
'''simple docstring'''
class 			SCREAMING_SNAKE_CASE__      :







				def __init__(       self						,				A_						,				A_						,				A_     )->		Any:







											'''simple docstring'''






											UpperCamelCase   			=     None
											UpperCamelCase   			=     None
											UpperCamelCase   			=     graph

											self._normalize_graph(A_						,				A_     )
											UpperCamelCase   			=     len(A_     )
											UpperCamelCase   			=     None







				def 						UpperCAmelCase_    (       self						,				A_						,				A_     )->		Tuple:







											'''simple docstring'''






											if sources is int:
																		UpperCamelCase   			=     [sources]
											if sinks is int:
																		UpperCamelCase   			=     [sinks]

											if len(A_     ) == 0 or len(A_     ) == 0:
																		return

											UpperCamelCase   			=     sources[0]
											UpperCamelCase   			=     sinks[0]

											# make fake vertex if there are more
											# than one source or sink
											if len(A_     ) > 1 or len(A_     ) > 1:
																		UpperCamelCase   			=     0
																		for i in sources:
																									max_input_flow += sum(self.graph[i]     )

																		UpperCamelCase   			=     len(self.graph     ) + 1
																		for room in self.graph:
																									room.insert(0						,				0     )
																		self.graph.insert(0						,				[0] * size     )
																		for i in sources:
																									UpperCamelCase   			=     max_input_flow
																		UpperCamelCase   			=     0

																		UpperCamelCase   			=     len(self.graph     ) + 1
																		for room in self.graph:
																									room.append(0     )
																		self.graph.append([0] * size     )
																		for i in sinks:
																									UpperCamelCase   			=     max_input_flow
																		UpperCamelCase   			=     size - 1







				def 						UpperCAmelCase_    (       self     )->		Optional[Any]:







											'''simple docstring'''






											if self.maximum_flow_algorithm is None:
																		raise Exception('You need to set maximum flow algorithm before.'     )
											if self.source_index is None or self.sink_index is None:
																		return 0

											self.maximum_flow_algorithm.execute()
											return self.maximum_flow_algorithm.getMaximumFlow()







				def 						UpperCAmelCase_    (       self						,				A_     )->		Any:







											'''simple docstring'''






											UpperCamelCase   			=     algorithm(self     )


class 			SCREAMING_SNAKE_CASE__      :







				def __init__(       self						,				A_     )->		Dict:







											'''simple docstring'''






											UpperCamelCase   			=     flow_network
											UpperCamelCase   			=     flow_network.verticesCount
											UpperCamelCase   			=     flow_network.sourceIndex
											UpperCamelCase   			=     flow_network.sinkIndex
											# it's just a reference, so you shouldn't change
											# it in your algorithms, use deep copy before doing that
											UpperCamelCase   			=     flow_network.graph
											UpperCamelCase   			=     False







				def 						UpperCAmelCase_    (       self     )->		Dict:







											'''simple docstring'''






											if not self.executed:
																		self._algorithm()
																		UpperCamelCase   			=     True







				def 						UpperCAmelCase_    (       self     )->		Dict:







											'''simple docstring'''






											pass


class 			SCREAMING_SNAKE_CASE__      (     snake_case_):







				def __init__(       self						,				A_     )->		List[str]:







											'''simple docstring'''






											super().__init__(A_     )
											# use this to save your result
											UpperCamelCase   			=     -1







				def 						UpperCAmelCase_    (       self     )->		Union[str, Any]:







											'''simple docstring'''






											if not self.executed:
																		raise Exception('You should execute algorithm before using its result!'     )

											return self.maximum_flow


class 			SCREAMING_SNAKE_CASE__      (     snake_case_):







				def __init__(       self						,				A_     )->		Optional[Any]:







											'''simple docstring'''






											super().__init__(A_     )

											UpperCamelCase   			=     [[0] * self.verticies_count for i in range(self.verticies_count     )]

											UpperCamelCase   			=     [0] * self.verticies_count
											UpperCamelCase   			=     [0] * self.verticies_count







				def 						UpperCAmelCase_    (       self     )->		Tuple:







											'''simple docstring'''






											UpperCamelCase   			=     self.verticies_count

											# push some substance to graph
											for nextvertex_index, bandwidth in enumerate(self.graph[self.source_index]     ):
																		self.preflow[self.source_index][nextvertex_index] += bandwidth
																		self.preflow[nextvertex_index][self.source_index] -= bandwidth
																		self.excesses[nextvertex_index] += bandwidth

											# Relabel-to-front selection rule
											UpperCamelCase   			=     [
											    i
											    for i in range(self.verticies_count     )
											    if i != self.source_index and i != self.sink_index
											]

