






import argparse

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


logging.set_verbosity_info()





def   SCREAMING_SNAKE_CASE__						(   snake_case_,  snake_case_,  snake_case_,  snake_case_,  snake_case_  )   ->  str:


 """simple docstring"""



 a		=			BertConfig.from_pretrained(
     '''bert-large-cased''',  vocab_size=snake_case_,  max_position_embeddings=5_1_2,  is_decoder=snake_case_,  add_cross_attention=snake_case_,  )
 a		=			bert_config.to_dict()
 del bert_config_dict["type_vocab_size"]
 a		=			BertGenerationConfig(**snake_case_  )
 if is_encoder:
  a		=			BertGenerationEncoder(snake_case_  )
 else:
  a		=			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__":
    UpperCamelCase__						:      str								=					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=50_358, type=int, help="""Vocab size of model""")
    UpperCamelCase__						:      Optional[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,
    )













def   SCREAMING_SNAKE_CASE__						(   snake_case_  )   ->  int:


	"""simple docstring"""



	return n * (2 * n - 1)





def   SCREAMING_SNAKE_CASE__						(   snake_case_  )   ->  bool:


	"""simple docstring"""



	a		=			(1 + 2_4 * n) ** 0.5
	return ((1 + root) / 6) % 1 == 0





def   SCREAMING_SNAKE_CASE__						(   snake_case_ = 1_4_4  )   ->  int:


	"""simple docstring"""



	a		=			start
	a		=			hexagonal_num(snake_case_  )
	while not is_pentagonal(snake_case_  ):
		n += 1
		a		=			hexagonal_num(snake_case_  )
	return num


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





