






from typing import TYPE_CHECKING

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


lowerCamelCase__							=     {"""configuration_dpt""": ["""DPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DPTConfig"""]}

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase__							=     ["""DPTFeatureExtractor"""]
     lowerCamelCase__							=     ["""DPTImageProcessor"""]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase__							=     [
         """DPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """DPTForDepthEstimation""",
         """DPTForSemanticSegmentation""",
         """DPTModel""",
         """DPTPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .feature_extraction_dpt import DPTFeatureExtractor
          from .image_processing_dpt import DPTImageProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_dpt import (
              DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
              DPTForDepthEstimation,
              DPTForSemanticSegmentation,
              DPTModel,
              DPTPreTrainedModel,
          )


else:
     import sys

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










from __future__ import annotations

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




@dataclass
class 			snake_case__   :

       '''simple docstring'''
       lowerCamelCase							:				int
       lowerCamelCase							:				int




class 			snake_case__   :

       '''simple docstring'''



       def __init__(							self			,						a__    ) ->      Any:






            '''simple docstring'''





            __snake_case			:list[list[Edge]]          =						[[] for _ in range(a__    )]
            __snake_case			:List[str]          =						size



       def __getitem__(							self			,						a__    ) ->      Iterator[Edge]:






            '''simple docstring'''





            return iter(self._graph[vertex]    )



       @property
       def 					__lowercase						(							self    ) ->      str:






            '''simple docstring'''





            return self._size



       def 					__lowercase						(							self			,						a__			,						a__			,						a__    ) ->      List[Any]:






            '''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 					__lowercase						(							self			,						a__			,						a__    ) ->      int | None:






            '''simple docstring'''





            __snake_case			:Optional[Any]          =						deque([start_vertex]    )
            __snake_case			:list[int | None]          =						[None] * self.size
            __snake_case			:Tuple          =						0

            while queue:
                 __snake_case			:List[Any]          =						queue.popleft()
                 __snake_case			:Optional[Any]          =						distances[current_vertex]
                 if current_distance is None:
                      continue

                 for edge in self[current_vertex]:
                      __snake_case			:Optional[Any]          =						current_distance + edge.weight
                      __snake_case			:Dict          =						distances[edge.destination_vertex]
                      if (
                          isinstance(a__			,						a__    )
                          and new_distance >= dest_vertex_distance
                      ):
                           continue
                      __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()


