Add files using upload-large-folder tool

data/conda_envs/evalclaw/lib/python3.11/site-packages/cryptography/__about__.py

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+# This file is dual licensed under the terms of the Apache License, Version
+# 2.0, and the BSD License. See the LICENSE file in the root of this repository
+# for complete details.
+
+from __future__ import annotations
+
+__all__ = [
+    "__author__",
+    "__copyright__",
+    "__version__",
+]
+
+__version__ = "46.0.5"
+
+
+__author__ = "The Python Cryptographic Authority and individual contributors"
+__copyright__ = f"Copyright 2013-2025 {__author__}"

data/conda_envs/evalclaw/lib/python3.11/site-packages/cryptography/__init__.py

@@ -0,0 +1,13 @@
+# This file is dual licensed under the terms of the Apache License, Version
+# 2.0, and the BSD License. See the LICENSE file in the root of this repository
+# for complete details.
+
+from __future__ import annotations
+
+from cryptography.__about__ import __author__, __copyright__, __version__
+
+__all__ = [
+    "__author__",
+    "__copyright__",
+    "__version__",
+]

data/conda_envs/evalclaw/lib/python3.11/site-packages/cryptography/exceptions.py

@@ -0,0 +1,52 @@
+# This file is dual licensed under the terms of the Apache License, Version
+# 2.0, and the BSD License. See the LICENSE file in the root of this repository
+# for complete details.
+
+from __future__ import annotations
+
+import typing
+
+from cryptography.hazmat.bindings._rust import exceptions as rust_exceptions
+
+if typing.TYPE_CHECKING:
+    from cryptography.hazmat.bindings._rust import openssl as rust_openssl
+
+_Reasons = rust_exceptions._Reasons
+
+
+class UnsupportedAlgorithm(Exception):
+    def __init__(self, message: str, reason: _Reasons | None = None) -> None:
+        super().__init__(message)
+        self._reason = reason
+
+
+class AlreadyFinalized(Exception):
+    pass
+
+
+class AlreadyUpdated(Exception):
+    pass
+
+
+class NotYetFinalized(Exception):
+    pass
+
+
+class InvalidTag(Exception):
+    pass
+
+
+class InvalidSignature(Exception):
+    pass
+
+
+class InternalError(Exception):
+    def __init__(
+        self, msg: str, err_code: list[rust_openssl.OpenSSLError]
+    ) -> None:
+        super().__init__(msg)
+        self.err_code = err_code
+
+
+class InvalidKey(Exception):
+    pass

data/conda_envs/evalclaw/lib/python3.11/site-packages/cryptography/fernet.py

@@ -0,0 +1,224 @@
+# This file is dual licensed under the terms of the Apache License, Version
+# 2.0, and the BSD License. See the LICENSE file in the root of this repository
+# for complete details.
+
+from __future__ import annotations
+
+import base64
+import binascii
+import os
+import time
+import typing
+from collections.abc import Iterable
+
+from cryptography import utils
+from cryptography.exceptions import InvalidSignature
+from cryptography.hazmat.primitives import hashes, padding
+from cryptography.hazmat.primitives.ciphers import Cipher, algorithms, modes
+from cryptography.hazmat.primitives.hmac import HMAC
+
+
+class InvalidToken(Exception):
+    pass
+
+
+_MAX_CLOCK_SKEW = 60
+
+
+class Fernet:
+    def __init__(
+        self,
+        key: bytes | str,
+        backend: typing.Any = None,
+    ) -> None:
+        try:
+            key = base64.urlsafe_b64decode(key)
+        except binascii.Error as exc:
+            raise ValueError(
+                "Fernet key must be 32 url-safe base64-encoded bytes."
+            ) from exc
+        if len(key) != 32:
+            raise ValueError(
+                "Fernet key must be 32 url-safe base64-encoded bytes."
+            )
+
+        self._signing_key = key[:16]
+        self._encryption_key = key[16:]
+
+    @classmethod
+    def generate_key(cls) -> bytes:
+        return base64.urlsafe_b64encode(os.urandom(32))
+
+    def encrypt(self, data: bytes) -> bytes:
+        return self.encrypt_at_time(data, int(time.time()))
+
+    def encrypt_at_time(self, data: bytes, current_time: int) -> bytes:
+        iv = os.urandom(16)
+        return self._encrypt_from_parts(data, current_time, iv)
+
+    def _encrypt_from_parts(
+        self, data: bytes, current_time: int, iv: bytes
+    ) -> bytes:
+        utils._check_bytes("data", data)
+
+        padder = padding.PKCS7(algorithms.AES.block_size).padder()
+        padded_data = padder.update(data) + padder.finalize()
+        encryptor = Cipher(
+            algorithms.AES(self._encryption_key),
+            modes.CBC(iv),
+        ).encryptor()
+        ciphertext = encryptor.update(padded_data) + encryptor.finalize()
+
+        basic_parts = (
+            b"\x80"
+            + current_time.to_bytes(length=8, byteorder="big")
+            + iv
+            + ciphertext
+        )
+
+        h = HMAC(self._signing_key, hashes.SHA256())
+        h.update(basic_parts)
+        hmac = h.finalize()
+        return base64.urlsafe_b64encode(basic_parts + hmac)
+
+    def decrypt(self, token: bytes | str, ttl: int | None = None) -> bytes:
+        timestamp, data = Fernet._get_unverified_token_data(token)
+        if ttl is None:
+            time_info = None
+        else:
+            time_info = (ttl, int(time.time()))
+        return self._decrypt_data(data, timestamp, time_info)
+
+    def decrypt_at_time(
+        self, token: bytes | str, ttl: int, current_time: int
+    ) -> bytes:
+        if ttl is None:
+            raise ValueError(
+                "decrypt_at_time() can only be used with a non-None ttl"
+            )
+        timestamp, data = Fernet._get_unverified_token_data(token)
+        return self._decrypt_data(data, timestamp, (ttl, current_time))
+
+    def extract_timestamp(self, token: bytes | str) -> int:
+        timestamp, data = Fernet._get_unverified_token_data(token)
+        # Verify the token was not tampered with.
+        self._verify_signature(data)
+        return timestamp
+
+    @staticmethod
+    def _get_unverified_token_data(token: bytes | str) -> tuple[int, bytes]:
+        if not isinstance(token, (str, bytes)):
+            raise TypeError("token must be bytes or str")
+
+        try:
+            data = base64.urlsafe_b64decode(token)
+        except (TypeError, binascii.Error):
+            raise InvalidToken
+
+        if not data or data[0] != 0x80:
+            raise InvalidToken
+
+        if len(data) < 9:
+            raise InvalidToken
+
+        timestamp = int.from_bytes(data[1:9], byteorder="big")
+        return timestamp, data
+
+    def _verify_signature(self, data: bytes) -> None:
+        h = HMAC(self._signing_key, hashes.SHA256())
+        h.update(data[:-32])
+        try:
+            h.verify(data[-32:])
+        except InvalidSignature:
+            raise InvalidToken
+
+    def _decrypt_data(
+        self,
+        data: bytes,
+        timestamp: int,
+        time_info: tuple[int, int] | None,
+    ) -> bytes:
+        if time_info is not None:
+            ttl, current_time = time_info
+            if timestamp + ttl < current_time:
+                raise InvalidToken
+
+            if current_time + _MAX_CLOCK_SKEW < timestamp:
+                raise InvalidToken
+
+        self._verify_signature(data)
+
+        iv = data[9:25]
+        ciphertext = data[25:-32]
+        decryptor = Cipher(
+            algorithms.AES(self._encryption_key), modes.CBC(iv)
+        ).decryptor()
+        plaintext_padded = decryptor.update(ciphertext)
+        try:
+            plaintext_padded += decryptor.finalize()
+        except ValueError:
+            raise InvalidToken
+        unpadder = padding.PKCS7(algorithms.AES.block_size).unpadder()
+
+        unpadded = unpadder.update(plaintext_padded)
+        try:
+            unpadded += unpadder.finalize()
+        except ValueError:
+            raise InvalidToken
+        return unpadded
+
+
+class MultiFernet:
+    def __init__(self, fernets: Iterable[Fernet]):
+        fernets = list(fernets)
+        if not fernets:
+            raise ValueError(
+                "MultiFernet requires at least one Fernet instance"
+            )
+        self._fernets = fernets
+
+    def encrypt(self, msg: bytes) -> bytes:
+        return self.encrypt_at_time(msg, int(time.time()))
+
+    def encrypt_at_time(self, msg: bytes, current_time: int) -> bytes:
+        return self._fernets[0].encrypt_at_time(msg, current_time)
+
+    def rotate(self, msg: bytes | str) -> bytes:
+        timestamp, data = Fernet._get_unverified_token_data(msg)
+        for f in self._fernets:
+            try:
+                p = f._decrypt_data(data, timestamp, None)
+                break
+            except InvalidToken:
+                pass
+        else:
+            raise InvalidToken
+
+        iv = os.urandom(16)
+        return self._fernets[0]._encrypt_from_parts(p, timestamp, iv)
+
+    def decrypt(self, msg: bytes | str, ttl: int | None = None) -> bytes:
+        for f in self._fernets:
+            try:
+                return f.decrypt(msg, ttl)
+            except InvalidToken:
+                pass
+        raise InvalidToken
+
+    def decrypt_at_time(
+        self, msg: bytes | str, ttl: int, current_time: int
+    ) -> bytes:
+        for f in self._fernets:
+            try:
+                return f.decrypt_at_time(msg, ttl, current_time)
+            except InvalidToken:
+                pass
+        raise InvalidToken
+
+    def extract_timestamp(self, msg: bytes | str) -> int:
+        for f in self._fernets:
+            try:
+                return f.extract_timestamp(msg)
+            except InvalidToken:
+                pass
+        raise InvalidToken

data/conda_envs/evalclaw/lib/python3.11/site-packages/cryptography/utils.py

@@ -0,0 +1,138 @@
+# This file is dual licensed under the terms of the Apache License, Version
+# 2.0, and the BSD License. See the LICENSE file in the root of this repository
+# for complete details.
+
+from __future__ import annotations
+
+import enum
+import sys
+import types
+import typing
+import warnings
+from collections.abc import Callable, Sequence
+
+
+# We use a UserWarning subclass, instead of DeprecationWarning, because CPython
+# decided deprecation warnings should be invisible by default.
+class CryptographyDeprecationWarning(UserWarning):
+    pass
+
+
+# Several APIs were deprecated with no specific end-of-life date because of the
+# ubiquity of their use. They should not be removed until we agree on when that
+# cycle ends.
+DeprecatedIn36 = CryptographyDeprecationWarning
+DeprecatedIn40 = CryptographyDeprecationWarning
+DeprecatedIn41 = CryptographyDeprecationWarning
+DeprecatedIn42 = CryptographyDeprecationWarning
+DeprecatedIn43 = CryptographyDeprecationWarning
+DeprecatedIn46 = CryptographyDeprecationWarning
+
+
+# If you're wondering why we don't use `Buffer`, it's because `Buffer` would
+# be more accurately named: Bufferable. It means something which has an
+# `__buffer__`. Which means you can't actually treat the result as a buffer
+# (and do things like take a `len()`).
+if sys.version_info >= (3, 9):
+    Buffer = typing.Union[bytes, bytearray, memoryview]
+else:
+    Buffer = typing.ByteString
+
+
+def _check_bytes(name: str, value: bytes) -> None:
+    if not isinstance(value, bytes):
+        raise TypeError(f"{name} must be bytes")
+
+
+def _check_byteslike(name: str, value: Buffer) -> None:
+    try:
+        memoryview(value)
+    except TypeError:
+        raise TypeError(f"{name} must be bytes-like")
+
+
+def int_to_bytes(integer: int, length: int | None = None) -> bytes:
+    if length == 0:
+        raise ValueError("length argument can't be 0")
+    return integer.to_bytes(
+        length or (integer.bit_length() + 7) // 8 or 1, "big"
+    )
+
+
+class InterfaceNotImplemented(Exception):
+    pass
+
+
+class _DeprecatedValue:
+    def __init__(self, value: object, message: str, warning_class):
+        self.value = value
+        self.message = message
+        self.warning_class = warning_class
+
+
+class _ModuleWithDeprecations(types.ModuleType):
+    def __init__(self, module: types.ModuleType):
+        super().__init__(module.__name__)
+        self.__dict__["_module"] = module
+
+    def __getattr__(self, attr: str) -> object:
+        obj = getattr(self._module, attr)
+        if isinstance(obj, _DeprecatedValue):
+            warnings.warn(obj.message, obj.warning_class, stacklevel=2)
+            obj = obj.value
+        return obj
+
+    def __setattr__(self, attr: str, value: object) -> None:
+        setattr(self._module, attr, value)
+
+    def __delattr__(self, attr: str) -> None:
+        obj = getattr(self._module, attr)
+        if isinstance(obj, _DeprecatedValue):
+            warnings.warn(obj.message, obj.warning_class, stacklevel=2)
+
+        delattr(self._module, attr)
+
+    def __dir__(self) -> Sequence[str]:
+        return ["_module", *dir(self._module)]
+
+
+def deprecated(
+    value: object,
+    module_name: str,
+    message: str,
+    warning_class: type[Warning],
+    name: str | None = None,
+) -> _DeprecatedValue:
+    module = sys.modules[module_name]
+    if not isinstance(module, _ModuleWithDeprecations):
+        sys.modules[module_name] = module = _ModuleWithDeprecations(module)
+    dv = _DeprecatedValue(value, message, warning_class)
+    # Maintain backwards compatibility with `name is None` for pyOpenSSL.
+    if name is not None:
+        setattr(module, name, dv)
+    return dv
+
+
+def cached_property(func: Callable) -> property:
+    cached_name = f"_cached_{func}"
+    sentinel = object()
+
+    def inner(instance: object):
+        cache = getattr(instance, cached_name, sentinel)
+        if cache is not sentinel:
+            return cache
+        result = func(instance)
+        setattr(instance, cached_name, result)
+        return result
+
+    return property(inner)
+
+
+# Python 3.10 changed representation of enums. We use well-defined object
+# representation and string representation from Python 3.9.
+class Enum(enum.Enum):
+    def __repr__(self) -> str:
+        return f"<{self.__class__.__name__}.{self._name_}: {self._value_!r}>"
+
+    def __str__(self) -> str:
+        return f"{self.__class__.__name__}.{self._name_}"

data/conda_envs/evalclaw/lib/python3.11/site-packages/googleapis_common_protos-1.75.0.dist-info/INSTALLER

@@ -0,0 +1 @@
+pip

data/conda_envs/evalclaw/lib/python3.11/site-packages/googleapis_common_protos-1.75.0.dist-info/METADATA

@@ -0,0 +1,215 @@
+Metadata-Version: 2.4
+Name: googleapis-common-protos
+Version: 1.75.0
+Summary: Common protobufs used in Google APIs
+Author-email: Google LLC <googleapis-packages@google.com>
+License: Apache 2.0
+Project-URL: Repository, https://github.com/googleapis/google-cloud-python/tree/main/packages/googleapis-common-protos
+Classifier: Development Status :: 5 - Production/Stable
+Classifier: Intended Audience :: Developers
+Classifier: License :: OSI Approved :: Apache Software License
+Classifier: Programming Language :: Python
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3.9
+Classifier: Programming Language :: Python :: 3.10
+Classifier: Programming Language :: Python :: 3.11
+Classifier: Programming Language :: Python :: 3.12
+Classifier: Programming Language :: Python :: 3.13
+Classifier: Programming Language :: Python :: 3.14
+Classifier: Operating System :: OS Independent
+Classifier: Topic :: Internet
+Requires-Python: >=3.9
+Description-Content-Type: text/x-rst
+License-File: LICENSE
+Requires-Dist: protobuf<8.0.0,>=4.25.8
+Provides-Extra: grpc
+Requires-Dist: grpcio<2.0.0,>=1.44.0; extra == "grpc"
+Dynamic: license-file
+
+Python Client for Service Config
+================================
+
+|stable| |pypi| |versions|
+
+- `Client Library Documentation`_
+
+.. |stable| image:: https://img.shields.io/badge/support-stable-gold.svg
+   :target: https://github.com/googleapis/google-cloud-python/blob/main/README.rst#stability-levels
+.. |pypi| image:: https://img.shields.io/pypi/v/googleapis-common-protos.svg
+   :target: https://pypi.org/project/googleapis-common-protos/
+.. |versions| image:: https://img.shields.io/pypi/pyversions/googleapis-common-protos.svg
+   :target: https://pypi.org/project/googleapis-common-protos/
+.. _Client Library Documentation: https://github.com/googleapis/google-cloud-python/tree/main/packages/googleapis-common-protos/summary_overview
+
+Quick Start
+-----------
+
+In order to use this library, you first need to go through the following steps:
+
+1. `Select or create a Cloud Platform project.`_
+2. `Enable billing for your project.`_
+3. `Set up Authentication.`_
+
+.. _Select or create a Cloud Platform project.: https://console.cloud.google.com/project
+.. _Enable billing for your project.: https://cloud.google.com/billing/docs/how-to/modify-project#enable_billing_for_a_project
+.. _Set up Authentication.: https://googleapis.dev/python/google-api-core/latest/auth.html
+
+Installation
+~~~~~~~~~~~~
+
+Install this library in a virtual environment using `venv`_. `venv`_ is a tool that
+creates isolated Python environments. These isolated environments can have separate
+versions of Python packages, which allows you to isolate one project's dependencies
+from the dependencies of other projects.
+
+With `venv`_, it's possible to install this library without needing system
+install permissions, and without clashing with the installed system
+dependencies.
+
+.. _`venv`: https://docs.python.org/3/library/venv.html
+
+
+Code samples and snippets
+~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Code samples and snippets live in the `samples/`_ folder.
+
+.. _samples/: https://github.com/googleapis/google-cloud-python/tree/main/packages/googleapis-common-protos/samples
+
+
+Supported Python Versions
+^^^^^^^^^^^^^^^^^^^^^^^^^
+Our client libraries are compatible with all current `active`_ and `maintenance`_ versions of
+Python.
+
+Python >= 3.9, including 3.14
+
+.. _active: https://devguide.python.org/devcycle/#in-development-main-branch
+.. _maintenance: https://devguide.python.org/devcycle/#maintenance-branches
+
+Unsupported Python Versions
+^^^^^^^^^^^^^^^^^^^^^^^^^^^
+Python <= 3.8
+
+If you are using an `end-of-life`_
+version of Python, we recommend that you update as soon as possible to an actively supported version.
+
+.. _end-of-life: https://devguide.python.org/devcycle/#end-of-life-branches
+
+Mac/Linux
+^^^^^^^^^
+
+.. code-block:: console
+
+    python3 -m venv <your-env>
+    source <your-env>/bin/activate
+    pip install googleapis-common-protos
+
+
+Windows
+^^^^^^^
+
+.. code-block:: console
+
+    py -m venv <your-env>
+    .\<your-env>\Scripts\activate
+    pip install googleapis-common-protos
+
+Next Steps
+~~~~~~~~~~
+
+-  Read the `Client Library Documentation`_ for Service Config
+   to see other available methods on the client.
+-  View this `README`_ to see the full list of Cloud
+   APIs that we cover.
+
+.. _README: https://github.com/googleapis/google-cloud-python/blob/main/README.rst
+
+Logging
+-------
+
+This library uses the standard Python :code:`logging` functionality to log some RPC events that could be of interest for debugging and monitoring purposes.
+Note the following:
+
+#. Logs may contain sensitive information. Take care to **restrict access to the logs** if they are saved, whether it be on local storage or on Google Cloud Logging.
+#. Google may refine the occurrence, level, and content of various log messages in this library without flagging such changes as breaking. **Do not depend on immutability of the logging events**.
+#. By default, the logging events from this library are not handled. You must **explicitly configure log handling** using one of the mechanisms below.
+
+Simple, environment-based configuration
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+To enable logging for this library without any changes in your code, set the :code:`GOOGLE_SDK_PYTHON_LOGGING_SCOPE` environment variable to a valid Google
+logging scope. This configures handling of logging events (at level :code:`logging.DEBUG` or higher) from this library in a default manner, emitting the logged
+messages in a structured format. It does not currently allow customizing the logging levels captured nor the handlers, formatters, etc. used for any logging
+event.
+
+A logging scope is a period-separated namespace that begins with :code:`google`, identifying the Python module or package to log.
+
+- Valid logging scopes: :code:`google`, :code:`google.cloud.asset.v1`, :code:`google.api`, :code:`google.auth`, etc.
+- Invalid logging scopes: :code:`foo`, :code:`123`, etc.
+
+**NOTE**: If the logging scope is invalid, the library does not set up any logging handlers.
+
+Environment-Based Examples
+^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+- Enabling the default handler for all Google-based loggers
+
+.. code-block:: console
+
+    export GOOGLE_SDK_PYTHON_LOGGING_SCOPE=google
+
+- Enabling the default handler for a specific Google module (for a client library called :code:`library_v1`):
+
+.. code-block:: console
+
+    export GOOGLE_SDK_PYTHON_LOGGING_SCOPE=google.cloud.library_v1
+
+
+Advanced, code-based configuration
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+You can also configure a valid logging scope using Python's standard `logging` mechanism.
+
+Code-Based Examples
+^^^^^^^^^^^^^^^^^^^
+
+- Configuring a handler for all Google-based loggers
+
+.. code-block:: python
+
+    import logging
+    
+    from google.cloud import library_v1
+    
+    base_logger = logging.getLogger("google")
+    base_logger.addHandler(logging.StreamHandler())
+    base_logger.setLevel(logging.DEBUG)
+
+- Configuring a handler for a specific Google module (for a client library called :code:`library_v1`):
+
+.. code-block:: python
+
+    import logging
+    
+    from google.cloud import library_v1
+    
+    base_logger = logging.getLogger("google.cloud.library_v1")
+    base_logger.addHandler(logging.StreamHandler())
+    base_logger.setLevel(logging.DEBUG)
+
+Logging details
+~~~~~~~~~~~~~~~
+
+#. Regardless of which of the mechanisms above you use to configure logging for this library, by default logging events are not propagated up to the root
+   logger from the `google`-level logger. If you need the events to be propagated to the root logger, you must explicitly set
+   :code:`logging.getLogger("google").propagate = True` in your code.
+#. You can mix the different logging configurations above for different Google modules. For example, you may want use a code-based logging configuration for
+   one library, but decide you need to also set up environment-based logging configuration for another library.
+
+   #. If you attempt to use both code-based and environment-based configuration for the same module, the environment-based configuration will be ineffectual
+      if the code -based configuration gets applied first.
+
+#. The Google-specific logging configurations (default handlers for environment-based configuration; not propagating logging events to the root logger) get
+   executed the first time *any* client library is instantiated in your application, and only if the affected loggers have not been previously configured.
+   (This is the reason for 2.i. above.)

data/conda_envs/evalclaw/lib/python3.11/site-packages/googleapis_common_protos-1.75.0.dist-info/RECORD

@@ -0,0 +1,269 @@
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+google/api/__pycache__/config_change_pb2.cpython-311.pyc,,
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data/conda_envs/evalclaw/lib/python3.11/site-packages/googleapis_common_protos-1.75.0.dist-info/WHEEL

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+Wheel-Version: 1.0
+Generator: setuptools (82.0.1)
+Root-Is-Purelib: true
+Tag: py3-none-any
+

data/conda_envs/evalclaw/lib/python3.11/site-packages/googleapis_common_protos-1.75.0.dist-info/top_level.txt

@@ -0,0 +1 @@
+google

data/conda_envs/evalclaw/lib/python3.11/site-packages/huggingface_hub/__init__.py

@@ -0,0 +1,1690 @@
+# Copyright 2020 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# ***********
+# `huggingface_hub` init has 2 modes:
+# - Normal usage:
+#       If imported to use it, all modules and functions are lazy-loaded. This means
+#       they exist at top level in module but are imported only the first time they are
+#       used. This way, `from huggingface_hub import something` will import `something`
+#       quickly without the hassle of importing all the features from `huggingface_hub`.
+# - Static check:
+#       If statically analyzed, all modules and functions are loaded normally. This way
+#       static typing check works properly as well as autocomplete in text editors and
+#       IDEs.
+#
+# The static model imports are done inside the `if TYPE_CHECKING:` statement at
+# the bottom of this file. Since module/functions imports are duplicated, it is
+# mandatory to make sure to add them twice when adding one. This is checked in the
+# `make quality` command.
+#
+# To update the static imports, please run the following command and commit the changes.
+# ```
+# # Use script
+# python utils/check_static_imports.py --update-file
+#
+# # Or run style on codebase
+# make style
+# ```
+#
+# ***********
+# Lazy loader vendored from https://github.com/scientific-python/lazy_loader
+import importlib
+import os
+import sys
+from typing import TYPE_CHECKING
+
+
+__version__ = "1.7.1"
+
+# Alphabetical order of definitions is ensured in tests
+# WARNING: any comment added in this dictionary definition will be lost when
+# re-generating the file !
+_SUBMOD_ATTRS = {
+    "_buckets": [
+        "BucketFile",
+        "BucketFileMetadata",
+        "BucketFolder",
+        "BucketInfo",
+        "BucketUrl",
+        "SyncOperation",
+        "SyncPlan",
+    ],
+    "_commit_scheduler": [
+        "CommitScheduler",
+    ],
+    "_eval_results": [
+        "EvalResultEntry",
+        "eval_result_entries_to_yaml",
+        "parse_eval_result_entries",
+    ],
+    "_inference_endpoints": [
+        "InferenceEndpoint",
+        "InferenceEndpointError",
+        "InferenceEndpointStatus",
+        "InferenceEndpointTimeoutError",
+        "InferenceEndpointType",
+    ],
+    "_jobs_api": [
+        "JobAccelerator",
+        "JobHardware",
+        "JobInfo",
+        "JobOwner",
+        "JobStage",
+        "JobStatus",
+    ],
+    "_login": [
+        "auth_list",
+        "auth_switch",
+        "interpreter_login",
+        "login",
+        "logout",
+        "notebook_login",
+    ],
+    "_oauth": [
+        "OAuthInfo",
+        "OAuthOrgInfo",
+        "OAuthUserInfo",
+        "attach_huggingface_oauth",
+        "parse_huggingface_oauth",
+    ],
+    "_snapshot_download": [
+        "snapshot_download",
+    ],
+    "_space_api": [
+        "SpaceHardware",
+        "SpaceRuntime",
+        "SpaceStage",
+        "SpaceStorage",
+        "SpaceVariable",
+    ],
+    "_tensorboard_logger": [
+        "HFSummaryWriter",
+    ],
+    "_webhooks_payload": [
+        "WebhookPayload",
+        "WebhookPayloadComment",
+        "WebhookPayloadDiscussion",
+        "WebhookPayloadDiscussionChanges",
+        "WebhookPayloadEvent",
+        "WebhookPayloadMovedTo",
+        "WebhookPayloadRepo",
+        "WebhookPayloadUrl",
+        "WebhookPayloadWebhook",
+    ],
+    "_webhooks_server": [
+        "WebhooksServer",
+        "webhook_endpoint",
+    ],
+    "cli._cli_utils": [
+        "check_cli_update",
+        "typer_factory",
+    ],
+    "community": [
+        "Discussion",
+        "DiscussionComment",
+        "DiscussionCommit",
+        "DiscussionEvent",
+        "DiscussionStatusChange",
+        "DiscussionTitleChange",
+        "DiscussionWithDetails",
+    ],
+    "constants": [
+        "CONFIG_NAME",
+        "FLAX_WEIGHTS_NAME",
+        "HUGGINGFACE_CO_URL_HOME",
+        "HUGGINGFACE_CO_URL_TEMPLATE",
+        "PYTORCH_WEIGHTS_NAME",
+        "REPO_TYPE_DATASET",
+        "REPO_TYPE_MODEL",
+        "REPO_TYPE_SPACE",
+        "TF2_WEIGHTS_NAME",
+        "TF_WEIGHTS_NAME",
+        "is_offline_mode",
+    ],
+    "fastai_utils": [
+        "_save_pretrained_fastai",
+        "from_pretrained_fastai",
+        "push_to_hub_fastai",
+    ],
+    "file_download": [
+        "DryRunFileInfo",
+        "HfFileMetadata",
+        "_CACHED_NO_EXIST",
+        "get_hf_file_metadata",
+        "hf_hub_download",
+        "hf_hub_url",
+        "try_to_load_from_cache",
+    ],
+    "hf_api": [
+        "Collection",
+        "CollectionItem",
+        "CommitInfo",
+        "CommitOperation",
+        "CommitOperationAdd",
+        "CommitOperationCopy",
+        "CommitOperationDelete",
+        "DatasetInfo",
+        "GitCommitInfo",
+        "GitRefInfo",
+        "GitRefs",
+        "HfApi",
+        "ModelInfo",
+        "Organization",
+        "RepoFile",
+        "RepoFolder",
+        "RepoUrl",
+        "SpaceInfo",
+        "User",
+        "UserLikes",
+        "WebhookInfo",
+        "WebhookWatchedItem",
+        "accept_access_request",
+        "add_collection_item",
+        "add_space_secret",
+        "add_space_variable",
+        "auth_check",
+        "batch_bucket_files",
+        "bucket_info",
+        "cancel_access_request",
+        "cancel_job",
+        "change_discussion_status",
+        "comment_discussion",
+        "create_branch",
+        "create_bucket",
+        "create_collection",
+        "create_commit",
+        "create_discussion",
+        "create_inference_endpoint",
+        "create_inference_endpoint_from_catalog",
+        "create_pull_request",
+        "create_repo",
+        "create_scheduled_job",
+        "create_scheduled_uv_job",
+        "create_tag",
+        "create_webhook",
+        "dataset_info",
+        "delete_branch",
+        "delete_bucket",
+        "delete_collection",
+        "delete_collection_item",
+        "delete_file",
+        "delete_folder",
+        "delete_inference_endpoint",
+        "delete_repo",
+        "delete_scheduled_job",
+        "delete_space_secret",
+        "delete_space_storage",
+        "delete_space_variable",
+        "delete_tag",
+        "delete_webhook",
+        "disable_space_dev_mode",
+        "disable_webhook",
+        "download_bucket_files",
+        "duplicate_repo",
+        "duplicate_space",
+        "edit_discussion_comment",
+        "enable_space_dev_mode",
+        "enable_webhook",
+        "fetch_job_logs",
+        "fetch_job_metrics",
+        "file_exists",
+        "get_bucket_file_metadata",
+        "get_bucket_paths_info",
+        "get_collection",
+        "get_dataset_tags",
+        "get_discussion_details",
+        "get_full_repo_name",
+        "get_inference_endpoint",
+        "get_local_safetensors_metadata",
+        "get_model_tags",
+        "get_organization_overview",
+        "get_paths_info",
+        "get_repo_discussions",
+        "get_safetensors_metadata",
+        "get_space_runtime",
+        "get_space_variables",
+        "get_user_overview",
+        "get_webhook",
+        "grant_access",
+        "inspect_job",
+        "inspect_scheduled_job",
+        "list_accepted_access_requests",
+        "list_bucket_tree",
+        "list_buckets",
+        "list_collections",
+        "list_daily_papers",
+        "list_dataset_parquet_files",
+        "list_datasets",
+        "list_inference_catalog",
+        "list_inference_endpoints",
+        "list_jobs",
+        "list_jobs_hardware",
+        "list_lfs_files",
+        "list_liked_repos",
+        "list_models",
+        "list_organization_followers",
+        "list_organization_members",
+        "list_papers",
+        "list_pending_access_requests",
+        "list_rejected_access_requests",
+        "list_repo_commits",
+        "list_repo_files",
+        "list_repo_likers",
+        "list_repo_refs",
+        "list_repo_tree",
+        "list_spaces",
+        "list_user_followers",
+        "list_user_following",
+        "list_webhooks",
+        "merge_pull_request",
+        "model_info",
+        "move_bucket",
+        "move_repo",
+        "paper_info",
+        "parse_local_safetensors_file_metadata",
+        "parse_safetensors_file_metadata",
+        "pause_inference_endpoint",
+        "pause_space",
+        "permanently_delete_lfs_files",
+        "preupload_lfs_files",
+        "reject_access_request",
+        "rename_discussion",
+        "repo_exists",
+        "repo_info",
+        "repo_type_and_id_from_hf_id",
+        "request_space_hardware",
+        "request_space_storage",
+        "restart_space",
+        "resume_inference_endpoint",
+        "resume_scheduled_job",
+        "revision_exists",
+        "run_as_future",
+        "run_job",
+        "run_uv_job",
+        "scale_to_zero_inference_endpoint",
+        "set_space_sleep_time",
+        "space_info",
+        "super_squash_history",
+        "suspend_scheduled_job",
+        "sync_bucket",
+        "unlike",
+        "update_collection_item",
+        "update_collection_metadata",
+        "update_inference_endpoint",
+        "update_repo_settings",
+        "update_webhook",
+        "upload_file",
+        "upload_folder",
+        "upload_large_folder",
+        "verify_repo_checksums",
+        "whoami",
+    ],
+    "hf_file_system": [
+        "HfFileSystem",
+        "HfFileSystemFile",
+        "HfFileSystemResolvedPath",
+        "HfFileSystemStreamFile",
+        "hffs",
+    ],
+    "hub_mixin": [
+        "ModelHubMixin",
+        "PyTorchModelHubMixin",
+    ],
+    "inference._client": [
+        "InferenceClient",
+        "InferenceTimeoutError",
+    ],
+    "inference._generated._async_client": [
+        "AsyncInferenceClient",
+    ],
+    "inference._generated.types": [
+        "AudioClassificationInput",
+        "AudioClassificationOutputElement",
+        "AudioClassificationOutputTransform",
+        "AudioClassificationParameters",
+        "AudioToAudioInput",
+        "AudioToAudioOutputElement",
+        "AutomaticSpeechRecognitionEarlyStoppingEnum",
+        "AutomaticSpeechRecognitionGenerationParameters",
+        "AutomaticSpeechRecognitionInput",
+        "AutomaticSpeechRecognitionOutput",
+        "AutomaticSpeechRecognitionOutputChunk",
+        "AutomaticSpeechRecognitionParameters",
+        "ChatCompletionInput",
+        "ChatCompletionInputFunctionDefinition",
+        "ChatCompletionInputFunctionName",
+        "ChatCompletionInputGrammarType",
+        "ChatCompletionInputJSONSchema",
+        "ChatCompletionInputMessage",
+        "ChatCompletionInputMessageChunk",
+        "ChatCompletionInputMessageChunkType",
+        "ChatCompletionInputResponseFormatJSONObject",
+        "ChatCompletionInputResponseFormatJSONSchema",
+        "ChatCompletionInputResponseFormatText",
+        "ChatCompletionInputStreamOptions",
+        "ChatCompletionInputTool",
+        "ChatCompletionInputToolCall",
+        "ChatCompletionInputToolChoiceClass",
+        "ChatCompletionInputToolChoiceEnum",
+        "ChatCompletionInputURL",
+        "ChatCompletionOutput",
+        "ChatCompletionOutputComplete",
+        "ChatCompletionOutputFunctionDefinition",
+        "ChatCompletionOutputLogprob",
+        "ChatCompletionOutputLogprobs",
+        "ChatCompletionOutputMessage",
+        "ChatCompletionOutputToolCall",
+        "ChatCompletionOutputTopLogprob",
+        "ChatCompletionOutputUsage",
+        "ChatCompletionStreamOutput",
+        "ChatCompletionStreamOutputChoice",
+        "ChatCompletionStreamOutputDelta",
+        "ChatCompletionStreamOutputDeltaToolCall",
+        "ChatCompletionStreamOutputFunction",
+        "ChatCompletionStreamOutputLogprob",
+        "ChatCompletionStreamOutputLogprobs",
+        "ChatCompletionStreamOutputTopLogprob",
+        "ChatCompletionStreamOutputUsage",
+        "DepthEstimationInput",
+        "DepthEstimationOutput",
+        "DocumentQuestionAnsweringInput",
+        "DocumentQuestionAnsweringInputData",
+        "DocumentQuestionAnsweringOutputElement",
+        "DocumentQuestionAnsweringParameters",
+        "FeatureExtractionInput",
+        "FeatureExtractionInputTruncationDirection",
+        "FillMaskInput",
+        "FillMaskOutputElement",
+        "FillMaskParameters",
+        "ImageClassificationInput",
+        "ImageClassificationOutputElement",
+        "ImageClassificationOutputTransform",
+        "ImageClassificationParameters",
+        "ImageSegmentationInput",
+        "ImageSegmentationOutputElement",
+        "ImageSegmentationParameters",
+        "ImageSegmentationSubtask",
+        "ImageTextToImageInput",
+        "ImageTextToImageOutput",
+        "ImageTextToImageParameters",
+        "ImageTextToImageTargetSize",
+        "ImageTextToVideoInput",
+        "ImageTextToVideoOutput",
+        "ImageTextToVideoParameters",
+        "ImageTextToVideoTargetSize",
+        "ImageToImageInput",
+        "ImageToImageOutput",
+        "ImageToImageParameters",
+        "ImageToImageTargetSize",
+        "ImageToTextEarlyStoppingEnum",
+        "ImageToTextGenerationParameters",
+        "ImageToTextInput",
+        "ImageToTextOutput",
+        "ImageToTextParameters",
+        "ImageToVideoInput",
+        "ImageToVideoOutput",
+        "ImageToVideoParameters",
+        "ImageToVideoTargetSize",
+        "ObjectDetectionBoundingBox",
+        "ObjectDetectionInput",
+        "ObjectDetectionOutputElement",
+        "ObjectDetectionParameters",
+        "Padding",
+        "QuestionAnsweringInput",
+        "QuestionAnsweringInputData",
+        "QuestionAnsweringOutputElement",
+        "QuestionAnsweringParameters",
+        "SentenceSimilarityInput",
+        "SentenceSimilarityInputData",
+        "SummarizationInput",
+        "SummarizationOutput",
+        "SummarizationParameters",
+        "SummarizationTruncationStrategy",
+        "TableQuestionAnsweringInput",
+        "TableQuestionAnsweringInputData",
+        "TableQuestionAnsweringOutputElement",
+        "TableQuestionAnsweringParameters",
+        "Text2TextGenerationInput",
+        "Text2TextGenerationOutput",
+        "Text2TextGenerationParameters",
+        "Text2TextGenerationTruncationStrategy",
+        "TextClassificationInput",
+        "TextClassificationOutputElement",
+        "TextClassificationOutputTransform",
+        "TextClassificationParameters",
+        "TextGenerationInput",
+        "TextGenerationInputGenerateParameters",
+        "TextGenerationInputGrammarType",
+        "TextGenerationOutput",
+        "TextGenerationOutputBestOfSequence",
+        "TextGenerationOutputDetails",
+        "TextGenerationOutputFinishReason",
+        "TextGenerationOutputPrefillToken",
+        "TextGenerationOutputToken",
+        "TextGenerationStreamOutput",
+        "TextGenerationStreamOutputStreamDetails",
+        "TextGenerationStreamOutputToken",
+        "TextToAudioEarlyStoppingEnum",
+        "TextToAudioGenerationParameters",
+        "TextToAudioInput",
+        "TextToAudioOutput",
+        "TextToAudioParameters",
+        "TextToImageInput",
+        "TextToImageOutput",
+        "TextToImageParameters",
+        "TextToSpeechEarlyStoppingEnum",
+        "TextToSpeechGenerationParameters",
+        "TextToSpeechInput",
+        "TextToSpeechOutput",
+        "TextToSpeechParameters",
+        "TextToVideoInput",
+        "TextToVideoOutput",
+        "TextToVideoParameters",
+        "TokenClassificationAggregationStrategy",
+        "TokenClassificationInput",
+        "TokenClassificationOutputElement",
+        "TokenClassificationParameters",
+        "TranslationInput",
+        "TranslationOutput",
+        "TranslationParameters",
+        "TranslationTruncationStrategy",
+        "TypeEnum",
+        "VideoClassificationInput",
+        "VideoClassificationOutputElement",
+        "VideoClassificationOutputTransform",
+        "VideoClassificationParameters",
+        "VisualQuestionAnsweringInput",
+        "VisualQuestionAnsweringInputData",
+        "VisualQuestionAnsweringOutputElement",
+        "VisualQuestionAnsweringParameters",
+        "ZeroShotClassificationInput",
+        "ZeroShotClassificationOutputElement",
+        "ZeroShotClassificationParameters",
+        "ZeroShotImageClassificationInput",
+        "ZeroShotImageClassificationOutputElement",
+        "ZeroShotImageClassificationParameters",
+        "ZeroShotObjectDetectionBoundingBox",
+        "ZeroShotObjectDetectionInput",
+        "ZeroShotObjectDetectionOutputElement",
+        "ZeroShotObjectDetectionParameters",
+    ],
+    "inference._mcp.agent": [
+        "Agent",
+    ],
+    "inference._mcp.mcp_client": [
+        "MCPClient",
+    ],
+    "repocard": [
+        "DatasetCard",
+        "ModelCard",
+        "RepoCard",
+        "SpaceCard",
+        "metadata_eval_result",
+        "metadata_load",
+        "metadata_save",
+        "metadata_update",
+    ],
+    "repocard_data": [
+        "CardData",
+        "DatasetCardData",
+        "EvalResult",
+        "ModelCardData",
+        "SpaceCardData",
+    ],
+    "serialization": [
+        "StateDictSplit",
+        "get_torch_storage_id",
+        "get_torch_storage_size",
+        "load_state_dict_from_file",
+        "load_torch_model",
+        "save_torch_model",
+        "save_torch_state_dict",
+        "split_state_dict_into_shards_factory",
+        "split_torch_state_dict_into_shards",
+    ],
+    "serialization._dduf": [
+        "DDUFEntry",
+        "export_entries_as_dduf",
+        "export_folder_as_dduf",
+        "read_dduf_file",
+    ],
+    "utils": [
+        "ASYNC_CLIENT_FACTORY_T",
+        "CLIENT_FACTORY_T",
+        "CacheNotFound",
+        "CachedFileInfo",
+        "CachedRepoInfo",
+        "CachedRevisionInfo",
+        "CorruptedCacheException",
+        "DeleteCacheStrategy",
+        "HFCacheInfo",
+        "cached_assets_path",
+        "close_session",
+        "dump_environment_info",
+        "get_async_session",
+        "get_session",
+        "get_token",
+        "hf_raise_for_status",
+        "logging",
+        "scan_cache_dir",
+        "set_async_client_factory",
+        "set_client_factory",
+    ],
+}
+
+# WARNING: __all__ is generated automatically, Any manual edit will be lost when re-generating this file !
+#
+# To update the static imports, please run the following command and commit the changes.
+# ```
+# # Use script
+# python utils/check_all_variable.py --update
+#
+# # Or run style on codebase
+# make style
+# ```
+
+__all__ = [
+    "ASYNC_CLIENT_FACTORY_T",
+    "Agent",
+    "AsyncInferenceClient",
+    "AudioClassificationInput",
+    "AudioClassificationOutputElement",
+    "AudioClassificationOutputTransform",
+    "AudioClassificationParameters",
+    "AudioToAudioInput",
+    "AudioToAudioOutputElement",
+    "AutomaticSpeechRecognitionEarlyStoppingEnum",
+    "AutomaticSpeechRecognitionGenerationParameters",
+    "AutomaticSpeechRecognitionInput",
+    "AutomaticSpeechRecognitionOutput",
+    "AutomaticSpeechRecognitionOutputChunk",
+    "AutomaticSpeechRecognitionParameters",
+    "BucketFile",
+    "BucketFileMetadata",
+    "BucketFolder",
+    "BucketInfo",
+    "BucketUrl",
+    "CLIENT_FACTORY_T",
+    "CONFIG_NAME",
+    "CacheNotFound",
+    "CachedFileInfo",
+    "CachedRepoInfo",
+    "CachedRevisionInfo",
+    "CardData",
+    "ChatCompletionInput",
+    "ChatCompletionInputFunctionDefinition",
+    "ChatCompletionInputFunctionName",
+    "ChatCompletionInputGrammarType",
+    "ChatCompletionInputJSONSchema",
+    "ChatCompletionInputMessage",
+    "ChatCompletionInputMessageChunk",
+    "ChatCompletionInputMessageChunkType",
+    "ChatCompletionInputResponseFormatJSONObject",
+    "ChatCompletionInputResponseFormatJSONSchema",
+    "ChatCompletionInputResponseFormatText",
+    "ChatCompletionInputStreamOptions",
+    "ChatCompletionInputTool",
+    "ChatCompletionInputToolCall",
+    "ChatCompletionInputToolChoiceClass",
+    "ChatCompletionInputToolChoiceEnum",
+    "ChatCompletionInputURL",
+    "ChatCompletionOutput",
+    "ChatCompletionOutputComplete",
+    "ChatCompletionOutputFunctionDefinition",
+    "ChatCompletionOutputLogprob",
+    "ChatCompletionOutputLogprobs",
+    "ChatCompletionOutputMessage",
+    "ChatCompletionOutputToolCall",
+    "ChatCompletionOutputTopLogprob",
+    "ChatCompletionOutputUsage",
+    "ChatCompletionStreamOutput",
+    "ChatCompletionStreamOutputChoice",
+    "ChatCompletionStreamOutputDelta",
+    "ChatCompletionStreamOutputDeltaToolCall",
+    "ChatCompletionStreamOutputFunction",
+    "ChatCompletionStreamOutputLogprob",
+    "ChatCompletionStreamOutputLogprobs",
+    "ChatCompletionStreamOutputTopLogprob",
+    "ChatCompletionStreamOutputUsage",
+    "Collection",
+    "CollectionItem",
+    "CommitInfo",
+    "CommitOperation",
+    "CommitOperationAdd",
+    "CommitOperationCopy",
+    "CommitOperationDelete",
+    "CommitScheduler",
+    "CorruptedCacheException",
+    "DDUFEntry",
+    "DatasetCard",
+    "DatasetCardData",
+    "DatasetInfo",
+    "DeleteCacheStrategy",
+    "DepthEstimationInput",
+    "DepthEstimationOutput",
+    "Discussion",
+    "DiscussionComment",
+    "DiscussionCommit",
+    "DiscussionEvent",
+    "DiscussionStatusChange",
+    "DiscussionTitleChange",
+    "DiscussionWithDetails",
+    "DocumentQuestionAnsweringInput",
+    "DocumentQuestionAnsweringInputData",
+    "DocumentQuestionAnsweringOutputElement",
+    "DocumentQuestionAnsweringParameters",
+    "DryRunFileInfo",
+    "EvalResult",
+    "EvalResultEntry",
+    "FLAX_WEIGHTS_NAME",
+    "FeatureExtractionInput",
+    "FeatureExtractionInputTruncationDirection",
+    "FillMaskInput",
+    "FillMaskOutputElement",
+    "FillMaskParameters",
+    "GitCommitInfo",
+    "GitRefInfo",
+    "GitRefs",
+    "HFCacheInfo",
+    "HFSummaryWriter",
+    "HUGGINGFACE_CO_URL_HOME",
+    "HUGGINGFACE_CO_URL_TEMPLATE",
+    "HfApi",
+    "HfFileMetadata",
+    "HfFileSystem",
+    "HfFileSystemFile",
+    "HfFileSystemResolvedPath",
+    "HfFileSystemStreamFile",
+    "ImageClassificationInput",
+    "ImageClassificationOutputElement",
+    "ImageClassificationOutputTransform",
+    "ImageClassificationParameters",
+    "ImageSegmentationInput",
+    "ImageSegmentationOutputElement",
+    "ImageSegmentationParameters",
+    "ImageSegmentationSubtask",
+    "ImageTextToImageInput",
+    "ImageTextToImageOutput",
+    "ImageTextToImageParameters",
+    "ImageTextToImageTargetSize",
+    "ImageTextToVideoInput",
+    "ImageTextToVideoOutput",
+    "ImageTextToVideoParameters",
+    "ImageTextToVideoTargetSize",
+    "ImageToImageInput",
+    "ImageToImageOutput",
+    "ImageToImageParameters",
+    "ImageToImageTargetSize",
+    "ImageToTextEarlyStoppingEnum",
+    "ImageToTextGenerationParameters",
+    "ImageToTextInput",
+    "ImageToTextOutput",
+    "ImageToTextParameters",
+    "ImageToVideoInput",
+    "ImageToVideoOutput",
+    "ImageToVideoParameters",
+    "ImageToVideoTargetSize",
+    "InferenceClient",
+    "InferenceEndpoint",
+    "InferenceEndpointError",
+    "InferenceEndpointStatus",
+    "InferenceEndpointTimeoutError",
+    "InferenceEndpointType",
+    "InferenceTimeoutError",
+    "JobAccelerator",
+    "JobHardware",
+    "JobInfo",
+    "JobOwner",
+    "JobStage",
+    "JobStatus",
+    "MCPClient",
+    "ModelCard",
+    "ModelCardData",
+    "ModelHubMixin",
+    "ModelInfo",
+    "OAuthInfo",
+    "OAuthOrgInfo",
+    "OAuthUserInfo",
+    "ObjectDetectionBoundingBox",
+    "ObjectDetectionInput",
+    "ObjectDetectionOutputElement",
+    "ObjectDetectionParameters",
+    "Organization",
+    "PYTORCH_WEIGHTS_NAME",
+    "Padding",
+    "PyTorchModelHubMixin",
+    "QuestionAnsweringInput",
+    "QuestionAnsweringInputData",
+    "QuestionAnsweringOutputElement",
+    "QuestionAnsweringParameters",
+    "REPO_TYPE_DATASET",
+    "REPO_TYPE_MODEL",
+    "REPO_TYPE_SPACE",
+    "RepoCard",
+    "RepoFile",
+    "RepoFolder",
+    "RepoUrl",
+    "SentenceSimilarityInput",
+    "SentenceSimilarityInputData",
+    "SpaceCard",
+    "SpaceCardData",
+    "SpaceHardware",
+    "SpaceInfo",
+    "SpaceRuntime",
+    "SpaceStage",
+    "SpaceStorage",
+    "SpaceVariable",
+    "StateDictSplit",
+    "SummarizationInput",
+    "SummarizationOutput",
+    "SummarizationParameters",
+    "SummarizationTruncationStrategy",
+    "SyncOperation",
+    "SyncPlan",
+    "TF2_WEIGHTS_NAME",
+    "TF_WEIGHTS_NAME",
+    "TableQuestionAnsweringInput",
+    "TableQuestionAnsweringInputData",
+    "TableQuestionAnsweringOutputElement",
+    "TableQuestionAnsweringParameters",
+    "Text2TextGenerationInput",
+    "Text2TextGenerationOutput",
+    "Text2TextGenerationParameters",
+    "Text2TextGenerationTruncationStrategy",
+    "TextClassificationInput",
+    "TextClassificationOutputElement",
+    "TextClassificationOutputTransform",
+    "TextClassificationParameters",
+    "TextGenerationInput",
+    "TextGenerationInputGenerateParameters",
+    "TextGenerationInputGrammarType",
+    "TextGenerationOutput",
+    "TextGenerationOutputBestOfSequence",
+    "TextGenerationOutputDetails",
+    "TextGenerationOutputFinishReason",
+    "TextGenerationOutputPrefillToken",
+    "TextGenerationOutputToken",
+    "TextGenerationStreamOutput",
+    "TextGenerationStreamOutputStreamDetails",
+    "TextGenerationStreamOutputToken",
+    "TextToAudioEarlyStoppingEnum",
+    "TextToAudioGenerationParameters",
+    "TextToAudioInput",
+    "TextToAudioOutput",
+    "TextToAudioParameters",
+    "TextToImageInput",
+    "TextToImageOutput",
+    "TextToImageParameters",
+    "TextToSpeechEarlyStoppingEnum",
+    "TextToSpeechGenerationParameters",
+    "TextToSpeechInput",
+    "TextToSpeechOutput",
+    "TextToSpeechParameters",
+    "TextToVideoInput",
+    "TextToVideoOutput",
+    "TextToVideoParameters",
+    "TokenClassificationAggregationStrategy",
+    "TokenClassificationInput",
+    "TokenClassificationOutputElement",
+    "TokenClassificationParameters",
+    "TranslationInput",
+    "TranslationOutput",
+    "TranslationParameters",
+    "TranslationTruncationStrategy",
+    "TypeEnum",
+    "User",
+    "UserLikes",
+    "VideoClassificationInput",
+    "VideoClassificationOutputElement",
+    "VideoClassificationOutputTransform",
+    "VideoClassificationParameters",
+    "VisualQuestionAnsweringInput",
+    "VisualQuestionAnsweringInputData",
+    "VisualQuestionAnsweringOutputElement",
+    "VisualQuestionAnsweringParameters",
+    "WebhookInfo",
+    "WebhookPayload",
+    "WebhookPayloadComment",
+    "WebhookPayloadDiscussion",
+    "WebhookPayloadDiscussionChanges",
+    "WebhookPayloadEvent",
+    "WebhookPayloadMovedTo",
+    "WebhookPayloadRepo",
+    "WebhookPayloadUrl",
+    "WebhookPayloadWebhook",
+    "WebhookWatchedItem",
+    "WebhooksServer",
+    "ZeroShotClassificationInput",
+    "ZeroShotClassificationOutputElement",
+    "ZeroShotClassificationParameters",
+    "ZeroShotImageClassificationInput",
+    "ZeroShotImageClassificationOutputElement",
+    "ZeroShotImageClassificationParameters",
+    "ZeroShotObjectDetectionBoundingBox",
+    "ZeroShotObjectDetectionInput",
+    "ZeroShotObjectDetectionOutputElement",
+    "ZeroShotObjectDetectionParameters",
+    "_CACHED_NO_EXIST",
+    "_save_pretrained_fastai",
+    "accept_access_request",
+    "add_collection_item",
+    "add_space_secret",
+    "add_space_variable",
+    "attach_huggingface_oauth",
+    "auth_check",
+    "auth_list",
+    "auth_switch",
+    "batch_bucket_files",
+    "bucket_info",
+    "cached_assets_path",
+    "cancel_access_request",
+    "cancel_job",
+    "change_discussion_status",
+    "check_cli_update",
+    "close_session",
+    "comment_discussion",
+    "create_branch",
+    "create_bucket",
+    "create_collection",
+    "create_commit",
+    "create_discussion",
+    "create_inference_endpoint",
+    "create_inference_endpoint_from_catalog",
+    "create_pull_request",
+    "create_repo",
+    "create_scheduled_job",
+    "create_scheduled_uv_job",
+    "create_tag",
+    "create_webhook",
+    "dataset_info",
+    "delete_branch",
+    "delete_bucket",
+    "delete_collection",
+    "delete_collection_item",
+    "delete_file",
+    "delete_folder",
+    "delete_inference_endpoint",
+    "delete_repo",
+    "delete_scheduled_job",
+    "delete_space_secret",
+    "delete_space_storage",
+    "delete_space_variable",
+    "delete_tag",
+    "delete_webhook",
+    "disable_space_dev_mode",
+    "disable_webhook",
+    "download_bucket_files",
+    "dump_environment_info",
+    "duplicate_repo",
+    "duplicate_space",
+    "edit_discussion_comment",
+    "enable_space_dev_mode",
+    "enable_webhook",
+    "eval_result_entries_to_yaml",
+    "export_entries_as_dduf",
+    "export_folder_as_dduf",
+    "fetch_job_logs",
+    "fetch_job_metrics",
+    "file_exists",
+    "from_pretrained_fastai",
+    "get_async_session",
+    "get_bucket_file_metadata",
+    "get_bucket_paths_info",
+    "get_collection",
+    "get_dataset_tags",
+    "get_discussion_details",
+    "get_full_repo_name",
+    "get_hf_file_metadata",
+    "get_inference_endpoint",
+    "get_local_safetensors_metadata",
+    "get_model_tags",
+    "get_organization_overview",
+    "get_paths_info",
+    "get_repo_discussions",
+    "get_safetensors_metadata",
+    "get_session",
+    "get_space_runtime",
+    "get_space_variables",
+    "get_token",
+    "get_torch_storage_id",
+    "get_torch_storage_size",
+    "get_user_overview",
+    "get_webhook",
+    "grant_access",
+    "hf_hub_download",
+    "hf_hub_url",
+    "hf_raise_for_status",
+    "hffs",
+    "inspect_job",
+    "inspect_scheduled_job",
+    "interpreter_login",
+    "is_offline_mode",
+    "list_accepted_access_requests",
+    "list_bucket_tree",
+    "list_buckets",
+    "list_collections",
+    "list_daily_papers",
+    "list_dataset_parquet_files",
+    "list_datasets",
+    "list_inference_catalog",
+    "list_inference_endpoints",
+    "list_jobs",
+    "list_jobs_hardware",
+    "list_lfs_files",
+    "list_liked_repos",
+    "list_models",
+    "list_organization_followers",
+    "list_organization_members",
+    "list_papers",
+    "list_pending_access_requests",
+    "list_rejected_access_requests",
+    "list_repo_commits",
+    "list_repo_files",
+    "list_repo_likers",
+    "list_repo_refs",
+    "list_repo_tree",
+    "list_spaces",
+    "list_user_followers",
+    "list_user_following",
+    "list_webhooks",
+    "load_state_dict_from_file",
+    "load_torch_model",
+    "logging",
+    "login",
+    "logout",
+    "merge_pull_request",
+    "metadata_eval_result",
+    "metadata_load",
+    "metadata_save",
+    "metadata_update",
+    "model_info",
+    "move_bucket",
+    "move_repo",
+    "notebook_login",
+    "paper_info",
+    "parse_eval_result_entries",
+    "parse_huggingface_oauth",
+    "parse_local_safetensors_file_metadata",
+    "parse_safetensors_file_metadata",
+    "pause_inference_endpoint",
+    "pause_space",
+    "permanently_delete_lfs_files",
+    "preupload_lfs_files",
+    "push_to_hub_fastai",
+    "read_dduf_file",
+    "reject_access_request",
+    "rename_discussion",
+    "repo_exists",
+    "repo_info",
+    "repo_type_and_id_from_hf_id",
+    "request_space_hardware",
+    "request_space_storage",
+    "restart_space",
+    "resume_inference_endpoint",
+    "resume_scheduled_job",
+    "revision_exists",
+    "run_as_future",
+    "run_job",
+    "run_uv_job",
+    "save_torch_model",
+    "save_torch_state_dict",
+    "scale_to_zero_inference_endpoint",
+    "scan_cache_dir",
+    "set_async_client_factory",
+    "set_client_factory",
+    "set_space_sleep_time",
+    "snapshot_download",
+    "space_info",
+    "split_state_dict_into_shards_factory",
+    "split_torch_state_dict_into_shards",
+    "super_squash_history",
+    "suspend_scheduled_job",
+    "sync_bucket",
+    "try_to_load_from_cache",
+    "typer_factory",
+    "unlike",
+    "update_collection_item",
+    "update_collection_metadata",
+    "update_inference_endpoint",
+    "update_repo_settings",
+    "update_webhook",
+    "upload_file",
+    "upload_folder",
+    "upload_large_folder",
+    "verify_repo_checksums",
+    "webhook_endpoint",
+    "whoami",
+]
+
+
+def _attach(package_name, submodules=None, submod_attrs=None):
+    """Attach lazily loaded submodules, functions, or other attributes.
+
+    Typically, modules import submodules and attributes as follows:
+
+    ```py
+    import mysubmodule
+    import anothersubmodule
+
+    from .foo import someattr
+    ```
+
+    The idea is to replace a package's `__getattr__`, `__dir__`, such that all imports
+    work exactly the way they would with normal imports, except that the import occurs
+    upon first use.
+
+    The typical way to call this function, replacing the above imports, is:
+
+    ```python
+    __getattr__, __dir__ = lazy.attach(
+        __name__,
+        ['mysubmodule', 'anothersubmodule'],
+        {'foo': ['someattr']}
+    )
+    ```
+    This functionality requires Python 3.7 or higher.
+
+    Args:
+        package_name (`str`):
+            Typically use `__name__`.
+        submodules (`set`):
+            List of submodules to attach.
+        submod_attrs (`dict`):
+            Dictionary of submodule -> list of attributes / functions.
+            These attributes are imported as they are used.
+
+    Returns:
+        __getattr__, __dir__, __all__
+
+    """
+    if submod_attrs is None:
+        submod_attrs = {}
+
+    if submodules is None:
+        submodules = set()
+    else:
+        submodules = set(submodules)
+
+    attr_to_modules = {attr: mod for mod, attrs in submod_attrs.items() for attr in attrs}
+
+    def __getattr__(name):
+        if name in submodules:
+            try:
+                return importlib.import_module(f"{package_name}.{name}")
+            except Exception as e:
+                print(f"Error importing {package_name}.{name}: {e}")
+                raise
+        elif name in attr_to_modules:
+            submod_path = f"{package_name}.{attr_to_modules[name]}"
+            try:
+                submod = importlib.import_module(submod_path)
+            except Exception as e:
+                print(f"Error importing {submod_path}: {e}")
+                raise
+            attr = getattr(submod, name)
+
+            # If the attribute lives in a file (module) with the same
+            # name as the attribute, ensure that the attribute and *not*
+            # the module is accessible on the package.
+            if name == attr_to_modules[name]:
+                pkg = sys.modules[package_name]
+                pkg.__dict__[name] = attr
+
+            return attr
+        else:
+            raise AttributeError(f"No {package_name} attribute {name}")
+
+    def __dir__():
+        return __all__
+
+    return __getattr__, __dir__
+
+
+__getattr__, __dir__ = _attach(__name__, submodules=[], submod_attrs=_SUBMOD_ATTRS)
+
+if os.environ.get("EAGER_IMPORT", ""):
+    for attr in __all__:
+        __getattr__(attr)
+
+# WARNING: any content below this statement is generated automatically. Any manual edit
+# will be lost when re-generating this file !
+#
+# To update the static imports, please run the following command and commit the changes.
+# ```
+# # Use script
+# python utils/check_static_imports.py --update
+#
+# # Or run style on codebase
+# make style
+# ```
+if TYPE_CHECKING:  # pragma: no cover
+    from ._buckets import (
+        BucketFile,  # noqa: F401
+        BucketFileMetadata,  # noqa: F401
+        BucketFolder,  # noqa: F401
+        BucketInfo,  # noqa: F401
+        BucketUrl,  # noqa: F401
+        SyncOperation,  # noqa: F401
+        SyncPlan,  # noqa: F401
+    )
+    from ._commit_scheduler import CommitScheduler  # noqa: F401
+    from ._eval_results import (
+        EvalResultEntry,  # noqa: F401
+        eval_result_entries_to_yaml,  # noqa: F401
+        parse_eval_result_entries,  # noqa: F401
+    )
+    from ._inference_endpoints import (
+        InferenceEndpoint,  # noqa: F401
+        InferenceEndpointError,  # noqa: F401
+        InferenceEndpointStatus,  # noqa: F401
+        InferenceEndpointTimeoutError,  # noqa: F401
+        InferenceEndpointType,  # noqa: F401
+    )
+    from ._jobs_api import (
+        JobAccelerator,  # noqa: F401
+        JobHardware,  # noqa: F401
+        JobInfo,  # noqa: F401
+        JobOwner,  # noqa: F401
+        JobStage,  # noqa: F401
+        JobStatus,  # noqa: F401
+    )
+    from ._login import (
+        auth_list,  # noqa: F401
+        auth_switch,  # noqa: F401
+        interpreter_login,  # noqa: F401
+        login,  # noqa: F401
+        logout,  # noqa: F401
+        notebook_login,  # noqa: F401
+    )
+    from ._oauth import (
+        OAuthInfo,  # noqa: F401
+        OAuthOrgInfo,  # noqa: F401
+        OAuthUserInfo,  # noqa: F401
+        attach_huggingface_oauth,  # noqa: F401
+        parse_huggingface_oauth,  # noqa: F401
+    )
+    from ._snapshot_download import snapshot_download  # noqa: F401
+    from ._space_api import (
+        SpaceHardware,  # noqa: F401
+        SpaceRuntime,  # noqa: F401
+        SpaceStage,  # noqa: F401
+        SpaceStorage,  # noqa: F401
+        SpaceVariable,  # noqa: F401
+    )
+    from ._tensorboard_logger import HFSummaryWriter  # noqa: F401
+    from ._webhooks_payload import (
+        WebhookPayload,  # noqa: F401
+        WebhookPayloadComment,  # noqa: F401
+        WebhookPayloadDiscussion,  # noqa: F401
+        WebhookPayloadDiscussionChanges,  # noqa: F401
+        WebhookPayloadEvent,  # noqa: F401
+        WebhookPayloadMovedTo,  # noqa: F401
+        WebhookPayloadRepo,  # noqa: F401
+        WebhookPayloadUrl,  # noqa: F401
+        WebhookPayloadWebhook,  # noqa: F401
+    )
+    from ._webhooks_server import (
+        WebhooksServer,  # noqa: F401
+        webhook_endpoint,  # noqa: F401
+    )
+    from .cli._cli_utils import (
+        check_cli_update,  # noqa: F401
+        typer_factory,  # noqa: F401
+    )
+    from .community import (
+        Discussion,  # noqa: F401
+        DiscussionComment,  # noqa: F401
+        DiscussionCommit,  # noqa: F401
+        DiscussionEvent,  # noqa: F401
+        DiscussionStatusChange,  # noqa: F401
+        DiscussionTitleChange,  # noqa: F401
+        DiscussionWithDetails,  # noqa: F401
+    )
+    from .constants import (
+        CONFIG_NAME,  # noqa: F401
+        FLAX_WEIGHTS_NAME,  # noqa: F401
+        HUGGINGFACE_CO_URL_HOME,  # noqa: F401
+        HUGGINGFACE_CO_URL_TEMPLATE,  # noqa: F401
+        PYTORCH_WEIGHTS_NAME,  # noqa: F401
+        REPO_TYPE_DATASET,  # noqa: F401
+        REPO_TYPE_MODEL,  # noqa: F401
+        REPO_TYPE_SPACE,  # noqa: F401
+        TF2_WEIGHTS_NAME,  # noqa: F401
+        TF_WEIGHTS_NAME,  # noqa: F401
+        is_offline_mode,  # noqa: F401
+    )
+    from .fastai_utils import (
+        _save_pretrained_fastai,  # noqa: F401
+        from_pretrained_fastai,  # noqa: F401
+        push_to_hub_fastai,  # noqa: F401
+    )
+    from .file_download import (
+        _CACHED_NO_EXIST,  # noqa: F401
+        DryRunFileInfo,  # noqa: F401
+        HfFileMetadata,  # noqa: F401
+        get_hf_file_metadata,  # noqa: F401
+        hf_hub_download,  # noqa: F401
+        hf_hub_url,  # noqa: F401
+        try_to_load_from_cache,  # noqa: F401
+    )
+    from .hf_api import (
+        Collection,  # noqa: F401
+        CollectionItem,  # noqa: F401
+        CommitInfo,  # noqa: F401
+        CommitOperation,  # noqa: F401
+        CommitOperationAdd,  # noqa: F401
+        CommitOperationCopy,  # noqa: F401
+        CommitOperationDelete,  # noqa: F401
+        DatasetInfo,  # noqa: F401
+        GitCommitInfo,  # noqa: F401
+        GitRefInfo,  # noqa: F401
+        GitRefs,  # noqa: F401
+        HfApi,  # noqa: F401
+        ModelInfo,  # noqa: F401
+        Organization,  # noqa: F401
+        RepoFile,  # noqa: F401
+        RepoFolder,  # noqa: F401
+        RepoUrl,  # noqa: F401
+        SpaceInfo,  # noqa: F401
+        User,  # noqa: F401
+        UserLikes,  # noqa: F401
+        WebhookInfo,  # noqa: F401
+        WebhookWatchedItem,  # noqa: F401
+        accept_access_request,  # noqa: F401
+        add_collection_item,  # noqa: F401
+        add_space_secret,  # noqa: F401
+        add_space_variable,  # noqa: F401
+        auth_check,  # noqa: F401
+        batch_bucket_files,  # noqa: F401
+        bucket_info,  # noqa: F401
+        cancel_access_request,  # noqa: F401
+        cancel_job,  # noqa: F401
+        change_discussion_status,  # noqa: F401
+        comment_discussion,  # noqa: F401
+        create_branch,  # noqa: F401
+        create_bucket,  # noqa: F401
+        create_collection,  # noqa: F401
+        create_commit,  # noqa: F401
+        create_discussion,  # noqa: F401
+        create_inference_endpoint,  # noqa: F401
+        create_inference_endpoint_from_catalog,  # noqa: F401
+        create_pull_request,  # noqa: F401
+        create_repo,  # noqa: F401
+        create_scheduled_job,  # noqa: F401
+        create_scheduled_uv_job,  # noqa: F401
+        create_tag,  # noqa: F401
+        create_webhook,  # noqa: F401
+        dataset_info,  # noqa: F401
+        delete_branch,  # noqa: F401
+        delete_bucket,  # noqa: F401
+        delete_collection,  # noqa: F401
+        delete_collection_item,  # noqa: F401
+        delete_file,  # noqa: F401
+        delete_folder,  # noqa: F401
+        delete_inference_endpoint,  # noqa: F401
+        delete_repo,  # noqa: F401
+        delete_scheduled_job,  # noqa: F401
+        delete_space_secret,  # noqa: F401
+        delete_space_storage,  # noqa: F401
+        delete_space_variable,  # noqa: F401
+        delete_tag,  # noqa: F401
+        delete_webhook,  # noqa: F401
+        disable_space_dev_mode,  # noqa: F401
+        disable_webhook,  # noqa: F401
+        download_bucket_files,  # noqa: F401
+        duplicate_repo,  # noqa: F401
+        duplicate_space,  # noqa: F401
+        edit_discussion_comment,  # noqa: F401
+        enable_space_dev_mode,  # noqa: F401
+        enable_webhook,  # noqa: F401
+        fetch_job_logs,  # noqa: F401
+        fetch_job_metrics,  # noqa: F401
+        file_exists,  # noqa: F401
+        get_bucket_file_metadata,  # noqa: F401
+        get_bucket_paths_info,  # noqa: F401
+        get_collection,  # noqa: F401
+        get_dataset_tags,  # noqa: F401
+        get_discussion_details,  # noqa: F401
+        get_full_repo_name,  # noqa: F401
+        get_inference_endpoint,  # noqa: F401
+        get_local_safetensors_metadata,  # noqa: F401
+        get_model_tags,  # noqa: F401
+        get_organization_overview,  # noqa: F401
+        get_paths_info,  # noqa: F401
+        get_repo_discussions,  # noqa: F401
+        get_safetensors_metadata,  # noqa: F401
+        get_space_runtime,  # noqa: F401
+        get_space_variables,  # noqa: F401
+        get_user_overview,  # noqa: F401
+        get_webhook,  # noqa: F401
+        grant_access,  # noqa: F401
+        inspect_job,  # noqa: F401
+        inspect_scheduled_job,  # noqa: F401
+        list_accepted_access_requests,  # noqa: F401
+        list_bucket_tree,  # noqa: F401
+        list_buckets,  # noqa: F401
+        list_collections,  # noqa: F401
+        list_daily_papers,  # noqa: F401
+        list_dataset_parquet_files,  # noqa: F401
+        list_datasets,  # noqa: F401
+        list_inference_catalog,  # noqa: F401
+        list_inference_endpoints,  # noqa: F401
+        list_jobs,  # noqa: F401
+        list_jobs_hardware,  # noqa: F401
+        list_lfs_files,  # noqa: F401
+        list_liked_repos,  # noqa: F401
+        list_models,  # noqa: F401
+        list_organization_followers,  # noqa: F401
+        list_organization_members,  # noqa: F401
+        list_papers,  # noqa: F401
+        list_pending_access_requests,  # noqa: F401
+        list_rejected_access_requests,  # noqa: F401
+        list_repo_commits,  # noqa: F401
+        list_repo_files,  # noqa: F401
+        list_repo_likers,  # noqa: F401
+        list_repo_refs,  # noqa: F401
+        list_repo_tree,  # noqa: F401
+        list_spaces,  # noqa: F401
+        list_user_followers,  # noqa: F401
+        list_user_following,  # noqa: F401
+        list_webhooks,  # noqa: F401
+        merge_pull_request,  # noqa: F401
+        model_info,  # noqa: F401
+        move_bucket,  # noqa: F401
+        move_repo,  # noqa: F401
+        paper_info,  # noqa: F401
+        parse_local_safetensors_file_metadata,  # noqa: F401
+        parse_safetensors_file_metadata,  # noqa: F401
+        pause_inference_endpoint,  # noqa: F401
+        pause_space,  # noqa: F401
+        permanently_delete_lfs_files,  # noqa: F401
+        preupload_lfs_files,  # noqa: F401
+        reject_access_request,  # noqa: F401
+        rename_discussion,  # noqa: F401
+        repo_exists,  # noqa: F401
+        repo_info,  # noqa: F401
+        repo_type_and_id_from_hf_id,  # noqa: F401
+        request_space_hardware,  # noqa: F401
+        request_space_storage,  # noqa: F401
+        restart_space,  # noqa: F401
+        resume_inference_endpoint,  # noqa: F401
+        resume_scheduled_job,  # noqa: F401
+        revision_exists,  # noqa: F401
+        run_as_future,  # noqa: F401
+        run_job,  # noqa: F401
+        run_uv_job,  # noqa: F401
+        scale_to_zero_inference_endpoint,  # noqa: F401
+        set_space_sleep_time,  # noqa: F401
+        space_info,  # noqa: F401
+        super_squash_history,  # noqa: F401
+        suspend_scheduled_job,  # noqa: F401
+        sync_bucket,  # noqa: F401
+        unlike,  # noqa: F401
+        update_collection_item,  # noqa: F401
+        update_collection_metadata,  # noqa: F401
+        update_inference_endpoint,  # noqa: F401
+        update_repo_settings,  # noqa: F401
+        update_webhook,  # noqa: F401
+        upload_file,  # noqa: F401
+        upload_folder,  # noqa: F401
+        upload_large_folder,  # noqa: F401
+        verify_repo_checksums,  # noqa: F401
+        whoami,  # noqa: F401
+    )
+    from .hf_file_system import (
+        HfFileSystem,  # noqa: F401
+        HfFileSystemFile,  # noqa: F401
+        HfFileSystemResolvedPath,  # noqa: F401
+        HfFileSystemStreamFile,  # noqa: F401
+        hffs,  # noqa: F401
+    )
+    from .hub_mixin import (
+        ModelHubMixin,  # noqa: F401
+        PyTorchModelHubMixin,  # noqa: F401
+    )
+    from .inference._client import (
+        InferenceClient,  # noqa: F401
+        InferenceTimeoutError,  # noqa: F401
+    )
+    from .inference._generated._async_client import AsyncInferenceClient  # noqa: F401
+    from .inference._generated.types import (
+        AudioClassificationInput,  # noqa: F401
+        AudioClassificationOutputElement,  # noqa: F401
+        AudioClassificationOutputTransform,  # noqa: F401
+        AudioClassificationParameters,  # noqa: F401
+        AudioToAudioInput,  # noqa: F401
+        AudioToAudioOutputElement,  # noqa: F401
+        AutomaticSpeechRecognitionEarlyStoppingEnum,  # noqa: F401
+        AutomaticSpeechRecognitionGenerationParameters,  # noqa: F401
+        AutomaticSpeechRecognitionInput,  # noqa: F401
+        AutomaticSpeechRecognitionOutput,  # noqa: F401
+        AutomaticSpeechRecognitionOutputChunk,  # noqa: F401
+        AutomaticSpeechRecognitionParameters,  # noqa: F401
+        ChatCompletionInput,  # noqa: F401
+        ChatCompletionInputFunctionDefinition,  # noqa: F401
+        ChatCompletionInputFunctionName,  # noqa: F401
+        ChatCompletionInputGrammarType,  # noqa: F401
+        ChatCompletionInputJSONSchema,  # noqa: F401
+        ChatCompletionInputMessage,  # noqa: F401
+        ChatCompletionInputMessageChunk,  # noqa: F401
+        ChatCompletionInputMessageChunkType,  # noqa: F401
+        ChatCompletionInputResponseFormatJSONObject,  # noqa: F401
+        ChatCompletionInputResponseFormatJSONSchema,  # noqa: F401
+        ChatCompletionInputResponseFormatText,  # noqa: F401
+        ChatCompletionInputStreamOptions,  # noqa: F401
+        ChatCompletionInputTool,  # noqa: F401
+        ChatCompletionInputToolCall,  # noqa: F401
+        ChatCompletionInputToolChoiceClass,  # noqa: F401
+        ChatCompletionInputToolChoiceEnum,  # noqa: F401
+        ChatCompletionInputURL,  # noqa: F401
+        ChatCompletionOutput,  # noqa: F401
+        ChatCompletionOutputComplete,  # noqa: F401
+        ChatCompletionOutputFunctionDefinition,  # noqa: F401
+        ChatCompletionOutputLogprob,  # noqa: F401
+        ChatCompletionOutputLogprobs,  # noqa: F401
+        ChatCompletionOutputMessage,  # noqa: F401
+        ChatCompletionOutputToolCall,  # noqa: F401
+        ChatCompletionOutputTopLogprob,  # noqa: F401
+        ChatCompletionOutputUsage,  # noqa: F401
+        ChatCompletionStreamOutput,  # noqa: F401
+        ChatCompletionStreamOutputChoice,  # noqa: F401
+        ChatCompletionStreamOutputDelta,  # noqa: F401
+        ChatCompletionStreamOutputDeltaToolCall,  # noqa: F401
+        ChatCompletionStreamOutputFunction,  # noqa: F401
+        ChatCompletionStreamOutputLogprob,  # noqa: F401
+        ChatCompletionStreamOutputLogprobs,  # noqa: F401
+        ChatCompletionStreamOutputTopLogprob,  # noqa: F401
+        ChatCompletionStreamOutputUsage,  # noqa: F401
+        DepthEstimationInput,  # noqa: F401
+        DepthEstimationOutput,  # noqa: F401
+        DocumentQuestionAnsweringInput,  # noqa: F401
+        DocumentQuestionAnsweringInputData,  # noqa: F401
+        DocumentQuestionAnsweringOutputElement,  # noqa: F401
+        DocumentQuestionAnsweringParameters,  # noqa: F401
+        FeatureExtractionInput,  # noqa: F401
+        FeatureExtractionInputTruncationDirection,  # noqa: F401
+        FillMaskInput,  # noqa: F401
+        FillMaskOutputElement,  # noqa: F401
+        FillMaskParameters,  # noqa: F401
+        ImageClassificationInput,  # noqa: F401
+        ImageClassificationOutputElement,  # noqa: F401
+        ImageClassificationOutputTransform,  # noqa: F401
+        ImageClassificationParameters,  # noqa: F401
+        ImageSegmentationInput,  # noqa: F401
+        ImageSegmentationOutputElement,  # noqa: F401
+        ImageSegmentationParameters,  # noqa: F401
+        ImageSegmentationSubtask,  # noqa: F401
+        ImageTextToImageInput,  # noqa: F401
+        ImageTextToImageOutput,  # noqa: F401
+        ImageTextToImageParameters,  # noqa: F401
+        ImageTextToImageTargetSize,  # noqa: F401
+        ImageTextToVideoInput,  # noqa: F401
+        ImageTextToVideoOutput,  # noqa: F401
+        ImageTextToVideoParameters,  # noqa: F401
+        ImageTextToVideoTargetSize,  # noqa: F401
+        ImageToImageInput,  # noqa: F401
+        ImageToImageOutput,  # noqa: F401
+        ImageToImageParameters,  # noqa: F401
+        ImageToImageTargetSize,  # noqa: F401
+        ImageToTextEarlyStoppingEnum,  # noqa: F401
+        ImageToTextGenerationParameters,  # noqa: F401
+        ImageToTextInput,  # noqa: F401
+        ImageToTextOutput,  # noqa: F401
+        ImageToTextParameters,  # noqa: F401
+        ImageToVideoInput,  # noqa: F401
+        ImageToVideoOutput,  # noqa: F401
+        ImageToVideoParameters,  # noqa: F401
+        ImageToVideoTargetSize,  # noqa: F401
+        ObjectDetectionBoundingBox,  # noqa: F401
+        ObjectDetectionInput,  # noqa: F401
+        ObjectDetectionOutputElement,  # noqa: F401
+        ObjectDetectionParameters,  # noqa: F401
+        Padding,  # noqa: F401
+        QuestionAnsweringInput,  # noqa: F401
+        QuestionAnsweringInputData,  # noqa: F401
+        QuestionAnsweringOutputElement,  # noqa: F401
+        QuestionAnsweringParameters,  # noqa: F401
+        SentenceSimilarityInput,  # noqa: F401
+        SentenceSimilarityInputData,  # noqa: F401
+        SummarizationInput,  # noqa: F401
+        SummarizationOutput,  # noqa: F401
+        SummarizationParameters,  # noqa: F401
+        SummarizationTruncationStrategy,  # noqa: F401
+        TableQuestionAnsweringInput,  # noqa: F401
+        TableQuestionAnsweringInputData,  # noqa: F401
+        TableQuestionAnsweringOutputElement,  # noqa: F401
+        TableQuestionAnsweringParameters,  # noqa: F401
+        Text2TextGenerationInput,  # noqa: F401
+        Text2TextGenerationOutput,  # noqa: F401
+        Text2TextGenerationParameters,  # noqa: F401
+        Text2TextGenerationTruncationStrategy,  # noqa: F401
+        TextClassificationInput,  # noqa: F401
+        TextClassificationOutputElement,  # noqa: F401
+        TextClassificationOutputTransform,  # noqa: F401
+        TextClassificationParameters,  # noqa: F401
+        TextGenerationInput,  # noqa: F401
+        TextGenerationInputGenerateParameters,  # noqa: F401
+        TextGenerationInputGrammarType,  # noqa: F401
+        TextGenerationOutput,  # noqa: F401
+        TextGenerationOutputBestOfSequence,  # noqa: F401
+        TextGenerationOutputDetails,  # noqa: F401
+        TextGenerationOutputFinishReason,  # noqa: F401
+        TextGenerationOutputPrefillToken,  # noqa: F401
+        TextGenerationOutputToken,  # noqa: F401
+        TextGenerationStreamOutput,  # noqa: F401
+        TextGenerationStreamOutputStreamDetails,  # noqa: F401
+        TextGenerationStreamOutputToken,  # noqa: F401
+        TextToAudioEarlyStoppingEnum,  # noqa: F401
+        TextToAudioGenerationParameters,  # noqa: F401
+        TextToAudioInput,  # noqa: F401
+        TextToAudioOutput,  # noqa: F401
+        TextToAudioParameters,  # noqa: F401
+        TextToImageInput,  # noqa: F401
+        TextToImageOutput,  # noqa: F401
+        TextToImageParameters,  # noqa: F401
+        TextToSpeechEarlyStoppingEnum,  # noqa: F401
+        TextToSpeechGenerationParameters,  # noqa: F401
+        TextToSpeechInput,  # noqa: F401
+        TextToSpeechOutput,  # noqa: F401
+        TextToSpeechParameters,  # noqa: F401
+        TextToVideoInput,  # noqa: F401
+        TextToVideoOutput,  # noqa: F401
+        TextToVideoParameters,  # noqa: F401
+        TokenClassificationAggregationStrategy,  # noqa: F401
+        TokenClassificationInput,  # noqa: F401
+        TokenClassificationOutputElement,  # noqa: F401
+        TokenClassificationParameters,  # noqa: F401
+        TranslationInput,  # noqa: F401
+        TranslationOutput,  # noqa: F401
+        TranslationParameters,  # noqa: F401
+        TranslationTruncationStrategy,  # noqa: F401
+        TypeEnum,  # noqa: F401
+        VideoClassificationInput,  # noqa: F401
+        VideoClassificationOutputElement,  # noqa: F401
+        VideoClassificationOutputTransform,  # noqa: F401
+        VideoClassificationParameters,  # noqa: F401
+        VisualQuestionAnsweringInput,  # noqa: F401
+        VisualQuestionAnsweringInputData,  # noqa: F401
+        VisualQuestionAnsweringOutputElement,  # noqa: F401
+        VisualQuestionAnsweringParameters,  # noqa: F401
+        ZeroShotClassificationInput,  # noqa: F401
+        ZeroShotClassificationOutputElement,  # noqa: F401
+        ZeroShotClassificationParameters,  # noqa: F401
+        ZeroShotImageClassificationInput,  # noqa: F401
+        ZeroShotImageClassificationOutputElement,  # noqa: F401
+        ZeroShotImageClassificationParameters,  # noqa: F401
+        ZeroShotObjectDetectionBoundingBox,  # noqa: F401
+        ZeroShotObjectDetectionInput,  # noqa: F401
+        ZeroShotObjectDetectionOutputElement,  # noqa: F401
+        ZeroShotObjectDetectionParameters,  # noqa: F401
+    )
+    from .inference._mcp.agent import Agent  # noqa: F401
+    from .inference._mcp.mcp_client import MCPClient  # noqa: F401
+    from .repocard import (
+        DatasetCard,  # noqa: F401
+        ModelCard,  # noqa: F401
+        RepoCard,  # noqa: F401
+        SpaceCard,  # noqa: F401
+        metadata_eval_result,  # noqa: F401
+        metadata_load,  # noqa: F401
+        metadata_save,  # noqa: F401
+        metadata_update,  # noqa: F401
+    )
+    from .repocard_data import (
+        CardData,  # noqa: F401
+        DatasetCardData,  # noqa: F401
+        EvalResult,  # noqa: F401
+        ModelCardData,  # noqa: F401
+        SpaceCardData,  # noqa: F401
+    )
+    from .serialization import (
+        StateDictSplit,  # noqa: F401
+        get_torch_storage_id,  # noqa: F401
+        get_torch_storage_size,  # noqa: F401
+        load_state_dict_from_file,  # noqa: F401
+        load_torch_model,  # noqa: F401
+        save_torch_model,  # noqa: F401
+        save_torch_state_dict,  # noqa: F401
+        split_state_dict_into_shards_factory,  # noqa: F401
+        split_torch_state_dict_into_shards,  # noqa: F401
+    )
+    from .serialization._dduf import (
+        DDUFEntry,  # noqa: F401
+        export_entries_as_dduf,  # noqa: F401
+        export_folder_as_dduf,  # noqa: F401
+        read_dduf_file,  # noqa: F401
+    )
+    from .utils import (
+        ASYNC_CLIENT_FACTORY_T,  # noqa: F401
+        CLIENT_FACTORY_T,  # noqa: F401
+        CachedFileInfo,  # noqa: F401
+        CachedRepoInfo,  # noqa: F401
+        CachedRevisionInfo,  # noqa: F401
+        CacheNotFound,  # noqa: F401
+        CorruptedCacheException,  # noqa: F401
+        DeleteCacheStrategy,  # noqa: F401
+        HFCacheInfo,  # noqa: F401
+        cached_assets_path,  # noqa: F401
+        close_session,  # noqa: F401
+        dump_environment_info,  # noqa: F401
+        get_async_session,  # noqa: F401
+        get_session,  # noqa: F401
+        get_token,  # noqa: F401
+        hf_raise_for_status,  # noqa: F401
+        logging,  # noqa: F401
+        scan_cache_dir,  # noqa: F401
+        set_async_client_factory,  # noqa: F401
+        set_client_factory,  # noqa: F401
+    )

