

from typing import Dict, Optional

import numpy as np

import datasets


_lowerCamelCase      :					Union[str, Any]					    =      '''
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.
'''

_lowerCamelCase      :					Tuple					    =      '''
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.        ])}
'''

_lowerCamelCase      :					str					    =      '''\
@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      A__					(			__A  :						Optional[int]     ,				__A  :						Optional[Any]     ,				__A  :						str     ,				__A  :						bool     ,				__A  :						Optional[Dict[int, int]] = None     ,				__A  :						bool = False     ,				)					->int:
      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(__A			)
      __A    =np.array(__A			)

      if reduce_labels:
            __A    =2_55
            __A    =label - 1
            __A    =2_55

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

      __A    =pred_label[pred_label == label]

      __A    =np.histogram(__A     ,				bins=__A     ,				range=(0, num_labels - 1)			)[0]
      __A    =np.histogram(__A     ,				bins=__A     ,				range=(0, num_labels - 1)			)[0]
      __A    =np.histogram(__A     ,				bins=__A     ,				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      A__					(			__A  :						str     ,				__A  :						List[Any]     ,				__A  :						Optional[Any]     ,				__A  :						bool     ,				__A  :						Optional[Dict[int, int]] = None     ,				__A  :						bool = False     ,				)					->Optional[Any]:
      __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(__A     ,				__A			):
            __A    ,		__A    ,		__A    ,		__A    =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      A__					(			__A  :						Any     ,				__A  :						int     ,				__A  :						Any     ,				__A  :						bool     ,				__A  :						Optional[int] = None     ,				__A  :						Optional[Dict[int, int]] = None     ,				__A  :						bool = False     ,				)					->Union[str, Any]:
      __A    ,		__A    ,		__A    ,		__A    =total_intersect_and_union(
          __A     ,				__A     ,				__A     ,				__A     ,				__A     ,				__A			)

      # 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(__A			)
      __A    =np.nanmean(__A			)
      __A    =all_acc
      __A    =iou
      __A    =acc

      if nan_to_num is not None:
            __A    ={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        lowerCAmelCase__  (					datasets.Metric			):



       '''simple docstring'''







       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             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     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ ,   lowercase__ ,   lowercase__ ,   lowercase__ = None ,   lowercase__ = None ,   lowercase__ = False ,   ):







             '''simple docstring'''



             __A    =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





from collections import deque





def      A__					(			__A  :						Optional[Any]			)					->Tuple:
      __A    =len(__A			)
      __A    =deque()
      __A    =[False for _ in range(__A			)]
      __A    =[-1 for _ in range(__A			)]
      __A    =index_of[:]

      def strong_connect(__A  :						Union[str, Any]     ,				__A  :						int     ,				__A  :						Optional[int]			):
            __A    =index  # the number when this node is seen
            __A    =index  # lowest rank node reachable from here
            index += 1
            stack.append(__A			)
            __A    =True

            for w in g[v]:
                  if index_of[w] == -1:
                        __A    =strong_connect(__A     ,				__A     ,				__A			)
                        __A    =(
                            lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
                        )
                  elif on_stack[w]:
                        __A    =(
                            lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
                        )

            if lowlink_of[v] == index_of[v]:
                  __A    =[]
                  __A    =stack.pop()
                  __A    =False
                  component.append(__A			)
                  while w != v:
                        __A    =stack.pop()
                        __A    =False
                        component.append(__A			)
                  components.append(__A			)
            return index

      __A    =[]
      for v in range(__A			):
            if index_of[v] == -1:
                  strong_connect(__A     ,				0     ,				__A			)

      return components





def      A__					(			__A  :						List[Any]     ,				__A  :						Optional[int]			)					->Tuple:
      __A    =[[] for _ in range(__A			)]
      for u, v in edges:
            g[u].append(__A			)
      return g


if __name__ == "__main__":
   # Test
   _lowerCamelCase      :					int					    =      7
   _lowerCamelCase      :					Tuple					    =      [0, 0, 1, 2, 3, 3, 4, 4, 6]
   _lowerCamelCase      :					Dict					    =      [1, 3, 2, 0, 1, 4, 5, 6, 5]
   _lowerCamelCase      :					Optional[Any]					    =      [(u, v) for u, v in zip(source, target)]
   _lowerCamelCase      :					Dict					    =      create_graph(n_vertices, edges)

   assert [[5], [6], [4], [3, 2, 1, 0]] == tarjan(g)


