from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, AutoModelForQuestionAnswering
from transformers import pipeline, PretrainedConfig

from huggingface_hub import PyTorchModelHubMixin

import torch
import torch.nn as nn
import torch.nn.functional as F
import os
import json
        
class HybridQAModel(nn.Module, PyTorchModelHubMixin):
    #config_class = HybridQAConfig
    def __init__(self, config):
        super().__init__()
        self.config = config
        self.load_models(config.extractive_id, config.generative_id)

    def can_generate(self):
        return False

    def load_models(self, extractive_id, generative_id):
        self.tokenizer_extractive = AutoTokenizer.from_pretrained(extractive_id)
        self.tokenizer_generative = AutoTokenizer.from_pretrained(generative_id)

        self.model_extractive = AutoModelForQuestionAnswering.from_pretrained(extractive_id)
        self.model_generative = AutoModelForSeq2SeqLM.from_pretrained(generative_id)
    
    def predict(self, question, context):
        result_gen, conf_gen = self.infer_generative(self.model_generative, self.tokenizer_generative, question)
        result_ext, conf_ext = self.infer_extractive(self.model_extractive, self.tokenizer_extractive, question, context)

        # print("Generative result: ",result_gen)
        # print("Confidence: ", conf_gen)
        # print("Extractive result: ", result_ext)
        # print("Confidence: ", conf_ext)

        if conf_gen > conf_ext:
            return {'guess':result_gen, 'confidence':conf_gen}
        else:
            return {'guess':result_ext, 'confidence':conf_ext}
        
    def infer_generative(self, model, tokenizer, input_text, **generate_kwargs):
        max_input_length = min(tokenizer.model_max_length, model.config.max_length)
        input_ids = tokenizer.encode(input_text, return_tensors="pt", max_length=max_input_length)

        with torch.no_grad():
            output_ids = model.generate(input_ids, **generate_kwargs)
        decoded_output = tokenizer.decode(output_ids[0], skip_special_tokens=True)

        output_probs = F.softmax(output_ids.float(), dim=-1).squeeze(0)
        entropy = -(output_probs * torch.log(output_probs)).sum(dim=-1)
        confidence_score = 1 - entropy.item()
            
        model.save_pretrained("./base_models")
        return decoded_output, confidence_score
    
    def infer_extractive(self, model, tokenizer, question, context):
        qa_pipeline = pipeline("question-answering", model=model, tokenizer=tokenizer)
        result = qa_pipeline(question=question, context=context)
        confidence_score = result['score']
        
        model.save_pretrained("./base_models")
        return result['answer'], confidence_score

    def save_pretrained(self, save_directory, **kwargs):
        if not os.path.exists(save_directory):
            os.makedirs(save_directory, exist_ok=True)
        self.config.save_pretrained(save_directory, **kwargs)
        self.model_extractive.save_pretrained(save_directory, **kwargs)
        self.tokenizer_extractive.save_pretrained(save_directory, **kwargs)
        self.model_generative.save_pretrained(save_directory, **kwargs)
        self.tokenizer_generative.save_pretrained(save_directory, **kwargs)

    def from_pretrained(cls, save_directory, *model_args, **model_kwargs):
        config = PretrainedConfig.from_pretrained(save_directory)
        model = HybridQAModel(config)
        
        model.model_extractive = AutoModelForQuestionAnswering.from_pretrained(save_directory)
        model.tokenizer_extractive = AutoTokenizer.from_pretrained(save_directory)
        model.model_generative = AutoModelForSeq2SeqLM.from_pretrained(save_directory)
        model.tokenizer_generative = AutoTokenizer.from_pretrained(save_directory)
        
        return model