






'''simple docstring'''







class 	SCREAMING_SNAKE_CASE					:






			def __init__( self      :			Union[str, Any]  ,						__SCREAMING_SNAKE_CASE      :			Union[str, Any]  ,						__SCREAMING_SNAKE_CASE      :			List[Any]  ,						__SCREAMING_SNAKE_CASE      :			Tuple					)			->			List[str]:
							a_		:  int											=      None
							a_		:  int											=      None
							a_		:  List[str]											=      graph

							self._normalize_graph(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					)
							a_		:  Tuple											=      len(__SCREAMING_SNAKE_CASE					)
							a_		:  int											=      None






			def 			SCREAMING_SNAKE_CASE      ( self      :			List[Any]  ,						__SCREAMING_SNAKE_CASE      :			int  ,						__SCREAMING_SNAKE_CASE      :			int					)			->			Optional[Any]:
							if sources is int:
											a_		:  Optional[int]											=      [sources]
							if sinks is int:
											a_		:  Optional[int]											=      [sinks]

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

							a_		:  List[Any]											=      sources[0]
							a_		:  Dict											=      sinks[0]

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

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

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






			def 			SCREAMING_SNAKE_CASE      ( self      :			Optional[int]					)			->			List[Any]:
							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 			SCREAMING_SNAKE_CASE      ( self      :			Optional[int]  ,						__SCREAMING_SNAKE_CASE      :			int					)			->			Optional[Any]:
							a_		:  Any											=      algorithm(self					)





class 	SCREAMING_SNAKE_CASE					:






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






			def 			SCREAMING_SNAKE_CASE      ( self      :			Any					)			->			Tuple:
							if not self.executed:
											self._algorithm()
											a_		:  Any											=      True







			def 			SCREAMING_SNAKE_CASE      ( self      :			List[str]					)			->			Tuple:
							pass





class 	SCREAMING_SNAKE_CASE					(					SCREAMING_SNAKE_CASE_       ):






			def __init__( self      :			str  ,						__SCREAMING_SNAKE_CASE      :			Dict					)			->			Dict:
							super().__init__(__SCREAMING_SNAKE_CASE					)
							# use this to save your result
							a_		:  Dict											=      -1







			def 			SCREAMING_SNAKE_CASE      ( self      :			Any					)			->			Tuple:
							if not self.executed:
											raise Exception('''You should execute algorithm before using its result!'''					)

							return self.maximum_flow





class 	SCREAMING_SNAKE_CASE					(					SCREAMING_SNAKE_CASE_       ):






			def __init__( self      :			List[Any]  ,						__SCREAMING_SNAKE_CASE      :			List[str]					)			->			List[Any]:
							super().__init__(__SCREAMING_SNAKE_CASE					)

							a_		:  List[str]											=      [[0] * self.verticies_count for i in range(self.verticies_count					)]

							a_		:  Dict											=      [0] * self.verticies_count
							a_		:  Any											=      [0] * self.verticies_count






			def 			SCREAMING_SNAKE_CASE      ( self      :			Any					)			->			Union[str, Any]:
							a_		:  Dict											=      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_		:  Optional[int]											=      [
							    i
							    for i in range(self.verticies_count					)
							    if i != self.source_index and i != self.sink_index
							]

							# move through list
							a_		:  List[Any]											=      0
							while i < len(__SCREAMING_SNAKE_CASE					):
											a_		:  Optional[int]											=      vertices_list[i]
											a_		:  Optional[Any]											=      self.heights[vertex_index]
											self.process_vertex(__SCREAMING_SNAKE_CASE					)
											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(__SCREAMING_SNAKE_CASE					)					)
															a_		:  Dict											=      0
											else:
															i += 1

							a_		:  Dict											=      sum(self.preflow[self.source_index]					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			Optional[Any]  ,						__SCREAMING_SNAKE_CASE      :			Optional[Any]					)			->			Union[str, Any]:
							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(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					)

											self.relabel(__SCREAMING_SNAKE_CASE					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			List[Any]  ,						__SCREAMING_SNAKE_CASE      :			str  ,						__SCREAMING_SNAKE_CASE      :			str					)			->			Tuple:
							a_		:  str											=      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 			SCREAMING_SNAKE_CASE      ( self      :			Dict  ,						__SCREAMING_SNAKE_CASE      :			Optional[Any]					)			->			Dict:
							a_		:  List[str]											=      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_		:  Optional[Any]											=      self.heights[to_index]

							if min_height is not None:
											a_		:  Union[str, Any]											=      min_height + 1


if __name__ == "__main__":
		__lowerCAmelCase       		=      [0]
		__lowerCAmelCase       		=      [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       		=      [[0, 7, 0, 0], [0, 0, 6, 0], [0, 0, 0, 8], [9, 0, 0, 0]]

		# prepare our network
		__lowerCAmelCase       		=      FlowNetwork(graph, entrances, exits)
		# set algorithm
		flow_network.set_maximum_flow_algorithm(PushRelabelExecutor)
		# and calculate
		__lowerCAmelCase       		=      flow_network.find_maximum_flow()

