"""simple docstring"""


from __future__ import annotations






def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""
  create_state_space_tree(lowercase   ,[]   ,0   ,[0 for i in range(len(lowercase		)		)]		)






def     __UpperCAmelCase					(			lowercase   ,lowercase   ,lowercase   ,lowercase   ,):







  """simple docstring"""

  if index == len(lowercase		):
    print(lowercase		)
    return

  for i in range(len(lowercase		)		):
    if not index_used[i]:
      current_sequence.append(sequence[i]		)
      _UpperCAmelCase						 =      True
      create_state_space_tree(lowercase   ,lowercase   ,index + 1   ,lowercase		)
      current_sequence.pop()
      _UpperCAmelCase						 =      False
UpperCAmelCase__  		=						[3, 1, 2, 4]
generate_all_permutations(sequence)

UpperCAmelCase__  		=						["A", "B", "C"]

generate_all_permutations(sequence_a)

"""simple docstring"""


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


UpperCAmelCase__  		=						logging.get_logger(__name__)

UpperCAmelCase__  		=						{
    """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        a				(		lowerCAmelCase_	,							lowerCAmelCase_   ):
    _snake_case     :  Union[str, Any]     							=							'nat'

    _snake_case     :  List[str]     							=							{
        'num_attention_heads': 'num_heads',
        'num_hidden_layers': 'num_layers',
    }


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

      _UpperCAmelCase						 =      patch_size
      _UpperCAmelCase						 =      num_channels
      _UpperCAmelCase						 =      embed_dim
      _UpperCAmelCase						 =      depths
      _UpperCAmelCase						 =      len(__lowerCAmelCase					)
      _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(__lowerCAmelCase					) - 1)					)
      _UpperCAmelCase						 =      layer_scale_init_value
      _UpperCAmelCase						 =      ["""stem"""] + [f'''stage{idx}''' for idx in range(1   ,				len(__lowerCAmelCase					) + 1					)]
      _UpperCAmelCase ,				_UpperCAmelCase						 =      get_aligned_output_features_output_indices(
          out_features=__lowerCAmelCase   ,				out_indices=__lowerCAmelCase   ,				stage_names=self.stage_names					)
