






'''simple docstring'''

class 							__magic_name__     :







	"""simple docstring"""



	def __init__(							self  ,      _lowercase  ,      _lowercase  ,      _lowercase       )						->  Union[str, Any]:
							lowercase_      :   str        =			None
							lowercase_      :   Dict        =			None
							lowercase_      :   str        =			graph

							self._normalize_graph(_lowercase  ,      _lowercase       )
							lowercase_      :   List[str]        =			len(_lowercase       )
							lowercase_      :   Optional[Any]        =			None
	def        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase       )						->  List[str]:
							if sources is int:
													lowercase_      :   Tuple        =			[sources]
							if sinks is int:
													lowercase_      :   Union[str, Any]        =			[sinks]

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

							lowercase_      :   Dict        =			sources[0]
							lowercase_      :   Union[str, Any]        =			sinks[0]

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

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

													lowercase_      :   Any        =			len(self.graph       ) + 1
													for room in self.graph:
																			room.append(0       )
													self.graph.append([0] * size       )
													for i in sinks:
																			lowercase_      :   str        =			max_input_flow
													lowercase_      :   str        =			size - 1
	def        lowerCamelCase__		(							self       )						->  int:
							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  ,      _lowercase       )						->  Optional[Any]:
							lowercase_      :   Union[str, Any]        =			algorithm(self       )






class 							__magic_name__     :







	"""simple docstring"""



	def __init__(							self  ,      _lowercase       )						->  Dict:
							lowercase_      :   List[Any]        =			flow_network
							lowercase_      :   Tuple        =			flow_network.verticesCount
							lowercase_      :   str        =			flow_network.sourceIndex
							lowercase_      :   Optional[int]        =			flow_network.sinkIndex
							# it's just a reference, so you shouldn't change
							# it in your algorithms, use deep copy before doing that
							lowercase_      :   Dict        =			flow_network.graph
							lowercase_      :   Optional[int]        =			False
	def        lowerCamelCase__		(							self       )						->  Optional[int]:
							if not self.executed:
													self._algorithm()
													lowercase_      :   str        =			True







	def        lowerCamelCase__		(							self       )						->  Any:
							pass






class 							__magic_name__     (	UpperCAmelCase_				):







	"""simple docstring"""



	def __init__(							self  ,      _lowercase       )						->  int:
							super().__init__(_lowercase       )
							# use this to save your result
							lowercase_      :   List[Any]        =			-1







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

							return self.maximum_flow






class 							__magic_name__     (	UpperCAmelCase_				):







	"""simple docstring"""



	def __init__(							self  ,      _lowercase       )						->  List[Any]:
							super().__init__(_lowercase       )

							lowercase_      :   Union[str, Any]        =			[[0] * self.verticies_count for i in range(self.verticies_count       )]

							lowercase_      :   Any        =			[0] * self.verticies_count
							lowercase_      :   List[Any]        =			[0] * self.verticies_count
	def        lowerCamelCase__		(							self       )						->  List[Any]:
							lowercase_      :   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
							lowercase_      :   str        =			[
							    i
							    for i in range(self.verticies_count       )
							    if i != self.source_index and i != self.sink_index
							]

							# move through list
							lowercase_      :   List[Any]        =			0
							while i < len(_lowercase       ):
													lowercase_      :   Any        =			vertices_list[i]
													lowercase_      :   Optional[Any]        =			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       )       )
																			lowercase_      :   Union[str, Any]        =			0
													else:
																			i += 1

							lowercase_      :   str        =			sum(self.preflow[self.source_index]       )
	def        lowerCamelCase__		(							self  ,      _lowercase       )						->  List[Any]:
							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        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase       )						->  List[Any]:
							lowercase_      :   List[str]        =			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  ,      _lowercase       )						->  Optional[int]:
							lowercase_      :   Union[str, 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):
																			lowercase_      :   Tuple        =			self.heights[to_index]

							if min_height is not None:
													lowercase_      :   Union[str, Any]        =			min_height + 1


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

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

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








'''simple docstring'''

from typing import Dict, Optional

import numpy as np

import datasets


A:      int  				=				"\nIoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union\nbetween the predicted segmentation and the ground truth. For binary (two classes) or multi-class segmentation,\nthe mean IoU of the image is calculated by taking the IoU of each class and averaging them.\n"

