from __future__ import annotations
from typing import TYPE_CHECKING, List

import logging
import json
import commentjson as cjson
import os
import sys
import requests
import urllib3
import traceback
import pathlib

from tqdm import tqdm
import colorama
from googlesearch import search
import asyncio
import aiohttp
from enum import Enum

from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain.callbacks.manager import BaseCallbackManager

from typing import Any, Dict, List, Optional, Union

from langchain.callbacks.base import BaseCallbackHandler
from langchain.input import print_text
from langchain.schema import AgentAction, AgentFinish, LLMResult
from threading import Thread, Condition
from collections import deque

from ..presets import *
from ..index_func import *
from ..utils import *
from .. import shared
from ..config import retrieve_proxy

class CallbackToIterator:
    def __init__(self):
        self.queue = deque()
        self.cond = Condition()
        self.finished = False

    def callback(self, result):
        with self.cond:
            self.queue.append(result)
            self.cond.notify()  # Wake up the generator.

    def __iter__(self):
        return self

    def __next__(self):
        with self.cond:
            while not self.queue and not self.finished:  # Wait for a value to be added to the queue.
                self.cond.wait()
            if not self.queue:
                raise StopIteration()
            return self.queue.popleft()

    def finish(self):
        with self.cond:
            self.finished = True
            self.cond.notify()  # Wake up the generator if it's waiting.

class ChuanhuCallbackHandler(BaseCallbackHandler):

    def __init__(self, callback) -> None:
        """Initialize callback handler."""
        self.callback = callback

    def on_agent_action(
        self, action: AgentAction, color: Optional[str] = None, **kwargs: Any
    ) -> Any:
        self.callback(action.log)

    def on_tool_end(
        self,
        output: str,
        color: Optional[str] = None,
        observation_prefix: Optional[str] = None,
        llm_prefix: Optional[str] = None,
        **kwargs: Any,
    ) -> None:
        """If not the final action, print out observation."""
        if observation_prefix is not None:
            self.callback(f"\n\n{observation_prefix}")
        self.callback(output)
        if llm_prefix is not None:
            self.callback(f"\n\n{llm_prefix}")

    def on_agent_finish(
        self, finish: AgentFinish, color: Optional[str] = None, **kwargs: Any
    ) -> None:
        self.callback(f"{finish.log}\n\n")

    def on_llm_new_token(self, token: str, **kwargs: Any) -> None:
        """Run on new LLM token. Only available when streaming is enabled."""
        self.callback(token)


class ModelType(Enum):
    Unknown = -1
    OpenAI = 0
    ChatGLM = 1
    LLaMA = 2
    XMChat = 3
    StableLM = 4
    MOSS = 5
    YuanAI = 6
    Minimax = 7
    ChuanhuAgent = 8

    @classmethod
    def get_type(cls, model_name: str):
        model_type = None
        model_name_lower = model_name.lower()
        if "gpt" in model_name_lower:
            model_type = ModelType.OpenAI
        elif "chatglm" in model_name_lower:
            model_type = ModelType.ChatGLM
        elif "llama" in model_name_lower or "alpaca" in model_name_lower:
            model_type = ModelType.LLaMA
        elif "xmchat" in model_name_lower:
            model_type = ModelType.XMChat
        elif "stablelm" in model_name_lower:
            model_type = ModelType.StableLM
        elif "moss" in model_name_lower:
            model_type = ModelType.MOSS
        elif "yuanai" in model_name_lower:
            model_type = ModelType.YuanAI
        elif "minimax" in model_name_lower:
            model_type = ModelType.Minimax
        elif "川虎助理" in model_name_lower:
            model_type = ModelType.ChuanhuAgent
        else:
            model_type = ModelType.Unknown
        return model_type


class BaseLLMModel:
    def __init__(
        self,
        model_name,
        system_prompt="",
        temperature=1.0,
        top_p=1.0,
        n_choices=1,
        stop=None,
        max_generation_token=None,
        presence_penalty=0,
        frequency_penalty=0,
        logit_bias=None,
        user="",
    ) -> None:
        self.history = []
        self.all_token_counts = []
        self.model_name = model_name
        self.model_type = ModelType.get_type(model_name)
        try:
            self.token_upper_limit = MODEL_TOKEN_LIMIT[model_name]
        except KeyError:
            self.token_upper_limit = DEFAULT_TOKEN_LIMIT
        self.interrupted = False
        self.system_prompt = system_prompt
        self.api_key = None
        self.need_api_key = False
        self.single_turn = False

