


'''simple docstring'''


class 		lowerCAmelCase__	:




			def __init__(						self     :   str		,     lowerCamelCase__     :   List[str]		,     lowerCamelCase__     :   Dict		,     lowerCamelCase__     :   int       )      ->List[str]:



					'''simple docstring'''







					_UpperCAmelCase						:				Union[str, Any]	    =					None
					_UpperCAmelCase						:				List[Any]	    =					None
					_UpperCAmelCase						:				str	    =					graph

					self._normalize_graph(lowerCamelCase__		,     lowerCamelCase__       )
					_UpperCAmelCase						:				str	    =					len(lowerCamelCase__       )
					_UpperCAmelCase						:				Any	    =					None




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



					'''simple docstring'''







					if sources is int:
							_UpperCAmelCase						:				Optional[Any]	    =					[sources]
					if sinks is int:
							_UpperCAmelCase						:				str	    =					[sinks]

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

					_UpperCAmelCase						:				Optional[Any]	    =					sources[0]
					_UpperCAmelCase						:				Optional[Any]	    =					sinks[0]

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

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

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




			def 							lowerCAmelCase__			(						self     :   List[str]       )      ->List[str]:



					'''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 							lowerCAmelCase__			(						self     :   Dict		,     lowerCamelCase__     :   Any       )      ->Tuple:



					'''simple docstring'''







					_UpperCAmelCase						:				List[str]	    =					algorithm(self       )






class 		lowerCAmelCase__	:




			def __init__(						self     :   str		,     lowerCamelCase__     :   Tuple       )      ->Any:



					'''simple docstring'''







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




			def 							lowerCAmelCase__			(						self     :   str       )      ->Optional[int]:



					'''simple docstring'''







					if not self.executed:
							self._algorithm()
							_UpperCAmelCase						:				Union[str, Any]	    =					True






			def 							lowerCAmelCase__			(						self     :   Optional[Any]       )      ->str:



					'''simple docstring'''







					pass






class 		lowerCAmelCase__	(	UpperCAmelCase__		):




			def __init__(						self     :   str		,     lowerCamelCase__     :   Dict       )      ->str:



					'''simple docstring'''







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






			def 							lowerCAmelCase__			(						self     :   Optional[Any]       )      ->Optional[Any]:



					'''simple docstring'''







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

					return self.maximum_flow






class 		lowerCAmelCase__	(	UpperCAmelCase__		):




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



					'''simple docstring'''







					super().__init__(lowerCamelCase__       )

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

					_UpperCAmelCase						:				List[str]	    =					[0] * self.verticies_count
					_UpperCAmelCase						:				List[str]	    =					[0] * self.verticies_count




			def 							lowerCAmelCase__			(						self     :   str       )      ->Optional[Any]:



					'''simple docstring'''







					_UpperCAmelCase						:				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
					_UpperCAmelCase						:				Any	    =					[
					    i
					    for i in range(self.verticies_count       )
					    if i != self.source_index and i != self.sink_index
					]

					# move through list
					_UpperCAmelCase						:				Dict	    =					0
					while i < len(lowerCamelCase__       ):
							_UpperCAmelCase						:				Optional[int]	    =					vertices_list[i]
							_UpperCAmelCase						:				str	    =					self.heights[vertex_index]
							self.process_vertex(lowerCamelCase__       )
							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(lowerCamelCase__       )       )
									_UpperCAmelCase						:				Tuple	    =					0
							else:
									i += 1

					_UpperCAmelCase						:				Dict	    =					sum(self.preflow[self.source_index]       )




			def 							lowerCAmelCase__			(						self     :   str		,     lowerCamelCase__     :   int       )      ->int:



					'''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(lowerCamelCase__		,     lowerCamelCase__       )

							self.relabel(lowerCamelCase__       )




			def 							lowerCAmelCase__			(						self     :   List[Any]		,     lowerCamelCase__     :   int		,     lowerCamelCase__     :   Dict       )      ->int:



					'''simple docstring'''







					_UpperCAmelCase						:				Optional[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 							lowerCAmelCase__			(						self     :   List[str]		,     lowerCamelCase__     :   List[str]       )      ->Dict:



					'''simple docstring'''







					_UpperCAmelCase						:				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):
									_UpperCAmelCase						:				Any	    =					self.heights[to_index]

					if min_height is not None:
							_UpperCAmelCase						:				Tuple	    =					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'''


import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


lowerCamelCase__					=   logging.get_logger(__name__)

lowerCamelCase__					=   {
    'ut/deta': 'https://huggingface.co/ut/deta/resolve/main/config.json',
}






class 		lowerCAmelCase__	(	UpperCAmelCase__		):
			lowerCAmelCase       :		Any				      =   "deta"
			lowerCAmelCase       :		Union[str, Any]				      =   {
			    "hidden_size": "d_model",
			    "num_attention_heads": "encoder_attention_heads",
			}




