from ...configuration_utils import PretrainedConfig
from ...utils import logging


a_    					=       logging.get_logger(__name__)

a_    					=       {
    'uw-madison/mra-base-512-4': 'https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json',
}





class 		_lowercase						(					snake_case_       ):
 lowercase				     =   'mra'

 def __init__( self	:							Tuple   ,      snake_case	:							Optional[int]=5_0_2_6_5   ,      snake_case	:							str=7_6_8   ,      snake_case	:							Optional[Any]=1_2   ,      snake_case	:							Tuple=1_2   ,      snake_case	:							Optional[Any]=3_0_7_2   ,      snake_case	:							Dict="gelu"   ,      snake_case	:							int=0.1   ,      snake_case	:							Tuple=0.1   ,      snake_case	:							List[str]=5_1_2   ,      snake_case	:							List[Any]=1   ,      snake_case	:							List[Any]=0.02   ,      snake_case	:							Optional[Any]=1e-5   ,      snake_case	:							Union[str, Any]="absolute"   ,      snake_case	:							str=4   ,      snake_case	:							int="full"   ,      snake_case	:							Tuple=0   ,      snake_case	:							Tuple=0   ,      snake_case	:							str=1   ,      snake_case	:							int=0   ,      snake_case	:							List[str]=2   ,      **snake_case	:							Union[str, Any]   ,      )   ->						Any:

  """simple docstring"""





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

  UpperCamelCase_					:						Tuple =					vocab_size
  UpperCamelCase_					:						Dict =					max_position_embeddings
  UpperCamelCase_					:						Union[str, Any] =					hidden_size
  UpperCamelCase_					:						Optional[Any] =					num_hidden_layers
  UpperCamelCase_					:						int =					num_attention_heads
  UpperCamelCase_					:						int =					intermediate_size
  UpperCamelCase_					:						Tuple =					hidden_act
  UpperCamelCase_					:						Any =					hidden_dropout_prob
  UpperCamelCase_					:						List[str] =					attention_probs_dropout_prob
  UpperCamelCase_					:						Dict =					initializer_range
  UpperCamelCase_					:						int =					type_vocab_size
  UpperCamelCase_					:						Union[str, Any] =					layer_norm_eps
  UpperCamelCase_					:						Dict =					position_embedding_type
  UpperCamelCase_					:						str =					block_per_row
  UpperCamelCase_					:						Optional[Any] =					approx_mode
  UpperCamelCase_					:						Dict =					initial_prior_first_n_blocks
  UpperCamelCase_					:						Optional[int] =					initial_prior_diagonal_n_blocks




from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments





def    __lowercase     (						):
 UpperCamelCase_					:						Optional[Any] =					HfArgumentParser(lowerCamelCase	)
 UpperCamelCase_					:						Tuple =					parser.parse_args_into_dataclasses()[0]
 UpperCamelCase_					:						Dict =					TensorFlowBenchmark(args=lowerCamelCase	)
 try:
  UpperCamelCase_					:						Any =					parser.parse_args_into_dataclasses()[0]
 except ValueError as e:
  UpperCamelCase_					:						Any =					'Arg --no_{0} is no longer used, please use --no-{0} instead.'
  UpperCamelCase_					:						Optional[int] =					' '.join(str(lowerCamelCase	).split(' '	)[:-1]	)
  UpperCamelCase_					:						Any =					''
  UpperCamelCase_					:						Any =					eval(str(lowerCamelCase	).split(' '	)[-1]	)
  UpperCamelCase_					:						List[Any] =					[]
  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(lowerCamelCase	)
  if len(lowerCamelCase	) > 0:
   UpperCamelCase_					:						List[str] =					full_error_msg + begin_error_msg + str(lowerCamelCase	)
  raise ValueError(lowerCamelCase	)
 benchmark.run()


if __name__ == "__main__":
   main()



