

class   _a					:




      """simple docstring"""

      def __init__(   self							:				Union[str, Any]						,			UpperCAmelCase							:				int						,			UpperCAmelCase							:				Any						,			UpperCAmelCase							:				Dict	):
            A_						  =				None
            A_						  =				None
            A_						  =				graph

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





      def 		__A	(   self							:				Union[str, Any]						,			UpperCAmelCase							:				List[str]						,			UpperCAmelCase							:				Tuple	):
            if sources is int:
                  A_						  =				[sources]
            if sinks is int:
                  A_						  =				[sinks]

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

            A_						  =				sources[0]
            A_						  =				sinks[0]

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

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

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





      def 		__A	(   self							:				str	):
            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 		__A	(   self							:				Tuple						,			UpperCAmelCase							:				List[Any]	):
            A_						  =				algorithm(self	)




class   _a					:




      """simple docstring"""

      def __init__(   self							:				Tuple						,			UpperCAmelCase							:				List[str]	):
            A_						  =				flow_network
            A_						  =				flow_network.verticesCount
            A_						  =				flow_network.sourceIndex
            A_						  =				flow_network.sinkIndex
            # it's just a reference, so you shouldn't change
            # it in your algorithms, use deep copy before doing that
            A_						  =				flow_network.graph
            A_						  =				False





      def 		__A	(   self							:				Optional[int]	):
            if not self.executed:
                  self._algorithm()
                  A_						  =				True







      def 		__A	(   self							:				Dict	):
            pass




class   _a					(			snake_case_				):




      """simple docstring"""

      def __init__(   self							:				Optional[Any]						,			UpperCAmelCase							:				List[Any]	):
            super().__init__(UpperCAmelCase	)
            # use this to save your result
            A_						  =				-1







      def 		__A	(   self							:				Tuple	):
            if not self.executed:
                  raise Exception("You should execute algorithm before using its result!"	)

            return self.maximum_flow




class   _a					(			snake_case_				):




      """simple docstring"""

      def __init__(   self							:				Tuple						,			UpperCAmelCase							:				Union[str, Any]	):
            super().__init__(UpperCAmelCase	)

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

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





      def 		__A	(   self							:				List[str]	):
            A_						  =				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
            A_						  =				[
                i
                for i in range(self.verticies_count	)
                if i != self.source_index and i != self.sink_index
            ]

            # move through list
            A_						  =				0
            while i < len(UpperCAmelCase	):
                  A_						  =				vertices_list[i]
                  A_						  =				self.heights[vertex_index]
                  self.process_vertex(UpperCAmelCase	)
                  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(UpperCAmelCase	)	)
                        A_						  =				0
                  else:
                        i += 1

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





      def 		__A	(   self							:				List[str]						,			UpperCAmelCase							:				Dict	):
            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(UpperCAmelCase						,			UpperCAmelCase	)

                  self.relabel(UpperCAmelCase	)





      def 		__A	(   self							:				Optional[Any]						,			UpperCAmelCase							:				Dict						,			UpperCAmelCase							:				str	):
            A_						  =				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 		__A	(   self							:				Optional[Any]						,			UpperCAmelCase							:				List[Any]	):
            A_						  =				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):
                        A_						  =				self.heights[to_index]

            if min_height is not None:
                  A_						  =				min_height + 1


if __name__ == "__main__":
      __a    :Tuple      =			[0]
      __a    :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],
      # ]
      __a    :List[str]      =			[[0, 7, 0, 0], [0, 0, 6, 0], [0, 0, 0, 8], [9, 0, 0, 0]]

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

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






import unittest

import numpy as np
import requests

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

       from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
       _lowerCamelCase      							=   False

if is_vision_available():
       from PIL import Image

       from transformers import PixaStructImageProcessor







class       __A							(   unittest.TestCase	):




      """simple docstring"""







      def __init__(		self   ,				a__   ,				a__=7   ,				a__=3   ,				a__=18   ,				a__=30   ,				a__=400   ,				a__=None   ,				a__=True   ,				a__=True   ,				a__=None   ,				):



             """simple docstring"""





