
"""simple docstring"""




from typing import Dict, Optional

import numpy as np

import datasets


A__      :	Tuple          =    """
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.
"""

A__      :	Optional[Any]          =    """
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.        ])}
"""

A__      :	Union[str, Any]          =    """\
@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__ (				lowerCAmelCase	:       Union[str, Any]       ,    lowerCAmelCase	:       Any       ,    lowerCAmelCase	:       Optional[Any]       ,    lowerCAmelCase	:       bool       ,    lowerCAmelCase	:       Optional[Dict[int, int]] = None       ,    lowerCAmelCase	:       bool = False       ,    ):






     '''simple docstring'''
     if label_map is not None:
          for old_id, new_id in label_map.items():
               UpperCAmelCase__							: Any 							=						new_id

    # turn into Numpy arrays
     UpperCAmelCase__							: Optional[int] 							=						np.array(lowerCAmelCase		)
     UpperCAmelCase__							: Tuple 							=						np.array(lowerCAmelCase		)

     if reduce_labels:
          UpperCAmelCase__							: Any 							=						255
          UpperCAmelCase__							: str 							=						label - 1
          UpperCAmelCase__							: str 							=						255

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

     UpperCAmelCase__							: Optional[Any] 							=						pred_label[pred_label == label]

     UpperCAmelCase__							: int 							=						np.histogram(lowerCAmelCase       ,    bins=lowerCAmelCase       ,    range=(0, num_labels - 1)		)[0]
     UpperCAmelCase__							: Union[str, Any] 							=						np.histogram(lowerCAmelCase       ,    bins=lowerCAmelCase       ,    range=(0, num_labels - 1)		)[0]
     UpperCAmelCase__							: List[Any] 							=						np.histogram(lowerCAmelCase       ,    bins=lowerCAmelCase       ,    range=(0, num_labels - 1)		)[0]

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

     return area_intersect, area_union, area_pred_label, area_label



def 							a__ (				lowerCAmelCase	:       Union[str, Any]       ,    lowerCAmelCase	:       Dict       ,    lowerCAmelCase	:       Union[str, Any]       ,    lowerCAmelCase	:       bool       ,    lowerCAmelCase	:       Optional[Dict[int, int]] = None       ,    lowerCAmelCase	:       bool = False       ,    ):






     '''simple docstring'''
     UpperCAmelCase__							: List[str] 							=						np.zeros((num_labels,)       ,    dtype=np.floataa		)
     UpperCAmelCase__							: List[str] 							=						np.zeros((num_labels,)       ,    dtype=np.floataa		)
     UpperCAmelCase__							: Tuple 							=						np.zeros((num_labels,)       ,    dtype=np.floataa		)
     UpperCAmelCase__							: List[Any] 							=						np.zeros((num_labels,)       ,    dtype=np.floataa		)
     for result, gt_seg_map in zip(lowerCAmelCase       ,    lowerCAmelCase		):
          UpperCAmelCase__							,	UpperCAmelCase__							,	UpperCAmelCase__							,	UpperCAmelCase__							: Union[str, Any] 							=						intersect_and_union(
              lowerCAmelCase       ,    lowerCAmelCase       ,    lowerCAmelCase       ,    lowerCAmelCase       ,    lowerCAmelCase       ,    lowerCAmelCase		)
          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__ (				lowerCAmelCase	:       Optional[int]       ,    lowerCAmelCase	:       List[str]       ,    lowerCAmelCase	:       List[str]       ,    lowerCAmelCase	:       bool       ,    lowerCAmelCase	:       Optional[int] = None       ,    lowerCAmelCase	:       Optional[Dict[int, int]] = None       ,    lowerCAmelCase	:       bool = False       ,    ):






     '''simple docstring'''
     UpperCAmelCase__							,	UpperCAmelCase__							,	UpperCAmelCase__							,	UpperCAmelCase__							: str 							=						total_intersect_and_union(
         lowerCAmelCase       ,    lowerCAmelCase       ,    lowerCAmelCase       ,    lowerCAmelCase       ,    lowerCAmelCase       ,    lowerCAmelCase		)

     # compute metrics
     UpperCAmelCase__							: Dict 							=						{}

     UpperCAmelCase__							: Dict 							=						total_area_intersect.sum() / total_area_label.sum()
     UpperCAmelCase__							: Any 							=						total_area_intersect / total_area_union
     UpperCAmelCase__							: Optional[int] 							=						total_area_intersect / total_area_label

     UpperCAmelCase__							: Optional[int] 							=						np.nanmean(lowerCAmelCase		)
     UpperCAmelCase__							: List[Any] 							=						np.nanmean(lowerCAmelCase		)
     UpperCAmelCase__							: Union[str, Any] 							=						all_acc
     UpperCAmelCase__							: Tuple 							=						iou
     UpperCAmelCase__							: Dict 							=						acc

     if nan_to_num is not None:
          UpperCAmelCase__							: Tuple 							=						{metric: np.nan_to_num(lowerCAmelCase       ,    nan=lowerCAmelCase		) for metric, metric_value in metrics.items()}

     return metrics







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


     '''simple docstring'''





     def  lowerCAmelCase__       (      self  )->					Optional[int]:
          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  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = False ,			)->					Dict:
          UpperCAmelCase__							: List[str] 							=						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





"""simple docstring"""




import math



def 							a__ (				lowerCAmelCase	:       int		):






     '''simple docstring'''

     if 1 < number < 4:
          # 2 and 3 are primes
          return True
     elif number < 2 or number % 2 == 0 or number % 3 == 0:
          # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
          return False

     # All primes number are in format of 6k +/- 1
     for i in range(5       ,    int(math.sqrt(lowerCAmelCase		) + 1		)       ,    6		):
          if number % i == 0 or number % (i + 2) == 0:
               return False
     return True



def 							a__ (				lowerCAmelCase	:       int = 1_0001		):






     '''simple docstring'''

     try:
          UpperCAmelCase__							: List[str] 							=						int(lowerCAmelCase		)
     except (TypeError, ValueError):
          raise TypeError("Parameter nth must be int or castable to int."		) from None
     if nth <= 0:
          raise ValueError("Parameter nth must be greater than or equal to one."		)
     UpperCAmelCase__							: list[int] 							=						[]
     UpperCAmelCase__							: str 							=						2
     while len(lowerCAmelCase		) < nth:
          if is_prime(lowerCAmelCase		):
               primes.append(lowerCAmelCase		)
               num += 1
          else:
               num += 1
     return primes[len(lowerCAmelCase		) - 1]


if __name__ == "__main__":
    print(f"""{solution() = }""")



