from __future__ import annotations

import asyncio
import typing

import openai
import openai.types.chat.chat_completion as chat_completion
import openai.types.chat.chat_completion_message_tool_call as chat_completion_message_tool_call
import httpx

from .. import entities, errors, requester
from ....core import entities as core_entities
from ... import entities as llm_entities
from ...tools import entities as tools_entities


class ModelScopeChatCompletions(requester.ProviderAPIRequester):
    """ModelScope ChatCompletion API 请求器"""

    client: openai.AsyncClient

    default_config: dict[str, typing.Any] = {
        'base_url': 'https://api-inference.modelscope.cn/v1',
        'timeout': 120,
    }

    async def initialize(self):
        self.client = openai.AsyncClient(
            api_key='',
            base_url=self.requester_cfg['base_url'],
            timeout=self.requester_cfg['timeout'],
            http_client=httpx.AsyncClient(trust_env=True, timeout=self.requester_cfg['timeout']),
        )

    async def _req(
        self,
        args: dict,
        extra_body: dict = {},
    ) -> chat_completion.ChatCompletion:
        args['stream'] = True

        chunk = None

        pending_content = ''

        tool_calls = []

        resp_gen: openai.AsyncStream = await self.client.chat.completions.create(**args, extra_body=extra_body)

        async for chunk in resp_gen:
            # print(chunk)
            if not chunk or not chunk.id or not chunk.choices or not chunk.choices[0] or not chunk.choices[0].delta:
                continue

            if chunk.choices[0].delta.content is not None:
                pending_content += chunk.choices[0].delta.content

            if chunk.choices[0].delta.tool_calls is not None:
                for tool_call in chunk.choices[0].delta.tool_calls:
                    if tool_call.function.arguments is None:
                        continue
                    for tc in tool_calls:
                        if tc.index == tool_call.index:
                            tc.function.arguments += tool_call.function.arguments
                            break
                    else:
                        tool_calls.append(tool_call)

            if chunk.choices[0].finish_reason is not None:
                break

        real_tool_calls = []

        for tc in tool_calls:
            function = chat_completion_message_tool_call.Function(
                name=tc.function.name, arguments=tc.function.arguments
            )
            real_tool_calls.append(
                chat_completion_message_tool_call.ChatCompletionMessageToolCall(
                    id=tc.id, function=function, type='function'
                )
            )

        return (
            chat_completion.ChatCompletion(
                id=chunk.id,
                object='chat.completion',
                created=chunk.created,
                choices=[
                    chat_completion.Choice(
                        index=0,
                        message=chat_completion.ChatCompletionMessage(
                            role='assistant',
                            content=pending_content,
                            tool_calls=real_tool_calls if len(real_tool_calls) > 0 else None,
                        ),
                        finish_reason=chunk.choices[0].finish_reason
                        if hasattr(chunk.choices[0], 'finish_reason') and chunk.choices[0].finish_reason is not None
                        else 'stop',
                        logprobs=chunk.choices[0].logprobs,
                    )
                ],
                model=chunk.model,
                service_tier=chunk.service_tier if hasattr(chunk, 'service_tier') else None,
                system_fingerprint=chunk.system_fingerprint if hasattr(chunk, 'system_fingerprint') else None,
                usage=chunk.usage if hasattr(chunk, 'usage') else None,
            )
            if chunk
            else None
        )

    async def _make_msg(
        self,
        chat_completion: chat_completion.ChatCompletion,
    ) -> llm_entities.Message:
        chatcmpl_message = chat_completion.choices[0].message.dict()

        # 确保 role 字段存在且不为 None
        if 'role' not in chatcmpl_message or chatcmpl_message['role'] is None:
            chatcmpl_message['role'] = 'assistant'

        message = llm_entities.Message(**chatcmpl_message)

        return message

    async def _closure(
        self,
        query: core_entities.Query,
        req_messages: list[dict],
        use_model: requester.RuntimeLLMModel,
        use_funcs: list[tools_entities.LLMFunction] = None,
        extra_args: dict[str, typing.Any] = {},
    ) -> llm_entities.Message:
        self.client.api_key = use_model.token_mgr.get_token()

        args = {}
        args['model'] = use_model.model_entity.name

        if use_funcs:
            tools = await self.ap.tool_mgr.generate_tools_for_openai(use_funcs)

