

"""simple docstring"""





__UpperCamelCase  :      Any							      =		[
    [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   __SCREAMING_SNAKE_CASE  (      A_						,     A_						,     A_						,     A_ ):
	# Return True if there is node that has not iterated.
	lowerCAmelCase__	:						Union[str, Any]  				=	[False] * len(A_ )
	lowerCAmelCase__	:						Union[str, Any]  				=	[s]
	lowerCAmelCase__	:						Union[str, Any]  				=	True

	while queue:
		lowerCAmelCase__	:						List[Any]  				=	queue.pop(0 )
		for ind in range(len(graph[u] ) ):
			if visited[ind] is False and graph[u][ind] > 0:
				queue.append(A_ )
				lowerCAmelCase__	:						Union[str, Any]  				=	True
				lowerCAmelCase__	:						str  				=	u

	return visited[t]

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_						,     A_ ):
	lowerCAmelCase__	:						List[Any]  				=	[-1] * (len(A_ ))
	lowerCAmelCase__	:						Any  				=	0
	lowerCAmelCase__	:						Any  				=	[]
	lowerCAmelCase__	:						Dict  				=	[i[:] for i in graph]  # Record original cut, copy.
	while bfs(A_						,     A_						,     A_						,     A_ ):
		lowerCAmelCase__	:						Optional[Any]  				=	float('''Inf''' )
		lowerCAmelCase__	:						List[Any]  				=	sink

		while s != source:
			# Find the minimum value in select path
			lowerCAmelCase__	:						Optional[Any]  				=	min(A_						,     graph[parent[s]][s] )
			lowerCAmelCase__	:						Dict  				=	parent[s]

		max_flow += path_flow
		lowerCAmelCase__	:						str  				=	sink

		while v != source:
			lowerCAmelCase__	:						List[Any]  				=	parent[v]
			graph[u][v] -= path_flow
			graph[v][u] += path_flow
			lowerCAmelCase__	:						List[Any]  				=	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))







"""simple docstring"""





import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
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 DetaImageProcessor







class 				SCREAMING_SNAKE_CASE					(		unittest.TestCase       ):






					"""simple docstring"""



					def __init__(       self			:				List[str]   ,lowercase_			:				Dict   ,lowercase_			:				Dict=7   ,lowercase_			:				Optional[int]=3   ,lowercase_			:				int=3_0   ,lowercase_			:				Optional[Any]=4_0_0   ,lowercase_			:				Any=True   ,lowercase_			:				List[str]=None   ,lowercase_			:				str=True   ,lowercase_			:				List[Any]=[0.5, 0.5, 0.5]   ,lowercase_			:				List[str]=[0.5, 0.5, 0.5]   ,lowercase_			:				Any=True   ,lowercase_			:				Union[str, Any]=1 / 2_5_5   ,lowercase_			:				str=True   ,):
						# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
						lowerCAmelCase__	:						str  				=	size if size is not None else {'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3}
						lowerCAmelCase__	:						Any  				=	parent
						lowerCAmelCase__	:						Tuple  				=	batch_size
						lowerCAmelCase__	:						List[str]  				=	num_channels
						lowerCAmelCase__	:						Optional[Any]  				=	min_resolution
						lowerCAmelCase__	:						Union[str, Any]  				=	max_resolution
						lowerCAmelCase__	:						Optional[int]  				=	do_resize
						lowerCAmelCase__	:						str  				=	size
						lowerCAmelCase__	:						Union[str, Any]  				=	do_normalize
						lowerCAmelCase__	:						List[str]  				=	image_mean
						lowerCAmelCase__	:						str  				=	image_std
						lowerCAmelCase__	:						Optional[Any]  				=	do_rescale
						lowerCAmelCase__	:						Union[str, Any]  				=	rescale_factor
						lowerCAmelCase__	:						Optional[Any]  				=	do_pad







					def        __lowerCAmelCase (       self			:				Optional[Any]		):
						return {
						    "do_resize": self.do_resize,
						    "size": self.size,
						    "do_normalize": self.do_normalize,
						    "image_mean": self.image_mean,
						    "image_std": self.image_std,
						    "do_rescale": self.do_rescale,
						    "rescale_factor": self.rescale_factor,
						    "do_pad": self.do_pad,
						}







