




"""simple docstring"""






from typing import Dict, Optional

import numpy as np

import datasets


lowercase__			=		"""
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.
"""

lowercase__			=		"""
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.        ])}
"""

lowercase__			=		"""\
@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 	_snake_case     (							lowercase__				,       lowercase__				,       lowercase__				,       lowercase__				,       lowercase__ = None				,       lowercase__ = False				,       ):
				if label_map is not None:
								for old_id, new_id in label_map.items():
												_lowerCamelCase	:     Any       =       new_id

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

				if reduce_labels:
								_lowerCamelCase	:     Union[str, Any]       =       255
								_lowerCamelCase	:     Optional[Any]       =       label - 1
								_lowerCamelCase	:     Tuple       =       255

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

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

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

				_lowerCamelCase	:     int       =       area_pred_label + area_label - area_intersect

				return area_intersect, area_union, area_pred_label, area_label







def 	_snake_case     (							lowercase__				,       lowercase__				,       lowercase__				,       lowercase__				,       lowercase__ = None				,       lowercase__ = False				,       ):
				_lowerCamelCase	:     List[str]       =       np.zeros((num_labels,)				,       dtype=np.floataa						)
				_lowerCamelCase	:     List[str]       =       np.zeros((num_labels,)				,       dtype=np.floataa						)
				_lowerCamelCase	:     Tuple       =       np.zeros((num_labels,)				,       dtype=np.floataa						)
				_lowerCamelCase	:     Optional[Any]       =       np.zeros((num_labels,)				,       dtype=np.floataa						)
				for result, gt_seg_map in zip(lowercase__				,       lowercase__						):
								_lowerCamelCase,					_lowerCamelCase,					_lowerCamelCase,					_lowerCamelCase	:     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 	_snake_case     (							lowercase__				,       lowercase__				,       lowercase__				,       lowercase__				,       lowercase__ = None				,       lowercase__ = None				,       lowercase__ = False				,       ):
				_lowerCamelCase,					_lowerCamelCase,					_lowerCamelCase,					_lowerCamelCase	:     Optional[int]       =       total_intersect_and_union(
				    lowercase__				,       lowercase__				,       lowercase__				,       lowercase__				,       lowercase__				,       lowercase__						)

				# compute metrics
				_lowerCamelCase	:     int       =       {}

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

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

				if nan_to_num is not None:
								_lowerCamelCase	:     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 lowerCAmelCase__      (	datasets.Metric		):






			'''simple docstring'''



			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    , ):
							_lowerCamelCase	:     List[Any]       =       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





"""simple docstring"""






import re







def 	_snake_case     (							lowercase__						):

				if len(re.findall('[ATCG]'				,       lowercase__						)						) != len(lowercase__						):
								raise ValueError('Invalid Strand'						)

				return dna.translate(dna.maketrans('ATCG'				,       'TAGC'						)						)


if __name__ == "__main__":
			import doctest

			doctest.testmod()