

class    _a						:


					'''simple docstring'''






					def __init__(			self   ,	A__   ,	A__   ,	A__      ):
						A__							:   List[str]					       =		None
						A__							:   List[Any]					       =		None
						A__							:   Any					       =		graph

						self._normalize_graph(A__   ,	A__      )
						A__							:   Optional[Any]					       =		len(A__      )
						A__							:   Union[str, Any]					       =		None






					def   __A      (			self   ,	A__   ,	A__      ):
						if sources is int:
							A__							:   Any					       =		[sources]
						if sinks is int:
							A__							:   Union[str, Any]					       =		[sinks]

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

						A__							:   Tuple					       =		sources[0]
						A__							:   List[str]					       =		sinks[0]

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

							A__							:   Dict					       =		len(self.graph      ) + 1
							for room in self.graph:
								room.insert(0   ,	0      )
							self.graph.insert(0   ,	[0] * size      )
							for i in sources:
								A__							:   Tuple					       =		max_input_flow
							A__							:   Union[str, Any]					       =		0

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






					def   __A      (			self      ):
						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   __A      (			self   ,	A__      ):
						A__							:   Optional[Any]					       =		algorithm(self      )



class    _a						:


					'''simple docstring'''






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






					def   __A      (			self      ):
						if not self.executed:
							self._algorithm()
							A__							:   List[Any]					       =		True






					def   __A      (			self      ):
						pass



class    _a						(__magic_name__    ):


					'''simple docstring'''






					def __init__(			self   ,	A__      ):
						super().__init__(A__      )
						# use this to save your result
						A__							:   Union[str, Any]					       =		-1






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

						return self.maximum_flow



class    _a						(__magic_name__    ):


					'''simple docstring'''






					def __init__(			self   ,	A__      ):
						super().__init__(A__      )

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

						A__							:   List[str]					       =		[0] * self.verticies_count
						A__							:   Dict					       =		[0] * self.verticies_count






					def   __A      (			self      ):
						A__							:   Union[str, 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
						A__							:   List[Any]					       =		[
						    i
						    for i in range(self.verticies_count      )
						    if i != self.source_index and i != self.sink_index
						]

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

						A__							:   Tuple					       =		sum(self.preflow[self.source_index]      )






					def   __A      (			self   ,	A__      ):
						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(A__   ,	A__      )

							self.relabel(A__      )






					def   __A      (			self   ,	A__   ,	A__      ):
						A__							:   List[Any]					       =		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   __A      (			self   ,	A__      ):
						A__							:   List[str]					       =		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):
								A__							:   int					       =		self.heights[to_index]

						if min_height is not None:
							A__							:   Optional[int]					       =		min_height + 1


if __name__ == "__main__":
	A_	:			int  	=      [0]
	A_	:			Optional[Any]  	=      [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_	:			Tuple  	=      [[0, 7, 0, 0], [0, 0, 6, 0], [0, 0, 0, 8], [9, 0, 0, 0]]

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

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






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


A_	:			Optional[int]  	=      {'configuration_fnet': ['FNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FNetConfig']}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_	:			Optional[Any]  	=      ['FNetTokenizer']

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_	:			Tuple  	=      ['FNetTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_	:			List[Any]  	=      [
	    'FNET_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'FNetForMaskedLM',
	    'FNetForMultipleChoice',
	    'FNetForNextSentencePrediction',
	    'FNetForPreTraining',
	    'FNetForQuestionAnswering',
	    'FNetForSequenceClassification',
	    'FNetForTokenClassification',
	    'FNetLayer',
	    'FNetModel',
	    'FNetPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_fnet import FNetTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_fnet_fast import FNetTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_fnet import (
		    FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
		    FNetForMaskedLM,
		    FNetForMultipleChoice,
		    FNetForNextSentencePrediction,
		    FNetForPreTraining,
		    FNetForQuestionAnswering,
		    FNetForSequenceClassification,
		    FNetForTokenClassification,
		    FNetLayer,
		    FNetModel,
		    FNetPreTrainedModel,
		)


else:
	import sys

	A_	:			Optional[Any]  	=      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



