




_lowerCamelCase					:     Optional[int]					  =  float("inf")
class 				__snake_case     :



				def __init__(					self					:						List[Any]						,		_UpperCAmelCase					:						Union[str, Any]					)     ->			List[Any]:





								'''simple docstring'''




								_lowerCAmelCase			:			Any        =					[0] * n
								_lowerCAmelCase			:			str        =					[0] * n
								_lowerCAmelCase			:			Optional[int]        =					[0] * n
								_lowerCAmelCase			:			List[Any]        =					[[] for _ in range(_UpperCAmelCase					)]



				def 					SCREAMING_SNAKE_CASE				(					self					:						int						,		_UpperCAmelCase					:						List[Any]						,		_UpperCAmelCase					:						Optional[Any]						,		_UpperCAmelCase					:						Any						,		_UpperCAmelCase					:						Union[str, Any]=0					)     ->			Any:





								'''simple docstring'''




								self.adj[a].append([b, len(self.adj[b]					), c, 0]					)
								self.adj[b].append([a, len(self.adj[a]					) - 1, rcap, 0]					)



				def 					SCREAMING_SNAKE_CASE				(					self					:						Any						,		_UpperCAmelCase					:						List[Any]						,		_UpperCAmelCase					:						Union[str, Any]						,		_UpperCAmelCase					:						Optional[int]					)     ->			Any:





								'''simple docstring'''




								if vertex == sink or not flow:
												return flow

								for i in range(self.ptr[vertex]						,		len(self.adj[vertex]					)					):
												_lowerCAmelCase			:			Optional[Any]        =					self.adj[vertex][i]
												if self.lvl[e[0]] == self.lvl[vertex] + 1:
																_lowerCAmelCase			:			str        =					self.depth_first_search(e[0]						,		_UpperCAmelCase						,		min(_UpperCAmelCase						,		e[2] - e[3]					)					)
																if p:
																				self.adj[vertex][i][3] += p
																				self.adj[e[0]][e[1]][3] -= p
																				return p
												_lowerCAmelCase			:			Any        =					self.ptr[vertex] + 1
								return 0



				def 					SCREAMING_SNAKE_CASE				(					self					:						List[Any]						,		_UpperCAmelCase					:						Union[str, Any]						,		_UpperCAmelCase					:						List[str]					)     ->			Tuple:





								'''simple docstring'''




								_lowerCAmelCase    ,			_lowerCAmelCase			:			Any        =					0, source
								for l in range(31					):  # noqa: E741  l = 30 maybe faster for random data
												while True:
																_lowerCAmelCase    ,			_lowerCAmelCase			:			Any        =					[0] * len(self.q					), [0] * len(self.q					)
																_lowerCAmelCase    ,			_lowerCAmelCase    ,			_lowerCAmelCase			:			int        =					0, 1, 1
																while qi < qe and not self.lvl[sink]:
																				_lowerCAmelCase			:			Any        =					self.q[qi]
																				qi += 1
																				for e in self.adj[v]:
																								if not self.lvl[e[0]] and (e[2] - e[3]) >> (30 - l):
																												_lowerCAmelCase			:			List[str]        =					e[0]
																												qe += 1
																												_lowerCAmelCase			:			Tuple        =					self.lvl[v] + 1

																_lowerCAmelCase			:			Any        =					self.depth_first_search(_UpperCAmelCase						,		_UpperCAmelCase						,		_UpperCAmelCase					)
																while p:
																				flow += p
																				_lowerCAmelCase			:			List[str]        =					self.depth_first_search(_UpperCAmelCase						,		_UpperCAmelCase						,		_UpperCAmelCase					)

																if not self.lvl[sink]:
																				break

								return flow






_lowerCamelCase					:     Optional[int]					  =  Dinic(1_0)
_lowerCamelCase					:     Union[str, Any]					  =  0
_lowerCamelCase					:     Tuple					  =  9
for vertex in range(1, 5):
	graph.add_edge(source, vertex, 1)
for vertex in range(5, 9):
	graph.add_edge(vertex, sink, 1)
for vertex in range(1, 5):
	graph.add_edge(vertex, vertex + 4, 1)

