




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




class 		__A   :

						def __init__(		self :Optional[Any]     ,			__snake_case :Optional[Any]			):
										'''simple docstring'''

										__magic_name__						:						int										=str(id_			)
										__magic_name__						:						str										=None
										__magic_name__						:						Any										=None
										__magic_name__						:						List[Any]										=[]
										__magic_name__						:						Optional[Any]										={}  # {vertex:distance}

						def __lt__(		self :List[Any]     ,			__snake_case :str			):
										'''simple docstring'''

										return self.key < other.key

						def __repr__(		self :List[str]			):
										'''simple docstring'''

										return self.id

						def      A__    (		self :List[Any]     ,			__snake_case :List[Any]			):
										'''simple docstring'''

										self.neighbors.append(__snake_case			)


						def      A__    (		self :Dict     ,			__snake_case :List[Any]     ,			__snake_case :Dict			):
										'''simple docstring'''

										__magic_name__						:						Dict										=weight




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase ):
				# add the neighbors:
				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]						,  lowerCamelCase )
				graph[b - 1].add_edge(graph[a - 1]						,  lowerCamelCase )




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase ):
				__magic_name__						:						List[str]										=[]
				for u in graph:
								__magic_name__						:						Any										=math.inf
								__magic_name__						:						Tuple										=None
				__magic_name__						:						Any										=0
				__magic_name__						:						Tuple										=graph[:]
				while q:
								__magic_name__						:						Tuple										=min(lowerCamelCase )
								q.remove(lowerCamelCase )
								for v in u.neighbors:
												if (v in q) and (u.edges[v.id] < v.key):
																__magic_name__						:						Optional[Any]										=u
																__magic_name__						:						int										=u.edges[v.id]
				for i in range(1						,  len(lowerCamelCase ) ):
								a.append((int(graph[i].id ) + 1, int(graph[i].pi.id ) + 1) )
				return a




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase ):
				for u in graph:
								__magic_name__						:						Any										=math.inf
								__magic_name__						:						str										=None
				__magic_name__						:						Union[str, Any]										=0

				__magic_name__						:						Union[str, Any]										=list(lowerCamelCase )
				hq.heapify(lowerCamelCase )

				while h:
								__magic_name__						:						str										=hq.heappop(lowerCamelCase )
								for v in u.neighbors:
												if (v in h) and (u.edges[v.id] < v.key):
																__magic_name__						:						List[str]										=u
																__magic_name__						:						str										=u.edges[v.id]
																hq.heapify(lowerCamelCase )

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




def 		lowerCAmelCase_ (						):
				pass


if __name__ == "__main__":
							import doctest

							doctest.testmod()










from typing import Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
						from PIL import Image

						from ..image_utils import load_image

if is_torch_available():
						from ..models.auto.modeling_auto import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING

UpperCamelCase__ :   List[str]						=							logging.get_logger(__name__)







@add_end_docstrings(lowerCamelCase_							)
class 				lowerCAmelCase_       (					lowerCamelCase_							):






				def __init__(			self      ,*snake_case__      ,**snake_case__ ):
							super().__init__(*snake_case__      ,**snake_case__ )
							self.check_model_type(snake_case__ )






				def 		snake_case	(			self      ,snake_case__=None      ,snake_case__=None      ,snake_case__=None      ,**snake_case__ ):
							SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      {}, {}
							if padding is not None:
										SCREAMING_SNAKE_CASE_   : Any					  =      padding
							if truncation is not None:
										SCREAMING_SNAKE_CASE_   : Tuple					  =      truncation
							if top_k is not None:
										SCREAMING_SNAKE_CASE_   : int					  =      top_k
							return preprocess_params, {}, postprocess_params






				def __call__(			self      ,snake_case__      ,snake_case__ = None      ,**snake_case__ ):
							if isinstance(snake_case__      ,(Image.Image, str) ) and isinstance(snake_case__      ,snake_case__ ):
										SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      {'image': image, 'question': question}
							else:
										SCREAMING_SNAKE_CASE_   : Optional[int]					  =      image
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      super().__call__(snake_case__      ,**snake_case__ )
							return results






				def 		snake_case	(			self      ,snake_case__      ,snake_case__=False      ,snake_case__=False ):
							SCREAMING_SNAKE_CASE_   : List[str]					  =      load_image(inputs['image'] )
							SCREAMING_SNAKE_CASE_   : Dict					  =      self.tokenizer(
							    inputs['question']      ,return_tensors=self.framework      ,padding=snake_case__      ,truncation=snake_case__ )
							SCREAMING_SNAKE_CASE_   : Any					  =      self.image_processor(images=snake_case__      ,return_tensors=self.framework )
							model_inputs.update(snake_case__ )
							return model_inputs






				def 		snake_case	(			self      ,snake_case__ ):
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      self.model(**snake_case__ )
							return model_outputs






				def 		snake_case	(			self      ,snake_case__      ,snake_case__=5 ):
							if top_k > self.model.config.num_labels:
										SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      self.model.config.num_labels

							if self.framework == "pt":
										SCREAMING_SNAKE_CASE_   : Any					  =      model_outputs.logits.sigmoid()[0]
										SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : Dict					  =      probs.topk(snake_case__ )
							else:
										raise ValueError(F'Unsupported framework: {self.framework}' )

							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      scores.tolist()
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      ids.tolist()
							return [{"score": score, "answer": self.model.config.idalabel[_id]} for score, _id in zip(snake_case__      ,snake_case__ )]



