


'''simple docstring'''



from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments






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


if __name__ == "__main__":
			main()







'''simple docstring'''



from __future__ import annotations

from typing import Dict

from ...configuration_utils import PretrainedConfig


__lowercase     :						List[Any]	=     {
    'susnato/ernie-m-base_pytorch': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json',
    'susnato/ernie-m-large_pytorch': 'https://huggingface.co/susnato/ernie-m-large_pytorch/blob/main/config.json',
}





class 							__UpperCamelCase    (		lowerCAmelCase_ ):
	A_           =   "ernie_m"
	A_           =   {"dropout": "classifier_dropout", "num_classes": "num_labels"}




	def __init__(       self						,    __a = 25_0002						,    __a = 768						,    __a = 12						,    __a = 12						,    __a = 3072						,    __a = "gelu"						,    __a = 0.1						,    __a = 0.1						,    __a = 514						,    __a = 0.02						,    __a = 1						,    __a = 1E-0_5						,    __a=None						,    __a=False						,    __a=0.0						,    **__a						,    ):

						'''simple docstring'''





						super().__init__(pad_token_id=__a						,    **__a							)
						__a      :	int    =  vocab_size
						__a      :	Dict    =  hidden_size
						__a      :	str    =  num_hidden_layers
						__a      :	Dict    =  num_attention_heads
						__a      :	List[str]    =  intermediate_size
						__a      :	Union[str, Any]    =  hidden_act
						__a      :	List[Any]    =  hidden_dropout_prob
						__a      :	str    =  attention_probs_dropout_prob
						__a      :	Any    =  max_position_embeddings
						__a      :	int    =  initializer_range
						__a      :	Dict    =  layer_norm_eps
						__a      :	int    =  classifier_dropout
						__a      :	Dict    =  is_decoder
						__a      :	int    =  act_dropout



