
'''simple docstring'''





a__		:   Union[str, Any]            =       float("inf")


class  UpperCamelCase__  :






	def __init__(			self  :Any	,				_A  :List[Any]			)				->			Optional[Any]:







							'''simple docstring'''



							__A       	=			[0] * n
							__A       	=			[0] * n
							__A       	=			[0] * n
							__A       	=			[[] for _ in range(_A			)]






	def 	lowercase_						(			self  :Dict	,				_A  :List[str]	,				_A  :str	,				_A  :Optional[int]	,				_A  :List[str]=0			)				->			str:







							'''simple docstring'''



							self.adj[a].append([b, len(self.adj[b]			), c, 0]			)
							self.adj[b].append([a, len(self.adj[a]			) - 1, rcap, 0]			)






	def 	lowercase_						(			self  :List[Any]	,				_A  :List[Any]	,				_A  :Optional[int]	,				_A  :str			)				->			List[Any]:







							'''simple docstring'''



							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]	,				_A	,				min(_A	,				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 	lowercase_						(			self  :Any	,				_A  :List[Any]	,				_A  :List[Any]			)				->			List[str]:







							'''simple docstring'''



							__A	,      __A       	=			0, source
							for l in range(31			):  # 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]) >> (30 - l):
																																					__A       	=			e[0]
																																					qe += 1
																																					__A       	=			self.lvl[v] + 1

																			__A       	=			self.depth_first_search(_A	,				_A	,				_A			)
																			while p:
																									flow += p
																									__A       	=			self.depth_first_search(_A	,				_A	,				_A			)

																			if not self.lvl[sink]:
																									break

							return flow
a__		:   Any            =       Dinic(1_0)
a__		:   Dict            =       0
a__		:   int            =       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))








'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
    is_vision_available,
)


a__		:   Optional[Any]            =       {
    "configuration_mobilevitv2": [
        "MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "MobileViTV2Config",
        "MobileViTV2OnnxConfig",
    ],
}


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a__		:   Optional[int]            =       [
     "MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST",
     "MobileViTV2ForImageClassification",
     "MobileViTV2ForSemanticSegmentation",
     "MobileViTV2Model",
     "MobileViTV2PreTrainedModel",
 ]

if TYPE_CHECKING:
 from .configuration_mobilevitva import (
     MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
     MobileViTVaConfig,
     MobileViTVaOnnxConfig,
 )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_mobilevitva import (
      MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
      MobileViTVaForImageClassification,
      MobileViTVaForSemanticSegmentation,
      MobileViTVaModel,
      MobileViTVaPreTrainedModel,
  )

else:
 import sys

 a__		:   List[str]            =       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






