




import copy

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


a_      						=   logging.get_logger(__name__)

a_      						=   {}

class        _lowerCamelCase  (	_SCREAMING_SNAKE_CASE      ):

      """simple docstring"""

      lowerCAmelCase__	:       List[str]			=    "fsmt_decoder"







      def __init__(   self    :       Any       ,				snake_case    :       Tuple=0       ,				snake_case    :       Dict=0 ):
          super().__init__()
          __UpperCamelCase     						=  vocab_size
          __UpperCamelCase     						=  bos_token_id







class        _lowerCamelCase  (	_SCREAMING_SNAKE_CASE      ):

      """simple docstring"""

      lowerCAmelCase__	:       Optional[Any]			=    "fsmt"
      lowerCAmelCase__	:       Optional[Any]			=    {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}







      def __init__(   self    :       str       ,				snake_case    :       Union[str, Any]=["en", "de"]       ,				snake_case    :       List[str]=42024       ,				snake_case    :       str=42024       ,				snake_case    :       List[Any]="relu"       ,				snake_case    :       str=1024       ,				snake_case    :       Any=200       ,				snake_case    :       Any=1024       ,				snake_case    :       str=4096       ,				snake_case    :       str=12       ,				snake_case    :       Dict=16       ,				snake_case    :       Any=0.0       ,				snake_case    :       Union[str, Any]=4096       ,				snake_case    :       Any=12       ,				snake_case    :       int=16       ,				snake_case    :       List[Any]=0.0       ,				snake_case    :       Optional[Any]=0.0       ,				snake_case    :       Optional[int]=0.1       ,				snake_case    :       str=0.0       ,				snake_case    :       Any=0.02       ,				snake_case    :       Any=2       ,				snake_case    :       Any=True       ,				snake_case    :       List[Any]=True       ,				snake_case    :       List[str]=False       ,				snake_case    :       List[str]=5       ,				snake_case    :       str=1.0       ,				snake_case    :       List[str]=False       ,				snake_case    :       Union[str, Any]=True       ,				snake_case    :       Union[str, Any]=1       ,				snake_case    :       List[Any]=0       ,				snake_case    :       Optional[int]=2       ,				snake_case    :       str=2       ,				**snake_case    :       List[Any]       ,				):
          __UpperCamelCase     						=  langs
          __UpperCamelCase     						=  src_vocab_size
          __UpperCamelCase     						=  tgt_vocab_size
          __UpperCamelCase     						=  d_model  # encoder_embed_dim and decoder_embed_dim

          __UpperCamelCase     						=  encoder_ffn_dim
          __UpperCamelCase     						=  __UpperCamelCase     						=  encoder_layers
          __UpperCamelCase     						=  encoder_attention_heads
          __UpperCamelCase     						=  encoder_layerdrop
          __UpperCamelCase     						=  decoder_layerdrop
          __UpperCamelCase     						=  decoder_ffn_dim
          __UpperCamelCase     						=  decoder_layers
          __UpperCamelCase     						=  decoder_attention_heads
          __UpperCamelCase     						=  max_position_embeddings
          __UpperCamelCase     						=  init_std  # Normal(0, this parameter)
          __UpperCamelCase     						=  activation_function

          __UpperCamelCase     						=  DecoderConfig(vocab_size=snake_case       ,				bos_token_id=snake_case )
          if "decoder" in common_kwargs:
              del common_kwargs["decoder"]

          __UpperCamelCase     						=  scale_embedding  # scale factor will be sqrt(d_model) if True

          # 3 Types of Dropout
          __UpperCamelCase     						=  attention_dropout
          __UpperCamelCase     						=  activation_dropout
          __UpperCamelCase     						=  dropout

          __UpperCamelCase     						=  use_cache
          super().__init__(
              pad_token_id=snake_case       ,				bos_token_id=snake_case       ,				eos_token_id=snake_case       ,				decoder_start_token_id=snake_case       ,				is_encoder_decoder=snake_case       ,				tie_word_embeddings=snake_case       ,				forced_eos_token_id=snake_case       ,				max_length=snake_case       ,				num_beams=snake_case       ,				length_penalty=snake_case       ,				early_stopping=snake_case       ,				**snake_case       ,				)







      def 					snake_case		(   self    :       Any ):
          __UpperCamelCase     						=  copy.deepcopy(self.__dict__ )
          __UpperCamelCase     						=  self.decoder.to_dict()
          __UpperCamelCase     						=  self.__class__.model_type
          return output












def     __SCREAMING_SNAKE_CASE      (     lowercase_    ,   lowercase_					)				->		int:

    """simple docstring"""


    __UpperCamelCase     						=  1.0
    for i in range(1    ,   r + 1					):
        ret *= (n + 1 - i) / i
    return round(lowercase_					)




def     __SCREAMING_SNAKE_CASE      (     lowercase_					)				->		int:

    """simple docstring"""


    return choose(8 + n    ,   lowercase_					) + choose(9 + n    ,   lowercase_					) - 10




def     __SCREAMING_SNAKE_CASE      (     lowercase_					)				->		int:

    """simple docstring"""


    return sum(non_bouncy_exact(lowercase_					) for i in range(1    ,   n + 1					)					)




def     __SCREAMING_SNAKE_CASE      (     lowercase_ = 1_00					)				->		int:

    """simple docstring"""


    return non_bouncy_upto(lowercase_					)


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