data/conda_envs/evalclaw/lib/python3.11/site-packages/huggingface_hub/_buckets.py

@@ -0,0 +1,1177 @@
+# coding=utf-8
+# Copyright 2026-present, the HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Shared logic for bucket operations.
+
+This module contains the core buckets logic used by both the CLI and the Python API.
+"""
+
+import fnmatch
+import json
+import mimetypes
+import os
+import sys
+import time
+from dataclasses import dataclass, field
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import TYPE_CHECKING, Any, Iterator, Literal, Optional, Union
+
+from . import constants, logging
+from .errors import BucketNotFoundError
+from .utils import XetFileData, disable_progress_bars, enable_progress_bars, parse_datetime
+from .utils._terminal import StatusLine
+
+
+if TYPE_CHECKING:
+    from .hf_api import HfApi
+
+
+logger = logging.get_logger(__name__)
+
+
+BUCKET_PREFIX = "hf://buckets/"
+_SYNC_TIME_WINDOW_MS = 1000  # 1s safety-window for file modification time comparisons
+
+
+# =============================================================================
+# Bucket data structures
+# =============================================================================
+
+
+def _split_bucket_id_and_prefix(path: str) -> tuple[str, str]:
+    """Split 'namespace/name(/optional/prefix)' into ('namespace/name', 'prefix').
+
+    Returns (bucket_id, prefix) where prefix may be empty string.
+    Raises ValueError if path doesn't contain at least namespace/name.
+    """
+    parts = path.split("/", 2)
+    if len(parts) < 2 or not parts[0] or not parts[1]:
+        raise ValueError(f"Invalid bucket path: '{path}'. Expected format: namespace/bucket_name")
+    bucket_id = f"{parts[0]}/{parts[1]}"
+    prefix = parts[2] if len(parts) > 2 else ""
+    return bucket_id, prefix
+
+
+@dataclass
+class BucketInfo:
+    """
+    Contains information about a bucket on the Hub. This object is returned by [`bucket_info`] and [`list_buckets`].
+
+    Attributes:
+        id (`str`):
+            ID of the bucket.
+        private (`bool`):
+            Is the bucket private.
+        created_at (`datetime`):
+            Date of creation of the bucket on the Hub.
+        size (`int`):
+            Size of the bucket in bytes.
+        total_files (`int`):
+            Total number of files in the bucket.
+    """
+
+    id: str
+    private: bool
+    created_at: datetime
+    size: int
+    total_files: int
+
+    def __init__(self, **kwargs):
+        self.id = kwargs.pop("id")
+        self.private = kwargs.pop("private")
+        self.created_at = parse_datetime(kwargs.pop("createdAt"))
+        self.size = kwargs.pop("size")
+        self.total_files = kwargs.pop("totalFiles")
+        self.__dict__.update(**kwargs)
+
+
+@dataclass
+class _BucketAddFile:
+    source: Union[str, Path, bytes]
+    destination: str
+
+    xet_hash: Optional[str] = field(default=None)
+    size: Optional[int] = field(default=None)
+    mtime: int = field(init=False)
+    content_type: Optional[str] = field(init=False)
+
+    def __post_init__(self) -> None:
+        self.content_type = None
+        if isinstance(self.source, (str, Path)):  # guess content type from source path
+            self.content_type = mimetypes.guess_type(self.source)[0]
+        if self.content_type is None:  # or default to destination path content type
+            self.content_type = mimetypes.guess_type(self.destination)[0]
+
+        self.mtime = int(
+            os.path.getmtime(self.source) * 1000 if not isinstance(self.source, bytes) else time.time() * 1000
+        )
+
+
+@dataclass
+class _BucketDeleteFile:
+    path: str
+
+
+@dataclass(frozen=True)
+class BucketFileMetadata:
+    """Data structure containing information about a file in a bucket.
+
+    Returned by [`get_bucket_file_metadata`].
+
+    Args:
+        size (`int`):
+            Size of the file in bytes.
+        xet_file_data (`XetFileData`):
+            Xet information for the file (hash and refresh route).
+    """
+
+    size: int
+    xet_file_data: XetFileData
+
+
+@dataclass
+class BucketUrl:
+    """Describes a bucket URL on the Hub.
+
+    `BucketUrl` is returned by [`create_bucket`]. At initialization, the URL is parsed to populate properties:
+    - endpoint (`str`)
+    - namespace (`str`)
+    - bucket_id (`str`)
+    - url (`str`)
+    - handle (`str`)
+
+    Args:
+        url (`str`):
+            String value of the bucket url.
+        endpoint (`str`, *optional*):
+            Endpoint of the Hub. Defaults to <https://huggingface.co>.
+    """
+
+    url: str
+    endpoint: str = ""
+    namespace: str = field(init=False)
+    bucket_id: str = field(init=False)
+    handle: str = field(init=False)
+
+    def __post_init__(self) -> None:
+        self.endpoint = self.endpoint or constants.ENDPOINT
+
+        # Parse URL: expected format is `{endpoint}/buckets/{namespace}/{bucket_name}`
+        url_path = self.url.replace(self.endpoint, "").strip("/")
+        # Remove leading "buckets/" prefix
+        if url_path.startswith("buckets/"):
+            url_path = url_path[len("buckets/") :]
+        bucket_id, prefix = _split_bucket_id_and_prefix(url_path)
+        if prefix:
+            raise ValueError(f"Unable to parse bucket URL: {self.url}")
+        self.namespace = bucket_id.split("/")[0]
+        self.bucket_id = bucket_id
+
+        self.handle = f"hf://buckets/{self.bucket_id}"
+
+
+@dataclass
+class BucketFile:
+    """
+    Contains information about a file in a bucket on the Hub. This object is returned by [`list_bucket_tree`].
+
+    Similar to [`RepoFile`] but for files in buckets.
+    """
+
+    type: Literal["file"]
+    path: str
+    size: int
+    xet_hash: str
+    mtime: Optional[datetime]
+    uploaded_at: Optional[datetime]
+
+    def __init__(self, **kwargs):
+        self.type = kwargs.pop("type")
+        self.path = kwargs.pop("path")
+        self.size = kwargs.pop("size")
+        self.xet_hash = kwargs.pop("xetHash")
+        mtime = kwargs.pop("mtime", None)
+        self.mtime = parse_datetime(mtime) if mtime else None
+        uploaded_at = kwargs.pop("uploadedAt", None)
+        self.uploaded_at = parse_datetime(uploaded_at) if uploaded_at else None
+
+
+@dataclass
+class BucketFolder:
+    """
+    Contains information about a directory in a bucket on the Hub. This object is returned by [`list_bucket_tree`].
+
+    Similar to [`RepoFolder`] but for directories in buckets.
+    """
+
+    type: Literal["directory"]
+    path: str
+    uploaded_at: Optional[datetime]
+
+    def __init__(self, **kwargs):
+        self.type = kwargs.pop("type")
+        self.path = kwargs.pop("path")
+        uploaded_at = kwargs.pop("uploadedAt", None) or kwargs.pop("uploaded_at", None)
+        self.uploaded_at = (
+            (uploaded_at if isinstance(uploaded_at, datetime) else parse_datetime(uploaded_at))
+            if uploaded_at
+            else None
+        )
+
+
+# =============================================================================
+# Bucket path parsing
+# =============================================================================
+
+
+def _parse_bucket_path(path: str) -> tuple[str, str]:
+    """Parse a bucket path like hf://buckets/namespace/bucket_name/prefix into (bucket_id, prefix).
+
+    Returns:
+        tuple: (bucket_id, prefix) where bucket_id is "namespace/bucket_name" and prefix may be empty string.
+    """
+    if not path.startswith(BUCKET_PREFIX):
+        raise ValueError(f"Invalid bucket path: {path}. Must start with {BUCKET_PREFIX}")
+    return _split_bucket_id_and_prefix(path.removeprefix(BUCKET_PREFIX))
+
+
+def _is_bucket_path(path: str) -> bool:
+    """Check if a path is a bucket path."""
+    return path.startswith(BUCKET_PREFIX)
+
+
+# =============================================================================
+# Sync data structures
+# =============================================================================
+
+
+@dataclass
+class SyncOperation:
+    """Represents a sync operation to be performed."""
+
+    action: Literal["upload", "download", "delete", "skip"]
+    path: str
+    size: Optional[int] = None
+    reason: str = ""
+    local_mtime: Optional[str] = None
+    remote_mtime: Optional[str] = None
+    bucket_file: Optional[BucketFile] = None  # BucketFile when available (not serialized to plan file)
+
+
+@dataclass
+class SyncPlan:
+    """Represents a complete sync plan."""
+
+    source: str
+    dest: str
+    timestamp: str
+    operations: list[SyncOperation] = field(default_factory=list)
+
+    def summary(self) -> dict[str, Union[int, str]]:
+        uploads = sum(1 for op in self.operations if op.action == "upload")
+        downloads = sum(1 for op in self.operations if op.action == "download")
+        deletes = sum(1 for op in self.operations if op.action == "delete")
+        skips = sum(1 for op in self.operations if op.action == "skip")
+        total_size = sum(op.size or 0 for op in self.operations if op.action in ("upload", "download"))
+        return {
+            "uploads": uploads,
+            "downloads": downloads,
+            "deletes": deletes,
+            "skips": skips,
+            "total_size": total_size,
+        }
+
+
+# =============================================================================
+# Filter matching
+# =============================================================================
+
+
+class FilterMatcher:
+    """Matches file paths against include/exclude patterns."""
+
+    def __init__(
+        self,
+        include_patterns: Optional[list[str]] = None,
+        exclude_patterns: Optional[list[str]] = None,
+        filter_rules: Optional[list[tuple[str, str]]] = None,
+    ):
+        """Initialize the filter matcher.
+
+        Args:
+            include_patterns: Patterns to include (from --include)
+            exclude_patterns: Patterns to exclude (from --exclude)
+            filter_rules: Rules from filter file as list of ("+"/"-", pattern) tuples
+        """
+        self.include_patterns = include_patterns or []
+        self.exclude_patterns = exclude_patterns or []
+        self.filter_rules = filter_rules or []
+
+    def matches(self, path: str) -> bool:
+        """Check if a path should be included based on the filter rules.
+
+        Filtering rules:
+        - Filters are evaluated in order, first matching rule decides
+        - If no rules match, include by default (unless include patterns are specified)
+        """
+        # First check filter rules from file (in order)
+        for sign, pattern in self.filter_rules:
+            if fnmatch.fnmatch(path, pattern):
+                return sign == "+"
+
+        # Then check CLI patterns
+        for pattern in self.exclude_patterns:
+            if fnmatch.fnmatch(path, pattern):
+                return False
+
+        for pattern in self.include_patterns:
+            if fnmatch.fnmatch(path, pattern):
+                return True
+
+        # If include patterns were specified but none matched, exclude
+        if self.include_patterns:
+            return False
+
+        # Default: include
+        return True
+
+
+def _parse_filter_file(filter_file: str) -> list[tuple[str, str]]:
+    """Parse a filter file and return a list of (sign, pattern) tuples.
+
+    Filter file format:
+    - Lines starting with "+" are include patterns
+    - Lines starting with "-" are exclude patterns
+    - Empty lines and lines starting with "#" are ignored
+    """
+    rules = []
+    with open(filter_file) as f:
+        for line in f:
+            line = line.strip()
+            if not line or line.startswith("#"):
+                continue
+            if line.startswith("+"):
+                rules.append(("+", line[1:].strip()))
+            elif line.startswith("-"):
+                rules.append(("-", line[1:].strip()))
+            else:
+                # Default to include if no prefix
+                rules.append(("+", line))
+    return rules
+
+
+# =============================================================================
+# File listing
+# =============================================================================
+
+
+def _list_local_files(local_path: str) -> Iterator[tuple[str, int, float]]:
+    """List all files in a local directory.
+
+    Yields:
+        tuple: (relative_path, size, mtime_ms) for each file
+    """
+    local_path = os.path.abspath(local_path)
+    if not os.path.isdir(local_path):
+        raise ValueError(f"Local path must be a directory: {local_path}")
+
+    for root, _, files in os.walk(local_path):
+        for filename in files:
+            full_path = os.path.join(root, filename)
+            rel_path = os.path.relpath(full_path, local_path)
+            # Normalize to forward slashes for consistency
+            rel_path = rel_path.replace(os.sep, "/")
+            size = os.path.getsize(full_path)
+            mtime_ms = os.path.getmtime(full_path) * 1000
+            yield rel_path, size, mtime_ms
+
+
+def _list_remote_files(api: "HfApi", bucket_id: str, prefix: str) -> Iterator[tuple[str, int, float, Any]]:
+    """List all files in a bucket with a given prefix.
+
+    Yields:
+        tuple: (relative_path, size, mtime_ms, bucket_file) for each file.
+            bucket_file is the BucketFile object from list_bucket_tree.
+    """
+    for item in api.list_bucket_tree(bucket_id, prefix=prefix or None, recursive=True):
+        if isinstance(item, BucketFolder):
+            continue
+        path = item.path
+        # Remove prefix from path to get relative path
+        # Only strip prefix if it's followed by "/" (directory boundary) or is exact match
+        if prefix:
+            if path.startswith(prefix + "/"):
+                rel_path = path[len(prefix) + 1 :]
+            elif path == prefix:
+                # Exact match: the file IS the prefix (e.g., single file download)
+                rel_path = path.rsplit("/", 1)[-1] if "/" in path else path
+            else:
+                # Path doesn't match prefix pattern (e.g., "submarine.txt" for prefix "sub")
+                # Skip this file - it was returned by the API but doesn't belong to this prefix
+                continue
+        else:
+            rel_path = path
+        mtime_ms = item.mtime.timestamp() * 1000 if item.mtime else 0
+        yield rel_path, item.size, mtime_ms, item
+
+
+# =============================================================================
+# Sync plan computation
+# =============================================================================
+
+
+def _mtime_to_iso(mtime_ms: float) -> str:
+    """Convert mtime in milliseconds to ISO format string."""
+    return datetime.fromtimestamp(mtime_ms / 1000, tz=timezone.utc).isoformat()
+
+
+def _compare_files_for_sync(
+    *,
+    path: str,
+    action: Literal["upload", "download"],
+    source_size: int,
+    source_mtime: float,
+    dest_size: int,
+    dest_mtime: float,
+    source_newer_label: str,
+    dest_newer_label: str,
+    ignore_sizes: bool,
+    ignore_times: bool,
+    ignore_existing: bool,
+    bucket_file: Optional[Any] = None,
+) -> SyncOperation:
+    """Compare source and dest files and return the appropriate sync operation.
+
+    This is a unified helper for both upload and download directions.
+
+    Args:
+        path: Relative file path
+        action: "upload" or "download"
+        source_size: Size of the source file (bytes)
+        source_mtime: Mtime of the source file (milliseconds)
+        dest_size: Size of the destination file (bytes)
+        dest_mtime: Mtime of the destination file (milliseconds)
+        source_newer_label: Label when source is newer (e.g., "local newer" or "remote newer")
+        dest_newer_label: Label when dest is newer (e.g., "remote newer" or "local newer")
+        ignore_sizes: Only compare mtime
+        ignore_times: Only compare size
+        ignore_existing: Skip files that exist on receiver
+        bucket_file: BucketFile object (for downloads only)
+
+    Returns:
+        SyncOperation describing the action to take
+    """
+    local_mtime_iso = _mtime_to_iso(source_mtime if action == "upload" else dest_mtime)
+    remote_mtime_iso = _mtime_to_iso(dest_mtime if action == "upload" else source_mtime)
+
+    base_kwargs: dict[str, Any] = {
+        "path": path,
+        "size": source_size,
+        "local_mtime": local_mtime_iso,
+        "remote_mtime": remote_mtime_iso,
+    }
+
+    if ignore_existing:
+        return SyncOperation(action="skip", reason="exists on receiver (--ignore-existing)", **base_kwargs)
+
+    size_differs = source_size != dest_size
+    source_newer = (source_mtime - dest_mtime) > _SYNC_TIME_WINDOW_MS
+
+    if ignore_sizes:
+        if source_newer:
+            return SyncOperation(action=action, reason=source_newer_label, bucket_file=bucket_file, **base_kwargs)
+        else:
+            dest_newer = (dest_mtime - source_mtime) > _SYNC_TIME_WINDOW_MS
+            skip_reason = dest_newer_label if dest_newer else "same mtime"
+            return SyncOperation(action="skip", reason=skip_reason, **base_kwargs)
+    elif ignore_times:
+        if size_differs:
+            return SyncOperation(action=action, reason="size differs", bucket_file=bucket_file, **base_kwargs)
+        else:
+            return SyncOperation(action="skip", reason="same size", **base_kwargs)
+    else:
+        if size_differs or source_newer:
+            reason = "size differs" if size_differs else source_newer_label
+            return SyncOperation(action=action, reason=reason, bucket_file=bucket_file, **base_kwargs)
+        else:
+            return SyncOperation(action="skip", reason="identical", **base_kwargs)
+
+
+def _compute_sync_plan(
+    source: str,
+    dest: str,
+    api: "HfApi",
+    delete: bool = False,
+    ignore_times: bool = False,
+    ignore_sizes: bool = False,
+    existing: bool = False,
+    ignore_existing: bool = False,
+    filter_matcher: Optional[FilterMatcher] = None,
+    status: Optional[Any] = None,
+) -> SyncPlan:
+    """Compute the sync plan by comparing source and destination.
+
+    Returns:
+        SyncPlan with all operations to be performed
+    """
+    filter_matcher = filter_matcher or FilterMatcher()
+    is_upload = not _is_bucket_path(source) and _is_bucket_path(dest)
+    is_download = _is_bucket_path(source) and not _is_bucket_path(dest)
+
+    if not is_upload and not is_download:
+        raise ValueError("One of source or dest must be a bucket path (hf://buckets/...) and the other must be local.")
+
+    plan = SyncPlan(
+        source=source,
+        dest=dest,
+        timestamp=datetime.now(timezone.utc).isoformat(),
+    )
+
+    remote_total: Optional[int] = None
+    if is_upload:
+        # Local -> Remote
+        local_path = os.path.abspath(source)
+        bucket_id, prefix = _parse_bucket_path(dest)
+
+        if not os.path.isdir(local_path):
+            raise ValueError(f"Source must be a directory: {local_path}")
+
+        # Get local and remote file lists
+        local_files = {}
+        for rel_path, size, mtime_ms in _list_local_files(local_path):
+            if filter_matcher.matches(rel_path):
+                local_files[rel_path] = (size, mtime_ms)
+            if status:
+                status.update(f"Scanning local directory ({len(local_files)} files)")
+        if status:
+            status.done(f"Scanning local directory ({len(local_files)} files)")
+
+        remote_files = {}
+        if status:
+            try:
+                remote_total = api.bucket_info(bucket_id).total_files
+            except Exception:
+                pass
+        try:
+            for rel_path, size, mtime_ms, _ in _list_remote_files(api, bucket_id, prefix):
+                if filter_matcher.matches(rel_path):
+                    remote_files[rel_path] = (size, mtime_ms)
+                if status:
+                    total_str = f"/{remote_total}" if remote_total is not None else ""
+                    status.update(f"Scanning remote bucket ({len(remote_files)}{total_str} files)")
+        except BucketNotFoundError:
+            # Bucket doesn't exist yet - this is expected for new uploads
+            logger.debug(f"Bucket '{bucket_id}' not found, treating as empty.")
+        if status:
+            status.done(f"Scanning remote bucket ({len(remote_files)} files)")
+
+        # Compare files
+        all_paths = set(local_files.keys()) | set(remote_files.keys())
+        if status:
+            status.done(f"Comparing files ({len(all_paths)} paths)")
+        for path in sorted(all_paths):
+            local_info = local_files.get(path)
+            remote_info = remote_files.get(path)
+
+            if local_info and not remote_info:
+                # New file
+                if existing:
+                    # --existing: skip new files
+                    plan.operations.append(
+                        SyncOperation(
+                            action="skip",
+                            path=path,
+                            size=local_info[0],
+                            reason="new file (--existing)",
+                            local_mtime=_mtime_to_iso(local_info[1]),
+                        )
+                    )
+                else:
+                    plan.operations.append(
+                        SyncOperation(
+                            action="upload",
+                            path=path,
+                            size=local_info[0],
+                            reason="new file",
+                            local_mtime=_mtime_to_iso(local_info[1]),
+                        )
+                    )
+            elif local_info and remote_info:
+                # File exists in both - use helper to determine action
+                local_size, local_mtime = local_info
+                remote_size, remote_mtime = remote_info
+                plan.operations.append(
+                    _compare_files_for_sync(
+                        path=path,
+                        action="upload",
+                        source_size=local_size,
+                        source_mtime=local_mtime,
+                        dest_size=remote_size,
+                        dest_mtime=remote_mtime,
+                        source_newer_label="local newer",
+                        dest_newer_label="remote newer",
+                        ignore_sizes=ignore_sizes,
+                        ignore_times=ignore_times,
+                        ignore_existing=ignore_existing,
+                    )
+                )
+            elif not local_info and remote_info and delete:
+                # File only in remote and --delete mode
+                plan.operations.append(
+                    SyncOperation(
+                        action="delete",
+                        path=path,
+                        size=remote_info[0],
+                        reason="not in source (--delete)",
+                        remote_mtime=_mtime_to_iso(remote_info[1]),
+                    )
+                )
+
+    else:
+        # Remote -> Local (download)
+        bucket_id, prefix = _parse_bucket_path(source)
+        local_path = os.path.abspath(dest)
+
+        # Get remote and local file lists
+        remote_files = {}
+        bucket_file_map: dict[str, Any] = {}
+        if status:
+            try:
+                remote_total = api.bucket_info(bucket_id).total_files
+            except Exception:
+                pass
+        for rel_path, size, mtime_ms, bucket_file in _list_remote_files(api, bucket_id, prefix):
+            if filter_matcher.matches(rel_path):
+                remote_files[rel_path] = (size, mtime_ms)
+                bucket_file_map[rel_path] = bucket_file
+            if status:
+                total_str = f"/{remote_total}" if remote_total is not None else ""
+                status.update(f"Scanning remote bucket ({len(remote_files)}{total_str} files)")
+        if status:
+            status.done(f"Scanning remote bucket ({len(remote_files)} files)")
+
+        local_files = {}
+        if os.path.isdir(local_path):
+            for rel_path, size, mtime_ms in _list_local_files(local_path):
+                if filter_matcher.matches(rel_path):
+                    local_files[rel_path] = (size, mtime_ms)
+                if status:
+                    status.update(f"Scanning local directory ({len(local_files)} files)")
+        if status:
+            status.done(f"Scanning local directory ({len(local_files)} files)")
+
+        # Compare files
+        all_paths = set(remote_files.keys()) | set(local_files.keys())
+        if status:
+            status.done(f"Comparing files ({len(all_paths)} paths)")
+        for path in sorted(all_paths):
+            remote_info = remote_files.get(path)
+            local_info = local_files.get(path)
+
+            if remote_info and not local_info:
+                # New file
+                if existing:
+                    # --existing: skip new files
+                    plan.operations.append(
+                        SyncOperation(
+                            action="skip",
+                            path=path,
+                            size=remote_info[0],
+                            reason="new file (--existing)",
+                            remote_mtime=_mtime_to_iso(remote_info[1]),
+                        )
+                    )
+                else:
+                    plan.operations.append(
+                        SyncOperation(
+                            action="download",
+                            path=path,
+                            size=remote_info[0],
+                            reason="new file",
+                            remote_mtime=_mtime_to_iso(remote_info[1]),
+                            bucket_file=bucket_file_map.get(path),
+                        )
+                    )
+            elif remote_info and local_info:
+                # File exists in both - use helper to determine action
+                remote_size, remote_mtime = remote_info
+                local_size, local_mtime = local_info
+                plan.operations.append(
+                    _compare_files_for_sync(
+                        path=path,
+                        action="download",
+                        source_size=remote_size,
+                        source_mtime=remote_mtime,
+                        dest_size=local_size,
+                        dest_mtime=local_mtime,
+                        source_newer_label="remote newer",
+                        dest_newer_label="local newer",
+                        ignore_sizes=ignore_sizes,
+                        ignore_times=ignore_times,
+                        ignore_existing=ignore_existing,
+                        bucket_file=bucket_file_map.get(path),
+                    )
+                )
+            elif not remote_info and local_info and delete:
+                # File only in local and --delete mode
+                plan.operations.append(
+                    SyncOperation(
+                        action="delete",
+                        path=path,
+                        size=local_info[0],
+                        reason="not in source (--delete)",
+                        local_mtime=_mtime_to_iso(local_info[1]),
+                    )
+                )
+
+    return plan
+
+
+# =============================================================================
+# Plan serialization
+# =============================================================================
+
+
+def _write_plan(plan: SyncPlan, f) -> None:
+    """Write a sync plan as JSONL to a file-like object."""
+    # Write header
+    header = {
+        "type": "header",
+        "source": plan.source,
+        "dest": plan.dest,
+        "timestamp": plan.timestamp,
+        "summary": plan.summary(),
+    }
+    f.write(json.dumps(header) + "\n")
+
+    # Write operations
+    for op in plan.operations:
+        op_dict: dict[str, Any] = {
+            "type": "operation",
+            "action": op.action,
+            "path": op.path,
+            "reason": op.reason,
+        }
+        if op.size is not None:
+            op_dict["size"] = op.size
+        if op.local_mtime is not None:
+            op_dict["local_mtime"] = op.local_mtime
+        if op.remote_mtime is not None:
+            op_dict["remote_mtime"] = op.remote_mtime
+        f.write(json.dumps(op_dict) + "\n")
+
+
+def _save_plan(plan: SyncPlan, plan_file: str) -> None:
+    """Save a sync plan to a JSONL file."""
+    with open(plan_file, "w") as f:
+        _write_plan(plan, f)
+
+
+def _load_plan(plan_file: str) -> SyncPlan:
+    """Load a sync plan from a JSONL file."""
+    with open(plan_file) as f:
+        lines = f.readlines()
+
+    if not lines:
+        raise ValueError(f"Empty plan file: {plan_file}")
+
+    # Parse header
+    header = json.loads(lines[0])
+    if header.get("type") != "header":
+        raise ValueError("Invalid plan file: expected header as first line")
+
+    plan = SyncPlan(
+        source=header["source"],
+        dest=header["dest"],
+        timestamp=header["timestamp"],
+    )
+
+    # Parse operations
+    for line in lines[1:]:
+        op_dict = json.loads(line)
+        if op_dict.get("type") != "operation":
+            continue
+        plan.operations.append(
+            SyncOperation(
+                action=op_dict["action"],
+                path=op_dict["path"],
+                size=op_dict.get("size"),
+                reason=op_dict.get("reason", ""),
+                local_mtime=op_dict.get("local_mtime"),
+                remote_mtime=op_dict.get("remote_mtime"),
+            )
+        )
+
+    return plan
+
+
+# =============================================================================
+# Plan execution
+# =============================================================================
+
+
+def _execute_plan(plan: SyncPlan, api: "HfApi", verbose: bool = False, status: Optional[Any] = None) -> None:
+    """Execute a sync plan."""
+    is_upload = not _is_bucket_path(plan.source) and _is_bucket_path(plan.dest)
+    is_download = _is_bucket_path(plan.source) and not _is_bucket_path(plan.dest)
+
+    if is_upload:
+        local_path = os.path.abspath(plan.source)
+        bucket_id, prefix = _parse_bucket_path(plan.dest)
+        prefix = prefix.rstrip("/")  # Avoid double slashes in remote paths
+
+        # Collect operations
+        add_files: list[tuple[Union[str, Path, bytes], str]] = []
+        delete_paths: list[str] = []
+
+        for op in plan.operations:
+            if op.action == "upload":
+                local_file = os.path.join(local_path, op.path)
+                remote_path = f"{prefix}/{op.path}" if prefix else op.path
+                if verbose:
+                    print(f"  Uploading: {op.path} ({op.reason})")
+                add_files.append((local_file, remote_path))
+            elif op.action == "delete":
+                remote_path = f"{prefix}/{op.path}" if prefix else op.path
+                if verbose:
+                    print(f"  Deleting: {op.path} ({op.reason})")
+                delete_paths.append(remote_path)
+            elif op.action == "skip" and verbose:
+                print(f"  Skipping: {op.path} ({op.reason})")
+
+        # Execute batch operations
+        if add_files or delete_paths:
+            if status:
+                parts = []
+                if add_files:
+                    parts.append(f"uploading {len(add_files)} files")
+                if delete_paths:
+                    parts.append(f"deleting {len(delete_paths)} files")
+                status.done(", ".join(parts).capitalize())
+            api.batch_bucket_files(
+                bucket_id,
+                add=add_files or None,
+                delete=delete_paths or None,
+            )
+
+    elif is_download:
+        bucket_id, prefix = _parse_bucket_path(plan.source)
+        prefix = prefix.rstrip("/")  # Avoid double slashes in remote paths
+        local_path = os.path.abspath(plan.dest)
+
+        # Ensure local directory exists
+        os.makedirs(local_path, exist_ok=True)
+
+        # Collect download operations
+        download_files: list[tuple[Union[str, BucketFile], Union[str, Path]]] = []
+        delete_files: list[str] = []
+
+        for op in plan.operations:
+            if op.action == "download":
+                local_file = os.path.join(local_path, op.path)
+                # Ensure parent directory exists
+                os.makedirs(os.path.dirname(local_file), exist_ok=True)
+                if verbose:
+                    print(f"  Downloading: {op.path} ({op.reason})")
+                # Use BucketFile when available (avoids extra metadata fetch per file)
+                if op.bucket_file is not None:
+                    download_files.append((op.bucket_file, local_file))
+                else:
+                    remote_path = f"{prefix}/{op.path}" if prefix else op.path
+                    download_files.append((remote_path, local_file))
+            elif op.action == "delete":
+                local_file = os.path.join(local_path, op.path)
+                if verbose:
+                    print(f"  Deleting: {op.path} ({op.reason})")
+                delete_files.append(local_file)
+            elif op.action == "skip" and verbose:
+                print(f"  Skipping: {op.path} ({op.reason})")
+
+        # Execute downloads
+        if len(download_files) > 0:
+            if status:
+                status.done(f"Downloading {len(download_files)} files")
+            api.download_bucket_files(bucket_id, download_files)
+
+        # Execute deletes
+        if status and delete_files:
+            status.done(f"Deleting {len(delete_files)} local files")
+        for file_path in delete_files:
+            if os.path.exists(file_path):
+                os.remove(file_path)
+                # Remove empty parent directories
+                parent = os.path.dirname(file_path)
+                while parent != local_path:
+                    try:
+                        os.rmdir(parent)
+                        parent = os.path.dirname(parent)
+                    except OSError:
+                        break
+
+
+def _print_plan_summary(plan: SyncPlan) -> None:
+    """Print a summary of the sync plan."""
+    summary = plan.summary()
+    print(f"Sync plan: {plan.source} -> {plan.dest}")
+    print(f"  Uploads: {summary['uploads']}")
+    print(f"  Downloads: {summary['downloads']}")
+    print(f"  Deletes: {summary['deletes']}")
+    print(f"  Skips: {summary['skips']}")
+
+
+# =============================================================================
+# Public sync function (Python API)
+# =============================================================================
+
+
+def sync_bucket_internal(
+    source: Optional[str] = None,
+    dest: Optional[str] = None,
+    *,
+    api: "HfApi",
+    delete: bool = False,
+    ignore_times: bool = False,
+    ignore_sizes: bool = False,
+    existing: bool = False,
+    ignore_existing: bool = False,
+    include: Optional[list[str]] = None,
+    exclude: Optional[list[str]] = None,
+    filter_from: Optional[str] = None,
+    plan: Optional[str] = None,
+    apply: Optional[str] = None,
+    dry_run: bool = False,
+    verbose: bool = False,
+    quiet: bool = False,
+    token: Union[bool, str, None] = None,
+) -> SyncPlan:
+    """Sync files between a local directory and a bucket.
+
+    This is equivalent to the ``hf buckets sync`` CLI command. One of ``source`` or ``dest`` must be a bucket path
+    (``hf://buckets/...``) and the other must be a local directory path.
+
+    Args:
+        source (`str`, *optional*):
+            Source path: local directory or ``hf://buckets/namespace/bucket_name(/prefix)``.
+            Required unless using ``apply``.
+        dest (`str`, *optional*):
+            Destination path: local directory or ``hf://buckets/namespace/bucket_name(/prefix)``.
+            Required unless using ``apply``.
+        api ([`HfApi`]):
+            The HfApi instance to use for API calls.
+        delete (`bool`, *optional*, defaults to `False`):
+            Delete destination files not present in source.
+        ignore_times (`bool`, *optional*, defaults to `False`):
+            Skip files only based on size, ignoring modification times.
+        ignore_sizes (`bool`, *optional*, defaults to `False`):
+            Skip files only based on modification times, ignoring sizes.
+        existing (`bool`, *optional*, defaults to `False`):
+            Skip creating new files on receiver (only update existing files).
+        ignore_existing (`bool`, *optional*, defaults to `False`):
+            Skip updating files that exist on receiver (only create new files).
+        include (`list[str]`, *optional*):
+            Include files matching patterns (fnmatch-style).
+        exclude (`list[str]`, *optional*):
+            Exclude files matching patterns (fnmatch-style).
+        filter_from (`str`, *optional*):
+            Path to a filter file with include/exclude rules.
+        plan (`str`, *optional*):
+            Save sync plan to this JSONL file instead of executing.
+        apply (`str`, *optional*):
+            Apply a previously saved plan file. When set, ``source`` and ``dest`` are not needed.
+        dry_run (`bool`, *optional*, defaults to `False`):
+            Print sync plan to stdout as JSONL without executing.
+        verbose (`bool`, *optional*, defaults to `False`):
+            Show detailed per-file operations.
+        quiet (`bool`, *optional*, defaults to `False`):
+            Suppress all output and progress bars.
+        token (Union[bool, str, None], optional):
+            A valid user access token. If not provided, the locally saved token will be used.
+
+    Returns:
+        [`SyncPlan`]: The computed (or loaded) sync plan.
+
+    Raises:
+        `ValueError`: If arguments are invalid (e.g., both paths are remote, conflicting options).
+
+    Example:
+        ```python
+        >>> from huggingface_hub import HfApi
+        >>> api = HfApi()
+
+        # Upload local directory to bucket
+        >>> api.sync_bucket("./data", "hf://buckets/username/my-bucket")
+
+        # Download bucket to local directory
+        >>> api.sync_bucket("hf://buckets/username/my-bucket", "./data")
+
+        # Sync with delete and filtering
+        >>> api.sync_bucket(
+        ...     "./data",
+        ...     "hf://buckets/username/my-bucket",
+        ...     delete=True,
+        ...     include=["*.safetensors"],
+        ... )
+
+        # Dry run: preview what would be synced
+        >>> plan = api.sync_bucket("./data", "hf://buckets/username/my-bucket", dry_run=True)
+        >>> plan.summary()
+        {'uploads': 3, 'downloads': 0, 'deletes': 0, 'skips': 1, 'total_size': 4096}
+
+        # Save plan for review, then apply
+        >>> api.sync_bucket("./data", "hf://buckets/username/my-bucket", plan="sync-plan.jsonl")
+        >>> api.sync_bucket(apply="sync-plan.jsonl")
+        ```
+    """
+    # Build API with token if needed
+    if token is not None:
+        from .hf_api import HfApi
+
+        api = HfApi(token=token)
+    # --- Apply mode ---
+    if apply:
+        if source or dest:
+            raise ValueError("Cannot specify source/dest when using apply.")
+        if plan is not None:
+            raise ValueError("Cannot specify both plan and apply.")
+        if delete:
+            raise ValueError("Cannot specify delete when using apply.")
+        if ignore_times:
+            raise ValueError("Cannot specify ignore_times when using apply.")
+        if ignore_sizes:
+            raise ValueError("Cannot specify ignore_sizes when using apply.")
+        if include:
+            raise ValueError("Cannot specify include when using apply.")
+        if exclude:
+            raise ValueError("Cannot specify exclude when using apply.")
+        if filter_from:
+            raise ValueError("Cannot specify filter_from when using apply.")
+        if existing:
+            raise ValueError("Cannot specify existing when using apply.")
+        if ignore_existing:
+            raise ValueError("Cannot specify ignore_existing when using apply.")
+        if dry_run:
+            raise ValueError("Cannot specify dry_run when using apply.")
+
+        sync_plan = _load_plan(apply)
+        status = StatusLine(enabled=not quiet)
+        if not quiet:
+            _print_plan_summary(sync_plan)
+            print("Executing plan...")
+
+        if quiet:
+            disable_progress_bars()
+        try:
+            _execute_plan(sync_plan, api, verbose=verbose, status=status)
+        finally:
+            if quiet:
+                enable_progress_bars()
+
+        if not quiet:
+            print("Sync completed.")
+
+        return sync_plan
+
+    # --- Normal mode ---
+    if not source or not dest:
+        raise ValueError("Both source and dest are required (unless using apply).")
+
+    source_is_bucket = _is_bucket_path(source)
+    dest_is_bucket = _is_bucket_path(dest)
+
+    if source_is_bucket and dest_is_bucket:
+        raise ValueError("Remote to remote sync is not supported. One path must be local.")
+
+    if not source_is_bucket and not dest_is_bucket:
+        raise ValueError("One of source or dest must be a bucket path (hf://buckets/...).")
+
+    if ignore_times and ignore_sizes:
+        raise ValueError("Cannot specify both ignore_times and ignore_sizes.")
+
+    if existing and ignore_existing:
+        raise ValueError("Cannot specify both existing and ignore_existing.")
+
+    if dry_run and plan:
+        raise ValueError("Cannot specify both dry_run and plan.")
+
+    # Validate local path
+    if source_is_bucket:
+        if os.path.exists(dest) and not os.path.isdir(dest):
+            raise ValueError(f"Destination must be a directory: {dest}")
+    else:
+        if not os.path.isdir(source):
+            raise ValueError(f"Source must be an existing directory: {source}")
+
+    # Build filter matcher
+    filter_rules = None
+    if filter_from:
+        filter_rules = _parse_filter_file(filter_from)
+
+    filter_matcher = FilterMatcher(
+        include_patterns=include,
+        exclude_patterns=exclude,
+        filter_rules=filter_rules,
+    )
+
+    # Compute sync plan
+    status = StatusLine(enabled=not quiet and not dry_run)
+    sync_plan = _compute_sync_plan(
+        source=source,
+        dest=dest,
+        api=api,
+        delete=delete,
+        ignore_times=ignore_times,
+        ignore_sizes=ignore_sizes,
+        existing=existing,
+        ignore_existing=ignore_existing,
+        filter_matcher=filter_matcher,
+        status=status,
+    )
+
+    if dry_run:
+        _write_plan(sync_plan, sys.stdout)
+        return sync_plan
+
+    if plan:
+        _save_plan(sync_plan, plan)
+        if not quiet:
+            _print_plan_summary(sync_plan)
+            print(f"Plan saved to: {plan}")
+        return sync_plan
+
+    # Execute plan
+    if not quiet:
+        _print_plan_summary(sync_plan)
+
+    summary = sync_plan.summary()
+    if summary["uploads"] == 0 and summary["downloads"] == 0 and summary["deletes"] == 0:
+        if not quiet:
+            print("Nothing to sync.")
+        return sync_plan
+
+    if not quiet:
+        print("Syncing...")
+
+    if quiet:
+        disable_progress_bars()
+    try:
+        _execute_plan(sync_plan, api, verbose=verbose, status=status)
+    finally:
+        if quiet:
+            enable_progress_bars()
+
+    if not quiet:
+        print("Sync completed.")
+
+    return sync_plan

data/conda_envs/evalclaw/lib/python3.11/site-packages/huggingface_hub/_commit_api.py

