


from typing import Dict, Optional

import numpy as np

import datasets


_snake_case					:		int      =    """
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					:		str      =    """
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					:		Optional[Any]      =    """\
@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     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase = None							,     __lowerCamelCase = False							,     ):







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

    # turn into Numpy arrays
							__snake_case		:      Dict										=       np.array(_A     )
							__snake_case		:      Any										=       np.array(_A     )

							if reduce_labels:
														__snake_case		:      Optional[int]										=       2_5_5
														__snake_case		:      Optional[int]										=       label - 1
														__snake_case		:      Optional[int]										=       2_5_5

							__snake_case		:      Any										=       label != ignore_index
							__snake_case		:      Dict										=       np.not_equal(_A							,     _A     )
							__snake_case		:      Dict										=       pred_label[mask]
							__snake_case		:      Union[str, Any]										=       np.array(_A     )[mask]

							__snake_case		:      Dict										=       pred_label[pred_label == label]

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

							__snake_case		:      List[str]										=       area_pred_label + area_label - area_intersect

							return area_intersect, area_union, area_pred_label, area_label







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase = None							,     __lowerCamelCase = False							,     ):
							__snake_case		:      Dict										=       np.zeros((num_labels,)							,     dtype=np.floataa     )
							__snake_case		:      Optional[Any]										=       np.zeros((num_labels,)							,     dtype=np.floataa     )
							__snake_case		:      str										=       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(_A							,     _A     ):
														__snake_case		:      int										=       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     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase = None							,     __lowerCamelCase = None							,     __lowerCamelCase = False							,     ):
							__snake_case		:      int										=       total_intersect_and_union(
							    _A							,     _A							,     _A							,     _A							,     _A							,     _A     )

							# compute metrics
							__snake_case		:      Optional[int]										=       {}

							__snake_case		:      str										=       total_area_intersect.sum() / total_area_label.sum()
							__snake_case		:      str										=       total_area_intersect / total_area_union
							__snake_case		:      Dict										=       total_area_intersect / total_area_label

							__snake_case		:      Optional[int]										=       np.nanmean(_A     )
							__snake_case		:      int										=       np.nanmean(_A     )
							__snake_case		:      Optional[int]										=       all_acc
							__snake_case		:      str										=       iou
							__snake_case		:      Any										=       acc

							if nan_to_num is not None:
														__snake_case		:      int										=       {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     a		(datasets.Metric				):




							"""simple docstring"""


							def    __snake_case (							self	:  Tuple    )     ->   Optional[int]:
														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    __snake_case (							self	:  List[str]   ,     lowerCamelCase	:  str   ,     lowerCamelCase	:  Tuple   ,     lowerCamelCase	:  Any   ,     lowerCamelCase	:  Union[str, Any]   ,     lowerCamelCase	:  Tuple = None   ,     lowerCamelCase	:  List[str] = None   ,     lowerCamelCase	:  List[Any] = False   ,     )     ->   int:
														__snake_case		:      Tuple										=       mean_iou(
														    results=snake_case__   ,     gt_seg_maps=snake_case__   ,     num_labels=snake_case__   ,     ignore_index=snake_case__   ,     nan_to_num=snake_case__   ,     label_map=snake_case__   ,     reduce_labels=snake_case__   ,     )
														return iou_result



from ..utils import DummyObject, requires_backends

class     a		(metaclass=_lowerCAmelCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							int					  =  ["speech"]

							def __init__(							self	:  List[Any]   ,     *lowerCamelCase	:  List[Any]   ,     **lowerCamelCase	:  Optional[Any]    )     ->   Dict:
														requires_backends(self   ,     ["speech"]    )

class     a		(metaclass=_lowerCAmelCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							Optional[Any]					  =  ["speech"]

							def __init__(							self	:  int   ,     *lowerCamelCase	:  List[Any]   ,     **lowerCamelCase	:  List[Any]    )     ->   Optional[int]:
														requires_backends(self   ,     ["speech"]    )

