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# -*- coding: utf-8 -*-
# Copyright (c) Alibaba, Inc. and its affiliates.

import json
from typing import Any, Dict, Generator, List, Union

import dashscope
from dashscope.aigc.generation import Generation
from dashscope.api_entities.chat_completion_types import (
    ChatCompletion,
    ChatCompletionChunk,
)
from dashscope.api_entities.dashscope_response import (
    GenerationResponse,
    Message,
)
from dashscope.client.base_api import BaseAioApi, CreateMixin
from dashscope.common.error import InputRequired, ModelRequired
from dashscope.common.utils import _get_task_group_and_task


class Completions(CreateMixin):
    """Support openai compatible chat completion interface."""

    SUB_PATH = ""

    @classmethod
    def create(
        cls,
        *,
        model: str,
        messages: List[Message],
        stream: bool = False,
        temperature: float = None,
        top_p: float = None,
        top_k: int = None,
        stop: Union[List[str], List[List[int]]] = None,
        max_tokens: int = None,
        repetition_penalty: float = None,
        api_key: str = None,
        workspace: str = None,
        extra_headers: Dict = None,
        extra_body: Dict = None,
        **kwargs,
    ) -> Union[ChatCompletion, Generator[ChatCompletionChunk, None, None]]:
        """Call openai compatible chat completion model service.
        Args:
            model (str): The requested model, such as qwen-long
            messages (list): The generation messages.
                examples:
                    [{'role': 'user',
                      'content': 'The weather is fine today.'},
                      {'role': 'assistant', 'content': 'Suitable for outings'}]
            stream(bool, `optional`): Enable server-sent events
                (ref: https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events/Using_server-sent_events)  # noqa E501  # pylint: disable=line-too-long
                the result will back partially[qwen-turbo,bailian-v1].
            temperature(float, `optional`): Used to control the degree
                of randomness and diversity. Specifically, the temperature
                value controls the degree to which the probability distribution
                of each candidate word is smoothed when generating text.
                A higher temperature value will reduce the peak value of
                the probability, allowing more low-probability words to be
                selected, and the generated results will be more diverse;
                while a lower temperature value will enhance the peak value
                of the probability, making it easier for high-probability
                words to be selected, the generated results are more
                deterministic.
            top_p(float, `optional`): A sampling strategy, called nucleus
                sampling, where the model considers the results of the
                tokens with top_p probability mass. So 0.1 means only
                the tokens comprising the top 10% probability mass are
                considered.
            top_k(int, `optional`): The size of the sample candidate set when generated.  # noqa E501  # pylint: disable=line-too-long
                For example, when the value is 50, only the 50 highest-scoring tokens  # noqa E501
                in a single generation form a randomly sampled candidate set. # noqa E501
                The larger the value, the higher the randomness generated;  # noqa E501
                the smaller the value, the higher the certainty generated. # noqa E501
                The default value is 0, which means the top_k policy is  # noqa E501
                not enabled. At this time, only the top_p policy takes effect. # noqa E501
            stop(list[str] or list[list[int]], `optional`): Used to control the generation to stop  # noqa E501  # pylint: disable=line-too-long
                when encountering setting str or token ids, the result will not include # noqa E501
                stop words or tokens.
            max_tokens(int, `optional`): The maximum token num expected to be output. It should be # noqa E501  # pylint: disable=line-too-long
                noted that the length generated by the model will only be less than max_tokens,  # noqa E501  # pylint: disable=line-too-long
                not necessarily equal to it. If max_tokens is set too large, the service will # noqa E501  # pylint: disable=line-too-long
                directly prompt that the length exceeds the limit. It is generally # noqa E501
                not recommended to set this value.
            repetition_penalty(float, `optional`): Used to control the repeatability when generating models.  # noqa E501  # pylint: disable=line-too-long
                Increasing repetition_penalty can reduce the duplication of model generation.  # noqa E501  # pylint: disable=line-too-long
                1.0 means no punishment.
            api_key (str, optional): The api api_key, can be None,
                if None, will get by default rule.
            workspace (str, optional): The bailian workspace id.
            **kwargs:
                timeout: set request timeout.
        Raises:
            InvalidInput: The history and auto_history are mutually exclusive.
        Returns:
            Union[ChatCompletion,
                  Generator[ChatCompletionChunk, None, None]]: If
            stream is True, return Generator, otherwise ChatCompletion.
        """
        if messages is None or not messages:
            raise InputRequired("Messages is required!")
        if model is None or not model:
            raise ModelRequired("Model is required!")
        data = {}
        data["model"] = model
        data["messages"] = messages
        if temperature is not None:
            data["temperature"] = temperature
        if top_p is not None:
            data["top_p"] = top_p
        if top_k is not None:
            data["top_k"] = top_k
        if stop is not None:
            data["stop"] = stop
        if max_tokens is not None:
            data[max_tokens] = max_tokens  # type: ignore[index]
        if repetition_penalty is not None:
            data["repetition_penalty"] = repetition_penalty
        if extra_body is not None and extra_body:
            data = {**data, **extra_body}