											# move through list
											UpperCamelCase   			=     0
											while i < len(A_     ):
																		UpperCamelCase   			=     vertices_list[i]
																		UpperCamelCase   			=     self.heights[vertex_index]
																		self.process_vertex(A_     )
																		if self.heights[vertex_index] > previous_height:
																									# if it was relabeled, swap elements
																									# and start from 0 index
																									vertices_list.insert(0						,				vertices_list.pop(A_     )     )
																									UpperCamelCase   			=     0
																		else:
																									i += 1

											UpperCamelCase   			=     sum(self.preflow[self.source_index]     )







				def 						UpperCAmelCase_    (       self						,				A_     )->		Dict:







											'''simple docstring'''






											while self.excesses[vertex_index] > 0:
																		for neighbour_index in range(self.verticies_count     ):
																									# if it's neighbour and current vertex is higher
																									if (
																									    self.graph[vertex_index][neighbour_index]
																									    - self.preflow[vertex_index][neighbour_index]
																									    > 0
																									    and self.heights[vertex_index] > self.heights[neighbour_index]
																									):
																																self.push(A_						,				A_     )

																		self.relabel(A_     )







				def 						UpperCAmelCase_    (       self						,				A_						,				A_     )->		str:







											'''simple docstring'''






											UpperCamelCase   			=     min(
											    self.excesses[from_index]						,				self.graph[from_index][to_index] - self.preflow[from_index][to_index]						,				)
											self.preflow[from_index][to_index] += preflow_delta
											self.preflow[to_index][from_index] -= preflow_delta
											self.excesses[from_index] -= preflow_delta
											self.excesses[to_index] += preflow_delta







				def 						UpperCAmelCase_    (       self						,				A_     )->		List[str]:







											'''simple docstring'''






											UpperCamelCase   			=     None
											for to_index in range(self.verticies_count     ):
																		if (
																		    self.graph[vertex_index][to_index]
																		    - self.preflow[vertex_index][to_index]
																		    > 0
																		) and (min_height is None or self.heights[to_index] < min_height):
																									UpperCamelCase   			=     self.heights[to_index]

											if min_height is not None:
																		UpperCamelCase   			=     min_height + 1


if __name__ == "__main__":
						lowerCAmelCase					:    int           = [0]
						lowerCAmelCase					:    Tuple           = [3]
						# graph = [
						#     [0, 0, 4, 6, 0, 0],
						#     [0, 0, 5, 2, 0, 0],
						#     [0, 0, 0, 0, 4, 4],
						#     [0, 0, 0, 0, 6, 6],
						#     [0, 0, 0, 0, 0, 0],
						#     [0, 0, 0, 0, 0, 0],
						# ]
						lowerCAmelCase					:    List[str]           = [[0, 7, 0, 0], [0, 0, 6, 0], [0, 0, 0, 8], [9, 0, 0, 0]]

						# prepare our network
						lowerCAmelCase					:    List[str]           = FlowNetwork(graph, entrances, exits)
						# set algorithm
						flow_network.set_maximum_flow_algorithm(PushRelabelExecutor)
						# and calculate
						lowerCAmelCase					:    int           = flow_network.find_maximum_flow()

						print(f"""maximum flow is {maximum_flow}""")






'''simple docstring'''
import unittest

import numpy as np

from transformers.testing_utils import is_flaky, 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 DonutImageProcessor


class 			SCREAMING_SNAKE_CASE__      (     unittest.TestCase):







				def __init__(       self						,				A_						,				A_=7						,				A_=3						,				A_=18						,				A_=30						,				A_=400						,				A_=True						,				A_=None						,				A_=True						,				A_=False						,				A_=True						,				A_=True						,				A_=[0.5, 0.5, 0.5]						,				A_=[0.5, 0.5, 0.5]						,				)->		Dict:







											'''simple docstring'''






											UpperCamelCase   			=     parent
											UpperCamelCase   			=     batch_size
											UpperCamelCase   			=     num_channels
											UpperCamelCase   			=     image_size
											UpperCamelCase   			=     min_resolution
											UpperCamelCase   			=     max_resolution
											UpperCamelCase   			=     do_resize
											UpperCamelCase   			=     size if size is not None else {'height': 18, 'width': 20}
											UpperCamelCase   			=     do_thumbnail
											UpperCamelCase   			=     do_align_axis
											UpperCamelCase   			=     do_pad
											UpperCamelCase   			=     do_normalize
											UpperCamelCase   			=     image_mean
											UpperCamelCase   			=     image_std







				def 						UpperCAmelCase_    (       self     )->		List[Any]:







