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


SCREAMING_SNAKE_CASE__							=				logging.get_logger(__name__)
class 	A__   (						UpperCAmelCase__    ,						UpperCAmelCase__				):
							lowerCAmelCase__		:						int    			=							"""maskformer-swin"""

							lowerCAmelCase__		:						str    			=							{
							    """num_attention_heads""": """num_heads""",
							    """num_hidden_layers""": """num_layers""",
							}





							def __init__( self  :   List[Any]     , _UpperCAmelCase  :   List[str]=2_24     , _UpperCAmelCase  :   Dict=4     , _UpperCAmelCase  :   str=3     , _UpperCAmelCase  :   Optional[int]=96     , _UpperCAmelCase  :   int=[2, 2, 6, 2]     , _UpperCAmelCase  :   List[str]=[3, 6, 12, 24]     , _UpperCAmelCase  :   List[str]=7     , _UpperCAmelCase  :   List[str]=4.0     , _UpperCAmelCase  :   List[Any]=True     , _UpperCAmelCase  :   Union[str, Any]=0.0     , _UpperCAmelCase  :   Union[str, Any]=0.0     , _UpperCAmelCase  :   int=0.1     , _UpperCAmelCase  :   List[str]="gelu"     , _UpperCAmelCase  :   Any=False     , _UpperCAmelCase  :   str=0.02     , _UpperCAmelCase  :   str=1e-5     , _UpperCAmelCase  :   Optional[Any]=None     , _UpperCAmelCase  :   List[Any]=None     , **_UpperCAmelCase  :   int     , )						->		Union[str, Any]:



											"""simple docstring"""


											super().__init__(**snake_case_				)

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












"""simple docstring"""




def 						UpperCAmelCase__		(     _UpperCAmelCase					):






  """simple docstring"""





  if isinstance(_UpperCAmelCase ,					_UpperCAmelCase					):
    raise TypeError('\'float\' object cannot be interpreted as an integer'					)
  if isinstance(_UpperCAmelCase ,					_UpperCAmelCase					):
    raise TypeError('\'str\' object cannot be interpreted as an integer'					)

  if num == 0:
    return "0b0"

  A_  :       str										=  False

  if num < 0:
    A_  :       Dict										=  True
    A_  :       Union[str, Any]										=  -num

  A_  :       list[int]										=  []
  while num > 0:
    binary.insert(0 ,					num % 2					)
    num >>= 1

  if negative:
    return "-0b" + "".join(str(_UpperCAmelCase					) for e in binary					)

  return "0b" + "".join(str(_UpperCAmelCase					) for e in binary					)


if __name__ == "__main__":
   import doctest

   doctest.testmod()