






'''simple docstring'''

from collections.abc import Callable






def 	_UpperCAmelCase    (  a		:	Callable[[float], float]						,			a		:	float						,			a		:	float							)			->			float:





						"""simple docstring"""
						lowercase_      :   float        =			a
						lowercase_      :   float        =			b
						if function(a							) == 0:  # one of the a or b is a root for the function
												return a
						elif function(a							) == 0:
												return b
						elif (
						    function(a							) * function(a							) > 0
						):  # if none of these are root and they are both positive or negative,
												# then this algorithm can't find the root
												raise ValueError('could not find root in given interval.'							)
						else:
												lowercase_      :   float        =			start + (end - start) / 2.0
												while abs(start - mid							) > 1_0**-7:  # until precisely equals to 10^-7
																		if function(a							) == 0:
																								return mid
																		elif function(a							) * function(a							) < 0:
																								lowercase_      :   Dict        =			mid
																		else:
																								lowercase_      :   Optional[Any]        =			mid
																		lowercase_      :   Any        =			start + (end - start) / 2.0
												return mid






def 	_UpperCAmelCase    (  a		:	float							)			->			float:





						"""simple docstring"""
						return x**3 - 2 * x - 5


if __name__ == "__main__":
						print(bisection(f, 1, 1_0_0_0))

						import doctest

						doctest.testmod()








'''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
