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

import json
from copy import deepcopy
from http import HTTPStatus
from typing import Any, Dict, Generator, List, Union

from dashscope.api_entities.dashscope_response import (
    ConversationResponse,
    Message,
    Role,
)
from dashscope.client.base_api import BaseApi
from dashscope.common.constants import DEPRECATED_MESSAGE, HISTORY, PROMPT
from dashscope.common.error import InputRequired, InvalidInput, ModelRequired
from dashscope.common.logging import logger
from dashscope.common.utils import _get_task_group_and_task


class HistoryItem(dict):
    """A conversation history item."""

    def __init__(self, role: str, text: str = None, **kwargs):
        """Init a history item.
        Args:
            role (str): The role name.
            text (str): The text history. Default ot None.
            kwargs: The key/value pair of history content.
        Raises:
            InvalidInput: The key and value must pair.
        """
        logger.warning(DEPRECATED_MESSAGE)
        self.role = role
        dict.__init__(self, {role: []})
        if text is not None:
            self[self.role].append({"text": text})
        for k, v in kwargs.items():
            self[self.role].append({k: v})

    def add(self, key: str, content: Any):
        """Add a key/value to history.
        Args:
            key (str): The key of the content.
            value (Any): The history content.
        """
        self[self.role].append({key: content})


class History(list):
    """Manage the conversation history."""

    def __init__(self, items: List[HistoryItem] = None):
        """Init a history with list of HistoryItems.
        Args:
            items (List[HistoryItem], optional): The history items.
                Defaults to None.
        """
        if items is not None:
            logger.warning(DEPRECATED_MESSAGE)
            list.__init__(items)
        else:
            list.__init__([])


def _history_to_qwen_format(history: History, n_history: int):
    """Convert history to Qwen-compatible simple format.
    Transforms conversation history into a simplified format where each
    entry contains user and bot messages as key-value pairs.
    Args:
        history: The conversation history to convert.
        n_history: Number of recent history items to include.
                   -1 means include all history.
    Returns:
        list: Simplified history in format:
              [{"user":"您好", "bot":"我是你的助手，很高兴为您服务"},
               {"user":"user input", "bot":"bot output"}]
    """
    simple_history = []
    user = None
    bot = None

    if n_history != -1 and len(history) >= 2 * n_history:
        history = history[len(history) - 2 * n_history :]

    for item in history:
        if "user" in item:
            user = item["user"][0]["text"]
        if "bot" in item:
            bot = item["bot"][0]["text"]
        if user is not None and bot is not None:
            simple_history.append({"user": user, "bot": bot})
            user = None
            bot = None
    return simple_history


class Conversation(BaseApi):
    """Conversational robot interface."""

    task = "generation"

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

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

        qwen_turbo = "qwen-turbo"
        qwen_plus = "qwen-plus"
        qwen_max = "qwen-max"

    def __init__(self, history: History = None) -> None:
        """Init a chat.
        Args:
            history (dict): The conversation initialization settings.
               will be recorded in the system history list.
                Defaults to None.
        """
        super().__init__()
        if history is None:
            self.history = History()
        else:
            logger.warning(DEPRECATED_MESSAGE)
            self.history = history  # type: ignore[has-type]

    # pylint: disable=arguments-renamed
    def call(  # type: ignore[override]
        self,
        model: str,
        prompt: Any = None,
        history: History = None,
        auto_history: bool = False,
        n_history: int = -1,
        api_key: str = None,
        messages: List[Message] = None,
        plugins: Union[str, Dict[str, Any]] = None,
        workspace: str = None,
        **kwargs,
    ) -> Union[
        ConversationResponse,
        Generator[
            ConversationResponse,
            None,
            None,
        ],
    ]:
        """Call conversational robot generator a response.
        Note: This method overrides BaseApi.call() as an instance method
        instead of a classmethod because Conversation maintains instance
        state (self.history). Pylint's arguments-renamed warning is
        disabled because the first parameter changes from 'cls' (in the
        classmethod) to 'self' (in the instance method). The type
        checker is instructed to ignore the signature incompatibility
        via type: ignore[override].
        Args:
            model (str): The request model.
            prompt (Any): The input prompt.
            history(History): The user provided history.
                Only works for this call, will not be recorded in the
                system history list. The ``history`` and ``auto_history``
                are mutually exclusive. Default to None.
            auto_history (bool): Call with the automatically maintenance
                conversation history list. The ``history`` and ``auto_history``
                are mutually exclusive.
            n_history (int): Number of latest history in conversation,
                -1 all history. Default to -1
            api_key (str, optional): The api api_key, if not present,
                will get by default rule(TODO: api key doc). Defaults to None.
            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(qwen-turbo, qwen-plus):
                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.
                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.
                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:
            InputRequired: The prompt cannot be empty.
            InvalidInput: The history and auto_history are mutually exclusive.
        Returns:
            Union[ConversationResponse,
                  Generator[ConversationResponse, None, None]]: If
            stream is True, return Generator, otherwise ConversationResponse.
        """
        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, _ = _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
        (
            input,  # pylint: disable=redefined-builtin
            parameters,
        ) = self._build_input_parameters(
            model,
            prompt,
            history,
            auto_history,
            n_history,
            messages,
            **kwargs,
        )
        response = super().call(
            model=model,
            task_group=task_group,
            task="text-generation",
            function="generation",
            api_key=api_key,
            input=input,
            workspace=workspace,
            **parameters,
        )
        is_stream = kwargs.get("stream", False)
        return self._handle_response(prompt, response, is_stream)

