






"""simple docstring"""



import copy
from typing import Dict, List, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


A_    =							{
    '''facebook/mask2former-swin-small-coco-instance''': (
        '''https://huggingface.co/facebook/mask2former-swin-small-coco-instance/blob/main/config.json'''
    )
    # See all Mask2Former models at https://huggingface.co/models?filter=mask2former
}

A_    =							logging.get_logger(__name__)






class 							__SCREAMING_SNAKE_CASE						(				UpperCamelCase		):
							snake_case_     =							'mask2former'
							snake_case_     =							['swin']
							snake_case_     =							{'hidden_size': 'hidden_dim'}


							def __init__(			self      :						Union[str, Any]			,    snake_case      :						Optional[Dict] = None			,    snake_case      :						int = 256			,    snake_case      :						int = 256			,    snake_case      :						int = 256			,    snake_case      :						int = 1024			,    snake_case      :						str = "relu"			,    snake_case      :						int = 6			,    snake_case      :						int = 10			,    snake_case      :						int = 8			,    snake_case      :						float = 0.0			,    snake_case      :						int = 2048			,    snake_case      :						bool = False			,    snake_case      :						bool = False			,    snake_case      :						int = 4			,    snake_case      :						int = 255			,    snake_case      :						int = 100			,    snake_case      :						float = 0.1			,    snake_case      :						float = 2.0			,    snake_case      :						float = 5.0			,    snake_case      :						float = 5.0			,    snake_case      :						int = 1_2544			,    snake_case      :						float = 3.0			,    snake_case      :						float = 0.75			,    snake_case      :						float = 0.02			,    snake_case      :						float = 1.0			,    snake_case      :						bool = True			,    snake_case      :						List[int] = [4, 8, 16, 32]			,    snake_case      :						bool = None			,    **snake_case      :						Dict			,    ):
													'''simple docstring'''





													if backbone_config is None:
																			logger.info("""`backbone_config` is `None`. Initializing the config with the default `Swin` backbone."""							)
																			A__						:  Union[str, Any]		  =  CONFIG_MAPPING["""swin"""](
																			    image_size=224			,    in_channels=3			,    patch_size=4			,    embed_dim=96			,    depths=[2, 2, 18, 2]			,    num_heads=[3, 6, 12, 24]			,    window_size=7			,    drop_path_rate=0.3			,    use_absolute_embeddings=snake_case			,    out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]			,    )

													if isinstance(snake_case			,    snake_case							):
																			A__						:  int		  =  backbone_config.pop("""model_type"""							)
																			A__						:  List[Any]		  =  CONFIG_MAPPING[backbone_model_type]
																			A__						:  Optional[int]		  =  config_class.from_dict(snake_case							)

													# verify that the backbone is supported
													if backbone_config.model_type not in self.backbones_supported:
																			logger.warning_once(
																			    F'Backbone {backbone_config.model_type} is not a supported model and may not be compatible with Mask2Former. '
																			    F'Supported model types: {",".join(self.backbones_supported							)}'							)

													A__						:  Dict		  =  backbone_config
													A__						:  Optional[Any]		  =  feature_size
													A__						:  List[Any]		  =  mask_feature_size
													A__						:  List[str]		  =  hidden_dim
													A__						:  Optional[int]		  =  encoder_feedforward_dim
													A__						:  Dict		  =  activation_function
													A__						:  str		  =  encoder_layers
													A__						:  Any		  =  decoder_layers
													A__						:  str		  =  num_attention_heads
													A__						:  str		  =  dropout
													A__						:  Union[str, Any]		  =  dim_feedforward
													A__						:  int		  =  pre_norm
													A__						:  int		  =  enforce_input_projection
													A__						:  Any		  =  common_stride
													A__						:  Any		  =  ignore_value
													A__						:  Tuple		  =  num_queries
													A__						:  Union[str, Any]		  =  no_object_weight
													A__						:  str		  =  class_weight
													A__						:  Optional[Any]		  =  mask_weight
													A__						:  List[str]		  =  dice_weight
													A__						:  List[Any]		  =  train_num_points
													A__						:  str		  =  oversample_ratio
													A__						:  Optional[Any]		  =  importance_sample_ratio
													A__						:  Union[str, Any]		  =  init_std
													A__						:  Dict		  =  init_xavier_std
													A__						:  str		  =  use_auxiliary_loss
													A__						:  Optional[int]		  =  feature_strides
													A__						:  str		  =  output_auxiliary_logits
													A__						:  List[Any]		  =  decoder_layers

													super().__init__(**snake_case							)


							@classmethod
							def 						_UpperCamelCase       (			cls      :						str			,    snake_case      :						PretrainedConfig			,    **snake_case      :						Union[str, Any]							):
													'''simple docstring'''





													return cls(
													    backbone_config=snake_case			,    **snake_case			,    )


							def 						_UpperCamelCase       (			self      :						Optional[int]							):
													'''simple docstring'''





													A__						:  str		  =  copy.deepcopy(self.__dict__							)
													A__						:  Optional[int]		  =  self.backbone_config.to_dict()
													A__						:  Tuple		  =  self.__class__.model_type
													return output








"""simple docstring"""



from timeit import timeit




def      _lowerCAmelCase		(							UpperCAmelCase__       :					int      )		->int:
						if number < 0:
												raise ValueError("""the value of input must not be negative"""      )
						A__						:  Optional[int]		  =  0
						while number:
												number &= number - 1
												result += 1
						return result




def      _lowerCAmelCase		(							UpperCAmelCase__       :					int      )		->int:
						if number < 0:
												raise ValueError("""the value of input must not be negative"""      )
						A__						:  Tuple		  =  0
						while number:
												if number % 2 == 1:
																		result += 1
												number >>= 1
						return result




def      _lowerCAmelCase		(							)		->None:

						def do_benchmark(UpperCAmelCase__       :					int      ) -> None:
												A__						:  Optional[int]		  =  """import __main__ as z"""
												print(f'Benchmark when {number = }:'      )
												print(f'{get_set_bits_count_using_modulo_operator(UpperCAmelCase__      ) = }'      )
												A__						:  Any		  =  timeit("""z.get_set_bits_count_using_modulo_operator(25)""",						setup=UpperCAmelCase__      )
												print(f'timeit() runs in {timing} seconds'      )
												print(f'{get_set_bits_count_using_brian_kernighans_algorithm(UpperCAmelCase__      ) = }'      )
												A__						:  List[str]		  =  timeit(
												    """z.get_set_bits_count_using_brian_kernighans_algorithm(25)""",						setup=UpperCAmelCase__,						)
												print(f'timeit() runs in {timing} seconds'      )

						for number in (2_5, 3_7, 5_8, 0):
												do_benchmark(UpperCAmelCase__      )
												print()


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						benchmark()






