
from typing import Dict, Optional

import numpy as np

import datasets


lowercase  : List[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.
"""

lowercase  : Optional[int]						   =							"""
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.        ])}
"""

lowercase  : Optional[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 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ = None ,     SCREAMING_SNAKE_CASE__ = False ,     )			->   Optional[int]:
       if label_map is not None:
              for old_id, new_id in label_map.items():
                     lowercase							:					int	     =				new_id

    # turn into Numpy arrays
       lowercase							:					Union[str, Any]	     =				np.array(SCREAMING_SNAKE_CASE__    )
       lowercase							:					List[Any]	     =				np.array(SCREAMING_SNAKE_CASE__    )

       if reduce_labels:
              lowercase							:					Optional[int]	     =				255
              lowercase							:					str	     =				label - 1
              lowercase							:					Union[str, Any]	     =				255

       lowercase							:					Dict	     =				label != ignore_index
       lowercase							:					Union[str, Any]	     =				np.not_equal(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )
       lowercase							:					List[str]	     =				pred_label[mask]
       lowercase							:					List[str]	     =				np.array(SCREAMING_SNAKE_CASE__    )[mask]

       lowercase							:					Tuple	     =				pred_label[pred_label == label]

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

       lowercase							:					str	     =				area_pred_label + area_label - area_intersect

       return area_intersect, area_union, area_pred_label, area_label







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ = None ,     SCREAMING_SNAKE_CASE__ = False ,     )			->   str:
       lowercase							:					List[str]	     =				np.zeros((num_labels,) ,     dtype=np.floataa    )
       lowercase							:					List[str]	     =				np.zeros((num_labels,) ,     dtype=np.floataa    )
       lowercase							:					Union[str, Any]	     =				np.zeros((num_labels,) ,     dtype=np.floataa    )
       lowercase							:					int	     =				np.zeros((num_labels,) ,     dtype=np.floataa    )
       for result, gt_seg_map in zip(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    ):
              lowercase     ,      lowercase     ,      lowercase     ,      lowercase							:					Optional[int]	     =				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 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ = None ,     SCREAMING_SNAKE_CASE__ = None ,     SCREAMING_SNAKE_CASE__ = False ,     )			->   Any:
       lowercase     ,      lowercase     ,      lowercase     ,      lowercase							:					Optional[Any]	     =				total_intersect_and_union(
           SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )

       # compute metrics
       lowercase							:					List[Any]	     =				{}

       lowercase							:					int	     =				total_area_intersect.sum() / total_area_label.sum()
       lowercase							:					int	     =				total_area_intersect / total_area_union
       lowercase							:					Dict	     =				total_area_intersect / total_area_label

       lowercase							:					Tuple	     =				np.nanmean(SCREAMING_SNAKE_CASE__    )
       lowercase							:					Union[str, Any]	     =				np.nanmean(SCREAMING_SNAKE_CASE__    )
       lowercase							:					Tuple	     =				all_acc
       lowercase							:					List[str]	     =				iou
       lowercase							:					Any	     =				acc

       if nan_to_num is not None:
              lowercase							:					Tuple	     =				{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				):



           '''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 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case = None		,snake_case = None		,snake_case = False		,):



           '''simple docstring'''







           lowercase							:					Union[str, Any]	     =				mean_iou(
               results=snake_case		,gt_seg_maps=snake_case		,num_labels=snake_case		,ignore_index=snake_case		,nan_to_num=snake_case		,label_map=snake_case		,reduce_labels=snake_case		,)
           return iou_result


def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   int:

       def count_of_possible_combinations(SCREAMING_SNAKE_CASE__    ) -> int:
              if target < 0:
                     return 0
              if target == 0:
                     return 1
              return sum(count_of_possible_combinations(target - item    ) for item in array    )

       return count_of_possible_combinations(SCREAMING_SNAKE_CASE__    )







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   int:

       def count_of_possible_combinations_with_dp_array(
           SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    ) -> int:
              if target < 0:
                     return 0
              if target == 0:
                     return 1
              if dp_array[target] != -1:
                     return dp_array[target]
              lowercase							:					Any	     =				sum(
                  count_of_possible_combinations_with_dp_array(target - item ,     SCREAMING_SNAKE_CASE__    )
                  for item in array    )
              lowercase							:					Optional[int]	     =				answer
              return answer

       lowercase							:					int	     =				[-1] * (target + 1)
       return count_of_possible_combinations_with_dp_array(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   int:
       lowercase							:					str	     =				[0] * (target + 1)
       lowercase							:					Tuple	     =				1

       for i in range(1 ,     target + 1    ):
              for j in range(SCREAMING_SNAKE_CASE__    ):
                     if i - array[j] >= 0:
                            dp_array[i] += dp_array[i - array[j]]

       return dp_array[target]


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  lowercase  : Any						   =							3
  lowercase  : Optional[Any]						   =							5
  lowercase  : Tuple						   =							[1, 2, 5]
  print(combination_sum_iv(n, array, target))
