

"""simple docstring"""


import unittest
from typing import Dict, List, Optional, Union

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import BridgeTowerImageProcessor





class lowerCamelCase__   (				unittest.TestCase    ):



    '''simple docstring'''




    def __init__( self ,lowerCamelCase_ ,lowerCamelCase_ = True ,lowerCamelCase_ = None ,lowerCamelCase_ = 3_2 ,lowerCamelCase_ = True ,lowerCamelCase_ = 1 / 2_5_5 ,lowerCamelCase_ = True ,lowerCamelCase_ = True ,lowerCamelCase_ = [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73] ,lowerCamelCase_ = [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11] ,lowerCamelCase_ = True ,lowerCamelCase_=7 ,lowerCamelCase_=3_0 ,lowerCamelCase_=4_0_0 ,lowerCamelCase_=3 ,)    ->      Optional[int]:
          A				  =		parent
          A				  =		do_resize
          A				  =		size if size is not None else {"""shortest_edge""": 2_8_8}
          A				  =		size_divisor
          A				  =		do_rescale
          A				  =		rescale_factor
          A				  =		do_normalize
          A				  =		do_center_crop
          A				  =		image_mean
          A				  =		image_std
          A				  =		do_pad
          A				  =		batch_size
          A				  =		num_channels
          A				  =		min_resolution
          A				  =		max_resolution




    def UpperCamelCase__		( self    )    ->      Union[str, Any]:
          return {
              "image_mean": self.image_mean,
              "image_std": self.image_std,
              "do_normalize": self.do_normalize,
              "do_resize": self.do_resize,
              "size": self.size,
              "size_divisor": self.size_divisor,
          }




    def UpperCamelCase__		( self ,lowerCamelCase_ ,lowerCamelCase_=False    )    ->      Optional[int]:
          if not batched:
                A				  =		self.size["""shortest_edge"""]
                A				  =		image_inputs[0]
                if isinstance(lowerCamelCase_ ,Image.Image    ):
                      A  ,			A				  =		image.size
                else:
                      A  ,			A				  =		image.shape[1], image.shape[2]
                A				  =		size / min(lowerCamelCase_ ,lowerCamelCase_    )
                if h < w:
                      A  ,			A				  =		size, scale * w
                else:
                      A  ,			A				  =		scale * h, size

                A				  =		int((1_3_3_3 / 8_0_0) * size    )
                if max(lowerCamelCase_ ,lowerCamelCase_    ) > max_size:
                      A				  =		max_size / max(lowerCamelCase_ ,lowerCamelCase_    )
                      A				  =		newh * scale
                      A				  =		neww * scale

                A  ,			A				  =		int(newh + 0.5    ), int(neww + 0.5    )
                A  ,			A				  =		(
                    newh // self.size_divisor * self.size_divisor,
                    neww // self.size_divisor * self.size_divisor,
                )

          else:
                A				  =		[]
                for image in image_inputs:
                      A  ,			A				  =		self.get_expected_values([image]    )
                      expected_values.append((expected_height, expected_width)    )
                A				  =		max(lowerCamelCase_ ,key=lambda lowerCamelCase_    : item[0]    )[0]
                A				  =		max(lowerCamelCase_ ,key=lambda lowerCamelCase_    : item[1]    )[1]

          return expected_height, expected_width





@require_torch
@require_vision
class lowerCamelCase__   (				SCREAMING_SNAKE_CASE     ,   unittest.TestCase    ):



    '''simple docstring'''




    _lowerCamelCase					=   BridgeTowerImageProcessor if is_vision_available() else None




    def UpperCamelCase__		( self    )    ->      List[Any]:
          A				  =		BridgeTowerImageProcessingTester(self    )




