


from __future__ import annotations

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







@dataclass
class 	__lowercase    :





							"""simple docstring"""







							_UpperCAmelCase      =						42
							_UpperCAmelCase      =						42







class 	__lowercase    :





							"""simple docstring"""







							def __init__(      self			,						lowerCAmelCase__       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     list[list[Edge]]					=					[[] for _ in range(lowerCAmelCase__       )]
										SCREAMING_SNAKE_CASE_		:     List[str]					=					size






							def __getitem__(      self			,						lowerCAmelCase__       ):





										"""simple docstring"""




										return iter(self._graph[vertex]       )






							@property
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										return self._size






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__       ):





										"""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(lowerCAmelCase__			,						lowerCAmelCase__       )       )






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__			,						lowerCAmelCase__       ):





										"""simple docstring"""




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

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

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


lowerCAmelCase__			:      str								={
    'configuration_clipseg': [
        'CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'CLIPSegConfig',
        'CLIPSegTextConfig',
        'CLIPSegVisionConfig',
    ],
    'processing_clipseg': ['CLIPSegProcessor'],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__			:      Optional[Any]								=[
				    'CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'CLIPSegModel',
				    'CLIPSegPreTrainedModel',
				    'CLIPSegTextModel',
				    'CLIPSegVisionModel',
				    'CLIPSegForImageSegmentation',
				]

if TYPE_CHECKING:
				from .configuration_clipseg import (
				    CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    CLIPSegConfig,
				    CLIPSegTextConfig,
				    CLIPSegVisionConfig,
				)
				from .processing_clipseg import CLIPSegProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_clipseg import (
								    CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
								    CLIPSegForImageSegmentation,
								    CLIPSegModel,
								    CLIPSegPreTrainedModel,
								    CLIPSegTextModel,
								    CLIPSegVisionModel,
								)

else:
				import sys

				lowerCAmelCase__			:      str								=_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





