






'''simple docstring'''





from typing import TYPE_CHECKING

import torch

from ..models.auto import AutoModelForVisualQuestionAnswering, AutoProcessor
from ..utils import requires_backends
from .base import PipelineTool


if TYPE_CHECKING:
  from PIL import Image
class 							A_    (						_a			):






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE							:			Tuple =					"""dandelin/vilt-b32-finetuned-vqa"""
 _SCREAMING_SNAKE_CASE							:			str =					(
     """This is a tool that answers a question about an image. It takes an input named `image` which should be the """
     """image containing the information, as well as a `question` which should be the question in English. It """
     """returns a text that is the answer to the question."""
 )
 _SCREAMING_SNAKE_CASE							:			Optional[Any] =					"""image_qa"""
 _SCREAMING_SNAKE_CASE							:			Dict =					AutoProcessor
 _SCREAMING_SNAKE_CASE							:			int =					AutoModelForVisualQuestionAnswering

 _SCREAMING_SNAKE_CASE							:			str =					["""image""", """text"""]
 _SCREAMING_SNAKE_CASE							:			List[Any] =					["""text"""]


 def __init__(	self							,  *_A							,  **_A) ->		Optional[Any]:

     """simple docstring"""



     requires_backends(self							,  ['''vision'''])
     super().__init__(*snake_case_							,  **snake_case_)


 def  snake_case__				(	self							,  _A							,  _A) ->		Any:

     """simple docstring"""



     return self.pre_processor(snake_case_							,  snake_case_							,  return_tensors='''pt''')


 def  snake_case__				(	self							,  _A) ->		Dict:

     """simple docstring"""



     with torch.no_grad():
         return self.model(**snake_case_).logits


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

     """simple docstring"""



     _UpperCAmelCase				:      int			  =				outputs.argmax(-1).item()
     return self.model.config.idalabel[idx]












import heapq

def      _lowerCamelCase	(						__A    :							dict )      ->					set[int]:
    _UpperCAmelCase				:      list[list]			  =				[]

    # for each node and his adjacency list add them and the rank of the node to queue
    # using heapq module the queue will be filled like a Priority Queue
    # heapq works with a min priority queue, so I used -1*len(v) to build it
    for key, value in graph.items():
        # O(log(n))
        heapq.heappush(__A		,		[-1 * len(__A ), (key, value)] )

    # chosen_vertices = set of chosen vertices
    _UpperCAmelCase				:      Union[str, Any]			  =				set()

    # while queue isn't empty and there are still edges
    #   (queue[0][0] is the rank of the node with max rank)
    while queue and queue[0][0] != 0:
        # extract vertex with max rank from queue and add it to chosen_vertices
        _UpperCAmelCase				:      Tuple			  =				heapq.heappop(__A )[1][0]
        chosen_vertices.add(__A )

        # Remove all arcs adjacent to argmax
        for elem in queue:
            # if v haven't adjacent node, skip
            if elem[0] == 0:
                continue
            # if argmax is reachable from elem
            # remove argmax from elem's adjacent list and update his rank
            if argmax in elem[1][1]:
                _UpperCAmelCase				:      Dict			  =				elem[1][1].index(__A )
                del elem[1][1][index]
                elem[0] += 1
        # re-order the queue
        heapq.heapify(__A )
    return chosen_vertices


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  SCREAMING_SNAKE_CASE 							=						{0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
  print(F'Minimum vertex cover:\n{greedy_min_vertex_cover(graph)}')