    @property
    def UpperCamelCase__		( self    )    ->      Any:
          return self.image_processor_tester.prepare_image_processor_dict()




    def UpperCamelCase__		( self    )    ->      Any:
          A				  =		self.image_processing_class(**self.image_processor_dict    )
          self.assertTrue(hasattr(lowerCamelCase_ ,"""image_mean"""    )    )
          self.assertTrue(hasattr(lowerCamelCase_ ,"""image_std"""    )    )
          self.assertTrue(hasattr(lowerCamelCase_ ,"""do_normalize"""    )    )
          self.assertTrue(hasattr(lowerCamelCase_ ,"""do_resize"""    )    )
          self.assertTrue(hasattr(lowerCamelCase_ ,"""size"""    )    )
          self.assertTrue(hasattr(lowerCamelCase_ ,"""size_divisor"""    )    )




    def UpperCamelCase__		( self    )    ->      Tuple:
          pass




    def UpperCamelCase__		( self    )    ->      Dict:
          # Initialize image processor
          A				  =		self.image_processing_class(**self.image_processor_dict    )
          # create random PIL images
          A				  =		prepare_image_inputs(self.image_processor_tester ,equal_resolution=lowerCamelCase_    )
          for image in image_inputs:
                self.assertIsInstance(lowerCamelCase_ ,Image.Image    )

          # Test not batched input
          A				  =		image_processing(image_inputs[0] ,return_tensors="""pt"""    ).pixel_values

          A  ,			A				  =		self.image_processor_tester.get_expected_values(lowerCamelCase_    )
          self.assertEqual(
              encoded_images.shape ,(1, self.image_processor_tester.num_channels, expected_height, expected_width) ,)

          # Test batched
          A				  =		image_processing(lowerCamelCase_ ,return_tensors="""pt"""    ).pixel_values

          A  ,			A				  =		self.image_processor_tester.get_expected_values(lowerCamelCase_ ,batched=lowerCamelCase_    )
          self.assertEqual(
              encoded_images.shape ,(
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  expected_height,
                  expected_width,
              ) ,)




    def UpperCamelCase__		( self    )    ->      Dict:
          # Initialize image processor
          A				  =		self.image_processing_class(**self.image_processor_dict    )
          # create random numpy tensors
          A				  =		prepare_image_inputs(self.image_processor_tester ,equal_resolution=lowerCamelCase_ ,numpify=lowerCamelCase_    )
          for image in image_inputs:
                self.assertIsInstance(lowerCamelCase_ ,np.ndarray    )

          # Test not batched input
          A				  =		image_processing(image_inputs[0] ,return_tensors="""pt"""    ).pixel_values

          A  ,			A				  =		self.image_processor_tester.get_expected_values(lowerCamelCase_    )
          self.assertEqual(
              encoded_images.shape ,(1, self.image_processor_tester.num_channels, expected_height, expected_width) ,)

          # Test batched
          A				  =		image_processing(lowerCamelCase_ ,return_tensors="""pt"""    ).pixel_values

          A  ,			A				  =		self.image_processor_tester.get_expected_values(lowerCamelCase_ ,batched=lowerCamelCase_    )
          self.assertEqual(
              encoded_images.shape ,(
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  expected_height,
                  expected_width,
              ) ,)




    def UpperCamelCase__		( self    )    ->      Tuple:
          # Initialize image processor
          A				  =		self.image_processing_class(**self.image_processor_dict    )
          # create random PyTorch tensors
          A				  =		prepare_image_inputs(self.image_processor_tester ,equal_resolution=lowerCamelCase_ ,torchify=lowerCamelCase_    )
          for image in image_inputs:
                self.assertIsInstance(lowerCamelCase_ ,torch.Tensor    )

          # Test not batched input
          A				  =		image_processing(image_inputs[0] ,return_tensors="""pt"""    ).pixel_values

          A  ,			A				  =		self.image_processor_tester.get_expected_values(lowerCamelCase_    )
          self.assertEqual(
              encoded_images.shape ,(1, self.image_processor_tester.num_channels, expected_height, expected_width) ,)

          # Test batched
          A				  =		image_processing(lowerCamelCase_ ,return_tensors="""pt"""    ).pixel_values

          A  ,			A				  =		self.image_processor_tester.get_expected_values(lowerCamelCase_ ,batched=lowerCamelCase_    )
          self.assertEqual(
              encoded_images.shape ,(
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  expected_height,
                  expected_width,
              ) ,)







"""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))