        if extra_headers is not None and extra_headers:
            kwargs = (
                {
                    "headers": extra_headers,
                }
                if kwargs
                else {
                    **kwargs,
                    **{
                        "headers": extra_headers,
                    },
                }
            )

        response = super().call(
            data=data,
            path="chat/completions",
            base_address=dashscope.base_compatible_api_url,
            api_key=api_key,
            flattened_output=True,
            stream=stream,
            workspace=workspace,
            **kwargs,
        )
        if stream:
            return (ChatCompletionChunk(**item) for _, item in response)
        else:
            return ChatCompletion(**response)


class AioGeneration(BaseAioApi):
    task = "text-generation"
    """API for AI-Generated Content(AIGC) models.
    """

    class Models:
        """@deprecated, use qwen_turbo instead"""

        qwen_v1 = "qwen-v1"
        """@deprecated, use qwen_plus instead"""
        qwen_plus_v1 = "qwen-plus-v1"

        bailian_v1 = "bailian-v1"
        dolly_12b_v2 = "dolly-12b-v2"
        qwen_turbo = "qwen-turbo"
        qwen_plus = "qwen-plus"
        qwen_max = "qwen-max"

    @classmethod
    # type: ignore[override]
    async def call(  # pylint: disable=arguments-renamed  # type: ignore[override] # noqa: E501
        cls,
        model: str,
        prompt: Any = None,
        history: list = None,
        api_key: str = None,
        messages: List[Message] = None,
        plugins: Union[str, Dict[str, Any]] = None,
        workspace: str = None,
        **kwargs,
    ) -> Union[GenerationResponse, Generator[GenerationResponse, None, None]]:
        """Call generation model service.
        Args:
            model (str): The requested model, such as qwen-turbo
            prompt (Any): The input prompt.
            history (list):The user provided history, deprecated
                examples:
                    [{'user':'The weather is fine today.',
                    'bot': 'Suitable for outings'}].
                Defaults to None.
            api_key (str, optional): The api api_key, can be None,
                if None, will get by default rule(TODO: api key doc).
            messages (list): The generation messages.
                examples:
                    [{'role': 'user',
                      'content': 'The weather is fine today.'},
                      {'role': 'assistant', 'content': 'Suitable for outings'}]
            plugins (Any): The plugin config. Can be plugins config str, or dict.
            **kwargs:
                stream(bool, `optional`): Enable server-sent events
                    (ref: https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events/Using_server-sent_events)  # noqa E501  # pylint: disable=line-too-long
                    the result will back partially[qwen-turbo,bailian-v1].
                temperature(float, `optional`): Used to control the degree
                    of randomness and diversity. Specifically, the temperature
                    value controls the degree to which the probability distribution
                    of each candidate word is smoothed when generating text.
                    A higher temperature value will reduce the peak value of
                    the probability, allowing more low-probability words to be
                    selected, and the generated results will be more diverse;
                    while a lower temperature value will enhance the peak value
                    of the probability, making it easier for high-probability
                    words to be selected, the generated results are more
                    deterministic, range(0, 2) .[qwen-turbo,qwen-plus].
                top_p(float, `optional`): A sampling strategy, called nucleus
                    sampling, where the model considers the results of the
                    tokens with top_p probability mass. So 0.1 means only
                    the tokens comprising the top 10% probability mass are
                    considered[qwen-turbo,bailian-v1].
                top_k(int, `optional`): The size of the sample candidate set when generated.  # noqa E501  # pylint: disable=line-too-long
                    For example, when the value is 50, only the 50 highest-scoring tokens  # noqa E501  # pylint: disable=line-too-long
                    in a single generation form a randomly sampled candidate set. # noqa E501