A:      List[str]  				=				"\nArgs:\n    predictions (`List[ndarray]`):\n        List of predicted segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.\n    references (`List[ndarray]`):\n        List of ground truth segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.\n    num_labels (`int`):\n        Number of classes (categories).\n    ignore_index (`int`):\n        Index that will be ignored during evaluation.\n    nan_to_num (`int`, *optional*):\n        If specified, NaN values will be replaced by the number defined by the user.\n    label_map (`dict`, *optional*):\n        If specified, dictionary mapping old label indices to new label indices.\n    reduce_labels (`bool`, *optional*, defaults to `False`):\n        Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0 is used for background,\n        and background itself is not included in all classes of a dataset (e.g. ADE20k). The background label will be replaced by 255.\n\nReturns:\n    `Dict[str, float | ndarray]` comprising various elements:\n    - *mean_iou* (`float`):\n        Mean Intersection-over-Union (IoU averaged over all categories).\n    - *mean_accuracy* (`float`):\n        Mean accuracy (averaged over all categories).\n    - *overall_accuracy* (`float`):\n        Overall accuracy on all images.\n    - *per_category_accuracy* (`ndarray` of shape `(num_labels,)`):\n        Per category accuracy.\n    - *per_category_iou* (`ndarray` of shape `(num_labels,)`):\n        Per category IoU.\n\nExamples:\n\n    >>> import numpy as np\n\n    >>> mean_iou = datasets.load_metric(\"mean_iou\")\n\n    >>> # suppose one has 3 different segmentation maps predicted\n    >>> predicted_1 = np.array([[1, 2], [3, 4], [5, 255]])\n    >>> actual_1 = np.array([[0, 3], [5, 4], [6, 255]])\n\n    >>> predicted_2 = np.array([[2, 7], [9, 2], [3, 6]])\n    >>> actual_2 = np.array([[1, 7], [9, 2], [3, 6]])\n\n    >>> predicted_3 = np.array([[2, 2, 3], [8, 2, 4], [3, 255, 2]])\n    >>> actual_3 = np.array([[1, 2, 2], [8, 2, 1], [3, 255, 1]])\n\n    >>> predicted = [predicted_1, predicted_2, predicted_3]\n    >>> ground_truth = [actual_1, actual_2, actual_3]\n\n    >>> results = mean_iou.compute(predictions=predicted, references=ground_truth, num_labels=10, ignore_index=255, reduce_labels=False)\n    >>> print(results) # doctest: +NORMALIZE_WHITESPACE\n    {'mean_iou': 0.47750000000000004, 'mean_accuracy': 0.5916666666666666, 'overall_accuracy': 0.5263157894736842, 'per_category_iou': array([0.   , 0.   , 0.375, 0.4  , 0.5  , 0.   , 0.5  , 1.   , 1.   , 1.   ]), 'per_category_accuracy': array([0.        , 0.        , 0.75      , 0.66666667, 1.        , 0.        , 0.5       , 1.        , 1.        , 1.        ])}\n"

A:      Union[str, Any]  				=				"\\n@software{MMSegmentation_Contributors_OpenMMLab_Semantic_Segmentation_2020,\nauthor = {{MMSegmentation Contributors}},\nlicense = {Apache-2.0},\nmonth = {7},\ntitle = {{OpenMMLab Semantic Segmentation Toolbox and Benchmark}},\nurl = {https://github.com/open-mmlab/mmsegmentation},\nyear = {2020}\n}"






def 	_UpperCAmelCase    (  a		:	str						,			a		:	Union[str, Any]						,			a		:	Dict						,			a		:	bool						,			a		:	Optional[Dict[int, int]] = None						,			a		:	bool = False						,			)			->			str:





						"""simple docstring"""
						if label_map is not None:
												for old_id, new_id in label_map.items():
																		lowercase_      :   Union[str, Any]        =			new_id

    # turn into Numpy arrays
						lowercase_      :   List[Any]        =			np.array(a							)
						lowercase_      :   Optional[Any]        =			np.array(a							)

						if reduce_labels:
												lowercase_      :   Any        =			2_5_5
												lowercase_      :   Dict        =			label - 1
												lowercase_      :   List[Any]        =			2_5_5

						lowercase_      :   Any        =			label != ignore_index
						lowercase_      :   List[Any]        =			np.not_equal(a						,			a							)
						lowercase_      :   Optional[int]        =			pred_label[mask]
						lowercase_      :   Union[str, Any]        =			np.array(a							)[mask]

						lowercase_      :   Optional[int]        =			pred_label[pred_label == label]

