




'''simple docstring'''


from __future__ import annotations

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

@dataclass
class       A   :
							lowercase_          =    42
							lowercase_          =    42



class       A   :







							def __init__(  self	:  Optional[int]							,      lowerCAmelCase_	:  int )      ->							int:


														"""simple docstring"""


														_a     					=    [[] for _ in range(lowerCAmelCase_ )]
														_a     					=    size







							def __getitem__(  self	:  Dict							,      lowerCAmelCase_	:  int )      ->							Iterator[Edge]:


														"""simple docstring"""


														return iter(self._graph[vertex] )







							@property
							def        __lowerCAmelCase      (  self	:  Tuple )      ->							List[Any]:


														"""simple docstring"""


														return self._size







							def        __lowerCAmelCase      (  self	:  Tuple							,      lowerCAmelCase_	:  int							,      lowerCAmelCase_	:  int							,      lowerCAmelCase_	:  int )      ->							Dict:


														"""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        __lowerCAmelCase      (  self	:  Any							,      lowerCAmelCase_	:  int							,      lowerCAmelCase_	:  int )      ->							int | None:


														"""simple docstring"""


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

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

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


_snake_case				:				int       							=		{'configuration_yolos': ['YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'YolosConfig', 'YolosOnnxConfig']}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case				:				List[str]       							=		['YolosFeatureExtractor']
				_snake_case				:				Optional[int]       							=		['YolosImageProcessor']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case				:				Tuple       							=		[
				    'YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'YolosForObjectDetection',
				    'YolosModel',
				    'YolosPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_yolos import YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP, YolosConfig, YolosOnnxConfig

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .feature_extraction_yolos import YolosFeatureExtractor
								from .image_processing_yolos import YolosImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_yolos import (
								    YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST,
								    YolosForObjectDetection,
								    YolosModel,
								    YolosPreTrainedModel,
								)


else:
				import sys

				_snake_case				:				Dict       							=		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

