
import argparse
import json

from transformers import GPTNeoConfig, GPTNeoForCausalLM, load_tf_weights_in_gpt_neo
from transformers.utils import logging


logging.set_verbosity_info()


def 		_lowerCamelCase       (   _a			,   _a			,   _a  ):
  """simple docstring"""




  _lowerCamelCase      =						json.load(open(_a			,   '''r'''  )  )
  _lowerCamelCase      =						GPTNeoConfig(
      hidden_size=config_json['''n_embd''']			,   num_layers=config_json['''n_layer''']			,   num_heads=config_json['''n_head''']			,   attention_types=config_json['''attention_types''']			,   max_position_embeddings=config_json['''n_positions''']			,   resid_dropout=config_json['''res_dropout''']			,   embed_dropout=config_json['''embed_dropout''']			,   attention_dropout=config_json['''attn_dropout''']			,   )
  print(F'''Building PyTorch model from configuration: {config}'''  )
  _lowerCamelCase      =						GPTNeoForCausalLM(_a  )

  # Load weights from tf checkpoint
  load_tf_weights_in_gpt_neo(_a			,   _a			,   _a  )

  # Save pytorch-model
  print(F'''Save PyTorch model to {pytorch_dump_path}'''  )
  model.save_pretrained(_a  )


if __name__ == "__main__":
  _UpperCAmelCase    			=					argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
  )
  parser.add_argument(
      "--config_file",
      default=None,
      type=str,
      required=True,
      help=(
          "The config json file corresponding to the pre-trained mesh-tf model. \n"
          "This specifies the model architecture."
      ),
  )
  parser.add_argument(
      "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
  )
  _UpperCAmelCase    			=					parser.parse_args()
  convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)





"""simple docstring"""


import bsa
import requests







def    UpperCAmelCase       (						UpperCAmelCase   )							-> dict[str, str]:
     return {
         "name": soup.ha.a.text,
         "genre": soup.find('span'		,   class_='genre'   ).text.strip(),
         "rating": soup.strong.text,
         "page_link": f'https://www.imdb.com{soup.a.get("href"   )}',
     }







def    UpperCAmelCase       (						UpperCAmelCase = 5   )							-> tuple:
     snake_case_   			=   int(float(UpperCAmelCase   )   )
     if num_movies < 1:
          return ()
     snake_case_   			=   (
         'https://www.imdb.com/search/title?title_type='
         f'feature&sort=num_votes,desc&count={num_movies}'
     )
     snake_case_   			=   bsa.BeautifulSoup(requests.get(UpperCAmelCase   ).content		,   'html.parser'   )
     return tuple(
         get_movie_data_from_soup(UpperCAmelCase   )
         for movie in source.find_all('div'		,   class_='lister-item mode-advanced'   )   )


if __name__ == "__main__":
    import json

    __UpperCamelCase										=     int(input('''How many movies would you like to see? '''))
    print(
        ''', '''.join(
            json.dumps(movie, indent=4) for movie in get_imdb_top_movies(num_movies)
        )
    )