        self.temperature = temperature
        self.top_p = top_p
        self.n_choices = n_choices
        self.stop_sequence = stop
        self.max_generation_token = None
        self.presence_penalty = presence_penalty
        self.frequency_penalty = frequency_penalty
        self.logit_bias = logit_bias
        self.user_identifier = user

    def get_answer_stream_iter(self):
        """stream predict, need to be implemented
        conversations are stored in self.history, with the most recent question, in OpenAI format
        should return a generator, each time give the next word (str) in the answer
        """
        logging.warning("stream predict not implemented, using at once predict instead")
        response, _ = self.get_answer_at_once()
        yield response

    def get_answer_at_once(self):
        """predict at once, need to be implemented
        conversations are stored in self.history, with the most recent question, in OpenAI format
        Should return:
        the answer (str)
        total token count (int)
        """
        logging.warning("at once predict not implemented, using stream predict instead")
        response_iter = self.get_answer_stream_iter()
        count = 0
        for response in response_iter:
            count += 1
        return response, sum(self.all_token_counts) + count

    def billing_info(self):
        """get billing infomation, inplement if needed"""
        logging.warning("billing info not implemented, using default")
        return BILLING_NOT_APPLICABLE_MSG

    def count_token(self, user_input):
        """get token count from input, implement if needed"""
        # logging.warning("token count not implemented, using default")
        return len(user_input)

    def stream_next_chatbot(self, inputs, chatbot, fake_input=None, display_append=""):
        def get_return_value():
            return chatbot, status_text

        status_text = i18n("开始实时传输回答……")
        if fake_input:
            chatbot.append((fake_input, ""))
        else:
            chatbot.append((inputs, ""))

        user_token_count = self.count_token(inputs)
        self.all_token_counts.append(user_token_count)
        logging.debug(f"输入token计数: {user_token_count}")

        stream_iter = self.get_answer_stream_iter()

        for partial_text in stream_iter:
            chatbot[-1] = (chatbot[-1][0], partial_text + display_append)
            self.all_token_counts[-1] += 1
            status_text = self.token_message()
            yield get_return_value()
            if self.interrupted:
                self.recover()
                break
        self.history.append(construct_assistant(partial_text))

    def next_chatbot_at_once(self, inputs, chatbot, fake_input=None, display_append=""):
        if fake_input:
            chatbot.append((fake_input, ""))
        else:
            chatbot.append((inputs, ""))
        if fake_input is not None:
            user_token_count = self.count_token(fake_input)
        else:
            user_token_count = self.count_token(inputs)
        self.all_token_counts.append(user_token_count)
        ai_reply, total_token_count = self.get_answer_at_once()
        self.history.append(construct_assistant(ai_reply))
        if fake_input is not None:
            self.history[-2] = construct_user(fake_input)
        chatbot[-1] = (chatbot[-1][0], ai_reply + display_append)
        if fake_input is not None:
            self.all_token_counts[-1] += count_token(construct_assistant(ai_reply))
        else:
            self.all_token_counts[-1] = total_token_count - sum(self.all_token_counts)
        status_text = self.token_message()
        return chatbot, status_text

    def handle_file_upload(self, files, chatbot, language):
        """if the model accepts multi modal input, implement this function"""
        status = gr.Markdown.update()
        if files:
            index = construct_index(self.api_key, file_src=files)
            status = i18n("索引构建完成")
        return gr.Files.update(), chatbot, status

    def summarize_index(self, files, chatbot, language):
        status = gr.Markdown.update()
        if files:
            index = construct_index(self.api_key, file_src=files)
            status = i18n("总结完成")
            logging.info(i18n("生成内容总结中……"))
            os.environ["OPENAI_API_KEY"] = self.api_key
            from langchain.chains.summarize import load_summarize_chain
            from langchain.prompts import PromptTemplate
            from langchain.chat_models import ChatOpenAI
            from langchain.callbacks import StdOutCallbackHandler
            prompt_template = "Write a concise summary of the following:\n\n{text}\n\nCONCISE SUMMARY IN " + language + ":"
            PROMPT = PromptTemplate(template=prompt_template, input_variables=["text"])
            llm = ChatOpenAI()
            chain = load_summarize_chain(llm, chain_type="map_reduce", return_intermediate_steps=True, map_prompt=PROMPT, combine_prompt=PROMPT)
            summary = chain({"input_documents": list(index.docstore.__dict__["_dict"].values())}, return_only_outputs=True)["output_text"]
            print(i18n("总结") + f": {summary}")
            chatbot.append([i18n("上传了")+str(len(files))+"个文件", summary])
        return chatbot, status

