


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__     :  List[str]				,							UpperCamelCase__     :  Any				,							UpperCamelCase__     :  Optional[int]				,							UpperCamelCase__     :  bool				,							UpperCamelCase__     :  Optional[Dict[int, int]] = None				,							UpperCamelCase__     :  bool = False				,							):
       if label_map is not None:
              for old_id, new_id in label_map.items():
                     _A      :       Union[str, Any]				    = new_id

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

       if reduce_labels:
              _A      :       str				    = 255
              _A      :       Union[str, Any]				    = label - 1
              _A      :       Optional[int]				    = 255

       _A      :       List[Any]				    = label != ignore_index
       _A      :       Any				    = np.not_equal(UpperCamelCase__				,							UpperCamelCase__						)
       _A      :       str				    = pred_label[mask]
       _A      :       Any				    = np.array(UpperCamelCase__						)[mask]

       _A      :       Tuple				    = pred_label[pred_label == label]

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

       _A      :       Tuple				    = area_pred_label + area_label - area_intersect

       return area_intersect, area_union, area_pred_label, area_label
def 							_UpperCAmelCase				(UpperCamelCase__     :  Tuple				,							UpperCamelCase__     :  Union[str, Any]				,							UpperCamelCase__     :  Any				,							UpperCamelCase__     :  bool				,							UpperCamelCase__     :  Optional[Dict[int, int]] = None				,							UpperCamelCase__     :  bool = False				,							):
       _A      :       List[Any]				    = np.zeros((num_labels,)				,							dtype=np.floataa						)
       _A      :       int				    = np.zeros((num_labels,)				,							dtype=np.floataa						)
       _A      :       Union[str, Any]				    = np.zeros((num_labels,)				,							dtype=np.floataa						)
       _A      :       int				    = np.zeros((num_labels,)				,							dtype=np.floataa						)
       for result, gt_seg_map in zip(UpperCamelCase__				,							UpperCamelCase__						):
              _A    ,			_A    ,			_A    ,			_A      :       str				    = 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__     :  List[Any]				,							UpperCamelCase__     :  List[str]				,							UpperCamelCase__     :  List[Any]				,							UpperCamelCase__     :  bool				,							UpperCamelCase__     :  Optional[int] = None				,							UpperCamelCase__     :  Optional[Dict[int, int]] = None				,							UpperCamelCase__     :  bool = False				,							):
       _A    ,			_A    ,			_A    ,			_A      :       Optional[int]				    = total_intersect_and_union(
           UpperCamelCase__				,							UpperCamelCase__				,							UpperCamelCase__				,							UpperCamelCase__				,							UpperCamelCase__				,							UpperCamelCase__						)

       # compute metrics
       _A      :       List[str]				    = {}

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

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

       if nan_to_num is not None:
              _A      :       List[Any]				    = {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      lowerCAmelCase__       (				datasets.Metric):




     '''simple docstring'''


     def  _lowerCamelCase (						self)			->				Dict:
            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 ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase = None ,						__lowerCamelCase = None ,						__lowerCamelCase = False ,						)			->				Optional[Any]:
            _A      :       int				    = mean_iou(
                results=__lowerCamelCase ,						gt_seg_maps=__lowerCamelCase ,						num_labels=__lowerCamelCase ,						ignore_index=__lowerCamelCase ,						nan_to_num=__lowerCamelCase ,						label_map=__lowerCamelCase ,						reduce_labels=__lowerCamelCase ,						)
            return iou_result




from __future__ import annotations
def 							_UpperCAmelCase				(UpperCamelCase__     :  list[int]				,							UpperCamelCase__     :  list[int]				,							UpperCamelCase__     :  int						):
       _A      :       Dict				    = list(range(len(UpperCamelCase__						)						)						)
       _A      :       Any				    = [v / w for v, w in zip(UpperCamelCase__				,							UpperCamelCase__						)]
       index.sort(key=lambda UpperCamelCase__						: ratio[i]				,							reverse=UpperCamelCase__						)

       _A      :       float				    = 0
       _A      :       list[float]				    = [0] * len(UpperCamelCase__						)
       for i in index:
              if weight[i] <= capacity:
                     _A      :       Union[str, Any]				    = 1
                     max_value += value[i]
                     capacity -= weight[i]
              else:
                     _A      :       Optional[Any]				    = capacity / weight[i]
                     max_value += value[i] * capacity / weight[i]
                     break

       return max_value, fractions


if __name__ == "__main__":
    import doctest

    doctest.testmod()