    def _handle_stream_response(self, prompt, responses):
        """Handle streaming response and update conversation history.
        Args:
            prompt: The user's input prompt.
            responses: Generator yielding response chunks.
        Yields:
            ConversationResponse: Parsed response objects.
        """
        for rsp in responses:
            rsp = ConversationResponse.from_api_response(rsp)
            yield rsp
        if (
            # pylint: disable=undefined-loop-variable
            rsp.status_code == HTTPStatus.OK
            and rsp.output.choices  # pylint: disable=undefined-loop-variable
            is None  # pylint: disable=undefined-loop-variable
        ):  # pylint: disable=undefined-loop-variable
            user_item = HistoryItem("user", text=prompt)
            bot_history_item = HistoryItem("bot", text=rsp.output.text)
            self.history.append(user_item)
            self.history.append(bot_history_item)

    def _handle_response(self, prompt, response, is_stream):
        """Handle API response and update conversation history.
        Args:
            prompt: The user's input prompt.
            response: The API response or response generator.
            is_stream: Whether the response is streaming.
        Returns:
            ConversationResponse or Generator: Parsed response.
        """
        if is_stream:
            return (
                rsp for rsp in self._handle_stream_response(prompt, response)
            )
        else:
            response = ConversationResponse.from_api_response(response)
            if (
                response.status_code == HTTPStatus.OK
                and response.output.choices is None
            ):
                user_item = HistoryItem("user", text=prompt)
                bot_history_item = HistoryItem(
                    "bot",
                    text=response.output["text"],
                )
                self.history.append(user_item)
                self.history.append(bot_history_item)
            return response

    def _build_input_parameters(
        self,
        model,
        prompt,
        history,
        auto_history,
        n_history,
        messages,
        **kwargs,
    ):
        """Build input data and parameters for API call.
        Args:
            model: The model name.
            prompt: The user's input prompt.
            history: User-provided conversation history.
            auto_history: Whether to use automatic history management.
            n_history: Number of history items to include.
            messages: List of message objects.
            **kwargs: Additional parameters.
        Returns:
            tuple: (input, parameters) for API call.
        """
        if model == Conversation.Models.qwen_v1:
            logger.warning(
                "Model %s is deprecated, use %s instead!",
                Conversation.Models.qwen_v1,
                Conversation.Models.qwen_turbo,
            )
        if model == Conversation.Models.qwen_plus_v1:
            logger.warning(
                "Model %s is deprecated, use %s instead!",
                Conversation.Models.qwen_plus_v1,
                Conversation.Models.qwen_plus,
            )
        parameters = {}
        if history is not None and auto_history:
            raise InvalidInput("auto_history is True, history must None")
        if history is not None:  # use user provided history or system.
            logger.warning(DEPRECATED_MESSAGE)
            input = {  # pylint: disable=redefined-builtin
                PROMPT: prompt,
                HISTORY: _history_to_qwen_format(history, n_history)
                if history
                else [],
            }
        elif auto_history:
            logger.warning(DEPRECATED_MESSAGE)
            input = {
                PROMPT: prompt,
                HISTORY: _history_to_qwen_format(self.history, n_history),
            }
        elif messages:
            msgs = deepcopy(messages)
            if prompt is not None and prompt:
                msgs.append({"role": Role.USER, "content": prompt})
            input = {"messages": msgs}
        else:
            input = {
                PROMPT: prompt,
            }
        # parameters
        if model.startswith("qwen"):
            enable_search = kwargs.pop("enable_search", False)
            if enable_search:
                parameters["enable_search"] = enable_search

        return input, {**parameters, **kwargs}

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

	import json
	from copy import deepcopy
	from http import HTTPStatus
	from typing import Any, Dict, Generator, List, Union

	from dashscope.api_entities.dashscope_response import (
	ConversationResponse,
	Message,
	Role,
	)
	from dashscope.client.base_api import BaseApi
	from dashscope.common.constants import DEPRECATED_MESSAGE, HISTORY, PROMPT
	from dashscope.common.error import InputRequired, InvalidInput, ModelRequired
	from dashscope.common.logging import logger
	from dashscope.common.utils import _get_task_group_and_task


	class HistoryItem(dict):
	"""A conversation history item."""