    def prepare_inputs(self, real_inputs, use_websearch, files, reply_language, chatbot):
        fake_inputs = None
        display_append = []
        limited_context = False
        fake_inputs = real_inputs
        if files:
            from langchain.embeddings.huggingface import HuggingFaceEmbeddings
            from langchain.vectorstores.base import VectorStoreRetriever
            limited_context = True
            msg = "加载索引中……"
            logging.info(msg)
            index = construct_index(self.api_key, file_src=files)
            assert index is not None, "获取索引失败"
            msg = "索引获取成功，生成回答中……"
            logging.info(msg)
            with retrieve_proxy():
                retriever = VectorStoreRetriever(vectorstore=index, search_type="similarity_score_threshold",search_kwargs={"k":6, "score_threshold": 0.5})
                relevant_documents = retriever.get_relevant_documents(real_inputs)
            reference_results = [[d.page_content.strip("�"), os.path.basename(d.metadata["source"])] for d in relevant_documents]
            reference_results = add_source_numbers(reference_results)
            display_append = add_details(reference_results)
            display_append = "\n\n" + "".join(display_append)
            real_inputs = (
                replace_today(PROMPT_TEMPLATE)
                .replace("{query_str}", real_inputs)
                .replace("{context_str}", "\n\n".join(reference_results))
                .replace("{reply_language}", reply_language)
            )
        elif use_websearch:
            limited_context = True
            search_results = [i for i in search(real_inputs, advanced=True)]
            reference_results = []
            for idx, result in enumerate(search_results):
                logging.debug(f"搜索结果{idx + 1}：{result}")
                domain_name = urllib3.util.parse_url(result.url).host
                reference_results.append([result.description, result.url])
                display_append.append(
                    # f"{idx+1}. [{domain_name}]({result['href']})\n"
                    f"<li><a href=\"{result.url}\" target=\"_blank\">{domain_name}</a></li>\n"
                )
            reference_results = add_source_numbers(reference_results)
            display_append = "<ol>\n\n" + "".join(display_append) + "</ol>"
            real_inputs = (
                replace_today(WEBSEARCH_PTOMPT_TEMPLATE)
                .replace("{query}", real_inputs)
                .replace("{web_results}", "\n\n".join(reference_results))
                .replace("{reply_language}", reply_language)
            )
        else:
            display_append = ""
        return limited_context, fake_inputs, display_append, real_inputs, chatbot

    def predict(
        self,
        inputs,
        chatbot,
        stream=False,
        use_websearch=False,
        files=None,
        reply_language="中文",
        should_check_token_count=True,
    ):  # repetition_penalty, top_k

        status_text = "开始生成回答……"
        logging.info(
            "输入为：" + colorama.Fore.BLUE + f"{inputs}" + colorama.Style.RESET_ALL
        )
        if should_check_token_count:
            yield chatbot + [(inputs, "")], status_text
        if reply_language == "跟随问题语言（不稳定）":
            reply_language = "the same language as the question, such as English, 中文, 日本語, Español, Français, or Deutsch."

        limited_context, fake_inputs, display_append, inputs, chatbot = self.prepare_inputs(real_inputs=inputs, use_websearch=use_websearch, files=files, reply_language=reply_language, chatbot=chatbot)
        yield chatbot + [(fake_inputs, "")], status_text

        if (
            self.need_api_key and
            self.api_key is None
            and not shared.state.multi_api_key
        ):
            status_text = STANDARD_ERROR_MSG + NO_APIKEY_MSG
            logging.info(status_text)
            chatbot.append((inputs, ""))
            if len(self.history) == 0:
                self.history.append(construct_user(inputs))
                self.history.append("")
                self.all_token_counts.append(0)
            else:
                self.history[-2] = construct_user(inputs)
            yield chatbot + [(inputs, "")], status_text
            return
        elif len(inputs.strip()) == 0:
            status_text = STANDARD_ERROR_MSG + NO_INPUT_MSG
            logging.info(status_text)
            yield chatbot + [(inputs, "")], status_text
            return

        if self.single_turn:
            self.history = []
            self.all_token_counts = []
        self.history.append(construct_user(inputs))

