
'''simple docstring'''






class     __snake_case		:
					def __init__(     self,    A,    A,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			str									= None
									lowerCamelCase					:			List[str]									= None
									lowerCamelCase					:			Tuple									= graph

									self._normalize_graph(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_      )
									lowerCamelCase					:			int									= len(SCREAMING_SNAKE_CASE_      )
									lowerCamelCase					:			List[str]									= None
					def     UpperCAmelCase_							(     self,    A,    A      ):

									"""simple docstring"""


									if sources is int:
													lowerCamelCase					:			Any									= [sources]
									if sinks is int:
													lowerCamelCase					:			int									= [sinks]

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

									lowerCamelCase					:			List[str]									= sources[0]
									lowerCamelCase					:			int									= sinks[0]

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

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

													lowerCamelCase					:			str									= len(self.graph      ) + 1
													for room in self.graph:
																	room.append(0      )
													self.graph.append([0] * size      )
													for i in sinks:
																	lowerCamelCase					:			List[str]									= max_input_flow
													lowerCamelCase					:			Dict									= size - 1
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									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     UpperCAmelCase_							(     self,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			Dict									= algorithm(self      )





class     __snake_case		:
					def __init__(     self,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			List[str]									= flow_network
									lowerCamelCase					:			str									= flow_network.verticesCount
									lowerCamelCase					:			Tuple									= flow_network.sourceIndex
									lowerCamelCase					:			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
									lowerCamelCase					:			List[str]									= flow_network.graph
									lowerCamelCase					:			str									= False
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									if not self.executed:
													self._algorithm()
													lowerCamelCase					:			Optional[int]									= True




					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									pass





class     __snake_case		(   __lowerCamelCase):
					def __init__(     self,    A      ):

									"""simple docstring"""


									super().__init__(SCREAMING_SNAKE_CASE_      )
									# use this to save your result
									lowerCamelCase					:			Tuple									= -1




					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


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

									return self.maximum_flow





class     __snake_case		(   __lowerCamelCase):
					def __init__(     self,    A      ):

									"""simple docstring"""


									super().__init__(SCREAMING_SNAKE_CASE_      )

									lowerCamelCase					:			Optional[Any]									= [[0] * self.verticies_count for i in range(self.verticies_count      )]

									lowerCamelCase					:			int									= [0] * self.verticies_count
									lowerCamelCase					:			Optional[Any]									= [0] * self.verticies_count
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Dict									= 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
									lowerCamelCase					:			Optional[int]									= [
									    i
									    for i in range(self.verticies_count      )
									    if i != self.source_index and i != self.sink_index
									]

									# move through list
									lowerCamelCase					:			int									= 0
									while i < len(SCREAMING_SNAKE_CASE_      ):
													lowerCamelCase					:			Tuple									= vertices_list[i]
													lowerCamelCase					:			Optional[Any]									= self.heights[vertex_index]
													self.process_vertex(SCREAMING_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(SCREAMING_SNAKE_CASE_      )      )
																	lowerCamelCase					:			int									= 0
													else:
																	i += 1

									lowerCamelCase					:			Any									= sum(self.preflow[self.source_index]      )
					def     UpperCAmelCase_							(     self,    A      ):

									"""simple docstring"""


									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(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_      )

													self.relabel(SCREAMING_SNAKE_CASE_      )
					def     UpperCAmelCase_							(     self,    A,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			Optional[int]									= 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     UpperCAmelCase_							(     self,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			Tuple									= 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):
																	lowerCamelCase					:			Tuple									= self.heights[to_index]

									if min_height is not None:
													lowerCamelCase					:			List[str]									= min_height + 1


if __name__ == "__main__":
					A			      =      [0]
					A			      =      [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			      =      [[0, 7, 0, 0], [0, 0, 6, 0], [0, 0, 0, 8], [9, 0, 0, 0]]

					# prepare our network
					A			      =      FlowNetwork(graph, entrances, exits)
					# set algorithm
					flow_network.set_maximum_flow_algorithm(PushRelabelExecutor)
					# and calculate
					A			      =      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_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


A			      =      {
    'configuration_clip': [
        'CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'CLIPConfig',
        'CLIPOnnxConfig',
        'CLIPTextConfig',
        'CLIPVisionConfig',
    ],
    'processing_clip': ['CLIPProcessor'],
    'tokenization_clip': ['CLIPTokenizer'],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A			      =      ['CLIPTokenizerFast']

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A			      =      ['CLIPFeatureExtractor']
					A			      =      ['CLIPImageProcessor']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A			      =      [
					    'CLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'CLIPModel',
					    'CLIPPreTrainedModel',
					    'CLIPTextModel',
					    'CLIPTextModelWithProjection',
					    'CLIPVisionModel',
					    'CLIPVisionModelWithProjection',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A			      =      [
					    'TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'TFCLIPModel',
					    'TFCLIPPreTrainedModel',
					    'TFCLIPTextModel',
					    'TFCLIPVisionModel',
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A			      =      [
					    'FlaxCLIPModel',
					    'FlaxCLIPPreTrainedModel',
					    'FlaxCLIPTextModel',
					    'FlaxCLIPTextPreTrainedModel',
					    'FlaxCLIPVisionModel',
					    'FlaxCLIPVisionPreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_clip import (
					    CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    CLIPConfig,
					    CLIPOnnxConfig,
					    CLIPTextConfig,
					    CLIPVisionConfig,
					)
					from .processing_clip import CLIPProcessor
					from .tokenization_clip import CLIPTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_clip_fast import CLIPTokenizerFast

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_clip import CLIPFeatureExtractor
										from .image_processing_clip import CLIPImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_clip import (
										    CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
										    CLIPModel,
										    CLIPPreTrainedModel,
										    CLIPTextModel,
										    CLIPTextModelWithProjection,
										    CLIPVisionModel,
										    CLIPVisionModelWithProjection,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_clip import (
										    TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFCLIPModel,
										    TFCLIPPreTrainedModel,
										    TFCLIPTextModel,
										    TFCLIPVisionModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_clip import (
										    FlaxCLIPModel,
										    FlaxCLIPPreTrainedModel,
										    FlaxCLIPTextModel,
										    FlaxCLIPTextPreTrainedModel,
										    FlaxCLIPVisionModel,
										    FlaxCLIPVisionPreTrainedModel,
										)


else:
					import sys

					A			      =      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