@@ -0,0 +1,975 @@
+"""
+Type definitions and utilities for the `create_commit` API
+"""
+
+import base64
+import io
+import os
+import warnings
+from collections import defaultdict
+from contextlib import contextmanager
+from dataclasses import dataclass, field
+from itertools import groupby
+from pathlib import Path, PurePosixPath
+from typing import TYPE_CHECKING, Any, BinaryIO, Iterable, Iterator, Literal, Optional, Union
+
+from tqdm.contrib.concurrent import thread_map
+
+from . import constants
+from .errors import EntryNotFoundError, HfHubHTTPError, XetAuthorizationError, XetRefreshTokenError
+from .file_download import hf_hub_url
+from .lfs import UploadInfo, lfs_upload, post_lfs_batch_info
+from .utils import (
+    FORBIDDEN_FOLDERS,
+    XetTokenType,
+    are_progress_bars_disabled,
+    chunk_iterable,
+    fetch_xet_connection_info_from_repo_info,
+    get_session,
+    hf_raise_for_status,
+    http_backoff,
+    logging,
+    sha,
+    tqdm_stream_file,
+    validate_hf_hub_args,
+)
+from .utils import tqdm as hf_tqdm
+from .utils._runtime import is_xet_available
+
+
+if TYPE_CHECKING:
+    from .hf_api import RepoFile
+
+
+logger = logging.get_logger(__name__)
+
+
+UploadMode = Literal["lfs", "regular"]
+
+# Max is 1,000 per request on the Hub for HfApi.get_paths_info
+# Otherwise we get:
+# HfHubHTTPError: 413 Client Error: Payload Too Large for url: https://huggingface.co/api/datasets/xxx (Request ID: xxx)\n\ntoo many parameters
+# See https://github.com/huggingface/huggingface_hub/issues/1503
+FETCH_LFS_BATCH_SIZE = 500
+
+UPLOAD_BATCH_MAX_NUM_FILES = 256
+
+
+@dataclass
+class CommitOperationDelete:
+    """
+    Data structure holding necessary info to delete a file or a folder from a repository
+    on the Hub.
+
+    Args:
+        path_in_repo (`str`):
+            Relative filepath in the repo, for example: `"checkpoints/1fec34a/weights.bin"`
+            for a file or `"checkpoints/1fec34a/"` for a folder.
+        is_folder (`bool` or `Literal["auto"]`, *optional*)
+            Whether the Delete Operation applies to a folder or not. If "auto", the path
+            type (file or folder) is guessed automatically by looking if path ends with
+            a "/" (folder) or not (file). To explicitly set the path type, you can set
+            `is_folder=True` or `is_folder=False`.
+    """
+
+    path_in_repo: str
+    is_folder: Union[bool, Literal["auto"]] = "auto"
+
+    def __post_init__(self):
+        self.path_in_repo = _validate_path_in_repo(self.path_in_repo)
+
+        if self.is_folder == "auto":
+            self.is_folder = self.path_in_repo.endswith("/")
+        if not isinstance(self.is_folder, bool):
+            raise ValueError(
+                f"Wrong value for `is_folder`. Must be one of [`True`, `False`, `'auto'`]. Got '{self.is_folder}'."
+            )
+
+
+@dataclass
+class CommitOperationCopy:
+    """
+    Data structure holding necessary info to copy a file in a repository on the Hub.
+
+    Limitations:
+      - Only LFS files can be copied. To copy a regular file, you need to download it locally and re-upload it
+      - Cross-repository copies are not supported.
+
+    Note: you can combine a [`CommitOperationCopy`] and a [`CommitOperationDelete`] to rename an LFS file on the Hub.
+
+    Args:
+        src_path_in_repo (`str`):
+            Relative filepath in the repo of the file to be copied, e.g. `"checkpoints/1fec34a/weights.bin"`.
+        path_in_repo (`str`):
+            Relative filepath in the repo where to copy the file, e.g. `"checkpoints/1fec34a/weights_copy.bin"`.
+        src_revision (`str`, *optional*):
+            The git revision of the file to be copied. Can be any valid git revision.
+            Default to the target commit revision.
+    """
+
+    src_path_in_repo: str
+    path_in_repo: str
+    src_revision: Optional[str] = None
+    # set to the OID of the file to be copied if it has already been uploaded
+    # useful to determine if a commit will be empty or not.
+    _src_oid: Optional[str] = None
+    # set to the OID of the file to copy to if it has already been uploaded
+    # useful to determine if a commit will be empty or not.
+    _dest_oid: Optional[str] = None
+
+    def __post_init__(self):
+        self.src_path_in_repo = _validate_path_in_repo(self.src_path_in_repo)
+        self.path_in_repo = _validate_path_in_repo(self.path_in_repo)
+
+
+@dataclass
+class CommitOperationAdd:
+    """
+    Data structure holding necessary info to upload a file to a repository on the Hub.
+
+    Args:
+        path_in_repo (`str`):
+            Relative filepath in the repo, for example: `"checkpoints/1fec34a/weights.bin"`
+        path_or_fileobj (`str`, `Path`, `bytes`, or `BinaryIO`):
+            Either:
+            - a path to a local file (as `str` or `pathlib.Path`) to upload
+            - a buffer of bytes (`bytes`) holding the content of the file to upload
+            - a "file object" (subclass of `io.BufferedIOBase`), typically obtained
+                with `open(path, "rb")`. It must support `seek()` and `tell()` methods.
+
+    Raises:
+        [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError)
+            If `path_or_fileobj` is not one of `str`, `Path`, `bytes` or `io.BufferedIOBase`.
+        [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError)
+            If `path_or_fileobj` is a `str` or `Path` but not a path to an existing file.
+        [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError)
+            If `path_or_fileobj` is a `io.BufferedIOBase` but it doesn't support both
+            `seek()` and `tell()`.
+    """
+
+    path_in_repo: str
+    path_or_fileobj: Union[str, Path, bytes, BinaryIO]
+    upload_info: UploadInfo = field(init=False, repr=False)
+
+    # Internal attributes
+
+    # set to "lfs" or "regular" once known
+    _upload_mode: Optional[UploadMode] = field(init=False, repr=False, default=None)
+
+    # set to True if .gitignore rules prevent the file from being uploaded as LFS
+    # (server-side check)
+    _should_ignore: Optional[bool] = field(init=False, repr=False, default=None)
+
+    # set to the remote OID of the file if it has already been uploaded
+    # useful to determine if a commit will be empty or not
+    _remote_oid: Optional[str] = field(init=False, repr=False, default=None)
+
+    # set to True once the file has been uploaded as LFS
+    _is_uploaded: bool = field(init=False, repr=False, default=False)
+
+    # set to True once the file has been committed
+    _is_committed: bool = field(init=False, repr=False, default=False)
+
+    def __post_init__(self) -> None:
+        """Validates `path_or_fileobj` and compute `upload_info`."""
+        self.path_in_repo = _validate_path_in_repo(self.path_in_repo)
+
+        # Validate `path_or_fileobj` value
+        if isinstance(self.path_or_fileobj, Path):
+            self.path_or_fileobj = str(self.path_or_fileobj)
+        if isinstance(self.path_or_fileobj, str):
+            path_or_fileobj = os.path.normpath(os.path.expanduser(self.path_or_fileobj))
+            if not os.path.isfile(path_or_fileobj):
+                raise ValueError(f"Provided path: '{path_or_fileobj}' is not a file on the local file system")
+        elif not isinstance(self.path_or_fileobj, (io.BufferedIOBase, bytes)):
+            # ^^ Inspired from: https://stackoverflow.com/questions/44584829/how-to-determine-if-file-is-opened-in-binary-or-text-mode
+            raise ValueError(
+                "path_or_fileobj must be either an instance of str, bytes or"
+                " io.BufferedIOBase. If you passed a file-like object, make sure it is"
+                " in binary mode."
+            )
+        if isinstance(self.path_or_fileobj, io.BufferedIOBase):
+            try:
+                self.path_or_fileobj.tell()
+                self.path_or_fileobj.seek(0, os.SEEK_CUR)
+            except (OSError, AttributeError) as exc:
+                raise ValueError(
+                    "path_or_fileobj is a file-like object but does not implement seek() and tell()"
+                ) from exc
+
+        # Compute "upload_info" attribute
+        if isinstance(self.path_or_fileobj, str):
+            self.upload_info = UploadInfo.from_path(self.path_or_fileobj)
+        elif isinstance(self.path_or_fileobj, bytes):
+            self.upload_info = UploadInfo.from_bytes(self.path_or_fileobj)
+        else:
+            self.upload_info = UploadInfo.from_fileobj(self.path_or_fileobj)
+
+    @contextmanager
+    def as_file(self, with_tqdm: bool = False) -> Iterator[BinaryIO]:
+        """
+        A context manager that yields a file-like object allowing to read the underlying
+        data behind `path_or_fileobj`.
+
+        Args:
+            with_tqdm (`bool`, *optional*, defaults to `False`):
+                If True, iterating over the file object will display a progress bar. Only
+                works if the file-like object is a path to a file. Pure bytes and buffers
+                are not supported.
+
+        Example:
+
+        ```python
+        >>> operation = CommitOperationAdd(
+        ...        path_in_repo="remote/dir/weights.h5",
+        ...        path_or_fileobj="./local/weights.h5",
+        ... )
+        CommitOperationAdd(path_in_repo='remote/dir/weights.h5', path_or_fileobj='./local/weights.h5')
+
+        >>> with operation.as_file() as file:
+        ...     content = file.read()
+
+        >>> with operation.as_file(with_tqdm=True) as file:
+        ...     while True:
+        ...         data = file.read(1024)
+        ...         if not data:
+        ...              break
+        config.json: 100%|█████████████████████████| 8.19k/8.19k [00:02<00:00, 3.72kB/s]
+
+        >>> with operation.as_file(with_tqdm=True) as file:
+        ...     httpx.put(..., data=file)
+        config.json: 100%|█████████████████████████| 8.19k/8.19k [00:02<00:00, 3.72kB/s]
+        ```
+        """
+        if isinstance(self.path_or_fileobj, str) or isinstance(self.path_or_fileobj, Path):
+            if with_tqdm:
+                with tqdm_stream_file(self.path_or_fileobj) as file:
+                    yield file
+            else:
+                with open(self.path_or_fileobj, "rb") as file:
+                    yield file
+        elif isinstance(self.path_or_fileobj, bytes):
+            yield io.BytesIO(self.path_or_fileobj)
+        elif isinstance(self.path_or_fileobj, io.BufferedIOBase):
+            prev_pos = self.path_or_fileobj.tell()
+            yield self.path_or_fileobj
+            self.path_or_fileobj.seek(prev_pos, io.SEEK_SET)
+
+    def b64content(self) -> bytes:
+        """
+        The base64-encoded content of `path_or_fileobj`
+
+        Returns: `bytes`
+        """
+        with self.as_file() as file:
+            return base64.b64encode(file.read())
+
+    @property
+    def _local_oid(self) -> Optional[str]:
+        """Return the OID of the local file.
+
+        This OID is then compared to `self._remote_oid` to check if the file has changed compared to the remote one.
+        If the file did not change, we won't upload it again to prevent empty commits.
+
+        For LFS files, the OID corresponds to the SHA256 of the file content (used a LFS ref).
+        For regular files, the OID corresponds to the SHA1 of the file content.
+        Note: this is slightly different to git OID computation since the oid of an LFS file is usually the git-SHA1 of the
+              pointer file content (not the actual file content). However, using the SHA256 is enough to detect changes
+              and more convenient client-side.
+        """
+        if self._upload_mode is None:
+            return None
+        elif self._upload_mode == "lfs":
+            return self.upload_info.sha256.hex()
+        else:
+            # Regular file => compute sha1
+            # => no need to read by chunk since the file is guaranteed to be <=5MB.
+            with self.as_file() as file:
+                return sha.git_hash(file.read())
+
+
+def _validate_path_in_repo(path_in_repo: str) -> str:
+    # Validate `path_in_repo` value to prevent a server-side issue
+    if path_in_repo.startswith("/"):
+        path_in_repo = path_in_repo[1:]
+    if path_in_repo == "." or path_in_repo == ".." or path_in_repo.startswith("../"):
+        raise ValueError(f"Invalid `path_in_repo` in CommitOperation: '{path_in_repo}'")
+    if path_in_repo.startswith("./"):
+        path_in_repo = path_in_repo[2:]
+    for forbidden in FORBIDDEN_FOLDERS:
+        if any(part == forbidden for part in path_in_repo.split("/")):
+            raise ValueError(
+                f"Invalid `path_in_repo` in CommitOperation: cannot update files under a '{forbidden}/' folder (path:"
+                f" '{path_in_repo}')."
+            )
+    return path_in_repo
+
+
+CommitOperation = Union[CommitOperationAdd, CommitOperationCopy, CommitOperationDelete]
+
+
+def _warn_on_overwriting_operations(operations: list[CommitOperation]) -> None:
+    """
+    Warn user when a list of operations is expected to overwrite itself in a single
+    commit.
+
+    Rules:
+    - If a filepath is updated by multiple `CommitOperationAdd` operations, a warning
+      message is triggered.
+    - If a filepath is updated at least once by a `CommitOperationAdd` and then deleted
+      by a `CommitOperationDelete`, a warning is triggered.
+    - If a `CommitOperationDelete` deletes a filepath that is then updated by a
+      `CommitOperationAdd`, no warning is triggered. This is usually useless (no need to
+      delete before upload) but can happen if a user deletes an entire folder and then
+      add new files to it.
+    """
+    nb_additions_per_path: dict[str, int] = defaultdict(int)
+    for operation in operations:
+        path_in_repo = operation.path_in_repo
+        if isinstance(operation, CommitOperationAdd):
+            if nb_additions_per_path[path_in_repo] > 0:
+                warnings.warn(
+                    "About to update multiple times the same file in the same commit:"
+                    f" '{path_in_repo}'. This can cause undesired inconsistencies in"
+                    " your repo."
+                )
+            nb_additions_per_path[path_in_repo] += 1
+            for parent in PurePosixPath(path_in_repo).parents:
+                # Also keep track of number of updated files per folder
+                # => warns if deleting a folder overwrite some contained files
+                nb_additions_per_path[str(parent)] += 1
+        if isinstance(operation, CommitOperationDelete):
+            if nb_additions_per_path[str(PurePosixPath(path_in_repo))] > 0:
+                if operation.is_folder:
+                    warnings.warn(
+                        "About to delete a folder containing files that have just been"
+                        f" updated within the same commit: '{path_in_repo}'. This can"
+                        " cause undesired inconsistencies in your repo."
+                    )
+                else:
+                    warnings.warn(
+                        "About to delete a file that have just been updated within the"
+                        f" same commit: '{path_in_repo}'. This can cause undesired"
+                        " inconsistencies in your repo."
+                    )
+
+
+@validate_hf_hub_args
+def _upload_files(
+    *,
+    additions: list[CommitOperationAdd],
+    repo_type: str,
+    repo_id: str,
+    headers: dict[str, str],
+    endpoint: Optional[str] = None,
+    num_threads: int = 5,
+    revision: Optional[str] = None,
+    create_pr: Optional[bool] = None,
+):
+    """
+    Negotiates per-file transfer (LFS vs Xet) and uploads in batches.
+    """
+    xet_additions: list[CommitOperationAdd] = []
+    lfs_actions: list[dict[str, Any]] = []
+    lfs_oid2addop: dict[str, CommitOperationAdd] = {}
+
+    for chunk in chunk_iterable(additions, chunk_size=UPLOAD_BATCH_MAX_NUM_FILES):
+        chunk_list = [op for op in chunk]
+
+        transfers: list[str] = ["basic", "multipart"]
+        has_buffered_io_data = any(isinstance(op.path_or_fileobj, io.BufferedIOBase) for op in chunk_list)
+        if is_xet_available():
+            if not has_buffered_io_data:
+                transfers.append("xet")
+            else:
+                logger.warning(
+                    "Uploading files as a binary IO buffer is not supported by Xet Storage. "
+                    "Falling back to HTTP upload."
+                )
+
+        actions_chunk, errors_chunk, chosen_transfer = post_lfs_batch_info(
+            upload_infos=[op.upload_info for op in chunk_list],
+            repo_id=repo_id,
+            repo_type=repo_type,
+            revision=revision,
+            endpoint=endpoint,
+            headers=headers,
+            token=None,  # already passed in 'headers'
+            transfers=transfers,
+        )
+        if errors_chunk:
+            message = "\n".join(
+                [
+                    f"Encountered error for file with OID {err.get('oid')}: `{err.get('error', {}).get('message')}"
+                    for err in errors_chunk
+                ]
+            )
+            raise ValueError(f"LFS batch API returned errors:\n{message}")
+
+        # If server returns a transfer we didn't offer (e.g "xet" while uploading from BytesIO),
+        # fall back to LFS for this chunk.
+        if chosen_transfer == "xet" and ("xet" in transfers):
+            xet_additions.extend(chunk_list)
+        else:
+            lfs_actions.extend(actions_chunk)
+            for op in chunk_list:
+                lfs_oid2addop[op.upload_info.sha256.hex()] = op
+
+    if len(lfs_actions) > 0:
+        _upload_lfs_files(
+            actions=lfs_actions,
+            oid2addop=lfs_oid2addop,
+            headers=headers,
+            endpoint=endpoint,
+            num_threads=num_threads,
+        )
+
+    if len(xet_additions) > 0:
+        _upload_xet_files(
+            additions=xet_additions,
+            repo_type=repo_type,
+            repo_id=repo_id,
+            headers=headers,
+            endpoint=endpoint,
+            revision=revision,
+            create_pr=create_pr,
+        )
+
+
+@validate_hf_hub_args
+def _upload_lfs_files(
+    *,
+    actions: list[dict[str, Any]],
+    oid2addop: dict[str, CommitOperationAdd],
+    headers: dict[str, str],
+    endpoint: Optional[str] = None,
+    num_threads: int = 5,
+):
+    """
+    Uploads the content of `additions` to the Hub using the large file storage protocol.
+
+    Relevant external documentation:
+        - LFS Batch API: https://github.com/git-lfs/git-lfs/blob/main/docs/api/batch.md
+
+    Args:
+        actions (`list[dict[str, Any]]`):
+            LFS batch actions returned by the server.
+        oid2addop (`dict[str, CommitOperationAdd]`):
+            A dictionary mapping the OID of the file to the corresponding `CommitOperationAdd` object.
+        headers (`dict[str, str]`):
+            Headers to use for the request, including authorization headers and user agent.
+        endpoint (`str`, *optional*):
+            The endpoint to use for the request. Defaults to `constants.ENDPOINT`.
+        num_threads (`int`, *optional*):
+            The number of concurrent threads to use when uploading. Defaults to 5.
+
+    Raises:
+        [`EnvironmentError`](https://docs.python.org/3/library/exceptions.html#EnvironmentError)
+            If an upload failed for any reason
+        [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError)
+            Type of the repo to upload to: `"model"`, `"dataset"` or `"space"`.
+        repo_id (`str`):
+            A namespace (user or an organization) and a repo name separated
+            by a `/`.
+        headers (`dict[str, str]`):
+            Headers to use for the request, including authorization headers and user agent.
+        num_threads (`int`, *optional*):
+            The number of concurrent threads to use when uploading. Defaults to 5.
+        revision (`str`, *optional*):
+            The git revision to upload to.
+
+    Raises:
+        [`EnvironmentError`](https://docs.python.org/3/library/exceptions.html#EnvironmentError)
+            If an upload failed for any reason
+        [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError)
+            If the server returns malformed responses
+        [`HfHubHTTPError`]
+            If the LFS batch endpoint returned an HTTP error.
+    """
+    # Filter out files already present upstream
+    filtered_actions = []
+    for action in actions:
+        if action.get("actions") is None:
+            logger.debug(
+                f"Content of file {oid2addop[action['oid']].path_in_repo} is already present upstream - skipping upload."
+            )
+        else:
+            filtered_actions.append(action)
+
+    # Upload according to server-provided actions
+    def _wrapped_lfs_upload(batch_action) -> None:
+        try:
+            operation = oid2addop[batch_action["oid"]]
+            lfs_upload(operation=operation, lfs_batch_action=batch_action, headers=headers, endpoint=endpoint)
+        except Exception as exc:
+            raise RuntimeError(f"Error while uploading '{operation.path_in_repo}' to the Hub.") from exc
+
+    if len(filtered_actions) == 1:
+        logger.debug("Uploading 1 LFS file to the Hub")
+        _wrapped_lfs_upload(filtered_actions[0])
+    else:
+        logger.debug(
+            f"Uploading {len(filtered_actions)} LFS files to the Hub using up to {num_threads} threads concurrently"
+        )
+        thread_map(
+            _wrapped_lfs_upload,
+            filtered_actions,
+            desc=f"Upload {len(filtered_actions)} LFS files",
+            max_workers=num_threads,
+            tqdm_class=hf_tqdm,
+        )
+
+
+@validate_hf_hub_args
+def _upload_xet_files(
+    *,
+    additions: list[CommitOperationAdd],
+    repo_type: str,
+    repo_id: str,
+    headers: dict[str, str],
+    endpoint: Optional[str] = None,
+    revision: Optional[str] = None,
+    create_pr: Optional[bool] = None,
+):
+    """
+    Uploads the content of `additions` to the Hub using the xet storage protocol.
+    This chunks the files and deduplicates the chunks before uploading them to xetcas storage.
+
+    Args:
+        additions (`` of `CommitOperationAdd`):
+            The files to be uploaded.
+        repo_type (`str`):
+            Type of the repo to upload to: `"model"`, `"dataset"` or `"space"`.
+        repo_id (`str`):
+            A namespace (user or an organization) and a repo name separated
+            by a `/`.
+        headers (`dict[str, str]`):
+            Headers to use for the request, including authorization headers and user agent.
+        endpoint: (`str`, *optional*):
+            The endpoint to use for the xetcas service. Defaults to `constants.ENDPOINT`.
+        revision (`str`, *optional*):
+            The git revision to upload to.
+        create_pr (`bool`, *optional*):
+            Whether or not to create a Pull Request with that commit.
+
+    Raises:
+        [`EnvironmentError`](https://docs.python.org/3/library/exceptions.html#EnvironmentError)
+            If an upload failed for any reason.
+        [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError)
+            If the server returns malformed responses or if the user is unauthorized to upload to xet storage.
+        [`HfHubHTTPError`]
+            If the LFS batch endpoint returned an HTTP error.
+
+    **How it works:**
+        The file download system uses Xet storage, which is a content-addressable storage system that breaks files into chunks
+            for efficient storage and transfer.
+
+        `hf_xet.upload_files` manages uploading files by:
+            - Taking a list of file paths to upload
+            - Breaking files into smaller chunks for efficient storage
+            - Avoiding duplicate storage by recognizing identical chunks across files
+            - Connecting to a storage server (CAS server) that manages these chunks
+
+        The upload process works like this:
+        1. Create a local folder at ~/.cache/huggingface/xet/chunk-cache to store file chunks for reuse.
+        2. Process files in parallel (up to 8 files at once):
+            2.1. Read the file content.
+            2.2. Split the file content into smaller chunks based on content patterns: each chunk gets a unique ID based on what's in it.
+            2.3. For each chunk:
+                - Check if it already exists in storage.
+                - Skip uploading chunks that already exist.
+            2.4. Group chunks into larger blocks for efficient transfer.
+            2.5. Upload these blocks to the storage server.
+            2.6. Create and upload information about how the file is structured.
+        3. Return reference files that contain information about the uploaded files, which can be used later to download them.
+    """
+    if len(additions) == 0:
+        return
+
+    # at this point, we know that hf_xet is installed
+    from hf_xet import upload_bytes, upload_files
+
+    from .utils._xet_progress_reporting import XetProgressReporter
+
+    try:
+        xet_connection_info = fetch_xet_connection_info_from_repo_info(
+            token_type=XetTokenType.WRITE,
+            repo_id=repo_id,
+            repo_type=repo_type,
+            revision=revision,
+            headers=headers,
+            endpoint=endpoint,
+            params={"create_pr": "1"} if create_pr else None,
+        )
+    except HfHubHTTPError as e:
+        if e.response.status_code == 401:
+            raise XetAuthorizationError(
+                f"You are unauthorized to upload to xet storage for {repo_type}/{repo_id}. "
+                f"Please check that you have configured your access token with write access to the repo."
+            ) from e
+        raise
+
+    xet_endpoint = xet_connection_info.endpoint
+    access_token_info = (xet_connection_info.access_token, xet_connection_info.expiration_unix_epoch)
+
+    def token_refresher() -> tuple[str, int]:
+        new_xet_connection = fetch_xet_connection_info_from_repo_info(
+            token_type=XetTokenType.WRITE,
+            repo_id=repo_id,
+            repo_type=repo_type,
+            revision=revision,
+            headers=headers,
+            endpoint=endpoint,
+            params={"create_pr": "1"} if create_pr else None,
+        )
+        if new_xet_connection is None:
+            raise XetRefreshTokenError("Failed to refresh xet token")
+        return new_xet_connection.access_token, new_xet_connection.expiration_unix_epoch
+
+    if not are_progress_bars_disabled():
+        progress = XetProgressReporter()
+        progress_callback = progress.update_progress
+    else:
+        progress, progress_callback = None, None
+
+    try:
+        all_bytes_ops = [op for op in additions if isinstance(op.path_or_fileobj, bytes)]
+        all_paths_ops = [op for op in additions if isinstance(op.path_or_fileobj, (str, Path))]
+
+        xet_headers = headers.copy()
+        xet_headers.pop("authorization", None)
+
+        if len(all_paths_ops) > 0:
+            all_paths = [str(op.path_or_fileobj) for op in all_paths_ops]
+            all_sha256s = [op.upload_info.sha256.hex() for op in all_paths_ops]
+            upload_files(
+                all_paths,
+                xet_endpoint,
+                access_token_info,
+                token_refresher,
+                progress_callback,
+                repo_type,
+                request_headers=xet_headers,
+                sha256s=all_sha256s,
+            )
+
+        if len(all_bytes_ops) > 0:
+            all_bytes = [op.path_or_fileobj for op in all_bytes_ops]
+            all_sha256s = [op.upload_info.sha256.hex() for op in all_bytes_ops]
+            upload_bytes(
+                all_bytes,
+                xet_endpoint,
+                access_token_info,
+                token_refresher,
+                progress_callback,
+                repo_type,
+                request_headers=xet_headers,
+                sha256s=all_sha256s,
+            )
+
+    finally:
+        if progress is not None:
+            progress.close(False)
+
+    return
+
+
+def _validate_preupload_info(preupload_info: dict):
+    files = preupload_info.get("files")
+    if not isinstance(files, list):
+        raise ValueError("preupload_info is improperly formatted")
+    for file_info in files:
+        if not (
+            isinstance(file_info, dict)
+            and isinstance(file_info.get("path"), str)
+            and isinstance(file_info.get("uploadMode"), str)
+            and (file_info["uploadMode"] in ("lfs", "regular"))
+        ):
+            raise ValueError("preupload_info is improperly formatted:")
+    return preupload_info
+
+
+@validate_hf_hub_args
+def _fetch_upload_modes(
+    additions: Iterable[CommitOperationAdd],
+    repo_type: str,
+    repo_id: str,
+    headers: dict[str, str],
+    revision: str,
+    endpoint: Optional[str] = None,
+    create_pr: bool = False,
+    gitignore_content: Optional[str] = None,
+) -> None:
+    """
+    Requests the Hub "preupload" endpoint to determine whether each input file should be uploaded as a regular git blob,
+    as a git LFS blob, or as a XET file. Input `additions` are mutated in-place with the upload mode.
+
+    Args:
+        additions (`Iterable` of :class:`CommitOperationAdd`):
+            Iterable of :class:`CommitOperationAdd` describing the files to
+            upload to the Hub.
+        repo_type (`str`):
+            Type of the repo to upload to: `"model"`, `"dataset"` or `"space"`.
+        repo_id (`str`):
+            A namespace (user or an organization) and a repo name separated
+            by a `/`.
+        headers (`dict[str, str]`):
+            Headers to use for the request, including authorization headers and user agent.
+        revision (`str`):
+            The git revision to upload the files to. Can be any valid git revision.
+        gitignore_content (`str`, *optional*):
+            The content of the `.gitignore` file to know which files should be ignored. The order of priority
+            is to first check if `gitignore_content` is passed, then check if the `.gitignore` file is present
+            in the list of files to commit and finally default to the `.gitignore` file already hosted on the Hub
+            (if any).
+    Raises:
+        [`~utils.HfHubHTTPError`]
+            If the Hub API returned an error.
+        [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError)
+            If the Hub API response is improperly formatted.
+    """
+    endpoint = endpoint if endpoint is not None else constants.ENDPOINT
+
+    # Fetch upload mode (LFS or regular) chunk by chunk.
+    upload_modes: dict[str, UploadMode] = {}
+    should_ignore_info: dict[str, bool] = {}
+    oid_info: dict[str, Optional[str]] = {}
+
+    for chunk in chunk_iterable(additions, 256):
+        payload: dict = {
+            "files": [
+                {
+                    "path": op.path_in_repo,
+                    "sample": base64.b64encode(op.upload_info.sample).decode("ascii"),
+                    "size": op.upload_info.size,
+                }
+                for op in chunk
+            ]
+        }
+        if gitignore_content is not None:
+            payload["gitIgnore"] = gitignore_content
+
+        resp = http_backoff(
+            "POST",
+            f"{endpoint}/api/{repo_type}s/{repo_id}/preupload/{revision}",
+            json=payload,
+            headers=headers,
+            params={"create_pr": "1"} if create_pr else None,
+        )
+        hf_raise_for_status(resp)
+        preupload_info = _validate_preupload_info(resp.json())
+        upload_modes.update(**{file["path"]: file["uploadMode"] for file in preupload_info["files"]})
+        should_ignore_info.update(**{file["path"]: file["shouldIgnore"] for file in preupload_info["files"]})
+        oid_info.update(**{file["path"]: file.get("oid") for file in preupload_info["files"]})
+
+    # Set upload mode for each addition operation
+    for addition in additions:
+        addition._upload_mode = upload_modes[addition.path_in_repo]
+        addition._should_ignore = should_ignore_info[addition.path_in_repo]
+        addition._remote_oid = oid_info[addition.path_in_repo]
+
+    # Empty files cannot be uploaded as LFS (S3 would fail with a 501 Not Implemented)
+    # => empty files are uploaded as "regular" to still allow users to commit them.
+    for addition in additions:
+        if addition.upload_info.size == 0:
+            addition._upload_mode = "regular"
+
+
+@validate_hf_hub_args
+def _fetch_files_to_copy(
+    copies: Iterable[CommitOperationCopy],
+    repo_type: str,
+    repo_id: str,
+    headers: dict[str, str],
+    revision: str,
+    endpoint: Optional[str] = None,
+) -> dict[tuple[str, Optional[str]], Union["RepoFile", bytes]]:
+    """
+    Fetch information about the files to copy.
+
+    For LFS files, we only need their metadata (file size and sha256) while for regular files
+    we need to download the raw content from the Hub.
+
+    Args:
+        copies (`Iterable` of :class:`CommitOperationCopy`):
+            Iterable of :class:`CommitOperationCopy` describing the files to
+            copy on the Hub.
+        repo_type (`str`):
+            Type of the repo to upload to: `"model"`, `"dataset"` or `"space"`.
+        repo_id (`str`):
+            A namespace (user or an organization) and a repo name separated
+            by a `/`.
+        headers (`dict[str, str]`):
+            Headers to use for the request, including authorization headers and user agent.
+        revision (`str`):
+            The git revision to upload the files to. Can be any valid git revision.
+
+    Returns: `dict[tuple[str, Optional[str]], Union[RepoFile, bytes]]]`
+        Key is the file path and revision of the file to copy.
+        Value is the raw content as bytes (for regular files) or the file information as a RepoFile (for LFS files).
+
+    Raises:
+        [`~utils.HfHubHTTPError`]
+            If the Hub API returned an error.
+        [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError)
+            If the Hub API response is improperly formatted.
+    """
+    from .hf_api import HfApi, RepoFolder
+
+    hf_api = HfApi(endpoint=endpoint, headers=headers)
+    files_to_copy: dict[tuple[str, Optional[str]], Union["RepoFile", bytes]] = {}
+    # Store (path, revision) -> oid mapping
+    oid_info: dict[tuple[str, Optional[str]], Optional[str]] = {}
+    # 1. Fetch OIDs for destination paths in batches.
+    dest_paths = [op.path_in_repo for op in copies]
+    for offset in range(0, len(dest_paths), FETCH_LFS_BATCH_SIZE):
+        dest_repo_files = hf_api.get_paths_info(
+            repo_id=repo_id,
+            paths=dest_paths[offset : offset + FETCH_LFS_BATCH_SIZE],
+            revision=revision,
+            repo_type=repo_type,
+        )
+        for file in dest_repo_files:
+            if not isinstance(file, RepoFolder):
+                oid_info[(file.path, revision)] = file.blob_id
+
+    # 2. Group by source revision and fetch source file info in batches.
+    for src_revision, operations in groupby(copies, key=lambda op: op.src_revision):
+        operations = list(operations)  # type: ignore
+        src_paths = [op.src_path_in_repo for op in operations]
+        for offset in range(0, len(src_paths), FETCH_LFS_BATCH_SIZE):
+            src_repo_files = hf_api.get_paths_info(
+                repo_id=repo_id,
+                paths=src_paths[offset : offset + FETCH_LFS_BATCH_SIZE],
+                revision=src_revision or revision,
+                repo_type=repo_type,
+            )
+
+            for src_repo_file in src_repo_files:
+                if isinstance(src_repo_file, RepoFolder):
+                    raise NotImplementedError("Copying a folder is not implemented.")
+                oid_info[(src_repo_file.path, src_revision)] = src_repo_file.blob_id
+                # If it's an LFS file, store the RepoFile object. Otherwise, download raw bytes.
+                if src_repo_file.lfs:
+                    files_to_copy[(src_repo_file.path, src_revision)] = src_repo_file
+                else:
+                    # TODO: (optimization) download regular files to copy concurrently
+                    url = hf_hub_url(
+                        endpoint=endpoint,
+                        repo_type=repo_type,
+                        repo_id=repo_id,
+                        revision=src_revision or revision,
+                        filename=src_repo_file.path,
+                    )
+                    response = get_session().get(url, headers=headers)
+                    hf_raise_for_status(response)
+                    files_to_copy[(src_repo_file.path, src_revision)] = response.content
+        # 3. Ensure all operations found a corresponding file in the Hub
+        #  and track src/dest OIDs for each operation.
+        for operation in operations:
+            if (operation.src_path_in_repo, src_revision) not in files_to_copy:
+                raise EntryNotFoundError(
+                    f"Cannot copy {operation.src_path_in_repo} at revision "
+                    f"{src_revision or revision}: file is missing on repo."
+                )
+            operation._src_oid = oid_info.get((operation.src_path_in_repo, operation.src_revision))
+            operation._dest_oid = oid_info.get((operation.path_in_repo, revision))
+    return files_to_copy
+
+
+def _prepare_commit_payload(
+    operations: Iterable[CommitOperation],
+    files_to_copy: dict[tuple[str, Optional[str]], Union["RepoFile", bytes]],
+    commit_message: str,
+    commit_description: Optional[str] = None,
+    parent_commit: Optional[str] = None,
+) -> Iterable[dict[str, Any]]:
+    """
+    Builds the payload to POST to the `/commit` API of the Hub.
+
+    Payload is returned as an iterator so that it can be streamed as a ndjson in the
+    POST request.
+
+    For more information, see:
+        - https://github.com/huggingface/huggingface_hub/issues/1085#issuecomment-1265208073
+        - http://ndjson.org/
+    """
+    commit_description = commit_description if commit_description is not None else ""
+
+    # 1. Send a header item with the commit metadata
+    header_value = {"summary": commit_message, "description": commit_description}
+    if parent_commit is not None:
+        header_value["parentCommit"] = parent_commit
+    yield {"key": "header", "value": header_value}
+
+    nb_ignored_files = 0
+
+    # 2. Send operations, one per line
+    for operation in operations:
+        # Skip ignored files
+        if isinstance(operation, CommitOperationAdd) and operation._should_ignore:
+            logger.debug(f"Skipping file '{operation.path_in_repo}' in commit (ignored by gitignore file).")
+            nb_ignored_files += 1
+            continue
+
+        # 2.a. Case adding a regular file
+        if isinstance(operation, CommitOperationAdd) and operation._upload_mode == "regular":
+            yield {
+                "key": "file",
+                "value": {
+                    "content": operation.b64content().decode(),
+                    "path": operation.path_in_repo,
+                    "encoding": "base64",
+                },
+            }
+        # 2.b. Case adding an LFS file
+        elif isinstance(operation, CommitOperationAdd) and operation._upload_mode == "lfs":
+            yield {
+                "key": "lfsFile",
+                "value": {
+                    "path": operation.path_in_repo,
+                    "algo": "sha256",
+                    "oid": operation.upload_info.sha256.hex(),
+                    "size": operation.upload_info.size,
+                },
+            }
+        # 2.c. Case deleting a file or folder
+        elif isinstance(operation, CommitOperationDelete):
+            yield {
+                "key": "deletedFolder" if operation.is_folder else "deletedFile",
+                "value": {"path": operation.path_in_repo},
+            }
+        # 2.d. Case copying a file or folder
+        elif isinstance(operation, CommitOperationCopy):
+            file_to_copy = files_to_copy[(operation.src_path_in_repo, operation.src_revision)]
+            if isinstance(file_to_copy, bytes):
+                yield {
+                    "key": "file",
+                    "value": {
+                        "content": base64.b64encode(file_to_copy).decode(),
+                        "path": operation.path_in_repo,
+                        "encoding": "base64",
+                    },
+                }
+            elif file_to_copy.lfs:
+                yield {
+                    "key": "lfsFile",
+                    "value": {
+                        "path": operation.path_in_repo,
+                        "algo": "sha256",
+                        "oid": file_to_copy.lfs.sha256,
+                    },
+                }
+            else:
+                raise ValueError(
+                    "Malformed files_to_copy (should be raw file content as bytes or RepoFile objects with LFS info."
+                )
+        # 2.e. Never expected to happen
+        else:
+            raise ValueError(
+                f"Unknown operation to commit. Operation: {operation}. Upload mode:"
+                f" {getattr(operation, '_upload_mode', None)}"
+            )
+
+    if nb_ignored_files > 0:
+        logger.info(f"Skipped {nb_ignored_files} file(s) in commit (ignored by gitignore file).")

data/conda_envs/evalclaw/lib/python3.11/site-packages/huggingface_hub/_commit_scheduler.py

@@ -0,0 +1,353 @@
+import atexit
+import logging
+import os
+import time
+from concurrent.futures import Future
+from dataclasses import dataclass
+from io import SEEK_END, SEEK_SET, BytesIO
+from pathlib import Path
+from threading import Lock, Thread
+from typing import Optional, Union
+
+from .hf_api import DEFAULT_IGNORE_PATTERNS, CommitInfo, CommitOperationAdd, HfApi
+from .utils import filter_repo_objects
+
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass(frozen=True)
+class _FileToUpload:
+    """Temporary dataclass to store info about files to upload. Not meant to be used directly."""
+
+    local_path: Path
+    path_in_repo: str
+    size_limit: int
+    last_modified: float
+
+
+class CommitScheduler:
+    """
+    Scheduler to upload a local folder to the Hub at regular intervals (e.g. push to hub every 5 minutes).
+
+    The recommended way to use the scheduler is to use it as a context manager. This ensures that the scheduler is
+    properly stopped and the last commit is triggered when the script ends. The scheduler can also be stopped manually
+    with the `stop` method. Checkout the [upload guide](https://huggingface.co/docs/huggingface_hub/guides/upload#scheduled-uploads)
+    to learn more about how to use it.
+
+    Args:
+        repo_id (`str`):
+            The id of the repo to commit to.
+        folder_path (`str` or `Path`):
+            Path to the local folder to upload regularly.
+        every (`int` or `float`, *optional*):
+            The number of minutes between each commit. Defaults to 5 minutes.
+        path_in_repo (`str`, *optional*):
+            Relative path of the directory in the repo, for example: `"checkpoints/"`. Defaults to the root folder
+            of the repository.
+        repo_type (`str`, *optional*):
+            The type of the repo to commit to. Defaults to `model`.
+        revision (`str`, *optional*):
+            The revision of the repo to commit to. Defaults to `main`.
+        private (`bool`, *optional*):
+            Whether to make the repo private. If `None` (default), the repo will be public unless the organization's default is private. This value is ignored if the repo already exists.
+        token (`str`, *optional*):
+            The token to use to commit to the repo. Defaults to the token saved on the machine.
+        allow_patterns (`list[str]` or `str`, *optional*):
+            If provided, only files matching at least one pattern are uploaded.
+        ignore_patterns (`list[str]` or `str`, *optional*):
+            If provided, files matching any of the patterns are not uploaded.
+        squash_history (`bool`, *optional*):
+            Whether to squash the history of the repo after each commit. Defaults to `False`. Squashing commits is
+            useful to avoid degraded performances on the repo when it grows too large.
+        hf_api (`HfApi`, *optional*):
+            The [`HfApi`] client to use to commit to the Hub. Can be set with custom settings (user agent, token,...).
+
+    Example:
+    ```py
+    >>> from pathlib import Path
+    >>> from huggingface_hub import CommitScheduler
+
+    # Scheduler uploads every 10 minutes
+    >>> csv_path = Path("watched_folder/data.csv")
+    >>> CommitScheduler(repo_id="test_scheduler", repo_type="dataset", folder_path=csv_path.parent, every=10)
+
+    >>> with csv_path.open("a") as f:
+    ...     f.write("first line")
+
+    # Some time later (...)
+    >>> with csv_path.open("a") as f:
+    ...     f.write("second line")
+    ```
+
+    Example using a context manager:
+    ```py
+    >>> from pathlib import Path
+    >>> from huggingface_hub import CommitScheduler
+
+    >>> with CommitScheduler(repo_id="test_scheduler", repo_type="dataset", folder_path="watched_folder", every=10) as scheduler:
+    ...     csv_path = Path("watched_folder/data.csv")
+    ...     with csv_path.open("a") as f:
+    ...         f.write("first line")
+    ...     (...)
+    ...     with csv_path.open("a") as f:
+    ...         f.write("second line")
+
+    # Scheduler is now stopped and last commit have been triggered
+    ```
+    """
+
+    def __init__(
+        self,
+        *,
+        repo_id: str,
+        folder_path: Union[str, Path],
+        every: Union[int, float] = 5,
+        path_in_repo: Optional[str] = None,
+        repo_type: Optional[str] = None,
+        revision: Optional[str] = None,
+        private: Optional[bool] = None,
+        token: Optional[str] = None,
+        allow_patterns: Optional[Union[list[str], str]] = None,
+        ignore_patterns: Optional[Union[list[str], str]] = None,
+        squash_history: bool = False,
+        hf_api: Optional["HfApi"] = None,
+    ) -> None:
+        self.api = hf_api or HfApi(token=token)
+
+        # Folder
+        self.folder_path = Path(folder_path).expanduser().resolve()
+        self.path_in_repo = path_in_repo or ""
+        self.allow_patterns = allow_patterns
+
+        if ignore_patterns is None:
+            ignore_patterns = []
+        elif isinstance(ignore_patterns, str):
+            ignore_patterns = [ignore_patterns]
+        self.ignore_patterns = ignore_patterns + DEFAULT_IGNORE_PATTERNS
+
+        if self.folder_path.is_file():
+            raise ValueError(f"'folder_path' must be a directory, not a file: '{self.folder_path}'.")
+        self.folder_path.mkdir(parents=True, exist_ok=True)
+
+        # Repository
+        repo_url = self.api.create_repo(repo_id=repo_id, private=private, repo_type=repo_type, exist_ok=True)
+        self.repo_id = repo_url.repo_id
+        self.repo_type = repo_type
+        self.revision = revision
+        self.token = token
+
+        # Keep track of already uploaded files
+        self.last_uploaded: dict[Path, float] = {}  # key is local path, value is timestamp
+
+        # Scheduler
+        if not every > 0:
+            raise ValueError(f"'every' must be a positive integer, not '{every}'.")
+        self.lock = Lock()
+        self.every = every
+        self.squash_history = squash_history
+
+        logger.info(f"Scheduled job to push '{self.folder_path}' to '{self.repo_id}' every {self.every} minutes.")
+        self._scheduler_thread = Thread(target=self._run_scheduler, daemon=True)
+        self._scheduler_thread.start()
+        atexit.register(self._push_to_hub)
+
+        self.__stopped = False
+
+    def stop(self) -> None:
+        """Stop the scheduler.
+
+        A stopped scheduler cannot be restarted. Mostly for tests purposes.
+        """
+        self.__stopped = True
+
+    def __enter__(self) -> "CommitScheduler":
+        return self
+
+    def __exit__(self, exc_type, exc_value, traceback) -> None:
+        # Upload last changes before exiting
+        self.trigger().result()
+        self.stop()
+        return
+
+    def _run_scheduler(self) -> None:
+        """Dumb thread waiting between each scheduled push to Hub."""
+        while True:
+            self.last_future = self.trigger()
+            time.sleep(self.every * 60)
+            if self.__stopped:
+                break
+
+    def trigger(self) -> Future:
+        """Trigger a `push_to_hub` and return a future.
+
+        This method is automatically called every `every` minutes. You can also call it manually to trigger a commit
+        immediately, without waiting for the next scheduled commit.
+        """
+        return self.api.run_as_future(self._push_to_hub)
+
+    def _push_to_hub(self) -> Optional[CommitInfo]:
+        if self.__stopped:  # If stopped, already scheduled commits are ignored
+            return None
+
+        logger.info("(Background) scheduled commit triggered.")
+        try:
+            value = self.push_to_hub()
+            if self.squash_history:
+                logger.info("(Background) squashing repo history.")
+                self.api.super_squash_history(repo_id=self.repo_id, repo_type=self.repo_type, branch=self.revision)
+            return value
+        except Exception as e:
+            logger.error(f"Error while pushing to Hub: {e}")  # Depending on the setup, error might be silenced
+            raise
+
+    def push_to_hub(self) -> Optional[CommitInfo]:
+        """
+        Push folder to the Hub and return the commit info.
+
+        > [!WARNING]
+        > This method is not meant to be called directly. It is run in the background by the scheduler, respecting a
+        > queue mechanism to avoid concurrent commits. Making a direct call to the method might lead to concurrency
+        > issues.
+
+        The default behavior of `push_to_hub` is to assume an append-only folder. It lists all files in the folder and
+        uploads only changed files. If no changes are found, the method returns without committing anything. If you want
+        to change this behavior, you can inherit from [`CommitScheduler`] and override this method. This can be useful
+        for example to compress data together in a single file before committing. For more details and examples, check
+        out our [integration guide](https://huggingface.co/docs/huggingface_hub/main/en/guides/upload#scheduled-uploads).
+        """
+        # Check files to upload (with lock)
+        with self.lock:
+            logger.debug("Listing files to upload for scheduled commit.")
+
+            # List files from folder (taken from `_prepare_upload_folder_additions`)
+            relpath_to_abspath = {
+                path.relative_to(self.folder_path).as_posix(): path
+                for path in sorted(self.folder_path.glob("**/*"))  # sorted to be deterministic
+                if path.is_file()
+            }
+            prefix = f"{self.path_in_repo.strip('/')}/" if self.path_in_repo else ""
+
+            # Filter with pattern + filter out unchanged files + retrieve current file size
+            files_to_upload: list[_FileToUpload] = []
+            for relpath in filter_repo_objects(
+                relpath_to_abspath.keys(), allow_patterns=self.allow_patterns, ignore_patterns=self.ignore_patterns
+            ):
+                local_path = relpath_to_abspath[relpath]
+                stat = local_path.stat()
+                if self.last_uploaded.get(local_path) is None or self.last_uploaded[local_path] != stat.st_mtime:
+                    files_to_upload.append(
+                        _FileToUpload(
+                            local_path=local_path,
+                            path_in_repo=prefix + relpath,
+                            size_limit=stat.st_size,
+                            last_modified=stat.st_mtime,
+                        )
+                    )
+
+        # Return if nothing to upload
+        if len(files_to_upload) == 0:
+            logger.debug("Dropping schedule commit: no changed file to upload.")
+            return None
+
+        # Convert `_FileToUpload` as `CommitOperationAdd` (=> compute file shas + limit to file size)
+        logger.debug("Removing unchanged files since previous scheduled commit.")
+        add_operations = [
+            CommitOperationAdd(
+                # Cap the file to its current size, even if the user append data to it while a scheduled commit is happening
+                path_or_fileobj=PartialFileIO(file_to_upload.local_path, size_limit=file_to_upload.size_limit),
+                path_in_repo=file_to_upload.path_in_repo,
+            )
+            for file_to_upload in files_to_upload
+        ]
+
+        # Upload files (append mode expected - no need for lock)
+        logger.debug("Uploading files for scheduled commit.")
+        commit_info = self.api.create_commit(
+            repo_id=self.repo_id,
+            repo_type=self.repo_type,
+            operations=add_operations,
+            commit_message="Scheduled Commit",
+            revision=self.revision,
+        )
+
+        # Successful commit: keep track of the latest "last_modified" for each file
+        for file in files_to_upload:
+            self.last_uploaded[file.local_path] = file.last_modified
+        return commit_info
+
+
+class PartialFileIO(BytesIO):
+    """A file-like object that reads only the first part of a file.
+
+    Useful to upload a file to the Hub when the user might still be appending data to it. Only the first part of the
+    file is uploaded (i.e. the part that was available when the filesystem was first scanned).
+
+    In practice, only used internally by the CommitScheduler to regularly push a folder to the Hub with minimal
+    disturbance for the user. The object is passed to `CommitOperationAdd`.
+
+    Only supports `read`, `tell` and `seek` methods.
+
+    Args:
+        file_path (`str` or `Path`):
+            Path to the file to read.
+        size_limit (`int`):
+            The maximum number of bytes to read from the file. If the file is larger than this, only the first part
+            will be read (and uploaded).
+    """
+
+    def __init__(self, file_path: Union[str, Path], size_limit: int) -> None:
+        self._file_path = Path(file_path)
+        self._file = self._file_path.open("rb")
+        self._size_limit = min(size_limit, os.fstat(self._file.fileno()).st_size)
+
+    def __del__(self) -> None:
+        self._file.close()
+        return super().__del__()
+
+    def __repr__(self) -> str:
+        return f"<PartialFileIO file_path={self._file_path} size_limit={self._size_limit}>"
+
+    def __len__(self) -> int:
+        return self._size_limit
+
+    def __getattribute__(self, name: str):
+        if name.startswith("_") or name in ("read", "tell", "seek", "fileno"):  # only 4 public methods supported
+            return super().__getattribute__(name)
+        raise NotImplementedError(f"PartialFileIO does not support '{name}'.")
+
+    def fileno(self):
+        raise AttributeError("PartialFileIO does not have a fileno.")
+
+    def tell(self) -> int:
+        """Return the current file position."""
+        return self._file.tell()
+
+    def seek(self, __offset: int, __whence: int = SEEK_SET) -> int:
+        """Change the stream position to the given offset.
+
+        Behavior is the same as a regular file, except that the position is capped to the size limit.
+        """
+        if __whence == SEEK_END:
+            # SEEK_END => set from the truncated end
+            __offset = len(self) + __offset
+            __whence = SEEK_SET
+
+        pos = self._file.seek(__offset, __whence)
+        if pos > self._size_limit:
+            return self._file.seek(self._size_limit)
+        return pos
+
+    def read(self, __size: Optional[int] = -1) -> bytes:
+        """Read at most `__size` bytes from the file.
+
+        Behavior is the same as a regular file, except that it is capped to the size limit.
+        """
+        current = self._file.tell()
+        if __size is None or __size < 0:
+            # Read until file limit
+            truncated_size = self._size_limit - current
+        else:
+            # Read until file limit or __size
+            truncated_size = min(__size, self._size_limit - current)
+        return self._file.read(truncated_size)

data/conda_envs/evalclaw/lib/python3.11/site-packages/huggingface_hub/_dataset_viewer.py

@@ -0,0 +1,173 @@
+# coding=utf-8
+# Copyright 2026 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import json
+import os
+import shutil
+import subprocess
+from dataclasses import dataclass
+from typing import TYPE_CHECKING, Any, Union
+
+from . import constants
+from .utils import get_token
+
+
+if TYPE_CHECKING:
+    import duckdb
+
+
+@dataclass(frozen=True)
+class DatasetParquetEntry:
+    """Represents a single parquet file available for a dataset on the Hub."""
+
+    config: str
+    split: str
+    url: str
+    size: int
+
+
+def execute_raw_sql_query(sql_query: str, *, token: Union[str, bool, None] = None) -> list[dict[str, Any]]:
+    normalized_query = sql_query.strip().rstrip(";").strip()
+    _raise_on_forbidden_query(normalized_query)
+
+    connection = None
+    try:
+        connection = _get_duckdb_connection(token=token)
+        relation = connection.sql(normalized_query)
+        if relation is None:
+            raise ValueError("SQL query must return rows.")
+
+        if isinstance(relation, _DuckDBCliRelation):
+            # DuckDB binary => run CLI => parse JSON
+            return relation.execute()
+        else:
+            # DuckDB Python API => fetch columns + rows => convert to dicts
+            columns = tuple(column[0] for column in relation.description)
+            rows = tuple(tuple(row) for row in relation.fetchall())
+            return [dict(zip(columns, row)) for row in rows]
+    finally:
+        if connection is not None:
+            connection.close()
+
+
+def _raise_on_forbidden_query(query: str) -> None:
+    if len(query) == 0:
+        raise ValueError("SQL query cannot be empty.")
+
+    # DuckDB CLI meta-commands are dot-prefixed words (e.g. `.shell`, `.output`).
+    # Let's forbid them for now but allow SQL expressions like `.5` that can legitimately start a line.
+    for line in query.splitlines():
+        stripped = line.lstrip()
+        if stripped.startswith(".") and stripped[1:2].isalpha():
+            raise ValueError("DuckDB CLI meta-commands are not allowed in SQL queries.")
+
+
+def _get_duckdb_connection(
+    token: Union[str, bool, None],
+) -> Union["duckdb.DuckDBPyConnection", "_DuckDBCliConnection"]:
+    try:
+        # If DuckDB is installed as a Python package, use it!
+        import duckdb
+    except ImportError as error:
+        # Otherwise, use the DuckDB CLI binary.
+        duckdb_binary = shutil.which("duckdb")
+        if duckdb_binary is None:
+            raise ImportError(
+                "DuckDB is required for `hf datasets sql`. Install the Python package with `pip install duckdb` or "
+                "install the DuckDB CLI binary (for example `brew install duckdb`)."
+            ) from error
+        return _DuckDBCliConnection(binary_path=duckdb_binary, token=token)
+
+    # Create a new connection (Python API).
+    connection = duckdb.connect()
+    try:
+        for statement in _build_duckdb_secret_statements(token):
+            connection.execute(statement)
+        return connection
+    except Exception:
+        connection.close()
+        raise
+
+
+@dataclass
+class _DuckDBCliConnection:
+    """DuckDB connection.
+
+    Mimics the DuckDB Python API, but runs the queries via the DuckDB CLI binary.
+    """
+
+    binary_path: str
+    token: Union[str, bool, None]
+
+    def __post_init__(self) -> None:
+        self._setup_statements = _build_duckdb_secret_statements(self.token)
+
+    def sql(self, query: str) -> "_DuckDBCliRelation":
+        return _DuckDBCliRelation(binary_path=self.binary_path, setup_statements=self._setup_statements, query=query)
+
+    def close(self) -> None:
+        pass
+
+
+@dataclass
+class _DuckDBCliRelation:
+    """DuckDB relation.
+
+    Mimics the DuckDB Python API, but runs the queries via the DuckDB CLI binary.
+    """
+
+    binary_path: str
+    setup_statements: list[str]
+    query: str
+
+    def execute(self) -> list[dict[str, Any]]:
+        # Build the DuckDB CLI input.
+        setup = []
+        if self.setup_statements:
+            setup = [
+                f".output {os.devnull}",
+                *(f"{stmt};" for stmt in self.setup_statements),
+                ".output",
+            ]
+        full_query = "\n".join(setup + [self.query + ";"])
+
+        # Run DuckDB binary
+        result = subprocess.run(
+            [self.binary_path, "-json"],
+            input=full_query,
+            capture_output=True,
+            text=True,
+            check=False,
+        )
+        if result.returncode != 0:
+            error_message = result.stderr.strip() or result.stdout.strip() or "DuckDB CLI command failed."
+            raise RuntimeError(error_message)
+
+        # Parse JSON output and return
+        return json.loads(result.stdout.strip())
+
+
+def _build_duckdb_secret_statements(token: Union[str, bool, None]) -> list[str]:
+    if token is None or token is True:
+        token = get_token()
+
+    if not token:
+        return []
+
+    escaped_token = token.replace("'", "''")
+    escaped_endpoint = constants.ENDPOINT.replace("'", "''")
+    return [
+        f"CREATE OR REPLACE SECRET hf_hub_token (TYPE HTTP, BEARER_TOKEN '{escaped_token}', SCOPE '{escaped_endpoint}')",
+        f"CREATE OR REPLACE SECRET hf_token (TYPE HUGGINGFACE, TOKEN '{escaped_token}')",
+    ]

data/conda_envs/evalclaw/lib/python3.11/site-packages/huggingface_hub/_eval_results.py

@@ -0,0 +1,211 @@
+"""Evaluation results utilities for the `.eval_results/*.yaml` format.
+
+See https://huggingface.co/docs/hub/eval-results for more details.
+Specifications are available at https://github.com/huggingface/hub-docs/blob/main/eval_results.yaml.
+"""
+
+from dataclasses import dataclass
+from typing import Any, Optional
+
+
+@dataclass
+class EvalResultEntry:
+    """
+    Evaluation result entry for the `.eval_results/*.yaml` format.
+
+    Represents evaluation scores stored in model repos that automatically appear on
+    the model page and the benchmark dataset's leaderboard.
+
+    For the legacy `model-index` format in `README.md`, use [`EvalResult`] instead.
+
+    See https://huggingface.co/docs/hub/eval-results for more details.
+
+    Args:
+        dataset_id (`str`):
+            Benchmark dataset ID from the Hub. Example: "cais/hle", "Idavidrein/gpqa".
+        task_id (`str`):
+            Task identifier within the benchmark. Example: "gpqa_diamond".
+        value (`Any`):
+            The metric value. Example: 20.90.
+        dataset_revision (`str`, *optional*):
+            Git SHA of the benchmark dataset.
+        verify_token (`str`, *optional*):
+            A signature that can be used to prove that evaluation is provably auditable and reproducible.
+        date (`str`, *optional*):
+            When the evaluation was run (ISO-8601 datetime). Defaults to git commit time.
+        source_url (`str`, *optional*):
+            Link to the evaluation source (e.g., https://huggingface.co/spaces/SaylorTwift/smollm3-mmlu-pro). Required if `source_name`, `source_user`, or `source_org` is provided.
+        source_name (`str`, *optional*):
+            Display name for the source. Example: "Eval Logs".
+        source_user (`str`, *optional*):
+            HF user name for attribution. Example: "celinah".
+        source_org (`str`, *optional*):
+            HF org name for attribution. Example: "cais".
+        notes (`str`, *optional*):
+            Details about the evaluation setup. Example: "tools", "no-tools", "chain-of-thought".
+
+    Example:
+        ```python
+        >>> from huggingface_hub import EvalResultEntry
+        >>> # Minimal example with required fields only
+        >>> result = EvalResultEntry(
+        ...     dataset_id="Idavidrein/gpqa",
+        ...     task_id="gpqa_diamond",
+        ...     value=0.412,
+        ... )
+        >>> # Full example with all fields
+        >>> result = EvalResultEntry(
+        ...     dataset_id="cais/hle",
+        ...     task_id="default",
+        ...     value=20.90,
+        ...     dataset_revision="5503434ddd753f426f4b38109466949a1217c2bb",
+        ...     verify_token="eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9...",
+        ...     date="2025-01-15T10:30:00Z",
+        ...     source_url="https://huggingface.co/datasets/cais/hle",
+        ...     source_name="CAIS HLE",
+        ...     source_org="cais",
+        ...     notes="no-tools",
+        ... )
+
+        ```
+    """
+
+    dataset_id: str
+    task_id: str
+    value: Any
+    dataset_revision: Optional[str] = None
+    verify_token: Optional[str] = None
+    date: Optional[str] = None
+    source_url: Optional[str] = None
+    source_name: Optional[str] = None
+    source_user: Optional[str] = None
+    source_org: Optional[str] = None
+    notes: Optional[str] = None
+
+    def __post_init__(self) -> None:
+        if (
+            self.source_name is not None or self.source_user is not None or self.source_org is not None
+        ) and self.source_url is None:
+            raise ValueError(
+                "If `source_name`, `source_user`, or `source_org` is provided, `source_url` must also be provided."
+            )
+
+
+def eval_result_entries_to_yaml(entries: list[EvalResultEntry]) -> list[dict[str, Any]]:
+    """Convert a list of [`EvalResultEntry`] objects to a YAML-serializable list of dicts.
+
+    This produces the format expected in `.eval_results/*.yaml` files.
+
+    Args:
+        entries (`list[EvalResultEntry]`):
+            List of evaluation result entries to serialize.
+
+    Returns:
+        `list[dict[str, Any]]`: A list of dictionaries ready to be dumped to YAML.
+
+    Example:
+        ```python
+        >>> from huggingface_hub import EvalResultEntry, eval_result_entries_to_yaml
+        >>> entries = [
+        ...     EvalResultEntry(dataset_id="cais/hle", task_id="default", value=20.90),
+        ...     EvalResultEntry(dataset_id="Idavidrein/gpqa", task_id="gpqa_diamond", value=0.412),
+        ... ]
+        >>> yaml_data = eval_result_entries_to_yaml(entries)
+        >>> yaml_data[0]
+        {'dataset': {'id': 'cais/hle', 'task_id': 'default'}, 'value': 20.9}
+
+        ```
+
+        To upload eval results to the Hub:
+        ```python
+        >>> import yaml
+        >>> from huggingface_hub import upload_file, EvalResultEntry, eval_result_entries_to_yaml
+        >>> entries = [
+        ...     EvalResultEntry(dataset_id="cais/hle", task_id="default", value=20.90),
+        ... ]
+        >>> yaml_content = yaml.dump(eval_result_entries_to_yaml(entries))
+        >>> upload_file(
+        ...     path_or_fileobj=yaml_content.encode(),
+        ...     path_in_repo=".eval_results/hle.yaml",
+        ...     repo_id="your-username/your-model",
+        ... )
+
+        ```
+    """
+    result = []
+    for entry in entries:
+        # build the dataset object
+        dataset: dict[str, Any] = {"id": entry.dataset_id, "task_id": entry.task_id}
+        if entry.dataset_revision is not None:
+            dataset["revision"] = entry.dataset_revision
+
+        data: dict[str, Any] = {"dataset": dataset, "value": entry.value}
+        if entry.verify_token is not None:
+            data["verifyToken"] = entry.verify_token
+        if entry.date is not None:
+            data["date"] = entry.date
+        # build the source object
+        if entry.source_url is not None:
+            source: dict[str, Any] = {"url": entry.source_url}
+            if entry.source_name is not None:
+                source["name"] = entry.source_name
+            if entry.source_user is not None:
+                source["user"] = entry.source_user
+            if entry.source_org is not None:
+                source["org"] = entry.source_org
+            data["source"] = source
+        if entry.notes is not None:
+            data["notes"] = entry.notes
+
+        result.append(data)
+    return result
+
+
+def parse_eval_result_entries(data: list[dict[str, Any]]) -> list[EvalResultEntry]:
+    """Parse a list of dicts into [`EvalResultEntry`] objects.
+
+    This parses the `.eval_results/*.yaml` format. For the legacy `model-index` format,
+    use [`model_index_to_eval_results`] instead.
+
+    Args:
+        data (`list[dict[str, Any]]`):
+            A list of dictionaries (e.g., parsed from YAML or API response).
+
+    Returns:
+        `list[EvalResultEntry]`: A list of evaluation result entry objects.
+
+    Example:
+        ```python
+        >>> from huggingface_hub import parse_eval_result_entries
+        >>> data = [
+        ...     {"dataset": {"id": "cais/hle", "task_id": "default"}, "value": 20.90},
+        ...     {"dataset": {"id": "Idavidrein/gpqa", "task_id": "gpqa_diamond"}, "value": 0.412},
+        ... ]
+        >>> entries = parse_eval_result_entries(data)
+        >>> entries[0].dataset_id
+        'cais/hle'
+        >>> entries[0].value
+        20.9
+
+        ```
+    """
+    entries = []
+    for item in data:
+        entry_data = item.get("data", item)
+        dataset = entry_data.get("dataset", {})
+        source = entry_data.get("source", {})
+        entry = EvalResultEntry(
+            dataset_id=dataset["id"],
+            value=entry_data["value"],
+            task_id=dataset["task_id"],
+            dataset_revision=dataset.get("revision"),
+            verify_token=entry_data.get("verifyToken"),
+            date=entry_data.get("date"),
+            source_url=source.get("url") if source else None,
+            source_name=source.get("name") if source else None,
+            source_user=source.get("user") if source else None,
+            source_org=source.get("org") if source else None,
+            notes=entry_data.get("notes"),
+        )
+        entries.append(entry)
+    return entries

data/conda_envs/evalclaw/lib/python3.11/site-packages/huggingface_hub/_jobs_api.py

@@ -0,0 +1,399 @@
+# coding=utf-8
+# Copyright 2025-present, the HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from dataclasses import dataclass
+from datetime import datetime
+from enum import Enum
+from typing import Any, Optional, Union
+
+from huggingface_hub import constants
+from huggingface_hub._space_api import SpaceHardware
+from huggingface_hub.utils._datetime import parse_datetime
+
+
+class JobStage(str, Enum):
+    """
+    Enumeration of possible stage of a Job on the Hub.
+
+    Value can be compared to a string:
+    ```py
+    assert JobStage.COMPLETED == "COMPLETED"
+    ```
+    Possible values are: `COMPLETED`, `CANCELED`, `ERROR`, `DELETED`, `RUNNING`.
+    Taken from https://github.com/huggingface/moon-landing/blob/main/server/job_types/JobInfo.ts#L61 (private url).
+    """
+
+    # Copied from moon-landing > server > lib > Job.ts
+    COMPLETED = "COMPLETED"
+    CANCELED = "CANCELED"
+    ERROR = "ERROR"
+    DELETED = "DELETED"
+    RUNNING = "RUNNING"
+
+
+@dataclass
+class JobStatus:
+    stage: JobStage
+    message: Optional[str]
+
+
+@dataclass
+class JobOwner:
+    id: str
+    name: str
+    type: str
+
+
+@dataclass
+class JobInfo:
+    """
+    Contains information about a Job.
+
+    Args:
+        id (`str`):
+            Job ID.
+        created_at (`datetime` or `None`):
+            When the Job was created.
+        docker_image (`str` or `None`):
+            The Docker image from Docker Hub used for the Job.
+            Can be None if space_id is present instead.
+        space_id (`str` or `None`):
+            The Docker image from Hugging Face Spaces used for the Job.
+            Can be None if docker_image is present instead.
+        command (`list[str]` or `None`):
+            Command of the Job, e.g. `["python", "-c", "print('hello world')"]`
+        arguments (`list[str]` or `None`):
+            Arguments passed to the command
+        environment (`dict[str]` or `None`):
+            Environment variables of the Job as a dictionary.
+        secrets (`dict[str]` or `None`):
+            Secret environment variables of the Job (encrypted).
+        flavor (`str` or `None`):
+            Flavor for the hardware, as in Hugging Face Spaces. See [`SpaceHardware`] for possible values.
+            E.g. `"cpu-basic"`.
+        labels (`dict[str, str]` or `None`):
+            Labels to attach to the job (key-value pairs).
+        status: (`JobStatus` or `None`):
+            Status of the Job, e.g. `JobStatus(stage="RUNNING", message=None)`
+            See [`JobStage`] for possible stage values.
+        owner: (`JobOwner` or `None`):
+            Owner of the Job, e.g. `JobOwner(id="5e9ecfc04957053f60648a3e", name="lhoestq", type="user")`
+
+    Example:
+
+    ```python
+    >>> from huggingface_hub import run_job
+    >>> job = run_job(
+    ...     image="python:3.12",
+    ...     command=["python", "-c", "print('Hello from the cloud!')"]
+    ... )
+    >>> job
+    JobInfo(id='687fb701029421ae5549d998', created_at=datetime.datetime(2025, 7, 22, 16, 6, 25, 79000, tzinfo=datetime.timezone.utc), docker_image='python:3.12', space_id=None, command=['python', '-c', "print('Hello from the cloud!')"], arguments=[], environment={}, secrets={}, flavor='cpu-basic', labels=None, status=JobStatus(stage='RUNNING', message=None), owner=JobOwner(id='5e9ecfc04957053f60648a3e', name='lhoestq', type='user'), endpoint='https://huggingface.co', url='https://huggingface.co/jobs/lhoestq/687fb701029421ae5549d998')
+    >>> job.id
+    '687fb701029421ae5549d998'
+    >>> job.url
+    'https://huggingface.co/jobs/lhoestq/687fb701029421ae5549d998'
+    >>> job.status.stage
+    'RUNNING'
+    ```
+    """
+
+    id: str
+    created_at: Optional[datetime]
+    docker_image: Optional[str]
+    space_id: Optional[str]
+    command: Optional[list[str]]
+    arguments: Optional[list[str]]
+    environment: Optional[dict[str, Any]]
+    secrets: Optional[dict[str, Any]]
+    flavor: Optional[SpaceHardware]
+    labels: Optional[dict[str, str]]
+    status: JobStatus
+    owner: JobOwner
+
+    # Inferred fields
+    endpoint: str
+    url: str
+
+    def __init__(self, **kwargs) -> None:
+        self.id = kwargs["id"]
+        created_at = kwargs.get("createdAt") or kwargs.get("created_at")
+        self.created_at = parse_datetime(created_at) if created_at else None
+        self.docker_image = kwargs.get("dockerImage") or kwargs.get("docker_image")
+        self.space_id = kwargs.get("spaceId") or kwargs.get("space_id")
+        owner = kwargs.get("owner", {})
+        self.owner = JobOwner(id=owner["id"], name=owner["name"], type=owner["type"])
+        self.command = kwargs.get("command")
+        self.arguments = kwargs.get("arguments")
+        self.environment = kwargs.get("environment")
+        self.secrets = kwargs.get("secrets")
+        self.flavor = kwargs.get("flavor")
+        self.labels = kwargs.get("labels")
+        status = kwargs.get("status", {})
+        self.status = JobStatus(stage=status["stage"], message=status.get("message"))
+
+        # Inferred fields
+        self.endpoint = kwargs.get("endpoint", constants.ENDPOINT)
+        self.url = f"{self.endpoint}/jobs/{self.owner.name}/{self.id}"
+
+
+@dataclass
+class JobSpec:
+    docker_image: Optional[str]
+    space_id: Optional[str]
+    command: Optional[list[str]]
+    arguments: Optional[list[str]]
+    environment: Optional[dict[str, Any]]
+    secrets: Optional[dict[str, Any]]
+    flavor: Optional[SpaceHardware]
+    timeout: Optional[int]
+    tags: Optional[list[str]]
+    arch: Optional[str]
+    labels: Optional[dict[str, str]]
+
+    def __init__(self, **kwargs) -> None:
+        self.docker_image = kwargs.get("dockerImage") or kwargs.get("docker_image")
+        self.space_id = kwargs.get("spaceId") or kwargs.get("space_id")
+        self.command = kwargs.get("command")
+        self.arguments = kwargs.get("arguments")
+        self.environment = kwargs.get("environment")
+        self.secrets = kwargs.get("secrets")
+        self.flavor = kwargs.get("flavor")
+        self.timeout = kwargs.get("timeout")
+        self.tags = kwargs.get("tags")
+        self.arch = kwargs.get("arch")
+        self.labels = kwargs.get("labels")
+
+
+@dataclass
+class LastJobInfo:
+    id: str
+    at: datetime
+
+    def __init__(self, **kwargs) -> None:
+        self.id = kwargs["id"]
+        self.at = parse_datetime(kwargs["at"])
+
+
+@dataclass
+class ScheduledJobStatus:
+    last_job: Optional[LastJobInfo]
+    next_job_run_at: Optional[datetime]
+
+    def __init__(self, **kwargs) -> None:
+        last_job = kwargs.get("lastJob") or kwargs.get("last_job")
+        self.last_job = LastJobInfo(**last_job) if last_job else None
+        next_job_run_at = kwargs.get("nextJobRunAt") or kwargs.get("next_job_run_at")
+        self.next_job_run_at = parse_datetime(str(next_job_run_at)) if next_job_run_at else None
+
+
+@dataclass
+class ScheduledJobInfo:
+    """
+    Contains information about a Job.
+
+    Args:
+        id (`str`):
+            Scheduled Job ID.
+        created_at (`datetime` or `None`):
+            When the scheduled Job was created.
+        tags (`list[str]` or `None`):
+            The tags of the scheduled Job.
+        schedule (`str` or `None`):
+            One of "@annually", "@yearly", "@monthly", "@weekly", "@daily", "@hourly", or a
+            CRON schedule expression (e.g., '0 9 * * 1' for 9 AM every Monday).
+        suspend (`bool` or `None`):
+            Whether the scheduled job is suspended (paused).
+        concurrency (`bool` or `None`):
+            Whether multiple instances of this Job can run concurrently.
+        status (`ScheduledJobStatus` or `None`):
+            Status of the scheduled Job.
+        owner: (`JobOwner` or `None`):
+            Owner of the scheduled Job, e.g. `JobOwner(id="5e9ecfc04957053f60648a3e", name="lhoestq", type="user")`
+        job_spec: (`JobSpec` or `None`):
+            Specifications of the Job.
+
+    Example:
+
+    ```python
+    >>> from huggingface_hub import run_job
+    >>> scheduled_job = create_scheduled_job(
+    ...     image="python:3.12",
+    ...     command=["python", "-c", "print('Hello from the cloud!')"],
+    ...     schedule="@hourly",
+    ... )
+    >>> scheduled_job.id
+    '687fb701029421ae5549d999'
+    >>> scheduled_job.status.next_job_run_at
+    datetime.datetime(2025, 7, 22, 17, 6, 25, 79000, tzinfo=datetime.timezone.utc)
+    ```
+    """
+
+    id: str
+    created_at: Optional[datetime]
+    job_spec: JobSpec
+    schedule: Optional[str]
+    suspend: Optional[bool]
+    concurrency: Optional[bool]
+    status: ScheduledJobStatus
+    owner: JobOwner
+
+    def __init__(self, **kwargs) -> None:
+        self.id = kwargs["id"]
+        created_at = kwargs.get("createdAt") or kwargs.get("created_at")
+        self.created_at = parse_datetime(created_at) if created_at else None
+        self.job_spec = JobSpec(**(kwargs.get("job_spec") or kwargs.get("jobSpec", {})))
+        self.schedule = kwargs.get("schedule")
+        self.suspend = kwargs.get("suspend")
+        self.concurrency = kwargs.get("concurrency")
+        status = kwargs.get("status", {})
+        self.status = ScheduledJobStatus(
+            last_job=status.get("last_job") or status.get("lastJob"),
+            next_job_run_at=status.get("next_job_run_at") or status.get("nextJobRunAt"),
+        )
+        owner = kwargs.get("owner", {})
+        self.owner = JobOwner(id=owner["id"], name=owner["name"], type=owner["type"])
+
+
+@dataclass
+class JobAccelerator:
+    """
+    Contains information about a Job accelerator (GPU).
+
+    Args:
+        type (`str`):
+            Type of accelerator, e.g. `"gpu"`.
+        model (`str`):
+            Model of accelerator, e.g. `"T4"`, `"A10G"`, `"A100"`, `"L4"`, `"L40S"`.
+        quantity (`str`):
+            Number of accelerators, e.g. `"1"`, `"2"`, `"4"`, `"8"`.
+        vram (`str`):
+            Total VRAM, e.g. `"16 GB"`, `"24 GB"`.
+        manufacturer (`str`):
+            Manufacturer of the accelerator, e.g. `"Nvidia"`.
+    """
+
+    type: str
+    model: str
+    quantity: str
+    vram: str
+    manufacturer: str
+
+    def __init__(self, **kwargs) -> None:
+        self.type = kwargs["type"]
+        self.model = kwargs["model"]
+        self.quantity = kwargs["quantity"]
+        self.vram = kwargs["vram"]
+        self.manufacturer = kwargs["manufacturer"]
+
+
+@dataclass
+class JobHardware:
+    """
+    Contains information about available Job hardware.
+
+    Args:
+        name (`str`):
+            Machine identifier, e.g. `"cpu-basic"`, `"a10g-large"`.
+        pretty_name (`str`):
+            Human-readable name, e.g. `"CPU Basic"`, `"Nvidia A10G - large"`.
+        cpu (`str`):
+            CPU specification, e.g. `"2 vCPU"`, `"12 vCPU"`.
+        ram (`str`):
+            RAM specification, e.g. `"16 GB"`, `"46 GB"`.
+        accelerator (`JobAccelerator` or `None`):
+            GPU/accelerator details if available.
+        unit_cost_micro_usd (`int`):
+            Cost in micro-dollars per unit, e.g. `167` (= $0.000167).
+        unit_cost_usd (`float`):
+            Cost in USD per unit, e.g. `0.000167`.
+        unit_label (`str`):
+            Cost unit period, e.g. `"minute"`.
+
+    Example:
+
+    ```python
+    >>> from huggingface_hub import list_jobs_hardware
+    >>> hardware_list = list_jobs_hardware()
+    >>> hardware_list[0]
+    JobHardware(name='cpu-basic', pretty_name='CPU Basic', cpu='2 vCPU', ram='16 GB', accelerator=None, unit_cost_micro_usd=167, unit_cost_usd=0.000167, unit_label='minute')
+    >>> hardware_list[0].name
+    'cpu-basic'
+    ```
+    """
+
+    name: str
+    pretty_name: str
+    cpu: str
+    ram: str
+    accelerator: Optional[JobAccelerator]
+    unit_cost_micro_usd: int
+    unit_cost_usd: float
+    unit_label: str
+
+    def __init__(self, **kwargs) -> None:
+        self.name = kwargs["name"]
+        self.pretty_name = kwargs["prettyName"]
+        self.cpu = kwargs["cpu"]
+        self.ram = kwargs["ram"]
+        accelerator = kwargs.get("accelerator")
+        self.accelerator = JobAccelerator(**accelerator) if accelerator else None
+        self.unit_cost_micro_usd = kwargs["unitCostMicroUSD"]
+        self.unit_cost_usd = kwargs["unitCostUSD"]
+        self.unit_label = kwargs["unitLabel"]
+
+
+def _create_job_spec(
+    *,
+    image: str,
+    command: list[str],
+    env: Optional[dict[str, Any]],
+    secrets: Optional[dict[str, Any]],
+    flavor: Optional[SpaceHardware],
+    timeout: Optional[Union[int, float, str]],
+    labels: Optional[dict[str, str]] = None,
+) -> dict[str, Any]:
+    # prepare job spec to send to HF Jobs API
+    job_spec: dict[str, Any] = {
+        "command": command,
+        "arguments": [],
+        "environment": env or {},
+        "flavor": flavor or SpaceHardware.CPU_BASIC,
+    }
+    # secrets are optional
+    if secrets:
+        job_spec["secrets"] = secrets
+    # timeout is optional
+    if timeout:
+        time_units_factors = {"s": 1, "m": 60, "h": 3600, "d": 3600 * 24}
+        if isinstance(timeout, str) and timeout[-1] in time_units_factors:
+            job_spec["timeoutSeconds"] = int(float(timeout[:-1]) * time_units_factors[timeout[-1]])
+        else:
+            job_spec["timeoutSeconds"] = int(timeout)
+    # labels are optional
+    if labels:
+        job_spec["labels"] = labels
+    # input is either from docker hub or from HF spaces
+    for prefix in (
+        "https://huggingface.co/spaces/",
+        "https://hf.co/spaces/",
+        "huggingface.co/spaces/",
+        "hf.co/spaces/",
+    ):
+        if image.startswith(prefix):
+            job_spec["spaceId"] = image[len(prefix) :]
+            break
+    else:
+        job_spec["dockerImage"] = image
+    return job_spec

data/conda_envs/evalclaw/lib/python3.11/site-packages/huggingface_hub/_local_folder.py

