



'''simple docstring'''





class 				snake_case :



							def __init__(				self     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_      )  ->							int:
											lowercase__		       =						None
											lowercase__		       =						None
											lowercase__		       =						graph

											self._normalize_graph(UpperCAmelCase_     ,UpperCAmelCase_      )
											lowercase__		       =						len(UpperCAmelCase_      )
											lowercase__		       =						None



							def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_      )  ->							Optional[int]:
											if sources is int:
															lowercase__		       =						[sources]
											if sinks is int:
															lowercase__		       =						[sinks]

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

											lowercase__		       =						sources[0]
											lowercase__		       =						sinks[0]

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

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

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



							def 							_a      (				self      )  ->							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 							_a      (				self     ,UpperCAmelCase_      )  ->							Dict:
											lowercase__		       =						algorithm(self      )





class 				snake_case :



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



							def 							_a      (				self      )  ->							List[Any]:
											if not self.executed:
															self._algorithm()
															lowercase__		       =						True







							def 							_a      (				self      )  ->							Dict:
											pass





class 				snake_case (UpperCamelCase   ):



							def __init__(				self     ,UpperCAmelCase_      )  ->							Tuple:
											super().__init__(UpperCAmelCase_      )
											# use this to save your result
											lowercase__		       =						-1







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

											return self.maximum_flow





class 				snake_case (UpperCamelCase   ):



							def __init__(				self     ,UpperCAmelCase_      )  ->							Dict:
											super().__init__(UpperCAmelCase_      )

											lowercase__		       =						[[0] * self.verticies_count for i in range(self.verticies_count      )]

											lowercase__		       =						[0] * self.verticies_count
											lowercase__		       =						[0] * self.verticies_count



							def 							_a      (				self      )  ->							Optional[int]:
											lowercase__		       =						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
											lowercase__		       =						[
											    i
											    for i in range(self.verticies_count      )
											    if i != self.source_index and i != self.sink_index
											]

											# move through list
											lowercase__		       =						0
											while i < len(UpperCAmelCase_      ):
															lowercase__		       =						vertices_list[i]
															lowercase__		       =						self.heights[vertex_index]
															self.process_vertex(UpperCAmelCase_      )
															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(UpperCAmelCase_      )      )
																			lowercase__		       =						0
															else:
																			i += 1

											lowercase__		       =						sum(self.preflow[self.source_index]      )



							def 							_a      (				self     ,UpperCAmelCase_      )  ->							str:
											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(UpperCAmelCase_     ,UpperCAmelCase_      )

															self.relabel(UpperCAmelCase_      )



							def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_      )  ->							str:
											lowercase__		       =						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     ,UpperCAmelCase_      )  ->							Optional[Any]:
											lowercase__		       =						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):
																			lowercase__		       =						self.heights[to_index]

											if min_height is not None:
															lowercase__		       =						min_height + 1


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

	# prepare our network
	SCREAMING_SNAKE_CASE__      					=     FlowNetwork(graph, entrances, exits)
	# set algorithm
	flow_network.set_maximum_flow_algorithm(PushRelabelExecutor)
	# and calculate
	SCREAMING_SNAKE_CASE__      					=     flow_network.find_maximum_flow()

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









'''simple docstring'''





import copy
import os
from typing import TYPE_CHECKING, List, Union


if TYPE_CHECKING:
 pass

from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE__      					=     logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__      					=     {
    "kakaobrain/align-base": "https://huggingface.co/kakaobrain/align-base/resolve/main/config.json",
}


class 				snake_case (UpperCamelCase   ):
       lowerCAmelCase__  :Optional[int]						   =					"align_text_model"



       def __init__(				self     ,UpperCAmelCase_=30_522     ,UpperCAmelCase_=768     ,UpperCAmelCase_=12     ,UpperCAmelCase_=12     ,UpperCAmelCase_=3_072     ,UpperCAmelCase_="gelu"     ,UpperCAmelCase_=0.1     ,UpperCAmelCase_=0.1     ,UpperCAmelCase_=512     ,UpperCAmelCase_=2     ,UpperCAmelCase_=0.02     ,UpperCAmelCase_=1E-1_2     ,UpperCAmelCase_=0     ,UpperCAmelCase_="absolute"     ,UpperCAmelCase_=True     ,**UpperCAmelCase_     ,)  ->							List[str]:
           super().__init__(**UpperCAmelCase_      )

           lowercase__		       =						vocab_size
           lowercase__		       =						hidden_size
           lowercase__		       =						num_hidden_layers
           lowercase__		       =						num_attention_heads
           lowercase__		       =						hidden_act
           lowercase__		       =						intermediate_size
           lowercase__		       =						hidden_dropout_prob
           lowercase__		       =						attention_probs_dropout_prob
           lowercase__		       =						max_position_embeddings
           lowercase__		       =						type_vocab_size
           lowercase__		       =						initializer_range
           lowercase__		       =						layer_norm_eps
           lowercase__		       =						position_embedding_type
           lowercase__		       =						use_cache
           lowercase__		       =						pad_token_id







       @classmethod
       def 							_a      (				cls     ,UpperCAmelCase_     ,**UpperCAmelCase_      )  ->							"PretrainedConfig":
           cls._set_token_in_kwargs(UpperCAmelCase_      )

           lowercase__       ,       lowercase__		       =						cls.get_config_dict(UpperCAmelCase_     ,**UpperCAmelCase_      )

