


from typing import Dict, Optional

import numpy as np

import datasets


snake_case    :   List[Any]	    =					'''\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'''

snake_case    :   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'''

snake_case    :   List[Any]	    =					'''\\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     __lowerCamelCase						(			UpperCAmelCase_						:		Dict			,  UpperCAmelCase_						:		List[str]			,  UpperCAmelCase_						:		List[str]			,  UpperCAmelCase_						:		bool			,  UpperCAmelCase_						:		Optional[Dict[int, int]] = None			,  UpperCAmelCase_						:		bool = False			,  ):




      """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						:int										=	np.array(__SCREAMING_SNAKE_CASE						)
      a						:List[Any]										=	np.array(__SCREAMING_SNAKE_CASE						)

      if reduce_labels:
            a						:int										=	255
            a						:Dict										=	label - 1
            a						:Optional[int]										=	255

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

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

      a						:str										=	np.histogram(__SCREAMING_SNAKE_CASE			,  bins=__SCREAMING_SNAKE_CASE			,  range=(0, num_labels - 1)						)[0]
      a						:Tuple										=	np.histogram(__SCREAMING_SNAKE_CASE			,  bins=__SCREAMING_SNAKE_CASE			,  range=(0, num_labels - 1)						)[0]
      a						:Union[str, Any]										=	np.histogram(__SCREAMING_SNAKE_CASE			,  bins=__SCREAMING_SNAKE_CASE			,  range=(0, num_labels - 1)						)[0]

      a						:Optional[int]										=	area_pred_label + area_label - area_intersect

      return area_intersect, area_union, area_pred_label, area_label
def     __lowerCamelCase						(			UpperCAmelCase_						:		Tuple			,  UpperCAmelCase_						:		Optional[int]			,  UpperCAmelCase_						:		Dict			,  UpperCAmelCase_						:		bool			,  UpperCAmelCase_						:		Optional[Dict[int, int]] = None			,  UpperCAmelCase_						:		bool = False			,  ):




      """simple docstring"""





      a						:int										=	np.zeros((num_labels,)			,  dtype=np.floataa						)
      a						:Union[str, Any]										=	np.zeros((num_labels,)			,  dtype=np.floataa						)
      a						:Optional[int]										=	np.zeros((num_labels,)			,  dtype=np.floataa						)
      a						:List[Any]										=	np.zeros((num_labels,)			,  dtype=np.floataa						)
      for result, gt_seg_map in zip(__SCREAMING_SNAKE_CASE			,  __SCREAMING_SNAKE_CASE						):
            a						:Tuple										=	intersect_and_union(
                __SCREAMING_SNAKE_CASE			,  __SCREAMING_SNAKE_CASE			,  __SCREAMING_SNAKE_CASE			,  __SCREAMING_SNAKE_CASE			,  __SCREAMING_SNAKE_CASE			,  __SCREAMING_SNAKE_CASE						)
            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						(			UpperCAmelCase_						:		int			,  UpperCAmelCase_						:		Optional[int]			,  UpperCAmelCase_						:		Optional[Any]			,  UpperCAmelCase_						:		bool			,  UpperCAmelCase_						:		Optional[int] = None			,  UpperCAmelCase_						:		Optional[Dict[int, int]] = None			,  UpperCAmelCase_						:		bool = False			,  ):




      """simple docstring"""





      a						:int										=	total_intersect_and_union(
          __SCREAMING_SNAKE_CASE			,  __SCREAMING_SNAKE_CASE			,  __SCREAMING_SNAKE_CASE			,  __SCREAMING_SNAKE_CASE			,  __SCREAMING_SNAKE_CASE			,  __SCREAMING_SNAKE_CASE						)

      # compute metrics
      a						:Tuple										=	{}

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

      a						:Optional[int]										=	np.nanmean(__SCREAMING_SNAKE_CASE						)
      a						:Union[str, Any]										=	np.nanmean(__SCREAMING_SNAKE_CASE						)
      a						:Union[str, Any]										=	all_acc
      a						:Dict										=	iou
      a						:Union[str, Any]										=	acc

      if nan_to_num is not None:
            a						:Optional[Any]										=	{metric: np.nan_to_num(__SCREAMING_SNAKE_CASE			,  nan=__SCREAMING_SNAKE_CASE						) for metric, metric_value in metrics.items()}

      return metrics







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




 def 	SCREAMING_SNAKE_CASE__    (		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 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase = None ,  _lowerCamelCase = None ,  _lowerCamelCase = False ,  ):
       a						:str										=	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









import math
def     __lowerCamelCase						(			UpperCAmelCase_						:		float			,  UpperCAmelCase_						:		float						):




      """simple docstring"""





      return math.pow(UpperCAmelCase_			,  2						) - a
def     __lowerCamelCase						(			UpperCAmelCase_						:		float						):




      """simple docstring"""





      return 2 * x
def     __lowerCamelCase						(			UpperCAmelCase_						:		float						):




      """simple docstring"""





      a						:int										=	2.0

      while start <= a:
            a						:int										=	math.pow(UpperCAmelCase_			,  2						)

      return start
def     __lowerCamelCase						(			UpperCAmelCase_						:		float			,  UpperCAmelCase_						:		int = 9999			,  UpperCAmelCase_						:		float = 0.00000000000001						):




      """simple docstring"""





      if a < 0:
            raise ValueError('''math domain error'''						)

      a						:List[Any]										=	get_initial_point(UpperCAmelCase_						)

      for _ in range(UpperCAmelCase_						):
            a						:Optional[int]										=	value
            a						:int										=	value - fx(UpperCAmelCase_			,  UpperCAmelCase_						) / fx_derivative(UpperCAmelCase_						)
            if abs(prev_value - value						) < tolerance:
                  return value

      return value


if __name__ == "__main__":
     from doctest import testmod

     testmod()





