





import copy

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


_UpperCAmelCase             =							logging.get_logger(__name__)






class        snake_case_    (		__lowercase						):
  A_      							=   'upernet'






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






   '''simple docstring'''

   super().__init__(**_snake_case      )

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

   __lowerCAmelCase					:	Tuple      =       backbone_config
   __lowerCAmelCase					:	Any      =       hidden_size
   __lowerCAmelCase					:	int      =       initializer_range
   __lowerCAmelCase					:	Any      =       pool_scales
   __lowerCAmelCase					:	Optional[Any]      =       use_auxiliary_head
   __lowerCAmelCase					:	Dict      =       auxiliary_loss_weight
   __lowerCAmelCase					:	Tuple      =       auxiliary_in_channels
   __lowerCAmelCase					:	int      =       auxiliary_channels
   __lowerCAmelCase					:	Optional[int]      =       auxiliary_num_convs
   __lowerCAmelCase					:	int      =       auxiliary_concat_input
   __lowerCAmelCase					:	str      =       loss_ignore_index






  def 				UpperCAmelCase__						(							self			:					List[Any]      )->Tuple:






   '''simple docstring'''

   __lowerCAmelCase					:	str      =       copy.deepcopy(self.__dict__      )
   __lowerCAmelCase					:	Optional[Any]      =       self.backbone_config.to_dict()
   __lowerCAmelCase					:	str      =       self.__class__.model_type
   return output






import random






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:list   ,   SCREAMING_SNAKE_CASE		:Dict   )      ->   tuple:
 __lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase					:	List[str]      =       [], [], []
 for element in data:
  if element < pivot:
   less.append(SCREAMING_SNAKE_CASE   )
  elif element > pivot:
   greater.append(SCREAMING_SNAKE_CASE   )
  else:
   equal.append(SCREAMING_SNAKE_CASE   )
 return less, equal, greater







def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:list   ,   SCREAMING_SNAKE_CASE		:int   )      ->   Dict:
 # index = len(items) // 2 when trying to find the median
 #   (value of index when items is sorted)

 # invalid input
 if index >= len(SCREAMING_SNAKE_CASE   ) or index < 0:
  return None

 __lowerCAmelCase					:	Union[str, Any]      =       items[random.randint(0   ,   len(SCREAMING_SNAKE_CASE   ) - 1   )]
 __lowerCAmelCase					:	int      =       0
 __lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase					:	Tuple      =       _partition(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )
 __lowerCAmelCase					:	Optional[int]      =       len(SCREAMING_SNAKE_CASE   )
 __lowerCAmelCase					:	List[str]      =       len(SCREAMING_SNAKE_CASE   )

 # index is the pivot
 if m <= index < m + count:
  return pivot
 # must be in smaller
 elif m > index:
  return quick_select(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )
 # must be in larger
 else:
  return quick_select(SCREAMING_SNAKE_CASE   ,   index - (m + count)   )