






"""simple docstring"""





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

A__					: Optional[int]=	logging.get_logger(__name__)
@add_end_docstrings(_a  )
class    __lowerCamelCase    (  _a  ):

   def __init__(					self ,				*snake_case_ ,				**snake_case_      )		->       str:
       super().__init__(*snake_case_ ,				**snake_case_      )
       self.check_model_type(snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_=None ,				snake_case_=None ,				snake_case_=None ,				**snake_case_      )		->       Dict:
       UpperCamelCase__       ,						UpperCamelCase__         =    {}, {}
       if padding is not None:
           UpperCamelCase__         =    padding
       if truncation is not None:
           UpperCamelCase__         =    truncation
       if top_k is not None:
           UpperCamelCase__         =    top_k
       return preprocess_params, {}, postprocess_params

   def __call__(					self ,				snake_case_ ,				snake_case_ = None ,				**snake_case_      )		->       Any:
       if isinstance(snake_case_ ,				(Image.Image, str)      ) and isinstance(snake_case_ ,				snake_case_      ):
           UpperCamelCase__         =    {'image': image, 'question': question}
       else:
           UpperCamelCase__         =    image
       UpperCamelCase__         =    super().__call__(snake_case_ ,				**snake_case_      )
       return results

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_=False ,				snake_case_=False      )		->       Tuple:
       UpperCamelCase__         =    load_image(inputs['image']      )
       UpperCamelCase__         =    self.tokenizer(
           inputs['question'] ,				return_tensors=self.framework ,				padding=snake_case_ ,				truncation=snake_case_      )
       UpperCamelCase__         =    self.image_processor(images=snake_case_ ,				return_tensors=self.framework      )
       model_inputs.update(snake_case_      )
       return model_inputs

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       List[str]:
       UpperCamelCase__         =    self.model(**snake_case_      )
       return model_outputs







   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_=5      )		->       int:
       if top_k > self.model.config.num_labels:
           UpperCamelCase__         =    self.model.config.num_labels

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

       UpperCamelCase__         =    scores.tolist()
       UpperCamelCase__         =    ids.tolist()
       return [{"score": score, "answer": self.model.config.idalabel[_id]} for score, _id in zip(snake_case_ ,				snake_case_      )]












"""simple docstring"""





from __future__ import annotations

from typing import Generic, TypeVar

A__					: Any=	TypeVar("""T""")
class    __lowerCamelCase    (  Generic[T]  ):

   def __init__(					self ,				snake_case_      )		->       None:
       UpperCamelCase__         =    data
       UpperCamelCase__         =    self
       UpperCamelCase__         =    0







class    __lowerCamelCase    (  Generic[T]  ):

   def __init__(					self      )		->       None:
       # map from node name to the node object
       UpperCamelCase__         =    {}

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       None:
       # create a new set with x as its member
       UpperCamelCase__         =    DisjointSetTreeNode(snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       DisjointSetTreeNode[T]:
       # find the set x belongs to (with path-compression)
       UpperCamelCase__         =    self.map[data]
       if elem_ref != elem_ref.parent:
           UpperCamelCase__         =    self.find_set(elem_ref.parent.data      )
       return elem_ref.parent

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_      )		->       None:
       # helper function for union operation
       if nodea.rank > nodea.rank:
           UpperCamelCase__         =    nodea
       else:
           UpperCamelCase__         =    nodea
           if nodea.rank == nodea.rank:
               nodea.rank += 1







   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_      )		->       None:
       # merge 2 disjoint sets
       self.link(self.find_set(snake_case_      ) ,				self.find_set(snake_case_      )      )







class    __lowerCamelCase    (  Generic[T]  ):

   def __init__(					self      )		->       None:
       # connections: map from the node to the neighbouring nodes (with weights)
       UpperCamelCase__         =    {}

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       None:
       # add a node ONLY if its not present in the graph
       if node not in self.connections:
           UpperCamelCase__         =    {}

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ ,				snake_case_      )		->       None:
       # add an edge with the given weight
       self.add_node(snake_case_      )
       self.add_node(snake_case_      )
       UpperCamelCase__         =    weight
       UpperCamelCase__         =    weight







   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       GraphUndirectedWeighted[T]:
       UpperCamelCase__         =    []
       UpperCamelCase__         =    set()
       for start in self.connections:
           for end in self.connections[start]:
               if (start, end) not in seen:
                   seen.add((end, start)      )
                   edges.append((start, end, self.connections[start][end])      )
       edges.sort(key=lambda snake_case_      : x[2]      )

       # creating the disjoint set
       UpperCamelCase__         =    DisjointSetTree[T]()
       for node in self.connections:
           disjoint_set.make_set(snake_case_      )

       # MST generation
       UpperCamelCase__         =    0
       UpperCamelCase__         =    0
       UpperCamelCase__         =    GraphUndirectedWeighted[T]()
       while num_edges < len(self.connections      ) - 1:
           UpperCamelCase__       ,						UpperCamelCase__       ,						UpperCamelCase__         =    edges[index]
           index += 1
           UpperCamelCase__         =    disjoint_set.find_set(snake_case_      )
           UpperCamelCase__         =    disjoint_set.find_set(snake_case_      )
           if parent_u != parent_v:
               num_edges += 1
               graph.add_edge(snake_case_ ,				snake_case_ ,				snake_case_      )
               disjoint_set.union(snake_case_ ,				snake_case_      )
       return graph




