code: 
  
  
  '''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))
  
  
  
style_context: 
  
  
  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				)
  
  
  