
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

lowerCamelCase__    :		Optional[Any]          =   logging.get_logger(__name__)



@add_end_docstrings(UpperCAmelCase_							)
class       _snake_case   (	UpperCAmelCase_							):


 def __init__(					self					,  *SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   super().__init__(*SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_)
   requires_backends(self					,  """vision""")
   self.check_model_type(SCREAMING_SNAKE_CASE_)


 def __call__(					self					,  SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   return super().__call__(SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_)


 def      lowercase__     (					self					,  **SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   return {}, {}, {}


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__  :   Optional[int]    		=      load_image(SCREAMING_SNAKE_CASE_)
   lowercase__  :   Tuple    		=      image.size
   lowercase__  :   Union[str, Any]    		=      self.image_processor(images=SCREAMING_SNAKE_CASE_					,  return_tensors=self.framework)
   return model_inputs


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__  :   Union[str, Any]    		=      self.model(**SCREAMING_SNAKE_CASE_)
   return model_outputs



 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__  :   Optional[Any]    		=      model_outputs.predicted_depth
   lowercase__  :   Dict    		=      torch.nn.functional.interpolate(
       predicted_depth.unsqueeze(1)					,  size=self.image_size[::-1]					,  mode="""bicubic"""					,  align_corners=SCREAMING_SNAKE_CASE_)
   lowercase__  :   Optional[Any]    		=      prediction.squeeze().cpu().numpy()
   lowercase__  :   Optional[int]    		=      (output * 2_55 / np.max(SCREAMING_SNAKE_CASE_)).astype("""uint8""")
   lowercase__  :   Optional[Any]    		=      Image.fromarray(SCREAMING_SNAKE_CASE_)
   lowercase__  :   Any    		=      {}
   lowercase__  :   Optional[int]    		=      predicted_depth
   lowercase__  :   Optional[Any]    		=      depth
   return output_dict








lowerCamelCase__    :		str          =   [
    [0, 1_6, 1_3, 0, 0, 0],
    [0, 0, 1_0, 1_2, 0, 0],
    [0, 4, 0, 0, 1_4, 0],
    [0, 0, 9, 0, 0, 2_0],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]

def 		UpperCamelCase	(      lowercase_ , lowercase_ , lowercase_ , lowercase_   )		->	str:


  '''simple docstring'''






  lowercase__  :   Any    		=      [False] * len(lowercase_   )
  lowercase__  :   List[Any]    		=      [s]
  lowercase__  :   int    		=      True

  while queue:
    lowercase__  :   Dict    		=      queue.pop(0   )
    for ind in range(len(graph[u]   )   ):
      if visited[ind] is False and graph[u][ind] > 0:
        queue.append(lowercase_   )
        lowercase__  :   Optional[Any]    		=      True
        lowercase__  :   int    		=      u

  return visited[t]

def 		UpperCamelCase	(      lowercase_ , lowercase_ , lowercase_   )		->	int:


  '''simple docstring'''






  lowercase__  :   Optional[int]    		=      [-1] * (len(lowercase_   ))
  lowercase__  :   Optional[Any]    		=      0
  lowercase__  :   List[Any]    		=      []
  lowercase__  :   Optional[Any]    		=      [i[:] for i in graph]  # Record original cut, copy.
  while bfs(lowercase_ , lowercase_ , lowercase_ , lowercase_   ):
    lowercase__  :   Optional[int]    		=      float("""Inf"""   )
    lowercase__  :   Tuple    		=      sink

    while s != source:
      # Find the minimum value in select path
      lowercase__  :   List[str]    		=      min(lowercase_ , graph[parent[s]][s]   )
      lowercase__  :   List[str]    		=      parent[s]

    max_flow += path_flow
    lowercase__  :   str    		=      sink

    while v != source:
      lowercase__  :   int    		=      parent[v]
      graph[u][v] -= path_flow
      graph[v][u] += path_flow
      lowercase__  :   Union[str, Any]    		=      parent[v]

  for i in range(len(lowercase_   )   ):
    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))






