

'''simple docstring'''




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


_lowerCAmelCase         =						logging.get_logger(__name__)

_lowerCAmelCase         =						{
    "facebook/nllb-moe-54B": "https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json",
}






class   _SCREAMING_SNAKE_CASE   (   __a     ):
 __SCREAMING_SNAKE_CASE					:Tuple      	=					"""nllb-moe"""
 __SCREAMING_SNAKE_CASE					:Tuple      	=					["""past_key_values"""]
 __SCREAMING_SNAKE_CASE					:Optional[int]      	=					{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}



 def __init__(   self		:   Optional[int]			,				a__		:   Tuple=12_8112			,				a__		:   List[str]=1024			,				a__		:   Dict=12			,				a__		:   List[Any]=4096			,				a__		:   Optional[Any]=16			,				a__		:   List[Any]=12			,				a__		:   Tuple=4096			,				a__		:   Tuple=16			,				a__		:   Dict=0.05			,				a__		:   Any=0.05			,				a__		:   int=True			,				a__		:   List[str]=True			,				a__		:   Optional[Any]="relu"			,				a__		:   Union[str, Any]=1024			,				a__		:   int=0.1			,				a__		:   List[Any]=0.1			,				a__		:   int=0.0			,				a__		:   Optional[int]=0.02			,				a__		:   List[str]=2			,				a__		:   Optional[int]=True			,				a__		:   Union[str, Any]=False			,				a__		:   Tuple="float32"			,				a__		:   Any=False			,				a__		:   List[Any]=128			,				a__		:   Union[str, Any]=64			,				a__		:   Optional[int]=4			,				a__		:   Union[str, Any]=4			,				a__		:   Dict=0.001			,				a__		:   Optional[Any]=0.001			,				a__		:   Optional[Any]="all"			,				a__		:   List[Any]=False			,				a__		:   Union[str, Any]=False			,				a__		:   Optional[int]=1.0			,				a__		:   Optional[int]=0.2			,				a__		:   int=1			,				a__		:   Optional[int]=0			,				a__		:   Optional[Any]=2			,				a__		:   Tuple=False			,				**a__		:   Dict			,				):
      __magic_name__						       =    vocab_size
      __magic_name__						       =    max_position_embeddings
      __magic_name__						       =    d_model
      __magic_name__						       =    encoder_ffn_dim
      __magic_name__						       =    encoder_layers
      __magic_name__						       =    encoder_attention_heads
      __magic_name__						       =    decoder_ffn_dim
      __magic_name__						       =    decoder_layers
      __magic_name__						       =    decoder_attention_heads
      __magic_name__						       =    dropout
      __magic_name__						       =    attention_dropout
      __magic_name__						       =    activation_dropout
      __magic_name__						       =    activation_function
      __magic_name__						       =    init_std
      __magic_name__						       =    encoder_layerdrop
      __magic_name__						       =    decoder_layerdrop
      __magic_name__						       =    use_cache
      __magic_name__						       =    encoder_layers
      __magic_name__						       =    scale_embedding  # scale factor will be sqrt(d_model) if True
      __magic_name__						       =    router_z_loss_coef
      __magic_name__						       =    router_aux_loss_coef
      __magic_name__						       =    decoder_sparse_step
      __magic_name__						       =    encoder_sparse_step
      __magic_name__						       =    num_experts
      __magic_name__						       =    expert_capacity
      __magic_name__						       =    router_bias
      if router_dtype not in ["float32", "float16", "bfloat16"]:
           raise ValueError(F'''`router_dtype` must be one of \'float32\', \'float16\' or \'bfloat16\', got {router_dtype}'''      )
      __magic_name__						       =    router_dtype

      __magic_name__						       =    router_ignore_padding_tokens
      __magic_name__						       =    batch_prioritized_routing
      __magic_name__						       =    second_expert_policy
      __magic_name__						       =    normalize_router_prob_before_dropping
      __magic_name__						       =    moe_eval_capacity_token_fraction
      __magic_name__						       =    moe_token_dropout
      __magic_name__						       =    output_router_logits
      super().__init__(
          pad_token_id=a__			,				bos_token_id=a__			,				eos_token_id=a__			,				is_encoder_decoder=a__			,				decoder_start_token_id=a__			,				**a__			,				)









'''simple docstring'''




import functools




def 						UpperCamelCase	(			a		,						a			)				->     int:






     '''simple docstring'''
     __magic_name__						       =    len(a			)
     __magic_name__						       =    len(a			)

     @functools.cache
     def min_distance(a		,						a			) -> int:
          # if first word index is overflow - delete all from the second word
          if indexa >= len_worda:
               return len_worda - indexa
          # if second word index is overflow - delete all from the first word
          if indexa >= len_worda:
               return len_worda - indexa
          __magic_name__						       =    int(worda[indexa] != worda[indexa]			)  # current letters not identical
          return min(
              1 + min_distance(indexa + 1		,						a			)		,						1 + min_distance(a		,						indexa + 1			)		,						diff + min_distance(indexa + 1		,						indexa + 1			)		,						)

     return min_distance(0		,						0			)


if __name__ == "__main__":
      import doctest

      doctest.testmod()






