from transformers import pipeline
# import transformers
# import torch
# import torch.nn as nn
# import tensorflow as tf
# from transformers import TFGPT2LMHeadModel ,GPT2Tokenizer, BitsAndBytesConfig
# tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
# model = TFGPT2LMHeadModel.from_pretrained('gpt2',pad_token_id = tokenizer.eos_token_id)
# def generate_text(inp):
#   input_ids = tokenizer.encode(inp,return_tensors = 'tf')
#   beam_output =  model.generate(input_ids, max_length = 100,num_beams = 5, no_repeat_ngram_size = 2, early_stopping = True)
#   output = tokenizer.decode(beam_output[0],skip_special_tokens = True, clean_up_tokenization_spaces = True)
#   return ".".join(output.split(".")[:-1]) + "."
qa_pipeline = pipeline("question-answering", model='deepset/roberta-base-squad2')