"""simple docstring"""




from math import cos, sin, sqrt, tau

from audio_filters.iir_filter import IIRFilter






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__ = 1 / sqrt(2				)				):






       """simple docstring"""





       A__	     =							tau * frequency / samplerate
       A__	     =							sin(snake_case_				)
       A__	     =							cos(snake_case_				)
       A__	     =							_sin / (2 * q_factor)

       A__	     =							(1 - _cos) / 2
       A__	     =							1 - _cos

       A__	     =							1 + alpha
       A__	     =							-2 * _cos
       A__	     =							1 - alpha

       A__	     =							IIRFilter(2				)
       filt.set_coefficients([aa, aa, aa]       ,    [ba, ba, ba]				)
       return filt






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__ = 1 / sqrt(2				)				):






       """simple docstring"""





       A__	     =							tau * frequency / samplerate
       A__	     =							sin(snake_case_				)
       A__	     =							cos(snake_case_				)
       A__	     =							_sin / (2 * q_factor)

       A__	     =							(1 + _cos) / 2
       A__	     =							-1 - _cos

       A__	     =							1 + alpha
       A__	     =							-2 * _cos
       A__	     =							1 - alpha

       A__	     =							IIRFilter(2				)
       filt.set_coefficients([aa, aa, aa]       ,    [ba, ba, ba]				)
       return filt






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__ = 1 / sqrt(2				)				):






       """simple docstring"""





       A__	     =							tau * frequency / samplerate
       A__	     =							sin(snake_case_				)
       A__	     =							cos(snake_case_				)
       A__	     =							_sin / (2 * q_factor)

       A__	     =							_sin / 2
       A__	     =							0
       A__	     =							-ba

       A__	     =							1 + alpha
       A__	     =							-2 * _cos
       A__	     =							1 - alpha

       A__	     =							IIRFilter(2				)
       filt.set_coefficients([aa, aa, aa]       ,    [ba, ba, ba]				)
       return filt






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__ = 1 / sqrt(2				)				):






       """simple docstring"""





       A__	     =							tau * frequency / samplerate
       A__	     =							sin(snake_case_				)
       A__	     =							cos(snake_case_				)
       A__	     =							_sin / (2 * q_factor)

       A__	     =							1 - alpha
       A__	     =							-2 * _cos
       A__	     =							1 + alpha

       A__	     =							IIRFilter(2				)
       filt.set_coefficients([ba, ba, ba]       ,    [ba, ba, ba]				)
       return filt






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__ = 1 / sqrt(2				)       ,    ):






       """simple docstring"""





       A__	     =							tau * frequency / samplerate
       A__	     =							sin(snake_case_				)
       A__	     =							cos(snake_case_				)
       A__	     =							_sin / (2 * q_factor)
       A__	     =							10 ** (gain_db / 40)

       A__	     =							1 + alpha * big_a
       A__	     =							-2 * _cos
       A__	     =							1 - alpha * big_a
       A__	     =							1 + alpha / big_a
       A__	     =							-2 * _cos
       A__	     =							1 - alpha / big_a

       A__	     =							IIRFilter(2				)
       filt.set_coefficients([aa, aa, aa]       ,    [ba, ba, ba]				)
       return filt






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__ = 1 / sqrt(2				)       ,    ):






       """simple docstring"""





       A__	     =							tau * frequency / samplerate
       A__	     =							sin(snake_case_				)
       A__	     =							cos(snake_case_				)
       A__	     =							_sin / (2 * q_factor)
       A__	     =							10 ** (gain_db / 40)
       A__	     =							(big_a + 1) - (big_a - 1) * _cos
       A__	     =							(big_a + 1) + (big_a - 1) * _cos
       A__	     =							(big_a - 1) - (big_a + 1) * _cos
       A__	     =							(big_a - 1) + (big_a + 1) * _cos
       A__	     =							2 * sqrt(snake_case_				) * alpha

       A__	     =							big_a * (pmc + aaa)
       A__	     =							2 * big_a * mpc
       A__	     =							big_a * (pmc - aaa)
       A__	     =							ppmc + aaa
       A__	     =							-2 * pmpc
       A__	     =							ppmc - aaa

       A__	     =							IIRFilter(2				)
       filt.set_coefficients([aa, aa, aa]       ,    [ba, ba, ba]				)
       return filt






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__ = 1 / sqrt(2				)       ,    ):






