


'''simple docstring'''
from __future__ import annotations

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

class     _lowerCamelCase       :




 '''simple docstring'''


 def __init__(   self   ,   __lowercase   ,   __lowercase    ):

  """simple docstring"""







  __A	:		Dict	  =      graph
  # mapping node to its parent in resulting breadth first tree
  __A	:		dict[str, str | None]	  =      {}
  __A	:		Union[str, Any]	  =      source_vertex


 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		str	  =      {self.source_vertex}
  __A	:		List[Any]	  =      None
  __A	:		int	  =      [self.source_vertex]  # first in first out queue

  while queue:
   __A	:		Tuple	  =      queue.pop(0    )
   for adjacent_vertex in self.graph[vertex]:
    if adjacent_vertex not in visited:
     visited.add(__lowercase    )
     __A	:		Dict	  =      vertex
     queue.append(__lowercase    )







 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  if target_vertex == self.source_vertex:
   return self.source_vertex

  __A	:		Tuple	  =      self.parent.get(__lowercase    )
  if target_vertex_parent is None:
   __A	:		Tuple	  =      (
       F"""No path from vertex: {self.source_vertex} to vertex: {target_vertex}"""
   )
   raise ValueError(__lowercase    )

  return self.shortest_path(__lowercase    ) + F"""->{target_vertex}"""


if __name__ == "__main__":
 UpperCAmelCase_			:  str										=   Graph(graph, 'G')
 g.breath_first_search()
 print(g.shortest_path('D'))
 print(g.shortest_path('G'))
 print(g.shortest_path('Foo'))










'''simple docstring'''
from typing import TYPE_CHECKING

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


UpperCAmelCase_			:  Any										=   {
    'configuration_maskformer': ['MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MaskFormerConfig'],
    'configuration_maskformer_swin': ['MaskFormerSwinConfig'],
}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase_			:  Tuple										=   ['MaskFormerFeatureExtractor']
 UpperCAmelCase_			:  List[str]										=   ['MaskFormerImageProcessor']


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase_			:  Optional[Any]										=   [
     'MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
     'MaskFormerForInstanceSegmentation',
     'MaskFormerModel',
     'MaskFormerPreTrainedModel',
 ]
 UpperCAmelCase_			:  Optional[int]										=   [
     'MaskFormerSwinBackbone',
     'MaskFormerSwinModel',
     'MaskFormerSwinPreTrainedModel',
 ]

if TYPE_CHECKING:
 from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
 from .configuration_maskformer_swin import MaskFormerSwinConfig

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_maskformer import MaskFormerFeatureExtractor
  from .image_processing_maskformer import MaskFormerImageProcessor
 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_maskformer import (
      MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      MaskFormerForInstanceSegmentation,
      MaskFormerModel,
      MaskFormerPreTrainedModel,
  )
  from .modeling_maskformer_swin import (
      MaskFormerSwinBackbone,
      MaskFormerSwinModel,
      MaskFormerSwinPreTrainedModel,
  )


else:
 import sys

 UpperCAmelCase_			:  Union[str, Any]										=   _LazyModule(__name__, globals()['__file__'], _import_structure)






