user_id == user.id, + ) + .one() + ) + except NoResultFound: + raise MlflowException( + f"Experiment permission with experiment_id={experiment_id} and " + f"username={username} not found", + RESOURCE_DOES_NOT_EXIST, + ) + except MultipleResultsFound: + raise MlflowException( + f"Found multiple experiment permissions with experiment_id={experiment_id} " + f"and username={username}", + INVALID_STATE, + ) + + def get_experiment_permission(self, experiment_id: str, username: str) -> ExperimentPermission: + with self.ManagedSessionMaker() as session: + return self._get_experiment_permission( + session, experiment_id, username + ).to_mlflow_entity() + + def list_experiment_permissions(self, username: str) -> list[ExperimentPermission]: + with self.ManagedSessionMaker() as session: + user = self._get_user(session, username=username) + perms = ( + session.query(SqlExperimentPermission) + .filter(SqlExperimentPermission.user_id == user.id) + .all() + ) + return [p.to_mlflow_entity() for p in perms] + + def update_experiment_permission( + self, experiment_id: str, username: str, permission: str + ) -> ExperimentPermission: + _validate_permission(permission) + with self.ManagedSessionMaker() as session: + perm = self._get_experiment_permission(session, experiment_id, username) + perm.permission = permission + return perm.to_mlflow_entity() + + def delete_experiment_permission(self, experiment_id: str, username: str): + with self.ManagedSessionMaker() as session: + perm = self._get_experiment_permission(session, experiment_id, username) + session.delete(perm) + + def create_registered_model_permission( + self, name: str, username: str, permission: str + ) -> RegisteredModelPermission: + _validate_permission(permission) + with self.ManagedSessionMaker() as session: + try: + user = self._get_user(session, username=username) + perm = SqlRegisteredModelPermission( + name=name, user_id=user.id, permission=permission + ) + session.add(perm) + session.flush() + return perm.to_mlflow_entity() + except IntegrityError as e: + raise MlflowException( + f"Registered model permission (name={name}, username={username}) " + f"already exists. Error: {e}", + RESOURCE_ALREADY_EXISTS, + ) + + def _get_registered_model_permission( + self, session, name: str, username: str + ) -> SqlRegisteredModelPermission: + try: + user = self._get_user(session, username=username) + return ( + session.query(SqlRegisteredModelPermission) + .filter( + SqlRegisteredModelPermission.name == name, + SqlRegisteredModelPermission.user_id == user.id, + ) + .one() + ) + except NoResultFound: + raise MlflowException( + f"Registered model permission with name={name} and username={username} not found", + RESOURCE_DOES_NOT_EXIST, + ) + except MultipleResultsFound: + raise MlflowException( + f"Found multiple registered model permissions with name={name} " + f"and username={username}", + INVALID_STATE, + ) + + def get_registered_model_permission( + self, name: str, username: str + ) -> RegisteredModelPermission: + with self.ManagedSessionMaker() as session: + return self._get_registered_model_permission(session, name, username).to_mlflow_entity() + + def list_registered_model_permissions(self, username: str) -> list[RegisteredModelPermission]: + with self.ManagedSessionMaker() as session: + user = self._get_user(session, username=username) + perms = ( + session.query(SqlRegisteredModelPermission) + .filter(SqlRegisteredModelPermission.user_id == user.id) + .all() + ) + return [p.to_mlflow_entity() for p in perms] + + def update_registered_model_permission( + self, name: str, username: str, permission: str + ) -> RegisteredModelPermission: + _validate_permission(permission) + with self.ManagedSessionMaker() as session: + perm = self._get_registered_model_permission(session, name, username) + perm.permission = permission + return perm.to_mlflow_entity() + + def delete_registered_model_permission(self, name: str, username: str): + with self.ManagedSessionMaker() as session: + perm = self._get_registered_model_permission(session, name, username) + session.delete(perm) + + def rename_registered_model_permissions(self, old_name: str, new_name: str): + with self.ManagedSessionMaker() as session: + perms = ( + session.query(SqlRegisteredModelPermission) + .filter(SqlRegisteredModelPermission.name == old_name) + .all() + ) + for perm in perms: + perm.name = new_name diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/fastapi_app.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/fastapi_app.py new file mode 100644 index 0000000000000000000000000000000000000000..11f4518e0c3d07a4b3108b1a43db49458cd4056c --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/fastapi_app.py @@ -0,0 +1,43 @@ +""" +FastAPI application wrapper for MLflow server. + +This module provides a FastAPI application that wraps the existing Flask application +using WSGIMiddleware to maintain 100% API compatibility while enabling future migration +to FastAPI endpoints. +""" + +from fastapi import FastAPI +from fastapi.middleware.wsgi import WSGIMiddleware + +from mlflow.server import app as flask_app +from mlflow.version import VERSION + + +def create_fastapi_app(): + """ + Create a FastAPI application that wraps the existing Flask app. + + Returns: + FastAPI application instance with the Flask app mounted via WSGIMiddleware. + """ + # Create FastAPI app with metadata + fastapi_app = FastAPI( + title="MLflow Tracking Server", + description="MLflow Tracking Server API", + version=VERSION, + # TODO: Enable API documentation when we have native FastAPI endpoints + # For now, disable docs since we only have Flask routes via WSGI + docs_url=None, + redoc_url=None, + openapi_url=None, + ) + + # Mount the entire Flask application at the root path + # This ensures compatibility with existing APIs + fastapi_app.mount("/", WSGIMiddleware(flask_app)) + + return fastapi_app + + +# Create the app instance that can be used by ASGI servers +app = create_fastapi_app() diff 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PLEASE DON'T MODIFY. +# Run python3 ./dev/proto_to_graphql/code_generator.py to regenerate. +import graphene +import mlflow +from mlflow.server.graphql.graphql_custom_scalars import LongString +from mlflow.server.graphql.graphql_errors import ApiError +from mlflow.utils.proto_json_utils import parse_dict + + +class MlflowDeploymentJobConnectionState(graphene.Enum): + DEPLOYMENT_JOB_CONNECTION_STATE_UNSPECIFIED = 1 + NOT_SET_UP = 2 + CONNECTED = 3 + NOT_FOUND = 4 + REQUIRED_PARAMETERS_CHANGED = 5 + + +class MlflowModelVersionDeploymentJobStateDeploymentJobRunState(graphene.Enum): + DEPLOYMENT_JOB_RUN_STATE_UNSPECIFIED = 1 + NO_VALID_DEPLOYMENT_JOB_FOUND = 2 + RUNNING = 3 + SUCCEEDED = 4 + FAILED = 5 + PENDING = 6 + APPROVAL = 7 + + +class MlflowModelVersionStatus(graphene.Enum): + PENDING_REGISTRATION = 1 + FAILED_REGISTRATION = 2 + READY = 3 + + +class MlflowRunStatus(graphene.Enum): + RUNNING = 1 + SCHEDULED = 2 + FINISHED = 3 + FAILED = 4 + KILLED = 5 + + +class MlflowViewType(graphene.Enum): + ACTIVE_ONLY = 1 + DELETED_ONLY = 2 + ALL = 3 + + +class MlflowModelVersionDeploymentJobState(graphene.ObjectType): + job_id = graphene.String() + run_id = graphene.String() + job_state = graphene.Field(MlflowDeploymentJobConnectionState) + run_state = graphene.Field(MlflowModelVersionDeploymentJobStateDeploymentJobRunState) + current_task_name = graphene.String() + + +class MlflowModelMetric(graphene.ObjectType): + key = graphene.String() + value = graphene.Float() + timestamp = LongString() + step = LongString() + dataset_name = graphene.String() + dataset_digest = graphene.String() + model_id = graphene.String() + run_id = graphene.String() + + +class MlflowModelParam(graphene.ObjectType): + name = graphene.String() + value = graphene.String() + + +class MlflowModelVersionTag(graphene.ObjectType): + key = graphene.String() + value = graphene.String() + + +class MlflowModelVersion(graphene.ObjectType): + name = graphene.String() + version = graphene.String() + creation_timestamp = LongString() + last_updated_timestamp = LongString() + user_id = graphene.String() + current_stage = graphene.String() + description = graphene.String() + source = graphene.String() + run_id = graphene.String() + status = graphene.Field(MlflowModelVersionStatus) + status_message = graphene.String() + tags = graphene.List(graphene.NonNull(MlflowModelVersionTag)) + run_link = graphene.String() + aliases = graphene.List(graphene.String) + model_id = graphene.String() + model_params = graphene.List(graphene.NonNull(MlflowModelParam)) + model_metrics = graphene.List(graphene.NonNull(MlflowModelMetric)) + deployment_job_state = graphene.Field(MlflowModelVersionDeploymentJobState) + + +class MlflowSearchModelVersionsResponse(graphene.ObjectType): + model_versions = graphene.List(graphene.NonNull(MlflowModelVersion)) + next_page_token = graphene.String() + apiError = graphene.Field(ApiError) + + +class MlflowDatasetSummary(graphene.ObjectType): + experiment_id = graphene.String() + name = graphene.String() + digest = graphene.String() + context = graphene.String() + + +class MlflowSearchDatasetsResponse(graphene.ObjectType): + dataset_summaries = graphene.List(graphene.NonNull(MlflowDatasetSummary)) + apiError = graphene.Field(ApiError) + + +class MlflowMetricWithRunId(graphene.ObjectType): + key = graphene.String() + value = graphene.Float() + timestamp = LongString() + step = LongString() + run_id = graphene.String() + + +class MlflowGetMetricHistoryBulkIntervalResponse(graphene.ObjectType): + metrics = graphene.List(graphene.NonNull(MlflowMetricWithRunId)) + apiError = graphene.Field(ApiError) + + +class MlflowFileInfo(graphene.ObjectType): + path = graphene.String() + is_dir = graphene.Boolean() + file_size = LongString() + + +class MlflowListArtifactsResponse(graphene.ObjectType): + root_uri = graphene.String() + files = graphene.List(graphene.NonNull(MlflowFileInfo)) + next_page_token = graphene.String() + apiError = graphene.Field(ApiError) + + +class MlflowModelOutput(graphene.ObjectType): + model_id = graphene.String() + step = LongString() + + +class MlflowRunOutputs(graphene.ObjectType): + model_outputs = graphene.List(graphene.NonNull(MlflowModelOutput)) + + +class MlflowModelInput(graphene.ObjectType): + model_id = graphene.String() + + +class MlflowDataset(graphene.ObjectType): + name = graphene.String() + digest = graphene.String() + source_type = graphene.String() + source = graphene.String() + schema = graphene.String() + profile = graphene.String() + + +class MlflowInputTag(graphene.ObjectType): + key = graphene.String() + value = graphene.String() + + +class MlflowDatasetInput(graphene.ObjectType): + tags = graphene.List(graphene.NonNull(MlflowInputTag)) + dataset = graphene.Field(MlflowDataset) + + +class MlflowRunInputs(graphene.ObjectType): + dataset_inputs = graphene.List(graphene.NonNull(MlflowDatasetInput)) + model_inputs = graphene.List(graphene.NonNull(MlflowModelInput)) + + +class MlflowRunTag(graphene.ObjectType): + key = graphene.String() + value = graphene.String() + + +class MlflowParam(graphene.ObjectType): + key = graphene.String() + value = graphene.String() + + +class MlflowMetric(graphene.ObjectType): + key = graphene.String() + value = graphene.Float() + timestamp = LongString() + step = LongString() + dataset_name = graphene.String() + dataset_digest = graphene.String() + model_id = graphene.String() + run_id = graphene.String() + + +class MlflowRunData(graphene.ObjectType): + metrics = graphene.List(graphene.NonNull('mlflow.server.graphql.graphql_schema_extensions.MlflowMetricExtension')) + params = graphene.List(graphene.NonNull(MlflowParam)) + tags = graphene.List(graphene.NonNull(MlflowRunTag)) + + +class MlflowRunInfo(graphene.ObjectType): + run_id = graphene.String() + run_uuid = graphene.String() + run_name = graphene.String() + experiment_id = graphene.String() + user_id = graphene.String() + status = graphene.Field(MlflowRunStatus) + start_time = LongString() + end_time = LongString() + artifact_uri = graphene.String() + lifecycle_stage = graphene.String() + + +class MlflowRun(graphene.ObjectType): + info = graphene.Field(MlflowRunInfo) + data = graphene.Field(MlflowRunData) + inputs = graphene.Field(MlflowRunInputs) + outputs = graphene.Field(MlflowRunOutputs) + + +class MlflowSearchRunsResponse(graphene.ObjectType): + runs = graphene.List(graphene.NonNull('mlflow.server.graphql.graphql_schema_extensions.MlflowRunExtension')) + next_page_token = graphene.String() + apiError = graphene.Field(ApiError) + + +class MlflowGetRunResponse(graphene.ObjectType): + run = graphene.Field('mlflow.server.graphql.graphql_schema_extensions.MlflowRunExtension') + apiError = graphene.Field(ApiError) + + +class MlflowExperimentTag(graphene.ObjectType): + key = graphene.String() + value = graphene.String() + + +class MlflowExperiment(graphene.ObjectType): + experiment_id = graphene.String() + name = graphene.String() + artifact_location = graphene.String() + lifecycle_stage = graphene.String() + last_update_time = LongString() + creation_time = LongString() + tags = graphene.List(graphene.NonNull(MlflowExperimentTag)) + + +class MlflowGetExperimentResponse(graphene.ObjectType): + experiment = graphene.Field(MlflowExperiment) + apiError = graphene.Field(ApiError) + + +class MlflowSearchModelVersionsInput(graphene.InputObjectType): + filter = graphene.String() + max_results = LongString() + order_by = graphene.List(graphene.String) + page_token = graphene.String() + + +class MlflowSearchDatasetsInput(graphene.InputObjectType): + experiment_ids = graphene.List(graphene.String) + + +class MlflowGetMetricHistoryBulkIntervalInput(graphene.InputObjectType): + run_ids = graphene.List(graphene.String) + metric_key = graphene.String() + start_step = graphene.Int() + end_step = graphene.Int() + max_results = graphene.Int() + + +class MlflowListArtifactsInput(graphene.InputObjectType): + run_id = graphene.String() + run_uuid = graphene.String() + path = graphene.String() + page_token = graphene.String() + + +class MlflowSearchRunsInput(graphene.InputObjectType): + experiment_ids = graphene.List(graphene.String) + filter = graphene.String() + run_view_type = graphene.Field(MlflowViewType) + max_results = graphene.Int() + order_by = graphene.List(graphene.String) + page_token = graphene.String() + + +class MlflowGetRunInput(graphene.InputObjectType): + run_id = graphene.String() + run_uuid = graphene.String() + + +class MlflowGetExperimentInput(graphene.InputObjectType): + experiment_id = graphene.String() + + +class QueryType(graphene.ObjectType): + mlflow_get_experiment = graphene.Field(MlflowGetExperimentResponse, input=MlflowGetExperimentInput()) + mlflow_get_metric_history_bulk_interval = graphene.Field(MlflowGetMetricHistoryBulkIntervalResponse, input=MlflowGetMetricHistoryBulkIntervalInput()) + mlflow_get_run = graphene.Field(MlflowGetRunResponse, input=MlflowGetRunInput()) + mlflow_list_artifacts = graphene.Field(MlflowListArtifactsResponse, input=MlflowListArtifactsInput()) + mlflow_search_model_versions = graphene.Field(MlflowSearchModelVersionsResponse, input=MlflowSearchModelVersionsInput()) + + def resolve_mlflow_get_experiment(self, info, input): + input_dict = vars(input) + request_message = mlflow.protos.service_pb2.GetExperiment() + parse_dict(input_dict, request_message) + return mlflow.server.handlers.get_experiment_impl(request_message) + + def resolve_mlflow_get_metric_history_bulk_interval(self, info, input): + input_dict = vars(input) + request_message = mlflow.protos.service_pb2.GetMetricHistoryBulkInterval() + parse_dict(input_dict, request_message) + return mlflow.server.handlers.get_metric_history_bulk_interval_impl(request_message) + + def resolve_mlflow_get_run(self, info, input): + input_dict = vars(input) + request_message = mlflow.protos.service_pb2.GetRun() + parse_dict(input_dict, request_message) + return mlflow.server.handlers.get_run_impl(request_message) + + def resolve_mlflow_list_artifacts(self, info, input): + input_dict = vars(input) + request_message = mlflow.protos.service_pb2.ListArtifacts() + parse_dict(input_dict, request_message) + return mlflow.server.handlers.list_artifacts_impl(request_message) + + def resolve_mlflow_search_model_versions(self, info, input): + input_dict = vars(input) + request_message = mlflow.protos.model_registry_pb2.SearchModelVersions() + parse_dict(input_dict, request_message) + return mlflow.server.handlers.search_model_versions_impl(request_message) + + +class MutationType(graphene.ObjectType): + mlflow_search_datasets = graphene.Field(MlflowSearchDatasetsResponse, input=MlflowSearchDatasetsInput()) + mlflow_search_runs = graphene.Field(MlflowSearchRunsResponse, input=MlflowSearchRunsInput()) + + def resolve_mlflow_search_datasets(self, info, input): + input_dict = vars(input) + request_message = mlflow.protos.service_pb2.SearchDatasets() + parse_dict(input_dict, request_message) + return mlflow.server.handlers.search_datasets_impl(request_message) + + def resolve_mlflow_search_runs(self, info, input): + input_dict = vars(input) + request_message = mlflow.protos.service_pb2.SearchRuns() + parse_dict(input_dict, request_message) + return mlflow.server.handlers.search_runs_impl(request_message) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/graphql/graphql_custom_scalars.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/graphql/graphql_custom_scalars.py new file mode 100644 index 0000000000000000000000000000000000000000..05bb08dd8cbed9eb01ce5dee5cfce75f6b47ac82 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/graphql/graphql_custom_scalars.py @@ -0,0 +1,24 @@ +import graphene +from graphql.language.ast import IntValueNode + + +class LongString(graphene.Scalar): + """ + LongString Scalar type to prevent truncation to max integer in JavaScript. + """ + + description = "Long converted to string to prevent truncation to max integer in JavaScript" + + @staticmethod + def serialize(long): + return str(long) + + @staticmethod + def parse_literal(node): + if isinstance(node, IntValueNode): + return int(node.value) + return None + + @staticmethod + def parse_value(value): + return int(value) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/graphql/graphql_errors.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/graphql/graphql_errors.py new file mode 100644 index 0000000000000000000000000000000000000000..38938808dc030bfa1b7d613c6087d424d46f761c --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/graphql/graphql_errors.py @@ -0,0 +1,15 @@ +import graphene + + +class ErrorDetail(graphene.ObjectType): + # NOTE: This is not an exhaustive list, might need to add more things in the future if needed. + field = graphene.String() + message = graphene.String() + + +class ApiError(graphene.ObjectType): + code = graphene.String() + message = graphene.String() + help_url = graphene.String() + trace_id = graphene.String() + error_details = graphene.List(ErrorDetail) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/graphql/graphql_no_batching.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/graphql/graphql_no_batching.py new file mode 100644 index 0000000000000000000000000000000000000000..3b62adff5111261b212b91ef6df99290e794562c --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/graphql/graphql_no_batching.py @@ -0,0 +1,89 @@ +from typing import NamedTuple + +from graphql.error import GraphQLError +from graphql.execution import ExecutionResult +from graphql.language.ast import DocumentNode, FieldNode + +from mlflow.environment_variables import ( + MLFLOW_SERVER_GRAPHQL_MAX_ALIASES, + MLFLOW_SERVER_GRAPHQL_MAX_ROOT_FIELDS, +) + +_MAX_DEPTH = 10 +_MAX_SELECTIONS = 1000 + + +class QueryInfo(NamedTuple): + root_fields: int + max_aliases: int + + +def scan_query(ast_node: DocumentNode) -> QueryInfo: + """ + Scan a GraphQL query and return its information. + """ + root_fields = 0 + max_aliases = 0 + total_selections = 0 + + for definition in ast_node.definitions: + if selection_set := getattr(definition, "selection_set", None): + stack = [(selection_set, 1)] + while stack: + selection_set, depth = stack.pop() + + # check current level depth + if depth > _MAX_DEPTH: + raise GraphQLError(f"Query exceeds maximum depth of {_MAX_DEPTH}") + + selections = getattr(selection_set, "selections", []) + + # check current level aliases + current_aliases = 0 + for selection in selections: + if isinstance(selection, FieldNode): + if depth == 1: + root_fields += 1 + if selection.alias: + current_aliases += 1 + if selection.selection_set: + stack.append((selection.selection_set, depth + 1)) + total_selections += 1 + if total_selections > _MAX_SELECTIONS: + raise GraphQLError( + f"Query exceeds maximum total selections of {_MAX_SELECTIONS}" + ) + max_aliases = max(max_aliases, current_aliases) + + return QueryInfo(root_fields, max_aliases) + + +def check_query_safety(ast_node: DocumentNode) -> ExecutionResult | None: + try: + query_info = scan_query(ast_node) + except GraphQLError as e: + return ExecutionResult( + data=None, + errors=[e], + ) + + if query_info.root_fields > MLFLOW_SERVER_GRAPHQL_MAX_ROOT_FIELDS.get(): + msg = "root fields" + env_var = MLFLOW_SERVER_GRAPHQL_MAX_ROOT_FIELDS + value = query_info.root_fields + elif query_info.max_aliases > MLFLOW_SERVER_GRAPHQL_MAX_ALIASES.get(): + msg = "aliases" + env_var = MLFLOW_SERVER_GRAPHQL_MAX_ALIASES + value = query_info.max_aliases + else: + return None + return ExecutionResult( + data=None, + errors=[ + GraphQLError( + f"GraphQL queries should have at most {env_var.get()} {msg}, " + f"got {value} {msg}. To increase the limit, set the " + f"{env_var.name} environment variable." + ) + ], + ) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/graphql/graphql_schema_extensions.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/graphql/graphql_schema_extensions.py new file mode 100644 index 0000000000000000000000000000000000000000..6abc9ea89cbad4e0f9a2574a9504e88aca4775d3 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/graphql/graphql_schema_extensions.py @@ -0,0 +1,95 @@ +import math + +import graphene +from graphql import ( + DirectiveLocation, + GraphQLArgument, + GraphQLDirective, + GraphQLNonNull, + GraphQLString, +) + +import mlflow +from mlflow.server.graphql.autogenerated_graphql_schema import ( + MlflowExperiment, + MlflowMetric, + MlflowModelVersion, + MlflowRun, + MlflowSearchRunsInput, + MlflowSearchRunsResponse, + MutationType, + QueryType, +) +from mlflow.utils.proto_json_utils import parse_dict + +# Component identifier, to keep compatible with Databricks in-house implementations. +ComponentDirective = GraphQLDirective( + name="component", + locations=[ + DirectiveLocation.QUERY, + DirectiveLocation.MUTATION, + ], + args={"name": GraphQLArgument(GraphQLNonNull(GraphQLString))}, +) + + +class Test(graphene.ObjectType): + output = graphene.String(description="Echoes the input string") + + +class TestMutation(graphene.ObjectType): + output = graphene.String(description="Echoes the input string") + + +class MlflowRunExtension(MlflowRun): + experiment = graphene.Field(MlflowExperiment) + model_versions = graphene.List(graphene.NonNull(MlflowModelVersion)) + + def resolve_experiment(self, info): + experiment_id = self.info.experiment_id + input_dict = {"experiment_id": experiment_id} + request_message = mlflow.protos.service_pb2.GetExperiment() + parse_dict(input_dict, request_message) + return mlflow.server.handlers.get_experiment_impl(request_message).experiment + + def resolve_model_versions(self, info): + run_id = self.info.run_id + input_dict = {"filter": f"run_id='{run_id}'"} + request_message = mlflow.protos.model_registry_pb2.SearchModelVersions() + parse_dict(input_dict, request_message) + return mlflow.server.handlers.search_model_versions_impl(request_message).model_versions + + +class MlflowMetricExtension(MlflowMetric): + value = graphene.Float() + + # metric values that are NaN will cause an error in graphQL validation as + # the type is Float. as a workaround, we return None if the value is NaN. + def resolve_value(self, info): + return None if math.isnan(self.value) else self.value + + +class Query(QueryType): + test = graphene.Field(Test, input_string=graphene.String(), description="Simple echoing field") + mlflow_search_runs = graphene.Field(MlflowSearchRunsResponse, input=MlflowSearchRunsInput()) + + def resolve_test(self, info, input_string): + return {"output": input_string} + + def resolve_mlflow_search_runs(self, info, input): + input_dict = vars(input) + request_message = mlflow.protos.service_pb2.SearchRuns() + parse_dict(input_dict, request_message) + return mlflow.server.handlers.search_runs_impl(request_message) + + +class Mutation(MutationType): + testMutation = graphene.Field( + TestMutation, input_string=graphene.String(), description="Simple echoing field" + ) + + def resolve_test_mutation(self, info, input_string): + return {"output": input_string} + + +schema = graphene.Schema(query=Query, mutation=Mutation, directives=[ComponentDirective]) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/handlers.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/handlers.py new file mode 100644 index 0000000000000000000000000000000000000000..27243d453a2a4e16449c6e678dff09aecc601359 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/handlers.py @@ -0,0 +1,3528 @@ +# Define all the service endpoint handlers here. +import bisect +import io +import json +import logging +import os +import pathlib +import posixpath +import re +import tempfile +import time +import urllib +from functools import wraps + +import requests +from flask import Response, current_app, jsonify, request, send_file +from google.protobuf import descriptor +from google.protobuf.json_format import ParseError + +from mlflow.entities import ( + Assessment, + DatasetInput, + Expectation, + ExperimentTag, + Feedback, + FileInfo, + Metric, + Param, + RunTag, + ViewType, +) +from mlflow.entities.logged_model import LoggedModel +from mlflow.entities.logged_model_input import LoggedModelInput +from mlflow.entities.logged_model_output import LoggedModelOutput +from mlflow.entities.logged_model_parameter import LoggedModelParameter +from mlflow.entities.logged_model_status import LoggedModelStatus +from mlflow.entities.logged_model_tag import LoggedModelTag +from mlflow.entities.model_registry import ModelVersionTag, RegisteredModelTag +from mlflow.entities.model_registry.prompt_version import IS_PROMPT_TAG_KEY +from mlflow.entities.multipart_upload import MultipartUploadPart +from mlflow.entities.trace_info import TraceInfo +from mlflow.entities.trace_info_v2 import TraceInfoV2 +from mlflow.entities.trace_status import TraceStatus +from mlflow.entities.webhook import WebhookAction, WebhookEntity, WebhookEvent, WebhookStatus +from mlflow.environment_variables import ( + MLFLOW_CREATE_MODEL_VERSION_SOURCE_VALIDATION_REGEX, + MLFLOW_DEPLOYMENTS_TARGET, +) +from mlflow.exceptions import MlflowException, _UnsupportedMultipartUploadException +from mlflow.models import Model +from mlflow.protos import databricks_pb2 +from mlflow.protos.databricks_pb2 import ( + BAD_REQUEST, + INVALID_PARAMETER_VALUE, + RESOURCE_DOES_NOT_EXIST, +) +from mlflow.protos.mlflow_artifacts_pb2 import ( + AbortMultipartUpload, + CompleteMultipartUpload, + CreateMultipartUpload, + DeleteArtifact, + DownloadArtifact, + MlflowArtifactsService, + UploadArtifact, +) +from mlflow.protos.mlflow_artifacts_pb2 import ( + ListArtifacts as ListArtifactsMlflowArtifacts, +) +from mlflow.protos.model_registry_pb2 import ( + CreateModelVersion, + CreateRegisteredModel, + DeleteModelVersion, + DeleteModelVersionTag, + DeleteRegisteredModel, + DeleteRegisteredModelAlias, + DeleteRegisteredModelTag, + GetLatestVersions, + GetModelVersion, + GetModelVersionByAlias, + GetModelVersionDownloadUri, + GetRegisteredModel, + ModelRegistryService, + RenameRegisteredModel, + SearchModelVersions, + SearchRegisteredModels, + SetModelVersionTag, + SetRegisteredModelAlias, + SetRegisteredModelTag, + TransitionModelVersionStage, + UpdateModelVersion, + UpdateRegisteredModel, +) +from mlflow.protos.service_pb2 import ( + CreateAssessment, + CreateExperiment, + CreateLoggedModel, + CreateRun, + DeleteAssessment, + DeleteExperiment, + DeleteExperimentTag, + DeleteLoggedModel, + DeleteLoggedModelTag, + DeleteRun, + DeleteTag, + DeleteTraces, + DeleteTracesV3, + DeleteTraceTag, + DeleteTraceTagV3, + EndTrace, + FinalizeLoggedModel, + GetAssessmentRequest, + GetExperiment, + GetExperimentByName, + GetLoggedModel, + GetMetricHistory, + GetMetricHistoryBulkInterval, + GetRun, + GetTraceInfo, + GetTraceInfoV3, + LinkTracesToRun, + ListArtifacts, + ListLoggedModelArtifacts, + LogBatch, + LogInputs, + LogLoggedModelParamsRequest, + LogMetric, + LogModel, + LogOutputs, + LogParam, + MlflowService, + RestoreExperiment, + RestoreRun, + SearchDatasets, + SearchExperiments, + SearchLoggedModels, + SearchRuns, + SearchTraces, + SearchTracesV3, + SetExperimentTag, + SetLoggedModelTags, + SetTag, + SetTraceTag, + SetTraceTagV3, + StartTrace, + StartTraceV3, + UpdateAssessment, + UpdateExperiment, + UpdateRun, +) +from mlflow.protos.service_pb2 import Trace as ProtoTrace +from mlflow.protos.webhooks_pb2 import ( + CreateWebhook, + DeleteWebhook, + GetWebhook, + ListWebhooks, + TestWebhook, + UpdateWebhook, + WebhookService, +) +from mlflow.server.validation import _validate_content_type +from mlflow.store.artifact.artifact_repo import MultipartUploadMixin +from mlflow.store.artifact.artifact_repository_registry import get_artifact_repository +from mlflow.store.db.db_types import DATABASE_ENGINES +from mlflow.tracing.utils.artifact_utils import ( + TRACE_DATA_FILE_NAME, + get_artifact_uri_for_trace, +) +from mlflow.tracking._model_registry import utils as registry_utils +from mlflow.tracking._model_registry.registry import ModelRegistryStoreRegistry +from mlflow.tracking._tracking_service import utils +from mlflow.tracking._tracking_service.registry import TrackingStoreRegistry +from mlflow.tracking.registry import UnsupportedModelRegistryStoreURIException +from mlflow.utils.file_utils import local_file_uri_to_path +from mlflow.utils.mime_type_utils import _guess_mime_type +from mlflow.utils.promptlab_utils import _create_promptlab_run_impl +from mlflow.utils.proto_json_utils import message_to_json, parse_dict +from mlflow.utils.string_utils import is_string_type +from mlflow.utils.uri import is_local_uri, validate_path_is_safe, validate_query_string +from mlflow.utils.validation import ( + _validate_batch_log_api_req, + invalid_value, + missing_value, +) +from mlflow.webhooks.delivery import deliver_webhook, test_webhook +from mlflow.webhooks.types import ( + ModelVersionAliasCreatedPayload, + ModelVersionAliasDeletedPayload, + ModelVersionCreatedPayload, + ModelVersionTagDeletedPayload, + ModelVersionTagSetPayload, + RegisteredModelCreatedPayload, +) + +_logger = logging.getLogger(__name__) +_tracking_store = None +_model_registry_store = None +_artifact_repo = None +STATIC_PREFIX_ENV_VAR = "_MLFLOW_STATIC_PREFIX" +MAX_RUNS_GET_METRIC_HISTORY_BULK = 100 +MAX_RESULTS_PER_RUN = 2500 +MAX_RESULTS_GET_METRIC_HISTORY = 25000 + + +class TrackingStoreRegistryWrapper(TrackingStoreRegistry): + def __init__(self): + super().__init__() + self.register("", self._get_file_store) + self.register("file", self._get_file_store) + for scheme in DATABASE_ENGINES: + self.register(scheme, self._get_sqlalchemy_store) + self.register_entrypoints() + + @classmethod + def _get_file_store(cls, store_uri, artifact_uri): + from mlflow.store.tracking.file_store import FileStore + + return FileStore(store_uri, artifact_uri) + + @classmethod + def _get_sqlalchemy_store(cls, store_uri, artifact_uri): + from mlflow.store.tracking.sqlalchemy_store import SqlAlchemyStore + + return SqlAlchemyStore(store_uri, artifact_uri) + + +class ModelRegistryStoreRegistryWrapper(ModelRegistryStoreRegistry): + def __init__(self): + super().__init__() + self.register("", self._get_file_store) + self.register("file", self._get_file_store) + for scheme in DATABASE_ENGINES: + self.register(scheme, self._get_sqlalchemy_store) + self.register_entrypoints() + + @classmethod + def _get_file_store(cls, store_uri): + from mlflow.store.model_registry.file_store import FileStore + + return FileStore(store_uri) + + @classmethod + def _get_sqlalchemy_store(cls, store_uri): + from mlflow.store.model_registry.sqlalchemy_store import SqlAlchemyStore + + return SqlAlchemyStore(store_uri) + + +_tracking_store_registry = TrackingStoreRegistryWrapper() +_model_registry_store_registry = ModelRegistryStoreRegistryWrapper() + + +def _get_artifact_repo_mlflow_artifacts(): + """ + Get an artifact repository specified by ``--artifacts-destination`` option for ``mlflow server`` + command. + """ + from mlflow.server import ARTIFACTS_DESTINATION_ENV_VAR + + global _artifact_repo + if _artifact_repo is None: + _artifact_repo = get_artifact_repository(os.environ[ARTIFACTS_DESTINATION_ENV_VAR]) + return _artifact_repo + + +def _get_trace_artifact_repo(trace_info: TraceInfo): + """ + Resolve the artifact repository for fetching data for the given trace. + + Args: + trace_info: The trace info object containing metadata about the trace. + """ + artifact_uri = get_artifact_uri_for_trace(trace_info) + + if _is_servable_proxied_run_artifact_root(artifact_uri): + # If the artifact location is a proxied run artifact root (e.g. mlflow-artifacts://...), + # we need to resolve it to the actual artifact location. + from mlflow.server import ARTIFACTS_DESTINATION_ENV_VAR + + path = _get_proxied_run_artifact_destination_path(artifact_uri) + if not path: + raise MlflowException( + f"Failed to resolve the proxied run artifact URI: {artifact_uri}. ", + "Trace artifact URI must contain subpath to the trace data directory.", + error_code=BAD_REQUEST, + ) + root = os.environ[ARTIFACTS_DESTINATION_ENV_VAR] + artifact_uri = posixpath.join(root, path) + + # We don't set it to global var unlike run artifact, because the artifact repo has + # to be created with full trace artifact URI including request_id. + # e.g. s3:///traces/ + artifact_repo = get_artifact_repository(artifact_uri) + else: + artifact_repo = get_artifact_repository(artifact_uri) + return artifact_repo + + +def _is_serving_proxied_artifacts(): + """ + Returns: + True if the MLflow server is serving proxied artifacts (i.e. acting as a proxy for + artifact upload / download / list operations), as would be enabled by specifying the + --serve-artifacts configuration option. False otherwise. + """ + from mlflow.server import SERVE_ARTIFACTS_ENV_VAR + + return os.environ.get(SERVE_ARTIFACTS_ENV_VAR, "false") == "true" + + +def _is_servable_proxied_run_artifact_root(run_artifact_root): + """ + Determines whether or not the following are true: + + - The specified Run artifact root is a proxied artifact root (i.e. an artifact root with scheme + ``http``, ``https``, or ``mlflow-artifacts``). + + - The MLflow server is capable of resolving and accessing the underlying storage location + corresponding to the proxied artifact root, allowing it to fulfill artifact list and + download requests by using this storage location directly. + + Args: + run_artifact_root: The Run artifact root location (URI). + + Returns: + True if the specified Run artifact root refers to proxied artifacts that can be + served by this MLflow server (i.e. the server has access to the destination and + can respond to list and download requests for the artifact). False otherwise. + """ + parsed_run_artifact_root = urllib.parse.urlparse(run_artifact_root) + # NB: If the run artifact root is a proxied artifact root (has scheme `http`, `https`, or + # `mlflow-artifacts`) *and* the MLflow server is configured to serve artifacts, the MLflow + # server always assumes that it has access to the underlying storage location for the proxied + # artifacts. This may not always be accurate. For example: + # + # An organization may initially use the MLflow server to serve Tracking API requests and proxy + # access to artifacts stored in Location A (via `mlflow server --serve-artifacts`). Then, for + # scalability and / or security purposes, the organization may decide to store artifacts in a + # new location B and set up a separate server (e.g. `mlflow server --artifacts-only`) to proxy + # access to artifacts stored in Location B. + # + # In this scenario, requests for artifacts stored in Location B that are sent to the original + # MLflow server will fail if the original MLflow server does not have access to Location B + # because it will assume that it can serve all proxied artifacts regardless of the underlying + # location. Such failures can be remediated by granting the original MLflow server access to + # Location B. + return ( + parsed_run_artifact_root.scheme in ["http", "https", "mlflow-artifacts"] + and _is_serving_proxied_artifacts() + ) + + +def _get_proxied_run_artifact_destination_path(proxied_artifact_root, relative_path=None): + """ + Resolves the specified proxied artifact location within a Run to a concrete storage location. + + Args: + proxied_artifact_root: The Run artifact root location (URI) with scheme ``http``, + ``https``, or `mlflow-artifacts` that can be resolved by the MLflow server to a + concrete storage location. + relative_path: The relative path of the destination within the specified + ``proxied_artifact_root``. If ``None``, the destination is assumed to be + the resolved ``proxied_artifact_root``. + + Returns: + The storage location of the specified artifact. + """ + parsed_proxied_artifact_root = urllib.parse.urlparse(proxied_artifact_root) + assert parsed_proxied_artifact_root.scheme in ["http", "https", "mlflow-artifacts"] + + if parsed_proxied_artifact_root.scheme == "mlflow-artifacts": + # If the proxied artifact root is an `mlflow-artifacts` URI, the run artifact root path is + # simply the path component of the URI, since the fully-qualified format of an + # `mlflow-artifacts` URI is `mlflow-artifacts:///path/to/artifact` + proxied_run_artifact_root_path = parsed_proxied_artifact_root.path.lstrip("/") + else: + # In this case, the proxied artifact root is an HTTP(S) URL referring to an mlflow-artifacts + # API route that can be used to download the artifact. These routes are always anchored at + # `/api/2.0/mlflow-artifacts/artifacts`. Accordingly, we split the path on this route anchor + # and interpret the rest of the path (everything after the route anchor) as the run artifact + # root path + mlflow_artifacts_http_route_anchor = "/api/2.0/mlflow-artifacts/artifacts/" + assert mlflow_artifacts_http_route_anchor in parsed_proxied_artifact_root.path + + proxied_run_artifact_root_path = parsed_proxied_artifact_root.path.split( + mlflow_artifacts_http_route_anchor + )[1].lstrip("/") + + return ( + posixpath.join(proxied_run_artifact_root_path, relative_path) + if relative_path is not None + else proxied_run_artifact_root_path + ) + + +def _get_tracking_store(backend_store_uri=None, default_artifact_root=None): + from mlflow.server import ARTIFACT_ROOT_ENV_VAR, BACKEND_STORE_URI_ENV_VAR + + global _tracking_store + if _tracking_store is None: + store_uri = backend_store_uri or os.environ.get(BACKEND_STORE_URI_ENV_VAR, None) + artifact_root = default_artifact_root or os.environ.get(ARTIFACT_ROOT_ENV_VAR, None) + _tracking_store = _tracking_store_registry.get_store(store_uri, artifact_root) + utils.set_tracking_uri(store_uri) + return _tracking_store + + +def _get_model_registry_store(registry_store_uri=None): + from mlflow.server import BACKEND_STORE_URI_ENV_VAR, REGISTRY_STORE_URI_ENV_VAR + + global _model_registry_store + if _model_registry_store is None: + store_uri = ( + registry_store_uri + or os.environ.get(REGISTRY_STORE_URI_ENV_VAR, None) + or os.environ.get(BACKEND_STORE_URI_ENV_VAR, None) + ) + _model_registry_store = _model_registry_store_registry.get_store(store_uri) + registry_utils.set_registry_uri(store_uri) + return _model_registry_store + + +def initialize_backend_stores( + backend_store_uri=None, registry_store_uri=None, default_artifact_root=None +): + _get_tracking_store(backend_store_uri, default_artifact_root) + try: + _get_model_registry_store(registry_store_uri) + except UnsupportedModelRegistryStoreURIException: + pass + + +def _assert_string(x): + assert isinstance(x, str) + + +def _assert_intlike(x): + try: + x = int(x) + except ValueError: + pass + + assert isinstance(x, int) + + +def _assert_bool(x): + assert isinstance(x, bool) + + +def _assert_floatlike(x): + try: + x = float(x) + except ValueError: + pass + + assert isinstance(x, float) + + +def _assert_array(x): + assert isinstance(x, list) + + +def _assert_map_key_present(x): + _assert_array(x) + for entry in x: + _assert_required(entry.get("key")) + + +def _assert_required(x, path=None): + if path is None: + assert x is not None + # When parsing JSON payloads via proto, absent string fields + # are expressed as empty strings + assert x != "" + else: + assert x is not None, missing_value(path) + assert x != "", missing_value(path) + + +def _assert_less_than_or_equal(x, max_value, message=None): + if x > max_value: + raise AssertionError(message) if message else AssertionError() + + +def _assert_intlike_within_range(x, min_value, max_value, message=None): + if not min_value <= x <= max_value: + raise AssertionError(message) if message else AssertionError() + + +def _assert_item_type_string(x): + assert all(isinstance(item, str) for item in x) + + +_TYPE_VALIDATORS = { + _assert_intlike, + _assert_string, + _assert_bool, + _assert_floatlike, + _assert_array, + _assert_item_type_string, +} + + +def _validate_param_against_schema(schema, param, value, proto_parsing_succeeded=False): + """ + Attempts to validate a single parameter against a specified schema. Examples of the elements of + the schema are type assertions and checks for required parameters. Returns None on validation + success. Otherwise, raises an MLFlowException if an assertion fails. This method is intended + to be called for side effects. + + Args: + schema: A list of functions to validate the parameter against. + param: The string name of the parameter being validated. + value: The corresponding value of the `param` being validated. + proto_parsing_succeeded: A boolean value indicating whether proto parsing succeeded. + If the proto was successfully parsed, we assume all of the types of the parameters in + the request body were correctly specified, and thus we skip validating types. If proto + parsing failed, then we validate types in addition to the rest of the schema. For + details, see https://github.com/mlflow/mlflow/pull/5458#issuecomment-1080880870. + """ + + for f in schema: + if f in _TYPE_VALIDATORS and proto_parsing_succeeded: + continue + + try: + f(value) + except AssertionError as e: + if e.args: + message = e.args[0] + elif f == _assert_required: + message = f"Missing value for required parameter '{param}'." + else: + message = invalid_value( + param, value, f" Hint: Value was of type '{type(value).__name__}'." + ) + raise MlflowException( + message=( + message + " See the API docs for more information about request parameters." + ), + error_code=INVALID_PARAMETER_VALUE, + ) + + return None + + +def _get_request_json(flask_request=request): + _validate_content_type(flask_request, ["application/json"]) + return flask_request.get_json(force=True, silent=True) + + +def _get_request_message(request_message, flask_request=request, schema=None): + if flask_request.method == "GET" and flask_request.args: + # Convert atomic values of repeated fields to lists before calling protobuf deserialization. + # Context: We parse the parameter string into a dictionary outside of protobuf since + # protobuf does not know how to read the query parameters directly. The query parser above + # has no type information and hence any parameter that occurs exactly once is parsed as an + # atomic value. Since protobuf requires that the values of repeated fields are lists, + # deserialization will fail unless we do the fix below. + request_json = {} + for field in request_message.DESCRIPTOR.fields: + if field.name not in flask_request.args: + continue + + if field.label == descriptor.FieldDescriptor.LABEL_REPEATED: + request_json[field.name] = flask_request.args.getlist(field.name) + else: + request_json[field.name] = flask_request.args.get(field.name) + else: + request_json = _get_request_json(flask_request) + + # Older clients may post their JSON double-encoded as strings, so the get_json + # above actually converts it to a string. Therefore, we check this condition + # (which we can tell for sure because any proper request should be a dictionary), + # and decode it a second time. + if is_string_type(request_json): + request_json = json.loads(request_json) + + # If request doesn't have json body then assume it's empty. + if request_json is None: + request_json = {} + + proto_parsing_succeeded = True + try: + parse_dict(request_json, request_message) + except ParseError: + proto_parsing_succeeded = False + + schema = schema or {} + for schema_key, schema_validation_fns in schema.items(): + if schema_key in request_json or _assert_required in schema_validation_fns: + value = request_json.get(schema_key) + if schema_key == "run_id" and value is None and "run_uuid" in request_json: + value = request_json.get("run_uuid") + _validate_param_against_schema( + schema=schema_validation_fns, + param=schema_key, + value=value, + proto_parsing_succeeded=proto_parsing_succeeded, + ) + + return request_message + + +def _response_with_file_attachment_headers(file_path, response): + mime_type = _guess_mime_type(file_path) + filename = pathlib.Path(file_path).name + response.mimetype = mime_type + content_disposition_header_name = "Content-Disposition" + if content_disposition_header_name not in response.headers: + response.headers[content_disposition_header_name] = f"attachment; filename={filename}" + response.headers["X-Content-Type-Options"] = "nosniff" + response.headers["Content-Type"] = mime_type + return response + + +def _send_artifact(artifact_repository, path): + file_path = os.path.abspath(artifact_repository.download_artifacts(path)) + # Always send artifacts as attachments to prevent the browser from displaying them on our web + # server's domain, which might enable XSS. + mime_type = _guess_mime_type(file_path) + file_sender_response = send_file(file_path, mimetype=mime_type, as_attachment=True) + return _response_with_file_attachment_headers(file_path, file_sender_response) + + +def catch_mlflow_exception(func): + @wraps(func) + def wrapper(*args, **kwargs): + try: + return func(*args, **kwargs) + except MlflowException as e: + response = Response(mimetype="application/json") + response.set_data(e.serialize_as_json()) + response.status_code = e.get_http_status_code() + return response + + return wrapper + + +def _disable_unless_serve_artifacts(func): + @wraps(func) + def wrapper(*args, **kwargs): + if not _is_serving_proxied_artifacts(): + return Response( + ( + f"Endpoint: {request.url_rule} disabled due to the mlflow server running " + "with `--no-serve-artifacts`. To enable artifacts server functionality, " + "run `mlflow server` with `--serve-artifacts`" + ), + 503, + ) + return func(*args, **kwargs) + + return wrapper + + +def _disable_if_artifacts_only(func): + @wraps(func) + def wrapper(*args, **kwargs): + from mlflow.server import ARTIFACTS_ONLY_ENV_VAR + + if os.environ.get(ARTIFACTS_ONLY_ENV_VAR): + return Response( + ( + f"Endpoint: {request.url_rule} disabled due to the mlflow server running " + "in `--artifacts-only` mode. To enable tracking server functionality, run " + "`mlflow server` without `--artifacts-only`" + ), + 503, + ) + return func(*args, **kwargs) + + return wrapper + + +@catch_mlflow_exception +def get_artifact_handler(): + run_id = request.args.get("run_id") or request.args.get("run_uuid") + path = request.args["path"] + path = validate_path_is_safe(path) + run = _get_tracking_store().get_run(run_id) + + if _is_servable_proxied_run_artifact_root(run.info.artifact_uri): + artifact_repo = _get_artifact_repo_mlflow_artifacts() + artifact_path = _get_proxied_run_artifact_destination_path( + proxied_artifact_root=run.info.artifact_uri, + relative_path=path, + ) + else: + artifact_repo = _get_artifact_repo(run) + artifact_path = path + + return _send_artifact(artifact_repo, artifact_path) + + +def _not_implemented(): + response = Response() + response.status_code = 404 + return response + + +# Tracking Server APIs + + +@catch_mlflow_exception +@_disable_if_artifacts_only +def _create_experiment(): + request_message = _get_request_message( + CreateExperiment(), + schema={ + "name": [_assert_required, _assert_string], + "artifact_location": [_assert_string], + "tags": [_assert_array], + }, + ) + + tags = [ExperimentTag(tag.key, tag.value) for tag in request_message.tags] + + # Validate query string in artifact location to prevent attacks + parsed_artifact_location = urllib.parse.urlparse(request_message.artifact_location) + if parsed_artifact_location.fragment or parsed_artifact_location.params: + raise MlflowException( + "'artifact_location' URL can't include fragments or params.", + error_code=INVALID_PARAMETER_VALUE, + ) + validate_query_string(parsed_artifact_location.query) + experiment_id = _get_tracking_store().create_experiment( + request_message.name, request_message.artifact_location, tags + ) + response_message = CreateExperiment.Response() + response_message.experiment_id = experiment_id + response = Response(mimetype="application/json") + response.set_data(message_to_json(response_message)) + return response + + +@catch_mlflow_exception +@_disable_if_artifacts_only +def _get_experiment(): + request_message = _get_request_message( + GetExperiment(), schema={"experiment_id": [_assert_required, _assert_string]} + ) + response_message = get_experiment_impl(request_message) + response = Response(mimetype="application/json") + response.set_data(message_to_json(response_message)) + return response + + +def get_experiment_impl(request_message): + response_message = GetExperiment.Response() + experiment = _get_tracking_store().get_experiment(request_message.experiment_id).to_proto() + response_message.experiment.MergeFrom(experiment) + return response_message + + +@catch_mlflow_exception +@_disable_if_artifacts_only +def _get_experiment_by_name(): + request_message = _get_request_message( + GetExperimentByName(), + schema={"experiment_name": [_assert_required, _assert_string]}, + ) + response_message = GetExperimentByName.Response() + store_exp = _get_tracking_store().get_experiment_by_name(request_message.experiment_name) + if store_exp is None: + raise MlflowException( + f"Could not find experiment with name '{request_message.experiment_name}'", + error_code=RESOURCE_DOES_NOT_EXIST, + ) + experiment = store_exp.to_proto() + response_message.experiment.MergeFrom(experiment) + response = Response(mimetype="application/json") + response.set_data(message_to_json(response_message)) + return response + + +@catch_mlflow_exception +@_disable_if_artifacts_only +def _delete_experiment(): + request_message = _get_request_message( + DeleteExperiment(), schema={"experiment_id": [_assert_required, _assert_string]} + ) + _get_tracking_store().delete_experiment(request_message.experiment_id) + response_message = DeleteExperiment.Response() + response = Response(mimetype="application/json") + response.set_data(message_to_json(response_message)) + return response + + +@catch_mlflow_exception +@_disable_if_artifacts_only +def _restore_experiment(): + request_message = _get_request_message( + RestoreExperiment(), + schema={"experiment_id": [_assert_required, _assert_string]}, + ) + _get_tracking_store().restore_experiment(request_message.experiment_id) + response_message = RestoreExperiment.Response() + response = Response(mimetype="application/json") + response.set_data(message_to_json(response_message)) + return response + + +@catch_mlflow_exception +@_disable_if_artifacts_only +def _update_experiment(): + request_message = _get_request_message( + UpdateExperiment(), + schema={ + "experiment_id": [_assert_required, _assert_string], + "new_name": [_assert_string, _assert_required], + }, + ) + if request_message.new_name: + _get_tracking_store().rename_experiment( + request_message.experiment_id, request_message.new_name + ) + response_message = UpdateExperiment.Response() + response = Response(mimetype="application/json") + response.set_data(message_to_json(response_message)) + return response + + +@catch_mlflow_exception +@_disable_if_artifacts_only +def _create_run(): + request_message = _get_request_message( + CreateRun(), + schema={ + "experiment_id": [_assert_string], + "start_time": [_assert_intlike], + "run_name": [_assert_string], + }, + ) + + tags = [RunTag(tag.key, tag.value) for tag in request_message.tags] + run = _get_tracking_store().create_run( + experiment_id=request_message.experiment_id, + user_id=request_message.user_id, + start_time=request_message.start_time, + tags=tags, + run_name=request_message.run_name, + ) + + response_message = CreateRun.Response() + response_message.run.MergeFrom(run.to_proto()) + response = Response(mimetype="application/json") + response.set_data(message_to_json(response_message)) + return response + + +@catch_mlflow_exception +@_disable_if_artifacts_only +def _update_run(): + request_message = _get_request_message( + UpdateRun(), + schema={ + "run_id": [_assert_required, _assert_string], + "end_time": [_assert_intlike], + "status": [_assert_string], + "run_name": [_assert_string], + }, + ) + run_id = request_message.run_id or request_message.run_uuid + run_name = request_message.run_name if request_message.HasField("run_name") else None + end_time = request_message.end_time if request_message.HasField("end_time") else None + status = request_message.status if request_message.HasField("status") else None + updated_info = _get_tracking_store().update_run_info(run_id, status, end_time, run_name) + response_message = UpdateRun.Response(run_info=updated_info.to_proto()) + response = Response(mimetype="application/json") + response.set_data(message_to_json(response_message)) + return response + + +@catch_mlflow_exception +@_disable_if_artifacts_only +def _delete_run(): + request_message = _get_request_message( + DeleteRun(), schema={"run_id": [_assert_required, _assert_string]} + ) + _get_tracking_store().delete_run(request_message.run_id) + response_message = DeleteRun.Response() + response = Response(mimetype="application/json") + response.set_data(message_to_json(response_message)) + return response + + +@catch_mlflow_exception +@_disable_if_artifacts_only +def _restore_run(): + request_message = _get_request_message( + RestoreRun(), schema={"run_id": [_assert_required, _assert_string]} + ) + _get_tracking_store().restore_run(request_message.run_id) + response_message = RestoreRun.Response() + response = Response(mimetype="application/json") + response.set_data(message_to_json(response_message)) + return response + + +@catch_mlflow_exception +@_disable_if_artifacts_only +def _log_metric(): + request_message = _get_request_message( + LogMetric(), + schema={ + "run_id": [_assert_required, _assert_string], + "key": [_assert_required, _assert_string], + "value": [_assert_required, _assert_floatlike], + "timestamp": [_assert_intlike, _assert_required], + "step": [_assert_intlike], + "model_id": [_assert_string], + "dataset_name": [_assert_string], + "dataset_digest": [_assert_string], + }, + ) + metric = Metric( + request_message.key, + request_message.value, + request_message.timestamp, + request_message.step, + request_message.model_id or None, + request_message.dataset_name or None, + request_message.dataset_digest or None, + request_message.run_id or None, + ) + run_id = request_message.run_id or request_message.run_uuid + _get_tracking_store().log_metric(run_id, metric) + response_message = LogMetric.Response() + response = Response(mimetype="application/json") + response.set_data(message_to_json(response_message)) + return response + + +@catch_mlflow_exception +@_disable_if_artifacts_only +def _log_param(): + request_message = _get_request_message( + LogParam(), + schema={ + "run_id": [_assert_required, _assert_string], + "key": [_assert_required, _assert_string], + "value": [_assert_string], + }, + ) + param = Param(request_message.key, request_message.value) + run_id = request_message.run_id or request_message.run_uuid + _get_tracking_store().log_param(run_id, param) + response_message = LogParam.Response() + response = Response(mimetype="application/json") + response.set_data(message_to_json(response_message)) + return response + + +@catch_mlflow_exception +@_disable_if_artifacts_only +def _log_inputs(): + request_message = _get_request_message( + LogInputs(), + schema={ + "run_id": [_assert_required, _assert_string], + "datasets": [_assert_array], + "models": [_assert_array], + }, + ) + run_id = request_message.run_id + datasets = [ + DatasetInput.from_proto(proto_dataset_input) + for proto_dataset_input in request_message.datasets + ] + models = ( + [ + LoggedModelInput.from_proto(proto_logged_model_input) + for proto_logged_model_input in request_message.models + ] + if request_message.models + else None + ) + + _get_tracking_store().log_inputs(run_id, datasets=datasets, models=models) + response_message = LogInputs.Response() + response = Response(mimetype="application/json") + response.set_data(message_to_json(response_message)) + return response + + +@catch_mlflow_exception +@_disable_if_artifacts_only +def _log_outputs(): + request_message = _get_request_message( + LogOutputs(), + schema={ + "run_id": [_assert_required, _assert_string], + "models": [_assert_required, _assert_array], + }, + ) + models = [LoggedModelOutput.from_proto(p) for p in request_message.models] + _get_tracking_store().log_outputs(run_id=request_message.run_id, models=models) + response_message = LogOutputs.Response() + return _wrap_response(response_message) + + +@catch_mlflow_exception +@_disable_if_artifacts_only +def _set_experiment_tag(): + request_message = _get_request_message( + SetExperimentTag(), + schema={ + "experiment_id": [_assert_required, _assert_string], + "key": [_assert_required, _assert_string], + "value": [_assert_string], + }, + ) + tag = ExperimentTag(request_message.key, request_message.value) + _get_tracking_store().set_experiment_tag(request_message.experiment_id, tag) + response_message = SetExperimentTag.Response() + response = Response(mimetype="application/json") + response.set_data(message_to_json(response_message)) + return response + + +@catch_mlflow_exception +@_disable_if_artifacts_only +def _delete_experiment_tag(): + request_message = _get_request_message( + DeleteExperimentTag(), + schema={ + "experiment_id": [_assert_required, _assert_string], + "key": [_assert_required, _assert_string], + }, + ) + _get_tracking_store().delete_experiment_tag(request_message.experiment_id, request_message.key) + response_message = DeleteExperimentTag.Response() + response = Response(mimetype="application/json") + response.set_data(message_to_json(response_message)) + return response + + +@catch_mlflow_exception +@_disable_if_artifacts_only +def _set_tag(): + request_message = _get_request_message( + SetTag(), + schema={ + "run_id": [_assert_required, _assert_string], + "key": [_assert_required, _assert_string], + "value": [_assert_string], + }, + ) + tag = RunTag(request_message.key, request_message.value) + run_id = request_message.run_id or request_message.run_uuid + _get_tracking_store().set_tag(run_id, tag) + response_message = SetTag.Response() + response = Response(mimetype="application/json") + response.set_data(message_to_json(response_message)) + return response + + +@catch_mlflow_exception +@_disable_if_artifacts_only +def _delete_tag(): + request_message = _get_request_message( + DeleteTag(), + schema={ + "run_id": [_assert_required, _assert_string], + "key": [_assert_required, _assert_string], + }, + ) + _get_tracking_store().delete_tag(request_message.run_id, request_message.key) + response_message = DeleteTag.Response() + response = Response(mimetype="application/json") + response.set_data(message_to_json(response_message)) + return response + + +@catch_mlflow_exception +@_disable_if_artifacts_only +def _get_run(): + request_message = _get_request_message( + GetRun(), schema={"run_id": [_assert_required, _assert_string]} + ) + response_message = get_run_impl(request_message) + response = Response(mimetype="application/json") + response.set_data(message_to_json(response_message)) + return response + + +def get_run_impl(request_message): + response_message = GetRun.Response() + run_id = request_message.run_id or request_message.run_uuid + response_message.run.MergeFrom(_get_tracking_store().get_run(run_id).to_proto()) + return response_message + + +@catch_mlflow_exception +@_disable_if_artifacts_only +def _search_runs(): + request_message = _get_request_message( + SearchRuns(), + schema={ + "experiment_ids": [_assert_array], + "filter": [_assert_string], + "max_results": [ + _assert_intlike, + lambda x: _assert_less_than_or_equal(int(x), 50000), + ], + "order_by": [_assert_array, _assert_item_type_string], + }, + ) + response_message = search_runs_impl(request_message) + response = Response(mimetype="application/json") + response.set_data(message_to_json(response_message)) + return response + + +def search_runs_impl(request_message): + response_message = SearchRuns.Response() + run_view_type = ViewType.ACTIVE_ONLY + if request_message.HasField("run_view_type"): + run_view_type = ViewType.from_proto(request_message.run_view_type) + filter_string = request_message.filter + max_results = request_message.max_results + experiment_ids = request_message.experiment_ids + order_by = request_message.order_by + page_token = request_message.page_token + run_entities = _get_tracking_store().search_runs( + experiment_ids, filter_string, run_view_type, max_results, order_by, page_token + ) + response_message.runs.extend([r.to_proto() for r in run_entities]) + if run_entities.token: + response_message.next_page_token = run_entities.token + return response_message + + +@catch_mlflow_exception +@_disable_if_artifacts_only +def _list_artifacts(): + request_message = _get_request_message( + ListArtifacts(), + schema={ + "run_id": [_assert_string, _assert_required], + "path": [_assert_string], + "page_token": [_assert_string], + }, + ) + response_message = list_artifacts_impl(request_message) + response = Response(mimetype="application/json") + response.set_data(message_to_json(response_message)) + return response + + +def list_artifacts_impl(request_message): + response_message = ListArtifacts.Response() + if request_message.HasField("path"): + path = request_message.path + path = validate_path_is_safe(path) + else: + path = None + run_id = request_message.run_id or request_message.run_uuid + run = _get_tracking_store().get_run(run_id) + + if _is_servable_proxied_run_artifact_root(run.info.artifact_uri): + artifact_entities = _list_artifacts_for_proxied_run_artifact_root( + proxied_artifact_root=run.info.artifact_uri, + relative_path=path, + ) + else: + artifact_entities = _get_artifact_repo(run).list_artifacts(path) + + response_message.files.extend([a.to_proto() for a in artifact_entities]) + response_message.root_uri = run.info.artifact_uri + return response_message + + +@catch_mlflow_exception +def _list_artifacts_for_proxied_run_artifact_root(proxied_artifact_root, relative_path=None): + """ + Lists artifacts from the specified ``relative_path`` within the specified proxied Run artifact + root (i.e. a Run artifact root with scheme ``http``, ``https``, or ``mlflow-artifacts``). + + Args: + proxied_artifact_root: The Run artifact root location (URI) with scheme ``http``, + ``https``, or ``mlflow-artifacts`` that can be resolved by the + MLflow server to a concrete storage location. + relative_path: The relative path within the specified ``proxied_artifact_root`` under + which to list artifact contents. If ``None``, artifacts are listed from + the ``proxied_artifact_root`` directory. + """ + parsed_proxied_artifact_root = urllib.parse.urlparse(proxied_artifact_root) + assert parsed_proxied_artifact_root.scheme in ["http", "https", "mlflow-artifacts"] + + artifact_destination_repo = _get_artifact_repo_mlflow_artifacts() + artifact_destination_path = _get_proxied_run_artifact_destination_path( + proxied_artifact_root=proxied_artifact_root, + relative_path=relative_path, + ) + + artifact_entities = [] + for file_info in artifact_destination_repo.list_artifacts(artifact_destination_path): + basename = posixpath.basename(file_info.path) + run_relative_artifact_path = ( + posixpath.join(relative_path, basename) if relative_path else basename + ) + artifact_entities.append( + FileInfo(run_relative_artifact_path, file_info.is_dir, file_info.file_size) + ) + + return artifact_entities + + +@catch_mlflow_exception +@_disable_if_artifacts_only +def _get_metric_history(): + request_message = _get_request_message( + GetMetricHistory(), + schema={ + "run_id": [_assert_string, _assert_required], + "metric_key": [_assert_string, _assert_required], + "page_token": [_assert_string], + }, + ) + response_message = GetMetricHistory.Response() + run_id = request_message.run_id or request_message.run_uuid + + max_results = request_message.max_results if request_message.max_results is not None else None + page_token = request_message.page_token if request_message.page_token else None + + metric_entities = _get_tracking_store().get_metric_history( + run_id, request_message.metric_key, max_results=max_results, page_token=page_token + ) + response_message.metrics.extend([m.to_proto() for m in metric_entities]) + + # Set next_page_token if available + if next_page_token := metric_entities.token: + response_message.next_page_token = next_page_token + + response = Response(mimetype="application/json") + response.set_data(message_to_json(response_message)) + return response + + +@catch_mlflow_exception +@_disable_if_artifacts_only +def get_metric_history_bulk_handler(): + MAX_HISTORY_RESULTS = 25000 + MAX_RUN_IDS_PER_REQUEST = 100 + run_ids = request.args.to_dict(flat=False).get("run_id", []) + if not run_ids: + raise MlflowException( + message="GetMetricHistoryBulk request must specify at least one run_id.", + error_code=INVALID_PARAMETER_VALUE, + ) + if len(run_ids) > MAX_RUN_IDS_PER_REQUEST: + raise MlflowException( + message=( + f"GetMetricHistoryBulk request cannot specify more than {MAX_RUN_IDS_PER_REQUEST}" + f" run_ids. Received {len(run_ids)} run_ids." + ), + error_code=INVALID_PARAMETER_VALUE, + ) + + metric_key = request.args.get("metric_key") + if metric_key is None: + raise MlflowException( + message="GetMetricHistoryBulk request must specify a metric_key.", + error_code=INVALID_PARAMETER_VALUE, + ) + + max_results = int(request.args.get("max_results", MAX_HISTORY_RESULTS)) + max_results = min(max_results, MAX_HISTORY_RESULTS) + + store = _get_tracking_store() + + def _default_history_bulk_impl(): + metrics_with_run_ids = [] + for run_id in sorted(run_ids): + metrics_for_run = sorted( + store.get_metric_history( + run_id=run_id, + metric_key=metric_key, + max_results=max_results, + ), + key=lambda metric: (metric.timestamp, metric.step, metric.value), + ) + metrics_with_run_ids.extend( + [ + { + "key": metric.key, + "value": metric.value, + "timestamp": metric.timestamp, + "step": metric.step, + "run_id": run_id, + } + for metric in metrics_for_run + ] + ) + return metrics_with_run_ids + + if hasattr(store, "get_metric_history_bulk"): + metrics_with_run_ids = [ + metric.to_dict() + for metric in store.get_metric_history_bulk( + run_ids=run_ids, + metric_key=metric_key, + max_results=max_results, + ) + ] + else: + metrics_with_run_ids = _default_history_bulk_impl() + + return { + "metrics": metrics_with_run_ids[:max_results], + } + + +def _get_sampled_steps_from_steps( + start_step: int, end_step: int, max_results: int, all_steps: list[int] +) -> set[int]: + # NOTE: all_steps should be sorted before + # being passed to this function + start_idx = bisect.bisect_left(all_steps, start_step) + end_idx = bisect.bisect_right(all_steps, end_step) + if end_idx - start_idx <= max_results: + return set(all_steps[start_idx:end_idx]) + + num_steps = end_idx - start_idx + interval = num_steps / max_results + sampled_steps = [] + + for i in range(0, max_results): + idx = start_idx + int(i * interval) + if idx < num_steps: + sampled_steps.append(all_steps[idx]) + + sampled_steps.append(all_steps[end_idx - 1]) + return set(sampled_steps) + + +@catch_mlflow_exception +@_disable_if_artifacts_only +def get_metric_history_bulk_interval_handler(): + request_message = _get_request_message( + GetMetricHistoryBulkInterval(), + schema={ + "run_ids": [ + _assert_required, + _assert_array, + _assert_item_type_string, + lambda x: _assert_less_than_or_equal( + len(x), + MAX_RUNS_GET_METRIC_HISTORY_BULK, + message=f"GetMetricHistoryBulkInterval request must specify at most " + f"{MAX_RUNS_GET_METRIC_HISTORY_BULK} run_ids. Received {len(x)} run_ids.", + ), + ], + "metric_key": [_assert_required, _assert_string], + "start_step": [_assert_intlike], + "end_step": [_assert_intlike], + "max_results": [ + _assert_intlike, + lambda x: _assert_intlike_within_range( + int(x), + 1, + MAX_RESULTS_PER_RUN, + message=f"max_results must be between 1 and {MAX_RESULTS_PER_RUN}.", + ), + ], + }, + ) + response_message = get_metric_history_bulk_interval_impl(request_message) + response = Response(mimetype="application/json") + response.set_data(message_to_json(response_message)) + return response + + +def get_metric_history_bulk_interval_impl(request_message): + args = request.args + run_ids = request_message.run_ids + metric_key = request_message.metric_key + max_results = int(args.get("max_results", MAX_RESULTS_PER_RUN)) + + store = _get_tracking_store() + + def _get_sampled_steps(run_ids, metric_key, max_results): + # cannot fetch from request_message as the default value is 0 + start_step = args.get("start_step") + end_step = args.get("end_step") + + # perform validation before any data fetching occurs + if start_step is not None and end_step is not None: + start_step = int(start_step) + end_step = int(end_step) + if start_step > end_step: + raise MlflowException.invalid_parameter_value( + "end_step must be greater than start_step. " + f"Found start_step={start_step} and end_step={end_step}." + ) + elif start_step is not None or end_step is not None: + raise MlflowException.invalid_parameter_value( + "If either start step or end step are specified, both must be specified." + ) + + # get a list of all steps for all runs. this is necessary + # because we can't assume that every step was logged, so + # sampling needs to be done on the steps that actually exist + all_runs = [ + [m.step for m in store.get_metric_history(run_id, metric_key)] for run_id in run_ids + ] + + # save mins and maxes to be added back later + all_mins_and_maxes = {step for run in all_runs if run for step in [min(run), max(run)]} + all_steps = sorted({step for sublist in all_runs for step in sublist}) + + # init start and end step if not provided in args + if start_step is None and end_step is None: + start_step = 0 + end_step = all_steps[-1] if all_steps else 0 + + # remove any steps outside of the range + all_mins_and_maxes = {step for step in all_mins_and_maxes if start_step <= step <= end_step} + + # doing extra iterations here shouldn't badly affect performance, + # since the number of steps at this point should be relatively small + # (MAX_RESULTS_PER_RUN + len(all_mins_and_maxes)) + sampled_steps = _get_sampled_steps_from_steps(start_step, end_step, max_results, all_steps) + return sorted(sampled_steps.union(all_mins_and_maxes)) + + def _default_history_bulk_interval_impl(): + steps = _get_sampled_steps(run_ids, metric_key, max_results) + metrics_with_run_ids = [] + for run_id in run_ids: + metrics_with_run_ids.extend( + store.get_metric_history_bulk_interval_from_steps( + run_id=run_id, + metric_key=metric_key, + steps=steps, + max_results=MAX_RESULTS_GET_METRIC_HISTORY, + ) + ) + return metrics_with_run_ids + + metrics_with_run_ids = _default_history_bulk_interval_impl() + + response_message = GetMetricHistoryBulkInterval.Response() + response_message.metrics.extend([m.to_proto() for m in metrics_with_run_ids]) + return response_message + + +@catch_mlflow_exception +@_disable_if_artifacts_only +def search_datasets_handler(): + request_message = _get_request_message( + SearchDatasets(), + ) + response_message = search_datasets_impl(request_message) + response = Response(mimetype="application/json") + response.set_data(message_to_json(response_message)) + return response + + +def search_datasets_impl(request_message): + MAX_EXPERIMENT_IDS_PER_REQUEST = 20 + _validate_content_type(request, ["application/json"]) + experiment_ids = request_message.experiment_ids or [] + if not experiment_ids: + raise MlflowException( + message="SearchDatasets request must specify at least one experiment_id.", + error_code=INVALID_PARAMETER_VALUE, + ) + if len(experiment_ids) > MAX_EXPERIMENT_IDS_PER_REQUEST: + raise MlflowException( + message=( + f"SearchDatasets request cannot specify more than {MAX_EXPERIMENT_IDS_PER_REQUEST}" + f" experiment_ids. Received {len(experiment_ids)} experiment_ids." + ), + error_code=INVALID_PARAMETER_VALUE, + ) + + store = _get_tracking_store() + + if hasattr(store, "_search_datasets"): + response_message = SearchDatasets.Response() + response_message.dataset_summaries.extend( + [summary.to_proto() for summary in store._search_datasets(experiment_ids)] + ) + return response_message + else: + return _not_implemented() + + +def _validate_gateway_path(method: str, gateway_path: str) -> None: + if not gateway_path: + raise MlflowException( + message="Deployments proxy request must specify a gateway_path.", + error_code=INVALID_PARAMETER_VALUE, + ) + elif method == "GET": + if gateway_path.strip("/") != "api/2.0/endpoints": + raise MlflowException( + message=f"Invalid gateway_path: {gateway_path} for method: {method}", + error_code=INVALID_PARAMETER_VALUE, + ) + elif method == "POST": + # For POST, gateway_path must be in the form of "gateway/{name}/invocations" + if not re.fullmatch(r"gateway/[^/]+/invocations", gateway_path.strip("/")): + raise MlflowException( + message=f"Invalid gateway_path: {gateway_path} for method: {method}", + error_code=INVALID_PARAMETER_VALUE, + ) + + +@catch_mlflow_exception +def gateway_proxy_handler(): + target_uri = MLFLOW_DEPLOYMENTS_TARGET.get() + if not target_uri: + # Pretend an empty gateway service is running + return {"endpoints": []} + + args = request.args if request.method == "GET" else request.json + gateway_path = args.get("gateway_path") + _validate_gateway_path(request.method, gateway_path) + json_data = args.get("json_data", None) + response = requests.request(request.method, f"{target_uri}/{gateway_path}", json=json_data) + if response.status_code == 200: + return response.json() + else: + raise MlflowException( + message=f"Deployments proxy request failed with error code {response.status_code}. " + f"Error message: {response.text}", + error_code=response.status_code, + ) + + +@catch_mlflow_exception +@_disable_if_artifacts_only +def create_promptlab_run_handler(): + def assert_arg_exists(arg_name, arg): + if not arg: + raise MlflowException( + message=f"CreatePromptlabRun request must specify {arg_name}.", + error_code=INVALID_PARAMETER_VALUE, + ) + + _validate_content_type(request, ["application/json"]) + + args = request.json + experiment_id = args.get("experiment_id") + assert_arg_exists("experiment_id", experiment_id) + run_name = args.get("run_name", None) + tags = args.get("tags", []) + prompt_template = args.get("prompt_template") + assert_arg_exists("prompt_template", prompt_template) + raw_prompt_parameters = args.get("prompt_parameters") + assert_arg_exists("prompt_parameters", raw_prompt_parameters) + prompt_parameters = [ + Param(param.get("key"), param.get("value")) for param in args.get("prompt_parameters") + ] + model_route = args.get("model_route") + assert_arg_exists("model_route", model_route) + raw_model_parameters = args.get("model_parameters", []) + model_parameters = [ + Param(param.get("key"), param.get("value")) for param in raw_model_parameters + ] + model_input = args.get("model_input") + assert_arg_exists("model_input", model_input) + model_output = args.get("model_output", None) + raw_model_output_parameters = args.get("model_output_parameters", []) + model_output_parameters = [ + Param(param.get("key"), param.get("value")) for param in raw_model_output_parameters + ] + mlflow_version = args.get("mlflow_version") + assert_arg_exists("mlflow_version", mlflow_version) + user_id = args.get("user_id", "unknown") + + # use current time if not provided + start_time = args.get("start_time", int(time.time() * 1000)) + + store = _get_tracking_store() + + run = _create_promptlab_run_impl( + store, + experiment_id=experiment_id, + run_name=run_name, + tags=tags, + prompt_template=prompt_template, + prompt_parameters=prompt_parameters, + model_route=model_route, + model_parameters=model_parameters, + model_input=model_input, + model_output=model_output, + model_output_parameters=model_output_parameters, + mlflow_version=mlflow_version, + user_id=user_id, + start_time=start_time, + ) + response_message = CreateRun.Response() + response_message.run.MergeFrom(run.to_proto()) + response = Response(mimetype="application/json") + response.set_data(message_to_json(response_message)) + return response + + +@catch_mlflow_exception +def upload_artifact_handler(): + args = request.args + run_uuid = args.get("run_uuid") + if not run_uuid: + raise MlflowException( + message="Request must specify run_uuid.", + error_code=INVALID_PARAMETER_VALUE, + ) + path = args.get("path") + if not path: + raise MlflowException( + message="Request must specify path.", + error_code=INVALID_PARAMETER_VALUE, + ) + path = validate_path_is_safe(path) + + if request.content_length and request.content_length > 10 * 1024 * 1024: + raise MlflowException( + message="Artifact size is too large. Max size is 10MB.", + error_code=INVALID_PARAMETER_VALUE, + ) + + data = request.data + if not data: + raise MlflowException( + message="Request must specify data.", + error_code=INVALID_PARAMETER_VALUE, + ) + + run = _get_tracking_store().get_run(run_uuid) + artifact_dir = run.info.artifact_uri + + basename = posixpath.basename(path) + dirname = posixpath.dirname(path) + + def _log_artifact_to_repo(file, run, dirname, artifact_dir): + if _is_servable_proxied_run_artifact_root(run.info.artifact_uri): + artifact_repo = _get_artifact_repo_mlflow_artifacts() + path_to_log = ( + os.path.join(run.info.experiment_id, run.info.run_id, "artifacts", dirname) + if dirname + else os.path.join(run.info.experiment_id, run.info.run_id, "artifacts") + ) + else: + artifact_repo = get_artifact_repository(artifact_dir) + path_to_log = dirname + + artifact_repo.log_artifact(file, path_to_log) + + with tempfile.TemporaryDirectory() as tmpdir: + dir_path = os.path.join(tmpdir, dirname) if dirname else tmpdir + file_path = os.path.join(dir_path, basename) + + os.makedirs(dir_path, exist_ok=True) + + with open(file_path, "wb") as f: + f.write(data) + + _log_artifact_to_repo(file_path, run, dirname, artifact_dir) + + return Response(mimetype="application/json") + + +@catch_mlflow_exception +@_disable_if_artifacts_only +def _search_experiments(): + request_message = _get_request_message( + SearchExperiments(), + schema={ + "view_type": [_assert_intlike], + "max_results": [_assert_intlike], + "order_by": [_assert_array], + "filter": [_assert_string], + "page_token": [_assert_string], + }, + ) + experiment_entities = _get_tracking_store().search_experiments( + view_type=request_message.view_type, + max_results=request_message.max_results, + order_by=request_message.order_by, + filter_string=request_message.filter, + page_token=request_message.page_token, + ) + response_message = SearchExperiments.Response() + response_message.experiments.extend([e.to_proto() for e in experiment_entities]) + if experiment_entities.token: + response_message.next_page_token = experiment_entities.token + response = Response(mimetype="application/json") + response.set_data(message_to_json(response_message)) + return response + + +@catch_mlflow_exception +def _get_artifact_repo(run): + return get_artifact_repository(run.info.artifact_uri) + + +@catch_mlflow_exception +@_disable_if_artifacts_only +def _log_batch(): + def _assert_metrics_fields_present(metrics): + for idx, m in enumerate(metrics): + _assert_required(m.get("key"), path=f"metrics[{idx}].key") + _assert_required(m.get("value"), path=f"metrics[{idx}].value") + _assert_required(m.get("timestamp"), path=f"metrics[{idx}].timestamp") + + def _assert_params_fields_present(params): + for idx, param in enumerate(params): + _assert_required(param.get("key"), path=f"params[{idx}].key") + + def _assert_tags_fields_present(tags): + for idx, tag in enumerate(tags): + _assert_required(tag.get("key"), path=f"tags[{idx}].key") + + _validate_batch_log_api_req(_get_request_json()) + request_message = _get_request_message( + LogBatch(), + schema={ + "run_id": [_assert_string, _assert_required], + "metrics": [_assert_array, _assert_metrics_fields_present], + "params": [_assert_array, _assert_params_fields_present], + "tags": [_assert_array, _assert_tags_fields_present], + }, + ) + metrics = [Metric.from_proto(proto_metric) for proto_metric in request_message.metrics] + params = [Param.from_proto(proto_param) for proto_param in request_message.params] + tags = [RunTag.from_proto(proto_tag) for proto_tag in request_message.tags] + _get_tracking_store().log_batch( + run_id=request_message.run_id, metrics=metrics, params=params, tags=tags + ) + response_message = LogBatch.Response() + response = Response(mimetype="application/json") + response.set_data(message_to_json(response_message)) + return response + + +@catch_mlflow_exception +@_disable_if_artifacts_only +def _log_model(): + request_message = _get_request_message( + LogModel(), + schema={ + "run_id": [_assert_string, _assert_required], + "model_json": [_assert_string, _assert_required], + }, + ) + try: + model = json.loads(request_message.model_json) + except Exception: + raise MlflowException( + f"Malformed model info. \n {request_message.model_json} \n is not a valid JSON.", + error_code=INVALID_PARAMETER_VALUE, + ) + + missing_fields = {"artifact_path", "flavors", "utc_time_created", "run_id"} - set(model.keys()) + + if missing_fields: + raise MlflowException( + f"Model json is missing mandatory fields: {missing_fields}", + error_code=INVALID_PARAMETER_VALUE, + ) + _get_tracking_store().record_logged_model( + run_id=request_message.run_id, mlflow_model=Model.from_dict(model) + ) + response_message = LogModel.Response() + response = Response(mimetype="application/json") + response.set_data(message_to_json(response_message)) + return response + + +def _wrap_response(response_message): + response = Response(mimetype="application/json") + response.set_data(message_to_json(response_message)) + return response + + +# Model Registry APIs + + +@catch_mlflow_exception +@_disable_if_artifacts_only +def _create_registered_model(): + request_message = _get_request_message( + CreateRegisteredModel(), + schema={ + "name": [_assert_string, _assert_required], + "tags": [_assert_array], + "description": [_assert_string], + }, + ) + store = _get_model_registry_store() + registered_model = store.create_registered_model( + name=request_message.name, + tags=request_message.tags, + description=request_message.description, + ) + response_message = CreateRegisteredModel.Response(registered_model=registered_model.to_proto()) + + deliver_webhook( + event=WebhookEvent(WebhookEntity.REGISTERED_MODEL, WebhookAction.CREATED), + payload=RegisteredModelCreatedPayload( + name=request_message.name, + tags={t.key: t.value for t in request_message.tags}, + description=request_message.description, + ), + store=store, + ) + + return _wrap_response(response_message) + + +@catch_mlflow_exception +@_disable_if_artifacts_only +def _get_registered_model(): + request_message = _get_request_message( + GetRegisteredModel(), schema={"name": [_assert_string, _assert_required]} + ) + registered_model = _get_model_registry_store().get_registered_model(name=request_message.name) + response_message = GetRegisteredModel.Response(registered_model=registered_model.to_proto()) + return _wrap_response(response_message) + + +@catch_mlflow_exception +@_disable_if_artifacts_only +def _update_registered_model(): + request_message = _get_request_message( + UpdateRegisteredModel(), + schema={ + "name": [_assert_string, _assert_required], + "description": [_assert_string], + }, + ) + name = request_message.name + new_description = request_message.description + registered_model = _get_model_registry_store().update_registered_model( + name=name, description=new_description + ) + response_message = UpdateRegisteredModel.Response(registered_model=registered_model.to_proto()) + return _wrap_response(response_message) + + +@catch_mlflow_exception +@_disable_if_artifacts_only +def _rename_registered_model(): + request_message = _get_request_message( + RenameRegisteredModel(), + schema={ + "name": [_assert_string, _assert_required], + "new_name": [_assert_string, _assert_required], + }, + ) + name = request_message.name + new_name = request_message.new_name + registered_model = _get_model_registry_store().rename_registered_model( + name=name, new_name=new_name + ) + response_message = RenameRegisteredModel.Response(registered_model=registered_model.to_proto()) + return _wrap_response(response_message) + + +@catch_mlflow_exception +@_disable_if_artifacts_only +def _delete_registered_model(): + request_message = _get_request_message( + DeleteRegisteredModel(), schema={"name": [_assert_string, _assert_required]} + ) + _get_model_registry_store().delete_registered_model(name=request_message.name) + return _wrap_response(DeleteRegisteredModel.Response()) + + +@catch_mlflow_exception +@_disable_if_artifacts_only +def _search_registered_models(): + request_message = _get_request_message( + SearchRegisteredModels(), + schema={ + "filter": [_assert_string], + "max_results": [ + _assert_intlike, + lambda x: _assert_less_than_or_equal(int(x), 1000), + ], + "order_by": [_assert_array, _assert_item_type_string], + "page_token": [_assert_string], + }, + ) + store = _get_model_registry_store() + registered_models = store.search_registered_models( + filter_string=request_message.filter, + max_results=request_message.max_results, + order_by=request_message.order_by, + page_token=request_message.page_token, + ) + response_message = SearchRegisteredModels.Response() + response_message.registered_models.extend([e.to_proto() for e in registered_models]) + if registered_models.token: + response_message.next_page_token = registered_models.token + return _wrap_response(response_message) + + +@catch_mlflow_exception +@_disable_if_artifacts_only +def _get_latest_versions(): + request_message = _get_request_message( + GetLatestVersions(), + schema={ + "name": [_assert_string, _assert_required], + "stages": [_assert_array, _assert_item_type_string], + }, + ) + latest_versions = _get_model_registry_store().get_latest_versions( + name=request_message.name, stages=request_message.stages + ) + response_message = GetLatestVersions.Response() + response_message.model_versions.extend([e.to_proto() for e in latest_versions]) + return _wrap_response(response_message) + + +@catch_mlflow_exception +@_disable_if_artifacts_only +def _set_registered_model_tag(): + request_message = _get_request_message( + SetRegisteredModelTag(), + schema={ + "name": [_assert_string, _assert_required], + "key": [_assert_string, _assert_required], + "value": [_assert_string], + }, + ) + tag = RegisteredModelTag(key=request_message.key, value=request_message.value) + _get_model_registry_store().set_registered_model_tag(name=request_message.name, tag=tag) + return _wrap_response(SetRegisteredModelTag.Response()) + + +@catch_mlflow_exception +@_disable_if_artifacts_only +def _delete_registered_model_tag(): + request_message = _get_request_message( + DeleteRegisteredModelTag(), + schema={ + "name": [_assert_string, _assert_required], + "key": [_assert_string, _assert_required], + }, + ) + _get_model_registry_store().delete_registered_model_tag( + name=request_message.name, key=request_message.key + ) + return _wrap_response(DeleteRegisteredModelTag.Response()) + + +def _validate_non_local_source_contains_relative_paths(source: str): + """ + Validation check to ensure that sources that are provided that conform to the schemes: + http, https, or mlflow-artifacts do not contain relative path designations that are intended + to access local file system paths on the tracking server. + + Example paths that this validation function is intended to find and raise an Exception if + passed: + "mlflow-artifacts://host:port/../../../../" + "http://host:port/api/2.0/mlflow-artifacts/artifacts/../../../../" + "https://host:port/api/2.0/mlflow-artifacts/artifacts/../../../../" + "/models/artifacts/../../../" + "s3:/my_bucket/models/path/../../other/path" + "file://path/to/../../../../some/where/you/should/not/be" + "mlflow-artifacts://host:port/..%2f..%2f..%2f..%2f" + "http://host:port/api/2.0/mlflow-artifacts/artifacts%00" + """ + invalid_source_error_message = ( + f"Invalid model version source: '{source}'. If supplying a source as an http, https, " + "local file path, ftp, objectstore, or mlflow-artifacts uri, an absolute path must be " + "provided without relative path references present. " + "Please provide an absolute path." + ) + + while (unquoted := urllib.parse.unquote_plus(source)) != source: + source = unquoted + source_path = re.sub(r"/+", "/", urllib.parse.urlparse(source).path.rstrip("/")) + if "\x00" in source_path or any(p == ".." for p in source.split("/")): + raise MlflowException(invalid_source_error_message, INVALID_PARAMETER_VALUE) + resolved_source = pathlib.Path(source_path).resolve().as_posix() + # NB: drive split is specifically for Windows since WindowsPath.resolve() will append the + # drive path of the pwd to a given path. We don't care about the drive here, though. + _, resolved_path = os.path.splitdrive(resolved_source) + + if resolved_path != source_path: + raise MlflowException(invalid_source_error_message, INVALID_PARAMETER_VALUE) + + +def _validate_source_run(source: str, run_id: str) -> None: + if is_local_uri(source): + if run_id: + store = _get_tracking_store() + run = store.get_run(run_id) + source = pathlib.Path(local_file_uri_to_path(source)).resolve() + if is_local_uri(run.info.artifact_uri): + run_artifact_dir = pathlib.Path( + local_file_uri_to_path(run.info.artifact_uri) + ).resolve() + if run_artifact_dir in [source, *source.parents]: + return + + raise MlflowException( + f"Invalid model version source: '{source}'. To use a local path as a model version " + "source, the run_id request parameter has to be specified and the local path has to be " + "contained within the artifact directory of the run specified by the run_id.", + INVALID_PARAMETER_VALUE, + ) + + # Checks if relative paths are present in the source (a security threat). If any are present, + # raises an Exception. + _validate_non_local_source_contains_relative_paths(source) + + +def _validate_source_model(source: str, model_id: str) -> None: + if is_local_uri(source): + if model_id: + store = _get_tracking_store() + model = store.get_logged_model(model_id) + source = pathlib.Path(local_file_uri_to_path(source)).resolve() + if is_local_uri(model.artifact_location): + run_artifact_dir = pathlib.Path( + local_file_uri_to_path(model.artifact_location) + ).resolve() + if run_artifact_dir in [source, *source.parents]: + return + + raise MlflowException( + f"Invalid model version source: '{source}'. To use a local path as a model version " + "source, the model_id request parameter has to be specified and the local path has to " + "be contained within the artifact directory of the run specified by the model_id.", + INVALID_PARAMETER_VALUE, + ) + + # Checks if relative paths are present in the source (a security threat). If any are present, + # raises an Exception. + _validate_non_local_source_contains_relative_paths(source) + + +@catch_mlflow_exception +@_disable_if_artifacts_only +def _create_model_version(): + request_message = _get_request_message( + CreateModelVersion(), + schema={ + "name": [_assert_string, _assert_required], + "source": [_assert_string, _assert_required], + "run_id": [_assert_string], + "tags": [_assert_array], + "run_link": [_assert_string], + "description": [_assert_string], + "model_id": [_assert_string], + }, + ) + + if request_message.source and ( + regex := MLFLOW_CREATE_MODEL_VERSION_SOURCE_VALIDATION_REGEX.get() + ): + if not re.search(regex, request_message.source): + raise MlflowException( + f"Invalid model version source: '{request_message.source}'.", + error_code=INVALID_PARAMETER_VALUE, + ) + + # If the model version is a prompt, we don't validate the source + is_prompt = _is_prompt_request(request_message) + if not is_prompt: + if request_message.model_id: + _validate_source_model(request_message.source, request_message.model_id) + else: + _validate_source_run(request_message.source, request_message.run_id) + + store = _get_model_registry_store() + model_version = store.create_model_version( + name=request_message.name, + source=request_message.source, + run_id=request_message.run_id, + run_link=request_message.run_link, + tags=request_message.tags, + description=request_message.description, + model_id=request_message.model_id, + ) + if not is_prompt and request_message.model_id: + tracking_store = _get_tracking_store() + tracking_store.set_model_versions_tags( + name=request_message.name, + version=model_version.version, + model_id=request_message.model_id, + ) + response_message = CreateModelVersion.Response(model_version=model_version.to_proto()) + + if not is_prompt: + deliver_webhook( + event=WebhookEvent(WebhookEntity.MODEL_VERSION, WebhookAction.CREATED), + payload=ModelVersionCreatedPayload( + name=request_message.name, + version=str(model_version.version), + source=request_message.source, + run_id=request_message.run_id or None, + tags={t.key: t.value for t in request_message.tags}, + description=request_message.description or None, + ), + store=store, + ) + + return _wrap_response(response_message) + + +def _is_prompt_request(request_message): + return any(tag.key == IS_PROMPT_TAG_KEY for tag in request_message.tags) + + +def _is_prompt(name: str) -> bool: + rm = _get_model_registry_store().get_registered_model(name=name) + return rm._is_prompt() + + +@catch_mlflow_exception +@_disable_if_artifacts_only +def get_model_version_artifact_handler(): + name = request.args.get("name") + version = request.args.get("version") + path = request.args["path"] + path = validate_path_is_safe(path) + artifact_uri = _get_model_registry_store().get_model_version_download_uri(name, version) + if _is_servable_proxied_run_artifact_root(artifact_uri): + artifact_repo = _get_artifact_repo_mlflow_artifacts() + artifact_path = _get_proxied_run_artifact_destination_path( + proxied_artifact_root=artifact_uri, + relative_path=path, + ) + else: + artifact_repo = get_artifact_repository(artifact_uri) + artifact_path = path + + return _send_artifact(artifact_repo, artifact_path) + + +@catch_mlflow_exception +@_disable_if_artifacts_only +def _get_model_version(): + request_message = _get_request_message( + GetModelVersion(), + schema={ + "name": [_assert_string, _assert_required], + "version": [_assert_string, _assert_required], + }, + ) + model_version = _get_model_registry_store().get_model_version( + name=request_message.name, version=request_message.version + ) + response_proto = model_version.to_proto() + response_message = GetModelVersion.Response(model_version=response_proto) + return _wrap_response(response_message) + + +@catch_mlflow_exception +@_disable_if_artifacts_only +def _update_model_version(): + request_message = _get_request_message( + UpdateModelVersion(), + schema={ + "name": [_assert_string, _assert_required], + "version": [_assert_string, _assert_required], + "description": [_assert_string], + }, + ) + new_description = None + if request_message.HasField("description"): + new_description = request_message.description + model_version = _get_model_registry_store().update_model_version( + name=request_message.name, + version=request_message.version, + description=new_description, + ) + return _wrap_response(UpdateModelVersion.Response(model_version=model_version.to_proto())) + + +@catch_mlflow_exception +@_disable_if_artifacts_only +def _transition_stage(): + request_message = _get_request_message( + TransitionModelVersionStage(), + schema={ + "name": [_assert_string, _assert_required], + "version": [_assert_string, _assert_required], + "stage": [_assert_string, _assert_required], + "archive_existing_versions": [_assert_bool], + }, + ) + model_version = _get_model_registry_store().transition_model_version_stage( + name=request_message.name, + version=request_message.version, + stage=request_message.stage, + archive_existing_versions=request_message.archive_existing_versions, + ) + return _wrap_response( + TransitionModelVersionStage.Response(model_version=model_version.to_proto()) + ) + + +@catch_mlflow_exception +@_disable_if_artifacts_only +def _delete_model_version(): + request_message = _get_request_message( + DeleteModelVersion(), + schema={ + "name": [_assert_string, _assert_required], + "version": [_assert_string, _assert_required], + }, + ) + _get_model_registry_store().delete_model_version( + name=request_message.name, version=request_message.version + ) + return _wrap_response(DeleteModelVersion.Response()) + + +@catch_mlflow_exception +@_disable_if_artifacts_only +def _get_model_version_download_uri(): + request_message = _get_request_message(GetModelVersionDownloadUri()) + download_uri = _get_model_registry_store().get_model_version_download_uri( + name=request_message.name, version=request_message.version + ) + response_message = GetModelVersionDownloadUri.Response(artifact_uri=download_uri) + return _wrap_response(response_message) + + +@catch_mlflow_exception +@_disable_if_artifacts_only +def _search_model_versions(): + request_message = _get_request_message( + SearchModelVersions(), + schema={ + "filter": [_assert_string], + "max_results": [ + _assert_intlike, + lambda x: _assert_less_than_or_equal(int(x), 200_000), + ], + "order_by": [_assert_array, _assert_item_type_string], + "page_token": [_assert_string], + }, + ) + response_message = search_model_versions_impl(request_message) + return _wrap_response(response_message) + + +def search_model_versions_impl(request_message): + store = _get_model_registry_store() + model_versions = store.search_model_versions( + filter_string=request_message.filter, + max_results=request_message.max_results, + order_by=request_message.order_by, + page_token=request_message.page_token, + ) + response_message = SearchModelVersions.Response() + response_message.model_versions.extend([e.to_proto() for e in model_versions]) + if model_versions.token: + response_message.next_page_token = model_versions.token + return response_message + + +@catch_mlflow_exception +@_disable_if_artifacts_only +def _set_model_version_tag(): + request_message = _get_request_message( + SetModelVersionTag(), + schema={ + "name": [_assert_string, _assert_required], + "version": [_assert_string, _assert_required], + "key": [_assert_string, _assert_required], + "value": [_assert_string], + }, + ) + tag = ModelVersionTag(key=request_message.key, value=request_message.value) + store = _get_model_registry_store() + store.set_model_version_tag(name=request_message.name, version=request_message.version, tag=tag) + + if not _is_prompt(request_message.name): + deliver_webhook( + event=WebhookEvent(WebhookEntity.MODEL_VERSION_TAG, WebhookAction.SET), + payload=ModelVersionTagSetPayload( + name=request_message.name, + version=request_message.version, + key=request_message.key, + value=request_message.value, + ), + store=store, + ) + + return _wrap_response(SetModelVersionTag.Response()) + + +@catch_mlflow_exception +@_disable_if_artifacts_only +def _delete_model_version_tag(): + request_message = _get_request_message( + DeleteModelVersionTag(), + schema={ + "name": [_assert_string, _assert_required], + "version": [_assert_string, _assert_required], + "key": [_assert_string, _assert_required], + }, + ) + store = _get_model_registry_store() + store.delete_model_version_tag( + name=request_message.name, + version=request_message.version, + key=request_message.key, + ) + + if not _is_prompt(request_message.name): + deliver_webhook( + event=WebhookEvent(WebhookEntity.MODEL_VERSION_TAG, WebhookAction.DELETED), + payload=ModelVersionTagDeletedPayload( + name=request_message.name, + version=request_message.version, + key=request_message.key, + ), + store=store, + ) + + return _wrap_response(DeleteModelVersionTag.Response()) + + +@catch_mlflow_exception +@_disable_if_artifacts_only +def _set_registered_model_alias(): + request_message = _get_request_message( + SetRegisteredModelAlias(), + schema={ + "name": [_assert_string, _assert_required], + "alias": [_assert_string, _assert_required], + "version": [_assert_string, _assert_required], + }, + ) + store = _get_model_registry_store() + store.set_registered_model_alias( + name=request_message.name, + alias=request_message.alias, + version=request_message.version, + ) + + if not _is_prompt(request_message.name): + deliver_webhook( + event=WebhookEvent(WebhookEntity.MODEL_VERSION_ALIAS, WebhookAction.CREATED), + payload=ModelVersionAliasCreatedPayload( + name=request_message.name, + alias=request_message.alias, + version=request_message.version, + ), + store=store, + ) + + return _wrap_response(SetRegisteredModelAlias.Response()) + + +@catch_mlflow_exception +@_disable_if_artifacts_only +def _delete_registered_model_alias(): + request_message = _get_request_message( + DeleteRegisteredModelAlias(), + schema={ + "name": [_assert_string, _assert_required], + "alias": [_assert_string, _assert_required], + }, + ) + store = _get_model_registry_store() + store.delete_registered_model_alias(name=request_message.name, alias=request_message.alias) + + if not _is_prompt(request_message.name): + deliver_webhook( + event=WebhookEvent(WebhookEntity.MODEL_VERSION_ALIAS, WebhookAction.DELETED), + payload=ModelVersionAliasDeletedPayload( + name=request_message.name, + alias=request_message.alias, + ), + store=store, + ) + + return _wrap_response(DeleteRegisteredModelAlias.Response()) + + +@catch_mlflow_exception +@_disable_if_artifacts_only +def _get_model_version_by_alias(): + request_message = _get_request_message( + GetModelVersionByAlias(), + schema={ + "name": [_assert_string, _assert_required], + "alias": [_assert_string, _assert_required], + }, + ) + model_version = _get_model_registry_store().get_model_version_by_alias( + name=request_message.name, alias=request_message.alias + ) + response_proto = model_version.to_proto() + response_message = GetModelVersionByAlias.Response(model_version=response_proto) + return _wrap_response(response_message) + + +# Webhook APIs +@catch_mlflow_exception +@_disable_if_artifacts_only +def _create_webhook(): + request_message = _get_request_message( + CreateWebhook(), + schema={ + "name": [_assert_string, _assert_required], + "url": [_assert_string, _assert_required], + "events": [_assert_array, _assert_required], + "description": [_assert_string], + "secret": [_assert_string], + "status": [_assert_string], + }, + ) + + webhook = _get_model_registry_store().create_webhook( + name=request_message.name, + url=request_message.url, + events=[WebhookEvent.from_proto(e) for e in request_message.events], + description=request_message.description or None, + secret=request_message.secret or None, + status=WebhookStatus.from_proto(request_message.status) if request_message.status else None, + ) + response_message = CreateWebhook.Response(webhook=webhook.to_proto()) + return _wrap_response(response_message) + + +@catch_mlflow_exception +@_disable_if_artifacts_only +def _list_webhooks(): + request_message = _get_request_message( + ListWebhooks(), + schema={ + "max_results": [_assert_intlike], + "page_token": [_assert_string], + }, + ) + webhooks_page = _get_model_registry_store().list_webhooks( + max_results=request_message.max_results, + page_token=request_message.page_token, + ) + response_message = ListWebhooks.Response( + webhooks=[w.to_proto() for w in webhooks_page], + next_page_token=webhooks_page.token, + ) + return _wrap_response(response_message) + + +@catch_mlflow_exception +@_disable_if_artifacts_only +def _get_webhook(webhook_id: str): + webhook = _get_model_registry_store().get_webhook(webhook_id=webhook_id) + response_message = GetWebhook.Response(webhook=webhook.to_proto()) + return _wrap_response(response_message) + + +@catch_mlflow_exception +@_disable_if_artifacts_only +def _update_webhook(webhook_id: str): + request_message = _get_request_message( + UpdateWebhook(), + schema={ + "name": [_assert_string], + "description": [_assert_string], + "url": [_assert_string], + "events": [_assert_array], + "secret": [_assert_string], + "status": [_assert_string], + }, + ) + webhook = _get_model_registry_store().update_webhook( + webhook_id=webhook_id, + name=request_message.name or None, + description=request_message.description or None, + url=request_message.url or None, + events=( + [WebhookEvent.from_proto(e) for e in request_message.events] + if request_message.events + else None + ), + secret=request_message.secret or None, + status=WebhookStatus.from_proto(request_message.status) if request_message.status else None, + ) + response_message = UpdateWebhook.Response(webhook=webhook.to_proto()) + return _wrap_response(response_message) + + +@catch_mlflow_exception +@_disable_if_artifacts_only +def _delete_webhook(webhook_id: str): + _get_model_registry_store().delete_webhook(webhook_id=webhook_id) + response_message = DeleteWebhook.Response() + return _wrap_response(response_message) + + +@catch_mlflow_exception +@_disable_if_artifacts_only +def _test_webhook(webhook_id: str): + request_message = _get_request_message(TestWebhook()) + event = ( + WebhookEvent.from_proto(request_message.event) + if request_message.HasField("event") + else None + ) + store = _get_model_registry_store() + webhook = store.get_webhook(webhook_id=webhook_id) + test_result = test_webhook(webhook=webhook, event=event) + response_message = TestWebhook.Response(result=test_result.to_proto()) + return _wrap_response(response_message) + + +# MLflow Artifacts APIs + + +@catch_mlflow_exception +@_disable_unless_serve_artifacts +def _download_artifact(artifact_path): + """ + A request handler for `GET /mlflow-artifacts/artifacts/` to download an artifact + from `artifact_path` (a relative path from the root artifact directory). + """ + artifact_path = validate_path_is_safe(artifact_path) + tmp_dir = tempfile.TemporaryDirectory() + artifact_repo = _get_artifact_repo_mlflow_artifacts() + dst = artifact_repo.download_artifacts(artifact_path, tmp_dir.name) + + # Ref: https://stackoverflow.com/a/24613980/6943581 + file_handle = open(dst, "rb") # noqa: SIM115 + + def stream_and_remove_file(): + yield from file_handle + file_handle.close() + tmp_dir.cleanup() + + file_sender_response = current_app.response_class(stream_and_remove_file()) + + return _response_with_file_attachment_headers(artifact_path, file_sender_response) + + +@catch_mlflow_exception +@_disable_unless_serve_artifacts +def _upload_artifact(artifact_path): + """ + A request handler for `PUT /mlflow-artifacts/artifacts/` to upload an artifact + to `artifact_path` (a relative path from the root artifact directory). + """ + artifact_path = validate_path_is_safe(artifact_path) + head, tail = posixpath.split(artifact_path) + with tempfile.TemporaryDirectory() as tmp_dir: + tmp_path = os.path.join(tmp_dir, tail) + with open(tmp_path, "wb") as f: + chunk_size = 1024 * 1024 # 1 MB + while True: + chunk = request.stream.read(chunk_size) + if len(chunk) == 0: + break + f.write(chunk) + + artifact_repo = _get_artifact_repo_mlflow_artifacts() + artifact_repo.log_artifact(tmp_path, artifact_path=head or None) + + return _wrap_response(UploadArtifact.Response()) + + +@catch_mlflow_exception +@_disable_unless_serve_artifacts +def _list_artifacts_mlflow_artifacts(): + """ + A request handler for `GET /mlflow-artifacts/artifacts?path=` to list artifacts in `path` + (a relative path from the root artifact directory). + """ + request_message = _get_request_message(ListArtifactsMlflowArtifacts()) + path = validate_path_is_safe(request_message.path) if request_message.HasField("path") else None + artifact_repo = _get_artifact_repo_mlflow_artifacts() + files = [] + for file_info in artifact_repo.list_artifacts(path): + basename = posixpath.basename(file_info.path) + new_file_info = FileInfo(basename, file_info.is_dir, file_info.file_size) + files.append(new_file_info.to_proto()) + response_message = ListArtifacts.Response() + response_message.files.extend(files) + response = Response(mimetype="application/json") + response.set_data(message_to_json(response_message)) + return response + + +@catch_mlflow_exception +@_disable_unless_serve_artifacts +def _delete_artifact_mlflow_artifacts(artifact_path): + """ + A request handler for `DELETE /mlflow-artifacts/artifacts?path=` to delete artifacts in + `path` (a relative path from the root artifact directory). + """ + artifact_path = validate_path_is_safe(artifact_path) + _get_request_message(DeleteArtifact()) + artifact_repo = _get_artifact_repo_mlflow_artifacts() + artifact_repo.delete_artifacts(artifact_path) + response_message = DeleteArtifact.Response() + response = Response(mimetype="application/json") + response.set_data(message_to_json(response_message)) + return response + + +@catch_mlflow_exception +def _graphql(): + from graphql import parse + + from mlflow.server.graphql.graphql_no_batching import check_query_safety + from mlflow.server.graphql.graphql_schema_extensions import schema + + # Extracting the query, variables, and operationName from the request + request_json = _get_request_json() + query = request_json.get("query") + variables = request_json.get("variables") + operation_name = request_json.get("operationName") + + node = parse(query) + if check_result := check_query_safety(node): + result = check_result + else: + # Executing the GraphQL query using the Graphene schema + result = schema.execute(query, variables=variables, operation_name=operation_name) + + # Convert execution result into json. + result_data = { + "data": result.data, + "errors": [error.message for error in result.errors] if result.errors else None, + } + + # Return the response + return jsonify(result_data) + + +def _validate_support_multipart_upload(artifact_repo): + if not isinstance(artifact_repo, MultipartUploadMixin): + raise _UnsupportedMultipartUploadException() + + +@catch_mlflow_exception +@_disable_unless_serve_artifacts +def _create_multipart_upload_artifact(artifact_path): + """ + A request handler for `POST /mlflow-artifacts/mpu/create` to create a multipart upload + to `artifact_path` (a relative path from the root artifact directory). + """ + artifact_path = validate_path_is_safe(artifact_path) + + request_message = _get_request_message( + CreateMultipartUpload(), + schema={ + "path": [_assert_required, _assert_string], + "num_parts": [_assert_intlike], + }, + ) + path = request_message.path + num_parts = request_message.num_parts + + artifact_repo = _get_artifact_repo_mlflow_artifacts() + _validate_support_multipart_upload(artifact_repo) + + create_response = artifact_repo.create_multipart_upload( + path, + num_parts, + artifact_path, + ) + response_message = create_response.to_proto() + response = Response(mimetype="application/json") + response.set_data(message_to_json(response_message)) + return response + + +@catch_mlflow_exception +@_disable_unless_serve_artifacts +def _complete_multipart_upload_artifact(artifact_path): + """ + A request handler for `POST /mlflow-artifacts/mpu/complete` to complete a multipart upload + to `artifact_path` (a relative path from the root artifact directory). + """ + artifact_path = validate_path_is_safe(artifact_path) + + request_message = _get_request_message( + CompleteMultipartUpload(), + schema={ + "path": [_assert_required, _assert_string], + "upload_id": [_assert_string], + "parts": [_assert_required], + }, + ) + path = request_message.path + upload_id = request_message.upload_id + parts = [MultipartUploadPart.from_proto(part) for part in request_message.parts] + + artifact_repo = _get_artifact_repo_mlflow_artifacts() + _validate_support_multipart_upload(artifact_repo) + + artifact_repo.complete_multipart_upload( + path, + upload_id, + parts, + artifact_path, + ) + return _wrap_response(CompleteMultipartUpload.Response()) + + +@catch_mlflow_exception +@_disable_unless_serve_artifacts +def _abort_multipart_upload_artifact(artifact_path): + """ + A request handler for `POST /mlflow-artifacts/mpu/abort` to abort a multipart upload + to `artifact_path` (a relative path from the root artifact directory). + """ + artifact_path = validate_path_is_safe(artifact_path) + + request_message = _get_request_message( + AbortMultipartUpload(), + schema={ + "path": [_assert_required, _assert_string], + "upload_id": [_assert_string], + }, + ) + path = request_message.path + upload_id = request_message.upload_id + + artifact_repo = _get_artifact_repo_mlflow_artifacts() + _validate_support_multipart_upload(artifact_repo) + + artifact_repo.abort_multipart_upload( + path, + upload_id, + artifact_path, + ) + return _wrap_response(AbortMultipartUpload.Response()) + + +# MLflow Tracing APIs + + +@catch_mlflow_exception +@_disable_if_artifacts_only +def _start_trace_v3(): + """ + A request handler for `POST /mlflow/traces` to create a new TraceInfo record in tracking store. + """ + request_message = _get_request_message( + StartTraceV3(), + schema={"trace": [_assert_required]}, + ) + trace_info = TraceInfo.from_proto(request_message.trace.trace_info) + trace_info = _get_tracking_store().start_trace(trace_info) + response_message = StartTraceV3.Response(trace=ProtoTrace(trace_info=trace_info.to_proto())) + return _wrap_response(response_message) + + +@catch_mlflow_exception +@_disable_if_artifacts_only +def _get_trace_info_v3(trace_id): + """ + A request handler for `GET /mlflow/traces/{trace_id}/info` to retrieve + an existing TraceInfo record from tracking store. + """ + trace_info = _get_tracking_store().get_trace_info(trace_id) + response_message = GetTraceInfoV3.Response(trace=ProtoTrace(trace_info=trace_info.to_proto())) + return _wrap_response(response_message) + + +@catch_mlflow_exception +@_disable_if_artifacts_only +def _search_traces_v3(): + """ + A request handler for `GET /mlflow/traces` to search for TraceInfo records in tracking store. + """ + request_message = _get_request_message( + SearchTracesV3(), + schema={ + "locations": [_assert_array, _assert_required], + "filter": [_assert_string], + "max_results": [ + _assert_intlike, + lambda x: _assert_less_than_or_equal(int(x), 500), + ], + "order_by": [_assert_array, _assert_item_type_string], + "page_token": [_assert_string], + }, + ) + experiment_ids = [] + for location in request_message.locations: + if location.HasField("mlflow_experiment"): + experiment_ids.append(location.mlflow_experiment.experiment_id) + + traces, token = _get_tracking_store().search_traces( + experiment_ids=experiment_ids, + filter_string=request_message.filter, + max_results=request_message.max_results, + order_by=request_message.order_by, + page_token=request_message.page_token, + ) + response_message = SearchTracesV3.Response() + response_message.traces.extend([e.to_proto() for e in traces]) + if token: + response_message.next_page_token = token + return _wrap_response(response_message) + + +@catch_mlflow_exception +@_disable_if_artifacts_only +def _delete_traces(): + """ + A request handler for `POST /mlflow/traces/delete-traces` to delete TraceInfo records + from tracking store. + """ + request_message = _get_request_message( + DeleteTraces(), + schema={ + "experiment_id": [_assert_string, _assert_required], + "max_timestamp_millis": [_assert_intlike], + "max_traces": [_assert_intlike], + "request_ids": [_assert_array, _assert_item_type_string], + }, + ) + + # NB: Interestingly, the field accessor for the message object returns the default + # value for optional field if it's not set. For example, `request_message.max_traces` + # returns 0 if max_traces is not specified in the request. This is not desirable, + # because null and 0 means completely opposite i.e. the former is 'delete nothing' + # while the latter is 'delete all'. To handle this, we need to explicitly check + # if the field is set or not using `HasField` method and return None if not. + def _get_nullable_field(field): + if request_message.HasField(field): + return getattr(request_message, field) + return None + + traces_deleted = _get_tracking_store().delete_traces( + experiment_id=request_message.experiment_id, + max_timestamp_millis=_get_nullable_field("max_timestamp_millis"), + max_traces=_get_nullable_field("max_traces"), + trace_ids=request_message.request_ids, + ) + return _wrap_response(DeleteTraces.Response(traces_deleted=traces_deleted)) + + +@catch_mlflow_exception +@_disable_if_artifacts_only +def _set_trace_tag(request_id): + """ + A request handler for `PATCH /mlflow/traces/{request_id}/tags` to set tags on a TraceInfo record + """ + request_message = _get_request_message( + SetTraceTag(), + schema={ + "key": [_assert_string, _assert_required], + "value": [_assert_string], + }, + ) + _get_tracking_store().set_trace_tag(request_id, request_message.key, request_message.value) + return _wrap_response(SetTraceTag.Response()) + + +@catch_mlflow_exception +@_disable_if_artifacts_only +def _delete_trace_tag(request_id): + """ + A request handler for `DELETE /mlflow/traces/{request_id}/tags` to delete tags from a TraceInfo + record. + """ + request_message = _get_request_message( + DeleteTraceTag(), + schema={ + "key": [_assert_string, _assert_required], + }, + ) + _get_tracking_store().delete_trace_tag(request_id, request_message.key) + return _wrap_response(DeleteTraceTag.Response()) + + +@catch_mlflow_exception +@_disable_if_artifacts_only +def _link_traces_to_run(): + """ + A request handler for `POST /mlflow/traces/link-to-run` to link traces to a run. + """ + request_message = _get_request_message( + LinkTracesToRun(), + schema={ + "trace_ids": [_assert_array, _assert_required, _assert_item_type_string], + "run_id": [_assert_string, _assert_required], + }, + ) + _get_tracking_store().link_traces_to_run( + trace_ids=request_message.trace_ids, + run_id=request_message.run_id, + ) + return _wrap_response(LinkTracesToRun.Response()) + + +@catch_mlflow_exception +@_disable_if_artifacts_only +def get_trace_artifact_handler(): + request_id = request.args.get("request_id") + + if not request_id: + raise MlflowException( + 'Request must include the "request_id" query parameter.', + error_code=BAD_REQUEST, + ) + + trace_info = _get_tracking_store().get_trace_info(request_id) + trace_data = _get_trace_artifact_repo(trace_info).download_trace_data() + + # Write data to a BytesIO buffer instead of needing to save a temp file + buf = io.BytesIO() + buf.write(json.dumps(trace_data).encode()) + buf.seek(0) + + file_sender_response = send_file( + buf, + mimetype="application/octet-stream", + as_attachment=True, + download_name=TRACE_DATA_FILE_NAME, + ) + return _response_with_file_attachment_headers(TRACE_DATA_FILE_NAME, file_sender_response) + + +# Assessments API handlers +@catch_mlflow_exception +@_disable_if_artifacts_only +def _create_assessment(trace_id): + """ + A request handler for `POST /mlflow/traces/{assessment.trace_id}/assessments` + to create a new assessment. + """ + request_message = _get_request_message( + CreateAssessment(), + schema={ + "assessment": [_assert_required], + }, + ) + + assessment = Assessment.from_proto(request_message.assessment) + assessment.trace_id = trace_id + created_assessment = _get_tracking_store().create_assessment(assessment) + + response_message = CreateAssessment.Response(assessment=created_assessment.to_proto()) + return _wrap_response(response_message) + + +@catch_mlflow_exception +@_disable_if_artifacts_only +def _get_assessment(trace_id, assessment_id): + """ + A request handler for `GET /mlflow/traces/{trace_id}/assessments/{assessment_id}` + to get an assessment. + """ + assessment = _get_tracking_store().get_assessment(trace_id, assessment_id) + + response_message = GetAssessmentRequest.Response(assessment=assessment.to_proto()) + return _wrap_response(response_message) + + +@catch_mlflow_exception +@_disable_if_artifacts_only +def _update_assessment(trace_id, assessment_id): + """ + A request handler for `PATCH /mlflow/traces/{trace_id}/assessments/{assessment_id}` + to update an assessment. + """ + request_message = _get_request_message( + UpdateAssessment(), + schema={ + "assessment": [_assert_required], + "update_mask": [_assert_required], + }, + ) + + assessment_proto = request_message.assessment + update_mask = request_message.update_mask + + kwargs = {} + + for path in update_mask.paths: + if path == "assessment_name": + kwargs["name"] = assessment_proto.assessment_name + elif path == "expectation": + kwargs["expectation"] = Expectation.from_proto(assessment_proto) + elif path == "feedback": + kwargs["feedback"] = Feedback.from_proto(assessment_proto) + elif path == "rationale": + kwargs["rationale"] = assessment_proto.rationale + elif path == "metadata": + kwargs["metadata"] = dict(assessment_proto.metadata) + elif path == "valid": + kwargs["valid"] = assessment_proto.valid + + updated_assessment = _get_tracking_store().update_assessment( + trace_id=trace_id, assessment_id=assessment_id, **kwargs + ) + + response_message = UpdateAssessment.Response(assessment=updated_assessment.to_proto()) + return _wrap_response(response_message) + + +@catch_mlflow_exception +@_disable_if_artifacts_only +def _delete_assessment(trace_id, assessment_id): + """ + A request handler for `DELETE /mlflow/traces/{trace_id}/assessments/{assessment_id}` + to delete an assessment. + """ + _get_tracking_store().delete_assessment(trace_id, assessment_id) + + response_message = DeleteAssessment.Response() + return _wrap_response(response_message) + + +# Deprecated MLflow Tracing APIs. Kept for backward compatibility but do not use. + + +@catch_mlflow_exception +@_disable_if_artifacts_only +def _deprecated_start_trace_v2(): + """ + A request handler for `POST /mlflow/traces` to create a new TraceInfo record in tracking store. + """ + request_message = _get_request_message( + StartTrace(), + schema={ + "experiment_id": [_assert_string], + "timestamp_ms": [_assert_intlike], + "request_metadata": [_assert_map_key_present], + "tags": [_assert_map_key_present], + }, + ) + request_metadata = {e.key: e.value for e in request_message.request_metadata} + tags = {e.key: e.value for e in request_message.tags} + + trace_info = _get_tracking_store().deprecated_start_trace_v2( + experiment_id=request_message.experiment_id, + timestamp_ms=request_message.timestamp_ms, + request_metadata=request_metadata, + tags=tags, + ) + response_message = StartTrace.Response(trace_info=trace_info.to_proto()) + return _wrap_response(response_message) + + +@catch_mlflow_exception +@_disable_if_artifacts_only +def _deprecated_end_trace_v2(request_id): + """ + A request handler for `PATCH /mlflow/traces/{request_id}` to mark an existing TraceInfo + record completed in tracking store. + """ + request_message = _get_request_message( + EndTrace(), + schema={ + "timestamp_ms": [_assert_intlike], + "status": [_assert_string], + "request_metadata": [_assert_map_key_present], + "tags": [_assert_map_key_present], + }, + ) + request_metadata = {e.key: e.value for e in request_message.request_metadata} + tags = {e.key: e.value for e in request_message.tags} + + trace_info = _get_tracking_store().deprecated_end_trace_v2( + request_id=request_id, + timestamp_ms=request_message.timestamp_ms, + status=TraceStatus.from_proto(request_message.status), + request_metadata=request_metadata, + tags=tags, + ) + + if isinstance(trace_info, TraceInfo): + trace_info = TraceInfoV2.from_v3(trace_info) + + response_message = EndTrace.Response(trace_info=trace_info.to_proto()) + return _wrap_response(response_message) + + +@catch_mlflow_exception +@_disable_if_artifacts_only +def _deprecated_get_trace_info_v2(request_id): + """ + A request handler for `GET /mlflow/traces/{request_id}/info` to retrieve + an existing TraceInfo record from tracking store. + """ + trace_info = _get_tracking_store().get_trace_info(request_id) + trace_info = TraceInfoV2.from_v3(trace_info) + response_message = GetTraceInfo.Response(trace_info=trace_info.to_proto()) + return _wrap_response(response_message) + + +@catch_mlflow_exception +@_disable_if_artifacts_only +def _deprecated_search_traces_v2(): + """ + A request handler for `GET /mlflow/traces` to search for TraceInfo records in tracking store. + """ + request_message = _get_request_message( + SearchTraces(), + schema={ + "experiment_ids": [ + _assert_array, + _assert_item_type_string, + _assert_required, + ], + "filter": [_assert_string], + "max_results": [ + _assert_intlike, + lambda x: _assert_less_than_or_equal(int(x), 500), + ], + "order_by": [_assert_array, _assert_item_type_string], + "page_token": [_assert_string], + }, + ) + traces, token = _get_tracking_store().search_traces( + experiment_ids=request_message.experiment_ids, + filter_string=request_message.filter, + max_results=request_message.max_results, + order_by=request_message.order_by, + page_token=request_message.page_token, + ) + traces = [TraceInfoV2.from_v3(t) for t in traces] + response_message = SearchTraces.Response() + response_message.traces.extend([e.to_proto() for e in traces]) + if token: + response_message.next_page_token = token + return _wrap_response(response_message) + + +# Logged Models APIs + + +@catch_mlflow_exception +@_disable_if_artifacts_only +def get_logged_model_artifact_handler(model_id: str): + artifact_file_path = request.args.get("artifact_file_path") + if not artifact_file_path: + raise MlflowException( + 'Request must include the "artifact_file_path" query parameter.', + error_code=BAD_REQUEST, + ) + validate_path_is_safe(artifact_file_path) + + logged_model: LoggedModel = _get_tracking_store().get_logged_model(model_id) + if _is_servable_proxied_run_artifact_root(logged_model.artifact_location): + artifact_repo = _get_artifact_repo_mlflow_artifacts() + artifact_path = _get_proxied_run_artifact_destination_path( + proxied_artifact_root=logged_model.artifact_location, + relative_path=artifact_file_path, + ) + else: + artifact_repo = get_artifact_repository(logged_model.artifact_location) + artifact_path = artifact_file_path + + return _send_artifact(artifact_repo, artifact_path) + + +@catch_mlflow_exception +@_disable_if_artifacts_only +def _create_logged_model(): + request_message = _get_request_message( + CreateLoggedModel(), + schema={ + "experiment_id": [_assert_string, _assert_required], + "name": [_assert_string], + "model_type": [_assert_string], + "source_run_id": [_assert_string], + "params": [_assert_array], + "tags": [_assert_array], + }, + ) + + model = _get_tracking_store().create_logged_model( + experiment_id=request_message.experiment_id, + name=request_message.name or None, + model_type=request_message.model_type, + source_run_id=request_message.source_run_id, + params=( + [LoggedModelParameter.from_proto(param) for param in request_message.params] + if request_message.params + else None + ), + tags=( + [LoggedModelTag(key=tag.key, value=tag.value) for tag in request_message.tags] + if request_message.tags + else None + ), + ) + response_message = CreateLoggedModel.Response(model=model.to_proto()) + return _wrap_response(response_message) + + +@catch_mlflow_exception +@_disable_if_artifacts_only +def _log_logged_model_params(model_id: str): + request_message = _get_request_message( + LogLoggedModelParamsRequest(), + schema={ + "model_id": [_assert_string, _assert_required], + "params": [_assert_array], + }, + ) + params = ( + [LoggedModelParameter.from_proto(param) for param in request_message.params] + if request_message.params + else [] + ) + _get_tracking_store().log_logged_model_params(model_id, params) + return _wrap_response(LogLoggedModelParamsRequest.Response()) + + +@catch_mlflow_exception +@_disable_if_artifacts_only +def _get_logged_model(model_id: str): + model = _get_tracking_store().get_logged_model(model_id) + response_message = GetLoggedModel.Response(model=model.to_proto()) + return _wrap_response(response_message) + + +@catch_mlflow_exception +@_disable_if_artifacts_only +def _finalize_logged_model(model_id: str): + request_message = _get_request_message( + FinalizeLoggedModel(), + schema={ + "model_id": [_assert_string, _assert_required], + "status": [_assert_intlike, _assert_required], + }, + ) + model = _get_tracking_store().finalize_logged_model( + request_message.model_id, LoggedModelStatus.from_int(request_message.status) + ) + response_message = FinalizeLoggedModel.Response(model=model.to_proto()) + return _wrap_response(response_message) + + +@catch_mlflow_exception +@_disable_if_artifacts_only +def _delete_logged_model(model_id: str): + _get_tracking_store().delete_logged_model(model_id) + return _wrap_response(DeleteLoggedModel.Response()) + + +@catch_mlflow_exception +@_disable_if_artifacts_only +def _set_logged_model_tags(model_id: str): + request_message = _get_request_message( + SetLoggedModelTags(), + schema={"tags": [_assert_array]}, + ) + tags = [LoggedModelTag(key=tag.key, value=tag.value) for tag in request_message.tags] + _get_tracking_store().set_logged_model_tags(model_id, tags) + return _wrap_response(SetLoggedModelTags.Response()) + + +@catch_mlflow_exception +@_disable_if_artifacts_only +def _delete_logged_model_tag(model_id: str, tag_key: str): + _get_tracking_store().delete_logged_model_tag(model_id, tag_key) + return _wrap_response(DeleteLoggedModelTag.Response()) + + +@catch_mlflow_exception +@_disable_if_artifacts_only +def _search_logged_models(): + request_message = _get_request_message( + SearchLoggedModels(), + schema={ + "experiment_ids": [ + _assert_array, + _assert_item_type_string, + _assert_required, + ], + "filter": [_assert_string], + "datasets": [_assert_array], + "max_results": [_assert_intlike], + "order_by": [_assert_array], + "page_token": [_assert_string], + }, + ) + models = _get_tracking_store().search_logged_models( + # Convert `RepeatedScalarContainer` objects (experiment_ids and order_by) to `list` + # to avoid serialization issues + experiment_ids=list(request_message.experiment_ids), + filter_string=request_message.filter or None, + datasets=( + [ + { + "dataset_name": d.dataset_name, + "dataset_digest": d.dataset_digest or None, + } + for d in request_message.datasets + ] + if request_message.datasets + else None + ), + max_results=request_message.max_results or None, + order_by=( + [ + { + "field_name": ob.field_name, + "ascending": ob.ascending, + "dataset_name": ob.dataset_name or None, + "dataset_digest": ob.dataset_digest or None, + } + for ob in request_message.order_by + ] + if request_message.order_by + else None + ), + page_token=request_message.page_token or None, + ) + response_message = SearchLoggedModels.Response() + response_message.models.extend([e.to_proto() for e in models]) + if models.token: + response_message.next_page_token = models.token + return _wrap_response(response_message) + + +@catch_mlflow_exception +@_disable_if_artifacts_only +def _list_logged_model_artifacts(model_id: str): + request_message = _get_request_message( + ListLoggedModelArtifacts(), + schema={"artifact_directory_path": [_assert_string]}, + ) + if request_message.HasField("artifact_directory_path"): + artifact_path = validate_path_is_safe(request_message.artifact_directory_path) + else: + artifact_path = None + + return _list_logged_model_artifacts_impl(model_id, artifact_path) + + +def _list_logged_model_artifacts_impl( + model_id: str, artifact_directory_path: str | None +) -> Response: + response = ListLoggedModelArtifacts.Response() + logged_model: LoggedModel = _get_tracking_store().get_logged_model(model_id) + if _is_servable_proxied_run_artifact_root(logged_model.artifact_location): + artifacts = _list_artifacts_for_proxied_run_artifact_root( + proxied_artifact_root=logged_model.artifact_location, + relative_path=artifact_directory_path, + ) + else: + artifacts = get_artifact_repository(logged_model.artifact_location).list_artifacts( + artifact_directory_path + ) + + response.files.extend([a.to_proto() for a in artifacts]) + response.root_uri = logged_model.artifact_location + return _wrap_response(response) + + +def _get_rest_path(base_path, version=2): + return f"/api/{version}.0{base_path}" + + +def _get_ajax_path(base_path, version=2): + return _add_static_prefix(f"/ajax-api/{version}.0{base_path}") + + +def _add_static_prefix(route: str) -> str: + if prefix := os.environ.get(STATIC_PREFIX_ENV_VAR): + return prefix.rstrip("/") + route + return route + + +def _get_paths(base_path, version=2): + """ + A service endpoints base path is typically something like /mlflow/experiment. + We should register paths like /api/2.0/mlflow/experiment and + /ajax-api/2.0/mlflow/experiment in the Flask router. + """ + base_path = _convert_path_parameter_to_flask_format(base_path) + return [_get_rest_path(base_path, version), _get_ajax_path(base_path, version)] + + +def _convert_path_parameter_to_flask_format(path): + """ + Converts path parameter format to Flask compatible format. + + Some protobuf endpoint paths contain parameters like /mlflow/trace/{request_id}. + This can be interpreted correctly by gRPC framework like Armeria, but Flask does + not understand it. Instead, we need to specify it with a different format, + like /mlflow/trace/. + """ + # Handle simple parameters like {trace_id} + path = re.sub(r"{(\w+)}", r"<\1>", path) + + # Handle Databricks-specific syntax like {assessment.trace_id} -> + # This is needed because Databricks can extract trace_id from request body, + # but Flask needs it in the URL path + return re.sub(r"{assessment\.trace_id}", r"", path) + + +def get_handler(request_class): + """ + Args: + request_class: The type of protobuf message + """ + return HANDLERS.get(request_class, _not_implemented) + + +def get_service_endpoints(service, get_handler): + ret = [] + for service_method in service.DESCRIPTOR.methods: + endpoints = service_method.GetOptions().Extensions[databricks_pb2.rpc].endpoints + for endpoint in endpoints: + for http_path in _get_paths(endpoint.path, version=endpoint.since.major): + handler = get_handler(service().GetRequestClass(service_method)) + ret.append((http_path, handler, [endpoint.method])) + return ret + + +def get_endpoints(get_handler=get_handler): + """ + Returns: + List of tuples (path, handler, methods) + """ + return ( + get_service_endpoints(MlflowService, get_handler) + + get_service_endpoints(ModelRegistryService, get_handler) + + get_service_endpoints(MlflowArtifactsService, get_handler) + + get_service_endpoints(WebhookService, get_handler) + + [(_add_static_prefix("/graphql"), _graphql, ["GET", "POST"])] + ) + + +HANDLERS = { + # Tracking Server APIs + CreateExperiment: _create_experiment, + GetExperiment: _get_experiment, + GetExperimentByName: _get_experiment_by_name, + DeleteExperiment: _delete_experiment, + RestoreExperiment: _restore_experiment, + UpdateExperiment: _update_experiment, + CreateRun: _create_run, + UpdateRun: _update_run, + DeleteRun: _delete_run, + RestoreRun: _restore_run, + LogParam: _log_param, + LogMetric: _log_metric, + SetExperimentTag: _set_experiment_tag, + DeleteExperimentTag: _delete_experiment_tag, + SetTag: _set_tag, + DeleteTag: _delete_tag, + LogBatch: _log_batch, + LogModel: _log_model, + GetRun: _get_run, + SearchRuns: _search_runs, + ListArtifacts: _list_artifacts, + GetMetricHistory: _get_metric_history, + GetMetricHistoryBulkInterval: get_metric_history_bulk_interval_handler, + SearchExperiments: _search_experiments, + LogInputs: _log_inputs, + LogOutputs: _log_outputs, + # Model Registry APIs + CreateRegisteredModel: _create_registered_model, + GetRegisteredModel: _get_registered_model, + DeleteRegisteredModel: _delete_registered_model, + UpdateRegisteredModel: _update_registered_model, + RenameRegisteredModel: _rename_registered_model, + SearchRegisteredModels: _search_registered_models, + GetLatestVersions: _get_latest_versions, + CreateModelVersion: _create_model_version, + GetModelVersion: _get_model_version, + DeleteModelVersion: _delete_model_version, + UpdateModelVersion: _update_model_version, + TransitionModelVersionStage: _transition_stage, + GetModelVersionDownloadUri: _get_model_version_download_uri, + SearchModelVersions: _search_model_versions, + SetRegisteredModelTag: _set_registered_model_tag, + DeleteRegisteredModelTag: _delete_registered_model_tag, + SetModelVersionTag: _set_model_version_tag, + DeleteModelVersionTag: _delete_model_version_tag, + SetRegisteredModelAlias: _set_registered_model_alias, + DeleteRegisteredModelAlias: _delete_registered_model_alias, + GetModelVersionByAlias: _get_model_version_by_alias, + # Webhook APIs + CreateWebhook: _create_webhook, + ListWebhooks: _list_webhooks, + GetWebhook: _get_webhook, + UpdateWebhook: _update_webhook, + DeleteWebhook: _delete_webhook, + TestWebhook: _test_webhook, + # MLflow Artifacts APIs + DownloadArtifact: _download_artifact, + UploadArtifact: _upload_artifact, + ListArtifactsMlflowArtifacts: _list_artifacts_mlflow_artifacts, + DeleteArtifact: _delete_artifact_mlflow_artifacts, + CreateMultipartUpload: _create_multipart_upload_artifact, + CompleteMultipartUpload: _complete_multipart_upload_artifact, + AbortMultipartUpload: _abort_multipart_upload_artifact, + # MLflow Tracing APIs (V3) + StartTraceV3: _start_trace_v3, + GetTraceInfoV3: _get_trace_info_v3, + SearchTracesV3: _search_traces_v3, + DeleteTracesV3: _delete_traces, + SetTraceTagV3: _set_trace_tag, + DeleteTraceTagV3: _delete_trace_tag, + LinkTracesToRun: _link_traces_to_run, + # Assessment APIs + CreateAssessment: _create_assessment, + GetAssessmentRequest: _get_assessment, + UpdateAssessment: _update_assessment, + DeleteAssessment: _delete_assessment, + # Legacy MLflow Tracing V2 APIs. Kept for backward compatibility but do not use. + StartTrace: _deprecated_start_trace_v2, + EndTrace: _deprecated_end_trace_v2, + GetTraceInfo: _deprecated_get_trace_info_v2, + SearchTraces: _deprecated_search_traces_v2, + DeleteTraces: _delete_traces, + SetTraceTag: _set_trace_tag, + DeleteTraceTag: _delete_trace_tag, + # Logged Models APIs + CreateLoggedModel: _create_logged_model, + GetLoggedModel: _get_logged_model, + FinalizeLoggedModel: _finalize_logged_model, + DeleteLoggedModel: _delete_logged_model, + SetLoggedModelTags: _set_logged_model_tags, + DeleteLoggedModelTag: _delete_logged_model_tag, + SearchLoggedModels: _search_logged_models, + ListLoggedModelArtifacts: _list_logged_model_artifacts, + LogLoggedModelParamsRequest: _log_logged_model_params, +} diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/js/build/asset-manifest.json 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****************************\n * Generic Styles\n ****************************\n*/\nag-grid, ag-grid-angular, ag-grid-ng2, ag-grid-polymer, ag-grid-aurelia {\n display: block;\n}\n\n.ag-hidden {\n display: none !important;\n}\n\n.ag-invisible {\n visibility: hidden !important;\n}\n\n.ag-drag-handle {\n cursor: -webkit-grab;\n cursor: grab;\n}\n\n.ag-column-drop-wrapper {\n display: -webkit-box;\n display: flex;\n}\n\n.ag-column-drop-horizontal-half-width {\n display: inline-block;\n width: 50% !important;\n}\n\n.ag-unselectable {\n -moz-user-select: none;\n -webkit-user-select: none;\n user-select: none;\n}\n\n.ag-selectable {\n -moz-user-select: text;\n -webkit-user-select: text;\n user-select: text;\n}\n\n.ag-tab {\n position: relative;\n}\n\n.ag-tab-guard {\n position: absolute;\n width: 0;\n height: 0;\n display: block;\n}\n\n.ag-select-agg-func-popup {\n position: absolute;\n}\n\n.ag-input-wrapper, .ag-picker-field-wrapper {\n display: -webkit-box;\n display: flex;\n -webkit-box-flex: 1;\n flex: 1 1 auto;\n -webkit-box-align: center;\n align-items: center;\n line-height: normal;\n position: relative;\n}\n\n.ag-shake-left-to-right {\n -webkit-animation-direction: alternate;\n animation-direction: alternate;\n -webkit-animation-duration: 0.2s;\n animation-duration: 0.2s;\n -webkit-animation-iteration-count: infinite;\n animation-iteration-count: infinite;\n -webkit-animation-name: ag-shake-left-to-right;\n animation-name: ag-shake-left-to-right;\n}\n\n@-webkit-keyframes ag-shake-left-to-right {\n from {\n padding-left: 6px;\n padding-right: 2px;\n }\n to {\n padding-left: 2px;\n padding-right: 6px;\n }\n}\n\n@keyframes ag-shake-left-to-right {\n from {\n padding-left: 6px;\n padding-right: 2px;\n }\n to {\n padding-left: 2px;\n padding-right: 6px;\n }\n}\n.ag-root-wrapper {\n cursor: default;\n position: relative;\n display: -webkit-box;\n display: flex;\n -webkit-box-orient: vertical;\n -webkit-box-direction: normal;\n flex-direction: column;\n overflow: hidden;\n}\n.ag-root-wrapper.ag-layout-normal {\n height: 100%;\n}\n\n.ag-watermark {\n position: absolute;\n bottom: 20px;\n right: 25px;\n opacity: 0.5;\n -webkit-transition: opacity 1s ease-out 3s;\n transition: opacity 1s ease-out 3s;\n}\n.ag-watermark::before {\n content: \"\";\n background-image: url(data:image/svg+xml;base64,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);\n background-repeat: no-repeat;\n background-size: 170px 40px;\n display: block;\n height: 40px;\n width: 170px;\n opacity: 0.5;\n}\n\n.ag-watermark-text {\n opacity: 0.5;\n font-weight: bold;\n font-family: Impact, sans-serif;\n font-size: 19px;\n padding-left: 0.7rem;\n}\n\n.ag-root-wrapper-body {\n display: -webkit-box;\n display: flex;\n -webkit-box-orient: horizontal;\n -webkit-box-direction: normal;\n flex-direction: row;\n}\n.ag-root-wrapper-body.ag-layout-normal {\n -webkit-box-flex: 1;\n flex: 1 1 auto;\n height: 0;\n min-height: 0;\n}\n\n.ag-root {\n position: relative;\n display: -webkit-box;\n display: flex;\n -webkit-box-orient: vertical;\n -webkit-box-direction: normal;\n flex-direction: column;\n}\n.ag-root.ag-layout-normal, .ag-root.ag-layout-auto-height {\n overflow: hidden;\n -webkit-box-flex: 1;\n flex: 1 1 auto;\n width: 0;\n}\n.ag-root.ag-layout-normal {\n height: 100%;\n}\n\n/**\n ****************************\n * Viewports\n ****************************\n*/\n.ag-header-viewport,\n.ag-floating-top-viewport,\n.ag-body-viewport,\n.ag-center-cols-viewport,\n.ag-floating-bottom-viewport,\n.ag-body-horizontal-scroll-viewport,\n.ag-virtual-list-viewport {\n position: relative;\n height: 100%;\n min-width: 0px;\n overflow: hidden;\n -webkit-box-flex: 1;\n flex: 1 1 auto;\n}\n\n.ag-body-viewport {\n display: -webkit-box;\n display: flex;\n}\n.ag-body-viewport.ag-layout-normal {\n overflow-y: auto;\n -webkit-overflow-scrolling: touch;\n}\n\n.ag-center-cols-viewport {\n width: 100%;\n overflow-x: auto;\n}\n\n.ag-body-horizontal-scroll-viewport {\n overflow-x: scroll;\n}\n\n.ag-virtual-list-viewport {\n overflow: auto;\n width: 100%;\n}\n\n/**\n ****************************\n * Containers\n ****************************\n*/\n.ag-header-container,\n.ag-floating-top-container,\n.ag-body-container,\n.ag-pinned-right-cols-container,\n.ag-center-cols-container,\n.ag-pinned-left-cols-container,\n.ag-floating-bottom-container,\n.ag-body-horizontal-scroll-container,\n.ag-full-width-container,\n.ag-floating-bottom-full-width-container,\n.ag-virtual-list-container {\n position: relative;\n}\n\n.ag-header-container, .ag-floating-top-container, .ag-floating-bottom-container {\n height: 100%;\n white-space: nowrap;\n}\n\n.ag-center-cols-container {\n display: block;\n}\n\n.ag-pinned-right-cols-container {\n display: block;\n}\n\n.ag-body-horizontal-scroll-container {\n height: 100%;\n}\n\n.ag-full-width-container,\n.ag-floating-top-full-width-container,\n.ag-floating-bottom-full-width-container {\n position: absolute;\n top: 0px;\n left: 0px;\n pointer-events: none;\n}\n\n.ag-full-width-container {\n width: 100%;\n}\n\n.ag-floating-bottom-full-width-container, .ag-floating-top-full-width-container {\n display: inline-block;\n overflow: hidden;\n height: 100%;\n width: 100%;\n}\n\n.ag-virtual-list-container {\n overflow: hidden;\n}\n\n/**\n ****************************\n * Scrollers\n ****************************\n*/\n.ag-center-cols-clipper {\n -webkit-box-flex: 1;\n flex: 1 1 auto;\n min-width: 0;\n overflow: hidden;\n min-height: 100%;\n -webkit-transform: translate3d(0, 0, 0);\n transform: translate3d(0, 0, 0);\n}\n\n.ag-body-horizontal-scroll {\n min-height: 0;\n min-width: 0;\n width: 100%;\n display: -webkit-box;\n display: flex;\n position: relative;\n}\n.ag-body-horizontal-scroll.ag-scrollbar-invisible {\n position: absolute;\n bottom: 0;\n left: 0;\n right: 0;\n pointer-events: none;\n}\n.ag-body-horizontal-scroll.ag-scrollbar-invisible.ag-scrollbar-scrolling, .ag-body-horizontal-scroll.ag-scrollbar-invisible.ag-scrollbar-active {\n pointer-events: all;\n}\n\n.ag-force-vertical-scroll {\n overflow-y: scroll !important;\n}\n\n.ag-horizontal-left-spacer, .ag-horizontal-right-spacer {\n height: 100%;\n min-width: 0;\n overflow-x: scroll;\n}\n.ag-horizontal-left-spacer.ag-scroller-corner, .ag-horizontal-right-spacer.ag-scroller-corner {\n overflow-x: hidden;\n}\n\n/**\n ****************************\n * Headers\n ****************************\n*/\n.ag-header, .ag-pinned-left-header, .ag-pinned-right-header {\n display: inline-block;\n overflow: hidden;\n position: relative;\n}\n\n.ag-header-cell-sortable {\n cursor: pointer;\n}\n\n.ag-header {\n display: -webkit-box;\n display: flex;\n width: 100%;\n white-space: nowrap;\n}\n\n.ag-pinned-left-header {\n height: 100%;\n}\n\n.ag-pinned-right-header {\n height: 100%;\n}\n\n.ag-header-row {\n position: absolute;\n overflow: hidden;\n}\n\n.ag-header-cell {\n display: -webkit-inline-box;\n display: inline-flex;\n -webkit-box-align: center;\n align-items: center;\n position: absolute;\n height: 100%;\n overflow: hidden;\n}\n\n.ag-header-cell.ag-header-active .ag-header-cell-menu-button {\n opacity: 1;\n}\n\n.ag-header-cell-menu-button:not(.ag-header-menu-always-show) {\n -webkit-transition: opacity 0.2s;\n transition: opacity 0.2s;\n opacity: 0;\n}\n\n.ag-header-group-cell-label, .ag-header-cell-label {\n display: -webkit-box;\n display: flex;\n -webkit-box-flex: 1;\n flex: 1 1 auto;\n overflow: hidden;\n -webkit-box-align: center;\n align-items: center;\n text-overflow: ellipsis;\n align-self: stretch;\n}\n\n.ag-header-cell-text {\n overflow: hidden;\n text-overflow: ellipsis;\n white-space: nowrap;\n}\n\n.ag-right-aligned-header .ag-header-cell-label {\n -webkit-box-orient: horizontal;\n -webkit-box-direction: reverse;\n flex-direction: row-reverse;\n}\n\n.ag-header-group-text {\n overflow: hidden;\n text-overflow: ellipsis;\n white-space: nowrap;\n}\n\n.ag-header-cell-resize {\n position: absolute;\n z-index: 2;\n height: 100%;\n width: 8px;\n top: 0;\n cursor: ew-resize;\n}\n.ag-ltr .ag-header-cell-resize {\n right: -4px;\n}\n.ag-rtl .ag-header-cell-resize {\n left: -4px;\n}\n\n.ag-pinned-left-header .ag-header-cell-resize {\n right: -4px;\n}\n\n.ag-pinned-right-header .ag-header-cell-resize {\n left: -4px;\n}\n\n.ag-header-select-all {\n display: -webkit-box;\n display: flex;\n}\n\n/**\n ****************************\n * Columns\n ****************************\n*/\n.ag-column-moving .ag-cell {\n -webkit-transition: left 0.2s;\n transition: left 0.2s;\n}\n.ag-column-moving .ag-header-cell {\n -webkit-transition: left 0.2s;\n transition: left 0.2s;\n}\n.ag-column-moving .ag-header-group-cell {\n -webkit-transition: left 0.2s, width 0.2s;\n transition: left 0.2s, width 0.2s;\n}\n\n/**\n ****************************\n * Column Panel\n ****************************\n*/\n.ag-column-panel {\n display: -webkit-box;\n display: flex;\n -webkit-box-orient: vertical;\n -webkit-box-direction: normal;\n flex-direction: column;\n overflow: hidden;\n -webkit-box-flex: 1;\n flex: 1 1 auto;\n}\n\n.ag-column-select {\n position: relative;\n display: -webkit-box;\n display: flex;\n -webkit-box-orient: vertical;\n -webkit-box-direction: normal;\n flex-direction: column;\n overflow: hidden;\n -webkit-box-flex: 3;\n flex: 3 1 0px;\n}\n\n.ag-column-select-header {\n position: relative;\n display: -webkit-box;\n display: flex;\n -webkit-box-flex: 0;\n flex: none;\n}\n\n.ag-column-select-header-icon {\n position: relative;\n}\n\n.ag-column-select-header-filter-wrapper {\n -webkit-box-flex: 1;\n flex: 1 1 auto;\n}\n\n.ag-column-select-header-filter {\n width: 100%;\n}\n\n.ag-column-select-list {\n -webkit-box-flex: 1;\n flex: 1 1 0px;\n overflow: hidden;\n}\n\n.ag-column-drop {\n position: relative;\n display: -webkit-inline-box;\n display: inline-flex;\n -webkit-box-align: center;\n align-items: center;\n overflow: auto;\n width: 100%;\n}\n\n.ag-column-drop-list {\n display: -webkit-box;\n display: flex;\n -webkit-box-align: center;\n align-items: center;\n}\n\n.ag-column-drop-cell {\n position: relative;\n display: -webkit-box;\n display: flex;\n -webkit-box-align: center;\n align-items: center;\n}\n\n.ag-column-drop-cell-text {\n overflow: hidden;\n -webkit-box-flex: 1;\n flex: 1 1 auto;\n text-overflow: ellipsis;\n white-space: nowrap;\n}\n\n.ag-column-drop-vertical {\n display: -webkit-box;\n display: flex;\n -webkit-box-orient: vertical;\n -webkit-box-direction: normal;\n flex-direction: column;\n overflow: hidden;\n -webkit-box-align: stretch;\n align-items: stretch;\n -webkit-box-flex: 1;\n flex: 1 1 0px;\n}\n\n.ag-column-drop-vertical-title-bar {\n display: -webkit-box;\n display: flex;\n -webkit-box-align: center;\n align-items: center;\n -webkit-box-flex: 0;\n flex: none;\n}\n\n.ag-column-drop-vertical-list {\n position: relative;\n -webkit-box-align: stretch;\n align-items: stretch;\n -webkit-box-flex: 1;\n flex-grow: 1;\n -webkit-box-orient: vertical;\n -webkit-box-direction: normal;\n flex-direction: column;\n overflow-x: auto;\n}\n.ag-column-drop-vertical-list > * {\n -webkit-box-flex: 0;\n flex: none;\n}\n\n.ag-column-drop-empty .ag-column-drop-vertical-list {\n overflow: hidden;\n}\n\n.ag-column-drop-vertical-empty-message {\n display: block;\n}\n\n.ag-column-drop.ag-column-drop-horizontal {\n white-space: nowrap;\n overflow: hidden;\n}\n\n.ag-column-drop-cell-button {\n cursor: pointer;\n}\n\n.ag-filter-toolpanel {\n -webkit-box-flex: 1;\n flex: 1 1 0px;\n min-width: 0;\n}\n\n.ag-filter-toolpanel-header {\n position: relative;\n}\n\n.ag-filter-toolpanel-header, .ag-filter-toolpanel-search {\n display: -webkit-box;\n display: flex;\n -webkit-box-align: center;\n align-items: center;\n}\n.ag-filter-toolpanel-header > *, .ag-filter-toolpanel-search > * {\n display: -webkit-box;\n display: flex;\n -webkit-box-align: center;\n align-items: center;\n}\n\n.ag-filter-apply-panel {\n display: -webkit-box;\n display: flex;\n -webkit-box-pack: end;\n justify-content: flex-end;\n overflow: hidden;\n}\n\n/**\n ****************************\n * Rows\n ****************************\n*/\n.ag-row-animation .ag-row {\n -webkit-transition: top 0.4s, background-color 0.1s, opacity 0.2s, -webkit-transform 0.4s;\n transition: top 0.4s, background-color 0.1s, opacity 0.2s, -webkit-transform 0.4s;\n transition: transform 0.4s, top 0.4s, background-color 0.1s, opacity 0.2s;\n transition: transform 0.4s, top 0.4s, background-color 0.1s, opacity 0.2s, -webkit-transform 0.4s;\n}\n\n.ag-row-animation .ag-row.ag-after-created {\n -webkit-transition: top 0.4s, height 0.4s, background-color 0.1s, opacity 0.2s, -webkit-transform 0.4s;\n transition: top 0.4s, height 0.4s, background-color 0.1s, opacity 0.2s, -webkit-transform 0.4s;\n transition: transform 0.4s, top 0.4s, height 0.4s, background-color 0.1s, opacity 0.2s;\n transition: transform 0.4s, top 0.4s, height 0.4s, background-color 0.1s, opacity 0.2s, -webkit-transform 0.4s;\n}\n\n.ag-row-no-animation .ag-row {\n -webkit-transition: background-color 0.1s;\n transition: background-color 0.1s;\n}\n\n.ag-row {\n white-space: nowrap;\n width: 100%;\n}\n\n.ag-row-loading {\n display: -webkit-box;\n display: flex;\n -webkit-box-align: center;\n align-items: center;\n}\n\n.ag-row-position-absolute {\n position: absolute;\n}\n\n.ag-row-position-relative {\n position: relative;\n}\n\n.ag-full-width-row {\n overflow: hidden;\n pointer-events: all;\n}\n\n.ag-row-inline-editing {\n z-index: 1;\n}\n\n.ag-row-dragging {\n z-index: 2;\n}\n\n.ag-stub-cell {\n display: -webkit-box;\n display: flex;\n -webkit-box-align: center;\n align-items: center;\n}\n\n/**\n ****************************\n * Cells\n ****************************\n*/\n.ag-cell {\n display: inline-block;\n position: absolute;\n white-space: nowrap;\n height: 100%;\n}\n\n.ag-cell-value {\n -webkit-box-flex: 1;\n flex: 1 1 auto;\n}\n\n.ag-cell-value, .ag-group-value {\n overflow: hidden;\n text-overflow: ellipsis;\n}\n\n.ag-cell-wrap-text {\n white-space: normal;\n word-break: break-all;\n}\n\n.ag-cell-wrapper {\n display: -webkit-box;\n display: flex;\n -webkit-box-align: center;\n align-items: center;\n}\n.ag-cell-wrapper.ag-row-group {\n -webkit-box-align: start;\n align-items: flex-start;\n}\n\n.ag-sparkline-wrapper {\n position: absolute;\n height: 100%;\n width: 100%;\n left: 0;\n top: 0;\n}\n\n.ag-full-width-row .ag-cell-wrapper.ag-row-group {\n height: 100%;\n -webkit-box-align: center;\n align-items: center;\n}\n\n.ag-cell-inline-editing {\n z-index: 1;\n}\n.ag-cell-inline-editing .ag-cell-wrapper,\n.ag-cell-inline-editing .ag-cell-edit-wrapper,\n.ag-cell-inline-editing .ag-cell-editor,\n.ag-cell-inline-editing .ag-cell-editor .ag-wrapper,\n.ag-cell-inline-editing .ag-cell-editor input {\n height: 100%;\n width: 100%;\n line-height: normal;\n}\n\n.ag-cell .ag-icon {\n display: inline-block;\n vertical-align: middle;\n}\n\n/**\n ****************************\n * Filters\n ****************************\n*/\n.ag-set-filter-item {\n display: -webkit-box;\n display: flex;\n -webkit-box-align: center;\n align-items: center;\n height: 100%;\n}\n\n.ag-set-filter-item-value {\n overflow: hidden;\n text-overflow: ellipsis;\n white-space: nowrap;\n}\n\n.ag-set-filter-item-checkbox {\n display: -webkit-box;\n display: flex;\n}\n\n.ag-filter-body-wrapper {\n display: -webkit-box;\n display: flex;\n -webkit-box-orient: vertical;\n -webkit-box-direction: normal;\n flex-direction: column;\n}\n\n.ag-filter-filter {\n -webkit-box-flex: 1;\n flex: 1 1 0px;\n}\n\n.ag-filter-condition {\n display: -webkit-box;\n display: flex;\n -webkit-box-pack: center;\n justify-content: center;\n}\n\n/**\n ****************************\n * Floating Filter\n ****************************\n*/\n.ag-floating-filter-body {\n position: relative;\n display: -webkit-box;\n display: flex;\n -webkit-box-flex: 1;\n flex: 1 1 auto;\n height: 100%;\n}\n\n.ag-floating-filter-full-body {\n display: -webkit-box;\n display: flex;\n -webkit-box-flex: 1;\n flex: 1 1 auto;\n height: 100%;\n width: 100%;\n -webkit-box-align: center;\n align-items: center;\n overflow: hidden;\n}\n\n.ag-floating-filter-full-body > div {\n -webkit-box-flex: 1;\n flex: 1 1 auto;\n}\n\n.ag-floating-filter-input {\n -webkit-box-align: center;\n align-items: center;\n display: -webkit-box;\n display: flex;\n width: 100%;\n}\n.ag-floating-filter-input > * {\n -webkit-box-flex: 1;\n flex: 1 1 auto;\n}\n\n.ag-floating-filter-button {\n display: -webkit-box;\n display: flex;\n -webkit-box-flex: 0;\n flex: none;\n}\n\n/**\n ****************************\n * Drag & Drop\n ****************************\n*/\n.ag-dnd-ghost {\n position: absolute;\n display: -webkit-inline-box;\n display: inline-flex;\n -webkit-box-align: center;\n align-items: center;\n cursor: move;\n white-space: nowrap;\n z-index: 9999;\n}\n\n/**\n ****************************\n * Overlay\n ****************************\n*/\n.ag-overlay {\n height: 100%;\n left: 0;\n pointer-events: none;\n position: absolute;\n top: 0;\n width: 100%;\n}\n\n.ag-overlay-panel {\n display: -webkit-box;\n display: flex;\n height: 100%;\n width: 100%;\n}\n\n.ag-overlay-wrapper {\n display: -webkit-box;\n display: flex;\n -webkit-box-flex: 0;\n flex: none;\n width: 100%;\n height: 100%;\n -webkit-box-align: center;\n align-items: center;\n -webkit-box-pack: center;\n justify-content: center;\n text-align: center;\n}\n\n.ag-overlay-loading-wrapper {\n pointer-events: all;\n}\n\n/**\n ****************************\n * Popup\n ****************************\n*/\n.ag-popup-child {\n z-index: 5;\n top: 0;\n}\n\n.ag-popup-editor {\n position: absolute;\n -webkit-user-select: none;\n -moz-user-select: none;\n user-select: none;\n z-index: 1;\n}\n\n.ag-large-text-input {\n display: block;\n}\n\n/**\n ****************************\n * Virtual Lists\n ****************************\n*/\n.ag-virtual-list-item {\n position: absolute;\n width: 100%;\n}\n\n/**\n ****************************\n * Floating Top and Bottom\n ****************************\n*/\n.ag-floating-top {\n overflow: hidden;\n white-space: nowrap;\n width: 100%;\n position: relative;\n display: -webkit-box;\n display: flex;\n}\n\n.ag-pinned-left-floating-top {\n display: inline-block;\n overflow: hidden;\n position: relative;\n min-width: 0px;\n}\n\n.ag-pinned-right-floating-top {\n display: inline-block;\n overflow: hidden;\n position: relative;\n min-width: 0px;\n}\n\n.ag-floating-bottom {\n overflow: hidden;\n white-space: nowrap;\n width: 100%;\n position: relative;\n display: -webkit-box;\n display: flex;\n}\n\n.ag-pinned-left-floating-bottom {\n display: inline-block;\n overflow: hidden;\n position: relative;\n min-width: 0px;\n}\n\n.ag-pinned-right-floating-bottom {\n display: inline-block;\n overflow: hidden;\n position: relative;\n min-width: 0px;\n}\n\n/**\n ****************************\n * Dialog\n ****************************\n*/\n.ag-dialog, .ag-panel {\n display: -webkit-box;\n display: flex;\n -webkit-box-orient: vertical;\n -webkit-box-direction: normal;\n flex-direction: column;\n position: relative;\n overflow: hidden;\n}\n\n.ag-panel-title-bar {\n display: -webkit-box;\n display: flex;\n -webkit-box-flex: 0;\n flex: none;\n -webkit-box-align: center;\n align-items: center;\n cursor: default;\n}\n\n.ag-panel-title-bar-title {\n -webkit-box-flex: 1;\n flex: 1 1 auto;\n}\n\n.ag-panel-title-bar-buttons {\n display: -webkit-box;\n display: flex;\n}\n\n.ag-panel-title-bar-button {\n cursor: pointer;\n}\n\n.ag-panel-content-wrapper {\n display: -webkit-box;\n display: flex;\n -webkit-box-flex: 1;\n flex: 1 1 auto;\n position: relative;\n overflow: hidden;\n}\n\n.ag-dialog {\n position: absolute;\n}\n\n.ag-resizer {\n position: absolute;\n pointer-events: none;\n -webkit-user-select: none;\n -moz-user-select: none;\n user-select: none;\n z-index: 1;\n}\n.ag-resizer.ag-resizer-topLeft {\n top: 0;\n left: 0;\n height: 5px;\n width: 5px;\n cursor: nwse-resize;\n}\n.ag-resizer.ag-resizer-top {\n top: 0;\n left: 5px;\n right: 5px;\n height: 5px;\n cursor: ns-resize;\n}\n.ag-resizer.ag-resizer-topRight {\n top: 0;\n right: 0;\n height: 5px;\n width: 5px;\n cursor: nesw-resize;\n}\n.ag-resizer.ag-resizer-right {\n top: 5px;\n right: 0;\n bottom: 5px;\n width: 5px;\n cursor: ew-resize;\n}\n.ag-resizer.ag-resizer-bottomRight {\n bottom: 0;\n right: 0;\n height: 5px;\n width: 5px;\n cursor: nwse-resize;\n}\n.ag-resizer.ag-resizer-bottom {\n bottom: 0;\n left: 5px;\n right: 5px;\n height: 5px;\n cursor: ns-resize;\n}\n.ag-resizer.ag-resizer-bottomLeft {\n bottom: 0;\n left: 0;\n height: 5px;\n width: 5px;\n cursor: nesw-resize;\n}\n.ag-resizer.ag-resizer-left {\n left: 0;\n top: 5px;\n bottom: 5px;\n width: 5px;\n cursor: ew-resize;\n}\n\n/**\n ****************************\n * Tooltip\n ****************************\n*/\n.ag-tooltip {\n position: absolute;\n pointer-events: none;\n z-index: 99999;\n}\n\n.ag-tooltip-custom {\n position: absolute;\n pointer-events: none;\n z-index: 99999;\n}\n\n/**\n ****************************\n * Animations\n ****************************\n*/\n.ag-value-slide-out {\n margin-right: 5px;\n opacity: 1;\n -webkit-transition: opacity 3s, margin-right 3s;\n transition: opacity 3s, margin-right 3s;\n -webkit-transition-timing-function: linear;\n transition-timing-function: linear;\n}\n\n.ag-value-slide-out-end {\n margin-right: 10px;\n opacity: 0;\n}\n\n.ag-opacity-zero {\n opacity: 0 !important;\n}\n\n/**\n ****************************\n * Menu\n ****************************\n*/\n.ag-menu {\n max-height: 100%;\n overflow-y: auto;\n position: absolute;\n -webkit-user-select: none;\n -moz-user-select: none;\n user-select: none;\n}\n\n.ag-menu-column-select-wrapper {\n height: 265px;\n overflow: auto;\n}\n.ag-menu-column-select-wrapper .ag-column-select {\n height: 100%;\n}\n\n.ag-menu-list {\n display: table;\n width: 100%;\n}\n\n.ag-menu-option, .ag-menu-separator {\n display: table-row;\n}\n\n.ag-menu-option-part, .ag-menu-separator-part {\n display: table-cell;\n vertical-align: middle;\n}\n\n.ag-menu-option-text {\n white-space: nowrap;\n}\n\n.ag-compact-menu-option {\n width: 100%;\n display: -webkit-box;\n display: flex;\n flex-wrap: nowrap;\n}\n\n.ag-compact-menu-option-text {\n white-space: nowrap;\n -webkit-box-flex: 1;\n flex: 1 1 auto;\n}\n\n/**\n ****************************\n * Rich Select\n ****************************\n*/\n.ag-rich-select {\n cursor: default;\n outline: none;\n}\n\n.ag-rich-select-value {\n display: -webkit-box;\n display: flex;\n -webkit-box-align: center;\n align-items: center;\n}\n\n.ag-rich-select-value-icon {\n -webkit-box-flex: 1;\n flex: 1 1 auto;\n -webkit-box-ordinal-group: 2;\n order: 1;\n}\n.ag-ltr .ag-rich-select-value-icon {\n text-align: right;\n}\n.ag-rtl .ag-rich-select-value-icon {\n text-align: left;\n}\n\n.ag-rich-select-list {\n position: relative;\n}\n\n.ag-rich-select-virtual-list-item {\n display: -webkit-box;\n display: flex;\n}\n\n.ag-rich-select-row {\n display: -webkit-box;\n display: flex;\n -webkit-box-flex: 1;\n flex: 1 1 auto;\n -webkit-box-align: center;\n align-items: center;\n white-space: nowrap;\n}\n\n/**\n ****************************\n * Pagination\n ****************************\n*/\n.ag-paging-panel {\n -webkit-box-align: center;\n align-items: center;\n display: -webkit-box;\n display: flex;\n -webkit-box-pack: end;\n justify-content: flex-end;\n}\n\n.ag-paging-page-summary-panel {\n display: -webkit-box;\n display: flex;\n -webkit-box-align: center;\n align-items: center;\n}\n\n.ag-paging-button {\n position: relative;\n}\n\n.ag-disabled .ag-paging-page-summary-panel {\n pointer-events: none;\n}\n\n/**\n ****************************\n * Tool Panel\n ****************************\n*/\n.ag-tool-panel-wrapper {\n display: -webkit-box;\n display: flex;\n overflow-y: auto;\n overflow-x: hidden;\n cursor: default;\n -webkit-user-select: none;\n -moz-user-select: none;\n user-select: none;\n}\n\n.ag-column-select-column,\n.ag-column-select-column-group,\n.ag-select-agg-func-item {\n position: relative;\n -webkit-box-align: center;\n align-items: center;\n display: -webkit-box;\n display: flex;\n -webkit-box-orient: horizontal;\n -webkit-box-direction: normal;\n flex-direction: row;\n flex-wrap: nowrap;\n text-overflow: ellipsis;\n white-space: nowrap;\n height: 100%;\n}\n.ag-column-select-column > *,\n.ag-column-select-column-group > *,\n.ag-select-agg-func-item > * {\n -webkit-box-flex: 0;\n flex: none;\n}\n\n.ag-column-select-checkbox {\n display: -webkit-box;\n display: flex;\n}\n\n.ag-tool-panel-horizontal-resize {\n cursor: ew-resize;\n height: 100%;\n position: absolute;\n top: 0;\n width: 5px;\n z-index: 1;\n}\n\n.ag-ltr .ag-side-bar-left .ag-tool-panel-horizontal-resize {\n right: -3px;\n}\n.ag-rtl .ag-side-bar-left .ag-tool-panel-horizontal-resize {\n left: -3px;\n}\n\n.ag-ltr .ag-side-bar-right .ag-tool-panel-horizontal-resize {\n left: -3px;\n}\n.ag-rtl .ag-side-bar-right .ag-tool-panel-horizontal-resize {\n right: -3px;\n}\n\n.ag-details-row {\n width: 100%;\n}\n\n.ag-details-row-fixed-height {\n height: 100%;\n}\n\n.ag-details-grid {\n width: 100%;\n}\n\n.ag-details-grid-fixed-height {\n height: 100%;\n}\n\n.ag-header-group-cell {\n display: -webkit-box;\n display: flex;\n -webkit-box-align: center;\n align-items: center;\n height: 100%;\n position: absolute;\n}\n\n.ag-cell-label-container {\n display: -webkit-box;\n display: flex;\n -webkit-box-pack: justify;\n justify-content: space-between;\n -webkit-box-orient: horizontal;\n -webkit-box-direction: reverse;\n flex-direction: row-reverse;\n -webkit-box-align: center;\n align-items: center;\n width: 100%;\n height: 100%;\n overflow: hidden;\n}\n\n.ag-right-aligned-header .ag-cell-label-container {\n -webkit-box-orient: horizontal;\n -webkit-box-direction: normal;\n flex-direction: row;\n}\n\n/**\n ****************************\n * Side Bar\n ****************************\n*/\n.ag-side-bar {\n display: -webkit-box;\n display: flex;\n -webkit-box-orient: horizontal;\n -webkit-box-direction: reverse;\n flex-direction: row-reverse;\n}\n\n.ag-side-bar-left {\n -webkit-box-ordinal-group: 0;\n order: -1;\n -webkit-box-orient: horizontal;\n -webkit-box-direction: normal;\n flex-direction: row;\n}\n\n.ag-side-button-button {\n position: relative;\n display: -webkit-box;\n display: flex;\n -webkit-box-orient: vertical;\n -webkit-box-direction: normal;\n flex-direction: column;\n -webkit-box-align: center;\n align-items: center;\n -webkit-box-pack: center;\n justify-content: center;\n flex-wrap: nowrap;\n white-space: nowrap;\n outline: none;\n cursor: pointer;\n}\n\n.ag-side-button-label {\n -webkit-writing-mode: vertical-lr;\n writing-mode: vertical-lr;\n}\n\n/**\n ****************************\n * Status Bar\n ****************************\n*/\n.ag-status-bar {\n display: -webkit-box;\n display: flex;\n -webkit-box-pack: justify;\n justify-content: space-between;\n overflow: hidden;\n}\n\n.ag-status-panel {\n display: -webkit-inline-box;\n display: inline-flex;\n}\n\n.ag-status-name-value {\n white-space: nowrap;\n}\n\n.ag-status-bar-left {\n display: -webkit-inline-box;\n display: inline-flex;\n}\n\n.ag-status-bar-center {\n display: -webkit-inline-box;\n display: inline-flex;\n}\n\n.ag-status-bar-right {\n display: -webkit-inline-box;\n display: inline-flex;\n}\n\n/**\n ****************************\n * Widgets\n ****************************\n*/\n.ag-icon {\n display: block;\n speak: none;\n}\n\n.ag-group {\n position: relative;\n width: 100%;\n}\n\n.ag-group-title-bar {\n display: -webkit-box;\n display: flex;\n -webkit-box-align: center;\n align-items: center;\n}\n\n.ag-group-title {\n display: block;\n -webkit-box-flex: 1;\n flex: 1 1 auto;\n min-width: 0;\n overflow: hidden;\n white-space: nowrap;\n text-overflow: ellipsis;\n}\n\n.ag-group-title-bar .ag-group-title {\n cursor: default;\n}\n\n.ag-group-toolbar {\n display: -webkit-box;\n display: flex;\n -webkit-box-align: center;\n align-items: center;\n}\n\n.ag-group-container {\n display: -webkit-box;\n display: flex;\n}\n\n.ag-disabled .ag-group-container {\n pointer-events: none;\n}\n\n.ag-group-container-horizontal {\n -webkit-box-orient: horizontal;\n -webkit-box-direction: normal;\n flex-direction: row;\n flex-wrap: wrap;\n}\n\n.ag-group-container-vertical {\n -webkit-box-orient: vertical;\n -webkit-box-direction: normal;\n flex-direction: column;\n}\n\n.ag-column-group-icons {\n display: block;\n}\n.ag-column-group-icons > * {\n cursor: pointer;\n}\n\n.ag-group-item-alignment-stretch .ag-group-item {\n -webkit-box-align: stretch;\n align-items: stretch;\n}\n\n.ag-group-item-alignment-start .ag-group-item {\n -webkit-box-align: start;\n align-items: flex-start;\n}\n\n.ag-group-item-alignment-end .ag-group-item {\n -webkit-box-align: end;\n align-items: flex-end;\n}\n\n.ag-toggle-button-icon {\n -webkit-transition: right 0.3s;\n transition: right 0.3s;\n position: absolute;\n top: -1px;\n}\n\n.ag-input-field, .ag-select {\n display: -webkit-box;\n display: flex;\n -webkit-box-orient: horizontal;\n -webkit-box-direction: normal;\n flex-direction: row;\n -webkit-box-align: center;\n align-items: center;\n}\n\n.ag-input-field-input {\n -webkit-box-flex: 1;\n flex: 1 1 auto;\n width: 100%;\n min-width: 0;\n}\n\n.ag-floating-filter-input .ag-input-field-input[type=date] {\n width: 1px;\n}\n\n.ag-range-field {\n display: -webkit-box;\n display: flex;\n -webkit-box-align: center;\n align-items: center;\n}\n\n.ag-angle-select {\n display: -webkit-box;\n display: flex;\n -webkit-box-align: center;\n align-items: center;\n}\n\n.ag-angle-select-wrapper {\n display: -webkit-box;\n display: flex;\n}\n\n.ag-angle-select-parent-circle {\n display: block;\n position: relative;\n}\n\n.ag-angle-select-child-circle {\n position: absolute;\n}\n\n.ag-slider-wrapper {\n display: -webkit-box;\n display: flex;\n}\n.ag-slider-wrapper .ag-input-field {\n -webkit-box-flex: 1;\n flex: 1 1 auto;\n}\n\n.ag-picker-field-display {\n -webkit-box-flex: 1;\n flex: 1 1 auto;\n}\n\n.ag-picker-field {\n display: -webkit-box;\n display: flex;\n -webkit-box-align: center;\n align-items: center;\n}\n\n.ag-picker-field-icon {\n display: -webkit-box;\n display: flex;\n border: 0;\n padding: 0;\n margin: 0;\n cursor: pointer;\n}\n\n.ag-picker-field-wrapper {\n overflow: hidden;\n}\n\n.ag-label-align-right .ag-label {\n -webkit-box-ordinal-group: 2;\n order: 1;\n}\n.ag-label-align-right > * {\n -webkit-box-flex: 0;\n flex: none;\n}\n\n.ag-label-align-top {\n -webkit-box-orient: vertical;\n -webkit-box-direction: normal;\n flex-direction: column;\n -webkit-box-align: start;\n align-items: flex-start;\n}\n.ag-label-align-top > * {\n align-self: stretch;\n}\n\n.ag-color-panel {\n width: 100%;\n display: -webkit-box;\n display: flex;\n -webkit-box-orient: vertical;\n -webkit-box-direction: normal;\n flex-direction: column;\n text-align: center;\n}\n\n.ag-spectrum-color {\n -webkit-box-flex: 1;\n flex: 1 1 auto;\n position: relative;\n overflow: hidden;\n cursor: default;\n}\n\n.ag-spectrum-fill {\n position: absolute;\n top: 0;\n left: 0;\n right: 0;\n bottom: 0;\n}\n\n.ag-spectrum-val {\n cursor: pointer;\n}\n\n.ag-spectrum-dragger {\n position: absolute;\n pointer-events: none;\n cursor: pointer;\n}\n\n.ag-spectrum-hue {\n cursor: default;\n background: -webkit-gradient(linear, right top, left top, color-stop(3%, #ff0000), color-stop(17%, #ffff00), color-stop(33%, #00ff00), color-stop(50%, #00ffff), color-stop(67%, #0000ff), color-stop(83%, #ff00ff), to(#ff0000));\n background: linear-gradient(to left, #ff0000 3%, #ffff00 17%, #00ff00 33%, #00ffff 50%, #0000ff 67%, #ff00ff 83%, #ff0000 100%);\n}\n\n.ag-spectrum-alpha {\n cursor: default;\n}\n\n.ag-spectrum-hue-background {\n width: 100%;\n height: 100%;\n}\n\n.ag-spectrum-alpha-background {\n background-image: -webkit-gradient(linear, left top, right top, from(rgba(0, 0, 0, 0)), to(rgb(0, 0, 0)));\n background-image: linear-gradient(to right, rgba(0, 0, 0, 0), rgb(0, 0, 0));\n width: 100%;\n height: 100%;\n}\n\n.ag-spectrum-tool {\n cursor: pointer;\n}\n\n.ag-spectrum-slider {\n position: absolute;\n pointer-events: none;\n}\n\n.ag-recent-colors {\n display: -webkit-box;\n display: flex;\n}\n\n.ag-recent-color {\n cursor: pointer;\n}\n\n.ag-ltr .ag-column-select-indent-1 {\n padding-left: 20px;\n}\n.ag-rtl .ag-column-select-indent-1 {\n padding-right: 20px;\n}\n\n.ag-ltr .ag-row-group-indent-1 {\n padding-left: 20px;\n}\n.ag-rtl .ag-row-group-indent-1 {\n padding-right: 20px;\n}\n\n.ag-ltr .ag-column-select-indent-2 {\n padding-left: 40px;\n}\n.ag-rtl .ag-column-select-indent-2 {\n padding-right: 40px;\n}\n\n.ag-ltr .ag-row-group-indent-2 {\n padding-left: 40px;\n}\n.ag-rtl .ag-row-group-indent-2 {\n padding-right: 40px;\n}\n\n.ag-ltr .ag-column-select-indent-3 {\n padding-left: 60px;\n}\n.ag-rtl .ag-column-select-indent-3 {\n padding-right: 60px;\n}\n\n.ag-ltr .ag-row-group-indent-3 {\n padding-left: 60px;\n}\n.ag-rtl .ag-row-group-indent-3 {\n padding-right: 60px;\n}\n\n.ag-ltr .ag-column-select-indent-4 {\n padding-left: 80px;\n}\n.ag-rtl .ag-column-select-indent-4 {\n padding-right: 80px;\n}\n\n.ag-ltr .ag-row-group-indent-4 {\n padding-left: 80px;\n}\n.ag-rtl .ag-row-group-indent-4 {\n padding-right: 80px;\n}\n\n.ag-ltr .ag-column-select-indent-5 {\n padding-left: 100px;\n}\n.ag-rtl .ag-column-select-indent-5 {\n padding-right: 100px;\n}\n\n.ag-ltr .ag-row-group-indent-5 {\n padding-left: 100px;\n}\n.ag-rtl .ag-row-group-indent-5 {\n padding-right: 100px;\n}\n\n.ag-ltr .ag-column-select-indent-6 {\n padding-left: 120px;\n}\n.ag-rtl .ag-column-select-indent-6 {\n padding-right: 120px;\n}\n\n.ag-ltr .ag-row-group-indent-6 {\n padding-left: 120px;\n}\n.ag-rtl .ag-row-group-indent-6 {\n padding-right: 120px;\n}\n\n.ag-ltr .ag-column-select-indent-7 {\n padding-left: 140px;\n}\n.ag-rtl .ag-column-select-indent-7 {\n padding-right: 140px;\n}\n\n.ag-ltr .ag-row-group-indent-7 {\n padding-left: 140px;\n}\n.ag-rtl .ag-row-group-indent-7 {\n padding-right: 140px;\n}\n\n.ag-ltr .ag-column-select-indent-8 {\n padding-left: 160px;\n}\n.ag-rtl .ag-column-select-indent-8 {\n padding-right: 160px;\n}\n\n.ag-ltr .ag-row-group-indent-8 {\n padding-left: 160px;\n}\n.ag-rtl .ag-row-group-indent-8 {\n padding-right: 160px;\n}\n\n.ag-ltr .ag-column-select-indent-9 {\n padding-left: 180px;\n}\n.ag-rtl .ag-column-select-indent-9 {\n padding-right: 180px;\n}\n\n.ag-ltr .ag-row-group-indent-9 {\n padding-left: 180px;\n}\n.ag-rtl .ag-row-group-indent-9 {\n padding-right: 180px;\n}\n\n.ag-ltr .ag-column-select-indent-10 {\n padding-left: 200px;\n}\n.ag-rtl .ag-column-select-indent-10 {\n padding-right: 200px;\n}\n\n.ag-ltr .ag-row-group-indent-10 {\n padding-left: 200px;\n}\n.ag-rtl .ag-row-group-indent-10 {\n padding-right: 200px;\n}\n\n.ag-ltr .ag-column-select-indent-11 {\n padding-left: 220px;\n}\n.ag-rtl .ag-column-select-indent-11 {\n padding-right: 220px;\n}\n\n.ag-ltr .ag-row-group-indent-11 {\n padding-left: 220px;\n}\n.ag-rtl .ag-row-group-indent-11 {\n padding-right: 220px;\n}\n\n.ag-ltr .ag-column-select-indent-12 {\n padding-left: 240px;\n}\n.ag-rtl .ag-column-select-indent-12 {\n padding-right: 240px;\n}\n\n.ag-ltr .ag-row-group-indent-12 {\n padding-left: 240px;\n}\n.ag-rtl .ag-row-group-indent-12 {\n padding-right: 240px;\n}\n\n.ag-ltr .ag-column-select-indent-13 {\n padding-left: 260px;\n}\n.ag-rtl .ag-column-select-indent-13 {\n padding-right: 260px;\n}\n\n.ag-ltr .ag-row-group-indent-13 {\n padding-left: 260px;\n}\n.ag-rtl .ag-row-group-indent-13 {\n padding-right: 260px;\n}\n\n.ag-ltr .ag-column-select-indent-14 {\n padding-left: 280px;\n}\n.ag-rtl .ag-column-select-indent-14 {\n padding-right: 280px;\n}\n\n.ag-ltr .ag-row-group-indent-14 {\n padding-left: 280px;\n}\n.ag-rtl .ag-row-group-indent-14 {\n padding-right: 280px;\n}\n\n.ag-ltr .ag-column-select-indent-15 {\n padding-left: 300px;\n}\n.ag-rtl .ag-column-select-indent-15 {\n padding-right: 300px;\n}\n\n.ag-ltr .ag-row-group-indent-15 {\n padding-left: 300px;\n}\n.ag-rtl .ag-row-group-indent-15 {\n padding-right: 300px;\n}\n\n.ag-ltr .ag-column-select-indent-16 {\n padding-left: 320px;\n}\n.ag-rtl .ag-column-select-indent-16 {\n padding-right: 320px;\n}\n\n.ag-ltr .ag-row-group-indent-16 {\n padding-left: 320px;\n}\n.ag-rtl .ag-row-group-indent-16 {\n padding-right: 320px;\n}\n\n.ag-ltr .ag-column-select-indent-17 {\n padding-left: 340px;\n}\n.ag-rtl .ag-column-select-indent-17 {\n padding-right: 340px;\n}\n\n.ag-ltr .ag-row-group-indent-17 {\n padding-left: 340px;\n}\n.ag-rtl .ag-row-group-indent-17 {\n padding-right: 340px;\n}\n\n.ag-ltr .ag-column-select-indent-18 {\n padding-left: 360px;\n}\n.ag-rtl .ag-column-select-indent-18 {\n padding-right: 360px;\n}\n\n.ag-ltr .ag-row-group-indent-18 {\n padding-left: 360px;\n}\n.ag-rtl .ag-row-group-indent-18 {\n padding-right: 360px;\n}\n\n.ag-ltr .ag-column-select-indent-19 {\n padding-left: 380px;\n}\n.ag-rtl .ag-column-select-indent-19 {\n padding-right: 380px;\n}\n\n.ag-ltr .ag-row-group-indent-19 {\n padding-left: 380px;\n}\n.ag-rtl .ag-row-group-indent-19 {\n padding-right: 380px;\n}\n\n.ag-ltr .ag-column-select-indent-20 {\n padding-left: 400px;\n}\n.ag-rtl .ag-column-select-indent-20 {\n padding-right: 400px;\n}\n\n.ag-ltr .ag-row-group-indent-20 {\n padding-left: 400px;\n}\n.ag-rtl .ag-row-group-indent-20 {\n padding-right: 400px;\n}\n\n.ag-ltr .ag-column-select-indent-21 {\n padding-left: 420px;\n}\n.ag-rtl .ag-column-select-indent-21 {\n padding-right: 420px;\n}\n\n.ag-ltr .ag-row-group-indent-21 {\n padding-left: 420px;\n}\n.ag-rtl .ag-row-group-indent-21 {\n padding-right: 420px;\n}\n\n.ag-ltr .ag-column-select-indent-22 {\n padding-left: 440px;\n}\n.ag-rtl .ag-column-select-indent-22 {\n padding-right: 440px;\n}\n\n.ag-ltr .ag-row-group-indent-22 {\n padding-left: 440px;\n}\n.ag-rtl .ag-row-group-indent-22 {\n padding-right: 440px;\n}\n\n.ag-ltr .ag-column-select-indent-23 {\n padding-left: 460px;\n}\n.ag-rtl .ag-column-select-indent-23 {\n padding-right: 460px;\n}\n\n.ag-ltr .ag-row-group-indent-23 {\n padding-left: 460px;\n}\n.ag-rtl .ag-row-group-indent-23 {\n padding-right: 460px;\n}\n\n.ag-ltr .ag-column-select-indent-24 {\n padding-left: 480px;\n}\n.ag-rtl .ag-column-select-indent-24 {\n padding-right: 480px;\n}\n\n.ag-ltr .ag-row-group-indent-24 {\n padding-left: 480px;\n}\n.ag-rtl .ag-row-group-indent-24 {\n padding-right: 480px;\n}\n\n.ag-ltr .ag-column-select-indent-25 {\n padding-left: 500px;\n}\n.ag-rtl .ag-column-select-indent-25 {\n padding-right: 500px;\n}\n\n.ag-ltr .ag-row-group-indent-25 {\n padding-left: 500px;\n}\n.ag-rtl .ag-row-group-indent-25 {\n padding-right: 500px;\n}\n\n.ag-ltr .ag-column-select-indent-26 {\n padding-left: 520px;\n}\n.ag-rtl .ag-column-select-indent-26 {\n padding-right: 520px;\n}\n\n.ag-ltr .ag-row-group-indent-26 {\n padding-left: 520px;\n}\n.ag-rtl .ag-row-group-indent-26 {\n padding-right: 520px;\n}\n\n.ag-ltr .ag-column-select-indent-27 {\n padding-left: 540px;\n}\n.ag-rtl .ag-column-select-indent-27 {\n padding-right: 540px;\n}\n\n.ag-ltr .ag-row-group-indent-27 {\n padding-left: 540px;\n}\n.ag-rtl .ag-row-group-indent-27 {\n padding-right: 540px;\n}\n\n.ag-ltr .ag-column-select-indent-28 {\n padding-left: 560px;\n}\n.ag-rtl .ag-column-select-indent-28 {\n padding-right: 560px;\n}\n\n.ag-ltr .ag-row-group-indent-28 {\n padding-left: 560px;\n}\n.ag-rtl .ag-row-group-indent-28 {\n padding-right: 560px;\n}\n\n.ag-ltr .ag-column-select-indent-29 {\n padding-left: 580px;\n}\n.ag-rtl .ag-column-select-indent-29 {\n padding-right: 580px;\n}\n\n.ag-ltr .ag-row-group-indent-29 {\n padding-left: 580px;\n}\n.ag-rtl .ag-row-group-indent-29 {\n padding-right: 580px;\n}\n\n.ag-ltr .ag-column-select-indent-30 {\n padding-left: 600px;\n}\n.ag-rtl .ag-column-select-indent-30 {\n padding-right: 600px;\n}\n\n.ag-ltr .ag-row-group-indent-30 {\n padding-left: 600px;\n}\n.ag-rtl .ag-row-group-indent-30 {\n padding-right: 600px;\n}\n\n.ag-ltr .ag-column-select-indent-31 {\n padding-left: 620px;\n}\n.ag-rtl .ag-column-select-indent-31 {\n padding-right: 620px;\n}\n\n.ag-ltr .ag-row-group-indent-31 {\n padding-left: 620px;\n}\n.ag-rtl .ag-row-group-indent-31 {\n padding-right: 620px;\n}\n\n.ag-ltr .ag-column-select-indent-32 {\n padding-left: 640px;\n}\n.ag-rtl .ag-column-select-indent-32 {\n padding-right: 640px;\n}\n\n.ag-ltr .ag-row-group-indent-32 {\n padding-left: 640px;\n}\n.ag-rtl .ag-row-group-indent-32 {\n padding-right: 640px;\n}\n\n.ag-ltr .ag-column-select-indent-33 {\n padding-left: 660px;\n}\n.ag-rtl .ag-column-select-indent-33 {\n padding-right: 660px;\n}\n\n.ag-ltr .ag-row-group-indent-33 {\n padding-left: 660px;\n}\n.ag-rtl .ag-row-group-indent-33 {\n padding-right: 660px;\n}\n\n.ag-ltr .ag-column-select-indent-34 {\n padding-left: 680px;\n}\n.ag-rtl .ag-column-select-indent-34 {\n padding-right: 680px;\n}\n\n.ag-ltr .ag-row-group-indent-34 {\n padding-left: 680px;\n}\n.ag-rtl .ag-row-group-indent-34 {\n padding-right: 680px;\n}\n\n.ag-ltr .ag-column-select-indent-35 {\n padding-left: 700px;\n}\n.ag-rtl .ag-column-select-indent-35 {\n padding-right: 700px;\n}\n\n.ag-ltr .ag-row-group-indent-35 {\n padding-left: 700px;\n}\n.ag-rtl .ag-row-group-indent-35 {\n padding-right: 700px;\n}\n\n.ag-ltr .ag-column-select-indent-36 {\n padding-left: 720px;\n}\n.ag-rtl .ag-column-select-indent-36 {\n padding-right: 720px;\n}\n\n.ag-ltr .ag-row-group-indent-36 {\n padding-left: 720px;\n}\n.ag-rtl .ag-row-group-indent-36 {\n padding-right: 720px;\n}\n\n.ag-ltr .ag-column-select-indent-37 {\n padding-left: 740px;\n}\n.ag-rtl .ag-column-select-indent-37 {\n padding-right: 740px;\n}\n\n.ag-ltr .ag-row-group-indent-37 {\n padding-left: 740px;\n}\n.ag-rtl .ag-row-group-indent-37 {\n padding-right: 740px;\n}\n\n.ag-ltr .ag-column-select-indent-38 {\n padding-left: 760px;\n}\n.ag-rtl .ag-column-select-indent-38 {\n padding-right: 760px;\n}\n\n.ag-ltr .ag-row-group-indent-38 {\n padding-left: 760px;\n}\n.ag-rtl .ag-row-group-indent-38 {\n padding-right: 760px;\n}\n\n.ag-ltr .ag-column-select-indent-39 {\n padding-left: 780px;\n}\n.ag-rtl .ag-column-select-indent-39 {\n padding-right: 780px;\n}\n\n.ag-ltr .ag-row-group-indent-39 {\n padding-left: 780px;\n}\n.ag-rtl .ag-row-group-indent-39 {\n padding-right: 780px;\n}\n\n.ag-ltr .ag-column-select-indent-40 {\n padding-left: 800px;\n}\n.ag-rtl .ag-column-select-indent-40 {\n padding-right: 800px;\n}\n\n.ag-ltr .ag-row-group-indent-40 {\n padding-left: 800px;\n}\n.ag-rtl .ag-row-group-indent-40 {\n padding-right: 800px;\n}\n\n.ag-ltr .ag-column-select-indent-41 {\n padding-left: 820px;\n}\n.ag-rtl .ag-column-select-indent-41 {\n padding-right: 820px;\n}\n\n.ag-ltr .ag-row-group-indent-41 {\n padding-left: 820px;\n}\n.ag-rtl .ag-row-group-indent-41 {\n padding-right: 820px;\n}\n\n.ag-ltr .ag-column-select-indent-42 {\n padding-left: 840px;\n}\n.ag-rtl .ag-column-select-indent-42 {\n padding-right: 840px;\n}\n\n.ag-ltr .ag-row-group-indent-42 {\n padding-left: 840px;\n}\n.ag-rtl .ag-row-group-indent-42 {\n padding-right: 840px;\n}\n\n.ag-ltr .ag-column-select-indent-43 {\n padding-left: 860px;\n}\n.ag-rtl .ag-column-select-indent-43 {\n padding-right: 860px;\n}\n\n.ag-ltr .ag-row-group-indent-43 {\n padding-left: 860px;\n}\n.ag-rtl .ag-row-group-indent-43 {\n padding-right: 860px;\n}\n\n.ag-ltr .ag-column-select-indent-44 {\n padding-left: 880px;\n}\n.ag-rtl .ag-column-select-indent-44 {\n padding-right: 880px;\n}\n\n.ag-ltr .ag-row-group-indent-44 {\n padding-left: 880px;\n}\n.ag-rtl .ag-row-group-indent-44 {\n padding-right: 880px;\n}\n\n.ag-ltr .ag-column-select-indent-45 {\n padding-left: 900px;\n}\n.ag-rtl .ag-column-select-indent-45 {\n padding-right: 900px;\n}\n\n.ag-ltr .ag-row-group-indent-45 {\n padding-left: 900px;\n}\n.ag-rtl .ag-row-group-indent-45 {\n padding-right: 900px;\n}\n\n.ag-ltr .ag-column-select-indent-46 {\n padding-left: 920px;\n}\n.ag-rtl .ag-column-select-indent-46 {\n padding-right: 920px;\n}\n\n.ag-ltr .ag-row-group-indent-46 {\n padding-left: 920px;\n}\n.ag-rtl .ag-row-group-indent-46 {\n padding-right: 920px;\n}\n\n.ag-ltr .ag-column-select-indent-47 {\n padding-left: 940px;\n}\n.ag-rtl .ag-column-select-indent-47 {\n padding-right: 940px;\n}\n\n.ag-ltr .ag-row-group-indent-47 {\n padding-left: 940px;\n}\n.ag-rtl .ag-row-group-indent-47 {\n padding-right: 940px;\n}\n\n.ag-ltr .ag-column-select-indent-48 {\n padding-left: 960px;\n}\n.ag-rtl .ag-column-select-indent-48 {\n padding-right: 960px;\n}\n\n.ag-ltr .ag-row-group-indent-48 {\n padding-left: 960px;\n}\n.ag-rtl .ag-row-group-indent-48 {\n padding-right: 960px;\n}\n\n.ag-ltr .ag-column-select-indent-49 {\n padding-left: 980px;\n}\n.ag-rtl .ag-column-select-indent-49 {\n padding-right: 980px;\n}\n\n.ag-ltr .ag-row-group-indent-49 {\n padding-left: 980px;\n}\n.ag-rtl .ag-row-group-indent-49 {\n padding-right: 980px;\n}\n\n.ag-ltr .ag-column-select-indent-50 {\n padding-left: 1000px;\n}\n.ag-rtl .ag-column-select-indent-50 {\n padding-right: 1000px;\n}\n\n.ag-ltr .ag-row-group-indent-50 {\n padding-left: 1000px;\n}\n.ag-rtl .ag-row-group-indent-50 {\n padding-right: 1000px;\n}\n\n.ag-ltr .ag-column-select-indent-51 {\n padding-left: 1020px;\n}\n.ag-rtl .ag-column-select-indent-51 {\n padding-right: 1020px;\n}\n\n.ag-ltr .ag-row-group-indent-51 {\n padding-left: 1020px;\n}\n.ag-rtl .ag-row-group-indent-51 {\n padding-right: 1020px;\n}\n\n.ag-ltr .ag-column-select-indent-52 {\n padding-left: 1040px;\n}\n.ag-rtl .ag-column-select-indent-52 {\n padding-right: 1040px;\n}\n\n.ag-ltr .ag-row-group-indent-52 {\n padding-left: 1040px;\n}\n.ag-rtl .ag-row-group-indent-52 {\n padding-right: 1040px;\n}\n\n.ag-ltr .ag-column-select-indent-53 {\n padding-left: 1060px;\n}\n.ag-rtl .ag-column-select-indent-53 {\n padding-right: 1060px;\n}\n\n.ag-ltr .ag-row-group-indent-53 {\n padding-left: 1060px;\n}\n.ag-rtl .ag-row-group-indent-53 {\n padding-right: 1060px;\n}\n\n.ag-ltr .ag-column-select-indent-54 {\n padding-left: 1080px;\n}\n.ag-rtl .ag-column-select-indent-54 {\n padding-right: 1080px;\n}\n\n.ag-ltr .ag-row-group-indent-54 {\n padding-left: 1080px;\n}\n.ag-rtl .ag-row-group-indent-54 {\n padding-right: 1080px;\n}\n\n.ag-ltr .ag-column-select-indent-55 {\n padding-left: 1100px;\n}\n.ag-rtl .ag-column-select-indent-55 {\n padding-right: 1100px;\n}\n\n.ag-ltr .ag-row-group-indent-55 {\n padding-left: 1100px;\n}\n.ag-rtl .ag-row-group-indent-55 {\n padding-right: 1100px;\n}\n\n.ag-ltr .ag-column-select-indent-56 {\n padding-left: 1120px;\n}\n.ag-rtl .ag-column-select-indent-56 {\n padding-right: 1120px;\n}\n\n.ag-ltr .ag-row-group-indent-56 {\n padding-left: 1120px;\n}\n.ag-rtl .ag-row-group-indent-56 {\n padding-right: 1120px;\n}\n\n.ag-ltr .ag-column-select-indent-57 {\n padding-left: 1140px;\n}\n.ag-rtl .ag-column-select-indent-57 {\n padding-right: 1140px;\n}\n\n.ag-ltr .ag-row-group-indent-57 {\n padding-left: 1140px;\n}\n.ag-rtl .ag-row-group-indent-57 {\n padding-right: 1140px;\n}\n\n.ag-ltr .ag-column-select-indent-58 {\n padding-left: 1160px;\n}\n.ag-rtl .ag-column-select-indent-58 {\n padding-right: 1160px;\n}\n\n.ag-ltr .ag-row-group-indent-58 {\n padding-left: 1160px;\n}\n.ag-rtl .ag-row-group-indent-58 {\n padding-right: 1160px;\n}\n\n.ag-ltr .ag-column-select-indent-59 {\n padding-left: 1180px;\n}\n.ag-rtl .ag-column-select-indent-59 {\n padding-right: 1180px;\n}\n\n.ag-ltr .ag-row-group-indent-59 {\n padding-left: 1180px;\n}\n.ag-rtl .ag-row-group-indent-59 {\n padding-right: 1180px;\n}\n\n.ag-ltr .ag-column-select-indent-60 {\n padding-left: 1200px;\n}\n.ag-rtl .ag-column-select-indent-60 {\n padding-right: 1200px;\n}\n\n.ag-ltr .ag-row-group-indent-60 {\n padding-left: 1200px;\n}\n.ag-rtl .ag-row-group-indent-60 {\n padding-right: 1200px;\n}\n\n.ag-ltr .ag-column-select-indent-61 {\n padding-left: 1220px;\n}\n.ag-rtl .ag-column-select-indent-61 {\n padding-right: 1220px;\n}\n\n.ag-ltr .ag-row-group-indent-61 {\n padding-left: 1220px;\n}\n.ag-rtl .ag-row-group-indent-61 {\n padding-right: 1220px;\n}\n\n.ag-ltr .ag-column-select-indent-62 {\n padding-left: 1240px;\n}\n.ag-rtl .ag-column-select-indent-62 {\n padding-right: 1240px;\n}\n\n.ag-ltr .ag-row-group-indent-62 {\n padding-left: 1240px;\n}\n.ag-rtl .ag-row-group-indent-62 {\n padding-right: 1240px;\n}\n\n.ag-ltr .ag-column-select-indent-63 {\n padding-left: 1260px;\n}\n.ag-rtl .ag-column-select-indent-63 {\n padding-right: 1260px;\n}\n\n.ag-ltr .ag-row-group-indent-63 {\n padding-left: 1260px;\n}\n.ag-rtl .ag-row-group-indent-63 {\n padding-right: 1260px;\n}\n\n.ag-ltr .ag-column-select-indent-64 {\n padding-left: 1280px;\n}\n.ag-rtl .ag-column-select-indent-64 {\n padding-right: 1280px;\n}\n\n.ag-ltr .ag-row-group-indent-64 {\n padding-left: 1280px;\n}\n.ag-rtl .ag-row-group-indent-64 {\n padding-right: 1280px;\n}\n\n.ag-ltr .ag-column-select-indent-65 {\n padding-left: 1300px;\n}\n.ag-rtl .ag-column-select-indent-65 {\n padding-right: 1300px;\n}\n\n.ag-ltr .ag-row-group-indent-65 {\n padding-left: 1300px;\n}\n.ag-rtl .ag-row-group-indent-65 {\n padding-right: 1300px;\n}\n\n.ag-ltr .ag-column-select-indent-66 {\n padding-left: 1320px;\n}\n.ag-rtl .ag-column-select-indent-66 {\n padding-right: 1320px;\n}\n\n.ag-ltr .ag-row-group-indent-66 {\n padding-left: 1320px;\n}\n.ag-rtl .ag-row-group-indent-66 {\n padding-right: 1320px;\n}\n\n.ag-ltr .ag-column-select-indent-67 {\n padding-left: 1340px;\n}\n.ag-rtl .ag-column-select-indent-67 {\n padding-right: 1340px;\n}\n\n.ag-ltr .ag-row-group-indent-67 {\n padding-left: 1340px;\n}\n.ag-rtl .ag-row-group-indent-67 {\n padding-right: 1340px;\n}\n\n.ag-ltr .ag-column-select-indent-68 {\n padding-left: 1360px;\n}\n.ag-rtl .ag-column-select-indent-68 {\n padding-right: 1360px;\n}\n\n.ag-ltr .ag-row-group-indent-68 {\n padding-left: 1360px;\n}\n.ag-rtl .ag-row-group-indent-68 {\n padding-right: 1360px;\n}\n\n.ag-ltr .ag-column-select-indent-69 {\n padding-left: 1380px;\n}\n.ag-rtl .ag-column-select-indent-69 {\n padding-right: 1380px;\n}\n\n.ag-ltr .ag-row-group-indent-69 {\n padding-left: 1380px;\n}\n.ag-rtl .ag-row-group-indent-69 {\n padding-right: 1380px;\n}\n\n.ag-ltr .ag-column-select-indent-70 {\n padding-left: 1400px;\n}\n.ag-rtl .ag-column-select-indent-70 {\n padding-right: 1400px;\n}\n\n.ag-ltr .ag-row-group-indent-70 {\n padding-left: 1400px;\n}\n.ag-rtl .ag-row-group-indent-70 {\n padding-right: 1400px;\n}\n\n.ag-ltr .ag-column-select-indent-71 {\n padding-left: 1420px;\n}\n.ag-rtl .ag-column-select-indent-71 {\n padding-right: 1420px;\n}\n\n.ag-ltr .ag-row-group-indent-71 {\n padding-left: 1420px;\n}\n.ag-rtl .ag-row-group-indent-71 {\n padding-right: 1420px;\n}\n\n.ag-ltr .ag-column-select-indent-72 {\n padding-left: 1440px;\n}\n.ag-rtl .ag-column-select-indent-72 {\n padding-right: 1440px;\n}\n\n.ag-ltr .ag-row-group-indent-72 {\n padding-left: 1440px;\n}\n.ag-rtl .ag-row-group-indent-72 {\n padding-right: 1440px;\n}\n\n.ag-ltr .ag-column-select-indent-73 {\n padding-left: 1460px;\n}\n.ag-rtl .ag-column-select-indent-73 {\n padding-right: 1460px;\n}\n\n.ag-ltr .ag-row-group-indent-73 {\n padding-left: 1460px;\n}\n.ag-rtl .ag-row-group-indent-73 {\n padding-right: 1460px;\n}\n\n.ag-ltr .ag-column-select-indent-74 {\n padding-left: 1480px;\n}\n.ag-rtl .ag-column-select-indent-74 {\n padding-right: 1480px;\n}\n\n.ag-ltr .ag-row-group-indent-74 {\n padding-left: 1480px;\n}\n.ag-rtl .ag-row-group-indent-74 {\n padding-right: 1480px;\n}\n\n.ag-ltr .ag-column-select-indent-75 {\n padding-left: 1500px;\n}\n.ag-rtl .ag-column-select-indent-75 {\n padding-right: 1500px;\n}\n\n.ag-ltr .ag-row-group-indent-75 {\n padding-left: 1500px;\n}\n.ag-rtl .ag-row-group-indent-75 {\n padding-right: 1500px;\n}\n\n.ag-ltr .ag-column-select-indent-76 {\n padding-left: 1520px;\n}\n.ag-rtl .ag-column-select-indent-76 {\n padding-right: 1520px;\n}\n\n.ag-ltr .ag-row-group-indent-76 {\n padding-left: 1520px;\n}\n.ag-rtl .ag-row-group-indent-76 {\n padding-right: 1520px;\n}\n\n.ag-ltr .ag-column-select-indent-77 {\n padding-left: 1540px;\n}\n.ag-rtl .ag-column-select-indent-77 {\n padding-right: 1540px;\n}\n\n.ag-ltr .ag-row-group-indent-77 {\n padding-left: 1540px;\n}\n.ag-rtl .ag-row-group-indent-77 {\n padding-right: 1540px;\n}\n\n.ag-ltr .ag-column-select-indent-78 {\n padding-left: 1560px;\n}\n.ag-rtl .ag-column-select-indent-78 {\n padding-right: 1560px;\n}\n\n.ag-ltr .ag-row-group-indent-78 {\n padding-left: 1560px;\n}\n.ag-rtl .ag-row-group-indent-78 {\n padding-right: 1560px;\n}\n\n.ag-ltr .ag-column-select-indent-79 {\n padding-left: 1580px;\n}\n.ag-rtl .ag-column-select-indent-79 {\n padding-right: 1580px;\n}\n\n.ag-ltr .ag-row-group-indent-79 {\n padding-left: 1580px;\n}\n.ag-rtl .ag-row-group-indent-79 {\n padding-right: 1580px;\n}\n\n.ag-ltr .ag-column-select-indent-80 {\n padding-left: 1600px;\n}\n.ag-rtl .ag-column-select-indent-80 {\n padding-right: 1600px;\n}\n\n.ag-ltr .ag-row-group-indent-80 {\n padding-left: 1600px;\n}\n.ag-rtl .ag-row-group-indent-80 {\n padding-right: 1600px;\n}\n\n.ag-ltr .ag-column-select-indent-81 {\n padding-left: 1620px;\n}\n.ag-rtl .ag-column-select-indent-81 {\n padding-right: 1620px;\n}\n\n.ag-ltr .ag-row-group-indent-81 {\n padding-left: 1620px;\n}\n.ag-rtl .ag-row-group-indent-81 {\n padding-right: 1620px;\n}\n\n.ag-ltr .ag-column-select-indent-82 {\n padding-left: 1640px;\n}\n.ag-rtl .ag-column-select-indent-82 {\n padding-right: 1640px;\n}\n\n.ag-ltr .ag-row-group-indent-82 {\n padding-left: 1640px;\n}\n.ag-rtl .ag-row-group-indent-82 {\n padding-right: 1640px;\n}\n\n.ag-ltr .ag-column-select-indent-83 {\n padding-left: 1660px;\n}\n.ag-rtl .ag-column-select-indent-83 {\n padding-right: 1660px;\n}\n\n.ag-ltr .ag-row-group-indent-83 {\n padding-left: 1660px;\n}\n.ag-rtl .ag-row-group-indent-83 {\n padding-right: 1660px;\n}\n\n.ag-ltr .ag-column-select-indent-84 {\n padding-left: 1680px;\n}\n.ag-rtl .ag-column-select-indent-84 {\n padding-right: 1680px;\n}\n\n.ag-ltr .ag-row-group-indent-84 {\n padding-left: 1680px;\n}\n.ag-rtl .ag-row-group-indent-84 {\n padding-right: 1680px;\n}\n\n.ag-ltr .ag-column-select-indent-85 {\n padding-left: 1700px;\n}\n.ag-rtl .ag-column-select-indent-85 {\n padding-right: 1700px;\n}\n\n.ag-ltr .ag-row-group-indent-85 {\n padding-left: 1700px;\n}\n.ag-rtl .ag-row-group-indent-85 {\n padding-right: 1700px;\n}\n\n.ag-ltr .ag-column-select-indent-86 {\n padding-left: 1720px;\n}\n.ag-rtl .ag-column-select-indent-86 {\n padding-right: 1720px;\n}\n\n.ag-ltr .ag-row-group-indent-86 {\n padding-left: 1720px;\n}\n.ag-rtl .ag-row-group-indent-86 {\n padding-right: 1720px;\n}\n\n.ag-ltr .ag-column-select-indent-87 {\n padding-left: 1740px;\n}\n.ag-rtl .ag-column-select-indent-87 {\n padding-right: 1740px;\n}\n\n.ag-ltr .ag-row-group-indent-87 {\n padding-left: 1740px;\n}\n.ag-rtl .ag-row-group-indent-87 {\n padding-right: 1740px;\n}\n\n.ag-ltr .ag-column-select-indent-88 {\n padding-left: 1760px;\n}\n.ag-rtl .ag-column-select-indent-88 {\n padding-right: 1760px;\n}\n\n.ag-ltr .ag-row-group-indent-88 {\n padding-left: 1760px;\n}\n.ag-rtl .ag-row-group-indent-88 {\n padding-right: 1760px;\n}\n\n.ag-ltr .ag-column-select-indent-89 {\n padding-left: 1780px;\n}\n.ag-rtl .ag-column-select-indent-89 {\n padding-right: 1780px;\n}\n\n.ag-ltr .ag-row-group-indent-89 {\n padding-left: 1780px;\n}\n.ag-rtl .ag-row-group-indent-89 {\n padding-right: 1780px;\n}\n\n.ag-ltr .ag-column-select-indent-90 {\n padding-left: 1800px;\n}\n.ag-rtl .ag-column-select-indent-90 {\n padding-right: 1800px;\n}\n\n.ag-ltr .ag-row-group-indent-90 {\n padding-left: 1800px;\n}\n.ag-rtl .ag-row-group-indent-90 {\n padding-right: 1800px;\n}\n\n.ag-ltr .ag-column-select-indent-91 {\n padding-left: 1820px;\n}\n.ag-rtl .ag-column-select-indent-91 {\n padding-right: 1820px;\n}\n\n.ag-ltr .ag-row-group-indent-91 {\n padding-left: 1820px;\n}\n.ag-rtl .ag-row-group-indent-91 {\n padding-right: 1820px;\n}\n\n.ag-ltr .ag-column-select-indent-92 {\n padding-left: 1840px;\n}\n.ag-rtl .ag-column-select-indent-92 {\n padding-right: 1840px;\n}\n\n.ag-ltr .ag-row-group-indent-92 {\n padding-left: 1840px;\n}\n.ag-rtl .ag-row-group-indent-92 {\n padding-right: 1840px;\n}\n\n.ag-ltr .ag-column-select-indent-93 {\n padding-left: 1860px;\n}\n.ag-rtl .ag-column-select-indent-93 {\n padding-right: 1860px;\n}\n\n.ag-ltr .ag-row-group-indent-93 {\n padding-left: 1860px;\n}\n.ag-rtl .ag-row-group-indent-93 {\n padding-right: 1860px;\n}\n\n.ag-ltr .ag-column-select-indent-94 {\n padding-left: 1880px;\n}\n.ag-rtl .ag-column-select-indent-94 {\n padding-right: 1880px;\n}\n\n.ag-ltr .ag-row-group-indent-94 {\n padding-left: 1880px;\n}\n.ag-rtl .ag-row-group-indent-94 {\n padding-right: 1880px;\n}\n\n.ag-ltr .ag-column-select-indent-95 {\n padding-left: 1900px;\n}\n.ag-rtl .ag-column-select-indent-95 {\n padding-right: 1900px;\n}\n\n.ag-ltr .ag-row-group-indent-95 {\n padding-left: 1900px;\n}\n.ag-rtl .ag-row-group-indent-95 {\n padding-right: 1900px;\n}\n\n.ag-ltr .ag-column-select-indent-96 {\n padding-left: 1920px;\n}\n.ag-rtl .ag-column-select-indent-96 {\n padding-right: 1920px;\n}\n\n.ag-ltr .ag-row-group-indent-96 {\n padding-left: 1920px;\n}\n.ag-rtl .ag-row-group-indent-96 {\n padding-right: 1920px;\n}\n\n.ag-ltr .ag-column-select-indent-97 {\n padding-left: 1940px;\n}\n.ag-rtl .ag-column-select-indent-97 {\n padding-right: 1940px;\n}\n\n.ag-ltr .ag-row-group-indent-97 {\n padding-left: 1940px;\n}\n.ag-rtl .ag-row-group-indent-97 {\n padding-right: 1940px;\n}\n\n.ag-ltr .ag-column-select-indent-98 {\n padding-left: 1960px;\n}\n.ag-rtl .ag-column-select-indent-98 {\n padding-right: 1960px;\n}\n\n.ag-ltr .ag-row-group-indent-98 {\n padding-left: 1960px;\n}\n.ag-rtl .ag-row-group-indent-98 {\n padding-right: 1960px;\n}\n\n.ag-ltr .ag-column-select-indent-99 {\n padding-left: 1980px;\n}\n.ag-rtl .ag-column-select-indent-99 {\n padding-right: 1980px;\n}\n\n.ag-ltr .ag-row-group-indent-99 {\n padding-left: 1980px;\n}\n.ag-rtl .ag-row-group-indent-99 {\n padding-right: 1980px;\n}\n\n.ag-ltr {\n direction: ltr;\n}\n.ag-ltr .ag-body, .ag-ltr .ag-floating-top, .ag-ltr .ag-floating-bottom, .ag-ltr .ag-header, .ag-ltr .ag-body-viewport, .ag-ltr .ag-body-horizontal-scroll {\n -webkit-box-orient: horizontal;\n -webkit-box-direction: normal;\n flex-direction: row;\n}\n\n.ag-rtl {\n direction: rtl;\n}\n.ag-rtl .ag-body, .ag-rtl .ag-floating-top, .ag-rtl .ag-floating-bottom, .ag-rtl .ag-header, .ag-rtl .ag-body-viewport, .ag-rtl .ag-body-horizontal-scroll {\n -webkit-box-orient: horizontal;\n -webkit-box-direction: reverse;\n flex-direction: row-reverse;\n}\n.ag-rtl .ag-icon-contracted,\n.ag-rtl .ag-icon-tree-closed {\n display: block;\n -webkit-transform: rotate(180deg);\n transform: rotate(180deg);\n}\n\n.ag-layout-print.ag-body-viewport {\n -webkit-box-flex: 0;\n flex: none;\n}\n.ag-layout-print.ag-root-wrapper {\n display: -webkit-inline-box;\n display: inline-flex;\n}\n.ag-layout-print .ag-center-cols-clipper {\n min-width: 100%;\n}\n.ag-layout-print .ag-body-horizontal-scroll {\n display: none;\n}\n.ag-layout-print.ag-force-vertical-scroll {\n overflow-y: visible !important;\n}\n\n@media print {\n .ag-root-wrapper.ag-layout-print,\n.ag-root-wrapper.ag-layout-print .ag-root-wrapper-body,\n.ag-root-wrapper.ag-layout-print .ag-root,\n.ag-root-wrapper.ag-layout-print .ag-body-viewport,\n.ag-root-wrapper.ag-layout-print .ag-center-cols-container,\n.ag-root-wrapper.ag-layout-print .ag-center-cols-viewport,\n.ag-root-wrapper.ag-layout-print .ag-center-cols-clipper,\n.ag-root-wrapper.ag-layout-print .ag-body-horizontal-scroll-viewport,\n.ag-root-wrapper.ag-layout-print .ag-virtual-list-viewport {\n height: auto !important;\n overflow: hidden !important;\n display: block !important;\n }\n .ag-root-wrapper.ag-layout-print .ag-row {\n page-break-inside: avoid;\n }\n}\n.ag-body .ag-body-viewport {\n -webkit-overflow-scrolling: touch;\n}\n\n.ag-chart {\n position: relative;\n display: -webkit-box;\n display: flex;\n overflow: hidden;\n width: 100%;\n height: 100%;\n}\n\n.ag-chart-components-wrapper {\n position: relative;\n display: -webkit-box;\n display: flex;\n -webkit-box-flex: 1;\n flex: 1 1 auto;\n overflow: hidden;\n}\n\n.ag-chart-title-edit {\n position: absolute;\n display: none;\n top: 0;\n left: 0;\n text-align: center;\n}\n\n.ag-chart-title-edit.currently-editing {\n display: inline-block;\n}\n\n.ag-chart-canvas-wrapper {\n position: relative;\n -webkit-box-flex: 1;\n flex: 1 1 auto;\n overflow: hidden;\n}\n\n.ag-charts-canvas {\n display: block;\n}\n\n.ag-chart-menu {\n position: absolute;\n top: 10px;\n width: 24px;\n overflow: hidden;\n display: -webkit-box;\n display: flex;\n -webkit-box-orient: vertical;\n -webkit-box-direction: normal;\n flex-direction: column;\n}\n.ag-ltr .ag-chart-menu {\n right: 20px;\n}\n.ag-rtl .ag-chart-menu {\n left: 20px;\n}\n\n.ag-chart-docked-container {\n position: relative;\n width: 0;\n min-width: 0;\n -webkit-transition: min-width 0.4s;\n transition: min-width 0.4s;\n}\n\n.ag-chart-menu-hidden ~ .ag-chart-docked-container {\n max-width: 0;\n overflow: hidden;\n}\n\n.ag-chart-tabbed-menu {\n width: 100%;\n height: 100%;\n display: -webkit-box;\n display: flex;\n -webkit-box-orient: vertical;\n -webkit-box-direction: normal;\n flex-direction: column;\n overflow: hidden;\n}\n\n.ag-chart-tabbed-menu-header {\n -webkit-box-flex: 0;\n flex: none;\n -webkit-user-select: none;\n -moz-user-select: none;\n user-select: none;\n cursor: default;\n}\n\n.ag-chart-tabbed-menu-body {\n display: -webkit-box;\n display: flex;\n -webkit-box-flex: 1;\n flex: 1 1 auto;\n -webkit-box-align: stretch;\n align-items: stretch;\n overflow: hidden;\n}\n\n.ag-chart-tab {\n width: 100%;\n overflow: hidden;\n overflow-y: auto;\n}\n\n.ag-chart-settings {\n overflow-x: hidden;\n}\n\n.ag-chart-settings-wrapper {\n position: relative;\n -webkit-box-orient: vertical;\n -webkit-box-direction: normal;\n flex-direction: column;\n width: 100%;\n height: 100%;\n display: -webkit-box;\n display: flex;\n overflow: hidden;\n}\n\n.ag-chart-settings-nav-bar {\n display: -webkit-box;\n display: flex;\n -webkit-box-align: center;\n align-items: center;\n width: 100%;\n height: 30px;\n padding: 0 10px;\n -webkit-user-select: none;\n -moz-user-select: none;\n user-select: none;\n}\n\n.ag-chart-settings-card-selector {\n display: -webkit-box;\n display: flex;\n -webkit-box-align: center;\n align-items: center;\n justify-content: space-around;\n -webkit-box-flex: 1;\n flex: 1 1 auto;\n height: 100%;\n padding: 0 10px;\n}\n\n.ag-chart-settings-card-item {\n cursor: pointer;\n width: 10px;\n height: 10px;\n background-color: #000;\n position: relative;\n}\n.ag-chart-settings-card-item.ag-not-selected {\n opacity: 0.2;\n}\n.ag-chart-settings-card-item::before {\n content: \" \";\n display: block;\n position: absolute;\n background-color: transparent;\n left: 50%;\n top: 50%;\n margin-left: -10px;\n margin-top: -10px;\n width: 20px;\n height: 20px;\n}\n\n.ag-chart-settings-prev,\n.ag-chart-settings-next {\n position: relative;\n -webkit-box-flex: 0;\n flex: none;\n}\n\n.ag-chart-settings-prev-button,\n.ag-chart-settings-next-button {\n position: absolute;\n top: 0;\n left: 0;\n width: 100%;\n height: 100%;\n cursor: pointer;\n opacity: 0;\n}\n\n.ag-chart-settings-mini-charts-container {\n position: relative;\n -webkit-box-flex: 1;\n flex: 1 1 auto;\n overflow-x: hidden;\n overflow-y: auto;\n}\n\n.ag-chart-settings-mini-wrapper {\n position: absolute;\n top: 0;\n left: 0;\n display: -webkit-box;\n display: flex;\n -webkit-box-orient: vertical;\n -webkit-box-direction: normal;\n flex-direction: column;\n width: 100%;\n min-height: 100%;\n overflow: hidden;\n}\n.ag-chart-settings-mini-wrapper.ag-animating {\n -webkit-transition: left 0.3s;\n transition: left 0.3s;\n -webkit-transition-timing-function: ease-in-out;\n transition-timing-function: ease-in-out;\n}\n\n.ag-chart-mini-thumbnail {\n cursor: pointer;\n}\n\n.ag-chart-mini-thumbnail-canvas {\n display: block;\n}\n\n.ag-chart-data-wrapper,\n.ag-chart-format-wrapper {\n display: -webkit-box;\n display: flex;\n -webkit-box-orient: vertical;\n -webkit-box-direction: normal;\n flex-direction: column;\n position: relative;\n -webkit-user-select: none;\n -moz-user-select: none;\n user-select: none;\n}\n\n.ag-chart-data-wrapper {\n height: 100%;\n overflow-y: auto;\n}\n\n.ag-chart-data-section,\n.ag-chart-format-section {\n display: -webkit-box;\n display: flex;\n margin: 0;\n}\n\n.ag-chart-empty-text {\n display: -webkit-box;\n display: flex;\n top: 0;\n width: 100%;\n height: 100%;\n -webkit-box-align: center;\n align-items: center;\n -webkit-box-pack: center;\n justify-content: center;\n}\n\n.ag-chart .ag-chart-menu {\n opacity: 0;\n pointer-events: none;\n}\n\n.ag-chart-menu-hidden:hover .ag-chart-menu {\n opacity: 1;\n pointer-events: all;\n}\n\n.ag-charts-font-size-color {\n display: -webkit-box;\n display: flex;\n align-self: stretch;\n -webkit-box-pack: justify;\n justify-content: space-between;\n}\n\n.ag-charts-data-group-item {\n position: relative;\n}\n\n.ag-date-time-list-page-title-bar {\n display: -webkit-box;\n display: flex;\n}\n\n.ag-date-time-list-page-column-labels-row,\n.ag-date-time-list-page-entries-row {\n display: -webkit-box;\n display: flex;\n}\n\n.ag-date-time-list-page-column-label,\n.ag-date-time-list-page-entry {\n flex-basis: 0;\n -webkit-box-flex: 1;\n flex-grow: 1;\n}\n\n.ag-date-time-list-page-entry {\n cursor: pointer;\n}\n",".ag-theme-balham {\n -webkit-font-smoothing: antialiased;\n color: #000;\n color: var(--ag-foreground-color, #000);\n font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Roboto, Oxygen-Sans, Ubuntu, Cantarell, \"Helvetica Neue\", sans-serif;\n font-size: 12px;\n line-height: normal;\n}\n@font-face {\n font-family: \"agGridBalham\";\n src: 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format(\"woff\");\n font-weight: normal;\n font-style: normal;\n}\n.ag-theme-balham .ag-icon {\n font-family: \"agGridBalham\";\n font-size: 16px;\n line-height: 16px;\n font-style: normal;\n font-weight: normal;\n font-variant: normal;\n text-transform: none;\n /* Better Font Rendering =========== */\n -webkit-font-smoothing: antialiased;\n -moz-osx-font-smoothing: grayscale;\n}\n.ag-theme-balham .ag-icon-aggregation::before {\n content: \"\\f101\";\n}\n.ag-theme-balham .ag-icon-arrows::before {\n content: \"\\f102\";\n}\n.ag-theme-balham .ag-icon-asc::before {\n content: \"\\f103\";\n}\n.ag-theme-balham .ag-icon-cancel::before {\n content: \"\\f104\";\n}\n.ag-theme-balham .ag-icon-chart::before {\n content: \"\\f105\";\n}\n.ag-theme-balham .ag-icon-color-picker::before {\n content: \"\\f109\";\n}\n.ag-theme-balham .ag-icon-columns::before {\n content: \"\\f10a\";\n}\n.ag-theme-balham .ag-icon-contracted::before {\n content: \"\\f10b\";\n}\n.ag-theme-balham .ag-icon-copy::before {\n content: \"\\f10c\";\n}\n.ag-theme-balham .ag-icon-cross::before {\n content: \"\\f10d\";\n}\n.ag-theme-balham .ag-icon-csv::before {\n content: \"\\f10e\";\n}\n.ag-theme-balham .ag-icon-desc::before {\n content: \"\\f10f\";\n}\n.ag-theme-balham .ag-icon-excel::before {\n content: \"\\f110\";\n}\n.ag-theme-balham .ag-icon-expanded::before {\n content: \"\\f111\";\n}\n.ag-theme-balham .ag-icon-eye-slash::before {\n content: \"\\f112\";\n}\n.ag-theme-balham .ag-icon-eye::before {\n content: \"\\f113\";\n}\n.ag-theme-balham .ag-icon-filter::before {\n content: \"\\f114\";\n}\n.ag-theme-balham .ag-icon-first::before {\n content: \"\\f115\";\n}\n.ag-theme-balham .ag-icon-grip::before {\n content: \"\\f116\";\n}\n.ag-theme-balham .ag-icon-group::before {\n content: \"\\f117\";\n}\n.ag-theme-balham .ag-icon-last::before {\n content: \"\\f118\";\n}\n.ag-theme-balham .ag-icon-left::before {\n content: \"\\f119\";\n}\n.ag-theme-balham .ag-icon-linked::before {\n content: \"\\f11a\";\n}\n.ag-theme-balham .ag-icon-loading::before {\n content: \"\\f11b\";\n}\n.ag-theme-balham .ag-icon-maximize::before {\n content: \"\\f11c\";\n}\n.ag-theme-balham .ag-icon-menu::before {\n content: \"\\f11d\";\n}\n.ag-theme-balham .ag-icon-minimize::before {\n content: \"\\f11e\";\n}\n.ag-theme-balham .ag-icon-next::before {\n content: \"\\f11f\";\n}\n.ag-theme-balham .ag-icon-none::before {\n content: \"\\f120\";\n}\n.ag-theme-balham .ag-icon-not-allowed::before {\n content: \"\\f121\";\n}\n.ag-theme-balham .ag-icon-paste::before {\n content: \"\\f122\";\n}\n.ag-theme-balham .ag-icon-pin::before {\n content: \"\\f123\";\n}\n.ag-theme-balham .ag-icon-pivot::before {\n content: \"\\f124\";\n}\n.ag-theme-balham .ag-icon-previous::before {\n content: \"\\f125\";\n}\n.ag-theme-balham .ag-icon-right::before {\n content: \"\\f128\";\n}\n.ag-theme-balham .ag-icon-save::before {\n content: \"\\f129\";\n}\n.ag-theme-balham .ag-icon-small-down::before {\n content: \"\\f12a\";\n}\n.ag-theme-balham .ag-icon-small-left::before {\n content: \"\\f12b\";\n}\n.ag-theme-balham .ag-icon-small-right::before {\n content: \"\\f12c\";\n}\n.ag-theme-balham .ag-icon-small-up::before {\n content: \"\\f12d\";\n}\n.ag-theme-balham .ag-icon-tick::before {\n content: \"\\f12e\";\n}\n.ag-theme-balham .ag-icon-tree-closed::before {\n content: \"\\f12f\";\n}\n.ag-theme-balham .ag-icon-tree-indeterminate::before {\n content: \"\\f130\";\n}\n.ag-theme-balham .ag-icon-tree-open::before {\n content: \"\\f131\";\n}\n.ag-theme-balham .ag-icon-unlinked::before {\n content: \"\\f132\";\n}\n.ag-theme-balham .ag-icon-row-drag::before {\n content: \"\\f116\";\n}\n.ag-theme-balham .ag-left-arrow::before {\n content: \"\\f119\";\n}\n.ag-theme-balham .ag-right-arrow::before {\n content: \"\\f128\";\n}\n.ag-theme-balham .ag-root-wrapper {\n background-color: white;\n background-color: var(--ag-background-color, white);\n}\n.ag-theme-balham [class^=ag-], .ag-theme-balham [class^=ag-]:focus, .ag-theme-balham [class^=ag-]:after, .ag-theme-balham [class^=ag-]:before {\n -webkit-box-sizing: border-box;\n box-sizing: border-box;\n outline: none;\n}\n.ag-theme-balham [class^=ag-]::-ms-clear {\n display: none;\n}\n.ag-theme-balham .ag-checkbox .ag-input-wrapper,\n.ag-theme-balham .ag-radio-button .ag-input-wrapper {\n overflow: visible;\n}\n.ag-theme-balham .ag-range-field .ag-input-wrapper {\n height: 100%;\n}\n.ag-theme-balham .ag-toggle-button {\n -webkit-box-flex: 0;\n flex: none;\n width: unset;\n min-width: unset;\n}\n.ag-theme-balham .ag-ltr .ag-label-align-right .ag-label {\n margin-left: 4px;\n}\n\n.ag-theme-balham .ag-rtl .ag-label-align-right .ag-label {\n margin-right: 4px;\n}\n\n.ag-theme-balham input[class^=ag-] {\n margin: 0;\n background-color: white;\n background-color: var(--ag-background-color, white);\n}\n.ag-theme-balham textarea[class^=ag-],\n.ag-theme-balham select[class^=ag-] {\n background-color: white;\n background-color: var(--ag-background-color, white);\n}\n.ag-theme-balham input[class^=ag-]:not([type]),\n.ag-theme-balham input[class^=ag-][type=text],\n.ag-theme-balham input[class^=ag-][type=number],\n.ag-theme-balham input[class^=ag-][type=tel],\n.ag-theme-balham input[class^=ag-][type=date],\n.ag-theme-balham input[class^=ag-][type=datetime-local],\n.ag-theme-balham textarea[class^=ag-] {\n font-size: inherit;\n line-height: inherit;\n color: inherit;\n border-width: 1px;\n border-style: solid;\n border-color: #95a5a6;\n border-color: var(--ag-input-border-color, #95a5a6);\n}\n.ag-theme-balham input[class^=ag-]:not([type]):disabled,\n.ag-theme-balham input[class^=ag-][type=text]:disabled,\n.ag-theme-balham input[class^=ag-][type=number]:disabled,\n.ag-theme-balham input[class^=ag-][type=tel]:disabled,\n.ag-theme-balham input[class^=ag-][type=date]:disabled,\n.ag-theme-balham input[class^=ag-][type=datetime-local]:disabled,\n.ag-theme-balham textarea[class^=ag-]:disabled {\n color: rgba(0, 0, 0, 0.38);\n color: var(--ag-disabled-foreground-color, rgba(0, 0, 0, 0.38));\n background-color: #ebebeb;\n background-color: var(--ag-input-disabled-background-color, #ebebeb);\n border-color: rgba(149, 165, 166, 0.3);\n border-color: var(--ag-input-disabled-border-color, rgba(149, 165, 166, 0.3));\n}\n.ag-theme-balham input[class^=ag-]:not([type]):focus,\n.ag-theme-balham input[class^=ag-][type=text]:focus,\n.ag-theme-balham input[class^=ag-][type=number]:focus,\n.ag-theme-balham input[class^=ag-][type=tel]:focus,\n.ag-theme-balham input[class^=ag-][type=date]:focus,\n.ag-theme-balham input[class^=ag-][type=datetime-local]:focus,\n.ag-theme-balham textarea[class^=ag-]:focus {\n outline: none;\n -webkit-box-shadow: 0 0 2px 1px #719ECE;\n box-shadow: 0 0 2px 1px #719ECE;\n border-color: #719ECE;\n border-color: var(--ag-input-focus-border-color, #719ECE);\n}\n.ag-theme-balham input[class^=ag-]:not([type]):invalid,\n.ag-theme-balham input[class^=ag-][type=text]:invalid,\n.ag-theme-balham input[class^=ag-][type=number]:invalid,\n.ag-theme-balham input[class^=ag-][type=tel]:invalid,\n.ag-theme-balham input[class^=ag-][type=date]:invalid,\n.ag-theme-balham input[class^=ag-][type=datetime-local]:invalid,\n.ag-theme-balham textarea[class^=ag-]:invalid {\n border-width: 2px;\n border-style: solid;\n border-color: #e02525;\n border-color: var(--ag-input-border-color-invalid, var(--ag-invalid-color, #e02525));\n}\n.ag-theme-balham input[class^=ag-][type=number] {\n -moz-appearance: textfield;\n}\n.ag-theme-balham input[class^=ag-][type=number]::-webkit-outer-spin-button, .ag-theme-balham input[class^=ag-][type=number]::-webkit-inner-spin-button {\n -webkit-appearance: none;\n margin: 0;\n}\n.ag-theme-balham input[class^=ag-][type=range] {\n padding: 0;\n}\n.ag-theme-balham input[class^=ag-][type=button]:focus, .ag-theme-balham button[class^=ag-]:focus {\n -webkit-box-shadow: 0 0 2px 1px #719ECE;\n box-shadow: 0 0 2px 1px #719ECE;\n}\n.ag-theme-balham .ag-drag-handle {\n color: rgba(0, 0, 0, 0.54);\n color: var(--ag-secondary-foreground-color, rgba(0, 0, 0, 0.54));\n}\n.ag-theme-balham .ag-list-item, .ag-theme-balham .ag-virtual-list-item {\n height: 24px;\n}\n.ag-theme-balham .ag-keyboard-focus .ag-virtual-list-item:focus {\n outline: none;\n}\n.ag-theme-balham .ag-keyboard-focus .ag-virtual-list-item:focus::after {\n content: \"\";\n position: absolute;\n background-color: transparent;\n pointer-events: none;\n top: 4px;\n left: 4px;\n display: block;\n width: calc(100% - 8px);\n height: calc(100% - 8px);\n border: 1px solid;\n border-color: #719ECE;\n border-color: var(--ag-input-focus-border-color, #719ECE);\n}\n.ag-theme-balham .ag-select-list {\n background-color: white;\n background-color: var(--ag-background-color, white);\n overflow-y: auto;\n overflow-x: hidden;\n}\n.ag-theme-balham .ag-list-item {\n display: -webkit-box;\n display: flex;\n -webkit-box-align: center;\n align-items: center;\n white-space: nowrap;\n overflow: hidden;\n text-overflow: ellipsis;\n}\n.ag-theme-balham .ag-list-item.ag-active-item {\n background-color: #ecf0f1;\n background-color: var(--ag-row-hover-color, #ecf0f1);\n}\n.ag-theme-balham .ag-select-list-item {\n padding-left: 4px;\n padding-right: 4px;\n cursor: default;\n -moz-user-select: none;\n -webkit-user-select: none;\n user-select: none;\n}\n.ag-theme-balham .ag-select-list-item span {\n white-space: nowrap;\n text-overflow: ellipsis;\n overflow: hidden;\n}\n.ag-theme-balham .ag-select .ag-picker-field-wrapper {\n background-color: white;\n background-color: var(--ag-background-color, white);\n min-height: 24px;\n cursor: default;\n}\n.ag-theme-balham .ag-select.ag-disabled .ag-picker-field-wrapper:focus {\n -webkit-box-shadow: none;\n box-shadow: none;\n}\n.ag-theme-balham .ag-select:not(.ag-cell-editor) {\n height: 24px;\n}\n.ag-theme-balham .ag-select .ag-picker-field-display {\n margin: 4px;\n white-space: nowrap;\n overflow: hidden;\n text-overflow: ellipsis;\n}\n.ag-theme-balham .ag-select .ag-picker-field-icon {\n display: -webkit-box;\n display: flex;\n -webkit-box-align: center;\n align-items: center;\n}\n.ag-theme-balham .ag-select.ag-disabled {\n opacity: 0.5;\n}\n.ag-theme-balham .ag-rich-select {\n background-color: #f5f7f7;\n background-color: var(--ag-control-panel-background-color, #f5f7f7);\n}\n.ag-theme-balham .ag-rich-select-list {\n width: 100%;\n min-width: 200px;\n height: 182px;\n}\n.ag-theme-balham .ag-rich-select-value {\n padding: 0 4px 0 12px;\n height: 28px;\n border-bottom: solid 1px;\n border-bottom-color: #bdc3c7;\n border-bottom-color: var(--ag-secondary-border-color, var(--ag-border-color, #bdc3c7));\n}\n.ag-theme-balham .ag-rich-select-virtual-list-item {\n cursor: default;\n height: 24px;\n}\n.ag-theme-balham .ag-rich-select-virtual-list-item:hover {\n background-color: #ecf0f1;\n background-color: var(--ag-row-hover-color, #ecf0f1);\n}\n.ag-theme-balham .ag-rich-select-row {\n padding-left: 12px;\n}\n.ag-theme-balham .ag-rich-select-row-selected {\n background-color: #b7e4ff;\n background-color: var(--ag-selected-row-background-color, #b7e4ff);\n}\n.ag-theme-balham .ag-row-drag,\n.ag-theme-balham .ag-selection-checkbox,\n.ag-theme-balham .ag-group-expanded,\n.ag-theme-balham .ag-group-contracted {\n color: rgba(0, 0, 0, 0.54);\n color: var(--ag-secondary-foreground-color, rgba(0, 0, 0, 0.54));\n}\n.ag-theme-balham .ag-ltr .ag-row-drag, .ag-theme-balham .ag-ltr .ag-selection-checkbox, .ag-theme-balham .ag-ltr .ag-group-expanded, .ag-theme-balham .ag-ltr .ag-group-contracted {\n margin-right: 12px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-drag, .ag-theme-balham .ag-rtl .ag-selection-checkbox, .ag-theme-balham .ag-rtl .ag-group-expanded, .ag-theme-balham .ag-rtl .ag-group-contracted {\n margin-left: 12px;\n}\n\n.ag-theme-balham .ag-cell-wrapper > *:not(.ag-cell-value):not(.ag-group-value) {\n height: min(var(--ag-line-height, 26px), 26px);\n display: -webkit-box;\n display: flex;\n -webkit-box-align: center;\n align-items: center;\n -webkit-box-flex: 0;\n flex: none;\n}\n.ag-theme-balham .ag-group-expanded,\n.ag-theme-balham .ag-group-contracted {\n cursor: pointer;\n}\n.ag-theme-balham .ag-group-title-bar-icon {\n cursor: pointer;\n -webkit-box-flex: 0;\n flex: none;\n color: rgba(0, 0, 0, 0.54);\n color: var(--ag-secondary-foreground-color, rgba(0, 0, 0, 0.54));\n}\n.ag-theme-balham .ag-ltr .ag-group-child-count {\n margin-left: 2px;\n}\n\n.ag-theme-balham .ag-rtl .ag-group-child-count {\n margin-right: 2px;\n}\n\n.ag-theme-balham .ag-group-title-bar {\n background-color: #e2e9eb;\n background-color: var(--ag-subheader-background-color, #e2e9eb);\n padding: 4px;\n}\n.ag-theme-balham .ag-group-toolbar {\n padding: 4px;\n}\n.ag-theme-balham .ag-disabled-group-title-bar, .ag-theme-balham .ag-disabled-group-container {\n opacity: 0.5;\n}\n.ag-theme-balham .group-item {\n margin: 2px 0;\n}\n.ag-theme-balham .ag-label {\n white-space: nowrap;\n}\n.ag-theme-balham .ag-ltr .ag-label {\n margin-right: 4px;\n}\n\n.ag-theme-balham .ag-rtl .ag-label {\n margin-left: 4px;\n}\n\n.ag-theme-balham .ag-label-align-top .ag-label {\n margin-bottom: 2px;\n}\n.ag-theme-balham .ag-ltr .ag-slider-field, .ag-theme-balham .ag-ltr .ag-angle-select-field {\n margin-right: 8px;\n}\n\n.ag-theme-balham .ag-rtl .ag-slider-field, .ag-theme-balham .ag-rtl .ag-angle-select-field {\n margin-left: 8px;\n}\n\n.ag-theme-balham .ag-angle-select-parent-circle {\n width: 24px;\n height: 24px;\n border-radius: 12px;\n border: solid 1px;\n border-color: #bdc3c7;\n border-color: var(--ag-border-color, #bdc3c7);\n background-color: white;\n background-color: var(--ag-background-color, white);\n}\n.ag-theme-balham .ag-angle-select-child-circle {\n top: 4px;\n left: 12px;\n width: 6px;\n height: 6px;\n margin-left: -3px;\n margin-top: -4px;\n border-radius: 3px;\n background-color: rgba(0, 0, 0, 0.54);\n background-color: var(--ag-secondary-foreground-color, rgba(0, 0, 0, 0.54));\n}\n.ag-theme-balham .ag-picker-field-wrapper {\n border: 1px solid;\n border-color: #bdc3c7;\n border-color: var(--ag-border-color, #bdc3c7);\n border-radius: 5px;\n}\n.ag-theme-balham .ag-picker-field-wrapper:focus {\n -webkit-box-shadow: 0 0 2px 1px #719ECE;\n box-shadow: 0 0 2px 1px #719ECE;\n}\n.ag-theme-balham .ag-picker-field-button {\n background-color: white;\n background-color: var(--ag-background-color, white);\n color: rgba(0, 0, 0, 0.54);\n color: var(--ag-secondary-foreground-color, rgba(0, 0, 0, 0.54));\n}\n.ag-theme-balham .ag-dialog.ag-color-dialog {\n border-radius: 5px;\n}\n.ag-theme-balham .ag-color-picker .ag-picker-field-display {\n height: 16px;\n}\n.ag-theme-balham .ag-color-panel {\n padding: 4px;\n}\n.ag-theme-balham .ag-spectrum-color {\n background-color: rgb(255, 0, 0);\n border-radius: 2px;\n}\n.ag-theme-balham .ag-spectrum-tools {\n padding: 10px;\n}\n.ag-theme-balham .ag-spectrum-sat {\n background-image: -webkit-gradient(linear, left top, right top, from(white), to(rgba(204, 154, 129, 0)));\n background-image: linear-gradient(to right, white, rgba(204, 154, 129, 0));\n}\n.ag-theme-balham .ag-spectrum-val {\n background-image: -webkit-gradient(linear, left bottom, left top, from(black), to(rgba(204, 154, 129, 0)));\n background-image: linear-gradient(to top, black, rgba(204, 154, 129, 0));\n}\n.ag-theme-balham .ag-spectrum-dragger {\n border-radius: 12px;\n height: 12px;\n width: 12px;\n border: 1px solid white;\n background: black;\n -webkit-box-shadow: 0 0 2px 0px rgba(0, 0, 0, 0.24);\n box-shadow: 0 0 2px 0px rgba(0, 0, 0, 0.24);\n}\n.ag-theme-balham .ag-spectrum-hue-background {\n border-radius: 2px;\n}\n.ag-theme-balham .ag-spectrum-alpha-background {\n border-radius: 2px;\n}\n.ag-theme-balham .ag-spectrum-tool {\n margin-bottom: 10px;\n height: 11px;\n border-radius: 2px;\n}\n.ag-theme-balham .ag-spectrum-slider {\n margin-top: -12px;\n width: 13px;\n height: 13px;\n border-radius: 13px;\n background-color: rgb(248, 248, 248);\n -webkit-box-shadow: 0 1px 4px 0 rgba(0, 0, 0, 0.37);\n box-shadow: 0 1px 4px 0 rgba(0, 0, 0, 0.37);\n}\n.ag-theme-balham .ag-recent-color {\n margin: 0 3px;\n}\n.ag-theme-balham .ag-recent-color:first-child {\n margin-left: 0;\n}\n.ag-theme-balham .ag-recent-color:last-child {\n margin-right: 0;\n}\n.ag-theme-balham.ag-dnd-ghost {\n border: solid 1px;\n border-color: #bdc3c7;\n border-color: var(--ag-border-color, #bdc3c7);\n background: white;\n background: var(--ag-background-color, white);\n border-radius: 2px;\n -webkit-box-shadow: none;\n box-shadow: none;\n padding: 4px;\n overflow: hidden;\n text-overflow: ellipsis;\n border: solid 1px;\n border-color: #bdc3c7;\n border-color: var(--ag-secondary-border-color, var(--ag-border-color, #bdc3c7));\n color: rgba(0, 0, 0, 0.54);\n color: var(--ag-secondary-foreground-color, rgba(0, 0, 0, 0.54));\n height: 32px !important;\n line-height: 32px;\n margin: 0;\n padding: 0 8px;\n -webkit-transform: translateY(8px);\n transform: translateY(8px);\n}\n.ag-theme-balham .ag-dnd-ghost-icon {\n margin-right: 4px;\n color: #000;\n color: var(--ag-foreground-color, #000);\n}\n.ag-theme-balham .ag-popup-child:not(.ag-tooltip-custom) {\n -webkit-box-shadow: 5px 5px 10px rgba(0, 0, 0, 0.3);\n box-shadow: 5px 5px 10px rgba(0, 0, 0, 0.3);\n}\n.ag-dragging-range-handle .ag-theme-balham .ag-dialog, .ag-dragging-fill-handle .ag-theme-balham .ag-dialog {\n opacity: 0.7;\n pointer-events: none;\n}\n.ag-theme-balham .ag-dialog {\n border-radius: 2px;\n border: solid 1px;\n border-color: #bdc3c7;\n border-color: var(--ag-border-color, #bdc3c7);\n}\n.ag-theme-balham .ag-panel {\n background-color: white;\n background-color: var(--ag-background-color, white);\n}\n.ag-theme-balham .ag-panel-title-bar {\n background-color: #f5f7f7;\n background-color: var(--ag-header-background-color, #f5f7f7);\n color: rgba(0, 0, 0, 0.54);\n color: var(--ag-header-foreground-color, var(--ag-secondary-foreground-color, rgba(0, 0, 0, 0.54)));\n height: 32px;\n padding: 4px 12px;\n border-bottom: solid 1px;\n border-bottom-color: #bdc3c7;\n border-bottom-color: var(--ag-border-color, #bdc3c7);\n}\n.ag-theme-balham .ag-ltr .ag-panel-title-bar-button {\n margin-left: 4px;\n}\n\n.ag-theme-balham .ag-rtl .ag-panel-title-bar-button {\n margin-right: 4px;\n}\n\n.ag-theme-balham .ag-tooltip {\n background-color: #f5f7f7;\n background-color: var(--ag-header-background-color, #f5f7f7);\n color: #000;\n color: var(--ag-foreground-color, #000);\n padding: 4px;\n border: solid 1px;\n border-color: #bdc3c7;\n border-color: var(--ag-border-color, #bdc3c7);\n border-radius: 2px;\n -webkit-transition: opacity 1s;\n transition: opacity 1s;\n white-space: normal;\n}\n.ag-theme-balham .ag-tooltip.ag-tooltip-hiding {\n opacity: 0;\n}\n.ag-theme-balham .ag-tooltip-custom {\n -webkit-transition: opacity 1s;\n transition: opacity 1s;\n}\n.ag-theme-balham .ag-tooltip-custom.ag-tooltip-hiding {\n opacity: 0;\n}\n.ag-theme-balham .ag-ltr .ag-column-select-indent-1 {\n padding-left: 20px;\n}\n\n.ag-theme-balham .ag-rtl .ag-column-select-indent-1 {\n padding-right: 20px;\n}\n\n.ag-theme-balham .ag-ltr .ag-column-select-indent-2 {\n padding-left: 40px;\n}\n\n.ag-theme-balham .ag-rtl .ag-column-select-indent-2 {\n padding-right: 40px;\n}\n\n.ag-theme-balham .ag-ltr .ag-column-select-indent-3 {\n padding-left: 60px;\n}\n\n.ag-theme-balham .ag-rtl .ag-column-select-indent-3 {\n padding-right: 60px;\n}\n\n.ag-theme-balham .ag-ltr .ag-column-select-indent-4 {\n padding-left: 80px;\n}\n\n.ag-theme-balham .ag-rtl .ag-column-select-indent-4 {\n padding-right: 80px;\n}\n\n.ag-theme-balham .ag-ltr .ag-column-select-indent-5 {\n padding-left: 100px;\n}\n\n.ag-theme-balham .ag-rtl .ag-column-select-indent-5 {\n padding-right: 100px;\n}\n\n.ag-theme-balham .ag-ltr .ag-column-select-indent-6 {\n padding-left: 120px;\n}\n\n.ag-theme-balham .ag-rtl .ag-column-select-indent-6 {\n padding-right: 120px;\n}\n\n.ag-theme-balham .ag-ltr .ag-column-select-indent-7 {\n padding-left: 140px;\n}\n\n.ag-theme-balham .ag-rtl .ag-column-select-indent-7 {\n padding-right: 140px;\n}\n\n.ag-theme-balham .ag-ltr .ag-column-select-indent-8 {\n padding-left: 160px;\n}\n\n.ag-theme-balham .ag-rtl .ag-column-select-indent-8 {\n padding-right: 160px;\n}\n\n.ag-theme-balham .ag-ltr .ag-column-select-indent-9 {\n padding-left: 180px;\n}\n\n.ag-theme-balham .ag-rtl .ag-column-select-indent-9 {\n padding-right: 180px;\n}\n\n.ag-theme-balham .ag-column-select-header-icon {\n cursor: pointer;\n}\n.ag-theme-balham .ag-keyboard-focus .ag-column-select-header-icon:focus {\n outline: none;\n}\n.ag-theme-balham .ag-keyboard-focus .ag-column-select-header-icon:focus::after {\n content: \"\";\n position: absolute;\n background-color: transparent;\n pointer-events: none;\n top: 0px;\n left: 0px;\n display: block;\n width: calc(100% - 0px);\n height: calc(100% - 0px);\n border: 1px solid;\n border-color: #719ECE;\n border-color: var(--ag-input-focus-border-color, #719ECE);\n}\n.ag-theme-balham .ag-ltr .ag-column-group-icons:not(:last-child), .ag-theme-balham .ag-ltr .ag-column-select-header-icon:not(:last-child), .ag-theme-balham .ag-ltr .ag-column-select-header-checkbox:not(:last-child), .ag-theme-balham .ag-ltr .ag-column-select-header-filter-wrapper:not(:last-child), .ag-theme-balham .ag-ltr .ag-column-select-checkbox:not(:last-child), .ag-theme-balham .ag-ltr .ag-column-select-column-drag-handle:not(:last-child), .ag-theme-balham .ag-ltr .ag-column-select-column-group-drag-handle:not(:last-child), .ag-theme-balham .ag-ltr .ag-column-select-column-label:not(:last-child) {\n margin-right: 8px;\n}\n\n.ag-theme-balham .ag-rtl .ag-column-group-icons:not(:last-child), .ag-theme-balham .ag-rtl .ag-column-select-header-icon:not(:last-child), .ag-theme-balham .ag-rtl .ag-column-select-header-checkbox:not(:last-child), .ag-theme-balham .ag-rtl .ag-column-select-header-filter-wrapper:not(:last-child), .ag-theme-balham .ag-rtl .ag-column-select-checkbox:not(:last-child), .ag-theme-balham .ag-rtl .ag-column-select-column-drag-handle:not(:last-child), .ag-theme-balham .ag-rtl .ag-column-select-column-group-drag-handle:not(:last-child), .ag-theme-balham .ag-rtl .ag-column-select-column-label:not(:last-child) {\n margin-left: 8px;\n}\n\n.ag-theme-balham .ag-keyboard-focus .ag-column-select-virtual-list-item:focus {\n outline: none;\n}\n.ag-theme-balham .ag-keyboard-focus .ag-column-select-virtual-list-item:focus::after {\n content: \"\";\n position: absolute;\n background-color: transparent;\n pointer-events: none;\n top: 1px;\n left: 1px;\n display: block;\n width: calc(100% - 2px);\n height: calc(100% - 2px);\n border: 1px solid;\n border-color: #719ECE;\n border-color: var(--ag-input-focus-border-color, #719ECE);\n}\n.ag-theme-balham .ag-column-select-column-group:not(:last-child),\n.ag-theme-balham .ag-column-select-column:not(:last-child) {\n margin-bottom: 4px;\n}\n.ag-theme-balham .ag-column-select-column-readonly,\n.ag-theme-balham .ag-column-select-column-group-readonly {\n color: rgba(0, 0, 0, 0.38);\n color: var(--ag-disabled-foreground-color, rgba(0, 0, 0, 0.38));\n pointer-events: none;\n}\n.ag-theme-balham .ag-ltr .ag-column-select-add-group-indent {\n margin-left: 24px;\n}\n\n.ag-theme-balham .ag-rtl .ag-column-select-add-group-indent {\n margin-right: 24px;\n}\n\n.ag-theme-balham .ag-column-select-virtual-list-viewport {\n padding: 3px 0px;\n}\n.ag-theme-balham .ag-column-select-virtual-list-item {\n padding: 0 6px;\n}\n.ag-theme-balham .ag-rtl {\n text-align: right;\n}\n.ag-theme-balham .ag-root-wrapper {\n border: solid 1px;\n border-color: #bdc3c7;\n border-color: var(--ag-border-color, #bdc3c7);\n}\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-1 {\n padding-left: 40px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-1 {\n padding-right: 40px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-1 {\n padding-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-1 {\n padding-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-1 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-1 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-2 {\n padding-left: 68px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-2 {\n padding-right: 68px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-2 {\n padding-left: 56px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-2 {\n padding-right: 56px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-2 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-2 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-3 {\n padding-left: 96px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-3 {\n padding-right: 96px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-3 {\n padding-left: 84px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-3 {\n padding-right: 84px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-3 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-3 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-4 {\n padding-left: 124px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-4 {\n padding-right: 124px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-4 {\n padding-left: 112px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-4 {\n padding-right: 112px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-4 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-4 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-5 {\n padding-left: 152px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-5 {\n padding-right: 152px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-5 {\n padding-left: 140px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-5 {\n padding-right: 140px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-5 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-5 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-6 {\n padding-left: 180px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-6 {\n padding-right: 180px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-6 {\n padding-left: 168px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-6 {\n padding-right: 168px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-6 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-6 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-7 {\n padding-left: 208px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-7 {\n padding-right: 208px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-7 {\n padding-left: 196px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-7 {\n padding-right: 196px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-7 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-7 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-8 {\n padding-left: 236px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-8 {\n padding-right: 236px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-8 {\n padding-left: 224px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-8 {\n padding-right: 224px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-8 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-8 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-9 {\n padding-left: 264px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-9 {\n padding-right: 264px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-9 {\n padding-left: 252px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-9 {\n padding-right: 252px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-9 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-9 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-10 {\n padding-left: 292px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-10 {\n padding-right: 292px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-10 {\n padding-left: 280px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-10 {\n padding-right: 280px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-10 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-10 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-11 {\n padding-left: 320px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-11 {\n padding-right: 320px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-11 {\n padding-left: 308px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-11 {\n padding-right: 308px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-11 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-11 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-12 {\n padding-left: 348px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-12 {\n padding-right: 348px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-12 {\n padding-left: 336px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-12 {\n padding-right: 336px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-12 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-12 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-13 {\n padding-left: 376px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-13 {\n padding-right: 376px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-13 {\n padding-left: 364px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-13 {\n padding-right: 364px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-13 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-13 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-14 {\n padding-left: 404px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-14 {\n padding-right: 404px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-14 {\n padding-left: 392px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-14 {\n padding-right: 392px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-14 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-14 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-15 {\n padding-left: 432px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-15 {\n padding-right: 432px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-15 {\n padding-left: 420px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-15 {\n padding-right: 420px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-15 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-15 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-16 {\n padding-left: 460px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-16 {\n padding-right: 460px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-16 {\n padding-left: 448px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-16 {\n padding-right: 448px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-16 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-16 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-17 {\n padding-left: 488px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-17 {\n padding-right: 488px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-17 {\n padding-left: 476px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-17 {\n padding-right: 476px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-17 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-17 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-18 {\n padding-left: 516px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-18 {\n padding-right: 516px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-18 {\n padding-left: 504px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-18 {\n padding-right: 504px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-18 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-18 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-19 {\n padding-left: 544px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-19 {\n padding-right: 544px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-19 {\n padding-left: 532px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-19 {\n padding-right: 532px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-19 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-19 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-20 {\n padding-left: 572px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-20 {\n padding-right: 572px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-20 {\n padding-left: 560px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-20 {\n padding-right: 560px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-20 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-20 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-21 {\n padding-left: 600px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-21 {\n padding-right: 600px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-21 {\n padding-left: 588px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-21 {\n padding-right: 588px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-21 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-21 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-22 {\n padding-left: 628px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-22 {\n padding-right: 628px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-22 {\n padding-left: 616px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-22 {\n padding-right: 616px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-22 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-22 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-23 {\n padding-left: 656px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-23 {\n padding-right: 656px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-23 {\n padding-left: 644px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-23 {\n padding-right: 644px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-23 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-23 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-24 {\n padding-left: 684px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-24 {\n padding-right: 684px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-24 {\n padding-left: 672px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-24 {\n padding-right: 672px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-24 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-24 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-25 {\n padding-left: 712px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-25 {\n padding-right: 712px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-25 {\n padding-left: 700px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-25 {\n padding-right: 700px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-25 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-25 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-26 {\n padding-left: 740px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-26 {\n padding-right: 740px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-26 {\n padding-left: 728px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-26 {\n padding-right: 728px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-26 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-26 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-27 {\n padding-left: 768px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-27 {\n padding-right: 768px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-27 {\n padding-left: 756px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-27 {\n padding-right: 756px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-27 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-27 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-28 {\n padding-left: 796px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-28 {\n padding-right: 796px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-28 {\n padding-left: 784px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-28 {\n padding-right: 784px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-28 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-28 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-29 {\n padding-left: 824px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-29 {\n padding-right: 824px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-29 {\n padding-left: 812px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-29 {\n padding-right: 812px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-29 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-29 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-30 {\n padding-left: 852px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-30 {\n padding-right: 852px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-30 {\n padding-left: 840px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-30 {\n padding-right: 840px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-30 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-30 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-31 {\n padding-left: 880px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-31 {\n padding-right: 880px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-31 {\n padding-left: 868px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-31 {\n padding-right: 868px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-31 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-31 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-32 {\n padding-left: 908px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-32 {\n padding-right: 908px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-32 {\n padding-left: 896px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-32 {\n padding-right: 896px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-32 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-32 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-33 {\n padding-left: 936px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-33 {\n padding-right: 936px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-33 {\n padding-left: 924px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-33 {\n padding-right: 924px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-33 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-33 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-34 {\n padding-left: 964px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-34 {\n padding-right: 964px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-34 {\n padding-left: 952px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-34 {\n padding-right: 952px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-34 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-34 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-35 {\n padding-left: 992px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-35 {\n padding-right: 992px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-35 {\n padding-left: 980px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-35 {\n padding-right: 980px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-35 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-35 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-36 {\n padding-left: 1020px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-36 {\n padding-right: 1020px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-36 {\n padding-left: 1008px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-36 {\n padding-right: 1008px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-36 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-36 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-37 {\n padding-left: 1048px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-37 {\n padding-right: 1048px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-37 {\n padding-left: 1036px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-37 {\n padding-right: 1036px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-37 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-37 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-38 {\n padding-left: 1076px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-38 {\n padding-right: 1076px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-38 {\n padding-left: 1064px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-38 {\n padding-right: 1064px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-38 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-38 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-39 {\n padding-left: 1104px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-39 {\n padding-right: 1104px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-39 {\n padding-left: 1092px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-39 {\n padding-right: 1092px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-39 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-39 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-40 {\n padding-left: 1132px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-40 {\n padding-right: 1132px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-40 {\n padding-left: 1120px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-40 {\n padding-right: 1120px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-40 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-40 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-41 {\n padding-left: 1160px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-41 {\n padding-right: 1160px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-41 {\n padding-left: 1148px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-41 {\n padding-right: 1148px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-41 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-41 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-42 {\n padding-left: 1188px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-42 {\n padding-right: 1188px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-42 {\n padding-left: 1176px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-42 {\n padding-right: 1176px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-42 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-42 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-43 {\n padding-left: 1216px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-43 {\n padding-right: 1216px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-43 {\n padding-left: 1204px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-43 {\n padding-right: 1204px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-43 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-43 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-44 {\n padding-left: 1244px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-44 {\n padding-right: 1244px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-44 {\n padding-left: 1232px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-44 {\n padding-right: 1232px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-44 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-44 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-45 {\n padding-left: 1272px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-45 {\n padding-right: 1272px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-45 {\n padding-left: 1260px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-45 {\n padding-right: 1260px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-45 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-45 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-46 {\n padding-left: 1300px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-46 {\n padding-right: 1300px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-46 {\n padding-left: 1288px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-46 {\n padding-right: 1288px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-46 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-46 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-47 {\n padding-left: 1328px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-47 {\n padding-right: 1328px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-47 {\n padding-left: 1316px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-47 {\n padding-right: 1316px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-47 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-47 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-48 {\n padding-left: 1356px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-48 {\n padding-right: 1356px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-48 {\n padding-left: 1344px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-48 {\n padding-right: 1344px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-48 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-48 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-49 {\n padding-left: 1384px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-49 {\n padding-right: 1384px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-49 {\n padding-left: 1372px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-49 {\n padding-right: 1372px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-49 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-49 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-50 {\n padding-left: 1412px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-50 {\n padding-right: 1412px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-50 {\n padding-left: 1400px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-50 {\n padding-right: 1400px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-50 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-50 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-51 {\n padding-left: 1440px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-51 {\n padding-right: 1440px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-51 {\n padding-left: 1428px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-51 {\n padding-right: 1428px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-51 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-51 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-52 {\n padding-left: 1468px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-52 {\n padding-right: 1468px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-52 {\n padding-left: 1456px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-52 {\n padding-right: 1456px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-52 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-52 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-53 {\n padding-left: 1496px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-53 {\n padding-right: 1496px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-53 {\n padding-left: 1484px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-53 {\n padding-right: 1484px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-53 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-53 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-54 {\n padding-left: 1524px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-54 {\n padding-right: 1524px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-54 {\n padding-left: 1512px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-54 {\n padding-right: 1512px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-54 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-54 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-55 {\n padding-left: 1552px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-55 {\n padding-right: 1552px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-55 {\n padding-left: 1540px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-55 {\n padding-right: 1540px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-55 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-55 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-56 {\n padding-left: 1580px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-56 {\n padding-right: 1580px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-56 {\n padding-left: 1568px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-56 {\n padding-right: 1568px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-56 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-56 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-57 {\n padding-left: 1608px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-57 {\n padding-right: 1608px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-57 {\n padding-left: 1596px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-57 {\n padding-right: 1596px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-57 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-57 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-58 {\n padding-left: 1636px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-58 {\n padding-right: 1636px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-58 {\n padding-left: 1624px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-58 {\n padding-right: 1624px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-58 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-58 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-59 {\n padding-left: 1664px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-59 {\n padding-right: 1664px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-59 {\n padding-left: 1652px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-59 {\n padding-right: 1652px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-59 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-59 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-60 {\n padding-left: 1692px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-60 {\n padding-right: 1692px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-60 {\n padding-left: 1680px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-60 {\n padding-right: 1680px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-60 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-60 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-61 {\n padding-left: 1720px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-61 {\n padding-right: 1720px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-61 {\n padding-left: 1708px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-61 {\n padding-right: 1708px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-61 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-61 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-62 {\n padding-left: 1748px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-62 {\n padding-right: 1748px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-62 {\n padding-left: 1736px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-62 {\n padding-right: 1736px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-62 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-62 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-63 {\n padding-left: 1776px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-63 {\n padding-right: 1776px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-63 {\n padding-left: 1764px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-63 {\n padding-right: 1764px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-63 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-63 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-64 {\n padding-left: 1804px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-64 {\n padding-right: 1804px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-64 {\n padding-left: 1792px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-64 {\n padding-right: 1792px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-64 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-64 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-65 {\n padding-left: 1832px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-65 {\n padding-right: 1832px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-65 {\n padding-left: 1820px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-65 {\n padding-right: 1820px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-65 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-65 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-66 {\n padding-left: 1860px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-66 {\n padding-right: 1860px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-66 {\n padding-left: 1848px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-66 {\n padding-right: 1848px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-66 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-66 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-67 {\n padding-left: 1888px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-67 {\n padding-right: 1888px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-67 {\n padding-left: 1876px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-67 {\n padding-right: 1876px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-67 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-67 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-68 {\n padding-left: 1916px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-68 {\n padding-right: 1916px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-68 {\n padding-left: 1904px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-68 {\n padding-right: 1904px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-68 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-68 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-69 {\n padding-left: 1944px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-69 {\n padding-right: 1944px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-69 {\n padding-left: 1932px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-69 {\n padding-right: 1932px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-69 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-69 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-70 {\n padding-left: 1972px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-70 {\n padding-right: 1972px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-70 {\n padding-left: 1960px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-70 {\n padding-right: 1960px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-70 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-70 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-71 {\n padding-left: 2000px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-71 {\n padding-right: 2000px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-71 {\n padding-left: 1988px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-71 {\n padding-right: 1988px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-71 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-71 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-72 {\n padding-left: 2028px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-72 {\n padding-right: 2028px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-72 {\n padding-left: 2016px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-72 {\n padding-right: 2016px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-72 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-72 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-73 {\n padding-left: 2056px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-73 {\n padding-right: 2056px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-73 {\n padding-left: 2044px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-73 {\n padding-right: 2044px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-73 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-73 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-74 {\n padding-left: 2084px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-74 {\n padding-right: 2084px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-74 {\n padding-left: 2072px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-74 {\n padding-right: 2072px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-74 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-74 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-75 {\n padding-left: 2112px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-75 {\n padding-right: 2112px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-75 {\n padding-left: 2100px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-75 {\n padding-right: 2100px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-75 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-75 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-76 {\n padding-left: 2140px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-76 {\n padding-right: 2140px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-76 {\n padding-left: 2128px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-76 {\n padding-right: 2128px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-76 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-76 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-77 {\n padding-left: 2168px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-77 {\n padding-right: 2168px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-77 {\n padding-left: 2156px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-77 {\n padding-right: 2156px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-77 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-77 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-78 {\n padding-left: 2196px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-78 {\n padding-right: 2196px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-78 {\n padding-left: 2184px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-78 {\n padding-right: 2184px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-78 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-78 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-79 {\n padding-left: 2224px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-79 {\n padding-right: 2224px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-79 {\n padding-left: 2212px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-79 {\n padding-right: 2212px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-79 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-79 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-80 {\n padding-left: 2252px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-80 {\n padding-right: 2252px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-80 {\n padding-left: 2240px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-80 {\n padding-right: 2240px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-80 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-80 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-81 {\n padding-left: 2280px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-81 {\n padding-right: 2280px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-81 {\n padding-left: 2268px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-81 {\n padding-right: 2268px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-81 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-81 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-82 {\n padding-left: 2308px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-82 {\n padding-right: 2308px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-82 {\n padding-left: 2296px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-82 {\n padding-right: 2296px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-82 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-82 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-83 {\n padding-left: 2336px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-83 {\n padding-right: 2336px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-83 {\n padding-left: 2324px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-83 {\n padding-right: 2324px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-83 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-83 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-84 {\n padding-left: 2364px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-84 {\n padding-right: 2364px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-84 {\n padding-left: 2352px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-84 {\n padding-right: 2352px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-84 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-84 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-85 {\n padding-left: 2392px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-85 {\n padding-right: 2392px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-85 {\n padding-left: 2380px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-85 {\n padding-right: 2380px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-85 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-85 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-86 {\n padding-left: 2420px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-86 {\n padding-right: 2420px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-86 {\n padding-left: 2408px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-86 {\n padding-right: 2408px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-86 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-86 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-87 {\n padding-left: 2448px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-87 {\n padding-right: 2448px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-87 {\n padding-left: 2436px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-87 {\n padding-right: 2436px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-87 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-87 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-88 {\n padding-left: 2476px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-88 {\n padding-right: 2476px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-88 {\n padding-left: 2464px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-88 {\n padding-right: 2464px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-88 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-88 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-89 {\n padding-left: 2504px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-89 {\n padding-right: 2504px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-89 {\n padding-left: 2492px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-89 {\n padding-right: 2492px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-89 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-89 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-90 {\n padding-left: 2532px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-90 {\n padding-right: 2532px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-90 {\n padding-left: 2520px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-90 {\n padding-right: 2520px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-90 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-90 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-91 {\n padding-left: 2560px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-91 {\n padding-right: 2560px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-91 {\n padding-left: 2548px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-91 {\n padding-right: 2548px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-91 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-91 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-92 {\n padding-left: 2588px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-92 {\n padding-right: 2588px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-92 {\n padding-left: 2576px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-92 {\n padding-right: 2576px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-92 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-92 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-93 {\n padding-left: 2616px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-93 {\n padding-right: 2616px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-93 {\n padding-left: 2604px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-93 {\n padding-right: 2604px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-93 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-93 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-94 {\n padding-left: 2644px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-94 {\n padding-right: 2644px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-94 {\n padding-left: 2632px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-94 {\n padding-right: 2632px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-94 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-94 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-95 {\n padding-left: 2672px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-95 {\n padding-right: 2672px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-95 {\n padding-left: 2660px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-95 {\n padding-right: 2660px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-95 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-95 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-96 {\n padding-left: 2700px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-96 {\n padding-right: 2700px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-96 {\n padding-left: 2688px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-96 {\n padding-right: 2688px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-96 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-96 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-97 {\n padding-left: 2728px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-97 {\n padding-right: 2728px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-97 {\n padding-left: 2716px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-97 {\n padding-right: 2716px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-97 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-97 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-98 {\n padding-left: 2756px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-98 {\n padding-right: 2756px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-98 {\n padding-left: 2744px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-98 {\n padding-right: 2744px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-98 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-98 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row > .ag-cell-wrapper.ag-row-group-indent-99 {\n padding-left: 2784px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row > .ag-cell-wrapper.ag-row-group-indent-99 {\n padding-right: 2784px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-indent-99 {\n padding-left: 2772px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-indent-99 {\n padding-right: 2772px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-level-99 .ag-pivot-leaf-group {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-level-99 .ag-pivot-leaf-group {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-ltr .ag-row-group-leaf-indent {\n margin-left: 28px;\n}\n\n.ag-theme-balham .ag-rtl .ag-row-group-leaf-indent {\n margin-right: 28px;\n}\n\n.ag-theme-balham .ag-value-change-delta {\n padding-right: 2px;\n}\n.ag-theme-balham .ag-value-change-delta-up {\n color: #43a047;\n color: var(--ag-value-change-delta-up-color, #43a047);\n}\n.ag-theme-balham .ag-value-change-delta-down {\n color: #e53935;\n color: var(--ag-value-change-delta-down-color, #e53935);\n}\n.ag-theme-balham .ag-value-change-value {\n background-color: transparent;\n border-radius: 1px;\n padding-left: 1px;\n padding-right: 1px;\n -webkit-transition: background-color 1s;\n transition: background-color 1s;\n}\n.ag-theme-balham .ag-value-change-value-highlight {\n background-color: rgba(22, 160, 133, 0.5);\n background-color: var(--ag-value-change-value-highlight-background-color, rgba(22, 160, 133, 0.5));\n -webkit-transition: background-color 0.1s;\n transition: background-color 0.1s;\n}\n.ag-theme-balham .ag-cell-data-changed {\n background-color: rgba(22, 160, 133, 0.5) !important;\n background-color: var(--ag-value-change-value-highlight-background-color, rgba(22, 160, 133, 0.5)) !important;\n}\n.ag-theme-balham .ag-cell-data-changed-animation {\n background-color: transparent;\n}\n.ag-theme-balham .ag-cell-highlight {\n background-color: #0091ea !important;\n background-color: var(--ag-range-selection-highlight-color, var(--ag-balham-active-color, #0091ea)) !important;\n}\n.ag-theme-balham .ag-row {\n height: 28px;\n background-color: white;\n background-color: var(--ag-background-color, white);\n color: #000;\n color: var(--ag-data-color, var(--ag-foreground-color, #000));\n border-width: 1px;\n border-color: #d9dcde;\n border-color: var(--ag-row-border-color, #d9dcde);\n border-bottom-style: solid;\n}\n.ag-theme-balham .ag-row-highlight-above::after, .ag-theme-balham .ag-row-highlight-below::after {\n content: \"\";\n position: absolute;\n width: calc(100% - 1px);\n height: 1px;\n background-color: #0091ea;\n background-color: var(--ag-range-selection-border-color, var(--ag-balham-active-color, #0091ea));\n left: 1px;\n}\n.ag-theme-balham .ag-row-highlight-above::after {\n top: -1px;\n}\n.ag-theme-balham .ag-row-highlight-above.ag-row-first::after {\n top: 0;\n}\n.ag-theme-balham .ag-row-highlight-below::after {\n bottom: 0px;\n}\n.ag-theme-balham .ag-row-odd {\n background-color: #fcfdfe;\n background-color: var(--ag-odd-row-background-color, #fcfdfe);\n}\n.ag-theme-balham .ag-body-horizontal-scroll:not(.ag-scrollbar-invisible) .ag-horizontal-left-spacer:not(.ag-scroller-corner) {\n border-right: solid 1px;\n border-right-color: #bdc3c7;\n border-right-color: var(--ag-border-color, #bdc3c7);\n}\n.ag-theme-balham .ag-body-horizontal-scroll:not(.ag-scrollbar-invisible) .ag-horizontal-right-spacer:not(.ag-scroller-corner) {\n border-left: solid 1px;\n border-left-color: #bdc3c7;\n border-left-color: var(--ag-border-color, #bdc3c7);\n}\n.ag-theme-balham .ag-row-hover {\n background-color: #ecf0f1;\n background-color: var(--ag-row-hover-color, #ecf0f1);\n}\n.ag-theme-balham .ag-column-hover {\n background-color: #ecf0f1;\n background-color: var(--ag-column-hover-color, #ecf0f1);\n}\n.ag-theme-balham .ag-ltr .ag-right-aligned-cell {\n text-align: right;\n}\n\n.ag-theme-balham .ag-rtl .ag-right-aligned-cell {\n text-align: left;\n}\n\n.ag-theme-balham .ag-ltr .ag-right-aligned-cell .ag-cell-value, .ag-theme-balham .ag-ltr .ag-right-aligned-cell .ag-group-value {\n margin-left: auto;\n}\n\n.ag-theme-balham .ag-rtl .ag-right-aligned-cell .ag-cell-value, .ag-theme-balham .ag-rtl .ag-right-aligned-cell .ag-group-value {\n margin-right: auto;\n}\n\n.ag-theme-balham .ag-cell, .ag-theme-balham .ag-full-width-row .ag-cell-wrapper.ag-row-group {\n border: 1px solid transparent;\n line-height: min(var(--ag-line-height, 26px), 26px);\n padding-left: 11px;\n padding-right: 11px;\n -webkit-font-smoothing: subpixel-antialiased;\n}\n.ag-theme-balham .ag-row > .ag-cell-wrapper {\n padding-left: 11px;\n padding-right: 11px;\n}\n.ag-theme-balham .ag-row-dragging {\n cursor: move;\n opacity: 0.5;\n}\n.ag-theme-balham .ag-cell-inline-editing {\n border: solid 1px;\n border-color: #bdc3c7;\n border-color: var(--ag-border-color, #bdc3c7);\n background: white;\n background: var(--ag-background-color, white);\n border-radius: 2px;\n -webkit-box-shadow: none;\n box-shadow: none;\n padding: 4px;\n padding: 0;\n height: 28px;\n background-color: #f5f7f7;\n background-color: var(--ag-control-panel-background-color, #f5f7f7);\n}\n.ag-theme-balham .ag-popup-editor {\n border: solid 1px;\n border-color: #bdc3c7;\n border-color: var(--ag-border-color, #bdc3c7);\n background: white;\n background: var(--ag-background-color, white);\n border-radius: 2px;\n -webkit-box-shadow: none;\n box-shadow: none;\n padding: 4px;\n background-color: #f5f7f7;\n background-color: var(--ag-control-panel-background-color, #f5f7f7);\n padding: 0;\n}\n.ag-theme-balham .ag-large-text-input {\n height: auto;\n padding: 12px;\n}\n.ag-theme-balham .ag-details-row {\n padding: 20px;\n background-color: white;\n background-color: var(--ag-background-color, white);\n}\n.ag-theme-balham .ag-layout-auto-height .ag-center-cols-clipper, .ag-theme-balham .ag-layout-auto-height .ag-center-cols-container, .ag-theme-balham .ag-layout-print .ag-center-cols-clipper, .ag-theme-balham .ag-layout-print .ag-center-cols-container {\n min-height: 50px;\n}\n.ag-theme-balham .ag-overlay-loading-wrapper {\n background-color: rgba(255, 255, 255, 0.66);\n background-color: var(--ag-modal-overlay-background-color, rgba(255, 255, 255, 0.66));\n}\n.ag-theme-balham .ag-overlay-loading-center {\n border: solid 1px;\n border-color: #bdc3c7;\n border-color: var(--ag-border-color, #bdc3c7);\n background: white;\n background: var(--ag-background-color, white);\n border-radius: 2px;\n -webkit-box-shadow: none;\n box-shadow: none;\n padding: 4px;\n}\n.ag-theme-balham .ag-overlay-no-rows-wrapper.ag-layout-auto-height {\n padding-top: 30px;\n}\n.ag-theme-balham .ag-loading {\n padding-left: 12px;\n display: -webkit-box;\n display: flex;\n height: 100%;\n -webkit-box-align: center;\n align-items: center;\n}\n.ag-theme-balham .ag-loading-icon {\n padding-right: 12px;\n}\n.ag-theme-balham .ag-icon-loading {\n -webkit-animation-name: spin;\n animation-name: spin;\n -webkit-animation-duration: 1000ms;\n animation-duration: 1000ms;\n -webkit-animation-iteration-count: infinite;\n animation-iteration-count: infinite;\n -webkit-animation-timing-function: linear;\n animation-timing-function: linear;\n}\n@-webkit-keyframes spin {\n from {\n -webkit-transform: rotate(0deg);\n transform: rotate(0deg);\n }\n to {\n -webkit-transform: rotate(360deg);\n transform: rotate(360deg);\n }\n}\n@keyframes spin {\n from {\n -webkit-transform: rotate(0deg);\n transform: rotate(0deg);\n }\n to {\n -webkit-transform: rotate(360deg);\n transform: rotate(360deg);\n }\n}\n.ag-theme-balham .ag-floating-top {\n border-bottom: solid 1px;\n border-bottom-color: #bdc3c7;\n border-bottom-color: var(--ag-border-color, #bdc3c7);\n}\n.ag-theme-balham .ag-floating-bottom {\n border-top: solid 1px;\n border-top-color: #bdc3c7;\n border-top-color: var(--ag-border-color, #bdc3c7);\n}\n.ag-theme-balham .ag-ltr .ag-cell {\n border-right: solid transparent;\n}\n\n.ag-theme-balham .ag-rtl .ag-cell {\n border-left: solid transparent;\n}\n\n.ag-theme-balham .ag-ltr .ag-cell {\n border-right-width: 1px;\n}\n\n.ag-theme-balham .ag-rtl .ag-cell {\n border-left-width: 1px;\n}\n\n.ag-theme-balham .ag-cell.ag-cell-first-right-pinned:not(.ag-cell-range-left):not(.ag-cell-range-single-cell) {\n border-left: solid 1px;\n border-left-color: #bdc3c7;\n border-left-color: var(--ag-border-color, #bdc3c7);\n}\n.ag-theme-balham .ag-cell.ag-cell-last-left-pinned:not(.ag-cell-range-right):not(.ag-cell-range-single-cell) {\n border-right: solid 1px;\n border-right-color: #bdc3c7;\n border-right-color: var(--ag-border-color, #bdc3c7);\n}\n.ag-theme-balham .ag-row-selected {\n background-color: #b7e4ff;\n background-color: var(--ag-selected-row-background-color, #b7e4ff);\n}\n.ag-theme-balham .ag-cell-range-selected:not(.ag-cell-focus),\n.ag-theme-balham .ag-body-viewport:not(.ag-has-focus) .ag-cell-range-single-cell:not(.ag-cell-inline-editing) {\n background-color: rgba(0, 145, 234, 0.2);\n background-color: var(--ag-range-selection-background-color, rgba(0, 145, 234, 0.2));\n}\n.ag-theme-balham .ag-cell-range-selected:not(.ag-cell-focus).ag-cell-range-chart,\n.ag-theme-balham .ag-body-viewport:not(.ag-has-focus) .ag-cell-range-single-cell:not(.ag-cell-inline-editing).ag-cell-range-chart {\n background-color: rgba(0, 88, 255, 0.1) !important;\n background-color: var(--ag-range-selection-chart-background-color, rgba(0, 88, 255, 0.1)) !important;\n}\n.ag-theme-balham .ag-cell-range-selected:not(.ag-cell-focus).ag-cell-range-chart.ag-cell-range-chart-category,\n.ag-theme-balham .ag-body-viewport:not(.ag-has-focus) .ag-cell-range-single-cell:not(.ag-cell-inline-editing).ag-cell-range-chart.ag-cell-range-chart-category {\n background-color: rgba(0, 255, 132, 0.1) !important;\n background-color: var(--ag-range-selection-chart-category-background-color, rgba(0, 255, 132, 0.1)) !important;\n}\n.ag-theme-balham .ag-cell-range-selected-1:not(.ag-cell-focus),\n.ag-theme-balham .ag-root:not(.ag-context-menu-open) .ag-body-viewport:not(.ag-has-focus) .ag-cell-range-selected-1:not(.ag-cell-inline-editing) {\n background-color: rgba(0, 145, 234, 0.2);\n background-color: var(--ag-range-selection-background-color-1, var(--ag-range-selection-background-color, rgba(0, 145, 234, 0.2)));\n}\n.ag-theme-balham .ag-cell-range-selected-2:not(.ag-cell-focus),\n.ag-theme-balham .ag-body-viewport:not(.ag-has-focus) .ag-cell-range-selected-2 {\n background-color: rgba(0, 145, 234, 0.36);\n background-color: var(--ag-range-selection-background-color-2, rgba(0, 145, 234, 0.36));\n}\n.ag-theme-balham .ag-cell-range-selected-3:not(.ag-cell-focus),\n.ag-theme-balham .ag-body-viewport:not(.ag-has-focus) .ag-cell-range-selected-3 {\n background-color: rgba(0, 145, 234, 0.488);\n background-color: var(--ag-range-selection-background-color-3, rgba(0, 145, 234, 0.488));\n}\n.ag-theme-balham .ag-cell-range-selected-4:not(.ag-cell-focus),\n.ag-theme-balham .ag-body-viewport:not(.ag-has-focus) .ag-cell-range-selected-4 {\n background-color: rgba(0, 145, 234, 0.5904);\n background-color: var(--ag-range-selection-background-color-4, rgba(0, 145, 234, 0.5904));\n}\n.ag-theme-balham .ag-cell.ag-cell-range-selected:not(.ag-cell-range-single-cell).ag-cell-range-top {\n border-top-color: #0091ea;\n border-top-color: var(--ag-range-selection-border-color, var(--ag-balham-active-color, #0091ea));\n}\n.ag-theme-balham .ag-cell.ag-cell-range-selected:not(.ag-cell-range-single-cell).ag-cell-range-right {\n border-right-color: #0091ea;\n border-right-color: var(--ag-range-selection-border-color, var(--ag-balham-active-color, #0091ea));\n}\n.ag-theme-balham .ag-cell.ag-cell-range-selected:not(.ag-cell-range-single-cell).ag-cell-range-bottom {\n border-bottom-color: #0091ea;\n border-bottom-color: var(--ag-range-selection-border-color, var(--ag-balham-active-color, #0091ea));\n}\n.ag-theme-balham .ag-cell.ag-cell-range-selected:not(.ag-cell-range-single-cell).ag-cell-range-left {\n border-left-color: #0091ea;\n border-left-color: var(--ag-range-selection-border-color, var(--ag-balham-active-color, #0091ea));\n}\n.ag-theme-balham .ag-ltr .ag-has-focus .ag-cell-focus:not(.ag-cell-range-selected),\n.ag-theme-balham .ag-ltr .ag-context-menu-open .ag-cell-focus:not(.ag-cell-range-selected),\n.ag-theme-balham .ag-ltr .ag-has-focus .ag-full-width-row.ag-row-focus .ag-cell-wrapper.ag-row-group,\n.ag-theme-balham .ag-ltr .ag-cell-range-single-cell,\n.ag-theme-balham .ag-ltr .ag-cell-range-single-cell.ag-cell-range-handle, .ag-theme-balham .ag-rtl .ag-has-focus .ag-cell-focus:not(.ag-cell-range-selected),\n.ag-theme-balham .ag-rtl .ag-context-menu-open .ag-cell-focus:not(.ag-cell-range-selected),\n.ag-theme-balham .ag-rtl .ag-has-focus .ag-full-width-row.ag-row-focus .ag-cell-wrapper.ag-row-group,\n.ag-theme-balham .ag-rtl .ag-cell-range-single-cell,\n.ag-theme-balham .ag-rtl .ag-cell-range-single-cell.ag-cell-range-handle {\n border: 1px solid;\n border-color: #0091ea;\n border-color: var(--ag-range-selection-border-color, var(--ag-balham-active-color, #0091ea));\n outline: initial;\n}\n.ag-theme-balham .ag-cell.ag-selection-fill-top,\n.ag-theme-balham .ag-cell.ag-selection-fill-top.ag-cell-range-selected {\n border-top: 1px dashed;\n border-top-color: #0091ea;\n border-top-color: var(--ag-range-selection-border-color, var(--ag-balham-active-color, #0091ea));\n}\n.ag-theme-balham .ag-ltr .ag-cell.ag-selection-fill-right, .ag-theme-balham .ag-ltr .ag-cell.ag-selection-fill-right.ag-cell-range-selected {\n border-right: 1px dashed;\n border-right-color: #0091ea;\n border-right-color: var(--ag-range-selection-border-color, var(--ag-balham-active-color, #0091ea));\n}\n\n.ag-theme-balham .ag-rtl .ag-cell.ag-selection-fill-right, .ag-theme-balham .ag-rtl .ag-cell.ag-selection-fill-right.ag-cell-range-selected {\n border-left: 1px dashed;\n border-left-color: #0091ea;\n border-left-color: var(--ag-range-selection-border-color, var(--ag-balham-active-color, #0091ea));\n}\n\n.ag-theme-balham .ag-cell.ag-selection-fill-bottom,\n.ag-theme-balham .ag-cell.ag-selection-fill-bottom.ag-cell-range-selected {\n border-bottom: 1px dashed;\n border-bottom-color: #0091ea;\n border-bottom-color: var(--ag-range-selection-border-color, var(--ag-balham-active-color, #0091ea));\n}\n.ag-theme-balham .ag-ltr .ag-cell.ag-selection-fill-left, .ag-theme-balham .ag-ltr .ag-cell.ag-selection-fill-left.ag-cell-range-selected {\n border-left: 1px dashed;\n border-left-color: #0091ea;\n border-left-color: var(--ag-range-selection-border-color, var(--ag-balham-active-color, #0091ea));\n}\n\n.ag-theme-balham .ag-rtl .ag-cell.ag-selection-fill-left, .ag-theme-balham .ag-rtl .ag-cell.ag-selection-fill-left.ag-cell-range-selected {\n border-right: 1px dashed;\n border-right-color: #0091ea;\n border-right-color: var(--ag-range-selection-border-color, var(--ag-balham-active-color, #0091ea));\n}\n\n.ag-theme-balham .ag-range-handle, .ag-theme-balham .ag-fill-handle {\n position: absolute;\n width: 6px;\n height: 6px;\n bottom: -1px;\n background-color: #0091ea;\n background-color: var(--ag-range-selection-border-color, var(--ag-balham-active-color, #0091ea));\n}\n.ag-theme-balham .ag-ltr .ag-range-handle, .ag-theme-balham .ag-ltr .ag-fill-handle {\n right: -1px;\n}\n\n.ag-theme-balham .ag-rtl .ag-range-handle, .ag-theme-balham .ag-rtl .ag-fill-handle {\n left: -1px;\n}\n\n.ag-theme-balham .ag-fill-handle {\n cursor: cell;\n}\n.ag-theme-balham .ag-range-handle {\n cursor: nwse-resize;\n}\n.ag-theme-balham .ag-cell-inline-editing {\n border-color: #719ECE !important;\n border-color: var(--ag-input-focus-border-color, #719ECE) !important;\n}\n.ag-theme-balham .ag-menu {\n border: solid 1px;\n border-color: #bdc3c7;\n border-color: var(--ag-border-color, #bdc3c7);\n background: white;\n background: var(--ag-background-color, white);\n border-radius: 2px;\n -webkit-box-shadow: none;\n box-shadow: none;\n padding: 4px;\n padding: 0;\n}\n.ag-theme-balham .ag-menu-list {\n cursor: default;\n padding: 4px 0;\n}\n.ag-theme-balham .ag-menu-separator {\n height: 9px;\n}\n.ag-theme-balham .ag-menu-separator-part::after {\n content: \"\";\n display: block;\n border-top: solid 1px;\n border-top-color: #bdc3c7;\n border-top-color: var(--ag-border-color, #bdc3c7);\n}\n.ag-theme-balham .ag-menu-option-active, .ag-theme-balham .ag-compact-menu-option-active {\n background-color: #ecf0f1;\n background-color: var(--ag-row-hover-color, #ecf0f1);\n}\n.ag-theme-balham .ag-menu-option-part, .ag-theme-balham .ag-compact-menu-option-part {\n line-height: 16px;\n padding: 6px 0;\n}\n.ag-theme-balham .ag-menu-option-disabled, .ag-theme-balham .ag-compact-menu-option-disabled {\n opacity: 0.5;\n}\n.ag-theme-balham .ag-menu-option-icon, .ag-theme-balham .ag-compact-menu-option-icon {\n width: 16px;\n}\n.ag-theme-balham .ag-ltr .ag-menu-option-icon, .ag-theme-balham .ag-ltr .ag-compact-menu-option-icon {\n padding-left: 8px;\n}\n\n.ag-theme-balham .ag-rtl .ag-menu-option-icon, .ag-theme-balham .ag-rtl .ag-compact-menu-option-icon {\n padding-right: 8px;\n}\n\n.ag-theme-balham .ag-menu-option-text, .ag-theme-balham .ag-compact-menu-option-text {\n padding-left: 8px;\n padding-right: 8px;\n}\n.ag-theme-balham .ag-ltr .ag-menu-option-shortcut, .ag-theme-balham .ag-ltr .ag-compact-menu-option-shortcut {\n padding-right: 4px;\n}\n\n.ag-theme-balham .ag-rtl .ag-menu-option-shortcut, .ag-theme-balham .ag-rtl .ag-compact-menu-option-shortcut {\n padding-left: 4px;\n}\n\n.ag-theme-balham .ag-menu-option-popup-pointer, .ag-theme-balham .ag-compact-menu-option-popup-pointer {\n padding-right: 4px;\n}\n.ag-theme-balham .ag-tabs {\n min-width: 220px;\n}\n.ag-theme-balham .ag-tabs-header {\n width: 100%;\n display: -webkit-box;\n display: flex;\n}\n.ag-theme-balham .ag-tab {\n border-bottom: 0 solid transparent;\n display: -webkit-box;\n display: flex;\n -webkit-box-flex: 0;\n flex: none;\n -webkit-box-align: center;\n align-items: center;\n -webkit-box-pack: center;\n justify-content: center;\n cursor: pointer;\n}\n.ag-theme-balham .ag-keyboard-focus .ag-tab:focus {\n outline: none;\n}\n.ag-theme-balham .ag-keyboard-focus .ag-tab:focus::after {\n content: \"\";\n position: absolute;\n background-color: transparent;\n pointer-events: none;\n top: 4px;\n left: 4px;\n display: block;\n width: calc(100% - 8px);\n height: calc(100% - 8px);\n border: 1px solid;\n border-color: #719ECE;\n border-color: var(--ag-input-focus-border-color, #719ECE);\n}\n.ag-theme-balham .ag-tab-selected {\n border-bottom-color: #0091ea;\n border-bottom-color: var(--ag-selected-tab-underline-color, var(--ag-range-selection-border-color, var(--ag-balham-active-color, #0091ea)));\n}\n.ag-theme-balham .ag-menu-header {\n color: rgba(0, 0, 0, 0.54);\n color: var(--ag-secondary-foreground-color, rgba(0, 0, 0, 0.54));\n}\n.ag-theme-balham .ag-filter-separator {\n border-top: solid 1px;\n border-top-color: #bdc3c7;\n border-top-color: var(--ag-border-color, #bdc3c7);\n}\n.ag-theme-balham .ag-menu:not(.ag-tabs) .ag-filter-select {\n min-width: 167px;\n}\n.ag-theme-balham .ag-tabs .ag-filter-select {\n min-width: 206px;\n}\n.ag-theme-balham .ag-filter-select .ag-picker-field-wrapper {\n width: 0;\n}\n.ag-theme-balham .ag-filter-condition-operator {\n height: 17px;\n}\n.ag-theme-balham .ag-ltr .ag-filter-condition-operator-or {\n margin-left: 8px;\n}\n\n.ag-theme-balham .ag-rtl .ag-filter-condition-operator-or {\n margin-right: 8px;\n}\n\n.ag-theme-balham .ag-set-filter-select-all {\n padding-top: 6px;\n}\n.ag-theme-balham .ag-set-filter-list, .ag-theme-balham .ag-filter-no-matches {\n height: 144px;\n}\n.ag-theme-balham .ag-set-filter-filter {\n margin-top: 6px;\n margin-left: 6px;\n margin-right: 6px;\n}\n.ag-theme-balham .ag-filter-to {\n margin-top: 4px;\n}\n.ag-theme-balham .ag-mini-filter {\n margin: 6px 6px;\n}\n.ag-theme-balham .ag-set-filter-item {\n margin: 0px 6px;\n}\n.ag-theme-balham .ag-ltr .ag-set-filter-item-value {\n margin-left: 6px;\n}\n\n.ag-theme-balham .ag-rtl .ag-set-filter-item-value {\n margin-right: 6px;\n}\n\n.ag-theme-balham .ag-filter-apply-panel {\n padding: 6px 6px;\n border-top: solid 1px;\n border-top-color: #bdc3c7;\n border-top-color: var(--ag-secondary-border-color, var(--ag-border-color, #bdc3c7));\n}\n.ag-theme-balham .ag-filter-apply-panel-button {\n line-height: 1.5;\n}\n.ag-theme-balham .ag-ltr .ag-filter-apply-panel-button {\n margin-left: 8px;\n}\n\n.ag-theme-balham .ag-rtl .ag-filter-apply-panel-button {\n margin-right: 8px;\n}\n\n.ag-theme-balham .ag-simple-filter-body-wrapper {\n padding: 6px 6px;\n padding-bottom: 2px;\n}\n.ag-theme-balham .ag-simple-filter-body-wrapper > * {\n margin-bottom: 4px;\n}\n.ag-theme-balham .ag-filter-no-matches {\n padding: 6px 6px;\n}\n.ag-theme-balham .ag-multi-filter-menu-item {\n margin: 4px 0;\n}\n.ag-theme-balham .ag-multi-filter-group-title-bar {\n padding: 8px 4px;\n background-color: transparent;\n}\n.ag-theme-balham .ag-keyboard-focus .ag-multi-filter-group-title-bar:focus {\n outline: none;\n}\n.ag-theme-balham .ag-keyboard-focus .ag-multi-filter-group-title-bar:focus::after {\n content: \"\";\n position: absolute;\n background-color: transparent;\n pointer-events: none;\n top: 4px;\n left: 4px;\n display: block;\n width: calc(100% - 8px);\n height: calc(100% - 8px);\n border: 1px solid;\n border-color: #719ECE;\n border-color: var(--ag-input-focus-border-color, #719ECE);\n}\n.ag-theme-balham .ag-side-bar {\n position: relative;\n}\n.ag-theme-balham .ag-tool-panel-wrapper {\n width: 200px;\n background-color: #f5f7f7;\n background-color: var(--ag-control-panel-background-color, #f5f7f7);\n}\n.ag-theme-balham .ag-side-buttons {\n padding-top: 16px;\n width: 20px;\n position: relative;\n color: #000;\n color: var(--ag-foreground-color, #000);\n overflow: hidden;\n}\n.ag-theme-balham button.ag-side-button-button {\n color: inherit;\n font-family: inherit;\n font-size: inherit;\n font-weight: inherit;\n line-height: inherit;\n background: transparent;\n padding: 8px 0 8px 0;\n width: calc(100% + 1px);\n margin: 0;\n min-height: 72px;\n background-position-y: center;\n background-position-x: center;\n background-repeat: no-repeat;\n border: none;\n border-top: solid 1px;\n border-top-color: #bdc3c7;\n border-top-color: var(--ag-border-color, #bdc3c7);\n border-bottom: solid 1px;\n border-bottom-color: #bdc3c7;\n border-bottom-color: var(--ag-border-color, #bdc3c7);\n}\n.ag-theme-balham button.ag-side-button-button:focus {\n -webkit-box-shadow: none;\n box-shadow: none;\n}\n.ag-theme-balham .ag-keyboard-focus .ag-side-button-button:focus {\n outline: none;\n}\n.ag-theme-balham .ag-keyboard-focus .ag-side-button-button:focus::after {\n content: \"\";\n position: absolute;\n background-color: transparent;\n pointer-events: none;\n top: 4px;\n left: 4px;\n display: block;\n width: calc(100% - 8px);\n height: calc(100% - 8px);\n border: 1px solid;\n border-color: #719ECE;\n border-color: var(--ag-input-focus-border-color, #719ECE);\n}\n.ag-theme-balham .ag-selected .ag-side-button-button {\n background-color: #f5f7f7;\n background-color: var(--ag-control-panel-background-color, #f5f7f7);\n border-top-color: #bdc3c7;\n border-top-color: var(--ag-border-color, #bdc3c7);\n border-bottom-color: #bdc3c7;\n border-bottom-color: var(--ag-border-color, #bdc3c7);\n}\n.ag-theme-balham .ag-side-button-icon-wrapper {\n margin-bottom: 3px;\n}\n.ag-theme-balham .ag-ltr .ag-side-bar-left,\n.ag-theme-balham .ag-rtl .ag-side-bar-right {\n border-right: solid 1px;\n border-right-color: #bdc3c7;\n border-right-color: var(--ag-border-color, #bdc3c7);\n}\n.ag-theme-balham .ag-ltr .ag-side-bar-left .ag-tool-panel-wrapper,\n.ag-theme-balham .ag-rtl .ag-side-bar-right .ag-tool-panel-wrapper {\n border-left: solid 1px;\n border-left-color: #bdc3c7;\n border-left-color: var(--ag-border-color, #bdc3c7);\n}\n.ag-theme-balham .ag-ltr .ag-side-bar-left .ag-side-button-button,\n.ag-theme-balham .ag-rtl .ag-side-bar-right .ag-side-button-button {\n border-right: 0 solid transparent;\n margin-right: -1px;\n padding-right: 1px;\n}\n.ag-theme-balham .ag-ltr .ag-side-bar-left .ag-selected .ag-side-button-button,\n.ag-theme-balham .ag-rtl .ag-side-bar-right .ag-selected .ag-side-button-button {\n border-right-color: #0091ea;\n border-right-color: var(--ag-selected-tab-underline-color, var(--ag-range-selection-border-color, var(--ag-balham-active-color, #0091ea)));\n}\n.ag-theme-balham .ag-rtl .ag-side-bar-left,\n.ag-theme-balham .ag-ltr .ag-side-bar-right {\n border-left: solid 1px;\n border-left-color: #bdc3c7;\n border-left-color: var(--ag-border-color, #bdc3c7);\n}\n.ag-theme-balham .ag-rtl .ag-side-bar-left .ag-tool-panel-wrapper,\n.ag-theme-balham .ag-ltr .ag-side-bar-right .ag-tool-panel-wrapper {\n border-right: solid 1px;\n border-right-color: #bdc3c7;\n border-right-color: var(--ag-border-color, #bdc3c7);\n}\n.ag-theme-balham .ag-rtl .ag-side-bar-left .ag-side-button-button,\n.ag-theme-balham .ag-ltr .ag-side-bar-right .ag-side-button-button {\n border-left: 0 solid transparent;\n margin-left: -1px;\n padding-left: 1px;\n}\n.ag-theme-balham .ag-rtl .ag-side-bar-left .ag-selected .ag-side-button-button,\n.ag-theme-balham .ag-ltr .ag-side-bar-right .ag-selected .ag-side-button-button {\n border-left-color: #0091ea;\n border-left-color: var(--ag-selected-tab-underline-color, var(--ag-range-selection-border-color, var(--ag-balham-active-color, #0091ea)));\n}\n.ag-theme-balham .ag-filter-toolpanel-header {\n height: 24px;\n}\n.ag-theme-balham .ag-ltr .ag-filter-toolpanel-header, .ag-theme-balham .ag-ltr .ag-filter-toolpanel-search {\n padding-left: 4px;\n}\n\n.ag-theme-balham .ag-rtl .ag-filter-toolpanel-header, .ag-theme-balham .ag-rtl .ag-filter-toolpanel-search {\n padding-right: 4px;\n}\n\n.ag-theme-balham .ag-keyboard-focus .ag-filter-toolpanel-header:focus {\n outline: none;\n}\n.ag-theme-balham .ag-keyboard-focus .ag-filter-toolpanel-header:focus::after {\n content: \"\";\n position: absolute;\n background-color: transparent;\n pointer-events: none;\n top: 4px;\n left: 4px;\n display: block;\n width: calc(100% - 8px);\n height: calc(100% - 8px);\n border: 1px solid;\n border-color: #719ECE;\n border-color: var(--ag-input-focus-border-color, #719ECE);\n}\n.ag-theme-balham .ag-filter-toolpanel-group.ag-has-filter > .ag-group-title-bar .ag-group-title::after {\n font-family: \"agGridBalham\";\n font-size: 16px;\n line-height: 16px;\n font-style: normal;\n font-weight: normal;\n font-variant: normal;\n text-transform: none;\n /* Better Font Rendering =========== */\n -webkit-font-smoothing: antialiased;\n -moz-osx-font-smoothing: grayscale;\n content: \"\\f114\";\n position: absolute;\n}\n.ag-theme-balham .ag-ltr .ag-filter-toolpanel-group.ag-has-filter > .ag-group-title-bar .ag-group-title::after {\n padding-left: 4px;\n}\n\n.ag-theme-balham .ag-rtl .ag-filter-toolpanel-group.ag-has-filter > .ag-group-title-bar .ag-group-title::after {\n padding-right: 4px;\n}\n\n.ag-theme-balham .ag-filter-toolpanel-group-level-0-header {\n height: 32px;\n}\n.ag-theme-balham .ag-filter-toolpanel-group-item {\n margin-top: 2px;\n margin-bottom: 2px;\n}\n.ag-theme-balham .ag-filter-toolpanel-search {\n height: 32px;\n}\n.ag-theme-balham .ag-filter-toolpanel-search-input {\n -webkit-box-flex: 1;\n flex-grow: 1;\n height: 16px;\n}\n.ag-theme-balham .ag-ltr .ag-filter-toolpanel-search-input {\n margin-right: 4px;\n}\n\n.ag-theme-balham .ag-rtl .ag-filter-toolpanel-search-input {\n margin-left: 4px;\n}\n\n.ag-theme-balham .ag-filter-toolpanel-group-level-0 {\n border-top: solid 1px;\n border-top-color: #bdc3c7;\n border-top-color: var(--ag-secondary-border-color, var(--ag-border-color, #bdc3c7));\n}\n.ag-theme-balham .ag-ltr .ag-filter-toolpanel-expand, .ag-theme-balham .ag-ltr .ag-filter-toolpanel-group-title-bar-icon {\n margin-right: 4px;\n}\n\n.ag-theme-balham .ag-rtl .ag-filter-toolpanel-expand, .ag-theme-balham .ag-rtl .ag-filter-toolpanel-group-title-bar-icon {\n margin-left: 4px;\n}\n\n.ag-theme-balham .ag-filter-toolpanel-group-level-1 .ag-filter-toolpanel-group-level-1-header.ag-filter-toolpanel-group-title-bar {\n background-color: transparent;\n}\n.ag-theme-balham .ag-ltr .ag-filter-toolpanel-group-level-1 .ag-filter-toolpanel-group-level-2-header {\n padding-left: 20px;\n}\n\n.ag-theme-balham .ag-rtl .ag-filter-toolpanel-group-level-1 .ag-filter-toolpanel-group-level-2-header {\n padding-right: 20px;\n}\n\n.ag-theme-balham .ag-filter-toolpanel-group-level-2 .ag-filter-toolpanel-group-level-2-header.ag-filter-toolpanel-group-title-bar {\n background-color: transparent;\n}\n.ag-theme-balham .ag-ltr .ag-filter-toolpanel-group-level-2 .ag-filter-toolpanel-group-level-3-header {\n padding-left: 36px;\n}\n\n.ag-theme-balham .ag-rtl .ag-filter-toolpanel-group-level-2 .ag-filter-toolpanel-group-level-3-header {\n padding-right: 36px;\n}\n\n.ag-theme-balham .ag-filter-toolpanel-group-level-3 .ag-filter-toolpanel-group-level-3-header.ag-filter-toolpanel-group-title-bar {\n background-color: transparent;\n}\n.ag-theme-balham .ag-ltr .ag-filter-toolpanel-group-level-3 .ag-filter-toolpanel-group-level-4-header {\n padding-left: 52px;\n}\n\n.ag-theme-balham .ag-rtl .ag-filter-toolpanel-group-level-3 .ag-filter-toolpanel-group-level-4-header {\n padding-right: 52px;\n}\n\n.ag-theme-balham .ag-filter-toolpanel-group-level-4 .ag-filter-toolpanel-group-level-4-header.ag-filter-toolpanel-group-title-bar {\n background-color: transparent;\n}\n.ag-theme-balham .ag-ltr .ag-filter-toolpanel-group-level-4 .ag-filter-toolpanel-group-level-5-header {\n padding-left: 68px;\n}\n\n.ag-theme-balham .ag-rtl .ag-filter-toolpanel-group-level-4 .ag-filter-toolpanel-group-level-5-header {\n padding-right: 68px;\n}\n\n.ag-theme-balham .ag-filter-toolpanel-group-level-5 .ag-filter-toolpanel-group-level-5-header.ag-filter-toolpanel-group-title-bar {\n background-color: transparent;\n}\n.ag-theme-balham .ag-ltr .ag-filter-toolpanel-group-level-5 .ag-filter-toolpanel-group-level-6-header {\n padding-left: 84px;\n}\n\n.ag-theme-balham .ag-rtl .ag-filter-toolpanel-group-level-5 .ag-filter-toolpanel-group-level-6-header {\n padding-right: 84px;\n}\n\n.ag-theme-balham .ag-filter-toolpanel-group-level-6 .ag-filter-toolpanel-group-level-6-header.ag-filter-toolpanel-group-title-bar {\n background-color: transparent;\n}\n.ag-theme-balham .ag-ltr .ag-filter-toolpanel-group-level-6 .ag-filter-toolpanel-group-level-7-header {\n padding-left: 100px;\n}\n\n.ag-theme-balham .ag-rtl .ag-filter-toolpanel-group-level-6 .ag-filter-toolpanel-group-level-7-header {\n padding-right: 100px;\n}\n\n.ag-theme-balham .ag-filter-toolpanel-group-level-7 .ag-filter-toolpanel-group-level-7-header.ag-filter-toolpanel-group-title-bar {\n background-color: transparent;\n}\n.ag-theme-balham .ag-ltr .ag-filter-toolpanel-group-level-7 .ag-filter-toolpanel-group-level-8-header {\n padding-left: 116px;\n}\n\n.ag-theme-balham .ag-rtl .ag-filter-toolpanel-group-level-7 .ag-filter-toolpanel-group-level-8-header {\n padding-right: 116px;\n}\n\n.ag-theme-balham .ag-filter-toolpanel-group-level-8 .ag-filter-toolpanel-group-level-8-header.ag-filter-toolpanel-group-title-bar {\n background-color: transparent;\n}\n.ag-theme-balham .ag-ltr .ag-filter-toolpanel-group-level-8 .ag-filter-toolpanel-group-level-9-header {\n padding-left: 132px;\n}\n\n.ag-theme-balham .ag-rtl .ag-filter-toolpanel-group-level-8 .ag-filter-toolpanel-group-level-9-header {\n padding-right: 132px;\n}\n\n.ag-theme-balham .ag-filter-toolpanel-group-level-9 .ag-filter-toolpanel-group-level-9-header.ag-filter-toolpanel-group-title-bar {\n background-color: transparent;\n}\n.ag-theme-balham .ag-ltr .ag-filter-toolpanel-group-level-9 .ag-filter-toolpanel-group-level-10-header {\n padding-left: 148px;\n}\n\n.ag-theme-balham .ag-rtl .ag-filter-toolpanel-group-level-9 .ag-filter-toolpanel-group-level-10-header {\n padding-right: 148px;\n}\n\n.ag-theme-balham .ag-filter-toolpanel-group-level-10 .ag-filter-toolpanel-group-level-10-header.ag-filter-toolpanel-group-title-bar {\n background-color: transparent;\n}\n.ag-theme-balham .ag-ltr .ag-filter-toolpanel-group-level-10 .ag-filter-toolpanel-group-level-11-header {\n padding-left: 164px;\n}\n\n.ag-theme-balham .ag-rtl .ag-filter-toolpanel-group-level-10 .ag-filter-toolpanel-group-level-11-header {\n padding-right: 164px;\n}\n\n.ag-theme-balham .ag-filter-toolpanel-instance-header.ag-filter-toolpanel-group-level-1-header {\n padding-left: 4px;\n}\n.ag-theme-balham .ag-filter-toolpanel-instance-filter {\n border-top: solid 1px;\n border-top-color: #bdc3c7;\n border-top-color: var(--ag-border-color, #bdc3c7);\n border-bottom: solid 1px;\n border-bottom-color: #bdc3c7;\n border-bottom-color: var(--ag-border-color, #bdc3c7);\n margin-top: 4px;\n}\n.ag-theme-balham .ag-ltr .ag-filter-toolpanel-instance-header-icon {\n margin-left: 4px;\n}\n\n.ag-theme-balham .ag-rtl .ag-filter-toolpanel-instance-header-icon {\n margin-right: 4px;\n}\n\n.ag-theme-balham .ag-pivot-mode-panel {\n min-height: 32px;\n height: 32px;\n display: -webkit-box;\n display: flex;\n}\n.ag-theme-balham .ag-pivot-mode-select {\n display: -webkit-box;\n display: flex;\n -webkit-box-align: center;\n align-items: center;\n}\n.ag-theme-balham .ag-ltr .ag-pivot-mode-select {\n margin-left: 6px;\n}\n\n.ag-theme-balham .ag-rtl .ag-pivot-mode-select {\n margin-right: 6px;\n}\n\n.ag-theme-balham .ag-keyboard-focus .ag-column-select-header:focus {\n outline: none;\n}\n.ag-theme-balham .ag-keyboard-focus .ag-column-select-header:focus::after {\n content: \"\";\n position: absolute;\n background-color: transparent;\n pointer-events: none;\n top: 4px;\n left: 4px;\n display: block;\n width: calc(100% - 8px);\n height: calc(100% - 8px);\n border: 1px solid;\n border-color: #719ECE;\n border-color: var(--ag-input-focus-border-color, #719ECE);\n}\n.ag-theme-balham .ag-column-select-header {\n height: 32px;\n -webkit-box-align: center;\n align-items: center;\n padding: 0 6px;\n border-bottom: solid 1px;\n border-bottom-color: #bdc3c7;\n border-bottom-color: var(--ag-secondary-border-color, var(--ag-border-color, #bdc3c7));\n}\n.ag-theme-balham .ag-column-panel-column-select {\n border-bottom: solid 1px;\n border-bottom-color: #bdc3c7;\n border-bottom-color: var(--ag-secondary-border-color, var(--ag-border-color, #bdc3c7));\n border-top: solid 1px;\n border-top-color: #bdc3c7;\n border-top-color: var(--ag-secondary-border-color, var(--ag-border-color, #bdc3c7));\n}\n.ag-theme-balham .ag-column-group-icons,\n.ag-theme-balham .ag-column-select-header-icon {\n color: rgba(0, 0, 0, 0.54);\n color: var(--ag-secondary-foreground-color, rgba(0, 0, 0, 0.54));\n}\n.ag-theme-balham .ag-column-select-list .ag-list-item-hovered::after {\n content: \"\";\n position: absolute;\n left: 0;\n right: 0;\n height: 1px;\n background-color: #0091ea;\n background-color: var(--ag-range-selection-border-color, var(--ag-balham-active-color, #0091ea));\n}\n.ag-theme-balham .ag-column-select-list .ag-item-highlight-top::after {\n top: 0;\n}\n.ag-theme-balham .ag-column-select-list .ag-item-highlight-bottom::after {\n bottom: 0;\n}\n.ag-theme-balham .ag-header {\n background-color: #f5f7f7;\n background-color: var(--ag-header-background-color, #f5f7f7);\n border-bottom: solid 1px;\n border-bottom-color: #bdc3c7;\n border-bottom-color: var(--ag-border-color, #bdc3c7);\n}\n.ag-theme-balham .ag-header-row {\n color: rgba(0, 0, 0, 0.54);\n color: var(--ag-header-foreground-color, var(--ag-secondary-foreground-color, rgba(0, 0, 0, 0.54)));\n height: 32px;\n}\n.ag-theme-balham .ag-pinned-right-header {\n border-left: solid 1px;\n border-left-color: #bdc3c7;\n border-left-color: var(--ag-border-color, #bdc3c7);\n}\n.ag-theme-balham .ag-pinned-left-header {\n border-right: solid 1px;\n border-right-color: #bdc3c7;\n border-right-color: var(--ag-border-color, #bdc3c7);\n}\n.ag-theme-balham .ag-ltr .ag-header-cell:not(.ag-right-aligned-header) .ag-header-label-icon {\n margin-left: 4px;\n}\n\n.ag-theme-balham .ag-rtl .ag-header-cell:not(.ag-right-aligned-header) .ag-header-label-icon {\n margin-right: 4px;\n}\n\n.ag-theme-balham .ag-ltr .ag-header-cell.ag-right-aligned-header .ag-header-label-icon {\n margin-right: 4px;\n}\n\n.ag-theme-balham .ag-rtl .ag-header-cell.ag-right-aligned-header .ag-header-label-icon {\n margin-left: 4px;\n}\n\n.ag-theme-balham .ag-header-cell,\n.ag-theme-balham .ag-header-group-cell {\n padding-left: 12px;\n padding-right: 12px;\n}\n.ag-theme-balham .ag-header-cell.ag-header-cell-moving,\n.ag-theme-balham .ag-header-group-cell.ag-header-cell-moving {\n background-color: white;\n background-color: var(--ag-header-cell-moving-background-color, var(--ag-background-color, white));\n}\n.ag-theme-balham .ag-keyboard-focus .ag-header-cell:focus {\n outline: none;\n}\n.ag-theme-balham .ag-keyboard-focus .ag-header-cell:focus::after {\n content: \"\";\n position: absolute;\n background-color: transparent;\n pointer-events: none;\n top: 4px;\n left: 4px;\n display: block;\n width: calc(100% - 8px);\n height: calc(100% - 8px);\n border: 1px solid;\n border-color: #719ECE;\n border-color: var(--ag-input-focus-border-color, #719ECE);\n}\n.ag-theme-balham .ag-keyboard-focus .ag-header-group-cell:focus {\n outline: none;\n}\n.ag-theme-balham .ag-keyboard-focus .ag-header-group-cell:focus::after {\n content: \"\";\n position: absolute;\n background-color: transparent;\n pointer-events: none;\n top: 4px;\n left: 4px;\n display: block;\n width: calc(100% - 8px);\n height: calc(100% - 8px);\n border: 1px solid;\n border-color: #719ECE;\n border-color: var(--ag-input-focus-border-color, #719ECE);\n}\n.ag-theme-balham .ag-header-icon {\n color: rgba(0, 0, 0, 0.54);\n color: var(--ag-secondary-foreground-color, rgba(0, 0, 0, 0.54));\n}\n.ag-theme-balham .ag-header-expand-icon {\n cursor: pointer;\n}\n.ag-theme-balham .ag-ltr .ag-header-expand-icon {\n padding-left: 4px;\n}\n\n.ag-theme-balham .ag-rtl .ag-header-expand-icon {\n padding-right: 4px;\n}\n\n.ag-theme-balham .ag-header-row:not(:first-child) .ag-header-cell,\n.ag-theme-balham .ag-header-row:not(:first-child) .ag-header-group-cell.ag-header-group-cell-with-group {\n border-top: solid 1px;\n border-top-color: #bdc3c7;\n border-top-color: var(--ag-border-color, #bdc3c7);\n}\n.ag-theme-balham .ag-header-cell::after,\n.ag-theme-balham .ag-header-group-cell::after {\n content: \"\";\n position: absolute;\n z-index: 1;\n display: block;\n width: 1px;\n height: 50%;\n top: calc(50% - 25%);\n background-color: rgba(189, 195, 199, 0.5);\n background-color: var(--ag-header-column-separator-color, rgba(189, 195, 199, 0.5));\n}\n.ag-theme-balham .ag-ltr .ag-header-cell::after, .ag-theme-balham .ag-ltr .ag-header-group-cell::after {\n right: 0;\n}\n\n.ag-theme-balham .ag-rtl .ag-header-cell::after, .ag-theme-balham .ag-rtl .ag-header-group-cell::after {\n left: 0;\n}\n\n.ag-theme-balham .ag-ltr .ag-header-select-all {\n margin-right: 12px;\n}\n\n.ag-theme-balham .ag-rtl .ag-header-select-all {\n margin-left: 12px;\n}\n\n.ag-theme-balham .ag-ltr .ag-floating-filter-button {\n margin-left: 12px;\n}\n\n.ag-theme-balham .ag-rtl .ag-floating-filter-button {\n margin-right: 12px;\n}\n\n.ag-theme-balham .ag-floating-filter-button-button {\n color: inherit;\n font-family: inherit;\n font-size: inherit;\n font-weight: inherit;\n line-height: inherit;\n -webkit-appearance: none;\n -moz-appearance: none;\n appearance: none;\n background: transparent;\n border: none;\n height: 16px;\n padding: 0;\n width: 16px;\n}\n.ag-theme-balham .ag-filter-loading {\n background-color: #f5f7f7;\n background-color: var(--ag-control-panel-background-color, #f5f7f7);\n height: 100%;\n padding: 6px 6px;\n position: absolute;\n width: 100%;\n z-index: 1;\n}\n.ag-theme-balham .ag-paging-panel {\n border-top: 1px solid;\n border-top-color: #bdc3c7;\n border-top-color: var(--ag-border-color, #bdc3c7);\n color: rgba(0, 0, 0, 0.54);\n color: var(--ag-secondary-foreground-color, rgba(0, 0, 0, 0.54));\n height: 32px;\n}\n.ag-theme-balham .ag-paging-panel > * {\n margin: 0 12px;\n}\n.ag-theme-balham .ag-paging-button {\n cursor: pointer;\n}\n.ag-theme-balham .ag-paging-button.ag-disabled {\n cursor: default;\n color: rgba(0, 0, 0, 0.38);\n color: var(--ag-disabled-foreground-color, rgba(0, 0, 0, 0.38));\n}\n.ag-theme-balham .ag-keyboard-focus .ag-paging-button:focus {\n outline: none;\n}\n.ag-theme-balham .ag-keyboard-focus .ag-paging-button:focus::after {\n content: \"\";\n position: absolute;\n background-color: transparent;\n pointer-events: none;\n top: 0px;\n left: 0px;\n display: block;\n width: calc(100% - 0px);\n height: calc(100% - 0px);\n border: 1px solid;\n border-color: #719ECE;\n border-color: var(--ag-input-focus-border-color, #719ECE);\n}\n.ag-theme-balham .ag-paging-button, .ag-theme-balham .ag-paging-description {\n margin: 0 4px;\n}\n.ag-theme-balham .ag-status-bar {\n border-top: solid 1px;\n border-top-color: #bdc3c7;\n border-top-color: var(--ag-border-color, #bdc3c7);\n color: rgba(0, 0, 0, 0.38);\n color: var(--ag-disabled-foreground-color, rgba(0, 0, 0, 0.38));\n padding-right: 16px;\n padding-left: 16px;\n line-height: 1.5;\n}\n.ag-theme-balham .ag-status-name-value-value {\n color: #000;\n color: var(--ag-foreground-color, #000);\n}\n.ag-theme-balham .ag-status-bar-center {\n text-align: center;\n}\n.ag-theme-balham .ag-status-name-value {\n margin-left: 4px;\n margin-right: 4px;\n padding-top: 8px;\n padding-bottom: 8px;\n}\n.ag-theme-balham .ag-column-drop-cell {\n background: #dddede;\n background: var(--ag-chip-background-color, #dddede);\n border-radius: 16px;\n height: 16px;\n padding: 0 2px;\n border: 1px solid transparent;\n}\n.ag-theme-balham .ag-keyboard-focus .ag-column-drop-cell:focus {\n outline: none;\n}\n.ag-theme-balham .ag-keyboard-focus .ag-column-drop-cell:focus::after {\n content: \"\";\n position: absolute;\n background-color: transparent;\n pointer-events: none;\n top: 2px;\n left: 2px;\n display: block;\n width: calc(100% - 4px);\n height: calc(100% - 4px);\n border: 1px solid;\n border-color: #719ECE;\n border-color: var(--ag-input-focus-border-color, #719ECE);\n}\n.ag-theme-balham .ag-column-drop-cell-text {\n margin: 0 4px;\n}\n.ag-theme-balham .ag-column-drop-cell-button {\n min-width: 16px;\n margin: 0 2px;\n color: rgba(0, 0, 0, 0.54);\n color: var(--ag-secondary-foreground-color, rgba(0, 0, 0, 0.54));\n}\n.ag-theme-balham .ag-column-drop-cell-drag-handle {\n margin-left: 8px;\n}\n.ag-theme-balham .ag-column-drop-cell-ghost {\n opacity: 0.5;\n}\n.ag-theme-balham .ag-column-drop-horizontal {\n background-color: #f5f7f7;\n background-color: var(--ag-control-panel-background-color, #f5f7f7);\n color: rgba(0, 0, 0, 0.54);\n color: var(--ag-secondary-foreground-color, rgba(0, 0, 0, 0.54));\n height: 28px;\n border-bottom: solid 1px;\n border-bottom-color: #bdc3c7;\n border-bottom-color: var(--ag-border-color, #bdc3c7);\n}\n.ag-theme-balham .ag-ltr .ag-column-drop-horizontal {\n padding-left: 12px;\n}\n\n.ag-theme-balham .ag-rtl .ag-column-drop-horizontal {\n padding-right: 12px;\n}\n\n.ag-theme-balham .ag-ltr .ag-column-drop-horizontal-half-width:not(:last-child) {\n border-right: solid 1px;\n border-right-color: #bdc3c7;\n border-right-color: var(--ag-border-color, #bdc3c7);\n}\n\n.ag-theme-balham .ag-rtl .ag-column-drop-horizontal-half-width:not(:last-child) {\n border-left: solid 1px;\n border-left-color: #bdc3c7;\n border-left-color: var(--ag-border-color, #bdc3c7);\n}\n\n.ag-theme-balham .ag-column-drop-horizontal-cell-separator {\n margin: 0 4px;\n color: rgba(0, 0, 0, 0.54);\n color: var(--ag-secondary-foreground-color, rgba(0, 0, 0, 0.54));\n}\n.ag-theme-balham .ag-column-drop-horizontal-empty-message {\n color: rgba(0, 0, 0, 0.38);\n color: var(--ag-disabled-foreground-color, rgba(0, 0, 0, 0.38));\n}\n.ag-theme-balham .ag-ltr .ag-column-drop-horizontal-icon {\n margin-right: 12px;\n}\n\n.ag-theme-balham .ag-rtl .ag-column-drop-horizontal-icon {\n margin-left: 12px;\n}\n\n.ag-theme-balham .ag-column-drop-vertical-list {\n padding-bottom: 4px;\n padding-right: 4px;\n padding-left: 4px;\n}\n.ag-theme-balham .ag-column-drop-vertical-cell {\n margin-top: 4px;\n}\n.ag-theme-balham .ag-column-drop-vertical {\n min-height: 50px;\n border-bottom: solid 1px;\n border-bottom-color: #bdc3c7;\n border-bottom-color: var(--ag-secondary-border-color, var(--ag-border-color, #bdc3c7));\n}\n.ag-theme-balham .ag-column-drop-vertical.ag-last-column-drop {\n border-bottom: none;\n}\n.ag-theme-balham .ag-column-drop-vertical-icon {\n margin-left: 4px;\n margin-right: 4px;\n}\n.ag-theme-balham .ag-column-drop-vertical-empty-message {\n position: absolute;\n top: 0;\n bottom: 0;\n left: 0;\n right: 0;\n overflow: hidden;\n color: rgba(0, 0, 0, 0.38);\n color: var(--ag-disabled-foreground-color, rgba(0, 0, 0, 0.38));\n margin-top: 4px;\n}\n.ag-theme-balham .ag-select-agg-func-popup {\n border: solid 1px;\n border-color: #bdc3c7;\n border-color: var(--ag-border-color, #bdc3c7);\n background: white;\n background: var(--ag-background-color, white);\n border-radius: 2px;\n -webkit-box-shadow: none;\n box-shadow: none;\n padding: 4px;\n background: white;\n background: var(--ag-background-color, white);\n height: 70px;\n padding: 0;\n}\n.ag-theme-balham .ag-select-agg-func-virtual-list-item {\n cursor: default;\n padding-left: 8px;\n}\n.ag-theme-balham .ag-select-agg-func-virtual-list-item:hover {\n background-color: #b7e4ff;\n background-color: var(--ag-selected-row-background-color, #b7e4ff);\n}\n.ag-theme-balham .ag-keyboard-focus .ag-select-agg-func-virtual-list-item:focus {\n outline: none;\n}\n.ag-theme-balham .ag-keyboard-focus .ag-select-agg-func-virtual-list-item:focus::after {\n content: \"\";\n position: absolute;\n background-color: transparent;\n pointer-events: none;\n top: 1px;\n left: 1px;\n display: block;\n width: calc(100% - 2px);\n height: calc(100% - 2px);\n border: 1px solid;\n border-color: #719ECE;\n border-color: var(--ag-input-focus-border-color, #719ECE);\n}\n.ag-theme-balham .ag-chart-menu {\n border-radius: 2px;\n background: white;\n background: var(--ag-background-color, white);\n}\n.ag-theme-balham .ag-chart-menu-icon {\n opacity: 0.5;\n line-height: 24px;\n font-size: 24px;\n width: 24px;\n height: 24px;\n margin: 2px 0;\n cursor: pointer;\n border-radius: 2px;\n color: rgba(0, 0, 0, 0.54);\n color: var(--ag-secondary-foreground-color, rgba(0, 0, 0, 0.54));\n}\n.ag-theme-balham .ag-chart-menu-icon:hover {\n opacity: 1;\n}\n.ag-theme-balham .ag-chart-mini-thumbnail {\n border: 1px solid;\n border-color: #bdc3c7;\n border-color: var(--ag-secondary-border-color, var(--ag-border-color, #bdc3c7));\n border-radius: 5px;\n margin: 5px;\n}\n.ag-theme-balham .ag-chart-mini-thumbnail:nth-last-child(3), .ag-theme-balham .ag-chart-mini-thumbnail:nth-last-child(3) ~ .ag-chart-mini-thumbnail {\n margin-left: auto;\n margin-right: auto;\n}\n.ag-theme-balham .ag-ltr .ag-chart-mini-thumbnail:first-child {\n margin-left: 0;\n}\n\n.ag-theme-balham .ag-rtl .ag-chart-mini-thumbnail:first-child {\n margin-right: 0;\n}\n\n.ag-theme-balham .ag-ltr .ag-chart-mini-thumbnail:last-child {\n margin-right: 0;\n}\n\n.ag-theme-balham .ag-rtl .ag-chart-mini-thumbnail:last-child {\n margin-left: 0;\n}\n\n.ag-theme-balham .ag-chart-mini-thumbnail.ag-selected {\n border-color: #0091ea;\n border-color: var(--ag-minichart-selected-chart-color, var(--ag-checkbox-checked-color, var(--ag-balham-active-color, #0091ea)));\n}\n.ag-theme-balham .ag-chart-settings-card-item {\n background: #000;\n background: var(--ag-foreground-color, #000);\n width: 8px;\n height: 8px;\n border-radius: 4px;\n}\n.ag-theme-balham .ag-chart-settings-card-item.ag-selected {\n background-color: #0091ea;\n background-color: var(--ag-minichart-selected-page-color, var(--ag-checkbox-checked-color, var(--ag-balham-active-color, #0091ea)));\n}\n.ag-theme-balham .ag-chart-data-column-drag-handle {\n margin-left: 4px;\n}\n.ag-theme-balham .ag-charts-settings-group-title-bar,\n.ag-theme-balham .ag-charts-data-group-title-bar,\n.ag-theme-balham .ag-charts-format-top-level-group-title-bar {\n border-top: solid 1px;\n border-top-color: #bdc3c7;\n border-top-color: var(--ag-secondary-border-color, var(--ag-border-color, #bdc3c7));\n}\n.ag-theme-balham .ag-charts-settings-group-container {\n padding: 4px;\n}\n.ag-theme-balham .ag-charts-data-group-container {\n padding: 3px 6px;\n}\n.ag-theme-balham .ag-charts-data-group-container .ag-charts-data-group-item:not(.ag-charts-format-sub-level-group) {\n height: 24px;\n}\n.ag-theme-balham .ag-charts-data-group-container .ag-list-item-hovered::after {\n content: \"\";\n position: absolute;\n left: 0;\n right: 0;\n height: 1px;\n background-color: #0091ea;\n background-color: var(--ag-range-selection-border-color, var(--ag-balham-active-color, #0091ea));\n}\n.ag-theme-balham .ag-charts-data-group-container .ag-item-highlight-top::after {\n top: 0;\n}\n.ag-theme-balham .ag-charts-data-group-container .ag-item-highlight-bottom::after {\n bottom: 0;\n}\n.ag-theme-balham .ag-charts-format-top-level-group-container {\n margin-left: 8px;\n padding: 4px;\n}\n.ag-theme-balham .ag-charts-format-top-level-group-item {\n margin: 4px 0;\n}\n.ag-theme-balham .ag-charts-format-sub-level-group-container {\n padding: 6px 6px;\n padding-bottom: 2px;\n}\n.ag-theme-balham .ag-charts-format-sub-level-group-container > * {\n margin-bottom: 4px;\n}\n.ag-theme-balham .ag-charts-group-container.ag-group-container-horizontal {\n padding: 4px;\n}\n.ag-theme-balham .ag-chart-data-section,\n.ag-theme-balham .ag-chart-format-section {\n display: -webkit-box;\n display: flex;\n margin: 0;\n}\n.ag-theme-balham .ag-chart-menu-panel {\n background-color: #f5f7f7;\n background-color: var(--ag-control-panel-background-color, #f5f7f7);\n}\n.ag-theme-balham .ag-ltr .ag-chart-menu-panel {\n border-left: solid 1px;\n border-left-color: #bdc3c7;\n border-left-color: var(--ag-border-color, #bdc3c7);\n}\n\n.ag-theme-balham .ag-rtl .ag-chart-menu-panel {\n border-right: solid 1px;\n border-right-color: #bdc3c7;\n border-right-color: var(--ag-border-color, #bdc3c7);\n}\n\n.ag-theme-balham .ag-date-time-list-page-title {\n -webkit-box-flex: 1;\n flex-grow: 1;\n text-align: center;\n}\n.ag-theme-balham .ag-date-time-list-page-column-label {\n text-align: center;\n}\n.ag-theme-balham .ag-date-time-list-page-entry {\n text-align: center;\n}\n.ag-theme-balham .ag-checkbox-input-wrapper {\n font-family: \"agGridBalham\";\n font-size: 16px;\n line-height: 16px;\n font-style: normal;\n font-weight: normal;\n font-variant: normal;\n text-transform: none;\n /* Better Font Rendering =========== */\n -webkit-font-smoothing: antialiased;\n -moz-osx-font-smoothing: grayscale;\n width: 16px;\n height: 16px;\n background-color: white;\n background-color: var(--ag-checkbox-background-color, var(--ag-background-color, white));\n border-radius: 3px;\n display: inline-block;\n vertical-align: middle;\n -webkit-box-flex: 0;\n flex: none;\n}\n.ag-theme-balham .ag-checkbox-input-wrapper input, .ag-theme-balham .ag-checkbox-input-wrapper input {\n -webkit-appearance: none;\n opacity: 0;\n width: 100%;\n height: 100%;\n}\n.ag-theme-balham .ag-checkbox-input-wrapper:focus-within, .ag-theme-balham .ag-checkbox-input-wrapper:active {\n outline: none;\n -webkit-box-shadow: 0 0 2px 1px #719ECE;\n box-shadow: 0 0 2px 1px #719ECE;\n}\n.ag-theme-balham .ag-checkbox-input-wrapper.ag-disabled {\n opacity: 0.5;\n}\n.ag-theme-balham .ag-checkbox-input-wrapper::after {\n content: \"\\f108\";\n color: #7f8c8d;\n color: var(--ag-checkbox-unchecked-color, #7f8c8d);\n position: absolute;\n top: 0;\n left: 0;\n pointer-events: none;\n}\n.ag-theme-balham .ag-checkbox-input-wrapper.ag-checked::after {\n content: \"\\f106\";\n color: #0091ea;\n color: var(--ag-checkbox-checked-color, var(--ag-balham-active-color, #0091ea));\n position: absolute;\n top: 0;\n left: 0;\n pointer-events: none;\n}\n.ag-theme-balham .ag-checkbox-input-wrapper.ag-indeterminate::after {\n content: \"\\f107\";\n color: #7f8c8d;\n color: var(--ag-checkbox-indeterminate-color, var(--ag-checkbox-unchecked-color, #7f8c8d));\n position: absolute;\n top: 0;\n left: 0;\n pointer-events: none;\n}\n.ag-theme-balham .ag-toggle-button-input-wrapper {\n -webkit-box-sizing: border-box;\n box-sizing: border-box;\n width: 32px;\n height: 16px;\n background-color: #7f8c8d;\n background-color: var(--ag-toggle-button-off-background-color, var(--ag-checkbox-unchecked-color, #7f8c8d));\n border-radius: 8px;\n position: relative;\n -webkit-box-flex: 0;\n flex: none;\n border: 1px solid;\n border-color: #7f8c8d;\n border-color: var(--ag-toggle-button-off-border-color, var(--ag-checkbox-unchecked-color, #7f8c8d));\n}\n.ag-theme-balham .ag-toggle-button-input-wrapper input {\n opacity: 0;\n height: 100%;\n width: 100%;\n}\n.ag-theme-balham .ag-toggle-button-input-wrapper:focus-within {\n outline: none;\n -webkit-box-shadow: 0 0 2px 1px #719ECE;\n box-shadow: 0 0 2px 1px #719ECE;\n}\n.ag-theme-balham .ag-toggle-button-input-wrapper.ag-disabled {\n opacity: 0.5;\n}\n.ag-theme-balham .ag-toggle-button-input-wrapper.ag-checked {\n background-color: #0091ea;\n background-color: var(--ag-toggle-button-on-background-color, var(--ag-checkbox-checked-color, var(--ag-balham-active-color, #0091ea)));\n border-color: #0091ea;\n border-color: var(--ag-toggle-button-on-border-color, var(--ag-checkbox-checked-color, var(--ag-balham-active-color, #0091ea)));\n}\n.ag-theme-balham .ag-toggle-button-input-wrapper::before {\n content: \" \";\n position: absolute;\n top: -1px;\n left: -1px;\n display: block;\n -webkit-box-sizing: border-box;\n box-sizing: border-box;\n height: 16px;\n width: 16px;\n background-color: white;\n background-color: var(--ag-toggle-button-switch-background-color, var(--ag-background-color, white));\n border-radius: 8px;\n -webkit-transition: left 100ms;\n transition: left 100ms;\n border: 1px solid;\n border-color: #7f8c8d;\n border-color: var(--ag-toggle-button-switch-border-color, var(--ag-toggle-button-off-border-color, var(--ag-checkbox-unchecked-color, #7f8c8d)));\n}\n.ag-theme-balham .ag-toggle-button-input-wrapper.ag-checked::before {\n left: calc(100% - 16px );\n border-color: #0091ea;\n border-color: var(--ag-toggle-button-on-border-color, var(--ag-checkbox-checked-color, var(--ag-balham-active-color, #0091ea)));\n}\n.ag-theme-balham .ag-radio-button-input-wrapper {\n font-family: \"agGridBalham\";\n font-size: 16px;\n line-height: 16px;\n font-style: normal;\n font-weight: normal;\n font-variant: normal;\n text-transform: none;\n /* Better Font Rendering =========== */\n -webkit-font-smoothing: antialiased;\n -moz-osx-font-smoothing: grayscale;\n width: 16px;\n height: 16px;\n background-color: white;\n background-color: var(--ag-checkbox-background-color, var(--ag-background-color, white));\n border-radius: 3px;\n display: inline-block;\n vertical-align: middle;\n -webkit-box-flex: 0;\n flex: none;\n border-radius: 16px;\n}\n.ag-theme-balham .ag-radio-button-input-wrapper input, .ag-theme-balham .ag-radio-button-input-wrapper input {\n -webkit-appearance: none;\n opacity: 0;\n width: 100%;\n height: 100%;\n}\n.ag-theme-balham .ag-radio-button-input-wrapper:focus-within, .ag-theme-balham .ag-radio-button-input-wrapper:active {\n outline: none;\n -webkit-box-shadow: 0 0 2px 1px #719ECE;\n box-shadow: 0 0 2px 1px #719ECE;\n}\n.ag-theme-balham .ag-radio-button-input-wrapper.ag-disabled {\n opacity: 0.5;\n}\n.ag-theme-balham .ag-radio-button-input-wrapper::after {\n content: \"\\f126\";\n color: #7f8c8d;\n color: var(--ag-checkbox-unchecked-color, #7f8c8d);\n position: absolute;\n top: 0;\n left: 0;\n pointer-events: none;\n}\n.ag-theme-balham .ag-radio-button-input-wrapper.ag-checked::after {\n content: \"\\f127\";\n color: #0091ea;\n color: var(--ag-checkbox-checked-color, var(--ag-balham-active-color, #0091ea));\n position: absolute;\n top: 0;\n left: 0;\n pointer-events: none;\n}\n.ag-theme-balham input[class^=ag-][type=range] {\n -webkit-appearance: none;\n width: 100%;\n height: 100%;\n background: none;\n overflow: visible;\n}\n.ag-theme-balham input[class^=ag-][type=range]::-webkit-slider-runnable-track {\n margin: 0;\n padding: 0;\n width: 100%;\n height: 3px;\n background-color: #bdc3c7;\n background-color: var(--ag-border-color, #bdc3c7);\n border-radius: 2px;\n border-radius: 3px;\n}\n.ag-theme-balham input[class^=ag-][type=range]::-moz-range-track {\n margin: 0;\n padding: 0;\n width: 100%;\n height: 3px;\n background-color: #bdc3c7;\n background-color: var(--ag-border-color, #bdc3c7);\n border-radius: 2px;\n border-radius: 3px;\n}\n.ag-theme-balham input[class^=ag-][type=range]::-ms-track {\n margin: 0;\n padding: 0;\n width: 100%;\n height: 3px;\n background-color: #bdc3c7;\n background-color: var(--ag-border-color, #bdc3c7);\n border-radius: 2px;\n border-radius: 3px;\n color: transparent;\n width: calc(100% - 2px);\n}\n.ag-theme-balham input[class^=ag-][type=range]::-webkit-slider-thumb {\n margin: 0;\n padding: 0;\n -webkit-appearance: none;\n width: 16px;\n height: 16px;\n background-color: white;\n background-color: var(--ag-background-color, white);\n border: 1px solid;\n border-color: #7f8c8d;\n border-color: var(--ag-checkbox-unchecked-color, #7f8c8d);\n border-radius: 16px;\n -webkit-transform: translateY(-6.5px);\n transform: translateY(-6.5px);\n}\n.ag-theme-balham input[class^=ag-][type=range]::-ms-thumb {\n margin: 0;\n padding: 0;\n -webkit-appearance: none;\n width: 16px;\n height: 16px;\n background-color: white;\n background-color: var(--ag-background-color, white);\n border: 1px solid;\n border-color: #7f8c8d;\n border-color: var(--ag-checkbox-unchecked-color, #7f8c8d);\n border-radius: 16px;\n}\n.ag-theme-balham input[class^=ag-][type=range]::-moz-ag-range-thumb {\n margin: 0;\n padding: 0;\n -webkit-appearance: none;\n width: 16px;\n height: 16px;\n background-color: white;\n background-color: var(--ag-background-color, white);\n border: 1px solid;\n border-color: #7f8c8d;\n border-color: var(--ag-checkbox-unchecked-color, #7f8c8d);\n border-radius: 16px;\n}\n.ag-theme-balham input[class^=ag-][type=range]:focus {\n outline: none;\n}\n.ag-theme-balham input[class^=ag-][type=range]:focus::-webkit-slider-thumb {\n -webkit-box-shadow: 0 0 2px 1px #719ECE;\n box-shadow: 0 0 2px 1px #719ECE;\n border-color: #0091ea;\n border-color: var(--ag-checkbox-checked-color, var(--ag-balham-active-color, #0091ea));\n}\n.ag-theme-balham input[class^=ag-][type=range]:focus::-ms-thumb {\n box-shadow: 0 0 2px 1px #719ECE;\n border-color: #0091ea;\n border-color: var(--ag-checkbox-checked-color, var(--ag-balham-active-color, #0091ea));\n}\n.ag-theme-balham input[class^=ag-][type=range]:focus::-moz-ag-range-thumb {\n box-shadow: 0 0 2px 1px #719ECE;\n border-color: #0091ea;\n border-color: var(--ag-checkbox-checked-color, var(--ag-balham-active-color, #0091ea));\n}\n.ag-theme-balham input[class^=ag-][type=range]:active::-webkit-slider-runnable-track {\n background-color: #719ECE;\n background-color: var(--ag-input-focus-border-color, #719ECE);\n}\n.ag-theme-balham input[class^=ag-][type=range]:active::-moz-ag-range-track {\n background-color: #719ECE;\n background-color: var(--ag-input-focus-border-color, #719ECE);\n}\n.ag-theme-balham input[class^=ag-][type=range]:active::-ms-track {\n background-color: #719ECE;\n background-color: var(--ag-input-focus-border-color, #719ECE);\n}\n.ag-theme-balham input[class^=ag-][type=range]:disabled {\n opacity: 0.5;\n}\n.ag-theme-balham .ag-filter-toolpanel-header,\n.ag-theme-balham .ag-filter-toolpanel-search,\n.ag-theme-balham .ag-status-bar,\n.ag-theme-balham .ag-header-row,\n.ag-theme-balham .ag-multi-filter-group-title-bar {\n font-weight: 600;\n color: rgba(0, 0, 0, 0.54);\n color: var(--ag-header-foreground-color, var(--ag-secondary-foreground-color, rgba(0, 0, 0, 0.54)));\n}\n.ag-theme-balham .ag-ltr input[class^=ag-]:not([type]),\n.ag-theme-balham .ag-ltr input[class^=ag-][type=text],\n.ag-theme-balham .ag-ltr input[class^=ag-][type=number],\n.ag-theme-balham .ag-ltr input[class^=ag-][type=tel],\n.ag-theme-balham .ag-ltr input[class^=ag-][type=date],\n.ag-theme-balham .ag-ltr input[class^=ag-][type=datetime-local],\n.ag-theme-balham .ag-ltr textarea[class^=ag-] {\n padding-left: 4px;\n}\n\n.ag-theme-balham .ag-rtl input[class^=ag-]:not([type]),\n.ag-theme-balham .ag-rtl input[class^=ag-][type=text],\n.ag-theme-balham .ag-rtl input[class^=ag-][type=number],\n.ag-theme-balham .ag-rtl input[class^=ag-][type=tel],\n.ag-theme-balham .ag-rtl input[class^=ag-][type=date],\n.ag-theme-balham .ag-rtl input[class^=ag-][type=datetime-local],\n.ag-theme-balham .ag-rtl textarea[class^=ag-] {\n padding-right: 4px;\n}\n\n.ag-theme-balham .ag-column-drop-vertical-empty-message, .ag-theme-balham .ag-status-bar {\n font-weight: 600;\n color: rgba(0, 0, 0, 0.38);\n color: var(--ag-disabled-foreground-color, rgba(0, 0, 0, 0.38));\n}\n.ag-theme-balham .ag-dnd-ghost {\n font-weight: 600;\n}\n.ag-theme-balham .ag-tab {\n border: 1px solid transparent;\n padding: 4px 8px;\n margin: 4px;\n margin-bottom: -1px;\n}\n.ag-theme-balham .ag-tab-selected {\n background-color: white;\n background-color: var(--ag-background-color, white);\n border-color: #bdc3c7;\n border-color: var(--ag-border-color, #bdc3c7);\n border-bottom-color: transparent;\n}\n.ag-theme-balham .ag-tabs-header {\n border-bottom: 1px solid;\n border-bottom-color: #bdc3c7;\n border-bottom-color: var(--ag-border-color, #bdc3c7);\n}\n.ag-theme-balham .ag-column-drop-cell {\n height: 24px;\n}\n.ag-theme-balham .ag-column-drop-vertical-title {\n color: #000;\n color: var(--ag-foreground-color, #000);\n}\n.ag-theme-balham .ag-column-drop-vertical-cell {\n margin-left: 8px;\n margin-right: 8px;\n}\n.ag-theme-balham .ag-column-drop-vertical-cell-text {\n margin-left: 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.rc-image-preview-body {\n bottom: 0;\n left: 0;\n overflow: hidden;\n position: absolute;\n right: 0;\n top: 0;\n}\n\n.mlflow-ui-container .rc-image-preview-img {\n cursor: grab;\n max-height: 100%;\n max-width: 100%;\n pointer-events: auto;\n transform: scaleX(1);\n -webkit-user-select: none;\n -moz-user-select: none;\n -ms-user-select: none;\n user-select: none;\n vertical-align: middle;\n}\n\n.mlflow-ui-container .rc-image-preview-img,\n.mlflow-ui-container .rc-image-preview-img-wrapper {\n transition: transform 0.3s cubic-bezier(0.215, 0.61, 0.355, 1) 0s;\n}\n\n.mlflow-ui-container .rc-image-preview-img-wrapper {\n bottom: 0;\n left: 0;\n position: absolute;\n right: 0;\n top: 0;\n}\n\n.mlflow-ui-container .rc-image-preview-img-wrapper:before {\n content: '';\n display: inline-block;\n height: 50%;\n margin-right: -1px;\n width: 1px;\n}\n\n.mlflow-ui-container .rc-image-preview-moving .mlflow-ui-container .rc-image-preview-img {\n cursor: grabbing;\n}\n\n.mlflow-ui-container .rc-image-preview-moving .mlflow-ui-container .rc-image-preview-img-wrapper {\n transition-duration: 0s;\n}\n\n.mlflow-ui-container .rc-image-preview-wrap {\n z-index: 1080;\n}\n\n.mlflow-ui-container .rc-image-preview-operations {\n font-feature-settings: 'tnum', 'tnum';\n align-items: center;\n background: rgba(0, 0, 0, 0.1);\n box-sizing: border-box;\n color: rgba(0, 0, 0, 0.85);\n color: hsla(0, 0%, 100%, 0.85);\n display: flex;\n flex-direction: row-reverse;\n font-size: 14px;\n font-variant: tabular-nums;\n line-height: 1.5715;\n list-style: none;\n margin: 0;\n padding: 0;\n pointer-events: auto;\n position: absolute;\n right: 0;\n top: 0;\n width: 100%;\n z-index: 1;\n}\n\n.mlflow-ui-container .rc-image-preview-operations-operation {\n cursor: pointer;\n margin-left: 12px;\n padding: 12px;\n}\n\n.mlflow-ui-container .rc-image-preview-operations-operation-disabled {\n color: hsla(0, 0%, 100%, 0.25);\n pointer-events: none;\n}\n\n.mlflow-ui-container .rc-image-preview-operations-operation:last-of-type {\n margin-left: 0;\n}\n\n.mlflow-ui-container .rc-image-preview-operations-icon {\n font-size: 18px;\n}\n\n.mlflow-ui-container .rc-image-preview-switch-left,\n.mlflow-ui-container .rc-image-preview-switch-right {\n align-items: center;\n background: rgba(0, 0, 0, 0.1);\n border-radius: 50%;\n color: hsla(0, 0%, 100%, 0.85);\n cursor: pointer;\n display: flex;\n height: 44px;\n justify-content: center;\n margin-top: -22px;\n pointer-events: auto;\n position: absolute;\n right: 10px;\n top: 50%;\n width: 44px;\n z-index: 1;\n}\n\n.mlflow-ui-container .rc-image-preview-switch-left-disabled,\n.mlflow-ui-container .rc-image-preview-switch-right-disabled {\n color: hsla(0, 0%, 100%, 0.25);\n cursor: not-allowed;\n}\n\n.mlflow-ui-container .rc-image-preview-switch-left-disabled > .anticon,\n.mlflow-ui-container .rc-image-preview-switch-right-disabled > .anticon {\n cursor: not-allowed;\n}\n\n.mlflow-ui-container .rc-image-preview-switch-left > .anticon,\n.mlflow-ui-container .rc-image-preview-switch-right > .anticon {\n font-size: 18px;\n}\n\n.mlflow-ui-container .rc-image-preview-switch-left {\n left: 10px;\n}\n\n.mlflow-ui-container .rc-image-preview-switch-right {\n right: 10px;\n}\n\n.mlflow-ui-container .fade-enter,\n.mlflow-ui-container .fade-appear {\n animation-duration: 0.3s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.mlflow-ui-container .fade-leave {\n animation-duration: 0.3s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.mlflow-ui-container .fade-enter.fade-enter-active,\n.mlflow-ui-container .fade-appear.fade-appear-active {\n animation-name: mlflow-rcImageFadeIn;\n animation-play-state: running;\n}\n.mlflow-ui-container .fade-leave.fade-leave-active {\n animation-name: mlflow-rcImageFadeOut;\n animation-play-state: running;\n pointer-events: none;\n}\n.mlflow-ui-container .fade-enter,\n.mlflow-ui-container .fade-appear {\n opacity: 0;\n animation-timing-function: linear;\n}\n.mlflow-ui-container .fade-leave {\n animation-timing-function: linear;\n}\n\n@keyframes mlflow-rcImageFadeIn {\n 0% {\n opacity: 0;\n }\n 100% {\n opacity: 1;\n }\n}\n\n@keyframes mlflow-rcImageFadeOut {\n 0% {\n opacity: 1;\n }\n 100% {\n opacity: 0;\n }\n}\n\n.mlflow-ui-container .zoom-enter,\n.mlflow-ui-container .zoom-appear {\n animation-duration: 0.3s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.mlflow-ui-container .zoom-leave {\n animation-duration: 0.3s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.mlflow-ui-container .zoom-enter.zoom-enter-active,\n.mlflow-ui-container .zoom-appear.zoom-appear-active {\n animation-name: mlflow-rcImageZoomIn;\n animation-play-state: running;\n}\n.mlflow-ui-container .zoom-leave.zoom-leave-active {\n animation-name: mlflow-rcImageZoomOut;\n animation-play-state: running;\n pointer-events: none;\n}\n.mlflow-ui-container .zoom-enter,\n.mlflow-ui-container .zoom-appear {\n transform: scale(0);\n opacity: 0;\n animation-timing-function: cubic-bezier(0.08, 0.82, 0.17, 1);\n}\n.mlflow-ui-container .zoom-leave {\n animation-timing-function: cubic-bezier(0.78, 0.14, 0.15, 0.86);\n}\n\n@keyframes mlflow-rcImageZoomIn {\n 0% {\n transform: scale(0.2);\n opacity: 0;\n }\n 100% {\n transform: scale(1);\n opacity: 1;\n }\n}\n\n@keyframes mlflow-rcImageZoomOut {\n 0% {\n transform: scale(1);\n }\n 100% {\n transform: scale(0.2);\n opacity: 0;\n }\n}\n","/* Overriding the table styles since antd tables take the full screen by default.