        try:
            if stream:
                logging.debug("使用流式传输")
                iter = self.stream_next_chatbot(
                    inputs,
                    chatbot,
                    fake_input=fake_inputs,
                    display_append=display_append,
                )
                for chatbot, status_text in iter:
                    yield chatbot, status_text
            else:
                logging.debug("不使用流式传输")
                chatbot, status_text = self.next_chatbot_at_once(
                    inputs,
                    chatbot,
                    fake_input=fake_inputs,
                    display_append=display_append,
                )
                yield chatbot, status_text
        except Exception as e:
            traceback.print_exc()
            status_text = STANDARD_ERROR_MSG + str(e)
            yield chatbot, status_text

        if len(self.history) > 1 and self.history[-1]["content"] != inputs:
            logging.info(
                "回答为："
                + colorama.Fore.BLUE
                + f"{self.history[-1]['content']}"
                + colorama.Style.RESET_ALL
            )

        if limited_context:
            # self.history = self.history[-4:]
            # self.all_token_counts = self.all_token_counts[-2:]
            self.history = []
            self.all_token_counts = []

        max_token = self.token_upper_limit - TOKEN_OFFSET

        if sum(self.all_token_counts) > max_token and should_check_token_count:
            count = 0
            while (
                sum(self.all_token_counts)
                > self.token_upper_limit * REDUCE_TOKEN_FACTOR
                and sum(self.all_token_counts) > 0
            ):
                count += 1
                del self.all_token_counts[0]
                del self.history[:2]
            logging.info(status_text)
            status_text = f"为了防止token超限，模型忘记了早期的 {count} 轮对话"
            yield chatbot, status_text

        self.auto_save(chatbot)

    def retry(
        self,
        chatbot,
        stream=False,
        use_websearch=False,
        files=None,
        reply_language="中文",
    ):
        logging.debug("重试中……")
        if len(self.history) > 0:
            inputs = self.history[-2]["content"]
            del self.history[-2:]
            self.all_token_counts.pop()
        elif len(chatbot) > 0:
            inputs = chatbot[-1][0]
        else:
            yield chatbot, f"{STANDARD_ERROR_MSG}上下文是空的"
            return

        iter = self.predict(
            inputs,
            chatbot,
            stream=stream,
            use_websearch=use_websearch,
            files=files,
            reply_language=reply_language,
        )
        for x in iter:
            yield x
        logging.debug("重试完毕")

    # def reduce_token_size(self, chatbot):
    #     logging.info("开始减少token数量……")
    #     chatbot, status_text = self.next_chatbot_at_once(
    #         summarize_prompt,
    #         chatbot
    #     )
    #     max_token_count = self.token_upper_limit * REDUCE_TOKEN_FACTOR
    #     num_chat = find_n(self.all_token_counts, max_token_count)
    #     logging.info(f"previous_token_count: {self.all_token_counts}, keeping {num_chat} chats")
    #     chatbot = chatbot[:-1]
    #     self.history = self.history[-2*num_chat:] if num_chat > 0 else []
    #     self.all_token_counts = self.all_token_counts[-num_chat:] if num_chat > 0 else []
    #     msg = f"保留了最近{num_chat}轮对话"
    #     logging.info(msg)
    #     logging.info("减少token数量完毕")
    #     return chatbot, msg + "，" + self.token_message(self.all_token_counts if len(self.all_token_counts) > 0 else [0])

    def interrupt(self):
        self.interrupted = True

    def recover(self):
        self.interrupted = False

    def set_token_upper_limit(self, new_upper_limit):
        self.token_upper_limit = new_upper_limit
        print(f"token上限设置为{new_upper_limit}")

    def set_temperature(self, new_temperature):
        self.temperature = new_temperature

    def set_top_p(self, new_top_p):
        self.top_p = new_top_p

    def set_n_choices(self, new_n_choices):
        self.n_choices = new_n_choices

    def set_stop_sequence(self, new_stop_sequence: str):
        new_stop_sequence = new_stop_sequence.split(",")
        self.stop_sequence = new_stop_sequence

    def set_max_tokens(self, new_max_tokens):
        self.max_generation_token = new_max_tokens

    def set_presence_penalty(self, new_presence_penalty):
        self.presence_penalty = new_presence_penalty

    def set_frequency_penalty(self, new_frequency_penalty):
        self.frequency_penalty = new_frequency_penalty

    def set_logit_bias(self, logit_bias):
        logit_bias = logit_bias.split()
        bias_map = {}
        encoding = tiktoken.get_encoding("cl100k_base")
        for line in logit_bias:
            word, bias_amount = line.split(":")
            if word:
                for token in encoding.encode(word):
                    bias_map[token] = float(bias_amount)
        self.logit_bias = bias_map

