

"""simple docstring"""


from typing import List, Union

import numpy as np

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
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():
  import torch

  from ..models.auto.modeling_auto import MODEL_FOR_DEPTH_ESTIMATION_MAPPING

UpperCAmelCase													=logging.get_logger(__name__)





@add_end_docstrings(SCREAMING_SNAKE_CASE    )
class lowerCamelCase__   (				SCREAMING_SNAKE_CASE    ):



    '''simple docstring'''




    def __init__( self ,*lowerCamelCase_ ,**lowerCamelCase_    )    ->      int:
          super().__init__(*lowerCamelCase_ ,**lowerCamelCase_    )
          requires_backends(self ,"""vision"""    )
          self.check_model_type(lowerCamelCase_    )




    def __call__( self ,lowerCamelCase_ ,**lowerCamelCase_    )    ->      Optional[Any]:
          return super().__call__(lowerCamelCase_ ,**lowerCamelCase_    )




    def UpperCamelCase__		( self ,**lowerCamelCase_    )    ->      int:
          return {}, {}, {}




    def UpperCamelCase__		( self ,lowerCamelCase_    )    ->      List[str]:
          A				  =		load_image(lowerCamelCase_    )
          A				  =		image.size
          A				  =		self.image_processor(images=lowerCamelCase_ ,return_tensors=self.framework    )
          return model_inputs




    def UpperCamelCase__		( self ,lowerCamelCase_    )    ->      str:
          A				  =		self.model(**lowerCamelCase_    )
          return model_outputs




    def UpperCamelCase__		( self ,lowerCamelCase_    )    ->      Dict:
          A				  =		model_outputs.predicted_depth
          A				  =		torch.nn.functional.interpolate(
              predicted_depth.unsqueeze(1    ) ,size=self.image_size[::-1] ,mode="""bicubic""" ,align_corners=lowerCamelCase_    )
          A				  =		prediction.squeeze().cpu().numpy()
          A				  =		(output * 2_5_5 / np.max(lowerCamelCase_    )).astype("""uint8"""    )
          A				  =		Image.fromarray(lowerCamelCase_    )
          A				  =		{}
          A				  =		predicted_depth
          A				  =		depth
          return output_dict







"""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))




