





"""simple docstring"""




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


SCREAMING_SNAKE_CASE_						       =	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_						       =	{
    """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 		UpperCamelCase__			(				lowerCAmelCase_       ):



     '''simple docstring'''

     __snake_case   :     Optional[int]		     =      "speech_to_text_2"
     __snake_case   :     List[Any]		     =      ["past_key_values"]
     __snake_case   :     Optional[Any]		     =      {"num_attention_heads": "decoder_attention_heads", "hidden_size": "d_model"}






     def __init__(							self						:   str						,lowerCamelCase__						:   Any=10000						,lowerCamelCase__						:   Optional[Any]=6						,lowerCamelCase__						:   Any=2048						,lowerCamelCase__						:   Union[str, Any]=4						,lowerCamelCase__						:   Optional[int]=0.0						,lowerCamelCase__						:   List[str]=True						,lowerCamelCase__						:   Optional[Any]="relu"						,lowerCamelCase__						:   Dict=256						,lowerCamelCase__						:   Dict=0.1						,lowerCamelCase__						:   Union[str, Any]=0.0						,lowerCamelCase__						:   str=0.0						,lowerCamelCase__						:   int=0.02						,lowerCamelCase__						:   Any=2						,lowerCamelCase__						:   Dict=True						,lowerCamelCase__						:   Dict=1						,lowerCamelCase__						:   Optional[Any]=0						,lowerCamelCase__						:   List[Any]=2						,lowerCamelCase__						:   Optional[Any]=1024						,**lowerCamelCase__						:   List[str]						,)					->		Union[str, Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						vocab_size
           SCREAMING_SNAKE_CASE		=						d_model
           SCREAMING_SNAKE_CASE		=						decoder_ffn_dim
           SCREAMING_SNAKE_CASE		=						decoder_layers
           SCREAMING_SNAKE_CASE		=						decoder_attention_heads
           SCREAMING_SNAKE_CASE		=						dropout
           SCREAMING_SNAKE_CASE		=						attention_dropout
           SCREAMING_SNAKE_CASE		=						activation_dropout
           SCREAMING_SNAKE_CASE		=						activation_function
           SCREAMING_SNAKE_CASE		=						init_std
           SCREAMING_SNAKE_CASE		=						decoder_layerdrop
           SCREAMING_SNAKE_CASE		=						use_cache
           SCREAMING_SNAKE_CASE		=						decoder_layers
           SCREAMING_SNAKE_CASE		=						scale_embedding  # scale factor will be sqrt(d_model) if True
           SCREAMING_SNAKE_CASE		=						max_target_positions

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









def        __lowercase       (				_SCREAMING_SNAKE_CASE = 50     )			->       int:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						[1] * (length + 1)

      for row_length in range(3			,						length + 1     ):
            for block_length in range(3			,						row_length + 1     ):
                  for block_start in range(row_length - block_length     ):
                        ways_number[row_length] += ways_number[
                            row_length - block_start - block_length - 1
                        ]

                  ways_number[row_length] += 1

      return ways_number[length]


if __name__ == "__main__":
      print(F'''{solution() = }''')


