




"""simple docstring"""






class lowerCAmelCase__			:






			'''simple docstring'''



			def __init__(						self						,   lowercase						,   lowercase						,   lowercase						):
							_lowerCamelCase  :    List[str]					=					None
							_lowerCamelCase  :    int					=					None
							_lowerCamelCase  :    List[Any]					=					graph

							self._normalize_graph(lowercase						,   lowercase						)
							_lowerCamelCase  :    str					=					len(lowercase						)
							_lowerCamelCase  :    Optional[Any]					=					None
			def       A_					(						self						,   lowercase						,   lowercase						):
							if sources is int:
											_lowerCamelCase  :    str					=					[sources]
							if sinks is int:
											_lowerCamelCase  :    str					=					[sinks]

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

							_lowerCamelCase  :    Optional[Any]					=					sources[0]
							_lowerCamelCase  :    Optional[int]					=					sinks[0]

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

											_lowerCamelCase  :    Optional[int]					=					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  :    List[Any]					=					0

											_lowerCamelCase  :    int					=					len(self.graph						) + 1
											for room in self.graph:
															room.append(0						)
											self.graph.append([0] * size						)
											for i in sinks:
															_lowerCamelCase  :    Dict					=					max_input_flow
											_lowerCamelCase  :    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						,   lowercase						):
							_lowerCamelCase  :    List[Any]					=					algorithm(self						)






class lowerCAmelCase__			:






			'''simple docstring'''



			def __init__(						self						,   lowercase						):
							_lowerCamelCase  :    Optional[Any]					=					flow_network
							_lowerCamelCase  :    List[str]					=					flow_network.verticesCount
							_lowerCamelCase  :    Dict					=					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  :    Tuple					=					flow_network.graph
							_lowerCamelCase  :    Optional[int]					=					False
			def       A_					(						self						):
							if not self.executed:
											self._algorithm()
											_lowerCamelCase  :    int					=					True






			def       A_					(						self						):
							pass






class lowerCAmelCase__			(  lowercase		):






			'''simple docstring'''



			def __init__(						self						,   lowercase						):
							super().__init__(lowercase						)
							# use this to save your result
							_lowerCamelCase  :    Dict					=					-1






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

							return self.maximum_flow






class lowerCAmelCase__			(  lowercase		):






			'''simple docstring'''



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

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

							_lowerCamelCase  :    Tuple					=					[0] * self.verticies_count
							_lowerCamelCase  :    int					=					[0] * self.verticies_count
			def       A_					(						self						):
							_lowerCamelCase  :    int					=					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  :    List[Any]					=					[
							    i
							    for i in range(self.verticies_count						)
							    if i != self.source_index and i != self.sink_index
							]

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

							_lowerCamelCase  :    int					=					sum(self.preflow[self.source_index]						)
			def       A_					(						self						,   lowercase						):
							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(lowercase						,   lowercase						)

											self.relabel(lowercase						)
			def       A_					(						self						,   lowercase						,   lowercase						):
							_lowerCamelCase  :    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						,   lowercase						):
							_lowerCamelCase  :    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):
															_lowerCamelCase  :    Optional[int]					=					self.heights[to_index]

							if min_height is not None:
											_lowerCamelCase  :    int					=					min_height + 1


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

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

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





"""simple docstring"""






from copy import deepcopy
from typing import Optional, Union

import numpy as np

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


if is_torch_available():
			import torch

if is_tf_available():
			import tensorflow as tf






class lowerCAmelCase__			(  lowercase		):






			'''simple docstring'''



			lowerCamelCase__   =    ["""image_processor"""]
			lowerCamelCase__   =    """SamImageProcessor"""
			def __init__(						self						,   lowercase						):
							super().__init__(lowercase						)
							_lowerCamelCase  :    str					=					self.image_processor
							_lowerCamelCase  :    Optional[int]					=					-10
							_lowerCamelCase  :    Optional[Any]					=					self.image_processor.size['longest_edge']
			def __call__(						self						,   lowercase=None						,   lowercase=None						,   lowercase=None						,   lowercase=None						,   lowercase = None						,   **lowercase						,   ):
							_lowerCamelCase  :    int					=					self.image_processor(
							    lowercase						,   return_tensors=lowercase						,   **lowercase						,   )

