





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


lowercase :			Tuple     		=    logging.get_logger(__name__)

lowercase :			str     		=    {
    """facebook/s2t-wav2vec2-large-en-de""": (
        """https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json"""
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}





class 					A__       (				A_      ):




   """simple docstring"""



   __A  :							int			    =    '''speech_to_text_2'''
   __A  :							Tuple			    =    ['''past_key_values''']
   __A  :							List[Any]			    =    {'''num_attention_heads''': '''decoder_attention_heads''', '''hidden_size''': '''d_model'''}

   def __init__(   self   ,    lowercase=1_0000   ,    lowercase=6   ,    lowercase=2048   ,    lowercase=4   ,    lowercase=0.0   ,    lowercase=True   ,    lowercase="relu"   ,    lowercase=256   ,    lowercase=0.1   ,    lowercase=0.0   ,    lowercase=0.0   ,    lowercase=0.02   ,    lowercase=2   ,    lowercase=True   ,    lowercase=1   ,    lowercase=0   ,    lowercase=2   ,    lowercase=1024   ,    **lowercase   ,    )     ->		Dict:







          '''simple docstring'''

          a__		:      Dict					=							vocab_size
          a__		:      str					=							d_model
          a__		:      List[str]					=							decoder_ffn_dim
          a__		:      Tuple					=							decoder_layers
          a__		:      str					=							decoder_attention_heads
          a__		:      Dict					=							dropout
          a__		:      Union[str, Any]					=							attention_dropout
          a__		:      int					=							activation_dropout
          a__		:      str					=							activation_function
          a__		:      Dict					=							init_std
          a__		:      Optional[Any]					=							decoder_layerdrop
          a__		:      Dict					=							use_cache
          a__		:      str					=							decoder_layers
          a__		:      str					=							scale_embedding  # scale factor will be sqrt(d_model) if True
          a__		:      Optional[Any]					=							max_target_positions

          super().__init__(
              pad_token_id=lowercase   ,    bos_token_id=lowercase   ,    eos_token_id=lowercase   ,    decoder_start_token_id=lowercase   ,    **lowercase   ,    )






"""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()

