




'''simple docstring'''





from typing import Dict, Optional

import numpy as np

import datasets


__UpperCAmelCase       =		"\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"

__UpperCAmelCase       =		"\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"

__UpperCAmelCase       =		"\\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       lowerCAmelCase_     (					__A						:							int ,			__A						:							List[str] ,			__A						:							Optional[Any] ,			__A						:							Optional[int] ,			__A						:							Dict = None ,			__A						:							Union[str, Any] = False ,			):




			'''simple docstring'''


			if label_map is not None:
						for old_id, new_id in label_map.items():
									snake_case:       Any        =       new_id

    # turn into Numpy arrays
			snake_case:       str        =       np.array(lowercase__ )
			snake_case:       List[Any]        =       np.array(lowercase__ )

			if reduce_labels:
						snake_case:       Union[str, Any]        =       2_55
						snake_case:       Optional[Any]        =       label - 1
						snake_case:       Tuple        =       2_55

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

			snake_case:       List[str]        =       pred_label[pred_label == label]

			snake_case:       Union[str, Any]        =       np.histogram(lowercase__ ,			bins=lowercase__ ,			range=(0, num_labels - 1) )[0]
			snake_case:       Optional[int]        =       np.histogram(lowercase__ ,			bins=lowercase__ ,			range=(0, num_labels - 1) )[0]
			snake_case:       Optional[int]        =       np.histogram(lowercase__ ,			bins=lowercase__ ,			range=(0, num_labels - 1) )[0]

			snake_case:       int        =       area_pred_label + area_label - area_intersect

			return area_intersect, area_union, area_pred_label, area_label





def       lowerCAmelCase_     (					__A						:							str ,			__A						:							Dict ,			__A						:							Tuple ,			__A						:							Tuple ,			__A						:							Dict = None ,			__A						:							List[str] = False ,			):




			'''simple docstring'''


			snake_case:       List[str]        =       np.zeros((num_labels,) ,			dtype=np.floataa )
			snake_case:       List[str]        =       np.zeros((num_labels,) ,			dtype=np.floataa )
			snake_case:       Tuple        =       np.zeros((num_labels,) ,			dtype=np.floataa )
			snake_case:       Optional[Any]        =       np.zeros((num_labels,) ,			dtype=np.floataa )
			for result, gt_seg_map in zip(lowercase__ ,			lowercase__ ):
						snake_case:       int        =       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       lowerCAmelCase_     (					__A						:							str ,			__A						:							str ,			__A						:							List[Any] ,			__A						:							Tuple ,			__A						:							Tuple = None ,			__A						:							Optional[int] = None ,			__A						:							List[Any] = False ,			):




			'''simple docstring'''


			snake_case:       Optional[int]        =       total_intersect_and_union(
			    lowercase__ ,			lowercase__ ,			lowercase__ ,			lowercase__ ,			lowercase__ ,			lowercase__ )

			# compute metrics
			snake_case:       int        =       {}

			snake_case:       Any        =       total_area_intersect.sum() / total_area_label.sum()
			snake_case:       int        =       total_area_intersect / total_area_union
			snake_case:       str        =       total_area_intersect / total_area_label

			snake_case:       List[Any]        =       np.nanmean(lowercase__ )
			snake_case:       Optional[Any]        =       np.nanmean(lowercase__ )
			snake_case:       Tuple        =       all_acc
			snake_case:       Dict        =       iou
			snake_case:       Tuple        =       acc

			if nan_to_num is not None:
						snake_case:       int        =       {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 		SCREAMING_SNAKE_CASE			(     datasets.Metric							):




				'''simple docstring'''





				def        _UpperCamelCase							(   self				):
							'''simple docstring'''







							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        _UpperCamelCase							(   self  ,						SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__ = None  ,						SCREAMING_SNAKE_CASE__ = None  ,						SCREAMING_SNAKE_CASE__ = False  ,						):
							'''simple docstring'''







							snake_case:       List[Any]        =       mean_iou(
							    results=SCREAMING_SNAKE_CASE__  ,						gt_seg_maps=SCREAMING_SNAKE_CASE__  ,						num_labels=SCREAMING_SNAKE_CASE__  ,						ignore_index=SCREAMING_SNAKE_CASE__  ,						nan_to_num=SCREAMING_SNAKE_CASE__  ,						label_map=SCREAMING_SNAKE_CASE__  ,						reduce_labels=SCREAMING_SNAKE_CASE__  ,						)
							return iou_result





"""simple docstring"""






from __future__ import annotations







def 	_snake_case     (							lowercase__						):
				_lowerCamelCase	:     int       =       len(lowercase__						)
				# We need to create solution object to save path.
				_lowerCamelCase	:     Tuple       =       [[0 for _ in range(lowercase__						)] for _ in range(lowercase__						)]
				_lowerCamelCase	:     Optional[Any]       =       run_maze(lowercase__				,       0				,       0				,       lowercase__						)
				if solved:
								print('\n'.join(str(lowercase__						) for row in solutions						)						)
				else:
								print('No solution exists!'						)
				return solved







def 	_snake_case     (							lowercase__				,       lowercase__				,       lowercase__				,       lowercase__						):
				_lowerCamelCase	:     Optional[int]       =       len(lowercase__						)
				# Final check point.
				if i == j == (size - 1):
								_lowerCamelCase	:     Optional[Any]       =       1
								return True

				_lowerCamelCase	:     List[str]       =       (not i < 0) and (not j < 0)  # Check lower bounds
				_lowerCamelCase	:     List[str]       =       (i < size) and (j < size)  # Check upper bounds

				if lower_flag and upper_flag:
								# check for already visited and block points.
								_lowerCamelCase	:     List[Any]       =       (not solutions[i][j]) and (not maze[i][j])
								if block_flag:
												# check visited
												_lowerCamelCase	:     Union[str, Any]       =       1

												# check for directions
												if (
												    run_maze(lowercase__				,       i + 1				,       lowercase__				,       lowercase__						)
												    or run_maze(lowercase__				,       lowercase__				,       j + 1				,       lowercase__						)
												    or run_maze(lowercase__				,       i - 1				,       lowercase__				,       lowercase__						)
												    or run_maze(lowercase__				,       lowercase__				,       j - 1				,       lowercase__						)
												):
																return True

												_lowerCamelCase	:     Dict       =       0
												return False
				return False


if __name__ == "__main__":
			import doctest

			doctest.testmod()