            if tools:
                args['tools'] = tools

        # 设置此次请求中的messages
        messages = req_messages.copy()

        # 检查vision
        for msg in messages:
            if 'content' in msg and isinstance(msg['content'], list):
                for me in msg['content']:
                    if me['type'] == 'image_base64':
                        me['image_url'] = {'url': me['image_base64']}
                        me['type'] = 'image_url'
                        del me['image_base64']

        args['messages'] = messages

        # 发送请求
        resp = await self._req(args, extra_body=extra_args)

        # 处理请求结果
        message = await self._make_msg(resp)

        return message

    async def _req_stream(
        self,
        args: dict,
        extra_body: dict = {},
    ) -> chat_completion.ChatCompletion:
        async for chunk in await self.client.chat.completions.create(**args, extra_body=extra_body):
            yield chunk

    async def _make_msg_chunk(self,
            delta: dict[str, typing.Any],
            idx: int,
            is_content: bool,
            is_think: bool,
    ) -> llm_entities.MessageChunk:
        # 处理流式chunk和完整响应的差异
        # print(chat_completion.choices[0])

        if 'role' not in delta or delta['role'] is None:
            delta['role'] = 'assistant'

        reasoning_content = delta['reasoning_content']

        delta['content'] = '' if delta['content'] is None else delta['content']
        # print(reasoning_content)

        # deepseek的reasoner模型

        if reasoning_content is not None and idx == 0:
            delta['content'] += f'<think>\n{reasoning_content}'
            is_think = True
        elif reasoning_content is None and idx != 0:
            if is_content:
                delta['content'] = delta['content']
            elif is_think:
                delta['content'] = f'\n<think>\n\n{delta["content"]}'
                is_content = True
                is_think = False
        elif reasoning_content is not None:
            delta['content'] = reasoning_content

        message = llm_entities.MessageChunk(**delta)

        return message, is_content, is_think

    async def _closure_stream(
        self,
        query: core_entities.Query,
        req_messages: list[dict],
        use_model: requester.RuntimeLLMModel,
        use_funcs: list[tools_entities.LLMFunction] = None,
        extra_args: dict[str, typing.Any] = {},
        remove_think: bool = False,
    ) -> llm_entities.Message | typing.AsyncGenerator[llm_entities.MessageChunk, None]:
        self.client.api_key = use_model.token_mgr.get_token()

        args = {}
        args['model'] = use_model.model_entity.name

        if use_funcs:
            tools = await self.ap.tool_mgr.generate_tools_for_openai(use_funcs)

            if tools:
                args['tools'] = tools

        # 设置此次请求中的messages
        messages = req_messages.copy()

        # 检查vision
        for msg in messages:
            if 'content' in msg and isinstance(msg['content'], list):
                for me in msg['content']:
                    if me['type'] == 'image_base64':
                        me['image_url'] = {'url': me['image_base64']}
                        me['type'] = 'image_url'
                        del me['image_base64']

        args['messages'] = messages

        current_content = ''
        args['stream'] = True
        chunk_idx = 0
        is_content = False
        is_think = False
        tool_calls_map: dict[str, llm_entities.ToolCall] = {}
        async for chunk in self._req_stream(args, extra_body=extra_args):
            if hasattr(chunk, 'choices'):
                # 完整响应模式
                choice = chunk.choices[0]
                delta = choice.delta.model_dump() if hasattr(choice, 'delta') else choice.message.model_dump()
            else:
                # 流式chunk模式
                delta = chunk.delta.model_dump() if hasattr(chunk, 'delta') else {}
            reasoning_content = delta['reasoning_content'] if 'reasoning_content' in delta else None
            delta['reasoning_content'] = None if reasoning_content == '' else reasoning_content  # 直接不管有没有思考消息，构造一个，方便去除思考判断
            if remove_think:
                if delta['reasoning_content'] is not None:
                    continue
            if ((delta['content'] == '' or delta.get('content', None) is None) and
                    (delta.get('reasoning_content', None) is None or delta['reasoning_content'] == '') and
                    chunk_idx == 0):  # 此处将第一条空消息排除，大部分模型第一条消息携带的是role,但是在role直接处理为ass
                continue
            # 处理流式消息
            delta_message, is_content, is_think = await self._make_msg_chunk(delta,
                                                                             chunk_idx,
                                                                             is_content,
                                                                             is_think)
            # 处理流式消息
            if delta_message.content:
                current_content += delta_message.content
                delta_message.content = current_content
                # delta_message.all_content = current_content
            if delta_message.tool_calls:
                for tool_call in delta_message.tool_calls:
                    if tool_call.id not in tool_calls_map:
                        tool_calls_map[tool_call.id] = llm_entities.ToolCall(
                            id=tool_call.id,
                            type=tool_call.type,
                            function=llm_entities.FunctionCall(
                                name=tool_call.function.name if tool_call.function else '', arguments=''
                            ),
                        )
                    if tool_call.function and tool_call.function.arguments:
                        # 流式处理中，工具调用参数可能分多个chunk返回，需要追加而不是覆盖
                        tool_calls_map[tool_call.id].function.arguments += tool_call.function.arguments

