'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A_     :						Any    							=logging.get_logger(__name__)

A_     :						Union[str, Any]    							={
    'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/config.json',
    'umberto-commoncrawl-cased-v1': (
        'https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json'
    ),
    'umberto-wikipedia-uncased-v1': (
        'https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json'
    ),
}


class     __UpperCAmelCase       ( _SCREAMING_SNAKE_CASE		):
  __A :				Any											= """camembert"""





  def __init__(	self    ,     _lowerCamelCase=3_0522    ,     _lowerCamelCase=768    ,     _lowerCamelCase=12    ,     _lowerCamelCase=12    ,     _lowerCamelCase=3072    ,     _lowerCamelCase="gelu"    ,     _lowerCamelCase=0.1    ,     _lowerCamelCase=0.1    ,     _lowerCamelCase=512    ,     _lowerCamelCase=2    ,     _lowerCamelCase=0.02    ,     _lowerCamelCase=1E-12    ,     _lowerCamelCase=1    ,     _lowerCamelCase=0    ,     _lowerCamelCase=2    ,     _lowerCamelCase="absolute"    ,     _lowerCamelCase=True    ,     _lowerCamelCase=None    ,     **_lowerCamelCase    ,     ):
     super().__init__(pad_token_id=__snake_case    ,     bos_token_id=__snake_case    ,     eos_token_id=__snake_case    ,     **__snake_case )

     lowerCAmelCase_			=      vocab_size
     lowerCAmelCase_			=      hidden_size
     lowerCAmelCase_			=      num_hidden_layers
     lowerCAmelCase_			=      num_attention_heads
     lowerCAmelCase_			=      hidden_act
     lowerCAmelCase_			=      intermediate_size
     lowerCAmelCase_			=      hidden_dropout_prob
     lowerCAmelCase_			=      attention_probs_dropout_prob
     lowerCAmelCase_			=      max_position_embeddings
     lowerCAmelCase_			=      type_vocab_size
     lowerCAmelCase_			=      initializer_range
     lowerCAmelCase_			=      layer_norm_eps
     lowerCAmelCase_			=      position_embedding_type
     lowerCAmelCase_			=      use_cache
     lowerCAmelCase_			=      classifier_dropout






class     __UpperCAmelCase       ( _SCREAMING_SNAKE_CASE		):





  @property
  def 						UpperCAmelCase_	(	self ):





     if self.task == "multiple-choice":
        lowerCAmelCase_			=      {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
     else:
        lowerCAmelCase_			=      {0: '''batch''', 1: '''sequence'''}
     return OrderedDict(
         [
             ('''input_ids''', dynamic_axis),
             ('''attention_mask''', dynamic_axis),
         ] )












from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments

def      UpperCAmelCase_    (    )	->				Optional[Any]:
     '''simple docstring'''


     A_          =       HfArgumentParser(_UpperCAmelCase				)
     A_          =       parser.parse_args_into_dataclasses()[0]
     A_          =       TensorFlowBenchmark(args=_UpperCAmelCase				)
     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(_UpperCAmelCase				).split(''' '''				)[:-1]				)
          A_          =       ''''''
          A_          =       eval(str(_UpperCAmelCase				).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(_UpperCAmelCase				)
          if len(_UpperCAmelCase				) > 0:
               A_          =       full_error_msg + begin_error_msg + str(_UpperCAmelCase				)
          raise ValueError(_UpperCAmelCase				)
     benchmark.run()


if __name__ == "__main__":
      main()



