






'''simple docstring'''

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


SCREAMING_SNAKE_CASE__											=     logging.get_logger(__name__)

SCREAMING_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        a_		(    lowerCamelCase       ):
  lowercase  	=   """speech_to_text"""
  lowercase  	=   ["""past_key_values"""]
  lowercase  	=   {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}

  def __init__(   self    ,	_SCREAMING_SNAKE_CASE=10000    ,	_SCREAMING_SNAKE_CASE=12    ,	_SCREAMING_SNAKE_CASE=2048    ,	_SCREAMING_SNAKE_CASE=4    ,	_SCREAMING_SNAKE_CASE=6    ,	_SCREAMING_SNAKE_CASE=2048    ,	_SCREAMING_SNAKE_CASE=4    ,	_SCREAMING_SNAKE_CASE=0.0    ,	_SCREAMING_SNAKE_CASE=0.0    ,	_SCREAMING_SNAKE_CASE=True    ,	_SCREAMING_SNAKE_CASE=True    ,	_SCREAMING_SNAKE_CASE="relu"    ,	_SCREAMING_SNAKE_CASE=256    ,	_SCREAMING_SNAKE_CASE=0.1    ,	_SCREAMING_SNAKE_CASE=0.0    ,	_SCREAMING_SNAKE_CASE=0.0    ,	_SCREAMING_SNAKE_CASE=0.0_2    ,	_SCREAMING_SNAKE_CASE=2    ,	_SCREAMING_SNAKE_CASE=True    ,	_SCREAMING_SNAKE_CASE=1    ,	_SCREAMING_SNAKE_CASE=0    ,	_SCREAMING_SNAKE_CASE=2    ,	_SCREAMING_SNAKE_CASE=6000    ,	_SCREAMING_SNAKE_CASE=1024    ,	_SCREAMING_SNAKE_CASE=2    ,	_SCREAMING_SNAKE_CASE=(5, 5)    ,	_SCREAMING_SNAKE_CASE=1024    ,	_SCREAMING_SNAKE_CASE=80    ,	_SCREAMING_SNAKE_CASE=1    ,	**_SCREAMING_SNAKE_CASE    ,	)						->   Tuple:



        """simple docstring"""






        UpperCamelCase       						=      vocab_size
        UpperCamelCase       						=      d_model
        UpperCamelCase       						=      encoder_ffn_dim
        UpperCamelCase       						=      encoder_layers
        UpperCamelCase       						=      encoder_attention_heads
        UpperCamelCase       						=      decoder_ffn_dim
        UpperCamelCase       						=      decoder_layers
        UpperCamelCase       						=      decoder_attention_heads
        UpperCamelCase       						=      dropout
        UpperCamelCase       						=      attention_dropout
        UpperCamelCase       						=      activation_dropout
        UpperCamelCase       						=      activation_function
        UpperCamelCase       						=      init_std
        UpperCamelCase       						=      encoder_layerdrop
        UpperCamelCase       						=      decoder_layerdrop
        UpperCamelCase       						=      use_cache
        UpperCamelCase       						=      encoder_layers
        UpperCamelCase       						=      scale_embedding  # scale factor will be sqrt(d_model) if True
        UpperCamelCase       						=      max_source_positions
        UpperCamelCase       						=      max_target_positions
        UpperCamelCase       						=      num_conv_layers
        UpperCamelCase       						=      list(_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      conv_channels
        UpperCamelCase       						=      input_feat_per_channel
        UpperCamelCase       						=      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=_SCREAMING_SNAKE_CASE    ,	bos_token_id=_SCREAMING_SNAKE_CASE    ,	eos_token_id=_SCREAMING_SNAKE_CASE    ,	is_encoder_decoder=_SCREAMING_SNAKE_CASE    ,	decoder_start_token_id=_SCREAMING_SNAKE_CASE    ,	**_SCREAMING_SNAKE_CASE    ,	)








'''simple docstring'''

from math import factorial







def 					lowercase__				(   __UpperCamelCase = 20					)->				int:
      UpperCamelCase       						=      2 * n  # middle entry of odd rows starting at row 3 is the solution for n = 1,
      # 2, 3,...
      UpperCamelCase       						=      n // 2

      return int(factorial(__UpperCamelCase					) / (factorial(__UpperCamelCase					) * factorial(n - k					))					)


if __name__ == "__main__":
     import sys

     if len(sys.argv) == 1:
          print(solution(2_0))
     else:
          try:
               SCREAMING_SNAKE_CASE__											=     int(sys.argv[1])
               print(solution(n))
          except ValueError:
               print('Invalid entry - please enter a number.')