\nWe would like to change it to auto to automatically grow based on the columns */\n\n.mlflow-html-table-view table {\n width: auto;\n min-width: 400px;\n}\n\n.mlflow-html-table-view th {\n width: auto;\n min-width: 200px;\n margin-right: 80px;\n font-size: 13px;\n color: #888;\n}\n",".mde-header {\n flex-shrink: 0;\n display: flex;\n flex-wrap: wrap;\n align-items: stretch;\n border-bottom: 1px solid #c8ccd0;\n border-radius: 2px 2px 0 0;\n background: #f9f9f9; }\n .mde-header .mde-tabs {\n display: flex;\n flex-direction: row; }\n .mde-header .mde-tabs button {\n border-radius: 2px;\n margin: 6px 3px;\n background-color: transparent;\n border: 1px solid transparent;\n cursor: pointer; }\n .mde-header .mde-tabs button:first-child {\n margin-left: 6px; }\n .mde-header .mde-tabs button.selected {\n border: 1px solid #c8ccd0; }\n .mde-header .svg-icon {\n width: 1em;\n height: 1em;\n display: inline-block;\n font-size: inherit;\n overflow: visible;\n vertical-align: -.125em; }\n .mde-header ul.mde-header-group {\n margin: 0;\n padding: 10px;\n list-style: none;\n display: flex;\n flex-wrap: nowrap; }\n .mde-header ul.mde-header-group.hidden {\n visibility: hidden; }\n .mde-header ul.mde-header-group li.mde-header-item {\n display: inline-block;\n position: relative;\n margin: 0 4px; }\n .mde-header ul.mde-header-group li.mde-header-item button {\n text-align: left;\n cursor: pointer;\n height: 22px;\n padding: 4px;\n margin: 0;\n border: none;\n background: none;\n color: #242729; }\n\n@keyframes tooltip-appear {\n from {\n opacity: 0; }\n to {\n opacity: 1; } }\n .mde-header ul.mde-header-group li.mde-header-item button.tooltipped:hover::before {\n animation-name: tooltip-appear;\n animation-duration: 0.2s;\n animation-delay: 0.5s;\n animation-fill-mode: forwards;\n opacity: 0;\n position: absolute;\n z-index: 1000001;\n width: 0;\n height: 0;\n color: rgba(0, 0, 0, 0.8);\n pointer-events: none;\n content: \"\";\n border: 5px solid transparent;\n top: -5px;\n right: 50%;\n bottom: auto;\n margin-right: -5px;\n border-top-color: rgba(0, 0, 0, 0.8); }\n .mde-header ul.mde-header-group li.mde-header-item button.tooltipped:hover::after {\n animation-name: tooltip-appear;\n animation-duration: 0.2s;\n animation-delay: 0.5s;\n animation-fill-mode: forwards;\n font-size: 11px;\n opacity: 0;\n position: absolute;\n z-index: 1000000;\n padding: 5px 8px;\n color: #fff;\n 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pointer-events: auto;\n position: absolute;\n right: 0;\n top: 0;\n width: 100%;\n z-index: 1;\n}\n\n.mlflow-ui-container .rc-image-preview-operations-operation {\n cursor: pointer;\n margin-left: 12px;\n padding: 12px;\n}\n\n.mlflow-ui-container .rc-image-preview-operations-operation-disabled {\n color: hsla(0, 0%, 100%, 0.25);\n pointer-events: none;\n}\n\n.mlflow-ui-container .rc-image-preview-operations-operation:last-of-type {\n margin-left: 0;\n}\n\n.mlflow-ui-container .rc-image-preview-operations-icon {\n font-size: 18px;\n}\n\n.mlflow-ui-container .rc-image-preview-switch-left,\n.mlflow-ui-container .rc-image-preview-switch-right {\n align-items: center;\n background: rgba(0, 0, 0, 0.1);\n border-radius: 50%;\n color: hsla(0, 0%, 100%, 0.85);\n cursor: pointer;\n display: flex;\n height: 44px;\n justify-content: center;\n margin-top: -22px;\n pointer-events: auto;\n position: absolute;\n right: 10px;\n top: 50%;\n width: 44px;\n z-index: 1;\n}\n\n.mlflow-ui-container 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.ant-select-dropdown-menu-item-group-title {\n color: #666;\n font-weight: bold;\n}\n\n.mlflow-model-select-dropdown .mlflow-create-new-model-option {\n border-top: 1px solid #ccc;\n}\n\n.mlflow-register-model-form .modal-explanatory-text {\n color: rgba(0, 0, 0, 0.52);\n font-size: 13px;\n}\n","/* Replaceing AntD Image */\n.mlflow-ui-container .rc-image-preview {\n height: 100%;\n pointer-events: none;\n text-align: center;\n}\n\n.mlflow-ui-container .rc-image-preview-mask {\n position: fixed;\n top: 0;\n left: 0;\n right: 0;\n bottom: 0;\n height: 100%;\n background-color: rgba(0, 0, 0, 0.45);\n z-index: 1000;\n}\n\n.mlflow-ui-container .rc-image-preview-mask img {\n max-width: 100%;\n max-height: 100%;\n position: absolute;\n top: 50%;\n left: 50%;\n transform: translate(-50%, -50%);\n}\n\n.mlflow-ui-container .rc-image-preview-mask {\n background-color: rgba(0, 0, 0, 0.45);\n bottom: 0;\n height: 100%;\n left: 0;\n position: fixed;\n right: 0;\n top: 0;\n z-index: 1000;\n}\n\n.mlflow-ui-container .rc-image-preview-mask-hidden {\n display: none;\n}\n\n.mlflow-ui-container .rc-image-preview-wrap {\n -webkit-overflow-scrolling: touch;\n bottom: 0;\n left: 0;\n outline: 0;\n overflow: auto;\n position: fixed;\n right: 0;\n top: 0px;\n}\n\n.mlflow-ui-container .rc-image-preview-body {\n bottom: 0;\n left: 0;\n overflow: hidden;\n position: absolute;\n right: 0;\n top: 0;\n}\n\n.mlflow-ui-container .rc-image-preview-img {\n cursor: grab;\n max-height: 100%;\n max-width: 100%;\n pointer-events: auto;\n transform: scaleX(1);\n -webkit-user-select: none;\n -moz-user-select: none;\n -ms-user-select: none;\n user-select: none;\n vertical-align: middle;\n}\n\n.mlflow-ui-container .rc-image-preview-img,\n.mlflow-ui-container .rc-image-preview-img-wrapper {\n transition: transform 0.3s cubic-bezier(0.215, 0.61, 0.355, 1) 0s;\n}\n\n.mlflow-ui-container .rc-image-preview-img-wrapper {\n bottom: 0;\n left: 0;\n position: absolute;\n right: 0;\n top: 0;\n}\n\n.mlflow-ui-container .rc-image-preview-img-wrapper:before {\n content: '';\n display: inline-block;\n height: 50%;\n margin-right: -1px;\n width: 1px;\n}\n\n.mlflow-ui-container .rc-image-preview-moving .mlflow-ui-container .rc-image-preview-img {\n cursor: grabbing;\n}\n\n.mlflow-ui-container .rc-image-preview-moving .mlflow-ui-container .rc-image-preview-img-wrapper {\n transition-duration: 0s;\n}\n\n.mlflow-ui-container .rc-image-preview-wrap {\n z-index: 1080;\n}\n\n.mlflow-ui-container .rc-image-preview-operations {\n font-feature-settings: 'tnum', 'tnum';\n align-items: center;\n background: rgba(0, 0, 0, 0.1);\n box-sizing: border-box;\n color: rgba(0, 0, 0, 0.85);\n color: hsla(0, 0%, 100%, 0.85);\n display: flex;\n flex-direction: row-reverse;\n font-size: 14px;\n font-variant: tabular-nums;\n line-height: 1.5715;\n list-style: none;\n margin: 0;\n padding: 0;\n pointer-events: auto;\n position: absolute;\n right: 0;\n top: 0;\n width: 100%;\n z-index: 1;\n}\n\n.mlflow-ui-container .rc-image-preview-operations-operation {\n cursor: pointer;\n margin-left: 12px;\n padding: 12px;\n}\n\n.mlflow-ui-container .rc-image-preview-operations-operation-disabled {\n color: hsla(0, 0%, 100%, 0.25);\n pointer-events: none;\n}\n\n.mlflow-ui-container .rc-image-preview-operations-operation:last-of-type {\n margin-left: 0;\n}\n\n.mlflow-ui-container .rc-image-preview-operations-icon {\n font-size: 18px;\n}\n\n.mlflow-ui-container .rc-image-preview-switch-left,\n.mlflow-ui-container .rc-image-preview-switch-right {\n align-items: center;\n background: rgba(0, 0, 0, 0.1);\n border-radius: 50%;\n color: hsla(0, 0%, 100%, 0.85);\n cursor: pointer;\n display: flex;\n height: 44px;\n justify-content: center;\n margin-top: -22px;\n pointer-events: auto;\n position: absolute;\n right: 10px;\n top: 50%;\n width: 44px;\n z-index: 1;\n}\n\n.mlflow-ui-container .rc-image-preview-switch-left-disabled,\n.mlflow-ui-container .rc-image-preview-switch-right-disabled {\n color: hsla(0, 0%, 100%, 0.25);\n cursor: not-allowed;\n}\n\n.mlflow-ui-container .rc-image-preview-switch-left-disabled > .anticon,\n.mlflow-ui-container .rc-image-preview-switch-right-disabled > .anticon {\n cursor: not-allowed;\n}\n\n.mlflow-ui-container .rc-image-preview-switch-left > .anticon,\n.mlflow-ui-container .rc-image-preview-switch-right > .anticon {\n font-size: 18px;\n}\n\n.mlflow-ui-container .rc-image-preview-switch-left {\n left: 10px;\n}\n\n.mlflow-ui-container .rc-image-preview-switch-right {\n right: 10px;\n}\n\n.mlflow-ui-container .fade-enter,\n.mlflow-ui-container .fade-appear {\n animation-duration: 0.3s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.mlflow-ui-container .fade-leave {\n animation-duration: 0.3s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.mlflow-ui-container .fade-enter.fade-enter-active,\n.mlflow-ui-container .fade-appear.fade-appear-active {\n animation-name: mlflow-rcImageFadeIn;\n animation-play-state: running;\n}\n.mlflow-ui-container .fade-leave.fade-leave-active {\n animation-name: mlflow-rcImageFadeOut;\n animation-play-state: running;\n pointer-events: none;\n}\n.mlflow-ui-container .fade-enter,\n.mlflow-ui-container .fade-appear {\n opacity: 0;\n animation-timing-function: linear;\n}\n.mlflow-ui-container .fade-leave {\n animation-timing-function: linear;\n}\n\n@keyframes mlflow-rcImageFadeIn {\n 0% {\n opacity: 0;\n }\n 100% {\n opacity: 1;\n }\n}\n\n@keyframes mlflow-rcImageFadeOut {\n 0% {\n opacity: 1;\n }\n 100% {\n opacity: 0;\n }\n}\n\n.mlflow-ui-container .zoom-enter,\n.mlflow-ui-container .zoom-appear {\n animation-duration: 0.3s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.mlflow-ui-container .zoom-leave {\n animation-duration: 0.3s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.mlflow-ui-container .zoom-enter.zoom-enter-active,\n.mlflow-ui-container .zoom-appear.zoom-appear-active {\n animation-name: mlflow-rcImageZoomIn;\n animation-play-state: running;\n}\n.mlflow-ui-container .zoom-leave.zoom-leave-active {\n animation-name: mlflow-rcImageZoomOut;\n animation-play-state: running;\n pointer-events: none;\n}\n.mlflow-ui-container .zoom-enter,\n.mlflow-ui-container .zoom-appear {\n transform: scale(0);\n opacity: 0;\n animation-timing-function: cubic-bezier(0.08, 0.82, 0.17, 1);\n}\n.mlflow-ui-container .zoom-leave {\n animation-timing-function: cubic-bezier(0.78, 0.14, 0.15, 0.86);\n}\n\n@keyframes mlflow-rcImageZoomIn {\n 0% {\n transform: scale(0.2);\n opacity: 0;\n }\n 100% {\n transform: scale(1);\n opacity: 1;\n }\n}\n\n@keyframes mlflow-rcImageZoomOut {\n 0% {\n transform: scale(1);\n }\n 100% {\n transform: scale(0.2);\n opacity: 0;\n }\n}\n",".mlflow-ShowArtifactPage .text-area {\n box-sizing: border-box;\n width: 100%;\n height: 100%;\n font-family: Menlo, Consolas, monospace;\n}\n\n.mlflow-ShowArtifactPage,\n.mlflow-ShowArtifactPage .text-area-border-box {\n width: 100%;\n height: 100%;\n overflow: hidden;\n}\n",".mlflow-html-iframe {\n border: none;\n}\n\n.mlflow-artifact-html-view {\n width: 100%;\n height: 100%;\n overflow: auto;\n}\n","/* Styles for antd `copyable` code snippets */\n\n.mlflow-ui-container .code-keyword {\n color: rgb(204, 120, 50);\n}\n.mlflow-ui-container .code {\n color: rgb(100, 110, 120);\n}\n.mlflow-ui-container .code-comment {\n color: rgb(140, 140, 140);\n}\n.mlflow-ui-container .code-string {\n color: rgb(106, 165, 89);\n}\n.mlflow-ui-container .code-number {\n color: rgb(104, 151, 187);\n}\n",".mlflow-show-artifact-logged-model-view {\n width: 100%;\n height: 100%;\n overflow: auto;\n}\n","div.mlflow-artifact-view {\n display: flex;\n overflow: hidden;\n}\n\n.mlflow-artifact-left {\n min-width: 200px;\n max-width: 400px;\n flex: 1;\n}\n\n.mlflow-artifact-left li {\n white-space: nowrap;\n}\n\n.mlflow-artifact-right {\n flex: 3;\n min-width: 400px;\n max-width: calc(100% - 200px); /* 200px is the min-width of .mlflow-artifact-left */\n\n overflow: hidden;\n display: flex;\n flex-direction: column;\n height: 100%;\n}\n\n.mlflow-artifact-info-left {\n flex: 1;\n max-width: 75%;\n}\n.mlflow-artifact-info-right {\n margin-left: auto;\n}\n\n.mlflow-artifact-info-path {\n display: flex;\n align-items: center;\n}\n\n.mlflow-artifact-info-text {\n min-width: 0;\n}\n\n.mlflow-artifact-info-size {\n overflow: hidden;\n text-overflow: ellipsis;\n}\n\n.mlflow-loading-spinner {\n height: 20px;\n opacity: 0;\n -webkit-animation: spin 3s linear infinite;\n -moz-animation: spin 3s linear infinite;\n animation: spin 3s linear infinite;\n}\n\n.mlflow-artifact-info-right .model-version-link {\n display: flex;\n align-items: baseline;\n max-width: 140px;\n padding-top: 1px;\n padding-left: 4px;\n}\n\n.mlflow-artifact-info-right .model-version-link .model-name {\n overflow: hidden;\n text-overflow: ellipsis;\n}\n\n.mlflow-artifact-info-right .model-version-info {\n font-size: 12px;\n}\n\n.mlflow-artifact-info-right .model-version-info .model-version-link-section {\n display: flex;\n align-items: center;\n}\n\n.mlflow-artifact-info-right .model-version-info .model-version-status-text {\n overflow: hidden;\n max-width: 160px;\n text-overflow: ellipsis;\n}\n",".mde-header {\n flex-shrink: 0;\n display: flex;\n flex-wrap: wrap;\n align-items: stretch;\n border-bottom: 1px solid #c8ccd0;\n border-radius: 2px 2px 0 0;\n background: #f9f9f9; }\n .mde-header .mde-tabs {\n display: flex;\n flex-direction: row; }\n .mde-header .mde-tabs button {\n border-radius: 2px;\n margin: 6px 3px;\n background-color: transparent;\n border: 1px solid transparent;\n cursor: pointer; }\n .mde-header .mde-tabs button:first-child {\n margin-left: 6px; }\n .mde-header .mde-tabs button.selected {\n border: 1px solid #c8ccd0; }\n .mde-header .svg-icon {\n width: 1em;\n height: 1em;\n display: inline-block;\n font-size: inherit;\n overflow: visible;\n vertical-align: -.125em; }\n .mde-header ul.mde-header-group {\n margin: 0;\n padding: 10px;\n list-style: none;\n display: flex;\n flex-wrap: nowrap; }\n .mde-header ul.mde-header-group.hidden {\n visibility: hidden; }\n .mde-header ul.mde-header-group li.mde-header-item {\n display: inline-block;\n position: relative;\n margin: 0 4px; }\n .mde-header ul.mde-header-group li.mde-header-item button {\n text-align: left;\n cursor: pointer;\n height: 22px;\n padding: 4px;\n margin: 0;\n border: none;\n background: none;\n color: #242729; }\n\n@keyframes tooltip-appear {\n from {\n opacity: 0; }\n to {\n opacity: 1; } }\n .mde-header ul.mde-header-group li.mde-header-item button.tooltipped:hover::before {\n animation-name: tooltip-appear;\n animation-duration: 0.2s;\n animation-delay: 0.5s;\n animation-fill-mode: forwards;\n opacity: 0;\n position: absolute;\n z-index: 1000001;\n width: 0;\n height: 0;\n color: rgba(0, 0, 0, 0.8);\n pointer-events: none;\n content: \"\";\n border: 5px solid transparent;\n top: -5px;\n 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paused;\n}\n.du-bois-light-move-up-enter.du-bois-light-move-up-enter-active,\n.du-bois-light-move-up-appear.du-bois-light-move-up-appear-active {\n animation-name: antMoveUpIn;\n animation-play-state: running;\n}\n.du-bois-light-move-up-leave.du-bois-light-move-up-leave-active {\n animation-name: antMoveUpOut;\n animation-play-state: running;\n pointer-events: none;\n}\n.du-bois-light-move-up-enter,\n.du-bois-light-move-up-appear {\n opacity: 0;\n animation-timing-function: cubic-bezier(0.08, 0.82, 0.17, 1);\n}\n.du-bois-light-move-up-leave {\n animation-timing-function: cubic-bezier(0.6, 0.04, 0.98, 0.34);\n}\n.du-bois-light-move-down-enter,\n.du-bois-light-move-down-appear {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-light-move-down-leave {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-light-move-down-enter.du-bois-light-move-down-enter-active,\n.du-bois-light-move-down-appear.du-bois-light-move-down-appear-active {\n animation-name: antMoveDownIn;\n animation-play-state: running;\n}\n.du-bois-light-move-down-leave.du-bois-light-move-down-leave-active {\n animation-name: antMoveDownOut;\n animation-play-state: running;\n pointer-events: none;\n}\n.du-bois-light-move-down-enter,\n.du-bois-light-move-down-appear {\n opacity: 0;\n animation-timing-function: cubic-bezier(0.08, 0.82, 0.17, 1);\n}\n.du-bois-light-move-down-leave {\n animation-timing-function: cubic-bezier(0.6, 0.04, 0.98, 0.34);\n}\n.du-bois-light-move-left-enter,\n.du-bois-light-move-left-appear {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-light-move-left-leave {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-light-move-left-enter.du-bois-light-move-left-enter-active,\n.du-bois-light-move-left-appear.du-bois-light-move-left-appear-active {\n animation-name: antMoveLeftIn;\n animation-play-state: running;\n}\n.du-bois-light-move-left-leave.du-bois-light-move-left-leave-active {\n animation-name: antMoveLeftOut;\n animation-play-state: running;\n pointer-events: none;\n}\n.du-bois-light-move-left-enter,\n.du-bois-light-move-left-appear {\n opacity: 0;\n animation-timing-function: cubic-bezier(0.08, 0.82, 0.17, 1);\n}\n.du-bois-light-move-left-leave {\n animation-timing-function: cubic-bezier(0.6, 0.04, 0.98, 0.34);\n}\n.du-bois-light-move-right-enter,\n.du-bois-light-move-right-appear {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-light-move-right-leave {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-light-move-right-enter.du-bois-light-move-right-enter-active,\n.du-bois-light-move-right-appear.du-bois-light-move-right-appear-active {\n animation-name: antMoveRightIn;\n animation-play-state: running;\n}\n.du-bois-light-move-right-leave.du-bois-light-move-right-leave-active {\n animation-name: antMoveRightOut;\n animation-play-state: running;\n pointer-events: none;\n}\n.du-bois-light-move-right-enter,\n.du-bois-light-move-right-appear {\n opacity: 0;\n animation-timing-function: cubic-bezier(0.08, 0.82, 0.17, 1);\n}\n.du-bois-light-move-right-leave {\n animation-timing-function: cubic-bezier(0.6, 0.04, 0.98, 0.34);\n}\n@keyframes antMoveDownIn {\n 0% {\n transform: translateY(100%);\n transform-origin: 0 0;\n opacity: 0;\n }\n 100% {\n transform: translateY(0%);\n transform-origin: 0 0;\n opacity: 1;\n }\n}\n@keyframes antMoveDownOut {\n 0% {\n transform: translateY(0%);\n transform-origin: 0 0;\n opacity: 1;\n }\n 100% {\n transform: translateY(100%);\n transform-origin: 0 0;\n opacity: 0;\n }\n}\n@keyframes antMoveLeftIn {\n 0% {\n transform: translateX(-100%);\n transform-origin: 0 0;\n opacity: 0;\n }\n 100% {\n transform: translateX(0%);\n transform-origin: 0 0;\n opacity: 1;\n }\n}\n@keyframes antMoveLeftOut {\n 0% {\n transform: translateX(0%);\n transform-origin: 0 0;\n opacity: 1;\n }\n 100% {\n transform: translateX(-100%);\n transform-origin: 0 0;\n opacity: 0;\n }\n}\n@keyframes antMoveRightIn {\n 0% {\n transform: translateX(100%);\n transform-origin: 0 0;\n opacity: 0;\n }\n 100% {\n transform: translateX(0%);\n transform-origin: 0 0;\n opacity: 1;\n }\n}\n@keyframes antMoveRightOut {\n 0% {\n transform: translateX(0%);\n transform-origin: 0 0;\n opacity: 1;\n }\n 100% {\n transform: translateX(100%);\n transform-origin: 0 0;\n opacity: 0;\n }\n}\n@keyframes antMoveUpIn {\n 0% {\n transform: translateY(-100%);\n transform-origin: 0 0;\n opacity: 0;\n }\n 100% {\n transform: translateY(0%);\n transform-origin: 0 0;\n opacity: 1;\n }\n}\n@keyframes antMoveUpOut {\n 0% {\n transform: translateY(0%);\n transform-origin: 0 0;\n opacity: 1;\n }\n 100% {\n transform: translateY(-100%);\n transform-origin: 0 0;\n opacity: 0;\n }\n}\n@keyframes loadingCircle {\n 100% {\n transform: rotate(360deg);\n }\n}\n[du-bois-light-click-animating='true'],\n[du-bois-light-click-animating-without-extra-node='true'] {\n position: relative;\n}\n[du-bois-light-click-animating-without-extra-node='true']::after,\n.du-bois-light-click-animating-node {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n display: block;\n border-radius: inherit;\n box-shadow: 0 0 0 0 #2272B4;\n box-shadow: 0 0 0 0 var(--antd-wave-shadow-color);\n opacity: 0.2;\n animation: fadeEffect 2s cubic-bezier(0.08, 0.82, 0.17, 1), waveEffect 0.4s cubic-bezier(0.08, 0.82, 0.17, 1);\n animation-fill-mode: forwards;\n content: '';\n pointer-events: none;\n}\n@keyframes waveEffect {\n 100% {\n box-shadow: 0 0 0 #2272B4;\n box-shadow: 0 0 0 6px var(--antd-wave-shadow-color);\n }\n}\n@keyframes fadeEffect {\n 100% {\n opacity: 0;\n }\n}\n.du-bois-light-slide-up-enter,\n.du-bois-light-slide-up-appear {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-light-slide-up-leave {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-light-slide-up-enter.du-bois-light-slide-up-enter-active,\n.du-bois-light-slide-up-appear.du-bois-light-slide-up-appear-active {\n animation-name: antSlideUpIn;\n animation-play-state: running;\n}\n.du-bois-light-slide-up-leave.du-bois-light-slide-up-leave-active {\n animation-name: antSlideUpOut;\n animation-play-state: running;\n pointer-events: none;\n}\n.du-bois-light-slide-up-enter,\n.du-bois-light-slide-up-appear {\n opacity: 0;\n animation-timing-function: cubic-bezier(0.23, 1, 0.32, 1);\n}\n.du-bois-light-slide-up-leave {\n animation-timing-function: cubic-bezier(0.755, 0.05, 0.855, 0.06);\n}\n.du-bois-light-slide-down-enter,\n.du-bois-light-slide-down-appear {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-light-slide-down-leave {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-light-slide-down-enter.du-bois-light-slide-down-enter-active,\n.du-bois-light-slide-down-appear.du-bois-light-slide-down-appear-active {\n animation-name: antSlideDownIn;\n animation-play-state: running;\n}\n.du-bois-light-slide-down-leave.du-bois-light-slide-down-leave-active {\n animation-name: antSlideDownOut;\n animation-play-state: running;\n pointer-events: none;\n}\n.du-bois-light-slide-down-enter,\n.du-bois-light-slide-down-appear {\n opacity: 0;\n animation-timing-function: cubic-bezier(0.23, 1, 0.32, 1);\n}\n.du-bois-light-slide-down-leave {\n animation-timing-function: cubic-bezier(0.755, 0.05, 0.855, 0.06);\n}\n.du-bois-light-slide-left-enter,\n.du-bois-light-slide-left-appear {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-light-slide-left-leave {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-light-slide-left-enter.du-bois-light-slide-left-enter-active,\n.du-bois-light-slide-left-appear.du-bois-light-slide-left-appear-active {\n animation-name: antSlideLeftIn;\n animation-play-state: running;\n}\n.du-bois-light-slide-left-leave.du-bois-light-slide-left-leave-active {\n animation-name: antSlideLeftOut;\n animation-play-state: running;\n pointer-events: none;\n}\n.du-bois-light-slide-left-enter,\n.du-bois-light-slide-left-appear {\n opacity: 0;\n animation-timing-function: cubic-bezier(0.23, 1, 0.32, 1);\n}\n.du-bois-light-slide-left-leave {\n animation-timing-function: cubic-bezier(0.755, 0.05, 0.855, 0.06);\n}\n.du-bois-light-slide-right-enter,\n.du-bois-light-slide-right-appear {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-light-slide-right-leave {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-light-slide-right-enter.du-bois-light-slide-right-enter-active,\n.du-bois-light-slide-right-appear.du-bois-light-slide-right-appear-active {\n animation-name: antSlideRightIn;\n animation-play-state: running;\n}\n.du-bois-light-slide-right-leave.du-bois-light-slide-right-leave-active {\n animation-name: antSlideRightOut;\n animation-play-state: running;\n pointer-events: none;\n}\n.du-bois-light-slide-right-enter,\n.du-bois-light-slide-right-appear {\n opacity: 0;\n animation-timing-function: cubic-bezier(0.23, 1, 0.32, 1);\n}\n.du-bois-light-slide-right-leave {\n animation-timing-function: cubic-bezier(0.755, 0.05, 0.855, 0.06);\n}\n@keyframes antSlideUpIn {\n 0% {\n transform: scaleY(0.8);\n transform-origin: 0% 0%;\n opacity: 0;\n }\n 100% {\n transform: scaleY(1);\n transform-origin: 0% 0%;\n opacity: 1;\n }\n}\n@keyframes antSlideUpOut {\n 0% {\n transform: scaleY(1);\n transform-origin: 0% 0%;\n opacity: 1;\n }\n 100% {\n transform: scaleY(0.8);\n transform-origin: 0% 0%;\n opacity: 0;\n }\n}\n@keyframes antSlideDownIn {\n 0% {\n transform: scaleY(0.8);\n transform-origin: 100% 100%;\n opacity: 0;\n }\n 100% {\n transform: scaleY(1);\n transform-origin: 100% 100%;\n opacity: 1;\n }\n}\n@keyframes antSlideDownOut {\n 0% {\n transform: scaleY(1);\n transform-origin: 100% 100%;\n opacity: 1;\n }\n 100% {\n transform: scaleY(0.8);\n transform-origin: 100% 100%;\n opacity: 0;\n }\n}\n@keyframes antSlideLeftIn {\n 0% {\n transform: scaleX(0.8);\n transform-origin: 0% 0%;\n opacity: 0;\n }\n 100% {\n transform: scaleX(1);\n transform-origin: 0% 0%;\n opacity: 1;\n }\n}\n@keyframes antSlideLeftOut {\n 0% {\n transform: scaleX(1);\n transform-origin: 0% 0%;\n opacity: 1;\n }\n 100% {\n transform: scaleX(0.8);\n transform-origin: 0% 0%;\n opacity: 0;\n }\n}\n@keyframes antSlideRightIn {\n 0% {\n transform: scaleX(0.8);\n transform-origin: 100% 0%;\n opacity: 0;\n }\n 100% {\n transform: scaleX(1);\n transform-origin: 100% 0%;\n opacity: 1;\n }\n}\n@keyframes antSlideRightOut {\n 0% {\n transform: scaleX(1);\n transform-origin: 100% 0%;\n opacity: 1;\n }\n 100% {\n transform: scaleX(0.8);\n transform-origin: 100% 0%;\n opacity: 0;\n }\n}\n.du-bois-light-zoom-enter,\n.du-bois-light-zoom-appear {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-light-zoom-leave {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-light-zoom-enter.du-bois-light-zoom-enter-active,\n.du-bois-light-zoom-appear.du-bois-light-zoom-appear-active {\n animation-name: antZoomIn;\n animation-play-state: running;\n}\n.du-bois-light-zoom-leave.du-bois-light-zoom-leave-active {\n animation-name: antZoomOut;\n animation-play-state: running;\n pointer-events: none;\n}\n.du-bois-light-zoom-enter,\n.du-bois-light-zoom-appear {\n transform: scale(0);\n opacity: 0;\n animation-timing-function: cubic-bezier(0.08, 0.82, 0.17, 1);\n}\n.du-bois-light-zoom-enter-prepare,\n.du-bois-light-zoom-appear-prepare {\n transform: none;\n}\n.du-bois-light-zoom-leave {\n animation-timing-function: cubic-bezier(0.78, 0.14, 0.15, 0.86);\n}\n.du-bois-light-zoom-big-enter,\n.du-bois-light-zoom-big-appear {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-light-zoom-big-leave {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-light-zoom-big-enter.du-bois-light-zoom-big-enter-active,\n.du-bois-light-zoom-big-appear.du-bois-light-zoom-big-appear-active {\n animation-name: antZoomBigIn;\n animation-play-state: running;\n}\n.du-bois-light-zoom-big-leave.du-bois-light-zoom-big-leave-active {\n animation-name: antZoomBigOut;\n animation-play-state: running;\n pointer-events: none;\n}\n.du-bois-light-zoom-big-enter,\n.du-bois-light-zoom-big-appear {\n transform: scale(0);\n opacity: 0;\n animation-timing-function: cubic-bezier(0.08, 0.82, 0.17, 1);\n}\n.du-bois-light-zoom-big-enter-prepare,\n.du-bois-light-zoom-big-appear-prepare {\n transform: none;\n}\n.du-bois-light-zoom-big-leave {\n animation-timing-function: cubic-bezier(0.78, 0.14, 0.15, 0.86);\n}\n.du-bois-light-zoom-big-fast-enter,\n.du-bois-light-zoom-big-fast-appear {\n animation-duration: 0.1s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-light-zoom-big-fast-leave {\n animation-duration: 0.1s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-light-zoom-big-fast-enter.du-bois-light-zoom-big-fast-enter-active,\n.du-bois-light-zoom-big-fast-appear.du-bois-light-zoom-big-fast-appear-active {\n animation-name: antZoomBigIn;\n animation-play-state: running;\n}\n.du-bois-light-zoom-big-fast-leave.du-bois-light-zoom-big-fast-leave-active {\n animation-name: antZoomBigOut;\n animation-play-state: running;\n pointer-events: none;\n}\n.du-bois-light-zoom-big-fast-enter,\n.du-bois-light-zoom-big-fast-appear {\n transform: scale(0);\n opacity: 0;\n animation-timing-function: cubic-bezier(0.08, 0.82, 0.17, 1);\n}\n.du-bois-light-zoom-big-fast-enter-prepare,\n.du-bois-light-zoom-big-fast-appear-prepare {\n transform: none;\n}\n.du-bois-light-zoom-big-fast-leave {\n animation-timing-function: cubic-bezier(0.78, 0.14, 0.15, 0.86);\n}\n.du-bois-light-zoom-up-enter,\n.du-bois-light-zoom-up-appear {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-light-zoom-up-leave {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-light-zoom-up-enter.du-bois-light-zoom-up-enter-active,\n.du-bois-light-zoom-up-appear.du-bois-light-zoom-up-appear-active {\n animation-name: antZoomUpIn;\n animation-play-state: running;\n}\n.du-bois-light-zoom-up-leave.du-bois-light-zoom-up-leave-active {\n animation-name: antZoomUpOut;\n animation-play-state: running;\n pointer-events: none;\n}\n.du-bois-light-zoom-up-enter,\n.du-bois-light-zoom-up-appear {\n transform: scale(0);\n opacity: 0;\n animation-timing-function: cubic-bezier(0.08, 0.82, 0.17, 1);\n}\n.du-bois-light-zoom-up-enter-prepare,\n.du-bois-light-zoom-up-appear-prepare {\n transform: none;\n}\n.du-bois-light-zoom-up-leave {\n animation-timing-function: cubic-bezier(0.78, 0.14, 0.15, 0.86);\n}\n.du-bois-light-zoom-down-enter,\n.du-bois-light-zoom-down-appear {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-light-zoom-down-leave {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-light-zoom-down-enter.du-bois-light-zoom-down-enter-active,\n.du-bois-light-zoom-down-appear.du-bois-light-zoom-down-appear-active {\n animation-name: antZoomDownIn;\n animation-play-state: running;\n}\n.du-bois-light-zoom-down-leave.du-bois-light-zoom-down-leave-active {\n animation-name: antZoomDownOut;\n animation-play-state: running;\n pointer-events: none;\n}\n.du-bois-light-zoom-down-enter,\n.du-bois-light-zoom-down-appear {\n transform: scale(0);\n opacity: 0;\n animation-timing-function: cubic-bezier(0.08, 0.82, 0.17, 1);\n}\n.du-bois-light-zoom-down-enter-prepare,\n.du-bois-light-zoom-down-appear-prepare {\n transform: none;\n}\n.du-bois-light-zoom-down-leave {\n animation-timing-function: cubic-bezier(0.78, 0.14, 0.15, 0.86);\n}\n.du-bois-light-zoom-left-enter,\n.du-bois-light-zoom-left-appear {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-light-zoom-left-leave {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-light-zoom-left-enter.du-bois-light-zoom-left-enter-active,\n.du-bois-light-zoom-left-appear.du-bois-light-zoom-left-appear-active {\n animation-name: antZoomLeftIn;\n animation-play-state: running;\n}\n.du-bois-light-zoom-left-leave.du-bois-light-zoom-left-leave-active {\n animation-name: antZoomLeftOut;\n animation-play-state: running;\n pointer-events: none;\n}\n.du-bois-light-zoom-left-enter,\n.du-bois-light-zoom-left-appear {\n transform: scale(0);\n opacity: 0;\n animation-timing-function: cubic-bezier(0.08, 0.82, 0.17, 1);\n}\n.du-bois-light-zoom-left-enter-prepare,\n.du-bois-light-zoom-left-appear-prepare {\n transform: none;\n}\n.du-bois-light-zoom-left-leave {\n animation-timing-function: cubic-bezier(0.78, 0.14, 0.15, 0.86);\n}\n.du-bois-light-zoom-right-enter,\n.du-bois-light-zoom-right-appear {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-light-zoom-right-leave {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-light-zoom-right-enter.du-bois-light-zoom-right-enter-active,\n.du-bois-light-zoom-right-appear.du-bois-light-zoom-right-appear-active {\n animation-name: antZoomRightIn;\n animation-play-state: running;\n}\n.du-bois-light-zoom-right-leave.du-bois-light-zoom-right-leave-active {\n animation-name: antZoomRightOut;\n animation-play-state: running;\n pointer-events: none;\n}\n.du-bois-light-zoom-right-enter,\n.du-bois-light-zoom-right-appear {\n transform: scale(0);\n opacity: 0;\n animation-timing-function: cubic-bezier(0.08, 0.82, 0.17, 1);\n}\n.du-bois-light-zoom-right-enter-prepare,\n.du-bois-light-zoom-right-appear-prepare {\n transform: none;\n}\n.du-bois-light-zoom-right-leave {\n animation-timing-function: cubic-bezier(0.78, 0.14, 0.15, 0.86);\n}\n@keyframes antZoomIn {\n 0% {\n transform: scale(0.2);\n opacity: 0;\n }\n 100% {\n transform: scale(1);\n opacity: 1;\n }\n}\n@keyframes antZoomOut {\n 0% {\n transform: scale(1);\n }\n 100% {\n transform: scale(0.2);\n opacity: 0;\n }\n}\n@keyframes antZoomBigIn {\n 0% {\n transform: scale(0.8);\n opacity: 0;\n }\n 100% {\n transform: scale(1);\n opacity: 1;\n }\n}\n@keyframes antZoomBigOut {\n 0% {\n transform: scale(1);\n }\n 100% {\n transform: scale(0.8);\n opacity: 0;\n }\n}\n@keyframes antZoomUpIn {\n 0% {\n transform: scale(0.8);\n transform-origin: 50% 0%;\n opacity: 0;\n }\n 100% {\n transform: scale(1);\n transform-origin: 50% 0%;\n }\n}\n@keyframes antZoomUpOut {\n 0% {\n transform: scale(1);\n transform-origin: 50% 0%;\n }\n 100% {\n transform: scale(0.8);\n transform-origin: 50% 0%;\n opacity: 0;\n }\n}\n@keyframes antZoomLeftIn {\n 0% {\n transform: scale(0.8);\n transform-origin: 0% 50%;\n opacity: 0;\n }\n 100% {\n transform: scale(1);\n transform-origin: 0% 50%;\n }\n}\n@keyframes antZoomLeftOut {\n 0% {\n transform: scale(1);\n transform-origin: 0% 50%;\n }\n 100% {\n transform: scale(0.8);\n transform-origin: 0% 50%;\n opacity: 0;\n }\n}\n@keyframes antZoomRightIn {\n 0% {\n transform: scale(0.8);\n transform-origin: 100% 50%;\n opacity: 0;\n }\n 100% {\n transform: scale(1);\n transform-origin: 100% 50%;\n }\n}\n@keyframes antZoomRightOut {\n 0% {\n transform: scale(1);\n transform-origin: 100% 50%;\n }\n 100% {\n transform: scale(0.8);\n transform-origin: 100% 50%;\n opacity: 0;\n }\n}\n@keyframes antZoomDownIn {\n 0% {\n transform: scale(0.8);\n transform-origin: 50% 100%;\n opacity: 0;\n }\n 100% {\n transform: scale(1);\n transform-origin: 50% 100%;\n }\n}\n@keyframes antZoomDownOut {\n 0% {\n transform: scale(1);\n transform-origin: 50% 100%;\n }\n 100% {\n transform: scale(0.8);\n transform-origin: 50% 100%;\n opacity: 0;\n }\n}\n.ant-motion-collapse-legacy {\n overflow: hidden;\n}\n.ant-motion-collapse-legacy-active {\n transition: height 0.2s cubic-bezier(0.645, 0.045, 0.355, 1), opacity 0.2s cubic-bezier(0.645, 0.045, 0.355, 1) !important;\n}\n.ant-motion-collapse {\n overflow: hidden;\n transition: height 0.2s cubic-bezier(0.645, 0.045, 0.355, 1), opacity 0.2s cubic-bezier(0.645, 0.045, 0.355, 1) !important;\n}\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-light-btn {\n line-height: 20px;\n position: relative;\n display: inline-block;\n font-weight: 400;\n white-space: nowrap;\n text-align: center;\n background-image: none;\n border: 1px solid transparent;\n box-shadow: 0 2px 0 rgba(0, 0, 0, 0.015);\n cursor: pointer;\n transition: all 0.3s cubic-bezier(0.645, 0.045, 0.355, 1);\n user-select: none;\n touch-action: manipulation;\n height: 40px;\n padding: 0 16px;\n font-size: 13px;\n border-radius: 4px;\n color: #11171C;\n background: #fff;\n border-color: #E8ECF0;\n}\n.du-bois-light-btn > .anticon {\n line-height: 1;\n}\n.du-bois-light-btn,\n.du-bois-light-btn:active,\n.du-bois-light-btn:focus {\n outline: 0;\n}\n.du-bois-light-btn:not([disabled]):hover {\n text-decoration: none;\n}\n.du-bois-light-btn:not([disabled]):active {\n outline: 0;\n box-shadow: none;\n}\n.du-bois-light-btn[disabled] {\n cursor: not-allowed;\n}\n.du-bois-light-btn[disabled] > * {\n pointer-events: none;\n}\n.du-bois-light-btn-lg {\n height: 40px;\n padding: 0 16px;\n font-size: 18px;\n border-radius: 4px;\n}\n.du-bois-light-btn-sm {\n height: 32px;\n padding: 0 16px;\n font-size: 13px;\n border-radius: 4px;\n}\n.du-bois-light-btn > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn:hover,\n.du-bois-light-btn:focus {\n color: #448dc2;\n background: #fff;\n border-color: #448dc2;\n}\n.du-bois-light-btn:hover > a:only-child,\n.du-bois-light-btn:focus > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn:hover > a:only-child::after,\n.du-bois-light-btn:focus > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn:active {\n color: #14538f;\n background: #fff;\n border-color: #14538f;\n}\n.du-bois-light-btn:active > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn[disabled],\n.du-bois-light-btn[disabled]:hover,\n.du-bois-light-btn[disabled]:focus,\n.du-bois-light-btn[disabled]:active {\n color: rgba(0, 0, 0, 0.25);\n background: #f5f5f5;\n border-color: #E8ECF0;\n text-shadow: none;\n box-shadow: none;\n}\n.du-bois-light-btn[disabled] > a:only-child,\n.du-bois-light-btn[disabled]:hover > a:only-child,\n.du-bois-light-btn[disabled]:focus > a:only-child,\n.du-bois-light-btn[disabled]:active > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn[disabled] > a:only-child::after,\n.du-bois-light-btn[disabled]:hover > a:only-child::after,\n.du-bois-light-btn[disabled]:focus > a:only-child::after,\n.du-bois-light-btn[disabled]:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn:hover,\n.du-bois-light-btn:focus,\n.du-bois-light-btn:active {\n text-decoration: none;\n background: #fff;\n}\n.du-bois-light-btn > span {\n display: inline-block;\n}\n.du-bois-light-btn-primary {\n color: #fff;\n background: #2272B4;\n border-color: #2272B4;\n text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.12);\n box-shadow: 0 2px 0 rgba(0, 0, 0, 0.045);\n}\n.du-bois-light-btn-primary > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-primary > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-primary:hover,\n.du-bois-light-btn-primary:focus {\n color: #fff;\n background: #448dc2;\n border-color: #448dc2;\n}\n.du-bois-light-btn-primary:hover > a:only-child,\n.du-bois-light-btn-primary:focus > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-primary:hover > a:only-child::after,\n.du-bois-light-btn-primary:focus > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-primary:active {\n color: #fff;\n background: #14538f;\n border-color: #14538f;\n}\n.du-bois-light-btn-primary:active > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-primary:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-primary[disabled],\n.du-bois-light-btn-primary[disabled]:hover,\n.du-bois-light-btn-primary[disabled]:focus,\n.du-bois-light-btn-primary[disabled]:active {\n color: rgba(0, 0, 0, 0.25);\n background: #f5f5f5;\n border-color: #E8ECF0;\n text-shadow: none;\n box-shadow: none;\n}\n.du-bois-light-btn-primary[disabled] > a:only-child,\n.du-bois-light-btn-primary[disabled]:hover > a:only-child,\n.du-bois-light-btn-primary[disabled]:focus > a:only-child,\n.du-bois-light-btn-primary[disabled]:active > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-primary[disabled] > a:only-child::after,\n.du-bois-light-btn-primary[disabled]:hover > a:only-child::after,\n.du-bois-light-btn-primary[disabled]:focus > a:only-child::after,\n.du-bois-light-btn-primary[disabled]:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-group .du-bois-light-btn-primary:not(:first-child):not(:last-child) {\n border-right-color: #448dc2;\n border-left-color: #448dc2;\n}\n.du-bois-light-btn-group .du-bois-light-btn-primary:not(:first-child):not(:last-child):disabled {\n border-color: #E8ECF0;\n}\n.du-bois-light-btn-group .du-bois-light-btn-primary:first-child:not(:last-child) {\n border-right-color: #448dc2;\n}\n.du-bois-light-btn-group .du-bois-light-btn-primary:first-child:not(:last-child)[disabled] {\n border-right-color: #E8ECF0;\n}\n.du-bois-light-btn-group .du-bois-light-btn-primary:last-child:not(:first-child),\n.du-bois-light-btn-group .du-bois-light-btn-primary + .du-bois-light-btn-primary {\n border-left-color: #448dc2;\n}\n.du-bois-light-btn-group .du-bois-light-btn-primary:last-child:not(:first-child)[disabled],\n.du-bois-light-btn-group .du-bois-light-btn-primary + .du-bois-light-btn-primary[disabled] {\n border-left-color: #E8ECF0;\n}\n.du-bois-light-btn-ghost {\n color: #11171C;\n background: transparent;\n border-color: #E8ECF0;\n}\n.du-bois-light-btn-ghost > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-ghost > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-ghost:hover,\n.du-bois-light-btn-ghost:focus {\n color: #448dc2;\n background: transparent;\n border-color: #448dc2;\n}\n.du-bois-light-btn-ghost:hover > a:only-child,\n.du-bois-light-btn-ghost:focus > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-ghost:hover > a:only-child::after,\n.du-bois-light-btn-ghost:focus > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-ghost:active {\n color: #14538f;\n background: transparent;\n border-color: #14538f;\n}\n.du-bois-light-btn-ghost:active > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-ghost:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-ghost[disabled],\n.du-bois-light-btn-ghost[disabled]:hover,\n.du-bois-light-btn-ghost[disabled]:focus,\n.du-bois-light-btn-ghost[disabled]:active {\n color: rgba(0, 0, 0, 0.25);\n background: #f5f5f5;\n border-color: #E8ECF0;\n text-shadow: none;\n box-shadow: none;\n}\n.du-bois-light-btn-ghost[disabled] > a:only-child,\n.du-bois-light-btn-ghost[disabled]:hover > a:only-child,\n.du-bois-light-btn-ghost[disabled]:focus > a:only-child,\n.du-bois-light-btn-ghost[disabled]:active > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-ghost[disabled] > a:only-child::after,\n.du-bois-light-btn-ghost[disabled]:hover > a:only-child::after,\n.du-bois-light-btn-ghost[disabled]:focus > a:only-child::after,\n.du-bois-light-btn-ghost[disabled]:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-dashed {\n color: #11171C;\n background: #fff;\n border-color: #E8ECF0;\n border-style: dashed;\n}\n.du-bois-light-btn-dashed > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-dashed > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-dashed:hover,\n.du-bois-light-btn-dashed:focus {\n color: #448dc2;\n background: #fff;\n border-color: #448dc2;\n}\n.du-bois-light-btn-dashed:hover > a:only-child,\n.du-bois-light-btn-dashed:focus > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-dashed:hover > a:only-child::after,\n.du-bois-light-btn-dashed:focus > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-dashed:active {\n color: #14538f;\n background: #fff;\n border-color: #14538f;\n}\n.du-bois-light-btn-dashed:active > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-dashed:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-dashed[disabled],\n.du-bois-light-btn-dashed[disabled]:hover,\n.du-bois-light-btn-dashed[disabled]:focus,\n.du-bois-light-btn-dashed[disabled]:active {\n color: rgba(0, 0, 0, 0.25);\n background: #f5f5f5;\n border-color: #E8ECF0;\n text-shadow: none;\n box-shadow: none;\n}\n.du-bois-light-btn-dashed[disabled] > a:only-child,\n.du-bois-light-btn-dashed[disabled]:hover > a:only-child,\n.du-bois-light-btn-dashed[disabled]:focus > a:only-child,\n.du-bois-light-btn-dashed[disabled]:active > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-dashed[disabled] > a:only-child::after,\n.du-bois-light-btn-dashed[disabled]:hover > a:only-child::after,\n.du-bois-light-btn-dashed[disabled]:focus > a:only-child::after,\n.du-bois-light-btn-dashed[disabled]:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-danger {\n color: #fff;\n background: #C82D4C;\n border-color: #C82D4C;\n text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.12);\n box-shadow: 0 2px 0 rgba(0, 0, 0, 0.045);\n}\n.du-bois-light-btn-danger > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-danger > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-danger:hover,\n.du-bois-light-btn-danger:focus {\n color: #fff;\n background: #d45368;\n border-color: #d45368;\n}\n.du-bois-light-btn-danger:hover > a:only-child,\n.du-bois-light-btn-danger:focus > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-danger:hover > a:only-child::after,\n.du-bois-light-btn-danger:focus > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-danger:active {\n color: #fff;\n background: #a11d3c;\n border-color: #a11d3c;\n}\n.du-bois-light-btn-danger:active > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-danger:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-danger[disabled],\n.du-bois-light-btn-danger[disabled]:hover,\n.du-bois-light-btn-danger[disabled]:focus,\n.du-bois-light-btn-danger[disabled]:active {\n color: rgba(0, 0, 0, 0.25);\n background: #f5f5f5;\n border-color: #E8ECF0;\n text-shadow: none;\n box-shadow: none;\n}\n.du-bois-light-btn-danger[disabled] > a:only-child,\n.du-bois-light-btn-danger[disabled]:hover > a:only-child,\n.du-bois-light-btn-danger[disabled]:focus > a:only-child,\n.du-bois-light-btn-danger[disabled]:active > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-danger[disabled] > a:only-child::after,\n.du-bois-light-btn-danger[disabled]:hover > a:only-child::after,\n.du-bois-light-btn-danger[disabled]:focus > a:only-child::after,\n.du-bois-light-btn-danger[disabled]:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-link {\n color: #2272B4;\n background: transparent;\n border-color: transparent;\n box-shadow: none;\n}\n.du-bois-light-btn-link > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-link > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-link:hover,\n.du-bois-light-btn-link:focus {\n color: #448dc2;\n background: transparent;\n border-color: #448dc2;\n}\n.du-bois-light-btn-link:hover > a:only-child,\n.du-bois-light-btn-link:focus > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-link:hover > a:only-child::after,\n.du-bois-light-btn-link:focus > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-link:active {\n color: #14538f;\n background: transparent;\n border-color: #14538f;\n}\n.du-bois-light-btn-link:active > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-link:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-link[disabled],\n.du-bois-light-btn-link[disabled]:hover,\n.du-bois-light-btn-link[disabled]:focus,\n.du-bois-light-btn-link[disabled]:active {\n color: rgba(0, 0, 0, 0.25);\n background: #f5f5f5;\n border-color: #E8ECF0;\n text-shadow: none;\n box-shadow: none;\n}\n.du-bois-light-btn-link[disabled] > a:only-child,\n.du-bois-light-btn-link[disabled]:hover > a:only-child,\n.du-bois-light-btn-link[disabled]:focus > a:only-child,\n.du-bois-light-btn-link[disabled]:active > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-link[disabled] > a:only-child::after,\n.du-bois-light-btn-link[disabled]:hover > a:only-child::after,\n.du-bois-light-btn-link[disabled]:focus > a:only-child::after,\n.du-bois-light-btn-link[disabled]:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-link:hover {\n background: transparent;\n}\n.du-bois-light-btn-link:hover,\n.du-bois-light-btn-link:focus,\n.du-bois-light-btn-link:active {\n border-color: transparent;\n}\n.du-bois-light-btn-link[disabled],\n.du-bois-light-btn-link[disabled]:hover,\n.du-bois-light-btn-link[disabled]:focus,\n.du-bois-light-btn-link[disabled]:active {\n color: rgba(0, 0, 0, 0.25);\n background: transparent;\n border-color: transparent;\n text-shadow: none;\n box-shadow: none;\n}\n.du-bois-light-btn-link[disabled] > a:only-child,\n.du-bois-light-btn-link[disabled]:hover > a:only-child,\n.du-bois-light-btn-link[disabled]:focus > a:only-child,\n.du-bois-light-btn-link[disabled]:active > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-link[disabled] > a:only-child::after,\n.du-bois-light-btn-link[disabled]:hover > a:only-child::after,\n.du-bois-light-btn-link[disabled]:focus > a:only-child::after,\n.du-bois-light-btn-link[disabled]:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-text {\n color: #11171C;\n background: transparent;\n border-color: transparent;\n box-shadow: none;\n}\n.du-bois-light-btn-text > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-text > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-text:hover,\n.du-bois-light-btn-text:focus {\n color: #448dc2;\n background: transparent;\n border-color: #448dc2;\n}\n.du-bois-light-btn-text:hover > a:only-child,\n.du-bois-light-btn-text:focus > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-text:hover > a:only-child::after,\n.du-bois-light-btn-text:focus > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-text:active {\n color: #14538f;\n background: transparent;\n border-color: #14538f;\n}\n.du-bois-light-btn-text:active > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-text:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-text[disabled],\n.du-bois-light-btn-text[disabled]:hover,\n.du-bois-light-btn-text[disabled]:focus,\n.du-bois-light-btn-text[disabled]:active {\n color: rgba(0, 0, 0, 0.25);\n background: #f5f5f5;\n border-color: #E8ECF0;\n text-shadow: none;\n box-shadow: none;\n}\n.du-bois-light-btn-text[disabled] > a:only-child,\n.du-bois-light-btn-text[disabled]:hover > a:only-child,\n.du-bois-light-btn-text[disabled]:focus > a:only-child,\n.du-bois-light-btn-text[disabled]:active > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-text[disabled] > a:only-child::after,\n.du-bois-light-btn-text[disabled]:hover > a:only-child::after,\n.du-bois-light-btn-text[disabled]:focus > a:only-child::after,\n.du-bois-light-btn-text[disabled]:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-text:hover,\n.du-bois-light-btn-text:focus {\n color: #11171C;\n background: rgba(0, 0, 0, 0.018);\n border-color: transparent;\n}\n.du-bois-light-btn-text:active {\n color: #11171C;\n background: rgba(0, 0, 0, 0.028);\n border-color: transparent;\n}\n.du-bois-light-btn-text[disabled],\n.du-bois-light-btn-text[disabled]:hover,\n.du-bois-light-btn-text[disabled]:focus,\n.du-bois-light-btn-text[disabled]:active {\n color: rgba(0, 0, 0, 0.25);\n background: transparent;\n border-color: transparent;\n text-shadow: none;\n box-shadow: none;\n}\n.du-bois-light-btn-text[disabled] > a:only-child,\n.du-bois-light-btn-text[disabled]:hover > a:only-child,\n.du-bois-light-btn-text[disabled]:focus > a:only-child,\n.du-bois-light-btn-text[disabled]:active > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-text[disabled] > a:only-child::after,\n.du-bois-light-btn-text[disabled]:hover > a:only-child::after,\n.du-bois-light-btn-text[disabled]:focus > a:only-child::after,\n.du-bois-light-btn-text[disabled]:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-dangerous {\n color: #C82D4C;\n background: #fff;\n border-color: #C82D4C;\n}\n.du-bois-light-btn-dangerous > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-dangerous > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-dangerous:hover,\n.du-bois-light-btn-dangerous:focus {\n color: #d45368;\n background: #fff;\n border-color: #d45368;\n}\n.du-bois-light-btn-dangerous:hover > a:only-child,\n.du-bois-light-btn-dangerous:focus > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-dangerous:hover > a:only-child::after,\n.du-bois-light-btn-dangerous:focus > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-dangerous:active {\n color: #a11d3c;\n background: #fff;\n border-color: #a11d3c;\n}\n.du-bois-light-btn-dangerous:active > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-dangerous:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-dangerous[disabled],\n.du-bois-light-btn-dangerous[disabled]:hover,\n.du-bois-light-btn-dangerous[disabled]:focus,\n.du-bois-light-btn-dangerous[disabled]:active {\n color: rgba(0, 0, 0, 0.25);\n background: #f5f5f5;\n border-color: #E8ECF0;\n text-shadow: none;\n box-shadow: none;\n}\n.du-bois-light-btn-dangerous[disabled] > a:only-child,\n.du-bois-light-btn-dangerous[disabled]:hover > a:only-child,\n.du-bois-light-btn-dangerous[disabled]:focus > a:only-child,\n.du-bois-light-btn-dangerous[disabled]:active > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-dangerous[disabled] > a:only-child::after,\n.du-bois-light-btn-dangerous[disabled]:hover > a:only-child::after,\n.du-bois-light-btn-dangerous[disabled]:focus > a:only-child::after,\n.du-bois-light-btn-dangerous[disabled]:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-primary {\n color: #fff;\n background: #C82D4C;\n border-color: #C82D4C;\n text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.12);\n box-shadow: 0 2px 0 rgba(0, 0, 0, 0.045);\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-primary > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-primary > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-primary:hover,\n.du-bois-light-btn-dangerous.du-bois-light-btn-primary:focus {\n color: #fff;\n background: #d45368;\n border-color: #d45368;\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-primary:hover > a:only-child,\n.du-bois-light-btn-dangerous.du-bois-light-btn-primary:focus > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-primary:hover > a:only-child::after,\n.du-bois-light-btn-dangerous.du-bois-light-btn-primary:focus > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-primary:active {\n color: #fff;\n background: #a11d3c;\n border-color: #a11d3c;\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-primary:active > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-primary:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-primary[disabled],\n.du-bois-light-btn-dangerous.du-bois-light-btn-primary[disabled]:hover,\n.du-bois-light-btn-dangerous.du-bois-light-btn-primary[disabled]:focus,\n.du-bois-light-btn-dangerous.du-bois-light-btn-primary[disabled]:active {\n color: rgba(0, 0, 0, 0.25);\n background: #f5f5f5;\n border-color: #E8ECF0;\n text-shadow: none;\n box-shadow: none;\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-primary[disabled] > a:only-child,\n.du-bois-light-btn-dangerous.du-bois-light-btn-primary[disabled]:hover > a:only-child,\n.du-bois-light-btn-dangerous.du-bois-light-btn-primary[disabled]:focus > a:only-child,\n.du-bois-light-btn-dangerous.du-bois-light-btn-primary[disabled]:active > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-primary[disabled] > a:only-child::after,\n.du-bois-light-btn-dangerous.du-bois-light-btn-primary[disabled]:hover > a:only-child::after,\n.du-bois-light-btn-dangerous.du-bois-light-btn-primary[disabled]:focus > a:only-child::after,\n.du-bois-light-btn-dangerous.du-bois-light-btn-primary[disabled]:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-link {\n color: #C82D4C;\n background: transparent;\n border-color: transparent;\n box-shadow: none;\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-link > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-link > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-link:hover,\n.du-bois-light-btn-dangerous.du-bois-light-btn-link:focus {\n color: #448dc2;\n background: transparent;\n border-color: #448dc2;\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-link:hover > a:only-child,\n.du-bois-light-btn-dangerous.du-bois-light-btn-link:focus > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-link:hover > a:only-child::after,\n.du-bois-light-btn-dangerous.du-bois-light-btn-link:focus > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-link:active {\n color: #14538f;\n background: transparent;\n border-color: #14538f;\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-link:active > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-link:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-link[disabled],\n.du-bois-light-btn-dangerous.du-bois-light-btn-link[disabled]:hover,\n.du-bois-light-btn-dangerous.du-bois-light-btn-link[disabled]:focus,\n.du-bois-light-btn-dangerous.du-bois-light-btn-link[disabled]:active {\n color: rgba(0, 0, 0, 0.25);\n background: #f5f5f5;\n border-color: #E8ECF0;\n text-shadow: none;\n box-shadow: none;\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-link[disabled] > a:only-child,\n.du-bois-light-btn-dangerous.du-bois-light-btn-link[disabled]:hover > a:only-child,\n.du-bois-light-btn-dangerous.du-bois-light-btn-link[disabled]:focus > a:only-child,\n.du-bois-light-btn-dangerous.du-bois-light-btn-link[disabled]:active > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-link[disabled] > a:only-child::after,\n.du-bois-light-btn-dangerous.du-bois-light-btn-link[disabled]:hover > a:only-child::after,\n.du-bois-light-btn-dangerous.du-bois-light-btn-link[disabled]:focus > a:only-child::after,\n.du-bois-light-btn-dangerous.du-bois-light-btn-link[disabled]:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-link:hover,\n.du-bois-light-btn-dangerous.du-bois-light-btn-link:focus {\n color: #d45368;\n background: transparent;\n border-color: transparent;\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-link:hover > a:only-child,\n.du-bois-light-btn-dangerous.du-bois-light-btn-link:focus > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-link:hover > a:only-child::after,\n.du-bois-light-btn-dangerous.du-bois-light-btn-link:focus > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-link:active {\n color: #a11d3c;\n background: transparent;\n border-color: transparent;\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-link:active > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-link:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-link[disabled],\n.du-bois-light-btn-dangerous.du-bois-light-btn-link[disabled]:hover,\n.du-bois-light-btn-dangerous.du-bois-light-btn-link[disabled]:focus,\n.du-bois-light-btn-dangerous.du-bois-light-btn-link[disabled]:active {\n color: rgba(0, 0, 0, 0.25);\n background: transparent;\n border-color: transparent;\n text-shadow: none;\n box-shadow: none;\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-link[disabled] > a:only-child,\n.du-bois-light-btn-dangerous.du-bois-light-btn-link[disabled]:hover > a:only-child,\n.du-bois-light-btn-dangerous.du-bois-light-btn-link[disabled]:focus > a:only-child,\n.du-bois-light-btn-dangerous.du-bois-light-btn-link[disabled]:active > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-link[disabled] > a:only-child::after,\n.du-bois-light-btn-dangerous.du-bois-light-btn-link[disabled]:hover > a:only-child::after,\n.du-bois-light-btn-dangerous.du-bois-light-btn-link[disabled]:focus > a:only-child::after,\n.du-bois-light-btn-dangerous.du-bois-light-btn-link[disabled]:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-text {\n color: #C82D4C;\n background: transparent;\n border-color: transparent;\n box-shadow: none;\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-text > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-text > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-text:hover,\n.du-bois-light-btn-dangerous.du-bois-light-btn-text:focus {\n color: #448dc2;\n background: transparent;\n border-color: #448dc2;\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-text:hover > a:only-child,\n.du-bois-light-btn-dangerous.du-bois-light-btn-text:focus > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-text:hover > a:only-child::after,\n.du-bois-light-btn-dangerous.du-bois-light-btn-text:focus > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-text:active {\n color: #14538f;\n background: transparent;\n border-color: #14538f;\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-text:active > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-text:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-text[disabled],\n.du-bois-light-btn-dangerous.du-bois-light-btn-text[disabled]:hover,\n.du-bois-light-btn-dangerous.du-bois-light-btn-text[disabled]:focus,\n.du-bois-light-btn-dangerous.du-bois-light-btn-text[disabled]:active {\n color: rgba(0, 0, 0, 0.25);\n background: #f5f5f5;\n border-color: #E8ECF0;\n text-shadow: none;\n box-shadow: none;\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-text[disabled] > a:only-child,\n.du-bois-light-btn-dangerous.du-bois-light-btn-text[disabled]:hover > a:only-child,\n.du-bois-light-btn-dangerous.du-bois-light-btn-text[disabled]:focus > a:only-child,\n.du-bois-light-btn-dangerous.du-bois-light-btn-text[disabled]:active > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-text[disabled] > a:only-child::after,\n.du-bois-light-btn-dangerous.du-bois-light-btn-text[disabled]:hover > a:only-child::after,\n.du-bois-light-btn-dangerous.du-bois-light-btn-text[disabled]:focus > a:only-child::after,\n.du-bois-light-btn-dangerous.du-bois-light-btn-text[disabled]:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-text:hover,\n.du-bois-light-btn-dangerous.du-bois-light-btn-text:focus {\n color: #d45368;\n background: rgba(0, 0, 0, 0.018);\n border-color: transparent;\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-text:hover > a:only-child,\n.du-bois-light-btn-dangerous.du-bois-light-btn-text:focus > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-text:hover > a:only-child::after,\n.du-bois-light-btn-dangerous.du-bois-light-btn-text:focus > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-text:active {\n color: #a11d3c;\n background: rgba(0, 0, 0, 0.028);\n border-color: transparent;\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-text:active > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-text:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-text[disabled],\n.du-bois-light-btn-dangerous.du-bois-light-btn-text[disabled]:hover,\n.du-bois-light-btn-dangerous.du-bois-light-btn-text[disabled]:focus,\n.du-bois-light-btn-dangerous.du-bois-light-btn-text[disabled]:active {\n color: rgba(0, 0, 0, 0.25);\n background: transparent;\n border-color: transparent;\n text-shadow: none;\n box-shadow: none;\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-text[disabled] > a:only-child,\n.du-bois-light-btn-dangerous.du-bois-light-btn-text[disabled]:hover > a:only-child,\n.du-bois-light-btn-dangerous.du-bois-light-btn-text[disabled]:focus > a:only-child,\n.du-bois-light-btn-dangerous.du-bois-light-btn-text[disabled]:active > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-text[disabled] > a:only-child::after,\n.du-bois-light-btn-dangerous.du-bois-light-btn-text[disabled]:hover > a:only-child::after,\n.du-bois-light-btn-dangerous.du-bois-light-btn-text[disabled]:focus > a:only-child::after,\n.du-bois-light-btn-dangerous.du-bois-light-btn-text[disabled]:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-icon-only {\n width: 40px;\n height: 40px;\n padding: 0 0;\n font-size: 15px;\n border-radius: 4px;\n vertical-align: -1px;\n}\n.du-bois-light-btn-icon-only > * {\n font-size: 15px;\n}\n.du-bois-light-btn-icon-only.du-bois-light-btn-lg {\n width: 40px;\n height: 40px;\n padding: 0 0;\n font-size: 20px;\n border-radius: 4px;\n}\n.du-bois-light-btn-icon-only.du-bois-light-btn-lg > * {\n font-size: 20px;\n}\n.du-bois-light-btn-icon-only.du-bois-light-btn-sm {\n width: 32px;\n height: 32px;\n padding: 0 0;\n font-size: 13px;\n border-radius: 4px;\n}\n.du-bois-light-btn-icon-only.du-bois-light-btn-sm > * {\n font-size: 13px;\n}\n.du-bois-light-btn-round {\n height: 40px;\n padding: 0 20px;\n font-size: 13px;\n border-radius: 40px;\n}\n.du-bois-light-btn-round.du-bois-light-btn-lg {\n height: 40px;\n padding: 0 20px;\n font-size: 18px;\n border-radius: 40px;\n}\n.du-bois-light-btn-round.du-bois-light-btn-sm {\n height: 32px;\n padding: 0 16px;\n font-size: 13px;\n border-radius: 32px;\n}\n.du-bois-light-btn-round.du-bois-light-btn-icon-only {\n width: auto;\n}\n.du-bois-light-btn-circle {\n min-width: 40px;\n padding-right: 0;\n padding-left: 0;\n text-align: center;\n border-radius: 50%;\n}\n.du-bois-light-btn-circle.du-bois-light-btn-lg {\n min-width: 40px;\n border-radius: 50%;\n}\n.du-bois-light-btn-circle.du-bois-light-btn-sm {\n min-width: 32px;\n border-radius: 50%;\n}\n.du-bois-light-btn::before {\n position: absolute;\n top: -1px;\n right: -1px;\n bottom: -1px;\n left: -1px;\n z-index: 1;\n display: none;\n background: #fff;\n border-radius: inherit;\n opacity: 0.35;\n transition: opacity 0.2s;\n content: '';\n pointer-events: none;\n}\n.du-bois-light-btn .anticon {\n transition: margin-left 0.3s cubic-bezier(0.645, 0.045, 0.355, 1);\n}\n.du-bois-light-btn .anticon.anticon-plus > svg,\n.du-bois-light-btn .anticon.anticon-minus > svg {\n shape-rendering: optimizeSpeed;\n}\n.du-bois-light-btn.du-bois-light-btn-loading {\n position: relative;\n}\n.du-bois-light-btn.du-bois-light-btn-loading:not([disabled]) {\n pointer-events: none;\n}\n.du-bois-light-btn.du-bois-light-btn-loading::before {\n display: block;\n}\n.du-bois-light-btn > .du-bois-light-btn-loading-icon {\n transition: all 0.3s cubic-bezier(0.645, 0.045, 0.355, 1);\n}\n.du-bois-light-btn > .du-bois-light-btn-loading-icon .anticon {\n padding-right: 4px;\n animation: none;\n}\n.du-bois-light-btn > .du-bois-light-btn-loading-icon .anticon svg {\n animation: loadingCircle 1s infinite linear;\n}\n.du-bois-light-btn > .du-bois-light-btn-loading-icon:only-child .anticon {\n padding-right: 0;\n}\n.du-bois-light-btn-group {\n position: relative;\n display: inline-flex;\n}\n.du-bois-light-btn-group > .du-bois-light-btn,\n.du-bois-light-btn-group > span > .du-bois-light-btn {\n position: relative;\n}\n.du-bois-light-btn-group > .du-bois-light-btn:hover,\n.du-bois-light-btn-group > span > .du-bois-light-btn:hover,\n.du-bois-light-btn-group > .du-bois-light-btn:focus,\n.du-bois-light-btn-group > span > .du-bois-light-btn:focus,\n.du-bois-light-btn-group > .du-bois-light-btn:active,\n.du-bois-light-btn-group > span > .du-bois-light-btn:active {\n z-index: 2;\n}\n.du-bois-light-btn-group > .du-bois-light-btn[disabled],\n.du-bois-light-btn-group > span > .du-bois-light-btn[disabled] {\n z-index: 0;\n}\n.du-bois-light-btn-group .du-bois-light-btn-icon-only {\n font-size: 13px;\n}\n.du-bois-light-btn-group-lg > .du-bois-light-btn,\n.du-bois-light-btn-group-lg > span > .du-bois-light-btn {\n height: 40px;\n padding: 0 16px;\n font-size: 18px;\n border-radius: 0;\n}\n.du-bois-light-btn-group-lg .du-bois-light-btn.du-bois-light-btn-icon-only {\n width: 40px;\n height: 40px;\n padding-right: 0;\n padding-left: 0;\n}\n.du-bois-light-btn-group-sm > .du-bois-light-btn,\n.du-bois-light-btn-group-sm > span > .du-bois-light-btn {\n height: 32px;\n padding: 0 16px;\n font-size: 13px;\n border-radius: 0;\n}\n.du-bois-light-btn-group-sm > .du-bois-light-btn > .anticon,\n.du-bois-light-btn-group-sm > span > .du-bois-light-btn > .anticon {\n font-size: 13px;\n}\n.du-bois-light-btn-group-sm .du-bois-light-btn.du-bois-light-btn-icon-only {\n width: 32px;\n height: 32px;\n padding-right: 0;\n padding-left: 0;\n}\n.du-bois-light-btn-group .du-bois-light-btn + .du-bois-light-btn,\n.du-bois-light-btn + .du-bois-light-btn-group,\n.du-bois-light-btn-group span + .du-bois-light-btn,\n.du-bois-light-btn-group .du-bois-light-btn + span,\n.du-bois-light-btn-group > span + span,\n.du-bois-light-btn-group + .du-bois-light-btn,\n.du-bois-light-btn-group + .du-bois-light-btn-group {\n margin-left: -1px;\n}\n.du-bois-light-btn-group .du-bois-light-btn-primary + .du-bois-light-btn:not(.du-bois-light-btn-primary):not([disabled]) {\n border-left-color: transparent;\n}\n.du-bois-light-btn-group .du-bois-light-btn {\n border-radius: 0;\n}\n.du-bois-light-btn-group > .du-bois-light-btn:first-child,\n.du-bois-light-btn-group > span:first-child > .du-bois-light-btn {\n margin-left: 0;\n}\n.du-bois-light-btn-group > .du-bois-light-btn:only-child {\n border-radius: 4px;\n}\n.du-bois-light-btn-group > span:only-child > .du-bois-light-btn {\n border-radius: 4px;\n}\n.du-bois-light-btn-group > .du-bois-light-btn:first-child:not(:last-child),\n.du-bois-light-btn-group > span:first-child:not(:last-child) > .du-bois-light-btn {\n border-top-left-radius: 4px;\n border-bottom-left-radius: 4px;\n}\n.du-bois-light-btn-group > .du-bois-light-btn:last-child:not(:first-child),\n.du-bois-light-btn-group > span:last-child:not(:first-child) > .du-bois-light-btn {\n border-top-right-radius: 4px;\n border-bottom-right-radius: 4px;\n}\n.du-bois-light-btn-group-sm > .du-bois-light-btn:only-child {\n border-radius: 4px;\n}\n.du-bois-light-btn-group-sm > span:only-child > .du-bois-light-btn {\n border-radius: 4px;\n}\n.du-bois-light-btn-group-sm > .du-bois-light-btn:first-child:not(:last-child),\n.du-bois-light-btn-group-sm > span:first-child:not(:last-child) > .du-bois-light-btn {\n border-top-left-radius: 4px;\n border-bottom-left-radius: 4px;\n}\n.du-bois-light-btn-group-sm > .du-bois-light-btn:last-child:not(:first-child),\n.du-bois-light-btn-group-sm > span:last-child:not(:first-child) > .du-bois-light-btn {\n border-top-right-radius: 4px;\n border-bottom-right-radius: 4px;\n}\n.du-bois-light-btn-group > .du-bois-light-btn-group {\n float: left;\n}\n.du-bois-light-btn-group > .du-bois-light-btn-group:not(:first-child):not(:last-child) > .du-bois-light-btn {\n border-radius: 0;\n}\n.du-bois-light-btn-group > .du-bois-light-btn-group:first-child:not(:last-child) > .du-bois-light-btn:last-child {\n padding-right: 8px;\n border-top-right-radius: 0;\n border-bottom-right-radius: 0;\n}\n.du-bois-light-btn-group > .du-bois-light-btn-group:last-child:not(:first-child) > .du-bois-light-btn:first-child {\n padding-left: 8px;\n border-top-left-radius: 0;\n border-bottom-left-radius: 0;\n}\n.du-bois-light-btn:focus > span,\n.du-bois-light-btn:active > span {\n position: relative;\n}\n.du-bois-light-btn > .anticon + span,\n.du-bois-light-btn > span + .anticon {\n margin-left: 8px;\n}\n.du-bois-light-btn-background-ghost {\n color: #fff;\n background: transparent !important;\n border-color: #fff;\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-primary {\n color: #2272B4;\n background: transparent;\n border-color: #2272B4;\n text-shadow: none;\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-primary > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-primary > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-primary:hover,\n.du-bois-light-btn-background-ghost.du-bois-light-btn-primary:focus {\n color: #448dc2;\n background: transparent;\n border-color: #448dc2;\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-primary:hover > a:only-child,\n.du-bois-light-btn-background-ghost.du-bois-light-btn-primary:focus > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-primary:hover > a:only-child::after,\n.du-bois-light-btn-background-ghost.du-bois-light-btn-primary:focus > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-primary:active {\n color: #14538f;\n background: transparent;\n border-color: #14538f;\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-primary:active > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-primary:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-primary[disabled],\n.du-bois-light-btn-background-ghost.du-bois-light-btn-primary[disabled]:hover,\n.du-bois-light-btn-background-ghost.du-bois-light-btn-primary[disabled]:focus,\n.du-bois-light-btn-background-ghost.du-bois-light-btn-primary[disabled]:active {\n color: rgba(0, 0, 0, 0.25);\n background: #f5f5f5;\n border-color: #E8ECF0;\n text-shadow: none;\n box-shadow: none;\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-primary[disabled] > a:only-child,\n.du-bois-light-btn-background-ghost.du-bois-light-btn-primary[disabled]:hover > a:only-child,\n.du-bois-light-btn-background-ghost.du-bois-light-btn-primary[disabled]:focus > a:only-child,\n.du-bois-light-btn-background-ghost.du-bois-light-btn-primary[disabled]:active > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-primary[disabled] > a:only-child::after,\n.du-bois-light-btn-background-ghost.du-bois-light-btn-primary[disabled]:hover > a:only-child::after,\n.du-bois-light-btn-background-ghost.du-bois-light-btn-primary[disabled]:focus > a:only-child::after,\n.du-bois-light-btn-background-ghost.du-bois-light-btn-primary[disabled]:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-danger {\n color: #C82D4C;\n background: transparent;\n border-color: #C82D4C;\n text-shadow: none;\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-danger > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-danger > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-danger:hover,\n.du-bois-light-btn-background-ghost.du-bois-light-btn-danger:focus {\n color: #d45368;\n background: transparent;\n border-color: #d45368;\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-danger:hover > a:only-child,\n.du-bois-light-btn-background-ghost.du-bois-light-btn-danger:focus > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-danger:hover > a:only-child::after,\n.du-bois-light-btn-background-ghost.du-bois-light-btn-danger:focus > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-danger:active {\n color: #a11d3c;\n background: transparent;\n border-color: #a11d3c;\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-danger:active > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-danger:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-danger[disabled],\n.du-bois-light-btn-background-ghost.du-bois-light-btn-danger[disabled]:hover,\n.du-bois-light-btn-background-ghost.du-bois-light-btn-danger[disabled]:focus,\n.du-bois-light-btn-background-ghost.du-bois-light-btn-danger[disabled]:active {\n color: rgba(0, 0, 0, 0.25);\n background: #f5f5f5;\n border-color: #E8ECF0;\n text-shadow: none;\n box-shadow: none;\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-danger[disabled] > a:only-child,\n.du-bois-light-btn-background-ghost.du-bois-light-btn-danger[disabled]:hover > a:only-child,\n.du-bois-light-btn-background-ghost.du-bois-light-btn-danger[disabled]:focus > a:only-child,\n.du-bois-light-btn-background-ghost.du-bois-light-btn-danger[disabled]:active > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-danger[disabled] > a:only-child::after,\n.du-bois-light-btn-background-ghost.du-bois-light-btn-danger[disabled]:hover > a:only-child::after,\n.du-bois-light-btn-background-ghost.du-bois-light-btn-danger[disabled]:focus > a:only-child::after,\n.du-bois-light-btn-background-ghost.du-bois-light-btn-danger[disabled]:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous {\n color: #C82D4C;\n background: transparent;\n border-color: #C82D4C;\n text-shadow: none;\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous:hover,\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous:focus {\n color: #d45368;\n background: transparent;\n border-color: #d45368;\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous:hover > a:only-child,\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous:focus > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous:hover > a:only-child::after,\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous:focus > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous:active {\n color: #a11d3c;\n background: transparent;\n border-color: #a11d3c;\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous:active > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous[disabled],\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous[disabled]:hover,\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous[disabled]:focus,\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous[disabled]:active {\n color: rgba(0, 0, 0, 0.25);\n background: #f5f5f5;\n border-color: #E8ECF0;\n text-shadow: none;\n box-shadow: none;\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous[disabled] > a:only-child,\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous[disabled]:hover > a:only-child,\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous[disabled]:focus > a:only-child,\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous[disabled]:active > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous[disabled] > a:only-child::after,\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous[disabled]:hover > a:only-child::after,\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous[disabled]:focus > a:only-child::after,\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous[disabled]:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous.du-bois-light-btn-link {\n color: #C82D4C;\n background: transparent;\n border-color: transparent;\n text-shadow: none;\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous.du-bois-light-btn-link > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous.du-bois-light-btn-link > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous.du-bois-light-btn-link:hover,\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous.du-bois-light-btn-link:focus {\n color: #d45368;\n background: transparent;\n border-color: transparent;\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous.du-bois-light-btn-link:hover > a:only-child,\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous.du-bois-light-btn-link:focus > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous.du-bois-light-btn-link:hover > a:only-child::after,\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous.du-bois-light-btn-link:focus > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous.du-bois-light-btn-link:active {\n color: #a11d3c;\n background: transparent;\n border-color: transparent;\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous.du-bois-light-btn-link:active > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous.du-bois-light-btn-link:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous.du-bois-light-btn-link[disabled],\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous.du-bois-light-btn-link[disabled]:hover,\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous.du-bois-light-btn-link[disabled]:focus,\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous.du-bois-light-btn-link[disabled]:active {\n color: rgba(0, 0, 0, 0.25);\n background: #f5f5f5;\n border-color: #E8ECF0;\n text-shadow: none;\n box-shadow: none;\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous.du-bois-light-btn-link[disabled] > a:only-child,\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous.du-bois-light-btn-link[disabled]:hover > a:only-child,\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous.du-bois-light-btn-link[disabled]:focus > a:only-child,\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous.du-bois-light-btn-link[disabled]:active > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous.du-bois-light-btn-link[disabled] > a:only-child::after,\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous.du-bois-light-btn-link[disabled]:hover > a:only-child::after,\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous.du-bois-light-btn-link[disabled]:focus > a:only-child::after,\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous.du-bois-light-btn-link[disabled]:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-two-chinese-chars::first-letter {\n letter-spacing: 0.34em;\n}\n.du-bois-light-btn-two-chinese-chars > *:not(.anticon) {\n margin-right: -0.34em;\n letter-spacing: 0.34em;\n}\n.du-bois-light-btn-block {\n width: 100%;\n}\n.du-bois-light-btn:empty {\n display: inline-block;\n width: 0;\n visibility: hidden;\n content: '\\a0';\n}\na.du-bois-light-btn {\n padding-top: 0.01px !important;\n line-height: 38px;\n}\na.du-bois-light-btn-lg {\n line-height: 38px;\n}\na.du-bois-light-btn-sm {\n line-height: 30px;\n}\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-light-alert {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #11171C;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: 'tnum';\n position: relative;\n display: flex;\n align-items: center;\n padding: 8px 15px;\n word-wrap: break-word;\n border-radius: 4px;\n}\n.du-bois-light-alert-content {\n flex: 1;\n min-width: 0;\n}\n.du-bois-light-alert-icon {\n margin-right: 8px;\n}\n.du-bois-light-alert-description {\n display: none;\n font-size: 13px;\n line-height: 21px;\n}\n.du-bois-light-alert-success {\n background-color: #b6c2b8;\n border: 1px solid #94a899;\n}\n.du-bois-light-alert-success .du-bois-light-alert-icon {\n color: #34824F;\n}\n.du-bois-light-alert-info {\n background-color: #F6F7F9;\n border: 1px solid #C0CDD8;\n}\n.du-bois-light-alert-info .du-bois-light-alert-icon {\n color: #5F7281;\n}\n.du-bois-light-alert-warning {\n background-color: #FFF9EB;\n border: 1px solid #F8D4A5;\n}\n.du-bois-light-alert-warning .du-bois-light-alert-icon {\n color: #BE501E;\n}\n.du-bois-light-alert-error {\n background-color: #FFF5F7;\n border: 1px solid #FBD0D8;\n}\n.du-bois-light-alert-error .du-bois-light-alert-icon {\n color: #C82D4C;\n}\n.du-bois-light-alert-error .du-bois-light-alert-description > pre {\n margin: 0;\n padding: 0;\n}\n.du-bois-light-alert-action {\n margin-left: 8px;\n}\n.du-bois-light-alert-close-icon {\n margin-left: 8px;\n padding: 0;\n overflow: hidden;\n font-size: 12px;\n line-height: 12px;\n background-color: transparent;\n border: none;\n outline: none;\n cursor: pointer;\n}\n.du-bois-light-alert-close-icon .anticon-close {\n color: #5F7281;\n transition: color 0.3s;\n}\n.du-bois-light-alert-close-icon .anticon-close:hover {\n color: rgba(0, 0, 0, 0.75);\n}\n.du-bois-light-alert-close-text {\n color: #5F7281;\n transition: color 0.3s;\n}\n.du-bois-light-alert-close-text:hover {\n color: rgba(0, 0, 0, 0.75);\n}\n.du-bois-light-alert-with-description {\n align-items: flex-start;\n padding: 7px 15px 7px 24px;\n}\n.du-bois-light-alert-with-description.du-bois-light-alert-no-icon {\n padding: 7px 15px;\n}\n.du-bois-light-alert-with-description .du-bois-light-alert-icon {\n margin-right: 7px;\n font-size: 24px;\n}\n.du-bois-light-alert-with-description .du-bois-light-alert-message {\n display: block;\n margin-bottom: 4px;\n color: inherit;\n font-size: 18px;\n}\n.du-bois-light-alert-message {\n color: inherit;\n}\n.du-bois-light-alert-with-description .du-bois-light-alert-description {\n display: block;\n}\n.du-bois-light-alert.du-bois-light-alert-motion-leave {\n overflow: hidden;\n opacity: 1;\n transition: max-height 0.3s cubic-bezier(0.78, 0.14, 0.15, 0.86), opacity 0.3s cubic-bezier(0.78, 0.14, 0.15, 0.86), padding-top 0.3s cubic-bezier(0.78, 0.14, 0.15, 0.86), padding-bottom 0.3s cubic-bezier(0.78, 0.14, 0.15, 0.86), margin-bottom 0.3s cubic-bezier(0.78, 0.14, 0.15, 0.86);\n}\n.du-bois-light-alert.du-bois-light-alert-motion-leave-active {\n max-height: 0;\n margin-bottom: 0 !important;\n padding-top: 0;\n padding-bottom: 0;\n opacity: 0;\n}\n.du-bois-light-alert-banner {\n margin-bottom: 0;\n border: 0;\n border-radius: 0;\n}\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n@keyframes antCheckboxEffect {\n 0% {\n transform: scale(1);\n opacity: 0.5;\n }\n 100% {\n transform: scale(1.6);\n opacity: 0;\n }\n}\n.du-bois-light-checkbox {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #11171C;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: 'tnum';\n position: relative;\n top: 0.2em;\n line-height: 1;\n white-space: nowrap;\n outline: none;\n cursor: pointer;\n}\n.du-bois-light-checkbox-wrapper:hover .du-bois-light-checkbox-inner,\n.du-bois-light-checkbox:hover .du-bois-light-checkbox-inner,\n.du-bois-light-checkbox-input:focus + .du-bois-light-checkbox-inner {\n border-color: #2272B4;\n}\n.du-bois-light-checkbox-checked::after {\n position: absolute;\n top: 0;\n left: 0;\n width: 100%;\n height: 100%;\n border: 1px solid #2272B4;\n border-radius: 4px;\n visibility: hidden;\n animation: antCheckboxEffect 0.36s ease-in-out;\n animation-fill-mode: backwards;\n content: '';\n}\n.du-bois-light-checkbox:hover::after,\n.du-bois-light-checkbox-wrapper:hover .du-bois-light-checkbox::after {\n visibility: visible;\n}\n.du-bois-light-checkbox-inner {\n position: relative;\n top: 0;\n left: 0;\n display: block;\n width: 16px;\n height: 16px;\n direction: ltr;\n background-color: #fff;\n border: 1px solid #E8ECF0;\n border-radius: 4px;\n border-collapse: separate;\n transition: all 0.3s;\n}\n.du-bois-light-checkbox-inner::after {\n position: absolute;\n top: 50%;\n left: 22%;\n display: table;\n width: 5.71428571px;\n height: 9.14285714px;\n border: 2px solid #fff;\n border-top: 0;\n border-left: 0;\n transform: rotate(45deg) scale(0) translate(-50%, -50%);\n opacity: 0;\n transition: all 0.1s cubic-bezier(0.71, -0.46, 0.88, 0.6), opacity 0.1s;\n content: ' ';\n}\n.du-bois-light-checkbox-input {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n z-index: 1;\n width: 100%;\n height: 100%;\n cursor: pointer;\n opacity: 0;\n}\n.du-bois-light-checkbox-checked .du-bois-light-checkbox-inner::after {\n position: absolute;\n display: table;\n border: 2px solid #fff;\n border-top: 0;\n border-left: 0;\n transform: rotate(45deg) scale(1) translate(-50%, -50%);\n opacity: 1;\n transition: all 0.2s cubic-bezier(0.12, 0.4, 0.29, 1.46) 0.1s;\n content: ' ';\n}\n.du-bois-light-checkbox-checked .du-bois-light-checkbox-inner {\n background-color: #2272B4;\n border-color: #2272B4;\n}\n.du-bois-light-checkbox-disabled {\n cursor: not-allowed;\n}\n.du-bois-light-checkbox-disabled.du-bois-light-checkbox-checked .du-bois-light-checkbox-inner::after {\n border-color: rgba(0, 0, 0, 0.25);\n animation-name: none;\n}\n.du-bois-light-checkbox-disabled .du-bois-light-checkbox-input {\n cursor: not-allowed;\n}\n.du-bois-light-checkbox-disabled .du-bois-light-checkbox-inner {\n background-color: #f5f5f5;\n border-color: #E8ECF0 !important;\n}\n.du-bois-light-checkbox-disabled .du-bois-light-checkbox-inner::after {\n border-color: #f5f5f5;\n border-collapse: separate;\n animation-name: none;\n}\n.du-bois-light-checkbox-disabled + span {\n color: rgba(0, 0, 0, 0.25);\n cursor: not-allowed;\n}\n.du-bois-light-checkbox-disabled:hover::after,\n.du-bois-light-checkbox-wrapper:hover .du-bois-light-checkbox-disabled::after {\n visibility: hidden;\n}\n.du-bois-light-checkbox-wrapper {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #11171C;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: 'tnum';\n display: inline-flex;\n align-items: baseline;\n line-height: unset;\n cursor: pointer;\n}\n.du-bois-light-checkbox-wrapper::after {\n display: inline-block;\n width: 0;\n overflow: hidden;\n content: '\\a0';\n}\n.du-bois-light-checkbox-wrapper.du-bois-light-checkbox-wrapper-disabled {\n cursor: not-allowed;\n}\n.du-bois-light-checkbox-wrapper + .du-bois-light-checkbox-wrapper {\n margin-left: 8px;\n}\n.du-bois-light-checkbox + span {\n padding-right: 8px;\n padding-left: 8px;\n}\n.du-bois-light-checkbox-group {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #11171C;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: 'tnum';\n display: inline-block;\n}\n.du-bois-light-checkbox-group-item {\n margin-right: 8px;\n}\n.du-bois-light-checkbox-group-item:last-child {\n margin-right: 0;\n}\n.du-bois-light-checkbox-group-item + .du-bois-light-checkbox-group-item {\n margin-left: 0;\n}\n.du-bois-light-checkbox-indeterminate .du-bois-light-checkbox-inner {\n background-color: #fff;\n border-color: #E8ECF0;\n}\n.du-bois-light-checkbox-indeterminate .du-bois-light-checkbox-inner::after {\n top: 50%;\n left: 50%;\n width: 8px;\n height: 8px;\n background-color: #2272B4;\n border: 0;\n transform: translate(-50%, -50%) scale(1);\n opacity: 1;\n content: ' ';\n}\n.du-bois-light-checkbox-indeterminate.du-bois-light-checkbox-disabled .du-bois-light-checkbox-inner::after {\n background-color: rgba(0, 0, 0, 0.25);\n border-color: rgba(0, 0, 0, 0.25);\n}\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-light-select-auto-complete {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #11171C;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: 'tnum';\n}\n.du-bois-light-select-auto-complete .du-bois-light-select-clear {\n right: 13px;\n}\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-light-select-single .du-bois-light-select-selector {\n display: flex;\n}\n.du-bois-light-select-single .du-bois-light-select-selector .du-bois-light-select-selection-search {\n position: absolute;\n top: 0;\n right: 12px;\n bottom: 0;\n left: 12px;\n}\n.du-bois-light-select-single .du-bois-light-select-selector .du-bois-light-select-selection-search-input {\n width: 100%;\n}\n.du-bois-light-select-single .du-bois-light-select-selector .du-bois-light-select-selection-item,\n.du-bois-light-select-single .du-bois-light-select-selector .du-bois-light-select-selection-placeholder {\n padding: 0;\n line-height: 38px;\n transition: all 0.3s;\n}\n@supports (-moz-appearance: meterbar) {\n .du-bois-light-select-single .du-bois-light-select-selector .du-bois-light-select-selection-item,\n .du-bois-light-select-single .du-bois-light-select-selector .du-bois-light-select-selection-placeholder {\n line-height: 38px;\n }\n}\n.du-bois-light-select-single .du-bois-light-select-selector .du-bois-light-select-selection-item {\n position: relative;\n user-select: none;\n}\n.du-bois-light-select-single .du-bois-light-select-selector .du-bois-light-select-selection-placeholder {\n pointer-events: none;\n}\n.du-bois-light-select-single .du-bois-light-select-selector::after,\n.du-bois-light-select-single .du-bois-light-select-selector .du-bois-light-select-selection-item::after,\n.du-bois-light-select-single .du-bois-light-select-selector .du-bois-light-select-selection-placeholder::after {\n display: inline-block;\n width: 0;\n visibility: hidden;\n content: '\\a0';\n}\n.du-bois-light-select-single.du-bois-light-select-show-arrow .du-bois-light-select-selection-search {\n right: 25px;\n}\n.du-bois-light-select-single.du-bois-light-select-show-arrow .du-bois-light-select-selection-item,\n.du-bois-light-select-single.du-bois-light-select-show-arrow .du-bois-light-select-selection-placeholder {\n padding-right: 17px;\n}\n.du-bois-light-select-single.du-bois-light-select-open .du-bois-light-select-selection-item {\n color: #bfbfbf;\n}\n.du-bois-light-select-single:not(.du-bois-light-select-customize-input) .du-bois-light-select-selector {\n width: 100%;\n height: 40px;\n padding: 0 12px;\n}\n.du-bois-light-select-single:not(.du-bois-light-select-customize-input) .du-bois-light-select-selector .du-bois-light-select-selection-search-input {\n height: 38px;\n}\n.du-bois-light-select-single:not(.du-bois-light-select-customize-input) .du-bois-light-select-selector::after {\n line-height: 38px;\n}\n.du-bois-light-select-single.du-bois-light-select-customize-input .du-bois-light-select-selector::after {\n display: none;\n}\n.du-bois-light-select-single.du-bois-light-select-customize-input .du-bois-light-select-selector .du-bois-light-select-selection-search {\n position: static;\n width: 100%;\n}\n.du-bois-light-select-single.du-bois-light-select-customize-input .du-bois-light-select-selector .du-bois-light-select-selection-placeholder {\n position: absolute;\n right: 0;\n left: 0;\n padding: 0 12px;\n}\n.du-bois-light-select-single.du-bois-light-select-customize-input .du-bois-light-select-selector .du-bois-light-select-selection-placeholder::after {\n display: none;\n}\n.du-bois-light-select-single.du-bois-light-select-lg:not(.du-bois-light-select-customize-input) .du-bois-light-select-selector {\n height: 40px;\n}\n.du-bois-light-select-single.du-bois-light-select-lg:not(.du-bois-light-select-customize-input) .du-bois-light-select-selector::after,\n.du-bois-light-select-single.du-bois-light-select-lg:not(.du-bois-light-select-customize-input) .du-bois-light-select-selector .du-bois-light-select-selection-item,\n.du-bois-light-select-single.du-bois-light-select-lg:not(.du-bois-light-select-customize-input) .du-bois-light-select-selector .du-bois-light-select-selection-placeholder {\n line-height: 38px;\n}\n.du-bois-light-select-single.du-bois-light-select-lg:not(.du-bois-light-select-customize-input):not(.du-bois-light-select-customize-input) .du-bois-light-select-selection-search-input {\n height: 38px;\n}\n.du-bois-light-select-single.du-bois-light-select-sm:not(.du-bois-light-select-customize-input) .du-bois-light-select-selector {\n height: 32px;\n}\n.du-bois-light-select-single.du-bois-light-select-sm:not(.du-bois-light-select-customize-input) .du-bois-light-select-selector::after,\n.du-bois-light-select-single.du-bois-light-select-sm:not(.du-bois-light-select-customize-input) .du-bois-light-select-selector .du-bois-light-select-selection-item,\n.du-bois-light-select-single.du-bois-light-select-sm:not(.du-bois-light-select-customize-input) .du-bois-light-select-selector .du-bois-light-select-selection-placeholder {\n line-height: 30px;\n}\n.du-bois-light-select-single.du-bois-light-select-sm:not(.du-bois-light-select-customize-input):not(.du-bois-light-select-customize-input) .du-bois-light-select-selection-search-input {\n height: 30px;\n}\n.du-bois-light-select-single.du-bois-light-select-sm:not(.du-bois-light-select-customize-input) .du-bois-light-select-selection-search {\n right: 12px;\n left: 12px;\n}\n.du-bois-light-select-single.du-bois-light-select-sm:not(.du-bois-light-select-customize-input) .du-bois-light-select-selector {\n padding: 0 12px;\n}\n.du-bois-light-select-single.du-bois-light-select-sm:not(.du-bois-light-select-customize-input).du-bois-light-select-show-arrow .du-bois-light-select-selection-search {\n right: 31.5px;\n}\n.du-bois-light-select-single.du-bois-light-select-sm:not(.du-bois-light-select-customize-input).du-bois-light-select-show-arrow .du-bois-light-select-selection-item,\n.du-bois-light-select-single.du-bois-light-select-sm:not(.du-bois-light-select-customize-input).du-bois-light-select-show-arrow .du-bois-light-select-selection-placeholder {\n padding-right: 19.5px;\n}\n.du-bois-light-select-single.du-bois-light-select-lg:not(.du-bois-light-select-customize-input) .du-bois-light-select-selector {\n padding: 0 12px;\n}\n/**\n * Do not merge `height` & `line-height` under style with `selection` & `search`,\n * since chrome may update to redesign with its align logic.\n */\n.du-bois-light-select-selection-overflow {\n position: relative;\n display: flex;\n flex: auto;\n flex-wrap: wrap;\n max-width: 100%;\n}\n.du-bois-light-select-selection-overflow-item {\n flex: none;\n align-self: center;\n max-width: 100%;\n}\n.du-bois-light-select-multiple .du-bois-light-select-selector {\n display: flex;\n flex-wrap: wrap;\n align-items: center;\n padding: 1px 5px;\n}\n.du-bois-light-select-show-search.du-bois-light-select-multiple .du-bois-light-select-selector {\n cursor: text;\n}\n.du-bois-light-select-disabled.du-bois-light-select-multiple .du-bois-light-select-selector {\n background: #f5f5f5;\n cursor: not-allowed;\n}\n.du-bois-light-select-multiple .du-bois-light-select-selector::after {\n display: inline-block;\n width: 0;\n margin: 3px 0;\n line-height: 30px;\n content: '\\a0';\n}\n.du-bois-light-select-multiple.du-bois-light-select-show-arrow .du-bois-light-select-selector,\n.du-bois-light-select-multiple.du-bois-light-select-allow-clear .du-bois-light-select-selector {\n padding-right: 20px;\n}\n.du-bois-light-select-multiple .du-bois-light-select-selection-item {\n position: relative;\n display: flex;\n flex: none;\n box-sizing: border-box;\n max-width: 100%;\n height: 30px;\n margin-top: 3px;\n margin-bottom: 3px;\n line-height: 28px;\n background: #f5f5f5;\n border: 1px solid #f0f0f0;\n border-radius: 4px;\n cursor: default;\n transition: font-size 0.3s, line-height 0.3s, height 0.3s;\n user-select: none;\n margin-inline-end: 5px;\n padding-inline-start: 4px;\n padding-inline-end: 2px;\n}\n.du-bois-light-select-disabled.du-bois-light-select-multiple .du-bois-light-select-selection-item {\n color: #bfbfbf;\n border-color: #E8ECF0;\n cursor: not-allowed;\n}\n.du-bois-light-select-multiple .du-bois-light-select-selection-item-content {\n display: inline-block;\n margin-right: 2px;\n overflow: hidden;\n white-space: pre;\n text-overflow: ellipsis;\n}\n.du-bois-light-select-multiple .du-bois-light-select-selection-item-remove {\n color: inherit;\n font-style: normal;\n line-height: 0;\n text-align: center;\n text-transform: none;\n vertical-align: -0.125em;\n text-rendering: optimizeLegibility;\n -webkit-font-smoothing: antialiased;\n -moz-osx-font-smoothing: grayscale;\n display: inline-block;\n color: #5F7281;\n font-weight: bold;\n font-size: 10px;\n line-height: inherit;\n cursor: pointer;\n}\n.du-bois-light-select-multiple .du-bois-light-select-selection-item-remove > * {\n line-height: 1;\n}\n.du-bois-light-select-multiple .du-bois-light-select-selection-item-remove svg {\n display: inline-block;\n}\n.du-bois-light-select-multiple .du-bois-light-select-selection-item-remove::before {\n display: none;\n}\n.du-bois-light-select-multiple .du-bois-light-select-selection-item-remove .du-bois-light-select-multiple .du-bois-light-select-selection-item-remove-icon {\n display: block;\n}\n.du-bois-light-select-multiple .du-bois-light-select-selection-item-remove > .anticon {\n vertical-align: -0.2em;\n}\n.du-bois-light-select-multiple .du-bois-light-select-selection-item-remove:hover {\n color: rgba(0, 0, 0, 0.75);\n}\n.du-bois-light-select-multiple .du-bois-light-select-selection-overflow-item + .du-bois-light-select-selection-overflow-item .du-bois-light-select-selection-search {\n margin-inline-start: 0;\n}\n.du-bois-light-select-multiple .du-bois-light-select-selection-search {\n position: relative;\n max-width: 100%;\n margin-top: 3px;\n margin-bottom: 3px;\n margin-inline-start: 7px;\n}\n.du-bois-light-select-multiple .du-bois-light-select-selection-search-input,\n.du-bois-light-select-multiple .du-bois-light-select-selection-search-mirror {\n height: 30px;\n font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, 'Helvetica Neue', Arial, 'Noto Sans', sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol', 'Noto Color Emoji';\n line-height: 30px;\n transition: all 0.3s;\n}\n.du-bois-light-select-multiple .du-bois-light-select-selection-search-input {\n width: 100%;\n min-width: 4.1px;\n}\n.du-bois-light-select-multiple .du-bois-light-select-selection-search-mirror {\n position: absolute;\n top: 0;\n left: 0;\n z-index: 999;\n white-space: pre;\n visibility: hidden;\n}\n.du-bois-light-select-multiple .du-bois-light-select-selection-placeholder {\n position: absolute;\n top: 50%;\n right: 12px;\n left: 12px;\n transform: translateY(-50%);\n transition: all 0.3s;\n}\n.du-bois-light-select-multiple.du-bois-light-select-lg .du-bois-light-select-selector::after {\n line-height: 30px;\n}\n.du-bois-light-select-multiple.du-bois-light-select-lg .du-bois-light-select-selection-item {\n height: 30px;\n line-height: 28px;\n}\n.du-bois-light-select-multiple.du-bois-light-select-lg .du-bois-light-select-selection-search {\n height: 30px;\n line-height: 30px;\n}\n.du-bois-light-select-multiple.du-bois-light-select-lg .du-bois-light-select-selection-search-input,\n.du-bois-light-select-multiple.du-bois-light-select-lg .du-bois-light-select-selection-search-mirror {\n height: 30px;\n line-height: 28px;\n}\n.du-bois-light-select-multiple.du-bois-light-select-sm .du-bois-light-select-selector::after {\n line-height: 22px;\n}\n.du-bois-light-select-multiple.du-bois-light-select-sm .du-bois-light-select-selection-item {\n height: 22px;\n line-height: 20px;\n}\n.du-bois-light-select-multiple.du-bois-light-select-sm .du-bois-light-select-selection-search {\n height: 22px;\n line-height: 22px;\n}\n.du-bois-light-select-multiple.du-bois-light-select-sm .du-bois-light-select-selection-search-input,\n.du-bois-light-select-multiple.du-bois-light-select-sm .du-bois-light-select-selection-search-mirror {\n height: 22px;\n line-height: 20px;\n}\n.du-bois-light-select-multiple.du-bois-light-select-sm .du-bois-light-select-selection-placeholder {\n left: 12px;\n}\n.du-bois-light-select-multiple.du-bois-light-select-sm .du-bois-light-select-selection-search {\n margin-inline-start: 3px;\n}\n.du-bois-light-select-multiple.du-bois-light-select-lg .du-bois-light-select-selection-item {\n height: 32px;\n line-height: 32px;\n}\n.du-bois-light-select-disabled .du-bois-light-select-selection-item-remove {\n display: none;\n}\n/* Reset search input style */\n.du-bois-light-select {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #11171C;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: 'tnum';\n position: relative;\n display: inline-block;\n cursor: pointer;\n}\n.du-bois-light-select:not(.du-bois-light-select-customize-input) .du-bois-light-select-selector {\n position: relative;\n background-color: #fff;\n border: 1px solid #E8ECF0;\n border-radius: 4px;\n transition: all 0.3s cubic-bezier(0.645, 0.045, 0.355, 1);\n}\n.du-bois-light-select:not(.du-bois-light-select-customize-input) .du-bois-light-select-selector input {\n cursor: pointer;\n}\n.du-bois-light-select-show-search.du-bois-light-select:not(.du-bois-light-select-customize-input) .du-bois-light-select-selector {\n cursor: text;\n}\n.du-bois-light-select-show-search.du-bois-light-select:not(.du-bois-light-select-customize-input) .du-bois-light-select-selector input {\n cursor: auto;\n}\n.du-bois-light-select-focused:not(.du-bois-light-select-disabled).du-bois-light-select:not(.du-bois-light-select-customize-input) .du-bois-light-select-selector {\n border-color: #448dc2;\n border-right-width: 1px !important;\n outline: 0;\n box-shadow: 0 0 0 2px rgba(34, 114, 180, 0.2);\n}\n.du-bois-light-select-disabled.du-bois-light-select:not(.du-bois-light-select-customize-input) .du-bois-light-select-selector {\n color: rgba(0, 0, 0, 0.25);\n background: #f5f5f5;\n cursor: not-allowed;\n}\n.du-bois-light-select-multiple.du-bois-light-select-disabled.du-bois-light-select:not(.du-bois-light-select-customize-input) .du-bois-light-select-selector {\n background: #f5f5f5;\n}\n.du-bois-light-select-disabled.du-bois-light-select:not(.du-bois-light-select-customize-input) .du-bois-light-select-selector input {\n cursor: not-allowed;\n}\n.du-bois-light-select:not(.du-bois-light-select-customize-input) .du-bois-light-select-selector .du-bois-light-select-selection-search-input {\n margin: 0;\n padding: 0;\n background: transparent;\n border: none;\n outline: none;\n appearance: none;\n}\n.du-bois-light-select:not(.du-bois-light-select-customize-input) .du-bois-light-select-selector .du-bois-light-select-selection-search-input::-webkit-search-cancel-button {\n display: none;\n -webkit-appearance: none;\n}\n.du-bois-light-select:not(.du-bois-light-select-disabled):hover .du-bois-light-select-selector {\n border-color: #448dc2;\n border-right-width: 1px !important;\n}\n.du-bois-light-select-selection-item {\n flex: 1;\n overflow: hidden;\n white-space: nowrap;\n text-overflow: ellipsis;\n}\n@media all and (-ms-high-contrast: none) {\n .du-bois-light-select-selection-item *::-ms-backdrop,\n .du-bois-light-select-selection-item {\n flex: auto;\n }\n}\n.du-bois-light-select-selection-placeholder {\n flex: 1;\n overflow: hidden;\n color: #bfbfbf;\n white-space: nowrap;\n text-overflow: ellipsis;\n pointer-events: none;\n}\n@media all and (-ms-high-contrast: none) {\n .du-bois-light-select-selection-placeholder *::-ms-backdrop,\n .du-bois-light-select-selection-placeholder {\n flex: auto;\n }\n}\n.du-bois-light-select-arrow {\n display: inline-block;\n color: inherit;\n font-style: normal;\n line-height: 0;\n text-transform: none;\n vertical-align: -0.125em;\n text-rendering: optimizeLegibility;\n -webkit-font-smoothing: antialiased;\n -moz-osx-font-smoothing: grayscale;\n position: absolute;\n top: 50%;\n right: 7px;\n width: 12px;\n height: 12px;\n margin-top: -6px;\n color: rgba(0, 0, 0, 0.25);\n font-size: 12px;\n line-height: 1;\n text-align: center;\n pointer-events: none;\n}\n.du-bois-light-select-arrow > * {\n line-height: 1;\n}\n.du-bois-light-select-arrow svg {\n display: inline-block;\n}\n.du-bois-light-select-arrow::before {\n display: none;\n}\n.du-bois-light-select-arrow .du-bois-light-select-arrow-icon {\n display: block;\n}\n.du-bois-light-select-arrow .anticon {\n vertical-align: top;\n transition: transform 0.3s;\n}\n.du-bois-light-select-arrow .anticon > svg {\n vertical-align: top;\n}\n.du-bois-light-select-arrow .anticon:not(.du-bois-light-select-suffix) {\n pointer-events: auto;\n}\n.du-bois-light-select-disabled .du-bois-light-select-arrow {\n cursor: not-allowed;\n}\n.du-bois-light-select-clear {\n position: absolute;\n top: 50%;\n right: 7px;\n z-index: 1;\n display: inline-block;\n width: 12px;\n height: 12px;\n margin-top: -6px;\n color: rgba(0, 0, 0, 0.25);\n font-size: 12px;\n font-style: normal;\n line-height: 1;\n text-align: center;\n text-transform: none;\n background: #fff;\n cursor: pointer;\n opacity: 0;\n transition: color 0.3s ease, opacity 0.15s ease;\n text-rendering: auto;\n}\n.du-bois-light-select-clear::before {\n display: block;\n}\n.du-bois-light-select-clear:hover {\n color: #5F7281;\n}\n.du-bois-light-select:hover .du-bois-light-select-clear {\n opacity: 1;\n}\n.du-bois-light-select-dropdown {\n margin: 0;\n padding: 0;\n color: #11171C;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: 'tnum';\n position: absolute;\n top: -9999px;\n left: -9999px;\n z-index: 1050;\n box-sizing: border-box;\n padding: 4px 0;\n overflow: hidden;\n font-size: 13px;\n font-variant: initial;\n background-color: #fff;\n border-radius: 4px;\n outline: none;\n box-shadow: 0 3px 6px -4px rgba(0, 0, 0, 0.12), 0 6px 16px 0 rgba(0, 0, 0, 0.08), 0 9px 28px 8px rgba(0, 0, 0, 0.05);\n}\n.du-bois-light-select-dropdown.du-bois-light-slide-up-enter.du-bois-light-slide-up-enter-active.du-bois-light-select-dropdown-placement-bottomLeft,\n.du-bois-light-select-dropdown.du-bois-light-slide-up-appear.du-bois-light-slide-up-appear-active.du-bois-light-select-dropdown-placement-bottomLeft {\n animation-name: antSlideUpIn;\n}\n.du-bois-light-select-dropdown.du-bois-light-slide-up-enter.du-bois-light-slide-up-enter-active.du-bois-light-select-dropdown-placement-topLeft,\n.du-bois-light-select-dropdown.du-bois-light-slide-up-appear.du-bois-light-slide-up-appear-active.du-bois-light-select-dropdown-placement-topLeft {\n animation-name: antSlideDownIn;\n}\n.du-bois-light-select-dropdown.du-bois-light-slide-up-leave.du-bois-light-slide-up-leave-active.du-bois-light-select-dropdown-placement-bottomLeft {\n animation-name: antSlideUpOut;\n}\n.du-bois-light-select-dropdown.du-bois-light-slide-up-leave.du-bois-light-slide-up-leave-active.du-bois-light-select-dropdown-placement-topLeft {\n animation-name: antSlideDownOut;\n}\n.du-bois-light-select-dropdown-hidden {\n display: none;\n}\n.du-bois-light-select-dropdown-empty {\n color: rgba(0, 0, 0, 0.25);\n}\n.du-bois-light-select-item-empty {\n position: relative;\n display: block;\n min-height: 32px;\n padding: 5px 8px;\n color: #11171C;\n font-weight: normal;\n font-size: 13px;\n line-height: 22px;\n color: rgba(0, 0, 0, 0.25);\n}\n.du-bois-light-select-item {\n position: relative;\n display: block;\n min-height: 32px;\n padding: 5px 8px;\n color: #11171C;\n font-weight: normal;\n font-size: 13px;\n line-height: 22px;\n cursor: pointer;\n transition: background 0.3s ease;\n}\n.du-bois-light-select-item-group {\n color: #5F7281;\n font-size: 12px;\n cursor: default;\n}\n.du-bois-light-select-item-option {\n display: flex;\n}\n.du-bois-light-select-item-option-content {\n flex: auto;\n overflow: hidden;\n white-space: nowrap;\n text-overflow: ellipsis;\n}\n.du-bois-light-select-item-option-state {\n flex: none;\n}\n.du-bois-light-select-item-option-active:not(.du-bois-light-select-item-option-disabled) {\n background-color: #f5f5f5;\n}\n.du-bois-light-select-item-option-selected:not(.du-bois-light-select-item-option-disabled) {\n color: #11171C;\n font-weight: 600;\n background-color: #e6f1f5;\n}\n.du-bois-light-select-item-option-selected:not(.du-bois-light-select-item-option-disabled) .du-bois-light-select-item-option-state {\n color: #2272B4;\n}\n.du-bois-light-select-item-option-disabled {\n color: rgba(0, 0, 0, 0.25);\n cursor: not-allowed;\n}\n.du-bois-light-select-item-option-disabled.du-bois-light-select-item-option-selected {\n background-color: #f5f5f5;\n}\n.du-bois-light-select-item-option-grouped {\n padding-left: 16px;\n}\n.du-bois-light-select-lg {\n font-size: 18px;\n}\n.du-bois-light-select-borderless .du-bois-light-select-selector {\n background-color: transparent !important;\n border-color: transparent !important;\n box-shadow: none !important;\n}\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-light-empty {\n margin: 0 8px;\n font-size: 13px;\n line-height: 20px;\n text-align: center;\n}\n.du-bois-light-empty-image {\n height: 100px;\n margin-bottom: 8px;\n}\n.du-bois-light-empty-image img {\n height: 100%;\n}\n.du-bois-light-empty-image svg {\n height: 100%;\n margin: auto;\n}\n.du-bois-light-empty-footer {\n margin-top: 16px;\n}\n.du-bois-light-empty-normal {\n margin: 32px 0;\n color: rgba(0, 0, 0, 0.25);\n}\n.du-bois-light-empty-normal .du-bois-light-empty-image {\n height: 40px;\n}\n.du-bois-light-empty-small {\n margin: 8px 0;\n color: rgba(0, 0, 0, 0.25);\n}\n.du-bois-light-empty-small .du-bois-light-empty-image {\n height: 35px;\n}\n.du-bois-light-empty-img-default-ellipse {\n fill: #f5f5f5;\n fill-opacity: 0.8;\n}\n.du-bois-light-empty-img-default-path-1 {\n fill: #aeb8c2;\n}\n.du-bois-light-empty-img-default-path-2 {\n fill: url(#linearGradient-1);\n}\n.du-bois-light-empty-img-default-path-3 {\n fill: #f5f5f7;\n}\n.du-bois-light-empty-img-default-path-4 {\n fill: #dce0e6;\n}\n.du-bois-light-empty-img-default-path-5 {\n fill: #dce0e6;\n}\n.du-bois-light-empty-img-default-g {\n fill: #fff;\n}\n.du-bois-light-empty-img-simple-ellipse {\n fill: #f5f5f5;\n}\n.du-bois-light-empty-img-simple-g {\n stroke: #d9d9d9;\n}\n.du-bois-light-empty-img-simple-path {\n fill: #fafafa;\n}\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-light-collapse {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #11171C;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: 'tnum';\n background-color: #fafafa;\n border: 1px solid #E8ECF0;\n border-bottom: 0;\n border-radius: 4px;\n}\n.du-bois-light-collapse > .du-bois-light-collapse-item {\n border-bottom: 1px solid #E8ECF0;\n}\n.du-bois-light-collapse > .du-bois-light-collapse-item:last-child,\n.du-bois-light-collapse > .du-bois-light-collapse-item:last-child > .du-bois-light-collapse-header {\n border-radius: 0 0 4px 4px;\n}\n.du-bois-light-collapse > .du-bois-light-collapse-item > .du-bois-light-collapse-header {\n position: relative;\n padding: 8px 8px;\n color: rgba(0, 0, 0, 0.85);\n line-height: 20px;\n cursor: pointer;\n transition: all 0.3s, visibility 0s;\n}\n.du-bois-light-collapse > .du-bois-light-collapse-item > .du-bois-light-collapse-header::before {\n display: table;\n content: '';\n}\n.du-bois-light-collapse > .du-bois-light-collapse-item > .du-bois-light-collapse-header::after {\n display: table;\n clear: both;\n content: '';\n}\n.du-bois-light-collapse > .du-bois-light-collapse-item > .du-bois-light-collapse-header .du-bois-light-collapse-arrow {\n display: inline-block;\n margin-right: 12px;\n font-size: 12px;\n vertical-align: -1px;\n}\n.du-bois-light-collapse > .du-bois-light-collapse-item > .du-bois-light-collapse-header .du-bois-light-collapse-arrow svg {\n transition: transform 0.24s;\n}\n.du-bois-light-collapse > .du-bois-light-collapse-item > .du-bois-light-collapse-header .du-bois-light-collapse-extra {\n float: right;\n}\n.du-bois-light-collapse > .du-bois-light-collapse-item > .du-bois-light-collapse-header:focus {\n outline: none;\n}\n.du-bois-light-collapse > .du-bois-light-collapse-item .du-bois-light-collapse-header-collapsible-only {\n cursor: default;\n}\n.du-bois-light-collapse > .du-bois-light-collapse-item .du-bois-light-collapse-header-collapsible-only .du-bois-light-collapse-header-text {\n cursor: pointer;\n}\n.du-bois-light-collapse > .du-bois-light-collapse-item.du-bois-light-collapse-no-arrow > .du-bois-light-collapse-header {\n padding-left: 12px;\n}\n.du-bois-light-collapse-icon-position-right > .du-bois-light-collapse-item > .du-bois-light-collapse-header {\n padding: 8px 8px;\n padding-right: 40px;\n}\n.du-bois-light-collapse-icon-position-right > .du-bois-light-collapse-item > .du-bois-light-collapse-header .du-bois-light-collapse-arrow {\n position: absolute;\n top: 50%;\n right: 8px;\n left: auto;\n margin: 0;\n transform: translateY(-50%);\n}\n.du-bois-light-collapse-content {\n color: #11171C;\n background-color: #fff;\n border-top: 1px solid #E8ECF0;\n}\n.du-bois-light-collapse-content > .du-bois-light-collapse-content-box {\n padding: 8px;\n}\n.du-bois-light-collapse-content-hidden {\n display: none;\n}\n.du-bois-light-collapse-item:last-child > .du-bois-light-collapse-content {\n border-radius: 0 0 4px 4px;\n}\n.du-bois-light-collapse-borderless {\n background-color: #fafafa;\n border: 0;\n}\n.du-bois-light-collapse-borderless > .du-bois-light-collapse-item {\n border-bottom: 1px solid #E8ECF0;\n}\n.du-bois-light-collapse-borderless > .du-bois-light-collapse-item:last-child,\n.du-bois-light-collapse-borderless > .du-bois-light-collapse-item:last-child .du-bois-light-collapse-header {\n border-radius: 0;\n}\n.du-bois-light-collapse-borderless > .du-bois-light-collapse-item > .du-bois-light-collapse-content {\n background-color: transparent;\n border-top: 0;\n}\n.du-bois-light-collapse-borderless > .du-bois-light-collapse-item > .du-bois-light-collapse-content > .du-bois-light-collapse-content-box {\n padding-top: 4px;\n}\n.du-bois-light-collapse-ghost {\n background-color: transparent;\n border: 0;\n}\n.du-bois-light-collapse-ghost > .du-bois-light-collapse-item {\n border-bottom: 0;\n}\n.du-bois-light-collapse-ghost > .du-bois-light-collapse-item > .du-bois-light-collapse-content {\n background-color: transparent;\n border-top: 0;\n}\n.du-bois-light-collapse-ghost > .du-bois-light-collapse-item > .du-bois-light-collapse-content > .du-bois-light-collapse-content-box {\n padding-top: 12px;\n padding-bottom: 12px;\n}\n.du-bois-light-collapse .du-bois-light-collapse-item-disabled > .du-bois-light-collapse-header,\n.du-bois-light-collapse .du-bois-light-collapse-item-disabled > .du-bois-light-collapse-header > .arrow {\n color: rgba(0, 0, 0, 0.25);\n cursor: not-allowed;\n}\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-light-picker {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #11171C;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: 'tnum';\n padding: 0 12px 0;\n position: relative;\n display: inline-flex;\n align-items: center;\n background: #fff;\n border: 1px solid #E8ECF0;\n border-radius: 4px;\n transition: border 0.3s, box-shadow 0.3s;\n}\n.du-bois-light-picker:hover,\n.du-bois-light-picker-focused {\n border-color: #448dc2;\n border-right-width: 1px !important;\n}\n.du-bois-light-picker-focused {\n border-color: #448dc2;\n border-right-width: 1px !important;\n outline: 0;\n box-shadow: 0 0 0 2px rgba(34, 114, 180, 0.2);\n}\n.du-bois-light-picker.du-bois-light-picker-disabled {\n background: #f5f5f5;\n border-color: #E8ECF0;\n cursor: not-allowed;\n}\n.du-bois-light-picker.du-bois-light-picker-disabled .du-bois-light-picker-suffix {\n color: rgba(0, 0, 0, 0.25);\n}\n.du-bois-light-picker.du-bois-light-picker-borderless {\n background-color: transparent !important;\n border-color: transparent !important;\n box-shadow: none !important;\n}\n.du-bois-light-picker-input {\n position: relative;\n display: inline-flex;\n align-items: center;\n width: 100%;\n}\n.du-bois-light-picker-input > input {\n position: relative;\n display: inline-block;\n width: 100%;\n min-width: 0;\n padding: 5px 12px;\n color: #11171C;\n font-size: 13px;\n line-height: 20px;\n background-color: #fff;\n background-image: none;\n border: 1px solid #E8ECF0;\n border-radius: 4px;\n transition: all 0.3s;\n flex: auto;\n min-width: 1px;\n height: auto;\n padding: 0;\n background: transparent;\n border: 0;\n}\n.du-bois-light-picker-input > input::-moz-placeholder {\n opacity: 1;\n}\n.du-bois-light-picker-input > input::placeholder {\n color: #bfbfbf;\n}\n.du-bois-light-picker-input > input:placeholder-shown {\n text-overflow: ellipsis;\n}\n.du-bois-light-picker-input > input:hover {\n border-color: #448dc2;\n border-right-width: 1px !important;\n}\n.du-bois-light-picker-input > input:focus,\n.du-bois-light-picker-input > input-focused {\n border-color: #448dc2;\n border-right-width: 1px !important;\n outline: 0;\n box-shadow: 0 0 0 2px rgba(34, 114, 180, 0.2);\n}\n.du-bois-light-picker-input > input-disabled {\n color: rgba(0, 0, 0, 0.25);\n background-color: #f5f5f5;\n cursor: not-allowed;\n opacity: 1;\n}\n.du-bois-light-picker-input > input-disabled:hover {\n border-color: #E8ECF0;\n border-right-width: 1px !important;\n}\n.du-bois-light-picker-input > input[disabled] {\n color: rgba(0, 0, 0, 0.25);\n background-color: #f5f5f5;\n cursor: not-allowed;\n opacity: 1;\n}\n.du-bois-light-picker-input > input[disabled]:hover {\n border-color: #E8ECF0;\n border-right-width: 1px !important;\n}\n.du-bois-light-picker-input > input-borderless,\n.du-bois-light-picker-input > input-borderless:hover,\n.du-bois-light-picker-input > input-borderless:focus,\n.du-bois-light-picker-input > input-borderless-focused,\n.du-bois-light-picker-input > input-borderless-disabled,\n.du-bois-light-picker-input > input-borderless[disabled] {\n background-color: transparent;\n border: none;\n box-shadow: none;\n}\ntextarea.du-bois-light-picker-input > input {\n max-width: 100%;\n height: auto;\n min-height: 40px;\n line-height: 20px;\n vertical-align: bottom;\n transition: all 0.3s, height 0s;\n}\n.du-bois-light-picker-input > input-lg {\n padding: 5px 12px;\n font-size: 18px;\n}\n.du-bois-light-picker-input > input-sm {\n padding: 5px 12px;\n}\n.du-bois-light-picker-input > input:focus {\n box-shadow: none;\n}\n.du-bois-light-picker-input > input[disabled] {\n background: transparent;\n}\n.du-bois-light-picker-input:hover .du-bois-light-picker-clear {\n opacity: 1;\n}\n.du-bois-light-picker-input-placeholder > input {\n color: #bfbfbf;\n}\n.du-bois-light-picker-large {\n padding: 0 12px 0;\n}\n.du-bois-light-picker-large .du-bois-light-picker-input > input {\n font-size: 18px;\n}\n.du-bois-light-picker-small {\n padding: 0 12px 0;\n}\n.du-bois-light-picker-suffix {\n align-self: center;\n margin-left: 2px;\n color: rgba(0, 0, 0, 0.25);\n line-height: 1;\n pointer-events: none;\n}\n.du-bois-light-picker-suffix > * {\n vertical-align: top;\n}\n.du-bois-light-picker-clear {\n position: absolute;\n top: 50%;\n right: 0;\n color: rgba(0, 0, 0, 0.25);\n line-height: 1;\n background: #fff;\n transform: translateY(-50%);\n cursor: pointer;\n opacity: 0;\n transition: opacity 0.3s, color 0.3s;\n}\n.du-bois-light-picker-clear > * {\n vertical-align: top;\n}\n.du-bois-light-picker-clear:hover {\n color: #5F7281;\n}\n.du-bois-light-picker-separator {\n position: relative;\n display: inline-block;\n width: 1em;\n height: 18px;\n color: rgba(0, 0, 0, 0.25);\n font-size: 18px;\n vertical-align: top;\n cursor: default;\n}\n.du-bois-light-picker-focused .du-bois-light-picker-separator {\n color: #5F7281;\n}\n.du-bois-light-picker-disabled .du-bois-light-picker-range-separator .du-bois-light-picker-separator {\n cursor: not-allowed;\n}\n.du-bois-light-picker-range {\n position: relative;\n display: inline-flex;\n}\n.du-bois-light-picker-range .du-bois-light-picker-clear {\n right: 12px;\n}\n.du-bois-light-picker-range:hover .du-bois-light-picker-clear {\n opacity: 1;\n}\n.du-bois-light-picker-range .du-bois-light-picker-active-bar {\n bottom: -1px;\n height: 2px;\n margin-left: 12px;\n background: #2272B4;\n opacity: 0;\n transition: all 0.3s ease-out;\n pointer-events: none;\n}\n.du-bois-light-picker-range.du-bois-light-picker-focused .du-bois-light-picker-active-bar {\n opacity: 1;\n}\n.du-bois-light-picker-range-separator {\n align-items: center;\n padding: 0 4px;\n line-height: 1;\n}\n.du-bois-light-picker-range.du-bois-light-picker-small .du-bois-light-picker-clear {\n right: 12px;\n}\n.du-bois-light-picker-range.du-bois-light-picker-small .du-bois-light-picker-active-bar {\n margin-left: 12px;\n}\n.du-bois-light-picker-dropdown {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #11171C;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: 'tnum';\n position: absolute;\n z-index: 1050;\n}\n.du-bois-light-picker-dropdown-hidden {\n display: none;\n}\n.du-bois-light-picker-dropdown-placement-bottomLeft .du-bois-light-picker-range-arrow {\n top: 1.66666667px;\n display: block;\n transform: rotate(-45deg);\n}\n.du-bois-light-picker-dropdown-placement-topLeft .du-bois-light-picker-range-arrow {\n bottom: 1.66666667px;\n display: block;\n transform: rotate(135deg);\n}\n.du-bois-light-picker-dropdown.du-bois-light-slide-up-enter.du-bois-light-slide-up-enter-active.du-bois-light-picker-dropdown-placement-topLeft,\n.du-bois-light-picker-dropdown.du-bois-light-slide-up-enter.du-bois-light-slide-up-enter-active.du-bois-light-picker-dropdown-placement-topRight,\n.du-bois-light-picker-dropdown.du-bois-light-slide-up-appear.du-bois-light-slide-up-appear-active.du-bois-light-picker-dropdown-placement-topLeft,\n.du-bois-light-picker-dropdown.du-bois-light-slide-up-appear.du-bois-light-slide-up-appear-active.du-bois-light-picker-dropdown-placement-topRight {\n animation-name: antSlideDownIn;\n}\n.du-bois-light-picker-dropdown.du-bois-light-slide-up-enter.du-bois-light-slide-up-enter-active.du-bois-light-picker-dropdown-placement-bottomLeft,\n.du-bois-light-picker-dropdown.du-bois-light-slide-up-enter.du-bois-light-slide-up-enter-active.du-bois-light-picker-dropdown-placement-bottomRight,\n.du-bois-light-picker-dropdown.du-bois-light-slide-up-appear.du-bois-light-slide-up-appear-active.du-bois-light-picker-dropdown-placement-bottomLeft,\n.du-bois-light-picker-dropdown.du-bois-light-slide-up-appear.du-bois-light-slide-up-appear-active.du-bois-light-picker-dropdown-placement-bottomRight {\n animation-name: antSlideUpIn;\n}\n.du-bois-light-picker-dropdown.du-bois-light-slide-up-leave.du-bois-light-slide-up-leave-active.du-bois-light-picker-dropdown-placement-topLeft,\n.du-bois-light-picker-dropdown.du-bois-light-slide-up-leave.du-bois-light-slide-up-leave-active.du-bois-light-picker-dropdown-placement-topRight {\n animation-name: antSlideDownOut;\n}\n.du-bois-light-picker-dropdown.du-bois-light-slide-up-leave.du-bois-light-slide-up-leave-active.du-bois-light-picker-dropdown-placement-bottomLeft,\n.du-bois-light-picker-dropdown.du-bois-light-slide-up-leave.du-bois-light-slide-up-leave-active.du-bois-light-picker-dropdown-placement-bottomRight {\n animation-name: antSlideUpOut;\n}\n.du-bois-light-picker-dropdown-range {\n padding: 6.66666667px 0;\n}\n.du-bois-light-picker-dropdown-range-hidden {\n display: none;\n}\n.du-bois-light-picker-dropdown .du-bois-light-picker-panel > .du-bois-light-picker-time-panel {\n padding-top: 2px;\n}\n.du-bois-light-picker-ranges {\n margin-bottom: 0;\n padding: 2px 8px;\n overflow: hidden;\n line-height: 36px;\n text-align: left;\n list-style: none;\n}\n.du-bois-light-picker-ranges > li {\n display: inline-block;\n}\n.du-bois-light-picker-ranges .du-bois-light-picker-preset > .du-bois-light-tag-blue {\n color: #2272B4;\n background: #e6f1f5;\n border-color: #93c2db;\n cursor: pointer;\n}\n.du-bois-light-picker-ranges .du-bois-light-picker-ok {\n float: right;\n margin-left: 4px;\n}\n.du-bois-light-picker-range-wrapper {\n display: flex;\n}\n.du-bois-light-picker-range-arrow {\n position: absolute;\n z-index: 1;\n display: none;\n width: 10px;\n height: 10px;\n margin-left: 18px;\n box-shadow: 2px -2px 6px rgba(0, 0, 0, 0.06);\n transition: left 0.3s ease-out;\n}\n.du-bois-light-picker-range-arrow::after {\n position: absolute;\n top: 1px;\n right: 1px;\n width: 10px;\n height: 10px;\n border: 5px solid #f0f0f0;\n border-color: #fff #fff transparent transparent;\n content: '';\n}\n.du-bois-light-picker-panel-container {\n overflow: hidden;\n vertical-align: top;\n background: #fff;\n border-radius: 4px;\n box-shadow: 0 3px 6px -4px rgba(0, 0, 0, 0.12), 0 6px 16px 0 rgba(0, 0, 0, 0.08), 0 9px 28px 8px rgba(0, 0, 0, 0.05);\n transition: margin 0.3s;\n}\n.du-bois-light-picker-panel-container .du-bois-light-picker-panels {\n display: inline-flex;\n flex-wrap: nowrap;\n direction: ltr;\n}\n.du-bois-light-picker-panel-container .du-bois-light-picker-panel {\n vertical-align: top;\n background: transparent;\n border-width: 0 0 1px 0;\n border-radius: 0;\n}\n.du-bois-light-picker-panel-container .du-bois-light-picker-panel .du-bois-light-picker-content,\n.du-bois-light-picker-panel-container .du-bois-light-picker-panel table {\n text-align: center;\n}\n.du-bois-light-picker-panel-container .du-bois-light-picker-panel-focused {\n border-color: #f0f0f0;\n}\n.du-bois-light-picker-panel {\n display: inline-flex;\n flex-direction: column;\n text-align: center;\n background: #fff;\n border: 1px solid #f0f0f0;\n border-radius: 4px;\n outline: none;\n}\n.du-bois-light-picker-panel-focused {\n border-color: #2272B4;\n}\n.du-bois-light-picker-decade-panel,\n.du-bois-light-picker-year-panel,\n.du-bois-light-picker-quarter-panel,\n.du-bois-light-picker-month-panel,\n.du-bois-light-picker-week-panel,\n.du-bois-light-picker-date-panel,\n.du-bois-light-picker-time-panel {\n display: flex;\n flex-direction: column;\n width: 272px;\n}\n.du-bois-light-picker-header {\n display: flex;\n padding: 0 4px;\n color: rgba(0, 0, 0, 0.85);\n border-bottom: 1px solid #f0f0f0;\n}\n.du-bois-light-picker-header > * {\n flex: none;\n}\n.du-bois-light-picker-header button {\n padding: 0;\n color: rgba(0, 0, 0, 0.25);\n line-height: 40px;\n background: transparent;\n border: 0;\n cursor: pointer;\n transition: color 0.3s;\n}\n.du-bois-light-picker-header > button {\n min-width: 1.6em;\n font-size: 13px;\n}\n.du-bois-light-picker-header > button:hover {\n color: #11171C;\n}\n.du-bois-light-picker-header-view {\n flex: auto;\n font-weight: 500;\n line-height: 40px;\n}\n.du-bois-light-picker-header-view button {\n color: inherit;\n font-weight: inherit;\n}\n.du-bois-light-picker-header-view button:not(:first-child) {\n margin-left: 4px;\n}\n.du-bois-light-picker-header-view button:hover {\n color: #2272B4;\n}\n.du-bois-light-picker-prev-icon,\n.du-bois-light-picker-next-icon,\n.du-bois-light-picker-super-prev-icon,\n.du-bois-light-picker-super-next-icon {\n position: relative;\n display: inline-block;\n width: 7px;\n height: 7px;\n}\n.du-bois-light-picker-prev-icon::before,\n.du-bois-light-picker-next-icon::before,\n.du-bois-light-picker-super-prev-icon::before,\n.du-bois-light-picker-super-next-icon::before {\n position: absolute;\n top: 0;\n left: 0;\n display: inline-block;\n width: 7px;\n height: 7px;\n border: 0 solid currentColor;\n border-width: 1.5px 0 0 1.5px;\n content: '';\n}\n.du-bois-light-picker-super-prev-icon::after,\n.du-bois-light-picker-super-next-icon::after {\n position: absolute;\n top: 4px;\n left: 4px;\n display: inline-block;\n width: 7px;\n height: 7px;\n border: 0 solid currentColor;\n border-width: 1.5px 0 0 1.5px;\n content: '';\n}\n.du-bois-light-picker-prev-icon,\n.du-bois-light-picker-super-prev-icon {\n transform: rotate(-45deg);\n}\n.du-bois-light-picker-next-icon,\n.du-bois-light-picker-super-next-icon {\n transform: rotate(135deg);\n}\n.du-bois-light-picker-content {\n width: 100%;\n table-layout: fixed;\n border-collapse: collapse;\n}\n.du-bois-light-picker-content th,\n.du-bois-light-picker-content td {\n position: relative;\n min-width: 24px;\n font-weight: 400;\n}\n.du-bois-light-picker-content th {\n height: 30px;\n color: #11171C;\n line-height: 30px;\n}\n.du-bois-light-picker-cell {\n padding: 3px 0;\n color: rgba(0, 0, 0, 0.25);\n cursor: pointer;\n}\n.du-bois-light-picker-cell-in-view {\n color: #11171C;\n}\n.du-bois-light-picker-cell::before {\n position: absolute;\n top: 50%;\n right: 0;\n left: 0;\n z-index: 1;\n height: 24px;\n transform: translateY(-50%);\n content: '';\n}\n.du-bois-light-picker-cell .du-bois-light-picker-cell-inner {\n position: relative;\n z-index: 2;\n display: inline-block;\n min-width: 24px;\n height: 24px;\n line-height: 24px;\n border-radius: 4px;\n transition: background 0.3s, border 0.3s;\n}\n.du-bois-light-picker-cell:hover:not(.du-bois-light-picker-cell-in-view) .du-bois-light-picker-cell-inner,\n.du-bois-light-picker-cell:hover:not(.du-bois-light-picker-cell-selected):not(.du-bois-light-picker-cell-range-start):not(.du-bois-light-picker-cell-range-end):not(.du-bois-light-picker-cell-range-hover-start):not(.du-bois-light-picker-cell-range-hover-end) .du-bois-light-picker-cell-inner {\n background: #f5f5f5;\n}\n.du-bois-light-picker-cell-in-view.du-bois-light-picker-cell-today .du-bois-light-picker-cell-inner::before {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n z-index: 1;\n border: 1px solid #2272B4;\n border-radius: 4px;\n content: '';\n}\n.du-bois-light-picker-cell-in-view.du-bois-light-picker-cell-in-range {\n position: relative;\n}\n.du-bois-light-picker-cell-in-view.du-bois-light-picker-cell-in-range::before {\n background: #e6f1f5;\n}\n.du-bois-light-picker-cell-in-view.du-bois-light-picker-cell-selected .du-bois-light-picker-cell-inner,\n.du-bois-light-picker-cell-in-view.du-bois-light-picker-cell-range-start .du-bois-light-picker-cell-inner,\n.du-bois-light-picker-cell-in-view.du-bois-light-picker-cell-range-end .du-bois-light-picker-cell-inner {\n color: #fff;\n background: #2272B4;\n}\n.du-bois-light-picker-cell-in-view.du-bois-light-picker-cell-range-start:not(.du-bois-light-picker-cell-range-start-single)::before,\n.du-bois-light-picker-cell-in-view.du-bois-light-picker-cell-range-end:not(.du-bois-light-picker-cell-range-end-single)::before {\n background: #e6f1f5;\n}\n.du-bois-light-picker-cell-in-view.du-bois-light-picker-cell-range-start::before {\n left: 50%;\n}\n.du-bois-light-picker-cell-in-view.du-bois-light-picker-cell-range-end::before {\n right: 50%;\n}\n.du-bois-light-picker-cell-in-view.du-bois-light-picker-cell-range-hover-start:not(.du-bois-light-picker-cell-in-range):not(.du-bois-light-picker-cell-range-start):not(.du-bois-light-picker-cell-range-end)::after,\n.du-bois-light-picker-cell-in-view.du-bois-light-picker-cell-range-hover-end:not(.du-bois-light-picker-cell-in-range):not(.du-bois-light-picker-cell-range-start):not(.du-bois-light-picker-cell-range-end)::after,\n.du-bois-light-picker-cell-in-view.du-bois-light-picker-cell-range-hover-start.du-bois-light-picker-cell-range-start-single::after,\n.du-bois-light-picker-cell-in-view.du-bois-light-picker-cell-range-hover-start.du-bois-light-picker-cell-range-start.du-bois-light-picker-cell-range-end.du-bois-light-picker-cell-range-end-near-hover::after,\n.du-bois-light-picker-cell-in-view.du-bois-light-picker-cell-range-hover-end.du-bois-light-picker-cell-range-start.du-bois-light-picker-cell-range-end.du-bois-light-picker-cell-range-start-near-hover::after,\n.du-bois-light-picker-cell-in-view.du-bois-light-picker-cell-range-hover-end.du-bois-light-picker-cell-range-end-single::after,\n.du-bois-light-picker-cell-in-view.du-bois-light-picker-cell-range-hover:not(.du-bois-light-picker-cell-in-range)::after {\n position: absolute;\n top: 50%;\n z-index: 0;\n height: 24px;\n border-top: 1px dashed #5ca4e0;\n border-bottom: 1px dashed #5ca4e0;\n transform: translateY(-50%);\n content: '';\n}\n.du-bois-light-picker-cell-range-hover-start::after,\n.du-bois-light-picker-cell-range-hover-end::after,\n.du-bois-light-picker-cell-range-hover::after {\n right: 0;\n left: 2px;\n}\n.du-bois-light-picker-cell-in-view.du-bois-light-picker-cell-in-range.du-bois-light-picker-cell-range-hover::before,\n.du-bois-light-picker-cell-in-view.du-bois-light-picker-cell-range-start.du-bois-light-picker-cell-range-hover::before,\n.du-bois-light-picker-cell-in-view.du-bois-light-picker-cell-range-end.du-bois-light-picker-cell-range-hover::before,\n.du-bois-light-picker-cell-in-view.du-bois-light-picker-cell-range-start:not(.du-bois-light-picker-cell-range-start-single).du-bois-light-picker-cell-range-hover-start::before,\n.du-bois-light-picker-cell-in-view.du-bois-light-picker-cell-range-end:not(.du-bois-light-picker-cell-range-end-single).du-bois-light-picker-cell-range-hover-end::before,\n.du-bois-light-picker-panel > :not(.du-bois-light-picker-date-panel) .du-bois-light-picker-cell-in-view.du-bois-light-picker-cell-in-range.du-bois-light-picker-cell-range-hover-start::before,\n.du-bois-light-picker-panel > :not(.du-bois-light-picker-date-panel) .du-bois-light-picker-cell-in-view.du-bois-light-picker-cell-in-range.du-bois-light-picker-cell-range-hover-end::before {\n background: #9cc8ec;\n}\n.du-bois-light-picker-cell-in-view.du-bois-light-picker-cell-range-start:not(.du-bois-light-picker-cell-range-start-single):not(.du-bois-light-picker-cell-range-end) .du-bois-light-picker-cell-inner {\n border-radius: 4px 0 0 4px;\n}\n.du-bois-light-picker-cell-in-view.du-bois-light-picker-cell-range-end:not(.du-bois-light-picker-cell-range-end-single):not(.du-bois-light-picker-cell-range-start) .du-bois-light-picker-cell-inner {\n border-radius: 0 4px 4px 0;\n}\n.du-bois-light-picker-date-panel .du-bois-light-picker-cell-in-view.du-bois-light-picker-cell-in-range.du-bois-light-picker-cell-range-hover-start .du-bois-light-picker-cell-inner::after,\n.du-bois-light-picker-date-panel .du-bois-light-picker-cell-in-view.du-bois-light-picker-cell-in-range.du-bois-light-picker-cell-range-hover-end .du-bois-light-picker-cell-inner::after {\n position: absolute;\n top: 0;\n bottom: 0;\n z-index: -1;\n background: #9cc8ec;\n content: '';\n}\n.du-bois-light-picker-date-panel .du-bois-light-picker-cell-in-view.du-bois-light-picker-cell-in-range.du-bois-light-picker-cell-range-hover-start .du-bois-light-picker-cell-inner::after {\n right: -6px;\n left: 0;\n}\n.du-bois-light-picker-date-panel .du-bois-light-picker-cell-in-view.du-bois-light-picker-cell-in-range.du-bois-light-picker-cell-range-hover-end .du-bois-light-picker-cell-inner::after {\n right: 0;\n left: -6px;\n}\n.du-bois-light-picker-cell-range-hover.du-bois-light-picker-cell-range-start::after {\n right: 50%;\n}\n.du-bois-light-picker-cell-range-hover.du-bois-light-picker-cell-range-end::after {\n left: 50%;\n}\ntr > .du-bois-light-picker-cell-in-view.du-bois-light-picker-cell-range-hover:first-child::after,\ntr > .du-bois-light-picker-cell-in-view.du-bois-light-picker-cell-range-hover-end:first-child::after,\n.du-bois-light-picker-cell-in-view.du-bois-light-picker-cell-start.du-bois-light-picker-cell-range-hover-edge-start.du-bois-light-picker-cell-range-hover-edge-start-near-range::after,\n.du-bois-light-picker-cell-in-view.du-bois-light-picker-cell-range-hover-edge-start:not(.du-bois-light-picker-cell-range-hover-edge-start-near-range)::after,\n.du-bois-light-picker-cell-in-view.du-bois-light-picker-cell-range-hover-start::after {\n left: 6px;\n border-left: 1px dashed #5ca4e0;\n border-top-left-radius: 4px;\n border-bottom-left-radius: 4px;\n}\ntr > .du-bois-light-picker-cell-in-view.du-bois-light-picker-cell-range-hover:last-child::after,\ntr > .du-bois-light-picker-cell-in-view.du-bois-light-picker-cell-range-hover-start:last-child::after,\n.du-bois-light-picker-cell-in-view.du-bois-light-picker-cell-end.du-bois-light-picker-cell-range-hover-edge-end.du-bois-light-picker-cell-range-hover-edge-end-near-range::after,\n.du-bois-light-picker-cell-in-view.du-bois-light-picker-cell-range-hover-edge-end:not(.du-bois-light-picker-cell-range-hover-edge-end-near-range)::after,\n.du-bois-light-picker-cell-in-view.du-bois-light-picker-cell-range-hover-end::after {\n right: 6px;\n border-right: 1px dashed #5ca4e0;\n border-top-right-radius: 4px;\n border-bottom-right-radius: 4px;\n}\n.du-bois-light-picker-cell-disabled {\n color: rgba(0, 0, 0, 0.25);\n pointer-events: none;\n}\n.du-bois-light-picker-cell-disabled .du-bois-light-picker-cell-inner {\n background: transparent;\n}\n.du-bois-light-picker-cell-disabled::before {\n background: rgba(0, 0, 0, 0.04);\n}\n.du-bois-light-picker-cell-disabled.du-bois-light-picker-cell-today .du-bois-light-picker-cell-inner::before {\n border-color: rgba(0, 0, 0, 0.25);\n}\n.du-bois-light-picker-decade-panel .du-bois-light-picker-content,\n.du-bois-light-picker-year-panel .du-bois-light-picker-content,\n.du-bois-light-picker-quarter-panel .du-bois-light-picker-content,\n.du-bois-light-picker-month-panel .du-bois-light-picker-content {\n height: 264px;\n}\n.du-bois-light-picker-decade-panel .du-bois-light-picker-cell-inner,\n.du-bois-light-picker-year-panel .du-bois-light-picker-cell-inner,\n.du-bois-light-picker-quarter-panel .du-bois-light-picker-cell-inner,\n.du-bois-light-picker-month-panel .du-bois-light-picker-cell-inner {\n padding: 0 4px;\n}\n.du-bois-light-picker-quarter-panel .du-bois-light-picker-content {\n height: 56px;\n}\n.du-bois-light-picker-footer {\n width: min-content;\n min-width: 100%;\n line-height: 38px;\n text-align: center;\n border-bottom: 1px solid transparent;\n}\n.du-bois-light-picker-panel .du-bois-light-picker-footer {\n border-top: 1px solid #f0f0f0;\n}\n.du-bois-light-picker-footer-extra {\n padding: 0 8px;\n line-height: 38px;\n text-align: left;\n}\n.du-bois-light-picker-footer-extra:not(:last-child) {\n border-bottom: 1px solid #f0f0f0;\n}\n.du-bois-light-picker-now {\n text-align: left;\n}\n.du-bois-light-picker-today-btn {\n color: #2272B4;\n}\n.du-bois-light-picker-today-btn:hover {\n color: #448dc2;\n}\n.du-bois-light-picker-today-btn:active {\n color: #14538f;\n}\n.du-bois-light-picker-today-btn.du-bois-light-picker-today-btn-disabled {\n color: rgba(0, 0, 0, 0.25);\n cursor: not-allowed;\n}\n.du-bois-light-picker-decade-panel .du-bois-light-picker-cell-inner {\n padding: 0 2px;\n}\n.du-bois-light-picker-decade-panel .du-bois-light-picker-cell::before {\n display: none;\n}\n.du-bois-light-picker-year-panel .du-bois-light-picker-body,\n.du-bois-light-picker-quarter-panel .du-bois-light-picker-body,\n.du-bois-light-picker-month-panel .du-bois-light-picker-body {\n padding: 0 4px;\n}\n.du-bois-light-picker-year-panel .du-bois-light-picker-cell-inner,\n.du-bois-light-picker-quarter-panel .du-bois-light-picker-cell-inner,\n.du-bois-light-picker-month-panel .du-bois-light-picker-cell-inner {\n width: 60px;\n}\n.du-bois-light-picker-year-panel .du-bois-light-picker-cell-range-hover-start::after,\n.du-bois-light-picker-quarter-panel .du-bois-light-picker-cell-range-hover-start::after,\n.du-bois-light-picker-month-panel .du-bois-light-picker-cell-range-hover-start::after {\n left: 14px;\n border-left: 1px dashed #5ca4e0;\n border-radius: 4px 0 0 4px;\n}\n.du-bois-light-picker-year-panel .du-bois-light-picker-cell-range-hover-end::after,\n.du-bois-light-picker-quarter-panel .du-bois-light-picker-cell-range-hover-end::after,\n.du-bois-light-picker-month-panel .du-bois-light-picker-cell-range-hover-end::after {\n right: 14px;\n border-right: 1px dashed #5ca4e0;\n border-radius: 0 4px 4px 0;\n}\n.du-bois-light-picker-week-panel .du-bois-light-picker-body {\n padding: 4px 8px;\n}\n.du-bois-light-picker-week-panel .du-bois-light-picker-cell:hover .du-bois-light-picker-cell-inner,\n.du-bois-light-picker-week-panel .du-bois-light-picker-cell-selected .du-bois-light-picker-cell-inner,\n.du-bois-light-picker-week-panel .du-bois-light-picker-cell .du-bois-light-picker-cell-inner {\n background: transparent !important;\n}\n.du-bois-light-picker-week-panel-row td {\n transition: background 0.3s;\n}\n.du-bois-light-picker-week-panel-row:hover td {\n background: #f5f5f5;\n}\n.du-bois-light-picker-week-panel-row-selected td,\n.du-bois-light-picker-week-panel-row-selected:hover td {\n background: #2272B4;\n}\n.du-bois-light-picker-week-panel-row-selected td.du-bois-light-picker-cell-week,\n.du-bois-light-picker-week-panel-row-selected:hover td.du-bois-light-picker-cell-week {\n color: rgba(255, 255, 255, 0.5);\n}\n.du-bois-light-picker-week-panel-row-selected td.du-bois-light-picker-cell-today .du-bois-light-picker-cell-inner::before,\n.du-bois-light-picker-week-panel-row-selected:hover td.du-bois-light-picker-cell-today .du-bois-light-picker-cell-inner::before {\n border-color: #fff;\n}\n.du-bois-light-picker-week-panel-row-selected td .du-bois-light-picker-cell-inner,\n.du-bois-light-picker-week-panel-row-selected:hover td .du-bois-light-picker-cell-inner {\n color: #fff;\n}\n.du-bois-light-picker-date-panel .du-bois-light-picker-body {\n padding: 4px 8px;\n}\n.du-bois-light-picker-date-panel .du-bois-light-picker-content {\n width: 252px;\n}\n.du-bois-light-picker-date-panel .du-bois-light-picker-content th {\n width: 36px;\n}\n.du-bois-light-picker-datetime-panel {\n display: flex;\n}\n.du-bois-light-picker-datetime-panel .du-bois-light-picker-time-panel {\n border-left: 1px solid #f0f0f0;\n}\n.du-bois-light-picker-datetime-panel .du-bois-light-picker-date-panel,\n.du-bois-light-picker-datetime-panel .du-bois-light-picker-time-panel {\n transition: opacity 0.3s;\n}\n.du-bois-light-picker-datetime-panel-active .du-bois-light-picker-date-panel,\n.du-bois-light-picker-datetime-panel-active .du-bois-light-picker-time-panel {\n opacity: 0.3;\n}\n.du-bois-light-picker-datetime-panel-active .du-bois-light-picker-date-panel-active,\n.du-bois-light-picker-datetime-panel-active .du-bois-light-picker-time-panel-active {\n opacity: 1;\n}\n.du-bois-light-picker-time-panel {\n width: auto;\n min-width: auto;\n}\n.du-bois-light-picker-time-panel .du-bois-light-picker-content {\n display: flex;\n flex: auto;\n height: 224px;\n}\n.du-bois-light-picker-time-panel-column {\n flex: 1 0 auto;\n width: 56px;\n margin: 0;\n padding: 0;\n overflow-y: hidden;\n text-align: left;\n list-style: none;\n transition: background 0.3s;\n}\n.du-bois-light-picker-time-panel-column::after {\n display: block;\n height: 196px;\n content: '';\n}\n.du-bois-light-picker-datetime-panel .du-bois-light-picker-time-panel-column::after {\n height: 198px;\n}\n.du-bois-light-picker-time-panel-column:not(:first-child) {\n border-left: 1px solid #f0f0f0;\n}\n.du-bois-light-picker-time-panel-column-active {\n background: rgba(230, 241, 245, 0.2);\n}\n.du-bois-light-picker-time-panel-column:hover {\n overflow-y: auto;\n}\n.du-bois-light-picker-time-panel-column > li {\n margin: 0;\n padding: 0;\n}\n.du-bois-light-picker-time-panel-column > li.du-bois-light-picker-time-panel-cell .du-bois-light-picker-time-panel-cell-inner {\n display: block;\n width: 100%;\n height: 28px;\n margin: 0;\n padding: 0 0 0 14px;\n color: #11171C;\n line-height: 28px;\n border-radius: 0;\n cursor: pointer;\n transition: background 0.3s;\n}\n.du-bois-light-picker-time-panel-column > li.du-bois-light-picker-time-panel-cell .du-bois-light-picker-time-panel-cell-inner:hover {\n background: #f5f5f5;\n}\n.du-bois-light-picker-time-panel-column > li.du-bois-light-picker-time-panel-cell-selected .du-bois-light-picker-time-panel-cell-inner {\n background: #e6f1f5;\n}\n.du-bois-light-picker-time-panel-column > li.du-bois-light-picker-time-panel-cell-disabled .du-bois-light-picker-time-panel-cell-inner {\n color: rgba(0, 0, 0, 0.25);\n background: transparent;\n cursor: not-allowed;\n}\n/* stylelint-disable-next-line */\n_:-ms-fullscreen .du-bois-light-picker-range-wrapper .du-bois-light-picker-month-panel .du-bois-light-picker-cell,\n:root .du-bois-light-picker-range-wrapper .du-bois-light-picker-month-panel .du-bois-light-picker-cell,\n_:-ms-fullscreen .du-bois-light-picker-range-wrapper .du-bois-light-picker-year-panel .du-bois-light-picker-cell,\n:root .du-bois-light-picker-range-wrapper .du-bois-light-picker-year-panel .du-bois-light-picker-cell {\n padding: 21px 0;\n}\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-light-tag {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #11171C;\n font-size: 13px;\n font-variant: tabular-nums;\n list-style: none;\n font-feature-settings: 'tnum';\n display: inline-block;\n height: auto;\n margin-right: 8px;\n padding: 0 7px;\n font-size: 12px;\n line-height: 20px;\n white-space: nowrap;\n background: #fafafa;\n border: 1px solid #E8ECF0;\n border-radius: 4px;\n opacity: 1;\n transition: all 0.3s;\n}\n.du-bois-light-tag,\n.du-bois-light-tag a,\n.du-bois-light-tag a:hover {\n color: #11171C;\n}\n.du-bois-light-tag > a:first-child:last-child {\n display: inline-block;\n margin: 0 -8px;\n padding: 0 8px;\n}\n.du-bois-light-tag-close-icon {\n margin-left: 3px;\n color: #5F7281;\n font-size: 10px;\n cursor: pointer;\n transition: all 0.3s;\n}\n.du-bois-light-tag-close-icon:hover {\n color: rgba(0, 0, 0, 0.85);\n}\n.du-bois-light-tag-has-color {\n border-color: transparent;\n}\n.du-bois-light-tag-has-color,\n.du-bois-light-tag-has-color a,\n.du-bois-light-tag-has-color a:hover,\n.du-bois-light-tag-has-color .anticon-close,\n.du-bois-light-tag-has-color .anticon-close:hover {\n color: #fff;\n}\n.du-bois-light-tag-checkable {\n background-color: transparent;\n border-color: transparent;\n cursor: pointer;\n}\n.du-bois-light-tag-checkable:not(.du-bois-light-tag-checkable-checked):hover {\n color: #2272B4;\n}\n.du-bois-light-tag-checkable:active,\n.du-bois-light-tag-checkable-checked {\n color: #fff;\n}\n.du-bois-light-tag-checkable-checked {\n background-color: #2272B4;\n}\n.du-bois-light-tag-checkable:active {\n background-color: #14538f;\n}\n.du-bois-light-tag-hidden {\n display: none;\n}\n.du-bois-light-tag-pink {\n color: #c41d7f;\n background: #fff0f6;\n border-color: #ffadd2;\n}\n.du-bois-light-tag-pink-inverse {\n color: #fff;\n background: #eb2f96;\n border-color: #eb2f96;\n}\n.du-bois-light-tag-magenta {\n color: #c41d7f;\n background: #fff0f6;\n border-color: #ffadd2;\n}\n.du-bois-light-tag-magenta-inverse {\n color: #fff;\n background: #eb2f96;\n border-color: #eb2f96;\n}\n.du-bois-light-tag-red {\n color: #cf1322;\n background: #fff1f0;\n border-color: #ffa39e;\n}\n.du-bois-light-tag-red-inverse {\n color: #fff;\n background: #f5222d;\n border-color: #f5222d;\n}\n.du-bois-light-tag-volcano {\n color: #d4380d;\n background: #fff2e8;\n border-color: #ffbb96;\n}\n.du-bois-light-tag-volcano-inverse {\n color: #fff;\n background: #fa541c;\n border-color: #fa541c;\n}\n.du-bois-light-tag-orange {\n color: #d46b08;\n background: #fff7e6;\n border-color: #ffd591;\n}\n.du-bois-light-tag-orange-inverse {\n color: #fff;\n background: #fa8c16;\n border-color: #fa8c16;\n}\n.du-bois-light-tag-yellow {\n color: #d4b106;\n background: #feffe6;\n border-color: #fffb8f;\n}\n.du-bois-light-tag-yellow-inverse {\n color: #fff;\n background: #fadb14;\n border-color: #fadb14;\n}\n.du-bois-light-tag-gold {\n color: #d48806;\n background: #fffbe6;\n border-color: #ffe58f;\n}\n.du-bois-light-tag-gold-inverse {\n color: #fff;\n background: #faad14;\n border-color: #faad14;\n}\n.du-bois-light-tag-cyan {\n color: #08979c;\n background: #e6fffb;\n border-color: #87e8de;\n}\n.du-bois-light-tag-cyan-inverse {\n color: #fff;\n background: #13c2c2;\n border-color: #13c2c2;\n}\n.du-bois-light-tag-lime {\n color: #7cb305;\n background: #fcffe6;\n border-color: #eaff8f;\n}\n.du-bois-light-tag-lime-inverse {\n color: #fff;\n background: #a0d911;\n border-color: #a0d911;\n}\n.du-bois-light-tag-green {\n color: #389e0d;\n background: #f6ffed;\n border-color: #b7eb8f;\n}\n.du-bois-light-tag-green-inverse {\n color: #fff;\n background: #52c41a;\n border-color: #52c41a;\n}\n.du-bois-light-tag-blue {\n color: #096dd9;\n background: #e6f7ff;\n border-color: #91d5ff;\n}\n.du-bois-light-tag-blue-inverse {\n color: #fff;\n background: #1890ff;\n border-color: #1890ff;\n}\n.du-bois-light-tag-geekblue {\n color: #1d39c4;\n background: #f0f5ff;\n border-color: #adc6ff;\n}\n.du-bois-light-tag-geekblue-inverse {\n color: #fff;\n background: #2f54eb;\n border-color: #2f54eb;\n}\n.du-bois-light-tag-purple {\n color: #531dab;\n background: #f9f0ff;\n border-color: #d3adf7;\n}\n.du-bois-light-tag-purple-inverse {\n color: #fff;\n background: #722ed1;\n border-color: #722ed1;\n}\n.du-bois-light-tag-success {\n color: #52c41a;\n background: #f6ffed;\n border-color: #b7eb8f;\n}\n.du-bois-light-tag-processing {\n color: #1890ff;\n background: #e6f7ff;\n border-color: #91d5ff;\n}\n.du-bois-light-tag-error {\n color: #f5222d;\n background: #fff1f0;\n border-color: #ffa39e;\n}\n.du-bois-light-tag-warning {\n color: #fa8c16;\n background: #fff7e6;\n border-color: #ffd591;\n}\n.du-bois-light-tag > .anticon + span,\n.du-bois-light-tag > span + .anticon {\n margin-left: 7px;\n}\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-light-dropdown-menu-item.du-bois-light-dropdown-menu-item-danger {\n color: #C82D4C;\n}\n.du-bois-light-dropdown-menu-item.du-bois-light-dropdown-menu-item-danger:hover {\n color: #fff;\n background-color: #C82D4C;\n}\n.du-bois-light-dropdown {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #11171C;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: 'tnum';\n position: absolute;\n top: -9999px;\n left: -9999px;\n z-index: 1050;\n display: block;\n}\n.du-bois-light-dropdown::before {\n position: absolute;\n top: -4px;\n right: 0;\n bottom: -4px;\n left: -7px;\n z-index: -9999;\n opacity: 0.0001;\n content: ' ';\n}\n.du-bois-light-dropdown-wrap {\n position: relative;\n}\n.du-bois-light-dropdown-wrap .du-bois-light-btn > .anticon-down {\n font-size: 10px;\n}\n.du-bois-light-dropdown-wrap .anticon-down::before {\n transition: transform 0.2s;\n}\n.du-bois-light-dropdown-wrap-open .anticon-down::before {\n transform: rotate(180deg);\n}\n.du-bois-light-dropdown-hidden,\n.du-bois-light-dropdown-menu-hidden,\n.du-bois-light-dropdown-menu-submenu-hidden {\n display: none;\n}\n.du-bois-light-dropdown-show-arrow.du-bois-light-dropdown-placement-topCenter,\n.du-bois-light-dropdown-show-arrow.du-bois-light-dropdown-placement-topLeft,\n.du-bois-light-dropdown-show-arrow.du-bois-light-dropdown-placement-topRight {\n padding-bottom: 10px;\n}\n.du-bois-light-dropdown-show-arrow.du-bois-light-dropdown-placement-bottomCenter,\n.du-bois-light-dropdown-show-arrow.du-bois-light-dropdown-placement-bottomLeft,\n.du-bois-light-dropdown-show-arrow.du-bois-light-dropdown-placement-bottomRight {\n padding-top: 10px;\n}\n.du-bois-light-dropdown-arrow {\n position: absolute;\n z-index: 1;\n display: block;\n width: 8.48528137px;\n height: 8.48528137px;\n background: transparent;\n border-style: solid;\n border-width: 4.24264069px;\n transform: rotate(45deg);\n}\n.du-bois-light-dropdown-placement-topCenter > .du-bois-light-dropdown-arrow,\n.du-bois-light-dropdown-placement-topLeft > .du-bois-light-dropdown-arrow,\n.du-bois-light-dropdown-placement-topRight > .du-bois-light-dropdown-arrow {\n bottom: 6.2px;\n border-top-color: transparent;\n border-right-color: #fff;\n border-bottom-color: #fff;\n border-left-color: transparent;\n box-shadow: 3px 3px 7px rgba(0, 0, 0, 0.07);\n}\n.du-bois-light-dropdown-placement-topCenter > .du-bois-light-dropdown-arrow {\n left: 50%;\n transform: translateX(-50%) rotate(45deg);\n}\n.du-bois-light-dropdown-placement-topLeft > .du-bois-light-dropdown-arrow {\n left: 16px;\n}\n.du-bois-light-dropdown-placement-topRight > .du-bois-light-dropdown-arrow {\n right: 16px;\n}\n.du-bois-light-dropdown-placement-bottomCenter > .du-bois-light-dropdown-arrow,\n.du-bois-light-dropdown-placement-bottomLeft > .du-bois-light-dropdown-arrow,\n.du-bois-light-dropdown-placement-bottomRight > .du-bois-light-dropdown-arrow {\n top: 6px;\n border-top-color: #fff;\n border-right-color: transparent;\n border-bottom-color: transparent;\n border-left-color: #fff;\n box-shadow: -2px -2px 5px rgba(0, 0, 0, 0.06);\n}\n.du-bois-light-dropdown-placement-bottomCenter > .du-bois-light-dropdown-arrow {\n left: 50%;\n transform: translateX(-50%) rotate(45deg);\n}\n.du-bois-light-dropdown-placement-bottomLeft > .du-bois-light-dropdown-arrow {\n left: 16px;\n}\n.du-bois-light-dropdown-placement-bottomRight > .du-bois-light-dropdown-arrow {\n right: 16px;\n}\n.du-bois-light-dropdown-menu {\n position: relative;\n margin: 0;\n padding: 4px 0;\n text-align: left;\n list-style-type: none;\n background-color: #fff;\n background-clip: padding-box;\n border-radius: 4px;\n outline: none;\n box-shadow: 0 3px 6px -4px rgba(0, 0, 0, 0.12), 0 6px 16px 0 rgba(0, 0, 0, 0.08), 0 9px 28px 8px rgba(0, 0, 0, 0.05);\n}\n.du-bois-light-dropdown-menu-item-group-title {\n padding: 5px 8px;\n color: #5F7281;\n transition: all 0.3s;\n}\n.du-bois-light-dropdown-menu-submenu-popup {\n position: absolute;\n z-index: 1050;\n background: transparent;\n box-shadow: none;\n transform-origin: 0 0;\n}\n.du-bois-light-dropdown-menu-submenu-popup ul,\n.du-bois-light-dropdown-menu-submenu-popup li {\n list-style: none;\n}\n.du-bois-light-dropdown-menu-submenu-popup ul {\n margin-right: 0.3em;\n margin-left: 0.3em;\n}\n.du-bois-light-dropdown-menu-item {\n position: relative;\n display: flex;\n align-items: center;\n}\n.du-bois-light-dropdown-menu-item-icon {\n min-width: 12px;\n margin-right: 8px;\n font-size: 12px;\n}\n.du-bois-light-dropdown-menu-title-content {\n flex: auto;\n}\n.du-bois-light-dropdown-menu-title-content > a {\n color: inherit;\n transition: all 0.3s;\n}\n.du-bois-light-dropdown-menu-title-content > a:hover {\n color: inherit;\n}\n.du-bois-light-dropdown-menu-title-content > a::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n content: '';\n}\n.du-bois-light-dropdown-menu-item,\n.du-bois-light-dropdown-menu-submenu-title {\n clear: both;\n margin: 0;\n padding: 5px 8px;\n color: #11171C;\n font-weight: normal;\n font-size: 13px;\n line-height: 22px;\n white-space: nowrap;\n cursor: pointer;\n transition: all 0.3s;\n}\n.du-bois-light-dropdown-menu-item-selected,\n.du-bois-light-dropdown-menu-submenu-title-selected {\n color: #2272B4;\n background-color: #e6f1f5;\n}\n.du-bois-light-dropdown-menu-item:hover,\n.du-bois-light-dropdown-menu-submenu-title:hover {\n background-color: #f5f5f5;\n}\n.du-bois-light-dropdown-menu-item-disabled,\n.du-bois-light-dropdown-menu-submenu-title-disabled {\n color: rgba(0, 0, 0, 0.25);\n cursor: not-allowed;\n}\n.du-bois-light-dropdown-menu-item-disabled:hover,\n.du-bois-light-dropdown-menu-submenu-title-disabled:hover {\n color: rgba(0, 0, 0, 0.25);\n background-color: #fff;\n cursor: not-allowed;\n}\n.du-bois-light-dropdown-menu-item-disabled a,\n.du-bois-light-dropdown-menu-submenu-title-disabled a {\n pointer-events: none;\n}\n.du-bois-light-dropdown-menu-item-divider,\n.du-bois-light-dropdown-menu-submenu-title-divider {\n height: 1px;\n margin: 4px 0;\n overflow: hidden;\n line-height: 0;\n background-color: #f0f0f0;\n}\n.du-bois-light-dropdown-menu-item .du-bois-light-dropdown-menu-submenu-expand-icon,\n.du-bois-light-dropdown-menu-submenu-title .du-bois-light-dropdown-menu-submenu-expand-icon {\n position: absolute;\n right: 4px;\n}\n.du-bois-light-dropdown-menu-item .du-bois-light-dropdown-menu-submenu-expand-icon .du-bois-light-dropdown-menu-submenu-arrow-icon,\n.du-bois-light-dropdown-menu-submenu-title .du-bois-light-dropdown-menu-submenu-expand-icon .du-bois-light-dropdown-menu-submenu-arrow-icon {\n margin-right: 0 !important;\n color: #5F7281;\n font-size: 10px;\n font-style: normal;\n}\n.du-bois-light-dropdown-menu-item-group-list {\n margin: 0 8px;\n padding: 0;\n list-style: none;\n}\n.du-bois-light-dropdown-menu-submenu-title {\n padding-right: 20px;\n}\n.du-bois-light-dropdown-menu-submenu-vertical {\n position: relative;\n}\n.du-bois-light-dropdown-menu-submenu-vertical > .du-bois-light-dropdown-menu {\n position: absolute;\n top: 0;\n left: 100%;\n min-width: 100%;\n margin-left: 4px;\n transform-origin: 0 0;\n}\n.du-bois-light-dropdown-menu-submenu.du-bois-light-dropdown-menu-submenu-disabled .du-bois-light-dropdown-menu-submenu-title,\n.du-bois-light-dropdown-menu-submenu.du-bois-light-dropdown-menu-submenu-disabled .du-bois-light-dropdown-menu-submenu-title .du-bois-light-dropdown-menu-submenu-arrow-icon {\n color: rgba(0, 0, 0, 0.25);\n background-color: #fff;\n cursor: not-allowed;\n}\n.du-bois-light-dropdown-menu-submenu-selected .du-bois-light-dropdown-menu-submenu-title {\n color: #2272B4;\n}\n.du-bois-light-dropdown.du-bois-light-slide-down-enter.du-bois-light-slide-down-enter-active.du-bois-light-dropdown-placement-bottomLeft,\n.du-bois-light-dropdown.du-bois-light-slide-down-appear.du-bois-light-slide-down-appear-active.du-bois-light-dropdown-placement-bottomLeft,\n.du-bois-light-dropdown.du-bois-light-slide-down-enter.du-bois-light-slide-down-enter-active.du-bois-light-dropdown-placement-bottomCenter,\n.du-bois-light-dropdown.du-bois-light-slide-down-appear.du-bois-light-slide-down-appear-active.du-bois-light-dropdown-placement-bottomCenter,\n.du-bois-light-dropdown.du-bois-light-slide-down-enter.du-bois-light-slide-down-enter-active.du-bois-light-dropdown-placement-bottomRight,\n.du-bois-light-dropdown.du-bois-light-slide-down-appear.du-bois-light-slide-down-appear-active.du-bois-light-dropdown-placement-bottomRight {\n animation-name: antSlideUpIn;\n}\n.du-bois-light-dropdown.du-bois-light-slide-up-enter.du-bois-light-slide-up-enter-active.du-bois-light-dropdown-placement-topLeft,\n.du-bois-light-dropdown.du-bois-light-slide-up-appear.du-bois-light-slide-up-appear-active.du-bois-light-dropdown-placement-topLeft,\n.du-bois-light-dropdown.du-bois-light-slide-up-enter.du-bois-light-slide-up-enter-active.du-bois-light-dropdown-placement-topCenter,\n.du-bois-light-dropdown.du-bois-light-slide-up-appear.du-bois-light-slide-up-appear-active.du-bois-light-dropdown-placement-topCenter,\n.du-bois-light-dropdown.du-bois-light-slide-up-enter.du-bois-light-slide-up-enter-active.du-bois-light-dropdown-placement-topRight,\n.du-bois-light-dropdown.du-bois-light-slide-up-appear.du-bois-light-slide-up-appear-active.du-bois-light-dropdown-placement-topRight {\n animation-name: antSlideDownIn;\n}\n.du-bois-light-dropdown.du-bois-light-slide-down-leave.du-bois-light-slide-down-leave-active.du-bois-light-dropdown-placement-bottomLeft,\n.du-bois-light-dropdown.du-bois-light-slide-down-leave.du-bois-light-slide-down-leave-active.du-bois-light-dropdown-placement-bottomCenter,\n.du-bois-light-dropdown.du-bois-light-slide-down-leave.du-bois-light-slide-down-leave-active.du-bois-light-dropdown-placement-bottomRight {\n animation-name: antSlideUpOut;\n}\n.du-bois-light-dropdown.du-bois-light-slide-up-leave.du-bois-light-slide-up-leave-active.du-bois-light-dropdown-placement-topLeft,\n.du-bois-light-dropdown.du-bois-light-slide-up-leave.du-bois-light-slide-up-leave-active.du-bois-light-dropdown-placement-topCenter,\n.du-bois-light-dropdown.du-bois-light-slide-up-leave.du-bois-light-slide-up-leave-active.du-bois-light-dropdown-placement-topRight {\n animation-name: antSlideDownOut;\n}\n.du-bois-light-dropdown-trigger > .anticon.anticon-down,\n.du-bois-light-dropdown-link > .anticon.anticon-down,\n.du-bois-light-dropdown-button > .anticon.anticon-down {\n font-size: 10px;\n vertical-align: baseline;\n}\n.du-bois-light-dropdown-button {\n white-space: nowrap;\n}\n.du-bois-light-dropdown-button.du-bois-light-btn-group > .du-bois-light-btn:last-child:not(:first-child):not(.du-bois-light-btn-icon-only) {\n padding-right: 4px;\n padding-left: 4px;\n}\n.du-bois-light-dropdown-menu-dark,\n.du-bois-light-dropdown-menu-dark .du-bois-light-dropdown-menu {\n background: #001529;\n}\n.du-bois-light-dropdown-menu-dark .du-bois-light-dropdown-menu-item,\n.du-bois-light-dropdown-menu-dark .du-bois-light-dropdown-menu-submenu-title,\n.du-bois-light-dropdown-menu-dark .du-bois-light-dropdown-menu-item > a,\n.du-bois-light-dropdown-menu-dark .du-bois-light-dropdown-menu-item > .anticon + span > a {\n color: rgba(255, 255, 255, 0.65);\n}\n.du-bois-light-dropdown-menu-dark .du-bois-light-dropdown-menu-item .du-bois-light-dropdown-menu-submenu-arrow::after,\n.du-bois-light-dropdown-menu-dark .du-bois-light-dropdown-menu-submenu-title .du-bois-light-dropdown-menu-submenu-arrow::after,\n.du-bois-light-dropdown-menu-dark .du-bois-light-dropdown-menu-item > a .du-bois-light-dropdown-menu-submenu-arrow::after,\n.du-bois-light-dropdown-menu-dark .du-bois-light-dropdown-menu-item > .anticon + span > a .du-bois-light-dropdown-menu-submenu-arrow::after {\n color: rgba(255, 255, 255, 0.65);\n}\n.du-bois-light-dropdown-menu-dark .du-bois-light-dropdown-menu-item:hover,\n.du-bois-light-dropdown-menu-dark .du-bois-light-dropdown-menu-submenu-title:hover,\n.du-bois-light-dropdown-menu-dark .du-bois-light-dropdown-menu-item > a:hover,\n.du-bois-light-dropdown-menu-dark .du-bois-light-dropdown-menu-item > .anticon + span > a:hover {\n color: #fff;\n background: transparent;\n}\n.du-bois-light-dropdown-menu-dark .du-bois-light-dropdown-menu-item-selected,\n.du-bois-light-dropdown-menu-dark .du-bois-light-dropdown-menu-item-selected:hover,\n.du-bois-light-dropdown-menu-dark .du-bois-light-dropdown-menu-item-selected > a {\n color: #fff;\n background: #2272B4;\n}\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-light-form-item .du-bois-light-mentions,\n.du-bois-light-form-item textarea.du-bois-light-input {\n height: auto;\n}\n.du-bois-light-form-item .du-bois-light-upload {\n background: transparent;\n}\n.du-bois-light-form-item .du-bois-light-upload.du-bois-light-upload-drag {\n background: #fafafa;\n}\n.du-bois-light-form-item input[type='radio'],\n.du-bois-light-form-item input[type='checkbox'] {\n width: 14px;\n height: 14px;\n}\n.du-bois-light-form-item .du-bois-light-radio-inline,\n.du-bois-light-form-item .du-bois-light-checkbox-inline {\n display: inline-block;\n margin-left: 8px;\n font-weight: normal;\n vertical-align: middle;\n cursor: pointer;\n}\n.du-bois-light-form-item .du-bois-light-radio-inline:first-child,\n.du-bois-light-form-item .du-bois-light-checkbox-inline:first-child {\n margin-left: 0;\n}\n.du-bois-light-form-item .du-bois-light-checkbox-vertical,\n.du-bois-light-form-item .du-bois-light-radio-vertical {\n display: block;\n}\n.du-bois-light-form-item .du-bois-light-checkbox-vertical + .du-bois-light-checkbox-vertical,\n.du-bois-light-form-item .du-bois-light-radio-vertical + .du-bois-light-radio-vertical {\n margin-left: 0;\n}\n.du-bois-light-form-item .du-bois-light-input-number + .du-bois-light-form-text {\n margin-left: 8px;\n}\n.du-bois-light-form-item .du-bois-light-input-number-handler-wrap {\n z-index: 2;\n}\n.du-bois-light-form-item .du-bois-light-select,\n.du-bois-light-form-item .du-bois-light-cascader-picker {\n width: 100%;\n}\n.du-bois-light-form-item .du-bois-light-picker-calendar-year-select,\n.du-bois-light-form-item .du-bois-light-picker-calendar-month-select,\n.du-bois-light-form-item .du-bois-light-input-group .du-bois-light-select,\n.du-bois-light-form-item .du-bois-light-input-group .du-bois-light-cascader-picker {\n width: auto;\n}\n.du-bois-light-form-inline {\n display: flex;\n flex-wrap: wrap;\n}\n.du-bois-light-form-inline .du-bois-light-form-item {\n flex: none;\n flex-wrap: nowrap;\n margin-right: 16px;\n margin-bottom: 0;\n}\n.du-bois-light-form-inline .du-bois-light-form-item-with-help {\n margin-bottom: 24px;\n}\n.du-bois-light-form-inline .du-bois-light-form-item > .du-bois-light-form-item-label,\n.du-bois-light-form-inline .du-bois-light-form-item > .du-bois-light-form-item-control {\n display: inline-block;\n vertical-align: top;\n}\n.du-bois-light-form-inline .du-bois-light-form-item > .du-bois-light-form-item-label {\n flex: none;\n}\n.du-bois-light-form-inline .du-bois-light-form-item .du-bois-light-form-text {\n display: inline-block;\n}\n.du-bois-light-form-inline .du-bois-light-form-item .du-bois-light-form-item-has-feedback {\n display: inline-block;\n}\n.du-bois-light-form-horizontal .du-bois-light-form-item-label {\n flex-grow: 0;\n}\n.du-bois-light-form-horizontal .du-bois-light-form-item-control {\n flex: 1 1 0;\n}\n.du-bois-light-form-vertical .du-bois-light-form-item {\n flex-direction: column;\n}\n.du-bois-light-form-vertical .du-bois-light-form-item-label > label {\n height: auto;\n}\n.du-bois-light-form-vertical .du-bois-light-form-item-label,\n.du-bois-light-col-24.du-bois-light-form-item-label,\n.du-bois-light-col-xl-24.du-bois-light-form-item-label {\n padding: 0 0 8px;\n line-height: 20px;\n white-space: initial;\n text-align: left;\n}\n.du-bois-light-form-vertical .du-bois-light-form-item-label > label,\n.du-bois-light-col-24.du-bois-light-form-item-label > label,\n.du-bois-light-col-xl-24.du-bois-light-form-item-label > label {\n margin: 0;\n}\n.du-bois-light-form-vertical .du-bois-light-form-item-label > label::after,\n.du-bois-light-col-24.du-bois-light-form-item-label > label::after,\n.du-bois-light-col-xl-24.du-bois-light-form-item-label > label::after {\n display: none;\n}\n@media (max-width: 575px) {\n .du-bois-light-form-item .du-bois-light-form-item-label {\n padding: 0 0 8px;\n line-height: 20px;\n white-space: initial;\n text-align: left;\n }\n .du-bois-light-form-item .du-bois-light-form-item-label > label {\n margin: 0;\n }\n .du-bois-light-form-item .du-bois-light-form-item-label > label::after {\n display: none;\n }\n .du-bois-light-form .du-bois-light-form-item {\n flex-wrap: wrap;\n }\n .du-bois-light-form .du-bois-light-form-item .du-bois-light-form-item-label,\n .du-bois-light-form .du-bois-light-form-item .du-bois-light-form-item-control {\n flex: 0 0 100%;\n max-width: 100%;\n }\n .du-bois-light-col-xs-24.du-bois-light-form-item-label {\n padding: 0 0 8px;\n line-height: 20px;\n white-space: initial;\n text-align: left;\n }\n .du-bois-light-col-xs-24.du-bois-light-form-item-label > label {\n margin: 0;\n }\n .du-bois-light-col-xs-24.du-bois-light-form-item-label > label::after {\n display: none;\n }\n}\n@media (max-width: 767px) {\n .du-bois-light-col-sm-24.du-bois-light-form-item-label {\n padding: 0 0 8px;\n line-height: 20px;\n white-space: initial;\n text-align: left;\n }\n .du-bois-light-col-sm-24.du-bois-light-form-item-label > label {\n margin: 0;\n }\n .du-bois-light-col-sm-24.du-bois-light-form-item-label > label::after {\n display: none;\n }\n}\n@media (max-width: 991px) {\n .du-bois-light-col-md-24.du-bois-light-form-item-label {\n padding: 0 0 8px;\n line-height: 20px;\n white-space: initial;\n text-align: left;\n }\n .du-bois-light-col-md-24.du-bois-light-form-item-label > label {\n margin: 0;\n }\n .du-bois-light-col-md-24.du-bois-light-form-item-label > label::after {\n display: none;\n }\n}\n@media (max-width: 1199px) {\n .du-bois-light-col-lg-24.du-bois-light-form-item-label {\n padding: 0 0 8px;\n line-height: 20px;\n white-space: initial;\n text-align: left;\n }\n .du-bois-light-col-lg-24.du-bois-light-form-item-label > label {\n margin: 0;\n }\n .du-bois-light-col-lg-24.du-bois-light-form-item-label > label::after {\n display: none;\n }\n}\n@media (max-width: 1599px) {\n .du-bois-light-col-xl-24.du-bois-light-form-item-label {\n padding: 0 0 8px;\n line-height: 20px;\n white-space: initial;\n text-align: left;\n }\n .du-bois-light-col-xl-24.du-bois-light-form-item-label > label {\n margin: 0;\n }\n .du-bois-light-col-xl-24.du-bois-light-form-item-label > label::after {\n display: none;\n }\n}\n.du-bois-light-form-item {\n /* Some non-status related component style is in `components.less` */\n /* To support leave along ErrorList. We add additional className to handle explain style */\n}\n.du-bois-light-form-item-explain.du-bois-light-form-item-explain-error {\n color: #C82D4C;\n}\n.du-bois-light-form-item-explain.du-bois-light-form-item-explain-warning {\n color: #AF5B23;\n}\n.du-bois-light-form-item-has-feedback .du-bois-light-input {\n padding-right: 24px;\n}\n.du-bois-light-form-item-has-feedback .du-bois-light-input-affix-wrapper .du-bois-light-input-suffix {\n padding-right: 18px;\n}\n.du-bois-light-form-item-has-feedback .du-bois-light-input-search:not(.du-bois-light-input-search-enter-button) .du-bois-light-input-suffix {\n right: 28px;\n}\n.du-bois-light-form-item-has-feedback .du-bois-light-switch {\n margin: 2px 0 4px;\n}\n.du-bois-light-form-item-has-feedback > .du-bois-light-select .du-bois-light-select-arrow,\n.du-bois-light-form-item-has-feedback > .du-bois-light-select .du-bois-light-select-clear,\n.du-bois-light-form-item-has-feedback :not(.du-bois-light-input-group-addon) > .du-bois-light-select .du-bois-light-select-arrow,\n.du-bois-light-form-item-has-feedback :not(.du-bois-light-input-group-addon) > .du-bois-light-select .du-bois-light-select-clear {\n right: 32px;\n}\n.du-bois-light-form-item-has-feedback > .du-bois-light-select .du-bois-light-select-selection-selected-value,\n.du-bois-light-form-item-has-feedback :not(.du-bois-light-input-group-addon) > .du-bois-light-select .du-bois-light-select-selection-selected-value {\n padding-right: 42px;\n}\n.du-bois-light-form-item-has-feedback .du-bois-light-cascader-picker-arrow {\n margin-right: 19px;\n}\n.du-bois-light-form-item-has-feedback .du-bois-light-cascader-picker-clear {\n right: 32px;\n}\n.du-bois-light-form-item-has-feedback .du-bois-light-picker {\n padding-right: 28.9px;\n}\n.du-bois-light-form-item-has-feedback .du-bois-light-picker-large {\n padding-right: 28.9px;\n}\n.du-bois-light-form-item-has-feedback .du-bois-light-picker-small {\n padding-right: 28.9px;\n}\n.du-bois-light-form-item-has-feedback.du-bois-light-form-item-has-success .du-bois-light-form-item-children-icon,\n.du-bois-light-form-item-has-feedback.du-bois-light-form-item-has-warning .du-bois-light-form-item-children-icon,\n.du-bois-light-form-item-has-feedback.du-bois-light-form-item-has-error .du-bois-light-form-item-children-icon,\n.du-bois-light-form-item-has-feedback.du-bois-light-form-item-is-validating .du-bois-light-form-item-children-icon {\n position: absolute;\n top: 50%;\n right: 0;\n z-index: 1;\n width: 40px;\n height: 20px;\n margin-top: -10px;\n font-size: 13px;\n line-height: 20px;\n text-align: center;\n visibility: visible;\n animation: zoomIn 0.3s cubic-bezier(0.12, 0.4, 0.29, 1.46);\n pointer-events: none;\n}\n.du-bois-light-form-item-has-success.du-bois-light-form-item-has-feedback .du-bois-light-form-item-children-icon {\n color: #34824F;\n animation-name: diffZoomIn1 !important;\n}\n.du-bois-light-form-item-has-warning .du-bois-light-form-item-split {\n color: #AF5B23;\n}\n.du-bois-light-form-item-has-warning :not(.du-bois-light-input-disabled).du-bois-light-input,\n.du-bois-light-form-item-has-warning :not(.du-bois-light-input-affix-wrapper-disabled).du-bois-light-input-affix-wrapper,\n.du-bois-light-form-item-has-warning :not(.du-bois-light-input-disabled).du-bois-light-input:hover,\n.du-bois-light-form-item-has-warning :not(.du-bois-light-input-affix-wrapper-disabled).du-bois-light-input-affix-wrapper:hover {\n background-color: #fff;\n border-color: #AF5B23;\n}\n.du-bois-light-form-item-has-warning :not(.du-bois-light-input-disabled).du-bois-light-input:focus,\n.du-bois-light-form-item-has-warning :not(.du-bois-light-input-affix-wrapper-disabled).du-bois-light-input-affix-wrapper:focus,\n.du-bois-light-form-item-has-warning :not(.du-bois-light-input-disabled).du-bois-light-input-focused,\n.du-bois-light-form-item-has-warning :not(.du-bois-light-input-affix-wrapper-disabled).du-bois-light-input-affix-wrapper-focused {\n border-color: #bd7844;\n border-right-width: 1px !important;\n outline: 0;\n box-shadow: 0 0 0 2px rgba(175, 91, 35, 0.2);\n}\n.du-bois-light-form-item-has-warning .du-bois-light-calendar-picker-open .du-bois-light-calendar-picker-input {\n border-color: #bd7844;\n border-right-width: 1px !important;\n outline: 0;\n box-shadow: 0 0 0 2px rgba(175, 91, 35, 0.2);\n}\n.du-bois-light-form-item-has-warning .du-bois-light-input-prefix {\n color: #AF5B23;\n}\n.du-bois-light-form-item-has-warning .du-bois-light-input-group-addon {\n color: #AF5B23;\n border-color: #AF5B23;\n}\n.du-bois-light-form-item-has-warning .has-feedback {\n color: #AF5B23;\n}\n.du-bois-light-form-item-has-warning.du-bois-light-form-item-has-feedback .du-bois-light-form-item-children-icon {\n color: #AF5B23;\n animation-name: diffZoomIn3 !important;\n}\n.du-bois-light-form-item-has-warning .du-bois-light-select:not(.du-bois-light-select-disabled):not(.du-bois-light-select-customize-input) .du-bois-light-select-selector {\n background-color: #fff;\n border-color: #AF5B23 !important;\n}\n.du-bois-light-form-item-has-warning .du-bois-light-select:not(.du-bois-light-select-disabled):not(.du-bois-light-select-customize-input).du-bois-light-select-open .du-bois-light-select-selector,\n.du-bois-light-form-item-has-warning .du-bois-light-select:not(.du-bois-light-select-disabled):not(.du-bois-light-select-customize-input).du-bois-light-select-focused .du-bois-light-select-selector {\n border-color: #bd7844;\n border-right-width: 1px !important;\n outline: 0;\n box-shadow: 0 0 0 2px rgba(175, 91, 35, 0.2);\n}\n.du-bois-light-form-item-has-warning .du-bois-light-input-number,\n.du-bois-light-form-item-has-warning .du-bois-light-picker {\n background-color: #fff;\n border-color: #AF5B23;\n}\n.du-bois-light-form-item-has-warning .du-bois-light-input-number-focused,\n.du-bois-light-form-item-has-warning .du-bois-light-picker-focused,\n.du-bois-light-form-item-has-warning .du-bois-light-input-number:focus,\n.du-bois-light-form-item-has-warning .du-bois-light-picker:focus {\n border-color: #bd7844;\n border-right-width: 1px !important;\n outline: 0;\n box-shadow: 0 0 0 2px rgba(175, 91, 35, 0.2);\n}\n.du-bois-light-form-item-has-warning .du-bois-light-input-number:not([disabled]):hover,\n.du-bois-light-form-item-has-warning .du-bois-light-picker:not([disabled]):hover {\n background-color: #fff;\n border-color: #AF5B23;\n}\n.du-bois-light-form-item-has-warning .du-bois-light-cascader-picker:focus .du-bois-light-cascader-input {\n border-color: #bd7844;\n border-right-width: 1px !important;\n outline: 0;\n box-shadow: 0 0 0 2px rgba(175, 91, 35, 0.2);\n}\n.du-bois-light-form-item-has-error .du-bois-light-form-item-split {\n color: #C82D4C;\n}\n.du-bois-light-form-item-has-error :not(.du-bois-light-input-disabled).du-bois-light-input,\n.du-bois-light-form-item-has-error :not(.du-bois-light-input-affix-wrapper-disabled).du-bois-light-input-affix-wrapper,\n.du-bois-light-form-item-has-error :not(.du-bois-light-input-disabled).du-bois-light-input:hover,\n.du-bois-light-form-item-has-error :not(.du-bois-light-input-affix-wrapper-disabled).du-bois-light-input-affix-wrapper:hover {\n background-color: #fff;\n border-color: #C82D4C;\n}\n.du-bois-light-form-item-has-error :not(.du-bois-light-input-disabled).du-bois-light-input:focus,\n.du-bois-light-form-item-has-error :not(.du-bois-light-input-affix-wrapper-disabled).du-bois-light-input-affix-wrapper:focus,\n.du-bois-light-form-item-has-error :not(.du-bois-light-input-disabled).du-bois-light-input-focused,\n.du-bois-light-form-item-has-error :not(.du-bois-light-input-affix-wrapper-disabled).du-bois-light-input-affix-wrapper-focused {\n border-color: #d45368;\n border-right-width: 1px !important;\n outline: 0;\n box-shadow: 0 0 0 2px rgba(200, 45, 76, 0.2);\n}\n.du-bois-light-form-item-has-error .du-bois-light-calendar-picker-open .du-bois-light-calendar-picker-input {\n border-color: #d45368;\n border-right-width: 1px !important;\n outline: 0;\n box-shadow: 0 0 0 2px rgba(200, 45, 76, 0.2);\n}\n.du-bois-light-form-item-has-error .du-bois-light-input-prefix {\n color: #C82D4C;\n}\n.du-bois-light-form-item-has-error .du-bois-light-input-group-addon {\n color: #C82D4C;\n border-color: #C82D4C;\n}\n.du-bois-light-form-item-has-error .has-feedback {\n color: #C82D4C;\n}\n.du-bois-light-form-item-has-error.du-bois-light-form-item-has-feedback .du-bois-light-form-item-children-icon {\n color: #C82D4C;\n animation-name: diffZoomIn2 !important;\n}\n.du-bois-light-form-item-has-error .du-bois-light-select:not(.du-bois-light-select-disabled):not(.du-bois-light-select-customize-input) .du-bois-light-select-selector {\n background-color: #fff;\n border-color: #C82D4C !important;\n}\n.du-bois-light-form-item-has-error .du-bois-light-select:not(.du-bois-light-select-disabled):not(.du-bois-light-select-customize-input).du-bois-light-select-open .du-bois-light-select-selector,\n.du-bois-light-form-item-has-error .du-bois-light-select:not(.du-bois-light-select-disabled):not(.du-bois-light-select-customize-input).du-bois-light-select-focused .du-bois-light-select-selector {\n border-color: #d45368;\n border-right-width: 1px !important;\n outline: 0;\n box-shadow: 0 0 0 2px rgba(200, 45, 76, 0.2);\n}\n.du-bois-light-form-item-has-error .du-bois-light-input-group-addon .du-bois-light-select.du-bois-light-select-single:not(.du-bois-light-select-customize-input) .du-bois-light-select-selector {\n background-color: inherit;\n border: 0;\n box-shadow: none;\n}\n.du-bois-light-form-item-has-error .du-bois-light-select.du-bois-light-select-auto-complete .du-bois-light-input:focus {\n border-color: #C82D4C;\n}\n.du-bois-light-form-item-has-error .du-bois-light-input-number,\n.du-bois-light-form-item-has-error .du-bois-light-picker {\n background-color: #fff;\n border-color: #C82D4C;\n}\n.du-bois-light-form-item-has-error .du-bois-light-input-number-focused,\n.du-bois-light-form-item-has-error .du-bois-light-picker-focused,\n.du-bois-light-form-item-has-error .du-bois-light-input-number:focus,\n.du-bois-light-form-item-has-error .du-bois-light-picker:focus {\n border-color: #d45368;\n border-right-width: 1px !important;\n outline: 0;\n box-shadow: 0 0 0 2px rgba(200, 45, 76, 0.2);\n}\n.du-bois-light-form-item-has-error .du-bois-light-input-number:not([disabled]):hover,\n.du-bois-light-form-item-has-error .du-bois-light-picker:not([disabled]):hover {\n background-color: #fff;\n border-color: #C82D4C;\n}\n.du-bois-light-form-item-has-error .du-bois-light-mention-wrapper .du-bois-light-mention-editor,\n.du-bois-light-form-item-has-error .du-bois-light-mention-wrapper .du-bois-light-mention-editor:not([disabled]):hover {\n background-color: #fff;\n border-color: #C82D4C;\n}\n.du-bois-light-form-item-has-error .du-bois-light-mention-wrapper.du-bois-light-mention-active:not([disabled]) .du-bois-light-mention-editor,\n.du-bois-light-form-item-has-error .du-bois-light-mention-wrapper .du-bois-light-mention-editor:not([disabled]):focus {\n border-color: #d45368;\n border-right-width: 1px !important;\n outline: 0;\n box-shadow: 0 0 0 2px rgba(200, 45, 76, 0.2);\n}\n.du-bois-light-form-item-has-error .du-bois-light-cascader-picker:hover .du-bois-light-cascader-picker-label:hover + .du-bois-light-cascader-input.du-bois-light-input {\n border-color: #C82D4C;\n}\n.du-bois-light-form-item-has-error .du-bois-light-cascader-picker:focus .du-bois-light-cascader-input {\n background-color: #fff;\n border-color: #d45368;\n border-right-width: 1px !important;\n outline: 0;\n box-shadow: 0 0 0 2px rgba(200, 45, 76, 0.2);\n}\n.du-bois-light-form-item-has-error .du-bois-light-transfer-list {\n border-color: #C82D4C;\n}\n.du-bois-light-form-item-has-error .du-bois-light-transfer-list-search:not([disabled]) {\n border-color: #E8ECF0;\n}\n.du-bois-light-form-item-has-error .du-bois-light-transfer-list-search:not([disabled]):hover {\n border-color: #448dc2;\n border-right-width: 1px !important;\n}\n.du-bois-light-form-item-has-error .du-bois-light-transfer-list-search:not([disabled]):focus {\n border-color: #448dc2;\n border-right-width: 1px !important;\n outline: 0;\n box-shadow: 0 0 0 2px rgba(34, 114, 180, 0.2);\n}\n.du-bois-light-form-item-has-error .du-bois-light-radio-button-wrapper {\n border-color: #C82D4C !important;\n}\n.du-bois-light-form-item-has-error .du-bois-light-radio-button-wrapper:not(:first-child)::before {\n background-color: #C82D4C;\n}\n.du-bois-light-form-item-is-validating.du-bois-light-form-item-has-feedback .du-bois-light-form-item-children-icon {\n display: inline-block;\n color: #2272B4;\n}\n.du-bois-light-form {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #11171C;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: 'tnum';\n}\n.du-bois-light-form legend {\n display: block;\n width: 100%;\n margin-bottom: 20px;\n padding: 0;\n color: #5F7281;\n font-size: 18px;\n line-height: inherit;\n border: 0;\n border-bottom: 1px solid #E8ECF0;\n}\n.du-bois-light-form label {\n font-size: 13px;\n}\n.du-bois-light-form input[type='search'] {\n box-sizing: border-box;\n}\n.du-bois-light-form input[type='radio'],\n.du-bois-light-form input[type='checkbox'] {\n line-height: normal;\n}\n.du-bois-light-form input[type='file'] {\n display: block;\n}\n.du-bois-light-form input[type='range'] {\n display: block;\n width: 100%;\n}\n.du-bois-light-form select[multiple],\n.du-bois-light-form select[size] {\n height: auto;\n}\n.du-bois-light-form input[type='file']:focus,\n.du-bois-light-form input[type='radio']:focus,\n.du-bois-light-form input[type='checkbox']:focus {\n outline: thin dotted;\n outline: 5px auto -webkit-focus-ring-color;\n outline-offset: -2px;\n}\n.du-bois-light-form output {\n display: block;\n padding-top: 15px;\n color: #11171C;\n font-size: 13px;\n line-height: 20px;\n}\n.du-bois-light-form .du-bois-light-form-text {\n display: inline-block;\n padding-right: 8px;\n}\n.du-bois-light-form-small .du-bois-light-form-item-label > label {\n height: 32px;\n}\n.du-bois-light-form-small .du-bois-light-form-item-control-input {\n min-height: 32px;\n}\n.du-bois-light-form-large .du-bois-light-form-item-label > label {\n height: 40px;\n}\n.du-bois-light-form-large .du-bois-light-form-item-control-input {\n min-height: 40px;\n}\n.du-bois-light-form-item {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #11171C;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: 'tnum';\n margin-bottom: 24px;\n vertical-align: top;\n}\n.du-bois-light-form-item-with-help {\n margin-bottom: 0;\n}\n.du-bois-light-form-item-hidden,\n.du-bois-light-form-item-hidden.du-bois-light-row {\n display: none;\n}\n.du-bois-light-form-item-label {\n display: inline-block;\n flex-grow: 0;\n overflow: hidden;\n white-space: nowrap;\n text-align: right;\n vertical-align: middle;\n}\n.du-bois-light-form-item-label-left {\n text-align: left;\n}\n.du-bois-light-form-item-label > label {\n position: relative;\n display: inline-flex;\n align-items: center;\n height: 40px;\n color: rgba(0, 0, 0, 0.85);\n font-size: 13px;\n}\n.du-bois-light-form-item-label > label > .anticon {\n font-size: 13px;\n vertical-align: top;\n}\n.du-bois-light-form-item-label > label.du-bois-light-form-item-required:not(.du-bois-light-form-item-required-mark-optional)::before {\n display: inline-block;\n margin-right: 4px;\n color: #ff4d4f;\n font-size: 13px;\n font-family: SimSun, sans-serif;\n line-height: 1;\n content: '*';\n}\n.du-bois-light-form-hide-required-mark .du-bois-light-form-item-label > label.du-bois-light-form-item-required:not(.du-bois-light-form-item-required-mark-optional)::before {\n display: none;\n}\n.du-bois-light-form-item-label > label .du-bois-light-form-item-optional {\n display: inline-block;\n margin-left: 4px;\n color: #5F7281;\n}\n.du-bois-light-form-hide-required-mark .du-bois-light-form-item-label > label .du-bois-light-form-item-optional {\n display: none;\n}\n.du-bois-light-form-item-label > label .du-bois-light-form-item-tooltip {\n color: #5F7281;\n cursor: help;\n writing-mode: horizontal-tb;\n margin-inline-start: 4px;\n}\n.du-bois-light-form-item-label > label::after {\n content: ':';\n position: relative;\n top: -0.5px;\n margin: 0 8px 0 2px;\n}\n.du-bois-light-form-item-label > label.du-bois-light-form-item-no-colon::after {\n content: ' ';\n}\n.du-bois-light-form-item-control {\n display: flex;\n flex-direction: column;\n flex-grow: 1;\n}\n.du-bois-light-form-item-control:first-child:not([class^='du-bois-light-col-']):not([class*=' du-bois-light-col-']) {\n width: 100%;\n}\n.du-bois-light-form-item-control-input {\n position: relative;\n display: flex;\n align-items: center;\n min-height: 40px;\n}\n.du-bois-light-form-item-control-input-content {\n flex: auto;\n max-width: 100%;\n}\n.du-bois-light-form-item-explain,\n.du-bois-light-form-item-extra {\n clear: both;\n min-height: 24px;\n color: #5F7281;\n font-size: 13px;\n line-height: 20px;\n transition: color 0.3s cubic-bezier(0.215, 0.61, 0.355, 1);\n margin-top: -118px;\n margin-bottom: -118px;\n}\n.du-bois-light-form-item .du-bois-light-input-textarea-show-count::after {\n margin-bottom: -22px;\n}\n.du-bois-light-show-help-enter,\n.du-bois-light-show-help-appear {\n animation-duration: 0.3s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-light-show-help-leave {\n animation-duration: 0.3s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-light-show-help-enter.du-bois-light-show-help-enter-active,\n.du-bois-light-show-help-appear.du-bois-light-show-help-appear-active {\n animation-name: antShowHelpIn;\n animation-play-state: running;\n}\n.du-bois-light-show-help-leave.du-bois-light-show-help-leave-active {\n animation-name: antShowHelpOut;\n animation-play-state: running;\n pointer-events: none;\n}\n.du-bois-light-show-help-enter,\n.du-bois-light-show-help-appear {\n opacity: 0;\n animation-timing-function: cubic-bezier(0.645, 0.045, 0.355, 1);\n}\n.du-bois-light-show-help-leave {\n animation-timing-function: cubic-bezier(0.645, 0.045, 0.355, 1);\n}\n@keyframes antShowHelpIn {\n 0% {\n transform: translateY(-5px);\n opacity: 0;\n }\n 100% {\n transform: translateY(0);\n opacity: 1;\n }\n}\n@keyframes antShowHelpOut {\n}\n@keyframes diffZoomIn1 {\n 0% {\n transform: scale(0);\n opacity: 0;\n }\n 100% {\n transform: scale(1);\n opacity: 1;\n }\n}\n@keyframes diffZoomIn2 {\n 0% {\n transform: scale(0);\n opacity: 0;\n }\n 100% {\n transform: scale(1);\n opacity: 1;\n }\n}\n@keyframes diffZoomIn3 {\n 0% {\n transform: scale(0);\n opacity: 0;\n }\n 100% {\n transform: scale(1);\n opacity: 1;\n }\n}\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-light-row {\n display: flex;\n flex-flow: row wrap;\n}\n.du-bois-light-row::before,\n.du-bois-light-row::after {\n display: flex;\n}\n.du-bois-light-row-no-wrap {\n flex-wrap: nowrap;\n}\n.du-bois-light-row-start {\n justify-content: flex-start;\n}\n.du-bois-light-row-center {\n justify-content: center;\n}\n.du-bois-light-row-end {\n justify-content: flex-end;\n}\n.du-bois-light-row-space-between {\n justify-content: space-between;\n}\n.du-bois-light-row-space-around {\n justify-content: space-around;\n}\n.du-bois-light-row-top {\n align-items: flex-start;\n}\n.du-bois-light-row-middle {\n align-items: center;\n}\n.du-bois-light-row-bottom {\n align-items: flex-end;\n}\n.du-bois-light-col {\n position: relative;\n max-width: 100%;\n min-height: 1px;\n}\n.du-bois-light-col-24 {\n display: block;\n flex: 0 0 100%;\n max-width: 100%;\n}\n.du-bois-light-col-push-24 {\n left: 100%;\n}\n.du-bois-light-col-pull-24 {\n right: 100%;\n}\n.du-bois-light-col-offset-24 {\n margin-left: 100%;\n}\n.du-bois-light-col-order-24 {\n order: 24;\n}\n.du-bois-light-col-23 {\n display: block;\n flex: 0 0 95.83333333%;\n max-width: 95.83333333%;\n}\n.du-bois-light-col-push-23 {\n left: 95.83333333%;\n}\n.du-bois-light-col-pull-23 {\n right: 95.83333333%;\n}\n.du-bois-light-col-offset-23 {\n margin-left: 95.83333333%;\n}\n.du-bois-light-col-order-23 {\n order: 23;\n}\n.du-bois-light-col-22 {\n display: block;\n flex: 0 0 91.66666667%;\n max-width: 91.66666667%;\n}\n.du-bois-light-col-push-22 {\n left: 91.66666667%;\n}\n.du-bois-light-col-pull-22 {\n right: 91.66666667%;\n}\n.du-bois-light-col-offset-22 {\n margin-left: 91.66666667%;\n}\n.du-bois-light-col-order-22 {\n order: 22;\n}\n.du-bois-light-col-21 {\n display: block;\n flex: 0 0 87.5%;\n max-width: 87.5%;\n}\n.du-bois-light-col-push-21 {\n left: 87.5%;\n}\n.du-bois-light-col-pull-21 {\n right: 87.5%;\n}\n.du-bois-light-col-offset-21 {\n margin-left: 87.5%;\n}\n.du-bois-light-col-order-21 {\n order: 21;\n}\n.du-bois-light-col-20 {\n display: block;\n flex: 0 0 83.33333333%;\n max-width: 83.33333333%;\n}\n.du-bois-light-col-push-20 {\n left: 83.33333333%;\n}\n.du-bois-light-col-pull-20 {\n right: 83.33333333%;\n}\n.du-bois-light-col-offset-20 {\n margin-left: 83.33333333%;\n}\n.du-bois-light-col-order-20 {\n order: 20;\n}\n.du-bois-light-col-19 {\n display: block;\n flex: 0 0 79.16666667%;\n max-width: 79.16666667%;\n}\n.du-bois-light-col-push-19 {\n left: 79.16666667%;\n}\n.du-bois-light-col-pull-19 {\n right: 79.16666667%;\n}\n.du-bois-light-col-offset-19 {\n margin-left: 79.16666667%;\n}\n.du-bois-light-col-order-19 {\n order: 19;\n}\n.du-bois-light-col-18 {\n display: block;\n flex: 0 0 75%;\n max-width: 75%;\n}\n.du-bois-light-col-push-18 {\n left: 75%;\n}\n.du-bois-light-col-pull-18 {\n right: 75%;\n}\n.du-bois-light-col-offset-18 {\n margin-left: 75%;\n}\n.du-bois-light-col-order-18 {\n order: 18;\n}\n.du-bois-light-col-17 {\n display: block;\n flex: 0 0 70.83333333%;\n max-width: 70.83333333%;\n}\n.du-bois-light-col-push-17 {\n left: 70.83333333%;\n}\n.du-bois-light-col-pull-17 {\n right: 70.83333333%;\n}\n.du-bois-light-col-offset-17 {\n margin-left: 70.83333333%;\n}\n.du-bois-light-col-order-17 {\n order: 17;\n}\n.du-bois-light-col-16 {\n display: block;\n flex: 0 0 66.66666667%;\n max-width: 66.66666667%;\n}\n.du-bois-light-col-push-16 {\n left: 66.66666667%;\n}\n.du-bois-light-col-pull-16 {\n right: 66.66666667%;\n}\n.du-bois-light-col-offset-16 {\n margin-left: 66.66666667%;\n}\n.du-bois-light-col-order-16 {\n order: 16;\n}\n.du-bois-light-col-15 {\n display: block;\n flex: 0 0 62.5%;\n max-width: 62.5%;\n}\n.du-bois-light-col-push-15 {\n left: 62.5%;\n}\n.du-bois-light-col-pull-15 {\n right: 62.5%;\n}\n.du-bois-light-col-offset-15 {\n margin-left: 62.5%;\n}\n.du-bois-light-col-order-15 {\n order: 15;\n}\n.du-bois-light-col-14 {\n display: block;\n flex: 0 0 58.33333333%;\n max-width: 58.33333333%;\n}\n.du-bois-light-col-push-14 {\n left: 58.33333333%;\n}\n.du-bois-light-col-pull-14 {\n right: 58.33333333%;\n}\n.du-bois-light-col-offset-14 {\n margin-left: 58.33333333%;\n}\n.du-bois-light-col-order-14 {\n order: 14;\n}\n.du-bois-light-col-13 {\n display: block;\n flex: 0 0 54.16666667%;\n max-width: 54.16666667%;\n}\n.du-bois-light-col-push-13 {\n left: 54.16666667%;\n}\n.du-bois-light-col-pull-13 {\n right: 54.16666667%;\n}\n.du-bois-light-col-offset-13 {\n margin-left: 54.16666667%;\n}\n.du-bois-light-col-order-13 {\n order: 13;\n}\n.du-bois-light-col-12 {\n display: block;\n flex: 0 0 50%;\n max-width: 50%;\n}\n.du-bois-light-col-push-12 {\n left: 50%;\n}\n.du-bois-light-col-pull-12 {\n right: 50%;\n}\n.du-bois-light-col-offset-12 {\n margin-left: 50%;\n}\n.du-bois-light-col-order-12 {\n order: 12;\n}\n.du-bois-light-col-11 {\n display: block;\n flex: 0 0 45.83333333%;\n max-width: 45.83333333%;\n}\n.du-bois-light-col-push-11 {\n left: 45.83333333%;\n}\n.du-bois-light-col-pull-11 {\n right: 45.83333333%;\n}\n.du-bois-light-col-offset-11 {\n margin-left: 45.83333333%;\n}\n.du-bois-light-col-order-11 {\n order: 11;\n}\n.du-bois-light-col-10 {\n display: block;\n flex: 0 0 41.66666667%;\n max-width: 41.66666667%;\n}\n.du-bois-light-col-push-10 {\n left: 41.66666667%;\n}\n.du-bois-light-col-pull-10 {\n right: 41.66666667%;\n}\n.du-bois-light-col-offset-10 {\n margin-left: 41.66666667%;\n}\n.du-bois-light-col-order-10 {\n order: 10;\n}\n.du-bois-light-col-9 {\n display: block;\n flex: 0 0 37.5%;\n max-width: 37.5%;\n}\n.du-bois-light-col-push-9 {\n left: 37.5%;\n}\n.du-bois-light-col-pull-9 {\n right: 37.5%;\n}\n.du-bois-light-col-offset-9 {\n margin-left: 37.5%;\n}\n.du-bois-light-col-order-9 {\n order: 9;\n}\n.du-bois-light-col-8 {\n display: block;\n flex: 0 0 33.33333333%;\n max-width: 33.33333333%;\n}\n.du-bois-light-col-push-8 {\n left: 33.33333333%;\n}\n.du-bois-light-col-pull-8 {\n right: 33.33333333%;\n}\n.du-bois-light-col-offset-8 {\n margin-left: 33.33333333%;\n}\n.du-bois-light-col-order-8 {\n order: 8;\n}\n.du-bois-light-col-7 {\n display: block;\n flex: 0 0 29.16666667%;\n max-width: 29.16666667%;\n}\n.du-bois-light-col-push-7 {\n left: 29.16666667%;\n}\n.du-bois-light-col-pull-7 {\n right: 29.16666667%;\n}\n.du-bois-light-col-offset-7 {\n margin-left: 29.16666667%;\n}\n.du-bois-light-col-order-7 {\n order: 7;\n}\n.du-bois-light-col-6 {\n display: block;\n flex: 0 0 25%;\n max-width: 25%;\n}\n.du-bois-light-col-push-6 {\n left: 25%;\n}\n.du-bois-light-col-pull-6 {\n right: 25%;\n}\n.du-bois-light-col-offset-6 {\n margin-left: 25%;\n}\n.du-bois-light-col-order-6 {\n order: 6;\n}\n.du-bois-light-col-5 {\n display: block;\n flex: 0 0 20.83333333%;\n max-width: 20.83333333%;\n}\n.du-bois-light-col-push-5 {\n left: 20.83333333%;\n}\n.du-bois-light-col-pull-5 {\n right: 20.83333333%;\n}\n.du-bois-light-col-offset-5 {\n margin-left: 20.83333333%;\n}\n.du-bois-light-col-order-5 {\n order: 5;\n}\n.du-bois-light-col-4 {\n display: block;\n flex: 0 0 16.66666667%;\n max-width: 16.66666667%;\n}\n.du-bois-light-col-push-4 {\n left: 16.66666667%;\n}\n.du-bois-light-col-pull-4 {\n right: 16.66666667%;\n}\n.du-bois-light-col-offset-4 {\n margin-left: 16.66666667%;\n}\n.du-bois-light-col-order-4 {\n order: 4;\n}\n.du-bois-light-col-3 {\n display: block;\n flex: 0 0 12.5%;\n max-width: 12.5%;\n}\n.du-bois-light-col-push-3 {\n left: 12.5%;\n}\n.du-bois-light-col-pull-3 {\n right: 12.5%;\n}\n.du-bois-light-col-offset-3 {\n margin-left: 12.5%;\n}\n.du-bois-light-col-order-3 {\n order: 3;\n}\n.du-bois-light-col-2 {\n display: block;\n flex: 0 0 8.33333333%;\n max-width: 8.33333333%;\n}\n.du-bois-light-col-push-2 {\n left: 8.33333333%;\n}\n.du-bois-light-col-pull-2 {\n right: 8.33333333%;\n}\n.du-bois-light-col-offset-2 {\n margin-left: 8.33333333%;\n}\n.du-bois-light-col-order-2 {\n order: 2;\n}\n.du-bois-light-col-1 {\n display: block;\n flex: 0 0 4.16666667%;\n max-width: 4.16666667%;\n}\n.du-bois-light-col-push-1 {\n left: 4.16666667%;\n}\n.du-bois-light-col-pull-1 {\n right: 4.16666667%;\n}\n.du-bois-light-col-offset-1 {\n margin-left: 4.16666667%;\n}\n.du-bois-light-col-order-1 {\n order: 1;\n}\n.du-bois-light-col-0 {\n display: none;\n}\n.du-bois-light-col-push-0 {\n left: auto;\n}\n.du-bois-light-col-pull-0 {\n right: auto;\n}\n.du-bois-light-col-push-0 {\n left: auto;\n}\n.du-bois-light-col-pull-0 {\n right: auto;\n}\n.du-bois-light-col-offset-0 {\n margin-left: 0;\n}\n.du-bois-light-col-order-0 {\n order: 0;\n}\n.du-bois-light-col-xs-24 {\n display: block;\n flex: 0 0 100%;\n max-width: 100%;\n}\n.du-bois-light-col-xs-push-24 {\n left: 100%;\n}\n.du-bois-light-col-xs-pull-24 {\n right: 100%;\n}\n.du-bois-light-col-xs-offset-24 {\n margin-left: 100%;\n}\n.du-bois-light-col-xs-order-24 {\n order: 24;\n}\n.du-bois-light-col-xs-23 {\n display: block;\n flex: 0 0 95.83333333%;\n max-width: 95.83333333%;\n}\n.du-bois-light-col-xs-push-23 {\n left: 95.83333333%;\n}\n.du-bois-light-col-xs-pull-23 {\n right: 95.83333333%;\n}\n.du-bois-light-col-xs-offset-23 {\n margin-left: 95.83333333%;\n}\n.du-bois-light-col-xs-order-23 {\n order: 23;\n}\n.du-bois-light-col-xs-22 {\n display: block;\n flex: 0 0 91.66666667%;\n max-width: 91.66666667%;\n}\n.du-bois-light-col-xs-push-22 {\n left: 91.66666667%;\n}\n.du-bois-light-col-xs-pull-22 {\n right: 91.66666667%;\n}\n.du-bois-light-col-xs-offset-22 {\n margin-left: 91.66666667%;\n}\n.du-bois-light-col-xs-order-22 {\n order: 22;\n}\n.du-bois-light-col-xs-21 {\n display: block;\n flex: 0 0 87.5%;\n max-width: 87.5%;\n}\n.du-bois-light-col-xs-push-21 {\n left: 87.5%;\n}\n.du-bois-light-col-xs-pull-21 {\n right: 87.5%;\n}\n.du-bois-light-col-xs-offset-21 {\n margin-left: 87.5%;\n}\n.du-bois-light-col-xs-order-21 {\n order: 21;\n}\n.du-bois-light-col-xs-20 {\n display: block;\n flex: 0 0 83.33333333%;\n max-width: 83.33333333%;\n}\n.du-bois-light-col-xs-push-20 {\n left: 83.33333333%;\n}\n.du-bois-light-col-xs-pull-20 {\n right: 83.33333333%;\n}\n.du-bois-light-col-xs-offset-20 {\n margin-left: 83.33333333%;\n}\n.du-bois-light-col-xs-order-20 {\n order: 20;\n}\n.du-bois-light-col-xs-19 {\n display: block;\n flex: 0 0 79.16666667%;\n max-width: 79.16666667%;\n}\n.du-bois-light-col-xs-push-19 {\n left: 79.16666667%;\n}\n.du-bois-light-col-xs-pull-19 {\n right: 79.16666667%;\n}\n.du-bois-light-col-xs-offset-19 {\n margin-left: 79.16666667%;\n}\n.du-bois-light-col-xs-order-19 {\n order: 19;\n}\n.du-bois-light-col-xs-18 {\n display: block;\n flex: 0 0 75%;\n max-width: 75%;\n}\n.du-bois-light-col-xs-push-18 {\n left: 75%;\n}\n.du-bois-light-col-xs-pull-18 {\n right: 75%;\n}\n.du-bois-light-col-xs-offset-18 {\n margin-left: 75%;\n}\n.du-bois-light-col-xs-order-18 {\n order: 18;\n}\n.du-bois-light-col-xs-17 {\n display: block;\n flex: 0 0 70.83333333%;\n max-width: 70.83333333%;\n}\n.du-bois-light-col-xs-push-17 {\n left: 70.83333333%;\n}\n.du-bois-light-col-xs-pull-17 {\n right: 70.83333333%;\n}\n.du-bois-light-col-xs-offset-17 {\n margin-left: 70.83333333%;\n}\n.du-bois-light-col-xs-order-17 {\n order: 17;\n}\n.du-bois-light-col-xs-16 {\n display: block;\n flex: 0 0 66.66666667%;\n max-width: 66.66666667%;\n}\n.du-bois-light-col-xs-push-16 {\n left: 66.66666667%;\n}\n.du-bois-light-col-xs-pull-16 {\n right: 66.66666667%;\n}\n.du-bois-light-col-xs-offset-16 {\n margin-left: 66.66666667%;\n}\n.du-bois-light-col-xs-order-16 {\n order: 16;\n}\n.du-bois-light-col-xs-15 {\n display: block;\n flex: 0 0 62.5%;\n max-width: 62.5%;\n}\n.du-bois-light-col-xs-push-15 {\n left: 62.5%;\n}\n.du-bois-light-col-xs-pull-15 {\n right: 62.5%;\n}\n.du-bois-light-col-xs-offset-15 {\n margin-left: 62.5%;\n}\n.du-bois-light-col-xs-order-15 {\n order: 15;\n}\n.du-bois-light-col-xs-14 {\n display: block;\n flex: 0 0 58.33333333%;\n max-width: 58.33333333%;\n}\n.du-bois-light-col-xs-push-14 {\n left: 58.33333333%;\n}\n.du-bois-light-col-xs-pull-14 {\n right: 58.33333333%;\n}\n.du-bois-light-col-xs-offset-14 {\n margin-left: 58.33333333%;\n}\n.du-bois-light-col-xs-order-14 {\n order: 14;\n}\n.du-bois-light-col-xs-13 {\n display: block;\n flex: 0 0 54.16666667%;\n max-width: 54.16666667%;\n}\n.du-bois-light-col-xs-push-13 {\n left: 54.16666667%;\n}\n.du-bois-light-col-xs-pull-13 {\n right: 54.16666667%;\n}\n.du-bois-light-col-xs-offset-13 {\n margin-left: 54.16666667%;\n}\n.du-bois-light-col-xs-order-13 {\n order: 13;\n}\n.du-bois-light-col-xs-12 {\n display: block;\n flex: 0 0 50%;\n max-width: 50%;\n}\n.du-bois-light-col-xs-push-12 {\n left: 50%;\n}\n.du-bois-light-col-xs-pull-12 {\n right: 50%;\n}\n.du-bois-light-col-xs-offset-12 {\n margin-left: 50%;\n}\n.du-bois-light-col-xs-order-12 {\n order: 12;\n}\n.du-bois-light-col-xs-11 {\n display: block;\n flex: 0 0 45.83333333%;\n max-width: 45.83333333%;\n}\n.du-bois-light-col-xs-push-11 {\n left: 45.83333333%;\n}\n.du-bois-light-col-xs-pull-11 {\n right: 45.83333333%;\n}\n.du-bois-light-col-xs-offset-11 {\n margin-left: 45.83333333%;\n}\n.du-bois-light-col-xs-order-11 {\n order: 11;\n}\n.du-bois-light-col-xs-10 {\n display: block;\n flex: 0 0 41.66666667%;\n max-width: 41.66666667%;\n}\n.du-bois-light-col-xs-push-10 {\n left: 41.66666667%;\n}\n.du-bois-light-col-xs-pull-10 {\n right: 41.66666667%;\n}\n.du-bois-light-col-xs-offset-10 {\n margin-left: 41.66666667%;\n}\n.du-bois-light-col-xs-order-10 {\n order: 10;\n}\n.du-bois-light-col-xs-9 {\n display: block;\n flex: 0 0 37.5%;\n max-width: 37.5%;\n}\n.du-bois-light-col-xs-push-9 {\n left: 37.5%;\n}\n.du-bois-light-col-xs-pull-9 {\n right: 37.5%;\n}\n.du-bois-light-col-xs-offset-9 {\n margin-left: 37.5%;\n}\n.du-bois-light-col-xs-order-9 {\n order: 9;\n}\n.du-bois-light-col-xs-8 {\n display: block;\n flex: 0 0 33.33333333%;\n max-width: 33.33333333%;\n}\n.du-bois-light-col-xs-push-8 {\n left: 33.33333333%;\n}\n.du-bois-light-col-xs-pull-8 {\n right: 33.33333333%;\n}\n.du-bois-light-col-xs-offset-8 {\n margin-left: 33.33333333%;\n}\n.du-bois-light-col-xs-order-8 {\n order: 8;\n}\n.du-bois-light-col-xs-7 {\n display: block;\n flex: 0 0 29.16666667%;\n max-width: 29.16666667%;\n}\n.du-bois-light-col-xs-push-7 {\n left: 29.16666667%;\n}\n.du-bois-light-col-xs-pull-7 {\n right: 29.16666667%;\n}\n.du-bois-light-col-xs-offset-7 {\n margin-left: 29.16666667%;\n}\n.du-bois-light-col-xs-order-7 {\n order: 7;\n}\n.du-bois-light-col-xs-6 {\n display: block;\n flex: 0 0 25%;\n max-width: 25%;\n}\n.du-bois-light-col-xs-push-6 {\n left: 25%;\n}\n.du-bois-light-col-xs-pull-6 {\n right: 25%;\n}\n.du-bois-light-col-xs-offset-6 {\n margin-left: 25%;\n}\n.du-bois-light-col-xs-order-6 {\n order: 6;\n}\n.du-bois-light-col-xs-5 {\n display: block;\n flex: 0 0 20.83333333%;\n max-width: 20.83333333%;\n}\n.du-bois-light-col-xs-push-5 {\n left: 20.83333333%;\n}\n.du-bois-light-col-xs-pull-5 {\n right: 20.83333333%;\n}\n.du-bois-light-col-xs-offset-5 {\n margin-left: 20.83333333%;\n}\n.du-bois-light-col-xs-order-5 {\n order: 5;\n}\n.du-bois-light-col-xs-4 {\n display: block;\n flex: 0 0 16.66666667%;\n max-width: 16.66666667%;\n}\n.du-bois-light-col-xs-push-4 {\n left: 16.66666667%;\n}\n.du-bois-light-col-xs-pull-4 {\n right: 16.66666667%;\n}\n.du-bois-light-col-xs-offset-4 {\n margin-left: 16.66666667%;\n}\n.du-bois-light-col-xs-order-4 {\n order: 4;\n}\n.du-bois-light-col-xs-3 {\n display: block;\n flex: 0 0 12.5%;\n max-width: 12.5%;\n}\n.du-bois-light-col-xs-push-3 {\n left: 12.5%;\n}\n.du-bois-light-col-xs-pull-3 {\n right: 12.5%;\n}\n.du-bois-light-col-xs-offset-3 {\n margin-left: 12.5%;\n}\n.du-bois-light-col-xs-order-3 {\n order: 3;\n}\n.du-bois-light-col-xs-2 {\n display: block;\n flex: 0 0 8.33333333%;\n max-width: 8.33333333%;\n}\n.du-bois-light-col-xs-push-2 {\n left: 8.33333333%;\n}\n.du-bois-light-col-xs-pull-2 {\n right: 8.33333333%;\n}\n.du-bois-light-col-xs-offset-2 {\n margin-left: 8.33333333%;\n}\n.du-bois-light-col-xs-order-2 {\n order: 2;\n}\n.du-bois-light-col-xs-1 {\n display: block;\n flex: 0 0 4.16666667%;\n max-width: 4.16666667%;\n}\n.du-bois-light-col-xs-push-1 {\n left: 4.16666667%;\n}\n.du-bois-light-col-xs-pull-1 {\n right: 4.16666667%;\n}\n.du-bois-light-col-xs-offset-1 {\n margin-left: 4.16666667%;\n}\n.du-bois-light-col-xs-order-1 {\n order: 1;\n}\n.du-bois-light-col-xs-0 {\n display: none;\n}\n.du-bois-light-col-push-0 {\n left: auto;\n}\n.du-bois-light-col-pull-0 {\n right: auto;\n}\n.du-bois-light-col-xs-push-0 {\n left: auto;\n}\n.du-bois-light-col-xs-pull-0 {\n right: auto;\n}\n.du-bois-light-col-xs-offset-0 {\n margin-left: 0;\n}\n.du-bois-light-col-xs-order-0 {\n order: 0;\n}\n@media (min-width: 576px) {\n .du-bois-light-col-sm-24 {\n display: block;\n flex: 0 0 100%;\n max-width: 100%;\n }\n .du-bois-light-col-sm-push-24 {\n left: 100%;\n }\n .du-bois-light-col-sm-pull-24 {\n right: 100%;\n }\n .du-bois-light-col-sm-offset-24 {\n margin-left: 100%;\n }\n .du-bois-light-col-sm-order-24 {\n order: 24;\n }\n .du-bois-light-col-sm-23 {\n display: block;\n flex: 0 0 95.83333333%;\n max-width: 95.83333333%;\n }\n .du-bois-light-col-sm-push-23 {\n left: 95.83333333%;\n }\n .du-bois-light-col-sm-pull-23 {\n right: 95.83333333%;\n }\n .du-bois-light-col-sm-offset-23 {\n margin-left: 95.83333333%;\n }\n .du-bois-light-col-sm-order-23 {\n order: 23;\n }\n .du-bois-light-col-sm-22 {\n display: block;\n flex: 0 0 91.66666667%;\n max-width: 91.66666667%;\n }\n .du-bois-light-col-sm-push-22 {\n left: 91.66666667%;\n }\n .du-bois-light-col-sm-pull-22 {\n right: 91.66666667%;\n }\n .du-bois-light-col-sm-offset-22 {\n margin-left: 91.66666667%;\n }\n .du-bois-light-col-sm-order-22 {\n order: 22;\n }\n .du-bois-light-col-sm-21 {\n display: block;\n flex: 0 0 87.5%;\n max-width: 87.5%;\n }\n .du-bois-light-col-sm-push-21 {\n left: 87.5%;\n }\n .du-bois-light-col-sm-pull-21 {\n right: 87.5%;\n }\n .du-bois-light-col-sm-offset-21 {\n margin-left: 87.5%;\n }\n .du-bois-light-col-sm-order-21 {\n order: 21;\n }\n .du-bois-light-col-sm-20 {\n display: block;\n flex: 0 0 83.33333333%;\n max-width: 83.33333333%;\n }\n .du-bois-light-col-sm-push-20 {\n left: 83.33333333%;\n }\n .du-bois-light-col-sm-pull-20 {\n right: 83.33333333%;\n }\n .du-bois-light-col-sm-offset-20 {\n margin-left: 83.33333333%;\n }\n .du-bois-light-col-sm-order-20 {\n order: 20;\n }\n .du-bois-light-col-sm-19 {\n display: block;\n flex: 0 0 79.16666667%;\n max-width: 79.16666667%;\n }\n .du-bois-light-col-sm-push-19 {\n left: 79.16666667%;\n }\n .du-bois-light-col-sm-pull-19 {\n right: 79.16666667%;\n }\n .du-bois-light-col-sm-offset-19 {\n margin-left: 79.16666667%;\n }\n .du-bois-light-col-sm-order-19 {\n order: 19;\n }\n .du-bois-light-col-sm-18 {\n display: block;\n flex: 0 0 75%;\n max-width: 75%;\n }\n .du-bois-light-col-sm-push-18 {\n left: 75%;\n }\n .du-bois-light-col-sm-pull-18 {\n right: 75%;\n }\n .du-bois-light-col-sm-offset-18 {\n margin-left: 75%;\n }\n .du-bois-light-col-sm-order-18 {\n order: 18;\n }\n .du-bois-light-col-sm-17 {\n display: block;\n flex: 0 0 70.83333333%;\n max-width: 70.83333333%;\n }\n .du-bois-light-col-sm-push-17 {\n left: 70.83333333%;\n }\n .du-bois-light-col-sm-pull-17 {\n right: 70.83333333%;\n }\n .du-bois-light-col-sm-offset-17 {\n margin-left: 70.83333333%;\n }\n .du-bois-light-col-sm-order-17 {\n order: 17;\n }\n .du-bois-light-col-sm-16 {\n display: block;\n flex: 0 0 66.66666667%;\n max-width: 66.66666667%;\n }\n .du-bois-light-col-sm-push-16 {\n left: 66.66666667%;\n }\n .du-bois-light-col-sm-pull-16 {\n right: 66.66666667%;\n }\n .du-bois-light-col-sm-offset-16 {\n margin-left: 66.66666667%;\n }\n .du-bois-light-col-sm-order-16 {\n order: 16;\n }\n .du-bois-light-col-sm-15 {\n display: block;\n flex: 0 0 62.5%;\n max-width: 62.5%;\n }\n .du-bois-light-col-sm-push-15 {\n left: 62.5%;\n }\n .du-bois-light-col-sm-pull-15 {\n right: 62.5%;\n }\n .du-bois-light-col-sm-offset-15 {\n margin-left: 62.5%;\n }\n .du-bois-light-col-sm-order-15 {\n order: 15;\n }\n .du-bois-light-col-sm-14 {\n display: block;\n flex: 0 0 58.33333333%;\n max-width: 58.33333333%;\n }\n .du-bois-light-col-sm-push-14 {\n left: 58.33333333%;\n }\n .du-bois-light-col-sm-pull-14 {\n right: 58.33333333%;\n }\n .du-bois-light-col-sm-offset-14 {\n margin-left: 58.33333333%;\n }\n .du-bois-light-col-sm-order-14 {\n order: 14;\n }\n .du-bois-light-col-sm-13 {\n display: block;\n flex: 0 0 54.16666667%;\n max-width: 54.16666667%;\n }\n .du-bois-light-col-sm-push-13 {\n left: 54.16666667%;\n }\n .du-bois-light-col-sm-pull-13 {\n right: 54.16666667%;\n }\n .du-bois-light-col-sm-offset-13 {\n margin-left: 54.16666667%;\n }\n .du-bois-light-col-sm-order-13 {\n order: 13;\n }\n .du-bois-light-col-sm-12 {\n display: block;\n flex: 0 0 50%;\n max-width: 50%;\n }\n .du-bois-light-col-sm-push-12 {\n left: 50%;\n }\n .du-bois-light-col-sm-pull-12 {\n right: 50%;\n }\n .du-bois-light-col-sm-offset-12 {\n margin-left: 50%;\n }\n .du-bois-light-col-sm-order-12 {\n order: 12;\n }\n .du-bois-light-col-sm-11 {\n display: block;\n flex: 0 0 45.83333333%;\n max-width: 45.83333333%;\n }\n .du-bois-light-col-sm-push-11 {\n left: 45.83333333%;\n }\n .du-bois-light-col-sm-pull-11 {\n right: 45.83333333%;\n }\n .du-bois-light-col-sm-offset-11 {\n margin-left: 45.83333333%;\n }\n .du-bois-light-col-sm-order-11 {\n order: 11;\n }\n .du-bois-light-col-sm-10 {\n display: block;\n flex: 0 0 41.66666667%;\n max-width: 41.66666667%;\n }\n .du-bois-light-col-sm-push-10 {\n left: 41.66666667%;\n }\n .du-bois-light-col-sm-pull-10 {\n right: 41.66666667%;\n }\n .du-bois-light-col-sm-offset-10 {\n margin-left: 41.66666667%;\n }\n .du-bois-light-col-sm-order-10 {\n order: 10;\n }\n .du-bois-light-col-sm-9 {\n display: block;\n flex: 0 0 37.5%;\n max-width: 37.5%;\n }\n .du-bois-light-col-sm-push-9 {\n left: 37.5%;\n }\n .du-bois-light-col-sm-pull-9 {\n right: 37.5%;\n }\n .du-bois-light-col-sm-offset-9 {\n margin-left: 37.5%;\n }\n .du-bois-light-col-sm-order-9 {\n order: 9;\n }\n .du-bois-light-col-sm-8 {\n display: block;\n flex: 0 0 33.33333333%;\n max-width: 33.33333333%;\n }\n .du-bois-light-col-sm-push-8 {\n left: 33.33333333%;\n }\n .du-bois-light-col-sm-pull-8 {\n right: 33.33333333%;\n }\n .du-bois-light-col-sm-offset-8 {\n margin-left: 33.33333333%;\n }\n .du-bois-light-col-sm-order-8 {\n order: 8;\n }\n .du-bois-light-col-sm-7 {\n display: block;\n flex: 0 0 29.16666667%;\n max-width: 29.16666667%;\n }\n .du-bois-light-col-sm-push-7 {\n left: 29.16666667%;\n }\n .du-bois-light-col-sm-pull-7 {\n right: 29.16666667%;\n }\n .du-bois-light-col-sm-offset-7 {\n margin-left: 29.16666667%;\n }\n .du-bois-light-col-sm-order-7 {\n order: 7;\n }\n .du-bois-light-col-sm-6 {\n display: block;\n flex: 0 0 25%;\n max-width: 25%;\n }\n .du-bois-light-col-sm-push-6 {\n left: 25%;\n }\n .du-bois-light-col-sm-pull-6 {\n right: 25%;\n }\n .du-bois-light-col-sm-offset-6 {\n margin-left: 25%;\n }\n .du-bois-light-col-sm-order-6 {\n order: 6;\n }\n .du-bois-light-col-sm-5 {\n display: block;\n flex: 0 0 20.83333333%;\n max-width: 20.83333333%;\n }\n .du-bois-light-col-sm-push-5 {\n left: 20.83333333%;\n }\n .du-bois-light-col-sm-pull-5 {\n right: 20.83333333%;\n }\n .du-bois-light-col-sm-offset-5 {\n margin-left: 20.83333333%;\n }\n .du-bois-light-col-sm-order-5 {\n order: 5;\n }\n .du-bois-light-col-sm-4 {\n display: block;\n flex: 0 0 16.66666667%;\n max-width: 16.66666667%;\n }\n .du-bois-light-col-sm-push-4 {\n left: 16.66666667%;\n }\n .du-bois-light-col-sm-pull-4 {\n right: 16.66666667%;\n }\n .du-bois-light-col-sm-offset-4 {\n margin-left: 16.66666667%;\n }\n .du-bois-light-col-sm-order-4 {\n order: 4;\n }\n .du-bois-light-col-sm-3 {\n display: block;\n flex: 0 0 12.5%;\n max-width: 12.5%;\n }\n .du-bois-light-col-sm-push-3 {\n left: 12.5%;\n }\n .du-bois-light-col-sm-pull-3 {\n right: 12.5%;\n }\n .du-bois-light-col-sm-offset-3 {\n margin-left: 12.5%;\n }\n .du-bois-light-col-sm-order-3 {\n order: 3;\n }\n .du-bois-light-col-sm-2 {\n display: block;\n flex: 0 0 8.33333333%;\n max-width: 8.33333333%;\n }\n .du-bois-light-col-sm-push-2 {\n left: 8.33333333%;\n }\n .du-bois-light-col-sm-pull-2 {\n right: 8.33333333%;\n }\n .du-bois-light-col-sm-offset-2 {\n margin-left: 8.33333333%;\n }\n .du-bois-light-col-sm-order-2 {\n order: 2;\n }\n .du-bois-light-col-sm-1 {\n display: block;\n flex: 0 0 4.16666667%;\n max-width: 4.16666667%;\n }\n .du-bois-light-col-sm-push-1 {\n left: 4.16666667%;\n }\n .du-bois-light-col-sm-pull-1 {\n right: 4.16666667%;\n }\n .du-bois-light-col-sm-offset-1 {\n margin-left: 4.16666667%;\n }\n .du-bois-light-col-sm-order-1 {\n order: 1;\n }\n .du-bois-light-col-sm-0 {\n display: none;\n }\n .du-bois-light-col-push-0 {\n left: auto;\n }\n .du-bois-light-col-pull-0 {\n right: auto;\n }\n .du-bois-light-col-sm-push-0 {\n left: auto;\n }\n .du-bois-light-col-sm-pull-0 {\n right: auto;\n }\n .du-bois-light-col-sm-offset-0 {\n margin-left: 0;\n }\n .du-bois-light-col-sm-order-0 {\n order: 0;\n }\n}\n@media (min-width: 768px) {\n .du-bois-light-col-md-24 {\n display: block;\n flex: 0 0 100%;\n max-width: 100%;\n }\n .du-bois-light-col-md-push-24 {\n left: 100%;\n }\n .du-bois-light-col-md-pull-24 {\n right: 100%;\n }\n .du-bois-light-col-md-offset-24 {\n margin-left: 100%;\n }\n .du-bois-light-col-md-order-24 {\n order: 24;\n }\n .du-bois-light-col-md-23 {\n display: block;\n flex: 0 0 95.83333333%;\n max-width: 95.83333333%;\n }\n .du-bois-light-col-md-push-23 {\n left: 95.83333333%;\n }\n .du-bois-light-col-md-pull-23 {\n right: 95.83333333%;\n }\n .du-bois-light-col-md-offset-23 {\n margin-left: 95.83333333%;\n }\n .du-bois-light-col-md-order-23 {\n order: 23;\n }\n .du-bois-light-col-md-22 {\n display: block;\n flex: 0 0 91.66666667%;\n max-width: 91.66666667%;\n }\n .du-bois-light-col-md-push-22 {\n left: 91.66666667%;\n }\n .du-bois-light-col-md-pull-22 {\n right: 91.66666667%;\n }\n .du-bois-light-col-md-offset-22 {\n margin-left: 91.66666667%;\n }\n .du-bois-light-col-md-order-22 {\n order: 22;\n }\n .du-bois-light-col-md-21 {\n display: block;\n flex: 0 0 87.5%;\n max-width: 87.5%;\n }\n .du-bois-light-col-md-push-21 {\n left: 87.5%;\n }\n .du-bois-light-col-md-pull-21 {\n right: 87.5%;\n }\n .du-bois-light-col-md-offset-21 {\n margin-left: 87.5%;\n }\n .du-bois-light-col-md-order-21 {\n order: 21;\n }\n .du-bois-light-col-md-20 {\n display: block;\n flex: 0 0 83.33333333%;\n max-width: 83.33333333%;\n }\n .du-bois-light-col-md-push-20 {\n left: 83.33333333%;\n }\n .du-bois-light-col-md-pull-20 {\n right: 83.33333333%;\n }\n .du-bois-light-col-md-offset-20 {\n margin-left: 83.33333333%;\n }\n .du-bois-light-col-md-order-20 {\n order: 20;\n }\n .du-bois-light-col-md-19 {\n display: block;\n flex: 0 0 79.16666667%;\n max-width: 79.16666667%;\n }\n .du-bois-light-col-md-push-19 {\n left: 79.16666667%;\n }\n .du-bois-light-col-md-pull-19 {\n right: 79.16666667%;\n }\n .du-bois-light-col-md-offset-19 {\n margin-left: 79.16666667%;\n }\n .du-bois-light-col-md-order-19 {\n order: 19;\n }\n .du-bois-light-col-md-18 {\n display: block;\n flex: 0 0 75%;\n max-width: 75%;\n }\n .du-bois-light-col-md-push-18 {\n left: 75%;\n }\n .du-bois-light-col-md-pull-18 {\n right: 75%;\n }\n .du-bois-light-col-md-offset-18 {\n margin-left: 75%;\n }\n .du-bois-light-col-md-order-18 {\n order: 18;\n }\n .du-bois-light-col-md-17 {\n display: block;\n flex: 0 0 70.83333333%;\n max-width: 70.83333333%;\n }\n .du-bois-light-col-md-push-17 {\n left: 70.83333333%;\n }\n .du-bois-light-col-md-pull-17 {\n right: 70.83333333%;\n }\n .du-bois-light-col-md-offset-17 {\n margin-left: 70.83333333%;\n }\n .du-bois-light-col-md-order-17 {\n order: 17;\n }\n .du-bois-light-col-md-16 {\n display: block;\n flex: 0 0 66.66666667%;\n max-width: 66.66666667%;\n }\n .du-bois-light-col-md-push-16 {\n left: 66.66666667%;\n }\n .du-bois-light-col-md-pull-16 {\n right: 66.66666667%;\n }\n .du-bois-light-col-md-offset-16 {\n margin-left: 66.66666667%;\n }\n .du-bois-light-col-md-order-16 {\n order: 16;\n }\n .du-bois-light-col-md-15 {\n display: block;\n flex: 0 0 62.5%;\n max-width: 62.5%;\n }\n .du-bois-light-col-md-push-15 {\n left: 62.5%;\n }\n .du-bois-light-col-md-pull-15 {\n right: 62.5%;\n }\n .du-bois-light-col-md-offset-15 {\n margin-left: 62.5%;\n }\n .du-bois-light-col-md-order-15 {\n order: 15;\n }\n .du-bois-light-col-md-14 {\n display: block;\n flex: 0 0 58.33333333%;\n max-width: 58.33333333%;\n }\n .du-bois-light-col-md-push-14 {\n left: 58.33333333%;\n }\n .du-bois-light-col-md-pull-14 {\n right: 58.33333333%;\n }\n .du-bois-light-col-md-offset-14 {\n margin-left: 58.33333333%;\n }\n .du-bois-light-col-md-order-14 {\n order: 14;\n }\n .du-bois-light-col-md-13 {\n display: block;\n flex: 0 0 54.16666667%;\n max-width: 54.16666667%;\n }\n .du-bois-light-col-md-push-13 {\n left: 54.16666667%;\n }\n .du-bois-light-col-md-pull-13 {\n right: 54.16666667%;\n }\n .du-bois-light-col-md-offset-13 {\n margin-left: 54.16666667%;\n }\n .du-bois-light-col-md-order-13 {\n order: 13;\n }\n .du-bois-light-col-md-12 {\n display: block;\n flex: 0 0 50%;\n max-width: 50%;\n }\n .du-bois-light-col-md-push-12 {\n left: 50%;\n }\n .du-bois-light-col-md-pull-12 {\n right: 50%;\n }\n .du-bois-light-col-md-offset-12 {\n margin-left: 50%;\n }\n .du-bois-light-col-md-order-12 {\n order: 12;\n }\n .du-bois-light-col-md-11 {\n display: block;\n flex: 0 0 45.83333333%;\n max-width: 45.83333333%;\n }\n .du-bois-light-col-md-push-11 {\n left: 45.83333333%;\n }\n .du-bois-light-col-md-pull-11 {\n right: 45.83333333%;\n }\n .du-bois-light-col-md-offset-11 {\n margin-left: 45.83333333%;\n }\n .du-bois-light-col-md-order-11 {\n order: 11;\n }\n .du-bois-light-col-md-10 {\n display: block;\n flex: 0 0 41.66666667%;\n max-width: 41.66666667%;\n }\n .du-bois-light-col-md-push-10 {\n left: 41.66666667%;\n }\n .du-bois-light-col-md-pull-10 {\n right: 41.66666667%;\n }\n .du-bois-light-col-md-offset-10 {\n margin-left: 41.66666667%;\n }\n .du-bois-light-col-md-order-10 {\n order: 10;\n }\n .du-bois-light-col-md-9 {\n display: block;\n flex: 0 0 37.5%;\n max-width: 37.5%;\n }\n .du-bois-light-col-md-push-9 {\n left: 37.5%;\n }\n .du-bois-light-col-md-pull-9 {\n right: 37.5%;\n }\n .du-bois-light-col-md-offset-9 {\n margin-left: 37.5%;\n }\n .du-bois-light-col-md-order-9 {\n order: 9;\n }\n .du-bois-light-col-md-8 {\n display: block;\n flex: 0 0 33.33333333%;\n max-width: 33.33333333%;\n }\n .du-bois-light-col-md-push-8 {\n left: 33.33333333%;\n }\n .du-bois-light-col-md-pull-8 {\n right: 33.33333333%;\n }\n .du-bois-light-col-md-offset-8 {\n margin-left: 33.33333333%;\n }\n .du-bois-light-col-md-order-8 {\n order: 8;\n }\n .du-bois-light-col-md-7 {\n display: block;\n flex: 0 0 29.16666667%;\n max-width: 29.16666667%;\n }\n .du-bois-light-col-md-push-7 {\n left: 29.16666667%;\n }\n .du-bois-light-col-md-pull-7 {\n right: 29.16666667%;\n }\n .du-bois-light-col-md-offset-7 {\n margin-left: 29.16666667%;\n }\n .du-bois-light-col-md-order-7 {\n order: 7;\n }\n .du-bois-light-col-md-6 {\n display: block;\n flex: 0 0 25%;\n max-width: 25%;\n }\n .du-bois-light-col-md-push-6 {\n left: 25%;\n }\n .du-bois-light-col-md-pull-6 {\n right: 25%;\n }\n .du-bois-light-col-md-offset-6 {\n margin-left: 25%;\n }\n .du-bois-light-col-md-order-6 {\n order: 6;\n }\n .du-bois-light-col-md-5 {\n display: block;\n flex: 0 0 20.83333333%;\n max-width: 20.83333333%;\n }\n .du-bois-light-col-md-push-5 {\n left: 20.83333333%;\n }\n .du-bois-light-col-md-pull-5 {\n right: 20.83333333%;\n }\n .du-bois-light-col-md-offset-5 {\n margin-left: 20.83333333%;\n }\n .du-bois-light-col-md-order-5 {\n order: 5;\n }\n .du-bois-light-col-md-4 {\n display: block;\n flex: 0 0 16.66666667%;\n max-width: 16.66666667%;\n }\n .du-bois-light-col-md-push-4 {\n left: 16.66666667%;\n }\n .du-bois-light-col-md-pull-4 {\n right: 16.66666667%;\n }\n .du-bois-light-col-md-offset-4 {\n margin-left: 16.66666667%;\n }\n .du-bois-light-col-md-order-4 {\n order: 4;\n }\n .du-bois-light-col-md-3 {\n display: block;\n flex: 0 0 12.5%;\n max-width: 12.5%;\n }\n .du-bois-light-col-md-push-3 {\n left: 12.5%;\n }\n .du-bois-light-col-md-pull-3 {\n right: 12.5%;\n }\n .du-bois-light-col-md-offset-3 {\n margin-left: 12.5%;\n }\n .du-bois-light-col-md-order-3 {\n order: 3;\n }\n .du-bois-light-col-md-2 {\n display: block;\n flex: 0 0 8.33333333%;\n max-width: 8.33333333%;\n }\n .du-bois-light-col-md-push-2 {\n left: 8.33333333%;\n }\n .du-bois-light-col-md-pull-2 {\n right: 8.33333333%;\n }\n .du-bois-light-col-md-offset-2 {\n margin-left: 8.33333333%;\n }\n .du-bois-light-col-md-order-2 {\n order: 2;\n }\n .du-bois-light-col-md-1 {\n display: block;\n flex: 0 0 4.16666667%;\n max-width: 4.16666667%;\n }\n .du-bois-light-col-md-push-1 {\n left: 4.16666667%;\n }\n .du-bois-light-col-md-pull-1 {\n right: 4.16666667%;\n }\n .du-bois-light-col-md-offset-1 {\n margin-left: 4.16666667%;\n }\n .du-bois-light-col-md-order-1 {\n order: 1;\n }\n .du-bois-light-col-md-0 {\n display: none;\n }\n .du-bois-light-col-push-0 {\n left: auto;\n }\n .du-bois-light-col-pull-0 {\n right: auto;\n }\n .du-bois-light-col-md-push-0 {\n left: auto;\n }\n .du-bois-light-col-md-pull-0 {\n right: auto;\n }\n .du-bois-light-col-md-offset-0 {\n margin-left: 0;\n }\n .du-bois-light-col-md-order-0 {\n order: 0;\n }\n}\n@media (min-width: 992px) {\n .du-bois-light-col-lg-24 {\n display: block;\n flex: 0 0 100%;\n max-width: 100%;\n }\n .du-bois-light-col-lg-push-24 {\n left: 100%;\n }\n .du-bois-light-col-lg-pull-24 {\n right: 100%;\n }\n .du-bois-light-col-lg-offset-24 {\n margin-left: 100%;\n }\n .du-bois-light-col-lg-order-24 {\n order: 24;\n }\n .du-bois-light-col-lg-23 {\n display: block;\n flex: 0 0 95.83333333%;\n max-width: 95.83333333%;\n }\n .du-bois-light-col-lg-push-23 {\n left: 95.83333333%;\n }\n .du-bois-light-col-lg-pull-23 {\n right: 95.83333333%;\n }\n .du-bois-light-col-lg-offset-23 {\n margin-left: 95.83333333%;\n }\n .du-bois-light-col-lg-order-23 {\n order: 23;\n }\n .du-bois-light-col-lg-22 {\n display: block;\n flex: 0 0 91.66666667%;\n max-width: 91.66666667%;\n }\n .du-bois-light-col-lg-push-22 {\n left: 91.66666667%;\n }\n .du-bois-light-col-lg-pull-22 {\n right: 91.66666667%;\n }\n .du-bois-light-col-lg-offset-22 {\n margin-left: 91.66666667%;\n }\n .du-bois-light-col-lg-order-22 {\n order: 22;\n }\n .du-bois-light-col-lg-21 {\n display: block;\n flex: 0 0 87.5%;\n max-width: 87.5%;\n }\n .du-bois-light-col-lg-push-21 {\n left: 87.5%;\n }\n .du-bois-light-col-lg-pull-21 {\n right: 87.5%;\n }\n .du-bois-light-col-lg-offset-21 {\n margin-left: 87.5%;\n }\n .du-bois-light-col-lg-order-21 {\n order: 21;\n }\n .du-bois-light-col-lg-20 {\n display: block;\n flex: 0 0 83.33333333%;\n max-width: 83.33333333%;\n }\n .du-bois-light-col-lg-push-20 {\n left: 83.33333333%;\n }\n .du-bois-light-col-lg-pull-20 {\n right: 83.33333333%;\n }\n .du-bois-light-col-lg-offset-20 {\n margin-left: 83.33333333%;\n }\n .du-bois-light-col-lg-order-20 {\n order: 20;\n }\n .du-bois-light-col-lg-19 {\n display: block;\n flex: 0 0 79.16666667%;\n max-width: 79.16666667%;\n }\n .du-bois-light-col-lg-push-19 {\n left: 79.16666667%;\n }\n .du-bois-light-col-lg-pull-19 {\n right: 79.16666667%;\n }\n .du-bois-light-col-lg-offset-19 {\n margin-left: 79.16666667%;\n }\n .du-bois-light-col-lg-order-19 {\n order: 19;\n }\n .du-bois-light-col-lg-18 {\n display: block;\n flex: 0 0 75%;\n max-width: 75%;\n }\n .du-bois-light-col-lg-push-18 {\n left: 75%;\n }\n .du-bois-light-col-lg-pull-18 {\n right: 75%;\n }\n .du-bois-light-col-lg-offset-18 {\n margin-left: 75%;\n }\n .du-bois-light-col-lg-order-18 {\n order: 18;\n }\n .du-bois-light-col-lg-17 {\n display: block;\n flex: 0 0 70.83333333%;\n max-width: 70.83333333%;\n }\n .du-bois-light-col-lg-push-17 {\n left: 70.83333333%;\n }\n .du-bois-light-col-lg-pull-17 {\n right: 70.83333333%;\n }\n .du-bois-light-col-lg-offset-17 {\n margin-left: 70.83333333%;\n }\n .du-bois-light-col-lg-order-17 {\n order: 17;\n }\n .du-bois-light-col-lg-16 {\n display: block;\n flex: 0 0 66.66666667%;\n max-width: 66.66666667%;\n }\n .du-bois-light-col-lg-push-16 {\n left: 66.66666667%;\n }\n .du-bois-light-col-lg-pull-16 {\n right: 66.66666667%;\n }\n .du-bois-light-col-lg-offset-16 {\n margin-left: 66.66666667%;\n }\n .du-bois-light-col-lg-order-16 {\n order: 16;\n }\n .du-bois-light-col-lg-15 {\n display: block;\n flex: 0 0 62.5%;\n max-width: 62.5%;\n }\n .du-bois-light-col-lg-push-15 {\n left: 62.5%;\n }\n .du-bois-light-col-lg-pull-15 {\n right: 62.5%;\n }\n .du-bois-light-col-lg-offset-15 {\n margin-left: 62.5%;\n }\n .du-bois-light-col-lg-order-15 {\n order: 15;\n }\n .du-bois-light-col-lg-14 {\n display: block;\n flex: 0 0 58.33333333%;\n max-width: 58.33333333%;\n }\n .du-bois-light-col-lg-push-14 {\n left: 58.33333333%;\n }\n .du-bois-light-col-lg-pull-14 {\n right: 58.33333333%;\n }\n .du-bois-light-col-lg-offset-14 {\n margin-left: 58.33333333%;\n }\n .du-bois-light-col-lg-order-14 {\n order: 14;\n }\n .du-bois-light-col-lg-13 {\n display: block;\n flex: 0 0 54.16666667%;\n max-width: 54.16666667%;\n }\n .du-bois-light-col-lg-push-13 {\n left: 54.16666667%;\n }\n .du-bois-light-col-lg-pull-13 {\n right: 54.16666667%;\n }\n .du-bois-light-col-lg-offset-13 {\n margin-left: 54.16666667%;\n }\n .du-bois-light-col-lg-order-13 {\n order: 13;\n }\n .du-bois-light-col-lg-12 {\n display: block;\n flex: 0 0 50%;\n max-width: 50%;\n }\n .du-bois-light-col-lg-push-12 {\n left: 50%;\n }\n .du-bois-light-col-lg-pull-12 {\n right: 50%;\n }\n .du-bois-light-col-lg-offset-12 {\n margin-left: 50%;\n }\n .du-bois-light-col-lg-order-12 {\n order: 12;\n }\n .du-bois-light-col-lg-11 {\n display: block;\n flex: 0 0 45.83333333%;\n max-width: 45.83333333%;\n }\n .du-bois-light-col-lg-push-11 {\n left: 45.83333333%;\n }\n .du-bois-light-col-lg-pull-11 {\n right: 45.83333333%;\n }\n .du-bois-light-col-lg-offset-11 {\n margin-left: 45.83333333%;\n }\n .du-bois-light-col-lg-order-11 {\n order: 11;\n }\n .du-bois-light-col-lg-10 {\n display: block;\n flex: 0 0 41.66666667%;\n max-width: 41.66666667%;\n }\n .du-bois-light-col-lg-push-10 {\n left: 41.66666667%;\n }\n .du-bois-light-col-lg-pull-10 {\n right: 41.66666667%;\n }\n .du-bois-light-col-lg-offset-10 {\n margin-left: 41.66666667%;\n }\n .du-bois-light-col-lg-order-10 {\n order: 10;\n }\n .du-bois-light-col-lg-9 {\n display: block;\n flex: 0 0 37.5%;\n max-width: 37.5%;\n }\n .du-bois-light-col-lg-push-9 {\n left: 37.5%;\n }\n .du-bois-light-col-lg-pull-9 {\n right: 37.5%;\n }\n .du-bois-light-col-lg-offset-9 {\n margin-left: 37.5%;\n }\n .du-bois-light-col-lg-order-9 {\n order: 9;\n }\n .du-bois-light-col-lg-8 {\n display: block;\n flex: 0 0 33.33333333%;\n max-width: 33.33333333%;\n }\n .du-bois-light-col-lg-push-8 {\n left: 33.33333333%;\n }\n .du-bois-light-col-lg-pull-8 {\n right: 33.33333333%;\n }\n .du-bois-light-col-lg-offset-8 {\n margin-left: 33.33333333%;\n }\n .du-bois-light-col-lg-order-8 {\n order: 8;\n }\n .du-bois-light-col-lg-7 {\n display: block;\n flex: 0 0 29.16666667%;\n max-width: 29.16666667%;\n }\n .du-bois-light-col-lg-push-7 {\n left: 29.16666667%;\n }\n .du-bois-light-col-lg-pull-7 {\n right: 29.16666667%;\n }\n .du-bois-light-col-lg-offset-7 {\n margin-left: 29.16666667%;\n }\n .du-bois-light-col-lg-order-7 {\n order: 7;\n }\n .du-bois-light-col-lg-6 {\n display: block;\n flex: 0 0 25%;\n max-width: 25%;\n }\n .du-bois-light-col-lg-push-6 {\n left: 25%;\n }\n .du-bois-light-col-lg-pull-6 {\n right: 25%;\n }\n .du-bois-light-col-lg-offset-6 {\n margin-left: 25%;\n }\n .du-bois-light-col-lg-order-6 {\n order: 6;\n }\n .du-bois-light-col-lg-5 {\n display: block;\n flex: 0 0 20.83333333%;\n max-width: 20.83333333%;\n }\n .du-bois-light-col-lg-push-5 {\n left: 20.83333333%;\n }\n .du-bois-light-col-lg-pull-5 {\n right: 20.83333333%;\n }\n .du-bois-light-col-lg-offset-5 {\n margin-left: 20.83333333%;\n }\n .du-bois-light-col-lg-order-5 {\n order: 5;\n }\n .du-bois-light-col-lg-4 {\n display: block;\n flex: 0 0 16.66666667%;\n max-width: 16.66666667%;\n }\n .du-bois-light-col-lg-push-4 {\n left: 16.66666667%;\n }\n .du-bois-light-col-lg-pull-4 {\n right: 16.66666667%;\n }\n .du-bois-light-col-lg-offset-4 {\n margin-left: 16.66666667%;\n }\n .du-bois-light-col-lg-order-4 {\n order: 4;\n }\n .du-bois-light-col-lg-3 {\n display: block;\n flex: 0 0 12.5%;\n max-width: 12.5%;\n }\n .du-bois-light-col-lg-push-3 {\n left: 12.5%;\n }\n .du-bois-light-col-lg-pull-3 {\n right: 12.5%;\n }\n .du-bois-light-col-lg-offset-3 {\n margin-left: 12.5%;\n }\n .du-bois-light-col-lg-order-3 {\n order: 3;\n }\n .du-bois-light-col-lg-2 {\n display: block;\n flex: 0 0 8.33333333%;\n max-width: 8.33333333%;\n }\n .du-bois-light-col-lg-push-2 {\n left: 8.33333333%;\n }\n .du-bois-light-col-lg-pull-2 {\n right: 8.33333333%;\n }\n .du-bois-light-col-lg-offset-2 {\n margin-left: 8.33333333%;\n }\n .du-bois-light-col-lg-order-2 {\n order: 2;\n }\n .du-bois-light-col-lg-1 {\n display: block;\n flex: 0 0 4.16666667%;\n max-width: 4.16666667%;\n }\n .du-bois-light-col-lg-push-1 {\n left: 4.16666667%;\n }\n .du-bois-light-col-lg-pull-1 {\n right: 4.16666667%;\n }\n .du-bois-light-col-lg-offset-1 {\n margin-left: 4.16666667%;\n }\n .du-bois-light-col-lg-order-1 {\n order: 1;\n }\n .du-bois-light-col-lg-0 {\n display: none;\n }\n .du-bois-light-col-push-0 {\n left: auto;\n }\n .du-bois-light-col-pull-0 {\n right: auto;\n }\n .du-bois-light-col-lg-push-0 {\n left: auto;\n }\n .du-bois-light-col-lg-pull-0 {\n right: auto;\n }\n .du-bois-light-col-lg-offset-0 {\n margin-left: 0;\n }\n .du-bois-light-col-lg-order-0 {\n order: 0;\n }\n}\n@media (min-width: 1200px) {\n .du-bois-light-col-xl-24 {\n display: block;\n flex: 0 0 100%;\n max-width: 100%;\n }\n .du-bois-light-col-xl-push-24 {\n left: 100%;\n }\n .du-bois-light-col-xl-pull-24 {\n right: 100%;\n }\n .du-bois-light-col-xl-offset-24 {\n margin-left: 100%;\n }\n .du-bois-light-col-xl-order-24 {\n order: 24;\n }\n .du-bois-light-col-xl-23 {\n display: block;\n flex: 0 0 95.83333333%;\n max-width: 95.83333333%;\n }\n .du-bois-light-col-xl-push-23 {\n left: 95.83333333%;\n }\n .du-bois-light-col-xl-pull-23 {\n right: 95.83333333%;\n }\n .du-bois-light-col-xl-offset-23 {\n margin-left: 95.83333333%;\n }\n .du-bois-light-col-xl-order-23 {\n order: 23;\n }\n .du-bois-light-col-xl-22 {\n display: block;\n flex: 0 0 91.66666667%;\n max-width: 91.66666667%;\n }\n .du-bois-light-col-xl-push-22 {\n left: 91.66666667%;\n }\n .du-bois-light-col-xl-pull-22 {\n right: 91.66666667%;\n }\n .du-bois-light-col-xl-offset-22 {\n margin-left: 91.66666667%;\n }\n .du-bois-light-col-xl-order-22 {\n order: 22;\n }\n .du-bois-light-col-xl-21 {\n display: block;\n flex: 0 0 87.5%;\n max-width: 87.5%;\n }\n .du-bois-light-col-xl-push-21 {\n left: 87.5%;\n }\n .du-bois-light-col-xl-pull-21 {\n right: 87.5%;\n }\n .du-bois-light-col-xl-offset-21 {\n margin-left: 87.5%;\n }\n .du-bois-light-col-xl-order-21 {\n order: 21;\n }\n .du-bois-light-col-xl-20 {\n display: block;\n flex: 0 0 83.33333333%;\n max-width: 83.33333333%;\n }\n .du-bois-light-col-xl-push-20 {\n left: 83.33333333%;\n }\n .du-bois-light-col-xl-pull-20 {\n right: 83.33333333%;\n }\n .du-bois-light-col-xl-offset-20 {\n margin-left: 83.33333333%;\n }\n .du-bois-light-col-xl-order-20 {\n order: 20;\n }\n .du-bois-light-col-xl-19 {\n display: block;\n flex: 0 0 79.16666667%;\n max-width: 79.16666667%;\n }\n .du-bois-light-col-xl-push-19 {\n left: 79.16666667%;\n }\n .du-bois-light-col-xl-pull-19 {\n right: 79.16666667%;\n }\n .du-bois-light-col-xl-offset-19 {\n margin-left: 79.16666667%;\n }\n .du-bois-light-col-xl-order-19 {\n order: 19;\n }\n .du-bois-light-col-xl-18 {\n display: block;\n flex: 0 0 75%;\n max-width: 75%;\n }\n .du-bois-light-col-xl-push-18 {\n left: 75%;\n }\n .du-bois-light-col-xl-pull-18 {\n right: 75%;\n }\n .du-bois-light-col-xl-offset-18 {\n margin-left: 75%;\n }\n .du-bois-light-col-xl-order-18 {\n order: 18;\n }\n .du-bois-light-col-xl-17 {\n display: block;\n flex: 0 0 70.83333333%;\n max-width: 70.83333333%;\n }\n .du-bois-light-col-xl-push-17 {\n left: 70.83333333%;\n }\n .du-bois-light-col-xl-pull-17 {\n right: 70.83333333%;\n }\n .du-bois-light-col-xl-offset-17 {\n margin-left: 70.83333333%;\n }\n .du-bois-light-col-xl-order-17 {\n order: 17;\n }\n .du-bois-light-col-xl-16 {\n display: block;\n flex: 0 0 66.66666667%;\n max-width: 66.66666667%;\n }\n .du-bois-light-col-xl-push-16 {\n left: 66.66666667%;\n }\n .du-bois-light-col-xl-pull-16 {\n right: 66.66666667%;\n }\n .du-bois-light-col-xl-offset-16 {\n margin-left: 66.66666667%;\n }\n .du-bois-light-col-xl-order-16 {\n order: 16;\n }\n .du-bois-light-col-xl-15 {\n display: block;\n flex: 0 0 62.5%;\n max-width: 62.5%;\n }\n .du-bois-light-col-xl-push-15 {\n left: 62.5%;\n }\n .du-bois-light-col-xl-pull-15 {\n right: 62.5%;\n }\n .du-bois-light-col-xl-offset-15 {\n margin-left: 62.5%;\n }\n .du-bois-light-col-xl-order-15 {\n order: 15;\n }\n .du-bois-light-col-xl-14 {\n display: block;\n flex: 0 0 58.33333333%;\n max-width: 58.33333333%;\n }\n .du-bois-light-col-xl-push-14 {\n left: 58.33333333%;\n }\n .du-bois-light-col-xl-pull-14 {\n right: 58.33333333%;\n }\n .du-bois-light-col-xl-offset-14 {\n margin-left: 58.33333333%;\n }\n .du-bois-light-col-xl-order-14 {\n order: 14;\n }\n .du-bois-light-col-xl-13 {\n display: block;\n flex: 0 0 54.16666667%;\n max-width: 54.16666667%;\n }\n .du-bois-light-col-xl-push-13 {\n left: 54.16666667%;\n }\n .du-bois-light-col-xl-pull-13 {\n right: 54.16666667%;\n }\n .du-bois-light-col-xl-offset-13 {\n margin-left: 54.16666667%;\n }\n .du-bois-light-col-xl-order-13 {\n order: 13;\n }\n .du-bois-light-col-xl-12 {\n display: block;\n flex: 0 0 50%;\n max-width: 50%;\n }\n .du-bois-light-col-xl-push-12 {\n left: 50%;\n }\n .du-bois-light-col-xl-pull-12 {\n right: 50%;\n }\n .du-bois-light-col-xl-offset-12 {\n margin-left: 50%;\n }\n .du-bois-light-col-xl-order-12 {\n order: 12;\n }\n .du-bois-light-col-xl-11 {\n display: block;\n flex: 0 0 45.83333333%;\n max-width: 45.83333333%;\n }\n .du-bois-light-col-xl-push-11 {\n left: 45.83333333%;\n }\n .du-bois-light-col-xl-pull-11 {\n right: 45.83333333%;\n }\n .du-bois-light-col-xl-offset-11 {\n margin-left: 45.83333333%;\n }\n .du-bois-light-col-xl-order-11 {\n order: 11;\n }\n .du-bois-light-col-xl-10 {\n display: block;\n flex: 0 0 41.66666667%;\n max-width: 41.66666667%;\n }\n .du-bois-light-col-xl-push-10 {\n left: 41.66666667%;\n }\n .du-bois-light-col-xl-pull-10 {\n right: 41.66666667%;\n }\n .du-bois-light-col-xl-offset-10 {\n margin-left: 41.66666667%;\n }\n .du-bois-light-col-xl-order-10 {\n order: 10;\n }\n .du-bois-light-col-xl-9 {\n display: block;\n flex: 0 0 37.5%;\n max-width: 37.5%;\n }\n .du-bois-light-col-xl-push-9 {\n left: 37.5%;\n }\n .du-bois-light-col-xl-pull-9 {\n right: 37.5%;\n }\n .du-bois-light-col-xl-offset-9 {\n margin-left: 37.5%;\n }\n .du-bois-light-col-xl-order-9 {\n order: 9;\n }\n .du-bois-light-col-xl-8 {\n display: block;\n flex: 0 0 33.33333333%;\n max-width: 33.33333333%;\n }\n .du-bois-light-col-xl-push-8 {\n left: 33.33333333%;\n }\n .du-bois-light-col-xl-pull-8 {\n right: 33.33333333%;\n }\n .du-bois-light-col-xl-offset-8 {\n margin-left: 33.33333333%;\n }\n .du-bois-light-col-xl-order-8 {\n order: 8;\n }\n .du-bois-light-col-xl-7 {\n display: block;\n flex: 0 0 29.16666667%;\n max-width: 29.16666667%;\n }\n .du-bois-light-col-xl-push-7 {\n left: 29.16666667%;\n }\n .du-bois-light-col-xl-pull-7 {\n right: 29.16666667%;\n }\n .du-bois-light-col-xl-offset-7 {\n margin-left: 29.16666667%;\n }\n .du-bois-light-col-xl-order-7 {\n order: 7;\n }\n .du-bois-light-col-xl-6 {\n display: block;\n flex: 0 0 25%;\n max-width: 25%;\n }\n .du-bois-light-col-xl-push-6 {\n left: 25%;\n }\n .du-bois-light-col-xl-pull-6 {\n right: 25%;\n }\n .du-bois-light-col-xl-offset-6 {\n margin-left: 25%;\n }\n .du-bois-light-col-xl-order-6 {\n order: 6;\n }\n .du-bois-light-col-xl-5 {\n display: block;\n flex: 0 0 20.83333333%;\n max-width: 20.83333333%;\n }\n .du-bois-light-col-xl-push-5 {\n left: 20.83333333%;\n }\n .du-bois-light-col-xl-pull-5 {\n right: 20.83333333%;\n }\n .du-bois-light-col-xl-offset-5 {\n margin-left: 20.83333333%;\n }\n .du-bois-light-col-xl-order-5 {\n order: 5;\n }\n .du-bois-light-col-xl-4 {\n display: block;\n flex: 0 0 16.66666667%;\n max-width: 16.66666667%;\n }\n .du-bois-light-col-xl-push-4 {\n left: 16.66666667%;\n }\n .du-bois-light-col-xl-pull-4 {\n right: 16.66666667%;\n }\n .du-bois-light-col-xl-offset-4 {\n margin-left: 16.66666667%;\n }\n .du-bois-light-col-xl-order-4 {\n order: 4;\n }\n .du-bois-light-col-xl-3 {\n display: block;\n flex: 0 0 12.5%;\n max-width: 12.5%;\n }\n .du-bois-light-col-xl-push-3 {\n left: 12.5%;\n }\n .du-bois-light-col-xl-pull-3 {\n right: 12.5%;\n }\n .du-bois-light-col-xl-offset-3 {\n margin-left: 12.5%;\n }\n .du-bois-light-col-xl-order-3 {\n order: 3;\n }\n .du-bois-light-col-xl-2 {\n display: block;\n flex: 0 0 8.33333333%;\n max-width: 8.33333333%;\n }\n .du-bois-light-col-xl-push-2 {\n left: 8.33333333%;\n }\n .du-bois-light-col-xl-pull-2 {\n right: 8.33333333%;\n }\n .du-bois-light-col-xl-offset-2 {\n margin-left: 8.33333333%;\n }\n .du-bois-light-col-xl-order-2 {\n order: 2;\n }\n .du-bois-light-col-xl-1 {\n display: block;\n flex: 0 0 4.16666667%;\n max-width: 4.16666667%;\n }\n .du-bois-light-col-xl-push-1 {\n left: 4.16666667%;\n }\n .du-bois-light-col-xl-pull-1 {\n right: 4.16666667%;\n }\n .du-bois-light-col-xl-offset-1 {\n margin-left: 4.16666667%;\n }\n .du-bois-light-col-xl-order-1 {\n order: 1;\n }\n .du-bois-light-col-xl-0 {\n display: none;\n }\n .du-bois-light-col-push-0 {\n left: auto;\n }\n .du-bois-light-col-pull-0 {\n right: auto;\n }\n .du-bois-light-col-xl-push-0 {\n left: auto;\n }\n .du-bois-light-col-xl-pull-0 {\n right: auto;\n }\n .du-bois-light-col-xl-offset-0 {\n margin-left: 0;\n }\n .du-bois-light-col-xl-order-0 {\n order: 0;\n }\n}\n@media (min-width: 1600px) {\n .du-bois-light-col-xxl-24 {\n display: block;\n flex: 0 0 100%;\n max-width: 100%;\n }\n .du-bois-light-col-xxl-push-24 {\n left: 100%;\n }\n .du-bois-light-col-xxl-pull-24 {\n right: 100%;\n }\n .du-bois-light-col-xxl-offset-24 {\n margin-left: 100%;\n }\n .du-bois-light-col-xxl-order-24 {\n order: 24;\n }\n .du-bois-light-col-xxl-23 {\n display: block;\n flex: 0 0 95.83333333%;\n max-width: 95.83333333%;\n }\n .du-bois-light-col-xxl-push-23 {\n left: 95.83333333%;\n }\n .du-bois-light-col-xxl-pull-23 {\n right: 95.83333333%;\n }\n .du-bois-light-col-xxl-offset-23 {\n margin-left: 95.83333333%;\n }\n .du-bois-light-col-xxl-order-23 {\n order: 23;\n }\n .du-bois-light-col-xxl-22 {\n display: block;\n flex: 0 0 91.66666667%;\n max-width: 91.66666667%;\n }\n .du-bois-light-col-xxl-push-22 {\n left: 91.66666667%;\n }\n .du-bois-light-col-xxl-pull-22 {\n right: 91.66666667%;\n }\n .du-bois-light-col-xxl-offset-22 {\n margin-left: 91.66666667%;\n }\n .du-bois-light-col-xxl-order-22 {\n order: 22;\n }\n .du-bois-light-col-xxl-21 {\n display: block;\n flex: 0 0 87.5%;\n max-width: 87.5%;\n }\n .du-bois-light-col-xxl-push-21 {\n left: 87.5%;\n }\n .du-bois-light-col-xxl-pull-21 {\n right: 87.5%;\n }\n .du-bois-light-col-xxl-offset-21 {\n margin-left: 87.5%;\n }\n .du-bois-light-col-xxl-order-21 {\n order: 21;\n }\n .du-bois-light-col-xxl-20 {\n display: block;\n flex: 0 0 83.33333333%;\n max-width: 83.33333333%;\n }\n .du-bois-light-col-xxl-push-20 {\n left: 83.33333333%;\n }\n .du-bois-light-col-xxl-pull-20 {\n right: 83.33333333%;\n }\n .du-bois-light-col-xxl-offset-20 {\n margin-left: 83.33333333%;\n }\n .du-bois-light-col-xxl-order-20 {\n order: 20;\n }\n .du-bois-light-col-xxl-19 {\n display: block;\n flex: 0 0 79.16666667%;\n max-width: 79.16666667%;\n }\n .du-bois-light-col-xxl-push-19 {\n left: 79.16666667%;\n }\n .du-bois-light-col-xxl-pull-19 {\n right: 79.16666667%;\n }\n .du-bois-light-col-xxl-offset-19 {\n margin-left: 79.16666667%;\n }\n .du-bois-light-col-xxl-order-19 {\n order: 19;\n }\n .du-bois-light-col-xxl-18 {\n display: block;\n flex: 0 0 75%;\n max-width: 75%;\n }\n .du-bois-light-col-xxl-push-18 {\n left: 75%;\n }\n .du-bois-light-col-xxl-pull-18 {\n right: 75%;\n }\n .du-bois-light-col-xxl-offset-18 {\n margin-left: 75%;\n }\n .du-bois-light-col-xxl-order-18 {\n order: 18;\n }\n .du-bois-light-col-xxl-17 {\n display: block;\n flex: 0 0 70.83333333%;\n max-width: 70.83333333%;\n }\n .du-bois-light-col-xxl-push-17 {\n left: 70.83333333%;\n }\n .du-bois-light-col-xxl-pull-17 {\n right: 70.83333333%;\n }\n .du-bois-light-col-xxl-offset-17 {\n margin-left: 70.83333333%;\n }\n .du-bois-light-col-xxl-order-17 {\n order: 17;\n }\n .du-bois-light-col-xxl-16 {\n display: block;\n flex: 0 0 66.66666667%;\n max-width: 66.66666667%;\n }\n .du-bois-light-col-xxl-push-16 {\n left: 66.66666667%;\n }\n .du-bois-light-col-xxl-pull-16 {\n right: 66.66666667%;\n }\n .du-bois-light-col-xxl-offset-16 {\n margin-left: 66.66666667%;\n }\n .du-bois-light-col-xxl-order-16 {\n order: 16;\n }\n .du-bois-light-col-xxl-15 {\n display: block;\n flex: 0 0 62.5%;\n max-width: 62.5%;\n }\n .du-bois-light-col-xxl-push-15 {\n left: 62.5%;\n }\n .du-bois-light-col-xxl-pull-15 {\n right: 62.5%;\n }\n .du-bois-light-col-xxl-offset-15 {\n margin-left: 62.5%;\n }\n .du-bois-light-col-xxl-order-15 {\n order: 15;\n }\n .du-bois-light-col-xxl-14 {\n display: block;\n flex: 0 0 58.33333333%;\n max-width: 58.33333333%;\n }\n .du-bois-light-col-xxl-push-14 {\n left: 58.33333333%;\n }\n .du-bois-light-col-xxl-pull-14 {\n right: 58.33333333%;\n }\n .du-bois-light-col-xxl-offset-14 {\n margin-left: 58.33333333%;\n }\n .du-bois-light-col-xxl-order-14 {\n order: 14;\n }\n .du-bois-light-col-xxl-13 {\n display: block;\n flex: 0 0 54.16666667%;\n max-width: 54.16666667%;\n }\n .du-bois-light-col-xxl-push-13 {\n left: 54.16666667%;\n }\n .du-bois-light-col-xxl-pull-13 {\n right: 54.16666667%;\n }\n .du-bois-light-col-xxl-offset-13 {\n margin-left: 54.16666667%;\n }\n .du-bois-light-col-xxl-order-13 {\n order: 13;\n }\n .du-bois-light-col-xxl-12 {\n display: block;\n flex: 0 0 50%;\n max-width: 50%;\n }\n .du-bois-light-col-xxl-push-12 {\n left: 50%;\n }\n .du-bois-light-col-xxl-pull-12 {\n right: 50%;\n }\n .du-bois-light-col-xxl-offset-12 {\n margin-left: 50%;\n }\n .du-bois-light-col-xxl-order-12 {\n order: 12;\n }\n .du-bois-light-col-xxl-11 {\n display: block;\n flex: 0 0 45.83333333%;\n max-width: 45.83333333%;\n }\n .du-bois-light-col-xxl-push-11 {\n left: 45.83333333%;\n }\n .du-bois-light-col-xxl-pull-11 {\n right: 45.83333333%;\n }\n .du-bois-light-col-xxl-offset-11 {\n margin-left: 45.83333333%;\n }\n .du-bois-light-col-xxl-order-11 {\n order: 11;\n }\n .du-bois-light-col-xxl-10 {\n display: block;\n flex: 0 0 41.66666667%;\n max-width: 41.66666667%;\n }\n .du-bois-light-col-xxl-push-10 {\n left: 41.66666667%;\n }\n .du-bois-light-col-xxl-pull-10 {\n right: 41.66666667%;\n }\n .du-bois-light-col-xxl-offset-10 {\n margin-left: 41.66666667%;\n }\n .du-bois-light-col-xxl-order-10 {\n order: 10;\n }\n .du-bois-light-col-xxl-9 {\n display: block;\n flex: 0 0 37.5%;\n max-width: 37.5%;\n }\n .du-bois-light-col-xxl-push-9 {\n left: 37.5%;\n }\n .du-bois-light-col-xxl-pull-9 {\n right: 37.5%;\n }\n .du-bois-light-col-xxl-offset-9 {\n margin-left: 37.5%;\n }\n .du-bois-light-col-xxl-order-9 {\n order: 9;\n }\n .du-bois-light-col-xxl-8 {\n display: block;\n flex: 0 0 33.33333333%;\n max-width: 33.33333333%;\n }\n .du-bois-light-col-xxl-push-8 {\n left: 33.33333333%;\n }\n .du-bois-light-col-xxl-pull-8 {\n right: 33.33333333%;\n }\n .du-bois-light-col-xxl-offset-8 {\n margin-left: 33.33333333%;\n }\n .du-bois-light-col-xxl-order-8 {\n order: 8;\n }\n .du-bois-light-col-xxl-7 {\n display: block;\n flex: 0 0 29.16666667%;\n max-width: 29.16666667%;\n }\n .du-bois-light-col-xxl-push-7 {\n left: 29.16666667%;\n }\n .du-bois-light-col-xxl-pull-7 {\n right: 29.16666667%;\n }\n .du-bois-light-col-xxl-offset-7 {\n margin-left: 29.16666667%;\n }\n .du-bois-light-col-xxl-order-7 {\n order: 7;\n }\n .du-bois-light-col-xxl-6 {\n display: block;\n flex: 0 0 25%;\n max-width: 25%;\n }\n .du-bois-light-col-xxl-push-6 {\n left: 25%;\n }\n .du-bois-light-col-xxl-pull-6 {\n right: 25%;\n }\n .du-bois-light-col-xxl-offset-6 {\n margin-left: 25%;\n }\n .du-bois-light-col-xxl-order-6 {\n order: 6;\n }\n .du-bois-light-col-xxl-5 {\n display: block;\n flex: 0 0 20.83333333%;\n max-width: 20.83333333%;\n }\n .du-bois-light-col-xxl-push-5 {\n left: 20.83333333%;\n }\n .du-bois-light-col-xxl-pull-5 {\n right: 20.83333333%;\n }\n .du-bois-light-col-xxl-offset-5 {\n margin-left: 20.83333333%;\n }\n .du-bois-light-col-xxl-order-5 {\n order: 5;\n }\n .du-bois-light-col-xxl-4 {\n display: block;\n flex: 0 0 16.66666667%;\n max-width: 16.66666667%;\n }\n .du-bois-light-col-xxl-push-4 {\n left: 16.66666667%;\n }\n .du-bois-light-col-xxl-pull-4 {\n right: 16.66666667%;\n }\n .du-bois-light-col-xxl-offset-4 {\n margin-left: 16.66666667%;\n }\n .du-bois-light-col-xxl-order-4 {\n order: 4;\n }\n .du-bois-light-col-xxl-3 {\n display: block;\n flex: 0 0 12.5%;\n max-width: 12.5%;\n }\n .du-bois-light-col-xxl-push-3 {\n left: 12.5%;\n }\n .du-bois-light-col-xxl-pull-3 {\n right: 12.5%;\n }\n .du-bois-light-col-xxl-offset-3 {\n margin-left: 12.5%;\n }\n .du-bois-light-col-xxl-order-3 {\n order: 3;\n }\n .du-bois-light-col-xxl-2 {\n display: block;\n flex: 0 0 8.33333333%;\n max-width: 8.33333333%;\n }\n .du-bois-light-col-xxl-push-2 {\n left: 8.33333333%;\n }\n .du-bois-light-col-xxl-pull-2 {\n right: 8.33333333%;\n }\n .du-bois-light-col-xxl-offset-2 {\n margin-left: 8.33333333%;\n }\n .du-bois-light-col-xxl-order-2 {\n order: 2;\n }\n .du-bois-light-col-xxl-1 {\n display: block;\n flex: 0 0 4.16666667%;\n max-width: 4.16666667%;\n }\n .du-bois-light-col-xxl-push-1 {\n left: 4.16666667%;\n }\n .du-bois-light-col-xxl-pull-1 {\n right: 4.16666667%;\n }\n .du-bois-light-col-xxl-offset-1 {\n margin-left: 4.16666667%;\n }\n .du-bois-light-col-xxl-order-1 {\n order: 1;\n }\n .du-bois-light-col-xxl-0 {\n display: none;\n }\n .du-bois-light-col-push-0 {\n left: auto;\n }\n .du-bois-light-col-pull-0 {\n right: auto;\n }\n .du-bois-light-col-xxl-push-0 {\n left: auto;\n }\n .du-bois-light-col-xxl-pull-0 {\n right: auto;\n }\n .du-bois-light-col-xxl-offset-0 {\n margin-left: 0;\n }\n .du-bois-light-col-xxl-order-0 {\n order: 0;\n }\n}\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-light-tooltip {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #11171C;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: 'tnum';\n position: absolute;\n z-index: 1070;\n display: block;\n width: max-content;\n max-width: 250px;\n visibility: visible;\n}\n.du-bois-light-tooltip-hidden {\n display: none;\n}\n.du-bois-light-tooltip-placement-top,\n.du-bois-light-tooltip-placement-topLeft,\n.du-bois-light-tooltip-placement-topRight {\n padding-bottom: 8px;\n}\n.du-bois-light-tooltip-placement-right,\n.du-bois-light-tooltip-placement-rightTop,\n.du-bois-light-tooltip-placement-rightBottom {\n padding-left: 8px;\n}\n.du-bois-light-tooltip-placement-bottom,\n.du-bois-light-tooltip-placement-bottomLeft,\n.du-bois-light-tooltip-placement-bottomRight {\n padding-top: 8px;\n}\n.du-bois-light-tooltip-placement-left,\n.du-bois-light-tooltip-placement-leftTop,\n.du-bois-light-tooltip-placement-leftBottom {\n padding-right: 8px;\n}\n.du-bois-light-tooltip-inner {\n min-width: 30px;\n min-height: 32px;\n padding: 6px 8px;\n color: #fff;\n text-align: left;\n text-decoration: none;\n word-wrap: break-word;\n background-color: rgba(0, 0, 0, 0.75);\n border-radius: 4px;\n box-shadow: 0 3px 6px -4px rgba(0, 0, 0, 0.12), 0 6px 16px 0 rgba(0, 0, 0, 0.08), 0 9px 28px 8px rgba(0, 0, 0, 0.05);\n}\n.du-bois-light-tooltip-arrow {\n position: absolute;\n display: block;\n width: 13.07106781px;\n height: 13.07106781px;\n overflow: hidden;\n background: transparent;\n pointer-events: none;\n}\n.du-bois-light-tooltip-arrow-content {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n display: block;\n width: 5px;\n height: 5px;\n margin: auto;\n background-color: rgba(0, 0, 0, 0.75);\n content: '';\n pointer-events: auto;\n}\n.du-bois-light-tooltip-placement-top .du-bois-light-tooltip-arrow,\n.du-bois-light-tooltip-placement-topLeft .du-bois-light-tooltip-arrow,\n.du-bois-light-tooltip-placement-topRight .du-bois-light-tooltip-arrow {\n bottom: -5.07106781px;\n}\n.du-bois-light-tooltip-placement-top .du-bois-light-tooltip-arrow-content,\n.du-bois-light-tooltip-placement-topLeft .du-bois-light-tooltip-arrow-content,\n.du-bois-light-tooltip-placement-topRight .du-bois-light-tooltip-arrow-content {\n box-shadow: 3px 3px 7px rgba(0, 0, 0, 0.07);\n transform: translateY(-6.53553391px) rotate(45deg);\n}\n.du-bois-light-tooltip-placement-top .du-bois-light-tooltip-arrow {\n left: 50%;\n transform: translateX(-50%);\n}\n.du-bois-light-tooltip-placement-topLeft .du-bois-light-tooltip-arrow {\n left: 13px;\n}\n.du-bois-light-tooltip-placement-topRight .du-bois-light-tooltip-arrow {\n right: 13px;\n}\n.du-bois-light-tooltip-placement-right .du-bois-light-tooltip-arrow,\n.du-bois-light-tooltip-placement-rightTop .du-bois-light-tooltip-arrow,\n.du-bois-light-tooltip-placement-rightBottom .du-bois-light-tooltip-arrow {\n left: -5.07106781px;\n}\n.du-bois-light-tooltip-placement-right .du-bois-light-tooltip-arrow-content,\n.du-bois-light-tooltip-placement-rightTop .du-bois-light-tooltip-arrow-content,\n.du-bois-light-tooltip-placement-rightBottom .du-bois-light-tooltip-arrow-content {\n box-shadow: -3px 3px 7px rgba(0, 0, 0, 0.07);\n transform: translateX(6.53553391px) rotate(45deg);\n}\n.du-bois-light-tooltip-placement-right .du-bois-light-tooltip-arrow {\n top: 50%;\n transform: translateY(-50%);\n}\n.du-bois-light-tooltip-placement-rightTop .du-bois-light-tooltip-arrow {\n top: 5px;\n}\n.du-bois-light-tooltip-placement-rightBottom .du-bois-light-tooltip-arrow {\n bottom: 5px;\n}\n.du-bois-light-tooltip-placement-left .du-bois-light-tooltip-arrow,\n.du-bois-light-tooltip-placement-leftTop .du-bois-light-tooltip-arrow,\n.du-bois-light-tooltip-placement-leftBottom .du-bois-light-tooltip-arrow {\n right: -5.07106781px;\n}\n.du-bois-light-tooltip-placement-left .du-bois-light-tooltip-arrow-content,\n.du-bois-light-tooltip-placement-leftTop .du-bois-light-tooltip-arrow-content,\n.du-bois-light-tooltip-placement-leftBottom .du-bois-light-tooltip-arrow-content {\n box-shadow: 3px -3px 7px rgba(0, 0, 0, 0.07);\n transform: translateX(-6.53553391px) rotate(45deg);\n}\n.du-bois-light-tooltip-placement-left .du-bois-light-tooltip-arrow {\n top: 50%;\n transform: translateY(-50%);\n}\n.du-bois-light-tooltip-placement-leftTop .du-bois-light-tooltip-arrow {\n top: 5px;\n}\n.du-bois-light-tooltip-placement-leftBottom .du-bois-light-tooltip-arrow {\n bottom: 5px;\n}\n.du-bois-light-tooltip-placement-bottom .du-bois-light-tooltip-arrow,\n.du-bois-light-tooltip-placement-bottomLeft .du-bois-light-tooltip-arrow,\n.du-bois-light-tooltip-placement-bottomRight .du-bois-light-tooltip-arrow {\n top: -5.07106781px;\n}\n.du-bois-light-tooltip-placement-bottom .du-bois-light-tooltip-arrow-content,\n.du-bois-light-tooltip-placement-bottomLeft .du-bois-light-tooltip-arrow-content,\n.du-bois-light-tooltip-placement-bottomRight .du-bois-light-tooltip-arrow-content {\n box-shadow: -3px -3px 7px rgba(0, 0, 0, 0.07);\n transform: translateY(6.53553391px) rotate(45deg);\n}\n.du-bois-light-tooltip-placement-bottom .du-bois-light-tooltip-arrow {\n left: 50%;\n transform: translateX(-50%);\n}\n.du-bois-light-tooltip-placement-bottomLeft .du-bois-light-tooltip-arrow {\n left: 13px;\n}\n.du-bois-light-tooltip-placement-bottomRight .du-bois-light-tooltip-arrow {\n right: 13px;\n}\n.du-bois-light-tooltip-pink .du-bois-light-tooltip-inner {\n background-color: #eb2f96;\n}\n.du-bois-light-tooltip-pink .du-bois-light-tooltip-arrow-content {\n background-color: #eb2f96;\n}\n.du-bois-light-tooltip-magenta .du-bois-light-tooltip-inner {\n background-color: #eb2f96;\n}\n.du-bois-light-tooltip-magenta .du-bois-light-tooltip-arrow-content {\n background-color: #eb2f96;\n}\n.du-bois-light-tooltip-red .du-bois-light-tooltip-inner {\n background-color: #f5222d;\n}\n.du-bois-light-tooltip-red .du-bois-light-tooltip-arrow-content {\n background-color: #f5222d;\n}\n.du-bois-light-tooltip-volcano .du-bois-light-tooltip-inner {\n background-color: #fa541c;\n}\n.du-bois-light-tooltip-volcano .du-bois-light-tooltip-arrow-content {\n background-color: #fa541c;\n}\n.du-bois-light-tooltip-orange .du-bois-light-tooltip-inner {\n background-color: #fa8c16;\n}\n.du-bois-light-tooltip-orange .du-bois-light-tooltip-arrow-content {\n background-color: #fa8c16;\n}\n.du-bois-light-tooltip-yellow .du-bois-light-tooltip-inner {\n background-color: #fadb14;\n}\n.du-bois-light-tooltip-yellow .du-bois-light-tooltip-arrow-content {\n background-color: #fadb14;\n}\n.du-bois-light-tooltip-gold .du-bois-light-tooltip-inner {\n background-color: #faad14;\n}\n.du-bois-light-tooltip-gold .du-bois-light-tooltip-arrow-content {\n background-color: #faad14;\n}\n.du-bois-light-tooltip-cyan .du-bois-light-tooltip-inner {\n background-color: #13c2c2;\n}\n.du-bois-light-tooltip-cyan .du-bois-light-tooltip-arrow-content {\n background-color: #13c2c2;\n}\n.du-bois-light-tooltip-lime .du-bois-light-tooltip-inner {\n background-color: #a0d911;\n}\n.du-bois-light-tooltip-lime .du-bois-light-tooltip-arrow-content {\n background-color: #a0d911;\n}\n.du-bois-light-tooltip-green .du-bois-light-tooltip-inner {\n background-color: #52c41a;\n}\n.du-bois-light-tooltip-green .du-bois-light-tooltip-arrow-content {\n background-color: #52c41a;\n}\n.du-bois-light-tooltip-blue .du-bois-light-tooltip-inner {\n background-color: #1890ff;\n}\n.du-bois-light-tooltip-blue .du-bois-light-tooltip-arrow-content {\n background-color: #1890ff;\n}\n.du-bois-light-tooltip-geekblue .du-bois-light-tooltip-inner {\n background-color: #2f54eb;\n}\n.du-bois-light-tooltip-geekblue .du-bois-light-tooltip-arrow-content {\n background-color: #2f54eb;\n}\n.du-bois-light-tooltip-purple .du-bois-light-tooltip-inner {\n background-color: #722ed1;\n}\n.du-bois-light-tooltip-purple .du-bois-light-tooltip-arrow-content {\n background-color: #722ed1;\n}\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-light-input-affix-wrapper {\n position: relative;\n display: inline-block;\n width: 100%;\n min-width: 0;\n padding: 5px 12px;\n color: #11171C;\n font-size: 13px;\n line-height: 20px;\n background-color: #fff;\n background-image: none;\n border: 1px solid #E8ECF0;\n border-radius: 4px;\n transition: all 0.3s;\n display: inline-flex;\n}\n.du-bois-light-input-affix-wrapper::-moz-placeholder {\n opacity: 1;\n}\n.du-bois-light-input-affix-wrapper::placeholder {\n color: #bfbfbf;\n}\n.du-bois-light-input-affix-wrapper:placeholder-shown {\n text-overflow: ellipsis;\n}\n.du-bois-light-input-affix-wrapper:hover {\n border-color: #448dc2;\n border-right-width: 1px !important;\n}\n.du-bois-light-input-affix-wrapper:focus,\n.du-bois-light-input-affix-wrapper-focused {\n border-color: #448dc2;\n border-right-width: 1px !important;\n outline: 0;\n box-shadow: 0 0 0 2px rgba(34, 114, 180, 0.2);\n}\n.du-bois-light-input-affix-wrapper-disabled {\n color: rgba(0, 0, 0, 0.25);\n background-color: #f5f5f5;\n cursor: not-allowed;\n opacity: 1;\n}\n.du-bois-light-input-affix-wrapper-disabled:hover {\n border-color: #E8ECF0;\n border-right-width: 1px !important;\n}\n.du-bois-light-input-affix-wrapper[disabled] {\n color: rgba(0, 0, 0, 0.25);\n background-color: #f5f5f5;\n cursor: not-allowed;\n opacity: 1;\n}\n.du-bois-light-input-affix-wrapper[disabled]:hover {\n border-color: #E8ECF0;\n border-right-width: 1px !important;\n}\n.du-bois-light-input-affix-wrapper-borderless,\n.du-bois-light-input-affix-wrapper-borderless:hover,\n.du-bois-light-input-affix-wrapper-borderless:focus,\n.du-bois-light-input-affix-wrapper-borderless-focused,\n.du-bois-light-input-affix-wrapper-borderless-disabled,\n.du-bois-light-input-affix-wrapper-borderless[disabled] {\n background-color: transparent;\n border: none;\n box-shadow: none;\n}\ntextarea.du-bois-light-input-affix-wrapper {\n max-width: 100%;\n height: auto;\n min-height: 40px;\n line-height: 20px;\n vertical-align: bottom;\n transition: all 0.3s, height 0s;\n}\n.du-bois-light-input-affix-wrapper-lg {\n padding: 5px 12px;\n font-size: 18px;\n}\n.du-bois-light-input-affix-wrapper-sm {\n padding: 5px 12px;\n}\n.du-bois-light-input-affix-wrapper:not(.du-bois-light-input-affix-wrapper-disabled):hover {\n border-color: #448dc2;\n border-right-width: 1px !important;\n z-index: 1;\n}\n.du-bois-light-input-search-with-button .du-bois-light-input-affix-wrapper:not(.du-bois-light-input-affix-wrapper-disabled):hover {\n z-index: 0;\n}\n.du-bois-light-input-affix-wrapper-focused,\n.du-bois-light-input-affix-wrapper:focus {\n z-index: 1;\n}\n.du-bois-light-input-affix-wrapper-disabled .du-bois-light-input[disabled] {\n background: transparent;\n}\n.du-bois-light-input-affix-wrapper > input.du-bois-light-input {\n padding: 0;\n border: none;\n outline: none;\n}\n.du-bois-light-input-affix-wrapper > input.du-bois-light-input:focus {\n box-shadow: none;\n}\n.du-bois-light-input-affix-wrapper::before {\n width: 0;\n visibility: hidden;\n content: '\\a0';\n}\n.du-bois-light-input-prefix,\n.du-bois-light-input-suffix {\n display: flex;\n flex: none;\n align-items: center;\n}\n.du-bois-light-input-prefix {\n margin-right: 4px;\n}\n.du-bois-light-input-suffix {\n margin-left: 4px;\n}\n.anticon.du-bois-light-input-clear-icon {\n margin: 0 4px;\n color: rgba(0, 0, 0, 0.25);\n font-size: 12px;\n vertical-align: -1px;\n cursor: pointer;\n transition: color 0.3s;\n}\n.anticon.du-bois-light-input-clear-icon:hover {\n color: #5F7281;\n}\n.anticon.du-bois-light-input-clear-icon:active {\n color: #11171C;\n}\n.anticon.du-bois-light-input-clear-icon-hidden {\n visibility: hidden;\n}\n.anticon.du-bois-light-input-clear-icon:last-child {\n margin-right: 0;\n}\n.du-bois-light-input-affix-wrapper-textarea-with-clear-btn {\n padding: 0 !important;\n border: 0 !important;\n}\n.du-bois-light-input-affix-wrapper-textarea-with-clear-btn .du-bois-light-input-clear-icon {\n position: absolute;\n top: 8px;\n right: 8px;\n z-index: 1;\n}\n.du-bois-light-input {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n font-variant: tabular-nums;\n list-style: none;\n font-feature-settings: 'tnum';\n position: relative;\n display: inline-block;\n width: 100%;\n min-width: 0;\n padding: 5px 12px;\n color: #11171C;\n font-size: 13px;\n line-height: 20px;\n background-color: #fff;\n background-image: none;\n border: 1px solid #E8ECF0;\n border-radius: 4px;\n transition: all 0.3s;\n}\n.du-bois-light-input::-moz-placeholder {\n opacity: 1;\n}\n.du-bois-light-input::placeholder {\n color: #bfbfbf;\n}\n.du-bois-light-input:placeholder-shown {\n text-overflow: ellipsis;\n}\n.du-bois-light-input:hover {\n border-color: #448dc2;\n border-right-width: 1px !important;\n}\n.du-bois-light-input:focus,\n.du-bois-light-input-focused {\n border-color: #448dc2;\n border-right-width: 1px !important;\n outline: 0;\n box-shadow: 0 0 0 2px rgba(34, 114, 180, 0.2);\n}\n.du-bois-light-input-disabled {\n color: rgba(0, 0, 0, 0.25);\n background-color: #f5f5f5;\n cursor: not-allowed;\n opacity: 1;\n}\n.du-bois-light-input-disabled:hover {\n border-color: #E8ECF0;\n border-right-width: 1px !important;\n}\n.du-bois-light-input[disabled] {\n color: rgba(0, 0, 0, 0.25);\n background-color: #f5f5f5;\n cursor: not-allowed;\n opacity: 1;\n}\n.du-bois-light-input[disabled]:hover {\n border-color: #E8ECF0;\n border-right-width: 1px !important;\n}\n.du-bois-light-input-borderless,\n.du-bois-light-input-borderless:hover,\n.du-bois-light-input-borderless:focus,\n.du-bois-light-input-borderless-focused,\n.du-bois-light-input-borderless-disabled,\n.du-bois-light-input-borderless[disabled] {\n background-color: transparent;\n border: none;\n box-shadow: none;\n}\ntextarea.du-bois-light-input {\n max-width: 100%;\n height: auto;\n min-height: 40px;\n line-height: 20px;\n vertical-align: bottom;\n transition: all 0.3s, height 0s;\n}\n.du-bois-light-input-lg {\n padding: 5px 12px;\n font-size: 18px;\n}\n.du-bois-light-input-sm {\n padding: 5px 12px;\n}\n.du-bois-light-input-group {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #11171C;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: 'tnum';\n position: relative;\n display: table;\n width: 100%;\n border-collapse: separate;\n border-spacing: 0;\n}\n.du-bois-light-input-group[class*='col-'] {\n float: none;\n padding-right: 0;\n padding-left: 0;\n}\n.du-bois-light-input-group > [class*='col-'] {\n padding-right: 8px;\n}\n.du-bois-light-input-group > [class*='col-']:last-child {\n padding-right: 0;\n}\n.du-bois-light-input-group-addon,\n.du-bois-light-input-group-wrap,\n.du-bois-light-input-group > .du-bois-light-input {\n display: table-cell;\n}\n.du-bois-light-input-group-addon:not(:first-child):not(:last-child),\n.du-bois-light-input-group-wrap:not(:first-child):not(:last-child),\n.du-bois-light-input-group > .du-bois-light-input:not(:first-child):not(:last-child) {\n border-radius: 0;\n}\n.du-bois-light-input-group-addon,\n.du-bois-light-input-group-wrap {\n width: 1px;\n white-space: nowrap;\n vertical-align: middle;\n}\n.du-bois-light-input-group-wrap > * {\n display: block !important;\n}\n.du-bois-light-input-group .du-bois-light-input {\n float: left;\n width: 100%;\n margin-bottom: 0;\n text-align: inherit;\n}\n.du-bois-light-input-group .du-bois-light-input:focus {\n z-index: 1;\n border-right-width: 1px;\n}\n.du-bois-light-input-group .du-bois-light-input:hover {\n z-index: 1;\n border-right-width: 1px;\n}\n.du-bois-light-input-search-with-button .du-bois-light-input-group .du-bois-light-input:hover {\n z-index: 0;\n}\n.du-bois-light-input-group-addon {\n position: relative;\n padding: 0 12px;\n color: #11171C;\n font-weight: normal;\n font-size: 13px;\n text-align: center;\n background-color: #fafafa;\n border: 1px solid #E8ECF0;\n border-radius: 4px;\n transition: all 0.3s;\n}\n.du-bois-light-input-group-addon .du-bois-light-select {\n margin: -6px -12px;\n}\n.du-bois-light-input-group-addon .du-bois-light-select.du-bois-light-select-single:not(.du-bois-light-select-customize-input) .du-bois-light-select-selector {\n background-color: inherit;\n border: 1px solid transparent;\n box-shadow: none;\n}\n.du-bois-light-input-group-addon .du-bois-light-select-open .du-bois-light-select-selector,\n.du-bois-light-input-group-addon .du-bois-light-select-focused .du-bois-light-select-selector {\n color: #2272B4;\n}\n.du-bois-light-input-group-addon .du-bois-light-cascader-picker {\n margin: -9px -8px;\n background-color: transparent;\n}\n.du-bois-light-input-group-addon .du-bois-light-cascader-picker .du-bois-light-cascader-input {\n text-align: left;\n border: 0;\n box-shadow: none;\n}\n.du-bois-light-input-group > .du-bois-light-input:first-child,\n.du-bois-light-input-group-addon:first-child {\n border-top-right-radius: 0;\n border-bottom-right-radius: 0;\n}\n.du-bois-light-input-group > .du-bois-light-input:first-child .du-bois-light-select .du-bois-light-select-selector,\n.du-bois-light-input-group-addon:first-child .du-bois-light-select .du-bois-light-select-selector {\n border-top-right-radius: 0;\n border-bottom-right-radius: 0;\n}\n.du-bois-light-input-group > .du-bois-light-input-affix-wrapper:not(:first-child) .du-bois-light-input {\n border-top-left-radius: 0;\n border-bottom-left-radius: 0;\n}\n.du-bois-light-input-group > .du-bois-light-input-affix-wrapper:not(:last-child) .du-bois-light-input {\n border-top-right-radius: 0;\n border-bottom-right-radius: 0;\n}\n.du-bois-light-input-group-addon:first-child {\n border-right: 0;\n}\n.du-bois-light-input-group-addon:last-child {\n border-left: 0;\n}\n.du-bois-light-input-group > .du-bois-light-input:last-child,\n.du-bois-light-input-group-addon:last-child {\n border-top-left-radius: 0;\n border-bottom-left-radius: 0;\n}\n.du-bois-light-input-group > .du-bois-light-input:last-child .du-bois-light-select .du-bois-light-select-selector,\n.du-bois-light-input-group-addon:last-child .du-bois-light-select .du-bois-light-select-selector {\n border-top-left-radius: 0;\n border-bottom-left-radius: 0;\n}\n.du-bois-light-input-group-lg .du-bois-light-input,\n.du-bois-light-input-group-lg > .du-bois-light-input-group-addon {\n padding: 5px 12px;\n font-size: 18px;\n}\n.du-bois-light-input-group-sm .du-bois-light-input,\n.du-bois-light-input-group-sm > .du-bois-light-input-group-addon {\n padding: 5px 12px;\n}\n.du-bois-light-input-group-lg .du-bois-light-select-single .du-bois-light-select-selector {\n height: 40px;\n}\n.du-bois-light-input-group-sm .du-bois-light-select-single .du-bois-light-select-selector {\n height: 32px;\n}\n.du-bois-light-input-group .du-bois-light-input-affix-wrapper:not(:first-child) {\n border-top-left-radius: 0;\n border-bottom-left-radius: 0;\n}\n.du-bois-light-input-group .du-bois-light-input-affix-wrapper:not(:last-child) {\n border-top-right-radius: 0;\n border-bottom-right-radius: 0;\n}\n.du-bois-light-input-search .du-bois-light-input-group .du-bois-light-input-affix-wrapper:not(:last-child) {\n border-top-left-radius: 4px;\n border-bottom-left-radius: 4px;\n}\n.du-bois-light-input-group.du-bois-light-input-group-compact {\n display: block;\n}\n.du-bois-light-input-group.du-bois-light-input-group-compact::before {\n display: table;\n content: '';\n}\n.du-bois-light-input-group.du-bois-light-input-group-compact::after {\n display: table;\n clear: both;\n content: '';\n}\n.du-bois-light-input-group.du-bois-light-input-group-compact-addon:not(:first-child):not(:last-child),\n.du-bois-light-input-group.du-bois-light-input-group-compact-wrap:not(:first-child):not(:last-child),\n.du-bois-light-input-group.du-bois-light-input-group-compact > .du-bois-light-input:not(:first-child):not(:last-child) {\n border-right-width: 1px;\n}\n.du-bois-light-input-group.du-bois-light-input-group-compact-addon:not(:first-child):not(:last-child):hover,\n.du-bois-light-input-group.du-bois-light-input-group-compact-wrap:not(:first-child):not(:last-child):hover,\n.du-bois-light-input-group.du-bois-light-input-group-compact > .du-bois-light-input:not(:first-child):not(:last-child):hover {\n z-index: 1;\n}\n.du-bois-light-input-group.du-bois-light-input-group-compact-addon:not(:first-child):not(:last-child):focus,\n.du-bois-light-input-group.du-bois-light-input-group-compact-wrap:not(:first-child):not(:last-child):focus,\n.du-bois-light-input-group.du-bois-light-input-group-compact > .du-bois-light-input:not(:first-child):not(:last-child):focus {\n z-index: 1;\n}\n.du-bois-light-input-group.du-bois-light-input-group-compact > * {\n display: inline-block;\n float: none;\n vertical-align: top;\n border-radius: 0;\n}\n.du-bois-light-input-group.du-bois-light-input-group-compact > .du-bois-light-input-affix-wrapper {\n display: inline-flex;\n}\n.du-bois-light-input-group.du-bois-light-input-group-compact > .du-bois-light-picker-range {\n display: inline-flex;\n}\n.du-bois-light-input-group.du-bois-light-input-group-compact > *:not(:last-child) {\n margin-right: -1px;\n border-right-width: 1px;\n}\n.du-bois-light-input-group.du-bois-light-input-group-compact .du-bois-light-input {\n float: none;\n}\n.du-bois-light-input-group.du-bois-light-input-group-compact > .du-bois-light-select > .du-bois-light-select-selector,\n.du-bois-light-input-group.du-bois-light-input-group-compact > .du-bois-light-select-auto-complete .du-bois-light-input,\n.du-bois-light-input-group.du-bois-light-input-group-compact > .du-bois-light-cascader-picker .du-bois-light-input,\n.du-bois-light-input-group.du-bois-light-input-group-compact > .du-bois-light-input-group-wrapper .du-bois-light-input {\n border-right-width: 1px;\n border-radius: 0;\n}\n.du-bois-light-input-group.du-bois-light-input-group-compact > .du-bois-light-select > .du-bois-light-select-selector:hover,\n.du-bois-light-input-group.du-bois-light-input-group-compact > .du-bois-light-select-auto-complete .du-bois-light-input:hover,\n.du-bois-light-input-group.du-bois-light-input-group-compact > .du-bois-light-cascader-picker .du-bois-light-input:hover,\n.du-bois-light-input-group.du-bois-light-input-group-compact > .du-bois-light-input-group-wrapper .du-bois-light-input:hover {\n z-index: 1;\n}\n.du-bois-light-input-group.du-bois-light-input-group-compact > .du-bois-light-select > .du-bois-light-select-selector:focus,\n.du-bois-light-input-group.du-bois-light-input-group-compact > .du-bois-light-select-auto-complete .du-bois-light-input:focus,\n.du-bois-light-input-group.du-bois-light-input-group-compact > .du-bois-light-cascader-picker .du-bois-light-input:focus,\n.du-bois-light-input-group.du-bois-light-input-group-compact > .du-bois-light-input-group-wrapper .du-bois-light-input:focus {\n z-index: 1;\n}\n.du-bois-light-input-group.du-bois-light-input-group-compact > .du-bois-light-select-focused {\n z-index: 1;\n}\n.du-bois-light-input-group.du-bois-light-input-group-compact > .du-bois-light-select > .du-bois-light-select-arrow {\n z-index: 1;\n}\n.du-bois-light-input-group.du-bois-light-input-group-compact > *:first-child,\n.du-bois-light-input-group.du-bois-light-input-group-compact > .du-bois-light-select:first-child > .du-bois-light-select-selector,\n.du-bois-light-input-group.du-bois-light-input-group-compact > .du-bois-light-select-auto-complete:first-child .du-bois-light-input,\n.du-bois-light-input-group.du-bois-light-input-group-compact > .du-bois-light-cascader-picker:first-child .du-bois-light-input {\n border-top-left-radius: 4px;\n border-bottom-left-radius: 4px;\n}\n.du-bois-light-input-group.du-bois-light-input-group-compact > *:last-child,\n.du-bois-light-input-group.du-bois-light-input-group-compact > .du-bois-light-select:last-child > .du-bois-light-select-selector,\n.du-bois-light-input-group.du-bois-light-input-group-compact > .du-bois-light-cascader-picker:last-child .du-bois-light-input,\n.du-bois-light-input-group.du-bois-light-input-group-compact > .du-bois-light-cascader-picker-focused:last-child .du-bois-light-input {\n border-right-width: 1px;\n border-top-right-radius: 4px;\n border-bottom-right-radius: 4px;\n}\n.du-bois-light-input-group.du-bois-light-input-group-compact > .du-bois-light-select-auto-complete .du-bois-light-input {\n vertical-align: top;\n}\n.du-bois-light-input-group.du-bois-light-input-group-compact .du-bois-light-input-group-wrapper + .du-bois-light-input-group-wrapper {\n margin-left: -1px;\n}\n.du-bois-light-input-group.du-bois-light-input-group-compact .du-bois-light-input-group-wrapper + .du-bois-light-input-group-wrapper .du-bois-light-input-affix-wrapper {\n border-radius: 0;\n}\n.du-bois-light-input-group.du-bois-light-input-group-compact .du-bois-light-input-group-wrapper:not(:last-child).du-bois-light-input-search > .du-bois-light-input-group > .du-bois-light-input-group-addon > .du-bois-light-input-search-button {\n border-radius: 0;\n}\n.du-bois-light-input-group.du-bois-light-input-group-compact .du-bois-light-input-group-wrapper:not(:last-child).du-bois-light-input-search > .du-bois-light-input-group > .du-bois-light-input {\n border-radius: 4px 0 0 4px;\n}\n.du-bois-light-input-group-wrapper {\n display: inline-block;\n width: 100%;\n text-align: start;\n vertical-align: top;\n}\n.du-bois-light-input-password-icon {\n color: #5F7281;\n cursor: pointer;\n transition: all 0.3s;\n}\n.du-bois-light-input-password-icon:hover {\n color: rgba(0, 0, 0, 0.85);\n}\n.du-bois-light-input[type='color'] {\n height: 40px;\n}\n.du-bois-light-input[type='color'].du-bois-light-input-lg {\n height: 40px;\n}\n.du-bois-light-input[type='color'].du-bois-light-input-sm {\n height: 32px;\n padding-top: 3px;\n padding-bottom: 3px;\n}\n.du-bois-light-input-textarea-show-count::after {\n float: right;\n color: #5F7281;\n white-space: nowrap;\n content: attr(data-count);\n pointer-events: none;\n}\n.du-bois-light-input-search .du-bois-light-input:hover,\n.du-bois-light-input-search .du-bois-light-input:focus {\n border-color: #448dc2;\n}\n.du-bois-light-input-search .du-bois-light-input:hover + .du-bois-light-input-group-addon .du-bois-light-input-search-button:not(.du-bois-light-btn-primary),\n.du-bois-light-input-search .du-bois-light-input:focus + .du-bois-light-input-group-addon .du-bois-light-input-search-button:not(.du-bois-light-btn-primary) {\n border-left-color: #448dc2;\n}\n.du-bois-light-input-search .du-bois-light-input-affix-wrapper {\n border-radius: 0;\n}\n.du-bois-light-input-search .du-bois-light-input-lg {\n line-height: 19.9998px;\n}\n.du-bois-light-input-search > .du-bois-light-input-group > .du-bois-light-input-group-addon:last-child {\n left: -1px;\n padding: 0;\n border: 0;\n}\n.du-bois-light-input-search > .du-bois-light-input-group > .du-bois-light-input-group-addon:last-child .du-bois-light-input-search-button {\n padding-top: 0;\n padding-bottom: 0;\n border-radius: 0 4px 4px 0;\n}\n.du-bois-light-input-search > .du-bois-light-input-group > .du-bois-light-input-group-addon:last-child .du-bois-light-input-search-button:not(.du-bois-light-btn-primary) {\n color: #5F7281;\n}\n.du-bois-light-input-search > .du-bois-light-input-group > .du-bois-light-input-group-addon:last-child .du-bois-light-input-search-button:not(.du-bois-light-btn-primary).du-bois-light-btn-loading::before {\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n}\n.du-bois-light-input-search-button {\n height: 40px;\n}\n.du-bois-light-input-search-button:hover,\n.du-bois-light-input-search-button:focus {\n z-index: 1;\n}\n.du-bois-light-input-search-large .du-bois-light-input-search-button {\n height: 40px;\n}\n.du-bois-light-input-search-small .du-bois-light-input-search-button {\n height: 32px;\n}\n@media screen and (-ms-high-contrast: active), (-ms-high-contrast: none) {\n .du-bois-light-input {\n height: 40px;\n }\n .du-bois-light-input-lg {\n height: 40px;\n }\n .du-bois-light-input-sm {\n height: 32px;\n }\n .du-bois-light-input-affix-wrapper > input.du-bois-light-input {\n height: auto;\n }\n}\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-light-layout {\n display: flex;\n flex: auto;\n flex-direction: column;\n /* fix firefox can't set height smaller than content on flex item */\n min-height: 0;\n background: #f0f2f5;\n}\n.du-bois-light-layout,\n.du-bois-light-layout * {\n box-sizing: border-box;\n}\n.du-bois-light-layout.du-bois-light-layout-has-sider {\n flex-direction: row;\n}\n.du-bois-light-layout.du-bois-light-layout-has-sider > .du-bois-light-layout,\n.du-bois-light-layout.du-bois-light-layout-has-sider > .du-bois-light-layout-content {\n width: 0;\n}\n.du-bois-light-layout-header,\n.du-bois-light-layout-footer {\n flex: 0 0 auto;\n}\n.du-bois-light-layout-header {\n height: 64px;\n padding: 0 50px;\n color: #11171C;\n line-height: 64px;\n background: #001529;\n}\n.du-bois-light-layout-footer {\n padding: 24px 50px;\n color: #11171C;\n font-size: 13px;\n background: #f0f2f5;\n}\n.du-bois-light-layout-content {\n flex: auto;\n /* fix firefox can't set height smaller than content on flex item */\n min-height: 0;\n}\n.du-bois-light-layout-sider {\n position: relative;\n /* fix firefox can't set width smaller than content on flex item */\n min-width: 0;\n background: #001529;\n transition: all 0.2s;\n}\n.du-bois-light-layout-sider-children {\n height: 100%;\n margin-top: -0.1px;\n padding-top: 0.1px;\n}\n.du-bois-light-layout-sider-children .du-bois-light-menu.du-bois-light-menu-inline-collapsed {\n width: auto;\n}\n.du-bois-light-layout-sider-has-trigger {\n padding-bottom: 48px;\n}\n.du-bois-light-layout-sider-right {\n order: 1;\n}\n.du-bois-light-layout-sider-trigger {\n position: fixed;\n bottom: 0;\n z-index: 1;\n height: 48px;\n color: #fff;\n line-height: 48px;\n text-align: center;\n background: #002140;\n cursor: pointer;\n transition: all 0.2s;\n}\n.du-bois-light-layout-sider-zero-width > * {\n overflow: hidden;\n}\n.du-bois-light-layout-sider-zero-width-trigger {\n position: absolute;\n top: 64px;\n right: -36px;\n z-index: 1;\n width: 36px;\n height: 42px;\n color: #fff;\n font-size: 18px;\n line-height: 42px;\n text-align: center;\n background: #001529;\n border-radius: 0 4px 4px 0;\n cursor: pointer;\n transition: background 0.3s ease;\n}\n.du-bois-light-layout-sider-zero-width-trigger::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n transition: all 0.3s;\n content: '';\n}\n.du-bois-light-layout-sider-zero-width-trigger:hover::after {\n background: rgba(255, 255, 255, 0.1);\n}\n.du-bois-light-layout-sider-zero-width-trigger-right {\n left: -36px;\n border-radius: 4px 0 0 4px;\n}\n.du-bois-light-layout-sider-light {\n background: #fff;\n}\n.du-bois-light-layout-sider-light .du-bois-light-layout-sider-trigger {\n color: #11171C;\n background: #fff;\n}\n.du-bois-light-layout-sider-light .du-bois-light-layout-sider-zero-width-trigger {\n color: #11171C;\n background: #fff;\n}\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-light-menu-item-danger.du-bois-light-menu-item {\n color: #C82D4C;\n}\n.du-bois-light-menu-item-danger.du-bois-light-menu-item:hover,\n.du-bois-light-menu-item-danger.du-bois-light-menu-item-active {\n color: #C82D4C;\n}\n.du-bois-light-menu-item-danger.du-bois-light-menu-item:active {\n background: #fff1f0;\n}\n.du-bois-light-menu-item-danger.du-bois-light-menu-item-selected {\n color: #C82D4C;\n}\n.du-bois-light-menu-item-danger.du-bois-light-menu-item-selected > a,\n.du-bois-light-menu-item-danger.du-bois-light-menu-item-selected > a:hover {\n color: #C82D4C;\n}\n.du-bois-light-menu:not(.du-bois-light-menu-horizontal) .du-bois-light-menu-item-danger.du-bois-light-menu-item-selected {\n background-color: #fff1f0;\n}\n.du-bois-light-menu-inline .du-bois-light-menu-item-danger.du-bois-light-menu-item::after {\n border-right-color: #C82D4C;\n}\n.du-bois-light-menu-dark .du-bois-light-menu-item-danger.du-bois-light-menu-item,\n.du-bois-light-menu-dark .du-bois-light-menu-item-danger.du-bois-light-menu-item:hover,\n.du-bois-light-menu-dark .du-bois-light-menu-item-danger.du-bois-light-menu-item > a {\n color: #C82D4C;\n}\n.du-bois-light-menu-dark.du-bois-light-menu-dark:not(.du-bois-light-menu-horizontal) .du-bois-light-menu-item-danger.du-bois-light-menu-item-selected {\n color: #fff;\n background-color: #C82D4C;\n}\n.du-bois-light-menu {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n font-variant: tabular-nums;\n line-height: 20px;\n font-feature-settings: 'tnum';\n margin-bottom: 0;\n padding-left: 0;\n color: #11171C;\n font-size: 13px;\n line-height: 0;\n text-align: left;\n list-style: none;\n background: #fff;\n outline: none;\n box-shadow: 0 3px 6px -4px rgba(0, 0, 0, 0.12), 0 6px 16px 0 rgba(0, 0, 0, 0.08), 0 9px 28px 8px rgba(0, 0, 0, 0.05);\n transition: background 0.3s, width 0.3s cubic-bezier(0.2, 0, 0, 1) 0s;\n}\n.du-bois-light-menu::before {\n display: table;\n content: '';\n}\n.du-bois-light-menu::after {\n display: table;\n clear: both;\n content: '';\n}\n.du-bois-light-menu.du-bois-light-menu-root:focus-visible {\n box-shadow: 0 0 0 2px rgba(34, 114, 180, 0.2);\n}\n.du-bois-light-menu ul,\n.du-bois-light-menu ol {\n margin: 0;\n padding: 0;\n list-style: none;\n}\n.du-bois-light-menu-overflow {\n display: flex;\n}\n.du-bois-light-menu-overflow-item {\n flex: none;\n}\n.du-bois-light-menu-hidden,\n.du-bois-light-menu-submenu-hidden {\n display: none;\n}\n.du-bois-light-menu-item-group-title {\n height: 20px;\n padding: 8px 16px;\n color: #5F7281;\n font-size: 13px;\n line-height: 20px;\n transition: all 0.3s;\n}\n.du-bois-light-menu-horizontal .du-bois-light-menu-submenu {\n transition: border-color 0.3s cubic-bezier(0.645, 0.045, 0.355, 1), background 0.3s cubic-bezier(0.645, 0.045, 0.355, 1);\n}\n.du-bois-light-menu-submenu,\n.du-bois-light-menu-submenu-inline {\n transition: border-color 0.3s cubic-bezier(0.645, 0.045, 0.355, 1), background 0.3s cubic-bezier(0.645, 0.045, 0.355, 1), padding 0.15s cubic-bezier(0.645, 0.045, 0.355, 1);\n}\n.du-bois-light-menu-submenu-selected {\n color: #2272B4;\n}\n.du-bois-light-menu-item:active,\n.du-bois-light-menu-submenu-title:active {\n background: #e6f1f5;\n}\n.du-bois-light-menu-submenu .du-bois-light-menu-sub {\n cursor: initial;\n transition: background 0.3s cubic-bezier(0.645, 0.045, 0.355, 1), padding 0.3s cubic-bezier(0.645, 0.045, 0.355, 1);\n}\n.du-bois-light-menu-title-content {\n transition: color 0.3s;\n}\n.du-bois-light-menu-item a {\n color: #11171C;\n}\n.du-bois-light-menu-item a:hover {\n color: #2272B4;\n}\n.du-bois-light-menu-item a::before {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background-color: transparent;\n content: '';\n}\n.du-bois-light-menu-item > .du-bois-light-badge a {\n color: #11171C;\n}\n.du-bois-light-menu-item > .du-bois-light-badge a:hover {\n color: #2272B4;\n}\n.du-bois-light-menu-item-divider {\n height: 1px;\n overflow: hidden;\n line-height: 0;\n background-color: #f0f0f0;\n}\n.du-bois-light-menu-horizontal .du-bois-light-menu-item,\n.du-bois-light-menu-horizontal .du-bois-light-menu-submenu {\n margin-top: -1px;\n}\n.du-bois-light-menu-horizontal > .du-bois-light-menu-item:hover,\n.du-bois-light-menu-horizontal > .du-bois-light-menu-item-active,\n.du-bois-light-menu-horizontal > .du-bois-light-menu-submenu .du-bois-light-menu-submenu-title:hover {\n background-color: transparent;\n}\n.du-bois-light-menu-item-selected {\n color: #2272B4;\n}\n.du-bois-light-menu-item-selected a,\n.du-bois-light-menu-item-selected a:hover {\n color: #2272B4;\n}\n.du-bois-light-menu:not(.du-bois-light-menu-horizontal) .du-bois-light-menu-item-selected {\n background-color: #e6f1f5;\n}\n.du-bois-light-menu-inline,\n.du-bois-light-menu-vertical,\n.du-bois-light-menu-vertical-left {\n border-right: 1px solid #f0f0f0;\n}\n.du-bois-light-menu-vertical-right {\n border-left: 1px solid #f0f0f0;\n}\n.du-bois-light-menu-vertical.du-bois-light-menu-sub,\n.du-bois-light-menu-vertical-left.du-bois-light-menu-sub,\n.du-bois-light-menu-vertical-right.du-bois-light-menu-sub {\n min-width: 160px;\n max-height: calc(100vh - 100px);\n padding: 0;\n overflow: hidden;\n border-right: 0;\n}\n.du-bois-light-menu-vertical.du-bois-light-menu-sub:not([class*='-active']),\n.du-bois-light-menu-vertical-left.du-bois-light-menu-sub:not([class*='-active']),\n.du-bois-light-menu-vertical-right.du-bois-light-menu-sub:not([class*='-active']) {\n overflow-x: hidden;\n overflow-y: auto;\n}\n.du-bois-light-menu-vertical.du-bois-light-menu-sub .du-bois-light-menu-item,\n.du-bois-light-menu-vertical-left.du-bois-light-menu-sub .du-bois-light-menu-item,\n.du-bois-light-menu-vertical-right.du-bois-light-menu-sub .du-bois-light-menu-item {\n left: 0;\n margin-left: 0;\n border-right: 0;\n}\n.du-bois-light-menu-vertical.du-bois-light-menu-sub .du-bois-light-menu-item::after,\n.du-bois-light-menu-vertical-left.du-bois-light-menu-sub .du-bois-light-menu-item::after,\n.du-bois-light-menu-vertical-right.du-bois-light-menu-sub .du-bois-light-menu-item::after {\n border-right: 0;\n}\n.du-bois-light-menu-vertical.du-bois-light-menu-sub > .du-bois-light-menu-item,\n.du-bois-light-menu-vertical-left.du-bois-light-menu-sub > .du-bois-light-menu-item,\n.du-bois-light-menu-vertical-right.du-bois-light-menu-sub > .du-bois-light-menu-item,\n.du-bois-light-menu-vertical.du-bois-light-menu-sub > .du-bois-light-menu-submenu,\n.du-bois-light-menu-vertical-left.du-bois-light-menu-sub > .du-bois-light-menu-submenu,\n.du-bois-light-menu-vertical-right.du-bois-light-menu-sub > .du-bois-light-menu-submenu {\n transform-origin: 0 0;\n}\n.du-bois-light-menu-horizontal.du-bois-light-menu-sub {\n min-width: 114px;\n}\n.du-bois-light-menu-horizontal .du-bois-light-menu-item,\n.du-bois-light-menu-horizontal .du-bois-light-menu-submenu-title {\n transition: border-color 0.3s, background 0.3s;\n}\n.du-bois-light-menu-item,\n.du-bois-light-menu-submenu-title {\n position: relative;\n display: block;\n margin: 0;\n padding: 0 20px;\n white-space: nowrap;\n cursor: pointer;\n transition: border-color 0.3s, background 0.3s, padding 0.3s cubic-bezier(0.645, 0.045, 0.355, 1);\n}\n.du-bois-light-menu-item .du-bois-light-menu-item-icon,\n.du-bois-light-menu-submenu-title .du-bois-light-menu-item-icon,\n.du-bois-light-menu-item .anticon,\n.du-bois-light-menu-submenu-title .anticon {\n min-width: 14px;\n font-size: 13px;\n transition: font-size 0.15s cubic-bezier(0.215, 0.61, 0.355, 1), margin 0.3s cubic-bezier(0.645, 0.045, 0.355, 1), color 0.3s;\n}\n.du-bois-light-menu-item .du-bois-light-menu-item-icon + span,\n.du-bois-light-menu-submenu-title .du-bois-light-menu-item-icon + span,\n.du-bois-light-menu-item .anticon + span,\n.du-bois-light-menu-submenu-title .anticon + span {\n margin-left: 10px;\n opacity: 1;\n transition: opacity 0.3s cubic-bezier(0.645, 0.045, 0.355, 1), margin 0.3s, color 0.3s;\n}\n.du-bois-light-menu-item .du-bois-light-menu-item-icon.svg,\n.du-bois-light-menu-submenu-title .du-bois-light-menu-item-icon.svg {\n vertical-align: -0.125em;\n}\n.du-bois-light-menu-item.du-bois-light-menu-item-only-child > .anticon,\n.du-bois-light-menu-submenu-title.du-bois-light-menu-item-only-child > .anticon,\n.du-bois-light-menu-item.du-bois-light-menu-item-only-child > .du-bois-light-menu-item-icon,\n.du-bois-light-menu-submenu-title.du-bois-light-menu-item-only-child > .du-bois-light-menu-item-icon {\n margin-right: 0;\n}\n.du-bois-light-menu-item:focus-visible,\n.du-bois-light-menu-submenu-title:focus-visible {\n box-shadow: 0 0 0 2px rgba(34, 114, 180, 0.2);\n}\n.du-bois-light-menu > .du-bois-light-menu-item-divider {\n height: 1px;\n margin: 1px 0;\n padding: 0;\n overflow: hidden;\n line-height: 0;\n background-color: #f0f0f0;\n}\n.du-bois-light-menu-submenu-popup {\n position: absolute;\n z-index: 1050;\n background: transparent;\n border-radius: 4px;\n box-shadow: none;\n transform-origin: 0 0;\n}\n.du-bois-light-menu-submenu-popup::before {\n position: absolute;\n top: -7px;\n right: 0;\n bottom: 0;\n left: 0;\n z-index: -1;\n width: 100%;\n height: 100%;\n opacity: 0.0001;\n content: ' ';\n}\n.du-bois-light-menu-submenu-placement-rightTop::before {\n top: 0;\n left: -7px;\n}\n.du-bois-light-menu-submenu > .du-bois-light-menu {\n background-color: #fff;\n border-radius: 4px;\n}\n.du-bois-light-menu-submenu > .du-bois-light-menu-submenu-title::after {\n transition: transform 0.3s cubic-bezier(0.645, 0.045, 0.355, 1);\n}\n.du-bois-light-menu-submenu-popup > .du-bois-light-menu {\n background-color: #fff;\n}\n.du-bois-light-menu-submenu-expand-icon,\n.du-bois-light-menu-submenu-arrow {\n position: absolute;\n top: 50%;\n right: 16px;\n width: 10px;\n color: #11171C;\n transform: translateY(-50%);\n transition: transform 0.3s cubic-bezier(0.645, 0.045, 0.355, 1);\n}\n.du-bois-light-menu-submenu-arrow::before,\n.du-bois-light-menu-submenu-arrow::after {\n position: absolute;\n width: 6px;\n height: 1.5px;\n background-color: currentColor;\n border-radius: 2px;\n transition: background 0.3s cubic-bezier(0.645, 0.045, 0.355, 1), transform 0.3s cubic-bezier(0.645, 0.045, 0.355, 1), top 0.3s cubic-bezier(0.645, 0.045, 0.355, 1), color 0.3s cubic-bezier(0.645, 0.045, 0.355, 1);\n content: '';\n}\n.du-bois-light-menu-submenu-arrow::before {\n transform: rotate(45deg) translateY(-2.5px);\n}\n.du-bois-light-menu-submenu-arrow::after {\n transform: rotate(-45deg) translateY(2.5px);\n}\n.du-bois-light-menu-submenu:hover > .du-bois-light-menu-submenu-title > .du-bois-light-menu-submenu-expand-icon,\n.du-bois-light-menu-submenu:hover > .du-bois-light-menu-submenu-title > .du-bois-light-menu-submenu-arrow {\n color: #2272B4;\n}\n.du-bois-light-menu-inline-collapsed .du-bois-light-menu-submenu-arrow::before,\n.du-bois-light-menu-submenu-inline .du-bois-light-menu-submenu-arrow::before {\n transform: rotate(-45deg) translateX(2.5px);\n}\n.du-bois-light-menu-inline-collapsed .du-bois-light-menu-submenu-arrow::after,\n.du-bois-light-menu-submenu-inline .du-bois-light-menu-submenu-arrow::after {\n transform: rotate(45deg) translateX(-2.5px);\n}\n.du-bois-light-menu-submenu-horizontal .du-bois-light-menu-submenu-arrow {\n display: none;\n}\n.du-bois-light-menu-submenu-open.du-bois-light-menu-submenu-inline > .du-bois-light-menu-submenu-title > .du-bois-light-menu-submenu-arrow {\n transform: translateY(-2px);\n}\n.du-bois-light-menu-submenu-open.du-bois-light-menu-submenu-inline > .du-bois-light-menu-submenu-title > .du-bois-light-menu-submenu-arrow::after {\n transform: rotate(-45deg) translateX(-2.5px);\n}\n.du-bois-light-menu-submenu-open.du-bois-light-menu-submenu-inline > .du-bois-light-menu-submenu-title > .du-bois-light-menu-submenu-arrow::before {\n transform: rotate(45deg) translateX(2.5px);\n}\n.du-bois-light-menu-vertical .du-bois-light-menu-submenu-selected,\n.du-bois-light-menu-vertical-left .du-bois-light-menu-submenu-selected,\n.du-bois-light-menu-vertical-right .du-bois-light-menu-submenu-selected {\n color: #2272B4;\n}\n.du-bois-light-menu-horizontal {\n line-height: 46px;\n border: 0;\n border-bottom: 1px solid #f0f0f0;\n box-shadow: none;\n}\n.du-bois-light-menu-horizontal:not(.du-bois-light-menu-dark) > .du-bois-light-menu-item,\n.du-bois-light-menu-horizontal:not(.du-bois-light-menu-dark) > .du-bois-light-menu-submenu {\n margin-top: -1px;\n margin-bottom: 0;\n padding: 0 20px;\n}\n.du-bois-light-menu-horizontal:not(.du-bois-light-menu-dark) > .du-bois-light-menu-item:hover,\n.du-bois-light-menu-horizontal:not(.du-bois-light-menu-dark) > .du-bois-light-menu-submenu:hover,\n.du-bois-light-menu-horizontal:not(.du-bois-light-menu-dark) > .du-bois-light-menu-item-active,\n.du-bois-light-menu-horizontal:not(.du-bois-light-menu-dark) > .du-bois-light-menu-submenu-active,\n.du-bois-light-menu-horizontal:not(.du-bois-light-menu-dark) > .du-bois-light-menu-item-open,\n.du-bois-light-menu-horizontal:not(.du-bois-light-menu-dark) > .du-bois-light-menu-submenu-open,\n.du-bois-light-menu-horizontal:not(.du-bois-light-menu-dark) > .du-bois-light-menu-item-selected,\n.du-bois-light-menu-horizontal:not(.du-bois-light-menu-dark) > .du-bois-light-menu-submenu-selected {\n color: #2272B4;\n}\n.du-bois-light-menu-horizontal:not(.du-bois-light-menu-dark) > .du-bois-light-menu-item:hover::after,\n.du-bois-light-menu-horizontal:not(.du-bois-light-menu-dark) > .du-bois-light-menu-submenu:hover::after,\n.du-bois-light-menu-horizontal:not(.du-bois-light-menu-dark) > .du-bois-light-menu-item-active::after,\n.du-bois-light-menu-horizontal:not(.du-bois-light-menu-dark) > .du-bois-light-menu-submenu-active::after,\n.du-bois-light-menu-horizontal:not(.du-bois-light-menu-dark) > .du-bois-light-menu-item-open::after,\n.du-bois-light-menu-horizontal:not(.du-bois-light-menu-dark) > .du-bois-light-menu-submenu-open::after,\n.du-bois-light-menu-horizontal:not(.du-bois-light-menu-dark) > .du-bois-light-menu-item-selected::after,\n.du-bois-light-menu-horizontal:not(.du-bois-light-menu-dark) > .du-bois-light-menu-submenu-selected::after {\n border-bottom: 2px solid #2272B4;\n}\n.du-bois-light-menu-horizontal > .du-bois-light-menu-item,\n.du-bois-light-menu-horizontal > .du-bois-light-menu-submenu {\n position: relative;\n top: 1px;\n display: inline-block;\n vertical-align: bottom;\n}\n.du-bois-light-menu-horizontal > .du-bois-light-menu-item::after,\n.du-bois-light-menu-horizontal > .du-bois-light-menu-submenu::after {\n position: absolute;\n right: 20px;\n bottom: 0;\n left: 20px;\n border-bottom: 2px solid transparent;\n transition: border-color 0.3s cubic-bezier(0.645, 0.045, 0.355, 1);\n content: '';\n}\n.du-bois-light-menu-horizontal > .du-bois-light-menu-submenu > .du-bois-light-menu-submenu-title {\n padding: 0;\n}\n.du-bois-light-menu-horizontal > .du-bois-light-menu-item a {\n color: #11171C;\n}\n.du-bois-light-menu-horizontal > .du-bois-light-menu-item a:hover {\n color: #2272B4;\n}\n.du-bois-light-menu-horizontal > .du-bois-light-menu-item a::before {\n bottom: -2px;\n}\n.du-bois-light-menu-horizontal > .du-bois-light-menu-item-selected a {\n color: #2272B4;\n}\n.du-bois-light-menu-horizontal::after {\n display: block;\n clear: both;\n height: 0;\n content: '\\20';\n}\n.du-bois-light-menu-vertical .du-bois-light-menu-item,\n.du-bois-light-menu-vertical-left .du-bois-light-menu-item,\n.du-bois-light-menu-vertical-right .du-bois-light-menu-item,\n.du-bois-light-menu-inline .du-bois-light-menu-item {\n position: relative;\n}\n.du-bois-light-menu-vertical .du-bois-light-menu-item::after,\n.du-bois-light-menu-vertical-left .du-bois-light-menu-item::after,\n.du-bois-light-menu-vertical-right .du-bois-light-menu-item::after,\n.du-bois-light-menu-inline .du-bois-light-menu-item::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n border-right: 3px solid #2272B4;\n transform: scaleY(0.0001);\n opacity: 0;\n transition: transform 0.15s cubic-bezier(0.215, 0.61, 0.355, 1), opacity 0.15s cubic-bezier(0.215, 0.61, 0.355, 1);\n content: '';\n}\n.du-bois-light-menu-vertical .du-bois-light-menu-item,\n.du-bois-light-menu-vertical-left .du-bois-light-menu-item,\n.du-bois-light-menu-vertical-right .du-bois-light-menu-item,\n.du-bois-light-menu-inline .du-bois-light-menu-item,\n.du-bois-light-menu-vertical .du-bois-light-menu-submenu-title,\n.du-bois-light-menu-vertical-left .du-bois-light-menu-submenu-title,\n.du-bois-light-menu-vertical-right .du-bois-light-menu-submenu-title,\n.du-bois-light-menu-inline .du-bois-light-menu-submenu-title {\n height: 40px;\n margin-top: 4px;\n margin-bottom: 4px;\n padding: 0 16px;\n overflow: hidden;\n line-height: 40px;\n text-overflow: ellipsis;\n}\n.du-bois-light-menu-vertical .du-bois-light-menu-submenu,\n.du-bois-light-menu-vertical-left .du-bois-light-menu-submenu,\n.du-bois-light-menu-vertical-right .du-bois-light-menu-submenu,\n.du-bois-light-menu-inline .du-bois-light-menu-submenu {\n padding-bottom: 0.02px;\n}\n.du-bois-light-menu-vertical .du-bois-light-menu-item:not(:last-child),\n.du-bois-light-menu-vertical-left .du-bois-light-menu-item:not(:last-child),\n.du-bois-light-menu-vertical-right .du-bois-light-menu-item:not(:last-child),\n.du-bois-light-menu-inline .du-bois-light-menu-item:not(:last-child) {\n margin-bottom: 8px;\n}\n.du-bois-light-menu-vertical > .du-bois-light-menu-item,\n.du-bois-light-menu-vertical-left > .du-bois-light-menu-item,\n.du-bois-light-menu-vertical-right > .du-bois-light-menu-item,\n.du-bois-light-menu-inline > .du-bois-light-menu-item,\n.du-bois-light-menu-vertical > .du-bois-light-menu-submenu > .du-bois-light-menu-submenu-title,\n.du-bois-light-menu-vertical-left > .du-bois-light-menu-submenu > .du-bois-light-menu-submenu-title,\n.du-bois-light-menu-vertical-right > .du-bois-light-menu-submenu > .du-bois-light-menu-submenu-title,\n.du-bois-light-menu-inline > .du-bois-light-menu-submenu > .du-bois-light-menu-submenu-title {\n height: 40px;\n line-height: 40px;\n}\n.du-bois-light-menu-vertical .du-bois-light-menu-item-group-list .du-bois-light-menu-submenu-title,\n.du-bois-light-menu-vertical .du-bois-light-menu-submenu-title {\n padding-right: 34px;\n}\n.du-bois-light-menu-inline {\n width: 100%;\n}\n.du-bois-light-menu-inline .du-bois-light-menu-selected::after,\n.du-bois-light-menu-inline .du-bois-light-menu-item-selected::after {\n transform: scaleY(1);\n opacity: 1;\n transition: transform 0.15s cubic-bezier(0.645, 0.045, 0.355, 1), opacity 0.15s cubic-bezier(0.645, 0.045, 0.355, 1);\n}\n.du-bois-light-menu-inline .du-bois-light-menu-item,\n.du-bois-light-menu-inline .du-bois-light-menu-submenu-title {\n width: calc(100% + 1px);\n}\n.du-bois-light-menu-inline .du-bois-light-menu-item-group-list .du-bois-light-menu-submenu-title,\n.du-bois-light-menu-inline .du-bois-light-menu-submenu-title {\n padding-right: 34px;\n}\n.du-bois-light-menu-inline.du-bois-light-menu-root .du-bois-light-menu-item,\n.du-bois-light-menu-inline.du-bois-light-menu-root .du-bois-light-menu-submenu-title {\n display: flex;\n align-items: center;\n transition: border-color 0.3s, background 0.3s, padding 0.1s cubic-bezier(0.215, 0.61, 0.355, 1);\n}\n.du-bois-light-menu-inline.du-bois-light-menu-root .du-bois-light-menu-item > .du-bois-light-menu-title-content,\n.du-bois-light-menu-inline.du-bois-light-menu-root .du-bois-light-menu-submenu-title > .du-bois-light-menu-title-content {\n flex: auto;\n min-width: 0;\n overflow: hidden;\n text-overflow: ellipsis;\n}\n.du-bois-light-menu-inline.du-bois-light-menu-root .du-bois-light-menu-item > *,\n.du-bois-light-menu-inline.du-bois-light-menu-root .du-bois-light-menu-submenu-title > * {\n flex: none;\n}\n.du-bois-light-menu.du-bois-light-menu-inline-collapsed {\n width: 80px;\n}\n.du-bois-light-menu.du-bois-light-menu-inline-collapsed > .du-bois-light-menu-item,\n.du-bois-light-menu.du-bois-light-menu-inline-collapsed > .du-bois-light-menu-item-group > .du-bois-light-menu-item-group-list > .du-bois-light-menu-item,\n.du-bois-light-menu.du-bois-light-menu-inline-collapsed > .du-bois-light-menu-item-group > .du-bois-light-menu-item-group-list > .du-bois-light-menu-submenu > .du-bois-light-menu-submenu-title,\n.du-bois-light-menu.du-bois-light-menu-inline-collapsed > .du-bois-light-menu-submenu > .du-bois-light-menu-submenu-title {\n left: 0;\n padding: 0 calc(50% - 18px / 2);\n text-overflow: clip;\n}\n.du-bois-light-menu.du-bois-light-menu-inline-collapsed > .du-bois-light-menu-item .du-bois-light-menu-submenu-arrow,\n.du-bois-light-menu.du-bois-light-menu-inline-collapsed > .du-bois-light-menu-item-group > .du-bois-light-menu-item-group-list > .du-bois-light-menu-item .du-bois-light-menu-submenu-arrow,\n.du-bois-light-menu.du-bois-light-menu-inline-collapsed > .du-bois-light-menu-item-group > .du-bois-light-menu-item-group-list > .du-bois-light-menu-submenu > .du-bois-light-menu-submenu-title .du-bois-light-menu-submenu-arrow,\n.du-bois-light-menu.du-bois-light-menu-inline-collapsed > .du-bois-light-menu-submenu > .du-bois-light-menu-submenu-title .du-bois-light-menu-submenu-arrow {\n opacity: 0;\n}\n.du-bois-light-menu.du-bois-light-menu-inline-collapsed > .du-bois-light-menu-item .du-bois-light-menu-item-icon,\n.du-bois-light-menu.du-bois-light-menu-inline-collapsed > .du-bois-light-menu-item-group > .du-bois-light-menu-item-group-list > .du-bois-light-menu-item .du-bois-light-menu-item-icon,\n.du-bois-light-menu.du-bois-light-menu-inline-collapsed > .du-bois-light-menu-item-group > .du-bois-light-menu-item-group-list > .du-bois-light-menu-submenu > .du-bois-light-menu-submenu-title .du-bois-light-menu-item-icon,\n.du-bois-light-menu.du-bois-light-menu-inline-collapsed > .du-bois-light-menu-submenu > .du-bois-light-menu-submenu-title .du-bois-light-menu-item-icon,\n.du-bois-light-menu.du-bois-light-menu-inline-collapsed > .du-bois-light-menu-item .anticon,\n.du-bois-light-menu.du-bois-light-menu-inline-collapsed > .du-bois-light-menu-item-group > .du-bois-light-menu-item-group-list > .du-bois-light-menu-item .anticon,\n.du-bois-light-menu.du-bois-light-menu-inline-collapsed > .du-bois-light-menu-item-group > .du-bois-light-menu-item-group-list > .du-bois-light-menu-submenu > .du-bois-light-menu-submenu-title .anticon,\n.du-bois-light-menu.du-bois-light-menu-inline-collapsed > .du-bois-light-menu-submenu > .du-bois-light-menu-submenu-title .anticon {\n margin: 0;\n font-size: 18px;\n line-height: 40px;\n}\n.du-bois-light-menu.du-bois-light-menu-inline-collapsed > .du-bois-light-menu-item .du-bois-light-menu-item-icon + span,\n.du-bois-light-menu.du-bois-light-menu-inline-collapsed > .du-bois-light-menu-item-group > .du-bois-light-menu-item-group-list > .du-bois-light-menu-item .du-bois-light-menu-item-icon + span,\n.du-bois-light-menu.du-bois-light-menu-inline-collapsed > .du-bois-light-menu-item-group > .du-bois-light-menu-item-group-list > .du-bois-light-menu-submenu > .du-bois-light-menu-submenu-title .du-bois-light-menu-item-icon + span,\n.du-bois-light-menu.du-bois-light-menu-inline-collapsed > .du-bois-light-menu-submenu > .du-bois-light-menu-submenu-title .du-bois-light-menu-item-icon + span,\n.du-bois-light-menu.du-bois-light-menu-inline-collapsed > .du-bois-light-menu-item .anticon + span,\n.du-bois-light-menu.du-bois-light-menu-inline-collapsed > .du-bois-light-menu-item-group > .du-bois-light-menu-item-group-list > .du-bois-light-menu-item .anticon + span,\n.du-bois-light-menu.du-bois-light-menu-inline-collapsed > .du-bois-light-menu-item-group > .du-bois-light-menu-item-group-list > .du-bois-light-menu-submenu > .du-bois-light-menu-submenu-title .anticon + span,\n.du-bois-light-menu.du-bois-light-menu-inline-collapsed > .du-bois-light-menu-submenu > .du-bois-light-menu-submenu-title .anticon + span {\n display: inline-block;\n opacity: 0;\n}\n.du-bois-light-menu.du-bois-light-menu-inline-collapsed .du-bois-light-menu-item-icon,\n.du-bois-light-menu.du-bois-light-menu-inline-collapsed .anticon {\n display: inline-block;\n}\n.du-bois-light-menu.du-bois-light-menu-inline-collapsed-tooltip {\n pointer-events: none;\n}\n.du-bois-light-menu.du-bois-light-menu-inline-collapsed-tooltip .du-bois-light-menu-item-icon,\n.du-bois-light-menu.du-bois-light-menu-inline-collapsed-tooltip .anticon {\n display: none;\n}\n.du-bois-light-menu.du-bois-light-menu-inline-collapsed-tooltip a {\n color: rgba(255, 255, 255, 0.85);\n}\n.du-bois-light-menu.du-bois-light-menu-inline-collapsed .du-bois-light-menu-item-group-title {\n padding-right: 4px;\n padding-left: 4px;\n overflow: hidden;\n white-space: nowrap;\n text-overflow: ellipsis;\n}\n.du-bois-light-menu-item-group-list {\n margin: 0;\n padding: 0;\n}\n.du-bois-light-menu-item-group-list .du-bois-light-menu-item,\n.du-bois-light-menu-item-group-list .du-bois-light-menu-submenu-title {\n padding: 0 16px 0 28px;\n}\n.du-bois-light-menu-root.du-bois-light-menu-vertical,\n.du-bois-light-menu-root.du-bois-light-menu-vertical-left,\n.du-bois-light-menu-root.du-bois-light-menu-vertical-right,\n.du-bois-light-menu-root.du-bois-light-menu-inline {\n box-shadow: none;\n}\n.du-bois-light-menu-root.du-bois-light-menu-inline-collapsed .du-bois-light-menu-item > .du-bois-light-menu-inline-collapsed-noicon,\n.du-bois-light-menu-root.du-bois-light-menu-inline-collapsed .du-bois-light-menu-submenu .du-bois-light-menu-submenu-title > .du-bois-light-menu-inline-collapsed-noicon {\n font-size: 18px;\n text-align: center;\n}\n.du-bois-light-menu-sub.du-bois-light-menu-inline {\n padding: 0;\n background: #fafafa;\n border: 0;\n border-radius: 0;\n box-shadow: none;\n}\n.du-bois-light-menu-sub.du-bois-light-menu-inline > .du-bois-light-menu-item,\n.du-bois-light-menu-sub.du-bois-light-menu-inline > .du-bois-light-menu-submenu > .du-bois-light-menu-submenu-title {\n height: 40px;\n line-height: 40px;\n list-style-position: inside;\n list-style-type: disc;\n}\n.du-bois-light-menu-sub.du-bois-light-menu-inline .du-bois-light-menu-item-group-title {\n padding-left: 32px;\n}\n.du-bois-light-menu-item-disabled,\n.du-bois-light-menu-submenu-disabled {\n color: rgba(0, 0, 0, 0.25) !important;\n background: none;\n cursor: not-allowed;\n}\n.du-bois-light-menu-item-disabled::after,\n.du-bois-light-menu-submenu-disabled::after {\n border-color: transparent !important;\n}\n.du-bois-light-menu-item-disabled a,\n.du-bois-light-menu-submenu-disabled a {\n color: rgba(0, 0, 0, 0.25) !important;\n pointer-events: none;\n}\n.du-bois-light-menu-item-disabled > .du-bois-light-menu-submenu-title,\n.du-bois-light-menu-submenu-disabled > .du-bois-light-menu-submenu-title {\n color: rgba(0, 0, 0, 0.25) !important;\n cursor: not-allowed;\n}\n.du-bois-light-menu-item-disabled > .du-bois-light-menu-submenu-title > .du-bois-light-menu-submenu-arrow::before,\n.du-bois-light-menu-submenu-disabled > .du-bois-light-menu-submenu-title > .du-bois-light-menu-submenu-arrow::before,\n.du-bois-light-menu-item-disabled > .du-bois-light-menu-submenu-title > .du-bois-light-menu-submenu-arrow::after,\n.du-bois-light-menu-submenu-disabled > .du-bois-light-menu-submenu-title > .du-bois-light-menu-submenu-arrow::after {\n background: rgba(0, 0, 0, 0.25) !important;\n}\n.du-bois-light-layout-header .du-bois-light-menu {\n line-height: inherit;\n}\n.du-bois-light-menu-light .du-bois-light-menu-item:hover,\n.du-bois-light-menu-light .du-bois-light-menu-item-active,\n.du-bois-light-menu-light .du-bois-light-menu:not(.du-bois-light-menu-inline) .du-bois-light-menu-submenu-open,\n.du-bois-light-menu-light .du-bois-light-menu-submenu-active,\n.du-bois-light-menu-light .du-bois-light-menu-submenu-title:hover {\n color: #2272B4;\n}\n.du-bois-light-menu.du-bois-light-menu-dark,\n.du-bois-light-menu-dark .du-bois-light-menu-sub,\n.du-bois-light-menu.du-bois-light-menu-dark .du-bois-light-menu-sub {\n color: rgba(255, 255, 255, 0.65);\n background: #001529;\n}\n.du-bois-light-menu.du-bois-light-menu-dark .du-bois-light-menu-submenu-title .du-bois-light-menu-submenu-arrow,\n.du-bois-light-menu-dark .du-bois-light-menu-sub .du-bois-light-menu-submenu-title .du-bois-light-menu-submenu-arrow,\n.du-bois-light-menu.du-bois-light-menu-dark .du-bois-light-menu-sub .du-bois-light-menu-submenu-title .du-bois-light-menu-submenu-arrow {\n opacity: 0.45;\n transition: all 0.3s;\n}\n.du-bois-light-menu.du-bois-light-menu-dark .du-bois-light-menu-submenu-title .du-bois-light-menu-submenu-arrow::after,\n.du-bois-light-menu-dark .du-bois-light-menu-sub .du-bois-light-menu-submenu-title .du-bois-light-menu-submenu-arrow::after,\n.du-bois-light-menu.du-bois-light-menu-dark .du-bois-light-menu-sub .du-bois-light-menu-submenu-title .du-bois-light-menu-submenu-arrow::after,\n.du-bois-light-menu.du-bois-light-menu-dark .du-bois-light-menu-submenu-title .du-bois-light-menu-submenu-arrow::before,\n.du-bois-light-menu-dark .du-bois-light-menu-sub .du-bois-light-menu-submenu-title .du-bois-light-menu-submenu-arrow::before,\n.du-bois-light-menu.du-bois-light-menu-dark .du-bois-light-menu-sub .du-bois-light-menu-submenu-title .du-bois-light-menu-submenu-arrow::before {\n background: #fff;\n}\n.du-bois-light-menu-dark.du-bois-light-menu-submenu-popup {\n background: transparent;\n}\n.du-bois-light-menu-dark .du-bois-light-menu-inline.du-bois-light-menu-sub {\n background: #000c17;\n}\n.du-bois-light-menu-dark.du-bois-light-menu-horizontal {\n border-bottom: 0;\n}\n.du-bois-light-menu-dark.du-bois-light-menu-horizontal > .du-bois-light-menu-item,\n.du-bois-light-menu-dark.du-bois-light-menu-horizontal > .du-bois-light-menu-submenu {\n top: 0;\n margin-top: 0;\n padding: 0 20px;\n border-color: #001529;\n border-bottom: 0;\n}\n.du-bois-light-menu-dark.du-bois-light-menu-horizontal > .du-bois-light-menu-item:hover {\n background-color: #2272B4;\n}\n.du-bois-light-menu-dark.du-bois-light-menu-horizontal > .du-bois-light-menu-item > a::before {\n bottom: 0;\n}\n.du-bois-light-menu-dark .du-bois-light-menu-item,\n.du-bois-light-menu-dark .du-bois-light-menu-item-group-title,\n.du-bois-light-menu-dark .du-bois-light-menu-item > a,\n.du-bois-light-menu-dark .du-bois-light-menu-item > span > a {\n color: rgba(255, 255, 255, 0.65);\n}\n.du-bois-light-menu-dark.du-bois-light-menu-inline,\n.du-bois-light-menu-dark.du-bois-light-menu-vertical,\n.du-bois-light-menu-dark.du-bois-light-menu-vertical-left,\n.du-bois-light-menu-dark.du-bois-light-menu-vertical-right {\n border-right: 0;\n}\n.du-bois-light-menu-dark.du-bois-light-menu-inline .du-bois-light-menu-item,\n.du-bois-light-menu-dark.du-bois-light-menu-vertical .du-bois-light-menu-item,\n.du-bois-light-menu-dark.du-bois-light-menu-vertical-left .du-bois-light-menu-item,\n.du-bois-light-menu-dark.du-bois-light-menu-vertical-right .du-bois-light-menu-item {\n left: 0;\n margin-left: 0;\n border-right: 0;\n}\n.du-bois-light-menu-dark.du-bois-light-menu-inline .du-bois-light-menu-item::after,\n.du-bois-light-menu-dark.du-bois-light-menu-vertical .du-bois-light-menu-item::after,\n.du-bois-light-menu-dark.du-bois-light-menu-vertical-left .du-bois-light-menu-item::after,\n.du-bois-light-menu-dark.du-bois-light-menu-vertical-right .du-bois-light-menu-item::after {\n border-right: 0;\n}\n.du-bois-light-menu-dark.du-bois-light-menu-inline .du-bois-light-menu-item,\n.du-bois-light-menu-dark.du-bois-light-menu-inline .du-bois-light-menu-submenu-title {\n width: 100%;\n}\n.du-bois-light-menu-dark .du-bois-light-menu-item:hover,\n.du-bois-light-menu-dark .du-bois-light-menu-item-active,\n.du-bois-light-menu-dark .du-bois-light-menu-submenu-active,\n.du-bois-light-menu-dark .du-bois-light-menu-submenu-open,\n.du-bois-light-menu-dark .du-bois-light-menu-submenu-selected,\n.du-bois-light-menu-dark .du-bois-light-menu-submenu-title:hover {\n color: #fff;\n background-color: transparent;\n}\n.du-bois-light-menu-dark .du-bois-light-menu-item:hover > a,\n.du-bois-light-menu-dark .du-bois-light-menu-item-active > a,\n.du-bois-light-menu-dark .du-bois-light-menu-submenu-active > a,\n.du-bois-light-menu-dark .du-bois-light-menu-submenu-open > a,\n.du-bois-light-menu-dark .du-bois-light-menu-submenu-selected > a,\n.du-bois-light-menu-dark .du-bois-light-menu-submenu-title:hover > a,\n.du-bois-light-menu-dark .du-bois-light-menu-item:hover > span > a,\n.du-bois-light-menu-dark .du-bois-light-menu-item-active > span > a,\n.du-bois-light-menu-dark .du-bois-light-menu-submenu-active > span > a,\n.du-bois-light-menu-dark .du-bois-light-menu-submenu-open > span > a,\n.du-bois-light-menu-dark .du-bois-light-menu-submenu-selected > span > a,\n.du-bois-light-menu-dark .du-bois-light-menu-submenu-title:hover > span > a {\n color: #fff;\n}\n.du-bois-light-menu-dark .du-bois-light-menu-item:hover > .du-bois-light-menu-submenu-title > .du-bois-light-menu-submenu-arrow,\n.du-bois-light-menu-dark .du-bois-light-menu-item-active > .du-bois-light-menu-submenu-title > .du-bois-light-menu-submenu-arrow,\n.du-bois-light-menu-dark .du-bois-light-menu-submenu-active > .du-bois-light-menu-submenu-title > .du-bois-light-menu-submenu-arrow,\n.du-bois-light-menu-dark .du-bois-light-menu-submenu-open > .du-bois-light-menu-submenu-title > .du-bois-light-menu-submenu-arrow,\n.du-bois-light-menu-dark .du-bois-light-menu-submenu-selected > .du-bois-light-menu-submenu-title > .du-bois-light-menu-submenu-arrow,\n.du-bois-light-menu-dark .du-bois-light-menu-submenu-title:hover > .du-bois-light-menu-submenu-title > .du-bois-light-menu-submenu-arrow {\n opacity: 1;\n}\n.du-bois-light-menu-dark .du-bois-light-menu-item:hover > .du-bois-light-menu-submenu-title > .du-bois-light-menu-submenu-arrow::after,\n.du-bois-light-menu-dark .du-bois-light-menu-item-active > .du-bois-light-menu-submenu-title > .du-bois-light-menu-submenu-arrow::after,\n.du-bois-light-menu-dark .du-bois-light-menu-submenu-active > .du-bois-light-menu-submenu-title > .du-bois-light-menu-submenu-arrow::after,\n.du-bois-light-menu-dark .du-bois-light-menu-submenu-open > .du-bois-light-menu-submenu-title > .du-bois-light-menu-submenu-arrow::after,\n.du-bois-light-menu-dark .du-bois-light-menu-submenu-selected > .du-bois-light-menu-submenu-title > .du-bois-light-menu-submenu-arrow::after,\n.du-bois-light-menu-dark .du-bois-light-menu-submenu-title:hover > .du-bois-light-menu-submenu-title > .du-bois-light-menu-submenu-arrow::after,\n.du-bois-light-menu-dark .du-bois-light-menu-item:hover > .du-bois-light-menu-submenu-title > .du-bois-light-menu-submenu-arrow::before,\n.du-bois-light-menu-dark .du-bois-light-menu-item-active > .du-bois-light-menu-submenu-title > .du-bois-light-menu-submenu-arrow::before,\n.du-bois-light-menu-dark .du-bois-light-menu-submenu-active > .du-bois-light-menu-submenu-title > .du-bois-light-menu-submenu-arrow::before,\n.du-bois-light-menu-dark .du-bois-light-menu-submenu-open > .du-bois-light-menu-submenu-title > .du-bois-light-menu-submenu-arrow::before,\n.du-bois-light-menu-dark .du-bois-light-menu-submenu-selected > .du-bois-light-menu-submenu-title > .du-bois-light-menu-submenu-arrow::before,\n.du-bois-light-menu-dark .du-bois-light-menu-submenu-title:hover > .du-bois-light-menu-submenu-title > .du-bois-light-menu-submenu-arrow::before {\n background: #fff;\n}\n.du-bois-light-menu-dark .du-bois-light-menu-item:hover {\n background-color: transparent;\n}\n.du-bois-light-menu-dark.du-bois-light-menu-dark:not(.du-bois-light-menu-horizontal) .du-bois-light-menu-item-selected {\n background-color: #2272B4;\n}\n.du-bois-light-menu-dark .du-bois-light-menu-item-selected {\n color: #fff;\n border-right: 0;\n}\n.du-bois-light-menu-dark .du-bois-light-menu-item-selected::after {\n border-right: 0;\n}\n.du-bois-light-menu-dark .du-bois-light-menu-item-selected > a,\n.du-bois-light-menu-dark .du-bois-light-menu-item-selected > span > a,\n.du-bois-light-menu-dark .du-bois-light-menu-item-selected > a:hover,\n.du-bois-light-menu-dark .du-bois-light-menu-item-selected > span > a:hover {\n color: #fff;\n}\n.du-bois-light-menu-dark .du-bois-light-menu-item-selected .du-bois-light-menu-item-icon,\n.du-bois-light-menu-dark .du-bois-light-menu-item-selected .anticon {\n color: #fff;\n}\n.du-bois-light-menu-dark .du-bois-light-menu-item-selected .du-bois-light-menu-item-icon + span,\n.du-bois-light-menu-dark .du-bois-light-menu-item-selected .anticon + span {\n color: #fff;\n}\n.du-bois-light-menu.du-bois-light-menu-dark .du-bois-light-menu-item-selected,\n.du-bois-light-menu-submenu-popup.du-bois-light-menu-dark .du-bois-light-menu-item-selected {\n background-color: #2272B4;\n}\n.du-bois-light-menu-dark .du-bois-light-menu-item-disabled,\n.du-bois-light-menu-dark .du-bois-light-menu-submenu-disabled,\n.du-bois-light-menu-dark .du-bois-light-menu-item-disabled > a,\n.du-bois-light-menu-dark .du-bois-light-menu-submenu-disabled > a,\n.du-bois-light-menu-dark .du-bois-light-menu-item-disabled > span > a,\n.du-bois-light-menu-dark .du-bois-light-menu-submenu-disabled > span > a {\n color: rgba(255, 255, 255, 0.35) !important;\n opacity: 0.8;\n}\n.du-bois-light-menu-dark .du-bois-light-menu-item-disabled > .du-bois-light-menu-submenu-title,\n.du-bois-light-menu-dark .du-bois-light-menu-submenu-disabled > .du-bois-light-menu-submenu-title {\n color: rgba(255, 255, 255, 0.35) !important;\n}\n.du-bois-light-menu-dark .du-bois-light-menu-item-disabled > .du-bois-light-menu-submenu-title > .du-bois-light-menu-submenu-arrow::before,\n.du-bois-light-menu-dark .du-bois-light-menu-submenu-disabled > .du-bois-light-menu-submenu-title > .du-bois-light-menu-submenu-arrow::before,\n.du-bois-light-menu-dark .du-bois-light-menu-item-disabled > .du-bois-light-menu-submenu-title > .du-bois-light-menu-submenu-arrow::after,\n.du-bois-light-menu-dark .du-bois-light-menu-submenu-disabled > .du-bois-light-menu-submenu-title > .du-bois-light-menu-submenu-arrow::after {\n background: rgba(255, 255, 255, 0.35) !important;\n}\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-light-modal {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #11171C;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: 'tnum';\n pointer-events: none;\n position: relative;\n top: 100px;\n width: auto;\n max-width: calc(100vw - 32px);\n margin: 0 auto;\n padding-bottom: 24px;\n}\n.du-bois-light-modal.du-bois-light-zoom-enter,\n.du-bois-light-modal.du-bois-lightzoom-appear {\n transform: none;\n opacity: 0;\n animation-duration: 0.3s;\n user-select: none;\n}\n.du-bois-light-modal-mask {\n position: fixed;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n z-index: 1000;\n height: 100%;\n background-color: rgba(0, 0, 0, 0.45);\n}\n.du-bois-light-modal-mask-hidden {\n display: none;\n}\n.du-bois-light-modal-wrap {\n position: fixed;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n overflow: auto;\n outline: 0;\n -webkit-overflow-scrolling: touch;\n}\n.du-bois-light-modal-wrap {\n z-index: 1000;\n}\n.du-bois-light-modal-title {\n margin: 0;\n color: rgba(0, 0, 0, 0.85);\n font-weight: 500;\n font-size: 18px;\n line-height: 22px;\n word-wrap: break-word;\n}\n.du-bois-light-modal-content {\n position: relative;\n background-color: #fff;\n background-clip: padding-box;\n border: 0;\n border-radius: 4px;\n box-shadow: 0 3px 6px -4px rgba(0, 0, 0, 0.12), 0 6px 16px 0 rgba(0, 0, 0, 0.08), 0 9px 28px 8px rgba(0, 0, 0, 0.05);\n pointer-events: auto;\n}\n.du-bois-light-modal-close {\n position: absolute;\n top: 0;\n right: 0;\n z-index: 10;\n padding: 0;\n color: #5F7281;\n font-weight: 700;\n line-height: 1;\n text-decoration: none;\n background: transparent;\n border: 0;\n outline: 0;\n cursor: pointer;\n transition: color 0.3s;\n}\n.du-bois-light-modal-close-x {\n display: block;\n width: 102px;\n height: 102px;\n font-size: 18px;\n font-style: normal;\n line-height: 102px;\n text-align: center;\n text-transform: none;\n text-rendering: auto;\n}\n.du-bois-light-modal-close:focus,\n.du-bois-light-modal-close:hover {\n color: rgba(0, 0, 0, 0.75);\n text-decoration: none;\n}\n.du-bois-light-modal-header {\n padding: 40px 40px 20px;\n color: #11171C;\n background: #fff;\n border-bottom: 0px solid #f0f0f0;\n border-radius: 4px 4px 0 0;\n}\n.du-bois-light-modal-body {\n padding: 0 40px;\n font-size: 13px;\n line-height: 20px;\n word-wrap: break-word;\n}\n.du-bois-light-modal-footer {\n padding: 0px 0px;\n text-align: right;\n background: transparent;\n border-top: 0px solid #f0f0f0;\n border-radius: 0 0 4px 4px;\n}\n.du-bois-light-modal-footer .du-bois-light-btn + .du-bois-light-btn:not(.du-bois-light-dropdown-trigger) {\n margin-bottom: 0;\n margin-left: 8px;\n}\n.du-bois-light-modal-open {\n overflow: hidden;\n}\n.du-bois-light-modal-centered {\n text-align: center;\n}\n.du-bois-light-modal-centered::before {\n display: inline-block;\n width: 0;\n height: 100%;\n vertical-align: middle;\n content: '';\n}\n.du-bois-light-modal-centered .du-bois-light-modal {\n top: 0;\n display: inline-block;\n text-align: left;\n vertical-align: middle;\n}\n@media (max-width: 767px) {\n .du-bois-light-modal {\n max-width: calc(100vw - 16px);\n margin: 8px auto;\n }\n .du-bois-light-modal-centered .du-bois-light-modal {\n flex: 1;\n }\n}\n.du-bois-light-modal-confirm .du-bois-light-modal-header {\n display: none;\n}\n.du-bois-light-modal-confirm .du-bois-light-modal-body {\n padding: 32px 32px 24px;\n}\n.du-bois-light-modal-confirm-body-wrapper::before {\n display: table;\n content: '';\n}\n.du-bois-light-modal-confirm-body-wrapper::after {\n display: table;\n clear: both;\n content: '';\n}\n.du-bois-light-modal-confirm-body .du-bois-light-modal-confirm-title {\n display: block;\n overflow: hidden;\n color: rgba(0, 0, 0, 0.85);\n font-weight: 500;\n font-size: 18px;\n line-height: 1.4;\n}\n.du-bois-light-modal-confirm-body .du-bois-light-modal-confirm-content {\n margin-top: 8px;\n color: #11171C;\n font-size: 13px;\n}\n.du-bois-light-modal-confirm-body > .anticon {\n float: left;\n margin-right: 16px;\n font-size: 22px;\n}\n.du-bois-light-modal-confirm-body > .anticon + .du-bois-light-modal-confirm-title + .du-bois-light-modal-confirm-content {\n margin-left: 38px;\n}\n.du-bois-light-modal-confirm .du-bois-light-modal-confirm-btns {\n float: right;\n margin-top: 24px;\n}\n.du-bois-light-modal-confirm .du-bois-light-modal-confirm-btns .du-bois-light-btn + .du-bois-light-btn {\n margin-bottom: 0;\n margin-left: 8px;\n}\n.du-bois-light-modal-confirm-error .du-bois-light-modal-confirm-body > .anticon {\n color: #C82D4C;\n}\n.du-bois-light-modal-confirm-warning .du-bois-light-modal-confirm-body > .anticon,\n.du-bois-light-modal-confirm-confirm .du-bois-light-modal-confirm-body > .anticon {\n color: #AF5B23;\n}\n.du-bois-light-modal-confirm-info .du-bois-light-modal-confirm-body > .anticon {\n color: #64727D;\n}\n.du-bois-light-modal-confirm-success .du-bois-light-modal-confirm-body > .anticon {\n color: #34824F;\n}\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-light-notification {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #11171C;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: 'tnum';\n position: fixed;\n z-index: 1010;\n margin-right: 24px;\n}\n.du-bois-light-notification-topLeft,\n.du-bois-light-notification-bottomLeft {\n margin-right: 0;\n margin-left: 24px;\n}\n.du-bois-light-notification-topLeft .du-bois-light-notification-fade-enter.du-bois-light-notification-fade-enter-active,\n.du-bois-light-notification-bottomLeft .du-bois-light-notification-fade-enter.du-bois-light-notification-fade-enter-active,\n.du-bois-light-notification-topLeft .du-bois-light-notification-fade-appear.du-bois-light-notification-fade-appear-active,\n.du-bois-light-notification-bottomLeft .du-bois-light-notification-fade-appear.du-bois-light-notification-fade-appear-active {\n animation-name: NotificationLeftFadeIn;\n}\n.du-bois-light-notification-close-icon {\n font-size: 13px;\n cursor: pointer;\n}\n.du-bois-light-notification-hook-holder {\n position: relative;\n}\n.du-bois-light-notification-notice {\n position: relative;\n width: 384px;\n max-width: calc(100vw - 24px * 2);\n margin-bottom: 16px;\n margin-left: auto;\n padding: 16px 24px;\n overflow: hidden;\n line-height: 20px;\n word-wrap: break-word;\n background: #fff;\n border-radius: 4px;\n box-shadow: 0 3px 6px -4px rgba(0, 0, 0, 0.12), 0 6px 16px 0 rgba(0, 0, 0, 0.08), 0 9px 28px 8px rgba(0, 0, 0, 0.05);\n}\n.du-bois-light-notification-topLeft .du-bois-light-notification-notice,\n.du-bois-light-notification-bottomLeft .du-bois-light-notification-notice {\n margin-right: auto;\n margin-left: 0;\n}\n.du-bois-light-notification-notice-message {\n margin-bottom: 8px;\n color: rgba(0, 0, 0, 0.85);\n font-size: 18px;\n line-height: 24px;\n}\n.du-bois-light-notification-notice-message-single-line-auto-margin {\n display: block;\n width: calc(384px - 24px * 2 - 24px - 48px - 100%);\n max-width: 4px;\n background-color: transparent;\n pointer-events: none;\n}\n.du-bois-light-notification-notice-message-single-line-auto-margin::before {\n display: block;\n content: '';\n}\n.du-bois-light-notification-notice-description {\n font-size: 13px;\n}\n.du-bois-light-notification-notice-closable .du-bois-light-notification-notice-message {\n padding-right: 24px;\n}\n.du-bois-light-notification-notice-with-icon .du-bois-light-notification-notice-message {\n margin-bottom: 4px;\n margin-left: 48px;\n font-size: 18px;\n}\n.du-bois-light-notification-notice-with-icon .du-bois-light-notification-notice-description {\n margin-left: 48px;\n font-size: 13px;\n}\n.du-bois-light-notification-notice-icon {\n position: absolute;\n margin-left: 4px;\n font-size: 24px;\n line-height: 24px;\n}\n.anticon.du-bois-light-notification-notice-icon-success {\n color: #34824F;\n}\n.anticon.du-bois-light-notification-notice-icon-info {\n color: #64727D;\n}\n.anticon.du-bois-light-notification-notice-icon-warning {\n color: #AF5B23;\n}\n.anticon.du-bois-light-notification-notice-icon-error {\n color: #C82D4C;\n}\n.du-bois-light-notification-notice-close {\n position: absolute;\n top: 16px;\n right: 22px;\n color: #5F7281;\n outline: none;\n}\n.du-bois-light-notification-notice-close:hover {\n color: #39444d;\n}\n.du-bois-light-notification-notice-btn {\n float: right;\n margin-top: 16px;\n}\n.du-bois-light-notification .notification-fade-effect {\n animation-duration: 0.24s;\n animation-timing-function: cubic-bezier(0.645, 0.045, 0.355, 1);\n animation-fill-mode: both;\n}\n.du-bois-light-notification-fade-enter,\n.du-bois-light-notification-fade-appear {\n animation-duration: 0.24s;\n animation-timing-function: cubic-bezier(0.645, 0.045, 0.355, 1);\n animation-fill-mode: both;\n opacity: 0;\n animation-play-state: paused;\n}\n.du-bois-light-notification-fade-leave {\n animation-duration: 0.24s;\n animation-timing-function: cubic-bezier(0.645, 0.045, 0.355, 1);\n animation-fill-mode: both;\n animation-duration: 0.2s;\n animation-play-state: paused;\n}\n.du-bois-light-notification-fade-enter.du-bois-light-notification-fade-enter-active,\n.du-bois-light-notification-fade-appear.du-bois-light-notification-fade-appear-active {\n animation-name: NotificationFadeIn;\n animation-play-state: running;\n}\n.du-bois-light-notification-fade-leave.du-bois-light-notification-fade-leave-active {\n animation-name: NotificationFadeOut;\n animation-play-state: running;\n}\n@keyframes NotificationFadeIn {\n 0% {\n left: 384px;\n opacity: 0;\n }\n 100% {\n left: 0;\n opacity: 1;\n }\n}\n@keyframes NotificationLeftFadeIn {\n 0% {\n right: 384px;\n opacity: 0;\n }\n 100% {\n right: 0;\n opacity: 1;\n }\n}\n@keyframes NotificationFadeOut {\n 0% {\n max-height: 150px;\n margin-bottom: 16px;\n opacity: 1;\n }\n 100% {\n max-height: 0;\n margin-bottom: 0;\n padding-top: 0;\n padding-bottom: 0;\n opacity: 0;\n }\n}\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-light-popover {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #11171C;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: 'tnum';\n position: absolute;\n top: 0;\n left: 0;\n z-index: 1030;\n font-weight: normal;\n white-space: normal;\n text-align: left;\n cursor: auto;\n user-select: text;\n}\n.du-bois-light-popover::after {\n position: absolute;\n background: rgba(255, 255, 255, 0.01);\n content: '';\n}\n.du-bois-light-popover-hidden {\n display: none;\n}\n.du-bois-light-popover-placement-top,\n.du-bois-light-popover-placement-topLeft,\n.du-bois-light-popover-placement-topRight {\n padding-bottom: 10px;\n}\n.du-bois-light-popover-placement-right,\n.du-bois-light-popover-placement-rightTop,\n.du-bois-light-popover-placement-rightBottom {\n padding-left: 10px;\n}\n.du-bois-light-popover-placement-bottom,\n.du-bois-light-popover-placement-bottomLeft,\n.du-bois-light-popover-placement-bottomRight {\n padding-top: 10px;\n}\n.du-bois-light-popover-placement-left,\n.du-bois-light-popover-placement-leftTop,\n.du-bois-light-popover-placement-leftBottom {\n padding-right: 10px;\n}\n.du-bois-light-popover-inner {\n background-color: #fff;\n background-clip: padding-box;\n border-radius: 4px;\n box-shadow: 0 3px 6px -4px rgba(0, 0, 0, 0.12), 0 6px 16px 0 rgba(0, 0, 0, 0.08), 0 9px 28px 8px rgba(0, 0, 0, 0.05);\n box-shadow: 0 0 8px rgba(0, 0, 0, 0.15) \\9;\n}\n@media screen and (-ms-high-contrast: active), (-ms-high-contrast: none) {\n .du-bois-light-popover {\n /* IE10+ */\n }\n .du-bois-light-popover-inner {\n box-shadow: 0 3px 6px -4px rgba(0, 0, 0, 0.12), 0 6px 16px 0 rgba(0, 0, 0, 0.08), 0 9px 28px 8px rgba(0, 0, 0, 0.05);\n }\n}\n.du-bois-light-popover-title {\n min-width: 177px;\n min-height: 32px;\n margin: 0;\n padding: 5px 8px 4px;\n color: rgba(0, 0, 0, 0.85);\n font-weight: 500;\n border-bottom: 1px solid #f0f0f0;\n}\n.du-bois-light-popover-inner-content {\n padding: 8px 8px;\n color: #11171C;\n}\n.du-bois-light-popover-message {\n position: relative;\n padding: 4px 0 12px;\n color: #11171C;\n font-size: 13px;\n}\n.du-bois-light-popover-message > .anticon {\n position: absolute;\n top: 127.5px;\n color: #AF5B23;\n font-size: 13px;\n}\n.du-bois-light-popover-message-title {\n padding-left: 21px;\n}\n.du-bois-light-popover-buttons {\n margin-bottom: 4px;\n text-align: right;\n}\n.du-bois-light-popover-buttons button {\n margin-left: 8px;\n}\n.du-bois-light-popover-arrow {\n position: absolute;\n display: block;\n width: 8.48528137px;\n height: 8.48528137px;\n overflow: hidden;\n background: transparent;\n pointer-events: none;\n}\n.du-bois-light-popover-arrow-content {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n display: block;\n width: 6px;\n height: 6px;\n margin: auto;\n background-color: #fff;\n content: '';\n pointer-events: auto;\n}\n.du-bois-light-popover-placement-top .du-bois-light-popover-arrow,\n.du-bois-light-popover-placement-topLeft .du-bois-light-popover-arrow,\n.du-bois-light-popover-placement-topRight .du-bois-light-popover-arrow {\n bottom: 1.51471863px;\n}\n.du-bois-light-popover-placement-top .du-bois-light-popover-arrow-content,\n.du-bois-light-popover-placement-topLeft .du-bois-light-popover-arrow-content,\n.du-bois-light-popover-placement-topRight .du-bois-light-popover-arrow-content {\n box-shadow: 3px 3px 7px rgba(0, 0, 0, 0.07);\n transform: translateY(-4.24264069px) rotate(45deg);\n}\n.du-bois-light-popover-placement-top .du-bois-light-popover-arrow {\n left: 50%;\n transform: translateX(-50%);\n}\n.du-bois-light-popover-placement-topLeft .du-bois-light-popover-arrow {\n left: 16px;\n}\n.du-bois-light-popover-placement-topRight .du-bois-light-popover-arrow {\n right: 16px;\n}\n.du-bois-light-popover-placement-right .du-bois-light-popover-arrow,\n.du-bois-light-popover-placement-rightTop .du-bois-light-popover-arrow,\n.du-bois-light-popover-placement-rightBottom .du-bois-light-popover-arrow {\n left: 1.51471863px;\n}\n.du-bois-light-popover-placement-right .du-bois-light-popover-arrow-content,\n.du-bois-light-popover-placement-rightTop .du-bois-light-popover-arrow-content,\n.du-bois-light-popover-placement-rightBottom .du-bois-light-popover-arrow-content {\n box-shadow: -3px 3px 7px rgba(0, 0, 0, 0.07);\n transform: translateX(4.24264069px) rotate(45deg);\n}\n.du-bois-light-popover-placement-right .du-bois-light-popover-arrow {\n top: 50%;\n transform: translateY(-50%);\n}\n.du-bois-light-popover-placement-rightTop .du-bois-light-popover-arrow {\n top: 12px;\n}\n.du-bois-light-popover-placement-rightBottom .du-bois-light-popover-arrow {\n bottom: 12px;\n}\n.du-bois-light-popover-placement-bottom .du-bois-light-popover-arrow,\n.du-bois-light-popover-placement-bottomLeft .du-bois-light-popover-arrow,\n.du-bois-light-popover-placement-bottomRight .du-bois-light-popover-arrow {\n top: 1.51471863px;\n}\n.du-bois-light-popover-placement-bottom .du-bois-light-popover-arrow-content,\n.du-bois-light-popover-placement-bottomLeft .du-bois-light-popover-arrow-content,\n.du-bois-light-popover-placement-bottomRight .du-bois-light-popover-arrow-content {\n box-shadow: -2px -2px 5px rgba(0, 0, 0, 0.06);\n transform: translateY(4.24264069px) rotate(45deg);\n}\n.du-bois-light-popover-placement-bottom .du-bois-light-popover-arrow {\n left: 50%;\n transform: translateX(-50%);\n}\n.du-bois-light-popover-placement-bottomLeft .du-bois-light-popover-arrow {\n left: 16px;\n}\n.du-bois-light-popover-placement-bottomRight .du-bois-light-popover-arrow {\n right: 16px;\n}\n.du-bois-light-popover-placement-left .du-bois-light-popover-arrow,\n.du-bois-light-popover-placement-leftTop .du-bois-light-popover-arrow,\n.du-bois-light-popover-placement-leftBottom .du-bois-light-popover-arrow {\n right: 1.51471863px;\n}\n.du-bois-light-popover-placement-left .du-bois-light-popover-arrow-content,\n.du-bois-light-popover-placement-leftTop .du-bois-light-popover-arrow-content,\n.du-bois-light-popover-placement-leftBottom .du-bois-light-popover-arrow-content {\n box-shadow: 3px -3px 7px rgba(0, 0, 0, 0.07);\n transform: translateX(-4.24264069px) rotate(45deg);\n}\n.du-bois-light-popover-placement-left .du-bois-light-popover-arrow {\n top: 50%;\n transform: translateY(-50%);\n}\n.du-bois-light-popover-placement-leftTop .du-bois-light-popover-arrow {\n top: 12px;\n}\n.du-bois-light-popover-placement-leftBottom .du-bois-light-popover-arrow {\n bottom: 12px;\n}\n.du-bois-light-popover-pink .du-bois-light-popover-inner {\n background-color: #eb2f96;\n}\n.du-bois-light-popover-pink .du-bois-light-popover-arrow-content {\n background-color: #eb2f96;\n}\n.du-bois-light-popover-magenta .du-bois-light-popover-inner {\n background-color: #eb2f96;\n}\n.du-bois-light-popover-magenta .du-bois-light-popover-arrow-content {\n background-color: #eb2f96;\n}\n.du-bois-light-popover-red .du-bois-light-popover-inner {\n background-color: #f5222d;\n}\n.du-bois-light-popover-red .du-bois-light-popover-arrow-content {\n background-color: #f5222d;\n}\n.du-bois-light-popover-volcano .du-bois-light-popover-inner {\n background-color: #fa541c;\n}\n.du-bois-light-popover-volcano .du-bois-light-popover-arrow-content {\n background-color: #fa541c;\n}\n.du-bois-light-popover-orange .du-bois-light-popover-inner {\n background-color: #fa8c16;\n}\n.du-bois-light-popover-orange .du-bois-light-popover-arrow-content {\n background-color: #fa8c16;\n}\n.du-bois-light-popover-yellow .du-bois-light-popover-inner {\n background-color: #fadb14;\n}\n.du-bois-light-popover-yellow .du-bois-light-popover-arrow-content {\n background-color: #fadb14;\n}\n.du-bois-light-popover-gold .du-bois-light-popover-inner {\n background-color: #faad14;\n}\n.du-bois-light-popover-gold .du-bois-light-popover-arrow-content {\n background-color: #faad14;\n}\n.du-bois-light-popover-cyan .du-bois-light-popover-inner {\n background-color: #13c2c2;\n}\n.du-bois-light-popover-cyan .du-bois-light-popover-arrow-content {\n background-color: #13c2c2;\n}\n.du-bois-light-popover-lime .du-bois-light-popover-inner {\n background-color: #a0d911;\n}\n.du-bois-light-popover-lime .du-bois-light-popover-arrow-content {\n background-color: #a0d911;\n}\n.du-bois-light-popover-green .du-bois-light-popover-inner {\n background-color: #52c41a;\n}\n.du-bois-light-popover-green .du-bois-light-popover-arrow-content {\n background-color: #52c41a;\n}\n.du-bois-light-popover-blue .du-bois-light-popover-inner {\n background-color: #1890ff;\n}\n.du-bois-light-popover-blue .du-bois-light-popover-arrow-content {\n background-color: #1890ff;\n}\n.du-bois-light-popover-geekblue .du-bois-light-popover-inner {\n background-color: #2f54eb;\n}\n.du-bois-light-popover-geekblue .du-bois-light-popover-arrow-content {\n background-color: #2f54eb;\n}\n.du-bois-light-popover-purple .du-bois-light-popover-inner {\n background-color: #722ed1;\n}\n.du-bois-light-popover-purple .du-bois-light-popover-arrow-content {\n background-color: #722ed1;\n}\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-light-radio-group {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #11171C;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: 'tnum';\n display: inline-block;\n font-size: 0;\n}\n.du-bois-light-radio-group .du-bois-light-badge-count {\n z-index: 1;\n}\n.du-bois-light-radio-group > .du-bois-light-badge:not(:first-child) > .du-bois-light-radio-button-wrapper {\n border-left: none;\n}\n.du-bois-light-radio-wrapper {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #11171C;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: 'tnum';\n position: relative;\n display: inline-flex;\n align-items: baseline;\n margin-right: 8px;\n cursor: pointer;\n}\n.du-bois-light-radio-wrapper::after {\n display: inline-block;\n width: 0;\n overflow: hidden;\n content: '\\a0';\n}\n.du-bois-light-radio {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #11171C;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: 'tnum';\n position: relative;\n top: 0.2em;\n display: inline-block;\n outline: none;\n cursor: pointer;\n}\n.du-bois-light-radio-wrapper:hover .du-bois-light-radio,\n.du-bois-light-radio:hover .du-bois-light-radio-inner,\n.du-bois-light-radio-input:focus + .du-bois-light-radio-inner {\n border-color: #2272B4;\n}\n.du-bois-light-radio-input:focus + .du-bois-light-radio-inner {\n box-shadow: 0 0 0 3px rgba(34, 114, 180, 0.08);\n}\n.du-bois-light-radio-checked::after {\n position: absolute;\n top: 0;\n left: 0;\n width: 100%;\n height: 100%;\n border: 1px solid #2272B4;\n border-radius: 50%;\n visibility: hidden;\n animation: antRadioEffect 0.36s ease-in-out;\n animation-fill-mode: both;\n content: '';\n}\n.du-bois-light-radio:hover::after,\n.du-bois-light-radio-wrapper:hover .du-bois-light-radio::after {\n visibility: visible;\n}\n.du-bois-light-radio-inner {\n position: relative;\n top: 0;\n left: 0;\n display: block;\n width: 16px;\n height: 16px;\n background-color: #fff;\n border-color: #E8ECF0;\n border-style: solid;\n border-width: 1px;\n border-radius: 50%;\n transition: all 0.3s;\n}\n.du-bois-light-radio-inner::after {\n position: absolute;\n top: 3px;\n left: 3px;\n display: block;\n width: 8px;\n height: 8px;\n background-color: #2272B4;\n border-top: 0;\n border-left: 0;\n border-radius: 8px;\n transform: scale(0);\n opacity: 0;\n transition: all 0.3s cubic-bezier(0.78, 0.14, 0.15, 0.86);\n content: ' ';\n}\n.du-bois-light-radio-input {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n z-index: 1;\n cursor: pointer;\n opacity: 0;\n}\n.du-bois-light-radio-checked .du-bois-light-radio-inner {\n border-color: #2272B4;\n}\n.du-bois-light-radio-checked .du-bois-light-radio-inner::after {\n transform: scale(1);\n opacity: 1;\n transition: all 0.3s cubic-bezier(0.78, 0.14, 0.15, 0.86);\n}\n.du-bois-light-radio-disabled {\n cursor: not-allowed;\n}\n.du-bois-light-radio-disabled .du-bois-light-radio-inner {\n background-color: #f5f5f5;\n border-color: #E8ECF0 !important;\n cursor: not-allowed;\n}\n.du-bois-light-radio-disabled .du-bois-light-radio-inner::after {\n background-color: rgba(0, 0, 0, 0.2);\n}\n.du-bois-light-radio-disabled .du-bois-light-radio-input {\n cursor: not-allowed;\n}\n.du-bois-light-radio-disabled + span {\n color: rgba(0, 0, 0, 0.25);\n cursor: not-allowed;\n}\nspan.du-bois-light-radio + * {\n padding-right: 8px;\n padding-left: 8px;\n}\n.du-bois-light-radio-button-wrapper {\n position: relative;\n display: inline-block;\n height: 40px;\n margin: 0;\n padding: 0 7px;\n color: #11171C;\n font-size: 13px;\n line-height: 38px;\n background: #fff;\n border: 1px solid #E8ECF0;\n border-top-width: 1.02px;\n border-left-width: 0;\n cursor: pointer;\n transition: color 0.3s, background 0.3s, border-color 0.3s, box-shadow 0.3s;\n}\n.du-bois-light-radio-button-wrapper a {\n color: #11171C;\n}\n.du-bois-light-radio-button-wrapper > .du-bois-light-radio-button {\n position: absolute;\n top: 0;\n left: 0;\n z-index: -1;\n width: 100%;\n height: 100%;\n}\n.du-bois-light-radio-group-large .du-bois-light-radio-button-wrapper {\n height: 40px;\n font-size: 18px;\n line-height: 38px;\n}\n.du-bois-light-radio-group-small .du-bois-light-radio-button-wrapper {\n height: 32px;\n padding: 0 3px;\n line-height: 30px;\n}\n.du-bois-light-radio-button-wrapper:not(:first-child)::before {\n position: absolute;\n top: -1px;\n left: -1px;\n display: block;\n box-sizing: content-box;\n width: 1px;\n height: 100%;\n padding: 1px 0;\n background-color: #E8ECF0;\n transition: background-color 0.3s;\n content: '';\n}\n.du-bois-light-radio-button-wrapper:first-child {\n border-left: 1px solid #E8ECF0;\n border-radius: 4px 0 0 4px;\n}\n.du-bois-light-radio-button-wrapper:last-child {\n border-radius: 0 4px 4px 0;\n}\n.du-bois-light-radio-button-wrapper:first-child:last-child {\n border-radius: 4px;\n}\n.du-bois-light-radio-button-wrapper:hover {\n position: relative;\n color: #2272B4;\n}\n.du-bois-light-radio-button-wrapper:focus-within {\n box-shadow: 0 0 0 3px rgba(34, 114, 180, 0.08);\n}\n.du-bois-light-radio-button-wrapper .du-bois-light-radio-inner,\n.du-bois-light-radio-button-wrapper input[type='checkbox'],\n.du-bois-light-radio-button-wrapper input[type='radio'] {\n width: 0;\n height: 0;\n opacity: 0;\n pointer-events: none;\n}\n.du-bois-light-radio-button-wrapper-checked:not(.du-bois-light-radio-button-wrapper-disabled) {\n z-index: 1;\n color: #2272B4;\n background: #fff;\n border-color: #2272B4;\n}\n.du-bois-light-radio-button-wrapper-checked:not(.du-bois-light-radio-button-wrapper-disabled)::before {\n background-color: #2272B4;\n}\n.du-bois-light-radio-button-wrapper-checked:not(.du-bois-light-radio-button-wrapper-disabled):first-child {\n border-color: #2272B4;\n}\n.du-bois-light-radio-button-wrapper-checked:not(.du-bois-light-radio-button-wrapper-disabled):hover {\n color: #448dc2;\n border-color: #448dc2;\n}\n.du-bois-light-radio-button-wrapper-checked:not(.du-bois-light-radio-button-wrapper-disabled):hover::before {\n background-color: #448dc2;\n}\n.du-bois-light-radio-button-wrapper-checked:not(.du-bois-light-radio-button-wrapper-disabled):active {\n color: #14538f;\n border-color: #14538f;\n}\n.du-bois-light-radio-button-wrapper-checked:not(.du-bois-light-radio-button-wrapper-disabled):active::before {\n background-color: #14538f;\n}\n.du-bois-light-radio-button-wrapper-checked:not(.du-bois-light-radio-button-wrapper-disabled):focus-within {\n box-shadow: 0 0 0 3px rgba(34, 114, 180, 0.08);\n}\n.du-bois-light-radio-group-solid .du-bois-light-radio-button-wrapper-checked:not(.du-bois-light-radio-button-wrapper-disabled) {\n color: #fff;\n background: #2272B4;\n border-color: #2272B4;\n}\n.du-bois-light-radio-group-solid .du-bois-light-radio-button-wrapper-checked:not(.du-bois-light-radio-button-wrapper-disabled):hover {\n color: #fff;\n background: #448dc2;\n border-color: #448dc2;\n}\n.du-bois-light-radio-group-solid .du-bois-light-radio-button-wrapper-checked:not(.du-bois-light-radio-button-wrapper-disabled):active {\n color: #fff;\n background: #14538f;\n border-color: #14538f;\n}\n.du-bois-light-radio-group-solid .du-bois-light-radio-button-wrapper-checked:not(.du-bois-light-radio-button-wrapper-disabled):focus-within {\n box-shadow: 0 0 0 3px rgba(34, 114, 180, 0.08);\n}\n.du-bois-light-radio-button-wrapper-disabled {\n color: rgba(0, 0, 0, 0.25);\n background-color: #f5f5f5;\n border-color: #E8ECF0;\n cursor: not-allowed;\n}\n.du-bois-light-radio-button-wrapper-disabled:first-child,\n.du-bois-light-radio-button-wrapper-disabled:hover {\n color: rgba(0, 0, 0, 0.25);\n background-color: #f5f5f5;\n border-color: #E8ECF0;\n}\n.du-bois-light-radio-button-wrapper-disabled:first-child {\n border-left-color: #E8ECF0;\n}\n.du-bois-light-radio-button-wrapper-disabled.du-bois-light-radio-button-wrapper-checked {\n color: rgba(0, 0, 0, 0.25);\n background-color: #e6e6e6;\n border-color: #E8ECF0;\n box-shadow: none;\n}\n@keyframes antRadioEffect {\n 0% {\n transform: scale(1);\n opacity: 0.5;\n }\n 100% {\n transform: scale(1.6);\n opacity: 0;\n }\n}\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-light-skeleton {\n display: table;\n width: 100%;\n}\n.du-bois-light-skeleton-header {\n display: table-cell;\n padding-right: 8px;\n vertical-align: top;\n}\n.du-bois-light-skeleton-header .du-bois-light-skeleton-avatar {\n display: inline-block;\n vertical-align: top;\n background: rgba(190, 190, 190, 0.2);\n width: 32px;\n height: 32px;\n line-height: 32px;\n}\n.du-bois-light-skeleton-header .du-bois-light-skeleton-avatar.du-bois-light-skeleton-avatar-circle {\n border-radius: 50%;\n}\n.du-bois-light-skeleton-header .du-bois-light-skeleton-avatar-lg {\n width: 40px;\n height: 40px;\n line-height: 40px;\n}\n.du-bois-light-skeleton-header .du-bois-light-skeleton-avatar-lg.du-bois-light-skeleton-avatar-circle {\n border-radius: 50%;\n}\n.du-bois-light-skeleton-header .du-bois-light-skeleton-avatar-sm {\n width: 24px;\n height: 24px;\n line-height: 24px;\n}\n.du-bois-light-skeleton-header .du-bois-light-skeleton-avatar-sm.du-bois-light-skeleton-avatar-circle {\n border-radius: 50%;\n}\n.du-bois-light-skeleton-content {\n display: table-cell;\n width: 100%;\n vertical-align: top;\n}\n.du-bois-light-skeleton-content .du-bois-light-skeleton-title {\n width: 100%;\n height: 16px;\n margin-top: 16px;\n background: rgba(190, 190, 190, 0.2);\n border-radius: 4px;\n}\n.du-bois-light-skeleton-content .du-bois-light-skeleton-title + .du-bois-light-skeleton-paragraph {\n margin-top: 24px;\n}\n.du-bois-light-skeleton-content .du-bois-light-skeleton-paragraph {\n padding: 0;\n}\n.du-bois-light-skeleton-content .du-bois-light-skeleton-paragraph > li {\n width: 100%;\n height: 16px;\n list-style: none;\n background: rgba(190, 190, 190, 0.2);\n border-radius: 4px;\n}\n.du-bois-light-skeleton-content .du-bois-light-skeleton-paragraph > li:last-child:not(:first-child):not(:nth-child(2)) {\n width: 61%;\n}\n.du-bois-light-skeleton-content .du-bois-light-skeleton-paragraph > li + li {\n margin-top: 16px;\n}\n.du-bois-light-skeleton-with-avatar .du-bois-light-skeleton-content .du-bois-light-skeleton-title {\n margin-top: 12px;\n}\n.du-bois-light-skeleton-with-avatar .du-bois-light-skeleton-content .du-bois-light-skeleton-title + .du-bois-light-skeleton-paragraph {\n margin-top: 28px;\n}\n.du-bois-light-skeleton-round .du-bois-light-skeleton-content .du-bois-light-skeleton-title,\n.du-bois-light-skeleton-round .du-bois-light-skeleton-content .du-bois-light-skeleton-paragraph > li {\n border-radius: 100px;\n}\n.du-bois-light-skeleton.du-bois-light-skeleton-active .du-bois-light-skeleton-content .du-bois-light-skeleton-title,\n.du-bois-light-skeleton.du-bois-light-skeleton-active .du-bois-light-skeleton-content .du-bois-light-skeleton-paragraph > li {\n background: linear-gradient(90deg, rgba(190, 190, 190, 0.2) 25%, rgba(129, 129, 129, 0.24) 37%, rgba(190, 190, 190, 0.2) 63%);\n background-size: 400% 100%;\n animation: du-bois-light-skeleton-loading 1.4s ease infinite;\n}\n.du-bois-light-skeleton.du-bois-light-skeleton-active .du-bois-light-skeleton-avatar {\n background: linear-gradient(90deg, rgba(190, 190, 190, 0.2) 25%, rgba(129, 129, 129, 0.24) 37%, rgba(190, 190, 190, 0.2) 63%);\n background-size: 400% 100%;\n animation: du-bois-light-skeleton-loading 1.4s ease infinite;\n}\n.du-bois-light-skeleton.du-bois-light-skeleton-active .du-bois-light-skeleton-button {\n background: linear-gradient(90deg, rgba(190, 190, 190, 0.2) 25%, rgba(129, 129, 129, 0.24) 37%, rgba(190, 190, 190, 0.2) 63%);\n background-size: 400% 100%;\n animation: du-bois-light-skeleton-loading 1.4s ease infinite;\n}\n.du-bois-light-skeleton.du-bois-light-skeleton-active .du-bois-light-skeleton-input {\n background: linear-gradient(90deg, rgba(190, 190, 190, 0.2) 25%, rgba(129, 129, 129, 0.24) 37%, rgba(190, 190, 190, 0.2) 63%);\n background-size: 400% 100%;\n animation: du-bois-light-skeleton-loading 1.4s ease infinite;\n}\n.du-bois-light-skeleton.du-bois-light-skeleton-active .du-bois-light-skeleton-image {\n background: linear-gradient(90deg, rgba(190, 190, 190, 0.2) 25%, rgba(129, 129, 129, 0.24) 37%, rgba(190, 190, 190, 0.2) 63%);\n background-size: 400% 100%;\n animation: du-bois-light-skeleton-loading 1.4s ease infinite;\n}\n.du-bois-light-skeleton-element {\n display: inline-block;\n width: auto;\n}\n.du-bois-light-skeleton-element .du-bois-light-skeleton-button {\n display: inline-block;\n vertical-align: top;\n background: rgba(190, 190, 190, 0.2);\n border-radius: 4px;\n width: 80px;\n height: 40px;\n line-height: 40px;\n}\n.du-bois-light-skeleton-element .du-bois-light-skeleton-button.du-bois-light-skeleton-button-circle {\n width: 40px;\n border-radius: 50%;\n}\n.du-bois-light-skeleton-element .du-bois-light-skeleton-button.du-bois-light-skeleton-button-round {\n border-radius: 40px;\n}\n.du-bois-light-skeleton-element .du-bois-light-skeleton-button-lg {\n width: 80px;\n height: 40px;\n line-height: 40px;\n}\n.du-bois-light-skeleton-element .du-bois-light-skeleton-button-lg.du-bois-light-skeleton-button-circle {\n width: 40px;\n border-radius: 50%;\n}\n.du-bois-light-skeleton-element .du-bois-light-skeleton-button-lg.du-bois-light-skeleton-button-round {\n border-radius: 40px;\n}\n.du-bois-light-skeleton-element .du-bois-light-skeleton-button-sm {\n width: 64px;\n height: 32px;\n line-height: 32px;\n}\n.du-bois-light-skeleton-element .du-bois-light-skeleton-button-sm.du-bois-light-skeleton-button-circle {\n width: 32px;\n border-radius: 50%;\n}\n.du-bois-light-skeleton-element .du-bois-light-skeleton-button-sm.du-bois-light-skeleton-button-round {\n border-radius: 32px;\n}\n.du-bois-light-skeleton-element .du-bois-light-skeleton-avatar {\n display: inline-block;\n vertical-align: top;\n background: rgba(190, 190, 190, 0.2);\n width: 32px;\n height: 32px;\n line-height: 32px;\n}\n.du-bois-light-skeleton-element .du-bois-light-skeleton-avatar.du-bois-light-skeleton-avatar-circle {\n border-radius: 50%;\n}\n.du-bois-light-skeleton-element .du-bois-light-skeleton-avatar-lg {\n width: 40px;\n height: 40px;\n line-height: 40px;\n}\n.du-bois-light-skeleton-element .du-bois-light-skeleton-avatar-lg.du-bois-light-skeleton-avatar-circle {\n border-radius: 50%;\n}\n.du-bois-light-skeleton-element .du-bois-light-skeleton-avatar-sm {\n width: 24px;\n height: 24px;\n line-height: 24px;\n}\n.du-bois-light-skeleton-element .du-bois-light-skeleton-avatar-sm.du-bois-light-skeleton-avatar-circle {\n border-radius: 50%;\n}\n.du-bois-light-skeleton-element .du-bois-light-skeleton-input {\n display: inline-block;\n vertical-align: top;\n background: rgba(190, 190, 190, 0.2);\n width: 100%;\n height: 40px;\n line-height: 40px;\n}\n.du-bois-light-skeleton-element .du-bois-light-skeleton-input-lg {\n width: 100%;\n height: 40px;\n line-height: 40px;\n}\n.du-bois-light-skeleton-element .du-bois-light-skeleton-input-sm {\n width: 100%;\n height: 32px;\n line-height: 32px;\n}\n.du-bois-light-skeleton-element .du-bois-light-skeleton-image {\n display: flex;\n align-items: center;\n justify-content: center;\n vertical-align: top;\n background: rgba(190, 190, 190, 0.2);\n width: 96px;\n height: 96px;\n line-height: 96px;\n}\n.du-bois-light-skeleton-element .du-bois-light-skeleton-image.du-bois-light-skeleton-image-circle {\n border-radius: 50%;\n}\n.du-bois-light-skeleton-element .du-bois-light-skeleton-image-path {\n fill: #bfbfbf;\n}\n.du-bois-light-skeleton-element .du-bois-light-skeleton-image-svg {\n width: 48px;\n height: 48px;\n line-height: 48px;\n max-width: 192px;\n max-height: 192px;\n}\n.du-bois-light-skeleton-element .du-bois-light-skeleton-image-svg.du-bois-light-skeleton-image-circle {\n border-radius: 50%;\n}\n@keyframes du-bois-light-skeleton-loading {\n 0% {\n background-position: 100% 50%;\n }\n 100% {\n background-position: 0 50%;\n }\n}\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-light-space {\n display: inline-flex;\n}\n.du-bois-light-space-vertical {\n flex-direction: column;\n}\n.du-bois-light-space-align-center {\n align-items: center;\n}\n.du-bois-light-space-align-start {\n align-items: flex-start;\n}\n.du-bois-light-space-align-end {\n align-items: flex-end;\n}\n.du-bois-light-space-align-baseline {\n align-items: baseline;\n}\n.du-bois-light-space-item:empty {\n display: none;\n}\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-light-steps {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #11171C;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: 'tnum';\n display: flex;\n width: 100%;\n font-size: 0;\n text-align: initial;\n}\n.du-bois-light-steps-item {\n position: relative;\n display: inline-block;\n flex: 1;\n overflow: hidden;\n vertical-align: top;\n}\n.du-bois-light-steps-item-container {\n outline: none;\n}\n.du-bois-light-steps-item:last-child {\n flex: none;\n}\n.du-bois-light-steps-item:last-child > .du-bois-light-steps-item-container > .du-bois-light-steps-item-tail,\n.du-bois-light-steps-item:last-child > .du-bois-light-steps-item-container > .du-bois-light-steps-item-content > .du-bois-light-steps-item-title::after {\n display: none;\n}\n.du-bois-light-steps-item-icon,\n.du-bois-light-steps-item-content {\n display: inline-block;\n vertical-align: top;\n}\n.du-bois-light-steps-item-icon {\n width: 32px;\n height: 32px;\n margin: 0 8px 0 0;\n font-size: 18px;\n font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, 'Helvetica Neue', Arial, 'Noto Sans', sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol', 'Noto Color Emoji';\n line-height: 32px;\n text-align: center;\n border: 1px solid rgba(0, 0, 0, 0.25);\n border-radius: 32px;\n transition: background-color 0.3s, border-color 0.3s;\n}\n.du-bois-light-steps-item-icon .du-bois-light-steps-icon {\n position: relative;\n top: -0.5px;\n color: #2272B4;\n line-height: 1;\n}\n.du-bois-light-steps-item-tail {\n position: absolute;\n top: 12px;\n left: 0;\n width: 100%;\n padding: 0 10px;\n}\n.du-bois-light-steps-item-tail::after {\n display: inline-block;\n width: 100%;\n height: 1px;\n background: #f0f0f0;\n border-radius: 1px;\n transition: background 0.3s;\n content: '';\n}\n.du-bois-light-steps-item-title {\n position: relative;\n display: inline-block;\n padding-right: 16px;\n color: #11171C;\n font-size: 18px;\n line-height: 40px;\n}\n.du-bois-light-steps-item-title::after {\n position: absolute;\n top: 20px;\n left: 100%;\n display: block;\n width: 9999px;\n height: 1px;\n background: #f0f0f0;\n content: '';\n}\n.du-bois-light-steps-item-subtitle {\n display: inline;\n margin-left: 8px;\n color: #5F7281;\n font-weight: normal;\n font-size: 13px;\n}\n.du-bois-light-steps-item-description {\n color: #5F7281;\n font-size: 13px;\n}\n.du-bois-light-steps-item-wait .du-bois-light-steps-item-icon {\n background-color: #fff;\n border-color: rgba(0, 0, 0, 0.25);\n}\n.du-bois-light-steps-item-wait .du-bois-light-steps-item-icon > .du-bois-light-steps-icon {\n color: rgba(0, 0, 0, 0.25);\n}\n.du-bois-light-steps-item-wait .du-bois-light-steps-item-icon > .du-bois-light-steps-icon .du-bois-light-steps-icon-dot {\n background: rgba(0, 0, 0, 0.25);\n}\n.du-bois-light-steps-item-wait > .du-bois-light-steps-item-container > .du-bois-light-steps-item-content > .du-bois-light-steps-item-title {\n color: #5F7281;\n}\n.du-bois-light-steps-item-wait > .du-bois-light-steps-item-container > .du-bois-light-steps-item-content > .du-bois-light-steps-item-title::after {\n background-color: #f0f0f0;\n}\n.du-bois-light-steps-item-wait > .du-bois-light-steps-item-container > .du-bois-light-steps-item-content > .du-bois-light-steps-item-description {\n color: #5F7281;\n}\n.du-bois-light-steps-item-wait > .du-bois-light-steps-item-container > .du-bois-light-steps-item-tail::after {\n background-color: #f0f0f0;\n}\n.du-bois-light-steps-item-process .du-bois-light-steps-item-icon {\n background-color: #fff;\n border-color: #2272B4;\n}\n.du-bois-light-steps-item-process .du-bois-light-steps-item-icon > .du-bois-light-steps-icon {\n color: #2272B4;\n}\n.du-bois-light-steps-item-process .du-bois-light-steps-item-icon > .du-bois-light-steps-icon .du-bois-light-steps-icon-dot {\n background: #2272B4;\n}\n.du-bois-light-steps-item-process > .du-bois-light-steps-item-container > .du-bois-light-steps-item-content > .du-bois-light-steps-item-title {\n color: rgba(0, 0, 0, 0.85);\n}\n.du-bois-light-steps-item-process > .du-bois-light-steps-item-container > .du-bois-light-steps-item-content > .du-bois-light-steps-item-title::after {\n background-color: #f0f0f0;\n}\n.du-bois-light-steps-item-process > .du-bois-light-steps-item-container > .du-bois-light-steps-item-content > .du-bois-light-steps-item-description {\n color: #11171C;\n}\n.du-bois-light-steps-item-process > .du-bois-light-steps-item-container > .du-bois-light-steps-item-tail::after {\n background-color: #f0f0f0;\n}\n.du-bois-light-steps-item-process > .du-bois-light-steps-item-container > .du-bois-light-steps-item-icon {\n background: #2272B4;\n}\n.du-bois-light-steps-item-process > .du-bois-light-steps-item-container > .du-bois-light-steps-item-icon .du-bois-light-steps-icon {\n color: #fff;\n}\n.du-bois-light-steps-item-process > .du-bois-light-steps-item-container > .du-bois-light-steps-item-title {\n font-weight: 500;\n}\n.du-bois-light-steps-item-finish .du-bois-light-steps-item-icon {\n background-color: #fff;\n border-color: #2272B4;\n}\n.du-bois-light-steps-item-finish .du-bois-light-steps-item-icon > .du-bois-light-steps-icon {\n color: #2272B4;\n}\n.du-bois-light-steps-item-finish .du-bois-light-steps-item-icon > .du-bois-light-steps-icon .du-bois-light-steps-icon-dot {\n background: #2272B4;\n}\n.du-bois-light-steps-item-finish > .du-bois-light-steps-item-container > .du-bois-light-steps-item-content > .du-bois-light-steps-item-title {\n color: #11171C;\n}\n.du-bois-light-steps-item-finish > .du-bois-light-steps-item-container > .du-bois-light-steps-item-content > .du-bois-light-steps-item-title::after {\n background-color: #2272B4;\n}\n.du-bois-light-steps-item-finish > .du-bois-light-steps-item-container > .du-bois-light-steps-item-content > .du-bois-light-steps-item-description {\n color: #5F7281;\n}\n.du-bois-light-steps-item-finish > .du-bois-light-steps-item-container > .du-bois-light-steps-item-tail::after {\n background-color: #2272B4;\n}\n.du-bois-light-steps-item-error .du-bois-light-steps-item-icon {\n background-color: #fff;\n border-color: #C82D4C;\n}\n.du-bois-light-steps-item-error .du-bois-light-steps-item-icon > .du-bois-light-steps-icon {\n color: #C82D4C;\n}\n.du-bois-light-steps-item-error .du-bois-light-steps-item-icon > .du-bois-light-steps-icon .du-bois-light-steps-icon-dot {\n background: #C82D4C;\n}\n.du-bois-light-steps-item-error > .du-bois-light-steps-item-container > .du-bois-light-steps-item-content > .du-bois-light-steps-item-title {\n color: #C82D4C;\n}\n.du-bois-light-steps-item-error > .du-bois-light-steps-item-container > .du-bois-light-steps-item-content > .du-bois-light-steps-item-title::after {\n background-color: #f0f0f0;\n}\n.du-bois-light-steps-item-error > .du-bois-light-steps-item-container > .du-bois-light-steps-item-content > .du-bois-light-steps-item-description {\n color: #C82D4C;\n}\n.du-bois-light-steps-item-error > .du-bois-light-steps-item-container > .du-bois-light-steps-item-tail::after {\n background-color: #f0f0f0;\n}\n.du-bois-light-steps-item.du-bois-light-steps-next-error .du-bois-light-steps-item-title::after {\n background: #C82D4C;\n}\n.du-bois-light-steps-item-disabled {\n cursor: not-allowed;\n}\n.du-bois-light-steps .du-bois-light-steps-item:not(.du-bois-light-steps-item-active) > .du-bois-light-steps-item-container[role='button'] {\n cursor: pointer;\n}\n.du-bois-light-steps .du-bois-light-steps-item:not(.du-bois-light-steps-item-active) > .du-bois-light-steps-item-container[role='button'] .du-bois-light-steps-item-title,\n.du-bois-light-steps .du-bois-light-steps-item:not(.du-bois-light-steps-item-active) > .du-bois-light-steps-item-container[role='button'] .du-bois-light-steps-item-subtitle,\n.du-bois-light-steps .du-bois-light-steps-item:not(.du-bois-light-steps-item-active) > .du-bois-light-steps-item-container[role='button'] .du-bois-light-steps-item-description,\n.du-bois-light-steps .du-bois-light-steps-item:not(.du-bois-light-steps-item-active) > .du-bois-light-steps-item-container[role='button'] .du-bois-light-steps-item-icon .du-bois-light-steps-icon {\n transition: color 0.3s;\n}\n.du-bois-light-steps .du-bois-light-steps-item:not(.du-bois-light-steps-item-active) > .du-bois-light-steps-item-container[role='button']:hover .du-bois-light-steps-item-title,\n.du-bois-light-steps .du-bois-light-steps-item:not(.du-bois-light-steps-item-active) > .du-bois-light-steps-item-container[role='button']:hover .du-bois-light-steps-item-subtitle,\n.du-bois-light-steps .du-bois-light-steps-item:not(.du-bois-light-steps-item-active) > .du-bois-light-steps-item-container[role='button']:hover .du-bois-light-steps-item-description {\n color: #2272B4;\n}\n.du-bois-light-steps .du-bois-light-steps-item:not(.du-bois-light-steps-item-active):not(.du-bois-light-steps-item-process) > .du-bois-light-steps-item-container[role='button']:hover .du-bois-light-steps-item-icon {\n border-color: #2272B4;\n}\n.du-bois-light-steps .du-bois-light-steps-item:not(.du-bois-light-steps-item-active):not(.du-bois-light-steps-item-process) > .du-bois-light-steps-item-container[role='button']:hover .du-bois-light-steps-item-icon .du-bois-light-steps-icon {\n color: #2272B4;\n}\n.du-bois-light-steps-horizontal:not(.du-bois-light-steps-label-vertical) .du-bois-light-steps-item {\n padding-left: 16px;\n white-space: nowrap;\n}\n.du-bois-light-steps-horizontal:not(.du-bois-light-steps-label-vertical) .du-bois-light-steps-item:first-child {\n padding-left: 0;\n}\n.du-bois-light-steps-horizontal:not(.du-bois-light-steps-label-vertical) .du-bois-light-steps-item:last-child .du-bois-light-steps-item-title {\n padding-right: 0;\n}\n.du-bois-light-steps-horizontal:not(.du-bois-light-steps-label-vertical) .du-bois-light-steps-item-tail {\n display: none;\n}\n.du-bois-light-steps-horizontal:not(.du-bois-light-steps-label-vertical) .du-bois-light-steps-item-description {\n max-width: 140px;\n white-space: normal;\n}\n.du-bois-light-steps-item-custom > .du-bois-light-steps-item-container > .du-bois-light-steps-item-icon {\n height: auto;\n background: none;\n border: 0;\n}\n.du-bois-light-steps-item-custom > .du-bois-light-steps-item-container > .du-bois-light-steps-item-icon > .du-bois-light-steps-icon {\n top: 0px;\n left: 0.5px;\n width: 32px;\n height: 32px;\n font-size: 24px;\n line-height: 32px;\n}\n.du-bois-light-steps-item-custom.du-bois-light-steps-item-process .du-bois-light-steps-item-icon > .du-bois-light-steps-icon {\n color: #2272B4;\n}\n.du-bois-light-steps:not(.du-bois-light-steps-vertical) .du-bois-light-steps-item-custom .du-bois-light-steps-item-icon {\n width: auto;\n background: none;\n}\n.du-bois-light-steps-small.du-bois-light-steps-horizontal:not(.du-bois-light-steps-label-vertical) .du-bois-light-steps-item {\n padding-left: 12px;\n}\n.du-bois-light-steps-small.du-bois-light-steps-horizontal:not(.du-bois-light-steps-label-vertical) .du-bois-light-steps-item:first-child {\n padding-left: 0;\n}\n.du-bois-light-steps-small .du-bois-light-steps-item-icon {\n width: 24px;\n height: 24px;\n margin: 0 8px 0 0;\n font-size: 12px;\n line-height: 24px;\n text-align: center;\n border-radius: 24px;\n}\n.du-bois-light-steps-small .du-bois-light-steps-item-title {\n padding-right: 12px;\n font-size: 13px;\n line-height: 24px;\n}\n.du-bois-light-steps-small .du-bois-light-steps-item-title::after {\n top: 12px;\n}\n.du-bois-light-steps-small .du-bois-light-steps-item-description {\n color: #5F7281;\n font-size: 13px;\n}\n.du-bois-light-steps-small .du-bois-light-steps-item-tail {\n top: 8px;\n}\n.du-bois-light-steps-small .du-bois-light-steps-item-custom .du-bois-light-steps-item-icon {\n width: inherit;\n height: inherit;\n line-height: inherit;\n background: none;\n border: 0;\n border-radius: 0;\n}\n.du-bois-light-steps-small .du-bois-light-steps-item-custom .du-bois-light-steps-item-icon > .du-bois-light-steps-icon {\n font-size: 24px;\n line-height: 24px;\n transform: none;\n}\n.du-bois-light-steps-vertical {\n display: flex;\n flex-direction: column;\n}\n.du-bois-light-steps-vertical > .du-bois-light-steps-item {\n display: block;\n flex: 1 0 auto;\n padding-left: 0;\n overflow: visible;\n}\n.du-bois-light-steps-vertical > .du-bois-light-steps-item .du-bois-light-steps-item-icon {\n float: left;\n margin-right: 16px;\n}\n.du-bois-light-steps-vertical > .du-bois-light-steps-item .du-bois-light-steps-item-content {\n display: block;\n min-height: 48px;\n overflow: hidden;\n}\n.du-bois-light-steps-vertical > .du-bois-light-steps-item .du-bois-light-steps-item-title {\n line-height: 32px;\n}\n.du-bois-light-steps-vertical > .du-bois-light-steps-item .du-bois-light-steps-item-description {\n padding-bottom: 12px;\n}\n.du-bois-light-steps-vertical > .du-bois-light-steps-item > .du-bois-light-steps-item-container > .du-bois-light-steps-item-tail {\n position: absolute;\n top: 0;\n left: 16px;\n width: 1px;\n height: 100%;\n padding: 38px 0 6px;\n}\n.du-bois-light-steps-vertical > .du-bois-light-steps-item > .du-bois-light-steps-item-container > .du-bois-light-steps-item-tail::after {\n width: 1px;\n height: 100%;\n}\n.du-bois-light-steps-vertical > .du-bois-light-steps-item:not(:last-child) > .du-bois-light-steps-item-container > .du-bois-light-steps-item-tail {\n display: block;\n}\n.du-bois-light-steps-vertical > .du-bois-light-steps-item > .du-bois-light-steps-item-container > .du-bois-light-steps-item-content > .du-bois-light-steps-item-title::after {\n display: none;\n}\n.du-bois-light-steps-vertical.du-bois-light-steps-small .du-bois-light-steps-item-container .du-bois-light-steps-item-tail {\n position: absolute;\n top: 0;\n left: 12px;\n padding: 30px 0 6px;\n}\n.du-bois-light-steps-vertical.du-bois-light-steps-small .du-bois-light-steps-item-container .du-bois-light-steps-item-title {\n line-height: 24px;\n}\n.du-bois-light-steps-label-vertical .du-bois-light-steps-item {\n overflow: visible;\n}\n.du-bois-light-steps-label-vertical .du-bois-light-steps-item-tail {\n margin-left: 58px;\n padding: 3.5px 24px;\n}\n.du-bois-light-steps-label-vertical .du-bois-light-steps-item-content {\n display: block;\n width: 116px;\n margin-top: 8px;\n text-align: center;\n}\n.du-bois-light-steps-label-vertical .du-bois-light-steps-item-icon {\n display: inline-block;\n margin-left: 42px;\n}\n.du-bois-light-steps-label-vertical .du-bois-light-steps-item-title {\n padding-right: 0;\n padding-left: 0;\n}\n.du-bois-light-steps-label-vertical .du-bois-light-steps-item-title::after {\n display: none;\n}\n.du-bois-light-steps-label-vertical .du-bois-light-steps-item-subtitle {\n display: block;\n margin-bottom: 4px;\n margin-left: 0;\n line-height: 20px;\n}\n.du-bois-light-steps-label-vertical.du-bois-light-steps-small:not(.du-bois-light-steps-dot) .du-bois-light-steps-item-icon {\n margin-left: 46px;\n}\n.du-bois-light-steps-dot .du-bois-light-steps-item-title,\n.du-bois-light-steps-dot.du-bois-light-steps-small .du-bois-light-steps-item-title {\n line-height: 20px;\n}\n.du-bois-light-steps-dot .du-bois-light-steps-item-tail,\n.du-bois-light-steps-dot.du-bois-light-steps-small .du-bois-light-steps-item-tail {\n top: 2px;\n width: 100%;\n margin: 0 0 0 70px;\n padding: 0;\n}\n.du-bois-light-steps-dot .du-bois-light-steps-item-tail::after,\n.du-bois-light-steps-dot.du-bois-light-steps-small .du-bois-light-steps-item-tail::after {\n width: calc(100% - 20px);\n height: 3px;\n margin-left: 12px;\n}\n.du-bois-light-steps-dot .du-bois-light-steps-item:first-child .du-bois-light-steps-icon-dot,\n.du-bois-light-steps-dot.du-bois-light-steps-small .du-bois-light-steps-item:first-child .du-bois-light-steps-icon-dot {\n left: 2px;\n}\n.du-bois-light-steps-dot .du-bois-light-steps-item-icon,\n.du-bois-light-steps-dot.du-bois-light-steps-small .du-bois-light-steps-item-icon {\n width: 8px;\n height: 8px;\n margin-left: 67px;\n padding-right: 0;\n line-height: 8px;\n background: transparent;\n border: 0;\n}\n.du-bois-light-steps-dot .du-bois-light-steps-item-icon .du-bois-light-steps-icon-dot,\n.du-bois-light-steps-dot.du-bois-light-steps-small .du-bois-light-steps-item-icon .du-bois-light-steps-icon-dot {\n position: relative;\n float: left;\n width: 100%;\n height: 100%;\n border-radius: 100px;\n transition: all 0.3s;\n /* expand hover area */\n}\n.du-bois-light-steps-dot .du-bois-light-steps-item-icon .du-bois-light-steps-icon-dot::after,\n.du-bois-light-steps-dot.du-bois-light-steps-small .du-bois-light-steps-item-icon .du-bois-light-steps-icon-dot::after {\n position: absolute;\n top: -12px;\n left: -26px;\n width: 60px;\n height: 32px;\n background: rgba(0, 0, 0, 0.001);\n content: '';\n}\n.du-bois-light-steps-dot .du-bois-light-steps-item-content,\n.du-bois-light-steps-dot.du-bois-light-steps-small .du-bois-light-steps-item-content {\n width: 140px;\n}\n.du-bois-light-steps-dot .du-bois-light-steps-item-process .du-bois-light-steps-item-icon,\n.du-bois-light-steps-dot.du-bois-light-steps-small .du-bois-light-steps-item-process .du-bois-light-steps-item-icon {\n position: relative;\n top: -1px;\n width: 10px;\n height: 10px;\n line-height: 10px;\n background: none;\n}\n.du-bois-light-steps-dot .du-bois-light-steps-item-process .du-bois-light-steps-icon:first-child .du-bois-light-steps-icon-dot,\n.du-bois-light-steps-dot.du-bois-light-steps-small .du-bois-light-steps-item-process .du-bois-light-steps-icon:first-child .du-bois-light-steps-icon-dot {\n left: 0;\n}\n.du-bois-light-steps-vertical.du-bois-light-steps-dot .du-bois-light-steps-item-icon {\n margin-top: 8px;\n margin-left: 0;\n background: none;\n}\n.du-bois-light-steps-vertical.du-bois-light-steps-dot .du-bois-light-steps-item > .du-bois-light-steps-item-container > .du-bois-light-steps-item-tail {\n top: 2px;\n left: -9px;\n margin: 0;\n padding: 22px 0 4px;\n}\n.du-bois-light-steps-vertical.du-bois-light-steps-dot .du-bois-light-steps-item:first-child .du-bois-light-steps-icon-dot {\n left: 0;\n}\n.du-bois-light-steps-vertical.du-bois-light-steps-dot .du-bois-light-steps-item-content {\n width: inherit;\n}\n.du-bois-light-steps-vertical.du-bois-light-steps-dot .du-bois-light-steps-item-process .du-bois-light-steps-item-container .du-bois-light-steps-item-icon .du-bois-light-steps-icon-dot {\n left: -2px;\n}\n.du-bois-light-steps-navigation {\n padding-top: 12px;\n}\n.du-bois-light-steps-navigation.du-bois-light-steps-small .du-bois-light-steps-item-container {\n margin-left: -12px;\n}\n.du-bois-light-steps-navigation .du-bois-light-steps-item {\n overflow: visible;\n text-align: center;\n}\n.du-bois-light-steps-navigation .du-bois-light-steps-item-container {\n display: inline-block;\n height: 100%;\n margin-left: -16px;\n padding-bottom: 12px;\n text-align: left;\n transition: opacity 0.3s;\n}\n.du-bois-light-steps-navigation .du-bois-light-steps-item-container .du-bois-light-steps-item-content {\n max-width: auto;\n}\n.du-bois-light-steps-navigation .du-bois-light-steps-item-container .du-bois-light-steps-item-title {\n max-width: 100%;\n padding-right: 0;\n overflow: hidden;\n white-space: nowrap;\n text-overflow: ellipsis;\n}\n.du-bois-light-steps-navigation .du-bois-light-steps-item-container .du-bois-light-steps-item-title::after {\n display: none;\n}\n.du-bois-light-steps-navigation .du-bois-light-steps-item:not(.du-bois-light-steps-item-active) .du-bois-light-steps-item-container[role='button'] {\n cursor: pointer;\n}\n.du-bois-light-steps-navigation .du-bois-light-steps-item:not(.du-bois-light-steps-item-active) .du-bois-light-steps-item-container[role='button']:hover {\n opacity: 0.85;\n}\n.du-bois-light-steps-navigation .du-bois-light-steps-item:last-child {\n flex: 1;\n}\n.du-bois-light-steps-navigation .du-bois-light-steps-item:last-child::after {\n display: none;\n}\n.du-bois-light-steps-navigation .du-bois-light-steps-item::after {\n position: absolute;\n top: 50%;\n left: 100%;\n display: inline-block;\n width: 12px;\n height: 12px;\n margin-top: -14px;\n margin-left: -2px;\n border: 1px solid rgba(0, 0, 0, 0.25);\n border-bottom: none;\n border-left: none;\n transform: rotate(45deg);\n content: '';\n}\n.du-bois-light-steps-navigation .du-bois-light-steps-item::before {\n position: absolute;\n bottom: 0;\n left: 50%;\n display: inline-block;\n width: 0;\n height: 2px;\n background-color: #2272B4;\n transition: width 0.3s, left 0.3s;\n transition-timing-function: ease-out;\n content: '';\n}\n.du-bois-light-steps-navigation .du-bois-light-steps-item.du-bois-light-steps-item-active::before {\n left: 0;\n width: 100%;\n}\n.du-bois-light-steps-navigation.du-bois-light-steps-vertical > .du-bois-light-steps-item {\n margin-right: 0 !important;\n}\n.du-bois-light-steps-navigation.du-bois-light-steps-vertical > .du-bois-light-steps-item::before {\n display: none;\n}\n.du-bois-light-steps-navigation.du-bois-light-steps-vertical > .du-bois-light-steps-item.du-bois-light-steps-item-active::before {\n top: 0;\n right: 0;\n left: unset;\n display: block;\n width: 3px;\n height: calc(100% - 24px);\n}\n.du-bois-light-steps-navigation.du-bois-light-steps-vertical > .du-bois-light-steps-item::after {\n position: relative;\n top: -2px;\n left: 50%;\n display: block;\n width: 8px;\n height: 8px;\n margin-bottom: 8px;\n text-align: center;\n transform: rotate(135deg);\n}\n.du-bois-light-steps-navigation.du-bois-light-steps-vertical > .du-bois-light-steps-item > .du-bois-light-steps-item-container > .du-bois-light-steps-item-tail {\n visibility: hidden;\n}\n.du-bois-light-steps-with-progress .du-bois-light-steps-item {\n padding-top: 4px;\n}\n.du-bois-light-steps-with-progress .du-bois-light-steps-item .du-bois-light-steps-item-tail {\n top: 4px !important;\n}\n.du-bois-light-steps-with-progress.du-bois-light-steps-horizontal .du-bois-light-steps-item:first-child {\n padding-bottom: 4px;\n padding-left: 4px;\n}\n.du-bois-light-steps-with-progress .du-bois-light-steps-item-icon {\n position: relative;\n}\n.du-bois-light-steps-with-progress .du-bois-light-steps-item-icon .du-bois-light-progress {\n position: absolute;\n top: -5px;\n right: -5px;\n bottom: -5px;\n left: -5px;\n}\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-light-progress {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #11171C;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: 'tnum';\n display: inline-block;\n}\n.du-bois-light-progress-line {\n position: relative;\n width: 100%;\n font-size: 13px;\n}\n.du-bois-light-progress-steps {\n display: inline-block;\n}\n.du-bois-light-progress-steps-outer {\n display: flex;\n flex-direction: row;\n align-items: center;\n}\n.du-bois-light-progress-steps-item {\n flex-shrink: 0;\n min-width: 2px;\n margin-right: 2px;\n background: #f3f3f3;\n transition: all 0.3s;\n}\n.du-bois-light-progress-steps-item-active {\n background: #1890ff;\n}\n.du-bois-light-progress-small.du-bois-light-progress-line,\n.du-bois-light-progress-small.du-bois-light-progress-line .du-bois-light-progress-text .anticon {\n font-size: 12px;\n}\n.du-bois-light-progress-outer {\n display: inline-block;\n width: 100%;\n margin-right: 0;\n padding-right: 0;\n}\n.du-bois-light-progress-show-info .du-bois-light-progress-outer {\n margin-right: calc(-2em - 8px);\n padding-right: calc(2em + 8px);\n}\n.du-bois-light-progress-inner {\n position: relative;\n display: inline-block;\n width: 100%;\n overflow: hidden;\n vertical-align: middle;\n background-color: #f5f5f5;\n border-radius: 100px;\n}\n.du-bois-light-progress-circle-trail {\n stroke: #f5f5f5;\n}\n.du-bois-light-progress-circle-path {\n animation: du-bois-light-progress-appear 0.3s;\n}\n.du-bois-light-progress-inner:not(.du-bois-light-progress-circle-gradient) .du-bois-light-progress-circle-path {\n stroke: #1890ff;\n}\n.du-bois-light-progress-success-bg,\n.du-bois-light-progress-bg {\n position: relative;\n background-color: #1890ff;\n border-radius: 100px;\n transition: all 0.4s cubic-bezier(0.08, 0.82, 0.17, 1) 0s;\n}\n.du-bois-light-progress-success-bg {\n position: absolute;\n top: 0;\n left: 0;\n background-color: #34824F;\n}\n.du-bois-light-progress-text {\n display: inline-block;\n width: 2em;\n margin-left: 8px;\n color: #11171C;\n font-size: 1em;\n line-height: 1;\n white-space: nowrap;\n text-align: left;\n vertical-align: middle;\n word-break: normal;\n}\n.du-bois-light-progress-text .anticon {\n font-size: 13px;\n}\n.du-bois-light-progress-status-active .du-bois-light-progress-bg::before {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: #fff;\n border-radius: 10px;\n opacity: 0;\n animation: du-bois-light-progress-active 2.4s cubic-bezier(0.23, 1, 0.32, 1) infinite;\n content: '';\n}\n.du-bois-light-progress-status-exception .du-bois-light-progress-bg {\n background-color: #C82D4C;\n}\n.du-bois-light-progress-status-exception .du-bois-light-progress-text {\n color: #C82D4C;\n}\n.du-bois-light-progress-status-exception .du-bois-light-progress-inner:not(.du-bois-light-progress-circle-gradient) .du-bois-light-progress-circle-path {\n stroke: #C82D4C;\n}\n.du-bois-light-progress-status-success .du-bois-light-progress-bg {\n background-color: #34824F;\n}\n.du-bois-light-progress-status-success .du-bois-light-progress-text {\n color: #34824F;\n}\n.du-bois-light-progress-status-success .du-bois-light-progress-inner:not(.du-bois-light-progress-circle-gradient) .du-bois-light-progress-circle-path {\n stroke: #34824F;\n}\n.du-bois-light-progress-circle .du-bois-light-progress-inner {\n position: relative;\n line-height: 1;\n background-color: transparent;\n}\n.du-bois-light-progress-circle .du-bois-light-progress-text {\n position: absolute;\n top: 50%;\n left: 50%;\n width: 100%;\n margin: 0;\n padding: 0;\n color: #11171C;\n font-size: 1em;\n line-height: 1;\n white-space: normal;\n text-align: center;\n transform: translate(-50%, -50%);\n}\n.du-bois-light-progress-circle .du-bois-light-progress-text .anticon {\n font-size: 1.16666667em;\n}\n.du-bois-light-progress-circle.du-bois-light-progress-status-exception .du-bois-light-progress-text {\n color: #C82D4C;\n}\n.du-bois-light-progress-circle.du-bois-light-progress-status-success .du-bois-light-progress-text {\n color: #34824F;\n}\n@keyframes du-bois-light-progress-active {\n 0% {\n transform: translateX(-100%) scaleX(0);\n opacity: 0.1;\n }\n 20% {\n transform: translateX(-100%) scaleX(0);\n opacity: 0.5;\n }\n 100% {\n transform: translateX(0) scaleX(1);\n opacity: 0;\n }\n}\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-light-table.du-bois-light-table-middle {\n font-size: 13px;\n}\n.du-bois-light-table.du-bois-light-table-middle .du-bois-light-table-title,\n.du-bois-light-table.du-bois-light-table-middle .du-bois-light-table-footer,\n.du-bois-light-table.du-bois-light-table-middle .du-bois-light-table-thead > tr > th,\n.du-bois-light-table.du-bois-light-table-middle .du-bois-light-table-tbody > tr > td,\n.du-bois-light-table.du-bois-light-table-middle tfoot > tr > th,\n.du-bois-light-table.du-bois-light-table-middle tfoot > tr > td {\n padding: 12px 8px;\n}\n.du-bois-light-table.du-bois-light-table-middle .du-bois-light-table-filter-trigger {\n margin-right: -4px;\n}\n.du-bois-light-table.du-bois-light-table-middle .du-bois-light-table-expanded-row-fixed {\n margin: -12px -8px;\n}\n.du-bois-light-table.du-bois-light-table-middle .du-bois-light-table-tbody .du-bois-light-table-wrapper:only-child .du-bois-light-table {\n margin: -12px -8px -12px 25px;\n}\n.du-bois-light-table.du-bois-light-table-small {\n font-size: 13px;\n}\n.du-bois-light-table.du-bois-light-table-small .du-bois-light-table-title,\n.du-bois-light-table.du-bois-light-table-small .du-bois-light-table-footer,\n.du-bois-light-table.du-bois-light-table-small .du-bois-light-table-thead > tr > th,\n.du-bois-light-table.du-bois-light-table-small .du-bois-light-table-tbody > tr > td,\n.du-bois-light-table.du-bois-light-table-small tfoot > tr > th,\n.du-bois-light-table.du-bois-light-table-small tfoot > tr > td {\n padding: 8px 8px;\n}\n.du-bois-light-table.du-bois-light-table-small .du-bois-light-table-filter-trigger {\n margin-right: -4px;\n}\n.du-bois-light-table.du-bois-light-table-small .du-bois-light-table-expanded-row-fixed {\n margin: -8px -8px;\n}\n.du-bois-light-table.du-bois-light-table-small .du-bois-light-table-tbody .du-bois-light-table-wrapper:only-child .du-bois-light-table {\n margin: -8px -8px -8px 25px;\n}\n.du-bois-light-table-small .du-bois-light-table-thead > tr > th {\n background-color: #fafafa;\n}\n.du-bois-light-table-small .du-bois-light-table-selection-column {\n width: 46px;\n min-width: 46px;\n}\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-title {\n border: 1px solid #f0f0f0;\n border-bottom: 0;\n}\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-container {\n border-left: 1px solid #f0f0f0;\n}\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-container > .du-bois-light-table-content > table > thead > tr > th,\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-container > .du-bois-light-table-header > table > thead > tr > th,\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-container > .du-bois-light-table-body > table > thead > tr > th,\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-container > .du-bois-light-table-summary > table > thead > tr > th,\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-container > .du-bois-light-table-content > table > tbody > tr > td,\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-container > .du-bois-light-table-header > table > tbody > tr > td,\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-container > .du-bois-light-table-body > table > tbody > tr > td,\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-container > .du-bois-light-table-summary > table > tbody > tr > td,\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-container > .du-bois-light-table-content > table > tfoot > tr > th,\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-container > .du-bois-light-table-header > table > tfoot > tr > th,\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-container > .du-bois-light-table-body > table > tfoot > tr > th,\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-container > .du-bois-light-table-summary > table > tfoot > tr > th,\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-container > .du-bois-light-table-content > table > tfoot > tr > td,\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-container > .du-bois-light-table-header > table > tfoot > tr > td,\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-container > .du-bois-light-table-body > table > tfoot > tr > td,\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-container > .du-bois-light-table-summary > table > tfoot > tr > td {\n border-right: 1px solid #f0f0f0;\n}\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-container > .du-bois-light-table-content > table > thead > tr:not(:last-child) > th,\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-container > .du-bois-light-table-header > table > thead > tr:not(:last-child) > th,\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-container > .du-bois-light-table-body > table > thead > tr:not(:last-child) > th,\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-container > .du-bois-light-table-summary > table > thead > tr:not(:last-child) > th {\n border-bottom: 1px solid #f0f0f0;\n}\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-container > .du-bois-light-table-content > table > thead > tr > th::before,\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-container > .du-bois-light-table-header > table > thead > tr > th::before,\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-container > .du-bois-light-table-body > table > thead > tr > th::before,\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-container > .du-bois-light-table-summary > table > thead > tr > th::before {\n background-color: transparent !important;\n}\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-container > .du-bois-light-table-content > table > thead > tr > .du-bois-light-table-cell-fix-right-first::after,\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-container > .du-bois-light-table-header > table > thead > tr > .du-bois-light-table-cell-fix-right-first::after,\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-container > .du-bois-light-table-body > table > thead > tr > .du-bois-light-table-cell-fix-right-first::after,\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-container > .du-bois-light-table-summary > table > thead > tr > .du-bois-light-table-cell-fix-right-first::after,\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-container > .du-bois-light-table-content > table > tbody > tr > .du-bois-light-table-cell-fix-right-first::after,\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-container > .du-bois-light-table-header > table > tbody > tr > .du-bois-light-table-cell-fix-right-first::after,\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-container > .du-bois-light-table-body > table > tbody > tr > .du-bois-light-table-cell-fix-right-first::after,\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-container > .du-bois-light-table-summary > table > tbody > tr > .du-bois-light-table-cell-fix-right-first::after,\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-container > .du-bois-light-table-content > table > tfoot > tr > .du-bois-light-table-cell-fix-right-first::after,\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-container > .du-bois-light-table-header > table > tfoot > tr > .du-bois-light-table-cell-fix-right-first::after,\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-container > .du-bois-light-table-body > table > tfoot > tr > .du-bois-light-table-cell-fix-right-first::after,\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-container > .du-bois-light-table-summary > table > tfoot > tr > .du-bois-light-table-cell-fix-right-first::after {\n border-right: 1px solid #f0f0f0;\n}\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-container > .du-bois-light-table-content > table > tbody > tr > td > .du-bois-light-table-expanded-row-fixed,\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-container > .du-bois-light-table-header > table > tbody > tr > td > .du-bois-light-table-expanded-row-fixed,\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-container > .du-bois-light-table-body > table > tbody > tr > td > .du-bois-light-table-expanded-row-fixed,\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-container > .du-bois-light-table-summary > table > tbody > tr > td > .du-bois-light-table-expanded-row-fixed {\n margin: -16px -17px;\n}\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-container > .du-bois-light-table-content > table > tbody > tr > td > .du-bois-light-table-expanded-row-fixed::after,\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-container > .du-bois-light-table-header > table > tbody > tr > td > .du-bois-light-table-expanded-row-fixed::after,\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-container > .du-bois-light-table-body > table > tbody > tr > td > .du-bois-light-table-expanded-row-fixed::after,\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-container > .du-bois-light-table-summary > table > tbody > tr > td > .du-bois-light-table-expanded-row-fixed::after {\n position: absolute;\n top: 0;\n right: 1px;\n bottom: 0;\n border-right: 1px solid #f0f0f0;\n content: '';\n}\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-container > .du-bois-light-table-content > table,\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-container > .du-bois-light-table-header > table {\n border-top: 1px solid #f0f0f0;\n}\n.du-bois-light-table.du-bois-light-table-bordered.du-bois-light-table-scroll-horizontal > .du-bois-light-table-container > .du-bois-light-table-body > table > tbody > tr.du-bois-light-table-expanded-row > td,\n.du-bois-light-table.du-bois-light-table-bordered.du-bois-light-table-scroll-horizontal > .du-bois-light-table-container > .du-bois-light-table-body > table > tbody > tr.du-bois-light-table-placeholder > td {\n border-right: 0;\n}\n.du-bois-light-table.du-bois-light-table-bordered.du-bois-light-table-middle > .du-bois-light-table-container > .du-bois-light-table-content > table > tbody > tr > td > .du-bois-light-table-expanded-row-fixed,\n.du-bois-light-table.du-bois-light-table-bordered.du-bois-light-table-middle > .du-bois-light-table-container > .du-bois-light-table-body > table > tbody > tr > td > .du-bois-light-table-expanded-row-fixed {\n margin: -12px -9px;\n}\n.du-bois-light-table.du-bois-light-table-bordered.du-bois-light-table-small > .du-bois-light-table-container > .du-bois-light-table-content > table > tbody > tr > td > .du-bois-light-table-expanded-row-fixed,\n.du-bois-light-table.du-bois-light-table-bordered.du-bois-light-table-small > .du-bois-light-table-container > .du-bois-light-table-body > table > tbody > tr > td > .du-bois-light-table-expanded-row-fixed {\n margin: -8px -9px;\n}\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-footer {\n border: 1px solid #f0f0f0;\n border-top: 0;\n}\n.du-bois-light-table-cell .du-bois-light-table-container:first-child {\n border-top: 0;\n}\n.du-bois-light-table-cell-scrollbar {\n box-shadow: 0 1px 0 1px #fafafa;\n}\n.du-bois-light-table-wrapper {\n clear: both;\n max-width: 100%;\n}\n.du-bois-light-table-wrapper::before {\n display: table;\n content: '';\n}\n.du-bois-light-table-wrapper::after {\n display: table;\n clear: both;\n content: '';\n}\n.du-bois-light-table {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #11171C;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: 'tnum';\n position: relative;\n font-size: 13px;\n background: #fff;\n border-radius: 4px;\n}\n.du-bois-light-table table {\n width: 100%;\n text-align: left;\n border-radius: 4px 4px 0 0;\n border-collapse: separate;\n border-spacing: 