						lowercase_      :   Optional[int]        =			np.histogram(a						,			bins=a						,			range=(0, num_labels - 1)							)[0]
						lowercase_      :   Optional[int]        =			np.histogram(a						,			bins=a						,			range=(0, num_labels - 1)							)[0]
						lowercase_      :   Dict        =			np.histogram(a						,			bins=a						,			range=(0, num_labels - 1)							)[0]

						lowercase_      :   Optional[Any]        =			area_pred_label + area_label - area_intersect

						return area_intersect, area_union, area_pred_label, area_label






def 	_UpperCAmelCase    (  a		:	int						,			a		:	Optional[Any]						,			a		:	Optional[int]						,			a		:	bool						,			a		:	Optional[Dict[int, int]] = None						,			a		:	bool = False						,			)			->			Dict:





						"""simple docstring"""
						lowercase_      :   Dict        =			np.zeros((num_labels,)						,			dtype=np.floataa							)
						lowercase_      :   List[str]        =			np.zeros((num_labels,)						,			dtype=np.floataa							)
						lowercase_      :   List[Any]        =			np.zeros((num_labels,)						,			dtype=np.floataa							)
						lowercase_      :   str        =			np.zeros((num_labels,)						,			dtype=np.floataa							)
						for result, gt_seg_map in zip(a						,			a							):
												lowercase_  ,      lowercase_  ,      lowercase_  ,      lowercase_      :   Tuple        =			intersect_and_union(
												    a						,			a						,			a						,			a						,			a						,			a							)
												total_area_intersect += area_intersect
												total_area_union += area_union
												total_area_pred_label += area_pred_label
												total_area_label += area_label
						return total_area_intersect, total_area_union, total_area_pred_label, total_area_label






def 	_UpperCAmelCase    (  a		:	Optional[Any]						,			a		:	List[str]						,			a		:	Optional[Any]						,			a		:	bool						,			a		:	Optional[int] = None						,			a		:	Optional[Dict[int, int]] = None						,			a		:	bool = False						,			)			->			Optional[int]:





						"""simple docstring"""
						lowercase_  ,      lowercase_  ,      lowercase_  ,      lowercase_      :   Optional[Any]        =			total_intersect_and_union(
						    a						,			a						,			a						,			a						,			a						,			a							)

						# compute metrics
						lowercase_      :   str        =			{}

						lowercase_      :   str        =			total_area_intersect.sum() / total_area_label.sum()
						lowercase_      :   Optional[Any]        =			total_area_intersect / total_area_union
						lowercase_      :   List[Any]        =			total_area_intersect / total_area_label

						lowercase_      :   Any        =			np.nanmean(a							)
						lowercase_      :   Optional[Any]        =			np.nanmean(a							)
						lowercase_      :   int        =			all_acc
						lowercase_      :   Union[str, Any]        =			iou
						lowercase_      :   Optional[Any]        =			acc

						if nan_to_num is not None:
												lowercase_      :   Optional[int]        =			{metric: np.nan_to_num(a						,			nan=a							) for metric, metric_value in metrics.items()}

						return metrics






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION,						_KWARGS_DESCRIPTION				)
class 							__magic_name__     (	datasets.Metric				):







	"""simple docstring"""



	def        lowerCamelCase__		(							self       )						->  int:
							return datasets.MetricInfo(
							    description=_DESCRIPTION  ,      citation=_CITATION  ,      inputs_description=_KWARGS_DESCRIPTION  ,      features=datasets.Features(
							        # 1st Seq - height dim, 2nd - width dim
							        {
							            'predictions': datasets.Sequence(datasets.Sequence(datasets.Value('uint16'       )       )       ),
							            'references': datasets.Sequence(datasets.Sequence(datasets.Value('uint16'       )       )       ),
							        }       )  ,      reference_urls=[
							        'https://github.com/open-mmlab/mmsegmentation/blob/71c201b1813267d78764f306a297ca717827c4bf/mmseg/core/evaluation/metrics.py'
							    ]  ,      )







	def        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase = None  ,      _lowercase = None  ,      _lowercase = False  ,      )						->  Tuple:
							lowercase_      :   Optional[int]        =			mean_iou(
							    results=_lowercase  ,      gt_seg_maps=_lowercase  ,      num_labels=_lowercase  ,      ignore_index=_lowercase  ,      nan_to_num=_lowercase  ,      label_map=_lowercase  ,      reduce_labels=_lowercase  ,      )
							return iou_result
