


from __future__ import annotations




def 						__SCREAMING_SNAKE_CASE       (					lowercase_			,					lowercase_			)		->    list[list[int]]:




						'''simple docstring'''







						__UpperCAmelCase :       list[list[int]]  						=  []
						__UpperCAmelCase :       list[int]  						=  []
						__UpperCAmelCase :       List[Any]  						=  0
						__UpperCAmelCase :       int  						=  sum(lowercase_			)
						create_state_space_tree(lowercase_			,					lowercase_			,					lowercase_			,					lowercase_			,					lowercase_			,					lowercase_			)
						return result




def 						__SCREAMING_SNAKE_CASE       (					lowercase_			,					lowercase_			,					lowercase_			,					lowercase_			,					lowercase_			,					lowercase_			,					)		->    None:




						'''simple docstring'''







						if sum(lowercase_			) > max_sum or (remaining_nums_sum + sum(lowercase_			)) < max_sum:
												return
						if sum(lowercase_			) == max_sum:
												result.append(lowercase_			)
												return
						for index in range(lowercase_			,					len(lowercase_			)			):
												create_state_space_tree(
												    lowercase_			,					lowercase_			,					index + 1			,					[*path, nums[index]]			,					lowercase_			,					remaining_nums_sum - nums[index]			,					)
lowerCAmelCase							=						[3, 34, 4, 12, 5, 2]
lowerCAmelCase							=						9
lowerCAmelCase							=						generate_sum_of_subsets_soln(nums, max_sum)





print(*result)





from typing import Dict, Optional

import numpy as np

import datasets


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

lowerCAmelCase							=						"""
Args:
    predictions (`List[ndarray]`):
        List of predicted segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.
    references (`List[ndarray]`):
        List of ground truth segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.
    num_labels (`int`):
        Number of classes (categories).
    ignore_index (`int`):
        Index that will be ignored during evaluation.
    nan_to_num (`int`, *optional*):
        If specified, NaN values will be replaced by the number defined by the user.
    label_map (`dict`, *optional*):
        If specified, dictionary mapping old label indices to new label indices.
    reduce_labels (`bool`, *optional*, defaults to `False`):
        Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0 is used for background,
        and background itself is not included in all classes of a dataset (e.g. ADE20k). The background label will be replaced by 255.

Returns:
    `Dict[str, float | ndarray]` comprising various elements:
    - *mean_iou* (`float`):
        Mean Intersection-over-Union (IoU averaged over all categories).
    - *mean_accuracy* (`float`):
        Mean accuracy (averaged over all categories).
    - *overall_accuracy* (`float`):
        Overall accuracy on all images.
    - *per_category_accuracy* (`ndarray` of shape `(num_labels,)`):
        Per category accuracy.
    - *per_category_iou* (`ndarray` of shape `(num_labels,)`):
        Per category IoU.

Examples:

    >>> import numpy as np

    >>> mean_iou = datasets.load_metric(\"mean_iou\")

    >>> # suppose one has 3 different segmentation maps predicted
    >>> predicted_1 = np.array([[1, 2], [3, 4], [5, 255]])
    >>> actual_1 = np.array([[0, 3], [5, 4], [6, 255]])

    >>> predicted_2 = np.array([[2, 7], [9, 2], [3, 6]])
    >>> actual_2 = np.array([[1, 7], [9, 2], [3, 6]])

    >>> predicted_3 = np.array([[2, 2, 3], [8, 2, 4], [3, 255, 2]])
    >>> actual_3 = np.array([[1, 2, 2], [8, 2, 1], [3, 255, 1]])

    >>> predicted = [predicted_1, predicted_2, predicted_3]
    >>> ground_truth = [actual_1, actual_2, actual_3]

    >>> results = mean_iou.compute(predictions=predicted, references=ground_truth, num_labels=10, ignore_index=255, reduce_labels=False)
    >>> print(results) # doctest: +NORMALIZE_WHITESPACE
    {'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.        ])}
"""

lowerCAmelCase							=						"""\
@software{MMSegmentation_Contributors_OpenMMLab_Semantic_Segmentation_2020,
author = {{MMSegmentation Contributors}},
license = {Apache-2.0},
month = {7},
title = {{OpenMMLab Semantic Segmentation Toolbox and Benchmark}},
url = {https://github.com/open-mmlab/mmsegmentation},
year = {2020}
}"""




def 						__SCREAMING_SNAKE_CASE       (					lowercase_			,					lowercase_			,					lowercase_			,					lowercase_			,					lowercase_ = None			,					lowercase_ = False			,					)		->    Optional[Any]:




