




'''simple docstring'''






import argparse

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


logging.set_verbosity_info()

def       __lowerCAmelCase (   snake_case__ ,				snake_case__ ,				snake_case__ ,				snake_case__ ,				snake_case__						):
   # Initialise PyTorch model
   __UpperCamelCase				:      Tuple									=       BertConfig.from_pretrained(
       "bert-large-cased" ,				vocab_size=snake_case__ ,				max_position_embeddings=512 ,				is_decoder=snake_case__ ,				add_cross_attention=snake_case__ ,				)
   __UpperCamelCase				:      str									=       bert_config.to_dict()
   del bert_config_dict["type_vocab_size"]
   __UpperCamelCase				:      int									=       BertGenerationConfig(**snake_case__						)
   if is_encoder:
      __UpperCamelCase				:      Optional[Any]									=       BertGenerationEncoder(snake_case__						)
   else:
      __UpperCamelCase				:      Any									=       BertGenerationDecoder(snake_case__						)
   print(F"Building PyTorch model from configuration: {config}"						)

   # Load weights from tf checkpoint
   load_tf_weights_in_bert_generation(
       snake_case__ ,				snake_case__ ,				model_class="bert" ,				is_encoder_named_decoder=snake_case__ ,				is_encoder=snake_case__ ,				)

   # Save pytorch-model
   print(F"Save PyTorch model and config to {pytorch_dump_path}"						)
   model.save_pretrained(snake_case__						)


if __name__ == "__main__":
    _lowerCAmelCase				     = 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=50358, type=int, help='''Vocab size of model''')
    _lowerCAmelCase				     = 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,
    )









'''simple docstring'''






def       __lowerCAmelCase (   snake_case__ = 4_000_000						):
			__UpperCamelCase				:      Tuple									=       1
			__UpperCamelCase				:      int									=       2
			__UpperCamelCase				:      str									=       0
			while j <= n:
						if j % 2 == 0:
									total += j
						__UpperCamelCase		, __UpperCamelCase				:      List[Any]									=       j, i + j

			return total


if __name__ == "__main__":
				print(f'{solution() = }')



