


'''simple docstring'''






from typing import Dict, Optional

import numpy as np

import datasets


__A	:	Any     =     '\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	:	Optional[int]     =     '\\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      (		lowerCamelCase_  :Any				,      lowerCamelCase_  :List[Any]				,      lowerCamelCase_  :Union[str, Any]				,      lowerCamelCase_  :Tuple				,      lowerCamelCase_  :Optional[Any] = None				,      lowerCamelCase_  :Tuple = False				,      ):







		'''simple docstring'''
		if label_map is not None:
				for old_id, new_id in label_map.items():
						snake_case_		:							Optional[int]			       =						new_id

    # turn into Numpy arrays
		snake_case_		:							Any			       =						np.array(lowerCamelCase__ )
		snake_case_		:							Optional[Any]			       =						np.array(lowerCamelCase__ )

		if reduce_labels:
				snake_case_		:							int			       =						2_55
				snake_case_		:							Optional[int]			       =						label - 1
				snake_case_		:							Optional[int]			       =						2_55

		snake_case_		:							List[Any]			       =						label != ignore_index
		snake_case_		:							Union[str, Any]			       =						np.not_equal(lowerCamelCase__				,      lowerCamelCase__ )
		snake_case_		:							Union[str, Any]			       =						pred_label[mask]
		snake_case_		:							List[Any]			       =						np.array(lowerCamelCase__ )[mask]

		snake_case_		:							str			       =						pred_label[pred_label == label]

		snake_case_		:							Dict			       =						np.histogram(lowerCamelCase__				,      bins=lowerCamelCase__				,      range=(0, num_labels - 1) )[0]
		snake_case_		:							Any			       =						np.histogram(lowerCamelCase__				,      bins=lowerCamelCase__				,      range=(0, num_labels - 1) )[0]
		snake_case_		:							List[Any]			       =						np.histogram(lowerCamelCase__				,      bins=lowerCamelCase__				,      range=(0, num_labels - 1) )[0]

		snake_case_		:							str			       =						area_pred_label + area_label - area_intersect

		return area_intersect, area_union, area_pred_label, area_label





def      UpperCAmelCase      (		lowerCamelCase_  :Optional[Any]				,      lowerCamelCase_  :List[Any]				,      lowerCamelCase_  :Union[str, Any]				,      lowerCamelCase_  :List[str]				,      lowerCamelCase_  :Optional[int] = None				,      lowerCamelCase_  :List[Any] = False				,      ):







		'''simple docstring'''
		snake_case_		:							Dict			       =						np.zeros((num_labels,)				,      dtype=np.floataa )
		snake_case_		:							Any			       =						np.zeros((num_labels,)				,      dtype=np.floataa )
		snake_case_		:							List[str]			       =						np.zeros((num_labels,)				,      dtype=np.floataa )
		snake_case_		:							Union[str, Any]			       =						np.zeros((num_labels,)				,      dtype=np.floataa )
		for result, gt_seg_map in zip(lowerCamelCase__				,      lowerCamelCase__ ):
				snake_case_       ,      snake_case_       ,      snake_case_       ,      snake_case_		:							str			       =						intersect_and_union(
				    lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__ )
				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      (		lowerCamelCase_  :Optional[Any]				,      lowerCamelCase_  :Optional[int]				,      lowerCamelCase_  :Dict				,      lowerCamelCase_  :str				,      lowerCamelCase_  :Tuple = None				,      lowerCamelCase_  :Optional[Any] = None				,      lowerCamelCase_  :Dict = False				,      ):







		'''simple docstring'''
		snake_case_       ,      snake_case_       ,      snake_case_       ,      snake_case_		:							Any			       =						total_intersect_and_union(
		    lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__ )

		# compute metrics
		snake_case_		:							List[Any]			       =						{}

		snake_case_		:							str			       =						total_area_intersect.sum() / total_area_label.sum()
		snake_case_		:							Union[str, Any]			       =						total_area_intersect / total_area_union
		snake_case_		:							Tuple			       =						total_area_intersect / total_area_label

		snake_case_		:							Any			       =						np.nanmean(lowerCamelCase__ )
		snake_case_		:							List[Any]			       =						np.nanmean(lowerCamelCase__ )
		snake_case_		:							Any			       =						all_acc
		snake_case_		:							int			       =						iou
		snake_case_		:							Optional[int]			       =						acc

		if nan_to_num is not None:
				snake_case_		:							List[str]			       =						{metric: np.nan_to_num(lowerCamelCase__				,      nan=lowerCamelCase__ ) for metric, metric_value in metrics.items()}

		return metrics






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





							def 		a__   (  self		:Tuple			):
									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 		a__   (  self		:str  ,_UpperCamelCase		:Optional[int]  ,_UpperCamelCase		:List[Any]  ,_UpperCamelCase		:int  ,_UpperCamelCase		:bool  ,_UpperCamelCase		:Optional[int] = None  ,_UpperCamelCase		:Optional[Dict[int, int]] = None  ,_UpperCamelCase		:bool = False  ,):
									snake_case_		:							Any			       =						mean_iou(
									    results=_UpperCamelCase  ,gt_seg_maps=_UpperCamelCase  ,num_labels=_UpperCamelCase  ,ignore_index=_UpperCamelCase  ,nan_to_num=_UpperCamelCase  ,label_map=_UpperCamelCase  ,reduce_labels=_UpperCamelCase  ,)
									return iou_result



'''simple docstring'''






from __future__ import annotations





def      UpperCAmelCase      (		lowerCamelCase_  :int ):







		'''simple docstring'''
		snake_case_		:							List[str]			       =						[True] * limit
		snake_case_		:							Union[str, Any]			       =						False
		snake_case_		:							List[str]			       =						False
		snake_case_		:							Any			       =						True

		for i in range(3				,      int(limit**0.5 + 1 )				,      2 ):
				snake_case_		:							Tuple			       =						i * 2
				while index < limit:
						snake_case_		:							Any			       =						False
						snake_case_		:							Optional[Any]			       =						index + i

		snake_case_		:							List[str]			       =						[2]

		for i in range(3				,      lowerCamelCase_				,      2 ):
				if is_prime[i]:
						primes.append(lowerCamelCase_ )

		return primes





def      UpperCAmelCase      (		lowerCamelCase_  :int = 1_00_00_00 ):







		'''simple docstring'''
		snake_case_		:							Optional[int]			       =						prime_sieve(lowerCamelCase_ )
		snake_case_		:							Optional[Any]			       =						0
		snake_case_		:							Optional[int]			       =						0

		for i in range(len(lowerCamelCase_ ) ):
				for j in range(i + length				,      len(lowerCamelCase_ ) ):
						snake_case_		:							Optional[int]			       =						sum(primes[i:j] )
						if sol >= ceiling:
								break

						if sol in primes:
								snake_case_		:							Optional[Any]			       =						j - i
								snake_case_		:							Tuple			       =						sol

		return largest


if __name__ == "__main__":
							print(F'{solution() = }')