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












'''simple docstring'''







import unittest

import numpy as np
import torch

from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()





class 	SCREAMING_SNAKE_CASE					(					SCREAMING_SNAKE_CASE_	,   unittest.TestCase       ):
			snake_case__  =				DDIMPipeline
			snake_case__  =				UNCONDITIONAL_IMAGE_GENERATION_PARAMS
			snake_case__  =				PipelineTesterMixin.required_optional_params - {
			    "num_images_per_prompt",
			    "latents",
			    "callback",
			    "callback_steps",
			}
			snake_case__  =				UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
			snake_case__  =				False






			def 			SCREAMING_SNAKE_CASE      ( self      :			Any					)			->			Optional[Any]:
							torch.manual_seed(0					)
							a_		:  int											=      UNetaDModel(
							    block_out_channels=(32, 64)  ,						layers_per_block=2  ,						sample_size=32  ,						in_channels=3  ,						out_channels=3  ,						down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')  ,						up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')  ,						)
							a_		:  str											=      DDIMScheduler()
							a_		:  Union[str, Any]											=      {'''unet''': unet, '''scheduler''': scheduler}
							return components






			def 			SCREAMING_SNAKE_CASE      ( self      :			Dict  ,						__SCREAMING_SNAKE_CASE      :			str  ,						__SCREAMING_SNAKE_CASE      :			Tuple=0					)			->			str:
							if str(__SCREAMING_SNAKE_CASE					).startswith('''mps'''					):
											a_		:  Dict											=      torch.manual_seed(__SCREAMING_SNAKE_CASE					)
							else:
											a_		:  Union[str, Any]											=      torch.Generator(device=__SCREAMING_SNAKE_CASE					).manual_seed(__SCREAMING_SNAKE_CASE					)
							a_		:  Union[str, Any]											=      {
							    '''batch_size''': 1,
							    '''generator''': generator,
							    '''num_inference_steps''': 2,
							    '''output_type''': '''numpy''',
							}
							return inputs






			def 			SCREAMING_SNAKE_CASE      ( self      :			Union[str, Any]					)			->			Union[str, Any]:
							a_		:  Dict											=      '''cpu'''

							a_		:  List[Any]											=      self.get_dummy_components()
							a_		:  List[str]											=      self.pipeline_class(**__SCREAMING_SNAKE_CASE					)
							pipe.to(__SCREAMING_SNAKE_CASE					)
							pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE					)

							a_		:  Tuple											=      self.get_dummy_inputs(__SCREAMING_SNAKE_CASE					)
							a_		:  Optional[Any]											=      pipe(**__SCREAMING_SNAKE_CASE					).images
							a_		:  List[str]											=      image[0, -3:, -3:, -1]

							self.assertEqual(image.shape  ,						(1, 32, 32, 3)					)
							a_		:  int											=      np.array(
							    [1.000e00, 5.717e-01, 4.717e-01, 1.000e00, 0.000e00, 1.000e00, 3.000e-04, 0.000e00, 9.000e-04]					)
							a_		:  Union[str, Any]											=      np.abs(image_slice.flatten() - expected_slice					).max()
							self.assertLessEqual(__SCREAMING_SNAKE_CASE  ,						1e-3					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			List[str]					)			->			str:
							super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			str					)			->			Optional[int]:
							super().test_save_load_local(expected_max_difference=3e-3					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			Any					)			->			List[str]:
							super().test_save_load_optional_components(expected_max_difference=3e-3					)







			def 			SCREAMING_SNAKE_CASE      ( self      :			str					)			->			Optional[int]:
							super().test_inference_batch_single_identical(expected_max_diff=3e-3					)





@slow
@require_torch_gpu
class 	SCREAMING_SNAKE_CASE					(					unittest.TestCase       ):






			def 			SCREAMING_SNAKE_CASE      ( self      :			str					)			->			Any:
							a_		:  Optional[Any]											=      '''google/ddpm-cifar10-32'''

							a_		:  Optional[Any]											=      UNetaDModel.from_pretrained(__SCREAMING_SNAKE_CASE					)
							a_		:  Dict											=      DDIMScheduler()

							a_		:  List[str]											=      DDIMPipeline(unet=__SCREAMING_SNAKE_CASE  ,						scheduler=__SCREAMING_SNAKE_CASE					)
							ddim.to(__SCREAMING_SNAKE_CASE					)
							ddim.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE					)

							a_		:  Tuple											=      torch.manual_seed(0					)
							a_		:  Tuple											=      ddim(generator=__SCREAMING_SNAKE_CASE  ,						eta=0.0  ,						output_type='''numpy'''					).images

							a_		:  List[Any]											=      image[0, -3:, -3:, -1]

							assert image.shape == (1, 32, 32, 3)
							a_		:  List[str]											=      np.array([0.1723, 0.1617, 0.1600, 0.1626, 0.1497, 0.1513, 0.1505, 0.1442, 0.1453]					)

							assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2







			def 			SCREAMING_SNAKE_CASE      ( self      :			Union[str, Any]					)			->			str:
							a_		:  int											=      '''google/ddpm-ema-bedroom-256'''

							a_		:  str											=      UNetaDModel.from_pretrained(__SCREAMING_SNAKE_CASE					)
							a_		:  Tuple											=      DDIMScheduler.from_pretrained(__SCREAMING_SNAKE_CASE					)

							a_		:  Any											=      DDIMPipeline(unet=__SCREAMING_SNAKE_CASE  ,						scheduler=__SCREAMING_SNAKE_CASE					)
							ddpm.to(__SCREAMING_SNAKE_CASE					)
							ddpm.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE					)

							a_		:  Tuple											=      torch.manual_seed(0					)
							a_		:  List[Any]											=      ddpm(generator=__SCREAMING_SNAKE_CASE  ,						output_type='''numpy'''					).images

							a_		:  Optional[int]											=      image[0, -3:, -3:, -1]

							assert image.shape == (1, 256, 256, 3)
							a_		:  Optional[Any]											=      np.array([0.0060, 0.0201, 0.0344, 0.0024, 0.0018, 0.0002, 0.0022, 0.0000, 0.0069]					)

							assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2




