





def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase					):







   """simple docstring"""
   if len(UpperCAmelCase					) != len(UpperCAmelCase					):
      raise ValueError('''String lengths must match!'''					)

   lowerCamelCase__       :						Union[str, Any]        =							0

   for chara, chara in zip(UpperCAmelCase      ,  UpperCAmelCase					):
      if chara != chara:
         count += 1

   return count


if __name__ == "__main__":
       import doctest

       doctest.testmod()










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 :   List[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        __SCREAMING_SNAKE_CASE      (			lowerCAmelCase_					,lowerCAmelCase_ ):
       _UpperCAmelCase				:  Dict			   =   "nat"

       _UpperCAmelCase				:  Tuple			   =   {
           "num_attention_heads": "num_heads",
           "num_hidden_layers": "num_layers",
       }







       def __init__(				self					:     List[Any]		, A					:     Union[str, Any]=4		, A					:     str=3		, A					:     List[Any]=6_4		, A					:     Optional[Any]=[3, 4, 6, 5]		, A					:     int=[2, 4, 8, 1_6]		, A					:     Optional[int]=7		, A					:     List[Any]=3.0		, A					:     str=True		, A					:     str=0.0		, A					:     Any=0.0		, A					:     int=0.1		, A					:     Tuple="gelu"		, A					:     List[Any]=0.02		, A					:     str=1e-5		, A					:     Optional[int]=0.0		, A					:     Optional[Any]=None		, A					:     Dict=None		, **A					:     str		, )       ->Union[str, Any]:
          super().__init__(**A      )

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



