

"""simple docstring"""




class    lowerCamelCase						:

			def __init__(					self				:						Union[str, Any]    ,				__UpperCAmelCase				:						Dict    ,				__UpperCAmelCase				:						Any    ,				__UpperCAmelCase				:						Optional[Any]       )   ->    List[str]:
						SCREAMING_SNAKE_CASE__								=      None
						SCREAMING_SNAKE_CASE__								=      None
						SCREAMING_SNAKE_CASE__								=      graph

						self._normalize_graph(__UpperCAmelCase    ,				__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      len(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      None

			def 					SCREAMING_SNAKE_CASE				(					self				:						List[Any]    ,				__UpperCAmelCase				:						Union[str, Any]    ,				__UpperCAmelCase				:						str       )   ->    List[str]:
						if sources is int:
									SCREAMING_SNAKE_CASE__								=      [sources]
						if sinks is int:
									SCREAMING_SNAKE_CASE__								=      [sinks]

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

						SCREAMING_SNAKE_CASE__								=      sources[0]
						SCREAMING_SNAKE_CASE__								=      sinks[0]

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

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

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

			def 					SCREAMING_SNAKE_CASE				(					self				:						List[str]       )   ->    Tuple:
						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[Any]    ,				__UpperCAmelCase				:						str       )   ->    Tuple:
						SCREAMING_SNAKE_CASE__								=      algorithm(self       )





class    lowerCamelCase						:

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

			def 					SCREAMING_SNAKE_CASE				(					self				:						List[Any]       )   ->    Tuple:
						if not self.executed:
									self._algorithm()
									SCREAMING_SNAKE_CASE__								=      True





			def 					SCREAMING_SNAKE_CASE				(					self				:						Tuple       )   ->    Any:
						pass





class    lowerCamelCase						(A__			):

			def __init__(					self				:						Optional[Any]    ,				__UpperCAmelCase				:						Optional[int]       )   ->    Optional[Any]:
						super().__init__(__UpperCAmelCase       )
						# use this to save your result
						SCREAMING_SNAKE_CASE__								=      -1





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

						return self.maximum_flow





class    lowerCamelCase						(A__			):

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

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

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

			def 					SCREAMING_SNAKE_CASE				(					self				:						Dict       )   ->    Optional[int]:
						SCREAMING_SNAKE_CASE__								=      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
						SCREAMING_SNAKE_CASE__								=      [
						    i
						    for i in range(self.verticies_count       )
						    if i != self.source_index and i != self.sink_index
						]

						# move through list
						SCREAMING_SNAKE_CASE__								=      0
						while i < len(__UpperCAmelCase       ):
									SCREAMING_SNAKE_CASE__								=      vertices_list[i]
									SCREAMING_SNAKE_CASE__								=      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       )       )
												SCREAMING_SNAKE_CASE__								=      0
									else:
												i += 1

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

			def 					SCREAMING_SNAKE_CASE				(					self				:						int    ,				__UpperCAmelCase				:						Tuple       )   ->    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(__UpperCAmelCase    ,				__UpperCAmelCase       )

									self.relabel(__UpperCAmelCase       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						str    ,				__UpperCAmelCase				:						int    ,				__UpperCAmelCase				:						Union[str, Any]       )   ->    str:
						SCREAMING_SNAKE_CASE__								=      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				:						Optional[Any]    ,				__UpperCAmelCase				:						Dict       )   ->    str:
						SCREAMING_SNAKE_CASE__								=      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):
												SCREAMING_SNAKE_CASE__								=      self.heights[to_index]

						if min_height is not None:
									SCREAMING_SNAKE_CASE__								=      min_height + 1


if __name__ == "__main__":
						A_							:			Union[str, Any]											= [0]
						A_							:			List[str]											= [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_							:			Union[str, Any]											= [[0, 7, 0, 0], [0, 0, 6, 0], [0, 0, 0, 8], [9, 0, 0, 0]]

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

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





"""simple docstring"""




import unittest

import numpy as np

from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    load_numpy,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)


if is_onnx_available():
						import onnxruntime as ort





@nightly
@require_onnxruntime
@require_torch_gpu
class    lowerCamelCase						(unittest.TestCase			):

			@property
			def 					SCREAMING_SNAKE_CASE				(					self				:						Dict       )   ->    str:
						return (
						    "CUDAExecutionProvider",
						    {
						        "gpu_mem_limit": "15000000000",  # 15GB
						        "arena_extend_strategy": "kSameAsRequested",
						    },
						)

			@property
			def 					SCREAMING_SNAKE_CASE				(					self				:						Tuple       )   ->    Optional[Any]:
						SCREAMING_SNAKE_CASE__								=      ort.SessionOptions()
						SCREAMING_SNAKE_CASE__								=      False
						return options





			def 					SCREAMING_SNAKE_CASE				(					self				:						Dict       )   ->    Any:
						SCREAMING_SNAKE_CASE__								=      load_image(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
						    """/in_paint/overture-creations-5sI6fQgYIuo.png"""       )
						SCREAMING_SNAKE_CASE__								=      load_image(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
						    """/in_paint/overture-creations-5sI6fQgYIuo_mask.png"""       )
						SCREAMING_SNAKE_CASE__								=      load_numpy(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
						    """/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy"""       )

						# using the PNDM scheduler by default
						SCREAMING_SNAKE_CASE__								=      OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
						    """CompVis/stable-diffusion-v1-4"""    ,				revision="""onnx"""    ,				safety_checker=__UpperCAmelCase    ,				feature_extractor=__UpperCAmelCase    ,				provider=self.gpu_provider    ,				sess_options=self.gpu_options    ,				)
						pipe.set_progress_bar_config(disable=__UpperCAmelCase       )

						SCREAMING_SNAKE_CASE__								=      """A red cat sitting on a park bench"""

						SCREAMING_SNAKE_CASE__								=      np.random.RandomState(0       )
						SCREAMING_SNAKE_CASE__								=      pipe(
						    prompt=__UpperCAmelCase    ,				image=__UpperCAmelCase    ,				mask_image=__UpperCAmelCase    ,				strength=0.75    ,				guidance_scale=7.5    ,				num_inference_steps=1_5    ,				generator=__UpperCAmelCase    ,				output_type="""np"""    ,				)

						SCREAMING_SNAKE_CASE__								=      output.images[0]

						assert image.shape == (5_1_2, 5_1_2, 3)
						assert np.abs(expected_image - image       ).max() < 1e-2


