
'''simple docstring'''
class 			SCREAMING_SNAKE_CASE__      :







				def __init__(       self						,				A_						,				A_						,				A_     )->		Any:







											'''simple docstring'''






											UpperCamelCase   			=     None
											UpperCamelCase   			=     None
											UpperCamelCase   			=     graph

											self._normalize_graph(A_						,				A_     )
											UpperCamelCase   			=     len(A_     )
											UpperCamelCase   			=     None







				def 						UpperCAmelCase_    (       self						,				A_						,				A_     )->		Tuple:







											'''simple docstring'''






											if sources is int:
																		UpperCamelCase   			=     [sources]
											if sinks is int:
																		UpperCamelCase   			=     [sinks]

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

											UpperCamelCase   			=     sources[0]
											UpperCamelCase   			=     sinks[0]

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

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

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







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







											'''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_     )->		Any:







											'''simple docstring'''






											UpperCamelCase   			=     algorithm(self     )


class 			SCREAMING_SNAKE_CASE__      :







				def __init__(       self						,				A_     )->		Dict:







											'''simple docstring'''






											UpperCamelCase   			=     flow_network
											UpperCamelCase   			=     flow_network.verticesCount
											UpperCamelCase   			=     flow_network.sourceIndex
											UpperCamelCase   			=     flow_network.sinkIndex
											# it's just a reference, so you shouldn't change
											# it in your algorithms, use deep copy before doing that
											UpperCamelCase   			=     flow_network.graph
											UpperCamelCase   			=     False







				def 						UpperCAmelCase_    (       self     )->		Dict:







											'''simple docstring'''






											if not self.executed:
																		self._algorithm()
																		UpperCamelCase   			=     True







				def 						UpperCAmelCase_    (       self     )->		Dict:







											'''simple docstring'''






											pass


class 			SCREAMING_SNAKE_CASE__      (     snake_case_):







				def __init__(       self						,				A_     )->		List[str]:







											'''simple docstring'''






											super().__init__(A_     )
											# use this to save your result
											UpperCamelCase   			=     -1







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







											'''simple docstring'''






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

											return self.maximum_flow


class 			SCREAMING_SNAKE_CASE__      (     snake_case_):







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







											'''simple docstring'''






											super().__init__(A_     )

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

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







				def 						UpperCAmelCase_    (       self     )->		Tuple:







											'''simple docstring'''






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

											# move through list
											UpperCamelCase   			=     0
											while i < len(A_     ):
																		UpperCamelCase   			=     vertices_list[i]
																		UpperCamelCase   			=     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_     )     )
																									UpperCamelCase   			=     0
																		else:
																									i += 1

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







				def 						UpperCAmelCase_    (       self						,				A_     )->		Dict:







											'''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(A_						,				A_     )

																		self.relabel(A_     )







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







											'''simple docstring'''






											UpperCamelCase   			=     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_     )->		List[str]:







											'''simple docstring'''






											UpperCamelCase   			=     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   			=     self.heights[to_index]

											if min_height is not None:
																		UpperCamelCase   			=     min_height + 1


if __name__ == "__main__":
						lowerCAmelCase					:    int           = [0]
						lowerCAmelCase					:    Tuple           = [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					:    List[str]           = [[0, 7, 0, 0], [0, 0, 6, 0], [0, 0, 0, 8], [9, 0, 0, 0]]

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

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











import warnings
from typing import List

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available




class 	UpperCamelCase_							(  UpperCamelCase__     ):
							lowerCamelCase_							     =       ["image_processor", "tokenizer"]
							lowerCamelCase_							     =       "OwlViTImageProcessor"
							lowerCamelCase_							     =       ("CLIPTokenizer", "CLIPTokenizerFast")


							def __init__(				self     :Optional[Any]				,					__A     :int=None				,					__A     :Optional[int]=None				,					**__A     :str		) -> Optional[int]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   None
									if "feature_extractor" in kwargs:
											warnings.warn(
											    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
											    """ instead."""				,					__A				,					)
											SCREAMING_SNAKE_CASE__								=   kwargs.pop("""feature_extractor"""		)

									SCREAMING_SNAKE_CASE__								=   image_processor if image_processor is not None else feature_extractor
									if image_processor is None:
											raise ValueError("""You need to specify an `image_processor`."""		)
									if tokenizer is None:
											raise ValueError("""You need to specify a `tokenizer`."""		)

									super().__init__(__A				,					__A		)


							def __call__(				self     :str				,					__A     :Dict=None				,					__A     :List[str]=None				,					__A     :str=None				,					__A     :Optional[int]="max_length"				,					__A     :Tuple="np"				,					**__A     :int		) -> Tuple:



									"""simple docstring"""



									if text is None and query_images is None and images is None:
											raise ValueError(
											    """You have to specify at least one text or query image or image. All three cannot be none."""		)

									if text is not None:
											if isinstance(__A				,					__A		) or (isinstance(__A				,					__A		) and not isinstance(text[0]				,					__A		)):
													SCREAMING_SNAKE_CASE__								=   [self.tokenizer(__A				,					padding=__A				,					return_tensors=__A				,					**__A		)]

											elif isinstance(__A				,					__A		) and isinstance(text[0]				,					__A		):
													SCREAMING_SNAKE_CASE__								=   []

