





from __future__ import annotations

from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass



@dataclass
class 				_UpperCAmelCase    :

  """simple docstring"""


  a_        = 42
  a_        = 42





class 				_UpperCAmelCase    :

  """simple docstring"""


  def __init__(				self :		str					,  lowerCAmelCase_ :		List[str]     )							->						List[str]:
     __lowerCAmelCase								=	[[] for _ in range(lowerCAmelCase__     )]
     __lowerCAmelCase								=	size






  def __getitem__(				self :		str					,  lowerCAmelCase_ :		List[str]     )							->						Iterator[Edge]:
     return iter(self._graph[vertex]     )






  @property
  def 				lowercase		(				self :		str     )							->						Union[str, Any]:
     return self._size






  def 				lowercase		(				self :		Dict					,  lowerCAmelCase_ :		str					,  lowerCAmelCase_ :		Union[str, Any]					,  lowerCAmelCase_ :		Any     )							->						Any:
     if weight not in (0, 1):
        raise ValueError('Edge weight must be either 0 or 1.'     )

     if to_vertex < 0 or to_vertex >= self.size:
        raise ValueError('Vertex indexes must be in [0; size).'     )

     self._graph[from_vertex].append(Edge(lowerCAmelCase__					,  lowerCAmelCase__     )     )






  def 				lowercase		(				self :		Dict					,  lowerCAmelCase_ :		List[str]					,  lowerCAmelCase_ :		int     )							->						int | None:
     __lowerCAmelCase								=	deque([start_vertex]     )
     __lowerCAmelCase								=	[None] * self.size
     __lowerCAmelCase								=	0

     while queue:
        __lowerCAmelCase								=	queue.popleft()
        __lowerCAmelCase								=	distances[current_vertex]
        if current_distance is None:
           continue

        for edge in self[current_vertex]:
           __lowerCAmelCase								=	current_distance + edge.weight
           __lowerCAmelCase								=	distances[edge.destination_vertex]
           if (
               isinstance(lowerCAmelCase__					,  lowerCAmelCase__     )
               and new_distance >= dest_vertex_distance
           ):
              continue
           __lowerCAmelCase								=	new_distance
           if edge.weight == 0:
              queue.appendleft(edge.destination_vertex     )
           else:
              queue.append(edge.destination_vertex     )

     if distances[finish_vertex] is None:
        raise ValueError('No path from start_vertex to finish_vertex.'     )

     return distances[finish_vertex]


if __name__ == "__main__":
     import doctest

     doctest.testmod()





from typing import TYPE_CHECKING

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


__lowerCAmelCase		       =							{
    '''configuration_canine''': ['''CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CanineConfig'''],
    '''tokenization_canine''': ['''CanineTokenizer'''],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __lowerCAmelCase		       =							[
     '''CANINE_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''CanineForMultipleChoice''',
     '''CanineForQuestionAnswering''',
     '''CanineForSequenceClassification''',
     '''CanineForTokenClassification''',
     '''CanineLayer''',
     '''CanineModel''',
     '''CaninePreTrainedModel''',
     '''load_tf_weights_in_canine''',
 ]


if TYPE_CHECKING:
 from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
 from .tokenization_canine import CanineTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_canine import (
      CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
      CanineForMultipleChoice,
      CanineForQuestionAnswering,
      CanineForSequenceClassification,
      CanineForTokenClassification,
      CanineLayer,
      CanineModel,
      CaninePreTrainedModel,
      load_tf_weights_in_canine,
  )


else:
 import sys

 __lowerCAmelCase		       =							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


