





from __future__ import annotations

from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class     UpperCamelCase__					:






		'''simple docstring'''






		__a       :       int
		__a       :       int




class     UpperCamelCase__					:






		'''simple docstring'''






		def __init__(							self,   snake_case__  )       -> List[Any]:






									"""simple docstring"""
									lowercase_	:   list[list[Edge]]  				=     [[] for _ in range(snake_case__  )]
									lowercase_	:   Any  				=     size






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






									"""simple docstring"""
									return iter(self._graph[vertex]  )






		@property
		def 						snake_case__				(							self  )       -> Union[str, Any]:






									"""simple docstring"""
									return self._size






		def 						snake_case__				(							self,   snake_case__,   snake_case__,   snake_case__  )       -> 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(snake_case__,   snake_case__  )  )






		def 						snake_case__				(							self,   snake_case__,   snake_case__  )       -> int | None:






									"""simple docstring"""
									lowercase_	:   List[str]  				=     deque([start_vertex]  )
									lowercase_	:   list[int | None]  				=     [None] * self.size
									lowercase_	:   Any  				=     0

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

																for edge in self[current_vertex]:
																							lowercase_	:   Optional[Any]  				=     current_distance + edge.weight
																							lowercase_	:   Optional[Any]  				=     distances[edge.destination_vertex]
																							if (
																							    isinstance(snake_case__,   snake_case__  )
																							    and new_distance >= dest_vertex_distance
																							):
																														continue
																							lowercase_	:   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, is_vision_available


UpperCAmelCase_							=				{
    """configuration_maskformer""": ["""MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MaskFormerConfig"""],
    """configuration_maskformer_swin""": ["""MaskFormerSwinConfig"""],
}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase_							=				["""MaskFormerFeatureExtractor"""]
							UpperCAmelCase_							=				["""MaskFormerImageProcessor"""]


try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase_							=				[
							    """MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """MaskFormerForInstanceSegmentation""",
							    """MaskFormerModel""",
							    """MaskFormerPreTrainedModel""",
							]
							UpperCAmelCase_							=				[
							    """MaskFormerSwinBackbone""",
							    """MaskFormerSwinModel""",
							    """MaskFormerSwinPreTrainedModel""",
							]

if TYPE_CHECKING:
							from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
							from .configuration_maskformer_swin import MaskFormerSwinConfig

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_maskformer import MaskFormerFeatureExtractor
														from .image_processing_maskformer import MaskFormerImageProcessor
							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_maskformer import (
														    MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MaskFormerForInstanceSegmentation,
														    MaskFormerModel,
														    MaskFormerPreTrainedModel,
														)
														from .modeling_maskformer_swin import (
														    MaskFormerSwinBackbone,
														    MaskFormerSwinModel,
														    MaskFormerSwinPreTrainedModel,
														)


else:
							import sys

							UpperCAmelCase_							=				_LazyModule(__name__, globals()["""__file__"""], _import_structure)