0;\n}\n.du-bois-light-table-thead > tr > th,\n.du-bois-light-table-tbody > tr > td,\n.du-bois-light-table tfoot > tr > th,\n.du-bois-light-table tfoot > tr > td {\n position: relative;\n padding: 16px 16px;\n overflow-wrap: break-word;\n}\n.du-bois-light-table-cell-ellipsis {\n overflow: hidden;\n white-space: nowrap;\n text-overflow: ellipsis;\n word-break: keep-all;\n}\n.du-bois-light-table-cell-ellipsis.du-bois-light-table-cell-fix-left-last,\n.du-bois-light-table-cell-ellipsis.du-bois-light-table-cell-fix-right-first {\n overflow: visible;\n}\n.du-bois-light-table-cell-ellipsis.du-bois-light-table-cell-fix-left-last .du-bois-light-table-cell-content,\n.du-bois-light-table-cell-ellipsis.du-bois-light-table-cell-fix-right-first .du-bois-light-table-cell-content {\n display: block;\n overflow: hidden;\n text-overflow: ellipsis;\n}\n.du-bois-light-table-cell-ellipsis .du-bois-light-table-column-title {\n overflow: hidden;\n text-overflow: ellipsis;\n word-break: keep-all;\n}\n.du-bois-light-table-title {\n padding: 16px 16px;\n}\n.du-bois-light-table-footer {\n padding: 16px 16px;\n color: rgba(0, 0, 0, 0.85);\n background: #fafafa;\n}\n.du-bois-light-table-thead > tr > th {\n position: relative;\n color: rgba(0, 0, 0, 0.85);\n font-weight: 500;\n text-align: left;\n background: #fafafa;\n border-bottom: 1px solid #f0f0f0;\n transition: background 0.3s ease;\n}\n.du-bois-light-table-thead > tr > th[colspan]:not([colspan='1']) {\n text-align: center;\n}\n.du-bois-light-table-thead > tr > th:not(:last-child):not(.du-bois-light-table-selection-column):not(.du-bois-light-table-row-expand-icon-cell):not([colspan])::before {\n position: absolute;\n top: 50%;\n right: 0;\n width: 1px;\n height: 1.6em;\n background-color: rgba(0, 0, 0, 0.06);\n transform: translateY(-50%);\n transition: background-color 0.3s;\n content: '';\n}\n.du-bois-light-table-thead > tr:not(:last-child) > th[colspan] {\n border-bottom: 0;\n}\n.du-bois-light-table-tbody > tr > td {\n border-bottom: 1px solid #f0f0f0;\n transition: background 0.3s;\n}\n.du-bois-light-table-tbody > tr > td > .du-bois-light-table-wrapper:only-child .du-bois-light-table,\n.du-bois-light-table-tbody > tr > td > .du-bois-light-table-expanded-row-fixed > .du-bois-light-table-wrapper:only-child .du-bois-light-table {\n margin: -16px -16px -16px 33px;\n}\n.du-bois-light-table-tbody > tr > td > .du-bois-light-table-wrapper:only-child .du-bois-light-table-tbody > tr:last-child > td,\n.du-bois-light-table-tbody > tr > td > .du-bois-light-table-expanded-row-fixed > .du-bois-light-table-wrapper:only-child .du-bois-light-table-tbody > tr:last-child > td {\n border-bottom: 0;\n}\n.du-bois-light-table-tbody > tr > td > .du-bois-light-table-wrapper:only-child .du-bois-light-table-tbody > tr:last-child > td:first-child,\n.du-bois-light-table-tbody > tr > td > .du-bois-light-table-expanded-row-fixed > .du-bois-light-table-wrapper:only-child .du-bois-light-table-tbody > tr:last-child > td:first-child,\n.du-bois-light-table-tbody > tr > td > .du-bois-light-table-wrapper:only-child .du-bois-light-table-tbody > tr:last-child > td:last-child,\n.du-bois-light-table-tbody > tr > td > .du-bois-light-table-expanded-row-fixed > .du-bois-light-table-wrapper:only-child .du-bois-light-table-tbody > tr:last-child > td:last-child {\n border-radius: 0;\n}\n.du-bois-light-table-tbody > tr.du-bois-light-table-row:hover > td {\n background: #fafafa;\n}\n.du-bois-light-table-tbody > tr.du-bois-light-table-row-selected > td {\n background: #e6f1f5;\n border-color: rgba(0, 0, 0, 0.03);\n}\n.du-bois-light-table-tbody > tr.du-bois-light-table-row-selected:hover > td {\n background: #dfedf2;\n}\n.du-bois-light-table-summary {\n position: relative;\n z-index: 2;\n background: #fff;\n}\ndiv.du-bois-light-table-summary {\n box-shadow: 0 -1px 0 #f0f0f0;\n}\n.du-bois-light-table-summary > tr > th,\n.du-bois-light-table-summary > tr > td {\n border-bottom: 1px solid #f0f0f0;\n}\n.du-bois-light-table-pagination.du-bois-light-pagination {\n margin: 16px 0;\n}\n.du-bois-light-table-pagination {\n display: flex;\n flex-wrap: wrap;\n row-gap: 4px;\n}\n.du-bois-light-table-pagination > * {\n flex: none;\n}\n.du-bois-light-table-pagination-left {\n justify-content: flex-start;\n}\n.du-bois-light-table-pagination-center {\n justify-content: center;\n}\n.du-bois-light-table-pagination-right {\n justify-content: flex-end;\n}\n.du-bois-light-table-thead th.du-bois-light-table-column-has-sorters {\n cursor: pointer;\n transition: all 0.3s;\n}\n.du-bois-light-table-thead th.du-bois-light-table-column-has-sorters:hover {\n background: rgba(0, 0, 0, 0.04);\n}\n.du-bois-light-table-thead th.du-bois-light-table-column-has-sorters:hover::before {\n background-color: transparent !important;\n}\n.du-bois-light-table-thead th.du-bois-light-table-column-has-sorters.du-bois-light-table-cell-fix-left:hover,\n.du-bois-light-table-thead th.du-bois-light-table-column-has-sorters.du-bois-light-table-cell-fix-right:hover {\n background: #f5f5f5;\n}\n.du-bois-light-table-thead th.du-bois-light-table-column-sort {\n background: #f5f5f5;\n}\n.du-bois-light-table-thead th.du-bois-light-table-column-sort::before {\n background-color: transparent !important;\n}\ntd.du-bois-light-table-column-sort {\n background: #fafafa;\n}\n.du-bois-light-table-column-title {\n position: relative;\n z-index: 1;\n flex: 1;\n}\n.du-bois-light-table-column-sorters {\n display: flex;\n flex: auto;\n align-items: center;\n justify-content: space-between;\n}\n.du-bois-light-table-column-sorters::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n width: 100%;\n height: 100%;\n content: '';\n}\n.du-bois-light-table-column-sorter {\n color: #bfbfbf;\n font-size: 0;\n transition: color 0.3s;\n}\n.du-bois-light-table-column-sorter-inner {\n display: inline-flex;\n flex-direction: column;\n align-items: center;\n}\n.du-bois-light-table-column-sorter-up,\n.du-bois-light-table-column-sorter-down {\n font-size: 11px;\n}\n.du-bois-light-table-column-sorter-up.active,\n.du-bois-light-table-column-sorter-down.active {\n color: #2272B4;\n}\n.du-bois-light-table-column-sorter-up + .du-bois-light-table-column-sorter-down {\n margin-top: -0.3em;\n}\n.du-bois-light-table-column-sorters:hover .du-bois-light-table-column-sorter {\n color: #a6a6a6;\n}\n.du-bois-light-table-filter-column {\n display: flex;\n justify-content: space-between;\n}\n.du-bois-light-table-filter-trigger {\n position: relative;\n display: flex;\n align-items: center;\n margin: -4px -8px -4px 4px;\n padding: 0 4px;\n color: #bfbfbf;\n font-size: 12px;\n border-radius: 4px;\n cursor: pointer;\n transition: all 0.3s;\n}\n.du-bois-light-table-filter-trigger:hover {\n color: #5F7281;\n background: rgba(0, 0, 0, 0.04);\n}\n.du-bois-light-table-filter-trigger.active {\n color: #2272B4;\n}\n.du-bois-light-table-filter-dropdown {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #11171C;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: 'tnum';\n min-width: 120px;\n background-color: #fff;\n border-radius: 4px;\n box-shadow: 0 3px 6px -4px rgba(0, 0, 0, 0.12), 0 6px 16px 0 rgba(0, 0, 0, 0.08), 0 9px 28px 8px rgba(0, 0, 0, 0.05);\n}\n.du-bois-light-table-filter-dropdown .du-bois-light-dropdown-menu {\n max-height: 264px;\n overflow-x: hidden;\n border: 0;\n box-shadow: none;\n}\n.du-bois-light-table-filter-dropdown-submenu > ul {\n max-height: calc(100vh - 130px);\n overflow-x: hidden;\n overflow-y: auto;\n}\n.du-bois-light-table-filter-dropdown .du-bois-light-checkbox-wrapper + span,\n.du-bois-light-table-filter-dropdown-submenu .du-bois-light-checkbox-wrapper + span {\n padding-left: 8px;\n}\n.du-bois-light-table-filter-dropdown-btns {\n display: flex;\n justify-content: space-between;\n padding: 7px 8px 7px 3px;\n overflow: hidden;\n background-color: inherit;\n border-top: 1px solid #f0f0f0;\n}\n.du-bois-light-table-selection-col {\n width: 32px;\n}\n.du-bois-light-table-bordered .du-bois-light-table-selection-col {\n width: 50px;\n}\ntable tr th.du-bois-light-table-selection-column,\ntable tr td.du-bois-light-table-selection-column {\n padding-right: 4px;\n padding-left: 4px;\n text-align: center;\n}\ntable tr th.du-bois-light-table-selection-column .du-bois-light-radio-wrapper,\ntable tr td.du-bois-light-table-selection-column .du-bois-light-radio-wrapper {\n margin-right: 0;\n}\ntable tr th.du-bois-light-table-selection-column::after {\n background-color: transparent !important;\n}\n.du-bois-light-table-selection {\n position: relative;\n display: inline-flex;\n flex-direction: column;\n}\n.du-bois-light-table-selection-extra {\n position: absolute;\n top: 0;\n z-index: 1;\n cursor: pointer;\n transition: all 0.3s;\n margin-inline-start: 100%;\n padding-inline-start: 0px;\n}\n.du-bois-light-table-selection-extra .anticon {\n color: #bfbfbf;\n font-size: 10px;\n}\n.du-bois-light-table-selection-extra .anticon:hover {\n color: #a6a6a6;\n}\n.du-bois-light-table-expand-icon-col {\n width: 48px;\n}\n.du-bois-light-table-row-expand-icon-cell {\n text-align: center;\n}\n.du-bois-light-table-row-indent {\n float: left;\n height: 1px;\n}\n.du-bois-light-table-row-expand-icon {\n color: #2272B4;\n text-decoration: none;\n cursor: pointer;\n transition: color 0.3s;\n position: relative;\n display: inline-flex;\n float: left;\n box-sizing: border-box;\n width: 17px;\n height: 17px;\n padding: 0;\n color: inherit;\n line-height: 17px;\n background: #fff;\n border: 1px solid #f0f0f0;\n border-radius: 4px;\n outline: none;\n transform: scale(0.94117647);\n transition: all 0.3s;\n user-select: none;\n}\n.du-bois-light-table-row-expand-icon:focus,\n.du-bois-light-table-row-expand-icon:hover {\n color: #448dc2;\n}\n.du-bois-light-table-row-expand-icon:active {\n color: #14538f;\n}\n.du-bois-light-table-row-expand-icon:focus,\n.du-bois-light-table-row-expand-icon:hover,\n.du-bois-light-table-row-expand-icon:active {\n border-color: currentColor;\n}\n.du-bois-light-table-row-expand-icon::before,\n.du-bois-light-table-row-expand-icon::after {\n position: absolute;\n background: currentColor;\n transition: transform 0.3s ease-out;\n content: '';\n}\n.du-bois-light-table-row-expand-icon::before {\n top: 7px;\n right: 3px;\n left: 3px;\n height: 1px;\n}\n.du-bois-light-table-row-expand-icon::after {\n top: 3px;\n bottom: 3px;\n left: 7px;\n width: 1px;\n transform: rotate(90deg);\n}\n.du-bois-light-table-row-expand-icon-collapsed::before {\n transform: rotate(-180deg);\n}\n.du-bois-light-table-row-expand-icon-collapsed::after {\n transform: rotate(0deg);\n}\n.du-bois-light-table-row-expand-icon-spaced {\n background: transparent;\n border: 0;\n visibility: hidden;\n}\n.du-bois-light-table-row-expand-icon-spaced::before,\n.du-bois-light-table-row-expand-icon-spaced::after {\n display: none;\n content: none;\n}\n.du-bois-light-table-row-indent + .du-bois-light-table-row-expand-icon {\n margin-top: 121.5px;\n margin-right: 4px;\n}\ntr.du-bois-light-table-expanded-row > td,\ntr.du-bois-light-table-expanded-row:hover > td {\n background: #fbfbfb;\n}\ntr.du-bois-light-table-expanded-row .du-bois-light-descriptions-view {\n display: flex;\n}\ntr.du-bois-light-table-expanded-row .du-bois-light-descriptions-view table {\n flex: auto;\n width: auto;\n}\n.du-bois-light-table .du-bois-light-table-expanded-row-fixed {\n position: relative;\n margin: -16px -16px;\n padding: 16px 16px;\n}\n.du-bois-light-table-tbody > tr.du-bois-light-table-placeholder {\n text-align: center;\n}\n.du-bois-light-table-empty .du-bois-light-table-tbody > tr.du-bois-light-table-placeholder {\n color: rgba(0, 0, 0, 0.25);\n}\n.du-bois-light-table-tbody > tr.du-bois-light-table-placeholder:hover > td {\n background: #fff;\n}\n.du-bois-light-table-cell-fix-left,\n.du-bois-light-table-cell-fix-right {\n position: -webkit-sticky !important;\n position: sticky !important;\n z-index: 2;\n background: #fff;\n}\n.du-bois-light-table-cell-fix-left-first::after,\n.du-bois-light-table-cell-fix-left-last::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: -1px;\n width: 30px;\n transform: translateX(100%);\n transition: box-shadow 0.3s;\n content: '';\n pointer-events: none;\n}\n.du-bois-light-table-cell-fix-right-first::after,\n.du-bois-light-table-cell-fix-right-last::after {\n position: absolute;\n top: 0;\n bottom: -1px;\n left: 0;\n width: 30px;\n transform: translateX(-100%);\n transition: box-shadow 0.3s;\n content: '';\n pointer-events: none;\n}\n.du-bois-light-table .du-bois-light-table-container::before,\n.du-bois-light-table .du-bois-light-table-container::after {\n position: absolute;\n top: 0;\n bottom: 0;\n z-index: 1;\n width: 30px;\n transition: box-shadow 0.3s;\n content: '';\n pointer-events: none;\n}\n.du-bois-light-table .du-bois-light-table-container::before {\n left: 0;\n}\n.du-bois-light-table .du-bois-light-table-container::after {\n right: 0;\n}\n.du-bois-light-table-ping-left:not(.du-bois-light-table-has-fix-left) .du-bois-light-table-container {\n position: relative;\n}\n.du-bois-light-table-ping-left:not(.du-bois-light-table-has-fix-left) .du-bois-light-table-container::before {\n box-shadow: inset 10px 0 8px -8px rgba(0, 0, 0, 0.15);\n}\n.du-bois-light-table-ping-left .du-bois-light-table-cell-fix-left-first::after,\n.du-bois-light-table-ping-left .du-bois-light-table-cell-fix-left-last::after {\n box-shadow: inset 10px 0 8px -8px rgba(0, 0, 0, 0.15);\n}\n.du-bois-light-table-ping-left .du-bois-light-table-cell-fix-left-last::before {\n background-color: transparent !important;\n}\n.du-bois-light-table-ping-right:not(.du-bois-light-table-has-fix-right) .du-bois-light-table-container {\n position: relative;\n}\n.du-bois-light-table-ping-right:not(.du-bois-light-table-has-fix-right) .du-bois-light-table-container::after {\n box-shadow: inset -10px 0 8px -8px rgba(0, 0, 0, 0.15);\n}\n.du-bois-light-table-ping-right .du-bois-light-table-cell-fix-right-first::after,\n.du-bois-light-table-ping-right .du-bois-light-table-cell-fix-right-last::after {\n box-shadow: inset -10px 0 8px -8px rgba(0, 0, 0, 0.15);\n}\n.du-bois-light-table-sticky-holder {\n position: sticky;\n z-index: 3;\n background: #fff;\n}\n.du-bois-light-table-sticky-scroll {\n position: sticky;\n bottom: 0;\n z-index: 3;\n display: flex;\n align-items: center;\n background: #ffffff;\n border-top: 1px solid #f0f0f0;\n opacity: 0.6;\n}\n.du-bois-light-table-sticky-scroll:hover {\n transform-origin: center bottom;\n}\n.du-bois-light-table-sticky-scroll-bar {\n height: 8px;\n background-color: rgba(0, 0, 0, 0.35);\n border-radius: 4px;\n}\n.du-bois-light-table-sticky-scroll-bar:hover {\n background-color: rgba(0, 0, 0, 0.8);\n}\n.du-bois-light-table-sticky-scroll-bar-active {\n background-color: rgba(0, 0, 0, 0.8);\n}\n@media all and (-ms-high-contrast: none) {\n .du-bois-light-table-ping-left .du-bois-light-table-cell-fix-left-last::after {\n box-shadow: none !important;\n }\n .du-bois-light-table-ping-right .du-bois-light-table-cell-fix-right-first::after {\n box-shadow: none !important;\n }\n}\n.du-bois-light-table {\n /* title + table */\n /* table */\n /* table + footer */\n}\n.du-bois-light-table-title {\n border-radius: 4px 4px 0 0;\n}\n.du-bois-light-table-title + .du-bois-light-table-container {\n border-top-left-radius: 0;\n border-top-right-radius: 0;\n}\n.du-bois-light-table-title + .du-bois-light-table-container table > thead > tr:first-child th:first-child {\n border-radius: 0;\n}\n.du-bois-light-table-title + .du-bois-light-table-container table > thead > tr:first-child th:last-child {\n border-radius: 0;\n}\n.du-bois-light-table-container {\n border-top-left-radius: 4px;\n border-top-right-radius: 4px;\n}\n.du-bois-light-table-container table > thead > tr:first-child th:first-child {\n border-top-left-radius: 4px;\n}\n.du-bois-light-table-container table > thead > tr:first-child th:last-child {\n border-top-right-radius: 4px;\n}\n.du-bois-light-table-footer {\n border-radius: 0 0 4px 4px;\n}\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-light-spin {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #11171C;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: 'tnum';\n position: absolute;\n display: none;\n color: #2272B4;\n text-align: center;\n vertical-align: middle;\n opacity: 0;\n transition: transform 0.3s cubic-bezier(0.78, 0.14, 0.15, 0.86);\n}\n.du-bois-light-spin-spinning {\n position: static;\n display: inline-block;\n opacity: 1;\n}\n.du-bois-light-spin-nested-loading {\n position: relative;\n}\n.du-bois-light-spin-nested-loading > div > .du-bois-light-spin {\n position: absolute;\n top: 0;\n left: 0;\n z-index: 4;\n display: block;\n width: 100%;\n height: 100%;\n max-height: 400px;\n}\n.du-bois-light-spin-nested-loading > div > .du-bois-light-spin .du-bois-light-spin-dot {\n position: absolute;\n top: 50%;\n left: 50%;\n margin: -10px;\n}\n.du-bois-light-spin-nested-loading > div > .du-bois-light-spin .du-bois-light-spin-text {\n position: absolute;\n top: 50%;\n width: 100%;\n padding-top: 5.5px;\n text-shadow: 0 1px 2px #fff;\n}\n.du-bois-light-spin-nested-loading > div > .du-bois-light-spin.du-bois-light-spin-show-text .du-bois-light-spin-dot {\n margin-top: -20px;\n}\n.du-bois-light-spin-nested-loading > div > .du-bois-light-spin-sm .du-bois-light-spin-dot {\n margin: -7px;\n}\n.du-bois-light-spin-nested-loading > div > .du-bois-light-spin-sm .du-bois-light-spin-text {\n padding-top: 2.5px;\n}\n.du-bois-light-spin-nested-loading > div > .du-bois-light-spin-sm.du-bois-light-spin-show-text .du-bois-light-spin-dot {\n margin-top: -17px;\n}\n.du-bois-light-spin-nested-loading > div > .du-bois-light-spin-lg .du-bois-light-spin-dot {\n margin: -16px;\n}\n.du-bois-light-spin-nested-loading > div > .du-bois-light-spin-lg .du-bois-light-spin-text {\n padding-top: 11.5px;\n}\n.du-bois-light-spin-nested-loading > div > .du-bois-light-spin-lg.du-bois-light-spin-show-text .du-bois-light-spin-dot {\n margin-top: -26px;\n}\n.du-bois-light-spin-container {\n position: relative;\n transition: opacity 0.3s;\n}\n.du-bois-light-spin-container::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n z-index: 10;\n display: none \\9;\n width: 100%;\n height: 100%;\n background: #fff;\n opacity: 0;\n transition: all 0.3s;\n content: '';\n pointer-events: none;\n}\n.du-bois-light-spin-blur {\n clear: both;\n overflow: hidden;\n opacity: 0.5;\n user-select: none;\n pointer-events: none;\n}\n.du-bois-light-spin-blur::after {\n opacity: 0.4;\n pointer-events: auto;\n}\n.du-bois-light-spin-tip {\n color: #5F7281;\n}\n.du-bois-light-spin-dot {\n position: relative;\n display: inline-block;\n font-size: 20px;\n width: 1em;\n height: 1em;\n}\n.du-bois-light-spin-dot-item {\n position: absolute;\n display: block;\n width: 9px;\n height: 9px;\n background-color: #2272B4;\n border-radius: 100%;\n transform: scale(0.75);\n transform-origin: 50% 50%;\n opacity: 0.3;\n animation: antSpinMove 1s infinite linear alternate;\n}\n.du-bois-light-spin-dot-item:nth-child(1) {\n top: 0;\n left: 0;\n}\n.du-bois-light-spin-dot-item:nth-child(2) {\n top: 0;\n right: 0;\n animation-delay: 0.4s;\n}\n.du-bois-light-spin-dot-item:nth-child(3) {\n right: 0;\n bottom: 0;\n animation-delay: 0.8s;\n}\n.du-bois-light-spin-dot-item:nth-child(4) {\n bottom: 0;\n left: 0;\n animation-delay: 1.2s;\n}\n.du-bois-light-spin-dot-spin {\n transform: rotate(45deg);\n animation: antRotate 1.2s infinite linear;\n}\n.du-bois-light-spin-sm .du-bois-light-spin-dot {\n font-size: 14px;\n}\n.du-bois-light-spin-sm .du-bois-light-spin-dot i {\n width: 6px;\n height: 6px;\n}\n.du-bois-light-spin-lg .du-bois-light-spin-dot {\n font-size: 32px;\n}\n.du-bois-light-spin-lg .du-bois-light-spin-dot i {\n width: 14px;\n height: 14px;\n}\n.du-bois-light-spin.du-bois-light-spin-show-text .du-bois-light-spin-text {\n display: block;\n}\n@media all and (-ms-high-contrast: none), (-ms-high-contrast: active) {\n /* IE10+ */\n .du-bois-light-spin-blur {\n background: #fff;\n opacity: 0.5;\n }\n}\n@keyframes antSpinMove {\n}\n@keyframes antRotate {\n}\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-light-pagination {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #11171C;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: 'tnum';\n}\n.du-bois-light-pagination ul,\n.du-bois-light-pagination ol {\n margin: 0;\n padding: 0;\n list-style: none;\n}\n.du-bois-light-pagination::after {\n display: block;\n clear: both;\n height: 0;\n overflow: hidden;\n visibility: hidden;\n content: ' ';\n}\n.du-bois-light-pagination-total-text {\n display: inline-block;\n height: 40px;\n margin-right: 8px;\n line-height: 38px;\n vertical-align: middle;\n}\n.du-bois-light-pagination-item {\n display: inline-block;\n min-width: 40px;\n height: 40px;\n margin-right: 8px;\n font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, 'Helvetica Neue', Arial, 'Noto Sans', sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol', 'Noto Color Emoji';\n line-height: 38px;\n text-align: center;\n vertical-align: middle;\n list-style: none;\n background-color: #fff;\n border: 1px solid #E8ECF0;\n border-radius: 4px;\n outline: 0;\n cursor: pointer;\n user-select: none;\n}\n.du-bois-light-pagination-item a {\n display: block;\n padding: 0 6px;\n color: #11171C;\n transition: none;\n}\n.du-bois-light-pagination-item a:hover {\n text-decoration: none;\n}\n.du-bois-light-pagination-item:focus-visible,\n.du-bois-light-pagination-item:hover {\n border-color: #2272B4;\n transition: all 0.3s;\n}\n.du-bois-light-pagination-item:focus-visible a,\n.du-bois-light-pagination-item:hover a {\n color: #2272B4;\n}\n.du-bois-light-pagination-item-active {\n font-weight: 500;\n background: #fff;\n border-color: #2272B4;\n}\n.du-bois-light-pagination-item-active a {\n color: #2272B4;\n}\n.du-bois-light-pagination-item-active:focus-visible,\n.du-bois-light-pagination-item-active:hover {\n border-color: #448dc2;\n}\n.du-bois-light-pagination-item-active:focus-visible a,\n.du-bois-light-pagination-item-active:hover a {\n color: #448dc2;\n}\n.du-bois-light-pagination-jump-prev,\n.du-bois-light-pagination-jump-next {\n outline: 0;\n}\n.du-bois-light-pagination-jump-prev .du-bois-light-pagination-item-container,\n.du-bois-light-pagination-jump-next .du-bois-light-pagination-item-container {\n position: relative;\n}\n.du-bois-light-pagination-jump-prev .du-bois-light-pagination-item-container .du-bois-light-pagination-item-link-icon,\n.du-bois-light-pagination-jump-next .du-bois-light-pagination-item-container .du-bois-light-pagination-item-link-icon {\n color: #2272B4;\n font-size: 12px;\n letter-spacing: -1px;\n opacity: 0;\n transition: all 0.2s;\n}\n.du-bois-light-pagination-jump-prev .du-bois-light-pagination-item-container .du-bois-light-pagination-item-link-icon-svg,\n.du-bois-light-pagination-jump-next .du-bois-light-pagination-item-container .du-bois-light-pagination-item-link-icon-svg {\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n margin: auto;\n}\n.du-bois-light-pagination-jump-prev .du-bois-light-pagination-item-container .du-bois-light-pagination-item-ellipsis,\n.du-bois-light-pagination-jump-next .du-bois-light-pagination-item-container .du-bois-light-pagination-item-ellipsis {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n display: block;\n margin: auto;\n color: rgba(0, 0, 0, 0.25);\n font-family: Arial, Helvetica, sans-serif;\n letter-spacing: 2px;\n text-align: center;\n text-indent: 0.13em;\n opacity: 1;\n transition: all 0.2s;\n}\n.du-bois-light-pagination-jump-prev:focus-visible .du-bois-light-pagination-item-link-icon,\n.du-bois-light-pagination-jump-next:focus-visible .du-bois-light-pagination-item-link-icon,\n.du-bois-light-pagination-jump-prev:hover .du-bois-light-pagination-item-link-icon,\n.du-bois-light-pagination-jump-next:hover .du-bois-light-pagination-item-link-icon {\n opacity: 1;\n}\n.du-bois-light-pagination-jump-prev:focus-visible .du-bois-light-pagination-item-ellipsis,\n.du-bois-light-pagination-jump-next:focus-visible .du-bois-light-pagination-item-ellipsis,\n.du-bois-light-pagination-jump-prev:hover .du-bois-light-pagination-item-ellipsis,\n.du-bois-light-pagination-jump-next:hover .du-bois-light-pagination-item-ellipsis {\n opacity: 0;\n}\n.du-bois-light-pagination-prev,\n.du-bois-light-pagination-jump-prev,\n.du-bois-light-pagination-jump-next {\n margin-right: 8px;\n}\n.du-bois-light-pagination-prev,\n.du-bois-light-pagination-next,\n.du-bois-light-pagination-jump-prev,\n.du-bois-light-pagination-jump-next {\n display: inline-block;\n min-width: 40px;\n height: 40px;\n color: #11171C;\n font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, 'Helvetica Neue', Arial, 'Noto Sans', sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol', 'Noto Color Emoji';\n line-height: 40px;\n text-align: center;\n vertical-align: middle;\n list-style: none;\n border-radius: 4px;\n cursor: pointer;\n transition: all 0.3s;\n}\n.du-bois-light-pagination-prev,\n.du-bois-light-pagination-next {\n font-family: Arial, Helvetica, sans-serif;\n outline: 0;\n}\n.du-bois-light-pagination-prev button,\n.du-bois-light-pagination-next button {\n color: #11171C;\n cursor: pointer;\n user-select: none;\n}\n.du-bois-light-pagination-prev:hover button,\n.du-bois-light-pagination-next:hover button {\n border-color: #448dc2;\n}\n.du-bois-light-pagination-prev .du-bois-light-pagination-item-link,\n.du-bois-light-pagination-next .du-bois-light-pagination-item-link {\n display: block;\n width: 100%;\n height: 100%;\n padding: 0;\n font-size: 12px;\n text-align: center;\n background-color: #fff;\n border: 1px solid #E8ECF0;\n border-radius: 4px;\n outline: none;\n transition: all 0.3s;\n}\n.du-bois-light-pagination-prev:focus-visible .du-bois-light-pagination-item-link,\n.du-bois-light-pagination-next:focus-visible .du-bois-light-pagination-item-link,\n.du-bois-light-pagination-prev:hover .du-bois-light-pagination-item-link,\n.du-bois-light-pagination-next:hover .du-bois-light-pagination-item-link {\n color: #2272B4;\n border-color: #2272B4;\n}\n.du-bois-light-pagination-disabled,\n.du-bois-light-pagination-disabled:hover,\n.du-bois-light-pagination-disabled:focus-visible {\n cursor: not-allowed;\n}\n.du-bois-light-pagination-disabled .du-bois-light-pagination-item-link,\n.du-bois-light-pagination-disabled:hover .du-bois-light-pagination-item-link,\n.du-bois-light-pagination-disabled:focus-visible .du-bois-light-pagination-item-link {\n color: rgba(0, 0, 0, 0.25);\n border-color: #E8ECF0;\n cursor: not-allowed;\n}\n.du-bois-light-pagination-slash {\n margin: 0 10px 0 5px;\n}\n.du-bois-light-pagination-options {\n display: inline-block;\n margin-left: 16px;\n vertical-align: middle;\n}\n@media all and (-ms-high-contrast: none) {\n .du-bois-light-pagination-options *::-ms-backdrop,\n .du-bois-light-pagination-options {\n vertical-align: top;\n }\n}\n.du-bois-light-pagination-options-size-changer.du-bois-light-select {\n display: inline-block;\n width: auto;\n}\n.du-bois-light-pagination-options-quick-jumper {\n display: inline-block;\n height: 40px;\n margin-left: 8px;\n line-height: 40px;\n vertical-align: top;\n}\n.du-bois-light-pagination-options-quick-jumper input {\n position: relative;\n display: inline-block;\n width: 100%;\n min-width: 0;\n padding: 5px 12px;\n color: #11171C;\n font-size: 13px;\n line-height: 20px;\n background-color: #fff;\n background-image: none;\n border: 1px solid #E8ECF0;\n border-radius: 4px;\n transition: all 0.3s;\n width: 50px;\n height: 40px;\n margin: 0 8px;\n}\n.du-bois-light-pagination-options-quick-jumper input::-moz-placeholder {\n opacity: 1;\n}\n.du-bois-light-pagination-options-quick-jumper input::placeholder {\n color: #bfbfbf;\n}\n.du-bois-light-pagination-options-quick-jumper input:placeholder-shown {\n text-overflow: ellipsis;\n}\n.du-bois-light-pagination-options-quick-jumper input:hover {\n border-color: #448dc2;\n border-right-width: 1px !important;\n}\n.du-bois-light-pagination-options-quick-jumper input:focus,\n.du-bois-light-pagination-options-quick-jumper input-focused {\n border-color: #448dc2;\n border-right-width: 1px !important;\n outline: 0;\n box-shadow: 0 0 0 2px rgba(34, 114, 180, 0.2);\n}\n.du-bois-light-pagination-options-quick-jumper input-disabled {\n color: rgba(0, 0, 0, 0.25);\n background-color: #f5f5f5;\n cursor: not-allowed;\n opacity: 1;\n}\n.du-bois-light-pagination-options-quick-jumper input-disabled:hover {\n border-color: #E8ECF0;\n border-right-width: 1px !important;\n}\n.du-bois-light-pagination-options-quick-jumper input[disabled] {\n color: rgba(0, 0, 0, 0.25);\n background-color: #f5f5f5;\n cursor: not-allowed;\n opacity: 1;\n}\n.du-bois-light-pagination-options-quick-jumper input[disabled]:hover {\n border-color: #E8ECF0;\n border-right-width: 1px !important;\n}\n.du-bois-light-pagination-options-quick-jumper input-borderless,\n.du-bois-light-pagination-options-quick-jumper input-borderless:hover,\n.du-bois-light-pagination-options-quick-jumper input-borderless:focus,\n.du-bois-light-pagination-options-quick-jumper input-borderless-focused,\n.du-bois-light-pagination-options-quick-jumper input-borderless-disabled,\n.du-bois-light-pagination-options-quick-jumper input-borderless[disabled] {\n background-color: transparent;\n border: none;\n box-shadow: none;\n}\ntextarea.du-bois-light-pagination-options-quick-jumper input {\n max-width: 100%;\n height: auto;\n min-height: 40px;\n line-height: 20px;\n vertical-align: bottom;\n transition: all 0.3s, height 0s;\n}\n.du-bois-light-pagination-options-quick-jumper input-lg {\n padding: 5px 12px;\n font-size: 18px;\n}\n.du-bois-light-pagination-options-quick-jumper input-sm {\n padding: 5px 12px;\n}\n.du-bois-light-pagination-simple .du-bois-light-pagination-prev,\n.du-bois-light-pagination-simple .du-bois-light-pagination-next {\n height: 24px;\n line-height: 24px;\n vertical-align: top;\n}\n.du-bois-light-pagination-simple .du-bois-light-pagination-prev .du-bois-light-pagination-item-link,\n.du-bois-light-pagination-simple .du-bois-light-pagination-next .du-bois-light-pagination-item-link {\n height: 24px;\n background-color: transparent;\n border: 0;\n}\n.du-bois-light-pagination-simple .du-bois-light-pagination-prev .du-bois-light-pagination-item-link::after,\n.du-bois-light-pagination-simple .du-bois-light-pagination-next .du-bois-light-pagination-item-link::after {\n height: 24px;\n line-height: 24px;\n}\n.du-bois-light-pagination-simple .du-bois-light-pagination-simple-pager {\n display: inline-block;\n height: 24px;\n margin-right: 8px;\n}\n.du-bois-light-pagination-simple .du-bois-light-pagination-simple-pager input {\n box-sizing: border-box;\n height: 100%;\n margin-right: 8px;\n padding: 0 6px;\n text-align: center;\n background-color: #fff;\n border: 1px solid #E8ECF0;\n border-radius: 4px;\n outline: none;\n transition: border-color 0.3s;\n}\n.du-bois-light-pagination-simple .du-bois-light-pagination-simple-pager input:hover {\n border-color: #2272B4;\n}\n.du-bois-light-pagination-simple .du-bois-light-pagination-simple-pager input[disabled] {\n color: rgba(0, 0, 0, 0.25);\n background: #f5f5f5;\n border-color: #E8ECF0;\n cursor: not-allowed;\n}\n.du-bois-light-pagination.mini .du-bois-light-pagination-total-text,\n.du-bois-light-pagination.mini .du-bois-light-pagination-simple-pager {\n height: 24px;\n line-height: 24px;\n}\n.du-bois-light-pagination.mini .du-bois-light-pagination-item {\n min-width: 24px;\n height: 24px;\n margin: 0;\n line-height: 22px;\n}\n.du-bois-light-pagination.mini .du-bois-light-pagination-item:not(.du-bois-light-pagination-item-active) {\n background: transparent;\n border-color: transparent;\n}\n.du-bois-light-pagination.mini .du-bois-light-pagination-prev,\n.du-bois-light-pagination.mini .du-bois-light-pagination-next {\n min-width: 24px;\n height: 24px;\n margin: 0;\n line-height: 24px;\n}\n.du-bois-light-pagination.mini .du-bois-light-pagination-prev .du-bois-light-pagination-item-link,\n.du-bois-light-pagination.mini .du-bois-light-pagination-next .du-bois-light-pagination-item-link {\n background: transparent;\n border-color: transparent;\n}\n.du-bois-light-pagination.mini .du-bois-light-pagination-prev .du-bois-light-pagination-item-link::after,\n.du-bois-light-pagination.mini .du-bois-light-pagination-next .du-bois-light-pagination-item-link::after {\n height: 24px;\n line-height: 24px;\n}\n.du-bois-light-pagination.mini .du-bois-light-pagination-jump-prev,\n.du-bois-light-pagination.mini .du-bois-light-pagination-jump-next {\n height: 24px;\n margin-right: 0;\n line-height: 24px;\n}\n.du-bois-light-pagination.mini .du-bois-light-pagination-options {\n margin-left: 2px;\n}\n.du-bois-light-pagination.mini .du-bois-light-pagination-options-size-changer {\n top: 0px;\n}\n.du-bois-light-pagination.mini .du-bois-light-pagination-options-quick-jumper {\n height: 24px;\n line-height: 24px;\n}\n.du-bois-light-pagination.mini .du-bois-light-pagination-options-quick-jumper input {\n padding: 5px 12px;\n width: 44px;\n height: 32px;\n}\n.du-bois-light-pagination.du-bois-light-pagination-disabled {\n cursor: not-allowed;\n}\n.du-bois-light-pagination.du-bois-light-pagination-disabled .du-bois-light-pagination-item {\n background: #f5f5f5;\n border-color: #E8ECF0;\n cursor: not-allowed;\n}\n.du-bois-light-pagination.du-bois-light-pagination-disabled .du-bois-light-pagination-item a {\n color: rgba(0, 0, 0, 0.25);\n background: transparent;\n border: none;\n cursor: not-allowed;\n}\n.du-bois-light-pagination.du-bois-light-pagination-disabled .du-bois-light-pagination-item-active {\n background: #e6e6e6;\n}\n.du-bois-light-pagination.du-bois-light-pagination-disabled .du-bois-light-pagination-item-active a {\n color: rgba(0, 0, 0, 0.25);\n}\n.du-bois-light-pagination.du-bois-light-pagination-disabled .du-bois-light-pagination-item-link {\n color: rgba(0, 0, 0, 0.25);\n background: #f5f5f5;\n border-color: #E8ECF0;\n cursor: not-allowed;\n}\n.du-bois-light-pagination-simple.du-bois-light-pagination.du-bois-light-pagination-disabled .du-bois-light-pagination-item-link {\n background: transparent;\n}\n.du-bois-light-pagination.du-bois-light-pagination-disabled .du-bois-light-pagination-item-link-icon {\n opacity: 0;\n}\n.du-bois-light-pagination.du-bois-light-pagination-disabled .du-bois-light-pagination-item-ellipsis {\n opacity: 1;\n}\n.du-bois-light-pagination.du-bois-light-pagination-disabled .du-bois-light-pagination-simple-pager {\n color: rgba(0, 0, 0, 0.25);\n}\n@media only screen and (max-width: 992px) {\n .du-bois-light-pagination-item-after-jump-prev,\n .du-bois-light-pagination-item-before-jump-next {\n display: none;\n }\n}\n@media only screen and (max-width: 576px) {\n .du-bois-light-pagination-options {\n display: none;\n }\n}\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-light-tabs-small > .du-bois-light-tabs-nav .du-bois-light-tabs-tab {\n padding: 4px 0;\n font-size: 13px;\n}\n.du-bois-light-tabs-large > .du-bois-light-tabs-nav .du-bois-light-tabs-tab {\n padding: 8px 0;\n font-size: 18px;\n}\n.du-bois-light-tabs-card.du-bois-light-tabs-small > .du-bois-light-tabs-nav .du-bois-light-tabs-tab {\n padding: 6px 8px;\n}\n.du-bois-light-tabs-card.du-bois-light-tabs-large > .du-bois-light-tabs-nav .du-bois-light-tabs-tab {\n padding: 7px 8px 6px;\n}\n.du-bois-light-tabs-top,\n.du-bois-light-tabs-bottom {\n flex-direction: column;\n}\n.du-bois-light-tabs-top > .du-bois-light-tabs-nav,\n.du-bois-light-tabs-bottom > .du-bois-light-tabs-nav,\n.du-bois-light-tabs-top > div > .du-bois-light-tabs-nav,\n.du-bois-light-tabs-bottom > div > .du-bois-light-tabs-nav {\n margin: 0 0 16px 0;\n}\n.du-bois-light-tabs-top > .du-bois-light-tabs-nav::before,\n.du-bois-light-tabs-bottom > .du-bois-light-tabs-nav::before,\n.du-bois-light-tabs-top > div > .du-bois-light-tabs-nav::before,\n.du-bois-light-tabs-bottom > div > .du-bois-light-tabs-nav::before {\n position: absolute;\n right: 0;\n left: 0;\n border-bottom: 1px solid #f0f0f0;\n content: '';\n}\n.du-bois-light-tabs-top > .du-bois-light-tabs-nav .du-bois-light-tabs-ink-bar,\n.du-bois-light-tabs-bottom > .du-bois-light-tabs-nav .du-bois-light-tabs-ink-bar,\n.du-bois-light-tabs-top > div > .du-bois-light-tabs-nav .du-bois-light-tabs-ink-bar,\n.du-bois-light-tabs-bottom > div > .du-bois-light-tabs-nav .du-bois-light-tabs-ink-bar {\n height: 2px;\n}\n.du-bois-light-tabs-top > .du-bois-light-tabs-nav .du-bois-light-tabs-ink-bar-animated,\n.du-bois-light-tabs-bottom > .du-bois-light-tabs-nav .du-bois-light-tabs-ink-bar-animated,\n.du-bois-light-tabs-top > div > .du-bois-light-tabs-nav .du-bois-light-tabs-ink-bar-animated,\n.du-bois-light-tabs-bottom > div > .du-bois-light-tabs-nav .du-bois-light-tabs-ink-bar-animated {\n transition: width 0.3s, left 0.3s, right 0.3s;\n}\n.du-bois-light-tabs-top > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap::before,\n.du-bois-light-tabs-bottom > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap::before,\n.du-bois-light-tabs-top > div > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap::before,\n.du-bois-light-tabs-bottom > div > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap::before,\n.du-bois-light-tabs-top > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap::after,\n.du-bois-light-tabs-bottom > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap::after,\n.du-bois-light-tabs-top > div > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap::after,\n.du-bois-light-tabs-bottom > div > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap::after {\n top: 0;\n bottom: 0;\n width: 30px;\n}\n.du-bois-light-tabs-top > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap::before,\n.du-bois-light-tabs-bottom > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap::before,\n.du-bois-light-tabs-top > div > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap::before,\n.du-bois-light-tabs-bottom > div > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap::before {\n left: 0;\n box-shadow: inset 10px 0 8px -8px rgba(0, 0, 0, 0.08);\n}\n.du-bois-light-tabs-top > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap::after,\n.du-bois-light-tabs-bottom > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap::after,\n.du-bois-light-tabs-top > div > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap::after,\n.du-bois-light-tabs-bottom > div > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap::after {\n right: 0;\n box-shadow: inset -10px 0 8px -8px rgba(0, 0, 0, 0.08);\n}\n.du-bois-light-tabs-top > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap.du-bois-light-tabs-nav-wrap-ping-left::before,\n.du-bois-light-tabs-bottom > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap.du-bois-light-tabs-nav-wrap-ping-left::before,\n.du-bois-light-tabs-top > div > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap.du-bois-light-tabs-nav-wrap-ping-left::before,\n.du-bois-light-tabs-bottom > div > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap.du-bois-light-tabs-nav-wrap-ping-left::before {\n opacity: 1;\n}\n.du-bois-light-tabs-top > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap.du-bois-light-tabs-nav-wrap-ping-right::after,\n.du-bois-light-tabs-bottom > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap.du-bois-light-tabs-nav-wrap-ping-right::after,\n.du-bois-light-tabs-top > div > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap.du-bois-light-tabs-nav-wrap-ping-right::after,\n.du-bois-light-tabs-bottom > div > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap.du-bois-light-tabs-nav-wrap-ping-right::after {\n opacity: 1;\n}\n.du-bois-light-tabs-top > .du-bois-light-tabs-nav::before,\n.du-bois-light-tabs-top > div > .du-bois-light-tabs-nav::before {\n bottom: 0;\n}\n.du-bois-light-tabs-top > .du-bois-light-tabs-nav .du-bois-light-tabs-ink-bar,\n.du-bois-light-tabs-top > div > .du-bois-light-tabs-nav .du-bois-light-tabs-ink-bar {\n bottom: 0;\n}\n.du-bois-light-tabs-bottom > .du-bois-light-tabs-nav,\n.du-bois-light-tabs-bottom > div > .du-bois-light-tabs-nav {\n order: 1;\n margin-top: 16px;\n margin-bottom: 0;\n}\n.du-bois-light-tabs-bottom > .du-bois-light-tabs-nav::before,\n.du-bois-light-tabs-bottom > div > .du-bois-light-tabs-nav::before {\n top: 0;\n}\n.du-bois-light-tabs-bottom > .du-bois-light-tabs-nav .du-bois-light-tabs-ink-bar,\n.du-bois-light-tabs-bottom > div > .du-bois-light-tabs-nav .du-bois-light-tabs-ink-bar {\n top: 0;\n}\n.du-bois-light-tabs-bottom > .du-bois-light-tabs-content-holder,\n.du-bois-light-tabs-bottom > div > .du-bois-light-tabs-content-holder {\n order: 0;\n}\n.du-bois-light-tabs-left > .du-bois-light-tabs-nav,\n.du-bois-light-tabs-right > .du-bois-light-tabs-nav,\n.du-bois-light-tabs-left > div > .du-bois-light-tabs-nav,\n.du-bois-light-tabs-right > div > .du-bois-light-tabs-nav {\n flex-direction: column;\n min-width: 50px;\n}\n.du-bois-light-tabs-left > .du-bois-light-tabs-nav .du-bois-light-tabs-tab,\n.du-bois-light-tabs-right > .du-bois-light-tabs-nav .du-bois-light-tabs-tab,\n.du-bois-light-tabs-left > div > .du-bois-light-tabs-nav .du-bois-light-tabs-tab,\n.du-bois-light-tabs-right > div > .du-bois-light-tabs-nav .du-bois-light-tabs-tab {\n padding: 4px 16px;\n text-align: center;\n}\n.du-bois-light-tabs-left > .du-bois-light-tabs-nav .du-bois-light-tabs-tab + .du-bois-light-tabs-tab,\n.du-bois-light-tabs-right > .du-bois-light-tabs-nav .du-bois-light-tabs-tab + .du-bois-light-tabs-tab,\n.du-bois-light-tabs-left > div > .du-bois-light-tabs-nav .du-bois-light-tabs-tab + .du-bois-light-tabs-tab,\n.du-bois-light-tabs-right > div > .du-bois-light-tabs-nav .du-bois-light-tabs-tab + .du-bois-light-tabs-tab {\n margin: 16px 0 0 0;\n}\n.du-bois-light-tabs-left > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap,\n.du-bois-light-tabs-right > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap,\n.du-bois-light-tabs-left > div > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap,\n.du-bois-light-tabs-right > div > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap {\n flex-direction: column;\n}\n.du-bois-light-tabs-left > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap::before,\n.du-bois-light-tabs-right > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap::before,\n.du-bois-light-tabs-left > div > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap::before,\n.du-bois-light-tabs-right > div > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap::before,\n.du-bois-light-tabs-left > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap::after,\n.du-bois-light-tabs-right > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap::after,\n.du-bois-light-tabs-left > div > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap::after,\n.du-bois-light-tabs-right > div > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap::after {\n right: 0;\n left: 0;\n height: 30px;\n}\n.du-bois-light-tabs-left > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap::before,\n.du-bois-light-tabs-right > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap::before,\n.du-bois-light-tabs-left > div > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap::before,\n.du-bois-light-tabs-right > div > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap::before {\n top: 0;\n box-shadow: inset 0 10px 8px -8px rgba(0, 0, 0, 0.08);\n}\n.du-bois-light-tabs-left > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap::after,\n.du-bois-light-tabs-right > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap::after,\n.du-bois-light-tabs-left > div > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap::after,\n.du-bois-light-tabs-right > div > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap::after {\n bottom: 0;\n box-shadow: inset 0 -10px 8px -8px rgba(0, 0, 0, 0.08);\n}\n.du-bois-light-tabs-left > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap.du-bois-light-tabs-nav-wrap-ping-top::before,\n.du-bois-light-tabs-right > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap.du-bois-light-tabs-nav-wrap-ping-top::before,\n.du-bois-light-tabs-left > div > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap.du-bois-light-tabs-nav-wrap-ping-top::before,\n.du-bois-light-tabs-right > div > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap.du-bois-light-tabs-nav-wrap-ping-top::before {\n opacity: 1;\n}\n.du-bois-light-tabs-left > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap.du-bois-light-tabs-nav-wrap-ping-bottom::after,\n.du-bois-light-tabs-right > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap.du-bois-light-tabs-nav-wrap-ping-bottom::after,\n.du-bois-light-tabs-left > div > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap.du-bois-light-tabs-nav-wrap-ping-bottom::after,\n.du-bois-light-tabs-right > div > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap.du-bois-light-tabs-nav-wrap-ping-bottom::after {\n opacity: 1;\n}\n.du-bois-light-tabs-left > .du-bois-light-tabs-nav .du-bois-light-tabs-ink-bar,\n.du-bois-light-tabs-right > .du-bois-light-tabs-nav .du-bois-light-tabs-ink-bar,\n.du-bois-light-tabs-left > div > .du-bois-light-tabs-nav .du-bois-light-tabs-ink-bar,\n.du-bois-light-tabs-right > div > .du-bois-light-tabs-nav .du-bois-light-tabs-ink-bar {\n width: 2px;\n}\n.du-bois-light-tabs-left > .du-bois-light-tabs-nav .du-bois-light-tabs-ink-bar-animated,\n.du-bois-light-tabs-right > .du-bois-light-tabs-nav .du-bois-light-tabs-ink-bar-animated,\n.du-bois-light-tabs-left > div > .du-bois-light-tabs-nav .du-bois-light-tabs-ink-bar-animated,\n.du-bois-light-tabs-right > div > .du-bois-light-tabs-nav .du-bois-light-tabs-ink-bar-animated {\n transition: height 0.3s, top 0.3s;\n}\n.du-bois-light-tabs-left > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-list,\n.du-bois-light-tabs-right > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-list,\n.du-bois-light-tabs-left > div > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-list,\n.du-bois-light-tabs-right > div > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-list,\n.du-bois-light-tabs-left > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-operations,\n.du-bois-light-tabs-right > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-operations,\n.du-bois-light-tabs-left > div > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-operations,\n.du-bois-light-tabs-right > div > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-operations {\n flex: 1 0 auto;\n flex-direction: column;\n}\n.du-bois-light-tabs-left > .du-bois-light-tabs-nav .du-bois-light-tabs-ink-bar,\n.du-bois-light-tabs-left > div > .du-bois-light-tabs-nav .du-bois-light-tabs-ink-bar {\n right: 0;\n}\n.du-bois-light-tabs-left > .du-bois-light-tabs-content-holder,\n.du-bois-light-tabs-left > div > .du-bois-light-tabs-content-holder {\n margin-left: -1px;\n border-left: 1px solid #f0f0f0;\n}\n.du-bois-light-tabs-left > .du-bois-light-tabs-content-holder > .du-bois-light-tabs-content > .du-bois-light-tabs-tabpane,\n.du-bois-light-tabs-left > div > .du-bois-light-tabs-content-holder > .du-bois-light-tabs-content > .du-bois-light-tabs-tabpane {\n padding-left: 16px;\n}\n.du-bois-light-tabs-right > .du-bois-light-tabs-nav,\n.du-bois-light-tabs-right > div > .du-bois-light-tabs-nav {\n order: 1;\n}\n.du-bois-light-tabs-right > .du-bois-light-tabs-nav .du-bois-light-tabs-ink-bar,\n.du-bois-light-tabs-right > div > .du-bois-light-tabs-nav .du-bois-light-tabs-ink-bar {\n left: 0;\n}\n.du-bois-light-tabs-right > .du-bois-light-tabs-content-holder,\n.du-bois-light-tabs-right > div > .du-bois-light-tabs-content-holder {\n order: 0;\n margin-right: -1px;\n border-right: 1px solid #f0f0f0;\n}\n.du-bois-light-tabs-right > .du-bois-light-tabs-content-holder > .du-bois-light-tabs-content > .du-bois-light-tabs-tabpane,\n.du-bois-light-tabs-right > div > .du-bois-light-tabs-content-holder > .du-bois-light-tabs-content > .du-bois-light-tabs-tabpane {\n padding-right: 16px;\n}\n.du-bois-light-tabs-dropdown {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #11171C;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: 'tnum';\n position: absolute;\n top: -9999px;\n left: -9999px;\n z-index: 1050;\n display: block;\n}\n.du-bois-light-tabs-dropdown-hidden {\n display: none;\n}\n.du-bois-light-tabs-dropdown-menu {\n max-height: 200px;\n margin: 0;\n padding: 4px 0;\n overflow-x: hidden;\n overflow-y: auto;\n text-align: left;\n list-style-type: none;\n background-color: #fff;\n background-clip: padding-box;\n border-radius: 4px;\n outline: none;\n box-shadow: 0 3px 6px -4px rgba(0, 0, 0, 0.12), 0 6px 16px 0 rgba(0, 0, 0, 0.08), 0 9px 28px 8px rgba(0, 0, 0, 0.05);\n}\n.du-bois-light-tabs-dropdown-menu-item {\n min-width: 120px;\n margin: 0;\n padding: 5px 8px;\n overflow: hidden;\n color: #11171C;\n font-weight: normal;\n font-size: 13px;\n line-height: 22px;\n white-space: nowrap;\n text-overflow: ellipsis;\n cursor: pointer;\n transition: all 0.3s;\n}\n.du-bois-light-tabs-dropdown-menu-item:hover {\n background: #f5f5f5;\n}\n.du-bois-light-tabs-dropdown-menu-item-disabled,\n.du-bois-light-tabs-dropdown-menu-item-disabled:hover {\n color: rgba(0, 0, 0, 0.25);\n background: transparent;\n cursor: not-allowed;\n}\n.du-bois-light-tabs-card > .du-bois-light-tabs-nav .du-bois-light-tabs-tab,\n.du-bois-light-tabs-card > div > .du-bois-light-tabs-nav .du-bois-light-tabs-tab {\n margin: 0;\n padding: -111px 8px;\n background: #fafafa;\n border: 1px solid #f0f0f0;\n transition: all 0.3s cubic-bezier(0.645, 0.045, 0.355, 1);\n}\n.du-bois-light-tabs-card > .du-bois-light-tabs-nav .du-bois-light-tabs-tab-active,\n.du-bois-light-tabs-card > div > .du-bois-light-tabs-nav .du-bois-light-tabs-tab-active {\n color: #2272B4;\n background: #fff;\n}\n.du-bois-light-tabs-card > .du-bois-light-tabs-nav .du-bois-light-tabs-ink-bar,\n.du-bois-light-tabs-card > div > .du-bois-light-tabs-nav .du-bois-light-tabs-ink-bar {\n visibility: hidden;\n}\n.du-bois-light-tabs-card.du-bois-light-tabs-top > .du-bois-light-tabs-nav .du-bois-light-tabs-tab + .du-bois-light-tabs-tab,\n.du-bois-light-tabs-card.du-bois-light-tabs-bottom > .du-bois-light-tabs-nav .du-bois-light-tabs-tab + .du-bois-light-tabs-tab,\n.du-bois-light-tabs-card.du-bois-light-tabs-top > div > .du-bois-light-tabs-nav .du-bois-light-tabs-tab + .du-bois-light-tabs-tab,\n.du-bois-light-tabs-card.du-bois-light-tabs-bottom > div > .du-bois-light-tabs-nav .du-bois-light-tabs-tab + .du-bois-light-tabs-tab {\n margin-left: 2px;\n}\n.du-bois-light-tabs-card.du-bois-light-tabs-top > .du-bois-light-tabs-nav .du-bois-light-tabs-tab,\n.du-bois-light-tabs-card.du-bois-light-tabs-top > div > .du-bois-light-tabs-nav .du-bois-light-tabs-tab {\n border-radius: 4px 4px 0 0;\n}\n.du-bois-light-tabs-card.du-bois-light-tabs-top > .du-bois-light-tabs-nav .du-bois-light-tabs-tab-active,\n.du-bois-light-tabs-card.du-bois-light-tabs-top > div > .du-bois-light-tabs-nav .du-bois-light-tabs-tab-active {\n border-bottom-color: #fff;\n}\n.du-bois-light-tabs-card.du-bois-light-tabs-bottom > .du-bois-light-tabs-nav .du-bois-light-tabs-tab,\n.du-bois-light-tabs-card.du-bois-light-tabs-bottom > div > .du-bois-light-tabs-nav .du-bois-light-tabs-tab {\n border-radius: 0 0 4px 4px;\n}\n.du-bois-light-tabs-card.du-bois-light-tabs-bottom > .du-bois-light-tabs-nav .du-bois-light-tabs-tab-active,\n.du-bois-light-tabs-card.du-bois-light-tabs-bottom > div > .du-bois-light-tabs-nav .du-bois-light-tabs-tab-active {\n border-top-color: #fff;\n}\n.du-bois-light-tabs-card.du-bois-light-tabs-left > .du-bois-light-tabs-nav .du-bois-light-tabs-tab + .du-bois-light-tabs-tab,\n.du-bois-light-tabs-card.du-bois-light-tabs-right > .du-bois-light-tabs-nav .du-bois-light-tabs-tab + .du-bois-light-tabs-tab,\n.du-bois-light-tabs-card.du-bois-light-tabs-left > div > .du-bois-light-tabs-nav .du-bois-light-tabs-tab + .du-bois-light-tabs-tab,\n.du-bois-light-tabs-card.du-bois-light-tabs-right > div > .du-bois-light-tabs-nav .du-bois-light-tabs-tab + .du-bois-light-tabs-tab {\n margin-top: 2px;\n}\n.du-bois-light-tabs-card.du-bois-light-tabs-left > .du-bois-light-tabs-nav .du-bois-light-tabs-tab,\n.du-bois-light-tabs-card.du-bois-light-tabs-left > div > .du-bois-light-tabs-nav .du-bois-light-tabs-tab {\n border-radius: 4px 0 0 4px;\n}\n.du-bois-light-tabs-card.du-bois-light-tabs-left > .du-bois-light-tabs-nav .du-bois-light-tabs-tab-active,\n.du-bois-light-tabs-card.du-bois-light-tabs-left > div > .du-bois-light-tabs-nav .du-bois-light-tabs-tab-active {\n border-right-color: #fff;\n}\n.du-bois-light-tabs-card.du-bois-light-tabs-right > .du-bois-light-tabs-nav .du-bois-light-tabs-tab,\n.du-bois-light-tabs-card.du-bois-light-tabs-right > div > .du-bois-light-tabs-nav .du-bois-light-tabs-tab {\n border-radius: 0 4px 4px 0;\n}\n.du-bois-light-tabs-card.du-bois-light-tabs-right > .du-bois-light-tabs-nav .du-bois-light-tabs-tab-active,\n.du-bois-light-tabs-card.du-bois-light-tabs-right > div > .du-bois-light-tabs-nav .du-bois-light-tabs-tab-active {\n border-left-color: #fff;\n}\n.du-bois-light-tabs {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #11171C;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: 'tnum';\n display: flex;\n overflow: hidden;\n}\n.du-bois-light-tabs > .du-bois-light-tabs-nav,\n.du-bois-light-tabs > div > .du-bois-light-tabs-nav {\n position: relative;\n display: flex;\n flex: none;\n align-items: center;\n}\n.du-bois-light-tabs > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap,\n.du-bois-light-tabs > div > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap {\n position: relative;\n display: inline-block;\n display: flex;\n flex: auto;\n align-self: stretch;\n overflow: hidden;\n white-space: nowrap;\n transform: translate(0);\n}\n.du-bois-light-tabs > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap::before,\n.du-bois-light-tabs > div > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap::before,\n.du-bois-light-tabs > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap::after,\n.du-bois-light-tabs > div > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap::after {\n position: absolute;\n z-index: 1;\n opacity: 0;\n transition: opacity 0.3s;\n content: '';\n pointer-events: none;\n}\n.du-bois-light-tabs > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-list,\n.du-bois-light-tabs > div > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-list {\n position: relative;\n display: flex;\n transition: transform 0.3s;\n}\n.du-bois-light-tabs > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-operations,\n.du-bois-light-tabs > div > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-operations {\n display: flex;\n align-self: stretch;\n}\n.du-bois-light-tabs > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-operations-hidden,\n.du-bois-light-tabs > div > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-operations-hidden {\n position: absolute;\n visibility: hidden;\n pointer-events: none;\n}\n.du-bois-light-tabs > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-more,\n.du-bois-light-tabs > div > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-more {\n position: relative;\n padding: -111px 8px;\n background: transparent;\n border: 0;\n}\n.du-bois-light-tabs > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-more::after,\n.du-bois-light-tabs > div > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-more::after {\n position: absolute;\n right: 0;\n bottom: 0;\n left: 0;\n height: 5px;\n transform: translateY(100%);\n content: '';\n}\n.du-bois-light-tabs > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-add,\n.du-bois-light-tabs > div > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-add {\n min-width: 40px;\n padding: 0 4px;\n background: #fafafa;\n border: 1px solid #f0f0f0;\n border-radius: 4px 4px 0 0;\n outline: none;\n cursor: pointer;\n transition: all 0.3s cubic-bezier(0.645, 0.045, 0.355, 1);\n}\n.du-bois-light-tabs > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-add:hover,\n.du-bois-light-tabs > div > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-add:hover {\n color: #448dc2;\n}\n.du-bois-light-tabs > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-add:active,\n.du-bois-light-tabs > div > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-add:active,\n.du-bois-light-tabs > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-add:focus,\n.du-bois-light-tabs > div > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-add:focus {\n color: #14538f;\n}\n.du-bois-light-tabs-extra-content {\n flex: none;\n}\n.du-bois-light-tabs-centered > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap:not([class*='du-bois-light-tabs-nav-wrap-ping']),\n.du-bois-light-tabs-centered > div > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap:not([class*='du-bois-light-tabs-nav-wrap-ping']) {\n justify-content: center;\n}\n.du-bois-light-tabs-ink-bar {\n position: absolute;\n background: #2272B4;\n pointer-events: none;\n}\n.du-bois-light-tabs-tab {\n position: relative;\n display: inline-flex;\n align-items: center;\n padding: 8px 0;\n font-size: 13px;\n background: transparent;\n border: 0;\n outline: none;\n cursor: pointer;\n}\n.du-bois-light-tabs-tab-btn:focus,\n.du-bois-light-tabs-tab-remove:focus,\n.du-bois-light-tabs-tab-btn:active,\n.du-bois-light-tabs-tab-remove:active {\n color: #14538f;\n}\n.du-bois-light-tabs-tab-btn {\n outline: none;\n transition: all 0.3s;\n}\n.du-bois-light-tabs-tab-remove {\n flex: none;\n margin-right: -4px;\n margin-left: 8px;\n color: #5F7281;\n font-size: 12px;\n background: transparent;\n border: none;\n outline: none;\n cursor: pointer;\n transition: all 0.3s;\n}\n.du-bois-light-tabs-tab-remove:hover {\n color: rgba(0, 0, 0, 0.85);\n}\n.du-bois-light-tabs-tab:hover {\n color: #448dc2;\n}\n.du-bois-light-tabs-tab.du-bois-light-tabs-tab-active .du-bois-light-tabs-tab-btn {\n color: #2272B4;\n text-shadow: 0 0 0.25px currentColor;\n}\n.du-bois-light-tabs-tab.du-bois-light-tabs-tab-disabled {\n color: rgba(0, 0, 0, 0.25);\n cursor: not-allowed;\n}\n.du-bois-light-tabs-tab.du-bois-light-tabs-tab-disabled .du-bois-light-tabs-tab-btn:focus,\n.du-bois-light-tabs-tab.du-bois-light-tabs-tab-disabled .du-bois-light-tabs-tab-remove:focus,\n.du-bois-light-tabs-tab.du-bois-light-tabs-tab-disabled .du-bois-light-tabs-tab-btn:active,\n.du-bois-light-tabs-tab.du-bois-light-tabs-tab-disabled .du-bois-light-tabs-tab-remove:active {\n color: rgba(0, 0, 0, 0.25);\n}\n.du-bois-light-tabs-tab .du-bois-light-tabs-tab-remove .anticon {\n margin: 0;\n}\n.du-bois-light-tabs-tab .anticon {\n margin-right: 12px;\n}\n.du-bois-light-tabs-tab + .du-bois-light-tabs-tab {\n margin: 0 0 0 32px;\n}\n.du-bois-light-tabs-content {\n display: flex;\n width: 100%;\n}\n.du-bois-light-tabs-content-holder {\n flex: auto;\n min-width: 0;\n min-height: 0;\n}\n.du-bois-light-tabs-content-animated {\n transition: margin 0.3s;\n}\n.du-bois-light-tabs-tabpane {\n flex: none;\n width: 100%;\n outline: none;\n}\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n@keyframes antCheckboxEffect {\n 0% {\n transform: scale(1);\n opacity: 0.5;\n }\n 100% {\n transform: scale(1.6);\n opacity: 0;\n }\n}\n.du-bois-light-tree-treenode-leaf-last .du-bois-light-tree-switcher-leaf-line::before {\n top: auto !important;\n bottom: auto !important;\n height: 14px !important;\n}\n.du-bois-light-tree.du-bois-light-tree-directory .du-bois-light-tree-treenode {\n position: relative;\n}\n.du-bois-light-tree.du-bois-light-tree-directory .du-bois-light-tree-treenode::before {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 4px;\n left: 0;\n transition: background-color 0.3s;\n content: '';\n pointer-events: none;\n}\n.du-bois-light-tree.du-bois-light-tree-directory .du-bois-light-tree-treenode:hover::before {\n background: #f5f5f5;\n}\n.du-bois-light-tree.du-bois-light-tree-directory .du-bois-light-tree-treenode > * {\n z-index: 1;\n}\n.du-bois-light-tree.du-bois-light-tree-directory .du-bois-light-tree-treenode .du-bois-light-tree-switcher {\n transition: color 0.3s;\n}\n.du-bois-light-tree.du-bois-light-tree-directory .du-bois-light-tree-treenode .du-bois-light-tree-node-content-wrapper {\n border-radius: 0;\n user-select: none;\n}\n.du-bois-light-tree.du-bois-light-tree-directory .du-bois-light-tree-treenode .du-bois-light-tree-node-content-wrapper:hover {\n background: transparent;\n}\n.du-bois-light-tree.du-bois-light-tree-directory .du-bois-light-tree-treenode .du-bois-light-tree-node-content-wrapper.du-bois-light-tree-node-selected {\n color: #fff;\n background: transparent;\n}\n.du-bois-light-tree.du-bois-light-tree-directory .du-bois-light-tree-treenode-selected:hover::before,\n.du-bois-light-tree.du-bois-light-tree-directory .du-bois-light-tree-treenode-selected::before {\n background: #2272B4;\n}\n.du-bois-light-tree.du-bois-light-tree-directory .du-bois-light-tree-treenode-selected .du-bois-light-tree-switcher {\n color: #fff;\n}\n.du-bois-light-tree.du-bois-light-tree-directory .du-bois-light-tree-treenode-selected .du-bois-light-tree-node-content-wrapper {\n color: #fff;\n background: transparent;\n}\n.du-bois-light-tree-checkbox {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #11171C;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: 'tnum';\n position: relative;\n top: 0.2em;\n line-height: 1;\n white-space: nowrap;\n outline: none;\n cursor: pointer;\n}\n.du-bois-light-tree-checkbox-wrapper:hover .du-bois-light-tree-checkbox-inner,\n.du-bois-light-tree-checkbox:hover .du-bois-light-tree-checkbox-inner,\n.du-bois-light-tree-checkbox-input:focus + .du-bois-light-tree-checkbox-inner {\n border-color: #2272B4;\n}\n.du-bois-light-tree-checkbox-checked::after {\n position: absolute;\n top: 0;\n left: 0;\n width: 100%;\n height: 100%;\n border: 1px solid #2272B4;\n border-radius: 4px;\n visibility: hidden;\n animation: antCheckboxEffect 0.36s ease-in-out;\n animation-fill-mode: backwards;\n content: '';\n}\n.du-bois-light-tree-checkbox:hover::after,\n.du-bois-light-tree-checkbox-wrapper:hover .du-bois-light-tree-checkbox::after {\n visibility: visible;\n}\n.du-bois-light-tree-checkbox-inner {\n position: relative;\n top: 0;\n left: 0;\n display: block;\n width: 16px;\n height: 16px;\n direction: ltr;\n background-color: #fff;\n border: 1px solid #E8ECF0;\n border-radius: 4px;\n border-collapse: separate;\n transition: all 0.3s;\n}\n.du-bois-light-tree-checkbox-inner::after {\n position: absolute;\n top: 50%;\n left: 22%;\n display: table;\n width: 5.71428571px;\n height: 9.14285714px;\n border: 2px solid #fff;\n border-top: 0;\n border-left: 0;\n transform: rotate(45deg) scale(0) translate(-50%, -50%);\n opacity: 0;\n transition: all 0.1s cubic-bezier(0.71, -0.46, 0.88, 0.6), opacity 0.1s;\n content: ' ';\n}\n.du-bois-light-tree-checkbox-input {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n z-index: 1;\n width: 100%;\n height: 100%;\n cursor: pointer;\n opacity: 0;\n}\n.du-bois-light-tree-checkbox-checked .du-bois-light-tree-checkbox-inner::after {\n position: absolute;\n display: table;\n border: 2px solid #fff;\n border-top: 0;\n border-left: 0;\n transform: rotate(45deg) scale(1) translate(-50%, -50%);\n opacity: 1;\n transition: all 0.2s cubic-bezier(0.12, 0.4, 0.29, 1.46) 0.1s;\n content: ' ';\n}\n.du-bois-light-tree-checkbox-checked .du-bois-light-tree-checkbox-inner {\n background-color: #2272B4;\n border-color: #2272B4;\n}\n.du-bois-light-tree-checkbox-disabled {\n cursor: not-allowed;\n}\n.du-bois-light-tree-checkbox-disabled.du-bois-light-tree-checkbox-checked .du-bois-light-tree-checkbox-inner::after {\n border-color: rgba(0, 0, 0, 0.25);\n animation-name: none;\n}\n.du-bois-light-tree-checkbox-disabled .du-bois-light-tree-checkbox-input {\n cursor: not-allowed;\n}\n.du-bois-light-tree-checkbox-disabled .du-bois-light-tree-checkbox-inner {\n background-color: #f5f5f5;\n border-color: #E8ECF0 !important;\n}\n.du-bois-light-tree-checkbox-disabled .du-bois-light-tree-checkbox-inner::after {\n border-color: #f5f5f5;\n border-collapse: separate;\n animation-name: none;\n}\n.du-bois-light-tree-checkbox-disabled + span {\n color: rgba(0, 0, 0, 0.25);\n cursor: not-allowed;\n}\n.du-bois-light-tree-checkbox-disabled:hover::after,\n.du-bois-light-tree-checkbox-wrapper:hover .du-bois-light-tree-checkbox-disabled::after {\n visibility: hidden;\n}\n.du-bois-light-tree-checkbox-wrapper {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #11171C;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: 'tnum';\n display: inline-flex;\n align-items: baseline;\n line-height: unset;\n cursor: pointer;\n}\n.du-bois-light-tree-checkbox-wrapper::after {\n display: inline-block;\n width: 0;\n overflow: hidden;\n content: '\\a0';\n}\n.du-bois-light-tree-checkbox-wrapper.du-bois-light-tree-checkbox-wrapper-disabled {\n cursor: not-allowed;\n}\n.du-bois-light-tree-checkbox-wrapper + .du-bois-light-tree-checkbox-wrapper {\n margin-left: 8px;\n}\n.du-bois-light-tree-checkbox + span {\n padding-right: 8px;\n padding-left: 8px;\n}\n.du-bois-light-tree-checkbox-group {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #11171C;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: 'tnum';\n display: inline-block;\n}\n.du-bois-light-tree-checkbox-group-item {\n margin-right: 8px;\n}\n.du-bois-light-tree-checkbox-group-item:last-child {\n margin-right: 0;\n}\n.du-bois-light-tree-checkbox-group-item + .du-bois-light-tree-checkbox-group-item {\n margin-left: 0;\n}\n.du-bois-light-tree-checkbox-indeterminate .du-bois-light-tree-checkbox-inner {\n background-color: #fff;\n border-color: #E8ECF0;\n}\n.du-bois-light-tree-checkbox-indeterminate .du-bois-light-tree-checkbox-inner::after {\n top: 50%;\n left: 50%;\n width: 8px;\n height: 8px;\n background-color: #2272B4;\n border: 0;\n transform: translate(-50%, -50%) scale(1);\n opacity: 1;\n content: ' ';\n}\n.du-bois-light-tree-checkbox-indeterminate.du-bois-light-tree-checkbox-disabled .du-bois-light-tree-checkbox-inner::after {\n background-color: rgba(0, 0, 0, 0.25);\n border-color: rgba(0, 0, 0, 0.25);\n}\n.du-bois-light-tree {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #11171C;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: 'tnum';\n background: #fff;\n border-radius: 4px;\n transition: background-color 0.3s;\n}\n.du-bois-light-tree-focused:not(:hover):not(.du-bois-light-tree-active-focused) {\n background: #e6f1f5;\n}\n.du-bois-light-tree-list-holder-inner {\n align-items: flex-start;\n}\n.du-bois-light-tree.du-bois-light-tree-block-node .du-bois-light-tree-list-holder-inner {\n align-items: stretch;\n}\n.du-bois-light-tree.du-bois-light-tree-block-node .du-bois-light-tree-list-holder-inner .du-bois-light-tree-node-content-wrapper {\n flex: auto;\n}\n.du-bois-light-tree .du-bois-light-tree-treenode {\n display: flex;\n align-items: flex-start;\n padding: 0 0 2px 0;\n outline: none;\n}\n.du-bois-light-tree .du-bois-light-tree-treenode-disabled .du-bois-light-tree-node-content-wrapper {\n color: rgba(0, 0, 0, 0.25);\n cursor: not-allowed;\n}\n.du-bois-light-tree .du-bois-light-tree-treenode-disabled .du-bois-light-tree-node-content-wrapper:hover {\n background: transparent;\n}\n.du-bois-light-tree .du-bois-light-tree-treenode-active .du-bois-light-tree-node-content-wrapper {\n background: #f5f5f5;\n}\n.du-bois-light-tree .du-bois-light-tree-treenode:not(.du-bois-light-tree .du-bois-light-tree-treenode-disabled).filter-node .du-bois-light-tree-title {\n color: inherit;\n font-weight: 500;\n}\n.du-bois-light-tree-indent {\n align-self: stretch;\n white-space: nowrap;\n user-select: none;\n}\n.du-bois-light-tree-indent-unit {\n display: inline-block;\n width: 24px;\n}\n.du-bois-light-tree-switcher {\n position: relative;\n flex: none;\n align-self: stretch;\n width: 24px;\n margin: 0;\n line-height: 24px;\n text-align: center;\n cursor: pointer;\n user-select: none;\n}\n.du-bois-light-tree-switcher .du-bois-light-tree-switcher-icon,\n.du-bois-light-tree-switcher .du-bois-light-select-tree-switcher-icon {\n display: inline-block;\n font-size: 10px;\n vertical-align: baseline;\n}\n.du-bois-light-tree-switcher .du-bois-light-tree-switcher-icon svg,\n.du-bois-light-tree-switcher .du-bois-light-select-tree-switcher-icon svg {\n transition: transform 0.3s;\n}\n.du-bois-light-tree-switcher-noop {\n cursor: default;\n}\n.du-bois-light-tree-switcher_close .du-bois-light-tree-switcher-icon svg {\n transform: rotate(-90deg);\n}\n.du-bois-light-tree-switcher-loading-icon {\n color: #2272B4;\n}\n.du-bois-light-tree-switcher-leaf-line {\n position: relative;\n z-index: 1;\n display: inline-block;\n width: 100%;\n height: 100%;\n}\n.du-bois-light-tree-switcher-leaf-line::before {\n position: absolute;\n top: 0;\n bottom: -2px;\n margin-left: -1px;\n border-left: 1px solid #d9d9d9;\n content: ' ';\n}\n.du-bois-light-tree-switcher-leaf-line::after {\n position: absolute;\n width: 10px;\n height: 14px;\n margin-left: -1px;\n border-bottom: 1px solid #d9d9d9;\n content: ' ';\n}\n.du-bois-light-tree-checkbox {\n top: initial;\n margin: 4px 8px 0 0;\n}\n.du-bois-light-tree .du-bois-light-tree-node-content-wrapper {\n position: relative;\n z-index: auto;\n min-height: 24px;\n margin: 0;\n padding: 0 4px;\n color: inherit;\n line-height: 24px;\n background: transparent;\n border-radius: 4px;\n cursor: pointer;\n transition: all 0.3s, border 0s, line-height 0s, box-shadow 0s;\n}\n.du-bois-light-tree .du-bois-light-tree-node-content-wrapper:hover {\n background-color: #f5f5f5;\n}\n.du-bois-light-tree .du-bois-light-tree-node-content-wrapper.du-bois-light-tree-node-selected {\n background-color: #c1dce8;\n}\n.du-bois-light-tree .du-bois-light-tree-node-content-wrapper .du-bois-light-tree-iconEle {\n display: inline-block;\n width: 24px;\n height: 24px;\n line-height: 24px;\n text-align: center;\n vertical-align: top;\n}\n.du-bois-light-tree .du-bois-light-tree-node-content-wrapper .du-bois-light-tree-iconEle:empty {\n display: none;\n}\n.du-bois-light-tree-unselectable .du-bois-light-tree-node-content-wrapper:hover {\n background-color: transparent;\n}\n.du-bois-light-tree-node-content-wrapper[draggable='true'] {\n line-height: 24px;\n user-select: none;\n}\n.du-bois-light-tree-node-content-wrapper[draggable='true'] .du-bois-light-tree-drop-indicator {\n position: absolute;\n z-index: 1;\n height: 2px;\n background-color: #2272B4;\n border-radius: 1px;\n pointer-events: none;\n}\n.du-bois-light-tree-node-content-wrapper[draggable='true'] .du-bois-light-tree-drop-indicator::after {\n position: absolute;\n top: -3px;\n left: -6px;\n width: 8px;\n height: 8px;\n background-color: transparent;\n border: 2px solid #2272B4;\n border-radius: 50%;\n content: '';\n}\n.du-bois-light-tree .du-bois-light-tree-treenode.drop-container > [draggable] {\n box-shadow: 0 0 0 2px #2272B4;\n}\n.du-bois-light-tree-show-line .du-bois-light-tree-indent-unit {\n position: relative;\n height: 100%;\n}\n.du-bois-light-tree-show-line .du-bois-light-tree-indent-unit::before {\n position: absolute;\n top: 0;\n right: 12px;\n bottom: -2px;\n border-right: 1px solid #E8ECF0;\n content: '';\n}\n.du-bois-light-tree-show-line .du-bois-light-tree-indent-unit-end::before {\n display: none;\n}\n.du-bois-light-tree-show-line .du-bois-light-tree-switcher {\n background: #fff;\n}\n.du-bois-light-tree-show-line .du-bois-light-tree-switcher-line-icon {\n vertical-align: -0.225em;\n}\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-light-typography {\n color: #11171C;\n overflow-wrap: break-word;\n}\n.du-bois-light-typography.du-bois-light-typography-secondary {\n color: #5F7281;\n}\n.du-bois-light-typography.du-bois-light-typography-success {\n color: #34824F;\n}\n.du-bois-light-typography.du-bois-light-typography-warning {\n color: #AF5B23;\n}\n.du-bois-light-typography.du-bois-light-typography-danger {\n color: #C82D4C;\n}\na.du-bois-light-typography.du-bois-light-typography-danger:active,\na.du-bois-light-typography.du-bois-light-typography-danger:focus,\na.du-bois-light-typography.du-bois-light-typography-danger:hover {\n color: #d45368;\n}\n.du-bois-light-typography.du-bois-light-typography-disabled {\n color: rgba(0, 0, 0, 0.25);\n cursor: not-allowed;\n user-select: none;\n}\ndiv.du-bois-light-typography,\n.du-bois-light-typography p {\n margin-bottom: 1em;\n}\nh1.du-bois-light-typography,\n.du-bois-light-typography h1 {\n margin-bottom: 0.5em;\n color: rgba(0, 0, 0, 0.85);\n font-weight: 600;\n font-size: 36px;\n line-height: 1.23;\n}\nh2.du-bois-light-typography,\n.du-bois-light-typography h2 {\n margin-bottom: 0.5em;\n color: rgba(0, 0, 0, 0.85);\n font-weight: 600;\n font-size: 28px;\n line-height: 1.35;\n}\nh3.du-bois-light-typography,\n.du-bois-light-typography h3 {\n margin-bottom: 0.5em;\n color: rgba(0, 0, 0, 0.85);\n font-weight: 600;\n font-size: 23px;\n line-height: 1.35;\n}\nh4.du-bois-light-typography,\n.du-bois-light-typography h4 {\n margin-bottom: 0.5em;\n color: rgba(0, 0, 0, 0.85);\n font-weight: 600;\n font-size: 19px;\n line-height: 1.4;\n}\nh5.du-bois-light-typography,\n.du-bois-light-typography h5 {\n margin-bottom: 0.5em;\n color: rgba(0, 0, 0, 0.85);\n font-weight: 600;\n font-size: 15px;\n line-height: 1.5;\n}\n.du-bois-light-typography + h1.du-bois-light-typography,\n.du-bois-light-typography + h2.du-bois-light-typography,\n.du-bois-light-typography + h3.du-bois-light-typography,\n.du-bois-light-typography + h4.du-bois-light-typography,\n.du-bois-light-typography + h5.du-bois-light-typography {\n margin-top: 1.2em;\n}\n.du-bois-light-typography div + h1,\n.du-bois-light-typography ul + h1,\n.du-bois-light-typography li + h1,\n.du-bois-light-typography p + h1,\n.du-bois-light-typography h1 + h1,\n.du-bois-light-typography h2 + h1,\n.du-bois-light-typography h3 + h1,\n.du-bois-light-typography h4 + h1,\n.du-bois-light-typography h5 + h1,\n.du-bois-light-typography div + h2,\n.du-bois-light-typography ul + h2,\n.du-bois-light-typography li + h2,\n.du-bois-light-typography p + h2,\n.du-bois-light-typography h1 + h2,\n.du-bois-light-typography h2 + h2,\n.du-bois-light-typography h3 + h2,\n.du-bois-light-typography h4 + h2,\n.du-bois-light-typography h5 + h2,\n.du-bois-light-typography div + h3,\n.du-bois-light-typography ul + h3,\n.du-bois-light-typography li + h3,\n.du-bois-light-typography p + h3,\n.du-bois-light-typography h1 + h3,\n.du-bois-light-typography h2 + h3,\n.du-bois-light-typography h3 + h3,\n.du-bois-light-typography h4 + h3,\n.du-bois-light-typography h5 + h3,\n.du-bois-light-typography div + h4,\n.du-bois-light-typography ul + h4,\n.du-bois-light-typography li + h4,\n.du-bois-light-typography p + h4,\n.du-bois-light-typography h1 + h4,\n.du-bois-light-typography h2 + h4,\n.du-bois-light-typography h3 + h4,\n.du-bois-light-typography h4 + h4,\n.du-bois-light-typography h5 + h4,\n.du-bois-light-typography div + h5,\n.du-bois-light-typography ul + h5,\n.du-bois-light-typography li + h5,\n.du-bois-light-typography p + h5,\n.du-bois-light-typography h1 + h5,\n.du-bois-light-typography h2 + h5,\n.du-bois-light-typography h3 + h5,\n.du-bois-light-typography h4 + h5,\n.du-bois-light-typography h5 + h5 {\n margin-top: 1.2em;\n}\na.du-bois-light-typography-ellipsis,\nspan.du-bois-light-typography-ellipsis {\n display: inline-block;\n}\na.du-bois-light-typography,\n.du-bois-light-typography a {\n color: #2272B4;\n outline: none;\n cursor: pointer;\n transition: color 0.3s;\n text-decoration: none;\n}\na.du-bois-light-typography:focus,\n.du-bois-light-typography a:focus,\na.du-bois-light-typography:hover,\n.du-bois-light-typography a:hover {\n color: #448dc2;\n}\na.du-bois-light-typography:active,\n.du-bois-light-typography a:active {\n color: #14538f;\n}\na.du-bois-light-typography:active,\n.du-bois-light-typography a:active,\na.du-bois-light-typography:hover,\n.du-bois-light-typography a:hover {\n text-decoration: none;\n}\na.du-bois-light-typography[disabled],\n.du-bois-light-typography a[disabled],\na.du-bois-light-typography.du-bois-light-typography-disabled,\n.du-bois-light-typography a.du-bois-light-typography-disabled {\n color: rgba(0, 0, 0, 0.25);\n cursor: not-allowed;\n}\na.du-bois-light-typography[disabled]:active,\n.du-bois-light-typography a[disabled]:active,\na.du-bois-light-typography.du-bois-light-typography-disabled:active,\n.du-bois-light-typography a.du-bois-light-typography-disabled:active,\na.du-bois-light-typography[disabled]:hover,\n.du-bois-light-typography a[disabled]:hover,\na.du-bois-light-typography.du-bois-light-typography-disabled:hover,\n.du-bois-light-typography a.du-bois-light-typography-disabled:hover {\n color: rgba(0, 0, 0, 0.25);\n}\na.du-bois-light-typography[disabled]:active,\n.du-bois-light-typography a[disabled]:active,\na.du-bois-light-typography.du-bois-light-typography-disabled:active,\n.du-bois-light-typography a.du-bois-light-typography-disabled:active {\n pointer-events: none;\n}\n.du-bois-light-typography code {\n margin: 0 0.2em;\n padding: 0.2em 0.4em 0.1em;\n font-size: 85%;\n background: rgba(150, 150, 150, 0.1);\n border: 1px solid rgba(100, 100, 100, 0.2);\n border-radius: 3px;\n}\n.du-bois-light-typography kbd {\n margin: 0 0.2em;\n padding: 0.15em 0.4em 0.1em;\n font-size: 90%;\n background: rgba(150, 150, 150, 0.06);\n border: 1px solid rgba(100, 100, 100, 0.2);\n border-bottom-width: 2px;\n border-radius: 3px;\n}\n.du-bois-light-typography mark {\n padding: 0;\n background-color: #ffe58f;\n}\n.du-bois-light-typography u,\n.du-bois-light-typography ins {\n text-decoration: underline;\n text-decoration-skip-ink: auto;\n}\n.du-bois-light-typography s,\n.du-bois-light-typography del {\n text-decoration: line-through;\n}\n.du-bois-light-typography strong {\n font-weight: 600;\n}\n.du-bois-light-typography-expand,\n.du-bois-light-typography-edit,\n.du-bois-light-typography-copy {\n color: #2272B4;\n text-decoration: none;\n outline: none;\n cursor: pointer;\n transition: color 0.3s;\n margin-left: 4px;\n}\n.du-bois-light-typography-expand:focus,\n.du-bois-light-typography-edit:focus,\n.du-bois-light-typography-copy:focus,\n.du-bois-light-typography-expand:hover,\n.du-bois-light-typography-edit:hover,\n.du-bois-light-typography-copy:hover {\n color: #448dc2;\n}\n.du-bois-light-typography-expand:active,\n.du-bois-light-typography-edit:active,\n.du-bois-light-typography-copy:active {\n color: #14538f;\n}\n.du-bois-light-typography-copy-success,\n.du-bois-light-typography-copy-success:hover,\n.du-bois-light-typography-copy-success:focus {\n color: #34824F;\n}\n.du-bois-light-typography-edit-content {\n position: relative;\n}\ndiv.du-bois-light-typography-edit-content {\n left: -13px;\n margin-top: -6px;\n margin-bottom: calc(1em - 5px - 1px);\n}\n.du-bois-light-typography-edit-content-confirm {\n position: absolute;\n right: 10px;\n bottom: 8px;\n color: #5F7281;\n pointer-events: none;\n}\n.du-bois-light-typography-edit-content textarea {\n -moz-transition: none;\n}\n.du-bois-light-typography ul,\n.du-bois-light-typography ol {\n margin: 0 0 1em 0;\n padding: 0;\n}\n.du-bois-light-typography ul li,\n.du-bois-light-typography ol li {\n margin: 0 0 0 20px;\n padding: 0 0 0 4px;\n}\n.du-bois-light-typography ul {\n list-style-type: circle;\n}\n.du-bois-light-typography ul ul {\n list-style-type: disc;\n}\n.du-bois-light-typography ol {\n list-style-type: decimal;\n}\n.du-bois-light-typography pre,\n.du-bois-light-typography blockquote {\n margin: 1em 0;\n}\n.du-bois-light-typography pre {\n padding: 0.4em 0.6em;\n white-space: pre-wrap;\n word-wrap: break-word;\n background: rgba(150, 150, 150, 0.1);\n border: 1px solid rgba(100, 100, 100, 0.2);\n border-radius: 3px;\n}\n.du-bois-light-typography pre code {\n display: inline;\n margin: 0;\n padding: 0;\n font-size: inherit;\n font-family: inherit;\n background: transparent;\n border: 0;\n}\n.du-bois-light-typography blockquote {\n padding: 0 0 0 0.6em;\n border-left: 4px solid rgba(100, 100, 100, 0.2);\n opacity: 0.85;\n}\n.du-bois-light-typography-single-line {\n white-space: nowrap;\n}\n.du-bois-light-typography-ellipsis-single-line {\n overflow: hidden;\n text-overflow: ellipsis;\n}\na.du-bois-light-typography-ellipsis-single-line,\nspan.du-bois-light-typography-ellipsis-single-line {\n vertical-align: bottom;\n}\n.du-bois-light-typography-ellipsis-multiple-line {\n display: -webkit-box;\n overflow: hidden;\n -webkit-line-clamp: 3;\n /*! autoprefixer: ignore next */\n -webkit-box-orient: vertical;\n}\n\n[data-dubois-show-outline] {\n outline: 1px dashed #308613 !important;\n outline-offset: 2px !important;\n}\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-light-switch {\n margin: 0;\n padding: 0;\n color: #11171C;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: 'tnum';\n position: relative;\n display: inline-block;\n box-sizing: border-box;\n min-width: 28px;\n height: 16px;\n line-height: 16px;\n vertical-align: middle;\n background-color: rgba(0, 0, 0, 0.25);\n border: 0;\n border-radius: 100px;\n cursor: pointer;\n transition: all 0.2s;\n user-select: none;\n}\n.du-bois-light-switch:focus {\n outline: 0;\n box-shadow: 0 0 0 2px rgba(0, 0, 0, 0.1);\n}\n.du-bois-light-switch-checked:focus {\n box-shadow: 0 0 0 2px rgba(34, 115, 181, 0.2);\n}\n.du-bois-light-switch:focus:hover {\n box-shadow: none;\n}\n.du-bois-light-switch-checked {\n background-color: #2273b5;\n}\n.du-bois-light-switch-loading,\n.du-bois-light-switch-disabled {\n cursor: not-allowed;\n opacity: 0.4;\n}\n.du-bois-light-switch-loading *,\n.du-bois-light-switch-disabled * {\n box-shadow: none;\n cursor: not-allowed;\n}\n.du-bois-light-switch-inner {\n display: block;\n margin: 0 5px 0 18px;\n color: #fff;\n font-size: 12px;\n transition: margin 0.2s;\n}\n.du-bois-light-switch-checked .du-bois-light-switch-inner {\n margin: 0 18px 0 5px;\n}\n.du-bois-light-switch-handle {\n position: absolute;\n top: 0px;\n left: 0px;\n width: 14px;\n height: 14px;\n transition: all 0.2s ease-in-out;\n}\n.du-bois-light-switch-handle::before {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background-color: white;\n border-radius: 7px;\n box-shadow: 0 2px 4px 0 rgba(0, 35, 11, 0.2);\n transition: all 0.2s ease-in-out;\n content: '';\n}\n.du-bois-light-switch-checked .du-bois-light-switch-handle {\n left: calc(100% - 14px - 0px);\n}\n.du-bois-light-switch:not(.du-bois-light-switch-disabled):active .du-bois-light-switch-handle::before {\n right: -30%;\n left: 0;\n}\n.du-bois-light-switch:not(.du-bois-light-switch-disabled):active.du-bois-light-switch-checked .du-bois-light-switch-handle::before {\n right: 0;\n left: -30%;\n}\n.du-bois-light-switch-loading-icon {\n position: relative;\n top: 0.5px;\n color: rgba(0, 0, 0, 0.65);\n vertical-align: top;\n}\n.du-bois-light-switch-checked .du-bois-light-switch-loading-icon {\n color: #2273b5;\n}\n.du-bois-light-switch-small {\n min-width: 28px;\n height: 16px;\n line-height: 16px;\n}\n.du-bois-light-switch-small .du-bois-light-switch-inner {\n margin: 0 5px 0 18px;\n font-size: 12px;\n}\n.du-bois-light-switch-small .du-bois-light-switch-handle {\n width: 12px;\n height: 12px;\n}\n.du-bois-light-switch-small .du-bois-light-switch-loading-icon {\n top: 1.5px;\n font-size: 9px;\n}\n.du-bois-light-switch-small.du-bois-light-switch-checked .du-bois-light-switch-inner {\n margin: 0 18px 0 5px;\n}\n.du-bois-light-switch-small.du-bois-light-switch-checked .du-bois-light-switch-handle {\n left: calc(100% - 12px - 0px);\n}\n.du-bois-light-switch {\n transition: none;\n border: 1px solid #64727d;\n background-color: white;\n margin-right: 8px;\n}\n.du-bois-light-switch .du-bois-light-switch-handle::before {\n box-shadow: 0px 0px 0px 1px #64727d;\n}\n.du-bois-light-switch-checked {\n border: 1px solid #2273b5;\n background-color: #2273b5;\n}\n.du-bois-light-switch-checked .du-bois-light-switch-handle::before {\n box-shadow: none;\n}\n.du-bois-light-switch:focus-visible {\n box-shadow: 0px 0px 0px 1px #2273b5;\n border: 1px solid #2273b5;\n}\n.du-bois-light-switch:focus-visible .du-bois-light-switch-handle::before {\n box-shadow: 0px 0px 0px 1px #2273b5;\n}\n.du-bois-light-switch-checked:focus-visible {\n box-shadow: 0px 0px 0px 1px #ffffff, 0px 0px 0px 3px #2273b5;\n}\n.du-bois-light-switch:hover {\n border: 1px solid #186099;\n background-color: rgba(34, 115, 181, 0.08);\n}\n.du-bois-light-switch:hover .du-bois-light-switch-handle::before {\n box-shadow: 0px 0px 0px 1px #186099;\n}\n.du-bois-light-switch-checked:hover {\n background-color: #186099;\n}\n.du-bois-light-switch:active {\n background-color: rgba(34, 115, 181, 0.16);\n}\n.du-bois-light-switch-checked:active {\n background-color: #0d4f85;\n}\n.du-bois-light-switch-disabled,\n.du-bois-light-switch-disabled:hover {\n border: 1px solid #a2aeb8;\n}\n.du-bois-light-switch-disabled .du-bois-light-switch-handle::before,\n.du-bois-light-switch-disabled:hover .du-bois-light-switch-handle::before {\n box-shadow: 0px 0px 0px 1px #a2aeb8;\n}\n.du-bois-light-switch-disabled.du-bois-light-switch-checked {\n background-color: #a2aeb8;\n}\n.du-bois-light-switch:not(.du-bois-light-switch-disabled):active .du-bois-light-switch-handle::before {\n right: 0;\n left: 0;\n}\n.du-bois-light-switch:not(.du-bois-light-switch-disabled):active.du-bois-light-switch-checked .du-bois-light-switch-handle::before {\n right: 0;\n left: 0;\n}\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-light-breadcrumb {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #11171C;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: 'tnum';\n color: #5F7281;\n font-size: 13px;\n}\n.du-bois-light-breadcrumb .anticon {\n font-size: 13px;\n}\n.du-bois-light-breadcrumb a {\n color: #5F7281;\n transition: color 0.3s;\n}\n.du-bois-light-breadcrumb a:hover {\n color: #448dc2;\n}\n.du-bois-light-breadcrumb > span:last-child {\n color: #11171C;\n}\n.du-bois-light-breadcrumb > span:last-child a {\n color: #11171C;\n}\n.du-bois-light-breadcrumb > span:last-child .du-bois-light-breadcrumb-separator {\n display: none;\n}\n.du-bois-light-breadcrumb-separator {\n margin: 0 4px;\n color: #5F7281;\n}\n.du-bois-light-breadcrumb-link > .anticon + span,\n.du-bois-light-breadcrumb-link > .anticon + a {\n margin-left: 4px;\n}\n.du-bois-light-breadcrumb-overlay-link > .anticon {\n margin-left: 4px;\n}\n.du-bois-light-breadcrumb a {\n color: #2272B4;\n text-decoration: none;\n outline: none;\n cursor: pointer;\n transition: color 0.3s;\n}\n.du-bois-light-breadcrumb a:focus,\n.du-bois-light-breadcrumb a:hover {\n color: #448dc2;\n}\n.du-bois-light-breadcrumb a:active {\n color: #14538f;\n}\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-light-notification {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #11171C;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: 'tnum';\n position: fixed;\n z-index: 1010;\n margin-right: 24px;\n}\n.du-bois-light-notification-topLeft,\n.du-bois-light-notification-bottomLeft {\n margin-right: 0;\n margin-left: 24px;\n}\n.du-bois-light-notification-topLeft .du-bois-light-notification-fade-enter.du-bois-light-notification-fade-enter-active,\n.du-bois-light-notification-bottomLeft .du-bois-light-notification-fade-enter.du-bois-light-notification-fade-enter-active,\n.du-bois-light-notification-topLeft .du-bois-light-notification-fade-appear.du-bois-light-notification-fade-appear-active,\n.du-bois-light-notification-bottomLeft .du-bois-light-notification-fade-appear.du-bois-light-notification-fade-appear-active {\n animation-name: NotificationLeftFadeIn;\n}\n.du-bois-light-notification-close-icon {\n font-size: 13px;\n cursor: pointer;\n}\n.du-bois-light-notification-hook-holder {\n position: relative;\n}\n.du-bois-light-notification-notice {\n position: relative;\n width: 384px;\n max-width: calc(100vw - 24px * 2);\n margin-bottom: 16px;\n margin-left: auto;\n padding: 16px 24px;\n overflow: hidden;\n line-height: 20px;\n word-wrap: break-word;\n background: #fff;\n border-radius: 4px;\n box-shadow: 0 3px 6px -4px rgba(0, 0, 0, 0.12), 0 6px 16px 0 rgba(0, 0, 0, 0.08), 0 9px 28px 8px rgba(0, 0, 0, 0.05);\n}\n.du-bois-light-notification-topLeft .du-bois-light-notification-notice,\n.du-bois-light-notification-bottomLeft .du-bois-light-notification-notice {\n margin-right: auto;\n margin-left: 0;\n}\n.du-bois-light-notification-notice-message {\n margin-bottom: 8px;\n color: rgba(0, 0, 0, 0.85);\n font-size: 18px;\n line-height: 24px;\n}\n.du-bois-light-notification-notice-message-single-line-auto-margin {\n display: block;\n width: calc(384px - 24px * 2 - 24px - 48px - 100%);\n max-width: 4px;\n background-color: transparent;\n pointer-events: none;\n}\n.du-bois-light-notification-notice-message-single-line-auto-margin::before {\n display: block;\n content: '';\n}\n.du-bois-light-notification-notice-description {\n font-size: 13px;\n}\n.du-bois-light-notification-notice-closable .du-bois-light-notification-notice-message {\n padding-right: 24px;\n}\n.du-bois-light-notification-notice-with-icon .du-bois-light-notification-notice-message {\n margin-bottom: 4px;\n margin-left: 48px;\n font-size: 18px;\n}\n.du-bois-light-notification-notice-with-icon .du-bois-light-notification-notice-description {\n margin-left: 48px;\n font-size: 13px;\n}\n.du-bois-light-notification-notice-icon {\n position: absolute;\n margin-left: 4px;\n font-size: 24px;\n line-height: 24px;\n}\n.anticon.du-bois-light-notification-notice-icon-success {\n color: #34824F;\n}\n.anticon.du-bois-light-notification-notice-icon-info {\n color: #64727D;\n}\n.anticon.du-bois-light-notification-notice-icon-warning {\n color: #AF5B23;\n}\n.anticon.du-bois-light-notification-notice-icon-error {\n color: #C82D4C;\n}\n.du-bois-light-notification-notice-close {\n position: absolute;\n top: 16px;\n right: 22px;\n color: #5F7281;\n outline: none;\n}\n.du-bois-light-notification-notice-close:hover {\n color: #39444d;\n}\n.du-bois-light-notification-notice-btn {\n float: right;\n margin-top: 16px;\n}\n.du-bois-light-notification .notification-fade-effect {\n animation-duration: 0.24s;\n animation-timing-function: cubic-bezier(0.645, 0.045, 0.355, 1);\n animation-fill-mode: both;\n}\n.du-bois-light-notification-fade-enter,\n.du-bois-light-notification-fade-appear {\n animation-duration: 0.24s;\n animation-timing-function: cubic-bezier(0.645, 0.045, 0.355, 1);\n animation-fill-mode: both;\n opacity: 0;\n animation-play-state: paused;\n}\n.du-bois-light-notification-fade-leave {\n animation-duration: 0.24s;\n animation-timing-function: cubic-bezier(0.645, 0.045, 0.355, 1);\n animation-fill-mode: both;\n animation-duration: 0.2s;\n animation-play-state: paused;\n}\n.du-bois-light-notification-fade-enter.du-bois-light-notification-fade-enter-active,\n.du-bois-light-notification-fade-appear.du-bois-light-notification-fade-appear-active {\n animation-name: NotificationFadeIn;\n animation-play-state: running;\n}\n.du-bois-light-notification-fade-leave.du-bois-light-notification-fade-leave-active {\n animation-name: NotificationFadeOut;\n animation-play-state: running;\n}\n@keyframes NotificationFadeIn {\n 0% {\n left: 384px;\n opacity: 0;\n }\n 100% {\n left: 0;\n opacity: 1;\n }\n}\n@keyframes NotificationLeftFadeIn {\n 0% {\n right: 384px;\n opacity: 0;\n }\n 100% {\n right: 0;\n opacity: 1;\n }\n}\n@keyframes NotificationFadeOut {\n 0% {\n max-height: 150px;\n margin-bottom: 16px;\n opacity: 1;\n }\n 100% {\n max-height: 0;\n margin-bottom: 0;\n padding-top: 0;\n padding-bottom: 0;\n opacity: 0;\n }\n}\n.du-bois-light-notification-notice {\n padding: 10px 40px 10px 32px;\n box-shadow: 0px 4px 16px rgba(46, 56, 64, 0.12);\n}\n.du-bois-light-notification-notice .du-bois-light-notification-notice-icon {\n font-size: 16px;\n width: 16px;\n height: 16px;\n margin-left: 0;\n left: 12px;\n top: 12px;\n line-height: 16px;\n}\n.du-bois-light-notification-notice .du-bois-light-notification-notice-close {\n top: 12px;\n right: 12px;\n width: 16px;\n height: 16px;\n line-height: 16px;\n}\n.du-bois-light-notification-notice .du-bois-light-notification-notice-close span[role='img'] {\n font-size: 16px;\n}\n.du-bois-light-notification-notice .du-bois-light-notification-notice-message {\n display: flex;\n align-items: center;\n min-height: 20px;\n line-height: 20px;\n font-weight: 600;\n}\n.du-bois-light-notification-notice .du-bois-light-notification-notice-message,\n.du-bois-light-notification-notice .du-bois-light-notification-notice-description {\n margin-left: unset;\n font-size: 13px;\n margin-bottom: 0;\n}\n\n.du-bois-light-typography a {\n color: #2272b4;\n}\n.du-bois-light-typography a:hover {\n color: #0e538b;\n}\n.du-bois-light-typography a:focus {\n color: #04355d;\n}\n.du-bois-dark-typography a {\n color: #4299e0;\n}\n.du-bois-dark-typography a:hover {\n color: #8acaff;\n}\n.du-bois-dark-typography a:focus {\n color: #bae1fc;\n}\n","[class^='du-bois-dark'] {\n box-sizing: border-box;\n}\n[class^='du-bois-dark'] *,\n[class^='du-bois-dark'] *::before,\n[class^='du-bois-dark'] *::after {\n box-sizing: border-box;\n}\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n[class^=du-bois-dark-]::-ms-clear,\n[class*= du-bois-dark-]::-ms-clear,\n[class^=du-bois-dark-] input::-ms-clear,\n[class*= du-bois-dark-] input::-ms-clear,\n[class^=du-bois-dark-] input::-ms-reveal,\n[class*= du-bois-dark-] input::-ms-reveal {\n display: none;\n}\n/* stylelint-disable at-rule-no-unknown */\n@-ms-viewport {\n width: device-width;\n}\nabbr[data-original-title] {\n text-decoration: underline;\n text-decoration: underline dotted;\n border-bottom: 0;\n cursor: help;\n}\n.anticon {\n display: inline-block;\n color: inherit;\n font-style: normal;\n line-height: 0;\n text-align: center;\n text-transform: none;\n vertical-align: -0.125em;\n text-rendering: optimizeLegibility;\n -webkit-font-smoothing: antialiased;\n -moz-osx-font-smoothing: grayscale;\n}\n.anticon > * {\n line-height: 1;\n}\n.anticon svg {\n display: inline-block;\n}\n.anticon::before {\n display: none;\n}\n.anticon .anticon-icon {\n display: block;\n}\n.anticon[tabindex] {\n cursor: pointer;\n}\n.anticon-spin::before {\n display: inline-block;\n animation: loadingCircle 1s infinite linear;\n}\n.anticon-spin {\n display: inline-block;\n animation: loadingCircle 1s infinite linear;\n}\n.du-bois-dark-fade-enter,\n.du-bois-dark-fade-appear {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-dark-fade-leave {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-dark-fade-enter.du-bois-dark-fade-enter-active,\n.du-bois-dark-fade-appear.du-bois-dark-fade-appear-active {\n animation-name: antFadeIn;\n animation-play-state: running;\n}\n.du-bois-dark-fade-leave.du-bois-dark-fade-leave-active {\n animation-name: antFadeOut;\n animation-play-state: running;\n pointer-events: none;\n}\n.du-bois-dark-fade-enter,\n.du-bois-dark-fade-appear {\n opacity: 0;\n animation-timing-function: linear;\n}\n.du-bois-dark-fade-leave {\n animation-timing-function: linear;\n}\n@keyframes antFadeIn {\n 0% {\n opacity: 0;\n }\n 100% {\n opacity: 1;\n }\n}\n@keyframes antFadeOut {\n 0% {\n opacity: 1;\n }\n 100% {\n opacity: 0;\n }\n}\n.du-bois-dark-move-up-enter,\n.du-bois-dark-move-up-appear {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-dark-move-up-leave {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-dark-move-up-enter.du-bois-dark-move-up-enter-active,\n.du-bois-dark-move-up-appear.du-bois-dark-move-up-appear-active {\n animation-name: antMoveUpIn;\n animation-play-state: running;\n}\n.du-bois-dark-move-up-leave.du-bois-dark-move-up-leave-active {\n animation-name: antMoveUpOut;\n animation-play-state: running;\n pointer-events: none;\n}\n.du-bois-dark-move-up-enter,\n.du-bois-dark-move-up-appear {\n opacity: 0;\n animation-timing-function: cubic-bezier(0.08, 0.82, 0.17, 1);\n}\n.du-bois-dark-move-up-leave {\n animation-timing-function: cubic-bezier(0.6, 0.04, 0.98, 0.34);\n}\n.du-bois-dark-move-down-enter,\n.du-bois-dark-move-down-appear {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-dark-move-down-leave {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-dark-move-down-enter.du-bois-dark-move-down-enter-active,\n.du-bois-dark-move-down-appear.du-bois-dark-move-down-appear-active {\n animation-name: antMoveDownIn;\n animation-play-state: running;\n}\n.du-bois-dark-move-down-leave.du-bois-dark-move-down-leave-active {\n animation-name: antMoveDownOut;\n animation-play-state: running;\n pointer-events: none;\n}\n.du-bois-dark-move-down-enter,\n.du-bois-dark-move-down-appear {\n opacity: 0;\n animation-timing-function: cubic-bezier(0.08, 0.82, 0.17, 1);\n}\n.du-bois-dark-move-down-leave {\n animation-timing-function: cubic-bezier(0.6, 0.04, 0.98, 0.34);\n}\n.du-bois-dark-move-left-enter,\n.du-bois-dark-move-left-appear {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-dark-move-left-leave {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-dark-move-left-enter.du-bois-dark-move-left-enter-active,\n.du-bois-dark-move-left-appear.du-bois-dark-move-left-appear-active {\n animation-name: antMoveLeftIn;\n animation-play-state: running;\n}\n.du-bois-dark-move-left-leave.du-bois-dark-move-left-leave-active {\n animation-name: antMoveLeftOut;\n animation-play-state: running;\n pointer-events: none;\n}\n.du-bois-dark-move-left-enter,\n.du-bois-dark-move-left-appear {\n opacity: 0;\n animation-timing-function: cubic-bezier(0.08, 0.82, 0.17, 1);\n}\n.du-bois-dark-move-left-leave {\n animation-timing-function: cubic-bezier(0.6, 0.04, 0.98, 0.34);\n}\n.du-bois-dark-move-right-enter,\n.du-bois-dark-move-right-appear {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-dark-move-right-leave {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-dark-move-right-enter.du-bois-dark-move-right-enter-active,\n.du-bois-dark-move-right-appear.du-bois-dark-move-right-appear-active {\n animation-name: antMoveRightIn;\n animation-play-state: running;\n}\n.du-bois-dark-move-right-leave.du-bois-dark-move-right-leave-active {\n animation-name: antMoveRightOut;\n animation-play-state: running;\n pointer-events: none;\n}\n.du-bois-dark-move-right-enter,\n.du-bois-dark-move-right-appear {\n opacity: 0;\n animation-timing-function: cubic-bezier(0.08, 0.82, 0.17, 1);\n}\n.du-bois-dark-move-right-leave {\n animation-timing-function: cubic-bezier(0.6, 0.04, 0.98, 0.34);\n}\n@keyframes antMoveDownIn {\n 0% {\n transform: translateY(100%);\n transform-origin: 0 0;\n opacity: 0;\n }\n 100% {\n transform: translateY(0%);\n transform-origin: 0 0;\n opacity: 1;\n }\n}\n@keyframes antMoveDownOut {\n 0% {\n transform: translateY(0%);\n transform-origin: 0 0;\n opacity: 1;\n }\n 100% {\n transform: translateY(100%);\n transform-origin: 0 0;\n opacity: 0;\n }\n}\n@keyframes antMoveLeftIn {\n 0% {\n transform: translateX(-100%);\n transform-origin: 0 0;\n opacity: 0;\n }\n 100% {\n transform: translateX(0%);\n transform-origin: 0 0;\n opacity: 1;\n }\n}\n@keyframes antMoveLeftOut {\n 0% {\n transform: translateX(0%);\n transform-origin: 0 0;\n opacity: 1;\n }\n 100% {\n transform: translateX(-100%);\n transform-origin: 0 0;\n opacity: 0;\n }\n}\n@keyframes antMoveRightIn {\n 0% {\n transform: translateX(100%);\n transform-origin: 0 0;\n opacity: 0;\n }\n 100% {\n transform: translateX(0%);\n transform-origin: 0 0;\n opacity: 1;\n }\n}\n@keyframes antMoveRightOut {\n 0% {\n transform: translateX(0%);\n transform-origin: 0 0;\n opacity: 1;\n }\n 100% {\n transform: translateX(100%);\n transform-origin: 0 0;\n opacity: 0;\n }\n}\n@keyframes antMoveUpIn {\n 0% {\n transform: translateY(-100%);\n transform-origin: 0 0;\n opacity: 0;\n }\n 100% {\n transform: translateY(0%);\n transform-origin: 0 0;\n opacity: 1;\n }\n}\n@keyframes antMoveUpOut {\n 0% {\n transform: translateY(0%);\n transform-origin: 0 0;\n opacity: 1;\n }\n 100% {\n transform: translateY(-100%);\n transform-origin: 0 0;\n opacity: 0;\n }\n}\n@keyframes loadingCircle {\n 100% {\n transform: rotate(360deg);\n }\n}\n[du-bois-dark-click-animating='true'],\n[du-bois-dark-click-animating-without-extra-node='true'] {\n position: relative;\n}\n[du-bois-dark-click-animating-without-extra-node='true']::after,\n.du-bois-dark-click-animating-node {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n display: block;\n border-radius: inherit;\n box-shadow: 0 0 0 0 #2272B4;\n box-shadow: 0 0 0 0 var(--antd-wave-shadow-color);\n opacity: 0.2;\n animation: fadeEffect 2s cubic-bezier(0.08, 0.82, 0.17, 1), waveEffect 0.4s cubic-bezier(0.08, 0.82, 0.17, 1);\n animation-fill-mode: forwards;\n content: '';\n pointer-events: none;\n}\n@keyframes waveEffect {\n 100% {\n box-shadow: 0 0 0 #2272B4;\n box-shadow: 0 0 0 6px var(--antd-wave-shadow-color);\n }\n}\n@keyframes fadeEffect {\n 100% {\n opacity: 0;\n }\n}\n.du-bois-dark-slide-up-enter,\n.du-bois-dark-slide-up-appear {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-dark-slide-up-leave {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-dark-slide-up-enter.du-bois-dark-slide-up-enter-active,\n.du-bois-dark-slide-up-appear.du-bois-dark-slide-up-appear-active {\n animation-name: antSlideUpIn;\n animation-play-state: running;\n}\n.du-bois-dark-slide-up-leave.du-bois-dark-slide-up-leave-active {\n animation-name: antSlideUpOut;\n animation-play-state: running;\n pointer-events: none;\n}\n.du-bois-dark-slide-up-enter,\n.du-bois-dark-slide-up-appear {\n opacity: 0;\n animation-timing-function: cubic-bezier(0.23, 1, 0.32, 1);\n}\n.du-bois-dark-slide-up-leave {\n animation-timing-function: cubic-bezier(0.755, 0.05, 0.855, 0.06);\n}\n.du-bois-dark-slide-down-enter,\n.du-bois-dark-slide-down-appear {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-dark-slide-down-leave {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-dark-slide-down-enter.du-bois-dark-slide-down-enter-active,\n.du-bois-dark-slide-down-appear.du-bois-dark-slide-down-appear-active {\n animation-name: antSlideDownIn;\n animation-play-state: running;\n}\n.du-bois-dark-slide-down-leave.du-bois-dark-slide-down-leave-active {\n animation-name: antSlideDownOut;\n animation-play-state: running;\n pointer-events: none;\n}\n.du-bois-dark-slide-down-enter,\n.du-bois-dark-slide-down-appear {\n opacity: 0;\n animation-timing-function: cubic-bezier(0.23, 1, 0.32, 1);\n}\n.du-bois-dark-slide-down-leave {\n animation-timing-function: cubic-bezier(0.755, 0.05, 0.855, 0.06);\n}\n.du-bois-dark-slide-left-enter,\n.du-bois-dark-slide-left-appear {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-dark-slide-left-leave {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-dark-slide-left-enter.du-bois-dark-slide-left-enter-active,\n.du-bois-dark-slide-left-appear.du-bois-dark-slide-left-appear-active {\n animation-name: antSlideLeftIn;\n animation-play-state: running;\n}\n.du-bois-dark-slide-left-leave.du-bois-dark-slide-left-leave-active {\n animation-name: antSlideLeftOut;\n animation-play-state: running;\n pointer-events: none;\n}\n.du-bois-dark-slide-left-enter,\n.du-bois-dark-slide-left-appear {\n opacity: 0;\n animation-timing-function: cubic-bezier(0.23, 1, 0.32, 1);\n}\n.du-bois-dark-slide-left-leave {\n animation-timing-function: cubic-bezier(0.755, 0.05, 0.855, 0.06);\n}\n.du-bois-dark-slide-right-enter,\n.du-bois-dark-slide-right-appear {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-dark-slide-right-leave {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-dark-slide-right-enter.du-bois-dark-slide-right-enter-active,\n.du-bois-dark-slide-right-appear.du-bois-dark-slide-right-appear-active {\n animation-name: antSlideRightIn;\n animation-play-state: running;\n}\n.du-bois-dark-slide-right-leave.du-bois-dark-slide-right-leave-active {\n animation-name: antSlideRightOut;\n animation-play-state: running;\n pointer-events: none;\n}\n.du-bois-dark-slide-right-enter,\n.du-bois-dark-slide-right-appear {\n opacity: 0;\n animation-timing-function: cubic-bezier(0.23, 1, 0.32, 1);\n}\n.du-bois-dark-slide-right-leave {\n animation-timing-function: cubic-bezier(0.755, 0.05, 0.855, 0.06);\n}\n@keyframes antSlideUpIn {\n 0% {\n transform: scaleY(0.8);\n transform-origin: 0% 0%;\n opacity: 0;\n }\n 100% {\n transform: scaleY(1);\n transform-origin: 0% 0%;\n opacity: 1;\n }\n}\n@keyframes antSlideUpOut {\n 0% {\n transform: scaleY(1);\n transform-origin: 0% 0%;\n opacity: 1;\n }\n 100% {\n transform: scaleY(0.8);\n transform-origin: 0% 0%;\n opacity: 0;\n }\n}\n@keyframes antSlideDownIn {\n 0% {\n transform: scaleY(0.8);\n transform-origin: 100% 100%;\n opacity: 0;\n }\n 100% {\n transform: scaleY(1);\n transform-origin: 100% 100%;\n opacity: 1;\n }\n}\n@keyframes antSlideDownOut {\n 0% {\n transform: scaleY(1);\n transform-origin: 100% 100%;\n opacity: 1;\n }\n 100% {\n transform: scaleY(0.8);\n transform-origin: 100% 100%;\n opacity: 0;\n }\n}\n@keyframes antSlideLeftIn {\n 0% {\n transform: scaleX(0.8);\n transform-origin: 0% 0%;\n opacity: 0;\n }\n 100% {\n transform: scaleX(1);\n transform-origin: 0% 0%;\n opacity: 1;\n }\n}\n@keyframes antSlideLeftOut {\n 0% {\n transform: scaleX(1);\n transform-origin: 0% 0%;\n opacity: 1;\n }\n 100% {\n transform: scaleX(0.8);\n transform-origin: 0% 0%;\n opacity: 0;\n }\n}\n@keyframes antSlideRightIn {\n 0% {\n transform: scaleX(0.8);\n transform-origin: 100% 0%;\n opacity: 0;\n }\n 100% {\n transform: scaleX(1);\n transform-origin: 100% 0%;\n opacity: 1;\n }\n}\n@keyframes antSlideRightOut {\n 0% {\n transform: scaleX(1);\n transform-origin: 100% 0%;\n opacity: 1;\n }\n 100% {\n transform: scaleX(0.8);\n transform-origin: 100% 0%;\n opacity: 0;\n }\n}\n.du-bois-dark-zoom-enter,\n.du-bois-dark-zoom-appear {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-dark-zoom-leave {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-dark-zoom-enter.du-bois-dark-zoom-enter-active,\n.du-bois-dark-zoom-appear.du-bois-dark-zoom-appear-active {\n animation-name: antZoomIn;\n animation-play-state: running;\n}\n.du-bois-dark-zoom-leave.du-bois-dark-zoom-leave-active {\n animation-name: antZoomOut;\n animation-play-state: running;\n pointer-events: none;\n}\n.du-bois-dark-zoom-enter,\n.du-bois-dark-zoom-appear {\n transform: scale(0);\n opacity: 0;\n animation-timing-function: cubic-bezier(0.08, 0.82, 0.17, 1);\n}\n.du-bois-dark-zoom-enter-prepare,\n.du-bois-dark-zoom-appear-prepare {\n transform: none;\n}\n.du-bois-dark-zoom-leave {\n animation-timing-function: cubic-bezier(0.78, 0.14, 0.15, 0.86);\n}\n.du-bois-dark-zoom-big-enter,\n.du-bois-dark-zoom-big-appear {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-dark-zoom-big-leave {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-dark-zoom-big-enter.du-bois-dark-zoom-big-enter-active,\n.du-bois-dark-zoom-big-appear.du-bois-dark-zoom-big-appear-active {\n animation-name: antZoomBigIn;\n animation-play-state: running;\n}\n.du-bois-dark-zoom-big-leave.du-bois-dark-zoom-big-leave-active {\n animation-name: antZoomBigOut;\n animation-play-state: running;\n pointer-events: none;\n}\n.du-bois-dark-zoom-big-enter,\n.du-bois-dark-zoom-big-appear {\n transform: scale(0);\n opacity: 0;\n animation-timing-function: cubic-bezier(0.08, 0.82, 0.17, 1);\n}\n.du-bois-dark-zoom-big-enter-prepare,\n.du-bois-dark-zoom-big-appear-prepare {\n transform: none;\n}\n.du-bois-dark-zoom-big-leave {\n animation-timing-function: cubic-bezier(0.78, 0.14, 0.15, 0.86);\n}\n.du-bois-dark-zoom-big-fast-enter,\n.du-bois-dark-zoom-big-fast-appear {\n animation-duration: 0.1s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-dark-zoom-big-fast-leave {\n animation-duration: 0.1s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-dark-zoom-big-fast-enter.du-bois-dark-zoom-big-fast-enter-active,\n.du-bois-dark-zoom-big-fast-appear.du-bois-dark-zoom-big-fast-appear-active {\n animation-name: antZoomBigIn;\n animation-play-state: running;\n}\n.du-bois-dark-zoom-big-fast-leave.du-bois-dark-zoom-big-fast-leave-active {\n animation-name: antZoomBigOut;\n animation-play-state: running;\n pointer-events: none;\n}\n.du-bois-dark-zoom-big-fast-enter,\n.du-bois-dark-zoom-big-fast-appear {\n transform: scale(0);\n opacity: 0;\n animation-timing-function: cubic-bezier(0.08, 0.82, 0.17, 1);\n}\n.du-bois-dark-zoom-big-fast-enter-prepare,\n.du-bois-dark-zoom-big-fast-appear-prepare {\n transform: none;\n}\n.du-bois-dark-zoom-big-fast-leave {\n animation-timing-function: cubic-bezier(0.78, 0.14, 0.15, 0.86);\n}\n.du-bois-dark-zoom-up-enter,\n.du-bois-dark-zoom-up-appear {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-dark-zoom-up-leave {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-dark-zoom-up-enter.du-bois-dark-zoom-up-enter-active,\n.du-bois-dark-zoom-up-appear.du-bois-dark-zoom-up-appear-active {\n animation-name: antZoomUpIn;\n animation-play-state: running;\n}\n.du-bois-dark-zoom-up-leave.du-bois-dark-zoom-up-leave-active {\n animation-name: antZoomUpOut;\n animation-play-state: running;\n pointer-events: none;\n}\n.du-bois-dark-zoom-up-enter,\n.du-bois-dark-zoom-up-appear {\n transform: scale(0);\n opacity: 0;\n animation-timing-function: cubic-bezier(0.08, 0.82, 0.17, 1);\n}\n.du-bois-dark-zoom-up-enter-prepare,\n.du-bois-dark-zoom-up-appear-prepare {\n transform: none;\n}\n.du-bois-dark-zoom-up-leave {\n animation-timing-function: cubic-bezier(0.78, 0.14, 0.15, 0.86);\n}\n.du-bois-dark-zoom-down-enter,\n.du-bois-dark-zoom-down-appear {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-dark-zoom-down-leave {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-dark-zoom-down-enter.du-bois-dark-zoom-down-enter-active,\n.du-bois-dark-zoom-down-appear.du-bois-dark-zoom-down-appear-active {\n animation-name: antZoomDownIn;\n animation-play-state: running;\n}\n.du-bois-dark-zoom-down-leave.du-bois-dark-zoom-down-leave-active {\n animation-name: antZoomDownOut;\n animation-play-state: running;\n pointer-events: none;\n}\n.du-bois-dark-zoom-down-enter,\n.du-bois-dark-zoom-down-appear {\n transform: scale(0);\n opacity: 0;\n animation-timing-function: cubic-bezier(0.08, 0.82, 0.17, 1);\n}\n.du-bois-dark-zoom-down-enter-prepare,\n.du-bois-dark-zoom-down-appear-prepare {\n transform: none;\n}\n.du-bois-dark-zoom-down-leave {\n animation-timing-function: cubic-bezier(0.78, 0.14, 0.15, 0.86);\n}\n.du-bois-dark-zoom-left-enter,\n.du-bois-dark-zoom-left-appear {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-dark-zoom-left-leave {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-dark-zoom-left-enter.du-bois-dark-zoom-left-enter-active,\n.du-bois-dark-zoom-left-appear.du-bois-dark-zoom-left-appear-active {\n animation-name: antZoomLeftIn;\n animation-play-state: running;\n}\n.du-bois-dark-zoom-left-leave.du-bois-dark-zoom-left-leave-active {\n animation-name: antZoomLeftOut;\n animation-play-state: running;\n pointer-events: none;\n}\n.du-bois-dark-zoom-left-enter,\n.du-bois-dark-zoom-left-appear {\n transform: scale(0);\n opacity: 0;\n animation-timing-function: cubic-bezier(0.08, 0.82, 0.17, 1);\n}\n.du-bois-dark-zoom-left-enter-prepare,\n.du-bois-dark-zoom-left-appear-prepare {\n transform: none;\n}\n.du-bois-dark-zoom-left-leave {\n animation-timing-function: cubic-bezier(0.78, 0.14, 0.15, 0.86);\n}\n.du-bois-dark-zoom-right-enter,\n.du-bois-dark-zoom-right-appear {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-dark-zoom-right-leave {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-dark-zoom-right-enter.du-bois-dark-zoom-right-enter-active,\n.du-bois-dark-zoom-right-appear.du-bois-dark-zoom-right-appear-active {\n animation-name: antZoomRightIn;\n animation-play-state: running;\n}\n.du-bois-dark-zoom-right-leave.du-bois-dark-zoom-right-leave-active {\n animation-name: antZoomRightOut;\n animation-play-state: running;\n pointer-events: none;\n}\n.du-bois-dark-zoom-right-enter,\n.du-bois-dark-zoom-right-appear {\n transform: scale(0);\n opacity: 0;\n animation-timing-function: cubic-bezier(0.08, 0.82, 0.17, 1);\n}\n.du-bois-dark-zoom-right-enter-prepare,\n.du-bois-dark-zoom-right-appear-prepare {\n transform: none;\n}\n.du-bois-dark-zoom-right-leave {\n animation-timing-function: cubic-bezier(0.78, 0.14, 0.15, 0.86);\n}\n@keyframes antZoomIn {\n 0% {\n transform: scale(0.2);\n opacity: 0;\n }\n 100% {\n transform: scale(1);\n opacity: 1;\n }\n}\n@keyframes antZoomOut {\n 0% {\n transform: scale(1);\n }\n 100% {\n transform: scale(0.2);\n opacity: 0;\n }\n}\n@keyframes antZoomBigIn {\n 0% {\n transform: scale(0.8);\n opacity: 0;\n }\n 100% {\n transform: scale(1);\n opacity: 1;\n }\n}\n@keyframes antZoomBigOut {\n 0% {\n transform: scale(1);\n }\n 100% {\n transform: scale(0.8);\n opacity: 0;\n }\n}\n@keyframes antZoomUpIn {\n 0% {\n transform: scale(0.8);\n transform-origin: 50% 0%;\n opacity: 0;\n }\n 100% {\n transform: scale(1);\n transform-origin: 50% 0%;\n }\n}\n@keyframes antZoomUpOut {\n 0% {\n transform: scale(1);\n transform-origin: 50% 0%;\n }\n 100% {\n transform: scale(0.8);\n transform-origin: 50% 0%;\n opacity: 0;\n }\n}\n@keyframes antZoomLeftIn {\n 0% {\n transform: scale(0.8);\n transform-origin: 0% 50%;\n opacity: 0;\n }\n 100% {\n transform: scale(1);\n transform-origin: 0% 50%;\n }\n}\n@keyframes antZoomLeftOut {\n 0% {\n transform: scale(1);\n transform-origin: 0% 50%;\n }\n 100% {\n transform: scale(0.8);\n transform-origin: 0% 50%;\n opacity: 0;\n }\n}\n@keyframes antZoomRightIn {\n 0% {\n transform: scale(0.8);\n transform-origin: 100% 50%;\n opacity: 0;\n }\n 100% {\n transform: scale(1);\n transform-origin: 100% 50%;\n }\n}\n@keyframes antZoomRightOut {\n 0% {\n transform: scale(1);\n transform-origin: 100% 50%;\n }\n 100% {\n transform: scale(0.8);\n transform-origin: 100% 50%;\n opacity: 0;\n }\n}\n@keyframes antZoomDownIn {\n 0% {\n transform: scale(0.8);\n transform-origin: 50% 100%;\n opacity: 0;\n }\n 100% {\n transform: scale(1);\n transform-origin: 50% 100%;\n }\n}\n@keyframes antZoomDownOut {\n 0% {\n transform: scale(1);\n transform-origin: 50% 100%;\n }\n 100% {\n transform: scale(0.8);\n transform-origin: 50% 100%;\n opacity: 0;\n }\n}\n.ant-motion-collapse-legacy {\n overflow: hidden;\n}\n.ant-motion-collapse-legacy-active {\n transition: height 0.2s cubic-bezier(0.645, 0.045, 0.355, 1), opacity 0.2s cubic-bezier(0.645, 0.045, 0.355, 1) !important;\n}\n.ant-motion-collapse {\n overflow: hidden;\n transition: height 0.2s cubic-bezier(0.645, 0.045, 0.355, 1), opacity 0.2s cubic-bezier(0.645, 0.045, 0.355, 1) !important;\n}\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-dark-btn {\n line-height: 20px;\n position: relative;\n display: inline-block;\n font-weight: 400;\n white-space: nowrap;\n text-align: center;\n background-image: none;\n border: 1px solid transparent;\n box-shadow: 0 2px 0 rgba(0, 0, 0, 0.015);\n cursor: pointer;\n transition: all 0.3s cubic-bezier(0.645, 0.045, 0.355, 1);\n user-select: none;\n touch-action: manipulation;\n height: 40px;\n padding: 0 16px;\n font-size: 13px;\n border-radius: 4px;\n color: #E8ECF0;\n background: transparent;\n border-color: #1F272D;\n}\n.du-bois-dark-btn > .anticon {\n line-height: 1;\n}\n.du-bois-dark-btn,\n.du-bois-dark-btn:active,\n.du-bois-dark-btn:focus {\n outline: 0;\n}\n.du-bois-dark-btn:not([disabled]):hover {\n text-decoration: none;\n}\n.du-bois-dark-btn:not([disabled]):active {\n outline: 0;\n box-shadow: none;\n}\n.du-bois-dark-btn[disabled] {\n cursor: not-allowed;\n}\n.du-bois-dark-btn[disabled] > * {\n pointer-events: none;\n}\n.du-bois-dark-btn-lg {\n height: 40px;\n padding: 0 16px;\n font-size: 18px;\n border-radius: 4px;\n}\n.du-bois-dark-btn-sm {\n height: 32px;\n padding: 0 16px;\n font-size: 13px;\n border-radius: 4px;\n}\n.du-bois-dark-btn > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn:hover,\n.du-bois-dark-btn:focus {\n color: #1d517c;\n background: transparent;\n border-color: #1d517c;\n}\n.du-bois-dark-btn:hover > a:only-child,\n.du-bois-dark-btn:focus > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn:hover > a:only-child::after,\n.du-bois-dark-btn:focus > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn:active {\n color: #3f81b1;\n background: transparent;\n border-color: #3f81b1;\n}\n.du-bois-dark-btn:active > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn[disabled],\n.du-bois-dark-btn[disabled]:hover,\n.du-bois-dark-btn[disabled]:focus,\n.du-bois-dark-btn[disabled]:active {\n color: rgba(255, 255, 255, 0.3);\n background: rgba(255, 255, 255, 0.08);\n border-color: #1F272D;\n text-shadow: none;\n box-shadow: none;\n}\n.du-bois-dark-btn[disabled] > a:only-child,\n.du-bois-dark-btn[disabled]:hover > a:only-child,\n.du-bois-dark-btn[disabled]:focus > a:only-child,\n.du-bois-dark-btn[disabled]:active > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn[disabled] > a:only-child::after,\n.du-bois-dark-btn[disabled]:hover > a:only-child::after,\n.du-bois-dark-btn[disabled]:focus > a:only-child::after,\n.du-bois-dark-btn[disabled]:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn:hover,\n.du-bois-dark-btn:focus,\n.du-bois-dark-btn:active {\n text-decoration: none;\n background: transparent;\n}\n.du-bois-dark-btn > span {\n display: inline-block;\n}\n.du-bois-dark-btn-primary {\n color: #fff;\n background: #2272B4;\n border-color: #2272B4;\n text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.12);\n box-shadow: 0 2px 0 rgba(0, 0, 0, 0.045);\n}\n.du-bois-dark-btn-primary > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-primary > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-primary:hover,\n.du-bois-dark-btn-primary:focus {\n color: #fff;\n background: #14538f;\n border-color: #14538f;\n}\n.du-bois-dark-btn-primary:hover > a:only-child,\n.du-bois-dark-btn-primary:focus > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-primary:hover > a:only-child::after,\n.du-bois-dark-btn-primary:focus > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-primary:active {\n color: #fff;\n background: #448dc2;\n border-color: #448dc2;\n}\n.du-bois-dark-btn-primary:active > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-primary:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-primary[disabled],\n.du-bois-dark-btn-primary[disabled]:hover,\n.du-bois-dark-btn-primary[disabled]:focus,\n.du-bois-dark-btn-primary[disabled]:active {\n color: rgba(255, 255, 255, 0.3);\n background: rgba(255, 255, 255, 0.08);\n border-color: #1F272D;\n text-shadow: none;\n box-shadow: none;\n}\n.du-bois-dark-btn-primary[disabled] > a:only-child,\n.du-bois-dark-btn-primary[disabled]:hover > a:only-child,\n.du-bois-dark-btn-primary[disabled]:focus > a:only-child,\n.du-bois-dark-btn-primary[disabled]:active > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-primary[disabled] > a:only-child::after,\n.du-bois-dark-btn-primary[disabled]:hover > a:only-child::after,\n.du-bois-dark-btn-primary[disabled]:focus > a:only-child::after,\n.du-bois-dark-btn-primary[disabled]:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-group .du-bois-dark-btn-primary:not(:first-child):not(:last-child) {\n border-right-color: #1d517c;\n border-left-color: #1d517c;\n}\n.du-bois-dark-btn-group .du-bois-dark-btn-primary:not(:first-child):not(:last-child):disabled {\n border-color: #1F272D;\n}\n.du-bois-dark-btn-group .du-bois-dark-btn-primary:first-child:not(:last-child) {\n border-right-color: #1d517c;\n}\n.du-bois-dark-btn-group .du-bois-dark-btn-primary:first-child:not(:last-child)[disabled] {\n border-right-color: #1F272D;\n}\n.du-bois-dark-btn-group .du-bois-dark-btn-primary:last-child:not(:first-child),\n.du-bois-dark-btn-group .du-bois-dark-btn-primary + .du-bois-dark-btn-primary {\n border-left-color: #1d517c;\n}\n.du-bois-dark-btn-group .du-bois-dark-btn-primary:last-child:not(:first-child)[disabled],\n.du-bois-dark-btn-group .du-bois-dark-btn-primary + .du-bois-dark-btn-primary[disabled] {\n border-left-color: #1F272D;\n}\n.du-bois-dark-btn-ghost {\n color: #E8ECF0;\n background: transparent;\n border-color: #1F272D;\n}\n.du-bois-dark-btn-ghost > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-ghost > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-ghost:hover,\n.du-bois-dark-btn-ghost:focus {\n color: #1d517c;\n background: transparent;\n border-color: #1d517c;\n}\n.du-bois-dark-btn-ghost:hover > a:only-child,\n.du-bois-dark-btn-ghost:focus > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-ghost:hover > a:only-child::after,\n.du-bois-dark-btn-ghost:focus > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-ghost:active {\n color: #3f81b1;\n background: transparent;\n border-color: #3f81b1;\n}\n.du-bois-dark-btn-ghost:active > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-ghost:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-ghost[disabled],\n.du-bois-dark-btn-ghost[disabled]:hover,\n.du-bois-dark-btn-ghost[disabled]:focus,\n.du-bois-dark-btn-ghost[disabled]:active {\n color: rgba(255, 255, 255, 0.3);\n background: rgba(255, 255, 255, 0.08);\n border-color: #1F272D;\n text-shadow: none;\n box-shadow: none;\n}\n.du-bois-dark-btn-ghost[disabled] > a:only-child,\n.du-bois-dark-btn-ghost[disabled]:hover > a:only-child,\n.du-bois-dark-btn-ghost[disabled]:focus > a:only-child,\n.du-bois-dark-btn-ghost[disabled]:active > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-ghost[disabled] > a:only-child::after,\n.du-bois-dark-btn-ghost[disabled]:hover > a:only-child::after,\n.du-bois-dark-btn-ghost[disabled]:focus > a:only-child::after,\n.du-bois-dark-btn-ghost[disabled]:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-dashed {\n color: #E8ECF0;\n background: transparent;\n border-color: #1F272D;\n border-style: dashed;\n}\n.du-bois-dark-btn-dashed > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-dashed > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-dashed:hover,\n.du-bois-dark-btn-dashed:focus {\n color: #1d517c;\n background: transparent;\n border-color: #1d517c;\n}\n.du-bois-dark-btn-dashed:hover > a:only-child,\n.du-bois-dark-btn-dashed:focus > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-dashed:hover > a:only-child::after,\n.du-bois-dark-btn-dashed:focus > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-dashed:active {\n color: #3f81b1;\n background: transparent;\n border-color: #3f81b1;\n}\n.du-bois-dark-btn-dashed:active > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-dashed:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-dashed[disabled],\n.du-bois-dark-btn-dashed[disabled]:hover,\n.du-bois-dark-btn-dashed[disabled]:focus,\n.du-bois-dark-btn-dashed[disabled]:active {\n color: rgba(255, 255, 255, 0.3);\n background: rgba(255, 255, 255, 0.08);\n border-color: #1F272D;\n text-shadow: none;\n box-shadow: none;\n}\n.du-bois-dark-btn-dashed[disabled] > a:only-child,\n.du-bois-dark-btn-dashed[disabled]:hover > a:only-child,\n.du-bois-dark-btn-dashed[disabled]:focus > a:only-child,\n.du-bois-dark-btn-dashed[disabled]:active > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-dashed[disabled] > a:only-child::after,\n.du-bois-dark-btn-dashed[disabled]:hover > a:only-child::after,\n.du-bois-dark-btn-dashed[disabled]:focus > a:only-child::after,\n.du-bois-dark-btn-dashed[disabled]:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-danger {\n color: #fff;\n background: #E65B77;\n border-color: #E65B77;\n text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.12);\n box-shadow: 0 2px 0 rgba(0, 0, 0, 0.045);\n}\n.du-bois-dark-btn-danger > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-danger > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-danger:hover,\n.du-bois-dark-btn-danger:focus {\n color: #fff;\n background: #bf4360;\n border-color: #bf4360;\n}\n.du-bois-dark-btn-danger:hover > a:only-child,\n.du-bois-dark-btn-danger:focus > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-danger:hover > a:only-child::after,\n.du-bois-dark-btn-danger:focus > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-danger:active {\n color: #fff;\n background: #f28899;\n border-color: #f28899;\n}\n.du-bois-dark-btn-danger:active > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-danger:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-danger[disabled],\n.du-bois-dark-btn-danger[disabled]:hover,\n.du-bois-dark-btn-danger[disabled]:focus,\n.du-bois-dark-btn-danger[disabled]:active {\n color: rgba(255, 255, 255, 0.3);\n background: rgba(255, 255, 255, 0.08);\n border-color: #1F272D;\n text-shadow: none;\n box-shadow: none;\n}\n.du-bois-dark-btn-danger[disabled] > a:only-child,\n.du-bois-dark-btn-danger[disabled]:hover > a:only-child,\n.du-bois-dark-btn-danger[disabled]:focus > a:only-child,\n.du-bois-dark-btn-danger[disabled]:active > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-danger[disabled] > a:only-child::after,\n.du-bois-dark-btn-danger[disabled]:hover > a:only-child::after,\n.du-bois-dark-btn-danger[disabled]:focus > a:only-child::after,\n.du-bois-dark-btn-danger[disabled]:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-link {\n color: #2272B4;\n background: transparent;\n border-color: transparent;\n box-shadow: none;\n}\n.du-bois-dark-btn-link > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-link > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-link:hover,\n.du-bois-dark-btn-link:focus {\n color: #1d517c;\n background: transparent;\n border-color: #1d517c;\n}\n.du-bois-dark-btn-link:hover > a:only-child,\n.du-bois-dark-btn-link:focus > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-link:hover > a:only-child::after,\n.du-bois-dark-btn-link:focus > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-link:active {\n color: #3f81b1;\n background: transparent;\n border-color: #3f81b1;\n}\n.du-bois-dark-btn-link:active > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-link:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-link[disabled],\n.du-bois-dark-btn-link[disabled]:hover,\n.du-bois-dark-btn-link[disabled]:focus,\n.du-bois-dark-btn-link[disabled]:active {\n color: rgba(255, 255, 255, 0.3);\n background: rgba(255, 255, 255, 0.08);\n border-color: #1F272D;\n text-shadow: none;\n box-shadow: none;\n}\n.du-bois-dark-btn-link[disabled] > a:only-child,\n.du-bois-dark-btn-link[disabled]:hover > a:only-child,\n.du-bois-dark-btn-link[disabled]:focus > a:only-child,\n.du-bois-dark-btn-link[disabled]:active > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-link[disabled] > a:only-child::after,\n.du-bois-dark-btn-link[disabled]:hover > a:only-child::after,\n.du-bois-dark-btn-link[disabled]:focus > a:only-child::after,\n.du-bois-dark-btn-link[disabled]:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-link:hover {\n background: transparent;\n}\n.du-bois-dark-btn-link:hover,\n.du-bois-dark-btn-link:focus,\n.du-bois-dark-btn-link:active {\n border-color: transparent;\n}\n.du-bois-dark-btn-link[disabled],\n.du-bois-dark-btn-link[disabled]:hover,\n.du-bois-dark-btn-link[disabled]:focus,\n.du-bois-dark-btn-link[disabled]:active {\n color: rgba(255, 255, 255, 0.3);\n background: transparent;\n border-color: transparent;\n text-shadow: none;\n box-shadow: none;\n}\n.du-bois-dark-btn-link[disabled] > a:only-child,\n.du-bois-dark-btn-link[disabled]:hover > a:only-child,\n.du-bois-dark-btn-link[disabled]:focus > a:only-child,\n.du-bois-dark-btn-link[disabled]:active > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-link[disabled] > a:only-child::after,\n.du-bois-dark-btn-link[disabled]:hover > a:only-child::after,\n.du-bois-dark-btn-link[disabled]:focus > a:only-child::after,\n.du-bois-dark-btn-link[disabled]:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-text {\n color: #E8ECF0;\n background: transparent;\n border-color: transparent;\n box-shadow: none;\n}\n.du-bois-dark-btn-text > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-text > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-text:hover,\n.du-bois-dark-btn-text:focus {\n color: #1d517c;\n background: transparent;\n border-color: #1d517c;\n}\n.du-bois-dark-btn-text:hover > a:only-child,\n.du-bois-dark-btn-text:focus > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-text:hover > a:only-child::after,\n.du-bois-dark-btn-text:focus > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-text:active {\n color: #3f81b1;\n background: transparent;\n border-color: #3f81b1;\n}\n.du-bois-dark-btn-text:active > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-text:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-text[disabled],\n.du-bois-dark-btn-text[disabled]:hover,\n.du-bois-dark-btn-text[disabled]:focus,\n.du-bois-dark-btn-text[disabled]:active {\n color: rgba(255, 255, 255, 0.3);\n background: rgba(255, 255, 255, 0.08);\n border-color: #1F272D;\n text-shadow: none;\n box-shadow: none;\n}\n.du-bois-dark-btn-text[disabled] > a:only-child,\n.du-bois-dark-btn-text[disabled]:hover > a:only-child,\n.du-bois-dark-btn-text[disabled]:focus > a:only-child,\n.du-bois-dark-btn-text[disabled]:active > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-text[disabled] > a:only-child::after,\n.du-bois-dark-btn-text[disabled]:hover > a:only-child::after,\n.du-bois-dark-btn-text[disabled]:focus > a:only-child::after,\n.du-bois-dark-btn-text[disabled]:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-text:hover,\n.du-bois-dark-btn-text:focus {\n color: #E8ECF0;\n background: rgba(255, 255, 255, 0.03);\n border-color: transparent;\n}\n.du-bois-dark-btn-text:active {\n color: #E8ECF0;\n background: rgba(255, 255, 255, 0.04);\n border-color: transparent;\n}\n.du-bois-dark-btn-text[disabled],\n.du-bois-dark-btn-text[disabled]:hover,\n.du-bois-dark-btn-text[disabled]:focus,\n.du-bois-dark-btn-text[disabled]:active {\n color: rgba(255, 255, 255, 0.3);\n background: transparent;\n border-color: transparent;\n text-shadow: none;\n box-shadow: none;\n}\n.du-bois-dark-btn-text[disabled] > a:only-child,\n.du-bois-dark-btn-text[disabled]:hover > a:only-child,\n.du-bois-dark-btn-text[disabled]:focus > a:only-child,\n.du-bois-dark-btn-text[disabled]:active > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-text[disabled] > a:only-child::after,\n.du-bois-dark-btn-text[disabled]:hover > a:only-child::after,\n.du-bois-dark-btn-text[disabled]:focus > a:only-child::after,\n.du-bois-dark-btn-text[disabled]:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-dangerous {\n color: #E65B77;\n background: transparent;\n border-color: #E65B77;\n}\n.du-bois-dark-btn-dangerous > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-dangerous > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-dangerous:hover,\n.du-bois-dark-btn-dangerous:focus {\n color: #bf4360;\n background: transparent;\n border-color: #bf4360;\n}\n.du-bois-dark-btn-dangerous:hover > a:only-child,\n.du-bois-dark-btn-dangerous:focus > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-dangerous:hover > a:only-child::after,\n.du-bois-dark-btn-dangerous:focus > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-dangerous:active {\n color: #f28899;\n background: transparent;\n border-color: #f28899;\n}\n.du-bois-dark-btn-dangerous:active > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-dangerous:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-dangerous[disabled],\n.du-bois-dark-btn-dangerous[disabled]:hover,\n.du-bois-dark-btn-dangerous[disabled]:focus,\n.du-bois-dark-btn-dangerous[disabled]:active {\n color: rgba(255, 255, 255, 0.3);\n background: rgba(255, 255, 255, 0.08);\n border-color: #1F272D;\n text-shadow: none;\n box-shadow: none;\n}\n.du-bois-dark-btn-dangerous[disabled] > a:only-child,\n.du-bois-dark-btn-dangerous[disabled]:hover > a:only-child,\n.du-bois-dark-btn-dangerous[disabled]:focus > a:only-child,\n.du-bois-dark-btn-dangerous[disabled]:active > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-dangerous[disabled] > a:only-child::after,\n.du-bois-dark-btn-dangerous[disabled]:hover > a:only-child::after,\n.du-bois-dark-btn-dangerous[disabled]:focus > a:only-child::after,\n.du-bois-dark-btn-dangerous[disabled]:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-primary {\n color: #fff;\n background: #E65B77;\n border-color: #E65B77;\n text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.12);\n box-shadow: 0 2px 0 rgba(0, 0, 0, 0.045);\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-primary > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-primary > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-primary:hover,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-primary:focus {\n color: #fff;\n background: #bf4360;\n border-color: #bf4360;\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-primary:hover > a:only-child,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-primary:focus > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-primary:hover > a:only-child::after,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-primary:focus > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-primary:active {\n color: #fff;\n background: #f28899;\n border-color: #f28899;\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-primary:active > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-primary:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-primary[disabled],\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-primary[disabled]:hover,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-primary[disabled]:focus,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-primary[disabled]:active {\n color: rgba(255, 255, 255, 0.3);\n background: rgba(255, 255, 255, 0.08);\n border-color: #1F272D;\n text-shadow: none;\n box-shadow: none;\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-primary[disabled] > a:only-child,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-primary[disabled]:hover > a:only-child,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-primary[disabled]:focus > a:only-child,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-primary[disabled]:active > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-primary[disabled] > a:only-child::after,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-primary[disabled]:hover > a:only-child::after,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-primary[disabled]:focus > a:only-child::after,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-primary[disabled]:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-link {\n color: #E65B77;\n background: transparent;\n border-color: transparent;\n box-shadow: none;\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-link > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-link > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-link:hover,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-link:focus {\n color: #1d517c;\n background: transparent;\n border-color: #1d517c;\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-link:hover > a:only-child,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-link:focus > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-link:hover > a:only-child::after,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-link:focus > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-link:active {\n color: #3f81b1;\n background: transparent;\n border-color: #3f81b1;\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-link:active > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-link:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-link[disabled],\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-link[disabled]:hover,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-link[disabled]:focus,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-link[disabled]:active {\n color: rgba(255, 255, 255, 0.3);\n background: rgba(255, 255, 255, 0.08);\n border-color: #1F272D;\n text-shadow: none;\n box-shadow: none;\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-link[disabled] > a:only-child,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-link[disabled]:hover > a:only-child,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-link[disabled]:focus > a:only-child,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-link[disabled]:active > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-link[disabled] > a:only-child::after,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-link[disabled]:hover > a:only-child::after,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-link[disabled]:focus > a:only-child::after,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-link[disabled]:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-link:hover,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-link:focus {\n color: #bf4360;\n background: transparent;\n border-color: transparent;\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-link:hover > a:only-child,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-link:focus > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-link:hover > a:only-child::after,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-link:focus > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-link:active {\n color: #f28899;\n background: transparent;\n border-color: transparent;\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-link:active > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-link:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-link[disabled],\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-link[disabled]:hover,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-link[disabled]:focus,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-link[disabled]:active {\n color: rgba(255, 255, 255, 0.3);\n background: transparent;\n border-color: transparent;\n text-shadow: none;\n box-shadow: none;\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-link[disabled] > a:only-child,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-link[disabled]:hover > a:only-child,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-link[disabled]:focus > a:only-child,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-link[disabled]:active > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-link[disabled] > a:only-child::after,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-link[disabled]:hover > a:only-child::after,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-link[disabled]:focus > a:only-child::after,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-link[disabled]:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-text {\n color: #E65B77;\n background: transparent;\n border-color: transparent;\n box-shadow: none;\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-text > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-text > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-text:hover,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-text:focus {\n color: #1d517c;\n background: transparent;\n border-color: #1d517c;\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-text:hover > a:only-child,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-text:focus > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-text:hover > a:only-child::after,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-text:focus > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-text:active {\n color: #3f81b1;\n background: transparent;\n border-color: #3f81b1;\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-text:active > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-text:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-text[disabled],\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-text[disabled]:hover,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-text[disabled]:focus,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-text[disabled]:active {\n color: rgba(255, 255, 255, 0.3);\n background: rgba(255, 255, 255, 0.08);\n border-color: #1F272D;\n text-shadow: none;\n box-shadow: none;\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-text[disabled] > a:only-child,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-text[disabled]:hover > a:only-child,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-text[disabled]:focus > a:only-child,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-text[disabled]:active > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-text[disabled] > a:only-child::after,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-text[disabled]:hover > a:only-child::after,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-text[disabled]:focus > a:only-child::after,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-text[disabled]:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-text:hover,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-text:focus {\n color: #bf4360;\n background: rgba(255, 255, 255, 0.03);\n border-color: transparent;\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-text:hover > a:only-child,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-text:focus > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-text:hover > a:only-child::after,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-text:focus > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-text:active {\n color: #f28899;\n background: rgba(255, 255, 255, 0.04);\n border-color: transparent;\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-text:active > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-text:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-text[disabled],\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-text[disabled]:hover,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-text[disabled]:focus,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-text[disabled]:active {\n color: rgba(255, 255, 255, 0.3);\n background: transparent;\n border-color: transparent;\n text-shadow: none;\n box-shadow: none;\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-text[disabled] > a:only-child,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-text[disabled]:hover > a:only-child,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-text[disabled]:focus > a:only-child,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-text[disabled]:active > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-text[disabled] > a:only-child::after,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-text[disabled]:hover > a:only-child::after,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-text[disabled]:focus > a:only-child::after,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-text[disabled]:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-icon-only {\n width: 40px;\n height: 40px;\n padding: 0 0;\n font-size: 15px;\n border-radius: 4px;\n vertical-align: -1px;\n}\n.du-bois-dark-btn-icon-only > * {\n font-size: 15px;\n}\n.du-bois-dark-btn-icon-only.du-bois-dark-btn-lg {\n width: 40px;\n height: 40px;\n padding: 0 0;\n font-size: 20px;\n border-radius: 4px;\n}\n.du-bois-dark-btn-icon-only.du-bois-dark-btn-lg > * {\n font-size: 20px;\n}\n.du-bois-dark-btn-icon-only.du-bois-dark-btn-sm {\n width: 32px;\n height: 32px;\n padding: 0 0;\n font-size: 13px;\n border-radius: 4px;\n}\n.du-bois-dark-btn-icon-only.du-bois-dark-btn-sm > * {\n font-size: 13px;\n}\n.du-bois-dark-btn-round {\n height: 40px;\n padding: 0 20px;\n font-size: 13px;\n border-radius: 40px;\n}\n.du-bois-dark-btn-round.du-bois-dark-btn-lg {\n height: 40px;\n padding: 0 20px;\n font-size: 18px;\n border-radius: 40px;\n}\n.du-bois-dark-btn-round.du-bois-dark-btn-sm {\n height: 32px;\n padding: 0 16px;\n font-size: 13px;\n border-radius: 32px;\n}\n.du-bois-dark-btn-round.du-bois-dark-btn-icon-only {\n width: auto;\n}\n.du-bois-dark-btn-circle {\n min-width: 40px;\n padding-right: 0;\n padding-left: 0;\n text-align: center;\n border-radius: 50%;\n}\n.du-bois-dark-btn-circle.du-bois-dark-btn-lg {\n min-width: 40px;\n border-radius: 50%;\n}\n.du-bois-dark-btn-circle.du-bois-dark-btn-sm {\n min-width: 32px;\n border-radius: 50%;\n}\n.du-bois-dark-btn::before {\n position: absolute;\n top: -1px;\n right: -1px;\n bottom: -1px;\n left: -1px;\n z-index: 1;\n display: none;\n background: #141414;\n border-radius: inherit;\n opacity: 0.35;\n transition: opacity 0.2s;\n content: '';\n pointer-events: none;\n}\n.du-bois-dark-btn .anticon {\n transition: margin-left 0.3s cubic-bezier(0.645, 0.045, 0.355, 1);\n}\n.du-bois-dark-btn .anticon.anticon-plus > svg,\n.du-bois-dark-btn .anticon.anticon-minus > svg {\n shape-rendering: optimizeSpeed;\n}\n.du-bois-dark-btn.du-bois-dark-btn-loading {\n position: relative;\n}\n.du-bois-dark-btn.du-bois-dark-btn-loading:not([disabled]) {\n pointer-events: none;\n}\n.du-bois-dark-btn.du-bois-dark-btn-loading::before {\n display: block;\n}\n.du-bois-dark-btn > .du-bois-dark-btn-loading-icon {\n transition: all 0.3s cubic-bezier(0.645, 0.045, 0.355, 1);\n}\n.du-bois-dark-btn > .du-bois-dark-btn-loading-icon .anticon {\n padding-right: 4px;\n animation: none;\n}\n.du-bois-dark-btn > .du-bois-dark-btn-loading-icon .anticon svg {\n animation: loadingCircle 1s infinite linear;\n}\n.du-bois-dark-btn > .du-bois-dark-btn-loading-icon:only-child .anticon {\n padding-right: 0;\n}\n.du-bois-dark-btn-group {\n position: relative;\n display: inline-flex;\n}\n.du-bois-dark-btn-group > .du-bois-dark-btn,\n.du-bois-dark-btn-group > span > .du-bois-dark-btn {\n position: relative;\n}\n.du-bois-dark-btn-group > .du-bois-dark-btn:hover,\n.du-bois-dark-btn-group > span > .du-bois-dark-btn:hover,\n.du-bois-dark-btn-group > .du-bois-dark-btn:focus,\n.du-bois-dark-btn-group > span > .du-bois-dark-btn:focus,\n.du-bois-dark-btn-group > .du-bois-dark-btn:active,\n.du-bois-dark-btn-group > span > .du-bois-dark-btn:active {\n z-index: 2;\n}\n.du-bois-dark-btn-group > .du-bois-dark-btn[disabled],\n.du-bois-dark-btn-group > span > .du-bois-dark-btn[disabled] {\n z-index: 0;\n}\n.du-bois-dark-btn-group .du-bois-dark-btn-icon-only {\n font-size: 13px;\n}\n.du-bois-dark-btn-group-lg > .du-bois-dark-btn,\n.du-bois-dark-btn-group-lg > span > .du-bois-dark-btn {\n height: 40px;\n padding: 0 16px;\n font-size: 18px;\n border-radius: 0;\n}\n.du-bois-dark-btn-group-lg .du-bois-dark-btn.du-bois-dark-btn-icon-only {\n width: 40px;\n height: 40px;\n padding-right: 0;\n padding-left: 0;\n}\n.du-bois-dark-btn-group-sm > .du-bois-dark-btn,\n.du-bois-dark-btn-group-sm > span > .du-bois-dark-btn {\n height: 32px;\n padding: 0 16px;\n font-size: 13px;\n border-radius: 0;\n}\n.du-bois-dark-btn-group-sm > .du-bois-dark-btn > .anticon,\n.du-bois-dark-btn-group-sm > span > .du-bois-dark-btn > .anticon {\n font-size: 13px;\n}\n.du-bois-dark-btn-group-sm .du-bois-dark-btn.du-bois-dark-btn-icon-only {\n width: 32px;\n height: 32px;\n padding-right: 0;\n padding-left: 0;\n}\n.du-bois-dark-btn-group .du-bois-dark-btn + .du-bois-dark-btn,\n.du-bois-dark-btn + .du-bois-dark-btn-group,\n.du-bois-dark-btn-group span + .du-bois-dark-btn,\n.du-bois-dark-btn-group .du-bois-dark-btn + span,\n.du-bois-dark-btn-group > span + span,\n.du-bois-dark-btn-group + .du-bois-dark-btn,\n.du-bois-dark-btn-group + .du-bois-dark-btn-group {\n margin-left: -1px;\n}\n.du-bois-dark-btn-group .du-bois-dark-btn-primary + .du-bois-dark-btn:not(.du-bois-dark-btn-primary):not([disabled]) {\n border-left-color: transparent;\n}\n.du-bois-dark-btn-group .du-bois-dark-btn {\n border-radius: 0;\n}\n.du-bois-dark-btn-group > .du-bois-dark-btn:first-child,\n.du-bois-dark-btn-group > span:first-child > .du-bois-dark-btn {\n margin-left: 0;\n}\n.du-bois-dark-btn-group > .du-bois-dark-btn:only-child {\n border-radius: 4px;\n}\n.du-bois-dark-btn-group > span:only-child > .du-bois-dark-btn {\n border-radius: 4px;\n}\n.du-bois-dark-btn-group > .du-bois-dark-btn:first-child:not(:last-child),\n.du-bois-dark-btn-group > span:first-child:not(:last-child) > .du-bois-dark-btn {\n border-top-left-radius: 4px;\n border-bottom-left-radius: 4px;\n}\n.du-bois-dark-btn-group > .du-bois-dark-btn:last-child:not(:first-child),\n.du-bois-dark-btn-group > span:last-child:not(:first-child) > .du-bois-dark-btn {\n border-top-right-radius: 4px;\n border-bottom-right-radius: 4px;\n}\n.du-bois-dark-btn-group-sm > .du-bois-dark-btn:only-child {\n border-radius: 4px;\n}\n.du-bois-dark-btn-group-sm > span:only-child > .du-bois-dark-btn {\n border-radius: 4px;\n}\n.du-bois-dark-btn-group-sm > .du-bois-dark-btn:first-child:not(:last-child),\n.du-bois-dark-btn-group-sm > span:first-child:not(:last-child) > .du-bois-dark-btn {\n border-top-left-radius: 4px;\n border-bottom-left-radius: 4px;\n}\n.du-bois-dark-btn-group-sm > .du-bois-dark-btn:last-child:not(:first-child),\n.du-bois-dark-btn-group-sm > span:last-child:not(:first-child) > .du-bois-dark-btn {\n border-top-right-radius: 4px;\n border-bottom-right-radius: 4px;\n}\n.du-bois-dark-btn-group > .du-bois-dark-btn-group {\n float: left;\n}\n.du-bois-dark-btn-group > .du-bois-dark-btn-group:not(:first-child):not(:last-child) > .du-bois-dark-btn {\n border-radius: 0;\n}\n.du-bois-dark-btn-group > .du-bois-dark-btn-group:first-child:not(:last-child) > .du-bois-dark-btn:last-child {\n padding-right: 8px;\n border-top-right-radius: 0;\n border-bottom-right-radius: 0;\n}\n.du-bois-dark-btn-group > .du-bois-dark-btn-group:last-child:not(:first-child) > .du-bois-dark-btn:first-child {\n padding-left: 8px;\n border-top-left-radius: 0;\n border-bottom-left-radius: 0;\n}\n.du-bois-dark-btn:focus > span,\n.du-bois-dark-btn:active > span {\n position: relative;\n}\n.du-bois-dark-btn > .anticon + span,\n.du-bois-dark-btn > span + .anticon {\n margin-left: 8px;\n}\n.du-bois-dark-btn-background-ghost {\n color: #E8ECF0;\n background: transparent !important;\n border-color: rgba(255, 255, 255, 0.25);\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-primary {\n color: #2272B4;\n background: transparent;\n border-color: #2272B4;\n text-shadow: none;\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-primary > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-primary > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-primary:hover,\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-primary:focus {\n color: #14538f;\n background: transparent;\n border-color: #14538f;\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-primary:hover > a:only-child,\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-primary:focus > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-primary:hover > a:only-child::after,\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-primary:focus > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-primary:active {\n color: #448dc2;\n background: transparent;\n border-color: #448dc2;\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-primary:active > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-primary:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-primary[disabled],\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-primary[disabled]:hover,\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-primary[disabled]:focus,\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-primary[disabled]:active {\n color: rgba(255, 255, 255, 0.3);\n background: rgba(255, 255, 255, 0.08);\n border-color: #1F272D;\n text-shadow: none;\n box-shadow: none;\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-primary[disabled] > a:only-child,\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-primary[disabled]:hover > a:only-child,\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-primary[disabled]:focus > a:only-child,\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-primary[disabled]:active > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-primary[disabled] > a:only-child::after,\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-primary[disabled]:hover > a:only-child::after,\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-primary[disabled]:focus > a:only-child::after,\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-primary[disabled]:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-danger {\n color: #E65B77;\n background: transparent;\n border-color: #E65B77;\n text-shadow: none;\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-danger > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-danger > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-danger:hover,\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-danger:focus {\n color: #bf4360;\n background: transparent;\n border-color: #bf4360;\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-danger:hover > a:only-child,\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-danger:focus > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-danger:hover > a:only-child::after,\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-danger:focus > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-danger:active {\n color: #f28899;\n background: transparent;\n border-color: #f28899;\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-danger:active > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-danger:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-danger[disabled],\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-danger[disabled]:hover,\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-danger[disabled]:focus,\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-danger[disabled]:active {\n color: rgba(255, 255, 255, 0.3);\n background: rgba(255, 255, 255, 0.08);\n border-color: #1F272D;\n text-shadow: none;\n box-shadow: none;\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-danger[disabled] > a:only-child,\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-danger[disabled]:hover > a:only-child,\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-danger[disabled]:focus > a:only-child,\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-danger[disabled]:active > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-danger[disabled] > a:only-child::after,\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-danger[disabled]:hover > a:only-child::after,\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-danger[disabled]:focus > a:only-child::after,\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-danger[disabled]:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous {\n color: #E65B77;\n background: transparent;\n border-color: #E65B77;\n text-shadow: none;\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous:hover,\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous:focus {\n color: #bf4360;\n background: transparent;\n border-color: #bf4360;\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous:hover > a:only-child,\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous:focus > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous:hover > a:only-child::after,\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous:focus > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous:active {\n color: #f28899;\n background: transparent;\n border-color: #f28899;\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous:active > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous[disabled],\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous[disabled]:hover,\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous[disabled]:focus,\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous[disabled]:active {\n color: rgba(255, 255, 255, 0.3);\n background: rgba(255, 255, 255, 0.08);\n border-color: #1F272D;\n text-shadow: none;\n box-shadow: none;\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous[disabled] > a:only-child,\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous[disabled]:hover > a:only-child,\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous[disabled]:focus > a:only-child,\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous[disabled]:active > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous[disabled] > a:only-child::after,\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous[disabled]:hover > a:only-child::after,\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous[disabled]:focus > a:only-child::after,\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous[disabled]:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous.du-bois-dark-btn-link {\n color: #E65B77;\n background: transparent;\n border-color: transparent;\n text-shadow: none;\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous.du-bois-dark-btn-link > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous.du-bois-dark-btn-link > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous.du-bois-dark-btn-link:hover,\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous.du-bois-dark-btn-link:focus {\n color: #bf4360;\n background: transparent;\n border-color: transparent;\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous.du-bois-dark-btn-link:hover > a:only-child,\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous.du-bois-dark-btn-link:focus > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous.du-bois-dark-btn-link:hover > a:only-child::after,\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous.du-bois-dark-btn-link:focus > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous.du-bois-dark-btn-link:active {\n color: #f28899;\n background: transparent;\n border-color: transparent;\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous.du-bois-dark-btn-link:active > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous.du-bois-dark-btn-link:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous.du-bois-dark-btn-link[disabled],\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous.du-bois-dark-btn-link[disabled]:hover,\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous.du-bois-dark-btn-link[disabled]:focus,\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous.du-bois-dark-btn-link[disabled]:active {\n color: rgba(255, 255, 255, 0.3);\n background: rgba(255, 255, 255, 0.08);\n border-color: #1F272D;\n text-shadow: none;\n box-shadow: none;\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous.du-bois-dark-btn-link[disabled] > a:only-child,\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous.du-bois-dark-btn-link[disabled]:hover > a:only-child,\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous.du-bois-dark-btn-link[disabled]:focus > a:only-child,\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous.du-bois-dark-btn-link[disabled]:active > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous.du-bois-dark-btn-link[disabled] > a:only-child::after,\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous.du-bois-dark-btn-link[disabled]:hover > a:only-child::after,\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous.du-bois-dark-btn-link[disabled]:focus > a:only-child::after,\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous.du-bois-dark-btn-link[disabled]:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-two-chinese-chars::first-letter {\n letter-spacing: 0.34em;\n}\n.du-bois-dark-btn-two-chinese-chars > *:not(.anticon) {\n margin-right: -0.34em;\n letter-spacing: 0.34em;\n}\n.du-bois-dark-btn-block {\n width: 100%;\n}\n.du-bois-dark-btn:empty {\n display: inline-block;\n width: 0;\n visibility: hidden;\n content: '\\a0';\n}\na.du-bois-dark-btn {\n padding-top: 0.01px !important;\n line-height: 38px;\n}\na.du-bois-dark-btn-lg {\n line-height: 38px;\n}\na.du-bois-dark-btn-sm {\n line-height: 30px;\n}\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-dark-alert {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #E8ECF0;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: tnum;\n position: relative;\n display: flex;\n align-items: center;\n padding: 8px 15px;\n word-wrap: break-word;\n border-radius: 4px;\n}\n.du-bois-dark-alert-content {\n flex: 1;\n min-width: 0;\n}\n.du-bois-dark-alert-icon {\n margin-right: 8px;\n}\n.du-bois-dark-alert-description {\n display: none;\n font-size: 13px;\n line-height: 21px;\n}\n.du-bois-dark-alert-success {\n background-color: #162312;\n border: 1px solid #274916;\n}\n.du-bois-dark-alert-success .du-bois-dark-alert-icon {\n color: #34824F;\n}\n.du-bois-dark-alert-info {\n background-color: transparent;\n border: 1px solid #C0CDD8;\n}\n.du-bois-dark-alert-info .du-bois-dark-alert-icon {\n color: #92A4B3;\n}\n.du-bois-dark-alert-warning {\n background-color: transparent;\n border: 1px solid #F8D4A5;\n}\n.du-bois-dark-alert-warning .du-bois-dark-alert-icon {\n color: #DE7921;\n}\n.du-bois-dark-alert-error {\n background-color: transparent;\n border: 1px solid #FBD0D8;\n}\n.du-bois-dark-alert-error .du-bois-dark-alert-icon {\n color: #E65B77;\n}\n.du-bois-dark-alert-error .du-bois-dark-alert-description > pre {\n margin: 0;\n padding: 0;\n}\n.du-bois-dark-alert-action {\n margin-left: 8px;\n}\n.du-bois-dark-alert-close-icon {\n margin-left: 8px;\n padding: 0;\n overflow: hidden;\n font-size: 12px;\n line-height: 12px;\n background-color: transparent;\n border: none;\n outline: none;\n cursor: pointer;\n}\n.du-bois-dark-alert-close-icon .anticon-close {\n color: #92A4B3;\n transition: color 0.3s;\n}\n.du-bois-dark-alert-close-icon .anticon-close:hover {\n color: rgba(255, 255, 255, 0.75);\n}\n.du-bois-dark-alert-close-text {\n color: #92A4B3;\n transition: color 0.3s;\n}\n.du-bois-dark-alert-close-text:hover {\n color: rgba(255, 255, 255, 0.75);\n}\n.du-bois-dark-alert-with-description {\n align-items: flex-start;\n padding: 7px 15px 7px 24px;\n}\n.du-bois-dark-alert-with-description.du-bois-dark-alert-no-icon {\n padding: 7px 15px;\n}\n.du-bois-dark-alert-with-description .du-bois-dark-alert-icon {\n margin-right: 7px;\n font-size: 24px;\n}\n.du-bois-dark-alert-with-description .du-bois-dark-alert-message {\n display: block;\n margin-bottom: 4px;\n color: inherit;\n font-size: 18px;\n}\n.du-bois-dark-alert-message {\n color: inherit;\n}\n.du-bois-dark-alert-with-description .du-bois-dark-alert-description {\n display: block;\n}\n.du-bois-dark-alert.du-bois-dark-alert-motion-leave {\n overflow: hidden;\n opacity: 1;\n transition: max-height 0.3s cubic-bezier(0.78, 0.14, 0.15, 0.86), opacity 0.3s cubic-bezier(0.78, 0.14, 0.15, 0.86), padding-top 0.3s cubic-bezier(0.78, 0.14, 0.15, 0.86), padding-bottom 0.3s cubic-bezier(0.78, 0.14, 0.15, 0.86), margin-bottom 0.3s cubic-bezier(0.78, 0.14, 0.15, 0.86);\n}\n.du-bois-dark-alert.du-bois-dark-alert-motion-leave-active {\n max-height: 0;\n margin-bottom: 0 !important;\n padding-top: 0;\n padding-bottom: 0;\n opacity: 0;\n}\n.du-bois-dark-alert-banner {\n margin-bottom: 0;\n border: 0;\n border-radius: 0;\n}\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n@keyframes antCheckboxEffect {\n 0% {\n transform: scale(1);\n opacity: 0.5;\n }\n 100% {\n transform: scale(1.6);\n opacity: 0;\n }\n}\n.du-bois-dark-checkbox {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #E8ECF0;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: tnum;\n position: relative;\n top: 0.2em;\n line-height: 1;\n white-space: nowrap;\n outline: none;\n cursor: pointer;\n}\n.du-bois-dark-checkbox-wrapper:hover .du-bois-dark-checkbox-inner,\n.du-bois-dark-checkbox:hover .du-bois-dark-checkbox-inner,\n.du-bois-dark-checkbox-input:focus + .du-bois-dark-checkbox-inner {\n border-color: #2272B4;\n}\n.du-bois-dark-checkbox-checked::after {\n position: absolute;\n top: 0;\n left: 0;\n width: 100%;\n height: 100%;\n border: 1px solid #2272B4;\n border-radius: 4px;\n visibility: hidden;\n animation: antCheckboxEffect 0.36s ease-in-out;\n animation-fill-mode: backwards;\n content: '';\n}\n.du-bois-dark-checkbox:hover::after,\n.du-bois-dark-checkbox-wrapper:hover .du-bois-dark-checkbox::after {\n visibility: visible;\n}\n.du-bois-dark-checkbox-inner {\n position: relative;\n top: 0;\n left: 0;\n display: block;\n width: 16px;\n height: 16px;\n direction: ltr;\n background-color: transparent;\n border: 1px solid #1F272D;\n border-radius: 4px;\n border-collapse: separate;\n transition: all 0.3s;\n}\n.du-bois-dark-checkbox-inner::after {\n position: absolute;\n top: 50%;\n left: 22%;\n display: table;\n width: 5.71428571px;\n height: 9.14285714px;\n border: 2px solid #fff;\n border-top: 0;\n border-left: 0;\n transform: rotate(45deg) scale(0) translate(-50%, -50%);\n opacity: 0;\n transition: all 0.1s cubic-bezier(0.71, -0.46, 0.88, 0.6), opacity 0.1s;\n content: ' ';\n}\n.du-bois-dark-checkbox-input {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n z-index: 1;\n width: 100%;\n height: 100%;\n cursor: pointer;\n opacity: 0;\n}\n.du-bois-dark-checkbox-checked .du-bois-dark-checkbox-inner::after {\n position: absolute;\n display: table;\n border: 2px solid #fff;\n border-top: 0;\n border-left: 0;\n transform: rotate(45deg) scale(1) translate(-50%, -50%);\n opacity: 1;\n transition: all 0.2s cubic-bezier(0.12, 0.4, 0.29, 1.46) 0.1s;\n content: ' ';\n}\n.du-bois-dark-checkbox-checked .du-bois-dark-checkbox-inner {\n background-color: #2272B4;\n border-color: #2272B4;\n}\n.du-bois-dark-checkbox-disabled {\n cursor: not-allowed;\n}\n.du-bois-dark-checkbox-disabled.du-bois-dark-checkbox-checked .du-bois-dark-checkbox-inner::after {\n border-color: rgba(255, 255, 255, 0.3);\n animation-name: none;\n}\n.du-bois-dark-checkbox-disabled .du-bois-dark-checkbox-input {\n cursor: not-allowed;\n}\n.du-bois-dark-checkbox-disabled .du-bois-dark-checkbox-inner {\n background-color: rgba(255, 255, 255, 0.08);\n border-color: #1F272D !important;\n}\n.du-bois-dark-checkbox-disabled .du-bois-dark-checkbox-inner::after {\n border-color: rgba(255, 255, 255, 0.08);\n border-collapse: separate;\n animation-name: none;\n}\n.du-bois-dark-checkbox-disabled + span {\n color: rgba(255, 255, 255, 0.3);\n cursor: not-allowed;\n}\n.du-bois-dark-checkbox-disabled:hover::after,\n.du-bois-dark-checkbox-wrapper:hover .du-bois-dark-checkbox-disabled::after {\n visibility: hidden;\n}\n.du-bois-dark-checkbox-wrapper {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #E8ECF0;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: tnum;\n display: inline-flex;\n align-items: baseline;\n line-height: unset;\n cursor: pointer;\n}\n.du-bois-dark-checkbox-wrapper::after {\n display: inline-block;\n width: 0;\n overflow: hidden;\n content: '\\a0';\n}\n.du-bois-dark-checkbox-wrapper.du-bois-dark-checkbox-wrapper-disabled {\n cursor: not-allowed;\n}\n.du-bois-dark-checkbox-wrapper + .du-bois-dark-checkbox-wrapper {\n margin-left: 8px;\n}\n.du-bois-dark-checkbox + span {\n padding-right: 8px;\n padding-left: 8px;\n}\n.du-bois-dark-checkbox-group {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #E8ECF0;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: tnum;\n display: inline-block;\n}\n.du-bois-dark-checkbox-group-item {\n margin-right: 8px;\n}\n.du-bois-dark-checkbox-group-item:last-child {\n margin-right: 0;\n}\n.du-bois-dark-checkbox-group-item + .du-bois-dark-checkbox-group-item {\n margin-left: 0;\n}\n.du-bois-dark-checkbox-indeterminate .du-bois-dark-checkbox-inner {\n background-color: transparent;\n border-color: #1F272D;\n}\n.du-bois-dark-checkbox-indeterminate .du-bois-dark-checkbox-inner::after {\n top: 50%;\n left: 50%;\n width: 8px;\n height: 8px;\n background-color: #2272B4;\n border: 0;\n transform: translate(-50%, -50%) scale(1);\n opacity: 1;\n content: ' ';\n}\n.du-bois-dark-checkbox-indeterminate.du-bois-dark-checkbox-disabled .du-bois-dark-checkbox-inner::after {\n background-color: rgba(255, 255, 255, 0.3);\n border-color: rgba(255, 255, 255, 0.3);\n}\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-dark-select-auto-complete {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #E8ECF0;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: tnum;\n}\n.du-bois-dark-select-auto-complete .du-bois-dark-select-clear {\n right: 13px;\n}\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-dark-select-single .du-bois-dark-select-selector {\n display: flex;\n}\n.du-bois-dark-select-single .du-bois-dark-select-selector .du-bois-dark-select-selection-search {\n position: absolute;\n top: 0;\n right: 12px;\n bottom: 0;\n left: 12px;\n}\n.du-bois-dark-select-single .du-bois-dark-select-selector .du-bois-dark-select-selection-search-input {\n width: 100%;\n}\n.du-bois-dark-select-single .du-bois-dark-select-selector .du-bois-dark-select-selection-item,\n.du-bois-dark-select-single .du-bois-dark-select-selector .du-bois-dark-select-selection-placeholder {\n padding: 0;\n line-height: 38px;\n transition: all 0.3s;\n}\n@supports (-moz-appearance: meterbar) {\n .du-bois-dark-select-single .du-bois-dark-select-selector .du-bois-dark-select-selection-item,\n .du-bois-dark-select-single .du-bois-dark-select-selector .du-bois-dark-select-selection-placeholder {\n line-height: 38px;\n }\n}\n.du-bois-dark-select-single .du-bois-dark-select-selector .du-bois-dark-select-selection-item {\n position: relative;\n user-select: none;\n}\n.du-bois-dark-select-single .du-bois-dark-select-selector .du-bois-dark-select-selection-placeholder {\n pointer-events: none;\n}\n.du-bois-dark-select-single .du-bois-dark-select-selector::after,\n.du-bois-dark-select-single .du-bois-dark-select-selector .du-bois-dark-select-selection-item::after,\n.du-bois-dark-select-single .du-bois-dark-select-selector .du-bois-dark-select-selection-placeholder::after {\n display: inline-block;\n width: 0;\n visibility: hidden;\n content: '\\a0';\n}\n.du-bois-dark-select-single.du-bois-dark-select-show-arrow .du-bois-dark-select-selection-search {\n right: 25px;\n}\n.du-bois-dark-select-single.du-bois-dark-select-show-arrow .du-bois-dark-select-selection-item,\n.du-bois-dark-select-single.du-bois-dark-select-show-arrow .du-bois-dark-select-selection-placeholder {\n padding-right: 17px;\n}\n.du-bois-dark-select-single.du-bois-dark-select-open .du-bois-dark-select-selection-item {\n color: rgba(255, 255, 255, 0.3);\n}\n.du-bois-dark-select-single:not(.du-bois-dark-select-customize-input) .du-bois-dark-select-selector {\n width: 100%;\n height: 40px;\n padding: 0 12px;\n}\n.du-bois-dark-select-single:not(.du-bois-dark-select-customize-input) .du-bois-dark-select-selector .du-bois-dark-select-selection-search-input {\n height: 38px;\n}\n.du-bois-dark-select-single:not(.du-bois-dark-select-customize-input) .du-bois-dark-select-selector::after {\n line-height: 38px;\n}\n.du-bois-dark-select-single.du-bois-dark-select-customize-input .du-bois-dark-select-selector::after {\n display: none;\n}\n.du-bois-dark-select-single.du-bois-dark-select-customize-input .du-bois-dark-select-selector .du-bois-dark-select-selection-search {\n position: static;\n width: 100%;\n}\n.du-bois-dark-select-single.du-bois-dark-select-customize-input .du-bois-dark-select-selector .du-bois-dark-select-selection-placeholder {\n position: absolute;\n right: 0;\n left: 0;\n padding: 0 12px;\n}\n.du-bois-dark-select-single.du-bois-dark-select-customize-input .du-bois-dark-select-selector .du-bois-dark-select-selection-placeholder::after {\n display: none;\n}\n.du-bois-dark-select-single.du-bois-dark-select-lg:not(.du-bois-dark-select-customize-input) .du-bois-dark-select-selector {\n height: 40px;\n}\n.du-bois-dark-select-single.du-bois-dark-select-lg:not(.du-bois-dark-select-customize-input) .du-bois-dark-select-selector::after,\n.du-bois-dark-select-single.du-bois-dark-select-lg:not(.du-bois-dark-select-customize-input) .du-bois-dark-select-selector .du-bois-dark-select-selection-item,\n.du-bois-dark-select-single.du-bois-dark-select-lg:not(.du-bois-dark-select-customize-input) .du-bois-dark-select-selector .du-bois-dark-select-selection-placeholder {\n line-height: 38px;\n}\n.du-bois-dark-select-single.du-bois-dark-select-lg:not(.du-bois-dark-select-customize-input):not(.du-bois-dark-select-customize-input) .du-bois-dark-select-selection-search-input {\n height: 38px;\n}\n.du-bois-dark-select-single.du-bois-dark-select-sm:not(.du-bois-dark-select-customize-input) .du-bois-dark-select-selector {\n height: 32px;\n}\n.du-bois-dark-select-single.du-bois-dark-select-sm:not(.du-bois-dark-select-customize-input) .du-bois-dark-select-selector::after,\n.du-bois-dark-select-single.du-bois-dark-select-sm:not(.du-bois-dark-select-customize-input) .du-bois-dark-select-selector .du-bois-dark-select-selection-item,\n.du-bois-dark-select-single.du-bois-dark-select-sm:not(.du-bois-dark-select-customize-input) .du-bois-dark-select-selector .du-bois-dark-select-selection-placeholder {\n line-height: 30px;\n}\n.du-bois-dark-select-single.du-bois-dark-select-sm:not(.du-bois-dark-select-customize-input):not(.du-bois-dark-select-customize-input) .du-bois-dark-select-selection-search-input {\n height: 30px;\n}\n.du-bois-dark-select-single.du-bois-dark-select-sm:not(.du-bois-dark-select-customize-input) .du-bois-dark-select-selection-search {\n right: 12px;\n left: 12px;\n}\n.du-bois-dark-select-single.du-bois-dark-select-sm:not(.du-bois-dark-select-customize-input) .du-bois-dark-select-selector {\n padding: 0 12px;\n}\n.du-bois-dark-select-single.du-bois-dark-select-sm:not(.du-bois-dark-select-customize-input).du-bois-dark-select-show-arrow .du-bois-dark-select-selection-search {\n right: 31.5px;\n}\n.du-bois-dark-select-single.du-bois-dark-select-sm:not(.du-bois-dark-select-customize-input).du-bois-dark-select-show-arrow .du-bois-dark-select-selection-item,\n.du-bois-dark-select-single.du-bois-dark-select-sm:not(.du-bois-dark-select-customize-input).du-bois-dark-select-show-arrow .du-bois-dark-select-selection-placeholder {\n padding-right: 19.5px;\n}\n.du-bois-dark-select-single.du-bois-dark-select-lg:not(.du-bois-dark-select-customize-input) .du-bois-dark-select-selector {\n padding: 0 12px;\n}\n/**\n * Do not merge `height` & `line-height` under style with `selection` & `search`,\n * since chrome may update to redesign with its align logic.\n */\n.du-bois-dark-select-selection-overflow {\n position: relative;\n display: flex;\n flex: auto;\n flex-wrap: wrap;\n max-width: 100%;\n}\n.du-bois-dark-select-selection-overflow-item {\n flex: none;\n align-self: center;\n max-width: 100%;\n}\n.du-bois-dark-select-multiple .du-bois-dark-select-selector {\n display: flex;\n flex-wrap: wrap;\n align-items: center;\n padding: 1px 5px;\n}\n.du-bois-dark-select-show-search.du-bois-dark-select-multiple .du-bois-dark-select-selector {\n cursor: text;\n}\n.du-bois-dark-select-disabled.du-bois-dark-select-multiple .du-bois-dark-select-selector {\n background: #141414;\n cursor: not-allowed;\n}\n.du-bois-dark-select-multiple .du-bois-dark-select-selector::after {\n display: inline-block;\n width: 0;\n margin: 3px 0;\n line-height: 30px;\n content: '\\a0';\n}\n.du-bois-dark-select-multiple.du-bois-dark-select-show-arrow .du-bois-dark-select-selector,\n.du-bois-dark-select-multiple.du-bois-dark-select-allow-clear .du-bois-dark-select-selector {\n padding-right: 20px;\n}\n.du-bois-dark-select-multiple .du-bois-dark-select-selection-item {\n position: relative;\n display: flex;\n flex: none;\n box-sizing: border-box;\n max-width: 100%;\n height: 30px;\n margin-top: 3px;\n margin-bottom: 3px;\n line-height: 28px;\n background: rgba(255, 255, 255, 0.08);\n border: 1px solid #303030;\n border-radius: 4px;\n cursor: default;\n transition: font-size 0.3s, line-height 0.3s, height 0.3s;\n user-select: none;\n margin-inline-end: 5px;\n padding-inline-start: 4px;\n padding-inline-end: 2px;\n}\n.du-bois-dark-select-disabled.du-bois-dark-select-multiple .du-bois-dark-select-selection-item {\n color: #595959;\n border-color: #1f1f1f;\n cursor: not-allowed;\n}\n.du-bois-dark-select-multiple .du-bois-dark-select-selection-item-content {\n display: inline-block;\n margin-right: 2px;\n overflow: hidden;\n white-space: pre;\n text-overflow: ellipsis;\n}\n.du-bois-dark-select-multiple .du-bois-dark-select-selection-item-remove {\n color: inherit;\n font-style: normal;\n line-height: 0;\n text-align: center;\n text-transform: none;\n vertical-align: -0.125em;\n text-rendering: optimizeLegibility;\n -webkit-font-smoothing: antialiased;\n -moz-osx-font-smoothing: grayscale;\n display: inline-block;\n color: #92A4B3;\n font-weight: bold;\n font-size: 10px;\n line-height: inherit;\n cursor: pointer;\n}\n.du-bois-dark-select-multiple .du-bois-dark-select-selection-item-remove > * {\n line-height: 1;\n}\n.du-bois-dark-select-multiple .du-bois-dark-select-selection-item-remove svg {\n display: inline-block;\n}\n.du-bois-dark-select-multiple .du-bois-dark-select-selection-item-remove::before {\n display: none;\n}\n.du-bois-dark-select-multiple .du-bois-dark-select-selection-item-remove .du-bois-dark-select-multiple .du-bois-dark-select-selection-item-remove-icon {\n display: block;\n}\n.du-bois-dark-select-multiple .du-bois-dark-select-selection-item-remove > .anticon {\n vertical-align: -0.2em;\n}\n.du-bois-dark-select-multiple .du-bois-dark-select-selection-item-remove:hover {\n color: rgba(255, 255, 255, 0.75);\n}\n.du-bois-dark-select-multiple .du-bois-dark-select-selection-overflow-item + .du-bois-dark-select-selection-overflow-item .du-bois-dark-select-selection-search {\n margin-inline-start: 0;\n}\n.du-bois-dark-select-multiple .du-bois-dark-select-selection-search {\n position: relative;\n max-width: 100%;\n margin-top: 3px;\n margin-bottom: 3px;\n margin-inline-start: 7px;\n}\n.du-bois-dark-select-multiple .du-bois-dark-select-selection-search-input,\n.du-bois-dark-select-multiple .du-bois-dark-select-selection-search-mirror {\n height: 30px;\n font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, 'Helvetica Neue', Arial, 'Noto Sans', sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol', 'Noto Color Emoji';\n line-height: 30px;\n transition: all 0.3s;\n}\n.du-bois-dark-select-multiple .du-bois-dark-select-selection-search-input {\n width: 100%;\n min-width: 4.1px;\n}\n.du-bois-dark-select-multiple .du-bois-dark-select-selection-search-mirror {\n position: absolute;\n top: 0;\n left: 0;\n z-index: 999;\n white-space: pre;\n visibility: hidden;\n}\n.du-bois-dark-select-multiple .du-bois-dark-select-selection-placeholder {\n position: absolute;\n top: 50%;\n right: 12px;\n left: 12px;\n transform: translateY(-50%);\n transition: all 0.3s;\n}\n.du-bois-dark-select-multiple.du-bois-dark-select-lg .du-bois-dark-select-selector::after {\n line-height: 30px;\n}\n.du-bois-dark-select-multiple.du-bois-dark-select-lg .du-bois-dark-select-selection-item {\n height: 30px;\n line-height: 28px;\n}\n.du-bois-dark-select-multiple.du-bois-dark-select-lg .du-bois-dark-select-selection-search {\n height: 30px;\n line-height: 30px;\n}\n.du-bois-dark-select-multiple.du-bois-dark-select-lg .du-bois-dark-select-selection-search-input,\n.du-bois-dark-select-multiple.du-bois-dark-select-lg .du-bois-dark-select-selection-search-mirror {\n height: 30px;\n line-height: 28px;\n}\n.du-bois-dark-select-multiple.du-bois-dark-select-sm .du-bois-dark-select-selector::after {\n line-height: 22px;\n}\n.du-bois-dark-select-multiple.du-bois-dark-select-sm .du-bois-dark-select-selection-item {\n height: 22px;\n line-height: 20px;\n}\n.du-bois-dark-select-multiple.du-bois-dark-select-sm .du-bois-dark-select-selection-search {\n height: 22px;\n line-height: 22px;\n}\n.du-bois-dark-select-multiple.du-bois-dark-select-sm .du-bois-dark-select-selection-search-input,\n.du-bois-dark-select-multiple.du-bois-dark-select-sm .du-bois-dark-select-selection-search-mirror {\n height: 22px;\n line-height: 20px;\n}\n.du-bois-dark-select-multiple.du-bois-dark-select-sm .du-bois-dark-select-selection-placeholder {\n left: 12px;\n}\n.du-bois-dark-select-multiple.du-bois-dark-select-sm .du-bois-dark-select-selection-search {\n margin-inline-start: 3px;\n}\n.du-bois-dark-select-multiple.du-bois-dark-select-lg .du-bois-dark-select-selection-item {\n height: 32px;\n line-height: 32px;\n}\n.du-bois-dark-select-disabled .du-bois-dark-select-selection-item-remove {\n display: none;\n}\n/* Reset search input style */\n.du-bois-dark-select {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #E8ECF0;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: tnum;\n position: relative;\n display: inline-block;\n cursor: pointer;\n}\n.du-bois-dark-select:not(.du-bois-dark-select-customize-input) .du-bois-dark-select-selector {\n position: relative;\n background-color: transparent;\n border: 1px solid #1F272D;\n border-radius: 4px;\n transition: all 0.3s cubic-bezier(0.645, 0.045, 0.355, 1);\n}\n.du-bois-dark-select:not(.du-bois-dark-select-customize-input) .du-bois-dark-select-selector input {\n cursor: pointer;\n}\n.du-bois-dark-select-show-search.du-bois-dark-select:not(.du-bois-dark-select-customize-input) .du-bois-dark-select-selector {\n cursor: text;\n}\n.du-bois-dark-select-show-search.du-bois-dark-select:not(.du-bois-dark-select-customize-input) .du-bois-dark-select-selector input {\n cursor: auto;\n}\n.du-bois-dark-select-focused:not(.du-bois-dark-select-disabled).du-bois-dark-select:not(.du-bois-dark-select-customize-input) .du-bois-dark-select-selector {\n border-color: #2272B4;\n border-right-width: 1px !important;\n outline: 0;\n box-shadow: 0 0 0 2px rgba(34, 114, 180, 0.2);\n}\n.du-bois-dark-select-disabled.du-bois-dark-select:not(.du-bois-dark-select-customize-input) .du-bois-dark-select-selector {\n color: rgba(255, 255, 255, 0.3);\n background: rgba(255, 255, 255, 0.08);\n cursor: not-allowed;\n}\n.du-bois-dark-select-multiple.du-bois-dark-select-disabled.du-bois-dark-select:not(.du-bois-dark-select-customize-input) .du-bois-dark-select-selector {\n background: #141414;\n}\n.du-bois-dark-select-disabled.du-bois-dark-select:not(.du-bois-dark-select-customize-input) .du-bois-dark-select-selector input {\n cursor: not-allowed;\n}\n.du-bois-dark-select:not(.du-bois-dark-select-customize-input) .du-bois-dark-select-selector .du-bois-dark-select-selection-search-input {\n margin: 0;\n padding: 0;\n background: transparent;\n border: none;\n outline: none;\n appearance: none;\n}\n.du-bois-dark-select:not(.du-bois-dark-select-customize-input) .du-bois-dark-select-selector .du-bois-dark-select-selection-search-input::-webkit-search-cancel-button {\n display: none;\n -webkit-appearance: none;\n}\n.du-bois-dark-select:not(.du-bois-dark-select-disabled):hover .du-bois-dark-select-selector {\n border-color: #1d517c;\n border-right-width: 1px !important;\n}\n.du-bois-dark-select-selection-item {\n flex: 1;\n overflow: hidden;\n white-space: nowrap;\n text-overflow: ellipsis;\n}\n@media all and (-ms-high-contrast: none) {\n .du-bois-dark-select-selection-item *::-ms-backdrop,\n .du-bois-dark-select-selection-item {\n flex: auto;\n }\n}\n.du-bois-dark-select-selection-placeholder {\n flex: 1;\n overflow: hidden;\n color: rgba(255, 255, 255, 0.3);\n white-space: nowrap;\n text-overflow: ellipsis;\n pointer-events: none;\n}\n@media all and (-ms-high-contrast: none) {\n .du-bois-dark-select-selection-placeholder *::-ms-backdrop,\n .du-bois-dark-select-selection-placeholder {\n flex: auto;\n }\n}\n.du-bois-dark-select-arrow {\n display: inline-block;\n color: inherit;\n font-style: normal;\n line-height: 0;\n text-transform: none;\n vertical-align: -0.125em;\n text-rendering: optimizeLegibility;\n -webkit-font-smoothing: antialiased;\n -moz-osx-font-smoothing: grayscale;\n position: absolute;\n top: 50%;\n right: 7px;\n width: 12px;\n height: 12px;\n margin-top: -6px;\n color: rgba(255, 255, 255, 0.3);\n font-size: 12px;\n line-height: 1;\n text-align: center;\n pointer-events: none;\n}\n.du-bois-dark-select-arrow > * {\n line-height: 1;\n}\n.du-bois-dark-select-arrow svg {\n display: inline-block;\n}\n.du-bois-dark-select-arrow::before {\n display: none;\n}\n.du-bois-dark-select-arrow .du-bois-dark-select-arrow-icon {\n display: block;\n}\n.du-bois-dark-select-arrow .anticon {\n vertical-align: top;\n transition: transform 0.3s;\n}\n.du-bois-dark-select-arrow .anticon > svg {\n vertical-align: top;\n}\n.du-bois-dark-select-arrow .anticon:not(.du-bois-dark-select-suffix) {\n pointer-events: auto;\n}\n.du-bois-dark-select-disabled .du-bois-dark-select-arrow {\n cursor: not-allowed;\n}\n.du-bois-dark-select-clear {\n position: absolute;\n top: 50%;\n right: 7px;\n z-index: 1;\n display: inline-block;\n width: 12px;\n height: 12px;\n margin-top: -6px;\n color: rgba(255, 255, 255, 0.3);\n font-size: 12px;\n font-style: normal;\n line-height: 1;\n text-align: center;\n text-transform: none;\n background: #141414;\n cursor: pointer;\n opacity: 0;\n transition: color 0.3s ease, opacity 0.15s ease;\n text-rendering: auto;\n}\n.du-bois-dark-select-clear::before {\n display: block;\n}\n.du-bois-dark-select-clear:hover {\n color: #92A4B3;\n}\n.du-bois-dark-select:hover .du-bois-dark-select-clear {\n opacity: 1;\n}\n.du-bois-dark-select-dropdown {\n margin: 0;\n padding: 0;\n color: #E8ECF0;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: tnum;\n position: absolute;\n top: -9999px;\n left: -9999px;\n z-index: 1050;\n box-sizing: border-box;\n padding: 4px 0;\n overflow: hidden;\n font-size: 13px;\n font-variant: initial;\n background-color: #1f1f1f;\n border-radius: 4px;\n outline: none;\n box-shadow: 0 3px 6px -4px rgba(0, 0, 0, 0.48), 0 6px 16px 0 rgba(0, 0, 0, 0.32), 0 9px 28px 8px rgba(0, 0, 0, 0.2);\n}\n.du-bois-dark-select-dropdown.du-bois-dark-slide-up-enter.du-bois-dark-slide-up-enter-active.du-bois-dark-select-dropdown-placement-bottomLeft,\n.du-bois-dark-select-dropdown.du-bois-dark-slide-up-appear.du-bois-dark-slide-up-appear-active.du-bois-dark-select-dropdown-placement-bottomLeft {\n animation-name: antSlideUpIn;\n}\n.du-bois-dark-select-dropdown.du-bois-dark-slide-up-enter.du-bois-dark-slide-up-enter-active.du-bois-dark-select-dropdown-placement-topLeft,\n.du-bois-dark-select-dropdown.du-bois-dark-slide-up-appear.du-bois-dark-slide-up-appear-active.du-bois-dark-select-dropdown-placement-topLeft {\n animation-name: antSlideDownIn;\n}\n.du-bois-dark-select-dropdown.du-bois-dark-slide-up-leave.du-bois-dark-slide-up-leave-active.du-bois-dark-select-dropdown-placement-bottomLeft {\n animation-name: antSlideUpOut;\n}\n.du-bois-dark-select-dropdown.du-bois-dark-slide-up-leave.du-bois-dark-slide-up-leave-active.du-bois-dark-select-dropdown-placement-topLeft {\n animation-name: antSlideDownOut;\n}\n.du-bois-dark-select-dropdown-hidden {\n display: none;\n}\n.du-bois-dark-select-dropdown-empty {\n color: rgba(255, 255, 255, 0.3);\n}\n.du-bois-dark-select-item-empty {\n position: relative;\n display: block;\n min-height: 32px;\n padding: 5px 8px;\n color: #E8ECF0;\n font-weight: normal;\n font-size: 13px;\n line-height: 22px;\n color: rgba(255, 255, 255, 0.3);\n}\n.du-bois-dark-select-item {\n position: relative;\n display: block;\n min-height: 32px;\n padding: 5px 8px;\n color: #E8ECF0;\n font-weight: normal;\n font-size: 13px;\n line-height: 22px;\n cursor: pointer;\n transition: background 0.3s ease;\n}\n.du-bois-dark-select-item-group {\n color: #92A4B3;\n font-size: 12px;\n cursor: default;\n}\n.du-bois-dark-select-item-option {\n display: flex;\n}\n.du-bois-dark-select-item-option-content {\n flex: auto;\n overflow: hidden;\n white-space: nowrap;\n text-overflow: ellipsis;\n}\n.du-bois-dark-select-item-option-state {\n flex: none;\n}\n.du-bois-dark-select-item-option-active:not(.du-bois-dark-select-item-option-disabled) {\n background-color: rgba(255, 255, 255, 0.08);\n}\n.du-bois-dark-select-item-option-selected:not(.du-bois-dark-select-item-option-disabled) {\n color: #E8ECF0;\n font-weight: 600;\n background-color: #121921;\n}\n.du-bois-dark-select-item-option-selected:not(.du-bois-dark-select-item-option-disabled) .du-bois-dark-select-item-option-state {\n color: #2272B4;\n}\n.du-bois-dark-select-item-option-disabled {\n color: rgba(255, 255, 255, 0.3);\n cursor: not-allowed;\n}\n.du-bois-dark-select-item-option-disabled.du-bois-dark-select-item-option-selected {\n background-color: #141414;\n}\n.du-bois-dark-select-item-option-grouped {\n padding-left: 16px;\n}\n.du-bois-dark-select-lg {\n font-size: 18px;\n}\n.du-bois-dark-select-borderless .du-bois-dark-select-selector {\n background-color: transparent !important;\n border-color: transparent !important;\n box-shadow: none !important;\n}\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-dark-empty {\n margin: 0 8px;\n font-size: 13px;\n line-height: 20px;\n text-align: center;\n}\n.du-bois-dark-empty-image {\n height: 100px;\n margin-bottom: 8px;\n}\n.du-bois-dark-empty-image img {\n height: 100%;\n}\n.du-bois-dark-empty-image svg {\n height: 100%;\n margin: auto;\n}\n.du-bois-dark-empty-footer {\n margin-top: 16px;\n}\n.du-bois-dark-empty-normal {\n margin: 32px 0;\n color: rgba(255, 255, 255, 0.3);\n}\n.du-bois-dark-empty-normal .du-bois-dark-empty-image {\n height: 40px;\n}\n.du-bois-dark-empty-small {\n margin: 8px 0;\n color: rgba(255, 255, 255, 0.3);\n}\n.du-bois-dark-empty-small .du-bois-dark-empty-image {\n height: 35px;\n}\n.du-bois-dark-empty-img-default-ellipse {\n fill: #fff;\n fill-opacity: 0.08;\n}\n.du-bois-dark-empty-img-default-path-1 {\n fill: #262626;\n}\n.du-bois-dark-empty-img-default-path-2 {\n fill: url(#linearGradient-1);\n}\n.du-bois-dark-empty-img-default-path-3 {\n fill: #595959;\n}\n.du-bois-dark-empty-img-default-path-4 {\n fill: #434343;\n}\n.du-bois-dark-empty-img-default-path-5 {\n fill: #595959;\n}\n.du-bois-dark-empty-img-default-g {\n fill: #434343;\n}\n.du-bois-dark-empty-img-simple-ellipse {\n fill: #fff;\n fill-opacity: 0.08;\n}\n.du-bois-dark-empty-img-simple-g {\n stroke: #434343;\n}\n.du-bois-dark-empty-img-simple-path {\n fill: #262626;\n stroke: #434343;\n}\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-dark-collapse {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #E8ECF0;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: tnum;\n background-color: rgba(255, 255, 255, 0.04);\n border: 1px solid #1F272D;\n border-bottom: 0;\n border-radius: 4px;\n}\n.du-bois-dark-collapse > .du-bois-dark-collapse-item {\n border-bottom: 1px solid #1F272D;\n}\n.du-bois-dark-collapse > .du-bois-dark-collapse-item:last-child,\n.du-bois-dark-collapse > .du-bois-dark-collapse-item:last-child > .du-bois-dark-collapse-header {\n border-radius: 0 0 4px 4px;\n}\n.du-bois-dark-collapse > .du-bois-dark-collapse-item > .du-bois-dark-collapse-header {\n position: relative;\n padding: 8px 8px;\n color: rgba(255, 255, 255, 0.85);\n line-height: 20px;\n cursor: pointer;\n transition: all 0.3s, visibility 0s;\n}\n.du-bois-dark-collapse > .du-bois-dark-collapse-item > .du-bois-dark-collapse-header::before {\n display: table;\n content: '';\n}\n.du-bois-dark-collapse > .du-bois-dark-collapse-item > .du-bois-dark-collapse-header::after {\n display: table;\n clear: both;\n content: '';\n}\n.du-bois-dark-collapse > .du-bois-dark-collapse-item > .du-bois-dark-collapse-header .du-bois-dark-collapse-arrow {\n display: inline-block;\n margin-right: 12px;\n font-size: 12px;\n vertical-align: -1px;\n}\n.du-bois-dark-collapse > .du-bois-dark-collapse-item > .du-bois-dark-collapse-header .du-bois-dark-collapse-arrow svg {\n transition: transform 0.24s;\n}\n.du-bois-dark-collapse > .du-bois-dark-collapse-item > .du-bois-dark-collapse-header .du-bois-dark-collapse-extra {\n float: right;\n}\n.du-bois-dark-collapse > .du-bois-dark-collapse-item > .du-bois-dark-collapse-header:focus {\n outline: none;\n}\n.du-bois-dark-collapse > .du-bois-dark-collapse-item .du-bois-dark-collapse-header-collapsible-only {\n cursor: default;\n}\n.du-bois-dark-collapse > .du-bois-dark-collapse-item .du-bois-dark-collapse-header-collapsible-only .du-bois-dark-collapse-header-text {\n cursor: pointer;\n}\n.du-bois-dark-collapse > .du-bois-dark-collapse-item.du-bois-dark-collapse-no-arrow > .du-bois-dark-collapse-header {\n padding-left: 12px;\n}\n.du-bois-dark-collapse-icon-position-right > .du-bois-dark-collapse-item > .du-bois-dark-collapse-header {\n padding: 8px 8px;\n padding-right: 40px;\n}\n.du-bois-dark-collapse-icon-position-right > .du-bois-dark-collapse-item > .du-bois-dark-collapse-header .du-bois-dark-collapse-arrow {\n position: absolute;\n top: 50%;\n right: 8px;\n left: auto;\n margin: 0;\n transform: translateY(-50%);\n}\n.du-bois-dark-collapse-content {\n color: #E8ECF0;\n background-color: #141414;\n border-top: 1px solid #1F272D;\n}\n.du-bois-dark-collapse-content > .du-bois-dark-collapse-content-box {\n padding: 8px;\n}\n.du-bois-dark-collapse-content-hidden {\n display: none;\n}\n.du-bois-dark-collapse-item:last-child > .du-bois-dark-collapse-content {\n border-radius: 0 0 4px 4px;\n}\n.du-bois-dark-collapse-borderless {\n background-color: rgba(255, 255, 255, 0.04);\n border: 0;\n}\n.du-bois-dark-collapse-borderless > .du-bois-dark-collapse-item {\n border-bottom: 1px solid #1F272D;\n}\n.du-bois-dark-collapse-borderless > .du-bois-dark-collapse-item:last-child,\n.du-bois-dark-collapse-borderless > .du-bois-dark-collapse-item:last-child .du-bois-dark-collapse-header {\n border-radius: 0;\n}\n.du-bois-dark-collapse-borderless > .du-bois-dark-collapse-item > .du-bois-dark-collapse-content {\n background-color: transparent;\n border-top: 0;\n}\n.du-bois-dark-collapse-borderless > .du-bois-dark-collapse-item > .du-bois-dark-collapse-content > .du-bois-dark-collapse-content-box {\n padding-top: 4px;\n}\n.du-bois-dark-collapse-ghost {\n background-color: transparent;\n border: 0;\n}\n.du-bois-dark-collapse-ghost > .du-bois-dark-collapse-item {\n border-bottom: 0;\n}\n.du-bois-dark-collapse-ghost > .du-bois-dark-collapse-item > .du-bois-dark-collapse-content {\n background-color: transparent;\n border-top: 0;\n}\n.du-bois-dark-collapse-ghost > .du-bois-dark-collapse-item > .du-bois-dark-collapse-content > .du-bois-dark-collapse-content-box {\n padding-top: 12px;\n padding-bottom: 12px;\n}\n.du-bois-dark-collapse .du-bois-dark-collapse-item-disabled > .du-bois-dark-collapse-header,\n.du-bois-dark-collapse .du-bois-dark-collapse-item-disabled > .du-bois-dark-collapse-header > .arrow {\n color: rgba(255, 255, 255, 0.3);\n cursor: not-allowed;\n}\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-dark-picker {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #E8ECF0;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: tnum;\n padding: 0 12px 0;\n position: relative;\n display: inline-flex;\n align-items: center;\n background: transparent;\n border: 1px solid #1F272D;\n border-radius: 4px;\n transition: border 0.3s, box-shadow 0.3s;\n}\n.du-bois-dark-picker:hover,\n.du-bois-dark-picker-focused {\n border-color: #1d517c;\n border-right-width: 1px !important;\n}\n.du-bois-dark-picker-focused {\n border-color: #2272B4;\n border-right-width: 1px !important;\n outline: 0;\n box-shadow: 0 0 0 2px rgba(34, 114, 180, 0.2);\n}\n.du-bois-dark-picker.du-bois-dark-picker-disabled {\n background: rgba(255, 255, 255, 0.08);\n border-color: #1F272D;\n cursor: not-allowed;\n}\n.du-bois-dark-picker.du-bois-dark-picker-disabled .du-bois-dark-picker-suffix {\n color: rgba(255, 255, 255, 0.3);\n}\n.du-bois-dark-picker.du-bois-dark-picker-borderless {\n background-color: transparent !important;\n border-color: transparent !important;\n box-shadow: none !important;\n}\n.du-bois-dark-picker-input {\n position: relative;\n display: inline-flex;\n align-items: center;\n width: 100%;\n}\n.du-bois-dark-picker-input > input {\n position: relative;\n display: inline-block;\n width: 100%;\n min-width: 0;\n padding: 5px 12px;\n color: #E8ECF0;\n font-size: 13px;\n line-height: 20px;\n background-color: transparent;\n background-image: none;\n border: 1px solid #1F272D;\n border-radius: 4px;\n transition: all 0.3s;\n flex: auto;\n min-width: 1px;\n height: auto;\n padding: 0;\n background: transparent;\n border: 0;\n}\n.du-bois-dark-picker-input > input::-moz-placeholder {\n opacity: 1;\n}\n.du-bois-dark-picker-input > input::placeholder {\n color: rgba(255, 255, 255, 0.3);\n}\n.du-bois-dark-picker-input > input:placeholder-shown {\n text-overflow: ellipsis;\n}\n.du-bois-dark-picker-input > input:hover {\n border-color: #1d517c;\n border-right-width: 1px !important;\n}\n.du-bois-dark-picker-input > input:focus,\n.du-bois-dark-picker-input > input-focused {\n border-color: #2272B4;\n border-right-width: 1px !important;\n outline: 0;\n box-shadow: 0 0 0 2px rgba(34, 114, 180, 0.2);\n}\n.du-bois-dark-picker-input > input-disabled {\n color: rgba(255, 255, 255, 0.3);\n background-color: rgba(255, 255, 255, 0.08);\n cursor: not-allowed;\n opacity: 1;\n}\n.du-bois-dark-picker-input > input-disabled:hover {\n border-color: #1F272D;\n border-right-width: 1px !important;\n}\n.du-bois-dark-picker-input > input[disabled] {\n color: rgba(255, 255, 255, 0.3);\n background-color: rgba(255, 255, 255, 0.08);\n cursor: not-allowed;\n opacity: 1;\n}\n.du-bois-dark-picker-input > input[disabled]:hover {\n border-color: #1F272D;\n border-right-width: 1px !important;\n}\n.du-bois-dark-picker-input > input-borderless,\n.du-bois-dark-picker-input > input-borderless:hover,\n.du-bois-dark-picker-input > input-borderless:focus,\n.du-bois-dark-picker-input > input-borderless-focused,\n.du-bois-dark-picker-input > input-borderless-disabled,\n.du-bois-dark-picker-input > input-borderless[disabled] {\n background-color: transparent;\n border: none;\n box-shadow: none;\n}\ntextarea.du-bois-dark-picker-input > input {\n max-width: 100%;\n height: auto;\n min-height: 40px;\n line-height: 20px;\n vertical-align: bottom;\n transition: all 0.3s, height 0s;\n}\n.du-bois-dark-picker-input > input-lg {\n padding: 5px 12px;\n font-size: 18px;\n}\n.du-bois-dark-picker-input > input-sm {\n padding: 5px 12px;\n}\n.du-bois-dark-picker-input > input:focus {\n box-shadow: none;\n}\n.du-bois-dark-picker-input > input[disabled] {\n background: transparent;\n}\n.du-bois-dark-picker-input:hover .du-bois-dark-picker-clear {\n opacity: 1;\n}\n.du-bois-dark-picker-input-placeholder > input {\n color: rgba(255, 255, 255, 0.3);\n}\n.du-bois-dark-picker-large {\n padding: 0 12px 0;\n}\n.du-bois-dark-picker-large .du-bois-dark-picker-input > input {\n font-size: 18px;\n}\n.du-bois-dark-picker-small {\n padding: 0 12px 0;\n}\n.du-bois-dark-picker-suffix {\n align-self: center;\n margin-left: 2px;\n color: rgba(255, 255, 255, 0.3);\n line-height: 1;\n pointer-events: none;\n}\n.du-bois-dark-picker-suffix > * {\n vertical-align: top;\n}\n.du-bois-dark-picker-clear {\n position: absolute;\n top: 50%;\n right: 0;\n color: rgba(255, 255, 255, 0.3);\n line-height: 1;\n background: #141414;\n transform: translateY(-50%);\n cursor: pointer;\n opacity: 0;\n transition: opacity 0.3s, color 0.3s;\n}\n.du-bois-dark-picker-clear > * {\n vertical-align: top;\n}\n.du-bois-dark-picker-clear:hover {\n color: #92A4B3;\n}\n.du-bois-dark-picker-separator {\n position: relative;\n display: inline-block;\n width: 1em;\n height: 18px;\n color: rgba(255, 255, 255, 0.3);\n font-size: 18px;\n vertical-align: top;\n cursor: default;\n}\n.du-bois-dark-picker-focused .du-bois-dark-picker-separator {\n color: #92A4B3;\n}\n.du-bois-dark-picker-disabled .du-bois-dark-picker-range-separator .du-bois-dark-picker-separator {\n cursor: not-allowed;\n}\n.du-bois-dark-picker-range {\n position: relative;\n display: inline-flex;\n}\n.du-bois-dark-picker-range .du-bois-dark-picker-clear {\n right: 12px;\n}\n.du-bois-dark-picker-range:hover .du-bois-dark-picker-clear {\n opacity: 1;\n}\n.du-bois-dark-picker-range .du-bois-dark-picker-active-bar {\n bottom: -1px;\n height: 2px;\n margin-left: 12px;\n background: #2272B4;\n opacity: 0;\n transition: all 0.3s ease-out;\n pointer-events: none;\n}\n.du-bois-dark-picker-range.du-bois-dark-picker-focused .du-bois-dark-picker-active-bar {\n opacity: 1;\n}\n.du-bois-dark-picker-range-separator {\n align-items: center;\n padding: 0 4px;\n line-height: 1;\n}\n.du-bois-dark-picker-range.du-bois-dark-picker-small .du-bois-dark-picker-clear {\n right: 12px;\n}\n.du-bois-dark-picker-range.du-bois-dark-picker-small .du-bois-dark-picker-active-bar {\n margin-left: 12px;\n}\n.du-bois-dark-picker-dropdown {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #E8ECF0;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: tnum;\n position: absolute;\n z-index: 1050;\n}\n.du-bois-dark-picker-dropdown-hidden {\n display: none;\n}\n.du-bois-dark-picker-dropdown-placement-bottomLeft .du-bois-dark-picker-range-arrow {\n top: 1.66666667px;\n display: block;\n transform: rotate(-45deg);\n}\n.du-bois-dark-picker-dropdown-placement-topLeft .du-bois-dark-picker-range-arrow {\n bottom: 1.66666667px;\n display: block;\n transform: rotate(135deg);\n}\n.du-bois-dark-picker-dropdown.du-bois-dark-slide-up-enter.du-bois-dark-slide-up-enter-active.du-bois-dark-picker-dropdown-placement-topLeft,\n.du-bois-dark-picker-dropdown.du-bois-dark-slide-up-enter.du-bois-dark-slide-up-enter-active.du-bois-dark-picker-dropdown-placement-topRight,\n.du-bois-dark-picker-dropdown.du-bois-dark-slide-up-appear.du-bois-dark-slide-up-appear-active.du-bois-dark-picker-dropdown-placement-topLeft,\n.du-bois-dark-picker-dropdown.du-bois-dark-slide-up-appear.du-bois-dark-slide-up-appear-active.du-bois-dark-picker-dropdown-placement-topRight {\n animation-name: antSlideDownIn;\n}\n.du-bois-dark-picker-dropdown.du-bois-dark-slide-up-enter.du-bois-dark-slide-up-enter-active.du-bois-dark-picker-dropdown-placement-bottomLeft,\n.du-bois-dark-picker-dropdown.du-bois-dark-slide-up-enter.du-bois-dark-slide-up-enter-active.du-bois-dark-picker-dropdown-placement-bottomRight,\n.du-bois-dark-picker-dropdown.du-bois-dark-slide-up-appear.du-bois-dark-slide-up-appear-active.du-bois-dark-picker-dropdown-placement-bottomLeft,\n.du-bois-dark-picker-dropdown.du-bois-dark-slide-up-appear.du-bois-dark-slide-up-appear-active.du-bois-dark-picker-dropdown-placement-bottomRight {\n animation-name: antSlideUpIn;\n}\n.du-bois-dark-picker-dropdown.du-bois-dark-slide-up-leave.du-bois-dark-slide-up-leave-active.du-bois-dark-picker-dropdown-placement-topLeft,\n.du-bois-dark-picker-dropdown.du-bois-dark-slide-up-leave.du-bois-dark-slide-up-leave-active.du-bois-dark-picker-dropdown-placement-topRight {\n animation-name: antSlideDownOut;\n}\n.du-bois-dark-picker-dropdown.du-bois-dark-slide-up-leave.du-bois-dark-slide-up-leave-active.du-bois-dark-picker-dropdown-placement-bottomLeft,\n.du-bois-dark-picker-dropdown.du-bois-dark-slide-up-leave.du-bois-dark-slide-up-leave-active.du-bois-dark-picker-dropdown-placement-bottomRight {\n animation-name: antSlideUpOut;\n}\n.du-bois-dark-picker-dropdown-range {\n padding: 6.66666667px 0;\n}\n.du-bois-dark-picker-dropdown-range-hidden {\n display: none;\n}\n.du-bois-dark-picker-dropdown .du-bois-dark-picker-panel > .du-bois-dark-picker-time-panel {\n padding-top: 2px;\n}\n.du-bois-dark-picker-ranges {\n margin-bottom: 0;\n padding: 2px 8px;\n overflow: hidden;\n line-height: 36px;\n text-align: left;\n list-style: none;\n}\n.du-bois-dark-picker-ranges > li {\n display: inline-block;\n}\n.du-bois-dark-picker-ranges .du-bois-dark-picker-preset > .du-bois-dark-tag-blue {\n color: #2272B4;\n background: #121921;\n border-color: #183044;\n cursor: pointer;\n}\n.du-bois-dark-picker-ranges .du-bois-dark-picker-ok {\n float: right;\n margin-left: 4px;\n}\n.du-bois-dark-picker-range-wrapper {\n display: flex;\n}\n.du-bois-dark-picker-range-arrow {\n position: absolute;\n z-index: 1;\n display: none;\n width: 10px;\n height: 10px;\n margin-left: 18px;\n box-shadow: 2px -2px 6px rgba(0, 0, 0, 0.06);\n transition: left 0.3s ease-out;\n}\n.du-bois-dark-picker-range-arrow::after {\n position: absolute;\n top: 1px;\n right: 1px;\n width: 10px;\n height: 10px;\n border: 5px solid #303030;\n border-color: #1f1f1f #1f1f1f transparent transparent;\n content: '';\n}\n.du-bois-dark-picker-panel-container {\n overflow: hidden;\n vertical-align: top;\n background: #1f1f1f;\n border-radius: 4px;\n box-shadow: 0 3px 6px -4px rgba(0, 0, 0, 0.48), 0 6px 16px 0 rgba(0, 0, 0, 0.32), 0 9px 28px 8px rgba(0, 0, 0, 0.2);\n transition: margin 0.3s;\n}\n.du-bois-dark-picker-panel-container .du-bois-dark-picker-panels {\n display: inline-flex;\n flex-wrap: nowrap;\n direction: ltr;\n}\n.du-bois-dark-picker-panel-container .du-bois-dark-picker-panel {\n vertical-align: top;\n background: transparent;\n border-width: 0 0 1px 0;\n border-radius: 0;\n}\n.du-bois-dark-picker-panel-container .du-bois-dark-picker-panel .du-bois-dark-picker-content,\n.du-bois-dark-picker-panel-container .du-bois-dark-picker-panel table {\n text-align: center;\n}\n.du-bois-dark-picker-panel-container .du-bois-dark-picker-panel-focused {\n border-color: #303030;\n}\n.du-bois-dark-picker-panel {\n display: inline-flex;\n flex-direction: column;\n text-align: center;\n background: #1f1f1f;\n border: 1px solid #303030;\n border-radius: 4px;\n outline: none;\n}\n.du-bois-dark-picker-panel-focused {\n border-color: #2272B4;\n}\n.du-bois-dark-picker-decade-panel,\n.du-bois-dark-picker-year-panel,\n.du-bois-dark-picker-quarter-panel,\n.du-bois-dark-picker-month-panel,\n.du-bois-dark-picker-week-panel,\n.du-bois-dark-picker-date-panel,\n.du-bois-dark-picker-time-panel {\n display: flex;\n flex-direction: column;\n width: 272px;\n}\n.du-bois-dark-picker-header {\n display: flex;\n padding: 0 4px;\n color: rgba(255, 255, 255, 0.85);\n border-bottom: 1px solid #303030;\n}\n.du-bois-dark-picker-header > * {\n flex: none;\n}\n.du-bois-dark-picker-header button {\n padding: 0;\n color: rgba(255, 255, 255, 0.3);\n line-height: 40px;\n background: transparent;\n border: 0;\n cursor: pointer;\n transition: color 0.3s;\n}\n.du-bois-dark-picker-header > button {\n min-width: 1.6em;\n font-size: 13px;\n}\n.du-bois-dark-picker-header > button:hover {\n color: #E8ECF0;\n}\n.du-bois-dark-picker-header-view {\n flex: auto;\n font-weight: 500;\n line-height: 40px;\n}\n.du-bois-dark-picker-header-view button {\n color: inherit;\n font-weight: inherit;\n}\n.du-bois-dark-picker-header-view button:not(:first-child) {\n margin-left: 4px;\n}\n.du-bois-dark-picker-header-view button:hover {\n color: #2272B4;\n}\n.du-bois-dark-picker-prev-icon,\n.du-bois-dark-picker-next-icon,\n.du-bois-dark-picker-super-prev-icon,\n.du-bois-dark-picker-super-next-icon {\n position: relative;\n display: inline-block;\n width: 7px;\n height: 7px;\n}\n.du-bois-dark-picker-prev-icon::before,\n.du-bois-dark-picker-next-icon::before,\n.du-bois-dark-picker-super-prev-icon::before,\n.du-bois-dark-picker-super-next-icon::before {\n position: absolute;\n top: 0;\n left: 0;\n display: inline-block;\n width: 7px;\n height: 7px;\n border: 0 solid currentColor;\n border-width: 1.5px 0 0 1.5px;\n content: '';\n}\n.du-bois-dark-picker-super-prev-icon::after,\n.du-bois-dark-picker-super-next-icon::after {\n position: absolute;\n top: 4px;\n left: 4px;\n display: inline-block;\n width: 7px;\n height: 7px;\n border: 0 solid currentColor;\n border-width: 1.5px 0 0 1.5px;\n content: '';\n}\n.du-bois-dark-picker-prev-icon,\n.du-bois-dark-picker-super-prev-icon {\n transform: rotate(-45deg);\n}\n.du-bois-dark-picker-next-icon,\n.du-bois-dark-picker-super-next-icon {\n transform: rotate(135deg);\n}\n.du-bois-dark-picker-content {\n width: 100%;\n table-layout: fixed;\n border-collapse: collapse;\n}\n.du-bois-dark-picker-content th,\n.du-bois-dark-picker-content td {\n position: relative;\n min-width: 24px;\n font-weight: 400;\n}\n.du-bois-dark-picker-content th {\n height: 30px;\n color: #E8ECF0;\n line-height: 30px;\n}\n.du-bois-dark-picker-cell {\n padding: 3px 0;\n color: rgba(255, 255, 255, 0.3);\n cursor: pointer;\n}\n.du-bois-dark-picker-cell-in-view {\n color: #E8ECF0;\n}\n.du-bois-dark-picker-cell::before {\n position: absolute;\n top: 50%;\n right: 0;\n left: 0;\n z-index: 1;\n height: 24px;\n transform: translateY(-50%);\n content: '';\n}\n.du-bois-dark-picker-cell .du-bois-dark-picker-cell-inner {\n position: relative;\n z-index: 2;\n display: inline-block;\n min-width: 24px;\n height: 24px;\n line-height: 24px;\n border-radius: 4px;\n transition: background 0.3s, border 0.3s;\n}\n.du-bois-dark-picker-cell:hover:not(.du-bois-dark-picker-cell-in-view) .du-bois-dark-picker-cell-inner,\n.du-bois-dark-picker-cell:hover:not(.du-bois-dark-picker-cell-selected):not(.du-bois-dark-picker-cell-range-start):not(.du-bois-dark-picker-cell-range-end):not(.du-bois-dark-picker-cell-range-hover-start):not(.du-bois-dark-picker-cell-range-hover-end) .du-bois-dark-picker-cell-inner {\n background: rgba(255, 255, 255, 0.08);\n}\n.du-bois-dark-picker-cell-in-view.du-bois-dark-picker-cell-today .du-bois-dark-picker-cell-inner::before {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n z-index: 1;\n border: 1px solid #2272B4;\n border-radius: 4px;\n content: '';\n}\n.du-bois-dark-picker-cell-in-view.du-bois-dark-picker-cell-in-range {\n position: relative;\n}\n.du-bois-dark-picker-cell-in-view.du-bois-dark-picker-cell-in-range::before {\n background: #121921;\n}\n.du-bois-dark-picker-cell-in-view.du-bois-dark-picker-cell-selected .du-bois-dark-picker-cell-inner,\n.du-bois-dark-picker-cell-in-view.du-bois-dark-picker-cell-range-start .du-bois-dark-picker-cell-inner,\n.du-bois-dark-picker-cell-in-view.du-bois-dark-picker-cell-range-end .du-bois-dark-picker-cell-inner {\n color: #fff;\n background: #2272B4;\n}\n.du-bois-dark-picker-cell-in-view.du-bois-dark-picker-cell-range-start:not(.du-bois-dark-picker-cell-range-start-single)::before,\n.du-bois-dark-picker-cell-in-view.du-bois-dark-picker-cell-range-end:not(.du-bois-dark-picker-cell-range-end-single)::before {\n background: #121921;\n}\n.du-bois-dark-picker-cell-in-view.du-bois-dark-picker-cell-range-start::before {\n left: 50%;\n}\n.du-bois-dark-picker-cell-in-view.du-bois-dark-picker-cell-range-end::before {\n right: 50%;\n}\n.du-bois-dark-picker-cell-in-view.du-bois-dark-picker-cell-range-hover-start:not(.du-bois-dark-picker-cell-in-range):not(.du-bois-dark-picker-cell-range-start):not(.du-bois-dark-picker-cell-range-end)::after,\n.du-bois-dark-picker-cell-in-view.du-bois-dark-picker-cell-range-hover-end:not(.du-bois-dark-picker-cell-in-range):not(.du-bois-dark-picker-cell-range-start):not(.du-bois-dark-picker-cell-range-end)::after,\n.du-bois-dark-picker-cell-in-view.du-bois-dark-picker-cell-range-hover-start.du-bois-dark-picker-cell-range-start-single::after,\n.du-bois-dark-picker-cell-in-view.du-bois-dark-picker-cell-range-hover-start.du-bois-dark-picker-cell-range-start.du-bois-dark-picker-cell-range-end.du-bois-dark-picker-cell-range-end-near-hover::after,\n.du-bois-dark-picker-cell-in-view.du-bois-dark-picker-cell-range-hover-end.du-bois-dark-picker-cell-range-start.du-bois-dark-picker-cell-range-end.du-bois-dark-picker-cell-range-start-near-hover::after,\n.du-bois-dark-picker-cell-in-view.du-bois-dark-picker-cell-range-hover-end.du-bois-dark-picker-cell-range-end-single::after,\n.du-bois-dark-picker-cell-in-view.du-bois-dark-picker-cell-range-hover:not(.du-bois-dark-picker-cell-in-range)::after {\n position: absolute;\n top: 50%;\n z-index: 0;\n height: 24px;\n border-top: 1px dashed #123c5e;\n border-bottom: 1px dashed #123c5e;\n transform: translateY(-50%);\n content: '';\n}\n.du-bois-dark-picker-cell-range-hover-start::after,\n.du-bois-dark-picker-cell-range-hover-end::after,\n.du-bois-dark-picker-cell-range-hover::after {\n right: 0;\n left: 2px;\n}\n.du-bois-dark-picker-cell-in-view.du-bois-dark-picker-cell-in-range.du-bois-dark-picker-cell-range-hover::before,\n.du-bois-dark-picker-cell-in-view.du-bois-dark-picker-cell-range-start.du-bois-dark-picker-cell-range-hover::before,\n.du-bois-dark-picker-cell-in-view.du-bois-dark-picker-cell-range-end.du-bois-dark-picker-cell-range-hover::before,\n.du-bois-dark-picker-cell-in-view.du-bois-dark-picker-cell-range-start:not(.du-bois-dark-picker-cell-range-start-single).du-bois-dark-picker-cell-range-hover-start::before,\n.du-bois-dark-picker-cell-in-view.du-bois-dark-picker-cell-range-end:not(.du-bois-dark-picker-cell-range-end-single).du-bois-dark-picker-cell-range-hover-end::before,\n.du-bois-dark-picker-panel > :not(.du-bois-dark-picker-date-panel) .du-bois-dark-picker-cell-in-view.du-bois-dark-picker-cell-in-range.du-bois-dark-picker-cell-range-hover-start::before,\n.du-bois-dark-picker-panel > :not(.du-bois-dark-picker-date-panel) .du-bois-dark-picker-cell-in-view.du-bois-dark-picker-cell-in-range.du-bois-dark-picker-cell-range-hover-end::before {\n background: #06131e;\n}\n.du-bois-dark-picker-cell-in-view.du-bois-dark-picker-cell-range-start:not(.du-bois-dark-picker-cell-range-start-single):not(.du-bois-dark-picker-cell-range-end) .du-bois-dark-picker-cell-inner {\n border-radius: 4px 0 0 4px;\n}\n.du-bois-dark-picker-cell-in-view.du-bois-dark-picker-cell-range-end:not(.du-bois-dark-picker-cell-range-end-single):not(.du-bois-dark-picker-cell-range-start) .du-bois-dark-picker-cell-inner {\n border-radius: 0 4px 4px 0;\n}\n.du-bois-dark-picker-date-panel .du-bois-dark-picker-cell-in-view.du-bois-dark-picker-cell-in-range.du-bois-dark-picker-cell-range-hover-start .du-bois-dark-picker-cell-inner::after,\n.du-bois-dark-picker-date-panel .du-bois-dark-picker-cell-in-view.du-bois-dark-picker-cell-in-range.du-bois-dark-picker-cell-range-hover-end .du-bois-dark-picker-cell-inner::after {\n position: absolute;\n top: 0;\n bottom: 0;\n z-index: -1;\n background: #06131e;\n content: '';\n}\n.du-bois-dark-picker-date-panel .du-bois-dark-picker-cell-in-view.du-bois-dark-picker-cell-in-range.du-bois-dark-picker-cell-range-hover-start .du-bois-dark-picker-cell-inner::after {\n right: -6px;\n left: 0;\n}\n.du-bois-dark-picker-date-panel .du-bois-dark-picker-cell-in-view.du-bois-dark-picker-cell-in-range.du-bois-dark-picker-cell-range-hover-end .du-bois-dark-picker-cell-inner::after {\n right: 0;\n left: -6px;\n}\n.du-bois-dark-picker-cell-range-hover.du-bois-dark-picker-cell-range-start::after {\n right: 50%;\n}\n.du-bois-dark-picker-cell-range-hover.du-bois-dark-picker-cell-range-end::after {\n left: 50%;\n}\ntr > .du-bois-dark-picker-cell-in-view.du-bois-dark-picker-cell-range-hover:first-child::after,\ntr > .du-bois-dark-picker-cell-in-view.du-bois-dark-picker-cell-range-hover-end:first-child::after,\n.du-bois-dark-picker-cell-in-view.du-bois-dark-picker-cell-start.du-bois-dark-picker-cell-range-hover-edge-start.du-bois-dark-picker-cell-range-hover-edge-start-near-range::after,\n.du-bois-dark-picker-cell-in-view.du-bois-dark-picker-cell-range-hover-edge-start:not(.du-bois-dark-picker-cell-range-hover-edge-start-near-range)::after,\n.du-bois-dark-picker-cell-in-view.du-bois-dark-picker-cell-range-hover-start::after {\n left: 6px;\n border-left: 1px dashed #123c5e;\n border-top-left-radius: 4px;\n border-bottom-left-radius: 4px;\n}\ntr > .du-bois-dark-picker-cell-in-view.du-bois-dark-picker-cell-range-hover:last-child::after,\ntr > .du-bois-dark-picker-cell-in-view.du-bois-dark-picker-cell-range-hover-start:last-child::after,\n.du-bois-dark-picker-cell-in-view.du-bois-dark-picker-cell-end.du-bois-dark-picker-cell-range-hover-edge-end.du-bois-dark-picker-cell-range-hover-edge-end-near-range::after,\n.du-bois-dark-picker-cell-in-view.du-bois-dark-picker-cell-range-hover-edge-end:not(.du-bois-dark-picker-cell-range-hover-edge-end-near-range)::after,\n.du-bois-dark-picker-cell-in-view.du-bois-dark-picker-cell-range-hover-end::after {\n right: 6px;\n border-right: 1px dashed #123c5e;\n border-top-right-radius: 4px;\n border-bottom-right-radius: 4px;\n}\n.du-bois-dark-picker-cell-disabled {\n color: rgba(255, 255, 255, 0.3);\n pointer-events: none;\n}\n.du-bois-dark-picker-cell-disabled .du-bois-dark-picker-cell-inner {\n background: transparent;\n}\n.du-bois-dark-picker-cell-disabled::before {\n background: #303030;\n}\n.du-bois-dark-picker-cell-disabled.du-bois-dark-picker-cell-today .du-bois-dark-picker-cell-inner::before {\n border-color: rgba(255, 255, 255, 0.3);\n}\n.du-bois-dark-picker-decade-panel .du-bois-dark-picker-content,\n.du-bois-dark-picker-year-panel .du-bois-dark-picker-content,\n.du-bois-dark-picker-quarter-panel .du-bois-dark-picker-content,\n.du-bois-dark-picker-month-panel .du-bois-dark-picker-content {\n height: 264px;\n}\n.du-bois-dark-picker-decade-panel .du-bois-dark-picker-cell-inner,\n.du-bois-dark-picker-year-panel .du-bois-dark-picker-cell-inner,\n.du-bois-dark-picker-quarter-panel .du-bois-dark-picker-cell-inner,\n.du-bois-dark-picker-month-panel .du-bois-dark-picker-cell-inner {\n padding: 0 4px;\n}\n.du-bois-dark-picker-quarter-panel .du-bois-dark-picker-content {\n height: 56px;\n}\n.du-bois-dark-picker-footer {\n width: min-content;\n min-width: 100%;\n line-height: 38px;\n text-align: center;\n border-bottom: 1px solid transparent;\n}\n.du-bois-dark-picker-panel .du-bois-dark-picker-footer {\n border-top: 1px solid #303030;\n}\n.du-bois-dark-picker-footer-extra {\n padding: 0 8px;\n line-height: 38px;\n text-align: left;\n}\n.du-bois-dark-picker-footer-extra:not(:last-child) {\n border-bottom: 1px solid #303030;\n}\n.du-bois-dark-picker-now {\n text-align: left;\n}\n.du-bois-dark-picker-today-btn {\n color: #2272B4;\n}\n.du-bois-dark-picker-today-btn:hover {\n color: #1d517c;\n}\n.du-bois-dark-picker-today-btn:active {\n color: #3f81b1;\n}\n.du-bois-dark-picker-today-btn.du-bois-dark-picker-today-btn-disabled {\n color: rgba(255, 255, 255, 0.3);\n cursor: not-allowed;\n}\n.du-bois-dark-picker-decade-panel .du-bois-dark-picker-cell-inner {\n padding: 0 2px;\n}\n.du-bois-dark-picker-decade-panel .du-bois-dark-picker-cell::before {\n display: none;\n}\n.du-bois-dark-picker-year-panel .du-bois-dark-picker-body,\n.du-bois-dark-picker-quarter-panel .du-bois-dark-picker-body,\n.du-bois-dark-picker-month-panel .du-bois-dark-picker-body {\n padding: 0 4px;\n}\n.du-bois-dark-picker-year-panel .du-bois-dark-picker-cell-inner,\n.du-bois-dark-picker-quarter-panel .du-bois-dark-picker-cell-inner,\n.du-bois-dark-picker-month-panel .du-bois-dark-picker-cell-inner {\n width: 60px;\n}\n.du-bois-dark-picker-year-panel .du-bois-dark-picker-cell-range-hover-start::after,\n.du-bois-dark-picker-quarter-panel .du-bois-dark-picker-cell-range-hover-start::after,\n.du-bois-dark-picker-month-panel .du-bois-dark-picker-cell-range-hover-start::after {\n left: 14px;\n border-left: 1px dashed #123c5e;\n border-radius: 4px 0 0 4px;\n}\n.du-bois-dark-picker-year-panel .du-bois-dark-picker-cell-range-hover-end::after,\n.du-bois-dark-picker-quarter-panel .du-bois-dark-picker-cell-range-hover-end::after,\n.du-bois-dark-picker-month-panel .du-bois-dark-picker-cell-range-hover-end::after {\n right: 14px;\n border-right: 1px dashed #123c5e;\n border-radius: 0 4px 4px 0;\n}\n.du-bois-dark-picker-week-panel .du-bois-dark-picker-body {\n padding: 4px 8px;\n}\n.du-bois-dark-picker-week-panel .du-bois-dark-picker-cell:hover .du-bois-dark-picker-cell-inner,\n.du-bois-dark-picker-week-panel .du-bois-dark-picker-cell-selected .du-bois-dark-picker-cell-inner,\n.du-bois-dark-picker-week-panel .du-bois-dark-picker-cell .du-bois-dark-picker-cell-inner {\n background: transparent !important;\n}\n.du-bois-dark-picker-week-panel-row td {\n transition: background 0.3s;\n}\n.du-bois-dark-picker-week-panel-row:hover td {\n background: rgba(255, 255, 255, 0.08);\n}\n.du-bois-dark-picker-week-panel-row-selected td,\n.du-bois-dark-picker-week-panel-row-selected:hover td {\n background: #2272B4;\n}\n.du-bois-dark-picker-week-panel-row-selected td.du-bois-dark-picker-cell-week,\n.du-bois-dark-picker-week-panel-row-selected:hover td.du-bois-dark-picker-cell-week {\n color: rgba(255, 255, 255, 0.5);\n}\n.du-bois-dark-picker-week-panel-row-selected td.du-bois-dark-picker-cell-today .du-bois-dark-picker-cell-inner::before,\n.du-bois-dark-picker-week-panel-row-selected:hover td.du-bois-dark-picker-cell-today .du-bois-dark-picker-cell-inner::before {\n border-color: #fff;\n}\n.du-bois-dark-picker-week-panel-row-selected td .du-bois-dark-picker-cell-inner,\n.du-bois-dark-picker-week-panel-row-selected:hover td .du-bois-dark-picker-cell-inner {\n color: #fff;\n}\n.du-bois-dark-picker-date-panel .du-bois-dark-picker-body {\n padding: 4px 8px;\n}\n.du-bois-dark-picker-date-panel .du-bois-dark-picker-content {\n width: 252px;\n}\n.du-bois-dark-picker-date-panel .du-bois-dark-picker-content th {\n width: 36px;\n}\n.du-bois-dark-picker-datetime-panel {\n display: flex;\n}\n.du-bois-dark-picker-datetime-panel .du-bois-dark-picker-time-panel {\n border-left: 1px solid #303030;\n}\n.du-bois-dark-picker-datetime-panel .du-bois-dark-picker-date-panel,\n.du-bois-dark-picker-datetime-panel .du-bois-dark-picker-time-panel {\n transition: opacity 0.3s;\n}\n.du-bois-dark-picker-datetime-panel-active .du-bois-dark-picker-date-panel,\n.du-bois-dark-picker-datetime-panel-active .du-bois-dark-picker-time-panel {\n opacity: 0.3;\n}\n.du-bois-dark-picker-datetime-panel-active .du-bois-dark-picker-date-panel-active,\n.du-bois-dark-picker-datetime-panel-active .du-bois-dark-picker-time-panel-active {\n opacity: 1;\n}\n.du-bois-dark-picker-time-panel {\n width: auto;\n min-width: auto;\n}\n.du-bois-dark-picker-time-panel .du-bois-dark-picker-content {\n display: flex;\n flex: auto;\n height: 224px;\n}\n.du-bois-dark-picker-time-panel-column {\n flex: 1 0 auto;\n width: 56px;\n margin: 0;\n padding: 0;\n overflow-y: hidden;\n text-align: left;\n list-style: none;\n transition: background 0.3s;\n}\n.du-bois-dark-picker-time-panel-column::after {\n display: block;\n height: 196px;\n content: '';\n}\n.du-bois-dark-picker-datetime-panel .du-bois-dark-picker-time-panel-column::after {\n height: 198px;\n}\n.du-bois-dark-picker-time-panel-column:not(:first-child) {\n border-left: 1px solid #303030;\n}\n.du-bois-dark-picker-time-panel-column-active {\n background: rgba(18, 25, 33, 0.2);\n}\n.du-bois-dark-picker-time-panel-column:hover {\n overflow-y: auto;\n}\n.du-bois-dark-picker-time-panel-column > li {\n margin: 0;\n padding: 0;\n}\n.du-bois-dark-picker-time-panel-column > li.du-bois-dark-picker-time-panel-cell .du-bois-dark-picker-time-panel-cell-inner {\n display: block;\n width: 100%;\n height: 28px;\n margin: 0;\n padding: 0 0 0 14px;\n color: #E8ECF0;\n line-height: 28px;\n border-radius: 0;\n cursor: pointer;\n transition: background 0.3s;\n}\n.du-bois-dark-picker-time-panel-column > li.du-bois-dark-picker-time-panel-cell .du-bois-dark-picker-time-panel-cell-inner:hover {\n background: rgba(255, 255, 255, 0.08);\n}\n.du-bois-dark-picker-time-panel-column > li.du-bois-dark-picker-time-panel-cell-selected .du-bois-dark-picker-time-panel-cell-inner {\n background: #121921;\n}\n.du-bois-dark-picker-time-panel-column > li.du-bois-dark-picker-time-panel-cell-disabled .du-bois-dark-picker-time-panel-cell-inner {\n color: rgba(255, 255, 255, 0.3);\n background: transparent;\n cursor: not-allowed;\n}\n/* stylelint-disable-next-line */\n_:-ms-fullscreen .du-bois-dark-picker-range-wrapper .du-bois-dark-picker-month-panel .du-bois-dark-picker-cell,\n:root .du-bois-dark-picker-range-wrapper .du-bois-dark-picker-month-panel .du-bois-dark-picker-cell,\n_:-ms-fullscreen .du-bois-dark-picker-range-wrapper .du-bois-dark-picker-year-panel .du-bois-dark-picker-cell,\n:root .du-bois-dark-picker-range-wrapper .du-bois-dark-picker-year-panel .du-bois-dark-picker-cell {\n padding: 21px 0;\n}\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-dark-tag {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #E8ECF0;\n font-size: 13px;\n font-variant: tabular-nums;\n list-style: none;\n font-feature-settings: tnum;\n display: inline-block;\n height: auto;\n margin-right: 8px;\n padding: 0 7px;\n font-size: 12px;\n line-height: 20px;\n white-space: nowrap;\n background: rgba(255, 255, 255, 0.04);\n border: 1px solid #1F272D;\n border-radius: 4px;\n opacity: 1;\n transition: all 0.3s;\n}\n.du-bois-dark-tag,\n.du-bois-dark-tag a,\n.du-bois-dark-tag a:hover {\n color: #E8ECF0;\n}\n.du-bois-dark-tag > a:first-child:last-child {\n display: inline-block;\n margin: 0 -8px;\n padding: 0 8px;\n}\n.du-bois-dark-tag-close-icon {\n margin-left: 3px;\n color: #92A4B3;\n font-size: 10px;\n cursor: pointer;\n transition: all 0.3s;\n}\n.du-bois-dark-tag-close-icon:hover {\n color: rgba(255, 255, 255, 0.85);\n}\n.du-bois-dark-tag-has-color {\n border-color: transparent;\n}\n.du-bois-dark-tag-has-color,\n.du-bois-dark-tag-has-color a,\n.du-bois-dark-tag-has-color a:hover,\n.du-bois-dark-tag-has-color .anticon-close,\n.du-bois-dark-tag-has-color .anticon-close:hover {\n color: #fff;\n}\n.du-bois-dark-tag-checkable {\n background-color: transparent;\n border-color: transparent;\n cursor: pointer;\n}\n.du-bois-dark-tag-checkable:not(.du-bois-dark-tag-checkable-checked):hover {\n color: #2272B4;\n}\n.du-bois-dark-tag-checkable:active,\n.du-bois-dark-tag-checkable-checked {\n color: #fff;\n}\n.du-bois-dark-tag-checkable-checked {\n background-color: #2272B4;\n}\n.du-bois-dark-tag-checkable:active {\n background-color: #3f81b1;\n}\n.du-bois-dark-tag-hidden {\n display: none;\n}\n.du-bois-dark-tag-pink {\n color: #e0529c;\n background: #291321;\n border-color: #551c3b;\n}\n.du-bois-dark-tag-pink-inverse {\n color: #fff;\n background: #cb2b83;\n border-color: #cb2b83;\n}\n.du-bois-dark-tag-magenta {\n color: #e0529c;\n background: #291321;\n border-color: #551c3b;\n}\n.du-bois-dark-tag-magenta-inverse {\n color: #fff;\n background: #cb2b83;\n border-color: #cb2b83;\n}\n.du-bois-dark-tag-red {\n color: #e84749;\n background: #2a1215;\n border-color: #58181c;\n}\n.du-bois-dark-tag-red-inverse {\n color: #fff;\n background: #d32029;\n border-color: #d32029;\n}\n.du-bois-dark-tag-volcano {\n color: #e87040;\n background: #2b1611;\n border-color: #592716;\n}\n.du-bois-dark-tag-volcano-inverse {\n color: #fff;\n background: #d84a1b;\n border-color: #d84a1b;\n}\n.du-bois-dark-tag-orange {\n color: #e89a3c;\n background: #2b1d11;\n border-color: #593815;\n}\n.du-bois-dark-tag-orange-inverse {\n color: #fff;\n background: #d87a16;\n border-color: #d87a16;\n}\n.du-bois-dark-tag-yellow {\n color: #e8d639;\n background: #2b2611;\n border-color: #595014;\n}\n.du-bois-dark-tag-yellow-inverse {\n color: #fff;\n background: #d8bd14;\n border-color: #d8bd14;\n}\n.du-bois-dark-tag-gold {\n color: #e8b339;\n background: #2b2111;\n border-color: #594214;\n}\n.du-bois-dark-tag-gold-inverse {\n color: #fff;\n background: #d89614;\n border-color: #d89614;\n}\n.du-bois-dark-tag-cyan {\n color: #33bcb7;\n background: #112123;\n border-color: #144848;\n}\n.du-bois-dark-tag-cyan-inverse {\n color: #fff;\n background: #13a8a8;\n border-color: #13a8a8;\n}\n.du-bois-dark-tag-lime {\n color: #a9d134;\n background: #1f2611;\n border-color: #3e4f13;\n}\n.du-bois-dark-tag-lime-inverse {\n color: #fff;\n background: #8bbb11;\n border-color: #8bbb11;\n}\n.du-bois-dark-tag-green {\n color: #6abe39;\n background: #162312;\n border-color: #274916;\n}\n.du-bois-dark-tag-green-inverse {\n color: #fff;\n background: #49aa19;\n border-color: #49aa19;\n}\n.du-bois-dark-tag-blue {\n color: #3c9ae8;\n background: #111d2c;\n border-color: #15395b;\n}\n.du-bois-dark-tag-blue-inverse {\n color: #fff;\n background: #177ddc;\n border-color: #177ddc;\n}\n.du-bois-dark-tag-geekblue {\n color: #5273e0;\n background: #131629;\n border-color: #1c2755;\n}\n.du-bois-dark-tag-geekblue-inverse {\n color: #fff;\n background: #2b4acb;\n border-color: #2b4acb;\n}\n.du-bois-dark-tag-purple {\n color: #854eca;\n background: #1a1325;\n border-color: #301c4d;\n}\n.du-bois-dark-tag-purple-inverse {\n color: #fff;\n background: #642ab5;\n border-color: #642ab5;\n}\n.du-bois-dark-tag-success {\n color: #49aa19;\n background: #162312;\n border-color: #274916;\n}\n.du-bois-dark-tag-processing {\n color: #177ddc;\n background: #111d2c;\n border-color: #15395b;\n}\n.du-bois-dark-tag-error {\n color: #d32029;\n background: #2a1215;\n border-color: #58181c;\n}\n.du-bois-dark-tag-warning {\n color: #d87a16;\n background: #2b1d11;\n border-color: #593815;\n}\n.du-bois-dark-tag > .anticon + span,\n.du-bois-dark-tag > span + .anticon {\n margin-left: 7px;\n}\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-dark-dropdown-menu-item.du-bois-dark-dropdown-menu-item-danger {\n color: #E65B77;\n}\n.du-bois-dark-dropdown-menu-item.du-bois-dark-dropdown-menu-item-danger:hover {\n color: #fff;\n background-color: #E65B77;\n}\n.du-bois-dark-dropdown {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #E8ECF0;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: tnum;\n position: absolute;\n top: -9999px;\n left: -9999px;\n z-index: 1050;\n display: block;\n}\n.du-bois-dark-dropdown::before {\n position: absolute;\n top: -4px;\n right: 0;\n bottom: -4px;\n left: -7px;\n z-index: -9999;\n opacity: 0.0001;\n content: ' ';\n}\n.du-bois-dark-dropdown-wrap {\n position: relative;\n}\n.du-bois-dark-dropdown-wrap .du-bois-dark-btn > .anticon-down {\n font-size: 10px;\n}\n.du-bois-dark-dropdown-wrap .anticon-down::before {\n transition: transform 0.2s;\n}\n.du-bois-dark-dropdown-wrap-open .anticon-down::before {\n transform: rotate(180deg);\n}\n.du-bois-dark-dropdown-hidden,\n.du-bois-dark-dropdown-menu-hidden,\n.du-bois-dark-dropdown-menu-submenu-hidden {\n display: none;\n}\n.du-bois-dark-dropdown-show-arrow.du-bois-dark-dropdown-placement-topCenter,\n.du-bois-dark-dropdown-show-arrow.du-bois-dark-dropdown-placement-topLeft,\n.du-bois-dark-dropdown-show-arrow.du-bois-dark-dropdown-placement-topRight {\n padding-bottom: 10px;\n}\n.du-bois-dark-dropdown-show-arrow.du-bois-dark-dropdown-placement-bottomCenter,\n.du-bois-dark-dropdown-show-arrow.du-bois-dark-dropdown-placement-bottomLeft,\n.du-bois-dark-dropdown-show-arrow.du-bois-dark-dropdown-placement-bottomRight {\n padding-top: 10px;\n}\n.du-bois-dark-dropdown-arrow {\n position: absolute;\n z-index: 1;\n display: block;\n width: 8.48528137px;\n height: 8.48528137px;\n background: transparent;\n border-style: solid;\n border-width: 4.24264069px;\n transform: rotate(45deg);\n}\n.du-bois-dark-dropdown-placement-topCenter > .du-bois-dark-dropdown-arrow,\n.du-bois-dark-dropdown-placement-topLeft > .du-bois-dark-dropdown-arrow,\n.du-bois-dark-dropdown-placement-topRight > .du-bois-dark-dropdown-arrow {\n bottom: 6.2px;\n border-top-color: transparent;\n border-right-color: #1f1f1f;\n border-bottom-color: #1f1f1f;\n border-left-color: transparent;\n box-shadow: 3px 3px 7px rgba(0, 0, 0, 0.07);\n}\n.du-bois-dark-dropdown-placement-topCenter > .du-bois-dark-dropdown-arrow {\n left: 50%;\n transform: translateX(-50%) rotate(45deg);\n}\n.du-bois-dark-dropdown-placement-topLeft > .du-bois-dark-dropdown-arrow {\n left: 16px;\n}\n.du-bois-dark-dropdown-placement-topRight > .du-bois-dark-dropdown-arrow {\n right: 16px;\n}\n.du-bois-dark-dropdown-placement-bottomCenter > .du-bois-dark-dropdown-arrow,\n.du-bois-dark-dropdown-placement-bottomLeft > .du-bois-dark-dropdown-arrow,\n.du-bois-dark-dropdown-placement-bottomRight > .du-bois-dark-dropdown-arrow {\n top: 6px;\n border-top-color: #1f1f1f;\n border-right-color: transparent;\n border-bottom-color: transparent;\n border-left-color: #1f1f1f;\n box-shadow: -2px -2px 5px rgba(0, 0, 0, 0.06);\n}\n.du-bois-dark-dropdown-placement-bottomCenter > .du-bois-dark-dropdown-arrow {\n left: 50%;\n transform: translateX(-50%) rotate(45deg);\n}\n.du-bois-dark-dropdown-placement-bottomLeft > .du-bois-dark-dropdown-arrow {\n left: 16px;\n}\n.du-bois-dark-dropdown-placement-bottomRight > .du-bois-dark-dropdown-arrow {\n right: 16px;\n}\n.du-bois-dark-dropdown-menu {\n position: relative;\n margin: 0;\n padding: 4px 0;\n text-align: left;\n list-style-type: none;\n background-color: #1f1f1f;\n background-clip: padding-box;\n border-radius: 4px;\n outline: none;\n box-shadow: 0 3px 6px -4px rgba(0, 0, 0, 0.48), 0 6px 16px 0 rgba(0, 0, 0, 0.32), 0 9px 28px 8px rgba(0, 0, 0, 0.2);\n}\n.du-bois-dark-dropdown-menu-item-group-title {\n padding: 5px 8px;\n color: #92A4B3;\n transition: all 0.3s;\n}\n.du-bois-dark-dropdown-menu-submenu-popup {\n position: absolute;\n z-index: 1050;\n background: transparent;\n box-shadow: none;\n transform-origin: 0 0;\n}\n.du-bois-dark-dropdown-menu-submenu-popup ul,\n.du-bois-dark-dropdown-menu-submenu-popup li {\n list-style: none;\n}\n.du-bois-dark-dropdown-menu-submenu-popup ul {\n margin-right: 0.3em;\n margin-left: 0.3em;\n}\n.du-bois-dark-dropdown-menu-item {\n position: relative;\n display: flex;\n align-items: center;\n}\n.du-bois-dark-dropdown-menu-item-icon {\n min-width: 12px;\n margin-right: 8px;\n font-size: 12px;\n}\n.du-bois-dark-dropdown-menu-title-content {\n flex: auto;\n}\n.du-bois-dark-dropdown-menu-title-content > a {\n color: inherit;\n transition: all 0.3s;\n}\n.du-bois-dark-dropdown-menu-title-content > a:hover {\n color: inherit;\n}\n.du-bois-dark-dropdown-menu-title-content > a::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n content: '';\n}\n.du-bois-dark-dropdown-menu-item,\n.du-bois-dark-dropdown-menu-submenu-title {\n clear: both;\n margin: 0;\n padding: 5px 8px;\n color: #E8ECF0;\n font-weight: normal;\n font-size: 13px;\n line-height: 22px;\n white-space: nowrap;\n cursor: pointer;\n transition: all 0.3s;\n}\n.du-bois-dark-dropdown-menu-item-selected,\n.du-bois-dark-dropdown-menu-submenu-title-selected {\n color: #2272B4;\n background-color: #121921;\n}\n.du-bois-dark-dropdown-menu-item:hover,\n.du-bois-dark-dropdown-menu-submenu-title:hover {\n background-color: rgba(255, 255, 255, 0.08);\n}\n.du-bois-dark-dropdown-menu-item-disabled,\n.du-bois-dark-dropdown-menu-submenu-title-disabled {\n color: rgba(255, 255, 255, 0.3);\n cursor: not-allowed;\n}\n.du-bois-dark-dropdown-menu-item-disabled:hover,\n.du-bois-dark-dropdown-menu-submenu-title-disabled:hover {\n color: rgba(255, 255, 255, 0.3);\n background-color: transparent;\n cursor: not-allowed;\n}\n.du-bois-dark-dropdown-menu-item-disabled a,\n.du-bois-dark-dropdown-menu-submenu-title-disabled a {\n pointer-events: none;\n}\n.du-bois-dark-dropdown-menu-item-divider,\n.du-bois-dark-dropdown-menu-submenu-title-divider {\n height: 1px;\n margin: 4px 0;\n overflow: hidden;\n line-height: 0;\n background-color: #303030;\n}\n.du-bois-dark-dropdown-menu-item .du-bois-dark-dropdown-menu-submenu-expand-icon,\n.du-bois-dark-dropdown-menu-submenu-title .du-bois-dark-dropdown-menu-submenu-expand-icon {\n position: absolute;\n right: 4px;\n}\n.du-bois-dark-dropdown-menu-item .du-bois-dark-dropdown-menu-submenu-expand-icon .du-bois-dark-dropdown-menu-submenu-arrow-icon,\n.du-bois-dark-dropdown-menu-submenu-title .du-bois-dark-dropdown-menu-submenu-expand-icon .du-bois-dark-dropdown-menu-submenu-arrow-icon {\n margin-right: 0 !important;\n color: #92A4B3;\n font-size: 10px;\n font-style: normal;\n}\n.du-bois-dark-dropdown-menu-item-group-list {\n margin: 0 8px;\n padding: 0;\n list-style: none;\n}\n.du-bois-dark-dropdown-menu-submenu-title {\n padding-right: 20px;\n}\n.du-bois-dark-dropdown-menu-submenu-vertical {\n position: relative;\n}\n.du-bois-dark-dropdown-menu-submenu-vertical > .du-bois-dark-dropdown-menu {\n position: absolute;\n top: 0;\n left: 100%;\n min-width: 100%;\n margin-left: 4px;\n transform-origin: 0 0;\n}\n.du-bois-dark-dropdown-menu-submenu.du-bois-dark-dropdown-menu-submenu-disabled .du-bois-dark-dropdown-menu-submenu-title,\n.du-bois-dark-dropdown-menu-submenu.du-bois-dark-dropdown-menu-submenu-disabled .du-bois-dark-dropdown-menu-submenu-title .du-bois-dark-dropdown-menu-submenu-arrow-icon {\n color: rgba(255, 255, 255, 0.3);\n background-color: transparent;\n cursor: not-allowed;\n}\n.du-bois-dark-dropdown-menu-submenu-selected .du-bois-dark-dropdown-menu-submenu-title {\n color: #2272B4;\n}\n.du-bois-dark-dropdown.du-bois-dark-slide-down-enter.du-bois-dark-slide-down-enter-active.du-bois-dark-dropdown-placement-bottomLeft,\n.du-bois-dark-dropdown.du-bois-dark-slide-down-appear.du-bois-dark-slide-down-appear-active.du-bois-dark-dropdown-placement-bottomLeft,\n.du-bois-dark-dropdown.du-bois-dark-slide-down-enter.du-bois-dark-slide-down-enter-active.du-bois-dark-dropdown-placement-bottomCenter,\n.du-bois-dark-dropdown.du-bois-dark-slide-down-appear.du-bois-dark-slide-down-appear-active.du-bois-dark-dropdown-placement-bottomCenter,\n.du-bois-dark-dropdown.du-bois-dark-slide-down-enter.du-bois-dark-slide-down-enter-active.du-bois-dark-dropdown-placement-bottomRight,\n.du-bois-dark-dropdown.du-bois-dark-slide-down-appear.du-bois-dark-slide-down-appear-active.du-bois-dark-dropdown-placement-bottomRight {\n animation-name: antSlideUpIn;\n}\n.du-bois-dark-dropdown.du-bois-dark-slide-up-enter.du-bois-dark-slide-up-enter-active.du-bois-dark-dropdown-placement-topLeft,\n.du-bois-dark-dropdown.du-bois-dark-slide-up-appear.du-bois-dark-slide-up-appear-active.du-bois-dark-dropdown-placement-topLeft,\n.du-bois-dark-dropdown.du-bois-dark-slide-up-enter.du-bois-dark-slide-up-enter-active.du-bois-dark-dropdown-placement-topCenter,\n.du-bois-dark-dropdown.du-bois-dark-slide-up-appear.du-bois-dark-slide-up-appear-active.du-bois-dark-dropdown-placement-topCenter,\n.du-bois-dark-dropdown.du-bois-dark-slide-up-enter.du-bois-dark-slide-up-enter-active.du-bois-dark-dropdown-placement-topRight,\n.du-bois-dark-dropdown.du-bois-dark-slide-up-appear.du-bois-dark-slide-up-appear-active.du-bois-dark-dropdown-placement-topRight {\n animation-name: antSlideDownIn;\n}\n.du-bois-dark-dropdown.du-bois-dark-slide-down-leave.du-bois-dark-slide-down-leave-active.du-bois-dark-dropdown-placement-bottomLeft,\n.du-bois-dark-dropdown.du-bois-dark-slide-down-leave.du-bois-dark-slide-down-leave-active.du-bois-dark-dropdown-placement-bottomCenter,\n.du-bois-dark-dropdown.du-bois-dark-slide-down-leave.du-bois-dark-slide-down-leave-active.du-bois-dark-dropdown-placement-bottomRight {\n animation-name: antSlideUpOut;\n}\n.du-bois-dark-dropdown.du-bois-dark-slide-up-leave.du-bois-dark-slide-up-leave-active.du-bois-dark-dropdown-placement-topLeft,\n.du-bois-dark-dropdown.du-bois-dark-slide-up-leave.du-bois-dark-slide-up-leave-active.du-bois-dark-dropdown-placement-topCenter,\n.du-bois-dark-dropdown.du-bois-dark-slide-up-leave.du-bois-dark-slide-up-leave-active.du-bois-dark-dropdown-placement-topRight {\n animation-name: antSlideDownOut;\n}\n.du-bois-dark-dropdown-trigger > .anticon.anticon-down,\n.du-bois-dark-dropdown-link > .anticon.anticon-down,\n.du-bois-dark-dropdown-button > .anticon.anticon-down {\n font-size: 10px;\n vertical-align: baseline;\n}\n.du-bois-dark-dropdown-button {\n white-space: nowrap;\n}\n.du-bois-dark-dropdown-button.du-bois-dark-btn-group > .du-bois-dark-btn:last-child:not(:first-child):not(.du-bois-dark-btn-icon-only) {\n padding-right: 4px;\n padding-left: 4px;\n}\n.du-bois-dark-dropdown-menu-dark,\n.du-bois-dark-dropdown-menu-dark .du-bois-dark-dropdown-menu {\n background: #1f1f1f;\n}\n.du-bois-dark-dropdown-menu-dark .du-bois-dark-dropdown-menu-item,\n.du-bois-dark-dropdown-menu-dark .du-bois-dark-dropdown-menu-submenu-title,\n.du-bois-dark-dropdown-menu-dark .du-bois-dark-dropdown-menu-item > a,\n.du-bois-dark-dropdown-menu-dark .du-bois-dark-dropdown-menu-item > .anticon + span > a {\n color: rgba(255, 255, 255, 0.65);\n}\n.du-bois-dark-dropdown-menu-dark .du-bois-dark-dropdown-menu-item .du-bois-dark-dropdown-menu-submenu-arrow::after,\n.du-bois-dark-dropdown-menu-dark .du-bois-dark-dropdown-menu-submenu-title .du-bois-dark-dropdown-menu-submenu-arrow::after,\n.du-bois-dark-dropdown-menu-dark .du-bois-dark-dropdown-menu-item > a .du-bois-dark-dropdown-menu-submenu-arrow::after,\n.du-bois-dark-dropdown-menu-dark .du-bois-dark-dropdown-menu-item > .anticon + span > a .du-bois-dark-dropdown-menu-submenu-arrow::after {\n color: rgba(255, 255, 255, 0.65);\n}\n.du-bois-dark-dropdown-menu-dark .du-bois-dark-dropdown-menu-item:hover,\n.du-bois-dark-dropdown-menu-dark .du-bois-dark-dropdown-menu-submenu-title:hover,\n.du-bois-dark-dropdown-menu-dark .du-bois-dark-dropdown-menu-item > a:hover,\n.du-bois-dark-dropdown-menu-dark .du-bois-dark-dropdown-menu-item > .anticon + span > a:hover {\n color: #fff;\n background: transparent;\n}\n.du-bois-dark-dropdown-menu-dark .du-bois-dark-dropdown-menu-item-selected,\n.du-bois-dark-dropdown-menu-dark .du-bois-dark-dropdown-menu-item-selected:hover,\n.du-bois-dark-dropdown-menu-dark .du-bois-dark-dropdown-menu-item-selected > a {\n color: #fff;\n background: #2272B4;\n}\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-dark-form-item .du-bois-dark-mentions,\n.du-bois-dark-form-item textarea.du-bois-dark-input {\n height: auto;\n}\n.du-bois-dark-form-item .du-bois-dark-upload {\n background: transparent;\n}\n.du-bois-dark-form-item .du-bois-dark-upload.du-bois-dark-upload-drag {\n background: rgba(255, 255, 255, 0.04);\n}\n.du-bois-dark-form-item input[type='radio'],\n.du-bois-dark-form-item input[type='checkbox'] {\n width: 14px;\n height: 14px;\n}\n.du-bois-dark-form-item .du-bois-dark-radio-inline,\n.du-bois-dark-form-item .du-bois-dark-checkbox-inline {\n display: inline-block;\n margin-left: 8px;\n font-weight: normal;\n vertical-align: middle;\n cursor: pointer;\n}\n.du-bois-dark-form-item .du-bois-dark-radio-inline:first-child,\n.du-bois-dark-form-item .du-bois-dark-checkbox-inline:first-child {\n margin-left: 0;\n}\n.du-bois-dark-form-item .du-bois-dark-checkbox-vertical,\n.du-bois-dark-form-item .du-bois-dark-radio-vertical {\n display: block;\n}\n.du-bois-dark-form-item .du-bois-dark-checkbox-vertical + .du-bois-dark-checkbox-vertical,\n.du-bois-dark-form-item .du-bois-dark-radio-vertical + .du-bois-dark-radio-vertical {\n margin-left: 0;\n}\n.du-bois-dark-form-item .du-bois-dark-input-number + .du-bois-dark-form-text {\n margin-left: 8px;\n}\n.du-bois-dark-form-item .du-bois-dark-input-number-handler-wrap {\n z-index: 2;\n}\n.du-bois-dark-form-item .du-bois-dark-select,\n.du-bois-dark-form-item .du-bois-dark-cascader-picker {\n width: 100%;\n}\n.du-bois-dark-form-item .du-bois-dark-picker-calendar-year-select,\n.du-bois-dark-form-item .du-bois-dark-picker-calendar-month-select,\n.du-bois-dark-form-item .du-bois-dark-input-group .du-bois-dark-select,\n.du-bois-dark-form-item .du-bois-dark-input-group .du-bois-dark-cascader-picker {\n width: auto;\n}\n.du-bois-dark-form-inline {\n display: flex;\n flex-wrap: wrap;\n}\n.du-bois-dark-form-inline .du-bois-dark-form-item {\n flex: none;\n flex-wrap: nowrap;\n margin-right: 16px;\n margin-bottom: 0;\n}\n.du-bois-dark-form-inline .du-bois-dark-form-item-with-help {\n margin-bottom: 24px;\n}\n.du-bois-dark-form-inline .du-bois-dark-form-item > .du-bois-dark-form-item-label,\n.du-bois-dark-form-inline .du-bois-dark-form-item > .du-bois-dark-form-item-control {\n display: inline-block;\n vertical-align: top;\n}\n.du-bois-dark-form-inline .du-bois-dark-form-item > .du-bois-dark-form-item-label {\n flex: none;\n}\n.du-bois-dark-form-inline .du-bois-dark-form-item .du-bois-dark-form-text {\n display: inline-block;\n}\n.du-bois-dark-form-inline .du-bois-dark-form-item .du-bois-dark-form-item-has-feedback {\n display: inline-block;\n}\n.du-bois-dark-form-horizontal .du-bois-dark-form-item-label {\n flex-grow: 0;\n}\n.du-bois-dark-form-horizontal .du-bois-dark-form-item-control {\n flex: 1 1 0;\n}\n.du-bois-dark-form-vertical .du-bois-dark-form-item {\n flex-direction: column;\n}\n.du-bois-dark-form-vertical .du-bois-dark-form-item-label > label {\n height: auto;\n}\n.du-bois-dark-form-vertical .du-bois-dark-form-item-label,\n.du-bois-dark-col-24.du-bois-dark-form-item-label,\n.du-bois-dark-col-xl-24.du-bois-dark-form-item-label {\n padding: 0 0 8px;\n line-height: 20px;\n white-space: initial;\n text-align: left;\n}\n.du-bois-dark-form-vertical .du-bois-dark-form-item-label > label,\n.du-bois-dark-col-24.du-bois-dark-form-item-label > label,\n.du-bois-dark-col-xl-24.du-bois-dark-form-item-label > label {\n margin: 0;\n}\n.du-bois-dark-form-vertical .du-bois-dark-form-item-label > label::after,\n.du-bois-dark-col-24.du-bois-dark-form-item-label > label::after,\n.du-bois-dark-col-xl-24.du-bois-dark-form-item-label > label::after {\n display: none;\n}\n@media (max-width: 575px) {\n .du-bois-dark-form-item .du-bois-dark-form-item-label {\n padding: 0 0 8px;\n line-height: 20px;\n white-space: initial;\n text-align: left;\n }\n .du-bois-dark-form-item .du-bois-dark-form-item-label > label {\n margin: 0;\n }\n .du-bois-dark-form-item .du-bois-dark-form-item-label > label::after {\n display: none;\n }\n .du-bois-dark-form .du-bois-dark-form-item {\n flex-wrap: wrap;\n }\n .du-bois-dark-form .du-bois-dark-form-item .du-bois-dark-form-item-label,\n .du-bois-dark-form .du-bois-dark-form-item .du-bois-dark-form-item-control {\n flex: 0 0 100%;\n max-width: 100%;\n }\n .du-bois-dark-col-xs-24.du-bois-dark-form-item-label {\n padding: 0 0 8px;\n line-height: 20px;\n white-space: initial;\n text-align: left;\n }\n .du-bois-dark-col-xs-24.du-bois-dark-form-item-label > label {\n margin: 0;\n }\n .du-bois-dark-col-xs-24.du-bois-dark-form-item-label > label::after {\n display: none;\n }\n}\n@media (max-width: 767px) {\n .du-bois-dark-col-sm-24.du-bois-dark-form-item-label {\n padding: 0 0 8px;\n line-height: 20px;\n white-space: initial;\n text-align: left;\n }\n .du-bois-dark-col-sm-24.du-bois-dark-form-item-label > label {\n margin: 0;\n }\n .du-bois-dark-col-sm-24.du-bois-dark-form-item-label > label::after {\n display: none;\n }\n}\n@media (max-width: 991px) {\n .du-bois-dark-col-md-24.du-bois-dark-form-item-label {\n padding: 0 0 8px;\n line-height: 20px;\n white-space: initial;\n text-align: left;\n }\n .du-bois-dark-col-md-24.du-bois-dark-form-item-label > label {\n margin: 0;\n }\n .du-bois-dark-col-md-24.du-bois-dark-form-item-label > label::after {\n display: none;\n }\n}\n@media (max-width: 1199px) {\n .du-bois-dark-col-lg-24.du-bois-dark-form-item-label {\n padding: 0 0 8px;\n line-height: 20px;\n white-space: initial;\n text-align: left;\n }\n .du-bois-dark-col-lg-24.du-bois-dark-form-item-label > label {\n margin: 0;\n }\n .du-bois-dark-col-lg-24.du-bois-dark-form-item-label > label::after {\n display: none;\n }\n}\n@media (max-width: 1599px) {\n .du-bois-dark-col-xl-24.du-bois-dark-form-item-label {\n padding: 0 0 8px;\n line-height: 20px;\n white-space: initial;\n text-align: left;\n }\n .du-bois-dark-col-xl-24.du-bois-dark-form-item-label > label {\n margin: 0;\n }\n .du-bois-dark-col-xl-24.du-bois-dark-form-item-label > label::after {\n display: none;\n }\n}\n.du-bois-dark-form-item {\n /* Some non-status related component style is in `components.less` */\n /* To support leave along ErrorList. We add additional className to handle explain style */\n}\n.du-bois-dark-form-item-explain.du-bois-dark-form-item-explain-error {\n color: #E65B77;\n}\n.du-bois-dark-form-item-explain.du-bois-dark-form-item-explain-warning {\n color: #AF5B23;\n}\n.du-bois-dark-form-item-has-feedback .du-bois-dark-input {\n padding-right: 24px;\n}\n.du-bois-dark-form-item-has-feedback .du-bois-dark-input-affix-wrapper .du-bois-dark-input-suffix {\n padding-right: 18px;\n}\n.du-bois-dark-form-item-has-feedback .du-bois-dark-input-search:not(.du-bois-dark-input-search-enter-button) .du-bois-dark-input-suffix {\n right: 28px;\n}\n.du-bois-dark-form-item-has-feedback .du-bois-dark-switch {\n margin: 2px 0 4px;\n}\n.du-bois-dark-form-item-has-feedback > .du-bois-dark-select .du-bois-dark-select-arrow,\n.du-bois-dark-form-item-has-feedback > .du-bois-dark-select .du-bois-dark-select-clear,\n.du-bois-dark-form-item-has-feedback :not(.du-bois-dark-input-group-addon) > .du-bois-dark-select .du-bois-dark-select-arrow,\n.du-bois-dark-form-item-has-feedback :not(.du-bois-dark-input-group-addon) > .du-bois-dark-select .du-bois-dark-select-clear {\n right: 32px;\n}\n.du-bois-dark-form-item-has-feedback > .du-bois-dark-select .du-bois-dark-select-selection-selected-value,\n.du-bois-dark-form-item-has-feedback :not(.du-bois-dark-input-group-addon) > .du-bois-dark-select .du-bois-dark-select-selection-selected-value {\n padding-right: 42px;\n}\n.du-bois-dark-form-item-has-feedback .du-bois-dark-cascader-picker-arrow {\n margin-right: 19px;\n}\n.du-bois-dark-form-item-has-feedback .du-bois-dark-cascader-picker-clear {\n right: 32px;\n}\n.du-bois-dark-form-item-has-feedback .du-bois-dark-picker {\n padding-right: 28.9px;\n}\n.du-bois-dark-form-item-has-feedback .du-bois-dark-picker-large {\n padding-right: 28.9px;\n}\n.du-bois-dark-form-item-has-feedback .du-bois-dark-picker-small {\n padding-right: 28.9px;\n}\n.du-bois-dark-form-item-has-feedback.du-bois-dark-form-item-has-success .du-bois-dark-form-item-children-icon,\n.du-bois-dark-form-item-has-feedback.du-bois-dark-form-item-has-warning .du-bois-dark-form-item-children-icon,\n.du-bois-dark-form-item-has-feedback.du-bois-dark-form-item-has-error .du-bois-dark-form-item-children-icon,\n.du-bois-dark-form-item-has-feedback.du-bois-dark-form-item-is-validating .du-bois-dark-form-item-children-icon {\n position: absolute;\n top: 50%;\n right: 0;\n z-index: 1;\n width: 40px;\n height: 20px;\n margin-top: -10px;\n font-size: 13px;\n line-height: 20px;\n text-align: center;\n visibility: visible;\n animation: zoomIn 0.3s cubic-bezier(0.12, 0.4, 0.29, 1.46);\n pointer-events: none;\n}\n.du-bois-dark-form-item-has-success.du-bois-dark-form-item-has-feedback .du-bois-dark-form-item-children-icon {\n color: #34824F;\n animation-name: diffZoomIn1 !important;\n}\n.du-bois-dark-form-item-has-warning .du-bois-dark-form-item-split {\n color: #AF5B23;\n}\n.du-bois-dark-form-item-has-warning :not(.du-bois-dark-input-disabled).du-bois-dark-input,\n.du-bois-dark-form-item-has-warning :not(.du-bois-dark-input-affix-wrapper-disabled).du-bois-dark-input-affix-wrapper,\n.du-bois-dark-form-item-has-warning :not(.du-bois-dark-input-disabled).du-bois-dark-input:hover,\n.du-bois-dark-form-item-has-warning :not(.du-bois-dark-input-affix-wrapper-disabled).du-bois-dark-input-affix-wrapper:hover {\n background-color: transparent;\n border-color: #AF5B23;\n}\n.du-bois-dark-form-item-has-warning :not(.du-bois-dark-input-disabled).du-bois-dark-input:focus,\n.du-bois-dark-form-item-has-warning :not(.du-bois-dark-input-affix-wrapper-disabled).du-bois-dark-input-affix-wrapper:focus,\n.du-bois-dark-form-item-has-warning :not(.du-bois-dark-input-disabled).du-bois-dark-input-focused,\n.du-bois-dark-form-item-has-warning :not(.du-bois-dark-input-affix-wrapper-disabled).du-bois-dark-input-affix-wrapper-focused {\n border-color: #AF5B23;\n border-right-width: 1px !important;\n outline: 0;\n box-shadow: 0 0 0 2px rgba(175, 91, 35, 0.2);\n}\n.du-bois-dark-form-item-has-warning .du-bois-dark-calendar-picker-open .du-bois-dark-calendar-picker-input {\n border-color: #AF5B23;\n border-right-width: 1px !important;\n outline: 0;\n box-shadow: 0 0 0 2px rgba(175, 91, 35, 0.2);\n}\n.du-bois-dark-form-item-has-warning .du-bois-dark-input-prefix {\n color: #AF5B23;\n}\n.du-bois-dark-form-item-has-warning .du-bois-dark-input-group-addon {\n color: #AF5B23;\n border-color: #AF5B23;\n}\n.du-bois-dark-form-item-has-warning .has-feedback {\n color: #AF5B23;\n}\n.du-bois-dark-form-item-has-warning.du-bois-dark-form-item-has-feedback .du-bois-dark-form-item-children-icon {\n color: #AF5B23;\n animation-name: diffZoomIn3 !important;\n}\n.du-bois-dark-form-item-has-warning .du-bois-dark-select:not(.du-bois-dark-select-disabled):not(.du-bois-dark-select-customize-input) .du-bois-dark-select-selector {\n background-color: transparent;\n border-color: #AF5B23 !important;\n}\n.du-bois-dark-form-item-has-warning .du-bois-dark-select:not(.du-bois-dark-select-disabled):not(.du-bois-dark-select-customize-input).du-bois-dark-select-open .du-bois-dark-select-selector,\n.du-bois-dark-form-item-has-warning .du-bois-dark-select:not(.du-bois-dark-select-disabled):not(.du-bois-dark-select-customize-input).du-bois-dark-select-focused .du-bois-dark-select-selector {\n border-color: #AF5B23;\n border-right-width: 1px !important;\n outline: 0;\n box-shadow: 0 0 0 2px rgba(175, 91, 35, 0.2);\n}\n.du-bois-dark-form-item-has-warning .du-bois-dark-input-number,\n.du-bois-dark-form-item-has-warning .du-bois-dark-picker {\n background-color: transparent;\n border-color: #AF5B23;\n}\n.du-bois-dark-form-item-has-warning .du-bois-dark-input-number-focused,\n.du-bois-dark-form-item-has-warning .du-bois-dark-picker-focused,\n.du-bois-dark-form-item-has-warning .du-bois-dark-input-number:focus,\n.du-bois-dark-form-item-has-warning .du-bois-dark-picker:focus {\n border-color: #AF5B23;\n border-right-width: 1px !important;\n outline: 0;\n box-shadow: 0 0 0 2px rgba(175, 91, 35, 0.2);\n}\n.du-bois-dark-form-item-has-warning .du-bois-dark-input-number:not([disabled]):hover,\n.du-bois-dark-form-item-has-warning .du-bois-dark-picker:not([disabled]):hover {\n background-color: transparent;\n border-color: #AF5B23;\n}\n.du-bois-dark-form-item-has-warning .du-bois-dark-cascader-picker:focus .du-bois-dark-cascader-input {\n border-color: #AF5B23;\n border-right-width: 1px !important;\n outline: 0;\n box-shadow: 0 0 0 2px rgba(175, 91, 35, 0.2);\n}\n.du-bois-dark-form-item-has-error .du-bois-dark-form-item-split {\n color: #E65B77;\n}\n.du-bois-dark-form-item-has-error :not(.du-bois-dark-input-disabled).du-bois-dark-input,\n.du-bois-dark-form-item-has-error :not(.du-bois-dark-input-affix-wrapper-disabled).du-bois-dark-input-affix-wrapper,\n.du-bois-dark-form-item-has-error :not(.du-bois-dark-input-disabled).du-bois-dark-input:hover,\n.du-bois-dark-form-item-has-error :not(.du-bois-dark-input-affix-wrapper-disabled).du-bois-dark-input-affix-wrapper:hover {\n background-color: transparent;\n border-color: #E65B77;\n}\n.du-bois-dark-form-item-has-error :not(.du-bois-dark-input-disabled).du-bois-dark-input:focus,\n.du-bois-dark-form-item-has-error :not(.du-bois-dark-input-affix-wrapper-disabled).du-bois-dark-input-affix-wrapper:focus,\n.du-bois-dark-form-item-has-error :not(.du-bois-dark-input-disabled).du-bois-dark-input-focused,\n.du-bois-dark-form-item-has-error :not(.du-bois-dark-input-affix-wrapper-disabled).du-bois-dark-input-affix-wrapper-focused {\n border-color: #E65B77;\n border-right-width: 1px !important;\n outline: 0;\n box-shadow: 0 0 0 2px rgba(230, 91, 119, 0.2);\n}\n.du-bois-dark-form-item-has-error .du-bois-dark-calendar-picker-open .du-bois-dark-calendar-picker-input {\n border-color: #E65B77;\n border-right-width: 1px !important;\n outline: 0;\n box-shadow: 0 0 0 2px rgba(230, 91, 119, 0.2);\n}\n.du-bois-dark-form-item-has-error .du-bois-dark-input-prefix {\n color: #E65B77;\n}\n.du-bois-dark-form-item-has-error .du-bois-dark-input-group-addon {\n color: #E65B77;\n border-color: #E65B77;\n}\n.du-bois-dark-form-item-has-error .has-feedback {\n color: #E65B77;\n}\n.du-bois-dark-form-item-has-error.du-bois-dark-form-item-has-feedback .du-bois-dark-form-item-children-icon {\n color: #E65B77;\n animation-name: diffZoomIn2 !important;\n}\n.du-bois-dark-form-item-has-error .du-bois-dark-select:not(.du-bois-dark-select-disabled):not(.du-bois-dark-select-customize-input) .du-bois-dark-select-selector {\n background-color: transparent;\n border-color: #E65B77 !important;\n}\n.du-bois-dark-form-item-has-error .du-bois-dark-select:not(.du-bois-dark-select-disabled):not(.du-bois-dark-select-customize-input).du-bois-dark-select-open .du-bois-dark-select-selector,\n.du-bois-dark-form-item-has-error .du-bois-dark-select:not(.du-bois-dark-select-disabled):not(.du-bois-dark-select-customize-input).du-bois-dark-select-focused .du-bois-dark-select-selector {\n border-color: #E65B77;\n border-right-width: 1px !important;\n outline: 0;\n box-shadow: 0 0 0 2px rgba(230, 91, 119, 0.2);\n}\n.du-bois-dark-form-item-has-error .du-bois-dark-input-group-addon .du-bois-dark-select.du-bois-dark-select-single:not(.du-bois-dark-select-customize-input) .du-bois-dark-select-selector {\n background-color: inherit;\n border: 0;\n box-shadow: none;\n}\n.du-bois-dark-form-item-has-error .du-bois-dark-select.du-bois-dark-select-auto-complete .du-bois-dark-input:focus {\n border-color: #E65B77;\n}\n.du-bois-dark-form-item-has-error .du-bois-dark-input-number,\n.du-bois-dark-form-item-has-error .du-bois-dark-picker {\n background-color: transparent;\n border-color: #E65B77;\n}\n.du-bois-dark-form-item-has-error .du-bois-dark-input-number-focused,\n.du-bois-dark-form-item-has-error .du-bois-dark-picker-focused,\n.du-bois-dark-form-item-has-error .du-bois-dark-input-number:focus,\n.du-bois-dark-form-item-has-error .du-bois-dark-picker:focus {\n border-color: #E65B77;\n border-right-width: 1px !important;\n outline: 0;\n box-shadow: 0 0 0 2px rgba(230, 91, 119, 0.2);\n}\n.du-bois-dark-form-item-has-error .du-bois-dark-input-number:not([disabled]):hover,\n.du-bois-dark-form-item-has-error .du-bois-dark-picker:not([disabled]):hover {\n background-color: transparent;\n border-color: #E65B77;\n}\n.du-bois-dark-form-item-has-error .du-bois-dark-mention-wrapper .du-bois-dark-mention-editor,\n.du-bois-dark-form-item-has-error .du-bois-dark-mention-wrapper .du-bois-dark-mention-editor:not([disabled]):hover {\n background-color: transparent;\n border-color: #E65B77;\n}\n.du-bois-dark-form-item-has-error .du-bois-dark-mention-wrapper.du-bois-dark-mention-active:not([disabled]) .du-bois-dark-mention-editor,\n.du-bois-dark-form-item-has-error .du-bois-dark-mention-wrapper .du-bois-dark-mention-editor:not([disabled]):focus {\n border-color: #E65B77;\n border-right-width: 1px !important;\n outline: 0;\n box-shadow: 0 0 0 2px rgba(230, 91, 119, 0.2);\n}\n.du-bois-dark-form-item-has-error .du-bois-dark-cascader-picker:hover .du-bois-dark-cascader-picker-label:hover + .du-bois-dark-cascader-input.du-bois-dark-input {\n border-color: #E65B77;\n}\n.du-bois-dark-form-item-has-error .du-bois-dark-cascader-picker:focus .du-bois-dark-cascader-input {\n background-color: transparent;\n border-color: #E65B77;\n border-right-width: 1px !important;\n outline: 0;\n box-shadow: 0 0 0 2px rgba(230, 91, 119, 0.2);\n}\n.du-bois-dark-form-item-has-error .du-bois-dark-transfer-list {\n border-color: #E65B77;\n}\n.du-bois-dark-form-item-has-error .du-bois-dark-transfer-list-search:not([disabled]) {\n border-color: #1F272D;\n}\n.du-bois-dark-form-item-has-error .du-bois-dark-transfer-list-search:not([disabled]):hover {\n border-color: #1d517c;\n border-right-width: 1px !important;\n}\n.du-bois-dark-form-item-has-error .du-bois-dark-transfer-list-search:not([disabled]):focus {\n border-color: #2272B4;\n border-right-width: 1px !important;\n outline: 0;\n box-shadow: 0 0 0 2px rgba(34, 114, 180, 0.2);\n}\n.du-bois-dark-form-item-has-error .du-bois-dark-radio-button-wrapper {\n border-color: #E65B77 !important;\n}\n.du-bois-dark-form-item-has-error .du-bois-dark-radio-button-wrapper:not(:first-child)::before {\n background-color: #E65B77;\n}\n.du-bois-dark-form-item-is-validating.du-bois-dark-form-item-has-feedback .du-bois-dark-form-item-children-icon {\n display: inline-block;\n color: #2272B4;\n}\n.du-bois-dark-form {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #E8ECF0;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: tnum;\n}\n.du-bois-dark-form legend {\n display: block;\n width: 100%;\n margin-bottom: 20px;\n padding: 0;\n color: #92A4B3;\n font-size: 18px;\n line-height: inherit;\n border: 0;\n border-bottom: 1px solid #1F272D;\n}\n.du-bois-dark-form label {\n font-size: 13px;\n}\n.du-bois-dark-form input[type='search'] {\n box-sizing: border-box;\n}\n.du-bois-dark-form input[type='radio'],\n.du-bois-dark-form input[type='checkbox'] {\n line-height: normal;\n}\n.du-bois-dark-form input[type='file'] {\n display: block;\n}\n.du-bois-dark-form input[type='range'] {\n display: block;\n width: 100%;\n}\n.du-bois-dark-form select[multiple],\n.du-bois-dark-form select[size] {\n height: auto;\n}\n.du-bois-dark-form input[type='file']:focus,\n.du-bois-dark-form input[type='radio']:focus,\n.du-bois-dark-form input[type='checkbox']:focus {\n outline: thin dotted;\n outline: 5px auto -webkit-focus-ring-color;\n outline-offset: -2px;\n}\n.du-bois-dark-form output {\n display: block;\n padding-top: 15px;\n color: #E8ECF0;\n font-size: 13px;\n line-height: 20px;\n}\n.du-bois-dark-form .du-bois-dark-form-text {\n display: inline-block;\n padding-right: 8px;\n}\n.du-bois-dark-form-small .du-bois-dark-form-item-label > label {\n height: 32px;\n}\n.du-bois-dark-form-small .du-bois-dark-form-item-control-input {\n min-height: 32px;\n}\n.du-bois-dark-form-large .du-bois-dark-form-item-label > label {\n height: 40px;\n}\n.du-bois-dark-form-large .du-bois-dark-form-item-control-input {\n min-height: 40px;\n}\n.du-bois-dark-form-item {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #E8ECF0;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: tnum;\n margin-bottom: 24px;\n vertical-align: top;\n}\n.du-bois-dark-form-item-with-help {\n margin-bottom: 0;\n}\n.du-bois-dark-form-item-hidden,\n.du-bois-dark-form-item-hidden.du-bois-dark-row {\n display: none;\n}\n.du-bois-dark-form-item-label {\n display: inline-block;\n flex-grow: 0;\n overflow: hidden;\n white-space: nowrap;\n text-align: right;\n vertical-align: middle;\n}\n.du-bois-dark-form-item-label-left {\n text-align: left;\n}\n.du-bois-dark-form-item-label > label {\n position: relative;\n display: inline-flex;\n align-items: center;\n height: 40px;\n color: rgba(255, 255, 255, 0.85);\n font-size: 13px;\n}\n.du-bois-dark-form-item-label > label > .anticon {\n font-size: 13px;\n vertical-align: top;\n}\n.du-bois-dark-form-item-label > label.du-bois-dark-form-item-required:not(.du-bois-dark-form-item-required-mark-optional)::before {\n display: inline-block;\n margin-right: 4px;\n color: #a61d24;\n font-size: 13px;\n font-family: SimSun, sans-serif;\n line-height: 1;\n content: '*';\n}\n.du-bois-dark-form-hide-required-mark .du-bois-dark-form-item-label > label.du-bois-dark-form-item-required:not(.du-bois-dark-form-item-required-mark-optional)::before {\n display: none;\n}\n.du-bois-dark-form-item-label > label .du-bois-dark-form-item-optional {\n display: inline-block;\n margin-left: 4px;\n color: #92A4B3;\n}\n.du-bois-dark-form-hide-required-mark .du-bois-dark-form-item-label > label .du-bois-dark-form-item-optional {\n display: none;\n}\n.du-bois-dark-form-item-label > label .du-bois-dark-form-item-tooltip {\n color: #92A4B3;\n cursor: help;\n writing-mode: horizontal-tb;\n margin-inline-start: 4px;\n}\n.du-bois-dark-form-item-label > label::after {\n content: ':';\n position: relative;\n top: -0.5px;\n margin: 0 8px 0 2px;\n}\n.du-bois-dark-form-item-label > label.du-bois-dark-form-item-no-colon::after {\n content: ' ';\n}\n.du-bois-dark-form-item-control {\n display: flex;\n flex-direction: column;\n flex-grow: 1;\n}\n.du-bois-dark-form-item-control:first-child:not([class^='du-bois-dark-col-']):not([class*=' du-bois-dark-col-']) {\n width: 100%;\n}\n.du-bois-dark-form-item-control-input {\n position: relative;\n display: flex;\n align-items: center;\n min-height: 40px;\n}\n.du-bois-dark-form-item-control-input-content {\n flex: auto;\n max-width: 100%;\n}\n.du-bois-dark-form-item-explain,\n.du-bois-dark-form-item-extra {\n clear: both;\n min-height: 24px;\n color: #92A4B3;\n font-size: 13px;\n line-height: 20px;\n transition: color 0.3s cubic-bezier(0.215, 0.61, 0.355, 1);\n margin-top: -118px;\n margin-bottom: -118px;\n}\n.du-bois-dark-form-item .du-bois-dark-input-textarea-show-count::after {\n margin-bottom: -22px;\n}\n.du-bois-dark-show-help-enter,\n.du-bois-dark-show-help-appear {\n animation-duration: 0.3s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-dark-show-help-leave {\n animation-duration: 0.3s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-dark-show-help-enter.du-bois-dark-show-help-enter-active,\n.du-bois-dark-show-help-appear.du-bois-dark-show-help-appear-active {\n animation-name: antShowHelpIn;\n animation-play-state: running;\n}\n.du-bois-dark-show-help-leave.du-bois-dark-show-help-leave-active {\n animation-name: antShowHelpOut;\n animation-play-state: running;\n pointer-events: none;\n}\n.du-bois-dark-show-help-enter,\n.du-bois-dark-show-help-appear {\n opacity: 0;\n animation-timing-function: cubic-bezier(0.645, 0.045, 0.355, 1);\n}\n.du-bois-dark-show-help-leave {\n animation-timing-function: cubic-bezier(0.645, 0.045, 0.355, 1);\n}\n@keyframes antShowHelpIn {\n 0% {\n transform: translateY(-5px);\n opacity: 0;\n }\n 100% {\n transform: translateY(0);\n opacity: 1;\n }\n}\n@keyframes antShowHelpOut {\n}\n@keyframes diffZoomIn1 {\n 0% {\n transform: scale(0);\n opacity: 0;\n }\n 100% {\n transform: scale(1);\n opacity: 1;\n }\n}\n@keyframes diffZoomIn2 {\n 0% {\n transform: scale(0);\n opacity: 0;\n }\n 100% {\n transform: scale(1);\n opacity: 1;\n }\n}\n@keyframes diffZoomIn3 {\n 0% {\n transform: scale(0);\n opacity: 0;\n }\n 100% {\n transform: scale(1);\n opacity: 1;\n }\n}\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-dark-row {\n display: flex;\n flex-flow: row wrap;\n}\n.du-bois-dark-row::before,\n.du-bois-dark-row::after {\n display: flex;\n}\n.du-bois-dark-row-no-wrap {\n flex-wrap: nowrap;\n}\n.du-bois-dark-row-start {\n justify-content: flex-start;\n}\n.du-bois-dark-row-center {\n justify-content: center;\n}\n.du-bois-dark-row-end {\n justify-content: flex-end;\n}\n.du-bois-dark-row-space-between {\n justify-content: space-between;\n}\n.du-bois-dark-row-space-around {\n justify-content: space-around;\n}\n.du-bois-dark-row-top {\n align-items: flex-start;\n}\n.du-bois-dark-row-middle {\n align-items: center;\n}\n.du-bois-dark-row-bottom {\n align-items: flex-end;\n}\n.du-bois-dark-col {\n position: relative;\n max-width: 100%;\n min-height: 1px;\n}\n.du-bois-dark-col-24 {\n display: block;\n flex: 0 0 100%;\n max-width: 100%;\n}\n.du-bois-dark-col-push-24 {\n left: 100%;\n}\n.du-bois-dark-col-pull-24 {\n right: 100%;\n}\n.du-bois-dark-col-offset-24 {\n margin-left: 100%;\n}\n.du-bois-dark-col-order-24 {\n order: 24;\n}\n.du-bois-dark-col-23 {\n display: block;\n flex: 0 0 95.83333333%;\n max-width: 95.83333333%;\n}\n.du-bois-dark-col-push-23 {\n left: 95.83333333%;\n}\n.du-bois-dark-col-pull-23 {\n right: 95.83333333%;\n}\n.du-bois-dark-col-offset-23 {\n margin-left: 95.83333333%;\n}\n.du-bois-dark-col-order-23 {\n order: 23;\n}\n.du-bois-dark-col-22 {\n display: block;\n flex: 0 0 91.66666667%;\n max-width: 91.66666667%;\n}\n.du-bois-dark-col-push-22 {\n left: 91.66666667%;\n}\n.du-bois-dark-col-pull-22 {\n right: 91.66666667%;\n}\n.du-bois-dark-col-offset-22 {\n margin-left: 91.66666667%;\n}\n.du-bois-dark-col-order-22 {\n order: 22;\n}\n.du-bois-dark-col-21 {\n display: block;\n flex: 0 0 87.5%;\n max-width: 87.5%;\n}\n.du-bois-dark-col-push-21 {\n left: 87.5%;\n}\n.du-bois-dark-col-pull-21 {\n right: 87.5%;\n}\n.du-bois-dark-col-offset-21 {\n margin-left: 87.5%;\n}\n.du-bois-dark-col-order-21 {\n order: 21;\n}\n.du-bois-dark-col-20 {\n display: block;\n flex: 0 0 83.33333333%;\n max-width: 83.33333333%;\n}\n.du-bois-dark-col-push-20 {\n left: 83.33333333%;\n}\n.du-bois-dark-col-pull-20 {\n right: 83.33333333%;\n}\n.du-bois-dark-col-offset-20 {\n margin-left: 83.33333333%;\n}\n.du-bois-dark-col-order-20 {\n order: 20;\n}\n.du-bois-dark-col-19 {\n display: block;\n flex: 0 0 79.16666667%;\n max-width: 79.16666667%;\n}\n.du-bois-dark-col-push-19 {\n left: 79.16666667%;\n}\n.du-bois-dark-col-pull-19 {\n right: 79.16666667%;\n}\n.du-bois-dark-col-offset-19 {\n margin-left: 79.16666667%;\n}\n.du-bois-dark-col-order-19 {\n order: 19;\n}\n.du-bois-dark-col-18 {\n display: block;\n flex: 0 0 75%;\n max-width: 75%;\n}\n.du-bois-dark-col-push-18 {\n left: 75%;\n}\n.du-bois-dark-col-pull-18 {\n right: 75%;\n}\n.du-bois-dark-col-offset-18 {\n margin-left: 75%;\n}\n.du-bois-dark-col-order-18 {\n order: 18;\n}\n.du-bois-dark-col-17 {\n display: block;\n flex: 0 0 70.83333333%;\n max-width: 70.83333333%;\n}\n.du-bois-dark-col-push-17 {\n left: 70.83333333%;\n}\n.du-bois-dark-col-pull-17 {\n right: 70.83333333%;\n}\n.du-bois-dark-col-offset-17 {\n margin-left: 70.83333333%;\n}\n.du-bois-dark-col-order-17 {\n order: 17;\n}\n.du-bois-dark-col-16 {\n display: block;\n flex: 0 0 66.66666667%;\n max-width: 66.66666667%;\n}\n.du-bois-dark-col-push-16 {\n left: 66.66666667%;\n}\n.du-bois-dark-col-pull-16 {\n right: 66.66666667%;\n}\n.du-bois-dark-col-offset-16 {\n margin-left: 66.66666667%;\n}\n.du-bois-dark-col-order-16 {\n order: 16;\n}\n.du-bois-dark-col-15 {\n display: block;\n flex: 0 0 62.5%;\n max-width: 62.5%;\n}\n.du-bois-dark-col-push-15 {\n left: 62.5%;\n}\n.du-bois-dark-col-pull-15 {\n right: 62.5%;\n}\n.du-bois-dark-col-offset-15 {\n margin-left: 62.5%;\n}\n.du-bois-dark-col-order-15 {\n order: 15;\n}\n.du-bois-dark-col-14 {\n display: block;\n flex: 0 0 58.33333333%;\n max-width: 58.33333333%;\n}\n.du-bois-dark-col-push-14 {\n left: 58.33333333%;\n}\n.du-bois-dark-col-pull-14 {\n right: 58.33333333%;\n}\n.du-bois-dark-col-offset-14 {\n margin-left: 58.33333333%;\n}\n.du-bois-dark-col-order-14 {\n order: 14;\n}\n.du-bois-dark-col-13 {\n display: block;\n flex: 0 0 54.16666667%;\n max-width: 54.16666667%;\n}\n.du-bois-dark-col-push-13 {\n left: 54.16666667%;\n}\n.du-bois-dark-col-pull-13 {\n right: 54.16666667%;\n}\n.du-bois-dark-col-offset-13 {\n margin-left: 54.16666667%;\n}\n.du-bois-dark-col-order-13 {\n order: 13;\n}\n.du-bois-dark-col-12 {\n display: block;\n flex: 0 0 50%;\n max-width: 50%;\n}\n.du-bois-dark-col-push-12 {\n left: 50%;\n}\n.du-bois-dark-col-pull-12 {\n right: 50%;\n}\n.du-bois-dark-col-offset-12 {\n margin-left: 50%;\n}\n.du-bois-dark-col-order-12 {\n order: 12;\n}\n.du-bois-dark-col-11 {\n display: block;\n flex: 0 0 45.83333333%;\n max-width: 45.83333333%;\n}\n.du-bois-dark-col-push-11 {\n left: 45.83333333%;\n}\n.du-bois-dark-col-pull-11 {\n right: 45.83333333%;\n}\n.du-bois-dark-col-offset-11 {\n margin-left: 45.83333333%;\n}\n.du-bois-dark-col-order-11 {\n order: 11;\n}\n.du-bois-dark-col-10 {\n display: block;\n flex: 0 0 41.66666667%;\n max-width: 41.66666667%;\n}\n.du-bois-dark-col-push-10 {\n left: 41.66666667%;\n}\n.du-bois-dark-col-pull-10 {\n right: 41.66666667%;\n}\n.du-bois-dark-col-offset-10 {\n margin-left: 41.66666667%;\n}\n.du-bois-dark-col-order-10 {\n order: 10;\n}\n.du-bois-dark-col-9 {\n display: block;\n flex: 0 0 37.5%;\n max-width: 37.5%;\n}\n.du-bois-dark-col-push-9 {\n left: 37.5%;\n}\n.du-bois-dark-col-pull-9 {\n right: 37.5%;\n}\n.du-bois-dark-col-offset-9 {\n margin-left: 37.5%;\n}\n.du-bois-dark-col-order-9 {\n order: 9;\n}\n.du-bois-dark-col-8 {\n display: block;\n flex: 0 0 33.33333333%;\n max-width: 33.33333333%;\n}\n.du-bois-dark-col-push-8 {\n left: 33.33333333%;\n}\n.du-bois-dark-col-pull-8 {\n right: 33.33333333%;\n}\n.du-bois-dark-col-offset-8 {\n margin-left: 33.33333333%;\n}\n.du-bois-dark-col-order-8 {\n order: 8;\n}\n.du-bois-dark-col-7 {\n display: block;\n flex: 0 0 29.16666667%;\n max-width: 29.16666667%;\n}\n.du-bois-dark-col-push-7 {\n left: 29.16666667%;\n}\n.du-bois-dark-col-pull-7 {\n right: 29.16666667%;\n}\n.du-bois-dark-col-offset-7 {\n margin-left: 29.16666667%;\n}\n.du-bois-dark-col-order-7 {\n order: 7;\n}\n.du-bois-dark-col-6 {\n display: block;\n flex: 0 0 25%;\n max-width: 25%;\n}\n.du-bois-dark-col-push-6 {\n left: 25%;\n}\n.du-bois-dark-col-pull-6 {\n right: 25%;\n}\n.du-bois-dark-col-offset-6 {\n margin-left: 25%;\n}\n.du-bois-dark-col-order-6 {\n order: 6;\n}\n.du-bois-dark-col-5 {\n display: block;\n flex: 0 0 20.83333333%;\n max-width: 20.83333333%;\n}\n.du-bois-dark-col-push-5 {\n left: 20.83333333%;\n}\n.du-bois-dark-col-pull-5 {\n right: 20.83333333%;\n}\n.du-bois-dark-col-offset-5 {\n margin-left: 20.83333333%;\n}\n.du-bois-dark-col-order-5 {\n order: 5;\n}\n.du-bois-dark-col-4 {\n display: block;\n flex: 0 0 16.66666667%;\n max-width: 16.66666667%;\n}\n.du-bois-dark-col-push-4 {\n left: 16.66666667%;\n}\n.du-bois-dark-col-pull-4 {\n right: 16.66666667%;\n}\n.du-bois-dark-col-offset-4 {\n margin-left: 16.66666667%;\n}\n.du-bois-dark-col-order-4 {\n order: 4;\n}\n.du-bois-dark-col-3 {\n display: block;\n flex: 0 0 12.5%;\n max-width: 12.5%;\n}\n.du-bois-dark-col-push-3 {\n left: 12.5%;\n}\n.du-bois-dark-col-pull-3 {\n right: 12.5%;\n}\n.du-bois-dark-col-offset-3 {\n margin-left: 12.5%;\n}\n.du-bois-dark-col-order-3 {\n order: 3;\n}\n.du-bois-dark-col-2 {\n display: block;\n flex: 0 0 8.33333333%;\n max-width: 8.33333333%;\n}\n.du-bois-dark-col-push-2 {\n left: 8.33333333%;\n}\n.du-bois-dark-col-pull-2 {\n right: 8.33333333%;\n}\n.du-bois-dark-col-offset-2 {\n margin-left: 8.33333333%;\n}\n.du-bois-dark-col-order-2 {\n order: 2;\n}\n.du-bois-dark-col-1 {\n display: block;\n flex: 0 0 4.16666667%;\n max-width: 4.16666667%;\n}\n.du-bois-dark-col-push-1 {\n left: 4.16666667%;\n}\n.du-bois-dark-col-pull-1 {\n right: 4.16666667%;\n}\n.du-bois-dark-col-offset-1 {\n margin-left: 4.16666667%;\n}\n.du-bois-dark-col-order-1 {\n order: 1;\n}\n.du-bois-dark-col-0 {\n display: none;\n}\n.du-bois-dark-col-push-0 {\n left: auto;\n}\n.du-bois-dark-col-pull-0 {\n right: auto;\n}\n.du-bois-dark-col-push-0 {\n left: auto;\n}\n.du-bois-dark-col-pull-0 {\n right: auto;\n}\n.du-bois-dark-col-offset-0 {\n margin-left: 0;\n}\n.du-bois-dark-col-order-0 {\n order: 0;\n}\n.du-bois-dark-col-xs-24 {\n display: block;\n flex: 0 0 100%;\n max-width: 100%;\n}\n.du-bois-dark-col-xs-push-24 {\n left: 100%;\n}\n.du-bois-dark-col-xs-pull-24 {\n right: 100%;\n}\n.du-bois-dark-col-xs-offset-24 {\n margin-left: 100%;\n}\n.du-bois-dark-col-xs-order-24 {\n order: 24;\n}\n.du-bois-dark-col-xs-23 {\n display: block;\n flex: 0 0 95.83333333%;\n max-width: 95.83333333%;\n}\n.du-bois-dark-col-xs-push-23 {\n left: 95.83333333%;\n}\n.du-bois-dark-col-xs-pull-23 {\n right: 95.83333333%;\n}\n.du-bois-dark-col-xs-offset-23 {\n margin-left: 95.83333333%;\n}\n.du-bois-dark-col-xs-order-23 {\n order: 23;\n}\n.du-bois-dark-col-xs-22 {\n display: block;\n flex: 0 0 91.66666667%;\n max-width: 91.66666667%;\n}\n.du-bois-dark-col-xs-push-22 {\n left: 91.66666667%;\n}\n.du-bois-dark-col-xs-pull-22 {\n right: 91.66666667%;\n}\n.du-bois-dark-col-xs-offset-22 {\n margin-left: 91.66666667%;\n}\n.du-bois-dark-col-xs-order-22 {\n order: 22;\n}\n.du-bois-dark-col-xs-21 {\n display: block;\n flex: 0 0 87.5%;\n max-width: 87.5%;\n}\n.du-bois-dark-col-xs-push-21 {\n left: 87.5%;\n}\n.du-bois-dark-col-xs-pull-21 {\n right: 87.5%;\n}\n.du-bois-dark-col-xs-offset-21 {\n margin-left: 87.5%;\n}\n.du-bois-dark-col-xs-order-21 {\n order: 21;\n}\n.du-bois-dark-col-xs-20 {\n display: block;\n flex: 0 0 83.33333333%;\n max-width: 83.33333333%;\n}\n.du-bois-dark-col-xs-push-20 {\n left: 83.33333333%;\n}\n.du-bois-dark-col-xs-pull-20 {\n right: 83.33333333%;\n}\n.du-bois-dark-col-xs-offset-20 {\n margin-left: 83.33333333%;\n}\n.du-bois-dark-col-xs-order-20 {\n order: 20;\n}\n.du-bois-dark-col-xs-19 {\n display: block;\n flex: 0 0 79.16666667%;\n max-width: 79.16666667%;\n}\n.du-bois-dark-col-xs-push-19 {\n left: 79.16666667%;\n}\n.du-bois-dark-col-xs-pull-19 {\n right: 79.16666667%;\n}\n.du-bois-dark-col-xs-offset-19 {\n margin-left: 79.16666667%;\n}\n.du-bois-dark-col-xs-order-19 {\n order: 19;\n}\n.du-bois-dark-col-xs-18 {\n display: block;\n flex: 0 0 75%;\n max-width: 75%;\n}\n.du-bois-dark-col-xs-push-18 {\n left: 75%;\n}\n.du-bois-dark-col-xs-pull-18 {\n right: 75%;\n}\n.du-bois-dark-col-xs-offset-18 {\n margin-left: 75%;\n}\n.du-bois-dark-col-xs-order-18 {\n order: 18;\n}\n.du-bois-dark-col-xs-17 {\n display: block;\n flex: 0 0 70.83333333%;\n max-width: 70.83333333%;\n}\n.du-bois-dark-col-xs-push-17 {\n left: 70.83333333%;\n}\n.du-bois-dark-col-xs-pull-17 {\n right: 70.83333333%;\n}\n.du-bois-dark-col-xs-offset-17 {\n margin-left: 70.83333333%;\n}\n.du-bois-dark-col-xs-order-17 {\n order: 17;\n}\n.du-bois-dark-col-xs-16 {\n display: block;\n flex: 0 0 66.66666667%;\n max-width: 66.66666667%;\n}\n.du-bois-dark-col-xs-push-16 {\n left: 66.66666667%;\n}\n.du-bois-dark-col-xs-pull-16 {\n right: 66.66666667%;\n}\n.du-bois-dark-col-xs-offset-16 {\n margin-left: 66.66666667%;\n}\n.du-bois-dark-col-xs-order-16 {\n order: 16;\n}\n.du-bois-dark-col-xs-15 {\n display: block;\n flex: 0 0 62.5%;\n max-width: 62.5%;\n}\n.du-bois-dark-col-xs-push-15 {\n left: 62.5%;\n}\n.du-bois-dark-col-xs-pull-15 {\n right: 62.5%;\n}\n.du-bois-dark-col-xs-offset-15 {\n margin-left: 62.5%;\n}\n.du-bois-dark-col-xs-order-15 {\n order: 15;\n}\n.du-bois-dark-col-xs-14 {\n display: block;\n flex: 0 0 58.33333333%;\n max-width: 58.33333333%;\n}\n.du-bois-dark-col-xs-push-14 {\n left: 58.33333333%;\n}\n.du-bois-dark-col-xs-pull-14 {\n right: 58.33333333%;\n}\n.du-bois-dark-col-xs-offset-14 {\n margin-left: 58.33333333%;\n}\n.du-bois-dark-col-xs-order-14 {\n order: 14;\n}\n.du-bois-dark-col-xs-13 {\n display: block;\n flex: 0 0 54.16666667%;\n max-width: 54.16666667%;\n}\n.du-bois-dark-col-xs-push-13 {\n left: 54.16666667%;\n}\n.du-bois-dark-col-xs-pull-13 {\n right: 54.16666667%;\n}\n.du-bois-dark-col-xs-offset-13 {\n margin-left: 54.16666667%;\n}\n.du-bois-dark-col-xs-order-13 {\n order: 13;\n}\n.du-bois-dark-col-xs-12 {\n display: block;\n flex: 0 0 50%;\n max-width: 50%;\n}\n.du-bois-dark-col-xs-push-12 {\n left: 50%;\n}\n.du-bois-dark-col-xs-pull-12 {\n right: 50%;\n}\n.du-bois-dark-col-xs-offset-12 {\n margin-left: 50%;\n}\n.du-bois-dark-col-xs-order-12 {\n order: 12;\n}\n.du-bois-dark-col-xs-11 {\n display: block;\n flex: 0 0 45.83333333%;\n max-width: 45.83333333%;\n}\n.du-bois-dark-col-xs-push-11 {\n left: 45.83333333%;\n}\n.du-bois-dark-col-xs-pull-11 {\n right: 45.83333333%;\n}\n.du-bois-dark-col-xs-offset-11 {\n margin-left: 45.83333333%;\n}\n.du-bois-dark-col-xs-order-11 {\n order: 11;\n}\n.du-bois-dark-col-xs-10 {\n display: block;\n flex: 0 0 41.66666667%;\n max-width: 41.66666667%;\n}\n.du-bois-dark-col-xs-push-10 {\n left: 41.66666667%;\n}\n.du-bois-dark-col-xs-pull-10 {\n right: 41.66666667%;\n}\n.du-bois-dark-col-xs-offset-10 {\n margin-left: 41.66666667%;\n}\n.du-bois-dark-col-xs-order-10 {\n order: 10;\n}\n.du-bois-dark-col-xs-9 {\n display: block;\n flex: 0 0 37.5%;\n max-width: 37.5%;\n}\n.du-bois-dark-col-xs-push-9 {\n left: 37.5%;\n}\n.du-bois-dark-col-xs-pull-9 {\n right: 37.5%;\n}\n.du-bois-dark-col-xs-offset-9 {\n margin-left: 37.5%;\n}\n.du-bois-dark-col-xs-order-9 {\n order: 9;\n}\n.du-bois-dark-col-xs-8 {\n display: block;\n flex: 0 0 33.33333333%;\n max-width: 33.33333333%;\n}\n.du-bois-dark-col-xs-push-8 {\n left: 33.33333333%;\n}\n.du-bois-dark-col-xs-pull-8 {\n right: 33.33333333%;\n}\n.du-bois-dark-col-xs-offset-8 {\n margin-left: 33.33333333%;\n}\n.du-bois-dark-col-xs-order-8 {\n order: 8;\n}\n.du-bois-dark-col-xs-7 {\n display: block;\n flex: 0 0 29.16666667%;\n max-width: 29.16666667%;\n}\n.du-bois-dark-col-xs-push-7 {\n left: 29.16666667%;\n}\n.du-bois-dark-col-xs-pull-7 {\n right: 29.16666667%;\n}\n.du-bois-dark-col-xs-offset-7 {\n margin-left: 29.16666667%;\n}\n.du-bois-dark-col-xs-order-7 {\n order: 7;\n}\n.du-bois-dark-col-xs-6 {\n display: block;\n flex: 0 0 25%;\n max-width: 25%;\n}\n.du-bois-dark-col-xs-push-6 {\n left: 25%;\n}\n.du-bois-dark-col-xs-pull-6 {\n right: 25%;\n}\n.du-bois-dark-col-xs-offset-6 {\n margin-left: 25%;\n}\n.du-bois-dark-col-xs-order-6 {\n order: 6;\n}\n.du-bois-dark-col-xs-5 {\n display: block;\n flex: 0 0 20.83333333%;\n max-width: 20.83333333%;\n}\n.du-bois-dark-col-xs-push-5 {\n left: 20.83333333%;\n}\n.du-bois-dark-col-xs-pull-5 {\n right: 20.83333333%;\n}\n.du-bois-dark-col-xs-offset-5 {\n margin-left: 20.83333333%;\n}\n.du-bois-dark-col-xs-order-5 {\n order: 5;\n}\n.du-bois-dark-col-xs-4 {\n display: block;\n flex: 0 0 16.66666667%;\n max-width: 16.66666667%;\n}\n.du-bois-dark-col-xs-push-4 {\n left: 16.66666667%;\n}\n.du-bois-dark-col-xs-pull-4 {\n right: 16.66666667%;\n}\n.du-bois-dark-col-xs-offset-4 {\n margin-left: 16.66666667%;\n}\n.du-bois-dark-col-xs-order-4 {\n order: 4;\n}\n.du-bois-dark-col-xs-3 {\n display: block;\n flex: 0 0 12.5%;\n max-width: 12.5%;\n}\n.du-bois-dark-col-xs-push-3 {\n left: 12.5%;\n}\n.du-bois-dark-col-xs-pull-3 {\n right: 12.5%;\n}\n.du-bois-dark-col-xs-offset-3 {\n margin-left: 12.5%;\n}\n.du-bois-dark-col-xs-order-3 {\n order: 3;\n}\n.du-bois-dark-col-xs-2 {\n display: block;\n flex: 0 0 8.33333333%;\n max-width: 8.33333333%;\n}\n.du-bois-dark-col-xs-push-2 {\n left: 8.33333333%;\n}\n.du-bois-dark-col-xs-pull-2 {\n right: 8.33333333%;\n}\n.du-bois-dark-col-xs-offset-2 {\n margin-left: 8.33333333%;\n}\n.du-bois-dark-col-xs-order-2 {\n order: 2;\n}\n.du-bois-dark-col-xs-1 {\n display: block;\n flex: 0 0 4.16666667%;\n max-width: 4.16666667%;\n}\n.du-bois-dark-col-xs-push-1 {\n left: 4.16666667%;\n}\n.du-bois-dark-col-xs-pull-1 {\n right: 4.16666667%;\n}\n.du-bois-dark-col-xs-offset-1 {\n margin-left: 4.16666667%;\n}\n.du-bois-dark-col-xs-order-1 {\n order: 1;\n}\n.du-bois-dark-col-xs-0 {\n display: none;\n}\n.du-bois-dark-col-push-0 {\n left: auto;\n}\n.du-bois-dark-col-pull-0 {\n right: auto;\n}\n.du-bois-dark-col-xs-push-0 {\n left: auto;\n}\n.du-bois-dark-col-xs-pull-0 {\n right: auto;\n}\n.du-bois-dark-col-xs-offset-0 {\n margin-left: 0;\n}\n.du-bois-dark-col-xs-order-0 {\n order: 0;\n}\n@media (min-width: 576px) {\n .du-bois-dark-col-sm-24 {\n display: block;\n flex: 0 0 100%;\n max-width: 100%;\n }\n .du-bois-dark-col-sm-push-24 {\n left: 100%;\n }\n .du-bois-dark-col-sm-pull-24 {\n right: 100%;\n }\n .du-bois-dark-col-sm-offset-24 {\n margin-left: 100%;\n }\n .du-bois-dark-col-sm-order-24 {\n order: 24;\n }\n .du-bois-dark-col-sm-23 {\n display: block;\n flex: 0 0 95.83333333%;\n max-width: 95.83333333%;\n }\n .du-bois-dark-col-sm-push-23 {\n left: 95.83333333%;\n }\n .du-bois-dark-col-sm-pull-23 {\n right: 95.83333333%;\n }\n .du-bois-dark-col-sm-offset-23 {\n margin-left: 95.83333333%;\n }\n .du-bois-dark-col-sm-order-23 {\n order: 23;\n }\n .du-bois-dark-col-sm-22 {\n display: block;\n flex: 0 0 91.66666667%;\n max-width: 91.66666667%;\n }\n .du-bois-dark-col-sm-push-22 {\n left: 91.66666667%;\n }\n .du-bois-dark-col-sm-pull-22 {\n right: 91.66666667%;\n }\n .du-bois-dark-col-sm-offset-22 {\n margin-left: 91.66666667%;\n }\n .du-bois-dark-col-sm-order-22 {\n order: 22;\n }\n .du-bois-dark-col-sm-21 {\n display: block;\n flex: 0 0 87.5%;\n max-width: 87.5%;\n }\n .du-bois-dark-col-sm-push-21 {\n left: 87.5%;\n }\n .du-bois-dark-col-sm-pull-21 {\n right: 87.5%;\n }\n .du-bois-dark-col-sm-offset-21 {\n margin-left: 87.5%;\n }\n .du-bois-dark-col-sm-order-21 {\n order: 21;\n }\n .du-bois-dark-col-sm-20 {\n display: block;\n flex: 0 0 83.33333333%;\n max-width: 83.33333333%;\n }\n .du-bois-dark-col-sm-push-20 {\n left: 83.33333333%;\n }\n .du-bois-dark-col-sm-pull-20 {\n right: 83.33333333%;\n }\n .du-bois-dark-col-sm-offset-20 {\n margin-left: 83.33333333%;\n }\n .du-bois-dark-col-sm-order-20 {\n order: 20;\n }\n .du-bois-dark-col-sm-19 {\n display: block;\n flex: 0 0 79.16666667%;\n max-width: 79.16666667%;\n }\n .du-bois-dark-col-sm-push-19 {\n left: 79.16666667%;\n }\n .du-bois-dark-col-sm-pull-19 {\n right: 79.16666667%;\n }\n .du-bois-dark-col-sm-offset-19 {\n margin-left: 79.16666667%;\n }\n .du-bois-dark-col-sm-order-19 {\n order: 19;\n }\n .du-bois-dark-col-sm-18 {\n display: block;\n flex: 0 0 75%;\n max-width: 75%;\n }\n .du-bois-dark-col-sm-push-18 {\n left: 75%;\n }\n .du-bois-dark-col-sm-pull-18 {\n right: 75%;\n }\n .du-bois-dark-col-sm-offset-18 {\n margin-left: 75%;\n }\n .du-bois-dark-col-sm-order-18 {\n order: 18;\n }\n .du-bois-dark-col-sm-17 {\n display: block;\n flex: 0 0 70.83333333%;\n max-width: 70.83333333%;\n }\n .du-bois-dark-col-sm-push-17 {\n left: 70.83333333%;\n }\n .du-bois-dark-col-sm-pull-17 {\n right: 70.83333333%;\n }\n .du-bois-dark-col-sm-offset-17 {\n margin-left: 70.83333333%;\n }\n .du-bois-dark-col-sm-order-17 {\n order: 17;\n }\n .du-bois-dark-col-sm-16 {\n display: block;\n flex: 0 0 66.66666667%;\n max-width: 66.66666667%;\n }\n .du-bois-dark-col-sm-push-16 {\n left: 66.66666667%;\n }\n .du-bois-dark-col-sm-pull-16 {\n right: 66.66666667%;\n }\n .du-bois-dark-col-sm-offset-16 {\n margin-left: 66.66666667%;\n }\n .du-bois-dark-col-sm-order-16 {\n order: 16;\n }\n .du-bois-dark-col-sm-15 {\n display: block;\n flex: 0 0 62.5%;\n max-width: 62.5%;\n }\n .du-bois-dark-col-sm-push-15 {\n left: 62.5%;\n }\n .du-bois-dark-col-sm-pull-15 {\n right: 62.5%;\n }\n .du-bois-dark-col-sm-offset-15 {\n margin-left: 62.5%;\n }\n .du-bois-dark-col-sm-order-15 {\n order: 15;\n }\n .du-bois-dark-col-sm-14 {\n display: block;\n flex: 0 0 58.33333333%;\n max-width: 58.33333333%;\n }\n .du-bois-dark-col-sm-push-14 {\n left: 58.33333333%;\n }\n .du-bois-dark-col-sm-pull-14 {\n right: 58.33333333%;\n }\n .du-bois-dark-col-sm-offset-14 {\n margin-left: 58.33333333%;\n }\n .du-bois-dark-col-sm-order-14 {\n order: 14;\n }\n .du-bois-dark-col-sm-13 {\n display: block;\n flex: 0 0 54.16666667%;\n max-width: 54.16666667%;\n }\n .du-bois-dark-col-sm-push-13 {\n left: 54.16666667%;\n }\n .du-bois-dark-col-sm-pull-13 {\n right: 54.16666667%;\n }\n .du-bois-dark-col-sm-offset-13 {\n margin-left: 54.16666667%;\n }\n .du-bois-dark-col-sm-order-13 {\n order: 13;\n }\n .du-bois-dark-col-sm-12 {\n display: block;\n flex: 0 0 50%;\n max-width: 50%;\n }\n .du-bois-dark-col-sm-push-12 {\n left: 50%;\n }\n .du-bois-dark-col-sm-pull-12 {\n right: 50%;\n }\n .du-bois-dark-col-sm-offset-12 {\n margin-left: 50%;\n }\n .du-bois-dark-col-sm-order-12 {\n order: 12;\n }\n .du-bois-dark-col-sm-11 {\n display: block;\n flex: 0 0 45.83333333%;\n max-width: 45.83333333%;\n }\n .du-bois-dark-col-sm-push-11 {\n left: 45.83333333%;\n }\n .du-bois-dark-col-sm-pull-11 {\n right: 45.83333333%;\n }\n .du-bois-dark-col-sm-offset-11 {\n margin-left: 45.83333333%;\n }\n .du-bois-dark-col-sm-order-11 {\n order: 11;\n }\n .du-bois-dark-col-sm-10 {\n display: block;\n flex: 0 0 41.66666667%;\n max-width: 41.66666667%;\n }\n .du-bois-dark-col-sm-push-10 {\n left: 41.66666667%;\n }\n .du-bois-dark-col-sm-pull-10 {\n right: 41.66666667%;\n }\n .du-bois-dark-col-sm-offset-10 {\n margin-left: 41.66666667%;\n }\n .du-bois-dark-col-sm-order-10 {\n order: 10;\n }\n .du-bois-dark-col-sm-9 {\n display: block;\n flex: 0 0 37.5%;\n max-width: 37.5%;\n }\n .du-bois-dark-col-sm-push-9 {\n left: 37.5%;\n }\n .du-bois-dark-col-sm-pull-9 {\n right: 37.5%;\n }\n .du-bois-dark-col-sm-offset-9 {\n margin-left: 37.5%;\n }\n .du-bois-dark-col-sm-order-9 {\n order: 9;\n }\n .du-bois-dark-col-sm-8 {\n display: block;\n flex: 0 0 33.33333333%;\n max-width: 33.33333333%;\n }\n .du-bois-dark-col-sm-push-8 {\n left: 33.33333333%;\n }\n .du-bois-dark-col-sm-pull-8 {\n right: 33.33333333%;\n }\n .du-bois-dark-col-sm-offset-8 {\n margin-left: 33.33333333%;\n }\n .du-bois-dark-col-sm-order-8 {\n order: 8;\n }\n .du-bois-dark-col-sm-7 {\n display: block;\n flex: 0 0 29.16666667%;\n max-width: 29.16666667%;\n }\n .du-bois-dark-col-sm-push-7 {\n left: 29.16666667%;\n }\n .du-bois-dark-col-sm-pull-7 {\n right: 29.16666667%;\n }\n .du-bois-dark-col-sm-offset-7 {\n margin-left: 29.16666667%;\n }\n .du-bois-dark-col-sm-order-7 {\n order: 7;\n }\n .du-bois-dark-col-sm-6 {\n display: block;\n flex: 0 0 25%;\n max-width: 25%;\n }\n .du-bois-dark-col-sm-push-6 {\n left: 25%;\n }\n .du-bois-dark-col-sm-pull-6 {\n right: 25%;\n }\n .du-bois-dark-col-sm-offset-6 {\n margin-left: 25%;\n }\n .du-bois-dark-col-sm-order-6 {\n order: 6;\n }\n .du-bois-dark-col-sm-5 {\n display: block;\n flex: 0 0 20.83333333%;\n max-width: 20.83333333%;\n }\n .du-bois-dark-col-sm-push-5 {\n left: 20.83333333%;\n }\n .du-bois-dark-col-sm-pull-5 {\n right: 20.83333333%;\n }\n .du-bois-dark-col-sm-offset-5 {\n margin-left: 20.83333333%;\n }\n .du-bois-dark-col-sm-order-5 {\n order: 5;\n }\n .du-bois-dark-col-sm-4 {\n display: block;\n flex: 0 0 16.66666667%;\n max-width: 16.66666667%;\n }\n .du-bois-dark-col-sm-push-4 {\n left: 16.66666667%;\n }\n .du-bois-dark-col-sm-pull-4 {\n right: 16.66666667%;\n }\n .du-bois-dark-col-sm-offset-4 {\n margin-left: 16.66666667%;\n }\n .du-bois-dark-col-sm-order-4 {\n order: 4;\n }\n .du-bois-dark-col-sm-3 {\n display: block;\n flex: 0 0 12.5%;\n max-width: 12.5%;\n }\n .du-bois-dark-col-sm-push-3 {\n left: 12.5%;\n }\n .du-bois-dark-col-sm-pull-3 {\n right: 12.5%;\n }\n .du-bois-dark-col-sm-offset-3 {\n margin-left: 12.5%;\n }\n .du-bois-dark-col-sm-order-3 {\n order: 3;\n }\n .du-bois-dark-col-sm-2 {\n display: block;\n flex: 0 0 8.33333333%;\n max-width: 8.33333333%;\n }\n .du-bois-dark-col-sm-push-2 {\n left: 8.33333333%;\n }\n .du-bois-dark-col-sm-pull-2 {\n right: 8.33333333%;\n }\n .du-bois-dark-col-sm-offset-2 {\n margin-left: 8.33333333%;\n }\n .du-bois-dark-col-sm-order-2 {\n order: 2;\n }\n .du-bois-dark-col-sm-1 {\n display: block;\n flex: 0 0 4.16666667%;\n max-width: 4.16666667%;\n }\n .du-bois-dark-col-sm-push-1 {\n left: 4.16666667%;\n }\n .du-bois-dark-col-sm-pull-1 {\n right: 4.16666667%;\n }\n .du-bois-dark-col-sm-offset-1 {\n margin-left: 4.16666667%;\n }\n .du-bois-dark-col-sm-order-1 {\n order: 1;\n }\n .du-bois-dark-col-sm-0 {\n display: none;\n }\n .du-bois-dark-col-push-0 {\n left: auto;\n }\n .du-bois-dark-col-pull-0 {\n right: auto;\n }\n .du-bois-dark-col-sm-push-0 {\n left: auto;\n }\n .du-bois-dark-col-sm-pull-0 {\n right: auto;\n }\n .du-bois-dark-col-sm-offset-0 {\n margin-left: 0;\n }\n .du-bois-dark-col-sm-order-0 {\n order: 0;\n }\n}\n@media (min-width: 768px) {\n .du-bois-dark-col-md-24 {\n display: block;\n flex: 0 0 100%;\n max-width: 100%;\n }\n .du-bois-dark-col-md-push-24 {\n left: 100%;\n }\n .du-bois-dark-col-md-pull-24 {\n right: 100%;\n }\n .du-bois-dark-col-md-offset-24 {\n margin-left: 100%;\n }\n .du-bois-dark-col-md-order-24 {\n order: 24;\n }\n .du-bois-dark-col-md-23 {\n display: block;\n flex: 0 0 95.83333333%;\n max-width: 95.83333333%;\n }\n .du-bois-dark-col-md-push-23 {\n left: 95.83333333%;\n }\n .du-bois-dark-col-md-pull-23 {\n right: 95.83333333%;\n }\n .du-bois-dark-col-md-offset-23 {\n margin-left: 95.83333333%;\n }\n .du-bois-dark-col-md-order-23 {\n order: 23;\n }\n .du-bois-dark-col-md-22 {\n display: block;\n flex: 0 0 91.66666667%;\n max-width: 91.66666667%;\n }\n .du-bois-dark-col-md-push-22 {\n left: 91.66666667%;\n }\n .du-bois-dark-col-md-pull-22 {\n right: 91.66666667%;\n }\n .du-bois-dark-col-md-offset-22 {\n margin-left: 91.66666667%;\n }\n .du-bois-dark-col-md-order-22 {\n order: 22;\n }\n .du-bois-dark-col-md-21 {\n display: block;\n flex: 0 0 87.5%;\n max-width: 87.5%;\n }\n .du-bois-dark-col-md-push-21 {\n left: 87.5%;\n }\n .du-bois-dark-col-md-pull-21 {\n right: 87.5%;\n }\n .du-bois-dark-col-md-offset-21 {\n margin-left: 87.5%;\n }\n .du-bois-dark-col-md-order-21 {\n order: 21;\n }\n .du-bois-dark-col-md-20 {\n display: block;\n flex: 0 0 83.33333333%;\n max-width: 83.33333333%;\n }\n .du-bois-dark-col-md-push-20 {\n left: 83.33333333%;\n }\n .du-bois-dark-col-md-pull-20 {\n right: 83.33333333%;\n }\n .du-bois-dark-col-md-offset-20 {\n margin-left: 83.33333333%;\n }\n .du-bois-dark-col-md-order-20 {\n order: 20;\n }\n .du-bois-dark-col-md-19 {\n display: block;\n flex: 0 0 79.16666667%;\n max-width: 79.16666667%;\n }\n .du-bois-dark-col-md-push-19 {\n left: 79.16666667%;\n }\n .du-bois-dark-col-md-pull-19 {\n right: 79.16666667%;\n }\n .du-bois-dark-col-md-offset-19 {\n margin-left: 79.16666667%;\n }\n .du-bois-dark-col-md-order-19 {\n order: 19;\n }\n .du-bois-dark-col-md-18 {\n display: block;\n flex: 0 0 75%;\n max-width: 75%;\n }\n .du-bois-dark-col-md-push-18 {\n left: 75%;\n }\n .du-bois-dark-col-md-pull-18 {\n right: 75%;\n }\n .du-bois-dark-col-md-offset-18 {\n margin-left: 75%;\n }\n .du-bois-dark-col-md-order-18 {\n order: 18;\n }\n .du-bois-dark-col-md-17 {\n display: block;\n flex: 0 0 70.83333333%;\n max-width: 70.83333333%;\n }\n .du-bois-dark-col-md-push-17 {\n left: 70.83333333%;\n }\n .du-bois-dark-col-md-pull-17 {\n right: 70.83333333%;\n }\n .du-bois-dark-col-md-offset-17 {\n margin-left: 70.83333333%;\n }\n .du-bois-dark-col-md-order-17 {\n order: 17;\n }\n .du-bois-dark-col-md-16 {\n display: block;\n flex: 0 0 66.66666667%;\n max-width: 66.66666667%;\n }\n .du-bois-dark-col-md-push-16 {\n left: 66.66666667%;\n }\n .du-bois-dark-col-md-pull-16 {\n right: 66.66666667%;\n }\n .du-bois-dark-col-md-offset-16 {\n margin-left: 66.66666667%;\n }\n .du-bois-dark-col-md-order-16 {\n order: 16;\n }\n .du-bois-dark-col-md-15 {\n display: block;\n flex: 0 0 62.5%;\n max-width: 62.5%;\n }\n .du-bois-dark-col-md-push-15 {\n left: 62.5%;\n }\n .du-bois-dark-col-md-pull-15 {\n right: 62.5%;\n }\n .du-bois-dark-col-md-offset-15 {\n margin-left: 62.5%;\n }\n .du-bois-dark-col-md-order-15 {\n order: 15;\n }\n .du-bois-dark-col-md-14 {\n display: block;\n flex: 0 0 58.33333333%;\n max-width: 58.33333333%;\n }\n .du-bois-dark-col-md-push-14 {\n left: 58.33333333%;\n }\n .du-bois-dark-col-md-pull-14 {\n right: 58.33333333%;\n }\n .du-bois-dark-col-md-offset-14 {\n margin-left: 58.33333333%;\n }\n .du-bois-dark-col-md-order-14 {\n order: 14;\n }\n .du-bois-dark-col-md-13 {\n display: block;\n flex: 0 0 54.16666667%;\n max-width: 54.16666667%;\n }\n .du-bois-dark-col-md-push-13 {\n left: 54.16666667%;\n }\n .du-bois-dark-col-md-pull-13 {\n right: 54.16666667%;\n }\n .du-bois-dark-col-md-offset-13 {\n margin-left: 54.16666667%;\n }\n .du-bois-dark-col-md-order-13 {\n order: 13;\n }\n .du-bois-dark-col-md-12 {\n display: block;\n flex: 0 0 50%;\n max-width: 50%;\n }\n .du-bois-dark-col-md-push-12 {\n left: 50%;\n }\n .du-bois-dark-col-md-pull-12 {\n right: 50%;\n }\n .du-bois-dark-col-md-offset-12 {\n margin-left: 50%;\n }\n .du-bois-dark-col-md-order-12 {\n order: 12;\n }\n .du-bois-dark-col-md-11 {\n display: block;\n flex: 0 0 45.83333333%;\n max-width: 45.83333333%;\n }\n .du-bois-dark-col-md-push-11 {\n left: 45.83333333%;\n }\n .du-bois-dark-col-md-pull-11 {\n right: 45.83333333%;\n }\n .du-bois-dark-col-md-offset-11 {\n margin-left: 45.83333333%;\n }\n .du-bois-dark-col-md-order-11 {\n order: 11;\n }\n .du-bois-dark-col-md-10 {\n display: block;\n flex: 0 0 41.66666667%;\n max-width: 41.66666667%;\n }\n .du-bois-dark-col-md-push-10 {\n left: 41.66666667%;\n }\n .du-bois-dark-col-md-pull-10 {\n right: 41.66666667%;\n }\n .du-bois-dark-col-md-offset-10 {\n margin-left: 41.66666667%;\n }\n .du-bois-dark-col-md-order-10 {\n order: 10;\n }\n .du-bois-dark-col-md-9 {\n display: block;\n flex: 0 0 37.5%;\n max-width: 37.5%;\n }\n .du-bois-dark-col-md-push-9 {\n left: 37.5%;\n }\n .du-bois-dark-col-md-pull-9 {\n right: 37.5%;\n }\n .du-bois-dark-col-md-offset-9 {\n margin-left: 37.5%;\n }\n .du-bois-dark-col-md-order-9 {\n order: 9;\n }\n .du-bois-dark-col-md-8 {\n display: block;\n flex: 0 0 33.33333333%;\n max-width: 33.33333333%;\n }\n .du-bois-dark-col-md-push-8 {\n left: 33.33333333%;\n }\n .du-bois-dark-col-md-pull-8 {\n right: 33.33333333%;\n }\n .du-bois-dark-col-md-offset-8 {\n margin-left: 33.33333333%;\n }\n .du-bois-dark-col-md-order-8 {\n order: 8;\n }\n .du-bois-dark-col-md-7 {\n display: block;\n flex: 0 0 29.16666667%;\n max-width: 29.16666667%;\n }\n .du-bois-dark-col-md-push-7 {\n left: 29.16666667%;\n }\n .du-bois-dark-col-md-pull-7 {\n right: 29.16666667%;\n }\n .du-bois-dark-col-md-offset-7 {\n margin-left: 29.16666667%;\n }\n .du-bois-dark-col-md-order-7 {\n order: 7;\n }\n .du-bois-dark-col-md-6 {\n display: block;\n flex: 0 0 25%;\n max-width: 25%;\n }\n .du-bois-dark-col-md-push-6 {\n left: 25%;\n }\n .du-bois-dark-col-md-pull-6 {\n right: 25%;\n }\n .du-bois-dark-col-md-offset-6 {\n margin-left: 25%;\n }\n .du-bois-dark-col-md-order-6 {\n order: 6;\n }\n .du-bois-dark-col-md-5 {\n display: block;\n flex: 0 0 20.83333333%;\n max-width: 20.83333333%;\n }\n .du-bois-dark-col-md-push-5 {\n left: 20.83333333%;\n }\n .du-bois-dark-col-md-pull-5 {\n right: 20.83333333%;\n }\n .du-bois-dark-col-md-offset-5 {\n margin-left: 20.83333333%;\n }\n .du-bois-dark-col-md-order-5 {\n order: 5;\n }\n .du-bois-dark-col-md-4 {\n display: block;\n flex: 0 0 16.66666667%;\n max-width: 16.66666667%;\n }\n .du-bois-dark-col-md-push-4 {\n left: 16.66666667%;\n }\n .du-bois-dark-col-md-pull-4 {\n right: 16.66666667%;\n }\n .du-bois-dark-col-md-offset-4 {\n margin-left: 16.66666667%;\n }\n .du-bois-dark-col-md-order-4 {\n order: 4;\n }\n .du-bois-dark-col-md-3 {\n display: block;\n flex: 0 0 12.5%;\n max-width: 12.5%;\n }\n .du-bois-dark-col-md-push-3 {\n left: 12.5%;\n }\n .du-bois-dark-col-md-pull-3 {\n right: 12.5%;\n }\n .du-bois-dark-col-md-offset-3 {\n margin-left: 12.5%;\n }\n .du-bois-dark-col-md-order-3 {\n order: 3;\n }\n .du-bois-dark-col-md-2 {\n display: block;\n flex: 0 0 8.33333333%;\n max-width: 8.33333333%;\n }\n .du-bois-dark-col-md-push-2 {\n left: 8.33333333%;\n }\n .du-bois-dark-col-md-pull-2 {\n right: 8.33333333%;\n }\n .du-bois-dark-col-md-offset-2 {\n margin-left: 8.33333333%;\n }\n .du-bois-dark-col-md-order-2 {\n order: 2;\n }\n .du-bois-dark-col-md-1 {\n display: block;\n flex: 0 0 4.16666667%;\n max-width: 4.16666667%;\n }\n .du-bois-dark-col-md-push-1 {\n left: 4.16666667%;\n }\n .du-bois-dark-col-md-pull-1 {\n right: 4.16666667%;\n }\n .du-bois-dark-col-md-offset-1 {\n margin-left: 4.16666667%;\n }\n .du-bois-dark-col-md-order-1 {\n order: 1;\n }\n .du-bois-dark-col-md-0 {\n display: none;\n }\n .du-bois-dark-col-push-0 {\n left: auto;\n }\n .du-bois-dark-col-pull-0 {\n right: auto;\n }\n .du-bois-dark-col-md-push-0 {\n left: auto;\n }\n .du-bois-dark-col-md-pull-0 {\n right: auto;\n }\n .du-bois-dark-col-md-offset-0 {\n margin-left: 0;\n }\n .du-bois-dark-col-md-order-0 {\n order: 0;\n }\n}\n@media (min-width: 992px) {\n .du-bois-dark-col-lg-24 {\n display: block;\n flex: 0 0 100%;\n max-width: 100%;\n }\n .du-bois-dark-col-lg-push-24 {\n left: 100%;\n }\n .du-bois-dark-col-lg-pull-24 {\n right: 100%;\n }\n .du-bois-dark-col-lg-offset-24 {\n margin-left: 100%;\n }\n .du-bois-dark-col-lg-order-24 {\n order: 24;\n }\n .du-bois-dark-col-lg-23 {\n display: block;\n flex: 0 0 95.83333333%;\n max-width: 95.83333333%;\n }\n .du-bois-dark-col-lg-push-23 {\n left: 95.83333333%;\n }\n .du-bois-dark-col-lg-pull-23 {\n right: 95.83333333%;\n }\n .du-bois-dark-col-lg-offset-23 {\n margin-left: 95.83333333%;\n }\n .du-bois-dark-col-lg-order-23 {\n order: 23;\n }\n .du-bois-dark-col-lg-22 {\n display: block;\n flex: 0 0 91.66666667%;\n max-width: 91.66666667%;\n }\n .du-bois-dark-col-lg-push-22 {\n left: 91.66666667%;\n }\n .du-bois-dark-col-lg-pull-22 {\n right: 91.66666667%;\n }\n .du-bois-dark-col-lg-offset-22 {\n margin-left: 91.66666667%;\n }\n .du-bois-dark-col-lg-order-22 {\n order: 22;\n }\n .du-bois-dark-col-lg-21 {\n display: block;\n flex: 0 0 87.5%;\n max-width: 87.5%;\n }\n .du-bois-dark-col-lg-push-21 {\n left: 87.5%;\n }\n .du-bois-dark-col-lg-pull-21 {\n right: 87.5%;\n }\n .du-bois-dark-col-lg-offset-21 {\n margin-left: 87.5%;\n }\n .du-bois-dark-col-lg-order-21 {\n order: 21;\n }\n .du-bois-dark-col-lg-20 {\n display: block;\n flex: 0 0 83.33333333%;\n max-width: 83.33333333%;\n }\n .du-bois-dark-col-lg-push-20 {\n left: 83.33333333%;\n }\n .du-bois-dark-col-lg-pull-20 {\n right: 83.33333333%;\n }\n .du-bois-dark-col-lg-offset-20 {\n margin-left: 83.33333333%;\n }\n .du-bois-dark-col-lg-order-20 {\n order: 20;\n }\n .du-bois-dark-col-lg-19 {\n display: block;\n flex: 0 0 79.16666667%;\n max-width: 79.16666667%;\n }\n .du-bois-dark-col-lg-push-19 {\n left: 79.16666667%;\n }\n .du-bois-dark-col-lg-pull-19 {\n right: 79.16666667%;\n }\n .du-bois-dark-col-lg-offset-19 {\n margin-left: 79.16666667%;\n }\n .du-bois-dark-col-lg-order-19 {\n order: 19;\n }\n .du-bois-dark-col-lg-18 {\n display: block;\n flex: 0 0 75%;\n max-width: 75%;\n }\n .du-bois-dark-col-lg-push-18 {\n left: 75%;\n }\n .du-bois-dark-col-lg-pull-18 {\n right: 75%;\n }\n .du-bois-dark-col-lg-offset-18 {\n margin-left: 75%;\n }\n .du-bois-dark-col-lg-order-18 {\n order: 18;\n }\n .du-bois-dark-col-lg-17 {\n display: block;\n flex: 0 0 70.83333333%;\n max-width: 70.83333333%;\n }\n .du-bois-dark-col-lg-push-17 {\n left: 70.83333333%;\n }\n .du-bois-dark-col-lg-pull-17 {\n right: 70.83333333%;\n }\n .du-bois-dark-col-lg-offset-17 {\n margin-left: 70.83333333%;\n }\n .du-bois-dark-col-lg-order-17 {\n order: 17;\n }\n .du-bois-dark-col-lg-16 {\n display: block;\n flex: 0 0 66.66666667%;\n max-width: 66.66666667%;\n }\n .du-bois-dark-col-lg-push-16 {\n left: 66.66666667%;\n }\n .du-bois-dark-col-lg-pull-16 {\n right: 66.66666667%;\n }\n .du-bois-dark-col-lg-offset-16 {\n margin-left: 66.66666667%;\n }\n .du-bois-dark-col-lg-order-16 {\n order: 16;\n }\n .du-bois-dark-col-lg-15 {\n display: block;\n flex: 0 0 62.5%;\n max-width: 62.5%;\n }\n .du-bois-dark-col-lg-push-15 {\n left: 62.5%;\n }\n .du-bois-dark-col-lg-pull-15 {\n right: 62.5%;\n }\n .du-bois-dark-col-lg-offset-15 {\n margin-left: 62.5%;\n }\n .du-bois-dark-col-lg-order-15 {\n order: 15;\n }\n .du-bois-dark-col-lg-14 {\n display: block;\n flex: 0 0 58.33333333%;\n max-width: 58.33333333%;\n }\n .du-bois-dark-col-lg-push-14 {\n left: 58.33333333%;\n }\n .du-bois-dark-col-lg-pull-14 {\n right: 58.33333333%;\n }\n .du-bois-dark-col-lg-offset-14 {\n margin-left: 58.33333333%;\n }\n .du-bois-dark-col-lg-order-14 {\n order: 14;\n }\n .du-bois-dark-col-lg-13 {\n display: block;\n flex: 0 0 54.16666667%;\n max-width: 54.16666667%;\n }\n .du-bois-dark-col-lg-push-13 {\n left: 54.16666667%;\n }\n .du-bois-dark-col-lg-pull-13 {\n right: 54.16666667%;\n }\n .du-bois-dark-col-lg-offset-13 {\n margin-left: 54.16666667%;\n }\n .du-bois-dark-col-lg-order-13 {\n order: 13;\n }\n .du-bois-dark-col-lg-12 {\n display: block;\n flex: 0 0 50%;\n max-width: 50%;\n }\n .du-bois-dark-col-lg-push-12 {\n left: 50%;\n }\n .du-bois-dark-col-lg-pull-12 {\n right: 50%;\n }\n .du-bois-dark-col-lg-offset-12 {\n margin-left: 50%;\n }\n .du-bois-dark-col-lg-order-12 {\n order: 12;\n }\n .du-bois-dark-col-lg-11 {\n display: block;\n flex: 0 0 45.83333333%;\n max-width: 45.83333333%;\n }\n .du-bois-dark-col-lg-push-11 {\n left: 45.83333333%;\n }\n .du-bois-dark-col-lg-pull-11 {\n right: 45.83333333%;\n }\n .du-bois-dark-col-lg-offset-11 {\n margin-left: 45.83333333%;\n }\n .du-bois-dark-col-lg-order-11 {\n order: 11;\n }\n .du-bois-dark-col-lg-10 {\n display: block;\n flex: 0 0 41.66666667%;\n max-width: 41.66666667%;\n }\n .du-bois-dark-col-lg-push-10 {\n left: 41.66666667%;\n }\n .du-bois-dark-col-lg-pull-10 {\n right: 41.66666667%;\n }\n .du-bois-dark-col-lg-offset-10 {\n margin-left: 41.66666667%;\n }\n .du-bois-dark-col-lg-order-10 {\n order: 10;\n }\n .du-bois-dark-col-lg-9 {\n display: block;\n flex: 0 0 37.5%;\n max-width: 37.5%;\n }\n .du-bois-dark-col-lg-push-9 {\n left: 37.5%;\n }\n .du-bois-dark-col-lg-pull-9 {\n right: 37.5%;\n }\n .du-bois-dark-col-lg-offset-9 {\n margin-left: 37.5%;\n }\n .du-bois-dark-col-lg-order-9 {\n order: 9;\n }\n .du-bois-dark-col-lg-8 {\n display: block;\n flex: 0 0 33.33333333%;\n max-width: 33.33333333%;\n }\n .du-bois-dark-col-lg-push-8 {\n left: 33.33333333%;\n }\n .du-bois-dark-col-lg-pull-8 {\n right: 33.33333333%;\n }\n .du-bois-dark-col-lg-offset-8 {\n margin-left: 33.33333333%;\n }\n .du-bois-dark-col-lg-order-8 {\n order: 8;\n }\n .du-bois-dark-col-lg-7 {\n display: block;\n flex: 0 0 29.16666667%;\n max-width: 29.16666667%;\n }\n .du-bois-dark-col-lg-push-7 {\n left: 29.16666667%;\n }\n .du-bois-dark-col-lg-pull-7 {\n right: 29.16666667%;\n }\n .du-bois-dark-col-lg-offset-7 {\n margin-left: 29.16666667%;\n }\n .du-bois-dark-col-lg-order-7 {\n order: 7;\n }\n .du-bois-dark-col-lg-6 {\n display: block;\n flex: 0 0 25%;\n max-width: 25%;\n }\n .du-bois-dark-col-lg-push-6 {\n left: 25%;\n }\n .du-bois-dark-col-lg-pull-6 {\n right: 25%;\n }\n .du-bois-dark-col-lg-offset-6 {\n margin-left: 25%;\n }\n .du-bois-dark-col-lg-order-6 {\n order: 6;\n }\n .du-bois-dark-col-lg-5 {\n display: block;\n flex: 0 0 20.83333333%;\n max-width: 20.83333333%;\n }\n .du-bois-dark-col-lg-push-5 {\n left: 20.83333333%;\n }\n .du-bois-dark-col-lg-pull-5 {\n right: 20.83333333%;\n }\n .du-bois-dark-col-lg-offset-5 {\n margin-left: 20.83333333%;\n }\n .du-bois-dark-col-lg-order-5 {\n order: 5;\n }\n .du-bois-dark-col-lg-4 {\n display: block;\n flex: 0 0 16.66666667%;\n max-width: 16.66666667%;\n }\n .du-bois-dark-col-lg-push-4 {\n left: 16.66666667%;\n }\n .du-bois-dark-col-lg-pull-4 {\n right: 16.66666667%;\n }\n .du-bois-dark-col-lg-offset-4 {\n margin-left: 16.66666667%;\n }\n .du-bois-dark-col-lg-order-4 {\n order: 4;\n }\n .du-bois-dark-col-lg-3 {\n display: block;\n flex: 0 0 12.5%;\n max-width: 12.5%;\n }\n .du-bois-dark-col-lg-push-3 {\n left: 12.5%;\n }\n .du-bois-dark-col-lg-pull-3 {\n right: 12.5%;\n }\n .du-bois-dark-col-lg-offset-3 {\n margin-left: 12.5%;\n }\n .du-bois-dark-col-lg-order-3 {\n order: 3;\n }\n .du-bois-dark-col-lg-2 {\n display: block;\n flex: 0 0 8.33333333%;\n max-width: 8.33333333%;\n }\n .du-bois-dark-col-lg-push-2 {\n left: 8.33333333%;\n }\n .du-bois-dark-col-lg-pull-2 {\n right: 8.33333333%;\n }\n .du-bois-dark-col-lg-offset-2 {\n margin-left: 8.33333333%;\n }\n .du-bois-dark-col-lg-order-2 {\n order: 2;\n }\n .du-bois-dark-col-lg-1 {\n display: block;\n flex: 0 0 4.16666667%;\n max-width: 4.16666667%;\n }\n .du-bois-dark-col-lg-push-1 {\n left: 4.16666667%;\n }\n .du-bois-dark-col-lg-pull-1 {\n right: 4.16666667%;\n }\n .du-bois-dark-col-lg-offset-1 {\n margin-left: 4.16666667%;\n }\n .du-bois-dark-col-lg-order-1 {\n order: 1;\n }\n .du-bois-dark-col-lg-0 {\n display: none;\n }\n .du-bois-dark-col-push-0 {\n left: auto;\n }\n .du-bois-dark-col-pull-0 {\n right: auto;\n }\n .du-bois-dark-col-lg-push-0 {\n left: auto;\n }\n .du-bois-dark-col-lg-pull-0 {\n right: auto;\n }\n .du-bois-dark-col-lg-offset-0 {\n margin-left: 0;\n }\n .du-bois-dark-col-lg-order-0 {\n order: 0;\n }\n}\n@media (min-width: 1200px) {\n .du-bois-dark-col-xl-24 {\n display: block;\n flex: 0 0 100%;\n max-width: 100%;\n }\n .du-bois-dark-col-xl-push-24 {\n left: 100%;\n }\n .du-bois-dark-col-xl-pull-24 {\n right: 100%;\n }\n .du-bois-dark-col-xl-offset-24 {\n margin-left: 100%;\n }\n .du-bois-dark-col-xl-order-24 {\n order: 24;\n }\n .du-bois-dark-col-xl-23 {\n display: block;\n flex: 0 0 95.83333333%;\n max-width: 95.83333333%;\n }\n .du-bois-dark-col-xl-push-23 {\n left: 95.83333333%;\n }\n .du-bois-dark-col-xl-pull-23 {\n right: 95.83333333%;\n }\n .du-bois-dark-col-xl-offset-23 {\n margin-left: 95.83333333%;\n }\n .du-bois-dark-col-xl-order-23 {\n order: 23;\n }\n .du-bois-dark-col-xl-22 {\n display: block;\n flex: 0 0 91.66666667%;\n max-width: 91.66666667%;\n }\n .du-bois-dark-col-xl-push-22 {\n left: 91.66666667%;\n }\n .du-bois-dark-col-xl-pull-22 {\n right: 91.66666667%;\n }\n .du-bois-dark-col-xl-offset-22 {\n margin-left: 91.66666667%;\n }\n .du-bois-dark-col-xl-order-22 {\n order: 22;\n }\n .du-bois-dark-col-xl-21 {\n display: block;\n flex: 0 0 87.5%;\n max-width: 87.5%;\n }\n .du-bois-dark-col-xl-push-21 {\n left: 87.5%;\n }\n .du-bois-dark-col-xl-pull-21 {\n right: 87.5%;\n }\n .du-bois-dark-col-xl-offset-21 {\n margin-left: 87.5%;\n }\n .du-bois-dark-col-xl-order-21 {\n order: 21;\n }\n .du-bois-dark-col-xl-20 {\n display: block;\n flex: 0 0 83.33333333%;\n max-width: 83.33333333%;\n }\n .du-bois-dark-col-xl-push-20 {\n left: 83.33333333%;\n }\n .du-bois-dark-col-xl-pull-20 {\n right: 83.33333333%;\n }\n .du-bois-dark-col-xl-offset-20 {\n margin-left: 83.33333333%;\n }\n .du-bois-dark-col-xl-order-20 {\n order: 20;\n }\n .du-bois-dark-col-xl-19 {\n display: block;\n flex: 0 0 79.16666667%;\n max-width: 79.16666667%;\n }\n .du-bois-dark-col-xl-push-19 {\n left: 79.16666667%;\n }\n .du-bois-dark-col-xl-pull-19 {\n right: 79.16666667%;\n }\n .du-bois-dark-col-xl-offset-19 {\n margin-left: 79.16666667%;\n }\n .du-bois-dark-col-xl-order-19 {\n order: 19;\n }\n .du-bois-dark-col-xl-18 {\n display: block;\n flex: 0 0 75%;\n max-width: 75%;\n }\n .du-bois-dark-col-xl-push-18 {\n left: 75%;\n }\n .du-bois-dark-col-xl-pull-18 {\n right: 75%;\n }\n .du-bois-dark-col-xl-offset-18 {\n margin-left: 75%;\n }\n .du-bois-dark-col-xl-order-18 {\n order: 18;\n }\n .du-bois-dark-col-xl-17 {\n display: block;\n flex: 0 0 70.83333333%;\n max-width: 70.83333333%;\n }\n .du-bois-dark-col-xl-push-17 {\n left: 70.83333333%;\n }\n .du-bois-dark-col-xl-pull-17 {\n right: 70.83333333%;\n }\n .du-bois-dark-col-xl-offset-17 {\n margin-left: 70.83333333%;\n }\n .du-bois-dark-col-xl-order-17 {\n order: 17;\n }\n .du-bois-dark-col-xl-16 {\n display: block;\n flex: 0 0 66.66666667%;\n max-width: 66.66666667%;\n }\n .du-bois-dark-col-xl-push-16 {\n left: 66.66666667%;\n }\n .du-bois-dark-col-xl-pull-16 {\n right: 66.66666667%;\n }\n .du-bois-dark-col-xl-offset-16 {\n margin-left: 66.66666667%;\n }\n .du-bois-dark-col-xl-order-16 {\n order: 16;\n }\n .du-bois-dark-col-xl-15 {\n display: block;\n flex: 0 0 62.5%;\n max-width: 62.5%;\n }\n .du-bois-dark-col-xl-push-15 {\n left: 62.5%;\n }\n .du-bois-dark-col-xl-pull-15 {\n right: 62.5%;\n }\n .du-bois-dark-col-xl-offset-15 {\n margin-left: 62.5%;\n }\n .du-bois-dark-col-xl-order-15 {\n order: 15;\n }\n .du-bois-dark-col-xl-14 {\n display: block;\n flex: 0 0 58.33333333%;\n max-width: 58.33333333%;\n }\n .du-bois-dark-col-xl-push-14 {\n left: 58.33333333%;\n }\n .du-bois-dark-col-xl-pull-14 {\n right: 58.33333333%;\n }\n .du-bois-dark-col-xl-offset-14 {\n margin-left: 58.33333333%;\n }\n .du-bois-dark-col-xl-order-14 {\n order: 14;\n }\n .du-bois-dark-col-xl-13 {\n display: block;\n flex: 0 0 54.16666667%;\n max-width: 54.16666667%;\n }\n .du-bois-dark-col-xl-push-13 {\n left: 54.16666667%;\n }\n .du-bois-dark-col-xl-pull-13 {\n right: 54.16666667%;\n }\n .du-bois-dark-col-xl-offset-13 {\n margin-left: 54.16666667%;\n }\n .du-bois-dark-col-xl-order-13 {\n order: 13;\n }\n .du-bois-dark-col-xl-12 {\n display: block;\n flex: 0 0 50%;\n max-width: 50%;\n }\n .du-bois-dark-col-xl-push-12 {\n left: 50%;\n }\n .du-bois-dark-col-xl-pull-12 {\n right: 50%;\n }\n .du-bois-dark-col-xl-offset-12 {\n margin-left: 50%;\n }\n .du-bois-dark-col-xl-order-12 {\n order: 12;\n }\n .du-bois-dark-col-xl-11 {\n display: block;\n flex: 0 0 45.83333333%;\n max-width: 45.83333333%;\n }\n .du-bois-dark-col-xl-push-11 {\n left: 45.83333333%;\n }\n .du-bois-dark-col-xl-pull-11 {\n right: 45.83333333%;\n }\n .du-bois-dark-col-xl-offset-11 {\n margin-left: 45.83333333%;\n }\n .du-bois-dark-col-xl-order-11 {\n order: 11;\n }\n .du-bois-dark-col-xl-10 {\n display: block;\n flex: 0 0 41.66666667%;\n max-width: 41.66666667%;\n }\n .du-bois-dark-col-xl-push-10 {\n left: 41.66666667%;\n }\n .du-bois-dark-col-xl-pull-10 {\n right: 41.66666667%;\n }\n .du-bois-dark-col-xl-offset-10 {\n margin-left: 41.66666667%;\n }\n .du-bois-dark-col-xl-order-10 {\n order: 10;\n }\n .du-bois-dark-col-xl-9 {\n display: block;\n flex: 0 0 37.5%;\n max-width: 37.5%;\n }\n .du-bois-dark-col-xl-push-9 {\n left: 37.5%;\n }\n .du-bois-dark-col-xl-pull-9 {\n right: 37.5%;\n }\n .du-bois-dark-col-xl-offset-9 {\n margin-left: 37.5%;\n }\n .du-bois-dark-col-xl-order-9 {\n order: 9;\n }\n .du-bois-dark-col-xl-8 {\n display: block;\n flex: 0 0 33.33333333%;\n max-width: 33.33333333%;\n }\n .du-bois-dark-col-xl-push-8 {\n left: 33.33333333%;\n }\n .du-bois-dark-col-xl-pull-8 {\n right: 33.33333333%;\n }\n .du-bois-dark-col-xl-offset-8 {\n margin-left: 33.33333333%;\n }\n .du-bois-dark-col-xl-order-8 {\n order: 8;\n }\n .du-bois-dark-col-xl-7 {\n display: block;\n flex: 0 0 29.16666667%;\n max-width: 29.16666667%;\n }\n .du-bois-dark-col-xl-push-7 {\n left: 29.16666667%;\n }\n .du-bois-dark-col-xl-pull-7 {\n right: 29.16666667%;\n }\n .du-bois-dark-col-xl-offset-7 {\n margin-left: 29.16666667%;\n }\n .du-bois-dark-col-xl-order-7 {\n order: 7;\n }\n .du-bois-dark-col-xl-6 {\n display: block;\n flex: 0 0 25%;\n max-width: 25%;\n }\n .du-bois-dark-col-xl-push-6 {\n left: 25%;\n }\n .du-bois-dark-col-xl-pull-6 {\n right: 25%;\n }\n .du-bois-dark-col-xl-offset-6 {\n margin-left: 25%;\n }\n .du-bois-dark-col-xl-order-6 {\n order: 6;\n }\n .du-bois-dark-col-xl-5 {\n display: block;\n flex: 0 0 20.83333333%;\n max-width: 20.83333333%;\n }\n .du-bois-dark-col-xl-push-5 {\n left: 20.83333333%;\n }\n .du-bois-dark-col-xl-pull-5 {\n right: 20.83333333%;\n }\n .du-bois-dark-col-xl-offset-5 {\n margin-left: 20.83333333%;\n }\n .du-bois-dark-col-xl-order-5 {\n order: 5;\n }\n .du-bois-dark-col-xl-4 {\n display: block;\n flex: 0 0 16.66666667%;\n max-width: 16.66666667%;\n }\n .du-bois-dark-col-xl-push-4 {\n left: 16.66666667%;\n }\n .du-bois-dark-col-xl-pull-4 {\n right: 16.66666667%;\n }\n .du-bois-dark-col-xl-offset-4 {\n margin-left: 16.66666667%;\n }\n .du-bois-dark-col-xl-order-4 {\n order: 4;\n }\n .du-bois-dark-col-xl-3 {\n display: block;\n flex: 0 0 12.5%;\n max-width: 12.5%;\n }\n .du-bois-dark-col-xl-push-3 {\n left: 12.5%;\n }\n .du-bois-dark-col-xl-pull-3 {\n right: 12.5%;\n }\n .du-bois-dark-col-xl-offset-3 {\n margin-left: 12.5%;\n }\n .du-bois-dark-col-xl-order-3 {\n order: 3;\n }\n .du-bois-dark-col-xl-2 {\n display: block;\n flex: 0 0 8.33333333%;\n max-width: 8.33333333%;\n }\n .du-bois-dark-col-xl-push-2 {\n left: 8.33333333%;\n }\n .du-bois-dark-col-xl-pull-2 {\n right: 8.33333333%;\n }\n .du-bois-dark-col-xl-offset-2 {\n margin-left: 8.33333333%;\n }\n .du-bois-dark-col-xl-order-2 {\n order: 2;\n }\n .du-bois-dark-col-xl-1 {\n display: block;\n flex: 0 0 4.16666667%;\n max-width: 4.16666667%;\n }\n .du-bois-dark-col-xl-push-1 {\n left: 4.16666667%;\n }\n .du-bois-dark-col-xl-pull-1 {\n right: 4.16666667%;\n }\n .du-bois-dark-col-xl-offset-1 {\n margin-left: 4.16666667%;\n }\n .du-bois-dark-col-xl-order-1 {\n order: 1;\n }\n .du-bois-dark-col-xl-0 {\n display: none;\n }\n .du-bois-dark-col-push-0 {\n left: auto;\n }\n .du-bois-dark-col-pull-0 {\n right: auto;\n }\n .du-bois-dark-col-xl-push-0 {\n left: auto;\n }\n .du-bois-dark-col-xl-pull-0 {\n right: auto;\n }\n .du-bois-dark-col-xl-offset-0 {\n margin-left: 0;\n }\n .du-bois-dark-col-xl-order-0 {\n order: 0;\n }\n}\n@media (min-width: 1600px) {\n .du-bois-dark-col-xxl-24 {\n display: block;\n flex: 0 0 100%;\n max-width: 100%;\n }\n .du-bois-dark-col-xxl-push-24 {\n left: 100%;\n }\n .du-bois-dark-col-xxl-pull-24 {\n right: 100%;\n }\n .du-bois-dark-col-xxl-offset-24 {\n margin-left: 100%;\n }\n .du-bois-dark-col-xxl-order-24 {\n order: 24;\n }\n .du-bois-dark-col-xxl-23 {\n display: block;\n flex: 0 0 95.83333333%;\n max-width: 95.83333333%;\n }\n .du-bois-dark-col-xxl-push-23 {\n left: 95.83333333%;\n }\n .du-bois-dark-col-xxl-pull-23 {\n right: 95.83333333%;\n }\n .du-bois-dark-col-xxl-offset-23 {\n margin-left: 95.83333333%;\n }\n .du-bois-dark-col-xxl-order-23 {\n order: 23;\n }\n .du-bois-dark-col-xxl-22 {\n display: block;\n flex: 0 0 91.66666667%;\n max-width: 91.66666667%;\n }\n .du-bois-dark-col-xxl-push-22 {\n left: 91.66666667%;\n }\n .du-bois-dark-col-xxl-pull-22 {\n right: 91.66666667%;\n }\n .du-bois-dark-col-xxl-offset-22 {\n margin-left: 91.66666667%;\n }\n .du-bois-dark-col-xxl-order-22 {\n order: 22;\n }\n .du-bois-dark-col-xxl-21 {\n display: block;\n flex: 0 0 87.5%;\n max-width: 87.5%;\n }\n .du-bois-dark-col-xxl-push-21 {\n left: 87.5%;\n }\n .du-bois-dark-col-xxl-pull-21 {\n right: 87.5%;\n }\n .du-bois-dark-col-xxl-offset-21 {\n margin-left: 87.5%;\n }\n .du-bois-dark-col-xxl-order-21 {\n order: 21;\n }\n .du-bois-dark-col-xxl-20 {\n display: block;\n flex: 0 0 83.33333333%;\n max-width: 83.33333333%;\n }\n .du-bois-dark-col-xxl-push-20 {\n left: 83.33333333%;\n }\n .du-bois-dark-col-xxl-pull-20 {\n right: 83.33333333%;\n }\n .du-bois-dark-col-xxl-offset-20 {\n margin-left: 83.33333333%;\n }\n .du-bois-dark-col-xxl-order-20 {\n order: 20;\n }\n .du-bois-dark-col-xxl-19 {\n display: block;\n flex: 0 0 79.16666667%;\n max-width: 79.16666667%;\n }\n .du-bois-dark-col-xxl-push-19 {\n left: 79.16666667%;\n }\n .du-bois-dark-col-xxl-pull-19 {\n right: 79.16666667%;\n }\n .du-bois-dark-col-xxl-offset-19 {\n margin-left: 79.16666667%;\n }\n .du-bois-dark-col-xxl-order-19 {\n order: 19;\n }\n .du-bois-dark-col-xxl-18 {\n display: block;\n flex: 0 0 75%;\n max-width: 75%;\n }\n .du-bois-dark-col-xxl-push-18 {\n left: 75%;\n }\n .du-bois-dark-col-xxl-pull-18 {\n right: 75%;\n }\n .du-bois-dark-col-xxl-offset-18 {\n margin-left: 75%;\n }\n .du-bois-dark-col-xxl-order-18 {\n order: 18;\n }\n .du-bois-dark-col-xxl-17 {\n display: block;\n flex: 0 0 70.83333333%;\n max-width: 70.83333333%;\n }\n .du-bois-dark-col-xxl-push-17 {\n left: 70.83333333%;\n }\n .du-bois-dark-col-xxl-pull-17 {\n right: 70.83333333%;\n }\n .du-bois-dark-col-xxl-offset-17 {\n margin-left: 70.83333333%;\n }\n .du-bois-dark-col-xxl-order-17 {\n order: 17;\n }\n .du-bois-dark-col-xxl-16 {\n display: block;\n flex: 0 0 66.66666667%;\n max-width: 66.66666667%;\n }\n .du-bois-dark-col-xxl-push-16 {\n left: 66.66666667%;\n }\n .du-bois-dark-col-xxl-pull-16 {\n right: 66.66666667%;\n }\n .du-bois-dark-col-xxl-offset-16 {\n margin-left: 66.66666667%;\n }\n .du-bois-dark-col-xxl-order-16 {\n order: 16;\n }\n .du-bois-dark-col-xxl-15 {\n display: block;\n flex: 0 0 62.5%;\n max-width: 62.5%;\n }\n .du-bois-dark-col-xxl-push-15 {\n left: 62.5%;\n }\n .du-bois-dark-col-xxl-pull-15 {\n right: 62.5%;\n }\n .du-bois-dark-col-xxl-offset-15 {\n margin-left: 62.5%;\n }\n .du-bois-dark-col-xxl-order-15 {\n order: 15;\n }\n .du-bois-dark-col-xxl-14 {\n display: block;\n flex: 0 0 58.33333333%;\n max-width: 58.33333333%;\n }\n .du-bois-dark-col-xxl-push-14 {\n left: 58.33333333%;\n }\n .du-bois-dark-col-xxl-pull-14 {\n right: 58.33333333%;\n }\n .du-bois-dark-col-xxl-offset-14 {\n margin-left: 58.33333333%;\n }\n .du-bois-dark-col-xxl-order-14 {\n order: 14;\n }\n .du-bois-dark-col-xxl-13 {\n display: block;\n flex: 0 0 54.16666667%;\n max-width: 54.16666667%;\n }\n .du-bois-dark-col-xxl-push-13 {\n left: 54.16666667%;\n }\n .du-bois-dark-col-xxl-pull-13 {\n right: 54.16666667%;\n }\n .du-bois-dark-col-xxl-offset-13 {\n margin-left: 54.16666667%;\n }\n .du-bois-dark-col-xxl-order-13 {\n order: 13;\n }\n .du-bois-dark-col-xxl-12 {\n display: block;\n flex: 0 0 50%;\n max-width: 50%;\n }\n .du-bois-dark-col-xxl-push-12 {\n left: 50%;\n }\n .du-bois-dark-col-xxl-pull-12 {\n right: 50%;\n }\n .du-bois-dark-col-xxl-offset-12 {\n margin-left: 50%;\n }\n .du-bois-dark-col-xxl-order-12 {\n order: 12;\n }\n .du-bois-dark-col-xxl-11 {\n display: block;\n flex: 0 0 45.83333333%;\n max-width: 45.83333333%;\n }\n .du-bois-dark-col-xxl-push-11 {\n left: 45.83333333%;\n }\n .du-bois-dark-col-xxl-pull-11 {\n right: 45.83333333%;\n }\n .du-bois-dark-col-xxl-offset-11 {\n margin-left: 45.83333333%;\n }\n .du-bois-dark-col-xxl-order-11 {\n order: 11;\n }\n .du-bois-dark-col-xxl-10 {\n display: block;\n flex: 0 0 41.66666667%;\n max-width: 41.66666667%;\n }\n .du-bois-dark-col-xxl-push-10 {\n left: 41.66666667%;\n }\n .du-bois-dark-col-xxl-pull-10 {\n right: 41.66666667%;\n }\n .du-bois-dark-col-xxl-offset-10 {\n margin-left: 41.66666667%;\n }\n .du-bois-dark-col-xxl-order-10 {\n order: 10;\n }\n .du-bois-dark-col-xxl-9 {\n display: block;\n flex: 0 0 37.5%;\n max-width: 37.5%;\n }\n .du-bois-dark-col-xxl-push-9 {\n left: 37.5%;\n }\n .du-bois-dark-col-xxl-pull-9 {\n right: 37.5%;\n }\n .du-bois-dark-col-xxl-offset-9 {\n margin-left: 37.5%;\n }\n .du-bois-dark-col-xxl-order-9 {\n order: 9;\n }\n .du-bois-dark-col-xxl-8 {\n display: block;\n flex: 0 0 33.33333333%;\n max-width: 33.33333333%;\n }\n .du-bois-dark-col-xxl-push-8 {\n left: 33.33333333%;\n }\n .du-bois-dark-col-xxl-pull-8 {\n right: 33.33333333%;\n }\n .du-bois-dark-col-xxl-offset-8 {\n margin-left: 33.33333333%;\n }\n .du-bois-dark-col-xxl-order-8 {\n order: 8;\n }\n .du-bois-dark-col-xxl-7 {\n display: block;\n flex: 0 0 29.16666667%;\n max-width: 29.16666667%;\n }\n .du-bois-dark-col-xxl-push-7 {\n left: 29.16666667%;\n }\n .du-bois-dark-col-xxl-pull-7 {\n right: 29.16666667%;\n }\n .du-bois-dark-col-xxl-offset-7 {\n margin-left: 29.16666667%;\n }\n .du-bois-dark-col-xxl-order-7 {\n order: 7;\n }\n .du-bois-dark-col-xxl-6 {\n display: block;\n flex: 0 0 25%;\n max-width: 25%;\n }\n .du-bois-dark-col-xxl-push-6 {\n left: 25%;\n }\n .du-bois-dark-col-xxl-pull-6 {\n right: 25%;\n }\n .du-bois-dark-col-xxl-offset-6 {\n margin-left: 25%;\n }\n .du-bois-dark-col-xxl-order-6 {\n order: 6;\n }\n .du-bois-dark-col-xxl-5 {\n display: block;\n flex: 0 0 20.83333333%;\n max-width: 20.83333333%;\n }\n .du-bois-dark-col-xxl-push-5 {\n left: 20.83333333%;\n }\n .du-bois-dark-col-xxl-pull-5 {\n right: 20.83333333%;\n }\n .du-bois-dark-col-xxl-offset-5 {\n margin-left: 20.83333333%;\n }\n .du-bois-dark-col-xxl-order-5 {\n order: 5;\n }\n .du-bois-dark-col-xxl-4 {\n display: block;\n flex: 0 0 16.66666667%;\n max-width: 16.66666667%;\n }\n .du-bois-dark-col-xxl-push-4 {\n left: 16.66666667%;\n }\n .du-bois-dark-col-xxl-pull-4 {\n right: 16.66666667%;\n }\n .du-bois-dark-col-xxl-offset-4 {\n margin-left: 16.66666667%;\n }\n .du-bois-dark-col-xxl-order-4 {\n order: 4;\n }\n .du-bois-dark-col-xxl-3 {\n display: block;\n flex: 0 0 12.5%;\n max-width: 12.5%;\n }\n .du-bois-dark-col-xxl-push-3 {\n left: 12.5%;\n }\n .du-bois-dark-col-xxl-pull-3 {\n right: 12.5%;\n }\n .du-bois-dark-col-xxl-offset-3 {\n margin-left: 12.5%;\n }\n .du-bois-dark-col-xxl-order-3 {\n order: 3;\n }\n .du-bois-dark-col-xxl-2 {\n display: block;\n flex: 0 0 8.33333333%;\n max-width: 8.33333333%;\n }\n .du-bois-dark-col-xxl-push-2 {\n left: 8.33333333%;\n }\n .du-bois-dark-col-xxl-pull-2 {\n right: 8.33333333%;\n }\n .du-bois-dark-col-xxl-offset-2 {\n margin-left: 8.33333333%;\n }\n .du-bois-dark-col-xxl-order-2 {\n order: 2;\n }\n .du-bois-dark-col-xxl-1 {\n display: block;\n flex: 0 0 4.16666667%;\n max-width: 4.16666667%;\n }\n .du-bois-dark-col-xxl-push-1 {\n left: 4.16666667%;\n }\n .du-bois-dark-col-xxl-pull-1 {\n right: 4.16666667%;\n }\n .du-bois-dark-col-xxl-offset-1 {\n margin-left: 4.16666667%;\n }\n .du-bois-dark-col-xxl-order-1 {\n order: 1;\n }\n .du-bois-dark-col-xxl-0 {\n display: none;\n }\n .du-bois-dark-col-push-0 {\n left: auto;\n }\n .du-bois-dark-col-pull-0 {\n right: auto;\n }\n .du-bois-dark-col-xxl-push-0 {\n left: auto;\n }\n .du-bois-dark-col-xxl-pull-0 {\n right: auto;\n }\n .du-bois-dark-col-xxl-offset-0 {\n margin-left: 0;\n }\n .du-bois-dark-col-xxl-order-0 {\n order: 0;\n }\n}\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-dark-tooltip {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #E8ECF0;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: tnum;\n position: absolute;\n z-index: 1070;\n display: block;\n width: max-content;\n max-width: 250px;\n visibility: visible;\n}\n.du-bois-dark-tooltip-hidden {\n display: none;\n}\n.du-bois-dark-tooltip-placement-top,\n.du-bois-dark-tooltip-placement-topLeft,\n.du-bois-dark-tooltip-placement-topRight {\n padding-bottom: 8px;\n}\n.du-bois-dark-tooltip-placement-right,\n.du-bois-dark-tooltip-placement-rightTop,\n.du-bois-dark-tooltip-placement-rightBottom {\n padding-left: 8px;\n}\n.du-bois-dark-tooltip-placement-bottom,\n.du-bois-dark-tooltip-placement-bottomLeft,\n.du-bois-dark-tooltip-placement-bottomRight {\n padding-top: 8px;\n}\n.du-bois-dark-tooltip-placement-left,\n.du-bois-dark-tooltip-placement-leftTop,\n.du-bois-dark-tooltip-placement-leftBottom {\n padding-right: 8px;\n}\n.du-bois-dark-tooltip-inner {\n min-width: 30px;\n min-height: 32px;\n padding: 6px 8px;\n color: #fff;\n text-align: left;\n text-decoration: none;\n word-wrap: break-word;\n background-color: #434343;\n border-radius: 4px;\n box-shadow: 0 3px 6px -4px rgba(0, 0, 0, 0.48), 0 6px 16px 0 rgba(0, 0, 0, 0.32), 0 9px 28px 8px rgba(0, 0, 0, 0.2);\n}\n.du-bois-dark-tooltip-arrow {\n position: absolute;\n display: block;\n width: 13.07106781px;\n height: 13.07106781px;\n overflow: hidden;\n background: transparent;\n pointer-events: none;\n}\n.du-bois-dark-tooltip-arrow-content {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n display: block;\n width: 5px;\n height: 5px;\n margin: auto;\n background-color: #434343;\n content: '';\n pointer-events: auto;\n}\n.du-bois-dark-tooltip-placement-top .du-bois-dark-tooltip-arrow,\n.du-bois-dark-tooltip-placement-topLeft .du-bois-dark-tooltip-arrow,\n.du-bois-dark-tooltip-placement-topRight .du-bois-dark-tooltip-arrow {\n bottom: -5.07106781px;\n}\n.du-bois-dark-tooltip-placement-top .du-bois-dark-tooltip-arrow-content,\n.du-bois-dark-tooltip-placement-topLeft .du-bois-dark-tooltip-arrow-content,\n.du-bois-dark-tooltip-placement-topRight .du-bois-dark-tooltip-arrow-content {\n box-shadow: 3px 3px 7px rgba(0, 0, 0, 0.07);\n transform: translateY(-6.53553391px) rotate(45deg);\n}\n.du-bois-dark-tooltip-placement-top .du-bois-dark-tooltip-arrow {\n left: 50%;\n transform: translateX(-50%);\n}\n.du-bois-dark-tooltip-placement-topLeft .du-bois-dark-tooltip-arrow {\n left: 13px;\n}\n.du-bois-dark-tooltip-placement-topRight .du-bois-dark-tooltip-arrow {\n right: 13px;\n}\n.du-bois-dark-tooltip-placement-right .du-bois-dark-tooltip-arrow,\n.du-bois-dark-tooltip-placement-rightTop .du-bois-dark-tooltip-arrow,\n.du-bois-dark-tooltip-placement-rightBottom .du-bois-dark-tooltip-arrow {\n left: -5.07106781px;\n}\n.du-bois-dark-tooltip-placement-right .du-bois-dark-tooltip-arrow-content,\n.du-bois-dark-tooltip-placement-rightTop .du-bois-dark-tooltip-arrow-content,\n.du-bois-dark-tooltip-placement-rightBottom .du-bois-dark-tooltip-arrow-content {\n box-shadow: -3px 3px 7px rgba(0, 0, 0, 0.07);\n transform: translateX(6.53553391px) rotate(45deg);\n}\n.du-bois-dark-tooltip-placement-right .du-bois-dark-tooltip-arrow {\n top: 50%;\n transform: translateY(-50%);\n}\n.du-bois-dark-tooltip-placement-rightTop .du-bois-dark-tooltip-arrow {\n top: 5px;\n}\n.du-bois-dark-tooltip-placement-rightBottom .du-bois-dark-tooltip-arrow {\n bottom: 5px;\n}\n.du-bois-dark-tooltip-placement-left .du-bois-dark-tooltip-arrow,\n.du-bois-dark-tooltip-placement-leftTop .du-bois-dark-tooltip-arrow,\n.du-bois-dark-tooltip-placement-leftBottom .du-bois-dark-tooltip-arrow {\n right: -5.07106781px;\n}\n.du-bois-dark-tooltip-placement-left .du-bois-dark-tooltip-arrow-content,\n.du-bois-dark-tooltip-placement-leftTop .du-bois-dark-tooltip-arrow-content,\n.du-bois-dark-tooltip-placement-leftBottom .du-bois-dark-tooltip-arrow-content {\n box-shadow: 3px -3px 7px rgba(0, 0, 0, 0.07);\n transform: translateX(-6.53553391px) rotate(45deg);\n}\n.du-bois-dark-tooltip-placement-left .du-bois-dark-tooltip-arrow {\n top: 50%;\n transform: translateY(-50%);\n}\n.du-bois-dark-tooltip-placement-leftTop .du-bois-dark-tooltip-arrow {\n top: 5px;\n}\n.du-bois-dark-tooltip-placement-leftBottom .du-bois-dark-tooltip-arrow {\n bottom: 5px;\n}\n.du-bois-dark-tooltip-placement-bottom .du-bois-dark-tooltip-arrow,\n.du-bois-dark-tooltip-placement-bottomLeft .du-bois-dark-tooltip-arrow,\n.du-bois-dark-tooltip-placement-bottomRight .du-bois-dark-tooltip-arrow {\n top: -5.07106781px;\n}\n.du-bois-dark-tooltip-placement-bottom .du-bois-dark-tooltip-arrow-content,\n.du-bois-dark-tooltip-placement-bottomLeft .du-bois-dark-tooltip-arrow-content,\n.du-bois-dark-tooltip-placement-bottomRight .du-bois-dark-tooltip-arrow-content {\n box-shadow: -3px -3px 7px rgba(0, 0, 0, 0.07);\n transform: translateY(6.53553391px) rotate(45deg);\n}\n.du-bois-dark-tooltip-placement-bottom .du-bois-dark-tooltip-arrow {\n left: 50%;\n transform: translateX(-50%);\n}\n.du-bois-dark-tooltip-placement-bottomLeft .du-bois-dark-tooltip-arrow {\n left: 13px;\n}\n.du-bois-dark-tooltip-placement-bottomRight .du-bois-dark-tooltip-arrow {\n right: 13px;\n}\n.du-bois-dark-tooltip-pink .du-bois-dark-tooltip-inner {\n background-color: #cb2b83;\n}\n.du-bois-dark-tooltip-pink .du-bois-dark-tooltip-arrow-content {\n background-color: #cb2b83;\n}\n.du-bois-dark-tooltip-magenta .du-bois-dark-tooltip-inner {\n background-color: #cb2b83;\n}\n.du-bois-dark-tooltip-magenta .du-bois-dark-tooltip-arrow-content {\n background-color: #cb2b83;\n}\n.du-bois-dark-tooltip-red .du-bois-dark-tooltip-inner {\n background-color: #d32029;\n}\n.du-bois-dark-tooltip-red .du-bois-dark-tooltip-arrow-content {\n background-color: #d32029;\n}\n.du-bois-dark-tooltip-volcano .du-bois-dark-tooltip-inner {\n background-color: #d84a1b;\n}\n.du-bois-dark-tooltip-volcano .du-bois-dark-tooltip-arrow-content {\n background-color: #d84a1b;\n}\n.du-bois-dark-tooltip-orange .du-bois-dark-tooltip-inner {\n background-color: #d87a16;\n}\n.du-bois-dark-tooltip-orange .du-bois-dark-tooltip-arrow-content {\n background-color: #d87a16;\n}\n.du-bois-dark-tooltip-yellow .du-bois-dark-tooltip-inner {\n background-color: #d8bd14;\n}\n.du-bois-dark-tooltip-yellow .du-bois-dark-tooltip-arrow-content {\n background-color: #d8bd14;\n}\n.du-bois-dark-tooltip-gold .du-bois-dark-tooltip-inner {\n background-color: #d89614;\n}\n.du-bois-dark-tooltip-gold .du-bois-dark-tooltip-arrow-content {\n background-color: #d89614;\n}\n.du-bois-dark-tooltip-cyan .du-bois-dark-tooltip-inner {\n background-color: #13a8a8;\n}\n.du-bois-dark-tooltip-cyan .du-bois-dark-tooltip-arrow-content {\n background-color: #13a8a8;\n}\n.du-bois-dark-tooltip-lime .du-bois-dark-tooltip-inner {\n background-color: #8bbb11;\n}\n.du-bois-dark-tooltip-lime .du-bois-dark-tooltip-arrow-content {\n background-color: #8bbb11;\n}\n.du-bois-dark-tooltip-green .du-bois-dark-tooltip-inner {\n background-color: #49aa19;\n}\n.du-bois-dark-tooltip-green .du-bois-dark-tooltip-arrow-content {\n background-color: #49aa19;\n}\n.du-bois-dark-tooltip-blue .du-bois-dark-tooltip-inner {\n background-color: #177ddc;\n}\n.du-bois-dark-tooltip-blue .du-bois-dark-tooltip-arrow-content {\n background-color: #177ddc;\n}\n.du-bois-dark-tooltip-geekblue .du-bois-dark-tooltip-inner {\n background-color: #2b4acb;\n}\n.du-bois-dark-tooltip-geekblue .du-bois-dark-tooltip-arrow-content {\n background-color: #2b4acb;\n}\n.du-bois-dark-tooltip-purple .du-bois-dark-tooltip-inner {\n background-color: #642ab5;\n}\n.du-bois-dark-tooltip-purple .du-bois-dark-tooltip-arrow-content {\n background-color: #642ab5;\n}\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-dark-input-affix-wrapper {\n position: relative;\n display: inline-block;\n width: 100%;\n min-width: 0;\n padding: 5px 12px;\n color: #E8ECF0;\n font-size: 13px;\n line-height: 20px;\n background-color: transparent;\n background-image: none;\n border: 1px solid #1F272D;\n border-radius: 4px;\n transition: all 0.3s;\n display: inline-flex;\n}\n.du-bois-dark-input-affix-wrapper::-moz-placeholder {\n opacity: 1;\n}\n.du-bois-dark-input-affix-wrapper::placeholder {\n color: rgba(255, 255, 255, 0.3);\n}\n.du-bois-dark-input-affix-wrapper:placeholder-shown {\n text-overflow: ellipsis;\n}\n.du-bois-dark-input-affix-wrapper:hover {\n border-color: #1d517c;\n border-right-width: 1px !important;\n}\n.du-bois-dark-input-affix-wrapper:focus,\n.du-bois-dark-input-affix-wrapper-focused {\n border-color: #2272B4;\n border-right-width: 1px !important;\n outline: 0;\n box-shadow: 0 0 0 2px rgba(34, 114, 180, 0.2);\n}\n.du-bois-dark-input-affix-wrapper-disabled {\n color: rgba(255, 255, 255, 0.3);\n background-color: rgba(255, 255, 255, 0.08);\n cursor: not-allowed;\n opacity: 1;\n}\n.du-bois-dark-input-affix-wrapper-disabled:hover {\n border-color: #1F272D;\n border-right-width: 1px !important;\n}\n.du-bois-dark-input-affix-wrapper[disabled] {\n color: rgba(255, 255, 255, 0.3);\n background-color: rgba(255, 255, 255, 0.08);\n cursor: not-allowed;\n opacity: 1;\n}\n.du-bois-dark-input-affix-wrapper[disabled]:hover {\n border-color: #1F272D;\n border-right-width: 1px !important;\n}\n.du-bois-dark-input-affix-wrapper-borderless,\n.du-bois-dark-input-affix-wrapper-borderless:hover,\n.du-bois-dark-input-affix-wrapper-borderless:focus,\n.du-bois-dark-input-affix-wrapper-borderless-focused,\n.du-bois-dark-input-affix-wrapper-borderless-disabled,\n.du-bois-dark-input-affix-wrapper-borderless[disabled] {\n background-color: transparent;\n border: none;\n box-shadow: none;\n}\ntextarea.du-bois-dark-input-affix-wrapper {\n max-width: 100%;\n height: auto;\n min-height: 40px;\n line-height: 20px;\n vertical-align: bottom;\n transition: all 0.3s, height 0s;\n}\n.du-bois-dark-input-affix-wrapper-lg {\n padding: 5px 12px;\n font-size: 18px;\n}\n.du-bois-dark-input-affix-wrapper-sm {\n padding: 5px 12px;\n}\n.du-bois-dark-input-affix-wrapper:not(.du-bois-dark-input-affix-wrapper-disabled):hover {\n border-color: #1d517c;\n border-right-width: 1px !important;\n z-index: 1;\n}\n.du-bois-dark-input-search-with-button .du-bois-dark-input-affix-wrapper:not(.du-bois-dark-input-affix-wrapper-disabled):hover {\n z-index: 0;\n}\n.du-bois-dark-input-affix-wrapper-focused,\n.du-bois-dark-input-affix-wrapper:focus {\n z-index: 1;\n}\n.du-bois-dark-input-affix-wrapper-disabled .du-bois-dark-input[disabled] {\n background: transparent;\n}\n.du-bois-dark-input-affix-wrapper > input.du-bois-dark-input {\n padding: 0;\n border: none;\n outline: none;\n}\n.du-bois-dark-input-affix-wrapper > input.du-bois-dark-input:focus {\n box-shadow: none;\n}\n.du-bois-dark-input-affix-wrapper::before {\n width: 0;\n visibility: hidden;\n content: '\\a0';\n}\n.du-bois-dark-input-prefix,\n.du-bois-dark-input-suffix {\n display: flex;\n flex: none;\n align-items: center;\n}\n.du-bois-dark-input-prefix {\n margin-right: 4px;\n}\n.du-bois-dark-input-suffix {\n margin-left: 4px;\n}\n.anticon.du-bois-dark-input-clear-icon {\n margin: 0 4px;\n color: rgba(255, 255, 255, 0.3);\n font-size: 12px;\n vertical-align: -1px;\n cursor: pointer;\n transition: color 0.3s;\n}\n.anticon.du-bois-dark-input-clear-icon:hover {\n color: #92A4B3;\n}\n.anticon.du-bois-dark-input-clear-icon:active {\n color: #E8ECF0;\n}\n.anticon.du-bois-dark-input-clear-icon-hidden {\n visibility: hidden;\n}\n.anticon.du-bois-dark-input-clear-icon:last-child {\n margin-right: 0;\n}\n.du-bois-dark-input-affix-wrapper-textarea-with-clear-btn {\n padding: 0 !important;\n border: 0 !important;\n}\n.du-bois-dark-input-affix-wrapper-textarea-with-clear-btn .du-bois-dark-input-clear-icon {\n position: absolute;\n top: 8px;\n right: 8px;\n z-index: 1;\n}\n.du-bois-dark-input {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n font-variant: tabular-nums;\n list-style: none;\n font-feature-settings: tnum;\n position: relative;\n display: inline-block;\n width: 100%;\n min-width: 0;\n padding: 5px 12px;\n color: #E8ECF0;\n font-size: 13px;\n line-height: 20px;\n background-color: transparent;\n background-image: none;\n border: 1px solid #1F272D;\n border-radius: 4px;\n transition: all 0.3s;\n}\n.du-bois-dark-input::-moz-placeholder {\n opacity: 1;\n}\n.du-bois-dark-input::placeholder {\n color: rgba(255, 255, 255, 0.3);\n}\n.du-bois-dark-input:placeholder-shown {\n text-overflow: ellipsis;\n}\n.du-bois-dark-input:hover {\n border-color: #1d517c;\n border-right-width: 1px !important;\n}\n.du-bois-dark-input:focus,\n.du-bois-dark-input-focused {\n border-color: #2272B4;\n border-right-width: 1px !important;\n outline: 0;\n box-shadow: 0 0 0 2px rgba(34, 114, 180, 0.2);\n}\n.du-bois-dark-input-disabled {\n color: rgba(255, 255, 255, 0.3);\n background-color: rgba(255, 255, 255, 0.08);\n cursor: not-allowed;\n opacity: 1;\n}\n.du-bois-dark-input-disabled:hover {\n border-color: #1F272D;\n border-right-width: 1px !important;\n}\n.du-bois-dark-input[disabled] {\n color: rgba(255, 255, 255, 0.3);\n background-color: rgba(255, 255, 255, 0.08);\n cursor: not-allowed;\n opacity: 1;\n}\n.du-bois-dark-input[disabled]:hover {\n border-color: #1F272D;\n border-right-width: 1px !important;\n}\n.du-bois-dark-input-borderless,\n.du-bois-dark-input-borderless:hover,\n.du-bois-dark-input-borderless:focus,\n.du-bois-dark-input-borderless-focused,\n.du-bois-dark-input-borderless-disabled,\n.du-bois-dark-input-borderless[disabled] {\n background-color: transparent;\n border: none;\n box-shadow: none;\n}\ntextarea.du-bois-dark-input {\n max-width: 100%;\n height: auto;\n min-height: 40px;\n line-height: 20px;\n vertical-align: bottom;\n transition: all 0.3s, height 0s;\n}\n.du-bois-dark-input-lg {\n padding: 5px 12px;\n font-size: 18px;\n}\n.du-bois-dark-input-sm {\n padding: 5px 12px;\n}\n.du-bois-dark-input-group {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #E8ECF0;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: tnum;\n position: relative;\n display: table;\n width: 100%;\n border-collapse: separate;\n border-spacing: 0;\n}\n.du-bois-dark-input-group[class*='col-'] {\n float: none;\n padding-right: 0;\n padding-left: 0;\n}\n.du-bois-dark-input-group > [class*='col-'] {\n padding-right: 8px;\n}\n.du-bois-dark-input-group > [class*='col-']:last-child {\n padding-right: 0;\n}\n.du-bois-dark-input-group-addon,\n.du-bois-dark-input-group-wrap,\n.du-bois-dark-input-group > .du-bois-dark-input {\n display: table-cell;\n}\n.du-bois-dark-input-group-addon:not(:first-child):not(:last-child),\n.du-bois-dark-input-group-wrap:not(:first-child):not(:last-child),\n.du-bois-dark-input-group > .du-bois-dark-input:not(:first-child):not(:last-child) {\n border-radius: 0;\n}\n.du-bois-dark-input-group-addon,\n.du-bois-dark-input-group-wrap {\n width: 1px;\n white-space: nowrap;\n vertical-align: middle;\n}\n.du-bois-dark-input-group-wrap > * {\n display: block !important;\n}\n.du-bois-dark-input-group .du-bois-dark-input {\n float: left;\n width: 100%;\n margin-bottom: 0;\n text-align: inherit;\n}\n.du-bois-dark-input-group .du-bois-dark-input:focus {\n z-index: 1;\n border-right-width: 1px;\n}\n.du-bois-dark-input-group .du-bois-dark-input:hover {\n z-index: 1;\n border-right-width: 1px;\n}\n.du-bois-dark-input-search-with-button .du-bois-dark-input-group .du-bois-dark-input:hover {\n z-index: 0;\n}\n.du-bois-dark-input-group-addon {\n position: relative;\n padding: 0 12px;\n color: #E8ECF0;\n font-weight: normal;\n font-size: 13px;\n text-align: center;\n background-color: rgba(255, 255, 255, 0.04);\n border: 1px solid #1F272D;\n border-radius: 4px;\n transition: all 0.3s;\n}\n.du-bois-dark-input-group-addon .du-bois-dark-select {\n margin: -6px -12px;\n}\n.du-bois-dark-input-group-addon .du-bois-dark-select.du-bois-dark-select-single:not(.du-bois-dark-select-customize-input) .du-bois-dark-select-selector {\n background-color: inherit;\n border: 1px solid transparent;\n box-shadow: none;\n}\n.du-bois-dark-input-group-addon .du-bois-dark-select-open .du-bois-dark-select-selector,\n.du-bois-dark-input-group-addon .du-bois-dark-select-focused .du-bois-dark-select-selector {\n color: #2272B4;\n}\n.du-bois-dark-input-group-addon .du-bois-dark-cascader-picker {\n margin: -9px -8px;\n background-color: transparent;\n}\n.du-bois-dark-input-group-addon .du-bois-dark-cascader-picker .du-bois-dark-cascader-input {\n text-align: left;\n border: 0;\n box-shadow: none;\n}\n.du-bois-dark-input-group > .du-bois-dark-input:first-child,\n.du-bois-dark-input-group-addon:first-child {\n border-top-right-radius: 0;\n border-bottom-right-radius: 0;\n}\n.du-bois-dark-input-group > .du-bois-dark-input:first-child .du-bois-dark-select .du-bois-dark-select-selector,\n.du-bois-dark-input-group-addon:first-child .du-bois-dark-select .du-bois-dark-select-selector {\n border-top-right-radius: 0;\n border-bottom-right-radius: 0;\n}\n.du-bois-dark-input-group > .du-bois-dark-input-affix-wrapper:not(:first-child) .du-bois-dark-input {\n border-top-left-radius: 0;\n border-bottom-left-radius: 0;\n}\n.du-bois-dark-input-group > .du-bois-dark-input-affix-wrapper:not(:last-child) .du-bois-dark-input {\n border-top-right-radius: 0;\n border-bottom-right-radius: 0;\n}\n.du-bois-dark-input-group-addon:first-child {\n border-right: 0;\n}\n.du-bois-dark-input-group-addon:last-child {\n border-left: 0;\n}\n.du-bois-dark-input-group > .du-bois-dark-input:last-child,\n.du-bois-dark-input-group-addon:last-child {\n border-top-left-radius: 0;\n border-bottom-left-radius: 0;\n}\n.du-bois-dark-input-group > .du-bois-dark-input:last-child .du-bois-dark-select .du-bois-dark-select-selector,\n.du-bois-dark-input-group-addon:last-child .du-bois-dark-select .du-bois-dark-select-selector {\n border-top-left-radius: 0;\n border-bottom-left-radius: 0;\n}\n.du-bois-dark-input-group-lg .du-bois-dark-input,\n.du-bois-dark-input-group-lg > .du-bois-dark-input-group-addon {\n padding: 5px 12px;\n font-size: 18px;\n}\n.du-bois-dark-input-group-sm .du-bois-dark-input,\n.du-bois-dark-input-group-sm > .du-bois-dark-input-group-addon {\n padding: 5px 12px;\n}\n.du-bois-dark-input-group-lg .du-bois-dark-select-single .du-bois-dark-select-selector {\n height: 40px;\n}\n.du-bois-dark-input-group-sm .du-bois-dark-select-single .du-bois-dark-select-selector {\n height: 32px;\n}\n.du-bois-dark-input-group .du-bois-dark-input-affix-wrapper:not(:first-child) {\n border-top-left-radius: 0;\n border-bottom-left-radius: 0;\n}\n.du-bois-dark-input-group .du-bois-dark-input-affix-wrapper:not(:last-child) {\n border-top-right-radius: 0;\n border-bottom-right-radius: 0;\n}\n.du-bois-dark-input-search .du-bois-dark-input-group .du-bois-dark-input-affix-wrapper:not(:last-child) {\n border-top-left-radius: 4px;\n border-bottom-left-radius: 4px;\n}\n.du-bois-dark-input-group.du-bois-dark-input-group-compact {\n display: block;\n}\n.du-bois-dark-input-group.du-bois-dark-input-group-compact::before {\n display: table;\n content: '';\n}\n.du-bois-dark-input-group.du-bois-dark-input-group-compact::after {\n display: table;\n clear: both;\n content: '';\n}\n.du-bois-dark-input-group.du-bois-dark-input-group-compact-addon:not(:first-child):not(:last-child),\n.du-bois-dark-input-group.du-bois-dark-input-group-compact-wrap:not(:first-child):not(:last-child),\n.du-bois-dark-input-group.du-bois-dark-input-group-compact > .du-bois-dark-input:not(:first-child):not(:last-child) {\n border-right-width: 1px;\n}\n.du-bois-dark-input-group.du-bois-dark-input-group-compact-addon:not(:first-child):not(:last-child):hover,\n.du-bois-dark-input-group.du-bois-dark-input-group-compact-wrap:not(:first-child):not(:last-child):hover,\n.du-bois-dark-input-group.du-bois-dark-input-group-compact > .du-bois-dark-input:not(:first-child):not(:last-child):hover {\n z-index: 1;\n}\n.du-bois-dark-input-group.du-bois-dark-input-group-compact-addon:not(:first-child):not(:last-child):focus,\n.du-bois-dark-input-group.du-bois-dark-input-group-compact-wrap:not(:first-child):not(:last-child):focus,\n.du-bois-dark-input-group.du-bois-dark-input-group-compact > .du-bois-dark-input:not(:first-child):not(:last-child):focus {\n z-index: 1;\n}\n.du-bois-dark-input-group.du-bois-dark-input-group-compact > * {\n display: inline-block;\n float: none;\n vertical-align: top;\n border-radius: 0;\n}\n.du-bois-dark-input-group.du-bois-dark-input-group-compact > .du-bois-dark-input-affix-wrapper {\n display: inline-flex;\n}\n.du-bois-dark-input-group.du-bois-dark-input-group-compact > .du-bois-dark-picker-range {\n display: inline-flex;\n}\n.du-bois-dark-input-group.du-bois-dark-input-group-compact > *:not(:last-child) {\n margin-right: -1px;\n border-right-width: 1px;\n}\n.du-bois-dark-input-group.du-bois-dark-input-group-compact .du-bois-dark-input {\n float: none;\n}\n.du-bois-dark-input-group.du-bois-dark-input-group-compact > .du-bois-dark-select > .du-bois-dark-select-selector,\n.du-bois-dark-input-group.du-bois-dark-input-group-compact > .du-bois-dark-select-auto-complete .du-bois-dark-input,\n.du-bois-dark-input-group.du-bois-dark-input-group-compact > .du-bois-dark-cascader-picker .du-bois-dark-input,\n.du-bois-dark-input-group.du-bois-dark-input-group-compact > .du-bois-dark-input-group-wrapper .du-bois-dark-input {\n border-right-width: 1px;\n border-radius: 0;\n}\n.du-bois-dark-input-group.du-bois-dark-input-group-compact > .du-bois-dark-select > .du-bois-dark-select-selector:hover,\n.du-bois-dark-input-group.du-bois-dark-input-group-compact > .du-bois-dark-select-auto-complete .du-bois-dark-input:hover,\n.du-bois-dark-input-group.du-bois-dark-input-group-compact > .du-bois-dark-cascader-picker .du-bois-dark-input:hover,\n.du-bois-dark-input-group.du-bois-dark-input-group-compact > .du-bois-dark-input-group-wrapper .du-bois-dark-input:hover {\n z-index: 1;\n}\n.du-bois-dark-input-group.du-bois-dark-input-group-compact > .du-bois-dark-select > .du-bois-dark-select-selector:focus,\n.du-bois-dark-input-group.du-bois-dark-input-group-compact > .du-bois-dark-select-auto-complete .du-bois-dark-input:focus,\n.du-bois-dark-input-group.du-bois-dark-input-group-compact > .du-bois-dark-cascader-picker .du-bois-dark-input:focus,\n.du-bois-dark-input-group.du-bois-dark-input-group-compact > .du-bois-dark-input-group-wrapper .du-bois-dark-input:focus {\n z-index: 1;\n}\n.du-bois-dark-input-group.du-bois-dark-input-group-compact > .du-bois-dark-select-focused {\n z-index: 1;\n}\n.du-bois-dark-input-group.du-bois-dark-input-group-compact > .du-bois-dark-select > .du-bois-dark-select-arrow {\n z-index: 1;\n}\n.du-bois-dark-input-group.du-bois-dark-input-group-compact > *:first-child,\n.du-bois-dark-input-group.du-bois-dark-input-group-compact > .du-bois-dark-select:first-child > .du-bois-dark-select-selector,\n.du-bois-dark-input-group.du-bois-dark-input-group-compact > .du-bois-dark-select-auto-complete:first-child .du-bois-dark-input,\n.du-bois-dark-input-group.du-bois-dark-input-group-compact > .du-bois-dark-cascader-picker:first-child .du-bois-dark-input {\n border-top-left-radius: 4px;\n border-bottom-left-radius: 4px;\n}\n.du-bois-dark-input-group.du-bois-dark-input-group-compact > *:last-child,\n.du-bois-dark-input-group.du-bois-dark-input-group-compact > .du-bois-dark-select:last-child > .du-bois-dark-select-selector,\n.du-bois-dark-input-group.du-bois-dark-input-group-compact > .du-bois-dark-cascader-picker:last-child .du-bois-dark-input,\n.du-bois-dark-input-group.du-bois-dark-input-group-compact > .du-bois-dark-cascader-picker-focused:last-child .du-bois-dark-input {\n border-right-width: 1px;\n border-top-right-radius: 4px;\n border-bottom-right-radius: 4px;\n}\n.du-bois-dark-input-group.du-bois-dark-input-group-compact > .du-bois-dark-select-auto-complete .du-bois-dark-input {\n vertical-align: top;\n}\n.du-bois-dark-input-group.du-bois-dark-input-group-compact .du-bois-dark-input-group-wrapper + .du-bois-dark-input-group-wrapper {\n margin-left: -1px;\n}\n.du-bois-dark-input-group.du-bois-dark-input-group-compact .du-bois-dark-input-group-wrapper + .du-bois-dark-input-group-wrapper .du-bois-dark-input-affix-wrapper {\n border-radius: 0;\n}\n.du-bois-dark-input-group.du-bois-dark-input-group-compact .du-bois-dark-input-group-wrapper:not(:last-child).du-bois-dark-input-search > .du-bois-dark-input-group > .du-bois-dark-input-group-addon > .du-bois-dark-input-search-button {\n border-radius: 0;\n}\n.du-bois-dark-input-group.du-bois-dark-input-group-compact .du-bois-dark-input-group-wrapper:not(:last-child).du-bois-dark-input-search > .du-bois-dark-input-group > .du-bois-dark-input {\n border-radius: 4px 0 0 4px;\n}\n.du-bois-dark-input-group-wrapper {\n display: inline-block;\n width: 100%;\n text-align: start;\n vertical-align: top;\n}\n.du-bois-dark-input-password-icon {\n color: #92A4B3;\n cursor: pointer;\n transition: all 0.3s;\n}\n.du-bois-dark-input-password-icon:hover {\n color: rgba(255, 255, 255, 0.85);\n}\n.du-bois-dark-input[type='color'] {\n height: 40px;\n}\n.du-bois-dark-input[type='color'].du-bois-dark-input-lg {\n height: 40px;\n}\n.du-bois-dark-input[type='color'].du-bois-dark-input-sm {\n height: 32px;\n padding-top: 3px;\n padding-bottom: 3px;\n}\n.du-bois-dark-input-textarea-show-count::after {\n float: right;\n color: #92A4B3;\n white-space: nowrap;\n content: attr(data-count);\n pointer-events: none;\n}\n.du-bois-dark-input-search .du-bois-dark-input:hover,\n.du-bois-dark-input-search .du-bois-dark-input:focus {\n border-color: #1d517c;\n}\n.du-bois-dark-input-search .du-bois-dark-input:hover + .du-bois-dark-input-group-addon .du-bois-dark-input-search-button:not(.du-bois-dark-btn-primary),\n.du-bois-dark-input-search .du-bois-dark-input:focus + .du-bois-dark-input-group-addon .du-bois-dark-input-search-button:not(.du-bois-dark-btn-primary) {\n border-left-color: #1d517c;\n}\n.du-bois-dark-input-search .du-bois-dark-input-affix-wrapper {\n border-radius: 0;\n}\n.du-bois-dark-input-search .du-bois-dark-input-lg {\n line-height: 19.9998px;\n}\n.du-bois-dark-input-search > .du-bois-dark-input-group > .du-bois-dark-input-group-addon:last-child {\n left: -1px;\n padding: 0;\n border: 0;\n}\n.du-bois-dark-input-search > .du-bois-dark-input-group > .du-bois-dark-input-group-addon:last-child .du-bois-dark-input-search-button {\n padding-top: 0;\n padding-bottom: 0;\n border-radius: 0 4px 4px 0;\n}\n.du-bois-dark-input-search > .du-bois-dark-input-group > .du-bois-dark-input-group-addon:last-child .du-bois-dark-input-search-button:not(.du-bois-dark-btn-primary) {\n color: #92A4B3;\n}\n.du-bois-dark-input-search > .du-bois-dark-input-group > .du-bois-dark-input-group-addon:last-child .du-bois-dark-input-search-button:not(.du-bois-dark-btn-primary).du-bois-dark-btn-loading::before {\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n}\n.du-bois-dark-input-search-button {\n height: 40px;\n}\n.du-bois-dark-input-search-button:hover,\n.du-bois-dark-input-search-button:focus {\n z-index: 1;\n}\n.du-bois-dark-input-search-large .du-bois-dark-input-search-button {\n height: 40px;\n}\n.du-bois-dark-input-search-small .du-bois-dark-input-search-button {\n height: 32px;\n}\n@media screen and (-ms-high-contrast: active), (-ms-high-contrast: none) {\n .du-bois-dark-input {\n height: 40px;\n }\n .du-bois-dark-input-lg {\n height: 40px;\n }\n .du-bois-dark-input-sm {\n height: 32px;\n }\n .du-bois-dark-input-affix-wrapper > input.du-bois-dark-input {\n height: auto;\n }\n}\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-dark-layout {\n display: flex;\n flex: auto;\n flex-direction: column;\n /* fix firefox can't set height smaller than content on flex item */\n min-height: 0;\n background: #11171C;\n}\n.du-bois-dark-layout,\n.du-bois-dark-layout * {\n box-sizing: border-box;\n}\n.du-bois-dark-layout.du-bois-dark-layout-has-sider {\n flex-direction: row;\n}\n.du-bois-dark-layout.du-bois-dark-layout-has-sider > .du-bois-dark-layout,\n.du-bois-dark-layout.du-bois-dark-layout-has-sider > .du-bois-dark-layout-content {\n width: 0;\n}\n.du-bois-dark-layout-header,\n.du-bois-dark-layout-footer {\n flex: 0 0 auto;\n}\n.du-bois-dark-layout-header {\n height: 64px;\n padding: 0 50px;\n color: #E8ECF0;\n line-height: 64px;\n background: #1f1f1f;\n}\n.du-bois-dark-layout-footer {\n padding: 24px 50px;\n color: #E8ECF0;\n font-size: 13px;\n background: #11171C;\n}\n.du-bois-dark-layout-content {\n flex: auto;\n /* fix firefox can't set height smaller than content on flex item */\n min-height: 0;\n}\n.du-bois-dark-layout-sider {\n position: relative;\n /* fix firefox can't set width smaller than content on flex item */\n min-width: 0;\n background: #1f1f1f;\n transition: all 0.2s;\n}\n.du-bois-dark-layout-sider-children {\n height: 100%;\n margin-top: -0.1px;\n padding-top: 0.1px;\n}\n.du-bois-dark-layout-sider-children .du-bois-dark-menu.du-bois-dark-menu-inline-collapsed {\n width: auto;\n}\n.du-bois-dark-layout-sider-has-trigger {\n padding-bottom: 48px;\n}\n.du-bois-dark-layout-sider-right {\n order: 1;\n}\n.du-bois-dark-layout-sider-trigger {\n position: fixed;\n bottom: 0;\n z-index: 1;\n height: 48px;\n color: #fff;\n line-height: 48px;\n text-align: center;\n background: #262626;\n cursor: pointer;\n transition: all 0.2s;\n}\n.du-bois-dark-layout-sider-zero-width > * {\n overflow: hidden;\n}\n.du-bois-dark-layout-sider-zero-width-trigger {\n position: absolute;\n top: 64px;\n right: -36px;\n z-index: 1;\n width: 36px;\n height: 42px;\n color: #fff;\n font-size: 18px;\n line-height: 42px;\n text-align: center;\n background: #1f1f1f;\n border-radius: 0 4px 4px 0;\n cursor: pointer;\n transition: background 0.3s ease;\n}\n.du-bois-dark-layout-sider-zero-width-trigger::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n transition: all 0.3s;\n content: '';\n}\n.du-bois-dark-layout-sider-zero-width-trigger:hover::after {\n background: rgba(255, 255, 255, 0.1);\n}\n.du-bois-dark-layout-sider-zero-width-trigger-right {\n left: -36px;\n border-radius: 4px 0 0 4px;\n}\n.du-bois-dark-layout-sider-light {\n background: #fff;\n}\n.du-bois-dark-layout-sider-light .du-bois-dark-layout-sider-trigger {\n color: #E8ECF0;\n background: #fff;\n}\n.du-bois-dark-layout-sider-light .du-bois-dark-layout-sider-zero-width-trigger {\n color: #E8ECF0;\n background: #fff;\n}\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-dark-menu-item-danger.du-bois-dark-menu-item {\n color: #E65B77;\n}\n.du-bois-dark-menu-item-danger.du-bois-dark-menu-item:hover,\n.du-bois-dark-menu-item-danger.du-bois-dark-menu-item-active {\n color: #E65B77;\n}\n.du-bois-dark-menu-item-danger.du-bois-dark-menu-item:active {\n background: #2a1215;\n}\n.du-bois-dark-menu-item-danger.du-bois-dark-menu-item-selected {\n color: #E65B77;\n}\n.du-bois-dark-menu-item-danger.du-bois-dark-menu-item-selected > a,\n.du-bois-dark-menu-item-danger.du-bois-dark-menu-item-selected > a:hover {\n color: #E65B77;\n}\n.du-bois-dark-menu:not(.du-bois-dark-menu-horizontal) .du-bois-dark-menu-item-danger.du-bois-dark-menu-item-selected {\n background-color: #2a1215;\n}\n.du-bois-dark-menu-inline .du-bois-dark-menu-item-danger.du-bois-dark-menu-item::after {\n border-right-color: #E65B77;\n}\n.du-bois-dark-menu-dark .du-bois-dark-menu-item-danger.du-bois-dark-menu-item,\n.du-bois-dark-menu-dark .du-bois-dark-menu-item-danger.du-bois-dark-menu-item:hover,\n.du-bois-dark-menu-dark .du-bois-dark-menu-item-danger.du-bois-dark-menu-item > a {\n color: #E65B77;\n}\n.du-bois-dark-menu-dark.du-bois-dark-menu-dark:not(.du-bois-dark-menu-horizontal) .du-bois-dark-menu-item-danger.du-bois-dark-menu-item-selected {\n color: #fff;\n background-color: #E65B77;\n}\n.du-bois-dark-menu {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n font-variant: tabular-nums;\n line-height: 20px;\n font-feature-settings: tnum;\n margin-bottom: 0;\n padding-left: 0;\n color: #E8ECF0;\n font-size: 13px;\n line-height: 0;\n text-align: left;\n list-style: none;\n background: #141414;\n outline: none;\n box-shadow: 0 3px 6px -4px rgba(0, 0, 0, 0.48), 0 6px 16px 0 rgba(0, 0, 0, 0.32), 0 9px 28px 8px rgba(0, 0, 0, 0.2);\n transition: background 0.3s, width 0.3s cubic-bezier(0.2, 0, 0, 1) 0s;\n}\n.du-bois-dark-menu::before {\n display: table;\n content: '';\n}\n.du-bois-dark-menu::after {\n display: table;\n clear: both;\n content: '';\n}\n.du-bois-dark-menu.du-bois-dark-menu-root:focus-visible {\n box-shadow: 0 0 0 2px rgba(34, 114, 180, 0.2);\n}\n.du-bois-dark-menu ul,\n.du-bois-dark-menu ol {\n margin: 0;\n padding: 0;\n list-style: none;\n}\n.du-bois-dark-menu-overflow {\n display: flex;\n}\n.du-bois-dark-menu-overflow-item {\n flex: none;\n}\n.du-bois-dark-menu-hidden,\n.du-bois-dark-menu-submenu-hidden {\n display: none;\n}\n.du-bois-dark-menu-item-group-title {\n height: 20px;\n padding: 8px 16px;\n color: #92A4B3;\n font-size: 13px;\n line-height: 20px;\n transition: all 0.3s;\n}\n.du-bois-dark-menu-horizontal .du-bois-dark-menu-submenu {\n transition: border-color 0.3s cubic-bezier(0.645, 0.045, 0.355, 1), background 0.3s cubic-bezier(0.645, 0.045, 0.355, 1);\n}\n.du-bois-dark-menu-submenu,\n.du-bois-dark-menu-submenu-inline {\n transition: border-color 0.3s cubic-bezier(0.645, 0.045, 0.355, 1), background 0.3s cubic-bezier(0.645, 0.045, 0.355, 1), padding 0.15s cubic-bezier(0.645, 0.045, 0.355, 1);\n}\n.du-bois-dark-menu-submenu-selected {\n color: #2272B4;\n}\n.du-bois-dark-menu-item:active,\n.du-bois-dark-menu-submenu-title:active {\n background: #121921;\n}\n.du-bois-dark-menu-submenu .du-bois-dark-menu-sub {\n cursor: initial;\n transition: background 0.3s cubic-bezier(0.645, 0.045, 0.355, 1), padding 0.3s cubic-bezier(0.645, 0.045, 0.355, 1);\n}\n.du-bois-dark-menu-title-content {\n transition: color 0.3s;\n}\n.du-bois-dark-menu-item a {\n color: #E8ECF0;\n}\n.du-bois-dark-menu-item a:hover {\n color: #2272B4;\n}\n.du-bois-dark-menu-item a::before {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background-color: transparent;\n content: '';\n}\n.du-bois-dark-menu-item > .du-bois-dark-badge a {\n color: #E8ECF0;\n}\n.du-bois-dark-menu-item > .du-bois-dark-badge a:hover {\n color: #2272B4;\n}\n.du-bois-dark-menu-item-divider {\n height: 1px;\n overflow: hidden;\n line-height: 0;\n background-color: #303030;\n}\n.du-bois-dark-menu-horizontal .du-bois-dark-menu-item,\n.du-bois-dark-menu-horizontal .du-bois-dark-menu-submenu {\n margin-top: -1px;\n}\n.du-bois-dark-menu-horizontal > .du-bois-dark-menu-item:hover,\n.du-bois-dark-menu-horizontal > .du-bois-dark-menu-item-active,\n.du-bois-dark-menu-horizontal > .du-bois-dark-menu-submenu .du-bois-dark-menu-submenu-title:hover {\n background-color: transparent;\n}\n.du-bois-dark-menu-item-selected {\n color: #2272B4;\n}\n.du-bois-dark-menu-item-selected a,\n.du-bois-dark-menu-item-selected a:hover {\n color: #2272B4;\n}\n.du-bois-dark-menu:not(.du-bois-dark-menu-horizontal) .du-bois-dark-menu-item-selected {\n background-color: #121921;\n}\n.du-bois-dark-menu-inline,\n.du-bois-dark-menu-vertical,\n.du-bois-dark-menu-vertical-left {\n border-right: 1px solid #303030;\n}\n.du-bois-dark-menu-vertical-right {\n border-left: 1px solid #303030;\n}\n.du-bois-dark-menu-vertical.du-bois-dark-menu-sub,\n.du-bois-dark-menu-vertical-left.du-bois-dark-menu-sub,\n.du-bois-dark-menu-vertical-right.du-bois-dark-menu-sub {\n min-width: 160px;\n max-height: calc(100vh - 100px);\n padding: 0;\n overflow: hidden;\n border-right: 0;\n}\n.du-bois-dark-menu-vertical.du-bois-dark-menu-sub:not([class*='-active']),\n.du-bois-dark-menu-vertical-left.du-bois-dark-menu-sub:not([class*='-active']),\n.du-bois-dark-menu-vertical-right.du-bois-dark-menu-sub:not([class*='-active']) {\n overflow-x: hidden;\n overflow-y: auto;\n}\n.du-bois-dark-menu-vertical.du-bois-dark-menu-sub .du-bois-dark-menu-item,\n.du-bois-dark-menu-vertical-left.du-bois-dark-menu-sub .du-bois-dark-menu-item,\n.du-bois-dark-menu-vertical-right.du-bois-dark-menu-sub .du-bois-dark-menu-item {\n left: 0;\n margin-left: 0;\n border-right: 0;\n}\n.du-bois-dark-menu-vertical.du-bois-dark-menu-sub .du-bois-dark-menu-item::after,\n.du-bois-dark-menu-vertical-left.du-bois-dark-menu-sub .du-bois-dark-menu-item::after,\n.du-bois-dark-menu-vertical-right.du-bois-dark-menu-sub .du-bois-dark-menu-item::after {\n border-right: 0;\n}\n.du-bois-dark-menu-vertical.du-bois-dark-menu-sub > .du-bois-dark-menu-item,\n.du-bois-dark-menu-vertical-left.du-bois-dark-menu-sub > .du-bois-dark-menu-item,\n.du-bois-dark-menu-vertical-right.du-bois-dark-menu-sub > .du-bois-dark-menu-item,\n.du-bois-dark-menu-vertical.du-bois-dark-menu-sub > .du-bois-dark-menu-submenu,\n.du-bois-dark-menu-vertical-left.du-bois-dark-menu-sub > .du-bois-dark-menu-submenu,\n.du-bois-dark-menu-vertical-right.du-bois-dark-menu-sub > .du-bois-dark-menu-submenu {\n transform-origin: 0 0;\n}\n.du-bois-dark-menu-horizontal.du-bois-dark-menu-sub {\n min-width: 114px;\n}\n.du-bois-dark-menu-horizontal .du-bois-dark-menu-item,\n.du-bois-dark-menu-horizontal .du-bois-dark-menu-submenu-title {\n transition: border-color 0.3s, background 0.3s;\n}\n.du-bois-dark-menu-item,\n.du-bois-dark-menu-submenu-title {\n position: relative;\n display: block;\n margin: 0;\n padding: 0 20px;\n white-space: nowrap;\n cursor: pointer;\n transition: border-color 0.3s, background 0.3s, padding 0.3s cubic-bezier(0.645, 0.045, 0.355, 1);\n}\n.du-bois-dark-menu-item .du-bois-dark-menu-item-icon,\n.du-bois-dark-menu-submenu-title .du-bois-dark-menu-item-icon,\n.du-bois-dark-menu-item .anticon,\n.du-bois-dark-menu-submenu-title .anticon {\n min-width: 14px;\n font-size: 13px;\n transition: font-size 0.15s cubic-bezier(0.215, 0.61, 0.355, 1), margin 0.3s cubic-bezier(0.645, 0.045, 0.355, 1), color 0.3s;\n}\n.du-bois-dark-menu-item .du-bois-dark-menu-item-icon + span,\n.du-bois-dark-menu-submenu-title .du-bois-dark-menu-item-icon + span,\n.du-bois-dark-menu-item .anticon + span,\n.du-bois-dark-menu-submenu-title .anticon + span {\n margin-left: 10px;\n opacity: 1;\n transition: opacity 0.3s cubic-bezier(0.645, 0.045, 0.355, 1), margin 0.3s, color 0.3s;\n}\n.du-bois-dark-menu-item .du-bois-dark-menu-item-icon.svg,\n.du-bois-dark-menu-submenu-title .du-bois-dark-menu-item-icon.svg {\n vertical-align: -0.125em;\n}\n.du-bois-dark-menu-item.du-bois-dark-menu-item-only-child > .anticon,\n.du-bois-dark-menu-submenu-title.du-bois-dark-menu-item-only-child > .anticon,\n.du-bois-dark-menu-item.du-bois-dark-menu-item-only-child > .du-bois-dark-menu-item-icon,\n.du-bois-dark-menu-submenu-title.du-bois-dark-menu-item-only-child > .du-bois-dark-menu-item-icon {\n margin-right: 0;\n}\n.du-bois-dark-menu-item:focus-visible,\n.du-bois-dark-menu-submenu-title:focus-visible {\n box-shadow: 0 0 0 2px rgba(34, 114, 180, 0.2);\n}\n.du-bois-dark-menu > .du-bois-dark-menu-item-divider {\n height: 1px;\n margin: 1px 0;\n padding: 0;\n overflow: hidden;\n line-height: 0;\n background-color: #303030;\n}\n.du-bois-dark-menu-submenu-popup {\n position: absolute;\n z-index: 1050;\n background: transparent;\n border-radius: 4px;\n box-shadow: none;\n transform-origin: 0 0;\n}\n.du-bois-dark-menu-submenu-popup::before {\n position: absolute;\n top: -7px;\n right: 0;\n bottom: 0;\n left: 0;\n z-index: -1;\n width: 100%;\n height: 100%;\n opacity: 0.0001;\n content: ' ';\n}\n.du-bois-dark-menu-submenu-placement-rightTop::before {\n top: 0;\n left: -7px;\n}\n.du-bois-dark-menu-submenu > .du-bois-dark-menu {\n background-color: #141414;\n border-radius: 4px;\n}\n.du-bois-dark-menu-submenu > .du-bois-dark-menu-submenu-title::after {\n transition: transform 0.3s cubic-bezier(0.645, 0.045, 0.355, 1);\n}\n.du-bois-dark-menu-submenu-popup > .du-bois-dark-menu {\n background-color: #1f1f1f;\n}\n.du-bois-dark-menu-submenu-expand-icon,\n.du-bois-dark-menu-submenu-arrow {\n position: absolute;\n top: 50%;\n right: 16px;\n width: 10px;\n color: #E8ECF0;\n transform: translateY(-50%);\n transition: transform 0.3s cubic-bezier(0.645, 0.045, 0.355, 1);\n}\n.du-bois-dark-menu-submenu-arrow::before,\n.du-bois-dark-menu-submenu-arrow::after {\n position: absolute;\n width: 6px;\n height: 1.5px;\n background-color: currentColor;\n border-radius: 2px;\n transition: background 0.3s cubic-bezier(0.645, 0.045, 0.355, 1), transform 0.3s cubic-bezier(0.645, 0.045, 0.355, 1), top 0.3s cubic-bezier(0.645, 0.045, 0.355, 1), color 0.3s cubic-bezier(0.645, 0.045, 0.355, 1);\n content: '';\n}\n.du-bois-dark-menu-submenu-arrow::before {\n transform: rotate(45deg) translateY(-2.5px);\n}\n.du-bois-dark-menu-submenu-arrow::after {\n transform: rotate(-45deg) translateY(2.5px);\n}\n.du-bois-dark-menu-submenu:hover > .du-bois-dark-menu-submenu-title > .du-bois-dark-menu-submenu-expand-icon,\n.du-bois-dark-menu-submenu:hover > .du-bois-dark-menu-submenu-title > .du-bois-dark-menu-submenu-arrow {\n color: #2272B4;\n}\n.du-bois-dark-menu-inline-collapsed .du-bois-dark-menu-submenu-arrow::before,\n.du-bois-dark-menu-submenu-inline .du-bois-dark-menu-submenu-arrow::before {\n transform: rotate(-45deg) translateX(2.5px);\n}\n.du-bois-dark-menu-inline-collapsed .du-bois-dark-menu-submenu-arrow::after,\n.du-bois-dark-menu-submenu-inline .du-bois-dark-menu-submenu-arrow::after {\n transform: rotate(45deg) translateX(-2.5px);\n}\n.du-bois-dark-menu-submenu-horizontal .du-bois-dark-menu-submenu-arrow {\n display: none;\n}\n.du-bois-dark-menu-submenu-open.du-bois-dark-menu-submenu-inline > .du-bois-dark-menu-submenu-title > .du-bois-dark-menu-submenu-arrow {\n transform: translateY(-2px);\n}\n.du-bois-dark-menu-submenu-open.du-bois-dark-menu-submenu-inline > .du-bois-dark-menu-submenu-title > .du-bois-dark-menu-submenu-arrow::after {\n transform: rotate(-45deg) translateX(-2.5px);\n}\n.du-bois-dark-menu-submenu-open.du-bois-dark-menu-submenu-inline > .du-bois-dark-menu-submenu-title > .du-bois-dark-menu-submenu-arrow::before {\n transform: rotate(45deg) translateX(2.5px);\n}\n.du-bois-dark-menu-vertical .du-bois-dark-menu-submenu-selected,\n.du-bois-dark-menu-vertical-left .du-bois-dark-menu-submenu-selected,\n.du-bois-dark-menu-vertical-right .du-bois-dark-menu-submenu-selected {\n color: #2272B4;\n}\n.du-bois-dark-menu-horizontal {\n line-height: 46px;\n border: 0;\n border-bottom: 1px solid #303030;\n box-shadow: none;\n}\n.du-bois-dark-menu-horizontal:not(.du-bois-dark-menu-dark) > .du-bois-dark-menu-item,\n.du-bois-dark-menu-horizontal:not(.du-bois-dark-menu-dark) > .du-bois-dark-menu-submenu {\n margin-top: -1px;\n margin-bottom: 0;\n padding: 0 20px;\n}\n.du-bois-dark-menu-horizontal:not(.du-bois-dark-menu-dark) > .du-bois-dark-menu-item:hover,\n.du-bois-dark-menu-horizontal:not(.du-bois-dark-menu-dark) > .du-bois-dark-menu-submenu:hover,\n.du-bois-dark-menu-horizontal:not(.du-bois-dark-menu-dark) > .du-bois-dark-menu-item-active,\n.du-bois-dark-menu-horizontal:not(.du-bois-dark-menu-dark) > .du-bois-dark-menu-submenu-active,\n.du-bois-dark-menu-horizontal:not(.du-bois-dark-menu-dark) > .du-bois-dark-menu-item-open,\n.du-bois-dark-menu-horizontal:not(.du-bois-dark-menu-dark) > .du-bois-dark-menu-submenu-open,\n.du-bois-dark-menu-horizontal:not(.du-bois-dark-menu-dark) > .du-bois-dark-menu-item-selected,\n.du-bois-dark-menu-horizontal:not(.du-bois-dark-menu-dark) > .du-bois-dark-menu-submenu-selected {\n color: #2272B4;\n}\n.du-bois-dark-menu-horizontal:not(.du-bois-dark-menu-dark) > .du-bois-dark-menu-item:hover::after,\n.du-bois-dark-menu-horizontal:not(.du-bois-dark-menu-dark) > .du-bois-dark-menu-submenu:hover::after,\n.du-bois-dark-menu-horizontal:not(.du-bois-dark-menu-dark) > .du-bois-dark-menu-item-active::after,\n.du-bois-dark-menu-horizontal:not(.du-bois-dark-menu-dark) > .du-bois-dark-menu-submenu-active::after,\n.du-bois-dark-menu-horizontal:not(.du-bois-dark-menu-dark) > .du-bois-dark-menu-item-open::after,\n.du-bois-dark-menu-horizontal:not(.du-bois-dark-menu-dark) > .du-bois-dark-menu-submenu-open::after,\n.du-bois-dark-menu-horizontal:not(.du-bois-dark-menu-dark) > .du-bois-dark-menu-item-selected::after,\n.du-bois-dark-menu-horizontal:not(.du-bois-dark-menu-dark) > .du-bois-dark-menu-submenu-selected::after {\n border-bottom: 2px solid #2272B4;\n}\n.du-bois-dark-menu-horizontal > .du-bois-dark-menu-item,\n.du-bois-dark-menu-horizontal > .du-bois-dark-menu-submenu {\n position: relative;\n top: 1px;\n display: inline-block;\n vertical-align: bottom;\n}\n.du-bois-dark-menu-horizontal > .du-bois-dark-menu-item::after,\n.du-bois-dark-menu-horizontal > .du-bois-dark-menu-submenu::after {\n position: absolute;\n right: 20px;\n bottom: 0;\n left: 20px;\n border-bottom: 2px solid transparent;\n transition: border-color 0.3s cubic-bezier(0.645, 0.045, 0.355, 1);\n content: '';\n}\n.du-bois-dark-menu-horizontal > .du-bois-dark-menu-submenu > .du-bois-dark-menu-submenu-title {\n padding: 0;\n}\n.du-bois-dark-menu-horizontal > .du-bois-dark-menu-item a {\n color: #E8ECF0;\n}\n.du-bois-dark-menu-horizontal > .du-bois-dark-menu-item a:hover {\n color: #2272B4;\n}\n.du-bois-dark-menu-horizontal > .du-bois-dark-menu-item a::before {\n bottom: -2px;\n}\n.du-bois-dark-menu-horizontal > .du-bois-dark-menu-item-selected a {\n color: #2272B4;\n}\n.du-bois-dark-menu-horizontal::after {\n display: block;\n clear: both;\n height: 0;\n content: '\\20';\n}\n.du-bois-dark-menu-vertical .du-bois-dark-menu-item,\n.du-bois-dark-menu-vertical-left .du-bois-dark-menu-item,\n.du-bois-dark-menu-vertical-right .du-bois-dark-menu-item,\n.du-bois-dark-menu-inline .du-bois-dark-menu-item {\n position: relative;\n}\n.du-bois-dark-menu-vertical .du-bois-dark-menu-item::after,\n.du-bois-dark-menu-vertical-left .du-bois-dark-menu-item::after,\n.du-bois-dark-menu-vertical-right .du-bois-dark-menu-item::after,\n.du-bois-dark-menu-inline .du-bois-dark-menu-item::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n border-right: 3px solid #2272B4;\n transform: scaleY(0.0001);\n opacity: 0;\n transition: transform 0.15s cubic-bezier(0.215, 0.61, 0.355, 1), opacity 0.15s cubic-bezier(0.215, 0.61, 0.355, 1);\n content: '';\n}\n.du-bois-dark-menu-vertical .du-bois-dark-menu-item,\n.du-bois-dark-menu-vertical-left .du-bois-dark-menu-item,\n.du-bois-dark-menu-vertical-right .du-bois-dark-menu-item,\n.du-bois-dark-menu-inline .du-bois-dark-menu-item,\n.du-bois-dark-menu-vertical .du-bois-dark-menu-submenu-title,\n.du-bois-dark-menu-vertical-left .du-bois-dark-menu-submenu-title,\n.du-bois-dark-menu-vertical-right .du-bois-dark-menu-submenu-title,\n.du-bois-dark-menu-inline .du-bois-dark-menu-submenu-title {\n height: 40px;\n margin-top: 4px;\n margin-bottom: 4px;\n padding: 0 16px;\n overflow: hidden;\n line-height: 40px;\n text-overflow: ellipsis;\n}\n.du-bois-dark-menu-vertical .du-bois-dark-menu-submenu,\n.du-bois-dark-menu-vertical-left .du-bois-dark-menu-submenu,\n.du-bois-dark-menu-vertical-right .du-bois-dark-menu-submenu,\n.du-bois-dark-menu-inline .du-bois-dark-menu-submenu {\n padding-bottom: 0.02px;\n}\n.du-bois-dark-menu-vertical .du-bois-dark-menu-item:not(:last-child),\n.du-bois-dark-menu-vertical-left .du-bois-dark-menu-item:not(:last-child),\n.du-bois-dark-menu-vertical-right .du-bois-dark-menu-item:not(:last-child),\n.du-bois-dark-menu-inline .du-bois-dark-menu-item:not(:last-child) {\n margin-bottom: 8px;\n}\n.du-bois-dark-menu-vertical > .du-bois-dark-menu-item,\n.du-bois-dark-menu-vertical-left > .du-bois-dark-menu-item,\n.du-bois-dark-menu-vertical-right > .du-bois-dark-menu-item,\n.du-bois-dark-menu-inline > .du-bois-dark-menu-item,\n.du-bois-dark-menu-vertical > .du-bois-dark-menu-submenu > .du-bois-dark-menu-submenu-title,\n.du-bois-dark-menu-vertical-left > .du-bois-dark-menu-submenu > .du-bois-dark-menu-submenu-title,\n.du-bois-dark-menu-vertical-right > .du-bois-dark-menu-submenu > .du-bois-dark-menu-submenu-title,\n.du-bois-dark-menu-inline > .du-bois-dark-menu-submenu > .du-bois-dark-menu-submenu-title {\n height: 40px;\n line-height: 40px;\n}\n.du-bois-dark-menu-vertical .du-bois-dark-menu-item-group-list .du-bois-dark-menu-submenu-title,\n.du-bois-dark-menu-vertical .du-bois-dark-menu-submenu-title {\n padding-right: 34px;\n}\n.du-bois-dark-menu-inline {\n width: 100%;\n}\n.du-bois-dark-menu-inline .du-bois-dark-menu-selected::after,\n.du-bois-dark-menu-inline .du-bois-dark-menu-item-selected::after {\n transform: scaleY(1);\n opacity: 1;\n transition: transform 0.15s cubic-bezier(0.645, 0.045, 0.355, 1), opacity 0.15s cubic-bezier(0.645, 0.045, 0.355, 1);\n}\n.du-bois-dark-menu-inline .du-bois-dark-menu-item,\n.du-bois-dark-menu-inline .du-bois-dark-menu-submenu-title {\n width: calc(100% + 1px);\n}\n.du-bois-dark-menu-inline .du-bois-dark-menu-item-group-list .du-bois-dark-menu-submenu-title,\n.du-bois-dark-menu-inline .du-bois-dark-menu-submenu-title {\n padding-right: 34px;\n}\n.du-bois-dark-menu-inline.du-bois-dark-menu-root .du-bois-dark-menu-item,\n.du-bois-dark-menu-inline.du-bois-dark-menu-root .du-bois-dark-menu-submenu-title {\n display: flex;\n align-items: center;\n transition: border-color 0.3s, background 0.3s, padding 0.1s cubic-bezier(0.215, 0.61, 0.355, 1);\n}\n.du-bois-dark-menu-inline.du-bois-dark-menu-root .du-bois-dark-menu-item > .du-bois-dark-menu-title-content,\n.du-bois-dark-menu-inline.du-bois-dark-menu-root .du-bois-dark-menu-submenu-title > .du-bois-dark-menu-title-content {\n flex: auto;\n min-width: 0;\n overflow: hidden;\n text-overflow: ellipsis;\n}\n.du-bois-dark-menu-inline.du-bois-dark-menu-root .du-bois-dark-menu-item > *,\n.du-bois-dark-menu-inline.du-bois-dark-menu-root .du-bois-dark-menu-submenu-title > * {\n flex: none;\n}\n.du-bois-dark-menu.du-bois-dark-menu-inline-collapsed {\n width: 80px;\n}\n.du-bois-dark-menu.du-bois-dark-menu-inline-collapsed > .du-bois-dark-menu-item,\n.du-bois-dark-menu.du-bois-dark-menu-inline-collapsed > .du-bois-dark-menu-item-group > .du-bois-dark-menu-item-group-list > .du-bois-dark-menu-item,\n.du-bois-dark-menu.du-bois-dark-menu-inline-collapsed > .du-bois-dark-menu-item-group > .du-bois-dark-menu-item-group-list > .du-bois-dark-menu-submenu > .du-bois-dark-menu-submenu-title,\n.du-bois-dark-menu.du-bois-dark-menu-inline-collapsed > .du-bois-dark-menu-submenu > .du-bois-dark-menu-submenu-title {\n left: 0;\n padding: 0 calc(50% - 18px / 2);\n text-overflow: clip;\n}\n.du-bois-dark-menu.du-bois-dark-menu-inline-collapsed > .du-bois-dark-menu-item .du-bois-dark-menu-submenu-arrow,\n.du-bois-dark-menu.du-bois-dark-menu-inline-collapsed > .du-bois-dark-menu-item-group > .du-bois-dark-menu-item-group-list > .du-bois-dark-menu-item .du-bois-dark-menu-submenu-arrow,\n.du-bois-dark-menu.du-bois-dark-menu-inline-collapsed > .du-bois-dark-menu-item-group > .du-bois-dark-menu-item-group-list > .du-bois-dark-menu-submenu > .du-bois-dark-menu-submenu-title .du-bois-dark-menu-submenu-arrow,\n.du-bois-dark-menu.du-bois-dark-menu-inline-collapsed > .du-bois-dark-menu-submenu > .du-bois-dark-menu-submenu-title .du-bois-dark-menu-submenu-arrow {\n opacity: 0;\n}\n.du-bois-dark-menu.du-bois-dark-menu-inline-collapsed > .du-bois-dark-menu-item .du-bois-dark-menu-item-icon,\n.du-bois-dark-menu.du-bois-dark-menu-inline-collapsed > .du-bois-dark-menu-item-group > .du-bois-dark-menu-item-group-list > .du-bois-dark-menu-item .du-bois-dark-menu-item-icon,\n.du-bois-dark-menu.du-bois-dark-menu-inline-collapsed > .du-bois-dark-menu-item-group > .du-bois-dark-menu-item-group-list > .du-bois-dark-menu-submenu > .du-bois-dark-menu-submenu-title .du-bois-dark-menu-item-icon,\n.du-bois-dark-menu.du-bois-dark-menu-inline-collapsed > .du-bois-dark-menu-submenu > .du-bois-dark-menu-submenu-title .du-bois-dark-menu-item-icon,\n.du-bois-dark-menu.du-bois-dark-menu-inline-collapsed > .du-bois-dark-menu-item .anticon,\n.du-bois-dark-menu.du-bois-dark-menu-inline-collapsed > .du-bois-dark-menu-item-group > .du-bois-dark-menu-item-group-list > .du-bois-dark-menu-item .anticon,\n.du-bois-dark-menu.du-bois-dark-menu-inline-collapsed > .du-bois-dark-menu-item-group > .du-bois-dark-menu-item-group-list > .du-bois-dark-menu-submenu > .du-bois-dark-menu-submenu-title .anticon,\n.du-bois-dark-menu.du-bois-dark-menu-inline-collapsed > .du-bois-dark-menu-submenu > .du-bois-dark-menu-submenu-title .anticon {\n margin: 0;\n font-size: 18px;\n line-height: 40px;\n}\n.du-bois-dark-menu.du-bois-dark-menu-inline-collapsed > .du-bois-dark-menu-item .du-bois-dark-menu-item-icon + span,\n.du-bois-dark-menu.du-bois-dark-menu-inline-collapsed > .du-bois-dark-menu-item-group > .du-bois-dark-menu-item-group-list > .du-bois-dark-menu-item .du-bois-dark-menu-item-icon + span,\n.du-bois-dark-menu.du-bois-dark-menu-inline-collapsed > .du-bois-dark-menu-item-group > .du-bois-dark-menu-item-group-list > .du-bois-dark-menu-submenu > .du-bois-dark-menu-submenu-title .du-bois-dark-menu-item-icon + span,\n.du-bois-dark-menu.du-bois-dark-menu-inline-collapsed > .du-bois-dark-menu-submenu > .du-bois-dark-menu-submenu-title .du-bois-dark-menu-item-icon + span,\n.du-bois-dark-menu.du-bois-dark-menu-inline-collapsed > .du-bois-dark-menu-item .anticon + span,\n.du-bois-dark-menu.du-bois-dark-menu-inline-collapsed > .du-bois-dark-menu-item-group > .du-bois-dark-menu-item-group-list > .du-bois-dark-menu-item .anticon + span,\n.du-bois-dark-menu.du-bois-dark-menu-inline-collapsed > .du-bois-dark-menu-item-group > .du-bois-dark-menu-item-group-list > .du-bois-dark-menu-submenu > .du-bois-dark-menu-submenu-title .anticon + span,\n.du-bois-dark-menu.du-bois-dark-menu-inline-collapsed > .du-bois-dark-menu-submenu > .du-bois-dark-menu-submenu-title .anticon + span {\n display: inline-block;\n opacity: 0;\n}\n.du-bois-dark-menu.du-bois-dark-menu-inline-collapsed .du-bois-dark-menu-item-icon,\n.du-bois-dark-menu.du-bois-dark-menu-inline-collapsed .anticon {\n display: inline-block;\n}\n.du-bois-dark-menu.du-bois-dark-menu-inline-collapsed-tooltip {\n pointer-events: none;\n}\n.du-bois-dark-menu.du-bois-dark-menu-inline-collapsed-tooltip .du-bois-dark-menu-item-icon,\n.du-bois-dark-menu.du-bois-dark-menu-inline-collapsed-tooltip .anticon {\n display: none;\n}\n.du-bois-dark-menu.du-bois-dark-menu-inline-collapsed-tooltip a {\n color: rgba(255, 255, 255, 0.85);\n}\n.du-bois-dark-menu.du-bois-dark-menu-inline-collapsed .du-bois-dark-menu-item-group-title {\n padding-right: 4px;\n padding-left: 4px;\n overflow: hidden;\n white-space: nowrap;\n text-overflow: ellipsis;\n}\n.du-bois-dark-menu-item-group-list {\n margin: 0;\n padding: 0;\n}\n.du-bois-dark-menu-item-group-list .du-bois-dark-menu-item,\n.du-bois-dark-menu-item-group-list .du-bois-dark-menu-submenu-title {\n padding: 0 16px 0 28px;\n}\n.du-bois-dark-menu-root.du-bois-dark-menu-vertical,\n.du-bois-dark-menu-root.du-bois-dark-menu-vertical-left,\n.du-bois-dark-menu-root.du-bois-dark-menu-vertical-right,\n.du-bois-dark-menu-root.du-bois-dark-menu-inline {\n box-shadow: none;\n}\n.du-bois-dark-menu-root.du-bois-dark-menu-inline-collapsed .du-bois-dark-menu-item > .du-bois-dark-menu-inline-collapsed-noicon,\n.du-bois-dark-menu-root.du-bois-dark-menu-inline-collapsed .du-bois-dark-menu-submenu .du-bois-dark-menu-submenu-title > .du-bois-dark-menu-inline-collapsed-noicon {\n font-size: 18px;\n text-align: center;\n}\n.du-bois-dark-menu-sub.du-bois-dark-menu-inline {\n padding: 0;\n background: rgba(255, 255, 255, 0.04);\n border: 0;\n border-radius: 0;\n box-shadow: none;\n}\n.du-bois-dark-menu-sub.du-bois-dark-menu-inline > .du-bois-dark-menu-item,\n.du-bois-dark-menu-sub.du-bois-dark-menu-inline > .du-bois-dark-menu-submenu > .du-bois-dark-menu-submenu-title {\n height: 40px;\n line-height: 40px;\n list-style-position: inside;\n list-style-type: disc;\n}\n.du-bois-dark-menu-sub.du-bois-dark-menu-inline .du-bois-dark-menu-item-group-title {\n padding-left: 32px;\n}\n.du-bois-dark-menu-item-disabled,\n.du-bois-dark-menu-submenu-disabled {\n color: rgba(255, 255, 255, 0.3) !important;\n background: none;\n cursor: not-allowed;\n}\n.du-bois-dark-menu-item-disabled::after,\n.du-bois-dark-menu-submenu-disabled::after {\n border-color: transparent !important;\n}\n.du-bois-dark-menu-item-disabled a,\n.du-bois-dark-menu-submenu-disabled a {\n color: rgba(255, 255, 255, 0.3) !important;\n pointer-events: none;\n}\n.du-bois-dark-menu-item-disabled > .du-bois-dark-menu-submenu-title,\n.du-bois-dark-menu-submenu-disabled > .du-bois-dark-menu-submenu-title {\n color: rgba(255, 255, 255, 0.3) !important;\n cursor: not-allowed;\n}\n.du-bois-dark-menu-item-disabled > .du-bois-dark-menu-submenu-title > .du-bois-dark-menu-submenu-arrow::before,\n.du-bois-dark-menu-submenu-disabled > .du-bois-dark-menu-submenu-title > .du-bois-dark-menu-submenu-arrow::before,\n.du-bois-dark-menu-item-disabled > .du-bois-dark-menu-submenu-title > .du-bois-dark-menu-submenu-arrow::after,\n.du-bois-dark-menu-submenu-disabled > .du-bois-dark-menu-submenu-title > .du-bois-dark-menu-submenu-arrow::after {\n background: rgba(255, 255, 255, 0.3) !important;\n}\n.du-bois-dark-layout-header .du-bois-dark-menu {\n line-height: inherit;\n}\n.du-bois-dark-menu-light .du-bois-dark-menu-item:hover,\n.du-bois-dark-menu-light .du-bois-dark-menu-item-active,\n.du-bois-dark-menu-light .du-bois-dark-menu:not(.du-bois-dark-menu-inline) .du-bois-dark-menu-submenu-open,\n.du-bois-dark-menu-light .du-bois-dark-menu-submenu-active,\n.du-bois-dark-menu-light .du-bois-dark-menu-submenu-title:hover {\n color: #2272B4;\n}\n.du-bois-dark-menu.du-bois-dark-menu-dark,\n.du-bois-dark-menu-dark .du-bois-dark-menu-sub,\n.du-bois-dark-menu.du-bois-dark-menu-dark .du-bois-dark-menu-sub {\n color: rgba(255, 255, 255, 0.65);\n background: #1f1f1f;\n}\n.du-bois-dark-menu.du-bois-dark-menu-dark .du-bois-dark-menu-submenu-title .du-bois-dark-menu-submenu-arrow,\n.du-bois-dark-menu-dark .du-bois-dark-menu-sub .du-bois-dark-menu-submenu-title .du-bois-dark-menu-submenu-arrow,\n.du-bois-dark-menu.du-bois-dark-menu-dark .du-bois-dark-menu-sub .du-bois-dark-menu-submenu-title .du-bois-dark-menu-submenu-arrow {\n opacity: 0.45;\n transition: all 0.3s;\n}\n.du-bois-dark-menu.du-bois-dark-menu-dark .du-bois-dark-menu-submenu-title .du-bois-dark-menu-submenu-arrow::after,\n.du-bois-dark-menu-dark .du-bois-dark-menu-sub .du-bois-dark-menu-submenu-title .du-bois-dark-menu-submenu-arrow::after,\n.du-bois-dark-menu.du-bois-dark-menu-dark .du-bois-dark-menu-sub .du-bois-dark-menu-submenu-title .du-bois-dark-menu-submenu-arrow::after,\n.du-bois-dark-menu.du-bois-dark-menu-dark .du-bois-dark-menu-submenu-title .du-bois-dark-menu-submenu-arrow::before,\n.du-bois-dark-menu-dark .du-bois-dark-menu-sub .du-bois-dark-menu-submenu-title .du-bois-dark-menu-submenu-arrow::before,\n.du-bois-dark-menu.du-bois-dark-menu-dark .du-bois-dark-menu-sub .du-bois-dark-menu-submenu-title .du-bois-dark-menu-submenu-arrow::before {\n background: #fff;\n}\n.du-bois-dark-menu-dark.du-bois-dark-menu-submenu-popup {\n background: transparent;\n}\n.du-bois-dark-menu-dark .du-bois-dark-menu-inline.du-bois-dark-menu-sub {\n background: #141414;\n}\n.du-bois-dark-menu-dark.du-bois-dark-menu-horizontal {\n border-bottom: 0;\n}\n.du-bois-dark-menu-dark.du-bois-dark-menu-horizontal > .du-bois-dark-menu-item,\n.du-bois-dark-menu-dark.du-bois-dark-menu-horizontal > .du-bois-dark-menu-submenu {\n top: 0;\n margin-top: 0;\n padding: 0 20px;\n border-color: #1f1f1f;\n border-bottom: 0;\n}\n.du-bois-dark-menu-dark.du-bois-dark-menu-horizontal > .du-bois-dark-menu-item:hover {\n background-color: #2272B4;\n}\n.du-bois-dark-menu-dark.du-bois-dark-menu-horizontal > .du-bois-dark-menu-item > a::before {\n bottom: 0;\n}\n.du-bois-dark-menu-dark .du-bois-dark-menu-item,\n.du-bois-dark-menu-dark .du-bois-dark-menu-item-group-title,\n.du-bois-dark-menu-dark .du-bois-dark-menu-item > a,\n.du-bois-dark-menu-dark .du-bois-dark-menu-item > span > a {\n color: rgba(255, 255, 255, 0.65);\n}\n.du-bois-dark-menu-dark.du-bois-dark-menu-inline,\n.du-bois-dark-menu-dark.du-bois-dark-menu-vertical,\n.du-bois-dark-menu-dark.du-bois-dark-menu-vertical-left,\n.du-bois-dark-menu-dark.du-bois-dark-menu-vertical-right {\n border-right: 0;\n}\n.du-bois-dark-menu-dark.du-bois-dark-menu-inline .du-bois-dark-menu-item,\n.du-bois-dark-menu-dark.du-bois-dark-menu-vertical .du-bois-dark-menu-item,\n.du-bois-dark-menu-dark.du-bois-dark-menu-vertical-left .du-bois-dark-menu-item,\n.du-bois-dark-menu-dark.du-bois-dark-menu-vertical-right .du-bois-dark-menu-item {\n left: 0;\n margin-left: 0;\n border-right: 0;\n}\n.du-bois-dark-menu-dark.du-bois-dark-menu-inline .du-bois-dark-menu-item::after,\n.du-bois-dark-menu-dark.du-bois-dark-menu-vertical .du-bois-dark-menu-item::after,\n.du-bois-dark-menu-dark.du-bois-dark-menu-vertical-left .du-bois-dark-menu-item::after,\n.du-bois-dark-menu-dark.du-bois-dark-menu-vertical-right .du-bois-dark-menu-item::after {\n border-right: 0;\n}\n.du-bois-dark-menu-dark.du-bois-dark-menu-inline .du-bois-dark-menu-item,\n.du-bois-dark-menu-dark.du-bois-dark-menu-inline .du-bois-dark-menu-submenu-title {\n width: 100%;\n}\n.du-bois-dark-menu-dark .du-bois-dark-menu-item:hover,\n.du-bois-dark-menu-dark .du-bois-dark-menu-item-active,\n.du-bois-dark-menu-dark .du-bois-dark-menu-submenu-active,\n.du-bois-dark-menu-dark .du-bois-dark-menu-submenu-open,\n.du-bois-dark-menu-dark .du-bois-dark-menu-submenu-selected,\n.du-bois-dark-menu-dark .du-bois-dark-menu-submenu-title:hover {\n color: #fff;\n background-color: transparent;\n}\n.du-bois-dark-menu-dark .du-bois-dark-menu-item:hover > a,\n.du-bois-dark-menu-dark .du-bois-dark-menu-item-active > a,\n.du-bois-dark-menu-dark .du-bois-dark-menu-submenu-active > a,\n.du-bois-dark-menu-dark .du-bois-dark-menu-submenu-open > a,\n.du-bois-dark-menu-dark .du-bois-dark-menu-submenu-selected > a,\n.du-bois-dark-menu-dark .du-bois-dark-menu-submenu-title:hover > a,\n.du-bois-dark-menu-dark .du-bois-dark-menu-item:hover > span > a,\n.du-bois-dark-menu-dark .du-bois-dark-menu-item-active > span > a,\n.du-bois-dark-menu-dark .du-bois-dark-menu-submenu-active > span > a,\n.du-bois-dark-menu-dark .du-bois-dark-menu-submenu-open > span > a,\n.du-bois-dark-menu-dark .du-bois-dark-menu-submenu-selected > span > a,\n.du-bois-dark-menu-dark .du-bois-dark-menu-submenu-title:hover > span > a {\n color: #fff;\n}\n.du-bois-dark-menu-dark .du-bois-dark-menu-item:hover > .du-bois-dark-menu-submenu-title > .du-bois-dark-menu-submenu-arrow,\n.du-bois-dark-menu-dark .du-bois-dark-menu-item-active > .du-bois-dark-menu-submenu-title > .du-bois-dark-menu-submenu-arrow,\n.du-bois-dark-menu-dark .du-bois-dark-menu-submenu-active > .du-bois-dark-menu-submenu-title > .du-bois-dark-menu-submenu-arrow,\n.du-bois-dark-menu-dark .du-bois-dark-menu-submenu-open > .du-bois-dark-menu-submenu-title > .du-bois-dark-menu-submenu-arrow,\n.du-bois-dark-menu-dark .du-bois-dark-menu-submenu-selected > .du-bois-dark-menu-submenu-title > .du-bois-dark-menu-submenu-arrow,\n.du-bois-dark-menu-dark .du-bois-dark-menu-submenu-title:hover > .du-bois-dark-menu-submenu-title > .du-bois-dark-menu-submenu-arrow {\n opacity: 1;\n}\n.du-bois-dark-menu-dark .du-bois-dark-menu-item:hover > .du-bois-dark-menu-submenu-title > .du-bois-dark-menu-submenu-arrow::after,\n.du-bois-dark-menu-dark .du-bois-dark-menu-item-active > .du-bois-dark-menu-submenu-title > .du-bois-dark-menu-submenu-arrow::after,\n.du-bois-dark-menu-dark .du-bois-dark-menu-submenu-active > .du-bois-dark-menu-submenu-title > .du-bois-dark-menu-submenu-arrow::after,\n.du-bois-dark-menu-dark .du-bois-dark-menu-submenu-open > .du-bois-dark-menu-submenu-title > .du-bois-dark-menu-submenu-arrow::after,\n.du-bois-dark-menu-dark .du-bois-dark-menu-submenu-selected > .du-bois-dark-menu-submenu-title > .du-bois-dark-menu-submenu-arrow::after,\n.du-bois-dark-menu-dark .du-bois-dark-menu-submenu-title:hover > .du-bois-dark-menu-submenu-title > .du-bois-dark-menu-submenu-arrow::after,\n.du-bois-dark-menu-dark .du-bois-dark-menu-item:hover > .du-bois-dark-menu-submenu-title > .du-bois-dark-menu-submenu-arrow::before,\n.du-bois-dark-menu-dark .du-bois-dark-menu-item-active > .du-bois-dark-menu-submenu-title > .du-bois-dark-menu-submenu-arrow::before,\n.du-bois-dark-menu-dark .du-bois-dark-menu-submenu-active > .du-bois-dark-menu-submenu-title > .du-bois-dark-menu-submenu-arrow::before,\n.du-bois-dark-menu-dark .du-bois-dark-menu-submenu-open > .du-bois-dark-menu-submenu-title > .du-bois-dark-menu-submenu-arrow::before,\n.du-bois-dark-menu-dark .du-bois-dark-menu-submenu-selected > .du-bois-dark-menu-submenu-title > .du-bois-dark-menu-submenu-arrow::before,\n.du-bois-dark-menu-dark .du-bois-dark-menu-submenu-title:hover > .du-bois-dark-menu-submenu-title > .du-bois-dark-menu-submenu-arrow::before {\n background: #fff;\n}\n.du-bois-dark-menu-dark .du-bois-dark-menu-item:hover {\n background-color: transparent;\n}\n.du-bois-dark-menu-dark.du-bois-dark-menu-dark:not(.du-bois-dark-menu-horizontal) .du-bois-dark-menu-item-selected {\n background-color: #2272B4;\n}\n.du-bois-dark-menu-dark .du-bois-dark-menu-item-selected {\n color: #fff;\n border-right: 0;\n}\n.du-bois-dark-menu-dark .du-bois-dark-menu-item-selected::after {\n border-right: 0;\n}\n.du-bois-dark-menu-dark .du-bois-dark-menu-item-selected > a,\n.du-bois-dark-menu-dark .du-bois-dark-menu-item-selected > span > a,\n.du-bois-dark-menu-dark .du-bois-dark-menu-item-selected > a:hover,\n.du-bois-dark-menu-dark .du-bois-dark-menu-item-selected > span > a:hover {\n color: #fff;\n}\n.du-bois-dark-menu-dark .du-bois-dark-menu-item-selected .du-bois-dark-menu-item-icon,\n.du-bois-dark-menu-dark .du-bois-dark-menu-item-selected .anticon {\n color: #fff;\n}\n.du-bois-dark-menu-dark .du-bois-dark-menu-item-selected .du-bois-dark-menu-item-icon + span,\n.du-bois-dark-menu-dark .du-bois-dark-menu-item-selected .anticon + span {\n color: #fff;\n}\n.du-bois-dark-menu.du-bois-dark-menu-dark .du-bois-dark-menu-item-selected,\n.du-bois-dark-menu-submenu-popup.du-bois-dark-menu-dark .du-bois-dark-menu-item-selected {\n background-color: #2272B4;\n}\n.du-bois-dark-menu-dark .du-bois-dark-menu-item-disabled,\n.du-bois-dark-menu-dark .du-bois-dark-menu-submenu-disabled,\n.du-bois-dark-menu-dark .du-bois-dark-menu-item-disabled > a,\n.du-bois-dark-menu-dark .du-bois-dark-menu-submenu-disabled > a,\n.du-bois-dark-menu-dark .du-bois-dark-menu-item-disabled > span > a,\n.du-bois-dark-menu-dark .du-bois-dark-menu-submenu-disabled > span > a {\n color: rgba(255, 255, 255, 0.3) !important;\n opacity: 0.8;\n}\n.du-bois-dark-menu-dark .du-bois-dark-menu-item-disabled > .du-bois-dark-menu-submenu-title,\n.du-bois-dark-menu-dark .du-bois-dark-menu-submenu-disabled > .du-bois-dark-menu-submenu-title {\n color: rgba(255, 255, 255, 0.3) !important;\n}\n.du-bois-dark-menu-dark .du-bois-dark-menu-item-disabled > .du-bois-dark-menu-submenu-title > .du-bois-dark-menu-submenu-arrow::before,\n.du-bois-dark-menu-dark .du-bois-dark-menu-submenu-disabled > .du-bois-dark-menu-submenu-title > .du-bois-dark-menu-submenu-arrow::before,\n.du-bois-dark-menu-dark .du-bois-dark-menu-item-disabled > .du-bois-dark-menu-submenu-title > .du-bois-dark-menu-submenu-arrow::after,\n.du-bois-dark-menu-dark .du-bois-dark-menu-submenu-disabled > .du-bois-dark-menu-submenu-title > .du-bois-dark-menu-submenu-arrow::after {\n background: rgba(255, 255, 255, 0.3) !important;\n}\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-dark-modal {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #E8ECF0;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: tnum;\n pointer-events: none;\n position: relative;\n top: 100px;\n width: auto;\n max-width: calc(100vw - 32px);\n margin: 0 auto;\n padding-bottom: 24px;\n}\n.du-bois-dark-modal.du-bois-dark-zoom-enter,\n.du-bois-dark-modal.du-bois-darkzoom-appear {\n transform: none;\n opacity: 0;\n animation-duration: 0.3s;\n user-select: none;\n}\n.du-bois-dark-modal-mask {\n position: fixed;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n z-index: 1000;\n height: 100%;\n background-color: rgba(0, 0, 0, 0.45);\n}\n.du-bois-dark-modal-mask-hidden {\n display: none;\n}\n.du-bois-dark-modal-wrap {\n position: fixed;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n overflow: auto;\n outline: 0;\n -webkit-overflow-scrolling: touch;\n}\n.du-bois-dark-modal-wrap {\n z-index: 1000;\n}\n.du-bois-dark-modal-title {\n margin: 0;\n color: rgba(255, 255, 255, 0.85);\n font-weight: 500;\n font-size: 18px;\n line-height: 22px;\n word-wrap: break-word;\n}\n.du-bois-dark-modal-content {\n position: relative;\n background-color: #1f1f1f;\n background-clip: padding-box;\n border: 0;\n border-radius: 4px;\n box-shadow: 0 3px 6px -4px rgba(0, 0, 0, 0.48), 0 6px 16px 0 rgba(0, 0, 0, 0.32), 0 9px 28px 8px rgba(0, 0, 0, 0.2);\n pointer-events: auto;\n}\n.du-bois-dark-modal-close {\n position: absolute;\n top: 0;\n right: 0;\n z-index: 10;\n padding: 0;\n color: #92A4B3;\n font-weight: 700;\n line-height: 1;\n text-decoration: none;\n background: transparent;\n border: 0;\n outline: 0;\n cursor: pointer;\n transition: color 0.3s;\n}\n.du-bois-dark-modal-close-x {\n display: block;\n width: 102px;\n height: 102px;\n font-size: 18px;\n font-style: normal;\n line-height: 102px;\n text-align: center;\n text-transform: none;\n text-rendering: auto;\n}\n.du-bois-dark-modal-close:focus,\n.du-bois-dark-modal-close:hover {\n color: rgba(255, 255, 255, 0.75);\n text-decoration: none;\n}\n.du-bois-dark-modal-header {\n padding: 40px 40px 20px;\n color: #E8ECF0;\n background: #1f1f1f;\n border-bottom: 0px solid #303030;\n border-radius: 4px 4px 0 0;\n}\n.du-bois-dark-modal-body {\n padding: 0 40px;\n font-size: 13px;\n line-height: 20px;\n word-wrap: break-word;\n}\n.du-bois-dark-modal-footer {\n padding: 0px 0px;\n text-align: right;\n background: transparent;\n border-top: 0px solid #303030;\n border-radius: 0 0 4px 4px;\n}\n.du-bois-dark-modal-footer .du-bois-dark-btn + .du-bois-dark-btn:not(.du-bois-dark-dropdown-trigger) {\n margin-bottom: 0;\n margin-left: 8px;\n}\n.du-bois-dark-modal-open {\n overflow: hidden;\n}\n.du-bois-dark-modal-centered {\n text-align: center;\n}\n.du-bois-dark-modal-centered::before {\n display: inline-block;\n width: 0;\n height: 100%;\n vertical-align: middle;\n content: '';\n}\n.du-bois-dark-modal-centered .du-bois-dark-modal {\n top: 0;\n display: inline-block;\n text-align: left;\n vertical-align: middle;\n}\n@media (max-width: 767px) {\n .du-bois-dark-modal {\n max-width: calc(100vw - 16px);\n margin: 8px auto;\n }\n .du-bois-dark-modal-centered .du-bois-dark-modal {\n flex: 1;\n }\n}\n.du-bois-dark-modal-confirm .du-bois-dark-modal-header {\n display: none;\n}\n.du-bois-dark-modal-confirm .du-bois-dark-modal-body {\n padding: 32px 32px 24px;\n}\n.du-bois-dark-modal-confirm-body-wrapper::before {\n display: table;\n content: '';\n}\n.du-bois-dark-modal-confirm-body-wrapper::after {\n display: table;\n clear: both;\n content: '';\n}\n.du-bois-dark-modal-confirm-body .du-bois-dark-modal-confirm-title {\n display: block;\n overflow: hidden;\n color: rgba(255, 255, 255, 0.85);\n font-weight: 500;\n font-size: 18px;\n line-height: 1.4;\n}\n.du-bois-dark-modal-confirm-body .du-bois-dark-modal-confirm-content {\n margin-top: 8px;\n color: #E8ECF0;\n font-size: 13px;\n}\n.du-bois-dark-modal-confirm-body > .anticon {\n float: left;\n margin-right: 16px;\n font-size: 22px;\n}\n.du-bois-dark-modal-confirm-body > .anticon + .du-bois-dark-modal-confirm-title + .du-bois-dark-modal-confirm-content {\n margin-left: 38px;\n}\n.du-bois-dark-modal-confirm .du-bois-dark-modal-confirm-btns {\n float: right;\n margin-top: 24px;\n}\n.du-bois-dark-modal-confirm .du-bois-dark-modal-confirm-btns .du-bois-dark-btn + .du-bois-dark-btn {\n margin-bottom: 0;\n margin-left: 8px;\n}\n.du-bois-dark-modal-confirm-error .du-bois-dark-modal-confirm-body > .anticon {\n color: #E65B77;\n}\n.du-bois-dark-modal-confirm-warning .du-bois-dark-modal-confirm-body > .anticon,\n.du-bois-dark-modal-confirm-confirm .du-bois-dark-modal-confirm-body > .anticon {\n color: #AF5B23;\n}\n.du-bois-dark-modal-confirm-info .du-bois-dark-modal-confirm-body > .anticon {\n color: #64727D;\n}\n.du-bois-dark-modal-confirm-success .du-bois-dark-modal-confirm-body > .anticon {\n color: #34824F;\n}\n.du-bois-dark-modal .du-bois-dark-picker-clear,\n.du-bois-dark-modal .du-bois-dark-slider-handle,\n.du-bois-dark-modal .du-bois-dark-anchor-wrapper,\n.du-bois-dark-modal .du-bois-dark-collapse-content,\n.du-bois-dark-modal .du-bois-dark-timeline-item-head,\n.du-bois-dark-modal .du-bois-dark-card {\n background-color: #1f1f1f;\n}\n.du-bois-dark-modal .du-bois-dark-transfer-list-header {\n background: #1f1f1f;\n border-bottom: 1px solid #3a3a3a;\n}\n.du-bois-dark-modal .du-bois-dark-transfer-list-content-item:not(.du-bois-dark-transfer-list-content-item-disabled):hover {\n background-color: rgba(255, 255, 255, 0.08);\n}\n.du-bois-dark-modal tr.du-bois-dark-table-expanded-row > td,\n.du-bois-dark-modal tr.du-bois-dark-table-expanded-row:hover > td {\n background: #272727;\n}\n.du-bois-dark-modal .du-bois-dark-table.du-bois-dark-table-small thead > tr > th {\n background-color: #1f1f1f;\n border-bottom: 1px solid #3a3a3a;\n}\n.du-bois-dark-modal .du-bois-dark-table {\n background-color: #1f1f1f;\n}\n.du-bois-dark-modal .du-bois-dark-table .du-bois-dark-table-row-expand-icon {\n border: 1px solid #3a3a3a;\n}\n.du-bois-dark-modal .du-bois-dark-table tfoot > tr > th,\n.du-bois-dark-modal .du-bois-dark-table tfoot > tr > td {\n border-bottom: 1px solid #3a3a3a;\n}\n.du-bois-dark-modal .du-bois-dark-table thead > tr > th {\n background-color: #272727;\n border-bottom: 1px solid #3a3a3a;\n}\n.du-bois-dark-modal .du-bois-dark-table tbody > tr > td {\n border-bottom: 1px solid #3a3a3a;\n}\n.du-bois-dark-modal .du-bois-dark-table tbody > tr > td.du-bois-dark-table-cell-fix-left,\n.du-bois-dark-modal .du-bois-dark-table tbody > tr > td.du-bois-dark-table-cell-fix-right {\n background-color: #1f1f1f;\n}\n.du-bois-dark-modal .du-bois-dark-table tbody > tr.du-bois-dark-table-row:hover > td {\n background: #303030;\n}\n.du-bois-dark-modal .du-bois-dark-table.du-bois-dark-table-bordered .du-bois-dark-table-title {\n border: 1px solid #3a3a3a;\n}\n.du-bois-dark-modal .du-bois-dark-table.du-bois-dark-table-bordered thead > tr > th,\n.du-bois-dark-modal .du-bois-dark-table.du-bois-dark-table-bordered tbody > tr > td,\n.du-bois-dark-modal .du-bois-dark-table.du-bois-dark-table-bordered tfoot > tr > th,\n.du-bois-dark-modal .du-bois-dark-table.du-bois-dark-table-bordered tfoot > tr > td {\n border-right: 1px solid #3a3a3a;\n}\n.du-bois-dark-modal .du-bois-dark-table.du-bois-dark-table-bordered .du-bois-dark-table-cell-fix-right-first::after {\n border-right: 1px solid #3a3a3a;\n}\n.du-bois-dark-modal .du-bois-dark-table.du-bois-dark-table-bordered table thead > tr:not(:last-child) > th {\n border-bottom: 1px solid #303030;\n}\n.du-bois-dark-modal .du-bois-dark-table.du-bois-dark-table-bordered .du-bois-dark-table-container {\n border: 1px solid #3a3a3a;\n}\n.du-bois-dark-modal .du-bois-dark-table.du-bois-dark-table-bordered .du-bois-dark-table-expanded-row-fixed::after {\n border-right: 1px solid #3a3a3a;\n}\n.du-bois-dark-modal .du-bois-dark-table.du-bois-dark-table-bordered .du-bois-dark-table-footer {\n border: 1px solid #3a3a3a;\n}\n.du-bois-dark-modal .du-bois-dark-table .du-bois-dark-table-filter-trigger-container-open {\n background-color: #525252;\n}\n.du-bois-dark-modal .du-bois-dark-picker-calendar-full {\n background-color: #1f1f1f;\n}\n.du-bois-dark-modal .du-bois-dark-picker-calendar-full .du-bois-dark-picker-panel {\n background-color: #1f1f1f;\n}\n.du-bois-dark-modal .du-bois-dark-picker-calendar-full .du-bois-dark-picker-panel .du-bois-dark-picker-calendar-date {\n border-top: 2px solid #3a3a3a;\n}\n.du-bois-dark-modal .du-bois-dark-tabs.du-bois-dark-tabs-card .du-bois-dark-tabs-card-bar .du-bois-dark-tabs-tab-active {\n background-color: #1f1f1f;\n border-bottom: 1px solid #1f1f1f;\n}\n.du-bois-dark-modal .du-bois-dark-badge-count {\n box-shadow: 0 0 0 1px #1f1f1f;\n}\n.du-bois-dark-modal .du-bois-dark-tree-show-line .du-bois-dark-tree-switcher {\n background: #1f1f1f;\n}\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-dark-notification .du-bois-dark-picker-clear,\n.du-bois-dark-notification .du-bois-dark-slider-handle,\n.du-bois-dark-notification .du-bois-dark-anchor-wrapper,\n.du-bois-dark-notification .du-bois-dark-collapse-content,\n.du-bois-dark-notification .du-bois-dark-timeline-item-head,\n.du-bois-dark-notification .du-bois-dark-card {\n background-color: #1f1f1f;\n}\n.du-bois-dark-notification .du-bois-dark-transfer-list-header {\n background: #1f1f1f;\n border-bottom: 1px solid #3a3a3a;\n}\n.du-bois-dark-notification .du-bois-dark-transfer-list-content-item:not(.du-bois-dark-transfer-list-content-item-disabled):hover {\n background-color: rgba(255, 255, 255, 0.08);\n}\n.du-bois-dark-notification tr.du-bois-dark-table-expanded-row > td,\n.du-bois-dark-notification tr.du-bois-dark-table-expanded-row:hover > td {\n background: #272727;\n}\n.du-bois-dark-notification .du-bois-dark-table.du-bois-dark-table-small thead > tr > th {\n background-color: #1f1f1f;\n border-bottom: 1px solid #3a3a3a;\n}\n.du-bois-dark-notification .du-bois-dark-table {\n background-color: #1f1f1f;\n}\n.du-bois-dark-notification .du-bois-dark-table .du-bois-dark-table-row-expand-icon {\n border: 1px solid #3a3a3a;\n}\n.du-bois-dark-notification .du-bois-dark-table tfoot > tr > th,\n.du-bois-dark-notification .du-bois-dark-table tfoot > tr > td {\n border-bottom: 1px solid #3a3a3a;\n}\n.du-bois-dark-notification .du-bois-dark-table thead > tr > th {\n background-color: #272727;\n border-bottom: 1px solid #3a3a3a;\n}\n.du-bois-dark-notification .du-bois-dark-table tbody > tr > td {\n border-bottom: 1px solid #3a3a3a;\n}\n.du-bois-dark-notification .du-bois-dark-table tbody > tr > td.du-bois-dark-table-cell-fix-left,\n.du-bois-dark-notification .du-bois-dark-table tbody > tr > td.du-bois-dark-table-cell-fix-right {\n background-color: #1f1f1f;\n}\n.du-bois-dark-notification .du-bois-dark-table tbody > tr.du-bois-dark-table-row:hover > td {\n background: #303030;\n}\n.du-bois-dark-notification .du-bois-dark-table.du-bois-dark-table-bordered .du-bois-dark-table-title {\n border: 1px solid #3a3a3a;\n}\n.du-bois-dark-notification .du-bois-dark-table.du-bois-dark-table-bordered thead > tr > th,\n.du-bois-dark-notification .du-bois-dark-table.du-bois-dark-table-bordered tbody > tr > td,\n.du-bois-dark-notification .du-bois-dark-table.du-bois-dark-table-bordered tfoot > tr > th,\n.du-bois-dark-notification .du-bois-dark-table.du-bois-dark-table-bordered tfoot > tr > td {\n border-right: 1px solid #3a3a3a;\n}\n.du-bois-dark-notification .du-bois-dark-table.du-bois-dark-table-bordered .du-bois-dark-table-cell-fix-right-first::after {\n border-right: 1px solid #3a3a3a;\n}\n.du-bois-dark-notification .du-bois-dark-table.du-bois-dark-table-bordered table thead > tr:not(:last-child) > th {\n border-bottom: 1px solid #303030;\n}\n.du-bois-dark-notification .du-bois-dark-table.du-bois-dark-table-bordered .du-bois-dark-table-container {\n border: 1px solid #3a3a3a;\n}\n.du-bois-dark-notification .du-bois-dark-table.du-bois-dark-table-bordered .du-bois-dark-table-expanded-row-fixed::after {\n border-right: 1px solid #3a3a3a;\n}\n.du-bois-dark-notification .du-bois-dark-table.du-bois-dark-table-bordered .du-bois-dark-table-footer {\n border: 1px solid #3a3a3a;\n}\n.du-bois-dark-notification .du-bois-dark-table .du-bois-dark-table-filter-trigger-container-open {\n background-color: #525252;\n}\n.du-bois-dark-notification .du-bois-dark-picker-calendar-full {\n background-color: #1f1f1f;\n}\n.du-bois-dark-notification .du-bois-dark-picker-calendar-full .du-bois-dark-picker-panel {\n background-color: #1f1f1f;\n}\n.du-bois-dark-notification .du-bois-dark-picker-calendar-full .du-bois-dark-picker-panel .du-bois-dark-picker-calendar-date {\n border-top: 2px solid #3a3a3a;\n}\n.du-bois-dark-notification .du-bois-dark-tabs.du-bois-dark-tabs-card .du-bois-dark-tabs-card-bar .du-bois-dark-tabs-tab-active {\n background-color: #1f1f1f;\n border-bottom: 1px solid #1f1f1f;\n}\n.du-bois-dark-notification .du-bois-dark-badge-count {\n box-shadow: 0 0 0 1px #1f1f1f;\n}\n.du-bois-dark-notification .du-bois-dark-tree-show-line .du-bois-dark-tree-switcher {\n background: #1f1f1f;\n}\n.du-bois-dark-notification {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #E8ECF0;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: tnum;\n position: fixed;\n z-index: 1010;\n margin-right: 24px;\n}\n.du-bois-dark-notification-topLeft,\n.du-bois-dark-notification-bottomLeft {\n margin-right: 0;\n margin-left: 24px;\n}\n.du-bois-dark-notification-topLeft .du-bois-dark-notification-fade-enter.du-bois-dark-notification-fade-enter-active,\n.du-bois-dark-notification-bottomLeft .du-bois-dark-notification-fade-enter.du-bois-dark-notification-fade-enter-active,\n.du-bois-dark-notification-topLeft .du-bois-dark-notification-fade-appear.du-bois-dark-notification-fade-appear-active,\n.du-bois-dark-notification-bottomLeft .du-bois-dark-notification-fade-appear.du-bois-dark-notification-fade-appear-active {\n animation-name: NotificationLeftFadeIn;\n}\n.du-bois-dark-notification-close-icon {\n font-size: 13px;\n cursor: pointer;\n}\n.du-bois-dark-notification-hook-holder {\n position: relative;\n}\n.du-bois-dark-notification-notice {\n position: relative;\n width: 384px;\n max-width: calc(100vw - 24px * 2);\n margin-bottom: 16px;\n margin-left: auto;\n padding: 16px 24px;\n overflow: hidden;\n line-height: 20px;\n word-wrap: break-word;\n background: #1f1f1f;\n border-radius: 4px;\n box-shadow: 0 3px 6px -4px rgba(0, 0, 0, 0.48), 0 6px 16px 0 rgba(0, 0, 0, 0.32), 0 9px 28px 8px rgba(0, 0, 0, 0.2);\n}\n.du-bois-dark-notification-topLeft .du-bois-dark-notification-notice,\n.du-bois-dark-notification-bottomLeft .du-bois-dark-notification-notice {\n margin-right: auto;\n margin-left: 0;\n}\n.du-bois-dark-notification-notice-message {\n margin-bottom: 8px;\n color: rgba(255, 255, 255, 0.85);\n font-size: 18px;\n line-height: 24px;\n}\n.du-bois-dark-notification-notice-message-single-line-auto-margin {\n display: block;\n width: calc(384px - 24px * 2 - 24px - 48px - 100%);\n max-width: 4px;\n background-color: transparent;\n pointer-events: none;\n}\n.du-bois-dark-notification-notice-message-single-line-auto-margin::before {\n display: block;\n content: '';\n}\n.du-bois-dark-notification-notice-description {\n font-size: 13px;\n}\n.du-bois-dark-notification-notice-closable .du-bois-dark-notification-notice-message {\n padding-right: 24px;\n}\n.du-bois-dark-notification-notice-with-icon .du-bois-dark-notification-notice-message {\n margin-bottom: 4px;\n margin-left: 48px;\n font-size: 18px;\n}\n.du-bois-dark-notification-notice-with-icon .du-bois-dark-notification-notice-description {\n margin-left: 48px;\n font-size: 13px;\n}\n.du-bois-dark-notification-notice-icon {\n position: absolute;\n margin-left: 4px;\n font-size: 24px;\n line-height: 24px;\n}\n.anticon.du-bois-dark-notification-notice-icon-success {\n color: #34824F;\n}\n.anticon.du-bois-dark-notification-notice-icon-info {\n color: #64727D;\n}\n.anticon.du-bois-dark-notification-notice-icon-warning {\n color: #AF5B23;\n}\n.anticon.du-bois-dark-notification-notice-icon-error {\n color: #E65B77;\n}\n.du-bois-dark-notification-notice-close {\n position: absolute;\n top: 16px;\n right: 22px;\n color: #92A4B3;\n outline: none;\n}\n.du-bois-dark-notification-notice-close:hover {\n color: rgba(255, 255, 255, 0.85);\n}\n.du-bois-dark-notification-notice-btn {\n float: right;\n margin-top: 16px;\n}\n.du-bois-dark-notification .notification-fade-effect {\n animation-duration: 0.24s;\n animation-timing-function: cubic-bezier(0.645, 0.045, 0.355, 1);\n animation-fill-mode: both;\n}\n.du-bois-dark-notification-fade-enter,\n.du-bois-dark-notification-fade-appear {\n animation-duration: 0.24s;\n animation-timing-function: cubic-bezier(0.645, 0.045, 0.355, 1);\n animation-fill-mode: both;\n opacity: 0;\n animation-play-state: paused;\n}\n.du-bois-dark-notification-fade-leave {\n animation-duration: 0.24s;\n animation-timing-function: cubic-bezier(0.645, 0.045, 0.355, 1);\n animation-fill-mode: both;\n animation-duration: 0.2s;\n animation-play-state: paused;\n}\n.du-bois-dark-notification-fade-enter.du-bois-dark-notification-fade-enter-active,\n.du-bois-dark-notification-fade-appear.du-bois-dark-notification-fade-appear-active {\n animation-name: NotificationFadeIn;\n animation-play-state: running;\n}\n.du-bois-dark-notification-fade-leave.du-bois-dark-notification-fade-leave-active {\n animation-name: NotificationFadeOut;\n animation-play-state: running;\n}\n@keyframes NotificationFadeIn {\n 0% {\n left: 384px;\n opacity: 0;\n }\n 100% {\n left: 0;\n opacity: 1;\n }\n}\n@keyframes NotificationLeftFadeIn {\n 0% {\n right: 384px;\n opacity: 0;\n }\n 100% {\n right: 0;\n opacity: 1;\n }\n}\n@keyframes NotificationFadeOut {\n 0% {\n max-height: 150px;\n margin-bottom: 16px;\n opacity: 1;\n }\n 100% {\n max-height: 0;\n margin-bottom: 0;\n padding-top: 0;\n padding-bottom: 0;\n opacity: 0;\n }\n}\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-dark-popover {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #E8ECF0;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: tnum;\n position: absolute;\n top: 0;\n left: 0;\n z-index: 1030;\n font-weight: normal;\n white-space: normal;\n text-align: left;\n cursor: auto;\n user-select: text;\n}\n.du-bois-dark-popover::after {\n position: absolute;\n background: rgba(255, 255, 255, 0.01);\n content: '';\n}\n.du-bois-dark-popover-hidden {\n display: none;\n}\n.du-bois-dark-popover-placement-top,\n.du-bois-dark-popover-placement-topLeft,\n.du-bois-dark-popover-placement-topRight {\n padding-bottom: 10px;\n}\n.du-bois-dark-popover-placement-right,\n.du-bois-dark-popover-placement-rightTop,\n.du-bois-dark-popover-placement-rightBottom {\n padding-left: 10px;\n}\n.du-bois-dark-popover-placement-bottom,\n.du-bois-dark-popover-placement-bottomLeft,\n.du-bois-dark-popover-placement-bottomRight {\n padding-top: 10px;\n}\n.du-bois-dark-popover-placement-left,\n.du-bois-dark-popover-placement-leftTop,\n.du-bois-dark-popover-placement-leftBottom {\n padding-right: 10px;\n}\n.du-bois-dark-popover-inner {\n background-color: #1f1f1f;\n background-clip: padding-box;\n border-radius: 4px;\n box-shadow: 0 3px 6px -4px rgba(0, 0, 0, 0.48), 0 6px 16px 0 rgba(0, 0, 0, 0.32), 0 9px 28px 8px rgba(0, 0, 0, 0.2);\n box-shadow: 0 0 8px rgba(0, 0, 0, 0.45) \\9;\n}\n@media screen and (-ms-high-contrast: active), (-ms-high-contrast: none) {\n .du-bois-dark-popover {\n /* IE10+ */\n }\n .du-bois-dark-popover-inner {\n box-shadow: 0 3px 6px -4px rgba(0, 0, 0, 0.48), 0 6px 16px 0 rgba(0, 0, 0, 0.32), 0 9px 28px 8px rgba(0, 0, 0, 0.2);\n }\n}\n.du-bois-dark-popover-title {\n min-width: 177px;\n min-height: 32px;\n margin: 0;\n padding: 5px 8px 4px;\n color: rgba(255, 255, 255, 0.85);\n font-weight: 500;\n border-bottom: 1px solid #303030;\n}\n.du-bois-dark-popover-inner-content {\n padding: 8px 8px;\n color: #E8ECF0;\n}\n.du-bois-dark-popover-message {\n position: relative;\n padding: 4px 0 12px;\n color: #E8ECF0;\n font-size: 13px;\n}\n.du-bois-dark-popover-message > .anticon {\n position: absolute;\n top: 127.5px;\n color: #AF5B23;\n font-size: 13px;\n}\n.du-bois-dark-popover-message-title {\n padding-left: 21px;\n}\n.du-bois-dark-popover-buttons {\n margin-bottom: 4px;\n text-align: right;\n}\n.du-bois-dark-popover-buttons button {\n margin-left: 8px;\n}\n.du-bois-dark-popover-arrow {\n position: absolute;\n display: block;\n width: 8.48528137px;\n height: 8.48528137px;\n overflow: hidden;\n background: transparent;\n pointer-events: none;\n}\n.du-bois-dark-popover-arrow-content {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n display: block;\n width: 6px;\n height: 6px;\n margin: auto;\n background-color: #1f1f1f;\n content: '';\n pointer-events: auto;\n}\n.du-bois-dark-popover-placement-top .du-bois-dark-popover-arrow,\n.du-bois-dark-popover-placement-topLeft .du-bois-dark-popover-arrow,\n.du-bois-dark-popover-placement-topRight .du-bois-dark-popover-arrow {\n bottom: 1.51471863px;\n}\n.du-bois-dark-popover-placement-top .du-bois-dark-popover-arrow-content,\n.du-bois-dark-popover-placement-topLeft .du-bois-dark-popover-arrow-content,\n.du-bois-dark-popover-placement-topRight .du-bois-dark-popover-arrow-content {\n box-shadow: 3px 3px 7px rgba(0, 0, 0, 0.07);\n transform: translateY(-4.24264069px) rotate(45deg);\n}\n.du-bois-dark-popover-placement-top .du-bois-dark-popover-arrow {\n left: 50%;\n transform: translateX(-50%);\n}\n.du-bois-dark-popover-placement-topLeft .du-bois-dark-popover-arrow {\n left: 16px;\n}\n.du-bois-dark-popover-placement-topRight .du-bois-dark-popover-arrow {\n right: 16px;\n}\n.du-bois-dark-popover-placement-right .du-bois-dark-popover-arrow,\n.du-bois-dark-popover-placement-rightTop .du-bois-dark-popover-arrow,\n.du-bois-dark-popover-placement-rightBottom .du-bois-dark-popover-arrow {\n left: 1.51471863px;\n}\n.du-bois-dark-popover-placement-right .du-bois-dark-popover-arrow-content,\n.du-bois-dark-popover-placement-rightTop .du-bois-dark-popover-arrow-content,\n.du-bois-dark-popover-placement-rightBottom .du-bois-dark-popover-arrow-content {\n box-shadow: -3px 3px 7px rgba(0, 0, 0, 0.07);\n transform: translateX(4.24264069px) rotate(45deg);\n}\n.du-bois-dark-popover-placement-right .du-bois-dark-popover-arrow {\n top: 50%;\n transform: translateY(-50%);\n}\n.du-bois-dark-popover-placement-rightTop .du-bois-dark-popover-arrow {\n top: 12px;\n}\n.du-bois-dark-popover-placement-rightBottom .du-bois-dark-popover-arrow {\n bottom: 12px;\n}\n.du-bois-dark-popover-placement-bottom .du-bois-dark-popover-arrow,\n.du-bois-dark-popover-placement-bottomLeft .du-bois-dark-popover-arrow,\n.du-bois-dark-popover-placement-bottomRight .du-bois-dark-popover-arrow {\n top: 1.51471863px;\n}\n.du-bois-dark-popover-placement-bottom .du-bois-dark-popover-arrow-content,\n.du-bois-dark-popover-placement-bottomLeft .du-bois-dark-popover-arrow-content,\n.du-bois-dark-popover-placement-bottomRight .du-bois-dark-popover-arrow-content {\n box-shadow: -2px -2px 5px rgba(0, 0, 0, 0.06);\n transform: translateY(4.24264069px) rotate(45deg);\n}\n.du-bois-dark-popover-placement-bottom .du-bois-dark-popover-arrow {\n left: 50%;\n transform: translateX(-50%);\n}\n.du-bois-dark-popover-placement-bottomLeft .du-bois-dark-popover-arrow {\n left: 16px;\n}\n.du-bois-dark-popover-placement-bottomRight .du-bois-dark-popover-arrow {\n right: 16px;\n}\n.du-bois-dark-popover-placement-left .du-bois-dark-popover-arrow,\n.du-bois-dark-popover-placement-leftTop .du-bois-dark-popover-arrow,\n.du-bois-dark-popover-placement-leftBottom .du-bois-dark-popover-arrow {\n right: 1.51471863px;\n}\n.du-bois-dark-popover-placement-left .du-bois-dark-popover-arrow-content,\n.du-bois-dark-popover-placement-leftTop .du-bois-dark-popover-arrow-content,\n.du-bois-dark-popover-placement-leftBottom .du-bois-dark-popover-arrow-content {\n box-shadow: 3px -3px 7px rgba(0, 0, 0, 0.07);\n transform: translateX(-4.24264069px) rotate(45deg);\n}\n.du-bois-dark-popover-placement-left .du-bois-dark-popover-arrow {\n top: 50%;\n transform: translateY(-50%);\n}\n.du-bois-dark-popover-placement-leftTop .du-bois-dark-popover-arrow {\n top: 12px;\n}\n.du-bois-dark-popover-placement-leftBottom .du-bois-dark-popover-arrow {\n bottom: 12px;\n}\n.du-bois-dark-popover-pink .du-bois-dark-popover-inner {\n background-color: #cb2b83;\n}\n.du-bois-dark-popover-pink .du-bois-dark-popover-arrow-content {\n background-color: #cb2b83;\n}\n.du-bois-dark-popover-magenta .du-bois-dark-popover-inner {\n background-color: #cb2b83;\n}\n.du-bois-dark-popover-magenta .du-bois-dark-popover-arrow-content {\n background-color: #cb2b83;\n}\n.du-bois-dark-popover-red .du-bois-dark-popover-inner {\n background-color: #d32029;\n}\n.du-bois-dark-popover-red .du-bois-dark-popover-arrow-content {\n background-color: #d32029;\n}\n.du-bois-dark-popover-volcano .du-bois-dark-popover-inner {\n background-color: #d84a1b;\n}\n.du-bois-dark-popover-volcano .du-bois-dark-popover-arrow-content {\n background-color: #d84a1b;\n}\n.du-bois-dark-popover-orange .du-bois-dark-popover-inner {\n background-color: #d87a16;\n}\n.du-bois-dark-popover-orange .du-bois-dark-popover-arrow-content {\n background-color: #d87a16;\n}\n.du-bois-dark-popover-yellow .du-bois-dark-popover-inner {\n background-color: #d8bd14;\n}\n.du-bois-dark-popover-yellow .du-bois-dark-popover-arrow-content {\n background-color: #d8bd14;\n}\n.du-bois-dark-popover-gold .du-bois-dark-popover-inner {\n background-color: #d89614;\n}\n.du-bois-dark-popover-gold .du-bois-dark-popover-arrow-content {\n background-color: #d89614;\n}\n.du-bois-dark-popover-cyan .du-bois-dark-popover-inner {\n background-color: #13a8a8;\n}\n.du-bois-dark-popover-cyan .du-bois-dark-popover-arrow-content {\n background-color: #13a8a8;\n}\n.du-bois-dark-popover-lime .du-bois-dark-popover-inner {\n background-color: #8bbb11;\n}\n.du-bois-dark-popover-lime .du-bois-dark-popover-arrow-content {\n background-color: #8bbb11;\n}\n.du-bois-dark-popover-green .du-bois-dark-popover-inner {\n background-color: #49aa19;\n}\n.du-bois-dark-popover-green .du-bois-dark-popover-arrow-content {\n background-color: #49aa19;\n}\n.du-bois-dark-popover-blue .du-bois-dark-popover-inner {\n background-color: #177ddc;\n}\n.du-bois-dark-popover-blue .du-bois-dark-popover-arrow-content {\n background-color: #177ddc;\n}\n.du-bois-dark-popover-geekblue .du-bois-dark-popover-inner {\n background-color: #2b4acb;\n}\n.du-bois-dark-popover-geekblue .du-bois-dark-popover-arrow-content {\n background-color: #2b4acb;\n}\n.du-bois-dark-popover-purple .du-bois-dark-popover-inner {\n background-color: #642ab5;\n}\n.du-bois-dark-popover-purple .du-bois-dark-popover-arrow-content {\n background-color: #642ab5;\n}\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-dark-radio-group {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #E8ECF0;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: tnum;\n display: inline-block;\n font-size: 0;\n}\n.du-bois-dark-radio-group .du-bois-dark-badge-count {\n z-index: 1;\n}\n.du-bois-dark-radio-group > .du-bois-dark-badge:not(:first-child) > .du-bois-dark-radio-button-wrapper {\n border-left: none;\n}\n.du-bois-dark-radio-wrapper {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #E8ECF0;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: tnum;\n position: relative;\n display: inline-flex;\n align-items: baseline;\n margin-right: 8px;\n cursor: pointer;\n}\n.du-bois-dark-radio-wrapper::after {\n display: inline-block;\n width: 0;\n overflow: hidden;\n content: '\\a0';\n}\n.du-bois-dark-radio {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #E8ECF0;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: tnum;\n position: relative;\n top: 0.2em;\n display: inline-block;\n outline: none;\n cursor: pointer;\n}\n.du-bois-dark-radio-wrapper:hover .du-bois-dark-radio,\n.du-bois-dark-radio:hover .du-bois-dark-radio-inner,\n.du-bois-dark-radio-input:focus + .du-bois-dark-radio-inner {\n border-color: #2272B4;\n}\n.du-bois-dark-radio-input:focus + .du-bois-dark-radio-inner {\n box-shadow: 0 0 0 3px rgba(34, 114, 180, 0.08);\n}\n.du-bois-dark-radio-checked::after {\n position: absolute;\n top: 0;\n left: 0;\n width: 100%;\n height: 100%;\n border: 1px solid #2272B4;\n border-radius: 50%;\n visibility: hidden;\n animation: antRadioEffect 0.36s ease-in-out;\n animation-fill-mode: both;\n content: '';\n}\n.du-bois-dark-radio:hover::after,\n.du-bois-dark-radio-wrapper:hover .du-bois-dark-radio::after {\n visibility: visible;\n}\n.du-bois-dark-radio-inner {\n position: relative;\n top: 0;\n left: 0;\n display: block;\n width: 16px;\n height: 16px;\n background-color: transparent;\n border-color: #1F272D;\n border-style: solid;\n border-width: 1px;\n border-radius: 50%;\n transition: all 0.3s;\n}\n.du-bois-dark-radio-inner::after {\n position: absolute;\n top: 3px;\n left: 3px;\n display: block;\n width: 8px;\n height: 8px;\n background-color: #2272B4;\n border-top: 0;\n border-left: 0;\n border-radius: 8px;\n transform: scale(0);\n opacity: 0;\n transition: all 0.3s cubic-bezier(0.78, 0.14, 0.15, 0.86);\n content: ' ';\n}\n.du-bois-dark-radio-input {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n z-index: 1;\n cursor: pointer;\n opacity: 0;\n}\n.du-bois-dark-radio-checked .du-bois-dark-radio-inner {\n border-color: #2272B4;\n}\n.du-bois-dark-radio-checked .du-bois-dark-radio-inner::after {\n transform: scale(1);\n opacity: 1;\n transition: all 0.3s cubic-bezier(0.78, 0.14, 0.15, 0.86);\n}\n.du-bois-dark-radio-disabled {\n cursor: not-allowed;\n}\n.du-bois-dark-radio-disabled .du-bois-dark-radio-inner {\n background-color: rgba(255, 255, 255, 0.08);\n border-color: #1F272D !important;\n cursor: not-allowed;\n}\n.du-bois-dark-radio-disabled .du-bois-dark-radio-inner::after {\n background-color: rgba(255, 255, 255, 0.2);\n}\n.du-bois-dark-radio-disabled .du-bois-dark-radio-input {\n cursor: not-allowed;\n}\n.du-bois-dark-radio-disabled + span {\n color: rgba(255, 255, 255, 0.3);\n cursor: not-allowed;\n}\nspan.du-bois-dark-radio + * {\n padding-right: 8px;\n padding-left: 8px;\n}\n.du-bois-dark-radio-button-wrapper {\n position: relative;\n display: inline-block;\n height: 40px;\n margin: 0;\n padding: 0 7px;\n color: #E8ECF0;\n font-size: 13px;\n line-height: 38px;\n background: transparent;\n border: 1px solid #1F272D;\n border-top-width: 1.02px;\n border-left-width: 0;\n cursor: pointer;\n transition: color 0.3s, background 0.3s, border-color 0.3s, box-shadow 0.3s;\n}\n.du-bois-dark-radio-button-wrapper a {\n color: #E8ECF0;\n}\n.du-bois-dark-radio-button-wrapper > .du-bois-dark-radio-button {\n position: absolute;\n top: 0;\n left: 0;\n z-index: -1;\n width: 100%;\n height: 100%;\n}\n.du-bois-dark-radio-group-large .du-bois-dark-radio-button-wrapper {\n height: 40px;\n font-size: 18px;\n line-height: 38px;\n}\n.du-bois-dark-radio-group-small .du-bois-dark-radio-button-wrapper {\n height: 32px;\n padding: 0 3px;\n line-height: 30px;\n}\n.du-bois-dark-radio-button-wrapper:not(:first-child)::before {\n position: absolute;\n top: -1px;\n left: -1px;\n display: block;\n box-sizing: content-box;\n width: 1px;\n height: 100%;\n padding: 1px 0;\n background-color: #1F272D;\n transition: background-color 0.3s;\n content: '';\n}\n.du-bois-dark-radio-button-wrapper:first-child {\n border-left: 1px solid #1F272D;\n border-radius: 4px 0 0 4px;\n}\n.du-bois-dark-radio-button-wrapper:last-child {\n border-radius: 0 4px 4px 0;\n}\n.du-bois-dark-radio-button-wrapper:first-child:last-child {\n border-radius: 4px;\n}\n.du-bois-dark-radio-button-wrapper:hover {\n position: relative;\n color: #2272B4;\n}\n.du-bois-dark-radio-button-wrapper:focus-within {\n box-shadow: 0 0 0 3px rgba(34, 114, 180, 0.08);\n}\n.du-bois-dark-radio-button-wrapper .du-bois-dark-radio-inner,\n.du-bois-dark-radio-button-wrapper input[type='checkbox'],\n.du-bois-dark-radio-button-wrapper input[type='radio'] {\n width: 0;\n height: 0;\n opacity: 0;\n pointer-events: none;\n}\n.du-bois-dark-radio-button-wrapper-checked:not(.du-bois-dark-radio-button-wrapper-disabled) {\n z-index: 1;\n color: #2272B4;\n background: transparent;\n border-color: #2272B4;\n}\n.du-bois-dark-radio-button-wrapper-checked:not(.du-bois-dark-radio-button-wrapper-disabled)::before {\n background-color: #2272B4;\n}\n.du-bois-dark-radio-button-wrapper-checked:not(.du-bois-dark-radio-button-wrapper-disabled):first-child {\n border-color: #2272B4;\n}\n.du-bois-dark-radio-button-wrapper-checked:not(.du-bois-dark-radio-button-wrapper-disabled):hover {\n color: #1d517c;\n border-color: #1d517c;\n}\n.du-bois-dark-radio-button-wrapper-checked:not(.du-bois-dark-radio-button-wrapper-disabled):hover::before {\n background-color: #1d517c;\n}\n.du-bois-dark-radio-button-wrapper-checked:not(.du-bois-dark-radio-button-wrapper-disabled):active {\n color: #3f81b1;\n border-color: #3f81b1;\n}\n.du-bois-dark-radio-button-wrapper-checked:not(.du-bois-dark-radio-button-wrapper-disabled):active::before {\n background-color: #3f81b1;\n}\n.du-bois-dark-radio-button-wrapper-checked:not(.du-bois-dark-radio-button-wrapper-disabled):focus-within {\n box-shadow: 0 0 0 3px rgba(34, 114, 180, 0.08);\n}\n.du-bois-dark-radio-group-solid .du-bois-dark-radio-button-wrapper-checked:not(.du-bois-dark-radio-button-wrapper-disabled) {\n color: #fff;\n background: #2272B4;\n border-color: #2272B4;\n}\n.du-bois-dark-radio-group-solid .du-bois-dark-radio-button-wrapper-checked:not(.du-bois-dark-radio-button-wrapper-disabled):hover {\n color: #fff;\n background: #1d517c;\n border-color: #1d517c;\n}\n.du-bois-dark-radio-group-solid .du-bois-dark-radio-button-wrapper-checked:not(.du-bois-dark-radio-button-wrapper-disabled):active {\n color: #fff;\n background: #3f81b1;\n border-color: #3f81b1;\n}\n.du-bois-dark-radio-group-solid .du-bois-dark-radio-button-wrapper-checked:not(.du-bois-dark-radio-button-wrapper-disabled):focus-within {\n box-shadow: 0 0 0 3px rgba(34, 114, 180, 0.08);\n}\n.du-bois-dark-radio-button-wrapper-disabled {\n color: rgba(255, 255, 255, 0.3);\n background-color: rgba(255, 255, 255, 0.08);\n border-color: #1F272D;\n cursor: not-allowed;\n}\n.du-bois-dark-radio-button-wrapper-disabled:first-child,\n.du-bois-dark-radio-button-wrapper-disabled:hover {\n color: rgba(255, 255, 255, 0.3);\n background-color: rgba(255, 255, 255, 0.08);\n border-color: #1F272D;\n}\n.du-bois-dark-radio-button-wrapper-disabled:first-child {\n border-left-color: #1F272D;\n}\n.du-bois-dark-radio-button-wrapper-disabled.du-bois-dark-radio-button-wrapper-checked {\n color: rgba(255, 255, 255, 0.3);\n background-color: rgba(255, 255, 255, 0.2);\n border-color: #1F272D;\n box-shadow: none;\n}\n@keyframes antRadioEffect {\n 0% {\n transform: scale(1);\n opacity: 0.5;\n }\n 100% {\n transform: scale(1.6);\n opacity: 0;\n }\n}\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-dark-skeleton {\n display: table;\n width: 100%;\n}\n.du-bois-dark-skeleton-header {\n display: table-cell;\n padding-right: 8px;\n vertical-align: top;\n}\n.du-bois-dark-skeleton-header .du-bois-dark-skeleton-avatar {\n display: inline-block;\n vertical-align: top;\n background: rgba(190, 190, 190, 0.2);\n width: 32px;\n height: 32px;\n line-height: 32px;\n}\n.du-bois-dark-skeleton-header .du-bois-dark-skeleton-avatar.du-bois-dark-skeleton-avatar-circle {\n border-radius: 50%;\n}\n.du-bois-dark-skeleton-header .du-bois-dark-skeleton-avatar-lg {\n width: 40px;\n height: 40px;\n line-height: 40px;\n}\n.du-bois-dark-skeleton-header .du-bois-dark-skeleton-avatar-lg.du-bois-dark-skeleton-avatar-circle {\n border-radius: 50%;\n}\n.du-bois-dark-skeleton-header .du-bois-dark-skeleton-avatar-sm {\n width: 24px;\n height: 24px;\n line-height: 24px;\n}\n.du-bois-dark-skeleton-header .du-bois-dark-skeleton-avatar-sm.du-bois-dark-skeleton-avatar-circle {\n border-radius: 50%;\n}\n.du-bois-dark-skeleton-content {\n display: table-cell;\n width: 100%;\n vertical-align: top;\n}\n.du-bois-dark-skeleton-content .du-bois-dark-skeleton-title {\n width: 100%;\n height: 16px;\n margin-top: 16px;\n background: rgba(190, 190, 190, 0.2);\n border-radius: 4px;\n}\n.du-bois-dark-skeleton-content .du-bois-dark-skeleton-title + .du-bois-dark-skeleton-paragraph {\n margin-top: 24px;\n}\n.du-bois-dark-skeleton-content .du-bois-dark-skeleton-paragraph {\n padding: 0;\n}\n.du-bois-dark-skeleton-content .du-bois-dark-skeleton-paragraph > li {\n width: 100%;\n height: 16px;\n list-style: none;\n background: rgba(190, 190, 190, 0.2);\n border-radius: 4px;\n}\n.du-bois-dark-skeleton-content .du-bois-dark-skeleton-paragraph > li:last-child:not(:first-child):not(:nth-child(2)) {\n width: 61%;\n}\n.du-bois-dark-skeleton-content .du-bois-dark-skeleton-paragraph > li + li {\n margin-top: 16px;\n}\n.du-bois-dark-skeleton-with-avatar .du-bois-dark-skeleton-content .du-bois-dark-skeleton-title {\n margin-top: 12px;\n}\n.du-bois-dark-skeleton-with-avatar .du-bois-dark-skeleton-content .du-bois-dark-skeleton-title + .du-bois-dark-skeleton-paragraph {\n margin-top: 28px;\n}\n.du-bois-dark-skeleton-round .du-bois-dark-skeleton-content .du-bois-dark-skeleton-title,\n.du-bois-dark-skeleton-round .du-bois-dark-skeleton-content .du-bois-dark-skeleton-paragraph > li {\n border-radius: 100px;\n}\n.du-bois-dark-skeleton.du-bois-dark-skeleton-active .du-bois-dark-skeleton-content .du-bois-dark-skeleton-title,\n.du-bois-dark-skeleton.du-bois-dark-skeleton-active .du-bois-dark-skeleton-content .du-bois-dark-skeleton-paragraph > li {\n background: linear-gradient(90deg, rgba(190, 190, 190, 0.2) 25%, rgba(255, 255, 255, 0.16) 37%, rgba(190, 190, 190, 0.2) 63%);\n background-size: 400% 100%;\n animation: du-bois-dark-skeleton-loading 1.4s ease infinite;\n}\n.du-bois-dark-skeleton.du-bois-dark-skeleton-active .du-bois-dark-skeleton-avatar {\n background: linear-gradient(90deg, rgba(190, 190, 190, 0.2) 25%, rgba(255, 255, 255, 0.16) 37%, rgba(190, 190, 190, 0.2) 63%);\n background-size: 400% 100%;\n animation: du-bois-dark-skeleton-loading 1.4s ease infinite;\n}\n.du-bois-dark-skeleton.du-bois-dark-skeleton-active .du-bois-dark-skeleton-button {\n background: linear-gradient(90deg, rgba(190, 190, 190, 0.2) 25%, rgba(255, 255, 255, 0.16) 37%, rgba(190, 190, 190, 0.2) 63%);\n background-size: 400% 100%;\n animation: du-bois-dark-skeleton-loading 1.4s ease infinite;\n}\n.du-bois-dark-skeleton.du-bois-dark-skeleton-active .du-bois-dark-skeleton-input {\n background: linear-gradient(90deg, rgba(190, 190, 190, 0.2) 25%, rgba(255, 255, 255, 0.16) 37%, rgba(190, 190, 190, 0.2) 63%);\n background-size: 400% 100%;\n animation: du-bois-dark-skeleton-loading 1.4s ease infinite;\n}\n.du-bois-dark-skeleton.du-bois-dark-skeleton-active .du-bois-dark-skeleton-image {\n background: linear-gradient(90deg, rgba(190, 190, 190, 0.2) 25%, rgba(255, 255, 255, 0.16) 37%, rgba(190, 190, 190, 0.2) 63%);\n background-size: 400% 100%;\n animation: du-bois-dark-skeleton-loading 1.4s ease infinite;\n}\n.du-bois-dark-skeleton-element {\n display: inline-block;\n width: auto;\n}\n.du-bois-dark-skeleton-element .du-bois-dark-skeleton-button {\n display: inline-block;\n vertical-align: top;\n background: rgba(190, 190, 190, 0.2);\n border-radius: 4px;\n width: 80px;\n height: 40px;\n line-height: 40px;\n}\n.du-bois-dark-skeleton-element .du-bois-dark-skeleton-button.du-bois-dark-skeleton-button-circle {\n width: 40px;\n border-radius: 50%;\n}\n.du-bois-dark-skeleton-element .du-bois-dark-skeleton-button.du-bois-dark-skeleton-button-round {\n border-radius: 40px;\n}\n.du-bois-dark-skeleton-element .du-bois-dark-skeleton-button-lg {\n width: 80px;\n height: 40px;\n line-height: 40px;\n}\n.du-bois-dark-skeleton-element .du-bois-dark-skeleton-button-lg.du-bois-dark-skeleton-button-circle {\n width: 40px;\n border-radius: 50%;\n}\n.du-bois-dark-skeleton-element .du-bois-dark-skeleton-button-lg.du-bois-dark-skeleton-button-round {\n border-radius: 40px;\n}\n.du-bois-dark-skeleton-element .du-bois-dark-skeleton-button-sm {\n width: 64px;\n height: 32px;\n line-height: 32px;\n}\n.du-bois-dark-skeleton-element .du-bois-dark-skeleton-button-sm.du-bois-dark-skeleton-button-circle {\n width: 32px;\n border-radius: 50%;\n}\n.du-bois-dark-skeleton-element .du-bois-dark-skeleton-button-sm.du-bois-dark-skeleton-button-round {\n border-radius: 32px;\n}\n.du-bois-dark-skeleton-element .du-bois-dark-skeleton-avatar {\n display: inline-block;\n vertical-align: top;\n background: rgba(190, 190, 190, 0.2);\n width: 32px;\n height: 32px;\n line-height: 32px;\n}\n.du-bois-dark-skeleton-element .du-bois-dark-skeleton-avatar.du-bois-dark-skeleton-avatar-circle {\n border-radius: 50%;\n}\n.du-bois-dark-skeleton-element .du-bois-dark-skeleton-avatar-lg {\n width: 40px;\n height: 40px;\n line-height: 40px;\n}\n.du-bois-dark-skeleton-element .du-bois-dark-skeleton-avatar-lg.du-bois-dark-skeleton-avatar-circle {\n border-radius: 50%;\n}\n.du-bois-dark-skeleton-element .du-bois-dark-skeleton-avatar-sm {\n width: 24px;\n height: 24px;\n line-height: 24px;\n}\n.du-bois-dark-skeleton-element .du-bois-dark-skeleton-avatar-sm.du-bois-dark-skeleton-avatar-circle {\n border-radius: 50%;\n}\n.du-bois-dark-skeleton-element .du-bois-dark-skeleton-input {\n display: inline-block;\n vertical-align: top;\n background: rgba(190, 190, 190, 0.2);\n width: 100%;\n height: 40px;\n line-height: 40px;\n}\n.du-bois-dark-skeleton-element .du-bois-dark-skeleton-input-lg {\n width: 100%;\n height: 40px;\n line-height: 40px;\n}\n.du-bois-dark-skeleton-element .du-bois-dark-skeleton-input-sm {\n width: 100%;\n height: 32px;\n line-height: 32px;\n}\n.du-bois-dark-skeleton-element .du-bois-dark-skeleton-image {\n display: flex;\n align-items: center;\n justify-content: center;\n vertical-align: top;\n background: rgba(190, 190, 190, 0.2);\n width: 96px;\n height: 96px;\n line-height: 96px;\n}\n.du-bois-dark-skeleton-element .du-bois-dark-skeleton-image.du-bois-dark-skeleton-image-circle {\n border-radius: 50%;\n}\n.du-bois-dark-skeleton-element .du-bois-dark-skeleton-image-path {\n fill: #bfbfbf;\n}\n.du-bois-dark-skeleton-element .du-bois-dark-skeleton-image-svg {\n width: 48px;\n height: 48px;\n line-height: 48px;\n max-width: 192px;\n max-height: 192px;\n}\n.du-bois-dark-skeleton-element .du-bois-dark-skeleton-image-svg.du-bois-dark-skeleton-image-circle {\n border-radius: 50%;\n}\n@keyframes du-bois-dark-skeleton-loading {\n 0% {\n background-position: 100% 50%;\n }\n 100% {\n background-position: 0 50%;\n }\n}\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-dark-space {\n display: inline-flex;\n}\n.du-bois-dark-space-vertical {\n flex-direction: column;\n}\n.du-bois-dark-space-align-center {\n align-items: center;\n}\n.du-bois-dark-space-align-start {\n align-items: flex-start;\n}\n.du-bois-dark-space-align-end {\n align-items: flex-end;\n}\n.du-bois-dark-space-align-baseline {\n align-items: baseline;\n}\n.du-bois-dark-space-item:empty {\n display: none;\n}\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-dark-steps {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #E8ECF0;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: tnum;\n display: flex;\n width: 100%;\n font-size: 0;\n text-align: initial;\n}\n.du-bois-dark-steps-item {\n position: relative;\n display: inline-block;\n flex: 1;\n overflow: hidden;\n vertical-align: top;\n}\n.du-bois-dark-steps-item-container {\n outline: none;\n}\n.du-bois-dark-steps-item:last-child {\n flex: none;\n}\n.du-bois-dark-steps-item:last-child > .du-bois-dark-steps-item-container > .du-bois-dark-steps-item-tail,\n.du-bois-dark-steps-item:last-child > .du-bois-dark-steps-item-container > .du-bois-dark-steps-item-content > .du-bois-dark-steps-item-title::after {\n display: none;\n}\n.du-bois-dark-steps-item-icon,\n.du-bois-dark-steps-item-content {\n display: inline-block;\n vertical-align: top;\n}\n.du-bois-dark-steps-item-icon {\n width: 32px;\n height: 32px;\n margin: 0 8px 0 0;\n font-size: 18px;\n font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, 'Helvetica Neue', Arial, 'Noto Sans', sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol', 'Noto Color Emoji';\n line-height: 32px;\n text-align: center;\n border: 1px solid rgba(255, 255, 255, 0.3);\n border-radius: 32px;\n transition: background-color 0.3s, border-color 0.3s;\n}\n.du-bois-dark-steps-item-icon .du-bois-dark-steps-icon {\n position: relative;\n top: -0.5px;\n color: #2272B4;\n line-height: 1;\n}\n.du-bois-dark-steps-item-tail {\n position: absolute;\n top: 12px;\n left: 0;\n width: 100%;\n padding: 0 10px;\n}\n.du-bois-dark-steps-item-tail::after {\n display: inline-block;\n width: 100%;\n height: 1px;\n background: #303030;\n border-radius: 1px;\n transition: background 0.3s;\n content: '';\n}\n.du-bois-dark-steps-item-title {\n position: relative;\n display: inline-block;\n padding-right: 16px;\n color: #E8ECF0;\n font-size: 18px;\n line-height: 40px;\n}\n.du-bois-dark-steps-item-title::after {\n position: absolute;\n top: 20px;\n left: 100%;\n display: block;\n width: 9999px;\n height: 1px;\n background: #303030;\n content: '';\n}\n.du-bois-dark-steps-item-subtitle {\n display: inline;\n margin-left: 8px;\n color: #92A4B3;\n font-weight: normal;\n font-size: 13px;\n}\n.du-bois-dark-steps-item-description {\n color: #92A4B3;\n font-size: 13px;\n}\n.du-bois-dark-steps-item-wait .du-bois-dark-steps-item-icon {\n background-color: transparent;\n border-color: rgba(255, 255, 255, 0.3);\n}\n.du-bois-dark-steps-item-wait .du-bois-dark-steps-item-icon > .du-bois-dark-steps-icon {\n color: rgba(255, 255, 255, 0.3);\n}\n.du-bois-dark-steps-item-wait .du-bois-dark-steps-item-icon > .du-bois-dark-steps-icon .du-bois-dark-steps-icon-dot {\n background: rgba(255, 255, 255, 0.3);\n}\n.du-bois-dark-steps-item-wait > .du-bois-dark-steps-item-container > .du-bois-dark-steps-item-content > .du-bois-dark-steps-item-title {\n color: #92A4B3;\n}\n.du-bois-dark-steps-item-wait > .du-bois-dark-steps-item-container > .du-bois-dark-steps-item-content > .du-bois-dark-steps-item-title::after {\n background-color: #303030;\n}\n.du-bois-dark-steps-item-wait > .du-bois-dark-steps-item-container > .du-bois-dark-steps-item-content > .du-bois-dark-steps-item-description {\n color: #92A4B3;\n}\n.du-bois-dark-steps-item-wait > .du-bois-dark-steps-item-container > .du-bois-dark-steps-item-tail::after {\n background-color: #303030;\n}\n.du-bois-dark-steps-item-process .du-bois-dark-steps-item-icon {\n background-color: transparent;\n border-color: #2272B4;\n}\n.du-bois-dark-steps-item-process .du-bois-dark-steps-item-icon > .du-bois-dark-steps-icon {\n color: #2272B4;\n}\n.du-bois-dark-steps-item-process .du-bois-dark-steps-item-icon > .du-bois-dark-steps-icon .du-bois-dark-steps-icon-dot {\n background: #2272B4;\n}\n.du-bois-dark-steps-item-process > .du-bois-dark-steps-item-container > .du-bois-dark-steps-item-content > .du-bois-dark-steps-item-title {\n color: rgba(255, 255, 255, 0.85);\n}\n.du-bois-dark-steps-item-process > .du-bois-dark-steps-item-container > .du-bois-dark-steps-item-content > .du-bois-dark-steps-item-title::after {\n background-color: #303030;\n}\n.du-bois-dark-steps-item-process > .du-bois-dark-steps-item-container > .du-bois-dark-steps-item-content > .du-bois-dark-steps-item-description {\n color: #E8ECF0;\n}\n.du-bois-dark-steps-item-process > .du-bois-dark-steps-item-container > .du-bois-dark-steps-item-tail::after {\n background-color: #303030;\n}\n.du-bois-dark-steps-item-process > .du-bois-dark-steps-item-container > .du-bois-dark-steps-item-icon {\n background: #2272B4;\n}\n.du-bois-dark-steps-item-process > .du-bois-dark-steps-item-container > .du-bois-dark-steps-item-icon .du-bois-dark-steps-icon {\n color: #fff;\n}\n.du-bois-dark-steps-item-process > .du-bois-dark-steps-item-container > .du-bois-dark-steps-item-title {\n font-weight: 500;\n}\n.du-bois-dark-steps-item-finish .du-bois-dark-steps-item-icon {\n background-color: transparent;\n border-color: #2272B4;\n}\n.du-bois-dark-steps-item-finish .du-bois-dark-steps-item-icon > .du-bois-dark-steps-icon {\n color: #2272B4;\n}\n.du-bois-dark-steps-item-finish .du-bois-dark-steps-item-icon > .du-bois-dark-steps-icon .du-bois-dark-steps-icon-dot {\n background: #2272B4;\n}\n.du-bois-dark-steps-item-finish > .du-bois-dark-steps-item-container > .du-bois-dark-steps-item-content > .du-bois-dark-steps-item-title {\n color: #E8ECF0;\n}\n.du-bois-dark-steps-item-finish > .du-bois-dark-steps-item-container > .du-bois-dark-steps-item-content > .du-bois-dark-steps-item-title::after {\n background-color: #2272B4;\n}\n.du-bois-dark-steps-item-finish > .du-bois-dark-steps-item-container > .du-bois-dark-steps-item-content > .du-bois-dark-steps-item-description {\n color: #92A4B3;\n}\n.du-bois-dark-steps-item-finish > .du-bois-dark-steps-item-container > .du-bois-dark-steps-item-tail::after {\n background-color: #2272B4;\n}\n.du-bois-dark-steps-item-error .du-bois-dark-steps-item-icon {\n background-color: transparent;\n border-color: #E65B77;\n}\n.du-bois-dark-steps-item-error .du-bois-dark-steps-item-icon > .du-bois-dark-steps-icon {\n color: #E65B77;\n}\n.du-bois-dark-steps-item-error .du-bois-dark-steps-item-icon > .du-bois-dark-steps-icon .du-bois-dark-steps-icon-dot {\n background: #E65B77;\n}\n.du-bois-dark-steps-item-error > .du-bois-dark-steps-item-container > .du-bois-dark-steps-item-content > .du-bois-dark-steps-item-title {\n color: #E65B77;\n}\n.du-bois-dark-steps-item-error > .du-bois-dark-steps-item-container > .du-bois-dark-steps-item-content > .du-bois-dark-steps-item-title::after {\n background-color: #303030;\n}\n.du-bois-dark-steps-item-error > .du-bois-dark-steps-item-container > .du-bois-dark-steps-item-content > .du-bois-dark-steps-item-description {\n color: #E65B77;\n}\n.du-bois-dark-steps-item-error > .du-bois-dark-steps-item-container > .du-bois-dark-steps-item-tail::after {\n background-color: #303030;\n}\n.du-bois-dark-steps-item.du-bois-dark-steps-next-error .du-bois-dark-steps-item-title::after {\n background: #E65B77;\n}\n.du-bois-dark-steps-item-disabled {\n cursor: not-allowed;\n}\n.du-bois-dark-steps .du-bois-dark-steps-item:not(.du-bois-dark-steps-item-active) > .du-bois-dark-steps-item-container[role='button'] {\n cursor: pointer;\n}\n.du-bois-dark-steps .du-bois-dark-steps-item:not(.du-bois-dark-steps-item-active) > .du-bois-dark-steps-item-container[role='button'] .du-bois-dark-steps-item-title,\n.du-bois-dark-steps .du-bois-dark-steps-item:not(.du-bois-dark-steps-item-active) > .du-bois-dark-steps-item-container[role='button'] .du-bois-dark-steps-item-subtitle,\n.du-bois-dark-steps .du-bois-dark-steps-item:not(.du-bois-dark-steps-item-active) > .du-bois-dark-steps-item-container[role='button'] .du-bois-dark-steps-item-description,\n.du-bois-dark-steps .du-bois-dark-steps-item:not(.du-bois-dark-steps-item-active) > .du-bois-dark-steps-item-container[role='button'] .du-bois-dark-steps-item-icon .du-bois-dark-steps-icon {\n transition: color 0.3s;\n}\n.du-bois-dark-steps .du-bois-dark-steps-item:not(.du-bois-dark-steps-item-active) > .du-bois-dark-steps-item-container[role='button']:hover .du-bois-dark-steps-item-title,\n.du-bois-dark-steps .du-bois-dark-steps-item:not(.du-bois-dark-steps-item-active) > .du-bois-dark-steps-item-container[role='button']:hover .du-bois-dark-steps-item-subtitle,\n.du-bois-dark-steps .du-bois-dark-steps-item:not(.du-bois-dark-steps-item-active) > .du-bois-dark-steps-item-container[role='button']:hover .du-bois-dark-steps-item-description {\n color: #2272B4;\n}\n.du-bois-dark-steps .du-bois-dark-steps-item:not(.du-bois-dark-steps-item-active):not(.du-bois-dark-steps-item-process) > .du-bois-dark-steps-item-container[role='button']:hover .du-bois-dark-steps-item-icon {\n border-color: #2272B4;\n}\n.du-bois-dark-steps .du-bois-dark-steps-item:not(.du-bois-dark-steps-item-active):not(.du-bois-dark-steps-item-process) > .du-bois-dark-steps-item-container[role='button']:hover .du-bois-dark-steps-item-icon .du-bois-dark-steps-icon {\n color: #2272B4;\n}\n.du-bois-dark-steps-horizontal:not(.du-bois-dark-steps-label-vertical) .du-bois-dark-steps-item {\n padding-left: 16px;\n white-space: nowrap;\n}\n.du-bois-dark-steps-horizontal:not(.du-bois-dark-steps-label-vertical) .du-bois-dark-steps-item:first-child {\n padding-left: 0;\n}\n.du-bois-dark-steps-horizontal:not(.du-bois-dark-steps-label-vertical) .du-bois-dark-steps-item:last-child .du-bois-dark-steps-item-title {\n padding-right: 0;\n}\n.du-bois-dark-steps-horizontal:not(.du-bois-dark-steps-label-vertical) .du-bois-dark-steps-item-tail {\n display: none;\n}\n.du-bois-dark-steps-horizontal:not(.du-bois-dark-steps-label-vertical) .du-bois-dark-steps-item-description {\n max-width: 140px;\n white-space: normal;\n}\n.du-bois-dark-steps-item-custom > .du-bois-dark-steps-item-container > .du-bois-dark-steps-item-icon {\n height: auto;\n background: none;\n border: 0;\n}\n.du-bois-dark-steps-item-custom > .du-bois-dark-steps-item-container > .du-bois-dark-steps-item-icon > .du-bois-dark-steps-icon {\n top: 0px;\n left: 0.5px;\n width: 32px;\n height: 32px;\n font-size: 24px;\n line-height: 32px;\n}\n.du-bois-dark-steps-item-custom.du-bois-dark-steps-item-process .du-bois-dark-steps-item-icon > .du-bois-dark-steps-icon {\n color: #2272B4;\n}\n.du-bois-dark-steps:not(.du-bois-dark-steps-vertical) .du-bois-dark-steps-item-custom .du-bois-dark-steps-item-icon {\n width: auto;\n background: none;\n}\n.du-bois-dark-steps-small.du-bois-dark-steps-horizontal:not(.du-bois-dark-steps-label-vertical) .du-bois-dark-steps-item {\n padding-left: 12px;\n}\n.du-bois-dark-steps-small.du-bois-dark-steps-horizontal:not(.du-bois-dark-steps-label-vertical) .du-bois-dark-steps-item:first-child {\n padding-left: 0;\n}\n.du-bois-dark-steps-small .du-bois-dark-steps-item-icon {\n width: 24px;\n height: 24px;\n margin: 0 8px 0 0;\n font-size: 12px;\n line-height: 24px;\n text-align: center;\n border-radius: 24px;\n}\n.du-bois-dark-steps-small .du-bois-dark-steps-item-title {\n padding-right: 12px;\n font-size: 13px;\n line-height: 24px;\n}\n.du-bois-dark-steps-small .du-bois-dark-steps-item-title::after {\n top: 12px;\n}\n.du-bois-dark-steps-small .du-bois-dark-steps-item-description {\n color: #92A4B3;\n font-size: 13px;\n}\n.du-bois-dark-steps-small .du-bois-dark-steps-item-tail {\n top: 8px;\n}\n.du-bois-dark-steps-small .du-bois-dark-steps-item-custom .du-bois-dark-steps-item-icon {\n width: inherit;\n height: inherit;\n line-height: inherit;\n background: none;\n border: 0;\n border-radius: 0;\n}\n.du-bois-dark-steps-small .du-bois-dark-steps-item-custom .du-bois-dark-steps-item-icon > .du-bois-dark-steps-icon {\n font-size: 24px;\n line-height: 24px;\n transform: none;\n}\n.du-bois-dark-steps-vertical {\n display: flex;\n flex-direction: column;\n}\n.du-bois-dark-steps-vertical > .du-bois-dark-steps-item {\n display: block;\n flex: 1 0 auto;\n padding-left: 0;\n overflow: visible;\n}\n.du-bois-dark-steps-vertical > .du-bois-dark-steps-item .du-bois-dark-steps-item-icon {\n float: left;\n margin-right: 16px;\n}\n.du-bois-dark-steps-vertical > .du-bois-dark-steps-item .du-bois-dark-steps-item-content {\n display: block;\n min-height: 48px;\n overflow: hidden;\n}\n.du-bois-dark-steps-vertical > .du-bois-dark-steps-item .du-bois-dark-steps-item-title {\n line-height: 32px;\n}\n.du-bois-dark-steps-vertical > .du-bois-dark-steps-item .du-bois-dark-steps-item-description {\n padding-bottom: 12px;\n}\n.du-bois-dark-steps-vertical > .du-bois-dark-steps-item > .du-bois-dark-steps-item-container > .du-bois-dark-steps-item-tail {\n position: absolute;\n top: 0;\n left: 16px;\n width: 1px;\n height: 100%;\n padding: 38px 0 6px;\n}\n.du-bois-dark-steps-vertical > .du-bois-dark-steps-item > .du-bois-dark-steps-item-container > .du-bois-dark-steps-item-tail::after {\n width: 1px;\n height: 100%;\n}\n.du-bois-dark-steps-vertical > .du-bois-dark-steps-item:not(:last-child) > .du-bois-dark-steps-item-container > .du-bois-dark-steps-item-tail {\n display: block;\n}\n.du-bois-dark-steps-vertical > .du-bois-dark-steps-item > .du-bois-dark-steps-item-container > .du-bois-dark-steps-item-content > .du-bois-dark-steps-item-title::after {\n display: none;\n}\n.du-bois-dark-steps-vertical.du-bois-dark-steps-small .du-bois-dark-steps-item-container .du-bois-dark-steps-item-tail {\n position: absolute;\n top: 0;\n left: 12px;\n padding: 30px 0 6px;\n}\n.du-bois-dark-steps-vertical.du-bois-dark-steps-small .du-bois-dark-steps-item-container .du-bois-dark-steps-item-title {\n line-height: 24px;\n}\n.du-bois-dark-steps-label-vertical .du-bois-dark-steps-item {\n overflow: visible;\n}\n.du-bois-dark-steps-label-vertical .du-bois-dark-steps-item-tail {\n margin-left: 58px;\n padding: 3.5px 24px;\n}\n.du-bois-dark-steps-label-vertical .du-bois-dark-steps-item-content {\n display: block;\n width: 116px;\n margin-top: 8px;\n text-align: center;\n}\n.du-bois-dark-steps-label-vertical .du-bois-dark-steps-item-icon {\n display: inline-block;\n margin-left: 42px;\n}\n.du-bois-dark-steps-label-vertical .du-bois-dark-steps-item-title {\n padding-right: 0;\n padding-left: 0;\n}\n.du-bois-dark-steps-label-vertical .du-bois-dark-steps-item-title::after {\n display: none;\n}\n.du-bois-dark-steps-label-vertical .du-bois-dark-steps-item-subtitle {\n display: block;\n margin-bottom: 4px;\n margin-left: 0;\n line-height: 20px;\n}\n.du-bois-dark-steps-label-vertical.du-bois-dark-steps-small:not(.du-bois-dark-steps-dot) .du-bois-dark-steps-item-icon {\n margin-left: 46px;\n}\n.du-bois-dark-steps-dot .du-bois-dark-steps-item-title,\n.du-bois-dark-steps-dot.du-bois-dark-steps-small .du-bois-dark-steps-item-title {\n line-height: 20px;\n}\n.du-bois-dark-steps-dot .du-bois-dark-steps-item-tail,\n.du-bois-dark-steps-dot.du-bois-dark-steps-small .du-bois-dark-steps-item-tail {\n top: 2px;\n width: 100%;\n margin: 0 0 0 70px;\n padding: 0;\n}\n.du-bois-dark-steps-dot .du-bois-dark-steps-item-tail::after,\n.du-bois-dark-steps-dot.du-bois-dark-steps-small .du-bois-dark-steps-item-tail::after {\n width: calc(100% - 20px);\n height: 3px;\n margin-left: 12px;\n}\n.du-bois-dark-steps-dot .du-bois-dark-steps-item:first-child .du-bois-dark-steps-icon-dot,\n.du-bois-dark-steps-dot.du-bois-dark-steps-small .du-bois-dark-steps-item:first-child .du-bois-dark-steps-icon-dot {\n left: 2px;\n}\n.du-bois-dark-steps-dot .du-bois-dark-steps-item-icon,\n.du-bois-dark-steps-dot.du-bois-dark-steps-small .du-bois-dark-steps-item-icon {\n width: 8px;\n height: 8px;\n margin-left: 67px;\n padding-right: 0;\n line-height: 8px;\n background: transparent;\n border: 0;\n}\n.du-bois-dark-steps-dot .du-bois-dark-steps-item-icon .du-bois-dark-steps-icon-dot,\n.du-bois-dark-steps-dot.du-bois-dark-steps-small .du-bois-dark-steps-item-icon .du-bois-dark-steps-icon-dot {\n position: relative;\n float: left;\n width: 100%;\n height: 100%;\n border-radius: 100px;\n transition: all 0.3s;\n /* expand hover area */\n}\n.du-bois-dark-steps-dot .du-bois-dark-steps-item-icon .du-bois-dark-steps-icon-dot::after,\n.du-bois-dark-steps-dot.du-bois-dark-steps-small .du-bois-dark-steps-item-icon .du-bois-dark-steps-icon-dot::after {\n position: absolute;\n top: -12px;\n left: -26px;\n width: 60px;\n height: 32px;\n background: rgba(0, 0, 0, 0.001);\n content: '';\n}\n.du-bois-dark-steps-dot .du-bois-dark-steps-item-content,\n.du-bois-dark-steps-dot.du-bois-dark-steps-small .du-bois-dark-steps-item-content {\n width: 140px;\n}\n.du-bois-dark-steps-dot .du-bois-dark-steps-item-process .du-bois-dark-steps-item-icon,\n.du-bois-dark-steps-dot.du-bois-dark-steps-small .du-bois-dark-steps-item-process .du-bois-dark-steps-item-icon {\n position: relative;\n top: -1px;\n width: 10px;\n height: 10px;\n line-height: 10px;\n background: none;\n}\n.du-bois-dark-steps-dot .du-bois-dark-steps-item-process .du-bois-dark-steps-icon:first-child .du-bois-dark-steps-icon-dot,\n.du-bois-dark-steps-dot.du-bois-dark-steps-small .du-bois-dark-steps-item-process .du-bois-dark-steps-icon:first-child .du-bois-dark-steps-icon-dot {\n left: 0;\n}\n.du-bois-dark-steps-vertical.du-bois-dark-steps-dot .du-bois-dark-steps-item-icon {\n margin-top: 8px;\n margin-left: 0;\n background: none;\n}\n.du-bois-dark-steps-vertical.du-bois-dark-steps-dot .du-bois-dark-steps-item > .du-bois-dark-steps-item-container > .du-bois-dark-steps-item-tail {\n top: 2px;\n left: -9px;\n margin: 0;\n padding: 22px 0 4px;\n}\n.du-bois-dark-steps-vertical.du-bois-dark-steps-dot .du-bois-dark-steps-item:first-child .du-bois-dark-steps-icon-dot {\n left: 0;\n}\n.du-bois-dark-steps-vertical.du-bois-dark-steps-dot .du-bois-dark-steps-item-content {\n width: inherit;\n}\n.du-bois-dark-steps-vertical.du-bois-dark-steps-dot .du-bois-dark-steps-item-process .du-bois-dark-steps-item-container .du-bois-dark-steps-item-icon .du-bois-dark-steps-icon-dot {\n left: -2px;\n}\n.du-bois-dark-steps-navigation {\n padding-top: 12px;\n}\n.du-bois-dark-steps-navigation.du-bois-dark-steps-small .du-bois-dark-steps-item-container {\n margin-left: -12px;\n}\n.du-bois-dark-steps-navigation .du-bois-dark-steps-item {\n overflow: visible;\n text-align: center;\n}\n.du-bois-dark-steps-navigation .du-bois-dark-steps-item-container {\n display: inline-block;\n height: 100%;\n margin-left: -16px;\n padding-bottom: 12px;\n text-align: left;\n transition: opacity 0.3s;\n}\n.du-bois-dark-steps-navigation .du-bois-dark-steps-item-container .du-bois-dark-steps-item-content {\n max-width: auto;\n}\n.du-bois-dark-steps-navigation .du-bois-dark-steps-item-container .du-bois-dark-steps-item-title {\n max-width: 100%;\n padding-right: 0;\n overflow: hidden;\n white-space: nowrap;\n text-overflow: ellipsis;\n}\n.du-bois-dark-steps-navigation .du-bois-dark-steps-item-container .du-bois-dark-steps-item-title::after {\n display: none;\n}\n.du-bois-dark-steps-navigation .du-bois-dark-steps-item:not(.du-bois-dark-steps-item-active) .du-bois-dark-steps-item-container[role='button'] {\n cursor: pointer;\n}\n.du-bois-dark-steps-navigation .du-bois-dark-steps-item:not(.du-bois-dark-steps-item-active) .du-bois-dark-steps-item-container[role='button']:hover {\n opacity: 0.85;\n}\n.du-bois-dark-steps-navigation .du-bois-dark-steps-item:last-child {\n flex: 1;\n}\n.du-bois-dark-steps-navigation .du-bois-dark-steps-item:last-child::after {\n display: none;\n}\n.du-bois-dark-steps-navigation .du-bois-dark-steps-item::after {\n position: absolute;\n top: 50%;\n left: 100%;\n display: inline-block;\n width: 12px;\n height: 12px;\n margin-top: -14px;\n margin-left: -2px;\n border: 1px solid rgba(255, 255, 255, 0.2);\n border-bottom: none;\n border-left: none;\n transform: rotate(45deg);\n content: '';\n}\n.du-bois-dark-steps-navigation .du-bois-dark-steps-item::before {\n position: absolute;\n bottom: 0;\n left: 50%;\n display: inline-block;\n width: 0;\n height: 2px;\n background-color: #2272B4;\n transition: width 0.3s, left 0.3s;\n transition-timing-function: ease-out;\n content: '';\n}\n.du-bois-dark-steps-navigation .du-bois-dark-steps-item.du-bois-dark-steps-item-active::before {\n left: 0;\n width: 100%;\n}\n.du-bois-dark-steps-navigation.du-bois-dark-steps-vertical > .du-bois-dark-steps-item {\n margin-right: 0 !important;\n}\n.du-bois-dark-steps-navigation.du-bois-dark-steps-vertical > .du-bois-dark-steps-item::before {\n display: none;\n}\n.du-bois-dark-steps-navigation.du-bois-dark-steps-vertical > .du-bois-dark-steps-item.du-bois-dark-steps-item-active::before {\n top: 0;\n right: 0;\n left: unset;\n display: block;\n width: 3px;\n height: calc(100% - 24px);\n}\n.du-bois-dark-steps-navigation.du-bois-dark-steps-vertical > .du-bois-dark-steps-item::after {\n position: relative;\n top: -2px;\n left: 50%;\n display: block;\n width: 8px;\n height: 8px;\n margin-bottom: 8px;\n text-align: center;\n transform: rotate(135deg);\n}\n.du-bois-dark-steps-navigation.du-bois-dark-steps-vertical > .du-bois-dark-steps-item > .du-bois-dark-steps-item-container > .du-bois-dark-steps-item-tail {\n visibility: hidden;\n}\n.du-bois-dark-steps-with-progress .du-bois-dark-steps-item {\n padding-top: 4px;\n}\n.du-bois-dark-steps-with-progress .du-bois-dark-steps-item .du-bois-dark-steps-item-tail {\n top: 4px !important;\n}\n.du-bois-dark-steps-with-progress.du-bois-dark-steps-horizontal .du-bois-dark-steps-item:first-child {\n padding-bottom: 4px;\n padding-left: 4px;\n}\n.du-bois-dark-steps-with-progress .du-bois-dark-steps-item-icon {\n position: relative;\n}\n.du-bois-dark-steps-with-progress .du-bois-dark-steps-item-icon .du-bois-dark-progress {\n position: absolute;\n top: -5px;\n right: -5px;\n bottom: -5px;\n left: -5px;\n}\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-dark-progress {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #E8ECF0;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: tnum;\n display: inline-block;\n}\n.du-bois-dark-progress-line {\n position: relative;\n width: 100%;\n font-size: 13px;\n}\n.du-bois-dark-progress-steps {\n display: inline-block;\n}\n.du-bois-dark-progress-steps-outer {\n display: flex;\n flex-direction: row;\n align-items: center;\n}\n.du-bois-dark-progress-steps-item {\n flex-shrink: 0;\n min-width: 2px;\n margin-right: 2px;\n background: rgba(255, 255, 255, 0.08);\n transition: all 0.3s;\n}\n.du-bois-dark-progress-steps-item-active {\n background: #177ddc;\n}\n.du-bois-dark-progress-small.du-bois-dark-progress-line,\n.du-bois-dark-progress-small.du-bois-dark-progress-line .du-bois-dark-progress-text .anticon {\n font-size: 12px;\n}\n.du-bois-dark-progress-outer {\n display: inline-block;\n width: 100%;\n margin-right: 0;\n padding-right: 0;\n}\n.du-bois-dark-progress-show-info .du-bois-dark-progress-outer {\n margin-right: calc(-2em - 8px);\n padding-right: calc(2em + 8px);\n}\n.du-bois-dark-progress-inner {\n position: relative;\n display: inline-block;\n width: 100%;\n overflow: hidden;\n vertical-align: middle;\n background-color: rgba(255, 255, 255, 0.08);\n border-radius: 100px;\n}\n.du-bois-dark-progress-circle-trail {\n stroke: rgba(255, 255, 255, 0.08);\n}\n.du-bois-dark-progress-circle-path {\n animation: du-bois-dark-progress-appear 0.3s;\n}\n.du-bois-dark-progress-inner:not(.du-bois-dark-progress-circle-gradient) .du-bois-dark-progress-circle-path {\n stroke: #177ddc;\n}\n.du-bois-dark-progress-success-bg,\n.du-bois-dark-progress-bg {\n position: relative;\n background-color: #177ddc;\n border-radius: 100px;\n transition: all 0.4s cubic-bezier(0.08, 0.82, 0.17, 1) 0s;\n}\n.du-bois-dark-progress-success-bg {\n position: absolute;\n top: 0;\n left: 0;\n background-color: #34824F;\n}\n.du-bois-dark-progress-text {\n display: inline-block;\n width: 2em;\n margin-left: 8px;\n color: #E8ECF0;\n font-size: 1em;\n line-height: 1;\n white-space: nowrap;\n text-align: left;\n vertical-align: middle;\n word-break: normal;\n}\n.du-bois-dark-progress-text .anticon {\n font-size: 13px;\n}\n.du-bois-dark-progress-status-active .du-bois-dark-progress-bg::before {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: #141414;\n border-radius: 10px;\n opacity: 0;\n animation: du-bois-dark-progress-active 2.4s cubic-bezier(0.23, 1, 0.32, 1) infinite;\n content: '';\n}\n.du-bois-dark-progress-status-exception .du-bois-dark-progress-bg {\n background-color: #E65B77;\n}\n.du-bois-dark-progress-status-exception .du-bois-dark-progress-text {\n color: #E65B77;\n}\n.du-bois-dark-progress-status-exception .du-bois-dark-progress-inner:not(.du-bois-dark-progress-circle-gradient) .du-bois-dark-progress-circle-path {\n stroke: #E65B77;\n}\n.du-bois-dark-progress-status-success .du-bois-dark-progress-bg {\n background-color: #34824F;\n}\n.du-bois-dark-progress-status-success .du-bois-dark-progress-text {\n color: #34824F;\n}\n.du-bois-dark-progress-status-success .du-bois-dark-progress-inner:not(.du-bois-dark-progress-circle-gradient) .du-bois-dark-progress-circle-path {\n stroke: #34824F;\n}\n.du-bois-dark-progress-circle .du-bois-dark-progress-inner {\n position: relative;\n line-height: 1;\n background-color: transparent;\n}\n.du-bois-dark-progress-circle .du-bois-dark-progress-text {\n position: absolute;\n top: 50%;\n left: 50%;\n width: 100%;\n margin: 0;\n padding: 0;\n color: #E8ECF0;\n font-size: 1em;\n line-height: 1;\n white-space: normal;\n text-align: center;\n transform: translate(-50%, -50%);\n}\n.du-bois-dark-progress-circle .du-bois-dark-progress-text .anticon {\n font-size: 1.16666667em;\n}\n.du-bois-dark-progress-circle.du-bois-dark-progress-status-exception .du-bois-dark-progress-text {\n color: #E65B77;\n}\n.du-bois-dark-progress-circle.du-bois-dark-progress-status-success .du-bois-dark-progress-text {\n color: #34824F;\n}\n@keyframes du-bois-dark-progress-active {\n 0% {\n transform: translateX(-100%) scaleX(0);\n opacity: 0.1;\n }\n 20% {\n transform: translateX(-100%) scaleX(0);\n opacity: 0.5;\n }\n 100% {\n transform: translateX(0) scaleX(1);\n opacity: 0;\n }\n}\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-dark-table.du-bois-dark-table-middle {\n font-size: 13px;\n}\n.du-bois-dark-table.du-bois-dark-table-middle .du-bois-dark-table-title,\n.du-bois-dark-table.du-bois-dark-table-middle .du-bois-dark-table-footer,\n.du-bois-dark-table.du-bois-dark-table-middle .du-bois-dark-table-thead > tr > th,\n.du-bois-dark-table.du-bois-dark-table-middle .du-bois-dark-table-tbody > tr > td,\n.du-bois-dark-table.du-bois-dark-table-middle tfoot > tr > th,\n.du-bois-dark-table.du-bois-dark-table-middle tfoot > tr > td {\n padding: 12px 8px;\n}\n.du-bois-dark-table.du-bois-dark-table-middle .du-bois-dark-table-filter-trigger {\n margin-right: -4px;\n}\n.du-bois-dark-table.du-bois-dark-table-middle .du-bois-dark-table-expanded-row-fixed {\n margin: -12px -8px;\n}\n.du-bois-dark-table.du-bois-dark-table-middle .du-bois-dark-table-tbody .du-bois-dark-table-wrapper:only-child .du-bois-dark-table {\n margin: -12px -8px -12px 25px;\n}\n.du-bois-dark-table.du-bois-dark-table-small {\n font-size: 13px;\n}\n.du-bois-dark-table.du-bois-dark-table-small .du-bois-dark-table-title,\n.du-bois-dark-table.du-bois-dark-table-small .du-bois-dark-table-footer,\n.du-bois-dark-table.du-bois-dark-table-small .du-bois-dark-table-thead > tr > th,\n.du-bois-dark-table.du-bois-dark-table-small .du-bois-dark-table-tbody > tr > td,\n.du-bois-dark-table.du-bois-dark-table-small tfoot > tr > th,\n.du-bois-dark-table.du-bois-dark-table-small tfoot > tr > td {\n padding: 8px 8px;\n}\n.du-bois-dark-table.du-bois-dark-table-small .du-bois-dark-table-filter-trigger {\n margin-right: -4px;\n}\n.du-bois-dark-table.du-bois-dark-table-small .du-bois-dark-table-expanded-row-fixed {\n margin: -8px -8px;\n}\n.du-bois-dark-table.du-bois-dark-table-small .du-bois-dark-table-tbody .du-bois-dark-table-wrapper:only-child .du-bois-dark-table {\n margin: -8px -8px -8px 25px;\n}\n.du-bois-dark-table-small .du-bois-dark-table-thead > tr > th {\n background-color: #1d1d1d;\n}\n.du-bois-dark-table-small .du-bois-dark-table-selection-column {\n width: 46px;\n min-width: 46px;\n}\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-title {\n border: 1px solid #303030;\n border-bottom: 0;\n}\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-container {\n border-left: 1px solid #303030;\n}\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-container > .du-bois-dark-table-content > table > thead > tr > th,\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-container > .du-bois-dark-table-header > table > thead > tr > th,\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-container > .du-bois-dark-table-body > table > thead > tr > th,\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-container > .du-bois-dark-table-summary > table > thead > tr > th,\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-container > .du-bois-dark-table-content > table > tbody > tr > td,\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-container > .du-bois-dark-table-header > table > tbody > tr > td,\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-container > .du-bois-dark-table-body > table > tbody > tr > td,\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-container > .du-bois-dark-table-summary > table > tbody > tr > td,\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-container > .du-bois-dark-table-content > table > tfoot > tr > th,\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-container > .du-bois-dark-table-header > table > tfoot > tr > th,\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-container > .du-bois-dark-table-body > table > tfoot > tr > th,\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-container > .du-bois-dark-table-summary > table > tfoot > tr > th,\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-container > .du-bois-dark-table-content > table > tfoot > tr > td,\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-container > .du-bois-dark-table-header > table > tfoot > tr > td,\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-container > .du-bois-dark-table-body > table > tfoot > tr > td,\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-container > .du-bois-dark-table-summary > table > tfoot > tr > td {\n border-right: 1px solid #303030;\n}\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-container > .du-bois-dark-table-content > table > thead > tr:not(:last-child) > th,\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-container > .du-bois-dark-table-header > table > thead > tr:not(:last-child) > th,\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-container > .du-bois-dark-table-body > table > thead > tr:not(:last-child) > th,\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-container > .du-bois-dark-table-summary > table > thead > tr:not(:last-child) > th {\n border-bottom: 1px solid #303030;\n}\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-container > .du-bois-dark-table-content > table > thead > tr > th::before,\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-container > .du-bois-dark-table-header > table > thead > tr > th::before,\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-container > .du-bois-dark-table-body > table > thead > tr > th::before,\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-container > .du-bois-dark-table-summary > table > thead > tr > th::before {\n background-color: transparent !important;\n}\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-container > .du-bois-dark-table-content > table > thead > tr > .du-bois-dark-table-cell-fix-right-first::after,\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-container > .du-bois-dark-table-header > table > thead > tr > .du-bois-dark-table-cell-fix-right-first::after,\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-container > .du-bois-dark-table-body > table > thead > tr > .du-bois-dark-table-cell-fix-right-first::after,\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-container > .du-bois-dark-table-summary > table > thead > tr > .du-bois-dark-table-cell-fix-right-first::after,\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-container > .du-bois-dark-table-content > table > tbody > tr > .du-bois-dark-table-cell-fix-right-first::after,\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-container > .du-bois-dark-table-header > table > tbody > tr > .du-bois-dark-table-cell-fix-right-first::after,\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-container > .du-bois-dark-table-body > table > tbody > tr > .du-bois-dark-table-cell-fix-right-first::after,\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-container > .du-bois-dark-table-summary > table > tbody > tr > .du-bois-dark-table-cell-fix-right-first::after,\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-container > .du-bois-dark-table-content > table > tfoot > tr > .du-bois-dark-table-cell-fix-right-first::after,\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-container > .du-bois-dark-table-header > table > tfoot > tr > .du-bois-dark-table-cell-fix-right-first::after,\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-container > .du-bois-dark-table-body > table > tfoot > tr > .du-bois-dark-table-cell-fix-right-first::after,\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-container > .du-bois-dark-table-summary > table > tfoot > tr > .du-bois-dark-table-cell-fix-right-first::after {\n border-right: 1px solid #303030;\n}\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-container > .du-bois-dark-table-content > table > tbody > tr > td > .du-bois-dark-table-expanded-row-fixed,\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-container > .du-bois-dark-table-header > table > tbody > tr > td > .du-bois-dark-table-expanded-row-fixed,\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-container > .du-bois-dark-table-body > table > tbody > tr > td > .du-bois-dark-table-expanded-row-fixed,\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-container > .du-bois-dark-table-summary > table > tbody > tr > td > .du-bois-dark-table-expanded-row-fixed {\n margin: -16px -17px;\n}\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-container > .du-bois-dark-table-content > table > tbody > tr > td > .du-bois-dark-table-expanded-row-fixed::after,\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-container > .du-bois-dark-table-header > table > tbody > tr > td > .du-bois-dark-table-expanded-row-fixed::after,\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-container > .du-bois-dark-table-body > table > tbody > tr > td > .du-bois-dark-table-expanded-row-fixed::after,\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-container > .du-bois-dark-table-summary > table > tbody > tr > td > .du-bois-dark-table-expanded-row-fixed::after {\n position: absolute;\n top: 0;\n right: 1px;\n bottom: 0;\n border-right: 1px solid #303030;\n content: '';\n}\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-container > .du-bois-dark-table-content > table,\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-container > .du-bois-dark-table-header > table {\n border-top: 1px solid #303030;\n}\n.du-bois-dark-table.du-bois-dark-table-bordered.du-bois-dark-table-scroll-horizontal > .du-bois-dark-table-container > .du-bois-dark-table-body > table > tbody > tr.du-bois-dark-table-expanded-row > td,\n.du-bois-dark-table.du-bois-dark-table-bordered.du-bois-dark-table-scroll-horizontal > .du-bois-dark-table-container > .du-bois-dark-table-body > table > tbody > tr.du-bois-dark-table-placeholder > td {\n border-right: 0;\n}\n.du-bois-dark-table.du-bois-dark-table-bordered.du-bois-dark-table-middle > .du-bois-dark-table-container > .du-bois-dark-table-content > table > tbody > tr > td > .du-bois-dark-table-expanded-row-fixed,\n.du-bois-dark-table.du-bois-dark-table-bordered.du-bois-dark-table-middle > .du-bois-dark-table-container > .du-bois-dark-table-body > table > tbody > tr > td > .du-bois-dark-table-expanded-row-fixed {\n margin: -12px -9px;\n}\n.du-bois-dark-table.du-bois-dark-table-bordered.du-bois-dark-table-small > .du-bois-dark-table-container > .du-bois-dark-table-content > table > tbody > tr > td > .du-bois-dark-table-expanded-row-fixed,\n.du-bois-dark-table.du-bois-dark-table-bordered.du-bois-dark-table-small > .du-bois-dark-table-container > .du-bois-dark-table-body > table > tbody > tr > td > .du-bois-dark-table-expanded-row-fixed {\n margin: -8px -9px;\n}\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-footer {\n border: 1px solid #303030;\n border-top: 0;\n}\n.du-bois-dark-table-cell .du-bois-dark-table-container:first-child {\n border-top: 0;\n}\n.du-bois-dark-table-cell-scrollbar {\n box-shadow: 0 1px 0 1px #1d1d1d;\n}\n.du-bois-dark-table-wrapper {\n clear: both;\n max-width: 100%;\n}\n.du-bois-dark-table-wrapper::before {\n display: table;\n content: '';\n}\n.du-bois-dark-table-wrapper::after {\n display: table;\n clear: both;\n content: '';\n}\n.du-bois-dark-table {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #E8ECF0;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: tnum;\n position: relative;\n font-size: 13px;\n background: #141414;\n border-radius: 4px;\n}\n.du-bois-dark-table table {\n width: 100%;\n text-align: left;\n border-radius: 4px 4px 0 0;\n border-collapse: separate;\n border-spacing: 0;\n}\n.du-bois-dark-table-thead > tr > th,\n.du-bois-dark-table-tbody > tr > td,\n.du-bois-dark-table tfoot > tr > th,\n.du-bois-dark-table tfoot > tr > td {\n position: relative;\n padding: 16px 16px;\n overflow-wrap: break-word;\n}\n.du-bois-dark-table-cell-ellipsis {\n overflow: hidden;\n white-space: nowrap;\n text-overflow: ellipsis;\n word-break: keep-all;\n}\n.du-bois-dark-table-cell-ellipsis.du-bois-dark-table-cell-fix-left-last,\n.du-bois-dark-table-cell-ellipsis.du-bois-dark-table-cell-fix-right-first {\n overflow: visible;\n}\n.du-bois-dark-table-cell-ellipsis.du-bois-dark-table-cell-fix-left-last .du-bois-dark-table-cell-content,\n.du-bois-dark-table-cell-ellipsis.du-bois-dark-table-cell-fix-right-first .du-bois-dark-table-cell-content {\n display: block;\n overflow: hidden;\n text-overflow: ellipsis;\n}\n.du-bois-dark-table-cell-ellipsis .du-bois-dark-table-column-title {\n overflow: hidden;\n text-overflow: ellipsis;\n word-break: keep-all;\n}\n.du-bois-dark-table-title {\n padding: 16px 16px;\n}\n.du-bois-dark-table-footer {\n padding: 16px 16px;\n color: rgba(255, 255, 255, 0.85);\n background: rgba(255, 255, 255, 0.04);\n}\n.du-bois-dark-table-thead > tr > th {\n position: relative;\n color: rgba(255, 255, 255, 0.85);\n font-weight: 500;\n text-align: left;\n background: #1d1d1d;\n border-bottom: 1px solid #303030;\n transition: background 0.3s ease;\n}\n.du-bois-dark-table-thead > tr > th[colspan]:not([colspan='1']) {\n text-align: center;\n}\n.du-bois-dark-table-thead > tr > th:not(:last-child):not(.du-bois-dark-table-selection-column):not(.du-bois-dark-table-row-expand-icon-cell):not([colspan])::before {\n position: absolute;\n top: 50%;\n right: 0;\n width: 1px;\n height: 1.6em;\n background-color: rgba(255, 255, 255, 0.08);\n transform: translateY(-50%);\n transition: background-color 0.3s;\n content: '';\n}\n.du-bois-dark-table-thead > tr:not(:last-child) > th[colspan] {\n border-bottom: 0;\n}\n.du-bois-dark-table-tbody > tr > td {\n border-bottom: 1px solid #303030;\n transition: background 0.3s;\n}\n.du-bois-dark-table-tbody > tr > td > .du-bois-dark-table-wrapper:only-child .du-bois-dark-table,\n.du-bois-dark-table-tbody > tr > td > .du-bois-dark-table-expanded-row-fixed > .du-bois-dark-table-wrapper:only-child .du-bois-dark-table {\n margin: -16px -16px -16px 33px;\n}\n.du-bois-dark-table-tbody > tr > td > .du-bois-dark-table-wrapper:only-child .du-bois-dark-table-tbody > tr:last-child > td,\n.du-bois-dark-table-tbody > tr > td > .du-bois-dark-table-expanded-row-fixed > .du-bois-dark-table-wrapper:only-child .du-bois-dark-table-tbody > tr:last-child > td {\n border-bottom: 0;\n}\n.du-bois-dark-table-tbody > tr > td > .du-bois-dark-table-wrapper:only-child .du-bois-dark-table-tbody > tr:last-child > td:first-child,\n.du-bois-dark-table-tbody > tr > td > .du-bois-dark-table-expanded-row-fixed > .du-bois-dark-table-wrapper:only-child .du-bois-dark-table-tbody > tr:last-child > td:first-child,\n.du-bois-dark-table-tbody > tr > td > .du-bois-dark-table-wrapper:only-child .du-bois-dark-table-tbody > tr:last-child > td:last-child,\n.du-bois-dark-table-tbody > tr > td > .du-bois-dark-table-expanded-row-fixed > .du-bois-dark-table-wrapper:only-child .du-bois-dark-table-tbody > tr:last-child > td:last-child {\n border-radius: 0;\n}\n.du-bois-dark-table-tbody > tr.du-bois-dark-table-row:hover > td {\n background: #262626;\n}\n.du-bois-dark-table-tbody > tr.du-bois-dark-table-row-selected > td {\n background: #121921;\n border-color: rgba(0, 0, 0, 0.03);\n}\n.du-bois-dark-table-tbody > tr.du-bois-dark-table-row-selected:hover > td {\n background: #0f141a;\n}\n.du-bois-dark-table-summary {\n position: relative;\n z-index: 2;\n background: #141414;\n}\ndiv.du-bois-dark-table-summary {\n box-shadow: 0 -1px 0 #303030;\n}\n.du-bois-dark-table-summary > tr > th,\n.du-bois-dark-table-summary > tr > td {\n border-bottom: 1px solid #303030;\n}\n.du-bois-dark-table-pagination.du-bois-dark-pagination {\n margin: 16px 0;\n}\n.du-bois-dark-table-pagination {\n display: flex;\n flex-wrap: wrap;\n row-gap: 4px;\n}\n.du-bois-dark-table-pagination > * {\n flex: none;\n}\n.du-bois-dark-table-pagination-left {\n justify-content: flex-start;\n}\n.du-bois-dark-table-pagination-center {\n justify-content: center;\n}\n.du-bois-dark-table-pagination-right {\n justify-content: flex-end;\n}\n.du-bois-dark-table-thead th.du-bois-dark-table-column-has-sorters {\n cursor: pointer;\n transition: all 0.3s;\n}\n.du-bois-dark-table-thead th.du-bois-dark-table-column-has-sorters:hover {\n background: #303030;\n}\n.du-bois-dark-table-thead th.du-bois-dark-table-column-has-sorters:hover::before {\n background-color: transparent !important;\n}\n.du-bois-dark-table-thead th.du-bois-dark-table-column-has-sorters.du-bois-dark-table-cell-fix-left:hover,\n.du-bois-dark-table-thead th.du-bois-dark-table-column-has-sorters.du-bois-dark-table-cell-fix-right:hover {\n background: #222;\n}\n.du-bois-dark-table-thead th.du-bois-dark-table-column-sort {\n background: #262626;\n}\n.du-bois-dark-table-thead th.du-bois-dark-table-column-sort::before {\n background-color: transparent !important;\n}\ntd.du-bois-dark-table-column-sort {\n background: rgba(255, 255, 255, 0.01);\n}\n.du-bois-dark-table-column-title {\n position: relative;\n z-index: 1;\n flex: 1;\n}\n.du-bois-dark-table-column-sorters {\n display: flex;\n flex: auto;\n align-items: center;\n justify-content: space-between;\n}\n.du-bois-dark-table-column-sorters::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n width: 100%;\n height: 100%;\n content: '';\n}\n.du-bois-dark-table-column-sorter {\n color: #bfbfbf;\n font-size: 0;\n transition: color 0.3s;\n}\n.du-bois-dark-table-column-sorter-inner {\n display: inline-flex;\n flex-direction: column;\n align-items: center;\n}\n.du-bois-dark-table-column-sorter-up,\n.du-bois-dark-table-column-sorter-down {\n font-size: 11px;\n}\n.du-bois-dark-table-column-sorter-up.active,\n.du-bois-dark-table-column-sorter-down.active {\n color: #2272B4;\n}\n.du-bois-dark-table-column-sorter-up + .du-bois-dark-table-column-sorter-down {\n margin-top: -0.3em;\n}\n.du-bois-dark-table-column-sorters:hover .du-bois-dark-table-column-sorter {\n color: #a6a6a6;\n}\n.du-bois-dark-table-filter-column {\n display: flex;\n justify-content: space-between;\n}\n.du-bois-dark-table-filter-trigger {\n position: relative;\n display: flex;\n align-items: center;\n margin: -4px -8px -4px 4px;\n padding: 0 4px;\n color: #bfbfbf;\n font-size: 12px;\n border-radius: 4px;\n cursor: pointer;\n transition: all 0.3s;\n}\n.du-bois-dark-table-filter-trigger:hover {\n color: #92A4B3;\n background: #434343;\n}\n.du-bois-dark-table-filter-trigger.active {\n color: #2272B4;\n}\n.du-bois-dark-table-filter-dropdown {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #E8ECF0;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: tnum;\n min-width: 120px;\n background-color: #1f1f1f;\n border-radius: 4px;\n box-shadow: 0 3px 6px -4px rgba(0, 0, 0, 0.48), 0 6px 16px 0 rgba(0, 0, 0, 0.32), 0 9px 28px 8px rgba(0, 0, 0, 0.2);\n}\n.du-bois-dark-table-filter-dropdown .du-bois-dark-dropdown-menu {\n max-height: 264px;\n overflow-x: hidden;\n border: 0;\n box-shadow: none;\n}\n.du-bois-dark-table-filter-dropdown-submenu > ul {\n max-height: calc(100vh - 130px);\n overflow-x: hidden;\n overflow-y: auto;\n}\n.du-bois-dark-table-filter-dropdown .du-bois-dark-checkbox-wrapper + span,\n.du-bois-dark-table-filter-dropdown-submenu .du-bois-dark-checkbox-wrapper + span {\n padding-left: 8px;\n}\n.du-bois-dark-table-filter-dropdown-btns {\n display: flex;\n justify-content: space-between;\n padding: 7px 8px 7px 3px;\n overflow: hidden;\n background-color: #1f1f1f;\n border-top: 1px solid #303030;\n}\n.du-bois-dark-table-selection-col {\n width: 32px;\n}\n.du-bois-dark-table-bordered .du-bois-dark-table-selection-col {\n width: 50px;\n}\ntable tr th.du-bois-dark-table-selection-column,\ntable tr td.du-bois-dark-table-selection-column {\n padding-right: 4px;\n padding-left: 4px;\n text-align: center;\n}\ntable tr th.du-bois-dark-table-selection-column .du-bois-dark-radio-wrapper,\ntable tr td.du-bois-dark-table-selection-column .du-bois-dark-radio-wrapper {\n margin-right: 0;\n}\ntable tr th.du-bois-dark-table-selection-column::after {\n background-color: transparent !important;\n}\n.du-bois-dark-table-selection {\n position: relative;\n display: inline-flex;\n flex-direction: column;\n}\n.du-bois-dark-table-selection-extra {\n position: absolute;\n top: 0;\n z-index: 1;\n cursor: pointer;\n transition: all 0.3s;\n margin-inline-start: 100%;\n padding-inline-start: 0px;\n}\n.du-bois-dark-table-selection-extra .anticon {\n color: #bfbfbf;\n font-size: 10px;\n}\n.du-bois-dark-table-selection-extra .anticon:hover {\n color: #a6a6a6;\n}\n.du-bois-dark-table-expand-icon-col {\n width: 48px;\n}\n.du-bois-dark-table-row-expand-icon-cell {\n text-align: center;\n}\n.du-bois-dark-table-row-indent {\n float: left;\n height: 1px;\n}\n.du-bois-dark-table-row-expand-icon {\n color: #2272B4;\n text-decoration: none;\n cursor: pointer;\n transition: color 0.3s;\n position: relative;\n display: inline-flex;\n float: left;\n box-sizing: border-box;\n width: 17px;\n height: 17px;\n padding: 0;\n color: inherit;\n line-height: 17px;\n background: transparent;\n border: 1px solid #303030;\n border-radius: 4px;\n outline: none;\n transform: scale(0.94117647);\n transition: all 0.3s;\n user-select: none;\n}\n.du-bois-dark-table-row-expand-icon:focus,\n.du-bois-dark-table-row-expand-icon:hover {\n color: #1d517c;\n}\n.du-bois-dark-table-row-expand-icon:active {\n color: #3f81b1;\n}\n.du-bois-dark-table-row-expand-icon:focus,\n.du-bois-dark-table-row-expand-icon:hover,\n.du-bois-dark-table-row-expand-icon:active {\n border-color: currentColor;\n}\n.du-bois-dark-table-row-expand-icon::before,\n.du-bois-dark-table-row-expand-icon::after {\n position: absolute;\n background: currentColor;\n transition: transform 0.3s ease-out;\n content: '';\n}\n.du-bois-dark-table-row-expand-icon::before {\n top: 7px;\n right: 3px;\n left: 3px;\n height: 1px;\n}\n.du-bois-dark-table-row-expand-icon::after {\n top: 3px;\n bottom: 3px;\n left: 7px;\n width: 1px;\n transform: rotate(90deg);\n}\n.du-bois-dark-table-row-expand-icon-collapsed::before {\n transform: rotate(-180deg);\n}\n.du-bois-dark-table-row-expand-icon-collapsed::after {\n transform: rotate(0deg);\n}\n.du-bois-dark-table-row-expand-icon-spaced {\n background: transparent;\n border: 0;\n visibility: hidden;\n}\n.du-bois-dark-table-row-expand-icon-spaced::before,\n.du-bois-dark-table-row-expand-icon-spaced::after {\n display: none;\n content: none;\n}\n.du-bois-dark-table-row-indent + .du-bois-dark-table-row-expand-icon {\n margin-top: 121.5px;\n margin-right: 4px;\n}\ntr.du-bois-dark-table-expanded-row > td,\ntr.du-bois-dark-table-expanded-row:hover > td {\n background: #1d1d1d;\n}\ntr.du-bois-dark-table-expanded-row .du-bois-dark-descriptions-view {\n display: flex;\n}\ntr.du-bois-dark-table-expanded-row .du-bois-dark-descriptions-view table {\n flex: auto;\n width: auto;\n}\n.du-bois-dark-table .du-bois-dark-table-expanded-row-fixed {\n position: relative;\n margin: -16px -16px;\n padding: 16px 16px;\n}\n.du-bois-dark-table-tbody > tr.du-bois-dark-table-placeholder {\n text-align: center;\n}\n.du-bois-dark-table-empty .du-bois-dark-table-tbody > tr.du-bois-dark-table-placeholder {\n color: rgba(255, 255, 255, 0.3);\n}\n.du-bois-dark-table-tbody > tr.du-bois-dark-table-placeholder:hover > td {\n background: #141414;\n}\n.du-bois-dark-table-cell-fix-left,\n.du-bois-dark-table-cell-fix-right {\n position: -webkit-sticky !important;\n position: sticky !important;\n z-index: 2;\n background: #141414;\n}\n.du-bois-dark-table-cell-fix-left-first::after,\n.du-bois-dark-table-cell-fix-left-last::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: -1px;\n width: 30px;\n transform: translateX(100%);\n transition: box-shadow 0.3s;\n content: '';\n pointer-events: none;\n}\n.du-bois-dark-table-cell-fix-right-first::after,\n.du-bois-dark-table-cell-fix-right-last::after {\n position: absolute;\n top: 0;\n bottom: -1px;\n left: 0;\n width: 30px;\n transform: translateX(-100%);\n transition: box-shadow 0.3s;\n content: '';\n pointer-events: none;\n}\n.du-bois-dark-table .du-bois-dark-table-container::before,\n.du-bois-dark-table .du-bois-dark-table-container::after {\n position: absolute;\n top: 0;\n bottom: 0;\n z-index: 1;\n width: 30px;\n transition: box-shadow 0.3s;\n content: '';\n pointer-events: none;\n}\n.du-bois-dark-table .du-bois-dark-table-container::before {\n left: 0;\n}\n.du-bois-dark-table .du-bois-dark-table-container::after {\n right: 0;\n}\n.du-bois-dark-table-ping-left:not(.du-bois-dark-table-has-fix-left) .du-bois-dark-table-container {\n position: relative;\n}\n.du-bois-dark-table-ping-left:not(.du-bois-dark-table-has-fix-left) .du-bois-dark-table-container::before {\n box-shadow: inset 10px 0 8px -8px rgba(0, 0, 0, 0.45);\n}\n.du-bois-dark-table-ping-left .du-bois-dark-table-cell-fix-left-first::after,\n.du-bois-dark-table-ping-left .du-bois-dark-table-cell-fix-left-last::after {\n box-shadow: inset 10px 0 8px -8px rgba(0, 0, 0, 0.45);\n}\n.du-bois-dark-table-ping-left .du-bois-dark-table-cell-fix-left-last::before {\n background-color: transparent !important;\n}\n.du-bois-dark-table-ping-right:not(.du-bois-dark-table-has-fix-right) .du-bois-dark-table-container {\n position: relative;\n}\n.du-bois-dark-table-ping-right:not(.du-bois-dark-table-has-fix-right) .du-bois-dark-table-container::after {\n box-shadow: inset -10px 0 8px -8px rgba(0, 0, 0, 0.45);\n}\n.du-bois-dark-table-ping-right .du-bois-dark-table-cell-fix-right-first::after,\n.du-bois-dark-table-ping-right .du-bois-dark-table-cell-fix-right-last::after {\n box-shadow: inset -10px 0 8px -8px rgba(0, 0, 0, 0.45);\n}\n.du-bois-dark-table-sticky-holder {\n position: sticky;\n z-index: 3;\n background: #141414;\n}\n.du-bois-dark-table-sticky-scroll {\n position: sticky;\n bottom: 0;\n z-index: 3;\n display: flex;\n align-items: center;\n background: #fcfcfc;\n border-top: 1px solid #303030;\n opacity: 0.6;\n}\n.du-bois-dark-table-sticky-scroll:hover {\n transform-origin: center bottom;\n}\n.du-bois-dark-table-sticky-scroll-bar {\n height: 8px;\n background-color: rgba(0, 0, 0, 0.35);\n border-radius: 4px;\n}\n.du-bois-dark-table-sticky-scroll-bar:hover {\n background-color: rgba(0, 0, 0, 0.8);\n}\n.du-bois-dark-table-sticky-scroll-bar-active {\n background-color: rgba(0, 0, 0, 0.8);\n}\n@media all and (-ms-high-contrast: none) {\n .du-bois-dark-table-ping-left .du-bois-dark-table-cell-fix-left-last::after {\n box-shadow: none !important;\n }\n .du-bois-dark-table-ping-right .du-bois-dark-table-cell-fix-right-first::after {\n box-shadow: none !important;\n }\n}\n.du-bois-dark-table {\n /* title + table */\n /* table */\n /* table + footer */\n}\n.du-bois-dark-table-title {\n border-radius: 4px 4px 0 0;\n}\n.du-bois-dark-table-title + .du-bois-dark-table-container {\n border-top-left-radius: 0;\n border-top-right-radius: 0;\n}\n.du-bois-dark-table-title + .du-bois-dark-table-container table > thead > tr:first-child th:first-child {\n border-radius: 0;\n}\n.du-bois-dark-table-title + .du-bois-dark-table-container table > thead > tr:first-child th:last-child {\n border-radius: 0;\n}\n.du-bois-dark-table-container {\n border-top-left-radius: 4px;\n border-top-right-radius: 4px;\n}\n.du-bois-dark-table-container table > thead > tr:first-child th:first-child {\n border-top-left-radius: 4px;\n}\n.du-bois-dark-table-container table > thead > tr:first-child th:last-child {\n border-top-right-radius: 4px;\n}\n.du-bois-dark-table-footer {\n border-radius: 0 0 4px 4px;\n}\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-dark-spin {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #E8ECF0;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: tnum;\n position: absolute;\n display: none;\n color: #2272B4;\n text-align: center;\n vertical-align: middle;\n opacity: 0;\n transition: transform 0.3s cubic-bezier(0.78, 0.14, 0.15, 0.86);\n}\n.du-bois-dark-spin-spinning {\n position: static;\n display: inline-block;\n opacity: 1;\n}\n.du-bois-dark-spin-nested-loading {\n position: relative;\n}\n.du-bois-dark-spin-nested-loading > div > .du-bois-dark-spin {\n position: absolute;\n top: 0;\n left: 0;\n z-index: 4;\n display: block;\n width: 100%;\n height: 100%;\n max-height: 400px;\n}\n.du-bois-dark-spin-nested-loading > div > .du-bois-dark-spin .du-bois-dark-spin-dot {\n position: absolute;\n top: 50%;\n left: 50%;\n margin: -10px;\n}\n.du-bois-dark-spin-nested-loading > div > .du-bois-dark-spin .du-bois-dark-spin-text {\n position: absolute;\n top: 50%;\n width: 100%;\n padding-top: 5.5px;\n text-shadow: 0 1px 2px #141414;\n}\n.du-bois-dark-spin-nested-loading > div > .du-bois-dark-spin.du-bois-dark-spin-show-text .du-bois-dark-spin-dot {\n margin-top: -20px;\n}\n.du-bois-dark-spin-nested-loading > div > .du-bois-dark-spin-sm .du-bois-dark-spin-dot {\n margin: -7px;\n}\n.du-bois-dark-spin-nested-loading > div > .du-bois-dark-spin-sm .du-bois-dark-spin-text {\n padding-top: 2.5px;\n}\n.du-bois-dark-spin-nested-loading > div > .du-bois-dark-spin-sm.du-bois-dark-spin-show-text .du-bois-dark-spin-dot {\n margin-top: -17px;\n}\n.du-bois-dark-spin-nested-loading > div > .du-bois-dark-spin-lg .du-bois-dark-spin-dot {\n margin: -16px;\n}\n.du-bois-dark-spin-nested-loading > div > .du-bois-dark-spin-lg .du-bois-dark-spin-text {\n padding-top: 11.5px;\n}\n.du-bois-dark-spin-nested-loading > div > .du-bois-dark-spin-lg.du-bois-dark-spin-show-text .du-bois-dark-spin-dot {\n margin-top: -26px;\n}\n.du-bois-dark-spin-container {\n position: relative;\n transition: opacity 0.3s;\n}\n.du-bois-dark-spin-container::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n z-index: 10;\n display: none \\9;\n width: 100%;\n height: 100%;\n background: #141414;\n opacity: 0;\n transition: all 0.3s;\n content: '';\n pointer-events: none;\n}\n.du-bois-dark-spin-blur {\n clear: both;\n overflow: hidden;\n opacity: 0.5;\n user-select: none;\n pointer-events: none;\n}\n.du-bois-dark-spin-blur::after {\n opacity: 0.4;\n pointer-events: auto;\n}\n.du-bois-dark-spin-tip {\n color: #92A4B3;\n}\n.du-bois-dark-spin-dot {\n position: relative;\n display: inline-block;\n font-size: 20px;\n width: 1em;\n height: 1em;\n}\n.du-bois-dark-spin-dot-item {\n position: absolute;\n display: block;\n width: 9px;\n height: 9px;\n background-color: #2272B4;\n border-radius: 100%;\n transform: scale(0.75);\n transform-origin: 50% 50%;\n opacity: 0.3;\n animation: antSpinMove 1s infinite linear alternate;\n}\n.du-bois-dark-spin-dot-item:nth-child(1) {\n top: 0;\n left: 0;\n}\n.du-bois-dark-spin-dot-item:nth-child(2) {\n top: 0;\n right: 0;\n animation-delay: 0.4s;\n}\n.du-bois-dark-spin-dot-item:nth-child(3) {\n right: 0;\n bottom: 0;\n animation-delay: 0.8s;\n}\n.du-bois-dark-spin-dot-item:nth-child(4) {\n bottom: 0;\n left: 0;\n animation-delay: 1.2s;\n}\n.du-bois-dark-spin-dot-spin {\n transform: rotate(45deg);\n animation: antRotate 1.2s infinite linear;\n}\n.du-bois-dark-spin-sm .du-bois-dark-spin-dot {\n font-size: 14px;\n}\n.du-bois-dark-spin-sm .du-bois-dark-spin-dot i {\n width: 6px;\n height: 6px;\n}\n.du-bois-dark-spin-lg .du-bois-dark-spin-dot {\n font-size: 32px;\n}\n.du-bois-dark-spin-lg .du-bois-dark-spin-dot i {\n width: 14px;\n height: 14px;\n}\n.du-bois-dark-spin.du-bois-dark-spin-show-text .du-bois-dark-spin-text {\n display: block;\n}\n@media all and (-ms-high-contrast: none), (-ms-high-contrast: active) {\n /* IE10+ */\n .du-bois-dark-spin-blur {\n background: #141414;\n opacity: 0.5;\n }\n}\n@keyframes antSpinMove {\n}\n@keyframes antRotate {\n}\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-dark-pagination {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #E8ECF0;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: tnum;\n}\n.du-bois-dark-pagination ul,\n.du-bois-dark-pagination ol {\n margin: 0;\n padding: 0;\n list-style: none;\n}\n.du-bois-dark-pagination::after {\n display: block;\n clear: both;\n height: 0;\n overflow: hidden;\n visibility: hidden;\n content: ' ';\n}\n.du-bois-dark-pagination-total-text {\n display: inline-block;\n height: 40px;\n margin-right: 8px;\n line-height: 38px;\n vertical-align: middle;\n}\n.du-bois-dark-pagination-item {\n display: inline-block;\n min-width: 40px;\n height: 40px;\n margin-right: 8px;\n font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, 'Helvetica Neue', Arial, 'Noto Sans', sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol', 'Noto Color Emoji';\n line-height: 38px;\n text-align: center;\n vertical-align: middle;\n list-style: none;\n background-color: transparent;\n border: 1px solid #1F272D;\n border-radius: 4px;\n outline: 0;\n cursor: pointer;\n user-select: none;\n}\n.du-bois-dark-pagination-item a {\n display: block;\n padding: 0 6px;\n color: #E8ECF0;\n transition: none;\n}\n.du-bois-dark-pagination-item a:hover {\n text-decoration: none;\n}\n.du-bois-dark-pagination-item:focus-visible,\n.du-bois-dark-pagination-item:hover {\n border-color: #2272B4;\n transition: all 0.3s;\n}\n.du-bois-dark-pagination-item:focus-visible a,\n.du-bois-dark-pagination-item:hover a {\n color: #2272B4;\n}\n.du-bois-dark-pagination-item-active {\n font-weight: 500;\n background: transparent;\n border-color: #2272B4;\n}\n.du-bois-dark-pagination-item-active a {\n color: #2272B4;\n}\n.du-bois-dark-pagination-item-active:focus-visible,\n.du-bois-dark-pagination-item-active:hover {\n border-color: #1d517c;\n}\n.du-bois-dark-pagination-item-active:focus-visible a,\n.du-bois-dark-pagination-item-active:hover a {\n color: #1d517c;\n}\n.du-bois-dark-pagination-jump-prev,\n.du-bois-dark-pagination-jump-next {\n outline: 0;\n}\n.du-bois-dark-pagination-jump-prev .du-bois-dark-pagination-item-container,\n.du-bois-dark-pagination-jump-next .du-bois-dark-pagination-item-container {\n position: relative;\n}\n.du-bois-dark-pagination-jump-prev .du-bois-dark-pagination-item-container .du-bois-dark-pagination-item-link-icon,\n.du-bois-dark-pagination-jump-next .du-bois-dark-pagination-item-container .du-bois-dark-pagination-item-link-icon {\n color: #2272B4;\n font-size: 12px;\n letter-spacing: -1px;\n opacity: 0;\n transition: all 0.2s;\n}\n.du-bois-dark-pagination-jump-prev .du-bois-dark-pagination-item-container .du-bois-dark-pagination-item-link-icon-svg,\n.du-bois-dark-pagination-jump-next .du-bois-dark-pagination-item-container .du-bois-dark-pagination-item-link-icon-svg {\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n margin: auto;\n}\n.du-bois-dark-pagination-jump-prev .du-bois-dark-pagination-item-container .du-bois-dark-pagination-item-ellipsis,\n.du-bois-dark-pagination-jump-next .du-bois-dark-pagination-item-container .du-bois-dark-pagination-item-ellipsis {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n display: block;\n margin: auto;\n color: rgba(255, 255, 255, 0.3);\n font-family: Arial, Helvetica, sans-serif;\n letter-spacing: 2px;\n text-align: center;\n text-indent: 0.13em;\n opacity: 1;\n transition: all 0.2s;\n}\n.du-bois-dark-pagination-jump-prev:focus-visible .du-bois-dark-pagination-item-link-icon,\n.du-bois-dark-pagination-jump-next:focus-visible .du-bois-dark-pagination-item-link-icon,\n.du-bois-dark-pagination-jump-prev:hover .du-bois-dark-pagination-item-link-icon,\n.du-bois-dark-pagination-jump-next:hover .du-bois-dark-pagination-item-link-icon {\n opacity: 1;\n}\n.du-bois-dark-pagination-jump-prev:focus-visible .du-bois-dark-pagination-item-ellipsis,\n.du-bois-dark-pagination-jump-next:focus-visible .du-bois-dark-pagination-item-ellipsis,\n.du-bois-dark-pagination-jump-prev:hover .du-bois-dark-pagination-item-ellipsis,\n.du-bois-dark-pagination-jump-next:hover .du-bois-dark-pagination-item-ellipsis {\n opacity: 0;\n}\n.du-bois-dark-pagination-prev,\n.du-bois-dark-pagination-jump-prev,\n.du-bois-dark-pagination-jump-next {\n margin-right: 8px;\n}\n.du-bois-dark-pagination-prev,\n.du-bois-dark-pagination-next,\n.du-bois-dark-pagination-jump-prev,\n.du-bois-dark-pagination-jump-next {\n display: inline-block;\n min-width: 40px;\n height: 40px;\n color: #E8ECF0;\n font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, 'Helvetica Neue', Arial, 'Noto Sans', sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol', 'Noto Color Emoji';\n line-height: 40px;\n text-align: center;\n vertical-align: middle;\n list-style: none;\n border-radius: 4px;\n cursor: pointer;\n transition: all 0.3s;\n}\n.du-bois-dark-pagination-prev,\n.du-bois-dark-pagination-next {\n font-family: Arial, Helvetica, sans-serif;\n outline: 0;\n}\n.du-bois-dark-pagination-prev button,\n.du-bois-dark-pagination-next button {\n color: #E8ECF0;\n cursor: pointer;\n user-select: none;\n}\n.du-bois-dark-pagination-prev:hover button,\n.du-bois-dark-pagination-next:hover button {\n border-color: #1d517c;\n}\n.du-bois-dark-pagination-prev .du-bois-dark-pagination-item-link,\n.du-bois-dark-pagination-next .du-bois-dark-pagination-item-link {\n display: block;\n width: 100%;\n height: 100%;\n padding: 0;\n font-size: 12px;\n text-align: center;\n background-color: transparent;\n border: 1px solid #1F272D;\n border-radius: 4px;\n outline: none;\n transition: all 0.3s;\n}\n.du-bois-dark-pagination-prev:focus-visible .du-bois-dark-pagination-item-link,\n.du-bois-dark-pagination-next:focus-visible .du-bois-dark-pagination-item-link,\n.du-bois-dark-pagination-prev:hover .du-bois-dark-pagination-item-link,\n.du-bois-dark-pagination-next:hover .du-bois-dark-pagination-item-link {\n color: #2272B4;\n border-color: #2272B4;\n}\n.du-bois-dark-pagination-disabled,\n.du-bois-dark-pagination-disabled:hover,\n.du-bois-dark-pagination-disabled:focus-visible {\n cursor: not-allowed;\n}\n.du-bois-dark-pagination-disabled .du-bois-dark-pagination-item-link,\n.du-bois-dark-pagination-disabled:hover .du-bois-dark-pagination-item-link,\n.du-bois-dark-pagination-disabled:focus-visible .du-bois-dark-pagination-item-link {\n color: rgba(255, 255, 255, 0.3);\n border-color: #1F272D;\n cursor: not-allowed;\n}\n.du-bois-dark-pagination-slash {\n margin: 0 10px 0 5px;\n}\n.du-bois-dark-pagination-options {\n display: inline-block;\n margin-left: 16px;\n vertical-align: middle;\n}\n@media all and (-ms-high-contrast: none) {\n .du-bois-dark-pagination-options *::-ms-backdrop,\n .du-bois-dark-pagination-options {\n vertical-align: top;\n }\n}\n.du-bois-dark-pagination-options-size-changer.du-bois-dark-select {\n display: inline-block;\n width: auto;\n}\n.du-bois-dark-pagination-options-quick-jumper {\n display: inline-block;\n height: 40px;\n margin-left: 8px;\n line-height: 40px;\n vertical-align: top;\n}\n.du-bois-dark-pagination-options-quick-jumper input {\n position: relative;\n display: inline-block;\n width: 100%;\n min-width: 0;\n padding: 5px 12px;\n color: #E8ECF0;\n font-size: 13px;\n line-height: 20px;\n background-color: transparent;\n background-image: none;\n border: 1px solid #1F272D;\n border-radius: 4px;\n transition: all 0.3s;\n width: 50px;\n height: 40px;\n margin: 0 8px;\n}\n.du-bois-dark-pagination-options-quick-jumper input::-moz-placeholder {\n opacity: 1;\n}\n.du-bois-dark-pagination-options-quick-jumper input::placeholder {\n color: rgba(255, 255, 255, 0.3);\n}\n.du-bois-dark-pagination-options-quick-jumper input:placeholder-shown {\n text-overflow: ellipsis;\n}\n.du-bois-dark-pagination-options-quick-jumper input:hover {\n border-color: #1d517c;\n border-right-width: 1px !important;\n}\n.du-bois-dark-pagination-options-quick-jumper input:focus,\n.du-bois-dark-pagination-options-quick-jumper input-focused {\n border-color: #2272B4;\n border-right-width: 1px !important;\n outline: 0;\n box-shadow: 0 0 0 2px rgba(34, 114, 180, 0.2);\n}\n.du-bois-dark-pagination-options-quick-jumper input-disabled {\n color: rgba(255, 255, 255, 0.3);\n background-color: rgba(255, 255, 255, 0.08);\n cursor: not-allowed;\n opacity: 1;\n}\n.du-bois-dark-pagination-options-quick-jumper input-disabled:hover {\n border-color: #1F272D;\n border-right-width: 1px !important;\n}\n.du-bois-dark-pagination-options-quick-jumper input[disabled] {\n color: rgba(255, 255, 255, 0.3);\n background-color: rgba(255, 255, 255, 0.08);\n cursor: not-allowed;\n opacity: 1;\n}\n.du-bois-dark-pagination-options-quick-jumper input[disabled]:hover {\n border-color: #1F272D;\n border-right-width: 1px !important;\n}\n.du-bois-dark-pagination-options-quick-jumper input-borderless,\n.du-bois-dark-pagination-options-quick-jumper input-borderless:hover,\n.du-bois-dark-pagination-options-quick-jumper input-borderless:focus,\n.du-bois-dark-pagination-options-quick-jumper input-borderless-focused,\n.du-bois-dark-pagination-options-quick-jumper input-borderless-disabled,\n.du-bois-dark-pagination-options-quick-jumper input-borderless[disabled] {\n background-color: transparent;\n border: none;\n box-shadow: none;\n}\ntextarea.du-bois-dark-pagination-options-quick-jumper input {\n max-width: 100%;\n height: auto;\n min-height: 40px;\n line-height: 20px;\n vertical-align: bottom;\n transition: all 0.3s, height 0s;\n}\n.du-bois-dark-pagination-options-quick-jumper input-lg {\n padding: 5px 12px;\n font-size: 18px;\n}\n.du-bois-dark-pagination-options-quick-jumper input-sm {\n padding: 5px 12px;\n}\n.du-bois-dark-pagination-simple .du-bois-dark-pagination-prev,\n.du-bois-dark-pagination-simple .du-bois-dark-pagination-next {\n height: 24px;\n line-height: 24px;\n vertical-align: top;\n}\n.du-bois-dark-pagination-simple .du-bois-dark-pagination-prev .du-bois-dark-pagination-item-link,\n.du-bois-dark-pagination-simple .du-bois-dark-pagination-next .du-bois-dark-pagination-item-link {\n height: 24px;\n background-color: transparent;\n border: 0;\n}\n.du-bois-dark-pagination-simple .du-bois-dark-pagination-prev .du-bois-dark-pagination-item-link::after,\n.du-bois-dark-pagination-simple .du-bois-dark-pagination-next .du-bois-dark-pagination-item-link::after {\n height: 24px;\n line-height: 24px;\n}\n.du-bois-dark-pagination-simple .du-bois-dark-pagination-simple-pager {\n display: inline-block;\n height: 24px;\n margin-right: 8px;\n}\n.du-bois-dark-pagination-simple .du-bois-dark-pagination-simple-pager input {\n box-sizing: border-box;\n height: 100%;\n margin-right: 8px;\n padding: 0 6px;\n text-align: center;\n background-color: transparent;\n border: 1px solid #1F272D;\n border-radius: 4px;\n outline: none;\n transition: border-color 0.3s;\n}\n.du-bois-dark-pagination-simple .du-bois-dark-pagination-simple-pager input:hover {\n border-color: #2272B4;\n}\n.du-bois-dark-pagination-simple .du-bois-dark-pagination-simple-pager input[disabled] {\n color: rgba(255, 255, 255, 0.3);\n background: rgba(255, 255, 255, 0.08);\n border-color: #1F272D;\n cursor: not-allowed;\n}\n.du-bois-dark-pagination.mini .du-bois-dark-pagination-total-text,\n.du-bois-dark-pagination.mini .du-bois-dark-pagination-simple-pager {\n height: 24px;\n line-height: 24px;\n}\n.du-bois-dark-pagination.mini .du-bois-dark-pagination-item {\n min-width: 24px;\n height: 24px;\n margin: 0;\n line-height: 22px;\n}\n.du-bois-dark-pagination.mini .du-bois-dark-pagination-item:not(.du-bois-dark-pagination-item-active) {\n background: transparent;\n border-color: transparent;\n}\n.du-bois-dark-pagination.mini .du-bois-dark-pagination-prev,\n.du-bois-dark-pagination.mini .du-bois-dark-pagination-next {\n min-width: 24px;\n height: 24px;\n margin: 0;\n line-height: 24px;\n}\n.du-bois-dark-pagination.mini .du-bois-dark-pagination-prev .du-bois-dark-pagination-item-link,\n.du-bois-dark-pagination.mini .du-bois-dark-pagination-next .du-bois-dark-pagination-item-link {\n background: transparent;\n border-color: transparent;\n}\n.du-bois-dark-pagination.mini .du-bois-dark-pagination-prev .du-bois-dark-pagination-item-link::after,\n.du-bois-dark-pagination.mini .du-bois-dark-pagination-next .du-bois-dark-pagination-item-link::after {\n height: 24px;\n line-height: 24px;\n}\n.du-bois-dark-pagination.mini .du-bois-dark-pagination-jump-prev,\n.du-bois-dark-pagination.mini .du-bois-dark-pagination-jump-next {\n height: 24px;\n margin-right: 0;\n line-height: 24px;\n}\n.du-bois-dark-pagination.mini .du-bois-dark-pagination-options {\n margin-left: 2px;\n}\n.du-bois-dark-pagination.mini .du-bois-dark-pagination-options-size-changer {\n top: 0px;\n}\n.du-bois-dark-pagination.mini .du-bois-dark-pagination-options-quick-jumper {\n height: 24px;\n line-height: 24px;\n}\n.du-bois-dark-pagination.mini .du-bois-dark-pagination-options-quick-jumper input {\n padding: 5px 12px;\n width: 44px;\n height: 32px;\n}\n.du-bois-dark-pagination.du-bois-dark-pagination-disabled {\n cursor: not-allowed;\n}\n.du-bois-dark-pagination.du-bois-dark-pagination-disabled .du-bois-dark-pagination-item {\n background: rgba(255, 255, 255, 0.08);\n border-color: #1F272D;\n cursor: not-allowed;\n}\n.du-bois-dark-pagination.du-bois-dark-pagination-disabled .du-bois-dark-pagination-item a {\n color: rgba(255, 255, 255, 0.3);\n background: transparent;\n border: none;\n cursor: not-allowed;\n}\n.du-bois-dark-pagination.du-bois-dark-pagination-disabled .du-bois-dark-pagination-item-active {\n background: rgba(255, 255, 255, 0.25);\n}\n.du-bois-dark-pagination.du-bois-dark-pagination-disabled .du-bois-dark-pagination-item-active a {\n color: #000;\n}\n.du-bois-dark-pagination.du-bois-dark-pagination-disabled .du-bois-dark-pagination-item-link {\n color: rgba(255, 255, 255, 0.3);\n background: rgba(255, 255, 255, 0.08);\n border-color: #1F272D;\n cursor: not-allowed;\n}\n.du-bois-dark-pagination-simple.du-bois-dark-pagination.du-bois-dark-pagination-disabled .du-bois-dark-pagination-item-link {\n background: transparent;\n}\n.du-bois-dark-pagination.du-bois-dark-pagination-disabled .du-bois-dark-pagination-item-link-icon {\n opacity: 0;\n}\n.du-bois-dark-pagination.du-bois-dark-pagination-disabled .du-bois-dark-pagination-item-ellipsis {\n opacity: 1;\n}\n.du-bois-dark-pagination.du-bois-dark-pagination-disabled .du-bois-dark-pagination-simple-pager {\n color: rgba(255, 255, 255, 0.3);\n}\n@media only screen and (max-width: 992px) {\n .du-bois-dark-pagination-item-after-jump-prev,\n .du-bois-dark-pagination-item-before-jump-next {\n display: none;\n }\n}\n@media only screen and (max-width: 576px) {\n .du-bois-dark-pagination-options {\n display: none;\n }\n}\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-dark-tabs-small > .du-bois-dark-tabs-nav .du-bois-dark-tabs-tab {\n padding: 4px 0;\n font-size: 13px;\n}\n.du-bois-dark-tabs-large > .du-bois-dark-tabs-nav .du-bois-dark-tabs-tab {\n padding: 8px 0;\n font-size: 18px;\n}\n.du-bois-dark-tabs-card.du-bois-dark-tabs-small > .du-bois-dark-tabs-nav .du-bois-dark-tabs-tab {\n padding: 6px 8px;\n}\n.du-bois-dark-tabs-card.du-bois-dark-tabs-large > .du-bois-dark-tabs-nav .du-bois-dark-tabs-tab {\n padding: 7px 8px 6px;\n}\n.du-bois-dark-tabs-top,\n.du-bois-dark-tabs-bottom {\n flex-direction: column;\n}\n.du-bois-dark-tabs-top > .du-bois-dark-tabs-nav,\n.du-bois-dark-tabs-bottom > .du-bois-dark-tabs-nav,\n.du-bois-dark-tabs-top > div > .du-bois-dark-tabs-nav,\n.du-bois-dark-tabs-bottom > div > .du-bois-dark-tabs-nav {\n margin: 0 0 16px 0;\n}\n.du-bois-dark-tabs-top > .du-bois-dark-tabs-nav::before,\n.du-bois-dark-tabs-bottom > .du-bois-dark-tabs-nav::before,\n.du-bois-dark-tabs-top > div > .du-bois-dark-tabs-nav::before,\n.du-bois-dark-tabs-bottom > div > .du-bois-dark-tabs-nav::before {\n position: absolute;\n right: 0;\n left: 0;\n border-bottom: 1px solid #303030;\n content: '';\n}\n.du-bois-dark-tabs-top > .du-bois-dark-tabs-nav .du-bois-dark-tabs-ink-bar,\n.du-bois-dark-tabs-bottom > .du-bois-dark-tabs-nav .du-bois-dark-tabs-ink-bar,\n.du-bois-dark-tabs-top > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-ink-bar,\n.du-bois-dark-tabs-bottom > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-ink-bar {\n height: 2px;\n}\n.du-bois-dark-tabs-top > .du-bois-dark-tabs-nav .du-bois-dark-tabs-ink-bar-animated,\n.du-bois-dark-tabs-bottom > .du-bois-dark-tabs-nav .du-bois-dark-tabs-ink-bar-animated,\n.du-bois-dark-tabs-top > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-ink-bar-animated,\n.du-bois-dark-tabs-bottom > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-ink-bar-animated {\n transition: width 0.3s, left 0.3s, right 0.3s;\n}\n.du-bois-dark-tabs-top > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap::before,\n.du-bois-dark-tabs-bottom > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap::before,\n.du-bois-dark-tabs-top > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap::before,\n.du-bois-dark-tabs-bottom > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap::before,\n.du-bois-dark-tabs-top > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap::after,\n.du-bois-dark-tabs-bottom > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap::after,\n.du-bois-dark-tabs-top > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap::after,\n.du-bois-dark-tabs-bottom > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap::after {\n top: 0;\n bottom: 0;\n width: 30px;\n}\n.du-bois-dark-tabs-top > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap::before,\n.du-bois-dark-tabs-bottom > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap::before,\n.du-bois-dark-tabs-top > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap::before,\n.du-bois-dark-tabs-bottom > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap::before {\n left: 0;\n box-shadow: inset 10px 0 8px -8px rgba(0, 0, 0, 0.08);\n}\n.du-bois-dark-tabs-top > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap::after,\n.du-bois-dark-tabs-bottom > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap::after,\n.du-bois-dark-tabs-top > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap::after,\n.du-bois-dark-tabs-bottom > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap::after {\n right: 0;\n box-shadow: inset -10px 0 8px -8px rgba(0, 0, 0, 0.08);\n}\n.du-bois-dark-tabs-top > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap.du-bois-dark-tabs-nav-wrap-ping-left::before,\n.du-bois-dark-tabs-bottom > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap.du-bois-dark-tabs-nav-wrap-ping-left::before,\n.du-bois-dark-tabs-top > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap.du-bois-dark-tabs-nav-wrap-ping-left::before,\n.du-bois-dark-tabs-bottom > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap.du-bois-dark-tabs-nav-wrap-ping-left::before {\n opacity: 1;\n}\n.du-bois-dark-tabs-top > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap.du-bois-dark-tabs-nav-wrap-ping-right::after,\n.du-bois-dark-tabs-bottom > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap.du-bois-dark-tabs-nav-wrap-ping-right::after,\n.du-bois-dark-tabs-top > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap.du-bois-dark-tabs-nav-wrap-ping-right::after,\n.du-bois-dark-tabs-bottom > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap.du-bois-dark-tabs-nav-wrap-ping-right::after {\n opacity: 1;\n}\n.du-bois-dark-tabs-top > .du-bois-dark-tabs-nav::before,\n.du-bois-dark-tabs-top > div > .du-bois-dark-tabs-nav::before {\n bottom: 0;\n}\n.du-bois-dark-tabs-top > .du-bois-dark-tabs-nav .du-bois-dark-tabs-ink-bar,\n.du-bois-dark-tabs-top > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-ink-bar {\n bottom: 0;\n}\n.du-bois-dark-tabs-bottom > .du-bois-dark-tabs-nav,\n.du-bois-dark-tabs-bottom > div > .du-bois-dark-tabs-nav {\n order: 1;\n margin-top: 16px;\n margin-bottom: 0;\n}\n.du-bois-dark-tabs-bottom > .du-bois-dark-tabs-nav::before,\n.du-bois-dark-tabs-bottom > div > .du-bois-dark-tabs-nav::before {\n top: 0;\n}\n.du-bois-dark-tabs-bottom > .du-bois-dark-tabs-nav .du-bois-dark-tabs-ink-bar,\n.du-bois-dark-tabs-bottom > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-ink-bar {\n top: 0;\n}\n.du-bois-dark-tabs-bottom > .du-bois-dark-tabs-content-holder,\n.du-bois-dark-tabs-bottom > div > .du-bois-dark-tabs-content-holder {\n order: 0;\n}\n.du-bois-dark-tabs-left > .du-bois-dark-tabs-nav,\n.du-bois-dark-tabs-right > .du-bois-dark-tabs-nav,\n.du-bois-dark-tabs-left > div > .du-bois-dark-tabs-nav,\n.du-bois-dark-tabs-right > div > .du-bois-dark-tabs-nav {\n flex-direction: column;\n min-width: 50px;\n}\n.du-bois-dark-tabs-left > .du-bois-dark-tabs-nav .du-bois-dark-tabs-tab,\n.du-bois-dark-tabs-right > .du-bois-dark-tabs-nav .du-bois-dark-tabs-tab,\n.du-bois-dark-tabs-left > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-tab,\n.du-bois-dark-tabs-right > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-tab {\n padding: 4px 16px;\n text-align: center;\n}\n.du-bois-dark-tabs-left > .du-bois-dark-tabs-nav .du-bois-dark-tabs-tab + .du-bois-dark-tabs-tab,\n.du-bois-dark-tabs-right > .du-bois-dark-tabs-nav .du-bois-dark-tabs-tab + .du-bois-dark-tabs-tab,\n.du-bois-dark-tabs-left > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-tab + .du-bois-dark-tabs-tab,\n.du-bois-dark-tabs-right > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-tab + .du-bois-dark-tabs-tab {\n margin: 16px 0 0 0;\n}\n.du-bois-dark-tabs-left > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap,\n.du-bois-dark-tabs-right > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap,\n.du-bois-dark-tabs-left > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap,\n.du-bois-dark-tabs-right > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap {\n flex-direction: column;\n}\n.du-bois-dark-tabs-left > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap::before,\n.du-bois-dark-tabs-right > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap::before,\n.du-bois-dark-tabs-left > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap::before,\n.du-bois-dark-tabs-right > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap::before,\n.du-bois-dark-tabs-left > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap::after,\n.du-bois-dark-tabs-right > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap::after,\n.du-bois-dark-tabs-left > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap::after,\n.du-bois-dark-tabs-right > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap::after {\n right: 0;\n left: 0;\n height: 30px;\n}\n.du-bois-dark-tabs-left > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap::before,\n.du-bois-dark-tabs-right > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap::before,\n.du-bois-dark-tabs-left > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap::before,\n.du-bois-dark-tabs-right > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap::before {\n top: 0;\n box-shadow: inset 0 10px 8px -8px rgba(0, 0, 0, 0.08);\n}\n.du-bois-dark-tabs-left > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap::after,\n.du-bois-dark-tabs-right > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap::after,\n.du-bois-dark-tabs-left > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap::after,\n.du-bois-dark-tabs-right > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap::after {\n bottom: 0;\n box-shadow: inset 0 -10px 8px -8px rgba(0, 0, 0, 0.08);\n}\n.du-bois-dark-tabs-left > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap.du-bois-dark-tabs-nav-wrap-ping-top::before,\n.du-bois-dark-tabs-right > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap.du-bois-dark-tabs-nav-wrap-ping-top::before,\n.du-bois-dark-tabs-left > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap.du-bois-dark-tabs-nav-wrap-ping-top::before,\n.du-bois-dark-tabs-right > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap.du-bois-dark-tabs-nav-wrap-ping-top::before {\n opacity: 1;\n}\n.du-bois-dark-tabs-left > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap.du-bois-dark-tabs-nav-wrap-ping-bottom::after,\n.du-bois-dark-tabs-right > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap.du-bois-dark-tabs-nav-wrap-ping-bottom::after,\n.du-bois-dark-tabs-left > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap.du-bois-dark-tabs-nav-wrap-ping-bottom::after,\n.du-bois-dark-tabs-right > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap.du-bois-dark-tabs-nav-wrap-ping-bottom::after {\n opacity: 1;\n}\n.du-bois-dark-tabs-left > .du-bois-dark-tabs-nav .du-bois-dark-tabs-ink-bar,\n.du-bois-dark-tabs-right > .du-bois-dark-tabs-nav .du-bois-dark-tabs-ink-bar,\n.du-bois-dark-tabs-left > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-ink-bar,\n.du-bois-dark-tabs-right > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-ink-bar {\n width: 2px;\n}\n.du-bois-dark-tabs-left > .du-bois-dark-tabs-nav .du-bois-dark-tabs-ink-bar-animated,\n.du-bois-dark-tabs-right > .du-bois-dark-tabs-nav .du-bois-dark-tabs-ink-bar-animated,\n.du-bois-dark-tabs-left > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-ink-bar-animated,\n.du-bois-dark-tabs-right > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-ink-bar-animated {\n transition: height 0.3s, top 0.3s;\n}\n.du-bois-dark-tabs-left > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-list,\n.du-bois-dark-tabs-right > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-list,\n.du-bois-dark-tabs-left > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-list,\n.du-bois-dark-tabs-right > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-list,\n.du-bois-dark-tabs-left > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-operations,\n.du-bois-dark-tabs-right > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-operations,\n.du-bois-dark-tabs-left > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-operations,\n.du-bois-dark-tabs-right > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-operations {\n flex: 1 0 auto;\n flex-direction: column;\n}\n.du-bois-dark-tabs-left > .du-bois-dark-tabs-nav .du-bois-dark-tabs-ink-bar,\n.du-bois-dark-tabs-left > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-ink-bar {\n right: 0;\n}\n.du-bois-dark-tabs-left > .du-bois-dark-tabs-content-holder,\n.du-bois-dark-tabs-left > div > .du-bois-dark-tabs-content-holder {\n margin-left: -1px;\n border-left: 1px solid #303030;\n}\n.du-bois-dark-tabs-left > .du-bois-dark-tabs-content-holder > .du-bois-dark-tabs-content > .du-bois-dark-tabs-tabpane,\n.du-bois-dark-tabs-left > div > .du-bois-dark-tabs-content-holder > .du-bois-dark-tabs-content > .du-bois-dark-tabs-tabpane {\n padding-left: 16px;\n}\n.du-bois-dark-tabs-right > .du-bois-dark-tabs-nav,\n.du-bois-dark-tabs-right > div > .du-bois-dark-tabs-nav {\n order: 1;\n}\n.du-bois-dark-tabs-right > .du-bois-dark-tabs-nav .du-bois-dark-tabs-ink-bar,\n.du-bois-dark-tabs-right > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-ink-bar {\n left: 0;\n}\n.du-bois-dark-tabs-right > .du-bois-dark-tabs-content-holder,\n.du-bois-dark-tabs-right > div > .du-bois-dark-tabs-content-holder {\n order: 0;\n margin-right: -1px;\n border-right: 1px solid #303030;\n}\n.du-bois-dark-tabs-right > .du-bois-dark-tabs-content-holder > .du-bois-dark-tabs-content > .du-bois-dark-tabs-tabpane,\n.du-bois-dark-tabs-right > div > .du-bois-dark-tabs-content-holder > .du-bois-dark-tabs-content > .du-bois-dark-tabs-tabpane {\n padding-right: 16px;\n}\n.du-bois-dark-tabs-dropdown {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #E8ECF0;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: tnum;\n position: absolute;\n top: -9999px;\n left: -9999px;\n z-index: 1050;\n display: block;\n}\n.du-bois-dark-tabs-dropdown-hidden {\n display: none;\n}\n.du-bois-dark-tabs-dropdown-menu {\n max-height: 200px;\n margin: 0;\n padding: 4px 0;\n overflow-x: hidden;\n overflow-y: auto;\n text-align: left;\n list-style-type: none;\n background-color: #1f1f1f;\n background-clip: padding-box;\n border-radius: 4px;\n outline: none;\n box-shadow: 0 3px 6px -4px rgba(0, 0, 0, 0.48), 0 6px 16px 0 rgba(0, 0, 0, 0.32), 0 9px 28px 8px rgba(0, 0, 0, 0.2);\n}\n.du-bois-dark-tabs-dropdown-menu-item {\n min-width: 120px;\n margin: 0;\n padding: 5px 8px;\n overflow: hidden;\n color: #E8ECF0;\n font-weight: normal;\n font-size: 13px;\n line-height: 22px;\n white-space: nowrap;\n text-overflow: ellipsis;\n cursor: pointer;\n transition: all 0.3s;\n}\n.du-bois-dark-tabs-dropdown-menu-item:hover {\n background: rgba(255, 255, 255, 0.08);\n}\n.du-bois-dark-tabs-dropdown-menu-item-disabled,\n.du-bois-dark-tabs-dropdown-menu-item-disabled:hover {\n color: rgba(255, 255, 255, 0.3);\n background: transparent;\n cursor: not-allowed;\n}\n.du-bois-dark-tabs-card > .du-bois-dark-tabs-nav .du-bois-dark-tabs-tab,\n.du-bois-dark-tabs-card > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-tab {\n margin: 0;\n padding: -111px 8px;\n background: rgba(255, 255, 255, 0.04);\n border: 1px solid #303030;\n transition: all 0.3s cubic-bezier(0.645, 0.045, 0.355, 1);\n}\n.du-bois-dark-tabs-card > .du-bois-dark-tabs-nav .du-bois-dark-tabs-tab-active,\n.du-bois-dark-tabs-card > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-tab-active {\n color: #2272B4;\n background: #141414;\n}\n.du-bois-dark-tabs-card > .du-bois-dark-tabs-nav .du-bois-dark-tabs-ink-bar,\n.du-bois-dark-tabs-card > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-ink-bar {\n visibility: hidden;\n}\n.du-bois-dark-tabs-card.du-bois-dark-tabs-top > .du-bois-dark-tabs-nav .du-bois-dark-tabs-tab + .du-bois-dark-tabs-tab,\n.du-bois-dark-tabs-card.du-bois-dark-tabs-bottom > .du-bois-dark-tabs-nav .du-bois-dark-tabs-tab + .du-bois-dark-tabs-tab,\n.du-bois-dark-tabs-card.du-bois-dark-tabs-top > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-tab + .du-bois-dark-tabs-tab,\n.du-bois-dark-tabs-card.du-bois-dark-tabs-bottom > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-tab + .du-bois-dark-tabs-tab {\n margin-left: 2px;\n}\n.du-bois-dark-tabs-card.du-bois-dark-tabs-top > .du-bois-dark-tabs-nav .du-bois-dark-tabs-tab,\n.du-bois-dark-tabs-card.du-bois-dark-tabs-top > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-tab {\n border-radius: 4px 4px 0 0;\n}\n.du-bois-dark-tabs-card.du-bois-dark-tabs-top > .du-bois-dark-tabs-nav .du-bois-dark-tabs-tab-active,\n.du-bois-dark-tabs-card.du-bois-dark-tabs-top > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-tab-active {\n border-bottom-color: #141414;\n}\n.du-bois-dark-tabs-card.du-bois-dark-tabs-bottom > .du-bois-dark-tabs-nav .du-bois-dark-tabs-tab,\n.du-bois-dark-tabs-card.du-bois-dark-tabs-bottom > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-tab {\n border-radius: 0 0 4px 4px;\n}\n.du-bois-dark-tabs-card.du-bois-dark-tabs-bottom > .du-bois-dark-tabs-nav .du-bois-dark-tabs-tab-active,\n.du-bois-dark-tabs-card.du-bois-dark-tabs-bottom > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-tab-active {\n border-top-color: #141414;\n}\n.du-bois-dark-tabs-card.du-bois-dark-tabs-left > .du-bois-dark-tabs-nav .du-bois-dark-tabs-tab + .du-bois-dark-tabs-tab,\n.du-bois-dark-tabs-card.du-bois-dark-tabs-right > .du-bois-dark-tabs-nav .du-bois-dark-tabs-tab + .du-bois-dark-tabs-tab,\n.du-bois-dark-tabs-card.du-bois-dark-tabs-left > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-tab + .du-bois-dark-tabs-tab,\n.du-bois-dark-tabs-card.du-bois-dark-tabs-right > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-tab + .du-bois-dark-tabs-tab {\n margin-top: 2px;\n}\n.du-bois-dark-tabs-card.du-bois-dark-tabs-left > .du-bois-dark-tabs-nav .du-bois-dark-tabs-tab,\n.du-bois-dark-tabs-card.du-bois-dark-tabs-left > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-tab {\n border-radius: 4px 0 0 4px;\n}\n.du-bois-dark-tabs-card.du-bois-dark-tabs-left > .du-bois-dark-tabs-nav .du-bois-dark-tabs-tab-active,\n.du-bois-dark-tabs-card.du-bois-dark-tabs-left > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-tab-active {\n border-right-color: #141414;\n}\n.du-bois-dark-tabs-card.du-bois-dark-tabs-right > .du-bois-dark-tabs-nav .du-bois-dark-tabs-tab,\n.du-bois-dark-tabs-card.du-bois-dark-tabs-right > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-tab {\n border-radius: 0 4px 4px 0;\n}\n.du-bois-dark-tabs-card.du-bois-dark-tabs-right > .du-bois-dark-tabs-nav .du-bois-dark-tabs-tab-active,\n.du-bois-dark-tabs-card.du-bois-dark-tabs-right > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-tab-active {\n border-left-color: #141414;\n}\n.du-bois-dark-tabs {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #E8ECF0;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: tnum;\n display: flex;\n overflow: hidden;\n}\n.du-bois-dark-tabs > .du-bois-dark-tabs-nav,\n.du-bois-dark-tabs > div > .du-bois-dark-tabs-nav {\n position: relative;\n display: flex;\n flex: none;\n align-items: center;\n}\n.du-bois-dark-tabs > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap,\n.du-bois-dark-tabs > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap {\n position: relative;\n display: inline-block;\n display: flex;\n flex: auto;\n align-self: stretch;\n overflow: hidden;\n white-space: nowrap;\n transform: translate(0);\n}\n.du-bois-dark-tabs > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap::before,\n.du-bois-dark-tabs > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap::before,\n.du-bois-dark-tabs > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap::after,\n.du-bois-dark-tabs > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap::after {\n position: absolute;\n z-index: 1;\n opacity: 0;\n transition: opacity 0.3s;\n content: '';\n pointer-events: none;\n}\n.du-bois-dark-tabs > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-list,\n.du-bois-dark-tabs > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-list {\n position: relative;\n display: flex;\n transition: transform 0.3s;\n}\n.du-bois-dark-tabs > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-operations,\n.du-bois-dark-tabs > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-operations {\n display: flex;\n align-self: stretch;\n}\n.du-bois-dark-tabs > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-operations-hidden,\n.du-bois-dark-tabs > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-operations-hidden {\n position: absolute;\n visibility: hidden;\n pointer-events: none;\n}\n.du-bois-dark-tabs > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-more,\n.du-bois-dark-tabs > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-more {\n position: relative;\n padding: -111px 8px;\n background: transparent;\n border: 0;\n}\n.du-bois-dark-tabs > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-more::after,\n.du-bois-dark-tabs > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-more::after {\n position: absolute;\n right: 0;\n bottom: 0;\n left: 0;\n height: 5px;\n transform: translateY(100%);\n content: '';\n}\n.du-bois-dark-tabs > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-add,\n.du-bois-dark-tabs > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-add {\n min-width: 40px;\n padding: 0 4px;\n background: rgba(255, 255, 255, 0.04);\n border: 1px solid #303030;\n border-radius: 4px 4px 0 0;\n outline: none;\n cursor: pointer;\n transition: all 0.3s cubic-bezier(0.645, 0.045, 0.355, 1);\n}\n.du-bois-dark-tabs > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-add:hover,\n.du-bois-dark-tabs > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-add:hover {\n color: #1d517c;\n}\n.du-bois-dark-tabs > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-add:active,\n.du-bois-dark-tabs > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-add:active,\n.du-bois-dark-tabs > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-add:focus,\n.du-bois-dark-tabs > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-add:focus {\n color: #3f81b1;\n}\n.du-bois-dark-tabs-extra-content {\n flex: none;\n}\n.du-bois-dark-tabs-centered > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap:not([class*='du-bois-dark-tabs-nav-wrap-ping']),\n.du-bois-dark-tabs-centered > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap:not([class*='du-bois-dark-tabs-nav-wrap-ping']) {\n justify-content: center;\n}\n.du-bois-dark-tabs-ink-bar {\n position: absolute;\n background: #2272B4;\n pointer-events: none;\n}\n.du-bois-dark-tabs-tab {\n position: relative;\n display: inline-flex;\n align-items: center;\n padding: 8px 0;\n font-size: 13px;\n background: transparent;\n border: 0;\n outline: none;\n cursor: pointer;\n}\n.du-bois-dark-tabs-tab-btn:focus,\n.du-bois-dark-tabs-tab-remove:focus,\n.du-bois-dark-tabs-tab-btn:active,\n.du-bois-dark-tabs-tab-remove:active {\n color: #3f81b1;\n}\n.du-bois-dark-tabs-tab-btn {\n outline: none;\n transition: all 0.3s;\n}\n.du-bois-dark-tabs-tab-remove {\n flex: none;\n margin-right: -4px;\n margin-left: 8px;\n color: #92A4B3;\n font-size: 12px;\n background: transparent;\n border: none;\n outline: none;\n cursor: pointer;\n transition: all 0.3s;\n}\n.du-bois-dark-tabs-tab-remove:hover {\n color: rgba(255, 255, 255, 0.85);\n}\n.du-bois-dark-tabs-tab:hover {\n color: #1d517c;\n}\n.du-bois-dark-tabs-tab.du-bois-dark-tabs-tab-active .du-bois-dark-tabs-tab-btn {\n color: #2272B4;\n text-shadow: 0 0 0.25px currentColor;\n}\n.du-bois-dark-tabs-tab.du-bois-dark-tabs-tab-disabled {\n color: rgba(255, 255, 255, 0.3);\n cursor: not-allowed;\n}\n.du-bois-dark-tabs-tab.du-bois-dark-tabs-tab-disabled .du-bois-dark-tabs-tab-btn:focus,\n.du-bois-dark-tabs-tab.du-bois-dark-tabs-tab-disabled .du-bois-dark-tabs-tab-remove:focus,\n.du-bois-dark-tabs-tab.du-bois-dark-tabs-tab-disabled .du-bois-dark-tabs-tab-btn:active,\n.du-bois-dark-tabs-tab.du-bois-dark-tabs-tab-disabled .du-bois-dark-tabs-tab-remove:active {\n color: rgba(255, 255, 255, 0.3);\n}\n.du-bois-dark-tabs-tab .du-bois-dark-tabs-tab-remove .anticon {\n margin: 0;\n}\n.du-bois-dark-tabs-tab .anticon {\n margin-right: 12px;\n}\n.du-bois-dark-tabs-tab + .du-bois-dark-tabs-tab {\n margin: 0 0 0 32px;\n}\n.du-bois-dark-tabs-content {\n display: flex;\n width: 100%;\n}\n.du-bois-dark-tabs-content-holder {\n flex: auto;\n min-width: 0;\n min-height: 0;\n}\n.du-bois-dark-tabs-content-animated {\n transition: margin 0.3s;\n}\n.du-bois-dark-tabs-tabpane {\n flex: none;\n width: 100%;\n outline: none;\n}\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n@keyframes antCheckboxEffect {\n 0% {\n transform: scale(1);\n opacity: 0.5;\n }\n 100% {\n transform: scale(1.6);\n opacity: 0;\n }\n}\n.du-bois-dark-tree-treenode-leaf-last .du-bois-dark-tree-switcher-leaf-line::before {\n top: auto !important;\n bottom: auto !important;\n height: 14px !important;\n}\n.du-bois-dark-tree.du-bois-dark-tree-directory .du-bois-dark-tree-treenode {\n position: relative;\n}\n.du-bois-dark-tree.du-bois-dark-tree-directory .du-bois-dark-tree-treenode::before {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 4px;\n left: 0;\n transition: background-color 0.3s;\n content: '';\n pointer-events: none;\n}\n.du-bois-dark-tree.du-bois-dark-tree-directory .du-bois-dark-tree-treenode:hover::before {\n background: rgba(255, 255, 255, 0.08);\n}\n.du-bois-dark-tree.du-bois-dark-tree-directory .du-bois-dark-tree-treenode > * {\n z-index: 1;\n}\n.du-bois-dark-tree.du-bois-dark-tree-directory .du-bois-dark-tree-treenode .du-bois-dark-tree-switcher {\n transition: color 0.3s;\n}\n.du-bois-dark-tree.du-bois-dark-tree-directory .du-bois-dark-tree-treenode .du-bois-dark-tree-node-content-wrapper {\n border-radius: 0;\n user-select: none;\n}\n.du-bois-dark-tree.du-bois-dark-tree-directory .du-bois-dark-tree-treenode .du-bois-dark-tree-node-content-wrapper:hover {\n background: transparent;\n}\n.du-bois-dark-tree.du-bois-dark-tree-directory .du-bois-dark-tree-treenode .du-bois-dark-tree-node-content-wrapper.du-bois-dark-tree-node-selected {\n color: #fff;\n background: transparent;\n}\n.du-bois-dark-tree.du-bois-dark-tree-directory .du-bois-dark-tree-treenode-selected:hover::before,\n.du-bois-dark-tree.du-bois-dark-tree-directory .du-bois-dark-tree-treenode-selected::before {\n background: #2272B4;\n}\n.du-bois-dark-tree.du-bois-dark-tree-directory .du-bois-dark-tree-treenode-selected .du-bois-dark-tree-switcher {\n color: #fff;\n}\n.du-bois-dark-tree.du-bois-dark-tree-directory .du-bois-dark-tree-treenode-selected .du-bois-dark-tree-node-content-wrapper {\n color: #fff;\n background: transparent;\n}\n.du-bois-dark-tree-checkbox {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #E8ECF0;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: tnum;\n position: relative;\n top: 0.2em;\n line-height: 1;\n white-space: nowrap;\n outline: none;\n cursor: pointer;\n}\n.du-bois-dark-tree-checkbox-wrapper:hover .du-bois-dark-tree-checkbox-inner,\n.du-bois-dark-tree-checkbox:hover .du-bois-dark-tree-checkbox-inner,\n.du-bois-dark-tree-checkbox-input:focus + .du-bois-dark-tree-checkbox-inner {\n border-color: #2272B4;\n}\n.du-bois-dark-tree-checkbox-checked::after {\n position: absolute;\n top: 0;\n left: 0;\n width: 100%;\n height: 100%;\n border: 1px solid #2272B4;\n border-radius: 4px;\n visibility: hidden;\n animation: antCheckboxEffect 0.36s ease-in-out;\n animation-fill-mode: backwards;\n content: '';\n}\n.du-bois-dark-tree-checkbox:hover::after,\n.du-bois-dark-tree-checkbox-wrapper:hover .du-bois-dark-tree-checkbox::after {\n visibility: visible;\n}\n.du-bois-dark-tree-checkbox-inner {\n position: relative;\n top: 0;\n left: 0;\n display: block;\n width: 16px;\n height: 16px;\n direction: ltr;\n background-color: transparent;\n border: 1px solid #1F272D;\n border-radius: 4px;\n border-collapse: separate;\n transition: all 0.3s;\n}\n.du-bois-dark-tree-checkbox-inner::after {\n position: absolute;\n top: 50%;\n left: 22%;\n display: table;\n width: 5.71428571px;\n height: 9.14285714px;\n border: 2px solid #fff;\n border-top: 0;\n border-left: 0;\n transform: rotate(45deg) scale(0) translate(-50%, -50%);\n opacity: 0;\n transition: all 0.1s cubic-bezier(0.71, -0.46, 0.88, 0.6), opacity 0.1s;\n content: ' ';\n}\n.du-bois-dark-tree-checkbox-input {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n z-index: 1;\n width: 100%;\n height: 100%;\n cursor: pointer;\n opacity: 0;\n}\n.du-bois-dark-tree-checkbox-checked .du-bois-dark-tree-checkbox-inner::after {\n position: absolute;\n display: table;\n border: 2px solid #fff;\n border-top: 0;\n border-left: 0;\n transform: rotate(45deg) scale(1) translate(-50%, -50%);\n opacity: 1;\n transition: all 0.2s cubic-bezier(0.12, 0.4, 0.29, 1.46) 0.1s;\n content: ' ';\n}\n.du-bois-dark-tree-checkbox-checked .du-bois-dark-tree-checkbox-inner {\n background-color: #2272B4;\n border-color: #2272B4;\n}\n.du-bois-dark-tree-checkbox-disabled {\n cursor: not-allowed;\n}\n.du-bois-dark-tree-checkbox-disabled.du-bois-dark-tree-checkbox-checked .du-bois-dark-tree-checkbox-inner::after {\n border-color: rgba(255, 255, 255, 0.3);\n animation-name: none;\n}\n.du-bois-dark-tree-checkbox-disabled .du-bois-dark-tree-checkbox-input {\n cursor: not-allowed;\n}\n.du-bois-dark-tree-checkbox-disabled .du-bois-dark-tree-checkbox-inner {\n background-color: rgba(255, 255, 255, 0.08);\n border-color: #1F272D !important;\n}\n.du-bois-dark-tree-checkbox-disabled .du-bois-dark-tree-checkbox-inner::after {\n border-color: rgba(255, 255, 255, 0.08);\n border-collapse: separate;\n animation-name: none;\n}\n.du-bois-dark-tree-checkbox-disabled + span {\n color: rgba(255, 255, 255, 0.3);\n cursor: not-allowed;\n}\n.du-bois-dark-tree-checkbox-disabled:hover::after,\n.du-bois-dark-tree-checkbox-wrapper:hover .du-bois-dark-tree-checkbox-disabled::after {\n visibility: hidden;\n}\n.du-bois-dark-tree-checkbox-wrapper {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #E8ECF0;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: tnum;\n display: inline-flex;\n align-items: baseline;\n line-height: unset;\n cursor: pointer;\n}\n.du-bois-dark-tree-checkbox-wrapper::after {\n display: inline-block;\n width: 0;\n overflow: hidden;\n content: '\\a0';\n}\n.du-bois-dark-tree-checkbox-wrapper.du-bois-dark-tree-checkbox-wrapper-disabled {\n cursor: not-allowed;\n}\n.du-bois-dark-tree-checkbox-wrapper + .du-bois-dark-tree-checkbox-wrapper {\n margin-left: 8px;\n}\n.du-bois-dark-tree-checkbox + span {\n padding-right: 8px;\n padding-left: 8px;\n}\n.du-bois-dark-tree-checkbox-group {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #E8ECF0;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: tnum;\n display: inline-block;\n}\n.du-bois-dark-tree-checkbox-group-item {\n margin-right: 8px;\n}\n.du-bois-dark-tree-checkbox-group-item:last-child {\n margin-right: 0;\n}\n.du-bois-dark-tree-checkbox-group-item + .du-bois-dark-tree-checkbox-group-item {\n margin-left: 0;\n}\n.du-bois-dark-tree-checkbox-indeterminate .du-bois-dark-tree-checkbox-inner {\n background-color: transparent;\n border-color: #1F272D;\n}\n.du-bois-dark-tree-checkbox-indeterminate .du-bois-dark-tree-checkbox-inner::after {\n top: 50%;\n left: 50%;\n width: 8px;\n height: 8px;\n background-color: #2272B4;\n border: 0;\n transform: translate(-50%, -50%) scale(1);\n opacity: 1;\n content: ' ';\n}\n.du-bois-dark-tree-checkbox-indeterminate.du-bois-dark-tree-checkbox-disabled .du-bois-dark-tree-checkbox-inner::after {\n background-color: rgba(255, 255, 255, 0.3);\n border-color: rgba(255, 255, 255, 0.3);\n}\n.du-bois-dark-tree {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #E8ECF0;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: tnum;\n background: transparent;\n border-radius: 4px;\n transition: background-color 0.3s;\n}\n.du-bois-dark-tree-focused:not(:hover):not(.du-bois-dark-tree-active-focused) {\n background: #121921;\n}\n.du-bois-dark-tree-list-holder-inner {\n align-items: flex-start;\n}\n.du-bois-dark-tree.du-bois-dark-tree-block-node .du-bois-dark-tree-list-holder-inner {\n align-items: stretch;\n}\n.du-bois-dark-tree.du-bois-dark-tree-block-node .du-bois-dark-tree-list-holder-inner .du-bois-dark-tree-node-content-wrapper {\n flex: auto;\n}\n.du-bois-dark-tree .du-bois-dark-tree-treenode {\n display: flex;\n align-items: flex-start;\n padding: 0 0 2px 0;\n outline: none;\n}\n.du-bois-dark-tree .du-bois-dark-tree-treenode-disabled .du-bois-dark-tree-node-content-wrapper {\n color: rgba(255, 255, 255, 0.3);\n cursor: not-allowed;\n}\n.du-bois-dark-tree .du-bois-dark-tree-treenode-disabled .du-bois-dark-tree-node-content-wrapper:hover {\n background: transparent;\n}\n.du-bois-dark-tree .du-bois-dark-tree-treenode-active .du-bois-dark-tree-node-content-wrapper {\n background: rgba(255, 255, 255, 0.08);\n}\n.du-bois-dark-tree .du-bois-dark-tree-treenode:not(.du-bois-dark-tree .du-bois-dark-tree-treenode-disabled).filter-node .du-bois-dark-tree-title {\n color: inherit;\n font-weight: 500;\n}\n.du-bois-dark-tree-indent {\n align-self: stretch;\n white-space: nowrap;\n user-select: none;\n}\n.du-bois-dark-tree-indent-unit {\n display: inline-block;\n width: 24px;\n}\n.du-bois-dark-tree-switcher {\n position: relative;\n flex: none;\n align-self: stretch;\n width: 24px;\n margin: 0;\n line-height: 24px;\n text-align: center;\n cursor: pointer;\n user-select: none;\n}\n.du-bois-dark-tree-switcher .du-bois-dark-tree-switcher-icon,\n.du-bois-dark-tree-switcher .du-bois-dark-select-tree-switcher-icon {\n display: inline-block;\n font-size: 10px;\n vertical-align: baseline;\n}\n.du-bois-dark-tree-switcher .du-bois-dark-tree-switcher-icon svg,\n.du-bois-dark-tree-switcher .du-bois-dark-select-tree-switcher-icon svg {\n transition: transform 0.3s;\n}\n.du-bois-dark-tree-switcher-noop {\n cursor: default;\n}\n.du-bois-dark-tree-switcher_close .du-bois-dark-tree-switcher-icon svg {\n transform: rotate(-90deg);\n}\n.du-bois-dark-tree-switcher-loading-icon {\n color: #2272B4;\n}\n.du-bois-dark-tree-switcher-leaf-line {\n position: relative;\n z-index: 1;\n display: inline-block;\n width: 100%;\n height: 100%;\n}\n.du-bois-dark-tree-switcher-leaf-line::before {\n position: absolute;\n top: 0;\n bottom: -2px;\n margin-left: -1px;\n border-left: 1px solid #d9d9d9;\n content: ' ';\n}\n.du-bois-dark-tree-switcher-leaf-line::after {\n position: absolute;\n width: 10px;\n height: 14px;\n margin-left: -1px;\n border-bottom: 1px solid #d9d9d9;\n content: ' ';\n}\n.du-bois-dark-tree-checkbox {\n top: initial;\n margin: 4px 8px 0 0;\n}\n.du-bois-dark-tree .du-bois-dark-tree-node-content-wrapper {\n position: relative;\n z-index: auto;\n min-height: 24px;\n margin: 0;\n padding: 0 4px;\n color: inherit;\n line-height: 24px;\n background: transparent;\n border-radius: 4px;\n cursor: pointer;\n transition: all 0.3s, border 0s, line-height 0s, box-shadow 0s;\n}\n.du-bois-dark-tree .du-bois-dark-tree-node-content-wrapper:hover {\n background-color: rgba(255, 255, 255, 0.08);\n}\n.du-bois-dark-tree .du-bois-dark-tree-node-content-wrapper.du-bois-dark-tree-node-selected {\n background-color: #142433;\n}\n.du-bois-dark-tree .du-bois-dark-tree-node-content-wrapper .du-bois-dark-tree-iconEle {\n display: inline-block;\n width: 24px;\n height: 24px;\n line-height: 24px;\n text-align: center;\n vertical-align: top;\n}\n.du-bois-dark-tree .du-bois-dark-tree-node-content-wrapper .du-bois-dark-tree-iconEle:empty {\n display: none;\n}\n.du-bois-dark-tree-unselectable .du-bois-dark-tree-node-content-wrapper:hover {\n background-color: transparent;\n}\n.du-bois-dark-tree-node-content-wrapper[draggable='true'] {\n line-height: 24px;\n user-select: none;\n}\n.du-bois-dark-tree-node-content-wrapper[draggable='true'] .du-bois-dark-tree-drop-indicator {\n position: absolute;\n z-index: 1;\n height: 2px;\n background-color: #2272B4;\n border-radius: 1px;\n pointer-events: none;\n}\n.du-bois-dark-tree-node-content-wrapper[draggable='true'] .du-bois-dark-tree-drop-indicator::after {\n position: absolute;\n top: -3px;\n left: -6px;\n width: 8px;\n height: 8px;\n background-color: transparent;\n border: 2px solid #2272B4;\n border-radius: 50%;\n content: '';\n}\n.du-bois-dark-tree .du-bois-dark-tree-treenode.drop-container > [draggable] {\n box-shadow: 0 0 0 2px #2272B4;\n}\n.du-bois-dark-tree-show-line .du-bois-dark-tree-indent-unit {\n position: relative;\n height: 100%;\n}\n.du-bois-dark-tree-show-line .du-bois-dark-tree-indent-unit::before {\n position: absolute;\n top: 0;\n right: 12px;\n bottom: -2px;\n border-right: 1px solid #1F272D;\n content: '';\n}\n.du-bois-dark-tree-show-line .du-bois-dark-tree-indent-unit-end::before {\n display: none;\n}\n.du-bois-dark-tree-show-line .du-bois-dark-tree-switcher {\n background: #141414;\n}\n.du-bois-dark-tree-show-line .du-bois-dark-tree-switcher-line-icon {\n vertical-align: -0.225em;\n}\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-dark-typography {\n color: #E8ECF0;\n overflow-wrap: break-word;\n}\n.du-bois-dark-typography.du-bois-dark-typography-secondary {\n color: #92A4B3;\n}\n.du-bois-dark-typography.du-bois-dark-typography-success {\n color: #34824F;\n}\n.du-bois-dark-typography.du-bois-dark-typography-warning {\n color: #AF5B23;\n}\n.du-bois-dark-typography.du-bois-dark-typography-danger {\n color: #E65B77;\n}\na.du-bois-dark-typography.du-bois-dark-typography-danger:active,\na.du-bois-dark-typography.du-bois-dark-typography-danger:focus,\na.du-bois-dark-typography.du-bois-dark-typography-danger:hover {\n color: #f28899;\n}\n.du-bois-dark-typography.du-bois-dark-typography-disabled {\n color: rgba(255, 255, 255, 0.3);\n cursor: not-allowed;\n user-select: none;\n}\ndiv.du-bois-dark-typography,\n.du-bois-dark-typography p {\n margin-bottom: 1em;\n}\nh1.du-bois-dark-typography,\n.du-bois-dark-typography h1 {\n margin-bottom: 0.5em;\n color: rgba(255, 255, 255, 0.85);\n font-weight: 600;\n font-size: 36px;\n line-height: 1.23;\n}\nh2.du-bois-dark-typography,\n.du-bois-dark-typography h2 {\n margin-bottom: 0.5em;\n color: rgba(255, 255, 255, 0.85);\n font-weight: 600;\n font-size: 28px;\n line-height: 1.35;\n}\nh3.du-bois-dark-typography,\n.du-bois-dark-typography h3 {\n margin-bottom: 0.5em;\n color: rgba(255, 255, 255, 0.85);\n font-weight: 600;\n font-size: 23px;\n line-height: 1.35;\n}\nh4.du-bois-dark-typography,\n.du-bois-dark-typography h4 {\n margin-bottom: 0.5em;\n color: rgba(255, 255, 255, 0.85);\n font-weight: 600;\n font-size: 19px;\n line-height: 1.4;\n}\nh5.du-bois-dark-typography,\n.du-bois-dark-typography h5 {\n margin-bottom: 0.5em;\n color: rgba(255, 255, 255, 0.85);\n font-weight: 600;\n font-size: 15px;\n line-height: 1.5;\n}\n.du-bois-dark-typography + h1.du-bois-dark-typography,\n.du-bois-dark-typography + h2.du-bois-dark-typography,\n.du-bois-dark-typography + h3.du-bois-dark-typography,\n.du-bois-dark-typography + h4.du-bois-dark-typography,\n.du-bois-dark-typography + h5.du-bois-dark-typography {\n margin-top: 1.2em;\n}\n.du-bois-dark-typography div + h1,\n.du-bois-dark-typography ul + h1,\n.du-bois-dark-typography li + h1,\n.du-bois-dark-typography p + h1,\n.du-bois-dark-typography h1 + h1,\n.du-bois-dark-typography h2 + h1,\n.du-bois-dark-typography h3 + h1,\n.du-bois-dark-typography h4 + h1,\n.du-bois-dark-typography h5 + h1,\n.du-bois-dark-typography div + h2,\n.du-bois-dark-typography ul + h2,\n.du-bois-dark-typography li + h2,\n.du-bois-dark-typography p + h2,\n.du-bois-dark-typography h1 + h2,\n.du-bois-dark-typography h2 + h2,\n.du-bois-dark-typography h3 + h2,\n.du-bois-dark-typography h4 + h2,\n.du-bois-dark-typography h5 + h2,\n.du-bois-dark-typography div + h3,\n.du-bois-dark-typography ul + h3,\n.du-bois-dark-typography li + h3,\n.du-bois-dark-typography p + h3,\n.du-bois-dark-typography h1 + h3,\n.du-bois-dark-typography h2 + h3,\n.du-bois-dark-typography h3 + h3,\n.du-bois-dark-typography h4 + h3,\n.du-bois-dark-typography h5 + h3,\n.du-bois-dark-typography div + h4,\n.du-bois-dark-typography ul + h4,\n.du-bois-dark-typography li + h4,\n.du-bois-dark-typography p + h4,\n.du-bois-dark-typography h1 + h4,\n.du-bois-dark-typography h2 + h4,\n.du-bois-dark-typography h3 + h4,\n.du-bois-dark-typography h4 + h4,\n.du-bois-dark-typography h5 + h4,\n.du-bois-dark-typography div + h5,\n.du-bois-dark-typography ul + h5,\n.du-bois-dark-typography li + h5,\n.du-bois-dark-typography p + h5,\n.du-bois-dark-typography h1 + h5,\n.du-bois-dark-typography h2 + h5,\n.du-bois-dark-typography h3 + h5,\n.du-bois-dark-typography h4 + h5,\n.du-bois-dark-typography h5 + h5 {\n margin-top: 1.2em;\n}\na.du-bois-dark-typography-ellipsis,\nspan.du-bois-dark-typography-ellipsis {\n display: inline-block;\n}\na.du-bois-dark-typography,\n.du-bois-dark-typography a {\n color: #2272B4;\n outline: none;\n cursor: pointer;\n transition: color 0.3s;\n text-decoration: none;\n}\na.du-bois-dark-typography:focus,\n.du-bois-dark-typography a:focus,\na.du-bois-dark-typography:hover,\n.du-bois-dark-typography a:hover {\n color: #1d517c;\n}\na.du-bois-dark-typography:active,\n.du-bois-dark-typography a:active {\n color: #3f81b1;\n}\na.du-bois-dark-typography:active,\n.du-bois-dark-typography a:active,\na.du-bois-dark-typography:hover,\n.du-bois-dark-typography a:hover {\n text-decoration: none;\n}\na.du-bois-dark-typography[disabled],\n.du-bois-dark-typography a[disabled],\na.du-bois-dark-typography.du-bois-dark-typography-disabled,\n.du-bois-dark-typography a.du-bois-dark-typography-disabled {\n color: rgba(255, 255, 255, 0.3);\n cursor: not-allowed;\n}\na.du-bois-dark-typography[disabled]:active,\n.du-bois-dark-typography a[disabled]:active,\na.du-bois-dark-typography.du-bois-dark-typography-disabled:active,\n.du-bois-dark-typography a.du-bois-dark-typography-disabled:active,\na.du-bois-dark-typography[disabled]:hover,\n.du-bois-dark-typography a[disabled]:hover,\na.du-bois-dark-typography.du-bois-dark-typography-disabled:hover,\n.du-bois-dark-typography a.du-bois-dark-typography-disabled:hover {\n color: rgba(255, 255, 255, 0.3);\n}\na.du-bois-dark-typography[disabled]:active,\n.du-bois-dark-typography a[disabled]:active,\na.du-bois-dark-typography.du-bois-dark-typography-disabled:active,\n.du-bois-dark-typography a.du-bois-dark-typography-disabled:active {\n pointer-events: none;\n}\n.du-bois-dark-typography code {\n margin: 0 0.2em;\n padding: 0.2em 0.4em 0.1em;\n font-size: 85%;\n background: rgba(150, 150, 150, 0.1);\n border: 1px solid rgba(100, 100, 100, 0.2);\n border-radius: 3px;\n}\n.du-bois-dark-typography kbd {\n margin: 0 0.2em;\n padding: 0.15em 0.4em 0.1em;\n font-size: 90%;\n background: rgba(150, 150, 150, 0.06);\n border: 1px solid rgba(100, 100, 100, 0.2);\n border-bottom-width: 2px;\n border-radius: 3px;\n}\n.du-bois-dark-typography mark {\n padding: 0;\n background-color: #594214;\n}\n.du-bois-dark-typography u,\n.du-bois-dark-typography ins {\n text-decoration: underline;\n text-decoration-skip-ink: auto;\n}\n.du-bois-dark-typography s,\n.du-bois-dark-typography del {\n text-decoration: line-through;\n}\n.du-bois-dark-typography strong {\n font-weight: 600;\n}\n.du-bois-dark-typography-expand,\n.du-bois-dark-typography-edit,\n.du-bois-dark-typography-copy {\n color: #2272B4;\n text-decoration: none;\n outline: none;\n cursor: pointer;\n transition: color 0.3s;\n margin-left: 4px;\n}\n.du-bois-dark-typography-expand:focus,\n.du-bois-dark-typography-edit:focus,\n.du-bois-dark-typography-copy:focus,\n.du-bois-dark-typography-expand:hover,\n.du-bois-dark-typography-edit:hover,\n.du-bois-dark-typography-copy:hover {\n color: #1d517c;\n}\n.du-bois-dark-typography-expand:active,\n.du-bois-dark-typography-edit:active,\n.du-bois-dark-typography-copy:active {\n color: #3f81b1;\n}\n.du-bois-dark-typography-copy-success,\n.du-bois-dark-typography-copy-success:hover,\n.du-bois-dark-typography-copy-success:focus {\n color: #34824F;\n}\n.du-bois-dark-typography-edit-content {\n position: relative;\n}\ndiv.du-bois-dark-typography-edit-content {\n left: -13px;\n margin-top: -6px;\n margin-bottom: calc(1em - 5px - 1px);\n}\n.du-bois-dark-typography-edit-content-confirm {\n position: absolute;\n right: 10px;\n bottom: 8px;\n color: #92A4B3;\n pointer-events: none;\n}\n.du-bois-dark-typography-edit-content textarea {\n -moz-transition: none;\n}\n.du-bois-dark-typography ul,\n.du-bois-dark-typography ol {\n margin: 0 0 1em 0;\n padding: 0;\n}\n.du-bois-dark-typography ul li,\n.du-bois-dark-typography ol li {\n margin: 0 0 0 20px;\n padding: 0 0 0 4px;\n}\n.du-bois-dark-typography ul {\n list-style-type: circle;\n}\n.du-bois-dark-typography ul ul {\n list-style-type: disc;\n}\n.du-bois-dark-typography ol {\n list-style-type: decimal;\n}\n.du-bois-dark-typography pre,\n.du-bois-dark-typography blockquote {\n margin: 1em 0;\n}\n.du-bois-dark-typography pre {\n padding: 0.4em 0.6em;\n white-space: pre-wrap;\n word-wrap: break-word;\n background: rgba(150, 150, 150, 0.1);\n border: 1px solid rgba(100, 100, 100, 0.2);\n border-radius: 3px;\n}\n.du-bois-dark-typography pre code {\n display: inline;\n margin: 0;\n padding: 0;\n font-size: inherit;\n font-family: inherit;\n background: transparent;\n border: 0;\n}\n.du-bois-dark-typography blockquote {\n padding: 0 0 0 0.6em;\n border-left: 4px solid rgba(100, 100, 100, 0.2);\n opacity: 0.85;\n}\n.du-bois-dark-typography-single-line {\n white-space: nowrap;\n}\n.du-bois-dark-typography-ellipsis-single-line {\n overflow: hidden;\n text-overflow: ellipsis;\n}\na.du-bois-dark-typography-ellipsis-single-line,\nspan.du-bois-dark-typography-ellipsis-single-line {\n vertical-align: bottom;\n}\n.du-bois-dark-typography-ellipsis-multiple-line {\n display: -webkit-box;\n overflow: hidden;\n -webkit-line-clamp: 3;\n /*! autoprefixer: ignore next */\n -webkit-box-orient: vertical;\n}\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n\n[data-dubois-show-outline] {\n outline: 1px dashed #308613 !important;\n outline-offset: 2px !important;\n}\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n.du-bois-dark-switch {\n margin: 0;\n padding: 0;\n color: #E8ECF0;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: tnum;\n position: relative;\n display: inline-block;\n box-sizing: border-box;\n min-width: 28px;\n height: 16px;\n line-height: 16px;\n vertical-align: middle;\n background-color: rgba(255, 255, 255, 0.3);\n border: 0;\n border-radius: 100px;\n cursor: pointer;\n transition: all 0.2s;\n user-select: none;\n}\n.du-bois-dark-switch:focus {\n outline: 0;\n box-shadow: 0 0 0 2px rgba(255, 255, 255, 0.1);\n}\n.du-bois-dark-switch-checked:focus {\n box-shadow: 0 0 0 2px rgba(34, 114, 180, 0.2);\n}\n.du-bois-dark-switch:focus:hover {\n box-shadow: none;\n}\n.du-bois-dark-switch-checked {\n background-color: #2272B4;\n}\n.du-bois-dark-switch-loading,\n.du-bois-dark-switch-disabled {\n cursor: not-allowed;\n opacity: 0.4;\n}\n.du-bois-dark-switch-loading *,\n.du-bois-dark-switch-disabled * {\n box-shadow: none;\n cursor: not-allowed;\n}\n.du-bois-dark-switch-inner {\n display: block;\n margin: 0 5px 0 18px;\n color: #fff;\n font-size: 12px;\n transition: margin 0.2s;\n}\n.du-bois-dark-switch-checked .du-bois-dark-switch-inner {\n margin: 0 18px 0 5px;\n}\n.du-bois-dark-switch-handle {\n position: absolute;\n top: 0px;\n left: 0px;\n width: 14px;\n height: 14px;\n transition: all 0.2s ease-in-out;\n}\n.du-bois-dark-switch-handle::before {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background-color: #fff;\n border-radius: 7px;\n box-shadow: 0 2px 4px 0 rgba(0, 35, 11, 0.2);\n transition: all 0.2s ease-in-out;\n content: '';\n}\n.du-bois-dark-switch-checked .du-bois-dark-switch-handle {\n left: calc(100% - 14px - 0px);\n}\n.du-bois-dark-switch:not(.du-bois-dark-switch-disabled):active .du-bois-dark-switch-handle::before {\n right: -30%;\n left: 0;\n}\n.du-bois-dark-switch:not(.du-bois-dark-switch-disabled):active.du-bois-dark-switch-checked .du-bois-dark-switch-handle::before {\n right: 0;\n left: -30%;\n}\n.du-bois-dark-switch-loading-icon {\n position: relative;\n top: 0.5px;\n color: rgba(0, 0, 0, 0.65);\n vertical-align: top;\n}\n.du-bois-dark-switch-checked .du-bois-dark-switch-loading-icon {\n color: #2272B4;\n}\n.du-bois-dark-switch-small {\n min-width: 28px;\n height: 16px;\n line-height: 16px;\n}\n.du-bois-dark-switch-small .du-bois-dark-switch-inner {\n margin: 0 5px 0 18px;\n font-size: 12px;\n}\n.du-bois-dark-switch-small .du-bois-dark-switch-handle {\n width: 12px;\n height: 12px;\n}\n.du-bois-dark-switch-small .du-bois-dark-switch-loading-icon {\n top: 1.5px;\n font-size: 9px;\n}\n.du-bois-dark-switch-small.du-bois-dark-switch-checked .du-bois-dark-switch-inner {\n margin: 0 18px 0 5px;\n}\n.du-bois-dark-switch-small.du-bois-dark-switch-checked .du-bois-dark-switch-handle {\n left: calc(100% - 12px - 0px);\n}\n.du-bois-dark-switch {\n transition: none;\n border: 1px solid #64727d;\n background-color: white;\n margin-right: 8px;\n}\n.du-bois-dark-switch .du-bois-dark-switch-handle::before {\n box-shadow: 0px 0px 0px 1px #64727d;\n}\n.du-bois-dark-switch-checked {\n border: 1px solid #2272B4;\n background-color: #2272B4;\n}\n.du-bois-dark-switch-checked .du-bois-dark-switch-handle::before {\n box-shadow: none;\n}\n.du-bois-dark-switch:focus-visible {\n box-shadow: 0px 0px 0px 1px #2272B4;\n border: 1px solid #2272B4;\n}\n.du-bois-dark-switch:focus-visible .du-bois-dark-switch-handle::before {\n box-shadow: 0px 0px 0px 1px #2272B4;\n}\n.du-bois-dark-switch-checked:focus-visible {\n box-shadow: 0px 0px 0px 1px #ffffff, 0px 0px 0px 3px #2272B4;\n}\n.du-bois-dark-switch:hover {\n border: 1px solid #186099;\n background-color: rgba(34, 114, 180, 0.08);\n}\n.du-bois-dark-switch:hover .du-bois-dark-switch-handle::before {\n box-shadow: 0px 0px 0px 1px #186099;\n}\n.du-bois-dark-switch-checked:hover {\n background-color: #186099;\n}\n.du-bois-dark-switch:active {\n background-color: rgba(34, 114, 180, 0.16);\n}\n.du-bois-dark-switch-checked:active {\n background-color: #0d4f85;\n}\n.du-bois-dark-switch-disabled,\n.du-bois-dark-switch-disabled:hover {\n border: 1px solid #a2aeb8;\n}\n.du-bois-dark-switch-disabled .du-bois-dark-switch-handle::before,\n.du-bois-dark-switch-disabled:hover .du-bois-dark-switch-handle::before {\n box-shadow: 0px 0px 0px 1px #a2aeb8;\n}\n.du-bois-dark-switch-disabled.du-bois-dark-switch-checked {\n background-color: #a2aeb8;\n}\n.du-bois-dark-switch:not(.du-bois-dark-switch-disabled):active .du-bois-dark-switch-handle::before {\n right: 0;\n left: 0;\n}\n.du-bois-dark-switch:not(.du-bois-dark-switch-disabled):active.du-bois-dark-switch-checked .du-bois-dark-switch-handle::before {\n right: 0;\n left: 0;\n}\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-dark-breadcrumb {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #E8ECF0;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: tnum;\n color: #92A4B3;\n font-size: 13px;\n}\n.du-bois-dark-breadcrumb .anticon {\n font-size: 13px;\n}\n.du-bois-dark-breadcrumb a {\n color: #92A4B3;\n transition: color 0.3s;\n}\n.du-bois-dark-breadcrumb a:hover {\n color: #1d517c;\n}\n.du-bois-dark-breadcrumb > span:last-child {\n color: #E8ECF0;\n}\n.du-bois-dark-breadcrumb > span:last-child a {\n color: #E8ECF0;\n}\n.du-bois-dark-breadcrumb > span:last-child .du-bois-dark-breadcrumb-separator {\n display: none;\n}\n.du-bois-dark-breadcrumb-separator {\n margin: 0 4px;\n color: #92A4B3;\n}\n.du-bois-dark-breadcrumb-link > .anticon + span,\n.du-bois-dark-breadcrumb-link > .anticon + a {\n margin-left: 4px;\n}\n.du-bois-dark-breadcrumb-overlay-link > .anticon {\n margin-left: 4px;\n}\n.du-bois-dark-breadcrumb a {\n color: #2272B4;\n text-decoration: none;\n outline: none;\n cursor: pointer;\n transition: color 0.3s;\n}\n.du-bois-dark-breadcrumb a:focus,\n.du-bois-dark-breadcrumb a:hover {\n color: #1d517c;\n}\n.du-bois-dark-breadcrumb a:active {\n color: #3f81b1;\n}\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n.du-bois-dark-notification .du-bois-dark-picker-clear,\n.du-bois-dark-notification .du-bois-dark-slider-handle,\n.du-bois-dark-notification .du-bois-dark-anchor-wrapper,\n.du-bois-dark-notification .du-bois-dark-collapse-content,\n.du-bois-dark-notification .du-bois-dark-timeline-item-head,\n.du-bois-dark-notification .du-bois-dark-card {\n background-color: #1f1f1f;\n}\n.du-bois-dark-notification .du-bois-dark-transfer-list-header {\n background: #1f1f1f;\n border-bottom: 1px solid #3a3a3a;\n}\n.du-bois-dark-notification .du-bois-dark-transfer-list-content-item:not(.du-bois-dark-transfer-list-content-item-disabled):hover {\n background-color: rgba(255, 255, 255, 0.08);\n}\n.du-bois-dark-notification tr.du-bois-dark-table-expanded-row > td,\n.du-bois-dark-notification tr.du-bois-dark-table-expanded-row:hover > td {\n background: #272727;\n}\n.du-bois-dark-notification .du-bois-dark-table.du-bois-dark-table-small thead > tr > th {\n background-color: #1f1f1f;\n border-bottom: 1px solid #3a3a3a;\n}\n.du-bois-dark-notification .du-bois-dark-table {\n background-color: #1f1f1f;\n}\n.du-bois-dark-notification .du-bois-dark-table .du-bois-dark-table-row-expand-icon {\n border: 1px solid #3a3a3a;\n}\n.du-bois-dark-notification .du-bois-dark-table tfoot > tr > th,\n.du-bois-dark-notification .du-bois-dark-table tfoot > tr > td {\n border-bottom: 1px solid #3a3a3a;\n}\n.du-bois-dark-notification .du-bois-dark-table thead > tr > th {\n background-color: #272727;\n border-bottom: 1px solid #3a3a3a;\n}\n.du-bois-dark-notification .du-bois-dark-table tbody > tr > td {\n border-bottom: 1px solid #3a3a3a;\n}\n.du-bois-dark-notification .du-bois-dark-table tbody > tr > td.du-bois-dark-table-cell-fix-left,\n.du-bois-dark-notification .du-bois-dark-table tbody > tr > td.du-bois-dark-table-cell-fix-right {\n background-color: #1f1f1f;\n}\n.du-bois-dark-notification .du-bois-dark-table tbody > tr.du-bois-dark-table-row:hover > td {\n background: #303030;\n}\n.du-bois-dark-notification .du-bois-dark-table.du-bois-dark-table-bordered .du-bois-dark-table-title {\n border: 1px solid #3a3a3a;\n}\n.du-bois-dark-notification .du-bois-dark-table.du-bois-dark-table-bordered thead > tr > th,\n.du-bois-dark-notification .du-bois-dark-table.du-bois-dark-table-bordered tbody > tr > td,\n.du-bois-dark-notification .du-bois-dark-table.du-bois-dark-table-bordered tfoot > tr > th,\n.du-bois-dark-notification .du-bois-dark-table.du-bois-dark-table-bordered tfoot > tr > td {\n border-right: 1px solid #3a3a3a;\n}\n.du-bois-dark-notification .du-bois-dark-table.du-bois-dark-table-bordered .du-bois-dark-table-cell-fix-right-first::after {\n border-right: 1px solid #3a3a3a;\n}\n.du-bois-dark-notification .du-bois-dark-table.du-bois-dark-table-bordered table thead > tr:not(:last-child) > th {\n border-bottom: 1px solid #303030;\n}\n.du-bois-dark-notification .du-bois-dark-table.du-bois-dark-table-bordered .du-bois-dark-table-container {\n border: 1px solid #3a3a3a;\n}\n.du-bois-dark-notification .du-bois-dark-table.du-bois-dark-table-bordered .du-bois-dark-table-expanded-row-fixed::after {\n border-right: 1px solid #3a3a3a;\n}\n.du-bois-dark-notification .du-bois-dark-table.du-bois-dark-table-bordered .du-bois-dark-table-footer {\n border: 1px solid #3a3a3a;\n}\n.du-bois-dark-notification .du-bois-dark-table .du-bois-dark-table-filter-trigger-container-open {\n background-color: #525252;\n}\n.du-bois-dark-notification .du-bois-dark-picker-calendar-full {\n background-color: #1f1f1f;\n}\n.du-bois-dark-notification .du-bois-dark-picker-calendar-full .du-bois-dark-picker-panel {\n background-color: #1f1f1f;\n}\n.du-bois-dark-notification .du-bois-dark-picker-calendar-full .du-bois-dark-picker-panel .du-bois-dark-picker-calendar-date {\n border-top: 2px solid #3a3a3a;\n}\n.du-bois-dark-notification .du-bois-dark-tabs.du-bois-dark-tabs-card .du-bois-dark-tabs-card-bar .du-bois-dark-tabs-tab-active {\n background-color: #1f1f1f;\n border-bottom: 1px solid #1f1f1f;\n}\n.du-bois-dark-notification .du-bois-dark-badge-count {\n box-shadow: 0 0 0 1px #1f1f1f;\n}\n.du-bois-dark-notification .du-bois-dark-tree-show-line .du-bois-dark-tree-switcher {\n background: #1f1f1f;\n}\n.du-bois-dark-notification {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #E8ECF0;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: tnum;\n position: fixed;\n z-index: 1010;\n margin-right: 24px;\n}\n.du-bois-dark-notification-topLeft,\n.du-bois-dark-notification-bottomLeft {\n margin-right: 0;\n margin-left: 24px;\n}\n.du-bois-dark-notification-topLeft .du-bois-dark-notification-fade-enter.du-bois-dark-notification-fade-enter-active,\n.du-bois-dark-notification-bottomLeft .du-bois-dark-notification-fade-enter.du-bois-dark-notification-fade-enter-active,\n.du-bois-dark-notification-topLeft .du-bois-dark-notification-fade-appear.du-bois-dark-notification-fade-appear-active,\n.du-bois-dark-notification-bottomLeft .du-bois-dark-notification-fade-appear.du-bois-dark-notification-fade-appear-active {\n animation-name: NotificationLeftFadeIn;\n}\n.du-bois-dark-notification-close-icon {\n font-size: 13px;\n cursor: pointer;\n}\n.du-bois-dark-notification-hook-holder {\n position: relative;\n}\n.du-bois-dark-notification-notice {\n position: relative;\n width: 384px;\n max-width: calc(100vw - 24px * 2);\n margin-bottom: 16px;\n margin-left: auto;\n padding: 16px 24px;\n overflow: hidden;\n line-height: 20px;\n word-wrap: break-word;\n background: #1f1f1f;\n border-radius: 4px;\n box-shadow: 0 3px 6px -4px rgba(0, 0, 0, 0.48), 0 6px 16px 0 rgba(0, 0, 0, 0.32), 0 9px 28px 8px rgba(0, 0, 0, 0.2);\n}\n.du-bois-dark-notification-topLeft .du-bois-dark-notification-notice,\n.du-bois-dark-notification-bottomLeft .du-bois-dark-notification-notice {\n margin-right: auto;\n margin-left: 0;\n}\n.du-bois-dark-notification-notice-message {\n margin-bottom: 8px;\n color: rgba(255, 255, 255, 0.85);\n font-size: 18px;\n line-height: 24px;\n}\n.du-bois-dark-notification-notice-message-single-line-auto-margin {\n display: block;\n width: calc(384px - 24px * 2 - 24px - 48px - 100%);\n max-width: 4px;\n background-color: transparent;\n pointer-events: none;\n}\n.du-bois-dark-notification-notice-message-single-line-auto-margin::before {\n display: block;\n content: '';\n}\n.du-bois-dark-notification-notice-description {\n font-size: 13px;\n}\n.du-bois-dark-notification-notice-closable .du-bois-dark-notification-notice-message {\n padding-right: 24px;\n}\n.du-bois-dark-notification-notice-with-icon .du-bois-dark-notification-notice-message {\n margin-bottom: 4px;\n margin-left: 48px;\n font-size: 18px;\n}\n.du-bois-dark-notification-notice-with-icon .du-bois-dark-notification-notice-description {\n margin-left: 48px;\n font-size: 13px;\n}\n.du-bois-dark-notification-notice-icon {\n position: absolute;\n margin-left: 4px;\n font-size: 24px;\n line-height: 24px;\n}\n.anticon.du-bois-dark-notification-notice-icon-success {\n color: #34824F;\n}\n.anticon.du-bois-dark-notification-notice-icon-info {\n color: #64727D;\n}\n.anticon.du-bois-dark-notification-notice-icon-warning {\n color: #AF5B23;\n}\n.anticon.du-bois-dark-notification-notice-icon-error {\n color: #E65B77;\n}\n.du-bois-dark-notification-notice-close {\n position: absolute;\n top: 16px;\n right: 22px;\n color: #92A4B3;\n outline: none;\n}\n.du-bois-dark-notification-notice-close:hover {\n color: rgba(255, 255, 255, 0.85);\n}\n.du-bois-dark-notification-notice-btn {\n float: right;\n margin-top: 16px;\n}\n.du-bois-dark-notification .notification-fade-effect {\n animation-duration: 0.24s;\n animation-timing-function: cubic-bezier(0.645, 0.045, 0.355, 1);\n animation-fill-mode: both;\n}\n.du-bois-dark-notification-fade-enter,\n.du-bois-dark-notification-fade-appear {\n animation-duration: 0.24s;\n animation-timing-function: cubic-bezier(0.645, 0.045, 0.355, 1);\n animation-fill-mode: both;\n opacity: 0;\n animation-play-state: paused;\n}\n.du-bois-dark-notification-fade-leave {\n animation-duration: 0.24s;\n animation-timing-function: cubic-bezier(0.645, 0.045, 0.355, 1);\n animation-fill-mode: both;\n animation-duration: 0.2s;\n animation-play-state: paused;\n}\n.du-bois-dark-notification-fade-enter.du-bois-dark-notification-fade-enter-active,\n.du-bois-dark-notification-fade-appear.du-bois-dark-notification-fade-appear-active {\n animation-name: NotificationFadeIn;\n animation-play-state: running;\n}\n.du-bois-dark-notification-fade-leave.du-bois-dark-notification-fade-leave-active {\n animation-name: NotificationFadeOut;\n animation-play-state: running;\n}\n@keyframes NotificationFadeIn {\n 0% {\n left: 384px;\n opacity: 0;\n }\n 100% {\n left: 0;\n opacity: 1;\n }\n}\n@keyframes NotificationLeftFadeIn {\n 0% {\n right: 384px;\n opacity: 0;\n }\n 100% {\n right: 0;\n opacity: 1;\n }\n}\n@keyframes NotificationFadeOut {\n 0% {\n max-height: 150px;\n margin-bottom: 16px;\n opacity: 1;\n }\n 100% {\n max-height: 0;\n margin-bottom: 0;\n padding-top: 0;\n padding-bottom: 0;\n opacity: 0;\n }\n}\n.du-bois-dark-notification-notice {\n padding: 10px 40px 10px 32px;\n box-shadow: 0px 4px 16px rgba(46, 56, 64, 0.12);\n}\n.du-bois-dark-notification-notice .du-bois-dark-notification-notice-icon {\n font-size: 16px;\n width: 16px;\n height: 16px;\n margin-left: 0;\n left: 12px;\n top: 12px;\n line-height: 16px;\n}\n.du-bois-dark-notification-notice .du-bois-dark-notification-notice-close {\n top: 12px;\n right: 12px;\n width: 16px;\n height: 16px;\n line-height: 16px;\n}\n.du-bois-dark-notification-notice .du-bois-dark-notification-notice-close span[role='img'] {\n font-size: 16px;\n}\n.du-bois-dark-notification-notice .du-bois-dark-notification-notice-message {\n display: flex;\n align-items: center;\n min-height: 20px;\n line-height: 20px;\n font-weight: 600;\n}\n.du-bois-dark-notification-notice .du-bois-dark-notification-notice-message,\n.du-bois-dark-notification-notice .du-bois-dark-notification-notice-description {\n margin-left: unset;\n font-size: 13px;\n margin-bottom: 0;\n}\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n\n.du-bois-light-typography a {\n color: #2272b4;\n}\n.du-bois-light-typography a:hover {\n color: #0e538b;\n}\n.du-bois-light-typography a:focus {\n color: #04355d;\n}\n.du-bois-dark-typography a {\n color: #4299e0;\n}\n.du-bois-dark-typography a:hover {\n color: #8acaff;\n}\n.du-bois-dark-typography a:focus {\n color: #bae1fc;\n}\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n"],"names":[],"sourceRoot":""} \ No newline at end of file diff --git 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margin-right: 10px;\n}\n\n.mlflow-metrics-plot-container .plot-controls .block-control {\n margin-top: 25px;\n}\n\n.mlflow-metrics-plot-container .metrics-plot-data {\n flex: 1;\n display: flex;\n flex-direction: column;\n}\n\n.mlflow-metrics-plot-container .metrics-plot-view-container {\n min-height: 500px;\n flex: 1;\n}\n\n.mlflow-metrics-plot-container .metrics-summary {\n margin: 20px 20px 20px 60px;\n}\n\n.mlflow-metrics-plot-container .metrics-summary .mlflow-html-table-view {\n margin-bottom: 25px;\n /* Shrink to fit, so that scroll bars are aligned with the edge of the table */\n display: inline-block;\n}\n\n/* Reset min-width which is overridden in HtmlTableView as this breaks the\n table layout when scrolling is enabled and widths are specified */\n.mlflow-metrics-plot-container .metrics-summary .mlflow-html-table-view th {\n min-width: auto;\n}\n","/* Overriding the table styles since antd tables take the full screen by default.\nWe would like to change it to auto to 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.ant-select-dropdown-menu-item-group-title {\n color: #666;\n font-weight: bold;\n}\n\n.mlflow-model-select-dropdown .mlflow-create-new-model-option {\n border-top: 1px solid #ccc;\n}\n\n.mlflow-register-model-form .modal-explanatory-text {\n color: rgba(0, 0, 0, 0.52);\n font-size: 13px;\n}\n",".mde-header {\n flex-shrink: 0;\n display: flex;\n flex-wrap: wrap;\n align-items: stretch;\n border-bottom: 1px solid #c8ccd0;\n border-radius: 2px 2px 0 0;\n background: #f9f9f9; }\n .mde-header .mde-tabs {\n display: flex;\n flex-direction: row; }\n .mde-header .mde-tabs button {\n border-radius: 2px;\n margin: 6px 3px;\n background-color: transparent;\n border: 1px solid transparent;\n cursor: pointer; }\n .mde-header .mde-tabs button:first-child {\n margin-left: 6px; }\n .mde-header .mde-tabs button.selected {\n border: 1px solid #c8ccd0; }\n .mde-header .svg-icon {\n width: 1em;\n height: 1em;\n display: inline-block;\n font-size: inherit;\n overflow: visible;\n vertical-align: -.125em; }\n .mde-header ul.mde-header-group {\n margin: 0;\n padding: 10px;\n list-style: none;\n display: flex;\n flex-wrap: nowrap; }\n .mde-header ul.mde-header-group.hidden {\n visibility: hidden; }\n .mde-header ul.mde-header-group li.mde-header-item {\n display: inline-block;\n position: relative;\n margin: 0 4px; }\n .mde-header ul.mde-header-group li.mde-header-item button {\n text-align: left;\n cursor: pointer;\n height: 22px;\n padding: 4px;\n margin: 0;\n border: none;\n background: none;\n color: #242729; }\n\n@keyframes tooltip-appear {\n from {\n opacity: 0; }\n to {\n opacity: 1; } }\n .mde-header ul.mde-header-group li.mde-header-item button.tooltipped:hover::before {\n animation-name: tooltip-appear;\n animation-duration: 0.2s;\n animation-delay: 0.5s;\n animation-fill-mode: forwards;\n opacity: 0;\n position: absolute;\n z-index: 1000001;\n width: 0;\n height: 0;\n color: rgba(0, 0, 0, 0.8);\n pointer-events: none;\n content: \"\";\n border: 5px solid transparent;\n top: -5px;\n right: 50%;\n bottom: auto;\n margin-right: -5px;\n border-top-color: rgba(0, 0, 0, 0.8); }\n .mde-header ul.mde-header-group li.mde-header-item button.tooltipped:hover::after {\n animation-name: tooltip-appear;\n animation-duration: 0.2s;\n animation-delay: 0.5s;\n animation-fill-mode: forwards;\n font-size: 11px;\n opacity: 0;\n position: absolute;\n z-index: 1000000;\n padding: 5px 8px;\n color: #fff;\n pointer-events: none;\n content: attr(aria-label);\n background: rgba(0, 0, 0, 0.8);\n border-radius: 3px;\n right: 50%;\n bottom: 100%;\n transform: translateX(50%);\n margin-bottom: 5px;\n white-space: nowrap; }\n\n.mde-textarea-wrapper {\n position: relative; }\n .mde-textarea-wrapper textarea.mde-text {\n width: 100%;\n border: 0;\n padding: 10px;\n vertical-align: top;\n resize: vertical;\n overflow-y: auto; }\n\n.mde-preview .mde-preview-content {\n padding: 10px; }\n .mde-preview .mde-preview-content p, .mde-preview .mde-preview-content blockquote, .mde-preview .mde-preview-content ul, .mde-preview .mde-preview-content ol, .mde-preview .mde-preview-content dl, .mde-preview .mde-preview-content table, .mde-preview .mde-preview-content pre {\n margin-top: 0;\n margin-bottom: 16px; }\n .mde-preview .mde-preview-content h1, .mde-preview .mde-preview-content h2, .mde-preview .mde-preview-content h3 {\n margin-top: 24px;\n margin-bottom: 16px;\n font-weight: 600;\n line-height: 1.25;\n border-bottom: 1px solid #eee;\n padding-bottom: 0.3em; }\n .mde-preview .mde-preview-content h1 {\n font-size: 1.6em; }\n .mde-preview .mde-preview-content h2 {\n font-size: 1.4em; }\n .mde-preview .mde-preview-content h3 {\n font-size: 1.2em; }\n .mde-preview .mde-preview-content ul, .mde-preview .mde-preview-content ol {\n padding-left: 2em; }\n .mde-preview .mde-preview-content blockquote {\n margin-left: 0;\n padding: 0 1em;\n color: #777;\n border-left: 0.25em solid #ddd; }\n .mde-preview .mde-preview-content blockquote > :first-child {\n margin-top: 0; }\n .mde-preview .mde-preview-content blockquote > :last-child {\n margin-bottom: 0; }\n .mde-preview .mde-preview-content code {\n padding: 0.2em 0 0.2em 0;\n margin: 0;\n font-size: 90%;\n background-color: rgba(0, 0, 0, 0.04);\n border-radius: 3px; }\n .mde-preview .mde-preview-content code::before, .mde-preview .mde-preview-content code::after {\n letter-spacing: -0.2em;\n content: \"\\00a0\"; }\n .mde-preview .mde-preview-content pre {\n padding: 16px;\n overflow: auto;\n font-size: 85%;\n line-height: 1.45;\n background-color: #f7f7f7;\n border-radius: 3px; }\n .mde-preview .mde-preview-content pre code {\n display: inline;\n padding: 0;\n margin: 0;\n overflow: visible;\n line-height: inherit;\n word-wrap: normal;\n background-color: transparent;\n border: 0; }\n .mde-preview .mde-preview-content pre code::before, .mde-preview .mde-preview-content pre code::after {\n content: none; }\n .mde-preview .mde-preview-content pre > code {\n padding: 0;\n margin: 0;\n font-size: 100%;\n word-break: normal;\n white-space: pre;\n background: transparent;\n border: 0; }\n .mde-preview .mde-preview-content a {\n color: #4078c0;\n text-decoration: none; }\n .mde-preview .mde-preview-content a:hover {\n text-decoration: underline; }\n .mde-preview .mde-preview-content > *:first-child {\n margin-top: 0 !important; }\n .mde-preview .mde-preview-content > *:last-child {\n margin-bottom: 0 !important; }\n .mde-preview .mde-preview-content::after {\n display: table;\n clear: both;\n content: \"\"; }\n .mde-preview .mde-preview-content table {\n display: block;\n width: 100%;\n border-spacing: 0;\n border-collapse: collapse; }\n .mde-preview .mde-preview-content table thead th {\n font-weight: bold; }\n .mde-preview .mde-preview-content table th, .mde-preview .mde-preview-content table td {\n padding: 6px 13px;\n border: 1px solid #c8ccd0; }\n\n.react-mde {\n border: 1px solid #c8ccd0;\n border-radius: 2px; }\n .react-mde * {\n box-sizing: border-box; }\n .react-mde .invisible {\n display: none; }\n .react-mde .image-tip {\n user-select: none;\n display: flex !important;\n padding: 7px 10px;\n margin: 0;\n font-size: 13px;\n line-height: 16px;\n color: gray;\n background-color: #f9f9f9;\n border-top: 1px solid #c8ccd0;\n position: relative; }\n .react-mde .image-tip .image-input {\n min-height: 0;\n opacity: .01;\n width: 100% !important;\n position: absolute;\n top: 0;\n left: 0;\n padding: 5px;\n cursor: pointer; }\n\nul.mde-suggestions {\n position: absolute;\n min-width: 180px;\n padding: 0;\n margin: 20px 0 0;\n list-style: none;\n cursor: pointer;\n background: #fff;\n border: 1px solid #c8ccd0;\n border-radius: 3px;\n box-shadow: 0 1px 5px rgba(27, 31, 35, 0.15); }\n ul.mde-suggestions li {\n padding: 4px 8px;\n border-bottom: 1px solid #e1e4e8; }\n ul.mde-suggestions li:first-child {\n border-top-left-radius: 2px;\n border-top-right-radius: 2px; }\n ul.mde-suggestions li:last-child {\n border-bottom-right-radius: 2px;\n border-bottom-left-radius: 2px; }\n ul.mde-suggestions li:hover, ul.mde-suggestions li[aria-selected=true] {\n color: white;\n background-color: #0366d6; }\n",".mlflow-editable-note-actions {\n margin-top: 16px;\n}\n\n.mlflow-editable-note-actions button + button {\n margin-left: 16px;\n}\n\n.mde-header {\n background: none;\n}\n","/* Replaceing AntD Image */\n.mlflow-ui-container .rc-image-preview {\n height: 100%;\n pointer-events: none;\n text-align: center;\n}\n\n.mlflow-ui-container .rc-image-preview-mask {\n position: fixed;\n top: 0;\n left: 0;\n right: 0;\n bottom: 0;\n height: 100%;\n background-color: rgba(0, 0, 0, 0.45);\n z-index: 1000;\n}\n\n.mlflow-ui-container .rc-image-preview-mask img {\n max-width: 100%;\n max-height: 100%;\n position: absolute;\n top: 50%;\n left: 50%;\n transform: translate(-50%, -50%);\n}\n\n.mlflow-ui-container .rc-image-preview-mask {\n background-color: rgba(0, 0, 0, 0.45);\n bottom: 0;\n height: 100%;\n left: 0;\n position: fixed;\n right: 0;\n top: 0;\n z-index: 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0;\n}\n\n.mlflow-ui-container .rc-image-preview-img-wrapper:before {\n content: '';\n display: inline-block;\n height: 50%;\n margin-right: -1px;\n width: 1px;\n}\n\n.mlflow-ui-container .rc-image-preview-moving .mlflow-ui-container .rc-image-preview-img {\n cursor: grabbing;\n}\n\n.mlflow-ui-container .rc-image-preview-moving .mlflow-ui-container .rc-image-preview-img-wrapper {\n transition-duration: 0s;\n}\n\n.mlflow-ui-container .rc-image-preview-wrap {\n z-index: 1080;\n}\n\n.mlflow-ui-container .rc-image-preview-operations {\n font-feature-settings: 'tnum', 'tnum';\n align-items: center;\n background: rgba(0, 0, 0, 0.1);\n box-sizing: border-box;\n color: rgba(0, 0, 0, 0.85);\n color: hsla(0, 0%, 100%, 0.85);\n display: flex;\n flex-direction: row-reverse;\n font-size: 14px;\n font-variant: tabular-nums;\n line-height: 1.5715;\n list-style: none;\n margin: 0;\n padding: 0;\n pointer-events: auto;\n position: absolute;\n right: 0;\n top: 0;\n width: 100%;\n z-index: 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{\n color: hsla(0, 0%, 100%, 0.25);\n cursor: not-allowed;\n}\n\n.mlflow-ui-container .rc-image-preview-switch-left-disabled > .anticon,\n.mlflow-ui-container .rc-image-preview-switch-right-disabled > .anticon {\n cursor: not-allowed;\n}\n\n.mlflow-ui-container .rc-image-preview-switch-left > .anticon,\n.mlflow-ui-container .rc-image-preview-switch-right > .anticon {\n font-size: 18px;\n}\n\n.mlflow-ui-container .rc-image-preview-switch-left {\n left: 10px;\n}\n\n.mlflow-ui-container .rc-image-preview-switch-right {\n right: 10px;\n}\n\n.mlflow-ui-container .fade-enter,\n.mlflow-ui-container .fade-appear {\n animation-duration: 0.3s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.mlflow-ui-container .fade-leave {\n animation-duration: 0.3s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.mlflow-ui-container .fade-enter.fade-enter-active,\n.mlflow-ui-container .fade-appear.fade-appear-active {\n animation-name: mlflow-rcImageFadeIn;\n animation-play-state: running;\n}\n.mlflow-ui-container .fade-leave.fade-leave-active {\n animation-name: mlflow-rcImageFadeOut;\n animation-play-state: running;\n pointer-events: none;\n}\n.mlflow-ui-container .fade-enter,\n.mlflow-ui-container .fade-appear {\n opacity: 0;\n animation-timing-function: linear;\n}\n.mlflow-ui-container .fade-leave {\n animation-timing-function: linear;\n}\n\n@keyframes mlflow-rcImageFadeIn {\n 0% {\n opacity: 0;\n }\n 100% {\n opacity: 1;\n }\n}\n\n@keyframes mlflow-rcImageFadeOut {\n 0% {\n opacity: 1;\n }\n 100% {\n opacity: 0;\n }\n}\n\n.mlflow-ui-container .zoom-enter,\n.mlflow-ui-container .zoom-appear {\n animation-duration: 0.3s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.mlflow-ui-container .zoom-leave {\n animation-duration: 0.3s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.mlflow-ui-container .zoom-enter.zoom-enter-active,\n.mlflow-ui-container .zoom-appear.zoom-appear-active {\n animation-name: mlflow-rcImageZoomIn;\n animation-play-state: running;\n}\n.mlflow-ui-container .zoom-leave.zoom-leave-active {\n animation-name: mlflow-rcImageZoomOut;\n animation-play-state: running;\n pointer-events: none;\n}\n.mlflow-ui-container .zoom-enter,\n.mlflow-ui-container .zoom-appear {\n transform: scale(0);\n opacity: 0;\n animation-timing-function: cubic-bezier(0.08, 0.82, 0.17, 1);\n}\n.mlflow-ui-container .zoom-leave {\n animation-timing-function: cubic-bezier(0.78, 0.14, 0.15, 0.86);\n}\n\n@keyframes mlflow-rcImageZoomIn {\n 0% {\n transform: scale(0.2);\n opacity: 0;\n }\n 100% {\n transform: scale(1);\n opacity: 1;\n }\n}\n\n@keyframes mlflow-rcImageZoomOut {\n 0% {\n transform: scale(1);\n }\n 100% {\n transform: scale(0.2);\n opacity: 0;\n }\n}\n",".mlflow-ShowArtifactPage .text-area {\n box-sizing: border-box;\n width: 100%;\n height: 100%;\n font-family: Menlo, Consolas, monospace;\n}\n\n.mlflow-ShowArtifactPage,\n.mlflow-ShowArtifactPage .text-area-border-box {\n width: 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.map-container {\n height: 100%;\n width: 100%;\n}\n\n.mlflow-ui-container .leaflet-container {\n height: 100%;\n width: 100%;\n}\n","/* required styles */\r\n\r\n.leaflet-pane,\r\n.leaflet-tile,\r\n.leaflet-marker-icon,\r\n.leaflet-marker-shadow,\r\n.leaflet-tile-container,\r\n.leaflet-pane > svg,\r\n.leaflet-pane > canvas,\r\n.leaflet-zoom-box,\r\n.leaflet-image-layer,\r\n.leaflet-layer {\r\n\tposition: absolute;\r\n\tleft: 0;\r\n\ttop: 0;\r\n\t}\r\n.leaflet-container {\r\n\toverflow: hidden;\r\n\t}\r\n.leaflet-tile,\r\n.leaflet-marker-icon,\r\n.leaflet-marker-shadow {\r\n\t-webkit-user-select: none;\r\n\t -moz-user-select: none;\r\n\t user-select: none;\r\n\t -webkit-user-drag: none;\r\n\t}\r\n/* Prevents IE11 from highlighting tiles in blue */\r\n.leaflet-tile::selection {\r\n\tbackground: transparent;\r\n}\r\n/* Safari renders non-retina tile on retina better with this, but Chrome is worse */\r\n.leaflet-safari .leaflet-tile {\r\n\timage-rendering: -webkit-optimize-contrast;\r\n\t}\r\n/* hack that prevents hw layers \"stretching\" when loading new tiles */\r\n.leaflet-safari .leaflet-tile-container {\r\n\twidth: 1600px;\r\n\theight: 1600px;\r\n\t-webkit-transform-origin: 0 0;\r\n\t}\r\n.leaflet-marker-icon,\r\n.leaflet-marker-shadow {\r\n\tdisplay: block;\r\n\t}\r\n/* .leaflet-container svg: reset svg max-width decleration shipped in Joomla! (joomla.org) 3.x */\r\n/* .leaflet-container img: map is broken in FF if you have max-width: 100% on tiles */\r\n.leaflet-container .leaflet-overlay-pane svg {\r\n\tmax-width: none !important;\r\n\tmax-height: none !important;\r\n\t}\r\n.leaflet-container .leaflet-marker-pane img,\r\n.leaflet-container .leaflet-shadow-pane img,\r\n.leaflet-container .leaflet-tile-pane img,\r\n.leaflet-container img.leaflet-image-layer,\r\n.leaflet-container .leaflet-tile {\r\n\tmax-width: none !important;\r\n\tmax-height: none !important;\r\n\twidth: auto;\r\n\tpadding: 0;\r\n\t}\r\n\r\n.leaflet-container.leaflet-touch-zoom {\r\n\t-ms-touch-action: pan-x pan-y;\r\n\ttouch-action: pan-x pan-y;\r\n\t}\r\n.leaflet-container.leaflet-touch-drag {\r\n\t-ms-touch-action: pinch-zoom;\r\n\t/* Fallback for FF which doesn't support pinch-zoom */\r\n\ttouch-action: none;\r\n\ttouch-action: pinch-zoom;\r\n}\r\n.leaflet-container.leaflet-touch-drag.leaflet-touch-zoom {\r\n\t-ms-touch-action: none;\r\n\ttouch-action: none;\r\n}\r\n.leaflet-container {\r\n\t-webkit-tap-highlight-color: transparent;\r\n}\r\n.leaflet-container a {\r\n\t-webkit-tap-highlight-color: rgba(51, 181, 229, 0.4);\r\n}\r\n.leaflet-tile {\r\n\tfilter: inherit;\r\n\tvisibility: hidden;\r\n\t}\r\n.leaflet-tile-loaded {\r\n\tvisibility: inherit;\r\n\t}\r\n.leaflet-zoom-box {\r\n\twidth: 0;\r\n\theight: 0;\r\n\t-moz-box-sizing: border-box;\r\n\t box-sizing: border-box;\r\n\tz-index: 800;\r\n\t}\r\n/* workaround for https://bugzilla.mozilla.org/show_bug.cgi?id=888319 */\r\n.leaflet-overlay-pane svg {\r\n\t-moz-user-select: none;\r\n\t}\r\n\r\n.leaflet-pane { z-index: 400; }\r\n\r\n.leaflet-tile-pane { z-index: 200; }\r\n.leaflet-overlay-pane { z-index: 400; }\r\n.leaflet-shadow-pane { z-index: 500; }\r\n.leaflet-marker-pane { z-index: 600; }\r\n.leaflet-tooltip-pane { z-index: 650; }\r\n.leaflet-popup-pane { z-index: 700; }\r\n\r\n.leaflet-map-pane canvas { z-index: 100; }\r\n.leaflet-map-pane svg { z-index: 200; }\r\n\r\n.leaflet-vml-shape {\r\n\twidth: 1px;\r\n\theight: 1px;\r\n\t}\r\n.lvml {\r\n\tbehavior: url(#default#VML);\r\n\tdisplay: inline-block;\r\n\tposition: absolute;\r\n\t}\r\n\r\n\r\n/* control positioning */\r\n\r\n.leaflet-control {\r\n\tposition: relative;\r\n\tz-index: 800;\r\n\tpointer-events: visiblePainted; /* IE 9-10 doesn't have auto */\r\n\tpointer-events: auto;\r\n\t}\r\n.leaflet-top,\r\n.leaflet-bottom {\r\n\tposition: absolute;\r\n\tz-index: 1000;\r\n\tpointer-events: none;\r\n\t}\r\n.leaflet-top {\r\n\ttop: 0;\r\n\t}\r\n.leaflet-right {\r\n\tright: 0;\r\n\t}\r\n.leaflet-bottom {\r\n\tbottom: 0;\r\n\t}\r\n.leaflet-left {\r\n\tleft: 0;\r\n\t}\r\n.leaflet-control {\r\n\tfloat: left;\r\n\tclear: both;\r\n\t}\r\n.leaflet-right .leaflet-control {\r\n\tfloat: right;\r\n\t}\r\n.leaflet-top .leaflet-control {\r\n\tmargin-top: 10px;\r\n\t}\r\n.leaflet-bottom .leaflet-control {\r\n\tmargin-bottom: 10px;\r\n\t}\r\n.leaflet-left .leaflet-control {\r\n\tmargin-left: 10px;\r\n\t}\r\n.leaflet-right .leaflet-control {\r\n\tmargin-right: 10px;\r\n\t}\r\n\r\n\r\n/* zoom and fade animations */\r\n\r\n.leaflet-fade-anim .leaflet-popup {\r\n\topacity: 0;\r\n\t-webkit-transition: opacity 0.2s linear;\r\n\t -moz-transition: opacity 0.2s linear;\r\n\t transition: opacity 0.2s linear;\r\n\t}\r\n.leaflet-fade-anim .leaflet-map-pane .leaflet-popup {\r\n\topacity: 1;\r\n\t}\r\n.leaflet-zoom-animated {\r\n\t-webkit-transform-origin: 0 0;\r\n\t -ms-transform-origin: 0 0;\r\n\t transform-origin: 0 0;\r\n\t}\r\nsvg.leaflet-zoom-animated {\r\n\twill-change: transform;\r\n}\r\n\r\n.leaflet-zoom-anim .leaflet-zoom-animated {\r\n\t-webkit-transition: -webkit-transform 0.25s cubic-bezier(0,0,0.25,1);\r\n\t -moz-transition: -moz-transform 0.25s cubic-bezier(0,0,0.25,1);\r\n\t transition: transform 0.25s cubic-bezier(0,0,0.25,1);\r\n\t}\r\n.leaflet-zoom-anim .leaflet-tile,\r\n.leaflet-pan-anim .leaflet-tile {\r\n\t-webkit-transition: none;\r\n\t -moz-transition: none;\r\n\t transition: none;\r\n\t}\r\n\r\n.leaflet-zoom-anim .leaflet-zoom-hide {\r\n\tvisibility: hidden;\r\n\t}\r\n\r\n\r\n/* cursors */\r\n\r\n.leaflet-interactive {\r\n\tcursor: pointer;\r\n\t}\r\n.leaflet-grab {\r\n\tcursor: -webkit-grab;\r\n\tcursor: -moz-grab;\r\n\tcursor: grab;\r\n\t}\r\n.leaflet-crosshair,\r\n.leaflet-crosshair .leaflet-interactive {\r\n\tcursor: crosshair;\r\n\t}\r\n.leaflet-popup-pane,\r\n.leaflet-control {\r\n\tcursor: auto;\r\n\t}\r\n.leaflet-dragging .leaflet-grab,\r\n.leaflet-dragging .leaflet-grab .leaflet-interactive,\r\n.leaflet-dragging .leaflet-marker-draggable {\r\n\tcursor: move;\r\n\tcursor: -webkit-grabbing;\r\n\tcursor: -moz-grabbing;\r\n\tcursor: grabbing;\r\n\t}\r\n\r\n/* marker & overlays interactivity */\r\n.leaflet-marker-icon,\r\n.leaflet-marker-shadow,\r\n.leaflet-image-layer,\r\n.leaflet-pane > svg path,\r\n.leaflet-tile-container {\r\n\tpointer-events: none;\r\n\t}\r\n\r\n.leaflet-marker-icon.leaflet-interactive,\r\n.leaflet-image-layer.leaflet-interactive,\r\n.leaflet-pane > svg path.leaflet-interactive,\r\nsvg.leaflet-image-layer.leaflet-interactive path {\r\n\tpointer-events: visiblePainted; /* IE 9-10 doesn't have auto */\r\n\tpointer-events: auto;\r\n\t}\r\n\r\n/* visual tweaks */\r\n\r\n.leaflet-container {\r\n\tbackground: #ddd;\r\n\toutline-offset: 1px;\r\n\t}\r\n.leaflet-container a {\r\n\tcolor: #0078A8;\r\n\t}\r\n.leaflet-zoom-box {\r\n\tborder: 2px dotted #38f;\r\n\tbackground: rgba(255,255,255,0.5);\r\n\t}\r\n\r\n\r\n/* general typography */\r\n.leaflet-container {\r\n\tfont-family: \"Helvetica Neue\", Arial, Helvetica, sans-serif;\r\n\tfont-size: 12px;\r\n\tfont-size: 0.75rem;\r\n\tline-height: 1.5;\r\n\t}\r\n\r\n\r\n/* general toolbar styles */\r\n\r\n.leaflet-bar {\r\n\tbox-shadow: 0 1px 5px rgba(0,0,0,0.65);\r\n\tborder-radius: 4px;\r\n\t}\r\n.leaflet-bar a {\r\n\tbackground-color: #fff;\r\n\tborder-bottom: 1px solid #ccc;\r\n\twidth: 26px;\r\n\theight: 26px;\r\n\tline-height: 26px;\r\n\tdisplay: block;\r\n\ttext-align: center;\r\n\ttext-decoration: none;\r\n\tcolor: black;\r\n\t}\r\n.leaflet-bar a,\r\n.leaflet-control-layers-toggle {\r\n\tbackground-position: 50% 50%;\r\n\tbackground-repeat: no-repeat;\r\n\tdisplay: block;\r\n\t}\r\n.leaflet-bar a:hover,\r\n.leaflet-bar a:focus {\r\n\tbackground-color: #f4f4f4;\r\n\t}\r\n.leaflet-bar a:first-child {\r\n\tborder-top-left-radius: 4px;\r\n\tborder-top-right-radius: 4px;\r\n\t}\r\n.leaflet-bar a:last-child {\r\n\tborder-bottom-left-radius: 4px;\r\n\tborder-bottom-right-radius: 4px;\r\n\tborder-bottom: none;\r\n\t}\r\n.leaflet-bar a.leaflet-disabled {\r\n\tcursor: default;\r\n\tbackground-color: #f4f4f4;\r\n\tcolor: #bbb;\r\n\t}\r\n\r\n.leaflet-touch .leaflet-bar a {\r\n\twidth: 30px;\r\n\theight: 30px;\r\n\tline-height: 30px;\r\n\t}\r\n.leaflet-touch .leaflet-bar a:first-child {\r\n\tborder-top-left-radius: 2px;\r\n\tborder-top-right-radius: 2px;\r\n\t}\r\n.leaflet-touch .leaflet-bar a:last-child {\r\n\tborder-bottom-left-radius: 2px;\r\n\tborder-bottom-right-radius: 2px;\r\n\t}\r\n\r\n/* zoom control */\r\n\r\n.leaflet-control-zoom-in,\r\n.leaflet-control-zoom-out {\r\n\tfont: bold 18px 'Lucida Console', Monaco, monospace;\r\n\ttext-indent: 1px;\r\n\t}\r\n\r\n.leaflet-touch .leaflet-control-zoom-in, .leaflet-touch .leaflet-control-zoom-out {\r\n\tfont-size: 22px;\r\n\t}\r\n\r\n\r\n/* layers control */\r\n\r\n.leaflet-control-layers {\r\n\tbox-shadow: 0 1px 5px rgba(0,0,0,0.4);\r\n\tbackground: #fff;\r\n\tborder-radius: 5px;\r\n\t}\r\n.leaflet-control-layers-toggle {\r\n\tbackground-image: url(images/layers.png);\r\n\twidth: 36px;\r\n\theight: 36px;\r\n\t}\r\n.leaflet-retina .leaflet-control-layers-toggle {\r\n\tbackground-image: url(images/layers-2x.png);\r\n\tbackground-size: 26px 26px;\r\n\t}\r\n.leaflet-touch .leaflet-control-layers-toggle {\r\n\twidth: 44px;\r\n\theight: 44px;\r\n\t}\r\n.leaflet-control-layers .leaflet-control-layers-list,\r\n.leaflet-control-layers-expanded .leaflet-control-layers-toggle {\r\n\tdisplay: none;\r\n\t}\r\n.leaflet-control-layers-expanded .leaflet-control-layers-list {\r\n\tdisplay: block;\r\n\tposition: relative;\r\n\t}\r\n.leaflet-control-layers-expanded {\r\n\tpadding: 6px 10px 6px 6px;\r\n\tcolor: #333;\r\n\tbackground: #fff;\r\n\t}\r\n.leaflet-control-layers-scrollbar {\r\n\toverflow-y: scroll;\r\n\toverflow-x: hidden;\r\n\tpadding-right: 5px;\r\n\t}\r\n.leaflet-control-layers-selector {\r\n\tmargin-top: 2px;\r\n\tposition: relative;\r\n\ttop: 1px;\r\n\t}\r\n.leaflet-control-layers label {\r\n\tdisplay: block;\r\n\tfont-size: 13px;\r\n\tfont-size: 1.08333em;\r\n\t}\r\n.leaflet-control-layers-separator {\r\n\theight: 0;\r\n\tborder-top: 1px solid #ddd;\r\n\tmargin: 5px -10px 5px -6px;\r\n\t}\r\n\r\n/* Default icon URLs */\r\n.leaflet-default-icon-path { /* used only in path-guessing heuristic, see L.Icon.Default */\r\n\tbackground-image: url(images/marker-icon.png);\r\n\t}\r\n\r\n\r\n/* attribution and scale controls */\r\n\r\n.leaflet-container .leaflet-control-attribution {\r\n\tbackground: #fff;\r\n\tbackground: rgba(255, 255, 255, 0.8);\r\n\tmargin: 0;\r\n\t}\r\n.leaflet-control-attribution,\r\n.leaflet-control-scale-line {\r\n\tpadding: 0 5px;\r\n\tcolor: #333;\r\n\tline-height: 1.4;\r\n\t}\r\n.leaflet-control-attribution a {\r\n\ttext-decoration: none;\r\n\t}\r\n.leaflet-control-attribution a:hover,\r\n.leaflet-control-attribution a:focus {\r\n\ttext-decoration: underline;\r\n\t}\r\n.leaflet-control-attribution svg {\r\n\tdisplay: inline !important;\r\n\t}\r\n.leaflet-left .leaflet-control-scale {\r\n\tmargin-left: 5px;\r\n\t}\r\n.leaflet-bottom .leaflet-control-scale {\r\n\tmargin-bottom: 5px;\r\n\t}\r\n.leaflet-control-scale-line {\r\n\tborder: 2px solid #777;\r\n\tborder-top: none;\r\n\tline-height: 1.1;\r\n\tpadding: 2px 5px 1px;\r\n\twhite-space: nowrap;\r\n\toverflow: hidden;\r\n\t-moz-box-sizing: border-box;\r\n\t box-sizing: border-box;\r\n\r\n\tbackground: #fff;\r\n\tbackground: rgba(255, 255, 255, 0.5);\r\n\t}\r\n.leaflet-control-scale-line:not(:first-child) {\r\n\tborder-top: 2px solid #777;\r\n\tborder-bottom: none;\r\n\tmargin-top: -2px;\r\n\t}\r\n.leaflet-control-scale-line:not(:first-child):not(:last-child) {\r\n\tborder-bottom: 2px solid #777;\r\n\t}\r\n\r\n.leaflet-touch .leaflet-control-attribution,\r\n.leaflet-touch .leaflet-control-layers,\r\n.leaflet-touch .leaflet-bar {\r\n\tbox-shadow: none;\r\n\t}\r\n.leaflet-touch .leaflet-control-layers,\r\n.leaflet-touch .leaflet-bar {\r\n\tborder: 2px solid rgba(0,0,0,0.2);\r\n\tbackground-clip: padding-box;\r\n\t}\r\n\r\n\r\n/* popup */\r\n\r\n.leaflet-popup {\r\n\tposition: absolute;\r\n\ttext-align: center;\r\n\tmargin-bottom: 20px;\r\n\t}\r\n.leaflet-popup-content-wrapper {\r\n\tpadding: 1px;\r\n\ttext-align: left;\r\n\tborder-radius: 12px;\r\n\t}\r\n.leaflet-popup-content {\r\n\tmargin: 13px 24px 13px 20px;\r\n\tline-height: 1.3;\r\n\tfont-size: 13px;\r\n\tfont-size: 1.08333em;\r\n\tmin-height: 1px;\r\n\t}\r\n.leaflet-popup-content p {\r\n\tmargin: 17px 0;\r\n\tmargin: 1.3em 0;\r\n\t}\r\n.leaflet-popup-tip-container {\r\n\twidth: 40px;\r\n\theight: 20px;\r\n\tposition: absolute;\r\n\tleft: 50%;\r\n\tmargin-top: -1px;\r\n\tmargin-left: -20px;\r\n\toverflow: hidden;\r\n\tpointer-events: none;\r\n\t}\r\n.leaflet-popup-tip {\r\n\twidth: 17px;\r\n\theight: 17px;\r\n\tpadding: 1px;\r\n\r\n\tmargin: -10px auto 0;\r\n\tpointer-events: auto;\r\n\r\n\t-webkit-transform: rotate(45deg);\r\n\t -moz-transform: rotate(45deg);\r\n\t -ms-transform: rotate(45deg);\r\n\t transform: rotate(45deg);\r\n\t}\r\n.leaflet-popup-content-wrapper,\r\n.leaflet-popup-tip {\r\n\tbackground: white;\r\n\tcolor: #333;\r\n\tbox-shadow: 0 3px 14px rgba(0,0,0,0.4);\r\n\t}\r\n.leaflet-container a.leaflet-popup-close-button {\r\n\tposition: absolute;\r\n\ttop: 0;\r\n\tright: 0;\r\n\tborder: none;\r\n\ttext-align: center;\r\n\twidth: 24px;\r\n\theight: 24px;\r\n\tfont: 16px/24px Tahoma, Verdana, sans-serif;\r\n\tcolor: #757575;\r\n\ttext-decoration: none;\r\n\tbackground: transparent;\r\n\t}\r\n.leaflet-container a.leaflet-popup-close-button:hover,\r\n.leaflet-container a.leaflet-popup-close-button:focus {\r\n\tcolor: #585858;\r\n\t}\r\n.leaflet-popup-scrolled {\r\n\toverflow: auto;\r\n\tborder-bottom: 1px solid #ddd;\r\n\tborder-top: 1px solid #ddd;\r\n\t}\r\n\r\n.leaflet-oldie .leaflet-popup-content-wrapper {\r\n\t-ms-zoom: 1;\r\n\t}\r\n.leaflet-oldie .leaflet-popup-tip {\r\n\twidth: 24px;\r\n\tmargin: 0 auto;\r\n\r\n\t-ms-filter: \"progid:DXImageTransform.Microsoft.Matrix(M11=0.70710678, M12=0.70710678, M21=-0.70710678, M22=0.70710678)\";\r\n\tfilter: progid:DXImageTransform.Microsoft.Matrix(M11=0.70710678, M12=0.70710678, M21=-0.70710678, M22=0.70710678);\r\n\t}\r\n\r\n.leaflet-oldie .leaflet-control-zoom,\r\n.leaflet-oldie .leaflet-control-layers,\r\n.leaflet-oldie .leaflet-popup-content-wrapper,\r\n.leaflet-oldie .leaflet-popup-tip {\r\n\tborder: 1px solid #999;\r\n\t}\r\n\r\n\r\n/* div icon */\r\n\r\n.leaflet-div-icon {\r\n\tbackground: #fff;\r\n\tborder: 1px solid #666;\r\n\t}\r\n\r\n\r\n/* Tooltip */\r\n/* Base styles for the element that has a tooltip */\r\n.leaflet-tooltip {\r\n\tposition: absolute;\r\n\tpadding: 6px;\r\n\tbackground-color: #fff;\r\n\tborder: 1px solid #fff;\r\n\tborder-radius: 3px;\r\n\tcolor: #222;\r\n\twhite-space: nowrap;\r\n\t-webkit-user-select: none;\r\n\t-moz-user-select: none;\r\n\t-ms-user-select: none;\r\n\tuser-select: none;\r\n\tpointer-events: none;\r\n\tbox-shadow: 0 1px 3px rgba(0,0,0,0.4);\r\n\t}\r\n.leaflet-tooltip.leaflet-interactive {\r\n\tcursor: pointer;\r\n\tpointer-events: auto;\r\n\t}\r\n.leaflet-tooltip-top:before,\r\n.leaflet-tooltip-bottom:before,\r\n.leaflet-tooltip-left:before,\r\n.leaflet-tooltip-right:before {\r\n\tposition: absolute;\r\n\tpointer-events: none;\r\n\tborder: 6px solid transparent;\r\n\tbackground: transparent;\r\n\tcontent: \"\";\r\n\t}\r\n\r\n/* Directions */\r\n\r\n.leaflet-tooltip-bottom {\r\n\tmargin-top: 6px;\r\n}\r\n.leaflet-tooltip-top {\r\n\tmargin-top: -6px;\r\n}\r\n.leaflet-tooltip-bottom:before,\r\n.leaflet-tooltip-top:before {\r\n\tleft: 50%;\r\n\tmargin-left: -6px;\r\n\t}\r\n.leaflet-tooltip-top:before {\r\n\tbottom: 0;\r\n\tmargin-bottom: -12px;\r\n\tborder-top-color: #fff;\r\n\t}\r\n.leaflet-tooltip-bottom:before {\r\n\ttop: 0;\r\n\tmargin-top: -12px;\r\n\tmargin-left: -6px;\r\n\tborder-bottom-color: #fff;\r\n\t}\r\n.leaflet-tooltip-left {\r\n\tmargin-left: -6px;\r\n}\r\n.leaflet-tooltip-right {\r\n\tmargin-left: 6px;\r\n}\r\n.leaflet-tooltip-left:before,\r\n.leaflet-tooltip-right:before {\r\n\ttop: 50%;\r\n\tmargin-top: -6px;\r\n\t}\r\n.leaflet-tooltip-left:before {\r\n\tright: 0;\r\n\tmargin-right: -12px;\r\n\tborder-left-color: #fff;\r\n\t}\r\n.leaflet-tooltip-right:before {\r\n\tleft: 0;\r\n\tmargin-left: 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.rc-image-preview-body {\n bottom: 0;\n left: 0;\n overflow: hidden;\n position: absolute;\n right: 0;\n top: 0;\n}\n\n.mlflow-ui-container .rc-image-preview-img {\n cursor: grab;\n max-height: 100%;\n max-width: 100%;\n pointer-events: auto;\n transform: scaleX(1);\n -webkit-user-select: none;\n -moz-user-select: none;\n -ms-user-select: none;\n user-select: none;\n vertical-align: middle;\n}\n\n.mlflow-ui-container .rc-image-preview-img,\n.mlflow-ui-container .rc-image-preview-img-wrapper {\n transition: transform 0.3s cubic-bezier(0.215, 0.61, 0.355, 1) 0s;\n}\n\n.mlflow-ui-container .rc-image-preview-img-wrapper {\n bottom: 0;\n left: 0;\n position: absolute;\n right: 0;\n top: 0;\n}\n\n.mlflow-ui-container .rc-image-preview-img-wrapper:before {\n content: '';\n display: inline-block;\n height: 50%;\n margin-right: -1px;\n width: 1px;\n}\n\n.mlflow-ui-container .rc-image-preview-moving .mlflow-ui-container .rc-image-preview-img {\n cursor: grabbing;\n}\n\n.mlflow-ui-container .rc-image-preview-moving .mlflow-ui-container .rc-image-preview-img-wrapper {\n transition-duration: 0s;\n}\n\n.mlflow-ui-container .rc-image-preview-wrap {\n z-index: 1080;\n}\n\n.mlflow-ui-container .rc-image-preview-operations {\n font-feature-settings: 'tnum', 'tnum';\n align-items: center;\n background: rgba(0, 0, 0, 0.1);\n box-sizing: border-box;\n color: rgba(0, 0, 0, 0.85);\n color: hsla(0, 0%, 100%, 0.85);\n display: flex;\n flex-direction: row-reverse;\n font-size: 14px;\n font-variant: tabular-nums;\n line-height: 1.5715;\n list-style: none;\n margin: 0;\n padding: 0;\n pointer-events: auto;\n position: absolute;\n right: 0;\n top: 0;\n width: 100%;\n z-index: 1;\n}\n\n.mlflow-ui-container .rc-image-preview-operations-operation {\n cursor: pointer;\n margin-left: 12px;\n padding: 12px;\n}\n\n.mlflow-ui-container .rc-image-preview-operations-operation-disabled {\n color: hsla(0, 0%, 100%, 0.25);\n pointer-events: none;\n}\n\n.mlflow-ui-container .rc-image-preview-operations-operation:last-of-type {\n margin-left: 0;\n}\n\n.mlflow-ui-container .rc-image-preview-operations-icon {\n font-size: 18px;\n}\n\n.mlflow-ui-container .rc-image-preview-switch-left,\n.mlflow-ui-container .rc-image-preview-switch-right {\n align-items: center;\n background: rgba(0, 0, 0, 0.1);\n border-radius: 50%;\n color: hsla(0, 0%, 100%, 0.85);\n cursor: pointer;\n display: flex;\n height: 44px;\n justify-content: center;\n margin-top: -22px;\n pointer-events: auto;\n position: absolute;\n right: 10px;\n top: 50%;\n width: 44px;\n z-index: 1;\n}\n\n.mlflow-ui-container .rc-image-preview-switch-left-disabled,\n.mlflow-ui-container .rc-image-preview-switch-right-disabled {\n color: hsla(0, 0%, 100%, 0.25);\n cursor: not-allowed;\n}\n\n.mlflow-ui-container .rc-image-preview-switch-left-disabled > .anticon,\n.mlflow-ui-container .rc-image-preview-switch-right-disabled > .anticon {\n cursor: not-allowed;\n}\n\n.mlflow-ui-container .rc-image-preview-switch-left > .anticon,\n.mlflow-ui-container .rc-image-preview-switch-right > .anticon {\n font-size: 18px;\n}\n\n.mlflow-ui-container .rc-image-preview-switch-left {\n left: 10px;\n}\n\n.mlflow-ui-container .rc-image-preview-switch-right {\n right: 10px;\n}\n\n.mlflow-ui-container .fade-enter,\n.mlflow-ui-container .fade-appear {\n animation-duration: 0.3s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.mlflow-ui-container .fade-leave {\n animation-duration: 0.3s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.mlflow-ui-container .fade-enter.fade-enter-active,\n.mlflow-ui-container .fade-appear.fade-appear-active {\n animation-name: mlflow-rcImageFadeIn;\n animation-play-state: running;\n}\n.mlflow-ui-container .fade-leave.fade-leave-active {\n animation-name: mlflow-rcImageFadeOut;\n animation-play-state: running;\n pointer-events: none;\n}\n.mlflow-ui-container .fade-enter,\n.mlflow-ui-container .fade-appear {\n opacity: 0;\n animation-timing-function: linear;\n}\n.mlflow-ui-container .fade-leave {\n animation-timing-function: linear;\n}\n\n@keyframes mlflow-rcImageFadeIn {\n 0% {\n opacity: 0;\n }\n 100% {\n opacity: 1;\n }\n}\n\n@keyframes mlflow-rcImageFadeOut {\n 0% {\n opacity: 1;\n }\n 100% {\n opacity: 0;\n }\n}\n\n.mlflow-ui-container .zoom-enter,\n.mlflow-ui-container .zoom-appear {\n animation-duration: 0.3s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.mlflow-ui-container .zoom-leave {\n animation-duration: 0.3s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.mlflow-ui-container .zoom-enter.zoom-enter-active,\n.mlflow-ui-container .zoom-appear.zoom-appear-active {\n animation-name: mlflow-rcImageZoomIn;\n animation-play-state: running;\n}\n.mlflow-ui-container .zoom-leave.zoom-leave-active {\n animation-name: mlflow-rcImageZoomOut;\n animation-play-state: running;\n pointer-events: none;\n}\n.mlflow-ui-container .zoom-enter,\n.mlflow-ui-container .zoom-appear {\n transform: scale(0);\n opacity: 0;\n animation-timing-function: cubic-bezier(0.08, 0.82, 0.17, 1);\n}\n.mlflow-ui-container .zoom-leave {\n animation-timing-function: cubic-bezier(0.78, 0.14, 0.15, 0.86);\n}\n\n@keyframes mlflow-rcImageZoomIn {\n 0% {\n transform: scale(0.2);\n opacity: 0;\n }\n 100% {\n transform: scale(1);\n opacity: 1;\n }\n}\n\n@keyframes mlflow-rcImageZoomOut {\n 0% {\n transform: scale(1);\n }\n 100% {\n transform: scale(0.2);\n opacity: 0;\n }\n}\n","/* Overriding the table styles since antd tables take the full screen by default.\nWe would like to change it to auto to automatically grow based on the columns */\n\n.mlflow-html-table-view table {\n width: auto;\n min-width: 400px;\n}\n\n.mlflow-html-table-view th {\n width: auto;\n min-width: 200px;\n margin-right: 80px;\n font-size: 13px;\n color: #888;\n}\n",".mde-header {\n flex-shrink: 0;\n display: flex;\n flex-wrap: wrap;\n align-items: stretch;\n border-bottom: 1px solid #c8ccd0;\n border-radius: 2px 2px 0 0;\n background: #f9f9f9; }\n .mde-header .mde-tabs {\n display: flex;\n flex-direction: row; }\n .mde-header .mde-tabs button {\n border-radius: 2px;\n margin: 6px 3px;\n background-color: transparent;\n border: 1px solid transparent;\n cursor: pointer; }\n .mde-header .mde-tabs button:first-child {\n margin-left: 6px; }\n .mde-header .mde-tabs button.selected {\n border: 1px solid #c8ccd0; }\n .mde-header .svg-icon {\n width: 1em;\n height: 1em;\n display: inline-block;\n font-size: inherit;\n overflow: visible;\n vertical-align: -.125em; }\n .mde-header ul.mde-header-group {\n margin: 0;\n padding: 10px;\n list-style: none;\n display: flex;\n flex-wrap: nowrap; }\n .mde-header ul.mde-header-group.hidden {\n visibility: hidden; }\n .mde-header ul.mde-header-group li.mde-header-item {\n display: inline-block;\n position: relative;\n margin: 0 4px; }\n .mde-header ul.mde-header-group li.mde-header-item button {\n text-align: left;\n cursor: pointer;\n height: 22px;\n padding: 4px;\n margin: 0;\n border: none;\n background: none;\n color: #242729; }\n\n@keyframes tooltip-appear {\n from {\n opacity: 0; }\n to {\n opacity: 1; } }\n .mde-header ul.mde-header-group li.mde-header-item button.tooltipped:hover::before {\n animation-name: tooltip-appear;\n animation-duration: 0.2s;\n animation-delay: 0.5s;\n animation-fill-mode: forwards;\n opacity: 0;\n position: absolute;\n z-index: 1000001;\n width: 0;\n height: 0;\n color: rgba(0, 0, 0, 0.8);\n pointer-events: none;\n content: \"\";\n border: 5px solid transparent;\n top: -5px;\n right: 50%;\n bottom: auto;\n margin-right: -5px;\n border-top-color: rgba(0, 0, 0, 0.8); }\n .mde-header ul.mde-header-group li.mde-header-item button.tooltipped:hover::after {\n animation-name: tooltip-appear;\n animation-duration: 0.2s;\n animation-delay: 0.5s;\n animation-fill-mode: forwards;\n font-size: 11px;\n opacity: 0;\n position: absolute;\n z-index: 1000000;\n padding: 5px 8px;\n color: #fff;\n 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* Font Awesome 4.7.0 by @davegandy - http://fontawesome.io - @fontawesome\n * License - http://fontawesome.io/license (Font: SIL OFL 1.1, CSS: MIT License)\n */\n/* FONT PATH\n * -------------------------- */\n@font-face {\n font-family: 'FontAwesome';\n src: url('../fonts/fontawesome-webfont.eot?v=4.7.0');\n src: url('../fonts/fontawesome-webfont.eot?#iefix&v=4.7.0') format('embedded-opentype'), url('../fonts/fontawesome-webfont.woff2?v=4.7.0') format('woff2'), url('../fonts/fontawesome-webfont.woff?v=4.7.0') format('woff'), url('../fonts/fontawesome-webfont.ttf?v=4.7.0') format('truetype'), url('../fonts/fontawesome-webfont.svg?v=4.7.0#fontawesomeregular') format('svg');\n font-weight: normal;\n font-style: normal;\n}\n.fa {\n display: inline-block;\n font: normal normal normal 14px/1 FontAwesome;\n font-size: inherit;\n text-rendering: auto;\n -webkit-font-smoothing: antialiased;\n -moz-osx-font-smoothing: grayscale;\n}\n/* makes the font 33% larger relative to the icon container */\n.fa-lg {\n font-size: 1.33333333em;\n line-height: 0.75em;\n vertical-align: -15%;\n}\n.fa-2x {\n font-size: 2em;\n}\n.fa-3x {\n font-size: 3em;\n}\n.fa-4x {\n font-size: 4em;\n}\n.fa-5x {\n font-size: 5em;\n}\n.fa-fw {\n width: 1.28571429em;\n text-align: center;\n}\n.fa-ul {\n padding-left: 0;\n margin-left: 2.14285714em;\n list-style-type: none;\n}\n.fa-ul > li {\n position: relative;\n}\n.fa-li {\n position: absolute;\n left: -2.14285714em;\n width: 2.14285714em;\n top: 0.14285714em;\n text-align: center;\n}\n.fa-li.fa-lg {\n left: -1.85714286em;\n}\n.fa-border {\n padding: .2em .25em .15em;\n border: solid 0.08em #eeeeee;\n border-radius: .1em;\n}\n.fa-pull-left {\n float: left;\n}\n.fa-pull-right {\n float: right;\n}\n.fa.fa-pull-left {\n margin-right: .3em;\n}\n.fa.fa-pull-right {\n margin-left: .3em;\n}\n/* Deprecated as of 4.4.0 */\n.pull-right {\n float: right;\n}\n.pull-left {\n float: left;\n}\n.fa.pull-left {\n margin-right: .3em;\n}\n.fa.pull-right {\n margin-left: .3em;\n}\n.fa-spin {\n -webkit-animation: fa-spin 2s infinite linear;\n animation: fa-spin 2s infinite linear;\n}\n.fa-pulse {\n -webkit-animation: fa-spin 1s infinite steps(8);\n animation: fa-spin 1s infinite steps(8);\n}\n@-webkit-keyframes fa-spin {\n 0% {\n -webkit-transform: rotate(0deg);\n transform: rotate(0deg);\n }\n 100% {\n -webkit-transform: rotate(359deg);\n transform: rotate(359deg);\n }\n}\n@keyframes fa-spin {\n 0% {\n -webkit-transform: rotate(0deg);\n transform: rotate(0deg);\n }\n 100% {\n -webkit-transform: rotate(359deg);\n transform: rotate(359deg);\n }\n}\n.fa-rotate-90 {\n -ms-filter: \"progid:DXImageTransform.Microsoft.BasicImage(rotation=1)\";\n -webkit-transform: rotate(90deg);\n -ms-transform: rotate(90deg);\n transform: rotate(90deg);\n}\n.fa-rotate-180 {\n -ms-filter: \"progid:DXImageTransform.Microsoft.BasicImage(rotation=2)\";\n -webkit-transform: rotate(180deg);\n -ms-transform: rotate(180deg);\n transform: rotate(180deg);\n}\n.fa-rotate-270 {\n 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inherit;\n}\n.fa-stack-2x {\n font-size: 2em;\n}\n.fa-inverse {\n color: #ffffff;\n}\n/* Font Awesome uses the Unicode Private Use Area (PUA) to ensure screen\n readers do not read off random characters that represent icons */\n.fa-glass:before {\n content: \"\\f000\";\n}\n.fa-music:before {\n content: \"\\f001\";\n}\n.fa-search:before {\n content: \"\\f002\";\n}\n.fa-envelope-o:before {\n content: \"\\f003\";\n}\n.fa-heart:before {\n content: \"\\f004\";\n}\n.fa-star:before {\n content: \"\\f005\";\n}\n.fa-star-o:before {\n content: \"\\f006\";\n}\n.fa-user:before {\n content: \"\\f007\";\n}\n.fa-film:before {\n content: \"\\f008\";\n}\n.fa-th-large:before {\n content: \"\\f009\";\n}\n.fa-th:before {\n content: \"\\f00a\";\n}\n.fa-th-list:before {\n content: \"\\f00b\";\n}\n.fa-check:before {\n content: \"\\f00c\";\n}\n.fa-remove:before,\n.fa-close:before,\n.fa-times:before {\n content: \"\\f00d\";\n}\n.fa-search-plus:before {\n content: \"\\f00e\";\n}\n.fa-search-minus:before {\n content: \"\\f010\";\n}\n.fa-power-off:before {\n content: \"\\f011\";\n}\n.fa-signal:before {\n content: \"\\f012\";\n}\n.fa-gear:before,\n.fa-cog:before {\n content: \"\\f013\";\n}\n.fa-trash-o:before {\n content: \"\\f014\";\n}\n.fa-home:before {\n content: \"\\f015\";\n}\n.fa-file-o:before {\n content: \"\\f016\";\n}\n.fa-clock-o:before {\n content: \"\\f017\";\n}\n.fa-road:before {\n content: \"\\f018\";\n}\n.fa-download:before {\n content: \"\\f019\";\n}\n.fa-arrow-circle-o-down:before {\n content: \"\\f01a\";\n}\n.fa-arrow-circle-o-up:before {\n content: \"\\f01b\";\n}\n.fa-inbox:before {\n content: \"\\f01c\";\n}\n.fa-play-circle-o:before {\n content: \"\\f01d\";\n}\n.fa-rotate-right:before,\n.fa-repeat:before {\n content: \"\\f01e\";\n}\n.fa-refresh:before {\n content: \"\\f021\";\n}\n.fa-list-alt:before {\n content: \"\\f022\";\n}\n.fa-lock:before {\n content: \"\\f023\";\n}\n.fa-flag:before {\n content: \"\\f024\";\n}\n.fa-headphones:before {\n content: \"\\f025\";\n}\n.fa-volume-off:before {\n content: \"\\f026\";\n}\n.fa-volume-down:before {\n content: \"\\f027\";\n}\n.fa-volume-up:before {\n content: \"\\f028\";\n}\n.fa-qrcode:before {\n content: \"\\f029\";\n}\n.fa-barcode:before {\n content: \"\\f02a\";\n}\n.fa-tag:before {\n content: \"\\f02b\";\n}\n.fa-tags:before {\n content: \"\\f02c\";\n}\n.fa-book:before {\n content: \"\\f02d\";\n}\n.fa-bookmark:before {\n content: \"\\f02e\";\n}\n.fa-print:before {\n content: \"\\f02f\";\n}\n.fa-camera:before {\n content: \"\\f030\";\n}\n.fa-font:before {\n content: \"\\f031\";\n}\n.fa-bold:before {\n content: \"\\f032\";\n}\n.fa-italic:before {\n content: \"\\f033\";\n}\n.fa-text-height:before {\n content: \"\\f034\";\n}\n.fa-text-width:before {\n content: \"\\f035\";\n}\n.fa-align-left:before {\n content: \"\\f036\";\n}\n.fa-align-center:before {\n content: \"\\f037\";\n}\n.fa-align-right:before {\n content: \"\\f038\";\n}\n.fa-align-justify:before {\n content: \"\\f039\";\n}\n.fa-list:before {\n content: \"\\f03a\";\n}\n.fa-dedent:before,\n.fa-outdent:before {\n content: \"\\f03b\";\n}\n.fa-indent:before {\n content: \"\\f03c\";\n}\n.fa-video-camera:before {\n content: \"\\f03d\";\n}\n.fa-photo:before,\n.fa-image:before,\n.fa-picture-o:before {\n content: \"\\f03e\";\n}\n.fa-pencil:before {\n content: \"\\f040\";\n}\n.fa-map-marker:before {\n content: \"\\f041\";\n}\n.fa-adjust:before {\n content: \"\\f042\";\n}\n.fa-tint:before {\n content: \"\\f043\";\n}\n.fa-edit:before,\n.fa-pencil-square-o:before {\n content: \"\\f044\";\n}\n.fa-share-square-o:before {\n content: \"\\f045\";\n}\n.fa-check-square-o:before {\n content: \"\\f046\";\n}\n.fa-arrows:before {\n content: \"\\f047\";\n}\n.fa-step-backward:before {\n content: \"\\f048\";\n}\n.fa-fast-backward:before {\n content: \"\\f049\";\n}\n.fa-backward:before {\n content: \"\\f04a\";\n}\n.fa-play:before {\n content: \"\\f04b\";\n}\n.fa-pause:before {\n content: \"\\f04c\";\n}\n.fa-stop:before {\n content: \"\\f04d\";\n}\n.fa-forward:before {\n content: \"\\f04e\";\n}\n.fa-fast-forward:before {\n content: \"\\f050\";\n}\n.fa-step-forward:before {\n content: \"\\f051\";\n}\n.fa-eject:before {\n content: \"\\f052\";\n}\n.fa-chevron-left:before {\n content: \"\\f053\";\n}\n.fa-chevron-right:before {\n content: \"\\f054\";\n}\n.fa-plus-circle:before {\n content: \"\\f055\";\n}\n.fa-minus-circle:before {\n content: \"\\f056\";\n}\n.fa-times-circle:before {\n content: \"\\f057\";\n}\n.fa-check-circle:before {\n content: \"\\f058\";\n}\n.fa-question-circle:before {\n content: \"\\f059\";\n}\n.fa-info-circle:before {\n content: \"\\f05a\";\n}\n.fa-crosshairs:before {\n content: \"\\f05b\";\n}\n.fa-times-circle-o:before {\n content: \"\\f05c\";\n}\n.fa-check-circle-o:before {\n content: \"\\f05d\";\n}\n.fa-ban:before {\n content: \"\\f05e\";\n}\n.fa-arrow-left:before {\n content: \"\\f060\";\n}\n.fa-arrow-right:before {\n content: \"\\f061\";\n}\n.fa-arrow-up:before {\n content: \"\\f062\";\n}\n.fa-arrow-down:before {\n content: \"\\f063\";\n}\n.fa-mail-forward:before,\n.fa-share:before {\n content: \"\\f064\";\n}\n.fa-expand:before {\n content: \"\\f065\";\n}\n.fa-compress:before {\n content: \"\\f066\";\n}\n.fa-plus:before {\n content: \"\\f067\";\n}\n.fa-minus:before {\n content: \"\\f068\";\n}\n.fa-asterisk:before {\n content: \"\\f069\";\n}\n.fa-exclamation-circle:before {\n content: \"\\f06a\";\n}\n.fa-gift:before {\n content: \"\\f06b\";\n}\n.fa-leaf:before {\n content: \"\\f06c\";\n}\n.fa-fire:before {\n content: \"\\f06d\";\n}\n.fa-eye:before {\n content: \"\\f06e\";\n}\n.fa-eye-slash:before {\n content: \"\\f070\";\n}\n.fa-warning:before,\n.fa-exclamation-triangle:before {\n content: \"\\f071\";\n}\n.fa-plane:before {\n content: \"\\f072\";\n}\n.fa-calendar:before {\n content: \"\\f073\";\n}\n.fa-random:before {\n content: \"\\f074\";\n}\n.fa-comment:before {\n content: \"\\f075\";\n}\n.fa-magnet:before {\n content: \"\\f076\";\n}\n.fa-chevron-up:before {\n content: \"\\f077\";\n}\n.fa-chevron-down:before {\n content: \"\\f078\";\n}\n.fa-retweet:before {\n content: \"\\f079\";\n}\n.fa-shopping-cart:before {\n content: \"\\f07a\";\n}\n.fa-folder:before {\n content: \"\\f07b\";\n}\n.fa-folder-open:before {\n content: \"\\f07c\";\n}\n.fa-arrows-v:before {\n content: \"\\f07d\";\n}\n.fa-arrows-h:before {\n content: \"\\f07e\";\n}\n.fa-bar-chart-o:before,\n.fa-bar-chart:before {\n content: \"\\f080\";\n}\n.fa-twitter-square:before {\n content: \"\\f081\";\n}\n.fa-facebook-square:before {\n content: \"\\f082\";\n}\n.fa-camera-retro:before {\n content: \"\\f083\";\n}\n.fa-key:before {\n content: \"\\f084\";\n}\n.fa-gears:before,\n.fa-cogs:before {\n content: \"\\f085\";\n}\n.fa-comments:before {\n content: \"\\f086\";\n}\n.fa-thumbs-o-up:before {\n content: \"\\f087\";\n}\n.fa-thumbs-o-down:before {\n content: \"\\f088\";\n}\n.fa-star-half:before {\n content: \"\\f089\";\n}\n.fa-heart-o:before {\n content: \"\\f08a\";\n}\n.fa-sign-out:before {\n content: \"\\f08b\";\n}\n.fa-linkedin-square:before {\n content: \"\\f08c\";\n}\n.fa-thumb-tack:before {\n content: \"\\f08d\";\n}\n.fa-external-link:before {\n content: \"\\f08e\";\n}\n.fa-sign-in:before {\n content: \"\\f090\";\n}\n.fa-trophy:before {\n content: \"\\f091\";\n}\n.fa-github-square:before {\n content: \"\\f092\";\n}\n.fa-upload:before {\n content: \"\\f093\";\n}\n.fa-lemon-o:before {\n content: \"\\f094\";\n}\n.fa-phone:before {\n content: \"\\f095\";\n}\n.fa-square-o:before {\n content: \"\\f096\";\n}\n.fa-bookmark-o:before {\n content: \"\\f097\";\n}\n.fa-phone-square:before {\n content: \"\\f098\";\n}\n.fa-twitter:before {\n content: \"\\f099\";\n}\n.fa-facebook-f:before,\n.fa-facebook:before {\n content: \"\\f09a\";\n}\n.fa-github:before {\n content: \"\\f09b\";\n}\n.fa-unlock:before {\n content: \"\\f09c\";\n}\n.fa-credit-card:before {\n content: \"\\f09d\";\n}\n.fa-feed:before,\n.fa-rss:before {\n content: \"\\f09e\";\n}\n.fa-hdd-o:before {\n content: \"\\f0a0\";\n}\n.fa-bullhorn:before {\n content: \"\\f0a1\";\n}\n.fa-bell:before {\n content: \"\\f0f3\";\n}\n.fa-certificate:before {\n content: \"\\f0a3\";\n}\n.fa-hand-o-right:before {\n content: \"\\f0a4\";\n}\n.fa-hand-o-left:before {\n content: \"\\f0a5\";\n}\n.fa-hand-o-up:before {\n content: \"\\f0a6\";\n}\n.fa-hand-o-down:before {\n content: \"\\f0a7\";\n}\n.fa-arrow-circle-left:before {\n content: \"\\f0a8\";\n}\n.fa-arrow-circle-right:before {\n content: \"\\f0a9\";\n}\n.fa-arrow-circle-up:before {\n content: \"\\f0aa\";\n}\n.fa-arrow-circle-down:before {\n content: \"\\f0ab\";\n}\n.fa-globe:before {\n content: \"\\f0ac\";\n}\n.fa-wrench:before {\n content: \"\\f0ad\";\n}\n.fa-tasks:before {\n content: \"\\f0ae\";\n}\n.fa-filter:before {\n content: \"\\f0b0\";\n}\n.fa-briefcase:before {\n content: \"\\f0b1\";\n}\n.fa-arrows-alt:before {\n content: \"\\f0b2\";\n}\n.fa-group:before,\n.fa-users:before {\n content: \"\\f0c0\";\n}\n.fa-chain:before,\n.fa-link:before {\n content: \"\\f0c1\";\n}\n.fa-cloud:before {\n content: \"\\f0c2\";\n}\n.fa-flask:before {\n content: \"\\f0c3\";\n}\n.fa-cut:before,\n.fa-scissors:before {\n content: \"\\f0c4\";\n}\n.fa-copy:before,\n.fa-files-o:before {\n content: \"\\f0c5\";\n}\n.fa-paperclip:before {\n content: \"\\f0c6\";\n}\n.fa-save:before,\n.fa-floppy-o:before {\n content: \"\\f0c7\";\n}\n.fa-square:before {\n content: \"\\f0c8\";\n}\n.fa-navicon:before,\n.fa-reorder:before,\n.fa-bars:before {\n content: \"\\f0c9\";\n}\n.fa-list-ul:before {\n content: \"\\f0ca\";\n}\n.fa-list-ol:before {\n content: \"\\f0cb\";\n}\n.fa-strikethrough:before {\n content: \"\\f0cc\";\n}\n.fa-underline:before {\n content: \"\\f0cd\";\n}\n.fa-table:before {\n content: \"\\f0ce\";\n}\n.fa-magic:before {\n content: \"\\f0d0\";\n}\n.fa-truck:before {\n content: \"\\f0d1\";\n}\n.fa-pinterest:before {\n content: \"\\f0d2\";\n}\n.fa-pinterest-square:before {\n content: \"\\f0d3\";\n}\n.fa-google-plus-square:before {\n content: \"\\f0d4\";\n}\n.fa-google-plus:before {\n content: \"\\f0d5\";\n}\n.fa-money:before {\n content: \"\\f0d6\";\n}\n.fa-caret-down:before {\n content: \"\\f0d7\";\n}\n.fa-caret-up:before {\n content: \"\\f0d8\";\n}\n.fa-caret-left:before {\n content: \"\\f0d9\";\n}\n.fa-caret-right:before {\n content: \"\\f0da\";\n}\n.fa-columns:before {\n content: \"\\f0db\";\n}\n.fa-unsorted:before,\n.fa-sort:before {\n content: \"\\f0dc\";\n}\n.fa-sort-down:before,\n.fa-sort-desc:before {\n content: \"\\f0dd\";\n}\n.fa-sort-up:before,\n.fa-sort-asc:before {\n content: \"\\f0de\";\n}\n.fa-envelope:before {\n content: \"\\f0e0\";\n}\n.fa-linkedin:before {\n content: \"\\f0e1\";\n}\n.fa-rotate-left:before,\n.fa-undo:before {\n content: \"\\f0e2\";\n}\n.fa-legal:before,\n.fa-gavel:before {\n content: \"\\f0e3\";\n}\n.fa-dashboard:before,\n.fa-tachometer:before {\n content: \"\\f0e4\";\n}\n.fa-comment-o:before {\n content: \"\\f0e5\";\n}\n.fa-comments-o:before {\n content: \"\\f0e6\";\n}\n.fa-flash:before,\n.fa-bolt:before {\n content: \"\\f0e7\";\n}\n.fa-sitemap:before {\n content: \"\\f0e8\";\n}\n.fa-umbrella:before {\n content: \"\\f0e9\";\n}\n.fa-paste:before,\n.fa-clipboard:before {\n content: \"\\f0ea\";\n}\n.fa-lightbulb-o:before {\n content: \"\\f0eb\";\n}\n.fa-exchange:before {\n content: \"\\f0ec\";\n}\n.fa-cloud-download:before {\n content: \"\\f0ed\";\n}\n.fa-cloud-upload:before {\n content: \"\\f0ee\";\n}\n.fa-user-md:before {\n content: \"\\f0f0\";\n}\n.fa-stethoscope:before {\n content: \"\\f0f1\";\n}\n.fa-suitcase:before {\n content: \"\\f0f2\";\n}\n.fa-bell-o:before {\n content: \"\\f0a2\";\n}\n.fa-coffee:before {\n content: \"\\f0f4\";\n}\n.fa-cutlery:before {\n content: \"\\f0f5\";\n}\n.fa-file-text-o:before {\n content: \"\\f0f6\";\n}\n.fa-building-o:before {\n content: \"\\f0f7\";\n}\n.fa-hospital-o:before {\n content: \"\\f0f8\";\n}\n.fa-ambulance:before {\n content: \"\\f0f9\";\n}\n.fa-medkit:before {\n content: \"\\f0fa\";\n}\n.fa-fighter-jet:before {\n content: \"\\f0fb\";\n}\n.fa-beer:before {\n content: \"\\f0fc\";\n}\n.fa-h-square:before {\n content: \"\\f0fd\";\n}\n.fa-plus-square:before {\n content: \"\\f0fe\";\n}\n.fa-angle-double-left:before {\n content: \"\\f100\";\n}\n.fa-angle-double-right:before {\n content: \"\\f101\";\n}\n.fa-angle-double-up:before {\n content: \"\\f102\";\n}\n.fa-angle-double-down:before {\n content: \"\\f103\";\n}\n.fa-angle-left:before {\n content: \"\\f104\";\n}\n.fa-angle-right:before {\n content: \"\\f105\";\n}\n.fa-angle-up:before {\n content: \"\\f106\";\n}\n.fa-angle-down:before {\n content: \"\\f107\";\n}\n.fa-desktop:before {\n content: \"\\f108\";\n}\n.fa-laptop:before {\n content: \"\\f109\";\n}\n.fa-tablet:before {\n content: \"\\f10a\";\n}\n.fa-mobile-phone:before,\n.fa-mobile:before {\n content: \"\\f10b\";\n}\n.fa-circle-o:before {\n content: \"\\f10c\";\n}\n.fa-quote-left:before {\n content: \"\\f10d\";\n}\n.fa-quote-right:before {\n content: \"\\f10e\";\n}\n.fa-spinner:before {\n content: \"\\f110\";\n}\n.fa-circle:before {\n content: \"\\f111\";\n}\n.fa-mail-reply:before,\n.fa-reply:before {\n content: \"\\f112\";\n}\n.fa-github-alt:before {\n content: \"\\f113\";\n}\n.fa-folder-o:before {\n content: \"\\f114\";\n}\n.fa-folder-open-o:before {\n content: \"\\f115\";\n}\n.fa-smile-o:before {\n content: \"\\f118\";\n}\n.fa-frown-o:before {\n content: \"\\f119\";\n}\n.fa-meh-o:before {\n content: \"\\f11a\";\n}\n.fa-gamepad:before {\n content: \"\\f11b\";\n}\n.fa-keyboard-o:before {\n content: \"\\f11c\";\n}\n.fa-flag-o:before {\n content: \"\\f11d\";\n}\n.fa-flag-checkered:before {\n content: \"\\f11e\";\n}\n.fa-terminal:before {\n content: \"\\f120\";\n}\n.fa-code:before {\n content: \"\\f121\";\n}\n.fa-mail-reply-all:before,\n.fa-reply-all:before {\n content: \"\\f122\";\n}\n.fa-star-half-empty:before,\n.fa-star-half-full:before,\n.fa-star-half-o:before {\n content: \"\\f123\";\n}\n.fa-location-arrow:before {\n content: \"\\f124\";\n}\n.fa-crop:before {\n content: \"\\f125\";\n}\n.fa-code-fork:before {\n content: \"\\f126\";\n}\n.fa-unlink:before,\n.fa-chain-broken:before {\n content: \"\\f127\";\n}\n.fa-question:before {\n content: \"\\f128\";\n}\n.fa-info:before {\n content: \"\\f129\";\n}\n.fa-exclamation:before {\n content: \"\\f12a\";\n}\n.fa-superscript:before {\n content: \"\\f12b\";\n}\n.fa-subscript:before {\n content: \"\\f12c\";\n}\n.fa-eraser:before {\n content: \"\\f12d\";\n}\n.fa-puzzle-piece:before {\n content: \"\\f12e\";\n}\n.fa-microphone:before {\n content: \"\\f130\";\n}\n.fa-microphone-slash:before {\n content: \"\\f131\";\n}\n.fa-shield:before {\n content: \"\\f132\";\n}\n.fa-calendar-o:before {\n content: \"\\f133\";\n}\n.fa-fire-extinguisher:before {\n content: \"\\f134\";\n}\n.fa-rocket:before {\n content: \"\\f135\";\n}\n.fa-maxcdn:before {\n content: \"\\f136\";\n}\n.fa-chevron-circle-left:before {\n content: \"\\f137\";\n}\n.fa-chevron-circle-right:before {\n content: \"\\f138\";\n}\n.fa-chevron-circle-up:before {\n content: \"\\f139\";\n}\n.fa-chevron-circle-down:before {\n content: \"\\f13a\";\n}\n.fa-html5:before {\n content: \"\\f13b\";\n}\n.fa-css3:before {\n content: \"\\f13c\";\n}\n.fa-anchor:before {\n content: \"\\f13d\";\n}\n.fa-unlock-alt:before {\n content: \"\\f13e\";\n}\n.fa-bullseye:before {\n content: \"\\f140\";\n}\n.fa-ellipsis-h:before {\n content: \"\\f141\";\n}\n.fa-ellipsis-v:before {\n content: \"\\f142\";\n}\n.fa-rss-square:before {\n content: \"\\f143\";\n}\n.fa-play-circle:before {\n content: \"\\f144\";\n}\n.fa-ticket:before {\n content: \"\\f145\";\n}\n.fa-minus-square:before {\n content: \"\\f146\";\n}\n.fa-minus-square-o:before {\n content: \"\\f147\";\n}\n.fa-level-up:before {\n content: \"\\f148\";\n}\n.fa-level-down:before {\n content: \"\\f149\";\n}\n.fa-check-square:before {\n content: \"\\f14a\";\n}\n.fa-pencil-square:before {\n content: \"\\f14b\";\n}\n.fa-external-link-square:before {\n content: \"\\f14c\";\n}\n.fa-share-square:before {\n content: \"\\f14d\";\n}\n.fa-compass:before {\n content: \"\\f14e\";\n}\n.fa-toggle-down:before,\n.fa-caret-square-o-down:before {\n content: \"\\f150\";\n}\n.fa-toggle-up:before,\n.fa-caret-square-o-up:before {\n content: \"\\f151\";\n}\n.fa-toggle-right:before,\n.fa-caret-square-o-right:before {\n content: \"\\f152\";\n}\n.fa-euro:before,\n.fa-eur:before {\n content: \"\\f153\";\n}\n.fa-gbp:before {\n content: \"\\f154\";\n}\n.fa-dollar:before,\n.fa-usd:before {\n content: \"\\f155\";\n}\n.fa-rupee:before,\n.fa-inr:before {\n content: \"\\f156\";\n}\n.fa-cny:before,\n.fa-rmb:before,\n.fa-yen:before,\n.fa-jpy:before {\n content: \"\\f157\";\n}\n.fa-ruble:before,\n.fa-rouble:before,\n.fa-rub:before {\n content: \"\\f158\";\n}\n.fa-won:before,\n.fa-krw:before {\n content: \"\\f159\";\n}\n.fa-bitcoin:before,\n.fa-btc:before {\n content: \"\\f15a\";\n}\n.fa-file:before {\n content: \"\\f15b\";\n}\n.fa-file-text:before {\n content: \"\\f15c\";\n}\n.fa-sort-alpha-asc:before {\n content: \"\\f15d\";\n}\n.fa-sort-alpha-desc:before {\n content: \"\\f15e\";\n}\n.fa-sort-amount-asc:before {\n content: \"\\f160\";\n}\n.fa-sort-amount-desc:before {\n content: \"\\f161\";\n}\n.fa-sort-numeric-asc:before {\n content: \"\\f162\";\n}\n.fa-sort-numeric-desc:before {\n content: \"\\f163\";\n}\n.fa-thumbs-up:before {\n content: \"\\f164\";\n}\n.fa-thumbs-down:before {\n content: \"\\f165\";\n}\n.fa-youtube-square:before {\n content: \"\\f166\";\n}\n.fa-youtube:before {\n content: \"\\f167\";\n}\n.fa-xing:before {\n content: \"\\f168\";\n}\n.fa-xing-square:before {\n content: \"\\f169\";\n}\n.fa-youtube-play:before {\n content: \"\\f16a\";\n}\n.fa-dropbox:before {\n content: \"\\f16b\";\n}\n.fa-stack-overflow:before {\n content: \"\\f16c\";\n}\n.fa-instagram:before {\n content: \"\\f16d\";\n}\n.fa-flickr:before {\n content: \"\\f16e\";\n}\n.fa-adn:before {\n content: \"\\f170\";\n}\n.fa-bitbucket:before {\n content: \"\\f171\";\n}\n.fa-bitbucket-square:before {\n content: \"\\f172\";\n}\n.fa-tumblr:before {\n content: \"\\f173\";\n}\n.fa-tumblr-square:before {\n content: \"\\f174\";\n}\n.fa-long-arrow-down:before {\n content: \"\\f175\";\n}\n.fa-long-arrow-up:before {\n content: \"\\f176\";\n}\n.fa-long-arrow-left:before {\n content: \"\\f177\";\n}\n.fa-long-arrow-right:before {\n content: \"\\f178\";\n}\n.fa-apple:before {\n content: \"\\f179\";\n}\n.fa-windows:before {\n content: \"\\f17a\";\n}\n.fa-android:before {\n content: \"\\f17b\";\n}\n.fa-linux:before {\n content: \"\\f17c\";\n}\n.fa-dribbble:before {\n content: \"\\f17d\";\n}\n.fa-skype:before {\n content: \"\\f17e\";\n}\n.fa-foursquare:before {\n content: \"\\f180\";\n}\n.fa-trello:before {\n content: \"\\f181\";\n}\n.fa-female:before {\n content: \"\\f182\";\n}\n.fa-male:before {\n content: \"\\f183\";\n}\n.fa-gittip:before,\n.fa-gratipay:before {\n content: \"\\f184\";\n}\n.fa-sun-o:before {\n content: \"\\f185\";\n}\n.fa-moon-o:before {\n content: \"\\f186\";\n}\n.fa-archive:before {\n content: \"\\f187\";\n}\n.fa-bug:before {\n content: \"\\f188\";\n}\n.fa-vk:before {\n content: \"\\f189\";\n}\n.fa-weibo:before {\n content: \"\\f18a\";\n}\n.fa-renren:before {\n content: \"\\f18b\";\n}\n.fa-pagelines:before {\n content: \"\\f18c\";\n}\n.fa-stack-exchange:before {\n content: \"\\f18d\";\n}\n.fa-arrow-circle-o-right:before {\n content: \"\\f18e\";\n}\n.fa-arrow-circle-o-left:before {\n content: \"\\f190\";\n}\n.fa-toggle-left:before,\n.fa-caret-square-o-left:before {\n content: \"\\f191\";\n}\n.fa-dot-circle-o:before {\n content: \"\\f192\";\n}\n.fa-wheelchair:before {\n content: \"\\f193\";\n}\n.fa-vimeo-square:before {\n content: \"\\f194\";\n}\n.fa-turkish-lira:before,\n.fa-try:before {\n content: \"\\f195\";\n}\n.fa-plus-square-o:before {\n content: \"\\f196\";\n}\n.fa-space-shuttle:before {\n content: \"\\f197\";\n}\n.fa-slack:before {\n content: \"\\f198\";\n}\n.fa-envelope-square:before {\n content: \"\\f199\";\n}\n.fa-wordpress:before {\n content: \"\\f19a\";\n}\n.fa-openid:before {\n content: \"\\f19b\";\n}\n.fa-institution:before,\n.fa-bank:before,\n.fa-university:before {\n content: \"\\f19c\";\n}\n.fa-mortar-board:before,\n.fa-graduation-cap:before {\n content: \"\\f19d\";\n}\n.fa-yahoo:before {\n content: \"\\f19e\";\n}\n.fa-google:before {\n content: \"\\f1a0\";\n}\n.fa-reddit:before {\n content: \"\\f1a1\";\n}\n.fa-reddit-square:before {\n content: \"\\f1a2\";\n}\n.fa-stumbleupon-circle:before {\n content: \"\\f1a3\";\n}\n.fa-stumbleupon:before {\n content: \"\\f1a4\";\n}\n.fa-delicious:before {\n content: \"\\f1a5\";\n}\n.fa-digg:before {\n content: \"\\f1a6\";\n}\n.fa-pied-piper-pp:before {\n content: \"\\f1a7\";\n}\n.fa-pied-piper-alt:before {\n content: \"\\f1a8\";\n}\n.fa-drupal:before {\n content: \"\\f1a9\";\n}\n.fa-joomla:before {\n content: \"\\f1aa\";\n}\n.fa-language:before {\n content: \"\\f1ab\";\n}\n.fa-fax:before {\n content: \"\\f1ac\";\n}\n.fa-building:before {\n content: \"\\f1ad\";\n}\n.fa-child:before {\n content: \"\\f1ae\";\n}\n.fa-paw:before {\n content: \"\\f1b0\";\n}\n.fa-spoon:before {\n content: \"\\f1b1\";\n}\n.fa-cube:before {\n content: \"\\f1b2\";\n}\n.fa-cubes:before {\n content: \"\\f1b3\";\n}\n.fa-behance:before {\n content: \"\\f1b4\";\n}\n.fa-behance-square:before {\n content: \"\\f1b5\";\n}\n.fa-steam:before {\n content: \"\\f1b6\";\n}\n.fa-steam-square:before {\n content: \"\\f1b7\";\n}\n.fa-recycle:before {\n content: \"\\f1b8\";\n}\n.fa-automobile:before,\n.fa-car:before {\n content: \"\\f1b9\";\n}\n.fa-cab:before,\n.fa-taxi:before {\n content: \"\\f1ba\";\n}\n.fa-tree:before {\n content: \"\\f1bb\";\n}\n.fa-spotify:before {\n content: \"\\f1bc\";\n}\n.fa-deviantart:before {\n content: \"\\f1bd\";\n}\n.fa-soundcloud:before {\n content: \"\\f1be\";\n}\n.fa-database:before {\n content: \"\\f1c0\";\n}\n.fa-file-pdf-o:before {\n content: \"\\f1c1\";\n}\n.fa-file-word-o:before {\n content: \"\\f1c2\";\n}\n.fa-file-excel-o:before {\n content: \"\\f1c3\";\n}\n.fa-file-powerpoint-o:before {\n content: \"\\f1c4\";\n}\n.fa-file-photo-o:before,\n.fa-file-picture-o:before,\n.fa-file-image-o:before {\n content: \"\\f1c5\";\n}\n.fa-file-zip-o:before,\n.fa-file-archive-o:before {\n content: \"\\f1c6\";\n}\n.fa-file-sound-o:before,\n.fa-file-audio-o:before {\n content: \"\\f1c7\";\n}\n.fa-file-movie-o:before,\n.fa-file-video-o:before {\n content: \"\\f1c8\";\n}\n.fa-file-code-o:before {\n content: \"\\f1c9\";\n}\n.fa-vine:before {\n content: \"\\f1ca\";\n}\n.fa-codepen:before {\n content: \"\\f1cb\";\n}\n.fa-jsfiddle:before {\n content: \"\\f1cc\";\n}\n.fa-life-bouy:before,\n.fa-life-buoy:before,\n.fa-life-saver:before,\n.fa-support:before,\n.fa-life-ring:before {\n content: \"\\f1cd\";\n}\n.fa-circle-o-notch:before {\n content: \"\\f1ce\";\n}\n.fa-ra:before,\n.fa-resistance:before,\n.fa-rebel:before {\n content: \"\\f1d0\";\n}\n.fa-ge:before,\n.fa-empire:before {\n content: \"\\f1d1\";\n}\n.fa-git-square:before {\n content: \"\\f1d2\";\n}\n.fa-git:before {\n content: \"\\f1d3\";\n}\n.fa-y-combinator-square:before,\n.fa-yc-square:before,\n.fa-hacker-news:before {\n content: \"\\f1d4\";\n}\n.fa-tencent-weibo:before {\n content: \"\\f1d5\";\n}\n.fa-qq:before {\n content: \"\\f1d6\";\n}\n.fa-wechat:before,\n.fa-weixin:before {\n content: \"\\f1d7\";\n}\n.fa-send:before,\n.fa-paper-plane:before {\n content: \"\\f1d8\";\n}\n.fa-send-o:before,\n.fa-paper-plane-o:before {\n content: \"\\f1d9\";\n}\n.fa-history:before {\n content: \"\\f1da\";\n}\n.fa-circle-thin:before {\n content: \"\\f1db\";\n}\n.fa-header:before {\n content: \"\\f1dc\";\n}\n.fa-paragraph:before {\n content: \"\\f1dd\";\n}\n.fa-sliders:before {\n content: \"\\f1de\";\n}\n.fa-share-alt:before {\n content: \"\\f1e0\";\n}\n.fa-share-alt-square:before {\n content: \"\\f1e1\";\n}\n.fa-bomb:before {\n content: \"\\f1e2\";\n}\n.fa-soccer-ball-o:before,\n.fa-futbol-o:before {\n content: \"\\f1e3\";\n}\n.fa-tty:before {\n content: \"\\f1e4\";\n}\n.fa-binoculars:before {\n content: \"\\f1e5\";\n}\n.fa-plug:before {\n content: \"\\f1e6\";\n}\n.fa-slideshare:before {\n content: \"\\f1e7\";\n}\n.fa-twitch:before {\n content: \"\\f1e8\";\n}\n.fa-yelp:before {\n content: \"\\f1e9\";\n}\n.fa-newspaper-o:before {\n content: \"\\f1ea\";\n}\n.fa-wifi:before {\n content: \"\\f1eb\";\n}\n.fa-calculator:before {\n content: \"\\f1ec\";\n}\n.fa-paypal:before {\n content: \"\\f1ed\";\n}\n.fa-google-wallet:before {\n content: \"\\f1ee\";\n}\n.fa-cc-visa:before {\n content: \"\\f1f0\";\n}\n.fa-cc-mastercard:before {\n content: \"\\f1f1\";\n}\n.fa-cc-discover:before {\n content: \"\\f1f2\";\n}\n.fa-cc-amex:before {\n content: \"\\f1f3\";\n}\n.fa-cc-paypal:before {\n content: \"\\f1f4\";\n}\n.fa-cc-stripe:before {\n content: \"\\f1f5\";\n}\n.fa-bell-slash:before {\n content: \"\\f1f6\";\n}\n.fa-bell-slash-o:before {\n content: \"\\f1f7\";\n}\n.fa-trash:before {\n content: \"\\f1f8\";\n}\n.fa-copyright:before {\n content: \"\\f1f9\";\n}\n.fa-at:before {\n content: \"\\f1fa\";\n}\n.fa-eyedropper:before {\n content: \"\\f1fb\";\n}\n.fa-paint-brush:before {\n content: \"\\f1fc\";\n}\n.fa-birthday-cake:before {\n content: \"\\f1fd\";\n}\n.fa-area-chart:before {\n content: \"\\f1fe\";\n}\n.fa-pie-chart:before {\n content: \"\\f200\";\n}\n.fa-line-chart:before {\n content: \"\\f201\";\n}\n.fa-lastfm:before {\n content: \"\\f202\";\n}\n.fa-lastfm-square:before {\n content: \"\\f203\";\n}\n.fa-toggle-off:before {\n content: \"\\f204\";\n}\n.fa-toggle-on:before {\n content: \"\\f205\";\n}\n.fa-bicycle:before {\n content: \"\\f206\";\n}\n.fa-bus:before {\n content: \"\\f207\";\n}\n.fa-ioxhost:before {\n content: \"\\f208\";\n}\n.fa-angellist:before {\n content: \"\\f209\";\n}\n.fa-cc:before {\n content: \"\\f20a\";\n}\n.fa-shekel:before,\n.fa-sheqel:before,\n.fa-ils:before {\n content: \"\\f20b\";\n}\n.fa-meanpath:before {\n content: \"\\f20c\";\n}\n.fa-buysellads:before {\n content: \"\\f20d\";\n}\n.fa-connectdevelop:before {\n content: \"\\f20e\";\n}\n.fa-dashcube:before {\n content: \"\\f210\";\n}\n.fa-forumbee:before {\n content: \"\\f211\";\n}\n.fa-leanpub:before {\n content: \"\\f212\";\n}\n.fa-sellsy:before {\n content: \"\\f213\";\n}\n.fa-shirtsinbulk:before {\n content: \"\\f214\";\n}\n.fa-simplybuilt:before {\n content: \"\\f215\";\n}\n.fa-skyatlas:before {\n content: \"\\f216\";\n}\n.fa-cart-plus:before {\n content: \"\\f217\";\n}\n.fa-cart-arrow-down:before {\n content: \"\\f218\";\n}\n.fa-diamond:before {\n content: \"\\f219\";\n}\n.fa-ship:before {\n content: \"\\f21a\";\n}\n.fa-user-secret:before {\n content: \"\\f21b\";\n}\n.fa-motorcycle:before {\n content: \"\\f21c\";\n}\n.fa-street-view:before {\n content: \"\\f21d\";\n}\n.fa-heartbeat:before {\n content: \"\\f21e\";\n}\n.fa-venus:before {\n content: \"\\f221\";\n}\n.fa-mars:before {\n content: \"\\f222\";\n}\n.fa-mercury:before {\n content: \"\\f223\";\n}\n.fa-intersex:before,\n.fa-transgender:before {\n content: \"\\f224\";\n}\n.fa-transgender-alt:before {\n content: \"\\f225\";\n}\n.fa-venus-double:before {\n content: \"\\f226\";\n}\n.fa-mars-double:before {\n content: \"\\f227\";\n}\n.fa-venus-mars:before {\n content: \"\\f228\";\n}\n.fa-mars-stroke:before {\n content: \"\\f229\";\n}\n.fa-mars-stroke-v:before {\n content: \"\\f22a\";\n}\n.fa-mars-stroke-h:before {\n content: \"\\f22b\";\n}\n.fa-neuter:before {\n content: \"\\f22c\";\n}\n.fa-genderless:before {\n content: \"\\f22d\";\n}\n.fa-facebook-official:before {\n content: \"\\f230\";\n}\n.fa-pinterest-p:before {\n content: \"\\f231\";\n}\n.fa-whatsapp:before {\n content: \"\\f232\";\n}\n.fa-server:before {\n content: \"\\f233\";\n}\n.fa-user-plus:before {\n content: \"\\f234\";\n}\n.fa-user-times:before {\n content: \"\\f235\";\n}\n.fa-hotel:before,\n.fa-bed:before {\n content: \"\\f236\";\n}\n.fa-viacoin:before {\n content: \"\\f237\";\n}\n.fa-train:before {\n content: \"\\f238\";\n}\n.fa-subway:before {\n content: \"\\f239\";\n}\n.fa-medium:before {\n content: \"\\f23a\";\n}\n.fa-yc:before,\n.fa-y-combinator:before {\n content: \"\\f23b\";\n}\n.fa-optin-monster:before {\n content: \"\\f23c\";\n}\n.fa-opencart:before {\n content: \"\\f23d\";\n}\n.fa-expeditedssl:before {\n content: \"\\f23e\";\n}\n.fa-battery-4:before,\n.fa-battery:before,\n.fa-battery-full:before {\n content: \"\\f240\";\n}\n.fa-battery-3:before,\n.fa-battery-three-quarters:before {\n content: \"\\f241\";\n}\n.fa-battery-2:before,\n.fa-battery-half:before {\n content: \"\\f242\";\n}\n.fa-battery-1:before,\n.fa-battery-quarter:before {\n content: \"\\f243\";\n}\n.fa-battery-0:before,\n.fa-battery-empty:before {\n content: \"\\f244\";\n}\n.fa-mouse-pointer:before {\n content: \"\\f245\";\n}\n.fa-i-cursor:before {\n content: \"\\f246\";\n}\n.fa-object-group:before {\n content: \"\\f247\";\n}\n.fa-object-ungroup:before {\n content: \"\\f248\";\n}\n.fa-sticky-note:before {\n content: \"\\f249\";\n}\n.fa-sticky-note-o:before {\n content: \"\\f24a\";\n}\n.fa-cc-jcb:before {\n content: \"\\f24b\";\n}\n.fa-cc-diners-club:before {\n content: \"\\f24c\";\n}\n.fa-clone:before {\n content: \"\\f24d\";\n}\n.fa-balance-scale:before {\n content: \"\\f24e\";\n}\n.fa-hourglass-o:before {\n content: \"\\f250\";\n}\n.fa-hourglass-1:before,\n.fa-hourglass-start:before {\n content: \"\\f251\";\n}\n.fa-hourglass-2:before,\n.fa-hourglass-half:before {\n content: \"\\f252\";\n}\n.fa-hourglass-3:before,\n.fa-hourglass-end:before {\n content: \"\\f253\";\n}\n.fa-hourglass:before {\n content: \"\\f254\";\n}\n.fa-hand-grab-o:before,\n.fa-hand-rock-o:before {\n content: \"\\f255\";\n}\n.fa-hand-stop-o:before,\n.fa-hand-paper-o:before {\n content: \"\\f256\";\n}\n.fa-hand-scissors-o:before {\n content: \"\\f257\";\n}\n.fa-hand-lizard-o:before {\n content: \"\\f258\";\n}\n.fa-hand-spock-o:before {\n content: \"\\f259\";\n}\n.fa-hand-pointer-o:before {\n content: \"\\f25a\";\n}\n.fa-hand-peace-o:before {\n content: \"\\f25b\";\n}\n.fa-trademark:before {\n content: \"\\f25c\";\n}\n.fa-registered:before {\n content: \"\\f25d\";\n}\n.fa-creative-commons:before {\n content: \"\\f25e\";\n}\n.fa-gg:before {\n content: \"\\f260\";\n}\n.fa-gg-circle:before {\n content: \"\\f261\";\n}\n.fa-tripadvisor:before {\n content: \"\\f262\";\n}\n.fa-odnoklassniki:before {\n content: \"\\f263\";\n}\n.fa-odnoklassniki-square:before {\n content: \"\\f264\";\n}\n.fa-get-pocket:before {\n content: \"\\f265\";\n}\n.fa-wikipedia-w:before {\n content: \"\\f266\";\n}\n.fa-safari:before {\n content: \"\\f267\";\n}\n.fa-chrome:before {\n content: \"\\f268\";\n}\n.fa-firefox:before {\n content: \"\\f269\";\n}\n.fa-opera:before {\n content: \"\\f26a\";\n}\n.fa-internet-explorer:before {\n content: \"\\f26b\";\n}\n.fa-tv:before,\n.fa-television:before {\n content: \"\\f26c\";\n}\n.fa-contao:before {\n content: \"\\f26d\";\n}\n.fa-500px:before {\n content: \"\\f26e\";\n}\n.fa-amazon:before {\n content: \"\\f270\";\n}\n.fa-calendar-plus-o:before {\n content: \"\\f271\";\n}\n.fa-calendar-minus-o:before {\n content: \"\\f272\";\n}\n.fa-calendar-times-o:before {\n content: \"\\f273\";\n}\n.fa-calendar-check-o:before {\n content: \"\\f274\";\n}\n.fa-industry:before {\n content: \"\\f275\";\n}\n.fa-map-pin:before {\n content: \"\\f276\";\n}\n.fa-map-signs:before {\n content: \"\\f277\";\n}\n.fa-map-o:before {\n content: \"\\f278\";\n}\n.fa-map:before {\n content: \"\\f279\";\n}\n.fa-commenting:before {\n content: \"\\f27a\";\n}\n.fa-commenting-o:before {\n content: \"\\f27b\";\n}\n.fa-houzz:before {\n content: \"\\f27c\";\n}\n.fa-vimeo:before {\n content: \"\\f27d\";\n}\n.fa-black-tie:before {\n content: \"\\f27e\";\n}\n.fa-fonticons:before {\n content: \"\\f280\";\n}\n.fa-reddit-alien:before {\n content: \"\\f281\";\n}\n.fa-edge:before {\n content: \"\\f282\";\n}\n.fa-credit-card-alt:before {\n content: \"\\f283\";\n}\n.fa-codiepie:before {\n content: \"\\f284\";\n}\n.fa-modx:before {\n content: \"\\f285\";\n}\n.fa-fort-awesome:before {\n content: \"\\f286\";\n}\n.fa-usb:before {\n content: \"\\f287\";\n}\n.fa-product-hunt:before {\n content: \"\\f288\";\n}\n.fa-mixcloud:before {\n content: \"\\f289\";\n}\n.fa-scribd:before {\n content: \"\\f28a\";\n}\n.fa-pause-circle:before {\n content: \"\\f28b\";\n}\n.fa-pause-circle-o:before {\n content: \"\\f28c\";\n}\n.fa-stop-circle:before {\n content: \"\\f28d\";\n}\n.fa-stop-circle-o:before {\n content: \"\\f28e\";\n}\n.fa-shopping-bag:before {\n content: \"\\f290\";\n}\n.fa-shopping-basket:before {\n content: \"\\f291\";\n}\n.fa-hashtag:before {\n content: \"\\f292\";\n}\n.fa-bluetooth:before {\n content: \"\\f293\";\n}\n.fa-bluetooth-b:before {\n content: \"\\f294\";\n}\n.fa-percent:before {\n content: \"\\f295\";\n}\n.fa-gitlab:before {\n content: \"\\f296\";\n}\n.fa-wpbeginner:before {\n content: \"\\f297\";\n}\n.fa-wpforms:before {\n content: \"\\f298\";\n}\n.fa-envira:before {\n content: \"\\f299\";\n}\n.fa-universal-access:before {\n content: \"\\f29a\";\n}\n.fa-wheelchair-alt:before {\n content: \"\\f29b\";\n}\n.fa-question-circle-o:before {\n content: \"\\f29c\";\n}\n.fa-blind:before {\n content: \"\\f29d\";\n}\n.fa-audio-description:before {\n content: \"\\f29e\";\n}\n.fa-volume-control-phone:before {\n content: \"\\f2a0\";\n}\n.fa-braille:before {\n content: \"\\f2a1\";\n}\n.fa-assistive-listening-systems:before {\n content: \"\\f2a2\";\n}\n.fa-asl-interpreting:before,\n.fa-american-sign-language-interpreting:before {\n content: \"\\f2a3\";\n}\n.fa-deafness:before,\n.fa-hard-of-hearing:before,\n.fa-deaf:before {\n content: \"\\f2a4\";\n}\n.fa-glide:before {\n content: \"\\f2a5\";\n}\n.fa-glide-g:before {\n content: \"\\f2a6\";\n}\n.fa-signing:before,\n.fa-sign-language:before {\n content: \"\\f2a7\";\n}\n.fa-low-vision:before {\n content: \"\\f2a8\";\n}\n.fa-viadeo:before {\n content: \"\\f2a9\";\n}\n.fa-viadeo-square:before {\n content: \"\\f2aa\";\n}\n.fa-snapchat:before {\n content: \"\\f2ab\";\n}\n.fa-snapchat-ghost:before {\n content: \"\\f2ac\";\n}\n.fa-snapchat-square:before {\n content: \"\\f2ad\";\n}\n.fa-pied-piper:before {\n content: \"\\f2ae\";\n}\n.fa-first-order:before {\n content: \"\\f2b0\";\n}\n.fa-yoast:before {\n content: \"\\f2b1\";\n}\n.fa-themeisle:before {\n content: \"\\f2b2\";\n}\n.fa-google-plus-circle:before,\n.fa-google-plus-official:before {\n content: \"\\f2b3\";\n}\n.fa-fa:before,\n.fa-font-awesome:before {\n content: \"\\f2b4\";\n}\n.fa-handshake-o:before {\n content: \"\\f2b5\";\n}\n.fa-envelope-open:before {\n content: \"\\f2b6\";\n}\n.fa-envelope-open-o:before {\n content: \"\\f2b7\";\n}\n.fa-linode:before {\n content: \"\\f2b8\";\n}\n.fa-address-book:before {\n content: \"\\f2b9\";\n}\n.fa-address-book-o:before {\n content: \"\\f2ba\";\n}\n.fa-vcard:before,\n.fa-address-card:before {\n content: \"\\f2bb\";\n}\n.fa-vcard-o:before,\n.fa-address-card-o:before {\n content: \"\\f2bc\";\n}\n.fa-user-circle:before {\n content: \"\\f2bd\";\n}\n.fa-user-circle-o:before {\n content: \"\\f2be\";\n}\n.fa-user-o:before {\n content: \"\\f2c0\";\n}\n.fa-id-badge:before {\n content: \"\\f2c1\";\n}\n.fa-drivers-license:before,\n.fa-id-card:before {\n content: \"\\f2c2\";\n}\n.fa-drivers-license-o:before,\n.fa-id-card-o:before {\n content: \"\\f2c3\";\n}\n.fa-quora:before {\n content: \"\\f2c4\";\n}\n.fa-free-code-camp:before {\n content: \"\\f2c5\";\n}\n.fa-telegram:before {\n content: \"\\f2c6\";\n}\n.fa-thermometer-4:before,\n.fa-thermometer:before,\n.fa-thermometer-full:before {\n content: \"\\f2c7\";\n}\n.fa-thermometer-3:before,\n.fa-thermometer-three-quarters:before {\n content: \"\\f2c8\";\n}\n.fa-thermometer-2:before,\n.fa-thermometer-half:before {\n content: \"\\f2c9\";\n}\n.fa-thermometer-1:before,\n.fa-thermometer-quarter:before {\n content: \"\\f2ca\";\n}\n.fa-thermometer-0:before,\n.fa-thermometer-empty:before {\n content: \"\\f2cb\";\n}\n.fa-shower:before {\n content: \"\\f2cc\";\n}\n.fa-bathtub:before,\n.fa-s15:before,\n.fa-bath:before {\n content: \"\\f2cd\";\n}\n.fa-podcast:before {\n content: \"\\f2ce\";\n}\n.fa-window-maximize:before {\n content: \"\\f2d0\";\n}\n.fa-window-minimize:before {\n content: \"\\f2d1\";\n}\n.fa-window-restore:before {\n content: \"\\f2d2\";\n}\n.fa-times-rectangle:before,\n.fa-window-close:before {\n content: \"\\f2d3\";\n}\n.fa-times-rectangle-o:before,\n.fa-window-close-o:before {\n content: \"\\f2d4\";\n}\n.fa-bandcamp:before {\n content: \"\\f2d5\";\n}\n.fa-grav:before {\n content: \"\\f2d6\";\n}\n.fa-etsy:before {\n content: \"\\f2d7\";\n}\n.fa-imdb:before {\n content: \"\\f2d8\";\n}\n.fa-ravelry:before {\n content: \"\\f2d9\";\n}\n.fa-eercast:before {\n content: \"\\f2da\";\n}\n.fa-microchip:before {\n content: \"\\f2db\";\n}\n.fa-snowflake-o:before {\n content: \"\\f2dc\";\n}\n.fa-superpowers:before {\n content: \"\\f2dd\";\n}\n.fa-wpexplorer:before {\n content: \"\\f2de\";\n}\n.fa-meetup:before {\n content: \"\\f2e0\";\n}\n.sr-only {\n position: absolute;\n width: 1px;\n height: 1px;\n padding: 0;\n margin: -1px;\n overflow: hidden;\n clip: rect(0, 0, 0, 0);\n border: 0;\n}\n.sr-only-focusable:active,\n.sr-only-focusable:focus {\n position: static;\n width: auto;\n height: auto;\n margin: 0;\n overflow: visible;\n clip: auto;\n}\n","[class^=ant-]::-ms-clear,\n[class*= ant-]::-ms-clear,\n[class^=ant-] input::-ms-clear,\n[class*= ant-] input::-ms-clear,\n[class^=ant-] input::-ms-reveal,\n[class*= ant-] input::-ms-reveal {\n display: none;\n}\nhtml,\nbody {\n width: 100%;\n height: 100%;\n}\ninput::-ms-clear,\ninput::-ms-reveal {\n display: none;\n}\n*,\n*::before,\n*::after {\n box-sizing: border-box;\n}\nhtml {\n font-family: sans-serif;\n line-height: 1.15;\n -webkit-text-size-adjust: 100%;\n -ms-text-size-adjust: 100%;\n -ms-overflow-style: scrollbar;\n -webkit-tap-highlight-color: rgba(0, 0, 0, 0);\n}\n@-ms-viewport {\n width: device-width;\n}\nbody {\n margin: 0;\n color: rgba(0, 0, 0, 0.85);\n font-size: 14px;\n font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, 'Helvetica Neue', Arial, 'Noto Sans', sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol', 'Noto Color Emoji';\n font-variant: tabular-nums;\n line-height: 1.5715;\n background-color: #fff;\n font-feature-settings: 'tnum';\n}\n[tabindex='-1']:focus {\n outline: none !important;\n}\nhr {\n box-sizing: content-box;\n height: 0;\n overflow: visible;\n}\nh1,\nh2,\nh3,\nh4,\nh5,\nh6 {\n margin-top: 0;\n margin-bottom: 0.5em;\n font-weight: 500;\n}\np {\n margin-top: 0;\n margin-bottom: 1em;\n}\nabbr[title],\nabbr[data-original-title] {\n text-decoration: underline;\n -webkit-text-decoration: underline dotted;\n text-decoration: underline dotted;\n border-bottom: 0;\n cursor: help;\n}\naddress {\n margin-bottom: 1em;\n font-style: normal;\n line-height: inherit;\n}\ninput[type='text'],\ninput[type='password'],\ninput[type='number'],\ntextarea {\n -webkit-appearance: none;\n}\nol,\nul,\ndl {\n margin-top: 0;\n margin-bottom: 1em;\n}\nol ol,\nul ul,\nol ul,\nul ol {\n margin-bottom: 0;\n}\ndt {\n font-weight: 500;\n}\ndd {\n margin-bottom: 0.5em;\n margin-left: 0;\n}\nblockquote {\n margin: 0 0 1em;\n}\ndfn {\n font-style: italic;\n}\nb,\nstrong {\n font-weight: bolder;\n}\nsmall {\n font-size: 80%;\n}\nsub,\nsup {\n position: relative;\n font-size: 75%;\n line-height: 0;\n vertical-align: baseline;\n}\nsub {\n bottom: -0.25em;\n}\nsup {\n top: -0.5em;\n}\na {\n color: #1890ff;\n text-decoration: none;\n background-color: transparent;\n outline: none;\n cursor: pointer;\n transition: color 0.3s;\n -webkit-text-decoration-skip: objects;\n}\na:hover {\n color: #40a9ff;\n}\na:active {\n color: #096dd9;\n}\na:active,\na:hover {\n text-decoration: none;\n outline: 0;\n}\na:focus {\n text-decoration: none;\n outline: 0;\n}\na[disabled] {\n color: rgba(0, 0, 0, 0.25);\n cursor: not-allowed;\n}\npre,\ncode,\nkbd,\nsamp {\n font-size: 1em;\n font-family: 'SFMono-Regular', Consolas, 'Liberation Mono', Menlo, Courier, monospace;\n}\npre {\n margin-top: 0;\n margin-bottom: 1em;\n overflow: auto;\n}\nfigure {\n margin: 0 0 1em;\n}\nimg {\n vertical-align: middle;\n border-style: none;\n}\nsvg:not(:root) {\n overflow: hidden;\n}\na,\narea,\nbutton,\n[role='button'],\ninput:not([type='range']),\nlabel,\nselect,\nsummary,\ntextarea {\n touch-action: manipulation;\n}\ntable {\n border-collapse: collapse;\n}\ncaption {\n padding-top: 0.75em;\n padding-bottom: 0.3em;\n color: rgba(0, 0, 0, 0.45);\n text-align: left;\n caption-side: bottom;\n}\ninput,\nbutton,\nselect,\noptgroup,\ntextarea {\n margin: 0;\n color: inherit;\n font-size: inherit;\n font-family: inherit;\n line-height: inherit;\n}\nbutton,\ninput {\n overflow: visible;\n}\nbutton,\nselect {\n text-transform: none;\n}\nbutton,\nhtml [type=\"button\"],\n[type=\"reset\"],\n[type=\"submit\"] {\n -webkit-appearance: button;\n}\nbutton::-moz-focus-inner,\n[type='button']::-moz-focus-inner,\n[type='reset']::-moz-focus-inner,\n[type='submit']::-moz-focus-inner {\n padding: 0;\n border-style: none;\n}\ninput[type='radio'],\ninput[type='checkbox'] {\n box-sizing: border-box;\n padding: 0;\n}\ninput[type='date'],\ninput[type='time'],\ninput[type='datetime-local'],\ninput[type='month'] {\n -webkit-appearance: listbox;\n}\ntextarea {\n overflow: auto;\n resize: vertical;\n}\nfieldset {\n min-width: 0;\n margin: 0;\n padding: 0;\n border: 0;\n}\nlegend {\n display: block;\n width: 100%;\n max-width: 100%;\n margin-bottom: 0.5em;\n padding: 0;\n color: inherit;\n font-size: 1.5em;\n line-height: inherit;\n white-space: normal;\n}\nprogress {\n vertical-align: baseline;\n}\n[type='number']::-webkit-inner-spin-button,\n[type='number']::-webkit-outer-spin-button {\n height: auto;\n}\n[type='search'] {\n outline-offset: -2px;\n -webkit-appearance: none;\n}\n[type='search']::-webkit-search-cancel-button,\n[type='search']::-webkit-search-decoration {\n -webkit-appearance: none;\n}\n::-webkit-file-upload-button {\n font: inherit;\n -webkit-appearance: button;\n}\noutput {\n display: inline-block;\n}\nsummary {\n display: list-item;\n}\ntemplate {\n display: none;\n}\n[hidden] {\n display: none !important;\n}\nmark {\n padding: 0.2em;\n background-color: #feffe6;\n}\n::-moz-selection {\n color: #fff;\n background: #1890ff;\n}\n::selection {\n color: #fff;\n background: #1890ff;\n}\n.clearfix::before {\n display: table;\n content: '';\n}\n.clearfix::after {\n display: table;\n clear: both;\n content: '';\n}\n",".mlflow-editable-note-actions {\n margin-top: 16px;\n}\n\n.mlflow-editable-note-actions button + button {\n margin-left: 16px;\n}\n\n.mde-header {\n background: none;\n}\n",".mlflow-table-endpoint-text {\n white-space: nowrap;\n text-overflow: ellipsis;\n display: block;\n overflow: hidden;\n}\n","/* >>> Extract to our own Alert wrapper component */\n.mlflow-status-alert {\n margin-bottom: 16px;\n border-radius: 2px;\n}\n\n.mlflow-status-alert .model-version-status-icon {\n margin-left: -3px;\n}\n\n.mlflow-status-alert.mlflow-status-alert-info {\n border-left: 2px solid #3895d3;\n}\n\n.mlflow-status-alert.mlflow-status-alert-error {\n border-left: 2px solid red;\n}\n\n.mlflow-version-follow-icon {\n margin-left: auto;\n}\n\n.ant-popover-content {\n max-width: 500px;\n}\n/* <<< Extract to our own Alert wrapper component */\n",".mlflow-stage-transition-dropdown .ant-tag {\n cursor: pointer;\n border-radius: 4px;\n}\n","@import 'components/ModelVersionTable.css';\n@import 'components/ModelVersionView.css';\n@import 'components/ModelStageTransitionDropdown.css';\n\n/** TODO(Zangr) migrate globally common components and styles into src/common folder */\n.mlflow-metadata-container {\n display: flex;\n flex-wrap: wrap;\n align-items: center;\n}\n\n.mlflow-metadata-entry {\n margin-bottom: 16px;\n}\n\n.mlflow-icon-fail {\n color: red;\n}\n","@import 'reset.css';\n@import 'common/components/EditableNote.css';\n@import 'model-registry/index.css';\n\na {\n color: #2374bb;\n}\na:hover,\na:focus {\n color: #005580;\n}\n\nbody {\n margin: 0;\n padding: 0;\n}\n\n#root {\n height: 100%;\n display: flex;\n flex-direction: column;\n}\n","[class^='du-bois-light'] {\n box-sizing: border-box;\n}\n[class^='du-bois-light'] *,\n[class^='du-bois-light'] *::before,\n[class^='du-bois-light'] *::after {\n box-sizing: border-box;\n}\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n[class^=du-bois-light-]::-ms-clear,\n[class*= du-bois-light-]::-ms-clear,\n[class^=du-bois-light-] input::-ms-clear,\n[class*= du-bois-light-] input::-ms-clear,\n[class^=du-bois-light-] input::-ms-reveal,\n[class*= du-bois-light-] input::-ms-reveal {\n display: none;\n}\n/* stylelint-disable at-rule-no-unknown */\n@-ms-viewport {\n width: device-width;\n}\nabbr[data-original-title] {\n text-decoration: underline;\n text-decoration: underline dotted;\n border-bottom: 0;\n cursor: help;\n}\n.anticon {\n display: inline-block;\n color: inherit;\n font-style: normal;\n line-height: 0;\n text-align: center;\n text-transform: none;\n vertical-align: -0.125em;\n text-rendering: optimizeLegibility;\n -webkit-font-smoothing: antialiased;\n -moz-osx-font-smoothing: grayscale;\n}\n.anticon > * {\n line-height: 1;\n}\n.anticon svg {\n display: inline-block;\n}\n.anticon::before {\n display: none;\n}\n.anticon .anticon-icon {\n display: block;\n}\n.anticon[tabindex] {\n cursor: pointer;\n}\n.anticon-spin::before {\n display: inline-block;\n animation: loadingCircle 1s infinite linear;\n}\n.anticon-spin {\n display: inline-block;\n animation: loadingCircle 1s infinite linear;\n}\n.du-bois-light-fade-enter,\n.du-bois-light-fade-appear {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-light-fade-leave {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-light-fade-enter.du-bois-light-fade-enter-active,\n.du-bois-light-fade-appear.du-bois-light-fade-appear-active {\n animation-name: antFadeIn;\n animation-play-state: running;\n}\n.du-bois-light-fade-leave.du-bois-light-fade-leave-active {\n animation-name: antFadeOut;\n animation-play-state: running;\n pointer-events: none;\n}\n.du-bois-light-fade-enter,\n.du-bois-light-fade-appear {\n opacity: 0;\n animation-timing-function: linear;\n}\n.du-bois-light-fade-leave {\n animation-timing-function: linear;\n}\n@keyframes antFadeIn {\n 0% {\n opacity: 0;\n }\n 100% {\n opacity: 1;\n }\n}\n@keyframes antFadeOut {\n 0% {\n opacity: 1;\n }\n 100% {\n opacity: 0;\n }\n}\n.du-bois-light-move-up-enter,\n.du-bois-light-move-up-appear {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-light-move-up-leave {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-light-move-up-enter.du-bois-light-move-up-enter-active,\n.du-bois-light-move-up-appear.du-bois-light-move-up-appear-active {\n animation-name: antMoveUpIn;\n animation-play-state: running;\n}\n.du-bois-light-move-up-leave.du-bois-light-move-up-leave-active {\n animation-name: antMoveUpOut;\n animation-play-state: running;\n pointer-events: none;\n}\n.du-bois-light-move-up-enter,\n.du-bois-light-move-up-appear {\n opacity: 0;\n animation-timing-function: cubic-bezier(0.08, 0.82, 0.17, 1);\n}\n.du-bois-light-move-up-leave {\n animation-timing-function: cubic-bezier(0.6, 0.04, 0.98, 0.34);\n}\n.du-bois-light-move-down-enter,\n.du-bois-light-move-down-appear {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-light-move-down-leave {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-light-move-down-enter.du-bois-light-move-down-enter-active,\n.du-bois-light-move-down-appear.du-bois-light-move-down-appear-active {\n animation-name: antMoveDownIn;\n animation-play-state: running;\n}\n.du-bois-light-move-down-leave.du-bois-light-move-down-leave-active {\n animation-name: antMoveDownOut;\n animation-play-state: running;\n pointer-events: none;\n}\n.du-bois-light-move-down-enter,\n.du-bois-light-move-down-appear {\n opacity: 0;\n animation-timing-function: cubic-bezier(0.08, 0.82, 0.17, 1);\n}\n.du-bois-light-move-down-leave {\n animation-timing-function: cubic-bezier(0.6, 0.04, 0.98, 0.34);\n}\n.du-bois-light-move-left-enter,\n.du-bois-light-move-left-appear {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-light-move-left-leave {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-light-move-left-enter.du-bois-light-move-left-enter-active,\n.du-bois-light-move-left-appear.du-bois-light-move-left-appear-active {\n animation-name: antMoveLeftIn;\n animation-play-state: running;\n}\n.du-bois-light-move-left-leave.du-bois-light-move-left-leave-active {\n animation-name: antMoveLeftOut;\n animation-play-state: running;\n pointer-events: none;\n}\n.du-bois-light-move-left-enter,\n.du-bois-light-move-left-appear {\n opacity: 0;\n animation-timing-function: cubic-bezier(0.08, 0.82, 0.17, 1);\n}\n.du-bois-light-move-left-leave {\n animation-timing-function: cubic-bezier(0.6, 0.04, 0.98, 0.34);\n}\n.du-bois-light-move-right-enter,\n.du-bois-light-move-right-appear {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-light-move-right-leave {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-light-move-right-enter.du-bois-light-move-right-enter-active,\n.du-bois-light-move-right-appear.du-bois-light-move-right-appear-active {\n animation-name: antMoveRightIn;\n animation-play-state: running;\n}\n.du-bois-light-move-right-leave.du-bois-light-move-right-leave-active {\n animation-name: antMoveRightOut;\n animation-play-state: running;\n pointer-events: none;\n}\n.du-bois-light-move-right-enter,\n.du-bois-light-move-right-appear {\n opacity: 0;\n animation-timing-function: cubic-bezier(0.08, 0.82, 0.17, 1);\n}\n.du-bois-light-move-right-leave {\n animation-timing-function: cubic-bezier(0.6, 0.04, 0.98, 0.34);\n}\n@keyframes antMoveDownIn {\n 0% {\n transform: translateY(100%);\n transform-origin: 0 0;\n opacity: 0;\n }\n 100% {\n transform: translateY(0%);\n transform-origin: 0 0;\n opacity: 1;\n }\n}\n@keyframes antMoveDownOut {\n 0% {\n transform: translateY(0%);\n transform-origin: 0 0;\n opacity: 1;\n }\n 100% {\n transform: translateY(100%);\n transform-origin: 0 0;\n opacity: 0;\n }\n}\n@keyframes antMoveLeftIn {\n 0% {\n transform: translateX(-100%);\n transform-origin: 0 0;\n opacity: 0;\n }\n 100% {\n transform: translateX(0%);\n transform-origin: 0 0;\n opacity: 1;\n }\n}\n@keyframes antMoveLeftOut {\n 0% {\n transform: translateX(0%);\n transform-origin: 0 0;\n opacity: 1;\n }\n 100% {\n transform: translateX(-100%);\n transform-origin: 0 0;\n opacity: 0;\n }\n}\n@keyframes antMoveRightIn {\n 0% {\n transform: translateX(100%);\n transform-origin: 0 0;\n opacity: 0;\n }\n 100% {\n transform: translateX(0%);\n transform-origin: 0 0;\n opacity: 1;\n }\n}\n@keyframes antMoveRightOut {\n 0% {\n transform: translateX(0%);\n transform-origin: 0 0;\n opacity: 1;\n }\n 100% {\n transform: translateX(100%);\n transform-origin: 0 0;\n opacity: 0;\n }\n}\n@keyframes antMoveUpIn {\n 0% {\n transform: translateY(-100%);\n transform-origin: 0 0;\n opacity: 0;\n }\n 100% {\n transform: translateY(0%);\n transform-origin: 0 0;\n opacity: 1;\n }\n}\n@keyframes antMoveUpOut {\n 0% {\n transform: translateY(0%);\n transform-origin: 0 0;\n opacity: 1;\n }\n 100% {\n transform: translateY(-100%);\n transform-origin: 0 0;\n opacity: 0;\n }\n}\n@keyframes loadingCircle {\n 100% {\n transform: rotate(360deg);\n }\n}\n[du-bois-light-click-animating='true'],\n[du-bois-light-click-animating-without-extra-node='true'] {\n position: relative;\n}\n[du-bois-light-click-animating-without-extra-node='true']::after,\n.du-bois-light-click-animating-node {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n display: block;\n border-radius: inherit;\n box-shadow: 0 0 0 0 #2272B4;\n box-shadow: 0 0 0 0 var(--antd-wave-shadow-color);\n opacity: 0.2;\n animation: fadeEffect 2s cubic-bezier(0.08, 0.82, 0.17, 1), waveEffect 0.4s cubic-bezier(0.08, 0.82, 0.17, 1);\n animation-fill-mode: forwards;\n content: '';\n pointer-events: none;\n}\n@keyframes waveEffect {\n 100% {\n box-shadow: 0 0 0 #2272B4;\n box-shadow: 0 0 0 6px var(--antd-wave-shadow-color);\n }\n}\n@keyframes fadeEffect {\n 100% {\n opacity: 0;\n }\n}\n.du-bois-light-slide-up-enter,\n.du-bois-light-slide-up-appear {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-light-slide-up-leave {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-light-slide-up-enter.du-bois-light-slide-up-enter-active,\n.du-bois-light-slide-up-appear.du-bois-light-slide-up-appear-active {\n animation-name: antSlideUpIn;\n animation-play-state: running;\n}\n.du-bois-light-slide-up-leave.du-bois-light-slide-up-leave-active {\n animation-name: antSlideUpOut;\n animation-play-state: running;\n pointer-events: none;\n}\n.du-bois-light-slide-up-enter,\n.du-bois-light-slide-up-appear {\n opacity: 0;\n animation-timing-function: cubic-bezier(0.23, 1, 0.32, 1);\n}\n.du-bois-light-slide-up-leave {\n animation-timing-function: cubic-bezier(0.755, 0.05, 0.855, 0.06);\n}\n.du-bois-light-slide-down-enter,\n.du-bois-light-slide-down-appear {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-light-slide-down-leave {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-light-slide-down-enter.du-bois-light-slide-down-enter-active,\n.du-bois-light-slide-down-appear.du-bois-light-slide-down-appear-active {\n animation-name: antSlideDownIn;\n animation-play-state: running;\n}\n.du-bois-light-slide-down-leave.du-bois-light-slide-down-leave-active {\n animation-name: antSlideDownOut;\n animation-play-state: running;\n pointer-events: none;\n}\n.du-bois-light-slide-down-enter,\n.du-bois-light-slide-down-appear {\n opacity: 0;\n animation-timing-function: cubic-bezier(0.23, 1, 0.32, 1);\n}\n.du-bois-light-slide-down-leave {\n animation-timing-function: cubic-bezier(0.755, 0.05, 0.855, 0.06);\n}\n.du-bois-light-slide-left-enter,\n.du-bois-light-slide-left-appear {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-light-slide-left-leave {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-light-slide-left-enter.du-bois-light-slide-left-enter-active,\n.du-bois-light-slide-left-appear.du-bois-light-slide-left-appear-active {\n animation-name: antSlideLeftIn;\n animation-play-state: running;\n}\n.du-bois-light-slide-left-leave.du-bois-light-slide-left-leave-active {\n animation-name: antSlideLeftOut;\n animation-play-state: running;\n pointer-events: none;\n}\n.du-bois-light-slide-left-enter,\n.du-bois-light-slide-left-appear {\n opacity: 0;\n animation-timing-function: cubic-bezier(0.23, 1, 0.32, 1);\n}\n.du-bois-light-slide-left-leave {\n animation-timing-function: cubic-bezier(0.755, 0.05, 0.855, 0.06);\n}\n.du-bois-light-slide-right-enter,\n.du-bois-light-slide-right-appear {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-light-slide-right-leave {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-light-slide-right-enter.du-bois-light-slide-right-enter-active,\n.du-bois-light-slide-right-appear.du-bois-light-slide-right-appear-active {\n animation-name: antSlideRightIn;\n animation-play-state: running;\n}\n.du-bois-light-slide-right-leave.du-bois-light-slide-right-leave-active {\n animation-name: antSlideRightOut;\n animation-play-state: running;\n pointer-events: none;\n}\n.du-bois-light-slide-right-enter,\n.du-bois-light-slide-right-appear {\n opacity: 0;\n animation-timing-function: cubic-bezier(0.23, 1, 0.32, 1);\n}\n.du-bois-light-slide-right-leave {\n animation-timing-function: cubic-bezier(0.755, 0.05, 0.855, 0.06);\n}\n@keyframes antSlideUpIn {\n 0% {\n transform: scaleY(0.8);\n transform-origin: 0% 0%;\n opacity: 0;\n }\n 100% {\n transform: scaleY(1);\n transform-origin: 0% 0%;\n opacity: 1;\n }\n}\n@keyframes antSlideUpOut {\n 0% {\n transform: scaleY(1);\n transform-origin: 0% 0%;\n opacity: 1;\n }\n 100% {\n transform: scaleY(0.8);\n transform-origin: 0% 0%;\n opacity: 0;\n }\n}\n@keyframes antSlideDownIn {\n 0% {\n transform: scaleY(0.8);\n transform-origin: 100% 100%;\n opacity: 0;\n }\n 100% {\n transform: scaleY(1);\n transform-origin: 100% 100%;\n opacity: 1;\n }\n}\n@keyframes antSlideDownOut {\n 0% {\n transform: scaleY(1);\n transform-origin: 100% 100%;\n opacity: 1;\n }\n 100% {\n transform: scaleY(0.8);\n transform-origin: 100% 100%;\n opacity: 0;\n }\n}\n@keyframes antSlideLeftIn {\n 0% {\n transform: scaleX(0.8);\n transform-origin: 0% 0%;\n opacity: 0;\n }\n 100% {\n transform: scaleX(1);\n transform-origin: 0% 0%;\n opacity: 1;\n }\n}\n@keyframes antSlideLeftOut {\n 0% {\n transform: scaleX(1);\n transform-origin: 0% 0%;\n opacity: 1;\n }\n 100% {\n transform: scaleX(0.8);\n transform-origin: 0% 0%;\n opacity: 0;\n }\n}\n@keyframes antSlideRightIn {\n 0% {\n transform: scaleX(0.8);\n transform-origin: 100% 0%;\n opacity: 0;\n }\n 100% {\n transform: scaleX(1);\n transform-origin: 100% 0%;\n opacity: 1;\n }\n}\n@keyframes antSlideRightOut {\n 0% {\n transform: scaleX(1);\n transform-origin: 100% 0%;\n opacity: 1;\n }\n 100% {\n transform: scaleX(0.8);\n transform-origin: 100% 0%;\n opacity: 0;\n }\n}\n.du-bois-light-zoom-enter,\n.du-bois-light-zoom-appear {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-light-zoom-leave {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-light-zoom-enter.du-bois-light-zoom-enter-active,\n.du-bois-light-zoom-appear.du-bois-light-zoom-appear-active {\n animation-name: antZoomIn;\n animation-play-state: running;\n}\n.du-bois-light-zoom-leave.du-bois-light-zoom-leave-active {\n animation-name: antZoomOut;\n animation-play-state: running;\n pointer-events: none;\n}\n.du-bois-light-zoom-enter,\n.du-bois-light-zoom-appear {\n transform: scale(0);\n opacity: 0;\n animation-timing-function: cubic-bezier(0.08, 0.82, 0.17, 1);\n}\n.du-bois-light-zoom-enter-prepare,\n.du-bois-light-zoom-appear-prepare {\n transform: none;\n}\n.du-bois-light-zoom-leave {\n animation-timing-function: cubic-bezier(0.78, 0.14, 0.15, 0.86);\n}\n.du-bois-light-zoom-big-enter,\n.du-bois-light-zoom-big-appear {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-light-zoom-big-leave {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-light-zoom-big-enter.du-bois-light-zoom-big-enter-active,\n.du-bois-light-zoom-big-appear.du-bois-light-zoom-big-appear-active {\n animation-name: antZoomBigIn;\n animation-play-state: running;\n}\n.du-bois-light-zoom-big-leave.du-bois-light-zoom-big-leave-active {\n animation-name: antZoomBigOut;\n animation-play-state: running;\n pointer-events: none;\n}\n.du-bois-light-zoom-big-enter,\n.du-bois-light-zoom-big-appear {\n transform: scale(0);\n opacity: 0;\n animation-timing-function: cubic-bezier(0.08, 0.82, 0.17, 1);\n}\n.du-bois-light-zoom-big-enter-prepare,\n.du-bois-light-zoom-big-appear-prepare {\n transform: none;\n}\n.du-bois-light-zoom-big-leave {\n animation-timing-function: cubic-bezier(0.78, 0.14, 0.15, 0.86);\n}\n.du-bois-light-zoom-big-fast-enter,\n.du-bois-light-zoom-big-fast-appear {\n animation-duration: 0.1s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-light-zoom-big-fast-leave {\n animation-duration: 0.1s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-light-zoom-big-fast-enter.du-bois-light-zoom-big-fast-enter-active,\n.du-bois-light-zoom-big-fast-appear.du-bois-light-zoom-big-fast-appear-active {\n animation-name: antZoomBigIn;\n animation-play-state: running;\n}\n.du-bois-light-zoom-big-fast-leave.du-bois-light-zoom-big-fast-leave-active {\n animation-name: antZoomBigOut;\n animation-play-state: running;\n pointer-events: none;\n}\n.du-bois-light-zoom-big-fast-enter,\n.du-bois-light-zoom-big-fast-appear {\n transform: scale(0);\n opacity: 0;\n animation-timing-function: cubic-bezier(0.08, 0.82, 0.17, 1);\n}\n.du-bois-light-zoom-big-fast-enter-prepare,\n.du-bois-light-zoom-big-fast-appear-prepare {\n transform: none;\n}\n.du-bois-light-zoom-big-fast-leave {\n animation-timing-function: cubic-bezier(0.78, 0.14, 0.15, 0.86);\n}\n.du-bois-light-zoom-up-enter,\n.du-bois-light-zoom-up-appear {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-light-zoom-up-leave {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-light-zoom-up-enter.du-bois-light-zoom-up-enter-active,\n.du-bois-light-zoom-up-appear.du-bois-light-zoom-up-appear-active {\n animation-name: antZoomUpIn;\n animation-play-state: running;\n}\n.du-bois-light-zoom-up-leave.du-bois-light-zoom-up-leave-active {\n animation-name: antZoomUpOut;\n animation-play-state: running;\n pointer-events: none;\n}\n.du-bois-light-zoom-up-enter,\n.du-bois-light-zoom-up-appear {\n transform: scale(0);\n opacity: 0;\n animation-timing-function: cubic-bezier(0.08, 0.82, 0.17, 1);\n}\n.du-bois-light-zoom-up-enter-prepare,\n.du-bois-light-zoom-up-appear-prepare {\n transform: none;\n}\n.du-bois-light-zoom-up-leave {\n animation-timing-function: cubic-bezier(0.78, 0.14, 0.15, 0.86);\n}\n.du-bois-light-zoom-down-enter,\n.du-bois-light-zoom-down-appear {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-light-zoom-down-leave {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-light-zoom-down-enter.du-bois-light-zoom-down-enter-active,\n.du-bois-light-zoom-down-appear.du-bois-light-zoom-down-appear-active {\n animation-name: antZoomDownIn;\n animation-play-state: running;\n}\n.du-bois-light-zoom-down-leave.du-bois-light-zoom-down-leave-active {\n animation-name: antZoomDownOut;\n animation-play-state: running;\n pointer-events: none;\n}\n.du-bois-light-zoom-down-enter,\n.du-bois-light-zoom-down-appear {\n transform: scale(0);\n opacity: 0;\n animation-timing-function: cubic-bezier(0.08, 0.82, 0.17, 1);\n}\n.du-bois-light-zoom-down-enter-prepare,\n.du-bois-light-zoom-down-appear-prepare {\n transform: none;\n}\n.du-bois-light-zoom-down-leave {\n animation-timing-function: cubic-bezier(0.78, 0.14, 0.15, 0.86);\n}\n.du-bois-light-zoom-left-enter,\n.du-bois-light-zoom-left-appear {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-light-zoom-left-leave {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-light-zoom-left-enter.du-bois-light-zoom-left-enter-active,\n.du-bois-light-zoom-left-appear.du-bois-light-zoom-left-appear-active {\n animation-name: antZoomLeftIn;\n animation-play-state: running;\n}\n.du-bois-light-zoom-left-leave.du-bois-light-zoom-left-leave-active {\n animation-name: antZoomLeftOut;\n animation-play-state: running;\n pointer-events: none;\n}\n.du-bois-light-zoom-left-enter,\n.du-bois-light-zoom-left-appear {\n transform: scale(0);\n opacity: 0;\n animation-timing-function: cubic-bezier(0.08, 0.82, 0.17, 1);\n}\n.du-bois-light-zoom-left-enter-prepare,\n.du-bois-light-zoom-left-appear-prepare {\n transform: none;\n}\n.du-bois-light-zoom-left-leave {\n animation-timing-function: cubic-bezier(0.78, 0.14, 0.15, 0.86);\n}\n.du-bois-light-zoom-right-enter,\n.du-bois-light-zoom-right-appear {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-light-zoom-right-leave {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-light-zoom-right-enter.du-bois-light-zoom-right-enter-active,\n.du-bois-light-zoom-right-appear.du-bois-light-zoom-right-appear-active {\n animation-name: antZoomRightIn;\n animation-play-state: running;\n}\n.du-bois-light-zoom-right-leave.du-bois-light-zoom-right-leave-active {\n animation-name: antZoomRightOut;\n animation-play-state: running;\n pointer-events: none;\n}\n.du-bois-light-zoom-right-enter,\n.du-bois-light-zoom-right-appear {\n transform: scale(0);\n opacity: 0;\n animation-timing-function: cubic-bezier(0.08, 0.82, 0.17, 1);\n}\n.du-bois-light-zoom-right-enter-prepare,\n.du-bois-light-zoom-right-appear-prepare {\n transform: none;\n}\n.du-bois-light-zoom-right-leave {\n animation-timing-function: cubic-bezier(0.78, 0.14, 0.15, 0.86);\n}\n@keyframes antZoomIn {\n 0% {\n transform: scale(0.2);\n opacity: 0;\n }\n 100% {\n transform: scale(1);\n opacity: 1;\n }\n}\n@keyframes antZoomOut {\n 0% {\n transform: scale(1);\n }\n 100% {\n transform: scale(0.2);\n opacity: 0;\n }\n}\n@keyframes antZoomBigIn {\n 0% {\n transform: scale(0.8);\n opacity: 0;\n }\n 100% {\n transform: scale(1);\n opacity: 1;\n }\n}\n@keyframes antZoomBigOut {\n 0% {\n transform: scale(1);\n }\n 100% {\n transform: scale(0.8);\n opacity: 0;\n }\n}\n@keyframes antZoomUpIn {\n 0% {\n transform: scale(0.8);\n transform-origin: 50% 0%;\n opacity: 0;\n }\n 100% {\n transform: scale(1);\n transform-origin: 50% 0%;\n }\n}\n@keyframes antZoomUpOut {\n 0% {\n transform: scale(1);\n transform-origin: 50% 0%;\n }\n 100% {\n transform: scale(0.8);\n transform-origin: 50% 0%;\n opacity: 0;\n }\n}\n@keyframes antZoomLeftIn {\n 0% {\n transform: scale(0.8);\n transform-origin: 0% 50%;\n opacity: 0;\n }\n 100% {\n transform: scale(1);\n transform-origin: 0% 50%;\n }\n}\n@keyframes antZoomLeftOut {\n 0% {\n transform: scale(1);\n transform-origin: 0% 50%;\n }\n 100% {\n transform: scale(0.8);\n transform-origin: 0% 50%;\n opacity: 0;\n }\n}\n@keyframes antZoomRightIn {\n 0% {\n transform: scale(0.8);\n transform-origin: 100% 50%;\n opacity: 0;\n }\n 100% {\n transform: scale(1);\n transform-origin: 100% 50%;\n }\n}\n@keyframes antZoomRightOut {\n 0% {\n transform: scale(1);\n transform-origin: 100% 50%;\n }\n 100% {\n transform: scale(0.8);\n transform-origin: 100% 50%;\n opacity: 0;\n }\n}\n@keyframes antZoomDownIn {\n 0% {\n transform: scale(0.8);\n transform-origin: 50% 100%;\n opacity: 0;\n }\n 100% {\n transform: scale(1);\n transform-origin: 50% 100%;\n }\n}\n@keyframes antZoomDownOut {\n 0% {\n transform: scale(1);\n transform-origin: 50% 100%;\n }\n 100% {\n transform: scale(0.8);\n transform-origin: 50% 100%;\n opacity: 0;\n }\n}\n.ant-motion-collapse-legacy {\n overflow: hidden;\n}\n.ant-motion-collapse-legacy-active {\n transition: height 0.2s cubic-bezier(0.645, 0.045, 0.355, 1), opacity 0.2s cubic-bezier(0.645, 0.045, 0.355, 1) !important;\n}\n.ant-motion-collapse {\n overflow: hidden;\n transition: height 0.2s cubic-bezier(0.645, 0.045, 0.355, 1), opacity 0.2s cubic-bezier(0.645, 0.045, 0.355, 1) !important;\n}\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-light-btn {\n line-height: 20px;\n position: relative;\n display: inline-block;\n font-weight: 400;\n white-space: nowrap;\n text-align: center;\n background-image: none;\n border: 1px solid transparent;\n box-shadow: 0 2px 0 rgba(0, 0, 0, 0.015);\n cursor: pointer;\n transition: all 0.3s cubic-bezier(0.645, 0.045, 0.355, 1);\n user-select: none;\n touch-action: manipulation;\n height: 40px;\n padding: 0 16px;\n font-size: 13px;\n border-radius: 4px;\n color: #11171C;\n background: #fff;\n border-color: #E8ECF0;\n}\n.du-bois-light-btn > .anticon {\n line-height: 1;\n}\n.du-bois-light-btn,\n.du-bois-light-btn:active,\n.du-bois-light-btn:focus {\n outline: 0;\n}\n.du-bois-light-btn:not([disabled]):hover {\n text-decoration: none;\n}\n.du-bois-light-btn:not([disabled]):active {\n outline: 0;\n box-shadow: none;\n}\n.du-bois-light-btn[disabled] {\n cursor: not-allowed;\n}\n.du-bois-light-btn[disabled] > * {\n pointer-events: none;\n}\n.du-bois-light-btn-lg {\n height: 40px;\n padding: 0 16px;\n font-size: 18px;\n border-radius: 4px;\n}\n.du-bois-light-btn-sm {\n height: 32px;\n padding: 0 16px;\n font-size: 13px;\n border-radius: 4px;\n}\n.du-bois-light-btn > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn:hover,\n.du-bois-light-btn:focus {\n color: #448dc2;\n background: #fff;\n border-color: #448dc2;\n}\n.du-bois-light-btn:hover > a:only-child,\n.du-bois-light-btn:focus > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn:hover > a:only-child::after,\n.du-bois-light-btn:focus > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn:active {\n color: #14538f;\n background: #fff;\n border-color: #14538f;\n}\n.du-bois-light-btn:active > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn[disabled],\n.du-bois-light-btn[disabled]:hover,\n.du-bois-light-btn[disabled]:focus,\n.du-bois-light-btn[disabled]:active {\n color: rgba(0, 0, 0, 0.25);\n background: #f5f5f5;\n border-color: #E8ECF0;\n text-shadow: none;\n box-shadow: none;\n}\n.du-bois-light-btn[disabled] > a:only-child,\n.du-bois-light-btn[disabled]:hover > a:only-child,\n.du-bois-light-btn[disabled]:focus > a:only-child,\n.du-bois-light-btn[disabled]:active > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn[disabled] > a:only-child::after,\n.du-bois-light-btn[disabled]:hover > a:only-child::after,\n.du-bois-light-btn[disabled]:focus > a:only-child::after,\n.du-bois-light-btn[disabled]:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn:hover,\n.du-bois-light-btn:focus,\n.du-bois-light-btn:active {\n text-decoration: none;\n background: #fff;\n}\n.du-bois-light-btn > span {\n display: inline-block;\n}\n.du-bois-light-btn-primary {\n color: #fff;\n background: #2272B4;\n border-color: #2272B4;\n text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.12);\n box-shadow: 0 2px 0 rgba(0, 0, 0, 0.045);\n}\n.du-bois-light-btn-primary > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-primary > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-primary:hover,\n.du-bois-light-btn-primary:focus {\n color: #fff;\n background: #448dc2;\n border-color: #448dc2;\n}\n.du-bois-light-btn-primary:hover > a:only-child,\n.du-bois-light-btn-primary:focus > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-primary:hover > a:only-child::after,\n.du-bois-light-btn-primary:focus > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-primary:active {\n color: #fff;\n background: #14538f;\n border-color: #14538f;\n}\n.du-bois-light-btn-primary:active > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-primary:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-primary[disabled],\n.du-bois-light-btn-primary[disabled]:hover,\n.du-bois-light-btn-primary[disabled]:focus,\n.du-bois-light-btn-primary[disabled]:active {\n color: rgba(0, 0, 0, 0.25);\n background: #f5f5f5;\n border-color: #E8ECF0;\n text-shadow: none;\n box-shadow: none;\n}\n.du-bois-light-btn-primary[disabled] > a:only-child,\n.du-bois-light-btn-primary[disabled]:hover > a:only-child,\n.du-bois-light-btn-primary[disabled]:focus > a:only-child,\n.du-bois-light-btn-primary[disabled]:active > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-primary[disabled] > a:only-child::after,\n.du-bois-light-btn-primary[disabled]:hover > a:only-child::after,\n.du-bois-light-btn-primary[disabled]:focus > a:only-child::after,\n.du-bois-light-btn-primary[disabled]:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-group .du-bois-light-btn-primary:not(:first-child):not(:last-child) {\n border-right-color: #448dc2;\n border-left-color: #448dc2;\n}\n.du-bois-light-btn-group .du-bois-light-btn-primary:not(:first-child):not(:last-child):disabled {\n border-color: #E8ECF0;\n}\n.du-bois-light-btn-group .du-bois-light-btn-primary:first-child:not(:last-child) {\n border-right-color: #448dc2;\n}\n.du-bois-light-btn-group .du-bois-light-btn-primary:first-child:not(:last-child)[disabled] {\n border-right-color: #E8ECF0;\n}\n.du-bois-light-btn-group .du-bois-light-btn-primary:last-child:not(:first-child),\n.du-bois-light-btn-group .du-bois-light-btn-primary + .du-bois-light-btn-primary {\n border-left-color: #448dc2;\n}\n.du-bois-light-btn-group .du-bois-light-btn-primary:last-child:not(:first-child)[disabled],\n.du-bois-light-btn-group .du-bois-light-btn-primary + .du-bois-light-btn-primary[disabled] {\n border-left-color: #E8ECF0;\n}\n.du-bois-light-btn-ghost {\n color: #11171C;\n background: transparent;\n border-color: #E8ECF0;\n}\n.du-bois-light-btn-ghost > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-ghost > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-ghost:hover,\n.du-bois-light-btn-ghost:focus {\n color: #448dc2;\n background: transparent;\n border-color: #448dc2;\n}\n.du-bois-light-btn-ghost:hover > a:only-child,\n.du-bois-light-btn-ghost:focus > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-ghost:hover > a:only-child::after,\n.du-bois-light-btn-ghost:focus > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-ghost:active {\n color: #14538f;\n background: transparent;\n border-color: #14538f;\n}\n.du-bois-light-btn-ghost:active > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-ghost:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-ghost[disabled],\n.du-bois-light-btn-ghost[disabled]:hover,\n.du-bois-light-btn-ghost[disabled]:focus,\n.du-bois-light-btn-ghost[disabled]:active {\n color: rgba(0, 0, 0, 0.25);\n background: #f5f5f5;\n border-color: #E8ECF0;\n text-shadow: none;\n box-shadow: none;\n}\n.du-bois-light-btn-ghost[disabled] > a:only-child,\n.du-bois-light-btn-ghost[disabled]:hover > a:only-child,\n.du-bois-light-btn-ghost[disabled]:focus > a:only-child,\n.du-bois-light-btn-ghost[disabled]:active > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-ghost[disabled] > a:only-child::after,\n.du-bois-light-btn-ghost[disabled]:hover > a:only-child::after,\n.du-bois-light-btn-ghost[disabled]:focus > a:only-child::after,\n.du-bois-light-btn-ghost[disabled]:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-dashed {\n color: #11171C;\n background: #fff;\n border-color: #E8ECF0;\n border-style: dashed;\n}\n.du-bois-light-btn-dashed > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-dashed > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-dashed:hover,\n.du-bois-light-btn-dashed:focus {\n color: #448dc2;\n background: #fff;\n border-color: #448dc2;\n}\n.du-bois-light-btn-dashed:hover > a:only-child,\n.du-bois-light-btn-dashed:focus > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-dashed:hover > a:only-child::after,\n.du-bois-light-btn-dashed:focus > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-dashed:active {\n color: #14538f;\n background: #fff;\n border-color: #14538f;\n}\n.du-bois-light-btn-dashed:active > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-dashed:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-dashed[disabled],\n.du-bois-light-btn-dashed[disabled]:hover,\n.du-bois-light-btn-dashed[disabled]:focus,\n.du-bois-light-btn-dashed[disabled]:active {\n color: rgba(0, 0, 0, 0.25);\n background: #f5f5f5;\n border-color: #E8ECF0;\n text-shadow: none;\n box-shadow: none;\n}\n.du-bois-light-btn-dashed[disabled] > a:only-child,\n.du-bois-light-btn-dashed[disabled]:hover > a:only-child,\n.du-bois-light-btn-dashed[disabled]:focus > a:only-child,\n.du-bois-light-btn-dashed[disabled]:active > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-dashed[disabled] > a:only-child::after,\n.du-bois-light-btn-dashed[disabled]:hover > a:only-child::after,\n.du-bois-light-btn-dashed[disabled]:focus > a:only-child::after,\n.du-bois-light-btn-dashed[disabled]:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-danger {\n color: #fff;\n background: #C82D4C;\n border-color: #C82D4C;\n text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.12);\n box-shadow: 0 2px 0 rgba(0, 0, 0, 0.045);\n}\n.du-bois-light-btn-danger > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-danger > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-danger:hover,\n.du-bois-light-btn-danger:focus {\n color: #fff;\n background: #d45368;\n border-color: #d45368;\n}\n.du-bois-light-btn-danger:hover > a:only-child,\n.du-bois-light-btn-danger:focus > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-danger:hover > a:only-child::after,\n.du-bois-light-btn-danger:focus > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-danger:active {\n color: #fff;\n background: #a11d3c;\n border-color: #a11d3c;\n}\n.du-bois-light-btn-danger:active > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-danger:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-danger[disabled],\n.du-bois-light-btn-danger[disabled]:hover,\n.du-bois-light-btn-danger[disabled]:focus,\n.du-bois-light-btn-danger[disabled]:active {\n color: rgba(0, 0, 0, 0.25);\n background: #f5f5f5;\n border-color: #E8ECF0;\n text-shadow: none;\n box-shadow: none;\n}\n.du-bois-light-btn-danger[disabled] > a:only-child,\n.du-bois-light-btn-danger[disabled]:hover > a:only-child,\n.du-bois-light-btn-danger[disabled]:focus > a:only-child,\n.du-bois-light-btn-danger[disabled]:active > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-danger[disabled] > a:only-child::after,\n.du-bois-light-btn-danger[disabled]:hover > a:only-child::after,\n.du-bois-light-btn-danger[disabled]:focus > a:only-child::after,\n.du-bois-light-btn-danger[disabled]:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-link {\n color: #2272B4;\n background: transparent;\n border-color: transparent;\n box-shadow: none;\n}\n.du-bois-light-btn-link > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-link > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-link:hover,\n.du-bois-light-btn-link:focus {\n color: #448dc2;\n background: transparent;\n border-color: #448dc2;\n}\n.du-bois-light-btn-link:hover > a:only-child,\n.du-bois-light-btn-link:focus > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-link:hover > a:only-child::after,\n.du-bois-light-btn-link:focus > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-link:active {\n color: #14538f;\n background: transparent;\n border-color: #14538f;\n}\n.du-bois-light-btn-link:active > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-link:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-link[disabled],\n.du-bois-light-btn-link[disabled]:hover,\n.du-bois-light-btn-link[disabled]:focus,\n.du-bois-light-btn-link[disabled]:active {\n color: rgba(0, 0, 0, 0.25);\n background: #f5f5f5;\n border-color: #E8ECF0;\n text-shadow: none;\n box-shadow: none;\n}\n.du-bois-light-btn-link[disabled] > a:only-child,\n.du-bois-light-btn-link[disabled]:hover > a:only-child,\n.du-bois-light-btn-link[disabled]:focus > a:only-child,\n.du-bois-light-btn-link[disabled]:active > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-link[disabled] > a:only-child::after,\n.du-bois-light-btn-link[disabled]:hover > a:only-child::after,\n.du-bois-light-btn-link[disabled]:focus > a:only-child::after,\n.du-bois-light-btn-link[disabled]:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-link:hover {\n background: transparent;\n}\n.du-bois-light-btn-link:hover,\n.du-bois-light-btn-link:focus,\n.du-bois-light-btn-link:active {\n border-color: transparent;\n}\n.du-bois-light-btn-link[disabled],\n.du-bois-light-btn-link[disabled]:hover,\n.du-bois-light-btn-link[disabled]:focus,\n.du-bois-light-btn-link[disabled]:active {\n color: rgba(0, 0, 0, 0.25);\n background: transparent;\n border-color: transparent;\n text-shadow: none;\n box-shadow: none;\n}\n.du-bois-light-btn-link[disabled] > a:only-child,\n.du-bois-light-btn-link[disabled]:hover > a:only-child,\n.du-bois-light-btn-link[disabled]:focus > a:only-child,\n.du-bois-light-btn-link[disabled]:active > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-link[disabled] > a:only-child::after,\n.du-bois-light-btn-link[disabled]:hover > a:only-child::after,\n.du-bois-light-btn-link[disabled]:focus > a:only-child::after,\n.du-bois-light-btn-link[disabled]:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-text {\n color: #11171C;\n background: transparent;\n border-color: transparent;\n box-shadow: none;\n}\n.du-bois-light-btn-text > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-text > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-text:hover,\n.du-bois-light-btn-text:focus {\n color: #448dc2;\n background: transparent;\n border-color: #448dc2;\n}\n.du-bois-light-btn-text:hover > a:only-child,\n.du-bois-light-btn-text:focus > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-text:hover > a:only-child::after,\n.du-bois-light-btn-text:focus > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-text:active {\n color: #14538f;\n background: transparent;\n border-color: #14538f;\n}\n.du-bois-light-btn-text:active > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-text:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-text[disabled],\n.du-bois-light-btn-text[disabled]:hover,\n.du-bois-light-btn-text[disabled]:focus,\n.du-bois-light-btn-text[disabled]:active {\n color: rgba(0, 0, 0, 0.25);\n background: #f5f5f5;\n border-color: #E8ECF0;\n text-shadow: none;\n box-shadow: none;\n}\n.du-bois-light-btn-text[disabled] > a:only-child,\n.du-bois-light-btn-text[disabled]:hover > a:only-child,\n.du-bois-light-btn-text[disabled]:focus > a:only-child,\n.du-bois-light-btn-text[disabled]:active > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-text[disabled] > a:only-child::after,\n.du-bois-light-btn-text[disabled]:hover > a:only-child::after,\n.du-bois-light-btn-text[disabled]:focus > a:only-child::after,\n.du-bois-light-btn-text[disabled]:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-text:hover,\n.du-bois-light-btn-text:focus {\n color: #11171C;\n background: rgba(0, 0, 0, 0.018);\n border-color: transparent;\n}\n.du-bois-light-btn-text:active {\n color: #11171C;\n background: rgba(0, 0, 0, 0.028);\n border-color: transparent;\n}\n.du-bois-light-btn-text[disabled],\n.du-bois-light-btn-text[disabled]:hover,\n.du-bois-light-btn-text[disabled]:focus,\n.du-bois-light-btn-text[disabled]:active {\n color: rgba(0, 0, 0, 0.25);\n background: transparent;\n border-color: transparent;\n text-shadow: none;\n box-shadow: none;\n}\n.du-bois-light-btn-text[disabled] > a:only-child,\n.du-bois-light-btn-text[disabled]:hover > a:only-child,\n.du-bois-light-btn-text[disabled]:focus > a:only-child,\n.du-bois-light-btn-text[disabled]:active > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-text[disabled] > a:only-child::after,\n.du-bois-light-btn-text[disabled]:hover > a:only-child::after,\n.du-bois-light-btn-text[disabled]:focus > a:only-child::after,\n.du-bois-light-btn-text[disabled]:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-dangerous {\n color: #C82D4C;\n background: #fff;\n border-color: #C82D4C;\n}\n.du-bois-light-btn-dangerous > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-dangerous > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-dangerous:hover,\n.du-bois-light-btn-dangerous:focus {\n color: #d45368;\n background: #fff;\n border-color: #d45368;\n}\n.du-bois-light-btn-dangerous:hover > a:only-child,\n.du-bois-light-btn-dangerous:focus > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-dangerous:hover > a:only-child::after,\n.du-bois-light-btn-dangerous:focus > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-dangerous:active {\n color: #a11d3c;\n background: #fff;\n border-color: #a11d3c;\n}\n.du-bois-light-btn-dangerous:active > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-dangerous:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-dangerous[disabled],\n.du-bois-light-btn-dangerous[disabled]:hover,\n.du-bois-light-btn-dangerous[disabled]:focus,\n.du-bois-light-btn-dangerous[disabled]:active {\n color: rgba(0, 0, 0, 0.25);\n background: #f5f5f5;\n border-color: #E8ECF0;\n text-shadow: none;\n box-shadow: none;\n}\n.du-bois-light-btn-dangerous[disabled] > a:only-child,\n.du-bois-light-btn-dangerous[disabled]:hover > a:only-child,\n.du-bois-light-btn-dangerous[disabled]:focus > a:only-child,\n.du-bois-light-btn-dangerous[disabled]:active > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-dangerous[disabled] > a:only-child::after,\n.du-bois-light-btn-dangerous[disabled]:hover > a:only-child::after,\n.du-bois-light-btn-dangerous[disabled]:focus > a:only-child::after,\n.du-bois-light-btn-dangerous[disabled]:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-primary {\n color: #fff;\n background: #C82D4C;\n border-color: #C82D4C;\n text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.12);\n box-shadow: 0 2px 0 rgba(0, 0, 0, 0.045);\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-primary > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-primary > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-primary:hover,\n.du-bois-light-btn-dangerous.du-bois-light-btn-primary:focus {\n color: #fff;\n background: #d45368;\n border-color: #d45368;\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-primary:hover > a:only-child,\n.du-bois-light-btn-dangerous.du-bois-light-btn-primary:focus > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-primary:hover > a:only-child::after,\n.du-bois-light-btn-dangerous.du-bois-light-btn-primary:focus > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-primary:active {\n color: #fff;\n background: #a11d3c;\n border-color: #a11d3c;\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-primary:active > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-primary:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-primary[disabled],\n.du-bois-light-btn-dangerous.du-bois-light-btn-primary[disabled]:hover,\n.du-bois-light-btn-dangerous.du-bois-light-btn-primary[disabled]:focus,\n.du-bois-light-btn-dangerous.du-bois-light-btn-primary[disabled]:active {\n color: rgba(0, 0, 0, 0.25);\n background: #f5f5f5;\n border-color: #E8ECF0;\n text-shadow: none;\n box-shadow: none;\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-primary[disabled] > a:only-child,\n.du-bois-light-btn-dangerous.du-bois-light-btn-primary[disabled]:hover > a:only-child,\n.du-bois-light-btn-dangerous.du-bois-light-btn-primary[disabled]:focus > a:only-child,\n.du-bois-light-btn-dangerous.du-bois-light-btn-primary[disabled]:active > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-primary[disabled] > a:only-child::after,\n.du-bois-light-btn-dangerous.du-bois-light-btn-primary[disabled]:hover > a:only-child::after,\n.du-bois-light-btn-dangerous.du-bois-light-btn-primary[disabled]:focus > a:only-child::after,\n.du-bois-light-btn-dangerous.du-bois-light-btn-primary[disabled]:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-link {\n color: #C82D4C;\n background: transparent;\n border-color: transparent;\n box-shadow: none;\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-link > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-link > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-link:hover,\n.du-bois-light-btn-dangerous.du-bois-light-btn-link:focus {\n color: #448dc2;\n background: transparent;\n border-color: #448dc2;\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-link:hover > a:only-child,\n.du-bois-light-btn-dangerous.du-bois-light-btn-link:focus > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-link:hover > a:only-child::after,\n.du-bois-light-btn-dangerous.du-bois-light-btn-link:focus > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-link:active {\n color: #14538f;\n background: transparent;\n border-color: #14538f;\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-link:active > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-link:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-link[disabled],\n.du-bois-light-btn-dangerous.du-bois-light-btn-link[disabled]:hover,\n.du-bois-light-btn-dangerous.du-bois-light-btn-link[disabled]:focus,\n.du-bois-light-btn-dangerous.du-bois-light-btn-link[disabled]:active {\n color: rgba(0, 0, 0, 0.25);\n background: #f5f5f5;\n border-color: #E8ECF0;\n text-shadow: none;\n box-shadow: none;\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-link[disabled] > a:only-child,\n.du-bois-light-btn-dangerous.du-bois-light-btn-link[disabled]:hover > a:only-child,\n.du-bois-light-btn-dangerous.du-bois-light-btn-link[disabled]:focus > a:only-child,\n.du-bois-light-btn-dangerous.du-bois-light-btn-link[disabled]:active > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-link[disabled] > a:only-child::after,\n.du-bois-light-btn-dangerous.du-bois-light-btn-link[disabled]:hover > a:only-child::after,\n.du-bois-light-btn-dangerous.du-bois-light-btn-link[disabled]:focus > a:only-child::after,\n.du-bois-light-btn-dangerous.du-bois-light-btn-link[disabled]:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-link:hover,\n.du-bois-light-btn-dangerous.du-bois-light-btn-link:focus {\n color: #d45368;\n background: transparent;\n border-color: transparent;\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-link:hover > a:only-child,\n.du-bois-light-btn-dangerous.du-bois-light-btn-link:focus > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-link:hover > a:only-child::after,\n.du-bois-light-btn-dangerous.du-bois-light-btn-link:focus > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-link:active {\n color: #a11d3c;\n background: transparent;\n border-color: transparent;\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-link:active > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-link:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-link[disabled],\n.du-bois-light-btn-dangerous.du-bois-light-btn-link[disabled]:hover,\n.du-bois-light-btn-dangerous.du-bois-light-btn-link[disabled]:focus,\n.du-bois-light-btn-dangerous.du-bois-light-btn-link[disabled]:active {\n color: rgba(0, 0, 0, 0.25);\n background: transparent;\n border-color: transparent;\n text-shadow: none;\n box-shadow: none;\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-link[disabled] > a:only-child,\n.du-bois-light-btn-dangerous.du-bois-light-btn-link[disabled]:hover > a:only-child,\n.du-bois-light-btn-dangerous.du-bois-light-btn-link[disabled]:focus > a:only-child,\n.du-bois-light-btn-dangerous.du-bois-light-btn-link[disabled]:active > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-link[disabled] > a:only-child::after,\n.du-bois-light-btn-dangerous.du-bois-light-btn-link[disabled]:hover > a:only-child::after,\n.du-bois-light-btn-dangerous.du-bois-light-btn-link[disabled]:focus > a:only-child::after,\n.du-bois-light-btn-dangerous.du-bois-light-btn-link[disabled]:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-text {\n color: #C82D4C;\n background: transparent;\n border-color: transparent;\n box-shadow: none;\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-text > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-text > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-text:hover,\n.du-bois-light-btn-dangerous.du-bois-light-btn-text:focus {\n color: #448dc2;\n background: transparent;\n border-color: #448dc2;\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-text:hover > a:only-child,\n.du-bois-light-btn-dangerous.du-bois-light-btn-text:focus > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-text:hover > a:only-child::after,\n.du-bois-light-btn-dangerous.du-bois-light-btn-text:focus > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-text:active {\n color: #14538f;\n background: transparent;\n border-color: #14538f;\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-text:active > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-text:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-text[disabled],\n.du-bois-light-btn-dangerous.du-bois-light-btn-text[disabled]:hover,\n.du-bois-light-btn-dangerous.du-bois-light-btn-text[disabled]:focus,\n.du-bois-light-btn-dangerous.du-bois-light-btn-text[disabled]:active {\n color: rgba(0, 0, 0, 0.25);\n background: #f5f5f5;\n border-color: #E8ECF0;\n text-shadow: none;\n box-shadow: none;\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-text[disabled] > a:only-child,\n.du-bois-light-btn-dangerous.du-bois-light-btn-text[disabled]:hover > a:only-child,\n.du-bois-light-btn-dangerous.du-bois-light-btn-text[disabled]:focus > a:only-child,\n.du-bois-light-btn-dangerous.du-bois-light-btn-text[disabled]:active > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-text[disabled] > a:only-child::after,\n.du-bois-light-btn-dangerous.du-bois-light-btn-text[disabled]:hover > a:only-child::after,\n.du-bois-light-btn-dangerous.du-bois-light-btn-text[disabled]:focus > a:only-child::after,\n.du-bois-light-btn-dangerous.du-bois-light-btn-text[disabled]:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-text:hover,\n.du-bois-light-btn-dangerous.du-bois-light-btn-text:focus {\n color: #d45368;\n background: rgba(0, 0, 0, 0.018);\n border-color: transparent;\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-text:hover > a:only-child,\n.du-bois-light-btn-dangerous.du-bois-light-btn-text:focus > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-text:hover > a:only-child::after,\n.du-bois-light-btn-dangerous.du-bois-light-btn-text:focus > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-text:active {\n color: #a11d3c;\n background: rgba(0, 0, 0, 0.028);\n border-color: transparent;\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-text:active > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-text:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-text[disabled],\n.du-bois-light-btn-dangerous.du-bois-light-btn-text[disabled]:hover,\n.du-bois-light-btn-dangerous.du-bois-light-btn-text[disabled]:focus,\n.du-bois-light-btn-dangerous.du-bois-light-btn-text[disabled]:active {\n color: rgba(0, 0, 0, 0.25);\n background: transparent;\n border-color: transparent;\n text-shadow: none;\n box-shadow: none;\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-text[disabled] > a:only-child,\n.du-bois-light-btn-dangerous.du-bois-light-btn-text[disabled]:hover > a:only-child,\n.du-bois-light-btn-dangerous.du-bois-light-btn-text[disabled]:focus > a:only-child,\n.du-bois-light-btn-dangerous.du-bois-light-btn-text[disabled]:active > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-dangerous.du-bois-light-btn-text[disabled] > a:only-child::after,\n.du-bois-light-btn-dangerous.du-bois-light-btn-text[disabled]:hover > a:only-child::after,\n.du-bois-light-btn-dangerous.du-bois-light-btn-text[disabled]:focus > a:only-child::after,\n.du-bois-light-btn-dangerous.du-bois-light-btn-text[disabled]:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-icon-only {\n width: 40px;\n height: 40px;\n padding: 0 0;\n font-size: 15px;\n border-radius: 4px;\n vertical-align: -1px;\n}\n.du-bois-light-btn-icon-only > * {\n font-size: 15px;\n}\n.du-bois-light-btn-icon-only.du-bois-light-btn-lg {\n width: 40px;\n height: 40px;\n padding: 0 0;\n font-size: 20px;\n border-radius: 4px;\n}\n.du-bois-light-btn-icon-only.du-bois-light-btn-lg > * {\n font-size: 20px;\n}\n.du-bois-light-btn-icon-only.du-bois-light-btn-sm {\n width: 32px;\n height: 32px;\n padding: 0 0;\n font-size: 13px;\n border-radius: 4px;\n}\n.du-bois-light-btn-icon-only.du-bois-light-btn-sm > * {\n font-size: 13px;\n}\n.du-bois-light-btn-round {\n height: 40px;\n padding: 0 20px;\n font-size: 13px;\n border-radius: 40px;\n}\n.du-bois-light-btn-round.du-bois-light-btn-lg {\n height: 40px;\n padding: 0 20px;\n font-size: 18px;\n border-radius: 40px;\n}\n.du-bois-light-btn-round.du-bois-light-btn-sm {\n height: 32px;\n padding: 0 16px;\n font-size: 13px;\n border-radius: 32px;\n}\n.du-bois-light-btn-round.du-bois-light-btn-icon-only {\n width: auto;\n}\n.du-bois-light-btn-circle {\n min-width: 40px;\n padding-right: 0;\n padding-left: 0;\n text-align: center;\n border-radius: 50%;\n}\n.du-bois-light-btn-circle.du-bois-light-btn-lg {\n min-width: 40px;\n border-radius: 50%;\n}\n.du-bois-light-btn-circle.du-bois-light-btn-sm {\n min-width: 32px;\n border-radius: 50%;\n}\n.du-bois-light-btn::before {\n position: absolute;\n top: -1px;\n right: -1px;\n bottom: -1px;\n left: -1px;\n z-index: 1;\n display: none;\n background: #fff;\n border-radius: inherit;\n opacity: 0.35;\n transition: opacity 0.2s;\n content: '';\n pointer-events: none;\n}\n.du-bois-light-btn .anticon {\n transition: margin-left 0.3s cubic-bezier(0.645, 0.045, 0.355, 1);\n}\n.du-bois-light-btn .anticon.anticon-plus > svg,\n.du-bois-light-btn .anticon.anticon-minus > svg {\n shape-rendering: optimizeSpeed;\n}\n.du-bois-light-btn.du-bois-light-btn-loading {\n position: relative;\n}\n.du-bois-light-btn.du-bois-light-btn-loading:not([disabled]) {\n pointer-events: none;\n}\n.du-bois-light-btn.du-bois-light-btn-loading::before {\n display: block;\n}\n.du-bois-light-btn > .du-bois-light-btn-loading-icon {\n transition: all 0.3s cubic-bezier(0.645, 0.045, 0.355, 1);\n}\n.du-bois-light-btn > .du-bois-light-btn-loading-icon .anticon {\n padding-right: 4px;\n animation: none;\n}\n.du-bois-light-btn > .du-bois-light-btn-loading-icon .anticon svg {\n animation: loadingCircle 1s infinite linear;\n}\n.du-bois-light-btn > .du-bois-light-btn-loading-icon:only-child .anticon {\n padding-right: 0;\n}\n.du-bois-light-btn-group {\n position: relative;\n display: inline-flex;\n}\n.du-bois-light-btn-group > .du-bois-light-btn,\n.du-bois-light-btn-group > span > .du-bois-light-btn {\n position: relative;\n}\n.du-bois-light-btn-group > .du-bois-light-btn:hover,\n.du-bois-light-btn-group > span > .du-bois-light-btn:hover,\n.du-bois-light-btn-group > .du-bois-light-btn:focus,\n.du-bois-light-btn-group > span > .du-bois-light-btn:focus,\n.du-bois-light-btn-group > .du-bois-light-btn:active,\n.du-bois-light-btn-group > span > .du-bois-light-btn:active {\n z-index: 2;\n}\n.du-bois-light-btn-group > .du-bois-light-btn[disabled],\n.du-bois-light-btn-group > span > .du-bois-light-btn[disabled] {\n z-index: 0;\n}\n.du-bois-light-btn-group .du-bois-light-btn-icon-only {\n font-size: 13px;\n}\n.du-bois-light-btn-group-lg > .du-bois-light-btn,\n.du-bois-light-btn-group-lg > span > .du-bois-light-btn {\n height: 40px;\n padding: 0 16px;\n font-size: 18px;\n border-radius: 0;\n}\n.du-bois-light-btn-group-lg .du-bois-light-btn.du-bois-light-btn-icon-only {\n width: 40px;\n height: 40px;\n padding-right: 0;\n padding-left: 0;\n}\n.du-bois-light-btn-group-sm > .du-bois-light-btn,\n.du-bois-light-btn-group-sm > span > .du-bois-light-btn {\n height: 32px;\n padding: 0 16px;\n font-size: 13px;\n border-radius: 0;\n}\n.du-bois-light-btn-group-sm > .du-bois-light-btn > .anticon,\n.du-bois-light-btn-group-sm > span > .du-bois-light-btn > .anticon {\n font-size: 13px;\n}\n.du-bois-light-btn-group-sm .du-bois-light-btn.du-bois-light-btn-icon-only {\n width: 32px;\n height: 32px;\n padding-right: 0;\n padding-left: 0;\n}\n.du-bois-light-btn-group .du-bois-light-btn + .du-bois-light-btn,\n.du-bois-light-btn + .du-bois-light-btn-group,\n.du-bois-light-btn-group span + .du-bois-light-btn,\n.du-bois-light-btn-group .du-bois-light-btn + span,\n.du-bois-light-btn-group > span + span,\n.du-bois-light-btn-group + .du-bois-light-btn,\n.du-bois-light-btn-group + .du-bois-light-btn-group {\n margin-left: -1px;\n}\n.du-bois-light-btn-group .du-bois-light-btn-primary + .du-bois-light-btn:not(.du-bois-light-btn-primary):not([disabled]) {\n border-left-color: transparent;\n}\n.du-bois-light-btn-group .du-bois-light-btn {\n border-radius: 0;\n}\n.du-bois-light-btn-group > .du-bois-light-btn:first-child,\n.du-bois-light-btn-group > span:first-child > .du-bois-light-btn {\n margin-left: 0;\n}\n.du-bois-light-btn-group > .du-bois-light-btn:only-child {\n border-radius: 4px;\n}\n.du-bois-light-btn-group > span:only-child > .du-bois-light-btn {\n border-radius: 4px;\n}\n.du-bois-light-btn-group > .du-bois-light-btn:first-child:not(:last-child),\n.du-bois-light-btn-group > span:first-child:not(:last-child) > .du-bois-light-btn {\n border-top-left-radius: 4px;\n border-bottom-left-radius: 4px;\n}\n.du-bois-light-btn-group > .du-bois-light-btn:last-child:not(:first-child),\n.du-bois-light-btn-group > span:last-child:not(:first-child) > .du-bois-light-btn {\n border-top-right-radius: 4px;\n border-bottom-right-radius: 4px;\n}\n.du-bois-light-btn-group-sm > .du-bois-light-btn:only-child {\n border-radius: 4px;\n}\n.du-bois-light-btn-group-sm > span:only-child > .du-bois-light-btn {\n border-radius: 4px;\n}\n.du-bois-light-btn-group-sm > .du-bois-light-btn:first-child:not(:last-child),\n.du-bois-light-btn-group-sm > span:first-child:not(:last-child) > .du-bois-light-btn {\n border-top-left-radius: 4px;\n border-bottom-left-radius: 4px;\n}\n.du-bois-light-btn-group-sm > .du-bois-light-btn:last-child:not(:first-child),\n.du-bois-light-btn-group-sm > span:last-child:not(:first-child) > .du-bois-light-btn {\n border-top-right-radius: 4px;\n border-bottom-right-radius: 4px;\n}\n.du-bois-light-btn-group > .du-bois-light-btn-group {\n float: left;\n}\n.du-bois-light-btn-group > .du-bois-light-btn-group:not(:first-child):not(:last-child) > .du-bois-light-btn {\n border-radius: 0;\n}\n.du-bois-light-btn-group > .du-bois-light-btn-group:first-child:not(:last-child) > .du-bois-light-btn:last-child {\n padding-right: 8px;\n border-top-right-radius: 0;\n border-bottom-right-radius: 0;\n}\n.du-bois-light-btn-group > .du-bois-light-btn-group:last-child:not(:first-child) > .du-bois-light-btn:first-child {\n padding-left: 8px;\n border-top-left-radius: 0;\n border-bottom-left-radius: 0;\n}\n.du-bois-light-btn:focus > span,\n.du-bois-light-btn:active > span {\n position: relative;\n}\n.du-bois-light-btn > .anticon + span,\n.du-bois-light-btn > span + .anticon {\n margin-left: 8px;\n}\n.du-bois-light-btn-background-ghost {\n color: #fff;\n background: transparent !important;\n border-color: #fff;\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-primary {\n color: #2272B4;\n background: transparent;\n border-color: #2272B4;\n text-shadow: none;\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-primary > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-primary > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-primary:hover,\n.du-bois-light-btn-background-ghost.du-bois-light-btn-primary:focus {\n color: #448dc2;\n background: transparent;\n border-color: #448dc2;\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-primary:hover > a:only-child,\n.du-bois-light-btn-background-ghost.du-bois-light-btn-primary:focus > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-primary:hover > a:only-child::after,\n.du-bois-light-btn-background-ghost.du-bois-light-btn-primary:focus > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-primary:active {\n color: #14538f;\n background: transparent;\n border-color: #14538f;\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-primary:active > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-primary:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-primary[disabled],\n.du-bois-light-btn-background-ghost.du-bois-light-btn-primary[disabled]:hover,\n.du-bois-light-btn-background-ghost.du-bois-light-btn-primary[disabled]:focus,\n.du-bois-light-btn-background-ghost.du-bois-light-btn-primary[disabled]:active {\n color: rgba(0, 0, 0, 0.25);\n background: #f5f5f5;\n border-color: #E8ECF0;\n text-shadow: none;\n box-shadow: none;\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-primary[disabled] > a:only-child,\n.du-bois-light-btn-background-ghost.du-bois-light-btn-primary[disabled]:hover > a:only-child,\n.du-bois-light-btn-background-ghost.du-bois-light-btn-primary[disabled]:focus > a:only-child,\n.du-bois-light-btn-background-ghost.du-bois-light-btn-primary[disabled]:active > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-primary[disabled] > a:only-child::after,\n.du-bois-light-btn-background-ghost.du-bois-light-btn-primary[disabled]:hover > a:only-child::after,\n.du-bois-light-btn-background-ghost.du-bois-light-btn-primary[disabled]:focus > a:only-child::after,\n.du-bois-light-btn-background-ghost.du-bois-light-btn-primary[disabled]:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-danger {\n color: #C82D4C;\n background: transparent;\n border-color: #C82D4C;\n text-shadow: none;\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-danger > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-danger > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-danger:hover,\n.du-bois-light-btn-background-ghost.du-bois-light-btn-danger:focus {\n color: #d45368;\n background: transparent;\n border-color: #d45368;\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-danger:hover > a:only-child,\n.du-bois-light-btn-background-ghost.du-bois-light-btn-danger:focus > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-danger:hover > a:only-child::after,\n.du-bois-light-btn-background-ghost.du-bois-light-btn-danger:focus > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-danger:active {\n color: #a11d3c;\n background: transparent;\n border-color: #a11d3c;\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-danger:active > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-danger:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-danger[disabled],\n.du-bois-light-btn-background-ghost.du-bois-light-btn-danger[disabled]:hover,\n.du-bois-light-btn-background-ghost.du-bois-light-btn-danger[disabled]:focus,\n.du-bois-light-btn-background-ghost.du-bois-light-btn-danger[disabled]:active {\n color: rgba(0, 0, 0, 0.25);\n background: #f5f5f5;\n border-color: #E8ECF0;\n text-shadow: none;\n box-shadow: none;\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-danger[disabled] > a:only-child,\n.du-bois-light-btn-background-ghost.du-bois-light-btn-danger[disabled]:hover > a:only-child,\n.du-bois-light-btn-background-ghost.du-bois-light-btn-danger[disabled]:focus > a:only-child,\n.du-bois-light-btn-background-ghost.du-bois-light-btn-danger[disabled]:active > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-danger[disabled] > a:only-child::after,\n.du-bois-light-btn-background-ghost.du-bois-light-btn-danger[disabled]:hover > a:only-child::after,\n.du-bois-light-btn-background-ghost.du-bois-light-btn-danger[disabled]:focus > a:only-child::after,\n.du-bois-light-btn-background-ghost.du-bois-light-btn-danger[disabled]:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous {\n color: #C82D4C;\n background: transparent;\n border-color: #C82D4C;\n text-shadow: none;\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous:hover,\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous:focus {\n color: #d45368;\n background: transparent;\n border-color: #d45368;\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous:hover > a:only-child,\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous:focus > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous:hover > a:only-child::after,\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous:focus > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous:active {\n color: #a11d3c;\n background: transparent;\n border-color: #a11d3c;\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous:active > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous[disabled],\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous[disabled]:hover,\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous[disabled]:focus,\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous[disabled]:active {\n color: rgba(0, 0, 0, 0.25);\n background: #f5f5f5;\n border-color: #E8ECF0;\n text-shadow: none;\n box-shadow: none;\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous[disabled] > a:only-child,\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous[disabled]:hover > a:only-child,\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous[disabled]:focus > a:only-child,\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous[disabled]:active > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous[disabled] > a:only-child::after,\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous[disabled]:hover > a:only-child::after,\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous[disabled]:focus > a:only-child::after,\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous[disabled]:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous.du-bois-light-btn-link {\n color: #C82D4C;\n background: transparent;\n border-color: transparent;\n text-shadow: none;\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous.du-bois-light-btn-link > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous.du-bois-light-btn-link > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous.du-bois-light-btn-link:hover,\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous.du-bois-light-btn-link:focus {\n color: #d45368;\n background: transparent;\n border-color: transparent;\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous.du-bois-light-btn-link:hover > a:only-child,\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous.du-bois-light-btn-link:focus > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous.du-bois-light-btn-link:hover > a:only-child::after,\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous.du-bois-light-btn-link:focus > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous.du-bois-light-btn-link:active {\n color: #a11d3c;\n background: transparent;\n border-color: transparent;\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous.du-bois-light-btn-link:active > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous.du-bois-light-btn-link:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous.du-bois-light-btn-link[disabled],\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous.du-bois-light-btn-link[disabled]:hover,\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous.du-bois-light-btn-link[disabled]:focus,\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous.du-bois-light-btn-link[disabled]:active {\n color: rgba(0, 0, 0, 0.25);\n background: #f5f5f5;\n border-color: #E8ECF0;\n text-shadow: none;\n box-shadow: none;\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous.du-bois-light-btn-link[disabled] > a:only-child,\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous.du-bois-light-btn-link[disabled]:hover > a:only-child,\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous.du-bois-light-btn-link[disabled]:focus > a:only-child,\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous.du-bois-light-btn-link[disabled]:active > a:only-child {\n color: currentColor;\n}\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous.du-bois-light-btn-link[disabled] > a:only-child::after,\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous.du-bois-light-btn-link[disabled]:hover > a:only-child::after,\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous.du-bois-light-btn-link[disabled]:focus > a:only-child::after,\n.du-bois-light-btn-background-ghost.du-bois-light-btn-dangerous.du-bois-light-btn-link[disabled]:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-light-btn-two-chinese-chars::first-letter {\n letter-spacing: 0.34em;\n}\n.du-bois-light-btn-two-chinese-chars > *:not(.anticon) {\n margin-right: -0.34em;\n letter-spacing: 0.34em;\n}\n.du-bois-light-btn-block {\n width: 100%;\n}\n.du-bois-light-btn:empty {\n display: inline-block;\n width: 0;\n visibility: hidden;\n content: '\\a0';\n}\na.du-bois-light-btn {\n padding-top: 0.01px !important;\n line-height: 38px;\n}\na.du-bois-light-btn-lg {\n line-height: 38px;\n}\na.du-bois-light-btn-sm {\n line-height: 30px;\n}\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-light-alert {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #11171C;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: 'tnum';\n position: relative;\n display: flex;\n align-items: center;\n padding: 8px 15px;\n word-wrap: break-word;\n border-radius: 4px;\n}\n.du-bois-light-alert-content {\n flex: 1;\n min-width: 0;\n}\n.du-bois-light-alert-icon {\n margin-right: 8px;\n}\n.du-bois-light-alert-description {\n display: none;\n font-size: 13px;\n line-height: 21px;\n}\n.du-bois-light-alert-success {\n background-color: #b6c2b8;\n border: 1px solid #94a899;\n}\n.du-bois-light-alert-success .du-bois-light-alert-icon {\n color: #34824F;\n}\n.du-bois-light-alert-info {\n background-color: #F6F7F9;\n border: 1px solid #C0CDD8;\n}\n.du-bois-light-alert-info .du-bois-light-alert-icon {\n color: #5F7281;\n}\n.du-bois-light-alert-warning {\n background-color: #FFF9EB;\n border: 1px solid #F8D4A5;\n}\n.du-bois-light-alert-warning .du-bois-light-alert-icon {\n color: #BE501E;\n}\n.du-bois-light-alert-error {\n background-color: #FFF5F7;\n border: 1px solid #FBD0D8;\n}\n.du-bois-light-alert-error .du-bois-light-alert-icon {\n color: #C82D4C;\n}\n.du-bois-light-alert-error .du-bois-light-alert-description > pre {\n margin: 0;\n padding: 0;\n}\n.du-bois-light-alert-action {\n margin-left: 8px;\n}\n.du-bois-light-alert-close-icon {\n margin-left: 8px;\n padding: 0;\n overflow: hidden;\n font-size: 12px;\n line-height: 12px;\n background-color: transparent;\n border: none;\n outline: none;\n cursor: pointer;\n}\n.du-bois-light-alert-close-icon .anticon-close {\n color: #5F7281;\n transition: color 0.3s;\n}\n.du-bois-light-alert-close-icon .anticon-close:hover {\n color: rgba(0, 0, 0, 0.75);\n}\n.du-bois-light-alert-close-text {\n color: #5F7281;\n transition: color 0.3s;\n}\n.du-bois-light-alert-close-text:hover {\n color: rgba(0, 0, 0, 0.75);\n}\n.du-bois-light-alert-with-description {\n align-items: flex-start;\n padding: 7px 15px 7px 24px;\n}\n.du-bois-light-alert-with-description.du-bois-light-alert-no-icon {\n padding: 7px 15px;\n}\n.du-bois-light-alert-with-description .du-bois-light-alert-icon {\n margin-right: 7px;\n font-size: 24px;\n}\n.du-bois-light-alert-with-description .du-bois-light-alert-message {\n display: block;\n margin-bottom: 4px;\n color: inherit;\n font-size: 18px;\n}\n.du-bois-light-alert-message {\n color: inherit;\n}\n.du-bois-light-alert-with-description .du-bois-light-alert-description {\n display: block;\n}\n.du-bois-light-alert.du-bois-light-alert-motion-leave {\n overflow: hidden;\n opacity: 1;\n transition: max-height 0.3s cubic-bezier(0.78, 0.14, 0.15, 0.86), opacity 0.3s cubic-bezier(0.78, 0.14, 0.15, 0.86), padding-top 0.3s cubic-bezier(0.78, 0.14, 0.15, 0.86), padding-bottom 0.3s cubic-bezier(0.78, 0.14, 0.15, 0.86), margin-bottom 0.3s cubic-bezier(0.78, 0.14, 0.15, 0.86);\n}\n.du-bois-light-alert.du-bois-light-alert-motion-leave-active {\n max-height: 0;\n margin-bottom: 0 !important;\n padding-top: 0;\n padding-bottom: 0;\n opacity: 0;\n}\n.du-bois-light-alert-banner {\n margin-bottom: 0;\n border: 0;\n border-radius: 0;\n}\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n@keyframes antCheckboxEffect {\n 0% {\n transform: scale(1);\n opacity: 0.5;\n }\n 100% {\n transform: scale(1.6);\n opacity: 0;\n }\n}\n.du-bois-light-checkbox {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #11171C;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: 'tnum';\n position: relative;\n top: 0.2em;\n line-height: 1;\n white-space: nowrap;\n outline: none;\n cursor: pointer;\n}\n.du-bois-light-checkbox-wrapper:hover .du-bois-light-checkbox-inner,\n.du-bois-light-checkbox:hover .du-bois-light-checkbox-inner,\n.du-bois-light-checkbox-input:focus + .du-bois-light-checkbox-inner {\n border-color: #2272B4;\n}\n.du-bois-light-checkbox-checked::after {\n position: absolute;\n top: 0;\n left: 0;\n width: 100%;\n height: 100%;\n border: 1px solid #2272B4;\n border-radius: 4px;\n visibility: hidden;\n animation: antCheckboxEffect 0.36s ease-in-out;\n animation-fill-mode: backwards;\n content: '';\n}\n.du-bois-light-checkbox:hover::after,\n.du-bois-light-checkbox-wrapper:hover .du-bois-light-checkbox::after {\n visibility: visible;\n}\n.du-bois-light-checkbox-inner {\n position: relative;\n top: 0;\n left: 0;\n display: block;\n width: 16px;\n height: 16px;\n direction: ltr;\n background-color: #fff;\n border: 1px solid #E8ECF0;\n border-radius: 4px;\n border-collapse: separate;\n transition: all 0.3s;\n}\n.du-bois-light-checkbox-inner::after {\n position: absolute;\n top: 50%;\n left: 22%;\n display: table;\n width: 5.71428571px;\n height: 9.14285714px;\n border: 2px solid #fff;\n border-top: 0;\n border-left: 0;\n transform: rotate(45deg) scale(0) translate(-50%, -50%);\n opacity: 0;\n transition: all 0.1s cubic-bezier(0.71, -0.46, 0.88, 0.6), opacity 0.1s;\n content: ' ';\n}\n.du-bois-light-checkbox-input {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n z-index: 1;\n width: 100%;\n height: 100%;\n cursor: pointer;\n opacity: 0;\n}\n.du-bois-light-checkbox-checked .du-bois-light-checkbox-inner::after {\n position: absolute;\n display: table;\n border: 2px solid #fff;\n border-top: 0;\n border-left: 0;\n transform: rotate(45deg) scale(1) translate(-50%, -50%);\n opacity: 1;\n transition: all 0.2s cubic-bezier(0.12, 0.4, 0.29, 1.46) 0.1s;\n content: ' ';\n}\n.du-bois-light-checkbox-checked .du-bois-light-checkbox-inner {\n background-color: #2272B4;\n border-color: #2272B4;\n}\n.du-bois-light-checkbox-disabled {\n cursor: not-allowed;\n}\n.du-bois-light-checkbox-disabled.du-bois-light-checkbox-checked .du-bois-light-checkbox-inner::after {\n border-color: rgba(0, 0, 0, 0.25);\n animation-name: none;\n}\n.du-bois-light-checkbox-disabled .du-bois-light-checkbox-input {\n cursor: not-allowed;\n}\n.du-bois-light-checkbox-disabled .du-bois-light-checkbox-inner {\n background-color: #f5f5f5;\n border-color: #E8ECF0 !important;\n}\n.du-bois-light-checkbox-disabled .du-bois-light-checkbox-inner::after {\n border-color: #f5f5f5;\n border-collapse: separate;\n animation-name: none;\n}\n.du-bois-light-checkbox-disabled + span {\n color: rgba(0, 0, 0, 0.25);\n cursor: not-allowed;\n}\n.du-bois-light-checkbox-disabled:hover::after,\n.du-bois-light-checkbox-wrapper:hover .du-bois-light-checkbox-disabled::after {\n visibility: hidden;\n}\n.du-bois-light-checkbox-wrapper {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #11171C;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: 'tnum';\n display: inline-flex;\n align-items: baseline;\n line-height: unset;\n cursor: pointer;\n}\n.du-bois-light-checkbox-wrapper::after {\n display: inline-block;\n width: 0;\n overflow: hidden;\n content: '\\a0';\n}\n.du-bois-light-checkbox-wrapper.du-bois-light-checkbox-wrapper-disabled {\n cursor: not-allowed;\n}\n.du-bois-light-checkbox-wrapper + .du-bois-light-checkbox-wrapper {\n margin-left: 8px;\n}\n.du-bois-light-checkbox + span {\n padding-right: 8px;\n padding-left: 8px;\n}\n.du-bois-light-checkbox-group {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #11171C;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: 'tnum';\n display: inline-block;\n}\n.du-bois-light-checkbox-group-item {\n margin-right: 8px;\n}\n.du-bois-light-checkbox-group-item:last-child {\n margin-right: 0;\n}\n.du-bois-light-checkbox-group-item + .du-bois-light-checkbox-group-item {\n margin-left: 0;\n}\n.du-bois-light-checkbox-indeterminate .du-bois-light-checkbox-inner {\n background-color: #fff;\n border-color: #E8ECF0;\n}\n.du-bois-light-checkbox-indeterminate .du-bois-light-checkbox-inner::after {\n top: 50%;\n left: 50%;\n width: 8px;\n height: 8px;\n background-color: #2272B4;\n border: 0;\n transform: translate(-50%, -50%) scale(1);\n opacity: 1;\n content: ' ';\n}\n.du-bois-light-checkbox-indeterminate.du-bois-light-checkbox-disabled .du-bois-light-checkbox-inner::after {\n background-color: rgba(0, 0, 0, 0.25);\n border-color: rgba(0, 0, 0, 0.25);\n}\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-light-select-auto-complete {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #11171C;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: 'tnum';\n}\n.du-bois-light-select-auto-complete .du-bois-light-select-clear {\n right: 13px;\n}\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-light-select-single .du-bois-light-select-selector {\n display: flex;\n}\n.du-bois-light-select-single .du-bois-light-select-selector .du-bois-light-select-selection-search {\n position: absolute;\n top: 0;\n right: 12px;\n bottom: 0;\n left: 12px;\n}\n.du-bois-light-select-single .du-bois-light-select-selector .du-bois-light-select-selection-search-input {\n width: 100%;\n}\n.du-bois-light-select-single .du-bois-light-select-selector .du-bois-light-select-selection-item,\n.du-bois-light-select-single .du-bois-light-select-selector .du-bois-light-select-selection-placeholder {\n padding: 0;\n line-height: 38px;\n transition: all 0.3s;\n}\n@supports (-moz-appearance: meterbar) {\n .du-bois-light-select-single .du-bois-light-select-selector .du-bois-light-select-selection-item,\n .du-bois-light-select-single .du-bois-light-select-selector .du-bois-light-select-selection-placeholder {\n line-height: 38px;\n }\n}\n.du-bois-light-select-single .du-bois-light-select-selector .du-bois-light-select-selection-item {\n position: relative;\n user-select: none;\n}\n.du-bois-light-select-single .du-bois-light-select-selector .du-bois-light-select-selection-placeholder {\n pointer-events: none;\n}\n.du-bois-light-select-single .du-bois-light-select-selector::after,\n.du-bois-light-select-single .du-bois-light-select-selector .du-bois-light-select-selection-item::after,\n.du-bois-light-select-single .du-bois-light-select-selector .du-bois-light-select-selection-placeholder::after {\n display: inline-block;\n width: 0;\n visibility: hidden;\n content: '\\a0';\n}\n.du-bois-light-select-single.du-bois-light-select-show-arrow .du-bois-light-select-selection-search {\n right: 25px;\n}\n.du-bois-light-select-single.du-bois-light-select-show-arrow .du-bois-light-select-selection-item,\n.du-bois-light-select-single.du-bois-light-select-show-arrow .du-bois-light-select-selection-placeholder {\n padding-right: 17px;\n}\n.du-bois-light-select-single.du-bois-light-select-open .du-bois-light-select-selection-item {\n color: #bfbfbf;\n}\n.du-bois-light-select-single:not(.du-bois-light-select-customize-input) .du-bois-light-select-selector {\n width: 100%;\n height: 40px;\n padding: 0 12px;\n}\n.du-bois-light-select-single:not(.du-bois-light-select-customize-input) .du-bois-light-select-selector .du-bois-light-select-selection-search-input {\n height: 38px;\n}\n.du-bois-light-select-single:not(.du-bois-light-select-customize-input) .du-bois-light-select-selector::after {\n line-height: 38px;\n}\n.du-bois-light-select-single.du-bois-light-select-customize-input .du-bois-light-select-selector::after {\n display: none;\n}\n.du-bois-light-select-single.du-bois-light-select-customize-input .du-bois-light-select-selector .du-bois-light-select-selection-search {\n position: static;\n width: 100%;\n}\n.du-bois-light-select-single.du-bois-light-select-customize-input .du-bois-light-select-selector .du-bois-light-select-selection-placeholder {\n position: absolute;\n right: 0;\n left: 0;\n padding: 0 12px;\n}\n.du-bois-light-select-single.du-bois-light-select-customize-input .du-bois-light-select-selector .du-bois-light-select-selection-placeholder::after {\n display: none;\n}\n.du-bois-light-select-single.du-bois-light-select-lg:not(.du-bois-light-select-customize-input) .du-bois-light-select-selector {\n height: 40px;\n}\n.du-bois-light-select-single.du-bois-light-select-lg:not(.du-bois-light-select-customize-input) .du-bois-light-select-selector::after,\n.du-bois-light-select-single.du-bois-light-select-lg:not(.du-bois-light-select-customize-input) .du-bois-light-select-selector .du-bois-light-select-selection-item,\n.du-bois-light-select-single.du-bois-light-select-lg:not(.du-bois-light-select-customize-input) .du-bois-light-select-selector .du-bois-light-select-selection-placeholder {\n line-height: 38px;\n}\n.du-bois-light-select-single.du-bois-light-select-lg:not(.du-bois-light-select-customize-input):not(.du-bois-light-select-customize-input) .du-bois-light-select-selection-search-input {\n height: 38px;\n}\n.du-bois-light-select-single.du-bois-light-select-sm:not(.du-bois-light-select-customize-input) .du-bois-light-select-selector {\n height: 32px;\n}\n.du-bois-light-select-single.du-bois-light-select-sm:not(.du-bois-light-select-customize-input) .du-bois-light-select-selector::after,\n.du-bois-light-select-single.du-bois-light-select-sm:not(.du-bois-light-select-customize-input) .du-bois-light-select-selector .du-bois-light-select-selection-item,\n.du-bois-light-select-single.du-bois-light-select-sm:not(.du-bois-light-select-customize-input) .du-bois-light-select-selector .du-bois-light-select-selection-placeholder {\n line-height: 30px;\n}\n.du-bois-light-select-single.du-bois-light-select-sm:not(.du-bois-light-select-customize-input):not(.du-bois-light-select-customize-input) .du-bois-light-select-selection-search-input {\n height: 30px;\n}\n.du-bois-light-select-single.du-bois-light-select-sm:not(.du-bois-light-select-customize-input) .du-bois-light-select-selection-search {\n right: 12px;\n left: 12px;\n}\n.du-bois-light-select-single.du-bois-light-select-sm:not(.du-bois-light-select-customize-input) .du-bois-light-select-selector {\n padding: 0 12px;\n}\n.du-bois-light-select-single.du-bois-light-select-sm:not(.du-bois-light-select-customize-input).du-bois-light-select-show-arrow .du-bois-light-select-selection-search {\n right: 31.5px;\n}\n.du-bois-light-select-single.du-bois-light-select-sm:not(.du-bois-light-select-customize-input).du-bois-light-select-show-arrow .du-bois-light-select-selection-item,\n.du-bois-light-select-single.du-bois-light-select-sm:not(.du-bois-light-select-customize-input).du-bois-light-select-show-arrow .du-bois-light-select-selection-placeholder {\n padding-right: 19.5px;\n}\n.du-bois-light-select-single.du-bois-light-select-lg:not(.du-bois-light-select-customize-input) .du-bois-light-select-selector {\n padding: 0 12px;\n}\n/**\n * Do not merge `height` & `line-height` under style with `selection` & `search`,\n * since chrome may update to redesign with its align logic.