           # get the text config dict if we are loading from AlignConfig
           if config_dict.get("model_type"      ) == "align":
               lowercase__		       =						config_dict["text_config"]

           if "model_type" in config_dict and hasattr(cls     ,"model_type"      ) and config_dict["model_type"] != cls.model_type:
               logger.warning(
                   F'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
                   F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''      )

           return cls.from_dict(UpperCAmelCase_     ,**UpperCAmelCase_      )





class 				snake_case (UpperCamelCase   ):
       lowerCAmelCase__  :Tuple						   =					"align_vision_model"



       def __init__(				self     ,UpperCAmelCase_ = 3     ,UpperCAmelCase_ = 600     ,UpperCAmelCase_ = 2.0     ,UpperCAmelCase_ = 3.1     ,UpperCAmelCase_ = 8     ,UpperCAmelCase_ = [3, 3, 5, 3, 5, 5, 3]     ,UpperCAmelCase_ = [32, 16, 24, 40, 80, 112, 192]     ,UpperCAmelCase_ = [16, 24, 40, 80, 112, 192, 320]     ,UpperCAmelCase_ = []     ,UpperCAmelCase_ = [1, 2, 2, 2, 1, 2, 1]     ,UpperCAmelCase_ = [1, 2, 2, 3, 3, 4, 1]     ,UpperCAmelCase_ = [1, 6, 6, 6, 6, 6, 6]     ,UpperCAmelCase_ = 0.25     ,UpperCAmelCase_ = "swish"     ,UpperCAmelCase_ = 2_560     ,UpperCAmelCase_ = "mean"     ,UpperCAmelCase_ = 0.02     ,UpperCAmelCase_ = 0.0_01     ,UpperCAmelCase_ = 0.99     ,UpperCAmelCase_ = 0.2     ,**UpperCAmelCase_     ,)  ->							Union[str, Any]:
           super().__init__(**UpperCAmelCase_      )

           lowercase__		       =						num_channels
           lowercase__		       =						image_size
           lowercase__		       =						width_coefficient
           lowercase__		       =						depth_coefficient
           lowercase__		       =						depth_divisor
           lowercase__		       =						kernel_sizes
           lowercase__		       =						in_channels
           lowercase__		       =						out_channels
           lowercase__		       =						depthwise_padding
           lowercase__		       =						strides
           lowercase__		       =						num_block_repeats
           lowercase__		       =						expand_ratios
           lowercase__		       =						squeeze_expansion_ratio
           lowercase__		       =						hidden_act
           lowercase__		       =						hidden_dim
           lowercase__		       =						pooling_type
           lowercase__		       =						initializer_range
           lowercase__		       =						batch_norm_eps
           lowercase__		       =						batch_norm_momentum
           lowercase__		       =						drop_connect_rate
           lowercase__		       =						sum(UpperCAmelCase_      ) * 4







       @classmethod
       def 							_a      (				cls     ,UpperCAmelCase_     ,**UpperCAmelCase_      )  ->							"PretrainedConfig":
           cls._set_token_in_kwargs(UpperCAmelCase_      )

           lowercase__       ,       lowercase__		       =						cls.get_config_dict(UpperCAmelCase_     ,**UpperCAmelCase_      )

           # get the vision config dict if we are loading from AlignConfig
           if config_dict.get("model_type"      ) == "align":
               lowercase__		       =						config_dict["vision_config"]

           if "model_type" in config_dict and hasattr(cls     ,"model_type"      ) and config_dict["model_type"] != cls.model_type:
               logger.warning(
                   F'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
                   F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''      )

           return cls.from_dict(UpperCAmelCase_     ,**UpperCAmelCase_      )





class 				snake_case (UpperCamelCase   ):
       lowerCAmelCase__  :Union[str, Any]						   =					"align"
       lowerCAmelCase__  :Tuple						   =					True



       def __init__(				self     ,UpperCAmelCase_=None     ,UpperCAmelCase_=None     ,UpperCAmelCase_=640     ,UpperCAmelCase_=1.0     ,UpperCAmelCase_=0.02     ,**UpperCAmelCase_     ,)  ->							Union[str, Any]:
           super().__init__(**UpperCAmelCase_      )

           if text_config is None:
               lowercase__		       =						{}
               logger.info("text_config is None. Initializing the AlignTextConfig with default values."      )

           if vision_config is None:
               lowercase__		       =						{}
               logger.info("vision_config is None. Initializing the AlignVisionConfig with default values."      )

           lowercase__		       =						AlignTextConfig(**UpperCAmelCase_      )
           lowercase__		       =						AlignVisionConfig(**UpperCAmelCase_      )

           lowercase__		       =						projection_dim
           lowercase__		       =						temperature_init_value
           lowercase__		       =						initializer_range



       @classmethod
       def 							_a      (				cls     ,UpperCAmelCase_     ,UpperCAmelCase_     ,**UpperCAmelCase_      )  ->							List[str]:
           return cls(text_config=text_config.to_dict()     ,vision_config=vision_config.to_dict()     ,**UpperCAmelCase_      )







       def 							_a      (				self      )  ->							List[Any]:
           lowercase__		       =						copy.deepcopy(self.__dict__      )
           lowercase__		       =						self.text_config.to_dict()
           lowercase__		       =						self.vision_config.to_dict()
           lowercase__		       =						self.__class__.model_type
           return output




