




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  snake_case(    __magic_name__			,		__magic_name__			,		__magic_name__			,		__magic_name__			,		__magic_name__ = None			,		__magic_name__ = False			,		)			->							Optional[int]:





     '''simple docstring'''


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

    # turn into Numpy arrays
     lowercase :       str       =			np.array(__magic_name__   )
     lowercase :       Optional[int]       =			np.array(__magic_name__   )

     if reduce_labels:
          lowercase :       Union[str, Any]       =			2_55
          lowercase :       Union[str, Any]       =			label - 1
          lowercase :       List[Any]       =			2_55

     lowercase :       Any       =			label != ignore_index
     lowercase :       List[Any]       =			np.not_equal(__magic_name__			,		__magic_name__   )
     lowercase :       Any       =			pred_label[mask]
     lowercase :       int       =			np.array(__magic_name__   )[mask]

     lowercase :       Optional[int]       =			pred_label[pred_label == label]

     lowercase :       int       =			np.histogram(__magic_name__			,		bins=__magic_name__			,		range=(0, num_labels - 1)   )[0]
     lowercase :       Any       =			np.histogram(__magic_name__			,		bins=__magic_name__			,		range=(0, num_labels - 1)   )[0]
     lowercase :       Optional[Any]       =			np.histogram(__magic_name__			,		bins=__magic_name__			,		range=(0, num_labels - 1)   )[0]

     lowercase :       Optional[Any]       =			area_pred_label + area_label - area_intersect

     return area_intersect, area_union, area_pred_label, area_label






def  snake_case(    __magic_name__			,		__magic_name__			,		__magic_name__			,		__magic_name__			,		__magic_name__ = None			,		__magic_name__ = False			,		)			->							Union[str, Any]:





     '''simple docstring'''


     lowercase :       str       =			np.zeros((num_labels,)			,		dtype=np.floataa   )
     lowercase :       Union[str, Any]       =			np.zeros((num_labels,)			,		dtype=np.floataa   )
     lowercase :       Tuple       =			np.zeros((num_labels,)			,		dtype=np.floataa   )
     lowercase :       str       =			np.zeros((num_labels,)			,		dtype=np.floataa   )
     for result, gt_seg_map in zip(__magic_name__			,		__magic_name__   ):
          lowercase :       Dict       =			intersect_and_union(
              __magic_name__			,		__magic_name__			,		__magic_name__			,		__magic_name__			,		__magic_name__			,		__magic_name__   )
          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(    __magic_name__			,		__magic_name__			,		__magic_name__			,		__magic_name__			,		__magic_name__ = None			,		__magic_name__ = None			,		__magic_name__ = False			,		)			->							Optional[Any]:





     '''simple docstring'''


     lowercase :       Optional[Any]       =			total_intersect_and_union(
         __magic_name__			,		__magic_name__			,		__magic_name__			,		__magic_name__			,		__magic_name__			,		__magic_name__   )

     # compute metrics
     lowercase :       Union[str, Any]       =			{}

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

     lowercase :       int       =			np.nanmean(__magic_name__   )
     lowercase :       int       =			np.nanmean(__magic_name__   )
     lowercase :       List[str]       =			all_acc
     lowercase :       Optional[Any]       =			iou
     lowercase :       Any       =			acc

     if nan_to_num is not None:
          lowercase :       Any       =			{metric: np.nan_to_num(__magic_name__			,		nan=__magic_name__   ) for metric, metric_value in metrics.items()}

     return metrics



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






  def  __a      (  self		:     Optional[int]				)		->   Tuple:






       """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  __a      (  self		:     List[Any]				,						_A		:     List[str]				,						_A		:     Tuple				,						_A		:     int				,						_A		:     bool				,						_A		:     Optional[int] = None				,						_A		:     Optional[Dict[int, int]] = None				,						_A		:     bool = False				,						)		->   str:






       """simple docstring"""







       lowercase :       List[str]       =			mean_iou(
           results=_A				,						gt_seg_maps=_A				,						num_labels=_A				,						ignore_index=_A				,						nan_to_num=_A				,						label_map=_A				,						reduce_labels=_A				,						)
       return iou_result









from string import ascii_uppercase

lowerCAmelCase_						=			{char: i for i, char in enumerate(ascii_uppercase)}
lowerCAmelCase_						=			dict(enumerate(ascii_uppercase))






def  snake_case(    __magic_name__			,		__magic_name__   )			->							str:





     '''simple docstring'''


     lowercase :       Optional[Any]       =			len(__magic_name__   )
     lowercase :       Any       =			0
     while True:
          if x == i:
               lowercase :       Any       =			0
          if len(__magic_name__   ) == len(__magic_name__   ):
               break
          key += key[i]
          i += 1
     return key






def  snake_case(    __magic_name__			,		__magic_name__   )			->							str:





     '''simple docstring'''


     lowercase :       str       =			''''''
     lowercase :       Dict       =			0
     for letter in message:
          if letter == " ":
               cipher_text += " "
          else:
               lowercase :       Dict       =			(dicta[letter] - dicta[key_new[i]]) % 26
               i += 1
               cipher_text += dicta[x]
     return cipher_text






def  snake_case(    __magic_name__			,		__magic_name__   )			->							str:





     '''simple docstring'''


     lowercase :       Any       =			''''''
     lowercase :       str       =			0
     for letter in cipher_text:
          if letter == " ":
               or_txt += " "
          else:
               lowercase :       Any       =			(dicta[letter] + dicta[key_new[i]] + 26) % 26
               i += 1
               or_txt += dicta[x]
     return or_txt






def  snake_case(    )			->							None:





     '''simple docstring'''


     lowercase :       Dict       =			'''THE GERMAN ATTACK'''
     lowercase :       Dict       =			'''SECRET'''
     lowercase :       Union[str, Any]       =			generate_key(__magic_name__			,		__magic_name__   )
     lowercase :       List[str]       =			cipher_text(__magic_name__			,		__magic_name__   )
     print(F"""Encrypted Text = {s}"""   )
     print(F"""Original Text = {original_text(__magic_name__			,		__magic_name__   )}"""   )


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 main()