


import copy

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


__UpperCamelCase							:							Union[str, Any]    							=					logging.get_logger(__name__)

__UpperCamelCase							:							List[Any]    							=					{}

class    __magic_name__						(  __lowerCAmelCase):
     A:		Any								=   "fsmt_decoder"


     def __init__(   self				:  str				,						lowerCamelCase__				:  Dict=0				,						lowerCamelCase__				:  List[Any]=0      )	->   Dict:





      '''simple docstring'''






      super().__init__()
      UpperCamelCase__       :     Optional[int] 							= vocab_size
      UpperCamelCase__       :     Union[str, Any] 							= bos_token_id



class    __magic_name__						(  __lowerCAmelCase):
     A:		str								=   "fsmt"
     A:		Any								=   {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}


     def __init__(   self				:  Any				,						lowerCamelCase__				:  List[str]=["en", "de"]				,						lowerCamelCase__				:  Optional[Any]=42024				,						lowerCamelCase__				:  List[str]=42024				,						lowerCamelCase__				:  Union[str, Any]="relu"				,						lowerCamelCase__				:  Union[str, Any]=1024				,						lowerCamelCase__				:  Dict=200				,						lowerCamelCase__				:  List[str]=1024				,						lowerCamelCase__				:  List[Any]=4096				,						lowerCamelCase__				:  Dict=12				,						lowerCamelCase__				:  str=16				,						lowerCamelCase__				:  List[Any]=0.0				,						lowerCamelCase__				:  Optional[int]=4096				,						lowerCamelCase__				:  Union[str, Any]=12				,						lowerCamelCase__				:  Optional[int]=16				,						lowerCamelCase__				:  Tuple=0.0				,						lowerCamelCase__				:  Dict=0.0				,						lowerCamelCase__				:  Tuple=0.1				,						lowerCamelCase__				:  Optional[Any]=0.0				,						lowerCamelCase__				:  Any=0.02				,						lowerCamelCase__				:  Optional[Any]=2				,						lowerCamelCase__				:  Union[str, Any]=True				,						lowerCamelCase__				:  Tuple=True				,						lowerCamelCase__				:  Optional[Any]=False				,						lowerCamelCase__				:  List[Any]=5				,						lowerCamelCase__				:  str=1.0				,						lowerCamelCase__				:  Dict=False				,						lowerCamelCase__				:  Optional[Any]=True				,						lowerCamelCase__				:  Any=1				,						lowerCamelCase__				:  List[str]=0				,						lowerCamelCase__				:  List[str]=2				,						lowerCamelCase__				:  Optional[int]=2				,						**lowerCamelCase__				:  List[str]				,						)	->   List[str]:





      '''simple docstring'''






      UpperCamelCase__       :     Dict 							= langs
      UpperCamelCase__       :     List[str] 							= src_vocab_size
      UpperCamelCase__       :     Union[str, Any] 							= tgt_vocab_size
      UpperCamelCase__       :     str 							= d_model  # encoder_embed_dim and decoder_embed_dim

      UpperCamelCase__       :     Union[str, Any] 							= encoder_ffn_dim
      UpperCamelCase__       :     Tuple 							= encoder_layers
      UpperCamelCase__       :     Dict 							= encoder_attention_heads
      UpperCamelCase__       :     List[str] 							= encoder_layerdrop
      UpperCamelCase__       :     Optional[Any] 							= decoder_layerdrop
      UpperCamelCase__       :     str 							= decoder_ffn_dim
      UpperCamelCase__       :     Optional[Any] 							= decoder_layers
      UpperCamelCase__       :     int 							= decoder_attention_heads
      UpperCamelCase__       :     List[str] 							= max_position_embeddings
      UpperCamelCase__       :     List[Any] 							= init_std  # Normal(0, this parameter)
      UpperCamelCase__       :     List[str] 							= activation_function

      UpperCamelCase__       :     List[Any] 							= DecoderConfig(vocab_size=lowerCamelCase__				,						bos_token_id=lowerCamelCase__      )
      if "decoder" in common_kwargs:
       del common_kwargs["decoder"]

      UpperCamelCase__       :     List[str] 							= scale_embedding  # scale factor will be sqrt(d_model) if True

      # 3 Types of Dropout
      UpperCamelCase__       :     Tuple 							= attention_dropout
      UpperCamelCase__       :     str 							= activation_dropout
      UpperCamelCase__       :     Tuple 							= dropout

      UpperCamelCase__       :     str 							= use_cache
      super().__init__(
          pad_token_id=lowerCamelCase__				,						bos_token_id=lowerCamelCase__				,						eos_token_id=lowerCamelCase__				,						decoder_start_token_id=lowerCamelCase__				,						is_encoder_decoder=lowerCamelCase__				,						tie_word_embeddings=lowerCamelCase__				,						forced_eos_token_id=lowerCamelCase__				,						max_length=lowerCamelCase__				,						num_beams=lowerCamelCase__				,						length_penalty=lowerCamelCase__				,						early_stopping=lowerCamelCase__				,						**lowerCamelCase__				,						)


     def   UpperCAmelCase__						(   self				:  List[str]      )	->   int:





      '''simple docstring'''






      UpperCamelCase__       :     str 							= copy.deepcopy(self.__dict__      )
      UpperCamelCase__       :     Tuple 							= self.decoder.to_dict()
      UpperCamelCase__       :     Any 							= self.__class__.model_type
      return output






import numpy as np

def       _a	(					SCREAMING_SNAKE_CASE						:					Tuple  ):







 """simple docstring"""



 UpperCamelCase__       :     str 							= np.exp(SCREAMING_SNAKE_CASE  )

 # Add up the all the exponentials
 UpperCamelCase__       :     Optional[int] 							= np.sum(SCREAMING_SNAKE_CASE  )

 # Divide every exponent by the sum of all exponents
 UpperCamelCase__       :     Optional[Any] 							= exponent_vector / sum_of_exponents

 return softmax_vector


if __name__ == "__main__":
       print(softmax((0,)))


