





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


UpperCAmelCase	:	int		     =       logging.get_logger(__name__)

UpperCAmelCase	:	Optional[int]		     =       {
    """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 				__lowerCAmelCase       (    _UpperCAmelCase):
      _lowercase				:					str						=			"speech_to_text"
      _lowercase				:					List[str]						=			["past_key_values"]
      _lowercase				:					Dict						=			{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}







      def __init__(    self		,      lowerCAmelCase__=1_0_0_0_0		,      lowerCAmelCase__=1_2		,      lowerCAmelCase__=2_0_4_8		,      lowerCAmelCase__=4		,      lowerCAmelCase__=6		,      lowerCAmelCase__=2_0_4_8		,      lowerCAmelCase__=4		,      lowerCAmelCase__=0.0		,      lowerCAmelCase__=0.0		,      lowerCAmelCase__=True		,      lowerCAmelCase__=True		,      lowerCAmelCase__="relu"		,      lowerCAmelCase__=2_5_6		,      lowerCAmelCase__=0.1		,      lowerCAmelCase__=0.0		,      lowerCAmelCase__=0.0		,      lowerCAmelCase__=0.02		,      lowerCAmelCase__=2		,      lowerCAmelCase__=True		,      lowerCAmelCase__=1		,      lowerCAmelCase__=0		,      lowerCAmelCase__=2		,      lowerCAmelCase__=6_0_0_0		,      lowerCAmelCase__=1_0_2_4		,      lowerCAmelCase__=2		,      lowerCAmelCase__=(5, 5)		,      lowerCAmelCase__=1_0_2_4		,      lowerCAmelCase__=8_0		,      lowerCAmelCase__=1		,      **lowerCAmelCase__		,      )   ->						Tuple:



        '''simple docstring'''




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







"""simple docstring"""






def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase )  ->  int:

   if not grid or not grid[0]:
      raise TypeError('''The grid does not contain the appropriate information''' )

   for cell_n in range(1       ,    len(grid[0] ) ):
      grid[0][cell_n] += grid[0][cell_n - 1]
   lowercase__:   Tuple					=      grid[0]

   for row_n in range(1       ,    len(__UpperCAmelCase ) ):
      lowercase__:   Tuple					=      grid[row_n]
      lowercase__:   Dict					=      fill_row(__UpperCAmelCase       ,    __UpperCAmelCase )
      lowercase__:   Union[str, Any]					=      grid[row_n]

   return grid[-1][-1]



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase )  ->  list:
   current_row[0] += row_above[0]
   for cell_n in range(1       ,    len(__UpperCAmelCase ) ):
      current_row[cell_n] += min(current_row[cell_n - 1]       ,    row_above[cell_n] )

   return current_row


if __name__ == "__main__":
       import doctest

       doctest.testmod()