                    The larger the value, the higher the randomness generated;  # noqa E501
                    the smaller the value, the higher the certainty generated. # noqa E501
                    The default value is 0, which means the top_k policy is  # noqa E501
                    not enabled. At this time, only the top_p policy takes effect. # noqa E501
                enable_search(bool, `optional`): Whether to enable web search(quark).  # noqa E501
                    Currently works best only on the first round of conversation.
                    Default to False, support model: [qwen-turbo].
                customized_model_id(str, required) The enterprise-specific
                    large model id, which needs to be generated from the
                    operation background of the enterprise-specific
                    large model product, support model: [bailian-v1].
                result_format(str, `optional`): [message|text] Set result result format. # noqa E501
                    Default result is text
                incremental_output(bool, `optional`): Used to control the streaming output mode. # noqa E501  # pylint: disable=line-too-long
                    If true, the subsequent output will include the previously input content. # noqa E501  # pylint: disable=line-too-long
                    Otherwise, the subsequent output will not include the previously output # noqa E501  # pylint: disable=line-too-long
                    content. Default false.
                stop(list[str] or list[list[int]], `optional`): Used to control the generation to stop  # noqa E501  # pylint: disable=line-too-long
                    when encountering setting str or token ids, the result will not include # noqa E501  # pylint: disable=line-too-long
                    stop words or tokens.
                max_tokens(int, `optional`): The maximum token num expected to be output. It should be # noqa E501  # pylint: disable=line-too-long
                    noted that the length generated by the model will only be less than max_tokens,  # noqa E501  # pylint: disable=line-too-long
                    not necessarily equal to it. If max_tokens is set too large, the service will # noqa E501  # pylint: disable=line-too-long
                    directly prompt that the length exceeds the limit. It is generally # noqa E501
                    not recommended to set this value.
                repetition_penalty(float, `optional`): Used to control the repeatability when generating models.  # noqa E501  # pylint: disable=line-too-long
                    Increasing repetition_penalty can reduce the duplication of model generation.  # noqa E501  # pylint: disable=line-too-long
                    1.0 means no punishment.
            workspace (str): The dashscope workspace id.
        Raises:
            InvalidInput: The history and auto_history are mutually exclusive.
        Returns:
            Union[GenerationResponse,
                  Generator[GenerationResponse, None, None]]: If
            stream is True, return Generator, otherwise GenerationResponse.
        """
        if (prompt is None or not prompt) and (
            messages is None or not messages
        ):
            raise InputRequired("prompt or messages is required!")
        if model is None or not model:
            raise ModelRequired("Model is required!")
        task_group, function = _get_task_group_and_task(__name__)
        if plugins is not None:
            headers = kwargs.pop("headers", {})
            if isinstance(plugins, str):
                headers["X-DashScope-Plugin"] = plugins
            else:
                headers["X-DashScope-Plugin"] = json.dumps(plugins)
            kwargs["headers"] = headers
        # pylint: disable=protected-access
        (
            input,  # pylint: disable=redefined-builtin
            parameters,
        ) = Generation._build_input_parameters(
            model,
            prompt,
            history,
            messages,
            **kwargs,
        )
        response = await super().call(
            model=model,
            task_group=task_group,
            task=Generation.task,
            function=function,
            api_key=api_key,
            input=input,
            workspace=workspace,
            **parameters,
        )
        is_stream = kwargs.get("stream", False)
        if is_stream:
            return (  # type: ignore[return-value]
                GenerationResponse.from_api_response(rsp)
                async for rsp in response
            )
        else:
            return GenerationResponse.from_api_response(response)

