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


__magic_name__            =  logging.get_logger(__name__)

__magic_name__            =  {
    '''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 					_SCREAMING_SNAKE_CASE  (      __A       ,					__A ):
      _A						:		int    	=     """nat"""

      _A						:		Tuple    	=     {
          """num_attention_heads""": """num_heads""",
          """num_hidden_layers""": """num_layers""",
      }
      def __init__(			self		,	lowerCamelCase=4		,	lowerCamelCase=3		,	lowerCamelCase=64		,	lowerCamelCase=[3, 4, 6, 5]		,	lowerCamelCase=[2, 4, 8, 16]		,	lowerCamelCase=7		,	lowerCamelCase=3.0		,	lowerCamelCase=True		,	lowerCamelCase=0.0		,	lowerCamelCase=0.0		,	lowerCamelCase=0.1		,	lowerCamelCase="gelu"		,	lowerCamelCase=0.0_2		,	lowerCamelCase=1e-5		,	lowerCamelCase=0.0		,	lowerCamelCase=None		,	lowerCamelCase=None		,	**lowerCamelCase		,	):
          super().__init__(**UpperCamelCase__ )

          snake_case__						=				patch_size
          snake_case__						=				num_channels
          snake_case__						=				embed_dim
          snake_case__						=				depths
          snake_case__						=				len(UpperCamelCase__ )
          snake_case__						=				num_heads
          snake_case__						=				kernel_size
          snake_case__						=				mlp_ratio
          snake_case__						=				qkv_bias
          snake_case__						=				hidden_dropout_prob
          snake_case__						=				attention_probs_dropout_prob
          snake_case__						=				drop_path_rate
          snake_case__						=				hidden_act
          snake_case__						=				layer_norm_eps
          snake_case__						=				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
          snake_case__						=				int(embed_dim * 2 ** (len(UpperCamelCase__ ) - 1) )
          snake_case__						=				layer_scale_init_value
          snake_case__						=				['stem'] + [F"""stage{idx}""" for idx in range(1		,	len(UpperCamelCase__ ) + 1 )]
          snake_case__						=				get_aligned_output_features_output_indices(
              out_features=UpperCamelCase__		,	out_indices=UpperCamelCase__		,	stage_names=self.stage_names )





from __future__ import annotations





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase							):
    if (voltage, current, resistance).count(0							) != 1:
        raise ValueError("One and only one argument must be 0"							)
    if resistance < 0:
        raise ValueError("Resistance cannot be negative"							)
    if voltage == 0:
        return {"voltage": float(current * resistance							)}
    elif current == 0:
        return {"current": voltage / resistance}
    elif resistance == 0:
        return {"resistance": voltage / current}
    else:
        raise ValueError("Exactly one argument must be 0"							)


if __name__ == "__main__":
     import doctest

     doctest.testmod()



