




'''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

lowerCAmelCase_     						=				logging.get_logger(__name__)

@add_end_docstrings(UpperCAmelCase      )
class 	_snake_case(			UpperCAmelCase      ):






 def __init__(self       :  Optional[int]    ,		*a       :  Optional[int]    ,		**a       :  Dict    )       ->   str:






    """simple docstring"""


    super().__init__(*lowerCamelCase_    ,		**lowerCamelCase_    )
    self.check_model_type(lowerCamelCase_    )






 def 						_UpperCamelCase (self       :  str    ,		a       :  Tuple=None    ,		a       :  List[str]=None    ,		a       :  Tuple=None    ,		**a       :  Optional[int]    )       ->   List[Any]:






    """simple docstring"""


    A__  ,			A__       =     {}, {}
    if padding is not None:
       A__       =     padding
    if truncation is not None:
       A__       =     truncation
    if top_k is not None:
       A__       =     top_k
    return preprocess_params, {}, postprocess_params






 def __call__(self       :  Tuple    ,		a       :  Dict    ,		a       :  List[Any] = None    ,		**a       :  Union[str, Any]    )       ->   Dict:






    """simple docstring"""


    if isinstance(lowerCamelCase_    ,		(Image.Image, str)    ) and isinstance(lowerCamelCase_    ,		lowerCamelCase_    ):
       A__       =     {'image': image, 'question': question}
    else:
       A__       =     image
    A__       =     super().__call__(lowerCamelCase_    ,		**lowerCamelCase_    )
    return results






 def 						_UpperCamelCase (self       :  str    ,		a       :  Dict    ,		a       :  Optional[int]=False    ,		a       :  List[Any]=False    )       ->   List[Any]:






    """simple docstring"""


    A__       =     load_image(inputs['image']    )
    A__       =     self.tokenizer(
        inputs['question']    ,		return_tensors=self.framework    ,		padding=lowerCamelCase_    ,		truncation=lowerCamelCase_    )
    A__       =     self.image_processor(images=lowerCamelCase_    ,		return_tensors=self.framework    )
    model_inputs.update(lowerCamelCase_    )
    return model_inputs






 def 						_UpperCamelCase (self       :  Dict    ,		a       :  str    )       ->   Union[str, Any]:






    """simple docstring"""


    A__       =     self.model(**lowerCamelCase_    )
    return model_outputs






 def 						_UpperCamelCase (self       :  List[str]    ,		a       :  Union[str, Any]    ,		a       :  Any=5    )       ->   int:






    """simple docstring"""


    if top_k > self.model.config.num_labels:
       A__       =     self.model.config.num_labels

    if self.framework == "pt":
       A__       =     model_outputs.logits.sigmoid()[0]
       A__  ,			A__       =     probs.topk(lowerCamelCase_    )
    else:
       raise ValueError(f"""Unsupported framework: {self.framework}"""    )

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





"""simple docstring"""


UpperCAmelCase													=[
    [0, 16, 13, 0, 0, 0],
    [0, 0, 10, 12, 0, 0],
    [0, 4, 0, 0, 14, 0],
    [0, 0, 9, 0, 0, 20],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]



def   _A					(	_a	:						Dict     ,   _a	:						int     ,   _a	:						Dict     ,   _a	:						List[Any]				):



      """simple docstring"""






      A				  =		[False] * len(_a				)
      A				  =		[s]
      A				  =		True

      while queue:
            A				  =		queue.pop(0				)
            for ind in range(len(graph[u]				)				):
                  if visited[ind] is False and graph[u][ind] > 0:
                        queue.append(_a				)
                        A				  =		True
                        A				  =		u

      return visited[t]



def   _A					(	_a	:						str     ,   _a	:						Optional[Any]     ,   _a	:						Optional[int]				):



      """simple docstring"""






      A				  =		[-1] * (len(_a				))
      A				  =		0
      A				  =		[]
      A				  =		[i[:] for i in graph]  # Record original cut, copy.
      while bfs(_a     ,   _a     ,   _a     ,   _a				):
            A				  =		float("""Inf"""				)
            A				  =		sink

            while s != source:
                  # Find the minimum value in select path
                  A				  =		min(_a     ,   graph[parent[s]][s]				)
                  A				  =		parent[s]

            max_flow += path_flow
            A				  =		sink

            while v != source:
                  A				  =		parent[v]
                  graph[u][v] -= path_flow
                  graph[v][u] += path_flow
                  A				  =		parent[v]

      for i in range(len(_a				)				):
            for j in range(len(graph[0]				)				):
                  if graph[i][j] == 0 and temp[i][j] > 0:
                        res.append((i, j)				)

      return res


if __name__ == "__main__":
  print(mincut(test_graph, source=0, sink=5))