	# -- coding: utf-8 --
	# Copyright (c) Alibaba, Inc. and its affiliates.

	import json
	from typing import Any, Dict, Generator, List, Union

	import dashscope
	from dashscope.aigc.generation import Generation
	from dashscope.api_entities.chat_completion_types import (
	ChatCompletion,
	ChatCompletionChunk,
	)
	from dashscope.api_entities.dashscope_response import (
	GenerationResponse,
	Message,
	)
	from dashscope.client.base_api import BaseAioApi, CreateMixin
	from dashscope.common.error import InputRequired, ModelRequired
	from dashscope.common.utils import _get_task_group_and_task


	class Completions(CreateMixin):
	"""Support openai compatible chat completion interface."""

	SUB_PATH = ""

	@classmethod
	def create(
	cls,
	*,
	model: str,
	messages: List[Message],
	stream: bool = False,
	temperature: float = None,
	top_p: float = None,
	top_k: int = None,
	stop: Union[List[str], List[List[int]]] = None,
	max_tokens: int = None,
	repetition_penalty: float = None,
	api_key: str = None,
	workspace: str = None,
	extra_headers: Dict = None,
	extra_body: Dict = None,
	**kwargs,
	) -> Union[ChatCompletion, Generator[ChatCompletionChunk, None, None]]:
	"""Call openai compatible chat completion model service.

	Args:
	model (str): The requested model, such as qwen-long
	messages (list): The generation messages.
	examples:
	[{'role': 'user',
	'content': 'The weather is fine today.'},
	{'role': 'assistant', 'content': 'Suitable for outings'}]
	stream(bool, `optional`): Enable server-sent events
	(ref: https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events/Using_server-sent_events) # noqa E501 # pylint: disable=line-too-long
	the result will back partially[qwen-turbo,bailian-v1].
	temperature(float, `optional`): Used to control the degree
	of randomness and diversity. Specifically, the temperature
	value controls the degree to which the probability distribution
	of each candidate word is smoothed when generating text.
	A higher temperature value will reduce the peak value of
	the probability, allowing more low-probability words to be
	selected, and the generated results will be more diverse;
	while a lower temperature value will enhance the peak value
	of the probability, making it easier for high-probability
	words to be selected, the generated results are more
	deterministic.
	top_p(float, `optional`): A sampling strategy, called nucleus
	sampling, where the model considers the results of the
	tokens with top_p probability mass. So 0.1 means only
	the tokens comprising the top 10% probability mass are
	considered.
	top_k(int, `optional`): The size of the sample candidate set when generated. # noqa E501 # pylint: disable=line-too-long
	For example, when the value is 50, only the 50 highest-scoring tokens # noqa E501
	in a single generation form a randomly sampled candidate set. # noqa E501
	The larger the value, the higher the randomness generated; # noqa E501
	the smaller the value, the higher the certainty generated. # noqa E501
	The default value is 0, which means the top_k policy is # noqa E501
	not enabled. At this time, only the top_p policy takes effect. # noqa E501
	stop(list[str] or list[list[int]], `optional`): Used to control the generation to stop # noqa E501 # pylint: disable=line-too-long
	when encountering setting str or token ids, the result will not include # noqa E501
	stop words or tokens.
	max_tokens(int, `optional`): The maximum token num expected to be output. It should be # noqa E501 # pylint: disable=line-too-long
	noted that the length generated by the model will only be less than max_tokens, # noqa E501 # pylint: disable=line-too-long
	not necessarily equal to it. If max_tokens is set too large, the service will # noqa E501 # pylint: disable=line-too-long
	directly prompt that the length exceeds the limit. It is generally # noqa E501
	not recommended to set this value.
	repetition_penalty(float, `optional`): Used to control the repeatability when generating models. # noqa E501 # pylint: disable=line-too-long
	Increasing repetition_penalty can reduce the duplication of model generation. # noqa E501 # pylint: disable=line-too-long
	1.0 means no punishment.
	api_key (str, optional): The api api_key, can be None,
	if None, will get by default rule.
	workspace (str, optional): The bailian workspace id.
	**kwargs:
	timeout: set request timeout.
	Raises:
	InvalidInput: The history and auto_history are mutually exclusive.