	def __init__(self, role: str, text: str = None, **kwargs):
	"""Init a history item.

	Args:
	role (str): The role name.
	text (str): The text history. Default ot None.
	kwargs: The key/value pair of history content.

	Raises:
	InvalidInput: The key and value must pair.
	"""
	logger.warning(DEPRECATED_MESSAGE)
	self.role = role
	dict.__init__(self, {role: []})
	if text is not None:
	self[self.role].append({"text": text})
	for k, v in kwargs.items():
	self[self.role].append({k: v})

	def add(self, key: str, content: Any):
	"""Add a key/value to history.

	Args:
	key (str): The key of the content.
	value (Any): The history content.
	"""
	self[self.role].append({key: content})


	class History(list):
	"""Manage the conversation history."""

	def __init__(self, items: List[HistoryItem] = None):
	"""Init a history with list of HistoryItems.

	Args:
	items (List[HistoryItem], optional): The history items.
	Defaults to None.
	"""
	if items is not None:
	logger.warning(DEPRECATED_MESSAGE)
	list.__init__(items)
	else:
	list.__init__([])


	def _history_to_qwen_format(history: History, n_history: int):
	"""Convert history to Qwen-compatible simple format.

	Transforms conversation history into a simplified format where each
	entry contains user and bot messages as key-value pairs.

	Args:
	history: The conversation history to convert.
	n_history: Number of recent history items to include.
	-1 means include all history.

	Returns:
	list: Simplified history in format:
	[{"user":"您好", "bot":"我是你的助手，很高兴为您服务"},
	{"user":"user input", "bot":"bot output"}]
	"""
	simple_history = []
	user = None
	bot = None

	if n_history != -1 and len(history) >= 2 * n_history:
	history = history[len(history) - 2 * n_history :]

	for item in history:
	if "user" in item:
	user = item["user"][0]["text"]
	if "bot" in item:
	bot = item["bot"][0]["text"]
	if user is not None and bot is not None:
	simple_history.append({"user": user, "bot": bot})
	user = None
	bot = None
	return simple_history


	class Conversation(BaseApi):
	"""Conversational robot interface."""

	task = "generation"

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

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

	qwen_turbo = "qwen-turbo"
	qwen_plus = "qwen-plus"
	qwen_max = "qwen-max"

	def __init__(self, history: History = None) -> None:
	"""Init a chat.

	Args:
	history (dict): The conversation initialization settings.
	will be recorded in the system history list.
	Defaults to None.
	"""
	super().__init__()
	if history is None:
	self.history = History()
	else:
	logger.warning(DEPRECATED_MESSAGE)
	self.history = history # type: ignore[has-type]

	# pylint: disable=arguments-renamed
	def call( # type: ignore[override]
	self,
	model: str,
	prompt: Any = None,
	history: History = None,
	auto_history: bool = False,
	n_history: int = -1,
	api_key: str = None,
	messages: List[Message] = None,
	plugins: Union[str, Dict[str, Any]] = None,
	workspace: str = None,
	**kwargs,
	) -> Union[
	ConversationResponse,
	Generator[
	ConversationResponse,
	None,
	None,
	],
	]:
	"""Call conversational robot generator a response.

	Note: This method overrides BaseApi.call() as an instance method
	instead of a classmethod because Conversation maintains instance
	state (self.history). Pylint's arguments-renamed warning is
	disabled because the first parameter changes from 'cls' (in the
	classmethod) to 'self' (in the instance method). The type
	checker is instructed to ignore the signature incompatibility
	via type: ignore[override].

	Args:
	model (str): The request model.
	prompt (Any): The input prompt.
	history(History): The user provided history.
	Only works for this call, will not be recorded in the
	system history list. The ``history`` and ``auto_history``
	are mutually exclusive. Default to None.
	auto_history (bool): Call with the automatically maintenance
	conversation history list. The ``history`` and ``auto_history``
	are mutually exclusive.
	n_history (int): Number of latest history in conversation,
	-1 all history. Default to -1
	api_key (str, optional): The api api_key, if not present,
	will get by default rule(TODO: api key doc). Defaults to None.
	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(qwen-turbo, qwen-plus):
	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.
	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.
	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:
	InputRequired: The prompt cannot be empty.
	InvalidInput: The history and auto_history are mutually exclusive.

