






from ...configuration_utils import PretrainedConfig


class       __lowercase      (	lowercase_  ):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE   						=				"bert-generation"




     def __init__(				self       :			Union[str, Any] ,	UpperCamelCase_       :			List[Any]=50_358 ,	UpperCamelCase_       :			Tuple=1_024 ,	UpperCamelCase_       :			List[Any]=24 ,	UpperCamelCase_       :			Optional[Any]=16 ,	UpperCamelCase_       :			str=4_096 ,	UpperCamelCase_       :			Dict="gelu" ,	UpperCamelCase_       :			Union[str, Any]=0.1 ,	UpperCamelCase_       :			Union[str, Any]=0.1 ,	UpperCamelCase_       :			int=512 ,	UpperCamelCase_       :			Optional[int]=0.02 ,	UpperCamelCase_       :			Any=1e-12 ,	UpperCamelCase_       :			Dict=0 ,	UpperCamelCase_       :			Tuple=2 ,	UpperCamelCase_       :			Any=1 ,	UpperCamelCase_       :			Union[str, Any]="absolute" ,	UpperCamelCase_       :			int=True ,	**UpperCamelCase_       :			Union[str, Any] ,	):


         """simple docstring"""



         super().__init__(pad_token_id=UpperCamelCase_ ,	bos_token_id=UpperCamelCase_ ,	eos_token_id=UpperCamelCase_ ,	**UpperCamelCase_				)

         __A         =	vocab_size
         __A         =	hidden_size
         __A         =	num_hidden_layers
         __A         =	num_attention_heads
         __A         =	hidden_act
         __A         =	intermediate_size
         __A         =	hidden_dropout_prob
         __A         =	attention_probs_dropout_prob
         __A         =	max_position_embeddings
         __A         =	initializer_range
         __A         =	layer_norm_eps
         __A         =	position_embedding_type
         __A         =	use_cache












from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments



def 			_SCREAMING_SNAKE_CASE						( )						->      Tuple:





    """simple docstring"""

    __A         =	HfArgumentParser(__lowercase    )
    __A         =	parser.parse_args_into_dataclasses()[0]
    __A         =	TensorFlowBenchmark(args=__lowercase    )
    try:
        __A         =	parser.parse_args_into_dataclasses()[0]
    except ValueError as e:
        __A         =	"""Arg --no_{0} is no longer used, please use --no-{0} instead."""
        __A         =	""" """.join(str(__lowercase    ).split(""" """    )[:-1]    )
        __A         =	""""""
        __A         =	eval(str(__lowercase    ).split(""" """    )[-1]    )
        __A         =	[]
        for arg in depreciated_args:
            # arg[2:] removes '--'
            if arg[2:] in TensorFlowBenchmark.deprecated_args:
                # arg[5:] removes '--no_'
                full_error_msg += arg_error_msg.format(arg[5:]    )
            else:
                wrong_args.append(__lowercase    )
        if len(__lowercase    ) > 0:
            __A         =	full_error_msg + begin_error_msg + str(__lowercase    )
        raise ValueError(__lowercase    )
    benchmark.run()


if __name__ == "__main__":
  main()