\n */\n.du-bois-light-select-selection-overflow {\n position: relative;\n display: flex;\n flex: auto;\n flex-wrap: wrap;\n max-width: 100%;\n}\n.du-bois-light-select-selection-overflow-item {\n flex: none;\n align-self: center;\n max-width: 100%;\n}\n.du-bois-light-select-multiple .du-bois-light-select-selector {\n display: flex;\n flex-wrap: wrap;\n align-items: center;\n padding: 1px 5px;\n}\n.du-bois-light-select-show-search.du-bois-light-select-multiple .du-bois-light-select-selector {\n cursor: text;\n}\n.du-bois-light-select-disabled.du-bois-light-select-multiple .du-bois-light-select-selector {\n background: #f5f5f5;\n cursor: not-allowed;\n}\n.du-bois-light-select-multiple .du-bois-light-select-selector::after {\n display: inline-block;\n width: 0;\n margin: 3px 0;\n line-height: 30px;\n content: '\\a0';\n}\n.du-bois-light-select-multiple.du-bois-light-select-show-arrow .du-bois-light-select-selector,\n.du-bois-light-select-multiple.du-bois-light-select-allow-clear .du-bois-light-select-selector {\n padding-right: 20px;\n}\n.du-bois-light-select-multiple .du-bois-light-select-selection-item {\n position: relative;\n display: flex;\n flex: none;\n box-sizing: border-box;\n max-width: 100%;\n height: 30px;\n margin-top: 3px;\n margin-bottom: 3px;\n line-height: 28px;\n background: #f5f5f5;\n border: 1px solid #f0f0f0;\n border-radius: 4px;\n cursor: default;\n transition: font-size 0.3s, line-height 0.3s, height 0.3s;\n user-select: none;\n margin-inline-end: 5px;\n padding-inline-start: 4px;\n padding-inline-end: 2px;\n}\n.du-bois-light-select-disabled.du-bois-light-select-multiple .du-bois-light-select-selection-item {\n color: #bfbfbf;\n border-color: #E8ECF0;\n cursor: not-allowed;\n}\n.du-bois-light-select-multiple .du-bois-light-select-selection-item-content {\n display: inline-block;\n margin-right: 2px;\n overflow: hidden;\n white-space: pre;\n text-overflow: ellipsis;\n}\n.du-bois-light-select-multiple .du-bois-light-select-selection-item-remove {\n color: inherit;\n font-style: normal;\n line-height: 0;\n text-align: center;\n text-transform: none;\n vertical-align: -0.125em;\n text-rendering: optimizeLegibility;\n -webkit-font-smoothing: antialiased;\n -moz-osx-font-smoothing: grayscale;\n display: inline-block;\n color: #5F7281;\n font-weight: bold;\n font-size: 10px;\n line-height: inherit;\n cursor: pointer;\n}\n.du-bois-light-select-multiple .du-bois-light-select-selection-item-remove > * {\n line-height: 1;\n}\n.du-bois-light-select-multiple .du-bois-light-select-selection-item-remove svg {\n display: inline-block;\n}\n.du-bois-light-select-multiple .du-bois-light-select-selection-item-remove::before {\n display: none;\n}\n.du-bois-light-select-multiple .du-bois-light-select-selection-item-remove .du-bois-light-select-multiple .du-bois-light-select-selection-item-remove-icon {\n display: block;\n}\n.du-bois-light-select-multiple .du-bois-light-select-selection-item-remove > .anticon {\n vertical-align: -0.2em;\n}\n.du-bois-light-select-multiple .du-bois-light-select-selection-item-remove:hover {\n color: rgba(0, 0, 0, 0.75);\n}\n.du-bois-light-select-multiple .du-bois-light-select-selection-overflow-item + .du-bois-light-select-selection-overflow-item .du-bois-light-select-selection-search {\n margin-inline-start: 0;\n}\n.du-bois-light-select-multiple .du-bois-light-select-selection-search {\n position: relative;\n max-width: 100%;\n margin-top: 3px;\n margin-bottom: 3px;\n margin-inline-start: 7px;\n}\n.du-bois-light-select-multiple .du-bois-light-select-selection-search-input,\n.du-bois-light-select-multiple .du-bois-light-select-selection-search-mirror {\n height: 30px;\n font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, 'Helvetica Neue', Arial, 'Noto Sans', sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol', 'Noto Color Emoji';\n line-height: 30px;\n transition: all 0.3s;\n}\n.du-bois-light-select-multiple .du-bois-light-select-selection-search-input {\n width: 100%;\n min-width: 4.1px;\n}\n.du-bois-light-select-multiple .du-bois-light-select-selection-search-mirror {\n position: absolute;\n top: 0;\n left: 0;\n z-index: 999;\n white-space: pre;\n visibility: hidden;\n}\n.du-bois-light-select-multiple .du-bois-light-select-selection-placeholder {\n position: absolute;\n top: 50%;\n right: 12px;\n left: 12px;\n transform: translateY(-50%);\n transition: all 0.3s;\n}\n.du-bois-light-select-multiple.du-bois-light-select-lg .du-bois-light-select-selector::after {\n line-height: 30px;\n}\n.du-bois-light-select-multiple.du-bois-light-select-lg .du-bois-light-select-selection-item {\n height: 30px;\n line-height: 28px;\n}\n.du-bois-light-select-multiple.du-bois-light-select-lg .du-bois-light-select-selection-search {\n height: 30px;\n line-height: 30px;\n}\n.du-bois-light-select-multiple.du-bois-light-select-lg .du-bois-light-select-selection-search-input,\n.du-bois-light-select-multiple.du-bois-light-select-lg .du-bois-light-select-selection-search-mirror {\n height: 30px;\n line-height: 28px;\n}\n.du-bois-light-select-multiple.du-bois-light-select-sm .du-bois-light-select-selector::after {\n line-height: 22px;\n}\n.du-bois-light-select-multiple.du-bois-light-select-sm .du-bois-light-select-selection-item {\n height: 22px;\n line-height: 20px;\n}\n.du-bois-light-select-multiple.du-bois-light-select-sm .du-bois-light-select-selection-search {\n height: 22px;\n line-height: 22px;\n}\n.du-bois-light-select-multiple.du-bois-light-select-sm .du-bois-light-select-selection-search-input,\n.du-bois-light-select-multiple.du-bois-light-select-sm .du-bois-light-select-selection-search-mirror {\n height: 22px;\n line-height: 20px;\n}\n.du-bois-light-select-multiple.du-bois-light-select-sm .du-bois-light-select-selection-placeholder {\n left: 12px;\n}\n.du-bois-light-select-multiple.du-bois-light-select-sm .du-bois-light-select-selection-search {\n margin-inline-start: 3px;\n}\n.du-bois-light-select-multiple.du-bois-light-select-lg .du-bois-light-select-selection-item {\n height: 32px;\n line-height: 32px;\n}\n.du-bois-light-select-disabled .du-bois-light-select-selection-item-remove {\n display: none;\n}\n/* Reset search input style */\n.du-bois-light-select {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #11171C;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: 'tnum';\n position: relative;\n display: inline-block;\n cursor: pointer;\n}\n.du-bois-light-select:not(.du-bois-light-select-customize-input) .du-bois-light-select-selector {\n position: relative;\n background-color: #fff;\n border: 1px solid #E8ECF0;\n border-radius: 4px;\n transition: all 0.3s cubic-bezier(0.645, 0.045, 0.355, 1);\n}\n.du-bois-light-select:not(.du-bois-light-select-customize-input) .du-bois-light-select-selector input {\n cursor: pointer;\n}\n.du-bois-light-select-show-search.du-bois-light-select:not(.du-bois-light-select-customize-input) .du-bois-light-select-selector {\n cursor: text;\n}\n.du-bois-light-select-show-search.du-bois-light-select:not(.du-bois-light-select-customize-input) .du-bois-light-select-selector input {\n cursor: auto;\n}\n.du-bois-light-select-focused:not(.du-bois-light-select-disabled).du-bois-light-select:not(.du-bois-light-select-customize-input) .du-bois-light-select-selector {\n border-color: #448dc2;\n border-right-width: 1px !important;\n outline: 0;\n box-shadow: 0 0 0 2px rgba(34, 114, 180, 0.2);\n}\n.du-bois-light-select-disabled.du-bois-light-select:not(.du-bois-light-select-customize-input) .du-bois-light-select-selector {\n color: rgba(0, 0, 0, 0.25);\n background: #f5f5f5;\n cursor: not-allowed;\n}\n.du-bois-light-select-multiple.du-bois-light-select-disabled.du-bois-light-select:not(.du-bois-light-select-customize-input) .du-bois-light-select-selector {\n background: #f5f5f5;\n}\n.du-bois-light-select-disabled.du-bois-light-select:not(.du-bois-light-select-customize-input) .du-bois-light-select-selector input {\n cursor: not-allowed;\n}\n.du-bois-light-select:not(.du-bois-light-select-customize-input) .du-bois-light-select-selector .du-bois-light-select-selection-search-input {\n margin: 0;\n padding: 0;\n background: transparent;\n border: none;\n outline: none;\n appearance: none;\n}\n.du-bois-light-select:not(.du-bois-light-select-customize-input) .du-bois-light-select-selector .du-bois-light-select-selection-search-input::-webkit-search-cancel-button {\n display: none;\n -webkit-appearance: none;\n}\n.du-bois-light-select:not(.du-bois-light-select-disabled):hover .du-bois-light-select-selector {\n border-color: #448dc2;\n border-right-width: 1px !important;\n}\n.du-bois-light-select-selection-item {\n flex: 1;\n overflow: hidden;\n white-space: nowrap;\n text-overflow: ellipsis;\n}\n@media all and (-ms-high-contrast: none) {\n .du-bois-light-select-selection-item *::-ms-backdrop,\n .du-bois-light-select-selection-item {\n flex: auto;\n }\n}\n.du-bois-light-select-selection-placeholder {\n flex: 1;\n overflow: hidden;\n color: #bfbfbf;\n white-space: nowrap;\n text-overflow: ellipsis;\n pointer-events: none;\n}\n@media all and (-ms-high-contrast: none) {\n .du-bois-light-select-selection-placeholder *::-ms-backdrop,\n .du-bois-light-select-selection-placeholder {\n flex: auto;\n }\n}\n.du-bois-light-select-arrow {\n display: inline-block;\n color: inherit;\n font-style: normal;\n line-height: 0;\n text-transform: none;\n vertical-align: -0.125em;\n text-rendering: optimizeLegibility;\n -webkit-font-smoothing: antialiased;\n -moz-osx-font-smoothing: grayscale;\n position: absolute;\n top: 50%;\n right: 7px;\n width: 12px;\n height: 12px;\n margin-top: -6px;\n color: rgba(0, 0, 0, 0.25);\n font-size: 12px;\n line-height: 1;\n text-align: center;\n pointer-events: none;\n}\n.du-bois-light-select-arrow > * {\n line-height: 1;\n}\n.du-bois-light-select-arrow svg {\n display: inline-block;\n}\n.du-bois-light-select-arrow::before {\n display: none;\n}\n.du-bois-light-select-arrow .du-bois-light-select-arrow-icon {\n display: block;\n}\n.du-bois-light-select-arrow .anticon {\n vertical-align: top;\n transition: transform 0.3s;\n}\n.du-bois-light-select-arrow .anticon > svg {\n vertical-align: top;\n}\n.du-bois-light-select-arrow .anticon:not(.du-bois-light-select-suffix) {\n pointer-events: auto;\n}\n.du-bois-light-select-disabled .du-bois-light-select-arrow {\n cursor: not-allowed;\n}\n.du-bois-light-select-clear {\n position: absolute;\n top: 50%;\n right: 7px;\n z-index: 1;\n display: inline-block;\n width: 12px;\n height: 12px;\n margin-top: -6px;\n color: rgba(0, 0, 0, 0.25);\n font-size: 12px;\n font-style: normal;\n line-height: 1;\n text-align: center;\n text-transform: none;\n background: #fff;\n cursor: pointer;\n opacity: 0;\n transition: color 0.3s ease, opacity 0.15s ease;\n text-rendering: auto;\n}\n.du-bois-light-select-clear::before {\n display: block;\n}\n.du-bois-light-select-clear:hover {\n color: #5F7281;\n}\n.du-bois-light-select:hover .du-bois-light-select-clear {\n opacity: 1;\n}\n.du-bois-light-select-dropdown {\n margin: 0;\n padding: 0;\n color: #11171C;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: 'tnum';\n position: absolute;\n top: -9999px;\n left: -9999px;\n z-index: 1050;\n box-sizing: border-box;\n padding: 4px 0;\n overflow: hidden;\n font-size: 13px;\n font-variant: initial;\n background-color: #fff;\n border-radius: 4px;\n outline: none;\n box-shadow: 0 3px 6px -4px rgba(0, 0, 0, 0.12), 0 6px 16px 0 rgba(0, 0, 0, 0.08), 0 9px 28px 8px rgba(0, 0, 0, 0.05);\n}\n.du-bois-light-select-dropdown.du-bois-light-slide-up-enter.du-bois-light-slide-up-enter-active.du-bois-light-select-dropdown-placement-bottomLeft,\n.du-bois-light-select-dropdown.du-bois-light-slide-up-appear.du-bois-light-slide-up-appear-active.du-bois-light-select-dropdown-placement-bottomLeft {\n animation-name: antSlideUpIn;\n}\n.du-bois-light-select-dropdown.du-bois-light-slide-up-enter.du-bois-light-slide-up-enter-active.du-bois-light-select-dropdown-placement-topLeft,\n.du-bois-light-select-dropdown.du-bois-light-slide-up-appear.du-bois-light-slide-up-appear-active.du-bois-light-select-dropdown-placement-topLeft {\n animation-name: antSlideDownIn;\n}\n.du-bois-light-select-dropdown.du-bois-light-slide-up-leave.du-bois-light-slide-up-leave-active.du-bois-light-select-dropdown-placement-bottomLeft {\n animation-name: antSlideUpOut;\n}\n.du-bois-light-select-dropdown.du-bois-light-slide-up-leave.du-bois-light-slide-up-leave-active.du-bois-light-select-dropdown-placement-topLeft {\n animation-name: antSlideDownOut;\n}\n.du-bois-light-select-dropdown-hidden {\n display: none;\n}\n.du-bois-light-select-dropdown-empty {\n color: rgba(0, 0, 0, 0.25);\n}\n.du-bois-light-select-item-empty {\n position: relative;\n display: block;\n min-height: 32px;\n padding: 5px 8px;\n color: #11171C;\n font-weight: normal;\n font-size: 13px;\n line-height: 22px;\n color: rgba(0, 0, 0, 0.25);\n}\n.du-bois-light-select-item {\n position: relative;\n display: block;\n min-height: 32px;\n padding: 5px 8px;\n color: #11171C;\n font-weight: normal;\n font-size: 13px;\n line-height: 22px;\n cursor: pointer;\n transition: background 0.3s ease;\n}\n.du-bois-light-select-item-group {\n color: #5F7281;\n font-size: 12px;\n cursor: default;\n}\n.du-bois-light-select-item-option {\n display: flex;\n}\n.du-bois-light-select-item-option-content {\n flex: auto;\n overflow: hidden;\n white-space: nowrap;\n text-overflow: ellipsis;\n}\n.du-bois-light-select-item-option-state {\n flex: none;\n}\n.du-bois-light-select-item-option-active:not(.du-bois-light-select-item-option-disabled) {\n background-color: #f5f5f5;\n}\n.du-bois-light-select-item-option-selected:not(.du-bois-light-select-item-option-disabled) {\n color: #11171C;\n font-weight: 600;\n background-color: #e6f1f5;\n}\n.du-bois-light-select-item-option-selected:not(.du-bois-light-select-item-option-disabled) .du-bois-light-select-item-option-state {\n color: #2272B4;\n}\n.du-bois-light-select-item-option-disabled {\n color: rgba(0, 0, 0, 0.25);\n cursor: not-allowed;\n}\n.du-bois-light-select-item-option-disabled.du-bois-light-select-item-option-selected {\n background-color: #f5f5f5;\n}\n.du-bois-light-select-item-option-grouped {\n padding-left: 16px;\n}\n.du-bois-light-select-lg {\n font-size: 18px;\n}\n.du-bois-light-select-borderless .du-bois-light-select-selector {\n background-color: transparent !important;\n border-color: transparent !important;\n box-shadow: none !important;\n}\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-light-empty {\n margin: 0 8px;\n font-size: 13px;\n line-height: 20px;\n text-align: center;\n}\n.du-bois-light-empty-image {\n height: 100px;\n margin-bottom: 8px;\n}\n.du-bois-light-empty-image img {\n height: 100%;\n}\n.du-bois-light-empty-image svg {\n height: 100%;\n margin: auto;\n}\n.du-bois-light-empty-footer {\n margin-top: 16px;\n}\n.du-bois-light-empty-normal {\n margin: 32px 0;\n color: rgba(0, 0, 0, 0.25);\n}\n.du-bois-light-empty-normal .du-bois-light-empty-image {\n height: 40px;\n}\n.du-bois-light-empty-small {\n margin: 8px 0;\n color: rgba(0, 0, 0, 0.25);\n}\n.du-bois-light-empty-small .du-bois-light-empty-image {\n height: 35px;\n}\n.du-bois-light-empty-img-default-ellipse {\n fill: #f5f5f5;\n fill-opacity: 0.8;\n}\n.du-bois-light-empty-img-default-path-1 {\n fill: #aeb8c2;\n}\n.du-bois-light-empty-img-default-path-2 {\n fill: url(#linearGradient-1);\n}\n.du-bois-light-empty-img-default-path-3 {\n fill: #f5f5f7;\n}\n.du-bois-light-empty-img-default-path-4 {\n fill: #dce0e6;\n}\n.du-bois-light-empty-img-default-path-5 {\n fill: #dce0e6;\n}\n.du-bois-light-empty-img-default-g {\n fill: #fff;\n}\n.du-bois-light-empty-img-simple-ellipse {\n fill: #f5f5f5;\n}\n.du-bois-light-empty-img-simple-g {\n stroke: #d9d9d9;\n}\n.du-bois-light-empty-img-simple-path {\n fill: #fafafa;\n}\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-light-collapse {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #11171C;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: 'tnum';\n background-color: #fafafa;\n border: 1px solid #E8ECF0;\n border-bottom: 0;\n border-radius: 4px;\n}\n.du-bois-light-collapse > .du-bois-light-collapse-item {\n border-bottom: 1px solid #E8ECF0;\n}\n.du-bois-light-collapse > .du-bois-light-collapse-item:last-child,\n.du-bois-light-collapse > .du-bois-light-collapse-item:last-child > .du-bois-light-collapse-header {\n border-radius: 0 0 4px 4px;\n}\n.du-bois-light-collapse > .du-bois-light-collapse-item > .du-bois-light-collapse-header {\n position: relative;\n padding: 8px 8px;\n color: rgba(0, 0, 0, 0.85);\n line-height: 20px;\n cursor: pointer;\n transition: all 0.3s, visibility 0s;\n}\n.du-bois-light-collapse > .du-bois-light-collapse-item > .du-bois-light-collapse-header::before {\n display: table;\n content: '';\n}\n.du-bois-light-collapse > .du-bois-light-collapse-item > .du-bois-light-collapse-header::after {\n display: table;\n clear: both;\n content: '';\n}\n.du-bois-light-collapse > .du-bois-light-collapse-item > .du-bois-light-collapse-header .du-bois-light-collapse-arrow {\n display: inline-block;\n margin-right: 12px;\n font-size: 12px;\n vertical-align: -1px;\n}\n.du-bois-light-collapse > .du-bois-light-collapse-item > .du-bois-light-collapse-header .du-bois-light-collapse-arrow svg {\n transition: transform 0.24s;\n}\n.du-bois-light-collapse > .du-bois-light-collapse-item > .du-bois-light-collapse-header .du-bois-light-collapse-extra {\n float: right;\n}\n.du-bois-light-collapse > .du-bois-light-collapse-item > .du-bois-light-collapse-header:focus {\n outline: none;\n}\n.du-bois-light-collapse > .du-bois-light-collapse-item .du-bois-light-collapse-header-collapsible-only {\n cursor: default;\n}\n.du-bois-light-collapse > .du-bois-light-collapse-item .du-bois-light-collapse-header-collapsible-only .du-bois-light-collapse-header-text {\n cursor: pointer;\n}\n.du-bois-light-collapse > .du-bois-light-collapse-item.du-bois-light-collapse-no-arrow > .du-bois-light-collapse-header {\n padding-left: 12px;\n}\n.du-bois-light-collapse-icon-position-right > .du-bois-light-collapse-item > .du-bois-light-collapse-header {\n padding: 8px 8px;\n padding-right: 40px;\n}\n.du-bois-light-collapse-icon-position-right > .du-bois-light-collapse-item > .du-bois-light-collapse-header .du-bois-light-collapse-arrow {\n position: absolute;\n top: 50%;\n right: 8px;\n left: auto;\n margin: 0;\n transform: translateY(-50%);\n}\n.du-bois-light-collapse-content {\n color: #11171C;\n background-color: #fff;\n border-top: 1px solid #E8ECF0;\n}\n.du-bois-light-collapse-content > .du-bois-light-collapse-content-box {\n padding: 8px;\n}\n.du-bois-light-collapse-content-hidden {\n display: none;\n}\n.du-bois-light-collapse-item:last-child > .du-bois-light-collapse-content {\n border-radius: 0 0 4px 4px;\n}\n.du-bois-light-collapse-borderless {\n background-color: #fafafa;\n border: 0;\n}\n.du-bois-light-collapse-borderless > .du-bois-light-collapse-item {\n border-bottom: 1px solid #E8ECF0;\n}\n.du-bois-light-collapse-borderless > .du-bois-light-collapse-item:last-child,\n.du-bois-light-collapse-borderless > .du-bois-light-collapse-item:last-child .du-bois-light-collapse-header {\n border-radius: 0;\n}\n.du-bois-light-collapse-borderless > .du-bois-light-collapse-item > .du-bois-light-collapse-content {\n background-color: transparent;\n border-top: 0;\n}\n.du-bois-light-collapse-borderless > .du-bois-light-collapse-item > .du-bois-light-collapse-content > .du-bois-light-collapse-content-box {\n padding-top: 4px;\n}\n.du-bois-light-collapse-ghost {\n background-color: transparent;\n border: 0;\n}\n.du-bois-light-collapse-ghost > .du-bois-light-collapse-item {\n border-bottom: 0;\n}\n.du-bois-light-collapse-ghost > .du-bois-light-collapse-item > .du-bois-light-collapse-content {\n background-color: transparent;\n border-top: 0;\n}\n.du-bois-light-collapse-ghost > .du-bois-light-collapse-item > .du-bois-light-collapse-content > .du-bois-light-collapse-content-box {\n padding-top: 12px;\n padding-bottom: 12px;\n}\n.du-bois-light-collapse .du-bois-light-collapse-item-disabled > .du-bois-light-collapse-header,\n.du-bois-light-collapse .du-bois-light-collapse-item-disabled > .du-bois-light-collapse-header > .arrow {\n color: rgba(0, 0, 0, 0.25);\n cursor: not-allowed;\n}\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-light-picker {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #11171C;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: 'tnum';\n padding: 0 12px 0;\n position: relative;\n display: inline-flex;\n align-items: center;\n background: #fff;\n border: 1px solid #E8ECF0;\n border-radius: 4px;\n transition: border 0.3s, box-shadow 0.3s;\n}\n.du-bois-light-picker:hover,\n.du-bois-light-picker-focused {\n border-color: #448dc2;\n border-right-width: 1px !important;\n}\n.du-bois-light-picker-focused {\n border-color: #448dc2;\n border-right-width: 1px !important;\n outline: 0;\n box-shadow: 0 0 0 2px rgba(34, 114, 180, 0.2);\n}\n.du-bois-light-picker.du-bois-light-picker-disabled {\n background: #f5f5f5;\n border-color: #E8ECF0;\n cursor: not-allowed;\n}\n.du-bois-light-picker.du-bois-light-picker-disabled .du-bois-light-picker-suffix {\n color: rgba(0, 0, 0, 0.25);\n}\n.du-bois-light-picker.du-bois-light-picker-borderless {\n background-color: transparent !important;\n border-color: transparent !important;\n box-shadow: none !important;\n}\n.du-bois-light-picker-input {\n position: relative;\n display: inline-flex;\n align-items: center;\n width: 100%;\n}\n.du-bois-light-picker-input > input {\n position: relative;\n display: inline-block;\n width: 100%;\n min-width: 0;\n padding: 5px 12px;\n color: #11171C;\n font-size: 13px;\n line-height: 20px;\n background-color: #fff;\n background-image: none;\n border: 1px solid #E8ECF0;\n border-radius: 4px;\n transition: all 0.3s;\n flex: auto;\n min-width: 1px;\n height: auto;\n padding: 0;\n background: transparent;\n border: 0;\n}\n.du-bois-light-picker-input > input::-moz-placeholder {\n opacity: 1;\n}\n.du-bois-light-picker-input > input::placeholder {\n color: #bfbfbf;\n}\n.du-bois-light-picker-input > input:placeholder-shown {\n text-overflow: ellipsis;\n}\n.du-bois-light-picker-input > input:hover {\n border-color: #448dc2;\n border-right-width: 1px !important;\n}\n.du-bois-light-picker-input > input:focus,\n.du-bois-light-picker-input > input-focused {\n border-color: #448dc2;\n border-right-width: 1px !important;\n outline: 0;\n box-shadow: 0 0 0 2px rgba(34, 114, 180, 0.2);\n}\n.du-bois-light-picker-input > input-disabled {\n color: rgba(0, 0, 0, 0.25);\n background-color: #f5f5f5;\n cursor: not-allowed;\n opacity: 1;\n}\n.du-bois-light-picker-input > input-disabled:hover {\n border-color: #E8ECF0;\n border-right-width: 1px !important;\n}\n.du-bois-light-picker-input > input[disabled] {\n color: rgba(0, 0, 0, 0.25);\n background-color: #f5f5f5;\n cursor: not-allowed;\n opacity: 1;\n}\n.du-bois-light-picker-input > input[disabled]:hover {\n border-color: #E8ECF0;\n border-right-width: 1px !important;\n}\n.du-bois-light-picker-input > input-borderless,\n.du-bois-light-picker-input > input-borderless:hover,\n.du-bois-light-picker-input > input-borderless:focus,\n.du-bois-light-picker-input > input-borderless-focused,\n.du-bois-light-picker-input > input-borderless-disabled,\n.du-bois-light-picker-input > input-borderless[disabled] {\n background-color: transparent;\n border: none;\n box-shadow: none;\n}\ntextarea.du-bois-light-picker-input > input {\n max-width: 100%;\n height: auto;\n min-height: 40px;\n line-height: 20px;\n vertical-align: bottom;\n transition: all 0.3s, height 0s;\n}\n.du-bois-light-picker-input > input-lg {\n padding: 5px 12px;\n font-size: 18px;\n}\n.du-bois-light-picker-input > input-sm {\n padding: 5px 12px;\n}\n.du-bois-light-picker-input > input:focus {\n box-shadow: none;\n}\n.du-bois-light-picker-input > input[disabled] {\n background: transparent;\n}\n.du-bois-light-picker-input:hover .du-bois-light-picker-clear {\n opacity: 1;\n}\n.du-bois-light-picker-input-placeholder > input {\n color: #bfbfbf;\n}\n.du-bois-light-picker-large {\n padding: 0 12px 0;\n}\n.du-bois-light-picker-large .du-bois-light-picker-input > input {\n font-size: 18px;\n}\n.du-bois-light-picker-small {\n padding: 0 12px 0;\n}\n.du-bois-light-picker-suffix {\n align-self: center;\n margin-left: 2px;\n color: rgba(0, 0, 0, 0.25);\n line-height: 1;\n pointer-events: none;\n}\n.du-bois-light-picker-suffix > * {\n vertical-align: top;\n}\n.du-bois-light-picker-clear {\n position: absolute;\n top: 50%;\n right: 0;\n color: rgba(0, 0, 0, 0.25);\n line-height: 1;\n background: #fff;\n transform: translateY(-50%);\n cursor: pointer;\n opacity: 0;\n transition: opacity 0.3s, color 0.3s;\n}\n.du-bois-light-picker-clear > * {\n vertical-align: top;\n}\n.du-bois-light-picker-clear:hover {\n color: #5F7281;\n}\n.du-bois-light-picker-separator {\n position: relative;\n display: inline-block;\n width: 1em;\n height: 18px;\n color: rgba(0, 0, 0, 0.25);\n font-size: 18px;\n vertical-align: top;\n cursor: default;\n}\n.du-bois-light-picker-focused .du-bois-light-picker-separator {\n color: #5F7281;\n}\n.du-bois-light-picker-disabled .du-bois-light-picker-range-separator .du-bois-light-picker-separator {\n cursor: not-allowed;\n}\n.du-bois-light-picker-range {\n position: relative;\n display: inline-flex;\n}\n.du-bois-light-picker-range .du-bois-light-picker-clear {\n right: 12px;\n}\n.du-bois-light-picker-range:hover .du-bois-light-picker-clear {\n opacity: 1;\n}\n.du-bois-light-picker-range .du-bois-light-picker-active-bar {\n bottom: -1px;\n height: 2px;\n margin-left: 12px;\n background: #2272B4;\n opacity: 0;\n transition: all 0.3s ease-out;\n pointer-events: none;\n}\n.du-bois-light-picker-range.du-bois-light-picker-focused .du-bois-light-picker-active-bar {\n opacity: 1;\n}\n.du-bois-light-picker-range-separator {\n align-items: center;\n padding: 0 4px;\n line-height: 1;\n}\n.du-bois-light-picker-range.du-bois-light-picker-small .du-bois-light-picker-clear {\n right: 12px;\n}\n.du-bois-light-picker-range.du-bois-light-picker-small .du-bois-light-picker-active-bar {\n margin-left: 12px;\n}\n.du-bois-light-picker-dropdown {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #11171C;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: 'tnum';\n position: absolute;\n z-index: 1050;\n}\n.du-bois-light-picker-dropdown-hidden {\n display: none;\n}\n.du-bois-light-picker-dropdown-placement-bottomLeft .du-bois-light-picker-range-arrow {\n top: 1.66666667px;\n display: block;\n transform: rotate(-45deg);\n}\n.du-bois-light-picker-dropdown-placement-topLeft .du-bois-light-picker-range-arrow {\n bottom: 1.66666667px;\n display: block;\n transform: rotate(135deg);\n}\n.du-bois-light-picker-dropdown.du-bois-light-slide-up-enter.du-bois-light-slide-up-enter-active.du-bois-light-picker-dropdown-placement-topLeft,\n.du-bois-light-picker-dropdown.du-bois-light-slide-up-enter.du-bois-light-slide-up-enter-active.du-bois-light-picker-dropdown-placement-topRight,\n.du-bois-light-picker-dropdown.du-bois-light-slide-up-appear.du-bois-light-slide-up-appear-active.du-bois-light-picker-dropdown-placement-topLeft,\n.du-bois-light-picker-dropdown.du-bois-light-slide-up-appear.du-bois-light-slide-up-appear-active.du-bois-light-picker-dropdown-placement-topRight {\n animation-name: antSlideDownIn;\n}\n.du-bois-light-picker-dropdown.du-bois-light-slide-up-enter.du-bois-light-slide-up-enter-active.du-bois-light-picker-dropdown-placement-bottomLeft,\n.du-bois-light-picker-dropdown.du-bois-light-slide-up-enter.du-bois-light-slide-up-enter-active.du-bois-light-picker-dropdown-placement-bottomRight,\n.du-bois-light-picker-dropdown.du-bois-light-slide-up-appear.du-bois-light-slide-up-appear-active.du-bois-light-picker-dropdown-placement-bottomLeft,\n.du-bois-light-picker-dropdown.du-bois-light-slide-up-appear.du-bois-light-slide-up-appear-active.du-bois-light-picker-dropdown-placement-bottomRight {\n animation-name: antSlideUpIn;\n}\n.du-bois-light-picker-dropdown.du-bois-light-slide-up-leave.du-bois-light-slide-up-leave-active.du-bois-light-picker-dropdown-placement-topLeft,\n.du-bois-light-picker-dropdown.du-bois-light-slide-up-leave.du-bois-light-slide-up-leave-active.du-bois-light-picker-dropdown-placement-topRight {\n animation-name: antSlideDownOut;\n}\n.du-bois-light-picker-dropdown.du-bois-light-slide-up-leave.du-bois-light-slide-up-leave-active.du-bois-light-picker-dropdown-placement-bottomLeft,\n.du-bois-light-picker-dropdown.du-bois-light-slide-up-leave.du-bois-light-slide-up-leave-active.du-bois-light-picker-dropdown-placement-bottomRight {\n animation-name: antSlideUpOut;\n}\n.du-bois-light-picker-dropdown-range {\n padding: 6.66666667px 0;\n}\n.du-bois-light-picker-dropdown-range-hidden {\n display: none;\n}\n.du-bois-light-picker-dropdown .du-bois-light-picker-panel > .du-bois-light-picker-time-panel {\n padding-top: 2px;\n}\n.du-bois-light-picker-ranges {\n margin-bottom: 0;\n padding: 2px 8px;\n overflow: hidden;\n line-height: 36px;\n text-align: left;\n list-style: none;\n}\n.du-bois-light-picker-ranges > li {\n display: inline-block;\n}\n.du-bois-light-picker-ranges .du-bois-light-picker-preset > .du-bois-light-tag-blue {\n color: #2272B4;\n background: #e6f1f5;\n border-color: #93c2db;\n cursor: pointer;\n}\n.du-bois-light-picker-ranges .du-bois-light-picker-ok {\n float: right;\n margin-left: 4px;\n}\n.du-bois-light-picker-range-wrapper {\n display: flex;\n}\n.du-bois-light-picker-range-arrow {\n position: absolute;\n z-index: 1;\n display: none;\n width: 10px;\n height: 10px;\n margin-left: 18px;\n box-shadow: 2px -2px 6px rgba(0, 0, 0, 0.06);\n transition: left 0.3s ease-out;\n}\n.du-bois-light-picker-range-arrow::after {\n position: absolute;\n top: 1px;\n right: 1px;\n width: 10px;\n height: 10px;\n border: 5px solid #f0f0f0;\n border-color: #fff #fff transparent transparent;\n content: '';\n}\n.du-bois-light-picker-panel-container {\n overflow: hidden;\n vertical-align: top;\n background: #fff;\n border-radius: 4px;\n box-shadow: 0 3px 6px -4px rgba(0, 0, 0, 0.12), 0 6px 16px 0 rgba(0, 0, 0, 0.08), 0 9px 28px 8px rgba(0, 0, 0, 0.05);\n transition: margin 0.3s;\n}\n.du-bois-light-picker-panel-container .du-bois-light-picker-panels {\n display: inline-flex;\n flex-wrap: nowrap;\n direction: ltr;\n}\n.du-bois-light-picker-panel-container .du-bois-light-picker-panel {\n vertical-align: top;\n background: transparent;\n border-width: 0 0 1px 0;\n border-radius: 0;\n}\n.du-bois-light-picker-panel-container .du-bois-light-picker-panel .du-bois-light-picker-content,\n.du-bois-light-picker-panel-container .du-bois-light-picker-panel table {\n text-align: center;\n}\n.du-bois-light-picker-panel-container .du-bois-light-picker-panel-focused {\n border-color: #f0f0f0;\n}\n.du-bois-light-picker-panel {\n display: inline-flex;\n flex-direction: column;\n text-align: center;\n background: #fff;\n border: 1px solid #f0f0f0;\n border-radius: 4px;\n outline: none;\n}\n.du-bois-light-picker-panel-focused {\n border-color: #2272B4;\n}\n.du-bois-light-picker-decade-panel,\n.du-bois-light-picker-year-panel,\n.du-bois-light-picker-quarter-panel,\n.du-bois-light-picker-month-panel,\n.du-bois-light-picker-week-panel,\n.du-bois-light-picker-date-panel,\n.du-bois-light-picker-time-panel {\n display: flex;\n flex-direction: column;\n width: 272px;\n}\n.du-bois-light-picker-header {\n display: flex;\n padding: 0 4px;\n color: rgba(0, 0, 0, 0.85);\n border-bottom: 1px solid #f0f0f0;\n}\n.du-bois-light-picker-header > * {\n flex: none;\n}\n.du-bois-light-picker-header button {\n padding: 0;\n color: rgba(0, 0, 0, 0.25);\n line-height: 40px;\n background: transparent;\n border: 0;\n cursor: pointer;\n transition: color 0.3s;\n}\n.du-bois-light-picker-header > button {\n min-width: 1.6em;\n font-size: 13px;\n}\n.du-bois-light-picker-header > button:hover {\n color: #11171C;\n}\n.du-bois-light-picker-header-view {\n flex: auto;\n font-weight: 500;\n line-height: 40px;\n}\n.du-bois-light-picker-header-view button {\n color: inherit;\n font-weight: inherit;\n}\n.du-bois-light-picker-header-view button:not(:first-child) {\n margin-left: 4px;\n}\n.du-bois-light-picker-header-view button:hover {\n color: #2272B4;\n}\n.du-bois-light-picker-prev-icon,\n.du-bois-light-picker-next-icon,\n.du-bois-light-picker-super-prev-icon,\n.du-bois-light-picker-super-next-icon {\n position: relative;\n display: inline-block;\n width: 7px;\n height: 7px;\n}\n.du-bois-light-picker-prev-icon::before,\n.du-bois-light-picker-next-icon::before,\n.du-bois-light-picker-super-prev-icon::before,\n.du-bois-light-picker-super-next-icon::before {\n position: absolute;\n top: 0;\n left: 0;\n display: inline-block;\n width: 7px;\n height: 7px;\n border: 0 solid currentColor;\n border-width: 1.5px 0 0 1.5px;\n content: '';\n}\n.du-bois-light-picker-super-prev-icon::after,\n.du-bois-light-picker-super-next-icon::after {\n position: absolute;\n top: 4px;\n left: 4px;\n display: inline-block;\n width: 7px;\n height: 7px;\n border: 0 solid currentColor;\n border-width: 1.5px 0 0 1.5px;\n content: '';\n}\n.du-bois-light-picker-prev-icon,\n.du-bois-light-picker-super-prev-icon {\n transform: rotate(-45deg);\n}\n.du-bois-light-picker-next-icon,\n.du-bois-light-picker-super-next-icon {\n transform: rotate(135deg);\n}\n.du-bois-light-picker-content {\n width: 100%;\n table-layout: fixed;\n border-collapse: collapse;\n}\n.du-bois-light-picker-content th,\n.du-bois-light-picker-content td {\n position: relative;\n min-width: 24px;\n font-weight: 400;\n}\n.du-bois-light-picker-content th {\n height: 30px;\n color: #11171C;\n line-height: 30px;\n}\n.du-bois-light-picker-cell {\n padding: 3px 0;\n color: rgba(0, 0, 0, 0.25);\n cursor: pointer;\n}\n.du-bois-light-picker-cell-in-view {\n color: #11171C;\n}\n.du-bois-light-picker-cell::before {\n position: absolute;\n top: 50%;\n right: 0;\n left: 0;\n z-index: 1;\n height: 24px;\n transform: translateY(-50%);\n content: '';\n}\n.du-bois-light-picker-cell .du-bois-light-picker-cell-inner {\n position: relative;\n z-index: 2;\n display: inline-block;\n min-width: 24px;\n height: 24px;\n line-height: 24px;\n border-radius: 4px;\n transition: background 0.3s, border 0.3s;\n}\n.du-bois-light-picker-cell:hover:not(.du-bois-light-picker-cell-in-view) .du-bois-light-picker-cell-inner,\n.du-bois-light-picker-cell:hover:not(.du-bois-light-picker-cell-selected):not(.du-bois-light-picker-cell-range-start):not(.du-bois-light-picker-cell-range-end):not(.du-bois-light-picker-cell-range-hover-start):not(.du-bois-light-picker-cell-range-hover-end) .du-bois-light-picker-cell-inner {\n background: #f5f5f5;\n}\n.du-bois-light-picker-cell-in-view.du-bois-light-picker-cell-today .du-bois-light-picker-cell-inner::before {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n z-index: 1;\n border: 1px solid #2272B4;\n border-radius: 4px;\n content: '';\n}\n.du-bois-light-picker-cell-in-view.du-bois-light-picker-cell-in-range {\n position: relative;\n}\n.du-bois-light-picker-cell-in-view.du-bois-light-picker-cell-in-range::before {\n background: #e6f1f5;\n}\n.du-bois-light-picker-cell-in-view.du-bois-light-picker-cell-selected .du-bois-light-picker-cell-inner,\n.du-bois-light-picker-cell-in-view.du-bois-light-picker-cell-range-start .du-bois-light-picker-cell-inner,\n.du-bois-light-picker-cell-in-view.du-bois-light-picker-cell-range-end .du-bois-light-picker-cell-inner {\n color: #fff;\n background: #2272B4;\n}\n.du-bois-light-picker-cell-in-view.du-bois-light-picker-cell-range-start:not(.du-bois-light-picker-cell-range-start-single)::before,\n.du-bois-light-picker-cell-in-view.du-bois-light-picker-cell-range-end:not(.du-bois-light-picker-cell-range-end-single)::before {\n background: #e6f1f5;\n}\n.du-bois-light-picker-cell-in-view.du-bois-light-picker-cell-range-start::before {\n left: 50%;\n}\n.du-bois-light-picker-cell-in-view.du-bois-light-picker-cell-range-end::before {\n right: 50%;\n}\n.du-bois-light-picker-cell-in-view.du-bois-light-picker-cell-range-hover-start:not(.du-bois-light-picker-cell-in-range):not(.du-bois-light-picker-cell-range-start):not(.du-bois-light-picker-cell-range-end)::after,\n.du-bois-light-picker-cell-in-view.du-bois-light-picker-cell-range-hover-end:not(.du-bois-light-picker-cell-in-range):not(.du-bois-light-picker-cell-range-start):not(.du-bois-light-picker-cell-range-end)::after,\n.du-bois-light-picker-cell-in-view.du-bois-light-picker-cell-range-hover-start.du-bois-light-picker-cell-range-start-single::after,\n.du-bois-light-picker-cell-in-view.du-bois-light-picker-cell-range-hover-start.du-bois-light-picker-cell-range-start.du-bois-light-picker-cell-range-end.du-bois-light-picker-cell-range-end-near-hover::after,\n.du-bois-light-picker-cell-in-view.du-bois-light-picker-cell-range-hover-end.du-bois-light-picker-cell-range-start.du-bois-light-picker-cell-range-end.du-bois-light-picker-cell-range-start-near-hover::after,\n.du-bois-light-picker-cell-in-view.du-bois-light-picker-cell-range-hover-end.du-bois-light-picker-cell-range-end-single::after,\n.du-bois-light-picker-cell-in-view.du-bois-light-picker-cell-range-hover:not(.du-bois-light-picker-cell-in-range)::after {\n position: absolute;\n top: 50%;\n z-index: 0;\n height: 24px;\n border-top: 1px dashed #5ca4e0;\n border-bottom: 1px dashed #5ca4e0;\n transform: translateY(-50%);\n content: '';\n}\n.du-bois-light-picker-cell-range-hover-start::after,\n.du-bois-light-picker-cell-range-hover-end::after,\n.du-bois-light-picker-cell-range-hover::after {\n right: 0;\n left: 2px;\n}\n.du-bois-light-picker-cell-in-view.du-bois-light-picker-cell-in-range.du-bois-light-picker-cell-range-hover::before,\n.du-bois-light-picker-cell-in-view.du-bois-light-picker-cell-range-start.du-bois-light-picker-cell-range-hover::before,\n.du-bois-light-picker-cell-in-view.du-bois-light-picker-cell-range-end.du-bois-light-picker-cell-range-hover::before,\n.du-bois-light-picker-cell-in-view.du-bois-light-picker-cell-range-start:not(.du-bois-light-picker-cell-range-start-single).du-bois-light-picker-cell-range-hover-start::before,\n.du-bois-light-picker-cell-in-view.du-bois-light-picker-cell-range-end:not(.du-bois-light-picker-cell-range-end-single).du-bois-light-picker-cell-range-hover-end::before,\n.du-bois-light-picker-panel > :not(.du-bois-light-picker-date-panel) .du-bois-light-picker-cell-in-view.du-bois-light-picker-cell-in-range.du-bois-light-picker-cell-range-hover-start::before,\n.du-bois-light-picker-panel > :not(.du-bois-light-picker-date-panel) .du-bois-light-picker-cell-in-view.du-bois-light-picker-cell-in-range.du-bois-light-picker-cell-range-hover-end::before {\n background: #9cc8ec;\n}\n.du-bois-light-picker-cell-in-view.du-bois-light-picker-cell-range-start:not(.du-bois-light-picker-cell-range-start-single):not(.du-bois-light-picker-cell-range-end) .du-bois-light-picker-cell-inner {\n border-radius: 4px 0 0 4px;\n}\n.du-bois-light-picker-cell-in-view.du-bois-light-picker-cell-range-end:not(.du-bois-light-picker-cell-range-end-single):not(.du-bois-light-picker-cell-range-start) .du-bois-light-picker-cell-inner {\n border-radius: 0 4px 4px 0;\n}\n.du-bois-light-picker-date-panel .du-bois-light-picker-cell-in-view.du-bois-light-picker-cell-in-range.du-bois-light-picker-cell-range-hover-start .du-bois-light-picker-cell-inner::after,\n.du-bois-light-picker-date-panel .du-bois-light-picker-cell-in-view.du-bois-light-picker-cell-in-range.du-bois-light-picker-cell-range-hover-end .du-bois-light-picker-cell-inner::after {\n position: absolute;\n top: 0;\n bottom: 0;\n z-index: -1;\n background: #9cc8ec;\n content: '';\n}\n.du-bois-light-picker-date-panel .du-bois-light-picker-cell-in-view.du-bois-light-picker-cell-in-range.du-bois-light-picker-cell-range-hover-start .du-bois-light-picker-cell-inner::after {\n right: -6px;\n left: 0;\n}\n.du-bois-light-picker-date-panel .du-bois-light-picker-cell-in-view.du-bois-light-picker-cell-in-range.du-bois-light-picker-cell-range-hover-end .du-bois-light-picker-cell-inner::after {\n right: 0;\n left: -6px;\n}\n.du-bois-light-picker-cell-range-hover.du-bois-light-picker-cell-range-start::after {\n right: 50%;\n}\n.du-bois-light-picker-cell-range-hover.du-bois-light-picker-cell-range-end::after {\n left: 50%;\n}\ntr > .du-bois-light-picker-cell-in-view.du-bois-light-picker-cell-range-hover:first-child::after,\ntr > .du-bois-light-picker-cell-in-view.du-bois-light-picker-cell-range-hover-end:first-child::after,\n.du-bois-light-picker-cell-in-view.du-bois-light-picker-cell-start.du-bois-light-picker-cell-range-hover-edge-start.du-bois-light-picker-cell-range-hover-edge-start-near-range::after,\n.du-bois-light-picker-cell-in-view.du-bois-light-picker-cell-range-hover-edge-start:not(.du-bois-light-picker-cell-range-hover-edge-start-near-range)::after,\n.du-bois-light-picker-cell-in-view.du-bois-light-picker-cell-range-hover-start::after {\n left: 6px;\n border-left: 1px dashed #5ca4e0;\n border-top-left-radius: 4px;\n border-bottom-left-radius: 4px;\n}\ntr > .du-bois-light-picker-cell-in-view.du-bois-light-picker-cell-range-hover:last-child::after,\ntr > .du-bois-light-picker-cell-in-view.du-bois-light-picker-cell-range-hover-start:last-child::after,\n.du-bois-light-picker-cell-in-view.du-bois-light-picker-cell-end.du-bois-light-picker-cell-range-hover-edge-end.du-bois-light-picker-cell-range-hover-edge-end-near-range::after,\n.du-bois-light-picker-cell-in-view.du-bois-light-picker-cell-range-hover-edge-end:not(.du-bois-light-picker-cell-range-hover-edge-end-near-range)::after,\n.du-bois-light-picker-cell-in-view.du-bois-light-picker-cell-range-hover-end::after {\n right: 6px;\n border-right: 1px dashed #5ca4e0;\n border-top-right-radius: 4px;\n border-bottom-right-radius: 4px;\n}\n.du-bois-light-picker-cell-disabled {\n color: rgba(0, 0, 0, 0.25);\n pointer-events: none;\n}\n.du-bois-light-picker-cell-disabled .du-bois-light-picker-cell-inner {\n background: transparent;\n}\n.du-bois-light-picker-cell-disabled::before {\n background: rgba(0, 0, 0, 0.04);\n}\n.du-bois-light-picker-cell-disabled.du-bois-light-picker-cell-today .du-bois-light-picker-cell-inner::before {\n border-color: rgba(0, 0, 0, 0.25);\n}\n.du-bois-light-picker-decade-panel .du-bois-light-picker-content,\n.du-bois-light-picker-year-panel .du-bois-light-picker-content,\n.du-bois-light-picker-quarter-panel .du-bois-light-picker-content,\n.du-bois-light-picker-month-panel .du-bois-light-picker-content {\n height: 264px;\n}\n.du-bois-light-picker-decade-panel .du-bois-light-picker-cell-inner,\n.du-bois-light-picker-year-panel .du-bois-light-picker-cell-inner,\n.du-bois-light-picker-quarter-panel .du-bois-light-picker-cell-inner,\n.du-bois-light-picker-month-panel .du-bois-light-picker-cell-inner {\n padding: 0 4px;\n}\n.du-bois-light-picker-quarter-panel .du-bois-light-picker-content {\n height: 56px;\n}\n.du-bois-light-picker-footer {\n width: min-content;\n min-width: 100%;\n line-height: 38px;\n text-align: center;\n border-bottom: 1px solid transparent;\n}\n.du-bois-light-picker-panel .du-bois-light-picker-footer {\n border-top: 1px solid #f0f0f0;\n}\n.du-bois-light-picker-footer-extra {\n padding: 0 8px;\n line-height: 38px;\n text-align: left;\n}\n.du-bois-light-picker-footer-extra:not(:last-child) {\n border-bottom: 1px solid #f0f0f0;\n}\n.du-bois-light-picker-now {\n text-align: left;\n}\n.du-bois-light-picker-today-btn {\n color: #2272B4;\n}\n.du-bois-light-picker-today-btn:hover {\n color: #448dc2;\n}\n.du-bois-light-picker-today-btn:active {\n color: #14538f;\n}\n.du-bois-light-picker-today-btn.du-bois-light-picker-today-btn-disabled {\n color: rgba(0, 0, 0, 0.25);\n cursor: not-allowed;\n}\n.du-bois-light-picker-decade-panel .du-bois-light-picker-cell-inner {\n padding: 0 2px;\n}\n.du-bois-light-picker-decade-panel .du-bois-light-picker-cell::before {\n display: none;\n}\n.du-bois-light-picker-year-panel .du-bois-light-picker-body,\n.du-bois-light-picker-quarter-panel .du-bois-light-picker-body,\n.du-bois-light-picker-month-panel .du-bois-light-picker-body {\n padding: 0 4px;\n}\n.du-bois-light-picker-year-panel .du-bois-light-picker-cell-inner,\n.du-bois-light-picker-quarter-panel .du-bois-light-picker-cell-inner,\n.du-bois-light-picker-month-panel .du-bois-light-picker-cell-inner {\n width: 60px;\n}\n.du-bois-light-picker-year-panel .du-bois-light-picker-cell-range-hover-start::after,\n.du-bois-light-picker-quarter-panel .du-bois-light-picker-cell-range-hover-start::after,\n.du-bois-light-picker-month-panel .du-bois-light-picker-cell-range-hover-start::after {\n left: 14px;\n border-left: 1px dashed #5ca4e0;\n border-radius: 4px 0 0 4px;\n}\n.du-bois-light-picker-year-panel .du-bois-light-picker-cell-range-hover-end::after,\n.du-bois-light-picker-quarter-panel .du-bois-light-picker-cell-range-hover-end::after,\n.du-bois-light-picker-month-panel .du-bois-light-picker-cell-range-hover-end::after {\n right: 14px;\n border-right: 1px dashed #5ca4e0;\n border-radius: 0 4px 4px 0;\n}\n.du-bois-light-picker-week-panel .du-bois-light-picker-body {\n padding: 4px 8px;\n}\n.du-bois-light-picker-week-panel .du-bois-light-picker-cell:hover .du-bois-light-picker-cell-inner,\n.du-bois-light-picker-week-panel .du-bois-light-picker-cell-selected .du-bois-light-picker-cell-inner,\n.du-bois-light-picker-week-panel .du-bois-light-picker-cell .du-bois-light-picker-cell-inner {\n background: transparent !important;\n}\n.du-bois-light-picker-week-panel-row td {\n transition: background 0.3s;\n}\n.du-bois-light-picker-week-panel-row:hover td {\n background: #f5f5f5;\n}\n.du-bois-light-picker-week-panel-row-selected td,\n.du-bois-light-picker-week-panel-row-selected:hover td {\n background: #2272B4;\n}\n.du-bois-light-picker-week-panel-row-selected td.du-bois-light-picker-cell-week,\n.du-bois-light-picker-week-panel-row-selected:hover td.du-bois-light-picker-cell-week {\n color: rgba(255, 255, 255, 0.5);\n}\n.du-bois-light-picker-week-panel-row-selected td.du-bois-light-picker-cell-today .du-bois-light-picker-cell-inner::before,\n.du-bois-light-picker-week-panel-row-selected:hover td.du-bois-light-picker-cell-today .du-bois-light-picker-cell-inner::before {\n border-color: #fff;\n}\n.du-bois-light-picker-week-panel-row-selected td .du-bois-light-picker-cell-inner,\n.du-bois-light-picker-week-panel-row-selected:hover td .du-bois-light-picker-cell-inner {\n color: #fff;\n}\n.du-bois-light-picker-date-panel .du-bois-light-picker-body {\n padding: 4px 8px;\n}\n.du-bois-light-picker-date-panel .du-bois-light-picker-content {\n width: 252px;\n}\n.du-bois-light-picker-date-panel .du-bois-light-picker-content th {\n width: 36px;\n}\n.du-bois-light-picker-datetime-panel {\n display: flex;\n}\n.du-bois-light-picker-datetime-panel .du-bois-light-picker-time-panel {\n border-left: 1px solid #f0f0f0;\n}\n.du-bois-light-picker-datetime-panel .du-bois-light-picker-date-panel,\n.du-bois-light-picker-datetime-panel .du-bois-light-picker-time-panel {\n transition: opacity 0.3s;\n}\n.du-bois-light-picker-datetime-panel-active .du-bois-light-picker-date-panel,\n.du-bois-light-picker-datetime-panel-active .du-bois-light-picker-time-panel {\n opacity: 0.3;\n}\n.du-bois-light-picker-datetime-panel-active .du-bois-light-picker-date-panel-active,\n.du-bois-light-picker-datetime-panel-active .du-bois-light-picker-time-panel-active {\n opacity: 1;\n}\n.du-bois-light-picker-time-panel {\n width: auto;\n min-width: auto;\n}\n.du-bois-light-picker-time-panel .du-bois-light-picker-content {\n display: flex;\n flex: auto;\n height: 224px;\n}\n.du-bois-light-picker-time-panel-column {\n flex: 1 0 auto;\n width: 56px;\n margin: 0;\n padding: 0;\n overflow-y: hidden;\n text-align: left;\n list-style: none;\n transition: background 0.3s;\n}\n.du-bois-light-picker-time-panel-column::after {\n display: block;\n height: 196px;\n content: '';\n}\n.du-bois-light-picker-datetime-panel .du-bois-light-picker-time-panel-column::after {\n height: 198px;\n}\n.du-bois-light-picker-time-panel-column:not(:first-child) {\n border-left: 1px solid #f0f0f0;\n}\n.du-bois-light-picker-time-panel-column-active {\n background: rgba(230, 241, 245, 0.2);\n}\n.du-bois-light-picker-time-panel-column:hover {\n overflow-y: auto;\n}\n.du-bois-light-picker-time-panel-column > li {\n margin: 0;\n padding: 0;\n}\n.du-bois-light-picker-time-panel-column > li.du-bois-light-picker-time-panel-cell .du-bois-light-picker-time-panel-cell-inner {\n display: block;\n width: 100%;\n height: 28px;\n margin: 0;\n padding: 0 0 0 14px;\n color: #11171C;\n line-height: 28px;\n border-radius: 0;\n cursor: pointer;\n transition: background 0.3s;\n}\n.du-bois-light-picker-time-panel-column > li.du-bois-light-picker-time-panel-cell .du-bois-light-picker-time-panel-cell-inner:hover {\n background: #f5f5f5;\n}\n.du-bois-light-picker-time-panel-column > li.du-bois-light-picker-time-panel-cell-selected .du-bois-light-picker-time-panel-cell-inner {\n background: #e6f1f5;\n}\n.du-bois-light-picker-time-panel-column > li.du-bois-light-picker-time-panel-cell-disabled .du-bois-light-picker-time-panel-cell-inner {\n color: rgba(0, 0, 0, 0.25);\n background: transparent;\n cursor: not-allowed;\n}\n/* stylelint-disable-next-line */\n_:-ms-fullscreen .du-bois-light-picker-range-wrapper .du-bois-light-picker-month-panel .du-bois-light-picker-cell,\n:root .du-bois-light-picker-range-wrapper .du-bois-light-picker-month-panel .du-bois-light-picker-cell,\n_:-ms-fullscreen .du-bois-light-picker-range-wrapper .du-bois-light-picker-year-panel .du-bois-light-picker-cell,\n:root .du-bois-light-picker-range-wrapper .du-bois-light-picker-year-panel .du-bois-light-picker-cell {\n padding: 21px 0;\n}\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-light-tag {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #11171C;\n font-size: 13px;\n font-variant: tabular-nums;\n list-style: none;\n font-feature-settings: 'tnum';\n display: inline-block;\n height: auto;\n margin-right: 8px;\n padding: 0 7px;\n font-size: 12px;\n line-height: 20px;\n white-space: nowrap;\n background: #fafafa;\n border: 1px solid #E8ECF0;\n border-radius: 4px;\n opacity: 1;\n transition: all 0.3s;\n}\n.du-bois-light-tag,\n.du-bois-light-tag a,\n.du-bois-light-tag a:hover {\n color: #11171C;\n}\n.du-bois-light-tag > a:first-child:last-child {\n display: inline-block;\n margin: 0 -8px;\n padding: 0 8px;\n}\n.du-bois-light-tag-close-icon {\n margin-left: 3px;\n color: #5F7281;\n font-size: 10px;\n cursor: pointer;\n transition: all 0.3s;\n}\n.du-bois-light-tag-close-icon:hover {\n color: rgba(0, 0, 0, 0.85);\n}\n.du-bois-light-tag-has-color {\n border-color: transparent;\n}\n.du-bois-light-tag-has-color,\n.du-bois-light-tag-has-color a,\n.du-bois-light-tag-has-color a:hover,\n.du-bois-light-tag-has-color .anticon-close,\n.du-bois-light-tag-has-color .anticon-close:hover {\n color: #fff;\n}\n.du-bois-light-tag-checkable {\n background-color: transparent;\n border-color: transparent;\n cursor: pointer;\n}\n.du-bois-light-tag-checkable:not(.du-bois-light-tag-checkable-checked):hover {\n color: #2272B4;\n}\n.du-bois-light-tag-checkable:active,\n.du-bois-light-tag-checkable-checked {\n color: #fff;\n}\n.du-bois-light-tag-checkable-checked {\n background-color: #2272B4;\n}\n.du-bois-light-tag-checkable:active {\n background-color: #14538f;\n}\n.du-bois-light-tag-hidden {\n display: none;\n}\n.du-bois-light-tag-pink {\n color: #c41d7f;\n background: #fff0f6;\n border-color: #ffadd2;\n}\n.du-bois-light-tag-pink-inverse {\n color: #fff;\n background: #eb2f96;\n border-color: #eb2f96;\n}\n.du-bois-light-tag-magenta {\n color: #c41d7f;\n background: #fff0f6;\n border-color: #ffadd2;\n}\n.du-bois-light-tag-magenta-inverse {\n color: #fff;\n background: #eb2f96;\n border-color: #eb2f96;\n}\n.du-bois-light-tag-red {\n color: #cf1322;\n background: #fff1f0;\n border-color: #ffa39e;\n}\n.du-bois-light-tag-red-inverse {\n color: #fff;\n background: #f5222d;\n border-color: #f5222d;\n}\n.du-bois-light-tag-volcano {\n color: #d4380d;\n background: #fff2e8;\n border-color: #ffbb96;\n}\n.du-bois-light-tag-volcano-inverse {\n color: #fff;\n background: #fa541c;\n border-color: #fa541c;\n}\n.du-bois-light-tag-orange {\n color: #d46b08;\n background: #fff7e6;\n border-color: #ffd591;\n}\n.du-bois-light-tag-orange-inverse {\n color: #fff;\n background: #fa8c16;\n border-color: #fa8c16;\n}\n.du-bois-light-tag-yellow {\n color: #d4b106;\n background: #feffe6;\n border-color: #fffb8f;\n}\n.du-bois-light-tag-yellow-inverse {\n color: #fff;\n background: #fadb14;\n border-color: #fadb14;\n}\n.du-bois-light-tag-gold {\n color: #d48806;\n background: #fffbe6;\n border-color: #ffe58f;\n}\n.du-bois-light-tag-gold-inverse {\n color: #fff;\n background: #faad14;\n border-color: #faad14;\n}\n.du-bois-light-tag-cyan {\n color: #08979c;\n background: #e6fffb;\n border-color: #87e8de;\n}\n.du-bois-light-tag-cyan-inverse {\n color: #fff;\n background: #13c2c2;\n border-color: #13c2c2;\n}\n.du-bois-light-tag-lime {\n color: #7cb305;\n background: #fcffe6;\n border-color: #eaff8f;\n}\n.du-bois-light-tag-lime-inverse {\n color: #fff;\n background: #a0d911;\n border-color: #a0d911;\n}\n.du-bois-light-tag-green {\n color: #389e0d;\n background: #f6ffed;\n border-color: #b7eb8f;\n}\n.du-bois-light-tag-green-inverse {\n color: #fff;\n background: #52c41a;\n border-color: #52c41a;\n}\n.du-bois-light-tag-blue {\n color: #096dd9;\n background: #e6f7ff;\n border-color: #91d5ff;\n}\n.du-bois-light-tag-blue-inverse {\n color: #fff;\n background: #1890ff;\n border-color: #1890ff;\n}\n.du-bois-light-tag-geekblue {\n color: #1d39c4;\n background: #f0f5ff;\n border-color: #adc6ff;\n}\n.du-bois-light-tag-geekblue-inverse {\n color: #fff;\n background: #2f54eb;\n border-color: #2f54eb;\n}\n.du-bois-light-tag-purple {\n color: #531dab;\n background: #f9f0ff;\n border-color: #d3adf7;\n}\n.du-bois-light-tag-purple-inverse {\n color: #fff;\n background: #722ed1;\n border-color: #722ed1;\n}\n.du-bois-light-tag-success {\n color: #52c41a;\n background: #f6ffed;\n border-color: #b7eb8f;\n}\n.du-bois-light-tag-processing {\n color: #1890ff;\n background: #e6f7ff;\n border-color: #91d5ff;\n}\n.du-bois-light-tag-error {\n color: #f5222d;\n background: #fff1f0;\n border-color: #ffa39e;\n}\n.du-bois-light-tag-warning {\n color: #fa8c16;\n background: #fff7e6;\n border-color: #ffd591;\n}\n.du-bois-light-tag > .anticon + span,\n.du-bois-light-tag > span + .anticon {\n margin-left: 7px;\n}\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-light-dropdown-menu-item.du-bois-light-dropdown-menu-item-danger {\n color: #C82D4C;\n}\n.du-bois-light-dropdown-menu-item.du-bois-light-dropdown-menu-item-danger:hover {\n color: #fff;\n background-color: #C82D4C;\n}\n.du-bois-light-dropdown {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #11171C;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: 'tnum';\n position: absolute;\n top: -9999px;\n left: -9999px;\n z-index: 1050;\n display: block;\n}\n.du-bois-light-dropdown::before {\n position: absolute;\n top: -4px;\n right: 0;\n bottom: -4px;\n left: -7px;\n z-index: -9999;\n opacity: 0.0001;\n content: ' ';\n}\n.du-bois-light-dropdown-wrap {\n position: relative;\n}\n.du-bois-light-dropdown-wrap .du-bois-light-btn > .anticon-down {\n font-size: 10px;\n}\n.du-bois-light-dropdown-wrap .anticon-down::before {\n transition: transform 0.2s;\n}\n.du-bois-light-dropdown-wrap-open .anticon-down::before {\n transform: rotate(180deg);\n}\n.du-bois-light-dropdown-hidden,\n.du-bois-light-dropdown-menu-hidden,\n.du-bois-light-dropdown-menu-submenu-hidden {\n display: none;\n}\n.du-bois-light-dropdown-show-arrow.du-bois-light-dropdown-placement-topCenter,\n.du-bois-light-dropdown-show-arrow.du-bois-light-dropdown-placement-topLeft,\n.du-bois-light-dropdown-show-arrow.du-bois-light-dropdown-placement-topRight {\n padding-bottom: 10px;\n}\n.du-bois-light-dropdown-show-arrow.du-bois-light-dropdown-placement-bottomCenter,\n.du-bois-light-dropdown-show-arrow.du-bois-light-dropdown-placement-bottomLeft,\n.du-bois-light-dropdown-show-arrow.du-bois-light-dropdown-placement-bottomRight {\n padding-top: 10px;\n}\n.du-bois-light-dropdown-arrow {\n position: absolute;\n z-index: 1;\n display: block;\n width: 8.48528137px;\n height: 8.48528137px;\n background: transparent;\n border-style: solid;\n border-width: 4.24264069px;\n transform: rotate(45deg);\n}\n.du-bois-light-dropdown-placement-topCenter > .du-bois-light-dropdown-arrow,\n.du-bois-light-dropdown-placement-topLeft > .du-bois-light-dropdown-arrow,\n.du-bois-light-dropdown-placement-topRight > .du-bois-light-dropdown-arrow {\n bottom: 6.2px;\n border-top-color: transparent;\n border-right-color: #fff;\n border-bottom-color: #fff;\n border-left-color: transparent;\n box-shadow: 3px 3px 7px rgba(0, 0, 0, 0.07);\n}\n.du-bois-light-dropdown-placement-topCenter > .du-bois-light-dropdown-arrow {\n left: 50%;\n transform: translateX(-50%) rotate(45deg);\n}\n.du-bois-light-dropdown-placement-topLeft > .du-bois-light-dropdown-arrow {\n left: 16px;\n}\n.du-bois-light-dropdown-placement-topRight > .du-bois-light-dropdown-arrow {\n right: 16px;\n}\n.du-bois-light-dropdown-placement-bottomCenter > .du-bois-light-dropdown-arrow,\n.du-bois-light-dropdown-placement-bottomLeft > .du-bois-light-dropdown-arrow,\n.du-bois-light-dropdown-placement-bottomRight > .du-bois-light-dropdown-arrow {\n top: 6px;\n border-top-color: #fff;\n border-right-color: transparent;\n border-bottom-color: transparent;\n border-left-color: #fff;\n box-shadow: -2px -2px 5px rgba(0, 0, 0, 0.06);\n}\n.du-bois-light-dropdown-placement-bottomCenter > .du-bois-light-dropdown-arrow {\n left: 50%;\n transform: translateX(-50%) rotate(45deg);\n}\n.du-bois-light-dropdown-placement-bottomLeft > .du-bois-light-dropdown-arrow {\n left: 16px;\n}\n.du-bois-light-dropdown-placement-bottomRight > .du-bois-light-dropdown-arrow {\n right: 16px;\n}\n.du-bois-light-dropdown-menu {\n position: relative;\n margin: 0;\n padding: 4px 0;\n text-align: left;\n list-style-type: none;\n background-color: #fff;\n background-clip: padding-box;\n border-radius: 4px;\n outline: none;\n box-shadow: 0 3px 6px -4px rgba(0, 0, 0, 0.12), 0 6px 16px 0 rgba(0, 0, 0, 0.08), 0 9px 28px 8px rgba(0, 0, 0, 0.05);\n}\n.du-bois-light-dropdown-menu-item-group-title {\n padding: 5px 8px;\n color: #5F7281;\n transition: all 0.3s;\n}\n.du-bois-light-dropdown-menu-submenu-popup {\n position: absolute;\n z-index: 1050;\n background: transparent;\n box-shadow: none;\n transform-origin: 0 0;\n}\n.du-bois-light-dropdown-menu-submenu-popup ul,\n.du-bois-light-dropdown-menu-submenu-popup li {\n list-style: none;\n}\n.du-bois-light-dropdown-menu-submenu-popup ul {\n margin-right: 0.3em;\n margin-left: 0.3em;\n}\n.du-bois-light-dropdown-menu-item {\n position: relative;\n display: flex;\n align-items: center;\n}\n.du-bois-light-dropdown-menu-item-icon {\n min-width: 12px;\n margin-right: 8px;\n font-size: 12px;\n}\n.du-bois-light-dropdown-menu-title-content {\n flex: auto;\n}\n.du-bois-light-dropdown-menu-title-content > a {\n color: inherit;\n transition: all 0.3s;\n}\n.du-bois-light-dropdown-menu-title-content > a:hover {\n color: inherit;\n}\n.du-bois-light-dropdown-menu-title-content > a::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n content: '';\n}\n.du-bois-light-dropdown-menu-item,\n.du-bois-light-dropdown-menu-submenu-title {\n clear: both;\n margin: 0;\n padding: 5px 8px;\n color: #11171C;\n font-weight: normal;\n font-size: 13px;\n line-height: 22px;\n white-space: nowrap;\n cursor: pointer;\n transition: all 0.3s;\n}\n.du-bois-light-dropdown-menu-item-selected,\n.du-bois-light-dropdown-menu-submenu-title-selected {\n color: #2272B4;\n background-color: #e6f1f5;\n}\n.du-bois-light-dropdown-menu-item:hover,\n.du-bois-light-dropdown-menu-submenu-title:hover {\n background-color: #f5f5f5;\n}\n.du-bois-light-dropdown-menu-item-disabled,\n.du-bois-light-dropdown-menu-submenu-title-disabled {\n color: rgba(0, 0, 0, 0.25);\n cursor: not-allowed;\n}\n.du-bois-light-dropdown-menu-item-disabled:hover,\n.du-bois-light-dropdown-menu-submenu-title-disabled:hover {\n color: rgba(0, 0, 0, 0.25);\n background-color: #fff;\n cursor: not-allowed;\n}\n.du-bois-light-dropdown-menu-item-disabled a,\n.du-bois-light-dropdown-menu-submenu-title-disabled a {\n pointer-events: none;\n}\n.du-bois-light-dropdown-menu-item-divider,\n.du-bois-light-dropdown-menu-submenu-title-divider {\n height: 1px;\n margin: 4px 0;\n overflow: hidden;\n line-height: 0;\n background-color: #f0f0f0;\n}\n.du-bois-light-dropdown-menu-item .du-bois-light-dropdown-menu-submenu-expand-icon,\n.du-bois-light-dropdown-menu-submenu-title .du-bois-light-dropdown-menu-submenu-expand-icon {\n position: absolute;\n right: 4px;\n}\n.du-bois-light-dropdown-menu-item .du-bois-light-dropdown-menu-submenu-expand-icon .du-bois-light-dropdown-menu-submenu-arrow-icon,\n.du-bois-light-dropdown-menu-submenu-title .du-bois-light-dropdown-menu-submenu-expand-icon .du-bois-light-dropdown-menu-submenu-arrow-icon {\n margin-right: 0 !important;\n color: #5F7281;\n font-size: 10px;\n font-style: normal;\n}\n.du-bois-light-dropdown-menu-item-group-list {\n margin: 0 8px;\n padding: 0;\n list-style: none;\n}\n.du-bois-light-dropdown-menu-submenu-title {\n padding-right: 20px;\n}\n.du-bois-light-dropdown-menu-submenu-vertical {\n position: relative;\n}\n.du-bois-light-dropdown-menu-submenu-vertical > .du-bois-light-dropdown-menu {\n position: absolute;\n top: 0;\n left: 100%;\n min-width: 100%;\n margin-left: 4px;\n transform-origin: 0 0;\n}\n.du-bois-light-dropdown-menu-submenu.du-bois-light-dropdown-menu-submenu-disabled .du-bois-light-dropdown-menu-submenu-title,\n.du-bois-light-dropdown-menu-submenu.du-bois-light-dropdown-menu-submenu-disabled .du-bois-light-dropdown-menu-submenu-title .du-bois-light-dropdown-menu-submenu-arrow-icon {\n color: rgba(0, 0, 0, 0.25);\n background-color: #fff;\n cursor: not-allowed;\n}\n.du-bois-light-dropdown-menu-submenu-selected .du-bois-light-dropdown-menu-submenu-title {\n color: #2272B4;\n}\n.du-bois-light-dropdown.du-bois-light-slide-down-enter.du-bois-light-slide-down-enter-active.du-bois-light-dropdown-placement-bottomLeft,\n.du-bois-light-dropdown.du-bois-light-slide-down-appear.du-bois-light-slide-down-appear-active.du-bois-light-dropdown-placement-bottomLeft,\n.du-bois-light-dropdown.du-bois-light-slide-down-enter.du-bois-light-slide-down-enter-active.du-bois-light-dropdown-placement-bottomCenter,\n.du-bois-light-dropdown.du-bois-light-slide-down-appear.du-bois-light-slide-down-appear-active.du-bois-light-dropdown-placement-bottomCenter,\n.du-bois-light-dropdown.du-bois-light-slide-down-enter.du-bois-light-slide-down-enter-active.du-bois-light-dropdown-placement-bottomRight,\n.du-bois-light-dropdown.du-bois-light-slide-down-appear.du-bois-light-slide-down-appear-active.du-bois-light-dropdown-placement-bottomRight {\n animation-name: antSlideUpIn;\n}\n.du-bois-light-dropdown.du-bois-light-slide-up-enter.du-bois-light-slide-up-enter-active.du-bois-light-dropdown-placement-topLeft,\n.du-bois-light-dropdown.du-bois-light-slide-up-appear.du-bois-light-slide-up-appear-active.du-bois-light-dropdown-placement-topLeft,\n.du-bois-light-dropdown.du-bois-light-slide-up-enter.du-bois-light-slide-up-enter-active.du-bois-light-dropdown-placement-topCenter,\n.du-bois-light-dropdown.du-bois-light-slide-up-appear.du-bois-light-slide-up-appear-active.du-bois-light-dropdown-placement-topCenter,\n.du-bois-light-dropdown.du-bois-light-slide-up-enter.du-bois-light-slide-up-enter-active.du-bois-light-dropdown-placement-topRight,\n.du-bois-light-dropdown.du-bois-light-slide-up-appear.du-bois-light-slide-up-appear-active.du-bois-light-dropdown-placement-topRight {\n animation-name: antSlideDownIn;\n}\n.du-bois-light-dropdown.du-bois-light-slide-down-leave.du-bois-light-slide-down-leave-active.du-bois-light-dropdown-placement-bottomLeft,\n.du-bois-light-dropdown.du-bois-light-slide-down-leave.du-bois-light-slide-down-leave-active.du-bois-light-dropdown-placement-bottomCenter,\n.du-bois-light-dropdown.du-bois-light-slide-down-leave.du-bois-light-slide-down-leave-active.du-bois-light-dropdown-placement-bottomRight {\n animation-name: antSlideUpOut;\n}\n.du-bois-light-dropdown.du-bois-light-slide-up-leave.du-bois-light-slide-up-leave-active.du-bois-light-dropdown-placement-topLeft,\n.du-bois-light-dropdown.du-bois-light-slide-up-leave.du-bois-light-slide-up-leave-active.du-bois-light-dropdown-placement-topCenter,\n.du-bois-light-dropdown.du-bois-light-slide-up-leave.du-bois-light-slide-up-leave-active.du-bois-light-dropdown-placement-topRight {\n animation-name: antSlideDownOut;\n}\n.du-bois-light-dropdown-trigger > .anticon.anticon-down,\n.du-bois-light-dropdown-link > .anticon.anticon-down,\n.du-bois-light-dropdown-button > .anticon.anticon-down {\n font-size: 10px;\n vertical-align: baseline;\n}\n.du-bois-light-dropdown-button {\n white-space: nowrap;\n}\n.du-bois-light-dropdown-button.du-bois-light-btn-group > .du-bois-light-btn:last-child:not(:first-child):not(.du-bois-light-btn-icon-only) {\n padding-right: 4px;\n padding-left: 4px;\n}\n.du-bois-light-dropdown-menu-dark,\n.du-bois-light-dropdown-menu-dark .du-bois-light-dropdown-menu {\n background: #001529;\n}\n.du-bois-light-dropdown-menu-dark .du-bois-light-dropdown-menu-item,\n.du-bois-light-dropdown-menu-dark .du-bois-light-dropdown-menu-submenu-title,\n.du-bois-light-dropdown-menu-dark .du-bois-light-dropdown-menu-item > a,\n.du-bois-light-dropdown-menu-dark .du-bois-light-dropdown-menu-item > .anticon + span > a {\n color: rgba(255, 255, 255, 0.65);\n}\n.du-bois-light-dropdown-menu-dark .du-bois-light-dropdown-menu-item .du-bois-light-dropdown-menu-submenu-arrow::after,\n.du-bois-light-dropdown-menu-dark .du-bois-light-dropdown-menu-submenu-title .du-bois-light-dropdown-menu-submenu-arrow::after,\n.du-bois-light-dropdown-menu-dark .du-bois-light-dropdown-menu-item > a .du-bois-light-dropdown-menu-submenu-arrow::after,\n.du-bois-light-dropdown-menu-dark .du-bois-light-dropdown-menu-item > .anticon + span > a .du-bois-light-dropdown-menu-submenu-arrow::after {\n color: rgba(255, 255, 255, 0.65);\n}\n.du-bois-light-dropdown-menu-dark .du-bois-light-dropdown-menu-item:hover,\n.du-bois-light-dropdown-menu-dark .du-bois-light-dropdown-menu-submenu-title:hover,\n.du-bois-light-dropdown-menu-dark .du-bois-light-dropdown-menu-item > a:hover,\n.du-bois-light-dropdown-menu-dark .du-bois-light-dropdown-menu-item > .anticon + span > a:hover {\n color: #fff;\n background: transparent;\n}\n.du-bois-light-dropdown-menu-dark .du-bois-light-dropdown-menu-item-selected,\n.du-bois-light-dropdown-menu-dark .du-bois-light-dropdown-menu-item-selected:hover,\n.du-bois-light-dropdown-menu-dark .du-bois-light-dropdown-menu-item-selected > a {\n color: #fff;\n background: #2272B4;\n}\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-light-form-item .du-bois-light-mentions,\n.du-bois-light-form-item textarea.du-bois-light-input {\n height: auto;\n}\n.du-bois-light-form-item .du-bois-light-upload {\n background: transparent;\n}\n.du-bois-light-form-item .du-bois-light-upload.du-bois-light-upload-drag {\n background: #fafafa;\n}\n.du-bois-light-form-item input[type='radio'],\n.du-bois-light-form-item input[type='checkbox'] {\n width: 14px;\n height: 14px;\n}\n.du-bois-light-form-item .du-bois-light-radio-inline,\n.du-bois-light-form-item .du-bois-light-checkbox-inline {\n display: inline-block;\n margin-left: 8px;\n font-weight: normal;\n vertical-align: middle;\n cursor: pointer;\n}\n.du-bois-light-form-item .du-bois-light-radio-inline:first-child,\n.du-bois-light-form-item .du-bois-light-checkbox-inline:first-child {\n margin-left: 0;\n}\n.du-bois-light-form-item .du-bois-light-checkbox-vertical,\n.du-bois-light-form-item .du-bois-light-radio-vertical {\n display: block;\n}\n.du-bois-light-form-item .du-bois-light-checkbox-vertical + .du-bois-light-checkbox-vertical,\n.du-bois-light-form-item .du-bois-light-radio-vertical + .du-bois-light-radio-vertical {\n margin-left: 0;\n}\n.du-bois-light-form-item .du-bois-light-input-number + .du-bois-light-form-text {\n margin-left: 8px;\n}\n.du-bois-light-form-item .du-bois-light-input-number-handler-wrap {\n z-index: 2;\n}\n.du-bois-light-form-item .du-bois-light-select,\n.du-bois-light-form-item .du-bois-light-cascader-picker {\n width: 100%;\n}\n.du-bois-light-form-item .du-bois-light-picker-calendar-year-select,\n.du-bois-light-form-item .du-bois-light-picker-calendar-month-select,\n.du-bois-light-form-item .du-bois-light-input-group .du-bois-light-select,\n.du-bois-light-form-item .du-bois-light-input-group .du-bois-light-cascader-picker {\n width: auto;\n}\n.du-bois-light-form-inline {\n display: flex;\n flex-wrap: wrap;\n}\n.du-bois-light-form-inline .du-bois-light-form-item {\n flex: none;\n flex-wrap: nowrap;\n margin-right: 16px;\n margin-bottom: 0;\n}\n.du-bois-light-form-inline .du-bois-light-form-item-with-help {\n margin-bottom: 24px;\n}\n.du-bois-light-form-inline .du-bois-light-form-item > .du-bois-light-form-item-label,\n.du-bois-light-form-inline .du-bois-light-form-item > .du-bois-light-form-item-control {\n display: inline-block;\n vertical-align: top;\n}\n.du-bois-light-form-inline .du-bois-light-form-item > .du-bois-light-form-item-label {\n flex: none;\n}\n.du-bois-light-form-inline .du-bois-light-form-item .du-bois-light-form-text {\n display: inline-block;\n}\n.du-bois-light-form-inline .du-bois-light-form-item .du-bois-light-form-item-has-feedback {\n display: inline-block;\n}\n.du-bois-light-form-horizontal .du-bois-light-form-item-label {\n flex-grow: 0;\n}\n.du-bois-light-form-horizontal .du-bois-light-form-item-control {\n flex: 1 1 0;\n}\n.du-bois-light-form-vertical .du-bois-light-form-item {\n flex-direction: column;\n}\n.du-bois-light-form-vertical .du-bois-light-form-item-label > label {\n height: auto;\n}\n.du-bois-light-form-vertical .du-bois-light-form-item-label,\n.du-bois-light-col-24.du-bois-light-form-item-label,\n.du-bois-light-col-xl-24.du-bois-light-form-item-label {\n padding: 0 0 8px;\n line-height: 20px;\n white-space: initial;\n text-align: left;\n}\n.du-bois-light-form-vertical .du-bois-light-form-item-label > label,\n.du-bois-light-col-24.du-bois-light-form-item-label > label,\n.du-bois-light-col-xl-24.du-bois-light-form-item-label > label {\n margin: 0;\n}\n.du-bois-light-form-vertical .du-bois-light-form-item-label > label::after,\n.du-bois-light-col-24.du-bois-light-form-item-label > label::after,\n.du-bois-light-col-xl-24.du-bois-light-form-item-label > label::after {\n display: none;\n}\n@media (max-width: 575px) {\n .du-bois-light-form-item .du-bois-light-form-item-label {\n padding: 0 0 8px;\n line-height: 20px;\n white-space: initial;\n text-align: left;\n }\n .du-bois-light-form-item .du-bois-light-form-item-label > label {\n margin: 0;\n }\n .du-bois-light-form-item .du-bois-light-form-item-label > label::after {\n display: none;\n }\n .du-bois-light-form .du-bois-light-form-item {\n flex-wrap: wrap;\n }\n .du-bois-light-form .du-bois-light-form-item .du-bois-light-form-item-label,\n .du-bois-light-form .du-bois-light-form-item .du-bois-light-form-item-control {\n flex: 0 0 100%;\n max-width: 100%;\n }\n .du-bois-light-col-xs-24.du-bois-light-form-item-label {\n padding: 0 0 8px;\n line-height: 20px;\n white-space: initial;\n text-align: left;\n }\n .du-bois-light-col-xs-24.du-bois-light-form-item-label > label {\n margin: 0;\n }\n .du-bois-light-col-xs-24.du-bois-light-form-item-label > label::after {\n display: none;\n }\n}\n@media (max-width: 767px) {\n .du-bois-light-col-sm-24.du-bois-light-form-item-label {\n padding: 0 0 8px;\n line-height: 20px;\n white-space: initial;\n text-align: left;\n }\n .du-bois-light-col-sm-24.du-bois-light-form-item-label > label {\n margin: 0;\n }\n .du-bois-light-col-sm-24.du-bois-light-form-item-label > label::after {\n display: none;\n }\n}\n@media (max-width: 991px) {\n .du-bois-light-col-md-24.du-bois-light-form-item-label {\n padding: 0 0 8px;\n line-height: 20px;\n white-space: initial;\n text-align: left;\n }\n .du-bois-light-col-md-24.du-bois-light-form-item-label > label {\n margin: 0;\n }\n .du-bois-light-col-md-24.du-bois-light-form-item-label > label::after {\n display: none;\n }\n}\n@media (max-width: 1199px) {\n .du-bois-light-col-lg-24.du-bois-light-form-item-label {\n padding: 0 0 8px;\n line-height: 20px;\n white-space: initial;\n text-align: left;\n }\n .du-bois-light-col-lg-24.du-bois-light-form-item-label > label {\n margin: 0;\n }\n .du-bois-light-col-lg-24.du-bois-light-form-item-label > label::after {\n display: none;\n }\n}\n@media (max-width: 1599px) {\n .du-bois-light-col-xl-24.du-bois-light-form-item-label {\n padding: 0 0 8px;\n line-height: 20px;\n white-space: initial;\n text-align: left;\n }\n .du-bois-light-col-xl-24.du-bois-light-form-item-label > label {\n margin: 0;\n }\n .du-bois-light-col-xl-24.du-bois-light-form-item-label > label::after {\n display: none;\n }\n}\n.du-bois-light-form-item {\n /* Some non-status related component style is in `components.less` */\n /* To support leave along ErrorList. We add additional className to handle explain style */\n}\n.du-bois-light-form-item-explain.du-bois-light-form-item-explain-error {\n color: #C82D4C;\n}\n.du-bois-light-form-item-explain.du-bois-light-form-item-explain-warning {\n color: #AF5B23;\n}\n.du-bois-light-form-item-has-feedback .du-bois-light-input {\n padding-right: 24px;\n}\n.du-bois-light-form-item-has-feedback .du-bois-light-input-affix-wrapper .du-bois-light-input-suffix {\n padding-right: 18px;\n}\n.du-bois-light-form-item-has-feedback .du-bois-light-input-search:not(.du-bois-light-input-search-enter-button) .du-bois-light-input-suffix {\n right: 28px;\n}\n.du-bois-light-form-item-has-feedback .du-bois-light-switch {\n margin: 2px 0 4px;\n}\n.du-bois-light-form-item-has-feedback > .du-bois-light-select .du-bois-light-select-arrow,\n.du-bois-light-form-item-has-feedback > .du-bois-light-select .du-bois-light-select-clear,\n.du-bois-light-form-item-has-feedback :not(.du-bois-light-input-group-addon) > .du-bois-light-select .du-bois-light-select-arrow,\n.du-bois-light-form-item-has-feedback :not(.du-bois-light-input-group-addon) > .du-bois-light-select .du-bois-light-select-clear {\n right: 32px;\n}\n.du-bois-light-form-item-has-feedback > .du-bois-light-select .du-bois-light-select-selection-selected-value,\n.du-bois-light-form-item-has-feedback :not(.du-bois-light-input-group-addon) > .du-bois-light-select .du-bois-light-select-selection-selected-value {\n padding-right: 42px;\n}\n.du-bois-light-form-item-has-feedback .du-bois-light-cascader-picker-arrow {\n margin-right: 19px;\n}\n.du-bois-light-form-item-has-feedback .du-bois-light-cascader-picker-clear {\n right: 32px;\n}\n.du-bois-light-form-item-has-feedback .du-bois-light-picker {\n padding-right: 28.9px;\n}\n.du-bois-light-form-item-has-feedback .du-bois-light-picker-large {\n padding-right: 28.9px;\n}\n.du-bois-light-form-item-has-feedback .du-bois-light-picker-small {\n padding-right: 28.9px;\n}\n.du-bois-light-form-item-has-feedback.du-bois-light-form-item-has-success .du-bois-light-form-item-children-icon,\n.du-bois-light-form-item-has-feedback.du-bois-light-form-item-has-warning .du-bois-light-form-item-children-icon,\n.du-bois-light-form-item-has-feedback.du-bois-light-form-item-has-error .du-bois-light-form-item-children-icon,\n.du-bois-light-form-item-has-feedback.du-bois-light-form-item-is-validating .du-bois-light-form-item-children-icon {\n position: absolute;\n top: 50%;\n right: 0;\n z-index: 1;\n width: 40px;\n height: 20px;\n margin-top: -10px;\n font-size: 13px;\n line-height: 20px;\n text-align: center;\n visibility: visible;\n animation: zoomIn 0.3s cubic-bezier(0.12, 0.4, 0.29, 1.46);\n pointer-events: none;\n}\n.du-bois-light-form-item-has-success.du-bois-light-form-item-has-feedback .du-bois-light-form-item-children-icon {\n color: #34824F;\n animation-name: diffZoomIn1 !important;\n}\n.du-bois-light-form-item-has-warning .du-bois-light-form-item-split {\n color: #AF5B23;\n}\n.du-bois-light-form-item-has-warning :not(.du-bois-light-input-disabled).du-bois-light-input,\n.du-bois-light-form-item-has-warning :not(.du-bois-light-input-affix-wrapper-disabled).du-bois-light-input-affix-wrapper,\n.du-bois-light-form-item-has-warning :not(.du-bois-light-input-disabled).du-bois-light-input:hover,\n.du-bois-light-form-item-has-warning :not(.du-bois-light-input-affix-wrapper-disabled).du-bois-light-input-affix-wrapper:hover {\n background-color: #fff;\n border-color: #AF5B23;\n}\n.du-bois-light-form-item-has-warning :not(.du-bois-light-input-disabled).du-bois-light-input:focus,\n.du-bois-light-form-item-has-warning :not(.du-bois-light-input-affix-wrapper-disabled).du-bois-light-input-affix-wrapper:focus,\n.du-bois-light-form-item-has-warning :not(.du-bois-light-input-disabled).du-bois-light-input-focused,\n.du-bois-light-form-item-has-warning :not(.du-bois-light-input-affix-wrapper-disabled).du-bois-light-input-affix-wrapper-focused {\n border-color: #bd7844;\n border-right-width: 1px !important;\n outline: 0;\n box-shadow: 0 0 0 2px rgba(175, 91, 35, 0.2);\n}\n.du-bois-light-form-item-has-warning .du-bois-light-calendar-picker-open .du-bois-light-calendar-picker-input {\n border-color: #bd7844;\n border-right-width: 1px !important;\n outline: 0;\n box-shadow: 0 0 0 2px rgba(175, 91, 35, 0.2);\n}\n.du-bois-light-form-item-has-warning .du-bois-light-input-prefix {\n color: #AF5B23;\n}\n.du-bois-light-form-item-has-warning .du-bois-light-input-group-addon {\n color: #AF5B23;\n border-color: #AF5B23;\n}\n.du-bois-light-form-item-has-warning .has-feedback {\n color: #AF5B23;\n}\n.du-bois-light-form-item-has-warning.du-bois-light-form-item-has-feedback .du-bois-light-form-item-children-icon {\n color: #AF5B23;\n animation-name: diffZoomIn3 !important;\n}\n.du-bois-light-form-item-has-warning .du-bois-light-select:not(.du-bois-light-select-disabled):not(.du-bois-light-select-customize-input) .du-bois-light-select-selector {\n background-color: #fff;\n border-color: #AF5B23 !important;\n}\n.du-bois-light-form-item-has-warning .du-bois-light-select:not(.du-bois-light-select-disabled):not(.du-bois-light-select-customize-input).du-bois-light-select-open .du-bois-light-select-selector,\n.du-bois-light-form-item-has-warning .du-bois-light-select:not(.du-bois-light-select-disabled):not(.du-bois-light-select-customize-input).du-bois-light-select-focused .du-bois-light-select-selector {\n border-color: #bd7844;\n border-right-width: 1px !important;\n outline: 0;\n box-shadow: 0 0 0 2px rgba(175, 91, 35, 0.2);\n}\n.du-bois-light-form-item-has-warning .du-bois-light-input-number,\n.du-bois-light-form-item-has-warning .du-bois-light-picker {\n background-color: #fff;\n border-color: #AF5B23;\n}\n.du-bois-light-form-item-has-warning .du-bois-light-input-number-focused,\n.du-bois-light-form-item-has-warning .du-bois-light-picker-focused,\n.du-bois-light-form-item-has-warning .du-bois-light-input-number:focus,\n.du-bois-light-form-item-has-warning .du-bois-light-picker:focus {\n border-color: #bd7844;\n border-right-width: 1px !important;\n outline: 0;\n box-shadow: 0 0 0 2px rgba(175, 91, 35, 0.2);\n}\n.du-bois-light-form-item-has-warning .du-bois-light-input-number:not([disabled]):hover,\n.du-bois-light-form-item-has-warning .du-bois-light-picker:not([disabled]):hover {\n background-color: #fff;\n border-color: #AF5B23;\n}\n.du-bois-light-form-item-has-warning .du-bois-light-cascader-picker:focus .du-bois-light-cascader-input {\n border-color: #bd7844;\n border-right-width: 1px !important;\n outline: 0;\n box-shadow: 0 0 0 2px rgba(175, 91, 35, 0.2);\n}\n.du-bois-light-form-item-has-error .du-bois-light-form-item-split {\n color: #C82D4C;\n}\n.du-bois-light-form-item-has-error :not(.du-bois-light-input-disabled).du-bois-light-input,\n.du-bois-light-form-item-has-error :not(.du-bois-light-input-affix-wrapper-disabled).du-bois-light-input-affix-wrapper,\n.du-bois-light-form-item-has-error :not(.du-bois-light-input-disabled).du-bois-light-input:hover,\n.du-bois-light-form-item-has-error :not(.du-bois-light-input-affix-wrapper-disabled).du-bois-light-input-affix-wrapper:hover {\n background-color: #fff;\n border-color: #C82D4C;\n}\n.du-bois-light-form-item-has-error :not(.du-bois-light-input-disabled).du-bois-light-input:focus,\n.du-bois-light-form-item-has-error :not(.du-bois-light-input-affix-wrapper-disabled).du-bois-light-input-affix-wrapper:focus,\n.du-bois-light-form-item-has-error :not(.du-bois-light-input-disabled).du-bois-light-input-focused,\n.du-bois-light-form-item-has-error :not(.du-bois-light-input-affix-wrapper-disabled).du-bois-light-input-affix-wrapper-focused {\n border-color: #d45368;\n border-right-width: 1px !important;\n outline: 0;\n box-shadow: 0 0 0 2px rgba(200, 45, 76, 0.2);\n}\n.du-bois-light-form-item-has-error .du-bois-light-calendar-picker-open .du-bois-light-calendar-picker-input {\n border-color: #d45368;\n border-right-width: 1px !important;\n outline: 0;\n box-shadow: 0 0 0 2px rgba(200, 45, 76, 0.2);\n}\n.du-bois-light-form-item-has-error .du-bois-light-input-prefix {\n color: #C82D4C;\n}\n.du-bois-light-form-item-has-error .du-bois-light-input-group-addon {\n color: #C82D4C;\n border-color: #C82D4C;\n}\n.du-bois-light-form-item-has-error .has-feedback {\n color: #C82D4C;\n}\n.du-bois-light-form-item-has-error.du-bois-light-form-item-has-feedback .du-bois-light-form-item-children-icon {\n color: #C82D4C;\n animation-name: diffZoomIn2 !important;\n}\n.du-bois-light-form-item-has-error .du-bois-light-select:not(.du-bois-light-select-disabled):not(.du-bois-light-select-customize-input) .du-bois-light-select-selector {\n background-color: #fff;\n border-color: #C82D4C !important;\n}\n.du-bois-light-form-item-has-error .du-bois-light-select:not(.du-bois-light-select-disabled):not(.du-bois-light-select-customize-input).du-bois-light-select-open .du-bois-light-select-selector,\n.du-bois-light-form-item-has-error .du-bois-light-select:not(.du-bois-light-select-disabled):not(.du-bois-light-select-customize-input).du-bois-light-select-focused .du-bois-light-select-selector {\n border-color: #d45368;\n border-right-width: 1px !important;\n outline: 0;\n box-shadow: 0 0 0 2px rgba(200, 45, 76, 0.2);\n}\n.du-bois-light-form-item-has-error .du-bois-light-input-group-addon .du-bois-light-select.du-bois-light-select-single:not(.du-bois-light-select-customize-input) .du-bois-light-select-selector {\n background-color: inherit;\n border: 0;\n box-shadow: none;\n}\n.du-bois-light-form-item-has-error .du-bois-light-select.du-bois-light-select-auto-complete .du-bois-light-input:focus {\n border-color: #C82D4C;\n}\n.du-bois-light-form-item-has-error .du-bois-light-input-number,\n.du-bois-light-form-item-has-error .du-bois-light-picker {\n background-color: #fff;\n border-color: #C82D4C;\n}\n.du-bois-light-form-item-has-error .du-bois-light-input-number-focused,\n.du-bois-light-form-item-has-error .du-bois-light-picker-focused,\n.du-bois-light-form-item-has-error .du-bois-light-input-number:focus,\n.du-bois-light-form-item-has-error .du-bois-light-picker:focus {\n border-color: #d45368;\n border-right-width: 1px !important;\n outline: 0;\n box-shadow: 0 0 0 2px rgba(200, 45, 76, 0.2);\n}\n.du-bois-light-form-item-has-error .du-bois-light-input-number:not([disabled]):hover,\n.du-bois-light-form-item-has-error .du-bois-light-picker:not([disabled]):hover {\n background-color: #fff;\n border-color: #C82D4C;\n}\n.du-bois-light-form-item-has-error .du-bois-light-mention-wrapper .du-bois-light-mention-editor,\n.du-bois-light-form-item-has-error .du-bois-light-mention-wrapper .du-bois-light-mention-editor:not([disabled]):hover {\n background-color: #fff;\n border-color: #C82D4C;\n}\n.du-bois-light-form-item-has-error .du-bois-light-mention-wrapper.du-bois-light-mention-active:not([disabled]) .du-bois-light-mention-editor,\n.du-bois-light-form-item-has-error .du-bois-light-mention-wrapper .du-bois-light-mention-editor:not([disabled]):focus {\n border-color: #d45368;\n border-right-width: 1px !important;\n outline: 0;\n box-shadow: 0 0 0 2px rgba(200, 45, 76, 0.2);\n}\n.du-bois-light-form-item-has-error .du-bois-light-cascader-picker:hover .du-bois-light-cascader-picker-label:hover + .du-bois-light-cascader-input.du-bois-light-input {\n border-color: #C82D4C;\n}\n.du-bois-light-form-item-has-error .du-bois-light-cascader-picker:focus .du-bois-light-cascader-input {\n background-color: #fff;\n border-color: #d45368;\n border-right-width: 1px !important;\n outline: 0;\n box-shadow: 0 0 0 2px rgba(200, 45, 76, 0.2);\n}\n.du-bois-light-form-item-has-error .du-bois-light-transfer-list {\n border-color: #C82D4C;\n}\n.du-bois-light-form-item-has-error .du-bois-light-transfer-list-search:not([disabled]) {\n border-color: #E8ECF0;\n}\n.du-bois-light-form-item-has-error .du-bois-light-transfer-list-search:not([disabled]):hover {\n border-color: #448dc2;\n border-right-width: 1px !important;\n}\n.du-bois-light-form-item-has-error .du-bois-light-transfer-list-search:not([disabled]):focus {\n border-color: #448dc2;\n border-right-width: 1px !important;\n outline: 0;\n box-shadow: 0 0 0 2px rgba(34, 114, 180, 0.2);\n}\n.du-bois-light-form-item-has-error .du-bois-light-radio-button-wrapper {\n border-color: #C82D4C !important;\n}\n.du-bois-light-form-item-has-error .du-bois-light-radio-button-wrapper:not(:first-child)::before {\n background-color: #C82D4C;\n}\n.du-bois-light-form-item-is-validating.du-bois-light-form-item-has-feedback .du-bois-light-form-item-children-icon {\n display: inline-block;\n color: #2272B4;\n}\n.du-bois-light-form {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #11171C;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: 'tnum';\n}\n.du-bois-light-form legend {\n display: block;\n width: 100%;\n margin-bottom: 20px;\n padding: 0;\n color: #5F7281;\n font-size: 18px;\n line-height: inherit;\n border: 0;\n border-bottom: 1px solid #E8ECF0;\n}\n.du-bois-light-form label {\n font-size: 13px;\n}\n.du-bois-light-form input[type='search'] {\n box-sizing: border-box;\n}\n.du-bois-light-form input[type='radio'],\n.du-bois-light-form input[type='checkbox'] {\n line-height: normal;\n}\n.du-bois-light-form input[type='file'] {\n display: block;\n}\n.du-bois-light-form input[type='range'] {\n display: block;\n width: 100%;\n}\n.du-bois-light-form select[multiple],\n.du-bois-light-form select[size] {\n height: auto;\n}\n.du-bois-light-form input[type='file']:focus,\n.du-bois-light-form input[type='radio']:focus,\n.du-bois-light-form input[type='checkbox']:focus {\n outline: thin dotted;\n outline: 5px auto -webkit-focus-ring-color;\n outline-offset: -2px;\n}\n.du-bois-light-form output {\n display: block;\n padding-top: 15px;\n color: #11171C;\n font-size: 13px;\n line-height: 20px;\n}\n.du-bois-light-form .du-bois-light-form-text {\n display: inline-block;\n padding-right: 8px;\n}\n.du-bois-light-form-small .du-bois-light-form-item-label > label {\n height: 32px;\n}\n.du-bois-light-form-small .du-bois-light-form-item-control-input {\n min-height: 32px;\n}\n.du-bois-light-form-large .du-bois-light-form-item-label > label {\n height: 40px;\n}\n.du-bois-light-form-large .du-bois-light-form-item-control-input {\n min-height: 40px;\n}\n.du-bois-light-form-item {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #11171C;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: 'tnum';\n margin-bottom: 24px;\n vertical-align: top;\n}\n.du-bois-light-form-item-with-help {\n margin-bottom: 0;\n}\n.du-bois-light-form-item-hidden,\n.du-bois-light-form-item-hidden.du-bois-light-row {\n display: none;\n}\n.du-bois-light-form-item-label {\n display: inline-block;\n flex-grow: 0;\n overflow: hidden;\n white-space: nowrap;\n text-align: right;\n vertical-align: middle;\n}\n.du-bois-light-form-item-label-left {\n text-align: left;\n}\n.du-bois-light-form-item-label > label {\n position: relative;\n display: inline-flex;\n align-items: center;\n height: 40px;\n color: rgba(0, 0, 0, 0.85);\n font-size: 13px;\n}\n.du-bois-light-form-item-label > label > .anticon {\n font-size: 13px;\n vertical-align: top;\n}\n.du-bois-light-form-item-label > label.du-bois-light-form-item-required:not(.du-bois-light-form-item-required-mark-optional)::before {\n display: inline-block;\n margin-right: 4px;\n color: #ff4d4f;\n font-size: 13px;\n font-family: SimSun, sans-serif;\n line-height: 1;\n content: '*';\n}\n.du-bois-light-form-hide-required-mark .du-bois-light-form-item-label > label.du-bois-light-form-item-required:not(.du-bois-light-form-item-required-mark-optional)::before {\n display: none;\n}\n.du-bois-light-form-item-label > label .du-bois-light-form-item-optional {\n display: inline-block;\n margin-left: 4px;\n color: #5F7281;\n}\n.du-bois-light-form-hide-required-mark .du-bois-light-form-item-label > label .du-bois-light-form-item-optional {\n display: none;\n}\n.du-bois-light-form-item-label > label .du-bois-light-form-item-tooltip {\n color: #5F7281;\n cursor: help;\n writing-mode: horizontal-tb;\n margin-inline-start: 4px;\n}\n.du-bois-light-form-item-label > label::after {\n content: ':';\n position: relative;\n top: -0.5px;\n margin: 0 8px 0 2px;\n}\n.du-bois-light-form-item-label > label.du-bois-light-form-item-no-colon::after {\n content: ' ';\n}\n.du-bois-light-form-item-control {\n display: flex;\n flex-direction: column;\n flex-grow: 1;\n}\n.du-bois-light-form-item-control:first-child:not([class^='du-bois-light-col-']):not([class*=' du-bois-light-col-']) {\n width: 100%;\n}\n.du-bois-light-form-item-control-input {\n position: relative;\n display: flex;\n align-items: center;\n min-height: 40px;\n}\n.du-bois-light-form-item-control-input-content {\n flex: auto;\n max-width: 100%;\n}\n.du-bois-light-form-item-explain,\n.du-bois-light-form-item-extra {\n clear: both;\n min-height: 24px;\n color: #5F7281;\n font-size: 13px;\n line-height: 20px;\n transition: color 0.3s cubic-bezier(0.215, 0.61, 0.355, 1);\n margin-top: -118px;\n margin-bottom: -118px;\n}\n.du-bois-light-form-item .du-bois-light-input-textarea-show-count::after {\n margin-bottom: -22px;\n}\n.du-bois-light-show-help-enter,\n.du-bois-light-show-help-appear {\n animation-duration: 0.3s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-light-show-help-leave {\n animation-duration: 0.3s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-light-show-help-enter.du-bois-light-show-help-enter-active,\n.du-bois-light-show-help-appear.du-bois-light-show-help-appear-active {\n animation-name: antShowHelpIn;\n animation-play-state: running;\n}\n.du-bois-light-show-help-leave.du-bois-light-show-help-leave-active {\n animation-name: antShowHelpOut;\n animation-play-state: running;\n pointer-events: none;\n}\n.du-bois-light-show-help-enter,\n.du-bois-light-show-help-appear {\n opacity: 0;\n animation-timing-function: cubic-bezier(0.645, 0.045, 0.355, 1);\n}\n.du-bois-light-show-help-leave {\n animation-timing-function: cubic-bezier(0.645, 0.045, 0.355, 1);\n}\n@keyframes antShowHelpIn {\n 0% {\n transform: translateY(-5px);\n opacity: 0;\n }\n 100% {\n transform: translateY(0);\n opacity: 1;\n }\n}\n@keyframes antShowHelpOut {\n}\n@keyframes diffZoomIn1 {\n 0% {\n transform: scale(0);\n opacity: 0;\n }\n 100% {\n transform: scale(1);\n opacity: 1;\n }\n}\n@keyframes diffZoomIn2 {\n 0% {\n transform: scale(0);\n opacity: 0;\n }\n 100% {\n transform: scale(1);\n opacity: 1;\n }\n}\n@keyframes diffZoomIn3 {\n 0% {\n transform: scale(0);\n opacity: 0;\n }\n 100% {\n transform: scale(1);\n opacity: 1;\n }\n}\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-light-row {\n display: flex;\n flex-flow: row wrap;\n}\n.du-bois-light-row::before,\n.du-bois-light-row::after {\n display: flex;\n}\n.du-bois-light-row-no-wrap {\n flex-wrap: nowrap;\n}\n.du-bois-light-row-start {\n justify-content: flex-start;\n}\n.du-bois-light-row-center {\n justify-content: center;\n}\n.du-bois-light-row-end {\n justify-content: flex-end;\n}\n.du-bois-light-row-space-between {\n justify-content: space-between;\n}\n.du-bois-light-row-space-around {\n justify-content: space-around;\n}\n.du-bois-light-row-top {\n align-items: flex-start;\n}\n.du-bois-light-row-middle {\n align-items: center;\n}\n.du-bois-light-row-bottom {\n align-items: flex-end;\n}\n.du-bois-light-col {\n position: relative;\n max-width: 100%;\n min-height: 1px;\n}\n.du-bois-light-col-24 {\n display: block;\n flex: 0 0 100%;\n max-width: 100%;\n}\n.du-bois-light-col-push-24 {\n left: 100%;\n}\n.du-bois-light-col-pull-24 {\n right: 100%;\n}\n.du-bois-light-col-offset-24 {\n margin-left: 100%;\n}\n.du-bois-light-col-order-24 {\n order: 24;\n}\n.du-bois-light-col-23 {\n display: block;\n flex: 0 0 95.83333333%;\n max-width: 95.83333333%;\n}\n.du-bois-light-col-push-23 {\n left: 95.83333333%;\n}\n.du-bois-light-col-pull-23 {\n right: 95.83333333%;\n}\n.du-bois-light-col-offset-23 {\n margin-left: 95.83333333%;\n}\n.du-bois-light-col-order-23 {\n order: 23;\n}\n.du-bois-light-col-22 {\n display: block;\n flex: 0 0 91.66666667%;\n max-width: 91.66666667%;\n}\n.du-bois-light-col-push-22 {\n left: 91.66666667%;\n}\n.du-bois-light-col-pull-22 {\n right: 91.66666667%;\n}\n.du-bois-light-col-offset-22 {\n margin-left: 91.66666667%;\n}\n.du-bois-light-col-order-22 {\n order: 22;\n}\n.du-bois-light-col-21 {\n display: block;\n flex: 0 0 87.5%;\n max-width: 87.5%;\n}\n.du-bois-light-col-push-21 {\n left: 87.5%;\n}\n.du-bois-light-col-pull-21 {\n right: 87.5%;\n}\n.du-bois-light-col-offset-21 {\n margin-left: 87.5%;\n}\n.du-bois-light-col-order-21 {\n order: 21;\n}\n.du-bois-light-col-20 {\n display: block;\n flex: 0 0 83.33333333%;\n max-width: 83.33333333%;\n}\n.du-bois-light-col-push-20 {\n left: 83.33333333%;\n}\n.du-bois-light-col-pull-20 {\n right: 83.33333333%;\n}\n.du-bois-light-col-offset-20 {\n margin-left: 83.33333333%;\n}\n.du-bois-light-col-order-20 {\n order: 20;\n}\n.du-bois-light-col-19 {\n display: block;\n flex: 0 0 79.16666667%;\n max-width: 79.16666667%;\n}\n.du-bois-light-col-push-19 {\n left: 79.16666667%;\n}\n.du-bois-light-col-pull-19 {\n right: 79.16666667%;\n}\n.du-bois-light-col-offset-19 {\n margin-left: 79.16666667%;\n}\n.du-bois-light-col-order-19 {\n order: 19;\n}\n.du-bois-light-col-18 {\n display: block;\n flex: 0 0 75%;\n max-width: 75%;\n}\n.du-bois-light-col-push-18 {\n left: 75%;\n}\n.du-bois-light-col-pull-18 {\n right: 75%;\n}\n.du-bois-light-col-offset-18 {\n margin-left: 75%;\n}\n.du-bois-light-col-order-18 {\n order: 18;\n}\n.du-bois-light-col-17 {\n display: block;\n flex: 0 0 70.83333333%;\n max-width: 70.83333333%;\n}\n.du-bois-light-col-push-17 {\n left: 70.83333333%;\n}\n.du-bois-light-col-pull-17 {\n right: 70.83333333%;\n}\n.du-bois-light-col-offset-17 {\n margin-left: 70.83333333%;\n}\n.du-bois-light-col-order-17 {\n order: 17;\n}\n.du-bois-light-col-16 {\n display: block;\n flex: 0 0 66.66666667%;\n max-width: 66.66666667%;\n}\n.du-bois-light-col-push-16 {\n left: 66.66666667%;\n}\n.du-bois-light-col-pull-16 {\n right: 66.66666667%;\n}\n.du-bois-light-col-offset-16 {\n margin-left: 66.66666667%;\n}\n.du-bois-light-col-order-16 {\n order: 16;\n}\n.du-bois-light-col-15 {\n display: block;\n flex: 0 0 62.5%;\n max-width: 62.5%;\n}\n.du-bois-light-col-push-15 {\n left: 62.5%;\n}\n.du-bois-light-col-pull-15 {\n right: 62.5%;\n}\n.du-bois-light-col-offset-15 {\n margin-left: 62.5%;\n}\n.du-bois-light-col-order-15 {\n order: 15;\n}\n.du-bois-light-col-14 {\n display: block;\n flex: 0 0 58.33333333%;\n max-width: 58.33333333%;\n}\n.du-bois-light-col-push-14 {\n left: 58.33333333%;\n}\n.du-bois-light-col-pull-14 {\n right: 58.33333333%;\n}\n.du-bois-light-col-offset-14 {\n margin-left: 58.33333333%;\n}\n.du-bois-light-col-order-14 {\n order: 14;\n}\n.du-bois-light-col-13 {\n display: block;\n flex: 0 0 54.16666667%;\n max-width: 54.16666667%;\n}\n.du-bois-light-col-push-13 {\n left: 54.16666667%;\n}\n.du-bois-light-col-pull-13 {\n right: 54.16666667%;\n}\n.du-bois-light-col-offset-13 {\n margin-left: 54.16666667%;\n}\n.du-bois-light-col-order-13 {\n order: 13;\n}\n.du-bois-light-col-12 {\n display: block;\n flex: 0 0 50%;\n max-width: 50%;\n}\n.du-bois-light-col-push-12 {\n left: 50%;\n}\n.du-bois-light-col-pull-12 {\n right: 50%;\n}\n.du-bois-light-col-offset-12 {\n margin-left: 50%;\n}\n.du-bois-light-col-order-12 {\n order: 12;\n}\n.du-bois-light-col-11 {\n display: block;\n flex: 0 0 45.83333333%;\n max-width: 45.83333333%;\n}\n.du-bois-light-col-push-11 {\n left: 45.83333333%;\n}\n.du-bois-light-col-pull-11 {\n right: 45.83333333%;\n}\n.du-bois-light-col-offset-11 {\n margin-left: 45.83333333%;\n}\n.du-bois-light-col-order-11 {\n order: 11;\n}\n.du-bois-light-col-10 {\n display: block;\n flex: 0 0 41.66666667%;\n max-width: 41.66666667%;\n}\n.du-bois-light-col-push-10 {\n left: 41.66666667%;\n}\n.du-bois-light-col-pull-10 {\n right: 41.66666667%;\n}\n.du-bois-light-col-offset-10 {\n margin-left: 41.66666667%;\n}\n.du-bois-light-col-order-10 {\n order: 10;\n}\n.du-bois-light-col-9 {\n display: block;\n flex: 0 0 37.5%;\n max-width: 37.5%;\n}\n.du-bois-light-col-push-9 {\n left: 37.5%;\n}\n.du-bois-light-col-pull-9 {\n right: 37.5%;\n}\n.du-bois-light-col-offset-9 {\n margin-left: 37.5%;\n}\n.du-bois-light-col-order-9 {\n order: 9;\n}\n.du-bois-light-col-8 {\n display: block;\n flex: 0 0 33.33333333%;\n max-width: 33.33333333%;\n}\n.du-bois-light-col-push-8 {\n left: 33.33333333%;\n}\n.du-bois-light-col-pull-8 {\n right: 33.33333333%;\n}\n.du-bois-light-col-offset-8 {\n margin-left: 33.33333333%;\n}\n.du-bois-light-col-order-8 {\n order: 8;\n}\n.du-bois-light-col-7 {\n display: block;\n flex: 0 0 29.16666667%;\n max-width: 29.16666667%;\n}\n.du-bois-light-col-push-7 {\n left: 29.16666667%;\n}\n.du-bois-light-col-pull-7 {\n right: 29.16666667%;\n}\n.du-bois-light-col-offset-7 {\n margin-left: 29.16666667%;\n}\n.du-bois-light-col-order-7 {\n order: 7;\n}\n.du-bois-light-col-6 {\n display: block;\n flex: 0 0 25%;\n max-width: 25%;\n}\n.du-bois-light-col-push-6 {\n left: 25%;\n}\n.du-bois-light-col-pull-6 {\n right: 25%;\n}\n.du-bois-light-col-offset-6 {\n margin-left: 25%;\n}\n.du-bois-light-col-order-6 {\n order: 6;\n}\n.du-bois-light-col-5 {\n display: block;\n flex: 0 0 20.83333333%;\n max-width: 20.83333333%;\n}\n.du-bois-light-col-push-5 {\n left: 20.83333333%;\n}\n.du-bois-light-col-pull-5 {\n right: 20.83333333%;\n}\n.du-bois-light-col-offset-5 {\n margin-left: 20.83333333%;\n}\n.du-bois-light-col-order-5 {\n order: 5;\n}\n.du-bois-light-col-4 {\n display: block;\n flex: 0 0 16.66666667%;\n max-width: 16.66666667%;\n}\n.du-bois-light-col-push-4 {\n left: 16.66666667%;\n}\n.du-bois-light-col-pull-4 {\n right: 16.66666667%;\n}\n.du-bois-light-col-offset-4 {\n margin-left: 16.66666667%;\n}\n.du-bois-light-col-order-4 {\n order: 4;\n}\n.du-bois-light-col-3 {\n display: block;\n flex: 0 0 12.5%;\n max-width: 12.5%;\n}\n.du-bois-light-col-push-3 {\n left: 12.5%;\n}\n.du-bois-light-col-pull-3 {\n right: 12.5%;\n}\n.du-bois-light-col-offset-3 {\n margin-left: 12.5%;\n}\n.du-bois-light-col-order-3 {\n order: 3;\n}\n.du-bois-light-col-2 {\n display: block;\n flex: 0 0 8.33333333%;\n max-width: 8.33333333%;\n}\n.du-bois-light-col-push-2 {\n left: 8.33333333%;\n}\n.du-bois-light-col-pull-2 {\n right: 8.33333333%;\n}\n.du-bois-light-col-offset-2 {\n margin-left: 8.33333333%;\n}\n.du-bois-light-col-order-2 {\n order: 2;\n}\n.du-bois-light-col-1 {\n display: block;\n flex: 0 0 4.16666667%;\n max-width: 4.16666667%;\n}\n.du-bois-light-col-push-1 {\n left: 4.16666667%;\n}\n.du-bois-light-col-pull-1 {\n right: 4.16666667%;\n}\n.du-bois-light-col-offset-1 {\n margin-left: 4.16666667%;\n}\n.du-bois-light-col-order-1 {\n order: 1;\n}\n.du-bois-light-col-0 {\n display: none;\n}\n.du-bois-light-col-push-0 {\n left: auto;\n}\n.du-bois-light-col-pull-0 {\n right: auto;\n}\n.du-bois-light-col-push-0 {\n left: auto;\n}\n.du-bois-light-col-pull-0 {\n right: auto;\n}\n.du-bois-light-col-offset-0 {\n margin-left: 0;\n}\n.du-bois-light-col-order-0 {\n order: 0;\n}\n.du-bois-light-col-xs-24 {\n display: block;\n flex: 0 0 100%;\n max-width: 100%;\n}\n.du-bois-light-col-xs-push-24 {\n left: 100%;\n}\n.du-bois-light-col-xs-pull-24 {\n right: 100%;\n}\n.du-bois-light-col-xs-offset-24 {\n margin-left: 100%;\n}\n.du-bois-light-col-xs-order-24 {\n order: 24;\n}\n.du-bois-light-col-xs-23 {\n display: block;\n flex: 0 0 95.83333333%;\n max-width: 95.83333333%;\n}\n.du-bois-light-col-xs-push-23 {\n left: 95.83333333%;\n}\n.du-bois-light-col-xs-pull-23 {\n right: 95.83333333%;\n}\n.du-bois-light-col-xs-offset-23 {\n margin-left: 95.83333333%;\n}\n.du-bois-light-col-xs-order-23 {\n order: 23;\n}\n.du-bois-light-col-xs-22 {\n display: block;\n flex: 0 0 91.66666667%;\n max-width: 91.66666667%;\n}\n.du-bois-light-col-xs-push-22 {\n left: 91.66666667%;\n}\n.du-bois-light-col-xs-pull-22 {\n right: 91.66666667%;\n}\n.du-bois-light-col-xs-offset-22 {\n margin-left: 91.66666667%;\n}\n.du-bois-light-col-xs-order-22 {\n order: 22;\n}\n.du-bois-light-col-xs-21 {\n display: block;\n flex: 0 0 87.5%;\n max-width: 87.5%;\n}\n.du-bois-light-col-xs-push-21 {\n left: 87.5%;\n}\n.du-bois-light-col-xs-pull-21 {\n right: 87.5%;\n}\n.du-bois-light-col-xs-offset-21 {\n margin-left: 87.5%;\n}\n.du-bois-light-col-xs-order-21 {\n order: 21;\n}\n.du-bois-light-col-xs-20 {\n display: block;\n flex: 0 0 83.33333333%;\n max-width: 83.33333333%;\n}\n.du-bois-light-col-xs-push-20 {\n left: 83.33333333%;\n}\n.du-bois-light-col-xs-pull-20 {\n right: 83.33333333%;\n}\n.du-bois-light-col-xs-offset-20 {\n margin-left: 83.33333333%;\n}\n.du-bois-light-col-xs-order-20 {\n order: 20;\n}\n.du-bois-light-col-xs-19 {\n display: block;\n flex: 0 0 79.16666667%;\n max-width: 79.16666667%;\n}\n.du-bois-light-col-xs-push-19 {\n left: 79.16666667%;\n}\n.du-bois-light-col-xs-pull-19 {\n right: 79.16666667%;\n}\n.du-bois-light-col-xs-offset-19 {\n margin-left: 79.16666667%;\n}\n.du-bois-light-col-xs-order-19 {\n order: 19;\n}\n.du-bois-light-col-xs-18 {\n display: block;\n flex: 0 0 75%;\n max-width: 75%;\n}\n.du-bois-light-col-xs-push-18 {\n left: 75%;\n}\n.du-bois-light-col-xs-pull-18 {\n right: 75%;\n}\n.du-bois-light-col-xs-offset-18 {\n margin-left: 75%;\n}\n.du-bois-light-col-xs-order-18 {\n order: 18;\n}\n.du-bois-light-col-xs-17 {\n display: block;\n flex: 0 0 70.83333333%;\n max-width: 70.83333333%;\n}\n.du-bois-light-col-xs-push-17 {\n left: 70.83333333%;\n}\n.du-bois-light-col-xs-pull-17 {\n right: 70.83333333%;\n}\n.du-bois-light-col-xs-offset-17 {\n margin-left: 70.83333333%;\n}\n.du-bois-light-col-xs-order-17 {\n order: 17;\n}\n.du-bois-light-col-xs-16 {\n display: block;\n flex: 0 0 66.66666667%;\n max-width: 66.66666667%;\n}\n.du-bois-light-col-xs-push-16 {\n left: 66.66666667%;\n}\n.du-bois-light-col-xs-pull-16 {\n right: 66.66666667%;\n}\n.du-bois-light-col-xs-offset-16 {\n margin-left: 66.66666667%;\n}\n.du-bois-light-col-xs-order-16 {\n order: 16;\n}\n.du-bois-light-col-xs-15 {\n display: block;\n flex: 0 0 62.5%;\n max-width: 62.5%;\n}\n.du-bois-light-col-xs-push-15 {\n left: 62.5%;\n}\n.du-bois-light-col-xs-pull-15 {\n right: 62.5%;\n}\n.du-bois-light-col-xs-offset-15 {\n margin-left: 62.5%;\n}\n.du-bois-light-col-xs-order-15 {\n order: 15;\n}\n.du-bois-light-col-xs-14 {\n display: block;\n flex: 0 0 58.33333333%;\n max-width: 58.33333333%;\n}\n.du-bois-light-col-xs-push-14 {\n left: 58.33333333%;\n}\n.du-bois-light-col-xs-pull-14 {\n right: 58.33333333%;\n}\n.du-bois-light-col-xs-offset-14 {\n margin-left: 58.33333333%;\n}\n.du-bois-light-col-xs-order-14 {\n order: 14;\n}\n.du-bois-light-col-xs-13 {\n display: block;\n flex: 0 0 54.16666667%;\n max-width: 54.16666667%;\n}\n.du-bois-light-col-xs-push-13 {\n left: 54.16666667%;\n}\n.du-bois-light-col-xs-pull-13 {\n right: 54.16666667%;\n}\n.du-bois-light-col-xs-offset-13 {\n margin-left: 54.16666667%;\n}\n.du-bois-light-col-xs-order-13 {\n order: 13;\n}\n.du-bois-light-col-xs-12 {\n display: block;\n flex: 0 0 50%;\n max-width: 50%;\n}\n.du-bois-light-col-xs-push-12 {\n left: 50%;\n}\n.du-bois-light-col-xs-pull-12 {\n right: 50%;\n}\n.du-bois-light-col-xs-offset-12 {\n margin-left: 50%;\n}\n.du-bois-light-col-xs-order-12 {\n order: 12;\n}\n.du-bois-light-col-xs-11 {\n display: block;\n flex: 0 0 45.83333333%;\n max-width: 45.83333333%;\n}\n.du-bois-light-col-xs-push-11 {\n left: 45.83333333%;\n}\n.du-bois-light-col-xs-pull-11 {\n right: 45.83333333%;\n}\n.du-bois-light-col-xs-offset-11 {\n margin-left: 45.83333333%;\n}\n.du-bois-light-col-xs-order-11 {\n order: 11;\n}\n.du-bois-light-col-xs-10 {\n display: block;\n flex: 0 0 41.66666667%;\n max-width: 41.66666667%;\n}\n.du-bois-light-col-xs-push-10 {\n left: 41.66666667%;\n}\n.du-bois-light-col-xs-pull-10 {\n right: 41.66666667%;\n}\n.du-bois-light-col-xs-offset-10 {\n margin-left: 41.66666667%;\n}\n.du-bois-light-col-xs-order-10 {\n order: 10;\n}\n.du-bois-light-col-xs-9 {\n display: block;\n flex: 0 0 37.5%;\n max-width: 37.5%;\n}\n.du-bois-light-col-xs-push-9 {\n left: 37.5%;\n}\n.du-bois-light-col-xs-pull-9 {\n right: 37.5%;\n}\n.du-bois-light-col-xs-offset-9 {\n margin-left: 37.5%;\n}\n.du-bois-light-col-xs-order-9 {\n order: 9;\n}\n.du-bois-light-col-xs-8 {\n display: block;\n flex: 0 0 33.33333333%;\n max-width: 33.33333333%;\n}\n.du-bois-light-col-xs-push-8 {\n left: 33.33333333%;\n}\n.du-bois-light-col-xs-pull-8 {\n right: 33.33333333%;\n}\n.du-bois-light-col-xs-offset-8 {\n margin-left: 33.33333333%;\n}\n.du-bois-light-col-xs-order-8 {\n order: 8;\n}\n.du-bois-light-col-xs-7 {\n display: block;\n flex: 0 0 29.16666667%;\n max-width: 29.16666667%;\n}\n.du-bois-light-col-xs-push-7 {\n left: 29.16666667%;\n}\n.du-bois-light-col-xs-pull-7 {\n right: 29.16666667%;\n}\n.du-bois-light-col-xs-offset-7 {\n margin-left: 29.16666667%;\n}\n.du-bois-light-col-xs-order-7 {\n order: 7;\n}\n.du-bois-light-col-xs-6 {\n display: block;\n flex: 0 0 25%;\n max-width: 25%;\n}\n.du-bois-light-col-xs-push-6 {\n left: 25%;\n}\n.du-bois-light-col-xs-pull-6 {\n right: 25%;\n}\n.du-bois-light-col-xs-offset-6 {\n margin-left: 25%;\n}\n.du-bois-light-col-xs-order-6 {\n order: 6;\n}\n.du-bois-light-col-xs-5 {\n display: block;\n flex: 0 0 20.83333333%;\n max-width: 20.83333333%;\n}\n.du-bois-light-col-xs-push-5 {\n left: 20.83333333%;\n}\n.du-bois-light-col-xs-pull-5 {\n right: 20.83333333%;\n}\n.du-bois-light-col-xs-offset-5 {\n margin-left: 20.83333333%;\n}\n.du-bois-light-col-xs-order-5 {\n order: 5;\n}\n.du-bois-light-col-xs-4 {\n display: block;\n flex: 0 0 16.66666667%;\n max-width: 16.66666667%;\n}\n.du-bois-light-col-xs-push-4 {\n left: 16.66666667%;\n}\n.du-bois-light-col-xs-pull-4 {\n right: 16.66666667%;\n}\n.du-bois-light-col-xs-offset-4 {\n margin-left: 16.66666667%;\n}\n.du-bois-light-col-xs-order-4 {\n order: 4;\n}\n.du-bois-light-col-xs-3 {\n display: block;\n flex: 0 0 12.5%;\n max-width: 12.5%;\n}\n.du-bois-light-col-xs-push-3 {\n left: 12.5%;\n}\n.du-bois-light-col-xs-pull-3 {\n right: 12.5%;\n}\n.du-bois-light-col-xs-offset-3 {\n margin-left: 12.5%;\n}\n.du-bois-light-col-xs-order-3 {\n order: 3;\n}\n.du-bois-light-col-xs-2 {\n display: block;\n flex: 0 0 8.33333333%;\n max-width: 8.33333333%;\n}\n.du-bois-light-col-xs-push-2 {\n left: 8.33333333%;\n}\n.du-bois-light-col-xs-pull-2 {\n right: 8.33333333%;\n}\n.du-bois-light-col-xs-offset-2 {\n margin-left: 8.33333333%;\n}\n.du-bois-light-col-xs-order-2 {\n order: 2;\n}\n.du-bois-light-col-xs-1 {\n display: block;\n flex: 0 0 4.16666667%;\n max-width: 4.16666667%;\n}\n.du-bois-light-col-xs-push-1 {\n left: 4.16666667%;\n}\n.du-bois-light-col-xs-pull-1 {\n right: 4.16666667%;\n}\n.du-bois-light-col-xs-offset-1 {\n margin-left: 4.16666667%;\n}\n.du-bois-light-col-xs-order-1 {\n order: 1;\n}\n.du-bois-light-col-xs-0 {\n display: none;\n}\n.du-bois-light-col-push-0 {\n left: auto;\n}\n.du-bois-light-col-pull-0 {\n right: auto;\n}\n.du-bois-light-col-xs-push-0 {\n left: auto;\n}\n.du-bois-light-col-xs-pull-0 {\n right: auto;\n}\n.du-bois-light-col-xs-offset-0 {\n margin-left: 0;\n}\n.du-bois-light-col-xs-order-0 {\n order: 0;\n}\n@media (min-width: 576px) {\n .du-bois-light-col-sm-24 {\n display: block;\n flex: 0 0 100%;\n max-width: 100%;\n }\n .du-bois-light-col-sm-push-24 {\n left: 100%;\n }\n .du-bois-light-col-sm-pull-24 {\n right: 100%;\n }\n .du-bois-light-col-sm-offset-24 {\n margin-left: 100%;\n }\n .du-bois-light-col-sm-order-24 {\n order: 24;\n }\n .du-bois-light-col-sm-23 {\n display: block;\n flex: 0 0 95.83333333%;\n max-width: 95.83333333%;\n }\n .du-bois-light-col-sm-push-23 {\n left: 95.83333333%;\n }\n .du-bois-light-col-sm-pull-23 {\n right: 95.83333333%;\n }\n .du-bois-light-col-sm-offset-23 {\n margin-left: 95.83333333%;\n }\n .du-bois-light-col-sm-order-23 {\n order: 23;\n }\n .du-bois-light-col-sm-22 {\n display: block;\n flex: 0 0 91.66666667%;\n max-width: 91.66666667%;\n }\n .du-bois-light-col-sm-push-22 {\n left: 91.66666667%;\n }\n .du-bois-light-col-sm-pull-22 {\n right: 91.66666667%;\n }\n .du-bois-light-col-sm-offset-22 {\n margin-left: 91.66666667%;\n }\n .du-bois-light-col-sm-order-22 {\n order: 22;\n }\n .du-bois-light-col-sm-21 {\n display: block;\n flex: 0 0 87.5%;\n max-width: 87.5%;\n }\n .du-bois-light-col-sm-push-21 {\n left: 87.5%;\n }\n .du-bois-light-col-sm-pull-21 {\n right: 87.5%;\n }\n .du-bois-light-col-sm-offset-21 {\n margin-left: 87.5%;\n }\n .du-bois-light-col-sm-order-21 {\n order: 21;\n }\n .du-bois-light-col-sm-20 {\n display: block;\n flex: 0 0 83.33333333%;\n max-width: 83.33333333%;\n }\n .du-bois-light-col-sm-push-20 {\n left: 83.33333333%;\n }\n .du-bois-light-col-sm-pull-20 {\n right: 83.33333333%;\n }\n .du-bois-light-col-sm-offset-20 {\n margin-left: 83.33333333%;\n }\n .du-bois-light-col-sm-order-20 {\n order: 20;\n }\n .du-bois-light-col-sm-19 {\n display: block;\n flex: 0 0 79.16666667%;\n max-width: 79.16666667%;\n }\n .du-bois-light-col-sm-push-19 {\n left: 79.16666667%;\n }\n .du-bois-light-col-sm-pull-19 {\n right: 79.16666667%;\n }\n .du-bois-light-col-sm-offset-19 {\n margin-left: 79.16666667%;\n }\n .du-bois-light-col-sm-order-19 {\n order: 19;\n }\n .du-bois-light-col-sm-18 {\n display: block;\n flex: 0 0 75%;\n max-width: 75%;\n }\n .du-bois-light-col-sm-push-18 {\n left: 75%;\n }\n .du-bois-light-col-sm-pull-18 {\n right: 75%;\n }\n .du-bois-light-col-sm-offset-18 {\n margin-left: 75%;\n }\n .du-bois-light-col-sm-order-18 {\n order: 18;\n }\n .du-bois-light-col-sm-17 {\n display: block;\n flex: 0 0 70.83333333%;\n max-width: 70.83333333%;\n }\n .du-bois-light-col-sm-push-17 {\n left: 70.83333333%;\n }\n .du-bois-light-col-sm-pull-17 {\n right: 70.83333333%;\n }\n .du-bois-light-col-sm-offset-17 {\n margin-left: 70.83333333%;\n }\n .du-bois-light-col-sm-order-17 {\n order: 17;\n }\n .du-bois-light-col-sm-16 {\n display: block;\n flex: 0 0 66.66666667%;\n max-width: 66.66666667%;\n }\n .du-bois-light-col-sm-push-16 {\n left: 66.66666667%;\n }\n .du-bois-light-col-sm-pull-16 {\n right: 66.66666667%;\n }\n .du-bois-light-col-sm-offset-16 {\n margin-left: 66.66666667%;\n }\n .du-bois-light-col-sm-order-16 {\n order: 16;\n }\n .du-bois-light-col-sm-15 {\n display: block;\n flex: 0 0 62.5%;\n max-width: 62.5%;\n }\n .du-bois-light-col-sm-push-15 {\n left: 62.5%;\n }\n .du-bois-light-col-sm-pull-15 {\n right: 62.5%;\n }\n .du-bois-light-col-sm-offset-15 {\n margin-left: 62.5%;\n }\n .du-bois-light-col-sm-order-15 {\n order: 15;\n }\n .du-bois-light-col-sm-14 {\n display: block;\n flex: 0 0 58.33333333%;\n max-width: 58.33333333%;\n }\n .du-bois-light-col-sm-push-14 {\n left: 58.33333333%;\n }\n .du-bois-light-col-sm-pull-14 {\n right: 58.33333333%;\n }\n .du-bois-light-col-sm-offset-14 {\n margin-left: 58.33333333%;\n }\n .du-bois-light-col-sm-order-14 {\n order: 14;\n }\n .du-bois-light-col-sm-13 {\n display: block;\n flex: 0 0 54.16666667%;\n max-width: 54.16666667%;\n }\n .du-bois-light-col-sm-push-13 {\n left: 54.16666667%;\n }\n .du-bois-light-col-sm-pull-13 {\n right: 54.16666667%;\n }\n .du-bois-light-col-sm-offset-13 {\n margin-left: 54.16666667%;\n }\n .du-bois-light-col-sm-order-13 {\n order: 13;\n }\n .du-bois-light-col-sm-12 {\n display: block;\n flex: 0 0 50%;\n max-width: 50%;\n }\n .du-bois-light-col-sm-push-12 {\n left: 50%;\n }\n .du-bois-light-col-sm-pull-12 {\n right: 50%;\n }\n .du-bois-light-col-sm-offset-12 {\n margin-left: 50%;\n }\n .du-bois-light-col-sm-order-12 {\n order: 12;\n }\n .du-bois-light-col-sm-11 {\n display: block;\n flex: 0 0 45.83333333%;\n max-width: 45.83333333%;\n }\n .du-bois-light-col-sm-push-11 {\n left: 45.83333333%;\n }\n .du-bois-light-col-sm-pull-11 {\n right: 45.83333333%;\n }\n .du-bois-light-col-sm-offset-11 {\n margin-left: 45.83333333%;\n }\n .du-bois-light-col-sm-order-11 {\n order: 11;\n }\n .du-bois-light-col-sm-10 {\n display: block;\n flex: 0 0 41.66666667%;\n max-width: 41.66666667%;\n }\n .du-bois-light-col-sm-push-10 {\n left: 41.66666667%;\n }\n .du-bois-light-col-sm-pull-10 {\n right: 41.66666667%;\n }\n .du-bois-light-col-sm-offset-10 {\n margin-left: 41.66666667%;\n }\n .du-bois-light-col-sm-order-10 {\n order: 10;\n }\n .du-bois-light-col-sm-9 {\n display: block;\n flex: 0 0 37.5%;\n max-width: 37.5%;\n }\n .du-bois-light-col-sm-push-9 {\n left: 37.5%;\n }\n .du-bois-light-col-sm-pull-9 {\n right: 37.5%;\n }\n .du-bois-light-col-sm-offset-9 {\n margin-left: 37.5%;\n }\n .du-bois-light-col-sm-order-9 {\n order: 9;\n }\n .du-bois-light-col-sm-8 {\n display: block;\n flex: 0 0 33.33333333%;\n max-width: 33.33333333%;\n }\n .du-bois-light-col-sm-push-8 {\n left: 33.33333333%;\n }\n .du-bois-light-col-sm-pull-8 {\n right: 33.33333333%;\n }\n .du-bois-light-col-sm-offset-8 {\n margin-left: 33.33333333%;\n }\n .du-bois-light-col-sm-order-8 {\n order: 8;\n }\n .du-bois-light-col-sm-7 {\n display: block;\n flex: 0 0 29.16666667%;\n max-width: 29.16666667%;\n }\n .du-bois-light-col-sm-push-7 {\n left: 29.16666667%;\n }\n .du-bois-light-col-sm-pull-7 {\n right: 29.16666667%;\n }\n .du-bois-light-col-sm-offset-7 {\n margin-left: 29.16666667%;\n }\n .du-bois-light-col-sm-order-7 {\n order: 7;\n }\n .du-bois-light-col-sm-6 {\n display: block;\n flex: 0 0 25%;\n max-width: 25%;\n }\n .du-bois-light-col-sm-push-6 {\n left: 25%;\n }\n .du-bois-light-col-sm-pull-6 {\n right: 25%;\n }\n .du-bois-light-col-sm-offset-6 {\n margin-left: 25%;\n }\n .du-bois-light-col-sm-order-6 {\n order: 6;\n }\n .du-bois-light-col-sm-5 {\n display: block;\n flex: 0 0 20.83333333%;\n max-width: 20.83333333%;\n }\n .du-bois-light-col-sm-push-5 {\n left: 20.83333333%;\n }\n .du-bois-light-col-sm-pull-5 {\n right: 20.83333333%;\n }\n .du-bois-light-col-sm-offset-5 {\n margin-left: 20.83333333%;\n }\n .du-bois-light-col-sm-order-5 {\n order: 5;\n }\n .du-bois-light-col-sm-4 {\n display: block;\n flex: 0 0 16.66666667%;\n max-width: 16.66666667%;\n }\n .du-bois-light-col-sm-push-4 {\n left: 16.66666667%;\n }\n .du-bois-light-col-sm-pull-4 {\n right: 16.66666667%;\n }\n .du-bois-light-col-sm-offset-4 {\n margin-left: 16.66666667%;\n }\n .du-bois-light-col-sm-order-4 {\n order: 4;\n }\n .du-bois-light-col-sm-3 {\n display: block;\n flex: 0 0 12.5%;\n max-width: 12.5%;\n }\n .du-bois-light-col-sm-push-3 {\n left: 12.5%;\n }\n .du-bois-light-col-sm-pull-3 {\n right: 12.5%;\n }\n .du-bois-light-col-sm-offset-3 {\n margin-left: 12.5%;\n }\n .du-bois-light-col-sm-order-3 {\n order: 3;\n }\n .du-bois-light-col-sm-2 {\n display: block;\n flex: 0 0 8.33333333%;\n max-width: 8.33333333%;\n }\n .du-bois-light-col-sm-push-2 {\n left: 8.33333333%;\n }\n .du-bois-light-col-sm-pull-2 {\n right: 8.33333333%;\n }\n .du-bois-light-col-sm-offset-2 {\n margin-left: 8.33333333%;\n }\n .du-bois-light-col-sm-order-2 {\n order: 2;\n }\n .du-bois-light-col-sm-1 {\n display: block;\n flex: 0 0 4.16666667%;\n max-width: 4.16666667%;\n }\n .du-bois-light-col-sm-push-1 {\n left: 4.16666667%;\n }\n .du-bois-light-col-sm-pull-1 {\n right: 4.16666667%;\n }\n .du-bois-light-col-sm-offset-1 {\n margin-left: 4.16666667%;\n }\n .du-bois-light-col-sm-order-1 {\n order: 1;\n }\n .du-bois-light-col-sm-0 {\n display: none;\n }\n .du-bois-light-col-push-0 {\n left: auto;\n }\n .du-bois-light-col-pull-0 {\n right: auto;\n }\n .du-bois-light-col-sm-push-0 {\n left: auto;\n }\n .du-bois-light-col-sm-pull-0 {\n right: auto;\n }\n .du-bois-light-col-sm-offset-0 {\n margin-left: 0;\n }\n .du-bois-light-col-sm-order-0 {\n order: 0;\n }\n}\n@media (min-width: 768px) {\n .du-bois-light-col-md-24 {\n display: block;\n flex: 0 0 100%;\n max-width: 100%;\n }\n .du-bois-light-col-md-push-24 {\n left: 100%;\n }\n .du-bois-light-col-md-pull-24 {\n right: 100%;\n }\n .du-bois-light-col-md-offset-24 {\n margin-left: 100%;\n }\n .du-bois-light-col-md-order-24 {\n order: 24;\n }\n .du-bois-light-col-md-23 {\n display: block;\n flex: 0 0 95.83333333%;\n max-width: 95.83333333%;\n }\n .du-bois-light-col-md-push-23 {\n left: 95.83333333%;\n }\n .du-bois-light-col-md-pull-23 {\n right: 95.83333333%;\n }\n .du-bois-light-col-md-offset-23 {\n margin-left: 95.83333333%;\n }\n .du-bois-light-col-md-order-23 {\n order: 23;\n }\n .du-bois-light-col-md-22 {\n display: block;\n flex: 0 0 91.66666667%;\n max-width: 91.66666667%;\n }\n .du-bois-light-col-md-push-22 {\n left: 91.66666667%;\n }\n .du-bois-light-col-md-pull-22 {\n right: 91.66666667%;\n }\n .du-bois-light-col-md-offset-22 {\n margin-left: 91.66666667%;\n }\n .du-bois-light-col-md-order-22 {\n order: 22;\n }\n .du-bois-light-col-md-21 {\n display: block;\n flex: 0 0 87.5%;\n max-width: 87.5%;\n }\n .du-bois-light-col-md-push-21 {\n left: 87.5%;\n }\n .du-bois-light-col-md-pull-21 {\n right: 87.5%;\n }\n .du-bois-light-col-md-offset-21 {\n margin-left: 87.5%;\n }\n .du-bois-light-col-md-order-21 {\n order: 21;\n }\n .du-bois-light-col-md-20 {\n display: block;\n flex: 0 0 83.33333333%;\n max-width: 83.33333333%;\n }\n .du-bois-light-col-md-push-20 {\n left: 83.33333333%;\n }\n .du-bois-light-col-md-pull-20 {\n right: 83.33333333%;\n }\n .du-bois-light-col-md-offset-20 {\n margin-left: 83.33333333%;\n }\n .du-bois-light-col-md-order-20 {\n order: 20;\n }\n .du-bois-light-col-md-19 {\n display: block;\n flex: 0 0 79.16666667%;\n max-width: 79.16666667%;\n }\n .du-bois-light-col-md-push-19 {\n left: 79.16666667%;\n }\n .du-bois-light-col-md-pull-19 {\n right: 79.16666667%;\n }\n .du-bois-light-col-md-offset-19 {\n margin-left: 79.16666667%;\n }\n .du-bois-light-col-md-order-19 {\n order: 19;\n }\n .du-bois-light-col-md-18 {\n display: block;\n flex: 0 0 75%;\n max-width: 75%;\n }\n .du-bois-light-col-md-push-18 {\n left: 75%;\n }\n .du-bois-light-col-md-pull-18 {\n right: 75%;\n }\n .du-bois-light-col-md-offset-18 {\n margin-left: 75%;\n }\n .du-bois-light-col-md-order-18 {\n order: 18;\n }\n .du-bois-light-col-md-17 {\n display: block;\n flex: 0 0 70.83333333%;\n max-width: 70.83333333%;\n }\n .du-bois-light-col-md-push-17 {\n left: 70.83333333%;\n }\n .du-bois-light-col-md-pull-17 {\n right: 70.83333333%;\n }\n .du-bois-light-col-md-offset-17 {\n margin-left: 70.83333333%;\n }\n .du-bois-light-col-md-order-17 {\n order: 17;\n }\n .du-bois-light-col-md-16 {\n display: block;\n flex: 0 0 66.66666667%;\n max-width: 66.66666667%;\n }\n .du-bois-light-col-md-push-16 {\n left: 66.66666667%;\n }\n .du-bois-light-col-md-pull-16 {\n right: 66.66666667%;\n }\n .du-bois-light-col-md-offset-16 {\n margin-left: 66.66666667%;\n }\n .du-bois-light-col-md-order-16 {\n order: 16;\n }\n .du-bois-light-col-md-15 {\n display: block;\n flex: 0 0 62.5%;\n max-width: 62.5%;\n }\n .du-bois-light-col-md-push-15 {\n left: 62.5%;\n }\n .du-bois-light-col-md-pull-15 {\n right: 62.5%;\n }\n .du-bois-light-col-md-offset-15 {\n margin-left: 62.5%;\n }\n .du-bois-light-col-md-order-15 {\n order: 15;\n }\n .du-bois-light-col-md-14 {\n display: block;\n flex: 0 0 58.33333333%;\n max-width: 58.33333333%;\n }\n .du-bois-light-col-md-push-14 {\n left: 58.33333333%;\n }\n .du-bois-light-col-md-pull-14 {\n right: 58.33333333%;\n }\n .du-bois-light-col-md-offset-14 {\n margin-left: 58.33333333%;\n }\n .du-bois-light-col-md-order-14 {\n order: 14;\n }\n .du-bois-light-col-md-13 {\n display: block;\n flex: 0 0 54.16666667%;\n max-width: 54.16666667%;\n }\n .du-bois-light-col-md-push-13 {\n left: 54.16666667%;\n }\n .du-bois-light-col-md-pull-13 {\n right: 54.16666667%;\n }\n .du-bois-light-col-md-offset-13 {\n margin-left: 54.16666667%;\n }\n .du-bois-light-col-md-order-13 {\n order: 13;\n }\n .du-bois-light-col-md-12 {\n display: block;\n flex: 0 0 50%;\n max-width: 50%;\n }\n .du-bois-light-col-md-push-12 {\n left: 50%;\n }\n .du-bois-light-col-md-pull-12 {\n right: 50%;\n }\n .du-bois-light-col-md-offset-12 {\n margin-left: 50%;\n }\n .du-bois-light-col-md-order-12 {\n order: 12;\n }\n .du-bois-light-col-md-11 {\n display: block;\n flex: 0 0 45.83333333%;\n max-width: 45.83333333%;\n }\n .du-bois-light-col-md-push-11 {\n left: 45.83333333%;\n }\n .du-bois-light-col-md-pull-11 {\n right: 45.83333333%;\n }\n .du-bois-light-col-md-offset-11 {\n margin-left: 45.83333333%;\n }\n .du-bois-light-col-md-order-11 {\n order: 11;\n }\n .du-bois-light-col-md-10 {\n display: block;\n flex: 0 0 41.66666667%;\n max-width: 41.66666667%;\n }\n .du-bois-light-col-md-push-10 {\n left: 41.66666667%;\n }\n .du-bois-light-col-md-pull-10 {\n right: 41.66666667%;\n }\n .du-bois-light-col-md-offset-10 {\n margin-left: 41.66666667%;\n }\n .du-bois-light-col-md-order-10 {\n order: 10;\n }\n .du-bois-light-col-md-9 {\n display: block;\n flex: 0 0 37.5%;\n max-width: 37.5%;\n }\n .du-bois-light-col-md-push-9 {\n left: 37.5%;\n }\n .du-bois-light-col-md-pull-9 {\n right: 37.5%;\n }\n .du-bois-light-col-md-offset-9 {\n margin-left: 37.5%;\n }\n .du-bois-light-col-md-order-9 {\n order: 9;\n }\n .du-bois-light-col-md-8 {\n display: block;\n flex: 0 0 33.33333333%;\n max-width: 33.33333333%;\n }\n .du-bois-light-col-md-push-8 {\n left: 33.33333333%;\n }\n .du-bois-light-col-md-pull-8 {\n right: 33.33333333%;\n }\n .du-bois-light-col-md-offset-8 {\n margin-left: 33.33333333%;\n }\n .du-bois-light-col-md-order-8 {\n order: 8;\n }\n .du-bois-light-col-md-7 {\n display: block;\n flex: 0 0 29.16666667%;\n max-width: 29.16666667%;\n }\n .du-bois-light-col-md-push-7 {\n left: 29.16666667%;\n }\n .du-bois-light-col-md-pull-7 {\n right: 29.16666667%;\n }\n .du-bois-light-col-md-offset-7 {\n margin-left: 29.16666667%;\n }\n .du-bois-light-col-md-order-7 {\n order: 7;\n }\n .du-bois-light-col-md-6 {\n display: block;\n flex: 0 0 25%;\n max-width: 25%;\n }\n .du-bois-light-col-md-push-6 {\n left: 25%;\n }\n .du-bois-light-col-md-pull-6 {\n right: 25%;\n }\n .du-bois-light-col-md-offset-6 {\n margin-left: 25%;\n }\n .du-bois-light-col-md-order-6 {\n order: 6;\n }\n .du-bois-light-col-md-5 {\n display: block;\n flex: 0 0 20.83333333%;\n max-width: 20.83333333%;\n }\n .du-bois-light-col-md-push-5 {\n left: 20.83333333%;\n }\n .du-bois-light-col-md-pull-5 {\n right: 20.83333333%;\n }\n .du-bois-light-col-md-offset-5 {\n margin-left: 20.83333333%;\n }\n .du-bois-light-col-md-order-5 {\n order: 5;\n }\n .du-bois-light-col-md-4 {\n display: block;\n flex: 0 0 16.66666667%;\n max-width: 16.66666667%;\n }\n .du-bois-light-col-md-push-4 {\n left: 16.66666667%;\n }\n .du-bois-light-col-md-pull-4 {\n right: 16.66666667%;\n }\n .du-bois-light-col-md-offset-4 {\n margin-left: 16.66666667%;\n }\n .du-bois-light-col-md-order-4 {\n order: 4;\n }\n .du-bois-light-col-md-3 {\n display: block;\n flex: 0 0 12.5%;\n max-width: 12.5%;\n }\n .du-bois-light-col-md-push-3 {\n left: 12.5%;\n }\n .du-bois-light-col-md-pull-3 {\n right: 12.5%;\n }\n .du-bois-light-col-md-offset-3 {\n margin-left: 12.5%;\n }\n .du-bois-light-col-md-order-3 {\n order: 3;\n }\n .du-bois-light-col-md-2 {\n display: block;\n flex: 0 0 8.33333333%;\n max-width: 8.33333333%;\n }\n .du-bois-light-col-md-push-2 {\n left: 8.33333333%;\n }\n .du-bois-light-col-md-pull-2 {\n right: 8.33333333%;\n }\n .du-bois-light-col-md-offset-2 {\n margin-left: 8.33333333%;\n }\n .du-bois-light-col-md-order-2 {\n order: 2;\n }\n .du-bois-light-col-md-1 {\n display: block;\n flex: 0 0 4.16666667%;\n max-width: 4.16666667%;\n }\n .du-bois-light-col-md-push-1 {\n left: 4.16666667%;\n }\n .du-bois-light-col-md-pull-1 {\n right: 4.16666667%;\n }\n .du-bois-light-col-md-offset-1 {\n margin-left: 4.16666667%;\n }\n .du-bois-light-col-md-order-1 {\n order: 1;\n }\n .du-bois-light-col-md-0 {\n display: none;\n }\n .du-bois-light-col-push-0 {\n left: auto;\n }\n .du-bois-light-col-pull-0 {\n right: auto;\n }\n .du-bois-light-col-md-push-0 {\n left: auto;\n }\n .du-bois-light-col-md-pull-0 {\n right: auto;\n }\n .du-bois-light-col-md-offset-0 {\n margin-left: 0;\n }\n .du-bois-light-col-md-order-0 {\n order: 0;\n }\n}\n@media (min-width: 992px) {\n .du-bois-light-col-lg-24 {\n display: block;\n flex: 0 0 100%;\n max-width: 100%;\n }\n .du-bois-light-col-lg-push-24 {\n left: 100%;\n }\n .du-bois-light-col-lg-pull-24 {\n right: 100%;\n }\n .du-bois-light-col-lg-offset-24 {\n margin-left: 100%;\n }\n .du-bois-light-col-lg-order-24 {\n order: 24;\n }\n .du-bois-light-col-lg-23 {\n display: block;\n flex: 0 0 95.83333333%;\n max-width: 95.83333333%;\n }\n .du-bois-light-col-lg-push-23 {\n left: 95.83333333%;\n }\n .du-bois-light-col-lg-pull-23 {\n right: 95.83333333%;\n }\n .du-bois-light-col-lg-offset-23 {\n margin-left: 95.83333333%;\n }\n .du-bois-light-col-lg-order-23 {\n order: 23;\n }\n .du-bois-light-col-lg-22 {\n display: block;\n flex: 0 0 91.66666667%;\n max-width: 91.66666667%;\n }\n .du-bois-light-col-lg-push-22 {\n left: 91.66666667%;\n }\n .du-bois-light-col-lg-pull-22 {\n right: 91.66666667%;\n }\n .du-bois-light-col-lg-offset-22 {\n margin-left: 91.66666667%;\n }\n .du-bois-light-col-lg-order-22 {\n order: 22;\n }\n .du-bois-light-col-lg-21 {\n display: block;\n flex: 0 0 87.5%;\n max-width: 87.5%;\n }\n .du-bois-light-col-lg-push-21 {\n left: 87.5%;\n }\n .du-bois-light-col-lg-pull-21 {\n right: 87.5%;\n }\n .du-bois-light-col-lg-offset-21 {\n margin-left: 87.5%;\n }\n .du-bois-light-col-lg-order-21 {\n order: 21;\n }\n .du-bois-light-col-lg-20 {\n display: block;\n flex: 0 0 83.33333333%;\n max-width: 83.33333333%;\n }\n .du-bois-light-col-lg-push-20 {\n left: 83.33333333%;\n }\n .du-bois-light-col-lg-pull-20 {\n right: 83.33333333%;\n }\n .du-bois-light-col-lg-offset-20 {\n margin-left: 83.33333333%;\n }\n .du-bois-light-col-lg-order-20 {\n order: 20;\n }\n .du-bois-light-col-lg-19 {\n display: block;\n flex: 0 0 79.16666667%;\n max-width: 79.16666667%;\n }\n .du-bois-light-col-lg-push-19 {\n left: 79.16666667%;\n }\n .du-bois-light-col-lg-pull-19 {\n right: 79.16666667%;\n }\n .du-bois-light-col-lg-offset-19 {\n margin-left: 79.16666667%;\n }\n .du-bois-light-col-lg-order-19 {\n order: 19;\n }\n .du-bois-light-col-lg-18 {\n display: block;\n flex: 0 0 75%;\n max-width: 75%;\n }\n .du-bois-light-col-lg-push-18 {\n left: 75%;\n }\n .du-bois-light-col-lg-pull-18 {\n right: 75%;\n }\n .du-bois-light-col-lg-offset-18 {\n margin-left: 75%;\n }\n .du-bois-light-col-lg-order-18 {\n order: 18;\n }\n .du-bois-light-col-lg-17 {\n display: block;\n flex: 0 0 70.83333333%;\n max-width: 70.83333333%;\n }\n .du-bois-light-col-lg-push-17 {\n left: 70.83333333%;\n }\n .du-bois-light-col-lg-pull-17 {\n right: 70.83333333%;\n }\n .du-bois-light-col-lg-offset-17 {\n margin-left: 70.83333333%;\n }\n .du-bois-light-col-lg-order-17 {\n order: 17;\n }\n .du-bois-light-col-lg-16 {\n display: block;\n flex: 0 0 66.66666667%;\n max-width: 66.66666667%;\n }\n .du-bois-light-col-lg-push-16 {\n left: 66.66666667%;\n }\n .du-bois-light-col-lg-pull-16 {\n right: 66.66666667%;\n }\n .du-bois-light-col-lg-offset-16 {\n margin-left: 66.66666667%;\n }\n .du-bois-light-col-lg-order-16 {\n order: 16;\n }\n .du-bois-light-col-lg-15 {\n display: block;\n flex: 0 0 62.5%;\n max-width: 62.5%;\n }\n .du-bois-light-col-lg-push-15 {\n left: 62.5%;\n }\n .du-bois-light-col-lg-pull-15 {\n right: 62.5%;\n }\n .du-bois-light-col-lg-offset-15 {\n margin-left: 62.5%;\n }\n .du-bois-light-col-lg-order-15 {\n order: 15;\n }\n .du-bois-light-col-lg-14 {\n display: block;\n flex: 0 0 58.33333333%;\n max-width: 58.33333333%;\n }\n .du-bois-light-col-lg-push-14 {\n left: 58.33333333%;\n }\n .du-bois-light-col-lg-pull-14 {\n right: 58.33333333%;\n }\n .du-bois-light-col-lg-offset-14 {\n margin-left: 58.33333333%;\n }\n .du-bois-light-col-lg-order-14 {\n order: 14;\n }\n .du-bois-light-col-lg-13 {\n display: block;\n flex: 0 0 54.16666667%;\n max-width: 54.16666667%;\n }\n .du-bois-light-col-lg-push-13 {\n left: 54.16666667%;\n }\n .du-bois-light-col-lg-pull-13 {\n right: 54.16666667%;\n }\n .du-bois-light-col-lg-offset-13 {\n margin-left: 54.16666667%;\n }\n .du-bois-light-col-lg-order-13 {\n order: 13;\n }\n .du-bois-light-col-lg-12 {\n display: block;\n flex: 0 0 50%;\n max-width: 50%;\n }\n .du-bois-light-col-lg-push-12 {\n left: 50%;\n }\n .du-bois-light-col-lg-pull-12 {\n right: 50%;\n }\n .du-bois-light-col-lg-offset-12 {\n margin-left: 50%;\n }\n .du-bois-light-col-lg-order-12 {\n order: 12;\n }\n .du-bois-light-col-lg-11 {\n display: block;\n flex: 0 0 45.83333333%;\n max-width: 45.83333333%;\n }\n .du-bois-light-col-lg-push-11 {\n left: 45.83333333%;\n }\n .du-bois-light-col-lg-pull-11 {\n right: 45.83333333%;\n }\n .du-bois-light-col-lg-offset-11 {\n margin-left: 45.83333333%;\n }\n .du-bois-light-col-lg-order-11 {\n order: 11;\n }\n .du-bois-light-col-lg-10 {\n display: block;\n flex: 0 0 41.66666667%;\n max-width: 41.66666667%;\n }\n .du-bois-light-col-lg-push-10 {\n left: 41.66666667%;\n }\n .du-bois-light-col-lg-pull-10 {\n right: 41.66666667%;\n }\n .du-bois-light-col-lg-offset-10 {\n margin-left: 41.66666667%;\n }\n .du-bois-light-col-lg-order-10 {\n order: 10;\n }\n .du-bois-light-col-lg-9 {\n display: block;\n flex: 0 0 37.5%;\n max-width: 37.5%;\n }\n .du-bois-light-col-lg-push-9 {\n left: 37.5%;\n }\n .du-bois-light-col-lg-pull-9 {\n right: 37.5%;\n }\n .du-bois-light-col-lg-offset-9 {\n margin-left: 37.5%;\n }\n .du-bois-light-col-lg-order-9 {\n order: 9;\n }\n .du-bois-light-col-lg-8 {\n display: block;\n flex: 0 0 33.33333333%;\n max-width: 33.33333333%;\n }\n .du-bois-light-col-lg-push-8 {\n left: 33.33333333%;\n }\n .du-bois-light-col-lg-pull-8 {\n right: 33.33333333%;\n }\n .du-bois-light-col-lg-offset-8 {\n margin-left: 33.33333333%;\n }\n .du-bois-light-col-lg-order-8 {\n order: 8;\n }\n .du-bois-light-col-lg-7 {\n display: block;\n flex: 0 0 29.16666667%;\n max-width: 29.16666667%;\n }\n .du-bois-light-col-lg-push-7 {\n left: 29.16666667%;\n }\n .du-bois-light-col-lg-pull-7 {\n right: 29.16666667%;\n }\n .du-bois-light-col-lg-offset-7 {\n margin-left: 29.16666667%;\n }\n .du-bois-light-col-lg-order-7 {\n order: 7;\n }\n .du-bois-light-col-lg-6 {\n display: block;\n flex: 0 0 25%;\n max-width: 25%;\n }\n .du-bois-light-col-lg-push-6 {\n left: 25%;\n }\n .du-bois-light-col-lg-pull-6 {\n right: 25%;\n }\n .du-bois-light-col-lg-offset-6 {\n margin-left: 25%;\n }\n .du-bois-light-col-lg-order-6 {\n order: 6;\n }\n .du-bois-light-col-lg-5 {\n display: block;\n flex: 0 0 20.83333333%;\n max-width: 20.83333333%;\n }\n .du-bois-light-col-lg-push-5 {\n left: 20.83333333%;\n }\n .du-bois-light-col-lg-pull-5 {\n right: 20.83333333%;\n }\n .du-bois-light-col-lg-offset-5 {\n margin-left: 20.83333333%;\n }\n .du-bois-light-col-lg-order-5 {\n order: 5;\n }\n .du-bois-light-col-lg-4 {\n display: block;\n flex: 0 0 16.66666667%;\n max-width: 16.66666667%;\n }\n .du-bois-light-col-lg-push-4 {\n left: 16.66666667%;\n }\n .du-bois-light-col-lg-pull-4 {\n right: 16.66666667%;\n }\n .du-bois-light-col-lg-offset-4 {\n margin-left: 16.66666667%;\n }\n .du-bois-light-col-lg-order-4 {\n order: 4;\n }\n .du-bois-light-col-lg-3 {\n display: block;\n flex: 0 0 12.5%;\n max-width: 12.5%;\n }\n .du-bois-light-col-lg-push-3 {\n left: 12.5%;\n }\n .du-bois-light-col-lg-pull-3 {\n right: 12.5%;\n }\n .du-bois-light-col-lg-offset-3 {\n margin-left: 12.5%;\n }\n .du-bois-light-col-lg-order-3 {\n order: 3;\n }\n .du-bois-light-col-lg-2 {\n display: block;\n flex: 0 0 8.33333333%;\n max-width: 8.33333333%;\n }\n .du-bois-light-col-lg-push-2 {\n left: 8.33333333%;\n }\n .du-bois-light-col-lg-pull-2 {\n right: 8.33333333%;\n }\n .du-bois-light-col-lg-offset-2 {\n margin-left: 8.33333333%;\n }\n .du-bois-light-col-lg-order-2 {\n order: 2;\n }\n .du-bois-light-col-lg-1 {\n display: block;\n flex: 0 0 4.16666667%;\n max-width: 4.16666667%;\n }\n .du-bois-light-col-lg-push-1 {\n left: 4.16666667%;\n }\n .du-bois-light-col-lg-pull-1 {\n right: 4.16666667%;\n }\n .du-bois-light-col-lg-offset-1 {\n margin-left: 4.16666667%;\n }\n .du-bois-light-col-lg-order-1 {\n order: 1;\n }\n .du-bois-light-col-lg-0 {\n display: none;\n }\n .du-bois-light-col-push-0 {\n left: auto;\n }\n .du-bois-light-col-pull-0 {\n right: auto;\n }\n .du-bois-light-col-lg-push-0 {\n left: auto;\n }\n .du-bois-light-col-lg-pull-0 {\n right: auto;\n }\n .du-bois-light-col-lg-offset-0 {\n margin-left: 0;\n }\n .du-bois-light-col-lg-order-0 {\n order: 0;\n }\n}\n@media (min-width: 1200px) {\n .du-bois-light-col-xl-24 {\n display: block;\n flex: 0 0 100%;\n max-width: 100%;\n }\n .du-bois-light-col-xl-push-24 {\n left: 100%;\n }\n .du-bois-light-col-xl-pull-24 {\n right: 100%;\n }\n .du-bois-light-col-xl-offset-24 {\n margin-left: 100%;\n }\n .du-bois-light-col-xl-order-24 {\n order: 24;\n }\n .du-bois-light-col-xl-23 {\n display: block;\n flex: 0 0 95.83333333%;\n max-width: 95.83333333%;\n }\n .du-bois-light-col-xl-push-23 {\n left: 95.83333333%;\n }\n .du-bois-light-col-xl-pull-23 {\n right: 95.83333333%;\n }\n .du-bois-light-col-xl-offset-23 {\n margin-left: 95.83333333%;\n }\n .du-bois-light-col-xl-order-23 {\n order: 23;\n }\n .du-bois-light-col-xl-22 {\n display: block;\n flex: 0 0 91.66666667%;\n max-width: 91.66666667%;\n }\n .du-bois-light-col-xl-push-22 {\n left: 91.66666667%;\n }\n .du-bois-light-col-xl-pull-22 {\n right: 91.66666667%;\n }\n .du-bois-light-col-xl-offset-22 {\n margin-left: 91.66666667%;\n }\n .du-bois-light-col-xl-order-22 {\n order: 22;\n }\n .du-bois-light-col-xl-21 {\n display: block;\n flex: 0 0 87.5%;\n max-width: 87.5%;\n }\n .du-bois-light-col-xl-push-21 {\n left: 87.5%;\n }\n .du-bois-light-col-xl-pull-21 {\n right: 87.5%;\n }\n .du-bois-light-col-xl-offset-21 {\n margin-left: 87.5%;\n }\n .du-bois-light-col-xl-order-21 {\n order: 21;\n }\n .du-bois-light-col-xl-20 {\n display: block;\n flex: 0 0 83.33333333%;\n max-width: 83.33333333%;\n }\n .du-bois-light-col-xl-push-20 {\n left: 83.33333333%;\n }\n .du-bois-light-col-xl-pull-20 {\n right: 83.33333333%;\n }\n .du-bois-light-col-xl-offset-20 {\n margin-left: 83.33333333%;\n }\n .du-bois-light-col-xl-order-20 {\n order: 20;\n }\n .du-bois-light-col-xl-19 {\n display: block;\n flex: 0 0 79.16666667%;\n max-width: 79.16666667%;\n }\n .du-bois-light-col-xl-push-19 {\n left: 79.16666667%;\n }\n .du-bois-light-col-xl-pull-19 {\n right: 79.16666667%;\n }\n .du-bois-light-col-xl-offset-19 {\n margin-left: 79.16666667%;\n }\n .du-bois-light-col-xl-order-19 {\n order: 19;\n }\n .du-bois-light-col-xl-18 {\n display: block;\n flex: 0 0 75%;\n max-width: 75%;\n }\n .du-bois-light-col-xl-push-18 {\n left: 75%;\n }\n .du-bois-light-col-xl-pull-18 {\n right: 75%;\n }\n .du-bois-light-col-xl-offset-18 {\n margin-left: 75%;\n }\n .du-bois-light-col-xl-order-18 {\n order: 18;\n }\n .du-bois-light-col-xl-17 {\n display: block;\n flex: 0 0 70.83333333%;\n max-width: 70.83333333%;\n }\n .du-bois-light-col-xl-push-17 {\n left: 70.83333333%;\n }\n .du-bois-light-col-xl-pull-17 {\n right: 70.83333333%;\n }\n .du-bois-light-col-xl-offset-17 {\n margin-left: 70.83333333%;\n }\n .du-bois-light-col-xl-order-17 {\n order: 17;\n }\n .du-bois-light-col-xl-16 {\n display: block;\n flex: 0 0 66.66666667%;\n max-width: 66.66666667%;\n }\n .du-bois-light-col-xl-push-16 {\n left: 66.66666667%;\n }\n .du-bois-light-col-xl-pull-16 {\n right: 66.66666667%;\n }\n .du-bois-light-col-xl-offset-16 {\n margin-left: 66.66666667%;\n }\n .du-bois-light-col-xl-order-16 {\n order: 16;\n }\n .du-bois-light-col-xl-15 {\n display: block;\n flex: 0 0 62.5%;\n max-width: 62.5%;\n }\n .du-bois-light-col-xl-push-15 {\n left: 62.5%;\n }\n .du-bois-light-col-xl-pull-15 {\n right: 62.5%;\n }\n .du-bois-light-col-xl-offset-15 {\n margin-left: 62.5%;\n }\n .du-bois-light-col-xl-order-15 {\n order: 15;\n }\n .du-bois-light-col-xl-14 {\n display: block;\n flex: 0 0 58.33333333%;\n max-width: 58.33333333%;\n }\n .du-bois-light-col-xl-push-14 {\n left: 58.33333333%;\n }\n .du-bois-light-col-xl-pull-14 {\n right: 58.33333333%;\n }\n .du-bois-light-col-xl-offset-14 {\n margin-left: 58.33333333%;\n }\n .du-bois-light-col-xl-order-14 {\n order: 14;\n }\n .du-bois-light-col-xl-13 {\n display: block;\n flex: 0 0 54.16666667%;\n max-width: 54.16666667%;\n }\n .du-bois-light-col-xl-push-13 {\n left: 54.16666667%;\n }\n .du-bois-light-col-xl-pull-13 {\n right: 54.16666667%;\n }\n .du-bois-light-col-xl-offset-13 {\n margin-left: 54.16666667%;\n }\n .du-bois-light-col-xl-order-13 {\n order: 13;\n }\n .du-bois-light-col-xl-12 {\n display: block;\n flex: 0 0 50%;\n max-width: 50%;\n }\n .du-bois-light-col-xl-push-12 {\n left: 50%;\n }\n .du-bois-light-col-xl-pull-12 {\n right: 50%;\n }\n .du-bois-light-col-xl-offset-12 {\n margin-left: 50%;\n }\n .du-bois-light-col-xl-order-12 {\n order: 12;\n }\n .du-bois-light-col-xl-11 {\n display: block;\n flex: 0 0 45.83333333%;\n max-width: 45.83333333%;\n }\n .du-bois-light-col-xl-push-11 {\n left: 45.83333333%;\n }\n .du-bois-light-col-xl-pull-11 {\n right: 45.83333333%;\n }\n .du-bois-light-col-xl-offset-11 {\n margin-left: 45.83333333%;\n }\n .du-bois-light-col-xl-order-11 {\n order: 11;\n }\n .du-bois-light-col-xl-10 {\n display: block;\n flex: 0 0 41.66666667%;\n max-width: 41.66666667%;\n }\n .du-bois-light-col-xl-push-10 {\n left: 41.66666667%;\n }\n .du-bois-light-col-xl-pull-10 {\n right: 41.66666667%;\n }\n .du-bois-light-col-xl-offset-10 {\n margin-left: 41.66666667%;\n }\n .du-bois-light-col-xl-order-10 {\n order: 10;\n }\n .du-bois-light-col-xl-9 {\n display: block;\n flex: 0 0 37.5%;\n max-width: 37.5%;\n }\n .du-bois-light-col-xl-push-9 {\n left: 37.5%;\n }\n .du-bois-light-col-xl-pull-9 {\n right: 37.5%;\n }\n .du-bois-light-col-xl-offset-9 {\n margin-left: 37.5%;\n }\n .du-bois-light-col-xl-order-9 {\n order: 9;\n }\n .du-bois-light-col-xl-8 {\n display: block;\n flex: 0 0 33.33333333%;\n max-width: 33.33333333%;\n }\n .du-bois-light-col-xl-push-8 {\n left: 33.33333333%;\n }\n .du-bois-light-col-xl-pull-8 {\n right: 33.33333333%;\n }\n .du-bois-light-col-xl-offset-8 {\n margin-left: 33.33333333%;\n }\n .du-bois-light-col-xl-order-8 {\n order: 8;\n }\n .du-bois-light-col-xl-7 {\n display: block;\n flex: 0 0 29.16666667%;\n max-width: 29.16666667%;\n }\n .du-bois-light-col-xl-push-7 {\n left: 29.16666667%;\n }\n .du-bois-light-col-xl-pull-7 {\n right: 29.16666667%;\n }\n .du-bois-light-col-xl-offset-7 {\n margin-left: 29.16666667%;\n }\n .du-bois-light-col-xl-order-7 {\n order: 7;\n }\n .du-bois-light-col-xl-6 {\n display: block;\n flex: 0 0 25%;\n max-width: 25%;\n }\n .du-bois-light-col-xl-push-6 {\n left: 25%;\n }\n .du-bois-light-col-xl-pull-6 {\n right: 25%;\n }\n .du-bois-light-col-xl-offset-6 {\n margin-left: 25%;\n }\n .du-bois-light-col-xl-order-6 {\n order: 6;\n }\n .du-bois-light-col-xl-5 {\n display: block;\n flex: 0 0 20.83333333%;\n max-width: 20.83333333%;\n }\n .du-bois-light-col-xl-push-5 {\n left: 20.83333333%;\n }\n .du-bois-light-col-xl-pull-5 {\n right: 20.83333333%;\n }\n .du-bois-light-col-xl-offset-5 {\n margin-left: 20.83333333%;\n }\n .du-bois-light-col-xl-order-5 {\n order: 5;\n }\n .du-bois-light-col-xl-4 {\n display: block;\n flex: 0 0 16.66666667%;\n max-width: 16.66666667%;\n }\n .du-bois-light-col-xl-push-4 {\n left: 16.66666667%;\n }\n .du-bois-light-col-xl-pull-4 {\n right: 16.66666667%;\n }\n .du-bois-light-col-xl-offset-4 {\n margin-left: 16.66666667%;\n }\n .du-bois-light-col-xl-order-4 {\n order: 4;\n }\n .du-bois-light-col-xl-3 {\n display: block;\n flex: 0 0 12.5%;\n max-width: 12.5%;\n }\n .du-bois-light-col-xl-push-3 {\n left: 12.5%;\n }\n .du-bois-light-col-xl-pull-3 {\n right: 12.5%;\n }\n .du-bois-light-col-xl-offset-3 {\n margin-left: 12.5%;\n }\n .du-bois-light-col-xl-order-3 {\n order: 3;\n }\n .du-bois-light-col-xl-2 {\n display: block;\n flex: 0 0 8.33333333%;\n max-width: 8.33333333%;\n }\n .du-bois-light-col-xl-push-2 {\n left: 8.33333333%;\n }\n .du-bois-light-col-xl-pull-2 {\n right: 8.33333333%;\n }\n .du-bois-light-col-xl-offset-2 {\n margin-left: 8.33333333%;\n }\n .du-bois-light-col-xl-order-2 {\n order: 2;\n }\n .du-bois-light-col-xl-1 {\n display: block;\n flex: 0 0 4.16666667%;\n max-width: 4.16666667%;\n }\n .du-bois-light-col-xl-push-1 {\n left: 4.16666667%;\n }\n .du-bois-light-col-xl-pull-1 {\n right: 4.16666667%;\n }\n .du-bois-light-col-xl-offset-1 {\n margin-left: 4.16666667%;\n }\n .du-bois-light-col-xl-order-1 {\n order: 1;\n }\n .du-bois-light-col-xl-0 {\n display: none;\n }\n .du-bois-light-col-push-0 {\n left: auto;\n }\n .du-bois-light-col-pull-0 {\n right: auto;\n }\n .du-bois-light-col-xl-push-0 {\n left: auto;\n }\n .du-bois-light-col-xl-pull-0 {\n right: auto;\n }\n .du-bois-light-col-xl-offset-0 {\n margin-left: 0;\n }\n .du-bois-light-col-xl-order-0 {\n order: 0;\n }\n}\n@media (min-width: 1600px) {\n .du-bois-light-col-xxl-24 {\n display: block;\n flex: 0 0 100%;\n max-width: 100%;\n }\n .du-bois-light-col-xxl-push-24 {\n left: 100%;\n }\n .du-bois-light-col-xxl-pull-24 {\n right: 100%;\n }\n .du-bois-light-col-xxl-offset-24 {\n margin-left: 100%;\n }\n .du-bois-light-col-xxl-order-24 {\n order: 24;\n }\n .du-bois-light-col-xxl-23 {\n display: block;\n flex: 0 0 95.83333333%;\n max-width: 95.83333333%;\n }\n .du-bois-light-col-xxl-push-23 {\n left: 95.83333333%;\n }\n .du-bois-light-col-xxl-pull-23 {\n right: 95.83333333%;\n }\n .du-bois-light-col-xxl-offset-23 {\n margin-left: 95.83333333%;\n }\n .du-bois-light-col-xxl-order-23 {\n order: 23;\n }\n .du-bois-light-col-xxl-22 {\n display: block;\n flex: 0 0 91.66666667%;\n max-width: 91.66666667%;\n }\n .du-bois-light-col-xxl-push-22 {\n left: 91.66666667%;\n }\n .du-bois-light-col-xxl-pull-22 {\n right: 91.66666667%;\n }\n .du-bois-light-col-xxl-offset-22 {\n margin-left: 91.66666667%;\n }\n .du-bois-light-col-xxl-order-22 {\n order: 22;\n }\n .du-bois-light-col-xxl-21 {\n display: block;\n flex: 0 0 87.5%;\n max-width: 87.5%;\n }\n .du-bois-light-col-xxl-push-21 {\n left: 87.5%;\n }\n .du-bois-light-col-xxl-pull-21 {\n right: 87.5%;\n }\n .du-bois-light-col-xxl-offset-21 {\n margin-left: 87.5%;\n }\n .du-bois-light-col-xxl-order-21 {\n order: 21;\n }\n .du-bois-light-col-xxl-20 {\n display: block;\n flex: 0 0 83.33333333%;\n max-width: 83.33333333%;\n }\n .du-bois-light-col-xxl-push-20 {\n left: 83.33333333%;\n }\n .du-bois-light-col-xxl-pull-20 {\n right: 83.33333333%;\n }\n .du-bois-light-col-xxl-offset-20 {\n margin-left: 83.33333333%;\n }\n .du-bois-light-col-xxl-order-20 {\n order: 20;\n }\n .du-bois-light-col-xxl-19 {\n display: block;\n flex: 0 0 79.16666667%;\n max-width: 79.16666667%;\n }\n .du-bois-light-col-xxl-push-19 {\n left: 79.16666667%;\n }\n .du-bois-light-col-xxl-pull-19 {\n right: 79.16666667%;\n }\n .du-bois-light-col-xxl-offset-19 {\n margin-left: 79.16666667%;\n }\n .du-bois-light-col-xxl-order-19 {\n order: 19;\n }\n .du-bois-light-col-xxl-18 {\n display: block;\n flex: 0 0 75%;\n max-width: 75%;\n }\n .du-bois-light-col-xxl-push-18 {\n left: 75%;\n }\n .du-bois-light-col-xxl-pull-18 {\n right: 75%;\n }\n .du-bois-light-col-xxl-offset-18 {\n margin-left: 75%;\n }\n .du-bois-light-col-xxl-order-18 {\n order: 18;\n }\n .du-bois-light-col-xxl-17 {\n display: block;\n flex: 0 0 70.83333333%;\n max-width: 70.83333333%;\n }\n .du-bois-light-col-xxl-push-17 {\n left: 70.83333333%;\n }\n .du-bois-light-col-xxl-pull-17 {\n right: 70.83333333%;\n }\n .du-bois-light-col-xxl-offset-17 {\n margin-left: 70.83333333%;\n }\n .du-bois-light-col-xxl-order-17 {\n order: 17;\n }\n .du-bois-light-col-xxl-16 {\n display: block;\n flex: 0 0 66.66666667%;\n max-width: 66.66666667%;\n }\n .du-bois-light-col-xxl-push-16 {\n left: 66.66666667%;\n }\n .du-bois-light-col-xxl-pull-16 {\n right: 66.66666667%;\n }\n .du-bois-light-col-xxl-offset-16 {\n margin-left: 66.66666667%;\n }\n .du-bois-light-col-xxl-order-16 {\n order: 16;\n }\n .du-bois-light-col-xxl-15 {\n display: block;\n flex: 0 0 62.5%;\n max-width: 62.5%;\n }\n .du-bois-light-col-xxl-push-15 {\n left: 62.5%;\n }\n .du-bois-light-col-xxl-pull-15 {\n right: 62.5%;\n }\n .du-bois-light-col-xxl-offset-15 {\n margin-left: 62.5%;\n }\n .du-bois-light-col-xxl-order-15 {\n order: 15;\n }\n .du-bois-light-col-xxl-14 {\n display: block;\n flex: 0 0 58.33333333%;\n max-width: 58.33333333%;\n }\n .du-bois-light-col-xxl-push-14 {\n left: 58.33333333%;\n }\n .du-bois-light-col-xxl-pull-14 {\n right: 58.33333333%;\n }\n .du-bois-light-col-xxl-offset-14 {\n margin-left: 58.33333333%;\n }\n .du-bois-light-col-xxl-order-14 {\n order: 14;\n }\n .du-bois-light-col-xxl-13 {\n display: block;\n flex: 0 0 54.16666667%;\n max-width: 54.16666667%;\n }\n .du-bois-light-col-xxl-push-13 {\n left: 54.16666667%;\n }\n .du-bois-light-col-xxl-pull-13 {\n right: 54.16666667%;\n }\n .du-bois-light-col-xxl-offset-13 {\n margin-left: 54.16666667%;\n }\n .du-bois-light-col-xxl-order-13 {\n order: 13;\n }\n .du-bois-light-col-xxl-12 {\n display: block;\n flex: 0 0 50%;\n max-width: 50%;\n }\n .du-bois-light-col-xxl-push-12 {\n left: 50%;\n }\n .du-bois-light-col-xxl-pull-12 {\n right: 50%;\n }\n .du-bois-light-col-xxl-offset-12 {\n margin-left: 50%;\n }\n .du-bois-light-col-xxl-order-12 {\n order: 12;\n }\n .du-bois-light-col-xxl-11 {\n display: block;\n flex: 0 0 45.83333333%;\n max-width: 45.83333333%;\n }\n .du-bois-light-col-xxl-push-11 {\n left: 45.83333333%;\n }\n .du-bois-light-col-xxl-pull-11 {\n right: 45.83333333%;\n }\n .du-bois-light-col-xxl-offset-11 {\n margin-left: 45.83333333%;\n }\n .du-bois-light-col-xxl-order-11 {\n order: 11;\n }\n .du-bois-light-col-xxl-10 {\n display: block;\n flex: 0 0 41.66666667%;\n max-width: 41.66666667%;\n }\n .du-bois-light-col-xxl-push-10 {\n left: 41.66666667%;\n }\n .du-bois-light-col-xxl-pull-10 {\n right: 41.66666667%;\n }\n .du-bois-light-col-xxl-offset-10 {\n margin-left: 41.66666667%;\n }\n .du-bois-light-col-xxl-order-10 {\n order: 10;\n }\n .du-bois-light-col-xxl-9 {\n display: block;\n flex: 0 0 37.5%;\n max-width: 37.5%;\n }\n .du-bois-light-col-xxl-push-9 {\n left: 37.5%;\n }\n .du-bois-light-col-xxl-pull-9 {\n right: 37.5%;\n }\n .du-bois-light-col-xxl-offset-9 {\n margin-left: 37.5%;\n }\n .du-bois-light-col-xxl-order-9 {\n order: 9;\n }\n .du-bois-light-col-xxl-8 {\n display: block;\n flex: 0 0 33.33333333%;\n max-width: 33.33333333%;\n }\n .du-bois-light-col-xxl-push-8 {\n left: 33.33333333%;\n }\n .du-bois-light-col-xxl-pull-8 {\n right: 33.33333333%;\n }\n .du-bois-light-col-xxl-offset-8 {\n margin-left: 33.33333333%;\n }\n .du-bois-light-col-xxl-order-8 {\n order: 8;\n }\n .du-bois-light-col-xxl-7 {\n display: block;\n flex: 0 0 29.16666667%;\n max-width: 29.16666667%;\n }\n .du-bois-light-col-xxl-push-7 {\n left: 29.16666667%;\n }\n .du-bois-light-col-xxl-pull-7 {\n right: 29.16666667%;\n }\n .du-bois-light-col-xxl-offset-7 {\n margin-left: 29.16666667%;\n }\n .du-bois-light-col-xxl-order-7 {\n order: 7;\n }\n .du-bois-light-col-xxl-6 {\n display: block;\n flex: 0 0 25%;\n max-width: 25%;\n }\n .du-bois-light-col-xxl-push-6 {\n left: 25%;\n }\n .du-bois-light-col-xxl-pull-6 {\n right: 25%;\n }\n .du-bois-light-col-xxl-offset-6 {\n margin-left: 25%;\n }\n .du-bois-light-col-xxl-order-6 {\n order: 6;\n }\n .du-bois-light-col-xxl-5 {\n display: block;\n flex: 0 0 20.83333333%;\n max-width: 20.83333333%;\n }\n .du-bois-light-col-xxl-push-5 {\n left: 20.83333333%;\n }\n .du-bois-light-col-xxl-pull-5 {\n right: 20.83333333%;\n }\n .du-bois-light-col-xxl-offset-5 {\n margin-left: 20.83333333%;\n }\n .du-bois-light-col-xxl-order-5 {\n order: 5;\n }\n .du-bois-light-col-xxl-4 {\n display: block;\n flex: 0 0 16.66666667%;\n max-width: 16.66666667%;\n }\n .du-bois-light-col-xxl-push-4 {\n left: 16.66666667%;\n }\n .du-bois-light-col-xxl-pull-4 {\n right: 16.66666667%;\n }\n .du-bois-light-col-xxl-offset-4 {\n margin-left: 16.66666667%;\n }\n .du-bois-light-col-xxl-order-4 {\n order: 4;\n }\n .du-bois-light-col-xxl-3 {\n display: block;\n flex: 0 0 12.5%;\n max-width: 12.5%;\n }\n .du-bois-light-col-xxl-push-3 {\n left: 12.5%;\n }\n .du-bois-light-col-xxl-pull-3 {\n right: 12.5%;\n }\n .du-bois-light-col-xxl-offset-3 {\n margin-left: 12.5%;\n }\n .du-bois-light-col-xxl-order-3 {\n order: 3;\n }\n .du-bois-light-col-xxl-2 {\n display: block;\n flex: 0 0 8.33333333%;\n max-width: 8.33333333%;\n }\n .du-bois-light-col-xxl-push-2 {\n left: 8.33333333%;\n }\n .du-bois-light-col-xxl-pull-2 {\n right: 8.33333333%;\n }\n .du-bois-light-col-xxl-offset-2 {\n margin-left: 8.33333333%;\n }\n .du-bois-light-col-xxl-order-2 {\n order: 2;\n }\n .du-bois-light-col-xxl-1 {\n display: block;\n flex: 0 0 4.16666667%;\n max-width: 4.16666667%;\n }\n .du-bois-light-col-xxl-push-1 {\n left: 4.16666667%;\n }\n .du-bois-light-col-xxl-pull-1 {\n right: 4.16666667%;\n }\n .du-bois-light-col-xxl-offset-1 {\n margin-left: 4.16666667%;\n }\n .du-bois-light-col-xxl-order-1 {\n order: 1;\n }\n .du-bois-light-col-xxl-0 {\n display: none;\n }\n .du-bois-light-col-push-0 {\n left: auto;\n }\n .du-bois-light-col-pull-0 {\n right: auto;\n }\n .du-bois-light-col-xxl-push-0 {\n left: auto;\n }\n .du-bois-light-col-xxl-pull-0 {\n right: auto;\n }\n .du-bois-light-col-xxl-offset-0 {\n margin-left: 0;\n }\n .du-bois-light-col-xxl-order-0 {\n order: 0;\n }\n}\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-light-tooltip {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #11171C;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: 'tnum';\n position: absolute;\n z-index: 1070;\n display: block;\n width: max-content;\n max-width: 250px;\n visibility: visible;\n}\n.du-bois-light-tooltip-hidden {\n display: none;\n}\n.du-bois-light-tooltip-placement-top,\n.du-bois-light-tooltip-placement-topLeft,\n.du-bois-light-tooltip-placement-topRight {\n padding-bottom: 8px;\n}\n.du-bois-light-tooltip-placement-right,\n.du-bois-light-tooltip-placement-rightTop,\n.du-bois-light-tooltip-placement-rightBottom {\n padding-left: 8px;\n}\n.du-bois-light-tooltip-placement-bottom,\n.du-bois-light-tooltip-placement-bottomLeft,\n.du-bois-light-tooltip-placement-bottomRight {\n padding-top: 8px;\n}\n.du-bois-light-tooltip-placement-left,\n.du-bois-light-tooltip-placement-leftTop,\n.du-bois-light-tooltip-placement-leftBottom {\n padding-right: 8px;\n}\n.du-bois-light-tooltip-inner {\n min-width: 30px;\n min-height: 32px;\n padding: 6px 8px;\n color: #fff;\n text-align: left;\n text-decoration: none;\n word-wrap: break-word;\n background-color: rgba(0, 0, 0, 0.75);\n border-radius: 4px;\n box-shadow: 0 3px 6px -4px rgba(0, 0, 0, 0.12), 0 6px 16px 0 rgba(0, 0, 0, 0.08), 0 9px 28px 8px rgba(0, 0, 0, 0.05);\n}\n.du-bois-light-tooltip-arrow {\n position: absolute;\n display: block;\n width: 13.07106781px;\n height: 13.07106781px;\n overflow: hidden;\n background: transparent;\n pointer-events: none;\n}\n.du-bois-light-tooltip-arrow-content {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n display: block;\n width: 5px;\n height: 5px;\n margin: auto;\n background-color: rgba(0, 0, 0, 0.75);\n content: '';\n pointer-events: auto;\n}\n.du-bois-light-tooltip-placement-top .du-bois-light-tooltip-arrow,\n.du-bois-light-tooltip-placement-topLeft .du-bois-light-tooltip-arrow,\n.du-bois-light-tooltip-placement-topRight .du-bois-light-tooltip-arrow {\n bottom: -5.07106781px;\n}\n.du-bois-light-tooltip-placement-top .du-bois-light-tooltip-arrow-content,\n.du-bois-light-tooltip-placement-topLeft .du-bois-light-tooltip-arrow-content,\n.du-bois-light-tooltip-placement-topRight .du-bois-light-tooltip-arrow-content {\n box-shadow: 3px 3px 7px rgba(0, 0, 0, 0.07);\n transform: translateY(-6.53553391px) rotate(45deg);\n}\n.du-bois-light-tooltip-placement-top .du-bois-light-tooltip-arrow {\n left: 50%;\n transform: translateX(-50%);\n}\n.du-bois-light-tooltip-placement-topLeft .du-bois-light-tooltip-arrow {\n left: 13px;\n}\n.du-bois-light-tooltip-placement-topRight .du-bois-light-tooltip-arrow {\n right: 13px;\n}\n.du-bois-light-tooltip-placement-right .du-bois-light-tooltip-arrow,\n.du-bois-light-tooltip-placement-rightTop .du-bois-light-tooltip-arrow,\n.du-bois-light-tooltip-placement-rightBottom .du-bois-light-tooltip-arrow {\n left: -5.07106781px;\n}\n.du-bois-light-tooltip-placement-right .du-bois-light-tooltip-arrow-content,\n.du-bois-light-tooltip-placement-rightTop .du-bois-light-tooltip-arrow-content,\n.du-bois-light-tooltip-placement-rightBottom .du-bois-light-tooltip-arrow-content {\n box-shadow: -3px 3px 7px rgba(0, 0, 0, 0.07);\n transform: translateX(6.53553391px) rotate(45deg);\n}\n.du-bois-light-tooltip-placement-right .du-bois-light-tooltip-arrow {\n top: 50%;\n transform: translateY(-50%);\n}\n.du-bois-light-tooltip-placement-rightTop .du-bois-light-tooltip-arrow {\n top: 5px;\n}\n.du-bois-light-tooltip-placement-rightBottom .du-bois-light-tooltip-arrow {\n bottom: 5px;\n}\n.du-bois-light-tooltip-placement-left .du-bois-light-tooltip-arrow,\n.du-bois-light-tooltip-placement-leftTop .du-bois-light-tooltip-arrow,\n.du-bois-light-tooltip-placement-leftBottom .du-bois-light-tooltip-arrow {\n right: -5.07106781px;\n}\n.du-bois-light-tooltip-placement-left .du-bois-light-tooltip-arrow-content,\n.du-bois-light-tooltip-placement-leftTop .du-bois-light-tooltip-arrow-content,\n.du-bois-light-tooltip-placement-leftBottom .du-bois-light-tooltip-arrow-content {\n box-shadow: 3px -3px 7px rgba(0, 0, 0, 0.07);\n transform: translateX(-6.53553391px) rotate(45deg);\n}\n.du-bois-light-tooltip-placement-left .du-bois-light-tooltip-arrow {\n top: 50%;\n transform: translateY(-50%);\n}\n.du-bois-light-tooltip-placement-leftTop .du-bois-light-tooltip-arrow {\n top: 5px;\n}\n.du-bois-light-tooltip-placement-leftBottom .du-bois-light-tooltip-arrow {\n bottom: 5px;\n}\n.du-bois-light-tooltip-placement-bottom .du-bois-light-tooltip-arrow,\n.du-bois-light-tooltip-placement-bottomLeft .du-bois-light-tooltip-arrow,\n.du-bois-light-tooltip-placement-bottomRight .du-bois-light-tooltip-arrow {\n top: -5.07106781px;\n}\n.du-bois-light-tooltip-placement-bottom .du-bois-light-tooltip-arrow-content,\n.du-bois-light-tooltip-placement-bottomLeft .du-bois-light-tooltip-arrow-content,\n.du-bois-light-tooltip-placement-bottomRight .du-bois-light-tooltip-arrow-content {\n box-shadow: -3px -3px 7px rgba(0, 0, 0, 0.07);\n transform: translateY(6.53553391px) rotate(45deg);\n}\n.du-bois-light-tooltip-placement-bottom .du-bois-light-tooltip-arrow {\n left: 50%;\n transform: translateX(-50%);\n}\n.du-bois-light-tooltip-placement-bottomLeft .du-bois-light-tooltip-arrow {\n left: 13px;\n}\n.du-bois-light-tooltip-placement-bottomRight .du-bois-light-tooltip-arrow {\n right: 13px;\n}\n.du-bois-light-tooltip-pink .du-bois-light-tooltip-inner {\n background-color: #eb2f96;\n}\n.du-bois-light-tooltip-pink .du-bois-light-tooltip-arrow-content {\n background-color: #eb2f96;\n}\n.du-bois-light-tooltip-magenta .du-bois-light-tooltip-inner {\n background-color: #eb2f96;\n}\n.du-bois-light-tooltip-magenta .du-bois-light-tooltip-arrow-content {\n background-color: #eb2f96;\n}\n.du-bois-light-tooltip-red .du-bois-light-tooltip-inner {\n background-color: #f5222d;\n}\n.du-bois-light-tooltip-red .du-bois-light-tooltip-arrow-content {\n background-color: #f5222d;\n}\n.du-bois-light-tooltip-volcano .du-bois-light-tooltip-inner {\n background-color: #fa541c;\n}\n.du-bois-light-tooltip-volcano .du-bois-light-tooltip-arrow-content {\n background-color: #fa541c;\n}\n.du-bois-light-tooltip-orange .du-bois-light-tooltip-inner {\n background-color: #fa8c16;\n}\n.du-bois-light-tooltip-orange .du-bois-light-tooltip-arrow-content {\n background-color: #fa8c16;\n}\n.du-bois-light-tooltip-yellow .du-bois-light-tooltip-inner {\n background-color: #fadb14;\n}\n.du-bois-light-tooltip-yellow .du-bois-light-tooltip-arrow-content {\n background-color: #fadb14;\n}\n.du-bois-light-tooltip-gold .du-bois-light-tooltip-inner {\n background-color: #faad14;\n}\n.du-bois-light-tooltip-gold .du-bois-light-tooltip-arrow-content {\n background-color: #faad14;\n}\n.du-bois-light-tooltip-cyan .du-bois-light-tooltip-inner {\n background-color: #13c2c2;\n}\n.du-bois-light-tooltip-cyan .du-bois-light-tooltip-arrow-content {\n background-color: #13c2c2;\n}\n.du-bois-light-tooltip-lime .du-bois-light-tooltip-inner {\n background-color: #a0d911;\n}\n.du-bois-light-tooltip-lime .du-bois-light-tooltip-arrow-content {\n background-color: #a0d911;\n}\n.du-bois-light-tooltip-green .du-bois-light-tooltip-inner {\n background-color: #52c41a;\n}\n.du-bois-light-tooltip-green .du-bois-light-tooltip-arrow-content {\n background-color: #52c41a;\n}\n.du-bois-light-tooltip-blue .du-bois-light-tooltip-inner {\n background-color: #1890ff;\n}\n.du-bois-light-tooltip-blue .du-bois-light-tooltip-arrow-content {\n background-color: #1890ff;\n}\n.du-bois-light-tooltip-geekblue .du-bois-light-tooltip-inner {\n background-color: #2f54eb;\n}\n.du-bois-light-tooltip-geekblue .du-bois-light-tooltip-arrow-content {\n background-color: #2f54eb;\n}\n.du-bois-light-tooltip-purple .du-bois-light-tooltip-inner {\n background-color: #722ed1;\n}\n.du-bois-light-tooltip-purple .du-bois-light-tooltip-arrow-content {\n background-color: #722ed1;\n}\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-light-input-affix-wrapper {\n position: relative;\n display: inline-block;\n width: 100%;\n min-width: 0;\n padding: 5px 12px;\n color: #11171C;\n font-size: 13px;\n line-height: 20px;\n background-color: #fff;\n background-image: none;\n border: 1px solid #E8ECF0;\n border-radius: 4px;\n transition: all 0.3s;\n display: inline-flex;\n}\n.du-bois-light-input-affix-wrapper::-moz-placeholder {\n opacity: 1;\n}\n.du-bois-light-input-affix-wrapper::placeholder {\n color: #bfbfbf;\n}\n.du-bois-light-input-affix-wrapper:placeholder-shown {\n text-overflow: ellipsis;\n}\n.du-bois-light-input-affix-wrapper:hover {\n border-color: #448dc2;\n border-right-width: 1px !important;\n}\n.du-bois-light-input-affix-wrapper:focus,\n.du-bois-light-input-affix-wrapper-focused {\n border-color: #448dc2;\n border-right-width: 1px !important;\n outline: 0;\n box-shadow: 0 0 0 2px rgba(34, 114, 180, 0.2);\n}\n.du-bois-light-input-affix-wrapper-disabled {\n color: rgba(0, 0, 0, 0.25);\n background-color: #f5f5f5;\n cursor: not-allowed;\n opacity: 1;\n}\n.du-bois-light-input-affix-wrapper-disabled:hover {\n border-color: #E8ECF0;\n border-right-width: 1px !important;\n}\n.du-bois-light-input-affix-wrapper[disabled] {\n color: rgba(0, 0, 0, 0.25);\n background-color: #f5f5f5;\n cursor: not-allowed;\n opacity: 1;\n}\n.du-bois-light-input-affix-wrapper[disabled]:hover {\n border-color: #E8ECF0;\n border-right-width: 1px !important;\n}\n.du-bois-light-input-affix-wrapper-borderless,\n.du-bois-light-input-affix-wrapper-borderless:hover,\n.du-bois-light-input-affix-wrapper-borderless:focus,\n.du-bois-light-input-affix-wrapper-borderless-focused,\n.du-bois-light-input-affix-wrapper-borderless-disabled,\n.du-bois-light-input-affix-wrapper-borderless[disabled] {\n background-color: transparent;\n border: none;\n box-shadow: none;\n}\ntextarea.du-bois-light-input-affix-wrapper {\n max-width: 100%;\n height: auto;\n min-height: 40px;\n line-height: 20px;\n vertical-align: bottom;\n transition: all 0.3s, height 0s;\n}\n.du-bois-light-input-affix-wrapper-lg {\n padding: 5px 12px;\n font-size: 18px;\n}\n.du-bois-light-input-affix-wrapper-sm {\n padding: 5px 12px;\n}\n.du-bois-light-input-affix-wrapper:not(.du-bois-light-input-affix-wrapper-disabled):hover {\n border-color: #448dc2;\n border-right-width: 1px !important;\n z-index: 1;\n}\n.du-bois-light-input-search-with-button .du-bois-light-input-affix-wrapper:not(.du-bois-light-input-affix-wrapper-disabled):hover {\n z-index: 0;\n}\n.du-bois-light-input-affix-wrapper-focused,\n.du-bois-light-input-affix-wrapper:focus {\n z-index: 1;\n}\n.du-bois-light-input-affix-wrapper-disabled .du-bois-light-input[disabled] {\n background: transparent;\n}\n.du-bois-light-input-affix-wrapper > input.du-bois-light-input {\n padding: 0;\n border: none;\n outline: none;\n}\n.du-bois-light-input-affix-wrapper > input.du-bois-light-input:focus {\n box-shadow: none;\n}\n.du-bois-light-input-affix-wrapper::before {\n width: 0;\n visibility: hidden;\n content: '\\a0';\n}\n.du-bois-light-input-prefix,\n.du-bois-light-input-suffix {\n display: flex;\n flex: none;\n align-items: center;\n}\n.du-bois-light-input-prefix {\n margin-right: 4px;\n}\n.du-bois-light-input-suffix {\n margin-left: 4px;\n}\n.anticon.du-bois-light-input-clear-icon {\n margin: 0 4px;\n color: rgba(0, 0, 0, 0.25);\n font-size: 12px;\n vertical-align: -1px;\n cursor: pointer;\n transition: color 0.3s;\n}\n.anticon.du-bois-light-input-clear-icon:hover {\n color: #5F7281;\n}\n.anticon.du-bois-light-input-clear-icon:active {\n color: #11171C;\n}\n.anticon.du-bois-light-input-clear-icon-hidden {\n visibility: hidden;\n}\n.anticon.du-bois-light-input-clear-icon:last-child {\n margin-right: 0;\n}\n.du-bois-light-input-affix-wrapper-textarea-with-clear-btn {\n padding: 0 !important;\n border: 0 !important;\n}\n.du-bois-light-input-affix-wrapper-textarea-with-clear-btn .du-bois-light-input-clear-icon {\n position: absolute;\n top: 8px;\n right: 8px;\n z-index: 1;\n}\n.du-bois-light-input {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n font-variant: tabular-nums;\n list-style: none;\n font-feature-settings: 'tnum';\n position: relative;\n display: inline-block;\n width: 100%;\n min-width: 0;\n padding: 5px 12px;\n color: #11171C;\n font-size: 13px;\n line-height: 20px;\n background-color: #fff;\n background-image: none;\n border: 1px solid #E8ECF0;\n border-radius: 4px;\n transition: all 0.3s;\n}\n.du-bois-light-input::-moz-placeholder {\n opacity: 1;\n}\n.du-bois-light-input::placeholder {\n color: #bfbfbf;\n}\n.du-bois-light-input:placeholder-shown {\n text-overflow: ellipsis;\n}\n.du-bois-light-input:hover {\n border-color: #448dc2;\n border-right-width: 1px !important;\n}\n.du-bois-light-input:focus,\n.du-bois-light-input-focused {\n border-color: #448dc2;\n border-right-width: 1px !important;\n outline: 0;\n box-shadow: 0 0 0 2px rgba(34, 114, 180, 0.2);\n}\n.du-bois-light-input-disabled {\n color: rgba(0, 0, 0, 0.25);\n background-color: #f5f5f5;\n cursor: not-allowed;\n opacity: 1;\n}\n.du-bois-light-input-disabled:hover {\n border-color: #E8ECF0;\n border-right-width: 1px !important;\n}\n.du-bois-light-input[disabled] {\n color: rgba(0, 0, 0, 0.25);\n background-color: #f5f5f5;\n cursor: not-allowed;\n opacity: 1;\n}\n.du-bois-light-input[disabled]:hover {\n border-color: #E8ECF0;\n border-right-width: 1px !important;\n}\n.du-bois-light-input-borderless,\n.du-bois-light-input-borderless:hover,\n.du-bois-light-input-borderless:focus,\n.du-bois-light-input-borderless-focused,\n.du-bois-light-input-borderless-disabled,\n.du-bois-light-input-borderless[disabled] {\n background-color: transparent;\n border: none;\n box-shadow: none;\n}\ntextarea.du-bois-light-input {\n max-width: 100%;\n height: auto;\n min-height: 40px;\n line-height: 20px;\n vertical-align: bottom;\n transition: all 0.3s, height 0s;\n}\n.du-bois-light-input-lg {\n padding: 5px 12px;\n font-size: 18px;\n}\n.du-bois-light-input-sm {\n padding: 5px 12px;\n}\n.du-bois-light-input-group {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #11171C;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: 'tnum';\n position: relative;\n display: table;\n width: 100%;\n border-collapse: separate;\n border-spacing: 0;\n}\n.du-bois-light-input-group[class*='col-'] {\n float: none;\n padding-right: 0;\n padding-left: 0;\n}\n.du-bois-light-input-group > [class*='col-'] {\n padding-right: 8px;\n}\n.du-bois-light-input-group > [class*='col-']:last-child {\n padding-right: 0;\n}\n.du-bois-light-input-group-addon,\n.du-bois-light-input-group-wrap,\n.du-bois-light-input-group > .du-bois-light-input {\n display: table-cell;\n}\n.du-bois-light-input-group-addon:not(:first-child):not(:last-child),\n.du-bois-light-input-group-wrap:not(:first-child):not(:last-child),\n.du-bois-light-input-group > .du-bois-light-input:not(:first-child):not(:last-child) {\n border-radius: 0;\n}\n.du-bois-light-input-group-addon,\n.du-bois-light-input-group-wrap {\n width: 1px;\n white-space: nowrap;\n vertical-align: middle;\n}\n.du-bois-light-input-group-wrap > * {\n display: block !important;\n}\n.du-bois-light-input-group .du-bois-light-input {\n float: left;\n width: 100%;\n margin-bottom: 0;\n text-align: inherit;\n}\n.du-bois-light-input-group .du-bois-light-input:focus {\n z-index: 1;\n border-right-width: 1px;\n}\n.du-bois-light-input-group .du-bois-light-input:hover {\n z-index: 1;\n border-right-width: 1px;\n}\n.du-bois-light-input-search-with-button .du-bois-light-input-group .du-bois-light-input:hover {\n z-index: 0;\n}\n.du-bois-light-input-group-addon {\n position: relative;\n padding: 0 12px;\n color: #11171C;\n font-weight: normal;\n font-size: 13px;\n text-align: center;\n background-color: #fafafa;\n border: 1px solid #E8ECF0;\n border-radius: 4px;\n transition: all 0.3s;\n}\n.du-bois-light-input-group-addon .du-bois-light-select {\n margin: -6px -12px;\n}\n.du-bois-light-input-group-addon .du-bois-light-select.du-bois-light-select-single:not(.du-bois-light-select-customize-input) .du-bois-light-select-selector {\n background-color: inherit;\n border: 1px solid transparent;\n box-shadow: none;\n}\n.du-bois-light-input-group-addon .du-bois-light-select-open .du-bois-light-select-selector,\n.du-bois-light-input-group-addon .du-bois-light-select-focused .du-bois-light-select-selector {\n color: #2272B4;\n}\n.du-bois-light-input-group-addon .du-bois-light-cascader-picker {\n margin: -9px -8px;\n background-color: transparent;\n}\n.du-bois-light-input-group-addon .du-bois-light-cascader-picker .du-bois-light-cascader-input {\n text-align: left;\n border: 0;\n box-shadow: none;\n}\n.du-bois-light-input-group > .du-bois-light-input:first-child,\n.du-bois-light-input-group-addon:first-child {\n border-top-right-radius: 0;\n border-bottom-right-radius: 0;\n}\n.du-bois-light-input-group > .du-bois-light-input:first-child .du-bois-light-select .du-bois-light-select-selector,\n.du-bois-light-input-group-addon:first-child .du-bois-light-select .du-bois-light-select-selector {\n border-top-right-radius: 0;\n border-bottom-right-radius: 0;\n}\n.du-bois-light-input-group > .du-bois-light-input-affix-wrapper:not(:first-child) .du-bois-light-input {\n border-top-left-radius: 0;\n border-bottom-left-radius: 0;\n}\n.du-bois-light-input-group > .du-bois-light-input-affix-wrapper:not(:last-child) .du-bois-light-input {\n border-top-right-radius: 0;\n border-bottom-right-radius: 0;\n}\n.du-bois-light-input-group-addon:first-child {\n border-right: 0;\n}\n.du-bois-light-input-group-addon:last-child {\n border-left: 0;\n}\n.du-bois-light-input-group > .du-bois-light-input:last-child,\n.du-bois-light-input-group-addon:last-child {\n border-top-left-radius: 0;\n border-bottom-left-radius: 0;\n}\n.du-bois-light-input-group > .du-bois-light-input:last-child .du-bois-light-select .du-bois-light-select-selector,\n.du-bois-light-input-group-addon:last-child .du-bois-light-select .du-bois-light-select-selector {\n border-top-left-radius: 0;\n border-bottom-left-radius: 0;\n}\n.du-bois-light-input-group-lg .du-bois-light-input,\n.du-bois-light-input-group-lg > .du-bois-light-input-group-addon {\n padding: 5px 12px;\n font-size: 18px;\n}\n.du-bois-light-input-group-sm .du-bois-light-input,\n.du-bois-light-input-group-sm > .du-bois-light-input-group-addon {\n padding: 5px 12px;\n}\n.du-bois-light-input-group-lg .du-bois-light-select-single .du-bois-light-select-selector {\n height: 40px;\n}\n.du-bois-light-input-group-sm .du-bois-light-select-single .du-bois-light-select-selector {\n height: 32px;\n}\n.du-bois-light-input-group .du-bois-light-input-affix-wrapper:not(:first-child) {\n border-top-left-radius: 0;\n border-bottom-left-radius: 0;\n}\n.du-bois-light-input-group .du-bois-light-input-affix-wrapper:not(:last-child) {\n border-top-right-radius: 0;\n border-bottom-right-radius: 0;\n}\n.du-bois-light-input-search .du-bois-light-input-group .du-bois-light-input-affix-wrapper:not(:last-child) {\n border-top-left-radius: 4px;\n border-bottom-left-radius: 4px;\n}\n.du-bois-light-input-group.du-bois-light-input-group-compact {\n display: block;\n}\n.du-bois-light-input-group.du-bois-light-input-group-compact::before {\n display: table;\n content: '';\n}\n.du-bois-light-input-group.du-bois-light-input-group-compact::after {\n display: table;\n clear: both;\n content: '';\n}\n.du-bois-light-input-group.du-bois-light-input-group-compact-addon:not(:first-child):not(:last-child),\n.du-bois-light-input-group.du-bois-light-input-group-compact-wrap:not(:first-child):not(:last-child),\n.du-bois-light-input-group.du-bois-light-input-group-compact > .du-bois-light-input:not(:first-child):not(:last-child) {\n border-right-width: 1px;\n}\n.du-bois-light-input-group.du-bois-light-input-group-compact-addon:not(:first-child):not(:last-child):hover,\n.du-bois-light-input-group.du-bois-light-input-group-compact-wrap:not(:first-child):not(:last-child):hover,\n.du-bois-light-input-group.du-bois-light-input-group-compact > .du-bois-light-input:not(:first-child):not(:last-child):hover {\n z-index: 1;\n}\n.du-bois-light-input-group.du-bois-light-input-group-compact-addon:not(:first-child):not(:last-child):focus,\n.du-bois-light-input-group.du-bois-light-input-group-compact-wrap:not(:first-child):not(:last-child):focus,\n.du-bois-light-input-group.du-bois-light-input-group-compact > .du-bois-light-input:not(:first-child):not(:last-child):focus {\n z-index: 1;\n}\n.du-bois-light-input-group.du-bois-light-input-group-compact > * {\n display: inline-block;\n float: none;\n vertical-align: top;\n border-radius: 0;\n}\n.du-bois-light-input-group.du-bois-light-input-group-compact > .du-bois-light-input-affix-wrapper {\n display: inline-flex;\n}\n.du-bois-light-input-group.du-bois-light-input-group-compact > .du-bois-light-picker-range {\n display: inline-flex;\n}\n.du-bois-light-input-group.du-bois-light-input-group-compact > *:not(:last-child) {\n margin-right: -1px;\n border-right-width: 1px;\n}\n.du-bois-light-input-group.du-bois-light-input-group-compact .du-bois-light-input {\n float: none;\n}\n.du-bois-light-input-group.du-bois-light-input-group-compact > .du-bois-light-select > .du-bois-light-select-selector,\n.du-bois-light-input-group.du-bois-light-input-group-compact > .du-bois-light-select-auto-complete .du-bois-light-input,\n.du-bois-light-input-group.du-bois-light-input-group-compact > .du-bois-light-cascader-picker .du-bois-light-input,\n.du-bois-light-input-group.du-bois-light-input-group-compact > .du-bois-light-input-group-wrapper .du-bois-light-input {\n border-right-width: 1px;\n border-radius: 0;\n}\n.du-bois-light-input-group.du-bois-light-input-group-compact > .du-bois-light-select > .du-bois-light-select-selector:hover,\n.du-bois-light-input-group.du-bois-light-input-group-compact > .du-bois-light-select-auto-complete .du-bois-light-input:hover,\n.du-bois-light-input-group.du-bois-light-input-group-compact > .du-bois-light-cascader-picker .du-bois-light-input:hover,\n.du-bois-light-input-group.du-bois-light-input-group-compact > .du-bois-light-input-group-wrapper .du-bois-light-input:hover {\n z-index: 1;\n}\n.du-bois-light-input-group.du-bois-light-input-group-compact > .du-bois-light-select > .du-bois-light-select-selector:focus,\n.du-bois-light-input-group.du-bois-light-input-group-compact > .du-bois-light-select-auto-complete .du-bois-light-input:focus,\n.du-bois-light-input-group.du-bois-light-input-group-compact > .du-bois-light-cascader-picker .du-bois-light-input:focus,\n.du-bois-light-input-group.du-bois-light-input-group-compact > .du-bois-light-input-group-wrapper .du-bois-light-input:focus {\n z-index: 1;\n}\n.du-bois-light-input-group.du-bois-light-input-group-compact > .du-bois-light-select-focused {\n z-index: 1;\n}\n.du-bois-light-input-group.du-bois-light-input-group-compact > .du-bois-light-select > .du-bois-light-select-arrow {\n z-index: 1;\n}\n.du-bois-light-input-group.du-bois-light-input-group-compact > *:first-child,\n.du-bois-light-input-group.du-bois-light-input-group-compact > .du-bois-light-select:first-child > .du-bois-light-select-selector,\n.du-bois-light-input-group.du-bois-light-input-group-compact > .du-bois-light-select-auto-complete:first-child .du-bois-light-input,\n.du-bois-light-input-group.du-bois-light-input-group-compact > .du-bois-light-cascader-picker:first-child .du-bois-light-input {\n border-top-left-radius: 4px;\n border-bottom-left-radius: 4px;\n}\n.du-bois-light-input-group.du-bois-light-input-group-compact > *:last-child,\n.du-bois-light-input-group.du-bois-light-input-group-compact > .du-bois-light-select:last-child > .du-bois-light-select-selector,\n.du-bois-light-input-group.du-bois-light-input-group-compact > .du-bois-light-cascader-picker:last-child .du-bois-light-input,\n.du-bois-light-input-group.du-bois-light-input-group-compact > .du-bois-light-cascader-picker-focused:last-child .du-bois-light-input {\n border-right-width: 1px;\n border-top-right-radius: 4px;\n border-bottom-right-radius: 4px;\n}\n.du-bois-light-input-group.du-bois-light-input-group-compact > .du-bois-light-select-auto-complete .du-bois-light-input {\n vertical-align: top;\n}\n.du-bois-light-input-group.du-bois-light-input-group-compact .du-bois-light-input-group-wrapper + .du-bois-light-input-group-wrapper {\n margin-left: -1px;\n}\n.du-bois-light-input-group.du-bois-light-input-group-compact .du-bois-light-input-group-wrapper + .du-bois-light-input-group-wrapper .du-bois-light-input-affix-wrapper {\n border-radius: 0;\n}\n.du-bois-light-input-group.du-bois-light-input-group-compact .du-bois-light-input-group-wrapper:not(:last-child).du-bois-light-input-search > .du-bois-light-input-group > .du-bois-light-input-group-addon > .du-bois-light-input-search-button {\n border-radius: 0;\n}\n.du-bois-light-input-group.du-bois-light-input-group-compact .du-bois-light-input-group-wrapper:not(:last-child).du-bois-light-input-search > .du-bois-light-input-group > .du-bois-light-input {\n border-radius: 4px 0 0 4px;\n}\n.du-bois-light-input-group-wrapper {\n display: inline-block;\n width: 100%;\n text-align: start;\n vertical-align: top;\n}\n.du-bois-light-input-password-icon {\n color: #5F7281;\n cursor: pointer;\n transition: all 0.3s;\n}\n.du-bois-light-input-password-icon:hover {\n color: rgba(0, 0, 0, 0.85);\n}\n.du-bois-light-input[type='color'] {\n height: 40px;\n}\n.du-bois-light-input[type='color'].du-bois-light-input-lg {\n height: 40px;\n}\n.du-bois-light-input[type='color'].du-bois-light-input-sm {\n height: 32px;\n padding-top: 3px;\n padding-bottom: 3px;\n}\n.du-bois-light-input-textarea-show-count::after {\n float: right;\n color: #5F7281;\n white-space: nowrap;\n content: attr(data-count);\n pointer-events: none;\n}\n.du-bois-light-input-search .du-bois-light-input:hover,\n.du-bois-light-input-search .du-bois-light-input:focus {\n border-color: #448dc2;\n}\n.du-bois-light-input-search .du-bois-light-input:hover + .du-bois-light-input-group-addon .du-bois-light-input-search-button:not(.du-bois-light-btn-primary),\n.du-bois-light-input-search .du-bois-light-input:focus + .du-bois-light-input-group-addon .du-bois-light-input-search-button:not(.du-bois-light-btn-primary) {\n border-left-color: #448dc2;\n}\n.du-bois-light-input-search .du-bois-light-input-affix-wrapper {\n border-radius: 0;\n}\n.du-bois-light-input-search .du-bois-light-input-lg {\n line-height: 19.9998px;\n}\n.du-bois-light-input-search > .du-bois-light-input-group > .du-bois-light-input-group-addon:last-child {\n left: -1px;\n padding: 0;\n border: 0;\n}\n.du-bois-light-input-search > .du-bois-light-input-group > .du-bois-light-input-group-addon:last-child .du-bois-light-input-search-button {\n padding-top: 0;\n padding-bottom: 0;\n border-radius: 0 4px 4px 0;\n}\n.du-bois-light-input-search > .du-bois-light-input-group > .du-bois-light-input-group-addon:last-child .du-bois-light-input-search-button:not(.du-bois-light-btn-primary) {\n color: #5F7281;\n}\n.du-bois-light-input-search > .du-bois-light-input-group > .du-bois-light-input-group-addon:last-child .du-bois-light-input-search-button:not(.du-bois-light-btn-primary).du-bois-light-btn-loading::before {\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n}\n.du-bois-light-input-search-button {\n height: 40px;\n}\n.du-bois-light-input-search-button:hover,\n.du-bois-light-input-search-button:focus {\n z-index: 1;\n}\n.du-bois-light-input-search-large .du-bois-light-input-search-button {\n height: 40px;\n}\n.du-bois-light-input-search-small .du-bois-light-input-search-button {\n height: 32px;\n}\n@media screen and (-ms-high-contrast: active), (-ms-high-contrast: none) {\n .du-bois-light-input {\n height: 40px;\n }\n .du-bois-light-input-lg {\n height: 40px;\n }\n .du-bois-light-input-sm {\n height: 32px;\n }\n .du-bois-light-input-affix-wrapper > input.du-bois-light-input {\n height: auto;\n }\n}\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-light-layout {\n display: flex;\n flex: auto;\n flex-direction: column;\n /* fix firefox can't set height smaller than content on flex item */\n min-height: 0;\n background: #f0f2f5;\n}\n.du-bois-light-layout,\n.du-bois-light-layout * {\n box-sizing: border-box;\n}\n.du-bois-light-layout.du-bois-light-layout-has-sider {\n flex-direction: row;\n}\n.du-bois-light-layout.du-bois-light-layout-has-sider > .du-bois-light-layout,\n.du-bois-light-layout.du-bois-light-layout-has-sider > .du-bois-light-layout-content {\n width: 0;\n}\n.du-bois-light-layout-header,\n.du-bois-light-layout-footer {\n flex: 0 0 auto;\n}\n.du-bois-light-layout-header {\n height: 64px;\n padding: 0 50px;\n color: #11171C;\n line-height: 64px;\n background: #001529;\n}\n.du-bois-light-layout-footer {\n padding: 24px 50px;\n color: #11171C;\n font-size: 13px;\n background: #f0f2f5;\n}\n.du-bois-light-layout-content {\n flex: auto;\n /* fix firefox can't set height smaller than content on flex item */\n min-height: 0;\n}\n.du-bois-light-layout-sider {\n position: relative;\n /* fix firefox can't set width smaller than content on flex item */\n min-width: 0;\n background: #001529;\n transition: all 0.2s;\n}\n.du-bois-light-layout-sider-children {\n height: 100%;\n margin-top: -0.1px;\n padding-top: 0.1px;\n}\n.du-bois-light-layout-sider-children .du-bois-light-menu.du-bois-light-menu-inline-collapsed {\n width: auto;\n}\n.du-bois-light-layout-sider-has-trigger {\n padding-bottom: 48px;\n}\n.du-bois-light-layout-sider-right {\n order: 1;\n}\n.du-bois-light-layout-sider-trigger {\n position: fixed;\n bottom: 0;\n z-index: 1;\n height: 48px;\n color: #fff;\n line-height: 48px;\n text-align: center;\n background: #002140;\n cursor: pointer;\n transition: all 0.2s;\n}\n.du-bois-light-layout-sider-zero-width > * {\n overflow: hidden;\n}\n.du-bois-light-layout-sider-zero-width-trigger {\n position: absolute;\n top: 64px;\n right: -36px;\n z-index: 1;\n width: 36px;\n height: 42px;\n color: #fff;\n font-size: 18px;\n line-height: 42px;\n text-align: center;\n background: #001529;\n border-radius: 0 4px 4px 0;\n cursor: pointer;\n transition: background 0.3s ease;\n}\n.du-bois-light-layout-sider-zero-width-trigger::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n transition: all 0.3s;\n content: '';\n}\n.du-bois-light-layout-sider-zero-width-trigger:hover::after {\n background: rgba(255, 255, 255, 0.1);\n}\n.du-bois-light-layout-sider-zero-width-trigger-right {\n left: -36px;\n border-radius: 4px 0 0 4px;\n}\n.du-bois-light-layout-sider-light {\n background: #fff;\n}\n.du-bois-light-layout-sider-light .du-bois-light-layout-sider-trigger {\n color: #11171C;\n background: #fff;\n}\n.du-bois-light-layout-sider-light .du-bois-light-layout-sider-zero-width-trigger {\n color: #11171C;\n background: #fff;\n}\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-light-menu-item-danger.du-bois-light-menu-item {\n color: #C82D4C;\n}\n.du-bois-light-menu-item-danger.du-bois-light-menu-item:hover,\n.du-bois-light-menu-item-danger.du-bois-light-menu-item-active {\n color: #C82D4C;\n}\n.du-bois-light-menu-item-danger.du-bois-light-menu-item:active {\n background: #fff1f0;\n}\n.du-bois-light-menu-item-danger.du-bois-light-menu-item-selected {\n color: #C82D4C;\n}\n.du-bois-light-menu-item-danger.du-bois-light-menu-item-selected > a,\n.du-bois-light-menu-item-danger.du-bois-light-menu-item-selected > a:hover {\n color: #C82D4C;\n}\n.du-bois-light-menu:not(.du-bois-light-menu-horizontal) .du-bois-light-menu-item-danger.du-bois-light-menu-item-selected {\n background-color: #fff1f0;\n}\n.du-bois-light-menu-inline .du-bois-light-menu-item-danger.du-bois-light-menu-item::after {\n border-right-color: #C82D4C;\n}\n.du-bois-light-menu-dark .du-bois-light-menu-item-danger.du-bois-light-menu-item,\n.du-bois-light-menu-dark .du-bois-light-menu-item-danger.du-bois-light-menu-item:hover,\n.du-bois-light-menu-dark .du-bois-light-menu-item-danger.du-bois-light-menu-item > a {\n color: #C82D4C;\n}\n.du-bois-light-menu-dark.du-bois-light-menu-dark:not(.du-bois-light-menu-horizontal) .du-bois-light-menu-item-danger.du-bois-light-menu-item-selected {\n color: #fff;\n background-color: #C82D4C;\n}\n.du-bois-light-menu {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n font-variant: tabular-nums;\n line-height: 20px;\n font-feature-settings: 'tnum';\n margin-bottom: 0;\n padding-left: 0;\n color: #11171C;\n font-size: 13px;\n line-height: 0;\n text-align: left;\n list-style: none;\n background: #fff;\n outline: none;\n box-shadow: 0 3px 6px -4px rgba(0, 0, 0, 0.12), 0 6px 16px 0 rgba(0, 0, 0, 0.08), 0 9px 28px 8px rgba(0, 0, 0, 0.05);\n transition: background 0.3s, width 0.3s cubic-bezier(0.2, 0, 0, 1) 0s;\n}\n.du-bois-light-menu::before {\n display: table;\n content: '';\n}\n.du-bois-light-menu::after {\n display: table;\n clear: both;\n content: '';\n}\n.du-bois-light-menu.du-bois-light-menu-root:focus-visible {\n box-shadow: 0 0 0 2px rgba(34, 114, 180, 0.2);\n}\n.du-bois-light-menu ul,\n.du-bois-light-menu ol {\n margin: 0;\n padding: 0;\n list-style: none;\n}\n.du-bois-light-menu-overflow {\n display: flex;\n}\n.du-bois-light-menu-overflow-item {\n flex: none;\n}\n.du-bois-light-menu-hidden,\n.du-bois-light-menu-submenu-hidden {\n display: none;\n}\n.du-bois-light-menu-item-group-title {\n height: 20px;\n padding: 8px 16px;\n color: #5F7281;\n font-size: 13px;\n line-height: 20px;\n transition: all 0.3s;\n}\n.du-bois-light-menu-horizontal .du-bois-light-menu-submenu {\n transition: border-color 0.3s cubic-bezier(0.645, 0.045, 0.355, 1), background 0.3s cubic-bezier(0.645, 0.045, 0.355, 1);\n}\n.du-bois-light-menu-submenu,\n.du-bois-light-menu-submenu-inline {\n transition: border-color 0.3s cubic-bezier(0.645, 0.045, 0.355, 1), background 0.3s cubic-bezier(0.645, 0.045, 0.355, 1), padding 0.15s cubic-bezier(0.645, 0.045, 0.355, 1);\n}\n.du-bois-light-menu-submenu-selected {\n color: #2272B4;\n}\n.du-bois-light-menu-item:active,\n.du-bois-light-menu-submenu-title:active {\n background: #e6f1f5;\n}\n.du-bois-light-menu-submenu .du-bois-light-menu-sub {\n cursor: initial;\n transition: background 0.3s cubic-bezier(0.645, 0.045, 0.355, 1), padding 0.3s cubic-bezier(0.645, 0.045, 0.355, 1);\n}\n.du-bois-light-menu-title-content {\n transition: color 0.3s;\n}\n.du-bois-light-menu-item a {\n color: #11171C;\n}\n.du-bois-light-menu-item a:hover {\n color: #2272B4;\n}\n.du-bois-light-menu-item a::before {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background-color: transparent;\n content: '';\n}\n.du-bois-light-menu-item > .du-bois-light-badge a {\n color: #11171C;\n}\n.du-bois-light-menu-item > .du-bois-light-badge a:hover {\n color: #2272B4;\n}\n.du-bois-light-menu-item-divider {\n height: 1px;\n overflow: hidden;\n line-height: 0;\n background-color: #f0f0f0;\n}\n.du-bois-light-menu-horizontal .du-bois-light-menu-item,\n.du-bois-light-menu-horizontal .du-bois-light-menu-submenu {\n margin-top: -1px;\n}\n.du-bois-light-menu-horizontal > .du-bois-light-menu-item:hover,\n.du-bois-light-menu-horizontal > .du-bois-light-menu-item-active,\n.du-bois-light-menu-horizontal > .du-bois-light-menu-submenu .du-bois-light-menu-submenu-title:hover {\n background-color: transparent;\n}\n.du-bois-light-menu-item-selected {\n color: #2272B4;\n}\n.du-bois-light-menu-item-selected a,\n.du-bois-light-menu-item-selected a:hover {\n color: #2272B4;\n}\n.du-bois-light-menu:not(.du-bois-light-menu-horizontal) .du-bois-light-menu-item-selected {\n background-color: #e6f1f5;\n}\n.du-bois-light-menu-inline,\n.du-bois-light-menu-vertical,\n.du-bois-light-menu-vertical-left {\n border-right: 1px solid #f0f0f0;\n}\n.du-bois-light-menu-vertical-right {\n border-left: 1px solid #f0f0f0;\n}\n.du-bois-light-menu-vertical.du-bois-light-menu-sub,\n.du-bois-light-menu-vertical-left.du-bois-light-menu-sub,\n.du-bois-light-menu-vertical-right.du-bois-light-menu-sub {\n min-width: 160px;\n max-height: calc(100vh - 100px);\n padding: 0;\n overflow: hidden;\n border-right: 0;\n}\n.du-bois-light-menu-vertical.du-bois-light-menu-sub:not([class*='-active']),\n.du-bois-light-menu-vertical-left.du-bois-light-menu-sub:not([class*='-active']),\n.du-bois-light-menu-vertical-right.du-bois-light-menu-sub:not([class*='-active']) {\n overflow-x: hidden;\n overflow-y: auto;\n}\n.du-bois-light-menu-vertical.du-bois-light-menu-sub .du-bois-light-menu-item,\n.du-bois-light-menu-vertical-left.du-bois-light-menu-sub .du-bois-light-menu-item,\n.du-bois-light-menu-vertical-right.du-bois-light-menu-sub .du-bois-light-menu-item {\n left: 0;\n margin-left: 0;\n border-right: 0;\n}\n.du-bois-light-menu-vertical.du-bois-light-menu-sub .du-bois-light-menu-item::after,\n.du-bois-light-menu-vertical-left.du-bois-light-menu-sub .du-bois-light-menu-item::after,\n.du-bois-light-menu-vertical-right.du-bois-light-menu-sub .du-bois-light-menu-item::after {\n border-right: 0;\n}\n.du-bois-light-menu-vertical.du-bois-light-menu-sub > .du-bois-light-menu-item,\n.du-bois-light-menu-vertical-left.du-bois-light-menu-sub > .du-bois-light-menu-item,\n.du-bois-light-menu-vertical-right.du-bois-light-menu-sub > .du-bois-light-menu-item,\n.du-bois-light-menu-vertical.du-bois-light-menu-sub > .du-bois-light-menu-submenu,\n.du-bois-light-menu-vertical-left.du-bois-light-menu-sub > .du-bois-light-menu-submenu,\n.du-bois-light-menu-vertical-right.du-bois-light-menu-sub > .du-bois-light-menu-submenu {\n transform-origin: 0 0;\n}\n.du-bois-light-menu-horizontal.du-bois-light-menu-sub {\n min-width: 114px;\n}\n.du-bois-light-menu-horizontal .du-bois-light-menu-item,\n.du-bois-light-menu-horizontal .du-bois-light-menu-submenu-title {\n transition: border-color 0.3s, background 0.3s;\n}\n.du-bois-light-menu-item,\n.du-bois-light-menu-submenu-title {\n position: relative;\n display: block;\n margin: 0;\n padding: 0 20px;\n white-space: nowrap;\n cursor: pointer;\n transition: border-color 0.3s, background 0.3s, padding 0.3s cubic-bezier(0.645, 0.045, 0.355, 1);\n}\n.du-bois-light-menu-item .du-bois-light-menu-item-icon,\n.du-bois-light-menu-submenu-title .du-bois-light-menu-item-icon,\n.du-bois-light-menu-item .anticon,\n.du-bois-light-menu-submenu-title .anticon {\n min-width: 14px;\n font-size: 13px;\n transition: font-size 0.15s cubic-bezier(0.215, 0.61, 0.355, 1), margin 0.3s cubic-bezier(0.645, 0.045, 0.355, 1), color 0.3s;\n}\n.du-bois-light-menu-item .du-bois-light-menu-item-icon + span,\n.du-bois-light-menu-submenu-title .du-bois-light-menu-item-icon + span,\n.du-bois-light-menu-item .anticon + span,\n.du-bois-light-menu-submenu-title .anticon + span {\n margin-left: 10px;\n opacity: 1;\n transition: opacity 0.3s cubic-bezier(0.645, 0.045, 0.355, 1), margin 0.3s, color 0.3s;\n}\n.du-bois-light-menu-item .du-bois-light-menu-item-icon.svg,\n.du-bois-light-menu-submenu-title .du-bois-light-menu-item-icon.svg {\n vertical-align: -0.125em;\n}\n.du-bois-light-menu-item.du-bois-light-menu-item-only-child > .anticon,\n.du-bois-light-menu-submenu-title.du-bois-light-menu-item-only-child > .anticon,\n.du-bois-light-menu-item.du-bois-light-menu-item-only-child > .du-bois-light-menu-item-icon,\n.du-bois-light-menu-submenu-title.du-bois-light-menu-item-only-child > .du-bois-light-menu-item-icon {\n margin-right: 0;\n}\n.du-bois-light-menu-item:focus-visible,\n.du-bois-light-menu-submenu-title:focus-visible {\n box-shadow: 0 0 0 2px rgba(34, 114, 180, 0.2);\n}\n.du-bois-light-menu > .du-bois-light-menu-item-divider {\n height: 1px;\n margin: 1px 0;\n padding: 0;\n overflow: hidden;\n line-height: 0;\n background-color: #f0f0f0;\n}\n.du-bois-light-menu-submenu-popup {\n position: absolute;\n z-index: 1050;\n background: transparent;\n border-radius: 4px;\n box-shadow: none;\n transform-origin: 0 0;\n}\n.du-bois-light-menu-submenu-popup::before {\n position: absolute;\n top: -7px;\n right: 0;\n bottom: 0;\n left: 0;\n z-index: -1;\n width: 100%;\n height: 100%;\n opacity: 0.0001;\n content: ' ';\n}\n.du-bois-light-menu-submenu-placement-rightTop::before {\n top: 0;\n left: -7px;\n}\n.du-bois-light-menu-submenu > .du-bois-light-menu {\n background-color: #fff;\n border-radius: 4px;\n}\n.du-bois-light-menu-submenu > .du-bois-light-menu-submenu-title::after {\n transition: transform 0.3s cubic-bezier(0.645, 0.045, 0.355, 1);\n}\n.du-bois-light-menu-submenu-popup > .du-bois-light-menu {\n background-color: #fff;\n}\n.du-bois-light-menu-submenu-expand-icon,\n.du-bois-light-menu-submenu-arrow {\n position: absolute;\n top: 50%;\n right: 16px;\n width: 10px;\n color: #11171C;\n transform: translateY(-50%);\n transition: transform 0.3s cubic-bezier(0.645, 0.045, 0.355, 1);\n}\n.du-bois-light-menu-submenu-arrow::before,\n.du-bois-light-menu-submenu-arrow::after {\n position: absolute;\n width: 6px;\n height: 1.5px;\n background-color: currentColor;\n border-radius: 2px;\n transition: background 0.3s cubic-bezier(0.645, 0.045, 0.355, 1), transform 0.3s cubic-bezier(0.645, 0.045, 0.355, 1), top 0.3s cubic-bezier(0.645, 0.045, 0.355, 1), color 0.3s cubic-bezier(0.645, 0.045, 0.355, 1);\n content: '';\n}\n.du-bois-light-menu-submenu-arrow::before {\n transform: rotate(45deg) translateY(-2.5px);\n}\n.du-bois-light-menu-submenu-arrow::after {\n transform: rotate(-45deg) translateY(2.5px);\n}\n.du-bois-light-menu-submenu:hover > .du-bois-light-menu-submenu-title > .du-bois-light-menu-submenu-expand-icon,\n.du-bois-light-menu-submenu:hover > .du-bois-light-menu-submenu-title > .du-bois-light-menu-submenu-arrow {\n color: #2272B4;\n}\n.du-bois-light-menu-inline-collapsed .du-bois-light-menu-submenu-arrow::before,\n.du-bois-light-menu-submenu-inline .du-bois-light-menu-submenu-arrow::before {\n transform: rotate(-45deg) translateX(2.5px);\n}\n.du-bois-light-menu-inline-collapsed .du-bois-light-menu-submenu-arrow::after,\n.du-bois-light-menu-submenu-inline .du-bois-light-menu-submenu-arrow::after {\n transform: rotate(45deg) translateX(-2.5px);\n}\n.du-bois-light-menu-submenu-horizontal .du-bois-light-menu-submenu-arrow {\n display: none;\n}\n.du-bois-light-menu-submenu-open.du-bois-light-menu-submenu-inline > .du-bois-light-menu-submenu-title > .du-bois-light-menu-submenu-arrow {\n transform: translateY(-2px);\n}\n.du-bois-light-menu-submenu-open.du-bois-light-menu-submenu-inline > .du-bois-light-menu-submenu-title > .du-bois-light-menu-submenu-arrow::after {\n transform: rotate(-45deg) translateX(-2.5px);\n}\n.du-bois-light-menu-submenu-open.du-bois-light-menu-submenu-inline > .du-bois-light-menu-submenu-title > .du-bois-light-menu-submenu-arrow::before {\n transform: rotate(45deg) translateX(2.5px);\n}\n.du-bois-light-menu-vertical .du-bois-light-menu-submenu-selected,\n.du-bois-light-menu-vertical-left .du-bois-light-menu-submenu-selected,\n.du-bois-light-menu-vertical-right .du-bois-light-menu-submenu-selected {\n color: #2272B4;\n}\n.du-bois-light-menu-horizontal {\n line-height: 46px;\n border: 0;\n border-bottom: 1px solid #f0f0f0;\n box-shadow: none;\n}\n.du-bois-light-menu-horizontal:not(.du-bois-light-menu-dark) > .du-bois-light-menu-item,\n.du-bois-light-menu-horizontal:not(.du-bois-light-menu-dark) > .du-bois-light-menu-submenu {\n margin-top: -1px;\n margin-bottom: 0;\n padding: 0 20px;\n}\n.du-bois-light-menu-horizontal:not(.du-bois-light-menu-dark) > .du-bois-light-menu-item:hover,\n.du-bois-light-menu-horizontal:not(.du-bois-light-menu-dark) > .du-bois-light-menu-submenu:hover,\n.du-bois-light-menu-horizontal:not(.du-bois-light-menu-dark) > .du-bois-light-menu-item-active,\n.du-bois-light-menu-horizontal:not(.du-bois-light-menu-dark) > .du-bois-light-menu-submenu-active,\n.du-bois-light-menu-horizontal:not(.du-bois-light-menu-dark) > .du-bois-light-menu-item-open,\n.du-bois-light-menu-horizontal:not(.du-bois-light-menu-dark) > .du-bois-light-menu-submenu-open,\n.du-bois-light-menu-horizontal:not(.du-bois-light-menu-dark) > .du-bois-light-menu-item-selected,\n.du-bois-light-menu-horizontal:not(.du-bois-light-menu-dark) > .du-bois-light-menu-submenu-selected {\n color: #2272B4;\n}\n.du-bois-light-menu-horizontal:not(.du-bois-light-menu-dark) > .du-bois-light-menu-item:hover::after,\n.du-bois-light-menu-horizontal:not(.du-bois-light-menu-dark) > .du-bois-light-menu-submenu:hover::after,\n.du-bois-light-menu-horizontal:not(.du-bois-light-menu-dark) > .du-bois-light-menu-item-active::after,\n.du-bois-light-menu-horizontal:not(.du-bois-light-menu-dark) > .du-bois-light-menu-submenu-active::after,\n.du-bois-light-menu-horizontal:not(.du-bois-light-menu-dark) > .du-bois-light-menu-item-open::after,\n.du-bois-light-menu-horizontal:not(.du-bois-light-menu-dark) > .du-bois-light-menu-submenu-open::after,\n.du-bois-light-menu-horizontal:not(.du-bois-light-menu-dark) > .du-bois-light-menu-item-selected::after,\n.du-bois-light-menu-horizontal:not(.du-bois-light-menu-dark) > .du-bois-light-menu-submenu-selected::after {\n border-bottom: 2px solid #2272B4;\n}\n.du-bois-light-menu-horizontal > .du-bois-light-menu-item,\n.du-bois-light-menu-horizontal > .du-bois-light-menu-submenu {\n position: relative;\n top: 1px;\n display: inline-block;\n vertical-align: bottom;\n}\n.du-bois-light-menu-horizontal > .du-bois-light-menu-item::after,\n.du-bois-light-menu-horizontal > .du-bois-light-menu-submenu::after {\n position: absolute;\n right: 20px;\n bottom: 0;\n left: 20px;\n border-bottom: 2px solid transparent;\n transition: border-color 0.3s cubic-bezier(0.645, 0.045, 0.355, 1);\n content: '';\n}\n.du-bois-light-menu-horizontal > .du-bois-light-menu-submenu > .du-bois-light-menu-submenu-title {\n padding: 0;\n}\n.du-bois-light-menu-horizontal > .du-bois-light-menu-item a {\n color: #11171C;\n}\n.du-bois-light-menu-horizontal > .du-bois-light-menu-item a:hover {\n color: #2272B4;\n}\n.du-bois-light-menu-horizontal > .du-bois-light-menu-item a::before {\n bottom: -2px;\n}\n.du-bois-light-menu-horizontal > .du-bois-light-menu-item-selected a {\n color: #2272B4;\n}\n.du-bois-light-menu-horizontal::after {\n display: block;\n clear: both;\n height: 0;\n content: '\\20';\n}\n.du-bois-light-menu-vertical .du-bois-light-menu-item,\n.du-bois-light-menu-vertical-left .du-bois-light-menu-item,\n.du-bois-light-menu-vertical-right .du-bois-light-menu-item,\n.du-bois-light-menu-inline .du-bois-light-menu-item {\n position: relative;\n}\n.du-bois-light-menu-vertical .du-bois-light-menu-item::after,\n.du-bois-light-menu-vertical-left .du-bois-light-menu-item::after,\n.du-bois-light-menu-vertical-right .du-bois-light-menu-item::after,\n.du-bois-light-menu-inline .du-bois-light-menu-item::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n border-right: 3px solid #2272B4;\n transform: scaleY(0.0001);\n opacity: 0;\n transition: transform 0.15s cubic-bezier(0.215, 0.61, 0.355, 1), opacity 0.15s cubic-bezier(0.215, 0.61, 0.355, 1);\n content: '';\n}\n.du-bois-light-menu-vertical .du-bois-light-menu-item,\n.du-bois-light-menu-vertical-left .du-bois-light-menu-item,\n.du-bois-light-menu-vertical-right .du-bois-light-menu-item,\n.du-bois-light-menu-inline .du-bois-light-menu-item,\n.du-bois-light-menu-vertical .du-bois-light-menu-submenu-title,\n.du-bois-light-menu-vertical-left .du-bois-light-menu-submenu-title,\n.du-bois-light-menu-vertical-right .du-bois-light-menu-submenu-title,\n.du-bois-light-menu-inline .du-bois-light-menu-submenu-title {\n height: 40px;\n margin-top: 4px;\n margin-bottom: 4px;\n padding: 0 16px;\n overflow: hidden;\n line-height: 40px;\n text-overflow: ellipsis;\n}\n.du-bois-light-menu-vertical .du-bois-light-menu-submenu,\n.du-bois-light-menu-vertical-left .du-bois-light-menu-submenu,\n.du-bois-light-menu-vertical-right .du-bois-light-menu-submenu,\n.du-bois-light-menu-inline .du-bois-light-menu-submenu {\n padding-bottom: 0.02px;\n}\n.du-bois-light-menu-vertical .du-bois-light-menu-item:not(:last-child),\n.du-bois-light-menu-vertical-left .du-bois-light-menu-item:not(:last-child),\n.du-bois-light-menu-vertical-right .du-bois-light-menu-item:not(:last-child),\n.du-bois-light-menu-inline .du-bois-light-menu-item:not(:last-child) {\n margin-bottom: 8px;\n}\n.du-bois-light-menu-vertical > .du-bois-light-menu-item,\n.du-bois-light-menu-vertical-left > .du-bois-light-menu-item,\n.du-bois-light-menu-vertical-right > .du-bois-light-menu-item,\n.du-bois-light-menu-inline > .du-bois-light-menu-item,\n.du-bois-light-menu-vertical > .du-bois-light-menu-submenu > .du-bois-light-menu-submenu-title,\n.du-bois-light-menu-vertical-left > .du-bois-light-menu-submenu > .du-bois-light-menu-submenu-title,\n.du-bois-light-menu-vertical-right > .du-bois-light-menu-submenu > .du-bois-light-menu-submenu-title,\n.du-bois-light-menu-inline > .du-bois-light-menu-submenu > .du-bois-light-menu-submenu-title {\n height: 40px;\n line-height: 40px;\n}\n.du-bois-light-menu-vertical .du-bois-light-menu-item-group-list .du-bois-light-menu-submenu-title,\n.du-bois-light-menu-vertical .du-bois-light-menu-submenu-title {\n padding-right: 34px;\n}\n.du-bois-light-menu-inline {\n width: 100%;\n}\n.du-bois-light-menu-inline .du-bois-light-menu-selected::after,\n.du-bois-light-menu-inline .du-bois-light-menu-item-selected::after {\n transform: scaleY(1);\n opacity: 1;\n transition: transform 0.15s cubic-bezier(0.645, 0.045, 0.355, 1), opacity 0.15s cubic-bezier(0.645, 0.045, 0.355, 1);\n}\n.du-bois-light-menu-inline .du-bois-light-menu-item,\n.du-bois-light-menu-inline .du-bois-light-menu-submenu-title {\n width: calc(100% + 1px);\n}\n.du-bois-light-menu-inline .du-bois-light-menu-item-group-list .du-bois-light-menu-submenu-title,\n.du-bois-light-menu-inline .du-bois-light-menu-submenu-title {\n padding-right: 34px;\n}\n.du-bois-light-menu-inline.du-bois-light-menu-root .du-bois-light-menu-item,\n.du-bois-light-menu-inline.du-bois-light-menu-root .du-bois-light-menu-submenu-title {\n display: flex;\n align-items: center;\n transition: border-color 0.3s, background 0.3s, padding 0.1s cubic-bezier(0.215, 0.61, 0.355, 1);\n}\n.du-bois-light-menu-inline.du-bois-light-menu-root .du-bois-light-menu-item > .du-bois-light-menu-title-content,\n.du-bois-light-menu-inline.du-bois-light-menu-root .du-bois-light-menu-submenu-title > .du-bois-light-menu-title-content {\n flex: auto;\n min-width: 0;\n overflow: hidden;\n text-overflow: ellipsis;\n}\n.du-bois-light-menu-inline.du-bois-light-menu-root .du-bois-light-menu-item > *,\n.du-bois-light-menu-inline.du-bois-light-menu-root .du-bois-light-menu-submenu-title > * {\n flex: none;\n}\n.du-bois-light-menu.du-bois-light-menu-inline-collapsed {\n width: 80px;\n}\n.du-bois-light-menu.du-bois-light-menu-inline-collapsed > .du-bois-light-menu-item,\n.du-bois-light-menu.du-bois-light-menu-inline-collapsed > .du-bois-light-menu-item-group > .du-bois-light-menu-item-group-list > .du-bois-light-menu-item,\n.du-bois-light-menu.du-bois-light-menu-inline-collapsed > .du-bois-light-menu-item-group > .du-bois-light-menu-item-group-list > .du-bois-light-menu-submenu > .du-bois-light-menu-submenu-title,\n.du-bois-light-menu.du-bois-light-menu-inline-collapsed > .du-bois-light-menu-submenu > .du-bois-light-menu-submenu-title {\n left: 0;\n padding: 0 calc(50% - 18px / 2);\n text-overflow: clip;\n}\n.du-bois-light-menu.du-bois-light-menu-inline-collapsed > .du-bois-light-menu-item .du-bois-light-menu-submenu-arrow,\n.du-bois-light-menu.du-bois-light-menu-inline-collapsed > .du-bois-light-menu-item-group > .du-bois-light-menu-item-group-list > .du-bois-light-menu-item .du-bois-light-menu-submenu-arrow,\n.du-bois-light-menu.du-bois-light-menu-inline-collapsed > .du-bois-light-menu-item-group > .du-bois-light-menu-item-group-list > .du-bois-light-menu-submenu > .du-bois-light-menu-submenu-title .du-bois-light-menu-submenu-arrow,\n.du-bois-light-menu.du-bois-light-menu-inline-collapsed > .du-bois-light-menu-submenu > .du-bois-light-menu-submenu-title .du-bois-light-menu-submenu-arrow {\n opacity: 0;\n}\n.du-bois-light-menu.du-bois-light-menu-inline-collapsed > .du-bois-light-menu-item .du-bois-light-menu-item-icon,\n.du-bois-light-menu.du-bois-light-menu-inline-collapsed > .du-bois-light-menu-item-group > .du-bois-light-menu-item-group-list > .du-bois-light-menu-item .du-bois-light-menu-item-icon,\n.du-bois-light-menu.du-bois-light-menu-inline-collapsed > .du-bois-light-menu-item-group > .du-bois-light-menu-item-group-list > .du-bois-light-menu-submenu > .du-bois-light-menu-submenu-title .du-bois-light-menu-item-icon,\n.du-bois-light-menu.du-bois-light-menu-inline-collapsed > .du-bois-light-menu-submenu > .du-bois-light-menu-submenu-title .du-bois-light-menu-item-icon,\n.du-bois-light-menu.du-bois-light-menu-inline-collapsed > .du-bois-light-menu-item .anticon,\n.du-bois-light-menu.du-bois-light-menu-inline-collapsed > .du-bois-light-menu-item-group > .du-bois-light-menu-item-group-list > .du-bois-light-menu-item .anticon,\n.du-bois-light-menu.du-bois-light-menu-inline-collapsed > .du-bois-light-menu-item-group > .du-bois-light-menu-item-group-list > .du-bois-light-menu-submenu > .du-bois-light-menu-submenu-title .anticon,\n.du-bois-light-menu.du-bois-light-menu-inline-collapsed > .du-bois-light-menu-submenu > .du-bois-light-menu-submenu-title .anticon {\n margin: 0;\n font-size: 18px;\n line-height: 40px;\n}\n.du-bois-light-menu.du-bois-light-menu-inline-collapsed > .du-bois-light-menu-item .du-bois-light-menu-item-icon + span,\n.du-bois-light-menu.du-bois-light-menu-inline-collapsed > .du-bois-light-menu-item-group > .du-bois-light-menu-item-group-list > .du-bois-light-menu-item .du-bois-light-menu-item-icon + span,\n.du-bois-light-menu.du-bois-light-menu-inline-collapsed > .du-bois-light-menu-item-group > .du-bois-light-menu-item-group-list > .du-bois-light-menu-submenu > .du-bois-light-menu-submenu-title .du-bois-light-menu-item-icon + span,\n.du-bois-light-menu.du-bois-light-menu-inline-collapsed > .du-bois-light-menu-submenu > .du-bois-light-menu-submenu-title .du-bois-light-menu-item-icon + span,\n.du-bois-light-menu.du-bois-light-menu-inline-collapsed > .du-bois-light-menu-item .anticon + span,\n.du-bois-light-menu.du-bois-light-menu-inline-collapsed > .du-bois-light-menu-item-group > .du-bois-light-menu-item-group-list > .du-bois-light-menu-item .anticon + span,\n.du-bois-light-menu.du-bois-light-menu-inline-collapsed > .du-bois-light-menu-item-group > .du-bois-light-menu-item-group-list > .du-bois-light-menu-submenu > .du-bois-light-menu-submenu-title .anticon + span,\n.du-bois-light-menu.du-bois-light-menu-inline-collapsed > .du-bois-light-menu-submenu > .du-bois-light-menu-submenu-title .anticon + span {\n display: inline-block;\n opacity: 0;\n}\n.du-bois-light-menu.du-bois-light-menu-inline-collapsed .du-bois-light-menu-item-icon,\n.du-bois-light-menu.du-bois-light-menu-inline-collapsed .anticon {\n display: inline-block;\n}\n.du-bois-light-menu.du-bois-light-menu-inline-collapsed-tooltip {\n pointer-events: none;\n}\n.du-bois-light-menu.du-bois-light-menu-inline-collapsed-tooltip .du-bois-light-menu-item-icon,\n.du-bois-light-menu.du-bois-light-menu-inline-collapsed-tooltip .anticon {\n display: none;\n}\n.du-bois-light-menu.du-bois-light-menu-inline-collapsed-tooltip a {\n color: rgba(255, 255, 255, 0.85);\n}\n.du-bois-light-menu.du-bois-light-menu-inline-collapsed .du-bois-light-menu-item-group-title {\n padding-right: 4px;\n padding-left: 4px;\n overflow: hidden;\n white-space: nowrap;\n text-overflow: ellipsis;\n}\n.du-bois-light-menu-item-group-list {\n margin: 0;\n padding: 0;\n}\n.du-bois-light-menu-item-group-list .du-bois-light-menu-item,\n.du-bois-light-menu-item-group-list .du-bois-light-menu-submenu-title {\n padding: 0 16px 0 28px;\n}\n.du-bois-light-menu-root.du-bois-light-menu-vertical,\n.du-bois-light-menu-root.du-bois-light-menu-vertical-left,\n.du-bois-light-menu-root.du-bois-light-menu-vertical-right,\n.du-bois-light-menu-root.du-bois-light-menu-inline {\n box-shadow: none;\n}\n.du-bois-light-menu-root.du-bois-light-menu-inline-collapsed .du-bois-light-menu-item > .du-bois-light-menu-inline-collapsed-noicon,\n.du-bois-light-menu-root.du-bois-light-menu-inline-collapsed .du-bois-light-menu-submenu .du-bois-light-menu-submenu-title > .du-bois-light-menu-inline-collapsed-noicon {\n font-size: 18px;\n text-align: center;\n}\n.du-bois-light-menu-sub.du-bois-light-menu-inline {\n padding: 0;\n background: #fafafa;\n border: 0;\n border-radius: 0;\n box-shadow: none;\n}\n.du-bois-light-menu-sub.du-bois-light-menu-inline > .du-bois-light-menu-item,\n.du-bois-light-menu-sub.du-bois-light-menu-inline > .du-bois-light-menu-submenu > .du-bois-light-menu-submenu-title {\n height: 40px;\n line-height: 40px;\n list-style-position: inside;\n list-style-type: disc;\n}\n.du-bois-light-menu-sub.du-bois-light-menu-inline .du-bois-light-menu-item-group-title {\n padding-left: 32px;\n}\n.du-bois-light-menu-item-disabled,\n.du-bois-light-menu-submenu-disabled {\n color: rgba(0, 0, 0, 0.25) !important;\n background: none;\n cursor: not-allowed;\n}\n.du-bois-light-menu-item-disabled::after,\n.du-bois-light-menu-submenu-disabled::after {\n border-color: transparent !important;\n}\n.du-bois-light-menu-item-disabled a,\n.du-bois-light-menu-submenu-disabled a {\n color: rgba(0, 0, 0, 0.25) !important;\n pointer-events: none;\n}\n.du-bois-light-menu-item-disabled > .du-bois-light-menu-submenu-title,\n.du-bois-light-menu-submenu-disabled > .du-bois-light-menu-submenu-title {\n color: rgba(0, 0, 0, 0.25) !important;\n cursor: not-allowed;\n}\n.du-bois-light-menu-item-disabled > .du-bois-light-menu-submenu-title > .du-bois-light-menu-submenu-arrow::before,\n.du-bois-light-menu-submenu-disabled > .du-bois-light-menu-submenu-title > .du-bois-light-menu-submenu-arrow::before,\n.du-bois-light-menu-item-disabled > .du-bois-light-menu-submenu-title > .du-bois-light-menu-submenu-arrow::after,\n.du-bois-light-menu-submenu-disabled > .du-bois-light-menu-submenu-title > .du-bois-light-menu-submenu-arrow::after {\n background: rgba(0, 0, 0, 0.25) !important;\n}\n.du-bois-light-layout-header .du-bois-light-menu {\n line-height: inherit;\n}\n.du-bois-light-menu-light .du-bois-light-menu-item:hover,\n.du-bois-light-menu-light .du-bois-light-menu-item-active,\n.du-bois-light-menu-light .du-bois-light-menu:not(.du-bois-light-menu-inline) .du-bois-light-menu-submenu-open,\n.du-bois-light-menu-light .du-bois-light-menu-submenu-active,\n.du-bois-light-menu-light .du-bois-light-menu-submenu-title:hover {\n color: #2272B4;\n}\n.du-bois-light-menu.du-bois-light-menu-dark,\n.du-bois-light-menu-dark .du-bois-light-menu-sub,\n.du-bois-light-menu.du-bois-light-menu-dark .du-bois-light-menu-sub {\n color: rgba(255, 255, 255, 0.65);\n background: #001529;\n}\n.du-bois-light-menu.du-bois-light-menu-dark .du-bois-light-menu-submenu-title .du-bois-light-menu-submenu-arrow,\n.du-bois-light-menu-dark .du-bois-light-menu-sub .du-bois-light-menu-submenu-title .du-bois-light-menu-submenu-arrow,\n.du-bois-light-menu.du-bois-light-menu-dark .du-bois-light-menu-sub .du-bois-light-menu-submenu-title .du-bois-light-menu-submenu-arrow {\n opacity: 0.45;\n transition: all 0.3s;\n}\n.du-bois-light-menu.du-bois-light-menu-dark .du-bois-light-menu-submenu-title .du-bois-light-menu-submenu-arrow::after,\n.du-bois-light-menu-dark .du-bois-light-menu-sub .du-bois-light-menu-submenu-title .du-bois-light-menu-submenu-arrow::after,\n.du-bois-light-menu.du-bois-light-menu-dark .du-bois-light-menu-sub .du-bois-light-menu-submenu-title .du-bois-light-menu-submenu-arrow::after,\n.du-bois-light-menu.du-bois-light-menu-dark .du-bois-light-menu-submenu-title .du-bois-light-menu-submenu-arrow::before,\n.du-bois-light-menu-dark .du-bois-light-menu-sub .du-bois-light-menu-submenu-title .du-bois-light-menu-submenu-arrow::before,\n.du-bois-light-menu.du-bois-light-menu-dark .du-bois-light-menu-sub .du-bois-light-menu-submenu-title .du-bois-light-menu-submenu-arrow::before {\n background: #fff;\n}\n.du-bois-light-menu-dark.du-bois-light-menu-submenu-popup {\n background: transparent;\n}\n.du-bois-light-menu-dark .du-bois-light-menu-inline.du-bois-light-menu-sub {\n background: #000c17;\n}\n.du-bois-light-menu-dark.du-bois-light-menu-horizontal {\n border-bottom: 0;\n}\n.du-bois-light-menu-dark.du-bois-light-menu-horizontal > .du-bois-light-menu-item,\n.du-bois-light-menu-dark.du-bois-light-menu-horizontal > .du-bois-light-menu-submenu {\n top: 0;\n margin-top: 0;\n padding: 0 20px;\n border-color: #001529;\n border-bottom: 0;\n}\n.du-bois-light-menu-dark.du-bois-light-menu-horizontal > .du-bois-light-menu-item:hover {\n background-color: #2272B4;\n}\n.du-bois-light-menu-dark.du-bois-light-menu-horizontal > .du-bois-light-menu-item > a::before {\n bottom: 0;\n}\n.du-bois-light-menu-dark .du-bois-light-menu-item,\n.du-bois-light-menu-dark .du-bois-light-menu-item-group-title,\n.du-bois-light-menu-dark .du-bois-light-menu-item > a,\n.du-bois-light-menu-dark .du-bois-light-menu-item > span > a {\n color: rgba(255, 255, 255, 0.65);\n}\n.du-bois-light-menu-dark.du-bois-light-menu-inline,\n.du-bois-light-menu-dark.du-bois-light-menu-vertical,\n.du-bois-light-menu-dark.du-bois-light-menu-vertical-left,\n.du-bois-light-menu-dark.du-bois-light-menu-vertical-right {\n border-right: 0;\n}\n.du-bois-light-menu-dark.du-bois-light-menu-inline .du-bois-light-menu-item,\n.du-bois-light-menu-dark.du-bois-light-menu-vertical .du-bois-light-menu-item,\n.du-bois-light-menu-dark.du-bois-light-menu-vertical-left .du-bois-light-menu-item,\n.du-bois-light-menu-dark.du-bois-light-menu-vertical-right .du-bois-light-menu-item {\n left: 0;\n margin-left: 0;\n border-right: 0;\n}\n.du-bois-light-menu-dark.du-bois-light-menu-inline .du-bois-light-menu-item::after,\n.du-bois-light-menu-dark.du-bois-light-menu-vertical .du-bois-light-menu-item::after,\n.du-bois-light-menu-dark.du-bois-light-menu-vertical-left .du-bois-light-menu-item::after,\n.du-bois-light-menu-dark.du-bois-light-menu-vertical-right .du-bois-light-menu-item::after {\n border-right: 0;\n}\n.du-bois-light-menu-dark.du-bois-light-menu-inline .du-bois-light-menu-item,\n.du-bois-light-menu-dark.du-bois-light-menu-inline .du-bois-light-menu-submenu-title {\n width: 100%;\n}\n.du-bois-light-menu-dark .du-bois-light-menu-item:hover,\n.du-bois-light-menu-dark .du-bois-light-menu-item-active,\n.du-bois-light-menu-dark .du-bois-light-menu-submenu-active,\n.du-bois-light-menu-dark .du-bois-light-menu-submenu-open,\n.du-bois-light-menu-dark .du-bois-light-menu-submenu-selected,\n.du-bois-light-menu-dark .du-bois-light-menu-submenu-title:hover {\n color: #fff;\n background-color: transparent;\n}\n.du-bois-light-menu-dark .du-bois-light-menu-item:hover > a,\n.du-bois-light-menu-dark .du-bois-light-menu-item-active > a,\n.du-bois-light-menu-dark .du-bois-light-menu-submenu-active > a,\n.du-bois-light-menu-dark .du-bois-light-menu-submenu-open > a,\n.du-bois-light-menu-dark .du-bois-light-menu-submenu-selected > a,\n.du-bois-light-menu-dark .du-bois-light-menu-submenu-title:hover > a,\n.du-bois-light-menu-dark .du-bois-light-menu-item:hover > span > a,\n.du-bois-light-menu-dark .du-bois-light-menu-item-active > span > a,\n.du-bois-light-menu-dark .du-bois-light-menu-submenu-active > span > a,\n.du-bois-light-menu-dark .du-bois-light-menu-submenu-open > span > a,\n.du-bois-light-menu-dark .du-bois-light-menu-submenu-selected > span > a,\n.du-bois-light-menu-dark .du-bois-light-menu-submenu-title:hover > span > a {\n color: #fff;\n}\n.du-bois-light-menu-dark .du-bois-light-menu-item:hover > .du-bois-light-menu-submenu-title > .du-bois-light-menu-submenu-arrow,\n.du-bois-light-menu-dark .du-bois-light-menu-item-active > .du-bois-light-menu-submenu-title > .du-bois-light-menu-submenu-arrow,\n.du-bois-light-menu-dark .du-bois-light-menu-submenu-active > .du-bois-light-menu-submenu-title > .du-bois-light-menu-submenu-arrow,\n.du-bois-light-menu-dark .du-bois-light-menu-submenu-open > .du-bois-light-menu-submenu-title > .du-bois-light-menu-submenu-arrow,\n.du-bois-light-menu-dark .du-bois-light-menu-submenu-selected > .du-bois-light-menu-submenu-title > .du-bois-light-menu-submenu-arrow,\n.du-bois-light-menu-dark .du-bois-light-menu-submenu-title:hover > .du-bois-light-menu-submenu-title > .du-bois-light-menu-submenu-arrow {\n opacity: 1;\n}\n.du-bois-light-menu-dark .du-bois-light-menu-item:hover > .du-bois-light-menu-submenu-title > .du-bois-light-menu-submenu-arrow::after,\n.du-bois-light-menu-dark .du-bois-light-menu-item-active > .du-bois-light-menu-submenu-title > .du-bois-light-menu-submenu-arrow::after,\n.du-bois-light-menu-dark .du-bois-light-menu-submenu-active > .du-bois-light-menu-submenu-title > .du-bois-light-menu-submenu-arrow::after,\n.du-bois-light-menu-dark .du-bois-light-menu-submenu-open > .du-bois-light-menu-submenu-title > .du-bois-light-menu-submenu-arrow::after,\n.du-bois-light-menu-dark .du-bois-light-menu-submenu-selected > .du-bois-light-menu-submenu-title > .du-bois-light-menu-submenu-arrow::after,\n.du-bois-light-menu-dark .du-bois-light-menu-submenu-title:hover > .du-bois-light-menu-submenu-title > .du-bois-light-menu-submenu-arrow::after,\n.du-bois-light-menu-dark .du-bois-light-menu-item:hover > .du-bois-light-menu-submenu-title > .du-bois-light-menu-submenu-arrow::before,\n.du-bois-light-menu-dark .du-bois-light-menu-item-active > .du-bois-light-menu-submenu-title > .du-bois-light-menu-submenu-arrow::before,\n.du-bois-light-menu-dark .du-bois-light-menu-submenu-active > .du-bois-light-menu-submenu-title > .du-bois-light-menu-submenu-arrow::before,\n.du-bois-light-menu-dark .du-bois-light-menu-submenu-open > .du-bois-light-menu-submenu-title > .du-bois-light-menu-submenu-arrow::before,\n.du-bois-light-menu-dark .du-bois-light-menu-submenu-selected > .du-bois-light-menu-submenu-title > .du-bois-light-menu-submenu-arrow::before,\n.du-bois-light-menu-dark .du-bois-light-menu-submenu-title:hover > .du-bois-light-menu-submenu-title > .du-bois-light-menu-submenu-arrow::before {\n background: #fff;\n}\n.du-bois-light-menu-dark .du-bois-light-menu-item:hover {\n background-color: transparent;\n}\n.du-bois-light-menu-dark.du-bois-light-menu-dark:not(.du-bois-light-menu-horizontal) .du-bois-light-menu-item-selected {\n background-color: #2272B4;\n}\n.du-bois-light-menu-dark .du-bois-light-menu-item-selected {\n color: #fff;\n border-right: 0;\n}\n.du-bois-light-menu-dark .du-bois-light-menu-item-selected::after {\n border-right: 0;\n}\n.du-bois-light-menu-dark .du-bois-light-menu-item-selected > a,\n.du-bois-light-menu-dark .du-bois-light-menu-item-selected > span > a,\n.du-bois-light-menu-dark .du-bois-light-menu-item-selected > a:hover,\n.du-bois-light-menu-dark .du-bois-light-menu-item-selected > span > a:hover {\n color: #fff;\n}\n.du-bois-light-menu-dark .du-bois-light-menu-item-selected .du-bois-light-menu-item-icon,\n.du-bois-light-menu-dark .du-bois-light-menu-item-selected .anticon {\n color: #fff;\n}\n.du-bois-light-menu-dark .du-bois-light-menu-item-selected .du-bois-light-menu-item-icon + span,\n.du-bois-light-menu-dark .du-bois-light-menu-item-selected .anticon + span {\n color: #fff;\n}\n.du-bois-light-menu.du-bois-light-menu-dark .du-bois-light-menu-item-selected,\n.du-bois-light-menu-submenu-popup.du-bois-light-menu-dark .du-bois-light-menu-item-selected {\n background-color: #2272B4;\n}\n.du-bois-light-menu-dark .du-bois-light-menu-item-disabled,\n.du-bois-light-menu-dark .du-bois-light-menu-submenu-disabled,\n.du-bois-light-menu-dark .du-bois-light-menu-item-disabled > a,\n.du-bois-light-menu-dark .du-bois-light-menu-submenu-disabled > a,\n.du-bois-light-menu-dark .du-bois-light-menu-item-disabled > span > a,\n.du-bois-light-menu-dark .du-bois-light-menu-submenu-disabled > span > a {\n color: rgba(255, 255, 255, 0.35) !important;\n opacity: 0.8;\n}\n.du-bois-light-menu-dark .du-bois-light-menu-item-disabled > .du-bois-light-menu-submenu-title,\n.du-bois-light-menu-dark .du-bois-light-menu-submenu-disabled > .du-bois-light-menu-submenu-title {\n color: rgba(255, 255, 255, 0.35) !important;\n}\n.du-bois-light-menu-dark .du-bois-light-menu-item-disabled > .du-bois-light-menu-submenu-title > .du-bois-light-menu-submenu-arrow::before,\n.du-bois-light-menu-dark .du-bois-light-menu-submenu-disabled > .du-bois-light-menu-submenu-title > .du-bois-light-menu-submenu-arrow::before,\n.du-bois-light-menu-dark .du-bois-light-menu-item-disabled > .du-bois-light-menu-submenu-title > .du-bois-light-menu-submenu-arrow::after,\n.du-bois-light-menu-dark .du-bois-light-menu-submenu-disabled > .du-bois-light-menu-submenu-title > .du-bois-light-menu-submenu-arrow::after {\n background: rgba(255, 255, 255, 0.35) !important;\n}\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-light-modal {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #11171C;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: 'tnum';\n pointer-events: none;\n position: relative;\n top: 100px;\n width: auto;\n max-width: calc(100vw - 32px);\n margin: 0 auto;\n padding-bottom: 24px;\n}\n.du-bois-light-modal.du-bois-light-zoom-enter,\n.du-bois-light-modal.du-bois-lightzoom-appear {\n transform: none;\n opacity: 0;\n animation-duration: 0.3s;\n user-select: none;\n}\n.du-bois-light-modal-mask {\n position: fixed;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n z-index: 1000;\n height: 100%;\n background-color: rgba(0, 0, 0, 0.45);\n}\n.du-bois-light-modal-mask-hidden {\n display: none;\n}\n.du-bois-light-modal-wrap {\n position: fixed;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n overflow: auto;\n outline: 0;\n -webkit-overflow-scrolling: touch;\n}\n.du-bois-light-modal-wrap {\n z-index: 1000;\n}\n.du-bois-light-modal-title {\n margin: 0;\n color: rgba(0, 0, 0, 0.85);\n font-weight: 500;\n font-size: 18px;\n line-height: 22px;\n word-wrap: break-word;\n}\n.du-bois-light-modal-content {\n position: relative;\n background-color: #fff;\n background-clip: padding-box;\n border: 0;\n border-radius: 4px;\n box-shadow: 0 3px 6px -4px rgba(0, 0, 0, 0.12), 0 6px 16px 0 rgba(0, 0, 0, 0.08), 0 9px 28px 8px rgba(0, 0, 0, 0.05);\n pointer-events: auto;\n}\n.du-bois-light-modal-close {\n position: absolute;\n top: 0;\n right: 0;\n z-index: 10;\n padding: 0;\n color: #5F7281;\n font-weight: 700;\n line-height: 1;\n text-decoration: none;\n background: transparent;\n border: 0;\n outline: 0;\n cursor: pointer;\n transition: color 0.3s;\n}\n.du-bois-light-modal-close-x {\n display: block;\n width: 102px;\n height: 102px;\n font-size: 18px;\n font-style: normal;\n line-height: 102px;\n text-align: center;\n text-transform: none;\n text-rendering: auto;\n}\n.du-bois-light-modal-close:focus,\n.du-bois-light-modal-close:hover {\n color: rgba(0, 0, 0, 0.75);\n text-decoration: none;\n}\n.du-bois-light-modal-header {\n padding: 40px 40px 20px;\n color: #11171C;\n background: #fff;\n border-bottom: 0px solid #f0f0f0;\n border-radius: 4px 4px 0 0;\n}\n.du-bois-light-modal-body {\n padding: 0 40px;\n font-size: 13px;\n line-height: 20px;\n word-wrap: break-word;\n}\n.du-bois-light-modal-footer {\n padding: 0px 0px;\n text-align: right;\n background: transparent;\n border-top: 0px solid #f0f0f0;\n border-radius: 0 0 4px 4px;\n}\n.du-bois-light-modal-footer .du-bois-light-btn + .du-bois-light-btn:not(.du-bois-light-dropdown-trigger) {\n margin-bottom: 0;\n margin-left: 8px;\n}\n.du-bois-light-modal-open {\n overflow: hidden;\n}\n.du-bois-light-modal-centered {\n text-align: center;\n}\n.du-bois-light-modal-centered::before {\n display: inline-block;\n width: 0;\n height: 100%;\n vertical-align: middle;\n content: '';\n}\n.du-bois-light-modal-centered .du-bois-light-modal {\n top: 0;\n display: inline-block;\n text-align: left;\n vertical-align: middle;\n}\n@media (max-width: 767px) {\n .du-bois-light-modal {\n max-width: calc(100vw - 16px);\n margin: 8px auto;\n }\n .du-bois-light-modal-centered .du-bois-light-modal {\n flex: 1;\n }\n}\n.du-bois-light-modal-confirm .du-bois-light-modal-header {\n display: none;\n}\n.du-bois-light-modal-confirm .du-bois-light-modal-body {\n padding: 32px 32px 24px;\n}\n.du-bois-light-modal-confirm-body-wrapper::before {\n display: table;\n content: '';\n}\n.du-bois-light-modal-confirm-body-wrapper::after {\n display: table;\n clear: both;\n content: '';\n}\n.du-bois-light-modal-confirm-body .du-bois-light-modal-confirm-title {\n display: block;\n overflow: hidden;\n color: rgba(0, 0, 0, 0.85);\n font-weight: 500;\n font-size: 18px;\n line-height: 1.4;\n}\n.du-bois-light-modal-confirm-body .du-bois-light-modal-confirm-content {\n margin-top: 8px;\n color: #11171C;\n font-size: 13px;\n}\n.du-bois-light-modal-confirm-body > .anticon {\n float: left;\n margin-right: 16px;\n font-size: 22px;\n}\n.du-bois-light-modal-confirm-body > .anticon + .du-bois-light-modal-confirm-title + .du-bois-light-modal-confirm-content {\n margin-left: 38px;\n}\n.du-bois-light-modal-confirm .du-bois-light-modal-confirm-btns {\n float: right;\n margin-top: 24px;\n}\n.du-bois-light-modal-confirm .du-bois-light-modal-confirm-btns .du-bois-light-btn + .du-bois-light-btn {\n margin-bottom: 0;\n margin-left: 8px;\n}\n.du-bois-light-modal-confirm-error .du-bois-light-modal-confirm-body > .anticon {\n color: #C82D4C;\n}\n.du-bois-light-modal-confirm-warning .du-bois-light-modal-confirm-body > .anticon,\n.du-bois-light-modal-confirm-confirm .du-bois-light-modal-confirm-body > .anticon {\n color: #AF5B23;\n}\n.du-bois-light-modal-confirm-info .du-bois-light-modal-confirm-body > .anticon {\n color: #64727D;\n}\n.du-bois-light-modal-confirm-success .du-bois-light-modal-confirm-body > .anticon {\n color: #34824F;\n}\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-light-notification {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #11171C;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: 'tnum';\n position: fixed;\n z-index: 1010;\n margin-right: 24px;\n}\n.du-bois-light-notification-topLeft,\n.du-bois-light-notification-bottomLeft {\n margin-right: 0;\n margin-left: 24px;\n}\n.du-bois-light-notification-topLeft .du-bois-light-notification-fade-enter.du-bois-light-notification-fade-enter-active,\n.du-bois-light-notification-bottomLeft .du-bois-light-notification-fade-enter.du-bois-light-notification-fade-enter-active,\n.du-bois-light-notification-topLeft .du-bois-light-notification-fade-appear.du-bois-light-notification-fade-appear-active,\n.du-bois-light-notification-bottomLeft .du-bois-light-notification-fade-appear.du-bois-light-notification-fade-appear-active {\n animation-name: NotificationLeftFadeIn;\n}\n.du-bois-light-notification-close-icon {\n font-size: 13px;\n cursor: pointer;\n}\n.du-bois-light-notification-hook-holder {\n position: relative;\n}\n.du-bois-light-notification-notice {\n position: relative;\n width: 384px;\n max-width: calc(100vw - 24px * 2);\n margin-bottom: 16px;\n margin-left: auto;\n padding: 16px 24px;\n overflow: hidden;\n line-height: 20px;\n word-wrap: break-word;\n background: #fff;\n border-radius: 4px;\n box-shadow: 0 3px 6px -4px rgba(0, 0, 0, 0.12), 0 6px 16px 0 rgba(0, 0, 0, 0.08), 0 9px 28px 8px rgba(0, 0, 0, 0.05);\n}\n.du-bois-light-notification-topLeft .du-bois-light-notification-notice,\n.du-bois-light-notification-bottomLeft .du-bois-light-notification-notice {\n margin-right: auto;\n margin-left: 0;\n}\n.du-bois-light-notification-notice-message {\n margin-bottom: 8px;\n color: rgba(0, 0, 0, 0.85);\n font-size: 18px;\n line-height: 24px;\n}\n.du-bois-light-notification-notice-message-single-line-auto-margin {\n display: block;\n width: calc(384px - 24px * 2 - 24px - 48px - 100%);\n max-width: 4px;\n background-color: transparent;\n pointer-events: none;\n}\n.du-bois-light-notification-notice-message-single-line-auto-margin::before {\n display: block;\n content: '';\n}\n.du-bois-light-notification-notice-description {\n font-size: 13px;\n}\n.du-bois-light-notification-notice-closable .du-bois-light-notification-notice-message {\n padding-right: 24px;\n}\n.du-bois-light-notification-notice-with-icon .du-bois-light-notification-notice-message {\n margin-bottom: 4px;\n margin-left: 48px;\n font-size: 18px;\n}\n.du-bois-light-notification-notice-with-icon .du-bois-light-notification-notice-description {\n margin-left: 48px;\n font-size: 13px;\n}\n.du-bois-light-notification-notice-icon {\n position: absolute;\n margin-left: 4px;\n font-size: 24px;\n line-height: 24px;\n}\n.anticon.du-bois-light-notification-notice-icon-success {\n color: #34824F;\n}\n.anticon.du-bois-light-notification-notice-icon-info {\n color: #64727D;\n}\n.anticon.du-bois-light-notification-notice-icon-warning {\n color: #AF5B23;\n}\n.anticon.du-bois-light-notification-notice-icon-error {\n color: #C82D4C;\n}\n.du-bois-light-notification-notice-close {\n position: absolute;\n top: 16px;\n right: 22px;\n color: #5F7281;\n outline: none;\n}\n.du-bois-light-notification-notice-close:hover {\n color: #39444d;\n}\n.du-bois-light-notification-notice-btn {\n float: right;\n margin-top: 16px;\n}\n.du-bois-light-notification .notification-fade-effect {\n animation-duration: 0.24s;\n animation-timing-function: cubic-bezier(0.645, 0.045, 0.355, 1);\n animation-fill-mode: both;\n}\n.du-bois-light-notification-fade-enter,\n.du-bois-light-notification-fade-appear {\n animation-duration: 0.24s;\n animation-timing-function: cubic-bezier(0.645, 0.045, 0.355, 1);\n animation-fill-mode: both;\n opacity: 0;\n animation-play-state: paused;\n}\n.du-bois-light-notification-fade-leave {\n animation-duration: 0.24s;\n animation-timing-function: cubic-bezier(0.645, 0.045, 0.355, 1);\n animation-fill-mode: both;\n animation-duration: 0.2s;\n animation-play-state: paused;\n}\n.du-bois-light-notification-fade-enter.du-bois-light-notification-fade-enter-active,\n.du-bois-light-notification-fade-appear.du-bois-light-notification-fade-appear-active {\n animation-name: NotificationFadeIn;\n animation-play-state: running;\n}\n.du-bois-light-notification-fade-leave.du-bois-light-notification-fade-leave-active {\n animation-name: NotificationFadeOut;\n animation-play-state: running;\n}\n@keyframes NotificationFadeIn {\n 0% {\n left: 384px;\n opacity: 0;\n }\n 100% {\n left: 0;\n opacity: 1;\n }\n}\n@keyframes NotificationLeftFadeIn {\n 0% {\n right: 384px;\n opacity: 0;\n }\n 100% {\n right: 0;\n opacity: 1;\n }\n}\n@keyframes NotificationFadeOut {\n 0% {\n max-height: 150px;\n margin-bottom: 16px;\n opacity: 1;\n }\n 100% {\n max-height: 0;\n margin-bottom: 0;\n padding-top: 0;\n padding-bottom: 0;\n opacity: 0;\n }\n}\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-light-popover {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #11171C;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: 'tnum';\n position: absolute;\n top: 0;\n left: 0;\n z-index: 1030;\n font-weight: normal;\n white-space: normal;\n text-align: left;\n cursor: auto;\n user-select: text;\n}\n.du-bois-light-popover::after {\n position: absolute;\n background: rgba(255, 255, 255, 0.01);\n content: '';\n}\n.du-bois-light-popover-hidden {\n display: none;\n}\n.du-bois-light-popover-placement-top,\n.du-bois-light-popover-placement-topLeft,\n.du-bois-light-popover-placement-topRight {\n padding-bottom: 10px;\n}\n.du-bois-light-popover-placement-right,\n.du-bois-light-popover-placement-rightTop,\n.du-bois-light-popover-placement-rightBottom {\n padding-left: 10px;\n}\n.du-bois-light-popover-placement-bottom,\n.du-bois-light-popover-placement-bottomLeft,\n.du-bois-light-popover-placement-bottomRight {\n padding-top: 10px;\n}\n.du-bois-light-popover-placement-left,\n.du-bois-light-popover-placement-leftTop,\n.du-bois-light-popover-placement-leftBottom {\n padding-right: 10px;\n}\n.du-bois-light-popover-inner {\n background-color: #fff;\n background-clip: padding-box;\n border-radius: 4px;\n box-shadow: 0 3px 6px -4px rgba(0, 0, 0, 0.12), 0 6px 16px 0 rgba(0, 0, 0, 0.08), 0 9px 28px 8px rgba(0, 0, 0, 0.05);\n box-shadow: 0 0 8px rgba(0, 0, 0, 0.15) \\9;\n}\n@media screen and (-ms-high-contrast: active), (-ms-high-contrast: none) {\n .du-bois-light-popover {\n /* IE10+ */\n }\n .du-bois-light-popover-inner {\n box-shadow: 0 3px 6px -4px rgba(0, 0, 0, 0.12), 0 6px 16px 0 rgba(0, 0, 0, 0.08), 0 9px 28px 8px rgba(0, 0, 0, 0.05);\n }\n}\n.du-bois-light-popover-title {\n min-width: 177px;\n min-height: 32px;\n margin: 0;\n padding: 5px 8px 4px;\n color: rgba(0, 0, 0, 0.85);\n font-weight: 500;\n border-bottom: 1px solid #f0f0f0;\n}\n.du-bois-light-popover-inner-content {\n padding: 8px 8px;\n color: #11171C;\n}\n.du-bois-light-popover-message {\n position: relative;\n padding: 4px 0 12px;\n color: #11171C;\n font-size: 13px;\n}\n.du-bois-light-popover-message > .anticon {\n position: absolute;\n top: 127.5px;\n color: #AF5B23;\n font-size: 13px;\n}\n.du-bois-light-popover-message-title {\n padding-left: 21px;\n}\n.du-bois-light-popover-buttons {\n margin-bottom: 4px;\n text-align: right;\n}\n.du-bois-light-popover-buttons button {\n margin-left: 8px;\n}\n.du-bois-light-popover-arrow {\n position: absolute;\n display: block;\n width: 8.48528137px;\n height: 8.48528137px;\n overflow: hidden;\n background: transparent;\n pointer-events: none;\n}\n.du-bois-light-popover-arrow-content {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n display: block;\n width: 6px;\n height: 6px;\n margin: auto;\n background-color: #fff;\n content: '';\n pointer-events: auto;\n}\n.du-bois-light-popover-placement-top .du-bois-light-popover-arrow,\n.du-bois-light-popover-placement-topLeft .du-bois-light-popover-arrow,\n.du-bois-light-popover-placement-topRight .du-bois-light-popover-arrow {\n bottom: 1.51471863px;\n}\n.du-bois-light-popover-placement-top .du-bois-light-popover-arrow-content,\n.du-bois-light-popover-placement-topLeft .du-bois-light-popover-arrow-content,\n.du-bois-light-popover-placement-topRight .du-bois-light-popover-arrow-content {\n box-shadow: 3px 3px 7px rgba(0, 0, 0, 0.07);\n transform: translateY(-4.24264069px) rotate(45deg);\n}\n.du-bois-light-popover-placement-top .du-bois-light-popover-arrow {\n left: 50%;\n transform: translateX(-50%);\n}\n.du-bois-light-popover-placement-topLeft .du-bois-light-popover-arrow {\n left: 16px;\n}\n.du-bois-light-popover-placement-topRight .du-bois-light-popover-arrow {\n right: 16px;\n}\n.du-bois-light-popover-placement-right .du-bois-light-popover-arrow,\n.du-bois-light-popover-placement-rightTop .du-bois-light-popover-arrow,\n.du-bois-light-popover-placement-rightBottom .du-bois-light-popover-arrow {\n left: 1.51471863px;\n}\n.du-bois-light-popover-placement-right .du-bois-light-popover-arrow-content,\n.du-bois-light-popover-placement-rightTop .du-bois-light-popover-arrow-content,\n.du-bois-light-popover-placement-rightBottom .du-bois-light-popover-arrow-content {\n box-shadow: -3px 3px 7px rgba(0, 0, 0, 0.07);\n transform: translateX(4.24264069px) rotate(45deg);\n}\n.du-bois-light-popover-placement-right .du-bois-light-popover-arrow {\n top: 50%;\n transform: translateY(-50%);\n}\n.du-bois-light-popover-placement-rightTop .du-bois-light-popover-arrow {\n top: 12px;\n}\n.du-bois-light-popover-placement-rightBottom .du-bois-light-popover-arrow {\n bottom: 12px;\n}\n.du-bois-light-popover-placement-bottom .du-bois-light-popover-arrow,\n.du-bois-light-popover-placement-bottomLeft .du-bois-light-popover-arrow,\n.du-bois-light-popover-placement-bottomRight .du-bois-light-popover-arrow {\n top: 1.51471863px;\n}\n.du-bois-light-popover-placement-bottom .du-bois-light-popover-arrow-content,\n.du-bois-light-popover-placement-bottomLeft .du-bois-light-popover-arrow-content,\n.du-bois-light-popover-placement-bottomRight .du-bois-light-popover-arrow-content {\n box-shadow: -2px -2px 5px rgba(0, 0, 0, 0.06);\n transform: translateY(4.24264069px) rotate(45deg);\n}\n.du-bois-light-popover-placement-bottom .du-bois-light-popover-arrow {\n left: 50%;\n transform: translateX(-50%);\n}\n.du-bois-light-popover-placement-bottomLeft .du-bois-light-popover-arrow {\n left: 16px;\n}\n.du-bois-light-popover-placement-bottomRight .du-bois-light-popover-arrow {\n right: 16px;\n}\n.du-bois-light-popover-placement-left .du-bois-light-popover-arrow,\n.du-bois-light-popover-placement-leftTop .du-bois-light-popover-arrow,\n.du-bois-light-popover-placement-leftBottom .du-bois-light-popover-arrow {\n right: 1.51471863px;\n}\n.du-bois-light-popover-placement-left .du-bois-light-popover-arrow-content,\n.du-bois-light-popover-placement-leftTop .du-bois-light-popover-arrow-content,\n.du-bois-light-popover-placement-leftBottom .du-bois-light-popover-arrow-content {\n box-shadow: 3px -3px 7px rgba(0, 0, 0, 0.07);\n transform: translateX(-4.24264069px) rotate(45deg);\n}\n.du-bois-light-popover-placement-left .du-bois-light-popover-arrow {\n top: 50%;\n transform: translateY(-50%);\n}\n.du-bois-light-popover-placement-leftTop .du-bois-light-popover-arrow {\n top: 12px;\n}\n.du-bois-light-popover-placement-leftBottom .du-bois-light-popover-arrow {\n bottom: 12px;\n}\n.du-bois-light-popover-pink .du-bois-light-popover-inner {\n background-color: #eb2f96;\n}\n.du-bois-light-popover-pink .du-bois-light-popover-arrow-content {\n background-color: #eb2f96;\n}\n.du-bois-light-popover-magenta .du-bois-light-popover-inner {\n background-color: #eb2f96;\n}\n.du-bois-light-popover-magenta .du-bois-light-popover-arrow-content {\n background-color: #eb2f96;\n}\n.du-bois-light-popover-red .du-bois-light-popover-inner {\n background-color: #f5222d;\n}\n.du-bois-light-popover-red .du-bois-light-popover-arrow-content {\n background-color: #f5222d;\n}\n.du-bois-light-popover-volcano .du-bois-light-popover-inner {\n background-color: #fa541c;\n}\n.du-bois-light-popover-volcano .du-bois-light-popover-arrow-content {\n background-color: #fa541c;\n}\n.du-bois-light-popover-orange .du-bois-light-popover-inner {\n background-color: #fa8c16;\n}\n.du-bois-light-popover-orange .du-bois-light-popover-arrow-content {\n background-color: #fa8c16;\n}\n.du-bois-light-popover-yellow .du-bois-light-popover-inner {\n background-color: #fadb14;\n}\n.du-bois-light-popover-yellow .du-bois-light-popover-arrow-content {\n background-color: #fadb14;\n}\n.du-bois-light-popover-gold .du-bois-light-popover-inner {\n background-color: #faad14;\n}\n.du-bois-light-popover-gold .du-bois-light-popover-arrow-content {\n background-color: #faad14;\n}\n.du-bois-light-popover-cyan .du-bois-light-popover-inner {\n background-color: #13c2c2;\n}\n.du-bois-light-popover-cyan .du-bois-light-popover-arrow-content {\n background-color: #13c2c2;\n}\n.du-bois-light-popover-lime .du-bois-light-popover-inner {\n background-color: #a0d911;\n}\n.du-bois-light-popover-lime .du-bois-light-popover-arrow-content {\n background-color: #a0d911;\n}\n.du-bois-light-popover-green .du-bois-light-popover-inner {\n background-color: #52c41a;\n}\n.du-bois-light-popover-green .du-bois-light-popover-arrow-content {\n background-color: #52c41a;\n}\n.du-bois-light-popover-blue .du-bois-light-popover-inner {\n background-color: #1890ff;\n}\n.du-bois-light-popover-blue .du-bois-light-popover-arrow-content {\n background-color: #1890ff;\n}\n.du-bois-light-popover-geekblue .du-bois-light-popover-inner {\n background-color: #2f54eb;\n}\n.du-bois-light-popover-geekblue .du-bois-light-popover-arrow-content {\n background-color: #2f54eb;\n}\n.du-bois-light-popover-purple .du-bois-light-popover-inner {\n background-color: #722ed1;\n}\n.du-bois-light-popover-purple .du-bois-light-popover-arrow-content {\n background-color: #722ed1;\n}\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-light-radio-group {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #11171C;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: 'tnum';\n display: inline-block;\n font-size: 0;\n}\n.du-bois-light-radio-group .du-bois-light-badge-count {\n z-index: 1;\n}\n.du-bois-light-radio-group > .du-bois-light-badge:not(:first-child) > .du-bois-light-radio-button-wrapper {\n border-left: none;\n}\n.du-bois-light-radio-wrapper {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #11171C;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: 'tnum';\n position: relative;\n display: inline-flex;\n align-items: baseline;\n margin-right: 8px;\n cursor: pointer;\n}\n.du-bois-light-radio-wrapper::after {\n display: inline-block;\n width: 0;\n overflow: hidden;\n content: '\\a0';\n}\n.du-bois-light-radio {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #11171C;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: 'tnum';\n position: relative;\n top: 0.2em;\n display: inline-block;\n outline: none;\n cursor: pointer;\n}\n.du-bois-light-radio-wrapper:hover .du-bois-light-radio,\n.du-bois-light-radio:hover .du-bois-light-radio-inner,\n.du-bois-light-radio-input:focus + .du-bois-light-radio-inner {\n border-color: #2272B4;\n}\n.du-bois-light-radio-input:focus + .du-bois-light-radio-inner {\n box-shadow: 0 0 0 3px rgba(34, 114, 180, 0.08);\n}\n.du-bois-light-radio-checked::after {\n position: absolute;\n top: 0;\n left: 0;\n width: 100%;\n height: 100%;\n border: 1px solid #2272B4;\n border-radius: 50%;\n visibility: hidden;\n animation: antRadioEffect 0.36s ease-in-out;\n animation-fill-mode: both;\n content: '';\n}\n.du-bois-light-radio:hover::after,\n.du-bois-light-radio-wrapper:hover .du-bois-light-radio::after {\n visibility: visible;\n}\n.du-bois-light-radio-inner {\n position: relative;\n top: 0;\n left: 0;\n display: block;\n width: 16px;\n height: 16px;\n background-color: #fff;\n border-color: #E8ECF0;\n border-style: solid;\n border-width: 1px;\n border-radius: 50%;\n transition: all 0.3s;\n}\n.du-bois-light-radio-inner::after {\n position: absolute;\n top: 3px;\n left: 3px;\n display: block;\n width: 8px;\n height: 8px;\n background-color: #2272B4;\n border-top: 0;\n border-left: 0;\n border-radius: 8px;\n transform: scale(0);\n opacity: 0;\n transition: all 0.3s cubic-bezier(0.78, 0.14, 0.15, 0.86);\n content: ' ';\n}\n.du-bois-light-radio-input {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n z-index: 1;\n cursor: pointer;\n opacity: 0;\n}\n.du-bois-light-radio-checked .du-bois-light-radio-inner {\n border-color: #2272B4;\n}\n.du-bois-light-radio-checked .du-bois-light-radio-inner::after {\n transform: scale(1);\n opacity: 1;\n transition: all 0.3s cubic-bezier(0.78, 0.14, 0.15, 0.86);\n}\n.du-bois-light-radio-disabled {\n cursor: not-allowed;\n}\n.du-bois-light-radio-disabled .du-bois-light-radio-inner {\n background-color: #f5f5f5;\n border-color: #E8ECF0 !important;\n cursor: not-allowed;\n}\n.du-bois-light-radio-disabled .du-bois-light-radio-inner::after {\n background-color: rgba(0, 0, 0, 0.2);\n}\n.du-bois-light-radio-disabled .du-bois-light-radio-input {\n cursor: not-allowed;\n}\n.du-bois-light-radio-disabled + span {\n color: rgba(0, 0, 0, 0.25);\n cursor: not-allowed;\n}\nspan.du-bois-light-radio + * {\n padding-right: 8px;\n padding-left: 8px;\n}\n.du-bois-light-radio-button-wrapper {\n position: relative;\n display: inline-block;\n height: 40px;\n margin: 0;\n padding: 0 7px;\n color: #11171C;\n font-size: 13px;\n line-height: 38px;\n background: #fff;\n border: 1px solid #E8ECF0;\n border-top-width: 1.02px;\n border-left-width: 0;\n cursor: pointer;\n transition: color 0.3s, background 0.3s, border-color 0.3s, box-shadow 0.3s;\n}\n.du-bois-light-radio-button-wrapper a {\n color: #11171C;\n}\n.du-bois-light-radio-button-wrapper > .du-bois-light-radio-button {\n position: absolute;\n top: 0;\n left: 0;\n z-index: -1;\n width: 100%;\n height: 100%;\n}\n.du-bois-light-radio-group-large .du-bois-light-radio-button-wrapper {\n height: 40px;\n font-size: 18px;\n line-height: 38px;\n}\n.du-bois-light-radio-group-small .du-bois-light-radio-button-wrapper {\n height: 32px;\n padding: 0 3px;\n line-height: 30px;\n}\n.du-bois-light-radio-button-wrapper:not(:first-child)::before {\n position: absolute;\n top: -1px;\n left: -1px;\n display: block;\n box-sizing: content-box;\n width: 1px;\n height: 100%;\n padding: 1px 0;\n background-color: #E8ECF0;\n transition: background-color 0.3s;\n content: '';\n}\n.du-bois-light-radio-button-wrapper:first-child {\n border-left: 1px solid #E8ECF0;\n border-radius: 4px 0 0 4px;\n}\n.du-bois-light-radio-button-wrapper:last-child {\n border-radius: 0 4px 4px 0;\n}\n.du-bois-light-radio-button-wrapper:first-child:last-child {\n border-radius: 4px;\n}\n.du-bois-light-radio-button-wrapper:hover {\n position: relative;\n color: #2272B4;\n}\n.du-bois-light-radio-button-wrapper:focus-within {\n box-shadow: 0 0 0 3px rgba(34, 114, 180, 0.08);\n}\n.du-bois-light-radio-button-wrapper .du-bois-light-radio-inner,\n.du-bois-light-radio-button-wrapper input[type='checkbox'],\n.du-bois-light-radio-button-wrapper input[type='radio'] {\n width: 0;\n height: 0;\n opacity: 0;\n pointer-events: none;\n}\n.du-bois-light-radio-button-wrapper-checked:not(.du-bois-light-radio-button-wrapper-disabled) {\n z-index: 1;\n color: #2272B4;\n background: #fff;\n border-color: #2272B4;\n}\n.du-bois-light-radio-button-wrapper-checked:not(.du-bois-light-radio-button-wrapper-disabled)::before {\n background-color: #2272B4;\n}\n.du-bois-light-radio-button-wrapper-checked:not(.du-bois-light-radio-button-wrapper-disabled):first-child {\n border-color: #2272B4;\n}\n.du-bois-light-radio-button-wrapper-checked:not(.du-bois-light-radio-button-wrapper-disabled):hover {\n color: #448dc2;\n border-color: #448dc2;\n}\n.du-bois-light-radio-button-wrapper-checked:not(.du-bois-light-radio-button-wrapper-disabled):hover::before {\n background-color: #448dc2;\n}\n.du-bois-light-radio-button-wrapper-checked:not(.du-bois-light-radio-button-wrapper-disabled):active {\n color: #14538f;\n border-color: #14538f;\n}\n.du-bois-light-radio-button-wrapper-checked:not(.du-bois-light-radio-button-wrapper-disabled):active::before {\n background-color: #14538f;\n}\n.du-bois-light-radio-button-wrapper-checked:not(.du-bois-light-radio-button-wrapper-disabled):focus-within {\n box-shadow: 0 0 0 3px rgba(34, 114, 180, 0.08);\n}\n.du-bois-light-radio-group-solid .du-bois-light-radio-button-wrapper-checked:not(.du-bois-light-radio-button-wrapper-disabled) {\n color: #fff;\n background: #2272B4;\n border-color: #2272B4;\n}\n.du-bois-light-radio-group-solid .du-bois-light-radio-button-wrapper-checked:not(.du-bois-light-radio-button-wrapper-disabled):hover {\n color: #fff;\n background: #448dc2;\n border-color: #448dc2;\n}\n.du-bois-light-radio-group-solid .du-bois-light-radio-button-wrapper-checked:not(.du-bois-light-radio-button-wrapper-disabled):active {\n color: #fff;\n background: #14538f;\n border-color: #14538f;\n}\n.du-bois-light-radio-group-solid .du-bois-light-radio-button-wrapper-checked:not(.du-bois-light-radio-button-wrapper-disabled):focus-within {\n box-shadow: 0 0 0 3px rgba(34, 114, 180, 0.08);\n}\n.du-bois-light-radio-button-wrapper-disabled {\n color: rgba(0, 0, 0, 0.25);\n background-color: #f5f5f5;\n border-color: #E8ECF0;\n cursor: not-allowed;\n}\n.du-bois-light-radio-button-wrapper-disabled:first-child,\n.du-bois-light-radio-button-wrapper-disabled:hover {\n color: rgba(0, 0, 0, 0.25);\n background-color: #f5f5f5;\n border-color: #E8ECF0;\n}\n.du-bois-light-radio-button-wrapper-disabled:first-child {\n border-left-color: #E8ECF0;\n}\n.du-bois-light-radio-button-wrapper-disabled.du-bois-light-radio-button-wrapper-checked {\n color: rgba(0, 0, 0, 0.25);\n background-color: #e6e6e6;\n border-color: #E8ECF0;\n box-shadow: none;\n}\n@keyframes antRadioEffect {\n 0% {\n transform: scale(1);\n opacity: 0.5;\n }\n 100% {\n transform: scale(1.6);\n opacity: 0;\n }\n}\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-light-skeleton {\n display: table;\n width: 100%;\n}\n.du-bois-light-skeleton-header {\n display: table-cell;\n padding-right: 8px;\n vertical-align: top;\n}\n.du-bois-light-skeleton-header .du-bois-light-skeleton-avatar {\n display: inline-block;\n vertical-align: top;\n background: rgba(190, 190, 190, 0.2);\n width: 32px;\n height: 32px;\n line-height: 32px;\n}\n.du-bois-light-skeleton-header .du-bois-light-skeleton-avatar.du-bois-light-skeleton-avatar-circle {\n border-radius: 50%;\n}\n.du-bois-light-skeleton-header .du-bois-light-skeleton-avatar-lg {\n width: 40px;\n height: 40px;\n line-height: 40px;\n}\n.du-bois-light-skeleton-header .du-bois-light-skeleton-avatar-lg.du-bois-light-skeleton-avatar-circle {\n border-radius: 50%;\n}\n.du-bois-light-skeleton-header .du-bois-light-skeleton-avatar-sm {\n width: 24px;\n height: 24px;\n line-height: 24px;\n}\n.du-bois-light-skeleton-header .du-bois-light-skeleton-avatar-sm.du-bois-light-skeleton-avatar-circle {\n border-radius: 50%;\n}\n.du-bois-light-skeleton-content {\n display: table-cell;\n width: 100%;\n vertical-align: top;\n}\n.du-bois-light-skeleton-content .du-bois-light-skeleton-title {\n width: 100%;\n height: 16px;\n margin-top: 16px;\n background: rgba(190, 190, 190, 0.2);\n border-radius: 4px;\n}\n.du-bois-light-skeleton-content .du-bois-light-skeleton-title + .du-bois-light-skeleton-paragraph {\n margin-top: 24px;\n}\n.du-bois-light-skeleton-content .du-bois-light-skeleton-paragraph {\n padding: 0;\n}\n.du-bois-light-skeleton-content .du-bois-light-skeleton-paragraph > li {\n width: 100%;\n height: 16px;\n list-style: none;\n background: rgba(190, 190, 190, 0.2);\n border-radius: 4px;\n}\n.du-bois-light-skeleton-content .du-bois-light-skeleton-paragraph > li:last-child:not(:first-child):not(:nth-child(2)) {\n width: 61%;\n}\n.du-bois-light-skeleton-content .du-bois-light-skeleton-paragraph > li + li {\n margin-top: 16px;\n}\n.du-bois-light-skeleton-with-avatar .du-bois-light-skeleton-content .du-bois-light-skeleton-title {\n margin-top: 12px;\n}\n.du-bois-light-skeleton-with-avatar .du-bois-light-skeleton-content .du-bois-light-skeleton-title + .du-bois-light-skeleton-paragraph {\n margin-top: 28px;\n}\n.du-bois-light-skeleton-round .du-bois-light-skeleton-content .du-bois-light-skeleton-title,\n.du-bois-light-skeleton-round .du-bois-light-skeleton-content .du-bois-light-skeleton-paragraph > li {\n border-radius: 100px;\n}\n.du-bois-light-skeleton.du-bois-light-skeleton-active .du-bois-light-skeleton-content .du-bois-light-skeleton-title,\n.du-bois-light-skeleton.du-bois-light-skeleton-active .du-bois-light-skeleton-content .du-bois-light-skeleton-paragraph > li {\n background: linear-gradient(90deg, rgba(190, 190, 190, 0.2) 25%, rgba(129, 129, 129, 0.24) 37%, rgba(190, 190, 190, 0.2) 63%);\n background-size: 400% 100%;\n animation: du-bois-light-skeleton-loading 1.4s ease infinite;\n}\n.du-bois-light-skeleton.du-bois-light-skeleton-active .du-bois-light-skeleton-avatar {\n background: linear-gradient(90deg, rgba(190, 190, 190, 0.2) 25%, rgba(129, 129, 129, 0.24) 37%, rgba(190, 190, 190, 0.2) 63%);\n background-size: 400% 100%;\n animation: du-bois-light-skeleton-loading 1.4s ease infinite;\n}\n.du-bois-light-skeleton.du-bois-light-skeleton-active .du-bois-light-skeleton-button {\n background: linear-gradient(90deg, rgba(190, 190, 190, 0.2) 25%, rgba(129, 129, 129, 0.24) 37%, rgba(190, 190, 190, 0.2) 63%);\n background-size: 400% 100%;\n animation: du-bois-light-skeleton-loading 1.4s ease infinite;\n}\n.du-bois-light-skeleton.du-bois-light-skeleton-active .du-bois-light-skeleton-input {\n background: linear-gradient(90deg, rgba(190, 190, 190, 0.2) 25%, rgba(129, 129, 129, 0.24) 37%, rgba(190, 190, 190, 0.2) 63%);\n background-size: 400% 100%;\n animation: du-bois-light-skeleton-loading 1.4s ease infinite;\n}\n.du-bois-light-skeleton.du-bois-light-skeleton-active .du-bois-light-skeleton-image {\n background: linear-gradient(90deg, rgba(190, 190, 190, 0.2) 25%, rgba(129, 129, 129, 0.24) 37%, rgba(190, 190, 190, 0.2) 63%);\n background-size: 400% 100%;\n animation: du-bois-light-skeleton-loading 1.4s ease infinite;\n}\n.du-bois-light-skeleton-element {\n display: inline-block;\n width: auto;\n}\n.du-bois-light-skeleton-element .du-bois-light-skeleton-button {\n display: inline-block;\n vertical-align: top;\n background: rgba(190, 190, 190, 0.2);\n border-radius: 4px;\n width: 80px;\n height: 40px;\n line-height: 40px;\n}\n.du-bois-light-skeleton-element .du-bois-light-skeleton-button.du-bois-light-skeleton-button-circle {\n width: 40px;\n border-radius: 50%;\n}\n.du-bois-light-skeleton-element .du-bois-light-skeleton-button.du-bois-light-skeleton-button-round {\n border-radius: 40px;\n}\n.du-bois-light-skeleton-element .du-bois-light-skeleton-button-lg {\n width: 80px;\n height: 40px;\n line-height: 40px;\n}\n.du-bois-light-skeleton-element .du-bois-light-skeleton-button-lg.du-bois-light-skeleton-button-circle {\n width: 40px;\n border-radius: 50%;\n}\n.du-bois-light-skeleton-element .du-bois-light-skeleton-button-lg.du-bois-light-skeleton-button-round {\n border-radius: 40px;\n}\n.du-bois-light-skeleton-element .du-bois-light-skeleton-button-sm {\n width: 64px;\n height: 32px;\n line-height: 32px;\n}\n.du-bois-light-skeleton-element .du-bois-light-skeleton-button-sm.du-bois-light-skeleton-button-circle {\n width: 32px;\n border-radius: 50%;\n}\n.du-bois-light-skeleton-element .du-bois-light-skeleton-button-sm.du-bois-light-skeleton-button-round {\n border-radius: 32px;\n}\n.du-bois-light-skeleton-element .du-bois-light-skeleton-avatar {\n display: inline-block;\n vertical-align: top;\n background: rgba(190, 190, 190, 0.2);\n width: 32px;\n height: 32px;\n line-height: 32px;\n}\n.du-bois-light-skeleton-element .du-bois-light-skeleton-avatar.du-bois-light-skeleton-avatar-circle {\n border-radius: 50%;\n}\n.du-bois-light-skeleton-element .du-bois-light-skeleton-avatar-lg {\n width: 40px;\n height: 40px;\n line-height: 40px;\n}\n.du-bois-light-skeleton-element .du-bois-light-skeleton-avatar-lg.du-bois-light-skeleton-avatar-circle {\n border-radius: 50%;\n}\n.du-bois-light-skeleton-element .du-bois-light-skeleton-avatar-sm {\n width: 24px;\n height: 24px;\n line-height: 24px;\n}\n.du-bois-light-skeleton-element .du-bois-light-skeleton-avatar-sm.du-bois-light-skeleton-avatar-circle {\n border-radius: 50%;\n}\n.du-bois-light-skeleton-element .du-bois-light-skeleton-input {\n display: inline-block;\n vertical-align: top;\n background: rgba(190, 190, 190, 0.2);\n width: 100%;\n height: 40px;\n line-height: 40px;\n}\n.du-bois-light-skeleton-element .du-bois-light-skeleton-input-lg {\n width: 100%;\n height: 40px;\n line-height: 40px;\n}\n.du-bois-light-skeleton-element .du-bois-light-skeleton-input-sm {\n width: 100%;\n height: 32px;\n line-height: 32px;\n}\n.du-bois-light-skeleton-element .du-bois-light-skeleton-image {\n display: flex;\n align-items: center;\n justify-content: center;\n vertical-align: top;\n background: rgba(190, 190, 190, 0.2);\n width: 96px;\n height: 96px;\n line-height: 96px;\n}\n.du-bois-light-skeleton-element .du-bois-light-skeleton-image.du-bois-light-skeleton-image-circle {\n border-radius: 50%;\n}\n.du-bois-light-skeleton-element .du-bois-light-skeleton-image-path {\n fill: #bfbfbf;\n}\n.du-bois-light-skeleton-element .du-bois-light-skeleton-image-svg {\n width: 48px;\n height: 48px;\n line-height: 48px;\n max-width: 192px;\n max-height: 192px;\n}\n.du-bois-light-skeleton-element .du-bois-light-skeleton-image-svg.du-bois-light-skeleton-image-circle {\n border-radius: 50%;\n}\n@keyframes du-bois-light-skeleton-loading {\n 0% {\n background-position: 100% 50%;\n }\n 100% {\n background-position: 0 50%;\n }\n}\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-light-space {\n display: inline-flex;\n}\n.du-bois-light-space-vertical {\n flex-direction: column;\n}\n.du-bois-light-space-align-center {\n align-items: center;\n}\n.du-bois-light-space-align-start {\n align-items: flex-start;\n}\n.du-bois-light-space-align-end {\n align-items: flex-end;\n}\n.du-bois-light-space-align-baseline {\n align-items: baseline;\n}\n.du-bois-light-space-item:empty {\n display: none;\n}\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-light-steps {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #11171C;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: 'tnum';\n display: flex;\n width: 100%;\n font-size: 0;\n text-align: initial;\n}\n.du-bois-light-steps-item {\n position: relative;\n display: inline-block;\n flex: 1;\n overflow: hidden;\n vertical-align: top;\n}\n.du-bois-light-steps-item-container {\n outline: none;\n}\n.du-bois-light-steps-item:last-child {\n flex: none;\n}\n.du-bois-light-steps-item:last-child > .du-bois-light-steps-item-container > .du-bois-light-steps-item-tail,\n.du-bois-light-steps-item:last-child > .du-bois-light-steps-item-container > .du-bois-light-steps-item-content > .du-bois-light-steps-item-title::after {\n display: none;\n}\n.du-bois-light-steps-item-icon,\n.du-bois-light-steps-item-content {\n display: inline-block;\n vertical-align: top;\n}\n.du-bois-light-steps-item-icon {\n width: 32px;\n height: 32px;\n margin: 0 8px 0 0;\n font-size: 18px;\n font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, 'Helvetica Neue', Arial, 'Noto Sans', sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol', 'Noto Color Emoji';\n line-height: 32px;\n text-align: center;\n border: 1px solid rgba(0, 0, 0, 0.25);\n border-radius: 32px;\n transition: background-color 0.3s, border-color 0.3s;\n}\n.du-bois-light-steps-item-icon .du-bois-light-steps-icon {\n position: relative;\n top: -0.5px;\n color: #2272B4;\n line-height: 1;\n}\n.du-bois-light-steps-item-tail {\n position: absolute;\n top: 12px;\n left: 0;\n width: 100%;\n padding: 0 10px;\n}\n.du-bois-light-steps-item-tail::after {\n display: inline-block;\n width: 100%;\n height: 1px;\n background: #f0f0f0;\n border-radius: 1px;\n transition: background 0.3s;\n content: '';\n}\n.du-bois-light-steps-item-title {\n position: relative;\n display: inline-block;\n padding-right: 16px;\n color: #11171C;\n font-size: 18px;\n line-height: 40px;\n}\n.du-bois-light-steps-item-title::after {\n position: absolute;\n top: 20px;\n left: 100%;\n display: block;\n width: 9999px;\n height: 1px;\n background: #f0f0f0;\n content: '';\n}\n.du-bois-light-steps-item-subtitle {\n display: inline;\n margin-left: 8px;\n color: #5F7281;\n font-weight: normal;\n font-size: 13px;\n}\n.du-bois-light-steps-item-description {\n color: #5F7281;\n font-size: 13px;\n}\n.du-bois-light-steps-item-wait .du-bois-light-steps-item-icon {\n background-color: #fff;\n border-color: rgba(0, 0, 0, 0.25);\n}\n.du-bois-light-steps-item-wait .du-bois-light-steps-item-icon > .du-bois-light-steps-icon {\n color: rgba(0, 0, 0, 0.25);\n}\n.du-bois-light-steps-item-wait .du-bois-light-steps-item-icon > .du-bois-light-steps-icon .du-bois-light-steps-icon-dot {\n background: rgba(0, 0, 0, 0.25);\n}\n.du-bois-light-steps-item-wait > .du-bois-light-steps-item-container > .du-bois-light-steps-item-content > .du-bois-light-steps-item-title {\n color: #5F7281;\n}\n.du-bois-light-steps-item-wait > .du-bois-light-steps-item-container > .du-bois-light-steps-item-content > .du-bois-light-steps-item-title::after {\n background-color: #f0f0f0;\n}\n.du-bois-light-steps-item-wait > .du-bois-light-steps-item-container > .du-bois-light-steps-item-content > .du-bois-light-steps-item-description {\n color: #5F7281;\n}\n.du-bois-light-steps-item-wait > .du-bois-light-steps-item-container > .du-bois-light-steps-item-tail::after {\n background-color: #f0f0f0;\n}\n.du-bois-light-steps-item-process .du-bois-light-steps-item-icon {\n background-color: #fff;\n border-color: #2272B4;\n}\n.du-bois-light-steps-item-process .du-bois-light-steps-item-icon > .du-bois-light-steps-icon {\n color: #2272B4;\n}\n.du-bois-light-steps-item-process .du-bois-light-steps-item-icon > .du-bois-light-steps-icon .du-bois-light-steps-icon-dot {\n background: #2272B4;\n}\n.du-bois-light-steps-item-process > .du-bois-light-steps-item-container > .du-bois-light-steps-item-content > .du-bois-light-steps-item-title {\n color: rgba(0, 0, 0, 0.85);\n}\n.du-bois-light-steps-item-process > .du-bois-light-steps-item-container > .du-bois-light-steps-item-content > .du-bois-light-steps-item-title::after {\n background-color: #f0f0f0;\n}\n.du-bois-light-steps-item-process > .du-bois-light-steps-item-container > .du-bois-light-steps-item-content > .du-bois-light-steps-item-description {\n color: #11171C;\n}\n.du-bois-light-steps-item-process > .du-bois-light-steps-item-container > .du-bois-light-steps-item-tail::after {\n background-color: #f0f0f0;\n}\n.du-bois-light-steps-item-process > .du-bois-light-steps-item-container > .du-bois-light-steps-item-icon {\n background: #2272B4;\n}\n.du-bois-light-steps-item-process > .du-bois-light-steps-item-container > .du-bois-light-steps-item-icon .du-bois-light-steps-icon {\n color: #fff;\n}\n.du-bois-light-steps-item-process > .du-bois-light-steps-item-container > .du-bois-light-steps-item-title {\n font-weight: 500;\n}\n.du-bois-light-steps-item-finish .du-bois-light-steps-item-icon {\n background-color: #fff;\n border-color: #2272B4;\n}\n.du-bois-light-steps-item-finish .du-bois-light-steps-item-icon > .du-bois-light-steps-icon {\n color: #2272B4;\n}\n.du-bois-light-steps-item-finish .du-bois-light-steps-item-icon > .du-bois-light-steps-icon .du-bois-light-steps-icon-dot {\n background: #2272B4;\n}\n.du-bois-light-steps-item-finish > .du-bois-light-steps-item-container > .du-bois-light-steps-item-content > .du-bois-light-steps-item-title {\n color: #11171C;\n}\n.du-bois-light-steps-item-finish > .du-bois-light-steps-item-container > .du-bois-light-steps-item-content > .du-bois-light-steps-item-title::after {\n background-color: #2272B4;\n}\n.du-bois-light-steps-item-finish > .du-bois-light-steps-item-container > .du-bois-light-steps-item-content > .du-bois-light-steps-item-description {\n color: #5F7281;\n}\n.du-bois-light-steps-item-finish > .du-bois-light-steps-item-container > .du-bois-light-steps-item-tail::after {\n background-color: #2272B4;\n}\n.du-bois-light-steps-item-error .du-bois-light-steps-item-icon {\n background-color: #fff;\n border-color: #C82D4C;\n}\n.du-bois-light-steps-item-error .du-bois-light-steps-item-icon > .du-bois-light-steps-icon {\n color: #C82D4C;\n}\n.du-bois-light-steps-item-error .du-bois-light-steps-item-icon > .du-bois-light-steps-icon .du-bois-light-steps-icon-dot {\n background: #C82D4C;\n}\n.du-bois-light-steps-item-error > .du-bois-light-steps-item-container > .du-bois-light-steps-item-content > .du-bois-light-steps-item-title {\n color: #C82D4C;\n}\n.du-bois-light-steps-item-error > .du-bois-light-steps-item-container > .du-bois-light-steps-item-content > .du-bois-light-steps-item-title::after {\n background-color: #f0f0f0;\n}\n.du-bois-light-steps-item-error > .du-bois-light-steps-item-container > .du-bois-light-steps-item-content > .du-bois-light-steps-item-description {\n color: #C82D4C;\n}\n.du-bois-light-steps-item-error > .du-bois-light-steps-item-container > .du-bois-light-steps-item-tail::after {\n background-color: #f0f0f0;\n}\n.du-bois-light-steps-item.du-bois-light-steps-next-error .du-bois-light-steps-item-title::after {\n background: #C82D4C;\n}\n.du-bois-light-steps-item-disabled {\n cursor: not-allowed;\n}\n.du-bois-light-steps .du-bois-light-steps-item:not(.du-bois-light-steps-item-active) > .du-bois-light-steps-item-container[role='button'] {\n cursor: pointer;\n}\n.du-bois-light-steps .du-bois-light-steps-item:not(.du-bois-light-steps-item-active) > .du-bois-light-steps-item-container[role='button'] .du-bois-light-steps-item-title,\n.du-bois-light-steps .du-bois-light-steps-item:not(.du-bois-light-steps-item-active) > .du-bois-light-steps-item-container[role='button'] .du-bois-light-steps-item-subtitle,\n.du-bois-light-steps .du-bois-light-steps-item:not(.du-bois-light-steps-item-active) > .du-bois-light-steps-item-container[role='button'] .du-bois-light-steps-item-description,\n.du-bois-light-steps .du-bois-light-steps-item:not(.du-bois-light-steps-item-active) > .du-bois-light-steps-item-container[role='button'] .du-bois-light-steps-item-icon .du-bois-light-steps-icon {\n transition: color 0.3s;\n}\n.du-bois-light-steps .du-bois-light-steps-item:not(.du-bois-light-steps-item-active) > .du-bois-light-steps-item-container[role='button']:hover .du-bois-light-steps-item-title,\n.du-bois-light-steps .du-bois-light-steps-item:not(.du-bois-light-steps-item-active) > .du-bois-light-steps-item-container[role='button']:hover .du-bois-light-steps-item-subtitle,\n.du-bois-light-steps .du-bois-light-steps-item:not(.du-bois-light-steps-item-active) > .du-bois-light-steps-item-container[role='button']:hover .du-bois-light-steps-item-description {\n color: #2272B4;\n}\n.du-bois-light-steps .du-bois-light-steps-item:not(.du-bois-light-steps-item-active):not(.du-bois-light-steps-item-process) > .du-bois-light-steps-item-container[role='button']:hover .du-bois-light-steps-item-icon {\n border-color: #2272B4;\n}\n.du-bois-light-steps .du-bois-light-steps-item:not(.du-bois-light-steps-item-active):not(.du-bois-light-steps-item-process) > .du-bois-light-steps-item-container[role='button']:hover .du-bois-light-steps-item-icon .du-bois-light-steps-icon {\n color: #2272B4;\n}\n.du-bois-light-steps-horizontal:not(.du-bois-light-steps-label-vertical) .du-bois-light-steps-item {\n padding-left: 16px;\n white-space: nowrap;\n}\n.du-bois-light-steps-horizontal:not(.du-bois-light-steps-label-vertical) .du-bois-light-steps-item:first-child {\n padding-left: 0;\n}\n.du-bois-light-steps-horizontal:not(.du-bois-light-steps-label-vertical) .du-bois-light-steps-item:last-child .du-bois-light-steps-item-title {\n padding-right: 0;\n}\n.du-bois-light-steps-horizontal:not(.du-bois-light-steps-label-vertical) .du-bois-light-steps-item-tail {\n display: none;\n}\n.du-bois-light-steps-horizontal:not(.du-bois-light-steps-label-vertical) .du-bois-light-steps-item-description {\n max-width: 140px;\n white-space: normal;\n}\n.du-bois-light-steps-item-custom > .du-bois-light-steps-item-container > .du-bois-light-steps-item-icon {\n height: auto;\n background: none;\n border: 0;\n}\n.du-bois-light-steps-item-custom > .du-bois-light-steps-item-container > .du-bois-light-steps-item-icon > .du-bois-light-steps-icon {\n top: 0px;\n left: 0.5px;\n width: 32px;\n height: 32px;\n font-size: 24px;\n line-height: 32px;\n}\n.du-bois-light-steps-item-custom.du-bois-light-steps-item-process .du-bois-light-steps-item-icon > .du-bois-light-steps-icon {\n color: #2272B4;\n}\n.du-bois-light-steps:not(.du-bois-light-steps-vertical) .du-bois-light-steps-item-custom .du-bois-light-steps-item-icon {\n width: auto;\n background: none;\n}\n.du-bois-light-steps-small.du-bois-light-steps-horizontal:not(.du-bois-light-steps-label-vertical) .du-bois-light-steps-item {\n padding-left: 12px;\n}\n.du-bois-light-steps-small.du-bois-light-steps-horizontal:not(.du-bois-light-steps-label-vertical) .du-bois-light-steps-item:first-child {\n padding-left: 0;\n}\n.du-bois-light-steps-small .du-bois-light-steps-item-icon {\n width: 24px;\n height: 24px;\n margin: 0 8px 0 0;\n font-size: 12px;\n line-height: 24px;\n text-align: center;\n border-radius: 24px;\n}\n.du-bois-light-steps-small .du-bois-light-steps-item-title {\n padding-right: 12px;\n font-size: 13px;\n line-height: 24px;\n}\n.du-bois-light-steps-small .du-bois-light-steps-item-title::after {\n top: 12px;\n}\n.du-bois-light-steps-small .du-bois-light-steps-item-description {\n color: #5F7281;\n font-size: 13px;\n}\n.du-bois-light-steps-small .du-bois-light-steps-item-tail {\n top: 8px;\n}\n.du-bois-light-steps-small .du-bois-light-steps-item-custom .du-bois-light-steps-item-icon {\n width: inherit;\n height: inherit;\n line-height: inherit;\n background: none;\n border: 0;\n border-radius: 0;\n}\n.du-bois-light-steps-small .du-bois-light-steps-item-custom .du-bois-light-steps-item-icon > .du-bois-light-steps-icon {\n font-size: 24px;\n line-height: 24px;\n transform: none;\n}\n.du-bois-light-steps-vertical {\n display: flex;\n flex-direction: column;\n}\n.du-bois-light-steps-vertical > .du-bois-light-steps-item {\n display: block;\n flex: 1 0 auto;\n padding-left: 0;\n overflow: visible;\n}\n.du-bois-light-steps-vertical > .du-bois-light-steps-item .du-bois-light-steps-item-icon {\n float: left;\n margin-right: 16px;\n}\n.du-bois-light-steps-vertical > .du-bois-light-steps-item .du-bois-light-steps-item-content {\n display: block;\n min-height: 48px;\n overflow: hidden;\n}\n.du-bois-light-steps-vertical > .du-bois-light-steps-item .du-bois-light-steps-item-title {\n line-height: 32px;\n}\n.du-bois-light-steps-vertical > .du-bois-light-steps-item .du-bois-light-steps-item-description {\n padding-bottom: 12px;\n}\n.du-bois-light-steps-vertical > .du-bois-light-steps-item > .du-bois-light-steps-item-container > .du-bois-light-steps-item-tail {\n position: absolute;\n top: 0;\n left: 16px;\n width: 1px;\n height: 100%;\n padding: 38px 0 6px;\n}\n.du-bois-light-steps-vertical > .du-bois-light-steps-item > .du-bois-light-steps-item-container > .du-bois-light-steps-item-tail::after {\n width: 1px;\n height: 100%;\n}\n.du-bois-light-steps-vertical > .du-bois-light-steps-item:not(:last-child) > .du-bois-light-steps-item-container > .du-bois-light-steps-item-tail {\n display: block;\n}\n.du-bois-light-steps-vertical > .du-bois-light-steps-item > .du-bois-light-steps-item-container > .du-bois-light-steps-item-content > .du-bois-light-steps-item-title::after {\n display: none;\n}\n.du-bois-light-steps-vertical.du-bois-light-steps-small .du-bois-light-steps-item-container .du-bois-light-steps-item-tail {\n position: absolute;\n top: 0;\n left: 12px;\n padding: 30px 0 6px;\n}\n.du-bois-light-steps-vertical.du-bois-light-steps-small .du-bois-light-steps-item-container .du-bois-light-steps-item-title {\n line-height: 24px;\n}\n.du-bois-light-steps-label-vertical .du-bois-light-steps-item {\n overflow: visible;\n}\n.du-bois-light-steps-label-vertical .du-bois-light-steps-item-tail {\n margin-left: 58px;\n padding: 3.5px 24px;\n}\n.du-bois-light-steps-label-vertical .du-bois-light-steps-item-content {\n display: block;\n width: 116px;\n margin-top: 8px;\n text-align: center;\n}\n.du-bois-light-steps-label-vertical .du-bois-light-steps-item-icon {\n display: inline-block;\n margin-left: 42px;\n}\n.du-bois-light-steps-label-vertical .du-bois-light-steps-item-title {\n padding-right: 0;\n padding-left: 0;\n}\n.du-bois-light-steps-label-vertical .du-bois-light-steps-item-title::after {\n display: none;\n}\n.du-bois-light-steps-label-vertical .du-bois-light-steps-item-subtitle {\n display: block;\n margin-bottom: 4px;\n margin-left: 0;\n line-height: 20px;\n}\n.du-bois-light-steps-label-vertical.du-bois-light-steps-small:not(.du-bois-light-steps-dot) .du-bois-light-steps-item-icon {\n margin-left: 46px;\n}\n.du-bois-light-steps-dot .du-bois-light-steps-item-title,\n.du-bois-light-steps-dot.du-bois-light-steps-small .du-bois-light-steps-item-title {\n line-height: 20px;\n}\n.du-bois-light-steps-dot .du-bois-light-steps-item-tail,\n.du-bois-light-steps-dot.du-bois-light-steps-small .du-bois-light-steps-item-tail {\n top: 2px;\n width: 100%;\n margin: 0 0 0 70px;\n padding: 0;\n}\n.du-bois-light-steps-dot .du-bois-light-steps-item-tail::after,\n.du-bois-light-steps-dot.du-bois-light-steps-small .du-bois-light-steps-item-tail::after {\n width: calc(100% - 20px);\n height: 3px;\n margin-left: 12px;\n}\n.du-bois-light-steps-dot .du-bois-light-steps-item:first-child .du-bois-light-steps-icon-dot,\n.du-bois-light-steps-dot.du-bois-light-steps-small .du-bois-light-steps-item:first-child .du-bois-light-steps-icon-dot {\n left: 2px;\n}\n.du-bois-light-steps-dot .du-bois-light-steps-item-icon,\n.du-bois-light-steps-dot.du-bois-light-steps-small .du-bois-light-steps-item-icon {\n width: 8px;\n height: 8px;\n margin-left: 67px;\n padding-right: 0;\n line-height: 8px;\n background: transparent;\n border: 0;\n}\n.du-bois-light-steps-dot .du-bois-light-steps-item-icon .du-bois-light-steps-icon-dot,\n.du-bois-light-steps-dot.du-bois-light-steps-small .du-bois-light-steps-item-icon .du-bois-light-steps-icon-dot {\n position: relative;\n float: left;\n width: 100%;\n height: 100%;\n border-radius: 100px;\n transition: all 0.3s;\n /* expand hover area */\n}\n.du-bois-light-steps-dot .du-bois-light-steps-item-icon .du-bois-light-steps-icon-dot::after,\n.du-bois-light-steps-dot.du-bois-light-steps-small .du-bois-light-steps-item-icon .du-bois-light-steps-icon-dot::after {\n position: absolute;\n top: -12px;\n left: -26px;\n width: 60px;\n height: 32px;\n background: rgba(0, 0, 0, 0.001);\n content: '';\n}\n.du-bois-light-steps-dot .du-bois-light-steps-item-content,\n.du-bois-light-steps-dot.du-bois-light-steps-small .du-bois-light-steps-item-content {\n width: 140px;\n}\n.du-bois-light-steps-dot .du-bois-light-steps-item-process .du-bois-light-steps-item-icon,\n.du-bois-light-steps-dot.du-bois-light-steps-small .du-bois-light-steps-item-process .du-bois-light-steps-item-icon {\n position: relative;\n top: -1px;\n width: 10px;\n height: 10px;\n line-height: 10px;\n background: none;\n}\n.du-bois-light-steps-dot .du-bois-light-steps-item-process .du-bois-light-steps-icon:first-child .du-bois-light-steps-icon-dot,\n.du-bois-light-steps-dot.du-bois-light-steps-small .du-bois-light-steps-item-process .du-bois-light-steps-icon:first-child .du-bois-light-steps-icon-dot {\n left: 0;\n}\n.du-bois-light-steps-vertical.du-bois-light-steps-dot .du-bois-light-steps-item-icon {\n margin-top: 8px;\n margin-left: 0;\n background: none;\n}\n.du-bois-light-steps-vertical.du-bois-light-steps-dot .du-bois-light-steps-item > .du-bois-light-steps-item-container > .du-bois-light-steps-item-tail {\n top: 2px;\n left: -9px;\n margin: 0;\n padding: 22px 0 4px;\n}\n.du-bois-light-steps-vertical.du-bois-light-steps-dot .du-bois-light-steps-item:first-child .du-bois-light-steps-icon-dot {\n left: 0;\n}\n.du-bois-light-steps-vertical.du-bois-light-steps-dot .du-bois-light-steps-item-content {\n width: inherit;\n}\n.du-bois-light-steps-vertical.du-bois-light-steps-dot .du-bois-light-steps-item-process .du-bois-light-steps-item-container .du-bois-light-steps-item-icon .du-bois-light-steps-icon-dot {\n left: -2px;\n}\n.du-bois-light-steps-navigation {\n padding-top: 12px;\n}\n.du-bois-light-steps-navigation.du-bois-light-steps-small .du-bois-light-steps-item-container {\n margin-left: -12px;\n}\n.du-bois-light-steps-navigation .du-bois-light-steps-item {\n overflow: visible;\n text-align: center;\n}\n.du-bois-light-steps-navigation .du-bois-light-steps-item-container {\n display: inline-block;\n height: 100%;\n margin-left: -16px;\n padding-bottom: 12px;\n text-align: left;\n transition: opacity 0.3s;\n}\n.du-bois-light-steps-navigation .du-bois-light-steps-item-container .du-bois-light-steps-item-content {\n max-width: auto;\n}\n.du-bois-light-steps-navigation .du-bois-light-steps-item-container .du-bois-light-steps-item-title {\n max-width: 100%;\n padding-right: 0;\n overflow: hidden;\n white-space: nowrap;\n text-overflow: ellipsis;\n}\n.du-bois-light-steps-navigation .du-bois-light-steps-item-container .du-bois-light-steps-item-title::after {\n display: none;\n}\n.du-bois-light-steps-navigation .du-bois-light-steps-item:not(.du-bois-light-steps-item-active) .du-bois-light-steps-item-container[role='button'] {\n cursor: pointer;\n}\n.du-bois-light-steps-navigation .du-bois-light-steps-item:not(.du-bois-light-steps-item-active) .du-bois-light-steps-item-container[role='button']:hover {\n opacity: 0.85;\n}\n.du-bois-light-steps-navigation .du-bois-light-steps-item:last-child {\n flex: 1;\n}\n.du-bois-light-steps-navigation .du-bois-light-steps-item:last-child::after {\n display: none;\n}\n.du-bois-light-steps-navigation .du-bois-light-steps-item::after {\n position: absolute;\n top: 50%;\n left: 100%;\n display: inline-block;\n width: 12px;\n height: 12px;\n margin-top: -14px;\n margin-left: -2px;\n border: 1px solid rgba(0, 0, 0, 0.25);\n border-bottom: none;\n border-left: none;\n transform: rotate(45deg);\n content: '';\n}\n.du-bois-light-steps-navigation .du-bois-light-steps-item::before {\n position: absolute;\n bottom: 0;\n left: 50%;\n display: inline-block;\n width: 0;\n height: 2px;\n background-color: #2272B4;\n transition: width 0.3s, left 0.3s;\n transition-timing-function: ease-out;\n content: '';\n}\n.du-bois-light-steps-navigation .du-bois-light-steps-item.du-bois-light-steps-item-active::before {\n left: 0;\n width: 100%;\n}\n.du-bois-light-steps-navigation.du-bois-light-steps-vertical > .du-bois-light-steps-item {\n margin-right: 0 !important;\n}\n.du-bois-light-steps-navigation.du-bois-light-steps-vertical > .du-bois-light-steps-item::before {\n display: none;\n}\n.du-bois-light-steps-navigation.du-bois-light-steps-vertical > .du-bois-light-steps-item.du-bois-light-steps-item-active::before {\n top: 0;\n right: 0;\n left: unset;\n display: block;\n width: 3px;\n height: calc(100% - 24px);\n}\n.du-bois-light-steps-navigation.du-bois-light-steps-vertical > .du-bois-light-steps-item::after {\n position: relative;\n top: -2px;\n left: 50%;\n display: block;\n width: 8px;\n height: 8px;\n margin-bottom: 8px;\n text-align: center;\n transform: rotate(135deg);\n}\n.du-bois-light-steps-navigation.du-bois-light-steps-vertical > .du-bois-light-steps-item > .du-bois-light-steps-item-container > .du-bois-light-steps-item-tail {\n visibility: hidden;\n}\n.du-bois-light-steps-with-progress .du-bois-light-steps-item {\n padding-top: 4px;\n}\n.du-bois-light-steps-with-progress .du-bois-light-steps-item .du-bois-light-steps-item-tail {\n top: 4px !important;\n}\n.du-bois-light-steps-with-progress.du-bois-light-steps-horizontal .du-bois-light-steps-item:first-child {\n padding-bottom: 4px;\n padding-left: 4px;\n}\n.du-bois-light-steps-with-progress .du-bois-light-steps-item-icon {\n position: relative;\n}\n.du-bois-light-steps-with-progress .du-bois-light-steps-item-icon .du-bois-light-progress {\n position: absolute;\n top: -5px;\n right: -5px;\n bottom: -5px;\n left: -5px;\n}\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-light-progress {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #11171C;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: 'tnum';\n display: inline-block;\n}\n.du-bois-light-progress-line {\n position: relative;\n width: 100%;\n font-size: 13px;\n}\n.du-bois-light-progress-steps {\n display: inline-block;\n}\n.du-bois-light-progress-steps-outer {\n display: flex;\n flex-direction: row;\n align-items: center;\n}\n.du-bois-light-progress-steps-item {\n flex-shrink: 0;\n min-width: 2px;\n margin-right: 2px;\n background: #f3f3f3;\n transition: all 0.3s;\n}\n.du-bois-light-progress-steps-item-active {\n background: #1890ff;\n}\n.du-bois-light-progress-small.du-bois-light-progress-line,\n.du-bois-light-progress-small.du-bois-light-progress-line .du-bois-light-progress-text .anticon {\n font-size: 12px;\n}\n.du-bois-light-progress-outer {\n display: inline-block;\n width: 100%;\n margin-right: 0;\n padding-right: 0;\n}\n.du-bois-light-progress-show-info .du-bois-light-progress-outer {\n margin-right: calc(-2em - 8px);\n padding-right: calc(2em + 8px);\n}\n.du-bois-light-progress-inner {\n position: relative;\n display: inline-block;\n width: 100%;\n overflow: hidden;\n vertical-align: middle;\n background-color: #f5f5f5;\n border-radius: 100px;\n}\n.du-bois-light-progress-circle-trail {\n stroke: #f5f5f5;\n}\n.du-bois-light-progress-circle-path {\n animation: du-bois-light-progress-appear 0.3s;\n}\n.du-bois-light-progress-inner:not(.du-bois-light-progress-circle-gradient) .du-bois-light-progress-circle-path {\n stroke: #1890ff;\n}\n.du-bois-light-progress-success-bg,\n.du-bois-light-progress-bg {\n position: relative;\n background-color: #1890ff;\n border-radius: 100px;\n transition: all 0.4s cubic-bezier(0.08, 0.82, 0.17, 1) 0s;\n}\n.du-bois-light-progress-success-bg {\n position: absolute;\n top: 0;\n left: 0;\n background-color: #34824F;\n}\n.du-bois-light-progress-text {\n display: inline-block;\n width: 2em;\n margin-left: 8px;\n color: #11171C;\n font-size: 1em;\n line-height: 1;\n white-space: nowrap;\n text-align: left;\n vertical-align: middle;\n word-break: normal;\n}\n.du-bois-light-progress-text .anticon {\n font-size: 13px;\n}\n.du-bois-light-progress-status-active .du-bois-light-progress-bg::before {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: #fff;\n border-radius: 10px;\n opacity: 0;\n animation: du-bois-light-progress-active 2.4s cubic-bezier(0.23, 1, 0.32, 1) infinite;\n content: '';\n}\n.du-bois-light-progress-status-exception .du-bois-light-progress-bg {\n background-color: #C82D4C;\n}\n.du-bois-light-progress-status-exception .du-bois-light-progress-text {\n color: #C82D4C;\n}\n.du-bois-light-progress-status-exception .du-bois-light-progress-inner:not(.du-bois-light-progress-circle-gradient) .du-bois-light-progress-circle-path {\n stroke: #C82D4C;\n}\n.du-bois-light-progress-status-success .du-bois-light-progress-bg {\n background-color: #34824F;\n}\n.du-bois-light-progress-status-success .du-bois-light-progress-text {\n color: #34824F;\n}\n.du-bois-light-progress-status-success .du-bois-light-progress-inner:not(.du-bois-light-progress-circle-gradient) .du-bois-light-progress-circle-path {\n stroke: #34824F;\n}\n.du-bois-light-progress-circle .du-bois-light-progress-inner {\n position: relative;\n line-height: 1;\n background-color: transparent;\n}\n.du-bois-light-progress-circle .du-bois-light-progress-text {\n position: absolute;\n top: 50%;\n left: 50%;\n width: 100%;\n margin: 0;\n padding: 0;\n color: #11171C;\n font-size: 1em;\n line-height: 1;\n white-space: normal;\n text-align: center;\n transform: translate(-50%, -50%);\n}\n.du-bois-light-progress-circle .du-bois-light-progress-text .anticon {\n font-size: 1.16666667em;\n}\n.du-bois-light-progress-circle.du-bois-light-progress-status-exception .du-bois-light-progress-text {\n color: #C82D4C;\n}\n.du-bois-light-progress-circle.du-bois-light-progress-status-success .du-bois-light-progress-text {\n color: #34824F;\n}\n@keyframes du-bois-light-progress-active {\n 0% {\n transform: translateX(-100%) scaleX(0);\n opacity: 0.1;\n }\n 20% {\n transform: translateX(-100%) scaleX(0);\n opacity: 0.5;\n }\n 100% {\n transform: translateX(0) scaleX(1);\n opacity: 0;\n }\n}\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-light-table.du-bois-light-table-middle {\n font-size: 13px;\n}\n.du-bois-light-table.du-bois-light-table-middle .du-bois-light-table-title,\n.du-bois-light-table.du-bois-light-table-middle .du-bois-light-table-footer,\n.du-bois-light-table.du-bois-light-table-middle .du-bois-light-table-thead > tr > th,\n.du-bois-light-table.du-bois-light-table-middle .du-bois-light-table-tbody > tr > td,\n.du-bois-light-table.du-bois-light-table-middle tfoot > tr > th,\n.du-bois-light-table.du-bois-light-table-middle tfoot > tr > td {\n padding: 12px 8px;\n}\n.du-bois-light-table.du-bois-light-table-middle .du-bois-light-table-filter-trigger {\n margin-right: -4px;\n}\n.du-bois-light-table.du-bois-light-table-middle .du-bois-light-table-expanded-row-fixed {\n margin: -12px -8px;\n}\n.du-bois-light-table.du-bois-light-table-middle .du-bois-light-table-tbody .du-bois-light-table-wrapper:only-child .du-bois-light-table {\n margin: -12px -8px -12px 25px;\n}\n.du-bois-light-table.du-bois-light-table-small {\n font-size: 13px;\n}\n.du-bois-light-table.du-bois-light-table-small .du-bois-light-table-title,\n.du-bois-light-table.du-bois-light-table-small .du-bois-light-table-footer,\n.du-bois-light-table.du-bois-light-table-small .du-bois-light-table-thead > tr > th,\n.du-bois-light-table.du-bois-light-table-small .du-bois-light-table-tbody > tr > td,\n.du-bois-light-table.du-bois-light-table-small tfoot > tr > th,\n.du-bois-light-table.du-bois-light-table-small tfoot > tr > td {\n padding: 8px 8px;\n}\n.du-bois-light-table.du-bois-light-table-small .du-bois-light-table-filter-trigger {\n margin-right: -4px;\n}\n.du-bois-light-table.du-bois-light-table-small .du-bois-light-table-expanded-row-fixed {\n margin: -8px -8px;\n}\n.du-bois-light-table.du-bois-light-table-small .du-bois-light-table-tbody .du-bois-light-table-wrapper:only-child .du-bois-light-table {\n margin: -8px -8px -8px 25px;\n}\n.du-bois-light-table-small .du-bois-light-table-thead > tr > th {\n background-color: #fafafa;\n}\n.du-bois-light-table-small .du-bois-light-table-selection-column {\n width: 46px;\n min-width: 46px;\n}\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-title {\n border: 1px solid #f0f0f0;\n border-bottom: 0;\n}\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-container {\n border-left: 1px solid #f0f0f0;\n}\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-container > .du-bois-light-table-content > table > thead > tr > th,\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-container > .du-bois-light-table-header > table > thead > tr > th,\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-container > .du-bois-light-table-body > table > thead > tr > th,\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-container > .du-bois-light-table-summary > table > thead > tr > th,\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-container > .du-bois-light-table-content > table > tbody > tr > td,\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-container > .du-bois-light-table-header > table > tbody > tr > td,\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-container > .du-bois-light-table-body > table > tbody > tr > td,\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-container > .du-bois-light-table-summary > table > tbody > tr > td,\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-container > .du-bois-light-table-content > table > tfoot > tr > th,\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-container > .du-bois-light-table-header > table > tfoot > tr > th,\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-container > .du-bois-light-table-body > table > tfoot > tr > th,\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-container > .du-bois-light-table-summary > table > tfoot > tr > th,\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-container > .du-bois-light-table-content > table > tfoot > tr > td,\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-container > .du-bois-light-table-header > table > tfoot > tr > td,\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-container > .du-bois-light-table-body > table > tfoot > tr > td,\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-container > .du-bois-light-table-summary > table > tfoot > tr > td {\n border-right: 1px solid #f0f0f0;\n}\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-container > .du-bois-light-table-content > table > thead > tr:not(:last-child) > th,\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-container > .du-bois-light-table-header > table > thead > tr:not(:last-child) > th,\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-container > .du-bois-light-table-body > table > thead > tr:not(:last-child) > th,\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-container > .du-bois-light-table-summary > table > thead > tr:not(:last-child) > th {\n border-bottom: 1px solid #f0f0f0;\n}\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-container > .du-bois-light-table-content > table > thead > tr > th::before,\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-container > .du-bois-light-table-header > table > thead > tr > th::before,\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-container > .du-bois-light-table-body > table > thead > tr > th::before,\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-container > .du-bois-light-table-summary > table > thead > tr > th::before {\n background-color: transparent !important;\n}\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-container > .du-bois-light-table-content > table > thead > tr > .du-bois-light-table-cell-fix-right-first::after,\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-container > .du-bois-light-table-header > table > thead > tr > .du-bois-light-table-cell-fix-right-first::after,\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-container > .du-bois-light-table-body > table > thead > tr > .du-bois-light-table-cell-fix-right-first::after,\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-container > .du-bois-light-table-summary > table > thead > tr > .du-bois-light-table-cell-fix-right-first::after,\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-container > .du-bois-light-table-content > table > tbody > tr > .du-bois-light-table-cell-fix-right-first::after,\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-container > .du-bois-light-table-header > table > tbody > tr > .du-bois-light-table-cell-fix-right-first::after,\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-container > .du-bois-light-table-body > table > tbody > tr > .du-bois-light-table-cell-fix-right-first::after,\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-container > .du-bois-light-table-summary > table > tbody > tr > .du-bois-light-table-cell-fix-right-first::after,\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-container > .du-bois-light-table-content > table > tfoot > tr > .du-bois-light-table-cell-fix-right-first::after,\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-container > .du-bois-light-table-header > table > tfoot > tr > .du-bois-light-table-cell-fix-right-first::after,\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-container > .du-bois-light-table-body > table > tfoot > tr > .du-bois-light-table-cell-fix-right-first::after,\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-container > .du-bois-light-table-summary > table > tfoot > tr > .du-bois-light-table-cell-fix-right-first::after {\n border-right: 1px solid #f0f0f0;\n}\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-container > .du-bois-light-table-content > table > tbody > tr > td > .du-bois-light-table-expanded-row-fixed,\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-container > .du-bois-light-table-header > table > tbody > tr > td > .du-bois-light-table-expanded-row-fixed,\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-container > .du-bois-light-table-body > table > tbody > tr > td > .du-bois-light-table-expanded-row-fixed,\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-container > .du-bois-light-table-summary > table > tbody > tr > td > .du-bois-light-table-expanded-row-fixed {\n margin: -16px -17px;\n}\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-container > .du-bois-light-table-content > table > tbody > tr > td > .du-bois-light-table-expanded-row-fixed::after,\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-container > .du-bois-light-table-header > table > tbody > tr > td > .du-bois-light-table-expanded-row-fixed::after,\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-container > .du-bois-light-table-body > table > tbody > tr > td > .du-bois-light-table-expanded-row-fixed::after,\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-container > .du-bois-light-table-summary > table > tbody > tr > td > .du-bois-light-table-expanded-row-fixed::after {\n position: absolute;\n top: 0;\n right: 1px;\n bottom: 0;\n border-right: 1px solid #f0f0f0;\n content: '';\n}\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-container > .du-bois-light-table-content > table,\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-container > .du-bois-light-table-header > table {\n border-top: 1px solid #f0f0f0;\n}\n.du-bois-light-table.du-bois-light-table-bordered.du-bois-light-table-scroll-horizontal > .du-bois-light-table-container > .du-bois-light-table-body > table > tbody > tr.du-bois-light-table-expanded-row > td,\n.du-bois-light-table.du-bois-light-table-bordered.du-bois-light-table-scroll-horizontal > .du-bois-light-table-container > .du-bois-light-table-body > table > tbody > tr.du-bois-light-table-placeholder > td {\n border-right: 0;\n}\n.du-bois-light-table.du-bois-light-table-bordered.du-bois-light-table-middle > .du-bois-light-table-container > .du-bois-light-table-content > table > tbody > tr > td > .du-bois-light-table-expanded-row-fixed,\n.du-bois-light-table.du-bois-light-table-bordered.du-bois-light-table-middle > .du-bois-light-table-container > .du-bois-light-table-body > table > tbody > tr > td > .du-bois-light-table-expanded-row-fixed {\n margin: -12px -9px;\n}\n.du-bois-light-table.du-bois-light-table-bordered.du-bois-light-table-small > .du-bois-light-table-container > .du-bois-light-table-content > table > tbody > tr > td > .du-bois-light-table-expanded-row-fixed,\n.du-bois-light-table.du-bois-light-table-bordered.du-bois-light-table-small > .du-bois-light-table-container > .du-bois-light-table-body > table > tbody > tr > td > .du-bois-light-table-expanded-row-fixed {\n margin: -8px -9px;\n}\n.du-bois-light-table.du-bois-light-table-bordered > .du-bois-light-table-footer {\n border: 1px solid #f0f0f0;\n border-top: 0;\n}\n.du-bois-light-table-cell .du-bois-light-table-container:first-child {\n border-top: 0;\n}\n.du-bois-light-table-cell-scrollbar {\n box-shadow: 0 1px 0 1px #fafafa;\n}\n.du-bois-light-table-wrapper {\n clear: both;\n max-width: 100%;\n}\n.du-bois-light-table-wrapper::before {\n display: table;\n content: '';\n}\n.du-bois-light-table-wrapper::after {\n display: table;\n clear: both;\n content: '';\n}\n.du-bois-light-table {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #11171C;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: 'tnum';\n position: relative;\n font-size: 13px;\n background: #fff;\n border-radius: 4px;\n}\n.du-bois-light-table table {\n width: 100%;\n text-align: left;\n border-radius: 4px 4px 0 0;\n border-collapse: separate;\n border-spacing: 0;\n}\n.du-bois-light-table-thead > tr > th,\n.du-bois-light-table-tbody > tr > td,\n.du-bois-light-table tfoot > tr > th,\n.du-bois-light-table tfoot > tr > td {\n position: relative;\n padding: 16px 16px;\n overflow-wrap: break-word;\n}\n.du-bois-light-table-cell-ellipsis {\n overflow: hidden;\n white-space: nowrap;\n text-overflow: ellipsis;\n word-break: keep-all;\n}\n.du-bois-light-table-cell-ellipsis.du-bois-light-table-cell-fix-left-last,\n.du-bois-light-table-cell-ellipsis.du-bois-light-table-cell-fix-right-first {\n overflow: visible;\n}\n.du-bois-light-table-cell-ellipsis.du-bois-light-table-cell-fix-left-last .du-bois-light-table-cell-content,\n.du-bois-light-table-cell-ellipsis.du-bois-light-table-cell-fix-right-first .du-bois-light-table-cell-content {\n display: block;\n overflow: hidden;\n text-overflow: ellipsis;\n}\n.du-bois-light-table-cell-ellipsis .du-bois-light-table-column-title {\n overflow: hidden;\n text-overflow: ellipsis;\n word-break: keep-all;\n}\n.du-bois-light-table-title {\n padding: 16px 16px;\n}\n.du-bois-light-table-footer {\n padding: 16px 16px;\n color: rgba(0, 0, 0, 0.85);\n background: #fafafa;\n}\n.du-bois-light-table-thead > tr > th {\n position: relative;\n color: rgba(0, 0, 0, 0.85);\n font-weight: 500;\n text-align: left;\n background: #fafafa;\n border-bottom: 1px solid #f0f0f0;\n transition: background 0.3s ease;\n}\n.du-bois-light-table-thead > tr > th[colspan]:not([colspan='1']) {\n text-align: center;\n}\n.du-bois-light-table-thead > tr > th:not(:last-child):not(.du-bois-light-table-selection-column):not(.du-bois-light-table-row-expand-icon-cell):not([colspan])::before {\n position: absolute;\n top: 50%;\n right: 0;\n width: 1px;\n height: 1.6em;\n background-color: rgba(0, 0, 0, 0.06);\n transform: translateY(-50%);\n transition: background-color 0.3s;\n content: '';\n}\n.du-bois-light-table-thead > tr:not(:last-child) > th[colspan] {\n border-bottom: 0;\n}\n.du-bois-light-table-tbody > tr > td {\n border-bottom: 1px solid #f0f0f0;\n transition: background 0.3s;\n}\n.du-bois-light-table-tbody > tr > td > .du-bois-light-table-wrapper:only-child .du-bois-light-table,\n.du-bois-light-table-tbody > tr > td > .du-bois-light-table-expanded-row-fixed > .du-bois-light-table-wrapper:only-child .du-bois-light-table {\n margin: -16px -16px -16px 33px;\n}\n.du-bois-light-table-tbody > tr > td > .du-bois-light-table-wrapper:only-child .du-bois-light-table-tbody > tr:last-child > td,\n.du-bois-light-table-tbody > tr > td > .du-bois-light-table-expanded-row-fixed > .du-bois-light-table-wrapper:only-child .du-bois-light-table-tbody > tr:last-child > td {\n border-bottom: 0;\n}\n.du-bois-light-table-tbody > tr > td > .du-bois-light-table-wrapper:only-child .du-bois-light-table-tbody > tr:last-child > td:first-child,\n.du-bois-light-table-tbody > tr > td > .du-bois-light-table-expanded-row-fixed > .du-bois-light-table-wrapper:only-child .du-bois-light-table-tbody > tr:last-child > td:first-child,\n.du-bois-light-table-tbody > tr > td > .du-bois-light-table-wrapper:only-child .du-bois-light-table-tbody > tr:last-child > td:last-child,\n.du-bois-light-table-tbody > tr > td > .du-bois-light-table-expanded-row-fixed > .du-bois-light-table-wrapper:only-child .du-bois-light-table-tbody > tr:last-child > td:last-child {\n border-radius: 0;\n}\n.du-bois-light-table-tbody > tr.du-bois-light-table-row:hover > td {\n background: #fafafa;\n}\n.du-bois-light-table-tbody > tr.du-bois-light-table-row-selected > td {\n background: #e6f1f5;\n border-color: rgba(0, 0, 0, 0.03);\n}\n.du-bois-light-table-tbody > tr.du-bois-light-table-row-selected:hover > td {\n background: #dfedf2;\n}\n.du-bois-light-table-summary {\n position: relative;\n z-index: 2;\n background: #fff;\n}\ndiv.du-bois-light-table-summary {\n box-shadow: 0 -1px 0 #f0f0f0;\n}\n.du-bois-light-table-summary > tr > th,\n.du-bois-light-table-summary > tr > td {\n border-bottom: 1px solid #f0f0f0;\n}\n.du-bois-light-table-pagination.du-bois-light-pagination {\n margin: 16px 0;\n}\n.du-bois-light-table-pagination {\n display: flex;\n flex-wrap: wrap;\n row-gap: 4px;\n}\n.du-bois-light-table-pagination > * {\n flex: none;\n}\n.du-bois-light-table-pagination-left {\n justify-content: flex-start;\n}\n.du-bois-light-table-pagination-center {\n justify-content: center;\n}\n.du-bois-light-table-pagination-right {\n justify-content: flex-end;\n}\n.du-bois-light-table-thead th.du-bois-light-table-column-has-sorters {\n cursor: pointer;\n transition: all 0.3s;\n}\n.du-bois-light-table-thead th.du-bois-light-table-column-has-sorters:hover {\n background: rgba(0, 0, 0, 0.04);\n}\n.du-bois-light-table-thead th.du-bois-light-table-column-has-sorters:hover::before {\n background-color: transparent !important;\n}\n.du-bois-light-table-thead th.du-bois-light-table-column-has-sorters.du-bois-light-table-cell-fix-left:hover,\n.du-bois-light-table-thead th.du-bois-light-table-column-has-sorters.du-bois-light-table-cell-fix-right:hover {\n background: #f5f5f5;\n}\n.du-bois-light-table-thead th.du-bois-light-table-column-sort {\n background: #f5f5f5;\n}\n.du-bois-light-table-thead th.du-bois-light-table-column-sort::before {\n background-color: transparent !important;\n}\ntd.du-bois-light-table-column-sort {\n background: #fafafa;\n}\n.du-bois-light-table-column-title {\n position: relative;\n z-index: 1;\n flex: 1;\n}\n.du-bois-light-table-column-sorters {\n display: flex;\n flex: auto;\n align-items: center;\n justify-content: space-between;\n}\n.du-bois-light-table-column-sorters::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n width: 100%;\n height: 100%;\n content: '';\n}\n.du-bois-light-table-column-sorter {\n color: #bfbfbf;\n font-size: 0;\n transition: color 0.3s;\n}\n.du-bois-light-table-column-sorter-inner {\n display: inline-flex;\n flex-direction: column;\n align-items: center;\n}\n.du-bois-light-table-column-sorter-up,\n.du-bois-light-table-column-sorter-down {\n font-size: 11px;\n}\n.du-bois-light-table-column-sorter-up.active,\n.du-bois-light-table-column-sorter-down.active {\n color: #2272B4;\n}\n.du-bois-light-table-column-sorter-up + .du-bois-light-table-column-sorter-down {\n margin-top: -0.3em;\n}\n.du-bois-light-table-column-sorters:hover .du-bois-light-table-column-sorter {\n color: #a6a6a6;\n}\n.du-bois-light-table-filter-column {\n display: flex;\n justify-content: space-between;\n}\n.du-bois-light-table-filter-trigger {\n position: relative;\n display: flex;\n align-items: center;\n margin: -4px -8px -4px 4px;\n padding: 0 4px;\n color: #bfbfbf;\n font-size: 12px;\n border-radius: 4px;\n cursor: pointer;\n transition: all 0.3s;\n}\n.du-bois-light-table-filter-trigger:hover {\n color: #5F7281;\n background: rgba(0, 0, 0, 0.04);\n}\n.du-bois-light-table-filter-trigger.active {\n color: #2272B4;\n}\n.du-bois-light-table-filter-dropdown {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #11171C;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: 'tnum';\n min-width: 120px;\n background-color: #fff;\n border-radius: 4px;\n box-shadow: 0 3px 6px -4px rgba(0, 0, 0, 0.12), 0 6px 16px 0 rgba(0, 0, 0, 0.08), 0 9px 28px 8px rgba(0, 0, 0, 0.05);\n}\n.du-bois-light-table-filter-dropdown .du-bois-light-dropdown-menu {\n max-height: 264px;\n overflow-x: hidden;\n border: 0;\n box-shadow: none;\n}\n.du-bois-light-table-filter-dropdown-submenu > ul {\n max-height: calc(100vh - 130px);\n overflow-x: hidden;\n overflow-y: auto;\n}\n.du-bois-light-table-filter-dropdown .du-bois-light-checkbox-wrapper + span,\n.du-bois-light-table-filter-dropdown-submenu .du-bois-light-checkbox-wrapper + span {\n padding-left: 8px;\n}\n.du-bois-light-table-filter-dropdown-btns {\n display: flex;\n justify-content: space-between;\n padding: 7px 8px 7px 3px;\n overflow: hidden;\n background-color: inherit;\n border-top: 1px solid #f0f0f0;\n}\n.du-bois-light-table-selection-col {\n width: 32px;\n}\n.du-bois-light-table-bordered .du-bois-light-table-selection-col {\n width: 50px;\n}\ntable tr th.du-bois-light-table-selection-column,\ntable tr td.du-bois-light-table-selection-column {\n padding-right: 4px;\n padding-left: 4px;\n text-align: center;\n}\ntable tr th.du-bois-light-table-selection-column .du-bois-light-radio-wrapper,\ntable tr td.du-bois-light-table-selection-column .du-bois-light-radio-wrapper {\n margin-right: 0;\n}\ntable tr th.du-bois-light-table-selection-column::after {\n background-color: transparent !important;\n}\n.du-bois-light-table-selection {\n position: relative;\n display: inline-flex;\n flex-direction: column;\n}\n.du-bois-light-table-selection-extra {\n position: absolute;\n top: 0;\n z-index: 1;\n cursor: pointer;\n transition: all 0.3s;\n margin-inline-start: 100%;\n padding-inline-start: 0px;\n}\n.du-bois-light-table-selection-extra .anticon {\n color: #bfbfbf;\n font-size: 10px;\n}\n.du-bois-light-table-selection-extra .anticon:hover {\n color: #a6a6a6;\n}\n.du-bois-light-table-expand-icon-col {\n width: 48px;\n}\n.du-bois-light-table-row-expand-icon-cell {\n text-align: center;\n}\n.du-bois-light-table-row-indent {\n float: left;\n height: 1px;\n}\n.du-bois-light-table-row-expand-icon {\n color: #2272B4;\n text-decoration: none;\n cursor: pointer;\n transition: color 0.3s;\n position: relative;\n display: inline-flex;\n float: left;\n box-sizing: border-box;\n width: 17px;\n height: 17px;\n padding: 0;\n color: inherit;\n line-height: 17px;\n background: #fff;\n border: 1px solid #f0f0f0;\n border-radius: 4px;\n outline: none;\n transform: scale(0.94117647);\n transition: all 0.3s;\n user-select: none;\n}\n.du-bois-light-table-row-expand-icon:focus,\n.du-bois-light-table-row-expand-icon:hover {\n color: #448dc2;\n}\n.du-bois-light-table-row-expand-icon:active {\n color: #14538f;\n}\n.du-bois-light-table-row-expand-icon:focus,\n.du-bois-light-table-row-expand-icon:hover,\n.du-bois-light-table-row-expand-icon:active {\n border-color: currentColor;\n}\n.du-bois-light-table-row-expand-icon::before,\n.du-bois-light-table-row-expand-icon::after {\n position: absolute;\n background: currentColor;\n transition: transform 0.3s ease-out;\n content: '';\n}\n.du-bois-light-table-row-expand-icon::before {\n top: 7px;\n right: 3px;\n left: 3px;\n height: 1px;\n}\n.du-bois-light-table-row-expand-icon::after {\n top: 3px;\n bottom: 3px;\n left: 7px;\n width: 1px;\n transform: rotate(90deg);\n}\n.du-bois-light-table-row-expand-icon-collapsed::before {\n transform: rotate(-180deg);\n}\n.du-bois-light-table-row-expand-icon-collapsed::after {\n transform: rotate(0deg);\n}\n.du-bois-light-table-row-expand-icon-spaced {\n background: transparent;\n border: 0;\n visibility: hidden;\n}\n.du-bois-light-table-row-expand-icon-spaced::before,\n.du-bois-light-table-row-expand-icon-spaced::after {\n display: none;\n content: none;\n}\n.du-bois-light-table-row-indent + .du-bois-light-table-row-expand-icon {\n margin-top: 121.5px;\n margin-right: 4px;\n}\ntr.du-bois-light-table-expanded-row > td,\ntr.du-bois-light-table-expanded-row:hover > td {\n background: #fbfbfb;\n}\ntr.du-bois-light-table-expanded-row .du-bois-light-descriptions-view {\n display: flex;\n}\ntr.du-bois-light-table-expanded-row .du-bois-light-descriptions-view table {\n flex: auto;\n width: auto;\n}\n.du-bois-light-table .du-bois-light-table-expanded-row-fixed {\n position: relative;\n margin: -16px -16px;\n padding: 16px 16px;\n}\n.du-bois-light-table-tbody > tr.du-bois-light-table-placeholder {\n text-align: center;\n}\n.du-bois-light-table-empty .du-bois-light-table-tbody > tr.du-bois-light-table-placeholder {\n color: rgba(0, 0, 0, 0.25);\n}\n.du-bois-light-table-tbody > tr.du-bois-light-table-placeholder:hover > td {\n background: #fff;\n}\n.du-bois-light-table-cell-fix-left,\n.du-bois-light-table-cell-fix-right {\n position: -webkit-sticky !important;\n position: sticky !important;\n z-index: 2;\n background: #fff;\n}\n.du-bois-light-table-cell-fix-left-first::after,\n.du-bois-light-table-cell-fix-left-last::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: -1px;\n width: 30px;\n transform: translateX(100%);\n transition: box-shadow 0.3s;\n content: '';\n pointer-events: none;\n}\n.du-bois-light-table-cell-fix-right-first::after,\n.du-bois-light-table-cell-fix-right-last::after {\n position: absolute;\n top: 0;\n bottom: -1px;\n left: 0;\n width: 30px;\n transform: translateX(-100%);\n transition: box-shadow 0.3s;\n content: '';\n pointer-events: none;\n}\n.du-bois-light-table .du-bois-light-table-container::before,\n.du-bois-light-table .du-bois-light-table-container::after {\n position: absolute;\n top: 0;\n bottom: 0;\n z-index: 1;\n width: 30px;\n transition: box-shadow 0.3s;\n content: '';\n pointer-events: none;\n}\n.du-bois-light-table .du-bois-light-table-container::before {\n left: 0;\n}\n.du-bois-light-table .du-bois-light-table-container::after {\n right: 0;\n}\n.du-bois-light-table-ping-left:not(.du-bois-light-table-has-fix-left) .du-bois-light-table-container {\n position: relative;\n}\n.du-bois-light-table-ping-left:not(.du-bois-light-table-has-fix-left) .du-bois-light-table-container::before {\n box-shadow: inset 10px 0 8px -8px rgba(0, 0, 0, 0.15);\n}\n.du-bois-light-table-ping-left .du-bois-light-table-cell-fix-left-first::after,\n.du-bois-light-table-ping-left .du-bois-light-table-cell-fix-left-last::after {\n box-shadow: inset 10px 0 8px -8px rgba(0, 0, 0, 0.15);\n}\n.du-bois-light-table-ping-left .du-bois-light-table-cell-fix-left-last::before {\n background-color: transparent !important;\n}\n.du-bois-light-table-ping-right:not(.du-bois-light-table-has-fix-right) .du-bois-light-table-container {\n position: relative;\n}\n.du-bois-light-table-ping-right:not(.du-bois-light-table-has-fix-right) .du-bois-light-table-container::after {\n box-shadow: inset -10px 0 8px -8px rgba(0, 0, 0, 0.15);\n}\n.du-bois-light-table-ping-right .du-bois-light-table-cell-fix-right-first::after,\n.du-bois-light-table-ping-right .du-bois-light-table-cell-fix-right-last::after {\n box-shadow: inset -10px 0 8px -8px rgba(0, 0, 0, 0.15);\n}\n.du-bois-light-table-sticky-holder {\n position: sticky;\n z-index: 3;\n background: #fff;\n}\n.du-bois-light-table-sticky-scroll {\n position: sticky;\n bottom: 0;\n z-index: 3;\n display: flex;\n align-items: center;\n background: #ffffff;\n border-top: 1px solid #f0f0f0;\n opacity: 0.6;\n}\n.du-bois-light-table-sticky-scroll:hover {\n transform-origin: center bottom;\n}\n.du-bois-light-table-sticky-scroll-bar {\n height: 8px;\n background-color: rgba(0, 0, 0, 0.35);\n border-radius: 4px;\n}\n.du-bois-light-table-sticky-scroll-bar:hover {\n background-color: rgba(0, 0, 0, 0.8);\n}\n.du-bois-light-table-sticky-scroll-bar-active {\n background-color: rgba(0, 0, 0, 0.8);\n}\n@media all and (-ms-high-contrast: none) {\n .du-bois-light-table-ping-left .du-bois-light-table-cell-fix-left-last::after {\n box-shadow: none !important;\n }\n .du-bois-light-table-ping-right .du-bois-light-table-cell-fix-right-first::after {\n box-shadow: none !important;\n }\n}\n.du-bois-light-table {\n /* title + table */\n /* table */\n /* table + footer */\n}\n.du-bois-light-table-title {\n border-radius: 4px 4px 0 0;\n}\n.du-bois-light-table-title + .du-bois-light-table-container {\n border-top-left-radius: 0;\n border-top-right-radius: 0;\n}\n.du-bois-light-table-title + .du-bois-light-table-container table > thead > tr:first-child th:first-child {\n border-radius: 0;\n}\n.du-bois-light-table-title + .du-bois-light-table-container table > thead > tr:first-child th:last-child {\n border-radius: 0;\n}\n.du-bois-light-table-container {\n border-top-left-radius: 4px;\n border-top-right-radius: 4px;\n}\n.du-bois-light-table-container table > thead > tr:first-child th:first-child {\n border-top-left-radius: 4px;\n}\n.du-bois-light-table-container table > thead > tr:first-child th:last-child {\n border-top-right-radius: 4px;\n}\n.du-bois-light-table-footer {\n border-radius: 0 0 4px 4px;\n}\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-light-spin {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #11171C;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: 'tnum';\n position: absolute;\n display: none;\n color: #2272B4;\n text-align: center;\n vertical-align: middle;\n opacity: 0;\n transition: transform 0.3s cubic-bezier(0.78, 0.14, 0.15, 0.86);\n}\n.du-bois-light-spin-spinning {\n position: static;\n display: inline-block;\n opacity: 1;\n}\n.du-bois-light-spin-nested-loading {\n position: relative;\n}\n.du-bois-light-spin-nested-loading > div > .du-bois-light-spin {\n position: absolute;\n top: 0;\n left: 0;\n z-index: 4;\n display: block;\n width: 100%;\n height: 100%;\n max-height: 400px;\n}\n.du-bois-light-spin-nested-loading > div > .du-bois-light-spin .du-bois-light-spin-dot {\n position: absolute;\n top: 50%;\n left: 50%;\n margin: -10px;\n}\n.du-bois-light-spin-nested-loading > div > .du-bois-light-spin .du-bois-light-spin-text {\n position: absolute;\n top: 50%;\n width: 100%;\n padding-top: 5.5px;\n text-shadow: 0 1px 2px #fff;\n}\n.du-bois-light-spin-nested-loading > div > .du-bois-light-spin.du-bois-light-spin-show-text .du-bois-light-spin-dot {\n margin-top: -20px;\n}\n.du-bois-light-spin-nested-loading > div > .du-bois-light-spin-sm .du-bois-light-spin-dot {\n margin: -7px;\n}\n.du-bois-light-spin-nested-loading > div > .du-bois-light-spin-sm .du-bois-light-spin-text {\n padding-top: 2.5px;\n}\n.du-bois-light-spin-nested-loading > div > .du-bois-light-spin-sm.du-bois-light-spin-show-text .du-bois-light-spin-dot {\n margin-top: -17px;\n}\n.du-bois-light-spin-nested-loading > div > .du-bois-light-spin-lg .du-bois-light-spin-dot {\n margin: -16px;\n}\n.du-bois-light-spin-nested-loading > div > .du-bois-light-spin-lg .du-bois-light-spin-text {\n padding-top: 11.5px;\n}\n.du-bois-light-spin-nested-loading > div > .du-bois-light-spin-lg.du-bois-light-spin-show-text .du-bois-light-spin-dot {\n margin-top: -26px;\n}\n.du-bois-light-spin-container {\n position: relative;\n transition: opacity 0.3s;\n}\n.du-bois-light-spin-container::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n z-index: 10;\n display: none \\9;\n width: 100%;\n height: 100%;\n background: #fff;\n opacity: 0;\n transition: all 0.3s;\n content: '';\n pointer-events: none;\n}\n.du-bois-light-spin-blur {\n clear: both;\n overflow: hidden;\n opacity: 0.5;\n user-select: none;\n pointer-events: none;\n}\n.du-bois-light-spin-blur::after {\n opacity: 0.4;\n pointer-events: auto;\n}\n.du-bois-light-spin-tip {\n color: #5F7281;\n}\n.du-bois-light-spin-dot {\n position: relative;\n display: inline-block;\n font-size: 20px;\n width: 1em;\n height: 1em;\n}\n.du-bois-light-spin-dot-item {\n position: absolute;\n display: block;\n width: 9px;\n height: 9px;\n background-color: #2272B4;\n border-radius: 100%;\n transform: scale(0.75);\n transform-origin: 50% 50%;\n opacity: 0.3;\n animation: antSpinMove 1s infinite linear alternate;\n}\n.du-bois-light-spin-dot-item:nth-child(1) {\n top: 0;\n left: 0;\n}\n.du-bois-light-spin-dot-item:nth-child(2) {\n top: 0;\n right: 0;\n animation-delay: 0.4s;\n}\n.du-bois-light-spin-dot-item:nth-child(3) {\n right: 0;\n bottom: 0;\n animation-delay: 0.8s;\n}\n.du-bois-light-spin-dot-item:nth-child(4) {\n bottom: 0;\n left: 0;\n animation-delay: 1.2s;\n}\n.du-bois-light-spin-dot-spin {\n transform: rotate(45deg);\n animation: antRotate 1.2s infinite linear;\n}\n.du-bois-light-spin-sm .du-bois-light-spin-dot {\n font-size: 14px;\n}\n.du-bois-light-spin-sm .du-bois-light-spin-dot i {\n width: 6px;\n height: 6px;\n}\n.du-bois-light-spin-lg .du-bois-light-spin-dot {\n font-size: 32px;\n}\n.du-bois-light-spin-lg .du-bois-light-spin-dot i {\n width: 14px;\n height: 14px;\n}\n.du-bois-light-spin.du-bois-light-spin-show-text .du-bois-light-spin-text {\n display: block;\n}\n@media all and (-ms-high-contrast: none), (-ms-high-contrast: active) {\n /* IE10+ */\n .du-bois-light-spin-blur {\n background: #fff;\n opacity: 0.5;\n }\n}\n@keyframes antSpinMove {\n}\n@keyframes antRotate {\n}\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-light-pagination {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #11171C;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: 'tnum';\n}\n.du-bois-light-pagination ul,\n.du-bois-light-pagination ol {\n margin: 0;\n padding: 0;\n list-style: none;\n}\n.du-bois-light-pagination::after {\n display: block;\n clear: both;\n height: 0;\n overflow: hidden;\n visibility: hidden;\n content: ' ';\n}\n.du-bois-light-pagination-total-text {\n display: inline-block;\n height: 40px;\n margin-right: 8px;\n line-height: 38px;\n vertical-align: middle;\n}\n.du-bois-light-pagination-item {\n display: inline-block;\n min-width: 40px;\n height: 40px;\n margin-right: 8px;\n font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, 'Helvetica Neue', Arial, 'Noto Sans', sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol', 'Noto Color Emoji';\n line-height: 38px;\n text-align: center;\n vertical-align: middle;\n list-style: none;\n background-color: #fff;\n border: 1px solid #E8ECF0;\n border-radius: 4px;\n outline: 0;\n cursor: pointer;\n user-select: none;\n}\n.du-bois-light-pagination-item a {\n display: block;\n padding: 0 6px;\n color: #11171C;\n transition: none;\n}\n.du-bois-light-pagination-item a:hover {\n text-decoration: none;\n}\n.du-bois-light-pagination-item:focus-visible,\n.du-bois-light-pagination-item:hover {\n border-color: #2272B4;\n transition: all 0.3s;\n}\n.du-bois-light-pagination-item:focus-visible a,\n.du-bois-light-pagination-item:hover a {\n color: #2272B4;\n}\n.du-bois-light-pagination-item-active {\n font-weight: 500;\n background: #fff;\n border-color: #2272B4;\n}\n.du-bois-light-pagination-item-active a {\n color: #2272B4;\n}\n.du-bois-light-pagination-item-active:focus-visible,\n.du-bois-light-pagination-item-active:hover {\n border-color: #448dc2;\n}\n.du-bois-light-pagination-item-active:focus-visible a,\n.du-bois-light-pagination-item-active:hover a {\n color: #448dc2;\n}\n.du-bois-light-pagination-jump-prev,\n.du-bois-light-pagination-jump-next {\n outline: 0;\n}\n.du-bois-light-pagination-jump-prev .du-bois-light-pagination-item-container,\n.du-bois-light-pagination-jump-next .du-bois-light-pagination-item-container {\n position: relative;\n}\n.du-bois-light-pagination-jump-prev .du-bois-light-pagination-item-container .du-bois-light-pagination-item-link-icon,\n.du-bois-light-pagination-jump-next .du-bois-light-pagination-item-container .du-bois-light-pagination-item-link-icon {\n color: #2272B4;\n font-size: 12px;\n letter-spacing: -1px;\n opacity: 0;\n transition: all 0.2s;\n}\n.du-bois-light-pagination-jump-prev .du-bois-light-pagination-item-container .du-bois-light-pagination-item-link-icon-svg,\n.du-bois-light-pagination-jump-next .du-bois-light-pagination-item-container .du-bois-light-pagination-item-link-icon-svg {\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n margin: auto;\n}\n.du-bois-light-pagination-jump-prev .du-bois-light-pagination-item-container .du-bois-light-pagination-item-ellipsis,\n.du-bois-light-pagination-jump-next .du-bois-light-pagination-item-container .du-bois-light-pagination-item-ellipsis {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n display: block;\n margin: auto;\n color: rgba(0, 0, 0, 0.25);\n font-family: Arial, Helvetica, sans-serif;\n letter-spacing: 2px;\n text-align: center;\n text-indent: 0.13em;\n opacity: 1;\n transition: all 0.2s;\n}\n.du-bois-light-pagination-jump-prev:focus-visible .du-bois-light-pagination-item-link-icon,\n.du-bois-light-pagination-jump-next:focus-visible .du-bois-light-pagination-item-link-icon,\n.du-bois-light-pagination-jump-prev:hover .du-bois-light-pagination-item-link-icon,\n.du-bois-light-pagination-jump-next:hover .du-bois-light-pagination-item-link-icon {\n opacity: 1;\n}\n.du-bois-light-pagination-jump-prev:focus-visible .du-bois-light-pagination-item-ellipsis,\n.du-bois-light-pagination-jump-next:focus-visible .du-bois-light-pagination-item-ellipsis,\n.du-bois-light-pagination-jump-prev:hover .du-bois-light-pagination-item-ellipsis,\n.du-bois-light-pagination-jump-next:hover .du-bois-light-pagination-item-ellipsis {\n opacity: 0;\n}\n.du-bois-light-pagination-prev,\n.du-bois-light-pagination-jump-prev,\n.du-bois-light-pagination-jump-next {\n margin-right: 8px;\n}\n.du-bois-light-pagination-prev,\n.du-bois-light-pagination-next,\n.du-bois-light-pagination-jump-prev,\n.du-bois-light-pagination-jump-next {\n display: inline-block;\n min-width: 40px;\n height: 40px;\n color: #11171C;\n font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, 'Helvetica Neue', Arial, 'Noto Sans', sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol', 'Noto Color Emoji';\n line-height: 40px;\n text-align: center;\n vertical-align: middle;\n list-style: none;\n border-radius: 4px;\n cursor: pointer;\n transition: all 0.3s;\n}\n.du-bois-light-pagination-prev,\n.du-bois-light-pagination-next {\n font-family: Arial, Helvetica, sans-serif;\n outline: 0;\n}\n.du-bois-light-pagination-prev button,\n.du-bois-light-pagination-next button {\n color: #11171C;\n cursor: pointer;\n user-select: none;\n}\n.du-bois-light-pagination-prev:hover button,\n.du-bois-light-pagination-next:hover button {\n border-color: #448dc2;\n}\n.du-bois-light-pagination-prev .du-bois-light-pagination-item-link,\n.du-bois-light-pagination-next .du-bois-light-pagination-item-link {\n display: block;\n width: 100%;\n height: 100%;\n padding: 0;\n font-size: 12px;\n text-align: center;\n background-color: #fff;\n border: 1px solid #E8ECF0;\n border-radius: 4px;\n outline: none;\n transition: all 0.3s;\n}\n.du-bois-light-pagination-prev:focus-visible .du-bois-light-pagination-item-link,\n.du-bois-light-pagination-next:focus-visible .du-bois-light-pagination-item-link,\n.du-bois-light-pagination-prev:hover .du-bois-light-pagination-item-link,\n.du-bois-light-pagination-next:hover .du-bois-light-pagination-item-link {\n color: #2272B4;\n border-color: #2272B4;\n}\n.du-bois-light-pagination-disabled,\n.du-bois-light-pagination-disabled:hover,\n.du-bois-light-pagination-disabled:focus-visible {\n cursor: not-allowed;\n}\n.du-bois-light-pagination-disabled .du-bois-light-pagination-item-link,\n.du-bois-light-pagination-disabled:hover .du-bois-light-pagination-item-link,\n.du-bois-light-pagination-disabled:focus-visible .du-bois-light-pagination-item-link {\n color: rgba(0, 0, 0, 0.25);\n border-color: #E8ECF0;\n cursor: not-allowed;\n}\n.du-bois-light-pagination-slash {\n margin: 0 10px 0 5px;\n}\n.du-bois-light-pagination-options {\n display: inline-block;\n margin-left: 16px;\n vertical-align: middle;\n}\n@media all and (-ms-high-contrast: none) {\n .du-bois-light-pagination-options *::-ms-backdrop,\n .du-bois-light-pagination-options {\n vertical-align: top;\n }\n}\n.du-bois-light-pagination-options-size-changer.du-bois-light-select {\n display: inline-block;\n width: auto;\n}\n.du-bois-light-pagination-options-quick-jumper {\n display: inline-block;\n height: 40px;\n margin-left: 8px;\n line-height: 40px;\n vertical-align: top;\n}\n.du-bois-light-pagination-options-quick-jumper input {\n position: relative;\n display: inline-block;\n width: 100%;\n min-width: 0;\n padding: 5px 12px;\n color: #11171C;\n font-size: 13px;\n line-height: 20px;\n background-color: #fff;\n background-image: none;\n border: 1px solid #E8ECF0;\n border-radius: 4px;\n transition: all 0.3s;\n width: 50px;\n height: 40px;\n margin: 0 8px;\n}\n.du-bois-light-pagination-options-quick-jumper input::-moz-placeholder {\n opacity: 1;\n}\n.du-bois-light-pagination-options-quick-jumper input::placeholder {\n color: #bfbfbf;\n}\n.du-bois-light-pagination-options-quick-jumper input:placeholder-shown {\n text-overflow: ellipsis;\n}\n.du-bois-light-pagination-options-quick-jumper input:hover {\n border-color: #448dc2;\n border-right-width: 1px !important;\n}\n.du-bois-light-pagination-options-quick-jumper input:focus,\n.du-bois-light-pagination-options-quick-jumper input-focused {\n border-color: #448dc2;\n border-right-width: 1px !important;\n outline: 0;\n box-shadow: 0 0 0 2px rgba(34, 114, 180, 0.2);\n}\n.du-bois-light-pagination-options-quick-jumper input-disabled {\n color: rgba(0, 0, 0, 0.25);\n background-color: #f5f5f5;\n cursor: not-allowed;\n opacity: 1;\n}\n.du-bois-light-pagination-options-quick-jumper input-disabled:hover {\n border-color: #E8ECF0;\n border-right-width: 1px !important;\n}\n.du-bois-light-pagination-options-quick-jumper input[disabled] {\n color: rgba(0, 0, 0, 0.25);\n background-color: #f5f5f5;\n cursor: not-allowed;\n opacity: 1;\n}\n.du-bois-light-pagination-options-quick-jumper input[disabled]:hover {\n border-color: #E8ECF0;\n border-right-width: 1px !important;\n}\n.du-bois-light-pagination-options-quick-jumper input-borderless,\n.du-bois-light-pagination-options-quick-jumper input-borderless:hover,\n.du-bois-light-pagination-options-quick-jumper input-borderless:focus,\n.du-bois-light-pagination-options-quick-jumper input-borderless-focused,\n.du-bois-light-pagination-options-quick-jumper input-borderless-disabled,\n.du-bois-light-pagination-options-quick-jumper input-borderless[disabled] {\n background-color: transparent;\n border: none;\n box-shadow: none;\n}\ntextarea.du-bois-light-pagination-options-quick-jumper input {\n max-width: 100%;\n height: auto;\n min-height: 40px;\n line-height: 20px;\n vertical-align: bottom;\n transition: all 0.3s, height 0s;\n}\n.du-bois-light-pagination-options-quick-jumper input-lg {\n padding: 5px 12px;\n font-size: 18px;\n}\n.du-bois-light-pagination-options-quick-jumper input-sm {\n padding: 5px 12px;\n}\n.du-bois-light-pagination-simple .du-bois-light-pagination-prev,\n.du-bois-light-pagination-simple .du-bois-light-pagination-next {\n height: 24px;\n line-height: 24px;\n vertical-align: top;\n}\n.du-bois-light-pagination-simple .du-bois-light-pagination-prev .du-bois-light-pagination-item-link,\n.du-bois-light-pagination-simple .du-bois-light-pagination-next .du-bois-light-pagination-item-link {\n height: 24px;\n background-color: transparent;\n border: 0;\n}\n.du-bois-light-pagination-simple .du-bois-light-pagination-prev .du-bois-light-pagination-item-link::after,\n.du-bois-light-pagination-simple .du-bois-light-pagination-next .du-bois-light-pagination-item-link::after {\n height: 24px;\n line-height: 24px;\n}\n.du-bois-light-pagination-simple .du-bois-light-pagination-simple-pager {\n display: inline-block;\n height: 24px;\n margin-right: 8px;\n}\n.du-bois-light-pagination-simple .du-bois-light-pagination-simple-pager input {\n box-sizing: border-box;\n height: 100%;\n margin-right: 8px;\n padding: 0 6px;\n text-align: center;\n background-color: #fff;\n border: 1px solid #E8ECF0;\n border-radius: 4px;\n outline: none;\n transition: border-color 0.3s;\n}\n.du-bois-light-pagination-simple .du-bois-light-pagination-simple-pager input:hover {\n border-color: #2272B4;\n}\n.du-bois-light-pagination-simple .du-bois-light-pagination-simple-pager input[disabled] {\n color: rgba(0, 0, 0, 0.25);\n background: #f5f5f5;\n border-color: #E8ECF0;\n cursor: not-allowed;\n}\n.du-bois-light-pagination.mini .du-bois-light-pagination-total-text,\n.du-bois-light-pagination.mini .du-bois-light-pagination-simple-pager {\n height: 24px;\n line-height: 24px;\n}\n.du-bois-light-pagination.mini .du-bois-light-pagination-item {\n min-width: 24px;\n height: 24px;\n margin: 0;\n line-height: 22px;\n}\n.du-bois-light-pagination.mini .du-bois-light-pagination-item:not(.du-bois-light-pagination-item-active) {\n background: transparent;\n border-color: transparent;\n}\n.du-bois-light-pagination.mini .du-bois-light-pagination-prev,\n.du-bois-light-pagination.mini .du-bois-light-pagination-next {\n min-width: 24px;\n height: 24px;\n margin: 0;\n line-height: 24px;\n}\n.du-bois-light-pagination.mini .du-bois-light-pagination-prev .du-bois-light-pagination-item-link,\n.du-bois-light-pagination.mini .du-bois-light-pagination-next .du-bois-light-pagination-item-link {\n background: transparent;\n border-color: transparent;\n}\n.du-bois-light-pagination.mini .du-bois-light-pagination-prev .du-bois-light-pagination-item-link::after,\n.du-bois-light-pagination.mini .du-bois-light-pagination-next .du-bois-light-pagination-item-link::after {\n height: 24px;\n line-height: 24px;\n}\n.du-bois-light-pagination.mini .du-bois-light-pagination-jump-prev,\n.du-bois-light-pagination.mini .du-bois-light-pagination-jump-next {\n height: 24px;\n margin-right: 0;\n line-height: 24px;\n}\n.du-bois-light-pagination.mini .du-bois-light-pagination-options {\n margin-left: 2px;\n}\n.du-bois-light-pagination.mini .du-bois-light-pagination-options-size-changer {\n top: 0px;\n}\n.du-bois-light-pagination.mini .du-bois-light-pagination-options-quick-jumper {\n height: 24px;\n line-height: 24px;\n}\n.du-bois-light-pagination.mini .du-bois-light-pagination-options-quick-jumper input {\n padding: 5px 12px;\n width: 44px;\n height: 32px;\n}\n.du-bois-light-pagination.du-bois-light-pagination-disabled {\n cursor: not-allowed;\n}\n.du-bois-light-pagination.du-bois-light-pagination-disabled .du-bois-light-pagination-item {\n background: #f5f5f5;\n border-color: #E8ECF0;\n cursor: not-allowed;\n}\n.du-bois-light-pagination.du-bois-light-pagination-disabled .du-bois-light-pagination-item a {\n color: rgba(0, 0, 0, 0.25);\n background: transparent;\n border: none;\n cursor: not-allowed;\n}\n.du-bois-light-pagination.du-bois-light-pagination-disabled .du-bois-light-pagination-item-active {\n background: #e6e6e6;\n}\n.du-bois-light-pagination.du-bois-light-pagination-disabled .du-bois-light-pagination-item-active a {\n color: rgba(0, 0, 0, 0.25);\n}\n.du-bois-light-pagination.du-bois-light-pagination-disabled .du-bois-light-pagination-item-link {\n color: rgba(0, 0, 0, 0.25);\n background: #f5f5f5;\n border-color: #E8ECF0;\n cursor: not-allowed;\n}\n.du-bois-light-pagination-simple.du-bois-light-pagination.du-bois-light-pagination-disabled .du-bois-light-pagination-item-link {\n background: transparent;\n}\n.du-bois-light-pagination.du-bois-light-pagination-disabled .du-bois-light-pagination-item-link-icon {\n opacity: 0;\n}\n.du-bois-light-pagination.du-bois-light-pagination-disabled .du-bois-light-pagination-item-ellipsis {\n opacity: 1;\n}\n.du-bois-light-pagination.du-bois-light-pagination-disabled .du-bois-light-pagination-simple-pager {\n color: rgba(0, 0, 0, 0.25);\n}\n@media only screen and (max-width: 992px) {\n .du-bois-light-pagination-item-after-jump-prev,\n .du-bois-light-pagination-item-before-jump-next {\n display: none;\n }\n}\n@media only screen and (max-width: 576px) {\n .du-bois-light-pagination-options {\n display: none;\n }\n}\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-light-tabs-small > .du-bois-light-tabs-nav .du-bois-light-tabs-tab {\n padding: 4px 0;\n font-size: 13px;\n}\n.du-bois-light-tabs-large > .du-bois-light-tabs-nav .du-bois-light-tabs-tab {\n padding: 8px 0;\n font-size: 18px;\n}\n.du-bois-light-tabs-card.du-bois-light-tabs-small > .du-bois-light-tabs-nav .du-bois-light-tabs-tab {\n padding: 6px 8px;\n}\n.du-bois-light-tabs-card.du-bois-light-tabs-large > .du-bois-light-tabs-nav .du-bois-light-tabs-tab {\n padding: 7px 8px 6px;\n}\n.du-bois-light-tabs-top,\n.du-bois-light-tabs-bottom {\n flex-direction: column;\n}\n.du-bois-light-tabs-top > .du-bois-light-tabs-nav,\n.du-bois-light-tabs-bottom > .du-bois-light-tabs-nav,\n.du-bois-light-tabs-top > div > .du-bois-light-tabs-nav,\n.du-bois-light-tabs-bottom > div > .du-bois-light-tabs-nav {\n margin: 0 0 16px 0;\n}\n.du-bois-light-tabs-top > .du-bois-light-tabs-nav::before,\n.du-bois-light-tabs-bottom > .du-bois-light-tabs-nav::before,\n.du-bois-light-tabs-top > div > .du-bois-light-tabs-nav::before,\n.du-bois-light-tabs-bottom > div > .du-bois-light-tabs-nav::before {\n position: absolute;\n right: 0;\n left: 0;\n border-bottom: 1px solid #f0f0f0;\n content: '';\n}\n.du-bois-light-tabs-top > .du-bois-light-tabs-nav .du-bois-light-tabs-ink-bar,\n.du-bois-light-tabs-bottom > .du-bois-light-tabs-nav .du-bois-light-tabs-ink-bar,\n.du-bois-light-tabs-top > div > .du-bois-light-tabs-nav .du-bois-light-tabs-ink-bar,\n.du-bois-light-tabs-bottom > div > .du-bois-light-tabs-nav .du-bois-light-tabs-ink-bar {\n height: 2px;\n}\n.du-bois-light-tabs-top > .du-bois-light-tabs-nav .du-bois-light-tabs-ink-bar-animated,\n.du-bois-light-tabs-bottom > .du-bois-light-tabs-nav .du-bois-light-tabs-ink-bar-animated,\n.du-bois-light-tabs-top > div > .du-bois-light-tabs-nav .du-bois-light-tabs-ink-bar-animated,\n.du-bois-light-tabs-bottom > div > .du-bois-light-tabs-nav .du-bois-light-tabs-ink-bar-animated {\n transition: width 0.3s, left 0.3s, right 0.3s;\n}\n.du-bois-light-tabs-top > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap::before,\n.du-bois-light-tabs-bottom > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap::before,\n.du-bois-light-tabs-top > div > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap::before,\n.du-bois-light-tabs-bottom > div > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap::before,\n.du-bois-light-tabs-top > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap::after,\n.du-bois-light-tabs-bottom > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap::after,\n.du-bois-light-tabs-top > div > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap::after,\n.du-bois-light-tabs-bottom > div > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap::after {\n top: 0;\n bottom: 0;\n width: 30px;\n}\n.du-bois-light-tabs-top > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap::before,\n.du-bois-light-tabs-bottom > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap::before,\n.du-bois-light-tabs-top > div > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap::before,\n.du-bois-light-tabs-bottom > div > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap::before {\n left: 0;\n box-shadow: inset 10px 0 8px -8px rgba(0, 0, 0, 0.08);\n}\n.du-bois-light-tabs-top > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap::after,\n.du-bois-light-tabs-bottom > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap::after,\n.du-bois-light-tabs-top > div > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap::after,\n.du-bois-light-tabs-bottom > div > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap::after {\n right: 0;\n box-shadow: inset -10px 0 8px -8px rgba(0, 0, 0, 0.08);\n}\n.du-bois-light-tabs-top > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap.du-bois-light-tabs-nav-wrap-ping-left::before,\n.du-bois-light-tabs-bottom > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap.du-bois-light-tabs-nav-wrap-ping-left::before,\n.du-bois-light-tabs-top > div > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap.du-bois-light-tabs-nav-wrap-ping-left::before,\n.du-bois-light-tabs-bottom > div > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap.du-bois-light-tabs-nav-wrap-ping-left::before {\n opacity: 1;\n}\n.du-bois-light-tabs-top > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap.du-bois-light-tabs-nav-wrap-ping-right::after,\n.du-bois-light-tabs-bottom > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap.du-bois-light-tabs-nav-wrap-ping-right::after,\n.du-bois-light-tabs-top > div > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap.du-bois-light-tabs-nav-wrap-ping-right::after,\n.du-bois-light-tabs-bottom > div > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap.du-bois-light-tabs-nav-wrap-ping-right::after {\n opacity: 1;\n}\n.du-bois-light-tabs-top > .du-bois-light-tabs-nav::before,\n.du-bois-light-tabs-top > div > .du-bois-light-tabs-nav::before {\n bottom: 0;\n}\n.du-bois-light-tabs-top > .du-bois-light-tabs-nav .du-bois-light-tabs-ink-bar,\n.du-bois-light-tabs-top > div > .du-bois-light-tabs-nav .du-bois-light-tabs-ink-bar {\n bottom: 0;\n}\n.du-bois-light-tabs-bottom > .du-bois-light-tabs-nav,\n.du-bois-light-tabs-bottom > div > .du-bois-light-tabs-nav {\n order: 1;\n margin-top: 16px;\n margin-bottom: 0;\n}\n.du-bois-light-tabs-bottom > .du-bois-light-tabs-nav::before,\n.du-bois-light-tabs-bottom > div > .du-bois-light-tabs-nav::before {\n top: 0;\n}\n.du-bois-light-tabs-bottom > .du-bois-light-tabs-nav .du-bois-light-tabs-ink-bar,\n.du-bois-light-tabs-bottom > div > .du-bois-light-tabs-nav .du-bois-light-tabs-ink-bar {\n top: 0;\n}\n.du-bois-light-tabs-bottom > .du-bois-light-tabs-content-holder,\n.du-bois-light-tabs-bottom > div > .du-bois-light-tabs-content-holder {\n order: 0;\n}\n.du-bois-light-tabs-left > .du-bois-light-tabs-nav,\n.du-bois-light-tabs-right > .du-bois-light-tabs-nav,\n.du-bois-light-tabs-left > div > .du-bois-light-tabs-nav,\n.du-bois-light-tabs-right > div > .du-bois-light-tabs-nav {\n flex-direction: column;\n min-width: 50px;\n}\n.du-bois-light-tabs-left > .du-bois-light-tabs-nav .du-bois-light-tabs-tab,\n.du-bois-light-tabs-right > .du-bois-light-tabs-nav .du-bois-light-tabs-tab,\n.du-bois-light-tabs-left > div > .du-bois-light-tabs-nav .du-bois-light-tabs-tab,\n.du-bois-light-tabs-right > div > .du-bois-light-tabs-nav .du-bois-light-tabs-tab {\n padding: 4px 16px;\n text-align: center;\n}\n.du-bois-light-tabs-left > .du-bois-light-tabs-nav .du-bois-light-tabs-tab + .du-bois-light-tabs-tab,\n.du-bois-light-tabs-right > .du-bois-light-tabs-nav .du-bois-light-tabs-tab + .du-bois-light-tabs-tab,\n.du-bois-light-tabs-left > div > .du-bois-light-tabs-nav .du-bois-light-tabs-tab + .du-bois-light-tabs-tab,\n.du-bois-light-tabs-right > div > .du-bois-light-tabs-nav .du-bois-light-tabs-tab + .du-bois-light-tabs-tab {\n margin: 16px 0 0 0;\n}\n.du-bois-light-tabs-left > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap,\n.du-bois-light-tabs-right > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap,\n.du-bois-light-tabs-left > div > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap,\n.du-bois-light-tabs-right > div > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap {\n flex-direction: column;\n}\n.du-bois-light-tabs-left > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap::before,\n.du-bois-light-tabs-right > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap::before,\n.du-bois-light-tabs-left > div > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap::before,\n.du-bois-light-tabs-right > div > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap::before,\n.du-bois-light-tabs-left > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap::after,\n.du-bois-light-tabs-right > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap::after,\n.du-bois-light-tabs-left > div > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap::after,\n.du-bois-light-tabs-right > div > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap::after {\n right: 0;\n left: 0;\n height: 30px;\n}\n.du-bois-light-tabs-left > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap::before,\n.du-bois-light-tabs-right > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap::before,\n.du-bois-light-tabs-left > div > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap::before,\n.du-bois-light-tabs-right > div > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap::before {\n top: 0;\n box-shadow: inset 0 10px 8px -8px rgba(0, 0, 0, 0.08);\n}\n.du-bois-light-tabs-left > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap::after,\n.du-bois-light-tabs-right > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap::after,\n.du-bois-light-tabs-left > div > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap::after,\n.du-bois-light-tabs-right > div > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap::after {\n bottom: 0;\n box-shadow: inset 0 -10px 8px -8px rgba(0, 0, 0, 0.08);\n}\n.du-bois-light-tabs-left > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap.du-bois-light-tabs-nav-wrap-ping-top::before,\n.du-bois-light-tabs-right > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap.du-bois-light-tabs-nav-wrap-ping-top::before,\n.du-bois-light-tabs-left > div > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap.du-bois-light-tabs-nav-wrap-ping-top::before,\n.du-bois-light-tabs-right > div > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap.du-bois-light-tabs-nav-wrap-ping-top::before {\n opacity: 1;\n}\n.du-bois-light-tabs-left > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap.du-bois-light-tabs-nav-wrap-ping-bottom::after,\n.du-bois-light-tabs-right > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap.du-bois-light-tabs-nav-wrap-ping-bottom::after,\n.du-bois-light-tabs-left > div > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap.du-bois-light-tabs-nav-wrap-ping-bottom::after,\n.du-bois-light-tabs-right > div > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap.du-bois-light-tabs-nav-wrap-ping-bottom::after {\n opacity: 1;\n}\n.du-bois-light-tabs-left > .du-bois-light-tabs-nav .du-bois-light-tabs-ink-bar,\n.du-bois-light-tabs-right > .du-bois-light-tabs-nav .du-bois-light-tabs-ink-bar,\n.du-bois-light-tabs-left > div > .du-bois-light-tabs-nav .du-bois-light-tabs-ink-bar,\n.du-bois-light-tabs-right > div > .du-bois-light-tabs-nav .du-bois-light-tabs-ink-bar {\n width: 2px;\n}\n.du-bois-light-tabs-left > .du-bois-light-tabs-nav .du-bois-light-tabs-ink-bar-animated,\n.du-bois-light-tabs-right > .du-bois-light-tabs-nav .du-bois-light-tabs-ink-bar-animated,\n.du-bois-light-tabs-left > div > .du-bois-light-tabs-nav .du-bois-light-tabs-ink-bar-animated,\n.du-bois-light-tabs-right > div > .du-bois-light-tabs-nav .du-bois-light-tabs-ink-bar-animated {\n transition: height 0.3s, top 0.3s;\n}\n.du-bois-light-tabs-left > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-list,\n.du-bois-light-tabs-right > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-list,\n.du-bois-light-tabs-left > div > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-list,\n.du-bois-light-tabs-right > div > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-list,\n.du-bois-light-tabs-left > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-operations,\n.du-bois-light-tabs-right > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-operations,\n.du-bois-light-tabs-left > div > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-operations,\n.du-bois-light-tabs-right > div > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-operations {\n flex: 1 0 auto;\n flex-direction: column;\n}\n.du-bois-light-tabs-left > .du-bois-light-tabs-nav .du-bois-light-tabs-ink-bar,\n.du-bois-light-tabs-left > div > .du-bois-light-tabs-nav .du-bois-light-tabs-ink-bar {\n right: 0;\n}\n.du-bois-light-tabs-left > .du-bois-light-tabs-content-holder,\n.du-bois-light-tabs-left > div > .du-bois-light-tabs-content-holder {\n margin-left: -1px;\n border-left: 1px solid #f0f0f0;\n}\n.du-bois-light-tabs-left > .du-bois-light-tabs-content-holder > .du-bois-light-tabs-content > .du-bois-light-tabs-tabpane,\n.du-bois-light-tabs-left > div > .du-bois-light-tabs-content-holder > .du-bois-light-tabs-content > .du-bois-light-tabs-tabpane {\n padding-left: 16px;\n}\n.du-bois-light-tabs-right > .du-bois-light-tabs-nav,\n.du-bois-light-tabs-right > div > .du-bois-light-tabs-nav {\n order: 1;\n}\n.du-bois-light-tabs-right > .du-bois-light-tabs-nav .du-bois-light-tabs-ink-bar,\n.du-bois-light-tabs-right > div > .du-bois-light-tabs-nav .du-bois-light-tabs-ink-bar {\n left: 0;\n}\n.du-bois-light-tabs-right > .du-bois-light-tabs-content-holder,\n.du-bois-light-tabs-right > div > .du-bois-light-tabs-content-holder {\n order: 0;\n margin-right: -1px;\n border-right: 1px solid #f0f0f0;\n}\n.du-bois-light-tabs-right > .du-bois-light-tabs-content-holder > .du-bois-light-tabs-content > .du-bois-light-tabs-tabpane,\n.du-bois-light-tabs-right > div > .du-bois-light-tabs-content-holder > .du-bois-light-tabs-content > .du-bois-light-tabs-tabpane {\n padding-right: 16px;\n}\n.du-bois-light-tabs-dropdown {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #11171C;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: 'tnum';\n position: absolute;\n top: -9999px;\n left: -9999px;\n z-index: 1050;\n display: block;\n}\n.du-bois-light-tabs-dropdown-hidden {\n display: none;\n}\n.du-bois-light-tabs-dropdown-menu {\n max-height: 200px;\n margin: 0;\n padding: 4px 0;\n overflow-x: hidden;\n overflow-y: auto;\n text-align: left;\n list-style-type: none;\n background-color: #fff;\n background-clip: padding-box;\n border-radius: 4px;\n outline: none;\n box-shadow: 0 3px 6px -4px rgba(0, 0, 0, 0.12), 0 6px 16px 0 rgba(0, 0, 0, 0.08), 0 9px 28px 8px rgba(0, 0, 0, 0.05);\n}\n.du-bois-light-tabs-dropdown-menu-item {\n min-width: 120px;\n margin: 0;\n padding: 5px 8px;\n overflow: hidden;\n color: #11171C;\n font-weight: normal;\n font-size: 13px;\n line-height: 22px;\n white-space: nowrap;\n text-overflow: ellipsis;\n cursor: pointer;\n transition: all 0.3s;\n}\n.du-bois-light-tabs-dropdown-menu-item:hover {\n background: #f5f5f5;\n}\n.du-bois-light-tabs-dropdown-menu-item-disabled,\n.du-bois-light-tabs-dropdown-menu-item-disabled:hover {\n color: rgba(0, 0, 0, 0.25);\n background: transparent;\n cursor: not-allowed;\n}\n.du-bois-light-tabs-card > .du-bois-light-tabs-nav .du-bois-light-tabs-tab,\n.du-bois-light-tabs-card > div > .du-bois-light-tabs-nav .du-bois-light-tabs-tab {\n margin: 0;\n padding: -111px 8px;\n background: #fafafa;\n border: 1px solid #f0f0f0;\n transition: all 0.3s cubic-bezier(0.645, 0.045, 0.355, 1);\n}\n.du-bois-light-tabs-card > .du-bois-light-tabs-nav .du-bois-light-tabs-tab-active,\n.du-bois-light-tabs-card > div > .du-bois-light-tabs-nav .du-bois-light-tabs-tab-active {\n color: #2272B4;\n background: #fff;\n}\n.du-bois-light-tabs-card > .du-bois-light-tabs-nav .du-bois-light-tabs-ink-bar,\n.du-bois-light-tabs-card > div > .du-bois-light-tabs-nav .du-bois-light-tabs-ink-bar {\n visibility: hidden;\n}\n.du-bois-light-tabs-card.du-bois-light-tabs-top > .du-bois-light-tabs-nav .du-bois-light-tabs-tab + .du-bois-light-tabs-tab,\n.du-bois-light-tabs-card.du-bois-light-tabs-bottom > .du-bois-light-tabs-nav .du-bois-light-tabs-tab + .du-bois-light-tabs-tab,\n.du-bois-light-tabs-card.du-bois-light-tabs-top > div > .du-bois-light-tabs-nav .du-bois-light-tabs-tab + .du-bois-light-tabs-tab,\n.du-bois-light-tabs-card.du-bois-light-tabs-bottom > div > .du-bois-light-tabs-nav .du-bois-light-tabs-tab + .du-bois-light-tabs-tab {\n margin-left: 2px;\n}\n.du-bois-light-tabs-card.du-bois-light-tabs-top > .du-bois-light-tabs-nav .du-bois-light-tabs-tab,\n.du-bois-light-tabs-card.du-bois-light-tabs-top > div > .du-bois-light-tabs-nav .du-bois-light-tabs-tab {\n border-radius: 4px 4px 0 0;\n}\n.du-bois-light-tabs-card.du-bois-light-tabs-top > .du-bois-light-tabs-nav .du-bois-light-tabs-tab-active,\n.du-bois-light-tabs-card.du-bois-light-tabs-top > div > .du-bois-light-tabs-nav .du-bois-light-tabs-tab-active {\n border-bottom-color: #fff;\n}\n.du-bois-light-tabs-card.du-bois-light-tabs-bottom > .du-bois-light-tabs-nav .du-bois-light-tabs-tab,\n.du-bois-light-tabs-card.du-bois-light-tabs-bottom > div > .du-bois-light-tabs-nav .du-bois-light-tabs-tab {\n border-radius: 0 0 4px 4px;\n}\n.du-bois-light-tabs-card.du-bois-light-tabs-bottom > .du-bois-light-tabs-nav .du-bois-light-tabs-tab-active,\n.du-bois-light-tabs-card.du-bois-light-tabs-bottom > div > .du-bois-light-tabs-nav .du-bois-light-tabs-tab-active {\n border-top-color: #fff;\n}\n.du-bois-light-tabs-card.du-bois-light-tabs-left > .du-bois-light-tabs-nav .du-bois-light-tabs-tab + .du-bois-light-tabs-tab,\n.du-bois-light-tabs-card.du-bois-light-tabs-right > .du-bois-light-tabs-nav .du-bois-light-tabs-tab + .du-bois-light-tabs-tab,\n.du-bois-light-tabs-card.du-bois-light-tabs-left > div > .du-bois-light-tabs-nav .du-bois-light-tabs-tab + .du-bois-light-tabs-tab,\n.du-bois-light-tabs-card.du-bois-light-tabs-right > div > .du-bois-light-tabs-nav .du-bois-light-tabs-tab + .du-bois-light-tabs-tab {\n margin-top: 2px;\n}\n.du-bois-light-tabs-card.du-bois-light-tabs-left > .du-bois-light-tabs-nav .du-bois-light-tabs-tab,\n.du-bois-light-tabs-card.du-bois-light-tabs-left > div > .du-bois-light-tabs-nav .du-bois-light-tabs-tab {\n border-radius: 4px 0 0 4px;\n}\n.du-bois-light-tabs-card.du-bois-light-tabs-left > .du-bois-light-tabs-nav .du-bois-light-tabs-tab-active,\n.du-bois-light-tabs-card.du-bois-light-tabs-left > div > .du-bois-light-tabs-nav .du-bois-light-tabs-tab-active {\n border-right-color: #fff;\n}\n.du-bois-light-tabs-card.du-bois-light-tabs-right > .du-bois-light-tabs-nav .du-bois-light-tabs-tab,\n.du-bois-light-tabs-card.du-bois-light-tabs-right > div > .du-bois-light-tabs-nav .du-bois-light-tabs-tab {\n border-radius: 0 4px 4px 0;\n}\n.du-bois-light-tabs-card.du-bois-light-tabs-right > .du-bois-light-tabs-nav .du-bois-light-tabs-tab-active,\n.du-bois-light-tabs-card.du-bois-light-tabs-right > div > .du-bois-light-tabs-nav .du-bois-light-tabs-tab-active {\n border-left-color: #fff;\n}\n.du-bois-light-tabs {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #11171C;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: 'tnum';\n display: flex;\n overflow: hidden;\n}\n.du-bois-light-tabs > .du-bois-light-tabs-nav,\n.du-bois-light-tabs > div > .du-bois-light-tabs-nav {\n position: relative;\n display: flex;\n flex: none;\n align-items: center;\n}\n.du-bois-light-tabs > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap,\n.du-bois-light-tabs > div > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap {\n position: relative;\n display: inline-block;\n display: flex;\n flex: auto;\n align-self: stretch;\n overflow: hidden;\n white-space: nowrap;\n transform: translate(0);\n}\n.du-bois-light-tabs > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap::before,\n.du-bois-light-tabs > div > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap::before,\n.du-bois-light-tabs > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap::after,\n.du-bois-light-tabs > div > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap::after {\n position: absolute;\n z-index: 1;\n opacity: 0;\n transition: opacity 0.3s;\n content: '';\n pointer-events: none;\n}\n.du-bois-light-tabs > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-list,\n.du-bois-light-tabs > div > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-list {\n position: relative;\n display: flex;\n transition: transform 0.3s;\n}\n.du-bois-light-tabs > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-operations,\n.du-bois-light-tabs > div > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-operations {\n display: flex;\n align-self: stretch;\n}\n.du-bois-light-tabs > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-operations-hidden,\n.du-bois-light-tabs > div > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-operations-hidden {\n position: absolute;\n visibility: hidden;\n pointer-events: none;\n}\n.du-bois-light-tabs > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-more,\n.du-bois-light-tabs > div > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-more {\n position: relative;\n padding: -111px 8px;\n background: transparent;\n border: 0;\n}\n.du-bois-light-tabs > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-more::after,\n.du-bois-light-tabs > div > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-more::after {\n position: absolute;\n right: 0;\n bottom: 0;\n left: 0;\n height: 5px;\n transform: translateY(100%);\n content: '';\n}\n.du-bois-light-tabs > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-add,\n.du-bois-light-tabs > div > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-add {\n min-width: 40px;\n padding: 0 4px;\n background: #fafafa;\n border: 1px solid #f0f0f0;\n border-radius: 4px 4px 0 0;\n outline: none;\n cursor: pointer;\n transition: all 0.3s cubic-bezier(0.645, 0.045, 0.355, 1);\n}\n.du-bois-light-tabs > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-add:hover,\n.du-bois-light-tabs > div > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-add:hover {\n color: #448dc2;\n}\n.du-bois-light-tabs > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-add:active,\n.du-bois-light-tabs > div > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-add:active,\n.du-bois-light-tabs > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-add:focus,\n.du-bois-light-tabs > div > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-add:focus {\n color: #14538f;\n}\n.du-bois-light-tabs-extra-content {\n flex: none;\n}\n.du-bois-light-tabs-centered > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap:not([class*='du-bois-light-tabs-nav-wrap-ping']),\n.du-bois-light-tabs-centered > div > .du-bois-light-tabs-nav .du-bois-light-tabs-nav-wrap:not([class*='du-bois-light-tabs-nav-wrap-ping']) {\n justify-content: center;\n}\n.du-bois-light-tabs-ink-bar {\n position: absolute;\n background: #2272B4;\n pointer-events: none;\n}\n.du-bois-light-tabs-tab {\n position: relative;\n display: inline-flex;\n align-items: center;\n padding: 8px 0;\n font-size: 13px;\n background: transparent;\n border: 0;\n outline: none;\n cursor: pointer;\n}\n.du-bois-light-tabs-tab-btn:focus,\n.du-bois-light-tabs-tab-remove:focus,\n.du-bois-light-tabs-tab-btn:active,\n.du-bois-light-tabs-tab-remove:active {\n color: #14538f;\n}\n.du-bois-light-tabs-tab-btn {\n outline: none;\n transition: all 0.3s;\n}\n.du-bois-light-tabs-tab-remove {\n flex: none;\n margin-right: -4px;\n margin-left: 8px;\n color: #5F7281;\n font-size: 12px;\n background: transparent;\n border: none;\n outline: none;\n cursor: pointer;\n transition: all 0.3s;\n}\n.du-bois-light-tabs-tab-remove:hover {\n color: rgba(0, 0, 0, 0.85);\n}\n.du-bois-light-tabs-tab:hover {\n color: #448dc2;\n}\n.du-bois-light-tabs-tab.du-bois-light-tabs-tab-active .du-bois-light-tabs-tab-btn {\n color: #2272B4;\n text-shadow: 0 0 0.25px currentColor;\n}\n.du-bois-light-tabs-tab.du-bois-light-tabs-tab-disabled {\n color: rgba(0, 0, 0, 0.25);\n cursor: not-allowed;\n}\n.du-bois-light-tabs-tab.du-bois-light-tabs-tab-disabled .du-bois-light-tabs-tab-btn:focus,\n.du-bois-light-tabs-tab.du-bois-light-tabs-tab-disabled .du-bois-light-tabs-tab-remove:focus,\n.du-bois-light-tabs-tab.du-bois-light-tabs-tab-disabled .du-bois-light-tabs-tab-btn:active,\n.du-bois-light-tabs-tab.du-bois-light-tabs-tab-disabled .du-bois-light-tabs-tab-remove:active {\n color: rgba(0, 0, 0, 0.25);\n}\n.du-bois-light-tabs-tab .du-bois-light-tabs-tab-remove .anticon {\n margin: 0;\n}\n.du-bois-light-tabs-tab .anticon {\n margin-right: 12px;\n}\n.du-bois-light-tabs-tab + .du-bois-light-tabs-tab {\n margin: 0 0 0 32px;\n}\n.du-bois-light-tabs-content {\n display: flex;\n width: 100%;\n}\n.du-bois-light-tabs-content-holder {\n flex: auto;\n min-width: 0;\n min-height: 0;\n}\n.du-bois-light-tabs-content-animated {\n transition: margin 0.3s;\n}\n.du-bois-light-tabs-tabpane {\n flex: none;\n width: 100%;\n outline: none;\n}\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n@keyframes antCheckboxEffect {\n 0% {\n transform: scale(1);\n opacity: 0.5;\n }\n 100% {\n transform: scale(1.6);\n opacity: 0;\n }\n}\n.du-bois-light-tree-treenode-leaf-last .du-bois-light-tree-switcher-leaf-line::before {\n top: auto !important;\n bottom: auto !important;\n height: 14px !important;\n}\n.du-bois-light-tree.du-bois-light-tree-directory .du-bois-light-tree-treenode {\n position: relative;\n}\n.du-bois-light-tree.du-bois-light-tree-directory .du-bois-light-tree-treenode::before {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 4px;\n left: 0;\n transition: background-color 0.3s;\n content: '';\n pointer-events: none;\n}\n.du-bois-light-tree.du-bois-light-tree-directory .du-bois-light-tree-treenode:hover::before {\n background: #f5f5f5;\n}\n.du-bois-light-tree.du-bois-light-tree-directory .du-bois-light-tree-treenode > * {\n z-index: 1;\n}\n.du-bois-light-tree.du-bois-light-tree-directory .du-bois-light-tree-treenode .du-bois-light-tree-switcher {\n transition: color 0.3s;\n}\n.du-bois-light-tree.du-bois-light-tree-directory .du-bois-light-tree-treenode .du-bois-light-tree-node-content-wrapper {\n border-radius: 0;\n user-select: none;\n}\n.du-bois-light-tree.du-bois-light-tree-directory .du-bois-light-tree-treenode .du-bois-light-tree-node-content-wrapper:hover {\n background: transparent;\n}\n.du-bois-light-tree.du-bois-light-tree-directory .du-bois-light-tree-treenode .du-bois-light-tree-node-content-wrapper.du-bois-light-tree-node-selected {\n color: #fff;\n background: transparent;\n}\n.du-bois-light-tree.du-bois-light-tree-directory .du-bois-light-tree-treenode-selected:hover::before,\n.du-bois-light-tree.du-bois-light-tree-directory .du-bois-light-tree-treenode-selected::before {\n background: #2272B4;\n}\n.du-bois-light-tree.du-bois-light-tree-directory .du-bois-light-tree-treenode-selected .du-bois-light-tree-switcher {\n color: #fff;\n}\n.du-bois-light-tree.du-bois-light-tree-directory .du-bois-light-tree-treenode-selected .du-bois-light-tree-node-content-wrapper {\n color: #fff;\n background: transparent;\n}\n.du-bois-light-tree-checkbox {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #11171C;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: 'tnum';\n position: relative;\n top: 0.2em;\n line-height: 1;\n white-space: nowrap;\n outline: none;\n cursor: pointer;\n}\n.du-bois-light-tree-checkbox-wrapper:hover .du-bois-light-tree-checkbox-inner,\n.du-bois-light-tree-checkbox:hover .du-bois-light-tree-checkbox-inner,\n.du-bois-light-tree-checkbox-input:focus + .du-bois-light-tree-checkbox-inner {\n border-color: #2272B4;\n}\n.du-bois-light-tree-checkbox-checked::after {\n position: absolute;\n top: 0;\n left: 0;\n width: 100%;\n height: 100%;\n border: 1px solid #2272B4;\n border-radius: 4px;\n visibility: hidden;\n animation: antCheckboxEffect 0.36s ease-in-out;\n animation-fill-mode: backwards;\n content: '';\n}\n.du-bois-light-tree-checkbox:hover::after,\n.du-bois-light-tree-checkbox-wrapper:hover .du-bois-light-tree-checkbox::after {\n visibility: visible;\n}\n.du-bois-light-tree-checkbox-inner {\n position: relative;\n top: 0;\n left: 0;\n display: block;\n width: 16px;\n height: 16px;\n direction: ltr;\n background-color: #fff;\n border: 1px solid #E8ECF0;\n border-radius: 4px;\n border-collapse: separate;\n transition: all 0.3s;\n}\n.du-bois-light-tree-checkbox-inner::after {\n position: absolute;\n top: 50%;\n left: 22%;\n display: table;\n width: 5.71428571px;\n height: 9.14285714px;\n border: 2px solid #fff;\n border-top: 0;\n border-left: 0;\n transform: rotate(45deg) scale(0) translate(-50%, -50%);\n opacity: 0;\n transition: all 0.1s cubic-bezier(0.71, -0.46, 0.88, 0.6), opacity 0.1s;\n content: ' ';\n}\n.du-bois-light-tree-checkbox-input {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n z-index: 1;\n width: 100%;\n height: 100%;\n cursor: pointer;\n opacity: 0;\n}\n.du-bois-light-tree-checkbox-checked .du-bois-light-tree-checkbox-inner::after {\n position: absolute;\n display: table;\n border: 2px solid #fff;\n border-top: 0;\n border-left: 0;\n transform: rotate(45deg) scale(1) translate(-50%, -50%);\n opacity: 1;\n transition: all 0.2s cubic-bezier(0.12, 0.4, 0.29, 1.46) 0.1s;\n content: ' ';\n}\n.du-bois-light-tree-checkbox-checked .du-bois-light-tree-checkbox-inner {\n background-color: #2272B4;\n border-color: #2272B4;\n}\n.du-bois-light-tree-checkbox-disabled {\n cursor: not-allowed;\n}\n.du-bois-light-tree-checkbox-disabled.du-bois-light-tree-checkbox-checked .du-bois-light-tree-checkbox-inner::after {\n border-color: rgba(0, 0, 0, 0.25);\n animation-name: none;\n}\n.du-bois-light-tree-checkbox-disabled .du-bois-light-tree-checkbox-input {\n cursor: not-allowed;\n}\n.du-bois-light-tree-checkbox-disabled .du-bois-light-tree-checkbox-inner {\n background-color: #f5f5f5;\n border-color: #E8ECF0 !important;\n}\n.du-bois-light-tree-checkbox-disabled .du-bois-light-tree-checkbox-inner::after {\n border-color: #f5f5f5;\n border-collapse: separate;\n animation-name: none;\n}\n.du-bois-light-tree-checkbox-disabled + span {\n color: rgba(0, 0, 0, 0.25);\n cursor: not-allowed;\n}\n.du-bois-light-tree-checkbox-disabled:hover::after,\n.du-bois-light-tree-checkbox-wrapper:hover .du-bois-light-tree-checkbox-disabled::after {\n visibility: hidden;\n}\n.du-bois-light-tree-checkbox-wrapper {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #11171C;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: 'tnum';\n display: inline-flex;\n align-items: baseline;\n line-height: unset;\n cursor: pointer;\n}\n.du-bois-light-tree-checkbox-wrapper::after {\n display: inline-block;\n width: 0;\n overflow: hidden;\n content: '\\a0';\n}\n.du-bois-light-tree-checkbox-wrapper.du-bois-light-tree-checkbox-wrapper-disabled {\n cursor: not-allowed;\n}\n.du-bois-light-tree-checkbox-wrapper + .du-bois-light-tree-checkbox-wrapper {\n margin-left: 8px;\n}\n.du-bois-light-tree-checkbox + span {\n padding-right: 8px;\n padding-left: 8px;\n}\n.du-bois-light-tree-checkbox-group {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #11171C;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: 'tnum';\n display: inline-block;\n}\n.du-bois-light-tree-checkbox-group-item {\n margin-right: 8px;\n}\n.du-bois-light-tree-checkbox-group-item:last-child {\n margin-right: 0;\n}\n.du-bois-light-tree-checkbox-group-item + .du-bois-light-tree-checkbox-group-item {\n margin-left: 0;\n}\n.du-bois-light-tree-checkbox-indeterminate .du-bois-light-tree-checkbox-inner {\n background-color: #fff;\n border-color: #E8ECF0;\n}\n.du-bois-light-tree-checkbox-indeterminate .du-bois-light-tree-checkbox-inner::after {\n top: 50%;\n left: 50%;\n width: 8px;\n height: 8px;\n background-color: #2272B4;\n border: 0;\n transform: translate(-50%, -50%) scale(1);\n opacity: 1;\n content: ' ';\n}\n.du-bois-light-tree-checkbox-indeterminate.du-bois-light-tree-checkbox-disabled .du-bois-light-tree-checkbox-inner::after {\n background-color: rgba(0, 0, 0, 0.25);\n border-color: rgba(0, 0, 0, 0.25);\n}\n.du-bois-light-tree {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #11171C;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: 'tnum';\n background: #fff;\n border-radius: 4px;\n transition: background-color 0.3s;\n}\n.du-bois-light-tree-focused:not(:hover):not(.du-bois-light-tree-active-focused) {\n background: #e6f1f5;\n}\n.du-bois-light-tree-list-holder-inner {\n align-items: flex-start;\n}\n.du-bois-light-tree.du-bois-light-tree-block-node .du-bois-light-tree-list-holder-inner {\n align-items: stretch;\n}\n.du-bois-light-tree.du-bois-light-tree-block-node .du-bois-light-tree-list-holder-inner .du-bois-light-tree-node-content-wrapper {\n flex: auto;\n}\n.du-bois-light-tree .du-bois-light-tree-treenode {\n display: flex;\n align-items: flex-start;\n padding: 0 0 2px 0;\n outline: none;\n}\n.du-bois-light-tree .du-bois-light-tree-treenode-disabled .du-bois-light-tree-node-content-wrapper {\n color: rgba(0, 0, 0, 0.25);\n cursor: not-allowed;\n}\n.du-bois-light-tree .du-bois-light-tree-treenode-disabled .du-bois-light-tree-node-content-wrapper:hover {\n background: transparent;\n}\n.du-bois-light-tree .du-bois-light-tree-treenode-active .du-bois-light-tree-node-content-wrapper {\n background: #f5f5f5;\n}\n.du-bois-light-tree .du-bois-light-tree-treenode:not(.du-bois-light-tree .du-bois-light-tree-treenode-disabled).filter-node .du-bois-light-tree-title {\n color: inherit;\n font-weight: 500;\n}\n.du-bois-light-tree-indent {\n align-self: stretch;\n white-space: nowrap;\n user-select: none;\n}\n.du-bois-light-tree-indent-unit {\n display: inline-block;\n width: 24px;\n}\n.du-bois-light-tree-switcher {\n position: relative;\n flex: none;\n align-self: stretch;\n width: 24px;\n margin: 0;\n line-height: 24px;\n text-align: center;\n cursor: pointer;\n user-select: none;\n}\n.du-bois-light-tree-switcher .du-bois-light-tree-switcher-icon,\n.du-bois-light-tree-switcher .du-bois-light-select-tree-switcher-icon {\n display: inline-block;\n font-size: 10px;\n vertical-align: baseline;\n}\n.du-bois-light-tree-switcher .du-bois-light-tree-switcher-icon svg,\n.du-bois-light-tree-switcher .du-bois-light-select-tree-switcher-icon svg {\n transition: transform 0.3s;\n}\n.du-bois-light-tree-switcher-noop {\n cursor: default;\n}\n.du-bois-light-tree-switcher_close .du-bois-light-tree-switcher-icon svg {\n transform: rotate(-90deg);\n}\n.du-bois-light-tree-switcher-loading-icon {\n color: #2272B4;\n}\n.du-bois-light-tree-switcher-leaf-line {\n position: relative;\n z-index: 1;\n display: inline-block;\n width: 100%;\n height: 100%;\n}\n.du-bois-light-tree-switcher-leaf-line::before {\n position: absolute;\n top: 0;\n bottom: -2px;\n margin-left: -1px;\n border-left: 1px solid #d9d9d9;\n content: ' ';\n}\n.du-bois-light-tree-switcher-leaf-line::after {\n position: absolute;\n width: 10px;\n height: 14px;\n margin-left: -1px;\n border-bottom: 1px solid #d9d9d9;\n content: ' ';\n}\n.du-bois-light-tree-checkbox {\n top: initial;\n margin: 4px 8px 0 0;\n}\n.du-bois-light-tree .du-bois-light-tree-node-content-wrapper {\n position: relative;\n z-index: auto;\n min-height: 24px;\n margin: 0;\n padding: 0 4px;\n color: inherit;\n line-height: 24px;\n background: transparent;\n border-radius: 4px;\n cursor: pointer;\n transition: all 0.3s, border 0s, line-height 0s, box-shadow 0s;\n}\n.du-bois-light-tree .du-bois-light-tree-node-content-wrapper:hover {\n background-color: #f5f5f5;\n}\n.du-bois-light-tree .du-bois-light-tree-node-content-wrapper.du-bois-light-tree-node-selected {\n background-color: #c1dce8;\n}\n.du-bois-light-tree .du-bois-light-tree-node-content-wrapper .du-bois-light-tree-iconEle {\n display: inline-block;\n width: 24px;\n height: 24px;\n line-height: 24px;\n text-align: center;\n vertical-align: top;\n}\n.du-bois-light-tree .du-bois-light-tree-node-content-wrapper .du-bois-light-tree-iconEle:empty {\n display: none;\n}\n.du-bois-light-tree-unselectable .du-bois-light-tree-node-content-wrapper:hover {\n background-color: transparent;\n}\n.du-bois-light-tree-node-content-wrapper[draggable='true'] {\n line-height: 24px;\n user-select: none;\n}\n.du-bois-light-tree-node-content-wrapper[draggable='true'] .du-bois-light-tree-drop-indicator {\n position: absolute;\n z-index: 1;\n height: 2px;\n background-color: #2272B4;\n border-radius: 1px;\n pointer-events: none;\n}\n.du-bois-light-tree-node-content-wrapper[draggable='true'] .du-bois-light-tree-drop-indicator::after {\n position: absolute;\n top: -3px;\n left: -6px;\n width: 8px;\n height: 8px;\n background-color: transparent;\n border: 2px solid #2272B4;\n border-radius: 50%;\n content: '';\n}\n.du-bois-light-tree .du-bois-light-tree-treenode.drop-container > [draggable] {\n box-shadow: 0 0 0 2px #2272B4;\n}\n.du-bois-light-tree-show-line .du-bois-light-tree-indent-unit {\n position: relative;\n height: 100%;\n}\n.du-bois-light-tree-show-line .du-bois-light-tree-indent-unit::before {\n position: absolute;\n top: 0;\n right: 12px;\n bottom: -2px;\n border-right: 1px solid #E8ECF0;\n content: '';\n}\n.du-bois-light-tree-show-line .du-bois-light-tree-indent-unit-end::before {\n display: none;\n}\n.du-bois-light-tree-show-line .du-bois-light-tree-switcher {\n background: #fff;\n}\n.du-bois-light-tree-show-line .du-bois-light-tree-switcher-line-icon {\n vertical-align: -0.225em;\n}\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-light-typography {\n color: #11171C;\n overflow-wrap: break-word;\n}\n.du-bois-light-typography.du-bois-light-typography-secondary {\n color: #5F7281;\n}\n.du-bois-light-typography.du-bois-light-typography-success {\n color: #34824F;\n}\n.du-bois-light-typography.du-bois-light-typography-warning {\n color: #AF5B23;\n}\n.du-bois-light-typography.du-bois-light-typography-danger {\n color: #C82D4C;\n}\na.du-bois-light-typography.du-bois-light-typography-danger:active,\na.du-bois-light-typography.du-bois-light-typography-danger:focus,\na.du-bois-light-typography.du-bois-light-typography-danger:hover {\n color: #d45368;\n}\n.du-bois-light-typography.du-bois-light-typography-disabled {\n color: rgba(0, 0, 0, 0.25);\n cursor: not-allowed;\n user-select: none;\n}\ndiv.du-bois-light-typography,\n.du-bois-light-typography p {\n margin-bottom: 1em;\n}\nh1.du-bois-light-typography,\n.du-bois-light-typography h1 {\n margin-bottom: 0.5em;\n color: rgba(0, 0, 0, 0.85);\n font-weight: 600;\n font-size: 36px;\n line-height: 1.23;\n}\nh2.du-bois-light-typography,\n.du-bois-light-typography h2 {\n margin-bottom: 0.5em;\n color: rgba(0, 0, 0, 0.85);\n font-weight: 600;\n font-size: 28px;\n line-height: 1.35;\n}\nh3.du-bois-light-typography,\n.du-bois-light-typography h3 {\n margin-bottom: 0.5em;\n color: rgba(0, 0, 0, 0.85);\n font-weight: 600;\n font-size: 23px;\n line-height: 1.35;\n}\nh4.du-bois-light-typography,\n.du-bois-light-typography h4 {\n margin-bottom: 0.5em;\n color: rgba(0, 0, 0, 0.85);\n font-weight: 600;\n font-size: 19px;\n line-height: 1.4;\n}\nh5.du-bois-light-typography,\n.du-bois-light-typography h5 {\n margin-bottom: 0.5em;\n color: rgba(0, 0, 0, 0.85);\n font-weight: 600;\n font-size: 15px;\n line-height: 1.5;\n}\n.du-bois-light-typography + h1.du-bois-light-typography,\n.du-bois-light-typography + h2.du-bois-light-typography,\n.du-bois-light-typography + h3.du-bois-light-typography,\n.du-bois-light-typography + h4.du-bois-light-typography,\n.du-bois-light-typography + h5.du-bois-light-typography {\n margin-top: 1.2em;\n}\n.du-bois-light-typography div + h1,\n.du-bois-light-typography ul + h1,\n.du-bois-light-typography li + h1,\n.du-bois-light-typography p + h1,\n.du-bois-light-typography h1 + h1,\n.du-bois-light-typography h2 + h1,\n.du-bois-light-typography h3 + h1,\n.du-bois-light-typography h4 + h1,\n.du-bois-light-typography h5 + h1,\n.du-bois-light-typography div + h2,\n.du-bois-light-typography ul + h2,\n.du-bois-light-typography li + h2,\n.du-bois-light-typography p + h2,\n.du-bois-light-typography h1 + h2,\n.du-bois-light-typography h2 + h2,\n.du-bois-light-typography h3 + h2,\n.du-bois-light-typography h4 + h2,\n.du-bois-light-typography h5 + h2,\n.du-bois-light-typography div + h3,\n.du-bois-light-typography ul + h3,\n.du-bois-light-typography li + h3,\n.du-bois-light-typography p + h3,\n.du-bois-light-typography h1 + h3,\n.du-bois-light-typography h2 + h3,\n.du-bois-light-typography h3 + h3,\n.du-bois-light-typography h4 + h3,\n.du-bois-light-typography h5 + h3,\n.du-bois-light-typography div + h4,\n.du-bois-light-typography ul + h4,\n.du-bois-light-typography li + h4,\n.du-bois-light-typography p + h4,\n.du-bois-light-typography h1 + h4,\n.du-bois-light-typography h2 + h4,\n.du-bois-light-typography h3 + h4,\n.du-bois-light-typography h4 + h4,\n.du-bois-light-typography h5 + h4,\n.du-bois-light-typography div + h5,\n.du-bois-light-typography ul + h5,\n.du-bois-light-typography li + h5,\n.du-bois-light-typography p + h5,\n.du-bois-light-typography h1 + h5,\n.du-bois-light-typography h2 + h5,\n.du-bois-light-typography h3 + h5,\n.du-bois-light-typography h4 + h5,\n.du-bois-light-typography h5 + h5 {\n margin-top: 1.2em;\n}\na.du-bois-light-typography-ellipsis,\nspan.du-bois-light-typography-ellipsis {\n display: inline-block;\n}\na.du-bois-light-typography,\n.du-bois-light-typography a {\n color: #2272B4;\n outline: none;\n cursor: pointer;\n transition: color 0.3s;\n text-decoration: none;\n}\na.du-bois-light-typography:focus,\n.du-bois-light-typography a:focus,\na.du-bois-light-typography:hover,\n.du-bois-light-typography a:hover {\n color: #448dc2;\n}\na.du-bois-light-typography:active,\n.du-bois-light-typography a:active {\n color: #14538f;\n}\na.du-bois-light-typography:active,\n.du-bois-light-typography a:active,\na.du-bois-light-typography:hover,\n.du-bois-light-typography a:hover {\n text-decoration: none;\n}\na.du-bois-light-typography[disabled],\n.du-bois-light-typography a[disabled],\na.du-bois-light-typography.du-bois-light-typography-disabled,\n.du-bois-light-typography a.du-bois-light-typography-disabled {\n color: rgba(0, 0, 0, 0.25);\n cursor: not-allowed;\n}\na.du-bois-light-typography[disabled]:active,\n.du-bois-light-typography a[disabled]:active,\na.du-bois-light-typography.du-bois-light-typography-disabled:active,\n.du-bois-light-typography a.du-bois-light-typography-disabled:active,\na.du-bois-light-typography[disabled]:hover,\n.du-bois-light-typography a[disabled]:hover,\na.du-bois-light-typography.du-bois-light-typography-disabled:hover,\n.du-bois-light-typography a.du-bois-light-typography-disabled:hover {\n color: rgba(0, 0, 0, 0.25);\n}\na.du-bois-light-typography[disabled]:active,\n.du-bois-light-typography a[disabled]:active,\na.du-bois-light-typography.du-bois-light-typography-disabled:active,\n.du-bois-light-typography a.du-bois-light-typography-disabled:active {\n pointer-events: none;\n}\n.du-bois-light-typography code {\n margin: 0 0.2em;\n padding: 0.2em 0.4em 0.1em;\n font-size: 85%;\n background: rgba(150, 150, 150, 0.1);\n border: 1px solid rgba(100, 100, 100, 0.2);\n border-radius: 3px;\n}\n.du-bois-light-typography kbd {\n margin: 0 0.2em;\n padding: 0.15em 0.4em 0.1em;\n font-size: 90%;\n background: rgba(150, 150, 150, 0.06);\n border: 1px solid rgba(100, 100, 100, 0.2);\n border-bottom-width: 2px;\n border-radius: 3px;\n}\n.du-bois-light-typography mark {\n padding: 0;\n background-color: #ffe58f;\n}\n.du-bois-light-typography u,\n.du-bois-light-typography ins {\n text-decoration: underline;\n text-decoration-skip-ink: auto;\n}\n.du-bois-light-typography s,\n.du-bois-light-typography del {\n text-decoration: line-through;\n}\n.du-bois-light-typography strong {\n font-weight: 600;\n}\n.du-bois-light-typography-expand,\n.du-bois-light-typography-edit,\n.du-bois-light-typography-copy {\n color: #2272B4;\n text-decoration: none;\n outline: none;\n cursor: pointer;\n transition: color 0.3s;\n margin-left: 4px;\n}\n.du-bois-light-typography-expand:focus,\n.du-bois-light-typography-edit:focus,\n.du-bois-light-typography-copy:focus,\n.du-bois-light-typography-expand:hover,\n.du-bois-light-typography-edit:hover,\n.du-bois-light-typography-copy:hover {\n color: #448dc2;\n}\n.du-bois-light-typography-expand:active,\n.du-bois-light-typography-edit:active,\n.du-bois-light-typography-copy:active {\n color: #14538f;\n}\n.du-bois-light-typography-copy-success,\n.du-bois-light-typography-copy-success:hover,\n.du-bois-light-typography-copy-success:focus {\n color: #34824F;\n}\n.du-bois-light-typography-edit-content {\n position: relative;\n}\ndiv.du-bois-light-typography-edit-content {\n left: -13px;\n margin-top: -6px;\n margin-bottom: calc(1em - 5px - 1px);\n}\n.du-bois-light-typography-edit-content-confirm {\n position: absolute;\n right: 10px;\n bottom: 8px;\n color: #5F7281;\n pointer-events: none;\n}\n.du-bois-light-typography-edit-content textarea {\n -moz-transition: none;\n}\n.du-bois-light-typography ul,\n.du-bois-light-typography ol {\n margin: 0 0 1em 0;\n padding: 0;\n}\n.du-bois-light-typography ul li,\n.du-bois-light-typography ol li {\n margin: 0 0 0 20px;\n padding: 0 0 0 4px;\n}\n.du-bois-light-typography ul {\n list-style-type: circle;\n}\n.du-bois-light-typography ul ul {\n list-style-type: disc;\n}\n.du-bois-light-typography ol {\n list-style-type: decimal;\n}\n.du-bois-light-typography pre,\n.du-bois-light-typography blockquote {\n margin: 1em 0;\n}\n.du-bois-light-typography pre {\n padding: 0.4em 0.6em;\n white-space: pre-wrap;\n word-wrap: break-word;\n background: rgba(150, 150, 150, 0.1);\n border: 1px solid rgba(100, 100, 100, 0.2);\n border-radius: 3px;\n}\n.du-bois-light-typography pre code {\n display: inline;\n margin: 0;\n padding: 0;\n font-size: inherit;\n font-family: inherit;\n background: transparent;\n border: 0;\n}\n.du-bois-light-typography blockquote {\n padding: 0 0 0 0.6em;\n border-left: 4px solid rgba(100, 100, 100, 0.2);\n opacity: 0.85;\n}\n.du-bois-light-typography-single-line {\n white-space: nowrap;\n}\n.du-bois-light-typography-ellipsis-single-line {\n overflow: hidden;\n text-overflow: ellipsis;\n}\na.du-bois-light-typography-ellipsis-single-line,\nspan.du-bois-light-typography-ellipsis-single-line {\n vertical-align: bottom;\n}\n.du-bois-light-typography-ellipsis-multiple-line {\n display: -webkit-box;\n overflow: hidden;\n -webkit-line-clamp: 3;\n /*! autoprefixer: ignore next */\n -webkit-box-orient: vertical;\n}\n\n[data-dubois-show-outline] {\n outline: 1px dashed #308613 !important;\n outline-offset: 2px !important;\n}\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-light-switch {\n margin: 0;\n padding: 0;\n color: #11171C;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: 'tnum';\n position: relative;\n display: inline-block;\n box-sizing: border-box;\n min-width: 28px;\n height: 16px;\n line-height: 16px;\n vertical-align: middle;\n background-color: rgba(0, 0, 0, 0.25);\n border: 0;\n border-radius: 100px;\n cursor: pointer;\n transition: all 0.2s;\n user-select: none;\n}\n.du-bois-light-switch:focus {\n outline: 0;\n box-shadow: 0 0 0 2px rgba(0, 0, 0, 0.1);\n}\n.du-bois-light-switch-checked:focus {\n box-shadow: 0 0 0 2px rgba(34, 115, 181, 0.2);\n}\n.du-bois-light-switch:focus:hover {\n box-shadow: none;\n}\n.du-bois-light-switch-checked {\n background-color: #2273b5;\n}\n.du-bois-light-switch-loading,\n.du-bois-light-switch-disabled {\n cursor: not-allowed;\n opacity: 0.4;\n}\n.du-bois-light-switch-loading *,\n.du-bois-light-switch-disabled * {\n box-shadow: none;\n cursor: not-allowed;\n}\n.du-bois-light-switch-inner {\n display: block;\n margin: 0 5px 0 18px;\n color: #fff;\n font-size: 12px;\n transition: margin 0.2s;\n}\n.du-bois-light-switch-checked .du-bois-light-switch-inner {\n margin: 0 18px 0 5px;\n}\n.du-bois-light-switch-handle {\n position: absolute;\n top: 0px;\n left: 0px;\n width: 14px;\n height: 14px;\n transition: all 0.2s ease-in-out;\n}\n.du-bois-light-switch-handle::before {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background-color: white;\n border-radius: 7px;\n box-shadow: 0 2px 4px 0 rgba(0, 35, 11, 0.2);\n transition: all 0.2s ease-in-out;\n content: '';\n}\n.du-bois-light-switch-checked .du-bois-light-switch-handle {\n left: calc(100% - 14px - 0px);\n}\n.du-bois-light-switch:not(.du-bois-light-switch-disabled):active .du-bois-light-switch-handle::before {\n right: -30%;\n left: 0;\n}\n.du-bois-light-switch:not(.du-bois-light-switch-disabled):active.du-bois-light-switch-checked .du-bois-light-switch-handle::before {\n right: 0;\n left: -30%;\n}\n.du-bois-light-switch-loading-icon {\n position: relative;\n top: 0.5px;\n color: rgba(0, 0, 0, 0.65);\n vertical-align: top;\n}\n.du-bois-light-switch-checked .du-bois-light-switch-loading-icon {\n color: #2273b5;\n}\n.du-bois-light-switch-small {\n min-width: 28px;\n height: 16px;\n line-height: 16px;\n}\n.du-bois-light-switch-small .du-bois-light-switch-inner {\n margin: 0 5px 0 18px;\n font-size: 12px;\n}\n.du-bois-light-switch-small .du-bois-light-switch-handle {\n width: 12px;\n height: 12px;\n}\n.du-bois-light-switch-small .du-bois-light-switch-loading-icon {\n top: 1.5px;\n font-size: 9px;\n}\n.du-bois-light-switch-small.du-bois-light-switch-checked .du-bois-light-switch-inner {\n margin: 0 18px 0 5px;\n}\n.du-bois-light-switch-small.du-bois-light-switch-checked .du-bois-light-switch-handle {\n left: calc(100% - 12px - 0px);\n}\n.du-bois-light-switch {\n transition: none;\n border: 1px solid #64727d;\n background-color: white;\n margin-right: 8px;\n}\n.du-bois-light-switch .du-bois-light-switch-handle::before {\n box-shadow: 0px 0px 0px 1px #64727d;\n}\n.du-bois-light-switch-checked {\n border: 1px solid #2273b5;\n background-color: #2273b5;\n}\n.du-bois-light-switch-checked .du-bois-light-switch-handle::before {\n box-shadow: none;\n}\n.du-bois-light-switch:focus-visible {\n box-shadow: 0px 0px 0px 1px #2273b5;\n border: 1px solid #2273b5;\n}\n.du-bois-light-switch:focus-visible .du-bois-light-switch-handle::before {\n box-shadow: 0px 0px 0px 1px #2273b5;\n}\n.du-bois-light-switch-checked:focus-visible {\n box-shadow: 0px 0px 0px 1px #ffffff, 0px 0px 0px 3px #2273b5;\n}\n.du-bois-light-switch:hover {\n border: 1px solid #186099;\n background-color: rgba(34, 115, 181, 0.08);\n}\n.du-bois-light-switch:hover .du-bois-light-switch-handle::before {\n box-shadow: 0px 0px 0px 1px #186099;\n}\n.du-bois-light-switch-checked:hover {\n background-color: #186099;\n}\n.du-bois-light-switch:active {\n background-color: rgba(34, 115, 181, 0.16);\n}\n.du-bois-light-switch-checked:active {\n background-color: #0d4f85;\n}\n.du-bois-light-switch-disabled,\n.du-bois-light-switch-disabled:hover {\n border: 1px solid #a2aeb8;\n}\n.du-bois-light-switch-disabled .du-bois-light-switch-handle::before,\n.du-bois-light-switch-disabled:hover .du-bois-light-switch-handle::before {\n box-shadow: 0px 0px 0px 1px #a2aeb8;\n}\n.du-bois-light-switch-disabled.du-bois-light-switch-checked {\n background-color: #a2aeb8;\n}\n.du-bois-light-switch:not(.du-bois-light-switch-disabled):active .du-bois-light-switch-handle::before {\n right: 0;\n left: 0;\n}\n.du-bois-light-switch:not(.du-bois-light-switch-disabled):active.du-bois-light-switch-checked .du-bois-light-switch-handle::before {\n right: 0;\n left: 0;\n}\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-light-breadcrumb {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #11171C;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: 'tnum';\n color: #5F7281;\n font-size: 13px;\n}\n.du-bois-light-breadcrumb .anticon {\n font-size: 13px;\n}\n.du-bois-light-breadcrumb a {\n color: #5F7281;\n transition: color 0.3s;\n}\n.du-bois-light-breadcrumb a:hover {\n color: #448dc2;\n}\n.du-bois-light-breadcrumb > span:last-child {\n color: #11171C;\n}\n.du-bois-light-breadcrumb > span:last-child a {\n color: #11171C;\n}\n.du-bois-light-breadcrumb > span:last-child .du-bois-light-breadcrumb-separator {\n display: none;\n}\n.du-bois-light-breadcrumb-separator {\n margin: 0 4px;\n color: #5F7281;\n}\n.du-bois-light-breadcrumb-link > .anticon + span,\n.du-bois-light-breadcrumb-link > .anticon + a {\n margin-left: 4px;\n}\n.du-bois-light-breadcrumb-overlay-link > .anticon {\n margin-left: 4px;\n}\n.du-bois-light-breadcrumb a {\n color: #2272B4;\n text-decoration: none;\n outline: none;\n cursor: pointer;\n transition: color 0.3s;\n}\n.du-bois-light-breadcrumb a:focus,\n.du-bois-light-breadcrumb a:hover {\n color: #448dc2;\n}\n.du-bois-light-breadcrumb a:active {\n color: #14538f;\n}\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-light-notification {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #11171C;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: 'tnum';\n position: fixed;\n z-index: 1010;\n margin-right: 24px;\n}\n.du-bois-light-notification-topLeft,\n.du-bois-light-notification-bottomLeft {\n margin-right: 0;\n margin-left: 24px;\n}\n.du-bois-light-notification-topLeft .du-bois-light-notification-fade-enter.du-bois-light-notification-fade-enter-active,\n.du-bois-light-notification-bottomLeft .du-bois-light-notification-fade-enter.du-bois-light-notification-fade-enter-active,\n.du-bois-light-notification-topLeft .du-bois-light-notification-fade-appear.du-bois-light-notification-fade-appear-active,\n.du-bois-light-notification-bottomLeft .du-bois-light-notification-fade-appear.du-bois-light-notification-fade-appear-active {\n animation-name: NotificationLeftFadeIn;\n}\n.du-bois-light-notification-close-icon {\n font-size: 13px;\n cursor: pointer;\n}\n.du-bois-light-notification-hook-holder {\n position: relative;\n}\n.du-bois-light-notification-notice {\n position: relative;\n width: 384px;\n max-width: calc(100vw - 24px * 2);\n margin-bottom: 16px;\n margin-left: auto;\n padding: 16px 24px;\n overflow: hidden;\n line-height: 20px;\n word-wrap: break-word;\n background: #fff;\n border-radius: 4px;\n box-shadow: 0 3px 6px -4px rgba(0, 0, 0, 0.12), 0 6px 16px 0 rgba(0, 0, 0, 0.08), 0 9px 28px 8px rgba(0, 0, 0, 0.05);\n}\n.du-bois-light-notification-topLeft .du-bois-light-notification-notice,\n.du-bois-light-notification-bottomLeft .du-bois-light-notification-notice {\n margin-right: auto;\n margin-left: 0;\n}\n.du-bois-light-notification-notice-message {\n margin-bottom: 8px;\n color: rgba(0, 0, 0, 0.85);\n font-size: 18px;\n line-height: 24px;\n}\n.du-bois-light-notification-notice-message-single-line-auto-margin {\n display: block;\n width: calc(384px - 24px * 2 - 24px - 48px - 100%);\n max-width: 4px;\n background-color: transparent;\n pointer-events: none;\n}\n.du-bois-light-notification-notice-message-single-line-auto-margin::before {\n display: block;\n content: '';\n}\n.du-bois-light-notification-notice-description {\n font-size: 13px;\n}\n.du-bois-light-notification-notice-closable .du-bois-light-notification-notice-message {\n padding-right: 24px;\n}\n.du-bois-light-notification-notice-with-icon .du-bois-light-notification-notice-message {\n margin-bottom: 4px;\n margin-left: 48px;\n font-size: 18px;\n}\n.du-bois-light-notification-notice-with-icon .du-bois-light-notification-notice-description {\n margin-left: 48px;\n font-size: 13px;\n}\n.du-bois-light-notification-notice-icon {\n position: absolute;\n margin-left: 4px;\n font-size: 24px;\n line-height: 24px;\n}\n.anticon.du-bois-light-notification-notice-icon-success {\n color: #34824F;\n}\n.anticon.du-bois-light-notification-notice-icon-info {\n color: #64727D;\n}\n.anticon.du-bois-light-notification-notice-icon-warning {\n color: #AF5B23;\n}\n.anticon.du-bois-light-notification-notice-icon-error {\n color: #C82D4C;\n}\n.du-bois-light-notification-notice-close {\n position: absolute;\n top: 16px;\n right: 22px;\n color: #5F7281;\n outline: none;\n}\n.du-bois-light-notification-notice-close:hover {\n color: #39444d;\n}\n.du-bois-light-notification-notice-btn {\n float: right;\n margin-top: 16px;\n}\n.du-bois-light-notification .notification-fade-effect {\n animation-duration: 0.24s;\n animation-timing-function: cubic-bezier(0.645, 0.045, 0.355, 1);\n animation-fill-mode: both;\n}\n.du-bois-light-notification-fade-enter,\n.du-bois-light-notification-fade-appear {\n animation-duration: 0.24s;\n animation-timing-function: cubic-bezier(0.645, 0.045, 0.355, 1);\n animation-fill-mode: both;\n opacity: 0;\n animation-play-state: paused;\n}\n.du-bois-light-notification-fade-leave {\n animation-duration: 0.24s;\n animation-timing-function: cubic-bezier(0.645, 0.045, 0.355, 1);\n animation-fill-mode: both;\n animation-duration: 0.2s;\n animation-play-state: paused;\n}\n.du-bois-light-notification-fade-enter.du-bois-light-notification-fade-enter-active,\n.du-bois-light-notification-fade-appear.du-bois-light-notification-fade-appear-active {\n animation-name: NotificationFadeIn;\n animation-play-state: running;\n}\n.du-bois-light-notification-fade-leave.du-bois-light-notification-fade-leave-active {\n animation-name: NotificationFadeOut;\n animation-play-state: running;\n}\n@keyframes NotificationFadeIn {\n 0% {\n left: 384px;\n opacity: 0;\n }\n 100% {\n left: 0;\n opacity: 1;\n }\n}\n@keyframes NotificationLeftFadeIn {\n 0% {\n right: 384px;\n opacity: 0;\n }\n 100% {\n right: 0;\n opacity: 1;\n }\n}\n@keyframes NotificationFadeOut {\n 0% {\n max-height: 150px;\n margin-bottom: 16px;\n opacity: 1;\n }\n 100% {\n max-height: 0;\n margin-bottom: 0;\n padding-top: 0;\n padding-bottom: 0;\n opacity: 0;\n }\n}\n.du-bois-light-notification-notice {\n padding: 10px 40px 10px 32px;\n box-shadow: 0px 4px 16px rgba(46, 56, 64, 0.12);\n}\n.du-bois-light-notification-notice .du-bois-light-notification-notice-icon {\n font-size: 16px;\n width: 16px;\n height: 16px;\n margin-left: 0;\n left: 12px;\n top: 12px;\n line-height: 16px;\n}\n.du-bois-light-notification-notice .du-bois-light-notification-notice-close {\n top: 12px;\n right: 12px;\n width: 16px;\n height: 16px;\n line-height: 16px;\n}\n.du-bois-light-notification-notice .du-bois-light-notification-notice-close span[role='img'] {\n font-size: 16px;\n}\n.du-bois-light-notification-notice .du-bois-light-notification-notice-message {\n display: flex;\n align-items: center;\n min-height: 20px;\n line-height: 20px;\n font-weight: 600;\n}\n.du-bois-light-notification-notice .du-bois-light-notification-notice-message,\n.du-bois-light-notification-notice .du-bois-light-notification-notice-description {\n margin-left: unset;\n font-size: 13px;\n margin-bottom: 0;\n}\n\n.du-bois-light-typography a {\n color: #2272b4;\n}\n.du-bois-light-typography a:hover {\n color: #0e538b;\n}\n.du-bois-light-typography a:focus {\n color: #04355d;\n}\n.du-bois-dark-typography a {\n color: #4299e0;\n}\n.du-bois-dark-typography a:hover {\n color: #8acaff;\n}\n.du-bois-dark-typography a:focus {\n color: #bae1fc;\n}\n","[class^='du-bois-dark'] {\n box-sizing: border-box;\n}\n[class^='du-bois-dark'] *,\n[class^='du-bois-dark'] *::before,\n[class^='du-bois-dark'] *::after {\n box-sizing: border-box;\n}\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n[class^=du-bois-dark-]::-ms-clear,\n[class*= du-bois-dark-]::-ms-clear,\n[class^=du-bois-dark-] input::-ms-clear,\n[class*= du-bois-dark-] input::-ms-clear,\n[class^=du-bois-dark-] input::-ms-reveal,\n[class*= du-bois-dark-] input::-ms-reveal {\n display: none;\n}\n/* stylelint-disable at-rule-no-unknown */\n@-ms-viewport {\n width: device-width;\n}\nabbr[data-original-title] {\n text-decoration: underline;\n text-decoration: underline dotted;\n border-bottom: 0;\n cursor: help;\n}\n.anticon {\n display: inline-block;\n color: inherit;\n font-style: normal;\n line-height: 0;\n text-align: center;\n text-transform: none;\n vertical-align: -0.125em;\n text-rendering: optimizeLegibility;\n -webkit-font-smoothing: antialiased;\n -moz-osx-font-smoothing: grayscale;\n}\n.anticon > * {\n line-height: 1;\n}\n.anticon svg {\n display: inline-block;\n}\n.anticon::before {\n display: none;\n}\n.anticon .anticon-icon {\n display: block;\n}\n.anticon[tabindex] {\n cursor: pointer;\n}\n.anticon-spin::before {\n display: inline-block;\n animation: loadingCircle 1s infinite linear;\n}\n.anticon-spin {\n display: inline-block;\n animation: loadingCircle 1s infinite linear;\n}\n.du-bois-dark-fade-enter,\n.du-bois-dark-fade-appear {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-dark-fade-leave {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-dark-fade-enter.du-bois-dark-fade-enter-active,\n.du-bois-dark-fade-appear.du-bois-dark-fade-appear-active {\n animation-name: antFadeIn;\n animation-play-state: running;\n}\n.du-bois-dark-fade-leave.du-bois-dark-fade-leave-active {\n animation-name: antFadeOut;\n animation-play-state: running;\n pointer-events: none;\n}\n.du-bois-dark-fade-enter,\n.du-bois-dark-fade-appear {\n opacity: 0;\n animation-timing-function: linear;\n}\n.du-bois-dark-fade-leave {\n animation-timing-function: linear;\n}\n@keyframes antFadeIn {\n 0% {\n opacity: 0;\n }\n 100% {\n opacity: 1;\n }\n}\n@keyframes antFadeOut {\n 0% {\n opacity: 1;\n }\n 100% {\n opacity: 0;\n }\n}\n.du-bois-dark-move-up-enter,\n.du-bois-dark-move-up-appear {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-dark-move-up-leave {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-dark-move-up-enter.du-bois-dark-move-up-enter-active,\n.du-bois-dark-move-up-appear.du-bois-dark-move-up-appear-active {\n animation-name: antMoveUpIn;\n animation-play-state: running;\n}\n.du-bois-dark-move-up-leave.du-bois-dark-move-up-leave-active {\n animation-name: antMoveUpOut;\n animation-play-state: running;\n pointer-events: none;\n}\n.du-bois-dark-move-up-enter,\n.du-bois-dark-move-up-appear {\n opacity: 0;\n animation-timing-function: cubic-bezier(0.08, 0.82, 0.17, 1);\n}\n.du-bois-dark-move-up-leave {\n animation-timing-function: cubic-bezier(0.6, 0.04, 0.98, 0.34);\n}\n.du-bois-dark-move-down-enter,\n.du-bois-dark-move-down-appear {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-dark-move-down-leave {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-dark-move-down-enter.du-bois-dark-move-down-enter-active,\n.du-bois-dark-move-down-appear.du-bois-dark-move-down-appear-active {\n animation-name: antMoveDownIn;\n animation-play-state: running;\n}\n.du-bois-dark-move-down-leave.du-bois-dark-move-down-leave-active {\n animation-name: antMoveDownOut;\n animation-play-state: running;\n pointer-events: none;\n}\n.du-bois-dark-move-down-enter,\n.du-bois-dark-move-down-appear {\n opacity: 0;\n animation-timing-function: cubic-bezier(0.08, 0.82, 0.17, 1);\n}\n.du-bois-dark-move-down-leave {\n animation-timing-function: cubic-bezier(0.6, 0.04, 0.98, 0.34);\n}\n.du-bois-dark-move-left-enter,\n.du-bois-dark-move-left-appear {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-dark-move-left-leave {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-dark-move-left-enter.du-bois-dark-move-left-enter-active,\n.du-bois-dark-move-left-appear.du-bois-dark-move-left-appear-active {\n animation-name: antMoveLeftIn;\n animation-play-state: running;\n}\n.du-bois-dark-move-left-leave.du-bois-dark-move-left-leave-active {\n animation-name: antMoveLeftOut;\n animation-play-state: running;\n pointer-events: none;\n}\n.du-bois-dark-move-left-enter,\n.du-bois-dark-move-left-appear {\n opacity: 0;\n animation-timing-function: cubic-bezier(0.08, 0.82, 0.17, 1);\n}\n.du-bois-dark-move-left-leave {\n animation-timing-function: cubic-bezier(0.6, 0.04, 0.98, 0.34);\n}\n.du-bois-dark-move-right-enter,\n.du-bois-dark-move-right-appear {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-dark-move-right-leave {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-dark-move-right-enter.du-bois-dark-move-right-enter-active,\n.du-bois-dark-move-right-appear.du-bois-dark-move-right-appear-active {\n animation-name: antMoveRightIn;\n animation-play-state: running;\n}\n.du-bois-dark-move-right-leave.du-bois-dark-move-right-leave-active {\n animation-name: antMoveRightOut;\n animation-play-state: running;\n pointer-events: none;\n}\n.du-bois-dark-move-right-enter,\n.du-bois-dark-move-right-appear {\n opacity: 0;\n animation-timing-function: cubic-bezier(0.08, 0.82, 0.17, 1);\n}\n.du-bois-dark-move-right-leave {\n animation-timing-function: cubic-bezier(0.6, 0.04, 0.98, 0.34);\n}\n@keyframes antMoveDownIn {\n 0% {\n transform: translateY(100%);\n transform-origin: 0 0;\n opacity: 0;\n }\n 100% {\n transform: translateY(0%);\n transform-origin: 0 0;\n opacity: 1;\n }\n}\n@keyframes antMoveDownOut {\n 0% {\n transform: translateY(0%);\n transform-origin: 0 0;\n opacity: 1;\n }\n 100% {\n transform: translateY(100%);\n transform-origin: 0 0;\n opacity: 0;\n }\n}\n@keyframes antMoveLeftIn {\n 0% {\n transform: translateX(-100%);\n transform-origin: 0 0;\n opacity: 0;\n }\n 100% {\n transform: translateX(0%);\n transform-origin: 0 0;\n opacity: 1;\n }\n}\n@keyframes antMoveLeftOut {\n 0% {\n transform: translateX(0%);\n transform-origin: 0 0;\n opacity: 1;\n }\n 100% {\n transform: translateX(-100%);\n transform-origin: 0 0;\n opacity: 0;\n }\n}\n@keyframes antMoveRightIn {\n 0% {\n transform: translateX(100%);\n transform-origin: 0 0;\n opacity: 0;\n }\n 100% {\n transform: translateX(0%);\n transform-origin: 0 0;\n opacity: 1;\n }\n}\n@keyframes antMoveRightOut {\n 0% {\n transform: translateX(0%);\n transform-origin: 0 0;\n opacity: 1;\n }\n 100% {\n transform: translateX(100%);\n transform-origin: 0 0;\n opacity: 0;\n }\n}\n@keyframes antMoveUpIn {\n 0% {\n transform: translateY(-100%);\n transform-origin: 0 0;\n opacity: 0;\n }\n 100% {\n transform: translateY(0%);\n transform-origin: 0 0;\n opacity: 1;\n }\n}\n@keyframes antMoveUpOut {\n 0% {\n transform: translateY(0%);\n transform-origin: 0 0;\n opacity: 1;\n }\n 100% {\n transform: translateY(-100%);\n transform-origin: 0 0;\n opacity: 0;\n }\n}\n@keyframes loadingCircle {\n 100% {\n transform: rotate(360deg);\n }\n}\n[du-bois-dark-click-animating='true'],\n[du-bois-dark-click-animating-without-extra-node='true'] {\n position: relative;\n}\n[du-bois-dark-click-animating-without-extra-node='true']::after,\n.du-bois-dark-click-animating-node {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n display: block;\n border-radius: inherit;\n box-shadow: 0 0 0 0 #2272B4;\n box-shadow: 0 0 0 0 var(--antd-wave-shadow-color);\n opacity: 0.2;\n animation: fadeEffect 2s cubic-bezier(0.08, 0.82, 0.17, 1), waveEffect 0.4s cubic-bezier(0.08, 0.82, 0.17, 1);\n animation-fill-mode: forwards;\n content: '';\n pointer-events: none;\n}\n@keyframes waveEffect {\n 100% {\n box-shadow: 0 0 0 #2272B4;\n box-shadow: 0 0 0 6px var(--antd-wave-shadow-color);\n }\n}\n@keyframes fadeEffect {\n 100% {\n opacity: 0;\n }\n}\n.du-bois-dark-slide-up-enter,\n.du-bois-dark-slide-up-appear {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-dark-slide-up-leave {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-dark-slide-up-enter.du-bois-dark-slide-up-enter-active,\n.du-bois-dark-slide-up-appear.du-bois-dark-slide-up-appear-active {\n animation-name: antSlideUpIn;\n animation-play-state: running;\n}\n.du-bois-dark-slide-up-leave.du-bois-dark-slide-up-leave-active {\n animation-name: antSlideUpOut;\n animation-play-state: running;\n pointer-events: none;\n}\n.du-bois-dark-slide-up-enter,\n.du-bois-dark-slide-up-appear {\n opacity: 0;\n animation-timing-function: cubic-bezier(0.23, 1, 0.32, 1);\n}\n.du-bois-dark-slide-up-leave {\n animation-timing-function: cubic-bezier(0.755, 0.05, 0.855, 0.06);\n}\n.du-bois-dark-slide-down-enter,\n.du-bois-dark-slide-down-appear {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-dark-slide-down-leave {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-dark-slide-down-enter.du-bois-dark-slide-down-enter-active,\n.du-bois-dark-slide-down-appear.du-bois-dark-slide-down-appear-active {\n animation-name: antSlideDownIn;\n animation-play-state: running;\n}\n.du-bois-dark-slide-down-leave.du-bois-dark-slide-down-leave-active {\n animation-name: antSlideDownOut;\n animation-play-state: running;\n pointer-events: none;\n}\n.du-bois-dark-slide-down-enter,\n.du-bois-dark-slide-down-appear {\n opacity: 0;\n animation-timing-function: cubic-bezier(0.23, 1, 0.32, 1);\n}\n.du-bois-dark-slide-down-leave {\n animation-timing-function: cubic-bezier(0.755, 0.05, 0.855, 0.06);\n}\n.du-bois-dark-slide-left-enter,\n.du-bois-dark-slide-left-appear {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-dark-slide-left-leave {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-dark-slide-left-enter.du-bois-dark-slide-left-enter-active,\n.du-bois-dark-slide-left-appear.du-bois-dark-slide-left-appear-active {\n animation-name: antSlideLeftIn;\n animation-play-state: running;\n}\n.du-bois-dark-slide-left-leave.du-bois-dark-slide-left-leave-active {\n animation-name: antSlideLeftOut;\n animation-play-state: running;\n pointer-events: none;\n}\n.du-bois-dark-slide-left-enter,\n.du-bois-dark-slide-left-appear {\n opacity: 0;\n animation-timing-function: cubic-bezier(0.23, 1, 0.32, 1);\n}\n.du-bois-dark-slide-left-leave {\n animation-timing-function: cubic-bezier(0.755, 0.05, 0.855, 0.06);\n}\n.du-bois-dark-slide-right-enter,\n.du-bois-dark-slide-right-appear {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-dark-slide-right-leave {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-dark-slide-right-enter.du-bois-dark-slide-right-enter-active,\n.du-bois-dark-slide-right-appear.du-bois-dark-slide-right-appear-active {\n animation-name: antSlideRightIn;\n animation-play-state: running;\n}\n.du-bois-dark-slide-right-leave.du-bois-dark-slide-right-leave-active {\n animation-name: antSlideRightOut;\n animation-play-state: running;\n pointer-events: none;\n}\n.du-bois-dark-slide-right-enter,\n.du-bois-dark-slide-right-appear {\n opacity: 0;\n animation-timing-function: cubic-bezier(0.23, 1, 0.32, 1);\n}\n.du-bois-dark-slide-right-leave {\n animation-timing-function: cubic-bezier(0.755, 0.05, 0.855, 0.06);\n}\n@keyframes antSlideUpIn {\n 0% {\n transform: scaleY(0.8);\n transform-origin: 0% 0%;\n opacity: 0;\n }\n 100% {\n transform: scaleY(1);\n transform-origin: 0% 0%;\n opacity: 1;\n }\n}\n@keyframes antSlideUpOut {\n 0% {\n transform: scaleY(1);\n transform-origin: 0% 0%;\n opacity: 1;\n }\n 100% {\n transform: scaleY(0.8);\n transform-origin: 0% 0%;\n opacity: 0;\n }\n}\n@keyframes antSlideDownIn {\n 0% {\n transform: scaleY(0.8);\n transform-origin: 100% 100%;\n opacity: 0;\n }\n 100% {\n transform: scaleY(1);\n transform-origin: 100% 100%;\n opacity: 1;\n }\n}\n@keyframes antSlideDownOut {\n 0% {\n transform: scaleY(1);\n transform-origin: 100% 100%;\n opacity: 1;\n }\n 100% {\n transform: scaleY(0.8);\n transform-origin: 100% 100%;\n opacity: 0;\n }\n}\n@keyframes antSlideLeftIn {\n 0% {\n transform: scaleX(0.8);\n transform-origin: 0% 0%;\n opacity: 0;\n }\n 100% {\n transform: scaleX(1);\n transform-origin: 0% 0%;\n opacity: 1;\n }\n}\n@keyframes antSlideLeftOut {\n 0% {\n transform: scaleX(1);\n transform-origin: 0% 0%;\n opacity: 1;\n }\n 100% {\n transform: scaleX(0.8);\n transform-origin: 0% 0%;\n opacity: 0;\n }\n}\n@keyframes antSlideRightIn {\n 0% {\n transform: scaleX(0.8);\n transform-origin: 100% 0%;\n opacity: 0;\n }\n 100% {\n transform: scaleX(1);\n transform-origin: 100% 0%;\n opacity: 1;\n }\n}\n@keyframes antSlideRightOut {\n 0% {\n transform: scaleX(1);\n transform-origin: 100% 0%;\n opacity: 1;\n }\n 100% {\n transform: scaleX(0.8);\n transform-origin: 100% 0%;\n opacity: 0;\n }\n}\n.du-bois-dark-zoom-enter,\n.du-bois-dark-zoom-appear {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-dark-zoom-leave {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-dark-zoom-enter.du-bois-dark-zoom-enter-active,\n.du-bois-dark-zoom-appear.du-bois-dark-zoom-appear-active {\n animation-name: antZoomIn;\n animation-play-state: running;\n}\n.du-bois-dark-zoom-leave.du-bois-dark-zoom-leave-active {\n animation-name: antZoomOut;\n animation-play-state: running;\n pointer-events: none;\n}\n.du-bois-dark-zoom-enter,\n.du-bois-dark-zoom-appear {\n transform: scale(0);\n opacity: 0;\n animation-timing-function: cubic-bezier(0.08, 0.82, 0.17, 1);\n}\n.du-bois-dark-zoom-enter-prepare,\n.du-bois-dark-zoom-appear-prepare {\n transform: none;\n}\n.du-bois-dark-zoom-leave {\n animation-timing-function: cubic-bezier(0.78, 0.14, 0.15, 0.86);\n}\n.du-bois-dark-zoom-big-enter,\n.du-bois-dark-zoom-big-appear {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-dark-zoom-big-leave {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-dark-zoom-big-enter.du-bois-dark-zoom-big-enter-active,\n.du-bois-dark-zoom-big-appear.du-bois-dark-zoom-big-appear-active {\n animation-name: antZoomBigIn;\n animation-play-state: running;\n}\n.du-bois-dark-zoom-big-leave.du-bois-dark-zoom-big-leave-active {\n animation-name: antZoomBigOut;\n animation-play-state: running;\n pointer-events: none;\n}\n.du-bois-dark-zoom-big-enter,\n.du-bois-dark-zoom-big-appear {\n transform: scale(0);\n opacity: 0;\n animation-timing-function: cubic-bezier(0.08, 0.82, 0.17, 1);\n}\n.du-bois-dark-zoom-big-enter-prepare,\n.du-bois-dark-zoom-big-appear-prepare {\n transform: none;\n}\n.du-bois-dark-zoom-big-leave {\n animation-timing-function: cubic-bezier(0.78, 0.14, 0.15, 0.86);\n}\n.du-bois-dark-zoom-big-fast-enter,\n.du-bois-dark-zoom-big-fast-appear {\n animation-duration: 0.1s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-dark-zoom-big-fast-leave {\n animation-duration: 0.1s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-dark-zoom-big-fast-enter.du-bois-dark-zoom-big-fast-enter-active,\n.du-bois-dark-zoom-big-fast-appear.du-bois-dark-zoom-big-fast-appear-active {\n animation-name: antZoomBigIn;\n animation-play-state: running;\n}\n.du-bois-dark-zoom-big-fast-leave.du-bois-dark-zoom-big-fast-leave-active {\n animation-name: antZoomBigOut;\n animation-play-state: running;\n pointer-events: none;\n}\n.du-bois-dark-zoom-big-fast-enter,\n.du-bois-dark-zoom-big-fast-appear {\n transform: scale(0);\n opacity: 0;\n animation-timing-function: cubic-bezier(0.08, 0.82, 0.17, 1);\n}\n.du-bois-dark-zoom-big-fast-enter-prepare,\n.du-bois-dark-zoom-big-fast-appear-prepare {\n transform: none;\n}\n.du-bois-dark-zoom-big-fast-leave {\n animation-timing-function: cubic-bezier(0.78, 0.14, 0.15, 0.86);\n}\n.du-bois-dark-zoom-up-enter,\n.du-bois-dark-zoom-up-appear {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-dark-zoom-up-leave {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-dark-zoom-up-enter.du-bois-dark-zoom-up-enter-active,\n.du-bois-dark-zoom-up-appear.du-bois-dark-zoom-up-appear-active {\n animation-name: antZoomUpIn;\n animation-play-state: running;\n}\n.du-bois-dark-zoom-up-leave.du-bois-dark-zoom-up-leave-active {\n animation-name: antZoomUpOut;\n animation-play-state: running;\n pointer-events: none;\n}\n.du-bois-dark-zoom-up-enter,\n.du-bois-dark-zoom-up-appear {\n transform: scale(0);\n opacity: 0;\n animation-timing-function: cubic-bezier(0.08, 0.82, 0.17, 1);\n}\n.du-bois-dark-zoom-up-enter-prepare,\n.du-bois-dark-zoom-up-appear-prepare {\n transform: none;\n}\n.du-bois-dark-zoom-up-leave {\n animation-timing-function: cubic-bezier(0.78, 0.14, 0.15, 0.86);\n}\n.du-bois-dark-zoom-down-enter,\n.du-bois-dark-zoom-down-appear {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-dark-zoom-down-leave {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-dark-zoom-down-enter.du-bois-dark-zoom-down-enter-active,\n.du-bois-dark-zoom-down-appear.du-bois-dark-zoom-down-appear-active {\n animation-name: antZoomDownIn;\n animation-play-state: running;\n}\n.du-bois-dark-zoom-down-leave.du-bois-dark-zoom-down-leave-active {\n animation-name: antZoomDownOut;\n animation-play-state: running;\n pointer-events: none;\n}\n.du-bois-dark-zoom-down-enter,\n.du-bois-dark-zoom-down-appear {\n transform: scale(0);\n opacity: 0;\n animation-timing-function: cubic-bezier(0.08, 0.82, 0.17, 1);\n}\n.du-bois-dark-zoom-down-enter-prepare,\n.du-bois-dark-zoom-down-appear-prepare {\n transform: none;\n}\n.du-bois-dark-zoom-down-leave {\n animation-timing-function: cubic-bezier(0.78, 0.14, 0.15, 0.86);\n}\n.du-bois-dark-zoom-left-enter,\n.du-bois-dark-zoom-left-appear {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-dark-zoom-left-leave {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-dark-zoom-left-enter.du-bois-dark-zoom-left-enter-active,\n.du-bois-dark-zoom-left-appear.du-bois-dark-zoom-left-appear-active {\n animation-name: antZoomLeftIn;\n animation-play-state: running;\n}\n.du-bois-dark-zoom-left-leave.du-bois-dark-zoom-left-leave-active {\n animation-name: antZoomLeftOut;\n animation-play-state: running;\n pointer-events: none;\n}\n.du-bois-dark-zoom-left-enter,\n.du-bois-dark-zoom-left-appear {\n transform: scale(0);\n opacity: 0;\n animation-timing-function: cubic-bezier(0.08, 0.82, 0.17, 1);\n}\n.du-bois-dark-zoom-left-enter-prepare,\n.du-bois-dark-zoom-left-appear-prepare {\n transform: none;\n}\n.du-bois-dark-zoom-left-leave {\n animation-timing-function: cubic-bezier(0.78, 0.14, 0.15, 0.86);\n}\n.du-bois-dark-zoom-right-enter,\n.du-bois-dark-zoom-right-appear {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-dark-zoom-right-leave {\n animation-duration: 0.2s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-dark-zoom-right-enter.du-bois-dark-zoom-right-enter-active,\n.du-bois-dark-zoom-right-appear.du-bois-dark-zoom-right-appear-active {\n animation-name: antZoomRightIn;\n animation-play-state: running;\n}\n.du-bois-dark-zoom-right-leave.du-bois-dark-zoom-right-leave-active {\n animation-name: antZoomRightOut;\n animation-play-state: running;\n pointer-events: none;\n}\n.du-bois-dark-zoom-right-enter,\n.du-bois-dark-zoom-right-appear {\n transform: scale(0);\n opacity: 0;\n animation-timing-function: cubic-bezier(0.08, 0.82, 0.17, 1);\n}\n.du-bois-dark-zoom-right-enter-prepare,\n.du-bois-dark-zoom-right-appear-prepare {\n transform: none;\n}\n.du-bois-dark-zoom-right-leave {\n animation-timing-function: cubic-bezier(0.78, 0.14, 0.15, 0.86);\n}\n@keyframes antZoomIn {\n 0% {\n transform: scale(0.2);\n opacity: 0;\n }\n 100% {\n transform: scale(1);\n opacity: 1;\n }\n}\n@keyframes antZoomOut {\n 0% {\n transform: scale(1);\n }\n 100% {\n transform: scale(0.2);\n opacity: 0;\n }\n}\n@keyframes antZoomBigIn {\n 0% {\n transform: scale(0.8);\n opacity: 0;\n }\n 100% {\n transform: scale(1);\n opacity: 1;\n }\n}\n@keyframes antZoomBigOut {\n 0% {\n transform: scale(1);\n }\n 100% {\n transform: scale(0.8);\n opacity: 0;\n }\n}\n@keyframes antZoomUpIn {\n 0% {\n transform: scale(0.8);\n transform-origin: 50% 0%;\n opacity: 0;\n }\n 100% {\n transform: scale(1);\n transform-origin: 50% 0%;\n }\n}\n@keyframes antZoomUpOut {\n 0% {\n transform: scale(1);\n transform-origin: 50% 0%;\n }\n 100% {\n transform: scale(0.8);\n transform-origin: 50% 0%;\n opacity: 0;\n }\n}\n@keyframes antZoomLeftIn {\n 0% {\n transform: scale(0.8);\n transform-origin: 0% 50%;\n opacity: 0;\n }\n 100% {\n transform: scale(1);\n transform-origin: 0% 50%;\n }\n}\n@keyframes antZoomLeftOut {\n 0% {\n transform: scale(1);\n transform-origin: 0% 50%;\n }\n 100% {\n transform: scale(0.8);\n transform-origin: 0% 50%;\n opacity: 0;\n }\n}\n@keyframes antZoomRightIn {\n 0% {\n transform: scale(0.8);\n transform-origin: 100% 50%;\n opacity: 0;\n }\n 100% {\n transform: scale(1);\n transform-origin: 100% 50%;\n }\n}\n@keyframes antZoomRightOut {\n 0% {\n transform: scale(1);\n transform-origin: 100% 50%;\n }\n 100% {\n transform: scale(0.8);\n transform-origin: 100% 50%;\n opacity: 0;\n }\n}\n@keyframes antZoomDownIn {\n 0% {\n transform: scale(0.8);\n transform-origin: 50% 100%;\n opacity: 0;\n }\n 100% {\n transform: scale(1);\n transform-origin: 50% 100%;\n }\n}\n@keyframes antZoomDownOut {\n 0% {\n transform: scale(1);\n transform-origin: 50% 100%;\n }\n 100% {\n transform: scale(0.8);\n transform-origin: 50% 100%;\n opacity: 0;\n }\n}\n.ant-motion-collapse-legacy {\n overflow: hidden;\n}\n.ant-motion-collapse-legacy-active {\n transition: height 0.2s cubic-bezier(0.645, 0.045, 0.355, 1), opacity 0.2s cubic-bezier(0.645, 0.045, 0.355, 1) !important;\n}\n.ant-motion-collapse {\n overflow: hidden;\n transition: height 0.2s cubic-bezier(0.645, 0.045, 0.355, 1), opacity 0.2s cubic-bezier(0.645, 0.045, 0.355, 1) !important;\n}\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-dark-btn {\n line-height: 20px;\n position: relative;\n display: inline-block;\n font-weight: 400;\n white-space: nowrap;\n text-align: center;\n background-image: none;\n border: 1px solid transparent;\n box-shadow: 0 2px 0 rgba(0, 0, 0, 0.015);\n cursor: pointer;\n transition: all 0.3s cubic-bezier(0.645, 0.045, 0.355, 1);\n user-select: none;\n touch-action: manipulation;\n height: 40px;\n padding: 0 16px;\n font-size: 13px;\n border-radius: 4px;\n color: #E8ECF0;\n background: transparent;\n border-color: #1F272D;\n}\n.du-bois-dark-btn > .anticon {\n line-height: 1;\n}\n.du-bois-dark-btn,\n.du-bois-dark-btn:active,\n.du-bois-dark-btn:focus {\n outline: 0;\n}\n.du-bois-dark-btn:not([disabled]):hover {\n text-decoration: none;\n}\n.du-bois-dark-btn:not([disabled]):active {\n outline: 0;\n box-shadow: none;\n}\n.du-bois-dark-btn[disabled] {\n cursor: not-allowed;\n}\n.du-bois-dark-btn[disabled] > * {\n pointer-events: none;\n}\n.du-bois-dark-btn-lg {\n height: 40px;\n padding: 0 16px;\n font-size: 18px;\n border-radius: 4px;\n}\n.du-bois-dark-btn-sm {\n height: 32px;\n padding: 0 16px;\n font-size: 13px;\n border-radius: 4px;\n}\n.du-bois-dark-btn > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn:hover,\n.du-bois-dark-btn:focus {\n color: #1d517c;\n background: transparent;\n border-color: #1d517c;\n}\n.du-bois-dark-btn:hover > a:only-child,\n.du-bois-dark-btn:focus > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn:hover > a:only-child::after,\n.du-bois-dark-btn:focus > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn:active {\n color: #3f81b1;\n background: transparent;\n border-color: #3f81b1;\n}\n.du-bois-dark-btn:active > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn[disabled],\n.du-bois-dark-btn[disabled]:hover,\n.du-bois-dark-btn[disabled]:focus,\n.du-bois-dark-btn[disabled]:active {\n color: rgba(255, 255, 255, 0.3);\n background: rgba(255, 255, 255, 0.08);\n border-color: #1F272D;\n text-shadow: none;\n box-shadow: none;\n}\n.du-bois-dark-btn[disabled] > a:only-child,\n.du-bois-dark-btn[disabled]:hover > a:only-child,\n.du-bois-dark-btn[disabled]:focus > a:only-child,\n.du-bois-dark-btn[disabled]:active > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn[disabled] > a:only-child::after,\n.du-bois-dark-btn[disabled]:hover > a:only-child::after,\n.du-bois-dark-btn[disabled]:focus > a:only-child::after,\n.du-bois-dark-btn[disabled]:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn:hover,\n.du-bois-dark-btn:focus,\n.du-bois-dark-btn:active {\n text-decoration: none;\n background: transparent;\n}\n.du-bois-dark-btn > span {\n display: inline-block;\n}\n.du-bois-dark-btn-primary {\n color: #fff;\n background: #2272B4;\n border-color: #2272B4;\n text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.12);\n box-shadow: 0 2px 0 rgba(0, 0, 0, 0.045);\n}\n.du-bois-dark-btn-primary > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-primary > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-primary:hover,\n.du-bois-dark-btn-primary:focus {\n color: #fff;\n background: #14538f;\n border-color: #14538f;\n}\n.du-bois-dark-btn-primary:hover > a:only-child,\n.du-bois-dark-btn-primary:focus > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-primary:hover > a:only-child::after,\n.du-bois-dark-btn-primary:focus > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-primary:active {\n color: #fff;\n background: #448dc2;\n border-color: #448dc2;\n}\n.du-bois-dark-btn-primary:active > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-primary:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-primary[disabled],\n.du-bois-dark-btn-primary[disabled]:hover,\n.du-bois-dark-btn-primary[disabled]:focus,\n.du-bois-dark-btn-primary[disabled]:active {\n color: rgba(255, 255, 255, 0.3);\n background: rgba(255, 255, 255, 0.08);\n border-color: #1F272D;\n text-shadow: none;\n box-shadow: none;\n}\n.du-bois-dark-btn-primary[disabled] > a:only-child,\n.du-bois-dark-btn-primary[disabled]:hover > a:only-child,\n.du-bois-dark-btn-primary[disabled]:focus > a:only-child,\n.du-bois-dark-btn-primary[disabled]:active > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-primary[disabled] > a:only-child::after,\n.du-bois-dark-btn-primary[disabled]:hover > a:only-child::after,\n.du-bois-dark-btn-primary[disabled]:focus > a:only-child::after,\n.du-bois-dark-btn-primary[disabled]:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-group .du-bois-dark-btn-primary:not(:first-child):not(:last-child) {\n border-right-color: #1d517c;\n border-left-color: #1d517c;\n}\n.du-bois-dark-btn-group .du-bois-dark-btn-primary:not(:first-child):not(:last-child):disabled {\n border-color: #1F272D;\n}\n.du-bois-dark-btn-group .du-bois-dark-btn-primary:first-child:not(:last-child) {\n border-right-color: #1d517c;\n}\n.du-bois-dark-btn-group .du-bois-dark-btn-primary:first-child:not(:last-child)[disabled] {\n border-right-color: #1F272D;\n}\n.du-bois-dark-btn-group .du-bois-dark-btn-primary:last-child:not(:first-child),\n.du-bois-dark-btn-group .du-bois-dark-btn-primary + .du-bois-dark-btn-primary {\n border-left-color: #1d517c;\n}\n.du-bois-dark-btn-group .du-bois-dark-btn-primary:last-child:not(:first-child)[disabled],\n.du-bois-dark-btn-group .du-bois-dark-btn-primary + .du-bois-dark-btn-primary[disabled] {\n border-left-color: #1F272D;\n}\n.du-bois-dark-btn-ghost {\n color: #E8ECF0;\n background: transparent;\n border-color: #1F272D;\n}\n.du-bois-dark-btn-ghost > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-ghost > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-ghost:hover,\n.du-bois-dark-btn-ghost:focus {\n color: #1d517c;\n background: transparent;\n border-color: #1d517c;\n}\n.du-bois-dark-btn-ghost:hover > a:only-child,\n.du-bois-dark-btn-ghost:focus > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-ghost:hover > a:only-child::after,\n.du-bois-dark-btn-ghost:focus > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-ghost:active {\n color: #3f81b1;\n background: transparent;\n border-color: #3f81b1;\n}\n.du-bois-dark-btn-ghost:active > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-ghost:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-ghost[disabled],\n.du-bois-dark-btn-ghost[disabled]:hover,\n.du-bois-dark-btn-ghost[disabled]:focus,\n.du-bois-dark-btn-ghost[disabled]:active {\n color: rgba(255, 255, 255, 0.3);\n background: rgba(255, 255, 255, 0.08);\n border-color: #1F272D;\n text-shadow: none;\n box-shadow: none;\n}\n.du-bois-dark-btn-ghost[disabled] > a:only-child,\n.du-bois-dark-btn-ghost[disabled]:hover > a:only-child,\n.du-bois-dark-btn-ghost[disabled]:focus > a:only-child,\n.du-bois-dark-btn-ghost[disabled]:active > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-ghost[disabled] > a:only-child::after,\n.du-bois-dark-btn-ghost[disabled]:hover > a:only-child::after,\n.du-bois-dark-btn-ghost[disabled]:focus > a:only-child::after,\n.du-bois-dark-btn-ghost[disabled]:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-dashed {\n color: #E8ECF0;\n background: transparent;\n border-color: #1F272D;\n border-style: dashed;\n}\n.du-bois-dark-btn-dashed > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-dashed > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-dashed:hover,\n.du-bois-dark-btn-dashed:focus {\n color: #1d517c;\n background: transparent;\n border-color: #1d517c;\n}\n.du-bois-dark-btn-dashed:hover > a:only-child,\n.du-bois-dark-btn-dashed:focus > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-dashed:hover > a:only-child::after,\n.du-bois-dark-btn-dashed:focus > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-dashed:active {\n color: #3f81b1;\n background: transparent;\n border-color: #3f81b1;\n}\n.du-bois-dark-btn-dashed:active > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-dashed:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-dashed[disabled],\n.du-bois-dark-btn-dashed[disabled]:hover,\n.du-bois-dark-btn-dashed[disabled]:focus,\n.du-bois-dark-btn-dashed[disabled]:active {\n color: rgba(255, 255, 255, 0.3);\n background: rgba(255, 255, 255, 0.08);\n border-color: #1F272D;\n text-shadow: none;\n box-shadow: none;\n}\n.du-bois-dark-btn-dashed[disabled] > a:only-child,\n.du-bois-dark-btn-dashed[disabled]:hover > a:only-child,\n.du-bois-dark-btn-dashed[disabled]:focus > a:only-child,\n.du-bois-dark-btn-dashed[disabled]:active > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-dashed[disabled] > a:only-child::after,\n.du-bois-dark-btn-dashed[disabled]:hover > a:only-child::after,\n.du-bois-dark-btn-dashed[disabled]:focus > a:only-child::after,\n.du-bois-dark-btn-dashed[disabled]:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-danger {\n color: #fff;\n background: #E65B77;\n border-color: #E65B77;\n text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.12);\n box-shadow: 0 2px 0 rgba(0, 0, 0, 0.045);\n}\n.du-bois-dark-btn-danger > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-danger > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-danger:hover,\n.du-bois-dark-btn-danger:focus {\n color: #fff;\n background: #bf4360;\n border-color: #bf4360;\n}\n.du-bois-dark-btn-danger:hover > a:only-child,\n.du-bois-dark-btn-danger:focus > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-danger:hover > a:only-child::after,\n.du-bois-dark-btn-danger:focus > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-danger:active {\n color: #fff;\n background: #f28899;\n border-color: #f28899;\n}\n.du-bois-dark-btn-danger:active > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-danger:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-danger[disabled],\n.du-bois-dark-btn-danger[disabled]:hover,\n.du-bois-dark-btn-danger[disabled]:focus,\n.du-bois-dark-btn-danger[disabled]:active {\n color: rgba(255, 255, 255, 0.3);\n background: rgba(255, 255, 255, 0.08);\n border-color: #1F272D;\n text-shadow: none;\n box-shadow: none;\n}\n.du-bois-dark-btn-danger[disabled] > a:only-child,\n.du-bois-dark-btn-danger[disabled]:hover > a:only-child,\n.du-bois-dark-btn-danger[disabled]:focus > a:only-child,\n.du-bois-dark-btn-danger[disabled]:active > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-danger[disabled] > a:only-child::after,\n.du-bois-dark-btn-danger[disabled]:hover > a:only-child::after,\n.du-bois-dark-btn-danger[disabled]:focus > a:only-child::after,\n.du-bois-dark-btn-danger[disabled]:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-link {\n color: #2272B4;\n background: transparent;\n border-color: transparent;\n box-shadow: none;\n}\n.du-bois-dark-btn-link > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-link > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-link:hover,\n.du-bois-dark-btn-link:focus {\n color: #1d517c;\n background: transparent;\n border-color: #1d517c;\n}\n.du-bois-dark-btn-link:hover > a:only-child,\n.du-bois-dark-btn-link:focus > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-link:hover > a:only-child::after,\n.du-bois-dark-btn-link:focus > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-link:active {\n color: #3f81b1;\n background: transparent;\n border-color: #3f81b1;\n}\n.du-bois-dark-btn-link:active > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-link:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-link[disabled],\n.du-bois-dark-btn-link[disabled]:hover,\n.du-bois-dark-btn-link[disabled]:focus,\n.du-bois-dark-btn-link[disabled]:active {\n color: rgba(255, 255, 255, 0.3);\n background: rgba(255, 255, 255, 0.08);\n border-color: #1F272D;\n text-shadow: none;\n box-shadow: none;\n}\n.du-bois-dark-btn-link[disabled] > a:only-child,\n.du-bois-dark-btn-link[disabled]:hover > a:only-child,\n.du-bois-dark-btn-link[disabled]:focus > a:only-child,\n.du-bois-dark-btn-link[disabled]:active > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-link[disabled] > a:only-child::after,\n.du-bois-dark-btn-link[disabled]:hover > a:only-child::after,\n.du-bois-dark-btn-link[disabled]:focus > a:only-child::after,\n.du-bois-dark-btn-link[disabled]:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-link:hover {\n background: transparent;\n}\n.du-bois-dark-btn-link:hover,\n.du-bois-dark-btn-link:focus,\n.du-bois-dark-btn-link:active {\n border-color: transparent;\n}\n.du-bois-dark-btn-link[disabled],\n.du-bois-dark-btn-link[disabled]:hover,\n.du-bois-dark-btn-link[disabled]:focus,\n.du-bois-dark-btn-link[disabled]:active {\n color: rgba(255, 255, 255, 0.3);\n background: transparent;\n border-color: transparent;\n text-shadow: none;\n box-shadow: none;\n}\n.du-bois-dark-btn-link[disabled] > a:only-child,\n.du-bois-dark-btn-link[disabled]:hover > a:only-child,\n.du-bois-dark-btn-link[disabled]:focus > a:only-child,\n.du-bois-dark-btn-link[disabled]:active > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-link[disabled] > a:only-child::after,\n.du-bois-dark-btn-link[disabled]:hover > a:only-child::after,\n.du-bois-dark-btn-link[disabled]:focus > a:only-child::after,\n.du-bois-dark-btn-link[disabled]:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-text {\n color: #E8ECF0;\n background: transparent;\n border-color: transparent;\n box-shadow: none;\n}\n.du-bois-dark-btn-text > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-text > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-text:hover,\n.du-bois-dark-btn-text:focus {\n color: #1d517c;\n background: transparent;\n border-color: #1d517c;\n}\n.du-bois-dark-btn-text:hover > a:only-child,\n.du-bois-dark-btn-text:focus > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-text:hover > a:only-child::after,\n.du-bois-dark-btn-text:focus > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-text:active {\n color: #3f81b1;\n background: transparent;\n border-color: #3f81b1;\n}\n.du-bois-dark-btn-text:active > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-text:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-text[disabled],\n.du-bois-dark-btn-text[disabled]:hover,\n.du-bois-dark-btn-text[disabled]:focus,\n.du-bois-dark-btn-text[disabled]:active {\n color: rgba(255, 255, 255, 0.3);\n background: rgba(255, 255, 255, 0.08);\n border-color: #1F272D;\n text-shadow: none;\n box-shadow: none;\n}\n.du-bois-dark-btn-text[disabled] > a:only-child,\n.du-bois-dark-btn-text[disabled]:hover > a:only-child,\n.du-bois-dark-btn-text[disabled]:focus > a:only-child,\n.du-bois-dark-btn-text[disabled]:active > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-text[disabled] > a:only-child::after,\n.du-bois-dark-btn-text[disabled]:hover > a:only-child::after,\n.du-bois-dark-btn-text[disabled]:focus > a:only-child::after,\n.du-bois-dark-btn-text[disabled]:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-text:hover,\n.du-bois-dark-btn-text:focus {\n color: #E8ECF0;\n background: rgba(255, 255, 255, 0.03);\n border-color: transparent;\n}\n.du-bois-dark-btn-text:active {\n color: #E8ECF0;\n background: rgba(255, 255, 255, 0.04);\n border-color: transparent;\n}\n.du-bois-dark-btn-text[disabled],\n.du-bois-dark-btn-text[disabled]:hover,\n.du-bois-dark-btn-text[disabled]:focus,\n.du-bois-dark-btn-text[disabled]:active {\n color: rgba(255, 255, 255, 0.3);\n background: transparent;\n border-color: transparent;\n text-shadow: none;\n box-shadow: none;\n}\n.du-bois-dark-btn-text[disabled] > a:only-child,\n.du-bois-dark-btn-text[disabled]:hover > a:only-child,\n.du-bois-dark-btn-text[disabled]:focus > a:only-child,\n.du-bois-dark-btn-text[disabled]:active > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-text[disabled] > a:only-child::after,\n.du-bois-dark-btn-text[disabled]:hover > a:only-child::after,\n.du-bois-dark-btn-text[disabled]:focus > a:only-child::after,\n.du-bois-dark-btn-text[disabled]:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-dangerous {\n color: #E65B77;\n background: transparent;\n border-color: #E65B77;\n}\n.du-bois-dark-btn-dangerous > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-dangerous > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-dangerous:hover,\n.du-bois-dark-btn-dangerous:focus {\n color: #bf4360;\n background: transparent;\n border-color: #bf4360;\n}\n.du-bois-dark-btn-dangerous:hover > a:only-child,\n.du-bois-dark-btn-dangerous:focus > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-dangerous:hover > a:only-child::after,\n.du-bois-dark-btn-dangerous:focus > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-dangerous:active {\n color: #f28899;\n background: transparent;\n border-color: #f28899;\n}\n.du-bois-dark-btn-dangerous:active > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-dangerous:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-dangerous[disabled],\n.du-bois-dark-btn-dangerous[disabled]:hover,\n.du-bois-dark-btn-dangerous[disabled]:focus,\n.du-bois-dark-btn-dangerous[disabled]:active {\n color: rgba(255, 255, 255, 0.3);\n background: rgba(255, 255, 255, 0.08);\n border-color: #1F272D;\n text-shadow: none;\n box-shadow: none;\n}\n.du-bois-dark-btn-dangerous[disabled] > a:only-child,\n.du-bois-dark-btn-dangerous[disabled]:hover > a:only-child,\n.du-bois-dark-btn-dangerous[disabled]:focus > a:only-child,\n.du-bois-dark-btn-dangerous[disabled]:active > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-dangerous[disabled] > a:only-child::after,\n.du-bois-dark-btn-dangerous[disabled]:hover > a:only-child::after,\n.du-bois-dark-btn-dangerous[disabled]:focus > a:only-child::after,\n.du-bois-dark-btn-dangerous[disabled]:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-primary {\n color: #fff;\n background: #E65B77;\n border-color: #E65B77;\n text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.12);\n box-shadow: 0 2px 0 rgba(0, 0, 0, 0.045);\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-primary > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-primary > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-primary:hover,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-primary:focus {\n color: #fff;\n background: #bf4360;\n border-color: #bf4360;\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-primary:hover > a:only-child,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-primary:focus > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-primary:hover > a:only-child::after,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-primary:focus > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-primary:active {\n color: #fff;\n background: #f28899;\n border-color: #f28899;\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-primary:active > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-primary:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-primary[disabled],\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-primary[disabled]:hover,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-primary[disabled]:focus,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-primary[disabled]:active {\n color: rgba(255, 255, 255, 0.3);\n background: rgba(255, 255, 255, 0.08);\n border-color: #1F272D;\n text-shadow: none;\n box-shadow: none;\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-primary[disabled] > a:only-child,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-primary[disabled]:hover > a:only-child,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-primary[disabled]:focus > a:only-child,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-primary[disabled]:active > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-primary[disabled] > a:only-child::after,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-primary[disabled]:hover > a:only-child::after,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-primary[disabled]:focus > a:only-child::after,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-primary[disabled]:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-link {\n color: #E65B77;\n background: transparent;\n border-color: transparent;\n box-shadow: none;\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-link > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-link > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-link:hover,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-link:focus {\n color: #1d517c;\n background: transparent;\n border-color: #1d517c;\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-link:hover > a:only-child,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-link:focus > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-link:hover > a:only-child::after,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-link:focus > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-link:active {\n color: #3f81b1;\n background: transparent;\n border-color: #3f81b1;\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-link:active > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-link:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-link[disabled],\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-link[disabled]:hover,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-link[disabled]:focus,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-link[disabled]:active {\n color: rgba(255, 255, 255, 0.3);\n background: rgba(255, 255, 255, 0.08);\n border-color: #1F272D;\n text-shadow: none;\n box-shadow: none;\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-link[disabled] > a:only-child,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-link[disabled]:hover > a:only-child,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-link[disabled]:focus > a:only-child,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-link[disabled]:active > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-link[disabled] > a:only-child::after,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-link[disabled]:hover > a:only-child::after,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-link[disabled]:focus > a:only-child::after,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-link[disabled]:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-link:hover,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-link:focus {\n color: #bf4360;\n background: transparent;\n border-color: transparent;\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-link:hover > a:only-child,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-link:focus > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-link:hover > a:only-child::after,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-link:focus > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-link:active {\n color: #f28899;\n background: transparent;\n border-color: transparent;\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-link:active > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-link:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-link[disabled],\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-link[disabled]:hover,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-link[disabled]:focus,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-link[disabled]:active {\n color: rgba(255, 255, 255, 0.3);\n background: transparent;\n border-color: transparent;\n text-shadow: none;\n box-shadow: none;\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-link[disabled] > a:only-child,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-link[disabled]:hover > a:only-child,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-link[disabled]:focus > a:only-child,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-link[disabled]:active > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-link[disabled] > a:only-child::after,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-link[disabled]:hover > a:only-child::after,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-link[disabled]:focus > a:only-child::after,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-link[disabled]:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-text {\n color: #E65B77;\n background: transparent;\n border-color: transparent;\n box-shadow: none;\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-text > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-text > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-text:hover,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-text:focus {\n color: #1d517c;\n background: transparent;\n border-color: #1d517c;\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-text:hover > a:only-child,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-text:focus > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-text:hover > a:only-child::after,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-text:focus > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-text:active {\n color: #3f81b1;\n background: transparent;\n border-color: #3f81b1;\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-text:active > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-text:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-text[disabled],\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-text[disabled]:hover,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-text[disabled]:focus,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-text[disabled]:active {\n color: rgba(255, 255, 255, 0.3);\n background: rgba(255, 255, 255, 0.08);\n border-color: #1F272D;\n text-shadow: none;\n box-shadow: none;\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-text[disabled] > a:only-child,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-text[disabled]:hover > a:only-child,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-text[disabled]:focus > a:only-child,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-text[disabled]:active > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-text[disabled] > a:only-child::after,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-text[disabled]:hover > a:only-child::after,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-text[disabled]:focus > a:only-child::after,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-text[disabled]:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-text:hover,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-text:focus {\n color: #bf4360;\n background: rgba(255, 255, 255, 0.03);\n border-color: transparent;\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-text:hover > a:only-child,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-text:focus > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-text:hover > a:only-child::after,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-text:focus > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-text:active {\n color: #f28899;\n background: rgba(255, 255, 255, 0.04);\n border-color: transparent;\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-text:active > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-text:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-text[disabled],\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-text[disabled]:hover,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-text[disabled]:focus,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-text[disabled]:active {\n color: rgba(255, 255, 255, 0.3);\n background: transparent;\n border-color: transparent;\n text-shadow: none;\n box-shadow: none;\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-text[disabled] > a:only-child,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-text[disabled]:hover > a:only-child,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-text[disabled]:focus > a:only-child,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-text[disabled]:active > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-text[disabled] > a:only-child::after,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-text[disabled]:hover > a:only-child::after,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-text[disabled]:focus > a:only-child::after,\n.du-bois-dark-btn-dangerous.du-bois-dark-btn-text[disabled]:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-icon-only {\n width: 40px;\n height: 40px;\n padding: 0 0;\n font-size: 15px;\n border-radius: 4px;\n vertical-align: -1px;\n}\n.du-bois-dark-btn-icon-only > * {\n font-size: 15px;\n}\n.du-bois-dark-btn-icon-only.du-bois-dark-btn-lg {\n width: 40px;\n height: 40px;\n padding: 0 0;\n font-size: 20px;\n border-radius: 4px;\n}\n.du-bois-dark-btn-icon-only.du-bois-dark-btn-lg > * {\n font-size: 20px;\n}\n.du-bois-dark-btn-icon-only.du-bois-dark-btn-sm {\n width: 32px;\n height: 32px;\n padding: 0 0;\n font-size: 13px;\n border-radius: 4px;\n}\n.du-bois-dark-btn-icon-only.du-bois-dark-btn-sm > * {\n font-size: 13px;\n}\n.du-bois-dark-btn-round {\n height: 40px;\n padding: 0 20px;\n font-size: 13px;\n border-radius: 40px;\n}\n.du-bois-dark-btn-round.du-bois-dark-btn-lg {\n height: 40px;\n padding: 0 20px;\n font-size: 18px;\n border-radius: 40px;\n}\n.du-bois-dark-btn-round.du-bois-dark-btn-sm {\n height: 32px;\n padding: 0 16px;\n font-size: 13px;\n border-radius: 32px;\n}\n.du-bois-dark-btn-round.du-bois-dark-btn-icon-only {\n width: auto;\n}\n.du-bois-dark-btn-circle {\n min-width: 40px;\n padding-right: 0;\n padding-left: 0;\n text-align: center;\n border-radius: 50%;\n}\n.du-bois-dark-btn-circle.du-bois-dark-btn-lg {\n min-width: 40px;\n border-radius: 50%;\n}\n.du-bois-dark-btn-circle.du-bois-dark-btn-sm {\n min-width: 32px;\n border-radius: 50%;\n}\n.du-bois-dark-btn::before {\n position: absolute;\n top: -1px;\n right: -1px;\n bottom: -1px;\n left: -1px;\n z-index: 1;\n display: none;\n background: #141414;\n border-radius: inherit;\n opacity: 0.35;\n transition: opacity 0.2s;\n content: '';\n pointer-events: none;\n}\n.du-bois-dark-btn .anticon {\n transition: margin-left 0.3s cubic-bezier(0.645, 0.045, 0.355, 1);\n}\n.du-bois-dark-btn .anticon.anticon-plus > svg,\n.du-bois-dark-btn .anticon.anticon-minus > svg {\n shape-rendering: optimizeSpeed;\n}\n.du-bois-dark-btn.du-bois-dark-btn-loading {\n position: relative;\n}\n.du-bois-dark-btn.du-bois-dark-btn-loading:not([disabled]) {\n pointer-events: none;\n}\n.du-bois-dark-btn.du-bois-dark-btn-loading::before {\n display: block;\n}\n.du-bois-dark-btn > .du-bois-dark-btn-loading-icon {\n transition: all 0.3s cubic-bezier(0.645, 0.045, 0.355, 1);\n}\n.du-bois-dark-btn > .du-bois-dark-btn-loading-icon .anticon {\n padding-right: 4px;\n animation: none;\n}\n.du-bois-dark-btn > .du-bois-dark-btn-loading-icon .anticon svg {\n animation: loadingCircle 1s infinite linear;\n}\n.du-bois-dark-btn > .du-bois-dark-btn-loading-icon:only-child .anticon {\n padding-right: 0;\n}\n.du-bois-dark-btn-group {\n position: relative;\n display: inline-flex;\n}\n.du-bois-dark-btn-group > .du-bois-dark-btn,\n.du-bois-dark-btn-group > span > .du-bois-dark-btn {\n position: relative;\n}\n.du-bois-dark-btn-group > .du-bois-dark-btn:hover,\n.du-bois-dark-btn-group > span > .du-bois-dark-btn:hover,\n.du-bois-dark-btn-group > .du-bois-dark-btn:focus,\n.du-bois-dark-btn-group > span > .du-bois-dark-btn:focus,\n.du-bois-dark-btn-group > .du-bois-dark-btn:active,\n.du-bois-dark-btn-group > span > .du-bois-dark-btn:active {\n z-index: 2;\n}\n.du-bois-dark-btn-group > .du-bois-dark-btn[disabled],\n.du-bois-dark-btn-group > span > .du-bois-dark-btn[disabled] {\n z-index: 0;\n}\n.du-bois-dark-btn-group .du-bois-dark-btn-icon-only {\n font-size: 13px;\n}\n.du-bois-dark-btn-group-lg > .du-bois-dark-btn,\n.du-bois-dark-btn-group-lg > span > .du-bois-dark-btn {\n height: 40px;\n padding: 0 16px;\n font-size: 18px;\n border-radius: 0;\n}\n.du-bois-dark-btn-group-lg .du-bois-dark-btn.du-bois-dark-btn-icon-only {\n width: 40px;\n height: 40px;\n padding-right: 0;\n padding-left: 0;\n}\n.du-bois-dark-btn-group-sm > .du-bois-dark-btn,\n.du-bois-dark-btn-group-sm > span > .du-bois-dark-btn {\n height: 32px;\n padding: 0 16px;\n font-size: 13px;\n border-radius: 0;\n}\n.du-bois-dark-btn-group-sm > .du-bois-dark-btn > .anticon,\n.du-bois-dark-btn-group-sm > span > .du-bois-dark-btn > .anticon {\n font-size: 13px;\n}\n.du-bois-dark-btn-group-sm .du-bois-dark-btn.du-bois-dark-btn-icon-only {\n width: 32px;\n height: 32px;\n padding-right: 0;\n padding-left: 0;\n}\n.du-bois-dark-btn-group .du-bois-dark-btn + .du-bois-dark-btn,\n.du-bois-dark-btn + .du-bois-dark-btn-group,\n.du-bois-dark-btn-group span + .du-bois-dark-btn,\n.du-bois-dark-btn-group .du-bois-dark-btn + span,\n.du-bois-dark-btn-group > span + span,\n.du-bois-dark-btn-group + .du-bois-dark-btn,\n.du-bois-dark-btn-group + .du-bois-dark-btn-group {\n margin-left: -1px;\n}\n.du-bois-dark-btn-group .du-bois-dark-btn-primary + .du-bois-dark-btn:not(.du-bois-dark-btn-primary):not([disabled]) {\n border-left-color: transparent;\n}\n.du-bois-dark-btn-group .du-bois-dark-btn {\n border-radius: 0;\n}\n.du-bois-dark-btn-group > .du-bois-dark-btn:first-child,\n.du-bois-dark-btn-group > span:first-child > .du-bois-dark-btn {\n margin-left: 0;\n}\n.du-bois-dark-btn-group > .du-bois-dark-btn:only-child {\n border-radius: 4px;\n}\n.du-bois-dark-btn-group > span:only-child > .du-bois-dark-btn {\n border-radius: 4px;\n}\n.du-bois-dark-btn-group > .du-bois-dark-btn:first-child:not(:last-child),\n.du-bois-dark-btn-group > span:first-child:not(:last-child) > .du-bois-dark-btn {\n border-top-left-radius: 4px;\n border-bottom-left-radius: 4px;\n}\n.du-bois-dark-btn-group > .du-bois-dark-btn:last-child:not(:first-child),\n.du-bois-dark-btn-group > span:last-child:not(:first-child) > .du-bois-dark-btn {\n border-top-right-radius: 4px;\n border-bottom-right-radius: 4px;\n}\n.du-bois-dark-btn-group-sm > .du-bois-dark-btn:only-child {\n border-radius: 4px;\n}\n.du-bois-dark-btn-group-sm > span:only-child > .du-bois-dark-btn {\n border-radius: 4px;\n}\n.du-bois-dark-btn-group-sm > .du-bois-dark-btn:first-child:not(:last-child),\n.du-bois-dark-btn-group-sm > span:first-child:not(:last-child) > .du-bois-dark-btn {\n border-top-left-radius: 4px;\n border-bottom-left-radius: 4px;\n}\n.du-bois-dark-btn-group-sm > .du-bois-dark-btn:last-child:not(:first-child),\n.du-bois-dark-btn-group-sm > span:last-child:not(:first-child) > .du-bois-dark-btn {\n border-top-right-radius: 4px;\n border-bottom-right-radius: 4px;\n}\n.du-bois-dark-btn-group > .du-bois-dark-btn-group {\n float: left;\n}\n.du-bois-dark-btn-group > .du-bois-dark-btn-group:not(:first-child):not(:last-child) > .du-bois-dark-btn {\n border-radius: 0;\n}\n.du-bois-dark-btn-group > .du-bois-dark-btn-group:first-child:not(:last-child) > .du-bois-dark-btn:last-child {\n padding-right: 8px;\n border-top-right-radius: 0;\n border-bottom-right-radius: 0;\n}\n.du-bois-dark-btn-group > .du-bois-dark-btn-group:last-child:not(:first-child) > .du-bois-dark-btn:first-child {\n padding-left: 8px;\n border-top-left-radius: 0;\n border-bottom-left-radius: 0;\n}\n.du-bois-dark-btn:focus > span,\n.du-bois-dark-btn:active > span {\n position: relative;\n}\n.du-bois-dark-btn > .anticon + span,\n.du-bois-dark-btn > span + .anticon {\n margin-left: 8px;\n}\n.du-bois-dark-btn-background-ghost {\n color: #E8ECF0;\n background: transparent !important;\n border-color: rgba(255, 255, 255, 0.25);\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-primary {\n color: #2272B4;\n background: transparent;\n border-color: #2272B4;\n text-shadow: none;\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-primary > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-primary > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-primary:hover,\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-primary:focus {\n color: #14538f;\n background: transparent;\n border-color: #14538f;\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-primary:hover > a:only-child,\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-primary:focus > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-primary:hover > a:only-child::after,\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-primary:focus > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-primary:active {\n color: #448dc2;\n background: transparent;\n border-color: #448dc2;\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-primary:active > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-primary:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-primary[disabled],\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-primary[disabled]:hover,\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-primary[disabled]:focus,\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-primary[disabled]:active {\n color: rgba(255, 255, 255, 0.3);\n background: rgba(255, 255, 255, 0.08);\n border-color: #1F272D;\n text-shadow: none;\n box-shadow: none;\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-primary[disabled] > a:only-child,\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-primary[disabled]:hover > a:only-child,\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-primary[disabled]:focus > a:only-child,\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-primary[disabled]:active > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-primary[disabled] > a:only-child::after,\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-primary[disabled]:hover > a:only-child::after,\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-primary[disabled]:focus > a:only-child::after,\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-primary[disabled]:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-danger {\n color: #E65B77;\n background: transparent;\n border-color: #E65B77;\n text-shadow: none;\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-danger > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-danger > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-danger:hover,\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-danger:focus {\n color: #bf4360;\n background: transparent;\n border-color: #bf4360;\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-danger:hover > a:only-child,\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-danger:focus > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-danger:hover > a:only-child::after,\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-danger:focus > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-danger:active {\n color: #f28899;\n background: transparent;\n border-color: #f28899;\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-danger:active > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-danger:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-danger[disabled],\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-danger[disabled]:hover,\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-danger[disabled]:focus,\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-danger[disabled]:active {\n color: rgba(255, 255, 255, 0.3);\n background: rgba(255, 255, 255, 0.08);\n border-color: #1F272D;\n text-shadow: none;\n box-shadow: none;\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-danger[disabled] > a:only-child,\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-danger[disabled]:hover > a:only-child,\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-danger[disabled]:focus > a:only-child,\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-danger[disabled]:active > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-danger[disabled] > a:only-child::after,\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-danger[disabled]:hover > a:only-child::after,\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-danger[disabled]:focus > a:only-child::after,\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-danger[disabled]:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous {\n color: #E65B77;\n background: transparent;\n border-color: #E65B77;\n text-shadow: none;\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous:hover,\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous:focus {\n color: #bf4360;\n background: transparent;\n border-color: #bf4360;\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous:hover > a:only-child,\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous:focus > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous:hover > a:only-child::after,\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous:focus > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous:active {\n color: #f28899;\n background: transparent;\n border-color: #f28899;\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous:active > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous[disabled],\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous[disabled]:hover,\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous[disabled]:focus,\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous[disabled]:active {\n color: rgba(255, 255, 255, 0.3);\n background: rgba(255, 255, 255, 0.08);\n border-color: #1F272D;\n text-shadow: none;\n box-shadow: none;\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous[disabled] > a:only-child,\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous[disabled]:hover > a:only-child,\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous[disabled]:focus > a:only-child,\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous[disabled]:active > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous[disabled] > a:only-child::after,\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous[disabled]:hover > a:only-child::after,\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous[disabled]:focus > a:only-child::after,\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous[disabled]:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous.du-bois-dark-btn-link {\n color: #E65B77;\n background: transparent;\n border-color: transparent;\n text-shadow: none;\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous.du-bois-dark-btn-link > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous.du-bois-dark-btn-link > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous.du-bois-dark-btn-link:hover,\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous.du-bois-dark-btn-link:focus {\n color: #bf4360;\n background: transparent;\n border-color: transparent;\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous.du-bois-dark-btn-link:hover > a:only-child,\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous.du-bois-dark-btn-link:focus > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous.du-bois-dark-btn-link:hover > a:only-child::after,\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous.du-bois-dark-btn-link:focus > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous.du-bois-dark-btn-link:active {\n color: #f28899;\n background: transparent;\n border-color: transparent;\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous.du-bois-dark-btn-link:active > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous.du-bois-dark-btn-link:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous.du-bois-dark-btn-link[disabled],\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous.du-bois-dark-btn-link[disabled]:hover,\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous.du-bois-dark-btn-link[disabled]:focus,\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous.du-bois-dark-btn-link[disabled]:active {\n color: rgba(255, 255, 255, 0.3);\n background: rgba(255, 255, 255, 0.08);\n border-color: #1F272D;\n text-shadow: none;\n box-shadow: none;\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous.du-bois-dark-btn-link[disabled] > a:only-child,\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous.du-bois-dark-btn-link[disabled]:hover > a:only-child,\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous.du-bois-dark-btn-link[disabled]:focus > a:only-child,\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous.du-bois-dark-btn-link[disabled]:active > a:only-child {\n color: currentColor;\n}\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous.du-bois-dark-btn-link[disabled] > a:only-child::after,\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous.du-bois-dark-btn-link[disabled]:hover > a:only-child::after,\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous.du-bois-dark-btn-link[disabled]:focus > a:only-child::after,\n.du-bois-dark-btn-background-ghost.du-bois-dark-btn-dangerous.du-bois-dark-btn-link[disabled]:active > a:only-child::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n content: '';\n}\n.du-bois-dark-btn-two-chinese-chars::first-letter {\n letter-spacing: 0.34em;\n}\n.du-bois-dark-btn-two-chinese-chars > *:not(.anticon) {\n margin-right: -0.34em;\n letter-spacing: 0.34em;\n}\n.du-bois-dark-btn-block {\n width: 100%;\n}\n.du-bois-dark-btn:empty {\n display: inline-block;\n width: 0;\n visibility: hidden;\n content: '\\a0';\n}\na.du-bois-dark-btn {\n padding-top: 0.01px !important;\n line-height: 38px;\n}\na.du-bois-dark-btn-lg {\n line-height: 38px;\n}\na.du-bois-dark-btn-sm {\n line-height: 30px;\n}\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-dark-alert {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #E8ECF0;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: tnum;\n position: relative;\n display: flex;\n align-items: center;\n padding: 8px 15px;\n word-wrap: break-word;\n border-radius: 4px;\n}\n.du-bois-dark-alert-content {\n flex: 1;\n min-width: 0;\n}\n.du-bois-dark-alert-icon {\n margin-right: 8px;\n}\n.du-bois-dark-alert-description {\n display: none;\n font-size: 13px;\n line-height: 21px;\n}\n.du-bois-dark-alert-success {\n background-color: #162312;\n border: 1px solid #274916;\n}\n.du-bois-dark-alert-success .du-bois-dark-alert-icon {\n color: #34824F;\n}\n.du-bois-dark-alert-info {\n background-color: transparent;\n border: 1px solid #C0CDD8;\n}\n.du-bois-dark-alert-info .du-bois-dark-alert-icon {\n color: #92A4B3;\n}\n.du-bois-dark-alert-warning {\n background-color: transparent;\n border: 1px solid #F8D4A5;\n}\n.du-bois-dark-alert-warning .du-bois-dark-alert-icon {\n color: #DE7921;\n}\n.du-bois-dark-alert-error {\n background-color: transparent;\n border: 1px solid #FBD0D8;\n}\n.du-bois-dark-alert-error .du-bois-dark-alert-icon {\n color: #E65B77;\n}\n.du-bois-dark-alert-error .du-bois-dark-alert-description > pre {\n margin: 0;\n padding: 0;\n}\n.du-bois-dark-alert-action {\n margin-left: 8px;\n}\n.du-bois-dark-alert-close-icon {\n margin-left: 8px;\n padding: 0;\n overflow: hidden;\n font-size: 12px;\n line-height: 12px;\n background-color: transparent;\n border: none;\n outline: none;\n cursor: pointer;\n}\n.du-bois-dark-alert-close-icon .anticon-close {\n color: #92A4B3;\n transition: color 0.3s;\n}\n.du-bois-dark-alert-close-icon .anticon-close:hover {\n color: rgba(255, 255, 255, 0.75);\n}\n.du-bois-dark-alert-close-text {\n color: #92A4B3;\n transition: color 0.3s;\n}\n.du-bois-dark-alert-close-text:hover {\n color: rgba(255, 255, 255, 0.75);\n}\n.du-bois-dark-alert-with-description {\n align-items: flex-start;\n padding: 7px 15px 7px 24px;\n}\n.du-bois-dark-alert-with-description.du-bois-dark-alert-no-icon {\n padding: 7px 15px;\n}\n.du-bois-dark-alert-with-description .du-bois-dark-alert-icon {\n margin-right: 7px;\n font-size: 24px;\n}\n.du-bois-dark-alert-with-description .du-bois-dark-alert-message {\n display: block;\n margin-bottom: 4px;\n color: inherit;\n font-size: 18px;\n}\n.du-bois-dark-alert-message {\n color: inherit;\n}\n.du-bois-dark-alert-with-description .du-bois-dark-alert-description {\n display: block;\n}\n.du-bois-dark-alert.du-bois-dark-alert-motion-leave {\n overflow: hidden;\n opacity: 1;\n transition: max-height 0.3s cubic-bezier(0.78, 0.14, 0.15, 0.86), opacity 0.3s cubic-bezier(0.78, 0.14, 0.15, 0.86), padding-top 0.3s cubic-bezier(0.78, 0.14, 0.15, 0.86), padding-bottom 0.3s cubic-bezier(0.78, 0.14, 0.15, 0.86), margin-bottom 0.3s cubic-bezier(0.78, 0.14, 0.15, 0.86);\n}\n.du-bois-dark-alert.du-bois-dark-alert-motion-leave-active {\n max-height: 0;\n margin-bottom: 0 !important;\n padding-top: 0;\n padding-bottom: 0;\n opacity: 0;\n}\n.du-bois-dark-alert-banner {\n margin-bottom: 0;\n border: 0;\n border-radius: 0;\n}\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n@keyframes antCheckboxEffect {\n 0% {\n transform: scale(1);\n opacity: 0.5;\n }\n 100% {\n transform: scale(1.6);\n opacity: 0;\n }\n}\n.du-bois-dark-checkbox {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #E8ECF0;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: tnum;\n position: relative;\n top: 0.2em;\n line-height: 1;\n white-space: nowrap;\n outline: none;\n cursor: pointer;\n}\n.du-bois-dark-checkbox-wrapper:hover .du-bois-dark-checkbox-inner,\n.du-bois-dark-checkbox:hover .du-bois-dark-checkbox-inner,\n.du-bois-dark-checkbox-input:focus + .du-bois-dark-checkbox-inner {\n border-color: #2272B4;\n}\n.du-bois-dark-checkbox-checked::after {\n position: absolute;\n top: 0;\n left: 0;\n width: 100%;\n height: 100%;\n border: 1px solid #2272B4;\n border-radius: 4px;\n visibility: hidden;\n animation: antCheckboxEffect 0.36s ease-in-out;\n animation-fill-mode: backwards;\n content: '';\n}\n.du-bois-dark-checkbox:hover::after,\n.du-bois-dark-checkbox-wrapper:hover .du-bois-dark-checkbox::after {\n visibility: visible;\n}\n.du-bois-dark-checkbox-inner {\n position: relative;\n top: 0;\n left: 0;\n display: block;\n width: 16px;\n height: 16px;\n direction: ltr;\n background-color: transparent;\n border: 1px solid #1F272D;\n border-radius: 4px;\n border-collapse: separate;\n transition: all 0.3s;\n}\n.du-bois-dark-checkbox-inner::after {\n position: absolute;\n top: 50%;\n left: 22%;\n display: table;\n width: 5.71428571px;\n height: 9.14285714px;\n border: 2px solid #fff;\n border-top: 0;\n border-left: 0;\n transform: rotate(45deg) scale(0) translate(-50%, -50%);\n opacity: 0;\n transition: all 0.1s cubic-bezier(0.71, -0.46, 0.88, 0.6), opacity 0.1s;\n content: ' ';\n}\n.du-bois-dark-checkbox-input {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n z-index: 1;\n width: 100%;\n height: 100%;\n cursor: pointer;\n opacity: 0;\n}\n.du-bois-dark-checkbox-checked .du-bois-dark-checkbox-inner::after {\n position: absolute;\n display: table;\n border: 2px solid #fff;\n border-top: 0;\n border-left: 0;\n transform: rotate(45deg) scale(1) translate(-50%, -50%);\n opacity: 1;\n transition: all 0.2s cubic-bezier(0.12, 0.4, 0.29, 1.46) 0.1s;\n content: ' ';\n}\n.du-bois-dark-checkbox-checked .du-bois-dark-checkbox-inner {\n background-color: #2272B4;\n border-color: #2272B4;\n}\n.du-bois-dark-checkbox-disabled {\n cursor: not-allowed;\n}\n.du-bois-dark-checkbox-disabled.du-bois-dark-checkbox-checked .du-bois-dark-checkbox-inner::after {\n border-color: rgba(255, 255, 255, 0.3);\n animation-name: none;\n}\n.du-bois-dark-checkbox-disabled .du-bois-dark-checkbox-input {\n cursor: not-allowed;\n}\n.du-bois-dark-checkbox-disabled .du-bois-dark-checkbox-inner {\n background-color: rgba(255, 255, 255, 0.08);\n border-color: #1F272D !important;\n}\n.du-bois-dark-checkbox-disabled .du-bois-dark-checkbox-inner::after {\n border-color: rgba(255, 255, 255, 0.08);\n border-collapse: separate;\n animation-name: none;\n}\n.du-bois-dark-checkbox-disabled + span {\n color: rgba(255, 255, 255, 0.3);\n cursor: not-allowed;\n}\n.du-bois-dark-checkbox-disabled:hover::after,\n.du-bois-dark-checkbox-wrapper:hover .du-bois-dark-checkbox-disabled::after {\n visibility: hidden;\n}\n.du-bois-dark-checkbox-wrapper {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #E8ECF0;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: tnum;\n display: inline-flex;\n align-items: baseline;\n line-height: unset;\n cursor: pointer;\n}\n.du-bois-dark-checkbox-wrapper::after {\n display: inline-block;\n width: 0;\n overflow: hidden;\n content: '\\a0';\n}\n.du-bois-dark-checkbox-wrapper.du-bois-dark-checkbox-wrapper-disabled {\n cursor: not-allowed;\n}\n.du-bois-dark-checkbox-wrapper + .du-bois-dark-checkbox-wrapper {\n margin-left: 8px;\n}\n.du-bois-dark-checkbox + span {\n padding-right: 8px;\n padding-left: 8px;\n}\n.du-bois-dark-checkbox-group {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #E8ECF0;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: tnum;\n display: inline-block;\n}\n.du-bois-dark-checkbox-group-item {\n margin-right: 8px;\n}\n.du-bois-dark-checkbox-group-item:last-child {\n margin-right: 0;\n}\n.du-bois-dark-checkbox-group-item + .du-bois-dark-checkbox-group-item {\n margin-left: 0;\n}\n.du-bois-dark-checkbox-indeterminate .du-bois-dark-checkbox-inner {\n background-color: transparent;\n border-color: #1F272D;\n}\n.du-bois-dark-checkbox-indeterminate .du-bois-dark-checkbox-inner::after {\n top: 50%;\n left: 50%;\n width: 8px;\n height: 8px;\n background-color: #2272B4;\n border: 0;\n transform: translate(-50%, -50%) scale(1);\n opacity: 1;\n content: ' ';\n}\n.du-bois-dark-checkbox-indeterminate.du-bois-dark-checkbox-disabled .du-bois-dark-checkbox-inner::after {\n background-color: rgba(255, 255, 255, 0.3);\n border-color: rgba(255, 255, 255, 0.3);\n}\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-dark-select-auto-complete {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #E8ECF0;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: tnum;\n}\n.du-bois-dark-select-auto-complete .du-bois-dark-select-clear {\n right: 13px;\n}\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-dark-select-single .du-bois-dark-select-selector {\n display: flex;\n}\n.du-bois-dark-select-single .du-bois-dark-select-selector .du-bois-dark-select-selection-search {\n position: absolute;\n top: 0;\n right: 12px;\n bottom: 0;\n left: 12px;\n}\n.du-bois-dark-select-single .du-bois-dark-select-selector .du-bois-dark-select-selection-search-input {\n width: 100%;\n}\n.du-bois-dark-select-single .du-bois-dark-select-selector .du-bois-dark-select-selection-item,\n.du-bois-dark-select-single .du-bois-dark-select-selector .du-bois-dark-select-selection-placeholder {\n padding: 0;\n line-height: 38px;\n transition: all 0.3s;\n}\n@supports (-moz-appearance: meterbar) {\n .du-bois-dark-select-single .du-bois-dark-select-selector .du-bois-dark-select-selection-item,\n .du-bois-dark-select-single .du-bois-dark-select-selector .du-bois-dark-select-selection-placeholder {\n line-height: 38px;\n }\n}\n.du-bois-dark-select-single .du-bois-dark-select-selector .du-bois-dark-select-selection-item {\n position: relative;\n user-select: none;\n}\n.du-bois-dark-select-single .du-bois-dark-select-selector .du-bois-dark-select-selection-placeholder {\n pointer-events: none;\n}\n.du-bois-dark-select-single .du-bois-dark-select-selector::after,\n.du-bois-dark-select-single .du-bois-dark-select-selector .du-bois-dark-select-selection-item::after,\n.du-bois-dark-select-single .du-bois-dark-select-selector .du-bois-dark-select-selection-placeholder::after {\n display: inline-block;\n width: 0;\n visibility: hidden;\n content: '\\a0';\n}\n.du-bois-dark-select-single.du-bois-dark-select-show-arrow .du-bois-dark-select-selection-search {\n right: 25px;\n}\n.du-bois-dark-select-single.du-bois-dark-select-show-arrow .du-bois-dark-select-selection-item,\n.du-bois-dark-select-single.du-bois-dark-select-show-arrow .du-bois-dark-select-selection-placeholder {\n padding-right: 17px;\n}\n.du-bois-dark-select-single.du-bois-dark-select-open .du-bois-dark-select-selection-item {\n color: rgba(255, 255, 255, 0.3);\n}\n.du-bois-dark-select-single:not(.du-bois-dark-select-customize-input) .du-bois-dark-select-selector {\n width: 100%;\n height: 40px;\n padding: 0 12px;\n}\n.du-bois-dark-select-single:not(.du-bois-dark-select-customize-input) .du-bois-dark-select-selector .du-bois-dark-select-selection-search-input {\n height: 38px;\n}\n.du-bois-dark-select-single:not(.du-bois-dark-select-customize-input) .du-bois-dark-select-selector::after {\n line-height: 38px;\n}\n.du-bois-dark-select-single.du-bois-dark-select-customize-input .du-bois-dark-select-selector::after {\n display: none;\n}\n.du-bois-dark-select-single.du-bois-dark-select-customize-input .du-bois-dark-select-selector .du-bois-dark-select-selection-search {\n position: static;\n width: 100%;\n}\n.du-bois-dark-select-single.du-bois-dark-select-customize-input .du-bois-dark-select-selector .du-bois-dark-select-selection-placeholder {\n position: absolute;\n right: 0;\n left: 0;\n padding: 0 12px;\n}\n.du-bois-dark-select-single.du-bois-dark-select-customize-input .du-bois-dark-select-selector .du-bois-dark-select-selection-placeholder::after {\n display: none;\n}\n.du-bois-dark-select-single.du-bois-dark-select-lg:not(.du-bois-dark-select-customize-input) .du-bois-dark-select-selector {\n height: 40px;\n}\n.du-bois-dark-select-single.du-bois-dark-select-lg:not(.du-bois-dark-select-customize-input) .du-bois-dark-select-selector::after,\n.du-bois-dark-select-single.du-bois-dark-select-lg:not(.du-bois-dark-select-customize-input) .du-bois-dark-select-selector .du-bois-dark-select-selection-item,\n.du-bois-dark-select-single.du-bois-dark-select-lg:not(.du-bois-dark-select-customize-input) .du-bois-dark-select-selector .du-bois-dark-select-selection-placeholder {\n line-height: 38px;\n}\n.du-bois-dark-select-single.du-bois-dark-select-lg:not(.du-bois-dark-select-customize-input):not(.du-bois-dark-select-customize-input) .du-bois-dark-select-selection-search-input {\n height: 38px;\n}\n.du-bois-dark-select-single.du-bois-dark-select-sm:not(.du-bois-dark-select-customize-input) .du-bois-dark-select-selector {\n height: 32px;\n}\n.du-bois-dark-select-single.du-bois-dark-select-sm:not(.du-bois-dark-select-customize-input) .du-bois-dark-select-selector::after,\n.du-bois-dark-select-single.du-bois-dark-select-sm:not(.du-bois-dark-select-customize-input) .du-bois-dark-select-selector .du-bois-dark-select-selection-item,\n.du-bois-dark-select-single.du-bois-dark-select-sm:not(.du-bois-dark-select-customize-input) .du-bois-dark-select-selector .du-bois-dark-select-selection-placeholder {\n line-height: 30px;\n}\n.du-bois-dark-select-single.du-bois-dark-select-sm:not(.du-bois-dark-select-customize-input):not(.du-bois-dark-select-customize-input) .du-bois-dark-select-selection-search-input {\n height: 30px;\n}\n.du-bois-dark-select-single.du-bois-dark-select-sm:not(.du-bois-dark-select-customize-input) .du-bois-dark-select-selection-search {\n right: 12px;\n left: 12px;\n}\n.du-bois-dark-select-single.du-bois-dark-select-sm:not(.du-bois-dark-select-customize-input) .du-bois-dark-select-selector {\n padding: 0 12px;\n}\n.du-bois-dark-select-single.du-bois-dark-select-sm:not(.du-bois-dark-select-customize-input).du-bois-dark-select-show-arrow .du-bois-dark-select-selection-search {\n right: 31.5px;\n}\n.du-bois-dark-select-single.du-bois-dark-select-sm:not(.du-bois-dark-select-customize-input).du-bois-dark-select-show-arrow .du-bois-dark-select-selection-item,\n.du-bois-dark-select-single.du-bois-dark-select-sm:not(.du-bois-dark-select-customize-input).du-bois-dark-select-show-arrow .du-bois-dark-select-selection-placeholder {\n padding-right: 19.5px;\n}\n.du-bois-dark-select-single.du-bois-dark-select-lg:not(.du-bois-dark-select-customize-input) .du-bois-dark-select-selector {\n padding: 0 12px;\n}\n/**\n * Do not merge `height` & `line-height` under style with `selection` & `search`,\n * since chrome may update to redesign with its align logic.\n */\n.du-bois-dark-select-selection-overflow {\n position: relative;\n display: flex;\n flex: auto;\n flex-wrap: wrap;\n max-width: 100%;\n}\n.du-bois-dark-select-selection-overflow-item {\n flex: none;\n align-self: center;\n max-width: 100%;\n}\n.du-bois-dark-select-multiple .du-bois-dark-select-selector {\n display: flex;\n flex-wrap: wrap;\n align-items: center;\n padding: 1px 5px;\n}\n.du-bois-dark-select-show-search.du-bois-dark-select-multiple .du-bois-dark-select-selector {\n cursor: text;\n}\n.du-bois-dark-select-disabled.du-bois-dark-select-multiple .du-bois-dark-select-selector {\n background: #141414;\n cursor: not-allowed;\n}\n.du-bois-dark-select-multiple .du-bois-dark-select-selector::after {\n display: inline-block;\n width: 0;\n margin: 3px 0;\n line-height: 30px;\n content: '\\a0';\n}\n.du-bois-dark-select-multiple.du-bois-dark-select-show-arrow .du-bois-dark-select-selector,\n.du-bois-dark-select-multiple.du-bois-dark-select-allow-clear .du-bois-dark-select-selector {\n padding-right: 20px;\n}\n.du-bois-dark-select-multiple .du-bois-dark-select-selection-item {\n position: relative;\n display: flex;\n flex: none;\n box-sizing: border-box;\n max-width: 100%;\n height: 30px;\n margin-top: 3px;\n margin-bottom: 3px;\n line-height: 28px;\n background: rgba(255, 255, 255, 0.08);\n border: 1px solid #303030;\n border-radius: 4px;\n cursor: default;\n transition: font-size 0.3s, line-height 0.3s, height 0.3s;\n user-select: none;\n margin-inline-end: 5px;\n padding-inline-start: 4px;\n padding-inline-end: 2px;\n}\n.du-bois-dark-select-disabled.du-bois-dark-select-multiple .du-bois-dark-select-selection-item {\n color: #595959;\n border-color: #1f1f1f;\n cursor: not-allowed;\n}\n.du-bois-dark-select-multiple .du-bois-dark-select-selection-item-content {\n display: inline-block;\n margin-right: 2px;\n overflow: hidden;\n white-space: pre;\n text-overflow: ellipsis;\n}\n.du-bois-dark-select-multiple .du-bois-dark-select-selection-item-remove {\n color: inherit;\n font-style: normal;\n line-height: 0;\n text-align: center;\n text-transform: none;\n vertical-align: -0.125em;\n text-rendering: optimizeLegibility;\n -webkit-font-smoothing: antialiased;\n -moz-osx-font-smoothing: grayscale;\n display: inline-block;\n color: #92A4B3;\n font-weight: bold;\n font-size: 10px;\n line-height: inherit;\n cursor: pointer;\n}\n.du-bois-dark-select-multiple .du-bois-dark-select-selection-item-remove > * {\n line-height: 1;\n}\n.du-bois-dark-select-multiple .du-bois-dark-select-selection-item-remove svg {\n display: inline-block;\n}\n.du-bois-dark-select-multiple .du-bois-dark-select-selection-item-remove::before {\n display: none;\n}\n.du-bois-dark-select-multiple .du-bois-dark-select-selection-item-remove .du-bois-dark-select-multiple .du-bois-dark-select-selection-item-remove-icon {\n display: block;\n}\n.du-bois-dark-select-multiple .du-bois-dark-select-selection-item-remove > .anticon {\n vertical-align: -0.2em;\n}\n.du-bois-dark-select-multiple .du-bois-dark-select-selection-item-remove:hover {\n color: rgba(255, 255, 255, 0.75);\n}\n.du-bois-dark-select-multiple .du-bois-dark-select-selection-overflow-item + .du-bois-dark-select-selection-overflow-item .du-bois-dark-select-selection-search {\n margin-inline-start: 0;\n}\n.du-bois-dark-select-multiple .du-bois-dark-select-selection-search {\n position: relative;\n max-width: 100%;\n margin-top: 3px;\n margin-bottom: 3px;\n margin-inline-start: 7px;\n}\n.du-bois-dark-select-multiple .du-bois-dark-select-selection-search-input,\n.du-bois-dark-select-multiple .du-bois-dark-select-selection-search-mirror {\n height: 30px;\n font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, 'Helvetica Neue', Arial, 'Noto Sans', sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol', 'Noto Color Emoji';\n line-height: 30px;\n transition: all 0.3s;\n}\n.du-bois-dark-select-multiple .du-bois-dark-select-selection-search-input {\n width: 100%;\n min-width: 4.1px;\n}\n.du-bois-dark-select-multiple .du-bois-dark-select-selection-search-mirror {\n position: absolute;\n top: 0;\n left: 0;\n z-index: 999;\n white-space: pre;\n visibility: hidden;\n}\n.du-bois-dark-select-multiple .du-bois-dark-select-selection-placeholder {\n position: absolute;\n top: 50%;\n right: 12px;\n left: 12px;\n transform: translateY(-50%);\n transition: all 0.3s;\n}\n.du-bois-dark-select-multiple.du-bois-dark-select-lg .du-bois-dark-select-selector::after {\n line-height: 30px;\n}\n.du-bois-dark-select-multiple.du-bois-dark-select-lg .du-bois-dark-select-selection-item {\n height: 30px;\n line-height: 28px;\n}\n.du-bois-dark-select-multiple.du-bois-dark-select-lg .du-bois-dark-select-selection-search {\n height: 30px;\n line-height: 30px;\n}\n.du-bois-dark-select-multiple.du-bois-dark-select-lg .du-bois-dark-select-selection-search-input,\n.du-bois-dark-select-multiple.du-bois-dark-select-lg .du-bois-dark-select-selection-search-mirror {\n height: 30px;\n line-height: 28px;\n}\n.du-bois-dark-select-multiple.du-bois-dark-select-sm .du-bois-dark-select-selector::after {\n line-height: 22px;\n}\n.du-bois-dark-select-multiple.du-bois-dark-select-sm .du-bois-dark-select-selection-item {\n height: 22px;\n line-height: 20px;\n}\n.du-bois-dark-select-multiple.du-bois-dark-select-sm .du-bois-dark-select-selection-search {\n height: 22px;\n line-height: 22px;\n}\n.du-bois-dark-select-multiple.du-bois-dark-select-sm .du-bois-dark-select-selection-search-input,\n.du-bois-dark-select-multiple.du-bois-dark-select-sm .du-bois-dark-select-selection-search-mirror {\n height: 22px;\n line-height: 20px;\n}\n.du-bois-dark-select-multiple.du-bois-dark-select-sm .du-bois-dark-select-selection-placeholder {\n left: 12px;\n}\n.du-bois-dark-select-multiple.du-bois-dark-select-sm .du-bois-dark-select-selection-search {\n margin-inline-start: 3px;\n}\n.du-bois-dark-select-multiple.du-bois-dark-select-lg .du-bois-dark-select-selection-item {\n height: 32px;\n line-height: 32px;\n}\n.du-bois-dark-select-disabled .du-bois-dark-select-selection-item-remove {\n display: none;\n}\n/* Reset search input style */\n.du-bois-dark-select {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #E8ECF0;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: tnum;\n position: relative;\n display: inline-block;\n cursor: pointer;\n}\n.du-bois-dark-select:not(.du-bois-dark-select-customize-input) .du-bois-dark-select-selector {\n position: relative;\n background-color: transparent;\n border: 1px solid #1F272D;\n border-radius: 4px;\n transition: all 0.3s cubic-bezier(0.645, 0.045, 0.355, 1);\n}\n.du-bois-dark-select:not(.du-bois-dark-select-customize-input) .du-bois-dark-select-selector input {\n cursor: pointer;\n}\n.du-bois-dark-select-show-search.du-bois-dark-select:not(.du-bois-dark-select-customize-input) .du-bois-dark-select-selector {\n cursor: text;\n}\n.du-bois-dark-select-show-search.du-bois-dark-select:not(.du-bois-dark-select-customize-input) .du-bois-dark-select-selector input {\n cursor: auto;\n}\n.du-bois-dark-select-focused:not(.du-bois-dark-select-disabled).du-bois-dark-select:not(.du-bois-dark-select-customize-input) .du-bois-dark-select-selector {\n border-color: #2272B4;\n border-right-width: 1px !important;\n outline: 0;\n box-shadow: 0 0 0 2px rgba(34, 114, 180, 0.2);\n}\n.du-bois-dark-select-disabled.du-bois-dark-select:not(.du-bois-dark-select-customize-input) .du-bois-dark-select-selector {\n color: rgba(255, 255, 255, 0.3);\n background: rgba(255, 255, 255, 0.08);\n cursor: not-allowed;\n}\n.du-bois-dark-select-multiple.du-bois-dark-select-disabled.du-bois-dark-select:not(.du-bois-dark-select-customize-input) .du-bois-dark-select-selector {\n background: #141414;\n}\n.du-bois-dark-select-disabled.du-bois-dark-select:not(.du-bois-dark-select-customize-input) .du-bois-dark-select-selector input {\n cursor: not-allowed;\n}\n.du-bois-dark-select:not(.du-bois-dark-select-customize-input) .du-bois-dark-select-selector .du-bois-dark-select-selection-search-input {\n margin: 0;\n padding: 0;\n background: transparent;\n border: none;\n outline: none;\n appearance: none;\n}\n.du-bois-dark-select:not(.du-bois-dark-select-customize-input) .du-bois-dark-select-selector .du-bois-dark-select-selection-search-input::-webkit-search-cancel-button {\n display: none;\n -webkit-appearance: none;\n}\n.du-bois-dark-select:not(.du-bois-dark-select-disabled):hover .du-bois-dark-select-selector {\n border-color: #1d517c;\n border-right-width: 1px !important;\n}\n.du-bois-dark-select-selection-item {\n flex: 1;\n overflow: hidden;\n white-space: nowrap;\n text-overflow: ellipsis;\n}\n@media all and (-ms-high-contrast: none) {\n .du-bois-dark-select-selection-item *::-ms-backdrop,\n .du-bois-dark-select-selection-item {\n flex: auto;\n }\n}\n.du-bois-dark-select-selection-placeholder {\n flex: 1;\n overflow: hidden;\n color: rgba(255, 255, 255, 0.3);\n white-space: nowrap;\n text-overflow: ellipsis;\n pointer-events: none;\n}\n@media all and (-ms-high-contrast: none) {\n .du-bois-dark-select-selection-placeholder *::-ms-backdrop,\n .du-bois-dark-select-selection-placeholder {\n flex: auto;\n }\n}\n.du-bois-dark-select-arrow {\n display: inline-block;\n color: inherit;\n font-style: normal;\n line-height: 0;\n text-transform: none;\n vertical-align: -0.125em;\n text-rendering: optimizeLegibility;\n -webkit-font-smoothing: antialiased;\n -moz-osx-font-smoothing: grayscale;\n position: absolute;\n top: 50%;\n right: 7px;\n width: 12px;\n height: 12px;\n margin-top: -6px;\n color: rgba(255, 255, 255, 0.3);\n font-size: 12px;\n line-height: 1;\n text-align: center;\n pointer-events: none;\n}\n.du-bois-dark-select-arrow > * {\n line-height: 1;\n}\n.du-bois-dark-select-arrow svg {\n display: inline-block;\n}\n.du-bois-dark-select-arrow::before {\n display: none;\n}\n.du-bois-dark-select-arrow .du-bois-dark-select-arrow-icon {\n display: block;\n}\n.du-bois-dark-select-arrow .anticon {\n vertical-align: top;\n transition: transform 0.3s;\n}\n.du-bois-dark-select-arrow .anticon > svg {\n vertical-align: top;\n}\n.du-bois-dark-select-arrow .anticon:not(.du-bois-dark-select-suffix) {\n pointer-events: auto;\n}\n.du-bois-dark-select-disabled .du-bois-dark-select-arrow {\n cursor: not-allowed;\n}\n.du-bois-dark-select-clear {\n position: absolute;\n top: 50%;\n right: 7px;\n z-index: 1;\n display: inline-block;\n width: 12px;\n height: 12px;\n margin-top: -6px;\n color: rgba(255, 255, 255, 0.3);\n font-size: 12px;\n font-style: normal;\n line-height: 1;\n text-align: center;\n text-transform: none;\n background: #141414;\n cursor: pointer;\n opacity: 0;\n transition: color 0.3s ease, opacity 0.15s ease;\n text-rendering: auto;\n}\n.du-bois-dark-select-clear::before {\n display: block;\n}\n.du-bois-dark-select-clear:hover {\n color: #92A4B3;\n}\n.du-bois-dark-select:hover .du-bois-dark-select-clear {\n opacity: 1;\n}\n.du-bois-dark-select-dropdown {\n margin: 0;\n padding: 0;\n color: #E8ECF0;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: tnum;\n position: absolute;\n top: -9999px;\n left: -9999px;\n z-index: 1050;\n box-sizing: border-box;\n padding: 4px 0;\n overflow: hidden;\n font-size: 13px;\n font-variant: initial;\n background-color: #1f1f1f;\n border-radius: 4px;\n outline: none;\n box-shadow: 0 3px 6px -4px rgba(0, 0, 0, 0.48), 0 6px 16px 0 rgba(0, 0, 0, 0.32), 0 9px 28px 8px rgba(0, 0, 0, 0.2);\n}\n.du-bois-dark-select-dropdown.du-bois-dark-slide-up-enter.du-bois-dark-slide-up-enter-active.du-bois-dark-select-dropdown-placement-bottomLeft,\n.du-bois-dark-select-dropdown.du-bois-dark-slide-up-appear.du-bois-dark-slide-up-appear-active.du-bois-dark-select-dropdown-placement-bottomLeft {\n animation-name: antSlideUpIn;\n}\n.du-bois-dark-select-dropdown.du-bois-dark-slide-up-enter.du-bois-dark-slide-up-enter-active.du-bois-dark-select-dropdown-placement-topLeft,\n.du-bois-dark-select-dropdown.du-bois-dark-slide-up-appear.du-bois-dark-slide-up-appear-active.du-bois-dark-select-dropdown-placement-topLeft {\n animation-name: antSlideDownIn;\n}\n.du-bois-dark-select-dropdown.du-bois-dark-slide-up-leave.du-bois-dark-slide-up-leave-active.du-bois-dark-select-dropdown-placement-bottomLeft {\n animation-name: antSlideUpOut;\n}\n.du-bois-dark-select-dropdown.du-bois-dark-slide-up-leave.du-bois-dark-slide-up-leave-active.du-bois-dark-select-dropdown-placement-topLeft {\n animation-name: antSlideDownOut;\n}\n.du-bois-dark-select-dropdown-hidden {\n display: none;\n}\n.du-bois-dark-select-dropdown-empty {\n color: rgba(255, 255, 255, 0.3);\n}\n.du-bois-dark-select-item-empty {\n position: relative;\n display: block;\n min-height: 32px;\n padding: 5px 8px;\n color: #E8ECF0;\n font-weight: normal;\n font-size: 13px;\n line-height: 22px;\n color: rgba(255, 255, 255, 0.3);\n}\n.du-bois-dark-select-item {\n position: relative;\n display: block;\n min-height: 32px;\n padding: 5px 8px;\n color: #E8ECF0;\n font-weight: normal;\n font-size: 13px;\n line-height: 22px;\n cursor: pointer;\n transition: background 0.3s ease;\n}\n.du-bois-dark-select-item-group {\n color: #92A4B3;\n font-size: 12px;\n cursor: default;\n}\n.du-bois-dark-select-item-option {\n display: flex;\n}\n.du-bois-dark-select-item-option-content {\n flex: auto;\n overflow: hidden;\n white-space: nowrap;\n text-overflow: ellipsis;\n}\n.du-bois-dark-select-item-option-state {\n flex: none;\n}\n.du-bois-dark-select-item-option-active:not(.du-bois-dark-select-item-option-disabled) {\n background-color: rgba(255, 255, 255, 0.08);\n}\n.du-bois-dark-select-item-option-selected:not(.du-bois-dark-select-item-option-disabled) {\n color: #E8ECF0;\n font-weight: 600;\n background-color: #121921;\n}\n.du-bois-dark-select-item-option-selected:not(.du-bois-dark-select-item-option-disabled) .du-bois-dark-select-item-option-state {\n color: #2272B4;\n}\n.du-bois-dark-select-item-option-disabled {\n color: rgba(255, 255, 255, 0.3);\n cursor: not-allowed;\n}\n.du-bois-dark-select-item-option-disabled.du-bois-dark-select-item-option-selected {\n background-color: #141414;\n}\n.du-bois-dark-select-item-option-grouped {\n padding-left: 16px;\n}\n.du-bois-dark-select-lg {\n font-size: 18px;\n}\n.du-bois-dark-select-borderless .du-bois-dark-select-selector {\n background-color: transparent !important;\n border-color: transparent !important;\n box-shadow: none !important;\n}\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-dark-empty {\n margin: 0 8px;\n font-size: 13px;\n line-height: 20px;\n text-align: center;\n}\n.du-bois-dark-empty-image {\n height: 100px;\n margin-bottom: 8px;\n}\n.du-bois-dark-empty-image img {\n height: 100%;\n}\n.du-bois-dark-empty-image svg {\n height: 100%;\n margin: auto;\n}\n.du-bois-dark-empty-footer {\n margin-top: 16px;\n}\n.du-bois-dark-empty-normal {\n margin: 32px 0;\n color: rgba(255, 255, 255, 0.3);\n}\n.du-bois-dark-empty-normal .du-bois-dark-empty-image {\n height: 40px;\n}\n.du-bois-dark-empty-small {\n margin: 8px 0;\n color: rgba(255, 255, 255, 0.3);\n}\n.du-bois-dark-empty-small .du-bois-dark-empty-image {\n height: 35px;\n}\n.du-bois-dark-empty-img-default-ellipse {\n fill: #fff;\n fill-opacity: 0.08;\n}\n.du-bois-dark-empty-img-default-path-1 {\n fill: #262626;\n}\n.du-bois-dark-empty-img-default-path-2 {\n fill: url(#linearGradient-1);\n}\n.du-bois-dark-empty-img-default-path-3 {\n fill: #595959;\n}\n.du-bois-dark-empty-img-default-path-4 {\n fill: #434343;\n}\n.du-bois-dark-empty-img-default-path-5 {\n fill: #595959;\n}\n.du-bois-dark-empty-img-default-g {\n fill: #434343;\n}\n.du-bois-dark-empty-img-simple-ellipse {\n fill: #fff;\n fill-opacity: 0.08;\n}\n.du-bois-dark-empty-img-simple-g {\n stroke: #434343;\n}\n.du-bois-dark-empty-img-simple-path {\n fill: #262626;\n stroke: #434343;\n}\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-dark-collapse {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #E8ECF0;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: tnum;\n background-color: rgba(255, 255, 255, 0.04);\n border: 1px solid #1F272D;\n border-bottom: 0;\n border-radius: 4px;\n}\n.du-bois-dark-collapse > .du-bois-dark-collapse-item {\n border-bottom: 1px solid #1F272D;\n}\n.du-bois-dark-collapse > .du-bois-dark-collapse-item:last-child,\n.du-bois-dark-collapse > .du-bois-dark-collapse-item:last-child > .du-bois-dark-collapse-header {\n border-radius: 0 0 4px 4px;\n}\n.du-bois-dark-collapse > .du-bois-dark-collapse-item > .du-bois-dark-collapse-header {\n position: relative;\n padding: 8px 8px;\n color: rgba(255, 255, 255, 0.85);\n line-height: 20px;\n cursor: pointer;\n transition: all 0.3s, visibility 0s;\n}\n.du-bois-dark-collapse > .du-bois-dark-collapse-item > .du-bois-dark-collapse-header::before {\n display: table;\n content: '';\n}\n.du-bois-dark-collapse > .du-bois-dark-collapse-item > .du-bois-dark-collapse-header::after {\n display: table;\n clear: both;\n content: '';\n}\n.du-bois-dark-collapse > .du-bois-dark-collapse-item > .du-bois-dark-collapse-header .du-bois-dark-collapse-arrow {\n display: inline-block;\n margin-right: 12px;\n font-size: 12px;\n vertical-align: -1px;\n}\n.du-bois-dark-collapse > .du-bois-dark-collapse-item > .du-bois-dark-collapse-header .du-bois-dark-collapse-arrow svg {\n transition: transform 0.24s;\n}\n.du-bois-dark-collapse > .du-bois-dark-collapse-item > .du-bois-dark-collapse-header .du-bois-dark-collapse-extra {\n float: right;\n}\n.du-bois-dark-collapse > .du-bois-dark-collapse-item > .du-bois-dark-collapse-header:focus {\n outline: none;\n}\n.du-bois-dark-collapse > .du-bois-dark-collapse-item .du-bois-dark-collapse-header-collapsible-only {\n cursor: default;\n}\n.du-bois-dark-collapse > .du-bois-dark-collapse-item .du-bois-dark-collapse-header-collapsible-only .du-bois-dark-collapse-header-text {\n cursor: pointer;\n}\n.du-bois-dark-collapse > .du-bois-dark-collapse-item.du-bois-dark-collapse-no-arrow > .du-bois-dark-collapse-header {\n padding-left: 12px;\n}\n.du-bois-dark-collapse-icon-position-right > .du-bois-dark-collapse-item > .du-bois-dark-collapse-header {\n padding: 8px 8px;\n padding-right: 40px;\n}\n.du-bois-dark-collapse-icon-position-right > .du-bois-dark-collapse-item > .du-bois-dark-collapse-header .du-bois-dark-collapse-arrow {\n position: absolute;\n top: 50%;\n right: 8px;\n left: auto;\n margin: 0;\n transform: translateY(-50%);\n}\n.du-bois-dark-collapse-content {\n color: #E8ECF0;\n background-color: #141414;\n border-top: 1px solid #1F272D;\n}\n.du-bois-dark-collapse-content > .du-bois-dark-collapse-content-box {\n padding: 8px;\n}\n.du-bois-dark-collapse-content-hidden {\n display: none;\n}\n.du-bois-dark-collapse-item:last-child > .du-bois-dark-collapse-content {\n border-radius: 0 0 4px 4px;\n}\n.du-bois-dark-collapse-borderless {\n background-color: rgba(255, 255, 255, 0.04);\n border: 0;\n}\n.du-bois-dark-collapse-borderless > .du-bois-dark-collapse-item {\n border-bottom: 1px solid #1F272D;\n}\n.du-bois-dark-collapse-borderless > .du-bois-dark-collapse-item:last-child,\n.du-bois-dark-collapse-borderless > .du-bois-dark-collapse-item:last-child .du-bois-dark-collapse-header {\n border-radius: 0;\n}\n.du-bois-dark-collapse-borderless > .du-bois-dark-collapse-item > .du-bois-dark-collapse-content {\n background-color: transparent;\n border-top: 0;\n}\n.du-bois-dark-collapse-borderless > .du-bois-dark-collapse-item > .du-bois-dark-collapse-content > .du-bois-dark-collapse-content-box {\n padding-top: 4px;\n}\n.du-bois-dark-collapse-ghost {\n background-color: transparent;\n border: 0;\n}\n.du-bois-dark-collapse-ghost > .du-bois-dark-collapse-item {\n border-bottom: 0;\n}\n.du-bois-dark-collapse-ghost > .du-bois-dark-collapse-item > .du-bois-dark-collapse-content {\n background-color: transparent;\n border-top: 0;\n}\n.du-bois-dark-collapse-ghost > .du-bois-dark-collapse-item > .du-bois-dark-collapse-content > .du-bois-dark-collapse-content-box {\n padding-top: 12px;\n padding-bottom: 12px;\n}\n.du-bois-dark-collapse .du-bois-dark-collapse-item-disabled > .du-bois-dark-collapse-header,\n.du-bois-dark-collapse .du-bois-dark-collapse-item-disabled > .du-bois-dark-collapse-header > .arrow {\n color: rgba(255, 255, 255, 0.3);\n cursor: not-allowed;\n}\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-dark-picker {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #E8ECF0;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: tnum;\n padding: 0 12px 0;\n position: relative;\n display: inline-flex;\n align-items: center;\n background: transparent;\n border: 1px solid #1F272D;\n border-radius: 4px;\n transition: border 0.3s, box-shadow 0.3s;\n}\n.du-bois-dark-picker:hover,\n.du-bois-dark-picker-focused {\n border-color: #1d517c;\n border-right-width: 1px !important;\n}\n.du-bois-dark-picker-focused {\n border-color: #2272B4;\n border-right-width: 1px !important;\n outline: 0;\n box-shadow: 0 0 0 2px rgba(34, 114, 180, 0.2);\n}\n.du-bois-dark-picker.du-bois-dark-picker-disabled {\n background: rgba(255, 255, 255, 0.08);\n border-color: #1F272D;\n cursor: not-allowed;\n}\n.du-bois-dark-picker.du-bois-dark-picker-disabled .du-bois-dark-picker-suffix {\n color: rgba(255, 255, 255, 0.3);\n}\n.du-bois-dark-picker.du-bois-dark-picker-borderless {\n background-color: transparent !important;\n border-color: transparent !important;\n box-shadow: none !important;\n}\n.du-bois-dark-picker-input {\n position: relative;\n display: inline-flex;\n align-items: center;\n width: 100%;\n}\n.du-bois-dark-picker-input > input {\n position: relative;\n display: inline-block;\n width: 100%;\n min-width: 0;\n padding: 5px 12px;\n color: #E8ECF0;\n font-size: 13px;\n line-height: 20px;\n background-color: transparent;\n background-image: none;\n border: 1px solid #1F272D;\n border-radius: 4px;\n transition: all 0.3s;\n flex: auto;\n min-width: 1px;\n height: auto;\n padding: 0;\n background: transparent;\n border: 0;\n}\n.du-bois-dark-picker-input > input::-moz-placeholder {\n opacity: 1;\n}\n.du-bois-dark-picker-input > input::placeholder {\n color: rgba(255, 255, 255, 0.3);\n}\n.du-bois-dark-picker-input > input:placeholder-shown {\n text-overflow: ellipsis;\n}\n.du-bois-dark-picker-input > input:hover {\n border-color: #1d517c;\n border-right-width: 1px !important;\n}\n.du-bois-dark-picker-input > input:focus,\n.du-bois-dark-picker-input > input-focused {\n border-color: #2272B4;\n border-right-width: 1px !important;\n outline: 0;\n box-shadow: 0 0 0 2px rgba(34, 114, 180, 0.2);\n}\n.du-bois-dark-picker-input > input-disabled {\n color: rgba(255, 255, 255, 0.3);\n background-color: rgba(255, 255, 255, 0.08);\n cursor: not-allowed;\n opacity: 1;\n}\n.du-bois-dark-picker-input > input-disabled:hover {\n border-color: #1F272D;\n border-right-width: 1px !important;\n}\n.du-bois-dark-picker-input > input[disabled] {\n color: rgba(255, 255, 255, 0.3);\n background-color: rgba(255, 255, 255, 0.08);\n cursor: not-allowed;\n opacity: 1;\n}\n.du-bois-dark-picker-input > input[disabled]:hover {\n border-color: #1F272D;\n border-right-width: 1px !important;\n}\n.du-bois-dark-picker-input > input-borderless,\n.du-bois-dark-picker-input > input-borderless:hover,\n.du-bois-dark-picker-input > input-borderless:focus,\n.du-bois-dark-picker-input > input-borderless-focused,\n.du-bois-dark-picker-input > input-borderless-disabled,\n.du-bois-dark-picker-input > input-borderless[disabled] {\n background-color: transparent;\n border: none;\n box-shadow: none;\n}\ntextarea.du-bois-dark-picker-input > input {\n max-width: 100%;\n height: auto;\n min-height: 40px;\n line-height: 20px;\n vertical-align: bottom;\n transition: all 0.3s, height 0s;\n}\n.du-bois-dark-picker-input > input-lg {\n padding: 5px 12px;\n font-size: 18px;\n}\n.du-bois-dark-picker-input > input-sm {\n padding: 5px 12px;\n}\n.du-bois-dark-picker-input > input:focus {\n box-shadow: none;\n}\n.du-bois-dark-picker-input > input[disabled] {\n background: transparent;\n}\n.du-bois-dark-picker-input:hover .du-bois-dark-picker-clear {\n opacity: 1;\n}\n.du-bois-dark-picker-input-placeholder > input {\n color: rgba(255, 255, 255, 0.3);\n}\n.du-bois-dark-picker-large {\n padding: 0 12px 0;\n}\n.du-bois-dark-picker-large .du-bois-dark-picker-input > input {\n font-size: 18px;\n}\n.du-bois-dark-picker-small {\n padding: 0 12px 0;\n}\n.du-bois-dark-picker-suffix {\n align-self: center;\n margin-left: 2px;\n color: rgba(255, 255, 255, 0.3);\n line-height: 1;\n pointer-events: none;\n}\n.du-bois-dark-picker-suffix > * {\n vertical-align: top;\n}\n.du-bois-dark-picker-clear {\n position: absolute;\n top: 50%;\n right: 0;\n color: rgba(255, 255, 255, 0.3);\n line-height: 1;\n background: #141414;\n transform: translateY(-50%);\n cursor: pointer;\n opacity: 0;\n transition: opacity 0.3s, color 0.3s;\n}\n.du-bois-dark-picker-clear > * {\n vertical-align: top;\n}\n.du-bois-dark-picker-clear:hover {\n color: #92A4B3;\n}\n.du-bois-dark-picker-separator {\n position: relative;\n display: inline-block;\n width: 1em;\n height: 18px;\n color: rgba(255, 255, 255, 0.3);\n font-size: 18px;\n vertical-align: top;\n cursor: default;\n}\n.du-bois-dark-picker-focused .du-bois-dark-picker-separator {\n color: #92A4B3;\n}\n.du-bois-dark-picker-disabled .du-bois-dark-picker-range-separator .du-bois-dark-picker-separator {\n cursor: not-allowed;\n}\n.du-bois-dark-picker-range {\n position: relative;\n display: inline-flex;\n}\n.du-bois-dark-picker-range .du-bois-dark-picker-clear {\n right: 12px;\n}\n.du-bois-dark-picker-range:hover .du-bois-dark-picker-clear {\n opacity: 1;\n}\n.du-bois-dark-picker-range .du-bois-dark-picker-active-bar {\n bottom: -1px;\n height: 2px;\n margin-left: 12px;\n background: #2272B4;\n opacity: 0;\n transition: all 0.3s ease-out;\n pointer-events: none;\n}\n.du-bois-dark-picker-range.du-bois-dark-picker-focused .du-bois-dark-picker-active-bar {\n opacity: 1;\n}\n.du-bois-dark-picker-range-separator {\n align-items: center;\n padding: 0 4px;\n line-height: 1;\n}\n.du-bois-dark-picker-range.du-bois-dark-picker-small .du-bois-dark-picker-clear {\n right: 12px;\n}\n.du-bois-dark-picker-range.du-bois-dark-picker-small .du-bois-dark-picker-active-bar {\n margin-left: 12px;\n}\n.du-bois-dark-picker-dropdown {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #E8ECF0;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: tnum;\n position: absolute;\n z-index: 1050;\n}\n.du-bois-dark-picker-dropdown-hidden {\n display: none;\n}\n.du-bois-dark-picker-dropdown-placement-bottomLeft .du-bois-dark-picker-range-arrow {\n top: 1.66666667px;\n display: block;\n transform: rotate(-45deg);\n}\n.du-bois-dark-picker-dropdown-placement-topLeft .du-bois-dark-picker-range-arrow {\n bottom: 1.66666667px;\n display: block;\n transform: rotate(135deg);\n}\n.du-bois-dark-picker-dropdown.du-bois-dark-slide-up-enter.du-bois-dark-slide-up-enter-active.du-bois-dark-picker-dropdown-placement-topLeft,\n.du-bois-dark-picker-dropdown.du-bois-dark-slide-up-enter.du-bois-dark-slide-up-enter-active.du-bois-dark-picker-dropdown-placement-topRight,\n.du-bois-dark-picker-dropdown.du-bois-dark-slide-up-appear.du-bois-dark-slide-up-appear-active.du-bois-dark-picker-dropdown-placement-topLeft,\n.du-bois-dark-picker-dropdown.du-bois-dark-slide-up-appear.du-bois-dark-slide-up-appear-active.du-bois-dark-picker-dropdown-placement-topRight {\n animation-name: antSlideDownIn;\n}\n.du-bois-dark-picker-dropdown.du-bois-dark-slide-up-enter.du-bois-dark-slide-up-enter-active.du-bois-dark-picker-dropdown-placement-bottomLeft,\n.du-bois-dark-picker-dropdown.du-bois-dark-slide-up-enter.du-bois-dark-slide-up-enter-active.du-bois-dark-picker-dropdown-placement-bottomRight,\n.du-bois-dark-picker-dropdown.du-bois-dark-slide-up-appear.du-bois-dark-slide-up-appear-active.du-bois-dark-picker-dropdown-placement-bottomLeft,\n.du-bois-dark-picker-dropdown.du-bois-dark-slide-up-appear.du-bois-dark-slide-up-appear-active.du-bois-dark-picker-dropdown-placement-bottomRight {\n animation-name: antSlideUpIn;\n}\n.du-bois-dark-picker-dropdown.du-bois-dark-slide-up-leave.du-bois-dark-slide-up-leave-active.du-bois-dark-picker-dropdown-placement-topLeft,\n.du-bois-dark-picker-dropdown.du-bois-dark-slide-up-leave.du-bois-dark-slide-up-leave-active.du-bois-dark-picker-dropdown-placement-topRight {\n animation-name: antSlideDownOut;\n}\n.du-bois-dark-picker-dropdown.du-bois-dark-slide-up-leave.du-bois-dark-slide-up-leave-active.du-bois-dark-picker-dropdown-placement-bottomLeft,\n.du-bois-dark-picker-dropdown.du-bois-dark-slide-up-leave.du-bois-dark-slide-up-leave-active.du-bois-dark-picker-dropdown-placement-bottomRight {\n animation-name: antSlideUpOut;\n}\n.du-bois-dark-picker-dropdown-range {\n padding: 6.66666667px 0;\n}\n.du-bois-dark-picker-dropdown-range-hidden {\n display: none;\n}\n.du-bois-dark-picker-dropdown .du-bois-dark-picker-panel > .du-bois-dark-picker-time-panel {\n padding-top: 2px;\n}\n.du-bois-dark-picker-ranges {\n margin-bottom: 0;\n padding: 2px 8px;\n overflow: hidden;\n line-height: 36px;\n text-align: left;\n list-style: none;\n}\n.du-bois-dark-picker-ranges > li {\n display: inline-block;\n}\n.du-bois-dark-picker-ranges .du-bois-dark-picker-preset > .du-bois-dark-tag-blue {\n color: #2272B4;\n background: #121921;\n border-color: #183044;\n cursor: pointer;\n}\n.du-bois-dark-picker-ranges .du-bois-dark-picker-ok {\n float: right;\n margin-left: 4px;\n}\n.du-bois-dark-picker-range-wrapper {\n display: flex;\n}\n.du-bois-dark-picker-range-arrow {\n position: absolute;\n z-index: 1;\n display: none;\n width: 10px;\n height: 10px;\n margin-left: 18px;\n box-shadow: 2px -2px 6px rgba(0, 0, 0, 0.06);\n transition: left 0.3s ease-out;\n}\n.du-bois-dark-picker-range-arrow::after {\n position: absolute;\n top: 1px;\n right: 1px;\n width: 10px;\n height: 10px;\n border: 5px solid #303030;\n border-color: #1f1f1f #1f1f1f transparent transparent;\n content: '';\n}\n.du-bois-dark-picker-panel-container {\n overflow: hidden;\n vertical-align: top;\n background: #1f1f1f;\n border-radius: 4px;\n box-shadow: 0 3px 6px -4px rgba(0, 0, 0, 0.48), 0 6px 16px 0 rgba(0, 0, 0, 0.32), 0 9px 28px 8px rgba(0, 0, 0, 0.2);\n transition: margin 0.3s;\n}\n.du-bois-dark-picker-panel-container .du-bois-dark-picker-panels {\n display: inline-flex;\n flex-wrap: nowrap;\n direction: ltr;\n}\n.du-bois-dark-picker-panel-container .du-bois-dark-picker-panel {\n vertical-align: top;\n background: transparent;\n border-width: 0 0 1px 0;\n border-radius: 0;\n}\n.du-bois-dark-picker-panel-container .du-bois-dark-picker-panel .du-bois-dark-picker-content,\n.du-bois-dark-picker-panel-container .du-bois-dark-picker-panel table {\n text-align: center;\n}\n.du-bois-dark-picker-panel-container .du-bois-dark-picker-panel-focused {\n border-color: #303030;\n}\n.du-bois-dark-picker-panel {\n display: inline-flex;\n flex-direction: column;\n text-align: center;\n background: #1f1f1f;\n border: 1px solid #303030;\n border-radius: 4px;\n outline: none;\n}\n.du-bois-dark-picker-panel-focused {\n border-color: #2272B4;\n}\n.du-bois-dark-picker-decade-panel,\n.du-bois-dark-picker-year-panel,\n.du-bois-dark-picker-quarter-panel,\n.du-bois-dark-picker-month-panel,\n.du-bois-dark-picker-week-panel,\n.du-bois-dark-picker-date-panel,\n.du-bois-dark-picker-time-panel {\n display: flex;\n flex-direction: column;\n width: 272px;\n}\n.du-bois-dark-picker-header {\n display: flex;\n padding: 0 4px;\n color: rgba(255, 255, 255, 0.85);\n border-bottom: 1px solid #303030;\n}\n.du-bois-dark-picker-header > * {\n flex: none;\n}\n.du-bois-dark-picker-header button {\n padding: 0;\n color: rgba(255, 255, 255, 0.3);\n line-height: 40px;\n background: transparent;\n border: 0;\n cursor: pointer;\n transition: color 0.3s;\n}\n.du-bois-dark-picker-header > button {\n min-width: 1.6em;\n font-size: 13px;\n}\n.du-bois-dark-picker-header > button:hover {\n color: #E8ECF0;\n}\n.du-bois-dark-picker-header-view {\n flex: auto;\n font-weight: 500;\n line-height: 40px;\n}\n.du-bois-dark-picker-header-view button {\n color: inherit;\n font-weight: inherit;\n}\n.du-bois-dark-picker-header-view button:not(:first-child) {\n margin-left: 4px;\n}\n.du-bois-dark-picker-header-view button:hover {\n color: #2272B4;\n}\n.du-bois-dark-picker-prev-icon,\n.du-bois-dark-picker-next-icon,\n.du-bois-dark-picker-super-prev-icon,\n.du-bois-dark-picker-super-next-icon {\n position: relative;\n display: inline-block;\n width: 7px;\n height: 7px;\n}\n.du-bois-dark-picker-prev-icon::before,\n.du-bois-dark-picker-next-icon::before,\n.du-bois-dark-picker-super-prev-icon::before,\n.du-bois-dark-picker-super-next-icon::before {\n position: absolute;\n top: 0;\n left: 0;\n display: inline-block;\n width: 7px;\n height: 7px;\n border: 0 solid currentColor;\n border-width: 1.5px 0 0 1.5px;\n content: '';\n}\n.du-bois-dark-picker-super-prev-icon::after,\n.du-bois-dark-picker-super-next-icon::after {\n position: absolute;\n top: 4px;\n left: 4px;\n display: inline-block;\n width: 7px;\n height: 7px;\n border: 0 solid currentColor;\n border-width: 1.5px 0 0 1.5px;\n content: '';\n}\n.du-bois-dark-picker-prev-icon,\n.du-bois-dark-picker-super-prev-icon {\n transform: rotate(-45deg);\n}\n.du-bois-dark-picker-next-icon,\n.du-bois-dark-picker-super-next-icon {\n transform: rotate(135deg);\n}\n.du-bois-dark-picker-content {\n width: 100%;\n table-layout: fixed;\n border-collapse: collapse;\n}\n.du-bois-dark-picker-content th,\n.du-bois-dark-picker-content td {\n position: relative;\n min-width: 24px;\n font-weight: 400;\n}\n.du-bois-dark-picker-content th {\n height: 30px;\n color: #E8ECF0;\n line-height: 30px;\n}\n.du-bois-dark-picker-cell {\n padding: 3px 0;\n color: rgba(255, 255, 255, 0.3);\n cursor: pointer;\n}\n.du-bois-dark-picker-cell-in-view {\n color: #E8ECF0;\n}\n.du-bois-dark-picker-cell::before {\n position: absolute;\n top: 50%;\n right: 0;\n left: 0;\n z-index: 1;\n height: 24px;\n transform: translateY(-50%);\n content: '';\n}\n.du-bois-dark-picker-cell .du-bois-dark-picker-cell-inner {\n position: relative;\n z-index: 2;\n display: inline-block;\n min-width: 24px;\n height: 24px;\n line-height: 24px;\n border-radius: 4px;\n transition: background 0.3s, border 0.3s;\n}\n.du-bois-dark-picker-cell:hover:not(.du-bois-dark-picker-cell-in-view) .du-bois-dark-picker-cell-inner,\n.du-bois-dark-picker-cell:hover:not(.du-bois-dark-picker-cell-selected):not(.du-bois-dark-picker-cell-range-start):not(.du-bois-dark-picker-cell-range-end):not(.du-bois-dark-picker-cell-range-hover-start):not(.du-bois-dark-picker-cell-range-hover-end) .du-bois-dark-picker-cell-inner {\n background: rgba(255, 255, 255, 0.08);\n}\n.du-bois-dark-picker-cell-in-view.du-bois-dark-picker-cell-today .du-bois-dark-picker-cell-inner::before {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n z-index: 1;\n border: 1px solid #2272B4;\n border-radius: 4px;\n content: '';\n}\n.du-bois-dark-picker-cell-in-view.du-bois-dark-picker-cell-in-range {\n position: relative;\n}\n.du-bois-dark-picker-cell-in-view.du-bois-dark-picker-cell-in-range::before {\n background: #121921;\n}\n.du-bois-dark-picker-cell-in-view.du-bois-dark-picker-cell-selected .du-bois-dark-picker-cell-inner,\n.du-bois-dark-picker-cell-in-view.du-bois-dark-picker-cell-range-start .du-bois-dark-picker-cell-inner,\n.du-bois-dark-picker-cell-in-view.du-bois-dark-picker-cell-range-end .du-bois-dark-picker-cell-inner {\n color: #fff;\n background: #2272B4;\n}\n.du-bois-dark-picker-cell-in-view.du-bois-dark-picker-cell-range-start:not(.du-bois-dark-picker-cell-range-start-single)::before,\n.du-bois-dark-picker-cell-in-view.du-bois-dark-picker-cell-range-end:not(.du-bois-dark-picker-cell-range-end-single)::before {\n background: #121921;\n}\n.du-bois-dark-picker-cell-in-view.du-bois-dark-picker-cell-range-start::before {\n left: 50%;\n}\n.du-bois-dark-picker-cell-in-view.du-bois-dark-picker-cell-range-end::before {\n right: 50%;\n}\n.du-bois-dark-picker-cell-in-view.du-bois-dark-picker-cell-range-hover-start:not(.du-bois-dark-picker-cell-in-range):not(.du-bois-dark-picker-cell-range-start):not(.du-bois-dark-picker-cell-range-end)::after,\n.du-bois-dark-picker-cell-in-view.du-bois-dark-picker-cell-range-hover-end:not(.du-bois-dark-picker-cell-in-range):not(.du-bois-dark-picker-cell-range-start):not(.du-bois-dark-picker-cell-range-end)::after,\n.du-bois-dark-picker-cell-in-view.du-bois-dark-picker-cell-range-hover-start.du-bois-dark-picker-cell-range-start-single::after,\n.du-bois-dark-picker-cell-in-view.du-bois-dark-picker-cell-range-hover-start.du-bois-dark-picker-cell-range-start.du-bois-dark-picker-cell-range-end.du-bois-dark-picker-cell-range-end-near-hover::after,\n.du-bois-dark-picker-cell-in-view.du-bois-dark-picker-cell-range-hover-end.du-bois-dark-picker-cell-range-start.du-bois-dark-picker-cell-range-end.du-bois-dark-picker-cell-range-start-near-hover::after,\n.du-bois-dark-picker-cell-in-view.du-bois-dark-picker-cell-range-hover-end.du-bois-dark-picker-cell-range-end-single::after,\n.du-bois-dark-picker-cell-in-view.du-bois-dark-picker-cell-range-hover:not(.du-bois-dark-picker-cell-in-range)::after {\n position: absolute;\n top: 50%;\n z-index: 0;\n height: 24px;\n border-top: 1px dashed #123c5e;\n border-bottom: 1px dashed #123c5e;\n transform: translateY(-50%);\n content: '';\n}\n.du-bois-dark-picker-cell-range-hover-start::after,\n.du-bois-dark-picker-cell-range-hover-end::after,\n.du-bois-dark-picker-cell-range-hover::after {\n right: 0;\n left: 2px;\n}\n.du-bois-dark-picker-cell-in-view.du-bois-dark-picker-cell-in-range.du-bois-dark-picker-cell-range-hover::before,\n.du-bois-dark-picker-cell-in-view.du-bois-dark-picker-cell-range-start.du-bois-dark-picker-cell-range-hover::before,\n.du-bois-dark-picker-cell-in-view.du-bois-dark-picker-cell-range-end.du-bois-dark-picker-cell-range-hover::before,\n.du-bois-dark-picker-cell-in-view.du-bois-dark-picker-cell-range-start:not(.du-bois-dark-picker-cell-range-start-single).du-bois-dark-picker-cell-range-hover-start::before,\n.du-bois-dark-picker-cell-in-view.du-bois-dark-picker-cell-range-end:not(.du-bois-dark-picker-cell-range-end-single).du-bois-dark-picker-cell-range-hover-end::before,\n.du-bois-dark-picker-panel > :not(.du-bois-dark-picker-date-panel) .du-bois-dark-picker-cell-in-view.du-bois-dark-picker-cell-in-range.du-bois-dark-picker-cell-range-hover-start::before,\n.du-bois-dark-picker-panel > :not(.du-bois-dark-picker-date-panel) .du-bois-dark-picker-cell-in-view.du-bois-dark-picker-cell-in-range.du-bois-dark-picker-cell-range-hover-end::before {\n background: #06131e;\n}\n.du-bois-dark-picker-cell-in-view.du-bois-dark-picker-cell-range-start:not(.du-bois-dark-picker-cell-range-start-single):not(.du-bois-dark-picker-cell-range-end) .du-bois-dark-picker-cell-inner {\n border-radius: 4px 0 0 4px;\n}\n.du-bois-dark-picker-cell-in-view.du-bois-dark-picker-cell-range-end:not(.du-bois-dark-picker-cell-range-end-single):not(.du-bois-dark-picker-cell-range-start) .du-bois-dark-picker-cell-inner {\n border-radius: 0 4px 4px 0;\n}\n.du-bois-dark-picker-date-panel .du-bois-dark-picker-cell-in-view.du-bois-dark-picker-cell-in-range.du-bois-dark-picker-cell-range-hover-start .du-bois-dark-picker-cell-inner::after,\n.du-bois-dark-picker-date-panel .du-bois-dark-picker-cell-in-view.du-bois-dark-picker-cell-in-range.du-bois-dark-picker-cell-range-hover-end .du-bois-dark-picker-cell-inner::after {\n position: absolute;\n top: 0;\n bottom: 0;\n z-index: -1;\n background: #06131e;\n content: '';\n}\n.du-bois-dark-picker-date-panel .du-bois-dark-picker-cell-in-view.du-bois-dark-picker-cell-in-range.du-bois-dark-picker-cell-range-hover-start .du-bois-dark-picker-cell-inner::after {\n right: -6px;\n left: 0;\n}\n.du-bois-dark-picker-date-panel .du-bois-dark-picker-cell-in-view.du-bois-dark-picker-cell-in-range.du-bois-dark-picker-cell-range-hover-end .du-bois-dark-picker-cell-inner::after {\n right: 0;\n left: -6px;\n}\n.du-bois-dark-picker-cell-range-hover.du-bois-dark-picker-cell-range-start::after {\n right: 50%;\n}\n.du-bois-dark-picker-cell-range-hover.du-bois-dark-picker-cell-range-end::after {\n left: 50%;\n}\ntr > .du-bois-dark-picker-cell-in-view.du-bois-dark-picker-cell-range-hover:first-child::after,\ntr > .du-bois-dark-picker-cell-in-view.du-bois-dark-picker-cell-range-hover-end:first-child::after,\n.du-bois-dark-picker-cell-in-view.du-bois-dark-picker-cell-start.du-bois-dark-picker-cell-range-hover-edge-start.du-bois-dark-picker-cell-range-hover-edge-start-near-range::after,\n.du-bois-dark-picker-cell-in-view.du-bois-dark-picker-cell-range-hover-edge-start:not(.du-bois-dark-picker-cell-range-hover-edge-start-near-range)::after,\n.du-bois-dark-picker-cell-in-view.du-bois-dark-picker-cell-range-hover-start::after {\n left: 6px;\n border-left: 1px dashed #123c5e;\n border-top-left-radius: 4px;\n border-bottom-left-radius: 4px;\n}\ntr > .du-bois-dark-picker-cell-in-view.du-bois-dark-picker-cell-range-hover:last-child::after,\ntr > .du-bois-dark-picker-cell-in-view.du-bois-dark-picker-cell-range-hover-start:last-child::after,\n.du-bois-dark-picker-cell-in-view.du-bois-dark-picker-cell-end.du-bois-dark-picker-cell-range-hover-edge-end.du-bois-dark-picker-cell-range-hover-edge-end-near-range::after,\n.du-bois-dark-picker-cell-in-view.du-bois-dark-picker-cell-range-hover-edge-end:not(.du-bois-dark-picker-cell-range-hover-edge-end-near-range)::after,\n.du-bois-dark-picker-cell-in-view.du-bois-dark-picker-cell-range-hover-end::after {\n right: 6px;\n border-right: 1px dashed #123c5e;\n border-top-right-radius: 4px;\n border-bottom-right-radius: 4px;\n}\n.du-bois-dark-picker-cell-disabled {\n color: rgba(255, 255, 255, 0.3);\n pointer-events: none;\n}\n.du-bois-dark-picker-cell-disabled .du-bois-dark-picker-cell-inner {\n background: transparent;\n}\n.du-bois-dark-picker-cell-disabled::before {\n background: #303030;\n}\n.du-bois-dark-picker-cell-disabled.du-bois-dark-picker-cell-today .du-bois-dark-picker-cell-inner::before {\n border-color: rgba(255, 255, 255, 0.3);\n}\n.du-bois-dark-picker-decade-panel .du-bois-dark-picker-content,\n.du-bois-dark-picker-year-panel .du-bois-dark-picker-content,\n.du-bois-dark-picker-quarter-panel .du-bois-dark-picker-content,\n.du-bois-dark-picker-month-panel .du-bois-dark-picker-content {\n height: 264px;\n}\n.du-bois-dark-picker-decade-panel .du-bois-dark-picker-cell-inner,\n.du-bois-dark-picker-year-panel .du-bois-dark-picker-cell-inner,\n.du-bois-dark-picker-quarter-panel .du-bois-dark-picker-cell-inner,\n.du-bois-dark-picker-month-panel .du-bois-dark-picker-cell-inner {\n padding: 0 4px;\n}\n.du-bois-dark-picker-quarter-panel .du-bois-dark-picker-content {\n height: 56px;\n}\n.du-bois-dark-picker-footer {\n width: min-content;\n min-width: 100%;\n line-height: 38px;\n text-align: center;\n border-bottom: 1px solid transparent;\n}\n.du-bois-dark-picker-panel .du-bois-dark-picker-footer {\n border-top: 1px solid #303030;\n}\n.du-bois-dark-picker-footer-extra {\n padding: 0 8px;\n line-height: 38px;\n text-align: left;\n}\n.du-bois-dark-picker-footer-extra:not(:last-child) {\n border-bottom: 1px solid #303030;\n}\n.du-bois-dark-picker-now {\n text-align: left;\n}\n.du-bois-dark-picker-today-btn {\n color: #2272B4;\n}\n.du-bois-dark-picker-today-btn:hover {\n color: #1d517c;\n}\n.du-bois-dark-picker-today-btn:active {\n color: #3f81b1;\n}\n.du-bois-dark-picker-today-btn.du-bois-dark-picker-today-btn-disabled {\n color: rgba(255, 255, 255, 0.3);\n cursor: not-allowed;\n}\n.du-bois-dark-picker-decade-panel .du-bois-dark-picker-cell-inner {\n padding: 0 2px;\n}\n.du-bois-dark-picker-decade-panel .du-bois-dark-picker-cell::before {\n display: none;\n}\n.du-bois-dark-picker-year-panel .du-bois-dark-picker-body,\n.du-bois-dark-picker-quarter-panel .du-bois-dark-picker-body,\n.du-bois-dark-picker-month-panel .du-bois-dark-picker-body {\n padding: 0 4px;\n}\n.du-bois-dark-picker-year-panel .du-bois-dark-picker-cell-inner,\n.du-bois-dark-picker-quarter-panel .du-bois-dark-picker-cell-inner,\n.du-bois-dark-picker-month-panel .du-bois-dark-picker-cell-inner {\n width: 60px;\n}\n.du-bois-dark-picker-year-panel .du-bois-dark-picker-cell-range-hover-start::after,\n.du-bois-dark-picker-quarter-panel .du-bois-dark-picker-cell-range-hover-start::after,\n.du-bois-dark-picker-month-panel .du-bois-dark-picker-cell-range-hover-start::after {\n left: 14px;\n border-left: 1px dashed #123c5e;\n border-radius: 4px 0 0 4px;\n}\n.du-bois-dark-picker-year-panel .du-bois-dark-picker-cell-range-hover-end::after,\n.du-bois-dark-picker-quarter-panel .du-bois-dark-picker-cell-range-hover-end::after,\n.du-bois-dark-picker-month-panel .du-bois-dark-picker-cell-range-hover-end::after {\n right: 14px;\n border-right: 1px dashed #123c5e;\n border-radius: 0 4px 4px 0;\n}\n.du-bois-dark-picker-week-panel .du-bois-dark-picker-body {\n padding: 4px 8px;\n}\n.du-bois-dark-picker-week-panel .du-bois-dark-picker-cell:hover .du-bois-dark-picker-cell-inner,\n.du-bois-dark-picker-week-panel .du-bois-dark-picker-cell-selected .du-bois-dark-picker-cell-inner,\n.du-bois-dark-picker-week-panel .du-bois-dark-picker-cell .du-bois-dark-picker-cell-inner {\n background: transparent !important;\n}\n.du-bois-dark-picker-week-panel-row td {\n transition: background 0.3s;\n}\n.du-bois-dark-picker-week-panel-row:hover td {\n background: rgba(255, 255, 255, 0.08);\n}\n.du-bois-dark-picker-week-panel-row-selected td,\n.du-bois-dark-picker-week-panel-row-selected:hover td {\n background: #2272B4;\n}\n.du-bois-dark-picker-week-panel-row-selected td.du-bois-dark-picker-cell-week,\n.du-bois-dark-picker-week-panel-row-selected:hover td.du-bois-dark-picker-cell-week {\n color: rgba(255, 255, 255, 0.5);\n}\n.du-bois-dark-picker-week-panel-row-selected td.du-bois-dark-picker-cell-today .du-bois-dark-picker-cell-inner::before,\n.du-bois-dark-picker-week-panel-row-selected:hover td.du-bois-dark-picker-cell-today .du-bois-dark-picker-cell-inner::before {\n border-color: #fff;\n}\n.du-bois-dark-picker-week-panel-row-selected td .du-bois-dark-picker-cell-inner,\n.du-bois-dark-picker-week-panel-row-selected:hover td .du-bois-dark-picker-cell-inner {\n color: #fff;\n}\n.du-bois-dark-picker-date-panel .du-bois-dark-picker-body {\n padding: 4px 8px;\n}\n.du-bois-dark-picker-date-panel .du-bois-dark-picker-content {\n width: 252px;\n}\n.du-bois-dark-picker-date-panel .du-bois-dark-picker-content th {\n width: 36px;\n}\n.du-bois-dark-picker-datetime-panel {\n display: flex;\n}\n.du-bois-dark-picker-datetime-panel .du-bois-dark-picker-time-panel {\n border-left: 1px solid #303030;\n}\n.du-bois-dark-picker-datetime-panel .du-bois-dark-picker-date-panel,\n.du-bois-dark-picker-datetime-panel .du-bois-dark-picker-time-panel {\n transition: opacity 0.3s;\n}\n.du-bois-dark-picker-datetime-panel-active .du-bois-dark-picker-date-panel,\n.du-bois-dark-picker-datetime-panel-active .du-bois-dark-picker-time-panel {\n opacity: 0.3;\n}\n.du-bois-dark-picker-datetime-panel-active .du-bois-dark-picker-date-panel-active,\n.du-bois-dark-picker-datetime-panel-active .du-bois-dark-picker-time-panel-active {\n opacity: 1;\n}\n.du-bois-dark-picker-time-panel {\n width: auto;\n min-width: auto;\n}\n.du-bois-dark-picker-time-panel .du-bois-dark-picker-content {\n display: flex;\n flex: auto;\n height: 224px;\n}\n.du-bois-dark-picker-time-panel-column {\n flex: 1 0 auto;\n width: 56px;\n margin: 0;\n padding: 0;\n overflow-y: hidden;\n text-align: left;\n list-style: none;\n transition: background 0.3s;\n}\n.du-bois-dark-picker-time-panel-column::after {\n display: block;\n height: 196px;\n content: '';\n}\n.du-bois-dark-picker-datetime-panel .du-bois-dark-picker-time-panel-column::after {\n height: 198px;\n}\n.du-bois-dark-picker-time-panel-column:not(:first-child) {\n border-left: 1px solid #303030;\n}\n.du-bois-dark-picker-time-panel-column-active {\n background: rgba(18, 25, 33, 0.2);\n}\n.du-bois-dark-picker-time-panel-column:hover {\n overflow-y: auto;\n}\n.du-bois-dark-picker-time-panel-column > li {\n margin: 0;\n padding: 0;\n}\n.du-bois-dark-picker-time-panel-column > li.du-bois-dark-picker-time-panel-cell .du-bois-dark-picker-time-panel-cell-inner {\n display: block;\n width: 100%;\n height: 28px;\n margin: 0;\n padding: 0 0 0 14px;\n color: #E8ECF0;\n line-height: 28px;\n border-radius: 0;\n cursor: pointer;\n transition: background 0.3s;\n}\n.du-bois-dark-picker-time-panel-column > li.du-bois-dark-picker-time-panel-cell .du-bois-dark-picker-time-panel-cell-inner:hover {\n background: rgba(255, 255, 255, 0.08);\n}\n.du-bois-dark-picker-time-panel-column > li.du-bois-dark-picker-time-panel-cell-selected .du-bois-dark-picker-time-panel-cell-inner {\n background: #121921;\n}\n.du-bois-dark-picker-time-panel-column > li.du-bois-dark-picker-time-panel-cell-disabled .du-bois-dark-picker-time-panel-cell-inner {\n color: rgba(255, 255, 255, 0.3);\n background: transparent;\n cursor: not-allowed;\n}\n/* stylelint-disable-next-line */\n_:-ms-fullscreen .du-bois-dark-picker-range-wrapper .du-bois-dark-picker-month-panel .du-bois-dark-picker-cell,\n:root .du-bois-dark-picker-range-wrapper .du-bois-dark-picker-month-panel .du-bois-dark-picker-cell,\n_:-ms-fullscreen .du-bois-dark-picker-range-wrapper .du-bois-dark-picker-year-panel .du-bois-dark-picker-cell,\n:root .du-bois-dark-picker-range-wrapper .du-bois-dark-picker-year-panel .du-bois-dark-picker-cell {\n padding: 21px 0;\n}\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-dark-tag {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #E8ECF0;\n font-size: 13px;\n font-variant: tabular-nums;\n list-style: none;\n font-feature-settings: tnum;\n display: inline-block;\n height: auto;\n margin-right: 8px;\n padding: 0 7px;\n font-size: 12px;\n line-height: 20px;\n white-space: nowrap;\n background: rgba(255, 255, 255, 0.04);\n border: 1px solid #1F272D;\n border-radius: 4px;\n opacity: 1;\n transition: all 0.3s;\n}\n.du-bois-dark-tag,\n.du-bois-dark-tag a,\n.du-bois-dark-tag a:hover {\n color: #E8ECF0;\n}\n.du-bois-dark-tag > a:first-child:last-child {\n display: inline-block;\n margin: 0 -8px;\n padding: 0 8px;\n}\n.du-bois-dark-tag-close-icon {\n margin-left: 3px;\n color: #92A4B3;\n font-size: 10px;\n cursor: pointer;\n transition: all 0.3s;\n}\n.du-bois-dark-tag-close-icon:hover {\n color: rgba(255, 255, 255, 0.85);\n}\n.du-bois-dark-tag-has-color {\n border-color: transparent;\n}\n.du-bois-dark-tag-has-color,\n.du-bois-dark-tag-has-color a,\n.du-bois-dark-tag-has-color a:hover,\n.du-bois-dark-tag-has-color .anticon-close,\n.du-bois-dark-tag-has-color .anticon-close:hover {\n color: #fff;\n}\n.du-bois-dark-tag-checkable {\n background-color: transparent;\n border-color: transparent;\n cursor: pointer;\n}\n.du-bois-dark-tag-checkable:not(.du-bois-dark-tag-checkable-checked):hover {\n color: #2272B4;\n}\n.du-bois-dark-tag-checkable:active,\n.du-bois-dark-tag-checkable-checked {\n color: #fff;\n}\n.du-bois-dark-tag-checkable-checked {\n background-color: #2272B4;\n}\n.du-bois-dark-tag-checkable:active {\n background-color: #3f81b1;\n}\n.du-bois-dark-tag-hidden {\n display: none;\n}\n.du-bois-dark-tag-pink {\n color: #e0529c;\n background: #291321;\n border-color: #551c3b;\n}\n.du-bois-dark-tag-pink-inverse {\n color: #fff;\n background: #cb2b83;\n border-color: #cb2b83;\n}\n.du-bois-dark-tag-magenta {\n color: #e0529c;\n background: #291321;\n border-color: #551c3b;\n}\n.du-bois-dark-tag-magenta-inverse {\n color: #fff;\n background: #cb2b83;\n border-color: #cb2b83;\n}\n.du-bois-dark-tag-red {\n color: #e84749;\n background: #2a1215;\n border-color: #58181c;\n}\n.du-bois-dark-tag-red-inverse {\n color: #fff;\n background: #d32029;\n border-color: #d32029;\n}\n.du-bois-dark-tag-volcano {\n color: #e87040;\n background: #2b1611;\n border-color: #592716;\n}\n.du-bois-dark-tag-volcano-inverse {\n color: #fff;\n background: #d84a1b;\n border-color: #d84a1b;\n}\n.du-bois-dark-tag-orange {\n color: #e89a3c;\n background: #2b1d11;\n border-color: #593815;\n}\n.du-bois-dark-tag-orange-inverse {\n color: #fff;\n background: #d87a16;\n border-color: #d87a16;\n}\n.du-bois-dark-tag-yellow {\n color: #e8d639;\n background: #2b2611;\n border-color: #595014;\n}\n.du-bois-dark-tag-yellow-inverse {\n color: #fff;\n background: #d8bd14;\n border-color: #d8bd14;\n}\n.du-bois-dark-tag-gold {\n color: #e8b339;\n background: #2b2111;\n border-color: #594214;\n}\n.du-bois-dark-tag-gold-inverse {\n color: #fff;\n background: #d89614;\n border-color: #d89614;\n}\n.du-bois-dark-tag-cyan {\n color: #33bcb7;\n background: #112123;\n border-color: #144848;\n}\n.du-bois-dark-tag-cyan-inverse {\n color: #fff;\n background: #13a8a8;\n border-color: #13a8a8;\n}\n.du-bois-dark-tag-lime {\n color: #a9d134;\n background: #1f2611;\n border-color: #3e4f13;\n}\n.du-bois-dark-tag-lime-inverse {\n color: #fff;\n background: #8bbb11;\n border-color: #8bbb11;\n}\n.du-bois-dark-tag-green {\n color: #6abe39;\n background: #162312;\n border-color: #274916;\n}\n.du-bois-dark-tag-green-inverse {\n color: #fff;\n background: #49aa19;\n border-color: #49aa19;\n}\n.du-bois-dark-tag-blue {\n color: #3c9ae8;\n background: #111d2c;\n border-color: #15395b;\n}\n.du-bois-dark-tag-blue-inverse {\n color: #fff;\n background: #177ddc;\n border-color: #177ddc;\n}\n.du-bois-dark-tag-geekblue {\n color: #5273e0;\n background: #131629;\n border-color: #1c2755;\n}\n.du-bois-dark-tag-geekblue-inverse {\n color: #fff;\n background: #2b4acb;\n border-color: #2b4acb;\n}\n.du-bois-dark-tag-purple {\n color: #854eca;\n background: #1a1325;\n border-color: #301c4d;\n}\n.du-bois-dark-tag-purple-inverse {\n color: #fff;\n background: #642ab5;\n border-color: #642ab5;\n}\n.du-bois-dark-tag-success {\n color: #49aa19;\n background: #162312;\n border-color: #274916;\n}\n.du-bois-dark-tag-processing {\n color: #177ddc;\n background: #111d2c;\n border-color: #15395b;\n}\n.du-bois-dark-tag-error {\n color: #d32029;\n background: #2a1215;\n border-color: #58181c;\n}\n.du-bois-dark-tag-warning {\n color: #d87a16;\n background: #2b1d11;\n border-color: #593815;\n}\n.du-bois-dark-tag > .anticon + span,\n.du-bois-dark-tag > span + .anticon {\n margin-left: 7px;\n}\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-dark-dropdown-menu-item.du-bois-dark-dropdown-menu-item-danger {\n color: #E65B77;\n}\n.du-bois-dark-dropdown-menu-item.du-bois-dark-dropdown-menu-item-danger:hover {\n color: #fff;\n background-color: #E65B77;\n}\n.du-bois-dark-dropdown {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #E8ECF0;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: tnum;\n position: absolute;\n top: -9999px;\n left: -9999px;\n z-index: 1050;\n display: block;\n}\n.du-bois-dark-dropdown::before {\n position: absolute;\n top: -4px;\n right: 0;\n bottom: -4px;\n left: -7px;\n z-index: -9999;\n opacity: 0.0001;\n content: ' ';\n}\n.du-bois-dark-dropdown-wrap {\n position: relative;\n}\n.du-bois-dark-dropdown-wrap .du-bois-dark-btn > .anticon-down {\n font-size: 10px;\n}\n.du-bois-dark-dropdown-wrap .anticon-down::before {\n transition: transform 0.2s;\n}\n.du-bois-dark-dropdown-wrap-open .anticon-down::before {\n transform: rotate(180deg);\n}\n.du-bois-dark-dropdown-hidden,\n.du-bois-dark-dropdown-menu-hidden,\n.du-bois-dark-dropdown-menu-submenu-hidden {\n display: none;\n}\n.du-bois-dark-dropdown-show-arrow.du-bois-dark-dropdown-placement-topCenter,\n.du-bois-dark-dropdown-show-arrow.du-bois-dark-dropdown-placement-topLeft,\n.du-bois-dark-dropdown-show-arrow.du-bois-dark-dropdown-placement-topRight {\n padding-bottom: 10px;\n}\n.du-bois-dark-dropdown-show-arrow.du-bois-dark-dropdown-placement-bottomCenter,\n.du-bois-dark-dropdown-show-arrow.du-bois-dark-dropdown-placement-bottomLeft,\n.du-bois-dark-dropdown-show-arrow.du-bois-dark-dropdown-placement-bottomRight {\n padding-top: 10px;\n}\n.du-bois-dark-dropdown-arrow {\n position: absolute;\n z-index: 1;\n display: block;\n width: 8.48528137px;\n height: 8.48528137px;\n background: transparent;\n border-style: solid;\n border-width: 4.24264069px;\n transform: rotate(45deg);\n}\n.du-bois-dark-dropdown-placement-topCenter > .du-bois-dark-dropdown-arrow,\n.du-bois-dark-dropdown-placement-topLeft > .du-bois-dark-dropdown-arrow,\n.du-bois-dark-dropdown-placement-topRight > .du-bois-dark-dropdown-arrow {\n bottom: 6.2px;\n border-top-color: transparent;\n border-right-color: #1f1f1f;\n border-bottom-color: #1f1f1f;\n border-left-color: transparent;\n box-shadow: 3px 3px 7px rgba(0, 0, 0, 0.07);\n}\n.du-bois-dark-dropdown-placement-topCenter > .du-bois-dark-dropdown-arrow {\n left: 50%;\n transform: translateX(-50%) rotate(45deg);\n}\n.du-bois-dark-dropdown-placement-topLeft > .du-bois-dark-dropdown-arrow {\n left: 16px;\n}\n.du-bois-dark-dropdown-placement-topRight > .du-bois-dark-dropdown-arrow {\n right: 16px;\n}\n.du-bois-dark-dropdown-placement-bottomCenter > .du-bois-dark-dropdown-arrow,\n.du-bois-dark-dropdown-placement-bottomLeft > .du-bois-dark-dropdown-arrow,\n.du-bois-dark-dropdown-placement-bottomRight > .du-bois-dark-dropdown-arrow {\n top: 6px;\n border-top-color: #1f1f1f;\n border-right-color: transparent;\n border-bottom-color: transparent;\n border-left-color: #1f1f1f;\n box-shadow: -2px -2px 5px rgba(0, 0, 0, 0.06);\n}\n.du-bois-dark-dropdown-placement-bottomCenter > .du-bois-dark-dropdown-arrow {\n left: 50%;\n transform: translateX(-50%) rotate(45deg);\n}\n.du-bois-dark-dropdown-placement-bottomLeft > .du-bois-dark-dropdown-arrow {\n left: 16px;\n}\n.du-bois-dark-dropdown-placement-bottomRight > .du-bois-dark-dropdown-arrow {\n right: 16px;\n}\n.du-bois-dark-dropdown-menu {\n position: relative;\n margin: 0;\n padding: 4px 0;\n text-align: left;\n list-style-type: none;\n background-color: #1f1f1f;\n background-clip: padding-box;\n border-radius: 4px;\n outline: none;\n box-shadow: 0 3px 6px -4px rgba(0, 0, 0, 0.48), 0 6px 16px 0 rgba(0, 0, 0, 0.32), 0 9px 28px 8px rgba(0, 0, 0, 0.2);\n}\n.du-bois-dark-dropdown-menu-item-group-title {\n padding: 5px 8px;\n color: #92A4B3;\n transition: all 0.3s;\n}\n.du-bois-dark-dropdown-menu-submenu-popup {\n position: absolute;\n z-index: 1050;\n background: transparent;\n box-shadow: none;\n transform-origin: 0 0;\n}\n.du-bois-dark-dropdown-menu-submenu-popup ul,\n.du-bois-dark-dropdown-menu-submenu-popup li {\n list-style: none;\n}\n.du-bois-dark-dropdown-menu-submenu-popup ul {\n margin-right: 0.3em;\n margin-left: 0.3em;\n}\n.du-bois-dark-dropdown-menu-item {\n position: relative;\n display: flex;\n align-items: center;\n}\n.du-bois-dark-dropdown-menu-item-icon {\n min-width: 12px;\n margin-right: 8px;\n font-size: 12px;\n}\n.du-bois-dark-dropdown-menu-title-content {\n flex: auto;\n}\n.du-bois-dark-dropdown-menu-title-content > a {\n color: inherit;\n transition: all 0.3s;\n}\n.du-bois-dark-dropdown-menu-title-content > a:hover {\n color: inherit;\n}\n.du-bois-dark-dropdown-menu-title-content > a::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n content: '';\n}\n.du-bois-dark-dropdown-menu-item,\n.du-bois-dark-dropdown-menu-submenu-title {\n clear: both;\n margin: 0;\n padding: 5px 8px;\n color: #E8ECF0;\n font-weight: normal;\n font-size: 13px;\n line-height: 22px;\n white-space: nowrap;\n cursor: pointer;\n transition: all 0.3s;\n}\n.du-bois-dark-dropdown-menu-item-selected,\n.du-bois-dark-dropdown-menu-submenu-title-selected {\n color: #2272B4;\n background-color: #121921;\n}\n.du-bois-dark-dropdown-menu-item:hover,\n.du-bois-dark-dropdown-menu-submenu-title:hover {\n background-color: rgba(255, 255, 255, 0.08);\n}\n.du-bois-dark-dropdown-menu-item-disabled,\n.du-bois-dark-dropdown-menu-submenu-title-disabled {\n color: rgba(255, 255, 255, 0.3);\n cursor: not-allowed;\n}\n.du-bois-dark-dropdown-menu-item-disabled:hover,\n.du-bois-dark-dropdown-menu-submenu-title-disabled:hover {\n color: rgba(255, 255, 255, 0.3);\n background-color: transparent;\n cursor: not-allowed;\n}\n.du-bois-dark-dropdown-menu-item-disabled a,\n.du-bois-dark-dropdown-menu-submenu-title-disabled a {\n pointer-events: none;\n}\n.du-bois-dark-dropdown-menu-item-divider,\n.du-bois-dark-dropdown-menu-submenu-title-divider {\n height: 1px;\n margin: 4px 0;\n overflow: hidden;\n line-height: 0;\n background-color: #303030;\n}\n.du-bois-dark-dropdown-menu-item .du-bois-dark-dropdown-menu-submenu-expand-icon,\n.du-bois-dark-dropdown-menu-submenu-title .du-bois-dark-dropdown-menu-submenu-expand-icon {\n position: absolute;\n right: 4px;\n}\n.du-bois-dark-dropdown-menu-item .du-bois-dark-dropdown-menu-submenu-expand-icon .du-bois-dark-dropdown-menu-submenu-arrow-icon,\n.du-bois-dark-dropdown-menu-submenu-title .du-bois-dark-dropdown-menu-submenu-expand-icon .du-bois-dark-dropdown-menu-submenu-arrow-icon {\n margin-right: 0 !important;\n color: #92A4B3;\n font-size: 10px;\n font-style: normal;\n}\n.du-bois-dark-dropdown-menu-item-group-list {\n margin: 0 8px;\n padding: 0;\n list-style: none;\n}\n.du-bois-dark-dropdown-menu-submenu-title {\n padding-right: 20px;\n}\n.du-bois-dark-dropdown-menu-submenu-vertical {\n position: relative;\n}\n.du-bois-dark-dropdown-menu-submenu-vertical > .du-bois-dark-dropdown-menu {\n position: absolute;\n top: 0;\n left: 100%;\n min-width: 100%;\n margin-left: 4px;\n transform-origin: 0 0;\n}\n.du-bois-dark-dropdown-menu-submenu.du-bois-dark-dropdown-menu-submenu-disabled .du-bois-dark-dropdown-menu-submenu-title,\n.du-bois-dark-dropdown-menu-submenu.du-bois-dark-dropdown-menu-submenu-disabled .du-bois-dark-dropdown-menu-submenu-title .du-bois-dark-dropdown-menu-submenu-arrow-icon {\n color: rgba(255, 255, 255, 0.3);\n background-color: transparent;\n cursor: not-allowed;\n}\n.du-bois-dark-dropdown-menu-submenu-selected .du-bois-dark-dropdown-menu-submenu-title {\n color: #2272B4;\n}\n.du-bois-dark-dropdown.du-bois-dark-slide-down-enter.du-bois-dark-slide-down-enter-active.du-bois-dark-dropdown-placement-bottomLeft,\n.du-bois-dark-dropdown.du-bois-dark-slide-down-appear.du-bois-dark-slide-down-appear-active.du-bois-dark-dropdown-placement-bottomLeft,\n.du-bois-dark-dropdown.du-bois-dark-slide-down-enter.du-bois-dark-slide-down-enter-active.du-bois-dark-dropdown-placement-bottomCenter,\n.du-bois-dark-dropdown.du-bois-dark-slide-down-appear.du-bois-dark-slide-down-appear-active.du-bois-dark-dropdown-placement-bottomCenter,\n.du-bois-dark-dropdown.du-bois-dark-slide-down-enter.du-bois-dark-slide-down-enter-active.du-bois-dark-dropdown-placement-bottomRight,\n.du-bois-dark-dropdown.du-bois-dark-slide-down-appear.du-bois-dark-slide-down-appear-active.du-bois-dark-dropdown-placement-bottomRight {\n animation-name: antSlideUpIn;\n}\n.du-bois-dark-dropdown.du-bois-dark-slide-up-enter.du-bois-dark-slide-up-enter-active.du-bois-dark-dropdown-placement-topLeft,\n.du-bois-dark-dropdown.du-bois-dark-slide-up-appear.du-bois-dark-slide-up-appear-active.du-bois-dark-dropdown-placement-topLeft,\n.du-bois-dark-dropdown.du-bois-dark-slide-up-enter.du-bois-dark-slide-up-enter-active.du-bois-dark-dropdown-placement-topCenter,\n.du-bois-dark-dropdown.du-bois-dark-slide-up-appear.du-bois-dark-slide-up-appear-active.du-bois-dark-dropdown-placement-topCenter,\n.du-bois-dark-dropdown.du-bois-dark-slide-up-enter.du-bois-dark-slide-up-enter-active.du-bois-dark-dropdown-placement-topRight,\n.du-bois-dark-dropdown.du-bois-dark-slide-up-appear.du-bois-dark-slide-up-appear-active.du-bois-dark-dropdown-placement-topRight {\n animation-name: antSlideDownIn;\n}\n.du-bois-dark-dropdown.du-bois-dark-slide-down-leave.du-bois-dark-slide-down-leave-active.du-bois-dark-dropdown-placement-bottomLeft,\n.du-bois-dark-dropdown.du-bois-dark-slide-down-leave.du-bois-dark-slide-down-leave-active.du-bois-dark-dropdown-placement-bottomCenter,\n.du-bois-dark-dropdown.du-bois-dark-slide-down-leave.du-bois-dark-slide-down-leave-active.du-bois-dark-dropdown-placement-bottomRight {\n animation-name: antSlideUpOut;\n}\n.du-bois-dark-dropdown.du-bois-dark-slide-up-leave.du-bois-dark-slide-up-leave-active.du-bois-dark-dropdown-placement-topLeft,\n.du-bois-dark-dropdown.du-bois-dark-slide-up-leave.du-bois-dark-slide-up-leave-active.du-bois-dark-dropdown-placement-topCenter,\n.du-bois-dark-dropdown.du-bois-dark-slide-up-leave.du-bois-dark-slide-up-leave-active.du-bois-dark-dropdown-placement-topRight {\n animation-name: antSlideDownOut;\n}\n.du-bois-dark-dropdown-trigger > .anticon.anticon-down,\n.du-bois-dark-dropdown-link > .anticon.anticon-down,\n.du-bois-dark-dropdown-button > .anticon.anticon-down {\n font-size: 10px;\n vertical-align: baseline;\n}\n.du-bois-dark-dropdown-button {\n white-space: nowrap;\n}\n.du-bois-dark-dropdown-button.du-bois-dark-btn-group > .du-bois-dark-btn:last-child:not(:first-child):not(.du-bois-dark-btn-icon-only) {\n padding-right: 4px;\n padding-left: 4px;\n}\n.du-bois-dark-dropdown-menu-dark,\n.du-bois-dark-dropdown-menu-dark .du-bois-dark-dropdown-menu {\n background: #1f1f1f;\n}\n.du-bois-dark-dropdown-menu-dark .du-bois-dark-dropdown-menu-item,\n.du-bois-dark-dropdown-menu-dark .du-bois-dark-dropdown-menu-submenu-title,\n.du-bois-dark-dropdown-menu-dark .du-bois-dark-dropdown-menu-item > a,\n.du-bois-dark-dropdown-menu-dark .du-bois-dark-dropdown-menu-item > .anticon + span > a {\n color: rgba(255, 255, 255, 0.65);\n}\n.du-bois-dark-dropdown-menu-dark .du-bois-dark-dropdown-menu-item .du-bois-dark-dropdown-menu-submenu-arrow::after,\n.du-bois-dark-dropdown-menu-dark .du-bois-dark-dropdown-menu-submenu-title .du-bois-dark-dropdown-menu-submenu-arrow::after,\n.du-bois-dark-dropdown-menu-dark .du-bois-dark-dropdown-menu-item > a .du-bois-dark-dropdown-menu-submenu-arrow::after,\n.du-bois-dark-dropdown-menu-dark .du-bois-dark-dropdown-menu-item > .anticon + span > a .du-bois-dark-dropdown-menu-submenu-arrow::after {\n color: rgba(255, 255, 255, 0.65);\n}\n.du-bois-dark-dropdown-menu-dark .du-bois-dark-dropdown-menu-item:hover,\n.du-bois-dark-dropdown-menu-dark .du-bois-dark-dropdown-menu-submenu-title:hover,\n.du-bois-dark-dropdown-menu-dark .du-bois-dark-dropdown-menu-item > a:hover,\n.du-bois-dark-dropdown-menu-dark .du-bois-dark-dropdown-menu-item > .anticon + span > a:hover {\n color: #fff;\n background: transparent;\n}\n.du-bois-dark-dropdown-menu-dark .du-bois-dark-dropdown-menu-item-selected,\n.du-bois-dark-dropdown-menu-dark .du-bois-dark-dropdown-menu-item-selected:hover,\n.du-bois-dark-dropdown-menu-dark .du-bois-dark-dropdown-menu-item-selected > a {\n color: #fff;\n background: #2272B4;\n}\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-dark-form-item .du-bois-dark-mentions,\n.du-bois-dark-form-item textarea.du-bois-dark-input {\n height: auto;\n}\n.du-bois-dark-form-item .du-bois-dark-upload {\n background: transparent;\n}\n.du-bois-dark-form-item .du-bois-dark-upload.du-bois-dark-upload-drag {\n background: rgba(255, 255, 255, 0.04);\n}\n.du-bois-dark-form-item input[type='radio'],\n.du-bois-dark-form-item input[type='checkbox'] {\n width: 14px;\n height: 14px;\n}\n.du-bois-dark-form-item .du-bois-dark-radio-inline,\n.du-bois-dark-form-item .du-bois-dark-checkbox-inline {\n display: inline-block;\n margin-left: 8px;\n font-weight: normal;\n vertical-align: middle;\n cursor: pointer;\n}\n.du-bois-dark-form-item .du-bois-dark-radio-inline:first-child,\n.du-bois-dark-form-item .du-bois-dark-checkbox-inline:first-child {\n margin-left: 0;\n}\n.du-bois-dark-form-item .du-bois-dark-checkbox-vertical,\n.du-bois-dark-form-item .du-bois-dark-radio-vertical {\n display: block;\n}\n.du-bois-dark-form-item .du-bois-dark-checkbox-vertical + .du-bois-dark-checkbox-vertical,\n.du-bois-dark-form-item .du-bois-dark-radio-vertical + .du-bois-dark-radio-vertical {\n margin-left: 0;\n}\n.du-bois-dark-form-item .du-bois-dark-input-number + .du-bois-dark-form-text {\n margin-left: 8px;\n}\n.du-bois-dark-form-item .du-bois-dark-input-number-handler-wrap {\n z-index: 2;\n}\n.du-bois-dark-form-item .du-bois-dark-select,\n.du-bois-dark-form-item .du-bois-dark-cascader-picker {\n width: 100%;\n}\n.du-bois-dark-form-item .du-bois-dark-picker-calendar-year-select,\n.du-bois-dark-form-item .du-bois-dark-picker-calendar-month-select,\n.du-bois-dark-form-item .du-bois-dark-input-group .du-bois-dark-select,\n.du-bois-dark-form-item .du-bois-dark-input-group .du-bois-dark-cascader-picker {\n width: auto;\n}\n.du-bois-dark-form-inline {\n display: flex;\n flex-wrap: wrap;\n}\n.du-bois-dark-form-inline .du-bois-dark-form-item {\n flex: none;\n flex-wrap: nowrap;\n margin-right: 16px;\n margin-bottom: 0;\n}\n.du-bois-dark-form-inline .du-bois-dark-form-item-with-help {\n margin-bottom: 24px;\n}\n.du-bois-dark-form-inline .du-bois-dark-form-item > .du-bois-dark-form-item-label,\n.du-bois-dark-form-inline .du-bois-dark-form-item > .du-bois-dark-form-item-control {\n display: inline-block;\n vertical-align: top;\n}\n.du-bois-dark-form-inline .du-bois-dark-form-item > .du-bois-dark-form-item-label {\n flex: none;\n}\n.du-bois-dark-form-inline .du-bois-dark-form-item .du-bois-dark-form-text {\n display: inline-block;\n}\n.du-bois-dark-form-inline .du-bois-dark-form-item .du-bois-dark-form-item-has-feedback {\n display: inline-block;\n}\n.du-bois-dark-form-horizontal .du-bois-dark-form-item-label {\n flex-grow: 0;\n}\n.du-bois-dark-form-horizontal .du-bois-dark-form-item-control {\n flex: 1 1 0;\n}\n.du-bois-dark-form-vertical .du-bois-dark-form-item {\n flex-direction: column;\n}\n.du-bois-dark-form-vertical .du-bois-dark-form-item-label > label {\n height: auto;\n}\n.du-bois-dark-form-vertical .du-bois-dark-form-item-label,\n.du-bois-dark-col-24.du-bois-dark-form-item-label,\n.du-bois-dark-col-xl-24.du-bois-dark-form-item-label {\n padding: 0 0 8px;\n line-height: 20px;\n white-space: initial;\n text-align: left;\n}\n.du-bois-dark-form-vertical .du-bois-dark-form-item-label > label,\n.du-bois-dark-col-24.du-bois-dark-form-item-label > label,\n.du-bois-dark-col-xl-24.du-bois-dark-form-item-label > label {\n margin: 0;\n}\n.du-bois-dark-form-vertical .du-bois-dark-form-item-label > label::after,\n.du-bois-dark-col-24.du-bois-dark-form-item-label > label::after,\n.du-bois-dark-col-xl-24.du-bois-dark-form-item-label > label::after {\n display: none;\n}\n@media (max-width: 575px) {\n .du-bois-dark-form-item .du-bois-dark-form-item-label {\n padding: 0 0 8px;\n line-height: 20px;\n white-space: initial;\n text-align: left;\n }\n .du-bois-dark-form-item .du-bois-dark-form-item-label > label {\n margin: 0;\n }\n .du-bois-dark-form-item .du-bois-dark-form-item-label > label::after {\n display: none;\n }\n .du-bois-dark-form .du-bois-dark-form-item {\n flex-wrap: wrap;\n }\n .du-bois-dark-form .du-bois-dark-form-item .du-bois-dark-form-item-label,\n .du-bois-dark-form .du-bois-dark-form-item .du-bois-dark-form-item-control {\n flex: 0 0 100%;\n max-width: 100%;\n }\n .du-bois-dark-col-xs-24.du-bois-dark-form-item-label {\n padding: 0 0 8px;\n line-height: 20px;\n white-space: initial;\n text-align: left;\n }\n .du-bois-dark-col-xs-24.du-bois-dark-form-item-label > label {\n margin: 0;\n }\n .du-bois-dark-col-xs-24.du-bois-dark-form-item-label > label::after {\n display: none;\n }\n}\n@media (max-width: 767px) {\n .du-bois-dark-col-sm-24.du-bois-dark-form-item-label {\n padding: 0 0 8px;\n line-height: 20px;\n white-space: initial;\n text-align: left;\n }\n .du-bois-dark-col-sm-24.du-bois-dark-form-item-label > label {\n margin: 0;\n }\n .du-bois-dark-col-sm-24.du-bois-dark-form-item-label > label::after {\n display: none;\n }\n}\n@media (max-width: 991px) {\n .du-bois-dark-col-md-24.du-bois-dark-form-item-label {\n padding: 0 0 8px;\n line-height: 20px;\n white-space: initial;\n text-align: left;\n }\n .du-bois-dark-col-md-24.du-bois-dark-form-item-label > label {\n margin: 0;\n }\n .du-bois-dark-col-md-24.du-bois-dark-form-item-label > label::after {\n display: none;\n }\n}\n@media (max-width: 1199px) {\n .du-bois-dark-col-lg-24.du-bois-dark-form-item-label {\n padding: 0 0 8px;\n line-height: 20px;\n white-space: initial;\n text-align: left;\n }\n .du-bois-dark-col-lg-24.du-bois-dark-form-item-label > label {\n margin: 0;\n }\n .du-bois-dark-col-lg-24.du-bois-dark-form-item-label > label::after {\n display: none;\n }\n}\n@media (max-width: 1599px) {\n .du-bois-dark-col-xl-24.du-bois-dark-form-item-label {\n padding: 0 0 8px;\n line-height: 20px;\n white-space: initial;\n text-align: left;\n }\n .du-bois-dark-col-xl-24.du-bois-dark-form-item-label > label {\n margin: 0;\n }\n .du-bois-dark-col-xl-24.du-bois-dark-form-item-label > label::after {\n display: none;\n }\n}\n.du-bois-dark-form-item {\n /* Some non-status related component style is in `components.less` */\n /* To support leave along ErrorList. We add additional className to handle explain style */\n}\n.du-bois-dark-form-item-explain.du-bois-dark-form-item-explain-error {\n color: #E65B77;\n}\n.du-bois-dark-form-item-explain.du-bois-dark-form-item-explain-warning {\n color: #AF5B23;\n}\n.du-bois-dark-form-item-has-feedback .du-bois-dark-input {\n padding-right: 24px;\n}\n.du-bois-dark-form-item-has-feedback .du-bois-dark-input-affix-wrapper .du-bois-dark-input-suffix {\n padding-right: 18px;\n}\n.du-bois-dark-form-item-has-feedback .du-bois-dark-input-search:not(.du-bois-dark-input-search-enter-button) .du-bois-dark-input-suffix {\n right: 28px;\n}\n.du-bois-dark-form-item-has-feedback .du-bois-dark-switch {\n margin: 2px 0 4px;\n}\n.du-bois-dark-form-item-has-feedback > .du-bois-dark-select .du-bois-dark-select-arrow,\n.du-bois-dark-form-item-has-feedback > .du-bois-dark-select .du-bois-dark-select-clear,\n.du-bois-dark-form-item-has-feedback :not(.du-bois-dark-input-group-addon) > .du-bois-dark-select .du-bois-dark-select-arrow,\n.du-bois-dark-form-item-has-feedback :not(.du-bois-dark-input-group-addon) > .du-bois-dark-select .du-bois-dark-select-clear {\n right: 32px;\n}\n.du-bois-dark-form-item-has-feedback > .du-bois-dark-select .du-bois-dark-select-selection-selected-value,\n.du-bois-dark-form-item-has-feedback :not(.du-bois-dark-input-group-addon) > .du-bois-dark-select .du-bois-dark-select-selection-selected-value {\n padding-right: 42px;\n}\n.du-bois-dark-form-item-has-feedback .du-bois-dark-cascader-picker-arrow {\n margin-right: 19px;\n}\n.du-bois-dark-form-item-has-feedback .du-bois-dark-cascader-picker-clear {\n right: 32px;\n}\n.du-bois-dark-form-item-has-feedback .du-bois-dark-picker {\n padding-right: 28.9px;\n}\n.du-bois-dark-form-item-has-feedback .du-bois-dark-picker-large {\n padding-right: 28.9px;\n}\n.du-bois-dark-form-item-has-feedback .du-bois-dark-picker-small {\n padding-right: 28.9px;\n}\n.du-bois-dark-form-item-has-feedback.du-bois-dark-form-item-has-success .du-bois-dark-form-item-children-icon,\n.du-bois-dark-form-item-has-feedback.du-bois-dark-form-item-has-warning .du-bois-dark-form-item-children-icon,\n.du-bois-dark-form-item-has-feedback.du-bois-dark-form-item-has-error .du-bois-dark-form-item-children-icon,\n.du-bois-dark-form-item-has-feedback.du-bois-dark-form-item-is-validating .du-bois-dark-form-item-children-icon {\n position: absolute;\n top: 50%;\n right: 0;\n z-index: 1;\n width: 40px;\n height: 20px;\n margin-top: -10px;\n font-size: 13px;\n line-height: 20px;\n text-align: center;\n visibility: visible;\n animation: zoomIn 0.3s cubic-bezier(0.12, 0.4, 0.29, 1.46);\n pointer-events: none;\n}\n.du-bois-dark-form-item-has-success.du-bois-dark-form-item-has-feedback .du-bois-dark-form-item-children-icon {\n color: #34824F;\n animation-name: diffZoomIn1 !important;\n}\n.du-bois-dark-form-item-has-warning .du-bois-dark-form-item-split {\n color: #AF5B23;\n}\n.du-bois-dark-form-item-has-warning :not(.du-bois-dark-input-disabled).du-bois-dark-input,\n.du-bois-dark-form-item-has-warning :not(.du-bois-dark-input-affix-wrapper-disabled).du-bois-dark-input-affix-wrapper,\n.du-bois-dark-form-item-has-warning :not(.du-bois-dark-input-disabled).du-bois-dark-input:hover,\n.du-bois-dark-form-item-has-warning :not(.du-bois-dark-input-affix-wrapper-disabled).du-bois-dark-input-affix-wrapper:hover {\n background-color: transparent;\n border-color: #AF5B23;\n}\n.du-bois-dark-form-item-has-warning :not(.du-bois-dark-input-disabled).du-bois-dark-input:focus,\n.du-bois-dark-form-item-has-warning :not(.du-bois-dark-input-affix-wrapper-disabled).du-bois-dark-input-affix-wrapper:focus,\n.du-bois-dark-form-item-has-warning :not(.du-bois-dark-input-disabled).du-bois-dark-input-focused,\n.du-bois-dark-form-item-has-warning :not(.du-bois-dark-input-affix-wrapper-disabled).du-bois-dark-input-affix-wrapper-focused {\n border-color: #AF5B23;\n border-right-width: 1px !important;\n outline: 0;\n box-shadow: 0 0 0 2px rgba(175, 91, 35, 0.2);\n}\n.du-bois-dark-form-item-has-warning .du-bois-dark-calendar-picker-open .du-bois-dark-calendar-picker-input {\n border-color: #AF5B23;\n border-right-width: 1px !important;\n outline: 0;\n box-shadow: 0 0 0 2px rgba(175, 91, 35, 0.2);\n}\n.du-bois-dark-form-item-has-warning .du-bois-dark-input-prefix {\n color: #AF5B23;\n}\n.du-bois-dark-form-item-has-warning .du-bois-dark-input-group-addon {\n color: #AF5B23;\n border-color: #AF5B23;\n}\n.du-bois-dark-form-item-has-warning .has-feedback {\n color: #AF5B23;\n}\n.du-bois-dark-form-item-has-warning.du-bois-dark-form-item-has-feedback .du-bois-dark-form-item-children-icon {\n color: #AF5B23;\n animation-name: diffZoomIn3 !important;\n}\n.du-bois-dark-form-item-has-warning .du-bois-dark-select:not(.du-bois-dark-select-disabled):not(.du-bois-dark-select-customize-input) .du-bois-dark-select-selector {\n background-color: transparent;\n border-color: #AF5B23 !important;\n}\n.du-bois-dark-form-item-has-warning .du-bois-dark-select:not(.du-bois-dark-select-disabled):not(.du-bois-dark-select-customize-input).du-bois-dark-select-open .du-bois-dark-select-selector,\n.du-bois-dark-form-item-has-warning .du-bois-dark-select:not(.du-bois-dark-select-disabled):not(.du-bois-dark-select-customize-input).du-bois-dark-select-focused .du-bois-dark-select-selector {\n border-color: #AF5B23;\n border-right-width: 1px !important;\n outline: 0;\n box-shadow: 0 0 0 2px rgba(175, 91, 35, 0.2);\n}\n.du-bois-dark-form-item-has-warning .du-bois-dark-input-number,\n.du-bois-dark-form-item-has-warning .du-bois-dark-picker {\n background-color: transparent;\n border-color: #AF5B23;\n}\n.du-bois-dark-form-item-has-warning .du-bois-dark-input-number-focused,\n.du-bois-dark-form-item-has-warning .du-bois-dark-picker-focused,\n.du-bois-dark-form-item-has-warning .du-bois-dark-input-number:focus,\n.du-bois-dark-form-item-has-warning .du-bois-dark-picker:focus {\n border-color: #AF5B23;\n border-right-width: 1px !important;\n outline: 0;\n box-shadow: 0 0 0 2px rgba(175, 91, 35, 0.2);\n}\n.du-bois-dark-form-item-has-warning .du-bois-dark-input-number:not([disabled]):hover,\n.du-bois-dark-form-item-has-warning .du-bois-dark-picker:not([disabled]):hover {\n background-color: transparent;\n border-color: #AF5B23;\n}\n.du-bois-dark-form-item-has-warning .du-bois-dark-cascader-picker:focus .du-bois-dark-cascader-input {\n border-color: #AF5B23;\n border-right-width: 1px !important;\n outline: 0;\n box-shadow: 0 0 0 2px rgba(175, 91, 35, 0.2);\n}\n.du-bois-dark-form-item-has-error .du-bois-dark-form-item-split {\n color: #E65B77;\n}\n.du-bois-dark-form-item-has-error :not(.du-bois-dark-input-disabled).du-bois-dark-input,\n.du-bois-dark-form-item-has-error :not(.du-bois-dark-input-affix-wrapper-disabled).du-bois-dark-input-affix-wrapper,\n.du-bois-dark-form-item-has-error :not(.du-bois-dark-input-disabled).du-bois-dark-input:hover,\n.du-bois-dark-form-item-has-error :not(.du-bois-dark-input-affix-wrapper-disabled).du-bois-dark-input-affix-wrapper:hover {\n background-color: transparent;\n border-color: #E65B77;\n}\n.du-bois-dark-form-item-has-error :not(.du-bois-dark-input-disabled).du-bois-dark-input:focus,\n.du-bois-dark-form-item-has-error :not(.du-bois-dark-input-affix-wrapper-disabled).du-bois-dark-input-affix-wrapper:focus,\n.du-bois-dark-form-item-has-error :not(.du-bois-dark-input-disabled).du-bois-dark-input-focused,\n.du-bois-dark-form-item-has-error :not(.du-bois-dark-input-affix-wrapper-disabled).du-bois-dark-input-affix-wrapper-focused {\n border-color: #E65B77;\n border-right-width: 1px !important;\n outline: 0;\n box-shadow: 0 0 0 2px rgba(230, 91, 119, 0.2);\n}\n.du-bois-dark-form-item-has-error .du-bois-dark-calendar-picker-open .du-bois-dark-calendar-picker-input {\n border-color: #E65B77;\n border-right-width: 1px !important;\n outline: 0;\n box-shadow: 0 0 0 2px rgba(230, 91, 119, 0.2);\n}\n.du-bois-dark-form-item-has-error .du-bois-dark-input-prefix {\n color: #E65B77;\n}\n.du-bois-dark-form-item-has-error .du-bois-dark-input-group-addon {\n color: #E65B77;\n border-color: #E65B77;\n}\n.du-bois-dark-form-item-has-error .has-feedback {\n color: #E65B77;\n}\n.du-bois-dark-form-item-has-error.du-bois-dark-form-item-has-feedback .du-bois-dark-form-item-children-icon {\n color: #E65B77;\n animation-name: diffZoomIn2 !important;\n}\n.du-bois-dark-form-item-has-error .du-bois-dark-select:not(.du-bois-dark-select-disabled):not(.du-bois-dark-select-customize-input) .du-bois-dark-select-selector {\n background-color: transparent;\n border-color: #E65B77 !important;\n}\n.du-bois-dark-form-item-has-error .du-bois-dark-select:not(.du-bois-dark-select-disabled):not(.du-bois-dark-select-customize-input).du-bois-dark-select-open .du-bois-dark-select-selector,\n.du-bois-dark-form-item-has-error .du-bois-dark-select:not(.du-bois-dark-select-disabled):not(.du-bois-dark-select-customize-input).du-bois-dark-select-focused .du-bois-dark-select-selector {\n border-color: #E65B77;\n border-right-width: 1px !important;\n outline: 0;\n box-shadow: 0 0 0 2px rgba(230, 91, 119, 0.2);\n}\n.du-bois-dark-form-item-has-error .du-bois-dark-input-group-addon .du-bois-dark-select.du-bois-dark-select-single:not(.du-bois-dark-select-customize-input) .du-bois-dark-select-selector {\n background-color: inherit;\n border: 0;\n box-shadow: none;\n}\n.du-bois-dark-form-item-has-error .du-bois-dark-select.du-bois-dark-select-auto-complete .du-bois-dark-input:focus {\n border-color: #E65B77;\n}\n.du-bois-dark-form-item-has-error .du-bois-dark-input-number,\n.du-bois-dark-form-item-has-error .du-bois-dark-picker {\n background-color: transparent;\n border-color: #E65B77;\n}\n.du-bois-dark-form-item-has-error .du-bois-dark-input-number-focused,\n.du-bois-dark-form-item-has-error .du-bois-dark-picker-focused,\n.du-bois-dark-form-item-has-error .du-bois-dark-input-number:focus,\n.du-bois-dark-form-item-has-error .du-bois-dark-picker:focus {\n border-color: #E65B77;\n border-right-width: 1px !important;\n outline: 0;\n box-shadow: 0 0 0 2px rgba(230, 91, 119, 0.2);\n}\n.du-bois-dark-form-item-has-error .du-bois-dark-input-number:not([disabled]):hover,\n.du-bois-dark-form-item-has-error .du-bois-dark-picker:not([disabled]):hover {\n background-color: transparent;\n border-color: #E65B77;\n}\n.du-bois-dark-form-item-has-error .du-bois-dark-mention-wrapper .du-bois-dark-mention-editor,\n.du-bois-dark-form-item-has-error .du-bois-dark-mention-wrapper .du-bois-dark-mention-editor:not([disabled]):hover {\n background-color: transparent;\n border-color: #E65B77;\n}\n.du-bois-dark-form-item-has-error .du-bois-dark-mention-wrapper.du-bois-dark-mention-active:not([disabled]) .du-bois-dark-mention-editor,\n.du-bois-dark-form-item-has-error .du-bois-dark-mention-wrapper .du-bois-dark-mention-editor:not([disabled]):focus {\n border-color: #E65B77;\n border-right-width: 1px !important;\n outline: 0;\n box-shadow: 0 0 0 2px rgba(230, 91, 119, 0.2);\n}\n.du-bois-dark-form-item-has-error .du-bois-dark-cascader-picker:hover .du-bois-dark-cascader-picker-label:hover + .du-bois-dark-cascader-input.du-bois-dark-input {\n border-color: #E65B77;\n}\n.du-bois-dark-form-item-has-error .du-bois-dark-cascader-picker:focus .du-bois-dark-cascader-input {\n background-color: transparent;\n border-color: #E65B77;\n border-right-width: 1px !important;\n outline: 0;\n box-shadow: 0 0 0 2px rgba(230, 91, 119, 0.2);\n}\n.du-bois-dark-form-item-has-error .du-bois-dark-transfer-list {\n border-color: #E65B77;\n}\n.du-bois-dark-form-item-has-error .du-bois-dark-transfer-list-search:not([disabled]) {\n border-color: #1F272D;\n}\n.du-bois-dark-form-item-has-error .du-bois-dark-transfer-list-search:not([disabled]):hover {\n border-color: #1d517c;\n border-right-width: 1px !important;\n}\n.du-bois-dark-form-item-has-error .du-bois-dark-transfer-list-search:not([disabled]):focus {\n border-color: #2272B4;\n border-right-width: 1px !important;\n outline: 0;\n box-shadow: 0 0 0 2px rgba(34, 114, 180, 0.2);\n}\n.du-bois-dark-form-item-has-error .du-bois-dark-radio-button-wrapper {\n border-color: #E65B77 !important;\n}\n.du-bois-dark-form-item-has-error .du-bois-dark-radio-button-wrapper:not(:first-child)::before {\n background-color: #E65B77;\n}\n.du-bois-dark-form-item-is-validating.du-bois-dark-form-item-has-feedback .du-bois-dark-form-item-children-icon {\n display: inline-block;\n color: #2272B4;\n}\n.du-bois-dark-form {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #E8ECF0;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: tnum;\n}\n.du-bois-dark-form legend {\n display: block;\n width: 100%;\n margin-bottom: 20px;\n padding: 0;\n color: #92A4B3;\n font-size: 18px;\n line-height: inherit;\n border: 0;\n border-bottom: 1px solid #1F272D;\n}\n.du-bois-dark-form label {\n font-size: 13px;\n}\n.du-bois-dark-form input[type='search'] {\n box-sizing: border-box;\n}\n.du-bois-dark-form input[type='radio'],\n.du-bois-dark-form input[type='checkbox'] {\n line-height: normal;\n}\n.du-bois-dark-form input[type='file'] {\n display: block;\n}\n.du-bois-dark-form input[type='range'] {\n display: block;\n width: 100%;\n}\n.du-bois-dark-form select[multiple],\n.du-bois-dark-form select[size] {\n height: auto;\n}\n.du-bois-dark-form input[type='file']:focus,\n.du-bois-dark-form input[type='radio']:focus,\n.du-bois-dark-form input[type='checkbox']:focus {\n outline: thin dotted;\n outline: 5px auto -webkit-focus-ring-color;\n outline-offset: -2px;\n}\n.du-bois-dark-form output {\n display: block;\n padding-top: 15px;\n color: #E8ECF0;\n font-size: 13px;\n line-height: 20px;\n}\n.du-bois-dark-form .du-bois-dark-form-text {\n display: inline-block;\n padding-right: 8px;\n}\n.du-bois-dark-form-small .du-bois-dark-form-item-label > label {\n height: 32px;\n}\n.du-bois-dark-form-small .du-bois-dark-form-item-control-input {\n min-height: 32px;\n}\n.du-bois-dark-form-large .du-bois-dark-form-item-label > label {\n height: 40px;\n}\n.du-bois-dark-form-large .du-bois-dark-form-item-control-input {\n min-height: 40px;\n}\n.du-bois-dark-form-item {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #E8ECF0;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: tnum;\n margin-bottom: 24px;\n vertical-align: top;\n}\n.du-bois-dark-form-item-with-help {\n margin-bottom: 0;\n}\n.du-bois-dark-form-item-hidden,\n.du-bois-dark-form-item-hidden.du-bois-dark-row {\n display: none;\n}\n.du-bois-dark-form-item-label {\n display: inline-block;\n flex-grow: 0;\n overflow: hidden;\n white-space: nowrap;\n text-align: right;\n vertical-align: middle;\n}\n.du-bois-dark-form-item-label-left {\n text-align: left;\n}\n.du-bois-dark-form-item-label > label {\n position: relative;\n display: inline-flex;\n align-items: center;\n height: 40px;\n color: rgba(255, 255, 255, 0.85);\n font-size: 13px;\n}\n.du-bois-dark-form-item-label > label > .anticon {\n font-size: 13px;\n vertical-align: top;\n}\n.du-bois-dark-form-item-label > label.du-bois-dark-form-item-required:not(.du-bois-dark-form-item-required-mark-optional)::before {\n display: inline-block;\n margin-right: 4px;\n color: #a61d24;\n font-size: 13px;\n font-family: SimSun, sans-serif;\n line-height: 1;\n content: '*';\n}\n.du-bois-dark-form-hide-required-mark .du-bois-dark-form-item-label > label.du-bois-dark-form-item-required:not(.du-bois-dark-form-item-required-mark-optional)::before {\n display: none;\n}\n.du-bois-dark-form-item-label > label .du-bois-dark-form-item-optional {\n display: inline-block;\n margin-left: 4px;\n color: #92A4B3;\n}\n.du-bois-dark-form-hide-required-mark .du-bois-dark-form-item-label > label .du-bois-dark-form-item-optional {\n display: none;\n}\n.du-bois-dark-form-item-label > label .du-bois-dark-form-item-tooltip {\n color: #92A4B3;\n cursor: help;\n writing-mode: horizontal-tb;\n margin-inline-start: 4px;\n}\n.du-bois-dark-form-item-label > label::after {\n content: ':';\n position: relative;\n top: -0.5px;\n margin: 0 8px 0 2px;\n}\n.du-bois-dark-form-item-label > label.du-bois-dark-form-item-no-colon::after {\n content: ' ';\n}\n.du-bois-dark-form-item-control {\n display: flex;\n flex-direction: column;\n flex-grow: 1;\n}\n.du-bois-dark-form-item-control:first-child:not([class^='du-bois-dark-col-']):not([class*=' du-bois-dark-col-']) {\n width: 100%;\n}\n.du-bois-dark-form-item-control-input {\n position: relative;\n display: flex;\n align-items: center;\n min-height: 40px;\n}\n.du-bois-dark-form-item-control-input-content {\n flex: auto;\n max-width: 100%;\n}\n.du-bois-dark-form-item-explain,\n.du-bois-dark-form-item-extra {\n clear: both;\n min-height: 24px;\n color: #92A4B3;\n font-size: 13px;\n line-height: 20px;\n transition: color 0.3s cubic-bezier(0.215, 0.61, 0.355, 1);\n margin-top: -118px;\n margin-bottom: -118px;\n}\n.du-bois-dark-form-item .du-bois-dark-input-textarea-show-count::after {\n margin-bottom: -22px;\n}\n.du-bois-dark-show-help-enter,\n.du-bois-dark-show-help-appear {\n animation-duration: 0.3s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-dark-show-help-leave {\n animation-duration: 0.3s;\n animation-fill-mode: both;\n animation-play-state: paused;\n}\n.du-bois-dark-show-help-enter.du-bois-dark-show-help-enter-active,\n.du-bois-dark-show-help-appear.du-bois-dark-show-help-appear-active {\n animation-name: antShowHelpIn;\n animation-play-state: running;\n}\n.du-bois-dark-show-help-leave.du-bois-dark-show-help-leave-active {\n animation-name: antShowHelpOut;\n animation-play-state: running;\n pointer-events: none;\n}\n.du-bois-dark-show-help-enter,\n.du-bois-dark-show-help-appear {\n opacity: 0;\n animation-timing-function: cubic-bezier(0.645, 0.045, 0.355, 1);\n}\n.du-bois-dark-show-help-leave {\n animation-timing-function: cubic-bezier(0.645, 0.045, 0.355, 1);\n}\n@keyframes antShowHelpIn {\n 0% {\n transform: translateY(-5px);\n opacity: 0;\n }\n 100% {\n transform: translateY(0);\n opacity: 1;\n }\n}\n@keyframes antShowHelpOut {\n}\n@keyframes diffZoomIn1 {\n 0% {\n transform: scale(0);\n opacity: 0;\n }\n 100% {\n transform: scale(1);\n opacity: 1;\n }\n}\n@keyframes diffZoomIn2 {\n 0% {\n transform: scale(0);\n opacity: 0;\n }\n 100% {\n transform: scale(1);\n opacity: 1;\n }\n}\n@keyframes diffZoomIn3 {\n 0% {\n transform: scale(0);\n opacity: 0;\n }\n 100% {\n transform: scale(1);\n opacity: 1;\n }\n}\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-dark-row {\n display: flex;\n flex-flow: row wrap;\n}\n.du-bois-dark-row::before,\n.du-bois-dark-row::after {\n display: flex;\n}\n.du-bois-dark-row-no-wrap {\n flex-wrap: nowrap;\n}\n.du-bois-dark-row-start {\n justify-content: flex-start;\n}\n.du-bois-dark-row-center {\n justify-content: center;\n}\n.du-bois-dark-row-end {\n justify-content: flex-end;\n}\n.du-bois-dark-row-space-between {\n justify-content: space-between;\n}\n.du-bois-dark-row-space-around {\n justify-content: space-around;\n}\n.du-bois-dark-row-top {\n align-items: flex-start;\n}\n.du-bois-dark-row-middle {\n align-items: center;\n}\n.du-bois-dark-row-bottom {\n align-items: flex-end;\n}\n.du-bois-dark-col {\n position: relative;\n max-width: 100%;\n min-height: 1px;\n}\n.du-bois-dark-col-24 {\n display: block;\n flex: 0 0 100%;\n max-width: 100%;\n}\n.du-bois-dark-col-push-24 {\n left: 100%;\n}\n.du-bois-dark-col-pull-24 {\n right: 100%;\n}\n.du-bois-dark-col-offset-24 {\n margin-left: 100%;\n}\n.du-bois-dark-col-order-24 {\n order: 24;\n}\n.du-bois-dark-col-23 {\n display: block;\n flex: 0 0 95.83333333%;\n max-width: 95.83333333%;\n}\n.du-bois-dark-col-push-23 {\n left: 95.83333333%;\n}\n.du-bois-dark-col-pull-23 {\n right: 95.83333333%;\n}\n.du-bois-dark-col-offset-23 {\n margin-left: 95.83333333%;\n}\n.du-bois-dark-col-order-23 {\n order: 23;\n}\n.du-bois-dark-col-22 {\n display: block;\n flex: 0 0 91.66666667%;\n max-width: 91.66666667%;\n}\n.du-bois-dark-col-push-22 {\n left: 91.66666667%;\n}\n.du-bois-dark-col-pull-22 {\n right: 91.66666667%;\n}\n.du-bois-dark-col-offset-22 {\n margin-left: 91.66666667%;\n}\n.du-bois-dark-col-order-22 {\n order: 22;\n}\n.du-bois-dark-col-21 {\n display: block;\n flex: 0 0 87.5%;\n max-width: 87.5%;\n}\n.du-bois-dark-col-push-21 {\n left: 87.5%;\n}\n.du-bois-dark-col-pull-21 {\n right: 87.5%;\n}\n.du-bois-dark-col-offset-21 {\n margin-left: 87.5%;\n}\n.du-bois-dark-col-order-21 {\n order: 21;\n}\n.du-bois-dark-col-20 {\n display: block;\n flex: 0 0 83.33333333%;\n max-width: 83.33333333%;\n}\n.du-bois-dark-col-push-20 {\n left: 83.33333333%;\n}\n.du-bois-dark-col-pull-20 {\n right: 83.33333333%;\n}\n.du-bois-dark-col-offset-20 {\n margin-left: 83.33333333%;\n}\n.du-bois-dark-col-order-20 {\n order: 20;\n}\n.du-bois-dark-col-19 {\n display: block;\n flex: 0 0 79.16666667%;\n max-width: 79.16666667%;\n}\n.du-bois-dark-col-push-19 {\n left: 79.16666667%;\n}\n.du-bois-dark-col-pull-19 {\n right: 79.16666667%;\n}\n.du-bois-dark-col-offset-19 {\n margin-left: 79.16666667%;\n}\n.du-bois-dark-col-order-19 {\n order: 19;\n}\n.du-bois-dark-col-18 {\n display: block;\n flex: 0 0 75%;\n max-width: 75%;\n}\n.du-bois-dark-col-push-18 {\n left: 75%;\n}\n.du-bois-dark-col-pull-18 {\n right: 75%;\n}\n.du-bois-dark-col-offset-18 {\n margin-left: 75%;\n}\n.du-bois-dark-col-order-18 {\n order: 18;\n}\n.du-bois-dark-col-17 {\n display: block;\n flex: 0 0 70.83333333%;\n max-width: 70.83333333%;\n}\n.du-bois-dark-col-push-17 {\n left: 70.83333333%;\n}\n.du-bois-dark-col-pull-17 {\n right: 70.83333333%;\n}\n.du-bois-dark-col-offset-17 {\n margin-left: 70.83333333%;\n}\n.du-bois-dark-col-order-17 {\n order: 17;\n}\n.du-bois-dark-col-16 {\n display: block;\n flex: 0 0 66.66666667%;\n max-width: 66.66666667%;\n}\n.du-bois-dark-col-push-16 {\n left: 66.66666667%;\n}\n.du-bois-dark-col-pull-16 {\n right: 66.66666667%;\n}\n.du-bois-dark-col-offset-16 {\n margin-left: 66.66666667%;\n}\n.du-bois-dark-col-order-16 {\n order: 16;\n}\n.du-bois-dark-col-15 {\n display: block;\n flex: 0 0 62.5%;\n max-width: 62.5%;\n}\n.du-bois-dark-col-push-15 {\n left: 62.5%;\n}\n.du-bois-dark-col-pull-15 {\n right: 62.5%;\n}\n.du-bois-dark-col-offset-15 {\n margin-left: 62.5%;\n}\n.du-bois-dark-col-order-15 {\n order: 15;\n}\n.du-bois-dark-col-14 {\n display: block;\n flex: 0 0 58.33333333%;\n max-width: 58.33333333%;\n}\n.du-bois-dark-col-push-14 {\n left: 58.33333333%;\n}\n.du-bois-dark-col-pull-14 {\n right: 58.33333333%;\n}\n.du-bois-dark-col-offset-14 {\n margin-left: 58.33333333%;\n}\n.du-bois-dark-col-order-14 {\n order: 14;\n}\n.du-bois-dark-col-13 {\n display: block;\n flex: 0 0 54.16666667%;\n max-width: 54.16666667%;\n}\n.du-bois-dark-col-push-13 {\n left: 54.16666667%;\n}\n.du-bois-dark-col-pull-13 {\n right: 54.16666667%;\n}\n.du-bois-dark-col-offset-13 {\n margin-left: 54.16666667%;\n}\n.du-bois-dark-col-order-13 {\n order: 13;\n}\n.du-bois-dark-col-12 {\n display: block;\n flex: 0 0 50%;\n max-width: 50%;\n}\n.du-bois-dark-col-push-12 {\n left: 50%;\n}\n.du-bois-dark-col-pull-12 {\n right: 50%;\n}\n.du-bois-dark-col-offset-12 {\n margin-left: 50%;\n}\n.du-bois-dark-col-order-12 {\n order: 12;\n}\n.du-bois-dark-col-11 {\n display: block;\n flex: 0 0 45.83333333%;\n max-width: 45.83333333%;\n}\n.du-bois-dark-col-push-11 {\n left: 45.83333333%;\n}\n.du-bois-dark-col-pull-11 {\n right: 45.83333333%;\n}\n.du-bois-dark-col-offset-11 {\n margin-left: 45.83333333%;\n}\n.du-bois-dark-col-order-11 {\n order: 11;\n}\n.du-bois-dark-col-10 {\n display: block;\n flex: 0 0 41.66666667%;\n max-width: 41.66666667%;\n}\n.du-bois-dark-col-push-10 {\n left: 41.66666667%;\n}\n.du-bois-dark-col-pull-10 {\n right: 41.66666667%;\n}\n.du-bois-dark-col-offset-10 {\n margin-left: 41.66666667%;\n}\n.du-bois-dark-col-order-10 {\n order: 10;\n}\n.du-bois-dark-col-9 {\n display: block;\n flex: 0 0 37.5%;\n max-width: 37.5%;\n}\n.du-bois-dark-col-push-9 {\n left: 37.5%;\n}\n.du-bois-dark-col-pull-9 {\n right: 37.5%;\n}\n.du-bois-dark-col-offset-9 {\n margin-left: 37.5%;\n}\n.du-bois-dark-col-order-9 {\n order: 9;\n}\n.du-bois-dark-col-8 {\n display: block;\n flex: 0 0 33.33333333%;\n max-width: 33.33333333%;\n}\n.du-bois-dark-col-push-8 {\n left: 33.33333333%;\n}\n.du-bois-dark-col-pull-8 {\n right: 33.33333333%;\n}\n.du-bois-dark-col-offset-8 {\n margin-left: 33.33333333%;\n}\n.du-bois-dark-col-order-8 {\n order: 8;\n}\n.du-bois-dark-col-7 {\n display: block;\n flex: 0 0 29.16666667%;\n max-width: 29.16666667%;\n}\n.du-bois-dark-col-push-7 {\n left: 29.16666667%;\n}\n.du-bois-dark-col-pull-7 {\n right: 29.16666667%;\n}\n.du-bois-dark-col-offset-7 {\n margin-left: 29.16666667%;\n}\n.du-bois-dark-col-order-7 {\n order: 7;\n}\n.du-bois-dark-col-6 {\n display: block;\n flex: 0 0 25%;\n max-width: 25%;\n}\n.du-bois-dark-col-push-6 {\n left: 25%;\n}\n.du-bois-dark-col-pull-6 {\n right: 25%;\n}\n.du-bois-dark-col-offset-6 {\n margin-left: 25%;\n}\n.du-bois-dark-col-order-6 {\n order: 6;\n}\n.du-bois-dark-col-5 {\n display: block;\n flex: 0 0 20.83333333%;\n max-width: 20.83333333%;\n}\n.du-bois-dark-col-push-5 {\n left: 20.83333333%;\n}\n.du-bois-dark-col-pull-5 {\n right: 20.83333333%;\n}\n.du-bois-dark-col-offset-5 {\n margin-left: 20.83333333%;\n}\n.du-bois-dark-col-order-5 {\n order: 5;\n}\n.du-bois-dark-col-4 {\n display: block;\n flex: 0 0 16.66666667%;\n max-width: 16.66666667%;\n}\n.du-bois-dark-col-push-4 {\n left: 16.66666667%;\n}\n.du-bois-dark-col-pull-4 {\n right: 16.66666667%;\n}\n.du-bois-dark-col-offset-4 {\n margin-left: 16.66666667%;\n}\n.du-bois-dark-col-order-4 {\n order: 4;\n}\n.du-bois-dark-col-3 {\n display: block;\n flex: 0 0 12.5%;\n max-width: 12.5%;\n}\n.du-bois-dark-col-push-3 {\n left: 12.5%;\n}\n.du-bois-dark-col-pull-3 {\n right: 12.5%;\n}\n.du-bois-dark-col-offset-3 {\n margin-left: 12.5%;\n}\n.du-bois-dark-col-order-3 {\n order: 3;\n}\n.du-bois-dark-col-2 {\n display: block;\n flex: 0 0 8.33333333%;\n max-width: 8.33333333%;\n}\n.du-bois-dark-col-push-2 {\n left: 8.33333333%;\n}\n.du-bois-dark-col-pull-2 {\n right: 8.33333333%;\n}\n.du-bois-dark-col-offset-2 {\n margin-left: 8.33333333%;\n}\n.du-bois-dark-col-order-2 {\n order: 2;\n}\n.du-bois-dark-col-1 {\n display: block;\n flex: 0 0 4.16666667%;\n max-width: 4.16666667%;\n}\n.du-bois-dark-col-push-1 {\n left: 4.16666667%;\n}\n.du-bois-dark-col-pull-1 {\n right: 4.16666667%;\n}\n.du-bois-dark-col-offset-1 {\n margin-left: 4.16666667%;\n}\n.du-bois-dark-col-order-1 {\n order: 1;\n}\n.du-bois-dark-col-0 {\n display: none;\n}\n.du-bois-dark-col-push-0 {\n left: auto;\n}\n.du-bois-dark-col-pull-0 {\n right: auto;\n}\n.du-bois-dark-col-push-0 {\n left: auto;\n}\n.du-bois-dark-col-pull-0 {\n right: auto;\n}\n.du-bois-dark-col-offset-0 {\n margin-left: 0;\n}\n.du-bois-dark-col-order-0 {\n order: 0;\n}\n.du-bois-dark-col-xs-24 {\n display: block;\n flex: 0 0 100%;\n max-width: 100%;\n}\n.du-bois-dark-col-xs-push-24 {\n left: 100%;\n}\n.du-bois-dark-col-xs-pull-24 {\n right: 100%;\n}\n.du-bois-dark-col-xs-offset-24 {\n margin-left: 100%;\n}\n.du-bois-dark-col-xs-order-24 {\n order: 24;\n}\n.du-bois-dark-col-xs-23 {\n display: block;\n flex: 0 0 95.83333333%;\n max-width: 95.83333333%;\n}\n.du-bois-dark-col-xs-push-23 {\n left: 95.83333333%;\n}\n.du-bois-dark-col-xs-pull-23 {\n right: 95.83333333%;\n}\n.du-bois-dark-col-xs-offset-23 {\n margin-left: 95.83333333%;\n}\n.du-bois-dark-col-xs-order-23 {\n order: 23;\n}\n.du-bois-dark-col-xs-22 {\n display: block;\n flex: 0 0 91.66666667%;\n max-width: 91.66666667%;\n}\n.du-bois-dark-col-xs-push-22 {\n left: 91.66666667%;\n}\n.du-bois-dark-col-xs-pull-22 {\n right: 91.66666667%;\n}\n.du-bois-dark-col-xs-offset-22 {\n margin-left: 91.66666667%;\n}\n.du-bois-dark-col-xs-order-22 {\n order: 22;\n}\n.du-bois-dark-col-xs-21 {\n display: block;\n flex: 0 0 87.5%;\n max-width: 87.5%;\n}\n.du-bois-dark-col-xs-push-21 {\n left: 87.5%;\n}\n.du-bois-dark-col-xs-pull-21 {\n right: 87.5%;\n}\n.du-bois-dark-col-xs-offset-21 {\n margin-left: 87.5%;\n}\n.du-bois-dark-col-xs-order-21 {\n order: 21;\n}\n.du-bois-dark-col-xs-20 {\n display: block;\n flex: 0 0 83.33333333%;\n max-width: 83.33333333%;\n}\n.du-bois-dark-col-xs-push-20 {\n left: 83.33333333%;\n}\n.du-bois-dark-col-xs-pull-20 {\n right: 83.33333333%;\n}\n.du-bois-dark-col-xs-offset-20 {\n margin-left: 83.33333333%;\n}\n.du-bois-dark-col-xs-order-20 {\n order: 20;\n}\n.du-bois-dark-col-xs-19 {\n display: block;\n flex: 0 0 79.16666667%;\n max-width: 79.16666667%;\n}\n.du-bois-dark-col-xs-push-19 {\n left: 79.16666667%;\n}\n.du-bois-dark-col-xs-pull-19 {\n right: 79.16666667%;\n}\n.du-bois-dark-col-xs-offset-19 {\n margin-left: 79.16666667%;\n}\n.du-bois-dark-col-xs-order-19 {\n order: 19;\n}\n.du-bois-dark-col-xs-18 {\n display: block;\n flex: 0 0 75%;\n max-width: 75%;\n}\n.du-bois-dark-col-xs-push-18 {\n left: 75%;\n}\n.du-bois-dark-col-xs-pull-18 {\n right: 75%;\n}\n.du-bois-dark-col-xs-offset-18 {\n margin-left: 75%;\n}\n.du-bois-dark-col-xs-order-18 {\n order: 18;\n}\n.du-bois-dark-col-xs-17 {\n display: block;\n flex: 0 0 70.83333333%;\n max-width: 70.83333333%;\n}\n.du-bois-dark-col-xs-push-17 {\n left: 70.83333333%;\n}\n.du-bois-dark-col-xs-pull-17 {\n right: 70.83333333%;\n}\n.du-bois-dark-col-xs-offset-17 {\n margin-left: 70.83333333%;\n}\n.du-bois-dark-col-xs-order-17 {\n order: 17;\n}\n.du-bois-dark-col-xs-16 {\n display: block;\n flex: 0 0 66.66666667%;\n max-width: 66.66666667%;\n}\n.du-bois-dark-col-xs-push-16 {\n left: 66.66666667%;\n}\n.du-bois-dark-col-xs-pull-16 {\n right: 66.66666667%;\n}\n.du-bois-dark-col-xs-offset-16 {\n margin-left: 66.66666667%;\n}\n.du-bois-dark-col-xs-order-16 {\n order: 16;\n}\n.du-bois-dark-col-xs-15 {\n display: block;\n flex: 0 0 62.5%;\n max-width: 62.5%;\n}\n.du-bois-dark-col-xs-push-15 {\n left: 62.5%;\n}\n.du-bois-dark-col-xs-pull-15 {\n right: 62.5%;\n}\n.du-bois-dark-col-xs-offset-15 {\n margin-left: 62.5%;\n}\n.du-bois-dark-col-xs-order-15 {\n order: 15;\n}\n.du-bois-dark-col-xs-14 {\n display: block;\n flex: 0 0 58.33333333%;\n max-width: 58.33333333%;\n}\n.du-bois-dark-col-xs-push-14 {\n left: 58.33333333%;\n}\n.du-bois-dark-col-xs-pull-14 {\n right: 58.33333333%;\n}\n.du-bois-dark-col-xs-offset-14 {\n margin-left: 58.33333333%;\n}\n.du-bois-dark-col-xs-order-14 {\n order: 14;\n}\n.du-bois-dark-col-xs-13 {\n display: block;\n flex: 0 0 54.16666667%;\n max-width: 54.16666667%;\n}\n.du-bois-dark-col-xs-push-13 {\n left: 54.16666667%;\n}\n.du-bois-dark-col-xs-pull-13 {\n right: 54.16666667%;\n}\n.du-bois-dark-col-xs-offset-13 {\n margin-left: 54.16666667%;\n}\n.du-bois-dark-col-xs-order-13 {\n order: 13;\n}\n.du-bois-dark-col-xs-12 {\n display: block;\n flex: 0 0 50%;\n max-width: 50%;\n}\n.du-bois-dark-col-xs-push-12 {\n left: 50%;\n}\n.du-bois-dark-col-xs-pull-12 {\n right: 50%;\n}\n.du-bois-dark-col-xs-offset-12 {\n margin-left: 50%;\n}\n.du-bois-dark-col-xs-order-12 {\n order: 12;\n}\n.du-bois-dark-col-xs-11 {\n display: block;\n flex: 0 0 45.83333333%;\n max-width: 45.83333333%;\n}\n.du-bois-dark-col-xs-push-11 {\n left: 45.83333333%;\n}\n.du-bois-dark-col-xs-pull-11 {\n right: 45.83333333%;\n}\n.du-bois-dark-col-xs-offset-11 {\n margin-left: 45.83333333%;\n}\n.du-bois-dark-col-xs-order-11 {\n order: 11;\n}\n.du-bois-dark-col-xs-10 {\n display: block;\n flex: 0 0 41.66666667%;\n max-width: 41.66666667%;\n}\n.du-bois-dark-col-xs-push-10 {\n left: 41.66666667%;\n}\n.du-bois-dark-col-xs-pull-10 {\n right: 41.66666667%;\n}\n.du-bois-dark-col-xs-offset-10 {\n margin-left: 41.66666667%;\n}\n.du-bois-dark-col-xs-order-10 {\n order: 10;\n}\n.du-bois-dark-col-xs-9 {\n display: block;\n flex: 0 0 37.5%;\n max-width: 37.5%;\n}\n.du-bois-dark-col-xs-push-9 {\n left: 37.5%;\n}\n.du-bois-dark-col-xs-pull-9 {\n right: 37.5%;\n}\n.du-bois-dark-col-xs-offset-9 {\n margin-left: 37.5%;\n}\n.du-bois-dark-col-xs-order-9 {\n order: 9;\n}\n.du-bois-dark-col-xs-8 {\n display: block;\n flex: 0 0 33.33333333%;\n max-width: 33.33333333%;\n}\n.du-bois-dark-col-xs-push-8 {\n left: 33.33333333%;\n}\n.du-bois-dark-col-xs-pull-8 {\n right: 33.33333333%;\n}\n.du-bois-dark-col-xs-offset-8 {\n margin-left: 33.33333333%;\n}\n.du-bois-dark-col-xs-order-8 {\n order: 8;\n}\n.du-bois-dark-col-xs-7 {\n display: block;\n flex: 0 0 29.16666667%;\n max-width: 29.16666667%;\n}\n.du-bois-dark-col-xs-push-7 {\n left: 29.16666667%;\n}\n.du-bois-dark-col-xs-pull-7 {\n right: 29.16666667%;\n}\n.du-bois-dark-col-xs-offset-7 {\n margin-left: 29.16666667%;\n}\n.du-bois-dark-col-xs-order-7 {\n order: 7;\n}\n.du-bois-dark-col-xs-6 {\n display: block;\n flex: 0 0 25%;\n max-width: 25%;\n}\n.du-bois-dark-col-xs-push-6 {\n left: 25%;\n}\n.du-bois-dark-col-xs-pull-6 {\n right: 25%;\n}\n.du-bois-dark-col-xs-offset-6 {\n margin-left: 25%;\n}\n.du-bois-dark-col-xs-order-6 {\n order: 6;\n}\n.du-bois-dark-col-xs-5 {\n display: block;\n flex: 0 0 20.83333333%;\n max-width: 20.83333333%;\n}\n.du-bois-dark-col-xs-push-5 {\n left: 20.83333333%;\n}\n.du-bois-dark-col-xs-pull-5 {\n right: 20.83333333%;\n}\n.du-bois-dark-col-xs-offset-5 {\n margin-left: 20.83333333%;\n}\n.du-bois-dark-col-xs-order-5 {\n order: 5;\n}\n.du-bois-dark-col-xs-4 {\n display: block;\n flex: 0 0 16.66666667%;\n max-width: 16.66666667%;\n}\n.du-bois-dark-col-xs-push-4 {\n left: 16.66666667%;\n}\n.du-bois-dark-col-xs-pull-4 {\n right: 16.66666667%;\n}\n.du-bois-dark-col-xs-offset-4 {\n margin-left: 16.66666667%;\n}\n.du-bois-dark-col-xs-order-4 {\n order: 4;\n}\n.du-bois-dark-col-xs-3 {\n display: block;\n flex: 0 0 12.5%;\n max-width: 12.5%;\n}\n.du-bois-dark-col-xs-push-3 {\n left: 12.5%;\n}\n.du-bois-dark-col-xs-pull-3 {\n right: 12.5%;\n}\n.du-bois-dark-col-xs-offset-3 {\n margin-left: 12.5%;\n}\n.du-bois-dark-col-xs-order-3 {\n order: 3;\n}\n.du-bois-dark-col-xs-2 {\n display: block;\n flex: 0 0 8.33333333%;\n max-width: 8.33333333%;\n}\n.du-bois-dark-col-xs-push-2 {\n left: 8.33333333%;\n}\n.du-bois-dark-col-xs-pull-2 {\n right: 8.33333333%;\n}\n.du-bois-dark-col-xs-offset-2 {\n margin-left: 8.33333333%;\n}\n.du-bois-dark-col-xs-order-2 {\n order: 2;\n}\n.du-bois-dark-col-xs-1 {\n display: block;\n flex: 0 0 4.16666667%;\n max-width: 4.16666667%;\n}\n.du-bois-dark-col-xs-push-1 {\n left: 4.16666667%;\n}\n.du-bois-dark-col-xs-pull-1 {\n right: 4.16666667%;\n}\n.du-bois-dark-col-xs-offset-1 {\n margin-left: 4.16666667%;\n}\n.du-bois-dark-col-xs-order-1 {\n order: 1;\n}\n.du-bois-dark-col-xs-0 {\n display: none;\n}\n.du-bois-dark-col-push-0 {\n left: auto;\n}\n.du-bois-dark-col-pull-0 {\n right: auto;\n}\n.du-bois-dark-col-xs-push-0 {\n left: auto;\n}\n.du-bois-dark-col-xs-pull-0 {\n right: auto;\n}\n.du-bois-dark-col-xs-offset-0 {\n margin-left: 0;\n}\n.du-bois-dark-col-xs-order-0 {\n order: 0;\n}\n@media (min-width: 576px) {\n .du-bois-dark-col-sm-24 {\n display: block;\n flex: 0 0 100%;\n max-width: 100%;\n }\n .du-bois-dark-col-sm-push-24 {\n left: 100%;\n }\n .du-bois-dark-col-sm-pull-24 {\n right: 100%;\n }\n .du-bois-dark-col-sm-offset-24 {\n margin-left: 100%;\n }\n .du-bois-dark-col-sm-order-24 {\n order: 24;\n }\n .du-bois-dark-col-sm-23 {\n display: block;\n flex: 0 0 95.83333333%;\n max-width: 95.83333333%;\n }\n .du-bois-dark-col-sm-push-23 {\n left: 95.83333333%;\n }\n .du-bois-dark-col-sm-pull-23 {\n right: 95.83333333%;\n }\n .du-bois-dark-col-sm-offset-23 {\n margin-left: 95.83333333%;\n }\n .du-bois-dark-col-sm-order-23 {\n order: 23;\n }\n .du-bois-dark-col-sm-22 {\n display: block;\n flex: 0 0 91.66666667%;\n max-width: 91.66666667%;\n }\n .du-bois-dark-col-sm-push-22 {\n left: 91.66666667%;\n }\n .du-bois-dark-col-sm-pull-22 {\n right: 91.66666667%;\n }\n .du-bois-dark-col-sm-offset-22 {\n margin-left: 91.66666667%;\n }\n .du-bois-dark-col-sm-order-22 {\n order: 22;\n }\n .du-bois-dark-col-sm-21 {\n display: block;\n flex: 0 0 87.5%;\n max-width: 87.5%;\n }\n .du-bois-dark-col-sm-push-21 {\n left: 87.5%;\n }\n .du-bois-dark-col-sm-pull-21 {\n right: 87.5%;\n }\n .du-bois-dark-col-sm-offset-21 {\n margin-left: 87.5%;\n }\n .du-bois-dark-col-sm-order-21 {\n order: 21;\n }\n .du-bois-dark-col-sm-20 {\n display: block;\n flex: 0 0 83.33333333%;\n max-width: 83.33333333%;\n }\n .du-bois-dark-col-sm-push-20 {\n left: 83.33333333%;\n }\n .du-bois-dark-col-sm-pull-20 {\n right: 83.33333333%;\n }\n .du-bois-dark-col-sm-offset-20 {\n margin-left: 83.33333333%;\n }\n .du-bois-dark-col-sm-order-20 {\n order: 20;\n }\n .du-bois-dark-col-sm-19 {\n display: block;\n flex: 0 0 79.16666667%;\n max-width: 79.16666667%;\n }\n .du-bois-dark-col-sm-push-19 {\n left: 79.16666667%;\n }\n .du-bois-dark-col-sm-pull-19 {\n right: 79.16666667%;\n }\n .du-bois-dark-col-sm-offset-19 {\n margin-left: 79.16666667%;\n }\n .du-bois-dark-col-sm-order-19 {\n order: 19;\n }\n .du-bois-dark-col-sm-18 {\n display: block;\n flex: 0 0 75%;\n max-width: 75%;\n }\n .du-bois-dark-col-sm-push-18 {\n left: 75%;\n }\n .du-bois-dark-col-sm-pull-18 {\n right: 75%;\n }\n .du-bois-dark-col-sm-offset-18 {\n margin-left: 75%;\n }\n .du-bois-dark-col-sm-order-18 {\n order: 18;\n }\n .du-bois-dark-col-sm-17 {\n display: block;\n flex: 0 0 70.83333333%;\n max-width: 70.83333333%;\n }\n .du-bois-dark-col-sm-push-17 {\n left: 70.83333333%;\n }\n .du-bois-dark-col-sm-pull-17 {\n right: 70.83333333%;\n }\n .du-bois-dark-col-sm-offset-17 {\n margin-left: 70.83333333%;\n }\n .du-bois-dark-col-sm-order-17 {\n order: 17;\n }\n .du-bois-dark-col-sm-16 {\n display: block;\n flex: 0 0 66.66666667%;\n max-width: 66.66666667%;\n }\n .du-bois-dark-col-sm-push-16 {\n left: 66.66666667%;\n }\n .du-bois-dark-col-sm-pull-16 {\n right: 66.66666667%;\n }\n .du-bois-dark-col-sm-offset-16 {\n margin-left: 66.66666667%;\n }\n .du-bois-dark-col-sm-order-16 {\n order: 16;\n }\n .du-bois-dark-col-sm-15 {\n display: block;\n flex: 0 0 62.5%;\n max-width: 62.5%;\n }\n .du-bois-dark-col-sm-push-15 {\n left: 62.5%;\n }\n .du-bois-dark-col-sm-pull-15 {\n right: 62.5%;\n }\n .du-bois-dark-col-sm-offset-15 {\n margin-left: 62.5%;\n }\n .du-bois-dark-col-sm-order-15 {\n order: 15;\n }\n .du-bois-dark-col-sm-14 {\n display: block;\n flex: 0 0 58.33333333%;\n max-width: 58.33333333%;\n }\n .du-bois-dark-col-sm-push-14 {\n left: 58.33333333%;\n }\n .du-bois-dark-col-sm-pull-14 {\n right: 58.33333333%;\n }\n .du-bois-dark-col-sm-offset-14 {\n margin-left: 58.33333333%;\n }\n .du-bois-dark-col-sm-order-14 {\n order: 14;\n }\n .du-bois-dark-col-sm-13 {\n display: block;\n flex: 0 0 54.16666667%;\n max-width: 54.16666667%;\n }\n .du-bois-dark-col-sm-push-13 {\n left: 54.16666667%;\n }\n .du-bois-dark-col-sm-pull-13 {\n right: 54.16666667%;\n }\n .du-bois-dark-col-sm-offset-13 {\n margin-left: 54.16666667%;\n }\n .du-bois-dark-col-sm-order-13 {\n order: 13;\n }\n .du-bois-dark-col-sm-12 {\n display: block;\n flex: 0 0 50%;\n max-width: 50%;\n }\n .du-bois-dark-col-sm-push-12 {\n left: 50%;\n }\n .du-bois-dark-col-sm-pull-12 {\n right: 50%;\n }\n .du-bois-dark-col-sm-offset-12 {\n margin-left: 50%;\n }\n .du-bois-dark-col-sm-order-12 {\n order: 12;\n }\n .du-bois-dark-col-sm-11 {\n display: block;\n flex: 0 0 45.83333333%;\n max-width: 45.83333333%;\n }\n .du-bois-dark-col-sm-push-11 {\n left: 45.83333333%;\n }\n .du-bois-dark-col-sm-pull-11 {\n right: 45.83333333%;\n }\n .du-bois-dark-col-sm-offset-11 {\n margin-left: 45.83333333%;\n }\n .du-bois-dark-col-sm-order-11 {\n order: 11;\n }\n .du-bois-dark-col-sm-10 {\n display: block;\n flex: 0 0 41.66666667%;\n max-width: 41.66666667%;\n }\n .du-bois-dark-col-sm-push-10 {\n left: 41.66666667%;\n }\n .du-bois-dark-col-sm-pull-10 {\n right: 41.66666667%;\n }\n .du-bois-dark-col-sm-offset-10 {\n margin-left: 41.66666667%;\n }\n .du-bois-dark-col-sm-order-10 {\n order: 10;\n }\n .du-bois-dark-col-sm-9 {\n display: block;\n flex: 0 0 37.5%;\n max-width: 37.5%;\n }\n .du-bois-dark-col-sm-push-9 {\n left: 37.5%;\n }\n .du-bois-dark-col-sm-pull-9 {\n right: 37.5%;\n }\n .du-bois-dark-col-sm-offset-9 {\n margin-left: 37.5%;\n }\n .du-bois-dark-col-sm-order-9 {\n order: 9;\n }\n .du-bois-dark-col-sm-8 {\n display: block;\n flex: 0 0 33.33333333%;\n max-width: 33.33333333%;\n }\n .du-bois-dark-col-sm-push-8 {\n left: 33.33333333%;\n }\n .du-bois-dark-col-sm-pull-8 {\n right: 33.33333333%;\n }\n .du-bois-dark-col-sm-offset-8 {\n margin-left: 33.33333333%;\n }\n .du-bois-dark-col-sm-order-8 {\n order: 8;\n }\n .du-bois-dark-col-sm-7 {\n display: block;\n flex: 0 0 29.16666667%;\n max-width: 29.16666667%;\n }\n .du-bois-dark-col-sm-push-7 {\n left: 29.16666667%;\n }\n .du-bois-dark-col-sm-pull-7 {\n right: 29.16666667%;\n }\n .du-bois-dark-col-sm-offset-7 {\n margin-left: 29.16666667%;\n }\n .du-bois-dark-col-sm-order-7 {\n order: 7;\n }\n .du-bois-dark-col-sm-6 {\n display: block;\n flex: 0 0 25%;\n max-width: 25%;\n }\n .du-bois-dark-col-sm-push-6 {\n left: 25%;\n }\n .du-bois-dark-col-sm-pull-6 {\n right: 25%;\n }\n .du-bois-dark-col-sm-offset-6 {\n margin-left: 25%;\n }\n .du-bois-dark-col-sm-order-6 {\n order: 6;\n }\n .du-bois-dark-col-sm-5 {\n display: block;\n flex: 0 0 20.83333333%;\n max-width: 20.83333333%;\n }\n .du-bois-dark-col-sm-push-5 {\n left: 20.83333333%;\n }\n .du-bois-dark-col-sm-pull-5 {\n right: 20.83333333%;\n }\n .du-bois-dark-col-sm-offset-5 {\n margin-left: 20.83333333%;\n }\n .du-bois-dark-col-sm-order-5 {\n order: 5;\n }\n .du-bois-dark-col-sm-4 {\n display: block;\n flex: 0 0 16.66666667%;\n max-width: 16.66666667%;\n }\n .du-bois-dark-col-sm-push-4 {\n left: 16.66666667%;\n }\n .du-bois-dark-col-sm-pull-4 {\n right: 16.66666667%;\n }\n .du-bois-dark-col-sm-offset-4 {\n margin-left: 16.66666667%;\n }\n .du-bois-dark-col-sm-order-4 {\n order: 4;\n }\n .du-bois-dark-col-sm-3 {\n display: block;\n flex: 0 0 12.5%;\n max-width: 12.5%;\n }\n .du-bois-dark-col-sm-push-3 {\n left: 12.5%;\n }\n .du-bois-dark-col-sm-pull-3 {\n right: 12.5%;\n }\n .du-bois-dark-col-sm-offset-3 {\n margin-left: 12.5%;\n }\n .du-bois-dark-col-sm-order-3 {\n order: 3;\n }\n .du-bois-dark-col-sm-2 {\n display: block;\n flex: 0 0 8.33333333%;\n max-width: 8.33333333%;\n }\n .du-bois-dark-col-sm-push-2 {\n left: 8.33333333%;\n }\n .du-bois-dark-col-sm-pull-2 {\n right: 8.33333333%;\n }\n .du-bois-dark-col-sm-offset-2 {\n margin-left: 8.33333333%;\n }\n .du-bois-dark-col-sm-order-2 {\n order: 2;\n }\n .du-bois-dark-col-sm-1 {\n display: block;\n flex: 0 0 4.16666667%;\n max-width: 4.16666667%;\n }\n .du-bois-dark-col-sm-push-1 {\n left: 4.16666667%;\n }\n .du-bois-dark-col-sm-pull-1 {\n right: 4.16666667%;\n }\n .du-bois-dark-col-sm-offset-1 {\n margin-left: 4.16666667%;\n }\n .du-bois-dark-col-sm-order-1 {\n order: 1;\n }\n .du-bois-dark-col-sm-0 {\n display: none;\n }\n .du-bois-dark-col-push-0 {\n left: auto;\n }\n .du-bois-dark-col-pull-0 {\n right: auto;\n }\n .du-bois-dark-col-sm-push-0 {\n left: auto;\n }\n .du-bois-dark-col-sm-pull-0 {\n right: auto;\n }\n .du-bois-dark-col-sm-offset-0 {\n margin-left: 0;\n }\n .du-bois-dark-col-sm-order-0 {\n order: 0;\n }\n}\n@media (min-width: 768px) {\n .du-bois-dark-col-md-24 {\n display: block;\n flex: 0 0 100%;\n max-width: 100%;\n }\n .du-bois-dark-col-md-push-24 {\n left: 100%;\n }\n .du-bois-dark-col-md-pull-24 {\n right: 100%;\n }\n .du-bois-dark-col-md-offset-24 {\n margin-left: 100%;\n }\n .du-bois-dark-col-md-order-24 {\n order: 24;\n }\n .du-bois-dark-col-md-23 {\n display: block;\n flex: 0 0 95.83333333%;\n max-width: 95.83333333%;\n }\n .du-bois-dark-col-md-push-23 {\n left: 95.83333333%;\n }\n .du-bois-dark-col-md-pull-23 {\n right: 95.83333333%;\n }\n .du-bois-dark-col-md-offset-23 {\n margin-left: 95.83333333%;\n }\n .du-bois-dark-col-md-order-23 {\n order: 23;\n }\n .du-bois-dark-col-md-22 {\n display: block;\n flex: 0 0 91.66666667%;\n max-width: 91.66666667%;\n }\n .du-bois-dark-col-md-push-22 {\n left: 91.66666667%;\n }\n .du-bois-dark-col-md-pull-22 {\n right: 91.66666667%;\n }\n .du-bois-dark-col-md-offset-22 {\n margin-left: 91.66666667%;\n }\n .du-bois-dark-col-md-order-22 {\n order: 22;\n }\n .du-bois-dark-col-md-21 {\n display: block;\n flex: 0 0 87.5%;\n max-width: 87.5%;\n }\n .du-bois-dark-col-md-push-21 {\n left: 87.5%;\n }\n .du-bois-dark-col-md-pull-21 {\n right: 87.5%;\n }\n .du-bois-dark-col-md-offset-21 {\n margin-left: 87.5%;\n }\n .du-bois-dark-col-md-order-21 {\n order: 21;\n }\n .du-bois-dark-col-md-20 {\n display: block;\n flex: 0 0 83.33333333%;\n max-width: 83.33333333%;\n }\n .du-bois-dark-col-md-push-20 {\n left: 83.33333333%;\n }\n .du-bois-dark-col-md-pull-20 {\n right: 83.33333333%;\n }\n .du-bois-dark-col-md-offset-20 {\n margin-left: 83.33333333%;\n }\n .du-bois-dark-col-md-order-20 {\n order: 20;\n }\n .du-bois-dark-col-md-19 {\n display: block;\n flex: 0 0 79.16666667%;\n max-width: 79.16666667%;\n }\n .du-bois-dark-col-md-push-19 {\n left: 79.16666667%;\n }\n .du-bois-dark-col-md-pull-19 {\n right: 79.16666667%;\n }\n .du-bois-dark-col-md-offset-19 {\n margin-left: 79.16666667%;\n }\n .du-bois-dark-col-md-order-19 {\n order: 19;\n }\n .du-bois-dark-col-md-18 {\n display: block;\n flex: 0 0 75%;\n max-width: 75%;\n }\n .du-bois-dark-col-md-push-18 {\n left: 75%;\n }\n .du-bois-dark-col-md-pull-18 {\n right: 75%;\n }\n .du-bois-dark-col-md-offset-18 {\n margin-left: 75%;\n }\n .du-bois-dark-col-md-order-18 {\n order: 18;\n }\n .du-bois-dark-col-md-17 {\n display: block;\n flex: 0 0 70.83333333%;\n max-width: 70.83333333%;\n }\n .du-bois-dark-col-md-push-17 {\n left: 70.83333333%;\n }\n .du-bois-dark-col-md-pull-17 {\n right: 70.83333333%;\n }\n .du-bois-dark-col-md-offset-17 {\n margin-left: 70.83333333%;\n }\n .du-bois-dark-col-md-order-17 {\n order: 17;\n }\n .du-bois-dark-col-md-16 {\n display: block;\n flex: 0 0 66.66666667%;\n max-width: 66.66666667%;\n }\n .du-bois-dark-col-md-push-16 {\n left: 66.66666667%;\n }\n .du-bois-dark-col-md-pull-16 {\n right: 66.66666667%;\n }\n .du-bois-dark-col-md-offset-16 {\n margin-left: 66.66666667%;\n }\n .du-bois-dark-col-md-order-16 {\n order: 16;\n }\n .du-bois-dark-col-md-15 {\n display: block;\n flex: 0 0 62.5%;\n max-width: 62.5%;\n }\n .du-bois-dark-col-md-push-15 {\n left: 62.5%;\n }\n .du-bois-dark-col-md-pull-15 {\n right: 62.5%;\n }\n .du-bois-dark-col-md-offset-15 {\n margin-left: 62.5%;\n }\n .du-bois-dark-col-md-order-15 {\n order: 15;\n }\n .du-bois-dark-col-md-14 {\n display: block;\n flex: 0 0 58.33333333%;\n max-width: 58.33333333%;\n }\n .du-bois-dark-col-md-push-14 {\n left: 58.33333333%;\n }\n .du-bois-dark-col-md-pull-14 {\n right: 58.33333333%;\n }\n .du-bois-dark-col-md-offset-14 {\n margin-left: 58.33333333%;\n }\n .du-bois-dark-col-md-order-14 {\n order: 14;\n }\n .du-bois-dark-col-md-13 {\n display: block;\n flex: 0 0 54.16666667%;\n max-width: 54.16666667%;\n }\n .du-bois-dark-col-md-push-13 {\n left: 54.16666667%;\n }\n .du-bois-dark-col-md-pull-13 {\n right: 54.16666667%;\n }\n .du-bois-dark-col-md-offset-13 {\n margin-left: 54.16666667%;\n }\n .du-bois-dark-col-md-order-13 {\n order: 13;\n }\n .du-bois-dark-col-md-12 {\n display: block;\n flex: 0 0 50%;\n max-width: 50%;\n }\n .du-bois-dark-col-md-push-12 {\n left: 50%;\n }\n .du-bois-dark-col-md-pull-12 {\n right: 50%;\n }\n .du-bois-dark-col-md-offset-12 {\n margin-left: 50%;\n }\n .du-bois-dark-col-md-order-12 {\n order: 12;\n }\n .du-bois-dark-col-md-11 {\n display: block;\n flex: 0 0 45.83333333%;\n max-width: 45.83333333%;\n }\n .du-bois-dark-col-md-push-11 {\n left: 45.83333333%;\n }\n .du-bois-dark-col-md-pull-11 {\n right: 45.83333333%;\n }\n .du-bois-dark-col-md-offset-11 {\n margin-left: 45.83333333%;\n }\n .du-bois-dark-col-md-order-11 {\n order: 11;\n }\n .du-bois-dark-col-md-10 {\n display: block;\n flex: 0 0 41.66666667%;\n max-width: 41.66666667%;\n }\n .du-bois-dark-col-md-push-10 {\n left: 41.66666667%;\n }\n .du-bois-dark-col-md-pull-10 {\n right: 41.66666667%;\n }\n .du-bois-dark-col-md-offset-10 {\n margin-left: 41.66666667%;\n }\n .du-bois-dark-col-md-order-10 {\n order: 10;\n }\n .du-bois-dark-col-md-9 {\n display: block;\n flex: 0 0 37.5%;\n max-width: 37.5%;\n }\n .du-bois-dark-col-md-push-9 {\n left: 37.5%;\n }\n .du-bois-dark-col-md-pull-9 {\n right: 37.5%;\n }\n .du-bois-dark-col-md-offset-9 {\n margin-left: 37.5%;\n }\n .du-bois-dark-col-md-order-9 {\n order: 9;\n }\n .du-bois-dark-col-md-8 {\n display: block;\n flex: 0 0 33.33333333%;\n max-width: 33.33333333%;\n }\n .du-bois-dark-col-md-push-8 {\n left: 33.33333333%;\n }\n .du-bois-dark-col-md-pull-8 {\n right: 33.33333333%;\n }\n .du-bois-dark-col-md-offset-8 {\n margin-left: 33.33333333%;\n }\n .du-bois-dark-col-md-order-8 {\n order: 8;\n }\n .du-bois-dark-col-md-7 {\n display: block;\n flex: 0 0 29.16666667%;\n max-width: 29.16666667%;\n }\n .du-bois-dark-col-md-push-7 {\n left: 29.16666667%;\n }\n .du-bois-dark-col-md-pull-7 {\n right: 29.16666667%;\n }\n .du-bois-dark-col-md-offset-7 {\n margin-left: 29.16666667%;\n }\n .du-bois-dark-col-md-order-7 {\n order: 7;\n }\n .du-bois-dark-col-md-6 {\n display: block;\n flex: 0 0 25%;\n max-width: 25%;\n }\n .du-bois-dark-col-md-push-6 {\n left: 25%;\n }\n .du-bois-dark-col-md-pull-6 {\n right: 25%;\n }\n .du-bois-dark-col-md-offset-6 {\n margin-left: 25%;\n }\n .du-bois-dark-col-md-order-6 {\n order: 6;\n }\n .du-bois-dark-col-md-5 {\n display: block;\n flex: 0 0 20.83333333%;\n max-width: 20.83333333%;\n }\n .du-bois-dark-col-md-push-5 {\n left: 20.83333333%;\n }\n .du-bois-dark-col-md-pull-5 {\n right: 20.83333333%;\n }\n .du-bois-dark-col-md-offset-5 {\n margin-left: 20.83333333%;\n }\n .du-bois-dark-col-md-order-5 {\n order: 5;\n }\n .du-bois-dark-col-md-4 {\n display: block;\n flex: 0 0 16.66666667%;\n max-width: 16.66666667%;\n }\n .du-bois-dark-col-md-push-4 {\n left: 16.66666667%;\n }\n .du-bois-dark-col-md-pull-4 {\n right: 16.66666667%;\n }\n .du-bois-dark-col-md-offset-4 {\n margin-left: 16.66666667%;\n }\n .du-bois-dark-col-md-order-4 {\n order: 4;\n }\n .du-bois-dark-col-md-3 {\n display: block;\n flex: 0 0 12.5%;\n max-width: 12.5%;\n }\n .du-bois-dark-col-md-push-3 {\n left: 12.5%;\n }\n .du-bois-dark-col-md-pull-3 {\n right: 12.5%;\n }\n .du-bois-dark-col-md-offset-3 {\n margin-left: 12.5%;\n }\n .du-bois-dark-col-md-order-3 {\n order: 3;\n }\n .du-bois-dark-col-md-2 {\n display: block;\n flex: 0 0 8.33333333%;\n max-width: 8.33333333%;\n }\n .du-bois-dark-col-md-push-2 {\n left: 8.33333333%;\n }\n .du-bois-dark-col-md-pull-2 {\n right: 8.33333333%;\n }\n .du-bois-dark-col-md-offset-2 {\n margin-left: 8.33333333%;\n }\n .du-bois-dark-col-md-order-2 {\n order: 2;\n }\n .du-bois-dark-col-md-1 {\n display: block;\n flex: 0 0 4.16666667%;\n max-width: 4.16666667%;\n }\n .du-bois-dark-col-md-push-1 {\n left: 4.16666667%;\n }\n .du-bois-dark-col-md-pull-1 {\n right: 4.16666667%;\n }\n .du-bois-dark-col-md-offset-1 {\n margin-left: 4.16666667%;\n }\n .du-bois-dark-col-md-order-1 {\n order: 1;\n }\n .du-bois-dark-col-md-0 {\n display: none;\n }\n .du-bois-dark-col-push-0 {\n left: auto;\n }\n .du-bois-dark-col-pull-0 {\n right: auto;\n }\n .du-bois-dark-col-md-push-0 {\n left: auto;\n }\n .du-bois-dark-col-md-pull-0 {\n right: auto;\n }\n .du-bois-dark-col-md-offset-0 {\n margin-left: 0;\n }\n .du-bois-dark-col-md-order-0 {\n order: 0;\n }\n}\n@media (min-width: 992px) {\n .du-bois-dark-col-lg-24 {\n display: block;\n flex: 0 0 100%;\n max-width: 100%;\n }\n .du-bois-dark-col-lg-push-24 {\n left: 100%;\n }\n .du-bois-dark-col-lg-pull-24 {\n right: 100%;\n }\n .du-bois-dark-col-lg-offset-24 {\n margin-left: 100%;\n }\n .du-bois-dark-col-lg-order-24 {\n order: 24;\n }\n .du-bois-dark-col-lg-23 {\n display: block;\n flex: 0 0 95.83333333%;\n max-width: 95.83333333%;\n }\n .du-bois-dark-col-lg-push-23 {\n left: 95.83333333%;\n }\n .du-bois-dark-col-lg-pull-23 {\n right: 95.83333333%;\n }\n .du-bois-dark-col-lg-offset-23 {\n margin-left: 95.83333333%;\n }\n .du-bois-dark-col-lg-order-23 {\n order: 23;\n }\n .du-bois-dark-col-lg-22 {\n display: block;\n flex: 0 0 91.66666667%;\n max-width: 91.66666667%;\n }\n .du-bois-dark-col-lg-push-22 {\n left: 91.66666667%;\n }\n .du-bois-dark-col-lg-pull-22 {\n right: 91.66666667%;\n }\n .du-bois-dark-col-lg-offset-22 {\n margin-left: 91.66666667%;\n }\n .du-bois-dark-col-lg-order-22 {\n order: 22;\n }\n .du-bois-dark-col-lg-21 {\n display: block;\n flex: 0 0 87.5%;\n max-width: 87.5%;\n }\n .du-bois-dark-col-lg-push-21 {\n left: 87.5%;\n }\n .du-bois-dark-col-lg-pull-21 {\n right: 87.5%;\n }\n .du-bois-dark-col-lg-offset-21 {\n margin-left: 87.5%;\n }\n .du-bois-dark-col-lg-order-21 {\n order: 21;\n }\n .du-bois-dark-col-lg-20 {\n display: block;\n flex: 0 0 83.33333333%;\n max-width: 83.33333333%;\n }\n .du-bois-dark-col-lg-push-20 {\n left: 83.33333333%;\n }\n .du-bois-dark-col-lg-pull-20 {\n right: 83.33333333%;\n }\n .du-bois-dark-col-lg-offset-20 {\n margin-left: 83.33333333%;\n }\n .du-bois-dark-col-lg-order-20 {\n order: 20;\n }\n .du-bois-dark-col-lg-19 {\n display: block;\n flex: 0 0 79.16666667%;\n max-width: 79.16666667%;\n }\n .du-bois-dark-col-lg-push-19 {\n left: 79.16666667%;\n }\n .du-bois-dark-col-lg-pull-19 {\n right: 79.16666667%;\n }\n .du-bois-dark-col-lg-offset-19 {\n margin-left: 79.16666667%;\n }\n .du-bois-dark-col-lg-order-19 {\n order: 19;\n }\n .du-bois-dark-col-lg-18 {\n display: block;\n flex: 0 0 75%;\n max-width: 75%;\n }\n .du-bois-dark-col-lg-push-18 {\n left: 75%;\n }\n .du-bois-dark-col-lg-pull-18 {\n right: 75%;\n }\n .du-bois-dark-col-lg-offset-18 {\n margin-left: 75%;\n }\n .du-bois-dark-col-lg-order-18 {\n order: 18;\n }\n .du-bois-dark-col-lg-17 {\n display: block;\n flex: 0 0 70.83333333%;\n max-width: 70.83333333%;\n }\n .du-bois-dark-col-lg-push-17 {\n left: 70.83333333%;\n }\n .du-bois-dark-col-lg-pull-17 {\n right: 70.83333333%;\n }\n .du-bois-dark-col-lg-offset-17 {\n margin-left: 70.83333333%;\n }\n .du-bois-dark-col-lg-order-17 {\n order: 17;\n }\n .du-bois-dark-col-lg-16 {\n display: block;\n flex: 0 0 66.66666667%;\n max-width: 66.66666667%;\n }\n .du-bois-dark-col-lg-push-16 {\n left: 66.66666667%;\n }\n .du-bois-dark-col-lg-pull-16 {\n right: 66.66666667%;\n }\n .du-bois-dark-col-lg-offset-16 {\n margin-left: 66.66666667%;\n }\n .du-bois-dark-col-lg-order-16 {\n order: 16;\n }\n .du-bois-dark-col-lg-15 {\n display: block;\n flex: 0 0 62.5%;\n max-width: 62.5%;\n }\n .du-bois-dark-col-lg-push-15 {\n left: 62.5%;\n }\n .du-bois-dark-col-lg-pull-15 {\n right: 62.5%;\n }\n .du-bois-dark-col-lg-offset-15 {\n margin-left: 62.5%;\n }\n .du-bois-dark-col-lg-order-15 {\n order: 15;\n }\n .du-bois-dark-col-lg-14 {\n display: block;\n flex: 0 0 58.33333333%;\n max-width: 58.33333333%;\n }\n .du-bois-dark-col-lg-push-14 {\n left: 58.33333333%;\n }\n .du-bois-dark-col-lg-pull-14 {\n right: 58.33333333%;\n }\n .du-bois-dark-col-lg-offset-14 {\n margin-left: 58.33333333%;\n }\n .du-bois-dark-col-lg-order-14 {\n order: 14;\n }\n .du-bois-dark-col-lg-13 {\n display: block;\n flex: 0 0 54.16666667%;\n max-width: 54.16666667%;\n }\n .du-bois-dark-col-lg-push-13 {\n left: 54.16666667%;\n }\n .du-bois-dark-col-lg-pull-13 {\n right: 54.16666667%;\n }\n .du-bois-dark-col-lg-offset-13 {\n margin-left: 54.16666667%;\n }\n .du-bois-dark-col-lg-order-13 {\n order: 13;\n }\n .du-bois-dark-col-lg-12 {\n display: block;\n flex: 0 0 50%;\n max-width: 50%;\n }\n .du-bois-dark-col-lg-push-12 {\n left: 50%;\n }\n .du-bois-dark-col-lg-pull-12 {\n right: 50%;\n }\n .du-bois-dark-col-lg-offset-12 {\n margin-left: 50%;\n }\n .du-bois-dark-col-lg-order-12 {\n order: 12;\n }\n .du-bois-dark-col-lg-11 {\n display: block;\n flex: 0 0 45.83333333%;\n max-width: 45.83333333%;\n }\n .du-bois-dark-col-lg-push-11 {\n left: 45.83333333%;\n }\n .du-bois-dark-col-lg-pull-11 {\n right: 45.83333333%;\n }\n .du-bois-dark-col-lg-offset-11 {\n margin-left: 45.83333333%;\n }\n .du-bois-dark-col-lg-order-11 {\n order: 11;\n }\n .du-bois-dark-col-lg-10 {\n display: block;\n flex: 0 0 41.66666667%;\n max-width: 41.66666667%;\n }\n .du-bois-dark-col-lg-push-10 {\n left: 41.66666667%;\n }\n .du-bois-dark-col-lg-pull-10 {\n right: 41.66666667%;\n }\n .du-bois-dark-col-lg-offset-10 {\n margin-left: 41.66666667%;\n }\n .du-bois-dark-col-lg-order-10 {\n order: 10;\n }\n .du-bois-dark-col-lg-9 {\n display: block;\n flex: 0 0 37.5%;\n max-width: 37.5%;\n }\n .du-bois-dark-col-lg-push-9 {\n left: 37.5%;\n }\n .du-bois-dark-col-lg-pull-9 {\n right: 37.5%;\n }\n .du-bois-dark-col-lg-offset-9 {\n margin-left: 37.5%;\n }\n .du-bois-dark-col-lg-order-9 {\n order: 9;\n }\n .du-bois-dark-col-lg-8 {\n display: block;\n flex: 0 0 33.33333333%;\n max-width: 33.33333333%;\n }\n .du-bois-dark-col-lg-push-8 {\n left: 33.33333333%;\n }\n .du-bois-dark-col-lg-pull-8 {\n right: 33.33333333%;\n }\n .du-bois-dark-col-lg-offset-8 {\n margin-left: 33.33333333%;\n }\n .du-bois-dark-col-lg-order-8 {\n order: 8;\n }\n .du-bois-dark-col-lg-7 {\n display: block;\n flex: 0 0 29.16666667%;\n max-width: 29.16666667%;\n }\n .du-bois-dark-col-lg-push-7 {\n left: 29.16666667%;\n }\n .du-bois-dark-col-lg-pull-7 {\n right: 29.16666667%;\n }\n .du-bois-dark-col-lg-offset-7 {\n margin-left: 29.16666667%;\n }\n .du-bois-dark-col-lg-order-7 {\n order: 7;\n }\n .du-bois-dark-col-lg-6 {\n display: block;\n flex: 0 0 25%;\n max-width: 25%;\n }\n .du-bois-dark-col-lg-push-6 {\n left: 25%;\n }\n .du-bois-dark-col-lg-pull-6 {\n right: 25%;\n }\n .du-bois-dark-col-lg-offset-6 {\n margin-left: 25%;\n }\n .du-bois-dark-col-lg-order-6 {\n order: 6;\n }\n .du-bois-dark-col-lg-5 {\n display: block;\n flex: 0 0 20.83333333%;\n max-width: 20.83333333%;\n }\n .du-bois-dark-col-lg-push-5 {\n left: 20.83333333%;\n }\n .du-bois-dark-col-lg-pull-5 {\n right: 20.83333333%;\n }\n .du-bois-dark-col-lg-offset-5 {\n margin-left: 20.83333333%;\n }\n .du-bois-dark-col-lg-order-5 {\n order: 5;\n }\n .du-bois-dark-col-lg-4 {\n display: block;\n flex: 0 0 16.66666667%;\n max-width: 16.66666667%;\n }\n .du-bois-dark-col-lg-push-4 {\n left: 16.66666667%;\n }\n .du-bois-dark-col-lg-pull-4 {\n right: 16.66666667%;\n }\n .du-bois-dark-col-lg-offset-4 {\n margin-left: 16.66666667%;\n }\n .du-bois-dark-col-lg-order-4 {\n order: 4;\n }\n .du-bois-dark-col-lg-3 {\n display: block;\n flex: 0 0 12.5%;\n max-width: 12.5%;\n }\n .du-bois-dark-col-lg-push-3 {\n left: 12.5%;\n }\n .du-bois-dark-col-lg-pull-3 {\n right: 12.5%;\n }\n .du-bois-dark-col-lg-offset-3 {\n margin-left: 12.5%;\n }\n .du-bois-dark-col-lg-order-3 {\n order: 3;\n }\n .du-bois-dark-col-lg-2 {\n display: block;\n flex: 0 0 8.33333333%;\n max-width: 8.33333333%;\n }\n .du-bois-dark-col-lg-push-2 {\n left: 8.33333333%;\n }\n .du-bois-dark-col-lg-pull-2 {\n right: 8.33333333%;\n }\n .du-bois-dark-col-lg-offset-2 {\n margin-left: 8.33333333%;\n }\n .du-bois-dark-col-lg-order-2 {\n order: 2;\n }\n .du-bois-dark-col-lg-1 {\n display: block;\n flex: 0 0 4.16666667%;\n max-width: 4.16666667%;\n }\n .du-bois-dark-col-lg-push-1 {\n left: 4.16666667%;\n }\n .du-bois-dark-col-lg-pull-1 {\n right: 4.16666667%;\n }\n .du-bois-dark-col-lg-offset-1 {\n margin-left: 4.16666667%;\n }\n .du-bois-dark-col-lg-order-1 {\n order: 1;\n }\n .du-bois-dark-col-lg-0 {\n display: none;\n }\n .du-bois-dark-col-push-0 {\n left: auto;\n }\n .du-bois-dark-col-pull-0 {\n right: auto;\n }\n .du-bois-dark-col-lg-push-0 {\n left: auto;\n }\n .du-bois-dark-col-lg-pull-0 {\n right: auto;\n }\n .du-bois-dark-col-lg-offset-0 {\n margin-left: 0;\n }\n .du-bois-dark-col-lg-order-0 {\n order: 0;\n }\n}\n@media (min-width: 1200px) {\n .du-bois-dark-col-xl-24 {\n display: block;\n flex: 0 0 100%;\n max-width: 100%;\n }\n .du-bois-dark-col-xl-push-24 {\n left: 100%;\n }\n .du-bois-dark-col-xl-pull-24 {\n right: 100%;\n }\n .du-bois-dark-col-xl-offset-24 {\n margin-left: 100%;\n }\n .du-bois-dark-col-xl-order-24 {\n order: 24;\n }\n .du-bois-dark-col-xl-23 {\n display: block;\n flex: 0 0 95.83333333%;\n max-width: 95.83333333%;\n }\n .du-bois-dark-col-xl-push-23 {\n left: 95.83333333%;\n }\n .du-bois-dark-col-xl-pull-23 {\n right: 95.83333333%;\n }\n .du-bois-dark-col-xl-offset-23 {\n margin-left: 95.83333333%;\n }\n .du-bois-dark-col-xl-order-23 {\n order: 23;\n }\n .du-bois-dark-col-xl-22 {\n display: block;\n flex: 0 0 91.66666667%;\n max-width: 91.66666667%;\n }\n .du-bois-dark-col-xl-push-22 {\n left: 91.66666667%;\n }\n .du-bois-dark-col-xl-pull-22 {\n right: 91.66666667%;\n }\n .du-bois-dark-col-xl-offset-22 {\n margin-left: 91.66666667%;\n }\n .du-bois-dark-col-xl-order-22 {\n order: 22;\n }\n .du-bois-dark-col-xl-21 {\n display: block;\n flex: 0 0 87.5%;\n max-width: 87.5%;\n }\n .du-bois-dark-col-xl-push-21 {\n left: 87.5%;\n }\n .du-bois-dark-col-xl-pull-21 {\n right: 87.5%;\n }\n .du-bois-dark-col-xl-offset-21 {\n margin-left: 87.5%;\n }\n .du-bois-dark-col-xl-order-21 {\n order: 21;\n }\n .du-bois-dark-col-xl-20 {\n display: block;\n flex: 0 0 83.33333333%;\n max-width: 83.33333333%;\n }\n .du-bois-dark-col-xl-push-20 {\n left: 83.33333333%;\n }\n .du-bois-dark-col-xl-pull-20 {\n right: 83.33333333%;\n }\n .du-bois-dark-col-xl-offset-20 {\n margin-left: 83.33333333%;\n }\n .du-bois-dark-col-xl-order-20 {\n order: 20;\n }\n .du-bois-dark-col-xl-19 {\n display: block;\n flex: 0 0 79.16666667%;\n max-width: 79.16666667%;\n }\n .du-bois-dark-col-xl-push-19 {\n left: 79.16666667%;\n }\n .du-bois-dark-col-xl-pull-19 {\n right: 79.16666667%;\n }\n .du-bois-dark-col-xl-offset-19 {\n margin-left: 79.16666667%;\n }\n .du-bois-dark-col-xl-order-19 {\n order: 19;\n }\n .du-bois-dark-col-xl-18 {\n display: block;\n flex: 0 0 75%;\n max-width: 75%;\n }\n .du-bois-dark-col-xl-push-18 {\n left: 75%;\n }\n .du-bois-dark-col-xl-pull-18 {\n right: 75%;\n }\n .du-bois-dark-col-xl-offset-18 {\n margin-left: 75%;\n }\n .du-bois-dark-col-xl-order-18 {\n order: 18;\n }\n .du-bois-dark-col-xl-17 {\n display: block;\n flex: 0 0 70.83333333%;\n max-width: 70.83333333%;\n }\n .du-bois-dark-col-xl-push-17 {\n left: 70.83333333%;\n }\n .du-bois-dark-col-xl-pull-17 {\n right: 70.83333333%;\n }\n .du-bois-dark-col-xl-offset-17 {\n margin-left: 70.83333333%;\n }\n .du-bois-dark-col-xl-order-17 {\n order: 17;\n }\n .du-bois-dark-col-xl-16 {\n display: block;\n flex: 0 0 66.66666667%;\n max-width: 66.66666667%;\n }\n .du-bois-dark-col-xl-push-16 {\n left: 66.66666667%;\n }\n .du-bois-dark-col-xl-pull-16 {\n right: 66.66666667%;\n }\n .du-bois-dark-col-xl-offset-16 {\n margin-left: 66.66666667%;\n }\n .du-bois-dark-col-xl-order-16 {\n order: 16;\n }\n .du-bois-dark-col-xl-15 {\n display: block;\n flex: 0 0 62.5%;\n max-width: 62.5%;\n }\n .du-bois-dark-col-xl-push-15 {\n left: 62.5%;\n }\n .du-bois-dark-col-xl-pull-15 {\n right: 62.5%;\n }\n .du-bois-dark-col-xl-offset-15 {\n margin-left: 62.5%;\n }\n .du-bois-dark-col-xl-order-15 {\n order: 15;\n }\n .du-bois-dark-col-xl-14 {\n display: block;\n flex: 0 0 58.33333333%;\n max-width: 58.33333333%;\n }\n .du-bois-dark-col-xl-push-14 {\n left: 58.33333333%;\n }\n .du-bois-dark-col-xl-pull-14 {\n right: 58.33333333%;\n }\n .du-bois-dark-col-xl-offset-14 {\n margin-left: 58.33333333%;\n }\n .du-bois-dark-col-xl-order-14 {\n order: 14;\n }\n .du-bois-dark-col-xl-13 {\n display: block;\n flex: 0 0 54.16666667%;\n max-width: 54.16666667%;\n }\n .du-bois-dark-col-xl-push-13 {\n left: 54.16666667%;\n }\n .du-bois-dark-col-xl-pull-13 {\n right: 54.16666667%;\n }\n .du-bois-dark-col-xl-offset-13 {\n margin-left: 54.16666667%;\n }\n .du-bois-dark-col-xl-order-13 {\n order: 13;\n }\n .du-bois-dark-col-xl-12 {\n display: block;\n flex: 0 0 50%;\n max-width: 50%;\n }\n .du-bois-dark-col-xl-push-12 {\n left: 50%;\n }\n .du-bois-dark-col-xl-pull-12 {\n right: 50%;\n }\n .du-bois-dark-col-xl-offset-12 {\n margin-left: 50%;\n }\n .du-bois-dark-col-xl-order-12 {\n order: 12;\n }\n .du-bois-dark-col-xl-11 {\n display: block;\n flex: 0 0 45.83333333%;\n max-width: 45.83333333%;\n }\n .du-bois-dark-col-xl-push-11 {\n left: 45.83333333%;\n }\n .du-bois-dark-col-xl-pull-11 {\n right: 45.83333333%;\n }\n .du-bois-dark-col-xl-offset-11 {\n margin-left: 45.83333333%;\n }\n .du-bois-dark-col-xl-order-11 {\n order: 11;\n }\n .du-bois-dark-col-xl-10 {\n display: block;\n flex: 0 0 41.66666667%;\n max-width: 41.66666667%;\n }\n .du-bois-dark-col-xl-push-10 {\n left: 41.66666667%;\n }\n .du-bois-dark-col-xl-pull-10 {\n right: 41.66666667%;\n }\n .du-bois-dark-col-xl-offset-10 {\n margin-left: 41.66666667%;\n }\n .du-bois-dark-col-xl-order-10 {\n order: 10;\n }\n .du-bois-dark-col-xl-9 {\n display: block;\n flex: 0 0 37.5%;\n max-width: 37.5%;\n }\n .du-bois-dark-col-xl-push-9 {\n left: 37.5%;\n }\n .du-bois-dark-col-xl-pull-9 {\n right: 37.5%;\n }\n .du-bois-dark-col-xl-offset-9 {\n margin-left: 37.5%;\n }\n .du-bois-dark-col-xl-order-9 {\n order: 9;\n }\n .du-bois-dark-col-xl-8 {\n display: block;\n flex: 0 0 33.33333333%;\n max-width: 33.33333333%;\n }\n .du-bois-dark-col-xl-push-8 {\n left: 33.33333333%;\n }\n .du-bois-dark-col-xl-pull-8 {\n right: 33.33333333%;\n }\n .du-bois-dark-col-xl-offset-8 {\n margin-left: 33.33333333%;\n }\n .du-bois-dark-col-xl-order-8 {\n order: 8;\n }\n .du-bois-dark-col-xl-7 {\n display: block;\n flex: 0 0 29.16666667%;\n max-width: 29.16666667%;\n }\n .du-bois-dark-col-xl-push-7 {\n left: 29.16666667%;\n }\n .du-bois-dark-col-xl-pull-7 {\n right: 29.16666667%;\n }\n .du-bois-dark-col-xl-offset-7 {\n margin-left: 29.16666667%;\n }\n .du-bois-dark-col-xl-order-7 {\n order: 7;\n }\n .du-bois-dark-col-xl-6 {\n display: block;\n flex: 0 0 25%;\n max-width: 25%;\n }\n .du-bois-dark-col-xl-push-6 {\n left: 25%;\n }\n .du-bois-dark-col-xl-pull-6 {\n right: 25%;\n }\n .du-bois-dark-col-xl-offset-6 {\n margin-left: 25%;\n }\n .du-bois-dark-col-xl-order-6 {\n order: 6;\n }\n .du-bois-dark-col-xl-5 {\n display: block;\n flex: 0 0 20.83333333%;\n max-width: 20.83333333%;\n }\n .du-bois-dark-col-xl-push-5 {\n left: 20.83333333%;\n }\n .du-bois-dark-col-xl-pull-5 {\n right: 20.83333333%;\n }\n .du-bois-dark-col-xl-offset-5 {\n margin-left: 20.83333333%;\n }\n .du-bois-dark-col-xl-order-5 {\n order: 5;\n }\n .du-bois-dark-col-xl-4 {\n display: block;\n flex: 0 0 16.66666667%;\n max-width: 16.66666667%;\n }\n .du-bois-dark-col-xl-push-4 {\n left: 16.66666667%;\n }\n .du-bois-dark-col-xl-pull-4 {\n right: 16.66666667%;\n }\n .du-bois-dark-col-xl-offset-4 {\n margin-left: 16.66666667%;\n }\n .du-bois-dark-col-xl-order-4 {\n order: 4;\n }\n .du-bois-dark-col-xl-3 {\n display: block;\n flex: 0 0 12.5%;\n max-width: 12.5%;\n }\n .du-bois-dark-col-xl-push-3 {\n left: 12.5%;\n }\n .du-bois-dark-col-xl-pull-3 {\n right: 12.5%;\n }\n .du-bois-dark-col-xl-offset-3 {\n margin-left: 12.5%;\n }\n .du-bois-dark-col-xl-order-3 {\n order: 3;\n }\n .du-bois-dark-col-xl-2 {\n display: block;\n flex: 0 0 8.33333333%;\n max-width: 8.33333333%;\n }\n .du-bois-dark-col-xl-push-2 {\n left: 8.33333333%;\n }\n .du-bois-dark-col-xl-pull-2 {\n right: 8.33333333%;\n }\n .du-bois-dark-col-xl-offset-2 {\n margin-left: 8.33333333%;\n }\n .du-bois-dark-col-xl-order-2 {\n order: 2;\n }\n .du-bois-dark-col-xl-1 {\n display: block;\n flex: 0 0 4.16666667%;\n max-width: 4.16666667%;\n }\n .du-bois-dark-col-xl-push-1 {\n left: 4.16666667%;\n }\n .du-bois-dark-col-xl-pull-1 {\n right: 4.16666667%;\n }\n .du-bois-dark-col-xl-offset-1 {\n margin-left: 4.16666667%;\n }\n .du-bois-dark-col-xl-order-1 {\n order: 1;\n }\n .du-bois-dark-col-xl-0 {\n display: none;\n }\n .du-bois-dark-col-push-0 {\n left: auto;\n }\n .du-bois-dark-col-pull-0 {\n right: auto;\n }\n .du-bois-dark-col-xl-push-0 {\n left: auto;\n }\n .du-bois-dark-col-xl-pull-0 {\n right: auto;\n }\n .du-bois-dark-col-xl-offset-0 {\n margin-left: 0;\n }\n .du-bois-dark-col-xl-order-0 {\n order: 0;\n }\n}\n@media (min-width: 1600px) {\n .du-bois-dark-col-xxl-24 {\n display: block;\n flex: 0 0 100%;\n max-width: 100%;\n }\n .du-bois-dark-col-xxl-push-24 {\n left: 100%;\n }\n .du-bois-dark-col-xxl-pull-24 {\n right: 100%;\n }\n .du-bois-dark-col-xxl-offset-24 {\n margin-left: 100%;\n }\n .du-bois-dark-col-xxl-order-24 {\n order: 24;\n }\n .du-bois-dark-col-xxl-23 {\n display: block;\n flex: 0 0 95.83333333%;\n max-width: 95.83333333%;\n }\n .du-bois-dark-col-xxl-push-23 {\n left: 95.83333333%;\n }\n .du-bois-dark-col-xxl-pull-23 {\n right: 95.83333333%;\n }\n .du-bois-dark-col-xxl-offset-23 {\n margin-left: 95.83333333%;\n }\n .du-bois-dark-col-xxl-order-23 {\n order: 23;\n }\n .du-bois-dark-col-xxl-22 {\n display: block;\n flex: 0 0 91.66666667%;\n max-width: 91.66666667%;\n }\n .du-bois-dark-col-xxl-push-22 {\n left: 91.66666667%;\n }\n .du-bois-dark-col-xxl-pull-22 {\n right: 91.66666667%;\n }\n .du-bois-dark-col-xxl-offset-22 {\n margin-left: 91.66666667%;\n }\n .du-bois-dark-col-xxl-order-22 {\n order: 22;\n }\n .du-bois-dark-col-xxl-21 {\n display: block;\n flex: 0 0 87.5%;\n max-width: 87.5%;\n }\n .du-bois-dark-col-xxl-push-21 {\n left: 87.5%;\n }\n .du-bois-dark-col-xxl-pull-21 {\n right: 87.5%;\n }\n .du-bois-dark-col-xxl-offset-21 {\n margin-left: 87.5%;\n }\n .du-bois-dark-col-xxl-order-21 {\n order: 21;\n }\n .du-bois-dark-col-xxl-20 {\n display: block;\n flex: 0 0 83.33333333%;\n max-width: 83.33333333%;\n }\n .du-bois-dark-col-xxl-push-20 {\n left: 83.33333333%;\n }\n .du-bois-dark-col-xxl-pull-20 {\n right: 83.33333333%;\n }\n .du-bois-dark-col-xxl-offset-20 {\n margin-left: 83.33333333%;\n }\n .du-bois-dark-col-xxl-order-20 {\n order: 20;\n }\n .du-bois-dark-col-xxl-19 {\n display: block;\n flex: 0 0 79.16666667%;\n max-width: 79.16666667%;\n }\n .du-bois-dark-col-xxl-push-19 {\n left: 79.16666667%;\n }\n .du-bois-dark-col-xxl-pull-19 {\n right: 79.16666667%;\n }\n .du-bois-dark-col-xxl-offset-19 {\n margin-left: 79.16666667%;\n }\n .du-bois-dark-col-xxl-order-19 {\n order: 19;\n }\n .du-bois-dark-col-xxl-18 {\n display: block;\n flex: 0 0 75%;\n max-width: 75%;\n }\n .du-bois-dark-col-xxl-push-18 {\n left: 75%;\n }\n .du-bois-dark-col-xxl-pull-18 {\n right: 75%;\n }\n .du-bois-dark-col-xxl-offset-18 {\n margin-left: 75%;\n }\n .du-bois-dark-col-xxl-order-18 {\n order: 18;\n }\n .du-bois-dark-col-xxl-17 {\n display: block;\n flex: 0 0 70.83333333%;\n max-width: 70.83333333%;\n }\n .du-bois-dark-col-xxl-push-17 {\n left: 70.83333333%;\n }\n .du-bois-dark-col-xxl-pull-17 {\n right: 70.83333333%;\n }\n .du-bois-dark-col-xxl-offset-17 {\n margin-left: 70.83333333%;\n }\n .du-bois-dark-col-xxl-order-17 {\n order: 17;\n }\n .du-bois-dark-col-xxl-16 {\n display: block;\n flex: 0 0 66.66666667%;\n max-width: 66.66666667%;\n }\n .du-bois-dark-col-xxl-push-16 {\n left: 66.66666667%;\n }\n .du-bois-dark-col-xxl-pull-16 {\n right: 66.66666667%;\n }\n .du-bois-dark-col-xxl-offset-16 {\n margin-left: 66.66666667%;\n }\n .du-bois-dark-col-xxl-order-16 {\n order: 16;\n }\n .du-bois-dark-col-xxl-15 {\n display: block;\n flex: 0 0 62.5%;\n max-width: 62.5%;\n }\n .du-bois-dark-col-xxl-push-15 {\n left: 62.5%;\n }\n .du-bois-dark-col-xxl-pull-15 {\n right: 62.5%;\n }\n .du-bois-dark-col-xxl-offset-15 {\n margin-left: 62.5%;\n }\n .du-bois-dark-col-xxl-order-15 {\n order: 15;\n }\n .du-bois-dark-col-xxl-14 {\n display: block;\n flex: 0 0 58.33333333%;\n max-width: 58.33333333%;\n }\n .du-bois-dark-col-xxl-push-14 {\n left: 58.33333333%;\n }\n .du-bois-dark-col-xxl-pull-14 {\n right: 58.33333333%;\n }\n .du-bois-dark-col-xxl-offset-14 {\n margin-left: 58.33333333%;\n }\n .du-bois-dark-col-xxl-order-14 {\n order: 14;\n }\n .du-bois-dark-col-xxl-13 {\n display: block;\n flex: 0 0 54.16666667%;\n max-width: 54.16666667%;\n }\n .du-bois-dark-col-xxl-push-13 {\n left: 54.16666667%;\n }\n .du-bois-dark-col-xxl-pull-13 {\n right: 54.16666667%;\n }\n .du-bois-dark-col-xxl-offset-13 {\n margin-left: 54.16666667%;\n }\n .du-bois-dark-col-xxl-order-13 {\n order: 13;\n }\n .du-bois-dark-col-xxl-12 {\n display: block;\n flex: 0 0 50%;\n max-width: 50%;\n }\n .du-bois-dark-col-xxl-push-12 {\n left: 50%;\n }\n .du-bois-dark-col-xxl-pull-12 {\n right: 50%;\n }\n .du-bois-dark-col-xxl-offset-12 {\n margin-left: 50%;\n }\n .du-bois-dark-col-xxl-order-12 {\n order: 12;\n }\n .du-bois-dark-col-xxl-11 {\n display: block;\n flex: 0 0 45.83333333%;\n max-width: 45.83333333%;\n }\n .du-bois-dark-col-xxl-push-11 {\n left: 45.83333333%;\n }\n .du-bois-dark-col-xxl-pull-11 {\n right: 45.83333333%;\n }\n .du-bois-dark-col-xxl-offset-11 {\n margin-left: 45.83333333%;\n }\n .du-bois-dark-col-xxl-order-11 {\n order: 11;\n }\n .du-bois-dark-col-xxl-10 {\n display: block;\n flex: 0 0 41.66666667%;\n max-width: 41.66666667%;\n }\n .du-bois-dark-col-xxl-push-10 {\n left: 41.66666667%;\n }\n .du-bois-dark-col-xxl-pull-10 {\n right: 41.66666667%;\n }\n .du-bois-dark-col-xxl-offset-10 {\n margin-left: 41.66666667%;\n }\n .du-bois-dark-col-xxl-order-10 {\n order: 10;\n }\n .du-bois-dark-col-xxl-9 {\n display: block;\n flex: 0 0 37.5%;\n max-width: 37.5%;\n }\n .du-bois-dark-col-xxl-push-9 {\n left: 37.5%;\n }\n .du-bois-dark-col-xxl-pull-9 {\n right: 37.5%;\n }\n .du-bois-dark-col-xxl-offset-9 {\n margin-left: 37.5%;\n }\n .du-bois-dark-col-xxl-order-9 {\n order: 9;\n }\n .du-bois-dark-col-xxl-8 {\n display: block;\n flex: 0 0 33.33333333%;\n max-width: 33.33333333%;\n }\n .du-bois-dark-col-xxl-push-8 {\n left: 33.33333333%;\n }\n .du-bois-dark-col-xxl-pull-8 {\n right: 33.33333333%;\n }\n .du-bois-dark-col-xxl-offset-8 {\n margin-left: 33.33333333%;\n }\n .du-bois-dark-col-xxl-order-8 {\n order: 8;\n }\n .du-bois-dark-col-xxl-7 {\n display: block;\n flex: 0 0 29.16666667%;\n max-width: 29.16666667%;\n }\n .du-bois-dark-col-xxl-push-7 {\n left: 29.16666667%;\n }\n .du-bois-dark-col-xxl-pull-7 {\n right: 29.16666667%;\n }\n .du-bois-dark-col-xxl-offset-7 {\n margin-left: 29.16666667%;\n }\n .du-bois-dark-col-xxl-order-7 {\n order: 7;\n }\n .du-bois-dark-col-xxl-6 {\n display: block;\n flex: 0 0 25%;\n max-width: 25%;\n }\n .du-bois-dark-col-xxl-push-6 {\n left: 25%;\n }\n .du-bois-dark-col-xxl-pull-6 {\n right: 25%;\n }\n .du-bois-dark-col-xxl-offset-6 {\n margin-left: 25%;\n }\n .du-bois-dark-col-xxl-order-6 {\n order: 6;\n }\n .du-bois-dark-col-xxl-5 {\n display: block;\n flex: 0 0 20.83333333%;\n max-width: 20.83333333%;\n }\n .du-bois-dark-col-xxl-push-5 {\n left: 20.83333333%;\n }\n .du-bois-dark-col-xxl-pull-5 {\n right: 20.83333333%;\n }\n .du-bois-dark-col-xxl-offset-5 {\n margin-left: 20.83333333%;\n }\n .du-bois-dark-col-xxl-order-5 {\n order: 5;\n }\n .du-bois-dark-col-xxl-4 {\n display: block;\n flex: 0 0 16.66666667%;\n max-width: 16.66666667%;\n }\n .du-bois-dark-col-xxl-push-4 {\n left: 16.66666667%;\n }\n .du-bois-dark-col-xxl-pull-4 {\n right: 16.66666667%;\n }\n .du-bois-dark-col-xxl-offset-4 {\n margin-left: 16.66666667%;\n }\n .du-bois-dark-col-xxl-order-4 {\n order: 4;\n }\n .du-bois-dark-col-xxl-3 {\n display: block;\n flex: 0 0 12.5%;\n max-width: 12.5%;\n }\n .du-bois-dark-col-xxl-push-3 {\n left: 12.5%;\n }\n .du-bois-dark-col-xxl-pull-3 {\n right: 12.5%;\n }\n .du-bois-dark-col-xxl-offset-3 {\n margin-left: 12.5%;\n }\n .du-bois-dark-col-xxl-order-3 {\n order: 3;\n }\n .du-bois-dark-col-xxl-2 {\n display: block;\n flex: 0 0 8.33333333%;\n max-width: 8.33333333%;\n }\n .du-bois-dark-col-xxl-push-2 {\n left: 8.33333333%;\n }\n .du-bois-dark-col-xxl-pull-2 {\n right: 8.33333333%;\n }\n .du-bois-dark-col-xxl-offset-2 {\n margin-left: 8.33333333%;\n }\n .du-bois-dark-col-xxl-order-2 {\n order: 2;\n }\n .du-bois-dark-col-xxl-1 {\n display: block;\n flex: 0 0 4.16666667%;\n max-width: 4.16666667%;\n }\n .du-bois-dark-col-xxl-push-1 {\n left: 4.16666667%;\n }\n .du-bois-dark-col-xxl-pull-1 {\n right: 4.16666667%;\n }\n .du-bois-dark-col-xxl-offset-1 {\n margin-left: 4.16666667%;\n }\n .du-bois-dark-col-xxl-order-1 {\n order: 1;\n }\n .du-bois-dark-col-xxl-0 {\n display: none;\n }\n .du-bois-dark-col-push-0 {\n left: auto;\n }\n .du-bois-dark-col-pull-0 {\n right: auto;\n }\n .du-bois-dark-col-xxl-push-0 {\n left: auto;\n }\n .du-bois-dark-col-xxl-pull-0 {\n right: auto;\n }\n .du-bois-dark-col-xxl-offset-0 {\n margin-left: 0;\n }\n .du-bois-dark-col-xxl-order-0 {\n order: 0;\n }\n}\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-dark-tooltip {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #E8ECF0;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: tnum;\n position: absolute;\n z-index: 1070;\n display: block;\n width: max-content;\n max-width: 250px;\n visibility: visible;\n}\n.du-bois-dark-tooltip-hidden {\n display: none;\n}\n.du-bois-dark-tooltip-placement-top,\n.du-bois-dark-tooltip-placement-topLeft,\n.du-bois-dark-tooltip-placement-topRight {\n padding-bottom: 8px;\n}\n.du-bois-dark-tooltip-placement-right,\n.du-bois-dark-tooltip-placement-rightTop,\n.du-bois-dark-tooltip-placement-rightBottom {\n padding-left: 8px;\n}\n.du-bois-dark-tooltip-placement-bottom,\n.du-bois-dark-tooltip-placement-bottomLeft,\n.du-bois-dark-tooltip-placement-bottomRight {\n padding-top: 8px;\n}\n.du-bois-dark-tooltip-placement-left,\n.du-bois-dark-tooltip-placement-leftTop,\n.du-bois-dark-tooltip-placement-leftBottom {\n padding-right: 8px;\n}\n.du-bois-dark-tooltip-inner {\n min-width: 30px;\n min-height: 32px;\n padding: 6px 8px;\n color: #fff;\n text-align: left;\n text-decoration: none;\n word-wrap: break-word;\n background-color: #434343;\n border-radius: 4px;\n box-shadow: 0 3px 6px -4px rgba(0, 0, 0, 0.48), 0 6px 16px 0 rgba(0, 0, 0, 0.32), 0 9px 28px 8px rgba(0, 0, 0, 0.2);\n}\n.du-bois-dark-tooltip-arrow {\n position: absolute;\n display: block;\n width: 13.07106781px;\n height: 13.07106781px;\n overflow: hidden;\n background: transparent;\n pointer-events: none;\n}\n.du-bois-dark-tooltip-arrow-content {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n display: block;\n width: 5px;\n height: 5px;\n margin: auto;\n background-color: #434343;\n content: '';\n pointer-events: auto;\n}\n.du-bois-dark-tooltip-placement-top .du-bois-dark-tooltip-arrow,\n.du-bois-dark-tooltip-placement-topLeft .du-bois-dark-tooltip-arrow,\n.du-bois-dark-tooltip-placement-topRight .du-bois-dark-tooltip-arrow {\n bottom: -5.07106781px;\n}\n.du-bois-dark-tooltip-placement-top .du-bois-dark-tooltip-arrow-content,\n.du-bois-dark-tooltip-placement-topLeft .du-bois-dark-tooltip-arrow-content,\n.du-bois-dark-tooltip-placement-topRight .du-bois-dark-tooltip-arrow-content {\n box-shadow: 3px 3px 7px rgba(0, 0, 0, 0.07);\n transform: translateY(-6.53553391px) rotate(45deg);\n}\n.du-bois-dark-tooltip-placement-top .du-bois-dark-tooltip-arrow {\n left: 50%;\n transform: translateX(-50%);\n}\n.du-bois-dark-tooltip-placement-topLeft .du-bois-dark-tooltip-arrow {\n left: 13px;\n}\n.du-bois-dark-tooltip-placement-topRight .du-bois-dark-tooltip-arrow {\n right: 13px;\n}\n.du-bois-dark-tooltip-placement-right .du-bois-dark-tooltip-arrow,\n.du-bois-dark-tooltip-placement-rightTop .du-bois-dark-tooltip-arrow,\n.du-bois-dark-tooltip-placement-rightBottom .du-bois-dark-tooltip-arrow {\n left: -5.07106781px;\n}\n.du-bois-dark-tooltip-placement-right .du-bois-dark-tooltip-arrow-content,\n.du-bois-dark-tooltip-placement-rightTop .du-bois-dark-tooltip-arrow-content,\n.du-bois-dark-tooltip-placement-rightBottom .du-bois-dark-tooltip-arrow-content {\n box-shadow: -3px 3px 7px rgba(0, 0, 0, 0.07);\n transform: translateX(6.53553391px) rotate(45deg);\n}\n.du-bois-dark-tooltip-placement-right .du-bois-dark-tooltip-arrow {\n top: 50%;\n transform: translateY(-50%);\n}\n.du-bois-dark-tooltip-placement-rightTop .du-bois-dark-tooltip-arrow {\n top: 5px;\n}\n.du-bois-dark-tooltip-placement-rightBottom .du-bois-dark-tooltip-arrow {\n bottom: 5px;\n}\n.du-bois-dark-tooltip-placement-left .du-bois-dark-tooltip-arrow,\n.du-bois-dark-tooltip-placement-leftTop .du-bois-dark-tooltip-arrow,\n.du-bois-dark-tooltip-placement-leftBottom .du-bois-dark-tooltip-arrow {\n right: -5.07106781px;\n}\n.du-bois-dark-tooltip-placement-left .du-bois-dark-tooltip-arrow-content,\n.du-bois-dark-tooltip-placement-leftTop .du-bois-dark-tooltip-arrow-content,\n.du-bois-dark-tooltip-placement-leftBottom .du-bois-dark-tooltip-arrow-content {\n box-shadow: 3px -3px 7px rgba(0, 0, 0, 0.07);\n transform: translateX(-6.53553391px) rotate(45deg);\n}\n.du-bois-dark-tooltip-placement-left .du-bois-dark-tooltip-arrow {\n top: 50%;\n transform: translateY(-50%);\n}\n.du-bois-dark-tooltip-placement-leftTop .du-bois-dark-tooltip-arrow {\n top: 5px;\n}\n.du-bois-dark-tooltip-placement-leftBottom .du-bois-dark-tooltip-arrow {\n bottom: 5px;\n}\n.du-bois-dark-tooltip-placement-bottom .du-bois-dark-tooltip-arrow,\n.du-bois-dark-tooltip-placement-bottomLeft .du-bois-dark-tooltip-arrow,\n.du-bois-dark-tooltip-placement-bottomRight .du-bois-dark-tooltip-arrow {\n top: -5.07106781px;\n}\n.du-bois-dark-tooltip-placement-bottom .du-bois-dark-tooltip-arrow-content,\n.du-bois-dark-tooltip-placement-bottomLeft .du-bois-dark-tooltip-arrow-content,\n.du-bois-dark-tooltip-placement-bottomRight .du-bois-dark-tooltip-arrow-content {\n box-shadow: -3px -3px 7px rgba(0, 0, 0, 0.07);\n transform: translateY(6.53553391px) rotate(45deg);\n}\n.du-bois-dark-tooltip-placement-bottom .du-bois-dark-tooltip-arrow {\n left: 50%;\n transform: translateX(-50%);\n}\n.du-bois-dark-tooltip-placement-bottomLeft .du-bois-dark-tooltip-arrow {\n left: 13px;\n}\n.du-bois-dark-tooltip-placement-bottomRight .du-bois-dark-tooltip-arrow {\n right: 13px;\n}\n.du-bois-dark-tooltip-pink .du-bois-dark-tooltip-inner {\n background-color: #cb2b83;\n}\n.du-bois-dark-tooltip-pink .du-bois-dark-tooltip-arrow-content {\n background-color: #cb2b83;\n}\n.du-bois-dark-tooltip-magenta .du-bois-dark-tooltip-inner {\n background-color: #cb2b83;\n}\n.du-bois-dark-tooltip-magenta .du-bois-dark-tooltip-arrow-content {\n background-color: #cb2b83;\n}\n.du-bois-dark-tooltip-red .du-bois-dark-tooltip-inner {\n background-color: #d32029;\n}\n.du-bois-dark-tooltip-red .du-bois-dark-tooltip-arrow-content {\n background-color: #d32029;\n}\n.du-bois-dark-tooltip-volcano .du-bois-dark-tooltip-inner {\n background-color: #d84a1b;\n}\n.du-bois-dark-tooltip-volcano .du-bois-dark-tooltip-arrow-content {\n background-color: #d84a1b;\n}\n.du-bois-dark-tooltip-orange .du-bois-dark-tooltip-inner {\n background-color: #d87a16;\n}\n.du-bois-dark-tooltip-orange .du-bois-dark-tooltip-arrow-content {\n background-color: #d87a16;\n}\n.du-bois-dark-tooltip-yellow .du-bois-dark-tooltip-inner {\n background-color: #d8bd14;\n}\n.du-bois-dark-tooltip-yellow .du-bois-dark-tooltip-arrow-content {\n background-color: #d8bd14;\n}\n.du-bois-dark-tooltip-gold .du-bois-dark-tooltip-inner {\n background-color: #d89614;\n}\n.du-bois-dark-tooltip-gold .du-bois-dark-tooltip-arrow-content {\n background-color: #d89614;\n}\n.du-bois-dark-tooltip-cyan .du-bois-dark-tooltip-inner {\n background-color: #13a8a8;\n}\n.du-bois-dark-tooltip-cyan .du-bois-dark-tooltip-arrow-content {\n background-color: #13a8a8;\n}\n.du-bois-dark-tooltip-lime .du-bois-dark-tooltip-inner {\n background-color: #8bbb11;\n}\n.du-bois-dark-tooltip-lime .du-bois-dark-tooltip-arrow-content {\n background-color: #8bbb11;\n}\n.du-bois-dark-tooltip-green .du-bois-dark-tooltip-inner {\n background-color: #49aa19;\n}\n.du-bois-dark-tooltip-green .du-bois-dark-tooltip-arrow-content {\n background-color: #49aa19;\n}\n.du-bois-dark-tooltip-blue .du-bois-dark-tooltip-inner {\n background-color: #177ddc;\n}\n.du-bois-dark-tooltip-blue .du-bois-dark-tooltip-arrow-content {\n background-color: #177ddc;\n}\n.du-bois-dark-tooltip-geekblue .du-bois-dark-tooltip-inner {\n background-color: #2b4acb;\n}\n.du-bois-dark-tooltip-geekblue .du-bois-dark-tooltip-arrow-content {\n background-color: #2b4acb;\n}\n.du-bois-dark-tooltip-purple .du-bois-dark-tooltip-inner {\n background-color: #642ab5;\n}\n.du-bois-dark-tooltip-purple .du-bois-dark-tooltip-arrow-content {\n background-color: #642ab5;\n}\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-dark-input-affix-wrapper {\n position: relative;\n display: inline-block;\n width: 100%;\n min-width: 0;\n padding: 5px 12px;\n color: #E8ECF0;\n font-size: 13px;\n line-height: 20px;\n background-color: transparent;\n background-image: none;\n border: 1px solid #1F272D;\n border-radius: 4px;\n transition: all 0.3s;\n display: inline-flex;\n}\n.du-bois-dark-input-affix-wrapper::-moz-placeholder {\n opacity: 1;\n}\n.du-bois-dark-input-affix-wrapper::placeholder {\n color: rgba(255, 255, 255, 0.3);\n}\n.du-bois-dark-input-affix-wrapper:placeholder-shown {\n text-overflow: ellipsis;\n}\n.du-bois-dark-input-affix-wrapper:hover {\n border-color: #1d517c;\n border-right-width: 1px !important;\n}\n.du-bois-dark-input-affix-wrapper:focus,\n.du-bois-dark-input-affix-wrapper-focused {\n border-color: #2272B4;\n border-right-width: 1px !important;\n outline: 0;\n box-shadow: 0 0 0 2px rgba(34, 114, 180, 0.2);\n}\n.du-bois-dark-input-affix-wrapper-disabled {\n color: rgba(255, 255, 255, 0.3);\n background-color: rgba(255, 255, 255, 0.08);\n cursor: not-allowed;\n opacity: 1;\n}\n.du-bois-dark-input-affix-wrapper-disabled:hover {\n border-color: #1F272D;\n border-right-width: 1px !important;\n}\n.du-bois-dark-input-affix-wrapper[disabled] {\n color: rgba(255, 255, 255, 0.3);\n background-color: rgba(255, 255, 255, 0.08);\n cursor: not-allowed;\n opacity: 1;\n}\n.du-bois-dark-input-affix-wrapper[disabled]:hover {\n border-color: #1F272D;\n border-right-width: 1px !important;\n}\n.du-bois-dark-input-affix-wrapper-borderless,\n.du-bois-dark-input-affix-wrapper-borderless:hover,\n.du-bois-dark-input-affix-wrapper-borderless:focus,\n.du-bois-dark-input-affix-wrapper-borderless-focused,\n.du-bois-dark-input-affix-wrapper-borderless-disabled,\n.du-bois-dark-input-affix-wrapper-borderless[disabled] {\n background-color: transparent;\n border: none;\n box-shadow: none;\n}\ntextarea.du-bois-dark-input-affix-wrapper {\n max-width: 100%;\n height: auto;\n min-height: 40px;\n line-height: 20px;\n vertical-align: bottom;\n transition: all 0.3s, height 0s;\n}\n.du-bois-dark-input-affix-wrapper-lg {\n padding: 5px 12px;\n font-size: 18px;\n}\n.du-bois-dark-input-affix-wrapper-sm {\n padding: 5px 12px;\n}\n.du-bois-dark-input-affix-wrapper:not(.du-bois-dark-input-affix-wrapper-disabled):hover {\n border-color: #1d517c;\n border-right-width: 1px !important;\n z-index: 1;\n}\n.du-bois-dark-input-search-with-button .du-bois-dark-input-affix-wrapper:not(.du-bois-dark-input-affix-wrapper-disabled):hover {\n z-index: 0;\n}\n.du-bois-dark-input-affix-wrapper-focused,\n.du-bois-dark-input-affix-wrapper:focus {\n z-index: 1;\n}\n.du-bois-dark-input-affix-wrapper-disabled .du-bois-dark-input[disabled] {\n background: transparent;\n}\n.du-bois-dark-input-affix-wrapper > input.du-bois-dark-input {\n padding: 0;\n border: none;\n outline: none;\n}\n.du-bois-dark-input-affix-wrapper > input.du-bois-dark-input:focus {\n box-shadow: none;\n}\n.du-bois-dark-input-affix-wrapper::before {\n width: 0;\n visibility: hidden;\n content: '\\a0';\n}\n.du-bois-dark-input-prefix,\n.du-bois-dark-input-suffix {\n display: flex;\n flex: none;\n align-items: center;\n}\n.du-bois-dark-input-prefix {\n margin-right: 4px;\n}\n.du-bois-dark-input-suffix {\n margin-left: 4px;\n}\n.anticon.du-bois-dark-input-clear-icon {\n margin: 0 4px;\n color: rgba(255, 255, 255, 0.3);\n font-size: 12px;\n vertical-align: -1px;\n cursor: pointer;\n transition: color 0.3s;\n}\n.anticon.du-bois-dark-input-clear-icon:hover {\n color: #92A4B3;\n}\n.anticon.du-bois-dark-input-clear-icon:active {\n color: #E8ECF0;\n}\n.anticon.du-bois-dark-input-clear-icon-hidden {\n visibility: hidden;\n}\n.anticon.du-bois-dark-input-clear-icon:last-child {\n margin-right: 0;\n}\n.du-bois-dark-input-affix-wrapper-textarea-with-clear-btn {\n padding: 0 !important;\n border: 0 !important;\n}\n.du-bois-dark-input-affix-wrapper-textarea-with-clear-btn .du-bois-dark-input-clear-icon {\n position: absolute;\n top: 8px;\n right: 8px;\n z-index: 1;\n}\n.du-bois-dark-input {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n font-variant: tabular-nums;\n list-style: none;\n font-feature-settings: tnum;\n position: relative;\n display: inline-block;\n width: 100%;\n min-width: 0;\n padding: 5px 12px;\n color: #E8ECF0;\n font-size: 13px;\n line-height: 20px;\n background-color: transparent;\n background-image: none;\n border: 1px solid #1F272D;\n border-radius: 4px;\n transition: all 0.3s;\n}\n.du-bois-dark-input::-moz-placeholder {\n opacity: 1;\n}\n.du-bois-dark-input::placeholder {\n color: rgba(255, 255, 255, 0.3);\n}\n.du-bois-dark-input:placeholder-shown {\n text-overflow: ellipsis;\n}\n.du-bois-dark-input:hover {\n border-color: #1d517c;\n border-right-width: 1px !important;\n}\n.du-bois-dark-input:focus,\n.du-bois-dark-input-focused {\n border-color: #2272B4;\n border-right-width: 1px !important;\n outline: 0;\n box-shadow: 0 0 0 2px rgba(34, 114, 180, 0.2);\n}\n.du-bois-dark-input-disabled {\n color: rgba(255, 255, 255, 0.3);\n background-color: rgba(255, 255, 255, 0.08);\n cursor: not-allowed;\n opacity: 1;\n}\n.du-bois-dark-input-disabled:hover {\n border-color: #1F272D;\n border-right-width: 1px !important;\n}\n.du-bois-dark-input[disabled] {\n color: rgba(255, 255, 255, 0.3);\n background-color: rgba(255, 255, 255, 0.08);\n cursor: not-allowed;\n opacity: 1;\n}\n.du-bois-dark-input[disabled]:hover {\n border-color: #1F272D;\n border-right-width: 1px !important;\n}\n.du-bois-dark-input-borderless,\n.du-bois-dark-input-borderless:hover,\n.du-bois-dark-input-borderless:focus,\n.du-bois-dark-input-borderless-focused,\n.du-bois-dark-input-borderless-disabled,\n.du-bois-dark-input-borderless[disabled] {\n background-color: transparent;\n border: none;\n box-shadow: none;\n}\ntextarea.du-bois-dark-input {\n max-width: 100%;\n height: auto;\n min-height: 40px;\n line-height: 20px;\n vertical-align: bottom;\n transition: all 0.3s, height 0s;\n}\n.du-bois-dark-input-lg {\n padding: 5px 12px;\n font-size: 18px;\n}\n.du-bois-dark-input-sm {\n padding: 5px 12px;\n}\n.du-bois-dark-input-group {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #E8ECF0;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: tnum;\n position: relative;\n display: table;\n width: 100%;\n border-collapse: separate;\n border-spacing: 0;\n}\n.du-bois-dark-input-group[class*='col-'] {\n float: none;\n padding-right: 0;\n padding-left: 0;\n}\n.du-bois-dark-input-group > [class*='col-'] {\n padding-right: 8px;\n}\n.du-bois-dark-input-group > [class*='col-']:last-child {\n padding-right: 0;\n}\n.du-bois-dark-input-group-addon,\n.du-bois-dark-input-group-wrap,\n.du-bois-dark-input-group > .du-bois-dark-input {\n display: table-cell;\n}\n.du-bois-dark-input-group-addon:not(:first-child):not(:last-child),\n.du-bois-dark-input-group-wrap:not(:first-child):not(:last-child),\n.du-bois-dark-input-group > .du-bois-dark-input:not(:first-child):not(:last-child) {\n border-radius: 0;\n}\n.du-bois-dark-input-group-addon,\n.du-bois-dark-input-group-wrap {\n width: 1px;\n white-space: nowrap;\n vertical-align: middle;\n}\n.du-bois-dark-input-group-wrap > * {\n display: block !important;\n}\n.du-bois-dark-input-group .du-bois-dark-input {\n float: left;\n width: 100%;\n margin-bottom: 0;\n text-align: inherit;\n}\n.du-bois-dark-input-group .du-bois-dark-input:focus {\n z-index: 1;\n border-right-width: 1px;\n}\n.du-bois-dark-input-group .du-bois-dark-input:hover {\n z-index: 1;\n border-right-width: 1px;\n}\n.du-bois-dark-input-search-with-button .du-bois-dark-input-group .du-bois-dark-input:hover {\n z-index: 0;\n}\n.du-bois-dark-input-group-addon {\n position: relative;\n padding: 0 12px;\n color: #E8ECF0;\n font-weight: normal;\n font-size: 13px;\n text-align: center;\n background-color: rgba(255, 255, 255, 0.04);\n border: 1px solid #1F272D;\n border-radius: 4px;\n transition: all 0.3s;\n}\n.du-bois-dark-input-group-addon .du-bois-dark-select {\n margin: -6px -12px;\n}\n.du-bois-dark-input-group-addon .du-bois-dark-select.du-bois-dark-select-single:not(.du-bois-dark-select-customize-input) .du-bois-dark-select-selector {\n background-color: inherit;\n border: 1px solid transparent;\n box-shadow: none;\n}\n.du-bois-dark-input-group-addon .du-bois-dark-select-open .du-bois-dark-select-selector,\n.du-bois-dark-input-group-addon .du-bois-dark-select-focused .du-bois-dark-select-selector {\n color: #2272B4;\n}\n.du-bois-dark-input-group-addon .du-bois-dark-cascader-picker {\n margin: -9px -8px;\n background-color: transparent;\n}\n.du-bois-dark-input-group-addon .du-bois-dark-cascader-picker .du-bois-dark-cascader-input {\n text-align: left;\n border: 0;\n box-shadow: none;\n}\n.du-bois-dark-input-group > .du-bois-dark-input:first-child,\n.du-bois-dark-input-group-addon:first-child {\n border-top-right-radius: 0;\n border-bottom-right-radius: 0;\n}\n.du-bois-dark-input-group > .du-bois-dark-input:first-child .du-bois-dark-select .du-bois-dark-select-selector,\n.du-bois-dark-input-group-addon:first-child .du-bois-dark-select .du-bois-dark-select-selector {\n border-top-right-radius: 0;\n border-bottom-right-radius: 0;\n}\n.du-bois-dark-input-group > .du-bois-dark-input-affix-wrapper:not(:first-child) .du-bois-dark-input {\n border-top-left-radius: 0;\n border-bottom-left-radius: 0;\n}\n.du-bois-dark-input-group > .du-bois-dark-input-affix-wrapper:not(:last-child) .du-bois-dark-input {\n border-top-right-radius: 0;\n border-bottom-right-radius: 0;\n}\n.du-bois-dark-input-group-addon:first-child {\n border-right: 0;\n}\n.du-bois-dark-input-group-addon:last-child {\n border-left: 0;\n}\n.du-bois-dark-input-group > .du-bois-dark-input:last-child,\n.du-bois-dark-input-group-addon:last-child {\n border-top-left-radius: 0;\n border-bottom-left-radius: 0;\n}\n.du-bois-dark-input-group > .du-bois-dark-input:last-child .du-bois-dark-select .du-bois-dark-select-selector,\n.du-bois-dark-input-group-addon:last-child .du-bois-dark-select .du-bois-dark-select-selector {\n border-top-left-radius: 0;\n border-bottom-left-radius: 0;\n}\n.du-bois-dark-input-group-lg .du-bois-dark-input,\n.du-bois-dark-input-group-lg > .du-bois-dark-input-group-addon {\n padding: 5px 12px;\n font-size: 18px;\n}\n.du-bois-dark-input-group-sm .du-bois-dark-input,\n.du-bois-dark-input-group-sm > .du-bois-dark-input-group-addon {\n padding: 5px 12px;\n}\n.du-bois-dark-input-group-lg .du-bois-dark-select-single .du-bois-dark-select-selector {\n height: 40px;\n}\n.du-bois-dark-input-group-sm .du-bois-dark-select-single .du-bois-dark-select-selector {\n height: 32px;\n}\n.du-bois-dark-input-group .du-bois-dark-input-affix-wrapper:not(:first-child) {\n border-top-left-radius: 0;\n border-bottom-left-radius: 0;\n}\n.du-bois-dark-input-group .du-bois-dark-input-affix-wrapper:not(:last-child) {\n border-top-right-radius: 0;\n border-bottom-right-radius: 0;\n}\n.du-bois-dark-input-search .du-bois-dark-input-group .du-bois-dark-input-affix-wrapper:not(:last-child) {\n border-top-left-radius: 4px;\n border-bottom-left-radius: 4px;\n}\n.du-bois-dark-input-group.du-bois-dark-input-group-compact {\n display: block;\n}\n.du-bois-dark-input-group.du-bois-dark-input-group-compact::before {\n display: table;\n content: '';\n}\n.du-bois-dark-input-group.du-bois-dark-input-group-compact::after {\n display: table;\n clear: both;\n content: '';\n}\n.du-bois-dark-input-group.du-bois-dark-input-group-compact-addon:not(:first-child):not(:last-child),\n.du-bois-dark-input-group.du-bois-dark-input-group-compact-wrap:not(:first-child):not(:last-child),\n.du-bois-dark-input-group.du-bois-dark-input-group-compact > .du-bois-dark-input:not(:first-child):not(:last-child) {\n border-right-width: 1px;\n}\n.du-bois-dark-input-group.du-bois-dark-input-group-compact-addon:not(:first-child):not(:last-child):hover,\n.du-bois-dark-input-group.du-bois-dark-input-group-compact-wrap:not(:first-child):not(:last-child):hover,\n.du-bois-dark-input-group.du-bois-dark-input-group-compact > .du-bois-dark-input:not(:first-child):not(:last-child):hover {\n z-index: 1;\n}\n.du-bois-dark-input-group.du-bois-dark-input-group-compact-addon:not(:first-child):not(:last-child):focus,\n.du-bois-dark-input-group.du-bois-dark-input-group-compact-wrap:not(:first-child):not(:last-child):focus,\n.du-bois-dark-input-group.du-bois-dark-input-group-compact > .du-bois-dark-input:not(:first-child):not(:last-child):focus {\n z-index: 1;\n}\n.du-bois-dark-input-group.du-bois-dark-input-group-compact > * {\n display: inline-block;\n float: none;\n vertical-align: top;\n border-radius: 0;\n}\n.du-bois-dark-input-group.du-bois-dark-input-group-compact > .du-bois-dark-input-affix-wrapper {\n display: inline-flex;\n}\n.du-bois-dark-input-group.du-bois-dark-input-group-compact > .du-bois-dark-picker-range {\n display: inline-flex;\n}\n.du-bois-dark-input-group.du-bois-dark-input-group-compact > *:not(:last-child) {\n margin-right: -1px;\n border-right-width: 1px;\n}\n.du-bois-dark-input-group.du-bois-dark-input-group-compact .du-bois-dark-input {\n float: none;\n}\n.du-bois-dark-input-group.du-bois-dark-input-group-compact > .du-bois-dark-select > .du-bois-dark-select-selector,\n.du-bois-dark-input-group.du-bois-dark-input-group-compact > .du-bois-dark-select-auto-complete .du-bois-dark-input,\n.du-bois-dark-input-group.du-bois-dark-input-group-compact > .du-bois-dark-cascader-picker .du-bois-dark-input,\n.du-bois-dark-input-group.du-bois-dark-input-group-compact > .du-bois-dark-input-group-wrapper .du-bois-dark-input {\n border-right-width: 1px;\n border-radius: 0;\n}\n.du-bois-dark-input-group.du-bois-dark-input-group-compact > .du-bois-dark-select > .du-bois-dark-select-selector:hover,\n.du-bois-dark-input-group.du-bois-dark-input-group-compact > .du-bois-dark-select-auto-complete .du-bois-dark-input:hover,\n.du-bois-dark-input-group.du-bois-dark-input-group-compact > .du-bois-dark-cascader-picker .du-bois-dark-input:hover,\n.du-bois-dark-input-group.du-bois-dark-input-group-compact > .du-bois-dark-input-group-wrapper .du-bois-dark-input:hover {\n z-index: 1;\n}\n.du-bois-dark-input-group.du-bois-dark-input-group-compact > .du-bois-dark-select > .du-bois-dark-select-selector:focus,\n.du-bois-dark-input-group.du-bois-dark-input-group-compact > .du-bois-dark-select-auto-complete .du-bois-dark-input:focus,\n.du-bois-dark-input-group.du-bois-dark-input-group-compact > .du-bois-dark-cascader-picker .du-bois-dark-input:focus,\n.du-bois-dark-input-group.du-bois-dark-input-group-compact > .du-bois-dark-input-group-wrapper .du-bois-dark-input:focus {\n z-index: 1;\n}\n.du-bois-dark-input-group.du-bois-dark-input-group-compact > .du-bois-dark-select-focused {\n z-index: 1;\n}\n.du-bois-dark-input-group.du-bois-dark-input-group-compact > .du-bois-dark-select > .du-bois-dark-select-arrow {\n z-index: 1;\n}\n.du-bois-dark-input-group.du-bois-dark-input-group-compact > *:first-child,\n.du-bois-dark-input-group.du-bois-dark-input-group-compact > .du-bois-dark-select:first-child > .du-bois-dark-select-selector,\n.du-bois-dark-input-group.du-bois-dark-input-group-compact > .du-bois-dark-select-auto-complete:first-child .du-bois-dark-input,\n.du-bois-dark-input-group.du-bois-dark-input-group-compact > .du-bois-dark-cascader-picker:first-child .du-bois-dark-input {\n border-top-left-radius: 4px;\n border-bottom-left-radius: 4px;\n}\n.du-bois-dark-input-group.du-bois-dark-input-group-compact > *:last-child,\n.du-bois-dark-input-group.du-bois-dark-input-group-compact > .du-bois-dark-select:last-child > .du-bois-dark-select-selector,\n.du-bois-dark-input-group.du-bois-dark-input-group-compact > .du-bois-dark-cascader-picker:last-child .du-bois-dark-input,\n.du-bois-dark-input-group.du-bois-dark-input-group-compact > .du-bois-dark-cascader-picker-focused:last-child .du-bois-dark-input {\n border-right-width: 1px;\n border-top-right-radius: 4px;\n border-bottom-right-radius: 4px;\n}\n.du-bois-dark-input-group.du-bois-dark-input-group-compact > .du-bois-dark-select-auto-complete .du-bois-dark-input {\n vertical-align: top;\n}\n.du-bois-dark-input-group.du-bois-dark-input-group-compact .du-bois-dark-input-group-wrapper + .du-bois-dark-input-group-wrapper {\n margin-left: -1px;\n}\n.du-bois-dark-input-group.du-bois-dark-input-group-compact .du-bois-dark-input-group-wrapper + .du-bois-dark-input-group-wrapper .du-bois-dark-input-affix-wrapper {\n border-radius: 0;\n}\n.du-bois-dark-input-group.du-bois-dark-input-group-compact .du-bois-dark-input-group-wrapper:not(:last-child).du-bois-dark-input-search > .du-bois-dark-input-group > .du-bois-dark-input-group-addon > .du-bois-dark-input-search-button {\n border-radius: 0;\n}\n.du-bois-dark-input-group.du-bois-dark-input-group-compact .du-bois-dark-input-group-wrapper:not(:last-child).du-bois-dark-input-search > .du-bois-dark-input-group > .du-bois-dark-input {\n border-radius: 4px 0 0 4px;\n}\n.du-bois-dark-input-group-wrapper {\n display: inline-block;\n width: 100%;\n text-align: start;\n vertical-align: top;\n}\n.du-bois-dark-input-password-icon {\n color: #92A4B3;\n cursor: pointer;\n transition: all 0.3s;\n}\n.du-bois-dark-input-password-icon:hover {\n color: rgba(255, 255, 255, 0.85);\n}\n.du-bois-dark-input[type='color'] {\n height: 40px;\n}\n.du-bois-dark-input[type='color'].du-bois-dark-input-lg {\n height: 40px;\n}\n.du-bois-dark-input[type='color'].du-bois-dark-input-sm {\n height: 32px;\n padding-top: 3px;\n padding-bottom: 3px;\n}\n.du-bois-dark-input-textarea-show-count::after {\n float: right;\n color: #92A4B3;\n white-space: nowrap;\n content: attr(data-count);\n pointer-events: none;\n}\n.du-bois-dark-input-search .du-bois-dark-input:hover,\n.du-bois-dark-input-search .du-bois-dark-input:focus {\n border-color: #1d517c;\n}\n.du-bois-dark-input-search .du-bois-dark-input:hover + .du-bois-dark-input-group-addon .du-bois-dark-input-search-button:not(.du-bois-dark-btn-primary),\n.du-bois-dark-input-search .du-bois-dark-input:focus + .du-bois-dark-input-group-addon .du-bois-dark-input-search-button:not(.du-bois-dark-btn-primary) {\n border-left-color: #1d517c;\n}\n.du-bois-dark-input-search .du-bois-dark-input-affix-wrapper {\n border-radius: 0;\n}\n.du-bois-dark-input-search .du-bois-dark-input-lg {\n line-height: 19.9998px;\n}\n.du-bois-dark-input-search > .du-bois-dark-input-group > .du-bois-dark-input-group-addon:last-child {\n left: -1px;\n padding: 0;\n border: 0;\n}\n.du-bois-dark-input-search > .du-bois-dark-input-group > .du-bois-dark-input-group-addon:last-child .du-bois-dark-input-search-button {\n padding-top: 0;\n padding-bottom: 0;\n border-radius: 0 4px 4px 0;\n}\n.du-bois-dark-input-search > .du-bois-dark-input-group > .du-bois-dark-input-group-addon:last-child .du-bois-dark-input-search-button:not(.du-bois-dark-btn-primary) {\n color: #92A4B3;\n}\n.du-bois-dark-input-search > .du-bois-dark-input-group > .du-bois-dark-input-group-addon:last-child .du-bois-dark-input-search-button:not(.du-bois-dark-btn-primary).du-bois-dark-btn-loading::before {\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n}\n.du-bois-dark-input-search-button {\n height: 40px;\n}\n.du-bois-dark-input-search-button:hover,\n.du-bois-dark-input-search-button:focus {\n z-index: 1;\n}\n.du-bois-dark-input-search-large .du-bois-dark-input-search-button {\n height: 40px;\n}\n.du-bois-dark-input-search-small .du-bois-dark-input-search-button {\n height: 32px;\n}\n@media screen and (-ms-high-contrast: active), (-ms-high-contrast: none) {\n .du-bois-dark-input {\n height: 40px;\n }\n .du-bois-dark-input-lg {\n height: 40px;\n }\n .du-bois-dark-input-sm {\n height: 32px;\n }\n .du-bois-dark-input-affix-wrapper > input.du-bois-dark-input {\n height: auto;\n }\n}\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-dark-layout {\n display: flex;\n flex: auto;\n flex-direction: column;\n /* fix firefox can't set height smaller than content on flex item */\n min-height: 0;\n background: #11171C;\n}\n.du-bois-dark-layout,\n.du-bois-dark-layout * {\n box-sizing: border-box;\n}\n.du-bois-dark-layout.du-bois-dark-layout-has-sider {\n flex-direction: row;\n}\n.du-bois-dark-layout.du-bois-dark-layout-has-sider > .du-bois-dark-layout,\n.du-bois-dark-layout.du-bois-dark-layout-has-sider > .du-bois-dark-layout-content {\n width: 0;\n}\n.du-bois-dark-layout-header,\n.du-bois-dark-layout-footer {\n flex: 0 0 auto;\n}\n.du-bois-dark-layout-header {\n height: 64px;\n padding: 0 50px;\n color: #E8ECF0;\n line-height: 64px;\n background: #1f1f1f;\n}\n.du-bois-dark-layout-footer {\n padding: 24px 50px;\n color: #E8ECF0;\n font-size: 13px;\n background: #11171C;\n}\n.du-bois-dark-layout-content {\n flex: auto;\n /* fix firefox can't set height smaller than content on flex item */\n min-height: 0;\n}\n.du-bois-dark-layout-sider {\n position: relative;\n /* fix firefox can't set width smaller than content on flex item */\n min-width: 0;\n background: #1f1f1f;\n transition: all 0.2s;\n}\n.du-bois-dark-layout-sider-children {\n height: 100%;\n margin-top: -0.1px;\n padding-top: 0.1px;\n}\n.du-bois-dark-layout-sider-children .du-bois-dark-menu.du-bois-dark-menu-inline-collapsed {\n width: auto;\n}\n.du-bois-dark-layout-sider-has-trigger {\n padding-bottom: 48px;\n}\n.du-bois-dark-layout-sider-right {\n order: 1;\n}\n.du-bois-dark-layout-sider-trigger {\n position: fixed;\n bottom: 0;\n z-index: 1;\n height: 48px;\n color: #fff;\n line-height: 48px;\n text-align: center;\n background: #262626;\n cursor: pointer;\n transition: all 0.2s;\n}\n.du-bois-dark-layout-sider-zero-width > * {\n overflow: hidden;\n}\n.du-bois-dark-layout-sider-zero-width-trigger {\n position: absolute;\n top: 64px;\n right: -36px;\n z-index: 1;\n width: 36px;\n height: 42px;\n color: #fff;\n font-size: 18px;\n line-height: 42px;\n text-align: center;\n background: #1f1f1f;\n border-radius: 0 4px 4px 0;\n cursor: pointer;\n transition: background 0.3s ease;\n}\n.du-bois-dark-layout-sider-zero-width-trigger::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: transparent;\n transition: all 0.3s;\n content: '';\n}\n.du-bois-dark-layout-sider-zero-width-trigger:hover::after {\n background: rgba(255, 255, 255, 0.1);\n}\n.du-bois-dark-layout-sider-zero-width-trigger-right {\n left: -36px;\n border-radius: 4px 0 0 4px;\n}\n.du-bois-dark-layout-sider-light {\n background: #fff;\n}\n.du-bois-dark-layout-sider-light .du-bois-dark-layout-sider-trigger {\n color: #E8ECF0;\n background: #fff;\n}\n.du-bois-dark-layout-sider-light .du-bois-dark-layout-sider-zero-width-trigger {\n color: #E8ECF0;\n background: #fff;\n}\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-dark-menu-item-danger.du-bois-dark-menu-item {\n color: #E65B77;\n}\n.du-bois-dark-menu-item-danger.du-bois-dark-menu-item:hover,\n.du-bois-dark-menu-item-danger.du-bois-dark-menu-item-active {\n color: #E65B77;\n}\n.du-bois-dark-menu-item-danger.du-bois-dark-menu-item:active {\n background: #2a1215;\n}\n.du-bois-dark-menu-item-danger.du-bois-dark-menu-item-selected {\n color: #E65B77;\n}\n.du-bois-dark-menu-item-danger.du-bois-dark-menu-item-selected > a,\n.du-bois-dark-menu-item-danger.du-bois-dark-menu-item-selected > a:hover {\n color: #E65B77;\n}\n.du-bois-dark-menu:not(.du-bois-dark-menu-horizontal) .du-bois-dark-menu-item-danger.du-bois-dark-menu-item-selected {\n background-color: #2a1215;\n}\n.du-bois-dark-menu-inline .du-bois-dark-menu-item-danger.du-bois-dark-menu-item::after {\n border-right-color: #E65B77;\n}\n.du-bois-dark-menu-dark .du-bois-dark-menu-item-danger.du-bois-dark-menu-item,\n.du-bois-dark-menu-dark .du-bois-dark-menu-item-danger.du-bois-dark-menu-item:hover,\n.du-bois-dark-menu-dark .du-bois-dark-menu-item-danger.du-bois-dark-menu-item > a {\n color: #E65B77;\n}\n.du-bois-dark-menu-dark.du-bois-dark-menu-dark:not(.du-bois-dark-menu-horizontal) .du-bois-dark-menu-item-danger.du-bois-dark-menu-item-selected {\n color: #fff;\n background-color: #E65B77;\n}\n.du-bois-dark-menu {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n font-variant: tabular-nums;\n line-height: 20px;\n font-feature-settings: tnum;\n margin-bottom: 0;\n padding-left: 0;\n color: #E8ECF0;\n font-size: 13px;\n line-height: 0;\n text-align: left;\n list-style: none;\n background: #141414;\n outline: none;\n box-shadow: 0 3px 6px -4px rgba(0, 0, 0, 0.48), 0 6px 16px 0 rgba(0, 0, 0, 0.32), 0 9px 28px 8px rgba(0, 0, 0, 0.2);\n transition: background 0.3s, width 0.3s cubic-bezier(0.2, 0, 0, 1) 0s;\n}\n.du-bois-dark-menu::before {\n display: table;\n content: '';\n}\n.du-bois-dark-menu::after {\n display: table;\n clear: both;\n content: '';\n}\n.du-bois-dark-menu.du-bois-dark-menu-root:focus-visible {\n box-shadow: 0 0 0 2px rgba(34, 114, 180, 0.2);\n}\n.du-bois-dark-menu ul,\n.du-bois-dark-menu ol {\n margin: 0;\n padding: 0;\n list-style: none;\n}\n.du-bois-dark-menu-overflow {\n display: flex;\n}\n.du-bois-dark-menu-overflow-item {\n flex: none;\n}\n.du-bois-dark-menu-hidden,\n.du-bois-dark-menu-submenu-hidden {\n display: none;\n}\n.du-bois-dark-menu-item-group-title {\n height: 20px;\n padding: 8px 16px;\n color: #92A4B3;\n font-size: 13px;\n line-height: 20px;\n transition: all 0.3s;\n}\n.du-bois-dark-menu-horizontal .du-bois-dark-menu-submenu {\n transition: border-color 0.3s cubic-bezier(0.645, 0.045, 0.355, 1), background 0.3s cubic-bezier(0.645, 0.045, 0.355, 1);\n}\n.du-bois-dark-menu-submenu,\n.du-bois-dark-menu-submenu-inline {\n transition: border-color 0.3s cubic-bezier(0.645, 0.045, 0.355, 1), background 0.3s cubic-bezier(0.645, 0.045, 0.355, 1), padding 0.15s cubic-bezier(0.645, 0.045, 0.355, 1);\n}\n.du-bois-dark-menu-submenu-selected {\n color: #2272B4;\n}\n.du-bois-dark-menu-item:active,\n.du-bois-dark-menu-submenu-title:active {\n background: #121921;\n}\n.du-bois-dark-menu-submenu .du-bois-dark-menu-sub {\n cursor: initial;\n transition: background 0.3s cubic-bezier(0.645, 0.045, 0.355, 1), padding 0.3s cubic-bezier(0.645, 0.045, 0.355, 1);\n}\n.du-bois-dark-menu-title-content {\n transition: color 0.3s;\n}\n.du-bois-dark-menu-item a {\n color: #E8ECF0;\n}\n.du-bois-dark-menu-item a:hover {\n color: #2272B4;\n}\n.du-bois-dark-menu-item a::before {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background-color: transparent;\n content: '';\n}\n.du-bois-dark-menu-item > .du-bois-dark-badge a {\n color: #E8ECF0;\n}\n.du-bois-dark-menu-item > .du-bois-dark-badge a:hover {\n color: #2272B4;\n}\n.du-bois-dark-menu-item-divider {\n height: 1px;\n overflow: hidden;\n line-height: 0;\n background-color: #303030;\n}\n.du-bois-dark-menu-horizontal .du-bois-dark-menu-item,\n.du-bois-dark-menu-horizontal .du-bois-dark-menu-submenu {\n margin-top: -1px;\n}\n.du-bois-dark-menu-horizontal > .du-bois-dark-menu-item:hover,\n.du-bois-dark-menu-horizontal > .du-bois-dark-menu-item-active,\n.du-bois-dark-menu-horizontal > .du-bois-dark-menu-submenu .du-bois-dark-menu-submenu-title:hover {\n background-color: transparent;\n}\n.du-bois-dark-menu-item-selected {\n color: #2272B4;\n}\n.du-bois-dark-menu-item-selected a,\n.du-bois-dark-menu-item-selected a:hover {\n color: #2272B4;\n}\n.du-bois-dark-menu:not(.du-bois-dark-menu-horizontal) .du-bois-dark-menu-item-selected {\n background-color: #121921;\n}\n.du-bois-dark-menu-inline,\n.du-bois-dark-menu-vertical,\n.du-bois-dark-menu-vertical-left {\n border-right: 1px solid #303030;\n}\n.du-bois-dark-menu-vertical-right {\n border-left: 1px solid #303030;\n}\n.du-bois-dark-menu-vertical.du-bois-dark-menu-sub,\n.du-bois-dark-menu-vertical-left.du-bois-dark-menu-sub,\n.du-bois-dark-menu-vertical-right.du-bois-dark-menu-sub {\n min-width: 160px;\n max-height: calc(100vh - 100px);\n padding: 0;\n overflow: hidden;\n border-right: 0;\n}\n.du-bois-dark-menu-vertical.du-bois-dark-menu-sub:not([class*='-active']),\n.du-bois-dark-menu-vertical-left.du-bois-dark-menu-sub:not([class*='-active']),\n.du-bois-dark-menu-vertical-right.du-bois-dark-menu-sub:not([class*='-active']) {\n overflow-x: hidden;\n overflow-y: auto;\n}\n.du-bois-dark-menu-vertical.du-bois-dark-menu-sub .du-bois-dark-menu-item,\n.du-bois-dark-menu-vertical-left.du-bois-dark-menu-sub .du-bois-dark-menu-item,\n.du-bois-dark-menu-vertical-right.du-bois-dark-menu-sub .du-bois-dark-menu-item {\n left: 0;\n margin-left: 0;\n border-right: 0;\n}\n.du-bois-dark-menu-vertical.du-bois-dark-menu-sub .du-bois-dark-menu-item::after,\n.du-bois-dark-menu-vertical-left.du-bois-dark-menu-sub .du-bois-dark-menu-item::after,\n.du-bois-dark-menu-vertical-right.du-bois-dark-menu-sub .du-bois-dark-menu-item::after {\n border-right: 0;\n}\n.du-bois-dark-menu-vertical.du-bois-dark-menu-sub > .du-bois-dark-menu-item,\n.du-bois-dark-menu-vertical-left.du-bois-dark-menu-sub > .du-bois-dark-menu-item,\n.du-bois-dark-menu-vertical-right.du-bois-dark-menu-sub > .du-bois-dark-menu-item,\n.du-bois-dark-menu-vertical.du-bois-dark-menu-sub > .du-bois-dark-menu-submenu,\n.du-bois-dark-menu-vertical-left.du-bois-dark-menu-sub > .du-bois-dark-menu-submenu,\n.du-bois-dark-menu-vertical-right.du-bois-dark-menu-sub > .du-bois-dark-menu-submenu {\n transform-origin: 0 0;\n}\n.du-bois-dark-menu-horizontal.du-bois-dark-menu-sub {\n min-width: 114px;\n}\n.du-bois-dark-menu-horizontal .du-bois-dark-menu-item,\n.du-bois-dark-menu-horizontal .du-bois-dark-menu-submenu-title {\n transition: border-color 0.3s, background 0.3s;\n}\n.du-bois-dark-menu-item,\n.du-bois-dark-menu-submenu-title {\n position: relative;\n display: block;\n margin: 0;\n padding: 0 20px;\n white-space: nowrap;\n cursor: pointer;\n transition: border-color 0.3s, background 0.3s, padding 0.3s cubic-bezier(0.645, 0.045, 0.355, 1);\n}\n.du-bois-dark-menu-item .du-bois-dark-menu-item-icon,\n.du-bois-dark-menu-submenu-title .du-bois-dark-menu-item-icon,\n.du-bois-dark-menu-item .anticon,\n.du-bois-dark-menu-submenu-title .anticon {\n min-width: 14px;\n font-size: 13px;\n transition: font-size 0.15s cubic-bezier(0.215, 0.61, 0.355, 1), margin 0.3s cubic-bezier(0.645, 0.045, 0.355, 1), color 0.3s;\n}\n.du-bois-dark-menu-item .du-bois-dark-menu-item-icon + span,\n.du-bois-dark-menu-submenu-title .du-bois-dark-menu-item-icon + span,\n.du-bois-dark-menu-item .anticon + span,\n.du-bois-dark-menu-submenu-title .anticon + span {\n margin-left: 10px;\n opacity: 1;\n transition: opacity 0.3s cubic-bezier(0.645, 0.045, 0.355, 1), margin 0.3s, color 0.3s;\n}\n.du-bois-dark-menu-item .du-bois-dark-menu-item-icon.svg,\n.du-bois-dark-menu-submenu-title .du-bois-dark-menu-item-icon.svg {\n vertical-align: -0.125em;\n}\n.du-bois-dark-menu-item.du-bois-dark-menu-item-only-child > .anticon,\n.du-bois-dark-menu-submenu-title.du-bois-dark-menu-item-only-child > .anticon,\n.du-bois-dark-menu-item.du-bois-dark-menu-item-only-child > .du-bois-dark-menu-item-icon,\n.du-bois-dark-menu-submenu-title.du-bois-dark-menu-item-only-child > .du-bois-dark-menu-item-icon {\n margin-right: 0;\n}\n.du-bois-dark-menu-item:focus-visible,\n.du-bois-dark-menu-submenu-title:focus-visible {\n box-shadow: 0 0 0 2px rgba(34, 114, 180, 0.2);\n}\n.du-bois-dark-menu > .du-bois-dark-menu-item-divider {\n height: 1px;\n margin: 1px 0;\n padding: 0;\n overflow: hidden;\n line-height: 0;\n background-color: #303030;\n}\n.du-bois-dark-menu-submenu-popup {\n position: absolute;\n z-index: 1050;\n background: transparent;\n border-radius: 4px;\n box-shadow: none;\n transform-origin: 0 0;\n}\n.du-bois-dark-menu-submenu-popup::before {\n position: absolute;\n top: -7px;\n right: 0;\n bottom: 0;\n left: 0;\n z-index: -1;\n width: 100%;\n height: 100%;\n opacity: 0.0001;\n content: ' ';\n}\n.du-bois-dark-menu-submenu-placement-rightTop::before {\n top: 0;\n left: -7px;\n}\n.du-bois-dark-menu-submenu > .du-bois-dark-menu {\n background-color: #141414;\n border-radius: 4px;\n}\n.du-bois-dark-menu-submenu > .du-bois-dark-menu-submenu-title::after {\n transition: transform 0.3s cubic-bezier(0.645, 0.045, 0.355, 1);\n}\n.du-bois-dark-menu-submenu-popup > .du-bois-dark-menu {\n background-color: #1f1f1f;\n}\n.du-bois-dark-menu-submenu-expand-icon,\n.du-bois-dark-menu-submenu-arrow {\n position: absolute;\n top: 50%;\n right: 16px;\n width: 10px;\n color: #E8ECF0;\n transform: translateY(-50%);\n transition: transform 0.3s cubic-bezier(0.645, 0.045, 0.355, 1);\n}\n.du-bois-dark-menu-submenu-arrow::before,\n.du-bois-dark-menu-submenu-arrow::after {\n position: absolute;\n width: 6px;\n height: 1.5px;\n background-color: currentColor;\n border-radius: 2px;\n transition: background 0.3s cubic-bezier(0.645, 0.045, 0.355, 1), transform 0.3s cubic-bezier(0.645, 0.045, 0.355, 1), top 0.3s cubic-bezier(0.645, 0.045, 0.355, 1), color 0.3s cubic-bezier(0.645, 0.045, 0.355, 1);\n content: '';\n}\n.du-bois-dark-menu-submenu-arrow::before {\n transform: rotate(45deg) translateY(-2.5px);\n}\n.du-bois-dark-menu-submenu-arrow::after {\n transform: rotate(-45deg) translateY(2.5px);\n}\n.du-bois-dark-menu-submenu:hover > .du-bois-dark-menu-submenu-title > .du-bois-dark-menu-submenu-expand-icon,\n.du-bois-dark-menu-submenu:hover > .du-bois-dark-menu-submenu-title > .du-bois-dark-menu-submenu-arrow {\n color: #2272B4;\n}\n.du-bois-dark-menu-inline-collapsed .du-bois-dark-menu-submenu-arrow::before,\n.du-bois-dark-menu-submenu-inline .du-bois-dark-menu-submenu-arrow::before {\n transform: rotate(-45deg) translateX(2.5px);\n}\n.du-bois-dark-menu-inline-collapsed .du-bois-dark-menu-submenu-arrow::after,\n.du-bois-dark-menu-submenu-inline .du-bois-dark-menu-submenu-arrow::after {\n transform: rotate(45deg) translateX(-2.5px);\n}\n.du-bois-dark-menu-submenu-horizontal .du-bois-dark-menu-submenu-arrow {\n display: none;\n}\n.du-bois-dark-menu-submenu-open.du-bois-dark-menu-submenu-inline > .du-bois-dark-menu-submenu-title > .du-bois-dark-menu-submenu-arrow {\n transform: translateY(-2px);\n}\n.du-bois-dark-menu-submenu-open.du-bois-dark-menu-submenu-inline > .du-bois-dark-menu-submenu-title > .du-bois-dark-menu-submenu-arrow::after {\n transform: rotate(-45deg) translateX(-2.5px);\n}\n.du-bois-dark-menu-submenu-open.du-bois-dark-menu-submenu-inline > .du-bois-dark-menu-submenu-title > .du-bois-dark-menu-submenu-arrow::before {\n transform: rotate(45deg) translateX(2.5px);\n}\n.du-bois-dark-menu-vertical .du-bois-dark-menu-submenu-selected,\n.du-bois-dark-menu-vertical-left .du-bois-dark-menu-submenu-selected,\n.du-bois-dark-menu-vertical-right .du-bois-dark-menu-submenu-selected {\n color: #2272B4;\n}\n.du-bois-dark-menu-horizontal {\n line-height: 46px;\n border: 0;\n border-bottom: 1px solid #303030;\n box-shadow: none;\n}\n.du-bois-dark-menu-horizontal:not(.du-bois-dark-menu-dark) > .du-bois-dark-menu-item,\n.du-bois-dark-menu-horizontal:not(.du-bois-dark-menu-dark) > .du-bois-dark-menu-submenu {\n margin-top: -1px;\n margin-bottom: 0;\n padding: 0 20px;\n}\n.du-bois-dark-menu-horizontal:not(.du-bois-dark-menu-dark) > .du-bois-dark-menu-item:hover,\n.du-bois-dark-menu-horizontal:not(.du-bois-dark-menu-dark) > .du-bois-dark-menu-submenu:hover,\n.du-bois-dark-menu-horizontal:not(.du-bois-dark-menu-dark) > .du-bois-dark-menu-item-active,\n.du-bois-dark-menu-horizontal:not(.du-bois-dark-menu-dark) > .du-bois-dark-menu-submenu-active,\n.du-bois-dark-menu-horizontal:not(.du-bois-dark-menu-dark) > .du-bois-dark-menu-item-open,\n.du-bois-dark-menu-horizontal:not(.du-bois-dark-menu-dark) > .du-bois-dark-menu-submenu-open,\n.du-bois-dark-menu-horizontal:not(.du-bois-dark-menu-dark) > .du-bois-dark-menu-item-selected,\n.du-bois-dark-menu-horizontal:not(.du-bois-dark-menu-dark) > .du-bois-dark-menu-submenu-selected {\n color: #2272B4;\n}\n.du-bois-dark-menu-horizontal:not(.du-bois-dark-menu-dark) > .du-bois-dark-menu-item:hover::after,\n.du-bois-dark-menu-horizontal:not(.du-bois-dark-menu-dark) > .du-bois-dark-menu-submenu:hover::after,\n.du-bois-dark-menu-horizontal:not(.du-bois-dark-menu-dark) > .du-bois-dark-menu-item-active::after,\n.du-bois-dark-menu-horizontal:not(.du-bois-dark-menu-dark) > .du-bois-dark-menu-submenu-active::after,\n.du-bois-dark-menu-horizontal:not(.du-bois-dark-menu-dark) > .du-bois-dark-menu-item-open::after,\n.du-bois-dark-menu-horizontal:not(.du-bois-dark-menu-dark) > .du-bois-dark-menu-submenu-open::after,\n.du-bois-dark-menu-horizontal:not(.du-bois-dark-menu-dark) > .du-bois-dark-menu-item-selected::after,\n.du-bois-dark-menu-horizontal:not(.du-bois-dark-menu-dark) > .du-bois-dark-menu-submenu-selected::after {\n border-bottom: 2px solid #2272B4;\n}\n.du-bois-dark-menu-horizontal > .du-bois-dark-menu-item,\n.du-bois-dark-menu-horizontal > .du-bois-dark-menu-submenu {\n position: relative;\n top: 1px;\n display: inline-block;\n vertical-align: bottom;\n}\n.du-bois-dark-menu-horizontal > .du-bois-dark-menu-item::after,\n.du-bois-dark-menu-horizontal > .du-bois-dark-menu-submenu::after {\n position: absolute;\n right: 20px;\n bottom: 0;\n left: 20px;\n border-bottom: 2px solid transparent;\n transition: border-color 0.3s cubic-bezier(0.645, 0.045, 0.355, 1);\n content: '';\n}\n.du-bois-dark-menu-horizontal > .du-bois-dark-menu-submenu > .du-bois-dark-menu-submenu-title {\n padding: 0;\n}\n.du-bois-dark-menu-horizontal > .du-bois-dark-menu-item a {\n color: #E8ECF0;\n}\n.du-bois-dark-menu-horizontal > .du-bois-dark-menu-item a:hover {\n color: #2272B4;\n}\n.du-bois-dark-menu-horizontal > .du-bois-dark-menu-item a::before {\n bottom: -2px;\n}\n.du-bois-dark-menu-horizontal > .du-bois-dark-menu-item-selected a {\n color: #2272B4;\n}\n.du-bois-dark-menu-horizontal::after {\n display: block;\n clear: both;\n height: 0;\n content: '\\20';\n}\n.du-bois-dark-menu-vertical .du-bois-dark-menu-item,\n.du-bois-dark-menu-vertical-left .du-bois-dark-menu-item,\n.du-bois-dark-menu-vertical-right .du-bois-dark-menu-item,\n.du-bois-dark-menu-inline .du-bois-dark-menu-item {\n position: relative;\n}\n.du-bois-dark-menu-vertical .du-bois-dark-menu-item::after,\n.du-bois-dark-menu-vertical-left .du-bois-dark-menu-item::after,\n.du-bois-dark-menu-vertical-right .du-bois-dark-menu-item::after,\n.du-bois-dark-menu-inline .du-bois-dark-menu-item::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n border-right: 3px solid #2272B4;\n transform: scaleY(0.0001);\n opacity: 0;\n transition: transform 0.15s cubic-bezier(0.215, 0.61, 0.355, 1), opacity 0.15s cubic-bezier(0.215, 0.61, 0.355, 1);\n content: '';\n}\n.du-bois-dark-menu-vertical .du-bois-dark-menu-item,\n.du-bois-dark-menu-vertical-left .du-bois-dark-menu-item,\n.du-bois-dark-menu-vertical-right .du-bois-dark-menu-item,\n.du-bois-dark-menu-inline .du-bois-dark-menu-item,\n.du-bois-dark-menu-vertical .du-bois-dark-menu-submenu-title,\n.du-bois-dark-menu-vertical-left .du-bois-dark-menu-submenu-title,\n.du-bois-dark-menu-vertical-right .du-bois-dark-menu-submenu-title,\n.du-bois-dark-menu-inline .du-bois-dark-menu-submenu-title {\n height: 40px;\n margin-top: 4px;\n margin-bottom: 4px;\n padding: 0 16px;\n overflow: hidden;\n line-height: 40px;\n text-overflow: ellipsis;\n}\n.du-bois-dark-menu-vertical .du-bois-dark-menu-submenu,\n.du-bois-dark-menu-vertical-left .du-bois-dark-menu-submenu,\n.du-bois-dark-menu-vertical-right .du-bois-dark-menu-submenu,\n.du-bois-dark-menu-inline .du-bois-dark-menu-submenu {\n padding-bottom: 0.02px;\n}\n.du-bois-dark-menu-vertical .du-bois-dark-menu-item:not(:last-child),\n.du-bois-dark-menu-vertical-left .du-bois-dark-menu-item:not(:last-child),\n.du-bois-dark-menu-vertical-right .du-bois-dark-menu-item:not(:last-child),\n.du-bois-dark-menu-inline .du-bois-dark-menu-item:not(:last-child) {\n margin-bottom: 8px;\n}\n.du-bois-dark-menu-vertical > .du-bois-dark-menu-item,\n.du-bois-dark-menu-vertical-left > .du-bois-dark-menu-item,\n.du-bois-dark-menu-vertical-right > .du-bois-dark-menu-item,\n.du-bois-dark-menu-inline > .du-bois-dark-menu-item,\n.du-bois-dark-menu-vertical > .du-bois-dark-menu-submenu > .du-bois-dark-menu-submenu-title,\n.du-bois-dark-menu-vertical-left > .du-bois-dark-menu-submenu > .du-bois-dark-menu-submenu-title,\n.du-bois-dark-menu-vertical-right > .du-bois-dark-menu-submenu > .du-bois-dark-menu-submenu-title,\n.du-bois-dark-menu-inline > .du-bois-dark-menu-submenu > .du-bois-dark-menu-submenu-title {\n height: 40px;\n line-height: 40px;\n}\n.du-bois-dark-menu-vertical .du-bois-dark-menu-item-group-list .du-bois-dark-menu-submenu-title,\n.du-bois-dark-menu-vertical .du-bois-dark-menu-submenu-title {\n padding-right: 34px;\n}\n.du-bois-dark-menu-inline {\n width: 100%;\n}\n.du-bois-dark-menu-inline .du-bois-dark-menu-selected::after,\n.du-bois-dark-menu-inline .du-bois-dark-menu-item-selected::after {\n transform: scaleY(1);\n opacity: 1;\n transition: transform 0.15s cubic-bezier(0.645, 0.045, 0.355, 1), opacity 0.15s cubic-bezier(0.645, 0.045, 0.355, 1);\n}\n.du-bois-dark-menu-inline .du-bois-dark-menu-item,\n.du-bois-dark-menu-inline .du-bois-dark-menu-submenu-title {\n width: calc(100% + 1px);\n}\n.du-bois-dark-menu-inline .du-bois-dark-menu-item-group-list .du-bois-dark-menu-submenu-title,\n.du-bois-dark-menu-inline .du-bois-dark-menu-submenu-title {\n padding-right: 34px;\n}\n.du-bois-dark-menu-inline.du-bois-dark-menu-root .du-bois-dark-menu-item,\n.du-bois-dark-menu-inline.du-bois-dark-menu-root .du-bois-dark-menu-submenu-title {\n display: flex;\n align-items: center;\n transition: border-color 0.3s, background 0.3s, padding 0.1s cubic-bezier(0.215, 0.61, 0.355, 1);\n}\n.du-bois-dark-menu-inline.du-bois-dark-menu-root .du-bois-dark-menu-item > .du-bois-dark-menu-title-content,\n.du-bois-dark-menu-inline.du-bois-dark-menu-root .du-bois-dark-menu-submenu-title > .du-bois-dark-menu-title-content {\n flex: auto;\n min-width: 0;\n overflow: hidden;\n text-overflow: ellipsis;\n}\n.du-bois-dark-menu-inline.du-bois-dark-menu-root .du-bois-dark-menu-item > *,\n.du-bois-dark-menu-inline.du-bois-dark-menu-root .du-bois-dark-menu-submenu-title > * {\n flex: none;\n}\n.du-bois-dark-menu.du-bois-dark-menu-inline-collapsed {\n width: 80px;\n}\n.du-bois-dark-menu.du-bois-dark-menu-inline-collapsed > .du-bois-dark-menu-item,\n.du-bois-dark-menu.du-bois-dark-menu-inline-collapsed > .du-bois-dark-menu-item-group > .du-bois-dark-menu-item-group-list > .du-bois-dark-menu-item,\n.du-bois-dark-menu.du-bois-dark-menu-inline-collapsed > .du-bois-dark-menu-item-group > .du-bois-dark-menu-item-group-list > .du-bois-dark-menu-submenu > .du-bois-dark-menu-submenu-title,\n.du-bois-dark-menu.du-bois-dark-menu-inline-collapsed > .du-bois-dark-menu-submenu > .du-bois-dark-menu-submenu-title {\n left: 0;\n padding: 0 calc(50% - 18px / 2);\n text-overflow: clip;\n}\n.du-bois-dark-menu.du-bois-dark-menu-inline-collapsed > .du-bois-dark-menu-item .du-bois-dark-menu-submenu-arrow,\n.du-bois-dark-menu.du-bois-dark-menu-inline-collapsed > .du-bois-dark-menu-item-group > .du-bois-dark-menu-item-group-list > .du-bois-dark-menu-item .du-bois-dark-menu-submenu-arrow,\n.du-bois-dark-menu.du-bois-dark-menu-inline-collapsed > .du-bois-dark-menu-item-group > .du-bois-dark-menu-item-group-list > .du-bois-dark-menu-submenu > .du-bois-dark-menu-submenu-title .du-bois-dark-menu-submenu-arrow,\n.du-bois-dark-menu.du-bois-dark-menu-inline-collapsed > .du-bois-dark-menu-submenu > .du-bois-dark-menu-submenu-title .du-bois-dark-menu-submenu-arrow {\n opacity: 0;\n}\n.du-bois-dark-menu.du-bois-dark-menu-inline-collapsed > .du-bois-dark-menu-item .du-bois-dark-menu-item-icon,\n.du-bois-dark-menu.du-bois-dark-menu-inline-collapsed > .du-bois-dark-menu-item-group > .du-bois-dark-menu-item-group-list > .du-bois-dark-menu-item .du-bois-dark-menu-item-icon,\n.du-bois-dark-menu.du-bois-dark-menu-inline-collapsed > .du-bois-dark-menu-item-group > .du-bois-dark-menu-item-group-list > .du-bois-dark-menu-submenu > .du-bois-dark-menu-submenu-title .du-bois-dark-menu-item-icon,\n.du-bois-dark-menu.du-bois-dark-menu-inline-collapsed > .du-bois-dark-menu-submenu > .du-bois-dark-menu-submenu-title .du-bois-dark-menu-item-icon,\n.du-bois-dark-menu.du-bois-dark-menu-inline-collapsed > .du-bois-dark-menu-item .anticon,\n.du-bois-dark-menu.du-bois-dark-menu-inline-collapsed > .du-bois-dark-menu-item-group > .du-bois-dark-menu-item-group-list > .du-bois-dark-menu-item .anticon,\n.du-bois-dark-menu.du-bois-dark-menu-inline-collapsed > .du-bois-dark-menu-item-group > .du-bois-dark-menu-item-group-list > .du-bois-dark-menu-submenu > .du-bois-dark-menu-submenu-title .anticon,\n.du-bois-dark-menu.du-bois-dark-menu-inline-collapsed > .du-bois-dark-menu-submenu > .du-bois-dark-menu-submenu-title .anticon {\n margin: 0;\n font-size: 18px;\n line-height: 40px;\n}\n.du-bois-dark-menu.du-bois-dark-menu-inline-collapsed > .du-bois-dark-menu-item .du-bois-dark-menu-item-icon + span,\n.du-bois-dark-menu.du-bois-dark-menu-inline-collapsed > .du-bois-dark-menu-item-group > .du-bois-dark-menu-item-group-list > .du-bois-dark-menu-item .du-bois-dark-menu-item-icon + span,\n.du-bois-dark-menu.du-bois-dark-menu-inline-collapsed > .du-bois-dark-menu-item-group > .du-bois-dark-menu-item-group-list > .du-bois-dark-menu-submenu > .du-bois-dark-menu-submenu-title .du-bois-dark-menu-item-icon + span,\n.du-bois-dark-menu.du-bois-dark-menu-inline-collapsed > .du-bois-dark-menu-submenu > .du-bois-dark-menu-submenu-title .du-bois-dark-menu-item-icon + span,\n.du-bois-dark-menu.du-bois-dark-menu-inline-collapsed > .du-bois-dark-menu-item .anticon + span,\n.du-bois-dark-menu.du-bois-dark-menu-inline-collapsed > .du-bois-dark-menu-item-group > .du-bois-dark-menu-item-group-list > .du-bois-dark-menu-item .anticon + span,\n.du-bois-dark-menu.du-bois-dark-menu-inline-collapsed > .du-bois-dark-menu-item-group > .du-bois-dark-menu-item-group-list > .du-bois-dark-menu-submenu > .du-bois-dark-menu-submenu-title .anticon + span,\n.du-bois-dark-menu.du-bois-dark-menu-inline-collapsed > .du-bois-dark-menu-submenu > .du-bois-dark-menu-submenu-title .anticon + span {\n display: inline-block;\n opacity: 0;\n}\n.du-bois-dark-menu.du-bois-dark-menu-inline-collapsed .du-bois-dark-menu-item-icon,\n.du-bois-dark-menu.du-bois-dark-menu-inline-collapsed .anticon {\n display: inline-block;\n}\n.du-bois-dark-menu.du-bois-dark-menu-inline-collapsed-tooltip {\n pointer-events: none;\n}\n.du-bois-dark-menu.du-bois-dark-menu-inline-collapsed-tooltip .du-bois-dark-menu-item-icon,\n.du-bois-dark-menu.du-bois-dark-menu-inline-collapsed-tooltip .anticon {\n display: none;\n}\n.du-bois-dark-menu.du-bois-dark-menu-inline-collapsed-tooltip a {\n color: rgba(255, 255, 255, 0.85);\n}\n.du-bois-dark-menu.du-bois-dark-menu-inline-collapsed .du-bois-dark-menu-item-group-title {\n padding-right: 4px;\n padding-left: 4px;\n overflow: hidden;\n white-space: nowrap;\n text-overflow: ellipsis;\n}\n.du-bois-dark-menu-item-group-list {\n margin: 0;\n padding: 0;\n}\n.du-bois-dark-menu-item-group-list .du-bois-dark-menu-item,\n.du-bois-dark-menu-item-group-list .du-bois-dark-menu-submenu-title {\n padding: 0 16px 0 28px;\n}\n.du-bois-dark-menu-root.du-bois-dark-menu-vertical,\n.du-bois-dark-menu-root.du-bois-dark-menu-vertical-left,\n.du-bois-dark-menu-root.du-bois-dark-menu-vertical-right,\n.du-bois-dark-menu-root.du-bois-dark-menu-inline {\n box-shadow: none;\n}\n.du-bois-dark-menu-root.du-bois-dark-menu-inline-collapsed .du-bois-dark-menu-item > .du-bois-dark-menu-inline-collapsed-noicon,\n.du-bois-dark-menu-root.du-bois-dark-menu-inline-collapsed .du-bois-dark-menu-submenu .du-bois-dark-menu-submenu-title > .du-bois-dark-menu-inline-collapsed-noicon {\n font-size: 18px;\n text-align: center;\n}\n.du-bois-dark-menu-sub.du-bois-dark-menu-inline {\n padding: 0;\n background: rgba(255, 255, 255, 0.04);\n border: 0;\n border-radius: 0;\n box-shadow: none;\n}\n.du-bois-dark-menu-sub.du-bois-dark-menu-inline > .du-bois-dark-menu-item,\n.du-bois-dark-menu-sub.du-bois-dark-menu-inline > .du-bois-dark-menu-submenu > .du-bois-dark-menu-submenu-title {\n height: 40px;\n line-height: 40px;\n list-style-position: inside;\n list-style-type: disc;\n}\n.du-bois-dark-menu-sub.du-bois-dark-menu-inline .du-bois-dark-menu-item-group-title {\n padding-left: 32px;\n}\n.du-bois-dark-menu-item-disabled,\n.du-bois-dark-menu-submenu-disabled {\n color: rgba(255, 255, 255, 0.3) !important;\n background: none;\n cursor: not-allowed;\n}\n.du-bois-dark-menu-item-disabled::after,\n.du-bois-dark-menu-submenu-disabled::after {\n border-color: transparent !important;\n}\n.du-bois-dark-menu-item-disabled a,\n.du-bois-dark-menu-submenu-disabled a {\n color: rgba(255, 255, 255, 0.3) !important;\n pointer-events: none;\n}\n.du-bois-dark-menu-item-disabled > .du-bois-dark-menu-submenu-title,\n.du-bois-dark-menu-submenu-disabled > .du-bois-dark-menu-submenu-title {\n color: rgba(255, 255, 255, 0.3) !important;\n cursor: not-allowed;\n}\n.du-bois-dark-menu-item-disabled > .du-bois-dark-menu-submenu-title > .du-bois-dark-menu-submenu-arrow::before,\n.du-bois-dark-menu-submenu-disabled > .du-bois-dark-menu-submenu-title > .du-bois-dark-menu-submenu-arrow::before,\n.du-bois-dark-menu-item-disabled > .du-bois-dark-menu-submenu-title > .du-bois-dark-menu-submenu-arrow::after,\n.du-bois-dark-menu-submenu-disabled > .du-bois-dark-menu-submenu-title > .du-bois-dark-menu-submenu-arrow::after {\n background: rgba(255, 255, 255, 0.3) !important;\n}\n.du-bois-dark-layout-header .du-bois-dark-menu {\n line-height: inherit;\n}\n.du-bois-dark-menu-light .du-bois-dark-menu-item:hover,\n.du-bois-dark-menu-light .du-bois-dark-menu-item-active,\n.du-bois-dark-menu-light .du-bois-dark-menu:not(.du-bois-dark-menu-inline) .du-bois-dark-menu-submenu-open,\n.du-bois-dark-menu-light .du-bois-dark-menu-submenu-active,\n.du-bois-dark-menu-light .du-bois-dark-menu-submenu-title:hover {\n color: #2272B4;\n}\n.du-bois-dark-menu.du-bois-dark-menu-dark,\n.du-bois-dark-menu-dark .du-bois-dark-menu-sub,\n.du-bois-dark-menu.du-bois-dark-menu-dark .du-bois-dark-menu-sub {\n color: rgba(255, 255, 255, 0.65);\n background: #1f1f1f;\n}\n.du-bois-dark-menu.du-bois-dark-menu-dark .du-bois-dark-menu-submenu-title .du-bois-dark-menu-submenu-arrow,\n.du-bois-dark-menu-dark .du-bois-dark-menu-sub .du-bois-dark-menu-submenu-title .du-bois-dark-menu-submenu-arrow,\n.du-bois-dark-menu.du-bois-dark-menu-dark .du-bois-dark-menu-sub .du-bois-dark-menu-submenu-title .du-bois-dark-menu-submenu-arrow {\n opacity: 0.45;\n transition: all 0.3s;\n}\n.du-bois-dark-menu.du-bois-dark-menu-dark .du-bois-dark-menu-submenu-title .du-bois-dark-menu-submenu-arrow::after,\n.du-bois-dark-menu-dark .du-bois-dark-menu-sub .du-bois-dark-menu-submenu-title .du-bois-dark-menu-submenu-arrow::after,\n.du-bois-dark-menu.du-bois-dark-menu-dark .du-bois-dark-menu-sub .du-bois-dark-menu-submenu-title .du-bois-dark-menu-submenu-arrow::after,\n.du-bois-dark-menu.du-bois-dark-menu-dark .du-bois-dark-menu-submenu-title .du-bois-dark-menu-submenu-arrow::before,\n.du-bois-dark-menu-dark .du-bois-dark-menu-sub .du-bois-dark-menu-submenu-title .du-bois-dark-menu-submenu-arrow::before,\n.du-bois-dark-menu.du-bois-dark-menu-dark .du-bois-dark-menu-sub .du-bois-dark-menu-submenu-title .du-bois-dark-menu-submenu-arrow::before {\n background: #fff;\n}\n.du-bois-dark-menu-dark.du-bois-dark-menu-submenu-popup {\n background: transparent;\n}\n.du-bois-dark-menu-dark .du-bois-dark-menu-inline.du-bois-dark-menu-sub {\n background: #141414;\n}\n.du-bois-dark-menu-dark.du-bois-dark-menu-horizontal {\n border-bottom: 0;\n}\n.du-bois-dark-menu-dark.du-bois-dark-menu-horizontal > .du-bois-dark-menu-item,\n.du-bois-dark-menu-dark.du-bois-dark-menu-horizontal > .du-bois-dark-menu-submenu {\n top: 0;\n margin-top: 0;\n padding: 0 20px;\n border-color: #1f1f1f;\n border-bottom: 0;\n}\n.du-bois-dark-menu-dark.du-bois-dark-menu-horizontal > .du-bois-dark-menu-item:hover {\n background-color: #2272B4;\n}\n.du-bois-dark-menu-dark.du-bois-dark-menu-horizontal > .du-bois-dark-menu-item > a::before {\n bottom: 0;\n}\n.du-bois-dark-menu-dark .du-bois-dark-menu-item,\n.du-bois-dark-menu-dark .du-bois-dark-menu-item-group-title,\n.du-bois-dark-menu-dark .du-bois-dark-menu-item > a,\n.du-bois-dark-menu-dark .du-bois-dark-menu-item > span > a {\n color: rgba(255, 255, 255, 0.65);\n}\n.du-bois-dark-menu-dark.du-bois-dark-menu-inline,\n.du-bois-dark-menu-dark.du-bois-dark-menu-vertical,\n.du-bois-dark-menu-dark.du-bois-dark-menu-vertical-left,\n.du-bois-dark-menu-dark.du-bois-dark-menu-vertical-right {\n border-right: 0;\n}\n.du-bois-dark-menu-dark.du-bois-dark-menu-inline .du-bois-dark-menu-item,\n.du-bois-dark-menu-dark.du-bois-dark-menu-vertical .du-bois-dark-menu-item,\n.du-bois-dark-menu-dark.du-bois-dark-menu-vertical-left .du-bois-dark-menu-item,\n.du-bois-dark-menu-dark.du-bois-dark-menu-vertical-right .du-bois-dark-menu-item {\n left: 0;\n margin-left: 0;\n border-right: 0;\n}\n.du-bois-dark-menu-dark.du-bois-dark-menu-inline .du-bois-dark-menu-item::after,\n.du-bois-dark-menu-dark.du-bois-dark-menu-vertical .du-bois-dark-menu-item::after,\n.du-bois-dark-menu-dark.du-bois-dark-menu-vertical-left .du-bois-dark-menu-item::after,\n.du-bois-dark-menu-dark.du-bois-dark-menu-vertical-right .du-bois-dark-menu-item::after {\n border-right: 0;\n}\n.du-bois-dark-menu-dark.du-bois-dark-menu-inline .du-bois-dark-menu-item,\n.du-bois-dark-menu-dark.du-bois-dark-menu-inline .du-bois-dark-menu-submenu-title {\n width: 100%;\n}\n.du-bois-dark-menu-dark .du-bois-dark-menu-item:hover,\n.du-bois-dark-menu-dark .du-bois-dark-menu-item-active,\n.du-bois-dark-menu-dark .du-bois-dark-menu-submenu-active,\n.du-bois-dark-menu-dark .du-bois-dark-menu-submenu-open,\n.du-bois-dark-menu-dark .du-bois-dark-menu-submenu-selected,\n.du-bois-dark-menu-dark .du-bois-dark-menu-submenu-title:hover {\n color: #fff;\n background-color: transparent;\n}\n.du-bois-dark-menu-dark .du-bois-dark-menu-item:hover > a,\n.du-bois-dark-menu-dark .du-bois-dark-menu-item-active > a,\n.du-bois-dark-menu-dark .du-bois-dark-menu-submenu-active > a,\n.du-bois-dark-menu-dark .du-bois-dark-menu-submenu-open > a,\n.du-bois-dark-menu-dark .du-bois-dark-menu-submenu-selected > a,\n.du-bois-dark-menu-dark .du-bois-dark-menu-submenu-title:hover > a,\n.du-bois-dark-menu-dark .du-bois-dark-menu-item:hover > span > a,\n.du-bois-dark-menu-dark .du-bois-dark-menu-item-active > span > a,\n.du-bois-dark-menu-dark .du-bois-dark-menu-submenu-active > span > a,\n.du-bois-dark-menu-dark .du-bois-dark-menu-submenu-open > span > a,\n.du-bois-dark-menu-dark .du-bois-dark-menu-submenu-selected > span > a,\n.du-bois-dark-menu-dark .du-bois-dark-menu-submenu-title:hover > span > a {\n color: #fff;\n}\n.du-bois-dark-menu-dark .du-bois-dark-menu-item:hover > .du-bois-dark-menu-submenu-title > .du-bois-dark-menu-submenu-arrow,\n.du-bois-dark-menu-dark .du-bois-dark-menu-item-active > .du-bois-dark-menu-submenu-title > .du-bois-dark-menu-submenu-arrow,\n.du-bois-dark-menu-dark .du-bois-dark-menu-submenu-active > .du-bois-dark-menu-submenu-title > .du-bois-dark-menu-submenu-arrow,\n.du-bois-dark-menu-dark .du-bois-dark-menu-submenu-open > .du-bois-dark-menu-submenu-title > .du-bois-dark-menu-submenu-arrow,\n.du-bois-dark-menu-dark .du-bois-dark-menu-submenu-selected > .du-bois-dark-menu-submenu-title > .du-bois-dark-menu-submenu-arrow,\n.du-bois-dark-menu-dark .du-bois-dark-menu-submenu-title:hover > .du-bois-dark-menu-submenu-title > .du-bois-dark-menu-submenu-arrow {\n opacity: 1;\n}\n.du-bois-dark-menu-dark .du-bois-dark-menu-item:hover > .du-bois-dark-menu-submenu-title > .du-bois-dark-menu-submenu-arrow::after,\n.du-bois-dark-menu-dark .du-bois-dark-menu-item-active > .du-bois-dark-menu-submenu-title > .du-bois-dark-menu-submenu-arrow::after,\n.du-bois-dark-menu-dark .du-bois-dark-menu-submenu-active > .du-bois-dark-menu-submenu-title > .du-bois-dark-menu-submenu-arrow::after,\n.du-bois-dark-menu-dark .du-bois-dark-menu-submenu-open > .du-bois-dark-menu-submenu-title > .du-bois-dark-menu-submenu-arrow::after,\n.du-bois-dark-menu-dark .du-bois-dark-menu-submenu-selected > .du-bois-dark-menu-submenu-title > .du-bois-dark-menu-submenu-arrow::after,\n.du-bois-dark-menu-dark .du-bois-dark-menu-submenu-title:hover > .du-bois-dark-menu-submenu-title > .du-bois-dark-menu-submenu-arrow::after,\n.du-bois-dark-menu-dark .du-bois-dark-menu-item:hover > .du-bois-dark-menu-submenu-title > .du-bois-dark-menu-submenu-arrow::before,\n.du-bois-dark-menu-dark .du-bois-dark-menu-item-active > .du-bois-dark-menu-submenu-title > .du-bois-dark-menu-submenu-arrow::before,\n.du-bois-dark-menu-dark .du-bois-dark-menu-submenu-active > .du-bois-dark-menu-submenu-title > .du-bois-dark-menu-submenu-arrow::before,\n.du-bois-dark-menu-dark .du-bois-dark-menu-submenu-open > .du-bois-dark-menu-submenu-title > .du-bois-dark-menu-submenu-arrow::before,\n.du-bois-dark-menu-dark .du-bois-dark-menu-submenu-selected > .du-bois-dark-menu-submenu-title > .du-bois-dark-menu-submenu-arrow::before,\n.du-bois-dark-menu-dark .du-bois-dark-menu-submenu-title:hover > .du-bois-dark-menu-submenu-title > .du-bois-dark-menu-submenu-arrow::before {\n background: #fff;\n}\n.du-bois-dark-menu-dark .du-bois-dark-menu-item:hover {\n background-color: transparent;\n}\n.du-bois-dark-menu-dark.du-bois-dark-menu-dark:not(.du-bois-dark-menu-horizontal) .du-bois-dark-menu-item-selected {\n background-color: #2272B4;\n}\n.du-bois-dark-menu-dark .du-bois-dark-menu-item-selected {\n color: #fff;\n border-right: 0;\n}\n.du-bois-dark-menu-dark .du-bois-dark-menu-item-selected::after {\n border-right: 0;\n}\n.du-bois-dark-menu-dark .du-bois-dark-menu-item-selected > a,\n.du-bois-dark-menu-dark .du-bois-dark-menu-item-selected > span > a,\n.du-bois-dark-menu-dark .du-bois-dark-menu-item-selected > a:hover,\n.du-bois-dark-menu-dark .du-bois-dark-menu-item-selected > span > a:hover {\n color: #fff;\n}\n.du-bois-dark-menu-dark .du-bois-dark-menu-item-selected .du-bois-dark-menu-item-icon,\n.du-bois-dark-menu-dark .du-bois-dark-menu-item-selected .anticon {\n color: #fff;\n}\n.du-bois-dark-menu-dark .du-bois-dark-menu-item-selected .du-bois-dark-menu-item-icon + span,\n.du-bois-dark-menu-dark .du-bois-dark-menu-item-selected .anticon + span {\n color: #fff;\n}\n.du-bois-dark-menu.du-bois-dark-menu-dark .du-bois-dark-menu-item-selected,\n.du-bois-dark-menu-submenu-popup.du-bois-dark-menu-dark .du-bois-dark-menu-item-selected {\n background-color: #2272B4;\n}\n.du-bois-dark-menu-dark .du-bois-dark-menu-item-disabled,\n.du-bois-dark-menu-dark .du-bois-dark-menu-submenu-disabled,\n.du-bois-dark-menu-dark .du-bois-dark-menu-item-disabled > a,\n.du-bois-dark-menu-dark .du-bois-dark-menu-submenu-disabled > a,\n.du-bois-dark-menu-dark .du-bois-dark-menu-item-disabled > span > a,\n.du-bois-dark-menu-dark .du-bois-dark-menu-submenu-disabled > span > a {\n color: rgba(255, 255, 255, 0.3) !important;\n opacity: 0.8;\n}\n.du-bois-dark-menu-dark .du-bois-dark-menu-item-disabled > .du-bois-dark-menu-submenu-title,\n.du-bois-dark-menu-dark .du-bois-dark-menu-submenu-disabled > .du-bois-dark-menu-submenu-title {\n color: rgba(255, 255, 255, 0.3) !important;\n}\n.du-bois-dark-menu-dark .du-bois-dark-menu-item-disabled > .du-bois-dark-menu-submenu-title > .du-bois-dark-menu-submenu-arrow::before,\n.du-bois-dark-menu-dark .du-bois-dark-menu-submenu-disabled > .du-bois-dark-menu-submenu-title > .du-bois-dark-menu-submenu-arrow::before,\n.du-bois-dark-menu-dark .du-bois-dark-menu-item-disabled > .du-bois-dark-menu-submenu-title > .du-bois-dark-menu-submenu-arrow::after,\n.du-bois-dark-menu-dark .du-bois-dark-menu-submenu-disabled > .du-bois-dark-menu-submenu-title > .du-bois-dark-menu-submenu-arrow::after {\n background: rgba(255, 255, 255, 0.3) !important;\n}\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-dark-modal {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #E8ECF0;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: tnum;\n pointer-events: none;\n position: relative;\n top: 100px;\n width: auto;\n max-width: calc(100vw - 32px);\n margin: 0 auto;\n padding-bottom: 24px;\n}\n.du-bois-dark-modal.du-bois-dark-zoom-enter,\n.du-bois-dark-modal.du-bois-darkzoom-appear {\n transform: none;\n opacity: 0;\n animation-duration: 0.3s;\n user-select: none;\n}\n.du-bois-dark-modal-mask {\n position: fixed;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n z-index: 1000;\n height: 100%;\n background-color: rgba(0, 0, 0, 0.45);\n}\n.du-bois-dark-modal-mask-hidden {\n display: none;\n}\n.du-bois-dark-modal-wrap {\n position: fixed;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n overflow: auto;\n outline: 0;\n -webkit-overflow-scrolling: touch;\n}\n.du-bois-dark-modal-wrap {\n z-index: 1000;\n}\n.du-bois-dark-modal-title {\n margin: 0;\n color: rgba(255, 255, 255, 0.85);\n font-weight: 500;\n font-size: 18px;\n line-height: 22px;\n word-wrap: break-word;\n}\n.du-bois-dark-modal-content {\n position: relative;\n background-color: #1f1f1f;\n background-clip: padding-box;\n border: 0;\n border-radius: 4px;\n box-shadow: 0 3px 6px -4px rgba(0, 0, 0, 0.48), 0 6px 16px 0 rgba(0, 0, 0, 0.32), 0 9px 28px 8px rgba(0, 0, 0, 0.2);\n pointer-events: auto;\n}\n.du-bois-dark-modal-close {\n position: absolute;\n top: 0;\n right: 0;\n z-index: 10;\n padding: 0;\n color: #92A4B3;\n font-weight: 700;\n line-height: 1;\n text-decoration: none;\n background: transparent;\n border: 0;\n outline: 0;\n cursor: pointer;\n transition: color 0.3s;\n}\n.du-bois-dark-modal-close-x {\n display: block;\n width: 102px;\n height: 102px;\n font-size: 18px;\n font-style: normal;\n line-height: 102px;\n text-align: center;\n text-transform: none;\n text-rendering: auto;\n}\n.du-bois-dark-modal-close:focus,\n.du-bois-dark-modal-close:hover {\n color: rgba(255, 255, 255, 0.75);\n text-decoration: none;\n}\n.du-bois-dark-modal-header {\n padding: 40px 40px 20px;\n color: #E8ECF0;\n background: #1f1f1f;\n border-bottom: 0px solid #303030;\n border-radius: 4px 4px 0 0;\n}\n.du-bois-dark-modal-body {\n padding: 0 40px;\n font-size: 13px;\n line-height: 20px;\n word-wrap: break-word;\n}\n.du-bois-dark-modal-footer {\n padding: 0px 0px;\n text-align: right;\n background: transparent;\n border-top: 0px solid #303030;\n border-radius: 0 0 4px 4px;\n}\n.du-bois-dark-modal-footer .du-bois-dark-btn + .du-bois-dark-btn:not(.du-bois-dark-dropdown-trigger) {\n margin-bottom: 0;\n margin-left: 8px;\n}\n.du-bois-dark-modal-open {\n overflow: hidden;\n}\n.du-bois-dark-modal-centered {\n text-align: center;\n}\n.du-bois-dark-modal-centered::before {\n display: inline-block;\n width: 0;\n height: 100%;\n vertical-align: middle;\n content: '';\n}\n.du-bois-dark-modal-centered .du-bois-dark-modal {\n top: 0;\n display: inline-block;\n text-align: left;\n vertical-align: middle;\n}\n@media (max-width: 767px) {\n .du-bois-dark-modal {\n max-width: calc(100vw - 16px);\n margin: 8px auto;\n }\n .du-bois-dark-modal-centered .du-bois-dark-modal {\n flex: 1;\n }\n}\n.du-bois-dark-modal-confirm .du-bois-dark-modal-header {\n display: none;\n}\n.du-bois-dark-modal-confirm .du-bois-dark-modal-body {\n padding: 32px 32px 24px;\n}\n.du-bois-dark-modal-confirm-body-wrapper::before {\n display: table;\n content: '';\n}\n.du-bois-dark-modal-confirm-body-wrapper::after {\n display: table;\n clear: both;\n content: '';\n}\n.du-bois-dark-modal-confirm-body .du-bois-dark-modal-confirm-title {\n display: block;\n overflow: hidden;\n color: rgba(255, 255, 255, 0.85);\n font-weight: 500;\n font-size: 18px;\n line-height: 1.4;\n}\n.du-bois-dark-modal-confirm-body .du-bois-dark-modal-confirm-content {\n margin-top: 8px;\n color: #E8ECF0;\n font-size: 13px;\n}\n.du-bois-dark-modal-confirm-body > .anticon {\n float: left;\n margin-right: 16px;\n font-size: 22px;\n}\n.du-bois-dark-modal-confirm-body > .anticon + .du-bois-dark-modal-confirm-title + .du-bois-dark-modal-confirm-content {\n margin-left: 38px;\n}\n.du-bois-dark-modal-confirm .du-bois-dark-modal-confirm-btns {\n float: right;\n margin-top: 24px;\n}\n.du-bois-dark-modal-confirm .du-bois-dark-modal-confirm-btns .du-bois-dark-btn + .du-bois-dark-btn {\n margin-bottom: 0;\n margin-left: 8px;\n}\n.du-bois-dark-modal-confirm-error .du-bois-dark-modal-confirm-body > .anticon {\n color: #E65B77;\n}\n.du-bois-dark-modal-confirm-warning .du-bois-dark-modal-confirm-body > .anticon,\n.du-bois-dark-modal-confirm-confirm .du-bois-dark-modal-confirm-body > .anticon {\n color: #AF5B23;\n}\n.du-bois-dark-modal-confirm-info .du-bois-dark-modal-confirm-body > .anticon {\n color: #64727D;\n}\n.du-bois-dark-modal-confirm-success .du-bois-dark-modal-confirm-body > .anticon {\n color: #34824F;\n}\n.du-bois-dark-modal .du-bois-dark-picker-clear,\n.du-bois-dark-modal .du-bois-dark-slider-handle,\n.du-bois-dark-modal .du-bois-dark-anchor-wrapper,\n.du-bois-dark-modal .du-bois-dark-collapse-content,\n.du-bois-dark-modal .du-bois-dark-timeline-item-head,\n.du-bois-dark-modal .du-bois-dark-card {\n background-color: #1f1f1f;\n}\n.du-bois-dark-modal .du-bois-dark-transfer-list-header {\n background: #1f1f1f;\n border-bottom: 1px solid #3a3a3a;\n}\n.du-bois-dark-modal .du-bois-dark-transfer-list-content-item:not(.du-bois-dark-transfer-list-content-item-disabled):hover {\n background-color: rgba(255, 255, 255, 0.08);\n}\n.du-bois-dark-modal tr.du-bois-dark-table-expanded-row > td,\n.du-bois-dark-modal tr.du-bois-dark-table-expanded-row:hover > td {\n background: #272727;\n}\n.du-bois-dark-modal .du-bois-dark-table.du-bois-dark-table-small thead > tr > th {\n background-color: #1f1f1f;\n border-bottom: 1px solid #3a3a3a;\n}\n.du-bois-dark-modal .du-bois-dark-table {\n background-color: #1f1f1f;\n}\n.du-bois-dark-modal .du-bois-dark-table .du-bois-dark-table-row-expand-icon {\n border: 1px solid #3a3a3a;\n}\n.du-bois-dark-modal .du-bois-dark-table tfoot > tr > th,\n.du-bois-dark-modal .du-bois-dark-table tfoot > tr > td {\n border-bottom: 1px solid #3a3a3a;\n}\n.du-bois-dark-modal .du-bois-dark-table thead > tr > th {\n background-color: #272727;\n border-bottom: 1px solid #3a3a3a;\n}\n.du-bois-dark-modal .du-bois-dark-table tbody > tr > td {\n border-bottom: 1px solid #3a3a3a;\n}\n.du-bois-dark-modal .du-bois-dark-table tbody > tr > td.du-bois-dark-table-cell-fix-left,\n.du-bois-dark-modal .du-bois-dark-table tbody > tr > td.du-bois-dark-table-cell-fix-right {\n background-color: #1f1f1f;\n}\n.du-bois-dark-modal .du-bois-dark-table tbody > tr.du-bois-dark-table-row:hover > td {\n background: #303030;\n}\n.du-bois-dark-modal .du-bois-dark-table.du-bois-dark-table-bordered .du-bois-dark-table-title {\n border: 1px solid #3a3a3a;\n}\n.du-bois-dark-modal .du-bois-dark-table.du-bois-dark-table-bordered thead > tr > th,\n.du-bois-dark-modal .du-bois-dark-table.du-bois-dark-table-bordered tbody > tr > td,\n.du-bois-dark-modal .du-bois-dark-table.du-bois-dark-table-bordered tfoot > tr > th,\n.du-bois-dark-modal .du-bois-dark-table.du-bois-dark-table-bordered tfoot > tr > td {\n border-right: 1px solid #3a3a3a;\n}\n.du-bois-dark-modal .du-bois-dark-table.du-bois-dark-table-bordered .du-bois-dark-table-cell-fix-right-first::after {\n border-right: 1px solid #3a3a3a;\n}\n.du-bois-dark-modal .du-bois-dark-table.du-bois-dark-table-bordered table thead > tr:not(:last-child) > th {\n border-bottom: 1px solid #303030;\n}\n.du-bois-dark-modal .du-bois-dark-table.du-bois-dark-table-bordered .du-bois-dark-table-container {\n border: 1px solid #3a3a3a;\n}\n.du-bois-dark-modal .du-bois-dark-table.du-bois-dark-table-bordered .du-bois-dark-table-expanded-row-fixed::after {\n border-right: 1px solid #3a3a3a;\n}\n.du-bois-dark-modal .du-bois-dark-table.du-bois-dark-table-bordered .du-bois-dark-table-footer {\n border: 1px solid #3a3a3a;\n}\n.du-bois-dark-modal .du-bois-dark-table .du-bois-dark-table-filter-trigger-container-open {\n background-color: #525252;\n}\n.du-bois-dark-modal .du-bois-dark-picker-calendar-full {\n background-color: #1f1f1f;\n}\n.du-bois-dark-modal .du-bois-dark-picker-calendar-full .du-bois-dark-picker-panel {\n background-color: #1f1f1f;\n}\n.du-bois-dark-modal .du-bois-dark-picker-calendar-full .du-bois-dark-picker-panel .du-bois-dark-picker-calendar-date {\n border-top: 2px solid #3a3a3a;\n}\n.du-bois-dark-modal .du-bois-dark-tabs.du-bois-dark-tabs-card .du-bois-dark-tabs-card-bar .du-bois-dark-tabs-tab-active {\n background-color: #1f1f1f;\n border-bottom: 1px solid #1f1f1f;\n}\n.du-bois-dark-modal .du-bois-dark-badge-count {\n box-shadow: 0 0 0 1px #1f1f1f;\n}\n.du-bois-dark-modal .du-bois-dark-tree-show-line .du-bois-dark-tree-switcher {\n background: #1f1f1f;\n}\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-dark-notification .du-bois-dark-picker-clear,\n.du-bois-dark-notification .du-bois-dark-slider-handle,\n.du-bois-dark-notification .du-bois-dark-anchor-wrapper,\n.du-bois-dark-notification .du-bois-dark-collapse-content,\n.du-bois-dark-notification .du-bois-dark-timeline-item-head,\n.du-bois-dark-notification .du-bois-dark-card {\n background-color: #1f1f1f;\n}\n.du-bois-dark-notification .du-bois-dark-transfer-list-header {\n background: #1f1f1f;\n border-bottom: 1px solid #3a3a3a;\n}\n.du-bois-dark-notification .du-bois-dark-transfer-list-content-item:not(.du-bois-dark-transfer-list-content-item-disabled):hover {\n background-color: rgba(255, 255, 255, 0.08);\n}\n.du-bois-dark-notification tr.du-bois-dark-table-expanded-row > td,\n.du-bois-dark-notification tr.du-bois-dark-table-expanded-row:hover > td {\n background: #272727;\n}\n.du-bois-dark-notification .du-bois-dark-table.du-bois-dark-table-small thead > tr > th {\n background-color: #1f1f1f;\n border-bottom: 1px solid #3a3a3a;\n}\n.du-bois-dark-notification .du-bois-dark-table {\n background-color: #1f1f1f;\n}\n.du-bois-dark-notification .du-bois-dark-table .du-bois-dark-table-row-expand-icon {\n border: 1px solid #3a3a3a;\n}\n.du-bois-dark-notification .du-bois-dark-table tfoot > tr > th,\n.du-bois-dark-notification .du-bois-dark-table tfoot > tr > td {\n border-bottom: 1px solid #3a3a3a;\n}\n.du-bois-dark-notification .du-bois-dark-table thead > tr > th {\n background-color: #272727;\n border-bottom: 1px solid #3a3a3a;\n}\n.du-bois-dark-notification .du-bois-dark-table tbody > tr > td {\n border-bottom: 1px solid #3a3a3a;\n}\n.du-bois-dark-notification .du-bois-dark-table tbody > tr > td.du-bois-dark-table-cell-fix-left,\n.du-bois-dark-notification .du-bois-dark-table tbody > tr > td.du-bois-dark-table-cell-fix-right {\n background-color: #1f1f1f;\n}\n.du-bois-dark-notification .du-bois-dark-table tbody > tr.du-bois-dark-table-row:hover > td {\n background: #303030;\n}\n.du-bois-dark-notification .du-bois-dark-table.du-bois-dark-table-bordered .du-bois-dark-table-title {\n border: 1px solid #3a3a3a;\n}\n.du-bois-dark-notification .du-bois-dark-table.du-bois-dark-table-bordered thead > tr > th,\n.du-bois-dark-notification .du-bois-dark-table.du-bois-dark-table-bordered tbody > tr > td,\n.du-bois-dark-notification .du-bois-dark-table.du-bois-dark-table-bordered tfoot > tr > th,\n.du-bois-dark-notification .du-bois-dark-table.du-bois-dark-table-bordered tfoot > tr > td {\n border-right: 1px solid #3a3a3a;\n}\n.du-bois-dark-notification .du-bois-dark-table.du-bois-dark-table-bordered .du-bois-dark-table-cell-fix-right-first::after {\n border-right: 1px solid #3a3a3a;\n}\n.du-bois-dark-notification .du-bois-dark-table.du-bois-dark-table-bordered table thead > tr:not(:last-child) > th {\n border-bottom: 1px solid #303030;\n}\n.du-bois-dark-notification .du-bois-dark-table.du-bois-dark-table-bordered .du-bois-dark-table-container {\n border: 1px solid #3a3a3a;\n}\n.du-bois-dark-notification .du-bois-dark-table.du-bois-dark-table-bordered .du-bois-dark-table-expanded-row-fixed::after {\n border-right: 1px solid #3a3a3a;\n}\n.du-bois-dark-notification .du-bois-dark-table.du-bois-dark-table-bordered .du-bois-dark-table-footer {\n border: 1px solid #3a3a3a;\n}\n.du-bois-dark-notification .du-bois-dark-table .du-bois-dark-table-filter-trigger-container-open {\n background-color: #525252;\n}\n.du-bois-dark-notification .du-bois-dark-picker-calendar-full {\n background-color: #1f1f1f;\n}\n.du-bois-dark-notification .du-bois-dark-picker-calendar-full .du-bois-dark-picker-panel {\n background-color: #1f1f1f;\n}\n.du-bois-dark-notification .du-bois-dark-picker-calendar-full .du-bois-dark-picker-panel .du-bois-dark-picker-calendar-date {\n border-top: 2px solid #3a3a3a;\n}\n.du-bois-dark-notification .du-bois-dark-tabs.du-bois-dark-tabs-card .du-bois-dark-tabs-card-bar .du-bois-dark-tabs-tab-active {\n background-color: #1f1f1f;\n border-bottom: 1px solid #1f1f1f;\n}\n.du-bois-dark-notification .du-bois-dark-badge-count {\n box-shadow: 0 0 0 1px #1f1f1f;\n}\n.du-bois-dark-notification .du-bois-dark-tree-show-line .du-bois-dark-tree-switcher {\n background: #1f1f1f;\n}\n.du-bois-dark-notification {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #E8ECF0;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: tnum;\n position: fixed;\n z-index: 1010;\n margin-right: 24px;\n}\n.du-bois-dark-notification-topLeft,\n.du-bois-dark-notification-bottomLeft {\n margin-right: 0;\n margin-left: 24px;\n}\n.du-bois-dark-notification-topLeft .du-bois-dark-notification-fade-enter.du-bois-dark-notification-fade-enter-active,\n.du-bois-dark-notification-bottomLeft .du-bois-dark-notification-fade-enter.du-bois-dark-notification-fade-enter-active,\n.du-bois-dark-notification-topLeft .du-bois-dark-notification-fade-appear.du-bois-dark-notification-fade-appear-active,\n.du-bois-dark-notification-bottomLeft .du-bois-dark-notification-fade-appear.du-bois-dark-notification-fade-appear-active {\n animation-name: NotificationLeftFadeIn;\n}\n.du-bois-dark-notification-close-icon {\n font-size: 13px;\n cursor: pointer;\n}\n.du-bois-dark-notification-hook-holder {\n position: relative;\n}\n.du-bois-dark-notification-notice {\n position: relative;\n width: 384px;\n max-width: calc(100vw - 24px * 2);\n margin-bottom: 16px;\n margin-left: auto;\n padding: 16px 24px;\n overflow: hidden;\n line-height: 20px;\n word-wrap: break-word;\n background: #1f1f1f;\n border-radius: 4px;\n box-shadow: 0 3px 6px -4px rgba(0, 0, 0, 0.48), 0 6px 16px 0 rgba(0, 0, 0, 0.32), 0 9px 28px 8px rgba(0, 0, 0, 0.2);\n}\n.du-bois-dark-notification-topLeft .du-bois-dark-notification-notice,\n.du-bois-dark-notification-bottomLeft .du-bois-dark-notification-notice {\n margin-right: auto;\n margin-left: 0;\n}\n.du-bois-dark-notification-notice-message {\n margin-bottom: 8px;\n color: rgba(255, 255, 255, 0.85);\n font-size: 18px;\n line-height: 24px;\n}\n.du-bois-dark-notification-notice-message-single-line-auto-margin {\n display: block;\n width: calc(384px - 24px * 2 - 24px - 48px - 100%);\n max-width: 4px;\n background-color: transparent;\n pointer-events: none;\n}\n.du-bois-dark-notification-notice-message-single-line-auto-margin::before {\n display: block;\n content: '';\n}\n.du-bois-dark-notification-notice-description {\n font-size: 13px;\n}\n.du-bois-dark-notification-notice-closable .du-bois-dark-notification-notice-message {\n padding-right: 24px;\n}\n.du-bois-dark-notification-notice-with-icon .du-bois-dark-notification-notice-message {\n margin-bottom: 4px;\n margin-left: 48px;\n font-size: 18px;\n}\n.du-bois-dark-notification-notice-with-icon .du-bois-dark-notification-notice-description {\n margin-left: 48px;\n font-size: 13px;\n}\n.du-bois-dark-notification-notice-icon {\n position: absolute;\n margin-left: 4px;\n font-size: 24px;\n line-height: 24px;\n}\n.anticon.du-bois-dark-notification-notice-icon-success {\n color: #34824F;\n}\n.anticon.du-bois-dark-notification-notice-icon-info {\n color: #64727D;\n}\n.anticon.du-bois-dark-notification-notice-icon-warning {\n color: #AF5B23;\n}\n.anticon.du-bois-dark-notification-notice-icon-error {\n color: #E65B77;\n}\n.du-bois-dark-notification-notice-close {\n position: absolute;\n top: 16px;\n right: 22px;\n color: #92A4B3;\n outline: none;\n}\n.du-bois-dark-notification-notice-close:hover {\n color: rgba(255, 255, 255, 0.85);\n}\n.du-bois-dark-notification-notice-btn {\n float: right;\n margin-top: 16px;\n}\n.du-bois-dark-notification .notification-fade-effect {\n animation-duration: 0.24s;\n animation-timing-function: cubic-bezier(0.645, 0.045, 0.355, 1);\n animation-fill-mode: both;\n}\n.du-bois-dark-notification-fade-enter,\n.du-bois-dark-notification-fade-appear {\n animation-duration: 0.24s;\n animation-timing-function: cubic-bezier(0.645, 0.045, 0.355, 1);\n animation-fill-mode: both;\n opacity: 0;\n animation-play-state: paused;\n}\n.du-bois-dark-notification-fade-leave {\n animation-duration: 0.24s;\n animation-timing-function: cubic-bezier(0.645, 0.045, 0.355, 1);\n animation-fill-mode: both;\n animation-duration: 0.2s;\n animation-play-state: paused;\n}\n.du-bois-dark-notification-fade-enter.du-bois-dark-notification-fade-enter-active,\n.du-bois-dark-notification-fade-appear.du-bois-dark-notification-fade-appear-active {\n animation-name: NotificationFadeIn;\n animation-play-state: running;\n}\n.du-bois-dark-notification-fade-leave.du-bois-dark-notification-fade-leave-active {\n animation-name: NotificationFadeOut;\n animation-play-state: running;\n}\n@keyframes NotificationFadeIn {\n 0% {\n left: 384px;\n opacity: 0;\n }\n 100% {\n left: 0;\n opacity: 1;\n }\n}\n@keyframes NotificationLeftFadeIn {\n 0% {\n right: 384px;\n opacity: 0;\n }\n 100% {\n right: 0;\n opacity: 1;\n }\n}\n@keyframes NotificationFadeOut {\n 0% {\n max-height: 150px;\n margin-bottom: 16px;\n opacity: 1;\n }\n 100% {\n max-height: 0;\n margin-bottom: 0;\n padding-top: 0;\n padding-bottom: 0;\n opacity: 0;\n }\n}\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-dark-popover {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #E8ECF0;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: tnum;\n position: absolute;\n top: 0;\n left: 0;\n z-index: 1030;\n font-weight: normal;\n white-space: normal;\n text-align: left;\n cursor: auto;\n user-select: text;\n}\n.du-bois-dark-popover::after {\n position: absolute;\n background: rgba(255, 255, 255, 0.01);\n content: '';\n}\n.du-bois-dark-popover-hidden {\n display: none;\n}\n.du-bois-dark-popover-placement-top,\n.du-bois-dark-popover-placement-topLeft,\n.du-bois-dark-popover-placement-topRight {\n padding-bottom: 10px;\n}\n.du-bois-dark-popover-placement-right,\n.du-bois-dark-popover-placement-rightTop,\n.du-bois-dark-popover-placement-rightBottom {\n padding-left: 10px;\n}\n.du-bois-dark-popover-placement-bottom,\n.du-bois-dark-popover-placement-bottomLeft,\n.du-bois-dark-popover-placement-bottomRight {\n padding-top: 10px;\n}\n.du-bois-dark-popover-placement-left,\n.du-bois-dark-popover-placement-leftTop,\n.du-bois-dark-popover-placement-leftBottom {\n padding-right: 10px;\n}\n.du-bois-dark-popover-inner {\n background-color: #1f1f1f;\n background-clip: padding-box;\n border-radius: 4px;\n box-shadow: 0 3px 6px -4px rgba(0, 0, 0, 0.48), 0 6px 16px 0 rgba(0, 0, 0, 0.32), 0 9px 28px 8px rgba(0, 0, 0, 0.2);\n box-shadow: 0 0 8px rgba(0, 0, 0, 0.45) \\9;\n}\n@media screen and (-ms-high-contrast: active), (-ms-high-contrast: none) {\n .du-bois-dark-popover {\n /* IE10+ */\n }\n .du-bois-dark-popover-inner {\n box-shadow: 0 3px 6px -4px rgba(0, 0, 0, 0.48), 0 6px 16px 0 rgba(0, 0, 0, 0.32), 0 9px 28px 8px rgba(0, 0, 0, 0.2);\n }\n}\n.du-bois-dark-popover-title {\n min-width: 177px;\n min-height: 32px;\n margin: 0;\n padding: 5px 8px 4px;\n color: rgba(255, 255, 255, 0.85);\n font-weight: 500;\n border-bottom: 1px solid #303030;\n}\n.du-bois-dark-popover-inner-content {\n padding: 8px 8px;\n color: #E8ECF0;\n}\n.du-bois-dark-popover-message {\n position: relative;\n padding: 4px 0 12px;\n color: #E8ECF0;\n font-size: 13px;\n}\n.du-bois-dark-popover-message > .anticon {\n position: absolute;\n top: 127.5px;\n color: #AF5B23;\n font-size: 13px;\n}\n.du-bois-dark-popover-message-title {\n padding-left: 21px;\n}\n.du-bois-dark-popover-buttons {\n margin-bottom: 4px;\n text-align: right;\n}\n.du-bois-dark-popover-buttons button {\n margin-left: 8px;\n}\n.du-bois-dark-popover-arrow {\n position: absolute;\n display: block;\n width: 8.48528137px;\n height: 8.48528137px;\n overflow: hidden;\n background: transparent;\n pointer-events: none;\n}\n.du-bois-dark-popover-arrow-content {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n display: block;\n width: 6px;\n height: 6px;\n margin: auto;\n background-color: #1f1f1f;\n content: '';\n pointer-events: auto;\n}\n.du-bois-dark-popover-placement-top .du-bois-dark-popover-arrow,\n.du-bois-dark-popover-placement-topLeft .du-bois-dark-popover-arrow,\n.du-bois-dark-popover-placement-topRight .du-bois-dark-popover-arrow {\n bottom: 1.51471863px;\n}\n.du-bois-dark-popover-placement-top .du-bois-dark-popover-arrow-content,\n.du-bois-dark-popover-placement-topLeft .du-bois-dark-popover-arrow-content,\n.du-bois-dark-popover-placement-topRight .du-bois-dark-popover-arrow-content {\n box-shadow: 3px 3px 7px rgba(0, 0, 0, 0.07);\n transform: translateY(-4.24264069px) rotate(45deg);\n}\n.du-bois-dark-popover-placement-top .du-bois-dark-popover-arrow {\n left: 50%;\n transform: translateX(-50%);\n}\n.du-bois-dark-popover-placement-topLeft .du-bois-dark-popover-arrow {\n left: 16px;\n}\n.du-bois-dark-popover-placement-topRight .du-bois-dark-popover-arrow {\n right: 16px;\n}\n.du-bois-dark-popover-placement-right .du-bois-dark-popover-arrow,\n.du-bois-dark-popover-placement-rightTop .du-bois-dark-popover-arrow,\n.du-bois-dark-popover-placement-rightBottom .du-bois-dark-popover-arrow {\n left: 1.51471863px;\n}\n.du-bois-dark-popover-placement-right .du-bois-dark-popover-arrow-content,\n.du-bois-dark-popover-placement-rightTop .du-bois-dark-popover-arrow-content,\n.du-bois-dark-popover-placement-rightBottom .du-bois-dark-popover-arrow-content {\n box-shadow: -3px 3px 7px rgba(0, 0, 0, 0.07);\n transform: translateX(4.24264069px) rotate(45deg);\n}\n.du-bois-dark-popover-placement-right .du-bois-dark-popover-arrow {\n top: 50%;\n transform: translateY(-50%);\n}\n.du-bois-dark-popover-placement-rightTop .du-bois-dark-popover-arrow {\n top: 12px;\n}\n.du-bois-dark-popover-placement-rightBottom .du-bois-dark-popover-arrow {\n bottom: 12px;\n}\n.du-bois-dark-popover-placement-bottom .du-bois-dark-popover-arrow,\n.du-bois-dark-popover-placement-bottomLeft .du-bois-dark-popover-arrow,\n.du-bois-dark-popover-placement-bottomRight .du-bois-dark-popover-arrow {\n top: 1.51471863px;\n}\n.du-bois-dark-popover-placement-bottom .du-bois-dark-popover-arrow-content,\n.du-bois-dark-popover-placement-bottomLeft .du-bois-dark-popover-arrow-content,\n.du-bois-dark-popover-placement-bottomRight .du-bois-dark-popover-arrow-content {\n box-shadow: -2px -2px 5px rgba(0, 0, 0, 0.06);\n transform: translateY(4.24264069px) rotate(45deg);\n}\n.du-bois-dark-popover-placement-bottom .du-bois-dark-popover-arrow {\n left: 50%;\n transform: translateX(-50%);\n}\n.du-bois-dark-popover-placement-bottomLeft .du-bois-dark-popover-arrow {\n left: 16px;\n}\n.du-bois-dark-popover-placement-bottomRight .du-bois-dark-popover-arrow {\n right: 16px;\n}\n.du-bois-dark-popover-placement-left .du-bois-dark-popover-arrow,\n.du-bois-dark-popover-placement-leftTop .du-bois-dark-popover-arrow,\n.du-bois-dark-popover-placement-leftBottom .du-bois-dark-popover-arrow {\n right: 1.51471863px;\n}\n.du-bois-dark-popover-placement-left .du-bois-dark-popover-arrow-content,\n.du-bois-dark-popover-placement-leftTop .du-bois-dark-popover-arrow-content,\n.du-bois-dark-popover-placement-leftBottom .du-bois-dark-popover-arrow-content {\n box-shadow: 3px -3px 7px rgba(0, 0, 0, 0.07);\n transform: translateX(-4.24264069px) rotate(45deg);\n}\n.du-bois-dark-popover-placement-left .du-bois-dark-popover-arrow {\n top: 50%;\n transform: translateY(-50%);\n}\n.du-bois-dark-popover-placement-leftTop .du-bois-dark-popover-arrow {\n top: 12px;\n}\n.du-bois-dark-popover-placement-leftBottom .du-bois-dark-popover-arrow {\n bottom: 12px;\n}\n.du-bois-dark-popover-pink .du-bois-dark-popover-inner {\n background-color: #cb2b83;\n}\n.du-bois-dark-popover-pink .du-bois-dark-popover-arrow-content {\n background-color: #cb2b83;\n}\n.du-bois-dark-popover-magenta .du-bois-dark-popover-inner {\n background-color: #cb2b83;\n}\n.du-bois-dark-popover-magenta .du-bois-dark-popover-arrow-content {\n background-color: #cb2b83;\n}\n.du-bois-dark-popover-red .du-bois-dark-popover-inner {\n background-color: #d32029;\n}\n.du-bois-dark-popover-red .du-bois-dark-popover-arrow-content {\n background-color: #d32029;\n}\n.du-bois-dark-popover-volcano .du-bois-dark-popover-inner {\n background-color: #d84a1b;\n}\n.du-bois-dark-popover-volcano .du-bois-dark-popover-arrow-content {\n background-color: #d84a1b;\n}\n.du-bois-dark-popover-orange .du-bois-dark-popover-inner {\n background-color: #d87a16;\n}\n.du-bois-dark-popover-orange .du-bois-dark-popover-arrow-content {\n background-color: #d87a16;\n}\n.du-bois-dark-popover-yellow .du-bois-dark-popover-inner {\n background-color: #d8bd14;\n}\n.du-bois-dark-popover-yellow .du-bois-dark-popover-arrow-content {\n background-color: #d8bd14;\n}\n.du-bois-dark-popover-gold .du-bois-dark-popover-inner {\n background-color: #d89614;\n}\n.du-bois-dark-popover-gold .du-bois-dark-popover-arrow-content {\n background-color: #d89614;\n}\n.du-bois-dark-popover-cyan .du-bois-dark-popover-inner {\n background-color: #13a8a8;\n}\n.du-bois-dark-popover-cyan .du-bois-dark-popover-arrow-content {\n background-color: #13a8a8;\n}\n.du-bois-dark-popover-lime .du-bois-dark-popover-inner {\n background-color: #8bbb11;\n}\n.du-bois-dark-popover-lime .du-bois-dark-popover-arrow-content {\n background-color: #8bbb11;\n}\n.du-bois-dark-popover-green .du-bois-dark-popover-inner {\n background-color: #49aa19;\n}\n.du-bois-dark-popover-green .du-bois-dark-popover-arrow-content {\n background-color: #49aa19;\n}\n.du-bois-dark-popover-blue .du-bois-dark-popover-inner {\n background-color: #177ddc;\n}\n.du-bois-dark-popover-blue .du-bois-dark-popover-arrow-content {\n background-color: #177ddc;\n}\n.du-bois-dark-popover-geekblue .du-bois-dark-popover-inner {\n background-color: #2b4acb;\n}\n.du-bois-dark-popover-geekblue .du-bois-dark-popover-arrow-content {\n background-color: #2b4acb;\n}\n.du-bois-dark-popover-purple .du-bois-dark-popover-inner {\n background-color: #642ab5;\n}\n.du-bois-dark-popover-purple .du-bois-dark-popover-arrow-content {\n background-color: #642ab5;\n}\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-dark-radio-group {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #E8ECF0;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: tnum;\n display: inline-block;\n font-size: 0;\n}\n.du-bois-dark-radio-group .du-bois-dark-badge-count {\n z-index: 1;\n}\n.du-bois-dark-radio-group > .du-bois-dark-badge:not(:first-child) > .du-bois-dark-radio-button-wrapper {\n border-left: none;\n}\n.du-bois-dark-radio-wrapper {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #E8ECF0;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: tnum;\n position: relative;\n display: inline-flex;\n align-items: baseline;\n margin-right: 8px;\n cursor: pointer;\n}\n.du-bois-dark-radio-wrapper::after {\n display: inline-block;\n width: 0;\n overflow: hidden;\n content: '\\a0';\n}\n.du-bois-dark-radio {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #E8ECF0;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: tnum;\n position: relative;\n top: 0.2em;\n display: inline-block;\n outline: none;\n cursor: pointer;\n}\n.du-bois-dark-radio-wrapper:hover .du-bois-dark-radio,\n.du-bois-dark-radio:hover .du-bois-dark-radio-inner,\n.du-bois-dark-radio-input:focus + .du-bois-dark-radio-inner {\n border-color: #2272B4;\n}\n.du-bois-dark-radio-input:focus + .du-bois-dark-radio-inner {\n box-shadow: 0 0 0 3px rgba(34, 114, 180, 0.08);\n}\n.du-bois-dark-radio-checked::after {\n position: absolute;\n top: 0;\n left: 0;\n width: 100%;\n height: 100%;\n border: 1px solid #2272B4;\n border-radius: 50%;\n visibility: hidden;\n animation: antRadioEffect 0.36s ease-in-out;\n animation-fill-mode: both;\n content: '';\n}\n.du-bois-dark-radio:hover::after,\n.du-bois-dark-radio-wrapper:hover .du-bois-dark-radio::after {\n visibility: visible;\n}\n.du-bois-dark-radio-inner {\n position: relative;\n top: 0;\n left: 0;\n display: block;\n width: 16px;\n height: 16px;\n background-color: transparent;\n border-color: #1F272D;\n border-style: solid;\n border-width: 1px;\n border-radius: 50%;\n transition: all 0.3s;\n}\n.du-bois-dark-radio-inner::after {\n position: absolute;\n top: 3px;\n left: 3px;\n display: block;\n width: 8px;\n height: 8px;\n background-color: #2272B4;\n border-top: 0;\n border-left: 0;\n border-radius: 8px;\n transform: scale(0);\n opacity: 0;\n transition: all 0.3s cubic-bezier(0.78, 0.14, 0.15, 0.86);\n content: ' ';\n}\n.du-bois-dark-radio-input {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n z-index: 1;\n cursor: pointer;\n opacity: 0;\n}\n.du-bois-dark-radio-checked .du-bois-dark-radio-inner {\n border-color: #2272B4;\n}\n.du-bois-dark-radio-checked .du-bois-dark-radio-inner::after {\n transform: scale(1);\n opacity: 1;\n transition: all 0.3s cubic-bezier(0.78, 0.14, 0.15, 0.86);\n}\n.du-bois-dark-radio-disabled {\n cursor: not-allowed;\n}\n.du-bois-dark-radio-disabled .du-bois-dark-radio-inner {\n background-color: rgba(255, 255, 255, 0.08);\n border-color: #1F272D !important;\n cursor: not-allowed;\n}\n.du-bois-dark-radio-disabled .du-bois-dark-radio-inner::after {\n background-color: rgba(255, 255, 255, 0.2);\n}\n.du-bois-dark-radio-disabled .du-bois-dark-radio-input {\n cursor: not-allowed;\n}\n.du-bois-dark-radio-disabled + span {\n color: rgba(255, 255, 255, 0.3);\n cursor: not-allowed;\n}\nspan.du-bois-dark-radio + * {\n padding-right: 8px;\n padding-left: 8px;\n}\n.du-bois-dark-radio-button-wrapper {\n position: relative;\n display: inline-block;\n height: 40px;\n margin: 0;\n padding: 0 7px;\n color: #E8ECF0;\n font-size: 13px;\n line-height: 38px;\n background: transparent;\n border: 1px solid #1F272D;\n border-top-width: 1.02px;\n border-left-width: 0;\n cursor: pointer;\n transition: color 0.3s, background 0.3s, border-color 0.3s, box-shadow 0.3s;\n}\n.du-bois-dark-radio-button-wrapper a {\n color: #E8ECF0;\n}\n.du-bois-dark-radio-button-wrapper > .du-bois-dark-radio-button {\n position: absolute;\n top: 0;\n left: 0;\n z-index: -1;\n width: 100%;\n height: 100%;\n}\n.du-bois-dark-radio-group-large .du-bois-dark-radio-button-wrapper {\n height: 40px;\n font-size: 18px;\n line-height: 38px;\n}\n.du-bois-dark-radio-group-small .du-bois-dark-radio-button-wrapper {\n height: 32px;\n padding: 0 3px;\n line-height: 30px;\n}\n.du-bois-dark-radio-button-wrapper:not(:first-child)::before {\n position: absolute;\n top: -1px;\n left: -1px;\n display: block;\n box-sizing: content-box;\n width: 1px;\n height: 100%;\n padding: 1px 0;\n background-color: #1F272D;\n transition: background-color 0.3s;\n content: '';\n}\n.du-bois-dark-radio-button-wrapper:first-child {\n border-left: 1px solid #1F272D;\n border-radius: 4px 0 0 4px;\n}\n.du-bois-dark-radio-button-wrapper:last-child {\n border-radius: 0 4px 4px 0;\n}\n.du-bois-dark-radio-button-wrapper:first-child:last-child {\n border-radius: 4px;\n}\n.du-bois-dark-radio-button-wrapper:hover {\n position: relative;\n color: #2272B4;\n}\n.du-bois-dark-radio-button-wrapper:focus-within {\n box-shadow: 0 0 0 3px rgba(34, 114, 180, 0.08);\n}\n.du-bois-dark-radio-button-wrapper .du-bois-dark-radio-inner,\n.du-bois-dark-radio-button-wrapper input[type='checkbox'],\n.du-bois-dark-radio-button-wrapper input[type='radio'] {\n width: 0;\n height: 0;\n opacity: 0;\n pointer-events: none;\n}\n.du-bois-dark-radio-button-wrapper-checked:not(.du-bois-dark-radio-button-wrapper-disabled) {\n z-index: 1;\n color: #2272B4;\n background: transparent;\n border-color: #2272B4;\n}\n.du-bois-dark-radio-button-wrapper-checked:not(.du-bois-dark-radio-button-wrapper-disabled)::before {\n background-color: #2272B4;\n}\n.du-bois-dark-radio-button-wrapper-checked:not(.du-bois-dark-radio-button-wrapper-disabled):first-child {\n border-color: #2272B4;\n}\n.du-bois-dark-radio-button-wrapper-checked:not(.du-bois-dark-radio-button-wrapper-disabled):hover {\n color: #1d517c;\n border-color: #1d517c;\n}\n.du-bois-dark-radio-button-wrapper-checked:not(.du-bois-dark-radio-button-wrapper-disabled):hover::before {\n background-color: #1d517c;\n}\n.du-bois-dark-radio-button-wrapper-checked:not(.du-bois-dark-radio-button-wrapper-disabled):active {\n color: #3f81b1;\n border-color: #3f81b1;\n}\n.du-bois-dark-radio-button-wrapper-checked:not(.du-bois-dark-radio-button-wrapper-disabled):active::before {\n background-color: #3f81b1;\n}\n.du-bois-dark-radio-button-wrapper-checked:not(.du-bois-dark-radio-button-wrapper-disabled):focus-within {\n box-shadow: 0 0 0 3px rgba(34, 114, 180, 0.08);\n}\n.du-bois-dark-radio-group-solid .du-bois-dark-radio-button-wrapper-checked:not(.du-bois-dark-radio-button-wrapper-disabled) {\n color: #fff;\n background: #2272B4;\n border-color: #2272B4;\n}\n.du-bois-dark-radio-group-solid .du-bois-dark-radio-button-wrapper-checked:not(.du-bois-dark-radio-button-wrapper-disabled):hover {\n color: #fff;\n background: #1d517c;\n border-color: #1d517c;\n}\n.du-bois-dark-radio-group-solid .du-bois-dark-radio-button-wrapper-checked:not(.du-bois-dark-radio-button-wrapper-disabled):active {\n color: #fff;\n background: #3f81b1;\n border-color: #3f81b1;\n}\n.du-bois-dark-radio-group-solid .du-bois-dark-radio-button-wrapper-checked:not(.du-bois-dark-radio-button-wrapper-disabled):focus-within {\n box-shadow: 0 0 0 3px rgba(34, 114, 180, 0.08);\n}\n.du-bois-dark-radio-button-wrapper-disabled {\n color: rgba(255, 255, 255, 0.3);\n background-color: rgba(255, 255, 255, 0.08);\n border-color: #1F272D;\n cursor: not-allowed;\n}\n.du-bois-dark-radio-button-wrapper-disabled:first-child,\n.du-bois-dark-radio-button-wrapper-disabled:hover {\n color: rgba(255, 255, 255, 0.3);\n background-color: rgba(255, 255, 255, 0.08);\n border-color: #1F272D;\n}\n.du-bois-dark-radio-button-wrapper-disabled:first-child {\n border-left-color: #1F272D;\n}\n.du-bois-dark-radio-button-wrapper-disabled.du-bois-dark-radio-button-wrapper-checked {\n color: rgba(255, 255, 255, 0.3);\n background-color: rgba(255, 255, 255, 0.2);\n border-color: #1F272D;\n box-shadow: none;\n}\n@keyframes antRadioEffect {\n 0% {\n transform: scale(1);\n opacity: 0.5;\n }\n 100% {\n transform: scale(1.6);\n opacity: 0;\n }\n}\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-dark-skeleton {\n display: table;\n width: 100%;\n}\n.du-bois-dark-skeleton-header {\n display: table-cell;\n padding-right: 8px;\n vertical-align: top;\n}\n.du-bois-dark-skeleton-header .du-bois-dark-skeleton-avatar {\n display: inline-block;\n vertical-align: top;\n background: rgba(190, 190, 190, 0.2);\n width: 32px;\n height: 32px;\n line-height: 32px;\n}\n.du-bois-dark-skeleton-header .du-bois-dark-skeleton-avatar.du-bois-dark-skeleton-avatar-circle {\n border-radius: 50%;\n}\n.du-bois-dark-skeleton-header .du-bois-dark-skeleton-avatar-lg {\n width: 40px;\n height: 40px;\n line-height: 40px;\n}\n.du-bois-dark-skeleton-header .du-bois-dark-skeleton-avatar-lg.du-bois-dark-skeleton-avatar-circle {\n border-radius: 50%;\n}\n.du-bois-dark-skeleton-header .du-bois-dark-skeleton-avatar-sm {\n width: 24px;\n height: 24px;\n line-height: 24px;\n}\n.du-bois-dark-skeleton-header .du-bois-dark-skeleton-avatar-sm.du-bois-dark-skeleton-avatar-circle {\n border-radius: 50%;\n}\n.du-bois-dark-skeleton-content {\n display: table-cell;\n width: 100%;\n vertical-align: top;\n}\n.du-bois-dark-skeleton-content .du-bois-dark-skeleton-title {\n width: 100%;\n height: 16px;\n margin-top: 16px;\n background: rgba(190, 190, 190, 0.2);\n border-radius: 4px;\n}\n.du-bois-dark-skeleton-content .du-bois-dark-skeleton-title + .du-bois-dark-skeleton-paragraph {\n margin-top: 24px;\n}\n.du-bois-dark-skeleton-content .du-bois-dark-skeleton-paragraph {\n padding: 0;\n}\n.du-bois-dark-skeleton-content .du-bois-dark-skeleton-paragraph > li {\n width: 100%;\n height: 16px;\n list-style: none;\n background: rgba(190, 190, 190, 0.2);\n border-radius: 4px;\n}\n.du-bois-dark-skeleton-content .du-bois-dark-skeleton-paragraph > li:last-child:not(:first-child):not(:nth-child(2)) {\n width: 61%;\n}\n.du-bois-dark-skeleton-content .du-bois-dark-skeleton-paragraph > li + li {\n margin-top: 16px;\n}\n.du-bois-dark-skeleton-with-avatar .du-bois-dark-skeleton-content .du-bois-dark-skeleton-title {\n margin-top: 12px;\n}\n.du-bois-dark-skeleton-with-avatar .du-bois-dark-skeleton-content .du-bois-dark-skeleton-title + .du-bois-dark-skeleton-paragraph {\n margin-top: 28px;\n}\n.du-bois-dark-skeleton-round .du-bois-dark-skeleton-content .du-bois-dark-skeleton-title,\n.du-bois-dark-skeleton-round .du-bois-dark-skeleton-content .du-bois-dark-skeleton-paragraph > li {\n border-radius: 100px;\n}\n.du-bois-dark-skeleton.du-bois-dark-skeleton-active .du-bois-dark-skeleton-content .du-bois-dark-skeleton-title,\n.du-bois-dark-skeleton.du-bois-dark-skeleton-active .du-bois-dark-skeleton-content .du-bois-dark-skeleton-paragraph > li {\n background: linear-gradient(90deg, rgba(190, 190, 190, 0.2) 25%, rgba(255, 255, 255, 0.16) 37%, rgba(190, 190, 190, 0.2) 63%);\n background-size: 400% 100%;\n animation: du-bois-dark-skeleton-loading 1.4s ease infinite;\n}\n.du-bois-dark-skeleton.du-bois-dark-skeleton-active .du-bois-dark-skeleton-avatar {\n background: linear-gradient(90deg, rgba(190, 190, 190, 0.2) 25%, rgba(255, 255, 255, 0.16) 37%, rgba(190, 190, 190, 0.2) 