					def        __lowerCAmelCase (       self			:				List[str]   ,lowercase_			:				List[Any]   ,lowercase_			:				int=False		):
						if not batched:
							lowerCAmelCase__	:						Tuple  				=	image_inputs[0]
							if isinstance(lowercase_   ,Image.Image		):
								lowerCAmelCase__				,lowerCAmelCase__	:						List[str]  				=	image.size
							else:
								lowerCAmelCase__				,lowerCAmelCase__	:						Optional[int]  				=	image.shape[1], image.shape[2]
							if w < h:
								lowerCAmelCase__	:						Any  				=	int(self.size['''shortest_edge'''] * h / w		)
								lowerCAmelCase__	:						str  				=	self.size['''shortest_edge''']
							elif w > h:
								lowerCAmelCase__	:						Union[str, Any]  				=	self.size['''shortest_edge''']
								lowerCAmelCase__	:						Dict  				=	int(self.size['''shortest_edge'''] * w / h		)
							else:
								lowerCAmelCase__	:						List[str]  				=	self.size['''shortest_edge''']
								lowerCAmelCase__	:						str  				=	self.size['''shortest_edge''']

						else:
							lowerCAmelCase__	:						Optional[Any]  				=	[]
							for image in image_inputs:
								lowerCAmelCase__				,lowerCAmelCase__	:						Any  				=	self.get_expected_values([image]		)
								expected_values.append((expected_height, expected_width)		)
							lowerCAmelCase__	:						List[str]  				=	max(lowercase_   ,key=lambda lowercase_		: item[0]		)[0]
							lowerCAmelCase__	:						Any  				=	max(lowercase_   ,key=lambda lowercase_		: item[1]		)[1]

						return expected_height, expected_width







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






					"""simple docstring"""



					lowercase__				 =       DetaImageProcessor if is_vision_available() else None







					def        __lowerCAmelCase (       self			:				Any		):
						lowerCAmelCase__	:						Optional[Any]  				=	DetaImageProcessingTester(self		)







					@property
					def        __lowerCAmelCase (       self			:				Any		):
						return self.image_processor_tester.prepare_image_processor_dict()







					def        __lowerCAmelCase (       self			:				List[str]		):
						lowerCAmelCase__	:						str  				=	self.image_processing_class(**self.image_processor_dict		)
						self.assertTrue(hasattr(lowercase_   ,'''image_mean'''		)		)
						self.assertTrue(hasattr(lowercase_   ,'''image_std'''		)		)
						self.assertTrue(hasattr(lowercase_   ,'''do_normalize'''		)		)
						self.assertTrue(hasattr(lowercase_   ,'''do_resize'''		)		)
						self.assertTrue(hasattr(lowercase_   ,'''do_rescale'''		)		)
						self.assertTrue(hasattr(lowercase_   ,'''do_pad'''		)		)
						self.assertTrue(hasattr(lowercase_   ,'''size'''		)		)







					def        __lowerCAmelCase (       self			:				Dict		):
						lowerCAmelCase__	:						Dict  				=	self.image_processing_class.from_dict(self.image_processor_dict		)
						self.assertEqual(image_processor.size   ,{'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3}		)
						self.assertEqual(image_processor.do_pad   ,lowercase_		)







					def        __lowerCAmelCase (       self			:				List[str]		):
						pass







					def        __lowerCAmelCase (       self			:				Union[str, Any]		):
						# Initialize image_processing
						lowerCAmelCase__	:						str  				=	self.image_processing_class(**self.image_processor_dict		)
						# create random PIL images
						lowerCAmelCase__	:						Union[str, Any]  				=	prepare_image_inputs(self.image_processor_tester   ,equal_resolution=lowercase_		)
						for image in image_inputs:
							self.assertIsInstance(lowercase_   ,Image.Image		)

						# Test not batched input
						lowerCAmelCase__	:						List[Any]  				=	image_processing(image_inputs[0]   ,return_tensors='''pt'''		).pixel_values

						lowerCAmelCase__				,lowerCAmelCase__	:						Tuple  				=	self.image_processor_tester.get_expected_values(lowercase_		)

						self.assertEqual(
						    encoded_images.shape   ,(1, self.image_processor_tester.num_channels, expected_height, expected_width)   ,)

