


from typing import Dict, Optional

import numpy as np

import datasets


UpperCAmelCase_				:			List[str]   =	'\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'

UpperCAmelCase_				:			List[str]   =	'\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'

UpperCAmelCase_				:			List[str]   =	'\\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 	SCREAMING_SNAKE_CASE_						(      __A			:      List[Any]  ,   __A			:      str  ,   __A			:      Union[str, Any]  ,   __A			:      bool  ,   __A			:      Optional[Dict[int, int]] = None  ,   __A			:      bool = False  ,   )     ->		Union[str, Any]:

       """simple docstring"""

       if label_map is not None:
              for old_id, new_id in label_map.items():
                     a_ :		List[Any]							=  new_id

    # turn into Numpy arrays
       a_ :		Any							=  np.array(__A    )
       a_ :		str							=  np.array(__A    )

       if reduce_labels:
              a_ :		Tuple							=  2_55
              a_ :		List[Any]							=  label - 1
              a_ :		str							=  2_55

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

       a_ :		Union[str, Any]							=  pred_label[pred_label == label]

       a_ :		Optional[int]							=  np.histogram(__A  ,   bins=__A  ,   range=(0, num_labels - 1)    )[0]
       a_ :		int							=  np.histogram(__A  ,   bins=__A  ,   range=(0, num_labels - 1)    )[0]
       a_ :		Tuple							=  np.histogram(__A  ,   bins=__A  ,   range=(0, num_labels - 1)    )[0]

       a_ :		List[Any]							=  area_pred_label + area_label - area_intersect

       return area_intersect, area_union, area_pred_label, area_label







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Optional[Any]  ,   __A			:      Dict  ,   __A			:      Tuple  ,   __A			:      bool  ,   __A			:      Optional[Dict[int, int]] = None  ,   __A			:      bool = False  ,   )     ->		Tuple:

       """simple docstring"""

       a_ :		Optional[Any]							=  np.zeros((num_labels,)  ,   dtype=np.floataa    )
       a_ :		int							=  np.zeros((num_labels,)  ,   dtype=np.floataa    )
       a_ :		Dict							=  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_ :		Any							=  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 	SCREAMING_SNAKE_CASE_						(      __A			:      List[Any]  ,   __A			:      Dict  ,   __A			:      Union[str, Any]  ,   __A			:      bool  ,   __A			:      Optional[int] = None  ,   __A			:      Optional[Dict[int, int]] = None  ,   __A			:      bool = False  ,   )     ->		Tuple:

       """simple docstring"""

       a_							,		a_							,		a_							,		a_ :		Any							=  total_intersect_and_union(
           __A  ,   __A  ,   __A  ,   __A  ,   __A  ,   __A    )

       # compute metrics
       a_ :		Tuple							=  {}

       a_ :		Tuple							=  total_area_intersect.sum() / total_area_label.sum()
       a_ :		int							=  total_area_intersect / total_area_union
       a_ :		Any							=  total_area_intersect / total_area_label

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

       if nan_to_num is not None:
              a_ :		List[str]							=  {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 			SCREAMING_SNAKE_CASE__  (    datasets.Metric      ):
      def 	SCREAMING_SNAKE_CASE							(	self    :	Optional[Any]					)		->      Any:
             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 	SCREAMING_SNAKE_CASE							(	self    :	List[Any]					,							SCREAMING_SNAKE_CASE__    :	List[Any]					,							SCREAMING_SNAKE_CASE__    :	Optional[int]					,							SCREAMING_SNAKE_CASE__    :	int					,							SCREAMING_SNAKE_CASE__    :	bool					,							SCREAMING_SNAKE_CASE__    :	Optional[int] = None					,							SCREAMING_SNAKE_CASE__    :	Optional[Dict[int, int]] = None					,							SCREAMING_SNAKE_CASE__    :	bool = False					,							)		->      str:
             a_ :		Any							=  mean_iou(
                 results=SCREAMING_SNAKE_CASE__					,							gt_seg_maps=SCREAMING_SNAKE_CASE__					,							num_labels=SCREAMING_SNAKE_CASE__					,							ignore_index=SCREAMING_SNAKE_CASE__					,							nan_to_num=SCREAMING_SNAKE_CASE__					,							label_map=SCREAMING_SNAKE_CASE__					,							reduce_labels=SCREAMING_SNAKE_CASE__					,							)
             return iou_result






def 	SCREAMING_SNAKE_CASE_						(      __A			:      list    )     ->		bool:

       """simple docstring"""

       if not isinstance(__A  ,   __A    ):
              raise ValueError('Input series is not valid, valid series - [2, 4, 6]'    )
       if len(__A    ) == 0:
              raise ValueError('Input list must be a non empty list'    )
       if len(__A    ) == 1:
              return True
       a_ :		Tuple							=  series[1] - series[0]
       for index in range(len(__A    ) - 1    ):
              if series[index + 1] - series[index] != common_diff:
                     return False
       return True







def 	SCREAMING_SNAKE_CASE_						(      __A			:      list    )     ->		float:

       """simple docstring"""

       if not isinstance(__A  ,   __A    ):
              raise ValueError('Input series is not valid, valid series - [2, 4, 6]'    )
       if len(__A    ) == 0:
              raise ValueError('Input list must be a non empty list'    )
       a_ :		str							=  0
       for val in series:
              answer += val
       return answer / len(__A    )


if __name__ == "__main__":
   import doctest

   doctest.testmod()