											'''simple docstring'''






											return {
											    "do_resize": self.do_resize,
											    "size": self.size,
											    "do_thumbnail": self.do_thumbnail,
											    "do_align_long_axis": self.do_align_axis,
											    "do_pad": self.do_pad,
											    "do_normalize": self.do_normalize,
											    "image_mean": self.image_mean,
											    "image_std": self.image_std,
											}


@require_torch
@require_vision
class 			SCREAMING_SNAKE_CASE__      (     snake_case_							,       unittest.TestCase):
				lowerCAmelCase_     			=							DonutImageProcessor if is_vision_available() else None







				def 						UpperCAmelCase_    (       self     )->		str:







											'''simple docstring'''






											UpperCamelCase   			=     DonutImageProcessingTester(self     )







				@property
				def 						UpperCAmelCase_    (       self     )->		str:







											'''simple docstring'''






											return self.image_processor_tester.prepare_image_processor_dict()







				def 						UpperCAmelCase_    (       self     )->		Optional[int]:







											'''simple docstring'''






											UpperCamelCase   			=     self.image_processing_class(**self.image_processor_dict     )
											self.assertTrue(hasattr(A_						,				'do_resize'     )     )
											self.assertTrue(hasattr(A_						,				'size'     )     )
											self.assertTrue(hasattr(A_						,				'do_thumbnail'     )     )
											self.assertTrue(hasattr(A_						,				'do_align_long_axis'     )     )
											self.assertTrue(hasattr(A_						,				'do_pad'     )     )
											self.assertTrue(hasattr(A_						,				'do_normalize'     )     )
											self.assertTrue(hasattr(A_						,				'image_mean'     )     )
											self.assertTrue(hasattr(A_						,				'image_std'     )     )







				def 						UpperCAmelCase_    (       self     )->		Optional[int]:







											'''simple docstring'''






											UpperCamelCase   			=     self.image_processing_class.from_dict(self.image_processor_dict     )
											self.assertEqual(image_processor.size						,				{'height': 18, 'width': 20}     )

											UpperCamelCase   			=     self.image_processing_class.from_dict(self.image_processor_dict						,				size=42     )
											self.assertEqual(image_processor.size						,				{'height': 42, 'width': 42}     )

											# Previous config had dimensions in (width, height) order
											UpperCamelCase   			=     self.image_processing_class.from_dict(self.image_processor_dict						,				size=(42, 84)     )
											self.assertEqual(image_processor.size						,				{'height': 84, 'width': 42}     )







				def 						UpperCAmelCase_    (       self     )->		Tuple:







											'''simple docstring'''






											pass







				@is_flaky()
				def 						UpperCAmelCase_    (       self     )->		Any:







											'''simple docstring'''






											UpperCamelCase   			=     self.image_processing_class(**self.image_processor_dict     )
											# create random PIL images
											UpperCamelCase   			=     prepare_image_inputs(self.image_processor_tester						,				equal_resolution=A_     )
											for image in image_inputs:
																		self.assertIsInstance(A_						,				Image.Image     )

											# Test not batched input
											UpperCamelCase   			=     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.size['height'],
											        self.image_processor_tester.size['width'],
											    )						,				)

											# Test batched
											UpperCamelCase   			=     image_processing(A_						,				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.size['height'],
											        self.image_processor_tester.size['width'],
											    )						,				)







				@is_flaky()
				def 						UpperCAmelCase_    (       self     )->		Optional[int]:







											'''simple docstring'''






											UpperCamelCase   			=     self.image_processing_class(**self.image_processor_dict     )
											# create random numpy tensors
											UpperCamelCase   			=     prepare_image_inputs(self.image_processor_tester						,				equal_resolution=A_						,				numpify=A_     )
											for image in image_inputs:
																		self.assertIsInstance(A_						,				np.ndarray     )

											# Test not batched input
											UpperCamelCase   			=     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.size['height'],
											        self.image_processor_tester.size['width'],
											    )						,				)

											# Test batched
											UpperCamelCase   			=     image_processing(A_						,				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.size['height'],
											        self.image_processor_tester.size['width'],
											    )						,				)







				@is_flaky()
				def 						UpperCAmelCase_    (       self     )->		Dict:







											'''simple docstring'''






											UpperCamelCase   			=     self.image_processing_class(**self.image_processor_dict     )
											# create random PyTorch tensors
											UpperCamelCase   			=     prepare_image_inputs(self.image_processor_tester						,				equal_resolution=A_						,				torchify=A_     )
											for image in image_inputs:
																		self.assertIsInstance(A_						,				torch.Tensor     )

											# Test not batched input
											UpperCamelCase   			=     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.size['height'],
											        self.image_processor_tester.size['width'],
											    )						,				)

											# Test batched
											UpperCamelCase   			=     image_processing(A_						,				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.size['height'],
											        self.image_processor_tester.size['width'],
											    )						,				)




