






from abc import ABC, abstractmethod
from argparse import ArgumentParser




class 							snake_case_       (    __UpperCamelCase      ):

						"""simple docstring"""



						@staticmethod
						@abstractmethod
						def        UpperCAmelCase__					(__UpperCAmelCase:					ArgumentParser   )    ->     Tuple:




											'''simple docstring'''



											raise NotImplementedError()



						@abstractmethod
						def        UpperCAmelCase__					(self:					List[str]   )    ->     List[str]:




											'''simple docstring'''



											raise NotImplementedError()













from typing import Dict, Optional

import numpy as np

import datasets


UpperCAmelCase__      			=						'''
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.
'''

UpperCAmelCase__      			=						'''
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.        ])}
'''

UpperCAmelCase__      			=						'''\
@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				,       )				->						Dict:



					"""simple docstring"""
					if label_map is not None:
										for old_id, new_id in label_map.items():
															__a					:						str      =					new_id

    # turn into Numpy arrays
					__a					:						Union[str, Any]      =					np.array(__A      )
					__a					:						Any      =					np.array(__A      )

					if reduce_labels:
										__a					:						Dict      =					255
										__a					:						Union[str, Any]      =					label - 1
										__a					:						int      =					255

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

					__a					:						Optional[int]      =					pred_label[pred_label == label]

					__a					:						Dict      =					np.histogram(__A				,       bins=__A				,       range=(0, num_labels - 1)      )[0]
					__a					:						Union[str, Any]      =					np.histogram(__A				,       bins=__A				,       range=(0, num_labels - 1)      )[0]
					__a					:						List[Any]      =					np.histogram(__A				,       bins=__A				,       range=(0, num_labels - 1)      )[0]

					__a					:						Optional[int]      =					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				,       )				->						Dict:



					"""simple docstring"""
					__a					:						Union[str, Any]      =					np.zeros((num_labels,)				,       dtype=np.floataa      )
					__a					:						List[str]      =					np.zeros((num_labels,)				,       dtype=np.floataa      )
					__a					:						Optional[Any]      =					np.zeros((num_labels,)				,       dtype=np.floataa      )
					__a					:						Optional[int]      =					np.zeros((num_labels,)				,       dtype=np.floataa      )
					for result, gt_seg_map in zip(__A				,       __A      ):
										__a      ,			__a      ,			__a      ,			__a					:						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				,       )				->						Optional[int]:



					"""simple docstring"""
					__a      ,			__a      ,			__a      ,			__a					:						Optional[int]      =					total_intersect_and_union(
					    __A				,       __A				,       __A				,       __A				,       __A				,       __A      )

					# compute metrics
					__a					:						Any      =					{}

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

					__a					:						Optional[int]      =					np.nanmean(__A      )
					__a					:						str      =					np.nanmean(__A      )
					__a					:						List[str]      =					all_acc
					__a					:						Dict      =					iou
					__a					:						Union[str, Any]      =					acc

					if nan_to_num is not None:
										__a					:						Tuple      =					{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 							snake_case_       (    datasets.Metric      ):

						"""simple docstring"""



						def        UpperCAmelCase__					(self:					List[str]   )    ->     Tuple:




											'''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:					Optional[int] ,					__UpperCAmelCase:					int ,					__UpperCAmelCase:					Dict ,					__UpperCAmelCase:					int ,					__UpperCAmelCase:					bool ,					__UpperCAmelCase:					Optional[int] = None ,					__UpperCAmelCase:					Optional[Dict[int, int]] = None ,					__UpperCAmelCase:					bool = False ,					)    ->     List[str]:




											'''simple docstring'''



											__a					:						str      =					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