						# Test batched
						lowerCAmelCase__				,lowerCAmelCase__	:						Optional[int]  				=	self.image_processor_tester.get_expected_values(lowercase_   ,batched=lowercase_		)

						lowerCAmelCase__	:						Optional[int]  				=	image_processing(lowercase_   ,return_tensors='''pt'''		).pixel_values
						self.assertEqual(
						    encoded_images.shape   ,(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    )   ,)







					def        __lowerCAmelCase (       self			:				Dict		):
						# Initialize image_processing
						lowerCAmelCase__	:						Optional[Any]  				=	self.image_processing_class(**self.image_processor_dict		)
						# create random numpy tensors
						lowerCAmelCase__	:						Tuple  				=	prepare_image_inputs(self.image_processor_tester   ,equal_resolution=lowercase_   ,numpify=lowercase_		)
						for image in image_inputs:
							self.assertIsInstance(lowercase_   ,np.ndarray		)

						# Test not batched input
						lowerCAmelCase__	:						List[str]  				=	image_processing(image_inputs[0]   ,return_tensors='''pt'''		).pixel_values

						lowerCAmelCase__				,lowerCAmelCase__	:						Any  				=	self.image_processor_tester.get_expected_values(lowercase_		)

						self.assertEqual(
						    encoded_images.shape   ,(1, self.image_processor_tester.num_channels, expected_height, expected_width)   ,)

						# Test batched
						lowerCAmelCase__	:						str  				=	image_processing(lowercase_   ,return_tensors='''pt'''		).pixel_values

						lowerCAmelCase__				,lowerCAmelCase__	:						Optional[Any]  				=	self.image_processor_tester.get_expected_values(lowercase_   ,batched=lowercase_		)

						self.assertEqual(
						    encoded_images.shape   ,(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    )   ,)







					def        __lowerCAmelCase (       self			:				Union[str, Any]		):
						# Initialize image_processing
						lowerCAmelCase__	:						Tuple  				=	self.image_processing_class(**self.image_processor_dict		)
						# create random PyTorch tensors
						lowerCAmelCase__	:						Any  				=	prepare_image_inputs(self.image_processor_tester   ,equal_resolution=lowercase_   ,torchify=lowercase_		)
						for image in image_inputs:
							self.assertIsInstance(lowercase_   ,torch.Tensor		)

						# Test not batched input
						lowerCAmelCase__	:						List[Any]  				=	image_processing(image_inputs[0]   ,return_tensors='''pt'''		).pixel_values

						lowerCAmelCase__				,lowerCAmelCase__	:						Any  				=	self.image_processor_tester.get_expected_values(lowercase_		)

						self.assertEqual(
						    encoded_images.shape   ,(1, self.image_processor_tester.num_channels, expected_height, expected_width)   ,)

						# Test batched
						lowerCAmelCase__	:						str  				=	image_processing(lowercase_   ,return_tensors='''pt'''		).pixel_values

						lowerCAmelCase__				,lowerCAmelCase__	:						List[Any]  				=	self.image_processor_tester.get_expected_values(lowercase_   ,batched=lowercase_		)

						self.assertEqual(
						    encoded_images.shape   ,(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    )   ,)







					@slow
					def        __lowerCAmelCase (       self			:				Tuple		):
						# prepare image and target
						lowerCAmelCase__	:						Tuple  				=	Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
						with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''   ,'''r'''		) as f:
							lowerCAmelCase__	:						Union[str, Any]  				=	json.loads(f.read()		)

						lowerCAmelCase__	:						str  				=	{'''image_id''': 3_9_7_6_9, '''annotations''': target}

						# encode them
						lowerCAmelCase__	:						Optional[Any]  				=	DetaImageProcessor()
						lowerCAmelCase__	:						Optional[int]  				=	image_processing(images=lowercase_   ,annotations=lowercase_   ,return_tensors='''pt'''		)