# Now we can know that is the maximum flow(source -> sink)
print(graph.max_flow(source, sink))







import unittest

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


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import TvltImageProcessor




def     __SCREAMING_SNAKE_CASE      (     lowercase_    ,   lowercase_=10    ,   lowercase_=10    ,   lowercase_=False    ,   lowercase_=False					)				->		Dict:

    """simple docstring"""


    __UpperCamelCase     						=  []
    for i in range(image_processor_tester.num_frames					):
        video.append(np.random.randint(2_55    ,   size=(image_processor_tester.num_channels, width, height)    ,   dtype=np.uinta					)					)

    if not numpify and not torchify:
        # PIL expects the channel dimension as last dimension
        __UpperCamelCase     						=  [Image.fromarray(np.moveaxis(lowercase_    ,   0    ,   -1					)					) for frame in video]

    if torchify:
        __UpperCamelCase     						=  [torch.from_numpy(lowercase_					) for frame in video]

    return video




def     __SCREAMING_SNAKE_CASE      (     lowercase_    ,   lowercase_=False    ,   lowercase_=False    ,   lowercase_=False					)				->		Tuple:

    """simple docstring"""


    assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

    __UpperCamelCase     						=  []
    for i in range(image_processor_tester.batch_size					):
        if equal_resolution:
            __UpperCamelCase     						=  __UpperCamelCase     						=  image_processor_tester.max_resolution
        else:
            __UpperCamelCase						,					__UpperCamelCase     						=  np.random.choice(
                np.arange(image_processor_tester.min_resolution    ,   image_processor_tester.max_resolution					)    ,   2					)
            __UpperCamelCase     						=  prepare_video(
                image_processor_tester=lowercase_    ,   width=lowercase_    ,   height=lowercase_    ,   numpify=lowercase_    ,   torchify=lowercase_    ,   )
        video_inputs.append(lowercase_					)

    return video_inputs

class        _lowerCamelCase  (	unittest.TestCase      ):

      """simple docstring"""

      def __init__(   self    :       List[str]       ,				snake_case    :       int       ,				snake_case    :       int=7       ,				snake_case    :       int=3       ,				snake_case    :       Any=4       ,				snake_case    :       Tuple=18       ,				snake_case    :       Optional[int]=30       ,				snake_case    :       List[Any]=400       ,				snake_case    :       str=True       ,				snake_case    :       str=None       ,				snake_case    :       Optional[Any]=True       ,				snake_case    :       str=[0.5, 0.5, 0.5]       ,				snake_case    :       Optional[int]=[0.5, 0.5, 0.5]       ,				snake_case    :       Any=True       ,				snake_case    :       str=None       ,				):
          __UpperCamelCase     						=  size if size is not None else {'''shortest_edge''': 18}
          __UpperCamelCase     						=  crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}

          __UpperCamelCase     						=  parent
          __UpperCamelCase     						=  batch_size
          __UpperCamelCase     						=  num_channels
          __UpperCamelCase     						=  num_frames
          __UpperCamelCase     						=  image_size
          __UpperCamelCase     						=  min_resolution
          __UpperCamelCase     						=  max_resolution
          __UpperCamelCase     						=  do_resize
          __UpperCamelCase     						=  size
          __UpperCamelCase     						=  do_normalize
          __UpperCamelCase     						=  image_mean
          __UpperCamelCase     						=  image_std
          __UpperCamelCase     						=  do_center_crop
          __UpperCamelCase     						=  crop_size







      def 					snake_case		(   self    :       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,
              "do_center_crop": self.do_center_crop,
              "crop_size": self.crop_size,
          }







@require_torch
@require_vision
class        _lowerCamelCase  (	_SCREAMING_SNAKE_CASE							,	unittest.TestCase      ):

      """simple docstring"""

      lowerCAmelCase__	:       str			=    TvltImageProcessor if is_vision_available() else None







      def 					snake_case		(   self    :       Dict ):
          __UpperCamelCase     						=  TvltImageProcessorTester(self )







