


'''simple docstring'''





lowercase_								=    float('inf')




class 					_UpperCamelCase :







							'''simple docstring'''







							def __init__(		self  :				List[Any]		,				SCREAMING_SNAKE_CASE_  :				List[str]		):
													_a     =			[0] * n
													_a     =			[0] * n
													_a     =			[0] * n
													_a     =			[[] for _ in range(SCREAMING_SNAKE_CASE_		)]







							def 		_UpperCAmelCase	(		self  :				str		,				SCREAMING_SNAKE_CASE_  :				Any		,				SCREAMING_SNAKE_CASE_  :				Tuple		,				SCREAMING_SNAKE_CASE_  :				Dict		,				SCREAMING_SNAKE_CASE_  :				int=0		):
													self.adj[a].append([b, len(self.adj[b]		), c, 0]		)
													self.adj[b].append([a, len(self.adj[a]		) - 1, rcap, 0]		)







							def 		_UpperCAmelCase	(		self  :				Optional[Any]		,				SCREAMING_SNAKE_CASE_  :				List[Any]		,				SCREAMING_SNAKE_CASE_  :				Union[str, Any]		,				SCREAMING_SNAKE_CASE_  :				Optional[int]		):
													if vertex == sink or not flow:
																			return flow

													for i in range(self.ptr[vertex]		,				len(self.adj[vertex]		)		):
																			_a     =			self.adj[vertex][i]
																			if self.lvl[e[0]] == self.lvl[vertex] + 1:
																									_a     =			self.depth_first_search(e[0]		,				SCREAMING_SNAKE_CASE_		,				min(SCREAMING_SNAKE_CASE_		,				e[2] - e[3]		)		)
																									if p:
																															self.adj[vertex][i][3] += p
																															self.adj[e[0]][e[1]][3] -= p
																															return p
																			_a     =			self.ptr[vertex] + 1
													return 0







							def 		_UpperCAmelCase	(		self  :				Optional[int]		,				SCREAMING_SNAKE_CASE_  :				str		,				SCREAMING_SNAKE_CASE_  :				str		):
													_a ,	_a     =			0, source
													for l in range(3_1		):  # noqa: E741  l = 30 maybe faster for random data
																			while True:
																									_a ,	_a     =			[0] * len(self.q		), [0] * len(self.q		)
																									_a ,	_a ,	_a     =			0, 1, 1
																									while qi < qe and not self.lvl[sink]:
																															_a     =			self.q[qi]
																															qi += 1
																															for e in self.adj[v]:
																																					if not self.lvl[e[0]] and (e[2] - e[3]) >> (3_0 - l):
																																											_a     =			e[0]
																																											qe += 1
																																											_a     =			self.lvl[v] + 1

																									_a     =			self.depth_first_search(SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		)
																									while p:
																															flow += p
																															_a     =			self.depth_first_search(SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		)

																									if not self.lvl[sink]:
																															break

													return flow




lowercase_								=    Dinic(10)
lowercase_								=    0
lowercase_								=    9
for vertex in range(1, 5):
		graph.add_edge(source, vertex, 1)
for vertex in range(5, 9):
		graph.add_edge(vertex, sink, 1)
for vertex in range(1, 5):
		graph.add_edge(vertex, vertex + 4, 1)

# Now we can know that is the maximum flow(source -> sink)
print(graph.max_flow(source, sink))






import numpy as np
import PIL
import torch
import torchvision.transforms as T
import torchvision.transforms.functional as TF
from PIL import Image


def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase							,     _UpperCAmelCase=256				)    ->  Dict:
						_a     =			min(img.size				)

						if s < target_image_size:
												raise ValueError(f"""min dim for image {s} < {target_image_size}"""				)

						_a     =			target_image_size / s
						_a     =			(round(r * img.size[1]				), round(r * img.size[0]				))
						_a     =			TF.resize(_UpperCAmelCase							,     _UpperCAmelCase							,     interpolation=PIL.Image.LANCZOS				)
						_a     =			TF.center_crop(_UpperCAmelCase							,     output_size=2 * [target_image_size]				)
						_a     =			torch.unsqueeze(T.ToTensor()(_UpperCAmelCase				)							,     0				)
						return img


def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase				)    ->  List[Any]:
						_a     =			2.0 * x - 1.0
						return x


def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase							,     _UpperCAmelCase=True							,     _UpperCAmelCase="RGB"				)    ->  Optional[Any]:
						_a     =			x.detach().cpu()
						if process:
												_a     =			post_process_tensor(_UpperCAmelCase				)
						_a     =			x.numpy()
						if process:
												_a     =			(255 * x).astype(np.uinta				)
						_a     =			Image.fromarray(_UpperCAmelCase				)
						if not x.mode == mode:
												_a     =			x.convert(_UpperCAmelCase				)
						return x


def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase				)    ->  Optional[Any]:
						_a     =			torch.clamp(_UpperCAmelCase							,     -1.0							,     1.0				)
						_a     =			(x + 1.0) / 2.0
						_a     =			x.permute(1							,     2							,     0				)
						return x






def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase				)    ->  str:
						_a     =			post_process_tensor(x.squeeze()				)
						return x.permute(2							,     0							,     1				).unsqueeze(0				)


