


'''simple docstring'''




from typing import Dict, Optional

import numpy as np

import datasets


_lowerCAmelCase  :		int		     =	'\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  :		List[str]		     =	'\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  :		List[str]		     =	'\\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 			__UpperCamelCase    (    _A    :				Any					,   _A    :				Optional[Any]					,   _A    :				Union[str, Any]					,   _A    :				bool					,   _A    :				Optional[Dict[int, int]] = None					,   _A    :				bool = False					,   )    ->      Any:




	"""simple docstring"""





	if label_map is not None:
		for old_id, new_id in label_map.items():
			lowerCAmelCase					:    str      							= new_id

    # turn into Numpy arrays
	lowerCAmelCase					:    List[str]      							= np.array(_A				)
	lowerCAmelCase					:    List[str]      							= np.array(_A				)

	if reduce_labels:
		lowerCAmelCase					:    List[Any]      							= 2_55
		lowerCAmelCase					:    Dict      							= label - 1
		lowerCAmelCase					:    int      							= 2_55

	lowerCAmelCase					:    Dict      							= label != ignore_index
	lowerCAmelCase					:    Optional[Any]      							= np.not_equal(_A					,   _A				)
	lowerCAmelCase					:    List[Any]      							= pred_label[mask]
	lowerCAmelCase					:    Any      							= np.array(_A				)[mask]

	lowerCAmelCase					:    List[str]      							= pred_label[pred_label == label]

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

	lowerCAmelCase					:    List[str]      							= area_pred_label + area_label - area_intersect

	return area_intersect, area_union, area_pred_label, area_label

def 			__UpperCamelCase    (    _A    :				Optional[int]					,   _A    :				Dict					,   _A    :				Dict					,   _A    :				bool					,   _A    :				Optional[Dict[int, int]] = None					,   _A    :				bool = False					,   )    ->      Optional[int]:




	"""simple docstring"""





	lowerCAmelCase					:    List[str]      							= np.zeros((num_labels,)					,   dtype=np.floataa				)
	lowerCAmelCase					:    Tuple      							= np.zeros((num_labels,)					,   dtype=np.floataa				)
	lowerCAmelCase					:    Any      							= np.zeros((num_labels,)					,   dtype=np.floataa				)
	lowerCAmelCase					:    Tuple      							= np.zeros((num_labels,)					,   dtype=np.floataa				)
	for result, gt_seg_map in zip(_A					,   _A				):
		lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase					:    Any      							= intersect_and_union(
		    _A					,   _A					,   _A					,   _A					,   _A					,   _A				)
		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 			__UpperCamelCase    (    _A    :				Tuple					,   _A    :				Dict					,   _A    :				Optional[int]					,   _A    :				bool					,   _A    :				Optional[int] = None					,   _A    :				Optional[Dict[int, int]] = None					,   _A    :				bool = False					,   )    ->      int:




	"""simple docstring"""





	lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase					:    List[str]      							= total_intersect_and_union(
	    _A					,   _A					,   _A					,   _A					,   _A					,   _A				)

	# compute metrics
	lowerCAmelCase					:    List[str]      							= {}

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

	lowerCAmelCase					:    Tuple      							= np.nanmean(_A				)
	lowerCAmelCase					:    Tuple      							= np.nanmean(_A				)
	lowerCAmelCase					:    int      							= all_acc
	lowerCAmelCase					:    Optional[Any]      							= iou
	lowerCAmelCase					:    str      							= acc

	if nan_to_num is not None:
		lowerCAmelCase					:    Optional[Any]      							= {metric: np.nan_to_num(_A					,   nan=_A				) for metric, metric_value in metrics.items()}

	return metrics






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



	def 				lowercase     (					self    ):
		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 ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ = None ,					snake_case__ = None ,					snake_case__ = False ,					):
		lowerCAmelCase					:    Optional[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




'''simple docstring'''




def 			__UpperCamelCase    (    _A    :				List[str]				)    ->      Optional[Any]:




	"""simple docstring"""





	if not head:
		return True
	# split the list to two parts
	lowerCAmelCase							,   lowerCAmelCase					:    str      							= head.next, head
	while fast and fast.next:
		lowerCAmelCase					:    Optional[int]      							= fast.next.next
		lowerCAmelCase					:    int      							= slow.next
	lowerCAmelCase					:    int      							= slow.next
	lowerCAmelCase					:    Optional[Any]      							= None  # Don't forget here! But forget still works!
	# reverse the second part
	lowerCAmelCase					:    List[Any]      							= None
	while second:
		lowerCAmelCase					:    List[Any]      							= second.next
		lowerCAmelCase					:    Union[str, Any]      							= node
		lowerCAmelCase					:    Optional[Any]      							= second
		lowerCAmelCase					:    Any      							= nxt
	# compare two parts
	# second part has the same or one less node
	while node:
		if node.val != head.val:
			return False
		lowerCAmelCase					:    Optional[Any]      							= node.next
		lowerCAmelCase					:    Tuple      							= head.next
	return True

def 			__UpperCamelCase    (    _A    :				Optional[Any]				)    ->      Optional[int]:




	"""simple docstring"""





	if not head or not head.next:
		return True

	# 1. Get the midpoint (slow)
	lowerCAmelCase					:    Optional[int]      							= head
	while fast and fast.next:
		lowerCAmelCase							,   lowerCAmelCase					:    Optional[Any]      							= fast.next.next, slow.next

	# 2. Push the second half into the stack
	lowerCAmelCase					:    Tuple      							= [slow.val]
	while slow.next:
		lowerCAmelCase					:    Tuple      							= slow.next
		stack.append(slow.val				)

	# 3. Comparison
	while stack:
		if stack.pop() != cur.val:
			return False
		lowerCAmelCase					:    Union[str, Any]      							= cur.next

	return True






def 			__UpperCamelCase    (    _A    :				Tuple				)    ->      Optional[int]:




	"""simple docstring"""





	if not head or not head.next:
		return True
	lowerCAmelCase					:    Optional[int]      							= {}
	lowerCAmelCase					:    int      							= 0
	while head:
		if head.val in d:
			d[head.val].append(_A				)
		else:
			lowerCAmelCase					:    Any      							= [pos]
		lowerCAmelCase					:    int      							= head.next
		pos += 1
	lowerCAmelCase					:    str      							= pos - 1
	lowerCAmelCase					:    Optional[Any]      							= 0
	for v in d.values():
		if len(_A				) % 2 != 0:
			middle += 1
		else:
			lowerCAmelCase					:    Any      							= 0
			for i in range(0					,   len(_A				)				):
				if v[i] + v[len(_A				) - 1 - step] != checksum:
					return False
				step += 1
		if middle > 1:
			return False
	return True
