import json
import logging
import os
from abc import ABC
from typing import Callable, List

import openai
from tenacity import (  # for exponential backoff
    before_sleep_log,
    retry,
    stop_after_attempt,
    wait_random_exponential,
)

from ..base_llm import BaseLLM
from ...schemas import *

logger = logging.getLogger(__name__)

MAX_PROMPT_LENGTH = 7000


@retry(wait=wait_random_exponential(min=1, max=10), stop=stop_after_attempt(10), reraise=True,
       before_sleep=before_sleep_log(logger, logging.WARNING))
def chatcompletion_with_backoff(**kwargs):
    return openai.ChatCompletion.create(**kwargs)


@retry(wait=wait_random_exponential(min=1, max=10), stop=stop_after_attempt(10), reraise=True,
       before_sleep=before_sleep_log(logger, logging.WARNING))
async def async_chatcompletion_with_backoff(**kwargs):
    async def _internal_coroutine():
        return await openai.ChatCompletion.acreate(**kwargs)

    return await _internal_coroutine()


class OptOpenAIClient(BaseLLM, ABC):
    """
    Wrapper class for OpenAI GPT API collections.

    :param model_name: The name of the model to use.
    :type model_name: str
    :param params: The parameters for the model.
    :type params: OptParamModel
    """

    model_name: str
    params: OptParamModel = OptParamModel()

    def __init__(self, **data):
        super().__init__(**data)
        openai.api_key = "EMPTY"
        openai.api_base = "http://localhost:8000/v1"

    @classmethod
    async def create(cls, config_data):
        return OptOpenAIClient(**config_data)

    def get_model_name(self) -> str:
        return self.model_name
    
    def get_model_param(self) -> OptParamModel:
        return self.params

    def completion(self, prompt: str, **kwargs) -> BaseCompletion:
        """
        Completion method for OpenAI GPT API.

        :param prompt: The prompt to use for completion.
        :type prompt: str
        :param kwargs: Additional keyword arguments.
        :type kwargs: dict
        :return: BaseCompletion object.
        :rtype: BaseCompletion

        """

        response = chatcompletion_with_backoff(
            model=self.model_name,
            # engine=self.get_model_name(),  # GPT-4
            messages=[
                {"role": "user", "content": prompt[-MAX_PROMPT_LENGTH:]}
            ],
            timeout=1000,
            **kwargs
        )

        return BaseCompletion(state="success",
                              content=response.choices[0].message["content"],
                              prompt_token=response.get("usage", {}).get("prompt_tokens", 0),
                              completion_token=response.get("usage", {}).get("completion_tokens", 0))

    async def async_completion(self, prompt: str, **kwargs) -> BaseCompletion:
        """
        Completion method for OpenAI GPT API.

        :param prompt: The prompt to use for completion.
        :type prompt: str
        :param kwargs: Additional keyword arguments.
        :type kwargs: dict
        :return: BaseCompletion object.
        :rtype: BaseCompletion

        """
        response = await async_chatcompletion_with_backoff(
            # engine=self.get_model_name(),  # GPT-4
            model=self.model_name,
            messages=[
                {"role": "user", "content": prompt[-MAX_PROMPT_LENGTH:]}
            ],
            timeout=1000,
            **kwargs
        )

        return BaseCompletion(state="success",
                              content=response.choices[0].message["content"],
                              prompt_token=response.get("usage", {}).get("prompt_tokens", 0),
                              completion_token=response.get("usage", {}).get("completion_tokens", 0))

    def chat_completion(self, message: List[dict]) -> ChatCompletion:
        """
        Chat completion method for OpenAI GPT API.

        :param message: The message to use for completion.
        :type message: List[dict]
        :return: ChatCompletion object.
        :rtype: ChatCompletion
        """
        try:
            # response = openai.ChatCompletion.create(
            #     engine=self.get_model_name(),  # GPT-4
            #     messages=message,
            #     timeout=1000,
            # )
            response = openai.ChatCompletion.create(
                n=self.params.n,
                model=self.model_name,
                messages=message,
                temperature=self.params.temperature,
                max_tokens=self.params.max_tokens,
                top_p=self.params.top_p,
                frequency_penalty=self.params.frequency_penalty,
                presence_penalty=self.params.presence_penalty,
            )
            return ChatCompletion(
                state="success",
                role=response.choices[0].message["role"],
                content=response.choices[0].message["content"],
                prompt_token=response.get("usage", {}).get("prompt_tokens", 0),
                completion_token=response.get("usage", {}).get("completion_tokens", 0),
            )
        except Exception as exception:
            print("Exception:", exception)
            return ChatCompletion(state="error", content=exception)

    def stream_chat_completion(self, message: List[dict], **kwargs):
        """
        Stream output chat completion for OpenAI GPT API.

        :param message: The message (scratchpad) to use for completion. Usually contains json of role and content.
        :type message: List[dict]
        :param kwargs: Additional keyword arguments.
        :type kwargs: dict
        :return: ChatCompletion object.
        :rtype: ChatCompletion
        """
        try:
            # response = openai.ChatCompletion.create(
            #     engine=self.get_model_name(),  # GPT-4
            #     messages=message,
            #     timeout=1000,
            #     **kwargs,
            # )
            response = openai.ChatCompletion.create(
                n=self.params.n,
                model=self.model_name,
                messages=message,
                temperature=self.params.temperature,
                max_tokens=self.params.max_tokens,
                top_p=self.params.top_p,
                frequency_penalty=self.params.frequency_penalty,
                presence_penalty=self.params.presence_penalty,
                stream=True,
                **kwargs
            )
            role = next(response).choices[0].delta["role"]
            messages = []
            ## TODO: Calculate prompt_token and for stream mode
            for resp in response:
                messages.append(resp.choices[0].delta.get("content", ""))
                yield ChatCompletion(
                    state="success",
                    role=role,
                    content=messages[-1],
                    prompt_token=0,
                    completion_token=0,
                )
        except Exception as exception:
            print("Exception:", exception)
            return ChatCompletion(state="error", content=exception)

    def function_chat_completion(
            self,
            message: List[dict],
            function_map: Dict[str, Callable],
            function_schema: List[Dict],
    ) -> ChatCompletionWithHistory:
        """
        Chat completion method for OpenAI GPT API.

