





"""simple docstring"""




from collections import namedtuple

UpperCamelCase_	 =namedtuple("""from_to""", """from_ to""")

UpperCamelCase_	 ={
    """millimeter""": """mm""",
    """centimeter""": """cm""",
    """meter""": """m""",
    """kilometer""": """km""",
    """inch""": """in""",
    """inche""": """in""",  # Trailing 's' has been stripped off
    """feet""": """ft""",
    """foot""": """ft""",
    """yard""": """yd""",
    """mile""": """mi""",
}

UpperCamelCase_	 ={
    """mm""": from_to(0.001, 1_000),
    """cm""": from_to(0.01, 100),
    """m""": from_to(1, 1),
    """km""": from_to(1_000, 0.001),
    """in""": from_to(0.0254, 39.3701),
    """ft""": from_to(0.3048, 3.2_8084),
    """yd""": from_to(0.9144, 1.0_9361),
    """mi""": from_to(1609.34, 0.0_0062_1371),
}





def 			a_							(				_lowercase			,		_lowercase			,		_lowercase  ):
						_UpperCamelCase     :   Dict      =       from_type.lower().rstrip('''s'''  )
						_UpperCamelCase     :   Optional[Any]      =       TYPE_CONVERSION.get(_lowercase			,		_lowercase  )
						_UpperCamelCase     :   Any      =       to_type.lower().rstrip('''s'''  )
						_UpperCamelCase     :   Optional[int]      =       TYPE_CONVERSION.get(_lowercase			,		_lowercase  )
						if new_from not in METRIC_CONVERSION:
												_UpperCamelCase     :   List[Any]      =       (
												    F"""Invalid 'from_type' value: {from_type!r}.\n"""
												    F"""Conversion abbreviations are: {', '.join(_lowercase  )}"""
												)
												raise ValueError(_lowercase  )
						if new_to not in METRIC_CONVERSION:
												_UpperCamelCase     :   int      =       (
												    F"""Invalid 'to_type' value: {to_type!r}.\n"""
												    F"""Conversion abbreviations are: {', '.join(_lowercase  )}"""
												)
												raise ValueError(_lowercase  )
						return value * METRIC_CONVERSION[new_from].from_ * METRIC_CONVERSION[new_to].to


if __name__ == "__main__":
						import doctest

						doctest.testmod()








import argparse

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


logging.set_verbosity_info()




def    UpperCAmelCase					(  lowercase__						:					int				,					lowercase__						:					str				,					lowercase__						:					Union[str, Any]				,					lowercase__						:					Optional[int]				,					lowercase__						:					Optional[Any]	):



			'''simple docstring'''





			a__					=  BertConfig.from_pretrained(
			    """bert-large-cased"""				,					vocab_size=lowercase__				,					max_position_embeddings=512				,					is_decoder=lowercase__				,					add_cross_attention=lowercase__				,					)
			a__					=  bert_config.to_dict()
			del bert_config_dict["type_vocab_size"]
			a__					=  BertGenerationConfig(**lowercase__	)
			if is_encoder:
						a__					=  BertGenerationEncoder(lowercase__	)
			else:
						a__					=  BertGenerationDecoder(lowercase__	)
			print(f'Building PyTorch model from configuration: {config}'	)

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

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


if __name__ == "__main__":
	_lowercase				:					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=5_03_58, type=int, help="""Vocab size of model""")
	_lowercase				:					List[str]     					=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,
	)