            chunk_idx += 1
            chunk_choices = getattr(chunk, 'choices', None)
            if chunk_choices and getattr(chunk_choices[0], 'finish_reason', None):
                delta_message.is_final = True
                delta_message.content = current_content

            yield delta_message
            # return

    async def invoke_llm(
        self,
        query: core_entities.Query,
        model: entities.LLMModelInfo,
        messages: typing.List[llm_entities.Message],
        funcs: typing.List[tools_entities.LLMFunction] = None,
        extra_args: dict[str, typing.Any] = {},
        remove_think: bool = False,
    ) -> llm_entities.Message:
        req_messages = []  # req_messages 仅用于类内，外部同步由 query.messages 进行
        for m in messages:
            msg_dict = m.dict(exclude_none=True)
            content = msg_dict.get('content')
            if isinstance(content, list):
                # 检查 content 列表中是否每个部分都是文本
                if all(isinstance(part, dict) and part.get('type') == 'text' for part in content):
                    # 将所有文本部分合并为一个字符串
                    msg_dict['content'] = '\n'.join(part['text'] for part in content)
            req_messages.append(msg_dict)

        try:
            return await self._closure(
                query=query, req_messages=req_messages, use_model=model, use_funcs=funcs, extra_args=extra_args
            )
        except asyncio.TimeoutError:
            raise errors.RequesterError('请求超时')
        except openai.BadRequestError as e:
            if 'context_length_exceeded' in e.message:
                raise errors.RequesterError(f'上文过长，请重置会话: {e.message}')
            else:
                raise errors.RequesterError(f'请求参数错误: {e.message}')
        except openai.AuthenticationError as e:
            raise errors.RequesterError(f'无效的 api-key: {e.message}')
        except openai.NotFoundError as e:
            raise errors.RequesterError(f'请求路径错误: {e.message}')
        except openai.RateLimitError as e:
            raise errors.RequesterError(f'请求过于频繁或余额不足: {e.message}')
        except openai.APIError as e:
            raise errors.RequesterError(f'请求错误: {e.message}')

    async def invoke_llm_stream(
        self,
        query: core_entities.Query,
        model: requester.RuntimeLLMModel,
        messages: typing.List[llm_entities.Message],
        funcs: typing.List[tools_entities.LLMFunction] = None,
        extra_args: dict[str, typing.Any] = {},
        remove_think: bool = False,
    ) -> llm_entities.MessageChunk:
        req_messages = []  # req_messages 仅用于类内，外部同步由 query.messages 进行
        for m in messages:
            msg_dict = m.dict(exclude_none=True)
            content = msg_dict.get('content')
            if isinstance(content, list):
                # 检查 content 列表中是否每个部分都是文本
                if all(isinstance(part, dict) and part.get('type') == 'text' for part in content):
                    # 将所有文本部分合并为一个字符串
                    msg_dict['content'] = '\n'.join(part['text'] for part in content)
            req_messages.append(msg_dict)

        try:
            async for item in self._closure_stream(
                query=query,
                req_messages=req_messages,
                use_model=model,
                use_funcs=funcs,
                extra_args=extra_args,
                remove_think=remove_think,
            ):
                yield item

        except asyncio.TimeoutError:
            raise errors.RequesterError('请求超时')
        except openai.BadRequestError as e:
            if 'context_length_exceeded' in e.message:
                raise errors.RequesterError(f'上文过长，请重置会话: {e.message}')
            else:
                raise errors.RequesterError(f'请求参数错误: {e.message}')
        except openai.AuthenticationError as e:
            raise errors.RequesterError(f'无效的 api-key: {e.message}')
        except openai.NotFoundError as e:
            raise errors.RequesterError(f'请求路径错误: {e.message}')
        except openai.RateLimitError as e:
            raise errors.RequesterError(f'请求过于频繁或余额不足: {e.message}')
        except openai.APIError as e:
            raise errors.RequesterError(f'请求错误: {e.message}')