


import argparse

from . import (
    BertConfig,
    BertGenerationConfig,
    BertGenerationDecoder,
    BertGenerationEncoder,
    load_tf_weights_in_bert_generation,
    logging,
)


logging.set_verbosity_info()






def  _UpperCAmelCase						( __lowerCamelCase			,  __lowerCamelCase			,  __lowerCamelCase			,  __lowerCamelCase			,  __lowerCamelCase							)		->Dict:


 '''simple docstring'''
 # Initialise PyTorch model
 snake_case :			int							=   BertConfig.from_pretrained(
     '''bert-large-cased'''			,  vocab_size=__lowerCamelCase			,  max_position_embeddings=512			,  is_decoder=__lowerCamelCase			,  add_cross_attention=__lowerCamelCase			,  )
 snake_case :			Optional[Any]							=   bert_config.to_dict()
 del bert_config_dict["type_vocab_size"]
 snake_case :			Union[str, Any]							=   BertGenerationConfig(**__lowerCamelCase							)
 if is_encoder:
  snake_case :			Dict							=   BertGenerationEncoder(__lowerCamelCase							)
 else:
  snake_case :			Optional[Any]							=   BertGenerationDecoder(__lowerCamelCase							)
 print(f'''Building PyTorch model from configuration: {config}'''							)

 # Load weights from tf checkpoint
 load_tf_weights_in_bert_generation(
     __lowerCamelCase			,  __lowerCamelCase			,  model_class='''bert'''			,  is_encoder_named_decoder=__lowerCamelCase			,  is_encoder=__lowerCamelCase			,  )

 # Save pytorch-model
 print(f'''Save PyTorch model and config to {pytorch_dump_path}'''							)
 model.save_pretrained(__lowerCamelCase							)


if __name__ == "__main__":
  a					:				int       =      argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--tf_hub_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
  )
  parser.add_argument(
      '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
  )
  parser.add_argument(
      '--is_encoder_named_decoder',
      action='store_true',
      help='If decoder has to be renamed to encoder in PyTorch model.',
  )
  parser.add_argument('--is_encoder', action='store_true', help='If model is an encoder.')
  parser.add_argument('--vocab_size', default=5_0358, type=int, help='Vocab size of model')
  a					:				Union[str, Any]       =      parser.parse_args()
  convert_tf_checkpoint_to_pytorch(
      args.tf_hub_path,
      args.pytorch_dump_path,
      args.is_encoder_named_decoder,
      args.vocab_size,
      is_encoder=args.is_encoder,
  )










import os






def  _UpperCAmelCase						( )		->Tuple:


 '''simple docstring'''
 snake_case :			Dict							=   0
 snake_case :			int							=   0
 with open(os.path.dirname(__lowerCamelCase							) + '''/p022_names.txt'''							) as file:
  snake_case :			int							=   str(file.readlines()[0]							)
  snake_case :			Any							=   name.replace('''"'''			,  ''''''							).split(''','''							)

 name.sort()
 for i in range(len(__lowerCamelCase							)							):
  for j in name[i]:
   temp_sum += ord(__lowerCamelCase							) - ord('''A'''							) + 1
  total_sum += (i + 1) * temp_sum
  snake_case :			List[Any]							=   0
 return total_sum


if __name__ == "__main__":
  print(solution())