	Returns:
	Union[ChatCompletion,
	Generator[ChatCompletionChunk, None, None]]: If
	stream is True, return Generator, otherwise ChatCompletion.
	"""
	if messages is None or not messages:
	raise InputRequired("Messages is required!")
	if model is None or not model:
	raise ModelRequired("Model is required!")
	data = {}
	data["model"] = model
	data["messages"] = messages
	if temperature is not None:
	data["temperature"] = temperature
	if top_p is not None:
	data["top_p"] = top_p
	if top_k is not None:
	data["top_k"] = top_k
	if stop is not None:
	data["stop"] = stop
	if max_tokens is not None:
	data[max_tokens] = max_tokens # type: ignore[index]
	if repetition_penalty is not None:
	data["repetition_penalty"] = repetition_penalty
	if extra_body is not None and extra_body:
	data = {data, extra_body}

	if extra_headers is not None and extra_headers:
	kwargs = (
	{
	"headers": extra_headers,
	}
	if kwargs
	else {
	**kwargs,
	**{
	"headers": extra_headers,
	},
	}
	)

	response = super().call(
	data=data,
	path="chat/completions",
	base_address=dashscope.base_compatible_api_url,
	api_key=api_key,
	flattened_output=True,
	stream=stream,
	workspace=workspace,
	**kwargs,
	)
	if stream:
	return (ChatCompletionChunk(**item) for _, item in response)
	else:
	return ChatCompletion(**response)


	class AioGeneration(BaseAioApi):
	task = "text-generation"
	"""API for AI-Generated Content(AIGC) models.

	"""

	class Models:
	"""@deprecated, use qwen_turbo instead"""

	qwen_v1 = "qwen-v1"
	"""@deprecated, use qwen_plus instead"""
	qwen_plus_v1 = "qwen-plus-v1"

	bailian_v1 = "bailian-v1"
	dolly_12b_v2 = "dolly-12b-v2"
	qwen_turbo = "qwen-turbo"
	qwen_plus = "qwen-plus"
	qwen_max = "qwen-max"

	@classmethod
	# type: ignore[override]
	async def call( # pylint: disable=arguments-renamed # type: ignore[override] # noqa: E501
	cls,
	model: str,
	prompt: Any = None,
	history: list = None,
	api_key: str = None,
	messages: List[Message] = None,
	plugins: Union[str, Dict[str, Any]] = None,
	workspace: str = None,
	**kwargs,
	) -> Union[GenerationResponse, Generator[GenerationResponse, None, None]]:
	"""Call generation model service.