             _lowerCamelCase				:  str						=		size if size is not None else {'''height''': 20, '''width''': 20}
             _lowerCamelCase				:  int						=		parent
             _lowerCamelCase				:  Tuple						=		batch_size
             _lowerCamelCase				:  Union[str, Any]						=		num_channels
             _lowerCamelCase				:  Tuple						=		image_size
             _lowerCamelCase				:  Dict						=		min_resolution
             _lowerCamelCase				:  int						=		max_resolution
             _lowerCamelCase				:  Dict						=		size
             _lowerCamelCase				:  Dict						=		do_normalize
             _lowerCamelCase				:  List[Any]						=		do_convert_rgb
             _lowerCamelCase				:  Optional[int]						=		[512, 1024, 2048, 4096]
             _lowerCamelCase				:  Optional[int]						=		patch_size if patch_size is not None else {'''height''': 16, '''width''': 16}


      def 					__snake_case      (		self):



             """simple docstring"""





             return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}



      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Dict						=		'''https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'''
             _lowerCamelCase				:  Union[str, Any]						=		Image.open(requests.get(a__   ,				stream=a__).raw).convert('''RGB''')
             return raw_image







@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11			,reason="""`Pix2StructImageProcessor` requires `torch>=1.11.0`."""			,)
@require_torch
@require_vision
class       __A							(   lowerCamelCase__			,unittest.TestCase	):




      """simple docstring"""







      UpperCAmelCase__							=   PixaStructImageProcessor if is_vision_available() else None


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  int						=		PixaStructImageProcessingTester(self)


      @property
      def 					__snake_case      (		self):



             """simple docstring"""





             return self.image_processor_tester.prepare_image_processor_dict()


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  int						=		self.image_processing_class(**self.image_processor_dict)
             self.assertTrue(hasattr(a__   ,				'''do_normalize'''))
             self.assertTrue(hasattr(a__   ,				'''do_convert_rgb'''))


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Any						=		self.image_processor_tester.prepare_dummy_image()

             _lowerCamelCase				:  List[Any]						=		self.image_processing_class(**self.image_processor_dict)
             _lowerCamelCase				:  Optional[int]						=		2048

             _lowerCamelCase				:  Any						=		image_processor(a__   ,				return_tensors='''pt'''   ,				max_patches=a__)
             self.assertTrue(torch.allclose(inputs.flattened_patches.mean()   ,				torch.tensor(0.0606)   ,				atol=1e-3   ,				rtol=1e-3))


      def 					__snake_case      (		self):



             """simple docstring"""





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

             # Test not batched input
             _lowerCamelCase				:  Optional[Any]						=		(
                 (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
                 * self.image_processor_tester.num_channels
             ) + 2

             for max_patch in self.image_processor_tester.max_patches:
                    # Test not batched input
                    _lowerCamelCase				:  Optional[int]						=		image_processor(
                        image_inputs[0]   ,				return_tensors='''pt'''   ,				max_patches=a__).flattened_patches
                    self.assertEqual(
                        encoded_images.shape   ,				(1, max_patch, expected_hidden_dim)   ,				)

                    # Test batched
                    _lowerCamelCase				:  Dict						=		image_processor(
                        a__   ,				return_tensors='''pt'''   ,				max_patches=a__).flattened_patches
                    self.assertEqual(
                        encoded_images.shape   ,				(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)   ,				)


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Dict						=		self.image_processing_class(**self.image_processor_dict)
             # create random PIL images
             _lowerCamelCase				:  Optional[int]						=		prepare_image_inputs(self.image_processor_tester   ,				equal_resolution=a__)
             for image in image_inputs:
                    self.assertIsInstance(a__   ,				Image.Image)

             # Test not batched input
             _lowerCamelCase				:  List[str]						=		(
                 (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
                 * self.image_processor_tester.num_channels
             ) + 2

             _lowerCamelCase				:  Dict						=		True

             for max_patch in self.image_processor_tester.max_patches:
                    # Test not batched input
                    with self.assertRaises(a__):
                           _lowerCamelCase				:  List[Any]						=		image_processor(
                               image_inputs[0]   ,				return_tensors='''pt'''   ,				max_patches=a__).flattened_patches

                    _lowerCamelCase				:  List[Any]						=		'''Hello'''

                    _lowerCamelCase				:  int						=		image_processor(
                        image_inputs[0]   ,				return_tensors='''pt'''   ,				max_patches=a__   ,				header_text=a__).flattened_patches
                    self.assertEqual(
                        encoded_images.shape   ,				(1, max_patch, expected_hidden_dim)   ,				)

