
'''simple docstring'''
from __future__ import annotations

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


@dataclass
class       _lowerCAmelCase   :







							'''simple docstring'''

							lowerCAmelCase_ 						=						42
							lowerCAmelCase_ 						=						42


class       _lowerCAmelCase   :







							'''simple docstring'''

							def __init__(self  ,  UpperCAmelCase  )					->       Optional[Any]:
														_snake_case       =  [[] for _ in range(UpperCAmelCase  )]
														_snake_case       =  size

							def __getitem__(self  ,  UpperCAmelCase  )					->       Iterator[Edge]:
														return iter(self._graph[vertex]  )

							@property
							def 	lowercase							(self  )					->       Union[str, Any]:
														return self._size

							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  )					->       Dict:
														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(UpperCAmelCase  ,  UpperCAmelCase  )  )





							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  )					->       int | None:
														_snake_case       =  deque([start_vertex]  )
														_snake_case       =  [None] * self.size
														_snake_case       =  0

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

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


__lowerCAmelCase												=	{
    'configuration_resnet': ['RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ResNetConfig', 'ResNetOnnxConfig']
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCAmelCase												=	[
				    'RESNET_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'ResNetForImageClassification',
				    'ResNetModel',
				    'ResNetPreTrainedModel',
				    'ResNetBackbone',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCAmelCase												=	[
				    'TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'TFResNetForImageClassification',
				    'TFResNetModel',
				    'TFResNetPreTrainedModel',
				]

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCAmelCase												=	[
				    'FlaxResNetForImageClassification',
				    'FlaxResNetModel',
				    'FlaxResNetPreTrainedModel',
				]

if TYPE_CHECKING:
				from .configuration_resnet import RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ResNetConfig, ResNetOnnxConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_resnet import (
								    RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
								    ResNetBackbone,
								    ResNetForImageClassification,
								    ResNetModel,
								    ResNetPreTrainedModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_resnet import (
								    TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFResNetForImageClassification,
								    TFResNetModel,
								    TFResNetPreTrainedModel,
								)

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel, FlaxResNetPreTrainedModel


else:
				import sys

				__lowerCAmelCase												=	_LazyModule(__name__, globals()['__file__'], _import_structure)