      @property
      def 					snake_case		(   self    :       Dict ):
          return self.image_processor_tester.prepare_image_processor_dict()







      def 					snake_case		(   self    :       int ):
          __UpperCamelCase     						=  self.image_processing_class(**self.image_processor_dict )
          self.assertTrue(hasattr(snake_case       ,				'''image_mean''' ) )
          self.assertTrue(hasattr(snake_case       ,				'''image_std''' ) )
          self.assertTrue(hasattr(snake_case       ,				'''do_normalize''' ) )
          self.assertTrue(hasattr(snake_case       ,				'''do_resize''' ) )
          self.assertTrue(hasattr(snake_case       ,				'''do_center_crop''' ) )
          self.assertTrue(hasattr(snake_case       ,				'''size''' ) )







      def 					snake_case		(   self    :       Union[str, Any] ):
          # Initialize image_processor
          __UpperCamelCase     						=  self.image_processing_class(**self.image_processor_dict )
          # create random PIL videos
          __UpperCamelCase     						=  prepare_video_inputs(self.image_processor_tester       ,				equal_resolution=snake_case )
          for video in video_inputs:
              self.assertIsInstance(snake_case       ,				snake_case )
              self.assertIsInstance(video[0]       ,				Image.Image )

          # Test not batched input
          __UpperCamelCase     						=  image_processor(video_inputs[0]       ,				return_tensors='''pt''' ).pixel_values
          self.assertEqual(
              encoded_videos.shape       ,				(
                  1,
                  self.image_processor_tester.num_frames,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['''height'''],
                  self.image_processor_tester.crop_size['''width'''],
              )       ,				)

          # Test batched
          __UpperCamelCase     						=  image_processor(snake_case       ,				return_tensors='''pt''' ).pixel_values
          self.assertEqual(
              encoded_videos.shape       ,				(
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_frames,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['''height'''],
                  self.image_processor_tester.crop_size['''width'''],
              )       ,				)







      def 					snake_case		(   self    :       List[Any] ):
          # Initialize image_processor
          __UpperCamelCase     						=  self.image_processing_class(**self.image_processor_dict )
          # create random numpy tensors
          __UpperCamelCase     						=  prepare_video_inputs(self.image_processor_tester       ,				equal_resolution=snake_case       ,				numpify=snake_case )
          for video in video_inputs:
              self.assertIsInstance(snake_case       ,				snake_case )
              self.assertIsInstance(video[0]       ,				np.ndarray )

          # Test not batched input
          __UpperCamelCase     						=  image_processor(video_inputs[0]       ,				return_tensors='''pt''' ).pixel_values
          self.assertEqual(
              encoded_videos.shape       ,				(
                  1,
                  self.image_processor_tester.num_frames,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['''height'''],
                  self.image_processor_tester.crop_size['''width'''],
              )       ,				)

          # Test batched
          __UpperCamelCase     						=  image_processor(snake_case       ,				return_tensors='''pt''' ).pixel_values
          self.assertEqual(
              encoded_videos.shape       ,				(
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_frames,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['''height'''],
                  self.image_processor_tester.crop_size['''width'''],
              )       ,				)







      def 					snake_case		(   self    :       Union[str, Any] ):
          # Initialize image_processor
          __UpperCamelCase     						=  self.image_processing_class(**self.image_processor_dict )
          # create random PyTorch tensors
          __UpperCamelCase     						=  prepare_video_inputs(self.image_processor_tester       ,				equal_resolution=snake_case       ,				torchify=snake_case )
          for video in video_inputs:
              self.assertIsInstance(snake_case       ,				snake_case )
              self.assertIsInstance(video[0]       ,				torch.Tensor )

          # Test not batched input
          __UpperCamelCase     						=  image_processor(video_inputs[0]       ,				return_tensors='''pt''' ).pixel_values
          self.assertEqual(
              encoded_videos.shape       ,				(
                  1,
                  self.image_processor_tester.num_frames,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['''height'''],
                  self.image_processor_tester.crop_size['''width'''],
              )       ,				)

          # Test batched
          __UpperCamelCase     						=  image_processor(snake_case       ,				return_tensors='''pt''' ).pixel_values
          self.assertEqual(
              encoded_videos.shape       ,				(
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_frames,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['''height'''],
                  self.image_processor_tester.crop_size['''width'''],
              )       ,				)