@@ -0,0 +1,451 @@
+# coding=utf-8
+# Copyright 2024-present, the HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Contains utilities to handle the `../.cache/huggingface` folder in local directories.
+
+First discussed in https://github.com/huggingface/huggingface_hub/issues/1738 to store
+download metadata when downloading files from the hub to a local directory (without
+using the cache).
+
+./.cache/huggingface folder structure:
+[4.0K]  data
+├── [4.0K]  .cache
+│   └── [4.0K]  huggingface
+│       └── [4.0K]  download
+│           ├── [  16]  file.parquet.metadata
+│           ├── [  16]  file.txt.metadata
+│           └── [4.0K]  folder
+│               └── [  16]  file.parquet.metadata
+│
+├── [6.5G]  file.parquet
+├── [1.5K]  file.txt
+└── [4.0K]  folder
+    └── [   16]  file.parquet
+
+
+Download metadata file structure:
+```
+# file.txt.metadata
+11c5a3d5811f50298f278a704980280950aedb10
+a16a55fda99d2f2e7b69cce5cf93ff4ad3049930
+1712656091.123
+
+# file.parquet.metadata
+11c5a3d5811f50298f278a704980280950aedb10
+7c5d3f4b8b76583b422fcb9189ad6c89d5d97a094541ce8932dce3ecabde1421
+1712656091.123
+}
+```
+"""
+
+import base64
+import hashlib
+import logging
+import os
+import time
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Optional
+
+from .utils import WeakFileLock
+
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class LocalDownloadFilePaths:
+    """
+    Paths to the files related to a download process in a local dir.
+
+    Returned by [`get_local_download_paths`].
+
+    Attributes:
+        file_path (`Path`):
+            Path where the file will be saved.
+        lock_path (`Path`):
+            Path to the lock file used to ensure atomicity when reading/writing metadata.
+        metadata_path (`Path`):
+            Path to the metadata file.
+    """
+
+    file_path: Path
+    lock_path: Path
+    metadata_path: Path
+
+    def incomplete_path(self, etag: str) -> Path:
+        """Return the path where a file will be temporarily downloaded before being moved to `file_path`."""
+        path = self.metadata_path.parent / f"{_short_hash(self.metadata_path.name)}.{etag}.incomplete"
+        resolved_path = str(path.resolve())
+        # Some Windows versions do not allow for paths longer than 255 characters.
+        # In this case, we must specify it as an extended path by using the "\\?\" prefix.
+        if os.name == "nt" and len(resolved_path) > 255 and not resolved_path.startswith("\\\\?\\"):
+            path = Path("\\\\?\\" + resolved_path)
+        return path
+
+
+@dataclass(frozen=True)
+class LocalUploadFilePaths:
+    """
+    Paths to the files related to an upload process in a local dir.
+
+    Returned by [`get_local_upload_paths`].
+
+    Attributes:
+        path_in_repo (`str`):
+            Path of the file in the repo.
+        file_path (`Path`):
+            Path where the file will be saved.
+        lock_path (`Path`):
+            Path to the lock file used to ensure atomicity when reading/writing metadata.
+        metadata_path (`Path`):
+            Path to the metadata file.
+    """
+
+    path_in_repo: str
+    file_path: Path
+    lock_path: Path
+    metadata_path: Path
+
+
+@dataclass
+class LocalDownloadFileMetadata:
+    """
+    Metadata about a file in the local directory related to a download process.
+
+    Attributes:
+        filename (`str`):
+            Path of the file in the repo.
+        commit_hash (`str`):
+            Commit hash of the file in the repo.
+        etag (`str`):
+            ETag of the file in the repo. Used to check if the file has changed.
+            For LFS files, this is the sha256 of the file. For regular files, it corresponds to the git hash.
+        timestamp (`int`):
+            Unix timestamp of when the metadata was saved i.e. when the metadata was accurate.
+    """
+
+    filename: str
+    commit_hash: str
+    etag: str
+    timestamp: float
+
+
+@dataclass
+class LocalUploadFileMetadata:
+    """
+    Metadata about a file in the local directory related to an upload process.
+    """
+
+    size: int
+
+    # Default values correspond to "we don't know yet"
+    timestamp: Optional[float] = None
+    should_ignore: Optional[bool] = None
+    sha256: Optional[str] = None
+    upload_mode: Optional[str] = None
+    remote_oid: Optional[str] = None
+    is_uploaded: bool = False
+    is_committed: bool = False
+
+    def save(self, paths: LocalUploadFilePaths) -> None:
+        """Save the metadata to disk."""
+        with WeakFileLock(paths.lock_path):
+            with paths.metadata_path.open("w") as f:
+                new_timestamp = time.time()
+                f.write(str(new_timestamp) + "\n")
+
+                f.write(str(self.size))  # never None
+                f.write("\n")
+
+                if self.should_ignore is not None:
+                    f.write(str(int(self.should_ignore)))
+                f.write("\n")
+
+                if self.sha256 is not None:
+                    f.write(self.sha256)
+                f.write("\n")
+
+                if self.upload_mode is not None:
+                    f.write(self.upload_mode)
+                f.write("\n")
+
+                if self.remote_oid is not None:
+                    f.write(self.remote_oid)
+                f.write("\n")
+
+                f.write(str(int(self.is_uploaded)) + "\n")
+                f.write(str(int(self.is_committed)) + "\n")
+
+            self.timestamp = new_timestamp
+
+
+def get_local_download_paths(local_dir: Path, filename: str) -> LocalDownloadFilePaths:
+    """Compute paths to the files related to a download process.
+
+    Folders containing the paths are all guaranteed to exist.
+
+    Args:
+        local_dir (`Path`):
+            Path to the local directory in which files are downloaded.
+        filename (`str`):
+            Path of the file in the repo.
+
+    Return:
+        [`LocalDownloadFilePaths`]: the paths to the files (file_path, lock_path, metadata_path, incomplete_path).
+    """
+    # filename is the path in the Hub repository (separated by '/')
+    # make sure to have a cross-platform transcription
+    sanitized_filename = os.path.join(*filename.split("/"))
+    if os.name == "nt":
+        if sanitized_filename.startswith("..\\") or "\\..\\" in sanitized_filename:
+            raise ValueError(
+                f"Invalid filename: cannot handle filename '{sanitized_filename}' on Windows. Please ask the repository"
+                " owner to rename this file."
+            )
+    file_path = local_dir / sanitized_filename
+    metadata_path = _huggingface_dir(local_dir) / "download" / f"{sanitized_filename}.metadata"
+    lock_path = metadata_path.with_suffix(".lock")
+
+    # Some Windows versions do not allow for paths longer than 255 characters.
+    # In this case, we must specify it as an extended path by using the "\\?\" prefix
+    if os.name == "nt":
+        if not str(local_dir).startswith("\\\\?\\") and len(os.path.abspath(lock_path)) > 255:
+            file_path = Path("\\\\?\\" + os.path.abspath(file_path))
+            lock_path = Path("\\\\?\\" + os.path.abspath(lock_path))
+            metadata_path = Path("\\\\?\\" + os.path.abspath(metadata_path))
+
+    file_path.parent.mkdir(parents=True, exist_ok=True)
+    metadata_path.parent.mkdir(parents=True, exist_ok=True)
+    return LocalDownloadFilePaths(file_path=file_path, lock_path=lock_path, metadata_path=metadata_path)
+
+
+def get_local_upload_paths(local_dir: Path, filename: str) -> LocalUploadFilePaths:
+    """Compute paths to the files related to an upload process.
+
+    Folders containing the paths are all guaranteed to exist.
+
+    Args:
+        local_dir (`Path`):
+            Path to the local directory that is uploaded.
+        filename (`str`):
+            Path of the file in the repo.
+
+    Return:
+        [`LocalUploadFilePaths`]: the paths to the files (file_path, lock_path, metadata_path).
+    """
+    # filename is the path in the Hub repository (separated by '/')
+    # make sure to have a cross-platform transcription
+    sanitized_filename = os.path.join(*filename.split("/"))
+    if os.name == "nt":
+        if sanitized_filename.startswith("..\\") or "\\..\\" in sanitized_filename:
+            raise ValueError(
+                f"Invalid filename: cannot handle filename '{sanitized_filename}' on Windows. Please ask the repository"
+                " owner to rename this file."
+            )
+    file_path = local_dir / sanitized_filename
+    metadata_path = _huggingface_dir(local_dir) / "upload" / f"{sanitized_filename}.metadata"
+    lock_path = metadata_path.with_suffix(".lock")
+
+    # Some Windows versions do not allow for paths longer than 255 characters.
+    # In this case, we must specify it as an extended path by using the "\\?\" prefix
+    if os.name == "nt":
+        if not str(local_dir).startswith("\\\\?\\") and len(os.path.abspath(lock_path)) > 255:
+            file_path = Path("\\\\?\\" + os.path.abspath(file_path))
+            lock_path = Path("\\\\?\\" + os.path.abspath(lock_path))
+            metadata_path = Path("\\\\?\\" + os.path.abspath(metadata_path))
+
+    file_path.parent.mkdir(parents=True, exist_ok=True)
+    metadata_path.parent.mkdir(parents=True, exist_ok=True)
+    return LocalUploadFilePaths(
+        path_in_repo=filename, file_path=file_path, lock_path=lock_path, metadata_path=metadata_path
+    )
+
+
+def read_download_metadata(local_dir: Path, filename: str) -> Optional[LocalDownloadFileMetadata]:
+    """Read metadata about a file in the local directory related to a download process.
+
+    Args:
+        local_dir (`Path`):
+            Path to the local directory in which files are downloaded.
+        filename (`str`):
+            Path of the file in the repo.
+
+    Return:
+        `[LocalDownloadFileMetadata]` or `None`: the metadata if it exists, `None` otherwise.
+    """
+    paths = get_local_download_paths(local_dir, filename)
+    with WeakFileLock(paths.lock_path):
+        if paths.metadata_path.exists():
+            try:
+                with paths.metadata_path.open() as f:
+                    commit_hash = f.readline().strip()
+                    etag = f.readline().strip()
+                    timestamp = float(f.readline().strip())
+                    metadata = LocalDownloadFileMetadata(
+                        filename=filename,
+                        commit_hash=commit_hash,
+                        etag=etag,
+                        timestamp=timestamp,
+                    )
+            except Exception as e:
+                # remove the metadata file if it is corrupted / not the right format
+                logger.warning(
+                    f"Invalid metadata file {paths.metadata_path}: {e}. Removing it from disk and continue."
+                )
+                try:
+                    paths.metadata_path.unlink()
+                except Exception as e:
+                    logger.warning(f"Could not remove corrupted metadata file {paths.metadata_path}: {e}")
+                return None
+
+            try:
+                # check if the file exists and hasn't been modified since the metadata was saved
+                stat = paths.file_path.stat()
+                if (
+                    stat.st_mtime - 1 <= metadata.timestamp
+                ):  # allow 1s difference as stat.st_mtime might not be precise
+                    return metadata
+                logger.info(f"Ignored metadata for '{filename}' (outdated). Will re-compute hash.")
+            except FileNotFoundError:
+                # file does not exist => metadata is outdated
+                return None
+    return None
+
+
+def read_upload_metadata(local_dir: Path, filename: str) -> LocalUploadFileMetadata:
+    """Read metadata about a file in the local directory related to an upload process.
+
+    TODO: factorize logic with `read_download_metadata`.
+
+    Args:
+        local_dir (`Path`):
+            Path to the local directory in which files are downloaded.
+        filename (`str`):
+            Path of the file in the repo.
+
+    Return:
+        `[LocalUploadFileMetadata]` or `None`: the metadata if it exists, `None` otherwise.
+    """
+    paths = get_local_upload_paths(local_dir, filename)
+    with WeakFileLock(paths.lock_path):
+        if paths.metadata_path.exists():
+            try:
+                with paths.metadata_path.open() as f:
+                    timestamp = float(f.readline().strip())
+
+                    size = int(f.readline().strip())  # never None
+
+                    _should_ignore = f.readline().strip()
+                    should_ignore = None if _should_ignore == "" else bool(int(_should_ignore))
+
+                    _sha256 = f.readline().strip()
+                    sha256 = None if _sha256 == "" else _sha256
+
+                    _upload_mode = f.readline().strip()
+                    upload_mode = None if _upload_mode == "" else _upload_mode
+                    if upload_mode not in (None, "regular", "lfs"):
+                        raise ValueError(f"Invalid upload mode in metadata {paths.path_in_repo}: {upload_mode}")
+
+                    _remote_oid = f.readline().strip()
+                    remote_oid = None if _remote_oid == "" else _remote_oid
+
+                    is_uploaded = bool(int(f.readline().strip()))
+                    is_committed = bool(int(f.readline().strip()))
+
+                    metadata = LocalUploadFileMetadata(
+                        timestamp=timestamp,
+                        size=size,
+                        should_ignore=should_ignore,
+                        sha256=sha256,
+                        upload_mode=upload_mode,
+                        remote_oid=remote_oid,
+                        is_uploaded=is_uploaded,
+                        is_committed=is_committed,
+                    )
+            except Exception as e:
+                # remove the metadata file if it is corrupted / not the right format
+                logger.warning(
+                    f"Invalid metadata file {paths.metadata_path}: {e}. Removing it from disk and continue."
+                )
+                try:
+                    paths.metadata_path.unlink()
+                except Exception as e:
+                    logger.warning(f"Could not remove corrupted metadata file {paths.metadata_path}: {e}")
+
+                # corrupted metadata => we don't know anything expect its size
+                return LocalUploadFileMetadata(size=paths.file_path.stat().st_size)
+
+            # TODO: can we do better?
+            if (
+                metadata.timestamp is not None
+                and metadata.is_uploaded  # file was uploaded
+                and not metadata.is_committed  # but not committed
+                and time.time() - metadata.timestamp > 20 * 3600  # and it's been more than 20 hours
+            ):  # => we consider it as garbage-collected by S3
+                metadata.is_uploaded = False
+
+            # check if the file exists and hasn't been modified since the metadata was saved
+            try:
+                if metadata.timestamp is not None and paths.file_path.stat().st_mtime <= metadata.timestamp:
+                    return metadata
+                logger.info(f"Ignored metadata for '{filename}' (outdated). Will re-compute hash.")
+            except FileNotFoundError:
+                # file does not exist => metadata is outdated
+                pass
+
+    # empty metadata => we don't know anything expect its size
+    return LocalUploadFileMetadata(size=paths.file_path.stat().st_size)
+
+
+def write_download_metadata(local_dir: Path, filename: str, commit_hash: str, etag: str) -> None:
+    """Write metadata about a file in the local directory related to a download process.
+
+    Args:
+        local_dir (`Path`):
+            Path to the local directory in which files are downloaded.
+    """
+    paths = get_local_download_paths(local_dir, filename)
+    with WeakFileLock(paths.lock_path):
+        with paths.metadata_path.open("w") as f:
+            f.write(f"{commit_hash}\n{etag}\n{time.time()}\n")
+
+
+def _huggingface_dir(local_dir: Path) -> Path:
+    """Return the path to the `.cache/huggingface` directory in a local directory."""
+    # Wrap in lru_cache to avoid overwriting the .gitignore file if called multiple times
+    path = local_dir / ".cache" / "huggingface"
+    path.mkdir(exist_ok=True, parents=True)
+
+    # Create a .gitignore file in the .cache/huggingface directory if it doesn't exist
+    # Should be thread-safe enough like this.
+    gitignore = path / ".gitignore"
+    gitignore_lock = path / ".gitignore.lock"
+    if not gitignore.exists():
+        try:
+            with WeakFileLock(gitignore_lock, timeout=0.1):
+                gitignore.write_text("*")
+        except IndexError:
+            pass
+        except OSError:  # TimeoutError, FileNotFoundError, PermissionError, etc.
+            pass
+        try:
+            gitignore_lock.unlink()
+        except OSError:
+            pass
+    return path
+
+
+def _short_hash(filename: str) -> str:
+    return base64.urlsafe_b64encode(hashlib.sha1(filename.encode()).digest()).decode()

data/conda_envs/evalclaw/lib/python3.11/site-packages/huggingface_hub/_login.py

@@ -0,0 +1,495 @@
+# Copyright 2020 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Contains methods to log in to the Hub."""
+
+import os
+import subprocess
+from getpass import getpass
+from pathlib import Path
+from typing import Optional
+
+import typer
+
+from . import constants
+from .utils import (
+    ANSI,
+    capture_output,
+    get_token,
+    is_google_colab,
+    is_notebook,
+    list_credential_helpers,
+    logging,
+    run_subprocess,
+    set_git_credential,
+    unset_git_credential,
+)
+from .utils._auth import (
+    _get_token_by_name,
+    _get_token_from_environment,
+    _get_token_from_file,
+    _get_token_from_google_colab,
+    _save_stored_tokens,
+    _save_token,
+    get_stored_tokens,
+)
+
+
+logger = logging.get_logger(__name__)
+
+_HF_LOGO_ASCII = """
+    _|    _|  _|    _|    _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|_|_|_|    _|_|      _|_|_|  _|_|_|_|
+    _|    _|  _|    _|  _|        _|          _|    _|_|    _|  _|            _|        _|    _|  _|        _|
+    _|_|_|_|  _|    _|  _|  _|_|  _|  _|_|    _|    _|  _|  _|  _|  _|_|      _|_|_|    _|_|_|_|  _|        _|_|_|
+    _|    _|  _|    _|  _|    _|  _|    _|    _|    _|    _|_|  _|    _|      _|        _|    _|  _|        _|
+    _|    _|    _|_|      _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|        _|    _|    _|_|_|  _|_|_|_|
+"""
+
+
+def login(
+    token: Optional[str] = None,
+    *,
+    add_to_git_credential: bool = False,
+    skip_if_logged_in: bool = True,
+) -> None:
+    """Login the machine to access the Hub.
+
+    The `token` is persisted in cache and set as a git credential. Once done, the machine
+    is logged in and the access token will be available across all `huggingface_hub`
+    components. If `token` is not provided, it will be prompted to the user either with
+    a widget (in a notebook) or via the terminal.
+
+    To log in from outside of a script, one can also use `hf auth login` which is
+    a cli command that wraps [`login`].
+
+    > [!TIP]
+    > [`login`] is a drop-in replacement method for [`notebook_login`] as it wraps and
+    > extends its capabilities.
+
+    > [!TIP]
+    > When the token is not passed, [`login`] will automatically detect if the script runs
+    > in a notebook or not. However, this detection might not be accurate due to the
+    > variety of notebooks that exists nowadays. If that is the case, you can always force
+    > the UI by using [`notebook_login`] or [`interpreter_login`].
+
+    Args:
+        token (`str`, *optional*):
+            User access token to generate from https://huggingface.co/settings/token.
+        add_to_git_credential (`bool`, defaults to `False`):
+            If `True`, token will be set as git credential. If no git credential helper
+            is configured, a warning will be displayed to the user. If `token` is `None`,
+            the value of `add_to_git_credential` is ignored and will be prompted again
+            to the end user.
+        skip_if_logged_in (`bool`, defaults to `True`):
+            If `True`, do not prompt for token if user is already logged in.
+            Set to `False` to force re-login. In CLI, use `--force` instead.
+    Raises:
+        [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError)
+            If an organization token is passed. Only personal account tokens are valid
+            to log in.
+        [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError)
+            If token is invalid.
+        [`ImportError`](https://docs.python.org/3/library/exceptions.html#ImportError)
+            If running in a notebook but `ipywidgets` is not installed.
+    """
+    if token is not None:
+        if not add_to_git_credential:
+            logger.info(
+                "The token has not been saved to the git credentials helper. Pass "
+                "`add_to_git_credential=True` in this function directly or "
+                "`--add-to-git-credential` if using via `hf`CLI if "
+                "you want to set the git credential as well."
+            )
+        _login(token, add_to_git_credential=add_to_git_credential)
+    elif is_notebook():
+        notebook_login(skip_if_logged_in=skip_if_logged_in)
+    else:
+        interpreter_login(skip_if_logged_in=skip_if_logged_in)
+
+
+def logout(token_name: Optional[str] = None) -> None:
+    """Logout the machine from the Hub.
+
+    Token is deleted from the machine and removed from git credential.
+
+    Args:
+        token_name (`str`, *optional*):
+            Name of the access token to logout from. If `None`, will log out from all saved access tokens.
+    Raises:
+        [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError):
+            If the access token name is not found.
+    """
+    if get_token() is None and not get_stored_tokens():  # No active token and no saved access tokens
+        logger.warning("Not logged in!")
+        return
+    if not token_name:
+        # Delete all saved access tokens and token
+        for file_path in (constants.HF_TOKEN_PATH, constants.HF_STORED_TOKENS_PATH):
+            try:
+                Path(file_path).unlink()
+            except FileNotFoundError:
+                pass
+        logger.info("Successfully logged out from all access tokens.")
+    else:
+        _logout_from_token(token_name)
+        logger.info(f"Successfully logged out from access token: {token_name}.")
+
+    unset_git_credential()
+
+    # Check if still logged in
+    if _get_token_from_google_colab() is not None:
+        raise EnvironmentError(
+            "You are automatically logged in using a Google Colab secret.\n"
+            "To log out, you must unset the `HF_TOKEN` secret in your Colab settings."
+        )
+    if _get_token_from_environment() is not None:
+        raise EnvironmentError(
+            "Token has been deleted from your machine but you are still logged in.\n"
+            "To log out, you must clear out both `HF_TOKEN` and `HUGGING_FACE_HUB_TOKEN` environment variables."
+        )
+
+
+def auth_switch(token_name: str, add_to_git_credential: bool = False) -> None:
+    """Switch to a different access token.
+
+    Args:
+        token_name (`str`):
+            Name of the access token to switch to.
+        add_to_git_credential (`bool`, defaults to `False`):
+            If `True`, token will be set as git credential. If no git credential helper
+            is configured, a warning will be displayed to the user. If `token` is `None`,
+            the value of `add_to_git_credential` is ignored and will be prompted again
+            to the end user.
+
+    Raises:
+        [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError):
+            If the access token name is not found.
+    """
+    token = _get_token_by_name(token_name)
+    if not token:
+        raise ValueError(f"Access token {token_name} not found in {constants.HF_STORED_TOKENS_PATH}")
+    # Write token to HF_TOKEN_PATH
+    _set_active_token(token_name, add_to_git_credential)
+    logger.info(f"The current active token is: {token_name}")
+    token_from_environment = _get_token_from_environment()
+    if token_from_environment is not None and token_from_environment != token:
+        logger.warning(
+            "The environment variable `HF_TOKEN` is set and will override the access token you've just switched to."
+        )
+
+
+def auth_list() -> None:
+    """List all stored access tokens."""
+    tokens = get_stored_tokens()
+
+    if not tokens:
+        if _get_token_from_environment():
+            logger.info("No stored access tokens found.")
+            logger.warning("Note: Environment variable `HF_TOKEN` is set and is the current active token.")
+        else:
+            logger.info("No access tokens found.")
+        return
+    # Find current token
+    current_token = get_token()
+    current_token_name = None
+    for token_name in tokens:
+        if tokens.get(token_name) == current_token:
+            current_token_name = token_name
+    # Print header
+    max_offset = max(len("token"), max(len(token) for token in tokens)) + 2
+    print(f"  {{:<{max_offset}}}| {{:<15}}".format("name", "token"))
+    print("-" * (max_offset + 2) + "|" + "-" * 15)
+
+    # Print saved access tokens
+    for token_name in tokens:
+        token = tokens.get(token_name, "<not set>")
+        masked_token = f"{token[:3]}****{token[-4:]}" if token != "<not set>" else token
+        is_current = "*" if token == current_token else " "
+
+        print(f"{is_current} {{:<{max_offset}}}| {{:<15}}".format(token_name, masked_token))
+
+    if _get_token_from_environment():
+        logger.warning(
+            "\nNote: Environment variable `HF_TOKEN` is set and is the current active token independently from the stored tokens listed above."
+        )
+    elif current_token_name is None:
+        logger.warning(
+            "\nNote: No active token is set and no environment variable `HF_TOKEN` is found. Use `hf auth login` to log in."
+        )
+
+
+###
+# Interpreter-based login (text)
+###
+
+
+def interpreter_login(*, skip_if_logged_in: bool = True) -> None:
+    """
+    Displays a prompt to log in to the HF website and store the token.
+
+    This is equivalent to [`login`] without passing a token when not run in a notebook.
+    [`interpreter_login`] is useful if you want to force the use of the terminal prompt
+    instead of a notebook widget.
+
+    For more details, see [`login`].
+
+    Args:
+        skip_if_logged_in (`bool`, defaults to `True`):
+            If `True`, do not prompt for token if user is already logged in.
+            Set to `False` to force re-login. In CLI, use `--force` instead.
+    """
+    if skip_if_logged_in and get_token() is not None:
+        logger.info("User is already logged in. Use `hf auth login --force` to force re-login.")
+        return
+
+    print(_HF_LOGO_ASCII)
+    if get_token() is not None:
+        logger.info(
+            "    A token is already saved on your machine. Run `hf auth whoami`"
+            " to get more information or `hf auth logout` if you want"
+            " to log out."
+        )
+        logger.info("    Setting a new token will erase the existing one.")
+
+    logger.info(
+        "    To log in, `huggingface_hub` requires a token generated from https://huggingface.co/settings/tokens ."
+    )
+    if os.name == "nt":
+        logger.info("Token can be pasted using 'Right-Click'.")
+    token = getpass("Enter your token (input will not be visible): ")
+    add_to_git_credential = typer.confirm("Add token as git credential?")
+
+    _login(token=token, add_to_git_credential=add_to_git_credential)
+
+
+###
+# Notebook-based login (widget)
+###
+
+NOTEBOOK_LOGIN_PASSWORD_HTML = """<center> <img
+src=https://huggingface.co/front/assets/huggingface_logo-noborder.svg
+alt='Hugging Face'> <br> Immediately click login after typing your password or
+it might be stored in plain text in this notebook file. </center>"""
+
+
+NOTEBOOK_LOGIN_TOKEN_HTML_START = """<center> <img
+src=https://huggingface.co/front/assets/huggingface_logo-noborder.svg
+alt='Hugging Face'> <br> Copy a token from <a
+href="https://huggingface.co/settings/tokens" target="_blank">your Hugging Face
+tokens page</a> and paste it below. <br> Immediately click login after copying
+your token or it might be stored in plain text in this notebook file. </center>"""
+
+
+NOTEBOOK_LOGIN_TOKEN_HTML_END = """
+<b>Pro Tip:</b> If you don't already have one, you can create a dedicated
+'notebooks' token with 'write' access, that you can then easily reuse for all
+notebooks. </center>"""
+
+
+def notebook_login(*, skip_if_logged_in: bool = True) -> None:
+    """
+    Displays a widget to log in to the HF website and store the token.
+
+    This is equivalent to [`login`] without passing a token when run in a notebook.
+    [`notebook_login`] is useful if you want to force the use of the notebook widget
+    instead of a prompt in the terminal.
+
+    For more details, see [`login`].
+
+    Args:
+        skip_if_logged_in (`bool`, defaults to `True`):
+            If `True`, do not prompt for token if user is already logged in.
+            Set to `False` to force re-login. In CLI, use `--force` instead.
+    """
+    try:
+        import ipywidgets.widgets as widgets  # type: ignore
+        from IPython.display import display  # type: ignore
+    except ImportError:
+        raise ImportError(
+            "The `notebook_login` function can only be used in a notebook (Jupyter or"
+            " Colab) and you need the `ipywidgets` module: `pip install ipywidgets`."
+        )
+    if skip_if_logged_in and get_token() is not None:
+        logger.info("User is already logged in. Use `hf auth login --force` to force re-login.")
+        return
+
+    box_layout = widgets.Layout(display="flex", flex_flow="column", align_items="center", width="50%")
+
+    token_widget = widgets.Password(description="Token:")
+    git_checkbox_widget = widgets.Checkbox(value=True, description="Add token as git credential?")
+    token_finish_button = widgets.Button(description="Login")
+
+    login_token_widget = widgets.VBox(
+        [
+            widgets.HTML(NOTEBOOK_LOGIN_TOKEN_HTML_START),
+            token_widget,
+            git_checkbox_widget,
+            token_finish_button,
+            widgets.HTML(NOTEBOOK_LOGIN_TOKEN_HTML_END),
+        ],
+        layout=box_layout,
+    )
+    display(login_token_widget)
+
+    # On click events
+    def login_token_event(t):
+        """Event handler for the login button."""
+        token = token_widget.value
+        add_to_git_credential = git_checkbox_widget.value
+        # Erase token and clear value to make sure it's not saved in the notebook.
+        token_widget.value = ""
+        # Hide inputs
+        login_token_widget.children = [widgets.Label("Connecting...")]
+        try:
+            with capture_output() as captured:
+                _login(token, add_to_git_credential=add_to_git_credential)
+            message = captured.getvalue()
+        except Exception as error:
+            message = str(error)
+        # Print result (success message or error)
+        login_token_widget.children = [widgets.Label(line) for line in message.split("\n") if line.strip()]
+
+    token_finish_button.on_click(login_token_event)
+
+
+###
+# Login private helpers
+###
+
+
+def _login(
+    token: str,
+    add_to_git_credential: bool,
+) -> None:
+    from .hf_api import whoami  # avoid circular import
+
+    if token.startswith("api_org"):
+        raise ValueError("You must use your personal account token, not an organization token.")
+
+    token_info = whoami(token)
+    permission = token_info["auth"]["accessToken"]["role"]
+    logger.info(f"Token is valid (permission: {permission}).")
+
+    token_name = token_info["auth"]["accessToken"]["displayName"]
+    # Store token locally
+    _save_token(token=token, token_name=token_name)
+    # Set active token
+    _set_active_token(token_name=token_name, add_to_git_credential=add_to_git_credential)
+    logger.info("Login successful.")
+    if _get_token_from_environment():
+        logger.warning(
+            "Note: Environment variable`HF_TOKEN` is set and is the current active token independently from the token you've just configured."
+        )
+    else:
+        logger.info(f"The current active token is: `{token_name}`")
+
+
+def _logout_from_token(token_name: str) -> None:
+    """Logout from a specific access token.
+
+    Args:
+        token_name (`str`):
+            The name of the access token to logout from.
+    Raises:
+        [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError):
+            If the access token name is not found.
+    """
+    stored_tokens = get_stored_tokens()
+    # If there is no access tokens saved or the access token name is not found, do nothing
+    if not stored_tokens or token_name not in stored_tokens:
+        return
+
+    token = stored_tokens.pop(token_name)
+    _save_stored_tokens(stored_tokens)
+
+    if token == _get_token_from_file():
+        logger.warning(f"Active token '{token_name}' has been deleted.")
+        Path(constants.HF_TOKEN_PATH).unlink(missing_ok=True)
+
+
+def _set_active_token(
+    token_name: str,
+    add_to_git_credential: bool,
+) -> None:
+    """Set the active access token.
+
+    Args:
+        token_name (`str`):
+            The name of the token to set as active.
+    """
+    token = _get_token_by_name(token_name)
+    if not token:
+        raise ValueError(f"Token {token_name} not found in {constants.HF_STORED_TOKENS_PATH}")
+    if add_to_git_credential:
+        if _is_git_credential_helper_configured():
+            set_git_credential(token)
+            logger.info(
+                "Your token has been saved in your configured git credential helpers"
+                + f" ({','.join(list_credential_helpers())})."
+            )
+        else:
+            logger.warning("Token has not been saved to git credential helper.")
+    # Write token to HF_TOKEN_PATH
+    path = Path(constants.HF_TOKEN_PATH)
+    path.parent.mkdir(parents=True, exist_ok=True)
+    path.write_text(token)
+    logger.info(f"Your token has been saved to {constants.HF_TOKEN_PATH}")
+
+
+def _is_git_credential_helper_configured() -> bool:
+    """Check if a git credential helper is configured.
+
+    Warns user if not the case (except for Google Colab where "store" is set by default
+    by `huggingface_hub`).
+    """
+    helpers = list_credential_helpers()
+    if len(helpers) > 0:
+        return True  # Do not warn: at least 1 helper is set
+
+    # Only in Google Colab to avoid the warning message
+    # See https://github.com/huggingface/huggingface_hub/issues/1043#issuecomment-1247010710
+    if is_google_colab():
+        _set_store_as_git_credential_helper_globally()
+        return True  # Do not warn: "store" is used by default in Google Colab
+
+    # Otherwise, warn user
+    print(
+        ANSI.red(
+            "Cannot authenticate through git-credential as no helper is defined on your"
+            " machine.\nYou might have to re-authenticate when pushing to the Hugging"
+            " Face Hub.\nRun the following command in your terminal in case you want to"
+            " set the 'store' credential helper as default.\n\ngit config --global"
+            " credential.helper store\n\nRead"
+            " https://git-scm.com/book/en/v2/Git-Tools-Credential-Storage for more"
+            " details."
+        )
+    )
+    return False
+
+
+def _set_store_as_git_credential_helper_globally() -> None:
+    """Set globally the credential.helper to `store`.
+
+    To be used only in Google Colab as we assume the user doesn't care about the git
+    credential config. It is the only particular case where we don't want to display the
+    warning message in [`notebook_login()`].
+
+    Related:
+    - https://github.com/huggingface/huggingface_hub/issues/1043
+    - https://github.com/huggingface/huggingface_hub/issues/1051
+    - https://git-scm.com/docs/git-credential-store
+    """
+    try:
+        run_subprocess("git config --global credential.helper store")
+    except subprocess.CalledProcessError as exc:
+        raise EnvironmentError(exc.stderr)

data/conda_envs/evalclaw/lib/python3.11/site-packages/huggingface_hub/_oauth.py

@@ -0,0 +1,460 @@
+import datetime
+import hashlib
+import logging
+import os
+import time
+import urllib.parse
+import warnings
+from dataclasses import dataclass
+from typing import TYPE_CHECKING, Literal, Optional, Union
+
+from . import constants
+from .hf_api import whoami
+from .utils import experimental, get_token
+
+
+logger = logging.getLogger(__name__)
+
+if TYPE_CHECKING:
+    import fastapi
+
+
+@dataclass
+class OAuthOrgInfo:
+    """
+    Information about an organization linked to a user logged in with OAuth.
+
+    Attributes:
+        sub (`str`):
+            Unique identifier for the org. OpenID Connect field.
+        name (`str`):
+            The org's full name. OpenID Connect field.
+        preferred_username (`str`):
+            The org's username. OpenID Connect field.
+        picture (`str`):
+            The org's profile picture URL. OpenID Connect field.
+        plan (`str`, *optional*):
+            The org's plan (e.g., "enterprise", "team"). Hugging Face field.
+        can_pay (`Optional[bool]`, *optional*):
+            Whether the org has a payment method set up. Hugging Face field.
+        role_in_org (`Optional[str]`, *optional*):
+            The user's role in the org. Hugging Face field.
+        security_restrictions (`Optional[list[Literal["ip", "token-policy", "mfa", "sso"]]]`, *optional*):
+            Array of security restrictions that the user hasn't completed for this org. Possible values: "ip", "token-policy", "mfa", "sso". Hugging Face field.
+    """
+
+    sub: str
+    name: str
+    preferred_username: str
+    picture: str
+    plan: Optional[str] = None
+    can_pay: Optional[bool] = None
+    role_in_org: Optional[str] = None
+    security_restrictions: Optional[list[Literal["ip", "token-policy", "mfa", "sso"]]] = None
+
+
+@dataclass
+class OAuthUserInfo:
+    """
+    Information about a user logged in with OAuth.
+
+    Attributes:
+        sub (`str`):
+            Unique identifier for the user, even in case of rename. OpenID Connect field.
+        name (`str`):
+            The user's full name. OpenID Connect field.
+        preferred_username (`str`):
+            The user's username. OpenID Connect field.
+        email_verified (`Optional[bool]`, *optional*):
+            Indicates if the user's email is verified. OpenID Connect field.
+        email (`Optional[str]`, *optional*):
+            The user's email address. OpenID Connect field.
+        picture (`str`):
+            The user's profile picture URL. OpenID Connect field.
+        profile (`str`):
+            The user's profile URL. OpenID Connect field.
+        website (`Optional[str]`, *optional*):
+            The user's website URL. OpenID Connect field.
+        is_pro (`bool`):
+            Whether the user is a pro user. Hugging Face field.
+        can_pay (`Optional[bool]`, *optional*):
+            Whether the user has a payment method set up. Hugging Face field.
+        orgs (`Optional[list[OrgInfo]]`, *optional*):
+            List of organizations the user is part of. Hugging Face field.
+    """
+
+    sub: str
+    name: str
+    preferred_username: str
+    email_verified: Optional[bool]
+    email: Optional[str]
+    picture: str
+    profile: str
+    website: Optional[str]
+    is_pro: bool
+    can_pay: Optional[bool]
+    orgs: Optional[list[OAuthOrgInfo]]
+
+
+@dataclass
+class OAuthInfo:
+    """
+    Information about the OAuth login.
+
+    Attributes:
+        access_token (`str`):
+            The access token.
+        access_token_expires_at (`datetime.datetime`):
+            The expiration date of the access token.
+        user_info ([`OAuthUserInfo`]):
+            The user information.
+        state (`str`, *optional*):
+            State passed to the OAuth provider in the original request to the OAuth provider.
+        scope (`str`):
+            Granted scope.
+    """
+
+    access_token: str
+    access_token_expires_at: datetime.datetime
+    user_info: OAuthUserInfo
+    state: Optional[str]
+    scope: str
+
+
+@experimental
+def attach_huggingface_oauth(app: "fastapi.FastAPI", route_prefix: str = "/"):
+    """
+    Add OAuth endpoints to a FastAPI app to enable OAuth login with Hugging Face.
+
+    How to use:
+    - Call this method on your FastAPI app to add the OAuth endpoints.
+    - Inside your route handlers, call `parse_huggingface_oauth(request)` to retrieve the OAuth info.
+    - If user is logged in, an [`OAuthInfo`] object is returned with the user's info. If not, `None` is returned.
+    - In your app, make sure to add links to `/oauth/huggingface/login` and `/oauth/huggingface/logout` for the user to log in and out.
+
+    Example:
+    ```py
+    from huggingface_hub import attach_huggingface_oauth, parse_huggingface_oauth
+
+    # Create a FastAPI app
+    app = FastAPI()
+
+    # Add OAuth endpoints to the FastAPI app
+    attach_huggingface_oauth(app)
+
+    # Add a route that greets the user if they are logged in
+    @app.get("/")
+    def greet_json(request: Request):
+        # Retrieve the OAuth info from the request
+        oauth_info = parse_huggingface_oauth(request)  # e.g. OAuthInfo dataclass
+        if oauth_info is None:
+            return {"msg": "Not logged in!"}
+        return {"msg": f"Hello, {oauth_info.user_info.preferred_username}!"}
+    ```
+    """
+    # TODO: handle generic case (handling OAuth in a non-Space environment with custom dev values) (low priority)
+
+    # Add SessionMiddleware to the FastAPI app to store the OAuth info in the session.
+    # Session Middleware requires a secret key to sign the cookies. Let's use a hash
+    # of the OAuth secret key to make it unique to the Space + updated in case OAuth
+    # config gets updated. When ran locally, we use an empty string as a secret key.
+    try:
+        from starlette.middleware.sessions import SessionMiddleware
+    except ImportError as e:
+        raise ImportError(
+            "Cannot initialize OAuth to due a missing library. Please run `pip install huggingface_hub[oauth]` or add "
+            "`huggingface_hub[oauth]` to your requirements.txt file in order to install the required dependencies."
+        ) from e
+    session_secret = (constants.OAUTH_CLIENT_SECRET or "") + "-v1"
+    app.add_middleware(
+        SessionMiddleware,  # type: ignore[arg-type]
+        secret_key=hashlib.sha256(session_secret.encode()).hexdigest(),
+        same_site="none",
+        https_only=True,
+    )  # type: ignore
+
+    # Add OAuth endpoints to the FastAPI app:
+    #   - {route_prefix}/oauth/huggingface/login
+    #   - {route_prefix}/oauth/huggingface/callback
+    #   - {route_prefix}/oauth/huggingface/logout
+    # If the app is running in a Space, OAuth is enabled normally.
+    # Otherwise, we mock the endpoints to make the user log in with a fake user profile - without any calls to hf.co.
+    route_prefix = route_prefix.strip("/")
+    if os.getenv("SPACE_ID") is not None:
+        logger.info("OAuth is enabled in the Space. Adding OAuth routes.")
+        _add_oauth_routes(app, route_prefix=route_prefix)
+    else:
+        logger.info("App is not running in a Space. Adding mocked OAuth routes.")
+        _add_mocked_oauth_routes(app, route_prefix=route_prefix)
+
+
+def parse_huggingface_oauth(request: "fastapi.Request") -> Optional[OAuthInfo]:
+    """
+    Returns the information from a logged-in user as a [`OAuthInfo`] object.
+
+    For flexibility and future-proofing, this method is very lax in its parsing and does not raise errors.
+    Missing fields are set to `None` without a warning.
+
+    Return `None`, if the user is not logged in (no info in session cookie).
+
+    See [`attach_huggingface_oauth`] for an example on how to use this method.
+    """
+    if "oauth_info" not in request.session:
+        logger.debug("No OAuth info in session.")
+        return None
+
+    logger.debug("Parsing OAuth info from session.")
+    oauth_data = request.session["oauth_info"]
+    user_data = oauth_data.get("userinfo", {})
+    orgs_data = user_data.get("orgs", [])
+
+    orgs = (
+        [
+            OAuthOrgInfo(
+                sub=org.get("sub"),
+                name=org.get("name"),
+                preferred_username=org.get("preferred_username"),
+                picture=org.get("picture"),
+                plan=org.get("plan"),
+                can_pay=org.get("canPay"),
+                role_in_org=org.get("roleInOrg"),
+                security_restrictions=org.get("securityRestrictions"),
+            )
+            for org in orgs_data
+        ]
+        if orgs_data
+        else None
+    )
+
+    user_info = OAuthUserInfo(
+        sub=user_data.get("sub"),
+        name=user_data.get("name"),
+        preferred_username=user_data.get("preferred_username"),
+        email_verified=user_data.get("email_verified"),
+        email=user_data.get("email"),
+        picture=user_data.get("picture"),
+        profile=user_data.get("profile"),
+        website=user_data.get("website"),
+        is_pro=user_data.get("isPro"),
+        can_pay=user_data.get("canPay"),
+        orgs=orgs,
+    )
+
+    return OAuthInfo(
+        access_token=oauth_data.get("access_token"),
+        access_token_expires_at=datetime.datetime.fromtimestamp(oauth_data.get("expires_at")),
+        user_info=user_info,
+        state=oauth_data.get("state"),
+        scope=oauth_data.get("scope"),
+    )
+
+
+def _add_oauth_routes(app: "fastapi.FastAPI", route_prefix: str) -> None:
+    """Add OAuth routes to the FastAPI app (login, callback handler and logout)."""
+    try:
+        import fastapi
+        from authlib.integrations.base_client.errors import MismatchingStateError
+        from authlib.integrations.starlette_client import OAuth
+        from fastapi.responses import RedirectResponse
+    except ImportError as e:
+        raise ImportError(
+            "Cannot initialize OAuth to due a missing library. Please run `pip install huggingface_hub[oauth]` or add "
+            "`huggingface_hub[oauth]` to your requirements.txt file."
+        ) from e
+
+    # Check environment variables
+    msg = (
+        "OAuth is required but '{}' environment variable is not set. Make sure you've enabled OAuth in your Space by"
+        " setting `hf_oauth: true` in the Space metadata."
+    )
+    if constants.OAUTH_CLIENT_ID is None:
+        raise ValueError(msg.format("OAUTH_CLIENT_ID"))
+    if constants.OAUTH_CLIENT_SECRET is None:
+        raise ValueError(msg.format("OAUTH_CLIENT_SECRET"))
+    if constants.OAUTH_SCOPES is None:
+        raise ValueError(msg.format("OAUTH_SCOPES"))
+    if constants.OPENID_PROVIDER_URL is None:
+        raise ValueError(msg.format("OPENID_PROVIDER_URL"))
+
+    # Register OAuth server
+    oauth = OAuth()
+    oauth.register(
+        name="huggingface",
+        client_id=constants.OAUTH_CLIENT_ID,
+        client_secret=constants.OAUTH_CLIENT_SECRET,
+        client_kwargs={"scope": constants.OAUTH_SCOPES},
+        server_metadata_url=constants.OPENID_PROVIDER_URL + "/.well-known/openid-configuration",
+    )
+
+    login_uri, callback_uri, logout_uri = _get_oauth_uris(route_prefix)
+
+    # Register OAuth endpoints
+    @app.get(login_uri)
+    async def oauth_login(request: fastapi.Request) -> RedirectResponse:
+        """Endpoint that redirects to HF OAuth page."""
+        redirect_uri = _generate_redirect_uri(request)
+        return await oauth.huggingface.authorize_redirect(request, redirect_uri)  # type: ignore
+
+    @app.get(callback_uri)
+    async def oauth_redirect_callback(request: fastapi.Request) -> RedirectResponse:
+        """Endpoint that handles the OAuth callback."""
+        try:
+            oauth_info = await oauth.huggingface.authorize_access_token(request)  # type: ignore
+        except MismatchingStateError:
+            # Parse query params
+            nb_redirects = int(request.query_params.get("_nb_redirects", 0))
+            target_url = request.query_params.get("_target_url")
+
+            # Build redirect URI with the same query params as before and bump nb_redirects count
+            query_params: dict[str, Union[int, str]] = {"_nb_redirects": nb_redirects + 1}
+            if target_url:
+                query_params["_target_url"] = target_url
+
+            redirect_uri = f"{login_uri}?{urllib.parse.urlencode(query_params)}"
+
+            # If the user is redirected more than 3 times, it is very likely that the cookie is not working properly.
+            # (e.g. browser is blocking third-party cookies in iframe). In this case, redirect the user in the
+            # non-iframe view.
+            if nb_redirects > constants.OAUTH_MAX_REDIRECTS:
+                host = os.environ.get("SPACE_HOST")
+                if host is None:  # cannot happen in a Space
+                    raise RuntimeError(
+                        "App is not running in a Space (SPACE_HOST environment variable is not set). Cannot redirect to non-iframe view."
+                    ) from None
+                host_url = "https://" + host.rstrip("/")
+                return RedirectResponse(host_url + redirect_uri)
+
+            # Redirect the user to the login page again
+            return RedirectResponse(redirect_uri)
+
+        # OAuth login worked => store the user info in the session and redirect
+        logger.debug("Successfully logged in with OAuth. Storing user info in session.")
+        request.session["oauth_info"] = oauth_info
+        return RedirectResponse(_get_redirect_target(request))
+
+    @app.get(logout_uri)
+    async def oauth_logout(request: fastapi.Request) -> RedirectResponse:
+        """Endpoint that logs out the user (e.g. delete info from cookie session)."""
+        logger.debug("Logged out with OAuth. Removing user info from session.")
+        request.session.pop("oauth_info", None)
+        return RedirectResponse(_get_redirect_target(request))
+
+
+def _add_mocked_oauth_routes(app: "fastapi.FastAPI", route_prefix: str = "/") -> None:
+    """Add fake oauth routes if app is run locally and OAuth is enabled.
+
+    Using OAuth will have the same behavior as in a Space but instead of authenticating with HF, a mocked user profile
+    is added to the session.
+    """
+    try:
+        import fastapi
+        from fastapi.responses import RedirectResponse
+        from starlette.datastructures import URL
+    except ImportError as e:
+        raise ImportError(
+            "Cannot initialize OAuth to due a missing library. Please run `pip install huggingface_hub[oauth]` or add "
+            "`huggingface_hub[oauth]` to your requirements.txt file."
+        ) from e
+
+    warnings.warn(
+        "OAuth is not supported outside of a Space environment. To help you debug your app locally, the oauth endpoints"
+        " are mocked to return your profile and token. To make it work, your machine must be logged in to Huggingface."
+    )
+    mocked_oauth_info = _get_mocked_oauth_info()
+
+    login_uri, callback_uri, logout_uri = _get_oauth_uris(route_prefix)
+
+    # Define OAuth routes
+    @app.get(login_uri)
+    async def oauth_login(request: fastapi.Request) -> RedirectResponse:
+        """Fake endpoint that redirects to HF OAuth page."""
+        # Define target (where to redirect after login)
+        redirect_uri = _generate_redirect_uri(request)
+        return RedirectResponse(callback_uri + "?" + urllib.parse.urlencode({"_target_url": redirect_uri}))
+
+    @app.get(callback_uri)
+    async def oauth_redirect_callback(request: fastapi.Request) -> RedirectResponse:
+        """Endpoint that handles the OAuth callback."""
+        request.session["oauth_info"] = mocked_oauth_info
+        return RedirectResponse(_get_redirect_target(request))
+
+    @app.get(logout_uri)
+    async def oauth_logout(request: fastapi.Request) -> RedirectResponse:
+        """Endpoint that logs out the user (e.g. delete cookie session)."""
+        request.session.pop("oauth_info", None)
+        logout_url = URL("/").include_query_params(**request.query_params)
+        return RedirectResponse(url=logout_url, status_code=302)  # see https://github.com/gradio-app/gradio/pull/9659
+
+
+def _generate_redirect_uri(request: "fastapi.Request") -> str:
+    if "_target_url" in request.query_params:
+        # if `_target_url` already in query params => respect it
+        target = request.query_params["_target_url"]
+    else:
+        # otherwise => keep query params
+        target = "/?" + urllib.parse.urlencode(request.query_params)
+
+    redirect_uri = request.url_for("oauth_redirect_callback").include_query_params(_target_url=target)
+    redirect_uri_as_str = str(redirect_uri)
+    if redirect_uri.netloc.endswith(".hf.space"):
+        # In Space, FastAPI redirect as http but we want https
+        redirect_uri_as_str = redirect_uri_as_str.replace("http://", "https://")
+    return redirect_uri_as_str
+
+
+def _get_redirect_target(request: "fastapi.Request", default_target: str = "/") -> str:
+    return request.query_params.get("_target_url", default_target)
+
+
+def _get_mocked_oauth_info() -> dict:
+    token = get_token()
+    if token is None:
+        raise ValueError(
+            "Your machine must be logged in to HF to debug an OAuth app locally. Please"
+            " run `hf auth login` or set `HF_TOKEN` as environment variable "
+            "with one of your access token. You can generate a new token in your "
+            "settings page (https://huggingface.co/settings/tokens)."
+        )
+
+    user = whoami()
+    if user["type"] != "user":
+        raise ValueError(
+            "Your machine is not logged in with a personal account. Please use a "
+            "personal access token. You can generate a new token in your settings page"
+            " (https://huggingface.co/settings/tokens)."
+        )
+
+    return {
+        "access_token": token,
+        "token_type": "bearer",
+        "expires_in": 8 * 60 * 60,  # 8 hours
+        "id_token": "FOOBAR",
+        "scope": "openid profile",
+        "refresh_token": "hf_oauth__refresh_token",
+        "expires_at": int(time.time()) + 8 * 60 * 60,  # 8 hours
+        "userinfo": {
+            "sub": "0123456789",
+            "name": user["fullname"],
+            "preferred_username": user["name"],
+            "profile": f"https://huggingface.co/{user['name']}",
+            "picture": user["avatarUrl"],
+            "website": "",
+            "aud": "00000000-0000-0000-0000-000000000000",
+            "auth_time": 1691672844,
+            "nonce": "aaaaaaaaaaaaaaaaaaa",
+            "iat": 1691672844,
+            "exp": 1691676444,
+            "iss": "https://huggingface.co",
+        },
+    }
+
+
+def _get_oauth_uris(route_prefix: str = "/") -> tuple[str, str, str]:
+    route_prefix = route_prefix.strip("/")
+    if route_prefix:
+        route_prefix = f"/{route_prefix}"
+    return (
+        f"{route_prefix}/oauth/huggingface/login",
+        f"{route_prefix}/oauth/huggingface/callback",
+        f"{route_prefix}/oauth/huggingface/logout",
+    )

data/conda_envs/evalclaw/lib/python3.11/site-packages/huggingface_hub/_snapshot_download.py

@@ -0,0 +1,466 @@
+import os
+from pathlib import Path
+from typing import Iterable, List, Literal, Optional, Union, overload
+
+import httpx
+from tqdm.auto import tqdm as base_tqdm
+from tqdm.contrib.concurrent import thread_map
+
+from . import constants
+from .errors import (
+    DryRunError,
+    GatedRepoError,
+    HfHubHTTPError,
+    LocalEntryNotFoundError,
+    RepositoryNotFoundError,
+    RevisionNotFoundError,
+)
+from .file_download import REGEX_COMMIT_HASH, DryRunFileInfo, hf_hub_download, repo_folder_name
+from .hf_api import DatasetInfo, HfApi, ModelInfo, RepoFile, SpaceInfo
+from .utils import OfflineModeIsEnabled, filter_repo_objects, is_tqdm_disabled, logging, validate_hf_hub_args
+from .utils import tqdm as hf_tqdm
+
+
+logger = logging.get_logger(__name__)
+
+LARGE_REPO_THRESHOLD = 1000  # After this limit, we don't consider `repo_info.siblings` to be reliable enough
+
+
+@overload
+def snapshot_download(
+    repo_id: str,
+    *,
+    repo_type: Optional[str] = None,
+    revision: Optional[str] = None,
+    cache_dir: Union[str, Path, None] = None,
+    local_dir: Union[str, Path, None] = None,
+    library_name: Optional[str] = None,
+    library_version: Optional[str] = None,
+    user_agent: Optional[Union[dict, str]] = None,
+    etag_timeout: float = constants.DEFAULT_ETAG_TIMEOUT,
+    force_download: bool = False,
+    token: Optional[Union[bool, str]] = None,
+    local_files_only: bool = False,
+    allow_patterns: Optional[Union[list[str], str]] = None,
+    ignore_patterns: Optional[Union[list[str], str]] = None,
+    max_workers: int = 8,
+    tqdm_class: Optional[type[base_tqdm]] = None,
+    headers: Optional[dict[str, str]] = None,
+    endpoint: Optional[str] = None,
+    dry_run: Literal[False] = False,
+) -> str: ...
+
+
+@overload
+def snapshot_download(
+    repo_id: str,
+    *,
+    repo_type: Optional[str] = None,
+    revision: Optional[str] = None,
+    cache_dir: Union[str, Path, None] = None,
+    local_dir: Union[str, Path, None] = None,
+    library_name: Optional[str] = None,
+    library_version: Optional[str] = None,
+    user_agent: Optional[Union[dict, str]] = None,
+    etag_timeout: float = constants.DEFAULT_ETAG_TIMEOUT,
+    force_download: bool = False,
+    token: Optional[Union[bool, str]] = None,
+    local_files_only: bool = False,
+    allow_patterns: Optional[Union[list[str], str]] = None,
+    ignore_patterns: Optional[Union[list[str], str]] = None,
+    max_workers: int = 8,
+    tqdm_class: Optional[type[base_tqdm]] = None,
+    headers: Optional[dict[str, str]] = None,
+    endpoint: Optional[str] = None,
+    dry_run: Literal[True] = True,
+) -> list[DryRunFileInfo]: ...
+
+
+@overload
+def snapshot_download(
+    repo_id: str,
+    *,
+    repo_type: Optional[str] = None,
+    revision: Optional[str] = None,
+    cache_dir: Union[str, Path, None] = None,
+    local_dir: Union[str, Path, None] = None,
+    library_name: Optional[str] = None,
+    library_version: Optional[str] = None,
+    user_agent: Optional[Union[dict, str]] = None,
+    etag_timeout: float = constants.DEFAULT_ETAG_TIMEOUT,
+    force_download: bool = False,
+    token: Optional[Union[bool, str]] = None,
+    local_files_only: bool = False,
+    allow_patterns: Optional[Union[list[str], str]] = None,
+    ignore_patterns: Optional[Union[list[str], str]] = None,
+    max_workers: int = 8,
+    tqdm_class: Optional[type[base_tqdm]] = None,
+    headers: Optional[dict[str, str]] = None,
+    endpoint: Optional[str] = None,
+    dry_run: bool = False,
+) -> Union[str, list[DryRunFileInfo]]: ...
+
+
+@validate_hf_hub_args
+def snapshot_download(
+    repo_id: str,
+    *,
+    repo_type: Optional[str] = None,
+    revision: Optional[str] = None,
+    cache_dir: Union[str, Path, None] = None,
+    local_dir: Union[str, Path, None] = None,
+    library_name: Optional[str] = None,
+    library_version: Optional[str] = None,
+    user_agent: Optional[Union[dict, str]] = None,
+    etag_timeout: float = constants.DEFAULT_ETAG_TIMEOUT,
+    force_download: bool = False,
+    token: Optional[Union[bool, str]] = None,
+    local_files_only: bool = False,
+    allow_patterns: Optional[Union[list[str], str]] = None,
+    ignore_patterns: Optional[Union[list[str], str]] = None,
+    max_workers: int = 8,
+    tqdm_class: Optional[type[base_tqdm]] = None,
+    headers: Optional[dict[str, str]] = None,
+    endpoint: Optional[str] = None,
+    dry_run: bool = False,
+) -> Union[str, list[DryRunFileInfo]]:
+    """Download repo files.
+
+    Download a whole snapshot of a repo's files at the specified revision. This is useful when you want all files from
+    a repo, because you don't know which ones you will need a priori. All files are nested inside a folder in order
+    to keep their actual filename relative to that folder. You can also filter which files to download using
+    `allow_patterns` and `ignore_patterns`.
+
+    If `local_dir` is provided, the file structure from the repo will be replicated in this location. When using this
+    option, the `cache_dir` will not be used and a `.cache/huggingface/` folder will be created at the root of `local_dir`
+    to store some metadata related to the downloaded files. While this mechanism is not as robust as the main
+    cache-system, it's optimized for regularly pulling the latest version of a repository.
+
+    An alternative would be to clone the repo but this requires git and git-lfs to be installed and properly
+    configured. It is also not possible to filter which files to download when cloning a repository using git.
+
+    Args:
+        repo_id (`str`):
+            A user or an organization name and a repo name separated by a `/`.
+        repo_type (`str`, *optional*):
+            Set to `"dataset"` or `"space"` if downloading from a dataset or space,
+            `None` or `"model"` if downloading from a model. Default is `None`.
+        revision (`str`, *optional*):
+            An optional Git revision id which can be a branch name, a tag, or a
+            commit hash.
+        cache_dir (`str`, `Path`, *optional*):
+            Path to the folder where cached files are stored.
+        local_dir (`str` or `Path`, *optional*):
+            If provided, the downloaded files will be placed under this directory.
+        library_name (`str`, *optional*):
+            The name of the library to which the object corresponds.
+        library_version (`str`, *optional*):
+            The version of the library.
+        user_agent (`str`, `dict`, *optional*):
+            The user-agent info in the form of a dictionary or a string.
+        etag_timeout (`float`, *optional*, defaults to `10`):
+            When fetching ETag, how many seconds to wait for the server to send
+            data before giving up which is passed to `httpx.request`.
+        force_download (`bool`, *optional*, defaults to `False`):
+            Whether the file should be downloaded even if it already exists in the local cache.
+        token (`str`, `bool`, *optional*):
+            A token to be used for the download.
+                - If `True`, the token is read from the HuggingFace config
+                  folder.
+                - If a string, it's used as the authentication token.
+        headers (`dict`, *optional*):
+            Additional headers to include in the request. Those headers take precedence over the others.
+        local_files_only (`bool`, *optional*, defaults to `False`):
+            If `True`, avoid downloading the file and return the path to the
+            local cached file if it exists.
+        allow_patterns (`list[str]` or `str`, *optional*):
+            If provided, only files matching at least one pattern are downloaded.
+        ignore_patterns (`list[str]` or `str`, *optional*):
+            If provided, files matching any of the patterns are not downloaded.
+        max_workers (`int`, *optional*):
+            Number of concurrent threads to download files (1 thread = 1 file download).
+            Defaults to 8.
+        tqdm_class (`tqdm`, *optional*):
+            If provided, overwrites the default behavior for the progress bar. Passed
+            argument must inherit from `tqdm.auto.tqdm` or at least mimic its behavior.
+            Note that the `tqdm_class` is not passed to each individual download.
+            Defaults to the custom HF progress bar that can be disabled by setting
+            `HF_HUB_DISABLE_PROGRESS_BARS` environment variable.
+        dry_run (`bool`, *optional*, defaults to `False`):
+            If `True`, perform a dry run without actually downloading the files. Returns a list of
+            [`DryRunFileInfo`] objects containing information about what would be downloaded.
+
+    Returns:
+        `str` or list of [`DryRunFileInfo`]:
+            - If `dry_run=False`: Local snapshot path.
+            - If `dry_run=True`: A list of [`DryRunFileInfo`] objects containing download information.
+
+    Raises:
+        [`~utils.RepositoryNotFoundError`]
+            If the repository to download from cannot be found. This may be because it doesn't exist,
+            or because it is set to `private` and you do not have access.
+        [`~utils.RevisionNotFoundError`]
+            If the revision to download from cannot be found.
+        [`EnvironmentError`](https://docs.python.org/3/library/exceptions.html#EnvironmentError)
+            If `token=True` and the token cannot be found.
+        [`OSError`](https://docs.python.org/3/library/exceptions.html#OSError) if
+            ETag cannot be determined.
+        [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError)
+            if some parameter value is invalid.
+    """
+    if cache_dir is None:
+        cache_dir = constants.HF_HUB_CACHE
+    if revision is None:
+        revision = constants.DEFAULT_REVISION
+    if isinstance(cache_dir, Path):
+        cache_dir = str(cache_dir)
+
+    if repo_type is None:
+        repo_type = "model"
+    if repo_type not in constants.REPO_TYPES:
+        raise ValueError(f"Invalid repo type: {repo_type}. Accepted repo types are: {str(constants.REPO_TYPES)}")
+
+    storage_folder = os.path.join(cache_dir, repo_folder_name(repo_id=repo_id, repo_type=repo_type))
+
+    api = HfApi(
+        library_name=library_name,
+        library_version=library_version,
+        user_agent=user_agent,
+        endpoint=endpoint,
+        headers=headers,
+        token=token,
+    )
+
+    repo_info: Union[ModelInfo, DatasetInfo, SpaceInfo, None] = None
+    api_call_error: Optional[Exception] = None
+    if not local_files_only:
+        # try/except logic to handle different errors => taken from `hf_hub_download`
+        try:
+            # if we have internet connection we want to list files to download
+            repo_info = api.repo_info(repo_id=repo_id, repo_type=repo_type, revision=revision)
+        except httpx.ProxyError:
+            # Actually raise on proxy error
+            raise
+        except (httpx.ConnectError, httpx.TimeoutException, OfflineModeIsEnabled) as error:
+            # Internet connection is down
+            # => will try to use local files only
+            api_call_error = error
+            pass
+        except RevisionNotFoundError:
+            # The repo was found but the revision doesn't exist on the Hub (never existed or got deleted)
+            raise
+        except HfHubHTTPError as error:
+            # Multiple reasons for an http error:
+            # - Repository is private and invalid/missing token sent
+            # - Repository is gated and invalid/missing token sent
+            # - Hub is down (error 500 or 504)
+            # => let's switch to 'local_files_only=True' to check if the files are already cached.
+            #    (if it's not the case, the error will be re-raised)
+            api_call_error = error
+            pass
+
+    # At this stage, if `repo_info` is None it means either:
+    # - internet connection is down
+    # - internet connection is deactivated (local_files_only=True or HF_HUB_OFFLINE=True)
+    # - repo is private/gated and invalid/missing token sent
+    # - Hub is down
+    # => let's look if we can find the appropriate folder in the cache:
+    #    - if the specified revision is a commit hash, look inside "snapshots".
+    #    - f the specified revision is a branch or tag, look inside "refs".
+    # => if local_dir is not None, we will return the path to the local folder if it exists.
+    if repo_info is None:
+        if dry_run:
+            raise DryRunError(
+                "Dry run cannot be performed as the repository cannot be accessed. Please check your internet connection or authentication token."
+            ) from api_call_error
+
+        # Try to get which commit hash corresponds to the specified revision
+        commit_hash = None
+        if REGEX_COMMIT_HASH.match(revision):
+            commit_hash = revision
+        else:
+            ref_path = os.path.join(storage_folder, "refs", revision)
+            if os.path.exists(ref_path):
+                # retrieve commit_hash from refs file
+                with open(ref_path) as f:
+                    commit_hash = f.read()
+
+        # Try to locate snapshot folder for this commit hash
+        if commit_hash is not None and local_dir is None:
+            snapshot_folder = os.path.join(storage_folder, "snapshots", commit_hash)
+            if os.path.exists(snapshot_folder):
+                # Snapshot folder exists => let's return it
+                # (but we can't check if all the files are actually there)
+                return snapshot_folder
+
+        # If local_dir is not None, return it if it exists and is not empty
+        if local_dir is not None:
+            local_dir = Path(local_dir)
+            if local_dir.is_dir() and any(local_dir.iterdir()):
+                logger.warning(
+                    f"Returning existing local_dir `{local_dir}` as remote repo cannot be accessed in `snapshot_download` ({api_call_error})."
+                )
+                return str(local_dir.resolve())
+        # If we couldn't find the appropriate folder on disk, raise an error.
+        if local_files_only:
+            raise LocalEntryNotFoundError(
+                "Cannot find an appropriate cached snapshot folder for the specified revision on the local disk and "
+                "outgoing traffic has been disabled. To enable repo look-ups and downloads online, pass "
+                "'local_files_only=False' as input."
+            )
+        elif isinstance(api_call_error, OfflineModeIsEnabled):
+            raise LocalEntryNotFoundError(
+                "Cannot find an appropriate cached snapshot folder for the specified revision on the local disk and "
+                "outgoing traffic has been disabled. To enable repo look-ups and downloads online, set "
+                "'HF_HUB_OFFLINE=0' as environment variable."
+            ) from api_call_error
+        elif isinstance(api_call_error, (RepositoryNotFoundError, GatedRepoError)) or (
+            isinstance(api_call_error, HfHubHTTPError) and api_call_error.response.status_code == 401
+        ):
+            # Repo not found, gated, or specific authentication error => let's raise the actual error
+            raise api_call_error
+        else:
+            # Otherwise: most likely a connection issue or Hub downtime => let's warn the user
+            raise LocalEntryNotFoundError(
+                f"Got: {api_call_error.__class__.__name__}: {api_call_error}"
+                "\nAn error happened while trying to locate the files on the Hub and we cannot find the appropriate"
+                " snapshot folder for the specified revision on the local disk. Please check your internet connection"
+                " and try again."
+            ) from api_call_error
+
+    # At this stage, internet connection is up and running
+    # => let's download the files!
+    assert repo_info.sha is not None, "Repo info returned from server must have a revision sha."
+
+    # Corner case: on very large repos, the siblings list in `repo_info` might not contain all files.
+    # In that case, we need to use the `list_repo_tree` method to prevent caching issues.
+    repo_files: Iterable[str] = [f.rfilename for f in repo_info.siblings] if repo_info.siblings is not None else []
+    unreliable_nb_files = (
+        repo_info.siblings is None or len(repo_info.siblings) == 0 or len(repo_info.siblings) > LARGE_REPO_THRESHOLD
+    )
+    if unreliable_nb_files:
+        logger.info(
+            "Number of files in the repo is unreliable. Using `list_repo_tree` to ensure all files are listed."
+        )
+        repo_files = (
+            f.rfilename
+            for f in api.list_repo_tree(repo_id=repo_id, recursive=True, revision=revision, repo_type=repo_type)
+            if isinstance(f, RepoFile)
+        )
+
+    filtered_repo_files: Iterable[str] = filter_repo_objects(
+        items=repo_files,
+        allow_patterns=allow_patterns,
+        ignore_patterns=ignore_patterns,
+    )
+
+    if not unreliable_nb_files:
+        filtered_repo_files = list(filtered_repo_files)
+        tqdm_desc = f"Fetching {len(filtered_repo_files)} files"
+    else:
+        tqdm_desc = "Fetching ... files"
+    if dry_run:
+        tqdm_desc = "[dry-run] " + tqdm_desc
+
+    commit_hash = repo_info.sha
+    snapshot_folder = os.path.join(storage_folder, "snapshots", commit_hash)
+    # if passed revision is not identical to commit_hash
+    # then revision has to be a branch name or tag name.
+    # In that case store a ref.
+    if revision != commit_hash:
+        ref_path = os.path.join(storage_folder, "refs", revision)
+        try:
+            os.makedirs(os.path.dirname(ref_path), exist_ok=True)
+            with open(ref_path, "w") as f:
+                f.write(commit_hash)
+        except OSError as e:
+            logger.warning(f"Ignored error while writing commit hash to {ref_path}: {e}.")
+
+    results: List[Union[str, DryRunFileInfo]] = []
+
+    # User can use its own tqdm class or the default one from `huggingface_hub.utils`
+    tqdm_class = tqdm_class or hf_tqdm
+
+    # Create a progress bar for the bytes downloaded
+    # This progress bar is shared across threads/files and gets updated each time we fetch
+    # metadata for a file.
+    bytes_progress = tqdm_class(
+        desc="Downloading (incomplete total...)",
+        disable=is_tqdm_disabled(log_level=logger.getEffectiveLevel()),
+        total=0,
+        initial=0,
+        unit="B",
+        unit_scale=True,
+        name="huggingface_hub.snapshot_download",
+    )
+
+    class _AggregatedTqdm:
+        """Fake tqdm object to aggregate progress into the parent `bytes_progress` bar.
+
+        In practice the `_AggregatedTqdm` object won't be displayed, it's just used to update
+        the `bytes_progress` bar from each thread/file download.
+        """
+
+        def __init__(self, *args, **kwargs):
+            # Adjust the total of the parent progress bar
+            total = kwargs.pop("total", None)
+            if total is not None:
+                bytes_progress.total += total
+                bytes_progress.refresh()
+
+            # Adjust initial of the parent progress bar
+            initial = kwargs.pop("initial", 0)
+            if initial:
+                bytes_progress.update(initial)
+
+        def __enter__(self):
+            return self
+
+        def __exit__(self, exc_type, exc_value, traceback):
+            pass
+
+        def update(self, n: Optional[Union[int, float]] = 1) -> None:
+            bytes_progress.update(n)
+
+    # we pass the commit_hash to hf_hub_download
+    # so no network call happens if we already
+    # have the file locally.
+    def _inner_hf_hub_download(repo_file: str) -> None:
+        results.append(
+            hf_hub_download(  # type: ignore
+                repo_id,
+                filename=repo_file,
+                repo_type=repo_type,
+                revision=commit_hash,
+                endpoint=endpoint,
+                cache_dir=cache_dir,
+                local_dir=local_dir,
+                library_name=library_name,
+                library_version=library_version,
+                user_agent=user_agent,
+                etag_timeout=etag_timeout,
+                force_download=force_download,
+                token=token,
+                headers=headers,
+                tqdm_class=_AggregatedTqdm,  # type: ignore
+                dry_run=dry_run,
+            )
+        )
+
+    thread_map(
+        _inner_hf_hub_download,
+        filtered_repo_files,
+        desc=tqdm_desc,
+        max_workers=max_workers,
+        tqdm_class=tqdm_class,
+    )
+
+    bytes_progress.set_description("Download complete")
+
+    if dry_run:
+        assert all(isinstance(r, DryRunFileInfo) for r in results)
+        return results  # type: ignore
+
+    if local_dir is not None:
+        return str(os.path.realpath(local_dir))
+    return snapshot_folder

data/conda_envs/evalclaw/lib/python3.11/site-packages/huggingface_hub/_space_api.py

@@ -0,0 +1,184 @@
+# coding=utf-8
+# Copyright 2019-present, the HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from dataclasses import dataclass
+from datetime import datetime
+from enum import Enum
+from typing import Literal, Optional
+
+from huggingface_hub.utils import parse_datetime
+
+
+class SpaceStage(str, Enum):
+    """
+    Enumeration of possible stage of a Space on the Hub.
+
+    Value can be compared to a string:
+    ```py
+    assert SpaceStage.BUILDING == "BUILDING"
+    ```
+
+    Taken from https://github.com/huggingface/moon-landing/blob/main/server/repo_types/SpaceInfo.ts#L61 (private url).
+    """
+
+    # Copied from moon-landing > server > repo_types > SpaceInfo.ts (private repo)
+    NO_APP_FILE = "NO_APP_FILE"
+    CONFIG_ERROR = "CONFIG_ERROR"
+    BUILDING = "BUILDING"
+    BUILD_ERROR = "BUILD_ERROR"
+    RUNNING = "RUNNING"
+    RUNNING_BUILDING = "RUNNING_BUILDING"
+    RUNTIME_ERROR = "RUNTIME_ERROR"
+    DELETING = "DELETING"
+    STOPPED = "STOPPED"
+    PAUSED = "PAUSED"
+    APP_STARTING = "APP_STARTING"
+    RUNNING_APP_STARTING = "RUNNING_APP_STARTING"
+
+
+class SpaceHardware(str, Enum):
+    """
+    Enumeration of hardwares available to run your Space on the Hub.
+
+    Value can be compared to a string:
+    ```py
+    assert SpaceHardware.CPU_BASIC == "cpu-basic"
+    ```
+
+    Taken from https://github.com/huggingface-internal/moon-landing/blob/main/server/repo_types/SpaceHardwareFlavor.ts (private url).
+    """
+
+    # CPU
+    CPU_BASIC = "cpu-basic"
+    CPU_UPGRADE = "cpu-upgrade"
+    CPU_XL = "cpu-xl"
+
+    # ZeroGPU
+    ZERO_A10G = "zero-a10g"
+
+    # GPU
+    T4_SMALL = "t4-small"
+    T4_MEDIUM = "t4-medium"
+    L4X1 = "l4x1"
+    L4X4 = "l4x4"
+    L40SX1 = "l40sx1"
+    L40SX4 = "l40sx4"
+    L40SX8 = "l40sx8"
+    A10G_SMALL = "a10g-small"
+    A10G_LARGE = "a10g-large"
+    A10G_LARGEX2 = "a10g-largex2"
+    A10G_LARGEX4 = "a10g-largex4"
+    A100_LARGE = "a100-large"
+    A100x4 = "a100x4"
+    A100x8 = "a100x8"
+
+
+class SpaceStorage(str, Enum):
+    """
+    Enumeration of persistent storage available for your Space on the Hub.
+
+    Value can be compared to a string:
+    ```py
+    assert SpaceStorage.SMALL == "small"
+    ```
+
+    Taken from https://github.com/huggingface/moon-landing/blob/main/server/repo_types/SpaceHardwareFlavor.ts#L24 (private url).
+    """
+
+    SMALL = "small"
+    MEDIUM = "medium"
+    LARGE = "large"
+
+
+@dataclass
+class SpaceHotReloading:
+    status: Literal["created", "canceled"]
+    replica_statuses: list[tuple[str, str]]  # See _hot_reloading_types.ApiCreateReloadResponse.res.status
+    raw: dict
+
+    def __init__(self, data: dict) -> None:
+        self.status = data["status"]
+        self.replica_statuses = data["replicaStatuses"]
+        self.raw = data
+
+
+@dataclass
+class SpaceRuntime:
+    """
+    Contains information about the current runtime of a Space.
+
+    Args:
+        stage (`str`):
+            Current stage of the space. Example: RUNNING.
+        hardware (`str` or `None`):
+            Current hardware of the space. Example: "cpu-basic". Can be `None` if Space
+            is `BUILDING` for the first time.
+        requested_hardware (`str` or `None`):
+            Requested hardware. Can be different from `hardware` especially if the request
+            has just been made. Example: "t4-medium". Can be `None` if no hardware has
+            been requested yet.
+        sleep_time (`int` or `None`):
+            Number of seconds the Space will be kept alive after the last request. By default (if value is `None`), the
+            Space will never go to sleep if it's running on an upgraded hardware, while it will go to sleep after 48
+            hours on a free 'cpu-basic' hardware. For more details, see https://huggingface.co/docs/hub/spaces-gpus#sleep-time.
+        raw (`dict`):
+            Raw response from the server. Contains more information about the Space
+            runtime like number of replicas, number of cpu, memory size,...
+    """
+
+    stage: SpaceStage
+    hardware: Optional[SpaceHardware]
+    requested_hardware: Optional[SpaceHardware]
+    sleep_time: Optional[int]
+    storage: Optional[SpaceStorage]
+    hot_reloading: Optional[SpaceHotReloading]
+    raw: dict
+
+    def __init__(self, data: dict) -> None:
+        self.stage = data["stage"]
+        self.hardware = data.get("hardware", {}).get("current")
+        self.requested_hardware = data.get("hardware", {}).get("requested")
+        self.sleep_time = data.get("gcTimeout")
+        self.storage = data.get("storage")
+        self.hot_reloading = SpaceHotReloading(raw_hr) if (raw_hr := data.get("hotReloading")) is not None else None
+        self.raw = data
+
+
+@dataclass
+class SpaceVariable:
+    """
+    Contains information about the current variables of a Space.
+
+    Args:
+        key (`str`):
+            Variable key. Example: `"MODEL_REPO_ID"`
+        value (`str`):
+            Variable value. Example: `"the_model_repo_id"`.
+        description (`str` or None):
+            Description of the variable. Example: `"Model Repo ID of the implemented model"`.
+        updatedAt (`datetime` or None):
+            datetime of the last update of the variable (if the variable has been updated at least once).
+    """
+
+    key: str
+    value: str
+    description: Optional[str]
+    updated_at: Optional[datetime]
+
+    def __init__(self, key: str, values: dict) -> None:
+        self.key = key
+        self.value = values["value"]
+        self.description = values.get("description")
+        updated_at = values.get("updatedAt")
+        self.updated_at = parse_datetime(updated_at) if updated_at is not None else None

data/conda_envs/evalclaw/lib/python3.11/site-packages/huggingface_hub/_tensorboard_logger.py

@@ -0,0 +1,190 @@
+# Copyright 2023 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Contains a logger to push training logs to the Hub, using Tensorboard."""
+
+from pathlib import Path
+from typing import Optional, Union
+
+from ._commit_scheduler import CommitScheduler
+from .errors import EntryNotFoundError
+from .repocard import ModelCard
+from .utils import experimental
+
+
+# Depending on user's setup, SummaryWriter can come either from 'tensorboardX'
+# or from 'torch.utils.tensorboard'. Both are compatible so let's try to load
+# from either of them.
+try:
+    from tensorboardX import SummaryWriter as _RuntimeSummaryWriter
+
+    is_summary_writer_available = True
+except ImportError:
+    try:
+        from torch.utils.tensorboard import SummaryWriter as _RuntimeSummaryWriter
+
+        is_summary_writer_available = True
+    except ImportError:
+        # Dummy class to avoid failing at import. Will raise on instance creation.
+        class _DummySummaryWriter:
+            pass
+
+        _RuntimeSummaryWriter = _DummySummaryWriter  # type: ignore[assignment]
+        is_summary_writer_available = False
+
+
+class HFSummaryWriter(_RuntimeSummaryWriter):
+    """
+    Wrapper around the tensorboard's `SummaryWriter` to push training logs to the Hub.
+
+    Data is logged locally and then pushed to the Hub asynchronously. Pushing data to the Hub is done in a separate
+    thread to avoid blocking the training script. In particular, if the upload fails for any reason (e.g. a connection
+    issue), the main script will not be interrupted. Data is automatically pushed to the Hub every `commit_every`
+    minutes (default to every 5 minutes).
+
+    > [!WARNING]
+    > `HFSummaryWriter` is experimental. Its API is subject to change in the future without prior notice.
+
+    Args:
+        repo_id (`str`):
+            The id of the repo to which the logs will be pushed.
+        logdir (`str`, *optional*):
+            The directory where the logs will be written. If not specified, a local directory will be created by the
+            underlying `SummaryWriter` object.
+        commit_every (`int` or `float`, *optional*):
+            The frequency (in minutes) at which the logs will be pushed to the Hub. Defaults to 5 minutes.
+        squash_history (`bool`, *optional*):
+            Whether to squash the history of the repo after each commit. Defaults to `False`. Squashing commits is
+            useful to avoid degraded performances on the repo when it grows too large.
+        repo_type (`str`, *optional*):
+            The type of the repo to which the logs will be pushed. Defaults to "model".
+        repo_revision (`str`, *optional*):
+            The revision of the repo to which the logs will be pushed. Defaults to "main".
+        repo_private (`bool`, *optional*):
+            Whether to make the repo private. If `None` (default), the repo will be public unless the organization's default is private. This value is ignored if the repo already exists.
+        path_in_repo (`str`, *optional*):
+            The path to the folder in the repo where the logs will be pushed. Defaults to "tensorboard/".
+        repo_allow_patterns (`list[str]` or `str`, *optional*):
+            A list of patterns to include in the upload. Defaults to `"*.tfevents.*"`. Check out the
+            [upload guide](https://huggingface.co/docs/huggingface_hub/guides/upload#upload-a-folder) for more details.
+        repo_ignore_patterns (`list[str]` or `str`, *optional*):
+            A list of patterns to exclude in the upload. Check out the
+            [upload guide](https://huggingface.co/docs/huggingface_hub/guides/upload#upload-a-folder) for more details.
+        token (`str`, *optional*):
+            Authentication token. Will default to the stored token. See https://huggingface.co/settings/token for more
+            details
+        kwargs:
+            Additional keyword arguments passed to `SummaryWriter`.
+
+    Examples:
+    ```diff
+    # Taken from https://pytorch.org/docs/stable/tensorboard.html
+    - from torch.utils.tensorboard import SummaryWriter
+    + from huggingface_hub import HFSummaryWriter
+
+    import numpy as np
+
+    - writer = SummaryWriter()
+    + writer = HFSummaryWriter(repo_id="username/my-trained-model")
+
+    for n_iter in range(100):
+        writer.add_scalar('Loss/train', np.random.random(), n_iter)
+        writer.add_scalar('Loss/test', np.random.random(), n_iter)
+        writer.add_scalar('Accuracy/train', np.random.random(), n_iter)
+        writer.add_scalar('Accuracy/test', np.random.random(), n_iter)
+    ```
+
+    ```py
+    >>> from huggingface_hub import HFSummaryWriter
+
+    # Logs are automatically pushed every 15 minutes (5 by default) + when exiting the context manager
+    >>> with HFSummaryWriter(repo_id="test_hf_logger", commit_every=15) as logger:
+    ...     logger.add_scalar("a", 1)
+    ...     logger.add_scalar("b", 2)
+    ```
+    """
+
+    @experimental
+    def __new__(cls, *args, **kwargs) -> "HFSummaryWriter":
+        if not is_summary_writer_available:
+            raise ImportError(
+                "You must have `tensorboard` installed to use `HFSummaryWriter`. Please run `pip install --upgrade"
+                " tensorboardX` first."
+            )
+        return super().__new__(cls)
+
+    def __init__(
+        self,
+        repo_id: str,
+        *,
+        logdir: Optional[str] = None,
+        commit_every: Union[int, float] = 5,
+        squash_history: bool = False,
+        repo_type: Optional[str] = None,
+        repo_revision: Optional[str] = None,
+        repo_private: Optional[bool] = None,
+        path_in_repo: Optional[str] = "tensorboard",
+        repo_allow_patterns: Optional[Union[list[str], str]] = "*.tfevents.*",
+        repo_ignore_patterns: Optional[Union[list[str], str]] = None,
+        token: Optional[str] = None,
+        **kwargs,
+    ):
+        # Initialize SummaryWriter
+        super().__init__(logdir=logdir, **kwargs)
+
+        # Check logdir has been correctly initialized and fail early otherwise. In practice, SummaryWriter takes care of it.
+        if not isinstance(self.logdir, str):
+            raise ValueError(f"`self.logdir` must be a string. Got '{self.logdir}' of type {type(self.logdir)}.")
+
+        # Append logdir name to `path_in_repo`
+        if path_in_repo is None or path_in_repo == "":
+            path_in_repo = Path(self.logdir).name
+        else:
+            path_in_repo = path_in_repo.strip("/") + "/" + Path(self.logdir).name
+
+        # Initialize scheduler
+        self.scheduler = CommitScheduler(
+            folder_path=self.logdir,
+            path_in_repo=path_in_repo,
+            repo_id=repo_id,
+            repo_type=repo_type,
+            revision=repo_revision,
+            private=repo_private,
+            token=token,
+            allow_patterns=repo_allow_patterns,
+            ignore_patterns=repo_ignore_patterns,
+            every=commit_every,
+            squash_history=squash_history,
+        )
+
+        # Exposing some high-level info at root level
+        self.repo_id = self.scheduler.repo_id
+        self.repo_type = self.scheduler.repo_type
+        self.repo_revision = self.scheduler.revision
+
+        # Add `hf-summary-writer` tag to the model card metadata
+        try:
+            card = ModelCard.load(repo_id_or_path=self.repo_id, repo_type=self.repo_type)
+        except EntryNotFoundError:
+            card = ModelCard("")
+        tags = card.data.get("tags", [])
+        if "hf-summary-writer" not in tags:
+            tags.append("hf-summary-writer")
+            card.data["tags"] = tags
+            card.push_to_hub(repo_id=self.repo_id, repo_type=self.repo_type)
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        """Push to hub in a non-blocking way when exiting the logger's context manager."""
+        super().__exit__(exc_type, exc_val, exc_tb)
+        future = self.scheduler.trigger()
+        future.result()

data/conda_envs/evalclaw/lib/python3.11/site-packages/huggingface_hub/_upload_large_folder.py