							# pop arguments that are not used in the foward but used nevertheless
							_lowerCamelCase  :    int					=					encoding_image_processor['original_sizes']

							if hasattr(lowercase						,   'numpy'						):  # Checks if Torch or TF tensor
											_lowerCamelCase  :    Tuple					=					original_sizes.numpy()

							_lowerCamelCase,				_lowerCamelCase,				_lowerCamelCase  :    Optional[Any]					=					self._check_and_preprocess_points(
							    input_points=lowercase						,   input_labels=lowercase						,   input_boxes=lowercase						,   )

							_lowerCamelCase  :    Tuple					=					self._normalize_and_convert(
							    lowercase						,   lowercase						,   input_points=lowercase						,   input_labels=lowercase						,   input_boxes=lowercase						,   return_tensors=lowercase						,   )

							return encoding_image_processor
			def       A_					(						self						,   lowercase						,   lowercase						,   lowercase=None						,   lowercase=None						,   lowercase=None						,   lowercase="pt"						,   ):
							if input_points is not None:
											if len(lowercase						) != len(lowercase						):
															_lowerCamelCase  :    str					=					[
															    self._normalize_coordinates(self.target_size						,   lowercase						,   original_sizes[0]						) for point in input_points
															]
											else:
															_lowerCamelCase  :    Optional[Any]					=					[
															    self._normalize_coordinates(self.target_size						,   lowercase						,   lowercase						)
															    for point, original_size in zip(lowercase						,   lowercase						)
															]
											# check that all arrays have the same shape
											if not all(point.shape == input_points[0].shape for point in input_points						):
															if input_labels is not None:
																			_lowerCamelCase,				_lowerCamelCase  :    List[str]					=					self._pad_points_and_labels(lowercase						,   lowercase						)

											_lowerCamelCase  :    Optional[int]					=					np.array(lowercase						)

							if input_labels is not None:
											_lowerCamelCase  :    List[Any]					=					np.array(lowercase						)

							if input_boxes is not None:
											if len(lowercase						) != len(lowercase						):
															_lowerCamelCase  :    Optional[int]					=					[
															    self._normalize_coordinates(self.target_size						,   lowercase						,   original_sizes[0]						,   is_bounding_box=lowercase						)
															    for box in input_boxes
															]
											else:
															_lowerCamelCase  :    Optional[int]					=					[
															    self._normalize_coordinates(self.target_size						,   lowercase						,   lowercase						,   is_bounding_box=lowercase						)
															    for box, original_size in zip(lowercase						,   lowercase						)
															]
											_lowerCamelCase  :    Any					=					np.array(lowercase						)

							if input_boxes is not None:
											if return_tensors == "pt":
															_lowerCamelCase  :    List[str]					=					torch.from_numpy(lowercase						)
															# boxes batch size of 1 by default
															_lowerCamelCase  :    str					=					input_boxes.unsqueeze(1						) if len(input_boxes.shape						) != 3 else input_boxes
											elif return_tensors == "tf":
															_lowerCamelCase  :    int					=					tf.convert_to_tensor(lowercase						)
															# boxes batch size of 1 by default
															_lowerCamelCase  :    Union[str, Any]					=					tf.expand_dims(lowercase						,   1						) if len(input_boxes.shape						) != 3 else input_boxes
											encoding_image_processor.update({'input_boxes': input_boxes}						)
							if input_points is not None:
											if return_tensors == "pt":
															_lowerCamelCase  :    Optional[Any]					=					torch.from_numpy(lowercase						)
															# point batch size of 1 by default
															_lowerCamelCase  :    Union[str, Any]					=					input_points.unsqueeze(1						) if len(input_points.shape						) != 4 else input_points
											elif return_tensors == "tf":
															_lowerCamelCase  :    List[str]					=					tf.convert_to_tensor(lowercase						)
															# point batch size of 1 by default
															_lowerCamelCase  :    Union[str, Any]					=					tf.expand_dims(lowercase						,   1						) if len(input_points.shape						) != 4 else input_points
											encoding_image_processor.update({'input_points': input_points}						)
							if input_labels is not None:
											if return_tensors == "pt":
															_lowerCamelCase  :    Union[str, Any]					=					torch.from_numpy(lowercase						)
															# point batch size of 1 by default
															_lowerCamelCase  :    Optional[Any]					=					input_labels.unsqueeze(1						) if len(input_labels.shape						) != 3 else input_labels
											elif return_tensors == "tf":
															_lowerCamelCase  :    Dict					=					tf.convert_to_tensor(lowercase						)
															# point batch size of 1 by default
															_lowerCamelCase  :    Any					=					tf.expand_dims(lowercase						,   1						) if len(input_labels.shape						) != 3 else input_labels
											encoding_image_processor.update({'input_labels': input_labels}						)

