import logging
from telegram import Update
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch
from telegram.ext import (
    CallbackContext,
)

NAME = "Conversation"

DESCRIPTION = """
Useful for building up conversation. 
Input: A normal chat text
Output: A text
"""

GET_CON = range(1)


class Conversation():
    tokenizer = AutoTokenizer.from_pretrained(
        "microsoft/GODEL-v1_1-large-seq2seq")
    model = AutoModelForSeq2SeqLM.from_pretrained(
        "microsoft/GODEL-v1_1-large-seq2seq")

    # async def talk(self, message: str):
    #     logging.info(f"{message}")
    #     chat_history_ids = torch.tensor([], dtype=torch.long)
    #     new_user_input_ids = self.tokenizer.encode(message + self.tokenizer.eos_token, return_tensors='pt')
    #     bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1)
    #     chat_history_ids =self.model.generate(bot_input_ids, max_length=1000, pad_token_id=self.tokenizer.eos_token_id)
    #     return "{}".format(self.tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True))
    def predict(self, input, history=[]):
       instruction = "Instruction: given a dialog context and related knowledge, you need to answer the question based on the knowledge."
       knowledge = '[KNOWLEDGE] ' + ''
       s = list(sum(history, ()))
       s.append(input)
       dialog = ' EOS '.join(s)
       query = f"{instruction} [CONTEXT] {dialog} {knowledge}"
       input_ids = self.tokenizer.encode(f"{query}", return_tensors='pt')
       print(input, s)
       output = self.model.generate(input_ids, max_length=128, min_length=8, top_p=0.9, do_sample=True).tolist()
       response = self.tokenizer.decode(output[0], skip_special_tokens=True)
       history.append((input, response))
       return response
    # def generate(self, instruction, knowledge, dialog):
    #     if knowledge != '':
    #         knowledge = '[KNOWLEDGE] ' + knowledge
    #     dialog = ' EOS '.join(dialog)
    #     query = f"{instruction} [CONTEXT] {dialog} {knowledge}"
    #     input_ids = self.tokenizer(f"{query}", return_tensors="pt").input_ids
    #     outputs = self.model.generate(
    #         input_ids, max_length=128,
    #         min_length=8,
    #         top_p=0.9,
    #         do_sample=True,
    #     )
    #     output = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
    #     return output

    async def process_conversation(self, update: Update, context: CallbackContext) -> int:
        message = update.message.text
        # instruction = f'Instruction: given a dialog context, you need to response empathically.'
        # knowledge = ''
        # dialog = []
        # dialog .append(message)
        text = self.predict(message)
        await update.message.reply_text(f'{text}')