						# verify pixel values
						lowerCAmelCase__	:						Dict  				=	torch.Size([1, 3, 8_0_0, 1_0_6_6]		)
						self.assertEqual(encoding['''pixel_values'''].shape   ,lowercase_		)

						lowerCAmelCase__	:						Any  				=	torch.tensor([0.2796, 0.3138, 0.3481]		)
						self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]   ,lowercase_   ,atol=1E-4		)		)

						# verify area
						lowerCAmelCase__	:						Tuple  				=	torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']   ,lowercase_		)		)
						# verify boxes
						lowerCAmelCase__	:						List[str]  				=	torch.Size([6, 4]		)
						self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape   ,lowercase_		)
						lowerCAmelCase__	:						Tuple  				=	torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]   ,lowercase_   ,atol=1E-3		)		)
						# verify image_id
						lowerCAmelCase__	:						Optional[int]  				=	torch.tensor([3_9_7_6_9]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']   ,lowercase_		)		)
						# verify is_crowd
						lowerCAmelCase__	:						List[str]  				=	torch.tensor([0, 0, 0, 0, 0, 0]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']   ,lowercase_		)		)
						# verify class_labels
						lowerCAmelCase__	:						Any  				=	torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']   ,lowercase_		)		)
						# verify orig_size
						lowerCAmelCase__	:						int  				=	torch.tensor([4_8_0, 6_4_0]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']   ,lowercase_		)		)
						# verify size
						lowerCAmelCase__	:						str  				=	torch.tensor([8_0_0, 1_0_6_6]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']   ,lowercase_		)		)







					@slow
					def        __lowerCAmelCase (       self			:				Any		):
						# prepare image, target and masks_path
						lowerCAmelCase__	:						Dict  				=	Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
						with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''   ,'''r'''		) as f:
							lowerCAmelCase__	:						str  				=	json.loads(f.read()		)

						lowerCAmelCase__	:						Tuple  				=	{'''file_name''': '''000000039769.png''', '''image_id''': 3_9_7_6_9, '''segments_info''': target}

						lowerCAmelCase__	:						Optional[Any]  				=	pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''		)

						# encode them
						lowerCAmelCase__	:						str  				=	DetaImageProcessor(format='''coco_panoptic'''		)
						lowerCAmelCase__	:						Optional[int]  				=	image_processing(images=lowercase_   ,annotations=lowercase_   ,masks_path=lowercase_   ,return_tensors='''pt'''		)

						# verify pixel values
						lowerCAmelCase__	:						Any  				=	torch.Size([1, 3, 8_0_0, 1_0_6_6]		)
						self.assertEqual(encoding['''pixel_values'''].shape   ,lowercase_		)

						lowerCAmelCase__	:						int  				=	torch.tensor([0.2796, 0.3138, 0.3481]		)
						self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]   ,lowercase_   ,atol=1E-4		)		)

						# verify area
						lowerCAmelCase__	:						Tuple  				=	torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']   ,lowercase_		)		)
						# verify boxes
						lowerCAmelCase__	:						Optional[Any]  				=	torch.Size([6, 4]		)
						self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape   ,lowercase_		)
						lowerCAmelCase__	:						str  				=	torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]   ,lowercase_   ,atol=1E-3		)		)
						# verify image_id
						lowerCAmelCase__	:						Union[str, Any]  				=	torch.tensor([3_9_7_6_9]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']   ,lowercase_		)		)
						# verify is_crowd
						lowerCAmelCase__	:						Union[str, Any]  				=	torch.tensor([0, 0, 0, 0, 0, 0]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']   ,lowercase_		)		)
						# verify class_labels
						lowerCAmelCase__	:						List[str]  				=	torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']   ,lowercase_		)		)
						# verify masks
						lowerCAmelCase__	:						Optional[int]  				=	8_2_2_8_7_3
						self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()   ,lowercase_		)
						# verify orig_size
						lowerCAmelCase__	:						List[Any]  				=	torch.tensor([4_8_0, 6_4_0]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']   ,lowercase_		)		)
						# verify size
						lowerCAmelCase__	:						Optional[Any]  				=	torch.tensor([8_0_0, 1_0_6_6]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']   ,lowercase_		)		)