@@ -0,0 +1,765 @@
+# coding=utf-8
+# Copyright 2024-present, the HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import enum
+import logging
+import os
+import queue
+import shutil
+import sys
+import threading
+import time
+import traceback
+from datetime import datetime
+from pathlib import Path
+from threading import Lock
+from typing import TYPE_CHECKING, Any, Optional, Union
+from urllib.parse import quote
+
+from ._commit_api import CommitOperationAdd, UploadInfo, _fetch_upload_modes
+from ._local_folder import LocalUploadFileMetadata, LocalUploadFilePaths, get_local_upload_paths, read_upload_metadata
+from .constants import DEFAULT_REVISION, REPO_TYPES
+from .utils import DEFAULT_IGNORE_PATTERNS, _format_size, filter_repo_objects, tqdm
+from .utils._runtime import is_xet_available
+from .utils.sha import sha_fileobj
+
+
+if TYPE_CHECKING:
+    from .hf_api import HfApi
+
+logger = logging.getLogger(__name__)
+
+WAITING_TIME_IF_NO_TASKS = 10  # seconds
+MAX_NB_FILES_FETCH_UPLOAD_MODE = 100
+COMMIT_SIZE_SCALE: list[int] = [20, 50, 75, 100, 125, 200, 250, 400, 600, 1000]
+
+UPLOAD_BATCH_SIZE_XET = 256  # Max 256 files per upload batch for XET-enabled repos
+UPLOAD_BATCH_SIZE_LFS = 1  # Otherwise, batches of 1 for regular LFS upload
+
+# Repository limits (from https://huggingface.co/docs/hub/repositories-recommendations)
+MAX_FILES_PER_REPO = 100_000  # Recommended maximum number of files per repository
+MAX_FILES_PER_FOLDER = 10_000  # Recommended maximum number of files per folder
+MAX_FILE_SIZE_GB = 200  # Recommended maximum for individual file size (split larger files)
+RECOMMENDED_FILE_SIZE_GB = 20  # Recommended maximum for individual file size
+
+
+def _validate_upload_limits(paths_list: list[LocalUploadFilePaths]) -> None:
+    """
+    Validate upload against repository limits and warn about potential issues.
+
+    Args:
+        paths_list: List of file paths to be uploaded
+
+    Warns about:
+        - Too many files in the repository (>100k)
+        - Too many entries (files or subdirectories) in a single folder (>10k)
+        - Files exceeding size limits (>20GB recommended, >200GB maximum)
+    """
+    logger.info("Running validation checks on files to upload...")
+
+    # Check 1: Total file count
+    if len(paths_list) > MAX_FILES_PER_REPO:
+        logger.warning(
+            f"You are about to upload {len(paths_list):,} files. "
+            f"This exceeds the recommended limit of {MAX_FILES_PER_REPO:,} files per repository.\n"
+            f"Consider:\n"
+            f"  - Splitting your data into multiple repositories\n"
+            f"  - Using fewer, larger files (e.g., parquet files)\n"
+            f"  - See: https://huggingface.co/docs/hub/repositories-recommendations"
+        )
+
+    # Check 2: Files and subdirectories per folder
+    # Track immediate children (files and subdirs) for each folder
+    from collections import defaultdict
+
+    entries_per_folder: dict[str, Any] = defaultdict(lambda: {"files": 0, "subdirs": set()})
+
+    for paths in paths_list:
+        path = Path(paths.path_in_repo)
+        parts = path.parts
+
+        # Count this file in its immediate parent directory
+        parent = str(path.parent) if str(path.parent) != "." else "."
+        entries_per_folder[parent]["files"] += 1
+
+        # Track immediate subdirectories for each parent folder
+        # Walk through the path components to track parent-child relationships
+        for i, child in enumerate(parts[:-1]):
+            parent = "." if i == 0 else "/".join(parts[:i])
+            entries_per_folder[parent]["subdirs"].add(child)
+
+    # Check limits for each folder
+    for folder, data in entries_per_folder.items():
+        file_count = data["files"]
+        subdir_count = len(data["subdirs"])
+        total_entries = file_count + subdir_count
+
+        if total_entries > MAX_FILES_PER_FOLDER:
+            folder_display = "root" if folder == "." else folder
+            logger.warning(
+                f"Folder '{folder_display}' contains {total_entries:,} entries "
+                f"({file_count:,} files and {subdir_count:,} subdirectories). "
+                f"This exceeds the recommended {MAX_FILES_PER_FOLDER:,} entries per folder.\n"
+                "Consider reorganising into sub-folders."
+            )
+
+    # Check 3: File sizes
+    large_files = []
+    very_large_files = []
+
+    for paths in paths_list:
+        size = paths.file_path.stat().st_size
+        size_gb = size / 1_000_000_000  # Use decimal GB as per Hub limits
+
+        if size_gb > MAX_FILE_SIZE_GB:
+            very_large_files.append((paths.path_in_repo, size_gb))
+        elif size_gb > RECOMMENDED_FILE_SIZE_GB:
+            large_files.append((paths.path_in_repo, size_gb))
+
+    # Warn about very large files (>200GB)
+    if very_large_files:
+        files_str = "\n  - ".join(f"{path}: {size:.1f}GB" for path, size in very_large_files[:5])
+        more_str = f"\n  ... and {len(very_large_files) - 5} more files" if len(very_large_files) > 5 else ""
+        logger.warning(
+            f"Found {len(very_large_files)} files exceeding the {MAX_FILE_SIZE_GB}GB recommended maximum:\n"
+            f"  - {files_str}{more_str}\n"
+            f"Consider splitting these files into smaller chunks."
+        )
+
+    # Warn about large files (>20GB)
+    if large_files:
+        files_str = "\n  - ".join(f"{path}: {size:.1f}GB" for path, size in large_files[:5])
+        more_str = f"\n  ... and {len(large_files) - 5} more files" if len(large_files) > 5 else ""
+        logger.warning(
+            f"Found {len(large_files)} files larger than {RECOMMENDED_FILE_SIZE_GB}GB (recommended limit):\n"
+            f"  - {files_str}{more_str}\n"
+            f"Large files may slow down loading and processing."
+        )
+
+    logger.info("Validation checks complete.")
+
+
+def upload_large_folder_internal(
+    api: "HfApi",
+    repo_id: str,
+    folder_path: Union[str, Path],
+    *,
+    repo_type: str,  # Repo type is required!
+    revision: Optional[str] = None,
+    private: Optional[bool] = None,
+    allow_patterns: Optional[Union[list[str], str]] = None,
+    ignore_patterns: Optional[Union[list[str], str]] = None,
+    num_workers: Optional[int] = None,
+    print_report: bool = True,
+    print_report_every: int = 60,
+):
+    """Upload a large folder to the Hub in the most resilient way possible.
+
+    See [`HfApi.upload_large_folder`] for the full documentation.
+    """
+    # 1. Check args and setup
+    if repo_type is None:
+        raise ValueError(
+            "For large uploads, `repo_type` is explicitly required. Please set it to `model`, `dataset` or `space`."
+            " If you are using the CLI, pass it as `--repo-type=model`."
+        )
+    if repo_type not in REPO_TYPES:
+        raise ValueError(f"Invalid repo type, must be one of {REPO_TYPES}")
+    if revision is None:
+        revision = DEFAULT_REVISION
+
+    folder_path = Path(folder_path).expanduser().resolve()
+    if not folder_path.is_dir():
+        raise ValueError(f"Provided path: '{folder_path}' is not a directory")
+
+    if ignore_patterns is None:
+        ignore_patterns = []
+    elif isinstance(ignore_patterns, str):
+        ignore_patterns = [ignore_patterns]
+    ignore_patterns += DEFAULT_IGNORE_PATTERNS
+
+    if num_workers is None:
+        nb_cores = os.cpu_count() or 1
+        num_workers = max(nb_cores // 2, 1)  # Use at most half of cpu cores
+
+    # 2. Create repo if missing
+    repo_url = api.create_repo(repo_id=repo_id, repo_type=repo_type, private=private, exist_ok=True)
+    logger.info(f"Repo created: {repo_url}")
+    repo_id = repo_url.repo_id
+
+    # Warn on too many commits
+    try:
+        commits = api.list_repo_commits(repo_id=repo_id, repo_type=repo_type, revision=revision)
+        commit_count = len(commits)
+        if commit_count > 500:
+            logger.warning(
+                f"\n{'=' * 80}\n"
+                f"WARNING: This repository has {commit_count} commits.\n"
+                f"Repositories with a large number of commits can experience performance issues.\n"
+                f"\n"
+                f"Consider squashing your commit history using `super_squash_history()`.\n"
+                "To do so, you need to stop this process, run the snippet below and restart the upload command."
+                f"  from huggingface_hub import super_squash_history\n"
+                f"  super_squash_history(repo_id='{repo_id}', repo_type='{repo_type}')\n"
+                f"\n"
+                f"Note: This is a non-revertible operation. See the documentation for more details:\n"
+                f"https://huggingface.co/docs/huggingface_hub/main/en/package_reference/hf_api#huggingface_hub.HfApi.super_squash_history\n"
+                f"{'=' * 80}\n"
+            )
+    except Exception as e:
+        # Don't fail the upload if we can't check commit count
+        logger.debug(f"Could not check commit count: {e}")
+
+    # 2.1 Check if xet is enabled to set batch file upload size
+    upload_batch_size = UPLOAD_BATCH_SIZE_XET if is_xet_available() else UPLOAD_BATCH_SIZE_LFS
+
+    # 3. List files to upload
+    filtered_paths_list = filter_repo_objects(
+        (path.relative_to(folder_path).as_posix() for path in folder_path.glob("**/*") if path.is_file()),
+        allow_patterns=allow_patterns,
+        ignore_patterns=ignore_patterns,
+    )
+    paths_list = [get_local_upload_paths(folder_path, relpath) for relpath in filtered_paths_list]
+    logger.info(f"Found {len(paths_list)} candidate files to upload")
+
+    # Validate upload against repository limits
+    _validate_upload_limits(paths_list)
+
+    logger.info("Starting upload...")
+
+    # Read metadata for each file
+    items = [
+        (paths, read_upload_metadata(folder_path, paths.path_in_repo))
+        for paths in tqdm(paths_list, desc="Recovering from metadata files")
+    ]
+
+    # 4. Start workers
+    status = LargeUploadStatus(items, upload_batch_size)
+    threads = [
+        threading.Thread(
+            target=_worker_job,
+            kwargs={
+                "status": status,
+                "api": api,
+                "repo_id": repo_id,
+                "repo_type": repo_type,
+                "revision": revision,
+            },
+        )
+        for _ in range(num_workers)
+    ]
+
+    for thread in threads:
+        thread.start()
+
+    # 5. Print regular reports
+    if print_report:
+        print("\n\n" + status.current_report())
+    last_report_ts = time.time()
+    while True:
+        time.sleep(1)
+        if time.time() - last_report_ts >= print_report_every:
+            if print_report:
+                _print_overwrite(status.current_report())
+            last_report_ts = time.time()
+        if status.is_done():
+            logging.info("Is done: exiting main loop")
+            break
+
+    for thread in threads:
+        thread.join()
+
+    logger.info(status.current_report())
+    logging.info("Upload is complete!")
+
+
+####################
+# Logic to manage workers and synchronize tasks
+####################
+
+
+class WorkerJob(enum.Enum):
+    SHA256 = enum.auto()
+    GET_UPLOAD_MODE = enum.auto()
+    PREUPLOAD_LFS = enum.auto()
+    COMMIT = enum.auto()
+    WAIT = enum.auto()  # if no tasks are available but we don't want to exit
+
+
+JOB_ITEM_T = tuple[LocalUploadFilePaths, LocalUploadFileMetadata]
+
+
+class LargeUploadStatus:
+    """Contains information, queues and tasks for a large upload process."""
+
+    def __init__(self, items: list[JOB_ITEM_T], upload_batch_size: int = 1):
+        self.items = items
+        self.queue_sha256: "queue.Queue[JOB_ITEM_T]" = queue.Queue()
+        self.queue_get_upload_mode: "queue.Queue[JOB_ITEM_T]" = queue.Queue()
+        self.queue_preupload_lfs: "queue.Queue[JOB_ITEM_T]" = queue.Queue()
+        self.queue_commit: "queue.Queue[JOB_ITEM_T]" = queue.Queue()
+        self.lock = Lock()
+
+        self.nb_workers_sha256: int = 0
+        self.nb_workers_get_upload_mode: int = 0
+        self.nb_workers_preupload_lfs: int = 0
+        self.upload_batch_size: int = upload_batch_size
+        self.nb_workers_commit: int = 0
+        self.nb_workers_waiting: int = 0
+        self.last_commit_attempt: Optional[float] = None
+
+        self._started_at = datetime.now()
+        self._chunk_idx: int = 1
+        self._chunk_lock: Lock = Lock()
+
+        # Setup queues
+        for item in self.items:
+            paths, metadata = item
+            if metadata.sha256 is None:
+                self.queue_sha256.put(item)
+            elif metadata.upload_mode is None:
+                self.queue_get_upload_mode.put(item)
+            elif metadata.upload_mode == "lfs" and not metadata.is_uploaded:
+                self.queue_preupload_lfs.put(item)
+            elif not metadata.is_committed:
+                self.queue_commit.put(item)
+            else:
+                logger.debug(f"Skipping file {paths.path_in_repo} (already uploaded and committed)")
+
+    def target_chunk(self) -> int:
+        with self._chunk_lock:
+            return COMMIT_SIZE_SCALE[self._chunk_idx]
+
+    def update_chunk(self, success: bool, nb_items: int, duration: float) -> None:
+        with self._chunk_lock:
+            if not success:
+                logger.warning(f"Failed to commit {nb_items} files at once. Will retry with less files in next batch.")
+                self._chunk_idx -= 1
+            elif nb_items >= COMMIT_SIZE_SCALE[self._chunk_idx] and duration < 40:
+                logger.info(f"Successfully committed {nb_items} at once. Increasing the limit for next batch.")
+                self._chunk_idx += 1
+
+            self._chunk_idx = max(0, min(self._chunk_idx, len(COMMIT_SIZE_SCALE) - 1))
+
+    def current_report(self) -> str:
+        """Generate a report of the current status of the large upload."""
+        nb_hashed = 0
+        size_hashed = 0
+        nb_preuploaded = 0
+        nb_lfs = 0
+        nb_lfs_unsure = 0
+        size_preuploaded = 0
+        nb_committed = 0
+        size_committed = 0
+        total_size = 0
+        ignored_files = 0
+        total_files = 0
+
+        with self.lock:
+            for _, metadata in self.items:
+                if metadata.should_ignore:
+                    ignored_files += 1
+                    continue
+                total_size += metadata.size
+                total_files += 1
+                if metadata.sha256 is not None:
+                    nb_hashed += 1
+                    size_hashed += metadata.size
+                if metadata.upload_mode == "lfs":
+                    nb_lfs += 1
+                if metadata.upload_mode is None:
+                    nb_lfs_unsure += 1
+                if metadata.is_uploaded:
+                    nb_preuploaded += 1
+                    size_preuploaded += metadata.size
+                if metadata.is_committed:
+                    nb_committed += 1
+                    size_committed += metadata.size
+            total_size_str = _format_size(total_size)
+
+            now = datetime.now()
+            now_str = now.strftime("%Y-%m-%d %H:%M:%S")
+            elapsed = now - self._started_at
+            elapsed_str = str(elapsed).split(".")[0]  # remove milliseconds
+
+            message = "\n" + "-" * 10
+            message += f" {now_str} ({elapsed_str}) "
+            message += "-" * 10 + "\n"
+
+            message += "Files:   "
+            message += f"hashed {nb_hashed}/{total_files} ({_format_size(size_hashed)}/{total_size_str}) | "
+            message += f"pre-uploaded: {nb_preuploaded}/{nb_lfs} ({_format_size(size_preuploaded)}/{total_size_str})"
+            if nb_lfs_unsure > 0:
+                message += f" (+{nb_lfs_unsure} unsure)"
+            message += f" | committed: {nb_committed}/{total_files} ({_format_size(size_committed)}/{total_size_str})"
+            message += f" | ignored: {ignored_files}\n"
+
+            message += "Workers: "
+            message += f"hashing: {self.nb_workers_sha256} | "
+            message += f"get upload mode: {self.nb_workers_get_upload_mode} | "
+            message += f"pre-uploading: {self.nb_workers_preupload_lfs} | "
+            message += f"committing: {self.nb_workers_commit} | "
+            message += f"waiting: {self.nb_workers_waiting}\n"
+            message += "-" * 51
+
+            return message
+
+    def is_done(self) -> bool:
+        with self.lock:
+            return all(metadata.is_committed or metadata.should_ignore for _, metadata in self.items)
+
+
+def _worker_job(
+    status: LargeUploadStatus,
+    api: "HfApi",
+    repo_id: str,
+    repo_type: str,
+    revision: str,
+):
+    """
+    Main process for a worker. The worker will perform tasks based on the priority list until all files are uploaded
+    and committed. If no tasks are available, the worker will wait for 10 seconds before checking again.
+
+    If a task fails for any reason, the item(s) are put back in the queue for another worker to pick up.
+
+    Read `upload_large_folder` docstring for more information on how tasks are prioritized.
+    """
+    while True:
+        next_job: Optional[tuple[WorkerJob, list[JOB_ITEM_T]]] = None
+
+        # Determine next task
+        next_job = _determine_next_job(status)
+        if next_job is None:
+            return
+        job, items = next_job
+
+        # Perform task
+        if job == WorkerJob.SHA256:
+            item = items[0]  # single item
+            try:
+                _compute_sha256(item)
+                status.queue_get_upload_mode.put(item)
+            except KeyboardInterrupt:
+                raise
+            except Exception as e:
+                logger.error(f"Failed to compute sha256: {e}")
+                traceback.format_exc()
+                status.queue_sha256.put(item)
+
+            with status.lock:
+                status.nb_workers_sha256 -= 1
+
+        elif job == WorkerJob.GET_UPLOAD_MODE:
+            try:
+                _get_upload_mode(items, api=api, repo_id=repo_id, repo_type=repo_type, revision=revision)
+            except KeyboardInterrupt:
+                raise
+            except Exception as e:
+                logger.error(f"Failed to get upload mode: {e}")
+                traceback.format_exc()
+
+            # Items are either:
+            # - dropped (if should_ignore)
+            # - put in LFS queue (if LFS)
+            # - put in commit queue (if regular)
+            # - or put back (if error occurred).
+            for item in items:
+                _, metadata = item
+                if metadata.should_ignore:
+                    continue
+                if metadata.upload_mode == "lfs":
+                    status.queue_preupload_lfs.put(item)
+                elif metadata.upload_mode == "regular":
+                    status.queue_commit.put(item)
+                else:
+                    status.queue_get_upload_mode.put(item)
+
+            with status.lock:
+                status.nb_workers_get_upload_mode -= 1
+
+        elif job == WorkerJob.PREUPLOAD_LFS:
+            try:
+                _preupload_lfs(items, api=api, repo_id=repo_id, repo_type=repo_type, revision=revision)
+                for item in items:
+                    status.queue_commit.put(item)
+            except KeyboardInterrupt:
+                raise
+            except Exception as e:
+                logger.error(f"Failed to preupload LFS: {e}")
+                traceback.format_exc()
+                for item in items:
+                    status.queue_preupload_lfs.put(item)
+
+            with status.lock:
+                status.nb_workers_preupload_lfs -= 1
+
+        elif job == WorkerJob.COMMIT:
+            start_ts = time.time()
+            success = True
+            try:
+                _commit(items, api=api, repo_id=repo_id, repo_type=repo_type, revision=revision)
+            except KeyboardInterrupt:
+                raise
+            except Exception as e:
+                logger.error(f"Failed to commit: {e}")
+                traceback.format_exc()
+                for item in items:
+                    status.queue_commit.put(item)
+                success = False
+            duration = time.time() - start_ts
+            status.update_chunk(success, len(items), duration)
+            with status.lock:
+                status.last_commit_attempt = time.time()
+                status.nb_workers_commit -= 1
+
+        elif job == WorkerJob.WAIT:
+            time.sleep(WAITING_TIME_IF_NO_TASKS)
+            with status.lock:
+                status.nb_workers_waiting -= 1
+
+
+def _determine_next_job(status: LargeUploadStatus) -> Optional[tuple[WorkerJob, list[JOB_ITEM_T]]]:
+    with status.lock:
+        # 1. Commit if more than 5 minutes since last commit attempt (and at least 1 file)
+        if (
+            status.nb_workers_commit == 0
+            and status.queue_commit.qsize() > 0
+            and status.last_commit_attempt is not None
+            and time.time() - status.last_commit_attempt > 5 * 60
+        ):
+            status.nb_workers_commit += 1
+            logger.debug("Job: commit (more than 5 minutes since last commit attempt)")
+            return (WorkerJob.COMMIT, _get_n(status.queue_commit, status.target_chunk()))
+
+        # 2. Commit if at least 100 files are ready to commit
+        elif status.nb_workers_commit == 0 and status.queue_commit.qsize() >= 150:
+            status.nb_workers_commit += 1
+            logger.debug("Job: commit (>100 files ready)")
+            return (WorkerJob.COMMIT, _get_n(status.queue_commit, status.target_chunk()))
+
+        # 3. Get upload mode if at least 100 files
+        elif status.queue_get_upload_mode.qsize() >= MAX_NB_FILES_FETCH_UPLOAD_MODE:
+            status.nb_workers_get_upload_mode += 1
+            logger.debug(f"Job: get upload mode (>{MAX_NB_FILES_FETCH_UPLOAD_MODE} files ready)")
+            return (WorkerJob.GET_UPLOAD_MODE, _get_n(status.queue_get_upload_mode, MAX_NB_FILES_FETCH_UPLOAD_MODE))
+
+        # 4. Preupload LFS file if at least `status.upload_batch_size` files and no worker is preuploading LFS
+        elif status.queue_preupload_lfs.qsize() >= status.upload_batch_size and status.nb_workers_preupload_lfs == 0:
+            status.nb_workers_preupload_lfs += 1
+            logger.debug("Job: preupload LFS (no other worker preuploading LFS)")
+            return (WorkerJob.PREUPLOAD_LFS, _get_n(status.queue_preupload_lfs, status.upload_batch_size))
+
+        # 5. Compute sha256 if at least 1 file and no worker is computing sha256
+        elif status.queue_sha256.qsize() > 0 and status.nb_workers_sha256 == 0:
+            status.nb_workers_sha256 += 1
+            logger.debug("Job: sha256 (no other worker computing sha256)")
+            return (WorkerJob.SHA256, _get_one(status.queue_sha256))
+
+        # 6. Get upload mode if at least 1 file and no worker is getting upload mode
+        elif status.queue_get_upload_mode.qsize() > 0 and status.nb_workers_get_upload_mode == 0:
+            status.nb_workers_get_upload_mode += 1
+            logger.debug("Job: get upload mode (no other worker getting upload mode)")
+            return (WorkerJob.GET_UPLOAD_MODE, _get_n(status.queue_get_upload_mode, MAX_NB_FILES_FETCH_UPLOAD_MODE))
+
+        # 7. Preupload LFS file if at least `status.upload_batch_size` files
+        elif status.queue_preupload_lfs.qsize() >= status.upload_batch_size:
+            status.nb_workers_preupload_lfs += 1
+            logger.debug("Job: preupload LFS")
+            return (WorkerJob.PREUPLOAD_LFS, _get_n(status.queue_preupload_lfs, status.upload_batch_size))
+
+        # 8. Compute sha256 if at least 1 file
+        elif status.queue_sha256.qsize() > 0:
+            status.nb_workers_sha256 += 1
+            logger.debug("Job: sha256")
+            return (WorkerJob.SHA256, _get_one(status.queue_sha256))
+
+        # 9. Get upload mode if at least 1 file
+        elif status.queue_get_upload_mode.qsize() > 0:
+            status.nb_workers_get_upload_mode += 1
+            logger.debug("Job: get upload mode")
+            return (WorkerJob.GET_UPLOAD_MODE, _get_n(status.queue_get_upload_mode, MAX_NB_FILES_FETCH_UPLOAD_MODE))
+
+        # 10. Preupload LFS file if at least 1 file
+        elif status.queue_preupload_lfs.qsize() > 0:
+            status.nb_workers_preupload_lfs += 1
+            logger.debug("Job: preupload LFS")
+            return (WorkerJob.PREUPLOAD_LFS, _get_n(status.queue_preupload_lfs, status.upload_batch_size))
+
+        # 11. Commit if at least 1 file and 1 min since last commit attempt
+        elif (
+            status.nb_workers_commit == 0
+            and status.queue_commit.qsize() > 0
+            and status.last_commit_attempt is not None
+            and time.time() - status.last_commit_attempt > 1 * 60
+        ):
+            status.nb_workers_commit += 1
+            logger.debug("Job: commit (1 min since last commit attempt)")
+            return (WorkerJob.COMMIT, _get_n(status.queue_commit, status.target_chunk()))
+
+        # 12. Commit if at least 1 file all other queues are empty and all workers are waiting
+        #     e.g. when it's the last commit
+        elif (
+            status.nb_workers_commit == 0
+            and status.queue_commit.qsize() > 0
+            and status.queue_sha256.qsize() == 0
+            and status.queue_get_upload_mode.qsize() == 0
+            and status.queue_preupload_lfs.qsize() == 0
+            and status.nb_workers_sha256 == 0
+            and status.nb_workers_get_upload_mode == 0
+            and status.nb_workers_preupload_lfs == 0
+        ):
+            status.nb_workers_commit += 1
+            logger.debug("Job: commit")
+            return (WorkerJob.COMMIT, _get_n(status.queue_commit, status.target_chunk()))
+
+        # 13. If all queues are empty, exit
+        elif all(metadata.is_committed or metadata.should_ignore for _, metadata in status.items):
+            logger.info("All files have been processed! Exiting worker.")
+            return None
+
+        # 14. If no task is available, wait
+        else:
+            status.nb_workers_waiting += 1
+            logger.debug(f"No task available, waiting... ({WAITING_TIME_IF_NO_TASKS}s)")
+            return (WorkerJob.WAIT, [])
+
+
+####################
+# Atomic jobs (sha256, get_upload_mode, preupload_lfs, commit)
+####################
+
+
+def _compute_sha256(item: JOB_ITEM_T) -> None:
+    """Compute sha256 of a file and save it in metadata."""
+    paths, metadata = item
+    if metadata.sha256 is None:
+        with paths.file_path.open("rb") as f:
+            metadata.sha256 = sha_fileobj(f).hex()
+    metadata.save(paths)
+
+
+def _get_upload_mode(items: list[JOB_ITEM_T], api: "HfApi", repo_id: str, repo_type: str, revision: str) -> None:
+    """Get upload mode for each file and update metadata.
+
+    Also receive info if the file should be ignored.
+    """
+    additions = [_build_hacky_operation(item) for item in items]
+    _fetch_upload_modes(
+        additions=additions,
+        repo_type=repo_type,
+        repo_id=repo_id,
+        headers=api._build_hf_headers(),
+        revision=quote(revision, safe=""),
+        endpoint=api.endpoint,
+    )
+    for item, addition in zip(items, additions):
+        paths, metadata = item
+        metadata.upload_mode = addition._upload_mode
+        metadata.should_ignore = addition._should_ignore
+        metadata.remote_oid = addition._remote_oid
+        metadata.save(paths)
+
+
+def _preupload_lfs(items: list[JOB_ITEM_T], api: "HfApi", repo_id: str, repo_type: str, revision: str) -> None:
+    """Preupload LFS files and update metadata."""
+    additions = [_build_hacky_operation(item) for item in items]
+    api.preupload_lfs_files(
+        repo_id=repo_id,
+        repo_type=repo_type,
+        revision=revision,
+        additions=additions,
+    )
+
+    for paths, metadata in items:
+        metadata.is_uploaded = True
+        metadata.save(paths)
+
+
+def _commit(items: list[JOB_ITEM_T], api: "HfApi", repo_id: str, repo_type: str, revision: str) -> None:
+    """Commit files to the repo."""
+    additions = [_build_hacky_operation(item) for item in items]
+    api.create_commit(
+        repo_id=repo_id,
+        repo_type=repo_type,
+        revision=revision,
+        operations=additions,
+        commit_message="Add files using upload-large-folder tool",
+    )
+    for paths, metadata in items:
+        metadata.is_committed = True
+        metadata.save(paths)
+
+
+####################
+# Hacks with CommitOperationAdd to bypass checks/sha256 calculation
+####################
+
+
+class HackyCommitOperationAdd(CommitOperationAdd):
+    def __post_init__(self) -> None:
+        if isinstance(self.path_or_fileobj, Path):
+            self.path_or_fileobj = str(self.path_or_fileobj)
+
+
+def _build_hacky_operation(item: JOB_ITEM_T) -> HackyCommitOperationAdd:
+    paths, metadata = item
+    operation = HackyCommitOperationAdd(path_in_repo=paths.path_in_repo, path_or_fileobj=paths.file_path)
+    with paths.file_path.open("rb") as file:
+        sample = file.peek(512)[:512]
+    if metadata.sha256 is None:
+        raise ValueError("sha256 must have been computed by now!")
+    operation.upload_info = UploadInfo(sha256=bytes.fromhex(metadata.sha256), size=metadata.size, sample=sample)
+    operation._upload_mode = metadata.upload_mode  # type: ignore[assignment]
+    operation._should_ignore = metadata.should_ignore
+    operation._remote_oid = metadata.remote_oid
+    return operation
+
+
+####################
+# Misc helpers
+####################
+
+
+def _get_one(queue: "queue.Queue[JOB_ITEM_T]") -> list[JOB_ITEM_T]:
+    return [queue.get()]
+
+
+def _get_n(queue: "queue.Queue[JOB_ITEM_T]", n: int) -> list[JOB_ITEM_T]:
+    return [queue.get() for _ in range(min(queue.qsize(), n))]
+
+
+def _print_overwrite(report: str) -> None:
+    """Print a report, overwriting the previous lines.
+
+    Since tqdm in using `sys.stderr` to (re-)write progress bars, we need to use `sys.stdout`
+    to print the report.
+
+    Note: works well only if no other process is writing to `sys.stdout`!
+    """
+    report += "\n"
+    # Get terminal width
+    terminal_width = shutil.get_terminal_size().columns
+
+    # Count number of lines that should be cleared
+    nb_lines = sum(len(line) // terminal_width + 1 for line in report.splitlines())
+
+    # Clear previous lines based on the number of lines in the report
+    for _ in range(nb_lines):
+        sys.stdout.write("\r\033[K")  # Clear line
+        sys.stdout.write("\033[F")  # Move cursor up one line
+
+    # Print the new report, filling remaining space with whitespace
+    sys.stdout.write(report)
+    sys.stdout.write(" " * (terminal_width - len(report.splitlines()[-1])))
+    sys.stdout.flush()

data/conda_envs/evalclaw/lib/python3.11/site-packages/huggingface_hub/_webhooks_payload.py

@@ -0,0 +1,137 @@
+# coding=utf-8
+# Copyright 2023-present, the HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Contains data structures to parse the webhooks payload."""
+
+from typing import Literal, Optional
+
+from .utils import is_pydantic_available
+
+
+if is_pydantic_available():
+    from pydantic import BaseModel
+else:
+    # Define a dummy BaseModel to avoid import errors when pydantic is not installed
+    # Import error will be raised when trying to use the class
+
+    class BaseModel:  # type: ignore [no-redef]
+        def __init__(self, *args, **kwargs) -> None:
+            raise ImportError(
+                "You must have `pydantic` installed to use `WebhookPayload`. This is an optional dependency that"
+                " should be installed separately. Please run `pip install --upgrade pydantic` and retry."
+            )
+
+
+# This is an adaptation of the ReportV3 interface implemented in moon-landing. V0, V1 and V2 have been ignored as they
+# are not in used anymore. To keep in sync when format is updated in
+# https://github.com/huggingface/moon-landing/blob/main/server/lib/HFWebhooks.ts (internal link).
+
+
+WebhookEvent_T = Literal[
+    "create",
+    "delete",
+    "move",
+    "update",
+]
+RepoChangeEvent_T = Literal[
+    "add",
+    "move",
+    "remove",
+    "update",
+]
+RepoType_T = Literal[
+    "dataset",
+    "model",
+    "space",
+]
+DiscussionStatus_T = Literal[
+    "closed",
+    "draft",
+    "open",
+    "merged",
+]
+SupportedWebhookVersion = Literal[3]
+
+
+class ObjectId(BaseModel):
+    id: str
+
+
+class WebhookPayloadUrl(BaseModel):
+    web: str
+    api: Optional[str] = None
+
+
+class WebhookPayloadMovedTo(BaseModel):
+    name: str
+    owner: ObjectId
+
+
+class WebhookPayloadWebhook(ObjectId):
+    version: SupportedWebhookVersion
+
+
+class WebhookPayloadEvent(BaseModel):
+    action: WebhookEvent_T
+    scope: str
+
+
+class WebhookPayloadDiscussionChanges(BaseModel):
+    base: str
+    mergeCommitId: Optional[str] = None
+
+
+class WebhookPayloadComment(ObjectId):
+    author: ObjectId
+    hidden: bool
+    content: Optional[str] = None
+    url: WebhookPayloadUrl
+
+
+class WebhookPayloadDiscussion(ObjectId):
+    num: int
+    author: ObjectId
+    url: WebhookPayloadUrl
+    title: str
+    isPullRequest: bool
+    status: DiscussionStatus_T
+    changes: Optional[WebhookPayloadDiscussionChanges] = None
+    pinned: Optional[bool] = None
+
+
+class WebhookPayloadRepo(ObjectId):
+    owner: ObjectId
+    head_sha: Optional[str] = None
+    name: str
+    private: bool
+    subdomain: Optional[str] = None
+    tags: Optional[list[str]] = None
+    type: Literal["dataset", "model", "space"]
+    url: WebhookPayloadUrl
+
+
+class WebhookPayloadUpdatedRef(BaseModel):
+    ref: str
+    oldSha: Optional[str] = None
+    newSha: Optional[str] = None
+
+
+class WebhookPayload(BaseModel):
+    event: WebhookPayloadEvent
+    repo: WebhookPayloadRepo
+    discussion: Optional[WebhookPayloadDiscussion] = None
+    comment: Optional[WebhookPayloadComment] = None
+    webhook: WebhookPayloadWebhook
+    movedTo: Optional[WebhookPayloadMovedTo] = None
+    updatedRefs: Optional[list[WebhookPayloadUpdatedRef]] = None

data/conda_envs/evalclaw/lib/python3.11/site-packages/huggingface_hub/_webhooks_server.py

@@ -0,0 +1,376 @@
+# coding=utf-8
+# Copyright 2023-present, the HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Contains `WebhooksServer` and `webhook_endpoint` to create a webhook server easily."""
+
+import atexit
+import inspect
+import os
+from functools import wraps
+from typing import TYPE_CHECKING, Any, Callable, Optional
+
+from .utils import experimental, is_fastapi_available, is_gradio_available
+
+
+if TYPE_CHECKING:
+    import gradio as gr
+    from fastapi import Request
+
+if is_fastapi_available():
+    from fastapi import FastAPI, Request
+    from fastapi.responses import JSONResponse
+else:
+    # Will fail at runtime if FastAPI is not available
+    FastAPI = Request = JSONResponse = None  # type: ignore
+
+
+_global_app: Optional["WebhooksServer"] = None
+_is_local = os.environ.get("SPACE_ID") is None
+
+
+@experimental
+class WebhooksServer:
+    """
+    The [`WebhooksServer`] class lets you create an instance of a Gradio app that can receive Huggingface webhooks.
+    These webhooks can be registered using the [`~WebhooksServer.add_webhook`] decorator. Webhook endpoints are added to
+    the app as a POST endpoint to the FastAPI router. Once all the webhooks are registered, the `launch` method has to be
+    called to start the app.
+
+    It is recommended to accept [`WebhookPayload`] as the first argument of the webhook function. It is a Pydantic
+    model that contains all the information about the webhook event. The data will be parsed automatically for you.
+
+    Check out the [webhooks guide](../guides/webhooks_server) for a step-by-step tutorial on how to set up your
+    WebhooksServer and deploy it on a Space.
+
+    > [!WARNING]
+    > `WebhooksServer` is experimental. Its API is subject to change in the future.
+
+    > [!WARNING]
+    > You must have `gradio` installed to use `WebhooksServer` (`pip install --upgrade gradio`).
+
+    Args:
+        ui (`gradio.Blocks`, optional):
+            A Gradio UI instance to be used as the Space landing page. If `None`, a UI displaying instructions
+            about the configured webhooks is created.
+        webhook_secret (`str`, optional):
+            A secret key to verify incoming webhook requests. You can set this value to any secret you want as long as
+            you also configure it in your [webhooks settings panel](https://huggingface.co/settings/webhooks). You
+            can also set this value as the `WEBHOOK_SECRET` environment variable. If no secret is provided, the
+            webhook endpoints are opened without any security.
+
+    Example:
+
+        ```python
+        import gradio as gr
+        from huggingface_hub import WebhooksServer, WebhookPayload
+
+        with gr.Blocks() as ui:
+            ...
+
+        app = WebhooksServer(ui=ui, webhook_secret="my_secret_key")
+
+        @app.add_webhook("/say_hello")
+        async def hello(payload: WebhookPayload):
+            return {"message": "hello"}
+
+        app.launch()
+        ```
+    """
+
+    def __new__(cls, *args, **kwargs) -> "WebhooksServer":
+        if not is_gradio_available():
+            raise ImportError(
+                "You must have `gradio` installed to use `WebhooksServer`. Please run `pip install --upgrade gradio`"
+                " first."
+            )
+        if not is_fastapi_available():
+            raise ImportError(
+                "You must have `fastapi` installed to use `WebhooksServer`. Please run `pip install --upgrade fastapi`"
+                " first."
+            )
+        return super().__new__(cls)
+
+    def __init__(
+        self,
+        ui: Optional["gr.Blocks"] = None,
+        webhook_secret: Optional[str] = None,
+    ) -> None:
+        self._ui = ui
+
+        self.webhook_secret = webhook_secret or os.getenv("WEBHOOK_SECRET")
+        self.registered_webhooks: dict[str, Callable] = {}
+        _warn_on_empty_secret(self.webhook_secret)
+
+    def add_webhook(self, path: Optional[str] = None) -> Callable:
+        """
+        Decorator to add a webhook to the [`WebhooksServer`] server.
+
+        Args:
+            path (`str`, optional):
+                The URL path to register the webhook function. If not provided, the function name will be used as the
+                path. In any case, all webhooks are registered under `/webhooks`.
+
+        Raises:
+            ValueError: If the provided path is already registered as a webhook.
+
+        Example:
+            ```python
+            from huggingface_hub import WebhooksServer, WebhookPayload
+
+            app = WebhooksServer()
+
+            @app.add_webhook
+            async def trigger_training(payload: WebhookPayload):
+                if payload.repo.type == "dataset" and payload.event.action == "update":
+                    # Trigger a training job if a dataset is updated
+                    ...
+
+            app.launch()
+        ```
+        """
+        # Usage: directly as decorator. Example: `@app.add_webhook`
+        if callable(path):
+            # If path is a function, it means it was used as a decorator without arguments
+            return self.add_webhook()(path)
+
+        # Usage: provide a path. Example: `@app.add_webhook(...)`
+        @wraps(FastAPI.post)
+        def _inner_post(*args, **kwargs):
+            func = args[0]
+            abs_path = f"/webhooks/{(path or func.__name__).strip('/')}"
+            if abs_path in self.registered_webhooks:
+                raise ValueError(f"Webhook {abs_path} already exists.")
+            self.registered_webhooks[abs_path] = func
+
+        return _inner_post
+
+    def launch(self, prevent_thread_lock: bool = False, **launch_kwargs: Any) -> None:
+        """Launch the Gradio app and register webhooks to the underlying FastAPI server.
+
+        Input parameters are forwarded to Gradio when launching the app.
+        """
+        ui = self._ui or self._get_default_ui()
+
+        # Start Gradio App
+        #   - as non-blocking so that webhooks can be added afterwards
+        #   - as shared if launch locally (to debug webhooks)
+        launch_kwargs.setdefault("share", _is_local)
+        self.fastapi_app, _, _ = ui.launch(prevent_thread_lock=True, **launch_kwargs)
+
+        # Register webhooks to FastAPI app
+        for path, func in self.registered_webhooks.items():
+            # Add secret check if required
+            if self.webhook_secret is not None:
+                func = _wrap_webhook_to_check_secret(func, webhook_secret=self.webhook_secret)
+
+            # Add route to FastAPI app
+            self.fastapi_app.post(path)(func)
+
+        # Print instructions and block main thread
+        space_host = os.environ.get("SPACE_HOST")
+        url = "https://" + space_host if space_host is not None else (ui.share_url or ui.local_url)
+        if url is None:
+            raise ValueError("Cannot find the URL of the app. Please provide a valid `ui` or update `gradio` version.")
+        url = url.strip("/")
+        message = "\nWebhooks are correctly setup and ready to use:"
+        message += "\n" + "\n".join(f"  - POST {url}{webhook}" for webhook in self.registered_webhooks)
+        message += "\nGo to https://huggingface.co/settings/webhooks to setup your webhooks."
+        print(message)
+
+        if not prevent_thread_lock:
+            ui.block_thread()
+
+    def _get_default_ui(self) -> "gr.Blocks":
+        """Default UI if not provided (lists webhooks and provides basic instructions)."""
+        import gradio as gr
+
+        with gr.Blocks() as ui:
+            gr.Markdown("# This is an app to process 🤗 Webhooks")
+            gr.Markdown(
+                "Webhooks are a foundation for MLOps-related features. They allow you to listen for new changes on"
+                " specific repos or to all repos belonging to particular set of users/organizations (not just your"
+                " repos, but any repo). Check out this [guide](https://huggingface.co/docs/hub/webhooks) to get to"
+                " know more about webhooks on the Huggingface Hub."
+            )
+            gr.Markdown(
+                f"{len(self.registered_webhooks)} webhook(s) are registered:"
+                + "\n\n"
+                + "\n ".join(
+                    f"- [{webhook_path}]({_get_webhook_doc_url(webhook.__name__, webhook_path)})"
+                    for webhook_path, webhook in self.registered_webhooks.items()
+                )
+            )
+            gr.Markdown(
+                "Go to https://huggingface.co/settings/webhooks to setup your webhooks."
+                + "\nYou app is running locally. Please look at the logs to check the full URL you need to set."
+                if _is_local
+                else (
+                    "\nThis app is running on a Space. You can find the corresponding URL in the options menu"
+                    " (top-right) > 'Embed the Space'. The URL looks like 'https://{username}-{repo_name}.hf.space'."
+                )
+            )
+        return ui
+
+
+@experimental
+def webhook_endpoint(path: Optional[str] = None) -> Callable:
+    """Decorator to start a [`WebhooksServer`] and register the decorated function as a webhook endpoint.
+
+    This is a helper to get started quickly. If you need more flexibility (custom landing page or webhook secret),
+    you can use [`WebhooksServer`] directly. You can register multiple webhook endpoints (to the same server) by using
+    this decorator multiple times.
+
+    Check out the [webhooks guide](../guides/webhooks_server) for a step-by-step tutorial on how to set up your
+    server and deploy it on a Space.
+
+    > [!WARNING]
+    > `webhook_endpoint` is experimental. Its API is subject to change in the future.
+
+    > [!WARNING]
+    > You must have `gradio` installed to use `webhook_endpoint` (`pip install --upgrade gradio`).
+
+    Args:
+        path (`str`, optional):
+            The URL path to register the webhook function. If not provided, the function name will be used as the path.
+            In any case, all webhooks are registered under `/webhooks`.
+
+    Examples:
+        The default usage is to register a function as a webhook endpoint. The function name will be used as the path.
+        The server will be started automatically at exit (i.e. at the end of the script).
+
+        ```python
+        from huggingface_hub import webhook_endpoint, WebhookPayload
+
+        @webhook_endpoint
+        async def trigger_training(payload: WebhookPayload):
+            if payload.repo.type == "dataset" and payload.event.action == "update":
+                # Trigger a training job if a dataset is updated
+                ...
+
+        # Server is automatically started at the end of the script.
+        ```
+
+        Advanced usage: register a function as a webhook endpoint and start the server manually. This is useful if you
+        are running it in a notebook.
+
+        ```python
+        from huggingface_hub import webhook_endpoint, WebhookPayload
+
+        @webhook_endpoint
+        async def trigger_training(payload: WebhookPayload):
+            if payload.repo.type == "dataset" and payload.event.action == "update":
+                # Trigger a training job if a dataset is updated
+                ...
+
+        # Start the server manually
+        trigger_training.launch()
+        ```
+    """
+    if callable(path):
+        # If path is a function, it means it was used as a decorator without arguments
+        return webhook_endpoint()(path)
+
+    @wraps(WebhooksServer.add_webhook)
+    def _inner(func: Callable) -> Callable:
+        app = _get_global_app()
+        app.add_webhook(path)(func)
+        if len(app.registered_webhooks) == 1:
+            # Register `app.launch` to run at exit (only once)
+            atexit.register(app.launch)
+
+        @wraps(app.launch)
+        def _launch_now():
+            # Run the app directly (without waiting atexit)
+            atexit.unregister(app.launch)
+            app.launch()
+
+        func.launch = _launch_now  # type: ignore
+        return func
+
+    return _inner
+
+
+def _get_global_app() -> WebhooksServer:
+    global _global_app
+    if _global_app is None:
+        _global_app = WebhooksServer()
+    return _global_app
+
+
+def _warn_on_empty_secret(webhook_secret: Optional[str]) -> None:
+    if webhook_secret is None:
+        print("Webhook secret is not defined. This means your webhook endpoints will be open to everyone.")
+        print(
+            "To add a secret, set `WEBHOOK_SECRET` as environment variable or pass it at initialization: "
+            "\n\t`app = WebhooksServer(webhook_secret='my_secret', ...)`"
+        )
+        print(
+            "For more details about webhook secrets, please refer to"
+            " https://huggingface.co/docs/hub/webhooks#webhook-secret."
+        )
+    else:
+        print("Webhook secret is correctly defined.")
+
+
+def _get_webhook_doc_url(webhook_name: str, webhook_path: str) -> str:
+    """Returns the anchor to a given webhook in the docs (experimental)"""
+    return "/docs#/default/" + webhook_name + webhook_path.replace("/", "_") + "_post"
+
+
+def _wrap_webhook_to_check_secret(func: Callable, webhook_secret: str) -> Callable:
+    """Wraps a webhook function to check the webhook secret before calling the function.
+
+    This is a hacky way to add the `request` parameter to the function signature. Since FastAPI based itself on route
+    parameters to inject the values to the function, we need to hack the function signature to retrieve the `Request`
+    object (and hence the headers). A far cleaner solution would be to use a middleware. However, since
+    `fastapi==0.90.1`, a middleware cannot be added once the app has started. And since the FastAPI app is started by
+    Gradio internals (and not by us), we cannot add a middleware.
+
+    This method is called only when a secret has been defined by the user. If a request is sent without the
+    "x-webhook-secret", the function will return a 401 error (unauthorized). If the header is sent but is incorrect,
+    the function will return a 403 error (forbidden).
+
+    Inspired by https://stackoverflow.com/a/33112180.
+    """
+    initial_sig = inspect.signature(func)
+
+    @wraps(func)
+    async def _protected_func(request: Request, **kwargs):
+        request_secret = request.headers.get("x-webhook-secret")
+        if request_secret is None:
+            return JSONResponse({"error": "x-webhook-secret header not set."}, status_code=401)
+        if request_secret != webhook_secret:
+            return JSONResponse({"error": "Invalid webhook secret."}, status_code=403)
+
+        # Inject `request` in kwargs if required
+        if "request" in initial_sig.parameters:
+            kwargs["request"] = request
+
+        # Handle both sync and async routes
+        if inspect.iscoroutinefunction(func):
+            return await func(**kwargs)
+        else:
+            return func(**kwargs)
+
+    # Update signature to include request
+    if "request" not in initial_sig.parameters:
+        _protected_func.__signature__ = initial_sig.replace(  # type: ignore
+            parameters=(
+                inspect.Parameter(name="request", kind=inspect.Parameter.POSITIONAL_OR_KEYWORD, annotation=Request),
+            )
+            + tuple(initial_sig.parameters.values())
+        )
+
+    # Return protected route
+    return _protected_func

data/conda_envs/evalclaw/lib/python3.11/site-packages/huggingface_hub/community.py

@@ -0,0 +1,363 @@
+"""
+Data structures to interact with Discussions and Pull Requests on the Hub.
+
+See [the Discussions and Pull Requests guide](https://huggingface.co/docs/hub/repositories-pull-requests-discussions)
+for more information on Pull Requests, Discussions, and the community tab.
+"""
+
+from dataclasses import dataclass
+from datetime import datetime
+from typing import Literal, Optional, TypedDict, Union
+
+from . import constants
+from .utils import parse_datetime
+
+
+DiscussionStatus = Literal["open", "closed", "merged", "draft"]
+
+
+@dataclass
+class Discussion:
+    """
+    A Discussion or Pull Request on the Hub.
+
+    This dataclass is not intended to be instantiated directly.
+
+    Attributes:
+        title (`str`):
+            The title of the Discussion / Pull Request
+        status (`str`):
+            The status of the Discussion / Pull Request.
+            It must be one of:
+                * `"open"`
+                * `"closed"`
+                * `"merged"` (only for Pull Requests )
+                * `"draft"` (only for Pull Requests )
+        num (`int`):
+            The number of the Discussion / Pull Request.
+        repo_id (`str`):
+            The id (`"{namespace}/{repo_name}"`) of the repo on which
+            the Discussion / Pull Request was open.
+        repo_type (`str`):
+            The type of the repo on which the Discussion / Pull Request was open.
+            Possible values are: `"model"`, `"dataset"`, `"space"`.
+        author (`str`):
+            The username of the Discussion / Pull Request author.
+            Can be `"deleted"` if the user has been deleted since.
+        is_pull_request (`bool`):
+            Whether or not this is a Pull Request.
+        created_at (`datetime`):
+            The `datetime` of creation of the Discussion / Pull Request.
+        endpoint (`str`):
+            Endpoint of the Hub. Default is https://huggingface.co.
+        git_reference (`str`, *optional*):
+            (property) Git reference to which changes can be pushed if this is a Pull Request, `None` otherwise.
+        url (`str`):
+            (property) URL of the discussion on the Hub.
+    """
+
+    title: str
+    status: DiscussionStatus
+    num: int
+    repo_id: str
+    repo_type: str
+    author: str
+    is_pull_request: bool
+    created_at: datetime
+    endpoint: str
+
+    @property
+    def git_reference(self) -> Optional[str]:
+        """
+        If this is a Pull Request , returns the git reference to which changes can be pushed.
+        Returns `None` otherwise.
+        """
+        if self.is_pull_request:
+            return f"refs/pr/{self.num}"
+        return None
+
+    @property
+    def url(self) -> str:
+        """Returns the URL of the discussion on the Hub."""
+        if self.repo_type is None or self.repo_type == constants.REPO_TYPE_MODEL:
+            return f"{self.endpoint}/{self.repo_id}/discussions/{self.num}"
+        return f"{self.endpoint}/{self.repo_type}s/{self.repo_id}/discussions/{self.num}"
+
+
+@dataclass
+class DiscussionWithDetails(Discussion):
+    """
+    Subclass of [`Discussion`].
+
+    Attributes:
+        title (`str`):
+            The title of the Discussion / Pull Request
+        status (`str`):
+            The status of the Discussion / Pull Request.
+            It can be one of:
+                * `"open"`
+                * `"closed"`
+                * `"merged"` (only for Pull Requests )
+                * `"draft"` (only for Pull Requests )
+        num (`int`):
+            The number of the Discussion / Pull Request.
+        repo_id (`str`):
+            The id (`"{namespace}/{repo_name}"`) of the repo on which
+            the Discussion / Pull Request was open.
+        repo_type (`str`):
+            The type of the repo on which the Discussion / Pull Request was open.
+            Possible values are: `"model"`, `"dataset"`, `"space"`.
+        author (`str`):
+            The username of the Discussion / Pull Request author.
+            Can be `"deleted"` if the user has been deleted since.
+        is_pull_request (`bool`):
+            Whether or not this is a Pull Request.
+        created_at (`datetime`):
+            The `datetime` of creation of the Discussion / Pull Request.
+        events (`list` of [`DiscussionEvent`])
+            The list of [`DiscussionEvents`] in this Discussion or Pull Request.
+        conflicting_files (`Union[list[str], bool, None]`, *optional*):
+            A list of conflicting files if this is a Pull Request.
+            `None` if `self.is_pull_request` is `False`.
+            `True` if there are conflicting files but the list can't be retrieved.
+        target_branch (`str`, *optional*):
+            The branch into which changes are to be merged if this is a
+            Pull Request . `None`  if `self.is_pull_request` is `False`.
+        merge_commit_oid (`str`, *optional*):
+            If this is a merged Pull Request , this is set to the OID / SHA of
+            the merge commit, `None` otherwise.
+        diff (`str`, *optional*):
+            The git diff if this is a Pull Request , `None` otherwise.
+        endpoint (`str`):
+            Endpoint of the Hub. Default is https://huggingface.co.
+        git_reference (`str`, *optional*):
+            (property) Git reference to which changes can be pushed if this is a Pull Request, `None` otherwise.
+        url (`str`):
+            (property) URL of the discussion on the Hub.
+    """
+
+    events: list["DiscussionEvent"]
+    conflicting_files: Union[list[str], bool, None]
+    target_branch: Optional[str]
+    merge_commit_oid: Optional[str]
+    diff: Optional[str]
+
+
+class DiscussionEventArgs(TypedDict):
+    id: str
+    type: str
+    created_at: datetime
+    author: str
+    _event: dict
+
+
+@dataclass
+class DiscussionEvent:
+    """
+    An event in a Discussion or Pull Request.
+
+    Use concrete classes:
+        * [`DiscussionComment`]
+        * [`DiscussionStatusChange`]
+        * [`DiscussionCommit`]
+        * [`DiscussionTitleChange`]
+
+    Attributes:
+        id (`str`):
+            The ID of the event. An hexadecimal string.
+        type (`str`):
+            The type of the event.
+        created_at (`datetime`):
+            A [`datetime`](https://docs.python.org/3/library/datetime.html?highlight=datetime#datetime.datetime)
+            object holding the creation timestamp for the event.
+        author (`str`):
+            The username of the Discussion / Pull Request author.
+            Can be `"deleted"` if the user has been deleted since.
+    """
+
+    id: str
+    type: str
+    created_at: datetime
+    author: str
+
+    _event: dict
+    """Stores the original event data, in case we need to access it later."""
+
+
+@dataclass
+class DiscussionComment(DiscussionEvent):
+    """A comment in a Discussion / Pull Request.
+
+    Subclass of [`DiscussionEvent`].
+
+
+    Attributes:
+        id (`str`):
+            The ID of the event. An hexadecimal string.
+        type (`str`):
+            The type of the event.
+        created_at (`datetime`):
+            A [`datetime`](https://docs.python.org/3/library/datetime.html?highlight=datetime#datetime.datetime)
+            object holding the creation timestamp for the event.
+        author (`str`):
+            The username of the Discussion / Pull Request author.
+            Can be `"deleted"` if the user has been deleted since.
+        content (`str`):
+            The raw markdown content of the comment. Mentions, links and images are not rendered.
+        edited (`bool`):
+            Whether or not this comment has been edited.
+        hidden (`bool`):
+            Whether or not this comment has been hidden.
+    """
+
+    content: str
+    edited: bool
+    hidden: bool
+
+    @property
+    def rendered(self) -> str:
+        """The rendered comment, as a HTML string"""
+        return self._event["data"]["latest"]["html"]
+
+    @property
+    def last_edited_at(self) -> datetime:
+        """The last edit time, as a `datetime` object."""
+        return parse_datetime(self._event["data"]["latest"]["updatedAt"])
+
+    @property
+    def last_edited_by(self) -> str:
+        """The last edit time, as a `datetime` object."""
+        return self._event["data"]["latest"].get("author", {}).get("name", "deleted")
+
+    @property
+    def edit_history(self) -> list[dict]:
+        """The edit history of the comment"""
+        return self._event["data"]["history"]
+
+    @property
+    def number_of_edits(self) -> int:
+        return len(self.edit_history)
+
+
+@dataclass
+class DiscussionStatusChange(DiscussionEvent):
+    """A change of status in a Discussion / Pull Request.
+
+    Subclass of [`DiscussionEvent`].
+
+    Attributes:
+        id (`str`):
+            The ID of the event. An hexadecimal string.
+        type (`str`):
+            The type of the event.
+        created_at (`datetime`):
+            A [`datetime`](https://docs.python.org/3/library/datetime.html?highlight=datetime#datetime.datetime)
+            object holding the creation timestamp for the event.
+        author (`str`):
+            The username of the Discussion / Pull Request author.
+            Can be `"deleted"` if the user has been deleted since.
+        new_status (`str`):
+            The status of the Discussion / Pull Request after the change.
+            It can be one of:
+                * `"open"`
+                * `"closed"`
+                * `"merged"` (only for Pull Requests )
+    """
+
+    new_status: str
+
+
+@dataclass
+class DiscussionCommit(DiscussionEvent):
+    """A commit in a Pull Request.
+
+    Subclass of [`DiscussionEvent`].
+
+    Attributes:
+        id (`str`):
+            The ID of the event. An hexadecimal string.
+        type (`str`):
+            The type of the event.
+        created_at (`datetime`):
+            A [`datetime`](https://docs.python.org/3/library/datetime.html?highlight=datetime#datetime.datetime)
+            object holding the creation timestamp for the event.
+        author (`str`):
+            The username of the Discussion / Pull Request author.
+            Can be `"deleted"` if the user has been deleted since.
+        summary (`str`):
+            The summary of the commit.
+        oid (`str`):
+            The OID / SHA of the commit, as a hexadecimal string.
+    """
+
+    summary: str
+    oid: str
+
+
+@dataclass
+class DiscussionTitleChange(DiscussionEvent):
+    """A rename event in a Discussion / Pull Request.
+
+    Subclass of [`DiscussionEvent`].
+
+    Attributes:
+        id (`str`):
+            The ID of the event. An hexadecimal string.
+        type (`str`):
+            The type of the event.
+        created_at (`datetime`):
+            A [`datetime`](https://docs.python.org/3/library/datetime.html?highlight=datetime#datetime.datetime)
+            object holding the creation timestamp for the event.
+        author (`str`):
+            The username of the Discussion / Pull Request author.
+            Can be `"deleted"` if the user has been deleted since.
+        old_title (`str`):
+            The previous title for the Discussion / Pull Request.
+        new_title (`str`):
+            The new title.
+    """
+
+    old_title: str
+    new_title: str
+
+
+def deserialize_event(event: dict) -> DiscussionEvent:
+    """Instantiates a [`DiscussionEvent`] from a dict"""
+    event_id: str = event["id"]
+    event_type: str = event["type"]
+    created_at = parse_datetime(event["createdAt"])
+
+    common_args: DiscussionEventArgs = {
+        "id": event_id,
+        "type": event_type,
+        "created_at": created_at,
+        "author": event.get("author", {}).get("name", "deleted"),
+        "_event": event,
+    }
+
+    if event_type == "comment":
+        return DiscussionComment(
+            **common_args,
+            edited=event["data"]["edited"],
+            hidden=event["data"]["hidden"],
+            content=event["data"]["latest"]["raw"],
+        )
+    if event_type == "status-change":
+        return DiscussionStatusChange(
+            **common_args,
+            new_status=event["data"]["status"],
+        )
+    if event_type == "commit":
+        return DiscussionCommit(
+            **common_args,
+            summary=event["data"]["subject"],
+            oid=event["data"]["oid"],
+        )
+    if event_type == "title-change":
+        return DiscussionTitleChange(
+            **common_args,
+            old_title=event["data"]["from"],
+            new_title=event["data"]["to"],
+        )
+
+    return DiscussionEvent(**common_args)

data/conda_envs/evalclaw/lib/python3.11/site-packages/huggingface_hub/constants.py

@@ -0,0 +1,283 @@
+import os
+import re
+import typing
+from typing import Literal, Optional
+
+
+# Possible values for env variables
+
+
+ENV_VARS_TRUE_VALUES = {"1", "ON", "YES", "TRUE"}
+ENV_VARS_TRUE_AND_AUTO_VALUES = ENV_VARS_TRUE_VALUES.union({"AUTO"})
+
+
+def _is_true(value: Optional[str]) -> bool:
+    if value is None:
+        return False
+    return value.upper() in ENV_VARS_TRUE_VALUES
+
+
+def _as_int(value: Optional[str]) -> Optional[int]:
+    if value is None:
+        return None
+    return int(value)
+
+
+# Constants for file downloads
+
+PYTORCH_WEIGHTS_NAME = "pytorch_model.bin"
+TF2_WEIGHTS_NAME = "tf_model.h5"
+TF_WEIGHTS_NAME = "model.ckpt"
+FLAX_WEIGHTS_NAME = "flax_model.msgpack"
+CONFIG_NAME = "config.json"
+REPOCARD_NAME = "README.md"
+EVAL_RESULTS_FOLDER = ".eval_results"
+DEFAULT_ETAG_TIMEOUT = 10
+DEFAULT_DOWNLOAD_TIMEOUT = 10
+DEFAULT_REQUEST_TIMEOUT = 10
+DOWNLOAD_CHUNK_SIZE = 10 * 1024 * 1024
+MAX_HTTP_DOWNLOAD_SIZE = 50 * 1000 * 1000 * 1000  # 50 GB
+
+# Constants for serialization
+
+PYTORCH_WEIGHTS_FILE_PATTERN = "pytorch_model{suffix}.bin"  # Unsafe pickle: use safetensors instead
+SAFETENSORS_WEIGHTS_FILE_PATTERN = "model{suffix}.safetensors"
+TF2_WEIGHTS_FILE_PATTERN = "tf_model{suffix}.h5"
+
+# Constants for safetensors repos
+
+SAFETENSORS_SINGLE_FILE = "model.safetensors"
+SAFETENSORS_INDEX_FILE = "model.safetensors.index.json"
+SAFETENSORS_MAX_HEADER_LENGTH = 25_000_000
+
+# Timeout of aquiring file lock and logging the attempt
+FILELOCK_LOG_EVERY_SECONDS = 10
+
+# Git-related constants
+
+DEFAULT_REVISION = "main"
+REGEX_COMMIT_OID = re.compile(r"[A-Fa-f0-9]{5,40}")
+
+HUGGINGFACE_CO_URL_HOME = "https://huggingface.co/"
+
+_staging_mode = _is_true(os.environ.get("HUGGINGFACE_CO_STAGING"))
+
+_HF_DEFAULT_ENDPOINT = "https://huggingface.co"
+_HF_DEFAULT_STAGING_ENDPOINT = "https://hub-ci.huggingface.co"
+ENDPOINT = os.getenv("HF_ENDPOINT", _HF_DEFAULT_ENDPOINT).rstrip("/")
+HUGGINGFACE_CO_URL_TEMPLATE = ENDPOINT + "/{repo_id}/resolve/{revision}/{filename}"
+
+if _staging_mode:
+    ENDPOINT = _HF_DEFAULT_STAGING_ENDPOINT
+    HUGGINGFACE_CO_URL_TEMPLATE = _HF_DEFAULT_STAGING_ENDPOINT + "/{repo_id}/resolve/{revision}/{filename}"
+
+DATASETS_SERVER_ENDPOINT = "https://datasets-server.huggingface.co"
+
+HUGGINGFACE_HEADER_X_REPO_COMMIT = "X-Repo-Commit"
+HUGGINGFACE_HEADER_X_LINKED_ETAG = "X-Linked-Etag"
+HUGGINGFACE_HEADER_X_LINKED_SIZE = "X-Linked-Size"
+HUGGINGFACE_HEADER_X_BILL_TO = "X-HF-Bill-To"
+
+INFERENCE_ENDPOINT = os.environ.get("HF_INFERENCE_ENDPOINT", "https://api-inference.huggingface.co")
+
+# See https://huggingface.co/docs/inference-endpoints/index
+INFERENCE_ENDPOINTS_ENDPOINT = "https://api.endpoints.huggingface.cloud/v2"
+INFERENCE_CATALOG_ENDPOINT = "https://endpoints.huggingface.co/api/catalog"
+
+# See https://api.endpoints.huggingface.cloud/#post-/v2/endpoint/-namespace-
+INFERENCE_ENDPOINT_IMAGE_KEYS = [
+    "custom",
+    "huggingface",
+    "huggingfaceNeuron",
+    "llamacpp",
+    "tei",
+    "tgi",
+    "tgiNeuron",
+]
+
+# Proxy for third-party providers
+INFERENCE_PROXY_TEMPLATE = "https://router.huggingface.co/{provider}"
+
+REPO_ID_SEPARATOR = "--"
+# ^ this substring is not allowed in repo_ids on hf.co
+# and is the canonical one we use for serialization of repo ids elsewhere.
+
+
+REPO_TYPE_DATASET = "dataset"
+REPO_TYPE_SPACE = "space"
+REPO_TYPE_MODEL = "model"
+REPO_TYPES = [None, REPO_TYPE_MODEL, REPO_TYPE_DATASET, REPO_TYPE_SPACE]
+SPACES_SDK_TYPES = ["gradio", "streamlit", "docker", "static"]
+
+REPO_TYPES_URL_PREFIXES = {
+    REPO_TYPE_DATASET: "datasets/",
+    REPO_TYPE_SPACE: "spaces/",
+}
+REPO_TYPES_MAPPING = {
+    "datasets": REPO_TYPE_DATASET,
+    "spaces": REPO_TYPE_SPACE,
+    "models": REPO_TYPE_MODEL,
+}
+
+DiscussionTypeFilter = Literal["all", "discussion", "pull_request"]
+DISCUSSION_TYPES: tuple[DiscussionTypeFilter, ...] = typing.get_args(DiscussionTypeFilter)
+DiscussionStatusFilter = Literal["all", "open", "closed"]
+DISCUSSION_STATUS: tuple[DiscussionTypeFilter, ...] = typing.get_args(DiscussionStatusFilter)
+
+# Webhook subscription types
+WEBHOOK_DOMAIN_T = Literal["repo", "discussions"]
+
+# default cache
+default_home = os.path.join(os.path.expanduser("~"), ".cache")
+HF_HOME = os.path.expandvars(
+    os.path.expanduser(
+        os.getenv(
+            "HF_HOME",
+            os.path.join(os.getenv("XDG_CACHE_HOME", default_home), "huggingface"),
+        )
+    )
+)
+
+default_cache_path = os.path.join(HF_HOME, "hub")
+default_assets_cache_path = os.path.join(HF_HOME, "assets")
+
+# Legacy env variables
+HUGGINGFACE_HUB_CACHE = os.getenv("HUGGINGFACE_HUB_CACHE", default_cache_path)
+HUGGINGFACE_ASSETS_CACHE = os.getenv("HUGGINGFACE_ASSETS_CACHE", default_assets_cache_path)
+
+# New env variables
+HF_HUB_CACHE = os.path.expandvars(
+    os.path.expanduser(
+        os.getenv(
+            "HF_HUB_CACHE",
+            HUGGINGFACE_HUB_CACHE,
+        )
+    )
+)
+HF_ASSETS_CACHE = os.path.expandvars(
+    os.path.expanduser(
+        os.getenv(
+            "HF_ASSETS_CACHE",
+            HUGGINGFACE_ASSETS_CACHE,
+        )
+    )
+)
+
+HF_HUB_OFFLINE = _is_true(os.environ.get("HF_HUB_OFFLINE") or os.environ.get("TRANSFORMERS_OFFLINE"))
+
+
+def is_offline_mode() -> bool:
+    """Returns whether we are in offline mode for the Hub.
+
+    When offline mode is enabled, all HTTP requests made with `get_session` will raise an `OfflineModeIsEnabled` exception.
+
+    Example:
+        ```py
+        from huggingface_hub import is_offline_mode
+
+        def list_files(repo_id: str):
+            if is_offline_mode():
+                ... # list files from local cache (degraded experience but still functional)
+            else:
+                ... # list files from Hub (complete experience)
+        ```
+    """
+    return HF_HUB_OFFLINE
+
+
+# File created to mark that the version check has been done.
+# Check is performed once per 24 hours at most.
+CHECK_FOR_UPDATE_DONE_PATH = os.path.join(HF_HOME, ".check_for_update_done")
+
+# If set, log level will be set to DEBUG and all requests made to the Hub will be logged
+# as curl commands for reproducibility.
+HF_DEBUG = _is_true(os.environ.get("HF_DEBUG"))
+
+# Opt-out from telemetry requests
+HF_HUB_DISABLE_TELEMETRY = (
+    _is_true(os.environ.get("HF_HUB_DISABLE_TELEMETRY"))  # HF-specific env variable
+    or _is_true(os.environ.get("DISABLE_TELEMETRY"))
+    or _is_true(os.environ.get("DO_NOT_TRACK"))  # https://consoledonottrack.com/
+)
+
+HF_TOKEN_PATH = os.path.expandvars(
+    os.path.expanduser(
+        os.getenv(
+            "HF_TOKEN_PATH",
+            os.path.join(HF_HOME, "token"),
+        )
+    )
+)
+HF_STORED_TOKENS_PATH = os.path.join(os.path.dirname(HF_TOKEN_PATH), "stored_tokens")
+
+if _staging_mode:
+    # In staging mode, we use a different cache to ensure we don't mix up production and staging data or tokens
+    # In practice in `huggingface_hub` tests, we monkeypatch these values with temporary directories. The following
+    # lines are only used in third-party libraries tests (e.g. `transformers`, `diffusers`, etc.).
+    _staging_home = os.path.join(os.path.expanduser("~"), ".cache", "huggingface_staging")
+    HUGGINGFACE_HUB_CACHE = os.path.join(_staging_home, "hub")
+    HF_TOKEN_PATH = os.path.join(_staging_home, "token")
+
+# Here, `True` will disable progress bars globally without possibility of enabling it
+# programmatically. `False` will enable them without possibility of disabling them.
+# If environment variable is not set (None), then the user is free to enable/disable
+# them programmatically.
+# TL;DR: env variable has priority over code
+__HF_HUB_DISABLE_PROGRESS_BARS = os.environ.get("HF_HUB_DISABLE_PROGRESS_BARS")
+HF_HUB_DISABLE_PROGRESS_BARS: Optional[bool] = (
+    _is_true(__HF_HUB_DISABLE_PROGRESS_BARS) if __HF_HUB_DISABLE_PROGRESS_BARS is not None else None
+)
+
+# Disable warning on machines that do not support symlinks (e.g. Windows non-developer)
+HF_HUB_DISABLE_SYMLINKS_WARNING: bool = _is_true(os.environ.get("HF_HUB_DISABLE_SYMLINKS_WARNING"))
+
+# Disable warning when using experimental features
+HF_HUB_DISABLE_EXPERIMENTAL_WARNING: bool = _is_true(os.environ.get("HF_HUB_DISABLE_EXPERIMENTAL_WARNING"))
+
+# Disable sending the cached token by default is all HTTP requests to the Hub
+HF_HUB_DISABLE_IMPLICIT_TOKEN: bool = _is_true(os.environ.get("HF_HUB_DISABLE_IMPLICIT_TOKEN"))
+
+HF_XET_HIGH_PERFORMANCE: bool = _is_true(os.environ.get("HF_XET_HIGH_PERFORMANCE"))
+
+# hf_transfer is not used anymore. Let's warn user is case they set the env variable
+if _is_true(os.environ.get("HF_HUB_ENABLE_HF_TRANSFER")) and not HF_XET_HIGH_PERFORMANCE:
+    import warnings
+
+    warnings.warn(
+        "The `HF_HUB_ENABLE_HF_TRANSFER` environment variable is deprecated as 'hf_transfer' is not used anymore. "
+        "Please use `HF_XET_HIGH_PERFORMANCE` instead to enable high performance transfer with Xet. "
+        "Visit https://huggingface.co/docs/huggingface_hub/package_reference/environment_variables#hfxethighperformance for more details.",
+        DeprecationWarning,
+    )
+
+# Used to override the etag timeout on a system level
+HF_HUB_ETAG_TIMEOUT: int = _as_int(os.environ.get("HF_HUB_ETAG_TIMEOUT")) or DEFAULT_ETAG_TIMEOUT
+
+# Used to override the get request timeout on a system level
+# Also used as a default timeout for other requests if not specified (kept the naming for legacy reasons)
+HF_HUB_DOWNLOAD_TIMEOUT: int = _as_int(os.environ.get("HF_HUB_DOWNLOAD_TIMEOUT")) or DEFAULT_DOWNLOAD_TIMEOUT
+
+# Allows to add information about the requester in the user-agent (e.g. partner name)
+HF_HUB_USER_AGENT_ORIGIN: Optional[str] = os.environ.get("HF_HUB_USER_AGENT_ORIGIN")
+
+# If OAuth didn't work after 2 redirects, there's likely a third-party cookie issue in the Space iframe view.
+# In this case, we redirect the user to the non-iframe view.
+OAUTH_MAX_REDIRECTS = 2
+
+# OAuth-related environment variables injected by the Space
+OAUTH_CLIENT_ID = os.environ.get("OAUTH_CLIENT_ID")
+OAUTH_CLIENT_SECRET = os.environ.get("OAUTH_CLIENT_SECRET")
+OAUTH_SCOPES = os.environ.get("OAUTH_SCOPES")
+OPENID_PROVIDER_URL = os.environ.get("OPENID_PROVIDER_URL")
+
+# Xet constants
+HUGGINGFACE_HEADER_X_XET_ENDPOINT = "X-Xet-Cas-Url"
+HUGGINGFACE_HEADER_X_XET_ACCESS_TOKEN = "X-Xet-Access-Token"
+HUGGINGFACE_HEADER_X_XET_EXPIRATION = "X-Xet-Token-Expiration"
+HUGGINGFACE_HEADER_X_XET_HASH = "X-Xet-Hash"
+HUGGINGFACE_HEADER_X_XET_REFRESH_ROUTE = "X-Xet-Refresh-Route"
+HUGGINGFACE_HEADER_LINK_XET_AUTH_KEY = "xet-auth"
+
+default_xet_cache_path = os.path.join(HF_HOME, "xet")
+HF_XET_CACHE = os.getenv("HF_XET_CACHE", default_xet_cache_path)
+HF_HUB_DISABLE_XET: bool = _is_true(os.environ.get("HF_HUB_DISABLE_XET"))

data/conda_envs/evalclaw/lib/python3.11/site-packages/huggingface_hub/dataclasses.py

