




'''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[Any]				,     lowerCAmelCase_			:    int   )     ->				str:


														"""simple docstring"""


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







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


														"""simple docstring"""


														return iter(self._graph[vertex]   )







							@property
							def   __lowerCAmelCase     (    self			:    str   )     ->				Tuple:


														"""simple docstring"""


														return self._size







							def   __lowerCAmelCase     (    self			:    Union[str, Any]				,     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			:    Tuple				,     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_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


a							   =				{
    'configuration_owlvit': [
        'OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'OwlViTConfig',
        'OwlViTOnnxConfig',
        'OwlViTTextConfig',
        'OwlViTVisionConfig',
    ],
    'processing_owlvit': ['OwlViTProcessor'],
}


try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a							   =				['OwlViTFeatureExtractor']
	a							   =				['OwlViTImageProcessor']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a							   =				[
	    'OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'OwlViTModel',
	    'OwlViTPreTrainedModel',
	    'OwlViTTextModel',
	    'OwlViTVisionModel',
	    'OwlViTForObjectDetection',
	]

if TYPE_CHECKING:
	from .configuration_owlvit import (
	    OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    OwlViTConfig,
	    OwlViTOnnxConfig,
	    OwlViTTextConfig,
	    OwlViTVisionConfig,
	)
	from .processing_owlvit import OwlViTProcessor

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_owlvit import OwlViTFeatureExtractor
		from .image_processing_owlvit import OwlViTImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_owlvit import (
		    OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    OwlViTForObjectDetection,
		    OwlViTModel,
		    OwlViTPreTrainedModel,
		    OwlViTTextModel,
		    OwlViTVisionModel,
		)

else:
	import sys

	a							   =				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




