
import copy

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


A     :	Optional[int]    = logging.get_logger(__name__)







class 			__A( a			):
				snake_case_						   =    '''upernet'''




				def __init__(     self		,     _snake_case=None		,     _snake_case=512		,     _snake_case=0.02		,     _snake_case=[1, 2, 3, 6]		,     _snake_case=True		,     _snake_case=0.4		,     _snake_case=384		,     _snake_case=256		,     _snake_case=1		,     _snake_case=False		,     _snake_case=255		,     **_snake_case		,     )    ->      Optional[Any]:


								'''simple docstring'''




								super().__init__(**__SCREAMING_SNAKE_CASE				)

								if backbone_config is None:
												logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''				)
												__a										=    CONFIG_MAPPING['''resnet'''](out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']				)
								elif isinstance(__SCREAMING_SNAKE_CASE		,     __SCREAMING_SNAKE_CASE				):
												__a										=    backbone_config.get('''model_type'''				)
												__a										=    CONFIG_MAPPING[backbone_model_type]
												__a										=    config_class.from_dict(__SCREAMING_SNAKE_CASE				)

								__a										=    backbone_config
								__a										=    hidden_size
								__a										=    initializer_range
								__a										=    pool_scales
								__a										=    use_auxiliary_head
								__a										=    auxiliary_loss_weight
								__a										=    auxiliary_in_channels
								__a										=    auxiliary_channels
								__a										=    auxiliary_num_convs
								__a										=    auxiliary_concat_input
								__a										=    loss_ignore_index




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      str:


								'''simple docstring'''




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


'''simple docstring'''



def    a__       (  a__						):




     """simple docstring"""


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

     if num == 0:
          return "0b0"

     __SCREAMING_SNAKE_CASE     =      False

     if num < 0:
          __SCREAMING_SNAKE_CASE     =      True
          __SCREAMING_SNAKE_CASE     =      -num

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

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

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


if __name__ == "__main__":
  import doctest

  doctest.testmod()






