
"""simple docstring"""

import copy
from typing import Dict, List, Optional

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


UpperCamelCase     :    List[Any]       =			{
    "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
}

UpperCamelCase     :    List[str]       =			logging.get_logger(__name__)

class __lowerCAmelCase		(				__SCREAMING_SNAKE_CASE      ):
     lowercase						       =						"mask2former"
     lowercase						       =						["swin"]
     lowercase						       =						{"hidden_size": "hidden_dim"}





     def __init__(     self     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = 256     ,					__UpperCAmelCase = 256     ,					__UpperCAmelCase = 256     ,					__UpperCAmelCase = 1024     ,					__UpperCAmelCase = "relu"     ,					__UpperCAmelCase = 6     ,					__UpperCAmelCase = 10     ,					__UpperCAmelCase = 8     ,					__UpperCAmelCase = 0.0     ,					__UpperCAmelCase = 2048     ,					__UpperCAmelCase = False     ,					__UpperCAmelCase = False     ,					__UpperCAmelCase = 4     ,					__UpperCAmelCase = 255     ,					__UpperCAmelCase = 100     ,					__UpperCAmelCase = 0.1     ,					__UpperCAmelCase = 2.0     ,					__UpperCAmelCase = 5.0     ,					__UpperCAmelCase = 5.0     ,					__UpperCAmelCase = 1_2544     ,					__UpperCAmelCase = 3.0     ,					__UpperCAmelCase = 0.7_5     ,					__UpperCAmelCase = 0.0_2     ,					__UpperCAmelCase = 1.0     ,					__UpperCAmelCase = True     ,					__UpperCAmelCase = [4, 8, 16, 32]     ,					__UpperCAmelCase = None     ,					**__UpperCAmelCase     ,					):

      '''simple docstring'''




      if backbone_config is None:
       logger.info('`backbone_config` is `None`. Initializing the config with the default `Swin` backbone.'				)
       __UpperCamelCase      =	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=__UpperCAmelCase     ,					out_features=['stage1', 'stage2', 'stage3', 'stage4']     ,					)

      if isinstance(__UpperCAmelCase     ,					__UpperCAmelCase				):
       __UpperCamelCase      =	backbone_config.pop('model_type'				)
       __UpperCamelCase      =	CONFIG_MAPPING[backbone_model_type]
       __UpperCamelCase      =	config_class.from_dict(__UpperCAmelCase				)

      # 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				)}'				)

      __UpperCamelCase      =	backbone_config
      __UpperCamelCase      =	feature_size
      __UpperCamelCase      =	mask_feature_size
      __UpperCamelCase      =	hidden_dim
      __UpperCamelCase      =	encoder_feedforward_dim
      __UpperCamelCase      =	activation_function
      __UpperCamelCase      =	encoder_layers
      __UpperCamelCase      =	decoder_layers
      __UpperCamelCase      =	num_attention_heads
      __UpperCamelCase      =	dropout
      __UpperCamelCase      =	dim_feedforward
      __UpperCamelCase      =	pre_norm
      __UpperCamelCase      =	enforce_input_projection
      __UpperCamelCase      =	common_stride
      __UpperCamelCase      =	ignore_value
      __UpperCamelCase      =	num_queries
      __UpperCamelCase      =	no_object_weight
      __UpperCamelCase      =	class_weight
      __UpperCamelCase      =	mask_weight
      __UpperCamelCase      =	dice_weight
      __UpperCamelCase      =	train_num_points
      __UpperCamelCase      =	oversample_ratio
      __UpperCamelCase      =	importance_sample_ratio
      __UpperCamelCase      =	init_std
      __UpperCamelCase      =	init_xavier_std
      __UpperCamelCase      =	use_auxiliary_loss
      __UpperCamelCase      =	feature_strides
      __UpperCamelCase      =	output_auxiliary_logits
      __UpperCamelCase      =	decoder_layers

      super().__init__(**__UpperCAmelCase				)





     @classmethod
     def 						UpperCAmelCase (     cls     ,					__UpperCAmelCase     ,					**__UpperCAmelCase				):

      '''simple docstring'''




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







     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	copy.deepcopy(self.__dict__				)
      __UpperCamelCase      =	self.backbone_config.to_dict()
      __UpperCamelCase      =	self.__class__.model_type
      return output





"""simple docstring"""

def 							A						(    snake_case				:int		)				->     int:
 __UpperCamelCase      =	abs(snake_case		)
 __UpperCamelCase      =	0
 while n > 0:
  res += n % 1_0
  n //= 1_0
 return res






def 							A						(    snake_case				:int		)				->     int:
 __UpperCamelCase      =	abs(snake_case		)
 return n if n < 1_0 else n % 1_0 + sum_of_digits(n // 1_0		)






def 							A						(    snake_case				:int		)				->     int:
 return sum(int(snake_case		) for c in str(abs(snake_case		)		)		)






def 							A						(    )				->     None:
 from collections.abc import Callable
 from timeit import timeit

 def benchmark_a_function(snake_case				:Callable    , snake_case				:int		) -> None:
  __UpperCamelCase      =	f'{func.__name__}({value})'
  __UpperCamelCase      =	timeit(f'__main__.{call}'    , setup='import __main__'		)
  print(f'{call:56} = {func(snake_case		)} -- {timing:.4f} seconds'		)

 for value in (2_6_2_1_4_4, 1_1_2_5_8_9_9_9_0_6_8_4_2_6_2_4, 1_2_6_7_6_5_0_6_0_0_2_2_8_2_2_9_4_0_1_4_9_6_7_0_3_2_0_5_3_7_6):
  for func in (sum_of_digits, sum_of_digits_recursion, sum_of_digits_compact):
   benchmark_a_function(snake_case    , snake_case		)
  print()


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      benchmark()



