

import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin

class  a     (		UpperCAmelCase ):
						_lowercase			  =	["image_processor", "tokenizer"]
						_lowercase			  =	"AutoImageProcessor"
						_lowercase			  =	"AutoTokenizer"


						def __init__(				self  ,    A_=None  ,    A_=None  ,    **A_		):







										'''simple docstring'''


										_UpperCAmelCase				:						List[str]       						=			None
										if "feature_extractor" in kwargs:
														warnings.warn(
														    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
														    " instead."  ,    A_  ,    )
														_UpperCAmelCase				:						int       						=			kwargs.pop("feature_extractor"		)

										_UpperCAmelCase				:						str       						=			image_processor if image_processor is not None else feature_extractor
										if image_processor is None:
														raise ValueError("You need to specify an `image_processor`."		)
										if tokenizer is None:
														raise ValueError("You need to specify a `tokenizer`."		)

										super().__init__(A_  ,    A_		)
										_UpperCAmelCase				:						int       						=			self.image_processor
										_UpperCAmelCase				:						int       						=			False


						def __call__(				self  ,    *A_  ,    **A_		):







										'''simple docstring'''


										if self._in_target_context_manager:
														return self.current_processor(*A_  ,    **A_		)

										_UpperCAmelCase				:						Dict       						=			kwargs.pop("images"  ,    A_		)
										_UpperCAmelCase				:						Any       						=			kwargs.pop("text"  ,    A_		)
										if len(A_		) > 0:
														_UpperCAmelCase				:						List[Any]       						=			args[0]
														_UpperCAmelCase				:						Any       						=			args[1:]

										if images is None and text is None:
														raise ValueError("You need to specify either an `images` or `text` input to process."		)

										if images is not None:
														_UpperCAmelCase				:						Any       						=			self.image_processor(A_  ,    *A_  ,    **A_		)
										if text is not None:
														_UpperCAmelCase				:						int       						=			self.tokenizer(A_  ,    **A_		)

										if text is None:
														return inputs
										elif images is None:
														return encodings
										else:
														_UpperCAmelCase				:						Optional[Any]       						=			encodings["input_ids"]
														return inputs


						def 				_UpperCAmelCase (				self  ,    *A_  ,    **A_		):







										'''simple docstring'''


										return self.tokenizer.batch_decode(*A_  ,    **A_		)


						def 				_UpperCAmelCase (				self  ,    *A_  ,    **A_		):







										'''simple docstring'''


										return self.tokenizer.decode(*A_  ,    **A_		)


						@contextmanager
						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										warnings.warn(
										    "`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
										    "labels by using the argument `text` of the regular `__call__` method (either in the same call as "
										    "your images inputs, or in a separate call."		)
										_UpperCAmelCase				:						Union[str, Any]       						=			True
										_UpperCAmelCase				:						int       						=			self.tokenizer
										yield
										_UpperCAmelCase				:						Optional[int]       						=			self.image_processor
										_UpperCAmelCase				:						Any       						=			False


						@property
						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										warnings.warn(
										    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."  ,    A_  ,    )
										return self.image_processor_class





						@property
						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										warnings.warn(
										    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."  ,    A_  ,    )
										return self.image_processor





from __future__ import annotations

class  a     :


						def __init__(				self  ,    A_		):







										'''simple docstring'''


										_UpperCAmelCase				:						List[Any]       						=			vertices
										_UpperCAmelCase				:						List[str]       						=			[[0] * vertices for _ in range(A_		)]


						def 				_UpperCAmelCase (				self  ,    A_		):







										'''simple docstring'''


										print("Vertex \t Distance from Source"		)
										for vertex in range(self.vertices		):
														print(A_  ,    "\t\t"  ,    distances_from_source[vertex]		)


						def 				_UpperCAmelCase (				self  ,    A_  ,    A_		):







										'''simple docstring'''


										_UpperCAmelCase				:						Tuple       						=			1e7
										_UpperCAmelCase				:						Any       						=			0

										# Search not nearest vertex not in the shortest path tree
										for vertex in range(self.vertices		):
														if distances_from_source[vertex] < minimum and visited[vertex] is False:
																		_UpperCAmelCase				:						List[Any]       						=			distances_from_source[vertex]
																		_UpperCAmelCase				:						Tuple       						=			vertex
										return min_index





						def 				_UpperCAmelCase (				self  ,    A_		):







										'''simple docstring'''


										_UpperCAmelCase				:						Union[str, Any]       						=			[int(1e7		)] * self.vertices  # distances from the source
										_UpperCAmelCase				:						Optional[Any]       						=			0
										_UpperCAmelCase				:						List[str]       						=			[False] * self.vertices

										for _ in range(self.vertices		):
														_UpperCAmelCase				:						str       						=			self.minimum_distance(A_  ,    A_		)
														_UpperCAmelCase				:						str       						=			True

														# Update dist value of the adjacent vertices
														# of the picked vertex only if the current
														# distance is greater than new distance and
														# the vertex in not in the shortest path tree
														for v in range(self.vertices		):
																		if (
																		    self.graph[u][v] > 0
																		    and visited[v] is False
																		    and distances[v] > distances[u] + self.graph[u][v]
																		):
																						_UpperCAmelCase				:						str       						=			distances[u] + self.graph[u][v]

										self.print_solution(A_		)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE_						=					Graph(9)
					SCREAMING_SNAKE_CASE_						=					[
					    [0, 4, 0, 0, 0, 0, 0, 8, 0],
					    [4, 0, 8, 0, 0, 0, 0, 11, 0],
					    [0, 8, 0, 7, 0, 4, 0, 0, 2],
					    [0, 0, 7, 0, 9, 14, 0, 0, 0],
					    [0, 0, 0, 9, 0, 10, 0, 0, 0],
					    [0, 0, 4, 14, 10, 0, 2, 0, 0],
					    [0, 0, 0, 0, 0, 2, 0, 1, 6],
					    [8, 11, 0, 0, 0, 0, 1, 0, 7],
					    [0, 0, 2, 0, 0, 0, 6, 7, 0],
					]
					graph.dijkstra(0)


