


'''simple docstring'''
from typing import Dict, Optional

import numpy as np

import datasets


__snake_case											=					'''
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.
'''

__snake_case											=					'''
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.        ])}
'''

__snake_case											=					'''\
@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       a				(      __a			, __a			, __a			, __a			, __a = None			, __a = False			, )  ->     int:



					'''simple docstring'''






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

    # turn into Numpy arrays
					UpperCamelCase__       :Dict							 =			np.array(__a       )
					UpperCamelCase__       :int							 =			np.array(__a       )

					if reduce_labels:
										UpperCamelCase__       :List[Any]							 =			255
										UpperCamelCase__       :int							 =			label - 1
										UpperCamelCase__       :int							 =			255

					UpperCamelCase__       :Tuple							 =			label != ignore_index
					UpperCamelCase__       :Optional[Any]							 =			np.not_equal(__a			, __a       )
					UpperCamelCase__       :str							 =			pred_label[mask]
					UpperCamelCase__       :Optional[int]							 =			np.array(__a       )[mask]

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

					UpperCamelCase__       :Optional[int]							 =			np.histogram(__a			, bins=__a			, range=(0, num_labels - 1)       )[0]
					UpperCamelCase__       :int							 =			np.histogram(__a			, bins=__a			, range=(0, num_labels - 1)       )[0]
					UpperCamelCase__       :Any							 =			np.histogram(__a			, bins=__a			, range=(0, num_labels - 1)       )[0]

					UpperCamelCase__       :Any							 =			area_pred_label + area_label - area_intersect

					return area_intersect, area_union, area_pred_label, area_label


def       a				(      __a			, __a			, __a			, __a			, __a = None			, __a = False			, )  ->     Union[str, Any]:



					'''simple docstring'''






					UpperCamelCase__       :Tuple							 =			np.zeros((num_labels,)			, dtype=np.floataa       )
					UpperCamelCase__       :List[Any]							 =			np.zeros((num_labels,)			, dtype=np.floataa       )
					UpperCamelCase__       :List[Any]							 =			np.zeros((num_labels,)			, dtype=np.floataa       )
					UpperCamelCase__       :Optional[Any]							 =			np.zeros((num_labels,)			, dtype=np.floataa       )
					for result, gt_seg_map in zip(__a			, __a       ):
										UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__       :Dict							 =			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       a				(      __a			, __a			, __a			, __a			, __a = None			, __a = None			, __a = False			, )  ->     int:



					'''simple docstring'''






					UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__       :List[str]							 =			total_intersect_and_union(
					    __a			, __a			, __a			, __a			, __a			, __a       )

					# compute metrics
					UpperCamelCase__       :Any							 =			{}

					UpperCamelCase__       :Optional[int]							 =			total_area_intersect.sum() / total_area_label.sum()
					UpperCamelCase__       :int							 =			total_area_intersect / total_area_union
					UpperCamelCase__       :Optional[Any]							 =			total_area_intersect / total_area_label

					UpperCamelCase__       :int							 =			np.nanmean(__a       )
					UpperCamelCase__       :Union[str, Any]							 =			np.nanmean(__a       )
					UpperCamelCase__       :List[Any]							 =			all_acc
					UpperCamelCase__       :Tuple							 =			iou
					UpperCamelCase__       :Tuple							 =			acc

					if nan_to_num is not None:
										UpperCamelCase__       :Dict							 =			{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        lowercase     (							datasets.Metric ):

		"""simple docstring"""

		def    lowerCAmelCase__    (		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    lowerCAmelCase__    (		self   ,       UpperCamelCase_   ,       UpperCamelCase_   ,       UpperCamelCase_   ,       UpperCamelCase_   ,       UpperCamelCase_ = None   ,       UpperCamelCase_ = None   ,       UpperCamelCase_ = False   ,       ):

							'''simple docstring'''


							UpperCamelCase__       :Dict							 =			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 math import ceil


def       a				(      __a			, __a       )  ->     Any:



					'''simple docstring'''






					UpperCamelCase__       :str							 =			list(range(0			, __a       )       )

					UpperCamelCase__       :Optional[int]							 =			[item for sublist in list(device_map.values()       ) for item in sublist]

					# Duplicate check
					UpperCamelCase__       :Optional[int]							 =			[]
					for i in device_map_blocks:
										if device_map_blocks.count(__a       ) > 1 and i not in duplicate_blocks:
															duplicate_blocks.append(__a       )
    # Missing blocks
					UpperCamelCase__       :List[str]							 =			[i for i in blocks if i not in device_map_blocks]
					UpperCamelCase__       :Optional[Any]							 =			[i for i in device_map_blocks if i not in blocks]

					if len(__a       ) != 0:
										raise ValueError(
										    '''Duplicate attention blocks specified in device_map. Attention blocks must be specified to one device.'''
										    ''' These attention blocks were specified more than once: ''' + str(__a       )       )
					if len(__a       ) != 0:
										raise ValueError(
										    '''There are attention blocks for this model that are not specified in the device_map. Add these attention '''
										    '''blocks to a device on the device_map: ''' + str(__a       )       )
					if len(__a       ) != 0:
										raise ValueError(
										    '''The device_map contains more attention blocks than this model has. Remove these from the device_map:'''
										    + str(__a       )       )







def       a				(      __a			, __a       )  ->     Tuple:



					'''simple docstring'''






					UpperCamelCase__       :Optional[Any]							 =			list(range(__a       )       )
					UpperCamelCase__       :Any							 =			int(ceil(n_layers / len(__a       )       )       )
					UpperCamelCase__       :List[Any]							 =			[layers[i : i + n_blocks] for i in range(0			, __a			, __a       )]

					return dict(zip(__a			, __a       )       )