



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


__lowerCamelCase         =			logging.get_logger(__name__)

__lowerCamelCase         =			{
    '''google/switch-base-8''': '''https://huggingface.co/google/switch-base-8/blob/main/config.json''',
}


class   snake_case_ (lowercase__  ):




       """simple docstring"""






       _lowerCamelCase			    =				"""switch_transformers"""
       _lowerCamelCase			    =				["""past_key_values"""]
       _lowerCamelCase			    =				{"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}



       def __init__(     self     ,lowercase=32128     ,lowercase=768     ,lowercase=64     ,lowercase=2048     ,lowercase=64     ,lowercase=12     ,lowercase=3     ,lowercase=12     ,lowercase=3     ,lowercase=12     ,lowercase=8     ,lowercase=False     ,lowercase=0.01     ,lowercase="float32"     ,lowercase=False     ,lowercase=32     ,lowercase=128     ,lowercase=0.1     ,lowercase=1E-6     ,lowercase=0.001     ,lowercase=0.001     ,lowercase=1.0     ,lowercase="relu"     ,lowercase=True     ,lowercase=False     ,lowercase=True     ,lowercase=0     ,lowercase=1     ,**lowercase     ,):

             """simple docstring"""





             UpperCAmelCase_  :	Any				      =		vocab_size
             UpperCAmelCase_  :	Any				      =		d_model
             UpperCAmelCase_  :	Union[str, Any]				      =		d_kv
             UpperCAmelCase_  :	Dict				      =		d_ff

             UpperCAmelCase_  :	Any				      =		num_sparse_encoder_layers

             UpperCAmelCase_  :	List[Any]				      =		num_layers
             UpperCAmelCase_  :	Union[str, Any]				      =		(
                 num_decoder_layers if num_decoder_layers is not None else self.num_layers
             )  # default = symmetry
             UpperCAmelCase_  :	List[Any]				      =		num_sparse_decoder_layers

             # This tells us, each how many encoder layer we'll have to set a sparse layer.
             if self.num_sparse_encoder_layers > 0:
                   UpperCAmelCase_  :	str				      =		self.num_layers // self.num_sparse_encoder_layers
             else:
                   UpperCAmelCase_  :	Tuple				      =		self.num_layers  # HACK: this will create 0 sparse layers

             # This tells us, each how many encoder layer we'll have to set a sparse layer.
             if self.num_sparse_decoder_layers > 0:
                   UpperCAmelCase_  :	List[str]				      =		self.num_decoder_layers // self.num_sparse_decoder_layers
             else:
                   UpperCAmelCase_  :	Optional[int]				      =		self.num_decoder_layers  # HACK: this will create 0 sparse layers

             UpperCAmelCase_  :	Optional[Any]				      =		num_heads
             UpperCAmelCase_  :	List[Any]				      =		num_experts
             UpperCAmelCase_  :	Optional[int]				      =		expert_capacity
             UpperCAmelCase_  :	Union[str, Any]				      =		router_bias
             UpperCAmelCase_  :	List[str]				      =		router_jitter_noise
             if router_dtype not in ["float32", "float16", "bfloat16"]:
                   raise ValueError(F"""`router_dtype` must be one of 'float32', 'float16' or 'bfloat16', got {router_dtype}""")
             UpperCAmelCase_  :	str				      =		router_dtype

             UpperCAmelCase_  :	int				      =		router_ignore_padding_tokens
             UpperCAmelCase_  :	str				      =		relative_attention_num_buckets
             UpperCAmelCase_  :	Optional[Any]				      =		relative_attention_max_distance

             UpperCAmelCase_  :	int				      =		dropout_rate
             UpperCAmelCase_  :	Union[str, Any]				      =		layer_norm_epsilon
             UpperCAmelCase_  :	Dict				      =		initializer_factor
             UpperCAmelCase_  :	Tuple				      =		feed_forward_proj
             UpperCAmelCase_  :	Optional[int]				      =		use_cache
             UpperCAmelCase_  :	List[Any]				      =		add_router_probs

             UpperCAmelCase_  :	List[str]				      =		router_z_loss_coef
             UpperCAmelCase_  :	Optional[int]				      =		router_aux_loss_coef

             UpperCAmelCase_  :	Optional[Any]				      =		self.feed_forward_proj.split("-")
             UpperCAmelCase_  :	Optional[Any]				      =		act_info[-1]
             UpperCAmelCase_  :	List[str]				      =		act_info[0] == "gated"

             if len(lowercase) > 1 and act_info[0] != "gated" or len(lowercase) > 2:
                   raise ValueError(
                       F"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
                       "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
                       "'gated-gelu' or 'relu'")

             # for backwards compatibility
             if feed_forward_proj == "gated-gelu":
                   UpperCAmelCase_  :	str				      =		"gelu_new"

             super().__init__(
                 pad_token_id=lowercase     ,eos_token_id=lowercase     ,is_encoder_decoder=lowercase     ,**lowercase     ,)





import operator







def    _snake_case (       __snake_case					,  __snake_case = False					,  __snake_case = None    )     ->     list:






      '''simple docstring'''







      UpperCAmelCase_  :	Optional[int]				      =		operator.lt if reverse else operator.gt
      UpperCAmelCase_  :	int				      =		solution or []

      if not arr:
            return solution

      UpperCAmelCase_  :	Union[str, Any]				      =		[arr.pop(0    )]
      for i, item in enumerate(__snake_case    ):
            if _operator(__snake_case					,  sublist[-1]    ):
                  sublist.append(__snake_case    )
                  arr.pop(__snake_case    )

    #  merging sublist into solution list
      if not solution:
            solution.extend(__snake_case    )
      else:
            while sublist:
                  UpperCAmelCase_  :	Optional[int]				      =		sublist.pop(0    )
                  for i, xx in enumerate(__snake_case    ):
                        if not _operator(__snake_case					,  __snake_case    ):
                              solution.insert(__snake_case					,  __snake_case    )
                              break
                  else:
                        solution.append(__snake_case    )

      strand_sort(__snake_case					,  __snake_case					,  __snake_case    )
      return solution


if __name__ == "__main__":
   assert strand_sort([4, 3, 5, 1, 2]) == [1, 2, 3, 4, 5]
   assert strand_sort([4, 3, 5, 1, 2], reverse=True) == [5, 4, 3, 2, 1]
