






"""simple docstring"""







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


lowerCAmelCase_	:    Optional[Any]            =      logging.get_logger(__name__)

lowerCAmelCase_	:    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   UpperCamelCase_ (       __lowercase   ,					__lowercase  ):
  _A       :					Optional[Any]				=	'nat'

  _A       :					int				=	{
      'num_attention_heads': 'num_heads',
      'num_hidden_layers': 'num_layers',
  }



  def __init__(      self ,		snake_case__=4 ,		snake_case__=3 ,		snake_case__=64 ,		snake_case__=[3, 4, 6, 5] ,		snake_case__=[2, 4, 8, 16] ,		snake_case__=7 ,		snake_case__=3.0 ,		snake_case__=True ,		snake_case__=0.0 ,		snake_case__=0.0 ,		snake_case__=0.1 ,		snake_case__="gelu" ,		snake_case__=0.02 ,		snake_case__=1e-5 ,		snake_case__=0.0 ,		snake_case__=None ,		snake_case__=None ,		**snake_case__ ,		)			->			int:



         """simple docstring"""





         super().__init__(**snake_case__				)

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








"""simple docstring"""







import random
from typing import Any






def       _lowerCAmelCase       (      lowerCAmelCase		):







       '''simple docstring'''
       for _ in range(len(lowerCAmelCase		)		):
              UpperCAmelCase	    =			random.randint(0	,      len(lowerCAmelCase		) - 1		)
              UpperCAmelCase	    =			random.randint(0	,      len(lowerCAmelCase		) - 1		)
              UpperCAmelCase    ,       UpperCAmelCase	    =			data[b], data[a]
       return data


if __name__ == "__main__":
 lowerCAmelCase_	:    Tuple            =      [0, 1, 2, 3, 4, 5, 6, 7]
 lowerCAmelCase_	:    List[str]            =      ['''python''', '''says''', '''hello''', '''!''']
 print('''Fisher-Yates Shuffle:''')
 print('''List''', integers, strings)
 print('''FY Shuffle''', fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))