							return encoding_image_processor
			def       A_					(						self						,   lowercase						,   lowercase						):
							_lowerCamelCase  :    Optional[int]					=					max([point.shape[0] for point in input_points]						)
							_lowerCamelCase  :    Optional[int]					=					[]
							for i, point in enumerate(lowercase						):
											if point.shape[0] != expected_nb_points:
															_lowerCamelCase  :    Tuple					=					np.concatenate(
															    [point, np.zeros((expected_nb_points - point.shape[0], 2)						) + self.point_pad_value]						,   axis=0						)
															_lowerCamelCase  :    Optional[Any]					=					np.append(input_labels[i]						,   [self.point_pad_value]						)
											processed_input_points.append(lowercase						)
							_lowerCamelCase  :    Any					=					processed_input_points
							return input_points, input_labels
			def       A_					(						self						,   lowercase						,   lowercase						,   lowercase						,   lowercase=False						):
							_lowerCamelCase,				_lowerCamelCase  :    List[str]					=					original_size
							_lowerCamelCase,				_lowerCamelCase  :    Optional[int]					=					self.image_processor._get_preprocess_shape(lowercase						,   longest_edge=lowercase						)
							_lowerCamelCase  :    Tuple					=					deepcopy(lowercase						).astype(lowercase						)

							if is_bounding_box:
											_lowerCamelCase  :    List[str]					=					coords.reshape(-1						,   2						,   2						)

							_lowerCamelCase  :    Optional[Any]					=					coords[..., 0] * (new_w / old_w)
							_lowerCamelCase  :    List[Any]					=					coords[..., 1] * (new_h / old_h)

							if is_bounding_box:
											_lowerCamelCase  :    Union[str, Any]					=					coords.reshape(-1						,   4						)

							return coords
			def       A_					(						self						,   lowercase=None						,   lowercase=None						,   lowercase=None						,   ):
							if input_points is not None:
											if hasattr(lowercase						,   'numpy'						):  # Checks for TF or Torch tensor
															_lowerCamelCase  :    int					=					input_points.numpy().tolist()

											if not isinstance(lowercase						,   lowercase						) or not isinstance(input_points[0]						,   lowercase						):
															raise ValueError('Input points must be a list of list of floating points.'						)
											_lowerCamelCase  :    Optional[int]					=					[np.array(lowercase						) for input_point in input_points]
							else:
											_lowerCamelCase  :    Optional[Any]					=					None

							if input_labels is not None:
											if hasattr(lowercase						,   'numpy'						):
															_lowerCamelCase  :    Any					=					input_labels.numpy().tolist()

											if not isinstance(lowercase						,   lowercase						) or not isinstance(input_labels[0]						,   lowercase						):
															raise ValueError('Input labels must be a list of list integers.'						)
											_lowerCamelCase  :    List[str]					=					[np.array(lowercase						) for label in input_labels]
							else:
											_lowerCamelCase  :    List[str]					=					None

							if input_boxes is not None:
											if hasattr(lowercase						,   'numpy'						):
															_lowerCamelCase  :    Optional[int]					=					input_boxes.numpy().tolist()

											if (
											    not isinstance(lowercase						,   lowercase						)
											    or not isinstance(input_boxes[0]						,   lowercase						)
											    or not isinstance(input_boxes[0][0]						,   lowercase						)
											):
															raise ValueError('Input boxes must be a list of list of list of floating points.'						)
											_lowerCamelCase  :    int					=					[np.array(lowercase						).astype(np.floataa						) for box in input_boxes]
							else:
											_lowerCamelCase  :    str					=					None

							return input_points, input_labels, input_boxes
			@property
			def       A_					(						self						):
							_lowerCamelCase  :    Optional[Any]					=					self.image_processor.model_input_names
							return list(dict.fromkeys(lowercase						)						)






			def       A_					(						self						,   *lowercase						,   **lowercase						):
							return self.image_processor.post_process_masks(*lowercase						,   **lowercase						)