63%);\n background-size: 400% 100%;\n animation: du-bois-dark-skeleton-loading 1.4s ease infinite;\n}\n.du-bois-dark-skeleton.du-bois-dark-skeleton-active .du-bois-dark-skeleton-button {\n background: linear-gradient(90deg, rgba(190, 190, 190, 0.2) 25%, rgba(255, 255, 255, 0.16) 37%, rgba(190, 190, 190, 0.2) 63%);\n background-size: 400% 100%;\n animation: du-bois-dark-skeleton-loading 1.4s ease infinite;\n}\n.du-bois-dark-skeleton.du-bois-dark-skeleton-active .du-bois-dark-skeleton-input {\n background: linear-gradient(90deg, rgba(190, 190, 190, 0.2) 25%, rgba(255, 255, 255, 0.16) 37%, rgba(190, 190, 190, 0.2) 63%);\n background-size: 400% 100%;\n animation: du-bois-dark-skeleton-loading 1.4s ease infinite;\n}\n.du-bois-dark-skeleton.du-bois-dark-skeleton-active .du-bois-dark-skeleton-image {\n background: linear-gradient(90deg, rgba(190, 190, 190, 0.2) 25%, rgba(255, 255, 255, 0.16) 37%, rgba(190, 190, 190, 0.2) 63%);\n background-size: 400% 100%;\n animation: du-bois-dark-skeleton-loading 1.4s ease infinite;\n}\n.du-bois-dark-skeleton-element {\n display: inline-block;\n width: auto;\n}\n.du-bois-dark-skeleton-element .du-bois-dark-skeleton-button {\n display: inline-block;\n vertical-align: top;\n background: rgba(190, 190, 190, 0.2);\n border-radius: 4px;\n width: 80px;\n height: 40px;\n line-height: 40px;\n}\n.du-bois-dark-skeleton-element .du-bois-dark-skeleton-button.du-bois-dark-skeleton-button-circle {\n width: 40px;\n border-radius: 50%;\n}\n.du-bois-dark-skeleton-element .du-bois-dark-skeleton-button.du-bois-dark-skeleton-button-round {\n border-radius: 40px;\n}\n.du-bois-dark-skeleton-element .du-bois-dark-skeleton-button-lg {\n width: 80px;\n height: 40px;\n line-height: 40px;\n}\n.du-bois-dark-skeleton-element .du-bois-dark-skeleton-button-lg.du-bois-dark-skeleton-button-circle {\n width: 40px;\n border-radius: 50%;\n}\n.du-bois-dark-skeleton-element .du-bois-dark-skeleton-button-lg.du-bois-dark-skeleton-button-round {\n border-radius: 40px;\n}\n.du-bois-dark-skeleton-element .du-bois-dark-skeleton-button-sm {\n width: 64px;\n height: 32px;\n line-height: 32px;\n}\n.du-bois-dark-skeleton-element .du-bois-dark-skeleton-button-sm.du-bois-dark-skeleton-button-circle {\n width: 32px;\n border-radius: 50%;\n}\n.du-bois-dark-skeleton-element .du-bois-dark-skeleton-button-sm.du-bois-dark-skeleton-button-round {\n border-radius: 32px;\n}\n.du-bois-dark-skeleton-element .du-bois-dark-skeleton-avatar {\n display: inline-block;\n vertical-align: top;\n background: rgba(190, 190, 190, 0.2);\n width: 32px;\n height: 32px;\n line-height: 32px;\n}\n.du-bois-dark-skeleton-element .du-bois-dark-skeleton-avatar.du-bois-dark-skeleton-avatar-circle {\n border-radius: 50%;\n}\n.du-bois-dark-skeleton-element .du-bois-dark-skeleton-avatar-lg {\n width: 40px;\n height: 40px;\n line-height: 40px;\n}\n.du-bois-dark-skeleton-element .du-bois-dark-skeleton-avatar-lg.du-bois-dark-skeleton-avatar-circle {\n border-radius: 50%;\n}\n.du-bois-dark-skeleton-element .du-bois-dark-skeleton-avatar-sm {\n width: 24px;\n height: 24px;\n line-height: 24px;\n}\n.du-bois-dark-skeleton-element .du-bois-dark-skeleton-avatar-sm.du-bois-dark-skeleton-avatar-circle {\n border-radius: 50%;\n}\n.du-bois-dark-skeleton-element .du-bois-dark-skeleton-input {\n display: inline-block;\n vertical-align: top;\n background: rgba(190, 190, 190, 0.2);\n width: 100%;\n height: 40px;\n line-height: 40px;\n}\n.du-bois-dark-skeleton-element .du-bois-dark-skeleton-input-lg {\n width: 100%;\n height: 40px;\n line-height: 40px;\n}\n.du-bois-dark-skeleton-element .du-bois-dark-skeleton-input-sm {\n width: 100%;\n height: 32px;\n line-height: 32px;\n}\n.du-bois-dark-skeleton-element .du-bois-dark-skeleton-image {\n display: flex;\n align-items: center;\n justify-content: center;\n vertical-align: top;\n background: rgba(190, 190, 190, 0.2);\n width: 96px;\n height: 96px;\n line-height: 96px;\n}\n.du-bois-dark-skeleton-element .du-bois-dark-skeleton-image.du-bois-dark-skeleton-image-circle {\n border-radius: 50%;\n}\n.du-bois-dark-skeleton-element .du-bois-dark-skeleton-image-path {\n fill: #bfbfbf;\n}\n.du-bois-dark-skeleton-element .du-bois-dark-skeleton-image-svg {\n width: 48px;\n height: 48px;\n line-height: 48px;\n max-width: 192px;\n max-height: 192px;\n}\n.du-bois-dark-skeleton-element .du-bois-dark-skeleton-image-svg.du-bois-dark-skeleton-image-circle {\n border-radius: 50%;\n}\n@keyframes du-bois-dark-skeleton-loading {\n 0% {\n background-position: 100% 50%;\n }\n 100% {\n background-position: 0 50%;\n }\n}\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-dark-space {\n display: inline-flex;\n}\n.du-bois-dark-space-vertical {\n flex-direction: column;\n}\n.du-bois-dark-space-align-center {\n align-items: center;\n}\n.du-bois-dark-space-align-start {\n align-items: flex-start;\n}\n.du-bois-dark-space-align-end {\n align-items: flex-end;\n}\n.du-bois-dark-space-align-baseline {\n align-items: baseline;\n}\n.du-bois-dark-space-item:empty {\n display: none;\n}\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-dark-steps {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #E8ECF0;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: tnum;\n display: flex;\n width: 100%;\n font-size: 0;\n text-align: initial;\n}\n.du-bois-dark-steps-item {\n position: relative;\n display: inline-block;\n flex: 1;\n overflow: hidden;\n vertical-align: top;\n}\n.du-bois-dark-steps-item-container {\n outline: none;\n}\n.du-bois-dark-steps-item:last-child {\n flex: none;\n}\n.du-bois-dark-steps-item:last-child > .du-bois-dark-steps-item-container > .du-bois-dark-steps-item-tail,\n.du-bois-dark-steps-item:last-child > .du-bois-dark-steps-item-container > .du-bois-dark-steps-item-content > .du-bois-dark-steps-item-title::after {\n display: none;\n}\n.du-bois-dark-steps-item-icon,\n.du-bois-dark-steps-item-content {\n display: inline-block;\n vertical-align: top;\n}\n.du-bois-dark-steps-item-icon {\n width: 32px;\n height: 32px;\n margin: 0 8px 0 0;\n font-size: 18px;\n font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, 'Helvetica Neue', Arial, 'Noto Sans', sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol', 'Noto Color Emoji';\n line-height: 32px;\n text-align: center;\n border: 1px solid rgba(255, 255, 255, 0.3);\n border-radius: 32px;\n transition: background-color 0.3s, border-color 0.3s;\n}\n.du-bois-dark-steps-item-icon .du-bois-dark-steps-icon {\n position: relative;\n top: -0.5px;\n color: #2272B4;\n line-height: 1;\n}\n.du-bois-dark-steps-item-tail {\n position: absolute;\n top: 12px;\n left: 0;\n width: 100%;\n padding: 0 10px;\n}\n.du-bois-dark-steps-item-tail::after {\n display: inline-block;\n width: 100%;\n height: 1px;\n background: #303030;\n border-radius: 1px;\n transition: background 0.3s;\n content: '';\n}\n.du-bois-dark-steps-item-title {\n position: relative;\n display: inline-block;\n padding-right: 16px;\n color: #E8ECF0;\n font-size: 18px;\n line-height: 40px;\n}\n.du-bois-dark-steps-item-title::after {\n position: absolute;\n top: 20px;\n left: 100%;\n display: block;\n width: 9999px;\n height: 1px;\n background: #303030;\n content: '';\n}\n.du-bois-dark-steps-item-subtitle {\n display: inline;\n margin-left: 8px;\n color: #92A4B3;\n font-weight: normal;\n font-size: 13px;\n}\n.du-bois-dark-steps-item-description {\n color: #92A4B3;\n font-size: 13px;\n}\n.du-bois-dark-steps-item-wait .du-bois-dark-steps-item-icon {\n background-color: transparent;\n border-color: rgba(255, 255, 255, 0.3);\n}\n.du-bois-dark-steps-item-wait .du-bois-dark-steps-item-icon > .du-bois-dark-steps-icon {\n color: rgba(255, 255, 255, 0.3);\n}\n.du-bois-dark-steps-item-wait .du-bois-dark-steps-item-icon > .du-bois-dark-steps-icon .du-bois-dark-steps-icon-dot {\n background: rgba(255, 255, 255, 0.3);\n}\n.du-bois-dark-steps-item-wait > .du-bois-dark-steps-item-container > .du-bois-dark-steps-item-content > .du-bois-dark-steps-item-title {\n color: #92A4B3;\n}\n.du-bois-dark-steps-item-wait > .du-bois-dark-steps-item-container > .du-bois-dark-steps-item-content > .du-bois-dark-steps-item-title::after {\n background-color: #303030;\n}\n.du-bois-dark-steps-item-wait > .du-bois-dark-steps-item-container > .du-bois-dark-steps-item-content > .du-bois-dark-steps-item-description {\n color: #92A4B3;\n}\n.du-bois-dark-steps-item-wait > .du-bois-dark-steps-item-container > .du-bois-dark-steps-item-tail::after {\n background-color: #303030;\n}\n.du-bois-dark-steps-item-process .du-bois-dark-steps-item-icon {\n background-color: transparent;\n border-color: #2272B4;\n}\n.du-bois-dark-steps-item-process .du-bois-dark-steps-item-icon > .du-bois-dark-steps-icon {\n color: #2272B4;\n}\n.du-bois-dark-steps-item-process .du-bois-dark-steps-item-icon > .du-bois-dark-steps-icon .du-bois-dark-steps-icon-dot {\n background: #2272B4;\n}\n.du-bois-dark-steps-item-process > .du-bois-dark-steps-item-container > .du-bois-dark-steps-item-content > .du-bois-dark-steps-item-title {\n color: rgba(255, 255, 255, 0.85);\n}\n.du-bois-dark-steps-item-process > .du-bois-dark-steps-item-container > .du-bois-dark-steps-item-content > .du-bois-dark-steps-item-title::after {\n background-color: #303030;\n}\n.du-bois-dark-steps-item-process > .du-bois-dark-steps-item-container > .du-bois-dark-steps-item-content > .du-bois-dark-steps-item-description {\n color: #E8ECF0;\n}\n.du-bois-dark-steps-item-process > .du-bois-dark-steps-item-container > .du-bois-dark-steps-item-tail::after {\n background-color: #303030;\n}\n.du-bois-dark-steps-item-process > .du-bois-dark-steps-item-container > .du-bois-dark-steps-item-icon {\n background: #2272B4;\n}\n.du-bois-dark-steps-item-process > .du-bois-dark-steps-item-container > .du-bois-dark-steps-item-icon .du-bois-dark-steps-icon {\n color: #fff;\n}\n.du-bois-dark-steps-item-process > .du-bois-dark-steps-item-container > .du-bois-dark-steps-item-title {\n font-weight: 500;\n}\n.du-bois-dark-steps-item-finish .du-bois-dark-steps-item-icon {\n background-color: transparent;\n border-color: #2272B4;\n}\n.du-bois-dark-steps-item-finish .du-bois-dark-steps-item-icon > .du-bois-dark-steps-icon {\n color: #2272B4;\n}\n.du-bois-dark-steps-item-finish .du-bois-dark-steps-item-icon > .du-bois-dark-steps-icon .du-bois-dark-steps-icon-dot {\n background: #2272B4;\n}\n.du-bois-dark-steps-item-finish > .du-bois-dark-steps-item-container > .du-bois-dark-steps-item-content > .du-bois-dark-steps-item-title {\n color: #E8ECF0;\n}\n.du-bois-dark-steps-item-finish > .du-bois-dark-steps-item-container > .du-bois-dark-steps-item-content > .du-bois-dark-steps-item-title::after {\n background-color: #2272B4;\n}\n.du-bois-dark-steps-item-finish > .du-bois-dark-steps-item-container > .du-bois-dark-steps-item-content > .du-bois-dark-steps-item-description {\n color: #92A4B3;\n}\n.du-bois-dark-steps-item-finish > .du-bois-dark-steps-item-container > .du-bois-dark-steps-item-tail::after {\n background-color: #2272B4;\n}\n.du-bois-dark-steps-item-error .du-bois-dark-steps-item-icon {\n background-color: transparent;\n border-color: #E65B77;\n}\n.du-bois-dark-steps-item-error .du-bois-dark-steps-item-icon > .du-bois-dark-steps-icon {\n color: #E65B77;\n}\n.du-bois-dark-steps-item-error .du-bois-dark-steps-item-icon > .du-bois-dark-steps-icon .du-bois-dark-steps-icon-dot {\n background: #E65B77;\n}\n.du-bois-dark-steps-item-error > .du-bois-dark-steps-item-container > .du-bois-dark-steps-item-content > .du-bois-dark-steps-item-title {\n color: #E65B77;\n}\n.du-bois-dark-steps-item-error > .du-bois-dark-steps-item-container > .du-bois-dark-steps-item-content > .du-bois-dark-steps-item-title::after {\n background-color: #303030;\n}\n.du-bois-dark-steps-item-error > .du-bois-dark-steps-item-container > .du-bois-dark-steps-item-content > .du-bois-dark-steps-item-description {\n color: #E65B77;\n}\n.du-bois-dark-steps-item-error > .du-bois-dark-steps-item-container > .du-bois-dark-steps-item-tail::after {\n background-color: #303030;\n}\n.du-bois-dark-steps-item.du-bois-dark-steps-next-error .du-bois-dark-steps-item-title::after {\n background: #E65B77;\n}\n.du-bois-dark-steps-item-disabled {\n cursor: not-allowed;\n}\n.du-bois-dark-steps .du-bois-dark-steps-item:not(.du-bois-dark-steps-item-active) > .du-bois-dark-steps-item-container[role='button'] {\n cursor: pointer;\n}\n.du-bois-dark-steps .du-bois-dark-steps-item:not(.du-bois-dark-steps-item-active) > .du-bois-dark-steps-item-container[role='button'] .du-bois-dark-steps-item-title,\n.du-bois-dark-steps .du-bois-dark-steps-item:not(.du-bois-dark-steps-item-active) > .du-bois-dark-steps-item-container[role='button'] .du-bois-dark-steps-item-subtitle,\n.du-bois-dark-steps .du-bois-dark-steps-item:not(.du-bois-dark-steps-item-active) > .du-bois-dark-steps-item-container[role='button'] .du-bois-dark-steps-item-description,\n.du-bois-dark-steps .du-bois-dark-steps-item:not(.du-bois-dark-steps-item-active) > .du-bois-dark-steps-item-container[role='button'] .du-bois-dark-steps-item-icon .du-bois-dark-steps-icon {\n transition: color 0.3s;\n}\n.du-bois-dark-steps .du-bois-dark-steps-item:not(.du-bois-dark-steps-item-active) > .du-bois-dark-steps-item-container[role='button']:hover .du-bois-dark-steps-item-title,\n.du-bois-dark-steps .du-bois-dark-steps-item:not(.du-bois-dark-steps-item-active) > .du-bois-dark-steps-item-container[role='button']:hover .du-bois-dark-steps-item-subtitle,\n.du-bois-dark-steps .du-bois-dark-steps-item:not(.du-bois-dark-steps-item-active) > .du-bois-dark-steps-item-container[role='button']:hover .du-bois-dark-steps-item-description {\n color: #2272B4;\n}\n.du-bois-dark-steps .du-bois-dark-steps-item:not(.du-bois-dark-steps-item-active):not(.du-bois-dark-steps-item-process) > .du-bois-dark-steps-item-container[role='button']:hover .du-bois-dark-steps-item-icon {\n border-color: #2272B4;\n}\n.du-bois-dark-steps .du-bois-dark-steps-item:not(.du-bois-dark-steps-item-active):not(.du-bois-dark-steps-item-process) > .du-bois-dark-steps-item-container[role='button']:hover .du-bois-dark-steps-item-icon .du-bois-dark-steps-icon {\n color: #2272B4;\n}\n.du-bois-dark-steps-horizontal:not(.du-bois-dark-steps-label-vertical) .du-bois-dark-steps-item {\n padding-left: 16px;\n white-space: nowrap;\n}\n.du-bois-dark-steps-horizontal:not(.du-bois-dark-steps-label-vertical) .du-bois-dark-steps-item:first-child {\n padding-left: 0;\n}\n.du-bois-dark-steps-horizontal:not(.du-bois-dark-steps-label-vertical) .du-bois-dark-steps-item:last-child .du-bois-dark-steps-item-title {\n padding-right: 0;\n}\n.du-bois-dark-steps-horizontal:not(.du-bois-dark-steps-label-vertical) .du-bois-dark-steps-item-tail {\n display: none;\n}\n.du-bois-dark-steps-horizontal:not(.du-bois-dark-steps-label-vertical) .du-bois-dark-steps-item-description {\n max-width: 140px;\n white-space: normal;\n}\n.du-bois-dark-steps-item-custom > .du-bois-dark-steps-item-container > .du-bois-dark-steps-item-icon {\n height: auto;\n background: none;\n border: 0;\n}\n.du-bois-dark-steps-item-custom > .du-bois-dark-steps-item-container > .du-bois-dark-steps-item-icon > .du-bois-dark-steps-icon {\n top: 0px;\n left: 0.5px;\n width: 32px;\n height: 32px;\n font-size: 24px;\n line-height: 32px;\n}\n.du-bois-dark-steps-item-custom.du-bois-dark-steps-item-process .du-bois-dark-steps-item-icon > .du-bois-dark-steps-icon {\n color: #2272B4;\n}\n.du-bois-dark-steps:not(.du-bois-dark-steps-vertical) .du-bois-dark-steps-item-custom .du-bois-dark-steps-item-icon {\n width: auto;\n background: none;\n}\n.du-bois-dark-steps-small.du-bois-dark-steps-horizontal:not(.du-bois-dark-steps-label-vertical) .du-bois-dark-steps-item {\n padding-left: 12px;\n}\n.du-bois-dark-steps-small.du-bois-dark-steps-horizontal:not(.du-bois-dark-steps-label-vertical) .du-bois-dark-steps-item:first-child {\n padding-left: 0;\n}\n.du-bois-dark-steps-small .du-bois-dark-steps-item-icon {\n width: 24px;\n height: 24px;\n margin: 0 8px 0 0;\n font-size: 12px;\n line-height: 24px;\n text-align: center;\n border-radius: 24px;\n}\n.du-bois-dark-steps-small .du-bois-dark-steps-item-title {\n padding-right: 12px;\n font-size: 13px;\n line-height: 24px;\n}\n.du-bois-dark-steps-small .du-bois-dark-steps-item-title::after {\n top: 12px;\n}\n.du-bois-dark-steps-small .du-bois-dark-steps-item-description {\n color: #92A4B3;\n font-size: 13px;\n}\n.du-bois-dark-steps-small .du-bois-dark-steps-item-tail {\n top: 8px;\n}\n.du-bois-dark-steps-small .du-bois-dark-steps-item-custom .du-bois-dark-steps-item-icon {\n width: inherit;\n height: inherit;\n line-height: inherit;\n background: none;\n border: 0;\n border-radius: 0;\n}\n.du-bois-dark-steps-small .du-bois-dark-steps-item-custom .du-bois-dark-steps-item-icon > .du-bois-dark-steps-icon {\n font-size: 24px;\n line-height: 24px;\n transform: none;\n}\n.du-bois-dark-steps-vertical {\n display: flex;\n flex-direction: column;\n}\n.du-bois-dark-steps-vertical > .du-bois-dark-steps-item {\n display: block;\n flex: 1 0 auto;\n padding-left: 0;\n overflow: visible;\n}\n.du-bois-dark-steps-vertical > .du-bois-dark-steps-item .du-bois-dark-steps-item-icon {\n float: left;\n margin-right: 16px;\n}\n.du-bois-dark-steps-vertical > .du-bois-dark-steps-item .du-bois-dark-steps-item-content {\n display: block;\n min-height: 48px;\n overflow: hidden;\n}\n.du-bois-dark-steps-vertical > .du-bois-dark-steps-item .du-bois-dark-steps-item-title {\n line-height: 32px;\n}\n.du-bois-dark-steps-vertical > .du-bois-dark-steps-item .du-bois-dark-steps-item-description {\n padding-bottom: 12px;\n}\n.du-bois-dark-steps-vertical > .du-bois-dark-steps-item > .du-bois-dark-steps-item-container > .du-bois-dark-steps-item-tail {\n position: absolute;\n top: 0;\n left: 16px;\n width: 1px;\n height: 100%;\n padding: 38px 0 6px;\n}\n.du-bois-dark-steps-vertical > .du-bois-dark-steps-item > .du-bois-dark-steps-item-container > .du-bois-dark-steps-item-tail::after {\n width: 1px;\n height: 100%;\n}\n.du-bois-dark-steps-vertical > .du-bois-dark-steps-item:not(:last-child) > .du-bois-dark-steps-item-container > .du-bois-dark-steps-item-tail {\n display: block;\n}\n.du-bois-dark-steps-vertical > .du-bois-dark-steps-item > .du-bois-dark-steps-item-container > .du-bois-dark-steps-item-content > .du-bois-dark-steps-item-title::after {\n display: none;\n}\n.du-bois-dark-steps-vertical.du-bois-dark-steps-small .du-bois-dark-steps-item-container .du-bois-dark-steps-item-tail {\n position: absolute;\n top: 0;\n left: 12px;\n padding: 30px 0 6px;\n}\n.du-bois-dark-steps-vertical.du-bois-dark-steps-small .du-bois-dark-steps-item-container .du-bois-dark-steps-item-title {\n line-height: 24px;\n}\n.du-bois-dark-steps-label-vertical .du-bois-dark-steps-item {\n overflow: visible;\n}\n.du-bois-dark-steps-label-vertical .du-bois-dark-steps-item-tail {\n margin-left: 58px;\n padding: 3.5px 24px;\n}\n.du-bois-dark-steps-label-vertical .du-bois-dark-steps-item-content {\n display: block;\n width: 116px;\n margin-top: 8px;\n text-align: center;\n}\n.du-bois-dark-steps-label-vertical .du-bois-dark-steps-item-icon {\n display: inline-block;\n margin-left: 42px;\n}\n.du-bois-dark-steps-label-vertical .du-bois-dark-steps-item-title {\n padding-right: 0;\n padding-left: 0;\n}\n.du-bois-dark-steps-label-vertical .du-bois-dark-steps-item-title::after {\n display: none;\n}\n.du-bois-dark-steps-label-vertical .du-bois-dark-steps-item-subtitle {\n display: block;\n margin-bottom: 4px;\n margin-left: 0;\n line-height: 20px;\n}\n.du-bois-dark-steps-label-vertical.du-bois-dark-steps-small:not(.du-bois-dark-steps-dot) .du-bois-dark-steps-item-icon {\n margin-left: 46px;\n}\n.du-bois-dark-steps-dot .du-bois-dark-steps-item-title,\n.du-bois-dark-steps-dot.du-bois-dark-steps-small .du-bois-dark-steps-item-title {\n line-height: 20px;\n}\n.du-bois-dark-steps-dot .du-bois-dark-steps-item-tail,\n.du-bois-dark-steps-dot.du-bois-dark-steps-small .du-bois-dark-steps-item-tail {\n top: 2px;\n width: 100%;\n margin: 0 0 0 70px;\n padding: 0;\n}\n.du-bois-dark-steps-dot .du-bois-dark-steps-item-tail::after,\n.du-bois-dark-steps-dot.du-bois-dark-steps-small .du-bois-dark-steps-item-tail::after {\n width: calc(100% - 20px);\n height: 3px;\n margin-left: 12px;\n}\n.du-bois-dark-steps-dot .du-bois-dark-steps-item:first-child .du-bois-dark-steps-icon-dot,\n.du-bois-dark-steps-dot.du-bois-dark-steps-small .du-bois-dark-steps-item:first-child .du-bois-dark-steps-icon-dot {\n left: 2px;\n}\n.du-bois-dark-steps-dot .du-bois-dark-steps-item-icon,\n.du-bois-dark-steps-dot.du-bois-dark-steps-small .du-bois-dark-steps-item-icon {\n width: 8px;\n height: 8px;\n margin-left: 67px;\n padding-right: 0;\n line-height: 8px;\n background: transparent;\n border: 0;\n}\n.du-bois-dark-steps-dot .du-bois-dark-steps-item-icon .du-bois-dark-steps-icon-dot,\n.du-bois-dark-steps-dot.du-bois-dark-steps-small .du-bois-dark-steps-item-icon .du-bois-dark-steps-icon-dot {\n position: relative;\n float: left;\n width: 100%;\n height: 100%;\n border-radius: 100px;\n transition: all 0.3s;\n /* expand hover area */\n}\n.du-bois-dark-steps-dot .du-bois-dark-steps-item-icon .du-bois-dark-steps-icon-dot::after,\n.du-bois-dark-steps-dot.du-bois-dark-steps-small .du-bois-dark-steps-item-icon .du-bois-dark-steps-icon-dot::after {\n position: absolute;\n top: -12px;\n left: -26px;\n width: 60px;\n height: 32px;\n background: rgba(0, 0, 0, 0.001);\n content: '';\n}\n.du-bois-dark-steps-dot .du-bois-dark-steps-item-content,\n.du-bois-dark-steps-dot.du-bois-dark-steps-small .du-bois-dark-steps-item-content {\n width: 140px;\n}\n.du-bois-dark-steps-dot .du-bois-dark-steps-item-process .du-bois-dark-steps-item-icon,\n.du-bois-dark-steps-dot.du-bois-dark-steps-small .du-bois-dark-steps-item-process .du-bois-dark-steps-item-icon {\n position: relative;\n top: -1px;\n width: 10px;\n height: 10px;\n line-height: 10px;\n background: none;\n}\n.du-bois-dark-steps-dot .du-bois-dark-steps-item-process .du-bois-dark-steps-icon:first-child .du-bois-dark-steps-icon-dot,\n.du-bois-dark-steps-dot.du-bois-dark-steps-small .du-bois-dark-steps-item-process .du-bois-dark-steps-icon:first-child .du-bois-dark-steps-icon-dot {\n left: 0;\n}\n.du-bois-dark-steps-vertical.du-bois-dark-steps-dot .du-bois-dark-steps-item-icon {\n margin-top: 8px;\n margin-left: 0;\n background: none;\n}\n.du-bois-dark-steps-vertical.du-bois-dark-steps-dot .du-bois-dark-steps-item > .du-bois-dark-steps-item-container > .du-bois-dark-steps-item-tail {\n top: 2px;\n left: -9px;\n margin: 0;\n padding: 22px 0 4px;\n}\n.du-bois-dark-steps-vertical.du-bois-dark-steps-dot .du-bois-dark-steps-item:first-child .du-bois-dark-steps-icon-dot {\n left: 0;\n}\n.du-bois-dark-steps-vertical.du-bois-dark-steps-dot .du-bois-dark-steps-item-content {\n width: inherit;\n}\n.du-bois-dark-steps-vertical.du-bois-dark-steps-dot .du-bois-dark-steps-item-process .du-bois-dark-steps-item-container .du-bois-dark-steps-item-icon .du-bois-dark-steps-icon-dot {\n left: -2px;\n}\n.du-bois-dark-steps-navigation {\n padding-top: 12px;\n}\n.du-bois-dark-steps-navigation.du-bois-dark-steps-small .du-bois-dark-steps-item-container {\n margin-left: -12px;\n}\n.du-bois-dark-steps-navigation .du-bois-dark-steps-item {\n overflow: visible;\n text-align: center;\n}\n.du-bois-dark-steps-navigation .du-bois-dark-steps-item-container {\n display: inline-block;\n height: 100%;\n margin-left: -16px;\n padding-bottom: 12px;\n text-align: left;\n transition: opacity 0.3s;\n}\n.du-bois-dark-steps-navigation .du-bois-dark-steps-item-container .du-bois-dark-steps-item-content {\n max-width: auto;\n}\n.du-bois-dark-steps-navigation .du-bois-dark-steps-item-container .du-bois-dark-steps-item-title {\n max-width: 100%;\n padding-right: 0;\n overflow: hidden;\n white-space: nowrap;\n text-overflow: ellipsis;\n}\n.du-bois-dark-steps-navigation .du-bois-dark-steps-item-container .du-bois-dark-steps-item-title::after {\n display: none;\n}\n.du-bois-dark-steps-navigation .du-bois-dark-steps-item:not(.du-bois-dark-steps-item-active) .du-bois-dark-steps-item-container[role='button'] {\n cursor: pointer;\n}\n.du-bois-dark-steps-navigation .du-bois-dark-steps-item:not(.du-bois-dark-steps-item-active) .du-bois-dark-steps-item-container[role='button']:hover {\n opacity: 0.85;\n}\n.du-bois-dark-steps-navigation .du-bois-dark-steps-item:last-child {\n flex: 1;\n}\n.du-bois-dark-steps-navigation .du-bois-dark-steps-item:last-child::after {\n display: none;\n}\n.du-bois-dark-steps-navigation .du-bois-dark-steps-item::after {\n position: absolute;\n top: 50%;\n left: 100%;\n display: inline-block;\n width: 12px;\n height: 12px;\n margin-top: -14px;\n margin-left: -2px;\n border: 1px solid rgba(255, 255, 255, 0.2);\n border-bottom: none;\n border-left: none;\n transform: rotate(45deg);\n content: '';\n}\n.du-bois-dark-steps-navigation .du-bois-dark-steps-item::before {\n position: absolute;\n bottom: 0;\n left: 50%;\n display: inline-block;\n width: 0;\n height: 2px;\n background-color: #2272B4;\n transition: width 0.3s, left 0.3s;\n transition-timing-function: ease-out;\n content: '';\n}\n.du-bois-dark-steps-navigation .du-bois-dark-steps-item.du-bois-dark-steps-item-active::before {\n left: 0;\n width: 100%;\n}\n.du-bois-dark-steps-navigation.du-bois-dark-steps-vertical > .du-bois-dark-steps-item {\n margin-right: 0 !important;\n}\n.du-bois-dark-steps-navigation.du-bois-dark-steps-vertical > .du-bois-dark-steps-item::before {\n display: none;\n}\n.du-bois-dark-steps-navigation.du-bois-dark-steps-vertical > .du-bois-dark-steps-item.du-bois-dark-steps-item-active::before {\n top: 0;\n right: 0;\n left: unset;\n display: block;\n width: 3px;\n height: calc(100% - 24px);\n}\n.du-bois-dark-steps-navigation.du-bois-dark-steps-vertical > .du-bois-dark-steps-item::after {\n position: relative;\n top: -2px;\n left: 50%;\n display: block;\n width: 8px;\n height: 8px;\n margin-bottom: 8px;\n text-align: center;\n transform: rotate(135deg);\n}\n.du-bois-dark-steps-navigation.du-bois-dark-steps-vertical > .du-bois-dark-steps-item > .du-bois-dark-steps-item-container > .du-bois-dark-steps-item-tail {\n visibility: hidden;\n}\n.du-bois-dark-steps-with-progress .du-bois-dark-steps-item {\n padding-top: 4px;\n}\n.du-bois-dark-steps-with-progress .du-bois-dark-steps-item .du-bois-dark-steps-item-tail {\n top: 4px !important;\n}\n.du-bois-dark-steps-with-progress.du-bois-dark-steps-horizontal .du-bois-dark-steps-item:first-child {\n padding-bottom: 4px;\n padding-left: 4px;\n}\n.du-bois-dark-steps-with-progress .du-bois-dark-steps-item-icon {\n position: relative;\n}\n.du-bois-dark-steps-with-progress .du-bois-dark-steps-item-icon .du-bois-dark-progress {\n position: absolute;\n top: -5px;\n right: -5px;\n bottom: -5px;\n left: -5px;\n}\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-dark-progress {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #E8ECF0;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: tnum;\n display: inline-block;\n}\n.du-bois-dark-progress-line {\n position: relative;\n width: 100%;\n font-size: 13px;\n}\n.du-bois-dark-progress-steps {\n display: inline-block;\n}\n.du-bois-dark-progress-steps-outer {\n display: flex;\n flex-direction: row;\n align-items: center;\n}\n.du-bois-dark-progress-steps-item {\n flex-shrink: 0;\n min-width: 2px;\n margin-right: 2px;\n background: rgba(255, 255, 255, 0.08);\n transition: all 0.3s;\n}\n.du-bois-dark-progress-steps-item-active {\n background: #177ddc;\n}\n.du-bois-dark-progress-small.du-bois-dark-progress-line,\n.du-bois-dark-progress-small.du-bois-dark-progress-line .du-bois-dark-progress-text .anticon {\n font-size: 12px;\n}\n.du-bois-dark-progress-outer {\n display: inline-block;\n width: 100%;\n margin-right: 0;\n padding-right: 0;\n}\n.du-bois-dark-progress-show-info .du-bois-dark-progress-outer {\n margin-right: calc(-2em - 8px);\n padding-right: calc(2em + 8px);\n}\n.du-bois-dark-progress-inner {\n position: relative;\n display: inline-block;\n width: 100%;\n overflow: hidden;\n vertical-align: middle;\n background-color: rgba(255, 255, 255, 0.08);\n border-radius: 100px;\n}\n.du-bois-dark-progress-circle-trail {\n stroke: rgba(255, 255, 255, 0.08);\n}\n.du-bois-dark-progress-circle-path {\n animation: du-bois-dark-progress-appear 0.3s;\n}\n.du-bois-dark-progress-inner:not(.du-bois-dark-progress-circle-gradient) .du-bois-dark-progress-circle-path {\n stroke: #177ddc;\n}\n.du-bois-dark-progress-success-bg,\n.du-bois-dark-progress-bg {\n position: relative;\n background-color: #177ddc;\n border-radius: 100px;\n transition: all 0.4s cubic-bezier(0.08, 0.82, 0.17, 1) 0s;\n}\n.du-bois-dark-progress-success-bg {\n position: absolute;\n top: 0;\n left: 0;\n background-color: #34824F;\n}\n.du-bois-dark-progress-text {\n display: inline-block;\n width: 2em;\n margin-left: 8px;\n color: #E8ECF0;\n font-size: 1em;\n line-height: 1;\n white-space: nowrap;\n text-align: left;\n vertical-align: middle;\n word-break: normal;\n}\n.du-bois-dark-progress-text .anticon {\n font-size: 13px;\n}\n.du-bois-dark-progress-status-active .du-bois-dark-progress-bg::before {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background: #141414;\n border-radius: 10px;\n opacity: 0;\n animation: du-bois-dark-progress-active 2.4s cubic-bezier(0.23, 1, 0.32, 1) infinite;\n content: '';\n}\n.du-bois-dark-progress-status-exception .du-bois-dark-progress-bg {\n background-color: #E65B77;\n}\n.du-bois-dark-progress-status-exception .du-bois-dark-progress-text {\n color: #E65B77;\n}\n.du-bois-dark-progress-status-exception .du-bois-dark-progress-inner:not(.du-bois-dark-progress-circle-gradient) .du-bois-dark-progress-circle-path {\n stroke: #E65B77;\n}\n.du-bois-dark-progress-status-success .du-bois-dark-progress-bg {\n background-color: #34824F;\n}\n.du-bois-dark-progress-status-success .du-bois-dark-progress-text {\n color: #34824F;\n}\n.du-bois-dark-progress-status-success .du-bois-dark-progress-inner:not(.du-bois-dark-progress-circle-gradient) .du-bois-dark-progress-circle-path {\n stroke: #34824F;\n}\n.du-bois-dark-progress-circle .du-bois-dark-progress-inner {\n position: relative;\n line-height: 1;\n background-color: transparent;\n}\n.du-bois-dark-progress-circle .du-bois-dark-progress-text {\n position: absolute;\n top: 50%;\n left: 50%;\n width: 100%;\n margin: 0;\n padding: 0;\n color: #E8ECF0;\n font-size: 1em;\n line-height: 1;\n white-space: normal;\n text-align: center;\n transform: translate(-50%, -50%);\n}\n.du-bois-dark-progress-circle .du-bois-dark-progress-text .anticon {\n font-size: 1.16666667em;\n}\n.du-bois-dark-progress-circle.du-bois-dark-progress-status-exception .du-bois-dark-progress-text {\n color: #E65B77;\n}\n.du-bois-dark-progress-circle.du-bois-dark-progress-status-success .du-bois-dark-progress-text {\n color: #34824F;\n}\n@keyframes du-bois-dark-progress-active {\n 0% {\n transform: translateX(-100%) scaleX(0);\n opacity: 0.1;\n }\n 20% {\n transform: translateX(-100%) scaleX(0);\n opacity: 0.5;\n }\n 100% {\n transform: translateX(0) scaleX(1);\n opacity: 0;\n }\n}\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-dark-table.du-bois-dark-table-middle {\n font-size: 13px;\n}\n.du-bois-dark-table.du-bois-dark-table-middle .du-bois-dark-table-title,\n.du-bois-dark-table.du-bois-dark-table-middle .du-bois-dark-table-footer,\n.du-bois-dark-table.du-bois-dark-table-middle .du-bois-dark-table-thead > tr > th,\n.du-bois-dark-table.du-bois-dark-table-middle .du-bois-dark-table-tbody > tr > td,\n.du-bois-dark-table.du-bois-dark-table-middle tfoot > tr > th,\n.du-bois-dark-table.du-bois-dark-table-middle tfoot > tr > td {\n padding: 12px 8px;\n}\n.du-bois-dark-table.du-bois-dark-table-middle .du-bois-dark-table-filter-trigger {\n margin-right: -4px;\n}\n.du-bois-dark-table.du-bois-dark-table-middle .du-bois-dark-table-expanded-row-fixed {\n margin: -12px -8px;\n}\n.du-bois-dark-table.du-bois-dark-table-middle .du-bois-dark-table-tbody .du-bois-dark-table-wrapper:only-child .du-bois-dark-table {\n margin: -12px -8px -12px 25px;\n}\n.du-bois-dark-table.du-bois-dark-table-small {\n font-size: 13px;\n}\n.du-bois-dark-table.du-bois-dark-table-small .du-bois-dark-table-title,\n.du-bois-dark-table.du-bois-dark-table-small .du-bois-dark-table-footer,\n.du-bois-dark-table.du-bois-dark-table-small .du-bois-dark-table-thead > tr > th,\n.du-bois-dark-table.du-bois-dark-table-small .du-bois-dark-table-tbody > tr > td,\n.du-bois-dark-table.du-bois-dark-table-small tfoot > tr > th,\n.du-bois-dark-table.du-bois-dark-table-small tfoot > tr > td {\n padding: 8px 8px;\n}\n.du-bois-dark-table.du-bois-dark-table-small .du-bois-dark-table-filter-trigger {\n margin-right: -4px;\n}\n.du-bois-dark-table.du-bois-dark-table-small .du-bois-dark-table-expanded-row-fixed {\n margin: -8px -8px;\n}\n.du-bois-dark-table.du-bois-dark-table-small .du-bois-dark-table-tbody .du-bois-dark-table-wrapper:only-child .du-bois-dark-table {\n margin: -8px -8px -8px 25px;\n}\n.du-bois-dark-table-small .du-bois-dark-table-thead > tr > th {\n background-color: #1d1d1d;\n}\n.du-bois-dark-table-small .du-bois-dark-table-selection-column {\n width: 46px;\n min-width: 46px;\n}\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-title {\n border: 1px solid #303030;\n border-bottom: 0;\n}\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-container {\n border-left: 1px solid #303030;\n}\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-container > .du-bois-dark-table-content > table > thead > tr > th,\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-container > .du-bois-dark-table-header > table > thead > tr > th,\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-container > .du-bois-dark-table-body > table > thead > tr > th,\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-container > .du-bois-dark-table-summary > table > thead > tr > th,\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-container > .du-bois-dark-table-content > table > tbody > tr > td,\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-container > .du-bois-dark-table-header > table > tbody > tr > td,\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-container > .du-bois-dark-table-body > table > tbody > tr > td,\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-container > .du-bois-dark-table-summary > table > tbody > tr > td,\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-container > .du-bois-dark-table-content > table > tfoot > tr > th,\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-container > .du-bois-dark-table-header > table > tfoot > tr > th,\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-container > .du-bois-dark-table-body > table > tfoot > tr > th,\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-container > .du-bois-dark-table-summary > table > tfoot > tr > th,\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-container > .du-bois-dark-table-content > table > tfoot > tr > td,\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-container > .du-bois-dark-table-header > table > tfoot > tr > td,\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-container > .du-bois-dark-table-body > table > tfoot > tr > td,\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-container > .du-bois-dark-table-summary > table > tfoot > tr > td {\n border-right: 1px solid #303030;\n}\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-container > .du-bois-dark-table-content > table > thead > tr:not(:last-child) > th,\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-container > .du-bois-dark-table-header > table > thead > tr:not(:last-child) > th,\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-container > .du-bois-dark-table-body > table > thead > tr:not(:last-child) > th,\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-container > .du-bois-dark-table-summary > table > thead > tr:not(:last-child) > th {\n border-bottom: 1px solid #303030;\n}\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-container > .du-bois-dark-table-content > table > thead > tr > th::before,\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-container > .du-bois-dark-table-header > table > thead > tr > th::before,\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-container > .du-bois-dark-table-body > table > thead > tr > th::before,\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-container > .du-bois-dark-table-summary > table > thead > tr > th::before {\n background-color: transparent !important;\n}\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-container > .du-bois-dark-table-content > table > thead > tr > .du-bois-dark-table-cell-fix-right-first::after,\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-container > .du-bois-dark-table-header > table > thead > tr > .du-bois-dark-table-cell-fix-right-first::after,\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-container > .du-bois-dark-table-body > table > thead > tr > .du-bois-dark-table-cell-fix-right-first::after,\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-container > .du-bois-dark-table-summary > table > thead > tr > .du-bois-dark-table-cell-fix-right-first::after,\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-container > .du-bois-dark-table-content > table > tbody > tr > .du-bois-dark-table-cell-fix-right-first::after,\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-container > .du-bois-dark-table-header > table > tbody > tr > .du-bois-dark-table-cell-fix-right-first::after,\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-container > .du-bois-dark-table-body > table > tbody > tr > .du-bois-dark-table-cell-fix-right-first::after,\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-container > .du-bois-dark-table-summary > table > tbody > tr > .du-bois-dark-table-cell-fix-right-first::after,\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-container > .du-bois-dark-table-content > table > tfoot > tr > .du-bois-dark-table-cell-fix-right-first::after,\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-container > .du-bois-dark-table-header > table > tfoot > tr > .du-bois-dark-table-cell-fix-right-first::after,\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-container > .du-bois-dark-table-body > table > tfoot > tr > .du-bois-dark-table-cell-fix-right-first::after,\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-container > .du-bois-dark-table-summary > table > tfoot > tr > .du-bois-dark-table-cell-fix-right-first::after {\n border-right: 1px solid #303030;\n}\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-container > .du-bois-dark-table-content > table > tbody > tr > td > .du-bois-dark-table-expanded-row-fixed,\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-container > .du-bois-dark-table-header > table > tbody > tr > td > .du-bois-dark-table-expanded-row-fixed,\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-container > .du-bois-dark-table-body > table > tbody > tr > td > .du-bois-dark-table-expanded-row-fixed,\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-container > .du-bois-dark-table-summary > table > tbody > tr > td > .du-bois-dark-table-expanded-row-fixed {\n margin: -16px -17px;\n}\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-container > .du-bois-dark-table-content > table > tbody > tr > td > .du-bois-dark-table-expanded-row-fixed::after,\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-container > .du-bois-dark-table-header > table > tbody > tr > td > .du-bois-dark-table-expanded-row-fixed::after,\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-container > .du-bois-dark-table-body > table > tbody > tr > td > .du-bois-dark-table-expanded-row-fixed::after,\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-container > .du-bois-dark-table-summary > table > tbody > tr > td > .du-bois-dark-table-expanded-row-fixed::after {\n position: absolute;\n top: 0;\n right: 1px;\n bottom: 0;\n border-right: 1px solid #303030;\n content: '';\n}\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-container > .du-bois-dark-table-content > table,\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-container > .du-bois-dark-table-header > table {\n border-top: 1px solid #303030;\n}\n.du-bois-dark-table.du-bois-dark-table-bordered.du-bois-dark-table-scroll-horizontal > .du-bois-dark-table-container > .du-bois-dark-table-body > table > tbody > tr.du-bois-dark-table-expanded-row > td,\n.du-bois-dark-table.du-bois-dark-table-bordered.du-bois-dark-table-scroll-horizontal > .du-bois-dark-table-container > .du-bois-dark-table-body > table > tbody > tr.du-bois-dark-table-placeholder > td {\n border-right: 0;\n}\n.du-bois-dark-table.du-bois-dark-table-bordered.du-bois-dark-table-middle > .du-bois-dark-table-container > .du-bois-dark-table-content > table > tbody > tr > td > .du-bois-dark-table-expanded-row-fixed,\n.du-bois-dark-table.du-bois-dark-table-bordered.du-bois-dark-table-middle > .du-bois-dark-table-container > .du-bois-dark-table-body > table > tbody > tr > td > .du-bois-dark-table-expanded-row-fixed {\n margin: -12px -9px;\n}\n.du-bois-dark-table.du-bois-dark-table-bordered.du-bois-dark-table-small > .du-bois-dark-table-container > .du-bois-dark-table-content > table > tbody > tr > td > .du-bois-dark-table-expanded-row-fixed,\n.du-bois-dark-table.du-bois-dark-table-bordered.du-bois-dark-table-small > .du-bois-dark-table-container > .du-bois-dark-table-body > table > tbody > tr > td > .du-bois-dark-table-expanded-row-fixed {\n margin: -8px -9px;\n}\n.du-bois-dark-table.du-bois-dark-table-bordered > .du-bois-dark-table-footer {\n border: 1px solid #303030;\n border-top: 0;\n}\n.du-bois-dark-table-cell .du-bois-dark-table-container:first-child {\n border-top: 0;\n}\n.du-bois-dark-table-cell-scrollbar {\n box-shadow: 0 1px 0 1px #1d1d1d;\n}\n.du-bois-dark-table-wrapper {\n clear: both;\n max-width: 100%;\n}\n.du-bois-dark-table-wrapper::before {\n display: table;\n content: '';\n}\n.du-bois-dark-table-wrapper::after {\n display: table;\n clear: both;\n content: '';\n}\n.du-bois-dark-table {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #E8ECF0;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: tnum;\n position: relative;\n font-size: 13px;\n background: #141414;\n border-radius: 4px;\n}\n.du-bois-dark-table table {\n width: 100%;\n text-align: left;\n border-radius: 4px 4px 0 0;\n border-collapse: separate;\n border-spacing: 0;\n}\n.du-bois-dark-table-thead > tr > th,\n.du-bois-dark-table-tbody > tr > td,\n.du-bois-dark-table tfoot > tr > th,\n.du-bois-dark-table tfoot > tr > td {\n position: relative;\n padding: 16px 16px;\n overflow-wrap: break-word;\n}\n.du-bois-dark-table-cell-ellipsis {\n overflow: hidden;\n white-space: nowrap;\n text-overflow: ellipsis;\n word-break: keep-all;\n}\n.du-bois-dark-table-cell-ellipsis.du-bois-dark-table-cell-fix-left-last,\n.du-bois-dark-table-cell-ellipsis.du-bois-dark-table-cell-fix-right-first {\n overflow: visible;\n}\n.du-bois-dark-table-cell-ellipsis.du-bois-dark-table-cell-fix-left-last .du-bois-dark-table-cell-content,\n.du-bois-dark-table-cell-ellipsis.du-bois-dark-table-cell-fix-right-first .du-bois-dark-table-cell-content {\n display: block;\n overflow: hidden;\n text-overflow: ellipsis;\n}\n.du-bois-dark-table-cell-ellipsis .du-bois-dark-table-column-title {\n overflow: hidden;\n text-overflow: ellipsis;\n word-break: keep-all;\n}\n.du-bois-dark-table-title {\n padding: 16px 16px;\n}\n.du-bois-dark-table-footer {\n padding: 16px 16px;\n color: rgba(255, 255, 255, 0.85);\n background: rgba(255, 255, 255, 0.04);\n}\n.du-bois-dark-table-thead > tr > th {\n position: relative;\n color: rgba(255, 255, 255, 0.85);\n font-weight: 500;\n text-align: left;\n background: #1d1d1d;\n border-bottom: 1px solid #303030;\n transition: background 0.3s ease;\n}\n.du-bois-dark-table-thead > tr > th[colspan]:not([colspan='1']) {\n text-align: center;\n}\n.du-bois-dark-table-thead > tr > th:not(:last-child):not(.du-bois-dark-table-selection-column):not(.du-bois-dark-table-row-expand-icon-cell):not([colspan])::before {\n position: absolute;\n top: 50%;\n right: 0;\n width: 1px;\n height: 1.6em;\n background-color: rgba(255, 255, 255, 0.08);\n transform: translateY(-50%);\n transition: background-color 0.3s;\n content: '';\n}\n.du-bois-dark-table-thead > tr:not(:last-child) > th[colspan] {\n border-bottom: 0;\n}\n.du-bois-dark-table-tbody > tr > td {\n border-bottom: 1px solid #303030;\n transition: background 0.3s;\n}\n.du-bois-dark-table-tbody > tr > td > .du-bois-dark-table-wrapper:only-child .du-bois-dark-table,\n.du-bois-dark-table-tbody > tr > td > .du-bois-dark-table-expanded-row-fixed > .du-bois-dark-table-wrapper:only-child .du-bois-dark-table {\n margin: -16px -16px -16px 33px;\n}\n.du-bois-dark-table-tbody > tr > td > .du-bois-dark-table-wrapper:only-child .du-bois-dark-table-tbody > tr:last-child > td,\n.du-bois-dark-table-tbody > tr > td > .du-bois-dark-table-expanded-row-fixed > .du-bois-dark-table-wrapper:only-child .du-bois-dark-table-tbody > tr:last-child > td {\n border-bottom: 0;\n}\n.du-bois-dark-table-tbody > tr > td > .du-bois-dark-table-wrapper:only-child .du-bois-dark-table-tbody > tr:last-child > td:first-child,\n.du-bois-dark-table-tbody > tr > td > .du-bois-dark-table-expanded-row-fixed > .du-bois-dark-table-wrapper:only-child .du-bois-dark-table-tbody > tr:last-child > td:first-child,\n.du-bois-dark-table-tbody > tr > td > .du-bois-dark-table-wrapper:only-child .du-bois-dark-table-tbody > tr:last-child > td:last-child,\n.du-bois-dark-table-tbody > tr > td > .du-bois-dark-table-expanded-row-fixed > .du-bois-dark-table-wrapper:only-child .du-bois-dark-table-tbody > tr:last-child > td:last-child {\n border-radius: 0;\n}\n.du-bois-dark-table-tbody > tr.du-bois-dark-table-row:hover > td {\n background: #262626;\n}\n.du-bois-dark-table-tbody > tr.du-bois-dark-table-row-selected > td {\n background: #121921;\n border-color: rgba(0, 0, 0, 0.03);\n}\n.du-bois-dark-table-tbody > tr.du-bois-dark-table-row-selected:hover > td {\n background: #0f141a;\n}\n.du-bois-dark-table-summary {\n position: relative;\n z-index: 2;\n background: #141414;\n}\ndiv.du-bois-dark-table-summary {\n box-shadow: 0 -1px 0 #303030;\n}\n.du-bois-dark-table-summary > tr > th,\n.du-bois-dark-table-summary > tr > td {\n border-bottom: 1px solid #303030;\n}\n.du-bois-dark-table-pagination.du-bois-dark-pagination {\n margin: 16px 0;\n}\n.du-bois-dark-table-pagination {\n display: flex;\n flex-wrap: wrap;\n row-gap: 4px;\n}\n.du-bois-dark-table-pagination > * {\n flex: none;\n}\n.du-bois-dark-table-pagination-left {\n justify-content: flex-start;\n}\n.du-bois-dark-table-pagination-center {\n justify-content: center;\n}\n.du-bois-dark-table-pagination-right {\n justify-content: flex-end;\n}\n.du-bois-dark-table-thead th.du-bois-dark-table-column-has-sorters {\n cursor: pointer;\n transition: all 0.3s;\n}\n.du-bois-dark-table-thead th.du-bois-dark-table-column-has-sorters:hover {\n background: #303030;\n}\n.du-bois-dark-table-thead th.du-bois-dark-table-column-has-sorters:hover::before {\n background-color: transparent !important;\n}\n.du-bois-dark-table-thead th.du-bois-dark-table-column-has-sorters.du-bois-dark-table-cell-fix-left:hover,\n.du-bois-dark-table-thead th.du-bois-dark-table-column-has-sorters.du-bois-dark-table-cell-fix-right:hover {\n background: #222;\n}\n.du-bois-dark-table-thead th.du-bois-dark-table-column-sort {\n background: #262626;\n}\n.du-bois-dark-table-thead th.du-bois-dark-table-column-sort::before {\n background-color: transparent !important;\n}\ntd.du-bois-dark-table-column-sort {\n background: rgba(255, 255, 255, 0.01);\n}\n.du-bois-dark-table-column-title {\n position: relative;\n z-index: 1;\n flex: 1;\n}\n.du-bois-dark-table-column-sorters {\n display: flex;\n flex: auto;\n align-items: center;\n justify-content: space-between;\n}\n.du-bois-dark-table-column-sorters::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n width: 100%;\n height: 100%;\n content: '';\n}\n.du-bois-dark-table-column-sorter {\n color: #bfbfbf;\n font-size: 0;\n transition: color 0.3s;\n}\n.du-bois-dark-table-column-sorter-inner {\n display: inline-flex;\n flex-direction: column;\n align-items: center;\n}\n.du-bois-dark-table-column-sorter-up,\n.du-bois-dark-table-column-sorter-down {\n font-size: 11px;\n}\n.du-bois-dark-table-column-sorter-up.active,\n.du-bois-dark-table-column-sorter-down.active {\n color: #2272B4;\n}\n.du-bois-dark-table-column-sorter-up + .du-bois-dark-table-column-sorter-down {\n margin-top: -0.3em;\n}\n.du-bois-dark-table-column-sorters:hover .du-bois-dark-table-column-sorter {\n color: #a6a6a6;\n}\n.du-bois-dark-table-filter-column {\n display: flex;\n justify-content: space-between;\n}\n.du-bois-dark-table-filter-trigger {\n position: relative;\n display: flex;\n align-items: center;\n margin: -4px -8px -4px 4px;\n padding: 0 4px;\n color: #bfbfbf;\n font-size: 12px;\n border-radius: 4px;\n cursor: pointer;\n transition: all 0.3s;\n}\n.du-bois-dark-table-filter-trigger:hover {\n color: #92A4B3;\n background: #434343;\n}\n.du-bois-dark-table-filter-trigger.active {\n color: #2272B4;\n}\n.du-bois-dark-table-filter-dropdown {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #E8ECF0;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: tnum;\n min-width: 120px;\n background-color: #1f1f1f;\n border-radius: 4px;\n box-shadow: 0 3px 6px -4px rgba(0, 0, 0, 0.48), 0 6px 16px 0 rgba(0, 0, 0, 0.32), 0 9px 28px 8px rgba(0, 0, 0, 0.2);\n}\n.du-bois-dark-table-filter-dropdown .du-bois-dark-dropdown-menu {\n max-height: 264px;\n overflow-x: hidden;\n border: 0;\n box-shadow: none;\n}\n.du-bois-dark-table-filter-dropdown-submenu > ul {\n max-height: calc(100vh - 130px);\n overflow-x: hidden;\n overflow-y: auto;\n}\n.du-bois-dark-table-filter-dropdown .du-bois-dark-checkbox-wrapper + span,\n.du-bois-dark-table-filter-dropdown-submenu .du-bois-dark-checkbox-wrapper + span {\n padding-left: 8px;\n}\n.du-bois-dark-table-filter-dropdown-btns {\n display: flex;\n justify-content: space-between;\n padding: 7px 8px 7px 3px;\n overflow: hidden;\n background-color: #1f1f1f;\n border-top: 1px solid #303030;\n}\n.du-bois-dark-table-selection-col {\n width: 32px;\n}\n.du-bois-dark-table-bordered .du-bois-dark-table-selection-col {\n width: 50px;\n}\ntable tr th.du-bois-dark-table-selection-column,\ntable tr td.du-bois-dark-table-selection-column {\n padding-right: 4px;\n padding-left: 4px;\n text-align: center;\n}\ntable tr th.du-bois-dark-table-selection-column .du-bois-dark-radio-wrapper,\ntable tr td.du-bois-dark-table-selection-column .du-bois-dark-radio-wrapper {\n margin-right: 0;\n}\ntable tr th.du-bois-dark-table-selection-column::after {\n background-color: transparent !important;\n}\n.du-bois-dark-table-selection {\n position: relative;\n display: inline-flex;\n flex-direction: column;\n}\n.du-bois-dark-table-selection-extra {\n position: absolute;\n top: 0;\n z-index: 1;\n cursor: pointer;\n transition: all 0.3s;\n margin-inline-start: 100%;\n padding-inline-start: 0px;\n}\n.du-bois-dark-table-selection-extra .anticon {\n color: #bfbfbf;\n font-size: 10px;\n}\n.du-bois-dark-table-selection-extra .anticon:hover {\n color: #a6a6a6;\n}\n.du-bois-dark-table-expand-icon-col {\n width: 48px;\n}\n.du-bois-dark-table-row-expand-icon-cell {\n text-align: center;\n}\n.du-bois-dark-table-row-indent {\n float: left;\n height: 1px;\n}\n.du-bois-dark-table-row-expand-icon {\n color: #2272B4;\n text-decoration: none;\n cursor: pointer;\n transition: color 0.3s;\n position: relative;\n display: inline-flex;\n float: left;\n box-sizing: border-box;\n width: 17px;\n height: 17px;\n padding: 0;\n color: inherit;\n line-height: 17px;\n background: transparent;\n border: 1px solid #303030;\n border-radius: 4px;\n outline: none;\n transform: scale(0.94117647);\n transition: all 0.3s;\n user-select: none;\n}\n.du-bois-dark-table-row-expand-icon:focus,\n.du-bois-dark-table-row-expand-icon:hover {\n color: #1d517c;\n}\n.du-bois-dark-table-row-expand-icon:active {\n color: #3f81b1;\n}\n.du-bois-dark-table-row-expand-icon:focus,\n.du-bois-dark-table-row-expand-icon:hover,\n.du-bois-dark-table-row-expand-icon:active {\n border-color: currentColor;\n}\n.du-bois-dark-table-row-expand-icon::before,\n.du-bois-dark-table-row-expand-icon::after {\n position: absolute;\n background: currentColor;\n transition: transform 0.3s ease-out;\n content: '';\n}\n.du-bois-dark-table-row-expand-icon::before {\n top: 7px;\n right: 3px;\n left: 3px;\n height: 1px;\n}\n.du-bois-dark-table-row-expand-icon::after {\n top: 3px;\n bottom: 3px;\n left: 7px;\n width: 1px;\n transform: rotate(90deg);\n}\n.du-bois-dark-table-row-expand-icon-collapsed::before {\n transform: rotate(-180deg);\n}\n.du-bois-dark-table-row-expand-icon-collapsed::after {\n transform: rotate(0deg);\n}\n.du-bois-dark-table-row-expand-icon-spaced {\n background: transparent;\n border: 0;\n visibility: hidden;\n}\n.du-bois-dark-table-row-expand-icon-spaced::before,\n.du-bois-dark-table-row-expand-icon-spaced::after {\n display: none;\n content: none;\n}\n.du-bois-dark-table-row-indent + .du-bois-dark-table-row-expand-icon {\n margin-top: 121.5px;\n margin-right: 4px;\n}\ntr.du-bois-dark-table-expanded-row > td,\ntr.du-bois-dark-table-expanded-row:hover > td {\n background: #1d1d1d;\n}\ntr.du-bois-dark-table-expanded-row .du-bois-dark-descriptions-view {\n display: flex;\n}\ntr.du-bois-dark-table-expanded-row .du-bois-dark-descriptions-view table {\n flex: auto;\n width: auto;\n}\n.du-bois-dark-table .du-bois-dark-table-expanded-row-fixed {\n position: relative;\n margin: -16px -16px;\n padding: 16px 16px;\n}\n.du-bois-dark-table-tbody > tr.du-bois-dark-table-placeholder {\n text-align: center;\n}\n.du-bois-dark-table-empty .du-bois-dark-table-tbody > tr.du-bois-dark-table-placeholder {\n color: rgba(255, 255, 255, 0.3);\n}\n.du-bois-dark-table-tbody > tr.du-bois-dark-table-placeholder:hover > td {\n background: #141414;\n}\n.du-bois-dark-table-cell-fix-left,\n.du-bois-dark-table-cell-fix-right {\n position: -webkit-sticky !important;\n position: sticky !important;\n z-index: 2;\n background: #141414;\n}\n.du-bois-dark-table-cell-fix-left-first::after,\n.du-bois-dark-table-cell-fix-left-last::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: -1px;\n width: 30px;\n transform: translateX(100%);\n transition: box-shadow 0.3s;\n content: '';\n pointer-events: none;\n}\n.du-bois-dark-table-cell-fix-right-first::after,\n.du-bois-dark-table-cell-fix-right-last::after {\n position: absolute;\n top: 0;\n bottom: -1px;\n left: 0;\n width: 30px;\n transform: translateX(-100%);\n transition: box-shadow 0.3s;\n content: '';\n pointer-events: none;\n}\n.du-bois-dark-table .du-bois-dark-table-container::before,\n.du-bois-dark-table .du-bois-dark-table-container::after {\n position: absolute;\n top: 0;\n bottom: 0;\n z-index: 1;\n width: 30px;\n transition: box-shadow 0.3s;\n content: '';\n pointer-events: none;\n}\n.du-bois-dark-table .du-bois-dark-table-container::before {\n left: 0;\n}\n.du-bois-dark-table .du-bois-dark-table-container::after {\n right: 0;\n}\n.du-bois-dark-table-ping-left:not(.du-bois-dark-table-has-fix-left) .du-bois-dark-table-container {\n position: relative;\n}\n.du-bois-dark-table-ping-left:not(.du-bois-dark-table-has-fix-left) .du-bois-dark-table-container::before {\n box-shadow: inset 10px 0 8px -8px rgba(0, 0, 0, 0.45);\n}\n.du-bois-dark-table-ping-left .du-bois-dark-table-cell-fix-left-first::after,\n.du-bois-dark-table-ping-left .du-bois-dark-table-cell-fix-left-last::after {\n box-shadow: inset 10px 0 8px -8px rgba(0, 0, 0, 0.45);\n}\n.du-bois-dark-table-ping-left .du-bois-dark-table-cell-fix-left-last::before {\n background-color: transparent !important;\n}\n.du-bois-dark-table-ping-right:not(.du-bois-dark-table-has-fix-right) .du-bois-dark-table-container {\n position: relative;\n}\n.du-bois-dark-table-ping-right:not(.du-bois-dark-table-has-fix-right) .du-bois-dark-table-container::after {\n box-shadow: inset -10px 0 8px -8px rgba(0, 0, 0, 0.45);\n}\n.du-bois-dark-table-ping-right .du-bois-dark-table-cell-fix-right-first::after,\n.du-bois-dark-table-ping-right .du-bois-dark-table-cell-fix-right-last::after {\n box-shadow: inset -10px 0 8px -8px rgba(0, 0, 0, 0.45);\n}\n.du-bois-dark-table-sticky-holder {\n position: sticky;\n z-index: 3;\n background: #141414;\n}\n.du-bois-dark-table-sticky-scroll {\n position: sticky;\n bottom: 0;\n z-index: 3;\n display: flex;\n align-items: center;\n background: #fcfcfc;\n border-top: 1px solid #303030;\n opacity: 0.6;\n}\n.du-bois-dark-table-sticky-scroll:hover {\n transform-origin: center bottom;\n}\n.du-bois-dark-table-sticky-scroll-bar {\n height: 8px;\n background-color: rgba(0, 0, 0, 0.35);\n border-radius: 4px;\n}\n.du-bois-dark-table-sticky-scroll-bar:hover {\n background-color: rgba(0, 0, 0, 0.8);\n}\n.du-bois-dark-table-sticky-scroll-bar-active {\n background-color: rgba(0, 0, 0, 0.8);\n}\n@media all and (-ms-high-contrast: none) {\n .du-bois-dark-table-ping-left .du-bois-dark-table-cell-fix-left-last::after {\n box-shadow: none !important;\n }\n .du-bois-dark-table-ping-right .du-bois-dark-table-cell-fix-right-first::after {\n box-shadow: none !important;\n }\n}\n.du-bois-dark-table {\n /* title + table */\n /* table */\n /* table + footer */\n}\n.du-bois-dark-table-title {\n border-radius: 4px 4px 0 0;\n}\n.du-bois-dark-table-title + .du-bois-dark-table-container {\n border-top-left-radius: 0;\n border-top-right-radius: 0;\n}\n.du-bois-dark-table-title + .du-bois-dark-table-container table > thead > tr:first-child th:first-child {\n border-radius: 0;\n}\n.du-bois-dark-table-title + .du-bois-dark-table-container table > thead > tr:first-child th:last-child {\n border-radius: 0;\n}\n.du-bois-dark-table-container {\n border-top-left-radius: 4px;\n border-top-right-radius: 4px;\n}\n.du-bois-dark-table-container table > thead > tr:first-child th:first-child {\n border-top-left-radius: 4px;\n}\n.du-bois-dark-table-container table > thead > tr:first-child th:last-child {\n border-top-right-radius: 4px;\n}\n.du-bois-dark-table-footer {\n border-radius: 0 0 4px 4px;\n}\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-dark-spin {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #E8ECF0;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: tnum;\n position: absolute;\n display: none;\n color: #2272B4;\n text-align: center;\n vertical-align: middle;\n opacity: 0;\n transition: transform 0.3s cubic-bezier(0.78, 0.14, 0.15, 0.86);\n}\n.du-bois-dark-spin-spinning {\n position: static;\n display: inline-block;\n opacity: 1;\n}\n.du-bois-dark-spin-nested-loading {\n position: relative;\n}\n.du-bois-dark-spin-nested-loading > div > .du-bois-dark-spin {\n position: absolute;\n top: 0;\n left: 0;\n z-index: 4;\n display: block;\n width: 100%;\n height: 100%;\n max-height: 400px;\n}\n.du-bois-dark-spin-nested-loading > div > .du-bois-dark-spin .du-bois-dark-spin-dot {\n position: absolute;\n top: 50%;\n left: 50%;\n margin: -10px;\n}\n.du-bois-dark-spin-nested-loading > div > .du-bois-dark-spin .du-bois-dark-spin-text {\n position: absolute;\n top: 50%;\n width: 100%;\n padding-top: 5.5px;\n text-shadow: 0 1px 2px #141414;\n}\n.du-bois-dark-spin-nested-loading > div > .du-bois-dark-spin.du-bois-dark-spin-show-text .du-bois-dark-spin-dot {\n margin-top: -20px;\n}\n.du-bois-dark-spin-nested-loading > div > .du-bois-dark-spin-sm .du-bois-dark-spin-dot {\n margin: -7px;\n}\n.du-bois-dark-spin-nested-loading > div > .du-bois-dark-spin-sm .du-bois-dark-spin-text {\n padding-top: 2.5px;\n}\n.du-bois-dark-spin-nested-loading > div > .du-bois-dark-spin-sm.du-bois-dark-spin-show-text .du-bois-dark-spin-dot {\n margin-top: -17px;\n}\n.du-bois-dark-spin-nested-loading > div > .du-bois-dark-spin-lg .du-bois-dark-spin-dot {\n margin: -16px;\n}\n.du-bois-dark-spin-nested-loading > div > .du-bois-dark-spin-lg .du-bois-dark-spin-text {\n padding-top: 11.5px;\n}\n.du-bois-dark-spin-nested-loading > div > .du-bois-dark-spin-lg.du-bois-dark-spin-show-text .du-bois-dark-spin-dot {\n margin-top: -26px;\n}\n.du-bois-dark-spin-container {\n position: relative;\n transition: opacity 0.3s;\n}\n.du-bois-dark-spin-container::after {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n z-index: 10;\n display: none \\9;\n width: 100%;\n height: 100%;\n background: #141414;\n opacity: 0;\n transition: all 0.3s;\n content: '';\n pointer-events: none;\n}\n.du-bois-dark-spin-blur {\n clear: both;\n overflow: hidden;\n opacity: 0.5;\n user-select: none;\n pointer-events: none;\n}\n.du-bois-dark-spin-blur::after {\n opacity: 0.4;\n pointer-events: auto;\n}\n.du-bois-dark-spin-tip {\n color: #92A4B3;\n}\n.du-bois-dark-spin-dot {\n position: relative;\n display: inline-block;\n font-size: 20px;\n width: 1em;\n height: 1em;\n}\n.du-bois-dark-spin-dot-item {\n position: absolute;\n display: block;\n width: 9px;\n height: 9px;\n background-color: #2272B4;\n border-radius: 100%;\n transform: scale(0.75);\n transform-origin: 50% 50%;\n opacity: 0.3;\n animation: antSpinMove 1s infinite linear alternate;\n}\n.du-bois-dark-spin-dot-item:nth-child(1) {\n top: 0;\n left: 0;\n}\n.du-bois-dark-spin-dot-item:nth-child(2) {\n top: 0;\n right: 0;\n animation-delay: 0.4s;\n}\n.du-bois-dark-spin-dot-item:nth-child(3) {\n right: 0;\n bottom: 0;\n animation-delay: 0.8s;\n}\n.du-bois-dark-spin-dot-item:nth-child(4) {\n bottom: 0;\n left: 0;\n animation-delay: 1.2s;\n}\n.du-bois-dark-spin-dot-spin {\n transform: rotate(45deg);\n animation: antRotate 1.2s infinite linear;\n}\n.du-bois-dark-spin-sm .du-bois-dark-spin-dot {\n font-size: 14px;\n}\n.du-bois-dark-spin-sm .du-bois-dark-spin-dot i {\n width: 6px;\n height: 6px;\n}\n.du-bois-dark-spin-lg .du-bois-dark-spin-dot {\n font-size: 32px;\n}\n.du-bois-dark-spin-lg .du-bois-dark-spin-dot i {\n width: 14px;\n height: 14px;\n}\n.du-bois-dark-spin.du-bois-dark-spin-show-text .du-bois-dark-spin-text {\n display: block;\n}\n@media all and (-ms-high-contrast: none), (-ms-high-contrast: active) {\n /* IE10+ */\n .du-bois-dark-spin-blur {\n background: #141414;\n opacity: 0.5;\n }\n}\n@keyframes antSpinMove {\n}\n@keyframes antRotate {\n}\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-dark-pagination {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #E8ECF0;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: tnum;\n}\n.du-bois-dark-pagination ul,\n.du-bois-dark-pagination ol {\n margin: 0;\n padding: 0;\n list-style: none;\n}\n.du-bois-dark-pagination::after {\n display: block;\n clear: both;\n height: 0;\n overflow: hidden;\n visibility: hidden;\n content: ' ';\n}\n.du-bois-dark-pagination-total-text {\n display: inline-block;\n height: 40px;\n margin-right: 8px;\n line-height: 38px;\n vertical-align: middle;\n}\n.du-bois-dark-pagination-item {\n display: inline-block;\n min-width: 40px;\n height: 40px;\n margin-right: 8px;\n font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, 'Helvetica Neue', Arial, 'Noto Sans', sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol', 'Noto Color Emoji';\n line-height: 38px;\n text-align: center;\n vertical-align: middle;\n list-style: none;\n background-color: transparent;\n border: 1px solid #1F272D;\n border-radius: 4px;\n outline: 0;\n cursor: pointer;\n user-select: none;\n}\n.du-bois-dark-pagination-item a {\n display: block;\n padding: 0 6px;\n color: #E8ECF0;\n transition: none;\n}\n.du-bois-dark-pagination-item a:hover {\n text-decoration: none;\n}\n.du-bois-dark-pagination-item:focus-visible,\n.du-bois-dark-pagination-item:hover {\n border-color: #2272B4;\n transition: all 0.3s;\n}\n.du-bois-dark-pagination-item:focus-visible a,\n.du-bois-dark-pagination-item:hover a {\n color: #2272B4;\n}\n.du-bois-dark-pagination-item-active {\n font-weight: 500;\n background: transparent;\n border-color: #2272B4;\n}\n.du-bois-dark-pagination-item-active a {\n color: #2272B4;\n}\n.du-bois-dark-pagination-item-active:focus-visible,\n.du-bois-dark-pagination-item-active:hover {\n border-color: #1d517c;\n}\n.du-bois-dark-pagination-item-active:focus-visible a,\n.du-bois-dark-pagination-item-active:hover a {\n color: #1d517c;\n}\n.du-bois-dark-pagination-jump-prev,\n.du-bois-dark-pagination-jump-next {\n outline: 0;\n}\n.du-bois-dark-pagination-jump-prev .du-bois-dark-pagination-item-container,\n.du-bois-dark-pagination-jump-next .du-bois-dark-pagination-item-container {\n position: relative;\n}\n.du-bois-dark-pagination-jump-prev .du-bois-dark-pagination-item-container .du-bois-dark-pagination-item-link-icon,\n.du-bois-dark-pagination-jump-next .du-bois-dark-pagination-item-container .du-bois-dark-pagination-item-link-icon {\n color: #2272B4;\n font-size: 12px;\n letter-spacing: -1px;\n opacity: 0;\n transition: all 0.2s;\n}\n.du-bois-dark-pagination-jump-prev .du-bois-dark-pagination-item-container .du-bois-dark-pagination-item-link-icon-svg,\n.du-bois-dark-pagination-jump-next .du-bois-dark-pagination-item-container .du-bois-dark-pagination-item-link-icon-svg {\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n margin: auto;\n}\n.du-bois-dark-pagination-jump-prev .du-bois-dark-pagination-item-container .du-bois-dark-pagination-item-ellipsis,\n.du-bois-dark-pagination-jump-next .du-bois-dark-pagination-item-container .du-bois-dark-pagination-item-ellipsis {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n display: block;\n margin: auto;\n color: rgba(255, 255, 255, 0.3);\n font-family: Arial, Helvetica, sans-serif;\n letter-spacing: 2px;\n text-align: center;\n text-indent: 0.13em;\n opacity: 1;\n transition: all 0.2s;\n}\n.du-bois-dark-pagination-jump-prev:focus-visible .du-bois-dark-pagination-item-link-icon,\n.du-bois-dark-pagination-jump-next:focus-visible .du-bois-dark-pagination-item-link-icon,\n.du-bois-dark-pagination-jump-prev:hover .du-bois-dark-pagination-item-link-icon,\n.du-bois-dark-pagination-jump-next:hover .du-bois-dark-pagination-item-link-icon {\n opacity: 1;\n}\n.du-bois-dark-pagination-jump-prev:focus-visible .du-bois-dark-pagination-item-ellipsis,\n.du-bois-dark-pagination-jump-next:focus-visible .du-bois-dark-pagination-item-ellipsis,\n.du-bois-dark-pagination-jump-prev:hover .du-bois-dark-pagination-item-ellipsis,\n.du-bois-dark-pagination-jump-next:hover .du-bois-dark-pagination-item-ellipsis {\n opacity: 0;\n}\n.du-bois-dark-pagination-prev,\n.du-bois-dark-pagination-jump-prev,\n.du-bois-dark-pagination-jump-next {\n margin-right: 8px;\n}\n.du-bois-dark-pagination-prev,\n.du-bois-dark-pagination-next,\n.du-bois-dark-pagination-jump-prev,\n.du-bois-dark-pagination-jump-next {\n display: inline-block;\n min-width: 40px;\n height: 40px;\n color: #E8ECF0;\n font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, 'Helvetica Neue', Arial, 'Noto Sans', sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol', 'Noto Color Emoji';\n line-height: 40px;\n text-align: center;\n vertical-align: middle;\n list-style: none;\n border-radius: 4px;\n cursor: pointer;\n transition: all 0.3s;\n}\n.du-bois-dark-pagination-prev,\n.du-bois-dark-pagination-next {\n font-family: Arial, Helvetica, sans-serif;\n outline: 0;\n}\n.du-bois-dark-pagination-prev button,\n.du-bois-dark-pagination-next button {\n color: #E8ECF0;\n cursor: pointer;\n user-select: none;\n}\n.du-bois-dark-pagination-prev:hover button,\n.du-bois-dark-pagination-next:hover button {\n border-color: #1d517c;\n}\n.du-bois-dark-pagination-prev .du-bois-dark-pagination-item-link,\n.du-bois-dark-pagination-next .du-bois-dark-pagination-item-link {\n display: block;\n width: 100%;\n height: 100%;\n padding: 0;\n font-size: 12px;\n text-align: center;\n background-color: transparent;\n border: 1px solid #1F272D;\n border-radius: 4px;\n outline: none;\n transition: all 0.3s;\n}\n.du-bois-dark-pagination-prev:focus-visible .du-bois-dark-pagination-item-link,\n.du-bois-dark-pagination-next:focus-visible .du-bois-dark-pagination-item-link,\n.du-bois-dark-pagination-prev:hover .du-bois-dark-pagination-item-link,\n.du-bois-dark-pagination-next:hover .du-bois-dark-pagination-item-link {\n color: #2272B4;\n border-color: #2272B4;\n}\n.du-bois-dark-pagination-disabled,\n.du-bois-dark-pagination-disabled:hover,\n.du-bois-dark-pagination-disabled:focus-visible {\n cursor: not-allowed;\n}\n.du-bois-dark-pagination-disabled .du-bois-dark-pagination-item-link,\n.du-bois-dark-pagination-disabled:hover .du-bois-dark-pagination-item-link,\n.du-bois-dark-pagination-disabled:focus-visible .du-bois-dark-pagination-item-link {\n color: rgba(255, 255, 255, 0.3);\n border-color: #1F272D;\n cursor: not-allowed;\n}\n.du-bois-dark-pagination-slash {\n margin: 0 10px 0 5px;\n}\n.du-bois-dark-pagination-options {\n display: inline-block;\n margin-left: 16px;\n vertical-align: middle;\n}\n@media all and (-ms-high-contrast: none) {\n .du-bois-dark-pagination-options *::-ms-backdrop,\n .du-bois-dark-pagination-options {\n vertical-align: top;\n }\n}\n.du-bois-dark-pagination-options-size-changer.du-bois-dark-select {\n display: inline-block;\n width: auto;\n}\n.du-bois-dark-pagination-options-quick-jumper {\n display: inline-block;\n height: 40px;\n margin-left: 8px;\n line-height: 40px;\n vertical-align: top;\n}\n.du-bois-dark-pagination-options-quick-jumper input {\n position: relative;\n display: inline-block;\n width: 100%;\n min-width: 0;\n padding: 5px 12px;\n color: #E8ECF0;\n font-size: 13px;\n line-height: 20px;\n background-color: transparent;\n background-image: none;\n border: 1px solid #1F272D;\n border-radius: 4px;\n transition: all 0.3s;\n width: 50px;\n height: 40px;\n margin: 0 8px;\n}\n.du-bois-dark-pagination-options-quick-jumper input::-moz-placeholder {\n opacity: 1;\n}\n.du-bois-dark-pagination-options-quick-jumper input::placeholder {\n color: rgba(255, 255, 255, 0.3);\n}\n.du-bois-dark-pagination-options-quick-jumper input:placeholder-shown {\n text-overflow: ellipsis;\n}\n.du-bois-dark-pagination-options-quick-jumper input:hover {\n border-color: #1d517c;\n border-right-width: 1px !important;\n}\n.du-bois-dark-pagination-options-quick-jumper input:focus,\n.du-bois-dark-pagination-options-quick-jumper input-focused {\n border-color: #2272B4;\n border-right-width: 1px !important;\n outline: 0;\n box-shadow: 0 0 0 2px rgba(34, 114, 180, 0.2);\n}\n.du-bois-dark-pagination-options-quick-jumper input-disabled {\n color: rgba(255, 255, 255, 0.3);\n background-color: rgba(255, 255, 255, 0.08);\n cursor: not-allowed;\n opacity: 1;\n}\n.du-bois-dark-pagination-options-quick-jumper input-disabled:hover {\n border-color: #1F272D;\n border-right-width: 1px !important;\n}\n.du-bois-dark-pagination-options-quick-jumper input[disabled] {\n color: rgba(255, 255, 255, 0.3);\n background-color: rgba(255, 255, 255, 0.08);\n cursor: not-allowed;\n opacity: 1;\n}\n.du-bois-dark-pagination-options-quick-jumper input[disabled]:hover {\n border-color: #1F272D;\n border-right-width: 1px !important;\n}\n.du-bois-dark-pagination-options-quick-jumper input-borderless,\n.du-bois-dark-pagination-options-quick-jumper input-borderless:hover,\n.du-bois-dark-pagination-options-quick-jumper input-borderless:focus,\n.du-bois-dark-pagination-options-quick-jumper input-borderless-focused,\n.du-bois-dark-pagination-options-quick-jumper input-borderless-disabled,\n.du-bois-dark-pagination-options-quick-jumper input-borderless[disabled] {\n background-color: transparent;\n border: none;\n box-shadow: none;\n}\ntextarea.du-bois-dark-pagination-options-quick-jumper input {\n max-width: 100%;\n height: auto;\n min-height: 40px;\n line-height: 20px;\n vertical-align: bottom;\n transition: all 0.3s, height 0s;\n}\n.du-bois-dark-pagination-options-quick-jumper input-lg {\n padding: 5px 12px;\n font-size: 18px;\n}\n.du-bois-dark-pagination-options-quick-jumper input-sm {\n padding: 5px 12px;\n}\n.du-bois-dark-pagination-simple .du-bois-dark-pagination-prev,\n.du-bois-dark-pagination-simple .du-bois-dark-pagination-next {\n height: 24px;\n line-height: 24px;\n vertical-align: top;\n}\n.du-bois-dark-pagination-simple .du-bois-dark-pagination-prev .du-bois-dark-pagination-item-link,\n.du-bois-dark-pagination-simple .du-bois-dark-pagination-next .du-bois-dark-pagination-item-link {\n height: 24px;\n background-color: transparent;\n border: 0;\n}\n.du-bois-dark-pagination-simple .du-bois-dark-pagination-prev .du-bois-dark-pagination-item-link::after,\n.du-bois-dark-pagination-simple .du-bois-dark-pagination-next .du-bois-dark-pagination-item-link::after {\n height: 24px;\n line-height: 24px;\n}\n.du-bois-dark-pagination-simple .du-bois-dark-pagination-simple-pager {\n display: inline-block;\n height: 24px;\n margin-right: 8px;\n}\n.du-bois-dark-pagination-simple .du-bois-dark-pagination-simple-pager input {\n box-sizing: border-box;\n height: 100%;\n margin-right: 8px;\n padding: 0 6px;\n text-align: center;\n background-color: transparent;\n border: 1px solid #1F272D;\n border-radius: 4px;\n outline: none;\n transition: border-color 0.3s;\n}\n.du-bois-dark-pagination-simple .du-bois-dark-pagination-simple-pager input:hover {\n border-color: #2272B4;\n}\n.du-bois-dark-pagination-simple .du-bois-dark-pagination-simple-pager input[disabled] {\n color: rgba(255, 255, 255, 0.3);\n background: rgba(255, 255, 255, 0.08);\n border-color: #1F272D;\n cursor: not-allowed;\n}\n.du-bois-dark-pagination.mini .du-bois-dark-pagination-total-text,\n.du-bois-dark-pagination.mini .du-bois-dark-pagination-simple-pager {\n height: 24px;\n line-height: 24px;\n}\n.du-bois-dark-pagination.mini .du-bois-dark-pagination-item {\n min-width: 24px;\n height: 24px;\n margin: 0;\n line-height: 22px;\n}\n.du-bois-dark-pagination.mini .du-bois-dark-pagination-item:not(.du-bois-dark-pagination-item-active) {\n background: transparent;\n border-color: transparent;\n}\n.du-bois-dark-pagination.mini .du-bois-dark-pagination-prev,\n.du-bois-dark-pagination.mini .du-bois-dark-pagination-next {\n min-width: 24px;\n height: 24px;\n margin: 0;\n line-height: 24px;\n}\n.du-bois-dark-pagination.mini .du-bois-dark-pagination-prev .du-bois-dark-pagination-item-link,\n.du-bois-dark-pagination.mini .du-bois-dark-pagination-next .du-bois-dark-pagination-item-link {\n background: transparent;\n border-color: transparent;\n}\n.du-bois-dark-pagination.mini .du-bois-dark-pagination-prev .du-bois-dark-pagination-item-link::after,\n.du-bois-dark-pagination.mini .du-bois-dark-pagination-next .du-bois-dark-pagination-item-link::after {\n height: 24px;\n line-height: 24px;\n}\n.du-bois-dark-pagination.mini .du-bois-dark-pagination-jump-prev,\n.du-bois-dark-pagination.mini .du-bois-dark-pagination-jump-next {\n height: 24px;\n margin-right: 0;\n line-height: 24px;\n}\n.du-bois-dark-pagination.mini .du-bois-dark-pagination-options {\n margin-left: 2px;\n}\n.du-bois-dark-pagination.mini .du-bois-dark-pagination-options-size-changer {\n top: 0px;\n}\n.du-bois-dark-pagination.mini .du-bois-dark-pagination-options-quick-jumper {\n height: 24px;\n line-height: 24px;\n}\n.du-bois-dark-pagination.mini .du-bois-dark-pagination-options-quick-jumper input {\n padding: 5px 12px;\n width: 44px;\n height: 32px;\n}\n.du-bois-dark-pagination.du-bois-dark-pagination-disabled {\n cursor: not-allowed;\n}\n.du-bois-dark-pagination.du-bois-dark-pagination-disabled .du-bois-dark-pagination-item {\n background: rgba(255, 255, 255, 0.08);\n border-color: #1F272D;\n cursor: not-allowed;\n}\n.du-bois-dark-pagination.du-bois-dark-pagination-disabled .du-bois-dark-pagination-item a {\n color: rgba(255, 255, 255, 0.3);\n background: transparent;\n border: none;\n cursor: not-allowed;\n}\n.du-bois-dark-pagination.du-bois-dark-pagination-disabled .du-bois-dark-pagination-item-active {\n background: rgba(255, 255, 255, 0.25);\n}\n.du-bois-dark-pagination.du-bois-dark-pagination-disabled .du-bois-dark-pagination-item-active a {\n color: #000;\n}\n.du-bois-dark-pagination.du-bois-dark-pagination-disabled .du-bois-dark-pagination-item-link {\n color: rgba(255, 255, 255, 0.3);\n background: rgba(255, 255, 255, 0.08);\n border-color: #1F272D;\n cursor: not-allowed;\n}\n.du-bois-dark-pagination-simple.du-bois-dark-pagination.du-bois-dark-pagination-disabled .du-bois-dark-pagination-item-link {\n background: transparent;\n}\n.du-bois-dark-pagination.du-bois-dark-pagination-disabled .du-bois-dark-pagination-item-link-icon {\n opacity: 0;\n}\n.du-bois-dark-pagination.du-bois-dark-pagination-disabled .du-bois-dark-pagination-item-ellipsis {\n opacity: 1;\n}\n.du-bois-dark-pagination.du-bois-dark-pagination-disabled .du-bois-dark-pagination-simple-pager {\n color: rgba(255, 255, 255, 0.3);\n}\n@media only screen and (max-width: 992px) {\n .du-bois-dark-pagination-item-after-jump-prev,\n .du-bois-dark-pagination-item-before-jump-next {\n display: none;\n }\n}\n@media only screen and (max-width: 576px) {\n .du-bois-dark-pagination-options {\n display: none;\n }\n}\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-dark-tabs-small > .du-bois-dark-tabs-nav .du-bois-dark-tabs-tab {\n padding: 4px 0;\n font-size: 13px;\n}\n.du-bois-dark-tabs-large > .du-bois-dark-tabs-nav .du-bois-dark-tabs-tab {\n padding: 8px 0;\n font-size: 18px;\n}\n.du-bois-dark-tabs-card.du-bois-dark-tabs-small > .du-bois-dark-tabs-nav .du-bois-dark-tabs-tab {\n padding: 6px 8px;\n}\n.du-bois-dark-tabs-card.du-bois-dark-tabs-large > .du-bois-dark-tabs-nav .du-bois-dark-tabs-tab {\n padding: 7px 8px 6px;\n}\n.du-bois-dark-tabs-top,\n.du-bois-dark-tabs-bottom {\n flex-direction: column;\n}\n.du-bois-dark-tabs-top > .du-bois-dark-tabs-nav,\n.du-bois-dark-tabs-bottom > .du-bois-dark-tabs-nav,\n.du-bois-dark-tabs-top > div > .du-bois-dark-tabs-nav,\n.du-bois-dark-tabs-bottom > div > .du-bois-dark-tabs-nav {\n margin: 0 0 16px 0;\n}\n.du-bois-dark-tabs-top > .du-bois-dark-tabs-nav::before,\n.du-bois-dark-tabs-bottom > .du-bois-dark-tabs-nav::before,\n.du-bois-dark-tabs-top > div > .du-bois-dark-tabs-nav::before,\n.du-bois-dark-tabs-bottom > div > .du-bois-dark-tabs-nav::before {\n position: absolute;\n right: 0;\n left: 0;\n border-bottom: 1px solid #303030;\n content: '';\n}\n.du-bois-dark-tabs-top > .du-bois-dark-tabs-nav .du-bois-dark-tabs-ink-bar,\n.du-bois-dark-tabs-bottom > .du-bois-dark-tabs-nav .du-bois-dark-tabs-ink-bar,\n.du-bois-dark-tabs-top > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-ink-bar,\n.du-bois-dark-tabs-bottom > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-ink-bar {\n height: 2px;\n}\n.du-bois-dark-tabs-top > .du-bois-dark-tabs-nav .du-bois-dark-tabs-ink-bar-animated,\n.du-bois-dark-tabs-bottom > .du-bois-dark-tabs-nav .du-bois-dark-tabs-ink-bar-animated,\n.du-bois-dark-tabs-top > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-ink-bar-animated,\n.du-bois-dark-tabs-bottom > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-ink-bar-animated {\n transition: width 0.3s, left 0.3s, right 0.3s;\n}\n.du-bois-dark-tabs-top > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap::before,\n.du-bois-dark-tabs-bottom > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap::before,\n.du-bois-dark-tabs-top > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap::before,\n.du-bois-dark-tabs-bottom > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap::before,\n.du-bois-dark-tabs-top > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap::after,\n.du-bois-dark-tabs-bottom > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap::after,\n.du-bois-dark-tabs-top > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap::after,\n.du-bois-dark-tabs-bottom > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap::after {\n top: 0;\n bottom: 0;\n width: 30px;\n}\n.du-bois-dark-tabs-top > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap::before,\n.du-bois-dark-tabs-bottom > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap::before,\n.du-bois-dark-tabs-top > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap::before,\n.du-bois-dark-tabs-bottom > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap::before {\n left: 0;\n box-shadow: inset 10px 0 8px -8px rgba(0, 0, 0, 0.08);\n}\n.du-bois-dark-tabs-top > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap::after,\n.du-bois-dark-tabs-bottom > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap::after,\n.du-bois-dark-tabs-top > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap::after,\n.du-bois-dark-tabs-bottom > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap::after {\n right: 0;\n box-shadow: inset -10px 0 8px -8px rgba(0, 0, 0, 0.08);\n}\n.du-bois-dark-tabs-top > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap.du-bois-dark-tabs-nav-wrap-ping-left::before,\n.du-bois-dark-tabs-bottom > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap.du-bois-dark-tabs-nav-wrap-ping-left::before,\n.du-bois-dark-tabs-top > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap.du-bois-dark-tabs-nav-wrap-ping-left::before,\n.du-bois-dark-tabs-bottom > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap.du-bois-dark-tabs-nav-wrap-ping-left::before {\n opacity: 1;\n}\n.du-bois-dark-tabs-top > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap.du-bois-dark-tabs-nav-wrap-ping-right::after,\n.du-bois-dark-tabs-bottom > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap.du-bois-dark-tabs-nav-wrap-ping-right::after,\n.du-bois-dark-tabs-top > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap.du-bois-dark-tabs-nav-wrap-ping-right::after,\n.du-bois-dark-tabs-bottom > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap.du-bois-dark-tabs-nav-wrap-ping-right::after {\n opacity: 1;\n}\n.du-bois-dark-tabs-top > .du-bois-dark-tabs-nav::before,\n.du-bois-dark-tabs-top > div > .du-bois-dark-tabs-nav::before {\n bottom: 0;\n}\n.du-bois-dark-tabs-top > .du-bois-dark-tabs-nav .du-bois-dark-tabs-ink-bar,\n.du-bois-dark-tabs-top > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-ink-bar {\n bottom: 0;\n}\n.du-bois-dark-tabs-bottom > .du-bois-dark-tabs-nav,\n.du-bois-dark-tabs-bottom > div > .du-bois-dark-tabs-nav {\n order: 1;\n margin-top: 16px;\n margin-bottom: 0;\n}\n.du-bois-dark-tabs-bottom > .du-bois-dark-tabs-nav::before,\n.du-bois-dark-tabs-bottom > div > .du-bois-dark-tabs-nav::before {\n top: 0;\n}\n.du-bois-dark-tabs-bottom > .du-bois-dark-tabs-nav .du-bois-dark-tabs-ink-bar,\n.du-bois-dark-tabs-bottom > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-ink-bar {\n top: 0;\n}\n.du-bois-dark-tabs-bottom > .du-bois-dark-tabs-content-holder,\n.du-bois-dark-tabs-bottom > div > .du-bois-dark-tabs-content-holder {\n order: 0;\n}\n.du-bois-dark-tabs-left > .du-bois-dark-tabs-nav,\n.du-bois-dark-tabs-right > .du-bois-dark-tabs-nav,\n.du-bois-dark-tabs-left > div > .du-bois-dark-tabs-nav,\n.du-bois-dark-tabs-right > div > .du-bois-dark-tabs-nav {\n flex-direction: column;\n min-width: 50px;\n}\n.du-bois-dark-tabs-left > .du-bois-dark-tabs-nav .du-bois-dark-tabs-tab,\n.du-bois-dark-tabs-right > .du-bois-dark-tabs-nav .du-bois-dark-tabs-tab,\n.du-bois-dark-tabs-left > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-tab,\n.du-bois-dark-tabs-right > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-tab {\n padding: 4px 16px;\n text-align: center;\n}\n.du-bois-dark-tabs-left > .du-bois-dark-tabs-nav .du-bois-dark-tabs-tab + .du-bois-dark-tabs-tab,\n.du-bois-dark-tabs-right > .du-bois-dark-tabs-nav .du-bois-dark-tabs-tab + .du-bois-dark-tabs-tab,\n.du-bois-dark-tabs-left > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-tab + .du-bois-dark-tabs-tab,\n.du-bois-dark-tabs-right > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-tab + .du-bois-dark-tabs-tab {\n margin: 16px 0 0 0;\n}\n.du-bois-dark-tabs-left > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap,\n.du-bois-dark-tabs-right > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap,\n.du-bois-dark-tabs-left > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap,\n.du-bois-dark-tabs-right > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap {\n flex-direction: column;\n}\n.du-bois-dark-tabs-left > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap::before,\n.du-bois-dark-tabs-right > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap::before,\n.du-bois-dark-tabs-left > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap::before,\n.du-bois-dark-tabs-right > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap::before,\n.du-bois-dark-tabs-left > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap::after,\n.du-bois-dark-tabs-right > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap::after,\n.du-bois-dark-tabs-left > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap::after,\n.du-bois-dark-tabs-right > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap::after {\n right: 0;\n left: 0;\n height: 30px;\n}\n.du-bois-dark-tabs-left > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap::before,\n.du-bois-dark-tabs-right > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap::before,\n.du-bois-dark-tabs-left > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap::before,\n.du-bois-dark-tabs-right > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap::before {\n top: 0;\n box-shadow: inset 0 10px 8px -8px rgba(0, 0, 0, 0.08);\n}\n.du-bois-dark-tabs-left > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap::after,\n.du-bois-dark-tabs-right > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap::after,\n.du-bois-dark-tabs-left > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap::after,\n.du-bois-dark-tabs-right > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap::after {\n bottom: 0;\n box-shadow: inset 0 -10px 8px -8px rgba(0, 0, 0, 0.08);\n}\n.du-bois-dark-tabs-left > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap.du-bois-dark-tabs-nav-wrap-ping-top::before,\n.du-bois-dark-tabs-right > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap.du-bois-dark-tabs-nav-wrap-ping-top::before,\n.du-bois-dark-tabs-left > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap.du-bois-dark-tabs-nav-wrap-ping-top::before,\n.du-bois-dark-tabs-right > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap.du-bois-dark-tabs-nav-wrap-ping-top::before {\n opacity: 1;\n}\n.du-bois-dark-tabs-left > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap.du-bois-dark-tabs-nav-wrap-ping-bottom::after,\n.du-bois-dark-tabs-right > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap.du-bois-dark-tabs-nav-wrap-ping-bottom::after,\n.du-bois-dark-tabs-left > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap.du-bois-dark-tabs-nav-wrap-ping-bottom::after,\n.du-bois-dark-tabs-right > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap.du-bois-dark-tabs-nav-wrap-ping-bottom::after {\n opacity: 1;\n}\n.du-bois-dark-tabs-left > .du-bois-dark-tabs-nav .du-bois-dark-tabs-ink-bar,\n.du-bois-dark-tabs-right > .du-bois-dark-tabs-nav .du-bois-dark-tabs-ink-bar,\n.du-bois-dark-tabs-left > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-ink-bar,\n.du-bois-dark-tabs-right > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-ink-bar {\n width: 2px;\n}\n.du-bois-dark-tabs-left > .du-bois-dark-tabs-nav .du-bois-dark-tabs-ink-bar-animated,\n.du-bois-dark-tabs-right > .du-bois-dark-tabs-nav .du-bois-dark-tabs-ink-bar-animated,\n.du-bois-dark-tabs-left > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-ink-bar-animated,\n.du-bois-dark-tabs-right > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-ink-bar-animated {\n transition: height 0.3s, top 0.3s;\n}\n.du-bois-dark-tabs-left > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-list,\n.du-bois-dark-tabs-right > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-list,\n.du-bois-dark-tabs-left > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-list,\n.du-bois-dark-tabs-right > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-list,\n.du-bois-dark-tabs-left > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-operations,\n.du-bois-dark-tabs-right > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-operations,\n.du-bois-dark-tabs-left > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-operations,\n.du-bois-dark-tabs-right > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-operations {\n flex: 1 0 auto;\n flex-direction: column;\n}\n.du-bois-dark-tabs-left > .du-bois-dark-tabs-nav .du-bois-dark-tabs-ink-bar,\n.du-bois-dark-tabs-left > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-ink-bar {\n right: 0;\n}\n.du-bois-dark-tabs-left > .du-bois-dark-tabs-content-holder,\n.du-bois-dark-tabs-left > div > .du-bois-dark-tabs-content-holder {\n margin-left: -1px;\n border-left: 1px solid #303030;\n}\n.du-bois-dark-tabs-left > .du-bois-dark-tabs-content-holder > .du-bois-dark-tabs-content > .du-bois-dark-tabs-tabpane,\n.du-bois-dark-tabs-left > div > .du-bois-dark-tabs-content-holder > .du-bois-dark-tabs-content > .du-bois-dark-tabs-tabpane {\n padding-left: 16px;\n}\n.du-bois-dark-tabs-right > .du-bois-dark-tabs-nav,\n.du-bois-dark-tabs-right > div > .du-bois-dark-tabs-nav {\n order: 1;\n}\n.du-bois-dark-tabs-right > .du-bois-dark-tabs-nav .du-bois-dark-tabs-ink-bar,\n.du-bois-dark-tabs-right > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-ink-bar {\n left: 0;\n}\n.du-bois-dark-tabs-right > .du-bois-dark-tabs-content-holder,\n.du-bois-dark-tabs-right > div > .du-bois-dark-tabs-content-holder {\n order: 0;\n margin-right: -1px;\n border-right: 1px solid #303030;\n}\n.du-bois-dark-tabs-right > .du-bois-dark-tabs-content-holder > .du-bois-dark-tabs-content > .du-bois-dark-tabs-tabpane,\n.du-bois-dark-tabs-right > div > .du-bois-dark-tabs-content-holder > .du-bois-dark-tabs-content > .du-bois-dark-tabs-tabpane {\n padding-right: 16px;\n}\n.du-bois-dark-tabs-dropdown {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #E8ECF0;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: tnum;\n position: absolute;\n top: -9999px;\n left: -9999px;\n z-index: 1050;\n display: block;\n}\n.du-bois-dark-tabs-dropdown-hidden {\n display: none;\n}\n.du-bois-dark-tabs-dropdown-menu {\n max-height: 200px;\n margin: 0;\n padding: 4px 0;\n overflow-x: hidden;\n overflow-y: auto;\n text-align: left;\n list-style-type: none;\n background-color: #1f1f1f;\n background-clip: padding-box;\n border-radius: 4px;\n outline: none;\n box-shadow: 0 3px 6px -4px rgba(0, 0, 0, 0.48), 0 6px 16px 0 rgba(0, 0, 0, 0.32), 0 9px 28px 8px rgba(0, 0, 0, 0.2);\n}\n.du-bois-dark-tabs-dropdown-menu-item {\n min-width: 120px;\n margin: 0;\n padding: 5px 8px;\n overflow: hidden;\n color: #E8ECF0;\n font-weight: normal;\n font-size: 13px;\n line-height: 22px;\n white-space: nowrap;\n text-overflow: ellipsis;\n cursor: pointer;\n transition: all 0.3s;\n}\n.du-bois-dark-tabs-dropdown-menu-item:hover {\n background: rgba(255, 255, 255, 0.08);\n}\n.du-bois-dark-tabs-dropdown-menu-item-disabled,\n.du-bois-dark-tabs-dropdown-menu-item-disabled:hover {\n color: rgba(255, 255, 255, 0.3);\n background: transparent;\n cursor: not-allowed;\n}\n.du-bois-dark-tabs-card > .du-bois-dark-tabs-nav .du-bois-dark-tabs-tab,\n.du-bois-dark-tabs-card > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-tab {\n margin: 0;\n padding: -111px 8px;\n background: rgba(255, 255, 255, 0.04);\n border: 1px solid #303030;\n transition: all 0.3s cubic-bezier(0.645, 0.045, 0.355, 1);\n}\n.du-bois-dark-tabs-card > .du-bois-dark-tabs-nav .du-bois-dark-tabs-tab-active,\n.du-bois-dark-tabs-card > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-tab-active {\n color: #2272B4;\n background: #141414;\n}\n.du-bois-dark-tabs-card > .du-bois-dark-tabs-nav .du-bois-dark-tabs-ink-bar,\n.du-bois-dark-tabs-card > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-ink-bar {\n visibility: hidden;\n}\n.du-bois-dark-tabs-card.du-bois-dark-tabs-top > .du-bois-dark-tabs-nav .du-bois-dark-tabs-tab + .du-bois-dark-tabs-tab,\n.du-bois-dark-tabs-card.du-bois-dark-tabs-bottom > .du-bois-dark-tabs-nav .du-bois-dark-tabs-tab + .du-bois-dark-tabs-tab,\n.du-bois-dark-tabs-card.du-bois-dark-tabs-top > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-tab + .du-bois-dark-tabs-tab,\n.du-bois-dark-tabs-card.du-bois-dark-tabs-bottom > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-tab + .du-bois-dark-tabs-tab {\n margin-left: 2px;\n}\n.du-bois-dark-tabs-card.du-bois-dark-tabs-top > .du-bois-dark-tabs-nav .du-bois-dark-tabs-tab,\n.du-bois-dark-tabs-card.du-bois-dark-tabs-top > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-tab {\n border-radius: 4px 4px 0 0;\n}\n.du-bois-dark-tabs-card.du-bois-dark-tabs-top > .du-bois-dark-tabs-nav .du-bois-dark-tabs-tab-active,\n.du-bois-dark-tabs-card.du-bois-dark-tabs-top > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-tab-active {\n border-bottom-color: #141414;\n}\n.du-bois-dark-tabs-card.du-bois-dark-tabs-bottom > .du-bois-dark-tabs-nav .du-bois-dark-tabs-tab,\n.du-bois-dark-tabs-card.du-bois-dark-tabs-bottom > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-tab {\n border-radius: 0 0 4px 4px;\n}\n.du-bois-dark-tabs-card.du-bois-dark-tabs-bottom > .du-bois-dark-tabs-nav .du-bois-dark-tabs-tab-active,\n.du-bois-dark-tabs-card.du-bois-dark-tabs-bottom > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-tab-active {\n border-top-color: #141414;\n}\n.du-bois-dark-tabs-card.du-bois-dark-tabs-left > .du-bois-dark-tabs-nav .du-bois-dark-tabs-tab + .du-bois-dark-tabs-tab,\n.du-bois-dark-tabs-card.du-bois-dark-tabs-right > .du-bois-dark-tabs-nav .du-bois-dark-tabs-tab + .du-bois-dark-tabs-tab,\n.du-bois-dark-tabs-card.du-bois-dark-tabs-left > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-tab + .du-bois-dark-tabs-tab,\n.du-bois-dark-tabs-card.du-bois-dark-tabs-right > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-tab + .du-bois-dark-tabs-tab {\n margin-top: 2px;\n}\n.du-bois-dark-tabs-card.du-bois-dark-tabs-left > .du-bois-dark-tabs-nav .du-bois-dark-tabs-tab,\n.du-bois-dark-tabs-card.du-bois-dark-tabs-left > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-tab {\n border-radius: 4px 0 0 4px;\n}\n.du-bois-dark-tabs-card.du-bois-dark-tabs-left > .du-bois-dark-tabs-nav .du-bois-dark-tabs-tab-active,\n.du-bois-dark-tabs-card.du-bois-dark-tabs-left > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-tab-active {\n border-right-color: #141414;\n}\n.du-bois-dark-tabs-card.du-bois-dark-tabs-right > .du-bois-dark-tabs-nav .du-bois-dark-tabs-tab,\n.du-bois-dark-tabs-card.du-bois-dark-tabs-right > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-tab {\n border-radius: 0 4px 4px 0;\n}\n.du-bois-dark-tabs-card.du-bois-dark-tabs-right > .du-bois-dark-tabs-nav .du-bois-dark-tabs-tab-active,\n.du-bois-dark-tabs-card.du-bois-dark-tabs-right > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-tab-active {\n border-left-color: #141414;\n}\n.du-bois-dark-tabs {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #E8ECF0;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: tnum;\n display: flex;\n overflow: hidden;\n}\n.du-bois-dark-tabs > .du-bois-dark-tabs-nav,\n.du-bois-dark-tabs > div > .du-bois-dark-tabs-nav {\n position: relative;\n display: flex;\n flex: none;\n align-items: center;\n}\n.du-bois-dark-tabs > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap,\n.du-bois-dark-tabs > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap {\n position: relative;\n display: inline-block;\n display: flex;\n flex: auto;\n align-self: stretch;\n overflow: hidden;\n white-space: nowrap;\n transform: translate(0);\n}\n.du-bois-dark-tabs > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap::before,\n.du-bois-dark-tabs > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap::before,\n.du-bois-dark-tabs > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap::after,\n.du-bois-dark-tabs > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap::after {\n position: absolute;\n z-index: 1;\n opacity: 0;\n transition: opacity 0.3s;\n content: '';\n pointer-events: none;\n}\n.du-bois-dark-tabs > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-list,\n.du-bois-dark-tabs > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-list {\n position: relative;\n display: flex;\n transition: transform 0.3s;\n}\n.du-bois-dark-tabs > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-operations,\n.du-bois-dark-tabs > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-operations {\n display: flex;\n align-self: stretch;\n}\n.du-bois-dark-tabs > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-operations-hidden,\n.du-bois-dark-tabs > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-operations-hidden {\n position: absolute;\n visibility: hidden;\n pointer-events: none;\n}\n.du-bois-dark-tabs > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-more,\n.du-bois-dark-tabs > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-more {\n position: relative;\n padding: -111px 8px;\n background: transparent;\n border: 0;\n}\n.du-bois-dark-tabs > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-more::after,\n.du-bois-dark-tabs > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-more::after {\n position: absolute;\n right: 0;\n bottom: 0;\n left: 0;\n height: 5px;\n transform: translateY(100%);\n content: '';\n}\n.du-bois-dark-tabs > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-add,\n.du-bois-dark-tabs > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-add {\n min-width: 40px;\n padding: 0 4px;\n background: rgba(255, 255, 255, 0.04);\n border: 1px solid #303030;\n border-radius: 4px 4px 0 0;\n outline: none;\n cursor: pointer;\n transition: all 0.3s cubic-bezier(0.645, 0.045, 0.355, 1);\n}\n.du-bois-dark-tabs > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-add:hover,\n.du-bois-dark-tabs > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-add:hover {\n color: #1d517c;\n}\n.du-bois-dark-tabs > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-add:active,\n.du-bois-dark-tabs > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-add:active,\n.du-bois-dark-tabs > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-add:focus,\n.du-bois-dark-tabs > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-add:focus {\n color: #3f81b1;\n}\n.du-bois-dark-tabs-extra-content {\n flex: none;\n}\n.du-bois-dark-tabs-centered > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap:not([class*='du-bois-dark-tabs-nav-wrap-ping']),\n.du-bois-dark-tabs-centered > div > .du-bois-dark-tabs-nav .du-bois-dark-tabs-nav-wrap:not([class*='du-bois-dark-tabs-nav-wrap-ping']) {\n justify-content: center;\n}\n.du-bois-dark-tabs-ink-bar {\n position: absolute;\n background: #2272B4;\n pointer-events: none;\n}\n.du-bois-dark-tabs-tab {\n position: relative;\n display: inline-flex;\n align-items: center;\n padding: 8px 0;\n font-size: 13px;\n background: transparent;\n border: 0;\n outline: none;\n cursor: pointer;\n}\n.du-bois-dark-tabs-tab-btn:focus,\n.du-bois-dark-tabs-tab-remove:focus,\n.du-bois-dark-tabs-tab-btn:active,\n.du-bois-dark-tabs-tab-remove:active {\n color: #3f81b1;\n}\n.du-bois-dark-tabs-tab-btn {\n outline: none;\n transition: all 0.3s;\n}\n.du-bois-dark-tabs-tab-remove {\n flex: none;\n margin-right: -4px;\n margin-left: 8px;\n color: #92A4B3;\n font-size: 12px;\n background: transparent;\n border: none;\n outline: none;\n cursor: pointer;\n transition: all 0.3s;\n}\n.du-bois-dark-tabs-tab-remove:hover {\n color: rgba(255, 255, 255, 0.85);\n}\n.du-bois-dark-tabs-tab:hover {\n color: #1d517c;\n}\n.du-bois-dark-tabs-tab.du-bois-dark-tabs-tab-active .du-bois-dark-tabs-tab-btn {\n color: #2272B4;\n text-shadow: 0 0 0.25px currentColor;\n}\n.du-bois-dark-tabs-tab.du-bois-dark-tabs-tab-disabled {\n color: rgba(255, 255, 255, 0.3);\n cursor: not-allowed;\n}\n.du-bois-dark-tabs-tab.du-bois-dark-tabs-tab-disabled .du-bois-dark-tabs-tab-btn:focus,\n.du-bois-dark-tabs-tab.du-bois-dark-tabs-tab-disabled .du-bois-dark-tabs-tab-remove:focus,\n.du-bois-dark-tabs-tab.du-bois-dark-tabs-tab-disabled .du-bois-dark-tabs-tab-btn:active,\n.du-bois-dark-tabs-tab.du-bois-dark-tabs-tab-disabled .du-bois-dark-tabs-tab-remove:active {\n color: rgba(255, 255, 255, 0.3);\n}\n.du-bois-dark-tabs-tab .du-bois-dark-tabs-tab-remove .anticon {\n margin: 0;\n}\n.du-bois-dark-tabs-tab .anticon {\n margin-right: 12px;\n}\n.du-bois-dark-tabs-tab + .du-bois-dark-tabs-tab {\n margin: 0 0 0 32px;\n}\n.du-bois-dark-tabs-content {\n display: flex;\n width: 100%;\n}\n.du-bois-dark-tabs-content-holder {\n flex: auto;\n min-width: 0;\n min-height: 0;\n}\n.du-bois-dark-tabs-content-animated {\n transition: margin 0.3s;\n}\n.du-bois-dark-tabs-tabpane {\n flex: none;\n width: 100%;\n outline: none;\n}\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n@keyframes antCheckboxEffect {\n 0% {\n transform: scale(1);\n opacity: 0.5;\n }\n 100% {\n transform: scale(1.6);\n opacity: 0;\n }\n}\n.du-bois-dark-tree-treenode-leaf-last .du-bois-dark-tree-switcher-leaf-line::before {\n top: auto !important;\n bottom: auto !important;\n height: 14px !important;\n}\n.du-bois-dark-tree.du-bois-dark-tree-directory .du-bois-dark-tree-treenode {\n position: relative;\n}\n.du-bois-dark-tree.du-bois-dark-tree-directory .du-bois-dark-tree-treenode::before {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 4px;\n left: 0;\n transition: background-color 0.3s;\n content: '';\n pointer-events: none;\n}\n.du-bois-dark-tree.du-bois-dark-tree-directory .du-bois-dark-tree-treenode:hover::before {\n background: rgba(255, 255, 255, 0.08);\n}\n.du-bois-dark-tree.du-bois-dark-tree-directory .du-bois-dark-tree-treenode > * {\n z-index: 1;\n}\n.du-bois-dark-tree.du-bois-dark-tree-directory .du-bois-dark-tree-treenode .du-bois-dark-tree-switcher {\n transition: color 0.3s;\n}\n.du-bois-dark-tree.du-bois-dark-tree-directory .du-bois-dark-tree-treenode .du-bois-dark-tree-node-content-wrapper {\n border-radius: 0;\n user-select: none;\n}\n.du-bois-dark-tree.du-bois-dark-tree-directory .du-bois-dark-tree-treenode .du-bois-dark-tree-node-content-wrapper:hover {\n background: transparent;\n}\n.du-bois-dark-tree.du-bois-dark-tree-directory .du-bois-dark-tree-treenode .du-bois-dark-tree-node-content-wrapper.du-bois-dark-tree-node-selected {\n color: #fff;\n background: transparent;\n}\n.du-bois-dark-tree.du-bois-dark-tree-directory .du-bois-dark-tree-treenode-selected:hover::before,\n.du-bois-dark-tree.du-bois-dark-tree-directory .du-bois-dark-tree-treenode-selected::before {\n background: #2272B4;\n}\n.du-bois-dark-tree.du-bois-dark-tree-directory .du-bois-dark-tree-treenode-selected .du-bois-dark-tree-switcher {\n color: #fff;\n}\n.du-bois-dark-tree.du-bois-dark-tree-directory .du-bois-dark-tree-treenode-selected .du-bois-dark-tree-node-content-wrapper {\n color: #fff;\n background: transparent;\n}\n.du-bois-dark-tree-checkbox {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #E8ECF0;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: tnum;\n position: relative;\n top: 0.2em;\n line-height: 1;\n white-space: nowrap;\n outline: none;\n cursor: pointer;\n}\n.du-bois-dark-tree-checkbox-wrapper:hover .du-bois-dark-tree-checkbox-inner,\n.du-bois-dark-tree-checkbox:hover .du-bois-dark-tree-checkbox-inner,\n.du-bois-dark-tree-checkbox-input:focus + .du-bois-dark-tree-checkbox-inner {\n border-color: #2272B4;\n}\n.du-bois-dark-tree-checkbox-checked::after {\n position: absolute;\n top: 0;\n left: 0;\n width: 100%;\n height: 100%;\n border: 1px solid #2272B4;\n border-radius: 4px;\n visibility: hidden;\n animation: antCheckboxEffect 0.36s ease-in-out;\n animation-fill-mode: backwards;\n content: '';\n}\n.du-bois-dark-tree-checkbox:hover::after,\n.du-bois-dark-tree-checkbox-wrapper:hover .du-bois-dark-tree-checkbox::after {\n visibility: visible;\n}\n.du-bois-dark-tree-checkbox-inner {\n position: relative;\n top: 0;\n left: 0;\n display: block;\n width: 16px;\n height: 16px;\n direction: ltr;\n background-color: transparent;\n border: 1px solid #1F272D;\n border-radius: 4px;\n border-collapse: separate;\n transition: all 0.3s;\n}\n.du-bois-dark-tree-checkbox-inner::after {\n position: absolute;\n top: 50%;\n left: 22%;\n display: table;\n width: 5.71428571px;\n height: 9.14285714px;\n border: 2px solid #fff;\n border-top: 0;\n border-left: 0;\n transform: rotate(45deg) scale(0) translate(-50%, -50%);\n opacity: 0;\n transition: all 0.1s cubic-bezier(0.71, -0.46, 0.88, 0.6), opacity 0.1s;\n content: ' ';\n}\n.du-bois-dark-tree-checkbox-input {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n z-index: 1;\n width: 100%;\n height: 100%;\n cursor: pointer;\n opacity: 0;\n}\n.du-bois-dark-tree-checkbox-checked .du-bois-dark-tree-checkbox-inner::after {\n position: absolute;\n display: table;\n border: 2px solid #fff;\n border-top: 0;\n border-left: 0;\n transform: rotate(45deg) scale(1) translate(-50%, -50%);\n opacity: 1;\n transition: all 0.2s cubic-bezier(0.12, 0.4, 0.29, 1.46) 0.1s;\n content: ' ';\n}\n.du-bois-dark-tree-checkbox-checked .du-bois-dark-tree-checkbox-inner {\n background-color: #2272B4;\n border-color: #2272B4;\n}\n.du-bois-dark-tree-checkbox-disabled {\n cursor: not-allowed;\n}\n.du-bois-dark-tree-checkbox-disabled.du-bois-dark-tree-checkbox-checked .du-bois-dark-tree-checkbox-inner::after {\n border-color: rgba(255, 255, 255, 0.3);\n animation-name: none;\n}\n.du-bois-dark-tree-checkbox-disabled .du-bois-dark-tree-checkbox-input {\n cursor: not-allowed;\n}\n.du-bois-dark-tree-checkbox-disabled .du-bois-dark-tree-checkbox-inner {\n background-color: rgba(255, 255, 255, 0.08);\n border-color: #1F272D !important;\n}\n.du-bois-dark-tree-checkbox-disabled .du-bois-dark-tree-checkbox-inner::after {\n border-color: rgba(255, 255, 255, 0.08);\n border-collapse: separate;\n animation-name: none;\n}\n.du-bois-dark-tree-checkbox-disabled + span {\n color: rgba(255, 255, 255, 0.3);\n cursor: not-allowed;\n}\n.du-bois-dark-tree-checkbox-disabled:hover::after,\n.du-bois-dark-tree-checkbox-wrapper:hover .du-bois-dark-tree-checkbox-disabled::after {\n visibility: hidden;\n}\n.du-bois-dark-tree-checkbox-wrapper {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #E8ECF0;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: tnum;\n display: inline-flex;\n align-items: baseline;\n line-height: unset;\n cursor: pointer;\n}\n.du-bois-dark-tree-checkbox-wrapper::after {\n display: inline-block;\n width: 0;\n overflow: hidden;\n content: '\\a0';\n}\n.du-bois-dark-tree-checkbox-wrapper.du-bois-dark-tree-checkbox-wrapper-disabled {\n cursor: not-allowed;\n}\n.du-bois-dark-tree-checkbox-wrapper + .du-bois-dark-tree-checkbox-wrapper {\n margin-left: 8px;\n}\n.du-bois-dark-tree-checkbox + span {\n padding-right: 8px;\n padding-left: 8px;\n}\n.du-bois-dark-tree-checkbox-group {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #E8ECF0;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: tnum;\n display: inline-block;\n}\n.du-bois-dark-tree-checkbox-group-item {\n margin-right: 8px;\n}\n.du-bois-dark-tree-checkbox-group-item:last-child {\n margin-right: 0;\n}\n.du-bois-dark-tree-checkbox-group-item + .du-bois-dark-tree-checkbox-group-item {\n margin-left: 0;\n}\n.du-bois-dark-tree-checkbox-indeterminate .du-bois-dark-tree-checkbox-inner {\n background-color: transparent;\n border-color: #1F272D;\n}\n.du-bois-dark-tree-checkbox-indeterminate .du-bois-dark-tree-checkbox-inner::after {\n top: 50%;\n left: 50%;\n width: 8px;\n height: 8px;\n background-color: #2272B4;\n border: 0;\n transform: translate(-50%, -50%) scale(1);\n opacity: 1;\n content: ' ';\n}\n.du-bois-dark-tree-checkbox-indeterminate.du-bois-dark-tree-checkbox-disabled .du-bois-dark-tree-checkbox-inner::after {\n background-color: rgba(255, 255, 255, 0.3);\n border-color: rgba(255, 255, 255, 0.3);\n}\n.du-bois-dark-tree {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #E8ECF0;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: tnum;\n background: transparent;\n border-radius: 4px;\n transition: background-color 0.3s;\n}\n.du-bois-dark-tree-focused:not(:hover):not(.du-bois-dark-tree-active-focused) {\n background: #121921;\n}\n.du-bois-dark-tree-list-holder-inner {\n align-items: flex-start;\n}\n.du-bois-dark-tree.du-bois-dark-tree-block-node .du-bois-dark-tree-list-holder-inner {\n align-items: stretch;\n}\n.du-bois-dark-tree.du-bois-dark-tree-block-node .du-bois-dark-tree-list-holder-inner .du-bois-dark-tree-node-content-wrapper {\n flex: auto;\n}\n.du-bois-dark-tree .du-bois-dark-tree-treenode {\n display: flex;\n align-items: flex-start;\n padding: 0 0 2px 0;\n outline: none;\n}\n.du-bois-dark-tree .du-bois-dark-tree-treenode-disabled .du-bois-dark-tree-node-content-wrapper {\n color: rgba(255, 255, 255, 0.3);\n cursor: not-allowed;\n}\n.du-bois-dark-tree .du-bois-dark-tree-treenode-disabled .du-bois-dark-tree-node-content-wrapper:hover {\n background: transparent;\n}\n.du-bois-dark-tree .du-bois-dark-tree-treenode-active .du-bois-dark-tree-node-content-wrapper {\n background: rgba(255, 255, 255, 0.08);\n}\n.du-bois-dark-tree .du-bois-dark-tree-treenode:not(.du-bois-dark-tree .du-bois-dark-tree-treenode-disabled).filter-node .du-bois-dark-tree-title {\n color: inherit;\n font-weight: 500;\n}\n.du-bois-dark-tree-indent {\n align-self: stretch;\n white-space: nowrap;\n user-select: none;\n}\n.du-bois-dark-tree-indent-unit {\n display: inline-block;\n width: 24px;\n}\n.du-bois-dark-tree-switcher {\n position: relative;\n flex: none;\n align-self: stretch;\n width: 24px;\n margin: 0;\n line-height: 24px;\n text-align: center;\n cursor: pointer;\n user-select: none;\n}\n.du-bois-dark-tree-switcher .du-bois-dark-tree-switcher-icon,\n.du-bois-dark-tree-switcher .du-bois-dark-select-tree-switcher-icon {\n display: inline-block;\n font-size: 10px;\n vertical-align: baseline;\n}\n.du-bois-dark-tree-switcher .du-bois-dark-tree-switcher-icon svg,\n.du-bois-dark-tree-switcher .du-bois-dark-select-tree-switcher-icon svg {\n transition: transform 0.3s;\n}\n.du-bois-dark-tree-switcher-noop {\n cursor: default;\n}\n.du-bois-dark-tree-switcher_close .du-bois-dark-tree-switcher-icon svg {\n transform: rotate(-90deg);\n}\n.du-bois-dark-tree-switcher-loading-icon {\n color: #2272B4;\n}\n.du-bois-dark-tree-switcher-leaf-line {\n position: relative;\n z-index: 1;\n display: inline-block;\n width: 100%;\n height: 100%;\n}\n.du-bois-dark-tree-switcher-leaf-line::before {\n position: absolute;\n top: 0;\n bottom: -2px;\n margin-left: -1px;\n border-left: 1px solid #d9d9d9;\n content: ' ';\n}\n.du-bois-dark-tree-switcher-leaf-line::after {\n position: absolute;\n width: 10px;\n height: 14px;\n margin-left: -1px;\n border-bottom: 1px solid #d9d9d9;\n content: ' ';\n}\n.du-bois-dark-tree-checkbox {\n top: initial;\n margin: 4px 8px 0 0;\n}\n.du-bois-dark-tree .du-bois-dark-tree-node-content-wrapper {\n position: relative;\n z-index: auto;\n min-height: 24px;\n margin: 0;\n padding: 0 4px;\n color: inherit;\n line-height: 24px;\n background: transparent;\n border-radius: 4px;\n cursor: pointer;\n transition: all 0.3s, border 0s, line-height 0s, box-shadow 0s;\n}\n.du-bois-dark-tree .du-bois-dark-tree-node-content-wrapper:hover {\n background-color: rgba(255, 255, 255, 0.08);\n}\n.du-bois-dark-tree .du-bois-dark-tree-node-content-wrapper.du-bois-dark-tree-node-selected {\n background-color: #142433;\n}\n.du-bois-dark-tree .du-bois-dark-tree-node-content-wrapper .du-bois-dark-tree-iconEle {\n display: inline-block;\n width: 24px;\n height: 24px;\n line-height: 24px;\n text-align: center;\n vertical-align: top;\n}\n.du-bois-dark-tree .du-bois-dark-tree-node-content-wrapper .du-bois-dark-tree-iconEle:empty {\n display: none;\n}\n.du-bois-dark-tree-unselectable .du-bois-dark-tree-node-content-wrapper:hover {\n background-color: transparent;\n}\n.du-bois-dark-tree-node-content-wrapper[draggable='true'] {\n line-height: 24px;\n user-select: none;\n}\n.du-bois-dark-tree-node-content-wrapper[draggable='true'] .du-bois-dark-tree-drop-indicator {\n position: absolute;\n z-index: 1;\n height: 2px;\n background-color: #2272B4;\n border-radius: 1px;\n pointer-events: none;\n}\n.du-bois-dark-tree-node-content-wrapper[draggable='true'] .du-bois-dark-tree-drop-indicator::after {\n position: absolute;\n top: -3px;\n left: -6px;\n width: 8px;\n height: 8px;\n background-color: transparent;\n border: 2px solid #2272B4;\n border-radius: 50%;\n content: '';\n}\n.du-bois-dark-tree .du-bois-dark-tree-treenode.drop-container > [draggable] {\n box-shadow: 0 0 0 2px #2272B4;\n}\n.du-bois-dark-tree-show-line .du-bois-dark-tree-indent-unit {\n position: relative;\n height: 100%;\n}\n.du-bois-dark-tree-show-line .du-bois-dark-tree-indent-unit::before {\n position: absolute;\n top: 0;\n right: 12px;\n bottom: -2px;\n border-right: 1px solid #1F272D;\n content: '';\n}\n.du-bois-dark-tree-show-line .du-bois-dark-tree-indent-unit-end::before {\n display: none;\n}\n.du-bois-dark-tree-show-line .du-bois-dark-tree-switcher {\n background: #141414;\n}\n.du-bois-dark-tree-show-line .du-bois-dark-tree-switcher-line-icon {\n vertical-align: -0.225em;\n}\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-dark-typography {\n color: #E8ECF0;\n overflow-wrap: break-word;\n}\n.du-bois-dark-typography.du-bois-dark-typography-secondary {\n color: #92A4B3;\n}\n.du-bois-dark-typography.du-bois-dark-typography-success {\n color: #34824F;\n}\n.du-bois-dark-typography.du-bois-dark-typography-warning {\n color: #AF5B23;\n}\n.du-bois-dark-typography.du-bois-dark-typography-danger {\n color: #E65B77;\n}\na.du-bois-dark-typography.du-bois-dark-typography-danger:active,\na.du-bois-dark-typography.du-bois-dark-typography-danger:focus,\na.du-bois-dark-typography.du-bois-dark-typography-danger:hover {\n color: #f28899;\n}\n.du-bois-dark-typography.du-bois-dark-typography-disabled {\n color: rgba(255, 255, 255, 0.3);\n cursor: not-allowed;\n user-select: none;\n}\ndiv.du-bois-dark-typography,\n.du-bois-dark-typography p {\n margin-bottom: 1em;\n}\nh1.du-bois-dark-typography,\n.du-bois-dark-typography h1 {\n margin-bottom: 0.5em;\n color: rgba(255, 255, 255, 0.85);\n font-weight: 600;\n font-size: 36px;\n line-height: 1.23;\n}\nh2.du-bois-dark-typography,\n.du-bois-dark-typography h2 {\n margin-bottom: 0.5em;\n color: rgba(255, 255, 255, 0.85);\n font-weight: 600;\n font-size: 28px;\n line-height: 1.35;\n}\nh3.du-bois-dark-typography,\n.du-bois-dark-typography h3 {\n margin-bottom: 0.5em;\n color: rgba(255, 255, 255, 0.85);\n font-weight: 600;\n font-size: 23px;\n line-height: 1.35;\n}\nh4.du-bois-dark-typography,\n.du-bois-dark-typography h4 {\n margin-bottom: 0.5em;\n color: rgba(255, 255, 255, 0.85);\n font-weight: 600;\n font-size: 19px;\n line-height: 1.4;\n}\nh5.du-bois-dark-typography,\n.du-bois-dark-typography h5 {\n margin-bottom: 0.5em;\n color: rgba(255, 255, 255, 0.85);\n font-weight: 600;\n font-size: 15px;\n line-height: 1.5;\n}\n.du-bois-dark-typography + h1.du-bois-dark-typography,\n.du-bois-dark-typography + h2.du-bois-dark-typography,\n.du-bois-dark-typography + h3.du-bois-dark-typography,\n.du-bois-dark-typography + h4.du-bois-dark-typography,\n.du-bois-dark-typography + h5.du-bois-dark-typography {\n margin-top: 1.2em;\n}\n.du-bois-dark-typography div + h1,\n.du-bois-dark-typography ul + h1,\n.du-bois-dark-typography li + h1,\n.du-bois-dark-typography p + h1,\n.du-bois-dark-typography h1 + h1,\n.du-bois-dark-typography h2 + h1,\n.du-bois-dark-typography h3 + h1,\n.du-bois-dark-typography h4 + h1,\n.du-bois-dark-typography h5 + h1,\n.du-bois-dark-typography div + h2,\n.du-bois-dark-typography ul + h2,\n.du-bois-dark-typography li + h2,\n.du-bois-dark-typography p + h2,\n.du-bois-dark-typography h1 + h2,\n.du-bois-dark-typography h2 + h2,\n.du-bois-dark-typography h3 + h2,\n.du-bois-dark-typography h4 + h2,\n.du-bois-dark-typography h5 + h2,\n.du-bois-dark-typography div + h3,\n.du-bois-dark-typography ul + h3,\n.du-bois-dark-typography li + h3,\n.du-bois-dark-typography p + h3,\n.du-bois-dark-typography h1 + h3,\n.du-bois-dark-typography h2 + h3,\n.du-bois-dark-typography h3 + h3,\n.du-bois-dark-typography h4 + h3,\n.du-bois-dark-typography h5 + h3,\n.du-bois-dark-typography div + h4,\n.du-bois-dark-typography ul + h4,\n.du-bois-dark-typography li + h4,\n.du-bois-dark-typography p + h4,\n.du-bois-dark-typography h1 + h4,\n.du-bois-dark-typography h2 + h4,\n.du-bois-dark-typography h3 + h4,\n.du-bois-dark-typography h4 + h4,\n.du-bois-dark-typography h5 + h4,\n.du-bois-dark-typography div + h5,\n.du-bois-dark-typography ul + h5,\n.du-bois-dark-typography li + h5,\n.du-bois-dark-typography p + h5,\n.du-bois-dark-typography h1 + h5,\n.du-bois-dark-typography h2 + h5,\n.du-bois-dark-typography h3 + h5,\n.du-bois-dark-typography h4 + h5,\n.du-bois-dark-typography h5 + h5 {\n margin-top: 1.2em;\n}\na.du-bois-dark-typography-ellipsis,\nspan.du-bois-dark-typography-ellipsis {\n display: inline-block;\n}\na.du-bois-dark-typography,\n.du-bois-dark-typography a {\n color: #2272B4;\n outline: none;\n cursor: pointer;\n transition: color 0.3s;\n text-decoration: none;\n}\na.du-bois-dark-typography:focus,\n.du-bois-dark-typography a:focus,\na.du-bois-dark-typography:hover,\n.du-bois-dark-typography a:hover {\n color: #1d517c;\n}\na.du-bois-dark-typography:active,\n.du-bois-dark-typography a:active {\n color: #3f81b1;\n}\na.du-bois-dark-typography:active,\n.du-bois-dark-typography a:active,\na.du-bois-dark-typography:hover,\n.du-bois-dark-typography a:hover {\n text-decoration: none;\n}\na.du-bois-dark-typography[disabled],\n.du-bois-dark-typography a[disabled],\na.du-bois-dark-typography.du-bois-dark-typography-disabled,\n.du-bois-dark-typography a.du-bois-dark-typography-disabled {\n color: rgba(255, 255, 255, 0.3);\n cursor: not-allowed;\n}\na.du-bois-dark-typography[disabled]:active,\n.du-bois-dark-typography a[disabled]:active,\na.du-bois-dark-typography.du-bois-dark-typography-disabled:active,\n.du-bois-dark-typography a.du-bois-dark-typography-disabled:active,\na.du-bois-dark-typography[disabled]:hover,\n.du-bois-dark-typography a[disabled]:hover,\na.du-bois-dark-typography.du-bois-dark-typography-disabled:hover,\n.du-bois-dark-typography a.du-bois-dark-typography-disabled:hover {\n color: rgba(255, 255, 255, 0.3);\n}\na.du-bois-dark-typography[disabled]:active,\n.du-bois-dark-typography a[disabled]:active,\na.du-bois-dark-typography.du-bois-dark-typography-disabled:active,\n.du-bois-dark-typography a.du-bois-dark-typography-disabled:active {\n pointer-events: none;\n}\n.du-bois-dark-typography code {\n margin: 0 0.2em;\n padding: 0.2em 0.4em 0.1em;\n font-size: 85%;\n background: rgba(150, 150, 150, 0.1);\n border: 1px solid rgba(100, 100, 100, 0.2);\n border-radius: 3px;\n}\n.du-bois-dark-typography kbd {\n margin: 0 0.2em;\n padding: 0.15em 0.4em 0.1em;\n font-size: 90%;\n background: rgba(150, 150, 150, 0.06);\n border: 1px solid rgba(100, 100, 100, 0.2);\n border-bottom-width: 2px;\n border-radius: 3px;\n}\n.du-bois-dark-typography mark {\n padding: 0;\n background-color: #594214;\n}\n.du-bois-dark-typography u,\n.du-bois-dark-typography ins {\n text-decoration: underline;\n text-decoration-skip-ink: auto;\n}\n.du-bois-dark-typography s,\n.du-bois-dark-typography del {\n text-decoration: line-through;\n}\n.du-bois-dark-typography strong {\n font-weight: 600;\n}\n.du-bois-dark-typography-expand,\n.du-bois-dark-typography-edit,\n.du-bois-dark-typography-copy {\n color: #2272B4;\n text-decoration: none;\n outline: none;\n cursor: pointer;\n transition: color 0.3s;\n margin-left: 4px;\n}\n.du-bois-dark-typography-expand:focus,\n.du-bois-dark-typography-edit:focus,\n.du-bois-dark-typography-copy:focus,\n.du-bois-dark-typography-expand:hover,\n.du-bois-dark-typography-edit:hover,\n.du-bois-dark-typography-copy:hover {\n color: #1d517c;\n}\n.du-bois-dark-typography-expand:active,\n.du-bois-dark-typography-edit:active,\n.du-bois-dark-typography-copy:active {\n color: #3f81b1;\n}\n.du-bois-dark-typography-copy-success,\n.du-bois-dark-typography-copy-success:hover,\n.du-bois-dark-typography-copy-success:focus {\n color: #34824F;\n}\n.du-bois-dark-typography-edit-content {\n position: relative;\n}\ndiv.du-bois-dark-typography-edit-content {\n left: -13px;\n margin-top: -6px;\n margin-bottom: calc(1em - 5px - 1px);\n}\n.du-bois-dark-typography-edit-content-confirm {\n position: absolute;\n right: 10px;\n bottom: 8px;\n color: #92A4B3;\n pointer-events: none;\n}\n.du-bois-dark-typography-edit-content textarea {\n -moz-transition: none;\n}\n.du-bois-dark-typography ul,\n.du-bois-dark-typography ol {\n margin: 0 0 1em 0;\n padding: 0;\n}\n.du-bois-dark-typography ul li,\n.du-bois-dark-typography ol li {\n margin: 0 0 0 20px;\n padding: 0 0 0 4px;\n}\n.du-bois-dark-typography ul {\n list-style-type: circle;\n}\n.du-bois-dark-typography ul ul {\n list-style-type: disc;\n}\n.du-bois-dark-typography ol {\n list-style-type: decimal;\n}\n.du-bois-dark-typography pre,\n.du-bois-dark-typography blockquote {\n margin: 1em 0;\n}\n.du-bois-dark-typography pre {\n padding: 0.4em 0.6em;\n white-space: pre-wrap;\n word-wrap: break-word;\n background: rgba(150, 150, 150, 0.1);\n border: 1px solid rgba(100, 100, 100, 0.2);\n border-radius: 3px;\n}\n.du-bois-dark-typography pre code {\n display: inline;\n margin: 0;\n padding: 0;\n font-size: inherit;\n font-family: inherit;\n background: transparent;\n border: 0;\n}\n.du-bois-dark-typography blockquote {\n padding: 0 0 0 0.6em;\n border-left: 4px solid rgba(100, 100, 100, 0.2);\n opacity: 0.85;\n}\n.du-bois-dark-typography-single-line {\n white-space: nowrap;\n}\n.du-bois-dark-typography-ellipsis-single-line {\n overflow: hidden;\n text-overflow: ellipsis;\n}\na.du-bois-dark-typography-ellipsis-single-line,\nspan.du-bois-dark-typography-ellipsis-single-line {\n vertical-align: bottom;\n}\n.du-bois-dark-typography-ellipsis-multiple-line {\n display: -webkit-box;\n overflow: hidden;\n -webkit-line-clamp: 3;\n /*! autoprefixer: ignore next */\n -webkit-box-orient: vertical;\n}\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n\n[data-dubois-show-outline] {\n outline: 1px dashed #308613 !important;\n outline-offset: 2px !important;\n}\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n.du-bois-dark-switch {\n margin: 0;\n padding: 0;\n color: #E8ECF0;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: tnum;\n position: relative;\n display: inline-block;\n box-sizing: border-box;\n min-width: 28px;\n height: 16px;\n line-height: 16px;\n vertical-align: middle;\n background-color: rgba(255, 255, 255, 0.3);\n border: 0;\n border-radius: 100px;\n cursor: pointer;\n transition: all 0.2s;\n user-select: none;\n}\n.du-bois-dark-switch:focus {\n outline: 0;\n box-shadow: 0 0 0 2px rgba(255, 255, 255, 0.1);\n}\n.du-bois-dark-switch-checked:focus {\n box-shadow: 0 0 0 2px rgba(34, 114, 180, 0.2);\n}\n.du-bois-dark-switch:focus:hover {\n box-shadow: none;\n}\n.du-bois-dark-switch-checked {\n background-color: #2272B4;\n}\n.du-bois-dark-switch-loading,\n.du-bois-dark-switch-disabled {\n cursor: not-allowed;\n opacity: 0.4;\n}\n.du-bois-dark-switch-loading *,\n.du-bois-dark-switch-disabled * {\n box-shadow: none;\n cursor: not-allowed;\n}\n.du-bois-dark-switch-inner {\n display: block;\n margin: 0 5px 0 18px;\n color: #fff;\n font-size: 12px;\n transition: margin 0.2s;\n}\n.du-bois-dark-switch-checked .du-bois-dark-switch-inner {\n margin: 0 18px 0 5px;\n}\n.du-bois-dark-switch-handle {\n position: absolute;\n top: 0px;\n left: 0px;\n width: 14px;\n height: 14px;\n transition: all 0.2s ease-in-out;\n}\n.du-bois-dark-switch-handle::before {\n position: absolute;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n background-color: #fff;\n border-radius: 7px;\n box-shadow: 0 2px 4px 0 rgba(0, 35, 11, 0.2);\n transition: all 0.2s ease-in-out;\n content: '';\n}\n.du-bois-dark-switch-checked .du-bois-dark-switch-handle {\n left: calc(100% - 14px - 0px);\n}\n.du-bois-dark-switch:not(.du-bois-dark-switch-disabled):active .du-bois-dark-switch-handle::before {\n right: -30%;\n left: 0;\n}\n.du-bois-dark-switch:not(.du-bois-dark-switch-disabled):active.du-bois-dark-switch-checked .du-bois-dark-switch-handle::before {\n right: 0;\n left: -30%;\n}\n.du-bois-dark-switch-loading-icon {\n position: relative;\n top: 0.5px;\n color: rgba(0, 0, 0, 0.65);\n vertical-align: top;\n}\n.du-bois-dark-switch-checked .du-bois-dark-switch-loading-icon {\n color: #2272B4;\n}\n.du-bois-dark-switch-small {\n min-width: 28px;\n height: 16px;\n line-height: 16px;\n}\n.du-bois-dark-switch-small .du-bois-dark-switch-inner {\n margin: 0 5px 0 18px;\n font-size: 12px;\n}\n.du-bois-dark-switch-small .du-bois-dark-switch-handle {\n width: 12px;\n height: 12px;\n}\n.du-bois-dark-switch-small .du-bois-dark-switch-loading-icon {\n top: 1.5px;\n font-size: 9px;\n}\n.du-bois-dark-switch-small.du-bois-dark-switch-checked .du-bois-dark-switch-inner {\n margin: 0 18px 0 5px;\n}\n.du-bois-dark-switch-small.du-bois-dark-switch-checked .du-bois-dark-switch-handle {\n left: calc(100% - 12px - 0px);\n}\n.du-bois-dark-switch {\n transition: none;\n border: 1px solid #64727d;\n background-color: white;\n margin-right: 8px;\n}\n.du-bois-dark-switch .du-bois-dark-switch-handle::before {\n box-shadow: 0px 0px 0px 1px #64727d;\n}\n.du-bois-dark-switch-checked {\n border: 1px solid #2272B4;\n background-color: #2272B4;\n}\n.du-bois-dark-switch-checked .du-bois-dark-switch-handle::before {\n box-shadow: none;\n}\n.du-bois-dark-switch:focus-visible {\n box-shadow: 0px 0px 0px 1px #2272B4;\n border: 1px solid #2272B4;\n}\n.du-bois-dark-switch:focus-visible .du-bois-dark-switch-handle::before {\n box-shadow: 0px 0px 0px 1px #2272B4;\n}\n.du-bois-dark-switch-checked:focus-visible {\n box-shadow: 0px 0px 0px 1px #ffffff, 0px 0px 0px 3px #2272B4;\n}\n.du-bois-dark-switch:hover {\n border: 1px solid #186099;\n background-color: rgba(34, 114, 180, 0.08);\n}\n.du-bois-dark-switch:hover .du-bois-dark-switch-handle::before {\n box-shadow: 0px 0px 0px 1px #186099;\n}\n.du-bois-dark-switch-checked:hover {\n background-color: #186099;\n}\n.du-bois-dark-switch:active {\n background-color: rgba(34, 114, 180, 0.16);\n}\n.du-bois-dark-switch-checked:active {\n background-color: #0d4f85;\n}\n.du-bois-dark-switch-disabled,\n.du-bois-dark-switch-disabled:hover {\n border: 1px solid #a2aeb8;\n}\n.du-bois-dark-switch-disabled .du-bois-dark-switch-handle::before,\n.du-bois-dark-switch-disabled:hover .du-bois-dark-switch-handle::before {\n box-shadow: 0px 0px 0px 1px #a2aeb8;\n}\n.du-bois-dark-switch-disabled.du-bois-dark-switch-checked {\n background-color: #a2aeb8;\n}\n.du-bois-dark-switch:not(.du-bois-dark-switch-disabled):active .du-bois-dark-switch-handle::before {\n right: 0;\n left: 0;\n}\n.du-bois-dark-switch:not(.du-bois-dark-switch-disabled):active.du-bois-dark-switch-checked .du-bois-dark-switch-handle::before {\n right: 0;\n left: 0;\n}\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n.du-bois-dark-breadcrumb {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #E8ECF0;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: tnum;\n color: #92A4B3;\n font-size: 13px;\n}\n.du-bois-dark-breadcrumb .anticon {\n font-size: 13px;\n}\n.du-bois-dark-breadcrumb a {\n color: #92A4B3;\n transition: color 0.3s;\n}\n.du-bois-dark-breadcrumb a:hover {\n color: #1d517c;\n}\n.du-bois-dark-breadcrumb > span:last-child {\n color: #E8ECF0;\n}\n.du-bois-dark-breadcrumb > span:last-child a {\n color: #E8ECF0;\n}\n.du-bois-dark-breadcrumb > span:last-child .du-bois-dark-breadcrumb-separator {\n display: none;\n}\n.du-bois-dark-breadcrumb-separator {\n margin: 0 4px;\n color: #92A4B3;\n}\n.du-bois-dark-breadcrumb-link > .anticon + span,\n.du-bois-dark-breadcrumb-link > .anticon + a {\n margin-left: 4px;\n}\n.du-bois-dark-breadcrumb-overlay-link > .anticon {\n margin-left: 4px;\n}\n.du-bois-dark-breadcrumb a {\n color: #2272B4;\n text-decoration: none;\n outline: none;\n cursor: pointer;\n transition: color 0.3s;\n}\n.du-bois-dark-breadcrumb a:focus,\n.du-bois-dark-breadcrumb a:hover {\n color: #1d517c;\n}\n.du-bois-dark-breadcrumb a:active {\n color: #3f81b1;\n}\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n\n/* stylelint-disable at-rule-empty-line-before,at-rule-name-space-after,at-rule-no-unknown */\n.du-bois-dark-notification .du-bois-dark-picker-clear,\n.du-bois-dark-notification .du-bois-dark-slider-handle,\n.du-bois-dark-notification .du-bois-dark-anchor-wrapper,\n.du-bois-dark-notification .du-bois-dark-collapse-content,\n.du-bois-dark-notification .du-bois-dark-timeline-item-head,\n.du-bois-dark-notification .du-bois-dark-card {\n background-color: #1f1f1f;\n}\n.du-bois-dark-notification .du-bois-dark-transfer-list-header {\n background: #1f1f1f;\n border-bottom: 1px solid #3a3a3a;\n}\n.du-bois-dark-notification .du-bois-dark-transfer-list-content-item:not(.du-bois-dark-transfer-list-content-item-disabled):hover {\n background-color: rgba(255, 255, 255, 0.08);\n}\n.du-bois-dark-notification tr.du-bois-dark-table-expanded-row > td,\n.du-bois-dark-notification tr.du-bois-dark-table-expanded-row:hover > td {\n background: #272727;\n}\n.du-bois-dark-notification .du-bois-dark-table.du-bois-dark-table-small thead > tr > th {\n background-color: #1f1f1f;\n border-bottom: 1px solid #3a3a3a;\n}\n.du-bois-dark-notification .du-bois-dark-table {\n background-color: #1f1f1f;\n}\n.du-bois-dark-notification .du-bois-dark-table .du-bois-dark-table-row-expand-icon {\n border: 1px solid #3a3a3a;\n}\n.du-bois-dark-notification .du-bois-dark-table tfoot > tr > th,\n.du-bois-dark-notification .du-bois-dark-table tfoot > tr > td {\n border-bottom: 1px solid #3a3a3a;\n}\n.du-bois-dark-notification .du-bois-dark-table thead > tr > th {\n background-color: #272727;\n border-bottom: 1px solid #3a3a3a;\n}\n.du-bois-dark-notification .du-bois-dark-table tbody > tr > td {\n border-bottom: 1px solid #3a3a3a;\n}\n.du-bois-dark-notification .du-bois-dark-table tbody > tr > td.du-bois-dark-table-cell-fix-left,\n.du-bois-dark-notification .du-bois-dark-table tbody > tr > td.du-bois-dark-table-cell-fix-right {\n background-color: #1f1f1f;\n}\n.du-bois-dark-notification .du-bois-dark-table tbody > tr.du-bois-dark-table-row:hover > td {\n background: #303030;\n}\n.du-bois-dark-notification .du-bois-dark-table.du-bois-dark-table-bordered .du-bois-dark-table-title {\n border: 1px solid #3a3a3a;\n}\n.du-bois-dark-notification .du-bois-dark-table.du-bois-dark-table-bordered thead > tr > th,\n.du-bois-dark-notification .du-bois-dark-table.du-bois-dark-table-bordered tbody > tr > td,\n.du-bois-dark-notification .du-bois-dark-table.du-bois-dark-table-bordered tfoot > tr > th,\n.du-bois-dark-notification .du-bois-dark-table.du-bois-dark-table-bordered tfoot > tr > td {\n border-right: 1px solid #3a3a3a;\n}\n.du-bois-dark-notification .du-bois-dark-table.du-bois-dark-table-bordered .du-bois-dark-table-cell-fix-right-first::after {\n border-right: 1px solid #3a3a3a;\n}\n.du-bois-dark-notification .du-bois-dark-table.du-bois-dark-table-bordered table thead > tr:not(:last-child) > th {\n border-bottom: 1px solid #303030;\n}\n.du-bois-dark-notification .du-bois-dark-table.du-bois-dark-table-bordered .du-bois-dark-table-container {\n border: 1px solid #3a3a3a;\n}\n.du-bois-dark-notification .du-bois-dark-table.du-bois-dark-table-bordered .du-bois-dark-table-expanded-row-fixed::after {\n border-right: 1px solid #3a3a3a;\n}\n.du-bois-dark-notification .du-bois-dark-table.du-bois-dark-table-bordered .du-bois-dark-table-footer {\n border: 1px solid #3a3a3a;\n}\n.du-bois-dark-notification .du-bois-dark-table .du-bois-dark-table-filter-trigger-container-open {\n background-color: #525252;\n}\n.du-bois-dark-notification .du-bois-dark-picker-calendar-full {\n background-color: #1f1f1f;\n}\n.du-bois-dark-notification .du-bois-dark-picker-calendar-full .du-bois-dark-picker-panel {\n background-color: #1f1f1f;\n}\n.du-bois-dark-notification .du-bois-dark-picker-calendar-full .du-bois-dark-picker-panel .du-bois-dark-picker-calendar-date {\n border-top: 2px solid #3a3a3a;\n}\n.du-bois-dark-notification .du-bois-dark-tabs.du-bois-dark-tabs-card .du-bois-dark-tabs-card-bar .du-bois-dark-tabs-tab-active {\n background-color: #1f1f1f;\n border-bottom: 1px solid #1f1f1f;\n}\n.du-bois-dark-notification .du-bois-dark-badge-count {\n box-shadow: 0 0 0 1px #1f1f1f;\n}\n.du-bois-dark-notification .du-bois-dark-tree-show-line .du-bois-dark-tree-switcher {\n background: #1f1f1f;\n}\n.du-bois-dark-notification {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n color: #E8ECF0;\n font-size: 13px;\n font-variant: tabular-nums;\n line-height: 20px;\n list-style: none;\n font-feature-settings: tnum;\n position: fixed;\n z-index: 1010;\n margin-right: 24px;\n}\n.du-bois-dark-notification-topLeft,\n.du-bois-dark-notification-bottomLeft {\n margin-right: 0;\n margin-left: 24px;\n}\n.du-bois-dark-notification-topLeft .du-bois-dark-notification-fade-enter.du-bois-dark-notification-fade-enter-active,\n.du-bois-dark-notification-bottomLeft .du-bois-dark-notification-fade-enter.du-bois-dark-notification-fade-enter-active,\n.du-bois-dark-notification-topLeft .du-bois-dark-notification-fade-appear.du-bois-dark-notification-fade-appear-active,\n.du-bois-dark-notification-bottomLeft .du-bois-dark-notification-fade-appear.du-bois-dark-notification-fade-appear-active {\n animation-name: NotificationLeftFadeIn;\n}\n.du-bois-dark-notification-close-icon {\n font-size: 13px;\n cursor: pointer;\n}\n.du-bois-dark-notification-hook-holder {\n position: relative;\n}\n.du-bois-dark-notification-notice {\n position: relative;\n width: 384px;\n max-width: calc(100vw - 24px * 2);\n margin-bottom: 16px;\n margin-left: auto;\n padding: 16px 24px;\n overflow: hidden;\n line-height: 20px;\n word-wrap: break-word;\n background: #1f1f1f;\n border-radius: 4px;\n box-shadow: 0 3px 6px -4px rgba(0, 0, 0, 0.48), 0 6px 16px 0 rgba(0, 0, 0, 0.32), 0 9px 28px 8px rgba(0, 0, 0, 0.2);\n}\n.du-bois-dark-notification-topLeft .du-bois-dark-notification-notice,\n.du-bois-dark-notification-bottomLeft .du-bois-dark-notification-notice {\n margin-right: auto;\n margin-left: 0;\n}\n.du-bois-dark-notification-notice-message {\n margin-bottom: 8px;\n color: rgba(255, 255, 255, 0.85);\n font-size: 18px;\n line-height: 24px;\n}\n.du-bois-dark-notification-notice-message-single-line-auto-margin {\n display: block;\n width: calc(384px - 24px * 2 - 24px - 48px - 100%);\n max-width: 4px;\n background-color: transparent;\n pointer-events: none;\n}\n.du-bois-dark-notification-notice-message-single-line-auto-margin::before {\n display: block;\n content: '';\n}\n.du-bois-dark-notification-notice-description {\n font-size: 13px;\n}\n.du-bois-dark-notification-notice-closable .du-bois-dark-notification-notice-message {\n padding-right: 24px;\n}\n.du-bois-dark-notification-notice-with-icon .du-bois-dark-notification-notice-message {\n margin-bottom: 4px;\n margin-left: 48px;\n font-size: 18px;\n}\n.du-bois-dark-notification-notice-with-icon .du-bois-dark-notification-notice-description {\n margin-left: 48px;\n font-size: 13px;\n}\n.du-bois-dark-notification-notice-icon {\n position: absolute;\n margin-left: 4px;\n font-size: 24px;\n line-height: 24px;\n}\n.anticon.du-bois-dark-notification-notice-icon-success {\n color: #34824F;\n}\n.anticon.du-bois-dark-notification-notice-icon-info {\n color: #64727D;\n}\n.anticon.du-bois-dark-notification-notice-icon-warning {\n color: #AF5B23;\n}\n.anticon.du-bois-dark-notification-notice-icon-error {\n color: #E65B77;\n}\n.du-bois-dark-notification-notice-close {\n position: absolute;\n top: 16px;\n right: 22px;\n color: #92A4B3;\n outline: none;\n}\n.du-bois-dark-notification-notice-close:hover {\n color: rgba(255, 255, 255, 0.85);\n}\n.du-bois-dark-notification-notice-btn {\n float: right;\n margin-top: 16px;\n}\n.du-bois-dark-notification .notification-fade-effect {\n animation-duration: 0.24s;\n animation-timing-function: cubic-bezier(0.645, 0.045, 0.355, 1);\n animation-fill-mode: both;\n}\n.du-bois-dark-notification-fade-enter,\n.du-bois-dark-notification-fade-appear {\n animation-duration: 0.24s;\n animation-timing-function: cubic-bezier(0.645, 0.045, 0.355, 1);\n animation-fill-mode: both;\n opacity: 0;\n animation-play-state: paused;\n}\n.du-bois-dark-notification-fade-leave {\n animation-duration: 0.24s;\n animation-timing-function: cubic-bezier(0.645, 0.045, 0.355, 1);\n animation-fill-mode: both;\n animation-duration: 0.2s;\n animation-play-state: paused;\n}\n.du-bois-dark-notification-fade-enter.du-bois-dark-notification-fade-enter-active,\n.du-bois-dark-notification-fade-appear.du-bois-dark-notification-fade-appear-active {\n animation-name: NotificationFadeIn;\n animation-play-state: running;\n}\n.du-bois-dark-notification-fade-leave.du-bois-dark-notification-fade-leave-active {\n animation-name: NotificationFadeOut;\n animation-play-state: running;\n}\n@keyframes NotificationFadeIn {\n 0% {\n left: 384px;\n opacity: 0;\n }\n 100% {\n left: 0;\n opacity: 1;\n }\n}\n@keyframes NotificationLeftFadeIn {\n 0% {\n right: 384px;\n opacity: 0;\n }\n 100% {\n right: 0;\n opacity: 1;\n }\n}\n@keyframes NotificationFadeOut {\n 0% {\n max-height: 150px;\n margin-bottom: 16px;\n opacity: 1;\n }\n 100% {\n max-height: 0;\n margin-bottom: 0;\n padding-top: 0;\n padding-bottom: 0;\n opacity: 0;\n }\n}\n.du-bois-dark-notification-notice {\n padding: 10px 40px 10px 32px;\n box-shadow: 0px 4px 16px rgba(46, 56, 64, 0.12);\n}\n.du-bois-dark-notification-notice .du-bois-dark-notification-notice-icon {\n font-size: 16px;\n width: 16px;\n height: 16px;\n margin-left: 0;\n left: 12px;\n top: 12px;\n line-height: 16px;\n}\n.du-bois-dark-notification-notice .du-bois-dark-notification-notice-close {\n top: 12px;\n right: 12px;\n width: 16px;\n height: 16px;\n line-height: 16px;\n}\n.du-bois-dark-notification-notice .du-bois-dark-notification-notice-close span[role='img'] {\n font-size: 16px;\n}\n.du-bois-dark-notification-notice .du-bois-dark-notification-notice-message {\n display: flex;\n align-items: center;\n min-height: 20px;\n line-height: 20px;\n font-weight: 600;\n}\n.du-bois-dark-notification-notice .du-bois-dark-notification-notice-message,\n.du-bois-dark-notification-notice .du-bois-dark-notification-notice-description {\n margin-left: unset;\n font-size: 13px;\n margin-bottom: 0;\n}\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n\n.du-bois-light-typography a {\n color: #2272b4;\n}\n.du-bois-light-typography a:hover {\n color: #0e538b;\n}\n.du-bois-light-typography a:focus {\n color: #04355d;\n}\n.du-bois-dark-typography a {\n color: #4299e0;\n}\n.du-bois-dark-typography a:hover {\n color: #8acaff;\n}\n.du-bois-dark-typography a:focus {\n color: #bae1fc;\n}\n/* stylelint-disable no-duplicate-selectors */\n/* stylelint-disable */\n/* stylelint-disable declaration-bang-space-before,no-duplicate-selectors,string-no-newline */\n",".mlflow-center {\n text-align: center;\n}\n\n.mlflow-error-image {\n margin: 12% auto 60px;\n display: block;\n}\n"],"names":[],"sourceRoot":""} \ No newline at end of file diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/js/build/static/js/1107.3b7eb241.chunk.js b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/js/build/static/js/1107.3b7eb241.chunk.js new file mode 100644 index 0000000000000000000000000000000000000000..4f3f5dd5f8fd28033342a2a6b2b6b8fe0724be43 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/js/build/static/js/1107.3b7eb241.chunk.js @@ -0,0 +1,11 @@ +"use strict";(self.webpackChunk_mlflow_mlflow=self.webpackChunk_mlflow_mlflow||[]).push([[1107],{3159:function(e,t,s){s.d(t,{S9:function(){return O},PK:function(){return B},nl:function(){return G},I$:function(){return j},Ay:function(){return z}});var i=s(89555),n=s(31014),r=s(10811),o=s(76010),a=s(68621),l=s(26809),c=s(9133),d=s.n(c),u=s(82645),h=s(26458),p=s(89933),m=s(64912),g=s(68048),y=s(50111);function v({columns:e,values:t,styles:s={},testId:i,scroll:n}){return(0,y.Y)(g.LegacyTable,{className:"mlflow-html-table-view","data-testid":i,dataSource:t,columns:e,scroll:n,size:"middle",pagination:!1,style:s})}var x=s(85017),A=s(48624),C=s(58481);class f extends n.Component{render(){const{runUuids:e}=this.props;return(0,y.Y)("div",{className:"metrics-summary",children:e.length>1?this.renderMetricTables():this.renderRunTable(e[0])})}renderRunTable(e){const{metricKeys:t,latestMetrics:s,minMetrics:i,maxMetrics:n,intl:r}=this.props,o=[{title:r.formatMessage({id:"YINJEO",defaultMessage:"Metric"}),dataIndex:"metricKey",sorter:(e,t)=>e.metricKeyt.metricKey?1:0,width:350},...this.dataColumns()];return 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W={getMetricHistoryApi:l.Ly,getRunApi:l.aO};var z=(0,P.h)((0,r.Ng)(((e,t)=>{const{runUuids:s}=t,i=s.filter((t=>"RUNNING"!==(0,h.K4)(t,e).status)),{latestMetricsByRunUuid:n,metricsByRunUuid:r}=e.entities,a=d().flatMap(s,(e=>{const t=n[e];return t?Object.keys(t):[]})),l=[...new Set(a)].sort(),c=[];let u=!1;const p=d().flatMap(s,(t=>{const s=o.A.getRunDisplayName((0,h.K4)(t,e),t);c.push(s);const i=r[t];return i?Object.keys(i).map((e=>{const n=i[e].map((e=>(0,V.rj)(e)));return n.some((({value:e})=>"number"===typeof e&&!isNaN(e)&&!isFinite(e)))&&(u=!0),{metricKey:e,history:n,runUuid:t,runDisplayName:s}})):[]}));return{runDisplayNames:c,latestMetricsByRunUuid:n,distinctMetricKeys:l,metricsWithRunInfoAndHistory:p,completedRunUuids:i,containsInfinities:u}}),W)(Z))},20193:function(e,t,s){s.d(t,{o:function(){return p}});var i=s(89555),n=s(59403),r=s(68048),o=s(70169),a=s(9133),l=s(31014),c=s(50111);const d=(e,t,s)=>(0,a.keys)(e).reduce(((e,s)=>Math.abs(Number(s)-t)"down"===s?Math.max(...Object.keys(e).filter((e=>Number(e)Number(e)>t)).map(Number));var h={name:"b9l38d",styles:"width:min-content"};const p=({max:e=1,min:t=0,step:s,marks:p,value:m,onChange:g,disabled:y,onAfterChange:v,componentId:x,className:A})=>{const{theme:C}=(0,n.u)(),f=!(0,a.isEmpty)(p),I=f&&Object.keys(p).length<10,[k,b]=(0,l.useState)(void 0);return(0,c.FD)("div",{css:(0,i.AH)({display:"flex",height:C.general.heightSm,gap:C.spacing.md,alignItems:"center"},""),children:[(0,c.FD)(r.Slider.Root,{disabled:y,css:(0,i.AH)({flex:1,position:"relative","span:last-child":{zIndex:2}},""),className:A,min:t,max:e,value:[null!==m&&void 0!==m?m:0],onValueCommit:([e])=>null===v||void 0===v?void 0:v(e),onKeyDown:e=>{if(f&&(e.preventDefault(),["ArrowLeft","ArrowRight","ArrowUp","ArrowDown"].includes(e.key))){const t=u(p,null!==m&&void 0!==m?m:0,"ArrowLeft"===e.key||"ArrowDown"===e.key?"down":"up");null===v||void 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e?e(!c,u,t,h):c||u||this.props.shouldOptimisticallyRender?p.length>0&&i?i:(u&&!n&&(r?r(h):d(h)),e):s||(0,l.Y)(o.y,{})}render(){return this.getRenderedContent()}}c.defaultProps={requests:[],requestIdsWith404sToIgnore:[],shouldOptimisticallyRender:!1};const d=e=>{throw console.error("ERROR",e),Error(`A request error occurred.: ${e.error}`)};t.Ay=(0,n.Ng)(((e,t)=>({requests:(0,r.EW)(t.requestIds,e)})))(c)},79445:function(e,t,s){s.d(t,{W:function(){return l}});var i=s(31014),n=s(68048),r=s(27705),o=s(50111);const a=i.lazy((()=>s.e(4850).then(s.bind(s,84850)))),l=({fallback:e,...t})=>(0,o.Y)(r.g,{children:(0,o.Y)(i.Suspense,{fallback:null!==e&&void 0!==e?e:(0,o.Y)(n.LegacySkeleton,{active:!0}),children:(0,o.Y)(a,{...t})})})},82645:function(e,t,s){s.d(t,{gT:function(){return C},mV:function(){return y}});var i=s(4473),n=s(68341),r=s(31014),o=s(76010),a=s(9133),l=s.n(a),c=s(47019),d=s(89933),u=s(3159),h=s(79445),p=s(32039),m=s(64912),g=s(50111);const y=(e,t)=>{if(t<=1||!e||e.length<=1||e.every((t=>t===e[0])))return e;const s=t/(d.W0+1),i=[];let n=0;for(let r=0;r{var e;const{metrics:t,xAxis:s,extraLayout:n}=this.props,r="log"===(null===n||void 0===n||null===(e=n.yaxis)||void 0===e?void 0:e.type),o={};t.forEach((e=>{const{metricKey:t,history:i}=e;o[t]=(0,p.cX)({xValues:C.getXValuesForLineChart(i,s,this.props.intl),yValues:i.map((e=>"number"===typeof e.value?e.value:Number(e.value))),isLogScale:r,stringFormatter:e=>this.props.intl.formatMessage(e,{metricKey:t})})})),(0,i.A)(this,x)[x]=o},Object.defineProperty(this,x,{writable:!0,value:{}}),this.getPlotPropsForLineChart=()=>{const{metrics:e,xAxis:t,showPoint:s,lineSmoothness:n,isComparing:r,deselectedCurves:a}=this.props,l=new Set(a),c=[],d=[],u={data:e.map((e=>{const{metricKey:a,runDisplayName:u,history:h,runUuid:p}=e,m=h.map((e=>"number"===typeof 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* NOTE: this code file was automatically migrated to TypeScript using ts-migrate and\n * may contain multiple `any` type annotations and `@ts-expect-error` directives.\n * If possible, please improve types while making changes to this file. If the type\n * annotations are already looking good, please remove this comment.\n */\n\nimport { LegacyTable } from '@databricks/design-system';\nimport './HtmlTableView.css';\n\ntype Props = {\n columns: any[];\n values: any[];\n styles?: any;\n testId?: string;\n scroll?: any;\n};\n\nexport function HtmlTableView({ columns, values, styles = {}, testId, scroll }: Props) {\n return (\n \n );\n}\n","/**\n * NOTE: this code file was automatically migrated to TypeScript using ts-migrate and\n * may contain multiple `any` type annotations and `@ts-expect-error` directives.\n * If possible, please improve types while making changes to this file. If the type\n * annotations are already looking good, please remove this comment.\n */\n\nimport React, { Fragment } from 'react';\nimport { connect } from 'react-redux';\nimport { injectIntl } from 'react-intl';\nimport { HtmlTableView } from './HtmlTableView';\nimport { getRunInfo } from '../reducers/Reducers';\nimport { getLatestMetrics, getMinMetrics, getMaxMetrics } from '../reducers/MetricReducer';\nimport Utils from '../../common/utils/Utils';\nimport { Link } from '../../common/utils/RoutingUtils';\nimport Routes from '../routes';\nimport type { RunInfoEntity } from '../types';\n\nconst maxTableHeight = 300;\n// Because we make the table body scrollable, column widths must be fixed\n// so that the header widths match the table body column widths.\nconst headerColWidth = 350;\nconst dataColWidth = 200;\n\ntype MetricsSummaryTableProps = {\n runUuids: string[];\n runExperimentIds: any;\n runDisplayNames: string[];\n metricKeys: string[];\n latestMetrics: any;\n minMetrics: any;\n maxMetrics: any;\n intl: {\n formatMessage: (...args: any[]) => any;\n };\n};\n\nclass MetricsSummaryTable extends React.Component {\n render() {\n const { runUuids } = this.props;\n return (\n
\n {runUuids.length > 1 ? this.renderMetricTables() : this.renderRunTable(runUuids[0])}\n
\n );\n }\n\n renderRunTable(runUuid: any) {\n const { metricKeys, latestMetrics, minMetrics, maxMetrics, intl } = this.props;\n const columns = [\n {\n title: intl.formatMessage({\n defaultMessage: 'Metric',\n description:\n // eslint-disable-next-line max-len\n 'Column title for the column displaying the metric names for a run',\n }),\n dataIndex: 'metricKey',\n sorter: (a: any, b: any) => (a.metricKey < b.metricKey ? -1 : a.metricKey > b.metricKey ? 1 : 0),\n width: headerColWidth,\n },\n ...this.dataColumns(),\n ];\n return metricKeys.length === 0 ? null : (\n \n );\n }\n\n renderMetricTables() {\n const { runExperimentIds, runUuids, runDisplayNames, metricKeys, latestMetrics, minMetrics, maxMetrics, intl } =\n this.props;\n const columns = [\n {\n title: intl.formatMessage({\n defaultMessage: 'Run',\n description:\n // eslint-disable-next-line max-len\n 'Column title for the column displaying the run names for a metric',\n }),\n dataIndex: 'runLink',\n sorter: (a: any, b: any) => (a.runName < b.runName ? -1 : a.runName > b.runName ? 1 : 0),\n width: headerColWidth,\n },\n ...this.dataColumns(),\n ];\n return metricKeys.map((metricKey) => {\n return (\n \n

{metricKey}

\n \n \n );\n });\n }\n\n dataColumns() {\n return [\n {\n title: this.props.intl.formatMessage({\n defaultMessage: 'Latest',\n description: 'Column title for the column displaying the latest metric values for a metric',\n }),\n dataIndex: 'latestFormatted',\n sorter: (a: any, b: any) => a.latestValue - b.latestValue,\n width: dataColWidth,\n ellipsis: true,\n },\n {\n title: this.props.intl.formatMessage({\n defaultMessage: 'Min',\n description: 'Column title for the column displaying the minimum metric values for a metric',\n }),\n dataIndex: 'minFormatted',\n sorter: (a: any, b: any) => a.minValue - b.minValue,\n width: dataColWidth,\n ellipsis: true,\n },\n {\n title: this.props.intl.formatMessage({\n defaultMessage: 'Max',\n description: 'Column title for the column displaying the maximum metric values for a metric',\n }),\n dataIndex: 'maxFormatted',\n sorter: (a: any, b: any) => a.maxValue - b.maxValue,\n width: dataColWidth,\n ellipsis: true,\n },\n ];\n }\n}\n\nconst getMetricValuesByRun = (\n metricKey: any,\n runExperimentIds: any,\n runUuids: any,\n runDisplayNames: any,\n latestMetrics: any,\n minMetrics: any,\n maxMetrics: any,\n intl: any,\n) => {\n return runUuids.map((runUuid: any, runIdx: any) => {\n const runName = runDisplayNames[runIdx];\n return {\n runName: runName,\n runLink: {runName},\n key: runUuid,\n ...rowData(runUuid, metricKey, latestMetrics, minMetrics, maxMetrics, intl),\n };\n });\n};\n\nconst getRunValuesByMetric = (\n runUuid: any,\n metricKeys: any,\n latestMetrics: any,\n minMetrics: any,\n maxMetrics: any,\n intl: any,\n) => {\n return metricKeys.map((metricKey: any) => {\n return {\n metricKey,\n key: metricKey,\n ...rowData(runUuid, metricKey, latestMetrics, minMetrics, maxMetrics, intl),\n };\n });\n};\n\nconst rowData = (runUuid: any, metricKey: any, latestMetrics: any, minMetrics: any, maxMetrics: any, intl: any) => {\n const latestMetric = getMetric(latestMetrics, runUuid, metricKey);\n const minMetric = getMetric(minMetrics, runUuid, metricKey);\n const maxMetric = getMetric(maxMetrics, runUuid, metricKey);\n const latestValue = getValue(latestMetric);\n const minValue = getValue(minMetric);\n const maxValue = getValue(maxMetric);\n return {\n latestFormatted: (\n \n {formatMetric(latestMetric, intl)}\n \n ),\n minFormatted: (\n \n {formatMetric(minMetric, intl)}\n \n ),\n maxFormatted: (\n \n {formatMetric(maxMetric, intl)}\n \n ),\n latestValue,\n minValue,\n maxValue,\n };\n};\n\nconst getMetric = (valuesMap: any, runUuid: any, metricKey: any) => valuesMap[runUuid] && valuesMap[runUuid][metricKey];\n\nconst getValue = (metric: any) => metric && metric.value;\n\nconst formatMetric = (metric: any, intl: any) =>\n metric === undefined\n ? ''\n : intl.formatMessage(\n {\n defaultMessage: '{value} (step={step})',\n description: 'Formats a metric value along with the step number it corresponds to',\n },\n {\n value: metric.value,\n step: metric.step,\n },\n );\n\nconst mapStateToProps = (state: any, ownProps: any) => {\n const { runUuids } = ownProps;\n const runExperimentIds = {};\n const latestMetrics = {};\n const minMetrics = {};\n const maxMetrics = {};\n runUuids.forEach((runUuid: any) => {\n const runInfo = getRunInfo(runUuid, state) as RunInfoEntity;\n // @ts-expect-error TS(7053): Element implicitly has an 'any' type because expre... Remove this comment to see the full error message\n runExperimentIds[runUuid] = runInfo && runInfo.experimentId;\n // @ts-expect-error TS(7053): Element implicitly has an 'any' type because expre... Remove this comment to see the full error message\n latestMetrics[runUuid] = getLatestMetrics(runUuid, state);\n // @ts-expect-error TS(7053): Element implicitly has an 'any' type because expre... Remove this comment to see the full error message\n minMetrics[runUuid] = getMinMetrics(runUuid, state);\n // @ts-expect-error TS(7053): Element implicitly has an 'any' type because expre... Remove this comment to see the full error message\n maxMetrics[runUuid] = getMaxMetrics(runUuid, state);\n });\n return { runExperimentIds, latestMetrics, minMetrics, maxMetrics };\n};\n\n// @ts-expect-error TS(2769): No overload matches this call.\nconst MetricsSummaryTableWithIntl = injectIntl(MetricsSummaryTable);\n\nexport default connect(mapStateToProps)(MetricsSummaryTableWithIntl);\n","import React from 'react';\nimport { Button } from '@databricks/design-system';\n\ntype Props = {\n icon: React.ReactNode;\n style?: React.CSSProperties;\n className?: string;\n onClick?: () => void;\n restProps?: unknown;\n};\n\nexport const IconButton = ({ icon, className, style, onClick, ...restProps }: Props) => {\n return (\n \n {icon}\n \n );\n};\n","import React from 'react';\nimport { Link } from '../../common/utils/RoutingUtils';\nimport Routes from '../routes';\nimport { IconButton } from '../../common/components/IconButton';\nimport Utils from '../../common/utils/Utils';\nimport { LegacyPopover } from '@databricks/design-system';\nimport { type RunItem } from '../types';\n\ntype Props = {\n experimentId: string;\n visible: boolean;\n x: number;\n y: number;\n runItems: RunItem[];\n handleClose: () => void;\n handleKeyDown: ({ key }: { key: string }) => void;\n handleVisibleChange: (visible: boolean) => void;\n};\n\nexport class RunLinksPopover extends React.Component {\n componentDidMount() {\n document.addEventListener('keydown', this.props.handleKeyDown);\n }\n\n componentWillUnmount() {\n document.removeEventListener('keydown', this.props.handleKeyDown);\n }\n\n renderContent = () => {\n const { experimentId, runItems } = this.props;\n return (\n
\n {runItems.map(({ name, runId, color, y }, index) => {\n const key = `${runId}-${index}`;\n const to = Routes.getRunPageRoute(experimentId, runId);\n return (\n \n

\n \n {`${name}, ${Utils.formatMetric(y)}`}\n

\n \n );\n })}\n
\n );\n };\n\n renderTitle = () => {\n const { handleClose } = this.props;\n return (\n
\n Jump to individual runs\n }\n onClick={handleClose}\n style={{ float: 'right', marginLeft: '7px' }}\n />\n
\n );\n };\n\n render() {\n const { visible, x, y, handleVisibleChange } = this.props;\n return (\n \n \n \n );\n }\n}\n","/**\n * NOTE: this code file was automatically migrated to TypeScript using ts-migrate and\n * may contain multiple `any` type annotations and `@ts-expect-error` directives.\n * If possible, please improve types while making changes to this file. If the type\n * annotations are already looking good, please remove this comment.\n */\n\nimport React from 'react';\nimport { connect } from 'react-redux';\nimport Utils from '../../common/utils/Utils';\nimport RequestStateWrapper from '../../common/components/RequestStateWrapper';\nimport { getMetricHistoryApi, getRunApi } from '../actions';\nimport _ from 'lodash';\nimport { MetricsPlotView } from './MetricsPlotView';\nimport { getRunInfo } from '../reducers/Reducers';\nimport { MetricsPlotControls, X_AXIS_WALL, X_AXIS_RELATIVE, X_AXIS_STEP } from './MetricsPlotControls';\nimport MetricsSummaryTable from './MetricsSummaryTable';\nimport qs from 'qs';\nimport { withRouterNext } from '../../common/utils/withRouterNext';\nimport Routes from '../routes';\nimport { RunLinksPopover } from './RunLinksPopover';\nimport { getUUID } from '../../common/utils/ActionUtils';\nimport { saveAs } from 'file-saver';\nimport { Spinner } from '@databricks/design-system';\nimport { normalizeMetricsHistoryEntry } from '../utils/MetricsUtils';\nimport type { Location, NavigateFunction } from '../../common/utils/RoutingUtils';\n\nexport const CHART_TYPE_LINE = 'line';\nexport const CHART_TYPE_BAR = 'bar';\n\nexport const METRICS_PLOT_POLLING_INTERVAL_MS = 10 * 1000; // 10 seconds\n// A run is considered as 'hanging' if its status is 'RUNNING' but its latest metric was logged\n// prior to this threshold. The metrics plot doesn't automatically update hanging runs.\nexport const METRICS_PLOT_HANGING_RUN_THRESHOLD_MS = 3600 * 24 * 7 * 1000; // 1 week\nconst MAXIMUM_METRIC_DATA_POINTS = 100_000;\nconst GET_METRIC_HISTORY_MAX_RESULTS = 25000;\n\nexport const convertMetricsToCsv = (metrics: any) => {\n const header = ['run_id', ...Object.keys(metrics[0].history[0])];\n const rows = metrics.flatMap(({ runUuid, history }: any) =>\n history.map((metric: any) => [runUuid, ...Object.values(metric)]),\n );\n return [header]\n .concat(rows)\n .map((row) => row.join(','))\n .join('\\n');\n};\n\ntype OwnMetricsPlotPanelProps = {\n experimentIds: string[];\n runUuids: string[];\n completedRunUuids: string[];\n metricKey: string;\n latestMetricsByRunUuid: any;\n distinctMetricKeys: string[];\n metricsWithRunInfoAndHistory: any[];\n getMetricHistoryApi: (...args: any[]) => any;\n getRunApi: (...args: any[]) => any;\n location: Location;\n navigate: NavigateFunction;\n runDisplayNames: string[];\n containsInfinities: boolean;\n};\n\ntype MetricsPlotPanelState = any;\n\ntype MetricsPlotPanelProps = OwnMetricsPlotPanelProps & typeof MetricsPlotPanel.defaultProps;\n\nexport class MetricsPlotPanel extends React.Component {\n _isMounted = false;\n\n static defaultProps = {\n containsInfinities: false,\n };\n\n displayPopover: any;\n intervalId: any;\n\n // The fields below are exposed as instance attributes rather than component state so that they\n // can be updated without triggering a rerender.\n //\n // ID of Javascript future (created via setTimeout()) used to trigger legend-click events after a\n // delay, to allow time for double-click events to occur\n legendClickTimeout = null;\n // Time (millis after Unix epoch) since last legend click - if two clicks occur in short\n // succession, we trigger a double-click event & cancel the pending single-click.\n prevLegendClickTime = (Math as any).inf;\n\n // Last curve ID clicked in the legend, used to determine if we're double-clicking on a specific\n // legend curve\n lastClickedLegendCurveId = null;\n\n // Max time interval (in milliseconds) between two successive clicks on the metric plot legend\n // that constitutes a double-click\n MAX_DOUBLE_CLICK_INTERVAL_MS = 300;\n\n // Delay (in ms) between when a user clicks on the metric plot legend & when event-handler logic\n // (to toggle display of the selected curve on or off) actually fires. Set to a larger value than\n // MAX_DOUBLE_CLICK_INTERVAL_MS to allow time for the double-click handler to fire before firing\n // a single-click event.\n SINGLE_CLICK_EVENT_DELAY_MS = this.MAX_DOUBLE_CLICK_INTERVAL_MS + 10;\n\n constructor(props: MetricsPlotPanelProps) {\n super(props);\n this.state = {\n historyRequestIds: [],\n popoverVisible: false,\n popoverX: 0,\n popoverY: 0,\n popoverRunItems: [],\n focused: true,\n loading: false,\n };\n this.displayPopover = false;\n this.intervalId = null;\n }\n\n hasMultipleExperiments() {\n return this.props.experimentIds && this.props.experimentIds.length > 1;\n }\n\n onFocus = () => {\n this.setState({ focused: true });\n };\n\n onBlur = () => {\n this.setState({ focused: false });\n };\n\n clearEventListeners = () => {\n // `window.removeEventListener` does nothing when called with an unregistered event listener:\n // https://developer.mozilla.org/en-US/docs/Web/API/EventTarget/removeEventListener\n window.removeEventListener('focus', this.onFocus);\n window.removeEventListener('blur', this.onBlur);\n };\n\n clearInterval = () => {\n // `clearInterval` does nothing when called with `null` or `undefine`:\n // https://www.w3.org/TR/2011/WD-html5-20110525/timers.html#dom-windowtimers-cleartimeout\n clearInterval(this.intervalId);\n this.intervalId = null;\n };\n\n allRunsCompleted = () => {\n return this.props.completedRunUuids.length === this.props.runUuids.length;\n };\n\n isHangingRunUuid = (activeRunUuid: any) => {\n const metrics = this.props.latestMetricsByRunUuid[activeRunUuid];\n if (!metrics) {\n return false;\n }\n // @ts-expect-error TS(2345): Argument of type '({ timestamp }: { timestamp: any... Remove this comment to see the full error message\n const timestamps = Object.values(metrics).map(({ timestamp }) => timestamp);\n // @ts-expect-error TS(2345): Argument of type 'unknown' is not assignable to pa... Remove this comment to see the full error message\n const latestTimestamp = Math.max(...timestamps);\n return new Date().getTime() - latestTimestamp > METRICS_PLOT_HANGING_RUN_THRESHOLD_MS;\n };\n\n getActiveRunUuids = () => {\n const { completedRunUuids, runUuids } = this.props;\n const activeRunUuids = _.difference(runUuids, completedRunUuids);\n return activeRunUuids.filter(_.negate(this.isHangingRunUuid)); // Exclude hanging runs\n };\n\n shouldPoll = () => {\n return !(this.allRunsCompleted() || this.getActiveRunUuids().length === 0);\n };\n\n componentDidMount() {\n this._isMounted = true;\n this.loadMetricHistory(this.props.runUuids, this.getUrlState().selectedMetricKeys);\n if (this.shouldPoll()) {\n // Set event listeners to detect when this component gains/loses focus,\n // e.g., a user switches to a different browser tab or app.\n window.addEventListener('blur', this.onBlur);\n window.addEventListener('focus', this.onFocus);\n this.intervalId = setInterval(() => {\n // Skip polling if this component is out of focus.\n // @ts-expect-error TS(4111): Property 'focused' comes from an index signature, ... Remove this comment to see the full error message\n if (this.state.focused) {\n const activeRunUuids = this.getActiveRunUuids();\n this.loadMetricHistory(activeRunUuids, this.getUrlState().selectedMetricKeys);\n this.loadRuns(activeRunUuids);\n\n if (!this.shouldPoll()) {\n this.clearEventListeners();\n this.clearInterval();\n }\n }\n }, METRICS_PLOT_POLLING_INTERVAL_MS);\n }\n }\n\n componentWillUnmount() {\n this._isMounted = false;\n this.clearEventListeners();\n this.clearInterval();\n }\n\n getUrlState() {\n return Utils.getMetricPlotStateFromUrl(this.props.location.search);\n }\n\n static predictChartType(metrics: any) {\n // Show bar chart when every metric has exactly 1 metric history\n if (metrics && metrics.length && _.every(metrics, (metric) => metric.history && metric.history.length === 1)) {\n return CHART_TYPE_BAR;\n }\n return CHART_TYPE_LINE;\n }\n\n static isComparing(search: any) {\n const params = qs.parse(search);\n const runs = params && params['?runs'];\n // @ts-expect-error TS(2345): Argument of type 'string | string[] | ParsedQs | P... Remove this comment to see the full error message\n return runs ? JSON.parse(runs).length > 1 : false;\n }\n\n // Update page URL from component state. Intended to be called after React applies component\n // state updates, e.g. in a setState callback\n updateUrlState = (updatedState: any) => {\n const { runUuids, metricKey, location, navigate } = this.props;\n // @ts-expect-error TS(2345): Argument of type 'string | string[] | ParsedQs | P... Remove this comment to see the full error message\n const experimentIds = JSON.parse(qs.parse(location.search)['experiments']);\n const newState = {\n ...this.getUrlState(),\n ...updatedState,\n };\n const {\n selectedXAxis,\n selectedMetricKeys,\n showPoint,\n yAxisLogScale,\n lineSmoothness,\n layout,\n deselectedCurves,\n lastLinearYAxisRange,\n } = newState;\n navigate(\n Routes.getMetricPageRoute(\n runUuids,\n metricKey,\n experimentIds,\n selectedMetricKeys,\n layout,\n selectedXAxis,\n yAxisLogScale,\n lineSmoothness,\n showPoint,\n deselectedCurves,\n lastLinearYAxisRange,\n ),\n {\n replace: true,\n },\n );\n };\n\n getNumTotalMetrics = () => {\n return this.props.metricsWithRunInfoAndHistory.map(({ history }) => history.length).reduce((a, b) => a + b, 0);\n };\n\n loadMetricHistory = (runUuids: any, metricKeys: any) => {\n if (this.getNumTotalMetrics() >= MAXIMUM_METRIC_DATA_POINTS) {\n Utils.logErrorAndNotifyUser(\n 'The total number of metric data points exceeded 100,000. Cannot fetch more metrics.',\n );\n return Promise.resolve([]);\n }\n this.setState({ loading: true });\n const promises = runUuids\n .flatMap((id: any) =>\n metricKeys.map((key: any) => ({\n runUuid: id,\n metricKey: key,\n })),\n )\n // Avoid fetching non existing metrics\n .filter(({ runUuid, metricKey }: any) => this.props.latestMetricsByRunUuid[runUuid].hasOwnProperty(metricKey))\n .map(async ({ runUuid, metricKey }: any) => {\n const requestIds = [];\n const id = getUUID();\n requestIds.push(id);\n const firstPageResp = await this.props.getMetricHistoryApi(\n runUuid,\n metricKey,\n GET_METRIC_HISTORY_MAX_RESULTS,\n undefined,\n id,\n );\n\n let nextPageToken = firstPageResp.value.next_page_token;\n while (nextPageToken) {\n if (this.getNumTotalMetrics() >= MAXIMUM_METRIC_DATA_POINTS) {\n return { requestIds, success: false };\n }\n\n const uid = getUUID();\n requestIds.push(uid);\n /* eslint-disable no-await-in-loop */\n const nextPageResp = await this.props.getMetricHistoryApi(\n runUuid,\n metricKey,\n GET_METRIC_HISTORY_MAX_RESULTS,\n nextPageToken,\n uid,\n );\n nextPageToken = nextPageResp.value.next_page_token;\n }\n return { requestIds, success: true };\n });\n return Promise.all(promises).then((results) => {\n // Ensure we don't set state if component is unmounted\n if (this._isMounted) {\n this.setState({ loading: false });\n }\n if (!results.every(({ success }) => success)) {\n Utils.logErrorAndNotifyUser(\n 'The total number of metric data points exceeded 100,000. Aborted fetching metrics.',\n );\n }\n return results.flatMap(({ requestIds }) => requestIds);\n });\n };\n\n loadRuns = (runUuids: any) => {\n const requestIds: any = [];\n runUuids.forEach((runUuid: any) => {\n const id = getUUID();\n this.props.getRunApi(runUuid);\n requestIds.push(id);\n });\n return requestIds;\n };\n\n getMetrics = () => {\n /* eslint-disable no-param-reassign */\n const state = this.getUrlState();\n const selectedMetricsSet = new Set(state.selectedMetricKeys);\n const { selectedXAxis } = state;\n const { metricsWithRunInfoAndHistory } = this.props;\n\n // Take only selected metrics\n const metrics = metricsWithRunInfoAndHistory.filter((m) => selectedMetricsSet.has(m.metricKey));\n\n // Sort metric history based on selected x-axis\n metrics.forEach((metric) => {\n const isStep = selectedXAxis === X_AXIS_STEP && metric.history[0] && _.isNumber(metric.history[0].step);\n // Metric history can be large. Doing an in-place here to save memory\n metric.history.sort(isStep ? Utils.compareByStepAndTimestamp : Utils.compareByTimestamp);\n });\n\n return metrics;\n };\n\n /**\n * Handle changes in the scale type of the y-axis\n * @param yAxisLogScale: Boolean - if true, y-axis should be converted to log scale, and if false,\n * y-axis scale should be converted to a linear scale.\n */\n handleYAxisLogScaleChange = (yAxisLogScale: any) => {\n const state = this.getUrlState();\n const newLayout = _.cloneDeep(state.layout);\n const newAxisType = yAxisLogScale ? 'log' : 'linear';\n\n // Handle special case of a linear y-axis scale with negative values converted to log scale &\n // now being restored to linear scale, by restoring the old linear-axis range from\n // state.linearYAxisRange. In particular, we assume that if state.linearYAxisRange\n // is non-empty, it contains a linear y axis range with negative values.\n if (!yAxisLogScale && (state as any).lastLinearYAxisRange && (state as any).lastLinearYAxisRange.length > 0) {\n newLayout.yaxis = {\n type: 'linear',\n range: (state as any).lastLinearYAxisRange,\n };\n this.updateUrlState({ layout: newLayout, lastLinearYAxisRange: [] });\n return;\n }\n\n // Otherwise, if plot previously had no y axis range configured, simply set the axis type to\n // log or linear scale appropriately\n if (!state.layout.yaxis || !state.layout.yaxis.range) {\n newLayout.yaxis = {\n type: newAxisType,\n autorange: true,\n ...(newAxisType === 'log' ? { exponentformat: 'e' } : {}),\n };\n this.updateUrlState({ layout: newLayout, lastLinearYAxisRange: [] });\n return;\n }\n\n // lastLinearYAxisRange contains the last range used for a linear-scale y-axis. We set\n // this state attribute if and only if we're converting from a linear-scale y-axis with\n // negative bounds to a log scale axis, so that we can restore the negative bounds if we\n // subsequently convert back to a linear scale axis. Otherwise, we reset this attribute to an\n // empty array\n let lastLinearYAxisRange = [];\n\n // At this point, we know the plot previously had a y axis specified with range bounds\n // Convert the range to/from log scale as appropriate\n const oldLayout = state.layout;\n const oldYRange = oldLayout.yaxis.range;\n if (yAxisLogScale) {\n if (oldYRange[0] <= 0) {\n lastLinearYAxisRange = oldYRange;\n // When converting to log scale, handle negative values (which have no log-scale\n // representation as taking the log of a negative number is not possible) as follows:\n // If bottom of old Y range is negative, then tell plotly to infer the log y-axis scale\n // (set 'autorange' to true), and preserve the old range in the lastLinearYAxisRange\n // state attribute so that we can restore it if the user converts back to a linear-scale\n // y axis. We defer to Plotly's autorange here under the assumption that it will produce\n // a reasonable y-axis log scale for plots containing negative values.\n newLayout.yaxis = {\n type: 'log',\n autorange: true,\n exponentformat: 'e',\n };\n } else {\n newLayout.yaxis = {\n type: 'log',\n range: [Math.log(oldYRange[0]) / Math.log(10), Math.log(oldYRange[1]) / Math.log(10)],\n exponentformat: 'e',\n };\n }\n } else {\n // Otherwise, convert from log to linear scale normally\n newLayout.yaxis = {\n type: 'linear',\n range: [Math.pow(10, oldYRange[0]), Math.pow(10, oldYRange[1])],\n };\n }\n this.updateUrlState({ layout: newLayout, lastLinearYAxisRange });\n };\n\n /**\n * Handle changes in the type of the metric plot's X axis (e.g. changes from wall-clock\n * scale to relative-time scale to step-based scale).\n * @param e: Selection event such that e.target.value is a string containing the new X axis type\n */\n handleXAxisChange = (e: any) => {\n // Set axis value type, & reset axis scaling via autorange\n const state = this.getUrlState();\n const axisEnumToPlotlyType = {\n [X_AXIS_WALL]: 'date',\n [X_AXIS_RELATIVE]: 'linear',\n [X_AXIS_STEP]: 'linear',\n };\n // @ts-expect-error TS(7053): Element implicitly has an 'any' type because expre... Remove this comment to see the full error message\n const axisType = axisEnumToPlotlyType[e.target.value] || 'linear';\n const newLayout = {\n ...state.layout,\n xaxis: {\n autorange: true,\n type: axisType,\n },\n };\n this.updateUrlState({ selectedXAxis: e.target.value, layout: newLayout });\n };\n\n getAxisType() {\n const state = this.getUrlState();\n return state.layout && state.layout.yaxis && state.layout.yaxis.type === 'log' ? 'log' : 'linear';\n }\n\n /**\n * Handle changes to metric plot layout (x & y axis ranges), e.g. specifically if the user\n * zooms in or out on the plot.\n *\n * @param newLayout: Object containing the new Plot layout. See\n * https://plot.ly/javascript/plotlyjs-events/#update-data for details on the object's fields\n * and schema.\n */\n handleLayoutChange = (newLayout: any) => {\n this.displayPopover = false;\n const state = this.getUrlState();\n // Unfortunately, we need to parse out the x & y axis range changes from the onLayout event...\n // see https://plot.ly/javascript/plotlyjs-events/#update-data\n const {\n 'xaxis.range[0]': newXRange0,\n 'xaxis.range[1]': newXRange1,\n 'yaxis.range[0]': newYRange0,\n 'yaxis.range[1]': newYRange1,\n 'xaxis.autorange': xAxisAutorange,\n 'yaxis.autorange': yAxisAutorange,\n 'yaxis.showspikes': yAxisShowSpikes,\n 'xaxis.showspikes': xAxisShowSpikes,\n ...restFields\n } = newLayout;\n\n let mergedLayout = {\n ...state.layout,\n ...restFields,\n };\n let lastLinearYAxisRange = [...(state as any).lastLinearYAxisRange];\n\n // Set fields for x axis\n const newXAxis = mergedLayout.xaxis || {};\n if (newXRange0 !== undefined && newXRange1 !== undefined) {\n newXAxis.range = [newXRange0, newXRange1];\n newXAxis.autorange = false;\n }\n if (xAxisShowSpikes) {\n newXAxis.showspikes = true;\n }\n if (xAxisAutorange) {\n newXAxis.autorange = true;\n }\n // Set fields for y axis\n const newYAxis = mergedLayout.yaxis || {};\n if (newYRange0 !== undefined && newYRange1 !== undefined) {\n newYAxis.range = [newYRange0, newYRange1];\n newYAxis.autorange = false;\n }\n if (yAxisShowSpikes) {\n newYAxis.showspikes = true;\n }\n if (yAxisAutorange) {\n lastLinearYAxisRange = [];\n const axisType = state.layout && state.layout.yaxis && state.layout.yaxis.type === 'log' ? 'log' : 'linear';\n newYAxis.autorange = true;\n newYAxis.type = axisType;\n }\n if (newYAxis.type === 'log') {\n newYAxis.exponentformat = 'e';\n }\n // Merge new X & Y axis info into layout\n mergedLayout = {\n ...mergedLayout,\n xaxis: newXAxis,\n yaxis: newYAxis,\n };\n this.updateUrlState({ layout: mergedLayout, lastLinearYAxisRange });\n };\n\n handleDownloadCsv = () => {\n const csv = convertMetricsToCsv(this.props.metricsWithRunInfoAndHistory);\n const blob = new Blob([csv], { type: 'application/csv;charset=utf-8' });\n saveAs(blob, 'metrics.csv');\n };\n\n // Return unique key identifying the curve or bar chart corresponding to the specified\n // Plotly plot data element\n static getCurveKey(plotDataElem: any) {\n // In bar charts, each legend item consists of a single run ID (all bars for that run are\n // associated with & toggled by that legend item)\n if (plotDataElem.type === 'bar') {\n return plotDataElem.runId;\n } else {\n // In line charts, each (run, metricKey) tuple has its own legend item, so construct\n // a unique legend item identifier by concatenating the run id & metric key\n return Utils.getCurveKey(plotDataElem.runId, plotDataElem.metricName);\n }\n }\n\n /**\n * Handle clicking on a single curve within the plot legend in order to toggle its display\n * on/off.\n */\n handleLegendClick = ({ curveNumber, data }: any) => {\n // If two clicks in short succession, trigger double-click event\n const state = this.getUrlState();\n const currentTime = Date.now();\n if (\n currentTime - this.prevLegendClickTime < this.MAX_DOUBLE_CLICK_INTERVAL_MS &&\n curveNumber === this.lastClickedLegendCurveId\n ) {\n this.handleLegendDoubleClick({ curveNumber, data });\n this.prevLegendClickTime = (Math as any).inf;\n } else {\n // Otherwise, record time of current click & trigger click event\n // Wait full double-click window to trigger setting state, and only if there was no\n // double-click do we run the single-click logic (we wait a little extra to be safe)\n const curveKey = MetricsPlotPanel.getCurveKey(data[curveNumber]);\n // @ts-expect-error TS(2322): Type 'number' is not assignable to type 'null'.\n this.legendClickTimeout = window.setTimeout(() => {\n const existingDeselectedCurves = new Set((state as any).deselectedCurves);\n if (existingDeselectedCurves.has(curveKey)) {\n existingDeselectedCurves.delete(curveKey);\n } else {\n existingDeselectedCurves.add(curveKey);\n }\n this.updateUrlState({ deselectedCurves: Array.from(existingDeselectedCurves) });\n }, this.SINGLE_CLICK_EVENT_DELAY_MS);\n this.prevLegendClickTime = currentTime;\n }\n this.lastClickedLegendCurveId = curveNumber;\n // Return false to disable plotly event handler\n return false;\n };\n\n /**\n * Handle double-clicking on a single curve within the plot legend in order to toggle display\n * of the selected curve on (and disable display of all other curves).\n */\n handleLegendDoubleClick = ({ curveNumber, data }: any) => {\n // @ts-expect-error TS(2769): No overload matches this call.\n window.clearTimeout(this.legendClickTimeout);\n // Exclude everything besides the current curve key\n const curveKey = MetricsPlotPanel.getCurveKey(data[curveNumber]);\n const allCurveKeys = data.map((elem: any) => MetricsPlotPanel.getCurveKey(elem));\n const newDeselectedCurves = allCurveKeys.filter((curvePair: any) => curvePair !== curveKey);\n this.updateUrlState({ deselectedCurves: newDeselectedCurves });\n return false;\n };\n\n handleMetricsSelectChange = (metricKeys: any) => {\n const existingMetricKeys = this.getUrlState().selectedMetricKeys || [];\n const newMetricKeys = metricKeys.filter((k: any) => !existingMetricKeys.includes(k));\n this.updateUrlState({ selectedMetricKeys: metricKeys });\n this.loadMetricHistory(this.props.runUuids, newMetricKeys).then((requestIds) => {\n this.setState({ loading: false });\n this.setState((prevState: any) => ({\n historyRequestIds: [...prevState.historyRequestIds, ...requestIds],\n }));\n });\n };\n\n handleShowPointChange = (showPoint: any) => this.updateUrlState({ showPoint });\n\n handleLineSmoothChange = (lineSmoothness: any) => this.updateUrlState({ lineSmoothness });\n\n handleKeyDownOnPopover = ({ key }: { key: string }) => {\n if (key === 'Escape') {\n this.setState({ popoverVisible: false });\n }\n };\n\n updatePopover = (data: any) => {\n this.displayPopover = !this.displayPopover;\n\n // Ignore double click.\n setTimeout(() => {\n if (this.displayPopover) {\n this.displayPopover = false;\n const { popoverVisible, popoverX, popoverY } = this.state;\n const {\n points,\n event: { clientX, clientY },\n } = data;\n const samePointClicked = popoverX === clientX && popoverY === clientY;\n const runItems = points\n .sort((a: any, b: any) => b.y - a.y)\n .map((point: any) => ({\n runId: point.data.runId,\n name: point.data.name,\n color: point.fullData.marker.color,\n y: point.y,\n }));\n\n this.setState({\n popoverVisible: !popoverVisible || !samePointClicked,\n popoverX: clientX,\n popoverY: clientY,\n popoverRunItems: runItems,\n });\n }\n }, 300);\n };\n\n render() {\n const { experimentIds, runUuids, runDisplayNames, distinctMetricKeys, location } = this.props;\n const { popoverVisible, popoverX, popoverY, popoverRunItems, loading } = this.state;\n const state = this.getUrlState();\n const { showPoint, selectedXAxis, selectedMetricKeys, lineSmoothness } = state;\n const yAxisLogScale = this.getAxisType() === 'log';\n const { historyRequestIds } = this.state;\n const metrics = this.getMetrics();\n const chartType = MetricsPlotPanel.predictChartType(metrics);\n return (\n
\n \n
\n \n {this.hasMultipleExperiments() ? null : (\n this.setState({ popoverVisible: false })}\n handleVisibleChange={(visible) => this.setState({ popoverVisible: visible })}\n />\n )}\n \n \n \n \n
\n
\n );\n }\n}\n\nconst mapStateToProps = (state: any, ownProps: any) => {\n const { runUuids } = ownProps;\n const completedRunUuids = runUuids.filter((runUuid: any) => getRunInfo(runUuid, state).status !== 'RUNNING');\n const { latestMetricsByRunUuid, metricsByRunUuid } = state.entities;\n\n // All metric keys from all runUuids, non-distinct\n const metricKeys = _.flatMap(runUuids, (runUuid) => {\n const latestMetrics = latestMetricsByRunUuid[runUuid];\n return latestMetrics ? Object.keys(latestMetrics) : [];\n });\n const distinctMetricKeys = [...new Set(metricKeys)].sort();\n const runDisplayNames: any = [];\n\n let containsInfinities = false;\n\n // Flat array of all metrics, with history and information of the run it belongs to\n // This is used for underlying MetricsPlotView & predicting chartType for MetricsPlotControls\n const metricsWithRunInfoAndHistory = _.flatMap(runUuids, (runUuid) => {\n const runDisplayName = Utils.getRunDisplayName(getRunInfo(runUuid, state), runUuid);\n runDisplayNames.push(runDisplayName);\n const metricsHistory = metricsByRunUuid[runUuid];\n return metricsHistory\n ? Object.keys(metricsHistory).map((metricKey) => {\n const history = metricsHistory[metricKey].map((entry: any) => normalizeMetricsHistoryEntry(entry));\n if (history.some(({ value }: any) => typeof value === 'number' && !isNaN(value) && !isFinite(value))) {\n containsInfinities = true;\n }\n return { metricKey, history, runUuid, runDisplayName };\n })\n : [];\n });\n\n return {\n runDisplayNames,\n latestMetricsByRunUuid,\n distinctMetricKeys,\n metricsWithRunInfoAndHistory,\n completedRunUuids,\n containsInfinities,\n };\n};\n\nconst mapDispatchToProps = { getMetricHistoryApi, getRunApi };\n\nexport default withRouterNext(connect(mapStateToProps, mapDispatchToProps)(MetricsPlotPanel));\n","import { useDesignSystemTheme } from '@databricks/design-system';\nimport { Input, Slider } from '@databricks/design-system';\nimport { clamp, isEmpty, isUndefined, keys } from 'lodash';\nimport { useState } from 'react';\n\nconst TRACK_SIZE = 20;\nconst THUMB_SIZE = 14;\nconst MARK_SIZE = 8;\nconst MARK_OFFSET_X = (THUMB_SIZE - MARK_SIZE) / 2;\nconst MARK_OFFSET_Y = (TRACK_SIZE - MARK_SIZE) / 2;\n\nconst ZINDEX_MARK = 1;\nconst ZINDEX_THUMB = 2;\n\nconst STEP_MARKS_DISPLAY_THRESHOLD = 10;\n\ninterface LineSmoothSliderProps {\n max?: number;\n min?: number;\n step?: number;\n marks?: Record;\n value: number | undefined;\n onChange: (value: number) => void;\n disabled?: boolean;\n componentId?: string;\n onAfterChange?: (value: number) => void;\n className?: string;\n}\n\n// Internal helper function: finds the closest value to the given value from the marks\nconst getClosestValue = (marks: Record, value: number, defaultValue: number) =>\n keys(marks).reduce(\n (prev, curr) => (Math.abs(Number(curr) - value) < Math.abs(prev - value) ? Number(curr) : Number(prev)),\n defaultValue,\n );\n\n// Internal helper function: finds the next value based on direction (down or up) from the marks\nconst getNextValue = (marks: Record, currentValue: number, direction: 'down' | 'up') =>\n direction === 'down'\n ? Math.max(\n ...Object.keys(marks)\n .filter((mark) => Number(mark) < currentValue)\n .map(Number),\n )\n : Math.min(\n ...Object.keys(marks)\n .filter((mark) => Number(mark) > currentValue)\n .map(Number),\n );\n\nexport const LineSmoothSlider = ({\n max = 1,\n min = 0,\n step,\n marks,\n value,\n onChange,\n disabled,\n onAfterChange,\n componentId,\n className,\n}: LineSmoothSliderProps) => {\n const { theme } = useDesignSystemTheme();\n const shouldUseMarks = !isEmpty(marks);\n const shouldDisplayMarks = shouldUseMarks && Object.keys(marks).length < STEP_MARKS_DISPLAY_THRESHOLD;\n\n // Temporary value is used to store the value of the input field before it is committed\n const [temporaryValue, setTemporaryValue] = useState(undefined);\n\n return (\n \n onAfterChange?.(newValue)}\n onKeyDown={(e) => {\n // If marks are used, we want to find the next value based on direction (arrow left/down or arrow right/up)\n if (shouldUseMarks) {\n e.preventDefault();\n\n if (['ArrowLeft', 'ArrowRight', 'ArrowUp', 'ArrowDown'].includes(e.key)) {\n const nextValue = getNextValue(\n marks,\n value ?? 0,\n e.key === 'ArrowLeft' || e.key === 'ArrowDown' ? 'down' : 'up',\n );\n\n onAfterChange?.(nextValue);\n onChange(nextValue);\n }\n }\n }}\n onValueChange={([newValue]) => {\n if (shouldUseMarks) {\n onChange(getClosestValue(marks, newValue, value ?? 0));\n return;\n }\n onChange(newValue);\n }}\n step={step ?? undefined}\n >\n {/* Render marks if needed */}\n {shouldDisplayMarks && (\n
\n {keys(marks).map((markPosition) => (\n \n ))}\n
\n )}\n \n \n \n \n \n {\n // If temporary value is set, we want to commit it to the value\n if (!isUndefined(temporaryValue)) {\n if (shouldUseMarks) {\n onAfterChange?.(getClosestValue(marks, temporaryValue, value ?? 0));\n onChange(getClosestValue(marks, temporaryValue, value ?? 0));\n } else {\n onAfterChange?.(clamp(temporaryValue, min, max));\n onChange(clamp(temporaryValue, min, max));\n }\n setTemporaryValue(undefined);\n }\n }}\n onChange={({ target, nativeEvent }) => {\n // If the input event is an input event, we want to set the temporary value\n // to be commited on blur instead of directly setting the value\n if (nativeEvent instanceof InputEvent) {\n setTemporaryValue(Number(target.value));\n return;\n }\n\n // If using marks, find the next value based on the direction of the change\n if (shouldUseMarks) {\n const nextValue = getNextValue(marks, value ?? 0, Number(target.value) < Number(value) ? 'down' : 'up');\n\n onChange(nextValue);\n return;\n }\n\n // If not using marks, clamp the value to the min and max\n onChange(clamp(Number(target.value), min, max));\n }}\n />\n \n );\n};\n","/**\n * NOTE: this code file was automatically migrated to TypeScript using ts-migrate and\n * may contain multiple `any` type annotations and `@ts-expect-error` directives.\n * If possible, please improve types while making changes to this file. If the type\n * annotations are already looking good, please remove this comment.\n */\n\nimport React from 'react';\nimport Utils from '../../utils/Utils';\n\ntype Props = {\n showServerError?: boolean;\n};\n\ntype State = any;\n\nexport class SectionErrorBoundary extends React.Component {\n state = { error: null };\n\n componentDidCatch(error: any, errorInfo: any) {\n this.setState({ error });\n // eslint-disable-next-line no-console -- TODO(FEINF-3587)\n console.error(error, errorInfo);\n }\n\n renderErrorMessage(error: any) {\n return this.props.showServerError ?
Error message: {error.message}
: '';\n }\n\n render() {\n const { children } = this.props;\n const { error } = this.state;\n if (error) {\n return (\n
\n

\n \n Something went wrong with this section. \n If this error persists, please report an issue \n {/* Reported during ESLint upgrade */}\n {/* eslint-disable-next-line react/jsx-no-target-blank */}\n \n here\n \n .{this.renderErrorMessage(error)}\n

\n
\n );\n }\n\n return children;\n }\n}\n\nconst classNames = {\n wrapper: {\n marginLeft: -2, // to align the failure icon with the collapsable section caret toggle\n },\n};\n","/**\n * NOTE: this code file was automatically migrated to TypeScript using ts-migrate and\n * may contain multiple `any` type annotations and `@ts-expect-error` directives.\n * If possible, please improve types while making changes to this file. If the type\n * annotations are already looking good, please remove this comment.\n */\n\nimport React, { Component } from 'react';\nimport { connect } from 'react-redux';\nimport { getApis } from '../../experiment-tracking/reducers/Reducers';\nimport { Spinner } from './Spinner';\nimport { ErrorCodes } from '../constants';\nimport { ErrorWrapper } from '../utils/ErrorWrapper';\nimport { ReduxState } from '../../redux-types';\n\nexport const DEFAULT_ERROR_MESSAGE = 'A request error occurred.';\n\ntype RequestStateWrapperProps = {\n children?: React.ReactNode;\n customSpinner?: React.ReactNode;\n shouldOptimisticallyRender?: boolean;\n requests: any[];\n requestIds?: string[];\n requestIdsWith404sToIgnore?: string[];\n description?: any; // TODO: PropTypes.oneOf(Object.values(LoadingDescription))\n permissionDeniedView?: React.ReactNode;\n suppressErrorThrow?: boolean;\n customRequestErrorHandlerFn?: (\n failedRequests: {\n id: string;\n active?: boolean;\n error: Error | ErrorWrapper;\n }[],\n ) => void;\n};\n\ntype RequestStateWrapperState = any;\n\nexport class RequestStateWrapper extends Component {\n static defaultProps = {\n requests: [],\n requestIdsWith404sToIgnore: [],\n shouldOptimisticallyRender: false,\n };\n\n state = {\n shouldRender: false,\n shouldRenderError: false,\n };\n\n static getErrorRequests(requests: any, requestIdsWith404sToIgnore: any) {\n return requests.filter((r: any) => {\n if (r.error !== undefined) {\n return !(\n requestIdsWith404sToIgnore &&\n requestIdsWith404sToIgnore.includes(r.id) &&\n r.error.getErrorCode() === ErrorCodes.RESOURCE_DOES_NOT_EXIST\n );\n }\n return false;\n });\n }\n\n static getDerivedStateFromProps(nextProps: any) {\n const shouldRender = nextProps.requests.length\n ? nextProps.requests.every((r: any) => r && r.active === false)\n : false;\n\n const requestErrors = RequestStateWrapper.getErrorRequests(\n nextProps.requests,\n nextProps.requestIdsWith404sToIgnore,\n );\n\n return {\n shouldRender,\n shouldRenderError: requestErrors.length > 0,\n requestErrors,\n };\n }\n\n getRenderedContent() {\n const { children, requests, customSpinner, permissionDeniedView, suppressErrorThrow, customRequestErrorHandlerFn } =\n this.props;\n // @ts-expect-error TS(2339): Property 'requestErrors' does not exist on type '{... Remove this comment to see the full error message\n const { shouldRender, shouldRenderError, requestErrors } = this.state;\n const permissionDeniedErrors = requestErrors.filter((failedRequest: any) => {\n return failedRequest.error.getErrorCode() === ErrorCodes.PERMISSION_DENIED;\n });\n\n if (typeof children === 'function') {\n return children(!shouldRender, shouldRenderError, requests, requestErrors);\n } else if (shouldRender || shouldRenderError || this.props.shouldOptimisticallyRender) {\n if (permissionDeniedErrors.length > 0 && permissionDeniedView) {\n return permissionDeniedView;\n }\n if (shouldRenderError && !suppressErrorThrow) {\n customRequestErrorHandlerFn ? customRequestErrorHandlerFn(requestErrors) : triggerError(requestErrors);\n }\n\n return children;\n }\n\n return customSpinner || ;\n }\n\n render() {\n return this.getRenderedContent();\n }\n}\n\nexport const triggerError = (requests: any) => {\n // This triggers the OOPS error boundary.\n // eslint-disable-next-line no-console -- TODO(FEINF-3587)\n console.error('ERROR', requests);\n throw Error(`${DEFAULT_ERROR_MESSAGE}: ${requests.error}`);\n};\n\nconst mapStateToProps = (state: ReduxState, ownProps: Omit) => ({\n requests: getApis(ownProps.requestIds, state),\n});\n\nexport default connect(mapStateToProps)(RequestStateWrapper);\n","/**\n * NOTE: this code file was automatically migrated to TypeScript using ts-migrate and\n * may contain multiple `any` type annotations and `@ts-expect-error` directives.\n * If possible, please improve types while making changes to this file. If the type\n * annotations are already looking good, please remove this comment.\n */\n\nimport React from 'react';\nimport { LegacySkeleton } from '@databricks/design-system';\nimport { SectionErrorBoundary } from '../../common/components/error-boundaries/SectionErrorBoundary';\n\nconst Plot = React.lazy(() => import('react-plotly.js'));\n\nexport const LazyPlot = ({ fallback, ...props }: any) => (\n \n }>\n \n \n \n);\n","/**\n * NOTE: this code file was automatically migrated to TypeScript using ts-migrate and\n * may contain multiple `any` type annotations and `@ts-expect-error` directives.\n * If possible, please improve types while making changes to this file. If the type\n * annotations are already looking good, please remove this comment.\n */\n\nimport React from 'react';\nimport Utils from '../../common/utils/Utils';\nimport _ from 'lodash';\nimport { saveAs } from 'file-saver';\nimport { X_AXIS_STEP, X_AXIS_RELATIVE, MAX_LINE_SMOOTHNESS } from './MetricsPlotControls';\nimport { CHART_TYPE_BAR, convertMetricsToCsv } from './MetricsPlotPanel';\nimport { LazyPlot } from './LazyPlot';\nimport { generateInfinityAnnotations } from '../utils/MetricsUtils';\nimport { injectIntl, IntlShape } from 'react-intl';\n\nconst MAX_RUN_NAME_DISPLAY_LENGTH = 24;\nconst EMA_THRESHOLD = 1;\n\nexport const EMA = (mArray: any, smoothingWeight: any) => {\n // If all elements in the set of metric values are constant, or if\n // the degree of smoothing is set to the minimum value, return the\n // original set of metric values\n if (smoothingWeight <= 1 || !mArray || mArray.length <= EMA_THRESHOLD || mArray.every((v: any) => v === mArray[0])) {\n return mArray;\n }\n\n const smoothness = smoothingWeight / (MAX_LINE_SMOOTHNESS + 1);\n const smoothedArray = [];\n let biasedElement = 0;\n for (let i = 0; i < mArray.length; i++) {\n if (!isNaN(mArray[i])) {\n biasedElement = biasedElement * smoothness + (1 - smoothness) * mArray[i];\n // To avoid biasing earlier elements toward smaller-than-accurate values, we divide\n // all elements by a `debiasedWeight` that asymptotically increases and approaches\n // 1 as the element index increases\n const debiasWeight = 1.0 - Math.pow(smoothness, i + 1);\n const debiasedElement = biasedElement / debiasWeight;\n smoothedArray.push(debiasedElement);\n } else {\n smoothedArray.push(mArray[i]);\n }\n }\n return smoothedArray;\n};\n\n// To avoid pulling in plotly.js (unlazily) and / or using a separate package, just duplicating here\n// Copied from https://github.com/plotly/plotly.js/blob/v2.5.1/src/fonts/ploticon.js#L100\nconst DISK_ICON = {\n width: 857.1,\n height: 1000,\n // eslint-disable-next-line max-len\n path: 'm214-7h429v214h-429v-214z m500 0h72v500q0 8-6 21t-11 20l-157 156q-5 6-19 12t-22 5v-232q0-22-15-38t-38-16h-322q-22 0-37 16t-16 38v232h-72v-714h72v232q0 22 16 38t37 16h465q22 0 38-16t15-38v-232z m-214 518v178q0 8-5 13t-13 5h-107q-7 0-13-5t-5-13v-178q0-8 5-13t13-5h107q7 0 13 5t5 13z m357-18v-518q0-22-15-38t-38-16h-750q-23 0-38 16t-16 38v750q0 22 16 38t38 16h517q23 0 50-12t42-26l156-157q16-15 27-42t11-49z',\n transform: 'matrix(1 0 0 -1 0 850)',\n};\n\ntype MetricsPlotViewImplProps = {\n runUuids: string[];\n runDisplayNames: string[];\n metrics: any[];\n xAxis: string;\n metricKeys: string[];\n showPoint: boolean;\n chartType: string;\n isComparing: boolean;\n lineSmoothness?: number;\n extraLayout?: any;\n onLayoutChange: (...args: any[]) => any;\n onClick: (...args: any[]) => any;\n onLegendClick: (...args: any[]) => any;\n onLegendDoubleClick: (...args: any[]) => any;\n deselectedCurves: string[];\n intl?: any;\n};\n\nexport class MetricsPlotViewImpl extends React.Component {\n static getLineLegend = (metricKey: any, runDisplayName: any, isComparing: any) => {\n let legend = metricKey;\n if (isComparing) {\n legend += `, ${Utils.truncateString(runDisplayName, MAX_RUN_NAME_DISPLAY_LENGTH)}`;\n }\n return legend;\n };\n\n static getXValuesForLineChart(history: any, xAxisType: any, intl?: IntlShape) {\n if (history.length === 0) {\n return [];\n }\n switch (xAxisType) {\n case X_AXIS_STEP:\n return history.map(({ step }: any) => step);\n case X_AXIS_RELATIVE: {\n // @ts-expect-error TS(2339): Property 'timestamp' does not exist on type '{ toS... Remove this comment to see the full error message\n const { timestamp: minTimestamp } = _.minBy(history, 'timestamp');\n return history.map(({ timestamp }: any) => (timestamp - minTimestamp) / 1000);\n }\n default: // X_AXIS_WALL\n return history.map(({ timestamp }: any) => timestamp);\n }\n }\n\n /**\n * Regenerates annotations and shapes for infinity and NaN values.\n * Best called infrequently. Ideally should be called only when data input changes.\n */\n regenerateInfinityAnnotations = () => {\n const { metrics, xAxis, extraLayout } = this.props;\n const isYAxisLog = extraLayout?.yaxis?.type === 'log';\n const annotationData = {};\n\n metrics.forEach((metric) => {\n const { metricKey, history } = metric;\n // @ts-expect-error TS(7053): Element implicitly has an 'any' type because expre... Remove this comment to see the full error message\n annotationData[metricKey] = generateInfinityAnnotations({\n xValues: (MetricsPlotView as any).getXValuesForLineChart(history, xAxis, this.props.intl),\n yValues: history.map((entry: any) => (typeof entry.value === 'number' ? entry.value : Number(entry.value))),\n isLogScale: isYAxisLog,\n stringFormatter: (value) => this.props.intl.formatMessage(value, { metricKey }),\n });\n });\n\n this.#annotationData = annotationData;\n };\n\n #annotationData = {};\n\n getPlotPropsForLineChart = () => {\n const { metrics, xAxis, showPoint, lineSmoothness, isComparing, deselectedCurves } = this.props;\n\n const deselectedCurvesSet = new Set(deselectedCurves);\n const shapes: any = [];\n const annotations: any = [];\n\n const data = metrics.map((metric) => {\n const { metricKey, runDisplayName, history, runUuid } = metric;\n const historyValues = history.map((entry: any) =>\n typeof entry.value === 'number' ? entry.value : Number(entry.value),\n );\n // For metrics with exactly one non-NaN item, we set `isSingleHistory` to `true` in order\n // to display the item as a point. For metrics with zero non-NaN items (i.e., empty metrics),\n // we also set `isSingleHistory` to `true` in order to populate the plot legend with a\n // point-style entry for each empty metric, although no data will be plotted for empty\n // metrics\n const isSingleHistory = historyValues.filter((value: any) => !isNaN(value)).length <= 1;\n\n const visible = !deselectedCurvesSet.has(Utils.getCurveKey(runUuid, metricKey)) ? true : 'legendonly';\n\n if (this.#annotationData && metricKey in this.#annotationData && visible === true) {\n // @ts-expect-error TS(7053): Element implicitly has an 'any' type because expre... Remove this comment to see the full error message\n shapes.push(...this.#annotationData[metricKey].shapes);\n // @ts-expect-error TS(7053): Element implicitly has an 'any' type because expre... Remove this comment to see the full error message\n annotations.push(...this.#annotationData[metricKey].annotations);\n }\n\n return {\n name: (MetricsPlotView as any).getLineLegend(metricKey, runDisplayName, isComparing),\n x: (MetricsPlotView as any).getXValuesForLineChart(history, xAxis),\n y: (isSingleHistory ? historyValues : EMA(historyValues, lineSmoothness)).map((entry: any) =>\n !isFinite(entry) ? NaN : entry,\n ),\n text: historyValues.map((value: any) => (isNaN(value) ? value : value.toFixed(5))),\n type: 'scattergl',\n mode: isSingleHistory ? 'markers' : 'lines+markers',\n marker: { opacity: isSingleHistory || showPoint ? 1 : 0 },\n hovertemplate: isSingleHistory || lineSmoothness === 1 ? '%{y}' : 'Value: %{text}
Smoothed: %{y}',\n visible: visible,\n runId: runUuid,\n metricName: metricKey,\n };\n });\n const props = { data };\n\n (props as any).layout = {\n ...(props as any).layout,\n ...this.props.extraLayout,\n shapes,\n annotations,\n };\n\n return props;\n };\n\n getPlotPropsForBarChart = () => {\n /* eslint-disable no-param-reassign */\n const { runUuids, runDisplayNames, deselectedCurves } = this.props;\n\n // A reverse lookup of `metricKey: { runUuid: value, metricKey }`\n const historyByMetricKey = this.props.metrics.reduce((map, metric) => {\n const { runUuid, metricKey, history } = metric;\n const value = history[0] && history[0].value;\n if (!map[metricKey]) {\n map[metricKey] = { metricKey, [runUuid]: value };\n } else {\n map[metricKey][runUuid] = value;\n }\n return map;\n }, {});\n\n const arrayOfHistorySortedByMetricKey = _.sortBy(Object.values(historyByMetricKey), 'metricKey');\n\n const sortedMetricKeys = arrayOfHistorySortedByMetricKey.map((history) => (history as any).metricKey);\n const deselectedCurvesSet = new Set(deselectedCurves);\n const data = runUuids.map((runUuid, i) => {\n const visibility = deselectedCurvesSet.has(runUuid) ? { visible: 'legendonly' } : {};\n return {\n name: Utils.truncateString(runDisplayNames[i], MAX_RUN_NAME_DISPLAY_LENGTH),\n x: sortedMetricKeys,\n y: arrayOfHistorySortedByMetricKey.map((history) => (history as any)[runUuid]),\n type: 'bar',\n runId: runUuid,\n ...visibility,\n };\n });\n\n const layout = { barmode: 'group' };\n const props = { data, layout };\n props.layout = {\n ...props.layout,\n ...this.props.extraLayout,\n };\n return props;\n };\n\n componentDidMount() {\n this.regenerateInfinityAnnotations();\n }\n\n componentDidUpdate() {\n /**\n * TODO: make sure that annotations are regenereated only when data changes.\n * In fact, all internal recalculations should be done only then.\n */\n this.regenerateInfinityAnnotations();\n }\n\n render() {\n const { onLayoutChange, onClick, onLegendClick, onLegendDoubleClick } = this.props;\n const plotProps =\n this.props.chartType === CHART_TYPE_BAR ? this.getPlotPropsForBarChart() : this.getPlotPropsForLineChart();\n\n return (\n
\n {\n const csv = convertMetricsToCsv(this.props.metrics);\n const blob = new Blob([csv], { type: 'application/csv;charset=utf-8' });\n saveAs(blob, 'metrics.csv');\n },\n },\n ],\n }}\n />\n
\n );\n }\n}\n\n// @ts-expect-error TS(2769): No overload matches this call.\nexport const MetricsPlotView = injectIntl(MetricsPlotViewImpl);\n","import { Theme } from '@emotion/react';\n\nexport interface ProgressProps {\n percent: number;\n format: (percent: number) => string;\n className?: string;\n}\n\n/**\n * Recreates basic features of antd's component.\n * Temporary solution, waiting for this component to be included in DuBois.\n */\nexport const Progress = (props: ProgressProps) => {\n return (\n
\n
\n
\n
\n {props.format(props.percent)}\n
\n );\n};\n\nconst styles = {\n wrapper: (theme: Theme) => ({ display: 'flex', alignItems: 'center', gap: theme.spacing.sm }),\n track: (theme: Theme) => ({\n backgroundColor: theme.colors.backgroundSecondary,\n height: theme.spacing.sm,\n flex: 1,\n borderRadius: theme.spacing.sm,\n }),\n progressTrack: (theme: Theme) => ({\n backgroundColor: theme.colors.primary,\n height: theme.spacing.sm,\n borderRadius: theme.spacing.sm,\n }),\n};\n","/**\n * NOTE: this code file was automatically migrated to TypeScript using ts-migrate and\n * may contain multiple `any` type annotations and `@ts-expect-error` directives.\n * If possible, please improve types while making changes to this file. If the type\n * annotations are already looking good, please remove this comment.\n */\n\nimport React from 'react';\nimport _ from 'lodash';\nimport { Button, LegacySelect, Switch, LegacyTooltip, Radio, QuestionMarkIcon } from '@databricks/design-system';\nimport { Progress } from '../../common/components/Progress';\nimport { CHART_TYPE_LINE, METRICS_PLOT_POLLING_INTERVAL_MS } from './MetricsPlotPanel';\n\nimport { FormattedMessage, injectIntl } from 'react-intl';\nimport { LineSmoothSlider } from './LineSmoothSlider';\n\nconst RadioGroup = Radio.Group;\nexport const X_AXIS_WALL = 'wall';\nexport const X_AXIS_STEP = 'step';\nexport const X_AXIS_RELATIVE = 'relative';\nexport const MAX_LINE_SMOOTHNESS = 100;\n\ntype Props = {\n distinctMetricKeys: string[];\n selectedMetricKeys: string[];\n selectedXAxis: string;\n handleXAxisChange: (...args: any[]) => any;\n handleShowPointChange: (...args: any[]) => any;\n handleMetricsSelectChange: (...args: any[]) => any;\n handleYAxisLogScaleChange: (...args: any[]) => any;\n handleLineSmoothChange: (value: number) => void;\n chartType: string;\n lineSmoothness: number;\n yAxisLogScale: boolean;\n showPoint: boolean;\n intl: {\n formatMessage: (...args: any[]) => any;\n };\n numRuns: number;\n numCompletedRuns: number;\n handleDownloadCsv: (...args: any[]) => any;\n disableSmoothnessControl: boolean;\n};\n\nclass MetricsPlotControlsImpl extends React.Component {\n static defaultProps = {\n disableSmoothnessControl: false,\n };\n\n handleMetricsSelectFilterChange = (text: any, option: any) =>\n option.props.title.toUpperCase().includes(text.toUpperCase());\n\n getAllMetricKeys = () => {\n const { distinctMetricKeys } = this.props;\n return distinctMetricKeys.map((metricKey) => ({\n title: metricKey,\n value: metricKey,\n key: metricKey,\n }));\n };\n\n render() {\n const { chartType, yAxisLogScale, lineSmoothness, showPoint, numRuns, numCompletedRuns, disableSmoothnessControl } =\n this.props;\n\n const lineSmoothnessTooltipText = (\n \n );\n const completedRunsTooltipText = (\n \n );\n return (\n \n {chartType === CHART_TYPE_LINE ? (\n
\n
\n
\n {' '}\n \n \n \n `${numCompletedRuns}/${numRuns}`}\n />\n
\n
\n
\n
\n \n
\n \n
\n {!disableSmoothnessControl && (\n
\n
\n {' '}\n \n \n \n
\n \n
\n )}\n
\n
\n \n
\n \n \n \n \n \n \n \n \n \n \n \n
\n
\n ) : null}\n
\n
\n \n
\n \n {this.getAllMetricKeys().map((key) => (\n \n {key.title}\n \n ))}\n \n
\n
\n
\n \n
\n \n
\n
\n \n \n \n \n
\n
\n );\n }\n}\n\nconst styles = {\n xAxisControls: (theme: any) => ({\n label: { marginTop: theme.spacing.xs, marginBottom: theme.spacing.xs },\n }),\n controlsWrapper: { minWidth: '20%', maxWidth: '30%' },\n axisSelector: { width: '100%' },\n centeredControlsWrapper: {\n // Make controls aligned to plotly line chart\n justifyContent: 'center',\n },\n};\n\n// @ts-expect-error TS(2769): No overload matches this call.\nexport const MetricsPlotControls = injectIntl(MetricsPlotControlsImpl);\n","import spinner from '../static/mlflow-spinner.png';\nimport { Interpolation, keyframes, Theme } from '@emotion/react';\n\ntype Props = {\n showImmediately?: boolean;\n};\n\nexport function Spinner({ showImmediately }: Props) {\n return (\n
styles.spinner(theme, showImmediately)}>\n \"Page\n
\n );\n}\n\nconst styles = {\n spinner: (theme: Theme, immediate?: boolean): Interpolation => ({\n width: 100,\n marginTop: 100,\n marginLeft: 'auto',\n marginRight: 'auto',\n\n img: {\n position: 'absolute',\n opacity: 0,\n top: '50%',\n left: '50%',\n width: theme.general.heightBase * 2,\n height: theme.general.heightBase * 2,\n marginTop: -theme.general.heightBase,\n marginLeft: -theme.general.heightBase,\n animation: `${keyframes`\n 0% {\n opacity: 1;\n }\n 100% {\n opacity: 1;\n -webkit-transform: rotate(360deg);\n transform: rotate(360deg);\n }\n `} 3s linear infinite`,\n animationDelay: immediate ? '0s' : '0.5s',\n },\n }),\n};\n"],"names":["HtmlTableView","columns","values","styles","testId","scroll","_jsx","LegacyTable","className","dataSource","size","pagination","style","MetricsSummaryTable","React","render","runUuids","this","props","children","length","renderMetricTables","renderRunTable","runUuid","metricKeys","latestMetrics","minMetrics","maxMetrics","intl","title","formatMessage","id","defaultMessage","dataIndex","sorter","a","b","metricKey","width","dataColumns","getRunValuesByMetric","y","runExperimentIds","runDisplayNames","runName","map","_jsxs","Fragment","getMetricValuesByRun","latestValue","ellipsis","minValue","maxValue","runIdx","runLink","Link","to","Routes","getRunPageRoute","key","rowData","_ref","name","_ref2","_ref3","latestMetric","getMetric","minMetric","maxMetric","getValue","latestFormatted","css","formatMetric","minFormatted","maxFormatted","valuesMap","metric","value","undefined","step","MetricsSummaryTableWithIntl","injectIntl","connect","mapStateToProps","state","ownProps","forEach","runInfo","getRunInfo","experimentId","getLatestMetrics","getMinMetrics","getMaxMetrics","IconButton","icon","onClick","restProps","Button","componentId","type","padding","RunLinksPopover","constructor","args","renderContent","runItems","runId","color","index","marginRight","Utils","renderTitle","handleClose","float","marginLeft","componentDidMount","document","addEventListener","handleKeyDown","componentWillUnmount","removeEventListener","visible","x","handleVisibleChange","LegacyPopover","content","placement","onVisibleChange","left","top","position","CHART_TYPE_LINE","CHART_TYPE_BAR","METRICS_PLOT_POLLING_INTERVAL_MS","MAXIMUM_METRIC_DATA_POINTS","convertMetricsToCsv","metrics","header","Object","keys","history","rows","flatMap","concat","row","join","MetricsPlotPanel","super","_isMounted","displayPopover","intervalId","legendClickTimeout","prevLegendClickTime","Math","inf","lastClickedLegendCurveId","MAX_DOUBLE_CLICK_INTERVAL_MS","SINGLE_CLICK_EVENT_DELAY_MS","onFocus","setState","focused","onBlur","clearEventListeners","window","clearInterval","allRunsCompleted","completedRunUuids","isHangingRunUuid","activeRunUuid","latestMetricsByRunUuid","timestamps","timestamp","latestTimestamp","max","Date","getTime","getActiveRunUuids","_","filter","shouldPoll","updateUrlState","updatedState","location","navigate","experimentIds","JSON","parse","qs","search","newState","getUrlState","selectedXAxis","selectedMetricKeys","showPoint","yAxisLogScale","lineSmoothness","layout","deselectedCurves","lastLinearYAxisRange","getMetricPageRoute","replace","getNumTotalMetrics","metricsWithRunInfoAndHistory","reduce","loadMetricHistory","logErrorAndNotifyUser","Promise","resolve","loading","promises","hasOwnProperty","async","requestIds","getUUID","push","nextPageToken","getMetricHistoryApi","next_page_token","success","uid","all","then","results","every","loadRuns","getRunApi","getMetrics","selectedMetricsSet","Set","m","has","isStep","X_AXIS_STEP","sort","compareByStepAndTimestamp","compareByTimestamp","handleYAxisLogScaleChange","newLayout","newAxisType","yaxis","range","autorange","exponentformat","oldYRange","log","pow","handleXAxisChange","e","axisType","X_AXIS_WALL","X_AXIS_RELATIVE","target","xaxis","handleLayoutChange","newXRange0","newXRange1","newYRange0","newYRange1","xAxisAutorange","yAxisAutorange","yAxisShowSpikes","xAxisShowSpikes","restFields","mergedLayout","newXAxis","showspikes","newYAxis","handleDownloadCsv","csv","blob","Blob","saveAs","handleLegendClick","curveNumber","data","currentTime","now","handleLegendDoubleClick","curveKey","getCurveKey","setTimeout","existingDeselectedCurves","delete","add","Array","from","clearTimeout","newDeselectedCurves","elem","curvePair","handleMetricsSelectChange","existingMetricKeys","newMetricKeys","k","includes","prevState","historyRequestIds","handleShowPointChange","handleLineSmoothChange","handleKeyDownOnPopover","popoverVisible","updatePopover","popoverX","popoverY","points","event","clientX","clientY","samePointClicked","point","fullData","marker","popoverRunItems","hasMultipleExperiments","setInterval","activeRunUuids","getMetricPlotStateFromUrl","predictChartType","isComparing","params","runs","getAxisType","plotDataElem","metricName","distinctMetricKeys","chartType","MetricsPlotControls","numRuns","numCompletedRuns","disableSmoothnessControl","containsInfinities","RequestStateWrapper","shouldOptimisticallyRender","Spinner","_css","visibility","MetricsPlotView","xAxis","extraLayout","onLayoutChange","onLegendClick","onLegendDoubleClick","defaultProps","mapDispatchToProps","withRouterNext","status","metricsByRunUuid","entities","runDisplayName","getRunDisplayName","metricsHistory","entry","normalizeMetricsHistoryEntry","some","isNaN","isFinite","getClosestValue","marks","defaultValue","prev","curr","abs","Number","getNextValue","currentValue","direction","mark","min","LineSmoothSlider","onChange","disabled","onAfterChange","theme","useDesignSystemTheme","shouldUseMarks",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type { ReactElement } from 'react';\nimport React, { useEffect, useRef, useState } from 'react';\nimport { useClipboard } from 'use-clipboard-copy';\n\nimport { CheckIcon, CopyIcon } from '@databricks/design-system';\nimport { useIntl } from '@databricks/i18n';\n\nexport interface CopyController {\n actionIcon: ReactElement;\n tooltipMessage: string;\n copy: () => void;\n copied: boolean;\n ariaLabel: string;\n tooltipOpen: boolean;\n handleTooltipOpenChange: (open: boolean) => void;\n}\n\n/**\n * Utility hook that is internal to web-shared, use: `Copyable` or `CopyActionButton`\n * or if it's a `CodeSnippet`, `SnippetCopyAction`\n */\nexport function useCopyController(text: string, copyTooltip?: string, onCopy?: () => void): CopyController {\n const intl = useIntl();\n\n const copyMessage = copyTooltip\n ? copyTooltip\n : intl.formatMessage({\n defaultMessage: 'Copy',\n description: 'Tooltip message displayed on copy action',\n });\n\n const copiedMessage = intl.formatMessage({\n defaultMessage: 'Copied',\n description: 'Tooltip message displayed on copy action after it has been clicked',\n });\n\n const clipboard = useClipboard();\n const copiedTimerIdRef = useRef();\n const [copied, setCopied] = useState(false);\n const [open, setOpen] = useState(false);\n\n useEffect(() => {\n return () => {\n window.clearTimeout(copiedTimerIdRef.current);\n };\n }, []);\n\n const copy = () => {\n clipboard.copy(text);\n window.clearTimeout(copiedTimerIdRef.current);\n setCopied(true);\n onCopy?.();\n copiedTimerIdRef.current = window.setTimeout(() => {\n setCopied(false);\n }, 3000);\n };\n\n return {\n actionIcon: copied ? : ,\n tooltipMessage: copied ? copiedMessage : copyMessage,\n copy,\n copied,\n ariaLabel: copyMessage,\n tooltipOpen: open || copied,\n handleTooltipOpenChange: setOpen,\n };\n}\n","export const shouldEnableAssessmentEditing = () => {\n return true;\n};\nexport const shouldEnableSummaryView = () => {\n return true;\n};\nexport const getIsMlflowTraceUIEnabled = () => {\n return true;\n};\n","/**\n * Adapted from `duotone-dark`\n * Ref: https://github.com/react-syntax-highlighter/react-syntax-highlighter/blob/b2457268891948f7005ccf539a70c000f0695bde/src/styles/prism/duotone-dark.js\n */\n\nconst databricksDuotoneDarkTheme = {\n 'code[class*=\"language-\"]': {\n fontFamily:\n 'Consolas, Menlo, Monaco, \"Andale Mono WT\", \"Andale Mono\", \"Lucida Console\", \"Lucida Sans Typewriter\", \"DejaVu Sans Mono\", \"Bitstream Vera Sans Mono\", \"Liberation Mono\", \"Nimbus Mono L\", \"Courier New\", Courier, monospace',\n fontSize: '14px',\n lineHeight: '1.375',\n direction: 'ltr',\n textAlign: 'left',\n whiteSpace: 'pre',\n wordSpacing: 'normal',\n wordBreak: 'normal',\n MozTabSize: '4',\n OTabSize: '4',\n tabSize: '4',\n WebkitHyphens: 'none',\n MozHyphens: 'none',\n msHyphens: 'none',\n hyphens: 'none',\n background: '#2a2734',\n color: '#5DFAFC', // D\n },\n 'pre[class*=\"language-\"]': {\n fontFamily:\n 'Consolas, Menlo, Monaco, \"Andale Mono WT\", \"Andale Mono\", \"Lucida Console\", \"Lucida Sans Typewriter\", \"DejaVu Sans Mono\", \"Bitstream Vera Sans Mono\", \"Liberation Mono\", \"Nimbus Mono L\", \"Courier New\", Courier, monospace',\n fontSize: '14px',\n lineHeight: '1.375',\n direction: 'ltr',\n textAlign: 'left',\n whiteSpace: 'pre',\n wordSpacing: 'normal',\n wordBreak: 'normal',\n MozTabSize: '4',\n OTabSize: '4',\n tabSize: '4',\n WebkitHyphens: 'none',\n MozHyphens: 'none',\n msHyphens: 'none',\n hyphens: 'none',\n background: '#2a2734',\n color: '#5DFAFC', // D\n padding: '1em',\n margin: '.5em 0',\n overflow: 'auto',\n },\n 'pre > code[class*=\"language-\"]': {\n fontSize: '1em',\n },\n 'pre[class*=\"language-\"]::-moz-selection': {\n textShadow: 'none',\n background: '#6a51e6',\n },\n 'pre[class*=\"language-\"] ::-moz-selection': {\n textShadow: 'none',\n background: '#6a51e6',\n },\n 'code[class*=\"language-\"]::-moz-selection': {\n textShadow: 'none',\n background: '#6a51e6',\n },\n 'code[class*=\"language-\"] ::-moz-selection': {\n textShadow: 'none',\n background: '#6a51e6',\n },\n 'pre[class*=\"language-\"]::selection': {\n textShadow: 'none',\n background: '#6a51e6',\n },\n 'pre[class*=\"language-\"] ::selection': {\n textShadow: 'none',\n background: '#6a51e6',\n },\n 'code[class*=\"language-\"]::selection': {\n textShadow: 'none',\n background: '#6a51e6',\n },\n 'code[class*=\"language-\"] ::selection': {\n textShadow: 'none',\n background: '#6a51e6',\n },\n ':not(pre) > code[class*=\"language-\"]': {\n padding: '.1em',\n borderRadius: '.3em',\n },\n comment: {\n color: '#6c6783',\n },\n prolog: {\n color: '#6c6783',\n },\n doctype: {\n color: '#6c6783',\n },\n cdata: {\n color: '#6c6783',\n },\n punctuation: {\n color: '#6c6783',\n },\n namespace: {\n Opacity: '.7',\n },\n tag: {\n color: '#3AACE2', // D\n },\n operator: {\n color: '#3AACE2', // D\n },\n number: {\n color: '#3AACE2', // D\n },\n property: {\n color: '#5DFAFC', // D\n },\n function: {\n color: '#5DFAFC', // D\n },\n 'tag-id': {\n color: '#eeebff',\n },\n selector: {\n color: '#eeebff',\n },\n 'atrule-id': {\n color: '#eeebff',\n },\n 'code.language-javascript': {\n color: '#c4b9fe',\n },\n 'attr-name': {\n color: '#c4b9fe',\n },\n 'code.language-css': {\n color: '#ffffff', // D\n },\n 'code.language-scss': {\n color: '#ffffff', // D\n },\n boolean: {\n color: '#ffffff', // D\n },\n string: {\n color: '#ffffff', // D\n },\n entity: {\n color: '#ffffff', // D\n cursor: 'help',\n },\n url: {\n color: '#ffffff', // D\n },\n '.language-css .token.string': {\n color: '#ffffff', // D\n },\n '.language-scss .token.string': {\n color: '#ffffff', // D\n },\n '.style .token.string': {\n color: '#ffffff', // D\n },\n 'attr-value': {\n color: '#ffffff', // D\n },\n keyword: {\n color: '#ffffff', // D\n },\n control: {\n color: '#ffffff', // D\n },\n directive: {\n color: '#ffffff', // D\n },\n unit: {\n color: '#ffffff', // D\n },\n statement: {\n color: '#ffffff', // D\n },\n regex: {\n color: '#ffffff', // D\n },\n atrule: {\n color: '#ffffff', // D\n },\n placeholder: {\n color: '#ffffff', // D\n },\n variable: {\n color: '#ffffff', // D\n },\n deleted: {\n textDecoration: 'line-through',\n },\n inserted: {\n borderBottom: '1px dotted #eeebff',\n textDecoration: 'none',\n },\n italic: {\n fontStyle: 'italic',\n },\n important: {\n fontWeight: 'bold',\n color: '#c4b9fe',\n },\n bold: {\n fontWeight: 'bold',\n },\n 'pre > code.highlight': {\n Outline: '.4em solid #8a75f5',\n OutlineOffset: '.4em',\n },\n '.line-numbers.line-numbers .line-numbers-rows': {\n borderRightColor: '#2c2937',\n },\n '.line-numbers .line-numbers-rows > span:before': {\n color: '#3c3949',\n },\n '.line-highlight.line-highlight': {\n background: 'linear-gradient(to right, rgba(224, 145, 66, 0.2) 70%, rgba(224, 145, 66, 0))',\n },\n};\n\nexport default databricksDuotoneDarkTheme;\n","/**\n * Adapted from `material-light`\n * Ref: https://github.com/react-syntax-highlighter/react-syntax-highlighter/blob/b2457268891948f7005ccf539a70c000f0695bde/src/styles/prism/material-light.js#L1\n *\n * This theme overwrites colors to be similiar to the `@databricks/editor` theme.\n */\n\nconst databricksLightTheme = {\n 'code[class*=\"language-\"]': {\n textAlign: 'left',\n whiteSpace: 'pre',\n wordSpacing: 'normal',\n wordBreak: 'normal',\n wordWrap: 'normal',\n color: 'rgb(77, 77, 76)', // D\n background: '#fafafa',\n fontFamily: 'Monaco, Menlo, Ubuntu Mono, Consolas, source-code-pro, monospace',\n fontSize: '12px', // D\n lineHeight: '1.5em',\n MozTabSize: '4',\n OTabSize: '4',\n tabSize: '4',\n WebkitHyphens: 'none',\n MozHyphens: 'none',\n msHyphens: 'none',\n hyphens: 'none',\n },\n 'pre[class*=\"language-\"]': {\n textAlign: 'left',\n whiteSpace: 'pre',\n wordSpacing: 'normal',\n wordBreak: 'normal',\n wordWrap: 'normal',\n color: 'rgb(77, 77, 76)', // D\n background: '#fafafa',\n fontFamily: 'Monaco, Menlo, Ubuntu Mono, Consolas, source-code-pro, monospace',\n fontSize: '12px', // D\n lineHeight: '1.5em',\n MozTabSize: '4',\n OTabSize: '4',\n tabSize: '4',\n WebkitHyphens: 'none',\n MozHyphens: 'none',\n msHyphens: 'none',\n hyphens: 'none',\n overflow: 'auto',\n position: 'relative',\n margin: '0.5em 0',\n padding: '1.25em 1em',\n },\n 'code[class*=\"language-\"]::-moz-selection': {\n background: '#cceae7',\n color: '#263238',\n },\n 'pre[class*=\"language-\"]::-moz-selection': {\n background: '#cceae7',\n color: '#263238',\n },\n 'code[class*=\"language-\"] ::-moz-selection': {\n background: '#cceae7',\n color: '#263238',\n },\n 'pre[class*=\"language-\"] ::-moz-selection': {\n background: '#cceae7',\n color: '#263238',\n },\n 'code[class*=\"language-\"]::selection': {\n background: '#cceae7',\n color: '#263238',\n },\n 'pre[class*=\"language-\"]::selection': {\n background: '#cceae7',\n color: '#263238',\n },\n 'code[class*=\"language-\"] ::selection': {\n background: '#cceae7',\n color: '#263238',\n },\n 'pre[class*=\"language-\"] ::selection': {\n background: '#cceae7',\n color: '#263238',\n },\n ':not(pre) > code[class*=\"language-\"]': {\n whiteSpace: 'normal',\n borderRadius: '0.2em',\n padding: '0.1em',\n },\n '.language-css > code': {\n color: '#f5871f', // D\n },\n '.language-sass > code': {\n color: '#f5871f', // D\n },\n '.language-scss > code': {\n color: '#f5871f', // D\n },\n '[class*=\"language-\"] .namespace': {\n Opacity: '0.7',\n },\n atrule: {\n color: '#7c4dff',\n },\n 'attr-name': {\n color: '#39adb5',\n },\n 'attr-value': {\n color: '#f6a434',\n },\n attribute: {\n color: '#f6a434',\n },\n boolean: {\n color: '#7c4dff', // D\n },\n builtin: {\n color: '#39adb5',\n },\n cdata: {\n color: '#39adb5',\n },\n char: {\n color: '#39adb5',\n },\n class: {\n color: '#39adb5',\n },\n 'class-name': {\n color: '#6182b8',\n },\n comment: {\n color: '#8e908c', // D\n },\n constant: {\n color: '#7c4dff', // D\n },\n deleted: {\n color: '#e53935',\n },\n doctype: {\n color: '#aabfc9',\n },\n entity: {\n color: '#e53935',\n },\n function: {\n color: '#4271ae', // D\n },\n hexcode: {\n color: '#f5871f', // D\n },\n id: {\n color: '#7c4dff',\n fontWeight: 'bold',\n },\n important: {\n color: '#7c4dff',\n fontWeight: 'bold',\n },\n inserted: {\n color: '#39adb5',\n },\n keyword: {\n color: '#8959a8', // D\n },\n number: {\n color: '#f5871f', // D\n },\n operator: {\n color: '#3e999f', // D\n },\n prolog: {\n color: '#aabfc9',\n },\n property: {\n color: '#39adb5',\n },\n 'pseudo-class': {\n color: '#f6a434',\n },\n 'pseudo-element': {\n color: '#f6a434',\n },\n punctuation: {\n color: 'rgb(77, 77, 76)', // D\n },\n regex: {\n color: '#6182b8',\n },\n selector: {\n color: '#e53935',\n },\n string: {\n color: '#3ba85f', // D\n },\n symbol: {\n color: '#7c4dff',\n },\n tag: {\n color: '#e53935',\n },\n unit: {\n color: '#f5871f', // D\n },\n url: {\n color: '#e53935',\n },\n variable: {\n color: '#c72d4c', // D\n },\n};\n\nexport default databricksLightTheme;\n","import { css } from '@emotion/react';\nimport type { ReactNode } from 'react';\nimport React from 'react';\n\nimport type { ButtonProps } from '@databricks/design-system';\nimport { Button, LegacyTooltip } from '@databricks/design-system';\n\ntype SnippetActionButtonProps = Pick & {\n tooltipMessage: NonNullable;\n};\n\nexport default function SnippetActionButton({ tooltipMessage, ...buttonProps }: SnippetActionButtonProps) {\n const style = css({\n zIndex: 1, // required for action buttons to be visible and float\n });\n return (\n \n \n \n );\n}\n","import React from 'react';\n\nimport type { ButtonProps } from '@databricks/design-system';\nimport { useCopyController } from '@databricks/web-shared/copy';\n\nimport SnippetActionButton from './SnippetActionButton';\n\nexport interface SnippetCopyActionProps extends ButtonProps {\n /**\n * The text to be copied into clipboard when action button is clicked.\n */\n copyText: string;\n onClick?: (e: React.MouseEvent) => void;\n}\n\nexport function SnippetCopyAction({ copyText, onClick, ...props }: SnippetCopyActionProps) {\n const { actionIcon, tooltipMessage, copy } = useCopyController(copyText);\n\n return (\n {\n copy();\n onClick?.(e);\n }}\n {...props}\n />\n );\n}\n","import { PrismLight as SyntaxHighlighter } from 'react-syntax-highlighter';\nimport go from 'react-syntax-highlighter/dist/cjs/languages/prism/go';\nimport java from 'react-syntax-highlighter/dist/cjs/languages/prism/java';\nimport javascript from 'react-syntax-highlighter/dist/cjs/languages/prism/javascript';\nimport json from 'react-syntax-highlighter/dist/cjs/languages/prism/json';\nimport python from 'react-syntax-highlighter/dist/cjs/languages/prism/python';\nimport yaml from 'react-syntax-highlighter/dist/cjs/languages/prism/yaml';\nimport sql from 'react-syntax-highlighter/dist/cjs/languages/prism/sql';\n\nSyntaxHighlighter.registerLanguage('sql', sql);\nSyntaxHighlighter.registerLanguage('java', java);\nSyntaxHighlighter.registerLanguage('python', python);\nSyntaxHighlighter.registerLanguage('go', go);\nSyntaxHighlighter.registerLanguage('javascript', javascript);\nSyntaxHighlighter.registerLanguage('yaml', yaml);\nSyntaxHighlighter.registerLanguage('json', json);\n\nimport duotoneDarkStyle from './theme/databricks-duotone-dark';\nimport lightStyle from './theme/databricks-light';\nimport { CSSProperties, ReactNode } from 'react';\nimport { pick } from 'lodash';\nexport type CodeSnippetTheme = 'duotoneDark' | 'light';\nexport const buttonBackgroundColorDark = 'rgba(140, 203, 255, 0)';\nexport const buttonColorDark = 'rgba(255, 255, 255, 0.84)';\nexport const buttonHoverColorDark = '#8ccbffcc';\nexport const buttonHoverBackgroundColorDark = 'rgba(140, 203, 255, 0.08)';\nexport const duboisAlertBackgroundColor = '#fff0f0';\nexport const snippetPadding = '24px';\nconst themesStyles: Record = {\n light: lightStyle,\n duotoneDark: duotoneDarkStyle,\n};\n\nexport type CodeSnippetLanguage = 'sql' | 'java' | 'python' | 'javascript' | 'go' | 'yaml' | 'text' | 'json';\n\nexport interface CodeSnippetProps {\n /**\n * The code string\n */\n children: string;\n /**\n * The actions that are displayed on the right top corner of the component\n * see `./actions` for built-in actions\n */\n actions?: NonNullable | NonNullable[];\n /**\n * The theme, default theme is `light`\n */\n theme?: CodeSnippetTheme;\n /**\n * Language of the code (`children`)\n */\n language: CodeSnippetLanguage;\n /**\n * Custom styles (passed to the internal `
`)\n   */\n  style?: CSSProperties;\n  /**\n   * Whether to show line numbers on the left or not\n   */\n  showLineNumbers?: boolean;\n  /**\n   * Custom styles for line numbers\n   */\n  lineNumberStyle?: CSSProperties;\n  /**\n   * Boolean to specify whether to style the  block with white-space: pre-wrap or white-space: pre\n   */\n  wrapLongLines?: boolean;\n  /**\n   * Boolean that determines whether or not each line of code should be wrapped in a parent element\n   */\n  wrapLines?: boolean;\n  /**\n   * Props to pass to the line elements\n   */\n  lineProps?: React.HTMLProps | undefined;\n  /**\n   * Custom tag to use for the `
` element\n   */\n  PreTag?: keyof JSX.IntrinsicElements | React.ComponentType | undefined;\n}\n\n/**\n * `CodeSnippet` is used for highlighting code, use this instead of\n */\nexport function CodeSnippet({\n  theme = 'light',\n  language,\n  actions,\n  style,\n  children,\n  showLineNumbers,\n  lineNumberStyle,\n  wrapLongLines,\n  wrapLines,\n  PreTag,\n}: CodeSnippetProps) {\n  const customStyle = {\n    border: 'none',\n    borderRadius: 0,\n    margin: 0,\n    padding: snippetPadding,\n    ...style,\n  };\n  return (\n    \n      {children}\n    \n  );\n}\n\nexport * from './actions/SnippetCopyAction';\n","export enum ErrorLogType {\n  UserInputError = 'UserInputError',\n  UnexpectedSystemStateError = 'UnexpectedSystemStateError',\n  ServerError = 'ServerError',\n  SessionError = 'SessionError',\n  NetworkError = 'NetworkError',\n  ApplicationError = 'ApplicationError',\n  UnknownError = 'UnknownError',\n}\n","export enum ErrorName {\n  BadRequestError = 'BadRequestError',\n  DatasetRunNotFoundError = 'DatasetRunNotFoundError',\n  FormValidationError = 'FormInputError',\n  GenericNetworkRequestError = 'GenericNetworkRequestError',\n  GraphQLGenericError = 'GraphQLGenericError',\n  InternalServerError = 'InternalServerError',\n  NotFoundError = 'NotFoundError',\n  PermissionError = 'PermissionError',\n  RateLimitedError = 'RateLimitedError',\n  RouteNotFoundError = 'RouteNotFoundError',\n  ServiceUnavailableError = 'ServiceUnavailableError',\n  UnauthorizedError = 'UnauthorizedError',\n  UnknownError = 'UnknownError',\n}\n","import { FormattedMessage } from 'react-intl';\n\n// eslint-disable-next-line no-restricted-imports\nimport type { ServerError } from '@apollo/client';\n\nimport { ErrorLogType } from './ErrorLogType';\nimport { ErrorName } from './ErrorName';\n\nexport type HandleableError = Error | string | Record | PredefinedError | Response;\n\nexport type CausableError = Error | string | Record;\n\nexport abstract class PredefinedError extends Error {\n  abstract errorLogType: ErrorLogType;\n  abstract errorName: ErrorName;\n  abstract displayMessage: React.ReactNode;\n  isUserError = false;\n\n  constructor(message?: string, cause?: CausableError) {\n    super(message);\n  }\n}\n\nexport const matchPredefinedError = (error: HandleableError) => {\n  if (error instanceof PredefinedError) {\n    return error;\n  }\n  if (error instanceof Error && ('networkError' in error || 'graphQLErrors' in error)) {\n    return matchPredefinedApolloError(error);\n  }\n\n  if (error instanceof Response) {\n    return matchPredefinedErrorFromResponse(error);\n  }\n\n  return new UnknownError(error);\n};\n\nexport function isServerError(e: unknown): e is ServerError {\n  return e instanceof Error && e.hasOwnProperty('response');\n}\n\nconst matchPredefinedApolloError = (error: Error) => {\n  // Some errors from Apollo mock provider may have `networkError` but are not `ServerError`\n  // only act on ServerError, which do have the response attached\n  if ('networkError' in error && isServerError(error.networkError)) {\n    return matchPredefinedErrorFromResponse(error.networkError.response, error.networkError);\n  }\n\n  return new GraphQLGenericError(error);\n};\n\nconst getNetworkRequestErrorDetailsFromResponse = (response: Response): NetworkRequestErrorDetails => {\n  const status = response.status;\n\n  return { status };\n};\n\nexport const matchPredefinedErrorFromResponse = (response: Response, originalError?: CausableError) => {\n  const errorDetails = NetworkRequestError.getNetworkRequestErrorDetailsFromResponse(response);\n  switch (response.status) {\n    case 400:\n      return new BadRequestError(errorDetails, originalError);\n    case 401:\n      return new UnauthorizedError(errorDetails, originalError);\n    case 403:\n      return new PermissionError(errorDetails, originalError);\n    case 404:\n      return new NotFoundError(errorDetails, originalError);\n    case 429:\n      return new RateLimitedError(errorDetails, originalError);\n    case 500:\n      return new InternalServerError(errorDetails, originalError);\n    case 503:\n      return new ServiceUnavailableError(errorDetails, originalError);\n    default:\n      return new GenericNetworkRequestError(errorDetails, originalError);\n  }\n};\n\ninterface NetworkRequestErrorDetails {\n  status?: number;\n  response?: Response;\n}\n\nexport abstract class NetworkRequestError extends PredefinedError {\n  status?: number;\n  response?: Response;\n\n  constructor(message: string, details: NetworkRequestErrorDetails, cause?: CausableError) {\n    super(message, cause);\n    this.status = details.status;\n    this.response = details.response;\n  }\n\n  static getNetworkRequestErrorDetailsFromResponse = getNetworkRequestErrorDetailsFromResponse;\n}\n\nexport class GenericNetworkRequestError extends NetworkRequestError {\n  errorLogType = ErrorLogType.ServerError;\n  errorName = ErrorName.GenericNetworkRequestError;\n  displayMessage = (\n    \n  );\n\n  constructor(details: NetworkRequestErrorDetails, cause?: CausableError) {\n    const message = 'A network error occurred.';\n\n    super(message, details, cause);\n  }\n}\n\nexport class GraphQLGenericError extends PredefinedError {\n  errorLogType = ErrorLogType.ApplicationError;\n  errorName = ErrorName.GraphQLGenericError;\n  displayMessage = (\n    \n  );\n\n  constructor(cause?: CausableError) {\n    const message = 'A GraphQL error occurred.';\n\n    super(message, cause);\n  }\n}\n\nexport class BadRequestError extends NetworkRequestError {\n  errorLogType = ErrorLogType.UserInputError;\n  errorName = ErrorName.BadRequestError;\n  displayMessage = (\n    \n  );\n\n  constructor(details: NetworkRequestErrorDetails, cause?: CausableError) {\n    const message = 'The request was invalid.';\n\n    super(message, details, cause);\n  }\n}\n\nexport class InternalServerError extends NetworkRequestError {\n  errorLogType = ErrorLogType.ServerError;\n  errorName = ErrorName.InternalServerError;\n  displayMessage = (\n    \n  );\n\n  constructor(details: NetworkRequestErrorDetails, cause?: CausableError) {\n    const message = 'Internal server error';\n\n    super(message, details, cause);\n  }\n}\n\nexport class NotFoundError extends NetworkRequestError {\n  errorLogType = ErrorLogType.UserInputError;\n  errorName = ErrorName.NotFoundError;\n\n  displayMessage = (\n    \n  );\n\n  isUserError = true;\n\n  constructor(details: NetworkRequestErrorDetails, cause?: CausableError) {\n    const message = 'The requested resource was not found.';\n\n    super(message, details, cause);\n  }\n}\n\nexport class PermissionError extends NetworkRequestError {\n  errorLogType = ErrorLogType.UnexpectedSystemStateError;\n  errorName = ErrorName.PermissionError;\n  displayMessage = (\n    \n  );\n  isUserError = true;\n\n  constructor(details: NetworkRequestErrorDetails, cause?: CausableError) {\n    const message = 'You do not have permission to access this resource.';\n\n    super(message, details, cause);\n  }\n}\n\nexport class RateLimitedError extends NetworkRequestError {\n  errorLogType = ErrorLogType.ServerError;\n  errorName = ErrorName.RateLimitedError;\n  displayMessage = (\n    \n  );\n\n  constructor(details: NetworkRequestErrorDetails, cause?: CausableError) {\n    const message = 'This request exceeds the maximum queries per second limit. Please wait and try again.';\n\n    super(message, details, cause);\n  }\n}\n\nexport class ServiceUnavailableError extends NetworkRequestError {\n  errorLogType = ErrorLogType.ServerError;\n  errorName = ErrorName.InternalServerError;\n  displayMessage = (\n    \n  );\n\n  constructor(details: NetworkRequestErrorDetails, cause?: CausableError) {\n    const message = 'Internal server error';\n\n    super(message, details, cause);\n  }\n}\n\nexport class UnauthorizedError extends NetworkRequestError {\n  errorLogType = ErrorLogType.SessionError;\n  errorName = ErrorName.UnauthorizedError;\n  displayMessage = (\n    \n  );\n\n  constructor(details: NetworkRequestErrorDetails, cause?: CausableError) {\n    const message = 'This request exceeds the maximum queries per second limit. Please wait and try again.';\n\n    super(message, details, cause);\n  }\n}\n\nexport class UnknownError extends PredefinedError {\n  errorLogType = ErrorLogType.UnknownError;\n  errorName = ErrorName.UnknownError;\n  displayMessage = (\n    \n  );\n\n  constructor(cause?: CausableError) {\n    const message = 'An unknown error occurred.';\n\n    super(message, cause);\n  }\n}\n\nexport class FormValidationError extends PredefinedError {\n  errorLogType = ErrorLogType.UserInputError;\n  errorName = ErrorName.FormValidationError;\n  isUserError = true;\n  displayMessage = (\n    \n  );\n\n  constructor(cause?: CausableError) {\n    const message = 'Incorrect form input.';\n\n    super(message, cause);\n  }\n}\n\n// have to be defined here to avoid circular dependencies\nexport class RouteNotFoundError extends PredefinedError {\n  errorLogType = ErrorLogType.UserInputError;\n  errorName = ErrorName.RouteNotFoundError;\n  isUserError = true;\n  displayMessage = (\n    \n  );\n  constructor() {\n    super('Page not found');\n  }\n}\n","import {\n  isNil,\n  omitBy,\n  mapValues,\n  isArray,\n  isString,\n  isNumber,\n  isBoolean,\n  escapeRegExp,\n  map,\n  every,\n  has,\n  compact,\n  keyBy,\n} from 'lodash';\n\nimport { ModelSpanType, ModelIconType, MLFLOW_TRACE_SCHEMA_VERSION_KEY } from './ModelTrace.types';\nimport type {\n  SearchMatch,\n  ModelTrace,\n  ModelTraceSpan,\n  ModelTraceSpanNode,\n  ModelTraceChatMessage,\n  ModelTraceChatResponse,\n  ModelTraceChatInput,\n  ModelTraceToolCall,\n  ModelTraceChatTool,\n  ModelTraceChatToolParamProperty,\n  RawModelTraceChatMessage,\n  ModelTraceContentType,\n  SpanFilterState,\n  ModelTraceSpanV3,\n  ModelTraceSpanV2,\n  ModelTraceInfoV3,\n  Assessment,\n  RetrieverDocument,\n  ModelTraceEvent,\n} from './ModelTrace.types';\nimport { ModelTraceExplorerIcon } from './ModelTraceExplorerIcon';\nimport {\n  normalizeAnthropicChatInput,\n  normalizeAnthropicChatOutput,\n  normalizeGeminiChatInput,\n  normalizeGeminiChatOutput,\n  normalizeOpenAIChatInput,\n  normalizeOpenAIChatResponse,\n  normalizeOpenAIResponsesInput,\n  normalizeOpenAIResponsesOutput,\n  normalizeLangchainChatInput,\n  normalizeLangchainChatResult,\n  normalizeLlamaIndexChatResponse,\n  normalizeDspyChatInput,\n  normalizeDspyChatOutput,\n} from './chat-utils';\nimport { normalizeLlamaIndexChatInput } from './chat-utils/llamaindex';\nimport { normalizeOpenAIAgentInput, normalizeOpenAIAgentOutput } from './chat-utils/openai';\nimport { normalizeAutogenChatInput, normalizeAutogenChatOutput } from './chat-utils/autogen';\nimport { normalizeBedrockChatInput, normalizeBedrockChatOutput } from './chat-utils/bedrock';\n\nexport const FETCH_TRACE_INFO_QUERY_KEY = 'model-trace-info-v3';\n\nexport const displayErrorNotification = (errorMessage: string) => {\n  // TODO: display error notification in OSS\n  return;\n};\n\nexport function getIconTypeForSpan(spanType: ModelSpanType | string): ModelIconType {\n  switch (spanType) {\n    case ModelSpanType.LLM:\n      return ModelIconType.MODELS;\n    case ModelSpanType.CHAIN:\n      return ModelIconType.CHAIN;\n    case ModelSpanType.AGENT:\n      return ModelIconType.AGENT;\n    case ModelSpanType.TOOL:\n      return ModelIconType.WRENCH;\n    case ModelSpanType.CHAT_MODEL:\n      return ModelIconType.MODELS;\n    case ModelSpanType.RETRIEVER:\n      return ModelIconType.SEARCH;\n    case ModelSpanType.PARSER:\n      return ModelIconType.CODE;\n    case ModelSpanType.EMBEDDING:\n      return ModelIconType.NUMBERS;\n    case ModelSpanType.RERANKER:\n      return ModelIconType.SORT;\n    case ModelSpanType.MEMORY:\n      return ModelIconType.SAVE;\n    case ModelSpanType.FUNCTION:\n      return ModelIconType.FUNCTION;\n    case ModelSpanType.UNKNOWN:\n      return ModelIconType.UNKNOWN;\n    default:\n      return ModelIconType.FUNCTION;\n  }\n}\n\nexport function getDisplayNameForSpanType(spanType: ModelSpanType | string): string {\n  switch (spanType) {\n    case ModelSpanType.LLM:\n      return 'LLM';\n    case ModelSpanType.CHAIN:\n      return 'Chain';\n    case ModelSpanType.AGENT:\n      return 'Agent';\n    case ModelSpanType.TOOL:\n      return 'Tool';\n    case ModelSpanType.CHAT_MODEL:\n      return 'Chat model';\n    case ModelSpanType.RETRIEVER:\n      return 'Retriever';\n    case ModelSpanType.PARSER:\n      return 'Parser';\n    case ModelSpanType.EMBEDDING:\n      return 'Embedding';\n    case ModelSpanType.RERANKER:\n      return 'Reranker';\n    case ModelSpanType.MEMORY:\n      return 'Memory';\n    case ModelSpanType.FUNCTION:\n      return 'Function';\n    case ModelSpanType.UNKNOWN:\n      return 'Unknown';\n    default:\n      return spanType;\n  }\n}\n\nexport function tryDeserializeAttribute(value: string): any {\n  try {\n    return JSON.parse(value);\n  } catch (e) {\n    return value;\n  }\n}\n\nexport const getMatchesFromEvent = (span: ModelTraceSpanNode, searchFilter: string): SearchMatch[] => {\n  const events = span.events;\n  if (!events) {\n    return [];\n  }\n\n  const matches: SearchMatch[] = [];\n  events.forEach((event, index) => {\n    const attributes = event.attributes;\n\n    if (!attributes) {\n      return;\n    }\n\n    Object.keys(attributes).forEach((attribute) => {\n      const isKeyMatch = attribute.toLowerCase().includes(searchFilter);\n      const key = getEventAttributeKey(event.name, index, attribute);\n\n      if (isKeyMatch) {\n        matches.push({\n          span,\n          section: 'events',\n          key,\n          isKeyMatch: true,\n          matchIndex: 0,\n        });\n      }\n\n      // event values can be arbitrary JSON\n      const value = JSON.stringify(attributes[attribute]).toLowerCase();\n      const numValueMatches = value.split(searchFilter).length - 1;\n      for (let i = 0; i < numValueMatches; i++) {\n        matches.push({\n          span,\n          section: 'events',\n          key,\n          isKeyMatch: false,\n          matchIndex: i,\n        });\n      }\n    });\n  });\n\n  return matches;\n};\n\n/**\n * This function extracts all the matches from a span based on the search filter,\n * and appends some necessary metadata that is necessary for the jump-to-search\n * function.\n */\nexport const getMatchesFromSpan = (span: ModelTraceSpanNode, searchFilter: string): SearchMatch[] => {\n  // if search filter is empty, don't generate matches\n  // because there will be nothing to highlight anyway\n  if (!searchFilter) {\n    return [];\n  }\n\n  const matches: SearchMatch[] = [];\n\n  const sections = {\n    inputs: span?.inputs,\n    outputs: span?.outputs,\n    attributes: span?.attributes,\n    events: span?.events,\n  };\n\n  map(sections, (section: any, label: 'inputs' | 'outputs' | 'attributes' | 'events') => {\n    if (label === 'events') {\n      matches.push(...getMatchesFromEvent(span, searchFilter));\n      return;\n    }\n\n    const sectionList = createListFromObject(section);\n    sectionList.forEach((item) => {\n      // NOTE: this ignores the fact that there might be multiple matches in a key\n      // for example, if the key is \"aaaaa\", and the search filter is \"a\". However,\n      // implementing support for this case would make the code needlessly complex.\n      // If we receive feedback that this is a problem, we can revisit this.\n      const isKeyMatch = item.key.toLowerCase().includes(searchFilter);\n      if (isKeyMatch) {\n        matches.push({\n          span: span,\n          section: label,\n          key: item.key,\n          isKeyMatch: true,\n          matchIndex: 0,\n        });\n      }\n\n      const numValueMatches = item.value.toLowerCase().split(searchFilter).length - 1;\n      for (let i = 0; i < numValueMatches; i++) {\n        matches.push({\n          span: span,\n          section: label,\n          key: item.key,\n          isKeyMatch: false,\n          matchIndex: i,\n        });\n      }\n    });\n  });\n  return matches;\n};\n\nexport function searchTree(\n  rootNode: ModelTraceSpanNode,\n  searchFilter: string,\n  spanFilterState: SpanFilterState,\n): {\n  filteredTreeNodes: ModelTraceSpanNode[];\n  matches: SearchMatch[];\n} {\n  const searchFilterLowercased = searchFilter.toLowerCase().trim();\n  const allSpanTypesSelected = Object.values(spanFilterState.spanTypeDisplayState).every(\n    (shouldDisplay) => shouldDisplay,\n  );\n  // if there is no search filter and all span types\n  // are selected, then we don't have to do any filtering.\n  if (searchFilterLowercased === '' && allSpanTypesSelected) {\n    return {\n      filteredTreeNodes: [rootNode],\n      matches: [],\n    };\n  }\n\n  const children = rootNode.children ?? [];\n  const filteredChildren: ModelTraceSpanNode[] = [];\n  const matches: SearchMatch[] = [];\n  children.forEach((child) => {\n    const { filteredTreeNodes: childNodes, matches: childMatches } = searchTree(\n      child,\n      searchFilterLowercased,\n      spanFilterState,\n    );\n\n    filteredChildren.push(...childNodes);\n    matches.push(...childMatches);\n  });\n\n  const spanName = ((rootNode.title as string) ?? '').toLowerCase();\n  const spanMatches = getMatchesFromSpan(rootNode, searchFilterLowercased);\n\n  // check if the span passes the text and type filters\n  const nodeMatchesSearch = spanMatches.length > 0 || spanName.includes(searchFilterLowercased);\n  const spanTypeIsDisplayed = rootNode.type ? spanFilterState.spanTypeDisplayState[rootNode.type] : true;\n  const nodePassesSpanFilters = nodeMatchesSearch && spanTypeIsDisplayed;\n\n  const hasMatchingChild = filteredChildren.length > 0;\n  const hasException = getSpanExceptionCount(rootNode) > 0;\n\n  const nodeShouldBeDisplayed =\n    nodePassesSpanFilters ||\n    // the `showParents` and `showExceptions` flags override the\n    // search filters, so we always show the node if they pass\n    (spanFilterState.showParents && hasMatchingChild) ||\n    (spanFilterState.showExceptions && hasException);\n\n  if (nodeShouldBeDisplayed) {\n    return {\n      filteredTreeNodes: [{ ...rootNode, children: filteredChildren }],\n      matches: spanMatches.concat(matches),\n    };\n  }\n\n  // otherwise cut the span out of the tree by returning the children directly\n  return {\n    filteredTreeNodes: filteredChildren,\n    matches,\n  };\n}\n\nexport function searchTreeBySpanId(\n  rootNode: ModelTraceSpanNode | null,\n  selectedSpanId?: string,\n): ModelTraceSpanNode | undefined {\n  if (isNil(selectedSpanId) || isNil(rootNode)) {\n    return undefined;\n  }\n\n  if (rootNode.key === selectedSpanId) {\n    return rootNode;\n  }\n\n  const children = rootNode.children ?? [];\n  for (const child of children) {\n    const matchedNode = searchTreeBySpanId(child, selectedSpanId);\n    if (matchedNode) {\n      return matchedNode;\n    }\n  }\n\n  return undefined;\n}\n\nconst getChatMessagesFromSpan = (\n  messagesAttributeValue: any,\n  inputs: any,\n  outputs: any,\n  spanAttributes?: Record,\n): ModelTraceChatMessage[] | undefined => {\n  // if the `mlflow.chat.messages` attribute is provided\n  // and in the correct format, return it as-is\n  // we allow content type to be content part list for the `mlflow.chat.messages` attribute\n  if (Array.isArray(messagesAttributeValue) && messagesAttributeValue.every(isRawModelTraceChatMessage)) {\n    return compact(messagesAttributeValue.map(prettyPrintChatMessage));\n  }\n\n  // otherwise, attempt to parse messages from inputs and outputs\n  // this is to support rich rendering for older versions of MLflow\n  // before the `mlflow.chat.messages` attribute was introduced\n  const messagesFromInputs = normalizeConversation(inputs, spanAttributes?.['mlflow.message.format']) ?? [];\n  const messagesFromOutputs = normalizeConversation(outputs, spanAttributes?.['mlflow.message.format']) ?? [];\n\n  // when either input or output is not chat messages, we do not set the chat message fiels.\n  if (messagesFromInputs.length === 0 || messagesFromOutputs.length === 0) {\n    return undefined;\n  }\n\n  return messagesFromInputs.concat(messagesFromOutputs);\n};\n\nconst getChatToolsFromSpan = (toolsAttributeValue: any, inputs: any): ModelTraceChatTool[] | undefined => {\n  // if the `mlflow.chat.tools` attribute is provided\n  // and in the correct format, return it as-is\n  if (Array.isArray(toolsAttributeValue) && toolsAttributeValue.every(isModelTraceChatTool)) {\n    return toolsAttributeValue;\n  }\n\n  // otherwise, attempt to parse tools from inputs\n  // TODO: support langchain format for tool inputs\n  if (Array.isArray(inputs?.tools) && inputs?.tools?.every(isModelTraceChatTool)) {\n    return inputs.tools;\n  }\n\n  return undefined;\n};\n\nexport const normalizeNewSpanData = (\n  span: ModelTraceSpan,\n  rootStartTime: number,\n  rootEndTime: number,\n  children: ModelTraceSpanNode[],\n  assessmentMap: Record,\n  traceId: string,\n): ModelTraceSpanNode => {\n  const spanType = tryDeserializeAttribute(span.attributes?.['mlflow.spanType']);\n  const inputs = tryDeserializeAttribute(span.attributes?.['mlflow.spanInputs']);\n  const outputs = tryDeserializeAttribute(span.attributes?.['mlflow.spanOutputs']);\n  const parentId = getModelTraceSpanParentId(span);\n  const spanId = getModelTraceSpanId(span);\n\n  const assessments = assessmentMap[spanId] ?? [];\n  if (!parentId) {\n    // assessments that are not associated with a specific\n    // span should be displayed at the root.\n    assessments.push(...(assessmentMap[''] ?? []));\n  }\n\n  // data that powers the \"chat\" tab\n  const messagesAttributeValue = tryDeserializeAttribute(span.attributes?.['mlflow.chat.messages']);\n  const messageFormat = tryDeserializeAttribute(span.attributes?.['mlflow.message.format']);\n  const spanAttributesForChat = messageFormat ? { 'mlflow.message.format': messageFormat } : undefined;\n  const chatMessages = getChatMessagesFromSpan(messagesAttributeValue, inputs, outputs, spanAttributesForChat);\n  const chatTools = getChatToolsFromSpan(tryDeserializeAttribute(span.attributes?.['mlflow.chat.tools']), inputs);\n\n  // remove other private mlflow attributes\n  const attributes = mapValues(\n    omitBy(span.attributes, (_, key) => key.startsWith('mlflow.')),\n    (value) => tryDeserializeAttribute(value),\n  );\n  const events = span.events;\n  const start = (Number(getModelTraceSpanStartTime(span)) - rootStartTime) / 1000;\n  const end = (Number(getModelTraceSpanEndTime(span) ?? rootEndTime) - rootStartTime) / 1000;\n\n  return {\n    title: span.name,\n    icon: (\n       0}\n        isRootSpan={!parentId}\n      />\n    ),\n    type: spanType,\n    key: spanId,\n    start,\n    end,\n    children,\n    inputs,\n    outputs,\n    attributes,\n    events,\n    chatMessages,\n    chatTools,\n    parentId,\n    assessments,\n    traceId,\n  };\n};\n\nconst base64ToHex = (base64: string): string => {\n  const binaryString = atob(base64);\n  const binaryLen = binaryString.length;\n  let hex = '';\n  for (let i = 0; i < binaryLen; i++) {\n    const charCode = binaryString.charCodeAt(i);\n    hex += charCode.toString(16).padStart(2, '0');\n  }\n  return hex;\n};\n\n// mlflow span ids are meant to be interpreted as hex strings\nexport const decodeSpanId = (spanId: string, isV3Span: boolean): string => {\n  if (isV3Span) {\n    // v3 span ids are base64 encoded\n    try {\n      return base64ToHex(spanId);\n    } catch (e) {\n      // if base64 decoding fails, just return the original spanId\n      return spanId;\n    }\n  }\n\n  // old V2 span ids (pre-March 2025) are in hex with a 0x prefix\n  if (spanId.startsWith('0x')) {\n    return spanId.slice(2);\n  }\n\n  // new V2 span ids have the prefix stripped\n  return spanId;\n};\n\nexport function isV3ModelTraceInfo(info: ModelTrace['info']): info is ModelTraceInfoV3 {\n  return 'trace_metadata' in info;\n}\n\nexport function isV3ModelTraceSpan(span: ModelTraceSpan): span is ModelTraceSpanV3 {\n  return 'start_time_unix_nano' in span;\n}\n\nexport function isV2ModelTraceSpan(span: ModelTraceSpan): span is ModelTraceSpanV2 {\n  return 'parent_id' in span;\n}\n\nexport function getModelTraceSpanId(span: ModelTraceSpan): string {\n  return isV3ModelTraceSpan(span) ? decodeSpanId(span.span_id, true) : decodeSpanId(span.context?.span_id ?? '', false);\n}\n\nexport function getModelTraceSpanParentId(span: ModelTraceSpan): string {\n  return isV3ModelTraceSpan(span)\n    ? decodeSpanId(span.parent_span_id ?? '', true)\n    : decodeSpanId(span.parent_id ?? '', false);\n}\n\nexport function getModelTraceSpanStartTime(span: ModelTraceSpan): number {\n  return isV3ModelTraceSpan(span) ? Number(span.start_time_unix_nano) : Number(span.start_time);\n}\n\nexport function getModelTraceSpanEndTime(span: ModelTraceSpan): number {\n  return isV3ModelTraceSpan(span) ? Number(span.end_time_unix_nano) : Number(span.end_time);\n}\n\nexport function getModelTraceId(trace: ModelTrace): string {\n  return isV3ModelTraceInfo(trace.info) ? trace.info.trace_id : trace.info.request_id ?? '';\n}\n\nexport function parseModelTraceToTree(trace: ModelTrace): ModelTraceSpanNode | null {\n  const traceId = getModelTraceId(trace);\n  const spans = trace.trace_data?.spans ?? trace.data.spans;\n  const spanMap: { [span_id: string]: ModelTraceSpan } = {};\n  const relationMap: { [span_id: string]: string[] } = {};\n\n  spans.forEach((span) => {\n    const spanId = getModelTraceSpanId(span);\n    spanMap[spanId] = span;\n    relationMap[spanId] = [];\n  });\n\n  spans.forEach((span) => {\n    const spanId = getModelTraceSpanId(span);\n    const parentId = getModelTraceSpanParentId(span);\n    if (parentId) {\n      if (!relationMap[parentId]) {\n        throw new Error('Tree structure is malformed!');\n      }\n      relationMap[parentId].push(spanId);\n    }\n  });\n\n  const rootSpan = spans.find((span) => !getModelTraceSpanParentId(span));\n  if (isNil(rootSpan)) {\n    return null;\n  }\n\n  const rootSpanId = getModelTraceSpanId(rootSpan);\n  function getSpanNodeFromData(span_id: string): ModelTraceSpanNode {\n    const span = spanMap[span_id];\n    // above we return if rootSpan is null, but for some\n    // reason typescript thinks it's still nullable here.\n    const rootStart = Number(getModelTraceSpanStartTime(rootSpan as ModelTraceSpan));\n    const rootEnd = Number(getModelTraceSpanEndTime(rootSpan as ModelTraceSpan));\n    const children = relationMap[span_id].map(getSpanNodeFromData);\n    const assessmentMap = getAssessmentMap(trace.info);\n\n    // not using `isV2Span` here because for legacy reasons,\n    // V1 and V2 are rolled into in the same type. \"parent_id\" is\n    // the way we distinguish between the two.\n    if (isV3ModelTraceSpan(span) || 'parent_id' in span) {\n      // reusing the same function for v2 and v3 as the changes are small\n      return normalizeNewSpanData(span, rootStart, rootEnd, children, assessmentMap, traceId);\n    }\n\n    // v1 spans\n    const spanType = span.span_type ?? ModelSpanType.UNKNOWN;\n    return {\n      title: span.name,\n      icon: ,\n      type: spanType as ModelSpanType,\n      key: span.context.span_id,\n      start: Number(span.start_time) - rootStart,\n      // default to the end of the root span if the span has no end time.\n      // this can happen if an exception was thrown in the span.\n      end: Number(span.end_time ?? rootEnd) - rootStart,\n      children: children,\n      inputs: span.inputs,\n      outputs: span.outputs,\n      attributes: span.attributes,\n      events: span.events,\n      parentId: span.parent_id ?? span.parent_span_id,\n      assessments: [],\n      traceId,\n    };\n  }\n\n  return getSpanNodeFromData(rootSpanId);\n}\n\n// returns a map of { [span_id: string] : Assessment[] }\nexport const getAssessmentMap = (traceInfo: ModelTrace['info']): Record => {\n  let assessments: Assessment[] = [];\n  if (isV3ModelTraceInfo(traceInfo)) {\n    assessments = traceInfo.assessments ?? [];\n  } else {\n    assessments = getAssessmentsFromTags(traceInfo.tags);\n  }\n\n  // hydrate the assessments with the overridden assessment objects\n  const assessmentsById = keyBy(assessments, 'assessment_id');\n  Object.values(assessmentsById).forEach((assessment) => {\n    if (assessment.overrides) {\n      const overriddenAssessment = assessmentsById[assessment.overrides];\n      assessment.overriddenAssessment = overriddenAssessment;\n    }\n  });\n\n  assessments = Object.values(assessmentsById);\n\n  // construct the map by reducing over the assessments\n  const assessmentMap = assessments.reduce((acc: Record, assessment: Assessment) => {\n    const spanId = assessment.span_id ?? '';\n    if (!acc[spanId]) {\n      acc[spanId] = [];\n    }\n    acc[spanId].push(assessment);\n    return acc;\n  }, {});\n\n  // sort the assessments by last_update_time\n  Object.keys(assessmentMap).forEach((spanId) => {\n    assessmentMap[spanId].sort(\n      (a, b) => new Date(b.last_update_time).getTime() - new Date(a.last_update_time).getTime(),\n    );\n  });\n\n  return assessmentMap;\n};\n\n// parses assessments out from the trace tags\nconst getAssessmentsFromTags = (tags: ModelTrace['info']['tags']): Assessment[] => {\n  if (!tags) {\n    return [];\n  }\n\n  const tagList = Array.isArray(tags) ? tags : Object.entries(tags).map(([key, value]) => ({ key, value }));\n\n  return tagList.filter(({ key }) => key.startsWith('mlflow.assessment.')).map(({ value }) => JSON.parse(value));\n};\n\n// this function attempts to extract the trace version from\n// a given source (either request_metadata or tags)\nexport function findTraceVersionByKey(\n  source: { [key: string]: string } | { key: string; value: string }[] | undefined,\n): string | undefined {\n  if (!source) {\n    return undefined;\n  }\n\n  if (isArray(source)) {\n    return source.find((tag) => tag.key === MLFLOW_TRACE_SCHEMA_VERSION_KEY)?.value;\n  }\n\n  return source[MLFLOW_TRACE_SCHEMA_VERSION_KEY];\n}\n\n// this function determines whether an object is a ModelTrace by asserting\n// that the object has the `data` and `info` fields, and that the\n// trace info contains the `mlflow.trace_schema.version` key\nexport const isModelTrace = (trace: any): trace is ModelTrace => {\n  const traceInfo = trace?.info;\n  const traceData = trace?.data;\n  if (!traceInfo || !traceData || !traceData?.spans) {\n    return false;\n  }\n\n  // request_metadata is for V2 traces, trace_metadata is for V3 traces\n  const metadata = traceInfo?.request_metadata ?? traceInfo?.trace_metadata;\n  if (metadata && findTraceVersionByKey(metadata)) {\n    return true;\n  }\n\n  const tags = traceInfo?.tags;\n  if (tags && findTraceVersionByKey(tags)) {\n    return true;\n  }\n\n  return false;\n};\n\nexport const createListFromObject = (\n  obj: { [key: string]: any } | string[] | string | boolean | number | undefined,\n) => {\n  if (isNil(obj)) {\n    return [];\n  }\n\n  if (Array.isArray(obj) || isString(obj) || isNumber(obj) || isBoolean(obj)) {\n    return [{ key: '', value: JSON.stringify(obj, null, 2) }];\n  }\n\n  return Object.entries(obj).map(([key, value]) => {\n    return { key, value: JSON.stringify(value, null, 2) };\n  });\n};\n\nexport const getHighlightedSpanComponents = ({\n  searchFilter,\n  data,\n  activeMatchBackgroundColor,\n  inactiveMatchBackgroundColor,\n  containsActiveMatch,\n  activeMatch,\n  scrollToActiveMatch,\n}: {\n  searchFilter: string;\n  data: string;\n  activeMatchBackgroundColor: string;\n  inactiveMatchBackgroundColor: string;\n  containsActiveMatch: boolean;\n  activeMatch: SearchMatch;\n  scrollToActiveMatch: (node: HTMLSpanElement) => void;\n}) => {\n  // splitting by regex retains the matches in the array,\n  // which makes it easier to handle stuff like preserving\n  // the original case of the match.\n  const regex = new RegExp(`(${escapeRegExp(searchFilter.trim())})`, 'gi');\n  const parts = data.split(regex);\n  const spans: React.ReactNode[] = [];\n  let matchIndex = 0;\n\n  for (let i = 0; i < parts.length; i++) {\n    if (parts[i].toLowerCase().includes(searchFilter.toLowerCase().trim())) {\n      const isActiveMatch = containsActiveMatch && activeMatch.matchIndex === matchIndex;\n      const backgroundColor = isActiveMatch ? activeMatchBackgroundColor : inactiveMatchBackgroundColor;\n      const span = (\n        \n          {parts[i]}\n        \n      );\n      matchIndex++;\n      spans.push(span);\n    } else {\n      spans.push(parts[i]);\n    }\n  }\n\n  return spans;\n};\n\nexport const isRetrieverDocument = (document: any): document is RetrieverDocument => {\n  return has(document, 'page_content');\n};\n\nexport const isRenderableRetrieverSpan = (span: ModelTraceSpanNode): boolean => {\n  return (\n    span.type === ModelSpanType.RETRIEVER &&\n    Array.isArray(span.outputs) &&\n    span.outputs.length > 0 &&\n    every(span.outputs, isRetrieverDocument)\n  );\n};\n\nexport const getEventAttributeKey = (name: string, index: number, attribute: string): string => {\n  return `${name}-${index}-${attribute}`;\n};\n\nexport const getSpanExceptionEvents = (span: ModelTraceSpanNode | ModelTraceSpan): ModelTraceEvent[] => {\n  return (span.events ?? []).filter((event) => event.name === 'exception');\n};\n\nexport const getSpanExceptionCount = (span: ModelTraceSpanNode | ModelTraceSpan): number => {\n  return getSpanExceptionEvents(span).length;\n};\n\nexport const isModelTraceChatToolParamProperty = (obj: any): obj is ModelTraceChatToolParamProperty => {\n  if (isNil(obj)) {\n    return false;\n  }\n\n  if (!isNil(obj.type) && !isString(obj.type)) {\n    return false;\n  }\n\n  if (!isNil(obj.description) && !isString(obj.description)) {\n    return false;\n  }\n\n  if (!isNil(obj.enum) && !Array.isArray(obj.enum)) {\n    return false;\n  }\n\n  return true;\n};\n\nexport const isModelTraceChatTool = (obj: any): obj is ModelTraceChatTool => {\n  if (isNil(obj) || obj.type !== 'function' || !has(obj, 'function.name')) {\n    return false;\n  }\n\n  // conditional validation for the `parameters` field\n  const parameters = obj.function?.parameters;\n  if (!isNil(parameters)) {\n    // if `required` is present, it must be a list of strings\n    const required = parameters.required;\n    if (!isNil(required) && (!Array.isArray(required) || !required.every(isString))) {\n      return false;\n    }\n\n    const properties = parameters.properties;\n    if (!isNil(properties) && !Object.values(properties).every(isModelTraceChatToolParamProperty)) {\n      return false;\n    }\n  }\n\n  return true;\n};\n\nexport const isModelTraceToolCall = (obj: any): obj is ModelTraceToolCall => {\n  return obj && isString(obj.id) && isString(obj.function?.arguments) && isString(obj.function?.name);\n};\n\nconst isContentPart = (part: any) => {\n  switch (part.type) {\n    case 'text':\n    case 'input_text':\n    case 'output_text':\n      return isString(part.text);\n    case 'image_url':\n      const { image_url } = part;\n      if (isNil(image_url)) {\n        return false;\n      }\n      return isString(image_url.url) && (isNil(image_url.detail) || ['auto', 'low', 'high'].includes(image_url.detail));\n    case 'input_audio':\n      const { input_audio } = part;\n      if (isNil(input_audio)) {\n        return false;\n      }\n      return isString(input_audio.data) && (isNil(input_audio.format) || ['wav', 'mp3'].includes(input_audio.format));\n    default:\n      return false;\n  }\n};\n\nconst isContentType = (content: any) => {\n  if (isNil(content) || isString(content)) {\n    return true;\n  }\n\n  if (isArray(content)) {\n    return content.every((part) => isContentPart(part));\n  }\n\n  return false;\n};\n\nexport const isModelTraceChatMessage = (message: any): message is ModelTraceChatMessage => {\n  if (!isRawModelTraceChatMessage(message)) {\n    return false;\n  }\n\n  return isNil(message.content) || isString(message.content);\n};\n\nexport const isRawModelTraceChatMessage = (message: any): message is RawModelTraceChatMessage => {\n  if (!message) {\n    return false;\n  }\n\n  if (message.tool_calls) {\n    if (!Array.isArray(message.tool_calls)) {\n      return false;\n    }\n\n    if (!message.tool_calls.every(isModelTraceToolCall)) {\n      return false;\n    }\n  }\n\n  if (message.type === 'reasoning') {\n    return true;\n  }\n\n  // verify if the message content is a valid content type or not\n  if (!isContentType(message.content)) {\n    return false;\n  }\n\n  return (\n    message.role === 'user' || message.role === 'assistant' || message.role === 'system' || message.role === 'tool'\n  );\n};\n\nexport const isModelTraceChatInput = (obj: any): obj is ModelTraceChatInput => {\n  return (\n    obj && Array.isArray(obj.messages) && obj.messages.length > 0 && obj.messages.every(isRawModelTraceChatMessage)\n  );\n};\n\nexport const isModelTraceChoices = (obj: any): obj is ModelTraceChatResponse['choices'] => {\n  return (\n    Array.isArray(obj) &&\n    obj.length > 0 &&\n    obj.every((choice: any) => has(choice, 'message') && isModelTraceChatMessage(choice.message))\n  );\n};\n\nexport const isModelTraceChatResponse = (obj: any): obj is ModelTraceChatResponse => {\n  return obj && isModelTraceChoices(obj.choices);\n};\n\n/**\n * Attempt to normalize a conversation, return null in case the format is unrecognized\n * TODO: move all chat parsing logic to the chat-utils folder to avoid cluttering this\n * utils file.\n *\n * Supported formats:\n *   1. Langchain chat inputs\n *   2. Langchain chat results\n *   3. OpenAI ChatCompletions inputs\n *   4. OpenAI ChatCompletions responses\n *   5. OpenAI Responses inputs\n *   6. OpenAI Responses output\n *   7. LlamaIndex chat responses\n *   8. DSPy chat inputs\n *   8. DSPy chat outputs\n *   9. Gemini inputs\n *  10. Gemini outputs\n *  11. Anthropic inputs\n *  12. Anthropic outputs\n */\nexport const normalizeConversation = (input: any, messageFormat?: string): ModelTraceChatMessage[] | null => {\n  // wrap in try/catch to avoid crashing the UI. we're doing a lot of type coercion\n  // and formatting, and it's possible that we miss some edge cases. in case of an error,\n  // simply return null to signify that the input is not a chat input.\n  try {\n    // if the input is already in the correct format, return it\n    if (Array.isArray(input) && input.length > 0 && input.every(isRawModelTraceChatMessage)) {\n      return compact(input.map(prettyPrintChatMessage));\n    }\n\n    switch (messageFormat) {\n      case 'langchain':\n        const langchainMessages = normalizeLangchainChatInput(input) ?? normalizeLangchainChatResult(input);\n        if (langchainMessages) return langchainMessages;\n        break;\n      case 'llamaindex':\n        const llamaIndexMessages = normalizeLlamaIndexChatInput(input) ?? normalizeLlamaIndexChatResponse(input);\n        if (llamaIndexMessages) return llamaIndexMessages;\n        break;\n      case 'openai':\n        const openAIMessages =\n          normalizeOpenAIChatInput(input) ??\n          normalizeOpenAIChatResponse(input) ??\n          normalizeOpenAIResponsesOutput(input) ??\n          normalizeOpenAIResponsesInput(input);\n        if (openAIMessages) return openAIMessages;\n        break;\n      case 'dspy':\n        const dspyMessages = normalizeDspyChatInput(input) ?? normalizeDspyChatOutput(input);\n        if (dspyMessages) return dspyMessages;\n        break;\n      case 'gemini':\n        const geminiMessages = normalizeGeminiChatInput(input) ?? normalizeGeminiChatOutput(input);\n        if (geminiMessages) return geminiMessages;\n        break;\n      case 'anthropic':\n        const anthropicMessages = normalizeAnthropicChatInput(input) ?? normalizeAnthropicChatOutput(input);\n        if (anthropicMessages) return anthropicMessages;\n        break;\n      case 'openai-agent':\n        const openAIAgentMessages = normalizeOpenAIAgentInput(input) ?? normalizeOpenAIAgentOutput(input);\n        if (openAIAgentMessages) return openAIAgentMessages;\n        break;\n      case 'autogen':\n        const autogenMessages = normalizeAutogenChatInput(input) ?? normalizeAutogenChatOutput(input);\n        if (autogenMessages) return autogenMessages;\n        break;\n      case 'bedrock':\n        const bedrockMessages = normalizeBedrockChatInput(input) ?? normalizeBedrockChatOutput(input);\n        if (bedrockMessages) return bedrockMessages;\n        break;\n      default:\n        // Fallback to OpenAI chat format\n        const chatMessages = normalizeOpenAIChatInput(input) ?? normalizeOpenAIChatResponse(input);\n        if (chatMessages) return chatMessages;\n        break;\n    }\n\n    return null;\n  } catch (e) {\n    return null;\n  }\n};\n\nexport const prettyPrintToolCall = (toolCall: ModelTraceToolCall): ModelTraceToolCall => {\n  // add some spacing to the arguments for better readability\n  let args = toolCall.function?.arguments;\n  try {\n    args = JSON.stringify(JSON.parse(args), null, 2);\n  } catch (e) {\n    // use original args\n  }\n  return {\n    id: toolCall.id,\n    function: {\n      arguments: args,\n      name: toolCall.function.name,\n    },\n  };\n};\n\nconst formatChatContent = (content?: ModelTraceContentType | null): string | undefined | null => {\n  if (isNil(content) || isString(content)) {\n    return content;\n  }\n\n  const contentParts = content\n    // eslint-disable-next-line array-callback-return\n    .map((part) => {\n      switch (part.type) {\n        case 'text':\n        case 'input_text':\n        case 'output_text':\n          return part.text;\n        case 'image_url':\n          const url = part?.image_url?.url;\n          return url ? `![](${url})` : '[image]';\n        case 'input_audio':\n          // raw encoded audio content is not displayed in the UI\n          return '[audio]';\n      }\n    })\n    .filter((part) => part !== undefined);\n\n  // Join with double line breaks for better visual separation\n  return contentParts.join('\\n\\n');\n};\n\nexport const prettyPrintChatMessage = (message: RawModelTraceChatMessage): ModelTraceChatMessage | null => {\n  // TODO: support rich rendering of reasoning messages\n  // for now, just return null and compact it away in the caller\n  // this is because we want to still render the rest of the message\n  // outputs.\n  if (message.type === 'reasoning') {\n    return null;\n  }\n\n  return {\n    ...message,\n    content: formatChatContent(message.content),\n    tool_calls: message.tool_calls?.map(prettyPrintToolCall),\n  };\n};\n","import type { TimelineTreeNode } from './timeline-tree';\n\nexport const MLFLOW_TRACE_SCHEMA_VERSION_KEY = 'mlflow.trace_schema.version';\n\n// column name for mlflow trace data in inference tables\nexport const INFERENCE_TABLE_RESPONSE_COLUMN_KEY = 'response';\nexport const INFERENCE_TABLE_TRACE_COLUMN_KEY = 'trace';\n\nexport type ModelTraceExplorerRenderMode = 'default' | 'json';\n\nexport enum ModelSpanType {\n  LLM = 'LLM',\n  CHAIN = 'CHAIN',\n  AGENT = 'AGENT',\n  TOOL = 'TOOL',\n  FUNCTION = 'FUNCTION',\n  CHAT_MODEL = 'CHAT_MODEL',\n  RETRIEVER = 'RETRIEVER',\n  PARSER = 'PARSER',\n  EMBEDDING = 'EMBEDDING',\n  RERANKER = 'RERANKER',\n  MEMORY = 'MEMORY',\n  UNKNOWN = 'UNKNOWN',\n}\n\nexport enum ModelIconType {\n  MODELS = 'models',\n  DOCUMENT = 'document',\n  CONNECT = 'connect',\n  SEARCH = 'search',\n  SORT = 'sort',\n  UNKNOWN = 'unknown',\n  FUNCTION = 'function',\n  CODE = 'code',\n  NUMBERS = 'numbers',\n  WRENCH = 'wrench',\n  AGENT = 'agent',\n  CHAIN = 'chain',\n  USER = 'user',\n  SYSTEM = 'system',\n  SAVE = 'save',\n}\n\n/**\n * Represents a single model trace span.\n * Based on https://github.com/mlflow/mlflow/blob/tracing/mlflow/entities/span.py\n *\n * TODO: clean up all deprecated fields after PrPr customers swap over to\n *       the latest version of mlflow tracing\n */\nexport type ModelTraceSpanV2 = {\n  context: {\n    span_id: string;\n    trace_id: string;\n  };\n  name: string;\n  /* deprecated, renamed to `parent_id` */\n  parent_span_id?: string | null;\n  parent_id?: string | null;\n  /* deprecated, contained in attributes['mlflow.spanType'] */\n  span_type?: ModelSpanType | string;\n  /* deprecated, migrated to `status_code` and `status_message` */\n  status?: ModelTraceStatus;\n  status_code?: string;\n  status_message?: string | null;\n  start_time: number;\n  end_time: number;\n  /* deprecated, contained in attributes['mlflow.spanInputs'] */\n  inputs?: any;\n  /* deprecated, contained in attributes['mlflow.spanOutputs'] */\n  outputs?: any;\n  attributes?: Record;\n  events?: ModelTraceEvent[];\n  /* metadata for ui usage logging */\n  type?: ModelSpanType;\n};\n\nexport type ModelTraceSpanV3 = {\n  trace_id: string;\n  span_id: string;\n  // can be empty\n  trace_state: string;\n  // can be empty or null\n  parent_span_id: string | null;\n  name: string;\n  start_time_unix_nano: string;\n  end_time_unix_nano: string;\n  status: {\n    code: ModelSpanStatusCode;\n    message?: string;\n  };\n  attributes: Record;\n  events?: ModelTraceEvent[];\n  /* metadata for ui usage logging */\n  type?: ModelSpanType;\n};\n\nexport type ModelTraceSpan = ModelTraceSpanV2 | ModelTraceSpanV3;\n\nexport type ModelTraceEvent = {\n  name: string;\n  /* deprecated as of v3, migrated to `time_unix_nano` */\n  timestamp?: number;\n  time_unix_nano?: number;\n  attributes?: Record;\n};\n\nexport type ModelTraceData = {\n  spans: ModelTraceSpan[];\n};\n\n/**\n * Represents a single model trace object.\n * Based on https://github.com/mlflow/mlflow/blob/8e44d102e9568d09d9dc376136d13a5a5d1ab46f/mlflow/tracing/types/model.py#L11\n */\nexport type ModelTrace = {\n  /* deprecated, renamed to `data` */\n  trace_data?: ModelTraceData;\n  /* deprecated, renamed to `info` */\n  trace_info?: ModelTraceInfo;\n  data: ModelTraceData;\n  info: ModelTraceInfoV3 | ModelTraceInfo | NotebookModelTraceInfo;\n};\n\n/**\n * Represents the trace data saved in an inference table.\n * https://github.com/databricks/universe/blob/fb8a572602161aa6387ac32593aa24a91518cc32/rag/serving/python/databricks/rag/unpacking/schemas.py#L133-L141\n */\nexport type ModelTraceInferenceTableData = {\n  app_version_id: string;\n  start_timestamp: string;\n  end_timestamp: string;\n  is_truncated: boolean;\n  [MLFLOW_TRACE_SCHEMA_VERSION_KEY]: number;\n  spans: (Omit & {\n    attributes: string;\n  })[];\n};\n\nexport type ModelTraceInfo = {\n  request_id?: string;\n  experiment_id?: string;\n  timestamp_ms?: number;\n  execution_time_ms?: number;\n  status?: ModelTraceStatus['description'];\n  attributes?: Record;\n  request_metadata?: { key: string; value: string }[];\n  tags?: { key: string; value: string }[];\n};\n\n// tags and request_metadata in the notebook view\n// (i.e. displayed directly from the python client)\n// are stored as an object rather than an array.\nexport type NotebookModelTraceInfo = Omit & {\n  tags?: { [key: string]: string };\n  request_metadata?: { [key: string]: string };\n};\n\nexport type ModelTraceLocationMlflowExperiment = {\n  type: 'MLFLOW_EXPERIMENT';\n  mlflow_experiment: {\n    experiment_id: string;\n  };\n};\n\nexport type ModelTraceLocationInferenceTable = {\n  type: 'INFERENCE_TABLE';\n  inference_table: {\n    full_table_name: string;\n  };\n};\n\nexport type ModelTraceLocation = ModelTraceLocationMlflowExperiment | ModelTraceLocationInferenceTable;\n\nexport type ModelTraceInfoV3 = {\n  trace_id: string;\n  client_request_id?: string;\n  trace_location: ModelTraceLocation;\n  request_preview?: string;\n  response_preview?: string;\n  // timestamp in a format like \"2025-02-19T09:52:23.140Z\"\n  request_time: string;\n  // formatted duration string like \"32.4s\"\n  execution_duration: string;\n  state: ModelTraceState;\n  trace_metadata: {\n    [key: string]: string;\n  };\n  assessments: Assessment[];\n  tags: {\n    [key: string]: string;\n  };\n};\n\nexport type ModelTraceState = 'STATE_UNSPECIFIED' | 'OK' | 'ERROR' | 'IN_PROGRESS';\n\nexport type ModelSpanStatusCode = 'STATUS_CODE_UNSET' | 'STATUS_CODE_OK' | 'STATUS_CODE_ERROR';\n\nexport type ModelTraceStatusUnset = {\n  description: 'UNSET';\n  status_code: 0;\n};\n\nexport type ModelTraceStatusOk = {\n  description: 'OK';\n  status_code: 1;\n};\n\nexport type ModelTraceStatusError = {\n  description: 'ERROR';\n  status_code: 2;\n};\n\nexport type ModelTraceStatusInProgress = {\n  description: 'IN_PROGRESS';\n  status_code: 3;\n};\n\nexport enum ModelTraceSpanType {\n  LLM = 'LLM',\n  CHAIN = 'CHAIN',\n  AGENT = 'AGENT',\n  TOOL = 'TOOL',\n  CHAT_MODEL = 'CHAT_MODEL',\n  RETRIEVER = 'RETRIEVER',\n  PARSER = 'PARSER',\n  EMBEDDING = 'EMBEDDING',\n  RERANKER = 'RERANKER',\n  MEMORY = 'MEMORY',\n  UNKNOWN = 'UNKNOWN',\n}\n\nexport type ModelTraceStatus =\n  | ModelTraceStatusUnset\n  | ModelTraceStatusOk\n  | ModelTraceStatusError\n  | ModelTraceStatusInProgress;\n\n/**\n * Represents a single node in the model trace tree.\n */\nexport interface ModelTraceSpanNode extends TimelineTreeNode, Pick {\n  assessments: Assessment[];\n  inputs?: any;\n  outputs?: any;\n  children?: ModelTraceSpanNode[];\n  chatMessages?: ModelTraceChatMessage[];\n  chatTools?: ModelTraceChatTool[];\n  parentId?: string | null;\n  traceId: string;\n}\n\nexport type ModelTraceExplorerTab = 'chat' | 'content' | 'attributes' | 'events';\n\nexport type SearchMatch = {\n  span: ModelTraceSpanNode;\n  section: 'inputs' | 'outputs' | 'attributes' | 'events';\n  key: string;\n  isKeyMatch: boolean;\n  matchIndex: number;\n};\n\nexport type SpanFilterState = {\n  // always show parents regardless of filter state\n  showParents: boolean;\n  // always show exceptions regardless of filter state\n  showExceptions: boolean;\n  // record of span_type: whether to show it\n  spanTypeDisplayState: Record;\n};\n\nexport interface RetrieverDocument {\n  metadata: {\n    doc_uri: string;\n    chunk_id: string;\n    [key: string]: any;\n  };\n  page_content: string;\n  [key: string]: any;\n}\n\nexport enum CodeSnippetRenderMode {\n  JSON = 'json',\n  TEXT = 'text',\n  MARKDOWN = 'markdown',\n}\n\ntype ModelTraceTextContentPart = {\n  type: 'text' | 'input_text' | 'output_text';\n  text: string;\n};\n\ntype ModelTraceImageUrl = {\n  url: string;\n  detail?: 'auto' | 'low' | 'high';\n};\n\ntype ModelTraceImageContentPart = {\n  type: 'image_url';\n  image_url: ModelTraceImageUrl;\n};\n\ntype ModelTraceInputAudio = {\n  data: string;\n  format: 'wav' | 'mp3';\n};\n\ntype ModelTraceAudioContentPart = {\n  type: 'input_audio';\n  input_audio: ModelTraceInputAudio;\n};\n\nexport type ModelTraceContentParts =\n  | ModelTraceTextContentPart\n  | ModelTraceImageContentPart\n  | ModelTraceAudioContentPart;\n\nexport type ModelTraceContentType = string | ModelTraceContentParts[];\n\n// We treat content as string in the tracing UI.\nexport type ModelTraceChatMessage = {\n  role: 'system' | 'user' | 'assistant' | 'tool' | 'function' | 'developer';\n  name?: string;\n  content?: string | null;\n  tool_calls?: ModelTraceToolCall[];\n  tool_call_id?: string;\n};\n\n// The actual chat message schema of mlflow contains string, null and content part list.\nexport type RawModelTraceChatMessage = Omit & {\n  // there are other types, but we don't support them yet\n  type?: 'message' | 'reasoning';\n  content?: ModelTraceContentType | null;\n};\n\nexport type ModelTraceChatToolParamProperty = {\n  type?: string;\n  description?: string;\n  enum?: string[];\n};\n\nexport type ModelTraceChatTool = {\n  type: 'function';\n  function: {\n    name: string;\n    description?: string;\n    parameters?: {\n      properties: {\n        [key: string]: ModelTraceChatToolParamProperty;\n      };\n      required?: string[];\n    };\n  };\n};\n\nexport type ModelTraceToolCall = {\n  id: string;\n  function: {\n    arguments: string;\n    name: string;\n  };\n};\n\n// aligned to the OpenAI format\nexport type ModelTraceChatResponse = {\n  choices: {\n    message: ModelTraceChatMessage;\n  }[];\n};\n\nexport type ModelTraceChatInput = {\n  messages: RawModelTraceChatMessage[];\n};\n\nexport type AssessmentSourceType = 'SOURCE_TYPE_UNSPECIFIED' | 'HUMAN' | 'LLM_JUDGE' | 'CODE';\n\nexport interface AssessmentSource {\n  source_type: AssessmentSourceType;\n  // Identifier for the source. 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\ No newline at end of file
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/js/build/static/js/1570.e6a5ccf7.chunk.js b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/js/build/static/js/1570.e6a5ccf7.chunk.js
new file mode 100644
index 0000000000000000000000000000000000000000..db212a16b2cc12544ea5909708402b598a5bb3eb
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/mlflow/server/js/build/static/js/1570.e6a5ccf7.chunk.js
@@ -0,0 +1,3 @@
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e=this.asyncFunctionsQueue.slice();this.asyncFunctionsQueue=[],e.forEach((function(e){return e()}))},Te([De(0,be("loggerFactory")),De(1,be("gridOptionsWrapper")),De(2,be("frameworkOverrides")),De(3,be("globalEventListener"))],e.prototype,"setBeans",null),e=Te([Ee("eventService")],e)}(),Ne=function(){function e(){}return e.ROW_BUFFER_SIZE=10,e.LAYOUT_INTERVAL=500,e.BATCH_WAIT_MILLIS=50,e.EXPORT_TYPE_DRAG_COPY="dragCopy",e.EXPORT_TYPE_CLIPBOARD="clipboard",e.EXPORT_TYPE_EXCEL="excel",e.EXPORT_TYPE_CSV="csv",e.ROW_MODEL_TYPE_INFINITE="infinite",e.ROW_MODEL_TYPE_VIEWPORT="viewport",e.ROW_MODEL_TYPE_CLIENT_SIDE="clientSide",e.ROW_MODEL_TYPE_SERVER_SIDE="serverSide",e.ALWAYS="always",e.ONLY_WHEN_GROUPING="onlyWhenGrouping",e.PINNED_TOP="top",e.PINNED_BOTTOM="bottom",e.DOM_LAYOUT_NORMAL="normal",e.DOM_LAYOUT_PRINT="print",e.DOM_LAYOUT_AUTO_HEIGHT="autoHeight",e.GROUP_AUTO_COLUMN_ID="ag-Grid-AutoColumn",e.SOURCE_PASTE="paste",e.PINNED_RIGHT="right",e.PINNED_LEFT="left",e.SORT_ASC="asc",e.SORT_DESC="desc",e.INPUT_SELECTOR="input, select, button, textarea",e.FOCUSABLE_SELECTOR="[tabindex], input, select, button, textarea",e.FOCUSABLE_EXCLUDE=".ag-hidden, .ag-hidden *, [disabled], .ag-disabled, .ag-disabled *",e}();!function(e){e.CommunityCoreModule="@ag-grid-community/core",e.CommunityAllModules="@ag-grid-community/all",e.InfiniteRowModelModule="@ag-grid-community/infinite-row-model",e.ClientSideRowModelModule="@ag-grid-community/client-side-row-model",e.CsvExportModule="@ag-grid-community/csv-export",e.EnterpriseCoreModule="@ag-grid-enterprise/core",e.EnterpriseAllModules="@ag-grid-enterprise/all",e.RowGroupingModule="@ag-grid-enterprise/row-grouping",e.ColumnToolPanelModule="@ag-grid-enterprise/column-tool-panel",e.FiltersToolPanelModule="@ag-grid-enterprise/filter-tool-panel",e.MenuModule="@ag-grid-enterprise/menu",e.SetFilterModule="@ag-grid-enterprise/set-filter",e.MultiFilterModule="@ag-grid-enterprise/multi-filter",e.StatusBarModule="@ag-grid-enterprise/status-bar",e.SideBarModule="@ag-grid-enterprise/side-bar",e.RangeSelectionModule="@ag-grid-enterprise/range-selection",e.MasterDetailModule="@ag-grid-enterprise/master-detail",e.RichSelectModule="@ag-grid-enterprise/rich-select",e.GridChartsModule="@ag-grid-enterprise/charts",e.ViewportRowModelModule="@ag-grid-enterprise/viewport-row-model",e.ServerSideRowModelModule="@ag-grid-enterprise/server-side-row-model",e.ExcelExportModule="@ag-grid-enterprise/excel-export",e.ClipboardModule="@ag-grid-enterprise/clipboard",e.SparklinesModule="@ag-grid-enterprise/sparklines",e.AngularModule="@ag-grid-community/angular",e.ReactModule="@ag-grid-community/react",e.VueModule="@ag-grid-community/vue",e.PolymerModule="@ag-grid-community/polymer"}(_e||(_e={}));var Fe=function(){function e(){}return e.register=function(t,o){void 0===o&&(o=!0),e.modulesMap[t.moduleName]=t,void 0===e.moduleBased?e.moduleBased=o:e.moduleBased!==o&&ne((function(){console.warn("AG Grid: You are mixing modules (i.e. @ag-grid-community/core) and packages (ag-grid-community) - you can only use one or the other of these mechanisms."),console.warn("Please see https://www.ag-grid.com/javascript-grid/packages-modules/ for more information.")}),"ModulePackageCheck")},e.registerModules=function(t,o){void 0===o&&(o=!0),t&&t.forEach((function(t){return e.register(t,o)}))},e.assertRegistered=function(t,o){if(this.isRegistered(t))return!0;var n,r=o+t;return n=e.moduleBased?"AG Grid: unable to use "+o+" as module "+t+" is not present. 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Check params.node.rowPinned. This was an unfortunate (but necessary) change we had to do to allow future plans we have of re-skinng the data grid in frameworks such as React, Angular and Vue. See https://www.ag-grid.com/javascript-grid/cell-rendering/#many-renderers-one-column","colDef.pinnedRowCellRenderer-deprecated"),e.pinnedRowCellRendererParams&&t("AG Grid: pinnedRowCellRenderer no longer exists, use cellRendererSelector if you want a different Cell Renderer for pinned rows. Check params.node.rowPinned. This was an unfortunate (but necessary) change we had to do to allow future plans we have of re-skinng the data grid in frameworks such as React, Angular and Vue. See https://www.ag-grid.com/javascript-grid/cell-rendering/#many-renderers-one-column","colDef.pinnedRowCellRenderer-deprecated"),e.pinnedRowCellRendererFramework&&t("AG Grid: pinnedRowCellRenderer no longer exists, use cellRendererSelector if you want a different Cell Renderer for pinned rows. Check params.node.rowPinned. This was an unfortunate (but necessary) change we had to do to allow future plans we have of re-skinng the data grid in frameworks such as React, Angular and Vue. See https://www.ag-grid.com/javascript-grid/cell-rendering/#many-renderers-one-column","colDef.pinnedRowCellRenderer-deprecated"),e.pinnedRowValueGetter&&t("AG Grid: pinnedRowCellRenderer is deprecated, use cellRendererSelector if you want a different Cell Renderer for pinned rows. Check params.node.rowPinned. This was an unfortunate (but necessary) change we had to do to allow future plans we have of re-skinng the data grid in frameworks such as React, Angular and Vue.","colDef.pinnedRowCellRenderer-deprecated")},e.prototype.addEventListener=function(e,t){this.eventService.addEventListener(e,t)},e.prototype.removeEventListener=function(e,t){this.eventService.removeEventListener(e,t)},e.prototype.createColumnFunctionCallbackParams=function(e){return{node:e,data:e.data,column:this,colDef:this.colDef,context:this.gridOptionsWrapper.getContext(),api:this.gridOptionsWrapper.getApi(),columnApi:this.gridOptionsWrapper.getColumnApi()}},e.prototype.isSuppressNavigable=function(e){if("boolean"===typeof this.colDef.suppressNavigable)return this.colDef.suppressNavigable;if("function"===typeof this.colDef.suppressNavigable){var t=this.createColumnFunctionCallbackParams(e);return(0,this.colDef.suppressNavigable)(t)}return!1},e.prototype.isCellEditable=function(e){return!(e.group&&!this.gridOptionsWrapper.isEnableGroupEdit())&&this.isColumnFunc(e,this.colDef.editable)},e.prototype.isSuppressFillHandle=function(){return!!D(this.colDef.suppressFillHandle)},e.prototype.isAutoHeight=function(){return!!D(this.colDef.autoHeight)},e.prototype.isRowDrag=function(e){return this.isColumnFunc(e,this.colDef.rowDrag)},e.prototype.isDndSource=function(e){return this.isColumnFunc(e,this.colDef.dndSource)},e.prototype.isCellCheckboxSelection=function(e){return this.isColumnFunc(e,this.colDef.checkboxSelection)},e.prototype.isSuppressPaste=function(e){return this.isColumnFunc(e,this.colDef?this.colDef.suppressPaste:null)},e.prototype.isResizable=function(){return!!D(this.colDef.resizable)},e.prototype.isColumnFunc=function(e,t){return"boolean"===typeof t?t:"function"===typeof t&&t(this.createColumnFunctionCallbackParams(e))},e.prototype.setMoving=function(t,o){void 0===o&&(o="api"),this.moving=t,this.eventService.dispatchEvent(this.createColumnEvent(e.EVENT_MOVING_CHANGED,o))},e.prototype.createColumnEvent=function(e,t){return{api:this.gridApi,columnApi:this.columnApi,type:e,column:this,columns:[this],source:t}},e.prototype.isMoving=function(){return this.moving},e.prototype.getSort=function(){return this.sort},e.prototype.setSort=function(t,o){void 0===o&&(o="api"),this.sort!==t&&(this.sort=t,this.eventService.dispatchEvent(this.createColumnEvent(e.EVENT_SORT_CHANGED,o)))},e.prototype.setMenuVisible=function(t,o){void 0===o&&(o="api"),this.menuVisible!==t&&(this.menuVisible=t,this.eventService.dispatchEvent(this.createColumnEvent(e.EVENT_MENU_VISIBLE_CHANGED,o)))},e.prototype.isMenuVisible=function(){return this.menuVisible},e.prototype.isSortAscending=function(){return this.sort===Ne.SORT_ASC},e.prototype.isSortDescending=function(){return this.sort===Ne.SORT_DESC},e.prototype.isSortNone=function(){return b(this.sort)},e.prototype.isSorting=function(){return O(this.sort)},e.prototype.getSortIndex=function(){return this.sortIndex},e.prototype.setSortIndex=function(e){this.sortIndex=e},e.prototype.setAggFunc=function(e){this.aggFunc=e},e.prototype.getAggFunc=function(){return this.aggFunc},e.prototype.getLeft=function(){return this.left},e.prototype.getOldLeft=function(){return this.oldLeft},e.prototype.getRight=function(){return this.left+this.actualWidth},e.prototype.setLeft=function(t,o){void 0===o&&(o="api"),this.oldLeft=this.left,this.left!==t&&(this.left=t,this.eventService.dispatchEvent(this.createColumnEvent(e.EVENT_LEFT_CHANGED,o)))},e.prototype.isFilterActive=function(){return this.filterActive},e.prototype.setFilterActive=function(t,o,n){void 0===o&&(o="api"),this.filterActive!==t&&(this.filterActive=t,this.eventService.dispatchEvent(this.createColumnEvent(e.EVENT_FILTER_ACTIVE_CHANGED,o)));var r=this.createColumnEvent(e.EVENT_FILTER_CHANGED,o);n&&K(r,n),this.eventService.dispatchEvent(r)},e.prototype.setPinned=function(e){!0===e||e===Ne.PINNED_LEFT?this.pinned=Ne.PINNED_LEFT:e===Ne.PINNED_RIGHT?this.pinned=Ne.PINNED_RIGHT:this.pinned=null},e.prototype.setFirstRightPinned=function(t,o){void 0===o&&(o="api"),this.firstRightPinned!==t&&(this.firstRightPinned=t,this.eventService.dispatchEvent(this.createColumnEvent(e.EVENT_FIRST_RIGHT_PINNED_CHANGED,o)))},e.prototype.setLastLeftPinned=function(t,o){void 0===o&&(o="api"),this.lastLeftPinned!==t&&(this.lastLeftPinned=t,this.eventService.dispatchEvent(this.createColumnEvent(e.EVENT_LAST_LEFT_PINNED_CHANGED,o)))},e.prototype.isFirstRightPinned=function(){return this.firstRightPinned},e.prototype.isLastLeftPinned=function(){return this.lastLeftPinned},e.prototype.isPinned=function(){return this.pinned===Ne.PINNED_LEFT||this.pinned===Ne.PINNED_RIGHT},e.prototype.isPinnedLeft=function(){return this.pinned===Ne.PINNED_LEFT},e.prototype.isPinnedRight=function(){return this.pinned===Ne.PINNED_RIGHT},e.prototype.getPinned=function(){return this.pinned},e.prototype.setVisible=function(t,o){void 0===o&&(o="api");var n=!0===t;this.visible!==n&&(this.visible=n,this.eventService.dispatchEvent(this.createColumnEvent(e.EVENT_VISIBLE_CHANGED,o)))},e.prototype.isVisible=function(){return this.visible},e.prototype.getColDef=function(){return this.colDef},e.prototype.getColumnGroupShow=function(){return this.colDef.columnGroupShow},e.prototype.getColId=function(){return this.colId},e.prototype.getId=function(){return this.getColId()},e.prototype.getUniqueId=function(){return this.getId()},e.prototype.getDefinition=function(){return this.colDef},e.prototype.getActualWidth=function(){return this.actualWidth},e.prototype.createBaseColDefParams=function(e){return{node:e,data:e.data,colDef:this.colDef,column:this,api:this.gridOptionsWrapper.getApi(),columnApi:this.gridOptionsWrapper.getColumnApi(),context:this.gridOptionsWrapper.getContext()}},e.prototype.getColSpan=function(e){if(b(this.colDef.colSpan))return 1;var t=this.createBaseColDefParams(e),o=this.colDef.colSpan(t);return Math.max(o,1)},e.prototype.getRowSpan=function(e){if(b(this.colDef.rowSpan))return 1;var t=this.createBaseColDefParams(e),o=this.colDef.rowSpan(t);return Math.max(o,1)},e.prototype.setActualWidth=function(e,t,o){void 0===t&&(t="api"),void 0===o&&(o=!1),null!=this.minWidth&&(e=Math.max(e,this.minWidth)),null!=this.maxWidth&&(e=Math.min(e,this.maxWidth)),this.actualWidth!==e&&(this.actualWidth=e,this.flex&&"flex"!==t&&"gridInitializing"!==t&&(this.flex=null),o||this.fireColumnWidthChangedEvent(t))},e.prototype.fireColumnWidthChangedEvent=function(t){this.eventService.dispatchEvent(this.createColumnEvent(e.EVENT_WIDTH_CHANGED,t))},e.prototype.isGreaterThanMax=function(e){return null!=this.maxWidth&&e>this.maxWidth},e.prototype.getMinWidth=function(){return this.minWidth},e.prototype.getMaxWidth=function(){return this.maxWidth},e.prototype.getFlex=function(){return this.flex||0},e.prototype.setFlex=function(e){this.flex!==e&&(this.flex=e)},e.prototype.setMinimum=function(e){void 0===e&&(e="api"),O(this.minWidth)&&this.setActualWidth(this.minWidth,e)},e.prototype.setRowGroupActive=function(t,o){void 0===o&&(o="api"),this.rowGroupActive!==t&&(this.rowGroupActive=t,this.eventService.dispatchEvent(this.createColumnEvent(e.EVENT_ROW_GROUP_CHANGED,o)))},e.prototype.isRowGroupActive=function(){return this.rowGroupActive},e.prototype.setPivotActive=function(t,o){void 0===o&&(o="api"),this.pivotActive!==t&&(this.pivotActive=t,this.eventService.dispatchEvent(this.createColumnEvent(e.EVENT_PIVOT_CHANGED,o)))},e.prototype.isPivotActive=function(){return this.pivotActive},e.prototype.isAnyFunctionActive=function(){return this.isPivotActive()||this.isRowGroupActive()||this.isValueActive()},e.prototype.isAnyFunctionAllowed=function(){return this.isAllowPivot()||this.isAllowRowGroup()||this.isAllowValue()},e.prototype.setValueActive=function(t,o){void 0===o&&(o="api"),this.aggregationActive!==t&&(this.aggregationActive=t,this.eventService.dispatchEvent(this.createColumnEvent(e.EVENT_VALUE_CHANGED,o)))},e.prototype.isValueActive=function(){return this.aggregationActive},e.prototype.isAllowPivot=function(){return!0===this.colDef.enablePivot},e.prototype.isAllowValue=function(){return!0===this.colDef.enableValue},e.prototype.isAllowRowGroup=function(){return!0===this.colDef.enableRowGroup},e.prototype.getMenuTabs=function(e){var t=this.getColDef().menuTabs;return null==t&&(t=e),t},e.prototype.isLockPosition=function(){return console.warn("AG Grid: since v21, col.isLockPosition() should not be used, please use col.getColDef().lockPosition instead."),!!this.colDef&&!!this.colDef.lockPosition},e.prototype.isLockVisible=function(){return console.warn("AG Grid: since v21, col.isLockVisible() should not be used, please use col.getColDef().lockVisible instead."),!!this.colDef&&!!this.colDef.lockVisible},e.prototype.isLockPinned=function(){return console.warn("AG Grid: since v21, col.isLockPinned() should not be used, please use col.getColDef().lockPinned 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e=this.getProvidedColumnGroup().getLeafColumns();return!(!e||0===e.length)&&e.every((function(e){return e.isMoving()}))},e.prototype.checkLeft=function(){if(this.displayedChildren.forEach((function(t){t instanceof e&&t.checkLeft()})),this.displayedChildren.length>0)if(this.gridOptionsWrapper.isEnableRtl()){var t=Ve(this.displayedChildren).getLeft();this.setLeft(t)}else{var o=this.displayedChildren[0].getLeft();this.setLeft(o)}else this.setLeft(null)},e.prototype.getLeft=function(){return this.left},e.prototype.getOldLeft=function(){return this.oldLeft},e.prototype.setLeft=function(t){this.oldLeft=t,this.left!==t&&(this.left=t,this.localEventService.dispatchEvent(this.createAgEvent(e.EVENT_LEFT_CHANGED)))},e.prototype.getPinned=function(){return this.pinned},e.prototype.createAgEvent=function(e){return{type:e}},e.prototype.addEventListener=function(e,t){this.localEventService.addEventListener(e,t)},e.prototype.removeEventListener=function(e,t){this.localEventService.removeEventListener(e,t)},e.prototype.getGroupId=function(){return this.groupId},e.prototype.getInstanceId=function(){return this.instanceId},e.prototype.isChildInThisGroupDeepSearch=function(t){var o=!1;return this.children.forEach((function(n){t===n&&(o=!0),n instanceof e&&n.isChildInThisGroupDeepSearch(t)&&(o=!0)})),o},e.prototype.getActualWidth=function(){var e=0;return this.displayedChildren&&this.displayedChildren.forEach((function(t){e+=t.getActualWidth()})),e},e.prototype.isResizable=function(){if(!this.displayedChildren)return!1;var e=!1;return this.displayedChildren.forEach((function(t){t.isResizable()&&(e=!0)})),e},e.prototype.getMinWidth=function(){var e=0;return 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this.isPadding()&&e&&e.isPadding()?1+e.getPaddingLevel():0},e.prototype.calculateDisplayedColumns=function(){var t=this;this.displayedChildren=[];for(var o=this;null!=o&&o.isPadding();)o=o.getParent();if(!(!!o&&o.providedColumnGroup.isExpandable()))return this.displayedChildren=this.children,void this.localEventService.dispatchEvent(this.createAgEvent(e.EVENT_DISPLAYED_CHILDREN_CHANGED));this.children.forEach((function(n){if(!(n instanceof e&&(!n.displayedChildren||!n.displayedChildren.length)))switch(n.getColumnGroupShow()){case e.HEADER_GROUP_SHOW_OPEN:o.providedColumnGroup.isExpanded()&&t.displayedChildren.push(n);break;case 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this.padding},e.prototype.setExpanded=function(t){this.expanded=void 0!==t&&t;var o={type:e.EVENT_EXPANDED_CHANGED};this.localEventService.dispatchEvent(o)},e.prototype.isExpandable=function(){return this.expandable},e.prototype.isExpanded=function(){return this.expanded},e.prototype.getGroupId=function(){return this.groupId},e.prototype.getId=function(){return this.getGroupId()},e.prototype.setChildren=function(e){this.children=e},e.prototype.getChildren=function(){return this.children},e.prototype.getColGroupDef=function(){return this.colGroupDef},e.prototype.getLeafColumns=function(){var e=[];return this.addLeafColumns(e),e},e.prototype.addLeafColumns=function(t){this.children&&this.children.forEach((function(o){o instanceof Ge?t.push(o):o instanceof e&&o.addLeafColumns(t)}))},e.prototype.getColumnGroupShow=function(){var e=this.colGroupDef;if(e)return e.columnGroupShow},e.prototype.setupExpandable=function(){var e=this;this.setExpandable(),this.getLeafColumns().forEach((function(t){return t.addEventListener(Ge.EVENT_VISIBLE_CHANGED,e.onColumnVisibilityChanged.bind(e))}))},e.prototype.setExpandable=function(){if(!this.isPadding()){for(var t=!1,o=!1,n=!1,r=this.findChildrenRemovingPadding(),i=0,s=r.length;i=0}function ht(e){for(var t=[],o=e.target;o;)t.push(o),o=o.parentElement;return t}function ft(e){e.__agGridEventPath=gt(e)}function gt(e){var t=e;return t.path?t.path:t.composedPath?t.composedPath():t.__agGridEventPath?t.__agGridEventPath:ht(e)}function yt(e,t,o,n){var r=Xe(it,o)?{passive:!0}:void 0;e&&e.addEventListener&&e.addEventListener(t,o,n,r)}var vt=function(e,t,o,n){var r,i=arguments.length,s=i<3?t:null===n?n=Object.getOwnPropertyDescriptor(t,o):n;if("object"===typeof Reflect&&"function"===typeof Reflect.decorate)s=Reflect.decorate(e,t,o,n);else for(var a=e.length-1;a>=0;a--)(r=e[a])&&(s=(i<3?r(s):i>3?r(t,o,s):r(t,o))||s);return 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o.destroyBean(e,t)})),[]},e.EVENT_DESTROYED="destroyed",vt([Re("frameworkOverrides")],e.prototype,"frameworkOverrides",void 0),vt([Re("context")],e.prototype,"context",void 0),vt([Re("eventService")],e.prototype,"eventService",void 0),vt([Re("gridOptionsWrapper")],e.prototype,"gridOptionsWrapper",void 0),vt([we],e.prototype,"destroy",null),e}(),Ct=function(){var e=function(t,o){return e=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(e,t){e.__proto__=t}||function(e,t){for(var o in t)t.hasOwnProperty(o)&&(e[o]=t[o])},e(t,o)};return function(t,o){function n(){this.constructor=t}e(t,o),t.prototype=null===o?Object.create(o):(n.prototype=o.prototype,new n)}}(),wt=function(e,t,o,n){var r,i=arguments.length,s=i<3?t:null===n?n=Object.getOwnPropertyDescriptor(t,o):n;if("object"===typeof Reflect&&"function"===typeof Reflect.decorate)s=Reflect.decorate(e,t,o,n);else for(var a=e.length-1;a>=0;a--)(r=e[a])&&(s=(i<3?r(s):i>3?r(t,o,s):r(t,o))||s);return i>3&&s&&Object.defineProperty(t,o,s),s},Et=function(e,t){return function(o,n){t(o,n,e)}},Rt=function(e){function t(){return null!==e&&e.apply(this,arguments)||this}return Ct(t,e),t.prototype.setBeans=function(e){this.logger=e.create("ColumnFactory")},t.prototype.createColumnTree=function(e,t,o){var n=new G,r=this.extractExistingTreeData(o),i=r.existingCols,s=r.existingGroups,a=r.existingColKeys;n.addExistingKeys(a);var l=this.recursivelyCreateColumns(e,0,t,i,n,s),u=this.findMaxDept(l,0);this.logger.log("Number of levels for grouped columns is "+u);var p=this.balanceColumnTree(l,0,u,n);return this.columnUtils.depthFirstOriginalTreeSearch(null,p,(function(e,t){e instanceof ot&&e.setupExpandable(),e.setOriginalParent(t)})),{columnTree:p,treeDept:u}},t.prototype.extractExistingTreeData=function(e){var t=[],o=[],n=[];return e&&this.columnUtils.depthFirstOriginalTreeSearch(null,e,(function(e){if(e instanceof ot){var r=e;o.push(r)}else{var i=e;n.push(i.getId()),t.push(i)}})),{existingCols:t,existingGroups:o,existingColKeys:n}},t.prototype.createForAutoGroups=function(e,t){var o=this;return e.map((function(e){return o.createAutoGroupTreeItem(t,e)}))},t.prototype.createAutoGroupTreeItem=function(e,t){for(var o=t,n=this.findDepth(e)-1;n>=0;n--){var r=new ot(null,"FAKE_PATH_"+t.getId()+"}_"+n,!0,n);this.context.createBean(r),r.setChildren([o]),o.setOriginalParent(r),o=r}return o},t.prototype.findDepth=function(e){for(var t=0,o=e;o&&o[0]&&o[0]instanceof ot;)t++,o=o[0].getChildren();return t},t.prototype.balanceColumnTree=function(e,t,o,n){for(var r=[],i=0;i=t;c--){var d=n.getUniqueKey(null,null),h=this.createMergedColGroupDef(null),f=new ot(h,d,!0,t);this.context.createBean(f),p&&p.setChildren([f]),p=f,u||(u=p)}if(u&&p){if(r.push(u),e.some((function(e){return e instanceof ot}))){p.setChildren([s]);continue}p.setChildren(e);break}r.push(s)}}return r},t.prototype.findMaxDept=function(e,t){for(var o=t,n=0;n"']/g,Pt={"&":"&","<":"<",">":">",'"':""","'":"'"};function _t(e){var t=String.fromCharCode;function o(e,o){return t(e>>o&63|128)}function n(e){if(e>=0&&e<=31&&10!==e)return"_x"+e.toString(16).toUpperCase().padStart(4,"0")+"_";if(0==(4294967168&e))return t(e);var n="";return 0==(4294965248&e)?n=t(e>>6&31|192):0==(4294901760&e)?(!function(e){if(e>=55296&&e<=57343)throw Error("Lone surrogate U+"+e.toString(16).toUpperCase()+" is not a scalar value")}(e),n=t(e>>12&15|224),n+=o(e,6)):0==(4292870144&e)&&(n=t(e>>18&7|240),n+=o(e,12),n+=o(e,6)),n+=t(63&e|128)}for(var r=function(e){var t=[];if(!e)return[];for(var o,n,r=e.length,i=0;i=55296&&o<=56319&&i1?e.substring(1,e.length):"")})).join(" ")}function It(e){var t=new Map;return e.forEach((function(e){return t.set(e[0],e[1])})),t}function Mt(e,t){var o=new Map;return e.forEach((function(e){return o.set(t(e),e)})),o}function Gt(e){var t=[];return e.forEach((function(e,o){return t.push(o)})),t}var Lt=function(){var e=function(t,o){return e=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(e,t){e.__proto__=t}||function(e,t){for(var o in t)t.hasOwnProperty(o)&&(e[o]=t[o])},e(t,o)};return function(t,o){function n(){this.constructor=t}e(t,o),t.prototype=null===o?Object.create(o):(n.prototype=o.prototype,new n)}}(),xt=function(e,t,o,n){var r,i=arguments.length,s=i<3?t:null===n?n=Object.getOwnPropertyDescriptor(t,o):n;if("object"===typeof Reflect&&"function"===typeof Reflect.decorate)s=Reflect.decorate(e,t,o,n);else for(var a=e.length-1;a>=0;a--)(r=e[a])&&(s=(i<3?r(s):i>3?r(t,o,s):r(t,o))||s);return i>3&&s&&Object.defineProperty(t,o,s),s},Vt=function(e,t){return function(o,n){t(o,n,e)}},Ht=function(e){var t="function"===typeof Symbol&&Symbol.iterator,o=t&&e[t],n=0;if(o)return o.call(e);if(e&&"number"===typeof e.length)return{next:function(){return e&&n>=e.length&&(e=void 0),{value:e&&e[n++],done:!e}}};throw new TypeError(t?"Object is not iterable.":"Symbol.iterator is not 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o},t.prototype.checkViewportColumns=function(){if(null!=this.displayedColumnsCenter){var e=this.viewportColumns.map((function(e){return e.getId()})).join("#");if(this.extractViewport(),e!==this.viewportColumns.map((function(e){return e.getId()})).join("#")){var t={type:St.EVENT_VIRTUAL_COLUMNS_CHANGED,api:this.gridApi,columnApi:this.columnApi};this.eventService.dispatchEvent(t)}}},t.prototype.setViewportPosition=function(e,t){(e!==this.scrollWidth||t!==this.scrollPosition||this.bodyWidthDirty)&&(this.scrollWidth=e,this.scrollPosition=t,this.bodyWidthDirty=!0,this.setViewport(),this.ready&&this.checkViewportColumns())},t.prototype.isPivotMode=function(){return this.pivotMode},t.prototype.isPivotSettingAllowed=function(e){return!e||!this.gridOptionsWrapper.isTreeData()||(console.warn("AG Grid: Pivot mode not available in conjunction Tree Data i.e. 'gridOptions.treeData: true'"),!1)},t.prototype.setPivotMode=function(e,t){if(void 0===t&&(t="api"),e!==this.pivotMode&&this.isPivotSettingAllowed(this.pivotMode)){this.pivotMode=e,this.autoGroupsNeedBuilding=!0,this.updateGridColumns(),this.updateDisplayedColumns(t);var o={type:St.EVENT_COLUMN_PIVOT_MODE_CHANGED,api:this.gridApi,columnApi:this.columnApi};this.eventService.dispatchEvent(o)}},t.prototype.getSecondaryPivotColumn=function(e,t){if(b(this.secondaryColumns))return null;var o=this.getPrimaryColumn(t),n=null;return this.secondaryColumns.forEach((function(t){var r=t.getColDef().pivotKeys,i=t.getColDef().pivotValueColumn;He(r,e)&&i===o&&(n=t)})),n},t.prototype.setBeans=function(e){this.logger=e.create("columnModel")},t.prototype.setFirstRightAndLastLeftPinned=function(e){var t,o;this.gridOptionsWrapper.isEnableRtl()?(t=this.displayedColumnsLeft?this.displayedColumnsLeft[0]:null,o=this.displayedColumnsRight?Ve(this.displayedColumnsRight):null):(t=this.displayedColumnsLeft?Ve(this.displayedColumnsLeft):null,o=this.displayedColumnsRight?this.displayedColumnsRight[0]:null),this.gridColumns.forEach((function(n){n.setLastLeftPinned(n===t,e),n.setFirstRightPinned(n===o,e)}))},t.prototype.autoSizeColumns=function(e){var t=this,o=e.columns,n=e.skipHeader,r=e.skipHeaderGroups,i=e.stopAtGroup,s=e.source,a=void 0===s?"api":s;this.animationFrameService.flushAllFrames();for(var l=[],u=-1,p=null!=n?n:this.gridOptionsWrapper.isSkipHeaderOnAutoSize(),c=null!=r?r:p;0!==u;)u=0,this.actionOnGridColumns(o,(function(e){if(l.indexOf(e)>=0)return!1;var o=t.autoWidthCalculator.getPreferredWidthForColumn(e,p);if(o>0){var n=t.normaliseColumnWidth(e,o);e.setActualWidth(n,a),l.push(e),u++}return!0}),a);c||this.autoSizeColumnGroupsByColumns(o,i),this.fireColumnResizedEvent(l,!0,"autosizeColumns")},t.prototype.fireColumnResizedEvent=function(e,t,o,n){if(void 0===n&&(n=null),e&&e.length){var r={type:St.EVENT_COLUMN_RESIZED,columns:e,column:1===e.length?e[0]:null,flexColumns:n,finished:t,api:this.gridApi,columnApi:this.columnApi,source:o};this.eventService.dispatchEvent(r)}},t.prototype.autoSizeColumn=function(e,t,o){void 0===o&&(o="api"),e&&this.autoSizeColumns({columns:[e],skipHeader:t,skipHeaderGroups:!0,source:o})},t.prototype.autoSizeColumnGroupsByColumns=function(e,t){var o,n,r,i,s,a=new Set;this.getGridColumns(e).forEach((function(e){for(var o=e.getParent();o&&o!=t;)o.isPadding()||a.add(o),o=o.getParent()}));try{for(var l=Ht(a),u=l.next();!u.done;u=l.next()){var p=u.value;try{for(var c=(r=void 0,Ht(this.ctrlsService.getHeaderRowContainerCtrls())),d=c.next();!d.done;d=c.next()){if(s=d.value.getHeaderCtrlForColumn(p))break}}catch(h){r={error:h}}finally{try{d&&!d.done&&(i=c.return)&&i.call(c)}finally{if(r)throw r.error}}s&&s.resizeLeafColumnsToFit()}}catch(f){o={error:f}}finally{try{u&&!u.done&&(n=l.return)&&n.call(l)}finally{if(o)throw o.error}}return[]},t.prototype.autoSizeAllColumns=function(e,t){void 0===t&&(t="api");var o=this.getAllDisplayedColumns();this.autoSizeColumns({columns:o,skipHeader:e,source:t})},t.prototype.getColumnsFromTree=function(e){var t=[],o=function(e){for(var n=0;n=0},t.prototype.getAllDisplayedColumns=function(){return this.displayedColumns},t.prototype.getViewportColumns=function(){return this.viewportColumns},t.prototype.getDisplayedLeftColumnsForRow=function(e){return this.colSpanActive?this.getDisplayedColumnsForRow(e,this.displayedColumnsLeft):this.displayedColumnsLeft},t.prototype.getDisplayedRightColumnsForRow=function(e){return this.colSpanActive?this.getDisplayedColumnsForRow(e,this.displayedColumnsRight):this.displayedColumnsRight},t.prototype.getDisplayedColumnsForRow=function(e,t,o,n){for(var r,i=[],s=null,a=function(a){var l,u=t[a],p=t.length-a,c=Math.min(u.getColSpan(e),p),d=[u];if(c>1){for(var h=c-1,f=1;f<=h;f++)d.push(t[a+f]);a+=h}if(o?(l=!1,d.forEach((function(e){o(e)&&(l=!0)}))):l=!0,l){if(0===i.length&&s)!!n&&n(u)&&i.push(s);i.push(u)}s=u,r=a},l=0;lt.viewportLeft}))},t.prototype.getAriaColumnIndex=function(e){return this.getAllGridColumns().indexOf(e)+1},t.prototype.isColumnInViewport=function(e){if(e.isAutoHeight())return!0;var t=e.getLeft()||0,o=t+e.getActualWidth(),n=this.viewportLeft-200,r=this.viewportRight+200;return!(tr&&o>r)},t.prototype.getDisplayedColumnsLeftWidth=function(){return this.getWidthOfColsInList(this.displayedColumnsLeft)},t.prototype.getDisplayedColumnsRightWidth=function(){return this.getWidthOfColsInList(this.displayedColumnsRight)},t.prototype.updatePrimaryColumnList=function(e,t,o,n,r,i){var s=this;if(void 0===i&&(i="api"),e&&!P(e)){var a=!1;if(e.forEach((function(e){var r=s.getPrimaryColumn(e);if(r){if(o){if(t.indexOf(r)>=0)return;t.push(r)}else{if(t.indexOf(r)<0)return;Ue(t,r)}n(r),a=!0}})),a){this.autoGroupsNeedBuilding&&this.updateGridColumns(),this.updateDisplayedColumns(i);var l={type:r,columns:t,column:1===t.length?t[0]:null,api:this.gridApi,columnApi:this.columnApi,source:i};this.eventService.dispatchEvent(l)}}},t.prototype.setRowGroupColumns=function(e,t){void 0===t&&(t="api"),this.autoGroupsNeedBuilding=!0,this.setPrimaryColumnList(e,this.rowGroupColumns,St.EVENT_COLUMN_ROW_GROUP_CHANGED,this.setRowGroupActive.bind(this),t)},t.prototype.setRowGroupActive=function(e,t,o){e!==t.isRowGroupActive()&&(t.setRowGroupActive(e,o),e||this.gridOptionsWrapper.isSuppressMakeColumnVisibleAfterUnGroup()||t.setVisible(!0,o))},t.prototype.addRowGroupColumn=function(e,t){void 0===t&&(t="api"),e&&this.addRowGroupColumns([e],t)},t.prototype.addRowGroupColumns=function(e,t){void 0===t&&(t="api"),this.autoGroupsNeedBuilding=!0,this.updatePrimaryColumnList(e,this.rowGroupColumns,!0,this.setRowGroupActive.bind(this,!0),St.EVENT_COLUMN_ROW_GROUP_CHANGED,t)},t.prototype.removeRowGroupColumns=function(e,t){void 0===t&&(t="api"),this.autoGroupsNeedBuilding=!0,this.updatePrimaryColumnList(e,this.rowGroupColumns,!1,this.setRowGroupActive.bind(this,!1),St.EVENT_COLUMN_ROW_GROUP_CHANGED,t)},t.prototype.removeRowGroupColumn=function(e,t){void 0===t&&(t="api"),e&&this.removeRowGroupColumns([e],t)},t.prototype.addPivotColumns=function(e,t){void 0===t&&(t="api"),this.updatePrimaryColumnList(e,this.pivotColumns,!0,(function(e){return e.setPivotActive(!0,t)}),St.EVENT_COLUMN_PIVOT_CHANGED,t)},t.prototype.setPivotColumns=function(e,t){void 0===t&&(t="api"),this.setPrimaryColumnList(e,this.pivotColumns,St.EVENT_COLUMN_PIVOT_CHANGED,(function(e,o){o.setPivotActive(e,t)}),t)},t.prototype.addPivotColumn=function(e,t){void 0===t&&(t="api"),this.addPivotColumns([e],t)},t.prototype.removePivotColumns=function(e,t){void 0===t&&(t="api"),this.updatePrimaryColumnList(e,this.pivotColumns,!1,(function(e){return e.setPivotActive(!1,t)}),St.EVENT_COLUMN_PIVOT_CHANGED,t)},t.prototype.removePivotColumn=function(e,t){void 0===t&&(t="api"),this.removePivotColumns([e],t)},t.prototype.setPrimaryColumnList=function(e,t,o,n,r){var i=this;t.length=0,O(e)&&e.forEach((function(e){var o=i.getPrimaryColumn(e);o&&t.push(o)})),(this.primaryColumns||[]).forEach((function(e){var o=t.indexOf(e)>=0;n(o,e)})),this.autoGroupsNeedBuilding&&this.updateGridColumns(),this.updateDisplayedColumns(r),this.fireColumnEvent(o,t,r)},t.prototype.setValueColumns=function(e,t){void 0===t&&(t="api"),this.setPrimaryColumnList(e,this.valueColumns,St.EVENT_COLUMN_VALUE_CHANGED,this.setValueActive.bind(this),t)},t.prototype.setValueActive=function(e,t,o){if(e!==t.isValueActive()&&(t.setValueActive(e,o),e&&!t.getAggFunc())){var n=this.aggFuncService.getDefaultAggFunc(t);t.setAggFunc(n)}},t.prototype.addValueColumns=function(e,t){void 0===t&&(t="api"),this.updatePrimaryColumnList(e,this.valueColumns,!0,this.setValueActive.bind(this,!0),St.EVENT_COLUMN_VALUE_CHANGED,t)},t.prototype.addValueColumn=function(e,t){void 0===t&&(t="api"),e&&this.addValueColumns([e],t)},t.prototype.removeValueColumn=function(e,t){void 0===t&&(t="api"),this.removeValueColumns([e],t)},t.prototype.removeValueColumns=function(e,t){void 0===t&&(t="api"),this.updatePrimaryColumnList(e,this.valueColumns,!1,this.setValueActive.bind(this,!1),St.EVENT_COLUMN_VALUE_CHANGED,t)},t.prototype.normaliseColumnWidth=function(e,t){var o=e.getMinWidth();O(o)&&t0?r+=o:i=!1})),o>=n&&(!i||o<=r)},t.prototype.resizeColumnSets=function(e){var t=this,o=e.resizeSets,n=e.finished,r=e.source;if(!o||o.every((function(e){return t.checkMinAndMaxWidthsForSet(e)}))){var i=[],s=[];o.forEach((function(e){var t=e.width,o=e.columns,n=e.ratios,a={},l={};o.forEach((function(e){return s.push(e)}));for(var u=!0,p=0,c=function(){if(++p>1e3)return console.error("AG Grid: infinite loop in resizeColumnSets"),"break";u=!1;var e=[],r=0,i=t;o.forEach((function(t,o){if(l[t.getId()])i-=a[t.getId()];else{e.push(t);var s=n[o];r+=s}}));var s=1/r;e.forEach((function(o,r){var p;r===e.length-1?p=i:(p=Math.round(n[r]*t*s),i-=p);var c=o.getMinWidth(),d=o.getMaxWidth();O(c)&&p0&&p>d&&(p=d,l[o.getId()]=!0,u=!0),a[o.getId()]=p}))};u;){if("break"===c())break}o.forEach((function(e){var t=a[e.getId()];e.getActualWidth()!==t&&(e.setActualWidth(t,r),i.push(e))}))}));var a=i.length>0,l=[];a&&(l=this.refreshFlexedColumns({resizingCols:s,skipSetLeft:!0}),this.setLeftValues(r),this.updateBodyWidths(),this.checkViewportColumns());var u=s.concat(l);(a||n)&&this.fireColumnResizedEvent(u,n,r,l)}else if(n){var p=o&&o.length>0?o[0].columns:null;this.fireColumnResizedEvent(p,n,r)}},t.prototype.setColumnAggFunc=function(e,t,o){if(void 0===o&&(o="api"),e){var n=this.getPrimaryColumn(e);n&&(n.setAggFunc(t),this.fireColumnEvent(St.EVENT_COLUMN_VALUE_CHANGED,[n],o))}},t.prototype.fireColumnEvent=function(e,t,o){var n={type:e,columns:t,column:t&&1==t.length?t[0]:null,api:this.gridApi,columnApi:this.columnApi,source:o};this.eventService.dispatchEvent(n)},t.prototype.moveRowGroupColumn=function(e,t,o){void 0===o&&(o="api");var n=this.rowGroupColumns[e];this.rowGroupColumns.splice(e,1),this.rowGroupColumns.splice(t,0,n);var r={type:St.EVENT_COLUMN_ROW_GROUP_CHANGED,columns:this.rowGroupColumns,column:1===this.rowGroupColumns.length?this.rowGroupColumns[0]:null,api:this.gridApi,columnApi:this.columnApi,source:o};this.eventService.dispatchEvent(r)},t.prototype.moveColumns=function(e,t,o){if(void 0===o&&(o="api"),this.columnAnimationService.start(),t>this.gridColumns.length-e.length)return console.warn("AG Grid: tried to insert columns in invalid location, toIndex = "+t),void console.warn("AG Grid: remember that you should not count the moving columns when calculating the new index");var n=this.getGridColumns(e);if(!!this.doesMovePassRules(n,t)){qe(this.gridColumns,n,t),this.updateDisplayedColumns(o);var r={type:St.EVENT_COLUMN_MOVED,columns:n,column:1===n.length?n[0]:null,toIndex:t,api:this.gridApi,columnApi:this.columnApi,source:o};this.eventService.dispatchEvent(r),this.columnAnimationService.finish()}},t.prototype.doesMovePassRules=function(e,t){var o=this.gridColumns.slice();return qe(o,e,t),!!this.doesMovePassMarryChildren(o)&&!!this.doesMovePassLockedPositions(o)},t.prototype.sortColumnsLikeGridColumns=function(e){var t=this;!e||e.length<=1||(e.filter((function(e){return t.gridColumns.indexOf(e)<0})).length>0||e.sort((function(e,o){return t.gridColumns.indexOf(e)-t.gridColumns.indexOf(o)})))},t.prototype.doesMovePassLockedPositions=function(e){var t=0,o=!0;return e.forEach((function(e){var n,r=(n=e.getColDef().lockPosition)?!0===n||"left"===n?0:2:1;rn.getLeafColumns().length-1&&(t=!1)}}})),t},t.prototype.moveColumn=function(e,t,o){void 0===o&&(o="api"),this.moveColumns([e],t,o)},t.prototype.moveColumnByIndex=function(e,t,o){void 0===o&&(o="api");var n=this.gridColumns[e];this.moveColumn(n,t,o)},t.prototype.getColumnDefs=function(){var e=this;if(this.primaryColumns){var t=this.primaryColumns.slice();return this.gridColsArePrimary?t.sort((function(t,o){return e.gridColumns.indexOf(t)-e.gridColumns.indexOf(o)})):this.lastPrimaryOrder&&t.sort((function(t,o){return e.lastPrimaryOrder.indexOf(t)-e.lastPrimaryOrder.indexOf(o)})),this.columnDefFactory.buildColumnDefs(t,this.rowGroupColumns,this.pivotColumns)}},t.prototype.getBodyContainerWidth=function(){return this.bodyWidth},t.prototype.getContainerWidth=function(e){switch(e){case Ne.PINNED_LEFT:return this.leftWidth;case Ne.PINNED_RIGHT:return this.rightWidth;default:return this.bodyWidth}},t.prototype.updateBodyWidths=function(){var e=this.getWidthOfColsInList(this.displayedColumnsCenter),t=this.getWidthOfColsInList(this.displayedColumnsLeft),o=this.getWidthOfColsInList(this.displayedColumnsRight);if(this.bodyWidthDirty=this.bodyWidth!==e,this.bodyWidth!==e||this.leftWidth!==t||this.rightWidth!==o){this.bodyWidth=e,this.leftWidth=t,this.rightWidth=o;var n={type:St.EVENT_DISPLAYED_COLUMNS_WIDTH_CHANGED,api:this.gridApi,columnApi:this.columnApi};this.eventService.dispatchEvent(n)}},t.prototype.getValueColumns=function(){return this.valueColumns?this.valueColumns:[]},t.prototype.getPivotColumns=function(){return this.pivotColumns?this.pivotColumns:[]},t.prototype.isPivotActive=function(){return this.pivotColumns&&this.pivotColumns.length>0&&this.pivotMode},t.prototype.getRowGroupColumns=function(){return this.rowGroupColumns?this.rowGroupColumns:[]},t.prototype.getDisplayedCenterColumns=function(){return this.displayedColumnsCenter},t.prototype.getDisplayedLeftColumns=function(){return this.displayedColumnsLeft},t.prototype.getDisplayedRightColumns=function(){return this.displayedColumnsRight},t.prototype.getDisplayedColumns=function(e){switch(e){case Ne.PINNED_LEFT:return this.getDisplayedLeftColumns();case Ne.PINNED_RIGHT:return this.getDisplayedRightColumns();default:return this.getDisplayedCenterColumns()}},t.prototype.getAllPrimaryColumns=function(){return this.primaryColumns?this.primaryColumns.slice():null},t.prototype.getSecondaryColumns=function(){return this.secondaryColumns?this.secondaryColumns.slice():null},t.prototype.getAllColumnsForQuickFilter=function(){return this.columnsForQuickFilter},t.prototype.getAllGridColumns=function(){return this.gridColumns},t.prototype.isEmpty=function(){return P(this.gridColumns)},t.prototype.isRowGroupEmpty=function(){return P(this.rowGroupColumns)},t.prototype.setColumnVisible=function(e,t,o){void 0===o&&(o="api"),this.setColumnsVisible([e],t,o)},t.prototype.setColumnsVisible=function(e,t,o){var n=this;void 0===t&&(t=!1),void 0===o&&(o="api"),this.columnAnimationService.start(),this.actionOnGridColumns(e,(function(e){return e.isVisible()!==t&&(e.setVisible(t,o),!0)}),o,(function(){return{type:St.EVENT_COLUMN_VISIBLE,visible:t,column:null,columns:null,api:n.gridApi,columnApi:n.columnApi,source:o}})),this.columnAnimationService.finish()},t.prototype.setColumnPinned=function(e,t,o){void 0===o&&(o="api"),e&&this.setColumnsPinned([e],t,o)},t.prototype.setColumnsPinned=function(e,t,o){var n,r=this;(void 0===o&&(o="api"),"print"!==this.gridOptionsWrapper.getDomLayout())?(this.columnAnimationService.start(),n=!0===t||t===Ne.PINNED_LEFT?Ne.PINNED_LEFT:t===Ne.PINNED_RIGHT?Ne.PINNED_RIGHT:null,this.actionOnGridColumns(e,(function(e){return e.getPinned()!==n&&(e.setPinned(n),!0)}),o,(function(){return{type:St.EVENT_COLUMN_PINNED,pinned:n,column:null,columns:null,api:r.gridApi,columnApi:r.columnApi,source:o}})),this.columnAnimationService.finish()):console.warn("Changing the column pinning status is not allowed with domLayout='print'")},t.prototype.actionOnGridColumns=function(e,t,o,n){var r=this;if(!P(e)){var i=[];if(e.forEach((function(e){var o=r.getGridColumn(e);o&&(!1!==t(o)&&i.push(o))})),i.length&&(this.updateDisplayedColumns(o),O(n)&&n)){var s=n();s.columns=i,s.column=1===i.length?i[0]:null,this.eventService.dispatchEvent(s)}}},t.prototype.getDisplayedColBefore=function(e){var t=this.getAllDisplayedColumns(),o=t.indexOf(e);return o>0?t[o-1]:null},t.prototype.getDisplayedColAfter=function(e){var t=this.getAllDisplayedColumns(),o=t.indexOf(e);return o0},t.prototype.isPinningRight=function(){return this.displayedColumnsRight.length>0},t.prototype.getPrimaryAndSecondaryAndAutoColumns=function(){var e;return(e=[]).concat.apply(e,kt([this.primaryColumns||[],this.groupAutoColumns||[],this.secondaryColumns||[]]))},t.prototype.getPrimaryAndAutoGroupCols=function(){var e;return(e=[]).concat.apply(e,kt([this.primaryColumns||[],this.groupAutoColumns||[]]))},t.prototype.getPrimaryAndSecondaryColumns=function(){var e;return(e=[]).concat.apply(e,kt([this.primaryColumns||[],this.secondaryColumns||[]]))},t.prototype.createStateItemFromColumn=function(e){var t=e.isRowGroupActive()?this.rowGroupColumns.indexOf(e):null,o=e.isPivotActive()?this.pivotColumns.indexOf(e):null,n=e.isValueActive()?e.getAggFunc():null,r=null!=e.getSort()?e.getSort():null,i=null!=e.getSortIndex()?e.getSortIndex():null,s=null!=e.getFlex()&&e.getFlex()>0?e.getFlex():null;return{colId:e.getColId(),width:e.getActualWidth(),hide:!e.isVisible(),pinned:e.getPinned(),sort:r,sortIndex:i,aggFunc:n,rowGroup:e.isRowGroupActive(),rowGroupIndex:t,pivot:e.isPivotActive(),pivotIndex:o,flex:s}},t.prototype.getColumnState=function(){if(b(this.primaryColumns)||!this.isAlive())return[];var e=this.getPrimaryAndSecondaryAndAutoColumns().map(this.createStateItemFromColumn.bind(this));return this.orderColumnStateList(e),e},t.prototype.orderColumnStateList=function(e){var t=It(this.gridColumns.map((function(e,t){return[e.getColId(),t]})));e.sort((function(e,o){return(t.has(e.colId)?t.get(e.colId):-1)-(t.has(o.colId)?t.get(o.colId):-1)}))},t.prototype.resetColumnState=function(e){void 0===e&&(e="api");var t=this.getColumnsFromTree(this.primaryColumnTree),o=[],n=1e3,r=1e3,i=[];this.groupAutoColumns&&(i=i.concat(this.groupAutoColumns)),t&&(i=i.concat(t)),i.forEach((function(e){var t=function(e,t){return null!=e?e:null!=t?t:null},i=e.getColDef(),s=t(i.sort,i.initialSort),a=t(i.sortIndex,i.initialSortIndex),l=t(i.hide,i.initialHide),u=t(i.pinned,i.initialPinned),p=t(i.width,i.initialWidth),c=t(i.flex,i.initialFlex),d=t(i.rowGroupIndex,i.initialRowGroupIndex),h=t(i.rowGroup,i.initialRowGroup);null!=d||null!=h&&0!=h||(d=null,h=null);var f=t(i.pivotIndex,i.initialPivotIndex),g=t(i.pivot,i.initialPivot);null!=f||null!=g&&0!=g||(f=null,g=null);var y=t(i.aggFunc,i.initialAggFunc),v={colId:e.getColId(),sort:s,sortIndex:a,hide:l,pinned:u,width:p,flex:c,rowGroup:h,rowGroupIndex:d,pivot:g,pivotIndex:f,aggFunc:y};b(d)&&h&&(v.rowGroupIndex=n++),b(f)&&g&&(v.pivotIndex=r++),o.push(v)})),this.applyColumnState({state:o,applyOrder:!0},e)},t.prototype.applyColumnState=function(e,t){var o=this;if(void 0===t&&(t="api"),P(this.primaryColumns))return!1;if(e&&e.state&&!e.state.forEach)return console.warn("AG Grid: applyColumnState() - the state attribute should be an array, however an array was not found. Please provide an array of items (one for each col you want to change) for state."),!1;var n=function(n,r,i){var s=o.compareColumnStatesAndRaiseEvents(t);o.autoGroupsNeedBuilding=!0;var a=r.slice(),l={},u={},p=[],c=[],d=0,h=o.rowGroupColumns.slice(),f=o.pivotColumns.slice();n.forEach((function(n){var r=n.colId||"";if(r.startsWith(Ne.GROUP_AUTO_COLUMN_ID))return p.push(n),void c.push(n);var s=i(r);s?(o.syncColumnWithStateItem(s,n,e.defaultState,l,u,!1,t),Ue(a,s)):(c.push(n),d+=1)}));var g=function(n){return o.syncColumnWithStateItem(n,null,e.defaultState,l,u,!1,t)};a.forEach(g);var y=function(e,t,o,n){var r=e[o.getId()],i=e[n.getId()],s=null!=r,a=null!=i;if(s&&a)return r-i;if(s)return-1;if(a)return 1;var l=t.indexOf(o),u=t.indexOf(n),p=l>=0;return p&&u>=0?l-u:p?-1:1};o.rowGroupColumns.sort(y.bind(o,l,h)),o.pivotColumns.sort(y.bind(o,u,f)),o.updateGridColumns();var v=o.groupAutoColumns?o.groupAutoColumns.slice():[];return p.forEach((function(n){var r=o.getAutoColumn(n.colId);Ue(v,r),o.syncColumnWithStateItem(r,n,e.defaultState,null,null,!0,t)})),v.forEach(g),o.applyOrderAfterApplyState(e),o.updateDisplayedColumns(t),o.dispatchEverythingChanged(t),s(),{unmatchedAndAutoStates:c,unmatchedCount:d}};this.columnAnimationService.start();var r=n(e.state||[],this.primaryColumns||[],(function(e){return o.getPrimaryColumn(e)})),i=r.unmatchedAndAutoStates,s=r.unmatchedCount;return(i.length>0||O(e.defaultState))&&(s=n(i,this.secondaryColumns||[],(function(e){return o.getSecondaryColumn(e)})).unmatchedCount),this.columnAnimationService.finish(),0===s},t.prototype.applyOrderAfterApplyState=function(e){var t=this;if(e.applyOrder&&e.state){var o=[],n={};e.state.forEach((function(e){if(e.colId&&!n[e.colId]){var r=t.gridColumnsMap[e.colId];r&&(o.push(r),n[e.colId]=!0)}}));var r=0;this.gridColumns.forEach((function(e){var t=e.getColId();null!=n[t]||(t.startsWith(Ne.GROUP_AUTO_COLUMN_ID)?Ke(o,e,r++):o.push(e))})),o=this.placeLockedColumns(o),this.doesMovePassMarryChildren(o)?this.gridColumns=o:console.warn("AG Grid: Applying column order broke a group where columns should be married together. Applying new order has been discarded.")}},t.prototype.compareColumnStatesAndRaiseEvents=function(e){var t=this,o={rowGroupColumns:this.rowGroupColumns.slice(),pivotColumns:this.pivotColumns.slice(),valueColumns:this.valueColumns.slice()},n=this.getColumnState(),r={};return n.forEach((function(e){r[e.colId]=e})),function(){if(!t.gridOptionsWrapper.isSuppressColumnStateEvents()){var i=t.getPrimaryAndSecondaryAndAutoColumns(),s=function(o,n,r,i){if(!He(n.map(i),r.map(i))){var s={type:o,columns:r,column:1===r.length?r[0]:null,api:t.gridApi,columnApi:t.columnApi,source:e};t.eventService.dispatchEvent(s)}},a=function(e){var t=[];return i.forEach((function(o){var n=r[o.getColId()];n&&e(n,o)&&t.push(o)})),t},l=function(e){return e.getColId()};s(St.EVENT_COLUMN_ROW_GROUP_CHANGED,o.rowGroupColumns,t.rowGroupColumns,l),s(St.EVENT_COLUMN_PIVOT_CHANGED,o.pivotColumns,t.pivotColumns,l);a((function(e,t){var o=null!=e.aggFunc,n=o!=t.isValueActive(),r=o&&e.aggFunc!=t.getAggFunc();return n||r})).length>0&&t.fireColumnEvent(St.EVENT_COLUMN_VALUE_CHANGED,t.valueColumns,e);t.fireColumnResizedEvent(a((function(e,t){return e.width!=t.getActualWidth()})),!0,e);t.raiseColumnPinnedEvent(a((function(e,t){return e.pinned!=t.getPinned()})),e);t.raiseColumnVisibleEvent(a((function(e,t){return e.hide==t.isVisible()})),e);a((function(e,t){return e.sort!=t.getSort()||e.sortIndex!=t.getSortIndex()})).length>0&&t.sortController.dispatchSortChangedEvents(e),t.raiseColumnMovedEvent(n,e)}}},t.prototype.raiseColumnPinnedEvent=function(e,t){if(e.length){var o=1===e.length?e[0]:null,n=this.getCommonValue(e,(function(e){return e.getPinned()})),r={type:St.EVENT_COLUMN_PINNED,pinned:null!=n?n:null,columns:e,column:o,api:this.gridApi,columnApi:this.columnApi,source:t};this.eventService.dispatchEvent(r)}},t.prototype.getCommonValue=function(e,t){if(e&&0!=e.length){for(var o=t(e[0]),n=1;n=p&&e.setActualWidth(d,s)}var h=a("sort").value1;void 0!==h&&(h===Ne.SORT_DESC||h===Ne.SORT_ASC?e.setSort(h,s):e.setSort(void 0,s));var f=a("sortIndex").value1;if(void 0!==f&&e.setSortIndex(f),!i&&e.isPrimary()){var g=a("aggFunc").value1;void 0!==g&&("string"===typeof g?(e.setAggFunc(g),e.isValueActive()||(e.setValueActive(!0,s),this.valueColumns.push(e))):(O(g)&&console.warn("AG Grid: stateItem.aggFunc must be a string. if using your own aggregation functions, register the functions first before using them in get/set state. This is because it is intended for the column state to be stored and retrieved as simple JSON."),e.isValueActive()&&(e.setValueActive(!1,s),Ue(this.valueColumns,e))));var y=a("rowGroup","rowGroupIndex"),v=y.value1,m=y.value2;void 0===v&&void 0===m||("number"===typeof m||v?(e.isRowGroupActive()||(e.setRowGroupActive(!0,s),this.rowGroupColumns.push(e)),n&&"number"===typeof m&&(n[e.getId()]=m)):e.isRowGroupActive()&&(e.setRowGroupActive(!1,s),Ue(this.rowGroupColumns,e)));var C=a("pivot","pivotIndex"),w=C.value1,E=C.value2;void 0===w&&void 0===E||("number"===typeof E||w?(e.isPivotActive()||(e.setPivotActive(!0,s),this.pivotColumns.push(e)),r&&"number"===typeof E&&(r[e.getId()]=E)):e.isPivotActive()&&(e.setPivotActive(!1,s),Ue(this.pivotColumns,e)))}}},t.prototype.getGridColumns=function(e){return this.getColumns(e,this.getGridColumn.bind(this))},t.prototype.getColumns=function(e,t){var o=[];return e&&e.forEach((function(e){var n=t(e);n&&o.push(n)})),o},t.prototype.getColumnWithValidation=function(e){if(null==e)return null;var t=this.getGridColumn(e);return t||console.warn("AG Grid: could not find column "+e),t},t.prototype.getPrimaryColumn=function(e){return this.primaryColumns?this.getColumn(e,this.primaryColumns,this.primaryColumnsMap):null},t.prototype.getGridColumn=function(e){return this.getColumn(e,this.gridColumns,this.gridColumnsMap)},t.prototype.getSecondaryColumn=function(e){return this.secondaryColumns?this.getColumn(e,this.secondaryColumns,this.secondaryColumnsMap):null},t.prototype.getColumn=function(e,t,o){if(!e)return null;if("string"==typeof e&&o[e])return o[e];for(var n=0;n=0:u?void 0!==d?d:!!(void 0!==f)&&(null!=f&&f>=0):t.indexOf(o)>=0)&&((u?null!=h||null!=f:null!=h)?a.push(o):l.push(o))}));var u=function(e){var t=n(e.getColDef()),o=r(e.getColDef());return null!=t?t:o};a.sort((function(e,t){var o=u(e),n=u(t);return o===n?0:o=0&&p.push(e)})),l.forEach((function(e){p.indexOf(e)<0&&p.push(e)})),t.forEach((function(e){p.indexOf(e)<0&&o(e,!1)})),p.forEach((function(e){t.indexOf(e)<0&&o(e,!0)})),p},t.prototype.extractPivotColumns=function(e,t){this.pivotColumns=this.extractColumns(t,this.pivotColumns,(function(t,o){return t.setPivotActive(o,e)}),(function(e){return e.pivotIndex}),(function(e){return e.initialPivotIndex}),(function(e){return e.pivot}),(function(e){return e.initialPivot}))},t.prototype.resetColumnGroupState=function(e){void 0===e&&(e="api");var t=[];this.columnUtils.depthFirstOriginalTreeSearch(null,this.primaryColumnTree,(function(e){if(e instanceof ot){var o=e.getColGroupDef(),n={groupId:e.getGroupId(),open:o?o.openByDefault:void 0};t.push(n)}})),this.setColumnGroupState(t,e)},t.prototype.getColumnGroupState=function(){var e=[];return this.columnUtils.depthFirstOriginalTreeSearch(null,this.gridBalancedTree,(function(t){t instanceof ot&&e.push({groupId:t.getGroupId(),open:t.isExpanded()})})),e},t.prototype.setColumnGroupState=function(e,t){var o=this;void 0===t&&(t="api"),this.columnAnimationService.start();var n=[];e.forEach((function(e){var t=e.groupId,r=e.open,i=o.getProvidedColumnGroup(t);i&&i.isExpanded()!==r&&(o.logger.log("columnGroupOpened("+i.getGroupId()+","+r+")"),i.setExpanded(r),n.push(i))})),this.updateGroupsAndDisplayedColumns(t),this.setFirstRightAndLastLeftPinned(t),n.forEach((function(e){var t={type:St.EVENT_COLUMN_GROUP_OPENED,columnGroup:e,api:o.gridApi,columnApi:o.columnApi};o.eventService.dispatchEvent(t)})),this.columnAnimationService.finish()},t.prototype.setColumnGroupOpened=function(e,t,o){var n;void 0===o&&(o="api"),n=e instanceof ot?e.getId():e||"",this.setColumnGroupState([{groupId:n,open:t}],o)},t.prototype.getProvidedColumnGroup=function(e){"string"!==typeof e&&console.error("AG Grid: group key must be a string");var t=null;return this.columnUtils.depthFirstOriginalTreeSearch(null,this.gridBalancedTree,(function(o){o instanceof ot&&o.getId()===e&&(t=o)})),t},t.prototype.calculateColumnsForDisplay=function(){var e=this;return this.pivotMode&&b(this.secondaryColumns)?this.gridColumns.filter((function(t){var o=e.groupAutoColumns&&Xe(e.groupAutoColumns,t),n=e.valueColumns&&Xe(e.valueColumns,t);return o||n})):this.gridColumns.filter((function(t){return e.groupAutoColumns&&Xe(e.groupAutoColumns,t)||t.isVisible()}))},t.prototype.checkColSpanActiveInCols=function(e){var t=!1;return e.forEach((function(e){O(e.getColDef().colSpan)&&(t=!0)})),t},t.prototype.calculateColumnsForGroupDisplay=function(){var e=this;this.groupDisplayColumns=[];var t=function(t){var o=t.getColDef();o&&O(o.showRowGroup)&&e.groupDisplayColumns.push(t)};this.gridColumns.forEach(t),this.groupAutoColumns&&this.groupAutoColumns.forEach(t)},t.prototype.getGroupDisplayColumns=function(){return this.groupDisplayColumns},t.prototype.updateDisplayedColumns=function(e){var t=this.calculateColumnsForDisplay();this.buildDisplayedTrees(t),this.calculateColumnsForGroupDisplay(),this.updateGroupsAndDisplayedColumns(e),this.setFirstRightAndLastLeftPinned(e)},t.prototype.isSecondaryColumnsPresent=function(){return O(this.secondaryColumns)},t.prototype.setSecondaryColumns=function(e,t){var o=this;void 0===t&&(t="api");var n=e&&e.length>0;if(n||!b(this.secondaryColumns)){if(n){this.processSecondaryColumnDefinitions(e);var r=this.columnFactory.createColumnTree(e,!1,this.secondaryBalancedTree||this.previousSecondaryColumns||void 0);this.secondaryBalancedTree=r.columnTree,this.secondaryHeaderRowCount=r.treeDept+1,this.secondaryColumns=this.getColumnsFromTree(this.secondaryBalancedTree),this.secondaryColumnsMap={},this.secondaryColumns.forEach((function(e){return o.secondaryColumnsMap[e.getId()]=e})),this.previousSecondaryColumns=null}else this.previousSecondaryColumns=this.secondaryBalancedTree,this.secondaryBalancedTree=null,this.secondaryHeaderRowCount=-1,this.secondaryColumns=null,this.secondaryColumnsMap={};this.updateGridColumns(),this.updateDisplayedColumns(t)}},t.prototype.processSecondaryColumnDefinitions=function(e){var t=this.gridOptionsWrapper.getProcessSecondaryColDefFunc(),o=this.gridOptionsWrapper.getProcessSecondaryColGroupDefFunc();if(t||o){var n=function(e){e.forEach((function(e){if(O(e.children)){var r=e;o&&o(r),n(r.children)}else{t&&t(e)}}))};e&&n(e)}},t.prototype.updateGridColumns=function(){var e=this;if(this.gridColsArePrimary?this.lastPrimaryOrder=this.gridColumns:this.lastSecondaryOrder=this.gridColumns,this.secondaryColumns&&this.secondaryBalancedTree){var t=this.secondaryColumns.every((function(t){return void 0!==e.gridColumnsMap[t.getColId()]}));this.gridBalancedTree=this.secondaryBalancedTree.slice(),this.gridHeaderRowCount=this.secondaryHeaderRowCount,this.gridColumns=this.secondaryColumns.slice(),this.gridColsArePrimary=!1,t&&this.orderGridColsLike(this.lastSecondaryOrder)}else this.primaryColumns&&(this.gridBalancedTree=this.primaryColumnTree.slice(),this.gridHeaderRowCount=this.primaryHeaderRowCount,this.gridColumns=this.primaryColumns.slice(),this.gridColsArePrimary=!0,this.orderGridColsLike(this.lastPrimaryOrder));this.addAutoGroupToGridColumns(),this.gridColumns=this.placeLockedColumns(this.gridColumns),this.setupQuickFilterColumns(),this.clearDisplayedAndViewportColumns(),this.colSpanActive=this.checkColSpanActiveInCols(this.gridColumns),this.gridColumnsMap={},this.gridColumns.forEach((function(t){return e.gridColumnsMap[t.getId()]=t})),this.setAutoHeightActive();var o={type:St.EVENT_GRID_COLUMNS_CHANGED,api:this.gridApi,columnApi:this.columnApi};this.eventService.dispatchEvent(o)},t.prototype.setAutoHeightActive=function(){if(this.autoHeightActive=this.gridColumns.filter((function(e){return e.isAutoHeight()})).length>0,this.autoHeightActive){this.autoHeightActiveAtLeastOnce=!0;var e=this.rowModel.getType();if(!(e===Ne.ROW_MODEL_TYPE_CLIENT_SIDE||e===Ne.ROW_MODEL_TYPE_SERVER_SIDE)){ne((function(){return console.warn("AG Grid - autoHeight columns only work with Client Side Row Model and Server Side Row Model.")}),"autoHeightActive.wrongRowModel")}}},t.prototype.orderGridColsLike=function(e){if(!b(e)){var t=It(e.map((function(e,t){return[e,t]}))),o=!0;if(this.gridColumns.forEach((function(e){t.has(e)&&(o=!1)})),!o){var n=It(this.gridColumns.map((function(e){return[e,!0]}))),r=e.filter((function(e){return n.has(e)})),i=It(r.map((function(e){return[e,!0]}))),s=this.gridColumns.filter((function(e){return!i.has(e)})),a=r.slice();s.forEach((function(e){var t=e.getOriginalParent();if(t){for(var o=[];!o.length&&t;){t.getLeafColumns().forEach((function(e){var t=a.indexOf(e)>=0,n=o.indexOf(e)<0;t&&n&&o.push(e)})),t=t.getOriginalParent()}if(o.length){var n=o.map((function(e){return a.indexOf(e)})),r=Math.max.apply(Math,kt(n));Ke(a,e,r+1)}else a.push(e)}else a.push(e)})),this.gridColumns=a}}},t.prototype.isPrimaryColumnGroupsPresent=function(){return this.primaryHeaderRowCount>1},t.prototype.setupQuickFilterColumns=function(){this.groupAutoColumns?this.columnsForQuickFilter=(this.primaryColumns||[]).concat(this.groupAutoColumns):this.primaryColumns&&(this.columnsForQuickFilter=this.primaryColumns)},t.prototype.placeLockedColumns=function(e){var t=[],o=[],n=[];return e.forEach((function(e){var r=e.getColDef().lockPosition;"right"===r?n.push(e):"left"===r||!0===r?t.push(e):o.push(e)})),kt(t,o,n)},t.prototype.addAutoGroupToGridColumns=function(){if(this.createGroupAutoColumnsIfNeeded(),!b(this.groupAutoColumns)){this.gridColumns=this.groupAutoColumns?this.groupAutoColumns.concat(this.gridColumns):this.gridColumns;var e=this.columnFactory.createForAutoGroups(this.groupAutoColumns,this.gridBalancedTree);this.gridBalancedTree=e.concat(this.gridBalancedTree)}},t.prototype.clearDisplayedAndViewportColumns=function(){this.displayedTreeLeft=[],this.displayedTreeRight=[],this.displayedTreeCentre=[],this.viewportRowLeft={},this.viewportRowRight={},this.viewportRowCenter={},this.displayedColumnsLeft=[],this.displayedColumnsRight=[],this.displayedColumnsCenter=[],this.displayedColumns=[],this.viewportColumns=[]},t.prototype.updateGroupsAndDisplayedColumns=function(e){this.updateOpenClosedVisibilityInColumnGroups(),this.deriveDisplayedColumns(e),this.refreshFlexedColumns(),this.extractViewport(),this.updateBodyWidths();var t={type:St.EVENT_DISPLAYED_COLUMNS_CHANGED,api:this.gridApi,columnApi:this.columnApi};this.eventService.dispatchEvent(t)},t.prototype.deriveDisplayedColumns=function(e){this.derivedDisplayedColumnsFromDisplayedTree(this.displayedTreeLeft,this.displayedColumnsLeft),this.derivedDisplayedColumnsFromDisplayedTree(this.displayedTreeCentre,this.displayedColumnsCenter),this.derivedDisplayedColumnsFromDisplayedTree(this.displayedTreeRight,this.displayedColumnsRight),this.joinDisplayedColumns(),this.setLeftValues(e),this.displayedAutoHeightCols=this.displayedColumns.filter((function(e){return e.isAutoHeight()}))},t.prototype.isAutoRowHeightActive=function(){return this.autoHeightActive},t.prototype.wasAutoRowHeightEverActive=function(){return this.autoHeightActiveAtLeastOnce},t.prototype.joinDisplayedColumns=function(){this.gridOptionsWrapper.isEnableRtl()?this.displayedColumns=this.displayedColumnsRight.concat(this.displayedColumnsCenter).concat(this.displayedColumnsLeft):this.displayedColumns=this.displayedColumnsLeft.concat(this.displayedColumnsCenter).concat(this.displayedColumnsRight)},t.prototype.setLeftValues=function(e){this.setLeftValuesOfColumns(e),this.setLeftValuesOfGroups()},t.prototype.setLeftValuesOfColumns=function(e){var t=this;if(this.primaryColumns){var o=this.primaryColumns.slice(0),n=this.gridOptionsWrapper.isEnableRtl();[this.displayedColumnsLeft,this.displayedColumnsRight,this.displayedColumnsCenter].forEach((function(r){if(n){var i=t.getWidthOfColsInList(r);r.forEach((function(t){i-=t.getActualWidth(),t.setLeft(i,e)}))}else{var s=0;r.forEach((function(t){t.setLeft(s,e),s+=t.getActualWidth()}))}ze(o,r)})),o.forEach((function(t){t.setLeft(null,e)}))}},t.prototype.setLeftValuesOfGroups=function(){[this.displayedTreeLeft,this.displayedTreeRight,this.displayedTreeCentre].forEach((function(e){e.forEach((function(e){e instanceof tt&&e.checkLeft()}))}))},t.prototype.derivedDisplayedColumnsFromDisplayedTree=function(e,t){t.length=0,this.columnUtils.depthFirstDisplayedColumnTreeSearch(e,(function(e){e instanceof Ge&&t.push(e)}))},t.prototype.extractViewportColumns=function(){this.suppressColumnVirtualisation?this.viewportColumnsCenter=this.displayedColumnsCenter:this.viewportColumnsCenter=this.filterOutColumnsWithinViewport(),this.viewportColumns=this.viewportColumnsCenter.concat(this.displayedColumnsLeft).concat(this.displayedColumnsRight)},t.prototype.getVirtualHeaderGroupRow=function(e,t){var o;switch(e){case Ne.PINNED_LEFT:o=this.viewportRowLeft[t];break;case Ne.PINNED_RIGHT:o=this.viewportRowRight[t];break;default:o=this.viewportRowCenter[t]}return b(o)&&(o=[]),o},t.prototype.extractViewportRows=function(){this.viewportRowLeft={},this.viewportRowRight={},this.viewportRowCenter={};var e={};this.viewportColumns.forEach((function(t){return e[t.getId()]=!0}));var t=function(o,n,r){for(var i=!1,s=0;sn;return e.getFlex()&&o},i=this.displayedColumnsCenter.filter((function(e){return!r(e)})),s=this.displayedColumnsCenter.filter((function(e){return r(e)})),a=[];if(!s.length)return[];var l,u=[];e:for(;;){var p=s.reduce((function(e,t){return e+t.getFlex()}),0);l=this.flexViewportWidth-this.getWidthOfColsInList(i);for(var c=0;cy&&(f=y),f){d.setActualWidth(f,o),Ue(s,d),a.push(d),i.push(d);continue e}u[c]=Math.round(h)}break}var v=l;return s.forEach((function(e,t){e.setActualWidth(Math.min(u[t],v),o),a.push(e),v-=u[t]})),e.skipSetLeft||this.setLeftValues(o),e.updateBodyWidths&&this.updateBodyWidths(),e.fireResizedEvent&&this.fireColumnResizedEvent(a,!0,o,s),this.flexColsCalculatedAtLestOnce||(this.gridOptionsWrapper.isRowModelDefault()&&this.rowModel.resetRowHeights(),this.flexColsCalculatedAtLestOnce=!0),s},t.prototype.sizeColumnsToFit=function(e,t,o){void 0===t&&(t="sizeColumnsToFit");var n=this.getAllDisplayedColumns();if(!(e<=0)&&n.length){var r=[],i=[];n.forEach((function(e){!0===e.getColDef().suppressSizeToFit?i.push(e):r.push(e)}));var s=r.slice(0),a=!1,l=function(e){Ue(r,e),i.push(e)};for(r.forEach((function(e){return e.resetActualWidth(t)}));!a;){a=!0;var u=e-this.getWidthOfColsInList(i);if(u<=0)r.forEach((function(e){e.setMinimum(t)}));else for(var p=u/this.getWidthOfColsInList(r),c=u,d=r.length-1;d>=0;d--){var h=r[d],f=h.getMinWidth(),g=h.getMaxWidth(),y=Math.round(h.getActualWidth()*p);O(f)&&y0||this.usingTreeData)&&!t&&!e){var o=this.groupAutoColumns||[],n=this.autoGroupColService.createAutoGroupColumns(o,this.rowGroupColumns);(!this.autoColsEqual(n,this.groupAutoColumns)||this.forceRecreateAutoGroups)&&(this.groupAutoColumns=n)}else this.groupAutoColumns=null}},t.prototype.autoColsEqual=function(e,t){return He(e,t,(function(e,t){return e.getColId()===t.getColId()}))},t.prototype.getWidthOfColsInList=function(e){return e.reduce((function(e,t){return e+t.getActualWidth()}),0)},t.prototype.getGridBalancedTree=function(){return this.gridBalancedTree},t.prototype.hasFloatingFilters=function(){return!!this.gridColumns&&this.gridColumns.some((function(e){return e.getColDef().floatingFilter}))},t.prototype.getFirstDisplayedColumn=function(){var e=this.gridOptionsWrapper.isEnableRtl(),t=["getDisplayedLeftColumns","getDisplayedCenterColumns","getDisplayedRightColumns"];e&&t.reverse();for(var o=0;o=0;a--)(r=e[a])&&(s=(i<3?r(s):i>3?r(t,o,s):r(t,o))||s);return i>3&&s&&Object.defineProperty(t,o,s),s},zt=function(e){function t(){return null!==e&&e.apply(this,arguments)||this}return jt(t,e),t.prototype.calculateColMinWidth=function(e){return null!=e.minWidth?e.minWidth:this.gridOptionsWrapper.getMinColWidth()},t.prototype.calculateColMaxWidth=function(e){return null!=e.maxWidth?e.maxWidth:this.gridOptionsWrapper.getMaxColWidth()||Number.MAX_SAFE_INTEGER},t.prototype.calculateColInitialWidth=function(e){var t,o=this.calculateColMinWidth(e),n=this.calculateColMaxWidth(e),r=T(e.width),i=T(e.initialWidth);return t=null!=r?r:null!=i?i:this.gridOptionsWrapper.getColWidth(),Math.max(Math.min(t,n),o)},t.prototype.getOriginalPathForColumn=function(e,t){var o=[],n=!1,r=function(t,i){for(var s=0;s=0;a--)(r=e[a])&&(s=(i<3?r(s):i>3?r(t,o,s):r(t,o))||s);return i>3&&s&&Object.defineProperty(t,o,s),s},qt=function(e){function t(){return null!==e&&e.apply(this,arguments)||this}return 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t=this.tooltipPopupDestroyFunc,o=this.tooltipComp;window.setTimeout((function(){t(),e.getContext().destroyBean(o)}),this.FADE_OUT_TOOLTIP_TIMEOUT),this.tooltipPopupDestroyFunc=void 0,this.tooltipComp=void 0},t.prototype.isLastTooltipHiddenRecently=function(){return(new Date).getTime()-t.lastTooltipHideTime=0;a--)(r=e[a])&&(s=(i<3?r(s):i>3?r(t,o,s):r(t,o))||s);return i>3&&s&&Object.defineProperty(t,o,s),s},zr=new Gr,Kr=function(){function e(e){this.cssClassStates={},this.getGui=e}return e.prototype.addCssClass=function(e){var t=this,o=(e||"").split(" ");o.length>1?o.forEach((function(e){return t.addCssClass(e)})):!0!==this.cssClassStates[e]&&e.length&&(this.getGui().classList.add(e),this.cssClassStates[e]=!0)},e.prototype.removeCssClass=function(e){var t=this,o=(e||"").split(" ");o.length>1?o.forEach((function(e){return 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t=this;this.tooltipText!=e&&(this.tooltipText&&(t.usingBrowserTooltips?t.getGui().removeAttribute("title"):t.tooltipFeature=t.destroyBean(t.tooltipFeature)),null!=e&&(this.tooltipText=e,this.tooltipText&&(t.usingBrowserTooltips?t.getGui().setAttribute("title",t.tooltipText):t.tooltipFeature=t.createBean(new Br(t)))))},t.prototype.createChildComponentsFromTags=function(e,t){var o=this;wn(e.childNodes).forEach((function(n){if(n instanceof HTMLElement){var r=o.createComponentFromElement(n,(function(e){e.getGui()&&o.copyAttributesFromNode(n,e.getGui())}),t);if(r){if(r.addItems&&n.children.length){o.createChildComponentsFromTags(n,t);var i=Array.prototype.slice.call(n.children);r.addItems(i)}o.swapComponentForNode(r,e,n)}else n.childNodes&&o.createChildComponentsFromTags(n,t)}}))},t.prototype.createComponentFromElement=function(e,o,n){var r=e.nodeName,i=n?n[e.getAttribute("ref")]:void 0,s=this.agStackComponentsRegistry.getComponentClass(r);if(s){t.elementGettingCreated=e;var a=new 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o=Zo(e);this.setTemplateFromElement(o,t)},t.prototype.setTemplateFromElement=function(e,t){this.eGui=e,this.eGui.__agComponent=this,this.wireQuerySelectors(),this.getContext()&&this.createChildComponentsFromTags(this.getGui(),t)},t.prototype.createChildComponentsPreConstruct=function(){this.getGui()&&this.createChildComponentsFromTags(this.getGui())},t.prototype.wireQuerySelectors=function(){var e=this;if(this.eGui){var t=this;this.iterateOverQuerySelectors((function(o){var n=function(e){return t[o.attributeName]=e};if(o.refSelector&&e.eGui.getAttribute("ref")===o.refSelector)n(e.eGui);else{var r=e.eGui.querySelector(o.querySelector);r&&n(r.__agComponent||r)}}))}},t.prototype.getGui=function(){return this.eGui},t.prototype.getFocusableElement=function(){return this.eGui},t.prototype.setParentComponent=function(e){this.parentComponent=e},t.prototype.getParentComponent=function(){return this.parentComponent},t.prototype.setGui=function(e){this.eGui=e},t.prototype.queryForHtmlElement=function(e){return this.eGui.querySelector(e)},t.prototype.queryForHtmlInputElement=function(e){return this.eGui.querySelector(e)},t.prototype.appendChild=function(e,t){if(t||(t=this.eGui),null!=e)if(Cn(e))t.appendChild(e);else{var o=e;t.appendChild(o.getGui()),this.addDestroyFunc(this.destroyBean.bind(this,o))}},t.prototype.isDisplayed=function(){return this.displayed},t.prototype.setVisible=function(e){e!==this.visible&&(this.visible=e,Vo(this.eGui,e))},t.prototype.setDisplayed=function(e){if(e!==this.displayed){this.displayed=e,xo(this.eGui,e);var o={type:t.EVENT_DISPLAYED_CHANGED,visible:this.displayed};this.dispatchEvent(o)}},t.prototype.destroy=function(){this.tooltipFeature&&(this.tooltipFeature=this.destroyBean(this.tooltipFeature)),e.prototype.destroy.call(this)},t.prototype.addGuiEventListener=function(e,t){var o=this;this.eGui.addEventListener(e,t),this.addDestroyFunc((function(){return o.eGui.removeEventListener(e,t)}))},t.prototype.addCssClass=function(e){this.cssClassManager.addCssClass(e)},t.prototype.removeCssClass=function(e){this.cssClassManager.removeCssClass(e)},t.prototype.containsCssClass=function(e){return this.cssClassManager.containsCssClass(e)},t.prototype.addOrRemoveCssClass=function(e,t){this.cssClassManager.addOrRemoveCssClass(e,t)},t.prototype.getAttribute=function(e){var t=this.eGui;return t?t.getAttribute(e):null},t.prototype.getRefElement=function(e){return this.queryForHtmlElement('[ref="'+e+'"]')},t.EVENT_DISPLAYED_CHANGED="displayedChanged",Ur([Re("agStackComponentsRegistry")],t.prototype,"agStackComponentsRegistry",void 0),Ur([me],t.prototype,"preConstructOnComponent",null),Ur([me],t.prototype,"createChildComponentsPreConstruct",null),t}(mt);function qr(e){return Qr.bind(this,e,void 0)}function Xr(e){return Qr.bind(this,"[ref="+e+"]",e)}function 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r=this;this.alive=!0,this.context=e,this.eParent=n,t.getDateCompDetails(o).newAgStackInstance().then((function(t){r.alive?(r.dateComp=t,t&&(n.appendChild(t.getGui()),t.afterGuiAttached&&t.afterGuiAttached(),r.tempValue&&t.setDate(r.tempValue),null!=r.disabled&&r.setDateCompDisabled(r.disabled))):e.destroyBean(t)}))}return e.prototype.destroy=function(){this.alive=!1,this.dateComp=this.context.destroyBean(this.dateComp)},e.prototype.getDate=function(){return this.dateComp?this.dateComp.getDate():this.tempValue},e.prototype.setDate=function(e){this.dateComp?this.dateComp.setDate(e):this.tempValue=e},e.prototype.setDisabled=function(e){this.dateComp?this.setDateCompDisabled(e):this.disabled=e},e.prototype.setDisplayed=function(e){xo(this.eParent,e)},e.prototype.setInputPlaceholder=function(e){this.dateComp&&this.dateComp.setInputPlaceholder&&this.dateComp.setInputPlaceholder(e)},e.prototype.setInputAriaLabel=function(e){this.dateComp&&this.dateComp.setInputAriaLabel&&this.dateComp.setInputAriaLabel(e)},e.prototype.afterGuiAttached=function(e){this.dateComp&&"function"===typeof this.dateComp.afterGuiAttached&&this.dateComp.afterGuiAttached(e)},e.prototype.setDateCompDisabled=function(e){null!=this.dateComp&&null!=this.dateComp.setDisabled&&this.dateComp.setDisabled(e)},e}(),oi=function(){return oi=Object.assign||function(e){for(var t,o=1,n=arguments.length;o=1){var t=this.filterOptions[0];"string"===typeof t?this.defaultOption=t:t.displayKey?this.defaultOption=t.displayKey:console.warn("AG Grid: invalid FilterOptionDef supplied as it doesn't contain a 'displayKey'")}else console.warn("AG Grid: no filter options for filter")},e.prototype.getDefaultOption=function(){return this.defaultOption},e.prototype.getCustomOption=function(e){return this.customFilterOptions[e]},e}(),ri={applyFilter:"Apply",clearFilter:"Clear",resetFilter:"Reset",cancelFilter:"Cancel",textFilter:"Text Filter",numberFilter:"Number Filter",dateFilter:"Date Filter",setFilter:"Set Filter",filterOoo:"Filter...",empty:"Choose One",equals:"Equals",notEqual:"Not equal",lessThan:"Less than",greaterThan:"Greater than",inRange:"In range",inRangeStart:"From",inRangeEnd:"To",lessThanOrEqual:"Less than or equals",greaterThanOrEqual:"Greater than or equals",contains:"Contains",notContains:"Not contains",startsWith:"Starts with",endsWith:"Ends with",blank:"Blank",notBlank:"Not blank",andCondition:"AND",orCondition:"OR",dateFormatOoo:"yyyy-mm-dd"},ii=function(){function e(){}return e.BACKSPACE="Backspace",e.TAB="Tab",e.ENTER="Enter",e.ESCAPE="Escape",e.SPACE=" ",e.LEFT="ArrowLeft",e.UP="ArrowUp",e.RIGHT="ArrowRight",e.DOWN="ArrowDown",e.DELETE="Delete",e.F2="F2",e.PAGE_UP="PageUp",e.PAGE_DOWN="PageDown",e.PAGE_HOME="Home",e.PAGE_END="End",e.A="KeyA",e.C="KeyC",e.V="KeyV",e.D="KeyD",e.Z="KeyZ",e.Y="KeyY",e}(),si=function(){var e=function(t,o){return e=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(e,t){e.__proto__=t}||function(e,t){for(var o in t)t.hasOwnProperty(o)&&(e[o]=t[o])},e(t,o)};return function(t,o){function n(){this.constructor=t}e(t,o),t.prototype=null===o?Object.create(o):(n.prototype=o.prototype,new n)}}(),ai=function(){return ai=Object.assign||function(e){for(var t,o=1,n=arguments.length;o=0;a--)(r=e[a])&&(s=(i<3?r(s):i>3?r(t,o,s):r(t,o))||s);return i>3&&s&&Object.defineProperty(t,o,s),s},ui=function(e){function t(t,o){void 0===o&&(o={});var n=e.call(this)||this;return n.eFocusableElement=t,n.callbacks=o,n.callbacks=ai({shouldStopEventPropagation:function(){return!1},onTabKeyDown:function(e){if(!e.defaultPrevented){var t=n.focusService.findNextFocusableElement(n.eFocusableElement,!1,e.shiftKey);t&&(t.focus(),e.preventDefault())}}},o),n}return si(t,e),t.prototype.postConstruct=function(){this.eFocusableElement.classList.add(t.FOCUS_MANAGED_CLASS),this.addKeyDownListeners(this.eFocusableElement),this.callbacks.onFocusIn&&this.addManagedListener(this.eFocusableElement,"focusin",this.callbacks.onFocusIn),this.callbacks.onFocusOut&&this.addManagedListener(this.eFocusableElement,"focusout",this.callbacks.onFocusOut)},t.prototype.addKeyDownListeners=function(e){var t=this;this.addManagedListener(e,"keydown",(function(e){e.defaultPrevented||lt(e)||(t.callbacks.shouldStopEventPropagation(e)?at(e):e.key===ii.TAB?t.callbacks.onTabKeyDown(e):t.callbacks.handleKeyDown&&t.callbacks.handleKeyDown(e))}))},t.FOCUS_MANAGED_CLASS="ag-focus-managed",li([Re("focusService")],t.prototype,"focusService",void 0),li([Ce],t.prototype,"postConstruct",null),t}(mt),pi=function(){var e=function(t,o){return e=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(e,t){e.__proto__=t}||function(e,t){for(var o in t)t.hasOwnProperty(o)&&(e[o]=t[o])},e(t,o)};return function(t,o){function n(){this.constructor=t}e(t,o),t.prototype=null===o?Object.create(o):(n.prototype=o.prototype,new n)}}(),ci=function(e,t,o,n){var r,i=arguments.length,s=i<3?t:null===n?n=Object.getOwnPropertyDescriptor(t,o):n;if("object"===typeof Reflect&&"function"===typeof Reflect.decorate)s=Reflect.decorate(e,t,o,n);else for(var a=e.length-1;a>=0;a--)(r=e[a])&&(s=(i<3?r(s):i>3?r(t,o,s):r(t,o))||s);return i>3&&s&&Object.defineProperty(t,o,s),s},di=function(e){function t(t){var o=e.call(this)||this;return o.filterNameKey=t,o.applyActive=!1,o.hidePopup=null,o.appliedModel=null,o}return pi(t,e),t.prototype.postConstruct=function(){this.resetTemplate(),this.createManagedBean(new ui(this.getFocusableElement(),{handleKeyDown:this.handleKeyDown.bind(this)}))},t.prototype.handleKeyDown=function(e){},t.prototype.getFilterTitle=function(){return this.translate(this.filterNameKey)},t.prototype.isFilterActive=function(){return!!this.appliedModel},t.prototype.resetTemplate=function(e){var t='\n
\n
\n '+this.createBodyTemplate()+"\n
\n
";this.setTemplate(t,e)},t.prototype.isReadOnly=function(){return!!this.providedFilterParams.readOnly},t.prototype.init=function(e){var t=this;this.setParams(e),this.resetUiToDefaults(!0).then((function(){t.updateUiVisibility(),t.setupOnBtApplyDebounce()}))},t.prototype.setParams=function(e){this.providedFilterParams=e,this.applyActive=t.isUseApplyButton(e),this.createButtonPanel()},t.prototype.createButtonPanel=function(){var e=this,t=this.providedFilterParams.buttons;if(!(!t||t.length<1||this.isReadOnly())){var o=document.createElement("div");o.classList.add("ag-filter-apply-panel");Nr(t).forEach((function(t){return function(t){var n,r;switch(t){case"apply":n=e.translate("applyFilter"),r=function(t){return e.onBtApply(!1,!1,t)};break;case"clear":n=e.translate("clearFilter"),r=function(){return e.onBtClear()};break;case"reset":n=e.translate("resetFilter"),r=function(){return e.onBtReset()};break;case"cancel":n=e.translate("cancelFilter"),r=function(t){e.onBtCancel(t)};break;default:return void console.warn("AG Grid: Unknown button type specified")}var i=Zo(''+n+"\n ");o.appendChild(i),e.addManagedListener(i,"click",r)}(t)})),this.getGui().appendChild(o)}},t.prototype.getDefaultDebounceMs=function(){return 0},t.prototype.setupOnBtApplyDebounce=function(){var e=t.getDebounceMs(this.providedFilterParams,this.getDefaultDebounceMs());this.onBtApplyDebounce=de(this.onBtApply.bind(this),e)},t.prototype.getModel=function(){return this.appliedModel?this.appliedModel:null},t.prototype.setModel=function(e){var t=this;return(null!=e?this.setModelIntoUi(e):this.resetUiToDefaults()).then((function(){t.updateUiVisibility(),t.applyModel()}))},t.prototype.onBtCancel=function(e){var t=this,o=this.getModel(),n=function(){t.onUiChanged(!1,"prevent"),t.providedFilterParams.closeOnApply&&t.close(e)};null!=o?this.setModelIntoUi(o).then(n):this.resetUiToDefaults().then(n)},t.prototype.onBtClear=function(){var e=this;this.resetUiToDefaults().then((function(){return e.onUiChanged()}))},t.prototype.onBtReset=function(){this.onBtClear(),this.onBtApply()},t.prototype.applyModel=function(){var e=this.getModelFromUi();if(!this.isModelValid(e))return!1;var t=this.appliedModel;return this.appliedModel=e,!this.areModelsEqual(t,e)},t.prototype.isModelValid=function(e){return!0},t.prototype.onBtApply=function(e,t,o){void 0===e&&(e=!1),void 0===t&&(t=!1),this.applyModel()&&this.providedFilterParams.filterChangedCallback({afterFloatingFilter:e,afterDataChange:t}),this.providedFilterParams.closeOnApply&&this.applyActive&&!e&&!t&&this.close(o)},t.prototype.onNewRowsLoaded=function(){},t.prototype.close=function(e){if(this.hidePopup){var t,o=e,n=o&&o.key;"Enter"!==n&&"Space"!==n||(t={keyboardEvent:o}),this.hidePopup(t),this.hidePopup=null}},t.prototype.onUiChanged=function(e,t){if(void 0===e&&(e=!1),this.updateUiVisibility(),this.providedFilterParams.filterModifiedCallback(),this.applyActive&&!this.isReadOnly){var o=this.isModelValid(this.getModelFromUi());Ho(this.getRefElement("applyFilterButton"),!o)}e&&!t||"immediately"===t?this.onBtApply(e):(this.applyActive||t)&&"debounce"!==t||this.onBtApplyDebounce()},t.prototype.afterGuiAttached=function(e){null!=e&&(this.hidePopup=e.hidePopup)},t.getDebounceMs=function(e,o){return t.isUseApplyButton(e)?(null!=e.debounceMs&&console.warn("AG Grid: debounceMs is ignored when apply button is present"),0):null!=e.debounceMs?e.debounceMs:o},t.isUseApplyButton=function(e){return!!e.buttons&&e.buttons.indexOf("apply")>=0},t.prototype.destroy=function(){this.hidePopup=null,e.prototype.destroy.call(this)},t.prototype.translate=function(e){return this.gridOptionsWrapper.getLocaleTextFunc()(e,ri[e])},t.prototype.getCellValue=function(e){var t=this.providedFilterParams,o=t.api,n=t.colDef,r=t.column,i=t.columnApi,s=t.context;return this.providedFilterParams.valueGetter({api:o,colDef:n,column:r,columnApi:i,context:s,data:e.data,getValue:function(t){return e.data[t]},node:e})},ci([Re("rowModel")],t.prototype,"rowModel",void 0),ci([Re("valueService")],t.prototype,"valueService",void 0),ci([Ce],t.prototype,"postConstruct",null),t}(Yr),hi=function(){var e=function(t,o){return e=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(e,t){e.__proto__=t}||function(e,t){for(var o in t)t.hasOwnProperty(o)&&(e[o]=t[o])},e(t,o)};return function(t,o){function n(){this.constructor=t}e(t,o),t.prototype=null===o?Object.create(o):(n.prototype=o.prototype,new n)}}(),fi=function(e,t,o,n){var r,i=arguments.length,s=i<3?t:null===n?n=Object.getOwnPropertyDescriptor(t,o):n;if("object"===typeof Reflect&&"function"===typeof Reflect.decorate)s=Reflect.decorate(e,t,o,n);else for(var a=e.length-1;a>=0;a--)(r=e[a])&&(s=(i<3?r(s):i>3?r(t,o,s):r(t,o))||s);return i>3&&s&&Object.defineProperty(t,o,s),s},gi=function(e){function t(t,o){var n=e.call(this,o)||this;return n.labelSeparator="",n.labelAlignment="left",n.label="",n.config=t||{},n}return hi(t,e),t.prototype.postConstruct=function(){this.addCssClass("ag-labeled"),this.eLabel.classList.add("ag-label");var e=this.config,t=e.labelSeparator,o=e.label,n=e.labelWidth,r=e.labelAlignment;null!=t&&this.setLabelSeparator(t),null!=o&&this.setLabel(o),null!=n&&this.setLabelWidth(n),this.setLabelAlignment(r||this.labelAlignment),this.refreshLabel()},t.prototype.refreshLabel=function(){Xo(this.eLabel),"string"===typeof this.label?this.eLabel.innerText=this.label+this.labelSeparator:this.label&&this.eLabel.appendChild(this.label),""===this.label?(this.eLabel.classList.add("ag-hidden"),In(this.eLabel,"presentation")):(this.eLabel.classList.remove("ag-hidden"),In(this.eLabel,null))},t.prototype.setLabelSeparator=function(e){return this.labelSeparator===e||(this.labelSeparator=e,null!=this.label&&this.refreshLabel()),this},t.prototype.getLabelId=function(){return this.eLabel.id=this.eLabel.id||"ag-"+this.getCompId()+"-label",this.eLabel.id},t.prototype.getLabel=function(){return this.label},t.prototype.setLabel=function(e){return this.label===e||(this.label=e,this.refreshLabel()),this},t.prototype.setLabelAlignment=function(e){var t=this.getGui().classList;return t.toggle("ag-label-align-left","left"===e),t.toggle("ag-label-align-right","right"===e),t.toggle("ag-label-align-top","top"===e),this},t.prototype.setLabelWidth=function(e){return null==this.label||dn(this.eLabel,e),this},fi([Ce],t.prototype,"postConstruct",null),t}(Yr),yi=function(){var e=function(t,o){return e=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(e,t){e.__proto__=t}||function(e,t){for(var o in t)t.hasOwnProperty(o)&&(e[o]=t[o])},e(t,o)};return function(t,o){function n(){this.constructor=t}e(t,o),t.prototype=null===o?Object.create(o):(n.prototype=o.prototype,new n)}}(),vi=function(e){function t(t,o,n){var r=e.call(this,t,o)||this;return r.className=n,r.disabled=!1,r}return yi(t,e),t.prototype.postConstruct=function(){e.prototype.postConstruct.call(this),this.className&&this.addCssClass(this.className)},t.prototype.onValueChange=function(e){var o=this;return this.addManagedListener(this,t.EVENT_CHANGED,(function(){return e(o.getValue())})),this},t.prototype.getWidth=function(){return this.getGui().clientWidth},t.prototype.setWidth=function(e){return hn(this.getGui(),e),this},t.prototype.getPreviousValue=function(){return this.previousValue},t.prototype.getValue=function(){return this.value},t.prototype.setValue=function(e,o){return this.value===e||(this.previousValue=this.value,this.value=e,o||this.dispatchEvent({type:t.EVENT_CHANGED})),this},t.prototype.setDisabled=function(e){e=!!e;var t=this.getGui();return Ho(t,e),t.classList.toggle("ag-disabled",e),this.disabled=e,this},t.prototype.isDisabled=function(){return!!this.disabled},t.EVENT_CHANGED="valueChange",t}(gi),mi=function(){var e=function(t,o){return e=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(e,t){e.__proto__=t}||function(e,t){for(var o in t)t.hasOwnProperty(o)&&(e[o]=t[o])},e(t,o)};return function(t,o){function n(){this.constructor=t}e(t,o),t.prototype=null===o?Object.create(o):(n.prototype=o.prototype,new n)}}(),Ci=function(e,t,o,n){var r,i=arguments.length,s=i<3?t:null===n?n=Object.getOwnPropertyDescriptor(t,o):n;if("object"===typeof Reflect&&"function"===typeof Reflect.decorate)s=Reflect.decorate(e,t,o,n);else for(var a=e.length-1;a>=0;a--)(r=e[a])&&(s=(i<3?r(s):i>3?r(t,o,s):r(t,o))||s);return i>3&&s&&Object.defineProperty(t,o,s),s},wi=function(e){function t(t,o,n,r){var i=e.call(this,t,'',o)||this;return i.pickerIcon=n,i.isPickerDisplayed=!1,i.isDestroyingPicker=!1,i.skipClick=!1,i}return mi(t,e),t.prototype.postConstruct=function(){var t=this;e.prototype.postConstruct.call(this);var o=this.getCompId()+"-display";this.eDisplayField.setAttribute("id",o),kn(this.eWrapper,o);var n=function(){t.skipClick?t.skipClick=!1:t.isDisabled()||(t.pickerComponent=t.showPicker())},r=this.getGui();if(this.addManagedListener(r,"mousedown",(function(e){!t.skipClick&&t.pickerComponent&&t.pickerComponent.isAlive()&&Jo(t.pickerComponent.getGui())&&r.contains(e.target)&&(t.skipClick=!0)})),this.addManagedListener(r,"keydown",(function(e){switch(e.key){case ii.UP:case ii.DOWN:case ii.ENTER:case ii.SPACE:n();case ii.ESCAPE:t.isPickerDisplayed&&e.preventDefault()}})),this.addManagedListener(this.eWrapper,"click",n),this.addManagedListener(this.eLabel,"click",n),this.pickerIcon){var i=Rr(this.pickerIcon,this.gridOptionsWrapper);i&&this.eIcon.appendChild(i)}},t.prototype.refreshLabel=function(){O(this.getLabel())?Hn(this.eWrapper,this.getLabelId()):this.eWrapper.removeAttribute("aria-labelledby"),e.prototype.refreshLabel.call(this)},t.prototype.setAriaLabel=function(e){return Vn(this.eWrapper,e),this},t.prototype.setInputWidth=function(e){return dn(this.eWrapper,e),this},t.prototype.getFocusableElement=function(){return this.eWrapper},Ci([Xr("eLabel")],t.prototype,"eLabel",void 0),Ci([Xr("eWrapper")],t.prototype,"eWrapper",void 0),Ci([Xr("eDisplayField")],t.prototype,"eDisplayField",void 0),Ci([Xr("eIcon")],t.prototype,"eIcon",void 0),t}(vi),Ei=function(){var e=function(t,o){return e=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(e,t){e.__proto__=t}||function(e,t){for(var o in t)t.hasOwnProperty(o)&&(e[o]=t[o])},e(t,o)};return function(t,o){function n(){this.constructor=t}e(t,o),t.prototype=null===o?Object.create(o):(n.prototype=o.prototype,new n)}}(),Ri=function(e,t,o,n){var r,i=arguments.length,s=i<3?t:null===n?n=Object.getOwnPropertyDescriptor(t,o):n;if("object"===typeof Reflect&&"function"===typeof Reflect.decorate)s=Reflect.decorate(e,t,o,n);else for(var a=e.length-1;a>=0;a--)(r=e[a])&&(s=(i<3?r(s):i>3?r(t,o,s):r(t,o))||s);return i>3&&s&&Object.defineProperty(t,o,s),s},Si=function(e){function t(t){void 0===t&&(t="default");var o=e.call(this,'
