
from typing import Dict, Optional

import numpy as np

import datasets


__a         =   '\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'

__a         =   '\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'

__a         =   '\\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 							a		(				snake_case__:   Optional[Any]		,  snake_case__:   str		,  snake_case__:   Optional[int]		,  snake_case__:   bool		,  snake_case__:   Optional[Dict[int, int]] = None		,  snake_case__:   bool = False		,  ):


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

    # turn into Numpy arrays
				lowercase_	    =   np.array(snake_case__   )
				lowercase_	    =   np.array(snake_case__   )

				if reduce_labels:
								lowercase_	    =   255
								lowercase_	    =   label - 1
								lowercase_	    =   255

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

				lowercase_	    =   pred_label[pred_label == label]

				lowercase_	    =   np.histogram(snake_case__		,  bins=snake_case__		,  range=(0, num_labels - 1)   )[0]
				lowercase_	    =   np.histogram(snake_case__		,  bins=snake_case__		,  range=(0, num_labels - 1)   )[0]
				lowercase_	    =   np.histogram(snake_case__		,  bins=snake_case__		,  range=(0, num_labels - 1)   )[0]

				lowercase_	    =   area_pred_label + area_label - area_intersect

				return area_intersect, area_union, area_pred_label, area_label




def 							a		(				snake_case__:   int		,  snake_case__:   Optional[int]		,  snake_case__:   Any		,  snake_case__:   bool		,  snake_case__:   Optional[Dict[int, int]] = None		,  snake_case__:   bool = False		,  ):


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


				'''simple docstring'''
				lowercase_    ,      lowercase_    ,      lowercase_    ,      lowercase_	    =   total_intersect_and_union(
				    snake_case__		,  snake_case__		,  snake_case__		,  snake_case__		,  snake_case__		,  snake_case__   )

				# compute metrics
				lowercase_	    =   {}

				lowercase_	    =   total_area_intersect.sum() / total_area_label.sum()
				lowercase_	    =   total_area_intersect / total_area_union
				lowercase_	    =   total_area_intersect / total_area_label

				lowercase_	    =   np.nanmean(snake_case__   )
				lowercase_	    =   np.nanmean(snake_case__   )
				lowercase_	    =   all_acc
				lowercase_	    =   iou
				lowercase_	    =   acc

				if nan_to_num is not None:
								lowercase_	    =   {metric: np.nan_to_num(snake_case__		,  nan=snake_case__   ) 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 		_lowercase    (			self    :     str			)			->	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 		_lowercase    (			self    :     int			,				SCREAMING_SNAKE_CASE_    :     Optional[int]			,				SCREAMING_SNAKE_CASE_    :     List[Any]			,				SCREAMING_SNAKE_CASE_    :     int			,				SCREAMING_SNAKE_CASE_    :     bool			,				SCREAMING_SNAKE_CASE_    :     Optional[int] = None			,				SCREAMING_SNAKE_CASE_    :     Optional[Dict[int, int]] = None			,				SCREAMING_SNAKE_CASE_    :     bool = False			,				)			->	str:
					lowercase_	    =   mean_iou(
					    results=SCREAMING_SNAKE_CASE_			,				gt_seg_maps=SCREAMING_SNAKE_CASE_			,				num_labels=SCREAMING_SNAKE_CASE_			,				ignore_index=SCREAMING_SNAKE_CASE_			,				nan_to_num=SCREAMING_SNAKE_CASE_			,				label_map=SCREAMING_SNAKE_CASE_			,				reduce_labels=SCREAMING_SNAKE_CASE_			,				)
					return iou_result







'''simple docstring'''

def 					__UpperCamelCase				(						lowercase__    :	int			):






     '''simple docstring'''





     if upper_limit < 0:
          raise ValueError('Limit for the Catalan sequence must be ≥ 0'			)

     __lowercase      		=[0] * (upper_limit + 1)

     # Base case: C(0) = C(1) = 1
     __lowercase      		=1
     if upper_limit > 0:
          __lowercase      		=1

     # Recurrence relation: C(i) = sum(C(j).C(i-j-1)), from j = 0 to i
     for i in range(2,       upper_limit + 1			):
          for j in range(lowercase__			):
               catalan_list[i] += catalan_list[j] * catalan_list[i - j - 1]

     return catalan_list


if __name__ == "__main__":
       print('''\n********* Catalan Numbers Using Dynamic Programming ************\n''')
       print('''\n*** Enter -1 at any time to quit ***''')
       print('''\nEnter the upper limit (≥ 0) for the Catalan number sequence: ''', end='''''')
       try:
              while True:
                     UpperCAmelCase					 =	int(input().strip())
                     if N < 0:
                            print('''\n********* Goodbye!! ************''')
                            break
                     else:
                            print(F'''The Catalan numbers from 0 through {N} are:''')
                            print(catalan_numbers(N))
                            print('''Try another upper limit for the sequence: ''', end='''''')
       except (NameError, ValueError):
              print('''\n********* Invalid input, goodbye! ************\n''')

       import doctest

       doctest.testmod()


