





from collections import OrderedDict
from typing import Mapping

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


lowercase :			Union[str, Any]     		=    logging.get_logger(__name__)

lowercase :			str     		=    {
    """facebook/xlm-roberta-xl""": """https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json""",
    """facebook/xlm-roberta-xxl""": """https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json""",
    # See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}





class 					A__       (				A_      ):




   """simple docstring"""



   __A  :							Any			    =    '''xlm-roberta-xl'''

   def __init__(   self   ,    lowercase=25_0880   ,    lowercase=2560   ,    lowercase=36   ,    lowercase=32   ,    lowercase=1_0240   ,    lowercase="gelu"   ,    lowercase=0.1   ,    lowercase=0.1   ,    lowercase=514   ,    lowercase=1   ,    lowercase=0.02   ,    lowercase=1e-05   ,    lowercase=1   ,    lowercase=0   ,    lowercase=2   ,    lowercase="absolute"   ,    lowercase=True   ,    lowercase=None   ,    **lowercase   ,    )     ->		Optional[int]:







          '''simple docstring'''

          super().__init__(pad_token_id=lowercase   ,    bos_token_id=lowercase   ,    eos_token_id=lowercase   ,    **lowercase)
          a__		:      List[str]					=							vocab_size
          a__		:      List[Any]					=							hidden_size
          a__		:      Any					=							num_hidden_layers
          a__		:      Any					=							num_attention_heads
          a__		:      Optional[int]					=							hidden_act
          a__		:      List[Any]					=							intermediate_size
          a__		:      Dict					=							hidden_dropout_prob
          a__		:      Tuple					=							attention_probs_dropout_prob
          a__		:      List[Any]					=							max_position_embeddings
          a__		:      int					=							type_vocab_size
          a__		:      Tuple					=							initializer_range
          a__		:      Union[str, Any]					=							layer_norm_eps
          a__		:      Optional[int]					=							position_embedding_type
          a__		:      Optional[int]					=							use_cache
          a__		:      List[str]					=							classifier_dropout





class 					A__       (				A_      ):




   """simple docstring"""



   @property
   def 				__lowercase						(   self)     ->		Mapping[str, Mapping[int, str]]:







          '''simple docstring'''

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






"""simple docstring"""
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments

def 				_snake_case      (   ):




   """simple docstring"""







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


if __name__ == "__main__":
 main()

