

"""simple docstring"""



def  _snake_case     (    UpperCAmelCase_  :					str = "The quick brown fox jumps over the lazy dog"					,   ):
     A__										=  set()

     # Replace all the whitespace in our sentence
     A__										=  input_str.replace(""" """					,   """"""			)
     for alpha in input_str:
          if "a" <= alpha.lower() <= "z":
               frequency.add(alpha.lower()			)
     return len(UpperCAmelCase_			) == 26





def  _snake_case     (    UpperCAmelCase_  :					str = "The quick brown fox jumps over the lazy dog"					,   ):
     A__										=  [False] * 26
     for char in input_str:
          if char.islower():
               A__										=  True
          elif char.isupper():
               A__										=  True
     return all(UpperCAmelCase_			)





def  _snake_case     (    UpperCAmelCase_  :					str = "The quick brown fox jumps over the lazy dog"					,   ):
     return len({char for char in input_str.lower() if char.isalpha()}			) == 26





def  _snake_case     (    ):
     from timeit import timeit

     A__										=  """from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest"""
     print(timeit("""is_pangram()"""					,   setup=UpperCAmelCase_			)			)
     print(timeit("""is_pangram_faster()"""					,   setup=UpperCAmelCase_			)			)
     print(timeit("""is_pangram_fastest()"""					,   setup=UpperCAmelCase_			)			)
     # 5.348480500048026, 2.6477354579837993, 1.8470395830227062
     # 5.036091582966037, 2.644472333951853,  1.8869528750656173


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 benchmark()









"""simple docstring"""



from typing import Dict, Optional

import numpy as np

import datasets


SCREAMING_SNAKE_CASE_					:	Tuple							  =						'\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'

SCREAMING_SNAKE_CASE_					:	Optional[int]							  =						'\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'

SCREAMING_SNAKE_CASE_					:	int							  =						'\\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  _snake_case     (    UpperCAmelCase_  :					str					,   UpperCAmelCase_  :					int					,   UpperCAmelCase_  :					Optional[Any]					,   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__										=  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  _snake_case     (    UpperCAmelCase_  :					int					,   UpperCAmelCase_  :					List[str]					,   UpperCAmelCase_  :					Any					,   UpperCAmelCase_  :					bool					,   UpperCAmelCase_  :					Optional[Dict[int, int]] = None					,   UpperCAmelCase_  :					bool = False					,   ):
     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  _snake_case     (    UpperCAmelCase_  :					Optional[int]					,   UpperCAmelCase_  :					Any					,   UpperCAmelCase_  :					Tuple					,   UpperCAmelCase_  :					bool					,   UpperCAmelCase_  :					Optional[int] = None					,   UpperCAmelCase_  :					Optional[Dict[int, int]] = None					,   UpperCAmelCase_  :					bool = False					,   ):
     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 					a						(       datasets.Metric				):



    """simple docstring"""



    def 				UpperCamelCase	(     self: str       ):






         """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: str    ,						UpperCamelCase: Union[str, Any]    ,						UpperCamelCase: Optional[Any]    ,						UpperCamelCase: int    ,						UpperCamelCase: bool    ,						UpperCamelCase: Optional[int] = None    ,						UpperCamelCase: Optional[Dict[int, int]] = None    ,						UpperCamelCase: bool = False    ,						):






         """simple docstring"""


         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






