






'''simple docstring'''



import heapq as hq
import math
from collections.abc import Iterator






class 	UpperCamelCase__:




				def __init__(		self      :			List[Any]				,   lowerCAmelCase      :			Any				)->					Optional[int]:

										"""simple docstring"""




										UpperCAmelCase					    =     str(id_				)
										UpperCAmelCase					    =     None
										UpperCAmelCase					    =     None
										UpperCAmelCase					    =     []
										UpperCAmelCase					    =     {}  # {vertex:distance}




				def __lt__(		self      :			Any				,   lowerCAmelCase      :			List[Any]				)->					Tuple:

										"""simple docstring"""




										return self.key < other.key




				def __repr__(		self      :			Optional[Any]				)->					str:

										"""simple docstring"""




										return self.id




				def 					a__(		self      :			Any				,   lowerCAmelCase      :			Optional[int]				)->					Optional[Any]:

										"""simple docstring"""




										self.neighbors.append(lowerCAmelCase				)





				def 					a__(		self      :			Any				,   lowerCAmelCase      :			int				,   lowerCAmelCase      :			List[str]				)->					Tuple:

										"""simple docstring"""




										UpperCAmelCase					    =     weight







def 				lowerCamelCase__				(	A       :      List[str]				,       A       :      Optional[Any]				,       A       :      List[str]				,       A       :      int					):




						'''simple docstring'''






						graph[a - 1].add_neighbor(graph[b - 1]					)
						graph[b - 1].add_neighbor(graph[a - 1]					)
						# add the edges:
						graph[a - 1].add_edge(graph[b - 1]				,       __snake_case					)
						graph[b - 1].add_edge(graph[a - 1]				,       __snake_case					)


def 				lowerCamelCase__				(	A       :      list				,       A       :      Vertex					):




						'''simple docstring'''






						UpperCAmelCase					    =     []
						for u in graph:
												UpperCAmelCase					    =     math.inf
												UpperCAmelCase					    =     None
						UpperCAmelCase					    =     0
						UpperCAmelCase					    =     graph[:]
						while q:
												UpperCAmelCase					    =     min(__snake_case					)
												q.remove(__snake_case					)
												for v in u.neighbors:
																		if (v in q) and (u.edges[v.id] < v.key):
																								UpperCAmelCase					    =     u
																								UpperCAmelCase					    =     u.edges[v.id]
						for i in range(1				,       len(__snake_case					)					):
												a.append((int(graph[i].id					) + 1, int(graph[i].pi.id					) + 1)					)
						return a


def 				lowerCamelCase__				(	A       :      list				,       A       :      Vertex					):




						'''simple docstring'''






						for u in graph:
												UpperCAmelCase					    =     math.inf
												UpperCAmelCase					    =     None
						UpperCAmelCase					    =     0

						UpperCAmelCase					    =     list(__snake_case					)
						hq.heapify(__snake_case					)

						while h:
												UpperCAmelCase					    =     hq.heappop(__snake_case					)
												for v in u.neighbors:
																		if (v in h) and (u.edges[v.id] < v.key):
																								UpperCAmelCase					    =     u
																								UpperCAmelCase					    =     u.edges[v.id]
																								hq.heapify(__snake_case					)

						for i in range(1				,       len(__snake_case					)					):
												yield (int(graph[i].id					) + 1, int(graph[i].pi.id					) + 1)


def 				lowerCamelCase__				(	):




						'''simple docstring'''








if __name__ == "__main__":
	import doctest

	doctest.testmod()









'''simple docstring'''



import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class 	UpperCamelCase__(       lowerCAmelCase	):
				__magic_name__    :				List[Any]  		=	["image_processor", "tokenizer"]
				__magic_name__    :				Tuple  		=	"ViTImageProcessor"
				__magic_name__    :				int  		=	("CLIPTokenizer", "CLIPTokenizerFast")




				def __init__(		self      :			List[str]				,   lowerCAmelCase      :			Tuple=None				,   lowerCAmelCase      :			List[str]=None				,   **lowerCAmelCase      :			Optional[int]				)->					Tuple:

										"""simple docstring"""




										UpperCAmelCase					    =     None
										if "feature_extractor" in kwargs:
																warnings.warn(
																    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
																    ''' instead.'''				,   lowerCAmelCase				,   )
																UpperCAmelCase					    =     kwargs.pop('''feature_extractor'''				)

										UpperCAmelCase					    =     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__(lowerCAmelCase				,   lowerCAmelCase				)




				def __call__(		self      :			int				,   lowerCAmelCase      :			Dict=None				,   lowerCAmelCase      :			Optional[int]=None				,   lowerCAmelCase      :			Union[str, Any]=None				,   lowerCAmelCase      :			int=None				,   **lowerCAmelCase      :			Tuple				)->					Optional[int]:

										"""simple docstring"""




										if text is None and visual_prompt is None and images is None:
																raise ValueError('''You have to specify either text, visual prompt or images.'''				)

										if text is not None and visual_prompt is not None:
																raise ValueError('''You have to specify exactly one type of prompt. Either text or visual prompt.'''				)

										if text is not None:
																UpperCAmelCase					    =     self.tokenizer(lowerCAmelCase				,   return_tensors=lowerCAmelCase				,   **lowerCAmelCase				)

										if visual_prompt is not None:
																UpperCAmelCase					    =     self.image_processor(lowerCAmelCase				,   return_tensors=lowerCAmelCase				,   **lowerCAmelCase				)

										if images is not None:
																UpperCAmelCase					    =     self.image_processor(lowerCAmelCase				,   return_tensors=lowerCAmelCase				,   **lowerCAmelCase				)

										if visual_prompt is not None and images is not None:
																UpperCAmelCase					    =     {
																    '''pixel_values''': image_features.pixel_values,
																    '''conditional_pixel_values''': prompt_features.pixel_values,
																}
																return encoding
										elif text is not None and images is not None:
																UpperCAmelCase					    =     image_features.pixel_values
																return encoding
										elif text is not None:
																return encoding
										elif visual_prompt is not None:
																UpperCAmelCase					    =     {
																    '''conditional_pixel_values''': prompt_features.pixel_values,
																}
																return encoding
										else:
																return BatchEncoding(data=dict(**lowerCAmelCase				)				,   tensor_type=lowerCAmelCase				)




				def 					a__(		self      :			Optional[int]				,   *lowerCAmelCase      :			List[str]				,   **lowerCAmelCase      :			Dict				)->					Tuple:

										"""simple docstring"""




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




				def 					a__(		self      :			List[Any]				,   *lowerCAmelCase      :			str				,   **lowerCAmelCase      :			List[Any]				)->					Optional[Any]:

										"""simple docstring"""




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




				@property
				def 					a__(		self      :			Any				)->					Optional[int]:

										"""simple docstring"""




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





				@property
				def 					a__(		self      :			str				)->					List[Any]:

										"""simple docstring"""




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





