


'''simple docstring'''







A__:    Optional[Any]					    =       {
    '''A''': ['''B''', '''C''', '''E'''],
    '''B''': ['''A''', '''D''', '''E'''],
    '''C''': ['''A''', '''F''', '''G'''],
    '''D''': ['''B'''],
    '''E''': ['''A''', '''B''', '''D'''],
    '''F''': ['''C'''],
    '''G''': ['''C'''],
}


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	dict    ,_UpperCAmelCase	:	Tuple    ,_UpperCAmelCase	:	Optional[Any]  )  ->			list[str]:
       _a    : List[Any]     					=set()
       # keep track of all the paths to be checked
       _a    : Tuple     					=[[start]]

       # return path if start is goal
       if start == goal:
              return [start]

       # keeps looping until all possible paths have been checked
       while queue:
              # pop the first path from the queue
              _a    : Optional[int]     					=queue.pop(0  )
              # get the last node from the path
              _a    : Any     					=path[-1]
              if node not in explored:
                     _a    : Dict     					=graph[node]
                     # go through all neighbour nodes, construct a new path and
                     # push it into the queue
                     for neighbour in neighbours:
                            _a    : Any     					=list(_UpperCAmelCase  )
                            new_path.append(_UpperCAmelCase  )
                            queue.append(_UpperCAmelCase  )
                            # return path if neighbour is goal
                            if neighbour == goal:
                                   return new_path

            # mark node as explored
                     explored.add(_UpperCAmelCase  )

    # in case there's no path between the 2 nodes
       return []


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	dict    ,_UpperCAmelCase	:	Any    ,_UpperCAmelCase	:	Optional[Any]  )  ->			int:
       if not graph or start not in graph or target not in graph:
              return -1
       if start == target:
              return 0
       _a    : str     					=[start]
       _a    : Optional[Any]     					=set(_UpperCAmelCase  )
       # Keep tab on distances from `start` node.
       _a    : int     					={start: 0, target: -1}
       while queue:
              _a    : str     					=queue.pop(0  )
              if node == target:
                     _a    : Optional[Any]     					=(
                         dist[node] if dist[target] == -1 else min(dist[target]    ,dist[node]  )
                     )
              for adjacent in graph[node]:
                     if adjacent not in visited:
                            visited.add(_UpperCAmelCase  )
                            queue.append(_UpperCAmelCase  )
                            _a    : str     					=dist[node] + 1
       return dist[target]


if __name__ == "__main__":
   print(bfs_shortest_path(demo_graph, '''G''', '''D'''))  # returns ['G', 'C', 'A', 'B', 'D']
   print(bfs_shortest_path_distance(demo_graph, '''G''', '''D'''))  # returns 4






'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


A__:    List[str]					    =       {
    '''configuration_chinese_clip''': [
        '''CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''ChineseCLIPConfig''',
        '''ChineseCLIPOnnxConfig''',
        '''ChineseCLIPTextConfig''',
        '''ChineseCLIPVisionConfig''',
    ],
    '''processing_chinese_clip''': ['''ChineseCLIPProcessor'''],
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A__:    Optional[int]					    =       ['''ChineseCLIPFeatureExtractor''']
   A__:    Any					    =       ['''ChineseCLIPImageProcessor''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A__:    Dict					    =       [
       '''CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''ChineseCLIPModel''',
       '''ChineseCLIPPreTrainedModel''',
       '''ChineseCLIPTextModel''',
       '''ChineseCLIPVisionModel''',
   ]

if TYPE_CHECKING:
   from .configuration_chinese_clip import (
       CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
       ChineseCLIPConfig,
       ChineseCLIPOnnxConfig,
       ChineseCLIPTextConfig,
       ChineseCLIPVisionConfig,
   )
   from .processing_chinese_clip import ChineseCLIPProcessor

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_chinese_clip import ChineseCLIPFeatureExtractor, ChineseCLIPImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_chinese_clip import (
          CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
          ChineseCLIPModel,
          ChineseCLIPPreTrainedModel,
          ChineseCLIPTextModel,
          ChineseCLIPVisionModel,
      )

else:
   import sys

   A__:    str					    =       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


