



"""simple docstring"""


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


__A      :							str   =      logging.get_logger(__name__)
class 	_a       (    lowercase__	,							lowercase__):
			"""simple docstring"""
			UpperCamelCase__						=		"""maskformer-swin"""

			UpperCamelCase__						=		{
			    """num_attention_heads""": """num_heads""",
			    """num_hidden_layers""": """num_layers""",
			}



			def __init__(						self   :			Union[str, Any] ,      __UpperCamelCase   :			Optional[Any]=2_2_4 ,      __UpperCamelCase   :			List[Any]=4 ,      __UpperCamelCase   :			Optional[int]=3 ,      __UpperCamelCase   :			Dict=9_6 ,      __UpperCamelCase   :			List[Any]=[2, 2, 6, 2] ,      __UpperCamelCase   :			Optional[int]=[3, 6, 1_2, 2_4] ,      __UpperCamelCase   :			int=7 ,      __UpperCamelCase   :			Optional[Any]=4.0 ,      __UpperCamelCase   :			Union[str, Any]=True ,      __UpperCamelCase   :			List[str]=0.0 ,      __UpperCamelCase   :			int=0.0 ,      __UpperCamelCase   :			List[Any]=0.1 ,      __UpperCamelCase   :			Any="gelu" ,      __UpperCamelCase   :			str=False ,      __UpperCamelCase   :			Dict=0.0_2 ,      __UpperCamelCase   :			List[Any]=1e-5 ,      __UpperCamelCase   :			Optional[Any]=None ,      __UpperCamelCase   :			Any=None ,      **__UpperCamelCase   :			Dict ,      )->int:
							super().__init__(**__lowerCamelCase      )

							_UpperCAmelCase							=			image_size
							_UpperCAmelCase							=			patch_size
							_UpperCAmelCase							=			num_channels
							_UpperCAmelCase							=			embed_dim
							_UpperCAmelCase							=			depths
							_UpperCAmelCase							=			len(__lowerCamelCase      )
							_UpperCAmelCase							=			num_heads
							_UpperCAmelCase							=			window_size
							_UpperCAmelCase							=			mlp_ratio
							_UpperCAmelCase							=			qkv_bias
							_UpperCAmelCase							=			hidden_dropout_prob
							_UpperCAmelCase							=			attention_probs_dropout_prob
							_UpperCAmelCase							=			drop_path_rate
							_UpperCAmelCase							=			hidden_act
							_UpperCAmelCase							=			use_absolute_embeddings
							_UpperCAmelCase							=			layer_norm_eps
							_UpperCAmelCase							=			initializer_range
							# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
							# this indicates the channel dimension after the last stage of the model
							_UpperCAmelCase							=			int(embed_dim * 2 ** (len(__lowerCamelCase      ) - 1)      )
							_UpperCAmelCase							=			["stem"] + [F'stage{idx}' for idx in range(1 ,      len(__lowerCamelCase      ) + 1      )]
							_UpperCAmelCase							=			get_aligned_output_features_output_indices(
							    out_features=__lowerCamelCase ,      out_indices=__lowerCamelCase ,      stage_names=self.stage_names      )







from __future__ import annotations

from typing import Any

def       lowercase_		(		_A			:    list[Any]						):

      """simple docstring"""




      create_state_space_tree(_A    ,					[]    ,					0						)

def       lowercase_		(		_A			:    list[Any]    ,					_A			:    list[Any]    ,					_A			:    int						):

      """simple docstring"""




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

      create_state_space_tree(_A    ,					_A    ,					index + 1						)
      current_subsequence.append(sequence[index]						)
      create_state_space_tree(_A    ,					_A    ,					index + 1						)
      current_subsequence.pop()


if __name__ == "__main__":
    A      :    list[Any]		=					[3, 1, 2, 4]
    generate_all_subsequences(seq)

    seq.clear()
    seq.extend(["A", "B", "C"])
    generate_all_subsequences(seq)


