


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


_snake_case			 =	logging.get_logger(__name__)

_snake_case			 =	{
    '''facebook/s2t-small-librispeech-asr''': (
        '''https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/config.json'''
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech_to_text
}
class       UpperCAmelCase_   (   UpperCamelCase       ):

   '''simple docstring'''




   __A					: Optional[Any]							=						"speech_to_text"
   __A					: Any							=						["past_key_values"]
   __A					: Optional[int]							=						{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}






   def __init__(						self		,							__A=1_0000		,							__A=12		,							__A=2048		,							__A=4		,							__A=6		,							__A=2048		,							__A=4		,							__A=0.0		,							__A=0.0		,							__A=True		,							__A=True		,							__A="relu"		,							__A=256		,							__A=0.1		,							__A=0.0		,							__A=0.0		,							__A=0.02		,							__A=2		,							__A=True		,							__A=1		,							__A=0		,							__A=2		,							__A=6000		,							__A=1024		,							__A=2		,							__A=(5, 5)		,							__A=1024		,							__A=80		,							__A=1		,							**__A		,							):






     """simple docstring"""





     lowerCamelCase					:   Any       =							vocab_size
     lowerCamelCase					:   List[str]       =							d_model
     lowerCamelCase					:   int       =							encoder_ffn_dim
     lowerCamelCase					:   str       =							encoder_layers
     lowerCamelCase					:   Optional[Any]       =							encoder_attention_heads
     lowerCamelCase					:   Optional[int]       =							decoder_ffn_dim
     lowerCamelCase					:   Any       =							decoder_layers
     lowerCamelCase					:   List[str]       =							decoder_attention_heads
     lowerCamelCase					:   Optional[int]       =							dropout
     lowerCamelCase					:   str       =							attention_dropout
     lowerCamelCase					:   Tuple       =							activation_dropout
     lowerCamelCase					:   Tuple       =							activation_function
     lowerCamelCase					:   Dict       =							init_std
     lowerCamelCase					:   List[str]       =							encoder_layerdrop
     lowerCamelCase					:   Optional[Any]       =							decoder_layerdrop
     lowerCamelCase					:   List[Any]       =							use_cache
     lowerCamelCase					:   Tuple       =							encoder_layers
     lowerCamelCase					:   Any       =							scale_embedding  # scale factor will be sqrt(d_model) if True
     lowerCamelCase					:   str       =							max_source_positions
     lowerCamelCase					:   Dict       =							max_target_positions
     lowerCamelCase					:   int       =							num_conv_layers
     lowerCamelCase					:   Dict       =							list(lowerCamelCase_					)
     lowerCamelCase					:   Tuple       =							conv_channels
     lowerCamelCase					:   Union[str, Any]       =							input_feat_per_channel
     lowerCamelCase					:   Optional[Any]       =							input_channels

     if len(self.conv_kernel_sizes					) != self.num_conv_layers:
       raise ValueError(
           "Configuration for convolutional module is incorrect. "
           "It is required that `len(config.conv_kernel_sizes)` == `config.num_conv_layers` "
           F"""but is `len(config.conv_kernel_sizes) = {len(self.conv_kernel_sizes					)}`, """
           F"""`config.num_conv_layers = {self.num_conv_layers}`."""					)

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






"""simple docstring"""


from __future__ import annotations



def   _A					(	_a	:						list[float]     ,   _a	:						list[float]				):



      """simple docstring"""






      A				  =		sorted(numsa + numsa				)
      A  ,			A				  =		divmod(len(_a				)     ,   2				)
      if mod == 1:
            return all_numbers[div]
      else:
            return (all_numbers[div] + all_numbers[div - 1]) / 2


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  UpperCAmelCase													=[float(x) for x in input("Enter the elements of first array: ").split()]
  UpperCAmelCase													=[float(x) for x in input("Enter the elements of second array: ").split()]
  print(f"""The median of two arrays is: {median_of_two_arrays(array_a, array_a)}""")