			def __init__(						self     :   Optional[Any]		,     lowerCamelCase__     :   str=None		,     lowerCamelCase__     :   str=9_00		,     lowerCamelCase__     :   Any=20_48		,     lowerCamelCase__     :   Optional[int]=6		,     lowerCamelCase__     :   str=20_48		,     lowerCamelCase__     :   Dict=8		,     lowerCamelCase__     :   Any=6		,     lowerCamelCase__     :   Union[str, Any]=10_24		,     lowerCamelCase__     :   Optional[int]=8		,     lowerCamelCase__     :   List[Any]=0.0		,     lowerCamelCase__     :   Tuple=True		,     lowerCamelCase__     :   int="relu"		,     lowerCamelCase__     :   str=2_56		,     lowerCamelCase__     :   Dict=0.1		,     lowerCamelCase__     :   Dict=0.0		,     lowerCamelCase__     :   Dict=0.0		,     lowerCamelCase__     :   Optional[int]=0.0_2		,     lowerCamelCase__     :   List[Any]=1.0		,     lowerCamelCase__     :   Optional[int]=True		,     lowerCamelCase__     :   Any=False		,     lowerCamelCase__     :   Any="sine"		,     lowerCamelCase__     :   str=5		,     lowerCamelCase__     :   Optional[int]=4		,     lowerCamelCase__     :   int=4		,     lowerCamelCase__     :   Optional[Any]=True		,     lowerCamelCase__     :   Dict=3_00		,     lowerCamelCase__     :   int=True		,     lowerCamelCase__     :   Dict=True		,     lowerCamelCase__     :   Union[str, Any]=1		,     lowerCamelCase__     :   Any=5		,     lowerCamelCase__     :   List[Any]=2		,     lowerCamelCase__     :   Union[str, Any]=1		,     lowerCamelCase__     :   str=1		,     lowerCamelCase__     :   Union[str, Any]=5		,     lowerCamelCase__     :   str=2		,     lowerCamelCase__     :   Optional[Any]=0.1		,     lowerCamelCase__     :   Union[str, Any]=0.2_5		,     **lowerCamelCase__     :   Optional[Any]		,     )      ->List[str]:



					'''simple docstring'''







					if backbone_config is None:
							logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."       )
							_UpperCAmelCase						:				int	    =					CONFIG_MAPPING["resnet"](out_features=["stage2", "stage3", "stage4"]       )
					else:
							if isinstance(lowerCamelCase__		,     lowerCamelCase__       ):
									_UpperCAmelCase						:				Any	    =					backbone_config.pop("model_type"       )
									_UpperCAmelCase						:				Optional[int]	    =					CONFIG_MAPPING[backbone_model_type]
									_UpperCAmelCase						:				List[str]	    =					config_class.from_dict(lowerCamelCase__       )

					_UpperCAmelCase						:				Optional[Any]	    =					backbone_config
					_UpperCAmelCase						:				Optional[int]	    =					num_queries
					_UpperCAmelCase						:				Union[str, Any]	    =					max_position_embeddings
					_UpperCAmelCase						:				Union[str, Any]	    =					d_model
					_UpperCAmelCase						:				str	    =					encoder_ffn_dim
					_UpperCAmelCase						:				Optional[int]	    =					encoder_layers
					_UpperCAmelCase						:				int	    =					encoder_attention_heads
					_UpperCAmelCase						:				Union[str, Any]	    =					decoder_ffn_dim
					_UpperCAmelCase						:				Tuple	    =					decoder_layers
					_UpperCAmelCase						:				Union[str, Any]	    =					decoder_attention_heads
					_UpperCAmelCase						:				Any	    =					dropout
					_UpperCAmelCase						:				List[str]	    =					attention_dropout
					_UpperCAmelCase						:				Union[str, Any]	    =					activation_dropout
					_UpperCAmelCase						:				Optional[int]	    =					activation_function
					_UpperCAmelCase						:				str	    =					init_std
					_UpperCAmelCase						:				Tuple	    =					init_xavier_std
					_UpperCAmelCase						:				Optional[Any]	    =					encoder_layerdrop
					_UpperCAmelCase						:				int	    =					auxiliary_loss
					_UpperCAmelCase						:				Union[str, Any]	    =					position_embedding_type
					# deformable attributes
					_UpperCAmelCase						:				List[Any]	    =					num_feature_levels
					_UpperCAmelCase						:				List[Any]	    =					encoder_n_points
					_UpperCAmelCase						:				Tuple	    =					decoder_n_points
					_UpperCAmelCase						:				Optional[int]	    =					two_stage
					_UpperCAmelCase						:				Dict	    =					two_stage_num_proposals
					_UpperCAmelCase						:				int	    =					with_box_refine
					_UpperCAmelCase						:				str	    =					assign_first_stage
					if two_stage is True and with_box_refine is False:
							raise ValueError("If two_stage is True, with_box_refine must be True."       )
					# Hungarian matcher
					_UpperCAmelCase						:				Optional[int]	    =					class_cost
					_UpperCAmelCase						:				Dict	    =					bbox_cost
					_UpperCAmelCase						:				int	    =					giou_cost
					# Loss coefficients
					_UpperCAmelCase						:				int	    =					mask_loss_coefficient
					_UpperCAmelCase						:				List[Any]	    =					dice_loss_coefficient
					_UpperCAmelCase						:				Dict	    =					bbox_loss_coefficient
					_UpperCAmelCase						:				int	    =					giou_loss_coefficient
					_UpperCAmelCase						:				Optional[Any]	    =					eos_coefficient
					_UpperCAmelCase						:				int	    =					focal_alpha
					super().__init__(is_encoder_decoder=lowerCamelCase__		,     **lowerCamelCase__       )




			@property
			def 							lowerCAmelCase__			(						self     :   int       )      ->int:



					'''simple docstring'''







					return self.encoder_attention_heads




			@property
			def 							lowerCAmelCase__			(						self     :   List[Any]       )      ->int:



					'''simple docstring'''







					return self.d_model






			def 							lowerCAmelCase__			(						self     :   Union[str, Any]       )      ->int:



					'''simple docstring'''







					_UpperCAmelCase						:				Optional[int]	    =					copy.deepcopy(self.__dict__       )
					_UpperCAmelCase						:				Tuple	    =					self.backbone_config.to_dict()
					_UpperCAmelCase						:				List[Any]	    =					self.__class__.model_type
					return output



