import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import time
import torch
from transformers import T5ForConditionalGeneration,T5Tokenizer
import random
import spacy
import zipfile
import os
import git
os.system('pip install git+https://github.com/boudinfl/pke.git')
os.system('python -m nltk.downloader universal_tagset')
os.system('python -m spacy download en')
os.system('wget https://github.com/explosion/sense2vec/releases/download/v1.0.0/s2v_reddit_2015_md.tar.gz')
os.system('tar -xvf  s2v_reddit_2015_md.tar.gz')
os.system('python -m spacy download en_core_web_sm')

import json
from sense2vec import Sense2Vec
import requests
from collections import OrderedDict
import string
import pke
import nltk
import numpy
import en_core_web_sm
from nltk import FreqDist
nltk.download('brown', quiet=True, force=True)
nltk.download('stopwords', quiet=True, force=True)
nltk.download('popular', quiet=True, force=True)
from nltk.corpus import stopwords
from nltk.corpus import brown
from similarity.normalized_levenshtein import NormalizedLevenshtein
from nltk.tokenize import sent_tokenize
from flashtext import KeywordProcessor
from encoding import beam_search_decoding
from mcq import tokenize_sentences
from mcq import get_keywords
from mcq import get_sentences_for_keyword
from mcq import generate_questions_mcq
from mcq import generate_normal_questions
import time




tokenizer = T5Tokenizer.from_pretrained('t5-large')
model = T5ForConditionalGeneration.from_pretrained('Parth/result')
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
# model.eval()
device = device
model = model
nlp = spacy.load('en_core_web_sm')
s2v = Sense2Vec().from_disk('s2v_old')
fdist = FreqDist(brown.words())
normalized_levenshtein = NormalizedLevenshtein()
def set_seed(seed):
        numpy.random.seed(seed)
        torch.manual_seed(seed)
        if torch.cuda.is_available():
            torch.cuda.manual_seed_all(seed)
set_seed(42)



def predict_mcq(payload):
        start = time.time()
        inp = {
            "input_text": payload.get("input_text"),
            "max_questions": payload.get("max_questions", 4)
        }

        text = inp['input_text']
        sentences = tokenize_sentences(text)
        joiner = " "
        modified_text = joiner.join(sentences)


        keywords = get_keywords(nlp,modified_text,inp['max_questions'],s2v,fdist,normalized_levenshtein,len(sentences) )


        keyword_sentence_mapping = get_sentences_for_keyword(keywords, sentences)

        for k in keyword_sentence_mapping.keys():
            text_snippet = " ".join(keyword_sentence_mapping[k][:3])
            keyword_sentence_mapping[k] = text_snippet


        final_output = {}

        if len(keyword_sentence_mapping.keys()) == 0:
            return final_output
        else:
            try:
                generated_questions = generate_questions_mcq(keyword_sentence_mapping,device,tokenizer,model,s2v,normalized_levenshtein)

            except:
                return final_output
            end = time.time()

            final_output["statement"] = modified_text
            final_output["questions"] = generated_questions["questions"]
            final_output["time_taken"] = end-start

            if torch.device=='cuda':
                torch.cuda.empty_cache()

            return final_output



def predict_shortq(payload):
        inp = {
            "input_text": payload.get("input_text"),
            "max_questions": payload.get("max_questions", 4)
        }

        text = inp['input_text']
        sentences = tokenize_sentences(text)
        joiner = " "
        modified_text = joiner.join(sentences)


        keywords = get_keywords(nlp,modified_text,inp['max_questions'],s2v,fdist,normalized_levenshtein,len(sentences) )


        keyword_sentence_mapping = get_sentences_for_keyword(keywords, sentences)

        for k in keyword_sentence_mapping.keys():
            text_snippet = " ".join(keyword_sentence_mapping[k][:3])
            keyword_sentence_mapping[k] = text_snippet

        final_output = {}

        if len(keyword_sentence_mapping.keys()) == 0:
            print('ZERO')
            return final_output
        else:

            generated_questions = generate_normal_questions(keyword_sentence_mapping,device,tokenizer,model)
            print(generated_questions)


        final_output["statement"] = modified_text
        final_output["questions"] = generated_questions["questions"]

        if torch.device=='cuda':
            torch.cuda.empty_cache()

        return final_output





def paraphrase(payload):
        start = time.time()
        inp = {
            "input_text": payload.get("input_text"),
            "max_questions": payload.get("max_questions", 3)
        }

        text = inp['input_text']
        num = inp['max_questions']

        sentence= text
        text= "paraphrase: " + sentence + " </s>"

        encoding = tokenizer.encode_plus(text,pad_to_max_length=True, return_tensors="pt")
        input_ids, attention_masks = encoding["input_ids"].to(device), encoding["attention_mask"].to(device)

        beam_outputs = model.generate(
            input_ids=input_ids,
            attention_mask=attention_masks,
            max_length= 50,
            num_beams=50,
            num_return_sequences=num,
            no_repeat_ngram_size=2,
            early_stopping=True
            )

#         print ("\nOriginal Question ::")
#         print (text)
#         print ("\n")
#         print ("Paraphrased Questions :: ")
        final_outputs =[]
        for beam_output in beam_outputs:
            sent = tokenizer.decode(beam_output, skip_special_tokens=True,clean_up_tokenization_spaces=True)
            if sent.lower() != sentence.lower() and sent not in final_outputs:
                final_outputs.append(sent)

        output= {}
        output['Question']= text
        output['Count']= num
        output['Paraphrased Questions']= final_outputs

        for i, final_output in enumerate(final_outputs):
            print("{}".format(i, final_output))

        if torch.device=='cuda':
            torch.cuda.empty_cache()

        return output