from concurrent.futures import ThreadPoolExecutor
from typing import Dict, List, Optional, Union

import requests

from opencompass.utils.prompt import PromptList

from .base_api import BaseAPIModel

PromptType = Union[PromptList, str]


class MiniMax(BaseAPIModel):
    """Model wrapper around MiniMax.

    Documentation: https://api.minimax.chat/document/guides/chat-pro

    Args:
        path (str): The name of MiniMax model.
            e.g. `abab5.5-chat`
        model_type (str): The type of the model
            e.g. `chat`
        group_id (str): The id of group(like the org ID of group)
        key (str): Authorization key.
        query_per_second (int): The maximum queries allowed per second
            between two consecutive calls of the API. Defaults to 1.
        max_seq_len (int): Unused here.
        meta_template (Dict, optional): The model's meta prompt
            template if needed, in case the requirement of injecting or
            wrapping of any meta instructions.
        retry (int): Number of retires if the API call fails. Defaults to 2.
    """

    def __init__(
        self,
        path: str,
        key: str,
        group_id: str,
        model_type: str = 'chat',
        url:
        str = 'https://api.minimax.chat/v1/text/chatcompletion_pro?GroupId=',
        query_per_second: int = 2,
        max_seq_len: int = 2048,
        meta_template: Optional[Dict] = None,
        retry: int = 2,
    ):
        super().__init__(path=path,
                         max_seq_len=max_seq_len,
                         query_per_second=query_per_second,
                         meta_template=meta_template,
                         retry=retry)
        self.headers = {
            'Authorization': f'Bearer {key}',
            'Content-Type': 'application/json',
        }
        self.type = model_type
        self.url = url + group_id
        self.model = path

    def generate(
        self,
        inputs: List[str or PromptList],
        max_out_len: int = 512,
    ) -> List[str]:
        """Generate results given a list of inputs.

        Args:
            inputs (List[str or PromptList]): A list of strings or PromptDicts.
                The PromptDict should be organized in OpenCompass'
                API format.
            max_out_len (int): The maximum length of the output.

        Returns:
            List[str]: A list of generated strings.
        """
        with ThreadPoolExecutor() as executor:
            results = list(
                executor.map(self._generate, inputs,
                             [max_out_len] * len(inputs)))
        self.flush()
        return results

    def _generate(
        self,
        input: str or PromptList,
        max_out_len: int = 512,
    ) -> str:
        """Generate results given an input.

        Args:
            inputs (str or PromptList): A string or PromptDict.
                The PromptDict should be organized in OpenCompass'
                API format.
            max_out_len (int): The maximum length of the output.

        Returns:
            str: The generated string.
        """
        assert isinstance(input, (str, PromptList))

        if isinstance(input, str):
            messages = [{
                'sender_type': 'USER',
                'sender_name': 'OpenCompass',
                'text': input
            }]
        else:
            messages = []
            for item in input:
                msg = {'text': item['prompt']}
                if item['role'] == 'HUMAN':
                    msg['sender_type'] = 'USER'
                    msg['sender_name'] = 'OpenCompass'
                elif item['role'] == 'BOT':
                    msg['sender_type'] = 'BOT'
                    msg['sender_name'] = 'MM智能助理'

                messages.append(msg)

        data = {
            'bot_setting': [{
                'bot_name':
                'MM智能助理',
                'content':
                'MM智能助理是一款由MiniMax自研的，没有调用其他产品的接口的大型语言模型。' +
                'MiniMax是一家中国科技公司，一直致力于进行大模型相关的研究。'
            }],
            'reply_constraints': {
                'sender_type': 'BOT',
                'sender_name': 'MM智能助理'
            },
            'model':
            self.model,
            'messages':
            messages
        }

        max_num_retries = 0
        while max_num_retries < self.retry:
            self.acquire()
            raw_response = requests.request('POST',
                                            url=self.url,
                                            headers=self.headers,
                                            json=data)
            response = raw_response.json()
            self.release()

            if response is None:
                print('Connection error, reconnect.')
                # if connect error, frequent requests will casuse
                # continuous unstable network, therefore wait here
                # to slow down the request
                self.wait()
                continue
            if raw_response.status_code == 200:
                # msg = json.load(response.text)
                # response
                msg = response['reply']
                return msg
            # sensitive content, prompt overlength, network error
            # or illegal prompt
            if (response.status_code == 1000 or response.status_code == 1001
                    or response.status_code == 1002
                    or response.status_code == 1004
                    or response.status_code == 1008
                    or response.status_code == 1013
                    or response.status_code == 1027
                    or response.status_code == 1039
                    or response.status_code == 2013):
                print(response.text)
                return ''
            print(response)
            max_num_retries += 1

        raise RuntimeError(response.text)
