



import copy

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


__UpperCAmelCase			  =			logging.get_logger(__name__)



class 							lowerCAmelCase_						(    a__				):
       UpperCAmelCase__							:   int  	=    "upernet"
       def __init__(     self,     SCREAMING_SNAKE_CASE_=None,     SCREAMING_SNAKE_CASE_=512,     SCREAMING_SNAKE_CASE_=0.02,     SCREAMING_SNAKE_CASE_=[1, 2, 3, 6],     SCREAMING_SNAKE_CASE_=True,     SCREAMING_SNAKE_CASE_=0.4,     SCREAMING_SNAKE_CASE_=384,     SCREAMING_SNAKE_CASE_=256,     SCREAMING_SNAKE_CASE_=1,     SCREAMING_SNAKE_CASE_=False,     SCREAMING_SNAKE_CASE_=255,     **SCREAMING_SNAKE_CASE_,     )	->						int:
          super().__init__(**SCREAMING_SNAKE_CASE_			)

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

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






       def    snake_case_			(     self			)	->						List[Any]:
          UpperCamelCase       :    Tuple		      =      copy.deepcopy(self.__dict__			)
          UpperCamelCase       :    Optional[Any]		      =      self.backbone_config.to_dict()
          UpperCamelCase       :    Dict		      =      self.__class__.model_type
          return output











'''simple docstring'''



from queue import PriorityQueue
from typing import Any

import numpy as np



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						dict,       SCREAMING_SNAKE_CASE__:						str,       SCREAMING_SNAKE_CASE__:						set,       SCREAMING_SNAKE_CASE__:						set,       SCREAMING_SNAKE_CASE__:						dict,       SCREAMING_SNAKE_CASE__:						dict,       SCREAMING_SNAKE_CASE__:						PriorityQueue,       SCREAMING_SNAKE_CASE__:						dict,       SCREAMING_SNAKE_CASE__:						float | int,       )	->      float | int:

   """simple docstring"""
   for nxt, d in graph[v]:
      if nxt in visited_forward:
         continue
      __a					  =     cst_fwd.get(SCREAMING_SNAKE_CASE__,       np.inf     )
      __a					  =     cst_fwd[v] + d
      if new_cost_f < old_cost_f:
         queue.put((new_cost_f, nxt)     )
         __a					  =     new_cost_f
         __a					  =     v
      if nxt in visited_backward:
         if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
            __a					  =     cst_fwd[v] + d + cst_bwd[nxt]
   return shortest_distance



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						str,       SCREAMING_SNAKE_CASE__:						str,       SCREAMING_SNAKE_CASE__:						dict,       SCREAMING_SNAKE_CASE__:						dict     )	->      int:

   """simple docstring"""
   __a					  =     -1

   __a					  =     set()
   __a					  =     set()
   __a					  =     {source: 0}
   __a					  =     {destination: 0}
   __a					  =     {source: None}
   __a					  =     {destination: None}
   __a					  =     PriorityQueue()
   __a					  =     PriorityQueue()

   __a					  =     np.inf

   queue_forward.put((0, source)     )
   queue_backward.put((0, destination)     )

   if source == destination:
      return 0

   while not queue_forward.empty() and not queue_backward.empty():
      __a  ,      __a					  =     queue_forward.get()
      visited_forward.add(SCREAMING_SNAKE_CASE__     )

      __a  ,      __a					  =     queue_backward.get()
      visited_backward.add(SCREAMING_SNAKE_CASE__     )

      __a					  =     pass_and_relaxation(
          SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       )

      __a					  =     pass_and_relaxation(
          SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       )

      if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
         break

   if shortest_distance != np.inf:
      __a					  =     shortest_distance
   return shortest_path_distance


__UpperCamelCase   :							Dict         =   {
    """B""": [["""C""", 1]],
    """C""": [["""D""", 1]],
    """D""": [["""F""", 1]],
    """E""": [["""B""", 1], ["""G""", 2]],
    """F""": [],
    """G""": [["""F""", 1]],
}
__UpperCamelCase   :							int         =   {
    """B""": [["""E""", 1]],
    """C""": [["""B""", 1]],
    """D""": [["""C""", 1]],
    """F""": [["""D""", 1], ["""G""", 1]],
    """E""": [[None, np.inf]],
    """G""": [["""E""", 2]],
}

if __name__ == "__main__":
 import doctest

 doctest.testmod()