						'''simple docstring'''







						if label_map is not None:
												for old_id, new_id in label_map.items():
																		__UpperCAmelCase :       List[str]  						=  new_id

    # turn into Numpy arrays
						__UpperCAmelCase :       Tuple  						=  np.array(lowercase_			)
						__UpperCAmelCase :       str  						=  np.array(lowercase_			)

						if reduce_labels:
												__UpperCAmelCase :       List[Any]  						=  255
												__UpperCAmelCase :       str  						=  label - 1
												__UpperCAmelCase :       Dict  						=  255

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

						__UpperCAmelCase :       Optional[Any]  						=  pred_label[pred_label == label]

						__UpperCAmelCase :       Optional[Any]  						=  np.histogram(lowercase_			,					bins=lowercase_			,					range=(0, num_labels - 1)			)[0]
						__UpperCAmelCase :       Any  						=  np.histogram(lowercase_			,					bins=lowercase_			,					range=(0, num_labels - 1)			)[0]
						__UpperCAmelCase :       List[str]  						=  np.histogram(lowercase_			,					bins=lowercase_			,					range=(0, num_labels - 1)			)[0]

						__UpperCAmelCase :       List[Any]  						=  area_pred_label + area_label - area_intersect

						return area_intersect, area_union, area_pred_label, area_label




def 						__SCREAMING_SNAKE_CASE       (					lowercase_			,					lowercase_			,					lowercase_			,					lowercase_			,					lowercase_ = None			,					lowercase_ = False			,					)		->    Any:




						'''simple docstring'''







						__UpperCAmelCase :       List[Any]  						=  np.zeros((num_labels,)			,					dtype=np.floataa			)
						__UpperCAmelCase :       List[Any]  						=  np.zeros((num_labels,)			,					dtype=np.floataa			)
						__UpperCAmelCase :       str  						=  np.zeros((num_labels,)			,					dtype=np.floataa			)
						__UpperCAmelCase :       str  						=  np.zeros((num_labels,)			,					dtype=np.floataa			)
						for result, gt_seg_map in zip(lowercase_			,					lowercase_			):
												__UpperCAmelCase						,			__UpperCAmelCase						,			__UpperCAmelCase						,			__UpperCAmelCase :       Tuple  						=  intersect_and_union(
												    lowercase_			,					lowercase_			,					lowercase_			,					lowercase_			,					lowercase_			,					lowercase_			)
												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 						__SCREAMING_SNAKE_CASE       (					lowercase_			,					lowercase_			,					lowercase_			,					lowercase_			,					lowercase_ = None			,					lowercase_ = None			,					lowercase_ = False			,					)		->    str:




						'''simple docstring'''







						__UpperCAmelCase						,			__UpperCAmelCase						,			__UpperCAmelCase						,			__UpperCAmelCase :       Any  						=  total_intersect_and_union(
						    lowercase_			,					lowercase_			,					lowercase_			,					lowercase_			,					lowercase_			,					lowercase_			)

						# compute metrics
						__UpperCAmelCase :       Any  						=  {}

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

						__UpperCAmelCase :       Optional[int]  						=  np.nanmean(lowercase_			)
						__UpperCAmelCase :       int  						=  np.nanmean(lowercase_			)
						__UpperCAmelCase :       List[str]  						=  all_acc
						__UpperCAmelCase :       Any  						=  iou
						__UpperCAmelCase :       str  						=  acc

						if nan_to_num is not None:
												__UpperCAmelCase :       Any  						=  {metric: np.nan_to_num(lowercase_			,					nan=lowercase_			) for metric, metric_value in metrics.items()}

						return metrics





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


	def    A(						self):
							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							,    lowercase__							,    lowercase__							,    lowercase__							,    lowercase__							,    lowercase__ = None							,    lowercase__ = None							,    lowercase__ = False							,    ):
							__UpperCAmelCase :       str  						=  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

