
'''simple docstring'''
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 					__SCREAMING_SNAKE_CASE      (  _SCREAMING_SNAKE_CASE							,					_SCREAMING_SNAKE_CASE							,					_SCREAMING_SNAKE_CASE							,					_SCREAMING_SNAKE_CASE							,					_SCREAMING_SNAKE_CASE = None							,					_SCREAMING_SNAKE_CASE = False							,					):
							if label_map is not None:
														for old_id, new_id in label_map.items():
																					_snake_case   =  new_id

    # turn into Numpy arrays
							_snake_case   =  np.array(lowercase__      )
							_snake_case   =  np.array(lowercase__      )

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

							_snake_case   =  label != ignore_index
							_snake_case   =  np.not_equal(lowercase__							,					lowercase__      )
							_snake_case   =  pred_label[mask]
							_snake_case   =  np.array(lowercase__      )[mask]

							_snake_case   =  pred_label[pred_label == label]

							_snake_case   =  np.histogram(lowercase__							,					bins=lowercase__							,					range=(0, num_labels - 1)      )[0]
							_snake_case   =  np.histogram(lowercase__							,					bins=lowercase__							,					range=(0, num_labels - 1)      )[0]
							_snake_case   =  np.histogram(lowercase__							,					bins=lowercase__							,					range=(0, num_labels - 1)      )[0]

							_snake_case   =  area_pred_label + area_label - area_intersect

							return area_intersect, area_union, area_pred_label, area_label






def 					__SCREAMING_SNAKE_CASE      (  _SCREAMING_SNAKE_CASE							,					_SCREAMING_SNAKE_CASE							,					_SCREAMING_SNAKE_CASE							,					_SCREAMING_SNAKE_CASE							,					_SCREAMING_SNAKE_CASE = None							,					_SCREAMING_SNAKE_CASE = False							,					):
							_snake_case   =  np.zeros((num_labels,)							,					dtype=np.floataa      )
							_snake_case   =  np.zeros((num_labels,)							,					dtype=np.floataa      )
							_snake_case   =  np.zeros((num_labels,)							,					dtype=np.floataa      )
							_snake_case   =  np.zeros((num_labels,)							,					dtype=np.floataa      )
							for result, gt_seg_map in zip(lowercase__							,					lowercase__      ):
														_snake_case   =  intersect_and_union(
														    lowercase__							,					lowercase__							,					lowercase__							,					lowercase__							,					lowercase__							,					lowercase__      )
														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 					__SCREAMING_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							,					):
							_snake_case   =  total_intersect_and_union(
							    lowercase__							,					lowercase__							,					lowercase__							,					lowercase__							,					lowercase__							,					lowercase__      )

							# compute metrics
							_snake_case   =  {}

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

							_snake_case   =  np.nanmean(lowercase__      )
							_snake_case   =  np.nanmean(lowercase__      )
							_snake_case   =  all_acc
							_snake_case   =  iou
							_snake_case   =  acc

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

							return metrics


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







							'''simple docstring'''

							def        lowercase							(self  )     ->   Optional[Any]:
														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  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase = None  ,  UpperCAmelCase = None  ,  UpperCAmelCase = False  ,  )     ->   Tuple:
														_snake_case   =  mean_iou(
														    results=lowerCAmelCase__  ,  gt_seg_maps=lowerCAmelCase__  ,  num_labels=lowerCAmelCase__  ,  ignore_index=lowerCAmelCase__  ,  nan_to_num=lowerCAmelCase__  ,  label_map=lowerCAmelCase__  ,  reduce_labels=lowerCAmelCase__  ,  )
														return iou_result



from __future__ import annotations

import bisect


def        _UpperCamelCase							(      lowercase__    ,       lowercase__    ,       lowercase__ = 0    ,       lowercase__ = -1      ):
						if hi < 0:
												__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		len(lowercase__      )

						while lo < hi:
												__SCREAMING_SNAKE_CASE						:     Any					=		lo + (hi - lo) // 2
												if sorted_collection[mid] < item:
																		__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		mid + 1
												else:
																		__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		mid

						return lo


def        _UpperCamelCase							(      lowercase__    ,       lowercase__    ,       lowercase__ = 0    ,       lowercase__ = -1      ):
						if hi < 0:
												__SCREAMING_SNAKE_CASE						:     List[Any]					=		len(lowercase__      )

						while lo < hi:
												__SCREAMING_SNAKE_CASE						:     Optional[int]					=		lo + (hi - lo) // 2
												if sorted_collection[mid] <= item:
																		__SCREAMING_SNAKE_CASE						:     Any					=		mid + 1
												else:
																		__SCREAMING_SNAKE_CASE						:     Optional[int]					=		mid

						return lo


def        _UpperCamelCase							(      lowercase__    ,       lowercase__    ,       lowercase__ = 0    ,       lowercase__ = -1      ):
						sorted_collection.insert(bisect_left(lowercase__    ,       lowercase__    ,       lowercase__    ,       lowercase__      )    ,       lowercase__      )


def        _UpperCamelCase							(      lowercase__    ,       lowercase__    ,       lowercase__ = 0    ,       lowercase__ = -1      ):
						sorted_collection.insert(bisect_right(lowercase__    ,       lowercase__    ,       lowercase__    ,       lowercase__      )    ,       lowercase__      )


def        _UpperCamelCase							(      lowercase__    ,       lowercase__      ):
						__SCREAMING_SNAKE_CASE						:     Any					=		0
						__SCREAMING_SNAKE_CASE						:     List[Any]					=		len(lowercase__      ) - 1

						while left <= right:
												__SCREAMING_SNAKE_CASE						:     str					=		left + (right - left) // 2
												__SCREAMING_SNAKE_CASE						:     List[str]					=		sorted_collection[midpoint]
												if current_item == item:
																		return midpoint
												elif item < current_item:
																		__SCREAMING_SNAKE_CASE						:     int					=		midpoint - 1
												else:
																		__SCREAMING_SNAKE_CASE						:     Dict					=		midpoint + 1
						return None


def        _UpperCamelCase							(      lowercase__    ,       lowercase__      ):
						__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		bisect.bisect_left(lowercase__    ,       lowercase__      )
						if index != len(lowercase__      ) and sorted_collection[index] == item:
												return index
						return None


def        _UpperCamelCase							(      lowercase__    ,       lowercase__    ,       lowercase__    ,       lowercase__      ):
						if right < left:
												return None

						__SCREAMING_SNAKE_CASE						:     int					=		left + (right - left) // 2

						if sorted_collection[midpoint] == item:
												return midpoint
						elif sorted_collection[midpoint] > item:
												return binary_search_by_recursion(lowercase__    ,       lowercase__    ,       lowercase__    ,       midpoint - 1      )
						else:
												return binary_search_by_recursion(lowercase__    ,       lowercase__    ,       midpoint + 1    ,       lowercase__      )


if __name__ == "__main__":
						__lowerCAmelCase	: Dict  				=input('Enter numbers separated by comma:\n').strip()
						__lowerCAmelCase	: str  				=sorted(int(item) for item in user_input.split(','))
						__lowerCAmelCase	: Tuple  				=int(input('Enter a single number to be found in the list:\n'))
						__lowerCAmelCase	: Tuple  				=binary_search(collection, target)
						if result is None:
												print(f"""{target} was not found in {collection}.""")
						else:
												print(f"""{target} was found at position {result} in {collection}.""")