	Args:
	model (str): The requested model, such as qwen-turbo
	prompt (Any): The input prompt.
	history (list):The user provided history, deprecated
	examples:
	[{'user':'The weather is fine today.',
	'bot': 'Suitable for outings'}].
	Defaults to None.
	api_key (str, optional): The api api_key, can be None,
	if None, will get by default rule(TODO: api key doc).
	messages (list): The generation messages.
	examples:
	[{'role': 'user',
	'content': 'The weather is fine today.'},
	{'role': 'assistant', 'content': 'Suitable for outings'}]
	plugins (Any): The plugin config. Can be plugins config str, or dict.
	**kwargs:
	stream(bool, `optional`): Enable server-sent events
	(ref: https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events/Using_server-sent_events) # noqa E501 # pylint: disable=line-too-long
	the result will back partially[qwen-turbo,bailian-v1].
	temperature(float, `optional`): Used to control the degree
	of randomness and diversity. Specifically, the temperature
	value controls the degree to which the probability distribution
	of each candidate word is smoothed when generating text.
	A higher temperature value will reduce the peak value of
	the probability, allowing more low-probability words to be
	selected, and the generated results will be more diverse;
	while a lower temperature value will enhance the peak value
	of the probability, making it easier for high-probability
	words to be selected, the generated results are more
	deterministic, range(0, 2) .[qwen-turbo,qwen-plus].
	top_p(float, `optional`): A sampling strategy, called nucleus
	sampling, where the model considers the results of the
	tokens with top_p probability mass. So 0.1 means only
	the tokens comprising the top 10% probability mass are
	considered[qwen-turbo,bailian-v1].
	top_k(int, `optional`): The size of the sample candidate set when generated. # noqa E501 # pylint: disable=line-too-long
	For example, when the value is 50, only the 50 highest-scoring tokens # noqa E501 # pylint: disable=line-too-long
	in a single generation form a randomly sampled candidate set. # noqa E501
	The larger the value, the higher the randomness generated; # noqa E501
	the smaller the value, the higher the certainty generated. # noqa E501
	The default value is 0, which means the top_k policy is # noqa E501
	not enabled. At this time, only the top_p policy takes effect. # noqa E501
	enable_search(bool, `optional`): Whether to enable web search(quark). # noqa E501
	Currently works best only on the first round of conversation.
	Default to False, support model: [qwen-turbo].
	customized_model_id(str, required) The enterprise-specific
	large model id, which needs to be generated from the
	operation background of the enterprise-specific
	large model product, support model: [bailian-v1].
	result_format(str, `optional`): [message\|text] Set result result format. # noqa E501
	Default result is text
	incremental_output(bool, `optional`): Used to control the streaming output mode. # noqa E501 # pylint: disable=line-too-long
	If true, the subsequent output will include the previously input content. # noqa E501 # pylint: disable=line-too-long
	Otherwise, the subsequent output will not include the previously output # noqa E501 # pylint: disable=line-too-long
	content. Default false.
	stop(list[str] or list[list[int]], `optional`): Used to control the generation to stop # noqa E501 # pylint: disable=line-too-long
	when encountering setting str or token ids, the result will not include # noqa E501 # pylint: disable=line-too-long
	stop words or tokens.
	max_tokens(int, `optional`): The maximum token num expected to be output. It should be # noqa E501 # pylint: disable=line-too-long
	noted that the length generated by the model will only be less than max_tokens, # noqa E501 # pylint: disable=line-too-long
	not necessarily equal to it. If max_tokens is set too large, the service will # noqa E501 # pylint: disable=line-too-long
	directly prompt that the length exceeds the limit. It is generally # noqa E501
	not recommended to set this value.
	repetition_penalty(float, `optional`): Used to control the repeatability when generating models. # noqa E501 # pylint: disable=line-too-long
	Increasing repetition_penalty can reduce the duplication of model generation. # noqa E501 # pylint: disable=line-too-long
	1.0 means no punishment.
	workspace (str): The dashscope workspace id.
	Raises:
	InvalidInput: The history and auto_history are mutually exclusive.

	Returns:
	Union[GenerationResponse,
	Generator[GenerationResponse, None, None]]: If
	stream is True, return Generator, otherwise GenerationResponse.
	"""
	if (prompt is None or not prompt) and (
	messages is None or not messages
	):
	raise InputRequired("prompt or messages is required!")
	if model is None or not model:
	raise ModelRequired("Model is required!")
	task_group, function = _get_task_group_and_task(__name__)
	if plugins is not None:
	headers = kwargs.pop("headers", {})
	if isinstance(plugins, str):
	headers["X-DashScope-Plugin"] = plugins
	else:
	headers["X-DashScope-Plugin"] = json.dumps(plugins)
	kwargs["headers"] = headers
	# pylint: disable=protected-access
	(
	input, # pylint: disable=redefined-builtin
	parameters,
	) = Generation._build_input_parameters(
	model,
	prompt,
	history,
	messages,
	**kwargs,
	)
	response = await super().call(
	model=model,
	task_group=task_group,
	task=Generation.task,
	function=function,
	api_key=api_key,
	input=input,
	workspace=workspace,
	**parameters,
	)
	is_stream = kwargs.get("stream", False)
	if is_stream:
	return ( # type: ignore[return-value]
	GenerationResponse.from_api_response(rsp)
	async for rsp in response
	)
	else:
	return GenerationResponse.from_api_response(response)