@@ -0,0 +1,667 @@
+import collections.abc
+import inspect
+import sys
+import types
+from dataclasses import _MISSING_TYPE, MISSING, Field, field, fields, make_dataclass
+from functools import lru_cache, wraps
+from typing import (
+    Annotated,
+    Any,
+    Callable,
+    ForwardRef,
+    Literal,
+    Optional,
+    Type,
+    TypeVar,
+    Union,
+    get_args,
+    get_origin,
+    overload,
+)
+
+
+try:
+    # Python 3.11+
+    from typing import NotRequired, Required  # type: ignore
+except ImportError:
+    try:
+        # In case typing_extensions is installed
+        from typing_extensions import NotRequired, Required  # type: ignore
+    except ImportError:
+        # Fallback: create dummy types that will never match
+        Required = type("Required", (), {})  # type: ignore
+        NotRequired = type("NotRequired", (), {})  # type: ignore
+
+from .errors import (
+    StrictDataclassClassValidationError,
+    StrictDataclassDefinitionError,
+    StrictDataclassFieldValidationError,
+)
+
+
+Validator_T = Callable[[Any], None]
+T = TypeVar("T")
+TypedDictType = TypeVar("TypedDictType", bound=dict[str, Any])
+
+_TYPED_DICT_DEFAULT_VALUE = object()  # used as default value in TypedDict fields (to distinguish from None)
+
+
+# The overload decorator helps type checkers understand the different return types
+@overload
+def strict(cls: Type[T]) -> Type[T]: ...
+
+
+@overload
+def strict(*, accept_kwargs: bool = False) -> Callable[[Type[T]], Type[T]]: ...
+
+
+def strict(
+    cls: Optional[Type[T]] = None, *, accept_kwargs: bool = False
+) -> Union[Type[T], Callable[[Type[T]], Type[T]]]:
+    """
+    Decorator to add strict validation to a dataclass.
+
+    This decorator must be used on top of `@dataclass` to ensure IDEs and static typing tools
+    recognize the class as a dataclass.
+
+    Can be used with or without arguments:
+    - `@strict`
+    - `@strict(accept_kwargs=True)`
+
+    Args:
+        cls:
+            The class to convert to a strict dataclass.
+        accept_kwargs (`bool`, *optional*):
+            If True, allows arbitrary keyword arguments in `__init__`. Defaults to False.
+
+    Returns:
+        The enhanced dataclass with strict validation on field assignment.
+
+    Example:
+    ```py
+    >>> from dataclasses import dataclass
+    >>> from huggingface_hub.dataclasses import as_validated_field, strict, validated_field
+
+    >>> @as_validated_field
+    >>> def positive_int(value: int):
+    ...     if not value >= 0:
+    ...         raise ValueError(f"Value must be positive, got {value}")
+
+    >>> @strict(accept_kwargs=True)
+    ... @dataclass
+    ... class User:
+    ...     name: str
+    ...     age: int = positive_int(default=10)
+
+    # Initialize
+    >>> User(name="John")
+    User(name='John', age=10)
+
+    # Extra kwargs are accepted
+    >>> User(name="John", age=30, lastname="Doe")
+    User(name='John', age=30, *lastname='Doe')
+
+    # Invalid type => raises
+    >>> User(name="John", age="30")
+    huggingface_hub.errors.StrictDataclassFieldValidationError: Validation error for field 'age':
+        TypeError: Field 'age' expected int, got str (value: '30')
+
+    # Invalid value => raises
+    >>> User(name="John", age=-1)
+    huggingface_hub.errors.StrictDataclassFieldValidationError: Validation error for field 'age':
+        ValueError: Value must be positive, got -1
+    ```
+    """
+
+    def wrap(cls: Type[T]) -> Type[T]:
+        if not hasattr(cls, "__dataclass_fields__"):
+            raise StrictDataclassDefinitionError(
+                f"Class '{cls.__name__}' must be a dataclass before applying @strict."
+            )
+
+        # List and store validators
+        field_validators: dict[str, list[Validator_T]] = {}
+        for f in fields(cls):  # type: ignore [arg-type]
+            validators = []
+            validators.append(_create_type_validator(f))
+            custom_validator = f.metadata.get("validator")
+            if custom_validator is not None:
+                if not isinstance(custom_validator, list):
+                    custom_validator = [custom_validator]
+                for validator in custom_validator:
+                    if not _is_validator(validator):
+                        raise StrictDataclassDefinitionError(
+                            f"Invalid validator for field '{f.name}': {validator}. Must be a callable taking a single argument."
+                        )
+                validators.extend(custom_validator)
+            field_validators[f.name] = validators
+        cls.__validators__ = field_validators  # type: ignore
+
+        # Override __setattr__ to validate fields on assignment
+        original_setattr = cls.__setattr__
+
+        def __strict_setattr__(self: Any, name: str, value: Any) -> None:
+            """Custom __setattr__ method for strict dataclasses."""
+            # Run all validators
+            for validator in self.__validators__.get(name, []):
+                try:
+                    validator(value)
+                except (ValueError, TypeError) as e:
+                    raise StrictDataclassFieldValidationError(field=name, cause=e) from e
+
+            # If validation passed, set the attribute
+            original_setattr(self, name, value)
+
+        cls.__setattr__ = __strict_setattr__  # type: ignore[method-assign]
+
+        if accept_kwargs:
+            # (optional) Override __init__ to accept arbitrary keyword arguments
+            original_init = cls.__init__
+
+            @wraps(original_init)
+            def __init__(self, *args, **kwargs: Any) -> None:
+                # Extract only the fields that are part of the dataclass
+                dataclass_fields = {f.name for f in fields(cls)}  # type: ignore [arg-type]
+                standard_kwargs = {k: v for k, v in kwargs.items() if k in dataclass_fields}
+
+                # User shouldn't define custom `__init__` when `accepts_kwargs`, and instead
+                # are advised to move field manipulation to `__post_init__` (e.g., derive new field from existing ones)
+                # We need to call bare `__init__` here without `__post_init__` but the``original_init`` would call
+                # post-init right away with no kwargs.
+                if len(args) > 0:
+                    raise ValueError(
+                        f"When `accept_kwargs=True`, {cls.__name__} accepts only keyword arguments, "
+                        f"but found `{len(args)}` positional args."
+                    )
+
+                for f in fields(cls):  # type: ignore
+                    if f.name in standard_kwargs:
+                        setattr(self, f.name, standard_kwargs[f.name])
+                    elif f.default is not MISSING:
+                        setattr(self, f.name, f.default)
+                    elif f.default_factory is not MISSING:
+                        setattr(self, f.name, f.default_factory())
+                    else:
+                        raise TypeError(f"Missing required field - '{f.name}'")
+
+                # Pass any additional kwargs to `__post_init__` and let the object
+                # decide whether to set the attr or use for different purposes (e.g. BC checks)
+                additional_kwargs = {}
+                for name, value in kwargs.items():
+                    if name not in dataclass_fields:
+                        additional_kwargs[name] = value
+
+                self.__post_init__(**additional_kwargs)
+
+            cls.__init__ = __init__  # type: ignore[method-assign]
+
+            # Define a default __post_init__ if not defined
+            if not hasattr(cls, "__post_init__"):
+
+                def __post_init__(self, **kwargs: Any) -> None:
+                    """Default __post_init__ to accept additional kwargs."""
+                    for name, value in kwargs.items():
+                        setattr(self, name, value)
+
+                cls.__post_init__ = __post_init__  # type: ignore
+
+            # (optional) Override __repr__ to include additional kwargs
+            original_repr = cls.__repr__
+
+            @wraps(original_repr)
+            def __repr__(self) -> str:
+                # Call the original __repr__ to get the standard fields
+                standard_repr = original_repr(self)
+
+                # Get additional kwargs
+                additional_kwargs = [
+                    # add a '*' in front of additional kwargs to let the user know they are not part of the dataclass
+                    f"*{k}={v!r}"
+                    for k, v in self.__dict__.items()
+                    if k not in cls.__dataclass_fields__  # type: ignore [attr-defined]
+                ]
+                additional_repr = ", ".join(additional_kwargs)
+
+                # Combine both representations
+                return f"{standard_repr[:-1]}, {additional_repr})" if additional_kwargs else standard_repr
+
+            if cls.__dataclass_params__.repr is True:  # type: ignore [attr-defined]
+                cls.__repr__ = __repr__  # type: ignore [method-assign]
+
+        # List all public methods starting with `validate_` => class validators.
+        class_validators = []
+
+        for name in dir(cls):
+            if not name.startswith("validate_"):
+                continue
+            method = getattr(cls, name)
+            if not callable(method):
+                continue
+            if len(inspect.signature(method).parameters) != 1:
+                raise StrictDataclassDefinitionError(
+                    f"Class '{cls.__name__}' has a class validator '{name}' that takes more than one argument."
+                    " Class validators must take only 'self' as an argument. Methods starting with 'validate_'"
+                    " are considered to be class validators."
+                )
+            class_validators.append(method)
+
+        cls.__class_validators__ = class_validators  # type: ignore [attr-defined]
+
+        # Add `validate` method to the class, but first check if it already exists
+        def validate(self: T) -> None:
+            """Run class validators on the instance."""
+            for validator in cls.__class_validators__:  # type: ignore [attr-defined]
+                try:
+                    validator(self)
+                except (ValueError, TypeError) as e:
+                    raise StrictDataclassClassValidationError(validator=validator.__name__, cause=e) from e
+
+        # Hack to be able to raise if `.validate()` already exists except if it was created by this decorator on a parent class
+        # (in which case we just override it)
+        validate.__is_defined_by_strict_decorator__ = True  # type: ignore [attr-defined]
+
+        if hasattr(cls, "validate"):
+            if not getattr(cls.validate, "__is_defined_by_strict_decorator__", False):  # type: ignore [attr-defined]
+                raise StrictDataclassDefinitionError(
+                    f"Class '{cls.__name__}' already implements a method called 'validate'."
+                    " This method name is reserved when using the @strict decorator on a dataclass."
+                    " If you want to keep your own method, please rename it."
+                )
+
+        cls.validate = validate  # type: ignore
+
+        # Run class validators after initialization
+        initial_init = cls.__init__
+
+        @wraps(initial_init)
+        def init_with_validate(self, *args, **kwargs) -> None:
+            """Run class validators after initialization."""
+            initial_init(self, *args, **kwargs)  # type: ignore [call-arg]
+            cls.validate(self)  # type: ignore [attr-defined]
+
+        setattr(cls, "__init__", init_with_validate)
+
+        return cls
+
+    # Return wrapped class or the decorator itself
+    return wrap(cls) if cls is not None else wrap
+
+
+def validate_typed_dict(schema: type[TypedDictType], data: dict) -> None:
+    """
+    Validate that a dictionary conforms to the types defined in a TypedDict class.
+
+    Under the hood, the typed dict is converted to a strict dataclass and validated using the `@strict` decorator.
+
+    Args:
+        schema (`type[TypedDictType]`):
+            The TypedDict class defining the expected structure and types.
+        data (`dict`):
+            The dictionary to validate.
+
+    Raises:
+        `StrictDataclassFieldValidationError`:
+            If any field in the dictionary does not conform to the expected type.
+
+    Example:
+    ```py
+    >>> from typing import Annotated, TypedDict
+    >>> from huggingface_hub.dataclasses import validate_typed_dict
+
+    >>> def positive_int(value: int):
+    ...     if not value >= 0:
+    ...         raise ValueError(f"Value must be positive, got {value}")
+
+    >>> class User(TypedDict):
+    ...     name: str
+    ...     age: Annotated[int, positive_int]
+
+    >>> # Valid data
+    >>> validate_typed_dict(User, {"name": "John", "age": 30})
+
+    >>> # Invalid type for age
+    >>> validate_typed_dict(User, {"name": "John", "age": "30"})
+    huggingface_hub.errors.StrictDataclassFieldValidationError: Validation error for field 'age':
+        TypeError: Field 'age' expected int, got str (value: '30')
+
+    >>> # Invalid value for age
+    >>> validate_typed_dict(User, {"name": "John", "age": -1})
+    huggingface_hub.errors.StrictDataclassFieldValidationError: Validation error for field 'age':
+        ValueError: Value must be positive, got -1
+    ```
+    """
+    # Convert typed dict to dataclass
+    strict_cls = _build_strict_cls_from_typed_dict(schema)
+
+    # Validate the data by instantiating the strict dataclass
+    strict_cls(**data)  # will raise if validation fails
+
+
+@lru_cache
+def _build_strict_cls_from_typed_dict(schema: type[TypedDictType]) -> Type:
+    # Extract type hints from the TypedDict class
+    type_hints = _get_typed_dict_annotations(schema)
+
+    # If the TypedDict is not total, wrap fields as NotRequired (unless explicitly Required or NotRequired)
+    if not getattr(schema, "__total__", True):
+        for key, value in type_hints.items():
+            origin = get_origin(value)
+
+            if origin is Annotated:
+                base, *meta = get_args(value)
+                if not _is_required_or_notrequired(base):
+                    base = NotRequired[base]
+                type_hints[key] = Annotated[tuple([base] + list(meta))]  # type: ignore
+            elif not _is_required_or_notrequired(value):
+                type_hints[key] = NotRequired[value]
+
+    # Convert type hints to dataclass fields
+    fields = []
+    for key, value in type_hints.items():
+        if get_origin(value) is Annotated:
+            base, *meta = get_args(value)
+            fields.append((key, base, field(default=_TYPED_DICT_DEFAULT_VALUE, metadata={"validator": meta[0]})))
+        else:
+            fields.append((key, value, field(default=_TYPED_DICT_DEFAULT_VALUE)))
+
+    # Create a strict dataclass from the TypedDict fields
+    return strict(make_dataclass(schema.__name__, fields))
+
+
+def _get_typed_dict_annotations(schema: type[TypedDictType]) -> dict[str, Any]:
+    """Extract type annotations from a TypedDict class."""
+    try:
+        # Available in Python 3.14+
+        import annotationlib
+
+        return annotationlib.get_annotations(schema)
+    except ImportError:
+        return {
+            # We do not use `get_type_hints` here to avoid evaluating ForwardRefs (which might fail).
+            # ForwardRefs are not validated by @strict anyway.
+            name: value if value is not None else type(None)
+            for name, value in schema.__dict__.get("__annotations__", {}).items()
+        }
+
+
+def validated_field(
+    validator: Union[list[Validator_T], Validator_T],
+    default: Union[Any, _MISSING_TYPE] = MISSING,
+    default_factory: Union[Callable[[], Any], _MISSING_TYPE] = MISSING,
+    init: bool = True,
+    repr: bool = True,
+    hash: Optional[bool] = None,
+    compare: bool = True,
+    metadata: Optional[dict] = None,
+    **kwargs: Any,
+) -> Any:
+    """
+    Create a dataclass field with a custom validator.
+
+    Useful to apply several checks to a field. If only applying one rule, check out the [`as_validated_field`] decorator.
+
+    Args:
+        validator (`Callable` or `list[Callable]`):
+            A method that takes a value as input and raises ValueError/TypeError if the value is invalid.
+            Can be a list of validators to apply multiple checks.
+        **kwargs:
+            Additional arguments to pass to `dataclasses.field()`.
+
+    Returns:
+        A field with the validator attached in metadata
+    """
+    if not isinstance(validator, list):
+        validator = [validator]
+    if metadata is None:
+        metadata = {}
+    metadata["validator"] = validator
+    return field(  # type: ignore
+        default=default,  # type: ignore [arg-type]
+        default_factory=default_factory,  # type: ignore [arg-type]
+        init=init,
+        repr=repr,
+        hash=hash,
+        compare=compare,
+        metadata=metadata,
+        **kwargs,
+    )
+
+
+def as_validated_field(validator: Validator_T):
+    """
+    Decorates a validator function as a [`validated_field`] (i.e. a dataclass field with a custom validator).
+
+    Args:
+        validator (`Callable`):
+            A method that takes a value as input and raises ValueError/TypeError if the value is invalid.
+    """
+
+    def _inner(
+        default: Union[Any, _MISSING_TYPE] = MISSING,
+        default_factory: Union[Callable[[], Any], _MISSING_TYPE] = MISSING,
+        init: bool = True,
+        repr: bool = True,
+        hash: Optional[bool] = None,
+        compare: bool = True,
+        metadata: Optional[dict] = None,
+        **kwargs: Any,
+    ):
+        return validated_field(
+            validator,
+            default=default,
+            default_factory=default_factory,
+            init=init,
+            repr=repr,
+            hash=hash,
+            compare=compare,
+            metadata=metadata,
+            **kwargs,
+        )
+
+    return _inner
+
+
+def type_validator(name: str, value: Any, expected_type: Any) -> None:
+    """Validate that 'value' matches 'expected_type'."""
+    origin = get_origin(expected_type)
+    args = get_args(expected_type)
+
+    if expected_type is Any:
+        return
+    elif validator := _BASIC_TYPE_VALIDATORS.get(origin):
+        validator(name, value, args)
+    elif isinstance(expected_type, type):  # simple types
+        _validate_simple_type(name, value, expected_type)
+    elif isinstance(expected_type, ForwardRef) or isinstance(expected_type, str):
+        return
+    elif origin is Required:
+        if value is _TYPED_DICT_DEFAULT_VALUE:
+            raise TypeError(f"Field '{name}' is required but missing.")
+        type_validator(name, value, args[0])
+    elif origin is NotRequired:
+        if value is _TYPED_DICT_DEFAULT_VALUE:
+            return
+        type_validator(name, value, args[0])
+    else:
+        raise TypeError(f"Unsupported type for field '{name}': {expected_type}")
+
+
+def _validate_union(name: str, value: Any, args: tuple[Any, ...]) -> None:
+    """Validate that value matches one of the types in a Union."""
+    errors = []
+    for t in args:
+        try:
+            type_validator(name, value, t)
+            return  # Valid if any type matches
+        except TypeError as e:
+            errors.append(str(e))
+
+    raise TypeError(
+        f"Field '{name}' with value {repr(value)} doesn't match any type in {args}. Errors: {'; '.join(errors)}"
+    )
+
+
+def _validate_literal(name: str, value: Any, args: tuple[Any, ...]) -> None:
+    """Validate Literal type."""
+    if value not in args:
+        raise TypeError(f"Field '{name}' expected one of {args}, got {value}")
+
+
+def _validate_list(name: str, value: Any, args: tuple[Any, ...]) -> None:
+    """Validate list[T] type."""
+    if not isinstance(value, list):
+        raise TypeError(f"Field '{name}' expected a list, got {type(value).__name__}")
+
+    # Validate each item in the list
+    item_type = args[0]
+    for i, item in enumerate(value):
+        try:
+            type_validator(f"{name}[{i}]", item, item_type)
+        except TypeError as e:
+            raise TypeError(f"Invalid item at index {i} in list '{name}'") from e
+
+
+def _validate_dict(name: str, value: Any, args: tuple[Any, ...]) -> None:
+    """Validate dict[K, V] type."""
+    if not isinstance(value, dict):
+        raise TypeError(f"Field '{name}' expected a dict, got {type(value).__name__}")
+
+    # Validate keys and values
+    key_type, value_type = args
+    for k, v in value.items():
+        try:
+            type_validator(f"{name}.key", k, key_type)
+            type_validator(f"{name}[{k!r}]", v, value_type)
+        except TypeError as e:
+            raise TypeError(f"Invalid key or value in dict '{name}'") from e
+
+
+def _validate_tuple(name: str, value: Any, args: tuple[Any, ...]) -> None:
+    """Validate Tuple type."""
+    if not isinstance(value, tuple):
+        raise TypeError(f"Field '{name}' expected a tuple, got {type(value).__name__}")
+
+    # Handle variable-length tuples: tuple[T, ...]
+    if len(args) == 2 and args[1] is Ellipsis:
+        for i, item in enumerate(value):
+            try:
+                type_validator(f"{name}[{i}]", item, args[0])
+            except TypeError as e:
+                raise TypeError(f"Invalid item at index {i} in tuple '{name}'") from e
+    # Handle fixed-length tuples: tuple[T1, T2, ...]
+    elif len(args) != len(value):
+        raise TypeError(f"Field '{name}' expected a tuple of length {len(args)}, got {len(value)}")
+    else:
+        for i, (item, expected) in enumerate(zip(value, args)):
+            try:
+                type_validator(f"{name}[{i}]", item, expected)
+            except TypeError as e:
+                raise TypeError(f"Invalid item at index {i} in tuple '{name}'") from e
+
+
+def _validate_set(name: str, value: Any, args: tuple[Any, ...]) -> None:
+    """Validate set[T] type."""
+    if not isinstance(value, set):
+        raise TypeError(f"Field '{name}' expected a set, got {type(value).__name__}")
+
+    # Validate each item in the set
+    item_type = args[0]
+    for i, item in enumerate(value):
+        try:
+            type_validator(f"{name} item", item, item_type)
+        except TypeError as e:
+            raise TypeError(f"Invalid item in set '{name}'") from e
+
+
+def _validate_sequence(name: str, value: Any, args: tuple[Any, ...]) -> None:
+    """Validate Sequence or Sequence[T] type."""
+    if not isinstance(value, collections.abc.Sequence):
+        raise TypeError(f"Field '{name}' expected a Sequence, got {type(value).__name__}")
+
+    # If no type argument is provided (i.e., just `Sequence`), skip item validation
+    if not args:
+        return
+
+    # Validate each item in the sequence
+    item_type = args[0]
+    for i, item in enumerate(value):
+        try:
+            type_validator(f"{name}[{i}]", item, item_type)
+        except TypeError as e:
+            raise TypeError(f"Invalid item at index {i} in sequence '{name}'") from e
+
+
+def _validate_simple_type(name: str, value: Any, expected_type: type) -> None:
+    """Validate simple type (int, str, etc.)."""
+    if not isinstance(value, expected_type):
+        raise TypeError(
+            f"Field '{name}' expected {expected_type.__name__}, got {type(value).__name__} (value: {repr(value)})"
+        )
+
+
+def _create_type_validator(field: Field) -> Validator_T:
+    """Create a type validator function for a field."""
+    # Hacky: we cannot use a lambda here because of reference issues
+
+    def validator(value: Any) -> None:
+        type_validator(field.name, value, field.type)
+
+    return validator
+
+
+def _is_validator(validator: Any) -> bool:
+    """Check if a function is a validator.
+
+    A validator is a Callable that can be called with a single positional argument.
+    The validator can have more arguments with default values.
+
+    Basically, returns True if `validator(value)` is possible.
+    """
+    if not callable(validator):
+        return False
+
+    signature = inspect.signature(validator)
+    parameters = list(signature.parameters.values())
+    if len(parameters) == 0:
+        return False
+    if parameters[0].kind not in (
+        inspect.Parameter.POSITIONAL_OR_KEYWORD,
+        inspect.Parameter.POSITIONAL_ONLY,
+        inspect.Parameter.VAR_POSITIONAL,
+    ):
+        return False
+    for parameter in parameters[1:]:
+        if parameter.default == inspect.Parameter.empty:
+            return False
+    return True
+
+
+def _is_required_or_notrequired(type_hint: Any) -> bool:
+    """Helper to check if a type is Required/NotRequired."""
+    return type_hint in (Required, NotRequired) or (get_origin(type_hint) in (Required, NotRequired))
+
+
+_BASIC_TYPE_VALIDATORS: dict[Any, Callable[[str, Any, tuple[Any, ...]], None]] = {
+    Union: _validate_union,
+    Literal: _validate_literal,
+    list: _validate_list,
+    dict: _validate_dict,
+    tuple: _validate_tuple,
+    set: _validate_set,
+    collections.abc.Sequence: _validate_sequence,
+}
+
+if sys.version_info >= (3, 10):
+    # TODO: make it first class citizen when bumping to Python 3.10+
+    _BASIC_TYPE_VALIDATORS[types.UnionType] = _validate_union  # x | y syntax, available only Python 3.10+
+
+
+__all__ = [
+    "strict",
+    "validate_typed_dict",
+    "validated_field",
+    "Validator_T",
+    "StrictDataclassClassValidationError",
+    "StrictDataclassDefinitionError",
+    "StrictDataclassFieldValidationError",
+]

data/conda_envs/evalclaw/lib/python3.11/site-packages/huggingface_hub/fastai_utils.py

@@ -0,0 +1,414 @@
+import json
+import os
+from pathlib import Path
+from pickle import DEFAULT_PROTOCOL, PicklingError
+from typing import Any, Optional, Union
+
+from packaging import version
+
+from huggingface_hub import constants, snapshot_download
+from huggingface_hub.hf_api import HfApi
+from huggingface_hub.utils import (
+    SoftTemporaryDirectory,
+    get_fastai_version,
+    get_fastcore_version,
+    get_python_version,
+)
+
+from .utils import logging, validate_hf_hub_args
+
+
+logger = logging.get_logger(__name__)
+
+
+def _check_fastai_fastcore_versions(
+    fastai_min_version: str = "2.4",
+    fastcore_min_version: str = "1.3.27",
+):
+    """
+    Checks that the installed fastai and fastcore versions are compatible for pickle serialization.
+
+    Args:
+        fastai_min_version (`str`, *optional*):
+            The minimum fastai version supported.
+        fastcore_min_version (`str`, *optional*):
+            The minimum fastcore version supported.
+
+    > [!TIP]
+    > Raises the following error:
+    >
+    >     - [`ImportError`](https://docs.python.org/3/library/exceptions.html#ImportError)
+    >       if the fastai or fastcore libraries are not available or are of an invalid version.
+    """
+
+    if (get_fastcore_version() or get_fastai_version()) == "N/A":
+        raise ImportError(
+            f"fastai>={fastai_min_version} and fastcore>={fastcore_min_version} are"
+            f" required. Currently using fastai=={get_fastai_version()} and"
+            f" fastcore=={get_fastcore_version()}."
+        )
+
+    current_fastai_version = version.Version(get_fastai_version())
+    current_fastcore_version = version.Version(get_fastcore_version())
+
+    if current_fastai_version < version.Version(fastai_min_version):
+        raise ImportError(
+            "`push_to_hub_fastai` and `from_pretrained_fastai` require a"
+            f" fastai>={fastai_min_version} version, but you are using fastai version"
+            f" {get_fastai_version()} which is incompatible. Upgrade with `pip install"
+            " fastai==2.5.6`."
+        )
+
+    if current_fastcore_version < version.Version(fastcore_min_version):
+        raise ImportError(
+            "`push_to_hub_fastai` and `from_pretrained_fastai` require a"
+            f" fastcore>={fastcore_min_version} version, but you are using fastcore"
+            f" version {get_fastcore_version()} which is incompatible. Upgrade with"
+            " `pip install fastcore==1.3.27`."
+        )
+
+
+def _check_fastai_fastcore_pyproject_versions(
+    storage_folder: str,
+    fastai_min_version: str = "2.4",
+    fastcore_min_version: str = "1.3.27",
+):
+    """
+    Checks that the `pyproject.toml` file in the directory `storage_folder` has fastai and fastcore versions
+    that are compatible with `from_pretrained_fastai` and `push_to_hub_fastai`. If `pyproject.toml` does not exist
+    or does not contain versions for fastai and fastcore, then it logs a warning.
+
+    Args:
+        storage_folder (`str`):
+            Folder to look for the `pyproject.toml` file.
+        fastai_min_version (`str`, *optional*):
+            The minimum fastai version supported.
+        fastcore_min_version (`str`, *optional*):
+            The minimum fastcore version supported.
+
+    > [!TIP]
+    > Raises the following errors:
+    >
+    >     - [`ImportError`](https://docs.python.org/3/library/exceptions.html#ImportError)
+    >       if the `toml` module is not installed.
+    >     - [`ImportError`](https://docs.python.org/3/library/exceptions.html#ImportError)
+    >       if the `pyproject.toml` indicates a lower than minimum supported version of fastai or fastcore.
+    """
+
+    try:
+        import toml
+    except ModuleNotFoundError:
+        raise ImportError(
+            "`push_to_hub_fastai` and `from_pretrained_fastai` require the toml module."
+            " Install it with `pip install toml`."
+        )
+
+    # Checks that a `pyproject.toml`, with `build-system` and `requires` sections, exists in the repository. If so, get a list of required packages.
+    if not os.path.isfile(f"{storage_folder}/pyproject.toml"):
+        logger.warning(
+            "There is no `pyproject.toml` in the repository that contains the fastai"
+            " `Learner`. The `pyproject.toml` would allow us to verify that your fastai"
+            " and fastcore versions are compatible with those of the model you want to"
+            " load."
+        )
+        return
+    pyproject_toml = toml.load(f"{storage_folder}/pyproject.toml")
+
+    if "build-system" not in pyproject_toml.keys():
+        logger.warning(
+            "There is no `build-system` section in the pyproject.toml of the repository"
+            " that contains the fastai `Learner`. The `build-system` would allow us to"
+            " verify that your fastai and fastcore versions are compatible with those"
+            " of the model you want to load."
+        )
+        return
+    build_system_toml = pyproject_toml["build-system"]
+
+    if "requires" not in build_system_toml.keys():
+        logger.warning(
+            "There is no `requires` section in the pyproject.toml of the repository"
+            " that contains the fastai `Learner`. The `requires` would allow us to"
+            " verify that your fastai and fastcore versions are compatible with those"
+            " of the model you want to load."
+        )
+        return
+    package_versions = build_system_toml["requires"]
+
+    # Extracts contains fastai and fastcore versions from `pyproject.toml` if available.
+    # If the package is specified but not the version (e.g. "fastai" instead of "fastai=2.4"), the default versions are the highest.
+    fastai_packages = [pck for pck in package_versions if pck.startswith("fastai")]
+    if len(fastai_packages) == 0:
+        logger.warning("The repository does not have a fastai version specified in the `pyproject.toml`.")
+    # fastai_version is an empty string if not specified
+    else:
+        fastai_version = str(fastai_packages[0]).partition("=")[2]
+        if fastai_version != "" and version.Version(fastai_version) < version.Version(fastai_min_version):
+            raise ImportError(
+                "`from_pretrained_fastai` requires"
+                f" fastai>={fastai_min_version} version but the model to load uses"
+                f" {fastai_version} which is incompatible."
+            )
+
+    fastcore_packages = [pck for pck in package_versions if pck.startswith("fastcore")]
+    if len(fastcore_packages) == 0:
+        logger.warning("The repository does not have a fastcore version specified in the `pyproject.toml`.")
+    # fastcore_version is an empty string if not specified
+    else:
+        fastcore_version = str(fastcore_packages[0]).partition("=")[2]
+        if fastcore_version != "" and version.Version(fastcore_version) < version.Version(fastcore_min_version):
+            raise ImportError(
+                "`from_pretrained_fastai` requires"
+                f" fastcore>={fastcore_min_version} version, but you are using fastcore"
+                f" version {fastcore_version} which is incompatible."
+            )
+
+
+README_TEMPLATE = """---
+tags:
+- fastai
+---
+
+# Amazing!
+
+🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
+
+# Some next steps
+1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
+
+2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)).
+
+3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)!
+
+Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card.
+
+
+---
+
+
+# Model card
+
+## Model description
+More information needed
+
+## Intended uses & limitations
+More information needed
+
+## Training and evaluation data
+More information needed
+"""
+
+PYPROJECT_TEMPLATE = f"""[build-system]
+requires = ["setuptools>=40.8.0", "wheel", "python={get_python_version()}", "fastai={get_fastai_version()}", "fastcore={get_fastcore_version()}"]
+build-backend = "setuptools.build_meta:__legacy__"
+"""
+
+
+def _create_model_card(repo_dir: Path):
+    """
+    Creates a model card for the repository.
+
+    Args:
+        repo_dir (`Path`):
+            Directory where model card is created.
+    """
+    readme_path = repo_dir / "README.md"
+
+    if not readme_path.exists():
+        with readme_path.open("w", encoding="utf-8") as f:
+            f.write(README_TEMPLATE)
+
+
+def _create_model_pyproject(repo_dir: Path):
+    """
+    Creates a `pyproject.toml` for the repository.
+
+    Args:
+        repo_dir (`Path`):
+            Directory where `pyproject.toml` is created.
+    """
+    pyproject_path = repo_dir / "pyproject.toml"
+
+    if not pyproject_path.exists():
+        with pyproject_path.open("w", encoding="utf-8") as f:
+            f.write(PYPROJECT_TEMPLATE)
+
+
+def _save_pretrained_fastai(
+    learner,
+    save_directory: Union[str, Path],
+    config: Optional[dict[str, Any]] = None,
+):
+    """
+    Saves a fastai learner to `save_directory` in pickle format using the default pickle protocol for the version of python used.
+
+    Args:
+        learner (`Learner`):
+            The `fastai.Learner` you'd like to save.
+        save_directory (`str` or `Path`):
+            Specific directory in which you want to save the fastai learner.
+        config (`dict`, *optional*):
+            Configuration object. Will be uploaded as a .json file. Example: 'https://huggingface.co/espejelomar/fastai-pet-breeds-classification/blob/main/config.json'.
+
+    > [!TIP]
+    > Raises the following error:
+    >
+    >     - [`RuntimeError`](https://docs.python.org/3/library/exceptions.html#RuntimeError)
+    >       if the config file provided is not a dictionary.
+    """
+    _check_fastai_fastcore_versions()
+
+    os.makedirs(save_directory, exist_ok=True)
+
+    # if the user provides config then we update it with the fastai and fastcore versions in CONFIG_TEMPLATE.
+    if config is not None:
+        if not isinstance(config, dict):
+            raise RuntimeError(f"Provided config should be a dict. Got: '{type(config)}'")
+        path = os.path.join(save_directory, constants.CONFIG_NAME)
+        with open(path, "w") as f:
+            json.dump(config, f)
+
+    _create_model_card(Path(save_directory))
+    _create_model_pyproject(Path(save_directory))
+
+    # learner.export saves the model in `self.path`.
+    learner.path = Path(save_directory)
+    os.makedirs(save_directory, exist_ok=True)
+    try:
+        learner.export(
+            fname="model.pkl",
+            pickle_protocol=DEFAULT_PROTOCOL,
+        )
+    except PicklingError:
+        raise PicklingError(
+            "You are using a lambda function, i.e., an anonymous function. `pickle`"
+            " cannot pickle function objects and requires that all functions have"
+            " names. One possible solution is to name the function."
+        )
+
+
+@validate_hf_hub_args
+def from_pretrained_fastai(
+    repo_id: str,
+    revision: Optional[str] = None,
+):
+    """
+    Load pretrained fastai model from the Hub or from a local directory.
+
+    Args:
+        repo_id (`str`):
+            The location where the pickled fastai.Learner is. It can be either of the two:
+                - Hosted on the Hugging Face Hub. E.g.: 'espejelomar/fatai-pet-breeds-classification' or 'distilgpt2'.
+                  You can add a `revision` by appending `@` at the end of `repo_id`. E.g.: `dbmdz/bert-base-german-cased@main`.
+                  Revision is the specific model version to use. Since we use a git-based system for storing models and other
+                  artifacts on the Hugging Face Hub, it can be a branch name, a tag name, or a commit id.
+                - Hosted locally. `repo_id` would be a directory containing the pickle and a pyproject.toml
+                  indicating the fastai and fastcore versions used to build the `fastai.Learner`. E.g.: `./my_model_directory/`.
+        revision (`str`, *optional*):
+            Revision at which the repo's files are downloaded. See documentation of `snapshot_download`.
+
+    Returns:
+        The `fastai.Learner` model in the `repo_id` repo.
+    """
+    _check_fastai_fastcore_versions()
+
+    # Load the `repo_id` repo.
+    # `snapshot_download` returns the folder where the model was stored.
+    # `cache_dir` will be the default '/root/.cache/huggingface/hub'
+    if not os.path.isdir(repo_id):
+        storage_folder = snapshot_download(
+            repo_id=repo_id,
+            revision=revision,
+            library_name="fastai",
+            library_version=get_fastai_version(),
+        )
+    else:
+        storage_folder = repo_id
+
+    _check_fastai_fastcore_pyproject_versions(storage_folder)
+
+    from fastai.learner import load_learner  # type: ignore
+
+    return load_learner(os.path.join(storage_folder, "model.pkl"))
+
+
+@validate_hf_hub_args
+def push_to_hub_fastai(
+    learner,
+    *,
+    repo_id: str,
+    commit_message: str = "Push FastAI model using huggingface_hub.",
+    private: Optional[bool] = None,
+    token: Optional[str] = None,
+    config: Optional[dict] = None,
+    branch: Optional[str] = None,
+    create_pr: Optional[bool] = None,
+    allow_patterns: Optional[Union[list[str], str]] = None,
+    ignore_patterns: Optional[Union[list[str], str]] = None,
+    delete_patterns: Optional[Union[list[str], str]] = None,
+    api_endpoint: Optional[str] = None,
+):
+    """
+    Upload learner checkpoint files to the Hub.
+
+    Use `allow_patterns` and `ignore_patterns` to precisely filter which files should be pushed to the hub. Use
+    `delete_patterns` to delete existing remote files in the same commit. See [`upload_folder`] reference for more
+    details.
+
+    Args:
+        learner (`Learner`):
+            The `fastai.Learner' you'd like to push to the Hub.
+        repo_id (`str`):
+            The repository id for your model in Hub in the format of "namespace/repo_name". The namespace can be your individual account or an organization to which you have write access (for example, 'stanfordnlp/stanza-de').
+        commit_message (`str`, *optional*):
+            Message to commit while pushing. Will default to :obj:`"add model"`.
+        private (`bool`, *optional*):
+            Whether or not the repository created should be private.
+            If `None` (default), will default to been public except if the organization's default is private.
+        token (`str`, *optional*):
+            The Hugging Face account token to use as HTTP bearer authorization for remote files. If :obj:`None`, the token will be asked by a prompt.
+        config (`dict`, *optional*):
+            Configuration object to be saved alongside the model weights.
+        branch (`str`, *optional*):
+            The git branch on which to push the model. This defaults to
+            the default branch as specified in your repository, which
+            defaults to `"main"`.
+        create_pr (`boolean`, *optional*):
+            Whether or not to create a Pull Request from `branch` with that commit.
+            Defaults to `False`.
+        api_endpoint (`str`, *optional*):
+            The API endpoint to use when pushing the model to the hub.
+        allow_patterns (`list[str]` or `str`, *optional*):
+            If provided, only files matching at least one pattern are pushed.
+        ignore_patterns (`list[str]` or `str`, *optional*):
+            If provided, files matching any of the patterns are not pushed.
+        delete_patterns (`list[str]` or `str`, *optional*):
+            If provided, remote files matching any of the patterns will be deleted from the repo.
+
+    Returns:
+        The url of the commit of your model in the given repository.
+
+    > [!TIP]
+    > Raises the following error:
+    >
+    >     - [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError)
+    >       if the user is not log on to the Hugging Face Hub.
+    """
+    _check_fastai_fastcore_versions()
+    api = HfApi(endpoint=api_endpoint)
+    repo_id = api.create_repo(repo_id=repo_id, token=token, private=private, exist_ok=True).repo_id
+
+    # Push the files to the repo in a single commit
+    with SoftTemporaryDirectory() as tmp:
+        saved_path = Path(tmp) / repo_id
+        _save_pretrained_fastai(learner, saved_path, config=config)
+        return api.upload_folder(
+            repo_id=repo_id,
+            token=token,
+            folder_path=saved_path,
+            commit_message=commit_message,
+            revision=branch,
+            create_pr=create_pr,
+            allow_patterns=allow_patterns,
+            ignore_patterns=ignore_patterns,
+            delete_patterns=delete_patterns,
+        )

data/conda_envs/evalclaw/lib/python3.11/site-packages/huggingface_hub/hf_file_system.py

