




"""simple docstring"""
import argparse

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


logging.set_verbosity_info()






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

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

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


if __name__ == "__main__":
    _SCREAMING_SNAKE_CASE						 =     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_0_3_5_8, type=int, help="""Vocab size of model""")
    _SCREAMING_SNAKE_CASE						 =     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'''




from itertools import combinations, product

def       __UpperCamelCase     (     _A     :					int       , _A     :					int       , _A     :					int       , _A     :					int					)     ->   bool:




	"""simple docstring"""





	if xa == ya == 0 or xa == ya == 0:
		return False
	lowerCAmelCase       :     Optional[Any]   				=			xa * xa + ya * ya
	lowerCAmelCase       :     Tuple   				=			xa * xa + ya * ya
	lowerCAmelCase       :     List[str]   				=			(xa - xa) * (xa - xa) + (ya - ya) * (ya - ya)
	return (
	    a_square + b_square == c_square
	    or a_square + c_square == b_square
	    or b_square + c_square == a_square
	)

def       __UpperCamelCase     (     _A     :					int = 50					)     ->   int:




	"""simple docstring"""





	return sum(
	    1
	    for pta, pta in combinations(product(range(limit + 1					)       , repeat=2					)       , 2					)
	    if is_right(*_A       , *_A					)					)


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