                    # Test batched
                    _lowerCamelCase				:  Dict						=		image_processor(
                        a__   ,				return_tensors='''pt'''   ,				max_patches=a__   ,				header_text=a__).flattened_patches
                    self.assertEqual(
                        encoded_images.shape   ,				(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)   ,				)


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  List[Any]						=		self.image_processing_class(**self.image_processor_dict)
             # create random numpy tensors
             _lowerCamelCase				:  Tuple						=		prepare_image_inputs(self.image_processor_tester   ,				equal_resolution=a__   ,				numpify=a__)
             for image in image_inputs:
                    self.assertIsInstance(a__   ,				np.ndarray)

             _lowerCamelCase				:  Any						=		(
                 (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
                 * self.image_processor_tester.num_channels
             ) + 2

             for max_patch in self.image_processor_tester.max_patches:
                    # Test not batched input
                    _lowerCamelCase				:  List[str]						=		image_processor(
                        image_inputs[0]   ,				return_tensors='''pt'''   ,				max_patches=a__).flattened_patches
                    self.assertEqual(
                        encoded_images.shape   ,				(1, max_patch, expected_hidden_dim)   ,				)

                    # Test batched
                    _lowerCamelCase				:  Tuple						=		image_processor(
                        a__   ,				return_tensors='''pt'''   ,				max_patches=a__).flattened_patches
                    self.assertEqual(
                        encoded_images.shape   ,				(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)   ,				)



      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Optional[int]						=		self.image_processing_class(**self.image_processor_dict)
             # create random PyTorch tensors
             _lowerCamelCase				:  str						=		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
             _lowerCamelCase				:  Tuple						=		(
                 (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
                 * self.image_processor_tester.num_channels
             ) + 2

             for max_patch in self.image_processor_tester.max_patches:
                    # Test not batched input
                    _lowerCamelCase				:  Optional[int]						=		image_processor(
                        image_inputs[0]   ,				return_tensors='''pt'''   ,				max_patches=a__).flattened_patches
                    self.assertEqual(
                        encoded_images.shape   ,				(1, max_patch, expected_hidden_dim)   ,				)

                    # Test batched
                    _lowerCamelCase				:  List[str]						=		image_processor(
                        a__   ,				return_tensors='''pt'''   ,				max_patches=a__).flattened_patches
                    self.assertEqual(
                        encoded_images.shape   ,				(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)   ,				)







@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11			,reason="""`Pix2StructImageProcessor` requires `torch>=1.11.0`."""			,)
@require_torch
@require_vision
class       __A							(   lowerCamelCase__			,unittest.TestCase	):




      """simple docstring"""







      UpperCAmelCase__							=   PixaStructImageProcessor if is_vision_available() else None


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Optional[Any]						=		PixaStructImageProcessingTester(self   ,				num_channels=4)
             _lowerCamelCase				:  Union[str, Any]						=		3


      @property
      def 					__snake_case      (		self):



             """simple docstring"""





             return self.image_processor_tester.prepare_image_processor_dict()


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  str						=		self.image_processing_class(**self.image_processor_dict)
             self.assertTrue(hasattr(a__   ,				'''do_normalize'''))
             self.assertTrue(hasattr(a__   ,				'''do_convert_rgb'''))



      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  List[str]						=		self.image_processing_class(**self.image_processor_dict)
             # create random PIL images
             _lowerCamelCase				:  Any						=		prepare_image_inputs(self.image_processor_tester   ,				equal_resolution=a__)
             for image in image_inputs:
                    self.assertIsInstance(a__   ,				Image.Image)

             # Test not batched input
             _lowerCamelCase				:  List[Any]						=		(
                 (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
                 * (self.image_processor_tester.num_channels - 1)
             ) + 2

             for max_patch in self.image_processor_tester.max_patches:
                    # Test not batched input
                    _lowerCamelCase				:  Union[str, Any]						=		image_processor(
                        image_inputs[0]   ,				return_tensors='''pt'''   ,				max_patches=a__).flattened_patches
                    self.assertEqual(
                        encoded_images.shape   ,				(1, max_patch, expected_hidden_dim)   ,				)

                    # Test batched
                    _lowerCamelCase				:  Union[str, Any]						=		image_processor(
                        a__   ,				return_tensors='''pt'''   ,				max_patches=a__).flattened_patches
                    self.assertEqual(
                        encoded_images.shape   ,				(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)   ,				)