	Returns:
	Union[ConversationResponse,
	Generator[ConversationResponse, None, None]]: If
	stream is True, return Generator, otherwise ConversationResponse.

	"""
	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, _ = _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
	(
	input, # pylint: disable=redefined-builtin
	parameters,
	) = self._build_input_parameters(
	model,
	prompt,
	history,
	auto_history,
	n_history,
	messages,
	**kwargs,
	)
	response = super().call(
	model=model,
	task_group=task_group,
	task="text-generation",
	function="generation",
	api_key=api_key,
	input=input,
	workspace=workspace,
	**parameters,
	)
	is_stream = kwargs.get("stream", False)
	return self._handle_response(prompt, response, is_stream)

	def _handle_stream_response(self, prompt, responses):
	"""Handle streaming response and update conversation history.

	Args:
	prompt: The user's input prompt.
	responses: Generator yielding response chunks.

	Yields:
	ConversationResponse: Parsed response objects.
	"""
	for rsp in responses:
	rsp = ConversationResponse.from_api_response(rsp)
	yield rsp
	if (
	# pylint: disable=undefined-loop-variable
	rsp.status_code == HTTPStatus.OK
	and rsp.output.choices # pylint: disable=undefined-loop-variable
	is None # pylint: disable=undefined-loop-variable
	): # pylint: disable=undefined-loop-variable
	user_item = HistoryItem("user", text=prompt)
	bot_history_item = HistoryItem("bot", text=rsp.output.text)
	self.history.append(user_item)
	self.history.append(bot_history_item)

	def _handle_response(self, prompt, response, is_stream):
	"""Handle API response and update conversation history.

	Args:
	prompt: The user's input prompt.
	response: The API response or response generator.
	is_stream: Whether the response is streaming.

	Returns:
	ConversationResponse or Generator: Parsed response.
	"""
	if is_stream:
	return (
	rsp for rsp in self._handle_stream_response(prompt, response)
	)
	else:
	response = ConversationResponse.from_api_response(response)
	if (
	response.status_code == HTTPStatus.OK
	and response.output.choices is None
	):
	user_item = HistoryItem("user", text=prompt)
	bot_history_item = HistoryItem(
	"bot",
	text=response.output["text"],
	)
	self.history.append(user_item)
	self.history.append(bot_history_item)
	return response

	def _build_input_parameters(
	self,
	model,
	prompt,
	history,
	auto_history,
	n_history,
	messages,
	**kwargs,
	):
	"""Build input data and parameters for API call.

	Args:
	model: The model name.
	prompt: The user's input prompt.
	history: User-provided conversation history.
	auto_history: Whether to use automatic history management.
	n_history: Number of history items to include.
	messages: List of message objects.
	**kwargs: Additional parameters.

	Returns:
	tuple: (input, parameters) for API call.
	"""
	if model == Conversation.Models.qwen_v1:
	logger.warning(
	"Model %s is deprecated, use %s instead!",
	Conversation.Models.qwen_v1,
	Conversation.Models.qwen_turbo,
	)
	if model == Conversation.Models.qwen_plus_v1:
	logger.warning(
	"Model %s is deprecated, use %s instead!",
	Conversation.Models.qwen_plus_v1,
	Conversation.Models.qwen_plus,
	)
	parameters = {}
	if history is not None and auto_history:
	raise InvalidInput("auto_history is True, history must None")
	if history is not None: # use user provided history or system.
	logger.warning(DEPRECATED_MESSAGE)
	input = { # pylint: disable=redefined-builtin
	PROMPT: prompt,
	HISTORY: _history_to_qwen_format(history, n_history)
	if history
	else [],
	}
	elif auto_history:
	logger.warning(DEPRECATED_MESSAGE)
	input = {
	PROMPT: prompt,
	HISTORY: _history_to_qwen_format(self.history, n_history),
	}
	elif messages:
	msgs = deepcopy(messages)
	if prompt is not None and prompt:
	msgs.append({"role": Role.USER, "content": prompt})
	input = {"messages": msgs}
	else:
	input = {
	PROMPT: prompt,
	}
	# parameters
	if model.startswith("qwen"):
	enable_search = kwargs.pop("enable_search", False)
	if enable_search:
	parameters["enable_search"] = enable_search

	return input, {parameters, kwargs}