    def set_user_identifier(self, new_user_identifier):
        self.user_identifier = new_user_identifier

    def set_system_prompt(self, new_system_prompt):
        self.system_prompt = new_system_prompt

    def set_key(self, new_access_key):
        self.api_key = new_access_key.strip()
        msg = i18n("API密钥更改为了") + hide_middle_chars(self.api_key)
        logging.info(msg)
        return self.api_key, msg

    def set_single_turn(self, new_single_turn):
        self.single_turn = new_single_turn

    def reset(self):
        self.history = []
        self.all_token_counts = []
        self.interrupted = False
        pathlib.Path(os.path.join(HISTORY_DIR, self.user_identifier, new_auto_history_filename(os.path.join(HISTORY_DIR, self.user_identifier)))).touch()
        return [], self.token_message([0])

    def delete_first_conversation(self):
        if self.history:
            del self.history[:2]
            del self.all_token_counts[0]
        return self.token_message()

    def delete_last_conversation(self, chatbot):
        if len(chatbot) > 0 and STANDARD_ERROR_MSG in chatbot[-1][1]:
            msg = "由于包含报错信息，只删除chatbot记录"
            chatbot.pop()
            return chatbot, self.history
        if len(self.history) > 0:
            self.history.pop()
            self.history.pop()
        if len(chatbot) > 0:
            msg = "删除了一组chatbot对话"
            chatbot.pop()
        if len(self.all_token_counts) > 0:
            msg = "删除了一组对话的token计数记录"
            self.all_token_counts.pop()
        msg = "删除了一组对话"
        return chatbot, msg

    def token_message(self, token_lst=None):
        if token_lst is None:
            token_lst = self.all_token_counts
        token_sum = 0
        for i in range(len(token_lst)):
            token_sum += sum(token_lst[: i + 1])
        return i18n("Token 计数: ") + f"{sum(token_lst)}" + i18n("，本次对话累计消耗了 ") + f"{token_sum} tokens"

    def save_chat_history(self, filename, chatbot, user_name):
        if filename == "":
            return
        if not filename.endswith(".json"):
            filename += ".json"
        return save_file(filename, self.system_prompt, self.history, chatbot, user_name)

    def auto_save(self, chatbot):
        history_file_path = get_history_filepath(self.user_identifier)
        save_file(history_file_path, self.system_prompt, self.history, chatbot, self.user_identifier)

    def export_markdown(self, filename, chatbot, user_name):
        if filename == "":
            return
        if not filename.endswith(".md"):
            filename += ".md"
        return save_file(filename, self.system_prompt, self.history, chatbot, user_name)

    def load_chat_history(self, filename, user_name):
        logging.debug(f"{user_name} 加载对话历史中……")
        logging.info(f"filename: {filename}")
        if type(filename) != str and filename is not None:
            filename = filename.name
        try:
            if "/" not in filename:
                history_file_path = os.path.join(HISTORY_DIR, user_name, filename)
            else:
                history_file_path = filename
            with open(history_file_path, "r") as f:
                json_s = json.load(f)
            try:
                if type(json_s["history"][0]) == str:
                    logging.info("历史记录格式为旧版，正在转换……")
                    new_history = []
                    for index, item in enumerate(json_s["history"]):
                        if index % 2 == 0:
                            new_history.append(construct_user(item))
                        else:
                            new_history.append(construct_assistant(item))
                    json_s["history"] = new_history
                    logging.info(new_history)
            except:
                pass
            logging.debug(f"{user_name} 加载对话历史完毕")
            self.history = json_s["history"]
            return os.path.basename(filename), json_s["system"], json_s["chatbot"]
        except:
            # 没有对话历史或者对话历史解析失败
            logging.info(f"没有找到对话历史记录 {filename}")
            return gr.update(), self.system_prompt, gr.update()

    def auto_load(self):
        if self.user_identifier == "":
            self.reset()
            return self.system_prompt, gr.update()
        history_file_path = get_history_filepath(self.user_identifier)
        filename, system_prompt, chatbot = self.load_chat_history(history_file_path, self.user_identifier)
        return system_prompt, chatbot


    def like(self):
        """like the last response, implement if needed
        """
        return gr.update()

    def dislike(self):
        """dislike the last response, implement if needed
        """
        return gr.update()