


'''simple docstring'''






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


__A   :    Tuple											= logging.get_logger(__name__)

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

      lowerCAmelCase_     : Tuple     =  patch_size
      lowerCAmelCase_     : str     =  num_channels
      lowerCAmelCase_     : Union[str, Any]     =  embed_dim
      lowerCAmelCase_     : Dict     =  depths
      lowerCAmelCase_     : str     =  len(lowerCamelCase						)
      lowerCAmelCase_     : Dict     =  num_heads
      lowerCAmelCase_     : str     =  kernel_size
      lowerCAmelCase_     : Optional[Any]     =  mlp_ratio
      lowerCAmelCase_     : Any     =  qkv_bias
      lowerCAmelCase_     : List[str]     =  hidden_dropout_prob
      lowerCAmelCase_     : List[str]     =  attention_probs_dropout_prob
      lowerCAmelCase_     : Tuple     =  drop_path_rate
      lowerCAmelCase_     : Optional[Any]     =  hidden_act
      lowerCAmelCase_     : Optional[Any]     =  layer_norm_eps
      lowerCAmelCase_     : List[str]     =  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_     : Any     =  int(embed_dim * 2 ** (len(lowerCamelCase						) - 1)						)
      lowerCAmelCase_     : Dict     =  layer_scale_init_value
      lowerCAmelCase_     : Optional[int]     =  ["""stem"""] + [F'stage{idx}' for idx in range(1       ,		len(lowerCamelCase						) + 1						)]
      lowerCAmelCase_,				lowerCAmelCase_     : List[Any]     =  get_aligned_output_features_output_indices(
          out_features=lowerCamelCase       ,		out_indices=lowerCamelCase       ,		stage_names=self.stage_names						)







'''simple docstring'''






from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors







def 							UpperCamelCase_     (       A__   :  int   ):
  '''simple docstring'''





  lowerCAmelCase_     : Any     =  prime_factors(A__   )
  if is_square_free(A__   ):
    return -1 if len(A__   ) % 2 else 1
  return 0


if __name__ == "__main__":
  import doctest

  doctest.testmod()



