






'''simple docstring'''





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


a__       =     logging.get_logger(__name__)


class    _lowerCAmelCase			(		_a     ,      _a      ):



				"""simple docstring"""



				_lowercase				:   Optional[Any]      =  """maskformer-swin"""

				_lowercase				:   int      =  {
				    """num_attention_heads""": """num_heads""",
				    """num_hidden_layers""": """num_layers""",
				}



				def __init__(      self    :    List[str]			,		UpperCamelCase__    :    Optional[int]=2_2_4			,		UpperCamelCase__    :    List[str]=4			,		UpperCamelCase__    :    int=3			,		UpperCamelCase__    :    int=9_6			,		UpperCamelCase__    :    Any=[2, 2, 6, 2]			,		UpperCamelCase__    :    Tuple=[3, 6, 1_2, 2_4]			,		UpperCamelCase__    :    str=7			,		UpperCamelCase__    :    Optional[int]=4.0			,		UpperCamelCase__    :    int=True			,		UpperCamelCase__    :    Optional[Any]=0.0			,		UpperCamelCase__    :    List[str]=0.0			,		UpperCamelCase__    :    Optional[int]=0.1			,		UpperCamelCase__    :    List[Any]="gelu"			,		UpperCamelCase__    :    Tuple=False			,		UpperCamelCase__    :    Dict=0.02			,		UpperCamelCase__    :    Tuple=1E-5			,		UpperCamelCase__    :    Optional[int]=None			,		UpperCamelCase__    :    Tuple=None			,		**UpperCamelCase__    :    List[Any]			,		):






											'''simple docstring'''







											super().__init__(**snake_case_)

											snake_case__	  =       image_size
											snake_case__	  =       patch_size
											snake_case__	  =       num_channels
											snake_case__	  =       embed_dim
											snake_case__	  =       depths
											snake_case__	  =       len(snake_case_)
											snake_case__	  =       num_heads
											snake_case__	  =       window_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__	  =       use_absolute_embeddings
											snake_case__	  =       layer_norm_eps
											snake_case__	  =       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
											snake_case__	  =       int(embed_dim * 2 ** (len(snake_case_) - 1))
											snake_case__	  =       ["""stem"""] + [F'''stage{idx}''' for idx in range(1			,		len(snake_case_) + 1)]
											snake_case__  , snake_case__	  =       get_aligned_output_features_output_indices(
											    out_features=snake_case_			,		out_indices=snake_case_			,		stage_names=self.stage_names)








import random
from typing import Any

def _UpperCAmelCase   (	a						:	list ):
							for _ in range(len(a ) ):
														snake_case__	  =       random.randint(0  ,					len(a ) - 1 )
														snake_case__	  =       random.randint(0  ,					len(a ) - 1 )
														snake_case__  , snake_case__	  =       data[b], data[a]
							return data


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