

from __future__ import annotations

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






@dataclass
class _UpperCamelCase							:
   '''simple docstring'''







   lowerCamelCase__           =42
   lowerCamelCase__           =42






class _UpperCamelCase							:
   '''simple docstring'''







   def __init__(			self							: Any      ,      a							: int	)							->       Any:

    """simple docstring"""

    SCREAMING_SNAKE_CASE   :      list[list[Edge]]				     =       [[] for _ in range(UpperCAmelCase_	)]
    SCREAMING_SNAKE_CASE   :      Dict				     =       size
   def __getitem__(			self							: Any      ,      a							: int	)							->       Optional[Any]:

    """simple docstring"""

    return iter(self._graph[vertex]	)
   @property
   def 							__UpperCamelCase (			self							: List[Any]	)							->       Union[str, Any]:

    """simple docstring"""

    return self._size
   def 							__UpperCamelCase (			self							: List[Any]      ,      a							: int      ,      a							: int      ,      a							: int	)							->       Optional[int]:

    """simple docstring"""

    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(UpperCAmelCase_      ,      UpperCAmelCase_	)	)






   def 							__UpperCamelCase (			self							: Tuple      ,      a							: int      ,      a							: int	)							->       Optional[int]:

    """simple docstring"""

    SCREAMING_SNAKE_CASE   :      Optional[int]				     =       deque([start_vertex]	)
    SCREAMING_SNAKE_CASE   :      list[int | None]				     =       [None] * self.size
    SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       0

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

     for edge in self[current_vertex]:
      SCREAMING_SNAKE_CASE   :      Optional[int]				     =       current_distance + edge.weight
      SCREAMING_SNAKE_CASE   :      Tuple				     =       distances[edge.destination_vertex]
      if (
          isinstance(UpperCAmelCase_      ,      UpperCAmelCase_	)
          and new_distance >= dest_vertex_distance
      ):
       continue
      SCREAMING_SNAKE_CASE   :      Dict				     =       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_torch_available, is_vision_available


a_						     =    {
    'configuration_bridgetower': [
        'BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BridgeTowerConfig',
        'BridgeTowerTextConfig',
        'BridgeTowerVisionConfig',
    ],
    'processing_bridgetower': ['BridgeTowerProcessor'],
}

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_						     =    ['BridgeTowerImageProcessor']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_						     =    [
			    'BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'BridgeTowerForContrastiveLearning',
			    'BridgeTowerForImageAndTextRetrieval',
			    'BridgeTowerForMaskedLM',
			    'BridgeTowerModel',
			    'BridgeTowerPreTrainedModel',
			]


if TYPE_CHECKING:
			from .configuration_bridgetower import (
			    BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    BridgeTowerConfig,
			    BridgeTowerTextConfig,
			    BridgeTowerVisionConfig,
			)
			from .processing_bridgetower import BridgeTowerProcessor

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .image_processing_bridgetower import BridgeTowerImageProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_bridgetower import (
						    BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST,
						    BridgeTowerForContrastiveLearning,
						    BridgeTowerForImageAndTextRetrieval,
						    BridgeTowerForMaskedLM,
						    BridgeTowerModel,
						    BridgeTowerPreTrainedModel,
						)


else:
			import sys

			a_						     =    _LazyModule(__name__, globals()['__file__'], _import_structure)