')||this;return o.cssIdentifier=t,o.options=[],o.itemEls=[],o}return Ei(t,e),t.prototype.init=function(){this.addManagedListener(this.getGui(),"keydown",this.handleKeyDown.bind(this))},t.prototype.handleKeyDown=function(e){var t=e.key;switch(t){case ii.ENTER:if(this.highlightedEl){var o=this.itemEls.indexOf(this.highlightedEl);this.setValueByIndex(o)}else this.setValue(this.getValue());break;case ii.DOWN:case ii.UP:var n=t===ii.DOWN,r=void 0;if(e.preventDefault(),this.highlightedEl){var i=this.itemEls.indexOf(this.highlightedEl)+(n?1:-1);i=Math.min(Math.max(i,0),this.itemEls.length-1),r=this.itemEls[i]}else r=this.itemEls[n?0:this.itemEls.length-1];this.highlightItem(r)}},t.prototype.addOptions=function(e){var t=this;return e.forEach((function(e){return t.addOption(e)})),this},t.prototype.addOption=function(e){var t=e.value,o=Nt(e.text||t);return this.options.push({value:t,text:o}),this.renderOption(t,o),this.updateIndices(),this},t.prototype.updateIndices=function(){var e=this.getGui().querySelectorAll(".ag-list-item");e.forEach((function(t,o){Yn(t,o+1),Kn(t,e.length)}))},t.prototype.renderOption=function(e,t){var o=this,n=document.createElement("div");In(n,"option"),n.classList.add("ag-list-item","ag-"+this.cssIdentifier+"-list-item"),n.innerHTML=""+t+"",n.tabIndex=-1,this.itemEls.push(n),this.addManagedListener(n,"mouseover",(function(){return o.highlightItem(n)})),this.addManagedListener(n,"mouseleave",(function(){return o.clearHighlighted()})),this.addManagedListener(n,"click",(function(){return o.setValue(e)})),this.getGui().appendChild(n)},t.prototype.setValue=function(e,t){if(this.value===e)return this.fireItemSelected(),this;if(null==e)return this.reset(),this;var o=this.options.findIndex((function(t){return t.value===e}));if(-1!==o){var n=this.options[o];this.value=n.value,this.displayValue=null!=n.text?n.text:n.value,this.highlightItem(this.itemEls[o]),t||this.fireChangeEvent()}return this},t.prototype.setValueByIndex=function(e){return this.setValue(this.options[e].value)},t.prototype.getValue=function(){return this.value},t.prototype.getDisplayValue=function(){return this.displayValue},t.prototype.refreshHighlighted=function(){var e=this;this.clearHighlighted();var t=this.options.findIndex((function(t){return t.value===e.value}));-1!==t&&this.highlightItem(this.itemEls[t])},t.prototype.reset=function(){this.value=null,this.displayValue=null,this.clearHighlighted(),this.fireChangeEvent()},t.prototype.highlightItem=function(e){e.offsetParent&&(this.clearHighlighted(),this.highlightedEl=e,this.highlightedEl.classList.add(t.ACTIVE_CLASS),or(this.highlightedEl,!0),this.highlightedEl.focus())},t.prototype.clearHighlighted=function(){this.highlightedEl&&this.highlightedEl.offsetParent&&(this.highlightedEl.classList.remove(t.ACTIVE_CLASS),or(this.highlightedEl,!1),this.highlightedEl=null)},t.prototype.fireChangeEvent=function(){this.dispatchEvent({type:vi.EVENT_CHANGED}),this.fireItemSelected()},t.prototype.fireItemSelected=function(){this.dispatchEvent({type:t.EVENT_ITEM_SELECTED})},t.EVENT_ITEM_SELECTED="selectedItem",t.ACTIVE_CLASS="ag-active-item",Ri([Ce],t.prototype,"init",null),t}(Yr),Oi=function(){var e=function(t,o){return e=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(e,t){e.__proto__=t}||function(e,t){for(var o in t)t.hasOwnProperty(o)&&(e[o]=t[o])},e(t,o)};return function(t,o){function n(){this.constructor=t}e(t,o),t.prototype=null===o?Object.create(o):(n.prototype=o.prototype,new n)}}(),bi=function(e,t,o,n){var r,i=arguments.length,s=i<3?t:null===n?n=Object.getOwnPropertyDescriptor(t,o):n;if("object"===typeof Reflect&&"function"===typeof Reflect.decorate)s=Reflect.decorate(e,t,o,n);else for(var a=e.length-1;a>=0;a--)(r=e[a])&&(s=(i<3?r(s):i>3?r(t,o,s):r(t,o))||s);return i>3&&s&&Object.defineProperty(t,o,s),s},Pi=function(e){function t(t){return e.call(this,t,"ag-select","smallDown","listbox")||this}return Oi(t,e),t.prototype.init=function(){var e=this;this.listComponent=this.createBean(new Si("select")),this.listComponent.setParentComponent(this),this.eWrapper.tabIndex=0,this.listComponent.addManagedListener(this.listComponent,Si.EVENT_ITEM_SELECTED,(function(){e.hideList&&e.hideList()})),this.listComponent.addManagedListener(this.listComponent,vi.EVENT_CHANGED,(function(){e.setValue(e.listComponent.getValue(),!1,!0),e.hideList&&e.hideList()}))},t.prototype.showPicker=function(){var e=this,t=this.listComponent.getGui(),o=this.gridOptionsWrapper.getDocument(),n=this.addManagedListener(o.body,"wheel",(function(o){!t.contains(o.target)&&e.hideList&&e.hideList()})),r=this.addManagedListener(t,"focusout",(function(o){!t.contains(o.relatedTarget)&&e.hideList&&e.hideList()})),i=this.gridOptionsWrapper.getLocaleTextFunc(),s=this.popupService.addPopup({modal:!0,eChild:t,closeOnEsc:!0,closedCallback:function(){e.hideList=null,e.isPickerDisplayed=!1,r(),n(),e.isAlive()&&(Un(e.eWrapper,!1),e.getFocusableElement().focus())},ariaLabel:i("ariaLabelSelectField","Select Field")});return s&&(this.hideList=s.hideFunc),this.isPickerDisplayed=!0,dn(t,zo(this.eWrapper)),Un(this.eWrapper,!0),t.style.maxHeight=Bo(this.popupService.getPopupParent())+"px",t.style.position="absolute",this.popupService.positionPopupUnderComponent({type:"ag-list",eventSource:this.eWrapper,ePopup:t,keepWithinBounds:!0}),this.listComponent.refreshHighlighted(),this.listComponent},t.prototype.addOptions=function(e){var t=this;return e.forEach((function(e){return t.addOption(e)})),this},t.prototype.addOption=function(e){return this.listComponent.addOption(e),this},t.prototype.setValue=function(t,o,n){return this.value===t?this:(n||this.listComponent.setValue(t,!0),this.listComponent.getValue()===this.getValue()?this:(this.eDisplayField.innerHTML=this.listComponent.getDisplayValue(),e.prototype.setValue.call(this,t,o)))},t.prototype.destroy=function(){this.hideList&&this.hideList(),this.destroyBean(this.listComponent),e.prototype.destroy.call(this)},bi([Re("popupService")],t.prototype,"popupService",void 0),bi([Ce],t.prototype,"init",null),t}(wi),_i=function(){var e=function(t,o){return e=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(e,t){e.__proto__=t}||function(e,t){for(var o in t)t.hasOwnProperty(o)&&(e[o]=t[o])},e(t,o)};return function(t,o){function n(){this.constructor=t}e(t,o),t.prototype=null===o?Object.create(o):(n.prototype=o.prototype,new n)}}(),Ti=function(e,t,o,n){var r,i=arguments.length,s=i<3?t:null===n?n=Object.getOwnPropertyDescriptor(t,o):n;if("object"===typeof Reflect&&"function"===typeof Reflect.decorate)s=Reflect.decorate(e,t,o,n);else for(var a=e.length-1;a>=0;a--)(r=e[a])&&(s=(i<3?r(s):i>3?r(t,o,s):r(t,o))||s);return i>3&&s&&Object.defineProperty(t,o,s),s},Di=function(e){function t(t,o,n,r){void 0===n&&(n="text"),void 0===r&&(r="input");var i=e.call(this,t,'\n
\n
\n \n
",o)||this;return i.inputType=n,i.displayFieldTag=r,i}return _i(t,e),t.prototype.postConstruct=function(){e.prototype.postConstruct.call(this),this.setInputType(),this.eLabel.classList.add(this.className+"-label"),this.eWrapper.classList.add(this.className+"-input-wrapper"),this.eInput.classList.add(this.className+"-input"),this.addCssClass("ag-input-field"),this.eInput.id=this.eInput.id||"ag-"+this.getCompId()+"-input";var t=this.config,o=t.width,n=t.value;null!=o&&this.setWidth(o),null!=n&&this.setValue(n),this.addInputListeners()},t.prototype.refreshLabel=function(){O(this.getLabel())?Hn(this.eInput,this.getLabelId()):this.eInput.removeAttribute("aria-labelledby"),e.prototype.refreshLabel.call(this)},t.prototype.addInputListeners=function(){var e=this;this.addManagedListener(this.eInput,"input",(function(t){return e.setValue(t.target.value)}))},t.prototype.setInputType=function(){"input"===this.displayFieldTag&&this.eInput.setAttribute("type",this.inputType)},t.prototype.getInputElement=function(){return this.eInput},t.prototype.setInputWidth=function(e){return dn(this.eWrapper,e),this},t.prototype.setInputName=function(e){return this.getInputElement().setAttribute("name",e),this},t.prototype.getFocusableElement=function(){return this.eInput},t.prototype.setMaxLength=function(e){return this.eInput.maxLength=e,this},t.prototype.setInputPlaceholder=function(e){return Sn(this.eInput,"placeholder",e),this},t.prototype.setInputAriaLabel=function(e){return Vn(this.eInput,e),this},t.prototype.setDisabled=function(t){return Ho(this.eInput,t),e.prototype.setDisabled.call(this,t)},Ti([Xr("eLabel")],t.prototype,"eLabel",void 0),Ti([Xr("eWrapper")],t.prototype,"eWrapper",void 0),Ti([Xr("eInput")],t.prototype,"eInput",void 0),t}(vi),Ai=function(){var e=function(t,o){return e=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(e,t){e.__proto__=t}||function(e,t){for(var o in t)t.hasOwnProperty(o)&&(e[o]=t[o])},e(t,o)};return function(t,o){function n(){this.constructor=t}e(t,o),t.prototype=null===o?Object.create(o):(n.prototype=o.prototype,new n)}}(),Ni=function(e,t,o,n){var r,i=arguments.length,s=i<3?t:null===n?n=Object.getOwnPropertyDescriptor(t,o):n;if("object"===typeof Reflect&&"function"===typeof Reflect.decorate)s=Reflect.decorate(e,t,o,n);else for(var a=e.length-1;a>=0;a--)(r=e[a])&&(s=(i<3?r(s):i>3?r(t,o,s):r(t,o))||s);return i>3&&s&&Object.defineProperty(t,o,s),s};!function(e){e[e.One=0]="One",e[e.Two=1]="Two"}($r||($r={}));var Fi,Ii=function(e){function t(){return null!==e&&e.apply(this,arguments)||this}return Ai(t,e),t.prototype.getNumberOfInputs=function(e){var o=this.optionsFactory.getCustomOption(e);if(o){var n=o.numberOfInputs;return null!=n?n:1}var r=[t.EMPTY,t.NOT_BLANK,t.BLANK];return e&&r.indexOf(e)>=0?0:e===t.IN_RANGE?2:1},t.prototype.onFloatingFilterChanged=function(e,t){this.setTypeFromFloatingFilter(e),this.setValueFromFloatingFilter(t),this.onUiChanged(!0)},t.prototype.setTypeFromFloatingFilter=function(e){this.eType1.setValue(e),this.eType2.setValue(this.optionsFactory.getDefaultOption()),(this.isDefaultOperator("AND")?this.eJoinOperatorAnd:this.eJoinOperatorOr).setValue(!0)},t.prototype.getModelFromUi=function(){return this.isConditionUiComplete($r.One)?this.isAllowTwoConditions()&&this.isConditionUiComplete($r.Two)?{filterType:this.getFilterType(),operator:this.getJoinOperator(),condition1:this.createCondition($r.One),condition2:this.createCondition($r.Two)}:this.createCondition($r.One):null},t.prototype.getConditionTypes=function(){return[this.eType1.getValue(),this.eType2.getValue()]},t.prototype.getJoinOperator=function(){return!0===this.eJoinOperatorOr.getValue()?"OR":"AND"},t.prototype.areModelsEqual=function(e,t){if(!e&&!t)return!0;if(!e&&t||e&&!t)return!1;var o,n=!e.operator,r=!t.operator;if(!n&&r||n&&!r)return!1;if(n){var i=e,s=t;o=this.areSimpleModelsEqual(i,s)}else{var a=e,l=t;o=a.operator===l.operator&&this.areSimpleModelsEqual(a.condition1,l.condition1)&&this.areSimpleModelsEqual(a.condition2,l.condition2)}return o},t.prototype.setModelIntoUi=function(e){if(e.operator){var t=e,o="OR"===t.operator;this.eJoinOperatorAnd.setValue(!o),this.eJoinOperatorOr.setValue(o),this.eType1.setValue(t.condition1.type),this.eType2.setValue(t.condition2.type),this.setConditionIntoUi(t.condition1,$r.One),this.setConditionIntoUi(t.condition2,$r.Two)}else{var n=e;this.eJoinOperatorAnd.setValue(this.isDefaultOperator("AND")),this.eJoinOperatorOr.setValue(this.isDefaultOperator("OR")),this.eType1.setValue(n.type),this.eType2.setValue(this.optionsFactory.getDefaultOption()),this.setConditionIntoUi(n,$r.One),this.setConditionIntoUi(null,$r.Two)}return xr.resolve()},t.prototype.doesFilterPass=function(e){var t=this,o=this.getModel();if(null==o)return!0;var n=o.operator,r=[];if(n){var i=o;r.push(i.condition1,i.condition2)}else r.push(o);return r[n&&"OR"===n?"some":"every"]((function(o){return t.individualConditionPasses(e,o)}))},t.prototype.setParams=function(t){e.prototype.setParams.call(this,t),this.optionsFactory=new ni,this.optionsFactory.init(t,this.getDefaultFilterOptions()),this.allowTwoConditions=!t.suppressAndOrCondition,this.alwaysShowBothConditions=!!t.alwaysShowBothConditions,this.defaultJoinOperator=this.getDefaultJoinOperator(t.defaultJoinOperator),this.putOptionsIntoDropdown(),this.addChangedListeners()},t.prototype.getDefaultJoinOperator=function(e){return Xe(["AND","OR"],e)?e:"AND"},t.prototype.putOptionsIntoDropdown=function(){var e=this,t=this.optionsFactory.getFilterOptions(),o=[this.eType1,this.eType2];t.forEach((function(t){var n="string"===typeof t?e.createBoilerplateListOption(t):e.createCustomListOption(t);o.forEach((function(e){return e.addOption(n)}))})),o.forEach((function(e){return e.setDisabled(t.length<=1)}))},t.prototype.createBoilerplateListOption=function(e){return{value:e,text:this.translate(e)}},t.prototype.createCustomListOption=function(e){var t=e.displayKey,o=this.optionsFactory.getCustomOption(e.displayKey);return{value:t,text:o?this.gridOptionsWrapper.getLocaleTextFunc()(o.displayKey,o.displayName):this.translate(t)}},t.prototype.isAllowTwoConditions=function(){return this.allowTwoConditions},t.prototype.createBodyTemplate=function(){return'\n \n '+this.createValueTemplate($r.One)+'\n
\n \n \n
\n \n '+this.createValueTemplate($r.Two)},t.prototype.getCssIdentifier=function(){return"simple-filter"},t.prototype.updateUiVisibility=function(){var e=this,t=[[this.eType1],[this.eType2,this.eJoinOperatorPanel,this.eJoinOperatorAnd,this.eJoinOperatorOr]],o=[this.eCondition1Body,this.eCondition2Body];t.forEach((function(t,o){var n=e.isConditionVisible(o),r=e.isConditionDisabled(o);t.forEach((function(e){e instanceof Di||e instanceof Pi?(e.setDisabled(r),e.setDisplayed(n)):(Ho(e,r),xo(e,n))}))})),o.forEach((function(t,o){xo(t,e.isConditionBodyVisible(o))})),this.forEachInput((function(t,o,n,r){e.setElementDisplayed(t,o1?"inRangeStart":0===n?"filterOoo":"inRangeEnd",a=0===n&&i>1?t("ariaFilterFromValue","Filter from value"):0===n?t("ariaFilterValue","Filter Value"):t("ariaFilterToValue","Filter to Value");o.setInputPlaceholder(e.translate(s)),o.setInputAriaLabel(a)}}))},t.prototype.setElementValue=function(e,t,o){e instanceof Di&&e.setValue(null!=t?String(t):null,o)},t.prototype.setElementDisplayed=function(e,t){e instanceof Yr&&xo(e.getGui(),t)},t.prototype.setElementDisabled=function(e,t){e instanceof Yr&&Ho(e.getGui(),t)},t.prototype.attachElementOnChange=function(e,t){e instanceof Di&&e.onValueChange(t)},t.prototype.forEachInput=function(e){var t=this,o=this.getInputs();this.getConditionTypes().forEach((function(n,r){for(var i=t.getNumberOfInputs(n),s=0;s0},t.prototype.isConditionUiComplete=function(e){return this.getConditionTypes()[e]!==t.EMPTY&&!this.getValues(e).some((function(e){return null==e}))},t.prototype.resetUiToDefaults=function(e){var t=this,o=this.gridOptionsWrapper.getLocaleTextFunc()("ariaFilteringOperator","Filtering operator"),n="ag-simple-filter-and-or-"+this.getCompId(),r=this.optionsFactory.getDefaultOption();return this.eType1.setValue(r,e).setAriaLabel(o).setDisabled(this.isReadOnly()),this.eType2.setValue(this.optionsFactory.getDefaultOption(),e).setAriaLabel(o).setDisabled(this.isReadOnly()),this.eJoinOperatorAnd.setValue(this.isDefaultOperator("AND"),e).setName(n).setLabel(this.translate("andCondition")).setDisabled(this.isReadOnly()),this.eJoinOperatorOr.setValue(this.isDefaultOperator("OR"),e).setName(n).setLabel(this.translate("orCondition")).setDisabled(this.isReadOnly()),this.forEachInput((function(o){t.setElementValue(o,null,e),t.setElementDisabled(o,t.isReadOnly())})),this.resetPlaceholder(),xr.resolve()},t.prototype.setConditionIntoUi=function(e,t){var o=this,n=this.mapValuesFromModel(e);this.forEachInput((function(e,r,i,s){i===t&&o.setElementValue(e,null!=n[r]?n[r]:null)}))},t.prototype.setValueFromFloatingFilter=function(e){var t=this;this.forEachInput((function(o,n,r,i){t.setElementValue(o,0===n&&0===r?e:null)}))},t.prototype.isDefaultOperator=function(e){return e===this.defaultJoinOperator},t.prototype.addChangedListeners=function(){var e=this;if(!this.isReadOnly()){var t=function(){return e.onUiChanged()};this.eType1.onValueChange(t),this.eType2.onValueChange(t),this.eJoinOperatorOr.onValueChange(t),this.eJoinOperatorAnd.onValueChange(t),this.forEachInput((function(o){e.attachElementOnChange(o,t)}))}},t.prototype.individualConditionPasses=function(e,t){var o=this.getCellValue(e.node),n=this.mapValuesFromModel(t),r=this.optionsFactory.getCustomOption(t.type),i=this.evaluateCustomFilter(r,n,o);return null!=i?i:null==o?this.evaluateNullValue(t.type):this.evaluateNonNullValue(n,o,t,e)},t.prototype.evaluateCustomFilter=function(e,t,o){if(null!=e){var n=e.predicate;return null==n||t.some((function(e){return null==e}))?void 0:n(t,o)}},t.prototype.isBlank=function(e){return null==e||"string"===typeof e&&0===e.trim().length},t.EMPTY="empty",t.BLANK="blank",t.NOT_BLANK="notBlank",t.EQUALS="equals",t.NOT_EQUAL="notEqual",t.LESS_THAN="lessThan",t.LESS_THAN_OR_EQUAL="lessThanOrEqual",t.GREATER_THAN="greaterThan",t.GREATER_THAN_OR_EQUAL="greaterThanOrEqual",t.IN_RANGE="inRange",t.CONTAINS="contains",t.NOT_CONTAINS="notContains",t.STARTS_WITH="startsWith",t.ENDS_WITH="endsWith",Ni([Xr("eOptions1")],t.prototype,"eType1",void 0),Ni([Xr("eOptions2")],t.prototype,"eType2",void 0),Ni([Xr("eJoinOperatorPanel")],t.prototype,"eJoinOperatorPanel",void 0),Ni([Xr("eJoinOperatorAnd")],t.prototype,"eJoinOperatorAnd",void 0),Ni([Xr("eJoinOperatorOr")],t.prototype,"eJoinOperatorOr",void 0),Ni([Xr("eCondition1Body")],t.prototype,"eCondition1Body",void 0),Ni([Xr("eCondition2Body")],t.prototype,"eCondition2Body",void 0),t}(di),Mi=function(){var e=function(t,o){return e=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(e,t){e.__proto__=t}||function(e,t){for(var o in t)t.hasOwnProperty(o)&&(e[o]=t[o])},e(t,o)};return function(t,o){function n(){this.constructor=t}e(t,o),t.prototype=null===o?Object.create(o):(n.prototype=o.prototype,new n)}}(),Gi=function(e){function t(){return null!==e&&e.apply(this,arguments)||this}return Mi(t,e),t.prototype.setParams=function(t){e.prototype.setParams.call(this,t),this.scalarFilterParams=t},t.prototype.evaluateNullValue=function(e){switch(e){case t.EQUALS:case t.NOT_EQUAL:if(this.scalarFilterParams.includeBlanksInEquals)return!0;break;case t.GREATER_THAN:case t.GREATER_THAN_OR_EQUAL:if(this.scalarFilterParams.includeBlanksInGreaterThan)return!0;break;case t.LESS_THAN:case t.LESS_THAN_OR_EQUAL:if(this.scalarFilterParams.includeBlanksInLessThan)return!0;break;case t.IN_RANGE:if(this.scalarFilterParams.includeBlanksInRange)return!0;break;case t.BLANK:return!0;case t.NOT_BLANK:return!1}return!1},t.prototype.evaluateNonNullValue=function(e,o,n){var r=this.comparator(),i=null!=e[0]?r(e[0],o):0;switch(n.type){case t.EQUALS:return 0===i;case t.NOT_EQUAL:return 0!==i;case t.GREATER_THAN:return i>0;case t.GREATER_THAN_OR_EQUAL:return i>=0;case t.LESS_THAN:return i<0;case t.LESS_THAN_OR_EQUAL:return i<=0;case t.IN_RANGE:var s=r(e[1],o);return this.scalarFilterParams.inRangeInclusive?i>=0&&s<=0:i>0&&s<0;case t.BLANK:return this.isBlank(o);case t.NOT_BLANK:return!this.isBlank(o);default:return console.warn('AG Grid: Unexpected type of filter "'+n.type+'", it looks like the filter was configured with incorrect Filter Options'),!0}},t}(Ii),Li=function(){var e=function(t,o){return e=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(e,t){e.__proto__=t}||function(e,t){for(var o in t)t.hasOwnProperty(o)&&(e[o]=t[o])},e(t,o)};return function(t,o){function n(){this.constructor=t}e(t,o),t.prototype=null===o?Object.create(o):(n.prototype=o.prototype,new n)}}(),xi=function(){return xi=Object.assign||function(e){for(var t,o=1,n=arguments.length;o=0;a--)(r=e[a])&&(s=(i<3?r(s):i>3?r(t,o,s):r(t,o))||s);return i>3&&s&&Object.defineProperty(t,o,s),s},Hi=1/0,Wi=function(e){function t(){var t=e.call(this,"dateFilter")||this;return t.minValidYear=1e3,t.maxValidYear=Hi,t}return Li(t,e),t.prototype.afterGuiAttached=function(t){e.prototype.afterGuiAttached.call(this,t),this.dateCondition1FromComp.afterGuiAttached(t)},t.prototype.mapValuesFromModel=function(e){var t=e||{},o=t.dateFrom,n=t.dateTo,r=t.type;return[o&&fr(o)||null,n&&fr(n)||null].slice(0,this.getNumberOfInputs(r))},t.prototype.comparator=function(){return this.dateFilterParams.comparator?this.dateFilterParams.comparator:this.defaultComparator.bind(this)},t.prototype.defaultComparator=function(e,t){return null==t||te?1:0},t.prototype.setParams=function(t){e.prototype.setParams.call(this,t),this.dateFilterParams=t;var o=function(e,o){if(null!=t[e]){if(!isNaN(t[e]))return null==t[e]?o:Number(t[e]);console.warn("AG Grid: DateFilter "+e+" is not a number")}return o};this.minValidYear=o("minValidYear",1e3),this.maxValidYear=o("maxValidYear",Hi),this.minValidYear>this.maxValidYear&&console.warn("AG Grid: DateFilter minValidYear should be <= maxValidYear"),this.createDateComponents()},t.prototype.createDateComponents=function(){var e=this,t=function(t){return new ti(e.getContext(),e.userComponentFactory,{onDateChanged:function(){return e.onUiChanged()},filterParams:e.dateFilterParams},t)};this.dateCondition1FromComp=t(this.eCondition1PanelFrom),this.dateCondition1ToComp=t(this.eCondition1PanelTo),this.dateCondition2FromComp=t(this.eCondition2PanelFrom),this.dateCondition2ToComp=t(this.eCondition2PanelTo),this.addDestroyFunc((function(){e.forEachInput((function(e){return e.destroy()}))}))},t.prototype.setElementValue=function(e,t,o){e.setDate(t)},t.prototype.setElementDisplayed=function(e,t){e.setDisplayed(t)},t.prototype.setElementDisabled=function(e,t){e.setDisabled(t)},t.prototype.getDefaultFilterOptions=function(){return t.DEFAULT_FILTER_OPTIONS},t.prototype.createValueTemplate=function(e){var t=e===$r.One?"1":"2";return'\n
\n
\n
\n
'},t.prototype.isConditionUiComplete=function(t){var o=this;if(!e.prototype.isConditionUiComplete.call(this,t))return!1;var n=!0;return this.forEachInput((function(e,r,i,s){var a;i!==t||!n||r>=s||(n=n&&(null!=(a=e.getDate())&&a.getUTCFullYear()>=o.minValidYear&&a.getUTCFullYear()<=o.maxValidYear))})),n},t.prototype.areSimpleModelsEqual=function(e,t){return e.dateFrom===t.dateFrom&&e.dateTo===t.dateTo&&e.type===t.type},t.prototype.getFilterType=function(){return"date"},t.prototype.createCondition=function(e){var t=this.getConditionTypes()[e],o={},n=this.getValues(e);return n.length>0&&(o.dateFrom=hr(n[0])),n.length>1&&(o.dateTo=hr(n[1])),xi({dateFrom:null,dateTo:null,filterType:this.getFilterType(),type:t},o)},t.prototype.resetPlaceholder=function(){var e=this.gridOptionsWrapper.getLocaleTextFunc(),t=this.translate("dateFormatOoo"),o=e("ariaFilterValue","Filter Value");this.forEachInput((function(e){e.setInputPlaceholder(t),e.setInputAriaLabel(o)}))},t.prototype.getInputs=function(){return[[this.dateCondition1FromComp,this.dateCondition1ToComp],[this.dateCondition2FromComp,this.dateCondition2ToComp]]},t.prototype.getValues=function(e){var t=[];return this.forEachInput((function(o,n,r,i){e===r&&n=0;a--)(r=e[a])&&(s=(i<3?r(s):i>3?r(t,o,s):r(t,o))||s);return i>3&&s&&Object.defineProperty(t,o,s),s},zi=function(e){function t(){return e.call(this,'\n ')||this}return ji(t,e),t.prototype.getDefaultFilterOptions=function(){return Wi.DEFAULT_FILTER_OPTIONS},t.prototype.conditionToString=function(e,t){var o=e.type,n=(t||{}).numberOfInputs,r=o==Ii.IN_RANGE||2===n,i=fr(e.dateFrom),s=fr(e.dateTo);return r?hr(i,!1)+"-"+hr(s,!1):null!=i?""+hr(i,!1):""+o},t.prototype.init=function(t){e.prototype.init.call(this,t),this.params=t,this.createDateComponent();var o=this.gridOptionsWrapper.getLocaleTextFunc();this.eReadOnlyText.setDisabled(!0).setInputAriaLabel(o("ariaDateFilterInput","Date Filter Input"))},t.prototype.setEditable=function(e){xo(this.eDateWrapper,e),xo(this.eReadOnlyText.getGui(),!e)},t.prototype.onParentModelChanged=function(t,o){if(!this.isEventFromFloatingFilter(o)){e.prototype.setLastTypeFromModel.call(this,t);var n=!this.isReadOnly()&&this.canWeEditAfterModelFromParentFilter(t);if(this.setEditable(n),n){if(t){var r=t;this.dateComp.setDate(fr(r.dateFrom))}else this.dateComp.setDate(null);this.eReadOnlyText.setValue("")}else this.eReadOnlyText.setValue(this.getTextFromModel(t)),this.dateComp.setDate(null)}},t.prototype.onDateChanged=function(){var e=this,t=hr(this.dateComp.getDate());this.params.parentFilterInstance((function(o){if(o){var n=fr(t);o.onFloatingFilterChanged(e.getLastType()||null,n)}}))},t.prototype.createDateComponent=function(){var e=this,t=di.getDebounceMs(this.params.filterParams,this.getDefaultDebounceMs()),o={onDateChanged:de(this.onDateChanged.bind(this),t),filterParams:this.params.column.getColDef().filterParams};this.dateComp=new ti(this.getContext(),this.userComponentFactory,o,this.eDateWrapper),this.addDestroyFunc((function(){return e.dateComp.destroy()}))},Ui([Re("userComponentFactory")],t.prototype,"userComponentFactory",void 0),Ui([Xr("eReadOnlyText")],t.prototype,"eReadOnlyText",void 0),Ui([Xr("eDateWrapper")],t.prototype,"eDateWrapper",void 0),t}(Bi),Ki=function(){var e=function(t,o){return e=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(e,t){e.__proto__=t}||function(e,t){for(var o in t)t.hasOwnProperty(o)&&(e[o]=t[o])},e(t,o)};return function(t,o){function n(){this.constructor=t}e(t,o),t.prototype=null===o?Object.create(o):(n.prototype=o.prototype,new n)}}(),Yi=function(e,t,o,n){var r,i=arguments.length,s=i<3?t:null===n?n=Object.getOwnPropertyDescriptor(t,o):n;if("object"===typeof Reflect&&"function"===typeof Reflect.decorate)s=Reflect.decorate(e,t,o,n);else for(var a=e.length-1;a>=0;a--)(r=e[a])&&(s=(i<3?r(s):i>3?r(t,o,s):r(t,o))||s);return i>3&&s&&Object.defineProperty(t,o,s),s},qi=function(e){function t(){return e.call(this,'\n
\n \n
')||this}return Ki(t,e),t.prototype.destroy=function(){e.prototype.destroy.call(this)},t.prototype.init=function(e){var t=this,o=this.gridOptionsWrapper.getDocument(),n=this.eDateInput.getInputElement();this.shouldUseBrowserDatePicker(e)&&(n.type="date"),this.addManagedListener(n,"mousedown",(function(){t.eDateInput.isDisabled()||n.focus()})),this.addManagedListener(n,"input",(function(n){n.target===o.activeElement&&(t.eDateInput.isDisabled()||e.onDateChanged())}));var r=e.filterParams||{},i=r.minValidYear,s=r.maxValidYear;i&&(n.min=i+"-01-01"),s&&(n.max=s+"-12-31")},t.prototype.getDate=function(){return fr(this.eDateInput.getValue())},t.prototype.setDate=function(e){this.eDateInput.setValue(hr(e,!1))},t.prototype.setInputPlaceholder=function(e){this.eDateInput.setInputPlaceholder(e)},t.prototype.setDisabled=function(e){this.eDateInput.setDisabled(e)},t.prototype.afterGuiAttached=function(e){e&&e.suppressFocus||this.eDateInput.getInputElement().focus()},t.prototype.shouldUseBrowserDatePicker=function(e){return e.filterParams&&null!=e.filterParams.browserDatePicker?e.filterParams.browserDatePicker:Oo()||bo()},Yi([Xr("eDateInput")],t.prototype,"eDateInput",void 0),t}(Yr),Xi=function(){var e=function(t,o){return e=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(e,t){e.__proto__=t}||function(e,t){for(var o in t)t.hasOwnProperty(o)&&(e[o]=t[o])},e(t,o)};return function(t,o){function n(){this.constructor=t}e(t,o),t.prototype=null===o?Object.create(o):(n.prototype=o.prototype,new n)}}(),Qi=function(e,t,o,n){var r,i=arguments.length,s=i<3?t:null===n?n=Object.getOwnPropertyDescriptor(t,o):n;if("object"===typeof Reflect&&"function"===typeof Reflect.decorate)s=Reflect.decorate(e,t,o,n);else for(var a=e.length-1;a>=0;a--)(r=e[a])&&(s=(i<3?r(s):i>3?r(t,o,s):r(t,o))||s);return i>3&&s&&Object.defineProperty(t,o,s),s},$i=function(e){function t(){return e.call(this,"numberFilter")||this}return Xi(t,e),t.prototype.mapValuesFromModel=function(e){var t=e||{},o=t.filter,n=t.filterTo,r=t.type;return[null==o?null:o,null==n?null:n].slice(0,this.getNumberOfInputs(r))},t.prototype.getDefaultDebounceMs=function(){return 500},t.prototype.comparator=function(){return function(e,t){return e===t?0:e\n <'+o+' class="ag-filter-from ag-filter-filter" ref="eValue-index0-'+t+'">\n <"+o+' class="ag-filter-to ag-filter-filter" ref="eValue-index1-'+t+'">\n "},t.prototype.getValues=function(e){var t=this,o=[];return 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t=[Ii.NOT_EQUAL,Ii.NOT_CONTAINS,Ii.BLANK];return!!e&&t.indexOf(e)>=0},t.prototype.evaluateNonNullValue=function(e,t,o,n){var r=this,i=e.map((function(e){return r.formatter(e)}))||[],s=this.formatter(t),a=this.textFilterParams,l=a.api,u=a.colDef,p=a.column,c=a.columnApi,d=a.context,h=a.textFormatter;if(o.type===Ii.BLANK)return this.isBlank(t);if(o.type===Ii.NOT_BLANK)return!this.isBlank(t);var f={api:l,colDef:u,column:p,columnApi:c,context:d,node:n.node,data:n.data,filterOption:o.type,value:s,textFormatter:h};return i.some((function(e){return r.matcher(Zi(Zi({},f),{filterText:e}))}))},t.DEFAULT_FILTER_OPTIONS=[Ii.CONTAINS,Ii.NOT_CONTAINS,Ii.EQUALS,Ii.NOT_EQUAL,Ii.STARTS_WITH,Ii.ENDS_WITH,Ii.BLANK,Ii.NOT_BLANK],t.DEFAULT_FORMATTER=function(e){return e},t.DEFAULT_LOWERCASE_FORMATTER=function(e){return null==e?null:e.toString().toLowerCase()},t.DEFAULT_MATCHER=function(e){var o=e.filterOption,n=e.value,r=e.filterText;if(null==r)return!1;switch(o){case t.CONTAINS:return n.indexOf(r)>=0;case 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Reflect.decorate)s=Reflect.decorate(e,t,o,n);else for(var a=e.length-1;a>=0;a--)(r=e[a])&&(s=(i<3?r(s):i>3?r(t,o,s):r(t,o))||s);return i>3&&s&&Object.defineProperty(t,o,s),s},rs=function(e){function t(){return null!==e&&e.apply(this,arguments)||this}return os(t,e),t.prototype.postConstruct=function(){this.resetTemplate()},t.prototype.resetTemplate=function(e){this.setTemplate('\n \n ',e)},t.prototype.getDefaultDebounceMs=function(){return 500},t.prototype.onParentModelChanged=function(e,t){this.isEventFromFloatingFilter(t)||(this.setLastTypeFromModel(e),this.eFloatingFilterInput.setValue(this.getTextFromModel(e)),this.setEditable(this.canWeEditAfterModelFromParentFilter(e)))},t.prototype.init=function(t){e.prototype.init.call(this,t),this.params=t,this.applyActive=di.isUseApplyButton(this.params.filterParams);var o=this.params.filterParams.allowedCharPattern;if(null!=o&&this.resetTemplate({eFloatingFilterInput:{allowedCharPattern:o}}),!this.isReadOnly()){var n=di.getDebounceMs(this.params.filterParams,this.getDefaultDebounceMs()),r=de(this.syncUpWithParentFilter.bind(this),n),i=this.eFloatingFilterInput.getGui();this.addManagedListener(i,"input",r),this.addManagedListener(i,"keypress",r),this.addManagedListener(i,"keydown",r)}var s=t.column.getDefinition();(this.isReadOnly()||s.filterParams&&s.filterParams.filterOptions&&1===s.filterParams.filterOptions.length&&"inRange"===s.filterParams.filterOptions[0])&&this.eFloatingFilterInput.setDisabled(!0);var a=this.columnModel.getDisplayNameForColumn(t.column,"header",!0),l=this.gridOptionsWrapper.getLocaleTextFunc();this.eFloatingFilterInput.setInputAriaLabel(a+" "+l("ariaFilterInput","Filter Input"))},t.prototype.syncUpWithParentFilter=function(e){var t=this,o=e.key===ii.ENTER;if(!this.applyActive||o){var n=this.eFloatingFilterInput.getValue();this.params.filterParams.trimInput&&(n=ts.trimInput(n),this.eFloatingFilterInput.setValue(n,!0)),this.params.parentFilterInstance((function(e){e&&e.onFloatingFilterChanged(t.getLastType()||null,n||null)}))}},t.prototype.conditionToString=function(e,t){var o=(t||{}).numberOfInputs;return e.type==Ii.IN_RANGE||2===o?e.filter+"-"+e.filterTo:null!=e.filter?""+e.filter:""+e.type},t.prototype.setEditable=function(e){this.eFloatingFilterInput.setDisabled(!e)},ns([Re("columnModel")],t.prototype,"columnModel",void 0),ns([Xr("eFloatingFilterInput")],t.prototype,"eFloatingFilterInput",void 0),ns([Ce],t.prototype,"postConstruct",null),t}(Bi),is=function(){var e=function(t,o){return e=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(e,t){e.__proto__=t}||function(e,t){for(var o in t)t.hasOwnProperty(o)&&(e[o]=t[o])},e(t,o)};return function(t,o){function n(){this.constructor=t}e(t,o),t.prototype=null===o?Object.create(o):(n.prototype=o.prototype,new n)}}(),ss=function(e){function t(){return null!==e&&e.apply(this,arguments)||this}return is(t,e),t.prototype.getDefaultFilterOptions=function(){return $i.DEFAULT_FILTER_OPTIONS},t}(rs),as=function(){var e=function(t,o){return e=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(e,t){e.__proto__=t}||function(e,t){for(var o in t)t.hasOwnProperty(o)&&(e[o]=t[o])},e(t,o)};return function(t,o){function n(){this.constructor=t}e(t,o),t.prototype=null===o?Object.create(o):(n.prototype=o.prototype,new n)}}(),ls=function(e){function t(){return null!==e&&e.apply(this,arguments)||this}return as(t,e),t.prototype.getDefaultFilterOptions=function(){return ts.DEFAULT_FILTER_OPTIONS},t}(rs),us=function(){function e(e,t){var o=this;void 0===t&&(t=!1),this.destroyFuncs=[],this.touching=!1,this.eventService=new Ae,this.eElement=e,this.preventMouseClick=t;var n=this.onTouchStart.bind(this),r=this.onTouchMove.bind(this),i=this.onTouchEnd.bind(this);this.eElement.addEventListener("touchstart",n,{passive:!0}),this.eElement.addEventListener("touchmove",r,{passive:!0}),this.eElement.addEventListener("touchend",i,{passive:!1}),this.destroyFuncs.push((function(){o.eElement.removeEventListener("touchstart",n,{passive:!0}),o.eElement.removeEventListener("touchmove",r,{passive:!0}),o.eElement.removeEventListener("touchend",i,{passive:!1})}))}return e.prototype.getActiveTouch=function(e){for(var t=0;t0)if(t-this.lastTapTime>e.DOUBLE_TAP_MILLIS){var o={type:e.EVENT_DOUBLE_TAP,touchStart:this.touchStart};this.eventService.dispatchEvent(o),this.lastTapTime=null}else this.lastTapTime=t;else this.lastTapTime=t},e.prototype.destroy=function(){this.destroyFuncs.forEach((function(e){return e()}))},e.EVENT_TAP="tap",e.EVENT_DOUBLE_TAP="doubleTap",e.EVENT_LONG_TAP="longTap",e.DOUBLE_TAP_MILLIS=500,e}(),ps=function(){var e=function(t,o){return e=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(e,t){e.__proto__=t}||function(e,t){for(var o in t)t.hasOwnProperty(o)&&(e[o]=t[o])},e(t,o)};return function(t,o){function n(){this.constructor=t}e(t,o),t.prototype=null===o?Object.create(o):(n.prototype=o.prototype,new n)}}(),cs=function(e,t,o,n){var r,i=arguments.length,s=i<3?t:null===n?n=Object.getOwnPropertyDescriptor(t,o):n;if("object"===typeof Reflect&&"function"===typeof Reflect.decorate)s=Reflect.decorate(e,t,o,n);else for(var a=e.length-1;a>=0;a--)(r=e[a])&&(s=(i<3?r(s):i>3?r(t,o,s):r(t,o))||s);return i>3&&s&&Object.defineProperty(t,o,s),s},ds=function(e){function t(){var t=null!==e&&e.apply(this,arguments)||this;return t.lastMovingChanged=0,t}return ps(t,e),t.prototype.destroy=function(){e.prototype.destroy.call(this)},t.prototype.refresh=function(e){return this.params=e,this.workOutTemplate()==this.currentTemplate&&(this.workOutShowMenu()==this.currentShowMenu&&(this.workOutSort()==this.currentSort&&(this.setDisplayName(e),!0)))},t.prototype.workOutTemplate=function(){var e=Le(this.params.template,t.TEMPLATE);return e=e&&e.trim?e.trim():e},t.prototype.init=function(e){this.params=e,this.currentTemplate=this.workOutTemplate(),this.setTemplate(this.currentTemplate),this.setupTap(),this.setupIcons(e.column),this.setMenu(),this.setupSort(),this.setupFilterIcon(),this.setDisplayName(e)},t.prototype.setDisplayName=function(e){if(this.currentDisplayName!=e.displayName){this.currentDisplayName=e.displayName;var t=Nt(this.currentDisplayName);this.eText&&(this.eText.innerHTML=t)}},t.prototype.setupIcons=function(e){this.addInIcon("sortAscending",this.eSortAsc,e),this.addInIcon("sortDescending",this.eSortDesc,e),this.addInIcon("sortUnSort",this.eSortNone,e),this.addInIcon("menu",this.eMenu,e),this.addInIcon("filter",this.eFilter,e)},t.prototype.addInIcon=function(e,t,o){if(null!=t){var n=Rr(e,this.gridOptionsWrapper,o);n&&t.appendChild(n)}},t.prototype.setupTap=function(){var e=this,t=this.gridOptionsWrapper;if(!t.isSuppressTouch()){var o=new us(this.getGui(),!0),n=t.isSuppressMenuHide(),r=n&&O(this.eMenu),i=r?new us(this.eMenu,!0):o;if(this.params.enableMenu){var s=r?"EVENT_TAP":"EVENT_LONG_TAP";this.addManagedListener(i,us[s],(function(o){t.getApi().showColumnMenuAfterMouseClick(e.params.column,o.touchStart)}))}if(this.params.enableSorting){this.addManagedListener(o,us.EVENT_TAP,(function(t){var o=t.touchStart.target;n&&e.eMenu.contains(o)||e.sortController.progressSort(e.params.column,!1,"uiColumnSorted")}))}this.addDestroyFunc((function(){return o.destroy()})),r&&this.addDestroyFunc((function(){return i.destroy()}))}},t.prototype.workOutShowMenu=function(){var e=!this.gridOptionsWrapper.isSuppressMenuHide(),t=Po()&&e;return this.params.enableMenu&&!t},t.prototype.setMenu=function(){var e=this;if(this.eMenu)if(this.currentShowMenu=this.workOutShowMenu(),this.currentShowMenu){var t=this.gridOptionsWrapper.isSuppressMenuHide();this.addManagedListener(this.eMenu,"click",(function(){return e.showMenu(e.eMenu)})),this.eMenu.classList.toggle("ag-header-menu-always-show",t)}else $o(this.eMenu)},t.prototype.showMenu=function(e){e||(e=this.eMenu),this.menuFactory.showMenuAfterButtonClick(this.params.column,e,"columnMenu")},t.prototype.removeSortIcons=function(){$o(this.eSortAsc),$o(this.eSortDesc),$o(this.eSortNone),$o(this.eSortOrder)},t.prototype.workOutSort=function(){return this.params.enableSorting},t.prototype.setupSort=function(){var e=this;if(this.currentSort=this.params.enableSorting,this.currentSort){var t=this.gridOptionsWrapper.isMultiSortKeyCtrl();this.addManagedListener(this.params.column,Ge.EVENT_MOVING_CHANGED,(function(){e.lastMovingChanged=(new Date).getTime()})),this.eLabel&&this.addManagedListener(this.eLabel,"click",(function(o){var n=e.params.column.isMoving(),r=(new Date).getTime()-e.lastMovingChanged<50;if(!(n||r)){var i=t?o.ctrlKey||o.metaKey:o.shiftKey;e.params.progressSort(i)}})),this.addManagedListener(this.params.column,Ge.EVENT_SORT_CHANGED,this.onSortChanged.bind(this)),this.onSortChanged(),this.addManagedListener(this.eventService,St.EVENT_SORT_CHANGED,this.setMultiSortOrder.bind(this)),this.setMultiSortOrder()}else this.removeSortIcons()},t.prototype.onSortChanged=function(){if(this.addOrRemoveCssClass("ag-header-cell-sorted-asc",this.params.column.isSortAscending()),this.addOrRemoveCssClass("ag-header-cell-sorted-desc",this.params.column.isSortDescending()),this.addOrRemoveCssClass("ag-header-cell-sorted-none",this.params.column.isSortNone()),this.eSortAsc&&this.eSortAsc.classList.toggle("ag-hidden",!this.params.column.isSortAscending()),this.eSortDesc&&this.eSortDesc.classList.toggle("ag-hidden",!this.params.column.isSortDescending()),this.eSortNone){var e=!this.params.column.getColDef().unSortIcon&&!this.gridOptionsWrapper.isUnSortIcon();this.eSortNone.classList.toggle("ag-hidden",e||!this.params.column.isSortNone())}},t.prototype.setMultiSortOrder=function(){if(this.eSortOrder){var e=this.params.column,t=this.sortController.getColumnsWithSortingOrdered(),o=t.indexOf(e),n=t.length>1,r=e.isSorting()&&n;xo(this.eSortOrder,r),o>=0?this.eSortOrder.innerHTML=(o+1).toString():Xo(this.eSortOrder)}},t.prototype.setupFilterIcon=function(){this.eFilter&&(this.addManagedListener(this.params.column,Ge.EVENT_FILTER_CHANGED,this.onFilterChanged.bind(this)),this.onFilterChanged())},t.prototype.onFilterChanged=function(){var e=this.params.column.isFilterActive();this.eFilter.classList.toggle("ag-hidden",!e)},t.TEMPLATE='',cs([Re("sortController")],t.prototype,"sortController",void 0),cs([Re("menuFactory")],t.prototype,"menuFactory",void 0),cs([Xr("eFilter")],t.prototype,"eFilter",void 0),cs([Xr("eSortAsc")],t.prototype,"eSortAsc",void 0),cs([Xr("eSortDesc")],t.prototype,"eSortDesc",void 0),cs([Xr("eSortNone")],t.prototype,"eSortNone",void 0),cs([Xr("eSortOrder")],t.prototype,"eSortOrder",void 0),cs([Xr("eMenu")],t.prototype,"eMenu",void 0),cs([Xr("eLabel")],t.prototype,"eLabel",void 0),cs([Xr("eText")],t.prototype,"eText",void 0),t}(Yr),hs=function(){var e=function(t,o){return e=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(e,t){e.__proto__=t}||function(e,t){for(var o in t)t.hasOwnProperty(o)&&(e[o]=t[o])},e(t,o)};return function(t,o){function n(){this.constructor=t}e(t,o),t.prototype=null===o?Object.create(o):(n.prototype=o.prototype,new n)}}(),fs=function(e,t,o,n){var r,i=arguments.length,s=i<3?t:null===n?n=Object.getOwnPropertyDescriptor(t,o):n;if("object"===typeof Reflect&&"function"===typeof Reflect.decorate)s=Reflect.decorate(e,t,o,n);else for(var a=e.length-1;a>=0;a--)(r=e[a])&&(s=(i<3?r(s):i>3?r(t,o,s):r(t,o))||s);return i>3&&s&&Object.defineProperty(t,o,s),s},gs=function(e){function t(){return e.call(this,t.TEMPLATE)||this}return hs(t,e),t.prototype.destroy=function(){e.prototype.destroy.call(this)},t.prototype.init=function(e){this.params=e,this.checkWarnings(),this.setupLabel(),this.addGroupExpandIcon(),this.setupExpandIcons()},t.prototype.checkWarnings=function(){if(this.params.template){ne((function(){return console.warn("AG Grid: A template was provided for Header Group Comp - templates are only supported for Header Comps (not groups)")}),"HeaderGroupComp.templateNotSupported")}},t.prototype.setupExpandIcons=function(){var e=this;this.addInIcon("columnGroupOpened","agOpened"),this.addInIcon("columnGroupClosed","agClosed");var t=function(t){if(!lt(t)){var o=!e.params.columnGroup.isExpanded();e.columnModel.setColumnGroupOpened(e.params.columnGroup.getProvidedColumnGroup(),o,"uiColumnExpanded")}};this.addTouchAndClickListeners(this.eCloseIcon,t),this.addTouchAndClickListeners(this.eOpenIcon,t);var 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r={type:e.EVENT_CELL_CHANGED,node:this,column:t,newValue:o,oldValue:n};this.dispatchLocalEvent(r)},e.prototype.resetQuickFilterAggregateText=function(){this.quickFilterAggregateText=null},e.prototype.isExpandable=function(){return!!(this.hasChildren()&&!this.footer||this.master)},e.prototype.isSelected=function(){return this.footer?this.sibling.isSelected():this.selected},e.prototype.depthFirstSearch=function(e){this.childrenAfterGroup&&this.childrenAfterGroup.forEach((function(t){return t.depthFirstSearch(e)})),e(this)},e.prototype.calculateSelectedFromChildren=function(){var e,t,o=!1,n=!1,r=!1;if(null===(e=this.childrenAfterGroup)||void 0===e?void 0:e.length){for(var i=0;i0){this.beans.selectionService.updateGroupsFromChildrenSelections();var d={type:St.EVENT_SELECTION_CHANGED,api:this.beans.gridApi,columnApi:this.beans.columnApi};this.beans.eventService.dispatchEvent(d)}n&&this.beans.selectionService.setLastSelectedNode(this)}return c},e.prototype.doRowRangeSelection=function(e){var t=this;void 0===e&&(e=!0);var o=this.beans.gridOptionsWrapper.isGroupSelectsChildren(),n=this.beans.selectionService.getLastSelectedNode(),r=this.beans.rowModel.getNodesInRangeForSelection(this,n),i=0;r.forEach((function(n){n.group&&o||!1===e&&t===n||n.selectThisNode(e)&&i++})),this.beans.selectionService.updateGroupsFromChildrenSelections();var s={type:St.EVENT_SELECTION_CHANGED,api:this.beans.gridApi,columnApi:this.beans.columnApi};return this.beans.eventService.dispatchEvent(s),i},e.prototype.isParentOfNode=function(e){for(var t=this.parent;t;){if(t===e)return!0;t=t.parent}return!1},e.prototype.selectThisNode=function(t){var o=!this.selectable&&t,n=this.selected===t;if(o||n)return!1;this.selected=t,this.eventService&&this.dispatchLocalEvent(this.createLocalRowEvent(e.EVENT_ROW_SELECTED));var r=this.createGlobalRowEvent(St.EVENT_ROW_SELECTED);return this.beans.eventService.dispatchEvent(r),!0},e.prototype.selectChildNodes=function(e,t){var o=t?this.childrenAfterAggFilter:this.childrenAfterGroup;if(b(o))return 0;for(var n=0,r=0;r=0;a--)(r=e[a])&&(s=(i<3?r(s):i>3?r(t,o,s):r(t,o))||s);return i>3&&s&&Object.defineProperty(t,o,s),s},js=function(e){function t(){return e.call(this,'\n ')||this}return ks(t,e),t.prototype.postConstruct=function(){this.eCheckbox.setPassive(!0)},t.prototype.getCheckboxId=function(){return this.eCheckbox.getInputElement().id},t.prototype.onDataChanged=function(){this.onSelectionChanged()},t.prototype.onSelectableChanged=function(){this.showOrHideSelect()},t.prototype.onSelectionChanged=function(){var e=this.gridOptionsWrapper.getLocaleTextFunc(),t=this.rowNode.isSelected(),o=void 0===t?e("ariaIndeterminate","indeterminate"):!0===t?e("ariaChecked","checked"):e("ariaUnchecked","unchecked"),n=e("ariaRowToggleSelection","Press Space to toggle row selection");this.eCheckbox.setValue(t,!0),this.eCheckbox.setInputAriaLabel(n+" ("+o+")")},t.prototype.onCheckedClicked=function(e){var t=this.gridOptionsWrapper.isGroupSelectsFiltered();return this.rowNode.setSelectedParams({newValue:!1,rangeSelect:e.shiftKey,groupSelectsFiltered:t})},t.prototype.onUncheckedClicked=function(e){var t=this.gridOptionsWrapper.isGroupSelectsFiltered();return this.rowNode.setSelectedParams({newValue:!0,rangeSelect:e.shiftKey,groupSelectsFiltered:t})},t.prototype.init=function(e){var t=this;if(this.rowNode=e.rowNode,this.column=e.column,this.onSelectionChanged(),this.addGuiEventListener("click",(function(e){return at(e)})),this.addGuiEventListener("dblclick",(function(e){return at(e)})),this.addManagedListener(this.eCheckbox.getInputElement(),"click",(function(e){var o=t.eCheckbox.getValue();void 0===t.eCheckbox.getPreviousValue()||void 0===o?0===t.onUncheckedClicked(e||{})&&t.onCheckedClicked(e):o?t.onCheckedClicked(e):t.onUncheckedClicked(e||{})})),this.addManagedListener(this.rowNode,Ws.EVENT_ROW_SELECTED,this.onSelectionChanged.bind(this)),this.addManagedListener(this.rowNode,Ws.EVENT_DATA_CHANGED,this.onDataChanged.bind(this)),this.addManagedListener(this.rowNode,Ws.EVENT_SELECTABLE_CHANGED,this.onSelectableChanged.bind(this)),this.gridOptionsWrapper.getIsRowSelectableFunc()||this.checkboxCallbackExists()){var o=this.showOrHideSelect.bind(this);this.addManagedListener(this.eventService,St.EVENT_DISPLAYED_COLUMNS_CHANGED,o),this.addManagedListener(this.rowNode,Ws.EVENT_DATA_CHANGED,o),this.addManagedListener(this.rowNode,Ws.EVENT_CELL_CHANGED,o),this.showOrHideSelect()}this.eCheckbox.getInputElement().setAttribute("tabindex","-1")},t.prototype.showOrHideSelect=function(){var e=this.rowNode.selectable;e&&this.checkboxCallbackExists()&&(e=this.column.isCellCheckboxSelection(this.rowNode)),this.setVisible(e)},t.prototype.checkboxCallbackExists=function(){var e=this.column?this.column.getColDef():null;return!!e&&"function"===typeof e.checkboxSelection},Bs([Xr("eCheckbox")],t.prototype,"eCheckbox",void 0),Bs([Ce],t.prototype,"postConstruct",null),t}(Yr),Us=function(){var e=function(t,o){return e=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(e,t){e.__proto__=t}||function(e,t){for(var o in t)t.hasOwnProperty(o)&&(e[o]=t[o])},e(t,o)};return function(t,o){function n(){this.constructor=t}e(t,o),t.prototype=null===o?Object.create(o):(n.prototype=o.prototype,new n)}}(),zs=function(e,t,o,n){var r,i=arguments.length,s=i<3?t:null===n?n=Object.getOwnPropertyDescriptor(t,o):n;if("object"===typeof Reflect&&"function"===typeof Reflect.decorate)s=Reflect.decorate(e,t,o,n);else for(var a=e.length-1;a>=0;a--)(r=e[a])&&(s=(i<3?r(s):i>3?r(t,o,s):r(t,o))||s);return i>3&&s&&Object.defineProperty(t,o,s),s},Ks=function(e){var t="function"===typeof Symbol&&Symbol.iterator,o=t&&e[t],n=0;if(o)return o.call(e);if(e&&"number"===typeof e.length)return{next:function(){return e&&n>=e.length&&(e=void 0),{value:e&&e[n++],done:!e}}};throw new TypeError(t?"Object is not iterable.":"Symbol.iterator is not defined.")};!function(e){e[e.ToolPanel=0]="ToolPanel",e[e.HeaderCell=1]="HeaderCell",e[e.RowDrag=2]="RowDrag",e[e.ChartPanel=3]="ChartPanel"}(xs||(xs={})),function(e){e[e.Up=0]="Up",e[e.Down=1]="Down"}(Vs||(Vs={})),function(e){e[e.Left=0]="Left",e[e.Right=1]="Right"}(Hs||(Hs={}));var Ys,qs=function(e){function t(){var t=null!==e&&e.apply(this,arguments)||this;return t.dragSourceAndParamsList=[],t.dropTargets=[],t}var o;return Us(t,e),o=t,t.prototype.init=function(){this.ePinnedIcon=Er("columnMovePin",this.gridOptionsWrapper,null),this.eHideIcon=Er("columnMoveHide",this.gridOptionsWrapper,null),this.eMoveIcon=Er("columnMoveMove",this.gridOptionsWrapper,null),this.eLeftIcon=Er("columnMoveLeft",this.gridOptionsWrapper,null),this.eRightIcon=Er("columnMoveRight",this.gridOptionsWrapper,null),this.eGroupIcon=Er("columnMoveGroup",this.gridOptionsWrapper,null),this.eAggregateIcon=Er("columnMoveValue",this.gridOptionsWrapper,null),this.ePivotIcon=Er("columnMovePivot",this.gridOptionsWrapper,null),this.eDropNotAllowedIcon=Er("dropNotAllowed",this.gridOptionsWrapper,null)},t.prototype.addDragSource=function(e,t){void 0===t&&(t=!1);var o={eElement:e.eElement,dragStartPixels:e.dragStartPixels,onDragStart:this.onDragStart.bind(this,e),onDragStop:this.onDragStop.bind(this),onDragging:this.onDragging.bind(this)};this.dragSourceAndParamsList.push({params:o,dragSource:e}),this.dragService.addDragSource(o,t)},t.prototype.removeDragSource=function(e){var t=this.dragSourceAndParamsList.find((function(t){return t.dragSource===e}));t&&(this.dragService.removeDragSource(t.params),Ue(this.dragSourceAndParamsList,t))},t.prototype.clearDragSourceParamsList=function(){var e=this;this.dragSourceAndParamsList.forEach((function(t){return e.dragService.removeDragSource(t.params)})),this.dragSourceAndParamsList.length=0},t.prototype.nudge=function(){this.dragging&&this.onDragging(this.eventLastTime,!0)},t.prototype.onDragStart=function(e,t){this.dragging=!0,this.dragSource=e,this.eventLastTime=t,this.dragItem=this.dragSource.getDragItem(),this.lastDropTarget=this.dragSource.dragSourceDropTarget,this.dragSource.onDragStarted&&this.dragSource.onDragStarted(),this.createGhost()},t.prototype.onDragStop=function(e){if(this.eventLastTime=null,this.dragging=!1,this.dragSource.onDragStopped&&this.dragSource.onDragStopped(),this.lastDropTarget&&this.lastDropTarget.onDragStop){var t=this.createDropTargetEvent(this.lastDropTarget,e,null,null,!1);this.lastDropTarget.onDragStop(t)}this.lastDropTarget=null,this.dragItem=null,this.removeGhost()},t.prototype.onDragging=function(e,t){var o=this,n=this.getHorizontalDirection(e),r=this.getVerticalDirection(e);this.eventLastTime=e,this.positionGhost(e);var i=this.dropTargets.filter((function(t){return o.isMouseOnDropTarget(e,t)})),s=i.length,a=null;if(s>0&&(a=1===s?i[0]:i.reduce((function(e,t){if(!e)return t;var o=e.getContainer(),n=t.getContainer();return o.contains(n)?t:e}))),a!==this.lastDropTarget)this.leaveLastTargetIfExists(e,n,r,t),this.enterDragTargetIfExists(a,e,n,r,t),this.lastDropTarget=a;else if(a&&a.onDragging){var l=this.createDropTargetEvent(a,e,n,r,t);a.onDragging(l)}},t.prototype.enterDragTargetIfExists=function(e,t,o,n,r){if(e){if(e.onDragEnter){var i=this.createDropTargetEvent(e,t,o,n,r);e.onDragEnter(i)}this.setGhostIcon(e.getIconName?e.getIconName():null)}},t.prototype.leaveLastTargetIfExists=function(e,t,o,n){if(this.lastDropTarget){if(this.lastDropTarget.onDragLeave){var r=this.createDropTargetEvent(this.lastDropTarget,e,t,o,n);this.lastDropTarget.onDragLeave(r)}this.setGhostIcon(null)}},t.prototype.getAllContainersFromDropTarget=function(e){var t=e.getSecondaryContainers?e.getSecondaryContainers():null,o=[[e.getContainer()]];return t?o.concat(t):o},t.prototype.allContainersIntersect=function(e,t){var o,n;try{for(var r=Ks(t),i=r.next();!i.done;i=r.next()){var s=i.value.getBoundingClientRect();if(0===s.width||0===s.height)return!1;var a=e.clientX>=s.left&&e.clientX=s.top&&e.clientYo?Hs.Left:Hs.Right},t.prototype.getVerticalDirection=function(e){var t=this.eventLastTime&&this.eventLastTime.clientY,o=e.clientY;return t===o?null:t>o?Vs.Up:Vs.Down},t.prototype.createDropTargetEvent=function(e,t,o,n,r){var i=e.getContainer(),s=i.getBoundingClientRect(),a=this,l=a.gridApi,u=a.columnApi,p=a.dragItem,c=a.dragSource;return{event:t,x:t.clientX-s.left,y:t.clientY-s.top,vDirection:n,hDirection:o,dragSource:c,fromNudge:r,dragItem:p,api:l,columnApi:u,dropZoneTarget:i}},t.prototype.positionGhost=function(e){var t=this.eGhost;if(t){var o=t.getBoundingClientRect().height,n=Io()-2,r=Mo()-2,i=e.pageY-o/2,s=e.pageX-10,a=this.gridOptionsWrapper.getDocument(),l=a.defaultView||window,u=l.pageYOffset||a.documentElement.scrollTop,p=l.pageXOffset||a.documentElement.scrollLeft;n>0&&s+t.clientWidth>n+p&&(s=n+p-t.clientWidth),s<0&&(s=0),r>0&&i+t.clientHeight>r+u&&(i=r+u-t.clientHeight),i<0&&(i=0),t.style.left=s+"px",t.style.top=i+"px"}},t.prototype.removeGhost=function(){this.eGhost&&this.eGhostParent&&this.eGhostParent.removeChild(this.eGhost),this.eGhost=null},t.prototype.createGhost=function(){this.eGhost=Zo(o.GHOST_TEMPLATE);var e=this.environment.getTheme().theme;e&&this.eGhost.classList.add(e),this.eGhostIcon=this.eGhost.querySelector(".ag-dnd-ghost-icon"),this.setGhostIcon(null);var t=this.eGhost.querySelector(".ag-dnd-ghost-label"),n=this.dragSource.dragItemName;se(n)&&(n=n()),t.innerHTML=Nt(n)||"",this.eGhost.style.height="25px",this.eGhost.style.top="20px",this.eGhost.style.left="20px";var r=this.gridOptionsWrapper.getDocument(),i=null;try{i=r.fullscreenElement}catch(s){}finally{i||(i=r.querySelector("body"))}this.eGhostParent=i,this.eGhostParent?this.eGhostParent.appendChild(this.eGhost):console.warn("AG Grid: could not find document body, it is needed for dragging columns")},t.prototype.setGhostIcon=function(e,t){void 0===t&&(t=!1),Xo(this.eGhostIcon);var n=null;switch(e||(e=this.dragSource.defaultIconName||o.ICON_NOT_ALLOWED),e){case o.ICON_PINNED:n=this.ePinnedIcon;break;case o.ICON_MOVE:n=this.eMoveIcon;break;case o.ICON_LEFT:n=this.eLeftIcon;break;case o.ICON_RIGHT:n=this.eRightIcon;break;case o.ICON_GROUP:n=this.eGroupIcon;break;case o.ICON_AGGREGATE:n=this.eAggregateIcon;break;case o.ICON_PIVOT:n=this.ePivotIcon;break;case o.ICON_NOT_ALLOWED:n=this.eDropNotAllowedIcon;break;case o.ICON_HIDE:n=this.eHideIcon}this.eGhostIcon.classList.toggle("ag-shake-left-to-right",t),n===this.eHideIcon&&this.gridOptionsWrapper.isSuppressDragLeaveHidesColumns()||n&&this.eGhostIcon.appendChild(n)},t.ICON_PINNED="pinned",t.ICON_MOVE="move",t.ICON_LEFT="left",t.ICON_RIGHT="right",t.ICON_GROUP="group",t.ICON_AGGREGATE="aggregate",t.ICON_PIVOT="pivot",t.ICON_NOT_ALLOWED="notAllowed",t.ICON_HIDE="hide",t.GHOST_TEMPLATE='
\n \n
\n
',zs([Re("dragService")],t.prototype,"dragService",void 0),zs([Re("environment")],t.prototype,"environment",void 0),zs([Re("columnApi")],t.prototype,"columnApi",void 0),zs([Re("gridApi")],t.prototype,"gridApi",void 0),zs([Ce],t.prototype,"init",null),zs([we],t.prototype,"clearDragSourceParamsList",null),t=o=zs([Ee("dragAndDropService")],t)}(mt),Xs=function(){var e=function(t,o){return e=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(e,t){e.__proto__=t}||function(e,t){for(var o in t)t.hasOwnProperty(o)&&(e[o]=t[o])},e(t,o)};return function(t,o){function n(){this.constructor=t}e(t,o),t.prototype=null===o?Object.create(o):(n.prototype=o.prototype,new n)}}(),Qs=function(e,t,o,n){var r,i=arguments.length,s=i<3?t:null===n?n=Object.getOwnPropertyDescriptor(t,o):n;if("object"===typeof Reflect&&"function"===typeof Reflect.decorate)s=Reflect.decorate(e,t,o,n);else for(var a=e.length-1;a>=0;a--)(r=e[a])&&(s=(i<3?r(s):i>3?r(t,o,s):r(t,o))||s);return i>3&&s&&Object.defineProperty(t,o,s),s},$s=function(e){function t(t,o,n,r,i,s){var a=e.call(this)||this;return a.cellValueFn=t,a.rowNode=o,a.column=n,a.customGui=r,a.dragStartPixels=i,a.suppressVisibilityChange=s,a.dragSource=null,a}return Xs(t,e),t.prototype.isCustomGui=function(){return null!=this.customGui},t.prototype.postConstruct=function(){if(this.customGui?this.setDragElement(this.customGui,this.dragStartPixels):(this.setTemplate(''),this.getGui().appendChild(Rr("rowDrag",this.beans.gridOptionsWrapper,null)),this.addDragSource()),this.checkCompatibility(),!this.suppressVisibilityChange){var e=this.beans.gridOptionsWrapper.isRowDragManaged()?new ea(this,this.beans,this.rowNode,this.column):new Zs(this,this.beans,this.rowNode,this.column);this.createManagedBean(e,this.beans.context)}},t.prototype.setDragElement=function(e,t){this.setTemplateFromElement(e),this.addDragSource(t)},t.prototype.getSelectedCount=function(){if(!this.beans.gridOptionsWrapper.isRowDragMultiRow())return 1;var e=this.beans.selectionService.getSelectedNodes();return-1!==e.indexOf(this.rowNode)?e.length:1},t.prototype.checkCompatibility=function(){var e=this.beans.gridOptionsWrapper.isRowDragManaged();this.beans.gridOptionsWrapper.isTreeData()&&e&&ne((function(){return console.warn("AG Grid: If using row drag with tree data, you cannot have rowDragManaged=true")}),"RowDragComp.managedAndTreeData")},t.prototype.addDragSource=function(e){var t=this;void 0===e&&(e=4),this.dragSource&&this.removeDragSource();var o={rowNode:this.rowNode,columns:this.column?[this.column]:void 0,defaultTextValue:this.cellValueFn()},n=this.column&&this.column.getColDef().rowDragText,r=this.gridOptionsWrapper.getLocaleTextFunc();this.dragSource={type:xs.RowDrag,eElement:this.getGui(),dragItemName:function(){var e=t.getSelectedCount();return n?n(o,e):1===e?t.cellValueFn():e+" "+r("rowDragRows","rows")},getDragItem:function(){return o},dragStartPixels:e,dragSourceDomDataKey:this.beans.gridOptionsWrapper.getDomDataKey()},this.beans.dragAndDropService.addDragSource(this.dragSource,!0)},t.prototype.removeDragSource=function(){this.dragSource&&this.beans.dragAndDropService.removeDragSource(this.dragSource),this.dragSource=null},Qs([Re("beans")],t.prototype,"beans",void 0),Qs([Ce],t.prototype,"postConstruct",null),Qs([we],t.prototype,"removeDragSource",null),t}(Yr),Js=function(e){function t(t,o,n){var r=e.call(this)||this;return r.parent=t,r.rowNode=o,r.column=n,r}return Xs(t,e),t.prototype.setDisplayedOrVisible=function(e){if(e)this.parent.setDisplayed(!1);else{var t=!0,o=!1;this.column&&(t=this.column.isRowDrag(this.rowNode)||this.parent.isCustomGui(),o=se(this.column.getColDef().rowDrag)),o?(this.parent.setDisplayed(!0),this.parent.setVisible(t)):(this.parent.setDisplayed(t),this.parent.setVisible(!0))}},t}(mt),Zs=function(e){function t(t,o,n,r){var i=e.call(this,t,n,r)||this;return i.beans=o,i}return Xs(t,e),t.prototype.postConstruct=function(){this.addManagedListener(this.beans.gridOptionsWrapper,"suppressRowDrag",this.onSuppressRowDrag.bind(this)),this.addManagedListener(this.rowNode,Ws.EVENT_DATA_CHANGED,this.workOutVisibility.bind(this)),this.addManagedListener(this.rowNode,Ws.EVENT_CELL_CHANGED,this.workOutVisibility.bind(this)),this.addManagedListener(this.rowNode,Ws.EVENT_CELL_CHANGED,this.workOutVisibility.bind(this)),this.addManagedListener(this.beans.eventService,St.EVENT_NEW_COLUMNS_LOADED,this.workOutVisibility.bind(this)),this.workOutVisibility()},t.prototype.onSuppressRowDrag=function(){this.workOutVisibility()},t.prototype.workOutVisibility=function(){var e=this.beans.gridOptionsWrapper.isSuppressRowDrag();this.setDisplayedOrVisible(e)},Qs([Ce],t.prototype,"postConstruct",null),t}(Js),ea=function(e){function t(t,o,n,r){var i=e.call(this,t,n,r)||this;return i.beans=o,i}return Xs(t,e),t.prototype.postConstruct=function(){this.addManagedListener(this.beans.eventService,St.EVENT_SORT_CHANGED,this.workOutVisibility.bind(this)),this.addManagedListener(this.beans.eventService,St.EVENT_FILTER_CHANGED,this.workOutVisibility.bind(this)),this.addManagedListener(this.beans.eventService,St.EVENT_COLUMN_ROW_GROUP_CHANGED,this.workOutVisibility.bind(this)),this.addManagedListener(this.beans.eventService,St.EVENT_NEW_COLUMNS_LOADED,this.workOutVisibility.bind(this)),this.addManagedListener(this.rowNode,Ws.EVENT_DATA_CHANGED,this.workOutVisibility.bind(this)),this.addManagedListener(this.rowNode,Ws.EVENT_CELL_CHANGED,this.workOutVisibility.bind(this)),this.addManagedListener(this.beans.gridOptionsWrapper,"suppressRowDrag",this.onSuppressRowDrag.bind(this)),this.workOutVisibility()},t.prototype.onSuppressRowDrag=function(){this.workOutVisibility()},t.prototype.workOutVisibility=function(){var e=this.beans.ctrlsService.getGridBodyCtrl().getRowDragFeature(),t=e&&e.shouldPreventRowMove(),o=this.beans.gridOptionsWrapper.isSuppressRowDrag(),n=this.beans.dragAndDropService.hasExternalDropZones(),r=t&&!n||o;this.setDisplayedOrVisible(r)},Qs([Ce],t.prototype,"postConstruct",null),t}(Js),ta=function(){var e=function(t,o){return e=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(e,t){e.__proto__=t}||function(e,t){for(var o in t)t.hasOwnProperty(o)&&(e[o]=t[o])},e(t,o)};return function(t,o){function n(){this.constructor=t}e(t,o),t.prototype=null===o?Object.create(o):(n.prototype=o.prototype,new n)}}(),oa=function(){return oa=Object.assign||function(e){for(var t,o=1,n=arguments.length;o=0;a--)(r=e[a])&&(s=(i<3?r(s):i>3?r(t,o,s):r(t,o))||s);return i>3&&s&&Object.defineProperty(t,o,s),s},ra=function(e){function t(){return null!==e&&e.apply(this,arguments)||this}return ta(t,e),t.prototype.init=function(e,t,o,n,r,i,s){this.params=s,this.eGui=t,this.eCheckbox=o,this.eExpanded=n,this.eContracted=r,this.comp=e,this.compClass=i;var a=this.isTopLevelFooter(),l=this.isEmbeddedRowMismatch(),u=null==s.value,p=!1;if(this.gridOptionsWrapper.isGroupIncludeFooter()&&this.gridOptionsWrapper.isGroupHideOpenParents()){var c=s.node;if(c.footer)p=(s.colDef&&s.colDef.showRowGroup)!==(c.rowGroupColumn&&c.rowGroupColumn.getColId())}this.cellIsBlank=!a&&(l||u||p),this.cellIsBlank||(this.setupShowingValueForOpenedParent(),this.findDisplayedGroupNode(),this.addFullWidthRowDraggerIfNeeded(),this.addExpandAndContract(),this.addCheckboxIfNeeded(),this.addValueElement(),this.setupIndent(),this.refreshAriaExpanded())},t.prototype.destroy=function(){e.prototype.destroy.call(this),this.expandListener=null},t.prototype.refreshAriaExpanded=function(){var e=this.params,t=e.node,o=e.eParentOfValue;if(this.expandListener&&(this.expandListener=this.expandListener()),this.isExpandable()){var n=function(){Un(o,!!t.expanded)};this.expandListener=this.addManagedListener(t,Ws.EVENT_EXPANDED_CHANGED,n)||null,n()}else zn(o)},t.prototype.isTopLevelFooter=function(){if(!this.gridOptionsWrapper.isGroupIncludeTotalFooter())return!1;if(null!=this.params.value||-1!=this.params.node.level)return!1;var e=this.params.colDef;if(null==e)return!0;if(!0===e.showRowGroup)return!0;var t=this.columnModel.getRowGroupColumns();return!t||0===t.length||t[0].getId()===e.showRowGroup},t.prototype.isEmbeddedRowMismatch=function(){if(!this.params.fullWidth||!this.gridOptionsWrapper.isEmbedFullWidthRows())return!1;var e=this.params.pinned===Ne.PINNED_LEFT,t=this.params.pinned===Ne.PINNED_RIGHT,o=!e&&!t;return this.gridOptionsWrapper.isEnableRtl()?this.columnModel.isPinningLeft()?!t:!o:this.columnModel.isPinningLeft()?!e:!o},t.prototype.findDisplayedGroupNode=function(){var e=this.params.column,t=this.params.node;if(this.showingValueForOpenedParent)for(var o=t.parent;null!=o;){if(o.rowGroupColumn&&e.isRowGroupDisplayed(o.rowGroupColumn.getId())){this.displayedGroupNode=o;break}o=o.parent}b(this.displayedGroupNode)&&(this.displayedGroupNode=t)},t.prototype.setupShowingValueForOpenedParent=function(){var e=this.params.node,t=this.params.column;if(this.gridOptionsWrapper.isGroupHideOpenParents())if(e.groupData){if(null!=e.rowGroupColumn){var o=e.rowGroupColumn.getId();if(t.isRowGroupDisplayed(o))return void(this.showingValueForOpenedParent=!1)}var n=null!=e.groupData[t.getId()];this.showingValueForOpenedParent=n}else this.showingValueForOpenedParent=!1;else this.showingValueForOpenedParent=!1},t.prototype.addValueElement=function(){this.displayedGroupNode.footer?this.addFooterValue():(this.addGroupValue(),this.addChildCount())},t.prototype.addGroupValue=function(){var e=this.adjustParamsWithDetailsFromRelatedColumn(),t=this.getInnerCompDetails(e),o=e.valueFormatted,n=e.value,r=null!=o?o:n;this.comp.setInnerRenderer(t,r)},t.prototype.adjustParamsWithDetailsFromRelatedColumn=function(){var e=this.displayedGroupNode.rowGroupColumn,t=this.params.column;if(!e)return this.params;if(null!=t&&!t.isRowGroupDisplayed(e.getId()))return this.params;var o=this.params,n=this.params,r=n.value,i=n.node,s=this.valueFormatterService.formatValue(e,i,r);return oa(oa({},o),{valueFormatted:s})},t.prototype.addFooterValue=function(){var e=this.params.footerValueGetter,t="";if(e){var o=x(this.params);o.value=this.params.value,"function"===typeof e?t=e(o):"string"===typeof e?t=this.expressionService.evaluate(e,o):console.warn("AG Grid: footerValueGetter should be either a function or a string (expression)")}else t="Total "+(null!=this.params.value?this.params.value:"");var n=this.getInnerCompDetails(this.params);this.comp.setInnerRenderer(n,t)},t.prototype.getInnerCompDetails=function(e){var t=this;if(e.fullWidth)return this.userComponentFactory.getFullWidthGroupRowInnerCellRenderer(this.gridOptions.groupRowRendererParams,e);var o=this.userComponentFactory.getInnerRendererDetails(e,e),n=function(e){return e&&e.componentClass==t.compClass};if(o&&!n(o))return o;var r=this.displayedGroupNode.rowGroupColumn,i=r?r.getColDef():void 0;if(i){var s=this.userComponentFactory.getCellRendererDetails(i,e);if(s&&!n(s))return s;if(n(s)&&i.cellRendererParams&&i.cellRendererParams.innerRenderer)return this.userComponentFactory.getInnerRendererDetails(i.cellRendererParams,e)}},t.prototype.addChildCount=function(){this.params.suppressCount||(this.addManagedListener(this.displayedGroupNode,Ws.EVENT_ALL_CHILDREN_COUNT_CHANGED,this.updateChildCount.bind(this)),this.updateChildCount())},t.prototype.updateChildCount=function(){var e=this.displayedGroupNode.allChildrenCount,t=this.isShowRowGroupForThisRow()&&null!=e&&e>=0?"("+e+")":"";this.comp.setChildCount(t)},t.prototype.isShowRowGroupForThisRow=function(){if(this.gridOptionsWrapper.isTreeData())return!0;var e=this.displayedGroupNode.rowGroupColumn;if(!e)return!1;var t=this.params.column;return null==t||t.isRowGroupDisplayed(e.getId())},t.prototype.addExpandAndContract=function(){var e=this.params,t=Rr("groupExpanded",this.gridOptionsWrapper,null),o=Rr("groupContracted",this.gridOptionsWrapper,null);t&&this.eExpanded.appendChild(t),o&&this.eContracted.appendChild(o);var n=e.eGridCell;this.gridOptionsWrapper.isEnableGroupEdit()||!this.isExpandable()||e.suppressDoubleClickExpand||this.addManagedListener(n,"dblclick",this.onCellDblClicked.bind(this)),this.addManagedListener(this.eExpanded,"click",this.onExpandClicked.bind(this)),this.addManagedListener(this.eContracted,"click",this.onExpandClicked.bind(this)),this.addManagedListener(n,"keydown",this.onKeyDown.bind(this)),this.addManagedListener(e.node,Ws.EVENT_EXPANDED_CHANGED,this.showExpandAndContractIcons.bind(this)),this.showExpandAndContractIcons();var r=this.onRowNodeIsExpandableChanged.bind(this);this.addManagedListener(this.displayedGroupNode,Ws.EVENT_ALL_CHILDREN_COUNT_CHANGED,r),this.addManagedListener(this.displayedGroupNode,Ws.EVENT_MASTER_CHANGED,r),this.addManagedListener(this.displayedGroupNode,Ws.EVENT_GROUP_CHANGED,r),this.addManagedListener(this.displayedGroupNode,Ws.EVENT_HAS_CHILDREN_CHANGED,r)},t.prototype.onExpandClicked=function(e){lt(e)||(at(e),this.onExpandOrContract(e))},t.prototype.onExpandOrContract=function(e){var t=this.displayedGroupNode,o=!t.expanded;t.setExpanded(o,e)},t.prototype.isExpandable=function(){if(this.showingValueForOpenedParent)return!0;var e=this.displayedGroupNode,t=this.columnModel.isPivotMode()&&e.leafGroup;if(!(e.isExpandable()&&!e.footer&&!t))return!1;var o=this.params.column;return null==o||"string"!==typeof o.getColDef().showRowGroup||this.isShowRowGroupForThisRow()},t.prototype.showExpandAndContractIcons=function(){var e=this,t=e.params,o=e.displayedGroupNode,n=e.columnModel,r=t.node,i=this.isExpandable();if(i){var s=!!this.showingValueForOpenedParent||r.expanded;this.comp.setExpandedDisplayed(s),this.comp.setContractedDisplayed(!s)}else this.comp.setExpandedDisplayed(!1),this.comp.setContractedDisplayed(!1);var a=n.isPivotMode(),l=a&&o.leafGroup,u=i&&!l,p=r.footer&&-1===r.level;this.comp.addOrRemoveCssClass("ag-cell-expandable",u),this.comp.addOrRemoveCssClass("ag-row-group",u),a?this.comp.addOrRemoveCssClass("ag-pivot-leaf-group",l):p||this.comp.addOrRemoveCssClass("ag-row-group-leaf-indent",!u)},t.prototype.onRowNodeIsExpandableChanged=function(){this.showExpandAndContractIcons(),this.setIndent(),this.refreshAriaExpanded()},t.prototype.setupIndent=function(){var e=this.params.node;this.params.suppressPadding||(this.addManagedListener(e,Ws.EVENT_UI_LEVEL_CHANGED,this.setIndent.bind(this)),this.setIndent())},t.prototype.setIndent=function(){if(!this.gridOptionsWrapper.isGroupHideOpenParents()){var e=this.params,t=e.node,o=!!e.colDef,n=this.gridOptionsWrapper.isTreeData(),r=!o||n||!0===e.colDef.showRowGroup?t.uiLevel:0;e.padding>=0&&ne((function(){return console.warn("AG Grid: cellRendererParams.padding no longer works, it was deprecated in since v14.2 and removed in v26, configuring padding for groupCellRenderer should be done with Sass variables and themes. Please see the AG Grid documentation page for Themes, in particular the property $row-group-indent-size.")}),"groupCellRenderer->doDeprecatedWay"),this.indentClass&&this.comp.addOrRemoveCssClass(this.indentClass,!1),this.indentClass="ag-row-group-indent-"+r,this.comp.addOrRemoveCssClass(this.indentClass,!0)}},t.prototype.addFullWidthRowDraggerIfNeeded=function(){var e=this;if(this.params.fullWidth&&this.params.rowDrag){var t=new $s((function(){return e.params.value}),this.params.node);this.createManagedBean(t,this.context),this.eGui.insertAdjacentElement("afterbegin",t.getGui())}},t.prototype.isUserWantsSelected=function(){var e=this.params.checkbox;return"function"===typeof e?e(this.params):!0===e},t.prototype.addCheckboxIfNeeded=function(){var e=this,t=this.displayedGroupNode,o=this.isUserWantsSelected()&&!t.footer&&!t.rowPinned&&!t.detail;if(o){var n=new 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xo(t.eExpanded,e)},setCheckboxVisible:function(e){return t.eCheckbox.classList.toggle("ag-invisible",!e)}},n=this.createManagedBean(new ra),r=!e.colDef,i=this.getGui();n.init(o,i,this.eCheckbox,this.eExpanded,this.eContracted,this.constructor,e),r&&In(i,"gridcell")},t.prototype.setRenderDetails=function(e,t){var o=this;if(e){var n=e.newAgStackInstance();if(!n)return;n.then((function(e){if(e){var t=function(){return o.context.destroyBean(e)};o.isAlive()?(o.eValue.appendChild(e.getGui()),o.addDestroyFunc(t)):t()}}))}else this.eValue.innerText=t},t.prototype.destroy=function(){this.getContext().destroyBean(this.innerCellRenderer),e.prototype.destroy.call(this)},t.prototype.refresh=function(){return!1},t.TEMPLATE='\n \n \n \n \n \n ',sa([Xr("eExpanded")],t.prototype,"eExpanded",void 0),sa([Xr("eContracted")],t.prototype,"eContracted",void 0),sa([Xr("eCheckbox")],t.prototype,"eCheckbox",void 0),sa([Xr("eValue")],t.prototype,"eValue",void 0),sa([Xr("eChildCount")],t.prototype,"eChildCount",void 0),t}(Yr),la=function(){var e=function(t,o){return e=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(e,t){e.__proto__=t}||function(e,t){for(var o in t)t.hasOwnProperty(o)&&(e[o]=t[o])},e(t,o)};return function(t,o){function n(){this.constructor=t}e(t,o),t.prototype=null===o?Object.create(o):(n.prototype=o.prototype,new n)}}(),ua=function(e,t,o,n){var r,i=arguments.length,s=i<3?t:null===n?n=Object.getOwnPropertyDescriptor(t,o):n;if("object"===typeof Reflect&&"function"===typeof Reflect.decorate)s=Reflect.decorate(e,t,o,n);else for(var a=e.length-1;a>=0;a--)(r=e[a])&&(s=(i<3?r(s):i>3?r(t,o,s):r(t,o))||s);return i>3&&s&&Object.defineProperty(t,o,s),s},pa=function(e){function t(){return e.call(this,t.TEMPLATE)||this}return la(t,e),t.prototype.init=function(e){e.node.failedLoad?this.setupFailed():this.setupLoading()},t.prototype.setupFailed=function(){this.eLoadingText.innerText="ERR"},t.prototype.setupLoading=function(){var e=Rr("groupLoading",this.gridOptionsWrapper,null);e&&this.eLoadingIcon.appendChild(e);var t=this.gridOptionsWrapper.getLocaleTextFunc();this.eLoadingText.innerText=t("loadingOoo","Loading")},t.prototype.refresh=function(e){return!1},t.prototype.destroy=function(){e.prototype.destroy.call(this)},t.TEMPLATE='
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You should call registerComponent"):this.agGridDefaults[o]=t},t.prototype.registerJsComponent=function(e,t){var o=this.translateIfDeprecated(e);this.fwComps[o]?console.error("Trying to register a component that you have already registered for frameworks: "+o):this.jsComps[o]=t},t.prototype.registerFwComponent=function(e,t){ne((function(){return console.warn("AG Grid: As of v27, registering components via grid property frameworkComponents is deprecated. Instead register both JavaScript AND Framework Components via the components property.")}),"UserComponentRegistry.frameworkComponentsDeprecated");var o=this.translateIfDeprecated(e);this.fwComps[o]=t},t.prototype.retrieve=function(e){var t=this.translateIfDeprecated(e),o=function(e,t){return{componentFromFramework:t,component:e}},n=this.getFrameworkOverrides().frameworkComponent(t);if(null!=n)return o(n,!0);var r=this.fwComps[t];if(r)return o(r,!0);var i=this.jsComps[t];if(i)return o(i,this.getFrameworkOverrides().isFrameworkComponent(i));var s=this.agGridDefaults[t];return s?o(s,!1):(Object.keys(this.agGridDefaults).indexOf(t)<0&&console.warn("AG Grid: Looking for component ["+t+"] but it wasn't found."),null)},t.prototype.translateIfDeprecated=function(e){var t=this.agDeprecatedNames[e];return null!=t?(ne((function(){console.warn("ag-grid. Since v15.0 component names have been renamed to be namespaced. 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r,i=arguments.length,s=i<3?t:null===n?n=Object.getOwnPropertyDescriptor(t,o):n;if("object"===typeof Reflect&&"function"===typeof Reflect.decorate)s=Reflect.decorate(e,t,o,n);else for(var a=e.length-1;a>=0;a--)(r=e[a])&&(s=(i<3?r(s):i>3?r(t,o,s):r(t,o))||s);return i>3&&s&&Object.defineProperty(t,o,s),s},Wa=function(e){function t(){return null!==e&&e.apply(this,arguments)||this}return Va(t,e),t.prototype.getHeaderCompDetails=function(e,t){return this.getCompDetails(e,Ea,"agColumnHeader",t)},t.prototype.getHeaderGroupCompDetails=function(e){var t=e.columnGroup.getColGroupDef();return this.getCompDetails(t,Ra,"agColumnGroupHeader",e)},t.prototype.getFullWidthCellRendererDetails=function(e){return this.getCompDetails(this.gridOptions,Ia,null,e,!0)},t.prototype.getFullWidthLoadingCellRendererDetails=function(e){return this.getCompDetails(this.gridOptions,Ma,"agLoadingCellRenderer",e,!0)},t.prototype.getFullWidthGroupCellRendererDetails=function(e){return this.getCompDetails(this.gridOptions,Ga,"agGroupRowRenderer",e,!0)},t.prototype.getFullWidthDetailCellRendererDetails=function(e){return this.getCompDetails(this.gridOptions,La,"agDetailCellRenderer",e,!0)},t.prototype.getInnerRendererDetails=function(e,t){return this.getCompDetails(e,ba,null,t)},t.prototype.getFullWidthGroupRowInnerCellRenderer=function(e,t){return this.getCompDetails(e,ba,null,t)},t.prototype.getCellRendererDetails=function(e,t){return this.getCompDetails(e,Sa,null,t)},t.prototype.getCellEditorDetails=function(e,t){return this.getCompDetails(e,Oa,"agCellEditor",t,!0)},t.prototype.getFilterDetails=function(e,t,o){return this.getCompDetails(e,Da,o,t,!0)},t.prototype.getDateCompDetails=function(e){return this.getCompDetails(this.gridOptions,wa,"agDateInput",e,!0)},t.prototype.getLoadingOverlayCompDetails=function(e){return this.getCompDetails(this.gridOptions,Pa,"agLoadingOverlay",e,!0)},t.prototype.getNoRowsOverlayCompDetails=function(e){return this.getCompDetails(this.gridOptions,_a,"agNoRowsOverlay",e,!0)},t.prototype.getTooltipCompDetails=function(e){return this.getCompDetails(e.colDef,Ta,"agTooltipComponent",e,!0)},t.prototype.getSetFilterCellRendererDetails=function(e,t){return this.getCompDetails(e,Sa,null,t)},t.prototype.getFloatingFilterCompDetails=function(e,t,o){return this.getCompDetails(e,Aa,o,t)},t.prototype.getToolPanelCompDetails=function(e,t){return this.getCompDetails(e,Na,null,t,!0)},t.prototype.getStatusPanelCompDetails=function(e,t){return this.getCompDetails(e,Fa,null,t,!0)},t.prototype.getCompDetails=function(e,t,o,n,r){var i=this;void 0===r&&(r=!1);var s=t.propertyName,a=t.cellRenderer,l=this.getCompKeys(e,t,n),u=l.compName,p=l.jsComp,c=l.fwComp,d=l.paramsFromSelector,h=l.popupFromSelector,f=l.popupPositionFromSelector,g=function(e){var t=i.userComponentRegistry.retrieve(e);t&&(p=t.componentFromFramework?void 0:t.component,c=t.componentFromFramework?t.component:void 0)};if(null!=u&&g(u),null==p&&null==c&&null!=o&&g(o),p&&a&&!this.agComponentUtils.doesImplementIComponent(p)&&(p=this.agComponentUtils.adaptFunction(s,p)),p||c){var y=this.mergeParamsWithApplicationProvidedParams(e,t,n,d),v=null==p,m=p||c;return{componentFromFramework:v,componentClass:m,params:y,type:t,popupFromSelector:h,popupPositionFromSelector:f,newAgStackInstance:function(){return i.newAgStackInstance(m,v,y,t)}}}r&&console.error("Could not find component "+u+", did you forget to configure this component?")},t.prototype.getCompKeys=function(e,t,o){var n,r,i,s,a,l,u=this,p=t.propertyName;if(e){var c=e,d=c[p+"Selector"],h=d?d(o):null,f=function(e,t){var o=function(){var e="AG Grid: As of v27, the property "+p+"Framework is deprecated. The property "+p+" can now be used for JavaScript AND Framework Components.";ne((function(){return console.warn(e)}),"UserComponentFactory."+p+"FrameworkDeprecated")};if("string"===typeof e)n=e;else if("string"===typeof t)o(),n=t;else if(null!=e&&!0!==e){u.getFrameworkOverrides().isFrameworkComponent(e)?i=e:r=e}else null!=t&&(o(),i=t)};if(h){if(null!=h.frameworkComponent){var g="AG Grid: As of v27, the return for "+p+"Selector has attributes [component, params] only. The attribute frameworkComponent is deprecated. You should now return back Framework Components using the 'component' attribute and the grid works out if it's a framework component or not.";ne((function(){return console.warn(g)}),"UserComponentFactory."+p+"FrameworkSelectorDeprecated"),f(h.frameworkComponent,void 0)}else f(h.component,void 0);s=h.params,a=h.popup,l=h.popupPosition}else f(c[p],c[p+"Framework"])}return{compName:n,jsComp:r,fwComp:i,paramsFromSelector:s,popupFromSelector:a,popupPositionFromSelector:l}},t.prototype.newAgStackInstance=function(e,t,o,n){var r,i=n.propertyName;if(!t)r=new e;else{var s=this.componentMetadataProvider.retrieve(i);r=this.frameworkComponentWrapper.wrap(e,s.mandatoryMethodList,s.optionalMethodList,n)}var a=this.initComponent(r,o);return null==a?xr.resolve(r):a.then((function(){return r}))},t.prototype.mergeParamsWithApplicationProvidedParams=function(e,t,o,n){void 0===n&&(n=null);var r={context:this.gridOptionsWrapper.getContext(),columnApi:this.gridOptionsWrapper.getColumnApi(),api:this.gridOptionsWrapper.getApi()};K(r,o);var i=e&&e[t.propertyName+"Params"];"function"===typeof i?K(r,i(o)):"object"===typeof i&&K(r,i);return K(r,n),r},t.prototype.initComponent=function(e,t){if(this.context.createBean(e),null!=e.init)return e.init(t)},t.prototype.getDefaultFloatingFilterType=function(e){if(null==e)return null;var t=null,o=this.getCompKeys(e,Da),n=o.compName,r=o.jsComp,i=o.fwComp;n?t=xa.getFloatingFilterType(n):null==r&&null==i&&!0===e.filter&&(t=Fe.isRegistered(_e.SetFilterModule)?"agSetColumnFloatingFilter":"agTextColumnFloatingFilter");return t},Ha([Re("gridOptions")],t.prototype,"gridOptions",void 0),Ha([Re("agComponentUtils")],t.prototype,"agComponentUtils",void 0),Ha([Re("componentMetadataProvider")],t.prototype,"componentMetadataProvider",void 0),Ha([Re("userComponentRegistry")],t.prototype,"userComponentRegistry",void 0),Ha([Se("frameworkComponentWrapper")],t.prototype,"frameworkComponentWrapper",void 0),t=Ha([Ee("userComponentFactory")],t)}(mt);!function(e){e[e.SINGLE_SHEET=0]="SINGLE_SHEET",e[e.MULTI_SHEET=1]="MULTI_SHEET"}(Ys||(Ys={}));var ka=function(){var e=function(t,o){return e=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(e,t){e.__proto__=t}||function(e,t){for(var o in t)t.hasOwnProperty(o)&&(e[o]=t[o])},e(t,o)};return function(t,o){function n(){this.constructor=t}e(t,o),t.prototype=null===o?Object.create(o):(n.prototype=o.prototype,new n)}}(),Ba=function(e,t,o,n){var r,i=arguments.length,s=i<3?t:null===n?n=Object.getOwnPropertyDescriptor(t,o):n;if("object"===typeof Reflect&&"function"===typeof Reflect.decorate)s=Reflect.decorate(e,t,o,n);else for(var a=e.length-1;a>=0;a--)(r=e[a])&&(s=(i<3?r(s):i>3?r(t,o,s):r(t,o))||s);return i>3&&s&&Object.defineProperty(t,o,s),s},ja=function(e){function t(){var t=null!==e&&e.apply(this,arguments)||this;return t.dragEndFunctions=[],t.dragSources=[],t}return ka(t,e),t.prototype.init=function(){this.logger=this.loggerFactory.create("DragService")},t.prototype.removeAllListeners=function(){this.dragSources.forEach(this.removeListener.bind(this)),this.dragSources.length=0},t.prototype.removeListener=function(e){var t=e.dragSource.eElement,o=e.mouseDownListener;if(t.removeEventListener("mousedown",o),e.touchEnabled){var n=e.touchStartListener;t.removeEventListener("touchstart",n,{passive:!0})}},t.prototype.removeDragSource=function(e){var t=this.dragSources.find((function(t){return t.dragSource===e}));t&&(this.removeListener(t),Ue(this.dragSources,t))},t.prototype.isDragging=function(){return this.dragging},t.prototype.addDragSource=function(e,t){var o=this;void 0===t&&(t=!1);var n=this.onMouseDown.bind(this,e);e.eElement.addEventListener("mousedown",n);var r=null,i=this.gridOptionsWrapper.isSuppressTouch();t&&!i&&(r=function(t){t.cancelable&&t.preventDefault(),o.onTouchStart(e,t)},e.eElement.addEventListener("touchstart",r,{passive:!0})),this.dragSources.push({dragSource:e,mouseDownListener:n,touchStartListener:r,touchEnabled:t})},t.prototype.onTouchStart=function(e,t){var o=this;this.currentDragParams=e,this.dragging=!1;var n=t.touches[0];this.touchLastTime=n,this.touchStart=n;var r=function(t){return o.onTouchUp(t,e.eElement)},i=e.eElement,s=[{target:document,type:"touchmove",listener:function(e){e.cancelable&&e.preventDefault()},options:{passive:!1}},{target:i,type:"touchmove",listener:function(t){return o.onTouchMove(t,e.eElement)},options:{passive:!0}},{target:i,type:"touchend",listener:r,options:{passive:!0}},{target:i,type:"touchcancel",listener:r,options:{passive:!0}}];this.addTemporaryEvents(s),0===e.dragStartPixels&&this.onCommonMove(n,this.touchStart,e.eElement)},t.prototype.onMouseDown=function(e,t){var o=this,n=t;if((!e.skipMouseEvent||!e.skipMouseEvent(t))&&!n._alreadyProcessedByDragService&&(n._alreadyProcessedByDragService=!0,0===t.button)){this.currentDragParams=e,this.dragging=!1,this.mouseStartEvent=t;var r=this.gridOptionsWrapper.getDocument(),i=[{target:r,type:"mousemove",listener:function(t){return o.onMouseMove(t,e.eElement)}},{target:r,type:"mouseup",listener:function(t){return o.onMouseUp(t,e.eElement)}},{target:r,type:"contextmenu",listener:function(e){return e.preventDefault()}}];this.addTemporaryEvents(i),0===e.dragStartPixels&&this.onMouseMove(t,e.eElement)}},t.prototype.addTemporaryEvents=function(e){e.forEach((function(e){var t=e.target,o=e.type,n=e.listener,r=e.options;t.addEventListener(o,n,r)})),this.dragEndFunctions.push((function(){e.forEach((function(e){var t=e.target,o=e.type,n=e.listener,r=e.options;t.removeEventListener(o,n,r)}))}))},t.prototype.isEventNearStartEvent=function(e,t){var o=this.currentDragParams.dragStartPixels;return Tr(e,t,O(o)?o:4)},t.prototype.getFirstActiveTouch=function(e){for(var t=0;t=0;a--)(r=e[a])&&(s=(i<3?r(s):i>3?r(t,o,s):r(t,o))||s);return i>3&&s&&Object.defineProperty(t,o,s),s},Ya=function(e){function t(){var t=null!==e&&e.apply(this,arguments)||this;return t.allColumnFilters=new Map,t.activeAggregateFilters=[],t.activeColumnFilters=[],t.quickFilter=null,t.quickFilterParts=null,t.processingFilterChange=!1,t}var o;return Ua(t,e),o=t,t.prototype.init=function(){var e=this;this.addManagedListener(this.eventService,St.EVENT_GRID_COLUMNS_CHANGED,(function(){return e.onColumnsChanged()})),this.addManagedListener(this.eventService,St.EVENT_COLUMN_VALUE_CHANGED,(function(){return e.refreshFiltersForAggregations()})),this.addManagedListener(this.eventService,St.EVENT_COLUMN_PIVOT_CHANGED,(function(){return e.refreshFiltersForAggregations()})),this.addManagedListener(this.eventService,St.EVENT_COLUMN_PIVOT_MODE_CHANGED,(function(){return e.refreshFiltersForAggregations()})),this.quickFilter=this.parseQuickFilter(this.gridOptionsWrapper.getQuickFilterText()),this.setQuickFilterParts(),this.allowShowChangeAfterFilter=this.gridOptionsWrapper.isAllowShowChangeAfterFilter()},t.prototype.setQuickFilterParts=function(){this.quickFilterParts=this.quickFilter?this.quickFilter.split(" "):null},t.prototype.setFilterModel=function(e){var t=this,o=[],n=this.getFilterModel();if(e){var r=Nr(Object.keys(e));this.allColumnFilters.forEach((function(n,i){var s=e[i];o.push(t.setModelOnFilterWrapper(n.filterPromise,s)),r.delete(i)})),r.forEach((function(n){var r=t.columnModel.getPrimaryColumn(n)||t.columnModel.getGridColumn(n);if(r)if(r.isFilterAllowed()){var i=t.getOrCreateFilterWrapper(r,"NO_UI");i?o.push(t.setModelOnFilterWrapper(i.filterPromise,e[n])):console.warn("AG-Grid: setFilterModel() - unable to fully apply model, unable to create filter for colId: "+n)}else console.warn("AG Grid: setFilterModel() - unable to fully apply model, filtering disabled for colId: "+n);else console.warn("AG Grid: setFilterModel() - no column found for colId: "+n)}))}else this.allColumnFilters.forEach((function(e){o.push(t.setModelOnFilterWrapper(e.filterPromise,null))}));xr.all(o).then((function(){var e=t.getFilterModel(),o=[];t.allColumnFilters.forEach((function(t,r){var i=n?n[r]:null,s=e?e[r]:null;Mr.jsonEquals(i,s)||o.push(t.column)})),o.length>0&&t.onFilterChanged({columns:o})}))},t.prototype.setModelOnFilterWrapper=function(e,t){return new xr((function(o){e.then((function(e){"function"!==typeof e.setModel&&(console.warn("AG Grid: filter missing setModel method, which is needed for setFilterModel"),o()),(e.setModel(t)||xr.resolve()).then((function(){return o()}))}))}))},t.prototype.getFilterModel=function(){var e={};return this.allColumnFilters.forEach((function(t,o){var n=t.filterPromise.resolveNow(null,(function(e){return e}));if(null==n)return null;if("function"===typeof n.getModel){var r=n.getModel();O(r)&&(e[o]=r)}else console.warn("AG Grid: filter API missing getModel method, which is needed for getFilterModel")})),e},t.prototype.isColumnFilterPresent=function(){return this.activeColumnFilters.length>0},t.prototype.isAggregateFilterPresent=function(){return!!this.activeAggregateFilters.length},t.prototype.doAggregateFiltersPass=function(e,t){return this.doColumnFiltersPass(e,t,!0)},t.prototype.updateActiveFilters=function(){var e=this;this.activeColumnFilters.length=0,this.activeAggregateFilters.length=0;var t=function(e){return!!e&&(e.isFilterActive?e.isFilterActive():(console.warn("AG Grid: Filter is missing isFilterActive() method"),!1))},o=!!this.gridOptionsWrapper.getGroupAggFiltering();this.allColumnFilters.forEach((function(n){if(n.filterPromise.resolveNow(!1,t)){var r=n.filterPromise.resolveNow(null,(function(e){return e}));!function(t){if(!t.isPrimary())return!0;var n=!e.columnModel.isPivotActive();return!(!t.isValueActive()||!n)&&(!!e.columnModel.isPivotMode()||o)}(n.column)?e.activeColumnFilters.push(r):e.activeAggregateFilters.push(r)}}))},t.prototype.updateFilterFlagInColumns=function(e,t){this.allColumnFilters.forEach((function(o){var n=o.filterPromise.resolveNow(!1,(function(e){return e.isFilterActive()}));o.column.setFilterActive(n,e,t)}))},t.prototype.isAnyFilterPresent=function(){return this.isQuickFilterPresent()||this.isColumnFilterPresent()||this.isAggregateFilterPresent()||this.gridOptionsWrapper.isExternalFilterPresent()},t.prototype.doColumnFiltersPass=function(e,t,o){for(var n=e.data,r=e.aggData,i=o?this.activeAggregateFilters:this.activeColumnFilters,s=o?r:n,a=0;a=0}))},t.prototype.doesRowPassQuickFilterCache=function(e,t){return e.quickFilterAggregateText||this.aggregateRowForQuickFilter(e),e.quickFilterAggregateText.indexOf(t)>=0},t.prototype.doesRowPassQuickFilter=function(e){var t=this,o=this.gridOptionsWrapper.isCacheQuickFilter();return this.quickFilterParts.every((function(n){return o?t.doesRowPassQuickFilterCache(e,n):t.doesRowPassQuickFilterNoCache(e,n)}))},t.prototype.doesRowPassAggregateFilters=function(e){return!(this.isAggregateFilterPresent()&&!this.doAggregateFiltersPass(e.rowNode,e.filterInstanceToSkip))},t.prototype.doesRowPassFilter=function(e){return!(this.isQuickFilterPresent()&&!this.doesRowPassQuickFilter(e.rowNode))&&(!(this.gridOptionsWrapper.isExternalFilterPresent()&&!this.gridOptionsWrapper.doesExternalFilterPass(e.rowNode))&&!(this.isColumnFilterPresent()&&!this.doColumnFiltersPass(e.rowNode,e.filterInstanceToSkip)))},t.prototype.getQuickFilterTextForColumn=function(e,t){var o=this.valueService.getValue(e,t,!0),n=e.getColDef();if(n.getQuickFilterText){var r={value:o,node:t,data:t.data,column:e,colDef:n,api:this.gridOptionsWrapper.getApi(),columnApi:this.gridOptionsWrapper.getColumnApi(),context:this.gridOptionsWrapper.getContext()};o=n.getQuickFilterText(r)}return O(o)?o.toString().toUpperCase():null},t.prototype.aggregateRowForQuickFilter=function(e){var t=this,n=[];this.columnModel.getAllColumnsForQuickFilter().forEach((function(o){var r=t.getQuickFilterTextForColumn(o,e);O(r)&&n.push(r)})),e.quickFilterAggregateText=n.join(o.QUICK_FILTER_SEPARATOR)},t.prototype.onNewRowsLoaded=function(e){this.allColumnFilters.forEach((function(e){e.filterPromise.then((function(e){e.onNewRowsLoaded&&e.onNewRowsLoaded()}))})),this.updateFilterFlagInColumns(e),this.updateActiveFilters()},t.prototype.createValueGetter=function(e){var t=this;return function(o){var n=o.node;return t.valueService.getValue(e,n,!0)}},t.prototype.getFilterComponent=function(e,t,o){var n;if(void 0===o&&(o=!0),o)return(null===(n=this.getOrCreateFilterWrapper(e,t))||void 0===n?void 0:n.filterPromise)||null;var r=this.cachedFilter(e);return r?r.filterPromise:null},t.prototype.isFilterActive=function(e){var t=this.cachedFilter(e);return!!t&&t.filterPromise.resolveNow(!1,(function(e){return e.isFilterActive()}))},t.prototype.getOrCreateFilterWrapper=function(e,t){if(!e.isFilterAllowed())return null;var o=this.cachedFilter(e);return o?"NO_UI"!==t&&this.putIntoGui(o,t):(o=this.createFilterWrapper(e,t),this.allColumnFilters.set(e.getColId(),o)),o},t.prototype.cachedFilter=function(e){return this.allColumnFilters.get(e.getColId())},t.prototype.createFilterInstance=function(e){var t,o=this,n=Fe.isRegistered(_e.SetFilterModule)?"agSetColumnFilter":"agTextColumnFilter",r=e.getColDef(),i=za(za({},this.createFilterParams(e,r)),{filterModifiedCallback:function(){var n={type:St.EVENT_FILTER_MODIFIED,api:o.gridApi,columnApi:o.columnApi,column:e,filterInstance:t};o.eventService.dispatchEvent(n)},filterChangedCallback:function(n){var r={filterInstance:t,additionalEventAttributes:n,columns:[e]};o.callOnFilterChangedOutsideRenderCycle(r)},doesRowPassOtherFilter:function(e){return o.doesRowPassOtherFilters(t,e)}}),s=this.userComponentFactory.getFilterDetails(r,i,n);if(!s)return null;var a=s.newAgStackInstance();return a&&a.then((function(e){return t=e})),a},t.prototype.createFilterParams=function(e,t){return{api:this.gridOptionsWrapper.getApi(),columnApi:this.gridOptionsWrapper.getColumnApi(),column:e,colDef:x(t),rowModel:this.rowModel,filterChangedCallback:function(){},filterModifiedCallback:function(){},valueGetter:this.createValueGetter(e),context:this.gridOptionsWrapper.getContext(),doesRowPassOtherFilter:function(){return!0}}},t.prototype.createFilterWrapper=function(e,t){var o={column:e,filterPromise:null,compiledElement:null,guiPromise:xr.resolve(null)};return o.filterPromise=this.createFilterInstance(e),o.filterPromise&&this.putIntoGui(o,t),o},t.prototype.putIntoGui=function(e,t){var o=this,n=document.createElement("div");n.className="ag-filter",e.guiPromise=new xr((function(r){e.filterPromise.then((function(i){var s=i.getGui();O(s)||console.warn("AG Grid: getGui method from filter returned "+s+", it should be a DOM element or an HTML template string."),"string"===typeof s&&(s=Zo(s)),n.appendChild(s),r(n),o.eventService.dispatchEvent({type:St.EVENT_FILTER_OPENED,column:e.column,source:t,eGui:n,api:o.gridApi,columnApi:o.columnApi})}))}))},t.prototype.onColumnsChanged=function(){var e=this,t=[];this.allColumnFilters.forEach((function(o,n){(o.column.isPrimary()?e.columnModel.getPrimaryColumn(n):e.columnModel.getGridColumn(n))||(t.push(o.column),e.disposeFilterWrapper(o,"filterDestroyed"))})),t.length>0&&this.onFilterChanged({columns:t})},t.prototype.destroyFilter=function(e,t){void 0===t&&(t="api");var o=this.allColumnFilters.get(e.getColId());o&&(this.disposeFilterWrapper(o,t),this.onFilterChanged({columns:[e]}))},t.prototype.disposeFilterWrapper=function(e,t){var o=this;e.filterPromise.then((function(n){(n.setModel(null)||xr.resolve()).then((function(){o.getContext().destroyBean(n),e.column.setFilterActive(!1,t),o.allColumnFilters.delete(e.column.getColId())}))}))},t.prototype.destroy=function(){var t=this;e.prototype.destroy.call(this),this.allColumnFilters.forEach((function(e){return t.disposeFilterWrapper(e,"filterDestroyed")}))},t.QUICK_FILTER_SEPARATOR="\n",Ka([Re("valueService")],t.prototype,"valueService",void 0),Ka([Re("columnModel")],t.prototype,"columnModel",void 0),Ka([Re("rowModel")],t.prototype,"rowModel",void 0),Ka([Re("columnApi")],t.prototype,"columnApi",void 0),Ka([Re("gridApi")],t.prototype,"gridApi",void 0),Ka([Re("userComponentFactory")],t.prototype,"userComponentFactory",void 0),Ka([Re("rowRenderer")],t.prototype,"rowRenderer",void 0),Ka([Ce],t.prototype,"init",null),Ka([we],t.prototype,"destroy",null),t=o=Ka([Ee("filterManager")],t)}(mt),qa=function(){var e=function(t,o){return e=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(e,t){e.__proto__=t}||function(e,t){for(var o in t)t.hasOwnProperty(o)&&(e[o]=t[o])},e(t,o)};return function(t,o){function n(){this.constructor=t}e(t,o),t.prototype=null===o?Object.create(o):(n.prototype=o.prototype,new n)}}(),Xa=function(e){function t(t,o){var n=e.call(this,t)||this;return n.ctrl=o,n}return qa(t,e),t.prototype.getCtrl=function(){return this.ctrl},t}(Yr),Qa=function(){var e=function(t,o){return e=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(e,t){e.__proto__=t}||function(e,t){for(var o in t)t.hasOwnProperty(o)&&(e[o]=t[o])},e(t,o)};return function(t,o){function n(){this.constructor=t}e(t,o),t.prototype=null===o?Object.create(o):(n.prototype=o.prototype,new n)}}(),$a=function(e,t,o,n){var r,i=arguments.length,s=i<3?t:null===n?n=Object.getOwnPropertyDescriptor(t,o):n;if("object"===typeof Reflect&&"function"===typeof Reflect.decorate)s=Reflect.decorate(e,t,o,n);else for(var a=e.length-1;a>=0;a--)(r=e[a])&&(s=(i<3?r(s):i>3?r(t,o,s):r(t,o))||s);return i>3&&s&&Object.defineProperty(t,o,s),s},Ja=function(e){function t(o){return e.call(this,t.TEMPLATE,o)||this}return Qa(t,e),t.prototype.postConstruct=function(){var e=this,t=this.getGui(),o={addOrRemoveCssClass:function(t,o){return e.addOrRemoveCssClass(t,o)},addOrRemoveBodyCssClass:function(t,o){return e.eFloatingFilterBody.classList.toggle(t,o)},addOrRemoveButtonWrapperCssClass:function(t,o){return e.eButtonWrapper.classList.toggle(t,o)},setCompDetails:function(t){return e.setCompDetails(t)},getFloatingFilterComp:function(){return e.compPromise},setWidth:function(e){return t.style.width=e},setMenuIcon:function(t){return e.eButtonShowMainFilter.appendChild(t)}};this.ctrl.setComp(o,t,this.eButtonShowMainFilter,this.eFloatingFilterBody)},t.prototype.setCompDetails=function(e){var t=this;this.compPromise=e.newAgStackInstance(),this.compPromise.then((function(e){return t.afterCompCreated(e)}))},t.prototype.afterCompCreated=function(e){var t=this;e&&(this.addDestroyFunc((function(){return t.context.destroyBean(e)})),this.isAlive()&&(this.eFloatingFilterBody.appendChild(e.getGui()),e.afterGuiAttached&&e.afterGuiAttached()))},t.TEMPLATE='
\n
\n \n
',$a([Xr("eFloatingFilterBody")],t.prototype,"eFloatingFilterBody",void 0),$a([Xr("eButtonWrapper")],t.prototype,"eButtonWrapper",void 0),$a([Xr("eButtonShowMainFilter")],t.prototype,"eButtonShowMainFilter",void 0),$a([Ce],t.prototype,"postConstruct",null),t}(Xa),Za=function(){function e(){}return e.parse=function(t){if(!t)return null;if(!0===t)return{toolPanels:[e.DEFAULT_COLUMN_COMP,e.DEFAULT_FILTER_COMP],defaultToolPanel:"columns"};if("string"===typeof t)return e.parse([t]);if(Array.isArray(t)){var o=[];return t.forEach((function(t){var n=e.DEFAULT_BY_KEY[t];n?o.push(n):console.warn("AG Grid: the key "+t+" is not a valid key for specifying a tool panel, valid keys are: "+Object.keys(e.DEFAULT_BY_KEY).join(","))})),0===o.length?null:{toolPanels:o,defaultToolPanel:o[0].id}}return{toolPanels:e.parseComponents(t.toolPanels),defaultToolPanel:t.defaultToolPanel,hiddenByDefault:t.hiddenByDefault,position:t.position}},e.parseComponents=function(t){var o=[];return t?(t.forEach((function(t){var n=null;if("string"===typeof t){var r=e.DEFAULT_BY_KEY[t];if(!r)return void console.warn("AG Grid: the key "+t+" is not a valid key for specifying a tool panel, valid keys are: "+Object.keys(e.DEFAULT_BY_KEY).join(","));n=r}else n=t;o.push(n)})),o):o},e.DEFAULT_COLUMN_COMP={id:"columns",labelDefault:"Columns",labelKey:"columns",iconKey:"columns",toolPanel:"agColumnsToolPanel"},e.DEFAULT_FILTER_COMP={id:"filters",labelDefault:"Filters",labelKey:"filters",iconKey:"filter",toolPanel:"agFiltersToolPanel"},e.DEFAULT_BY_KEY={columns:e.DEFAULT_COLUMN_COMP,filters:e.DEFAULT_FILTER_COMP},e}(),el=function(){return el=Object.assign||function(e){for(var t,o=1,n=arguments.length;o=0;a--)(r=e[a])&&(s=(i<3?r(s):i>3?r(t,o,s):r(t,o))||s);return i>3&&s&&Object.defineProperty(t,o,s),s},ol=function(e,t){return function(o,n){t(o,n,e)}},nl=function(e,t){var o="function"===typeof Symbol&&e[Symbol.iterator];if(!o)return e;var n,r,i=o.call(e),s=[];try{for(;(void 0===t||t-- >0)&&!(n=i.next()).done;)s.push(n.value)}catch(a){r={error:a}}finally{try{n&&!n.done&&(o=i.return)&&o.call(i)}finally{if(r)throw r.error}}return s},rl=function(){for(var e=[],t=0;t0?o:t}var ll,ul=function(){function e(){this.propertyEventService=new Ae,this.domDataKey="__AG_"+Math.random().toString(),this.destroyed=!1}var t;return t=e,e.prototype.agWire=function(e,t){this.gridOptions.api=e,this.gridOptions.columnApi=t,this.checkForDeprecated(),this.checkForViolations()},e.prototype.destroy=function(){this.gridOptions.api=null,this.gridOptions.columnApi=null,this.destroyed=!0},e.prototype.init=function(){var e=this;!0!==this.gridOptions.suppressPropertyNamesCheck&&(this.checkGridOptionsProperties(),this.checkColumnDefProperties()),null!=this.gridOptions.sideBar&&(this.gridOptions.sideBar=Za.parse(this.gridOptions.sideBar));var t=this.useAsyncEvents();if(this.eventService.addGlobalListener(this.globalEventHandler.bind(this),t),this.isGroupSelectsChildren()&&this.isSuppressParentsInRowNodes()&&console.warn("AG Grid: 'groupSelectsChildren' does not work with 'suppressParentsInRowNodes', this selection method needs the part in rowNode to work"),this.isGroupSelectsChildren()&&(this.isRowSelectionMulti()||console.warn("AG Grid: rowSelection must be 'multiple' for groupSelectsChildren to make sense"),this.isRowModelServerSide()&&console.warn("AG Grid: group selects children is NOT support for Server Side Row Model. This is because the rows are lazy loaded, so selecting a group is not possible asthe grid has no way of knowing what the children are.")),this.isGroupRemoveSingleChildren()&&this.isGroupHideOpenParents()&&console.warn("AG Grid: groupRemoveSingleChildren and groupHideOpenParents do not work with each other, you need to pick one. And don't ask us how to us these together on our support forum either you will get the same answer!"),this.isRowModelServerSide()){var o=function(e){return"AG Grid: '"+e+"' is not supported on the Server-Side Row Model"};O(this.gridOptions.groupDefaultExpanded)&&console.warn(o("groupDefaultExpanded")),O(this.gridOptions.groupDefaultExpanded)&&console.warn(o("groupIncludeFooter")),O(this.gridOptions.groupDefaultExpanded)&&console.warn(o("groupIncludeTotalFooter"))}this.isEnableRangeSelection()&&Fe.assertRegistered(_e.RangeSelectionModule,"enableRangeSelection"),this.isEnableRangeSelection()||!this.isEnableRangeHandle()&&!this.isEnableFillHandle()||console.warn("AG Grid: 'enableRangeHandle' and 'enableFillHandle' will not work unless 'enableRangeSelection' is set to true");var n=function(t){e.gridOptions.icons&&e.gridOptions.icons[t]&&console.warn("gridOptions.icons."+t+" is no longer supported. For information on how to style checkboxes and radio buttons, see https://www.ag-grid.com/javascript-grid-icons/")};n("radioButtonOff"),n("radioButtonOn"),n("checkboxChecked"),n("checkboxUnchecked"),n("checkboxIndeterminate"),this.getScrollbarWidth()},e.prototype.checkColumnDefProperties=function(){var e=this;null!=this.gridOptions.columnDefs&&this.gridOptions.columnDefs.forEach((function(t){var o=Object.getOwnPropertyNames(t),n=rl(ao.ALL_PROPERTIES,ao.FRAMEWORK_PROPERTIES);e.checkProperties(o,n,n,"colDef","https://www.ag-grid.com/javascript-grid-column-properties/")}))},e.prototype.checkGridOptionsProperties=function(){var e=Object.getOwnPropertyNames(this.gridOptions),t=rl($t.ALL_PROPERTIES,$t.FRAMEWORK_PROPERTIES,M(St).map((function(e){return to.getCallbackForEvent(e)}))),o=rl(t,["api","columnApi"]);this.checkProperties(e,o,t,"gridOptions","https://www.ag-grid.com/javascript-data-grid/grid-options/")},e.prototype.checkProperties=function(e,t,o,n,r){var i=gr(e,t,o);L(i,(function(e,t){console.warn("ag-grid: invalid "+n+" property '"+e+"' did you mean any of these: "+t.slice(0,8).join(", "))})),Object.keys(i).length>0&&console.warn("ag-grid: to see all the valid "+n+" properties please check: "+r)},e.prototype.mergeGridCommonParams=function(e){var t=this;if(e){return function(o){var n=el(el({},o),{api:t.getApi(),columnApi:t.getColumnApi(),context:t.getContext()});return e(n)}}return e},e.prototype.getDomDataKey=function(){return this.domDataKey},e.prototype.getDomData=function(e,t){var o=e[this.getDomDataKey()];return o?o[t]:void 0},e.prototype.setDomData=function(e,t,o){var n=this.getDomDataKey(),r=e[n];b(r)&&(r={},e[n]=r),r[t]=o},e.prototype.isRowSelection=function(){return"single"===this.gridOptions.rowSelection||"multiple"===this.gridOptions.rowSelection},e.prototype.isSuppressRowDeselection=function(){return il(this.gridOptions.suppressRowDeselection)},e.prototype.isRowSelectionMulti=function(){return"multiple"===this.gridOptions.rowSelection},e.prototype.isRowMultiSelectWithClick=function(){return il(this.gridOptions.rowMultiSelectWithClick)},e.prototype.getContext=function(){return this.gridOptions.context},e.prototype.isPivotMode=function(){return il(this.gridOptions.pivotMode)},e.prototype.isSuppressExpandablePivotGroups=function(){return il(this.gridOptions.suppressExpandablePivotGroups)},e.prototype.getPivotColumnGroupTotals=function(){return this.gridOptions.pivotColumnGroupTotals},e.prototype.getPivotRowTotals=function(){return this.gridOptions.pivotRowTotals},e.prototype.isRowModelInfinite=function(){return this.gridOptions.rowModelType===Ne.ROW_MODEL_TYPE_INFINITE},e.prototype.isRowModelViewport=function(){return this.gridOptions.rowModelType===Ne.ROW_MODEL_TYPE_VIEWPORT},e.prototype.isRowModelServerSide=function(){return this.gridOptions.rowModelType===Ne.ROW_MODEL_TYPE_SERVER_SIDE},e.prototype.isRowModelDefault=function(){return b(this.gridOptions.rowModelType)||this.gridOptions.rowModelType===Ne.ROW_MODEL_TYPE_CLIENT_SIDE},e.prototype.isFullRowEdit=function(){return"fullRow"===this.gridOptions.editType},e.prototype.isSuppressFocusAfterRefresh=function(){return il(this.gridOptions.suppressFocusAfterRefresh)},e.prototype.isSuppressBrowserResizeObserver=function(){return il(this.gridOptions.suppressBrowserResizeObserver)},e.prototype.isSuppressMaintainUnsortedOrder=function(){return il(this.gridOptions.suppressMaintainUnsortedOrder)},e.prototype.isSuppressClearOnFillReduction=function(){return il(this.gridOptions.suppressClearOnFillReduction)},e.prototype.isShowToolPanel=function(){return il(this.gridOptions.sideBar&&Array.isArray(this.getSideBar().toolPanels))},e.prototype.getSideBar=function(){return this.gridOptions.sideBar},e.prototype.isSuppressTouch=function(){return il(this.gridOptions.suppressTouch)},e.prototype.isMaintainColumnOrder=function(){return il(this.gridOptions.maintainColumnOrder)},e.prototype.isSuppressRowTransform=function(){return il(this.gridOptions.suppressRowTransform)},e.prototype.isSuppressColumnStateEvents=function(){return il(this.gridOptions.suppressColumnStateEvents)},e.prototype.isAllowDragFromColumnsToolPanel=function(){return il(this.gridOptions.allowDragFromColumnsToolPanel)},e.prototype.useAsyncEvents=function(){return!il(this.gridOptions.suppressAsyncEvents)},e.prototype.isEnableCellChangeFlash=function(){return il(this.gridOptions.enableCellChangeFlash)},e.prototype.getCellFlashDelay=function(){return this.gridOptions.cellFlashDelay||500},e.prototype.getCellFadeDelay=function(){return this.gridOptions.cellFadeDelay||1e3},e.prototype.isGroupSelectsChildren=function(){return il(this.gridOptions.groupSelectsChildren)},e.prototype.isSuppressRowHoverHighlight=function(){return il(this.gridOptions.suppressRowHoverHighlight)},e.prototype.isColumnHoverHighlight=function(){return il(this.gridOptions.columnHoverHighlight)},e.prototype.isGroupSelectsFiltered=function(){return il(this.gridOptions.groupSelectsFiltered)},e.prototype.isGroupHideOpenParents=function(){return il(this.gridOptions.groupHideOpenParents)},e.prototype.isGroupMaintainOrder=function(){return il(this.gridOptions.groupMaintainOrder)},e.prototype.getAutoGroupColumnDef=function(){return this.gridOptions.autoGroupColumnDef},e.prototype.isGroupMultiAutoColumn=function(){return this.gridOptions.groupDisplayType?this.matchesGroupDisplayType("multipleColumns",this.gridOptions.groupDisplayType):il(this.gridOptions.groupHideOpenParents)},e.prototype.isGroupUseEntireRow=function(e){return!e&&(!!this.gridOptions.groupDisplayType&&this.matchesGroupDisplayType("groupRows",this.gridOptions.groupDisplayType))},e.prototype.isGroupSuppressAutoColumn=function(){return!(!this.gridOptions.groupDisplayType||!this.matchesGroupDisplayType("custom",this.gridOptions.groupDisplayType))||!!this.gridOptions.treeDataDisplayType&&this.matchesTreeDataDisplayType("custom",this.gridOptions.treeDataDisplayType)},e.prototype.isGroupRemoveSingleChildren=function(){return il(this.gridOptions.groupRemoveSingleChildren)},e.prototype.isGroupRemoveLowestSingleChildren=function(){return il(this.gridOptions.groupRemoveLowestSingleChildren)},e.prototype.isGroupIncludeFooter=function(){return il(this.gridOptions.groupIncludeFooter)},e.prototype.isGroupIncludeTotalFooter=function(){return il(this.gridOptions.groupIncludeTotalFooter)},e.prototype.isGroupSuppressBlankHeader=function(){return il(this.gridOptions.groupSuppressBlankHeader)},e.prototype.isSuppressRowClickSelection=function(){return il(this.gridOptions.suppressRowClickSelection)},e.prototype.isSuppressCellFocus=function(){return il(this.gridOptions.suppressCellFocus)},e.prototype.isSuppressMultiSort=function(){return il(this.gridOptions.suppressMultiSort)},e.prototype.isAlwaysMultiSort=function(){return il(this.gridOptions.alwaysMultiSort)},e.prototype.isMultiSortKeyCtrl=function(){return"ctrl"===this.gridOptions.multiSortKey},e.prototype.isPivotSuppressAutoColumn=function(){return il(this.gridOptions.pivotSuppressAutoColumn)},e.prototype.isSuppressDragLeaveHidesColumns=function(){return il(this.gridOptions.suppressDragLeaveHidesColumns)},e.prototype.isSuppressScrollOnNewData=function(){return il(this.gridOptions.suppressScrollOnNewData)},e.prototype.isSuppressScrollWhenPopupsAreOpen=function(){return il(this.gridOptions.suppressScrollWhenPopupsAreOpen)},e.prototype.isRowDragEntireRow=function(){return il(this.gridOptions.rowDragEntireRow)},e.prototype.isSuppressRowDrag=function(){return il(this.gridOptions.suppressRowDrag)},e.prototype.isRowDragManaged=function(){return il(this.gridOptions.rowDragManaged)},e.prototype.isSuppressMoveWhenRowDragging=function(){return il(this.gridOptions.suppressMoveWhenRowDragging)},e.prototype.isRowDragMultiRow=function(){return il(this.gridOptions.rowDragMultiRow)},e.prototype.getDomLayout=function(){var e=this.gridOptions.domLayout||Ne.DOM_LAYOUT_NORMAL;return-1===[Ne.DOM_LAYOUT_PRINT,Ne.DOM_LAYOUT_AUTO_HEIGHT,Ne.DOM_LAYOUT_NORMAL].indexOf(e)?(ne((function(){return console.warn("AG Grid: "+e+" is not valid for DOM Layout, valid values are "+Ne.DOM_LAYOUT_NORMAL+", "+Ne.DOM_LAYOUT_AUTO_HEIGHT+" and "+Ne.DOM_LAYOUT_PRINT)}),"warn about dom layout values"),Ne.DOM_LAYOUT_NORMAL):e},e.prototype.isSuppressHorizontalScroll=function(){return il(this.gridOptions.suppressHorizontalScroll)},e.prototype.isSuppressMaxRenderedRowRestriction=function(){return il(this.gridOptions.suppressMaxRenderedRowRestriction)},e.prototype.isExcludeChildrenWhenTreeDataFiltering=function(){return il(this.gridOptions.excludeChildrenWhenTreeDataFiltering)},e.prototype.isAlwaysShowHorizontalScroll=function(){return il(this.gridOptions.alwaysShowHorizontalScroll)},e.prototype.isAlwaysShowVerticalScroll=function(){return il(this.gridOptions.alwaysShowVerticalScroll)},e.prototype.isDebounceVerticalScrollbar=function(){return il(this.gridOptions.debounceVerticalScrollbar)},e.prototype.isSuppressLoadingOverlay=function(){return il(this.gridOptions.suppressLoadingOverlay)},e.prototype.isSuppressNoRowsOverlay=function(){return il(this.gridOptions.suppressNoRowsOverlay)},e.prototype.isSuppressFieldDotNotation=function(){return il(this.gridOptions.suppressFieldDotNotation)},e.prototype.getPinnedTopRowData=function(){return this.gridOptions.pinnedTopRowData},e.prototype.getPinnedBottomRowData=function(){return this.gridOptions.pinnedBottomRowData},e.prototype.isFunctionsPassive=function(){return il(this.gridOptions.functionsPassive)},e.prototype.isSuppressChangeDetection=function(){return il(this.gridOptions.suppressChangeDetection)},e.prototype.isSuppressAnimationFrame=function(){return il(this.gridOptions.suppressAnimationFrame)},e.prototype.getQuickFilterText=function(){return this.gridOptions.quickFilterText},e.prototype.isCacheQuickFilter=function(){return il(this.gridOptions.cacheQuickFilter)},e.prototype.isUnSortIcon=function(){return il(this.gridOptions.unSortIcon)},e.prototype.isSuppressMenuHide=function(){return il(this.gridOptions.suppressMenuHide)},e.prototype.isEnterMovesDownAfterEdit=function(){return il(this.gridOptions.enterMovesDownAfterEdit)},e.prototype.isEnterMovesDown=function(){return il(this.gridOptions.enterMovesDown)},e.prototype.isUndoRedoCellEditing=function(){return il(this.gridOptions.undoRedoCellEditing)},e.prototype.getUndoRedoCellEditingLimit=function(){return sl(this.gridOptions.undoRedoCellEditingLimit)},e.prototype.getRowStyle=function(){return this.gridOptions.rowStyle},e.prototype.getRowClass=function(){return this.gridOptions.rowClass},e.prototype.getRowStyleFunc=function(){return this.mergeGridCommonParams(this.gridOptions.getRowStyle)},e.prototype.getRowClassFunc=function(){return this.mergeGridCommonParams(this.gridOptions.getRowClass)},e.prototype.rowClassRules=function(){return this.gridOptions.rowClassRules},e.prototype.getServerSideStoreType=function(){return this.gridOptions.serverSideStoreType},e.prototype.getServerSideStoreParamsFunc=function(){return this.mergeGridCommonParams(this.gridOptions.getServerSideStoreParams)},e.prototype.getCreateChartContainerFunc=function(){return this.mergeGridCommonParams(this.gridOptions.createChartContainer)},e.prototype.getPopupParent=function(){return this.gridOptions.popupParent},e.prototype.getBlockLoadDebounceMillis=function(){return this.gridOptions.blockLoadDebounceMillis},e.prototype.getPostProcessPopupFunc=function(){return this.mergeGridCommonParams(this.gridOptions.postProcessPopup)},e.prototype.getPaginationNumberFormatterFunc=function(){return this.mergeGridCommonParams(this.gridOptions.paginationNumberFormatter)},e.prototype.getChildCountFunc=function(){return this.gridOptions.getChildCount},e.prototype.getIsApplyServerSideTransactionFunc=function(){return this.mergeGridCommonParams(this.gridOptions.isApplyServerSideTransaction)},e.prototype.getInitialGroupOrderComparator=function(){var e=this.gridOptions,t=e.initialGroupOrderComparator,o=e.defaultGroupOrderComparator;return t?this.mergeGridCommonParams(t):o?function(e){return o(e.nodeA,e.nodeB)}:void 0},e.prototype.getIsFullWidthCellFunc=function(){var e=this.gridOptions,t=e.isFullWidthRow,o=e.isFullWidthCell;return t?this.mergeGridCommonParams(t):o?function(e){return o(e.rowNode)}:void 0},e.prototype.getFullWidthCellRendererParams=function(){return this.gridOptions.fullWidthCellRendererParams},e.prototype.isEmbedFullWidthRows=function(){return il(this.gridOptions.embedFullWidthRows)||il(this.gridOptions.deprecatedEmbedFullWidthRows)},e.prototype.isDetailRowAutoHeight=function(){return il(this.gridOptions.detailRowAutoHeight)},e.prototype.getSuppressKeyboardEventFunc=function(){return this.gridOptions.suppressKeyboardEvent},e.prototype.getBusinessKeyForNodeFunc=function(){return this.gridOptions.getBusinessKeyForNode},e.prototype.getApi=function(){return this.gridOptions.api},e.prototype.getColumnApi=function(){return this.gridOptions.columnApi},e.prototype.isReadOnlyEdit=function(){return il(this.gridOptions.readOnlyEdit)},e.prototype.isImmutableData=function(){var e=null!=this.gridOptions.getRowId,t=il(this.gridOptions.immutableData);return!il(this.gridOptions.resetRowDataOnUpdate)&&(e||t)},e.prototype.isEnsureDomOrder=function(){return il(this.gridOptions.ensureDomOrder)},e.prototype.isEnableCharts=function(){return!!il(this.gridOptions.enableCharts)&&Fe.assertRegistered(_e.GridChartsModule,"enableCharts")},e.prototype.getColResizeDefault=function(){return this.gridOptions.colResizeDefault},e.prototype.isSingleClickEdit=function(){return il(this.gridOptions.singleClickEdit)},e.prototype.isSuppressClickEdit=function(){return il(this.gridOptions.suppressClickEdit)},e.prototype.isStopEditingWhenCellsLoseFocus=function(){return il(this.gridOptions.stopEditingWhenCellsLoseFocus)},e.prototype.getGroupDefaultExpanded=function(){return this.gridOptions.groupDefaultExpanded},e.prototype.getMaxConcurrentDatasourceRequests=function(){var e=sl(this.gridOptions.maxConcurrentDatasourceRequests);return null==e?2:e<=0?void 0:e},e.prototype.getMaxBlocksInCache=function(){return this.gridOptions.maxBlocksInCache},e.prototype.getCacheOverflowSize=function(){return this.gridOptions.cacheOverflowSize},e.prototype.getPaginationPageSize=function(){return sl(this.gridOptions.paginationPageSize)},e.prototype.isPaginateChildRows=function(){return!(!this.isGroupRemoveSingleChildren()&&!this.isGroupRemoveLowestSingleChildren())||il(this.gridOptions.paginateChildRows)},e.prototype.getCacheBlockSize=function(){return al(this.gridOptions.cacheBlockSize)},e.prototype.getInfiniteInitialRowCount=function(){return this.gridOptions.infiniteInitialRowCount},e.prototype.isPurgeClosedRowNodes=function(){return il(this.gridOptions.purgeClosedRowNodes)},e.prototype.isSuppressPaginationPanel=function(){return il(this.gridOptions.suppressPaginationPanel)},e.prototype.getRowData=function(){return this.gridOptions.rowData},e.prototype.isEnableRtl=function(){return il(this.gridOptions.enableRtl)},e.prototype.getRowGroupPanelShow=function(){return this.gridOptions.rowGroupPanelShow},e.prototype.getPivotPanelShow=function(){return this.gridOptions.pivotPanelShow},e.prototype.isAngularCompileRows=function(){return il(this.gridOptions.angularCompileRows)},e.prototype.isAngularCompileFilters=function(){return il(this.gridOptions.angularCompileFilters)},e.prototype.isDebug=function(){return il(this.gridOptions.debug)},e.prototype.getColumnDefs=function(){return this.gridOptions.columnDefs},e.prototype.getColumnTypes=function(){return this.gridOptions.columnTypes},e.prototype.getDatasource=function(){return this.gridOptions.datasource},e.prototype.getViewportDatasource=function(){return this.gridOptions.viewportDatasource},e.prototype.getServerSideDatasource=function(){return this.gridOptions.serverSideDatasource},e.prototype.isAccentedSort=function(){return il(this.gridOptions.accentedSort)},e.prototype.isEnableBrowserTooltips=function(){return il(this.gridOptions.enableBrowserTooltips)},e.prototype.isEnableCellExpressions=function(){return il(this.gridOptions.enableCellExpressions)},e.prototype.isEnableGroupEdit=function(){return il(this.gridOptions.enableGroupEdit)},e.prototype.isSuppressMiddleClickScrolls=function(){return il(this.gridOptions.suppressMiddleClickScrolls)},e.prototype.isPreventDefaultOnContextMenu=function(){return il(this.gridOptions.preventDefaultOnContextMenu)},e.prototype.isSuppressPreventDefaultOnMouseWheel=function(){return il(this.gridOptions.suppressPreventDefaultOnMouseWheel)},e.prototype.isSuppressColumnVirtualisation=function(){return il(this.gridOptions.suppressColumnVirtualisation)},e.prototype.isSuppressRowVirtualisation=function(){return il(this.gridOptions.suppressRowVirtualisation)},e.prototype.isSuppressContextMenu=function(){return il(this.gridOptions.suppressContextMenu)},e.prototype.isAllowContextMenuWithControlKey=function(){return il(this.gridOptions.allowContextMenuWithControlKey)},e.prototype.isSuppressCopyRowsToClipboard=function(){return il(this.gridOptions.suppressCopyRowsToClipboard)},e.prototype.isSuppressCopySingleCellRanges=function(){return il(this.gridOptions.suppressCopySingleCellRanges)},e.prototype.isCopyHeadersToClipboard=function(){return il(this.gridOptions.copyHeadersToClipboard)},e.prototype.isCopyGroupHeadersToClipboard=function(){return il(this.gridOptions.copyGroupHeadersToClipboard)},e.prototype.isSuppressClipboardPaste=function(){return il(this.gridOptions.suppressClipboardPaste)},e.prototype.isSuppressLastEmptyLineOnPaste=function(){return il(this.gridOptions.suppressLastEmptyLineOnPaste)},e.prototype.isPagination=function(){return il(this.gridOptions.pagination)},e.prototype.isSuppressEnterpriseResetOnNewColumns=function(){return il(this.gridOptions.suppressEnterpriseResetOnNewColumns)},e.prototype.getProcessDataFromClipboardFunc=function(){return this.mergeGridCommonParams(this.gridOptions.processDataFromClipboard)},e.prototype.getAsyncTransactionWaitMillis=function(){return O(this.gridOptions.asyncTransactionWaitMillis)?this.gridOptions.asyncTransactionWaitMillis:Ne.BATCH_WAIT_MILLIS},e.prototype.isSuppressMovableColumns=function(){return il(this.gridOptions.suppressMovableColumns)},e.prototype.isAnimateRows=function(){return!this.isEnsureDomOrder()&&il(this.gridOptions.animateRows)},e.prototype.isSuppressColumnMoveAnimation=function(){return il(this.gridOptions.suppressColumnMoveAnimation)},e.prototype.isSuppressAggFuncInHeader=function(){return il(this.gridOptions.suppressAggFuncInHeader)},e.prototype.isSuppressAggAtRootLevel=function(){return il(this.gridOptions.suppressAggAtRootLevel)},e.prototype.isSuppressAggFilteredOnly=function(){return void 0!==this.getGroupAggFiltering()||il(this.gridOptions.suppressAggFilteredOnly)},e.prototype.isRemovePivotHeaderRowWhenSingleValueColumn=function(){return il(this.gridOptions.removePivotHeaderRowWhenSingleValueColumn)},e.prototype.isShowOpenedGroup=function(){return il(this.gridOptions.showOpenedGroup)},e.prototype.isReactUi=function(){return il(this.gridOptions.reactUi)},e.prototype.isSuppressReactUi=function(){return il(this.gridOptions.suppressReactUi)},e.prototype.isEnableRangeSelection=function(){return Fe.isRegistered(_e.RangeSelectionModule)&&il(this.gridOptions.enableRangeSelection)},e.prototype.isEnableRangeHandle=function(){return il(this.gridOptions.enableRangeHandle)},e.prototype.isEnableFillHandle=function(){return il(this.gridOptions.enableFillHandle)},e.prototype.getFillHandleDirection=function(){var e=this.gridOptions.fillHandleDirection;return e?"x"!==e&&"y"!==e&&"xy"!==e?(ne((function(){return console.warn("AG Grid: valid values for fillHandleDirection are 'x', 'y' and 'xy'. Default to 'xy'.")}),"warn invalid fill direction"),"xy"):e:"xy"},e.prototype.getFillOperation=function(){return this.mergeGridCommonParams(this.gridOptions.fillOperation)},e.prototype.isSuppressMultiRangeSelection=function(){return il(this.gridOptions.suppressMultiRangeSelection)},e.prototype.isPaginationAutoPageSize=function(){return il(this.gridOptions.paginationAutoPageSize)},e.prototype.isRememberGroupStateWhenNewData=function(){return il(this.gridOptions.rememberGroupStateWhenNewData)},e.prototype.getIcons=function(){return this.gridOptions.icons},e.prototype.getGroupAggFiltering=function(){var e=this.gridOptions.groupAggFiltering;return"function"===typeof e?this.mergeGridCommonParams(e):il(e)?function(){return!0}:void 0},e.prototype.getAggFuncs=function(){return this.gridOptions.aggFuncs},e.prototype.getSortingOrder=function(){return this.gridOptions.sortingOrder},e.prototype.getAlignedGrids=function(){return this.gridOptions.alignedGrids},e.prototype.isMasterDetail=function(){return!!il(this.gridOptions.masterDetail)&&Fe.assertRegistered(_e.MasterDetailModule,"masterDetail")},e.prototype.isKeepDetailRows=function(){return il(this.gridOptions.keepDetailRows)},e.prototype.getKeepDetailRowsCount=function(){var e=this.gridOptions.keepDetailRowsCount;return O(e)&&e>0?this.gridOptions.keepDetailRowsCount:10},e.prototype.getIsRowMasterFunc=function(){return this.gridOptions.isRowMaster},e.prototype.getIsRowSelectableFunc=function(){return this.gridOptions.isRowSelectable},e.prototype.getGroupRowRendererParams=function(){return this.gridOptions.groupRowRendererParams},e.prototype.getOverlayLoadingTemplate=function(){return this.gridOptions.overlayLoadingTemplate},e.prototype.getOverlayNoRowsTemplate=function(){return this.gridOptions.overlayNoRowsTemplate},e.prototype.isSuppressAutoSize=function(){return il(this.gridOptions.suppressAutoSize)},e.prototype.isEnableCellTextSelection=function(){return il(this.gridOptions.enableCellTextSelection)},e.prototype.isSuppressParentsInRowNodes=function(){return il(this.gridOptions.suppressParentsInRowNodes)},e.prototype.isSuppressClipboardApi=function(){return il(this.gridOptions.suppressClipboardApi)},e.prototype.isFunctionsReadOnly=function(){return il(this.gridOptions.functionsReadOnly)},e.prototype.isEnableCellTextSelect=function(){return il(this.gridOptions.enableCellTextSelection)},e.prototype.getDefaultColDef=function(){return this.gridOptions.defaultColDef},e.prototype.getDefaultColGroupDef=function(){return this.gridOptions.defaultColGroupDef},e.prototype.getDefaultExportParams=function(e){return this.gridOptions.defaultExportParams?(console.warn("AG Grid: Since v25.2 `defaultExportParams` has been replaced by `default"+At(e)+"ExportParams`'"),this.gridOptions.defaultExportParams):"csv"===e&&this.gridOptions.defaultCsvExportParams?this.gridOptions.defaultCsvExportParams:"excel"===e&&this.gridOptions.defaultExcelExportParams?this.gridOptions.defaultExcelExportParams:void 0},e.prototype.isSuppressCsvExport=function(){return il(this.gridOptions.suppressCsvExport)},e.prototype.isAllowShowChangeAfterFilter=function(){return il(this.gridOptions.allowShowChangeAfterFilter)},e.prototype.isSuppressExcelExport=function(){return il(this.gridOptions.suppressExcelExport)},e.prototype.isSuppressMakeColumnVisibleAfterUnGroup=function(){return il(this.gridOptions.suppressMakeColumnVisibleAfterUnGroup)},e.prototype.getDataPathFunc=function(){return this.gridOptions.getDataPath},e.prototype.getIsServerSideGroupFunc=function(){return this.gridOptions.isServerSideGroup},e.prototype.getIsServerSideGroupOpenByDefaultFunc=function(){return this.mergeGridCommonParams(this.gridOptions.isServerSideGroupOpenByDefault)},e.prototype.getIsGroupOpenByDefaultFunc=function(){return this.mergeGridCommonParams(this.gridOptions.isGroupOpenByDefault)},e.prototype.getServerSideGroupKeyFunc=function(){return this.gridOptions.getServerSideGroupKey},e.prototype.getGroupRowAggFunc=function(){var e=this.gridOptions,t=e.getGroupRowAgg,o=e.groupRowAggNodes;return t?this.mergeGridCommonParams(t):o?function(e){return o(e.nodes)}:void 0},e.prototype.getContextMenuItemsFunc=function(){return this.mergeGridCommonParams(this.gridOptions.getContextMenuItems)},e.prototype.getMainMenuItemsFunc=function(){return this.mergeGridCommonParams(this.gridOptions.getMainMenuItems)},e.prototype.getRowIdFunc=function(){var e=this.gridOptions,t=e.getRowId,o=e.getRowNodeId;return t?this.mergeGridCommonParams(t):o?function(e){return o(e.data)}:void 0},e.prototype.getNavigateToNextHeaderFunc=function(){return this.mergeGridCommonParams(this.gridOptions.navigateToNextHeader)},e.prototype.getTabToNextHeaderFunc=function(){return this.mergeGridCommonParams(this.gridOptions.tabToNextHeader)},e.prototype.getNavigateToNextCellFunc=function(){return this.mergeGridCommonParams(this.gridOptions.navigateToNextCell)},e.prototype.getTabToNextCellFunc=function(){return this.mergeGridCommonParams(this.gridOptions.tabToNextCell)},e.prototype.getGridTabIndex=function(){return(this.gridOptions.tabIndex||0).toString()},e.prototype.isTreeData=function(){return!!il(this.gridOptions.treeData)&&Fe.assertRegistered(_e.RowGroupingModule,"Tree Data")},e.prototype.isValueCache=function(){return il(this.gridOptions.valueCache)},e.prototype.isValueCacheNeverExpires=function(){return il(this.gridOptions.valueCacheNeverExpires)},e.prototype.isDeltaSort=function(){return il(this.gridOptions.deltaSort)},e.prototype.isAggregateOnlyChangedColumns=function(){return il(this.gridOptions.aggregateOnlyChangedColumns)},e.prototype.getProcessSecondaryColDefFunc=function(){return this.gridOptions.processSecondaryColDef},e.prototype.getProcessSecondaryColGroupDefFunc=function(){return this.gridOptions.processSecondaryColGroupDef},e.prototype.getSendToClipboardFunc=function(){return this.mergeGridCommonParams(this.gridOptions.sendToClipboard)},e.prototype.getProcessRowPostCreateFunc=function(){return this.mergeGridCommonParams(this.gridOptions.processRowPostCreate)},e.prototype.getProcessCellForClipboardFunc=function(){return this.mergeGridCommonParams(this.gridOptions.processCellForClipboard)},e.prototype.getProcessHeaderForClipboardFunc=function(){return this.mergeGridCommonParams(this.gridOptions.processHeaderForClipboard)},e.prototype.getProcessGroupHeaderForClipboardFunc=function(){return this.mergeGridCommonParams(this.gridOptions.processGroupHeaderForClipboard)},e.prototype.getProcessCellFromClipboardFunc=function(){return this.mergeGridCommonParams(this.gridOptions.processCellFromClipboard)},e.prototype.getViewportRowModelPageSize=function(){return al(this.gridOptions.viewportRowModelPageSize,5)},e.prototype.getViewportRowModelBufferSize=function(){return e=this.gridOptions.viewportRowModelBufferSize,t=5,e>=0?e:t;var e,t},e.prototype.isServerSideSortingAlwaysResets=function(){return il(this.gridOptions.serverSideSortingAlwaysResets)},e.prototype.isServerSideFilteringAlwaysResets=function(){return il(this.gridOptions.serverSideFilteringAlwaysResets)},e.prototype.getPostSortFunc=function(){var e=this.gridOptions,t=e.postSortRows,o=e.postSort;return t?this.mergeGridCommonParams(t):o?function(e){return o(e.nodes)}:void 0},e.prototype.getChartToolbarItemsFunc=function(){return this.mergeGridCommonParams(this.gridOptions.getChartToolbarItems)},e.prototype.getChartThemeOverrides=function(){return this.gridOptions.chartThemeOverrides},e.prototype.getCustomChartThemes=function(){return this.gridOptions.customChartThemes},e.prototype.getChartThemes=function(){return this.gridOptions.chartThemes||["ag-default","ag-material","ag-pastel","ag-vivid","ag-solar"]},e.prototype.getClipboardDelimiter=function(){return O(this.gridOptions.clipboardDelimiter)?this.gridOptions.clipboardDelimiter:"\t"},e.prototype.setProperty=function(e,t,o){void 0===o&&(o=!1);var n=this.gridOptions,r=n[e];if(o||r!==t){n[e]=t;var i={type:e,currentValue:t,previousValue:r};this.propertyEventService.dispatchEvent(i)}},e.prototype.addEventListener=function(e,t){this.propertyEventService.addEventListener(e,t)},e.prototype.removeEventListener=function(e,t){this.propertyEventService.removeEventListener(e,t)},e.prototype.isSkipHeaderOnAutoSize=function(){return!!this.gridOptions.skipHeaderOnAutoSize},e.prototype.getAutoSizePadding=function(){var e=this.gridOptions.autoSizePadding;return null!=e&&e>=0?e:20},e.prototype.getHeaderHeight=function(){return"number"===typeof this.gridOptions.headerHeight?this.gridOptions.headerHeight:this.getFromTheme(25,"headerHeight")},e.prototype.getFloatingFiltersHeight=function(){return"number"===typeof this.gridOptions.floatingFiltersHeight?this.gridOptions.floatingFiltersHeight:this.getFromTheme(25,"headerHeight")},e.prototype.getGroupHeaderHeight=function(){return"number"===typeof this.gridOptions.groupHeaderHeight?this.gridOptions.groupHeaderHeight:this.getHeaderHeight()},e.prototype.getPivotHeaderHeight=function(){return"number"===typeof this.gridOptions.pivotHeaderHeight?this.gridOptions.pivotHeaderHeight:this.getHeaderHeight()},e.prototype.getPivotGroupHeaderHeight=function(){return"number"===typeof this.gridOptions.pivotGroupHeaderHeight?this.gridOptions.pivotGroupHeaderHeight:this.getGroupHeaderHeight()},e.prototype.isExternalFilterPresent=function(){return"function"===typeof this.gridOptions.isExternalFilterPresent&&this.gridOptions.isExternalFilterPresent({api:this.getApi(),columnApi:this.getColumnApi(),context:this.getContext()})},e.prototype.doesExternalFilterPass=function(e){return"function"===typeof this.gridOptions.doesExternalFilterPass&&this.gridOptions.doesExternalFilterPass(e)},e.prototype.getTooltipDelay=function(e){var t=this.gridOptions,o=t.tooltipShowDelay,n=t.tooltipHideDelay,r="show"===e?o:n,i=At(e);return O(r)?(r<0&&ne((function(){return console.warn("ag-grid: tooltip"+i+"Delay should not be lower than 0")}),"tooltip"+i+"DelayWarn"),Math.max(200,r)):null},e.prototype.isTooltipMouseTrack=function(){return il(this.gridOptions.tooltipMouseTrack)},e.prototype.isSuppressModelUpdateAfterUpdateTransaction=function(){return il(this.gridOptions.suppressModelUpdateAfterUpdateTransaction)},e.prototype.getDocument=function(){var e=null;return this.gridOptions.getDocument&&O(this.gridOptions.getDocument)?e=this.gridOptions.getDocument():this.eGridDiv&&(e=this.eGridDiv.ownerDocument),e&&O(e)?e:document},e.prototype.getMinColWidth=function(){var e=this.gridOptions.minColWidth;if(O(e)&&e>t.MIN_COL_WIDTH)return this.gridOptions.minColWidth;var o=this.getFromTheme(null,"headerCellMinWidth");return O(o)?Math.max(o,t.MIN_COL_WIDTH):t.MIN_COL_WIDTH},e.prototype.getMaxColWidth=function(){return this.gridOptions.maxColWidth&&this.gridOptions.maxColWidth>t.MIN_COL_WIDTH?this.gridOptions.maxColWidth:null},e.prototype.getColWidth=function(){return"number"!==typeof this.gridOptions.colWidth||this.gridOptions.colWidth=0?this.gridOptions.scrollbarWidth:Do();null!=e&&(this.scrollbarWidth=e,this.eventService.dispatchEvent({type:St.EVENT_SCROLLBAR_WIDTH_CHANGED}))}return this.scrollbarWidth},e.prototype.checkForDeprecated=function(){var e=this.gridOptions;e.deprecatedEmbedFullWidthRows&&console.warn("AG Grid: since v21.2, deprecatedEmbedFullWidthRows has been replaced with embedFullWidthRows."),e.rowDeselection&&console.warn("AG Grid: since v24.x, rowDeselection is deprecated and the behaviour is true by default. Please use `suppressRowDeselection` to prevent rows from being deselected."),e.enableMultiRowDragging&&(e.rowDragMultiRow=!0,delete e.enableMultiRowDragging,console.warn("AG Grid: since v26.1, `enableMultiRowDragging` is deprecated. Please use `rowDragMultiRow`."));var t=function(t,o,n){null!=e[t]&&(console.warn("AG Grid: since version "+n+", '"+t+"' is deprecated / renamed, please use the new property name '"+o+"' instead."),null==e[o]&&(e[o]=e[t]))};if(t("batchUpdateWaitMillis","asyncTransactionWaitMillis","23.1.x"),t("deltaRowDataMode","immutableData","23.1.x"),(e.immutableColumns||e.deltaColumnMode)&&console.warn("AG Grid: since v24.0, immutableColumns and deltaColumnMode properties are gone. The grid now works like this as default. To keep column order maintained, set grid property applyColumnDefOrder=true"),t("suppressSetColumnStateEvents","suppressColumnStateEvents","24.0.x"),e.groupRowInnerRenderer||e.groupRowInnerRendererParams||e.groupRowInnerRendererFramework){console.warn("AG Grid: since v24.0, grid properties groupRowInnerRenderer, groupRowInnerRendererFramework and groupRowInnerRendererParams are no longer used."),console.warn(" Instead use the grid properties groupRowRendererParams.innerRenderer, groupRowRendererParams.innerRendererFramework and groupRowRendererParams.innerRendererParams."),console.warn(" For example instead of this:"),console.warn(' groupRowInnerRenderer: "myRenderer"'),console.warn(" groupRowInnerRendererParams: {x: a}"),console.warn(" Replace with this:"),console.warn(" groupRowRendererParams: {"),console.warn(' innerRenderer: "myRenderer",'),console.warn(" innerRendererParams: {x: a}"),console.warn(" }"),console.warn(" We have copied the properties over for you. However to stop this error message, please change your application code."),e.groupRowRendererParams||(e.groupRowRendererParams={});var o=e.groupRowRendererParams;e.groupRowInnerRenderer&&(o.innerRenderer=e.groupRowInnerRenderer),e.groupRowInnerRendererParams&&(o.innerRendererParams=e.groupRowInnerRendererParams),e.groupRowInnerRendererFramework&&(o.innerRendererFramework=e.groupRowInnerRendererFramework)}if(e.rememberGroupStateWhenNewData&&console.warn("AG Grid: since v24.0, grid property rememberGroupStateWhenNewData is deprecated. This feature was provided before Transaction Updates worked (which keep group state). Now that transaction updates are possible and they keep group state, this feature is no longer needed."),e.detailCellRendererParams&&e.detailCellRendererParams.autoHeight&&(console.warn("AG Grid: since v24.1, grid property detailCellRendererParams.autoHeight is replaced with grid property detailRowAutoHeight. This allows this feature to work when you provide a custom DetailCellRenderer"),e.detailRowAutoHeight=!0),e.suppressKeyboardEvent&&console.warn("AG Grid: since v24.1 suppressKeyboardEvent in the gridOptions has been deprecated and will be removed in\n future versions of AG Grid. If you need this to be set for every column use the defaultColDef property."),e.suppressEnterpriseResetOnNewColumns&&console.warn("AG Grid: since v25, grid property suppressEnterpriseResetOnNewColumns is deprecated. This was a temporary property to allow changing columns in Server Side Row Model without triggering a reload. Now that it is possible to dynamically change columns in the grid, this is no longer needed."),e.suppressColumnStateEvents&&console.warn('AG Grid: since v25, grid property suppressColumnStateEvents no longer works due to a refactor that we did. It should be possible to achieve similar using event.source, which would be "api" if the event was due to setting column state via the API'),e.defaultExportParams&&console.warn("AG Grid: since v25.2, the grid property `defaultExportParams` has been replaced by `defaultCsvExportParams` and `defaultExcelExportParams`."),e.stopEditingWhenGridLosesFocus&&(console.warn("AG Grid: since v25.2.2, the grid property `stopEditingWhenGridLosesFocus` has been replaced by `stopEditingWhenCellsLoseFocus`."),e.stopEditingWhenCellsLoseFocus=!0),e.applyColumnDefOrder&&console.warn("AG Grid: since v26.0, the grid property `applyColumnDefOrder` is no longer needed, as this is the default behaviour. To turn this behaviour off, set maintainColumnOrder=true"),e.groupMultiAutoColumn&&(console.warn("AG Grid: since v26.0, the grid property `groupMultiAutoColumn` has been replaced by `groupDisplayType = 'multipleColumns'`"),e.groupDisplayType="multipleColumns"),e.groupUseEntireRow&&(console.warn("AG Grid: since v26.0, the grid property `groupUseEntireRow` has been replaced by `groupDisplayType = 'groupRows'`"),e.groupDisplayType="groupRows"),e.groupSuppressAutoColumn){var n=e.treeData?"treeDataDisplayType":"groupDisplayType";console.warn("AG Grid: since v26.0, the grid property `groupSuppressAutoColumn` has been replaced by `"+n+" = 'custom'`"),e.groupDisplayType="custom"}e.defaultGroupOrderComparator&&console.warn("AG Grid: since v27.2, the grid property `defaultGroupOrderComparator` is deprecated and has been replaced by `initialGroupOrderComparator` and now receives a single params object."),e.defaultGroupSortComparator&&(console.warn("AG Grid: since v26.0, the grid property `defaultGroupSortComparator` has been replaced by `initialGroupOrderComparator`"),e.defaultGroupOrderComparator=e.defaultGroupSortComparator),e.groupRowAggNodes&&console.warn("AG Grid: since v27.2, the grid property `groupRowAggNodes` is deprecated and has been replaced by `getGroupRowAgg` and now receives a single params object."),e.postSort&&console.warn("AG Grid: since v27.2, the grid property `postSort` is deprecated and has been replaced by `postSortRows` and now receives a single params object."),e.isFullWidthCell&&console.warn("AG Grid: since v27.2, the grid property `isFullWidthCell` is deprecated and has been replaced by `isFullWidthRow` and now receives a single params object."),e.localeTextFunc&&console.warn("AG Grid: since v27.2, the grid property `localeTextFunc` is deprecated and has been replaced by `getLocaleText` and now receives a single params object."),e.colWidth&&console.warn("AG Grid: since v26.1, the grid property `colWidth` is deprecated and should be set via `defaultColDef.width`."),e.minColWidth&&console.warn("AG Grid: since v26.1, the grid property `minColWidth` is deprecated and should be set via `defaultColDef.minWidth`."),e.maxColWidth&&console.warn("AG Grid: since v26.1, the grid property `maxColWidth` is deprecated and should be set via `defaultColDef.maxWidth`."),e.reactUi&&console.warn("AG Grid: since v27.0, React UI is on by default, so no need for reactUi=true. To turn it off, set suppressReactUi=true."),e.suppressReactUi&&console.warn("AG Grid: The legacy React rendering engine is deprecated and will be removed in the next major version of the grid."),e.suppressCellSelection&&(console.warn("AG Grid: since v27.0, `suppressCellSelection` has been replaced by `suppressCellFocus`."),e.suppressCellFocus=e.suppressCellSelection),e.getRowNodeId&&console.warn("AG Grid: since v27.1, `getRowNodeId` is deprecated and has been replaced by `getRowId`. The difference: if getRowId() is implemented then immutable data is enabled by default."),e.immutableData&&(e.getRowId?console.warn("AG Grid: since v27.1, `immutableData` is deprecated. With the `getRowId` callback implemented, immutable data is enabled by default so you can remove `immutableData=true`."):console.warn("AG Grid: since v27.1, `immutableData` is deprecated. To enable immutable data you must implement the `getRowId()` callback.")),e.clipboardDeliminator&&(console.warn("AG Grid: since v27.1, `clipboardDeliminator` has been replaced by `clipboardDelimiter`."),e.clipboardDelimiter=e.clipboardDeliminator)},e.prototype.checkForViolations=function(){this.isTreeData()&&this.treeDataViolations()},e.prototype.treeDataViolations=function(){this.isRowModelDefault()&&b(this.getDataPathFunc())&&console.warn("AG Grid: property usingTreeData=true with rowModel=clientSide, but you did not provide getDataPath function, please provide getDataPath function if using tree data."),this.isRowModelServerSide()&&(b(this.getIsServerSideGroupFunc())&&console.warn("AG Grid: property usingTreeData=true with rowModel=serverSide, but you did not provide isServerSideGroup function, please provide isServerSideGroup function if using tree data."),b(this.getServerSideGroupKeyFunc())&&console.warn("AG Grid: property usingTreeData=true with rowModel=serverSide, but you did not provide getServerSideGroupKey function, please provide getServerSideGroupKey function if using tree data."))},e.prototype.getLocaleTextFunc=function(){var e=this,t=this.gridOptions,o=t.localeText,n=t.getLocaleText,r=t.localeTextFunc;return n?function(t,o,r){var i={key:t,defaultValue:o,variableValues:r,api:e.getApi(),columnApi:e.getColumnApi(),context:e.getContext()};return n(i)}:r||function(e,t,n){var r=o&&o[e];if(r&&n&&n.length)for(var i=0;!(i>=n.length);){if(-1===r.indexOf("${variable}"))break;r=r.replace("${variable}",n[i++])}return null!==r&&void 0!==r?r:t}},e.prototype.globalEventHandler=function(e,t){if(!this.destroyed){var o=to.getCallbackForEvent(e);"function"===typeof this.gridOptions[o]&&this.gridOptions[o](t)}},e.prototype.setRowHeightVariable=function(e){var t=e+"px";this.eGridDiv.style.getPropertyValue("--ag-line-height").trim()!=t&&this.eGridDiv.style.setProperty("--ag-line-height",t)},e.prototype.getRowHeightAsNumber=function(){if(!this.gridOptions.rowHeight||b(this.gridOptions.rowHeight))return this.getDefaultRowHeight();var e=this.gridOptions.rowHeight;return e&&this.isNumeric(e)?(this.setRowHeightVariable(e),e):(console.warn("AG Grid row height must be a number if not using standard row model"),this.getDefaultRowHeight())},e.prototype.isGetRowHeightFunction=function(){return"function"===typeof this.gridOptions.getRowHeight},e.prototype.getRowHeightForNode=function(e,t,o){if(void 0===t&&(t=!1),null==o&&(o=this.getDefaultRowHeight()),this.isGetRowHeightFunction()){if(t)return{height:o,estimated:!0};var n={node:e,data:e.data},r=this.mergeGridCommonParams(this.gridOptions.getRowHeight)(n);if(this.isNumeric(r))return 0===r&&ne((function(){return console.warn("AG Grid: The return of `getRowHeight` cannot be zero. If the intention is to hide rows, use a filter instead.")}),"invalidRowHeight"),{height:Math.max(1,r),estimated:!1}}return e.detail&&this.isMasterDetail()?this.isDetailRowAutoHeight()?{height:1,estimated:!1}:this.isNumeric(this.gridOptions.detailRowHeight)?{height:this.gridOptions.detailRowHeight,estimated:!1}:{height:300,estimated:!1}:{height:this.gridOptions.rowHeight&&this.isNumeric(this.gridOptions.rowHeight)?this.gridOptions.rowHeight:o,estimated:!1}},e.prototype.isDynamicRowHeight=function(){return"function"===typeof this.gridOptions.getRowHeight},e.prototype.getListItemHeight=function(){return this.getFromTheme(20,"listItemHeight")},e.prototype.chartMenuPanelWidth=function(){return this.environment.chartMenuPanelWidth()},e.prototype.isNumeric=function(e){return!isNaN(e)&&"number"===typeof e&&isFinite(e)},e.prototype.getFromTheme=function(e,t){var o=this.environment.getTheme().theme;return o&&0===o.indexOf("ag-theme")?this.environment.getSassVariable(o,t):e},e.prototype.getDefaultRowHeight=function(){return this.getFromTheme(25,"rowHeight")},e.prototype.matchesGroupDisplayType=function(e,t){var o=["groupRows","multipleColumns","custom","singleColumn"];return o.indexOf(t)<0?(console.warn("AG Grid: '"+t+"' is not a valid groupDisplayType value - possible values are: '"+o.join("', '")+"'"),!1):t===e},e.prototype.matchesTreeDataDisplayType=function(e,t){var o=["auto","custom"];return o.indexOf(t)<0?(console.warn("AG Grid: '"+t+"' is not a valid treeDataDisplayType value - possible values are: '"+o.join("', '")+"'"),!1):t===e},e.MIN_COL_WIDTH=10,e.PROP_HEADER_HEIGHT="headerHeight",e.PROP_GROUP_REMOVE_SINGLE_CHILDREN="groupRemoveSingleChildren",e.PROP_GROUP_REMOVE_LOWEST_SINGLE_CHILDREN="groupRemoveLowestSingleChildren",e.PROP_PIVOT_HEADER_HEIGHT="pivotHeaderHeight",e.PROP_SUPPRESS_CLIPBOARD_PASTE="suppressClipboardPaste",e.PROP_GROUP_HEADER_HEIGHT="groupHeaderHeight",e.PROP_PIVOT_GROUP_HEADER_HEIGHT="pivotGroupHeaderHeight",e.PROP_NAVIGATE_TO_NEXT_CELL="navigateToNextCell",e.PROP_TAB_TO_NEXT_CELL="tabToNextCell",e.PROP_NAVIGATE_TO_NEXT_HEADER="navigateToNextHeader",e.PROP_TAB_TO_NEXT_HEADER="tabToNextHeader",e.PROP_IS_EXTERNAL_FILTER_PRESENT="isExternalFilterPresent",e.PROP_DOES_EXTERNAL_FILTER_PASS="doesExternalFilterPass",e.PROP_FLOATING_FILTERS_HEIGHT="floatingFiltersHeight",e.PROP_SUPPRESS_ROW_CLICK_SELECTION="suppressRowClickSelection",e.PROP_SUPPRESS_ROW_DRAG="suppressRowDrag",e.PROP_SUPPRESS_MOVE_WHEN_ROW_DRAG="suppressMoveWhenRowDragging",e.PROP_GET_ROW_CLASS="getRowClass",e.PROP_GET_ROW_STYLE="getRowStyle",e.PROP_GET_ROW_HEIGHT="getRowHeight",e.PROP_POPUP_PARENT="popupParent",e.PROP_DOM_LAYOUT="domLayout",e.PROP_ROW_CLASS="rowClass",e.PROP_FILL_HANDLE_DIRECTION="fillHandleDirection",e.PROP_GROUP_ROW_AGG_NODES="groupRowAggNodes",e.PROP_GET_GROUP_ROW_AGG="getGroupRowAgg",e.PROP_GET_BUSINESS_KEY_FOR_NODE="getBusinessKeyForNode",e.PROP_GET_CHILD_COUNT="getChildCount",e.PROP_PROCESS_ROW_POST_CREATE="processRowPostCreate",e.PROP_GET_ROW_NODE_ID="getRowNodeId",e.PROP_GET_ROW_ID="getRowId",e.PROP_IS_FULL_WIDTH_CELL="isFullWidthCell",e.PROP_IS_FULL_WIDTH_ROW="isFullWidthRow",e.PROP_IS_ROW_SELECTABLE="isRowSelectable",e.PROP_IS_ROW_MASTER="isRowMaster",e.PROP_POST_SORT="postSort",e.PROP_POST_SORT_ROWS="postSortRows",e.PROP_GET_DOCUMENT="getDocument",e.PROP_POST_PROCESS_POPUP="postProcessPopup",e.PROP_DEFAULT_GROUP_ORDER_COMPARATOR="defaultGroupOrderComparator",e.PROP_INITIAL_GROUP_ORDER_COMPARATOR="initialGroupOrderComparator",e.PROP_PAGINATION_NUMBER_FORMATTER="paginationNumberFormatter",e.PROP_GET_CONTEXT_MENU_ITEMS="getContextMenuItems",e.PROP_GET_MAIN_MENU_ITEMS="getMainMenuItems",e.PROP_PROCESS_CELL_FOR_CLIPBOARD="processCellForClipboard",e.PROP_PROCESS_CELL_FROM_CLIPBOARD="processCellFromClipboard",e.PROP_SEND_TO_CLIPBOARD="sendToClipboard",e.PROP_PROCESS_TO_SECONDARY_COLDEF="processSecondaryColDef",e.PROP_PROCESS_SECONDARY_COL_GROUP_DEF="processSecondaryColGroupDef",e.PROP_GET_CHART_TOOLBAR_ITEMS="getChartToolbarItems",e.PROP_GET_SERVER_SIDE_STORE_PARAMS="getServerSideStoreParams",e.PROP_IS_SERVER_SIDE_GROUPS_OPEN_BY_DEFAULT="isServerSideGroupOpenByDefault",e.PROP_IS_APPLY_SERVER_SIDE_TRANSACTION="isApplyServerSideTransaction",e.PROP_IS_SERVER_SIDE_GROUP="isServerSideGroup",e.PROP_GET_SERVER_SIDE_GROUP_KEY="getServerSideGroupKey",tl([Re("gridOptions")],e.prototype,"gridOptions",void 0),tl([Re("eventService")],e.prototype,"eventService",void 0),tl([Re("environment")],e.prototype,"environment",void 0),tl([Re("eGridDiv")],e.prototype,"eGridDiv",void 0),tl([ol(0,be("gridApi")),ol(1,be("columnApi"))],e.prototype,"agWire",null),tl([we],e.prototype,"destroy",null),tl([Ce],e.prototype,"init",null),e=t=tl([Ee("gridOptionsWrapper")],e)}(),pl=function(){var e=function(t,o){return e=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(e,t){e.__proto__=t}||function(e,t){for(var o in t)t.hasOwnProperty(o)&&(e[o]=t[o])},e(t,o)};return function(t,o){function n(){this.constructor=t}e(t,o),t.prototype=null===o?Object.create(o):(n.prototype=o.prototype,new n)}}(),cl=function(e,t,o,n){var r,i=arguments.length,s=i<3?t:null===n?n=Object.getOwnPropertyDescriptor(t,o):n;if("object"===typeof Reflect&&"function"===typeof Reflect.decorate)s=Reflect.decorate(e,t,o,n);else for(var a=e.length-1;a>=0;a--)(r=e[a])&&(s=(i<3?r(s):i>3?r(t,o,s):r(t,o))||s);return i>3&&s&&Object.defineProperty(t,o,s),s};!function(e){e.AUTO_HEIGHT="ag-layout-auto-height",e.NORMAL="ag-layout-normal",e.PRINT="ag-layout-print"}(ll||(ll={}));var dl,hl=function(e){function t(t){var o=e.call(this)||this;return o.view=t,o}return pl(t,e),t.prototype.postConstruct=function(){this.addManagedListener(this.gridOptionsWrapper,ul.PROP_DOM_LAYOUT,this.updateLayoutClasses.bind(this)),this.updateLayoutClasses()},t.prototype.updateLayoutClasses=function(){var e=this.gridOptionsWrapper.getDomLayout(),t={autoHeight:e===Ne.DOM_LAYOUT_AUTO_HEIGHT,normal:e===Ne.DOM_LAYOUT_NORMAL,print:e===Ne.DOM_LAYOUT_PRINT},o=t.autoHeight?ll.AUTO_HEIGHT:t.print?ll.PRINT:ll.NORMAL;this.view.updateLayoutClasses(o,t)},cl([Re("gridOptionsWrapper")],t.prototype,"gridOptionsWrapper",void 0),cl([Ce],t.prototype,"postConstruct",null),t}(mt),fl=function(){var e=function(t,o){return e=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(e,t){e.__proto__=t}||function(e,t){for(var o in t)t.hasOwnProperty(o)&&(e[o]=t[o])},e(t,o)};return function(t,o){function n(){this.constructor=t}e(t,o),t.prototype=null===o?Object.create(o):(n.prototype=o.prototype,new n)}}(),gl=function(e,t,o,n){var r,i=arguments.length,s=i<3?t:null===n?n=Object.getOwnPropertyDescriptor(t,o):n;if("object"===typeof Reflect&&"function"===typeof Reflect.decorate)s=Reflect.decorate(e,t,o,n);else for(var a=e.length-1;a>=0;a--)(r=e[a])&&(s=(i<3?r(s):i>3?r(t,o,s):r(t,o))||s);return i>3&&s&&Object.defineProperty(t,o,s),s},yl=function(e){function t(t){var o=e.call(this)||this;return o.scrollLeft=-1,o.nextScrollTop=-1,o.scrollTop=-1,o.eBodyViewport=t,o.resetLastHorizontalScrollElementDebounced=de(o.resetLastHorizontalScrollElement.bind(o),500),o}return fl(t,e),t.prototype.postConstruct=function(){var e=this;this.enableRtl=this.gridOptionsWrapper.isEnableRtl(),this.addManagedListener(this.eventService,St.EVENT_DISPLAYED_COLUMNS_WIDTH_CHANGED,this.onDisplayedColumnsWidthChanged.bind(this)),this.ctrlsService.whenReady((function(t){e.centerRowContainerCtrl=t.centerRowContainerCtrl,e.onDisplayedColumnsWidthChanged(),e.addScrollListener()}))},t.prototype.addScrollListener=function(){var e=this.ctrlsService.getFakeHScrollCtrl();this.addManagedListener(this.centerRowContainerCtrl.getViewportElement(),"scroll",this.onCenterViewportScroll.bind(this)),this.addManagedListener(e.getViewport(),"scroll",this.onFakeHorizontalScroll.bind(this));var t=this.gridOptionsWrapper.isDebounceVerticalScrollbar()?de(this.onVerticalScroll.bind(this),100):this.onVerticalScroll.bind(this);this.addManagedListener(this.eBodyViewport,"scroll",t)},t.prototype.onDisplayedColumnsWidthChanged=function(){this.enableRtl&&this.horizontallyScrollHeaderCenterAndFloatingCenter()},t.prototype.horizontallyScrollHeaderCenterAndFloatingCenter=function(e){if(!(null==this.centerRowContainerCtrl)){void 0===e&&(e=this.centerRowContainerCtrl.getCenterViewportScrollLeft());var t=this.enableRtl?e:-e,o=this.ctrlsService.getTopCenterRowContainerCtrl(),n=this.ctrlsService.getBottomCenterRowContainerCtrl(),r=this.ctrlsService.getFakeHScrollCtrl();this.ctrlsService.getHeaderRowContainerCtrl().setHorizontalScroll(t),n.setContainerTranslateX(t),o.setContainerTranslateX(t),qo(this.lastHorizontalScrollElement===this.centerRowContainerCtrl.getViewportElement()?r.getViewport():this.centerRowContainerCtrl.getViewportElement(),Math.abs(e),this.enableRtl)}},t.prototype.isControllingScroll=function(e){return this.lastHorizontalScrollElement?e===this.lastHorizontalScrollElement:(this.lastHorizontalScrollElement=e,!0)},t.prototype.onFakeHorizontalScroll=function(){var e=this.ctrlsService.getFakeHScrollCtrl().getViewport();this.isControllingScroll(e)&&this.onBodyHorizontalScroll(e)},t.prototype.onCenterViewportScroll=function(){var e=this.centerRowContainerCtrl.getViewportElement();this.isControllingScroll(e)&&this.onBodyHorizontalScroll(e)},t.prototype.onBodyHorizontalScroll=function(e){var t=this.centerRowContainerCtrl.getViewportElement().scrollLeft;this.shouldBlockScrollUpdate("horizontal",t,!0)||(this.doHorizontalScroll(Math.round(Yo(e,this.enableRtl))),this.resetLastHorizontalScrollElementDebounced())},t.prototype.onVerticalScroll=function(){var e=this.eBodyViewport.scrollTop;this.shouldBlockScrollUpdate("vertical",e,!0)||(this.animationFrameService.setScrollTop(e),this.nextScrollTop=e,this.gridOptionsWrapper.isSuppressAnimationFrame()?(this.scrollTop=this.nextScrollTop,this.redrawRowsAfterScroll()):this.animationFrameService.schedule())},t.prototype.resetLastHorizontalScrollElement=function(){this.lastHorizontalScrollElement=null},t.prototype.doHorizontalScroll=function(e){var t=Yo(this.ctrlsService.getFakeHScrollCtrl().getViewport(),this.enableRtl);this.scrollLeft===e&&e===t||(this.scrollLeft=e,this.fireScrollEvent("horizontal"),this.horizontallyScrollHeaderCenterAndFloatingCenter(e),this.onHorizontalViewportChanged())},t.prototype.fireScrollEvent=function(e){var t=this,o={type:St.EVENT_BODY_SCROLL,api:this.gridApi,columnApi:this.columnApi,direction:e,left:this.scrollLeft,top:this.scrollTop};this.eventService.dispatchEvent(o),window.clearTimeout(this.scrollTimer),this.scrollTimer=void 0,this.scrollTimer=window.setTimeout((function(){var e=Object.assign({},o,{type:St.EVENT_BODY_SCROLL_END});t.eventService.dispatchEvent(e)}),100)},t.prototype.shouldBlockScrollUpdate=function(e,t,o){if(void 0===o&&(o=!1),o&&!Po())return!1;if("vertical"===e){var n=Bo(this.eBodyViewport),r=this.eBodyViewport.scrollHeight;if(t<0||t+n>r)return!0}if("horizontal"===e){var i=this.centerRowContainerCtrl.getCenterWidth(),s=this.centerRowContainerCtrl.getViewportElement().scrollWidth;if(this.enableRtl&&Ko()){if(t>0)return!0}else if(t<0)return!0;if(Math.abs(t)+i>s)return!0}return!1},t.prototype.redrawRowsAfterScroll=function(){this.fireScrollEvent("vertical")},t.prototype.onHorizontalViewportChanged=function(){this.centerRowContainerCtrl.onHorizontalViewportChanged()},t.prototype.checkScrollLeft=function(){this.scrollLeft!==this.centerRowContainerCtrl.getCenterViewportScrollLeft()&&this.onBodyHorizontalScroll(this.centerRowContainerCtrl.getViewportElement())},t.prototype.executeAnimationFrameScroll=function(){var e=this.scrollTop!=this.nextScrollTop;return e&&(this.scrollTop=this.nextScrollTop,this.redrawRowsAfterScroll()),e},t.prototype.setHorizontalScrollPosition=function(e){var t=this.centerRowContainerCtrl.getViewportElement().scrollWidth-this.centerRowContainerCtrl.getCenterWidth();this.shouldBlockScrollUpdate("horizontal",e)&&(e=this.enableRtl&&Ko()?e>0?0:t:Math.min(Math.max(e,0),t)),qo(this.centerRowContainerCtrl.getViewportElement(),Math.abs(e),this.enableRtl),this.doHorizontalScroll(e)},t.prototype.setVerticalScrollPosition=function(e){this.eBodyViewport.scrollTop=e},t.prototype.getVScrollPosition=function(){return{top:this.eBodyViewport.scrollTop,bottom:this.eBodyViewport.scrollTop+this.eBodyViewport.offsetHeight}},t.prototype.getHScrollPosition=function(){return this.centerRowContainerCtrl.getHScrollPosition()},t.prototype.isHorizontalScrollShowing=function(){return this.centerRowContainerCtrl.isHorizontalScrollShowing()},t.prototype.scrollHorizontally=function(e){var t=this.centerRowContainerCtrl.getViewportElement().scrollLeft;return this.setHorizontalScrollPosition(t+e),this.centerRowContainerCtrl.getViewportElement().scrollLeft-t},t.prototype.scrollToTop=function(){this.eBodyViewport.scrollTop=0},t.prototype.ensureNodeVisible=function(e,t){void 0===t&&(t=null);for(var o=this.rowModel.getRowCount(),n="function"===typeof e,r=-1,i=0;i=0&&this.ensureIndexVisible(r,t)},t.prototype.ensureIndexVisible=function(e,t){if(this.gridOptionsWrapper.getDomLayout()!==Ne.DOM_LAYOUT_PRINT){var o=this.paginationProxy.getRowCount();if("number"!==typeof e||e<0||e>=o)console.warn("invalid row index for ensureIndexVisible: "+e);else{this.gridOptionsWrapper.isPagination()&&!this.gridOptionsWrapper.isSuppressPaginationPanel()||this.paginationProxy.goToPageWithIndex(e);var n,r=this.paginationProxy.getRow(e);do{var i=r.rowTop,s=r.rowHeight,a=this.paginationProxy.getPixelOffset(),l=r.rowTop-a,u=l+r.rowHeight,p=this.getVScrollPosition(),c=this.heightScaler.getDivStretchOffset(),d=p.top+c,h=p.bottom+c,f=h-d,g=this.heightScaler.getScrollPositionForPixel(l),y=this.heightScaler.getScrollPositionForPixel(u-f),v=Math.min((g+y)/2,l),m=null;"top"===t?m=g:"bottom"===t?m=y:"middle"===t?m=v:d>l?m=g:hs:ni}},t.prototype.getColumnBounds=function(e){var t=this.enableRtl,o=this.columnModel.getBodyContainerWidth(),n=e.getActualWidth(),r=e.getLeft(),i=t?-1:1,s=t?o-r:r;return{colLeft:s,colMiddle:s+n/2*i,colRight:s+n*i}},t.prototype.getViewportBounds=function(){var e=this.centerRowContainerCtrl.getCenterWidth(),t=this.centerRowContainerCtrl.getCenterViewportScrollLeft();return{start:t,end:e+t,width:e}},gl([Re("ctrlsService")],t.prototype,"ctrlsService",void 0),gl([Re("animationFrameService")],t.prototype,"animationFrameService",void 0),gl([Re("columnApi")],t.prototype,"columnApi",void 0),gl([Re("gridApi")],t.prototype,"gridApi",void 0),gl([Re("paginationProxy")],t.prototype,"paginationProxy",void 0),gl([Re("rowModel")],t.prototype,"rowModel",void 0),gl([Re("rowContainerHeightService")],t.prototype,"heightScaler",void 0),gl([Re("rowRenderer")],t.prototype,"rowRenderer",void 0),gl([Re("columnModel")],t.prototype,"columnModel",void 0),gl([Ce],t.prototype,"postConstruct",null),t}(mt),vl=function(){function e(e){this.tickingInterval=null,this.onScrollCallback=null,this.scrollContainer=e.scrollContainer,this.scrollHorizontally=-1!==e.scrollAxis.indexOf("x"),this.scrollVertically=-1!==e.scrollAxis.indexOf("y"),this.scrollByTick=null!=e.scrollByTick?e.scrollByTick:20,e.onScrollCallback&&(this.onScrollCallback=e.onScrollCallback),this.scrollVertically&&(this.getVerticalPosition=e.getVerticalPosition,this.setVerticalPosition=e.setVerticalPosition),this.scrollHorizontally&&(this.getHorizontalPosition=e.getHorizontalPosition,this.setHorizontalPosition=e.setHorizontalPosition),this.shouldSkipVerticalScroll=e.shouldSkipVerticalScroll||function(){return!1},this.shouldSkipHorizontalScroll=e.shouldSkipHorizontalScroll||function(){return!1}}return e.prototype.check=function(e,t){void 0===t&&(t=!1);var o=t||this.shouldSkipVerticalScroll();if(!o||!this.shouldSkipHorizontalScroll()){var n=this.scrollContainer.getBoundingClientRect(),r=this.scrollByTick;this.tickLeft=e.clientXn.right-r,this.tickUp=e.clientYn.bottom-r&&!o,this.tickLeft||this.tickRight||this.tickUp||this.tickDown?this.ensureTickingStarted():this.ensureCleared()}},e.prototype.ensureTickingStarted=function(){null===this.tickingInterval&&(this.tickingInterval=window.setInterval(this.doTick.bind(this),100),this.tickCount=0)},e.prototype.doTick=function(){var e;if(this.tickCount++,e=this.tickCount>20?200:this.tickCount>10?80:40,this.scrollVertically){var t=this.getVerticalPosition();this.tickUp&&this.setVerticalPosition(t-e),this.tickDown&&this.setVerticalPosition(t+e)}if(this.scrollHorizontally){var o=this.getHorizontalPosition();this.tickLeft&&this.setHorizontalPosition(o-e),this.tickRight&&this.setHorizontalPosition(o+e)}this.onScrollCallback&&this.onScrollCallback()},e.prototype.ensureCleared=function(){this.tickingInterval&&(window.clearInterval(this.tickingInterval),this.tickingInterval=null)},e}(),ml=function(){var e=function(t,o){return e=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(e,t){e.__proto__=t}||function(e,t){for(var o in t)t.hasOwnProperty(o)&&(e[o]=t[o])},e(t,o)};return function(t,o){function n(){this.constructor=t}e(t,o),t.prototype=null===o?Object.create(o):(n.prototype=o.prototype,new n)}}(),Cl=function(){return Cl=Object.assign||function(e){for(var t,o=1,n=arguments.length;o=0;a--)(r=e[a])&&(s=(i<3?r(s):i>3?r(t,o,s):r(t,o))||s);return i>3&&s&&Object.defineProperty(t,o,s),s},El=function(e,t){var o="function"===typeof Symbol&&e[Symbol.iterator];if(!o)return e;var n,r,i=o.call(e),s=[];try{for(;(void 0===t||t-- >0)&&!(n=i.next()).done;)s.push(n.value)}catch(a){r={error:a}}finally{try{n&&!n.done&&(o=i.return)&&o.call(i)}finally{if(r)throw r.error}}return s},Rl=function(){for(var e=[],t=0;tthis.paginationProxy.getCurrentPageHeight()||(i=this.rowModel.getRowIndexAtPixel(r),o=this.rowModel.getRow(i)),t.vDirection){case Vs.Down:n="down";break;case Vs.Up:n="up";break;default:n=null}return{type:e,api:this.gridOptionsWrapper.getApi(),columnApi:this.gridOptionsWrapper.getColumnApi(),event:t.event,node:t.dragItem.rowNode,nodes:t.dragItem.rowNodes,overIndex:i,overNode:o,y:r,vDirection:n}},t.prototype.dispatchGridEvent=function(e,t){var o=this.draggingToRowDragEvent(e,t);this.eventService.dispatchEvent(o)},t.prototype.onDragLeave=function(e){this.dispatchGridEvent(St.EVENT_ROW_DRAG_LEAVE,e),this.stopDragging(e),this.gridOptionsWrapper.isRowDragManaged()&&this.clearRowHighlight(),this.isFromThisGrid(e)&&(this.isMultiRowDrag=!1)},t.prototype.onDragStop=function(e){this.dispatchGridEvent(St.EVENT_ROW_DRAG_END,e),this.stopDragging(e),!this.gridOptionsWrapper.isRowDragManaged()||!this.gridOptionsWrapper.isSuppressMoveWhenRowDragging()&&this.isFromThisGrid(e)||this.isDropZoneWithinThisGrid(e)||this.moveRowAndClearHighlight(e)},t.prototype.stopDragging=function(e){this.autoScrollService.ensureCleared(),this.getRowNodes(e).forEach((function(e){e.setDragging(!1)}))},wl([Re("dragAndDropService")],t.prototype,"dragAndDropService",void 0),wl([Re("rowModel")],t.prototype,"rowModel",void 0),wl([Re("paginationProxy")],t.prototype,"paginationProxy",void 0),wl([Re("columnModel")],t.prototype,"columnModel",void 0),wl([Re("focusService")],t.prototype,"focusService",void 0),wl([Re("sortController")],t.prototype,"sortController",void 0),wl([Re("filterManager")],t.prototype,"filterManager",void 0),wl([Re("selectionService")],t.prototype,"selectionService",void 0),wl([Re("mouseEventService")],t.prototype,"mouseEventService",void 0),wl([Re("ctrlsService")],t.prototype,"ctrlsService",void 0),wl([Se("rangeService")],t.prototype,"rangeService",void 0),wl([Re("columnApi")],t.prototype,"columnApi",void 0),wl([Re("gridApi")],t.prototype,"gridApi",void 0),wl([Ce],t.prototype,"postConstruct",null),t}(mt),Ol=function(){var e=function(t,o){return e=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(e,t){e.__proto__=t}||function(e,t){for(var o in t)t.hasOwnProperty(o)&&(e[o]=t[o])},e(t,o)};return function(t,o){function n(){this.constructor=t}e(t,o),t.prototype=null===o?Object.create(o):(n.prototype=o.prototype,new n)}}(),bl=function(e,t,o,n){var r,i=arguments.length,s=i<3?t:null===n?n=Object.getOwnPropertyDescriptor(t,o):n;if("object"===typeof Reflect&&"function"===typeof Reflect.decorate)s=Reflect.decorate(e,t,o,n);else for(var a=e.length-1;a>=0;a--)(r=e[a])&&(s=(i<3?r(s):i>3?r(t,o,s):r(t,o))||s);return i>3&&s&&Object.defineProperty(t,o,s),s};!function(e){e.ANIMATION_ON="ag-row-animation",e.ANIMATION_OFF="ag-row-no-animation"}(dl||(dl={}));var Pl,_l,Tl="ag-selectable",Dl="ag-force-vertical-scroll",Al="ag-column-moving",Nl=function(e){function t(){return null!==e&&e.apply(this,arguments)||this}return Ol(t,e),t.prototype.getScrollFeature=function(){return this.bodyScrollFeature},t.prototype.getBodyViewportElement=function(){return this.eBodyViewport},t.prototype.setComp=function(e,t,o,n,r){this.comp=e,this.eGridBody=t,this.eBodyViewport=o,this.eTop=n,this.eBottom=r,this.setCellTextSelection(this.gridOptionsWrapper.isEnableCellTextSelect()),this.createManagedBean(new hl(this.comp)),this.bodyScrollFeature=this.createManagedBean(new yl(this.eBodyViewport)),this.addRowDragListener(),this.setupRowAnimationCssClass(),this.addEventListeners(),this.addFocusListeners([n,o,r]),this.onGridColumnsChanged(),this.addBodyViewportListener(),this.setFloatingHeights(),this.disableBrowserDragging(),this.addStopEditingWhenGridLosesFocus(),this.ctrlsService.registerGridBodyCtrl(this)},t.prototype.getComp=function(){return this.comp},t.prototype.addEventListeners=function(){this.addManagedListener(this.eventService,St.EVENT_GRID_COLUMNS_CHANGED,this.onGridColumnsChanged.bind(this)),this.addManagedListener(this.eventService,St.EVENT_SCROLL_VISIBILITY_CHANGED,this.onScrollVisibilityChanged.bind(this)),this.addManagedListener(this.eventService,St.EVENT_PINNED_ROW_DATA_CHANGED,this.setFloatingHeights.bind(this))},t.prototype.addFocusListeners=function(e){var t=this;e.forEach((function(e){t.addManagedListener(e,"focusin",(function(){e.classList.add("ag-has-focus")})),t.addManagedListener(e,"focusout",(function(t){e.contains(t.relatedTarget)||e.classList.remove("ag-has-focus")}))}))},t.prototype.setColumnMovingCss=function(e){this.comp.setColumnMovingCss(e?Al:null,e)},t.prototype.setCellTextSelection=function(e){void 0===e&&(e=!1);var t=e?Tl:null;this.comp.setCellSelectableCss(t,e)},t.prototype.onScrollVisibilityChanged=function(){var e=this.scrollVisibleService.isVerticalScrollShowing();this.setVerticalScrollPaddingVisible(e)},t.prototype.onGridColumnsChanged=function(){var e=this.columnModel.getAllGridColumns();this.comp.setColumnCount(e?e.length:0)},t.prototype.disableBrowserDragging=function(){this.addManagedListener(this.eGridBody,"dragstart",(function(e){if(e.target instanceof HTMLImageElement)return e.preventDefault(),!1}))},t.prototype.addStopEditingWhenGridLosesFocus=function(){var e=this;if(this.gridOptionsWrapper.isStopEditingWhenCellsLoseFocus()){var t=function(t){var n=t.relatedTarget;if(null!==_o(n)){var r=o.some((function(e){return e.contains(n)}))&&e.mouseEventService.isElementInThisGrid(n);if(!r){var i=e.popupService;r=i.getActivePopups().some((function(e){return e.contains(n)}))||i.isElementWithinCustomPopup(n)}r||e.rowRenderer.stopEditing()}else e.rowRenderer.stopEditing()},o=[this.eBodyViewport,this.eBottom,this.eTop];o.forEach((function(o){return e.addManagedListener(o,"focusout",t)}))}},t.prototype.updateRowCount=function(){var e=this.headerNavigationService.getHeaderRowCount(),t=this.paginationProxy.getType(),o=-1;t===Ne.ROW_MODEL_TYPE_CLIENT_SIDE&&(o=0,this.paginationProxy.forEachNode((function(e){e.group||o++})));var n=-1===o?-1:e+o;this.comp.setRowCount(n)},t.prototype.registerBodyViewportResizeListener=function(e){this.comp.registerBodyViewportResizeListener(e)},t.prototype.setVerticalScrollPaddingVisible=function(e){var t=e?"scroll":"hidden";this.comp.setPinnedTopBottomOverflowY(t)},t.prototype.isVerticalScrollShowing=function(){var e=this.gridOptionsWrapper.isAlwaysShowVerticalScroll(),t=e?Dl:null;return this.comp.setAlwaysVerticalScrollClass(t,e),e||cn(this.eBodyViewport)},t.prototype.setupRowAnimationCssClass=function(){var e=this,t=function(){var t=e.gridOptionsWrapper.isAnimateRows()&&!e.rowContainerHeightService.isStretching(),o=t?dl.ANIMATION_ON:dl.ANIMATION_OFF;e.comp.setRowAnimationCssOnBodyViewport(o,t)};t(),this.addManagedListener(this.eventService,St.EVENT_HEIGHT_SCALE_CHANGED,t)},t.prototype.getGridBodyElement=function(){return this.eGridBody},t.prototype.addBodyViewportListener=function(){var e=this;this.addManagedListener(this.eBodyViewport,"contextmenu",(function(t){e.gridOptionsWrapper.isPreventDefaultOnContextMenu()&&t.preventDefault();var o=t.target;o!==e.eBodyViewport&&o!==e.ctrlsService.getCenterRowContainerCtrl().getViewportElement()||e.contextMenuFactory&&e.contextMenuFactory.onContextMenu(t,null,null,null,null,e.eGridBody)})),this.addManagedListener(this.eBodyViewport,"wheel",this.onWheel.bind(this))},t.prototype.onWheel=function(e){this.gridOptionsWrapper.isSuppressScrollWhenPopupsAreOpen()&&this.popupService.hasAnchoredPopup()&&e.preventDefault()},t.prototype.getGui=function(){return this.eGridBody},t.prototype.scrollVertically=function(e){var t=this.eBodyViewport.scrollTop;return this.bodyScrollFeature.setVerticalScrollPosition(t+e),this.eBodyViewport.scrollTop-t},t.prototype.addRowDragListener=function(){this.rowDragFeature=this.createManagedBean(new Sl(this.eBodyViewport)),this.dragAndDropService.addDropTarget(this.rowDragFeature)},t.prototype.getRowDragFeature=function(){return this.rowDragFeature},t.prototype.setFloatingHeights=function(){var e=this.pinnedRowModel,t=e.getPinnedTopTotalHeight();t&&(t+=1);var o=e.getPinnedBottomTotalHeight();o&&(o+=1),this.comp.setTopHeight(t),this.comp.setBottomHeight(o),this.comp.setTopDisplay(t?"inherit":"none"),this.comp.setBottomDisplay(o?"inherit":"none")},t.prototype.sizeColumnsToFit=function(e){var t=this,o=this.isVerticalScrollShowing()?this.gridOptionsWrapper.getScrollbarWidth():0,n=jo(this.eBodyViewport)-o;n>0?this.columnModel.sizeColumnsToFit(n,"sizeColumnsToFit"):void 0===e?window.setTimeout((function(){t.sizeColumnsToFit(100)}),0):100===e?window.setTimeout((function(){t.sizeColumnsToFit(500)}),100):500===e?window.setTimeout((function(){t.sizeColumnsToFit(-1)}),500):console.warn("AG Grid: tried to call sizeColumnsToFit() but the grid is coming back with zero width, maybe the grid is not visible yet on the screen?")},t.prototype.addScrollEventListener=function(e){this.eBodyViewport.addEventListener("scroll",e)},t.prototype.removeScrollEventListener=function(e){this.eBodyViewport.removeEventListener("scroll",e)},bl([Re("rowContainerHeightService")],t.prototype,"rowContainerHeightService",void 0),bl([Re("ctrlsService")],t.prototype,"ctrlsService",void 0),bl([Re("columnModel")],t.prototype,"columnModel",void 0),bl([Re("scrollVisibleService")],t.prototype,"scrollVisibleService",void 0),bl([Se("contextMenuFactory")],t.prototype,"contextMenuFactory",void 0),bl([Re("headerNavigationService")],t.prototype,"headerNavigationService",void 0),bl([Re("paginationProxy")],t.prototype,"paginationProxy",void 0),bl([Re("dragAndDropService")],t.prototype,"dragAndDropService",void 0),bl([Re("pinnedRowModel")],t.prototype,"pinnedRowModel",void 0),bl([Re("rowRenderer")],t.prototype,"rowRenderer",void 0),bl([Re("popupService")],t.prototype,"popupService",void 0),bl([Re("mouseEventService")],t.prototype,"mouseEventService",void 0),t}(mt);!function(e){e[e.FILL=0]="FILL",e[e.RANGE=1]="RANGE"}(Pl||(Pl={})),function(e){e[e.VALUE=0]="VALUE",e[e.DIMENSION=1]="DIMENSION"}(_l||(_l={}));var Fl,Il="ag-cell-range-selected",Ml=function(){function e(e,t){this.beans=e,this.cellCtrl=t}return e.prototype.setComp=function(e,t){this.cellComp=e,this.eGui=t,this.onRangeSelectionChanged()},e.prototype.onRangeSelectionChanged=function(){this.cellComp&&(this.rangeCount=this.beans.rangeService.getCellRangeCount(this.cellCtrl.getCellPosition()),this.hasChartRange=this.getHasChartRange(),this.cellComp.addOrRemoveCssClass(Il,0!==this.rangeCount),this.cellComp.addOrRemoveCssClass(Il+"-1",1===this.rangeCount),this.cellComp.addOrRemoveCssClass(Il+"-2",2===this.rangeCount),this.cellComp.addOrRemoveCssClass(Il+"-3",3===this.rangeCount),this.cellComp.addOrRemoveCssClass(Il+"-4",this.rangeCount>=4),this.cellComp.addOrRemoveCssClass("ag-cell-range-chart",this.hasChartRange),or(this.eGui,this.rangeCount>0||void 0),this.cellComp.addOrRemoveCssClass("ag-cell-range-single-cell",this.isSingleCell()),this.updateRangeBorders(),this.refreshHandle())},e.prototype.updateRangeBorders=function(){var e=this.getRangeBorders(),t=this.isSingleCell(),o=!t&&e.top,n=!t&&e.right,r=!t&&e.bottom,i=!t&&e.left;this.cellComp.addOrRemoveCssClass("ag-cell-range-top",o),this.cellComp.addOrRemoveCssClass("ag-cell-range-right",n),this.cellComp.addOrRemoveCssClass("ag-cell-range-bottom",r),this.cellComp.addOrRemoveCssClass("ag-cell-range-left",i)},e.prototype.isSingleCell=function(){var e=this.beans.rangeService;return 1===this.rangeCount&&e&&!e.isMoreThanOneCell()},e.prototype.getHasChartRange=function(){var e=this.beans.rangeService;if(!this.rangeCount||!e)return!1;var t=e.getCellRanges();return t.length>0&&t.every((function(e){return Xe([_l.DIMENSION,_l.VALUE],e.type)}))},e.prototype.updateRangeBordersIfRangeCount=function(){this.rangeCount>0&&(this.updateRangeBorders(),this.refreshHandle())},e.prototype.getRangeBorders=function(){var e,t,o=this,n=this.beans.gridOptionsWrapper.isEnableRtl(),r=!1,i=!1,s=!1,a=!1,l=this.cellCtrl.getCellPosition().column,u=this.beans,p=u.rangeService,c=u.columnModel;n?(e=c.getDisplayedColAfter(l),t=c.getDisplayedColBefore(l)):(e=c.getDisplayedColBefore(l),t=c.getDisplayedColAfter(l));var d=p.getCellRanges().filter((function(e){return p.isCellInSpecificRange(o.cellCtrl.getCellPosition(),e)}));e||(a=!0),t||(i=!0);for(var h=0;h=0;a--)(r=e[a])&&(s=(i<3?r(s):i>3?r(t,o,s):r(t,o))||s);return i>3&&s&&Object.defineProperty(t,o,s),s},Bl=function(){function e(){}return e.prototype.postConstruct=function(){this.doingMasterDetail=this.gridOptionsWrapper.isMasterDetail(),this.gridOptionsWrapper.isRowModelDefault()&&(this.clientSideRowModel=this.rowModel),this.gridOptionsWrapper.isRowModelServerSide()&&(this.serverSideRowModel=this.rowModel)},kl([Re("resizeObserverService")],e.prototype,"resizeObserverService",void 0),kl([Re("paginationProxy")],e.prototype,"paginationProxy",void 0),kl([Re("context")],e.prototype,"context",void 0),kl([Re("columnApi")],e.prototype,"columnApi",void 0),kl([Re("gridApi")],e.prototype,"gridApi",void 0),kl([Re("gridOptionsWrapper")],e.prototype,"gridOptionsWrapper",void 0),kl([Re("expressionService")],e.prototype,"expressionService",void 0),kl([Re("rowRenderer")],e.prototype,"rowRenderer",void 0),kl([Re("templateService")],e.prototype,"templateService",void 0),kl([Re("valueService")],e.prototype,"valueService",void 0),kl([Re("eventService")],e.prototype,"eventService",void 0),kl([Re("columnModel")],e.prototype,"columnModel",void 0),kl([Re("headerNavigationService")],e.prototype,"headerNavigationService",void 0),kl([Re("navigationService")],e.prototype,"navigationService",void 0),kl([Re("columnAnimationService")],e.prototype,"columnAnimationService",void 0),kl([Se("rangeService")],e.prototype,"rangeService",void 0),kl([Re("focusService")],e.prototype,"focusService",void 0),kl([Se("contextMenuFactory")],e.prototype,"contextMenuFactory",void 0),kl([Re("popupService")],e.prototype,"popupService",void 0),kl([Re("valueFormatterService")],e.prototype,"valueFormatterService",void 0),kl([Re("stylingService")],e.prototype,"stylingService",void 0),kl([Re("columnHoverService")],e.prototype,"columnHoverService",void 0),kl([Re("userComponentFactory")],e.prototype,"userComponentFactory",void 0),kl([Re("userComponentRegistry")],e.prototype,"userComponentRegistry",void 0),kl([Re("animationFrameService")],e.prototype,"animationFrameService",void 0),kl([Re("dragAndDropService")],e.prototype,"dragAndDropService",void 0),kl([Re("sortController")],e.prototype,"sortController",void 0),kl([Re("filterManager")],e.prototype,"filterManager",void 0),kl([Re("rowContainerHeightService")],e.prototype,"rowContainerHeightService",void 0),kl([Re("frameworkOverrides")],e.prototype,"frameworkOverrides",void 0),kl([Re("cellPositionUtils")],e.prototype,"cellPositionUtils",void 0),kl([Re("rowPositionUtils")],e.prototype,"rowPositionUtils",void 0),kl([Re("selectionService")],e.prototype,"selectionService",void 0),kl([Se("selectionHandleFactory")],e.prototype,"selectionHandleFactory",void 0),kl([Re("rowCssClassCalculator")],e.prototype,"rowCssClassCalculator",void 0),kl([Re("rowModel")],e.prototype,"rowModel",void 0),kl([Re("ctrlsService")],e.prototype,"ctrlsService",void 0),kl([Re("ctrlsFactory")],e.prototype,"ctrlsFactory",void 0),kl([Re("agStackComponentsRegistry")],e.prototype,"agStackComponentsRegistry",void 0),kl([Re("valueCache")],e.prototype,"valueCache",void 0),kl([Re("rowNodeEventThrottle")],e.prototype,"rowNodeEventThrottle",void 0),kl([Ce],e.prototype,"postConstruct",null),e=kl([Ee("beans")],e)}(),jl=function(){var e=function(t,o){return e=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(e,t){e.__proto__=t}||function(e,t){for(var o in t)t.hasOwnProperty(o)&&(e[o]=t[o])},e(t,o)};return function(t,o){function n(){this.constructor=t}e(t,o),t.prototype=null===o?Object.create(o):(n.prototype=o.prototype,new n)}}(),Ul=function(e){function t(t,o,n){var r=e.call(this)||this;return r.cellCtrl=t,r.beans=o,r.column=n,r}return jl(t,e),t.prototype.onMouseEvent=function(e,t){if(!lt(t))switch(e){case"click":this.onCellClicked(t);break;case"mousedown":case"touchstart":this.onMouseDown(t);break;case"dblclick":this.onCellDoubleClicked(t);break;case"mouseout":this.onMouseOut(t);break;case"mouseover":this.onMouseOver(t)}},t.prototype.onCellClicked=function(e){if(this.isDoubleClickOnIPad())return this.onCellDoubleClicked(e),void e.preventDefault();var t=this.beans,o=t.eventService,n=t.gridOptionsWrapper,r=this.cellCtrl.createEvent(e,St.EVENT_CELL_CLICKED);o.dispatchEvent(r);var i=this.column.getColDef();i.onCellClicked&&window.setTimeout((function(){return i.onCellClicked(r)}),0),(n.isSingleClickEdit()||i.singleClickEdit)&&!n.isSuppressClickEdit()&&this.cellCtrl.startRowOrCellEdit()},t.prototype.isDoubleClickOnIPad=function(){if(!Po()||ut("dblclick"))return!1;var e=(new Date).getTime(),t=e-this.lastIPadMouseClickEvent<200;return this.lastIPadMouseClickEvent=e,t},t.prototype.onCellDoubleClicked=function(e){var t=this.column.getColDef(),o=this.cellCtrl.createEvent(e,St.EVENT_CELL_DOUBLE_CLICKED);this.beans.eventService.dispatchEvent(o),"function"===typeof t.onCellDoubleClicked&&window.setTimeout((function(){return t.onCellDoubleClicked(o)}),0),!this.beans.gridOptionsWrapper.isSingleClickEdit()&&!this.beans.gridOptionsWrapper.isSuppressClickEdit()&&this.cellCtrl.startRowOrCellEdit(null,null,e)},t.prototype.onMouseDown=function(e){var t=e.ctrlKey,o=e.metaKey,n=e.shiftKey,r=e.target,i=this.beans,s=i.eventService,a=i.rangeService;if(!this.isRightClickInExistingRange(e)){var l=a&&0!=a.getCellRanges().length;if(!n||!l){var u=Ro()&&!this.cellCtrl.isEditing()&&!Lo(r);this.cellCtrl.focusCell(u)}if(n&&l&&e.preventDefault(),!this.containsWidget(r)){if(a){var p=this.cellCtrl.getCellPosition();if(n)a.extendLatestRangeToCell(p);else{var c=t||o;a.setRangeToCell(p,c)}}s.dispatchEvent(this.cellCtrl.createEvent(e,St.EVENT_CELL_MOUSE_DOWN))}}},t.prototype.isRightClickInExistingRange=function(e){var t=this.beans.rangeService;if(t&&(t.isCellInAnyRange(this.cellCtrl.getCellPosition())&&2===e.button))return!0;return!1},t.prototype.containsWidget=function(e){return Wo(e,"ag-selection-checkbox",3)},t.prototype.onMouseOut=function(e){if(!this.mouseStayingInsideCell(e)){var t=this.cellCtrl.createEvent(e,St.EVENT_CELL_MOUSE_OUT);this.beans.eventService.dispatchEvent(t),this.beans.columnHoverService.clearMouseOver()}},t.prototype.onMouseOver=function(e){if(!this.mouseStayingInsideCell(e)){var t=this.cellCtrl.createEvent(e,St.EVENT_CELL_MOUSE_OVER);this.beans.eventService.dispatchEvent(t),this.beans.columnHoverService.setMouseOver([this.column])}},t.prototype.mouseStayingInsideCell=function(e){if(!e.target||!e.relatedTarget)return!1;var t=this.cellCtrl.getGui(),o=t.contains(e.target),n=t.contains(e.relatedTarget);return o&&n},t.prototype.destroy=function(){},t}(Bl),zl=function(){var e=function(t,o){return e=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(e,t){e.__proto__=t}||function(e,t){for(var o in t)t.hasOwnProperty(o)&&(e[o]=t[o])},e(t,o)};return function(t,o){function n(){this.constructor=t}e(t,o),t.prototype=null===o?Object.create(o):(n.prototype=o.prototype,new n)}}(),Kl=function(e){function t(t,o,n,r,i){var s=e.call(this)||this;return s.cellCtrl=t,s.beans=o,s.rowNode=r,s.rowCtrl=i,s}return zl(t,e),t.prototype.setComp=function(e){this.eGui=e},t.prototype.onKeyDown=function(e){var t=e.key;switch(t){case ii.ENTER:this.onEnterKeyDown(e);break;case ii.F2:this.onF2KeyDown(e);break;case ii.ESCAPE:this.onEscapeKeyDown(e);break;case ii.TAB:this.onTabKeyDown(e);break;case ii.BACKSPACE:case ii.DELETE:this.onBackspaceOrDeleteKeyPressed(t,e);break;case ii.DOWN:case ii.UP:case ii.RIGHT:case ii.LEFT:this.onNavigationKeyPressed(e,t)}},t.prototype.onNavigationKeyPressed=function(e,t){this.cellCtrl.isEditing()||(e.shiftKey&&this.cellCtrl.isRangeSelectionEnabled()?this.onShiftRangeSelect(e):this.beans.navigationService.navigateToNextCell(e,t,this.cellCtrl.getCellPosition(),!0),e.preventDefault())},t.prototype.onShiftRangeSelect=function(e){if(this.beans.rangeService){var t=this.beans.rangeService.extendLatestRangeInDirection(e);t&&this.beans.navigationService.ensureCellVisible(t)}},t.prototype.onTabKeyDown=function(e){this.beans.navigationService.onTabKeyDown(this.cellCtrl,e)},t.prototype.onBackspaceOrDeleteKeyPressed=function(e,t){this.cellCtrl.isEditing()||this.cellCtrl.startRowOrCellEdit(e,void 0,t)},t.prototype.onEnterKeyDown=function(e){this.cellCtrl.isEditing()||this.rowCtrl.isEditing()?this.cellCtrl.stopEditingAndFocus():this.beans.gridOptionsWrapper.isEnterMovesDown()?this.beans.navigationService.navigateToNextCell(null,ii.DOWN,this.cellCtrl.getCellPosition(),!1):(this.cellCtrl.startRowOrCellEdit(ii.ENTER,void 0,e),this.cellCtrl.isEditing()&&e.preventDefault())},t.prototype.onF2KeyDown=function(e){this.cellCtrl.isEditing()||this.cellCtrl.startRowOrCellEdit(ii.F2,void 0,e)},t.prototype.onEscapeKeyDown=function(e){this.cellCtrl.isEditing()&&(this.cellCtrl.stopRowOrCellEdit(!0),this.cellCtrl.focusCell(!0))},t.prototype.onKeyPress=function(e){if(!(e.target!==this.eGui)&&!this.cellCtrl.isEditing()){var t=String.fromCharCode(e.charCode);" "===t?this.onSpaceKeyPressed(e):br(e)&&(this.cellCtrl.startRowOrCellEdit(null,t,e),e.preventDefault())}},t.prototype.onSpaceKeyPressed=function(e){var t=this.beans.gridOptionsWrapper;if(!this.cellCtrl.isEditing()&&t.isRowSelection()){var o=this.rowNode.isSelected(),n=!o;if(n||!t.isSuppressRowDeselection()){var r=this.beans.gridOptionsWrapper.isGroupSelectsFiltered(),i=this.rowNode.setSelectedParams({newValue:n,rangeSelect:e.shiftKey,groupSelectsFiltered:r});void 0===o&&0===i&&this.rowNode.setSelectedParams({newValue:!1,rangeSelect:e.shiftKey,groupSelectsFiltered:r})}}e.preventDefault()},t.prototype.destroy=function(){},t}(mt),Yl=function(){var e=function(t,o){return e=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(e,t){e.__proto__=t}||function(e,t){for(var o in t)t.hasOwnProperty(o)&&(e[o]=t[o])},e(t,o)};return function(t,o){function n(){this.constructor=t}e(t,o),t.prototype=null===o?Object.create(o):(n.prototype=o.prototype,new n)}}(),ql=function(e,t,o,n){var r,i=arguments.length,s=i<3?t:null===n?n=Object.getOwnPropertyDescriptor(t,o):n;if("object"===typeof Reflect&&"function"===typeof Reflect.decorate)s=Reflect.decorate(e,t,o,n);else for(var a=e.length-1;a>=0;a--)(r=e[a])&&(s=(i<3?r(s):i>3?r(t,o,s):r(t,o))||s);return i>3&&s&&Object.defineProperty(t,o,s),s},Xl=function(e){function t(t,o,n,r){var i=e.call(this,'
')||this;return i.rowNode=t,i.column=o,i.beans=n,i.eCell=r,i}return Yl(t,e),t.prototype.postConstruct=function(){this.getGui().appendChild(Rr("rowDrag",this.beans.gridOptionsWrapper,null)),this.addGuiEventListener("mousedown",(function(e){e.stopPropagation()})),this.addDragSource(),this.checkVisibility()},t.prototype.addDragSource=function(){this.addGuiEventListener("dragstart",this.onDragStart.bind(this))},t.prototype.onDragStart=function(e){var t=this,o=this.column.getColDef().dndSourceOnRowDrag;e.dataTransfer.setDragImage(this.eCell,0,0);o?o({rowNode:this.rowNode,dragEvent:e,api:this.gridOptionsWrapper.getApi(),columnApi:this.gridOptionsWrapper.getColumnApi(),context:this.gridOptionsWrapper.getContext()}):function(){try{var o=JSON.stringify(t.rowNode.data);e.dataTransfer.setData("application/json",o),e.dataTransfer.setData("text/plain",o)}catch(n){}}()},t.prototype.checkVisibility=function(){var e=this.column.isDndSource(this.rowNode);this.setDisplayed(e)},ql([Ce],t.prototype,"postConstruct",null),t}(Yr),Ql=function(){var e=function(t,o){return e=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(e,t){e.__proto__=t}||function(e,t){for(var o in t)t.hasOwnProperty(o)&&(e[o]=t[o])},e(t,o)};return function(t,o){function n(){this.constructor=t}e(t,o),t.prototype=null===o?Object.create(o):(n.prototype=o.prototype,new n)}}(),$l=function(){return $l=Object.assign||function(e){for(var t,o=1,n=arguments.length;o0)&&!(n=i.next()).done;)s.push(n.value)}catch(a){r={error:a}}finally{try{n&&!n.done&&(o=i.return)&&o.call(i)}finally{if(r)throw r.error}}return s},nu=function(){for(var e=[],t=0;t=0)&&o}return o},t.prototype.setAnimateFlags=function(e){if(e){var t=O(this.rowNode.oldRowTop);this.slideRowIn=t,this.fadeRowIn=!t}else this.slideRowIn=!1,this.fadeRowIn=!1},t.prototype.isEditing=function(){return this.editingRow},t.prototype.stopRowEditing=function(e){this.stopEditing(e)},t.prototype.isFullWidth=function(){return this.rowType!==Fl.Normal},t.prototype.getRowType=function(){return this.rowType},t.prototype.refreshFullWidth=function(){var e=this,t=function(t,o){if(!t)return!0;var n=t.rowComp.getFullWidthCellRenderer();if(!n)return!1;if(!n.refresh)return!1;var r=e.createFullWidthParams(t.element,o);return n.refresh(r)},o=t(this.fullWidthGui,null),n=t(this.centerGui,null),r=t(this.leftGui,Ne.PINNED_LEFT),i=t(this.rightGui,Ne.PINNED_RIGHT);return o&&n&&r&&i},t.prototype.addListeners=function(){this.addManagedListener(this.rowNode,Ws.EVENT_HEIGHT_CHANGED,this.onRowHeightChanged.bind(this)),this.addManagedListener(this.rowNode,Ws.EVENT_ROW_SELECTED,this.onRowSelected.bind(this)),this.addManagedListener(this.rowNode,Ws.EVENT_ROW_INDEX_CHANGED,this.onRowIndexChanged.bind(this)),this.addManagedListener(this.rowNode,Ws.EVENT_TOP_CHANGED,this.onTopChanged.bind(this)),this.addManagedListener(this.rowNode,Ws.EVENT_EXPANDED_CHANGED,this.updateExpandedCss.bind(this)),this.addManagedListener(this.rowNode,Ws.EVENT_HAS_CHILDREN_CHANGED,this.updateExpandedCss.bind(this)),this.addManagedListener(this.rowNode,Ws.EVENT_DATA_CHANGED,this.onRowNodeDataChanged.bind(this)),this.addManagedListener(this.rowNode,Ws.EVENT_CELL_CHANGED,this.onRowNodeCellChanged.bind(this)),this.addManagedListener(this.rowNode,Ws.EVENT_HIGHLIGHT_CHANGED,this.onRowNodeHighlightChanged.bind(this)),this.addManagedListener(this.rowNode,Ws.EVENT_DRAGGING_CHANGED,this.onRowNodeDraggingChanged.bind(this)),this.addManagedListener(this.rowNode,Ws.EVENT_UI_LEVEL_CHANGED,this.onUiLevelChanged.bind(this));var e=this.beans.eventService;this.addManagedListener(e,St.EVENT_PAGINATION_PIXEL_OFFSET_CHANGED,this.onPaginationPixelOffsetChanged.bind(this)),this.addManagedListener(e,St.EVENT_HEIGHT_SCALE_CHANGED,this.onTopChanged.bind(this)),this.addManagedListener(e,St.EVENT_DISPLAYED_COLUMNS_CHANGED,this.onDisplayedColumnsChanged.bind(this)),this.addManagedListener(e,St.EVENT_VIRTUAL_COLUMNS_CHANGED,this.onVirtualColumnsChanged.bind(this)),this.addManagedListener(e,St.EVENT_CELL_FOCUSED,this.onCellFocusChanged.bind(this)),this.addManagedListener(e,St.EVENT_PAGINATION_CHANGED,this.onPaginationChanged.bind(this)),this.addManagedListener(e,St.EVENT_MODEL_UPDATED,this.onModelUpdated.bind(this)),this.addManagedListener(e,St.EVENT_COLUMN_MOVED,this.onColumnMoved.bind(this)),this.addListenersForCellComps()},t.prototype.onColumnMoved=function(){this.updateColumnLists()},t.prototype.addListenersForCellComps=function(){var e=this;this.addManagedListener(this.rowNode,Ws.EVENT_ROW_INDEX_CHANGED,(function(){e.getAllCellCtrls().forEach((function(e){return e.onRowIndexChanged()}))})),this.addManagedListener(this.rowNode,Ws.EVENT_CELL_CHANGED,(function(t){e.getAllCellCtrls().forEach((function(e){return e.onCellChanged(t)}))}))},t.prototype.onRowNodeDataChanged=function(e){this.getAllCellCtrls().forEach((function(t){return t.refreshCell({suppressFlash:!e.update,newData:!e.update})})),this.onRowSelected(),this.postProcessCss()},t.prototype.onRowNodeCellChanged=function(){this.postProcessCss()},t.prototype.postProcessCss=function(){this.setStylesFromGridOptions(),this.postProcessClassesFromGridOptions(),this.postProcessRowClassRules(),this.postProcessRowDragging()},t.prototype.onRowNodeHighlightChanged=function(){var e=this.rowNode.highlighted;this.allRowGuis.forEach((function(t){var o=e===Fi.Above,n=e===Fi.Below;t.rowComp.addOrRemoveCssClass("ag-row-highlight-above",o),t.rowComp.addOrRemoveCssClass("ag-row-highlight-below",n)}))},t.prototype.onRowNodeDraggingChanged=function(){this.postProcessRowDragging()},t.prototype.postProcessRowDragging=function(){var e=this.rowNode.dragging;this.allRowGuis.forEach((function(t){return t.rowComp.addOrRemoveCssClass("ag-row-dragging",e)}))},t.prototype.updateExpandedCss=function(){var e=this.rowNode.isExpandable(),t=1==this.rowNode.expanded;this.allRowGuis.forEach((function(o){o.rowComp.addOrRemoveCssClass("ag-row-group",e),o.rowComp.addOrRemoveCssClass("ag-row-group-expanded",e&&t),o.rowComp.addOrRemoveCssClass("ag-row-group-contracted",e&&!t),Un(o.element,e&&t)}))},t.prototype.onDisplayedColumnsChanged=function(){this.updateColumnLists(!0),this.beans.columnModel.wasAutoRowHeightEverActive()&&this.rowNode.checkAutoHeights()},t.prototype.onVirtualColumnsChanged=function(){this.updateColumnLists()},t.prototype.getRowPosition=function(){return{rowPinned:S(this.rowNode.rowPinned),rowIndex:this.rowNode.rowIndex}},t.prototype.onKeyboardNavigate=function(e){var t=this.allRowGuis.find((function(t){return t.element.contains(e.target)}));if((t?t.element:null)===e.target){var o=this.rowNode,n=this.beans.focusService.getFocusedCell(),r={rowIndex:o.rowIndex,rowPinned:o.rowPinned,column:n&&n.column};this.beans.navigationService.navigateToNextCell(e,e.key,r,!0),e.preventDefault()}},t.prototype.onTabKeyDown=function(e){if(!e.defaultPrevented&&!lt(e)){var t=this.allRowGuis.find((function(t){return t.element.contains(e.target)})),o=t?t.element:null,n=o===e.target,r=null;n||(r=this.beans.focusService.findNextFocusableElement(o,!1,e.shiftKey)),(this.isFullWidth()&&n||!r)&&this.beans.navigationService.onTabKeyDown(this,e)}},t.prototype.onFullWidthRowFocused=function(e){var t,o=this.rowNode,n=this.isFullWidth()&&e.rowIndex===o.rowIndex&&e.rowPinned==o.rowPinned,r=this.fullWidthGui?this.fullWidthGui.element:null===(t=this.centerGui)||void 0===t?void 0:t.element;r&&(r.classList.toggle("ag-full-width-focus",n),n&&r.focus({preventScroll:!0}))},t.prototype.refreshCell=function(e){this.centerCellCtrls=this.removeCellCtrl(this.centerCellCtrls,e),this.leftCellCtrls=this.removeCellCtrl(this.leftCellCtrls,e),this.rightCellCtrls=this.removeCellCtrl(this.rightCellCtrls,e),this.updateColumnLists()},t.prototype.removeCellCtrl=function(e,t){var o={list:[],map:{}};return e.list.forEach((function(e){e!==t&&(o.list.push(e),o.map[e.getInstanceId()]=e)})),o},t.prototype.onMouseEvent=function(e,t){switch(e){case"dblclick":this.onRowDblClick(t);break;case"click":this.onRowClick(t);break;case"touchstart":case"mousedown":this.onRowMouseDown(t)}},t.prototype.createRowEvent=function(e,t){return{type:e,node:this.rowNode,data:this.rowNode.data,rowIndex:this.rowNode.rowIndex,rowPinned:this.rowNode.rowPinned,context:this.beans.gridOptionsWrapper.getContext(),api:this.beans.gridOptionsWrapper.getApi(),columnApi:this.beans.gridOptionsWrapper.getColumnApi(),event:t}},t.prototype.createRowEventWithSource=function(e,t){var o=this.createRowEvent(e,t);return o.source=this,o},t.prototype.onRowDblClick=function(e){if(!lt(e)){var t=this.createRowEventWithSource(St.EVENT_ROW_DOUBLE_CLICKED,e);this.beans.eventService.dispatchEvent(t)}},t.prototype.onRowMouseDown=function(e){if(this.lastMouseDownOnDragger=Wo(e.target,"ag-row-drag",3),this.isFullWidth()){var t=this.rowNode,o=this.beans.columnModel;this.beans.focusService.setFocusedCell(t.rowIndex,o.getAllDisplayedColumns()[0],t.rowPinned,!0)}},t.prototype.onRowClick=function(e){if(!(lt(e)||this.lastMouseDownOnDragger)){var t=this.createRowEventWithSource(St.EVENT_ROW_CLICKED,e);this.beans.eventService.dispatchEvent(t);var o=e.ctrlKey||e.metaKey,n=e.shiftKey;if(!(this.beans.gridOptionsWrapper.isGroupSelectsChildren()&&this.rowNode.group||!this.rowNode.selectable||this.rowNode.rowPinned||!this.beans.gridOptionsWrapper.isRowSelection()||this.beans.gridOptionsWrapper.isSuppressRowClickSelection())){var r=this.beans.gridOptionsWrapper.isRowMultiSelectWithClick(),i=!this.beans.gridOptionsWrapper.isSuppressRowDeselection();if(this.rowNode.isSelected())r?this.rowNode.setSelectedParams({newValue:!1}):o?i&&this.rowNode.setSelectedParams({newValue:!1}):this.rowNode.setSelectedParams({newValue:!0,clearSelection:!n,rangeSelect:n});else{var s=!r&&!o;this.rowNode.setSelectedParams({newValue:!0,clearSelection:s,rangeSelect:n})}}}},t.prototype.setupDetailRowAutoHeight=function(e){var t=this;if(this.rowType===Fl.FullWidthDetail&&this.beans.gridOptionsWrapper.isDetailRowAutoHeight()){var o=function(){var o=e.clientHeight;if(null!=o&&o>0){t.beans.frameworkOverrides.setTimeout((function(){t.rowNode.setRowHeight(o),t.beans.clientSideRowModel?t.beans.clientSideRowModel.onRowHeightChanged():t.beans.serverSideRowModel&&t.beans.serverSideRowModel.onRowHeightChanged()}),0)}},n=this.beans.resizeObserverService.observeResize(e,o);this.addDestroyFunc(n),o()}},t.prototype.createFullWidthParams=function(e,t){var o=this;return{fullWidth:!0,data:this.rowNode.data,node:this.rowNode,value:this.rowNode.key,valueFormatted:this.rowNode.key,rowIndex:this.rowNode.rowIndex,api:this.beans.gridOptionsWrapper.getApi(),columnApi:this.beans.gridOptionsWrapper.getColumnApi(),context:this.beans.gridOptionsWrapper.getContext(),eGridCell:e,eParentOfValue:e,pinned:t,addRenderedRowListener:this.addEventListener.bind(this),registerRowDragger:function(e,t,n,r){return o.addFullWidthRowDragging(e,t,n,r)}}},t.prototype.addFullWidthRowDragging=function(e,t,o,n){if(void 0===o&&(o=""),this.isFullWidth()){var r=new $s((function(){return o}),this.rowNode,void 0,e,t,n);this.createManagedBean(r,this.beans.context)}},t.prototype.onUiLevelChanged=function(){var e=this.beans.rowCssClassCalculator.calculateRowLevel(this.rowNode);if(this.rowLevel!=e){var t="ag-row-level-"+e,o="ag-row-level-"+this.rowLevel;this.allRowGuis.forEach((function(e){e.rowComp.addOrRemoveCssClass(t,!0),e.rowComp.addOrRemoveCssClass(o,!1)}))}this.rowLevel=e},t.prototype.isFirstRowOnPage=function(){return this.rowNode.rowIndex===this.beans.paginationProxy.getPageFirstRow()},t.prototype.isLastRowOnPage=function(){return this.rowNode.rowIndex===this.beans.paginationProxy.getPageLastRow()},t.prototype.onModelUpdated=function(){this.refreshFirstAndLastRowStyles()},t.prototype.refreshFirstAndLastRowStyles=function(){var e=this.isFirstRowOnPage(),t=this.isLastRowOnPage();this.firstRowOnPage!==e&&(this.firstRowOnPage=e,this.allRowGuis.forEach((function(t){return t.rowComp.addOrRemoveCssClass("ag-row-first",e)}))),this.lastRowOnPage!==t&&(this.lastRowOnPage=t,this.allRowGuis.forEach((function(e){return e.rowComp.addOrRemoveCssClass("ag-row-last",t)})))},t.prototype.stopEditing=function(e){void 0===e&&(e=!1);var t=this.getAllCellCtrls().map((function(t){return t.stopEditing(e)}));if(this.editingRow){if(!e&&t.some((function(e){return e}))){var o=this.createRowEvent(St.EVENT_ROW_VALUE_CHANGED);this.beans.eventService.dispatchEvent(o)}this.setEditingRow(!1)}},t.prototype.setInlineEditingCss=function(e){this.allRowGuis.forEach((function(t){t.rowComp.addOrRemoveCssClass("ag-row-inline-editing",e),t.rowComp.addOrRemoveCssClass("ag-row-not-inline-editing",!e)}))},t.prototype.setEditingRow=function(e){this.editingRow=e,this.allRowGuis.forEach((function(t){return t.rowComp.addOrRemoveCssClass("ag-row-editing",e)}));var t=e?this.createRowEvent(St.EVENT_ROW_EDITING_STARTED):this.createRowEvent(St.EVENT_ROW_EDITING_STOPPED);this.beans.eventService.dispatchEvent(t)},t.prototype.startRowEditing=function(e,t,o,n){void 0===e&&(e=null),void 0===t&&(t=null),void 0===o&&(o=null),void 0===n&&(n=null),this.editingRow||(this.getAllCellCtrls().forEach((function(r){var i=r===o;i?r.startEditing(e,t,i,n):r.startEditing(null,null,i,n)})),this.setEditingRow(!0))},t.prototype.getAllCellCtrls=function(){return nu(this.centerCellCtrls.list,this.leftCellCtrls.list,this.rightCellCtrls.list)},t.prototype.postProcessClassesFromGridOptions=function(){var e=this,t=this.beans.rowCssClassCalculator.processClassesFromGridOptions(this.rowNode);t&&t.length&&t.forEach((function(t){e.allRowGuis.forEach((function(e){return e.rowComp.addOrRemoveCssClass(t,!0)}))}))},t.prototype.postProcessRowClassRules=function(){var e=this;this.beans.rowCssClassCalculator.processRowClassRules(this.rowNode,(function(t){e.allRowGuis.forEach((function(e){return e.rowComp.addOrRemoveCssClass(t,!0)}))}),(function(t){e.allRowGuis.forEach((function(e){return e.rowComp.addOrRemoveCssClass(t,!1)}))}))},t.prototype.setStylesFromGridOptions=function(){var e=this.processStylesFromGridOptions();this.allRowGuis.forEach((function(t){return t.rowComp.setUserStyles(e)}))},t.prototype.getRowBusinessKey=function(){var e=this.beans.gridOptionsWrapper.getBusinessKeyForNodeFunc();if("function"===typeof e)return e(this.rowNode)},t.prototype.getPinnedForContainer=function(e){return e===iu.LEFT?Ne.PINNED_LEFT:e===iu.RIGHT?Ne.PINNED_RIGHT:null},t.prototype.getInitialRowClasses=function(e){var t=this.getPinnedForContainer(e),o={rowNode:this.rowNode,rowFocused:this.rowFocused,fadeRowIn:this.fadeRowIn,rowIsEven:this.rowNode.rowIndex%2===0,rowLevel:this.rowLevel,fullWidthRow:this.isFullWidth(),firstRowOnPage:this.isFirstRowOnPage(),lastRowOnPage:this.isLastRowOnPage(),printLayout:this.printLayout,expandable:this.rowNode.isExpandable(),pinned:t};return this.beans.rowCssClassCalculator.getInitialRowClasses(o)},t.prototype.processStylesFromGridOptions=function(){var e=this.beans.gridOptionsWrapper.getRowStyle();if(!e||"function"!==typeof e){var t,o=this.beans.gridOptionsWrapper.getRowStyleFunc();if(o)t=o({data:this.rowNode.data,node:this.rowNode,rowIndex:this.rowNode.rowIndex});return Object.assign({},e,t)}console.warn("AG Grid: rowStyle should be an object of key/value styles, not be a function, use getRowStyle() instead")},t.prototype.onRowSelected=function(){var e=this,t=!!this.rowNode.isSelected();this.allRowGuis.forEach((function(o){o.rowComp.addOrRemoveCssClass("ag-row-selected",t),or(o.element,!!t||void 0);var n=e.createAriaLabel();Vn(o.element,null==n?"":n)}))},t.prototype.createAriaLabel=function(){var e=this.rowNode.isSelected();if(!e||!this.beans.gridOptionsWrapper.isSuppressRowDeselection())return this.beans.gridOptionsWrapper.getLocaleTextFunc()(e?"ariaRowDeselect":"ariaRowSelect","Press SPACE to "+(e?"deselect":"select")+" this row.")},t.prototype.isUseAnimationFrameForCreate=function(){return this.useAnimationFrameForCreate},t.prototype.addHoverFunctionality=function(e){var t=this;this.active&&(this.addManagedListener(e,"mouseenter",(function(){return t.rowNode.onMouseEnter()})),this.addManagedListener(e,"mouseleave",(function(){return t.rowNode.onMouseLeave()})),this.addManagedListener(this.rowNode,Ws.EVENT_MOUSE_ENTER,(function(){t.beans.gridOptionsWrapper.isSuppressRowHoverHighlight()||e.classList.add("ag-row-hover")})),this.addManagedListener(this.rowNode,Ws.EVENT_MOUSE_LEAVE,(function(){e.classList.remove("ag-row-hover")})))},t.prototype.roundRowTopToBounds=function(e){var t=this.beans.ctrlsService.getGridBodyCtrl().getScrollFeature().getVScrollPosition(),o=this.applyPaginationOffset(t.top,!0)-100,n=this.applyPaginationOffset(t.bottom,!0)+100;return Math.min(Math.max(o,e),n)},t.prototype.getFrameworkOverrides=function(){return this.beans.frameworkOverrides},t.prototype.onRowHeightChanged=function(){if(null!=this.rowNode.rowHeight){var e=this.rowNode.rowHeight,t=this.beans.gridOptionsWrapper.getDefaultRowHeight(),o=this.beans.gridOptionsWrapper.isGetRowHeightFunction()?this.beans.gridOptionsWrapper.getRowHeightForNode(this.rowNode).height:void 0,n=o?Math.min(t,o)-2+"px":void 0;this.allRowGuis.forEach((function(t){t.element.style.height=e+"px",n&&t.element.style.setProperty("--ag-line-height",n)}))}},t.prototype.addEventListener=function(t,o){"renderedRowRemoved"!==t&&"rowRemoved"!==t||(t=St.EVENT_VIRTUAL_ROW_REMOVED,console.warn("AG Grid: Since version 11, event renderedRowRemoved is now called "+St.EVENT_VIRTUAL_ROW_REMOVED)),e.prototype.addEventListener.call(this,t,o)},t.prototype.removeEventListener=function(t,o){"renderedRowRemoved"!==t&&"rowRemoved"!==t||(t=St.EVENT_VIRTUAL_ROW_REMOVED,console.warn("AG Grid: Since version 11, event renderedRowRemoved and rowRemoved is now called "+St.EVENT_VIRTUAL_ROW_REMOVED)),e.prototype.removeEventListener.call(this,t,o)},t.prototype.destroyFirstPass=function(){this.active=!1,this.setupRemoveAnimation();var t=this.createRowEvent(St.EVENT_VIRTUAL_ROW_REMOVED);this.dispatchEvent(t),this.beans.eventService.dispatchEvent(t),e.prototype.destroy.call(this)},t.prototype.setupRemoveAnimation=function(){if(null!=this.rowNode.rowTop){var e=this.roundRowTopToBounds(this.rowNode.rowTop);this.setRowTop(e)}else this.allRowGuis.forEach((function(e){return e.rowComp.addOrRemoveCssClass("ag-opacity-zero",!0)}))},t.prototype.destroySecondPass=function(){this.allRowGuis.length=0;var e=function(e){return e.list.forEach((function(e){return e.destroy()})),{list:[],map:{}}};this.centerCellCtrls=e(this.centerCellCtrls),this.leftCellCtrls=e(this.leftCellCtrls),this.rightCellCtrls=e(this.rightCellCtrls)},t.prototype.setFocusedClasses=function(){var e=this;this.allRowGuis.forEach((function(t){t.rowComp.addOrRemoveCssClass("ag-row-focus",e.rowFocused),t.rowComp.addOrRemoveCssClass("ag-row-no-focus",!e.rowFocused)}))},t.prototype.onCellFocusChanged=function(){var e=this.beans.focusService.isRowFocused(this.rowNode.rowIndex,this.rowNode.rowPinned);e!==this.rowFocused&&(this.rowFocused=e,this.setFocusedClasses()),!e&&this.editingRow&&this.stopEditing(!1)},t.prototype.onPaginationChanged=function(){var e=this.beans.paginationProxy.getCurrentPage();this.paginationPage!==e&&(this.paginationPage=e,this.onTopChanged()),this.refreshFirstAndLastRowStyles()},t.prototype.onTopChanged=function(){this.setRowTop(this.rowNode.rowTop)},t.prototype.onPaginationPixelOffsetChanged=function(){this.onTopChanged()},t.prototype.applyPaginationOffset=function(e,t){return void 0===t&&(t=!1),this.rowNode.isRowPinned()?e:e+this.beans.paginationProxy.getPixelOffset()*(t?1:-1)},t.prototype.setRowTop=function(e){if(!this.printLayout&&O(e)){var t=this.applyPaginationOffset(e),o=(this.rowNode.isRowPinned()?t:this.beans.rowContainerHeightService.getRealPixelPosition(t))+"px";this.setRowTopStyle(o)}},t.prototype.getInitialRowTop=function(){return this.initialTop},t.prototype.getInitialTransform=function(){return this.initialTransform},t.prototype.setInitialRowTop=function(){if(this.printLayout)return"";var e=this.slideRowIn?this.roundRowTopToBounds(this.rowNode.oldRowTop):this.rowNode.rowTop,t=this.applyPaginationOffset(e),o=(this.rowNode.isRowPinned()?t:this.beans.rowContainerHeightService.getRealPixelPosition(t))+"px";this.beans.gridOptionsWrapper.isSuppressRowTransform()?this.initialTop=o:this.initialTransform="translateY("+o+")"},t.prototype.setRowTopStyle=function(e){var t=this.beans.gridOptionsWrapper.isSuppressRowTransform();this.allRowGuis.forEach((function(o){return t?o.rowComp.setTop(e):o.rowComp.setTransform("translateY("+e+")")}))},t.prototype.getRowNode=function(){return this.rowNode},t.prototype.getCellCtrl=function(e){var t=null;return this.getAllCellCtrls().forEach((function(o){o.getColumn()==e&&(t=o)})),null!=t||this.getAllCellCtrls().forEach((function(o){o.getColSpanningList().indexOf(e)>=0&&(t=o)})),t},t.prototype.onRowIndexChanged=function(){null!=this.rowNode.rowIndex&&(this.onCellFocusChanged(),this.updateRowIndexes(),this.postProcessCss())},t.prototype.updateRowIndexes=function(){var e=this.rowNode.getRowIndexString(),t=this.beans.headerNavigationService.getHeaderRowCount(),o=this.rowNode.rowIndex%2===0,n=t+this.rowNode.rowIndex+1;this.allRowGuis.forEach((function(t){t.rowComp.setRowIndex(e),t.rowComp.addOrRemoveCssClass("ag-row-even",o),t.rowComp.addOrRemoveCssClass("ag-row-odd",!o),Qn(t.element,n)}))},t.prototype.getPinnedLeftRowElement=function(){return this.leftGui?this.leftGui.element:void 0},t.prototype.getPinnedRightRowElement=function(){return this.rightGui?this.rightGui.element:void 0},t.prototype.getBodyRowElement=function(){return this.centerGui?this.centerGui.element:void 0},t.prototype.getFullWidthRowElement=function(){return this.fullWidthGui?this.fullWidthGui.element:void 0},t.DOM_DATA_KEY_ROW_CTRL="renderedRow",t}(mt),lu=function(){var e=function(t,o){return e=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(e,t){e.__proto__=t}||function(e,t){for(var o in t)t.hasOwnProperty(o)&&(e[o]=t[o])},e(t,o)};return function(t,o){function n(){this.constructor=t}e(t,o),t.prototype=null===o?Object.create(o):(n.prototype=o.prototype,new n)}}(),uu=function(e,t,o,n){var r,i=arguments.length,s=i<3?t:null===n?n=Object.getOwnPropertyDescriptor(t,o):n;if("object"===typeof Reflect&&"function"===typeof Reflect.decorate)s=Reflect.decorate(e,t,o,n);else for(var a=e.length-1;a>=0;a--)(r=e[a])&&(s=(i<3?r(s):i>3?r(t,o,s):r(t,o))||s);return i>3&&s&&Object.defineProperty(t,o,s),s},pu=function(e,t){var o="function"===typeof Symbol&&e[Symbol.iterator];if(!o)return e;var n,r,i=o.call(e),s=[];try{for(;(void 0===t||t-- >0)&&!(n=i.next()).done;)s.push(n.value)}catch(a){r={error:a}}finally{try{n&&!n.done&&(o=i.return)&&o.call(i)}finally{if(r)throw r.error}}return s},cu=function(e){function t(t){var o=e.call(this)||this;return o.element=t,o}return lu(t,e),t.prototype.postConstruct=function(){this.addMouseListeners(),this.mockContextMenuForIPad(),this.addKeyboardEvents()},t.prototype.addKeyboardEvents=function(){var e=this;["keydown","keypress"].forEach((function(t){var o=e.processKeyboardEvent.bind(e,t);e.addManagedListener(e.element,t,o)}))},t.prototype.addMouseListeners=function(){var e=this;["dblclick","contextmenu","mouseover","mouseout","click",ut("touchstart")?"touchstart":"mousedown"].forEach((function(t){var o=e.processMouseEvent.bind(e,t);e.addManagedListener(e.element,t,o)}))},t.prototype.processMouseEvent=function(e,t){if(this.mouseEventService.isEventFromThisGrid(t)&&!lt(t)){var o=this.getRowForEvent(t),n=this.mouseEventService.getRenderedCellForEvent(t);"contextmenu"===e?this.handleContextMenuMouseEvent(t,null,o,n):(n&&n.onMouseEvent(e,t),o&&o.onMouseEvent(e,t))}},t.prototype.mockContextMenuForIPad=function(){var e=this;if(Po()){var t=new us(this.element);this.addManagedListener(t,us.EVENT_LONG_TAP,(function(t){var o=e.getRowForEvent(t.touchEvent),n=e.mouseEventService.getRenderedCellForEvent(t.touchEvent);e.handleContextMenuMouseEvent(null,t.touchEvent,o,n)})),this.addDestroyFunc((function(){return t.destroy()}))}},t.prototype.getRowForEvent=function(e){for(var t=e.target;t;){var o=this.gridOptionsWrapper.getDomData(t,au.DOM_DATA_KEY_ROW_CTRL);if(o)return o;t=t.parentElement}return null},t.prototype.handleContextMenuMouseEvent=function(e,t,o,n){var r=o?o.getRowNode():null,i=n?n.getColumn():null,s=null;if(i){var a=e||t;n.dispatchCellContextMenuEvent(a),s=this.valueService.getValue(i,r)}var l=this.ctrlsService.getGridBodyCtrl(),u=n?n.getGui():l.getGridBodyElement();this.contextMenuFactory&&this.contextMenuFactory.onContextMenu(e,t,r,i,s,u)},t.prototype.processKeyboardEvent=function(e,t){var o=pt(this.gridOptionsWrapper,t,eu.DOM_DATA_KEY_CELL_CTRL),n=pt(this.gridOptionsWrapper,t,au.DOM_DATA_KEY_ROW_CTRL);t.defaultPrevented||(o?this.processCellKeyboardEvent(o,e,t):n&&n.isFullWidth()&&this.processFullWidthRowKeyboardEvent(n,e,t))},t.prototype.processCellKeyboardEvent=function(e,t,o){var n=e.getRowNode(),r=e.getColumn(),i=e.isEditing();if(!Pr(this.gridOptionsWrapper,o,n,r,i))switch(t){case"keydown":!i&&this.navigationService.handlePageScrollingKey(o)||e.onKeyDown(o),this.doGridOperations(o,e.isEditing());break;case"keypress":e.onKeyPress(o)}if("keydown"===t){var s=e.createEvent(o,St.EVENT_CELL_KEY_DOWN);this.eventService.dispatchEvent(s)}if("keypress"===t){var a=e.createEvent(o,St.EVENT_CELL_KEY_PRESS);this.eventService.dispatchEvent(a)}},t.prototype.processFullWidthRowKeyboardEvent=function(e,t,o){var n=e.getRowNode(),r=this.focusService.getFocusedCell(),i=r&&r.column;if(!Pr(this.gridOptionsWrapper,o,n,i,!1)){var s=o.key;if("keydown"===t)switch(s){case ii.UP:case ii.DOWN:e.onKeyboardNavigate(o);break;case ii.TAB:e.onTabKeyDown(o)}}if("keydown"===t){var a=e.createRowEvent(St.EVENT_CELL_KEY_DOWN,o);this.eventService.dispatchEvent(a)}if("keypress"===t){var l=e.createRowEvent(St.EVENT_CELL_KEY_PRESS,o);this.eventService.dispatchEvent(l)}},t.prototype.doGridOperations=function(e,t){if((e.ctrlKey||e.metaKey)&&!t&&this.mouseEventService.isEventFromThisGrid(e))switch(e.code){case ii.A:return this.onCtrlAndA(e);case ii.C:return this.onCtrlAndC(e);case ii.V:return this.onCtrlAndV();case ii.D:return this.onCtrlAndD(e);case ii.Z:return e.shiftKey?this.undoRedoService.redo():this.undoRedoService.undo();case ii.Y:return this.undoRedoService.redo()}},t.prototype.onCtrlAndA=function(e){var t=this,o=t.pinnedRowModel,n=t.paginationProxy,r=t.rangeService,i=Ne.PINNED_BOTTOM,s=Ne.PINNED_TOP;if(r&&n.isRowsToRender()){var a=pu([o.isEmpty(s),o.isEmpty(i)],2),l=a[0]?null:s,u=void 0,p=void 0;a[1]?(u=null,p=this.paginationProxy.getRowCount()-1):(u=i,p=o.getPinnedBottomRowData().length-1);var c=this.columnModel.getAllDisplayedColumns();if(P(c))return;r.setCellRange({rowStartIndex:0,rowStartPinned:l,rowEndIndex:p,rowEndPinned:u,columnStart:c[0],columnEnd:Ve(c)})}e.preventDefault()},t.prototype.onCtrlAndC=function(e){this.clipboardService&&!this.gridOptionsWrapper.isEnableCellTextSelection()&&(this.clipboardService.copyToClipboard(),e.preventDefault())},t.prototype.onCtrlAndV=function(){Fe.isRegistered(_e.ClipboardModule)&&!this.gridOptionsWrapper.isSuppressClipboardPaste()&&this.clipboardService.pasteFromClipboard()},t.prototype.onCtrlAndD=function(e){Fe.isRegistered(_e.ClipboardModule)&&!this.gridOptionsWrapper.isSuppressClipboardPaste()&&this.clipboardService.copyRangeDown(),e.preventDefault()},uu([Re("mouseEventService")],t.prototype,"mouseEventService",void 0),uu([Re("valueService")],t.prototype,"valueService",void 0),uu([Se("contextMenuFactory")],t.prototype,"contextMenuFactory",void 0),uu([Re("ctrlsService")],t.prototype,"ctrlsService",void 0),uu([Re("navigationService")],t.prototype,"navigationService",void 0),uu([Re("focusService")],t.prototype,"focusService",void 0),uu([Re("undoRedoService")],t.prototype,"undoRedoService",void 0),uu([Re("columnModel")],t.prototype,"columnModel",void 0),uu([Re("paginationProxy")],t.prototype,"paginationProxy",void 0),uu([Re("pinnedRowModel")],t.prototype,"pinnedRowModel",void 0),uu([Se("rangeService")],t.prototype,"rangeService",void 0),uu([Se("clipboardService")],t.prototype,"clipboardService",void 0),uu([Ce],t.prototype,"postConstruct",null),t}(mt),du=function(){var e=function(t,o){return e=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(e,t){e.__proto__=t}||function(e,t){for(var o in t)t.hasOwnProperty(o)&&(e[o]=t[o])},e(t,o)};return function(t,o){function n(){this.constructor=t}e(t,o),t.prototype=null===o?Object.create(o):(n.prototype=o.prototype,new n)}}(),hu=function(e,t,o,n){var r,i=arguments.length,s=i<3?t:null===n?n=Object.getOwnPropertyDescriptor(t,o):n;if("object"===typeof Reflect&&"function"===typeof Reflect.decorate)s=Reflect.decorate(e,t,o,n);else for(var a=e.length-1;a>=0;a--)(r=e[a])&&(s=(i<3?r(s):i>3?r(t,o,s):r(t,o))||s);return i>3&&s&&Object.defineProperty(t,o,s),s},fu=function(e){function t(t){var o=e.call(this)||this;return o.centerContainerCtrl=t,o}return du(t,e),t.prototype.postConstruct=function(){var e=this;this.ctrlsService.whenReady((function(){e.gridBodyCtrl=e.ctrlsService.getGridBodyCtrl(),e.listenForResize()})),this.addManagedListener(this.eventService,St.EVENT_SCROLLBAR_WIDTH_CHANGED,this.onScrollbarWidthChanged.bind(this))},t.prototype.listenForResize=function(){var e=this,t=function(){return e.onCenterViewportResized()};this.centerContainerCtrl.registerViewportResizeListener(t),this.gridBodyCtrl.registerBodyViewportResizeListener(t)},t.prototype.onScrollbarWidthChanged=function(){this.checkViewportAndScrolls()},t.prototype.onCenterViewportResized=function(){if(this.centerContainerCtrl.isViewportVisible()){this.checkViewportAndScrolls();var e=this.centerContainerCtrl.getCenterWidth();e!==this.centerWidth&&(this.centerWidth=e,this.columnModel.refreshFlexedColumns({viewportWidth:this.centerWidth,updateBodyWidths:!0,fireResizedEvent:!0}))}else this.bodyHeight=0},t.prototype.checkViewportAndScrolls=function(){this.updateScrollVisibleService(),this.checkBodyHeight(),this.onHorizontalViewportChanged(),this.gridBodyCtrl.getScrollFeature().checkScrollLeft()},t.prototype.getBodyHeight=function(){return this.bodyHeight},t.prototype.checkBodyHeight=function(){var e=Bo(this.gridBodyCtrl.getBodyViewportElement());if(this.bodyHeight!==e){this.bodyHeight=e;var t={type:St.EVENT_BODY_HEIGHT_CHANGED,api:this.gridApi,columnApi:this.columnApi};this.eventService.dispatchEvent(t)}},t.prototype.updateScrollVisibleService=function(){this.updateScrollVisibleServiceImpl(),setTimeout(this.updateScrollVisibleServiceImpl.bind(this),500)},t.prototype.updateScrollVisibleServiceImpl=function(){var e={horizontalScrollShowing:this.isHorizontalScrollShowing(),verticalScrollShowing:this.gridBodyCtrl.isVerticalScrollShowing()};this.scrollVisibleService.setScrollsVisible(e),this.gridBodyCtrl.setVerticalScrollPaddingVisible(e.verticalScrollShowing)},t.prototype.isHorizontalScrollShowing=function(){return this.gridOptionsWrapper.isAlwaysShowHorizontalScroll()||this.centerContainerCtrl.isViewportHScrollShowing()},t.prototype.onHorizontalViewportChanged=function(){var e=this.centerContainerCtrl.getCenterWidth(),t=this.centerContainerCtrl.getViewportScrollLeft();this.columnModel.setViewportPosition(e,t)},hu([Re("ctrlsService")],t.prototype,"ctrlsService",void 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t={dragStartPixels:0,eElement:this.eContainer,onDragStart:this.rangeService.onDragStart.bind(this.rangeService),onDragStop:this.rangeService.onDragStop.bind(this.rangeService),onDragging:this.rangeService.onDragging.bind(this.rangeService)};this.dragService.addDragSource(t),this.addDestroyFunc((function(){return e.dragService.removeDragSource(t)}))}},bu([Se("rangeService")],t.prototype,"rangeService",void 0),bu([Re("dragService")],t.prototype,"dragService",void 0),bu([Ce],t.prototype,"postConstruct",null),t}(mt),_u=function(){var e=function(t,o){return e=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(e,t){e.__proto__=t}||function(e,t){for(var o in t)t.hasOwnProperty(o)&&(e[o]=t[o])},e(t,o)};return function(t,o){function n(){this.constructor=t}e(t,o),t.prototype=null===o?Object.create(o):(n.prototype=o.prototype,new n)}}(),Tu=function(e,t,o,n){var r,i=arguments.length,s=i<3?t:null===n?n=Object.getOwnPropertyDescriptor(t,o):n;if("object"===typeof 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e=function(t,o){return e=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(e,t){e.__proto__=t}||function(e,t){for(var o in t)t.hasOwnProperty(o)&&(e[o]=t[o])},e(t,o)};return function(t,o){function n(){this.constructor=t}e(t,o),t.prototype=null===o?Object.create(o):(n.prototype=o.prototype,new n)}}(),Nu=function(e,t,o,n){var r,i=arguments.length,s=i<3?t:null===n?n=Object.getOwnPropertyDescriptor(t,o):n;if("object"===typeof Reflect&&"function"===typeof Reflect.decorate)s=Reflect.decorate(e,t,o,n);else for(var a=e.length-1;a>=0;a--)(r=e[a])&&(s=(i<3?r(s):i>3?r(t,o,s):r(t,o))||s);return i>3&&s&&Object.defineProperty(t,o,s),s},Fu=function(e,t){var o="function"===typeof Symbol&&e[Symbol.iterator];if(!o)return e;var n,r,i=o.call(e),s=[];try{for(;(void 0===t||t-- >0)&&!(n=i.next()).done;)s.push(n.value)}catch(a){r={error:a}}finally{try{n&&!n.done&&(o=i.return)&&o.call(i)}finally{if(r)throw r.error}}return s},Iu=function(){for(var e=[],t=0;t=0&&t()},t.prototype.getContainerElement=function(){return this.eContainer},t.prototype.getViewportSizeFeature=function(){return this.viewportSizeFeature},t.prototype.setComp=function(e,t,o,n){var r=this;this.comp=e,this.eContainer=t,this.eViewport=o,this.eWrapper=n,this.createManagedBean(new cu(this.eContainer)),this.addPreventScrollWhileDragging(),this.listenOnDomOrder(),this.stopHScrollOnPinnedRows();var i=[ru.TOP_CENTER,ru.TOP_LEFT,ru.TOP_RIGHT],s=[ru.BOTTOM_CENTER,ru.BOTTOM_LEFT,ru.BOTTOM_RIGHT],a=[ru.CENTER,ru.LEFT,ru.RIGHT],l=Iu(i,s,a),u=[ru.CENTER,ru.LEFT,ru.RIGHT,ru.FULL_WIDTH],p=[ru.CENTER,ru.TOP_CENTER,ru.BOTTOM_CENTER],c=[ru.LEFT,ru.BOTTOM_LEFT,ru.TOP_LEFT],d=[ru.RIGHT,ru.BOTTOM_RIGHT,ru.TOP_RIGHT];this.forContainers(c,(function(){return r.createManagedBean(new vu(r.eContainer))})),this.forContainers(d,(function(){return r.createManagedBean(new wu(r.eContainer))})),this.forContainers(u,(function(){return r.createManagedBean(new Su(r.eContainer,r.eWrapper))})),this.forContainers(l,(function(){return r.createManagedBean(new Pu(r.eContainer))})),this.forContainers(p,(function(){return r.createManagedBean(new Du((function(e){return r.comp.setContainerWidth(e+"px")})))})),this.addListeners(),this.registerWithCtrlsService()},t.prototype.addListeners=function(){var e=this;this.addManagedListener(this.eventService,St.EVENT_SCROLL_VISIBILITY_CHANGED,(function(){return e.onScrollVisibilityChanged()})),this.addManagedListener(this.eventService,St.EVENT_DISPLAYED_COLUMNS_CHANGED,(function(){return e.onDisplayedColumnsChanged()})),this.addManagedListener(this.eventService,St.EVENT_DISPLAYED_COLUMNS_WIDTH_CHANGED,(function(){return e.onDisplayedColumnsWidthChanged()})),this.addManagedListener(this.eventService,St.EVENT_DISPLAYED_ROWS_CHANGED,(function(){return e.onDisplayedRowsChanged()})),this.onScrollVisibilityChanged(),this.onDisplayedColumnsChanged(),this.onDisplayedColumnsWidthChanged(),this.onDisplayedRowsChanged()},t.prototype.listenOnDomOrder=function(){var e=this,t=function(){return e.comp.setDomOrder(e.gridOptionsWrapper.isEnsureDomOrder())};this.addManagedListener(this.gridOptionsWrapper,ul.PROP_DOM_LAYOUT,t),t()},t.prototype.stopHScrollOnPinnedRows=function(){var e=this;this.forContainers([ru.TOP_CENTER,ru.BOTTOM_CENTER],(function(){e.addManagedListener(e.eViewport,"scroll",(function(){return e.eViewport.scrollLeft=0}))}))},t.prototype.onDisplayedColumnsChanged=function(){var e=this;this.forContainers([ru.CENTER],(function(){return e.onHorizontalViewportChanged()}))},t.prototype.onDisplayedColumnsWidthChanged=function(){var e=this;this.forContainers([ru.CENTER],(function(){return e.onHorizontalViewportChanged()}))},t.prototype.onScrollVisibilityChanged=function(){if(this.name===ru.CENTER){var e=this.scrollVisibleService.isHorizontalScrollShowing()&&this.gridOptionsWrapper.getScrollbarWidth()||0,t=0==e?"100%":"calc(100% + "+e+"px)";this.comp.setViewportHeight(t)}},t.prototype.addPreventScrollWhileDragging=function(){var e=this,t=function(t){e.dragService.isDragging()&&t.cancelable&&t.preventDefault()};this.eContainer.addEventListener("touchmove",t,{passive:!1}),this.addDestroyFunc((function(){return e.eContainer.removeEventListener("touchmove",t)}))},t.prototype.onHorizontalViewportChanged=function(){var e=this.getCenterWidth(),t=this.getCenterViewportScrollLeft();this.columnModel.setViewportPosition(e,t)},t.prototype.getCenterWidth=function(){return jo(this.eViewport)},t.prototype.getCenterViewportScrollLeft=function(){return Yo(this.eViewport,this.enableRtl)},t.prototype.registerViewportResizeListener=function(e){var t=this.resizeObserverService.observeResize(this.eViewport,e);this.addDestroyFunc((function(){return t()}))},t.prototype.isViewportVisible=function(){return Jo(this.eViewport)},t.prototype.isViewportHScrollShowing=function(){return pn(this.eViewport)},t.prototype.getViewportScrollLeft=function(){return Yo(this.eViewport,this.enableRtl)},t.prototype.isHorizontalScrollShowing=function(){return this.gridOptionsWrapper.isAlwaysShowHorizontalScroll()||pn(this.eViewport)},t.prototype.getViewportElement=function(){return this.eViewport},t.prototype.setContainerTranslateX=function(e){this.eContainer.style.transform="translateX("+e+"px)"},t.prototype.getHScrollPosition=function(){return{left:this.eViewport.scrollLeft,right:this.eViewport.scrollLeft+this.eViewport.offsetWidth}},t.prototype.setCenterViewportScrollLeft=function(e){qo(this.eViewport,e,this.enableRtl)},t.prototype.onDisplayedRowsChanged=function(){var e=this,t=this.name===ru.TOP_FULL_WIDTH||this.name===ru.BOTTOM_FULL_WIDTH||this.name===ru.FULL_WIDTH,o=this.getRowCtrls().filter((function(o){var n=o.isFullWidth(),r=e.gridOptionsWrapper.getDomLayout()===Ne.DOM_LAYOUT_PRINT,i=e.embedFullWidthRows||r;return t?!i&&n:i||!n}));this.comp.setRowCtrls(o)},t.prototype.getRowCtrls=function(){switch(this.name){case ru.TOP_CENTER:case ru.TOP_LEFT:case ru.TOP_RIGHT:case ru.TOP_FULL_WIDTH:return this.rowRenderer.getTopRowCtrls();case ru.BOTTOM_CENTER:case ru.BOTTOM_LEFT:case ru.BOTTOM_RIGHT:case ru.BOTTOM_FULL_WIDTH:return this.rowRenderer.getBottomRowCtrls();default:return this.rowRenderer.getRowCtrls()}},Nu([Re("scrollVisibleService")],t.prototype,"scrollVisibleService",void 0),Nu([Re("dragService")],t.prototype,"dragService",void 0),Nu([Re("ctrlsService")],t.prototype,"ctrlsService",void 0),Nu([Re("columnModel")],t.prototype,"columnModel",void 0),Nu([Re("resizeObserverService")],t.prototype,"resizeObserverService",void 0),Nu([Re("rowRenderer")],t.prototype,"rowRenderer",void 0),Nu([Ce],t.prototype,"postConstruct",null),t}(mt),Hu=function(){var e=function(t,o){return e=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(e,t){e.__proto__=t}||function(e,t){for(var o in t)t.hasOwnProperty(o)&&(e[o]=t[o])},e(t,o)};return function(t,o){function n(){this.constructor=t}e(t,o),t.prototype=null===o?Object.create(o):(n.prototype=o.prototype,new n)}}(),Wu=function(e,t,o,n){var r,i=arguments.length,s=i<3?t:null===n?n=Object.getOwnPropertyDescriptor(t,o):n;if("object"===typeof Reflect&&"function"===typeof Reflect.decorate)s=Reflect.decorate(e,t,o,n);else for(var a=e.length-1;a>=0;a--)(r=e[a])&&(s=(i<3?r(s):i>3?r(t,o,s):r(t,o))||s);return i>3&&s&&Object.defineProperty(t,o,s),s},ku='
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n=e.eBodyViewport.classList;n.toggle(ll.AUTO_HEIGHT,o.autoHeight),n.toggle(ll.NORMAL,o.normal),n.toggle(ll.PRINT,o.print),e.addOrRemoveCssClass(ll.AUTO_HEIGHT,o.autoHeight),e.addOrRemoveCssClass(ll.NORMAL,o.normal),e.addOrRemoveCssClass(ll.PRINT,o.print)},setAlwaysVerticalScrollClass:function(t,o){return e.eBodyViewport.classList.toggle(Dl,o)},registerBodyViewportResizeListener:function(t){var o=e.resizeObserverService.observeResize(e.eBodyViewport,t);e.addDestroyFunc((function(){return o()}))},setPinnedTopBottomOverflowY:function(t){return e.eTop.style.overflowY=e.eBottom.style.overflowY=t},setCellSelectableCss:function(t,o){[e.eTop,e.eBodyViewport,e.eBottom].forEach((function(e){return e.classList.toggle(Tl,o)}))}};this.ctrl=this.createManagedBean(new Nl),this.ctrl.setComp(o,this.getGui(),this.eBodyViewport,this.eTop,this.eBottom),(this.rangeService||this.gridOptionsWrapper.isRowSelectionMulti())&&qn(this.getGui(),!0)},t.prototype.setRowAnimationCssOnBodyViewport=function(e,t){var o=this.eBodyViewport.classList;o.toggle(dl.ANIMATION_ON,t),o.toggle(dl.ANIMATION_OFF,!t)},t.prototype.getFloatingTopBottom=function(){return[this.eTop,this.eBottom]},Wu([Re("resizeObserverService")],t.prototype,"resizeObserverService",void 0),Wu([Se("rangeService")],t.prototype,"rangeService",void 0),Wu([Xr("eBodyViewport")],t.prototype,"eBodyViewport",void 0),Wu([Xr("eTop")],t.prototype,"eTop",void 0),Wu([Xr("eBottom")],t.prototype,"eBottom",void 0),Wu([Xr("gridHeader")],t.prototype,"headerRootComp",void 0),Wu([Ce],t.prototype,"init",null),t}(Yr),ju=function(){var e=function(t,o){return e=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(e,t){e.__proto__=t}||function(e,t){for(var o in t)t.hasOwnProperty(o)&&(e[o]=t[o])},e(t,o)};return function(t,o){function n(){this.constructor=t}e(t,o),t.prototype=null===o?Object.create(o):(n.prototype=o.prototype,new n)}}(),Uu=function(e,t,o,n){var r,i=arguments.length,s=i<3?t:null===n?n=Object.getOwnPropertyDescriptor(t,o):n;if("object"===typeof Reflect&&"function"===typeof Reflect.decorate)s=Reflect.decorate(e,t,o,n);else for(var a=e.length-1;a>=0;a--)(r=e[a])&&(s=(i<3?r(s):i>3?r(t,o,s):r(t,o))||s);return i>3&&s&&Object.defineProperty(t,o,s),s},zu=function(e){function t(){return null!==e&&e.apply(this,arguments)||this}return ju(t,e),t.prototype.postConstruct=function(){this.addManagedListener(this.eventService,St.EVENT_DISPLAYED_COLUMNS_CHANGED,this.onDisplayedColumnsChanged.bind(this)),this.addManagedListener(this.eventService,St.EVENT_DISPLAYED_COLUMNS_WIDTH_CHANGED,this.onDisplayedColumnsWidthChanged.bind(this))},t.prototype.onDisplayedColumnsChanged=function(){this.update()},t.prototype.onDisplayedColumnsWidthChanged=function(){this.update()},t.prototype.update=function(){this.updateImpl(),setTimeout(this.updateImpl.bind(this),500)},t.prototype.updateImpl=function(){var e=this.ctrlsService.getCenterRowContainerCtrl();if(e){var t={horizontalScrollShowing:e.isHorizontalScrollShowing(),verticalScrollShowing:this.isVerticalScrollShowing()};this.setScrollsVisible(t)}},t.prototype.setScrollsVisible=function(e){if(this.horizontalScrollShowing!==e.horizontalScrollShowing||this.verticalScrollShowing!==e.verticalScrollShowing){this.horizontalScrollShowing=e.horizontalScrollShowing,this.verticalScrollShowing=e.verticalScrollShowing;var t={type:St.EVENT_SCROLL_VISIBILITY_CHANGED,api:this.gridApi,columnApi:this.columnApi};this.eventService.dispatchEvent(t)}},t.prototype.isHorizontalScrollShowing=function(){return this.horizontalScrollShowing},t.prototype.isVerticalScrollShowing=function(){return this.verticalScrollShowing},Uu([Re("columnApi")],t.prototype,"columnApi",void 0),Uu([Re("gridApi")],t.prototype,"gridApi",void 0),Uu([Re("ctrlsService")],t.prototype,"ctrlsService",void 0),Uu([Ce],t.prototype,"postConstruct",null),t=Uu([Ee("scrollVisibleService")],t)}(mt),Ku=function(){var e=function(t,o){return e=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(e,t){e.__proto__=t}||function(e,t){for(var o in t)t.hasOwnProperty(o)&&(e[o]=t[o])},e(t,o)};return function(t,o){function n(){this.constructor=t}e(t,o),t.prototype=null===o?Object.create(o):(n.prototype=o.prototype,new n)}}(),Yu=function(e,t,o,n){var r,i=arguments.length,s=i<3?t:null===n?n=Object.getOwnPropertyDescriptor(t,o):n;if("object"===typeof Reflect&&"function"===typeof Reflect.decorate)s=Reflect.decorate(e,t,o,n);else for(var a=e.length-1;a>=0;a--)(r=e[a])&&(s=(i<3?r(s):i>3?r(t,o,s):r(t,o))||s);return i>3&&s&&Object.defineProperty(t,o,s),s},qu=function(e){function t(){var t=null!==e&&e.apply(this,arguments)||this;return t.gridInstanceId=o.gridInstanceSequence.next(),t}var o;return Ku(t,e),o=t,t.prototype.stampTopLevelGridCompWithGridInstance=function(e){e[o.GRID_DOM_KEY]=this.gridInstanceId},t.prototype.getRenderedCellForEvent=function(e){return pt(this.gridOptionsWrapper,e,eu.DOM_DATA_KEY_CELL_CTRL)},t.prototype.isEventFromThisGrid=function(e){return this.isElementInThisGrid(e.target)},t.prototype.isElementInThisGrid=function(e){for(var t=e;t;){var n=t[o.GRID_DOM_KEY];if(O(n))return n===this.gridInstanceId;t=t.parentElement}return!1},t.prototype.getCellPositionForEvent=function(e){var t=this.getRenderedCellForEvent(e);return t?t.getCellPosition():null},t.prototype.getNormalisedPosition=function(e){var t,o,n=this.gridOptionsWrapper.getDomLayout()===Ne.DOM_LAYOUT_NORMAL,r=e;if(null!=r.clientX||null!=r.clientY?(t=r.clientX,o=r.clientY):(t=r.x,o=r.y),n){var i=this.ctrlsService.getGridBodyCtrl(),s=i.getScrollFeature().getVScrollPosition();t+=i.getScrollFeature().getHScrollPosition().left,o+=s.top}return{x:t,y:o}},t.gridInstanceSequence=new Gr,t.GRID_DOM_KEY="__ag_grid_instance",Yu([Re("ctrlsService")],t.prototype,"ctrlsService",void 0),t=o=Yu([Ee("mouseEventService")],t)}(mt),Xu=function(){var e=function(t,o){return e=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(e,t){e.__proto__=t}||function(e,t){for(var o in t)t.hasOwnProperty(o)&&(e[o]=t[o])},e(t,o)};return function(t,o){function n(){this.constructor=t}e(t,o),t.prototype=null===o?Object.create(o):(n.prototype=o.prototype,new n)}}(),Qu=function(){return Qu=Object.assign||function(e){for(var t,o=1,n=arguments.length;o=0;a--)(r=e[a])&&(s=(i<3?r(s):i>3?r(t,o,s):r(t,o))||s);return i>3&&s&&Object.defineProperty(t,o,s),s},Ju=function(e){function t(){var t=e.call(this)||this;return t.onPageDown=he(t.onPageDown,100),t.onPageUp=he(t.onPageUp,100),t}return Xu(t,e),t.prototype.postConstruct=function(){var e=this;this.ctrlsService.whenReady((function(t){e.gridBodyCon=t.gridBodyCtrl}))},t.prototype.handlePageScrollingKey=function(e){var t=e.key,o=e.altKey,n=e.ctrlKey||e.metaKey,r=!!this.rangeService&&e.shiftKey,i=this.mouseEventService.getCellPositionForEvent(e);if(!i)return!1;var s=!1;switch(t){case ii.PAGE_HOME:case ii.PAGE_END:n||o||(this.onHomeOrEndKey(t),s=!0);break;case ii.LEFT:case ii.RIGHT:case ii.UP:case ii.DOWN:!n||o||r||(this.onCtrlUpDownLeftRight(t,i),s=!0);break;case ii.PAGE_DOWN:n||o||(this.onPageDown(i),s=!0);break;case ii.PAGE_UP:n||o||(this.onPageUp(i),s=!0)}return s&&e.preventDefault(),s},t.prototype.navigateTo=function(e){var t=e.scrollIndex,o=e.scrollType,n=e.scrollColumn,r=e.focusIndex,i=e.focusColumn;if(O(n)&&!n.isPinned()&&this.gridBodyCon.getScrollFeature().ensureColumnVisible(n),O(t)&&this.gridBodyCon.getScrollFeature().ensureIndexVisible(t,o),this.animationFrameService.flushAllFrames(),this.focusService.setFocusedCell(r,i,null,!0),this.rangeService){var s={rowIndex:r,rowPinned:null,column:i};this.rangeService.setRangeToCell(s)}},t.prototype.onPageDown=function(e){var t=this.ctrlsService.getGridBodyCtrl().getScrollFeature().getVScrollPosition(),o=this.getViewportHeight(),n=this.paginationProxy.getPixelOffset(),r=t.top+o,i=this.paginationProxy.getRowIndexAtPixel(r+n);this.columnModel.isAutoRowHeightActive()?this.navigateToNextPageWithAutoHeight(e,i):this.navigateToNextPage(e,i)},t.prototype.onPageUp=function(e){var t=this.ctrlsService.getGridBodyCtrl().getScrollFeature().getVScrollPosition(),o=this.paginationProxy.getPixelOffset(),n=t.top,r=this.paginationProxy.getRowIndexAtPixel(n+o);this.columnModel.isAutoRowHeightActive()?this.navigateToNextPageWithAutoHeight(e,r,!0):this.navigateToNextPage(e,r,!0)},t.prototype.navigateToNextPage=function(e,t,o){var n,r;void 0===o&&(o=!1);var i,s=this.getViewportHeight(),a=this.paginationProxy.getPageFirstRow(),l=this.paginationProxy.getPageLastRow(),u=this.paginationProxy.getPixelOffset(),p=this.paginationProxy.getRow(e.rowIndex),c=o?(null===(n=p)||void 0===n?void 0:n.rowHeight)-s-u:s-u,d=(null===(r=p)||void 0===r?void 0:r.rowTop)+c,h=this.paginationProxy.getRowIndexAtPixel(d+u);if(h===e.rowIndex){var f=o?-1:1;t=h=e.rowIndex+f}o?(i="bottom",hl&&(h=l),t>l&&(t=l)),this.isRowTallerThanView(h)&&(t=h,i="top"),this.navigateTo({scrollIndex:t,scrollType:i,scrollColumn:null,focusIndex:h,focusColumn:e.column})},t.prototype.navigateToNextPageWithAutoHeight=function(e,t,o){var n=this;void 0===o&&(o=!1),this.navigateTo({scrollIndex:t,scrollType:o?"bottom":"top",scrollColumn:null,focusIndex:t,focusColumn:e.column}),setTimeout((function(){var r=n.getNextFocusIndexForAutoHeight(e,o);n.navigateTo({scrollIndex:t,scrollType:o?"bottom":"top",scrollColumn:null,focusIndex:r,focusColumn:e.column})}),50)},t.prototype.getNextFocusIndexForAutoHeight=function(e,t){var o;void 0===t&&(t=!1);for(var n=t?-1:1,r=this.getViewportHeight(),i=this.paginationProxy.getPageLastRow(),s=0,a=e.rowIndex;a>=0&&a<=i;){var l=this.paginationProxy.getRow(a);if(l){var u=null!==(o=l.rowHeight)&&void 0!==o?o:0;if(s+u>r)break;s+=u}a+=n}return Math.max(0,Math.min(a,i))},t.prototype.getViewportHeight=function(){var e=this.ctrlsService.getGridBodyCtrl().getScrollFeature().getVScrollPosition(),t=this.gridOptionsWrapper.getScrollbarWidth(),o=e.bottom-e.top;return this.ctrlsService.getCenterRowContainerCtrl().isHorizontalScrollShowing()&&(o-=t),o},t.prototype.isRowTallerThanView=function(e){var t=this.paginationProxy.getRow(e);if(!t)return!1;var o=t.rowHeight;return"number"===typeof o&&o>this.getViewportHeight()},t.prototype.onCtrlUpDownLeftRight=function(e,t){var o=this.cellNavigationService.getNextCellToFocus(e,t,!0),n=o.rowIndex,r=o.column;this.navigateTo({scrollIndex:n,scrollType:null,scrollColumn:r,focusIndex:n,focusColumn:r})},t.prototype.onHomeOrEndKey=function(e){var t=e===ii.PAGE_HOME,o=this.columnModel.getAllDisplayedColumns(),n=t?o[0]:Ve(o),r=t?this.paginationProxy.getPageFirstRow():this.paginationProxy.getPageLastRow();this.navigateTo({scrollIndex:r,scrollType:null,scrollColumn:n,focusIndex:r,focusColumn:n})},t.prototype.onTabKeyDown=function(e,t){var o=t.shiftKey;if(this.tabToNextCellCommon(e,o,t))t.preventDefault();else if(o){var n=e.getRowPosition(),r=n.rowIndex;(n.rowPinned?0===r:r===this.paginationProxy.getPageFirstRow())&&(t.preventDefault(),this.focusService.focusLastHeader(t))}else e instanceof eu&&e.focusCell(!0),this.focusService.focusNextGridCoreContainer(o)&&t.preventDefault()},t.prototype.tabToNextCell=function(e,t){var o=this.focusService.getFocusedCell();if(!o)return!1;var n=this.getCellByPosition(o);return!!(n||(n=this.rowRenderer.getRowByPosition(o))&&n.isFullWidth())&&this.tabToNextCellCommon(n,e,t)},t.prototype.tabToNextCellCommon=function(e,t,o){var n=e.isEditing();if(!n&&e instanceof eu){var r=e.getRowCtrl();r&&(n=r.isEditing())}return(n?this.gridOptionsWrapper.isFullRowEdit()?this.moveToNextEditingRow(e,t,o):this.moveToNextEditingCell(e,t,o):this.moveToNextCellNotEditing(e,t))||!!this.focusService.getFocusedHeader()},t.prototype.moveToNextEditingCell=function(e,t,o){void 0===o&&(o=null);var n=e.getCellPosition();e.stopEditing();var r=this.findNextCellToFocusOn(n,t,!0);return null!=r&&(r.startEditing(null,null,!0,o),r.focusCell(!1),!0)},t.prototype.moveToNextEditingRow=function(e,t,o){void 0===o&&(o=null);var n=e.getCellPosition(),r=this.findNextCellToFocusOn(n,t,!0);if(null==r)return!1;var i=r.getCellPosition(),s=this.isCellEditable(n),a=this.isCellEditable(i),l=i&&n.rowIndex===i.rowIndex&&n.rowPinned===i.rowPinned;(s&&e.setFocusOutOnEditor(),l)||(e.getRowCtrl().stopEditing(),r.getRowCtrl().startRowEditing(void 0,void 0,void 0,o));return a?(r.setFocusInOnEditor(),r.focusCell()):r.focusCell(!0),!0},t.prototype.moveToNextCellNotEditing=function(e,t){var o,n=this.columnModel.getAllDisplayedColumns();o=e instanceof au?Qu(Qu({},e.getRowPosition()),{column:t?n[0]:Ve(n)}):e.getCellPosition();var r=this.findNextCellToFocusOn(o,t,!1);if(r instanceof eu)r.focusCell(!0);else if(r)return this.tryToFocusFullWidthRow(r.getRowPosition(),t);return O(r)},t.prototype.findNextCellToFocusOn=function(e,t,o){for(var n=e;;){e!==n&&(e=n),t||(n=this.getLastCellOfColSpan(n)),n=this.cellNavigationService.getNextTabbedCell(n,t);var r=this.gridOptionsWrapper.getTabToNextCellFunc();if(O(r)){var i=r({backwards:t,editing:o,previousCellPosition:e,nextCellPosition:n||null});O(i)?(i.floating&&(ne((function(){console.warn("AG Grid: tabToNextCellFunc return type should have attributes: rowIndex, rowPinned, column. However you had 'floating', maybe you meant 'rowPinned'?")}),"no floating in userCell"),i.rowPinned=i.floating),n={rowIndex:i.rowIndex,column:i.column,rowPinned:i.rowPinned}):n=null}if(!n)return null;if(n.rowIndex<0){var s=this.headerNavigationService.getHeaderRowCount();return this.focusService.focusHeaderPosition({headerPosition:{headerRowIndex:s+n.rowIndex,column:n.column}}),null}var a=this.gridOptionsWrapper.isFullRowEdit();if(o&&!a)if(!this.isCellEditable(n))continue;this.ensureCellVisible(n);var l=this.getCellByPosition(n);if(l){if(!l.isSuppressNavigable())return this.rangeService&&this.rangeService.setRangeToCell(n),l}else{var u=this.rowRenderer.getRowByPosition(n);if(u&&u.isFullWidth())return u}}},t.prototype.isCellEditable=function(e){var t=this.lookupRowNodeForCell(e);return!!t&&e.column.isCellEditable(t)},t.prototype.getCellByPosition=function(e){var t=this.rowRenderer.getRowByPosition(e);return t?t.getCellCtrl(e.column):null},t.prototype.lookupRowNodeForCell=function(e){return e.rowPinned===Ne.PINNED_TOP?this.pinnedRowModel.getPinnedTopRow(e.rowIndex):e.rowPinned===Ne.PINNED_BOTTOM?this.pinnedRowModel.getPinnedBottomRow(e.rowIndex):this.paginationProxy.getRow(e.rowIndex)},t.prototype.navigateToNextCell=function(e,t,o,n){for(var r=o,i=!1;r&&(r===o||!this.isValidNavigateCell(r));)this.gridOptionsWrapper.isEnableRtl()?t===ii.LEFT&&(r=this.getLastCellOfColSpan(r)):t===ii.RIGHT&&(r=this.getLastCellOfColSpan(r)),i=b(r=this.cellNavigationService.getNextCellToFocus(t,r));if(i&&e&&e.key===ii.UP&&(r={rowIndex:-1,rowPinned:null,column:o.column}),n){var s=this.gridOptionsWrapper.getNavigateToNextCellFunc();if(O(s)){var a=s({key:t,previousCellPosition:o,nextCellPosition:r||null,event:e});O(a)?(a.floating&&(ne((function(){console.warn("AG Grid: tabToNextCellFunc return type should have attributes: rowIndex, rowPinned, column. However you had 'floating', maybe you meant 'rowPinned'?")}),"no floating in userCell"),a.rowPinned=a.floating),r={rowPinned:a.rowPinned,rowIndex:a.rowIndex,column:a.column}):r=null}}if(r)if(r.rowIndex<0){var l=this.headerNavigationService.getHeaderRowCount();this.focusService.focusHeaderPosition({headerPosition:{headerRowIndex:l+r.rowIndex,column:o.column},event:e||void 0})}else{var u=this.getNormalisedPosition(r);u?this.focusPosition(u):this.tryToFocusFullWidthRow(r)}},t.prototype.getNormalisedPosition=function(e){this.ensureCellVisible(e);var t=this.getCellByPosition(e);return t?(e=t.getCellPosition(),this.ensureCellVisible(e),e):null},t.prototype.tryToFocusFullWidthRow=function(e,t){void 0===t&&(t=!1);var o=this.columnModel.getAllDisplayedColumns(),n=this.rowRenderer.getRowByPosition(e);if(!n||!n.isFullWidth())return!1;var r=this.focusService.getFocusedCell(),i={rowIndex:e.rowIndex,rowPinned:e.rowPinned,column:e.column||(t?Ve(o):o[0])};this.focusPosition(i);var s=null!=r&&this.rowPositionUtils.before(i,r),a={type:St.EVENT_FULL_WIDTH_ROW_FOCUSED,api:this.gridApi,columnApi:this.columnApi,rowIndex:i.rowIndex,rowPinned:i.rowPinned,column:i.column,isFullWidthCell:!0,floating:i.rowPinned,fromBelow:s};return this.eventService.dispatchEvent(a),!0},t.prototype.focusPosition=function(e){this.focusService.setFocusedCell(e.rowIndex,e.column,e.rowPinned,!0),this.rangeService&&this.rangeService.setRangeToCell(e)},t.prototype.isValidNavigateCell=function(e){return!!this.rowPositionUtils.getRowNode(e)},t.prototype.getLastCellOfColSpan=function(e){var t=this.getCellByPosition(e);if(!t)return e;var o=t.getColSpanningList();return 1===o.length?e:{rowIndex:e.rowIndex,column:Ve(o),rowPinned:e.rowPinned}},t.prototype.ensureCellVisible=function(e){b(e.rowPinned)&&this.gridBodyCon.getScrollFeature().ensureIndexVisible(e.rowIndex),e.column.isPinned()||this.gridBodyCon.getScrollFeature().ensureColumnVisible(e.column),this.gridBodyCon.getScrollFeature().horizontallyScrollHeaderCenterAndFloatingCenter(),this.animationFrameService.flushAllFrames()},$u([Re("columnApi")],t.prototype,"columnApi",void 0),$u([Re("gridApi")],t.prototype,"gridApi",void 0),$u([Re("mouseEventService")],t.prototype,"mouseEventService",void 0),$u([Re("paginationProxy")],t.prototype,"paginationProxy",void 0),$u([Re("focusService")],t.prototype,"focusService",void 0),$u([Re("animationFrameService")],t.prototype,"animationFrameService",void 0),$u([Se("rangeService")],t.prototype,"rangeService",void 0),$u([Re("columnModel")],t.prototype,"columnModel",void 0),$u([Re("ctrlsService")],t.prototype,"ctrlsService",void 0),$u([Re("rowRenderer")],t.prototype,"rowRenderer",void 0),$u([Re("headerNavigationService")],t.prototype,"headerNavigationService",void 0),$u([Re("rowPositionUtils")],t.prototype,"rowPositionUtils",void 0),$u([Re("cellNavigationService")],t.prototype,"cellNavigationService",void 0),$u([Re("pinnedRowModel")],t.prototype,"pinnedRowModel",void 0),$u([Ce],t.prototype,"postConstruct",null),t=$u([Ee("navigationService")],t)}(mt),Zu=function(){var e=function(t,o){return e=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(e,t){e.__proto__=t}||function(e,t){for(var o in t)t.hasOwnProperty(o)&&(e[o]=t[o])},e(t,o)};return function(t,o){function n(){this.constructor=t}e(t,o),t.prototype=null===o?Object.create(o):(n.prototype=o.prototype,new n)}}(),ep=function(e,t,o,n){var r,i=arguments.length,s=i<3?t:null===n?n=Object.getOwnPropertyDescriptor(t,o):n;if("object"===typeof Reflect&&"function"===typeof Reflect.decorate)s=Reflect.decorate(e,t,o,n);else for(var a=e.length-1;a>=0;a--)(r=e[a])&&(s=(i<3?r(s):i>3?r(t,o,s):r(t,o))||s);return i>3&&s&&Object.defineProperty(t,o,s),s},tp=function(e){function t(t){var o=e.call(this,'
')||this;return o.params=t,o}return Zu(t,e),t.prototype.postConstruct=function(){this.gridOptionsWrapper.setDomData(this.getGui(),t.DOM_KEY_POPUP_EDITOR_WRAPPER,!0),this.addKeyDownListener()},t.prototype.addKeyDownListener=function(){var e=this,t=this.getGui(),o=this.params;this.addManagedListener(t,"keydown",(function(t){Pr(e.gridOptionsWrapper,t,o.node,o.column,!0)||o.onKeyDown(t)}))},t.DOM_KEY_POPUP_EDITOR_WRAPPER="popupEditorWrapper",ep([Ce],t.prototype,"postConstruct",null),t}(vs),op=function(){var e=function(t,o){return e=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(e,t){e.__proto__=t}||function(e,t){for(var o in t)t.hasOwnProperty(o)&&(e[o]=t[o])},e(t,o)};return function(t,o){function n(){this.constructor=t}e(t,o),t.prototype=null===o?Object.create(o):(n.prototype=o.prototype,new n)}}(),np=function(e){function t(t,o,n,r,i){var s=e.call(this)||this;s.rendererVersion=0,s.editorVersion=0,s.beans=t,s.column=o.getColumn(),s.rowNode=o.getRowNode(),s.rowCtrl=o.getRowCtrl(),s.eRow=r,s.setTemplate('
');var a=s.getGui();s.forceWrapper=o.isForceWrapper(),s.refreshWrapper(!1);var l=function(e,t,o){var n=o||a;null!=t&&""!=t?n.setAttribute(e,t):n.removeAttribute(e)},u={addOrRemoveCssClass:function(e,t){return s.addOrRemoveCssClass(e,t)},setUserStyles:function(e){return un(a,e)},getFocusableElement:function(){return s.getFocusableElement()},setTabIndex:function(e){return l("tabindex",e.toString())},setRole:function(e){return In(a,e)},setColId:function(e){return l("col-id",e)},setTitle:function(e){return l("title",e)},setIncludeSelection:function(e){return s.includeSelection=e},setIncludeRowDrag:function(e){return s.includeRowDrag=e},setIncludeDndSource:function(e){return s.includeDndSource=e},setRenderDetails:function(e,t,o){return s.setRenderDetails(e,t,o)},setEditDetails:function(e,t,o){return s.setEditDetails(e,t,o)},getCellEditor:function(){return s.cellEditor||null},getCellRenderer:function(){return s.cellRenderer||null},getParentOfValue:function(){return s.getParentOfValue()}};return s.cellCtrl=o,o.setComp(u,s.getGui(),s.eCellWrapper,n,i),s}return op(t,e),t.prototype.getParentOfValue=function(){return this.eCellValue?this.eCellValue:this.eCellWrapper?this.eCellWrapper:this.getGui()},t.prototype.setRenderDetails=function(e,t,o){if(!(this.cellEditor&&!this.cellEditorPopupWrapper)){this.firstRender=null==this.firstRender;var n=this.refreshWrapper(!1);if(e)!(o||n)&&this.refreshCellRenderer(e)||(this.destroyRenderer(),this.createCellRendererInstance(e));else this.destroyRenderer(),this.insertValueWithoutCellRenderer(t)}},t.prototype.setEditDetails=function(e,t,o){e?this.createCellEditorInstance(e,t,o):this.destroyEditor()},t.prototype.removeControls=function(){this.checkboxSelectionComp=this.beans.context.destroyBean(this.checkboxSelectionComp),this.dndSourceComp=this.beans.context.destroyBean(this.dndSourceComp),this.rowDraggingComp=this.beans.context.destroyBean(this.rowDraggingComp)},t.prototype.refreshWrapper=function(e){var t=this.includeRowDrag||this.includeDndSource||this.includeSelection,o=t||this.forceWrapper,n=o&&null==this.eCellWrapper;n&&(this.eCellWrapper=Zo(''),this.getGui().appendChild(this.eCellWrapper));var r=!o&&null!=this.eCellWrapper;r&&($o(this.eCellWrapper),this.eCellWrapper=void 0),this.addOrRemoveCssClass("ag-cell-value",!o);var i=!e&&o,s=i&&null==this.eCellValue;s&&(this.eCellValue=Zo(''),this.eCellWrapper.appendChild(this.eCellValue));var a=!i&&null!=this.eCellValue;a&&($o(this.eCellValue),this.eCellValue=void 0);var l=n||r||s||a;return l&&this.removeControls(),!e&&t&&this.addControls(),l},t.prototype.addControls=function(){var e=this.eCellValue.id="cell-"+this.getCompId(),t=[];this.includeRowDrag&&null==this.rowDraggingComp&&(this.rowDraggingComp=this.cellCtrl.createRowDragComp(),this.rowDraggingComp&&this.eCellWrapper.insertBefore(this.rowDraggingComp.getGui(),this.eCellValue)),this.includeDndSource&&null==this.dndSourceComp&&(this.dndSourceComp=this.cellCtrl.createDndSource(),this.eCellWrapper.insertBefore(this.dndSourceComp.getGui(),this.eCellValue)),this.includeSelection&&(null==this.checkboxSelectionComp&&(this.checkboxSelectionComp=this.cellCtrl.createSelectionCheckbox(),this.eCellWrapper.insertBefore(this.checkboxSelectionComp.getGui(),this.eCellValue)),t.push(this.checkboxSelectionComp.getCheckboxId())),t.push(e),kn(this.getGui(),t.join(" "))},t.prototype.createCellEditorInstance=function(e,t,o){var n=this,r=this.editorVersion,i=e.newAgStackInstance();if(i){var s=e.params;i.then((function(e){return n.afterCellEditorCreated(r,e,s,t,o)})),b(this.cellEditor)&&s.cellStartedEdit&&this.cellCtrl.focusCell(!0)}},t.prototype.insertValueWithoutCellRenderer=function(e){var t=this.getParentOfValue();Xo(t);var o=null!=e?Nt(e):null;null!=o&&(t.innerHTML=o)},t.prototype.destroyEditorAndRenderer=function(){this.destroyRenderer(),this.destroyEditor()},t.prototype.destroyRenderer=function(){var e=this.beans.context;this.cellRenderer=e.destroyBean(this.cellRenderer),$o(this.cellRendererGui),this.cellRendererGui=null,this.rendererVersion++},t.prototype.destroyEditor=function(){var e=this.beans.context;this.hideEditorPopup&&this.hideEditorPopup(),this.hideEditorPopup=void 0,this.cellEditor=e.destroyBean(this.cellEditor),this.cellEditorPopupWrapper=e.destroyBean(this.cellEditorPopupWrapper),$o(this.cellEditorGui),this.cellEditorGui=null,this.editorVersion++},t.prototype.refreshCellRenderer=function(e){if(null==this.cellRenderer||null==this.cellRenderer.refresh)return!1;if(this.cellRendererClass!==e.componentClass)return!1;var t=this.cellRenderer.refresh(e.params);return!0===t||void 0===t},t.prototype.createCellRendererInstance=function(e){var t=this,o=this.beans.gridOptionsWrapper.isAngularCompileRows(),n=this.beans.gridOptionsWrapper.isSuppressAnimationFrame(),r=!o&&!n,i=this.rendererVersion,s=e.componentClass,a=function(){if(!(t.rendererVersion!==i||!t.isAlive())){var o=e.newAgStackInstance(),n=t.afterCellRendererCreated.bind(t,i,s);o&&o.then(n)}};r&&this.firstRender?this.beans.animationFrameService.createTask(a,this.rowNode.rowIndex,"createTasksP2"):a()},t.prototype.getCtrl=function(){return this.cellCtrl},t.prototype.getRowCtrl=function(){return this.rowCtrl},t.prototype.getCellRenderer=function(){return this.cellRenderer},t.prototype.getCellEditor=function(){return this.cellEditor},t.prototype.afterCellRendererCreated=function(e,t,o){if(!this.isAlive()||e!==this.rendererVersion)this.beans.context.destroyBean(o);else if(this.cellRenderer=o,this.cellRendererClass=t,this.cellRendererGui=this.cellRenderer.getGui(),null!=this.cellRendererGui){var n=this.getParentOfValue();Xo(n),n.appendChild(this.cellRendererGui)}},t.prototype.afterCellEditorCreated=function(e,t,o,n,r){if(e!==this.editorVersion)this.beans.context.destroyBean(t);else{if(t.isCancelBeforeStart&&t.isCancelBeforeStart())return this.beans.context.destroyBean(t),void this.cellCtrl.stopEditing();if(!t.getGui)return console.warn("AG Grid: cellEditor for column "+this.column.getId()+" is missing getGui() method"),void this.beans.context.destroyBean(t);this.cellEditor=t,this.cellEditorGui=t.getGui(),n||void 0!==t.isPopup&&t.isPopup()?(n||this.cellCtrl.hackSayEditingInPopup(),this.addPopupCellEditor(o,r)):this.addInCellEditor(),t.afterGuiAttached&&t.afterGuiAttached()}},t.prototype.addInCellEditor=function(){var e=this.getGui(),t=this.beans.gridOptionsWrapper.getDocument();(e.contains(t.activeElement)&&e.focus(),this.destroyRenderer(),this.refreshWrapper(!0),this.clearParentOfValue(),this.cellEditorGui)&&this.getParentOfValue().appendChild(this.cellEditorGui)},t.prototype.addPopupCellEditor=function(e,t){var o=this;this.beans.gridOptionsWrapper.isFullRowEdit()&&console.warn("AG Grid: popup cellEditor does not work with fullRowEdit - you cannot use them both - either turn off fullRowEdit, or stop using popup editors.");var n=this.cellEditor;this.cellEditorPopupWrapper=this.beans.context.createBean(new tp(e));var r=this.cellEditorPopupWrapper.getGui();this.cellEditorGui&&r.appendChild(this.cellEditorGui);var i=this.beans.popupService,s=this.beans.gridOptionsWrapper.isStopEditingWhenCellsLoseFocus(),a=null!=t?t:n.getPopupPosition?n.getPopupPosition():"over",l={column:this.column,rowNode:this.rowNode,type:"popupCellEditor",eventSource:this.getGui(),ePopup:r,keepWithinBounds:!0},u="under"===a?i.positionPopupUnderComponent.bind(i,l):i.positionPopupOverComponent.bind(i,l),p=this.beans.gridOptionsWrapper.getLocaleTextFunc(),c=i.addPopup({modal:s,eChild:r,closeOnEsc:!0,closedCallback:function(){o.cellCtrl.onPopupEditorClosed()},anchorToElement:this.getGui(),positionCallback:u,ariaLabel:p("ariaLabelCellEditor","Cell Editor")});c&&(this.hideEditorPopup=c.hideFunc)},t.prototype.detach=function(){this.eRow.removeChild(this.getGui())},t.prototype.destroy=function(){this.cellCtrl.stopEditing(),this.destroyEditorAndRenderer(),this.removeControls(),e.prototype.destroy.call(this)},t.prototype.clearParentOfValue=function(){var e=this.getGui(),t=this.beans.gridOptionsWrapper.getDocument();e.contains(t.activeElement)&&e.focus({preventScroll:!0}),Xo(this.getParentOfValue())},t}(Yr),rp=function(){var e=function(t,o){return e=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(e,t){e.__proto__=t}||function(e,t){for(var o in t)t.hasOwnProperty(o)&&(e[o]=t[o])},e(t,o)};return function(t,o){function n(){this.constructor=t}e(t,o),t.prototype=null===o?Object.create(o):(n.prototype=o.prototype,new n)}}(),ip=function(e){function t(t,o,n){var r=e.call(this)||this;r.cellComps={},r.beans=o,r.rowCtrl=t,r.setTemplate('
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'}())||this;return t.rowComps={},t.name=Yr.elementGettingCreated.getAttribute("name"),t.type=Mu(t.name),t}return sp(t,e),t.prototype.postConstruct=function(){var e=this,t={setViewportHeight:function(t){return e.eViewport.style.height=t},setRowCtrls:function(t){return e.setRowCtrls(t)},setDomOrder:function(t){e.domOrder=t},setContainerWidth:function(t){return e.eContainer.style.width=t}};this.createManagedBean(new Vu(this.name)).setComp(t,this.eContainer,this.eViewport,this.eWrapper)},t.prototype.preDestroy=function(){this.setRowCtrls([])},t.prototype.setRowCtrls=function(e){var t=this,o=ap({},this.rowComps);this.rowComps={},this.lastPlacedElement=null;e.forEach((function(e){var n=e.getInstanceId(),r=o[n];if(r)t.rowComps[n]=r,delete o[n],t.ensureDomOrder(r.getGui());else{var i=t.newRowComp(e);t.rowComps[n]=i,t.appendRow(i.getGui())}})),z(o).forEach((function(e){t.eContainer.removeChild(e.getGui()),e.destroy()})),In(this.eContainer,e.length?"rowgroup":"presentation")},t.prototype.appendRow=function(e){this.domOrder?an(this.eContainer,e,this.lastPlacedElement):this.eContainer.appendChild(e),this.lastPlacedElement=e},t.prototype.ensureDomOrder=function(e){this.domOrder&&(rn(this.eContainer,e,this.lastPlacedElement),this.lastPlacedElement=e)},t.prototype.newRowComp=function(e){Vu.getPinned(this.name);return new ip(e,this.beans,this.type)},lp([Re("beans")],t.prototype,"beans",void 0),lp([Xr("eViewport")],t.prototype,"eViewport",void 0),lp([Xr("eContainer")],t.prototype,"eContainer",void 0),lp([Xr("eWrapper")],t.prototype,"eWrapper",void 0),lp([Ce],t.prototype,"postConstruct",null),lp([we],t.prototype,"preDestroy",null),t}(Yr),cp=function(e,t,o,n){var r,i=arguments.length,s=i<3?t:null===n?n=Object.getOwnPropertyDescriptor(t,o):n;if("object"===typeof Reflect&&"function"===typeof Reflect.decorate)s=Reflect.decorate(e,t,o,n);else for(var a=e.length-1;a>=0;a--)(r=e[a])&&(s=(i<3?r(s):i>3?r(t,o,s):r(t,o))||s);return i>3&&s&&Object.defineProperty(t,o,s),s},dp=function(){function e(e){this.columnsToAggregate=[],this.columnsToGroup=[],this.columnsToPivot=[],this.pinned=e}return e.prototype.onDragEnter=function(e){var t=this;if(this.clearColumnsList(),!this.gridOptionsWrapper.isFunctionsReadOnly()){var o=e.dragItem.columns;o&&o.forEach((function(e){e.isPrimary()&&(e.isAnyFunctionActive()||(e.isAllowValue()?t.columnsToAggregate.push(e):e.isAllowRowGroup()?t.columnsToGroup.push(e):e.isAllowPivot()&&t.columnsToPivot.push(e)))}))}},e.prototype.getIconName=function(){return this.columnsToAggregate.length+this.columnsToGroup.length+this.columnsToPivot.length>0?this.pinned?qs.ICON_PINNED:qs.ICON_MOVE:null},e.prototype.onDragLeave=function(e){this.clearColumnsList()},e.prototype.clearColumnsList=function(){this.columnsToAggregate.length=0,this.columnsToGroup.length=0,this.columnsToPivot.length=0},e.prototype.onDragging=function(e){},e.prototype.onDragStop=function(e){this.columnsToAggregate.length>0&&this.columnModel.addValueColumns(this.columnsToAggregate,"toolPanelDragAndDrop"),this.columnsToGroup.length>0&&this.columnModel.addRowGroupColumns(this.columnsToGroup,"toolPanelDragAndDrop"),this.columnsToPivot.length>0&&this.columnModel.addPivotColumns(this.columnsToPivot,"toolPanelDragAndDrop")},cp([Re("columnModel")],e.prototype,"columnModel",void 0),cp([Re("gridOptionsWrapper")],e.prototype,"gridOptionsWrapper",void 0),e}(),hp=function(e,t,o,n){var r,i=arguments.length,s=i<3?t:null===n?n=Object.getOwnPropertyDescriptor(t,o):n;if("object"===typeof Reflect&&"function"===typeof Reflect.decorate)s=Reflect.decorate(e,t,o,n);else for(var a=e.length-1;a>=0;a--)(r=e[a])&&(s=(i<3?r(s):i>3?r(t,o,s):r(t,o))||s);return i>3&&s&&Object.defineProperty(t,o,s),s},fp=function(){function e(e,t){this.needToMoveLeft=!1,this.needToMoveRight=!1,this.pinned=e,this.eContainer=t,this.centerContainer=!O(e)}return e.prototype.init=function(){var e=this;this.ctrlsService.whenReady((function(){e.gridBodyCon=e.ctrlsService.getGridBodyCtrl()}))},e.prototype.getIconName=function(){return this.pinned?qs.ICON_PINNED:qs.ICON_MOVE},e.prototype.onDragEnter=function(e){var t=e.dragItem.columns;if(e.dragSource.type===xs.ToolPanel)this.setColumnsVisible(t,!0,"uiColumnDragged");else{var o=e.dragItem.visibleState,n=(t||[]).filter((function(e){return o[e.getId()]}));this.setColumnsVisible(n,!0,"uiColumnDragged")}this.setColumnsPinned(t,this.pinned,"uiColumnDragged"),this.onDragging(e,!0)},e.prototype.onDragLeave=function(e){if(!this.gridOptionsWrapper.isSuppressDragLeaveHidesColumns()&&!e.fromNudge){var t=e.dragSource.getDragItem().columns;this.setColumnsVisible(t,!1,"uiColumnDragged")}this.ensureIntervalCleared()},e.prototype.setColumnsVisible=function(e,t,o){if(void 0===o&&(o="api"),e){var n=e.filter((function(e){return!e.getColDef().lockVisible}));this.columnModel.setColumnsVisible(n,t,o)}},e.prototype.setColumnsPinned=function(e,t,o){if(void 0===o&&(o="api"),e){var n=e.filter((function(e){return!e.getColDef().lockPinned}));this.columnModel.setColumnsPinned(n,t,o)}},e.prototype.onDragStop=function(){this.ensureIntervalCleared()},e.prototype.normaliseX=function(e){this.gridOptionsWrapper.isEnableRtl()&&(e=this.eContainer.clientWidth-e);return this.centerContainer&&(e+=this.ctrlsService.getCenterRowContainerCtrl().getCenterViewportScrollLeft()),e},e.prototype.checkCenterForScrolling=function(e){if(this.centerContainer){var t=this.ctrlsService.getCenterRowContainerCtrl().getCenterViewportScrollLeft(),o=t+this.ctrlsService.getCenterRowContainerCtrl().getCenterWidth();this.gridOptionsWrapper.isEnableRtl()?(this.needToMoveRight=eo-50):(this.needToMoveLeft=eo-50),this.needToMoveLeft||this.needToMoveRight?this.ensureIntervalStarted():this.ensureIntervalCleared()}},e.prototype.onDragging=function(e,t){var o=this;if(void 0===t&&(t=!1),this.lastDraggingEvent=e,!b(e.hDirection)){var n=this.normaliseX(e.x);t||this.checkCenterForScrolling(n);var r=this.normaliseDirection(e.hDirection),i=e.dragSource.type,s=e.dragSource.getDragItem().columns;s=s.filter((function(e){return!e.getColDef().lockPinned||e.getPinned()==o.pinned})),this.attemptMoveColumns(i,s,r,n,t)}},e.prototype.normaliseDirection=function(e){if(!this.gridOptionsWrapper.isEnableRtl())return e;switch(e){case Hs.Left:return Hs.Right;case Hs.Right:return Hs.Left;default:console.error("AG Grid: Unknown direction "+e)}},e.prototype.calculateOldIndex=function(e){var t=this.columnModel.getAllGridColumns(),o=Be(e.map((function(e){return t.indexOf(e)}))),n=o[0];return Ve(o)-n!==o.length-1?null:n},e.prototype.attemptMoveColumns=function(e,t,o,n,r){var i=o===Hs.Left,s=o===Hs.Right,a=t.slice();this.columnModel.sortColumnsLikeGridColumns(a);var l=this.calculateValidMoves(a,s,n),u=this.calculateOldIndex(a);if(0!==l.length){var p=l[0],c=null!==u&&!r;if(e==xs.HeaderCell&&(c=null!==u),c){if(i&&p>=u)return;if(s&&p<=u)return}for(var d=0;d0){for(var h=0;h0){var f=l[p-1];r=u.indexOf(f)+1}else-1===(r=u.indexOf(l[0]))&&(r=0);var g=[r],y=function(e,t){return e-t};if(t){for(var v=r+1,m=s.length-1;v<=m;)g.push(v),v++;g.sort(y)}else{v=r,m=s.length-1;for(var C=s[v];v<=m&&this.isColumnHidden(i,C);)v++,g.push(v),C=s[v];v=r-1;for(;v>=0;)g.push(v),v--;g.sort(y).reverse()}return g},e.prototype.isColumnHidden=function(e,t){return e.indexOf(t)<0},e.prototype.ensureIntervalStarted=function(){this.movingIntervalId||(this.intervalCount=0,this.failedMoveAttempts=0,this.movingIntervalId=window.setInterval(this.moveInterval.bind(this),100),this.needToMoveLeft?this.dragAndDropService.setGhostIcon(qs.ICON_LEFT,!0):this.dragAndDropService.setGhostIcon(qs.ICON_RIGHT,!0))},e.prototype.ensureIntervalCleared=function(){this.movingIntervalId&&(window.clearInterval(this.movingIntervalId),this.movingIntervalId=null,this.dragAndDropService.setGhostIcon(qs.ICON_MOVE))},e.prototype.moveInterval=function(){var e;this.intervalCount++,(e=10+5*this.intervalCount)>100&&(e=100);var t=null,o=this.gridBodyCon.getScrollFeature();if(this.needToMoveLeft?t=o.scrollHorizontally(-e):this.needToMoveRight&&(t=o.scrollHorizontally(e)),0!==t)this.onDragging(this.lastDraggingEvent),this.failedMoveAttempts=0;else{this.failedMoveAttempts++;var n=this.lastDraggingEvent.dragItem.columns.filter((function(e){return!e.getColDef().lockPinned}));if(n.length>0&&(this.dragAndDropService.setGhostIcon(qs.ICON_PINNED),this.failedMoveAttempts>7)){var r=this.needToMoveLeft?Ne.PINNED_LEFT:Ne.PINNED_RIGHT;this.setColumnsPinned(n,r,"uiColumnDragged"),this.dragAndDropService.nudge()}}},hp([Re("columnModel")],e.prototype,"columnModel",void 0),hp([Re("dragAndDropService")],e.prototype,"dragAndDropService",void 0),hp([Re("gridOptionsWrapper")],e.prototype,"gridOptionsWrapper",void 0),hp([Re("ctrlsService")],e.prototype,"ctrlsService",void 0),hp([Ce],e.prototype,"init",null),e}(),gp=function(){var e=function(t,o){return e=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(e,t){e.__proto__=t}||function(e,t){for(var o in t)t.hasOwnProperty(o)&&(e[o]=t[o])},e(t,o)};return function(t,o){function n(){this.constructor=t}e(t,o),t.prototype=null===o?Object.create(o):(n.prototype=o.prototype,new n)}}(),yp=function(e,t,o,n){var r,i=arguments.length,s=i<3?t:null===n?n=Object.getOwnPropertyDescriptor(t,o):n;if("object"===typeof Reflect&&"function"===typeof Reflect.decorate)s=Reflect.decorate(e,t,o,n);else for(var a=e.length-1;a>=0;a--)(r=e[a])&&(s=(i<3?r(s):i>3?r(t,o,s):r(t,o))||s);return i>3&&s&&Object.defineProperty(t,o,s),s},vp=function(e){function t(t,o){var n=e.call(this)||this;return n.pinned=t,n.eContainer=o,n}return gp(t,e),t.prototype.postConstruct=function(){var e=this;this.ctrlsService.whenReady((function(t){switch(e.pinned){case Ne.PINNED_LEFT:e.eSecondaryContainers=[[t.gridBodyCtrl.getBodyViewportElement(),t.leftRowContainerCtrl.getContainerElement()],[t.bottomLeftRowContainerCtrl.getContainerElement()],[t.topLeftRowContainerCtrl.getContainerElement()]];break;case Ne.PINNED_RIGHT:e.eSecondaryContainers=[[t.gridBodyCtrl.getBodyViewportElement(),t.rightRowContainerCtrl.getContainerElement()],[t.bottomRightRowContainerCtrl.getContainerElement()],[t.topRightRowContainerCtrl.getContainerElement()]];break;default:e.eSecondaryContainers=[[t.gridBodyCtrl.getBodyViewportElement(),t.centerRowContainerCtrl.getViewportElement()],[t.bottomCenterRowContainerCtrl.getViewportElement()],[t.topCenterRowContainerCtrl.getViewportElement()]]}}))},t.prototype.isInterestedIn=function(e){return e===xs.HeaderCell||e===xs.ToolPanel&&this.gridOptionsWrapper.isAllowDragFromColumnsToolPanel()},t.prototype.getSecondaryContainers=function(){return this.eSecondaryContainers},t.prototype.getContainer=function(){return this.eContainer},t.prototype.init=function(){this.moveColumnFeature=this.createManagedBean(new fp(this.pinned,this.eContainer)),this.bodyDropPivotTarget=this.createManagedBean(new dp(this.pinned)),this.dragAndDropService.addDropTarget(this)},t.prototype.getIconName=function(){return this.currentDropListener.getIconName()},t.prototype.isDropColumnInPivotMode=function(e){return this.columnModel.isPivotMode()&&e.dragSource.type===xs.ToolPanel},t.prototype.onDragEnter=function(e){this.currentDropListener=this.isDropColumnInPivotMode(e)?this.bodyDropPivotTarget:this.moveColumnFeature,this.currentDropListener.onDragEnter(e)},t.prototype.onDragLeave=function(e){this.currentDropListener.onDragLeave(e)},t.prototype.onDragging=function(e){this.currentDropListener.onDragging(e)},t.prototype.onDragStop=function(e){this.currentDropListener.onDragStop(e)},yp([Re("dragAndDropService")],t.prototype,"dragAndDropService",void 0),yp([Re("columnModel")],t.prototype,"columnModel",void 0),yp([Re("ctrlsService")],t.prototype,"ctrlsService",void 0),yp([Ce],t.prototype,"postConstruct",null),yp([Ce],t.prototype,"init",null),t}(mt),mp=function(e,t){var o="function"===typeof Symbol&&e[Symbol.iterator];if(!o)return e;var n,r,i=o.call(e),s=[];try{for(;(void 0===t||t-- >0)&&!(n=i.next()).done;)s.push(n.value)}catch(a){r={error:a}}finally{try{n&&!n.done&&(o=i.return)&&o.call(i)}finally{if(r)throw r.error}}return s},Cp=function(){for(var e=[],t=0;t=0;a--)(r=e[a])&&(s=(i<3?r(s):i>3?r(t,o,s):r(t,o))||s);return i>3&&s&&Object.defineProperty(t,o,s),s},Sp=function(e){function t(o){var n=e.call(this,t.TEMPLATE,o)||this;return n.headerCompVersion=0,n.column=o.getColumnGroupChild(),n.pinned=o.getPinned(),n}return Ep(t,e),t.prototype.postConstruct=function(){var e=this,t=this.getGui(),o=function(e,o,n){var r=n||t;null!=o&&""!=o?r.setAttribute(e,o):r.removeAttribute(e)},n={setWidth:function(e){return t.style.width=e},addOrRemoveCssClass:function(t,o){return e.addOrRemoveCssClass(t,o)},setColId:function(e){return o("col-id",e)},setTitle:function(e){return o("title",e)},setAriaDescription:function(e){return Wn(t,e)},setAriaSort:function(e){return e?er(t,e):tr(t)},setUserCompDetails:function(t){return e.setUserCompDetails(t)},getUserCompInstance:function(){return e.headerComp}};this.ctrl.setComp(n,this.getGui(),this.eResize);var r=this.ctrl.getSelectAllGui();this.eResize.insertAdjacentElement("afterend",r)},t.prototype.destroyHeaderComp=function(){this.headerComp&&(this.getGui().removeChild(this.headerCompGui),this.headerComp=this.destroyBean(this.headerComp),this.headerCompGui=void 0)},t.prototype.setUserCompDetails=function(e){var t=this;this.headerCompVersion++;var o=this.headerCompVersion;e.newAgStackInstance().then((function(e){return t.afterCompCreated(o,e)}))},t.prototype.afterCompCreated=function(e,t){e==this.headerCompVersion&&this.isAlive()?(this.destroyHeaderComp(),this.headerComp=t,this.headerCompGui=t.getGui(),this.getGui().appendChild(this.headerCompGui),this.ctrl.setDragSource(this.headerCompGui)):this.destroyBean(t)},t.TEMPLATE='
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',Rp([Xr("eResize")],t.prototype,"eResize",void 0),Rp([Ce],t.prototype,"postConstruct",null),Rp([we],t.prototype,"destroyHeaderComp",null),t}(Xa),Op=function(){var e=function(t,o){return e=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(e,t){e.__proto__=t}||function(e,t){for(var o in t)t.hasOwnProperty(o)&&(e[o]=t[o])},e(t,o)};return function(t,o){function n(){this.constructor=t}e(t,o),t.prototype=null===o?Object.create(o):(n.prototype=o.prototype,new n)}}(),bp=function(e,t,o,n){var r,i=arguments.length,s=i<3?t:null===n?n=Object.getOwnPropertyDescriptor(t,o):n;if("object"===typeof Reflect&&"function"===typeof Reflect.decorate)s=Reflect.decorate(e,t,o,n);else for(var a=e.length-1;a>=0;a--)(r=e[a])&&(s=(i<3?r(s):i>3?r(t,o,s):r(t,o))||s);return i>3&&s&&Object.defineProperty(t,o,s),s},Pp=function(e){function t(o){return e.call(this,t.TEMPLATE,o)||this}return Op(t,e),t.prototype.postConstruct=function(){var e=this,t=this.getGui(),o=function(e,o){return void 0!=o?t.setAttribute(e,o):t.removeAttribute(e)},n={addOrRemoveCssClass:function(t,o){return e.addOrRemoveCssClass(t,o)},addOrRemoveResizableCssClass:function(t,o){return e.eResize.classList.toggle(t,o)},setWidth:function(e){return t.style.width=e},setColId:function(e){return t.setAttribute("col-id",e)},setAriaExpanded:function(e){return o("aria-expanded",e)},setTitle:function(e){return o("title",e)},setUserCompDetails:function(t){return e.setUserCompDetails(t)}};this.ctrl.setComp(n,t,this.eResize)},t.prototype.setUserCompDetails=function(e){var t=this;e.newAgStackInstance().then((function(e){return t.afterHeaderCompCreated(e)}))},t.prototype.afterHeaderCompCreated=function(e){var t=this,o=function(){return t.destroyBean(e)};this.isAlive()?(this.getGui().appendChild(e.getGui()),this.addDestroyFunc(o),this.ctrl.setDragSource(e.getGui())):o()},t.TEMPLATE='
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',bp([Re("userComponentFactory")],t.prototype,"userComponentFactory",void 0),bp([Xr("eResize")],t.prototype,"eResize",void 0),bp([Ce],t.prototype,"postConstruct",null),t}(Xa),_p=function(){var e=function(t,o){return e=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(e,t){e.__proto__=t}||function(e,t){for(var o in t)t.hasOwnProperty(o)&&(e[o]=t[o])},e(t,o)};return function(t,o){function n(){this.constructor=t}e(t,o),t.prototype=null===o?Object.create(o):(n.prototype=o.prototype,new n)}}(),Tp=function(e,t,o,n){var r,i=arguments.length,s=i<3?t:null===n?n=Object.getOwnPropertyDescriptor(t,o):n;if("object"===typeof Reflect&&"function"===typeof Reflect.decorate)s=Reflect.decorate(e,t,o,n);else for(var a=e.length-1;a>=0;a--)(r=e[a])&&(s=(i<3?r(s):i>3?r(t,o,s):r(t,o))||s);return i>3&&s&&Object.defineProperty(t,o,s),s};!function(e){e.COLUMN_GROUP="group",e.COLUMN="column",e.FLOATING_FILTER="filter"}(up||(up={}));var Dp,Ap=function(e){function t(t){var o=e.call(this)||this;o.headerComps={};var n=t.getType()==up.COLUMN_GROUP?"ag-header-row-column-group":t.getType()==up.FLOATING_FILTER?"ag-header-row-column-filter":"ag-header-row-column";return o.setTemplate('
'),o.ctrl=t,o}return _p(t,e),t.prototype.init=function(){var e=this,t={setTransform:function(t){return e.getGui().style.transform=t},setHeight:function(t){return e.getGui().style.height=t},setTop:function(t){return e.getGui().style.top=t},setHeaderCtrls:function(t){return e.setHeaderCtrls(t)},setWidth:function(t){return e.getGui().style.width=t},setAriaRowIndex:function(t){return Qn(e.getGui(),t)}};this.ctrl.setComp(t)},t.prototype.destroyHeaderCtrls=function(){this.setHeaderCtrls([])},t.prototype.setHeaderCtrls=function(e){var t=this;if(this.isAlive()){var o=this.headerComps;if(this.headerComps={},e.forEach((function(e){var n=e.getInstanceId(),r=o[n];delete o[n],null==r&&(r=t.createHeaderComp(e),t.getGui().appendChild(r.getGui())),t.headerComps[n]=r})),L(o,(function(e,o){t.getGui().removeChild(o.getGui()),t.destroyBean(o)})),this.gridOptionsWrapper.isEnsureDomOrder()){var n=z(this.headerComps);n.sort((function(e,t){return e.getCtrl().getColumnGroupChild().getLeft()-t.getCtrl().getColumnGroupChild().getLeft()}));var r=n.map((function(e){return e.getGui()}));sn(this.getGui(),r)}}},t.prototype.createHeaderComp=function(e){var t;switch(this.ctrl.getType()){case up.COLUMN_GROUP:t=new Pp(e);break;case up.FLOATING_FILTER:t=new Ja(e);break;default:t=new Sp(e)}return this.createBean(t),t.setParentComponent(this),t},Tp([Ce],t.prototype,"init",null),Tp([we],t.prototype,"destroyHeaderCtrls",null),t}(Yr),Np=function(){var e=function(t,o){return e=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(e,t){e.__proto__=t}||function(e,t){for(var o in t)t.hasOwnProperty(o)&&(e[o]=t[o])},e(t,o)};return function(t,o){function n(){this.constructor=t}e(t,o),t.prototype=null===o?Object.create(o):(n.prototype=o.prototype,new n)}}(),Fp=function(e,t,o,n){var r,i=arguments.length,s=i<3?t:null===n?n=Object.getOwnPropertyDescriptor(t,o):n;if("object"===typeof Reflect&&"function"===typeof Reflect.decorate)s=Reflect.decorate(e,t,o,n);else for(var a=e.length-1;a>=0;a--)(r=e[a])&&(s=(i<3?r(s):i>3?r(t,o,s):r(t,o))||s);return i>3&&s&&Object.defineProperty(t,o,s),s},Ip=0,Mp=function(e){function t(t,o){var n=e.call(this)||this;return n.lastFocusEvent=null,n.columnGroupChild=t,n.parentRowCtrl=o,n.instanceId=t.getUniqueId()+"-"+Ip++,n}return Np(t,e),t.prototype.shouldStopEventPropagation=function(e){var t=this.focusService.getFocusedHeader(),o=t.headerRowIndex,n=t.column;return _r(this.gridOptionsWrapper,e,o,n)},t.prototype.setGui=function(e){this.eGui=e,this.addDomData()},t.prototype.addDomData=function(){var e=this,o=t.DOM_DATA_KEY_HEADER_CTRL;this.gridOptionsWrapper.setDomData(this.eGui,o,this),this.addDestroyFunc((function(){return e.gridOptionsWrapper.setDomData(e.eGui,o,null)}))},t.prototype.getGui=function(){return this.eGui},t.prototype.focus=function(e){return!!this.eGui&&(this.lastFocusEvent=e||null,this.eGui.focus(),!0)},t.prototype.getRowIndex=function(){return this.parentRowCtrl.getRowIndex()},t.prototype.getParentRowCtrl=function(){return this.parentRowCtrl},t.prototype.getPinned=function(){return this.parentRowCtrl.getPinned()},t.prototype.getInstanceId=function(){return this.instanceId},t.prototype.getColumnGroupChild=function(){return this.columnGroupChild},t.DOM_DATA_KEY_HEADER_CTRL="headerCtrl",Fp([Re("focusService")],t.prototype,"focusService",void 0),t}(mt);!function(e){e.EVERYTHING="group",e.FILTER="filter",e.SORT="sort",e.MAP="map",e.AGGREGATE="aggregate",e.FILTER_AGGREGATES="filter_aggregates",e.PIVOT="pivot",e.NOTHING="nothing"}(Dp||(Dp={}));var Gp=function(e,t,o,n){var r,i=arguments.length,s=i<3?t:null===n?n=Object.getOwnPropertyDescriptor(t,o):n;if("object"===typeof Reflect&&"function"===typeof Reflect.decorate)s=Reflect.decorate(e,t,o,n);else for(var a=e.length-1;a>=0;a--)(r=e[a])&&(s=(i<3?r(s):i>3?r(t,o,s):r(t,o))||s);return i>3&&s&&Object.defineProperty(t,o,s),s};function Lp(e){var t=e;return null!=t&&null!=t.getFrameworkComponentInstance?t.getFrameworkComponentInstance():e}var xp,Vp=function(){function e(){this.detailGridInfoMap={},this.destroyCalled=!1}return e.prototype.registerOverlayWrapperComp=function(e){this.overlayWrapperComp=e},e.prototype.registerSideBarComp=function(e){this.sideBarComp=e},e.prototype.init=function(){var e=this;switch(this.rowModel.getType()){case Ne.ROW_MODEL_TYPE_CLIENT_SIDE:this.clientSideRowModel=this.rowModel;break;case Ne.ROW_MODEL_TYPE_INFINITE:this.infiniteRowModel=this.rowModel;break;case Ne.ROW_MODEL_TYPE_SERVER_SIDE:this.serverSideRowModel=this.rowModel}this.ctrlsService.whenReady((function(){e.gridBodyCtrl=e.ctrlsService.getGridBodyCtrl()}))},e.prototype.__getAlignedGridService=function(){return this.alignedGridsService},e.prototype.__getContext=function(){return this.context},e.prototype.addDetailGridInfo=function(e,t){this.detailGridInfoMap[e]=t},e.prototype.removeDetailGridInfo=function(e){this.detailGridInfoMap[e]=void 0},e.prototype.getDetailGridInfo=function(e){return this.detailGridInfoMap[e]},e.prototype.forEachDetailGridInfo=function(e){var t=0;L(this.detailGridInfoMap,(function(o,n){O(n)&&(e(n,t),t++)}))},e.prototype.getDataAsCsv=function(e){if(Fe.assertRegistered(_e.CsvExportModule,"api.getDataAsCsv"))return this.csvCreator.getDataAsCsv(e)},e.prototype.exportDataAsCsv=function(e){Fe.assertRegistered(_e.CsvExportModule,"api.exportDataAsCSv")&&this.csvCreator.exportDataAsCsv(e)},e.prototype.getExcelExportMode=function(e){var t=this.gridOptionsWrapper.getDefaultExportParams("excel");return Object.assign({exportMode:"xlsx"},t,e).exportMode},e.prototype.getDataAsExcel=function(e){if(Fe.assertRegistered(_e.ExcelExportModule,"api.getDataAsExcel")){var t=this.getExcelExportMode(e);if(this.excelCreator.getFactoryMode(t)!==Ys.MULTI_SHEET)return this.excelCreator.getDataAsExcel(e);console.warn("AG Grid: The Excel Exporter is currently on Multi Sheet mode. End that operation by calling `api.getMultipleSheetAsExcel()` or `api.exportMultipleSheetsAsExcel()`")}},e.prototype.exportDataAsExcel=function(e){if(Fe.assertRegistered(_e.ExcelExportModule,"api.exportDataAsExcel")){var t=this.getExcelExportMode(e);this.excelCreator.getFactoryMode(t)!==Ys.MULTI_SHEET?this.excelCreator.exportDataAsExcel(e):console.warn("AG Grid: The Excel Exporter is currently on Multi Sheet mode. End that operation by calling `api.getMultipleSheetAsExcel()` or `api.exportMultipleSheetsAsExcel()`")}},e.prototype.getSheetDataForExcel=function(e){if(Fe.assertRegistered(_e.ExcelExportModule,"api.getSheetDataForExcel")){var t=this.getExcelExportMode(e);return this.excelCreator.setFactoryMode(Ys.MULTI_SHEET,t),this.excelCreator.getSheetDataForExcel(e)}},e.prototype.getMultipleSheetsAsExcel=function(e){if(Fe.assertRegistered(_e.ExcelExportModule,"api.getMultipleSheetsAsExcel"))return this.excelCreator.getMultipleSheetsAsExcel(e)},e.prototype.exportMultipleSheetsAsExcel=function(e){if(Fe.assertRegistered(_e.ExcelExportModule,"api.exportMultipleSheetsAsExcel"))return this.excelCreator.exportMultipleSheetsAsExcel(e)},e.prototype.setEnterpriseDatasource=function(e){console.warn("AG Grid: since version 18.x, api.setEnterpriseDatasource() should be replaced with api.setServerSideDatasource()"),this.setServerSideDatasource(e)},e.prototype.setGridAriaProperty=function(e,t){if(e){var o=this.ctrlsService.getGridBodyCtrl().getGui(),n="aria-"+e;null===t?o.removeAttribute(n):o.setAttribute(n,t)}},e.prototype.setServerSideDatasource=function(e){this.serverSideRowModel?this.serverSideRowModel.setDatasource(e):console.warn("AG Grid: you can only use an enterprise datasource when gridOptions.rowModelType is '"+Ne.ROW_MODEL_TYPE_SERVER_SIDE+"'")},e.prototype.setDatasource=function(e){this.gridOptionsWrapper.isRowModelInfinite()?this.rowModel.setDatasource(e):console.warn("AG Grid: you can only use a datasource when gridOptions.rowModelType is '"+Ne.ROW_MODEL_TYPE_INFINITE+"'")},e.prototype.setViewportDatasource=function(e){this.gridOptionsWrapper.isRowModelViewport()?this.rowModel.setViewportDatasource(e):console.warn("AG Grid: you can only use a viewport datasource when gridOptions.rowModelType is '"+Ne.ROW_MODEL_TYPE_VIEWPORT+"'")},e.prototype.setRowData=function(e){null==this.immutableService?console.warn("AG Grid: you can only set rowData when using the Client Side Row Model"):this.immutableService.isActive()?this.immutableService.setRowData(e):(this.selectionService.reset(),this.clientSideRowModel.setRowData(e))},e.prototype.setFloatingTopRowData=function(e){console.warn("AG Grid: since v12, api.setFloatingTopRowData() is now api.setPinnedTopRowData()"),this.setPinnedTopRowData(e)},e.prototype.setFloatingBottomRowData=function(e){console.warn("AG Grid: since v12, api.setFloatingBottomRowData() is now api.setPinnedBottomRowData()"),this.setPinnedBottomRowData(e)},e.prototype.getFloatingTopRowCount=function(){return console.warn("AG Grid: since v12, api.getFloatingTopRowCount() is now api.getPinnedTopRowCount()"),this.getPinnedTopRowCount()},e.prototype.getFloatingBottomRowCount=function(){return console.warn("AG Grid: since v12, api.getFloatingBottomRowCount() is now api.getPinnedBottomRowCount()"),this.getPinnedBottomRowCount()},e.prototype.getFloatingTopRow=function(e){return console.warn("AG Grid: since v12, api.getFloatingTopRow() is now api.getPinnedTopRow()"),this.getPinnedTopRow(e)},e.prototype.getFloatingBottomRow=function(e){return console.warn("AG Grid: since v12, api.getFloatingBottomRow() is now api.getPinnedBottomRow()"),this.getPinnedBottomRow(e)},e.prototype.setPinnedTopRowData=function(e){this.pinnedRowModel.setPinnedTopRowData(e)},e.prototype.setPinnedBottomRowData=function(e){this.pinnedRowModel.setPinnedBottomRowData(e)},e.prototype.getPinnedTopRowCount=function(){return this.pinnedRowModel.getPinnedTopRowCount()},e.prototype.getPinnedBottomRowCount=function(){return this.pinnedRowModel.getPinnedBottomRowCount()},e.prototype.getPinnedTopRow=function(e){return this.pinnedRowModel.getPinnedTopRow(e)},e.prototype.getPinnedBottomRow=function(e){return this.pinnedRowModel.getPinnedBottomRow(e)},e.prototype.setColumnDefs=function(e,t){void 0===t&&(t="api"),this.columnModel.setColumnDefs(e,t)},e.prototype.setAutoGroupColumnDef=function(e,t){void 0===t&&(t="api"),this.gridOptionsWrapper.setProperty("autoGroupColumnDef",e,!0)},e.prototype.setDefaultColDef=function(e,t){void 0===t&&(t="api"),this.gridOptionsWrapper.setProperty("defaultColDef",e,!0)},e.prototype.expireValueCache=function(){this.valueCache.expire()},e.prototype.getVerticalPixelRange=function(){return this.gridBodyCtrl.getScrollFeature().getVScrollPosition()},e.prototype.getHorizontalPixelRange=function(){return this.gridBodyCtrl.getScrollFeature().getHScrollPosition()},e.prototype.setAlwaysShowHorizontalScroll=function(e){this.gridOptionsWrapper.setProperty("alwaysShowHorizontalScroll",e)},e.prototype.setAlwaysShowVerticalScroll=function(e){this.gridOptionsWrapper.setProperty("alwaysShowVerticalScroll",e)},e.prototype.refreshToolPanel=function(){this.sideBarComp&&this.sideBarComp.refresh()},e.prototype.refreshCells=function(e){void 0===e&&(e={}),Array.isArray(e)?console.warn("since AG Grid v11.1, refreshCells() now takes parameters, please see the documentation."):this.rowRenderer.refreshCells(e)},e.prototype.flashCells=function(e){void 0===e&&(e={}),this.rowRenderer.flashCells(e)},e.prototype.redrawRows=function(e){void 0===e&&(e={});var t=e?e.rowNodes:void 0;this.rowRenderer.redrawRows(t)},e.prototype.setFunctionsReadOnly=function(e){this.gridOptionsWrapper.setProperty("functionsReadOnly",e)},e.prototype.refreshHeader=function(){this.ctrlsService.getHeaderRowContainerCtrls().forEach((function(e){return e.refresh()}))},e.prototype.isAnyFilterPresent=function(){return this.filterManager.isAnyFilterPresent()},e.prototype.isColumnFilterPresent=function(){return this.filterManager.isColumnFilterPresent()||this.filterManager.isAggregateFilterPresent()},e.prototype.isQuickFilterPresent=function(){return this.filterManager.isQuickFilterPresent()},e.prototype.getModel=function(){return this.rowModel},e.prototype.setRowNodeExpanded=function(e,t){e&&e.setExpanded(t)},e.prototype.onGroupExpandedOrCollapsed=function(e){b(this.clientSideRowModel)&&console.warn("AG Grid: cannot call onGroupExpandedOrCollapsed unless using normal row model"),O(e)&&console.warn("AG Grid: api.onGroupExpandedOrCollapsed - refreshFromIndex parameter is no longer used, the grid will refresh all rows"),this.clientSideRowModel.refreshModel({step:Dp.MAP})},e.prototype.refreshInMemoryRowModel=function(e){console.warn("ag-grid: since version 18.x, api.refreshInMemoryRowModel() should be replaced with api.refreshClientSideRowModel()"),this.refreshClientSideRowModel(e)},e.prototype.refreshClientSideRowModel=function(e){b(this.clientSideRowModel)&&console.warn("cannot call refreshClientSideRowModel unless using normal row model");var t=Dp.EVERYTHING,o={group:Dp.EVERYTHING,filter:Dp.FILTER,map:Dp.MAP,aggregate:Dp.AGGREGATE,sort:Dp.SORT,pivot:Dp.PIVOT};if(O(e)&&(t=o[e]),b(t))console.error("AG Grid: invalid step "+e+", available steps are "+Object.keys(o).join(", "));else{var n={step:t,keepRenderedRows:!0,keepEditingRows:!0,animate:!this.gridOptionsWrapper.isSuppressAnimationFrame()};this.clientSideRowModel.refreshModel(n)}},e.prototype.isAnimationFrameQueueEmpty=function(){return this.animationFrameService.isQueueEmpty()},e.prototype.flushAllAnimationFrames=function(){this.animationFrameService.flushAllFrames()},e.prototype.getRowNode=function(e){return this.rowModel.getRowNode(e)},e.prototype.getSizesForCurrentTheme=function(){return{rowHeight:this.gridOptionsWrapper.getRowHeightAsNumber(),headerHeight:this.gridOptionsWrapper.getHeaderHeight()}},e.prototype.expandAll=function(){this.clientSideRowModel?this.clientSideRowModel.expandOrCollapseAll(!0):this.serverSideRowModel?this.serverSideRowModel.expandAll(!0):console.warn("AG Grid: expandAll only works with Client Side Row Model and Server Side Row Model")},e.prototype.collapseAll=function(){this.clientSideRowModel?this.clientSideRowModel.expandOrCollapseAll(!1):this.serverSideRowModel?this.serverSideRowModel.expandAll(!1):console.warn("AG Grid: collapseAll only works with Client Side Row Model and Server Side Row Model")},e.prototype.getToolPanelInstance=function(e){if(this.sideBarComp)return Lp(this.sideBarComp.getToolPanelInstance(e));console.warn("AG Grid: toolPanel is only available in AG Grid Enterprise")},e.prototype.addVirtualRowListener=function(e,t,o){"string"!==typeof e&&console.warn("AG Grid: addVirtualRowListener is deprecated, please use addRenderedRowListener."),this.addRenderedRowListener(e,t,o)},e.prototype.addRenderedRowListener=function(e,t,o){"virtualRowSelected"===e&&console.warn("AG Grid: event virtualRowSelected is deprecated, to register for individual row\n selection events, add a listener directly to the row node."),this.rowRenderer.addRenderedRowListener(e,t,o)},e.prototype.setQuickFilter=function(e){this.filterManager.setQuickFilter(e)},e.prototype.selectIndex=function(e,t,o){console.warn("AG Grid: do not use api for selection, call node.setSelected(value) instead"),o&&console.warn("AG Grid: suppressEvents is no longer supported, stop listening for the event if you no longer want it"),this.selectionService.selectIndex(e,t)},e.prototype.deselectIndex=function(e,t){void 0===t&&(t=!1),console.warn("AG Grid: do not use api for selection, call node.setSelected(value) instead"),t&&console.warn("AG Grid: suppressEvents is no longer supported, stop listening for the event if you no longer want it"),this.selectionService.deselectIndex(e)},e.prototype.selectNode=function(e,t,o){void 0===t&&(t=!1),void 0===o&&(o=!1),console.warn("AG Grid: API for selection is deprecated, call node.setSelected(value) instead"),o&&console.warn("AG Grid: suppressEvents is no longer supported, stop listening for the event if you no longer want it"),e.setSelectedParams({newValue:!0,clearSelection:!t})},e.prototype.deselectNode=function(e,t){void 0===t&&(t=!1),console.warn("AG Grid: API for selection is deprecated, call node.setSelected(value) instead"),t&&console.warn("AG Grid: suppressEvents is no longer supported, stop listening for the event if you no longer want it"),e.setSelectedParams({newValue:!1})},e.prototype.selectAll=function(){this.selectionService.selectAllRowNodes()},e.prototype.deselectAll=function(){this.selectionService.deselectAllRowNodes()},e.prototype.selectAllFiltered=function(){this.selectionService.selectAllRowNodes(!0)},e.prototype.deselectAllFiltered=function(){this.selectionService.deselectAllRowNodes(!0)},e.prototype.recomputeAggregates=function(){b(this.clientSideRowModel)&&console.warn("cannot call recomputeAggregates unless using normal row model"),console.warn("recomputeAggregates is deprecated, please call api.refreshClientSideRowModel('aggregate') instead"),this.clientSideRowModel.refreshModel({step:Dp.AGGREGATE})},e.prototype.sizeColumnsToFit=function(){this.gridBodyCtrl.sizeColumnsToFit()},e.prototype.showLoadingOverlay=function(){this.overlayWrapperComp.showLoadingOverlay()},e.prototype.showNoRowsOverlay=function(){this.overlayWrapperComp.showNoRowsOverlay()},e.prototype.hideOverlay=function(){this.overlayWrapperComp.hideOverlay()},e.prototype.isNodeSelected=function(e){return console.warn("AG Grid: no need to call api.isNodeSelected(), just call node.isSelected() instead"),e.isSelected()},e.prototype.getSelectedNodesById=function(){return console.error("AG Grid: since version 3.4, getSelectedNodesById no longer exists, use getSelectedNodes() instead"),null},e.prototype.getSelectedNodes=function(){return this.selectionService.getSelectedNodes()},e.prototype.getSelectedRows=function(){return this.selectionService.getSelectedRows()},e.prototype.getBestCostNodeSelection=function(){return this.selectionService.getBestCostNodeSelection()},e.prototype.getRenderedNodes=function(){return this.rowRenderer.getRenderedNodes()},e.prototype.ensureColIndexVisible=function(e){console.warn("AG Grid: ensureColIndexVisible(index) no longer supported, use ensureColumnVisible(colKey) instead.")},e.prototype.ensureColumnVisible=function(e,t){void 0===t&&(t="auto"),this.gridBodyCtrl.getScrollFeature().ensureColumnVisible(e,t)},e.prototype.ensureIndexVisible=function(e,t){this.gridBodyCtrl.getScrollFeature().ensureIndexVisible(e,t)},e.prototype.ensureNodeVisible=function(e,t){void 0===t&&(t=null),this.gridBodyCtrl.getScrollFeature().ensureNodeVisible(e,t)},e.prototype.forEachLeafNode=function(e){b(this.clientSideRowModel)&&console.warn("cannot call forEachNode unless using normal row model"),this.clientSideRowModel.forEachLeafNode(e)},e.prototype.forEachNode=function(e){this.rowModel.forEachNode(e)},e.prototype.forEachNodeAfterFilter=function(e){b(this.clientSideRowModel)&&console.warn("cannot call forEachNodeAfterFilter unless using normal row model"),this.clientSideRowModel.forEachNodeAfterFilter(e)},e.prototype.forEachNodeAfterFilterAndSort=function(e){b(this.clientSideRowModel)&&console.warn("cannot call forEachNodeAfterFilterAndSort unless using normal row model"),this.clientSideRowModel.forEachNodeAfterFilterAndSort(e)},e.prototype.getFilterInstance=function(e,t){var o=this.getFilterInstanceImpl(e,(function(e){if(t){var o=Lp(e);t(o)}}));return Lp(o)},e.prototype.getFilterInstanceImpl=function(e,t){var o=this.columnModel.getPrimaryColumn(e);if(o){var n=this.filterManager.getFilterComponent(o,"NO_UI"),r=n&&n.resolveNow(null,(function(e){return e}));return r?setTimeout(t,0,r):n&&n.then((function(e){t(e)})),r}},e.prototype.destroyFilter=function(e){var t=this.columnModel.getPrimaryColumn(e);if(t)return this.filterManager.destroyFilter(t,"filterDestroyed")},e.prototype.getStatusPanel=function(e){if(this.statusBarService)return Lp(this.statusBarService.getStatusPanel(e))},e.prototype.getColumnDef=function(e){var t=this.columnModel.getPrimaryColumn(e);return t?t.getColDef():null},e.prototype.getColumnDefs=function(){return this.columnModel.getColumnDefs()},e.prototype.onFilterChanged=function(){this.filterManager.onFilterChanged()},e.prototype.onSortChanged=function(){this.sortController.onSortChanged("api")},e.prototype.setFilterModel=function(e){this.filterManager.setFilterModel(e)},e.prototype.getFilterModel=function(){return this.filterManager.getFilterModel()},e.prototype.getFocusedCell=function(){return this.focusService.getFocusedCell()},e.prototype.clearFocusedCell=function(){return this.focusService.clearFocusedCell()},e.prototype.setFocusedCell=function(e,t,o){this.focusService.setFocusedCell(e,t,o,!0)},e.prototype.setSuppressRowDrag=function(e){this.gridOptionsWrapper.setProperty(ul.PROP_SUPPRESS_ROW_DRAG,e)},e.prototype.setSuppressMoveWhenRowDragging=function(e){this.gridOptionsWrapper.setProperty(ul.PROP_SUPPRESS_MOVE_WHEN_ROW_DRAG,e)},e.prototype.setSuppressRowClickSelection=function(e){this.gridOptionsWrapper.setProperty(ul.PROP_SUPPRESS_ROW_CLICK_SELECTION,e)},e.prototype.addRowDropZone=function(e){this.gridBodyCtrl.getRowDragFeature().addRowDropZone(e)},e.prototype.removeRowDropZone=function(e){var t=this.dragAndDropService.findExternalZone(e);t&&this.dragAndDropService.removeDropTarget(t)},e.prototype.getRowDropZoneParams=function(e){return this.gridBodyCtrl.getRowDragFeature().getRowDropZone(e)},e.prototype.setHeaderHeight=function(e){this.gridOptionsWrapper.setProperty(ul.PROP_HEADER_HEIGHT,e)},e.prototype.setDomLayout=function(e){this.gridOptionsWrapper.setProperty(ul.PROP_DOM_LAYOUT,e)},e.prototype.setEnableCellTextSelection=function(e){this.gridBodyCtrl.setCellTextSelection(e)},e.prototype.setFillHandleDirection=function(e){this.gridOptionsWrapper.setProperty(ul.PROP_FILL_HANDLE_DIRECTION,e)},e.prototype.setGroupHeaderHeight=function(e){this.gridOptionsWrapper.setProperty(ul.PROP_GROUP_HEADER_HEIGHT,e)},e.prototype.setFloatingFiltersHeight=function(e){this.gridOptionsWrapper.setProperty(ul.PROP_FLOATING_FILTERS_HEIGHT,e)},e.prototype.setPivotHeaderHeight=function(e){this.gridOptionsWrapper.setProperty(ul.PROP_PIVOT_HEADER_HEIGHT,e)},e.prototype.setPivotGroupHeaderHeight=function(e){this.gridOptionsWrapper.setProperty(ul.PROP_PIVOT_GROUP_HEADER_HEIGHT,e)},e.prototype.setIsExternalFilterPresent=function(e){this.gridOptionsWrapper.setProperty(ul.PROP_IS_EXTERNAL_FILTER_PRESENT,e)},e.prototype.setDoesExternalFilterPass=function(e){this.gridOptionsWrapper.setProperty(ul.PROP_DOES_EXTERNAL_FILTER_PASS,e)},e.prototype.setNavigateToNextCell=function(e){this.gridOptionsWrapper.setProperty(ul.PROP_NAVIGATE_TO_NEXT_CELL,e)},e.prototype.setTabToNextCell=function(e){this.gridOptionsWrapper.setProperty(ul.PROP_TAB_TO_NEXT_CELL,e)},e.prototype.setTabToNextHeader=function(e){this.gridOptionsWrapper.setProperty(ul.PROP_TAB_TO_NEXT_HEADER,e)},e.prototype.setNavigateToNextHeader=function(e){this.gridOptionsWrapper.setProperty(ul.PROP_NAVIGATE_TO_NEXT_HEADER,e)},e.prototype.setGroupRowAggNodes=function(e){this.gridOptionsWrapper.setProperty(ul.PROP_GROUP_ROW_AGG_NODES,e)},e.prototype.setGetGroupRowAgg=function(e){this.gridOptionsWrapper.setProperty(ul.PROP_GET_GROUP_ROW_AGG,e)},e.prototype.setGetBusinessKeyForNode=function(e){this.gridOptionsWrapper.setProperty(ul.PROP_GET_BUSINESS_KEY_FOR_NODE,e)},e.prototype.setGetChildCount=function(e){this.gridOptionsWrapper.setProperty(ul.PROP_GET_CHILD_COUNT,e)},e.prototype.setProcessRowPostCreate=function(e){this.gridOptionsWrapper.setProperty(ul.PROP_PROCESS_ROW_POST_CREATE,e)},e.prototype.setGetRowNodeId=function(e){this.gridOptionsWrapper.setProperty(ul.PROP_GET_ROW_NODE_ID,e)},e.prototype.setGetRowId=function(e){this.gridOptionsWrapper.setProperty(ul.PROP_GET_ROW_ID,e)},e.prototype.setGetRowClass=function(e){this.gridOptionsWrapper.setProperty(ul.PROP_GET_ROW_CLASS,e)},e.prototype.setIsFullWidthCell=function(e){this.gridOptionsWrapper.setProperty(ul.PROP_IS_FULL_WIDTH_CELL,e)},e.prototype.setIsFullWidthRow=function(e){this.gridOptionsWrapper.setProperty(ul.PROP_IS_FULL_WIDTH_ROW,e)},e.prototype.setIsRowSelectable=function(e){this.gridOptionsWrapper.setProperty(ul.PROP_IS_ROW_SELECTABLE,e)},e.prototype.setIsRowMaster=function(e){this.gridOptionsWrapper.setProperty(ul.PROP_IS_ROW_MASTER,e)},e.prototype.setPostSort=function(e){this.gridOptionsWrapper.setProperty(ul.PROP_POST_SORT,e)},e.prototype.setPostSortRows=function(e){this.gridOptionsWrapper.setProperty(ul.PROP_POST_SORT_ROWS,e)},e.prototype.setGetDocument=function(e){this.gridOptionsWrapper.setProperty(ul.PROP_GET_DOCUMENT,e)},e.prototype.setGetContextMenuItems=function(e){this.gridOptionsWrapper.setProperty(ul.PROP_GET_CONTEXT_MENU_ITEMS,e)},e.prototype.setGetMainMenuItems=function(e){this.gridOptionsWrapper.setProperty(ul.PROP_GET_MAIN_MENU_ITEMS,e)},e.prototype.setProcessCellForClipboard=function(e){this.gridOptionsWrapper.setProperty(ul.PROP_PROCESS_CELL_FOR_CLIPBOARD,e)},e.prototype.setSendToClipboard=function(e){this.gridOptionsWrapper.setProperty(ul.PROP_SEND_TO_CLIPBOARD,e)},e.prototype.setProcessCellFromClipboard=function(e){this.gridOptionsWrapper.setProperty(ul.PROP_PROCESS_CELL_FROM_CLIPBOARD,e)},e.prototype.setProcessSecondaryColDef=function(e){this.gridOptionsWrapper.setProperty(ul.PROP_PROCESS_TO_SECONDARY_COLDEF,e)},e.prototype.setProcessSecondaryColGroupDef=function(e){this.gridOptionsWrapper.setProperty(ul.PROP_PROCESS_SECONDARY_COL_GROUP_DEF,e)},e.prototype.setPostProcessPopup=function(e){this.gridOptionsWrapper.setProperty(ul.PROP_POST_PROCESS_POPUP,e)},e.prototype.setDefaultGroupOrderComparator=function(e){this.gridOptionsWrapper.setProperty(ul.PROP_DEFAULT_GROUP_ORDER_COMPARATOR,e)},e.prototype.setInitialGroupOrderComparator=function(e){this.gridOptionsWrapper.setProperty(ul.PROP_INITIAL_GROUP_ORDER_COMPARATOR,e)},e.prototype.setGetChartToolbarItems=function(e){this.gridOptionsWrapper.setProperty(ul.PROP_GET_CHART_TOOLBAR_ITEMS,e)},e.prototype.setPaginationNumberFormatter=function(e){this.gridOptionsWrapper.setProperty(ul.PROP_PAGINATION_NUMBER_FORMATTER,e)},e.prototype.setGetServerSideStoreParams=function(e){this.gridOptionsWrapper.setProperty(ul.PROP_GET_SERVER_SIDE_STORE_PARAMS,e)},e.prototype.setIsServerSideGroupOpenByDefault=function(e){this.gridOptionsWrapper.setProperty(ul.PROP_IS_SERVER_SIDE_GROUPS_OPEN_BY_DEFAULT,e)},e.prototype.setIsApplyServerSideTransaction=function(e){this.gridOptionsWrapper.setProperty(ul.PROP_IS_APPLY_SERVER_SIDE_TRANSACTION,e)},e.prototype.setIsServerSideGroup=function(e){this.gridOptionsWrapper.setProperty(ul.PROP_IS_SERVER_SIDE_GROUP,e)},e.prototype.setGetServerSideGroupKey=function(e){this.gridOptionsWrapper.setProperty(ul.PROP_GET_SERVER_SIDE_GROUP_KEY,e)},e.prototype.setGetRowStyle=function(e){this.gridOptionsWrapper.setProperty(ul.PROP_GET_ROW_STYLE,e)},e.prototype.setGetRowHeight=function(e){this.gridOptionsWrapper.setProperty(ul.PROP_GET_ROW_HEIGHT,e)},e.prototype.isSideBarVisible=function(){return!!this.sideBarComp&&this.sideBarComp.isDisplayed()},e.prototype.setSideBarVisible=function(e){this.sideBarComp?this.sideBarComp.setDisplayed(e):e&&console.warn("AG Grid: sideBar is not loaded")},e.prototype.setSideBarPosition=function(e){this.sideBarComp?this.sideBarComp.setSideBarPosition(e):console.warn("AG Grid: sideBar is not loaded")},e.prototype.openToolPanel=function(e){this.sideBarComp?this.sideBarComp.openToolPanel(e):console.warn("AG Grid: toolPanel is only available in AG Grid Enterprise")},e.prototype.closeToolPanel=function(){this.sideBarComp?this.sideBarComp.close():console.warn("AG Grid: toolPanel is only available in AG Grid Enterprise")},e.prototype.getOpenedToolPanel=function(){return this.sideBarComp?this.sideBarComp.openedItem():null},e.prototype.getSideBar=function(){return this.gridOptionsWrapper.getSideBar()},e.prototype.setSideBar=function(e){this.gridOptionsWrapper.setProperty("sideBar",Za.parse(e))},e.prototype.setSuppressClipboardPaste=function(e){this.gridOptionsWrapper.setProperty(ul.PROP_SUPPRESS_CLIPBOARD_PASTE,e)},e.prototype.isToolPanelShowing=function(){return this.sideBarComp.isToolPanelShowing()},e.prototype.doLayout=function(){ne((function(){return console.warn("AG Grid - since version 25.1, doLayout was taken out, as it's not needed. The grid responds to grid size changes automatically")}),"doLayoutDeprecated")},e.prototype.resetRowHeights=function(){if(O(this.clientSideRowModel)){if(this.columnModel.isAutoRowHeightActive())return void console.warn("AG Grid: calling gridApi.resetRowHeights() makes no sense when using Auto Row Height.");this.clientSideRowModel.resetRowHeights()}},e.prototype.setGroupRemoveSingleChildren=function(e){this.gridOptionsWrapper.setProperty(ul.PROP_GROUP_REMOVE_SINGLE_CHILDREN,e)},e.prototype.setGroupRemoveLowestSingleChildren=function(e){this.gridOptionsWrapper.setProperty(ul.PROP_GROUP_REMOVE_LOWEST_SINGLE_CHILDREN,e)},e.prototype.onRowHeightChanged=function(){this.clientSideRowModel?this.clientSideRowModel.onRowHeightChanged():this.serverSideRowModel&&this.serverSideRowModel.onRowHeightChanged()},e.prototype.getValue=function(e,t){var o=this.columnModel.getPrimaryColumn(e);return b(o)&&(o=this.columnModel.getGridColumn(e)),b(o)?null:this.valueService.getValue(o,t)},e.prototype.addEventListener=function(e,t){var o=this.gridOptionsWrapper.useAsyncEvents();this.eventService.addEventListener(e,t,o)},e.prototype.addGlobalListener=function(e){var t=this.gridOptionsWrapper.useAsyncEvents();this.eventService.addGlobalListener(e,t)},e.prototype.removeEventListener=function(e,t){var o=this.gridOptionsWrapper.useAsyncEvents();this.eventService.removeEventListener(e,t,o)},e.prototype.removeGlobalListener=function(e){var t=this.gridOptionsWrapper.useAsyncEvents();this.eventService.removeGlobalListener(e,t)},e.prototype.dispatchEvent=function(e){this.eventService.dispatchEvent(e)},e.prototype.destroy=function(){if(!this.destroyCalled){this.destroyCalled=!0;var e=this.ctrlsService.getGridCtrl();e&&e.destroyGridUi(),this.context.destroy()}},e.prototype.cleanDownReferencesToAvoidMemoryLeakInCaseApplicationIsKeepingReferenceToDestroyedGrid=function(){setTimeout(J.bind(window,this,"Grid API"),100)},e.prototype.warnIfDestroyed=function(e){return this.destroyCalled&&console.warn("AG Grid: Grid API method "+e+" was called on a grid that was destroyed."),this.destroyCalled},e.prototype.resetQuickFilter=function(){this.warnIfDestroyed("resetQuickFilter")||this.rowModel.forEachNode((function(e){return e.quickFilterAggregateText=null}))},e.prototype.getRangeSelections=function(){return console.warn("AG Grid: in v20.1.x, api.getRangeSelections() is gone, please use getCellRanges() instead.\n We had to change how cell selections works a small bit to allow charting to integrate. The return type of\n getCellRanges() is a bit different, please check the AG Grid documentation."),null},e.prototype.getCellRanges=function(){return this.rangeService?this.rangeService.getCellRanges():(console.warn("AG Grid: cell range selection is only available in AG Grid Enterprise"),null)},e.prototype.camelCaseToHumanReadable=function(e){return Ft(e)},e.prototype.addRangeSelection=function(e){console.warn("AG Grid: As of version 21.x, range selection changed slightly to allow charting integration. Please call api.addCellRange() instead of api.addRangeSelection()")},e.prototype.addCellRange=function(e){this.rangeService||console.warn("AG Grid: cell range selection is only available in AG Grid Enterprise"),this.rangeService.addCellRange(e)},e.prototype.clearRangeSelection=function(){this.rangeService||console.warn("AG Grid: cell range selection is only available in AG Grid Enterprise"),this.rangeService.removeAllCellRanges()},e.prototype.undoCellEditing=function(){this.undoRedoService.undo()},e.prototype.redoCellEditing=function(){this.undoRedoService.redo()},e.prototype.getCurrentUndoSize=function(){return this.undoRedoService.getCurrentUndoStackSize()},e.prototype.getCurrentRedoSize=function(){return this.undoRedoService.getCurrentRedoStackSize()},e.prototype.getChartModels=function(){if(Fe.assertRegistered(_e.RangeSelectionModule,"api.getChartModels")&&Fe.assertRegistered(_e.GridChartsModule,"api.getChartModels"))return this.chartService.getChartModels()},e.prototype.getChartRef=function(e){if(Fe.assertRegistered(_e.RangeSelectionModule,"api.getChartRef")&&Fe.assertRegistered(_e.GridChartsModule,"api.getChartRef"))return this.chartService.getChartRef(e)},e.prototype.getChartImageDataURL=function(e){if(Fe.assertRegistered(_e.RangeSelectionModule,"api.getChartImageDataURL")&&Fe.assertRegistered(_e.GridChartsModule,"api.getChartImageDataURL"))return this.chartService.getChartImageDataURL(e)},e.prototype.createRangeChart=function(e){if(Fe.assertRegistered(_e.RangeSelectionModule,"api.createRangeChart")&&Fe.assertRegistered(_e.GridChartsModule,"api.createRangeChart"))return this.chartService.createRangeChart(e)},e.prototype.createCrossFilterChart=function(e){if(Fe.assertRegistered(_e.RangeSelectionModule,"api.createCrossFilterChart")&&Fe.assertRegistered(_e.GridChartsModule,"api.createCrossFilterChart"))return this.chartService.createCrossFilterChart(e)},e.prototype.restoreChart=function(e,t){if(Fe.assertRegistered(_e.RangeSelectionModule,"api.restoreChart")&&Fe.assertRegistered(_e.GridChartsModule,"api.restoreChart"))return this.chartService.restoreChart(e,t)},e.prototype.createPivotChart=function(e){if(Fe.assertRegistered(_e.RangeSelectionModule,"api.createPivotChart")&&Fe.assertRegistered(_e.GridChartsModule,"api.createPivotChart"))return this.chartService.createPivotChart(e)},e.prototype.copySelectedRowsToClipboard=function(e){this.clipboardService||console.warn("AG Grid: clipboard is only available in AG Grid Enterprise"),this.clipboardService.copySelectedRowsToClipboard(e)},e.prototype.copySelectedRangeToClipboard=function(e){this.clipboardService||console.warn("AG Grid: clipboard is only available in AG Grid Enterprise"),this.clipboardService.copySelectedRangeToClipboard(e)},e.prototype.copySelectedRangeDown=function(){this.clipboardService||console.warn("AG Grid: clipboard is only available in AG Grid Enterprise"),this.clipboardService.copyRangeDown()},e.prototype.showColumnMenuAfterButtonClick=function(e,t){var o=this.columnModel.getGridColumn(e);this.menuFactory.showMenuAfterButtonClick(o,t,"columnMenu")},e.prototype.showColumnMenuAfterMouseClick=function(e,t){var o=this.columnModel.getGridColumn(e);o||(o=this.columnModel.getPrimaryColumn(e)),o?this.menuFactory.showMenuAfterMouseEvent(o,t):console.error("AG Grid: column '"+e+"' not found")},e.prototype.hidePopupMenu=function(){this.contextMenuFactory&&this.contextMenuFactory.hideActiveMenu(),this.menuFactory.hideActiveMenu()},e.prototype.setPopupParent=function(e){this.gridOptionsWrapper.setProperty(ul.PROP_POPUP_PARENT,e)},e.prototype.tabToNextCell=function(e){return this.navigationService.tabToNextCell(!1,e)},e.prototype.tabToPreviousCell=function(e){return this.navigationService.tabToNextCell(!0,e)},e.prototype.getCellRendererInstances=function(e){return void 0===e&&(e={}),this.rowRenderer.getCellRendererInstances(e).map(Lp)},e.prototype.getCellEditorInstances=function(e){return void 0===e&&(e={}),this.rowRenderer.getCellEditorInstances(e).map(Lp)},e.prototype.getEditingCells=function(){return this.rowRenderer.getEditingCells()},e.prototype.stopEditing=function(e){void 0===e&&(e=!1),this.rowRenderer.stopEditing(e)},e.prototype.startEditingCell=function(e){var t=this.columnModel.getGridColumn(e.colKey);if(t){var o={rowIndex:e.rowIndex,rowPinned:e.rowPinned||null,column:t};null==e.rowPinned&&this.gridBodyCtrl.getScrollFeature().ensureIndexVisible(e.rowIndex);var n=this.navigationService.getCellByPosition(o);n&&n.startRowOrCellEdit(e.key,e.charPress)}else console.warn("AG Grid: no column found for "+e.colKey)},e.prototype.addAggFunc=function(e,t){this.aggFuncService&&this.aggFuncService.addAggFunc(e,t)},e.prototype.addAggFuncs=function(e){this.aggFuncService&&this.aggFuncService.addAggFuncs(e)},e.prototype.clearAggFuncs=function(){this.aggFuncService&&this.aggFuncService.clear()},e.prototype.applyServerSideTransaction=function(e){if(this.serverSideTransactionManager)return this.serverSideTransactionManager.applyTransaction(e);console.warn("AG Grid: Cannot apply Server Side Transaction if not using the Server Side Row Model.")},e.prototype.applyServerSideTransactionAsync=function(e,t){if(this.serverSideTransactionManager)return this.serverSideTransactionManager.applyTransactionAsync(e,t);console.warn("AG Grid: Cannot apply Server Side Transaction if not using the Server Side Row Model.")},e.prototype.retryServerSideLoads=function(){this.serverSideRowModel?this.serverSideRowModel.retryLoads():console.warn("AG Grid: API retryServerSideLoads() can only be used when using Server-Side Row Model.")},e.prototype.flushServerSideAsyncTransactions=function(){if(this.serverSideTransactionManager)return this.serverSideTransactionManager.flushAsyncTransactions();console.warn("AG Grid: Cannot flush Server Side Transaction if not using the Server Side Row Model.")},e.prototype.applyTransaction=function(e){if(this.clientSideRowModel){var t=this.clientSideRowModel.updateRowData(e);return this.rowRenderer.refreshFullWidthRows(t.update),this.gridOptionsWrapper.isSuppressChangeDetection()||this.rowRenderer.refreshCells(),t}console.error("AG Grid: updateRowData() only works with ClientSideRowModel. Working with InfiniteRowModel was deprecated in v23.1 and removed in v24.1")},e.prototype.setDeltaSort=function(e){this.gridOptionsWrapper.setProperty("deltaSort",e)},e.prototype.updateRowData=function(e){return ne((function(){return console.warn("AG Grid: as of v23.1, grid API updateRowData(transaction) is now called applyTransaction(transaction). updateRowData is deprecated and will be removed in a future major release.")}),"updateRowData deprecated"),this.applyTransaction(e)},e.prototype.applyTransactionAsync=function(e,t){this.clientSideRowModel?this.clientSideRowModel.batchUpdateRowData(e,t):console.error("AG Grid: api.applyTransactionAsync() only works with ClientSideRowModel.")},e.prototype.flushAsyncTransactions=function(){this.clientSideRowModel?this.clientSideRowModel.flushAsyncTransactions():console.error("AG Grid: api.applyTransactionAsync() only works with ClientSideRowModel.")},e.prototype.batchUpdateRowData=function(e,t){ne((function(){return console.warn("AG Grid: as of v23.1, grid API batchUpdateRowData(transaction, callback) is now called applyTransactionAsync(transaction, callback). batchUpdateRowData is deprecated and will be removed in a future major release.")}),"batchUpdateRowData deprecated"),this.applyTransactionAsync(e,t)},e.prototype.insertItemsAtIndex=function(e,t,o){void 0===o&&(o=!1),console.warn("AG Grid: insertItemsAtIndex() is deprecated, use updateRowData(transaction) instead."),this.updateRowData({add:t,addIndex:e,update:null,remove:null})},e.prototype.removeItems=function(e,t){void 0===t&&(t=!1),console.warn("AG Grid: removeItems() is deprecated, use updateRowData(transaction) instead.");var o=e.map((function(e){return e.data}));this.updateRowData({add:null,addIndex:null,update:null,remove:o})},e.prototype.addItems=function(e,t){void 0===t&&(t=!1),console.warn("AG Grid: addItems() is deprecated, use updateRowData(transaction) instead."),this.updateRowData({add:e,addIndex:null,update:null,remove:null})},e.prototype.refreshVirtualPageCache=function(){console.warn("AG Grid: refreshVirtualPageCache() is now called refreshInfiniteCache(), please call refreshInfiniteCache() instead"),this.refreshInfiniteCache()},e.prototype.refreshInfinitePageCache=function(){console.warn("AG Grid: refreshInfinitePageCache() is now called refreshInfiniteCache(), please call refreshInfiniteCache() instead"),this.refreshInfiniteCache()},e.prototype.refreshInfiniteCache=function(){this.infiniteRowModel?this.infiniteRowModel.refreshCache():console.warn("AG Grid: api.refreshInfiniteCache is only available when rowModelType='infinite'.")},e.prototype.purgeVirtualPageCache=function(){console.warn("AG Grid: purgeVirtualPageCache() is now called purgeInfiniteCache(), please call purgeInfiniteCache() instead"),this.purgeInfinitePageCache()},e.prototype.purgeInfinitePageCache=function(){console.warn("AG Grid: purgeInfinitePageCache() is now called purgeInfiniteCache(), please call purgeInfiniteCache() instead"),this.purgeInfiniteCache()},e.prototype.purgeInfiniteCache=function(){this.infiniteRowModel?this.infiniteRowModel.purgeCache():console.warn("AG Grid: api.purgeInfiniteCache is only available when rowModelType='infinite'.")},e.prototype.purgeEnterpriseCache=function(e){console.warn("ag-grid: since version 18.x, api.purgeEnterpriseCache() should be replaced with api.purgeServerSideCache()"),this.purgeServerSideCache(e)},e.prototype.purgeServerSideCache=function(e){void 0===e&&(e=[]),this.serverSideRowModel?(console.warn("AG Grid: since v25.0, api.purgeServerSideCache is deprecated. Please use api.refreshServerSideStore({purge: true}) instead."),this.refreshServerSideStore({route:e,purge:!0})):console.warn("AG Grid: api.purgeServerSideCache is only available when rowModelType='serverSide'.")},e.prototype.refreshServerSideStore=function(e){this.serverSideRowModel?this.serverSideRowModel.refreshStore(e):console.warn("AG Grid: api.refreshServerSideStore is only available when rowModelType='serverSide'.")},e.prototype.getServerSideStoreState=function(){return this.serverSideRowModel?this.serverSideRowModel.getStoreState():(console.warn("AG Grid: api.getServerSideStoreState is only available when rowModelType='serverSide'."),[])},e.prototype.getVirtualRowCount=function(){return console.warn("AG Grid: getVirtualRowCount() is now called getInfiniteRowCount(), please call getInfiniteRowCount() instead"),this.getInfiniteRowCount()},e.prototype.getInfiniteRowCount=function(){if(this.infiniteRowModel)return this.infiniteRowModel.getRowCount();console.warn("AG Grid: api.getVirtualRowCount is only available when rowModelType='virtual'.")},e.prototype.isMaxRowFound=function(){return console.warn("AG Grid: api.isLastRowIndexKnown is deprecated, please use api.isLastRowIndexKnown()"),this.isLastRowIndexKnown()},e.prototype.isLastRowIndexKnown=function(){if(this.infiniteRowModel)return this.infiniteRowModel.isLastRowIndexKnown();console.warn("AG Grid: api.isMaxRowFound is only available when rowModelType='virtual'.")},e.prototype.setVirtualRowCount=function(e,t){console.warn("AG Grid: setVirtualRowCount() is now called setInfiniteRowCount(), please call setInfiniteRowCount() instead"),this.setRowCount(e,t)},e.prototype.setInfiniteRowCount=function(e,t){console.warn("AG Grid: setInfiniteRowCount() is now called setRowCount(), please call setRowCount() instead"),this.setRowCount(e,t)},e.prototype.setRowCount=function(e,t){this.infiniteRowModel?this.infiniteRowModel.setRowCount(e,t):console.warn("AG Grid: api.setRowCount is only available for Infinite Row Model.")},e.prototype.getVirtualPageState=function(){return console.warn("AG Grid: getVirtualPageState() is now called getCacheBlockState(), please call getCacheBlockState() instead"),this.getCacheBlockState()},e.prototype.getInfinitePageState=function(){return console.warn("AG Grid: getInfinitePageState() is now called getCacheBlockState(), please call getCacheBlockState() instead"),this.getCacheBlockState()},e.prototype.getCacheBlockState=function(){return this.rowNodeBlockLoader.getBlockState()},e.prototype.checkGridSize=function(){console.warn("in AG Grid v25.2.0, checkGridSize() was removed, as it was legacy and didn't do anything uesful.")},e.prototype.getFirstRenderedRow=function(){return console.warn("in AG Grid v12, getFirstRenderedRow() was renamed to getFirstDisplayedRow()"),this.getFirstDisplayedRow()},e.prototype.getFirstDisplayedRow=function(){return this.rowRenderer.getFirstVirtualRenderedRow()},e.prototype.getLastRenderedRow=function(){return console.warn("in AG Grid v12, getLastRenderedRow() was renamed to getLastDisplayedRow()"),this.getLastDisplayedRow()},e.prototype.getLastDisplayedRow=function(){return this.rowRenderer.getLastVirtualRenderedRow()},e.prototype.getDisplayedRowAtIndex=function(e){return this.rowModel.getRow(e)},e.prototype.getDisplayedRowCount=function(){return this.rowModel.getRowCount()},e.prototype.paginationIsLastPageFound=function(){return this.paginationProxy.isLastPageFound()},e.prototype.paginationGetPageSize=function(){return this.paginationProxy.getPageSize()},e.prototype.paginationSetPageSize=function(e){this.gridOptionsWrapper.setProperty("paginationPageSize",e)},e.prototype.paginationGetCurrentPage=function(){return this.paginationProxy.getCurrentPage()},e.prototype.paginationGetTotalPages=function(){return this.paginationProxy.getTotalPages()},e.prototype.paginationGetRowCount=function(){return this.paginationProxy.getMasterRowCount()},e.prototype.paginationGoToNextPage=function(){this.paginationProxy.goToNextPage()},e.prototype.paginationGoToPreviousPage=function(){this.paginationProxy.goToPreviousPage()},e.prototype.paginationGoToFirstPage=function(){this.paginationProxy.goToFirstPage()},e.prototype.paginationGoToLastPage=function(){this.paginationProxy.goToLastPage()},e.prototype.paginationGoToPage=function(e){this.paginationProxy.goToPage(e)},e.prototype.setRowClass=function(e){this.gridOptionsWrapper.setProperty(ul.PROP_ROW_CLASS,e)},Gp([Se("immutableService")],e.prototype,"immutableService",void 0),Gp([Se("csvCreator")],e.prototype,"csvCreator",void 0),Gp([Se("excelCreator")],e.prototype,"excelCreator",void 0),Gp([Re("rowRenderer")],e.prototype,"rowRenderer",void 0),Gp([Re("navigationService")],e.prototype,"navigationService",void 0),Gp([Re("filterManager")],e.prototype,"filterManager",void 0),Gp([Re("columnModel")],e.prototype,"columnModel",void 0),Gp([Re("selectionService")],e.prototype,"selectionService",void 0),Gp([Re("gridOptionsWrapper")],e.prototype,"gridOptionsWrapper",void 0),Gp([Re("valueService")],e.prototype,"valueService",void 0),Gp([Re("alignedGridsService")],e.prototype,"alignedGridsService",void 0),Gp([Re("eventService")],e.prototype,"eventService",void 0),Gp([Re("pinnedRowModel")],e.prototype,"pinnedRowModel",void 0),Gp([Re("context")],e.prototype,"context",void 0),Gp([Re("rowModel")],e.prototype,"rowModel",void 0),Gp([Re("sortController")],e.prototype,"sortController",void 0),Gp([Re("paginationProxy")],e.prototype,"paginationProxy",void 0),Gp([Re("focusService")],e.prototype,"focusService",void 0),Gp([Re("dragAndDropService")],e.prototype,"dragAndDropService",void 0),Gp([Se("rangeService")],e.prototype,"rangeService",void 0),Gp([Se("clipboardService")],e.prototype,"clipboardService",void 0),Gp([Se("aggFuncService")],e.prototype,"aggFuncService",void 0),Gp([Re("menuFactory")],e.prototype,"menuFactory",void 0),Gp([Se("contextMenuFactory")],e.prototype,"contextMenuFactory",void 0),Gp([Re("valueCache")],e.prototype,"valueCache",void 0),Gp([Re("animationFrameService")],e.prototype,"animationFrameService",void 0),Gp([Se("statusBarService")],e.prototype,"statusBarService",void 0),Gp([Se("chartService")],e.prototype,"chartService",void 0),Gp([Se("undoRedoService")],e.prototype,"undoRedoService",void 0),Gp([Se("rowNodeBlockLoader")],e.prototype,"rowNodeBlockLoader",void 0),Gp([Se("ssrmTransactionManager")],e.prototype,"serverSideTransactionManager",void 0),Gp([Re("ctrlsService")],e.prototype,"ctrlsService",void 0),Gp([Se("frameworkComponentWrapper")],e.prototype,"frameworkComponentWrapper",void 0),Gp([Ce],e.prototype,"init",null),Gp([we],e.prototype,"cleanDownReferencesToAvoidMemoryLeakInCaseApplicationIsKeepingReferenceToDestroyedGrid",null),e=Gp([Ee("gridApi")],e)}(),Hp=function(){var e=function(t,o){return e=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(e,t){e.__proto__=t}||function(e,t){for(var o in t)t.hasOwnProperty(o)&&(e[o]=t[o])},e(t,o)};return function(t,o){function n(){this.constructor=t}e(t,o),t.prototype=null===o?Object.create(o):(n.prototype=o.prototype,new n)}}(),Wp=function(e,t,o,n){var r,i=arguments.length,s=i<3?t:null===n?n=Object.getOwnPropertyDescriptor(t,o):n;if("object"===typeof Reflect&&"function"===typeof Reflect.decorate)s=Reflect.decorate(e,t,o,n);else for(var a=e.length-1;a>=0;a--)(r=e[a])&&(s=(i<3?r(s):i>3?r(t,o,s):r(t,o))||s);return i>3&&s&&Object.defineProperty(t,o,s),s},kp=function(e){function t(t,o,n,r){var i=e.call(this)||this;return i.columnOrGroup=t,i.eCell=o,i.ariaEl=i.eCell.querySelector("[role=columnheader]")||i.eCell,i.colsSpanning=r,i.beans=n,i}return Hp(t,e),t.prototype.setColsSpanning=function(e){this.colsSpanning=e,this.onLeftChanged()},t.prototype.getColumnOrGroup=function(){return this.beans.gridOptionsWrapper.isEnableRtl()&&this.colsSpanning?Ve(this.colsSpanning):this.columnOrGroup},t.prototype.postConstruct=function(){this.addManagedListener(this.columnOrGroup,Ge.EVENT_LEFT_CHANGED,this.onLeftChanged.bind(this)),this.setLeftFirstTime(),this.addManagedListener(this.eventService,St.EVENT_DISPLAYED_COLUMNS_WIDTH_CHANGED,this.onLeftChanged.bind(this)),this.addManagedListener(this.beans.gridOptionsWrapper,ul.PROP_DOM_LAYOUT,this.onLeftChanged.bind(this))},t.prototype.setLeftFirstTime=function(){var e=this.beans.gridOptionsWrapper.isSuppressColumnMoveAnimation(),t=O(this.columnOrGroup.getOldLeft());this.beans.columnAnimationService.isActive()&&t&&!e?this.animateInLeft():this.onLeftChanged()},t.prototype.animateInLeft=function(){var e=this,t=this.getColumnOrGroup(),o=t.getLeft(),n=t.getOldLeft(),r=this.modifyLeftForPrintLayout(t,n),i=this.modifyLeftForPrintLayout(t,o);this.setLeft(r),this.actualLeft=i,this.beans.columnAnimationService.executeNextVMTurn((function(){e.actualLeft===i&&e.setLeft(i)}))},t.prototype.onLeftChanged=function(){var e=this.getColumnOrGroup(),t=e.getLeft();this.actualLeft=this.modifyLeftForPrintLayout(e,t),this.setLeft(this.actualLeft)},t.prototype.modifyLeftForPrintLayout=function(e,t){if(!(this.beans.gridOptionsWrapper.getDomLayout()===Ne.DOM_LAYOUT_PRINT))return t;if(e.getPinned()===Ne.PINNED_LEFT)return t;var o=this.beans.columnModel.getDisplayedColumnsLeftWidth();return e.getPinned()===Ne.PINNED_RIGHT?o+this.beans.columnModel.getBodyContainerWidth()+t:o+t},t.prototype.setLeft=function(e){var t;if(O(e)&&(this.eCell.style.left=e+"px"),this.columnOrGroup instanceof Ge)t=this.columnOrGroup;else{var o=this.columnOrGroup.getLeafColumns();if(!o.length)return;o.length>1&&Zn(this.ariaEl,o.length),t=o[0]}var n=this.beans.columnModel.getAriaColumnIndex(t);Jn(this.ariaEl,n)},Wp([Ce],t.prototype,"postConstruct",null),t}(mt),Bp=function(){var e=function(t,o){return e=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(e,t){e.__proto__=t}||function(e,t){for(var o in t)t.hasOwnProperty(o)&&(e[o]=t[o])},e(t,o)};return function(t,o){function n(){this.constructor=t}e(t,o),t.prototype=null===o?Object.create(o):(n.prototype=o.prototype,new n)}}(),jp=function(e,t,o,n){var r,i=arguments.length,s=i<3?t:null===n?n=Object.getOwnPropertyDescriptor(t,o):n;if("object"===typeof Reflect&&"function"===typeof Reflect.decorate)s=Reflect.decorate(e,t,o,n);else for(var a=e.length-1;a>=0;a--)(r=e[a])&&(s=(i<3?r(s):i>3?r(t,o,s):r(t,o))||s);return i>3&&s&&Object.defineProperty(t,o,s),s},Up=function(e){function t(t,o){var n=e.call(this)||this;return n.columns=t,n.element=o,n}return Bp(t,e),t.prototype.postConstruct=function(){this.gridOptionsWrapper.isColumnHoverHighlight()&&this.addMouseHoverListeners()},t.prototype.addMouseHoverListeners=function(){this.addManagedListener(this.element,"mouseout",this.onMouseOut.bind(this)),this.addManagedListener(this.element,"mouseover",this.onMouseOver.bind(this))},t.prototype.onMouseOut=function(){this.columnHoverService.clearMouseOver()},t.prototype.onMouseOver=function(){this.columnHoverService.setMouseOver(this.columns)},jp([Re("columnHoverService")],t.prototype,"columnHoverService",void 0),jp([Ce],t.prototype,"postConstruct",null),t}(mt),zp=function(){var e=function(t,o){return e=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(e,t){e.__proto__=t}||function(e,t){for(var o in t)t.hasOwnProperty(o)&&(e[o]=t[o])},e(t,o)};return function(t,o){function n(){this.constructor=t}e(t,o),t.prototype=null===o?Object.create(o):(n.prototype=o.prototype,new n)}}(),Kp=function(e,t,o,n){var r,i=arguments.length,s=i<3?t:null===n?n=Object.getOwnPropertyDescriptor(t,o):n;if("object"===typeof Reflect&&"function"===typeof Reflect.decorate)s=Reflect.decorate(e,t,o,n);else for(var a=e.length-1;a>=0;a--)(r=e[a])&&(s=(i<3?r(s):i>3?r(t,o,s):r(t,o))||s);return i>3&&s&&Object.defineProperty(t,o,s),s},Yp=function(e){function t(t,o){var n=e.call(this,t,o)||this;return n.column=t,n}return zp(t,e),t.prototype.setComp=function(t,o,n,r){e.prototype.setGui.call(this,o),this.comp=t,this.eButtonShowMainFilter=n,this.eFloatingFilterBody=r;var i=this.column.getColDef(),s=!!i.filter||!!i.filterFramework,a=!!i.floatingFilter;this.active=s&&a,this.setupWidth(),this.setupLeft(),this.setupHover(),this.setupFocus(),this.setupUserComp(),this.setupSyncWithFilter(),this.setupUi(),this.addManagedListener(this.eButtonShowMainFilter,"click",this.showParentFilter.bind(this))},t.prototype.setupUi=function(){if(this.comp.addOrRemoveButtonWrapperCssClass("ag-hidden",!this.active||this.suppressFilterButton),this.active){this.comp.addOrRemoveBodyCssClass("ag-floating-filter-full-body",this.suppressFilterButton),this.comp.addOrRemoveBodyCssClass("ag-floating-filter-body",!this.suppressFilterButton);var e=Rr("filter",this.gridOptionsWrapper,this.column);e&&this.eButtonShowMainFilter.appendChild(e)}},t.prototype.setupFocus=function(){this.createManagedBean(new ui(this.eGui,{shouldStopEventPropagation:this.shouldStopEventPropagation.bind(this),onTabKeyDown:this.onTabKeyDown.bind(this),handleKeyDown:this.handleKeyDown.bind(this),onFocusIn:this.onFocusIn.bind(this)}))},t.prototype.onTabKeyDown=function(e){if(!(this.gridOptionsWrapper.getDocument().activeElement===this.eGui)){var t=this.focusService.findNextFocusableElement(this.eGui,null,e.shiftKey);if(t)return this.beans.headerNavigationService.scrollToColumn(this.column),e.preventDefault(),void t.focus();var o=this.findNextColumnWithFloatingFilter(e.shiftKey);o&&this.focusService.focusHeaderPosition({headerPosition:{headerRowIndex:this.getParentRowCtrl().getRowIndex(),column:o},event:e})&&e.preventDefault()}},t.prototype.findNextColumnWithFloatingFilter=function(e){var t=this.beans.columnModel,o=this.column;do{if(!(o=e?t.getDisplayedColBefore(o):t.getDisplayedColAfter(o)))break}while(!o.getColDef().filter||!o.getColDef().floatingFilter);return o},t.prototype.handleKeyDown=function(e){var t=this.gridOptionsWrapper.getDocument().activeElement===this.eGui;switch(e.key){case ii.UP:case ii.DOWN:t||e.preventDefault();case ii.LEFT:case ii.RIGHT:if(t)return;e.stopPropagation();case ii.ENTER:t&&this.focusService.focusInto(this.eGui)&&e.preventDefault();break;case ii.ESCAPE:t||this.eGui.focus()}},t.prototype.onFocusIn=function(e){if(!this.eGui.contains(e.relatedTarget)){var t=!!e.relatedTarget&&!e.relatedTarget.classList.contains("ag-floating-filter"),o=!!e.relatedTarget&&Wo(e.relatedTarget,"ag-floating-filter");if(t&&o&&e.target===this.eGui){var n=this.lastFocusEvent,r=!(!n||n.key!==ii.TAB);if(n&&r){var i=n.shiftKey;this.focusService.focusInto(this.eGui,i)}}var s=this.getRowIndex();this.beans.focusService.setFocusedHeader(s,this.column)}},t.prototype.setupHover=function(){var e=this;this.createManagedBean(new Up([this.column],this.eGui));var t=function(){if(e.gridOptionsWrapper.isColumnHoverHighlight()){var t=e.columnHoverService.isHovered(e.column);e.comp.addOrRemoveCssClass("ag-column-hover",t)}};this.addManagedListener(this.eventService,St.EVENT_COLUMN_HOVER_CHANGED,t),t()},t.prototype.setupLeft=function(){var e=new kp(this.column,this.eGui,this.beans);this.createManagedBean(e)},t.prototype.setupUserComp=function(){var e=this;if(this.active){var t=this.column.getColDef(),o=this.filterManager.createFilterParams(this.column,t),n=this.userComponentFactory.mergeParamsWithApplicationProvidedParams(t,Da,o),r=this.userComponentFactory.getDefaultFloatingFilterType(t);null==r&&(r="agReadOnlyFloatingFilter");var i={column:this.column,filterParams:n,currentParentModel:function(){return e.currentParentModel()},parentFilterInstance:function(t){return e.parentFilterInstance(t)},showParentFilter:function(){return e.showParentFilter()},suppressFilterButton:!1};this.suppressFilterButton=!!t.floatingFilterComponentParams&&!!t.floatingFilterComponentParams.suppressFilterButton;var s=this.userComponentFactory.getFloatingFilterCompDetails(t,i,r);s&&this.comp.setCompDetails(s)}},t.prototype.currentParentModel=function(){var e=this.getFilterComponent(!1);return e?e.resolveNow(null,(function(e){return e&&e.getModel()})):null},t.prototype.getFilterComponent=function(e){return void 0===e&&(e=!0),this.filterManager.getFilterComponent(this.column,"NO_UI",e)},t.prototype.parentFilterInstance=function(e){var t=this.getFilterComponent();null!=t&&t.then((function(t){e(Lp(t))}))},t.prototype.showParentFilter=function(){var e=this.suppressFilterButton?this.eFloatingFilterBody:this.eButtonShowMainFilter;this.menuFactory.showMenuAfterButtonClick(this.column,e,"floatingFilter","filterMenuTab",["filterMenuTab"])},t.prototype.setupSyncWithFilter=function(){var e=this;if(this.active){var t=function(t){var o=e.comp.getFloatingFilterComp();if(o){var n=e.currentParentModel();o.then((function(e){e&&e.onParentModelChanged(n,t)}))}};this.addManagedListener(this.column,Ge.EVENT_FILTER_CHANGED,t),this.filterManager.isFilterActive(this.column)&&t(null)}},t.prototype.setupWidth=function(){var e=this,t=function(){var t=e.column.getActualWidth()+"px";e.comp.setWidth(t)};this.addManagedListener(this.column,Ge.EVENT_WIDTH_CHANGED,t),t()},Kp([Re("userComponentFactory")],t.prototype,"userComponentFactory",void 0),Kp([Re("filterManager")],t.prototype,"filterManager",void 0),Kp([Re("columnHoverService")],t.prototype,"columnHoverService",void 0),Kp([Re("gridApi")],t.prototype,"gridApi",void 0),Kp([Re("menuFactory")],t.prototype,"menuFactory",void 0),Kp([Re("beans")],t.prototype,"beans",void 0),t}(Mp),qp=function(){var e=function(t,o){return e=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(e,t){e.__proto__=t}||function(e,t){for(var o in t)t.hasOwnProperty(o)&&(e[o]=t[o])},e(t,o)};return function(t,o){function n(){this.constructor=t}e(t,o),t.prototype=null===o?Object.create(o):(n.prototype=o.prototype,new n)}}(),Xp=function(e,t,o,n){var r,i=arguments.length,s=i<3?t:null===n?n=Object.getOwnPropertyDescriptor(t,o):n;if("object"===typeof Reflect&&"function"===typeof Reflect.decorate)s=Reflect.decorate(e,t,o,n);else for(var a=e.length-1;a>=0;a--)(r=e[a])&&(s=(i<3?r(s):i>3?r(t,o,s):r(t,o))||s);return i>3&&s&&Object.defineProperty(t,o,s),s},Qp=function(e){function t(t,o,n,r,i){var s=e.call(this)||this;return s.pinned=t,s.column=o,s.eResize=n,s.comp=r,s.ctrl=i,s}return qp(t,e),t.prototype.postConstruct=function(){var e,t,o=this,n=this.column.getColDef(),r=[],i=function(){r.forEach((function(e){return e()})),r.length=0},s=function(){var s=o.column.isResizable(),a=!o.gridOptionsWrapper.isSuppressAutoSize()&&!n.suppressAutoSize;(s!==e||a!==t)&&(e=s,t=a,i(),function(){if(xo(o.eResize,e),e){var n=o.horizontalResizeService.addResizeBar({eResizeBar:o.eResize,onResizeStart:o.onResizeStart.bind(o),onResizing:o.onResizing.bind(o,!1),onResizeEnd:o.onResizing.bind(o,!0)});if(r.push(n),t){var i=o.gridOptionsWrapper.isSkipHeaderOnAutoSize(),s=function(){o.columnModel.autoSizeColumn(o.column,i,"uiColumnResized")};o.eResize.addEventListener("dblclick",s);var a=new us(o.eResize);a.addEventListener(us.EVENT_DOUBLE_TAP,s),o.addDestroyFunc((function(){o.eResize.removeEventListener("dblclick",s),a.removeEventListener(us.EVENT_DOUBLE_TAP,s),a.destroy()}))}}}())};s(),this.addDestroyFunc(i),this.ctrl.addRefreshFunction(s)},t.prototype.onResizing=function(e,t){var o=this.normaliseResizeAmount(t),n=[{key:this.column,newWidth:this.resizeStartWidth+o}];this.columnModel.setColumnWidths(n,this.resizeWithShiftKey,e,"uiColumnDragged"),e&&this.comp.addOrRemoveCssClass("ag-column-resizing",!1)},t.prototype.onResizeStart=function(e){this.resizeStartWidth=this.column.getActualWidth(),this.resizeWithShiftKey=e,this.comp.addOrRemoveCssClass("ag-column-resizing",!0)},t.prototype.normaliseResizeAmount=function(e){var t=e,o=this.pinned!==Ne.PINNED_LEFT,n=this.pinned===Ne.PINNED_RIGHT;return this.gridOptionsWrapper.isEnableRtl()?o&&(t*=-1):n&&(t*=-1),t},Xp([Re("horizontalResizeService")],t.prototype,"horizontalResizeService",void 0),Xp([Re("columnModel")],t.prototype,"columnModel",void 0),Xp([Ce],t.prototype,"postConstruct",null),t}(mt),$p=function(){var e=function(t,o){return e=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(e,t){e.__proto__=t}||function(e,t){for(var o in t)t.hasOwnProperty(o)&&(e[o]=t[o])},e(t,o)};return function(t,o){function n(){this.constructor=t}e(t,o),t.prototype=null===o?Object.create(o):(n.prototype=o.prototype,new n)}}(),Jp=function(e){function t(t,o,n){void 0===o&&(o="ag-checkbox"),void 0===n&&(n="checkbox");var r=e.call(this,t,o,n)||this;return r.labelAlignment="right",r.selected=!1,r.readOnly=!1,r.passive=!1,r}return $p(t,e),t.prototype.addInputListeners=function(){this.addManagedListener(this.eInput,"click",this.onCheckboxClick.bind(this)),this.addManagedListener(this.eLabel,"click",this.toggle.bind(this))},t.prototype.getNextValue=function(){return void 0===this.selected||!this.selected},t.prototype.setPassive=function(e){this.passive=e},t.prototype.isReadOnly=function(){return this.readOnly},t.prototype.setReadOnly=function(e){this.eWrapper.classList.toggle("ag-disabled",e),this.eInput.disabled=e,this.readOnly=e},t.prototype.setDisabled=function(t){return this.eWrapper.classList.toggle("ag-disabled",t),e.prototype.setDisabled.call(this,t)},t.prototype.toggle=function(){if(!this.eInput.disabled){var e=this.isSelected(),t=this.getNextValue();this.passive?this.dispatchChange(t,e):this.setValue(t)}},t.prototype.getValue=function(){return this.isSelected()},t.prototype.setValue=function(e,t){return this.refreshSelectedClass(e),this.setSelected(e,t),this},t.prototype.setName=function(e){return this.getInputElement().name=e,this},t.prototype.isSelected=function(){return this.selected},t.prototype.setSelected=function(e,t){this.isSelected()!==e&&(this.previousValue=this.isSelected(),e=this.selected="boolean"===typeof e?e:void 0,this.eInput.checked=e,this.eInput.indeterminate=void 0===e,t||this.dispatchChange(this.selected,this.previousValue))},t.prototype.dispatchChange=function(e,o,n){this.dispatchEvent({type:t.EVENT_CHANGED,selected:e,previousValue:o,event:n});var r=this.getInputElement(),i={type:St.EVENT_CHECKBOX_CHANGED,id:r.id,name:r.name,selected:e,previousValue:o};this.eventService.dispatchEvent(i)},t.prototype.onCheckboxClick=function(e){if(!this.passive&&!this.eInput.disabled){var t=this.isSelected(),o=this.selected=e.target.checked;this.refreshSelectedClass(o),this.dispatchChange(o,t,e)}},t.prototype.refreshSelectedClass=function(e){this.eWrapper.classList.toggle("ag-checked",!0===e),this.eWrapper.classList.toggle("ag-indeterminate",null==e)},t}(Di),Zp=function(){var e=function(t,o){return e=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(e,t){e.__proto__=t}||function(e,t){for(var o in t)t.hasOwnProperty(o)&&(e[o]=t[o])},e(t,o)};return function(t,o){function n(){this.constructor=t}e(t,o),t.prototype=null===o?Object.create(o):(n.prototype=o.prototype,new n)}}(),ec=function(e,t,o,n){var r,i=arguments.length,s=i<3?t:null===n?n=Object.getOwnPropertyDescriptor(t,o):n;if("object"===typeof Reflect&&"function"===typeof Reflect.decorate)s=Reflect.decorate(e,t,o,n);else for(var a=e.length-1;a>=0;a--)(r=e[a])&&(s=(i<3?r(s):i>3?r(t,o,s):r(t,o))||s);return i>3&&s&&Object.defineProperty(t,o,s),s},tc=function(e){function t(t){var o=e.call(this)||this;o.cbSelectAllVisible=!1,o.processingEventFromCheckbox=!1,o.column=t;var n=t.getColDef();return o.filteredOnly=!!n&&!!n.headerCheckboxSelectionFilteredOnly,o}return Zp(t,e),t.prototype.onSpaceKeyPressed=function(e){var t=this.cbSelectAll,o=this.gridOptionsWrapper.getDocument();t.isDisplayed()&&!t.getGui().contains(o.activeElement)&&(e.preventDefault(),t.setValue(!t.getValue()))},t.prototype.getCheckboxGui=function(){return this.cbSelectAll.getGui()},t.prototype.setComp=function(e){this.headerCellCtrl=e,this.cbSelectAll=this.createManagedBean(new Jp),this.cbSelectAll.addCssClass("ag-header-select-all"),In(this.cbSelectAll.getGui(),"presentation"),this.showOrHideSelectAll(),this.addManagedListener(this.eventService,St.EVENT_NEW_COLUMNS_LOADED,this.showOrHideSelectAll.bind(this)),this.addManagedListener(this.eventService,St.EVENT_DISPLAYED_COLUMNS_CHANGED,this.showOrHideSelectAll.bind(this)),this.addManagedListener(this.eventService,St.EVENT_SELECTION_CHANGED,this.onSelectionChanged.bind(this)),this.addManagedListener(this.eventService,St.EVENT_MODEL_UPDATED,this.onModelChanged.bind(this)),this.addManagedListener(this.cbSelectAll,Jp.EVENT_CHANGED,this.onCbSelectAll.bind(this)),this.cbSelectAll.getInputElement().setAttribute("tabindex","-1"),this.refreshSelectAllLabel()},t.prototype.showOrHideSelectAll=function(){this.cbSelectAllVisible=this.isCheckboxSelection(),this.cbSelectAll.setDisplayed(this.cbSelectAllVisible),this.cbSelectAllVisible&&(this.checkRightRowModelType(),this.updateStateOfCheckbox()),this.refreshSelectAllLabel()},t.prototype.onModelChanged=function(){this.cbSelectAllVisible&&this.updateStateOfCheckbox()},t.prototype.onSelectionChanged=function(){this.cbSelectAllVisible&&this.updateStateOfCheckbox()},t.prototype.getNextCheckboxState=function(e){return(0!==e.selected||0!==e.notSelected)&&(e.selected>0&&e.notSelected>0?null:e.selected>0)},t.prototype.updateStateOfCheckbox=function(){if(!this.processingEventFromCheckbox){this.processingEventFromCheckbox=!0;var e=this.getSelectionCount(),t=this.getNextCheckboxState(e);this.cbSelectAll.setValue(t),this.refreshSelectAllLabel(),this.processingEventFromCheckbox=!1}},t.prototype.refreshSelectAllLabel=function(){if(this.cbSelectAllVisible){var e=this.gridOptionsWrapper.getLocaleTextFunc(),t=this.cbSelectAll.getValue()?e("ariaChecked","checked"):e("ariaUnchecked","unchecked"),o=e("ariaRowSelectAll","Press Space to toggle all rows selection");this.headerCellCtrl.setAriaDescriptionProperty("selectAll",o+" ("+t+")")}else this.headerCellCtrl.setAriaDescriptionProperty("selectAll",null);this.headerCellCtrl.refreshAriaDescription()},t.prototype.getSelectionCount=function(){var e=this,t=0,o=0,n=function(n){e.gridOptionsWrapper.isGroupSelectsChildren()&&n.group||(n.isSelected()?t++:n.selectable&&o++)};return this.filteredOnly?this.gridApi.forEachNodeAfterFilter(n):this.gridApi.forEachNode(n),{notSelected:o,selected:t}},t.prototype.checkRightRowModelType=function(){var e=this.rowModel.getType();e===Ne.ROW_MODEL_TYPE_CLIENT_SIDE||console.warn("AG Grid: selectAllCheckbox is only available if using normal row model, you are using "+e)},t.prototype.onCbSelectAll=function(){this.processingEventFromCheckbox||this.cbSelectAllVisible&&(this.cbSelectAll.getValue()?this.selectionService.selectAllRowNodes(this.filteredOnly):this.selectionService.deselectAllRowNodes(this.filteredOnly))},t.prototype.isCheckboxSelection=function(){var e=this.column.getColDef().headerCheckboxSelection;"function"===typeof e&&(e=e({column:this.column,colDef:this.column.getColDef(),columnApi:this.columnApi,api:this.gridApi,context:this.gridOptionsWrapper.getContext()}));return!!e&&(this.gridOptionsWrapper.isRowModelServerSide()?(console.warn("AG Grid: headerCheckboxSelection is not supported for Server Side Row Model"),!1):this.gridOptionsWrapper.isRowModelInfinite()?(console.warn("AG Grid: headerCheckboxSelection is not supported for Infinite Row Model"),!1):!this.gridOptionsWrapper.isRowModelViewport()||(console.warn("AG Grid: headerCheckboxSelection is not supported for Viewport Row Model"),!1))},ec([Re("gridApi")],t.prototype,"gridApi",void 0),ec([Re("columnApi")],t.prototype,"columnApi",void 0),ec([Re("rowModel")],t.prototype,"rowModel",void 0),ec([Re("selectionService")],t.prototype,"selectionService",void 0),t}(mt),oc=function(){var e=function(t,o){return e=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(e,t){e.__proto__=t}||function(e,t){for(var o in t)t.hasOwnProperty(o)&&(e[o]=t[o])},e(t,o)};return function(t,o){function n(){this.constructor=t}e(t,o),t.prototype=null===o?Object.create(o):(n.prototype=o.prototype,new n)}}(),nc=function(e,t,o,n){var r,i=arguments.length,s=i<3?t:null===n?n=Object.getOwnPropertyDescriptor(t,o):n;if("object"===typeof Reflect&&"function"===typeof Reflect.decorate)s=Reflect.decorate(e,t,o,n);else for(var a=e.length-1;a>=0;a--)(r=e[a])&&(s=(i<3?r(s):i>3?r(t,o,s):r(t,o))||s);return i>3&&s&&Object.defineProperty(t,o,s),s},rc=function(e){function t(t,o){var n=e.call(this,t,o)||this;return n.refreshFunctions=[],n.userHeaderClasses=new Set,n.ariaDescriptionProperties=new Map,n.column=t,n}return oc(t,e),t.prototype.setComp=function(t,o,n){var r=this;e.prototype.setGui.call(this,o),this.comp=t,this.colDefVersion=this.columnModel.getColDefVersion(),this.updateState(),this.setupWidth(),this.setupMovingCss(),this.setupMenuClass(),this.setupSortableClass(),this.addColumnHoverListener(),this.setupFilterCss(),this.setupColId(),this.setupClassesFromColDef(),this.setupTooltip(),this.addActiveHeaderMouseListeners(),this.setupSelectAll(),this.setupUserComp(),this.refreshAria(),this.createManagedBean(new Qp(this.getPinned(),this.column,n,t,this)),this.createManagedBean(new Up([this.column],o)),this.createManagedBean(new kp(this.column,o,this.beans)),this.createManagedBean(new ui(o,{shouldStopEventPropagation:function(e){return r.shouldStopEventPropagation(e)},onTabKeyDown:function(){return null},handleKeyDown:this.handleKeyDown.bind(this),onFocusIn:this.onFocusIn.bind(this),onFocusOut:this.onFocusOut.bind(this)})),this.addManagedListener(this.eventService,St.EVENT_NEW_COLUMNS_LOADED,this.onNewColumnsLoaded.bind(this)),this.addManagedListener(this.eventService,St.EVENT_COLUMN_VALUE_CHANGED,this.onColumnValueChanged.bind(this)),this.addManagedListener(this.eventService,St.EVENT_COLUMN_ROW_GROUP_CHANGED,this.onColumnRowGroupChanged.bind(this)),this.addManagedListener(this.eventService,St.EVENT_COLUMN_PIVOT_CHANGED,this.onColumnPivotChanged.bind(this))},t.prototype.setupUserComp=function(){var e=this.lookupUserCompDetails();this.setCompDetails(e)},t.prototype.setCompDetails=function(e){this.userCompDetails=e,this.comp.setUserCompDetails(e)},t.prototype.lookupUserCompDetails=function(){var e=this.createParams(),t=this.column.getColDef();return this.userComponentFactory.getHeaderCompDetails(t,e)},t.prototype.createParams=function(){var e=this,t=this.column.getColDef();return{column:this.column,displayName:this.displayName,enableSorting:t.sortable,enableMenu:this.menuEnabled,showColumnMenu:function(t){e.gridApi.showColumnMenuAfterButtonClick(e.column,t)},progressSort:function(t){e.sortController.progressSort(e.column,!!t,"uiColumnSorted")},setSort:function(t,o){e.sortController.setSortForColumn(e.column,t,!!o,"uiColumnSorted")},api:this.gridApi,columnApi:this.columnApi,context:this.gridOptionsWrapper.getContext(),eGridHeader:this.getGui()}},t.prototype.setupSelectAll=function(){this.selectAllFeature=this.createManagedBean(new tc(this.column)),this.selectAllFeature.setComp(this)},t.prototype.getSelectAllGui=function(){return this.selectAllFeature.getCheckboxGui()},t.prototype.handleKeyDown=function(e){e.key===ii.SPACE&&this.selectAllFeature.onSpaceKeyPressed(e),e.key===ii.ENTER&&this.onEnterKeyPressed(e)},t.prototype.onEnterKeyPressed=function(e){var t=this.comp.getUserCompInstance();if(t)if(e.ctrlKey||e.metaKey)this.menuEnabled&&t.showMenu&&(e.preventDefault(),t.showMenu());else if(this.sortable){var o=e.shiftKey;this.sortController.progressSort(this.column,o,"uiColumnSorted")}},t.prototype.isMenuEnabled=function(){return this.menuEnabled},t.prototype.onFocusIn=function(e){if(!this.getGui().contains(e.relatedTarget)){var t=this.getRowIndex();this.focusService.setFocusedHeader(t,this.column)}this.setActiveHeader(!0)},t.prototype.onFocusOut=function(e){this.getGui().contains(e.relatedTarget)||this.setActiveHeader(!1)},t.prototype.setupTooltip=function(){var e=this,t={getColumn:function(){return e.column},getColDef:function(){return e.column.getColDef()},getGui:function(){return e.eGui},getLocation:function(){return"header"},getTooltipValue:function(){return e.column.getColDef().headerTooltip}},o=this.createManagedBean(new Wl(t,this.beans));o.setComp(this.comp),this.refreshFunctions.push((function(){return o.refreshToolTip()}))},t.prototype.setupClassesFromColDef=function(){var e=this,t=function(){var t=e.column.getColDef(),o=e.gridOptionsWrapper,n=wp.getHeaderClassesFromColDef(t,o,e.column,null),r=e.userHeaderClasses;e.userHeaderClasses=new Set(n),n.forEach((function(t){r.has(t)?r.delete(t):e.comp.addOrRemoveCssClass(t,!0)})),r.forEach((function(t){return e.comp.addOrRemoveCssClass(t,!1)}))};this.refreshFunctions.push(t),t()},t.prototype.setDragSource=function(e){var t=this;this.dragSourceElement=e,this.removeDragSource(),e&&this.draggable&&(this.moveDragSource={type:xs.HeaderCell,eElement:e,defaultIconName:qs.ICON_HIDE,getDragItem:function(){return t.createDragItem()},dragItemName:this.displayName,onDragStarted:function(){return t.column.setMoving(!0,"uiColumnMoved")},onDragStopped:function(){return t.column.setMoving(!1,"uiColumnMoved")}},this.dragAndDropService.addDragSource(this.moveDragSource,!0))},t.prototype.createDragItem=function(){var e={};return e[this.column.getId()]=this.column.isVisible(),{columns:[this.column],visibleState:e}},t.prototype.removeDragSource=function(){this.moveDragSource&&(this.dragAndDropService.removeDragSource(this.moveDragSource),this.moveDragSource=void 0)},t.prototype.onNewColumnsLoaded=function(){var e=this.columnModel.getColDefVersion();e!=this.colDefVersion&&(this.colDefVersion=e,this.refresh())},t.prototype.updateState=function(){var e=this.column.getColDef();this.menuEnabled=this.menuFactory.isMenuEnabled(this.column)&&!e.suppressMenu,this.sortable=e.sortable,this.displayName=this.calculateDisplayName(),this.draggable=this.workOutDraggable()},t.prototype.addRefreshFunction=function(e){this.refreshFunctions.push(e)},t.prototype.refresh=function(){this.updateState(),this.refreshHeaderComp(),this.refreshAria(),this.refreshFunctions.forEach((function(e){return e()}))},t.prototype.refreshHeaderComp=function(){var e=this.lookupUserCompDetails();!!(null!=this.comp.getUserCompInstance()&&this.userCompDetails.componentClass==e.componentClass)&&this.attemptHeaderCompRefresh(e.params)?this.setDragSource(this.dragSourceElement):this.setCompDetails(e)},t.prototype.attemptHeaderCompRefresh=function(e){var t=this.comp.getUserCompInstance();return!!t&&(!!t.refresh&&t.refresh(e))},t.prototype.calculateDisplayName=function(){return this.columnModel.getDisplayNameForColumn(this.column,"header",!0)},t.prototype.checkDisplayName=function(){this.displayName!==this.calculateDisplayName()&&this.refresh()},t.prototype.workOutDraggable=function(){var e=this.column.getColDef();return!!(!this.gridOptionsWrapper.isSuppressMovableColumns()&&!e.suppressMovable&&!e.lockPosition)||!!e.enableRowGroup||!!e.enablePivot},t.prototype.onColumnRowGroupChanged=function(){this.checkDisplayName()},t.prototype.onColumnPivotChanged=function(){this.checkDisplayName()},t.prototype.onColumnValueChanged=function(){this.checkDisplayName()},t.prototype.setupWidth=function(){var e=this,t=function(){e.comp.setWidth(e.column.getActualWidth()+"px")};this.addManagedListener(this.column,Ge.EVENT_WIDTH_CHANGED,t),t()},t.prototype.setupMovingCss=function(){var e=this,t=function(){e.comp.addOrRemoveCssClass("ag-header-cell-moving",e.column.isMoving())};this.addManagedListener(this.column,Ge.EVENT_MOVING_CHANGED,t),t()},t.prototype.setupMenuClass=function(){var e=this,t=function(){e.comp.addOrRemoveCssClass("ag-column-menu-visible",e.column.isMenuVisible())};this.addManagedListener(this.column,Ge.EVENT_MENU_VISIBLE_CHANGED,t),t()},t.prototype.setupSortableClass=function(){var e=this,t=function(){e.comp.addOrRemoveCssClass("ag-header-cell-sortable",!!e.sortable)};t(),this.addRefreshFunction(t),this.addManagedListener(this.column,Ge.EVENT_SORT_CHANGED,this.refreshAriaSort.bind(this))},t.prototype.refreshAriaSort=function(){if(this.sortable){var e=this.gridOptionsWrapper.getLocaleTextFunc();this.comp.setAriaSort(Mn(this.column)),this.setAriaDescriptionProperty("sort",e("ariaSortableColumn","Press ENTER to sort."))}else this.comp.setAriaSort(),this.setAriaDescriptionProperty("sort",null)},t.prototype.refreshAriaMenu=function(){if(this.menuEnabled){var e=this.gridOptionsWrapper.getLocaleTextFunc();this.setAriaDescriptionProperty("menu",e("ariaMenuColumn","Press CTRL ENTER to open column menu."))}else this.setAriaDescriptionProperty("menu",null)},t.prototype.setAriaDescriptionProperty=function(e,t){null!=t?this.ariaDescriptionProperties.set(e,t):this.ariaDescriptionProperties.delete(e)},t.prototype.refreshAriaDescription=function(){var e=Array.from(this.ariaDescriptionProperties.values());this.comp.setAriaDescription(e.length?e.join(" "):void 0)},t.prototype.refreshAria=function(){this.refreshAriaSort(),this.refreshAriaMenu(),this.refreshAriaDescription()},t.prototype.addColumnHoverListener=function(){var e=this,t=function(){if(e.gridOptionsWrapper.isColumnHoverHighlight()){var t=e.columnHoverService.isHovered(e.column);e.comp.addOrRemoveCssClass("ag-column-hover",t)}};this.addManagedListener(this.eventService,St.EVENT_COLUMN_HOVER_CHANGED,t),t()},t.prototype.setupFilterCss=function(){var e=this,t=function(){e.comp.addOrRemoveCssClass("ag-header-cell-filtered",e.column.isFilterActive())};this.addManagedListener(this.column,Ge.EVENT_FILTER_ACTIVE_CHANGED,t),t()},t.prototype.setupColId=function(){this.comp.setColId(this.column.getColId())},t.prototype.addActiveHeaderMouseListeners=function(){var e=this,t=function(t){return e.setActiveHeader("mouseenter"===t.type)};this.addManagedListener(this.getGui(),"mouseenter",t),this.addManagedListener(this.getGui(),"mouseleave",t)},t.prototype.setActiveHeader=function(e){this.comp.addOrRemoveCssClass("ag-header-active",e)},nc([Re("columnModel")],t.prototype,"columnModel",void 0),nc([Re("columnHoverService")],t.prototype,"columnHoverService",void 0),nc([Re("beans")],t.prototype,"beans",void 0),nc([Re("sortController")],t.prototype,"sortController",void 0),nc([Re("menuFactory")],t.prototype,"menuFactory",void 0),nc([Re("dragAndDropService")],t.prototype,"dragAndDropService",void 0),nc([Re("gridApi")],t.prototype,"gridApi",void 0),nc([Re("columnApi")],t.prototype,"columnApi",void 0),nc([Re("userComponentFactory")],t.prototype,"userComponentFactory",void 0),nc([we],t.prototype,"removeDragSource",null),t}(Mp),ic=function(){var e=function(t,o){return e=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(e,t){e.__proto__=t}||function(e,t){for(var o in t)t.hasOwnProperty(o)&&(e[o]=t[o])},e(t,o)};return function(t,o){function n(){this.constructor=t}e(t,o),t.prototype=null===o?Object.create(o):(n.prototype=o.prototype,new n)}}(),sc=function(e,t,o,n){var r,i=arguments.length,s=i<3?t:null===n?n=Object.getOwnPropertyDescriptor(t,o):n;if("object"===typeof Reflect&&"function"===typeof Reflect.decorate)s=Reflect.decorate(e,t,o,n);else for(var a=e.length-1;a>=0;a--)(r=e[a])&&(s=(i<3?r(s):i>3?r(t,o,s):r(t,o))||s);return i>3&&s&&Object.defineProperty(t,o,s),s},ac=function(e){function t(t,o,n,r){var i=e.call(this)||this;return i.eResize=o,i.comp=t,i.pinned=n,i.columnGroup=r,i}return ic(t,e),t.prototype.postConstruct=function(){var e=this;if(this.columnGroup.isResizable()){var t=this.horizontalResizeService.addResizeBar({eResizeBar:this.eResize,onResizeStart:this.onResizeStart.bind(this),onResizing:this.onResizing.bind(this,!1),onResizeEnd:this.onResizing.bind(this,!0)});if(this.addDestroyFunc(t),!this.gridOptionsWrapper.isSuppressAutoSize()){var o=this.gridOptionsWrapper.isSkipHeaderOnAutoSize();this.eResize.addEventListener("dblclick",(function(){var t=[];e.columnGroup.getDisplayedLeafColumns().forEach((function(e){e.getColDef().suppressAutoSize||t.push(e.getColId())})),t.length>0&&e.columnModel.autoSizeColumns({columns:t,skipHeader:o,stopAtGroup:e.columnGroup,source:"uiColumnResized"}),e.resizeLeafColumnsToFit()}))}}else this.comp.addOrRemoveResizableCssClass("ag-hidden",!0)},t.prototype.onResizeStart=function(e){var t=this;this.calculateInitialValues();var o=null;if(e&&(o=this.columnModel.getDisplayedGroupAfter(this.columnGroup)),o){var n=o.getDisplayedLeafColumns();this.resizeTakeFromCols=n.filter((function(e){return e.isResizable()})),this.resizeTakeFromStartWidth=0,this.resizeTakeFromCols.forEach((function(e){return t.resizeTakeFromStartWidth+=e.getActualWidth()})),this.resizeTakeFromRatios=[],this.resizeTakeFromCols.forEach((function(e){return t.resizeTakeFromRatios.push(e.getActualWidth()/t.resizeTakeFromStartWidth)}))}else this.resizeTakeFromCols=null,this.resizeTakeFromStartWidth=null,this.resizeTakeFromRatios=null;this.comp.addOrRemoveCssClass("ag-column-resizing",!0)},t.prototype.onResizing=function(e,t){var o=this.normaliseDragChange(t),n=this.resizeStartWidth+o;this.resizeColumns(n,e)},t.prototype.resizeLeafColumnsToFit=function(){var e=this.autoWidthCalculator.getPreferredWidthForColumnGroup(this.columnGroup);this.calculateInitialValues(),e>this.resizeStartWidth&&this.resizeColumns(e,!0)},t.prototype.resizeColumns=function(e,t){void 0===t&&(t=!0);var o=[];if(o.push({columns:this.resizeCols,ratios:this.resizeRatios,width:e}),this.resizeTakeFromCols){var n=e-this.resizeStartWidth;o.push({columns:this.resizeTakeFromCols,ratios:this.resizeTakeFromRatios,width:this.resizeTakeFromStartWidth-n})}this.columnModel.resizeColumnSets({resizeSets:o,finished:t,source:"uiColumnDragged"}),t&&this.comp.addOrRemoveCssClass("ag-column-resizing",!1)},t.prototype.calculateInitialValues=function(){var e=this,t=this.columnGroup.getDisplayedLeafColumns();this.resizeCols=t.filter((function(e){return e.isResizable()})),this.resizeStartWidth=0,this.resizeCols.forEach((function(t){return e.resizeStartWidth+=t.getActualWidth()})),this.resizeRatios=[],this.resizeCols.forEach((function(t){return e.resizeRatios.push(t.getActualWidth()/e.resizeStartWidth)}))},t.prototype.normaliseDragChange=function(e){var t=e;return this.gridOptionsWrapper.isEnableRtl()?this.pinned!==Ne.PINNED_LEFT&&(t*=-1):this.pinned===Ne.PINNED_RIGHT&&(t*=-1),t},sc([Re("horizontalResizeService")],t.prototype,"horizontalResizeService",void 0),sc([Re("autoWidthCalculator")],t.prototype,"autoWidthCalculator",void 0),sc([Re("columnModel")],t.prototype,"columnModel",void 0),sc([Ce],t.prototype,"postConstruct",null),t}(mt),lc=function(){var e=function(t,o){return e=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(e,t){e.__proto__=t}||function(e,t){for(var o in t)t.hasOwnProperty(o)&&(e[o]=t[o])},e(t,o)};return function(t,o){function n(){this.constructor=t}e(t,o),t.prototype=null===o?Object.create(o):(n.prototype=o.prototype,new n)}}(),uc=function(e,t,o,n){var r,i=arguments.length,s=i<3?t:null===n?n=Object.getOwnPropertyDescriptor(t,o):n;if("object"===typeof Reflect&&"function"===typeof Reflect.decorate)s=Reflect.decorate(e,t,o,n);else for(var a=e.length-1;a>=0;a--)(r=e[a])&&(s=(i<3?r(s):i>3?r(t,o,s):r(t,o))||s);return i>3&&s&&Object.defineProperty(t,o,s),s},pc=function(e){function t(t,o){var n=e.call(this)||this;return n.removeChildListenersFuncs=[],n.columnGroup=o,n.comp=t,n}return lc(t,e),t.prototype.postConstruct=function(){this.addListenersToChildrenColumns(),this.addManagedListener(this.columnGroup,tt.EVENT_DISPLAYED_CHILDREN_CHANGED,this.onDisplayedChildrenChanged.bind(this)),this.onWidthChanged(),this.addDestroyFunc(this.removeListenersOnChildrenColumns.bind(this))},t.prototype.addListenersToChildrenColumns=function(){var e=this;this.removeListenersOnChildrenColumns();var t=this.onWidthChanged.bind(this);this.columnGroup.getLeafColumns().forEach((function(o){o.addEventListener(Ge.EVENT_WIDTH_CHANGED,t),o.addEventListener(Ge.EVENT_VISIBLE_CHANGED,t),e.removeChildListenersFuncs.push((function(){o.removeEventListener(Ge.EVENT_WIDTH_CHANGED,t),o.removeEventListener(Ge.EVENT_VISIBLE_CHANGED,t)}))}))},t.prototype.removeListenersOnChildrenColumns=function(){this.removeChildListenersFuncs.forEach((function(e){return e()})),this.removeChildListenersFuncs=[]},t.prototype.onDisplayedChildrenChanged=function(){this.addListenersToChildrenColumns(),this.onWidthChanged()},t.prototype.onWidthChanged=function(){this.comp.setWidth(this.columnGroup.getActualWidth()+"px")},uc([Ce],t.prototype,"postConstruct",null),t}(mt),cc=function(){var e=function(t,o){return e=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(e,t){e.__proto__=t}||function(e,t){for(var o in t)t.hasOwnProperty(o)&&(e[o]=t[o])},e(t,o)};return function(t,o){function n(){this.constructor=t}e(t,o),t.prototype=null===o?Object.create(o):(n.prototype=o.prototype,new n)}}(),dc=function(e,t,o,n){var r,i=arguments.length,s=i<3?t:null===n?n=Object.getOwnPropertyDescriptor(t,o):n;if("object"===typeof Reflect&&"function"===typeof Reflect.decorate)s=Reflect.decorate(e,t,o,n);else for(var a=e.length-1;a>=0;a--)(r=e[a])&&(s=(i<3?r(s):i>3?r(t,o,s):r(t,o))||s);return i>3&&s&&Object.defineProperty(t,o,s),s},hc=function(e){function t(t,o){var n=e.call(this,t,o)||this;return n.columnGroup=t,n}return cc(t,e),t.prototype.setComp=function(t,o,n){e.prototype.setGui.call(this,o),this.comp=t,this.displayName=this.columnModel.getDisplayNameForColumnGroup(this.columnGroup,"header"),this.addClasses(),this.addAttributes(),this.setupMovingCss(),this.setupExpandable(),this.setupTooltip(),this.setupUserComp();var r=this.getParentRowCtrl().getPinned(),i=this.columnGroup.getProvidedColumnGroup().getLeafColumns();this.createManagedBean(new Up(i,o)),this.createManagedBean(new kp(this.columnGroup,o,this.beans)),this.createManagedBean(new pc(t,this.columnGroup)),this.groupResizeFeature=this.createManagedBean(new ac(t,n,r,this.columnGroup)),this.createManagedBean(new ui(o,{shouldStopEventPropagation:this.shouldStopEventPropagation.bind(this),onTabKeyDown:function(){},handleKeyDown:this.handleKeyDown.bind(this),onFocusIn:this.onFocusIn.bind(this)}))},t.prototype.resizeLeafColumnsToFit=function(){this.groupResizeFeature.onResizeStart(!1),this.groupResizeFeature.resizeLeafColumnsToFit()},t.prototype.setupUserComp=function(){var e=this,t=this.displayName,o={displayName:this.displayName,columnGroup:this.columnGroup,setExpanded:function(t){e.columnModel.setColumnGroupOpened(e.columnGroup.getProvidedColumnGroup(),t,"gridInitializing")},api:this.gridApi,columnApi:this.columnApi,context:this.gridOptionsWrapper.getContext()};if(!t){for(var n=this.columnGroup,r=n.getLeafColumns();n.getParent()&&n.getParent().getLeafColumns().length===r.length;)n=n.getParent();var i=n.getColGroupDef();i&&(t=i.headerName),t||(t=r?this.columnModel.getDisplayNameForColumn(r[0],"header",!0):"")}var s=this.userComponentFactory.getHeaderGroupCompDetails(o);this.comp.setUserCompDetails(s)},t.prototype.setupTooltip=function(){var e=this,t=this.columnGroup.getColGroupDef(),o={getColumn:function(){return e.columnGroup},getGui:function(){return e.eGui},getLocation:function(){return"headerGroup"},getTooltipValue:function(){return t&&t.headerTooltip}};t&&(o.getColDef=function(){return t}),this.createManagedBean(new Wl(o,this.beans)).setComp(this.comp)},t.prototype.setupExpandable=function(){var e=this.columnGroup.getProvidedColumnGroup();this.refreshExpanded(),this.addManagedListener(e,ot.EVENT_EXPANDABLE_CHANGED,this.refreshExpanded.bind(this)),this.addManagedListener(e,ot.EVENT_EXPANDED_CHANGED,this.refreshExpanded.bind(this))},t.prototype.refreshExpanded=function(){var e=this.columnGroup;this.expandable=e.isExpandable();var t=e.isExpanded();this.expandable?this.comp.setAriaExpanded(t?"true":"false"):this.comp.setAriaExpanded(void 0)},t.prototype.addAttributes=function(){this.comp.setColId(this.columnGroup.getUniqueId())},t.prototype.addClasses=function(){var e=this,t=this.columnGroup.getColGroupDef(),o=wp.getHeaderClassesFromColDef(t,this.gridOptionsWrapper,null,this.columnGroup);o.push(this.columnGroup.isPadding()?"ag-header-group-cell-no-group":"ag-header-group-cell-with-group"),o.forEach((function(t){return e.comp.addOrRemoveCssClass(t,!0)}))},t.prototype.setupMovingCss=function(){var e=this,t=this.columnGroup.getProvidedColumnGroup().getLeafColumns(),o=function(){return e.comp.addOrRemoveCssClass("ag-header-cell-moving",e.columnGroup.isMoving())};t.forEach((function(t){e.addManagedListener(t,Ge.EVENT_MOVING_CHANGED,o)})),o()},t.prototype.onFocusIn=function(e){if(!this.eGui.contains(e.relatedTarget)){var t=this.getRowIndex();this.beans.focusService.setFocusedHeader(t,this.columnGroup)}},t.prototype.handleKeyDown=function(e){var t=this.gridOptionsWrapper.getDocument().activeElement===this.eGui;if(this.expandable&&t&&e.key===ii.ENTER){var o=this.columnGroup,n=!o.isExpanded();this.columnModel.setColumnGroupOpened(o.getProvidedColumnGroup(),n,"uiColumnExpanded")}},t.prototype.setDragSource=function(e){var t=this;if(!this.isSuppressMoving()){var o=this.columnGroup.getProvidedColumnGroup().getLeafColumns(),n={type:xs.HeaderCell,eElement:e,defaultIconName:qs.ICON_HIDE,dragItemName:this.displayName,getDragItem:this.getDragItemForGroup.bind(this),onDragStarted:function(){return o.forEach((function(e){return e.setMoving(!0,"uiColumnDragged")}))},onDragStopped:function(){return o.forEach((function(e){return e.setMoving(!1,"uiColumnDragged")}))}};this.dragAndDropService.addDragSource(n,!0),this.addDestroyFunc((function(){return t.dragAndDropService.removeDragSource(n)}))}},t.prototype.getDragItemForGroup=function(){var e=this.columnGroup.getProvidedColumnGroup().getLeafColumns(),t={};e.forEach((function(e){return t[e.getId()]=e.isVisible()}));var o=[];return this.columnModel.getAllDisplayedColumns().forEach((function(t){e.indexOf(t)>=0&&(o.push(t),Ue(e,t))})),e.forEach((function(e){return o.push(e)})),{columns:o,visibleState:t}},t.prototype.isSuppressMoving=function(){var e=!1;return this.columnGroup.getLeafColumns().forEach((function(t){(t.getColDef().suppressMovable||t.getColDef().lockPosition)&&(e=!0)})),e||this.gridOptionsWrapper.isSuppressMovableColumns()},dc([Re("beans")],t.prototype,"beans",void 0),dc([Re("columnModel")],t.prototype,"columnModel",void 0),dc([Re("dragAndDropService")],t.prototype,"dragAndDropService",void 0),dc([Re("userComponentFactory")],t.prototype,"userComponentFactory",void 0),dc([Re("gridApi")],t.prototype,"gridApi",void 0),dc([Re("columnApi")],t.prototype,"columnApi",void 0),t}(Mp),fc=function(){var e=function(t,o){return e=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(e,t){e.__proto__=t}||function(e,t){for(var o in t)t.hasOwnProperty(o)&&(e[o]=t[o])},e(t,o)};return function(t,o){function n(){this.constructor=t}e(t,o),t.prototype=null===o?Object.create(o):(n.prototype=o.prototype,new n)}}(),gc=function(e,t,o,n){var r,i=arguments.length,s=i<3?t:null===n?n=Object.getOwnPropertyDescriptor(t,o):n;if("object"===typeof Reflect&&"function"===typeof Reflect.decorate)s=Reflect.decorate(e,t,o,n);else for(var a=e.length-1;a>=0;a--)(r=e[a])&&(s=(i<3?r(s):i>3?r(t,o,s):r(t,o))||s);return i>3&&s&&Object.defineProperty(t,o,s),s},yc=0,vc=function(e){function t(t,o,n){var r=e.call(this)||this;return r.instanceId=yc++,r.headerCellCtrls={},r.rowIndex=t,r.pinned=o,r.type=n,r}return fc(t,e),t.prototype.getInstanceId=function(){return this.instanceId},t.prototype.setComp=function(e){this.comp=e,this.onRowHeightChanged(),this.onVirtualColumnsChanged(),this.setWidth(),this.addEventListeners(),So()&&this.comp.setTransform("translateZ(0)"),e.setAriaRowIndex(this.rowIndex+1)},t.prototype.addEventListeners=function(){this.addManagedListener(this.eventService,St.EVENT_COLUMN_RESIZED,this.onColumnResized.bind(this)),this.addManagedListener(this.gridOptionsWrapper,ul.PROP_DOM_LAYOUT,this.onDisplayedColumnsChanged.bind(this)),this.addManagedListener(this.eventService,St.EVENT_DISPLAYED_COLUMNS_CHANGED,this.onDisplayedColumnsChanged.bind(this)),this.addManagedListener(this.eventService,St.EVENT_VIRTUAL_COLUMNS_CHANGED,this.onVirtualColumnsChanged.bind(this)),this.addManagedListener(this.gridOptionsWrapper,ul.PROP_HEADER_HEIGHT,this.onRowHeightChanged.bind(this)),this.addManagedListener(this.gridOptionsWrapper,ul.PROP_PIVOT_HEADER_HEIGHT,this.onRowHeightChanged.bind(this)),this.addManagedListener(this.gridOptionsWrapper,ul.PROP_GROUP_HEADER_HEIGHT,this.onRowHeightChanged.bind(this)),this.addManagedListener(this.gridOptionsWrapper,ul.PROP_PIVOT_GROUP_HEADER_HEIGHT,this.onRowHeightChanged.bind(this)),this.addManagedListener(this.gridOptionsWrapper,ul.PROP_FLOATING_FILTERS_HEIGHT,this.onRowHeightChanged.bind(this))},t.prototype.getHeaderCellCtrl=function(e){return M(this.headerCellCtrls).find((function(t){return t.getColumnGroupChild()===e}))},t.prototype.onDisplayedColumnsChanged=function(){this.onVirtualColumnsChanged(),this.setWidth()},t.prototype.getType=function(){return this.type},t.prototype.onColumnResized=function(){this.setWidth()},t.prototype.setWidth=function(){var e=this.getWidthForRow();this.comp.setWidth(e+"px")},t.prototype.getWidthForRow=function(){return this.gridOptionsWrapper.getDomLayout()===Ne.DOM_LAYOUT_PRINT?null!=this.pinned?0:this.columnModel.getContainerWidth(Ne.PINNED_RIGHT)+this.columnModel.getContainerWidth(Ne.PINNED_LEFT)+this.columnModel.getContainerWidth(null):this.columnModel.getContainerWidth(this.pinned)},t.prototype.onRowHeightChanged=function(){var e,t,o=this.columnModel.getHeaderRowCount(),n=[],r=0;this.columnModel.hasFloatingFilters()&&(o++,r=1),this.columnModel.isPivotMode()?(e=this.gridOptionsWrapper.getPivotGroupHeaderHeight(),t=this.gridOptionsWrapper.getPivotHeaderHeight()):(e=this.gridOptionsWrapper.getGroupHeaderHeight(),t=this.gridOptionsWrapper.getHeaderHeight());for(var i=o-(1+r),s=0;s=0;a--)(r=e[a])&&(s=(i<3?r(s):i>3?r(t,o,s):r(t,o))||s);return i>3&&s&&Object.defineProperty(t,o,s),s},wc=function(e,t){var o="function"===typeof Symbol&&e[Symbol.iterator];if(!o)return e;var n,r,i=o.call(e),s=[];try{for(;(void 0===t||t-- >0)&&!(n=i.next()).done;)s.push(n.value)}catch(a){r={error:a}}finally{try{n&&!n.done&&(o=i.return)&&o.call(i)}finally{if(r)throw r.error}}return s},Ec=function(){for(var e=[],t=0;t=0;a--)(r=e[a])&&(s=(i<3?r(s):i>3?r(t,o,s):r(t,o))||s);return i>3&&s&&Object.defineProperty(t,o,s),s},bc=function(e){function t(t){var o=e.call(this)||this;return o.headerRowComps={},o.rowCompsList=[],o.pinned=t,o}return Sc(t,e),t.prototype.init=function(){var e=this;this.selectAndSetTemplate();var t={addOrRemoveCssClass:function(t,o){return e.addOrRemoveCssClass(t,o)},setCtrls:function(t){return e.setCtrls(t)},setCenterWidth:function(t){return e.eCenterContainer.style.width=t},setContainerTransform:function(t){return e.eCenterContainer.style.transform=t},setPinnedContainerWidth:function(t){var o=e.getGui();o.style.width=t,o.style.maxWidth=t,o.style.minWidth=t}};this.createManagedBean(new Rc(this.pinned)).setComp(t,this.getGui())},t.prototype.selectAndSetTemplate=function(){var e=this.pinned==Ne.PINNED_LEFT,o=this.pinned==Ne.PINNED_RIGHT,n=e?t.PINNED_LEFT_TEMPLATE:o?t.PINNED_RIGHT_TEMPLATE:t.CENTER_TEMPLATE;this.setTemplate(n),this.eRowContainer=this.eCenterContainer?this.eCenterContainer:this.getGui()},t.prototype.destroyRowComps=function(){this.setCtrls([])},t.prototype.destroyRowComp=function(e){this.destroyBean(e),this.eRowContainer.removeChild(e.getGui())},t.prototype.setCtrls=function(e){var t,o=this,n=this.headerRowComps;this.headerRowComps={},this.rowCompsList=[];e.forEach((function(e){var r=e.getInstanceId(),i=n[r];delete n[r];var s=i||o.createBean(new Ap(e));o.headerRowComps[r]=s,o.rowCompsList.push(s),function(e){var n=e.getGui();n.parentElement!=o.eRowContainer&&o.eRowContainer.appendChild(n),t&&rn(o.eRowContainer,n,t),t=n}(s)})),z(n).forEach((function(e){return o.destroyRowComp(e)}))},t.PINNED_LEFT_TEMPLATE='