													# Maximum number of queries across batch
													SCREAMING_SNAKE_CASE__								=   max([len(__A		) for t in text]		)

													# Pad all batch samples to max number of text queries
													for t in text:
															if len(__A		) != max_num_queries:
																	SCREAMING_SNAKE_CASE__								=   t + [""" """] * (max_num_queries - len(__A		))

															SCREAMING_SNAKE_CASE__								=   self.tokenizer(__A				,					padding=__A				,					return_tensors=__A				,					**__A		)
															encodings.append(__A		)
											else:
													raise TypeError("""Input text should be a string, a list of strings or a nested list of strings"""		)

											if return_tensors == "np":
													SCREAMING_SNAKE_CASE__								=   np.concatenate([encoding["""input_ids"""] for encoding in encodings]				,					axis=0		)
													SCREAMING_SNAKE_CASE__								=   np.concatenate([encoding["""attention_mask"""] for encoding in encodings]				,					axis=0		)

											elif return_tensors == "jax" and is_flax_available():
													import jax.numpy as jnp

													SCREAMING_SNAKE_CASE__								=   jnp.concatenate([encoding["""input_ids"""] for encoding in encodings]				,					axis=0		)
													SCREAMING_SNAKE_CASE__								=   jnp.concatenate([encoding["""attention_mask"""] for encoding in encodings]				,					axis=0		)

											elif return_tensors == "pt" and is_torch_available():
													import torch

													SCREAMING_SNAKE_CASE__								=   torch.cat([encoding["""input_ids"""] for encoding in encodings]				,					dim=0		)
													SCREAMING_SNAKE_CASE__								=   torch.cat([encoding["""attention_mask"""] for encoding in encodings]				,					dim=0		)

											elif return_tensors == "tf" and is_tf_available():
													import tensorflow as tf

													SCREAMING_SNAKE_CASE__								=   tf.stack([encoding["""input_ids"""] for encoding in encodings]				,					axis=0		)
													SCREAMING_SNAKE_CASE__								=   tf.stack([encoding["""attention_mask"""] for encoding in encodings]				,					axis=0		)

											else:
													raise ValueError("""Target return tensor type could not be returned"""		)

											SCREAMING_SNAKE_CASE__								=   BatchEncoding()
											SCREAMING_SNAKE_CASE__								=   input_ids
											SCREAMING_SNAKE_CASE__								=   attention_mask

									if query_images is not None:
											SCREAMING_SNAKE_CASE__								=   BatchEncoding()
											SCREAMING_SNAKE_CASE__								=   self.image_processor(
											    __A				,					return_tensors=__A				,					**__A		).pixel_values
											SCREAMING_SNAKE_CASE__								=   query_pixel_values

									if images is not None:
											SCREAMING_SNAKE_CASE__								=   self.image_processor(__A				,					return_tensors=__A				,					**__A		)

									if text is not None and images is not None:
											SCREAMING_SNAKE_CASE__								=   image_features.pixel_values
											return encoding
									elif query_images is not None and images is not None:
											SCREAMING_SNAKE_CASE__								=   image_features.pixel_values
											return encoding
									elif text is not None or query_images is not None:
											return encoding
									else:
											return BatchEncoding(data=dict(**__A		)				,					tensor_type=__A		)


							def   _snake_case	(				self     :List[Any]				,					*__A     :Dict				,					**__A     :Dict		) -> Optional[int]:



									"""simple docstring"""



									return self.image_processor.post_process(*__A				,					**__A		)


							def   _snake_case	(				self     :Optional[int]				,					*__A     :Dict				,					**__A     :List[str]		) -> Optional[Any]:



									"""simple docstring"""



									return self.image_processor.post_process_object_detection(*__A				,					**__A		)


							def   _snake_case	(				self     :str				,					*__A     :List[str]				,					**__A     :Union[str, Any]		) -> Any:



									"""simple docstring"""



									return self.image_processor.post_process_image_guided_detection(*__A				,					**__A		)


							def   _snake_case	(				self     :Dict				,					*__A     :List[str]				,					**__A     :List[str]		) -> int:



									"""simple docstring"""



									return self.tokenizer.batch_decode(*__A				,					**__A		)


							def   _snake_case	(				self     :Dict				,					*__A     :Dict				,					**__A     :List[str]		) -> str:



									"""simple docstring"""



									return self.tokenizer.decode(*__A				,					**__A		)


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



									"""simple docstring"""



									warnings.warn(
									    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""				,					__A				,					)
									return self.image_processor_class


							@property
							def   _snake_case	(				self     :Any		) -> Optional[Any]:



									"""simple docstring"""



									warnings.warn(
									    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""				,					__A				,					)
									return self.image_processor