@@ -0,0 +1,1454 @@
+import os
+import re
+import tempfile
+import threading
+from collections import deque
+from contextlib import ExitStack
+from copy import deepcopy
+from dataclasses import dataclass, field
+from datetime import datetime
+from itertools import chain
+from pathlib import Path, PurePosixPath
+from typing import Any, Iterable, Iterator, NoReturn, Optional, Union
+from urllib.parse import quote, unquote
+
+import fsspec
+import httpx
+from fsspec.callbacks import _DEFAULT_CALLBACK, NoOpCallback, TqdmCallback
+from fsspec.utils import isfilelike
+
+from . import constants
+from ._commit_api import CommitOperationCopy, CommitOperationDelete
+from .errors import (
+    BucketNotFoundError,
+    EntryNotFoundError,
+    HfHubHTTPError,
+    RepositoryNotFoundError,
+    RevisionNotFoundError,
+)
+from .file_download import hf_hub_url, http_get
+from .hf_api import BucketFile, BucketFolder, HfApi, LastCommitInfo, RepoFile, RepoFolder
+from .utils import HFValidationError, hf_raise_for_status, http_backoff, http_stream_backoff
+from .utils.insecure_hashlib import md5
+
+
+# Regex used to match special revisions with "/" in them (see #1710)
+SPECIAL_REFS_REVISION_REGEX = re.compile(
+    r"""
+    (^refs\/convert\/\w+)     # `refs/convert/parquet` revisions
+    |
+    (^refs\/pr\/\d+)          # PR revisions
+    """,
+    re.VERBOSE,
+)
+
+
+@dataclass
+class HfFileSystemResolvedPath:
+    """Top level Data structure containing information about a resolved Hugging Face file system path."""
+
+    root: str
+    path: str
+
+    def unresolve(self) -> str:
+        return f"{self.root}/{self.path}".rstrip("/")
+
+
+@dataclass
+class HfFileSystemResolvedRepositoryPath(HfFileSystemResolvedPath):
+    """Data structure containing information about a resolved path in a repository."""
+
+    repo_type: str
+    repo_id: str
+    revision: str
+    path_in_repo: str
+    root: str = field(init=False)
+    path: str = field(init=False)
+    # The part placed after '@' in the initial path. It can be a quoted or unquoted refs revision.
+    # Used to reconstruct the unresolved path to return to the user.
+    _raw_revision: Optional[str] = field(default=None, repr=False)
+
+    def __post_init__(self):
+        repo_path = constants.REPO_TYPES_URL_PREFIXES.get(self.repo_type, "") + self.repo_id
+        if self._raw_revision:
+            self.root = f"{repo_path}@{self._raw_revision}"
+        elif self.revision != constants.DEFAULT_REVISION:
+            self.root = f"{repo_path}@{safe_revision(self.revision)}"
+        else:
+            self.root = repo_path
+        self.path = self.path_in_repo
+
+
+@dataclass
+class HfFileSystemResolvedBucketPath(HfFileSystemResolvedPath):
+    """Data structure containing information about a resolved path in a bucket."""
+
+    bucket_id: str
+    root: str = field(init=False)
+
+    def __post_init__(self):
+        self.root = "buckets/" + self.bucket_id
+
+
+# We need to improve fsspec.spec._Cached which is AbstractFileSystem's metaclass
+_cached_base: Any = type(fsspec.AbstractFileSystem)
+
+
+class _Cached(_cached_base):
+    """
+    Metaclass for caching HfFileSystem instances according to the args.
+
+    This creates an additional reference to the filesystem, which prevents the
+    filesystem from being garbage collected when all *user* references go away.
+    A call to the :meth:`AbstractFileSystem.clear_instance_cache` must *also*
+    be made for a filesystem instance to be garbage collected.
+
+    This is a slightly modified version of `fsspec.spec._Cached` to improve it.
+    In particular in `_tokenize` the pid isn't taken into account for the
+    `fs_token` used to identify cached instances. The `fs_token` logic is also
+    robust to defaults values and the order of the args. Finally new instances
+    reuse the states from sister instances in the main thread.
+    """
+
+    def __init__(cls, *args, **kwargs):
+        # Hack: override https://github.com/fsspec/filesystem_spec/blob/dcb167e8f50e6273d4cfdfc4cab8fc5aa4c958bf/fsspec/spec.py#L53
+        super().__init__(*args, **kwargs)
+        # Note: we intentionally create a reference here, to avoid garbage
+        # collecting instances when all other references are gone. To really
+        # delete a FileSystem, the cache must be cleared.
+        cls._cache = {}
+
+    def __call__(cls, *args, **kwargs):
+        # Hack: override https://github.com/fsspec/filesystem_spec/blob/dcb167e8f50e6273d4cfdfc4cab8fc5aa4c958bf/fsspec/spec.py#L65
+        skip = kwargs.pop("skip_instance_cache", False)
+        fs_token = cls._tokenize(cls, threading.get_ident(), *args, **kwargs)
+        fs_token_main_thread = cls._tokenize(cls, threading.main_thread().ident, *args, **kwargs)
+        if not skip and cls.cachable and fs_token in cls._cache:
+            # reuse cached instance
+            cls._latest = fs_token
+            return cls._cache[fs_token]
+        else:
+            # create new instance
+            obj = type.__call__(cls, *args, **kwargs)
+            if not skip and cls.cachable and fs_token_main_thread in cls._cache:
+                # reuse the cache from the main thread instance in the new instance
+                instance_state = cls._cache[fs_token_main_thread]._get_instance_state()
+                for attr, state_value in instance_state.items():
+                    setattr(obj, attr, state_value)
+            obj._fs_token_ = fs_token
+            obj.storage_args = args
+            obj.storage_options = kwargs
+            if cls.cachable and not skip:
+                cls._latest = fs_token
+                cls._cache[fs_token] = obj
+            return obj
+
+
+class HfFileSystem(fsspec.AbstractFileSystem, metaclass=_Cached):
+    """
+    Access a remote Hugging Face Hub repository as if were a local file system.
+
+    > [!WARNING]
+    > [`HfFileSystem`] provides fsspec compatibility, which is useful for libraries that require it (e.g., reading
+    >     Hugging Face datasets directly with `pandas`). However, it introduces additional overhead due to this compatibility
+    >     layer. For better performance and reliability, it's recommended to use `HfApi` methods when possible.
+
+    The file system supports paths for the `hf://` protocol, which follows those URL schemes:
+
+    * Models, Datasets and Spaces repositories:
+
+        ```
+        hf://<repo-id>[@<revision>]/<path/in/repo>
+        hf://datasets/<repo-id>[@<revision>]/<path/in/repo>
+        hf://spaces/<repo-id>[@<revision>]/<path/in/repo>
+        ```
+
+    * Buckets (generic storage):
+
+        ```
+        hf://buckets/<bucket-id>/<path/in/bucket>
+        ```
+
+    Note: when using the [`HfFileSystem`] directly, passing the `hf://` protocol prefix is optional in paths.
+
+    Args:
+        endpoint (`str`, *optional*):
+                Endpoint of the Hub. Defaults to <https://huggingface.co>.
+        token (`bool` or `str`, *optional*):
+            A valid user access token (string). Defaults to the locally saved
+            token, which is the recommended method for authentication (see
+            https://huggingface.co/docs/huggingface_hub/quick-start#authentication).
+            To disable authentication, pass `False`.
+        block_size (`int`, *optional*):
+            Block size for reading and writing files.
+        expand_info (`bool`, *optional*):
+            Whether to expand the information of the files.
+        **storage_options (`dict`, *optional*):
+            Additional options for the filesystem. See [fsspec documentation](https://filesystem-spec.readthedocs.io/en/latest/api.html#fsspec.spec.AbstractFileSystem.__init__).
+
+    Usage:
+
+    ```python
+    >>> from huggingface_hub import hffs
+
+    >>> # List files
+    >>> hffs.glob("my-username/my-model/*.bin")
+    ['my-username/my-model/pytorch_model.bin']
+    >>> hffs.ls("datasets/my-username/my-dataset", detail=False)
+    ['datasets/my-username/my-dataset/.gitattributes', 'datasets/my-username/my-dataset/README.md', 'datasets/my-username/my-dataset/data.json']
+
+    >>> # Read/write files
+    >>> with hffs.open("my-username/my-model/pytorch_model.bin") as f:
+    ...     data = f.read()
+    >>> with hffs.open("my-username/my-model/pytorch_model.bin", "wb") as f:
+    ...     f.write(data)
+    ```
+
+    Specify a token for authentication:
+    ```python
+    >>> from huggingface_hub import HfFileSystem
+    >>> hffs = HfFileSystem(token=token)
+    ```
+    """
+
+    root_marker = ""
+    protocol = "hf"
+
+    def __init__(
+        self,
+        *args,
+        endpoint: Optional[str] = None,
+        token: Union[bool, str, None] = None,
+        block_size: Optional[int] = None,
+        expand_info: Optional[bool] = None,
+        **storage_options,
+    ):
+        super().__init__(*args, **storage_options)
+        self.endpoint = endpoint or constants.ENDPOINT
+        self.token = token
+        self._api = HfApi(endpoint=endpoint, token=token)
+        self.block_size = block_size
+        self.expand_info = expand_info
+        # Maps (repo_type, repo_id, revision) to a 2-tuple with:
+        #  * the 1st element indicating whether the repository and the revision exist
+        #  * the 2nd element being the exception raised if the repository or revision doesn't exist
+        self._repo_and_revision_exists_cache: dict[
+            tuple[str, str, Optional[str]], tuple[bool, Optional[Exception]]
+        ] = {}
+        # Same for buckets
+        self._bucket_exists_cache: dict[str, tuple[bool, Optional[Exception]]] = {}
+        # Note: special case for buckets: revision is always None
+        # Maps parent directory path to path infos
+        self.dircache: dict[str, list[dict[str, Any]]] = {}
+
+    @classmethod
+    def _tokenize(cls, threading_ident: int, *args, **kwargs) -> str:
+        """Deterministic token for caching"""
+        # make fs_token robust to default values and to kwargs order
+        kwargs["endpoint"] = kwargs.get("endpoint") or constants.ENDPOINT
+        kwargs["token"] = kwargs.get("token")
+        kwargs = {key: kwargs[key] for key in sorted(kwargs)}
+        # contrary to fsspec, we don't include pid here
+        tokenize_args = (cls, threading_ident, args, kwargs)
+        h = md5(str(tokenize_args).encode())
+        return h.hexdigest()
+
+    def _repo_and_revision_exist(
+        self, repo_type: str, repo_id: str, revision: Optional[str]
+    ) -> tuple[bool, Optional[Exception]]:
+        if (repo_type, repo_id, revision) not in self._repo_and_revision_exists_cache:
+            try:
+                self._api.repo_info(
+                    repo_id, revision=revision, repo_type=repo_type, timeout=constants.HF_HUB_ETAG_TIMEOUT
+                )
+            except (RepositoryNotFoundError, HFValidationError) as e:
+                self._repo_and_revision_exists_cache[(repo_type, repo_id, revision)] = False, e
+                self._repo_and_revision_exists_cache[(repo_type, repo_id, None)] = False, e
+            except RevisionNotFoundError as e:
+                self._repo_and_revision_exists_cache[(repo_type, repo_id, revision)] = False, e
+                self._repo_and_revision_exists_cache[(repo_type, repo_id, None)] = True, None
+            else:
+                self._repo_and_revision_exists_cache[(repo_type, repo_id, revision)] = True, None
+                self._repo_and_revision_exists_cache[(repo_type, repo_id, None)] = True, None
+        return self._repo_and_revision_exists_cache[(repo_type, repo_id, revision)]
+
+    def _bucket_exists(self, bucket_id: str) -> tuple[bool, Optional[Exception]]:
+        if bucket_id not in self._bucket_exists_cache:
+            try:
+                self._api.bucket_info(bucket_id)
+            except BucketNotFoundError as e:
+                self._bucket_exists_cache[bucket_id] = False, e
+            else:
+                self._bucket_exists_cache[bucket_id] = True, None
+        return self._bucket_exists_cache[bucket_id]
+
+    def resolve_path(
+        self, path: str, revision: Optional[str] = None
+    ) -> Union[HfFileSystemResolvedRepositoryPath, HfFileSystemResolvedBucketPath]:
+        """
+        Resolve a Hugging Face file system path into its components.
+
+        Args:
+            path (`str`):
+                Path to resolve.
+            revision (`str`, *optional*):
+                The revision of the repo to resolve. Defaults to the revision specified in the path.
+
+        Returns:
+            [`HfFileSystemResolvedPath`]: Resolved path information containing `repo_type`, `repo_id`, `revision` and `path_in_repo`.
+
+        Raises:
+            `ValueError`:
+                If path contains conflicting revision information.
+            `NotImplementedError`:
+                If trying to list repositories.
+        """
+
+        def _align_revision_in_path_with_revision(
+            revision_in_path: Optional[str], revision: Optional[str]
+        ) -> Optional[str]:
+            if revision is not None:
+                if revision_in_path is not None and revision_in_path != revision:
+                    raise ValueError(
+                        f'Revision specified in path ("{revision_in_path}") and in `revision` argument ("{revision}")'
+                        " are not the same."
+                    )
+            else:
+                revision = revision_in_path
+            return revision
+
+        path = self._strip_protocol(path)
+        if not path:
+            # can't list repositories at root
+            raise NotImplementedError("Access to buckets and repositories lists is not implemented.")
+        elif path.split("/")[0] == "buckets":
+            bucket_id = "/".join(path.split("/")[1:3])
+            path = "/".join(path.split("/")[3:])
+            bucket_exists, err = self._bucket_exists(bucket_id)
+            if not bucket_exists:
+                _raise_file_not_found(path, err)
+            return HfFileSystemResolvedBucketPath(bucket_id=bucket_id, path=path)
+        elif path.split("/")[0] + "/" in constants.REPO_TYPES_URL_PREFIXES.values():
+            if "/" not in path:
+                # can't list repositories at the repository type level
+                raise NotImplementedError("Access to repositories lists is not implemented.")
+            repo_type, path = path.split("/", 1)
+            repo_type = constants.REPO_TYPES_MAPPING[repo_type]
+        else:
+            repo_type = constants.REPO_TYPE_MODEL
+        if path.count("/") > 0:
+            if "@" in "/".join(path.split("/")[:2]):
+                repo_id, revision_in_path = path.split("@", 1)
+                if "/" in revision_in_path:
+                    match = SPECIAL_REFS_REVISION_REGEX.search(revision_in_path)
+                    if match is not None and revision in (None, match.group()):
+                        # Handle `refs/convert/parquet` and PR revisions separately
+                        path_in_repo = SPECIAL_REFS_REVISION_REGEX.sub("", revision_in_path).lstrip("/")
+                        revision_in_path = match.group()
+                    else:
+                        revision_in_path, path_in_repo = revision_in_path.split("/", 1)
+                else:
+                    path_in_repo = ""
+                revision = _align_revision_in_path_with_revision(unquote(revision_in_path), revision)
+                repo_and_revision_exist, err = self._repo_and_revision_exist(repo_type, repo_id, revision)
+                if not repo_and_revision_exist:
+                    _raise_file_not_found(path, err)
+            else:
+                revision_in_path = None
+                repo_id_with_namespace = "/".join(path.split("/")[:2])
+                path_in_repo_with_namespace = "/".join(path.split("/")[2:])
+                repo_id_without_namespace = path.split("/")[0]
+                path_in_repo_without_namespace = "/".join(path.split("/")[1:])
+                repo_id = repo_id_with_namespace
+                path_in_repo = path_in_repo_with_namespace
+                repo_and_revision_exist, err = self._repo_and_revision_exist(repo_type, repo_id, revision)
+                if not repo_and_revision_exist:
+                    if isinstance(err, (RepositoryNotFoundError, HFValidationError)):
+                        repo_id = repo_id_without_namespace
+                        path_in_repo = path_in_repo_without_namespace
+                        repo_and_revision_exist, _ = self._repo_and_revision_exist(repo_type, repo_id, revision)
+                        if not repo_and_revision_exist:
+                            _raise_file_not_found(path, err)
+                    else:
+                        _raise_file_not_found(path, err)
+        else:
+            repo_id = path
+            path_in_repo = ""
+            if "@" in path:
+                repo_id, revision_in_path = path.split("@", 1)
+                revision = _align_revision_in_path_with_revision(unquote(revision_in_path), revision)
+            else:
+                revision_in_path = None
+            repo_and_revision_exist, _ = self._repo_and_revision_exist(repo_type, repo_id, revision)
+            if not repo_and_revision_exist:
+                raise NotImplementedError("Access to repositories lists is not implemented.")
+
+        revision = revision if revision is not None else constants.DEFAULT_REVISION
+        return HfFileSystemResolvedRepositoryPath(
+            repo_type, repo_id, revision, path_in_repo, _raw_revision=revision_in_path
+        )
+
+    def invalidate_cache(self, path: Optional[str] = None) -> None:
+        """
+        Clear the cache for a given path.
+
+        For more details, refer to [fsspec documentation](https://filesystem-spec.readthedocs.io/en/latest/api.html#fsspec.spec.AbstractFileSystem.invalidate_cache).
+
+        Args:
+            path (`str`, *optional*):
+                Path to clear from cache. If not provided, clear the entire cache.
+
+        """
+        if not path:
+            self.dircache.clear()
+            self._repo_and_revision_exists_cache.clear()
+        else:
+            resolved_path = self.resolve_path(path)
+            path = resolved_path.unresolve()
+            while path:
+                self.dircache.pop(path, None)
+                path = self._parent(path)
+
+            # Only clear repo cache if path is to repo root
+            if not resolved_path.path:
+                if isinstance(resolved_path, HfFileSystemResolvedRepositoryPath):
+                    self._repo_and_revision_exists_cache.pop(
+                        (resolved_path.repo_type, resolved_path.repo_id, None), None
+                    )
+                    self._repo_and_revision_exists_cache.pop(
+                        (resolved_path.repo_type, resolved_path.repo_id, resolved_path.revision), None
+                    )
+                else:
+                    self._bucket_exists_cache.pop(resolved_path.bucket_id, None)
+
+    def _open(  # type: ignore[override]
+        self,
+        path: str,
+        mode: str = "rb",
+        block_size: Optional[int] = None,
+        revision: Optional[str] = None,
+        **kwargs,
+    ) -> Union["HfFileSystemFile", "HfFileSystemStreamFile"]:
+        block_size = block_size if block_size is not None else self.block_size
+        if block_size is not None:
+            kwargs["block_size"] = block_size
+        if "a" in mode:
+            raise NotImplementedError("Appending to remote files is not yet supported.")
+        if block_size == 0:
+            return HfFileSystemStreamFile(self, path, mode=mode, revision=revision, **kwargs)
+        else:
+            return HfFileSystemFile(self, path, mode=mode, revision=revision, **kwargs)
+
+    def _rm(self, path: str, revision: Optional[str] = None, **kwargs) -> None:
+        resolved_path = self.resolve_path(path, revision=revision)
+        if isinstance(resolved_path, HfFileSystemResolvedBucketPath):
+            self._api.batch_bucket_files(resolved_path.bucket_id, delete=[resolved_path.path])
+        else:
+            self._api.delete_file(
+                path_in_repo=resolved_path.path_in_repo,
+                repo_id=resolved_path.repo_id,
+                token=self.token,
+                repo_type=resolved_path.repo_type,
+                revision=resolved_path.revision,
+                commit_message=kwargs.get("commit_message"),
+                commit_description=kwargs.get("commit_description"),
+            )
+        self.invalidate_cache(path=resolved_path.unresolve())
+
+    def rm(
+        self,
+        path: str,
+        recursive: bool = False,
+        maxdepth: Optional[int] = None,
+        revision: Optional[str] = None,
+        **kwargs,
+    ) -> None:
+        """
+        Delete files from a repository.
+
+        For more details, refer to [fsspec documentation](https://filesystem-spec.readthedocs.io/en/latest/api.html#fsspec.spec.AbstractFileSystem.rm).
+
+        > [!WARNING]
+        > Note: When possible, use `HfApi.delete_file()` for better performance.
+
+        Args:
+            path (`str`):
+                Path to delete.
+            recursive (`bool`, *optional*):
+                If True, delete directory and all its contents. Defaults to False.
+            maxdepth (`int`, *optional*):
+                Maximum number of subdirectories to visit when deleting recursively.
+            revision (`str`, *optional*):
+                The git revision to delete from.
+
+        """
+        resolved_path = self.resolve_path(path, revision=revision)
+        paths = self.expand_path(path, recursive=recursive, maxdepth=maxdepth, revision=revision)
+        if isinstance(resolved_path, HfFileSystemResolvedBucketPath):
+            delete = [self.resolve_path(path).path for path in paths if not self.isdir(path)]
+            self._api.batch_bucket_files(resolved_path.bucket_id, delete=delete)
+        else:
+            paths_in_repo = [self.resolve_path(path).path for path in paths if not self.isdir(path)]
+            operations = [CommitOperationDelete(path_in_repo=path_in_repo) for path_in_repo in paths_in_repo]
+            commit_message = f"Delete {path} "
+            commit_message += "recursively " if recursive else ""
+            commit_message += f"up to depth {maxdepth} " if maxdepth is not None else ""
+            # TODO: use `commit_description` to list all the deleted paths?
+            self._api.create_commit(
+                repo_id=resolved_path.repo_id,
+                repo_type=resolved_path.repo_type,
+                token=self.token,
+                operations=operations,
+                revision=resolved_path.revision,
+                commit_message=kwargs.get("commit_message", commit_message),
+                commit_description=kwargs.get("commit_description"),
+            )
+        self.invalidate_cache(path=resolved_path.unresolve())
+
+    def ls(
+        self, path: str, detail: bool = True, refresh: bool = False, revision: Optional[str] = None, **kwargs
+    ) -> list[Union[str, dict[str, Any]]]:
+        """
+        List the contents of a directory.
+
+        For more details, refer to [fsspec documentation](https://filesystem-spec.readthedocs.io/en/latest/api.html#fsspec.spec.AbstractFileSystem.ls).
+
+        > [!WARNING]
+        > Note: When possible, use `HfApi.list_repo_tree()` for better performance.
+
+        Args:
+            path (`str`):
+                Path to the directory.
+            detail (`bool`, *optional*):
+                If True, returns a list of dictionaries containing file information. If False,
+                returns a list of file paths. Defaults to True.
+            refresh (`bool`, *optional*):
+                If True, bypass the cache and fetch the latest data. Defaults to False.
+            revision (`str`, *optional*):
+                The git revision to list from.
+
+        Returns:
+            `list[Union[str, dict[str, Any]]]`: List of file paths (if detail=False) or list of file information
+            dictionaries (if detail=True).
+        """
+        resolved_path = self.resolve_path(path, revision=revision)
+        path = resolved_path.unresolve()
+        try:
+            out = self._ls_tree(path, refresh=refresh, revision=revision, **kwargs)
+        except EntryNotFoundError:
+            # Path could be a file
+            if not resolved_path.path:
+                _raise_file_not_found(path, None)
+            out = self._ls_tree(self._parent(path), refresh=refresh, revision=revision, **kwargs)
+            out = [o for o in out if o["name"] == path]
+            if len(out) == 0:
+                _raise_file_not_found(path, None)
+        return out if detail else [o["name"] for o in out]
+
+    def _ls_tree(
+        self,
+        path: str,
+        recursive: bool = False,
+        refresh: bool = False,
+        revision: Optional[str] = None,
+        expand_info: Optional[bool] = None,
+        maxdepth: Optional[int] = None,
+    ):
+        expand_info = (
+            expand_info if expand_info is not None else (self.expand_info if self.expand_info is not None else False)
+        )
+        resolved_path = self.resolve_path(path, revision=revision)
+        path = resolved_path.unresolve()
+        root_path = resolved_path.root
+        maxdepth = maxdepth if recursive else 1
+
+        out = []
+        if path in self.dircache and not refresh:
+            cached_path_infos = self.dircache[path]
+            out.extend(cached_path_infos)
+            dirs_not_in_dircache = []
+            if recursive:
+                # Use BFS to traverse the cache and build the "recursive "output
+                # (The Hub uses a so-called "tree first" strategy for the tree endpoint but we sort the output to follow the spec so the result is (eventually) the same)
+                depth = 2
+                dirs_to_visit = deque(
+                    [(depth, path_info) for path_info in cached_path_infos if path_info["type"] == "directory"]
+                )
+                while dirs_to_visit:
+                    depth, dir_info = dirs_to_visit.popleft()
+                    if maxdepth is None or depth <= maxdepth:
+                        if dir_info["name"] not in self.dircache:
+                            dirs_not_in_dircache.append(dir_info["name"])
+                        else:
+                            cached_path_infos = self.dircache[dir_info["name"]]
+                            out.extend(cached_path_infos)
+                            dirs_to_visit.extend(
+                                [
+                                    (depth + 1, path_info)
+                                    for path_info in cached_path_infos
+                                    if path_info["type"] == "directory"
+                                ]
+                            )
+
+            dirs_not_expanded = []
+            if expand_info and isinstance(resolved_path, HfFileSystemResolvedRepositoryPath):
+                # Check if there are directories in repos with non-expanded entries
+                dirs_not_expanded = [self._parent(o["name"]) for o in out if o["last_commit"] is None]
+
+            if (recursive and dirs_not_in_dircache) or (expand_info and dirs_not_expanded):
+                # If the dircache is incomplete, find the common path of the missing and non-expanded entries
+                # and extend the output with the result of `_ls_tree(common_path, recursive=True)`
+                common_prefix = os.path.commonprefix(dirs_not_in_dircache + dirs_not_expanded)
+                # Get the parent directory if the common prefix itself is not a directory
+                common_path = (
+                    common_prefix.rstrip("/")
+                    if common_prefix.endswith("/")
+                    or common_prefix == root_path
+                    or common_prefix in chain(dirs_not_in_dircache, dirs_not_expanded)
+                    else self._parent(common_prefix)
+                )
+                if maxdepth is not None:
+                    common_path_depth = common_path[len(path) :].count("/")
+                    maxdepth -= common_path_depth
+                out = [o for o in out if not o["name"].startswith(common_path + "/")]
+                for cached_path in list(self.dircache):
+                    if cached_path.startswith(common_path + "/"):
+                        self.dircache.pop(cached_path, None)
+                self.dircache.pop(common_path, None)
+                out.extend(
+                    self._ls_tree(
+                        common_path,
+                        recursive=recursive,
+                        refresh=True,
+                        revision=revision,
+                        expand_info=expand_info,
+                        maxdepth=maxdepth,
+                    )
+                )
+        else:
+            tree: Iterable[Union[RepoFile, RepoFolder, BucketFile, BucketFolder]]
+            if isinstance(resolved_path, HfFileSystemResolvedBucketPath):
+                tree = self._list_bucket_tree_with_folders(
+                    resolved_path.bucket_id,
+                    prefix=resolved_path.path,
+                    recursive=recursive,
+                )
+            else:
+                tree = self._api.list_repo_tree(
+                    resolved_path.repo_id,
+                    resolved_path.path,
+                    recursive=recursive,
+                    expand=expand_info,
+                    revision=resolved_path.revision,
+                    repo_type=resolved_path.repo_type,
+                )
+            for path_info in tree:
+                cache_path = root_path + "/" + path_info.path
+                if isinstance(path_info, RepoFile):
+                    cache_path_info = {
+                        "name": cache_path,
+                        "size": path_info.size,
+                        "type": "file",
+                        "blob_id": path_info.blob_id,
+                        "lfs": path_info.lfs,
+                        "xet_hash": path_info.xet_hash,
+                        "last_commit": path_info.last_commit,
+                        "security": path_info.security,
+                    }
+                elif isinstance(path_info, BucketFile):
+                    cache_path_info = {
+                        "name": cache_path,
+                        "size": path_info.size,
+                        "type": "file",
+                        "xet_hash": path_info.xet_hash,
+                        "mtime": path_info.mtime,
+                        "uploaded_at": path_info.uploaded_at,
+                    }
+                elif isinstance(path_info, RepoFolder):
+                    cache_path_info = {
+                        "name": cache_path,
+                        "size": 0,
+                        "type": "directory",
+                        "tree_id": path_info.tree_id,
+                        "last_commit": path_info.last_commit,
+                    }
+                else:
+                    cache_path_info = {
+                        "name": cache_path,
+                        "size": 0,
+                        "type": "directory",
+                        "uploaded_at": path_info.uploaded_at,
+                    }
+                parent_path = self._parent(cache_path_info["name"])
+                self.dircache.setdefault(parent_path, []).append(cache_path_info)
+                depth = cache_path[len(path) :].count("/")
+                if maxdepth is None or depth <= maxdepth:
+                    out.append(cache_path_info)
+        return out
+
+    def _list_bucket_tree_with_folders(
+        self, bucket_id: str, prefix: str, recursive: bool
+    ) -> Iterable[Union[BucketFile, BucketFolder]]:
+        """Same as `HfApi.list_bucket_tree` but always includes folders"""
+        bucket_files = self._api.list_bucket_tree(bucket_id, prefix, recursive=recursive)
+        bucket_folders: dict[str, BucketFolder] = {}
+        min_depth = 1 + prefix.count("/") if prefix else 0
+        out: list[Union[BucketFile, BucketFolder]] = []
+
+        for bucket_entry in bucket_files:
+            out.append(bucket_entry)
+
+            # If recursive=False, both files and folders are returned by the server => nothing to do
+            if not recursive:
+                continue
+
+            # Otherwise, let's rebuild BucketFolders manually
+            for parent_bucket_folder_str in list(PurePosixPath(bucket_entry.path).parents)[: -min_depth - 1]:
+                parent_bucket_folder = BucketFolder(
+                    type="directory", path=str(parent_bucket_folder_str), uploaded_at=bucket_entry.uploaded_at
+                )
+
+                # If folder not visited yet, add it
+                if parent_bucket_folder.path not in bucket_folders:
+                    out.append(parent_bucket_folder)
+                    bucket_folders[parent_bucket_folder.path] = parent_bucket_folder
+                    continue
+
+                # Otherwise, get back BucketFolder object and update its 'uploaded_at'
+                if parent_bucket_folder.uploaded_at is not None:
+                    bucket_folder = bucket_folders[parent_bucket_folder.path]
+                    if bucket_folder.uploaded_at is None or (
+                        bucket_folder.uploaded_at < parent_bucket_folder.uploaded_at
+                    ):
+                        bucket_folder.uploaded_at = parent_bucket_folder.uploaded_at
+
+        if not out:
+            raise EntryNotFoundError(f"File not found in bucket '{bucket_id}': '{prefix}'")
+        return out
+
+    def walk(self, path: str, *args, **kwargs) -> Iterator[tuple[str, list[str], list[str]]]:
+        """
+        Return all files below the given path.
+
+        For more details, refer to [fsspec documentation](https://filesystem-spec.readthedocs.io/en/latest/api.html#fsspec.spec.AbstractFileSystem.walk).
+
+        Args:
+            path (`str`):
+                Root path to list files from.
+
+        Returns:
+            `Iterator[tuple[str, list[str], list[str]]]`: An iterator of (path, list of directory names, list of file names) tuples.
+        """
+        path = self.resolve_path(path, revision=kwargs.get("revision")).unresolve()
+        yield from super().walk(path, *args, **kwargs)
+
+    def glob(self, path: str, maxdepth: Optional[int] = None, **kwargs) -> list[str]:
+        """
+        Find files by glob-matching.
+
+        For more details, refer to [fsspec documentation](https://filesystem-spec.readthedocs.io/en/latest/api.html#fsspec.spec.AbstractFileSystem.glob).
+
+        Args:
+            path (`str`):
+                Path pattern to match.
+
+        Returns:
+            `list[str]`: List of paths matching the pattern.
+        """
+        path = self.resolve_path(path, revision=kwargs.get("revision")).unresolve()
+        return super().glob(path, maxdepth=maxdepth, **kwargs)
+
+    def find(
+        self,
+        path: str,
+        maxdepth: Optional[int] = None,
+        withdirs: bool = False,
+        detail: bool = False,
+        refresh: bool = False,
+        revision: Optional[str] = None,
+        **kwargs,
+    ) -> Union[list[str], dict[str, dict[str, Any]]]:
+        """
+        List all files below path.
+
+        For more details, refer to [fsspec documentation](https://filesystem-spec.readthedocs.io/en/latest/api.html#fsspec.spec.AbstractFileSystem.find).
+
+        Args:
+            path (`str`):
+                Root path to list files from.
+            maxdepth (`int`, *optional*):
+                Maximum depth to descend into subdirectories.
+            withdirs (`bool`, *optional*):
+                Include directory paths in the output. Defaults to False.
+            detail (`bool`, *optional*):
+                If True, returns a dict mapping paths to file information. Defaults to False.
+            refresh (`bool`, *optional*):
+                If True, bypass the cache and fetch the latest data. Defaults to False.
+            revision (`str`, *optional*):
+                The git revision to list from.
+
+        Returns:
+            `Union[list[str], dict[str, dict[str, Any]]]`: List of paths or dict of file information.
+        """
+        if maxdepth is not None and maxdepth < 1:
+            raise ValueError("maxdepth must be at least 1")
+        resolved_path = self.resolve_path(path, revision=revision)
+        path = resolved_path.unresolve()
+        try:
+            out = self._ls_tree(path, recursive=True, refresh=refresh, maxdepth=maxdepth, **kwargs)
+        except EntryNotFoundError:
+            # Path could be a file
+            try:
+                if self.info(path, revision=revision, **kwargs)["type"] == "file":
+                    out = {path: {}}
+                else:
+                    out = {}
+            except FileNotFoundError:
+                out = {}
+        else:
+            if not withdirs:
+                out = [o for o in out if o["type"] != "directory"]
+            else:
+                # If `withdirs=True`, include the directory itself to be consistent with the spec
+                path_info = self.info(path, **kwargs)
+                out = [path_info] + out if path_info["type"] == "directory" else out
+            out = {o["name"]: o for o in out}
+        names = sorted(out)
+        if not detail:
+            return names
+        else:
+            return {name: out[name] for name in names}
+
+    def cp_file(self, path1: str, path2: str, revision: Optional[str] = None, **kwargs) -> None:
+        """
+        Copy a file within or between repositories.
+
+        > [!WARNING]
+        > Note: When possible, use `HfApi.upload_file()` for better performance.
+
+        Args:
+            path1 (`str`):
+                Source path to copy from.
+            path2 (`str`):
+                Destination path to copy to.
+            revision (`str`, *optional*):
+                The git revision to copy from.
+
+        """
+        resolved_path1 = self.resolve_path(path1, revision=revision)
+        resolved_path2 = self.resolve_path(path2, revision=revision)
+        if isinstance(resolved_path1, HfFileSystemResolvedBucketPath) or isinstance(
+            resolved_path2, HfFileSystemResolvedBucketPath
+        ):
+            raise NotImplementedError("Copy from/to buckets is not available yet")
+
+        same_repo = (
+            resolved_path1.repo_type == resolved_path2.repo_type and resolved_path1.repo_id == resolved_path2.repo_id
+        )
+
+        if same_repo:
+            commit_message = f"Copy {path1} to {path2}"
+            self._api.create_commit(
+                repo_id=resolved_path1.repo_id,
+                repo_type=resolved_path1.repo_type,
+                revision=resolved_path2.revision,
+                commit_message=kwargs.get("commit_message", commit_message),
+                commit_description=kwargs.get("commit_description", ""),
+                operations=[
+                    CommitOperationCopy(
+                        src_path_in_repo=resolved_path1.path_in_repo,
+                        path_in_repo=resolved_path2.path_in_repo,
+                        src_revision=resolved_path1.revision,
+                    )
+                ],
+            )
+        else:
+            with self.open(path1, "rb", revision=resolved_path1.revision) as f:
+                content = f.read()
+            commit_message = f"Copy {path1} to {path2}"
+            self._api.upload_file(
+                path_or_fileobj=content,
+                path_in_repo=resolved_path2.path_in_repo,
+                repo_id=resolved_path2.repo_id,
+                token=self.token,
+                repo_type=resolved_path2.repo_type,
+                revision=resolved_path2.revision,
+                commit_message=kwargs.get("commit_message", commit_message),
+                commit_description=kwargs.get("commit_description"),
+            )
+        self.invalidate_cache(path=resolved_path1.unresolve())
+        self.invalidate_cache(path=resolved_path2.unresolve())
+
+    def modified(self, path: str, **kwargs) -> datetime:
+        """
+        Get the last modified time of a file.
+
+        For more details, refer to [fsspec documentation](https://filesystem-spec.readthedocs.io/en/latest/api.html#fsspec.spec.AbstractFileSystem.modified).
+
+        Args:
+            path (`str`):
+                Path to the file.
+
+        Returns:
+            `datetime`: Last modified time of the file.
+        """
+        info = self.info(path, **{**kwargs, "expand_info": True})  # type: ignore
+        if "last_commit" in info:
+            if info["last_commit"] is None:
+                raise NotImplementedError(f"'modified' is not implemented for repository paths like '{path}'")
+            return info["last_commit"].date
+        elif "mtime" in info and info["mtime"]:
+            return info["mtime"]
+        elif "uploaded_at" in info and info["uploaded_at"]:
+            return info["uploaded_at"]
+        else:
+            raise NotImplementedError(f"Cannot determined 'modified' for path '{path}' (info: {info})")
+
+    def info(self, path: str, refresh: bool = False, revision: Optional[str] = None, **kwargs) -> dict[str, Any]:
+        """
+        Get information about a file or directory.
+
+        For more details, refer to [fsspec documentation](https://filesystem-spec.readthedocs.io/en/latest/api.html#fsspec.spec.AbstractFileSystem.info).
+
+        > [!WARNING]
+        > Note: When possible, use `HfApi.get_paths_info()` or `HfApi.repo_info()`  for better performance
+        > (or `HfApi.get_bucket_paths_info()` or `HfApi.bucket_info()` for buckets)
+
+        Args:
+            path (`str`):
+                Path to get info for.
+            refresh (`bool`, *optional*):
+                If True, bypass the cache and fetch the latest data. Defaults to False.
+            revision (`str`, *optional*):
+                The git revision to get info from.
+
+        Returns:
+            `dict[str, Any]`: Dictionary containing file information (type, size, commit info, etc.).
+
+        """
+        resolved_path = self.resolve_path(path, revision=revision)
+        path = resolved_path.unresolve()
+        expand_info = kwargs.get(
+            "expand_info", self.expand_info if self.expand_info is not None else False
+        )  # don't expose it as a parameter in the public API to follow the spec
+        out: Optional[dict[str, Any]]
+        if not resolved_path.path:
+            # Path is the root directory
+            out = {
+                "name": path,
+                "size": 0,
+                "type": "directory",
+            }
+            if isinstance(resolved_path, HfFileSystemResolvedRepositoryPath):
+                out["last_commit"] = None
+            if isinstance(resolved_path, HfFileSystemResolvedRepositoryPath) and expand_info:
+                last_commit = self._api.list_repo_commits(
+                    resolved_path.repo_id, repo_type=resolved_path.repo_type, revision=resolved_path.revision
+                )[-1]
+                out = {
+                    **out,
+                    "tree_id": None,  # TODO: tree_id of the root directory?
+                    "last_commit": LastCommitInfo(
+                        oid=last_commit.commit_id, title=last_commit.title, date=last_commit.created_at
+                    ),
+                }
+        elif isinstance(resolved_path, HfFileSystemResolvedBucketPath):
+            parent_path = self._parent(path)
+            # Fill the cache with cheap call
+            self.ls(parent_path, refresh=refresh)
+            out1 = [o for o in self.dircache[parent_path] if o["name"] == path]
+            if not out1:
+                _raise_file_not_found(path, None)
+            out = out1[0]
+        else:
+            out = None
+            parent_path = self._parent(path)
+            if not expand_info and parent_path not in self.dircache:
+                # Fill the cache with cheap call
+                self.ls(parent_path)
+            if parent_path in self.dircache:
+                # Check if the path is in the cache
+                out1 = [o for o in self.dircache[parent_path] if o["name"] == path]
+                if not out1:
+                    _raise_file_not_found(path, None)
+                out = out1[0]
+            if refresh or out is None or (expand_info and out and out["last_commit"] is None):
+                paths_info = self._api.get_paths_info(
+                    resolved_path.repo_id,
+                    resolved_path.path_in_repo,
+                    expand=expand_info,
+                    revision=resolved_path.revision,
+                    repo_type=resolved_path.repo_type,
+                )
+                if not paths_info:
+                    _raise_file_not_found(path, None)
+                path_info = paths_info[0]
+                root_path = HfFileSystemResolvedRepositoryPath(
+                    resolved_path.repo_type,
+                    resolved_path.repo_id,
+                    resolved_path.revision,
+                    path_in_repo="",
+                    _raw_revision=resolved_path._raw_revision,
+                ).unresolve()
+                if isinstance(path_info, RepoFile):
+                    out = {
+                        "name": root_path + "/" + path_info.path,
+                        "size": path_info.size,
+                        "type": "file",
+                        "blob_id": path_info.blob_id,
+                        "lfs": path_info.lfs,
+                        "xet_hash": path_info.xet_hash,
+                        "last_commit": path_info.last_commit,
+                        "security": path_info.security,
+                    }
+                else:
+                    out = {
+                        "name": root_path + "/" + path_info.path,
+                        "size": 0,
+                        "type": "directory",
+                        "tree_id": path_info.tree_id,
+                        "last_commit": path_info.last_commit,
+                    }
+                if not expand_info:
+                    out = {k: out[k] for k in ["name", "size", "type"]}
+        assert out is not None
+        return out
+
+    def exists(self, path, **kwargs):
+        """
+        Check if a file exists.
+
+        For more details, refer to [fsspec documentation](https://filesystem-spec.readthedocs.io/en/latest/api.html#fsspec.spec.AbstractFileSystem.exists).
+
+        > [!WARNING]
+        > Note: When possible, use `HfApi.file_exists()` for better performance.
+
+        Args:
+            path (`str`):
+                Path to check.
+
+        Returns:
+            `bool`: True if file exists, False otherwise.
+        """
+        try:
+            if kwargs.get("refresh", False):
+                self.invalidate_cache(path)
+
+            self.info(path, **kwargs)
+            return True
+        except:  # noqa: E722
+            return False
+
+    def isdir(self, path):
+        """
+        Check if a path is a directory.
+
+        For more details, refer to [fsspec documentation](https://filesystem-spec.readthedocs.io/en/latest/api.html#fsspec.spec.AbstractFileSystem.isdir).
+
+        Args:
+            path (`str`):
+                Path to check.
+
+        Returns:
+            `bool`: True if path is a directory, False otherwise.
+        """
+        try:
+            return self.info(path)["type"] == "directory"
+        except OSError:
+            return False
+
+    def isfile(self, path):
+        """
+        Check if a path is a file.
+
+        For more details, refer to [fsspec documentation](https://filesystem-spec.readthedocs.io/en/latest/api.html#fsspec.spec.AbstractFileSystem.isfile).
+
+        Args:
+            path (`str`):
+                Path to check.
+
+        Returns:
+            `bool`: True if path is a file, False otherwise.
+        """
+        try:
+            return self.info(path)["type"] == "file"
+        except:  # noqa: E722
+            return False
+
+    def url(self, path: str) -> str:
+        """
+        Get the HTTP URL of the given path.
+
+        Args:
+            path (`str`):
+                Path to get URL for.
+
+        Returns:
+            `str`: HTTP URL to access the file or directory on the Hub.
+        """
+        resolved_path = self.resolve_path(path)
+        if isinstance(resolved_path, HfFileSystemResolvedBucketPath):
+            url = f"{self.endpoint}/buckets/{resolved_path.bucket_id}/resolve/{quote(resolved_path.path)}"
+        else:
+            url = hf_hub_url(
+                resolved_path.repo_id,
+                resolved_path.path_in_repo,
+                repo_type=resolved_path.repo_type,
+                revision=resolved_path.revision,
+                endpoint=self.endpoint,
+            )
+        if self.isdir(path):
+            url = url.replace("/resolve/", "/tree/", 1)
+        return url
+
+    def get_file(self, rpath, lpath, callback=_DEFAULT_CALLBACK, outfile=None, **kwargs) -> None:
+        """
+        Copy single remote file to local.
+
+        > [!WARNING]
+        > Note: When possible, use `HfApi.hf_hub_download()` or `HfApi.download_bucket_files` for better performance.
+
+        Args:
+            rpath (`str`):
+                Remote path to download from.
+            lpath (`str`):
+                Local path to download to.
+            callback (`Callback`, *optional*):
+                Optional callback to track download progress. Defaults to no callback.
+            outfile (`IO`, *optional*):
+                Optional file-like object to write to. If provided, `lpath` is ignored.
+
+        """
+        revision = kwargs.get("revision")
+        unhandled_kwargs = set(kwargs.keys()) - {"revision"}
+        if not isinstance(callback, (NoOpCallback, TqdmCallback)) or len(unhandled_kwargs) > 0:
+            # for now, let's not handle custom callbacks
+            # and let's not handle custom kwargs
+            return super().get_file(rpath, lpath, callback=callback, outfile=outfile, **kwargs)
+
+        # Taken from https://github.com/fsspec/filesystem_spec/blob/47b445ae4c284a82dd15e0287b1ffc410e8fc470/fsspec/spec.py#L883
+        if isfilelike(lpath):
+            outfile = lpath
+        elif self.isdir(rpath):
+            os.makedirs(lpath, exist_ok=True)
+            return None
+
+        if isinstance(lpath, (str, Path)):  # otherwise, let's assume it's a file-like object
+            os.makedirs(os.path.dirname(lpath), exist_ok=True)
+
+        # Open file if not already open
+        close_file = False
+        if outfile is None:
+            outfile = open(lpath, "wb")
+            close_file = True
+        initial_pos = outfile.tell()
+
+        # Custom implementation of `get_file` to use `http_get`.
+        resolve_remote_path = self.resolve_path(rpath, revision=revision)
+        expected_size = self.info(rpath, revision=revision)["size"]
+        callback.set_size(expected_size)
+        try:
+            http_get(
+                url=self.url(resolve_remote_path.unresolve()),
+                temp_file=outfile,  # type: ignore[arg-type]
+                displayed_filename=rpath,
+                expected_size=expected_size,
+                resume_size=0,
+                headers=self._api._build_hf_headers(),
+                _tqdm_bar=callback.tqdm if isinstance(callback, TqdmCallback) else None,
+            )
+            outfile.seek(initial_pos)
+        finally:
+            # Close file only if we opened it ourselves
+            if close_file:
+                outfile.close()
+
+    @property
+    def transaction(self):
+        """A context within which files are committed together upon exit
+
+        Requires the file class to implement `.commit()` and `.discard()`
+        for the normal and exception cases.
+        """
+        # Taken from https://github.com/fsspec/filesystem_spec/blob/3fbb6fee33b46cccb015607630843dea049d3243/fsspec/spec.py#L231
+        # See https://github.com/huggingface/huggingface_hub/issues/1733
+        raise NotImplementedError("Transactional commits are not supported.")
+
+    def start_transaction(self):
+        """Begin write transaction for deferring files, non-context version"""
+        # Taken from https://github.com/fsspec/filesystem_spec/blob/3fbb6fee33b46cccb015607630843dea049d3243/fsspec/spec.py#L241
+        # See https://github.com/huggingface/huggingface_hub/issues/1733
+        raise NotImplementedError("Transactional commits are not supported.")
+
+    def __reduce__(self):
+        # re-populate the instance cache at HfFileSystem._cache and re-populate the state of every instance
+        return make_instance, (
+            type(self),
+            self.storage_args,
+            self.storage_options,
+            self._get_instance_state(),
+        )
+
+    def _get_instance_state(self):
+        return {
+            "dircache": deepcopy(self.dircache),
+            "_repo_and_revision_exists_cache": deepcopy(self._repo_and_revision_exists_cache),
+            "_bucket_exists_cache": deepcopy(self._bucket_exists_cache),
+        }
+
+
+class HfFileSystemFile(fsspec.spec.AbstractBufferedFile):
+    def __init__(self, fs: HfFileSystem, path: str, revision: Optional[str] = None, **kwargs):
+        try:
+            self.resolved_path = fs.resolve_path(path, revision=revision)
+        except FileNotFoundError as e:
+            if "w" in kwargs.get("mode", ""):
+                raise FileNotFoundError(
+                    f"{e}.\nMake sure the repository and revision exist before writing data."
+                ) from e
+            raise
+        super().__init__(fs, self.resolved_path.unresolve(), **kwargs)
+        self.fs: HfFileSystem
+
+    def __del__(self):
+        if not hasattr(self, "resolved_path"):
+            # Means that the constructor failed. Nothing to do.
+            return
+        return super().__del__()
+
+    def _fetch_range(self, start: int, end: int) -> bytes:
+        headers = {
+            "range": f"bytes={start}-{end - 1}",
+            **self.fs._api._build_hf_headers(),
+        }
+        url = self.url()
+        r = http_backoff("GET", url, headers=headers, timeout=constants.HF_HUB_DOWNLOAD_TIMEOUT)
+        hf_raise_for_status(r)
+        return r.content
+
+    def _initiate_upload(self) -> None:
+        self.temp_file = tempfile.NamedTemporaryFile(prefix="hffs-", delete=False)
+
+    def _upload_chunk(self, final: bool = False) -> None:
+        self.buffer.seek(0)
+        block = self.buffer.read()
+        self.temp_file.write(block)
+        if final:
+            self.temp_file.close()
+            if isinstance(self.resolved_path, HfFileSystemResolvedBucketPath):
+                self.fs._api.batch_bucket_files(
+                    self.resolved_path.bucket_id, add=[(self.temp_file.name, self.resolved_path.path)]
+                )
+            else:
+                self.fs._api.upload_file(
+                    path_or_fileobj=self.temp_file.name,
+                    path_in_repo=self.resolved_path.path_in_repo,
+                    repo_id=self.resolved_path.repo_id,
+                    token=self.fs.token,
+                    repo_type=self.resolved_path.repo_type,
+                    revision=self.resolved_path.revision,
+                    commit_message=self.kwargs.get("commit_message"),
+                    commit_description=self.kwargs.get("commit_description"),
+                )
+            os.remove(self.temp_file.name)
+            self.fs.invalidate_cache(
+                path=self.resolved_path.unresolve(),
+            )
+
+    def read(self, length=-1):
+        """Read remote file.
+
+        If `length` is not provided or is -1, the entire file is downloaded and read. On POSIX systems the file is
+        loaded in memory directly. Otherwise, the file is downloaded to a temporary file and read from there.
+        """
+        if self.mode == "rb" and (length is None or length == -1) and self.loc == 0:
+            with self.fs.open(self.path, "rb", block_size=0) as f:  # block_size=0 enables fast streaming
+                out = f.read()
+                self.loc += len(out)
+                return out
+        return super().read(length)
+
+    def url(self) -> str:
+        return self.fs.url(self.path)
+
+
+class HfFileSystemStreamFile(fsspec.spec.AbstractBufferedFile):
+    def __init__(
+        self,
+        fs: HfFileSystem,
+        path: str,
+        mode: str = "rb",
+        revision: Optional[str] = None,
+        block_size: int = 0,
+        cache_type: str = "none",
+        **kwargs,
+    ):
+        if block_size != 0:
+            raise ValueError(f"HfFileSystemStreamFile only supports block_size=0 but got {block_size}")
+        if cache_type != "none":
+            raise ValueError(f"HfFileSystemStreamFile only supports cache_type='none' but got {cache_type}")
+        if "w" in mode:
+            raise ValueError(f"HfFileSystemStreamFile only supports reading but got mode='{mode}'")
+        try:
+            self.resolved_path = fs.resolve_path(path, revision=revision)
+        except FileNotFoundError as e:
+            if "w" in kwargs.get("mode", ""):
+                raise FileNotFoundError(
+                    f"{e}.\nMake sure the repository and revision exist before writing data."
+                ) from e
+        # avoid an unnecessary .info() call to instantiate .details
+        self.details = {"name": self.resolved_path.unresolve(), "size": None}
+        super().__init__(
+            fs, self.resolved_path.unresolve(), mode=mode, block_size=block_size, cache_type=cache_type, **kwargs
+        )
+        self.response: Optional[httpx.Response] = None
+        self.fs: HfFileSystem
+        self._exit_stack = ExitStack()
+        # streaming state
+        self._stream_iterator: Optional[Iterator[bytes]] = None
+        self._stream_buffer = bytearray()
+
+    def seek(self, loc: int, whence: int = 0):
+        if loc == 0 and whence == 1:
+            return
+        if loc == self.loc and whence == 0:
+            return
+        raise ValueError("Cannot seek streaming HF file")
+
+    def read(self, length: int = -1):
+        """Read the remote file.
+
+        If the file is already open, we reuse the connection.
+        Otherwise, open a new connection and read from it.
+
+        If reading the stream fails, we retry with a new connection.
+        """
+        if self.response is None:
+            self._open_connection()
+
+        retried_once = False
+        while True:
+            try:
+                if self.response is None or self._stream_iterator is None:
+                    return b""  # Already read the entire file
+                out = self._read_from_stream(self._stream_iterator, length)
+                self.loc += len(out)
+                return out
+            except Exception:
+                if self.response is not None:
+                    self.response.close()
+                if retried_once:  # Already retried once, give up
+                    raise
+                # First failure, retry with range header
+                self._open_connection()
+                retried_once = True
+
+    def _read_from_stream(self, iterator: Iterator[bytes], length: int = -1) -> bytes:
+        """Read up to `length` bytes from stream buffer and stream.
+
+        If length < 0, read until EOF.
+
+        If EOF is reached before length, fewer bytes may be returned.
+        """
+        if length == 0:
+            return b""
+
+        if length < 0:
+            buf = bytearray(self._stream_buffer)
+            self._stream_buffer.clear()
+            for chunk in iterator:
+                buf.extend(chunk)
+            return bytes(buf)
+
+        if length <= len(self._stream_buffer):
+            result = bytes(self._stream_buffer[:length])
+            del self._stream_buffer[:length]
+            return result
+
+        buf = bytearray(self._stream_buffer)
+        self._stream_buffer.clear()
+        for chunk in iterator:
+            need = length - len(buf)
+            if need > len(chunk):
+                buf.extend(chunk)
+            else:
+                buf.extend(chunk[:need])
+                self._stream_buffer.extend(chunk[need:])
+                break
+        return bytes(buf)
+
+    def url(self) -> str:
+        return self.fs.url(self.path)
+
+    def __del__(self):
+        if not hasattr(self, "resolved_path"):
+            # Means that the constructor failed. Nothing to do.
+            return
+        self._exit_stack.close()
+        return super().__del__()
+
+    def __reduce__(self):
+        return reopen, (self.fs, self.path, self.mode, self.blocksize, self.cache.name)
+
+    def _open_connection(self):
+        """Open a connection to the remote file."""
+        # reset streaming state
+        self._stream_buffer.clear()
+        self._stream_iterator = None
+
+        url = self.url()
+        headers = self.fs._api._build_hf_headers()
+        if self.loc > 0:
+            headers["Range"] = f"bytes={self.loc}-"
+        self.response = self._exit_stack.enter_context(
+            http_stream_backoff(
+                "GET",
+                url,
+                headers=headers,
+                timeout=constants.HF_HUB_DOWNLOAD_TIMEOUT,
+            )
+        )
+
+        try:
+            hf_raise_for_status(self.response)
+        except HfHubHTTPError as e:
+            if e.response.status_code == 416:
+                # Range not satisfiable => means that we have already read the entire file
+                self.response = None
+                return
+            raise
+
+        self._stream_iterator = self.response.iter_bytes()
+
+
+def safe_revision(revision: str) -> str:
+    return revision if SPECIAL_REFS_REVISION_REGEX.match(revision) else safe_quote(revision)
+
+
+def safe_quote(s: str) -> str:
+    return quote(s, safe="")
+
+
+def _raise_file_not_found(path: str, err: Optional[Exception]) -> NoReturn:
+    msg = path
+    if isinstance(err, RepositoryNotFoundError):
+        msg = f"{path} (repository not found)"
+    elif isinstance(err, RevisionNotFoundError):
+        msg = f"{path} (revision not found)"
+    elif isinstance(err, HFValidationError):
+        msg = f"{path} (invalid repository id)"
+    raise FileNotFoundError(msg) from err
+
+
+def reopen(fs: HfFileSystem, path: str, mode: str, block_size: int, cache_type: str):
+    return fs.open(path, mode=mode, block_size=block_size, cache_type=cache_type)
+
+
+def make_instance(cls, args, kwargs, instance_state):
+    fs = cls(*args, **kwargs)
+    for attr, state_value in instance_state.items():
+        setattr(fs, attr, state_value)
+    return fs
+
+
+hffs = HfFileSystem()

data/conda_envs/evalclaw/lib/python3.11/site-packages/huggingface_hub/hub_mixin.py

@@ -0,0 +1,831 @@
+import inspect
+import json
+import os
+from dataclasses import Field, asdict, dataclass, is_dataclass
+from pathlib import Path
+from typing import Any, Callable, ClassVar, Optional, Protocol, Type, TypeVar, Union
+
+import packaging.version
+
+from . import constants
+from .errors import EntryNotFoundError, HfHubHTTPError
+from .file_download import hf_hub_download
+from .hf_api import HfApi
+from .repocard import ModelCard, ModelCardData
+from .utils import (
+    SoftTemporaryDirectory,
+    is_jsonable,
+    is_safetensors_available,
+    is_simple_optional_type,
+    is_torch_available,
+    logging,
+    unwrap_simple_optional_type,
+    validate_hf_hub_args,
+)
+
+
+if is_torch_available():
+    import torch  # type: ignore
+
+if is_safetensors_available():
+    import safetensors
+    from safetensors.torch import load_model as load_model_as_safetensor
+    from safetensors.torch import save_model as save_model_as_safetensor
+
+
+logger = logging.get_logger(__name__)
+
+
+# Type alias for dataclass instances, copied from https://github.com/python/typeshed/blob/9f28171658b9ca6c32a7cb93fbb99fc92b17858b/stdlib/_typeshed/__init__.pyi#L349
+class DataclassInstance(Protocol):
+    __dataclass_fields__: ClassVar[dict[str, Field]]
+
+
+# Generic variable that is either ModelHubMixin or a subclass thereof
+T = TypeVar("T", bound="ModelHubMixin")
+# Generic variable to represent an args type
+ARGS_T = TypeVar("ARGS_T")
+ENCODER_T = Callable[[ARGS_T], Any]
+DECODER_T = Callable[[Any], ARGS_T]
+CODER_T = tuple[ENCODER_T, DECODER_T]
+
+
+DEFAULT_MODEL_CARD = """
+---
+# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
+# Doc / guide: https://huggingface.co/docs/hub/model-cards
+{{ card_data }}
+---
+
+This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
+- Code: {{ repo_url | default("[More Information Needed]", true) }}
+- Paper: {{ paper_url | default("[More Information Needed]", true) }}
+- Docs: {{ docs_url | default("[More Information Needed]", true) }}
+"""
+
+
+@dataclass
+class MixinInfo:
+    model_card_template: str
+    model_card_data: ModelCardData
+    docs_url: Optional[str] = None
+    paper_url: Optional[str] = None
+    repo_url: Optional[str] = None
+
+
+class ModelHubMixin:
+    """
+    A generic mixin to integrate ANY machine learning framework with the Hub.
+
+    To integrate your framework, your model class must inherit from this class. Custom logic for saving/loading models
+    have to be overwritten in  [`_from_pretrained`] and [`_save_pretrained`]. [`PyTorchModelHubMixin`] is a good example
+    of mixin integration with the Hub. Check out our [integration guide](../guides/integrations) for more instructions.
+
+    When inheriting from [`ModelHubMixin`], you can define class-level attributes. These attributes are not passed to
+    `__init__` but to the class definition itself. This is useful to define metadata about the library integrating
+    [`ModelHubMixin`].
+
+    For more details on how to integrate the mixin with your library, checkout the [integration guide](../guides/integrations).
+
+    Args:
+        repo_url (`str`, *optional*):
+            URL of the library repository. Used to generate model card.
+        paper_url (`str`, *optional*):
+            URL of the library paper. Used to generate model card.
+        docs_url (`str`, *optional*):
+            URL of the library documentation. Used to generate model card.
+        model_card_template (`str`, *optional*):
+            Template of the model card. Used to generate model card. Defaults to a generic template.
+        language (`str` or `list[str]`, *optional*):
+            Language supported by the library. Used to generate model card.
+        library_name (`str`, *optional*):
+            Name of the library integrating ModelHubMixin. Used to generate model card.
+        license (`str`, *optional*):
+            License of the library integrating ModelHubMixin. Used to generate model card.
+            E.g: "apache-2.0"
+        license_name (`str`, *optional*):
+            Name of the library integrating ModelHubMixin. Used to generate model card.
+            Only used if `license` is set to `other`.
+            E.g: "coqui-public-model-license".
+        license_link (`str`, *optional*):
+            URL to the license of the library integrating ModelHubMixin. Used to generate model card.
+            Only used if `license` is set to `other` and `license_name` is set.
+            E.g: "https://coqui.ai/cpml".
+        pipeline_tag (`str`, *optional*):
+            Tag of the pipeline. Used to generate model card. E.g. "text-classification".
+        tags (`list[str]`, *optional*):
+            Tags to be added to the model card. Used to generate model card. E.g. ["computer-vision"]
+        coders (`dict[Type, tuple[Callable, Callable]]`, *optional*):
+            Dictionary of custom types and their encoders/decoders. Used to encode/decode arguments that are not
+            jsonable by default. E.g. dataclasses, argparse.Namespace, OmegaConf, etc.
+
+    Example:
+
+    ```python
+    >>> from huggingface_hub import ModelHubMixin
+
+    # Inherit from ModelHubMixin
+    >>> class MyCustomModel(
+    ...         ModelHubMixin,
+    ...         library_name="my-library",
+    ...         tags=["computer-vision"],
+    ...         repo_url="https://github.com/huggingface/my-cool-library",
+    ...         paper_url="https://arxiv.org/abs/2304.12244",
+    ...         docs_url="https://huggingface.co/docs/my-cool-library",
+    ...         # ^ optional metadata to generate model card
+    ...     ):
+    ...     def __init__(self, size: int = 512, device: str = "cpu"):
+    ...         # define how to initialize your model
+    ...         super().__init__()
+    ...         ...
+    ...
+    ...     def _save_pretrained(self, save_directory: Path) -> None:
+    ...         # define how to serialize your model
+    ...         ...
+    ...
+    ...     @classmethod
+    ...     def from_pretrained(
+    ...         cls: type[T],
+    ...         pretrained_model_name_or_path: Union[str, Path],
+    ...         *,
+    ...         force_download: bool = False,
+    ...         token: Optional[Union[str, bool]] = None,
+    ...         cache_dir: Optional[Union[str, Path]] = None,
+    ...         local_files_only: bool = False,
+    ...         revision: Optional[str] = None,
+    ...         **model_kwargs,
+    ...     ) -> T:
+    ...         # define how to deserialize your model
+    ...         ...
+
+    >>> model = MyCustomModel(size=256, device="gpu")
+
+    # Save model weights to local directory
+    >>> model.save_pretrained("my-awesome-model")
+
+    # Push model weights to the Hub
+    >>> model.push_to_hub("my-awesome-model")
+
+    # Download and initialize weights from the Hub
+    >>> reloaded_model = MyCustomModel.from_pretrained("username/my-awesome-model")
+    >>> reloaded_model.size
+    256
+
+    # Model card has been correctly populated
+    >>> from huggingface_hub import ModelCard
+    >>> card = ModelCard.load("username/my-awesome-model")
+    >>> card.data.tags
+    ["x-custom-tag", "pytorch_model_hub_mixin", "model_hub_mixin"]
+    >>> card.data.library_name
+    "my-library"
+    ```
+    """
+
+    _hub_mixin_config: Optional[Union[dict, DataclassInstance]] = None
+    # ^ optional config attribute automatically set in `from_pretrained`
+    _hub_mixin_info: MixinInfo
+    # ^ information about the library integrating ModelHubMixin (used to generate model card)
+    _hub_mixin_inject_config: bool  # whether `_from_pretrained` expects `config` or not
+    _hub_mixin_init_parameters: dict[str, inspect.Parameter]  # __init__ parameters
+    _hub_mixin_jsonable_default_values: dict[str, Any]  # default values for __init__ parameters
+    _hub_mixin_jsonable_custom_types: tuple[Type, ...]  # custom types that can be encoded/decoded
+    _hub_mixin_coders: dict[Type, CODER_T]  # encoders/decoders for custom types
+    # ^ internal values to handle config
+
+    def __init_subclass__(
+        cls,
+        *,
+        # Generic info for model card
+        repo_url: Optional[str] = None,
+        paper_url: Optional[str] = None,
+        docs_url: Optional[str] = None,
+        # Model card template
+        model_card_template: str = DEFAULT_MODEL_CARD,
+        # Model card metadata
+        language: Optional[list[str]] = None,
+        library_name: Optional[str] = None,
+        license: Optional[str] = None,
+        license_name: Optional[str] = None,
+        license_link: Optional[str] = None,
+        pipeline_tag: Optional[str] = None,
+        tags: Optional[list[str]] = None,
+        # How to encode/decode arguments with custom type into a JSON config?
+        coders: Optional[
+            dict[Type, CODER_T]
+            # Key is a type.
+            # Value is a tuple (encoder, decoder).
+            # Example: {MyCustomType: (lambda x: x.value, lambda data: MyCustomType(data))}
+        ] = None,
+    ) -> None:
+        """Inspect __init__ signature only once when subclassing + handle modelcard."""
+        super().__init_subclass__()
+
+        # Will be reused when creating modelcard
+        tags = tags or []
+        tags.append("model_hub_mixin")
+
+        # Initialize MixinInfo if not existent
+        info = MixinInfo(model_card_template=model_card_template, model_card_data=ModelCardData())
+
+        # If parent class has a MixinInfo, inherit from it as a copy
+        if hasattr(cls, "_hub_mixin_info"):
+            # Inherit model card template from parent class if not explicitly set
+            if model_card_template == DEFAULT_MODEL_CARD:
+                info.model_card_template = cls._hub_mixin_info.model_card_template
+
+            # Inherit from parent model card data
+            info.model_card_data = ModelCardData(**cls._hub_mixin_info.model_card_data.to_dict())
+
+            # Inherit other info
+            info.docs_url = cls._hub_mixin_info.docs_url
+            info.paper_url = cls._hub_mixin_info.paper_url
+            info.repo_url = cls._hub_mixin_info.repo_url
+        cls._hub_mixin_info = info
+
+        # Update MixinInfo with metadata
+        if model_card_template is not None and model_card_template != DEFAULT_MODEL_CARD:
+            info.model_card_template = model_card_template
+        if repo_url is not None:
+            info.repo_url = repo_url
+        if paper_url is not None:
+            info.paper_url = paper_url
+        if docs_url is not None:
+            info.docs_url = docs_url
+        if language is not None:
+            info.model_card_data.language = language
+        if library_name is not None:
+            info.model_card_data.library_name = library_name
+        if license is not None:
+            info.model_card_data.license = license
+        if license_name is not None:
+            info.model_card_data.license_name = license_name
+        if license_link is not None:
+            info.model_card_data.license_link = license_link
+        if pipeline_tag is not None:
+            info.model_card_data.pipeline_tag = pipeline_tag
+        if tags is not None:
+            normalized_tags = list(tags)
+            if info.model_card_data.tags is not None:
+                info.model_card_data.tags.extend(normalized_tags)
+            else:
+                info.model_card_data.tags = normalized_tags
+
+        if info.model_card_data.tags is not None:
+            info.model_card_data.tags = sorted(set(info.model_card_data.tags))
+
+        # Handle encoders/decoders for args
+        cls._hub_mixin_coders = coders or {}
+        cls._hub_mixin_jsonable_custom_types = tuple(cls._hub_mixin_coders.keys())
+
+        # Inspect __init__ signature to handle config
+        cls._hub_mixin_init_parameters = dict(inspect.signature(cls.__init__).parameters)
+        cls._hub_mixin_jsonable_default_values = {
+            param.name: cls._encode_arg(param.default)
+            for param in cls._hub_mixin_init_parameters.values()
+            if param.default is not inspect.Parameter.empty and cls._is_jsonable(param.default)
+        }
+        cls._hub_mixin_inject_config = "config" in inspect.signature(cls._from_pretrained).parameters
+
+    def __new__(cls: type[T], *args, **kwargs) -> T:
+        """Create a new instance of the class and handle config.
+
+        3 cases:
+        - If `self._hub_mixin_config` is already set, do nothing.
+        - If `config` is passed as a dataclass, set it as `self._hub_mixin_config`.
+        - Otherwise, build `self._hub_mixin_config` from default values and passed values.
+        """
+        instance = super().__new__(cls)
+
+        # If `config` is already set, return early
+        if instance._hub_mixin_config is not None:
+            return instance
+
+        # Infer passed values
+        passed_values = {
+            **{
+                key: value
+                for key, value in zip(
+                    # [1:] to skip `self` parameter
+                    list(cls._hub_mixin_init_parameters)[1:],
+                    args,
+                )
+            },
+            **kwargs,
+        }
+
+        # If config passed as dataclass => set it and return early
+        if is_dataclass(passed_values.get("config")):
+            instance._hub_mixin_config = passed_values["config"]
+            return instance
+
+        # Otherwise, build config from default + passed values
+        init_config = {
+            # default values
+            **cls._hub_mixin_jsonable_default_values,
+            # passed values
+            **{
+                key: cls._encode_arg(value)  # Encode custom types as jsonable value
+                for key, value in passed_values.items()
+                if instance._is_jsonable(value)  # Only if jsonable or we have a custom encoder
+            },
+        }
+        passed_config = init_config.pop("config", {})
+
+        # Populate `init_config` with provided config
+        if isinstance(passed_config, dict):
+            init_config.update(passed_config)
+
+        # Set `config` attribute and return
+        if init_config != {}:
+            instance._hub_mixin_config = init_config
+        return instance
+
+    @classmethod
+    def _is_jsonable(cls, value: Any) -> bool:
+        """Check if a value is JSON serializable."""
+        if is_dataclass(value):
+            return True
+        if isinstance(value, cls._hub_mixin_jsonable_custom_types):
+            return True
+        return is_jsonable(value)
+
+    @classmethod
+    def _encode_arg(cls, arg: Any) -> Any:
+        """Encode an argument into a JSON serializable format."""
+        if is_dataclass(arg):
+            return asdict(arg)  # type: ignore[arg-type]
+        for type_, (encoder, _) in cls._hub_mixin_coders.items():
+            if isinstance(arg, type_):
+                if arg is None:
+                    return None
+                return encoder(arg)
+        return arg
+
+    @classmethod
+    def _decode_arg(cls, expected_type: type[ARGS_T], value: Any) -> Optional[ARGS_T]:
+        """Decode a JSON serializable value into an argument."""
+        if is_simple_optional_type(expected_type):
+            if value is None:
+                return None
+            expected_type = unwrap_simple_optional_type(expected_type)  # type: ignore[assignment]
+        # Dataclass => handle it
+        if is_dataclass(expected_type):
+            return _load_dataclass(expected_type, value)  # type: ignore[return-value]
+        # Otherwise => check custom decoders
+        for type_, (_, decoder) in cls._hub_mixin_coders.items():
+            if inspect.isclass(expected_type) and issubclass(expected_type, type_):
+                return decoder(value)
+        # Otherwise => don't decode
+        return value
+
+    def save_pretrained(
+        self,
+        save_directory: Union[str, Path],
+        *,
+        config: Optional[Union[dict, DataclassInstance]] = None,
+        repo_id: Optional[str] = None,
+        push_to_hub: bool = False,
+        model_card_kwargs: Optional[dict[str, Any]] = None,
+        **push_to_hub_kwargs,
+    ) -> Optional[str]:
+        """
+        Save weights in local directory.
+
+        Args:
+            save_directory (`str` or `Path`):
+                Path to directory in which the model weights and configuration will be saved.
+            config (`dict` or `DataclassInstance`, *optional*):
+                Model configuration specified as a key/value dictionary or a dataclass instance.
+            push_to_hub (`bool`, *optional*, defaults to `False`):
+                Whether or not to push your model to the Huggingface Hub after saving it.
+            repo_id (`str`, *optional*):
+                ID of your repository on the Hub. Used only if `push_to_hub=True`. Will default to the folder name if
+                not provided.
+            model_card_kwargs (`dict[str, Any]`, *optional*):
+                Additional arguments passed to the model card template to customize the model card.
+            push_to_hub_kwargs:
+                Additional key word arguments passed along to the [`~ModelHubMixin.push_to_hub`] method.
+        Returns:
+            `str` or `None`: url of the commit on the Hub if `push_to_hub=True`, `None` otherwise.
+        """
+        save_directory = Path(save_directory)
+        save_directory.mkdir(parents=True, exist_ok=True)
+
+        # Remove config.json if already exists. After `_save_pretrained` we don't want to overwrite config.json
+        # as it might have been saved by the custom `_save_pretrained` already. However we do want to overwrite
+        # an existing config.json if it was not saved by `_save_pretrained`.
+        config_path = save_directory / constants.CONFIG_NAME
+        config_path.unlink(missing_ok=True)
+
+        # save model weights/files (framework-specific)
+        self._save_pretrained(save_directory)
+
+        # save config (if provided and if not serialized yet in `_save_pretrained`)
+        if config is None:
+            config = self._hub_mixin_config
+        if config is not None:
+            if is_dataclass(config):
+                config = asdict(config)  # type: ignore[arg-type]
+            if not config_path.exists():
+                config_str = json.dumps(config, sort_keys=True, indent=2)
+                config_path.write_text(config_str)
+
+        # save model card
+        model_card_path = save_directory / "README.md"
+        model_card_kwargs = model_card_kwargs if model_card_kwargs is not None else {}
+        if not model_card_path.exists():  # do not overwrite if already exists
+            self.generate_model_card(**model_card_kwargs).save(save_directory / "README.md")
+
+        # push to the Hub if required
+        if push_to_hub:
+            kwargs = push_to_hub_kwargs.copy()  # soft-copy to avoid mutating input
+            if config is not None:  # kwarg for `push_to_hub`
+                kwargs["config"] = config
+            if repo_id is None:
+                repo_id = save_directory.name  # Defaults to `save_directory` name
+            return self.push_to_hub(repo_id=repo_id, model_card_kwargs=model_card_kwargs, **kwargs)
+        return None
+
+    def _save_pretrained(self, save_directory: Path) -> None:
+        """
+        Overwrite this method in subclass to define how to save your model.
+        Check out our [integration guide](../guides/integrations) for instructions.
+
+        Args:
+            save_directory (`str` or `Path`):
+                Path to directory in which the model weights and configuration will be saved.
+        """
+        raise NotImplementedError
+
+    @classmethod
+    @validate_hf_hub_args
+    def from_pretrained(
+        cls: type[T],
+        pretrained_model_name_or_path: Union[str, Path],
+        *,
+        force_download: bool = False,
+        token: Optional[Union[str, bool]] = None,
+        cache_dir: Optional[Union[str, Path]] = None,
+        local_files_only: bool = False,
+        revision: Optional[str] = None,
+        **model_kwargs,
+    ) -> T:
+        """
+        Download a model from the Huggingface Hub and instantiate it.
+
+        Args:
+            pretrained_model_name_or_path (`str`, `Path`):
+                - Either the `model_id` (string) of a model hosted on the Hub, e.g. `bigscience/bloom`.
+                - Or a path to a `directory` containing model weights saved using
+                    [`~transformers.PreTrainedModel.save_pretrained`], e.g., `../path/to/my_model_directory/`.
+            revision (`str`, *optional*):
+                Revision of the model on the Hub. Can be a branch name, a git tag or any commit id.
+                Defaults to the latest commit on `main` branch.
+            force_download (`bool`, *optional*, defaults to `False`):
+                Whether to force (re-)downloading the model weights and configuration files from the Hub, overriding
+                the existing cache.
+            token (`str` or `bool`, *optional*):
+                The token to use as HTTP bearer authorization for remote files. By default, it will use the token
+                cached when running `hf auth login`.
+            cache_dir (`str`, `Path`, *optional*):
+                Path to the folder where cached files are stored.
+            local_files_only (`bool`, *optional*, defaults to `False`):
+                If `True`, avoid downloading the file and return the path to the local cached file if it exists.
+            model_kwargs (`dict`, *optional*):
+                Additional kwargs to pass to the model during initialization.
+        """
+        model_id = str(pretrained_model_name_or_path)
+        config_file: Optional[str] = None
+        if os.path.isdir(model_id):
+            if constants.CONFIG_NAME in os.listdir(model_id):
+                config_file = os.path.join(model_id, constants.CONFIG_NAME)
+            else:
+                logger.warning(f"{constants.CONFIG_NAME} not found in {Path(model_id).resolve()}")
+        else:
+            try:
+                config_file = hf_hub_download(
+                    repo_id=model_id,
+                    filename=constants.CONFIG_NAME,
+                    revision=revision,
+                    cache_dir=cache_dir,
+                    force_download=force_download,
+                    token=token,
+                    local_files_only=local_files_only,
+                )
+            except HfHubHTTPError as e:
+                logger.info(f"{constants.CONFIG_NAME} not found on the HuggingFace Hub: {str(e)}")
+
+        # Read config
+        config = None
+        if config_file is not None:
+            with open(config_file, "r", encoding="utf-8") as f:
+                config = json.load(f)
+
+            # Decode custom types in config
+            for key, value in config.items():
+                if key in cls._hub_mixin_init_parameters:
+                    expected_type = cls._hub_mixin_init_parameters[key].annotation
+                    if expected_type is not inspect.Parameter.empty:
+                        config[key] = cls._decode_arg(expected_type, value)
+
+            # Populate model_kwargs from config
+            for param in cls._hub_mixin_init_parameters.values():
+                if param.name not in model_kwargs and param.name in config:
+                    model_kwargs[param.name] = config[param.name]
+
+            # Check if `config` argument was passed at init
+            if "config" in cls._hub_mixin_init_parameters and "config" not in model_kwargs:
+                # Decode `config` argument if it was passed
+                config_annotation = cls._hub_mixin_init_parameters["config"].annotation
+                config = cls._decode_arg(config_annotation, config)
+
+                # Forward config to model initialization
+                model_kwargs["config"] = config
+
+            # Inject config if `**kwargs` are expected
+            if is_dataclass(cls):
+                for key in cls.__dataclass_fields__:
+                    if key not in model_kwargs and key in config:
+                        model_kwargs[key] = config[key]
+            elif any(param.kind == inspect.Parameter.VAR_KEYWORD for param in cls._hub_mixin_init_parameters.values()):
+                for key, value in config.items():  # type: ignore[union-attr]
+                    if key not in model_kwargs:
+                        model_kwargs[key] = value
+
+            # Finally, also inject if `_from_pretrained` expects it
+            if cls._hub_mixin_inject_config and "config" not in model_kwargs:
+                model_kwargs["config"] = config
+
+        instance = cls._from_pretrained(
+            model_id=str(model_id),
+            revision=revision,
+            cache_dir=cache_dir,
+            force_download=force_download,
+            local_files_only=local_files_only,
+            token=token,
+            **model_kwargs,
+        )
+
+        # Implicitly set the config as instance attribute if not already set by the class
+        # This way `config` will be available when calling `save_pretrained` or `push_to_hub`.
+        if config is not None and (getattr(instance, "_hub_mixin_config", None) in (None, {})):
+            instance._hub_mixin_config = config
+
+        return instance
+
+    @classmethod
+    def _from_pretrained(
+        cls: type[T],
+        *,
+        model_id: str,
+        revision: Optional[str],
+        cache_dir: Optional[Union[str, Path]],
+        force_download: bool,
+        local_files_only: bool,
+        token: Optional[Union[str, bool]],
+        **model_kwargs,
+    ) -> T:
+        """Overwrite this method in subclass to define how to load your model from pretrained.
+
+        Use [`hf_hub_download`] or [`snapshot_download`] to download files from the Hub before loading them. Most
+        args taken as input can be directly passed to those 2 methods. If needed, you can add more arguments to this
+        method using "model_kwargs". For example [`PyTorchModelHubMixin._from_pretrained`] takes as input a `map_location`
+        parameter to set on which device the model should be loaded.
+
+        Check out our [integration guide](../guides/integrations) for more instructions.
+
+        Args:
+            model_id (`str`):
+                ID of the model to load from the Huggingface Hub (e.g. `bigscience/bloom`).
+            revision (`str`, *optional*):
+                Revision of the model on the Hub. Can be a branch name, a git tag or any commit id. Defaults to the
+                latest commit on `main` branch.
+            force_download (`bool`, *optional*, defaults to `False`):
+                Whether to force (re-)downloading the model weights and configuration files from the Hub, overriding
+                the existing cache.
+            token (`str` or `bool`, *optional*):
+                The token to use as HTTP bearer authorization for remote files. By default, it will use the token
+                cached when running `hf auth login`.
+            cache_dir (`str`, `Path`, *optional*):
+                Path to the folder where cached files are stored.
+            local_files_only (`bool`, *optional*, defaults to `False`):
+                If `True`, avoid downloading the file and return the path to the local cached file if it exists.
+            model_kwargs:
+                Additional keyword arguments passed along to the [`~ModelHubMixin._from_pretrained`] method.
+        """
+        raise NotImplementedError
+
+    @validate_hf_hub_args
+    def push_to_hub(
+        self,
+        repo_id: str,
+        *,
+        config: Optional[Union[dict, DataclassInstance]] = None,
+        commit_message: str = "Push model using huggingface_hub.",
+        private: Optional[bool] = None,
+        token: Optional[str] = None,
+        branch: Optional[str] = None,
+        create_pr: Optional[bool] = None,
+        allow_patterns: Optional[Union[list[str], str]] = None,
+        ignore_patterns: Optional[Union[list[str], str]] = None,
+        delete_patterns: Optional[Union[list[str], str]] = None,
+        model_card_kwargs: Optional[dict[str, Any]] = None,
+    ) -> str:
+        """
+        Upload model checkpoint to the Hub.
+
+        Use `allow_patterns` and `ignore_patterns` to precisely filter which files should be pushed to the hub. Use
+        `delete_patterns` to delete existing remote files in the same commit. See [`upload_folder`] reference for more
+        details.
+
+        Args:
+            repo_id (`str`):
+                ID of the repository to push to (example: `"username/my-model"`).
+            config (`dict` or `DataclassInstance`, *optional*):
+                Model configuration specified as a key/value dictionary or a dataclass instance.
+            commit_message (`str`, *optional*):
+                Message to commit while pushing.
+            private (`bool`, *optional*):
+                Whether the repository created should be private.
+                If `None` (default), the repo will be public unless the organization's default is private.
+            token (`str`, *optional*):
+                The token to use as HTTP bearer authorization for remote files. By default, it will use the token
+                cached when running `hf auth login`.
+            branch (`str`, *optional*):
+                The git branch on which to push the model. This defaults to `"main"`.
+            create_pr (`boolean`, *optional*):
+                Whether or not to create a Pull Request from `branch` with that commit. Defaults to `False`.
+            allow_patterns (`list[str]` or `str`, *optional*):
+                If provided, only files matching at least one pattern are pushed.
+            ignore_patterns (`list[str]` or `str`, *optional*):
+                If provided, files matching any of the patterns are not pushed.
+            delete_patterns (`list[str]` or `str`, *optional*):
+                If provided, remote files matching any of the patterns will be deleted from the repo.
+            model_card_kwargs (`dict[str, Any]`, *optional*):
+                Additional arguments passed to the model card template to customize the model card.
+
+        Returns:
+            The url of the commit of your model in the given repository.
+        """
+        api = HfApi(token=token)
+        repo_id = api.create_repo(repo_id=repo_id, private=private, exist_ok=True).repo_id
+
+        # Push the files to the repo in a single commit
+        with SoftTemporaryDirectory() as tmp:
+            saved_path = Path(tmp) / repo_id
+            self.save_pretrained(saved_path, config=config, model_card_kwargs=model_card_kwargs)
+            return api.upload_folder(
+                repo_id=repo_id,
+                repo_type="model",
+                folder_path=saved_path,
+                commit_message=commit_message,
+                revision=branch,
+                create_pr=create_pr,
+                allow_patterns=allow_patterns,
+                ignore_patterns=ignore_patterns,
+                delete_patterns=delete_patterns,
+            )
+
+    def generate_model_card(self, *args, **kwargs) -> ModelCard:
+        card = ModelCard.from_template(
+            card_data=self._hub_mixin_info.model_card_data,
+            template_str=self._hub_mixin_info.model_card_template,
+            repo_url=self._hub_mixin_info.repo_url,
+            paper_url=self._hub_mixin_info.paper_url,
+            docs_url=self._hub_mixin_info.docs_url,
+            **kwargs,
+        )
+        return card
+
+
+class PyTorchModelHubMixin(ModelHubMixin):
+    """
+    Implementation of [`ModelHubMixin`] to provide model Hub upload/download capabilities to PyTorch models. The model
+    is set in evaluation mode by default using `model.eval()` (dropout modules are deactivated). To train the model,
+    you should first set it back in training mode with `model.train()`.
+
+    See [`ModelHubMixin`] for more details on how to use the mixin.
+
+    Example:
+
+    ```python
+    >>> import torch
+    >>> import torch.nn as nn
+    >>> from huggingface_hub import PyTorchModelHubMixin
+
+    >>> class MyModel(
+    ...         nn.Module,
+    ...         PyTorchModelHubMixin,
+    ...         library_name="keras-nlp",
+    ...         repo_url="https://github.com/keras-team/keras-nlp",
+    ...         paper_url="https://arxiv.org/abs/2304.12244",
+    ...         docs_url="https://keras.io/keras_nlp/",
+    ...         # ^ optional metadata to generate model card
+    ...     ):
+    ...     def __init__(self, hidden_size: int = 512, vocab_size: int = 30000, output_size: int = 4):
+    ...         super().__init__()
+    ...         self.param = nn.Parameter(torch.rand(hidden_size, vocab_size))
+    ...         self.linear = nn.Linear(output_size, vocab_size)
+
+    ...     def forward(self, x):
+    ...         return self.linear(x + self.param)
+    >>> model = MyModel(hidden_size=256)
+
+    # Save model weights to local directory
+    >>> model.save_pretrained("my-awesome-model")
+
+    # Push model weights to the Hub
+    >>> model.push_to_hub("my-awesome-model")
+
+    # Download and initialize weights from the Hub
+    >>> model = MyModel.from_pretrained("username/my-awesome-model")
+    >>> model.hidden_size
+    256
+    ```
+    """
+
+    def __init_subclass__(cls, *args, tags: Optional[list[str]] = None, **kwargs) -> None:
+        tags = tags or []
+        tags.append("pytorch_model_hub_mixin")
+        kwargs["tags"] = tags
+        return super().__init_subclass__(*args, **kwargs)
+
+    def _save_pretrained(self, save_directory: Path) -> None:
+        """Save weights from a Pytorch model to a local directory."""
+        model_to_save = self.module if hasattr(self, "module") else self  # type: ignore
+        save_model_as_safetensor(model_to_save, str(save_directory / constants.SAFETENSORS_SINGLE_FILE))  # type: ignore [arg-type]
+
+    @classmethod
+    def _from_pretrained(
+        cls,
+        *,
+        model_id: str,
+        revision: Optional[str],
+        cache_dir: Optional[Union[str, Path]],
+        force_download: bool,
+        local_files_only: bool,
+        token: Union[str, bool, None],
+        map_location: str = "cpu",
+        strict: bool = False,
+        **model_kwargs,
+    ):
+        """Load Pytorch pretrained weights and return the loaded model."""
+        model = cls(**model_kwargs)
+        if os.path.isdir(model_id):
+            print("Loading weights from local directory")
+            model_file = os.path.join(model_id, constants.SAFETENSORS_SINGLE_FILE)
+            return cls._load_as_safetensor(model, model_file, map_location, strict)
+        else:
+            try:
+                model_file = hf_hub_download(
+                    repo_id=model_id,
+                    filename=constants.SAFETENSORS_SINGLE_FILE,
+                    revision=revision,
+                    cache_dir=cache_dir,
+                    force_download=force_download,
+                    token=token,
+                    local_files_only=local_files_only,
+                )
+                return cls._load_as_safetensor(model, model_file, map_location, strict)
+            except EntryNotFoundError:
+                model_file = hf_hub_download(
+                    repo_id=model_id,
+                    filename=constants.PYTORCH_WEIGHTS_NAME,
+                    revision=revision,
+                    cache_dir=cache_dir,
+                    force_download=force_download,
+                    token=token,
+                    local_files_only=local_files_only,
+                )
+                return cls._load_as_pickle(model, model_file, map_location, strict)
+
+    @classmethod
+    def _load_as_pickle(cls, model: T, model_file: str, map_location: str, strict: bool) -> T:
+        state_dict = torch.load(model_file, map_location=torch.device(map_location), weights_only=True)
+        model.load_state_dict(state_dict, strict=strict)  # type: ignore
+        model.eval()  # type: ignore
+        return model
+
+    @classmethod
+    def _load_as_safetensor(cls, model: T, model_file: str, map_location: str, strict: bool) -> T:
+        if packaging.version.parse(safetensors.__version__) < packaging.version.parse("0.4.3"):  # type: ignore [attr-defined]
+            load_model_as_safetensor(model, model_file, strict=strict)  # type: ignore [arg-type]
+            if map_location != "cpu":
+                logger.warning(
+                    "Loading model weights on other devices than 'cpu' is not supported natively in your version of safetensors."
+                    " This means that the model is loaded on 'cpu' first and then copied to the device."
+                    " This leads to a slower loading time."
+                    " Please update safetensors to version 0.4.3 or above for improved performance."
+                )
+                model.to(map_location)  # type: ignore [attr-defined]
+        else:
+            safetensors.torch.load_model(model, model_file, strict=strict, device=map_location)  # type: ignore [arg-type]
+        return model
+
+
+def _load_dataclass(datacls: type[DataclassInstance], data: dict) -> DataclassInstance:
+    """Load a dataclass instance from a dictionary.
+
+    Fields not expected by the dataclass are ignored.
+    """
+    return datacls(**{k: v for k, v in data.items() if k in datacls.__dataclass_fields__})

data/conda_envs/evalclaw/lib/python3.11/site-packages/huggingface_hub/lfs.py

@@ -0,0 +1,395 @@
+# coding=utf-8
+# Copyright 2019-present, the HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Git LFS related type definitions and utilities"""
+
+import io
+import re
+from dataclasses import dataclass
+from math import ceil
+from os.path import getsize
+from typing import TYPE_CHECKING, BinaryIO, Iterable, Optional, TypedDict
+from urllib.parse import unquote
+
+from huggingface_hub import constants
+
+from .utils import (
+    build_hf_headers,
+    fix_hf_endpoint_in_url,
+    hf_raise_for_status,
+    http_backoff,
+    logging,
+    validate_hf_hub_args,
+)
+from .utils._lfs import SliceFileObj
+from .utils.sha import sha256, sha_fileobj
+
+
+if TYPE_CHECKING:
+    from ._commit_api import CommitOperationAdd
+
+logger = logging.get_logger(__name__)
+
+OID_REGEX = re.compile(r"^[0-9a-f]{40}$")
+
+LFS_MULTIPART_UPLOAD_COMMAND = "lfs-multipart-upload"
+
+LFS_HEADERS = {
+    "Accept": "application/vnd.git-lfs+json",
+    "Content-Type": "application/vnd.git-lfs+json",
+}
+
+
+@dataclass
+class UploadInfo:
+    """
+    Dataclass holding required information to determine whether a blob
+    should be uploaded to the hub using the LFS protocol or the regular protocol
+
+    Args:
+        sha256 (`bytes`):
+            SHA256 hash of the blob
+        size (`int`):
+            Size in bytes of the blob
+        sample (`bytes`):
+            First 512 bytes of the blob
+    """
+
+    sha256: bytes
+    size: int
+    sample: bytes
+
+    @classmethod
+    def from_path(cls, path: str):
+        size = getsize(path)
+        with io.open(path, "rb") as file:
+            sample = file.peek(512)[:512]
+            sha = sha_fileobj(file)
+        return cls(size=size, sha256=sha, sample=sample)
+
+    @classmethod
+    def from_bytes(cls, data: bytes):
+        sha = sha256(data).digest()
+        return cls(size=len(data), sample=data[:512], sha256=sha)
+
+    @classmethod
+    def from_fileobj(cls, fileobj: BinaryIO):
+        sample = fileobj.read(512)
+        fileobj.seek(0, io.SEEK_SET)
+        sha = sha_fileobj(fileobj)
+        size = fileobj.tell()
+        fileobj.seek(0, io.SEEK_SET)
+        return cls(size=size, sha256=sha, sample=sample)
+
+
+@validate_hf_hub_args
+def post_lfs_batch_info(
+    upload_infos: Iterable[UploadInfo],
+    token: Optional[str],
+    repo_type: str,
+    repo_id: str,
+    revision: Optional[str] = None,
+    endpoint: Optional[str] = None,
+    headers: Optional[dict[str, str]] = None,
+    transfers: Optional[list[str]] = None,
+) -> tuple[list[dict], list[dict], Optional[str]]:
+    """
+    Requests the LFS batch endpoint to retrieve upload instructions
+
+    Learn more: https://github.com/git-lfs/git-lfs/blob/main/docs/api/batch.md
+
+    Args:
+        upload_infos (`Iterable` of `UploadInfo`):
+            `UploadInfo` for the files that are being uploaded, typically obtained
+            from `CommitOperationAdd.upload_info`
+        repo_type (`str`):
+            Type of the repo to upload to: `"model"`, `"dataset"` or `"space"`.
+        repo_id (`str`):
+            A namespace (user or an organization) and a repo name separated
+            by a `/`.
+        revision (`str`, *optional*):
+            The git revision to upload to.
+        headers (`dict`, *optional*):
+            Additional headers to include in the request
+        transfers (`list`, *optional*):
+            List of transfer methods to use. Defaults to ["basic", "multipart"].
+
+    Returns:
+        `LfsBatchInfo`: 3-tuple:
+            - First element is the list of upload instructions from the server
+            - Second element is a list of errors, if any
+            - Third element is the chosen transfer adapter if provided by the server (e.g. "basic", "multipart", "xet")
+
+    Raises:
+        [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError)
+            If an argument is invalid or the server response is malformed.
+        [`HfHubHTTPError`]
+            If the server returned an error.
+    """
+    endpoint = endpoint if endpoint is not None else constants.ENDPOINT
+    url_prefix = ""
+    if repo_type in constants.REPO_TYPES_URL_PREFIXES:
+        url_prefix = constants.REPO_TYPES_URL_PREFIXES[repo_type]
+    batch_url = f"{endpoint}/{url_prefix}{repo_id}.git/info/lfs/objects/batch"
+    payload: dict = {
+        "operation": "upload",
+        "transfers": transfers if transfers is not None else ["basic", "multipart"],
+        "objects": [
+            {
+                "oid": upload.sha256.hex(),
+                "size": upload.size,
+            }
+            for upload in upload_infos
+        ],
+        "hash_algo": "sha256",
+    }
+    if revision is not None:
+        payload["ref"] = {"name": unquote(revision)}  # revision has been previously 'quoted'
+
+    headers = {
+        **LFS_HEADERS,
+        **build_hf_headers(token=token),
+        **(headers or {}),
+    }
+    resp = http_backoff("POST", batch_url, headers=headers, json=payload)
+    hf_raise_for_status(resp)
+    batch_info = resp.json()
+
+    objects = batch_info.get("objects", None)
+    if not isinstance(objects, list):
+        raise ValueError("Malformed response from server")
+
+    chosen_transfer = batch_info.get("transfer")
+    chosen_transfer = chosen_transfer if isinstance(chosen_transfer, str) else None
+
+    return (
+        [_validate_batch_actions(obj) for obj in objects if "error" not in obj],
+        [_validate_batch_error(obj) for obj in objects if "error" in obj],
+        chosen_transfer,
+    )
+
+
+class PayloadPartT(TypedDict):
+    partNumber: int
+    etag: str
+
+
+class CompletionPayloadT(TypedDict):
+    """Payload that will be sent to the Hub when uploading multi-part."""
+
+    oid: str
+    parts: list[PayloadPartT]
+
+
+def lfs_upload(
+    operation: "CommitOperationAdd",
+    lfs_batch_action: dict,
+    token: Optional[str] = None,
+    headers: Optional[dict[str, str]] = None,
+    endpoint: Optional[str] = None,
+) -> None:
+    """
+    Handles uploading a given object to the Hub with the LFS protocol.
+
+    Can be a No-op if the content of the file is already present on the hub large file storage.
+
+    Args:
+        operation (`CommitOperationAdd`):
+            The add operation triggering this upload.
+        lfs_batch_action (`dict`):
+            Upload instructions from the LFS batch endpoint for this object. See [`~utils.lfs.post_lfs_batch_info`] for
+            more details.
+        headers (`dict`, *optional*):
+            Headers to include in the request, including authentication and user agent headers.
+
+    Raises:
+        [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError)
+            If `lfs_batch_action` is improperly formatted
+        [`HfHubHTTPError`]
+            If the upload resulted in an error
+    """
+    # 0. If LFS file is already present, skip upload
+    _validate_batch_actions(lfs_batch_action)
+    actions = lfs_batch_action.get("actions")
+    if actions is None:
+        # The file was already uploaded
+        logger.debug(f"Content of file {operation.path_in_repo} is already present upstream - skipping upload")
+        return
+
+    # 1. Validate server response (check required keys in dict)
+    upload_action = lfs_batch_action["actions"]["upload"]
+    _validate_lfs_action(upload_action)
+    verify_action = lfs_batch_action["actions"].get("verify")
+    if verify_action is not None:
+        _validate_lfs_action(verify_action)
+
+    # 2. Upload file (either single part or multi-part)
+    header = upload_action.get("header", {})
+    chunk_size = header.get("chunk_size")
+    upload_url = fix_hf_endpoint_in_url(upload_action["href"], endpoint=endpoint)
+    if chunk_size is not None:
+        try:
+            chunk_size = int(chunk_size)
+        except (ValueError, TypeError):
+            raise ValueError(
+                f"Malformed response from LFS batch endpoint: `chunk_size` should be an integer. Got '{chunk_size}'."
+            )
+        _upload_multi_part(operation=operation, header=header, chunk_size=chunk_size, upload_url=upload_url)
+    else:
+        _upload_single_part(operation=operation, upload_url=upload_url)
+
+    # 3. Verify upload went well
+    if verify_action is not None:
+        _validate_lfs_action(verify_action)
+        verify_url = fix_hf_endpoint_in_url(verify_action["href"], endpoint)
+        verify_resp = http_backoff(
+            "POST",
+            verify_url,
+            headers=build_hf_headers(token=token, headers=headers),
+            json={"oid": operation.upload_info.sha256.hex(), "size": operation.upload_info.size},
+        )
+        hf_raise_for_status(verify_resp)
+    logger.debug(f"{operation.path_in_repo}: Upload successful")
+
+
+def _validate_lfs_action(lfs_action: dict):
+    """validates response from the LFS batch endpoint"""
+    if not (
+        isinstance(lfs_action.get("href"), str)
+        and (lfs_action.get("header") is None or isinstance(lfs_action.get("header"), dict))
+    ):
+        raise ValueError("lfs_action is improperly formatted")
+    return lfs_action
+
+
+def _validate_batch_actions(lfs_batch_actions: dict):
+    """validates response from the LFS batch endpoint"""
+    if not (isinstance(lfs_batch_actions.get("oid"), str) and isinstance(lfs_batch_actions.get("size"), int)):
+        raise ValueError("lfs_batch_actions is improperly formatted")
+
+    upload_action = lfs_batch_actions.get("actions", {}).get("upload")
+    verify_action = lfs_batch_actions.get("actions", {}).get("verify")
+    if upload_action is not None:
+        _validate_lfs_action(upload_action)
+    if verify_action is not None:
+        _validate_lfs_action(verify_action)
+    return lfs_batch_actions
+
+
+def _validate_batch_error(lfs_batch_error: dict):
+    """validates response from the LFS batch endpoint"""
+    if not (isinstance(lfs_batch_error.get("oid"), str) and isinstance(lfs_batch_error.get("size"), int)):
+        raise ValueError("lfs_batch_error is improperly formatted")
+    error_info = lfs_batch_error.get("error")
+    if not (
+        isinstance(error_info, dict)
+        and isinstance(error_info.get("message"), str)
+        and isinstance(error_info.get("code"), int)
+    ):
+        raise ValueError("lfs_batch_error is improperly formatted")
+    return lfs_batch_error
+
+
+def _upload_single_part(operation: "CommitOperationAdd", upload_url: str) -> None:
+    """
+    Uploads `fileobj` as a single PUT HTTP request (basic LFS transfer protocol)
+
+    Args:
+        upload_url (`str`):
+            The URL to PUT the file to.
+        fileobj:
+            The file-like object holding the data to upload.
+
+    Raises:
+        [`HfHubHTTPError`]
+            If the upload resulted in an error.
+    """
+    with operation.as_file(with_tqdm=True) as fileobj:
+        # S3 might raise a transient 500 error -> let's retry if that happens
+        response = http_backoff("PUT", upload_url, data=fileobj)
+        hf_raise_for_status(response)
+
+
+def _upload_multi_part(operation: "CommitOperationAdd", header: dict, chunk_size: int, upload_url: str) -> None:
+    """
+    Uploads file using HF multipart LFS transfer protocol.
+    """
+    # 1. Get upload URLs for each part
+    sorted_parts_urls = _get_sorted_parts_urls(header=header, upload_info=operation.upload_info, chunk_size=chunk_size)
+
+    # 2. Upload parts (pure Python)
+    response_headers = _upload_parts_iteratively(
+        operation=operation, sorted_parts_urls=sorted_parts_urls, chunk_size=chunk_size
+    )
+
+    # 3. Send completion request
+    # NOTE: `upload_url` is the Hub completion endpoint (not the S3 upload URLs).
+    completion_res = http_backoff(
+        "POST",
+        upload_url,
+        json=_get_completion_payload(response_headers, operation.upload_info.sha256.hex()),
+        headers=LFS_HEADERS,
+    )
+    hf_raise_for_status(completion_res)
+
+
+def _get_sorted_parts_urls(header: dict, upload_info: UploadInfo, chunk_size: int) -> list[str]:
+    sorted_part_upload_urls = [
+        upload_url
+        for _, upload_url in sorted(
+            [
+                (int(part_num, 10), upload_url)
+                for part_num, upload_url in header.items()
+                if part_num.isdigit() and len(part_num) > 0
+            ],
+            key=lambda t: t[0],
+        )
+    ]
+    num_parts = len(sorted_part_upload_urls)
+    if num_parts != ceil(upload_info.size / chunk_size):
+        raise ValueError("Invalid server response to upload large LFS file")
+    return sorted_part_upload_urls
+
+
+def _get_completion_payload(response_headers: list[dict], oid: str) -> CompletionPayloadT:
+    parts: list[PayloadPartT] = []
+    for part_number, header in enumerate(response_headers):
+        etag = header.get("etag")
+        if etag is None or etag == "":
+            raise ValueError(f"Invalid etag (`{etag}`) returned for part {part_number + 1}")
+        parts.append(
+            {
+                "partNumber": part_number + 1,
+                "etag": etag,
+            }
+        )
+    return {"oid": oid, "parts": parts}
+
+
+def _upload_parts_iteratively(
+    operation: "CommitOperationAdd", sorted_parts_urls: list[str], chunk_size: int
+) -> list[dict]:
+    headers = []
+    with operation.as_file(with_tqdm=True) as fileobj:
+        for part_idx, part_upload_url in enumerate(sorted_parts_urls):
+            with SliceFileObj(
+                fileobj,
+                seek_from=chunk_size * part_idx,
+                read_limit=chunk_size,
+            ) as fileobj_slice:
+                # S3 might raise a transient 500 error -> let's retry if that happens
+                part_upload_res = http_backoff("PUT", part_upload_url, data=fileobj_slice)
+                hf_raise_for_status(part_upload_res)
+                headers.append(part_upload_res.headers)
+    return headers  # type: ignore

data/conda_envs/evalclaw/lib/python3.11/site-packages/huggingface_hub/repocard.py

