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, prepare_image_inputs


if is_torch_available():
	import torch

if is_vision_available():
	from PIL import Image

	from transformers import PoolFormerImageProcessor


class 						a__							(       unittest.TestCase		):



			def __init__(						self				,						UpperCAmelCase				,						UpperCAmelCase=7				,						UpperCAmelCase=3				,						UpperCAmelCase=3_0				,						UpperCAmelCase=4_0_0				,						UpperCAmelCase=True				,						UpperCAmelCase=None				,						UpperCAmelCase=0.9				,						UpperCAmelCase=None				,						UpperCAmelCase=True				,						UpperCAmelCase=[0.5, 0.5, 0.5]				,						UpperCAmelCase=[0.5, 0.5, 0.5]				,						)			->   Optional[int]:
								__a		 =       size if size is not None else {'shortest_edge': 3_0}
								__a		 =       crop_size if crop_size is not None else {'height': 3_0, 'width': 3_0}
								__a		 =       parent
								__a		 =       batch_size
								__a		 =       num_channels
								__a		 =       min_resolution
								__a		 =       max_resolution
								__a		 =       do_resize_and_center_crop
								__a		 =       size
								__a		 =       crop_pct
								__a		 =       crop_size
								__a		 =       do_normalize
								__a		 =       image_mean
								__a		 =       image_std







			def __SCREAMING_SNAKE_CASE							(						self   )			->   Dict:
								return {
								    "size": self.size,
								    "do_resize_and_center_crop": self.do_resize_and_center_crop,
								    "crop_pct": self.crop_pct,
								    "crop_size": self.crop_size,
								    "do_normalize": self.do_normalize,
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								}






@require_torch
@require_vision
class 						a__							(       __snake_case				,						unittest.TestCase		):
			A__    :       Tuple        =      PoolFormerImageProcessor if is_vision_available() else None



			def __SCREAMING_SNAKE_CASE							(						self   )			->   List[Any]:
								__a		 =       PoolFormerImageProcessingTester(self   )



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



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Tuple:
								__a		 =       self.image_processing_class(**self.image_processor_dict   )
								self.assertTrue(hasattr(UpperCAmelCase				,						'do_resize_and_center_crop'   )   )
								self.assertTrue(hasattr(UpperCAmelCase				,						'size'   )   )
								self.assertTrue(hasattr(UpperCAmelCase				,						'crop_pct'   )   )
								self.assertTrue(hasattr(UpperCAmelCase				,						'do_normalize'   )   )
								self.assertTrue(hasattr(UpperCAmelCase				,						'image_mean'   )   )
								self.assertTrue(hasattr(UpperCAmelCase				,						'image_std'   )   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   List[Any]:
								__a		 =       self.image_processing_class.from_dict(self.image_processor_dict   )
								self.assertEqual(image_processor.size				,						{'shortest_edge': 3_0}   )
								self.assertEqual(image_processor.crop_size				,						{'height': 3_0, 'width': 3_0}   )

								__a		 =       self.image_processing_class.from_dict(self.image_processor_dict				,						size=4_2				,						crop_size=8_4   )
								self.assertEqual(image_processor.size				,						{'shortest_edge': 4_2}   )
								self.assertEqual(image_processor.crop_size				,						{'height': 8_4, 'width': 8_4}   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Any:
								pass



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Tuple:
								# Initialize image_processing
								__a		 =       self.image_processing_class(**self.image_processor_dict   )
								# create random PIL images
								__a		 =       prepare_image_inputs(self.image_processor_tester				,						equal_resolution=UpperCAmelCase   )
								for image in image_inputs:
													self.assertIsInstance(UpperCAmelCase				,						Image.Image   )

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

								self.assertEqual(
								    encoded_images.shape				,						(
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['height'],
								        self.image_processor_tester.crop_size['width'],
								    )				,						)

								# Test batched
								__a		 =       image_processing(UpperCAmelCase				,						return_tensors='pt'   ).pixel_values
								self.assertEqual(
								    encoded_images.shape				,						(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['height'],
								        self.image_processor_tester.crop_size['width'],
								    )				,						)



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Dict:
								# Initialize image_processing
								__a		 =       self.image_processing_class(**self.image_processor_dict   )
								# create random numpy tensors
								__a		 =       prepare_image_inputs(self.image_processor_tester				,						equal_resolution=UpperCAmelCase				,						numpify=UpperCAmelCase   )
								for image in image_inputs:
													self.assertIsInstance(UpperCAmelCase				,						np.ndarray   )

								# Test not batched input
								__a		 =       image_processing(image_inputs[0]				,						return_tensors='pt'   ).pixel_values
								self.assertEqual(
								    encoded_images.shape				,						(
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['height'],
								        self.image_processor_tester.crop_size['width'],
								    )				,						)

								# Test batched
								__a		 =       image_processing(UpperCAmelCase				,						return_tensors='pt'   ).pixel_values
								self.assertEqual(
								    encoded_images.shape				,						(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['height'],
								        self.image_processor_tester.crop_size['width'],
								    )				,						)







			def __SCREAMING_SNAKE_CASE							(						self   )			->   List[Any]:
								# Initialize image_processing
								__a		 =       self.image_processing_class(**self.image_processor_dict   )
								# create random PyTorch tensors
								__a		 =       prepare_image_inputs(self.image_processor_tester				,						equal_resolution=UpperCAmelCase				,						torchify=UpperCAmelCase   )
								for image in image_inputs:
													self.assertIsInstance(UpperCAmelCase				,						torch.Tensor   )

								# Test not batched input
								__a		 =       image_processing(image_inputs[0]				,						return_tensors='pt'   ).pixel_values
								self.assertEqual(
								    encoded_images.shape				,						(
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['height'],
								        self.image_processor_tester.crop_size['width'],
								    )				,						)

								# Test batched
								__a		 =       image_processing(UpperCAmelCase				,						return_tensors='pt'   ).pixel_values
								self.assertEqual(
								    encoded_images.shape				,						(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['height'],
								        self.image_processor_tester.crop_size['width'],
								    )				,						)





def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase						):
					# Return True if there is node that has not iterated.
					__a		 =       [False] * len(__lowerCamelCase						)
					__a		 =       []
					queue.append(__lowerCamelCase						)
					__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(__lowerCamelCase						)
																				__a		 =       True
																				__a		 =       u

					return visited[t]
def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase						):
					# This array is filled by BFS and to store path
					__a		 =       [-1] * (len(__lowerCamelCase						))
					__a		 =       0
					while bfs(__lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase						):
										__a		 =       float('Inf'						)
										__a		 =       sink

										while s != source:
															# Find the minimum value in select path
															__a		 =       min(__lowerCamelCase				,    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]
					return max_flow


lowerCamelCase_    :		str							=	[
    [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],
]

lowerCamelCase_						,  lowerCamelCase_    :		Union[str, Any]							=	0, 5



print(ford_fulkerson(graph, source, sink))




