

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


__UpperCAmelCase								=			logging.get_logger(__name__)

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



class SCREAMING_SNAKE_CASE     (  a_			):
  """simple docstring"""






  lowerCamelCase   :      Union[str, Any]										="nllb-moe"
  lowerCamelCase   :      Any										=["past_key_values"]
  lowerCamelCase   :      Optional[Any]										={"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}







  def __init__(      self			:   Any	,							lowerCAmelCase			:   Any=12_81_12	,							lowerCAmelCase			:   Optional[int]=10_24	,							lowerCAmelCase			:   Any=12	,							lowerCAmelCase			:   Optional[Any]=40_96	,							lowerCAmelCase			:   Dict=16	,							lowerCAmelCase			:   str=12	,							lowerCAmelCase			:   Optional[Any]=40_96	,							lowerCAmelCase			:   Dict=16	,							lowerCAmelCase			:   Optional[Any]=0.05	,							lowerCAmelCase			:   Dict=0.05	,							lowerCAmelCase			:   str=True	,							lowerCAmelCase			:   List[str]=True	,							lowerCAmelCase			:   Any="relu"	,							lowerCAmelCase			:   Union[str, Any]=10_24	,							lowerCAmelCase			:   Dict=0.1	,							lowerCAmelCase			:   str=0.1	,							lowerCAmelCase			:   Union[str, Any]=0.0	,							lowerCAmelCase			:   Any=0.02	,							lowerCAmelCase			:   List[Any]=2	,							lowerCAmelCase			:   List[Any]=True	,							lowerCAmelCase			:   int=False	,							lowerCAmelCase			:   Optional[Any]="float32"	,							lowerCAmelCase			:   Union[str, Any]=False	,							lowerCAmelCase			:   Optional[Any]=1_28	,							lowerCAmelCase			:   Union[str, Any]=64	,							lowerCAmelCase			:   List[Any]=4	,							lowerCAmelCase			:   Dict=4	,							lowerCAmelCase			:   Any=0.001	,							lowerCAmelCase			:   List[Any]=0.001	,							lowerCAmelCase			:   Optional[Any]="all"	,							lowerCAmelCase			:   List[Any]=False	,							lowerCAmelCase			:   List[Any]=False	,							lowerCAmelCase			:   Union[str, Any]=1.0	,							lowerCAmelCase			:   Any=0.2	,							lowerCAmelCase			:   int=1	,							lowerCAmelCase			:   Optional[int]=0	,							lowerCAmelCase			:   str=2	,							lowerCAmelCase			:   int=False	,							**lowerCAmelCase			:   Optional[int]	,							)							->     Union[str, Any]:






     """simple docstring"""







     __lowerCAmelCase  :      int    =						vocab_size
     __lowerCAmelCase  :      List[Any]    =						max_position_embeddings
     __lowerCAmelCase  :      Optional[Any]    =						d_model
     __lowerCAmelCase  :      Tuple    =						encoder_ffn_dim
     __lowerCAmelCase  :      int    =						encoder_layers
     __lowerCAmelCase  :      List[Any]    =						encoder_attention_heads
     __lowerCAmelCase  :      str    =						decoder_ffn_dim
     __lowerCAmelCase  :      Optional[Any]    =						decoder_layers
     __lowerCAmelCase  :      int    =						decoder_attention_heads
     __lowerCAmelCase  :      List[Any]    =						dropout
     __lowerCAmelCase  :      Dict    =						attention_dropout
     __lowerCAmelCase  :      Optional[Any]    =						activation_dropout
     __lowerCAmelCase  :      Dict    =						activation_function
     __lowerCAmelCase  :      str    =						init_std
     __lowerCAmelCase  :      Any    =						encoder_layerdrop
     __lowerCAmelCase  :      List[str]    =						decoder_layerdrop
     __lowerCAmelCase  :      Dict    =						use_cache
     __lowerCAmelCase  :      Union[str, Any]    =						encoder_layers
     __lowerCAmelCase  :      Union[str, Any]    =						scale_embedding  # scale factor will be sqrt(d_model) if True
     __lowerCAmelCase  :      Any    =						router_z_loss_coef
     __lowerCAmelCase  :      str    =						router_aux_loss_coef
     __lowerCAmelCase  :      int    =						decoder_sparse_step
     __lowerCAmelCase  :      int    =						encoder_sparse_step
     __lowerCAmelCase  :      Union[str, Any]    =						num_experts
     __lowerCAmelCase  :      Union[str, Any]    =						expert_capacity
     __lowerCAmelCase  :      int    =						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}'''   )
     __lowerCAmelCase  :      Dict    =						router_dtype

     __lowerCAmelCase  :      int    =						router_ignore_padding_tokens
     __lowerCAmelCase  :      List[Any]    =						batch_prioritized_routing
     __lowerCAmelCase  :      Optional[Any]    =						second_expert_policy
     __lowerCAmelCase  :      Union[str, Any]    =						normalize_router_prob_before_dropping
     __lowerCAmelCase  :      int    =						moe_eval_capacity_token_fraction
     __lowerCAmelCase  :      Tuple    =						moe_token_dropout
     __lowerCAmelCase  :      Union[str, Any]    =						output_router_logits
     super().__init__(
         pad_token_id=lowerCAmelCase	,							bos_token_id=lowerCAmelCase	,							eos_token_id=lowerCAmelCase	,							is_encoder_decoder=lowerCAmelCase	,							decoder_start_token_id=lowerCAmelCase	,							**lowerCAmelCase	,							)





from __future__ import annotations





def      snake_case_				(__A					:						list[float] ,       __A					:						list[float]					)      ->     float:
   __lowerCAmelCase  :      Union[str, Any]    =						sorted(numsa + numsa					)
   __lowerCAmelCase   ,__lowerCAmelCase  :      Optional[int]    =						divmod(len(__A					) ,       2					)
   if mod == 1:
      return all_numbers[div]
   else:
      return (all_numbers[div] + all_numbers[div - 1]) / 2


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   __UpperCAmelCase								=			[float(x) for x in input("""Enter the elements of first array: """).split()]
   __UpperCAmelCase								=			[float(x) for x in input("""Enter the elements of second array: """).split()]
   print(F'The median of two arrays is: {median_of_two_arrays(array_a, array_a)}')