@@ -0,0 +1,826 @@
+import os
+import re
+from pathlib import Path
+from typing import Any, Literal, Optional, Union
+
+import yaml
+
+from huggingface_hub.file_download import hf_hub_download
+from huggingface_hub.hf_api import upload_file
+from huggingface_hub.repocard_data import (
+    CardData,
+    DatasetCardData,
+    EvalResult,
+    ModelCardData,
+    SpaceCardData,
+    eval_results_to_model_index,
+    model_index_to_eval_results,
+)
+from huggingface_hub.utils import HfHubHTTPError, get_session, hf_raise_for_status, is_jinja_available, yaml_dump
+
+from . import constants
+from .errors import EntryNotFoundError
+from .utils import SoftTemporaryDirectory, logging, validate_hf_hub_args
+
+
+logger = logging.get_logger(__name__)
+
+
+TEMPLATE_MODELCARD_PATH = Path(__file__).parent / "templates" / "modelcard_template.md"
+TEMPLATE_DATASETCARD_PATH = Path(__file__).parent / "templates" / "datasetcard_template.md"
+
+# exact same regex as in the Hub server. Please keep in sync.
+# See https://github.com/huggingface/moon-landing/blob/main/server/lib/ViewMarkdown.ts#L18
+REGEX_YAML_BLOCK = re.compile(r"^(\s*---[\r\n]+)([\S\s]*?)([\r\n]+---(\r\n|\n|$))")
+
+
+class RepoCard:
+    card_data_class = CardData
+    default_template_path = TEMPLATE_MODELCARD_PATH
+    repo_type = "model"
+
+    def __init__(self, content: str, ignore_metadata_errors: bool = False):
+        """Initialize a RepoCard from string content. The content should be a
+        Markdown file with a YAML block at the beginning and a Markdown body.
+
+        Args:
+            content (`str`): The content of the Markdown file.
+
+        Example:
+            ```python
+            >>> from huggingface_hub.repocard import RepoCard
+            >>> text = '''
+            ... ---
+            ... language: en
+            ... license: mit
+            ... ---
+            ...
+            ... # My repo
+            ... '''
+            >>> card = RepoCard(text)
+            >>> card.data.to_dict()
+            {'language': 'en', 'license': 'mit'}
+            >>> card.text
+            '\\n# My repo\\n'
+
+            ```
+        > [!TIP]
+        > Raises the following error:
+        >
+        >     - [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError)
+        >       when the content of the repo card metadata is not a dictionary.
+        """
+
+        # Set the content of the RepoCard, as well as underlying .data and .text attributes.
+        # See the `content` property setter for more details.
+        self.ignore_metadata_errors = ignore_metadata_errors
+        self.content = content
+
+    @property
+    def content(self):
+        """The content of the RepoCard, including the YAML block and the Markdown body."""
+        line_break = _detect_line_ending(self._content) or "\n"
+        return f"---{line_break}{self.data.to_yaml(line_break=line_break, original_order=self._original_order)}{line_break}---{line_break}{self.text}"
+
+    @content.setter
+    def content(self, content: str):
+        """Set the content of the RepoCard."""
+        self._content = content
+
+        match = REGEX_YAML_BLOCK.search(content)
+        if match:
+            # Metadata found in the YAML block
+            yaml_block = match.group(2)
+            self.text = content[match.end() :]
+            data_dict = yaml.safe_load(yaml_block)
+
+            if data_dict is None:
+                data_dict = {}
+
+            # The YAML block's data should be a dictionary
+            if not isinstance(data_dict, dict):
+                raise ValueError("repo card metadata block should be a dict")
+        else:
+            # Model card without metadata... create empty metadata
+            logger.warning("Repo card metadata block was not found. Setting CardData to empty.")
+            data_dict = {}
+            self.text = content
+
+        self.data = self.card_data_class(**data_dict, ignore_metadata_errors=self.ignore_metadata_errors)
+        self._original_order = list(data_dict.keys())
+
+    def __str__(self):
+        return self.content
+
+    def save(self, filepath: Union[Path, str]):
+        r"""Save a RepoCard to a file.
+
+        Args:
+            filepath (`Union[Path, str]`): Filepath to the markdown file to save.
+
+        Example:
+            ```python
+            >>> from huggingface_hub.repocard import RepoCard
+            >>> card = RepoCard("---\nlanguage: en\n---\n# This is a test repo card")
+            >>> card.save("/tmp/test.md")
+
+            ```
+        """
+        filepath = Path(filepath)
+        filepath.parent.mkdir(parents=True, exist_ok=True)
+        # Preserve newlines as in the existing file.
+        with open(filepath, mode="w", newline="", encoding="utf-8") as f:
+            f.write(str(self))
+
+    @classmethod
+    def load(
+        cls,
+        repo_id_or_path: Union[str, Path],
+        repo_type: Optional[str] = None,
+        token: Optional[str] = None,
+        ignore_metadata_errors: bool = False,
+    ):
+        """Initialize a RepoCard from a Hugging Face Hub repo's README.md or a local filepath.
+
+        Args:
+            repo_id_or_path (`Union[str, Path]`):
+                The repo ID associated with a Hugging Face Hub repo or a local filepath.
+            repo_type (`str`, *optional*):
+                The type of Hugging Face repo to push to. Defaults to None, which will use "model". Other options
+                are "dataset" and "space". Not used when loading from a local filepath. If this is called from a child
+                class, the default value will be the child class's `repo_type`.
+            token (`str`, *optional*):
+                Authentication token, obtained with `huggingface_hub.HfApi.login` method. Will default to the stored token.
+            ignore_metadata_errors (`str`):
+                If True, errors while parsing the metadata section will be ignored. Some information might be lost during
+                the process. Use it at your own risk.
+
+        Returns:
+            [`huggingface_hub.repocard.RepoCard`]: The RepoCard (or subclass) initialized from the repo's
+                README.md file or filepath.
+
+        Example:
+            ```python
+            >>> from huggingface_hub.repocard import RepoCard
+            >>> card = RepoCard.load("nateraw/food")
+            >>> assert card.data.tags == ["generated_from_trainer", "image-classification", "pytorch"]
+
+            ```
+        """
+
+        if Path(repo_id_or_path).is_file():
+            card_path = Path(repo_id_or_path)
+        elif isinstance(repo_id_or_path, str):
+            card_path = Path(
+                hf_hub_download(
+                    repo_id_or_path,
+                    constants.REPOCARD_NAME,
+                    repo_type=repo_type or cls.repo_type,
+                    token=token,
+                )
+            )
+        else:
+            raise ValueError(f"Cannot load RepoCard: path not found on disk ({repo_id_or_path}).")
+
+        # Preserve newlines in the existing file.
+        with card_path.open(mode="r", newline="", encoding="utf-8") as f:
+            return cls(f.read(), ignore_metadata_errors=ignore_metadata_errors)
+
+    def validate(self, repo_type: Optional[str] = None):
+        """Validates card against Hugging Face Hub's card validation logic.
+        Using this function requires access to the internet, so it is only called
+        internally by [`huggingface_hub.repocard.RepoCard.push_to_hub`].
+
+        Args:
+            repo_type (`str`, *optional*, defaults to "model"):
+                The type of Hugging Face repo to push to. Options are "model", "dataset", and "space".
+                If this function is called from a child class, the default will be the child class's `repo_type`.
+
+        > [!TIP]
+        > Raises the following errors:
+        >
+        >     - [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError)
+        >       if the card fails validation checks.
+        >     - [`HTTPError`](https://requests.readthedocs.io/en/latest/api/#requests.HTTPError)
+        >       if the request to the Hub API fails for any other reason.
+        """
+
+        # If repo type is provided, otherwise, use the repo type of the card.
+        repo_type = repo_type or self.repo_type
+
+        body = {
+            "repoType": repo_type,
+            "content": str(self),
+        }
+        headers = {"Accept": "text/plain"}
+
+        try:
+            response = get_session().post("https://huggingface.co/api/validate-yaml", json=body, headers=headers)
+            hf_raise_for_status(response)
+        except HfHubHTTPError as exc:
+            if response.status_code == 400:
+                raise ValueError(response.text)
+            else:
+                raise exc
+
+    def push_to_hub(
+        self,
+        repo_id: str,
+        token: Optional[str] = None,
+        repo_type: Optional[str] = None,
+        commit_message: Optional[str] = None,
+        commit_description: Optional[str] = None,
+        revision: Optional[str] = None,
+        create_pr: Optional[bool] = None,
+        parent_commit: Optional[str] = None,
+    ):
+        """Push a RepoCard to a Hugging Face Hub repo.
+
+        Args:
+            repo_id (`str`):
+                The repo ID of the Hugging Face Hub repo to push to. Example: "nateraw/food".
+            token (`str`, *optional*):
+                Authentication token, obtained with `huggingface_hub.HfApi.login` method. Will default to
+                the stored token.
+            repo_type (`str`, *optional*, defaults to "model"):
+                The type of Hugging Face repo to push to. Options are "model", "dataset", and "space". If this
+                function is called by a child class, it will default to the child class's `repo_type`.
+            commit_message (`str`, *optional*):
+                The summary / title / first line of the generated commit.
+            commit_description (`str`, *optional*)
+                The description of the generated commit.
+            revision (`str`, *optional*):
+                The git revision to commit from. Defaults to the head of the `"main"` branch.
+            create_pr (`bool`, *optional*):
+                Whether or not to create a Pull Request with this commit. Defaults to `False`.
+            parent_commit (`str`, *optional*):
+                The OID / SHA of the parent commit, as a hexadecimal string. Shorthands (7 first characters) are also supported.
+                If specified and `create_pr` is `False`, the commit will fail if `revision` does not point to `parent_commit`.
+                If specified and `create_pr` is `True`, the pull request will be created from `parent_commit`.
+                Specifying `parent_commit` ensures the repo has not changed before committing the changes, and can be
+                especially useful if the repo is updated / committed too concurrently.
+        Returns:
+            `str`: URL of the commit which updated the card metadata.
+        """
+
+        # If repo type is provided, otherwise, use the repo type of the card.
+        repo_type = repo_type or self.repo_type
+
+        # Validate card before pushing to hub
+        self.validate(repo_type=repo_type)
+
+        with SoftTemporaryDirectory() as tmpdir:
+            tmp_path = Path(tmpdir) / constants.REPOCARD_NAME
+            tmp_path.write_text(str(self), encoding="utf-8")
+            url = upload_file(
+                path_or_fileobj=str(tmp_path),
+                path_in_repo=constants.REPOCARD_NAME,
+                repo_id=repo_id,
+                token=token,
+                repo_type=repo_type,
+                commit_message=commit_message,
+                commit_description=commit_description,
+                create_pr=create_pr,
+                revision=revision,
+                parent_commit=parent_commit,
+            )
+        return url
+
+    @classmethod
+    def from_template(
+        cls,
+        card_data: CardData,
+        template_path: Optional[str] = None,
+        template_str: Optional[str] = None,
+        **template_kwargs,
+    ):
+        """Initialize a RepoCard from a template. By default, it uses the default template.
+
+        Templates are Jinja2 templates that can be customized by passing keyword arguments.
+
+        Args:
+            card_data (`huggingface_hub.CardData`):
+                A huggingface_hub.CardData instance containing the metadata you want to include in the YAML
+                header of the repo card on the Hugging Face Hub.
+            template_path (`str`, *optional*):
+                A path to a markdown file with optional Jinja template variables that can be filled
+                in with `template_kwargs`. Defaults to the default template.
+
+        Returns:
+            [`huggingface_hub.repocard.RepoCard`]: A RepoCard instance with the specified card data and content from the
+            template.
+        """
+        if is_jinja_available():
+            import jinja2
+        else:
+            raise ImportError(
+                "Using RepoCard.from_template requires Jinja2 to be installed. Please"
+                " install it with `pip install Jinja2`."
+            )
+
+        kwargs = card_data.to_dict().copy()
+        kwargs.update(template_kwargs)  # Template_kwargs have priority
+
+        if template_path is not None:
+            template_str = Path(template_path).read_text()
+        if template_str is None:
+            template_str = Path(cls.default_template_path).read_text()
+        template = jinja2.Template(template_str)
+        content = template.render(card_data=card_data.to_yaml(), **kwargs)
+        return cls(content)
+
+
+class ModelCard(RepoCard):
+    card_data_class = ModelCardData  # type: ignore[assignment]
+    default_template_path = TEMPLATE_MODELCARD_PATH
+    repo_type = "model"
+
+    @classmethod
+    def from_template(  # type: ignore # violates Liskov property but easier to use
+        cls,
+        card_data: ModelCardData,
+        template_path: Optional[str] = None,
+        template_str: Optional[str] = None,
+        **template_kwargs,
+    ):
+        """Initialize a ModelCard from a template. By default, it uses the default template, which can be found here:
+        https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md
+
+        Templates are Jinja2 templates that can be customized by passing keyword arguments.
+
+        Args:
+            card_data (`huggingface_hub.ModelCardData`):
+                A huggingface_hub.ModelCardData instance containing the metadata you want to include in the YAML
+                header of the model card on the Hugging Face Hub.
+            template_path (`str`, *optional*):
+                A path to a markdown file with optional Jinja template variables that can be filled
+                in with `template_kwargs`. Defaults to the default template.
+
+        Returns:
+            [`huggingface_hub.ModelCard`]: A ModelCard instance with the specified card data and content from the
+            template.
+
+        Example:
+            ```python
+            >>> from huggingface_hub import ModelCard, ModelCardData, EvalResult
+
+            >>> # Using the Default Template
+            >>> card_data = ModelCardData(
+            ...     language='en',
+            ...     license='mit',
+            ...     library_name='timm',
+            ...     tags=['image-classification', 'resnet'],
+            ...     datasets=['beans'],
+            ...     metrics=['accuracy'],
+            ... )
+            >>> card = ModelCard.from_template(
+            ...     card_data,
+            ...     model_description='This model does x + y...'
+            ... )
+
+            >>> # Including Evaluation Results
+            >>> card_data = ModelCardData(
+            ...     language='en',
+            ...     tags=['image-classification', 'resnet'],
+            ...     eval_results=[
+            ...         EvalResult(
+            ...             task_type='image-classification',
+            ...             dataset_type='beans',
+            ...             dataset_name='Beans',
+            ...             metric_type='accuracy',
+            ...             metric_value=0.9,
+            ...         ),
+            ...     ],
+            ...     model_name='my-cool-model',
+            ... )
+            >>> card = ModelCard.from_template(card_data)
+
+            >>> # Using a Custom Template
+            >>> card_data = ModelCardData(
+            ...     language='en',
+            ...     tags=['image-classification', 'resnet']
+            ... )
+            >>> card = ModelCard.from_template(
+            ...     card_data=card_data,
+            ...     template_path='./src/huggingface_hub/templates/modelcard_template.md',
+            ...     custom_template_var='custom value',  # will be replaced in template if it exists
+            ... )
+
+            ```
+        """
+        return super().from_template(card_data, template_path, template_str, **template_kwargs)
+
+
+class DatasetCard(RepoCard):
+    card_data_class = DatasetCardData  # type: ignore[assignment]
+    default_template_path = TEMPLATE_DATASETCARD_PATH
+    repo_type = "dataset"
+
+    @classmethod
+    def from_template(  # type: ignore # violates Liskov property but easier to use
+        cls,
+        card_data: DatasetCardData,
+        template_path: Optional[str] = None,
+        template_str: Optional[str] = None,
+        **template_kwargs,
+    ):
+        """Initialize a DatasetCard from a template. By default, it uses the default template, which can be found here:
+        https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md
+
+        Templates are Jinja2 templates that can be customized by passing keyword arguments.
+
+        Args:
+            card_data (`huggingface_hub.DatasetCardData`):
+                A huggingface_hub.DatasetCardData instance containing the metadata you want to include in the YAML
+                header of the dataset card on the Hugging Face Hub.
+            template_path (`str`, *optional*):
+                A path to a markdown file with optional Jinja template variables that can be filled
+                in with `template_kwargs`. Defaults to the default template.
+
+        Returns:
+            [`huggingface_hub.DatasetCard`]: A DatasetCard instance with the specified card data and content from the
+            template.
+
+        Example:
+            ```python
+            >>> from huggingface_hub import DatasetCard, DatasetCardData
+
+            >>> # Using the Default Template
+            >>> card_data = DatasetCardData(
+            ...     language='en',
+            ...     license='mit',
+            ...     annotations_creators='crowdsourced',
+            ...     task_categories=['text-classification'],
+            ...     task_ids=['sentiment-classification', 'text-scoring'],
+            ...     multilinguality='monolingual',
+            ...     pretty_name='My Text Classification Dataset',
+            ... )
+            >>> card = DatasetCard.from_template(
+            ...     card_data,
+            ...     pretty_name=card_data.pretty_name,
+            ... )
+
+            >>> # Using a Custom Template
+            >>> card_data = DatasetCardData(
+            ...     language='en',
+            ...     license='mit',
+            ... )
+            >>> card = DatasetCard.from_template(
+            ...     card_data=card_data,
+            ...     template_path='./src/huggingface_hub/templates/datasetcard_template.md',
+            ...     custom_template_var='custom value',  # will be replaced in template if it exists
+            ... )
+
+            ```
+        """
+        return super().from_template(card_data, template_path, template_str, **template_kwargs)
+
+
+class SpaceCard(RepoCard):
+    card_data_class = SpaceCardData  # type: ignore[assignment]
+    default_template_path = TEMPLATE_MODELCARD_PATH
+    repo_type = "space"
+
+
+def _detect_line_ending(content: str) -> Literal["\r", "\n", "\r\n", None]:  # noqa: F722
+    """Detect the line ending of a string. Used by RepoCard to avoid making huge diff on newlines.
+
+    Uses same implementation as in Hub server, keep it in sync.
+
+    Returns:
+        str: The detected line ending of the string.
+    """
+    cr = content.count("\r")
+    lf = content.count("\n")
+    crlf = content.count("\r\n")
+    if cr + lf == 0:
+        return None
+    if crlf == cr and crlf == lf:
+        return "\r\n"
+    if cr > lf:
+        return "\r"
+    else:
+        return "\n"
+
+
+def metadata_load(local_path: Union[str, Path]) -> Optional[dict]:
+    content = Path(local_path).read_text()
+    match = REGEX_YAML_BLOCK.search(content)
+    if match:
+        yaml_block = match.group(2)
+        data = yaml.safe_load(yaml_block)
+        if data is None or isinstance(data, dict):
+            return data
+        raise ValueError("repo card metadata block should be a dict")
+    else:
+        return None
+
+
+def metadata_save(local_path: Union[str, Path], data: dict) -> None:
+    """
+    Save the metadata dict in the upper YAML part Trying to preserve newlines as
+    in the existing file. Docs about open() with newline="" parameter:
+    https://docs.python.org/3/library/functions.html?highlight=open#open Does
+    not work with "^M" linebreaks, which are replaced by \n
+    """
+    line_break = "\n"
+    content = ""
+    # try to detect existing newline character
+    if os.path.exists(local_path):
+        with open(local_path, "r", newline="", encoding="utf8") as readme:
+            content = readme.read()
+            if isinstance(readme.newlines, tuple):
+                line_break = readme.newlines[0]
+            elif isinstance(readme.newlines, str):
+                line_break = readme.newlines
+
+    # creates a new file if it not
+    with open(local_path, "w", newline="", encoding="utf8") as readme:
+        data_yaml = yaml_dump(data, sort_keys=False, line_break=line_break)
+        # sort_keys: keep dict order
+        match = REGEX_YAML_BLOCK.search(content)
+        if match:
+            output = content[: match.start()] + f"---{line_break}{data_yaml}---{line_break}" + content[match.end() :]
+        else:
+            output = f"---{line_break}{data_yaml}---{line_break}{content}"
+
+        readme.write(output)
+        readme.close()
+
+
+def metadata_eval_result(
+    *,
+    model_pretty_name: str,
+    task_pretty_name: str,
+    task_id: str,
+    metrics_pretty_name: str,
+    metrics_id: str,
+    metrics_value: Any,
+    dataset_pretty_name: str,
+    dataset_id: str,
+    metrics_config: Optional[str] = None,
+    metrics_verified: bool = False,
+    dataset_config: Optional[str] = None,
+    dataset_split: Optional[str] = None,
+    dataset_revision: Optional[str] = None,
+    metrics_verification_token: Optional[str] = None,
+) -> dict:
+    """
+    Creates a metadata dict with the result from a model evaluated on a dataset.
+
+    Args:
+        model_pretty_name (`str`):
+            The name of the model in natural language.
+        task_pretty_name (`str`):
+            The name of a task in natural language.
+        task_id (`str`):
+            Example: automatic-speech-recognition. A task id.
+        metrics_pretty_name (`str`):
+            A name for the metric in natural language. Example: Test WER.
+        metrics_id (`str`):
+            Example: wer. A metric id from https://hf.co/metrics.
+        metrics_value (`Any`):
+            The value from the metric. Example: 20.0 or "20.0 ± 1.2".
+        dataset_pretty_name (`str`):
+            The name of the dataset in natural language.
+        dataset_id (`str`):
+            Example: common_voice. A dataset id from https://hf.co/datasets.
+        metrics_config (`str`, *optional*):
+            The name of the metric configuration used in `load_metric()`.
+            Example: bleurt-large-512 in `load_metric("bleurt", "bleurt-large-512")`.
+        metrics_verified (`bool`, *optional*, defaults to `False`):
+            Indicates whether the metrics originate from Hugging Face's [evaluation service](https://huggingface.co/spaces/autoevaluate/model-evaluator) or not. Automatically computed by Hugging Face, do not set.
+        dataset_config (`str`, *optional*):
+            Example: fr. The name of the dataset configuration used in `load_dataset()`.
+        dataset_split (`str`, *optional*):
+            Example: test. The name of the dataset split used in `load_dataset()`.
+        dataset_revision (`str`, *optional*):
+            Example: 5503434ddd753f426f4b38109466949a1217c2bb. The name of the dataset dataset revision
+            used in `load_dataset()`.
+        metrics_verification_token (`bool`, *optional*):
+            A JSON Web Token that is used to verify whether the metrics originate from Hugging Face's [evaluation service](https://huggingface.co/spaces/autoevaluate/model-evaluator) or not.
+
+    Returns:
+        `dict`: a metadata dict with the result from a model evaluated on a dataset.
+
+    Example:
+        ```python
+        >>> from huggingface_hub import metadata_eval_result
+        >>> results = metadata_eval_result(
+        ...         model_pretty_name="RoBERTa fine-tuned on ReactionGIF",
+        ...         task_pretty_name="Text Classification",
+        ...         task_id="text-classification",
+        ...         metrics_pretty_name="Accuracy",
+        ...         metrics_id="accuracy",
+        ...         metrics_value=0.2662102282047272,
+        ...         dataset_pretty_name="ReactionJPEG",
+        ...         dataset_id="julien-c/reactionjpeg",
+        ...         dataset_config="default",
+        ...         dataset_split="test",
+        ... )
+        >>> results == {
+        ...     'model-index': [
+        ...         {
+        ...             'name': 'RoBERTa fine-tuned on ReactionGIF',
+        ...             'results': [
+        ...                 {
+        ...                     'task': {
+        ...                         'type': 'text-classification',
+        ...                         'name': 'Text Classification'
+        ...                     },
+        ...                     'dataset': {
+        ...                         'name': 'ReactionJPEG',
+        ...                         'type': 'julien-c/reactionjpeg',
+        ...                         'config': 'default',
+        ...                         'split': 'test'
+        ...                     },
+        ...                     'metrics': [
+        ...                         {
+        ...                             'type': 'accuracy',
+        ...                             'value': 0.2662102282047272,
+        ...                             'name': 'Accuracy',
+        ...                             'verified': False
+        ...                         }
+        ...                     ]
+        ...                 }
+        ...             ]
+        ...         }
+        ...     ]
+        ... }
+        True
+
+        ```
+    """
+
+    return {
+        "model-index": eval_results_to_model_index(
+            model_name=model_pretty_name,
+            eval_results=[
+                EvalResult(
+                    task_name=task_pretty_name,
+                    task_type=task_id,
+                    metric_name=metrics_pretty_name,
+                    metric_type=metrics_id,
+                    metric_value=metrics_value,
+                    dataset_name=dataset_pretty_name,
+                    dataset_type=dataset_id,
+                    metric_config=metrics_config,
+                    verified=metrics_verified,
+                    verify_token=metrics_verification_token,
+                    dataset_config=dataset_config,
+                    dataset_split=dataset_split,
+                    dataset_revision=dataset_revision,
+                )
+            ],
+        )
+    }
+
+
+@validate_hf_hub_args
+def metadata_update(
+    repo_id: str,
+    metadata: dict,
+    *,
+    repo_type: Optional[str] = None,
+    overwrite: bool = False,
+    token: Optional[str] = None,
+    commit_message: Optional[str] = None,
+    commit_description: Optional[str] = None,
+    revision: Optional[str] = None,
+    create_pr: bool = False,
+    parent_commit: Optional[str] = None,
+) -> str:
+    """
+    Updates the metadata in the README.md of a repository on the Hugging Face Hub.
+    If the README.md file doesn't exist yet, a new one is created with metadata and
+    the default ModelCard or DatasetCard template. For `space` repo, an error is thrown
+    as a Space cannot exist without a `README.md` file.
+
+    Args:
+        repo_id (`str`):
+            The name of the repository.
+        metadata (`dict`):
+            A dictionary containing the metadata to be updated.
+        repo_type (`str`, *optional*):
+            Set to `"dataset"` or `"space"` if updating to a dataset or space,
+            `None` or `"model"` if updating to a model. Default is `None`.
+        overwrite (`bool`, *optional*, defaults to `False`):
+            If set to `True` an existing field can be overwritten, otherwise
+            attempting to overwrite an existing field will cause an error.
+        token (`str`, *optional*):
+            The Hugging Face authentication token.
+        commit_message (`str`, *optional*):
+            The summary / title / first line of the generated commit. Defaults to
+            `f"Update metadata with huggingface_hub"`
+        commit_description (`str` *optional*)
+            The description of the generated commit
+        revision (`str`, *optional*):
+            The git revision to commit from. Defaults to the head of the
+            `"main"` branch.
+        create_pr (`boolean`, *optional*):
+            Whether or not to create a Pull Request from `revision` with that commit.
+            Defaults to `False`.
+        parent_commit (`str`, *optional*):
+            The OID / SHA of the parent commit, as a hexadecimal string. Shorthands (7 first characters) are also supported.
+            If specified and `create_pr` is `False`, the commit will fail if `revision` does not point to `parent_commit`.
+            If specified and `create_pr` is `True`, the pull request will be created from `parent_commit`.
+            Specifying `parent_commit` ensures the repo has not changed before committing the changes, and can be
+            especially useful if the repo is updated / committed too concurrently.
+    Returns:
+        `str`: URL of the commit which updated the card metadata.
+
+    Example:
+        ```python
+        >>> from huggingface_hub import metadata_update
+        >>> metadata = {'model-index': [{'name': 'RoBERTa fine-tuned on ReactionGIF',
+        ...             'results': [{'dataset': {'name': 'ReactionGIF',
+        ...                                      'type': 'julien-c/reactiongif'},
+        ...                           'metrics': [{'name': 'Recall',
+        ...                                        'type': 'recall',
+        ...                                        'value': 0.7762102282047272}],
+        ...                          'task': {'name': 'Text Classification',
+        ...                                   'type': 'text-classification'}}]}]}
+        >>> url = metadata_update("hf-internal-testing/reactiongif-roberta-card", metadata)
+
+        ```
+    """
+    commit_message = commit_message if commit_message is not None else "Update metadata with huggingface_hub"
+
+    # Card class given repo_type
+    card_class: type[RepoCard]
+    if repo_type is None or repo_type == "model":
+        card_class = ModelCard
+    elif repo_type == "dataset":
+        card_class = DatasetCard
+    elif repo_type == "space":
+        card_class = RepoCard
+    else:
+        raise ValueError(f"Unknown repo_type: {repo_type}")
+
+    # Either load repo_card from the Hub or create an empty one.
+    # NOTE: Will not create the repo if it doesn't exist.
+    try:
+        card = card_class.load(repo_id, token=token, repo_type=repo_type)
+    except EntryNotFoundError:
+        if repo_type == "space":
+            raise ValueError("Cannot update metadata on a Space that doesn't contain a `README.md` file.")
+
+        # Initialize a ModelCard or DatasetCard from default template and no data.
+        # Cast to the concrete expected card type to satisfy type checkers.
+        card = card_class.from_template(CardData())  # type: ignore[return-value]
+
+    for key, value in metadata.items():
+        if key == "model-index":
+            # if the new metadata doesn't include a name, either use existing one or repo name
+            if "name" not in value[0]:
+                value[0]["name"] = getattr(card, "model_name", repo_id)
+            model_name, new_results = model_index_to_eval_results(value)
+            if card.data.eval_results is None:
+                card.data.eval_results = new_results
+                card.data.model_name = model_name
+            else:
+                existing_results = card.data.eval_results
+
+                # Iterate over new results
+                #   Iterate over existing results
+                #       If both results describe the same metric but value is different:
+                #           If overwrite=True: overwrite the metric value
+                #           Else: raise ValueError
+                #       Else: append new result to existing ones.
+                for new_result in new_results:
+                    result_found = False
+                    for existing_result in existing_results:
+                        if new_result.is_equal_except_value(existing_result):
+                            if new_result != existing_result and not overwrite:
+                                raise ValueError(
+                                    "You passed a new value for the existing metric"
+                                    f" 'name: {new_result.metric_name}, type: "
+                                    f"{new_result.metric_type}'. Set `overwrite=True`"
+                                    " to overwrite existing metrics."
+                                )
+                            result_found = True
+                            existing_result.metric_value = new_result.metric_value
+                            if existing_result.verified is True:
+                                existing_result.verify_token = new_result.verify_token
+                    if not result_found:
+                        card.data.eval_results.append(new_result)
+        else:
+            # Any metadata that is not a result metric
+            if card.data.get(key) is not None and not overwrite and card.data.get(key) != value:
+                raise ValueError(
+                    f"You passed a new value for the existing meta data field '{key}'."
+                    " Set `overwrite=True` to overwrite existing metadata."
+                )
+            else:
+                card.data[key] = value
+
+    return card.push_to_hub(
+        repo_id,
+        token=token,
+        repo_type=repo_type,
+        commit_message=commit_message,
+        commit_description=commit_description,
+        create_pr=create_pr,
+        revision=revision,
+        parent_commit=parent_commit,
+    )

data/conda_envs/evalclaw/lib/python3.11/site-packages/huggingface_hub/repocard_data.py

@@ -0,0 +1,770 @@
+import copy
+from collections import defaultdict
+from dataclasses import dataclass
+from typing import Any, Optional, Union
+
+from huggingface_hub.utils import logging, yaml_dump
+
+
+logger = logging.get_logger(__name__)
+
+
+@dataclass
+class EvalResult:
+    """
+    Flattened representation of individual evaluation results found in model-index of Model Cards.
+
+    For more information on the model-index spec, see https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1.
+
+    Args:
+        task_type (`str`):
+            The task identifier. Example: "image-classification".
+        dataset_type (`str`):
+            The dataset identifier. Example: "common_voice". Use dataset id from https://hf.co/datasets.
+        dataset_name (`str`):
+            A pretty name for the dataset. Example: "Common Voice (French)".
+        metric_type (`str`):
+            The metric identifier. Example: "wer". Use metric id from https://hf.co/metrics.
+        metric_value (`Any`):
+            The metric value. Example: 0.9 or "20.0 ± 1.2".
+        task_name (`str`, *optional*):
+            A pretty name for the task. Example: "Speech Recognition".
+        dataset_config (`str`, *optional*):
+            The name of the dataset configuration used in `load_dataset()`.
+            Example: fr in `load_dataset("common_voice", "fr")`. See the `datasets` docs for more info:
+            https://hf.co/docs/datasets/package_reference/loading_methods#datasets.load_dataset.name
+        dataset_split (`str`, *optional*):
+            The split used in `load_dataset()`. Example: "test".
+        dataset_revision (`str`, *optional*):
+            The revision (AKA Git Sha) of the dataset used in `load_dataset()`.
+            Example: 5503434ddd753f426f4b38109466949a1217c2bb
+        dataset_args (`dict[str, Any]`, *optional*):
+            The arguments passed during `Metric.compute()`. Example for `bleu`: `{"max_order": 4}`
+        metric_name (`str`, *optional*):
+            A pretty name for the metric. Example: "Test WER".
+        metric_config (`str`, *optional*):
+            The name of the metric configuration used in `load_metric()`.
+            Example: bleurt-large-512 in `load_metric("bleurt", "bleurt-large-512")`.
+            See the `datasets` docs for more info: https://huggingface.co/docs/datasets/v2.1.0/en/loading#load-configurations
+        metric_args (`dict[str, Any]`, *optional*):
+            The arguments passed during `Metric.compute()`. Example for `bleu`: max_order: 4
+        verified (`bool`, *optional*):
+            Indicates whether the metrics originate from Hugging Face's [evaluation service](https://huggingface.co/spaces/autoevaluate/model-evaluator) or not. Automatically computed by Hugging Face, do not set.
+        verify_token (`str`, *optional*):
+            A JSON Web Token that is used to verify whether the metrics originate from Hugging Face's [evaluation service](https://huggingface.co/spaces/autoevaluate/model-evaluator) or not.
+        source_name (`str`, *optional*):
+            The name of the source of the evaluation result. Example: "Open LLM Leaderboard".
+        source_url (`str`, *optional*):
+            The URL of the source of the evaluation result. Example: "https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard".
+    """
+
+    # Required
+
+    # The task identifier
+    # Example: automatic-speech-recognition
+    task_type: str
+
+    # The dataset identifier
+    # Example: common_voice. Use dataset id from https://hf.co/datasets
+    dataset_type: str
+
+    # A pretty name for the dataset.
+    # Example: Common Voice (French)
+    dataset_name: str
+
+    # The metric identifier
+    # Example: wer. Use metric id from https://hf.co/metrics
+    metric_type: str
+
+    # Value of the metric.
+    # Example: 20.0 or "20.0 ± 1.2"
+    metric_value: Any
+
+    # Optional
+
+    # A pretty name for the task.
+    # Example: Speech Recognition
+    task_name: Optional[str] = None
+
+    # The name of the dataset configuration used in `load_dataset()`.
+    # Example: fr in `load_dataset("common_voice", "fr")`.
+    # See the `datasets` docs for more info:
+    # https://huggingface.co/docs/datasets/package_reference/loading_methods#datasets.load_dataset.name
+    dataset_config: Optional[str] = None
+
+    # The split used in `load_dataset()`.
+    # Example: test
+    dataset_split: Optional[str] = None
+
+    # The revision (AKA Git Sha) of the dataset used in `load_dataset()`.
+    # Example: 5503434ddd753f426f4b38109466949a1217c2bb
+    dataset_revision: Optional[str] = None
+
+    # The arguments passed during `Metric.compute()`.
+    # Example for `bleu`: max_order: 4
+    dataset_args: Optional[dict[str, Any]] = None
+
+    # A pretty name for the metric.
+    # Example: Test WER
+    metric_name: Optional[str] = None
+
+    # The name of the metric configuration used in `load_metric()`.
+    # Example: bleurt-large-512 in `load_metric("bleurt", "bleurt-large-512")`.
+    # See the `datasets` docs for more info: https://huggingface.co/docs/datasets/v2.1.0/en/loading#load-configurations
+    metric_config: Optional[str] = None
+
+    # The arguments passed during `Metric.compute()`.
+    # Example for `bleu`: max_order: 4
+    metric_args: Optional[dict[str, Any]] = None
+
+    # Indicates whether the metrics originate from Hugging Face's [evaluation service](https://huggingface.co/spaces/autoevaluate/model-evaluator) or not. Automatically computed by Hugging Face, do not set.
+    verified: Optional[bool] = None
+
+    # A JSON Web Token that is used to verify whether the metrics originate from Hugging Face's [evaluation service](https://huggingface.co/spaces/autoevaluate/model-evaluator) or not.
+    verify_token: Optional[str] = None
+
+    # The name of the source of the evaluation result.
+    # Example: Open LLM Leaderboard
+    source_name: Optional[str] = None
+
+    # The URL of the source of the evaluation result.
+    # Example: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard
+    source_url: Optional[str] = None
+
+    @property
+    def unique_identifier(self) -> tuple:
+        """Returns a tuple that uniquely identifies this evaluation."""
+        return (
+            self.task_type,
+            self.dataset_type,
+            self.dataset_config,
+            self.dataset_split,
+            self.dataset_revision,
+        )
+
+    def is_equal_except_value(self, other: "EvalResult") -> bool:
+        """
+        Return True if `self` and `other` describe exactly the same metric but with a
+        different value.
+        """
+        for key, _ in self.__dict__.items():
+            if key == "metric_value":
+                continue
+            # For metrics computed by Hugging Face's evaluation service, `verify_token` is derived from `metric_value`,
+            # so we exclude it here in the comparison.
+            if key != "verify_token" and getattr(self, key) != getattr(other, key):
+                return False
+        return True
+
+    def __post_init__(self) -> None:
+        if self.source_name is not None and self.source_url is None:
+            raise ValueError("If `source_name` is provided, `source_url` must also be provided.")
+
+
+@dataclass
+class CardData:
+    """Structure containing metadata from a RepoCard.
+
+    [`CardData`] is the parent class of [`ModelCardData`] and [`DatasetCardData`].
+
+    Metadata can be exported as a dictionary or YAML. Export can be customized to alter the representation of the data
+    (example: flatten evaluation results). `CardData` behaves as a dictionary (can get, pop, set values) but do not
+    inherit from `dict` to allow this export step.
+    """
+
+    def __init__(self, ignore_metadata_errors: bool = False, **kwargs):
+        self.__dict__.update(kwargs)
+
+    def to_dict(self):
+        """Converts CardData to a dict.
+
+        Returns:
+            `dict`: CardData represented as a dictionary ready to be dumped to a YAML
+            block for inclusion in a README.md file.
+        """
+
+        data_dict = copy.deepcopy(self.__dict__)
+        self._to_dict(data_dict)
+        return {key: value for key, value in data_dict.items() if value is not None}
+
+    def _to_dict(self, data_dict):
+        """Use this method in child classes to alter the dict representation of the data. Alter the dict in-place.
+
+        Args:
+            data_dict (`dict`): The raw dict representation of the card data.
+        """
+        pass
+
+    def to_yaml(self, line_break=None, original_order: Optional[list[str]] = None) -> str:
+        """Dumps CardData to a YAML block for inclusion in a README.md file.
+
+        Args:
+            line_break (str, *optional*):
+                The line break to use when dumping to yaml.
+
+        Returns:
+            `str`: CardData represented as a YAML block.
+        """
+        if original_order:
+            self.__dict__ = {
+                k: self.__dict__[k]
+                for k in original_order + list(set(self.__dict__.keys()) - set(original_order))
+                if k in self.__dict__
+            }
+        return yaml_dump(self.to_dict(), sort_keys=False, line_break=line_break).strip()
+
+    def __repr__(self):
+        return repr(self.__dict__)
+
+    def __str__(self):
+        return self.to_yaml()
+
+    def get(self, key: str, default: Any = None) -> Any:
+        """Get value for a given metadata key."""
+        value = self.__dict__.get(key)
+        return default if value is None else value
+
+    def pop(self, key: str, default: Any = None) -> Any:
+        """Pop value for a given metadata key."""
+        return self.__dict__.pop(key, default)
+
+    def __getitem__(self, key: str) -> Any:
+        """Get value for a given metadata key."""
+        return self.__dict__[key]
+
+    def __setitem__(self, key: str, value: Any) -> None:
+        """Set value for a given metadata key."""
+        self.__dict__[key] = value
+
+    def __contains__(self, key: str) -> bool:
+        """Check if a given metadata key is set."""
+        return key in self.__dict__
+
+    def __len__(self) -> int:
+        """Return the number of metadata keys set."""
+        return len(self.__dict__)
+
+
+def _validate_eval_results(
+    eval_results: Optional[Union[EvalResult, list[EvalResult]]],
+    model_name: Optional[str],
+) -> list[EvalResult]:
+    if eval_results is None:
+        return []
+    if isinstance(eval_results, EvalResult):
+        eval_results = [eval_results]
+    if not isinstance(eval_results, list) or not all(isinstance(r, EvalResult) for r in eval_results):
+        raise ValueError(
+            f"`eval_results` should be of type `EvalResult` or a list of `EvalResult`, got {type(eval_results)}."
+        )
+    if model_name is None:
+        raise ValueError("Passing `eval_results` requires `model_name` to be set.")
+    return eval_results
+
+
+class ModelCardData(CardData):
+    """Model Card Metadata that is used by Hugging Face Hub when included at the top of your README.md
+
+    Args:
+        base_model (`str` or `list[str]`, *optional*):
+            The identifier of the base model from which the model derives. This is applicable for example if your model is a
+            fine-tune or adapter of an existing model. The value must be the ID of a model on the Hub (or a list of IDs
+            if your model derives from multiple models). Defaults to None.
+        datasets (`Union[str, list[str]]`, *optional*):
+            Dataset or list of datasets that were used to train this model. Should be a dataset ID
+            found on https://hf.co/datasets. Defaults to None.
+        eval_results (`Union[list[EvalResult], EvalResult]`, *optional*):
+            List of `huggingface_hub.EvalResult` that define evaluation results of the model. If provided,
+            `model_name` is used to as a name on PapersWithCode's leaderboards. Defaults to `None`.
+        language (`Union[str, list[str]]`, *optional*):
+            Language of model's training data or metadata. It must be an ISO 639-1, 639-2 or
+            639-3 code (two/three letters), or a special value like "code", "multilingual". Defaults to `None`.
+        library_name (`str`, *optional*):
+            Name of library used by this model. Example: keras or any library from
+            https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/src/model-libraries.ts.
+            Defaults to None.
+        license (`str`, *optional*):
+            License of this model. Example: apache-2.0 or any license from
+            https://huggingface.co/docs/hub/repositories-licenses. Defaults to None.
+        license_name (`str`, *optional*):
+            Name of the license of this model. Defaults to None. To be used in conjunction with `license_link`.
+            Common licenses (Apache-2.0, MIT, CC-BY-SA-4.0) do not need a name. In that case, use `license` instead.
+        license_link (`str`, *optional*):
+            Link to the license of this model. Defaults to None. To be used in conjunction with `license_name`.
+            Common licenses (Apache-2.0, MIT, CC-BY-SA-4.0) do not need a link. In that case, use `license` instead.
+        metrics (`list[str]`, *optional*):
+            List of metrics used to evaluate this model. Should be a metric name that can be found
+            at https://hf.co/metrics. Example: 'accuracy'. Defaults to None.
+        model_name (`str`, *optional*):
+            A name for this model. It is used along with
+            `eval_results` to construct the `model-index` within the card's metadata. The name
+            you supply here is what will be used on PapersWithCode's leaderboards. If None is provided
+            then the repo name is used as a default. Defaults to None.
+        pipeline_tag (`str`, *optional*):
+            The pipeline tag associated with the model. Example: "text-classification".
+        tags (`list[str]`, *optional*):
+            List of tags to add to your model that can be used when filtering on the Hugging
+            Face Hub. Defaults to None.
+        ignore_metadata_errors (`str`):
+            If True, errors while parsing the metadata section will be ignored. Some information might be lost during
+            the process. Use it at your own risk.
+        kwargs (`dict`, *optional*):
+            Additional metadata that will be added to the model card. Defaults to None.
+
+    Example:
+        ```python
+        >>> from huggingface_hub import ModelCardData
+        >>> card_data = ModelCardData(
+        ...     language="en",
+        ...     license="mit",
+        ...     library_name="timm",
+        ...     tags=['image-classification', 'resnet'],
+        ... )
+        >>> card_data.to_dict()
+        {'language': 'en', 'license': 'mit', 'library_name': 'timm', 'tags': ['image-classification', 'resnet']}
+
+        ```
+    """
+
+    def __init__(
+        self,
+        *,
+        base_model: Optional[Union[str, list[str]]] = None,
+        datasets: Optional[Union[str, list[str]]] = None,
+        eval_results: Optional[list[EvalResult]] = None,
+        language: Optional[Union[str, list[str]]] = None,
+        library_name: Optional[str] = None,
+        license: Optional[str] = None,
+        license_name: Optional[str] = None,
+        license_link: Optional[str] = None,
+        metrics: Optional[list[str]] = None,
+        model_name: Optional[str] = None,
+        pipeline_tag: Optional[str] = None,
+        tags: Optional[list[str]] = None,
+        ignore_metadata_errors: bool = False,
+        **kwargs,
+    ):
+        self.base_model = base_model
+        self.datasets = datasets
+        self.eval_results = eval_results
+        self.language = language
+        self.library_name = library_name
+        self.license = license
+        self.license_name = license_name
+        self.license_link = license_link
+        self.metrics = metrics
+        self.model_name = model_name
+        self.pipeline_tag = pipeline_tag
+        self.tags = _to_unique_list(tags)
+
+        model_index = kwargs.pop("model-index", None)
+        if model_index:
+            try:
+                model_name, eval_results = model_index_to_eval_results(model_index)
+                self.model_name = model_name
+                self.eval_results = eval_results
+            except (KeyError, TypeError) as error:
+                if ignore_metadata_errors:
+                    logger.warning("Invalid model-index. Not loading eval results into CardData.")
+                else:
+                    raise ValueError(
+                        f"Invalid `model_index` in metadata cannot be parsed: {error.__class__} {error}. Pass"
+                        " `ignore_metadata_errors=True` to ignore this error while loading a Model Card. Warning:"
+                        " some information will be lost. Use it at your own risk."
+                    )
+
+        super().__init__(**kwargs)
+
+        if self.eval_results:
+            try:
+                self.eval_results = _validate_eval_results(self.eval_results, self.model_name)
+            except Exception as e:
+                if ignore_metadata_errors:
+                    logger.warning(f"Failed to validate eval_results: {e}. Not loading eval results into CardData.")
+                else:
+                    raise ValueError(f"Failed to validate eval_results: {e}") from e
+
+    def _to_dict(self, data_dict):
+        """Format the internal data dict. In this case, we convert eval results to a valid model index"""
+        if self.eval_results is not None:
+            data_dict["model-index"] = eval_results_to_model_index(self.model_name, self.eval_results)  # type: ignore
+            del data_dict["eval_results"], data_dict["model_name"]
+
+
+class DatasetCardData(CardData):
+    """Dataset Card Metadata that is used by Hugging Face Hub when included at the top of your README.md
+
+    Args:
+        language (`list[str]`, *optional*):
+            Language of dataset's data or metadata. It must be an ISO 639-1, 639-2 or
+            639-3 code (two/three letters), or a special value like "code", "multilingual".
+        license (`Union[str, list[str]]`, *optional*):
+            License(s) of this dataset. Example: apache-2.0 or any license from
+            https://huggingface.co/docs/hub/repositories-licenses.
+        annotations_creators (`Union[str, list[str]]`, *optional*):
+            How the annotations for the dataset were created.
+            Options are: 'found', 'crowdsourced', 'expert-generated', 'machine-generated', 'no-annotation', 'other'.
+        language_creators (`Union[str, list[str]]`, *optional*):
+            How the text-based data in the dataset was created.
+            Options are: 'found', 'crowdsourced', 'expert-generated', 'machine-generated', 'other'
+        multilinguality (`Union[str, list[str]]`, *optional*):
+            Whether the dataset is multilingual.
+            Options are: 'monolingual', 'multilingual', 'translation', 'other'.
+        size_categories (`Union[str, list[str]]`, *optional*):
+            The number of examples in the dataset. Options are: 'n<1K', '1K<n<10K', '10K<n<100K',
+            '100K<n<1M', '1M<n<10M', '10M<n<100M', '100M<n<1B', '1B<n<10B', '10B<n<100B', '100B<n<1T', 'n>1T', and 'other'.
+        source_datasets (`list[str]]`, *optional*):
+            Indicates whether the dataset is an original dataset or extended from another existing dataset.
+            Options are: 'original' and 'extended'.
+        task_categories (`Union[str, list[str]]`, *optional*):
+            What categories of task does the dataset support?
+        task_ids (`Union[str, list[str]]`, *optional*):
+            What specific tasks does the dataset support?
+        paperswithcode_id (`str`, *optional*):
+            ID of the dataset on PapersWithCode.
+        pretty_name (`str`, *optional*):
+            A more human-readable name for the dataset. (ex. "Cats vs. Dogs")
+        train_eval_index (`dict`, *optional*):
+            A dictionary that describes the necessary spec for doing evaluation on the Hub.
+            If not provided, it will be gathered from the 'train-eval-index' key of the kwargs.
+        config_names (`Union[str, list[str]]`, *optional*):
+            A list of the available dataset configs for the dataset.
+    """
+
+    def __init__(
+        self,
+        *,
+        language: Optional[Union[str, list[str]]] = None,
+        license: Optional[Union[str, list[str]]] = None,
+        annotations_creators: Optional[Union[str, list[str]]] = None,
+        language_creators: Optional[Union[str, list[str]]] = None,
+        multilinguality: Optional[Union[str, list[str]]] = None,
+        size_categories: Optional[Union[str, list[str]]] = None,
+        source_datasets: Optional[list[str]] = None,
+        task_categories: Optional[Union[str, list[str]]] = None,
+        task_ids: Optional[Union[str, list[str]]] = None,
+        paperswithcode_id: Optional[str] = None,
+        pretty_name: Optional[str] = None,
+        train_eval_index: Optional[dict] = None,
+        config_names: Optional[Union[str, list[str]]] = None,
+        ignore_metadata_errors: bool = False,
+        **kwargs,
+    ):
+        self.annotations_creators = annotations_creators
+        self.language_creators = language_creators
+        self.language = language
+        self.license = license
+        self.multilinguality = multilinguality
+        self.size_categories = size_categories
+        self.source_datasets = source_datasets
+        self.task_categories = task_categories
+        self.task_ids = task_ids
+        self.paperswithcode_id = paperswithcode_id
+        self.pretty_name = pretty_name
+        self.config_names = config_names
+
+        # TODO - maybe handle this similarly to EvalResult?
+        self.train_eval_index = train_eval_index or kwargs.pop("train-eval-index", None)
+        super().__init__(**kwargs)
+
+    def _to_dict(self, data_dict):
+        data_dict["train-eval-index"] = data_dict.pop("train_eval_index")
+
+
+class SpaceCardData(CardData):
+    """Space Card Metadata that is used by Hugging Face Hub when included at the top of your README.md
+
+    To get an exhaustive reference of Spaces configuration, please visit https://huggingface.co/docs/hub/spaces-config-reference#spaces-configuration-reference.
+
+    Args:
+        title (`str`, *optional*)
+            Title of the Space.
+        sdk (`str`, *optional*)
+            SDK of the Space (one of `gradio`, `streamlit`, `docker`, or `static`).
+        sdk_version (`str`, *optional*)
+            Version of the used SDK (if Gradio/Streamlit sdk).
+        python_version (`str`, *optional*)
+            Python version used in the Space (if Gradio/Streamlit sdk).
+        app_file (`str`, *optional*)
+            Path to your main application file (which contains either gradio or streamlit Python code, or static html code).
+            Path is relative to the root of the repository.
+        app_port (`str`, *optional*)
+            Port on which your application is running. Used only if sdk is `docker`.
+        license (`str`, *optional*)
+            License of this model. Example: apache-2.0 or any license from
+            https://huggingface.co/docs/hub/repositories-licenses.
+        duplicated_from (`str`, *optional*)
+            ID of the original Space if this is a duplicated Space.
+        models (list[`str`], *optional*)
+            List of models related to this Space. Should be a dataset ID found on https://hf.co/models.
+        datasets (`list[str]`, *optional*)
+            List of datasets related to this Space. Should be a dataset ID found on https://hf.co/datasets.
+        tags (`list[str]`, *optional*)
+            List of tags to add to your Space that can be used when filtering on the Hub.
+        ignore_metadata_errors (`str`):
+            If True, errors while parsing the metadata section will be ignored. Some information might be lost during
+            the process. Use it at your own risk.
+        kwargs (`dict`, *optional*):
+            Additional metadata that will be added to the space card.
+
+    Example:
+        ```python
+        >>> from huggingface_hub import SpaceCardData
+        >>> card_data = SpaceCardData(
+        ...     title="Dreambooth Training",
+        ...     license="mit",
+        ...     sdk="gradio",
+        ...     duplicated_from="multimodalart/dreambooth-training"
+        ... )
+        >>> card_data.to_dict()
+        {'title': 'Dreambooth Training', 'sdk': 'gradio', 'license': 'mit', 'duplicated_from': 'multimodalart/dreambooth-training'}
+        ```
+    """
+
+    def __init__(
+        self,
+        *,
+        title: Optional[str] = None,
+        sdk: Optional[str] = None,
+        sdk_version: Optional[str] = None,
+        python_version: Optional[str] = None,
+        app_file: Optional[str] = None,
+        app_port: Optional[int] = None,
+        license: Optional[str] = None,
+        duplicated_from: Optional[str] = None,
+        models: Optional[list[str]] = None,
+        datasets: Optional[list[str]] = None,
+        tags: Optional[list[str]] = None,
+        ignore_metadata_errors: bool = False,
+        **kwargs,
+    ):
+        self.title = title
+        self.sdk = sdk
+        self.sdk_version = sdk_version
+        self.python_version = python_version
+        self.app_file = app_file
+        self.app_port = app_port
+        self.license = license
+        self.duplicated_from = duplicated_from
+        self.models = models
+        self.datasets = datasets
+        self.tags = _to_unique_list(tags)
+        super().__init__(**kwargs)
+
+
+def model_index_to_eval_results(model_index: list[dict[str, Any]]) -> tuple[str, list[EvalResult]]:
+    """Takes in a model index and returns the model name and a list of `huggingface_hub.EvalResult` objects.
+
+    A detailed spec of the model index can be found here:
+    https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
+
+    Args:
+        model_index (`list[dict[str, Any]]`):
+            A model index data structure, likely coming from a README.md file on the
+            Hugging Face Hub.
+
+    Returns:
+        model_name (`str`):
+            The name of the model as found in the model index. This is used as the
+            identifier for the model on leaderboards like PapersWithCode.
+        eval_results (`list[EvalResult]`):
+            A list of `huggingface_hub.EvalResult` objects containing the metrics
+            reported in the provided model_index.
+
+    Example:
+        ```python
+        >>> from huggingface_hub.repocard_data import model_index_to_eval_results
+        >>> # Define a minimal model index
+        >>> model_index = [
+        ...     {
+        ...         "name": "my-cool-model",
+        ...         "results": [
+        ...             {
+        ...                 "task": {
+        ...                     "type": "image-classification"
+        ...                 },
+        ...                 "dataset": {
+        ...                     "type": "beans",
+        ...                     "name": "Beans"
+        ...                 },
+        ...                 "metrics": [
+        ...                     {
+        ...                         "type": "accuracy",
+        ...                         "value": 0.9
+        ...                     }
+        ...                 ]
+        ...             }
+        ...         ]
+        ...     }
+        ... ]
+        >>> model_name, eval_results = model_index_to_eval_results(model_index)
+        >>> model_name
+        'my-cool-model'
+        >>> eval_results[0].task_type
+        'image-classification'
+        >>> eval_results[0].metric_type
+        'accuracy'
+
+        ```
+    """
+
+    eval_results = []
+    for elem in model_index:
+        name = elem["name"]
+        results = elem["results"]
+        for result in results:
+            task_type = result["task"]["type"]
+            task_name = result["task"].get("name")
+            dataset_type = result["dataset"]["type"]
+            dataset_name = result["dataset"]["name"]
+            dataset_config = result["dataset"].get("config")
+            dataset_split = result["dataset"].get("split")
+            dataset_revision = result["dataset"].get("revision")
+            dataset_args = result["dataset"].get("args")
+            source_name = result.get("source", {}).get("name")
+            source_url = result.get("source", {}).get("url")
+
+            for metric in result["metrics"]:
+                metric_type = metric["type"]
+                metric_value = metric["value"]
+                metric_name = metric.get("name")
+                metric_args = metric.get("args")
+                metric_config = metric.get("config")
+                verified = metric.get("verified")
+                verify_token = metric.get("verifyToken")
+
+                eval_result = EvalResult(
+                    task_type=task_type,  # Required
+                    dataset_type=dataset_type,  # Required
+                    dataset_name=dataset_name,  # Required
+                    metric_type=metric_type,  # Required
+                    metric_value=metric_value,  # Required
+                    task_name=task_name,
+                    dataset_config=dataset_config,
+                    dataset_split=dataset_split,
+                    dataset_revision=dataset_revision,
+                    dataset_args=dataset_args,
+                    metric_name=metric_name,
+                    metric_args=metric_args,
+                    metric_config=metric_config,
+                    verified=verified,
+                    verify_token=verify_token,
+                    source_name=source_name,
+                    source_url=source_url,
+                )
+                eval_results.append(eval_result)
+    return name, eval_results
+
+
+def _remove_none(obj):
+    """
+    Recursively remove `None` values from a dict. Borrowed from: https://stackoverflow.com/a/20558778
+    """
+    if isinstance(obj, (list, tuple, set)):
+        return type(obj)(_remove_none(x) for x in obj if x is not None)
+    elif isinstance(obj, dict):
+        return type(obj)((_remove_none(k), _remove_none(v)) for k, v in obj.items() if k is not None and v is not None)
+    else:
+        return obj
+
+
+def eval_results_to_model_index(model_name: str, eval_results: list[EvalResult]) -> list[dict[str, Any]]:
+    """Takes in given model name and list of `huggingface_hub.EvalResult` and returns a
+    valid model-index that will be compatible with the format expected by the
+    Hugging Face Hub.
+
+    Args:
+        model_name (`str`):
+            Name of the model (ex. "my-cool-model"). This is used as the identifier
+            for the model on leaderboards like PapersWithCode.
+        eval_results (`list[EvalResult]`):
+            List of `huggingface_hub.EvalResult` objects containing the metrics to be
+            reported in the model-index.
+
+    Returns:
+        model_index (`list[dict[str, Any]]`): The eval_results converted to a model-index.
+
+    Example:
+        ```python
+        >>> from huggingface_hub.repocard_data import eval_results_to_model_index, EvalResult
+        >>> # Define minimal eval_results
+        >>> eval_results = [
+        ...     EvalResult(
+        ...         task_type="image-classification",  # Required
+        ...         dataset_type="beans",  # Required
+        ...         dataset_name="Beans",  # Required
+        ...         metric_type="accuracy",  # Required
+        ...         metric_value=0.9,  # Required
+        ...     )
+        ... ]
+        >>> eval_results_to_model_index("my-cool-model", eval_results)
+        [{'name': 'my-cool-model', 'results': [{'task': {'type': 'image-classification'}, 'dataset': {'name': 'Beans', 'type': 'beans'}, 'metrics': [{'type': 'accuracy', 'value': 0.9}]}]}]
+
+        ```
+    """
+
+    # Metrics are reported on a unique task-and-dataset basis.
+    # Here, we make a map of those pairs and the associated EvalResults.
+    task_and_ds_types_map: dict[Any, list[EvalResult]] = defaultdict(list)
+    for eval_result in eval_results:
+        task_and_ds_types_map[eval_result.unique_identifier].append(eval_result)
+
+    # Use the map from above to generate the model index data.
+    model_index_data: list[dict[str, Any]] = []
+    for results in task_and_ds_types_map.values():
+        # All items from `results` share same metadata
+        sample_result = results[0]
+        data: dict[str, Any] = {
+            "task": {
+                "type": sample_result.task_type,
+                "name": sample_result.task_name,
+            },
+            "dataset": {
+                "name": sample_result.dataset_name,
+                "type": sample_result.dataset_type,
+                "config": sample_result.dataset_config,
+                "split": sample_result.dataset_split,
+                "revision": sample_result.dataset_revision,
+                "args": sample_result.dataset_args,
+            },
+            "metrics": [
+                {
+                    "type": result.metric_type,
+                    "value": result.metric_value,
+                    "name": result.metric_name,
+                    "config": result.metric_config,
+                    "args": result.metric_args,
+                    "verified": result.verified,
+                    "verifyToken": result.verify_token,
+                }
+                for result in results
+            ],
+        }
+        if sample_result.source_url is not None:
+            source: dict[str, str] = {
+                "url": sample_result.source_url,
+            }
+            if sample_result.source_name is not None:
+                source["name"] = sample_result.source_name
+            data["source"] = source
+        model_index_data.append(data)
+
+    # TODO - Check if there cases where this list is longer than one?
+    # Finally, the model index itself is list of dicts.
+    model_index = [
+        {
+            "name": model_name,
+            "results": model_index_data,
+        }
+    ]
+    return _remove_none(model_index)
+
+
+def _to_unique_list(tags: Optional[list[str]]) -> Optional[list[str]]:
+    if tags is None:
+        return tags
+    unique_tags = []  # make tags unique + keep order explicitly
+    for tag in tags:
+        if tag not in unique_tags:
+            unique_tags.append(tag)
+    return unique_tags

data/conda_envs/evalclaw/lib/python3.11/site-packages/ipykernel-7.2.0.dist-info/INSTALLER

@@ -0,0 +1 @@
+pip

data/conda_envs/evalclaw/lib/python3.11/site-packages/ipykernel-7.2.0.dist-info/METADATA

@@ -0,0 +1,121 @@
+Metadata-Version: 2.4
+Name: ipykernel
+Version: 7.2.0
+Summary: IPython Kernel for Jupyter
+Project-URL: Homepage, https://ipython.org
+Project-URL: Documentation, https://ipykernel.readthedocs.io
+Project-URL: Source, https://github.com/ipython/ipykernel
+Project-URL: Tracker, https://github.com/ipython/ipykernel/issues
+Author-email: IPython Development Team <ipython-dev@scipy.org>
+License-Expression: BSD-3-Clause
+License-File: LICENSE
+Keywords: Interactive,Interpreter,Shell,Web
+Classifier: Intended Audience :: Developers
+Classifier: Intended Audience :: Science/Research
+Classifier: Intended Audience :: System Administrators
+Classifier: Programming Language :: Python
+Classifier: Programming Language :: Python :: 3
+Requires-Python: >=3.10
+Requires-Dist: appnope>=0.1.2; platform_system == 'Darwin'
+Requires-Dist: comm>=0.1.1
+Requires-Dist: debugpy>=1.6.5
+Requires-Dist: ipython>=7.23.1
+Requires-Dist: jupyter-client>=8.8.0
+Requires-Dist: jupyter-core!=6.0.*,>=5.1
+Requires-Dist: matplotlib-inline>=0.1
+Requires-Dist: nest-asyncio>=1.4
+Requires-Dist: packaging>=22
+Requires-Dist: psutil>=5.7
+Requires-Dist: pyzmq>=25
+Requires-Dist: tornado>=6.4.1
+Requires-Dist: traitlets>=5.4.0
+Provides-Extra: cov
+Requires-Dist: coverage[toml]; extra == 'cov'
+Requires-Dist: matplotlib; extra == 'cov'
+Requires-Dist: pytest-cov; extra == 'cov'
+Requires-Dist: trio; extra == 'cov'
+Provides-Extra: docs
+Requires-Dist: intersphinx-registry; extra == 'docs'
+Requires-Dist: myst-parser; extra == 'docs'
+Requires-Dist: pydata-sphinx-theme; extra == 'docs'
+Requires-Dist: sphinx-autodoc-typehints; extra == 'docs'
+Requires-Dist: sphinx<8.2.0; extra == 'docs'
+Requires-Dist: sphinxcontrib-github-alt; extra == 'docs'
+Requires-Dist: sphinxcontrib-spelling; extra == 'docs'
+Requires-Dist: trio; extra == 'docs'
+Provides-Extra: pyqt5
+Requires-Dist: pyqt5; extra == 'pyqt5'
+Provides-Extra: pyside6
+Requires-Dist: pyside6; extra == 'pyside6'
+Provides-Extra: test
+Requires-Dist: flaky; extra == 'test'
+Requires-Dist: ipyparallel; extra == 'test'
+Requires-Dist: pre-commit; extra == 'test'
+Requires-Dist: pytest-asyncio>=0.23.5; extra == 'test'
+Requires-Dist: pytest-cov; extra == 'test'
+Requires-Dist: pytest-timeout; extra == 'test'
+Requires-Dist: pytest<10,>=7.0; extra == 'test'
+Description-Content-Type: text/markdown
+
+# IPython Kernel for Jupyter
+
+[![Build Status](https://github.com/ipython/ipykernel/actions/workflows/ci.yml/badge.svg?query=branch%3Amain++)](https://github.com/ipython/ipykernel/actions/workflows/ci.yml/badge.svg?query=branch%3Amain++)
+[![Documentation Status](https://readthedocs.org/projects/ipykernel/badge/?version=latest)](http://ipykernel.readthedocs.io/en/latest/?badge=latest)
+
+This package provides the IPython kernel for Jupyter.
+
+## Installation from source
+
+1. `git clone`
+1. `cd ipykernel`
+1. `pip install -e ".[test]"`
+
+After that, all normal `ipython` commands will use this newly-installed version of the kernel.
+
+## Running tests
+
+Follow the instructions from `Installation from source`.
+
+and then from the root directory
+
+```bash
+pytest
+```
+
+## Running tests with coverage
+
+Follow the instructions from `Installation from source`.
+
+and then from the root directory
+
+```bash
+pytest -vv -s --cov ipykernel --cov-branch --cov-report term-missing:skip-covered --durations 10
+```
+
+## About the IPython Development Team
+
+The IPython Development Team is the set of all contributors to the IPython project.
+This includes all of the IPython subprojects.
+
+The core team that coordinates development on GitHub can be found here:
+https://github.com/ipython/.
+
+## Our Copyright Policy
+
+IPython uses a shared copyright model. Each contributor maintains copyright
+over their contributions to IPython. But, it is important to note that these
+contributions are typically only changes to the repositories. Thus, the IPython
+source code, in its entirety is not the copyright of any single person or
+institution. Instead, it is the collective copyright of the entire IPython
+Development Team. If individual contributors want to maintain a record of what
+changes/contributions they have specific copyright on, they should indicate
+their copyright in the commit message of the change, when they commit the
+change to one of the IPython repositories.
+
+With this in mind, the following banner should be used in any source code file
+to indicate the copyright and license terms:
+
+```
+# Copyright (c) IPython Development Team.
+# Distributed under the terms of the Modified BSD License.
+```

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data/conda_envs/evalclaw/lib/python3.11/site-packages/ipykernel-7.2.0.dist-info/WHEEL

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+Generator: hatchling 1.28.0
+Root-Is-Purelib: true
+Tag: py3-none-any

data/conda_envs/evalclaw/lib/python3.11/site-packages/ipython-9.10.0.dist-info/INSTALLER

@@ -0,0 +1 @@
+pip

data/conda_envs/evalclaw/lib/python3.11/site-packages/ipython-9.10.0.dist-info/METADATA

@@ -0,0 +1,115 @@
+Metadata-Version: 2.4
+Name: ipython
+Version: 9.10.0
+Summary: IPython: Productive Interactive Computing
+Author: The IPython Development Team
+Author-email: ipython-dev@python.org
+License-Expression: BSD-3-Clause
+Project-URL: Homepage, https://ipython.org
+Project-URL: Documentation, https://ipython.readthedocs.io/
+Project-URL: Funding, https://jupyter.org/about#donate
+Project-URL: Source, https://github.com/ipython/ipython
+Project-URL: Tracker, https://github.com/ipython/ipython/issues
+Keywords: Interactive,Interpreter,Shell,Embedding
+Platform: Linux
+Platform: Mac OSX
+Platform: Windows
+Classifier: Framework :: IPython
+Classifier: Framework :: Jupyter
+Classifier: Intended Audience :: Developers
+Classifier: Intended Audience :: Science/Research
+Classifier: Programming Language :: Python
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3 :: Only
+Classifier: Topic :: System :: Shells
+Requires-Python: >=3.11
+Description-Content-Type: text/x-rst
+License-File: LICENSE
+License-File: COPYING.rst
+Requires-Dist: colorama>=0.4.4; sys_platform == "win32"
+Requires-Dist: decorator>=4.3.2
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data/conda_envs/evalclaw/lib/python3.11/site-packages/ipython-9.10.0.dist-info/top_level.txt

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data/conda_envs/evalclaw/lib/python3.11/site-packages/ipython_pygments_lexers-1.1.1.dist-info/INSTALLER

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