

'''simple docstring'''



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


UpperCAmelCase					:			Tuple       =	logging.get_logger(__name__)

UpperCAmelCase					:			Any       =	{
    '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 		lowerCAmelCase__  ( __lowercase   ):



 """simple docstring"""



 lowerCAmelCase__       = "speech_to_text_2"
 lowerCAmelCase__       = ["past_key_values"]
 lowerCAmelCase__       = {"num_attention_heads": "decoder_attention_heads", "hidden_size": "d_model"}
 def __init__(						self   :			Dict   ,				__SCREAMING_SNAKE_CASE   :			Dict=10_000   ,				__SCREAMING_SNAKE_CASE   :			Any=6   ,				__SCREAMING_SNAKE_CASE   :			Optional[int]=2_048   ,				__SCREAMING_SNAKE_CASE   :			str=4   ,				__SCREAMING_SNAKE_CASE   :			int=0.0   ,				__SCREAMING_SNAKE_CASE   :			Tuple=True   ,				__SCREAMING_SNAKE_CASE   :			Dict="relu"   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]=256   ,				__SCREAMING_SNAKE_CASE   :			str=0.1   ,				__SCREAMING_SNAKE_CASE   :			List[Any]=0.0   ,				__SCREAMING_SNAKE_CASE   :			Tuple=0.0   ,				__SCREAMING_SNAKE_CASE   :			Optional[int]=0.02   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]=2   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]=True   ,				__SCREAMING_SNAKE_CASE   :			Optional[int]=1   ,				__SCREAMING_SNAKE_CASE   :			Optional[int]=0   ,				__SCREAMING_SNAKE_CASE   :			List[Any]=2   ,				__SCREAMING_SNAKE_CASE   :			str=1_024   ,				**__SCREAMING_SNAKE_CASE   :			str   ,				)    ->					Optional[int]:


      """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=_A   ,				bos_token_id=_A   ,				eos_token_id=_A   ,				decoder_start_token_id=_A   ,				**_A   ,				)







def 					_snake_case	(  __snake_case = 100						):
					_UpperCamelCase			     =     (n * (n + 1) // 2) ** 2
					_UpperCamelCase			     =     n * (n + 1) * (2 * n + 1) // 6
					return sum_cubes - sum_squares


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