        :param message: The message to use for completion.
        :type message: List[dict]
        :param function_map: The function map to use for completion.
        :type function_map: Dict[str, Callable]
        :param function_schema: The function schema to use for completion.
        :type function_schema: List[Dict]
        :return: ChatCompletionWithHistory object.
        :rtype: ChatCompletionWithHistory
        """
        assert len(function_schema) == len(function_map)
        try:
            # response = openai.ChatCompletion.create(
            #     engine=self.get_model_name(),  # GPT-4
            #     messages=message,
            #     functions=function_schema,
            #     timeout=1000,
            # )
            response = openai.ChatCompletion.create(
                n=self.params.n,
                model=self.model_name,
                messages=message,
                functions=function_schema,
                temperature=self.params.temperature,
                max_tokens=self.params.max_tokens,
                top_p=self.params.top_p,
                frequency_penalty=self.params.frequency_penalty,
                presence_penalty=self.params.presence_penalty,
            )
            response_message = response.choices[0]["message"]

            if response_message.get("function_call"):
                function_name = response_message["function_call"]["name"]
                fuction_to_call = function_map[function_name]
                function_args = json.loads(
                    response_message["function_call"]["arguments"]
                )
                function_response = fuction_to_call(**function_args)

                # Postprocess function response
                if isinstance(function_response, str):
                    plugin_cost = 0
                    plugin_token = 0
                elif isinstance(function_response, AgentOutput):
                    plugin_cost = function_response.cost
                    plugin_token = function_response.token_usage
                    function_response = function_response.output
                else:
                    raise Exception(
                        "Invalid tool response type. Must be on of [AgentOutput, str]"
                    )

                message.append(dict(response_message))
                message.append(
                    {
                        "role": "function",
                        "name": function_name,
                        "content": function_response,
                    }
                )
                second_response = openai.ChatCompletion.create(
                    model=self.get_model_name(),
                    messages=message,
                )
                message.append(dict(second_response.choices[0].message))
                return ChatCompletionWithHistory(
                    state="success",
                    role=second_response.choices[0].message["role"],
                    content=second_response.choices[0].message["content"],
                    prompt_token=response.get("usage", {}).get("prompt_tokens", 0)
                                 + second_response.get("usage", {}).get("prompt_tokens", 0),
                    completion_token=response.get("usage", {}).get(
                        "completion_tokens", 0
                    )
                                     + second_response.get("usage", {}).get("completion_tokens", 0),
                    message_scratchpad=message,
                    plugin_cost=plugin_cost,
                    plugin_token=plugin_token,
                )
            else:
                message.append(dict(response_message))
                return ChatCompletionWithHistory(
                    state="success",
                    role=response.choices[0].message["role"],
                    content=response.choices[0].message["content"],
                    prompt_token=response.get("usage", {}).get("prompt_tokens", 0),
                    completion_token=response.get("usage", {}).get(
                        "completion_tokens", 0
                    ),
                    message_scratchpad=message,
                )

        except Exception as exception:
            print("Exception:", exception)
            return ChatCompletionWithHistory(state="error", content=str(exception))

    def function_chat_stream_completion(
            self,
            message: List[dict],
            function_map: Dict[str, Callable],
            function_schema: List[Dict],
    ) -> ChatCompletionWithHistory:
        assert len(function_schema) == len(function_map)
        try:
            response = openai.ChatCompletion.create(
                n=self.params.n,
                model=self.get_model_name(),
                messages=message,
                functions=function_schema,
                temperature=self.params.temperature,
                max_tokens=self.params.max_tokens,
                top_p=self.params.top_p,
                frequency_penalty=self.params.frequency_penalty,
                presence_penalty=self.params.presence_penalty,
                stream=True,
            )
            tmp = next(response)
            role = tmp.choices[0].delta["role"]
            _type = (
                "function_call"
                if tmp.choices[0].delta["content"] is None
                else "content"
            )
            if _type == "function_call":
                name = tmp.choices[0].delta["function_call"]["name"]
                yield _type, ChatCompletionWithHistory(
                    state="success",
                    role=role,
                    content="{" + f'"name":"{name}", "arguments":',
                    message_scratchpad=message,
                )
            for resp in response:
                # print(resp)
                content = resp.choices[0].delta.get(_type, "")
                if isinstance(content, dict):
                    content = content["arguments"]
                yield _type, ChatCompletionWithHistory(
                    state="success",
                    role=role,
                    content=content,
                    message_scratchpad=message,
                )

            # result = ''.join(messages)
            # if _type == "function_call":
            #     result = json.loads(result)
            #     function_name = result["name"]
            #     fuction_to_call = function_map[function_name]
            #     function_args = result["arguments"]
            #     function_response = fuction_to_call(**function_args)
            #
            #     # Postprocess function response
            #     if isinstance(function_response, AgentOutput):
            #         function_response = function_response.output
            #     message.append({"role": "function",
            #                     "name": function_name,
            #                     "content": function_response})
            #     second_response = self.function_chat_stream_completion(message=message,function_map=function_map,function_schema=function_schema)
            #     message.append(dict(second_response.choices[0].message))

        except Exception as e:
            logger.error(f"Failed to get response {str(e)}", exc_info=True)
            raise e