




import argparse

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


logging.set_verbosity_info()





def      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__   )     ->     Optional[Any]:







				'''simple docstring'''


				__lowerCAmelCase					=  BertConfig.from_pretrained(
				    """bert-large-cased"""						,			vocab_size=UpperCamelCase__						,			max_position_embeddings=5_12						,			is_decoder=UpperCamelCase__						,			add_cross_attention=UpperCamelCase__						,			)
				__lowerCAmelCase					=  bert_config.to_dict()
				del bert_config_dict["type_vocab_size"]
				__lowerCAmelCase					=  BertGenerationConfig(**UpperCamelCase__   )
				if is_encoder:
								__lowerCAmelCase					=  BertGenerationEncoder(UpperCamelCase__   )
				else:
								__lowerCAmelCase					=  BertGenerationDecoder(UpperCamelCase__   )
				print(F'''Building PyTorch model from configuration: {config}'''   )

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

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


if __name__ == "__main__":
	__A						:		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")
	__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,
	)








from __future__ import annotations





def 				__a							(       __A						):
    SCREAMING_SNAKE_CASE__         =    {1}
    SCREAMING_SNAKE_CASE__         =    [1]
    for _ in range(2			,							depth + 1						):
        SCREAMING_SNAKE_CASE__         =    [*previous_coefficients, 0]
        SCREAMING_SNAKE_CASE__         =    [0, *previous_coefficients]
        SCREAMING_SNAKE_CASE__         =    []
        for x, y in zip(__A			,							__A						):
            coefficients.add(x + y						)
            previous_coefficients.append(x + y						)
    return coefficients





def 				__a							(       __A						):
    SCREAMING_SNAKE_CASE__         =    set()
    for number in unique_coefficients:
        SCREAMING_SNAKE_CASE__         =    2
        SCREAMING_SNAKE_CASE__         =    number
        while divisor**2 <= copy_number:
            SCREAMING_SNAKE_CASE__         =    0
            while copy_number % divisor == 0:
                copy_number //= divisor
                multiplicity += 1
            if multiplicity >= 2:
                non_squarefrees.add(__A						)
                break
            divisor += 1

    return unique_coefficients.difference(__A						)





def 				__a							(       __A = 51						):
    SCREAMING_SNAKE_CASE__         =    get_pascal_triangle_unique_coefficients(__A						)
    SCREAMING_SNAKE_CASE__         =    get_squarefrees(__A						)
    return sum(__A						)


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


