


from __future__ import annotations

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



@dataclass
class 	__lowerCamelCase			:
       _lowercase											=     42
       _lowercase											=     42



class 	__lowerCamelCase			:







       def __init__(			self: List[str],A_: int					):






            '''simple docstring'''




            __UpperCamelCase =			[[] for _ in range(A_					)]
            __UpperCamelCase =			size







       def __getitem__(			self: Any,A_: int					):






            '''simple docstring'''




            return iter(self._graph[vertex]					)







       @property
       def 		snake_case_	(			self: Union[str, Any]					):






            '''simple docstring'''




            return self._size







       def 		snake_case_	(			self: Any,A_: int,A_: int,A_: 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(A_,A_					)					)







       def 		snake_case_	(			self: Optional[Any],A_: int,A_: int					):






            '''simple docstring'''




            __UpperCamelCase =			deque([start_vertex]					)
            __UpperCamelCase =			[None] * self.size
            __UpperCamelCase =			0

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

                 for edge in self[current_vertex]:
                      __UpperCamelCase =			current_distance + edge.weight
                      __UpperCamelCase =			distances[edge.destination_vertex]
                      if (
                          isinstance(A_,A_					)
                          and new_distance >= dest_vertex_distance
                      ):
                           continue
                      __UpperCamelCase =			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()



"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


UpperCAmelCase__			=    {"""configuration_vit_mae""": ["""VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViTMAEConfig"""]}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase__			=    [
        """VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """ViTMAEForPreTraining""",
        """ViTMAELayer""",
        """ViTMAEModel""",
        """ViTMAEPreTrainedModel""",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase__			=    [
        """TFViTMAEForPreTraining""",
        """TFViTMAEModel""",
        """TFViTMAEPreTrainedModel""",
    ]

if TYPE_CHECKING:
    from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_vit_mae import (
            VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
            ViTMAEForPreTraining,
            ViTMAELayer,
            ViTMAEModel,
            ViTMAEPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel


else:
    import sys

    UpperCAmelCase__			=    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
