






"""simple docstring"""





class       __A		:

					def __init__(			self  :      Optional[int]     ,		__snake_case  :      int     ,		__snake_case  :      Dict     ,		__snake_case  :      Optional[int]					)			->							List[str]:
											__magic_name__:	Any							=       None
											__magic_name__:	int							=       None
											__magic_name__:	Tuple							=       graph

											self._normalize_graph(__snake_case     ,		__snake_case					)
											__magic_name__:	List[Any]							=       len(__snake_case					)
											__magic_name__:	Optional[int]							=       None

					def   lowerCamelCase__   (			self  :      Optional[int]     ,		__snake_case  :      List[Any]     ,		__snake_case  :      Tuple					)			->							Optional[int]:
											if sources is int:
																	__magic_name__:	List[Any]							=       [sources]
											if sinks is int:
																	__magic_name__:	Dict							=       [sinks]

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

											__magic_name__:	Optional[int]							=       sources[0]
											__magic_name__:	str							=       sinks[0]

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

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

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

					def   lowerCamelCase__   (			self  :      List[str]					)			->							Union[str, 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   lowerCamelCase__   (			self  :      List[Any]     ,		__snake_case  :      int					)			->							Any:
											__magic_name__:	Union[str, Any]							=       algorithm(self					)



class       __A		:

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

					def   lowerCamelCase__   (			self  :      Dict					)			->							Any:
											if not self.executed:
																	self._algorithm()
																	__magic_name__:	int							=       True






					def   lowerCamelCase__   (			self  :      Optional[Any]					)			->							Dict:
											pass



class       __A		(   SCREAMING_SNAKE_CASE_			):

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






					def   lowerCamelCase__   (			self  :      Dict					)			->							List[Any]:
											if not self.executed:
																	raise Exception("""You should execute algorithm before using its result!"""					)

											return self.maximum_flow



class       __A		(   SCREAMING_SNAKE_CASE_			):

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

											__magic_name__:	Dict							=       [[0] * self.verticies_count for i in range(self.verticies_count					)]

											__magic_name__:	str							=       [0] * self.verticies_count
											__magic_name__:	Dict							=       [0] * self.verticies_count

					def   lowerCamelCase__   (			self  :      Any					)			->							List[str]:
											__magic_name__:	Optional[Any]							=       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
											__magic_name__:	Optional[Any]							=       [
											    i
											    for i in range(self.verticies_count					)
											    if i != self.source_index and i != self.sink_index
											]

											# move through list
											__magic_name__:	Union[str, Any]							=       0
											while i < len(__snake_case					):
																	__magic_name__:	Tuple							=       vertices_list[i]
																	__magic_name__:	str							=       self.heights[vertex_index]
																	self.process_vertex(__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(__snake_case					)					)
																							__magic_name__:	List[Any]							=       0
																	else:
																							i += 1

											__magic_name__:	List[str]							=       sum(self.preflow[self.source_index]					)

					def   lowerCamelCase__   (			self  :      List[Any]     ,		__snake_case  :      str					)			->							Optional[int]:
											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(__snake_case     ,		__snake_case					)

																	self.relabel(__snake_case					)

					def   lowerCamelCase__   (			self  :      Optional[Any]     ,		__snake_case  :      int     ,		__snake_case  :      Optional[int]					)			->							str:
											__magic_name__:	Dict							=       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   lowerCamelCase__   (			self  :      List[Any]     ,		__snake_case  :      List[str]					)			->							Union[str, Any]:
											__magic_name__:	List[Any]							=       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):
																							__magic_name__:	Optional[int]							=       self.heights[to_index]

											if min_height is not None:
																	__magic_name__:	List[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'''
from typing import TYPE_CHECKING

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


lowercase__						       =      {
    "configuration_poolformer": [
        "POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "PoolFormerConfig",
        "PoolFormerOnnxConfig",
    ]
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowercase__						       =      ["PoolFormerFeatureExtractor"]
						lowercase__						       =      ["PoolFormerImageProcessor"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowercase__						       =      [
						    "POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "PoolFormerForImageClassification",
						    "PoolFormerModel",
						    "PoolFormerPreTrainedModel",
						]


if TYPE_CHECKING:
						from .configuration_poolformer import (
						    POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    PoolFormerConfig,
						    PoolFormerOnnxConfig,
						)

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_poolformer import PoolFormerFeatureExtractor
												from .image_processing_poolformer import PoolFormerImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_poolformer import (
												    POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    PoolFormerForImageClassification,
												    PoolFormerModel,
												    PoolFormerPreTrainedModel,
												)


else:
						import sys

						lowercase__						       =      _LazyModule(__name__, globals()["__file__"], _import_structure)


