


'''simple docstring'''






from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


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

__A					:  Dict											=      {
    "shi-labs/nat-mini-in1k-224": "https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json",
    # See all Nat models at https://huggingface.co/models?filter=nat
}






class    __snake_case       (   _SCREAMING_SNAKE_CASE	,_SCREAMING_SNAKE_CASE):







    """simple docstring"""



    lowercase    =					'nat'

    lowercase    =					{
        'num_attention_heads': 'num_heads',
        'num_hidden_layers': 'num_layers',
    }

    def __init__(   self    :  Tuple    ,						lowerCamelCase    :  List[Any]=4    ,						lowerCamelCase    :  Optional[int]=3    ,						lowerCamelCase    :  Tuple=64    ,						lowerCamelCase    :  Dict=[3, 4, 6, 5]    ,						lowerCamelCase    :  Dict=[2, 4, 8, 16]    ,						lowerCamelCase    :  List[Any]=7    ,						lowerCamelCase    :  str=3.0    ,						lowerCamelCase    :  List[str]=True    ,						lowerCamelCase    :  Union[str, Any]=0.0    ,						lowerCamelCase    :  List[Any]=0.0    ,						lowerCamelCase    :  Union[str, Any]=0.1    ,						lowerCamelCase    :  str="gelu"    ,						lowerCamelCase    :  Union[str, Any]=0.02    ,						lowerCamelCase    :  Optional[Any]=1E-5    ,						lowerCamelCase    :  Dict=0.0    ,						lowerCamelCase    :  Optional[Any]=None    ,						lowerCamelCase    :  int=None    ,						**lowerCamelCase    :  str    ,						)   ->  str:
      super().__init__(**lowerCamelCase		)

      lowerCAmelCase_			:      Optional[int]			=   patch_size
      lowerCAmelCase_			:      Tuple			=   num_channels
      lowerCAmelCase_			:      Optional[int]			=   embed_dim
      lowerCAmelCase_			:      Optional[int]			=   depths
      lowerCAmelCase_			:      List[Any]			=   len(lowerCamelCase		)
      lowerCAmelCase_			:      Optional[Any]			=   num_heads
      lowerCAmelCase_			:      Any			=   kernel_size
      lowerCAmelCase_			:      List[str]			=   mlp_ratio
      lowerCAmelCase_			:      List[Any]			=   qkv_bias
      lowerCAmelCase_			:      Tuple			=   hidden_dropout_prob
      lowerCAmelCase_			:      str			=   attention_probs_dropout_prob
      lowerCAmelCase_			:      Optional[Any]			=   drop_path_rate
      lowerCAmelCase_			:      Union[str, Any]			=   hidden_act
      lowerCAmelCase_			:      int			=   layer_norm_eps
      lowerCAmelCase_			:      Tuple			=   initializer_range
      # we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
      # this indicates the channel dimension after the last stage of the model
      lowerCAmelCase_			:      str			=   int(embed_dim * 2 ** (len(lowerCamelCase		) - 1)		)
      lowerCAmelCase_			:      Optional[int]			=   layer_scale_init_value
      lowerCAmelCase_			:      Optional[Any]			=   ["""stem"""] + [F'stage{idx}' for idx in range(1    ,						len(lowerCamelCase		) + 1		)]
      lowerCAmelCase_,							lowerCAmelCase_			:      Union[str, Any]			=   get_aligned_output_features_output_indices(
          out_features=lowerCamelCase    ,						out_indices=lowerCamelCase    ,						stage_names=self.stage_names		)







'''simple docstring'''






def 				UpperCamelCase_			(    A__					:   str							,    A__					:   list[str]					):
  '''simple docstring'''





  lowerCAmelCase_			:      Dict			=   """"""
  for word_or_phrase in separated:
    if not isinstance(A__							,    A__					):
      raise Exception("""join() accepts only strings to be joined"""					)
    joined += word_or_phrase + separator
  return joined.strip(A__					)


if __name__ == "__main__":
  from doctest import testmod

  testmod()