       """simple docstring"""





       A__	     =							tau * frequency / samplerate
       A__	     =							sin(snake_case_				)
       A__	     =							cos(snake_case_				)
       A__	     =							_sin / (2 * q_factor)
       A__	     =							10 ** (gain_db / 40)
       A__	     =							(big_a + 1) - (big_a - 1) * _cos
       A__	     =							(big_a + 1) + (big_a - 1) * _cos
       A__	     =							(big_a - 1) - (big_a + 1) * _cos
       A__	     =							(big_a - 1) + (big_a + 1) * _cos
       A__	     =							2 * sqrt(snake_case_				) * alpha

       A__	     =							big_a * (ppmc + aaa)
       A__	     =							-2 * big_a * pmpc
       A__	     =							big_a * (ppmc - aaa)
       A__	     =							pmc + aaa
       A__	     =							2 * mpc
       A__	     =							pmc - aaa

       A__	     =							IIRFilter(2				)
       filt.set_coefficients([aa, aa, aa]       ,    [ba, ba, ba]				)
       return filt






"""simple docstring"""




from typing import Dict, Optional

import numpy as np

import datasets


__lowerCamelCase											=  "\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"

__lowerCamelCase											=  "\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"

__lowerCamelCase											=  "\\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   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__ = None       ,    UpperCamelCase__ = False       ,    ):






							"""simple docstring"""





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

    # turn into Numpy arrays
							A__	     =							np.array(UpperCamelCase__				)
							A__	     =							np.array(UpperCamelCase__				)

							if reduce_labels:
														A__	     =							255
														A__	     =							label - 1
														A__	     =							255

							A__	     =							label != ignore_index
							A__	     =							np.not_equal(UpperCamelCase__       ,    UpperCamelCase__				)
							A__	     =							pred_label[mask]
							A__	     =							np.array(UpperCamelCase__				)[mask]

							A__	     =							pred_label[pred_label == label]

							A__	     =							np.histogram(UpperCamelCase__       ,    bins=UpperCamelCase__       ,    range=(0, num_labels - 1)				)[0]
							A__	     =							np.histogram(UpperCamelCase__       ,    bins=UpperCamelCase__       ,    range=(0, num_labels - 1)				)[0]
							A__	     =							np.histogram(UpperCamelCase__       ,    bins=UpperCamelCase__       ,    range=(0, num_labels - 1)				)[0]

							A__	     =							area_pred_label + area_label - area_intersect

							return area_intersect, area_union, area_pred_label, area_label






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__ = None       ,    UpperCamelCase__ = False       ,    ):






							"""simple docstring"""





							A__	     =							np.zeros((num_labels,)       ,    dtype=np.floataa				)
							A__	     =							np.zeros((num_labels,)       ,    dtype=np.floataa				)
							A__	     =							np.zeros((num_labels,)       ,    dtype=np.floataa				)
							A__	     =							np.zeros((num_labels,)       ,    dtype=np.floataa				)
							for result, gt_seg_map in zip(UpperCamelCase__       ,    UpperCamelCase__				):
														A__     ,  A__     ,  A__     ,  A__	     =							intersect_and_union(
														    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__				)
														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   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__ = None       ,    UpperCamelCase__ = None       ,    UpperCamelCase__ = False       ,    ):






							"""simple docstring"""





							A__     ,  A__     ,  A__     ,  A__	     =							total_intersect_and_union(
							    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__				)

							# compute metrics
							A__	     =							{}

							A__	     =							total_area_intersect.sum() / total_area_label.sum()
							A__	     =							total_area_intersect / total_area_union
							A__	     =							total_area_intersect / total_area_label

							A__	     =							np.nanmean(UpperCamelCase__				)
							A__	     =							np.nanmean(UpperCamelCase__				)
							A__	     =							all_acc
							A__	     =							iou
							A__	     =							acc

							if nan_to_num is not None:
														A__	     =							{metric: np.nan_to_num(UpperCamelCase__       ,    nan=UpperCamelCase__				) for metric, metric_value in metrics.items()}

							return metrics


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION		,						_KWARGS_DESCRIPTION )
class     UpperCamelCase__(			datasets.Metric ):





						def 				snake_case__	(       self     )					->				str:
													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		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase = None		,__UpperCAmelCase = None		,__UpperCAmelCase = False		,)					->				Tuple:
													A__	     =							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





