


"""simple docstring"""
from typing import Dict, Optional

import numpy as np

import datasets


SCREAMING_SNAKE_CASE :    Optional[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"""

SCREAMING_SNAKE_CASE :    Tuple									=				"""\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"""

SCREAMING_SNAKE_CASE :    Optional[Any]									=				"""\\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 		lowercase							(     _snake_case						:      int     ,   _snake_case						:      List[Any]     ,   _snake_case						:      List[str]     ,   _snake_case						:      bool     ,   _snake_case						:      Optional[Dict[int, int]] = None     ,   _snake_case						:      bool = False     ,   )     ->Union[str, Any]:
	"""simple docstring"""





	if label_map is not None:
		for old_id, new_id in label_map.items():
			__snake_case	:						List[str]						=				new_id

    # turn into Numpy arrays
	__snake_case	:						Dict						=				np.array(SCREAMING_SNAKE_CASE__     )
	__snake_case	:						int						=				np.array(SCREAMING_SNAKE_CASE__     )

	if reduce_labels:
		__snake_case	:						str						=				255
		__snake_case	:						List[str]						=				label - 1
		__snake_case	:						str						=				255

	__snake_case	:						List[Any]						=				label != ignore_index
	__snake_case	:						int						=				np.not_equal(SCREAMING_SNAKE_CASE__     ,   SCREAMING_SNAKE_CASE__     )
	__snake_case	:						List[Any]						=				pred_label[mask]
	__snake_case	:						Any						=				np.array(SCREAMING_SNAKE_CASE__     )[mask]

	__snake_case	:						str						=				pred_label[pred_label == label]

	__snake_case	:						Tuple						=				np.histogram(SCREAMING_SNAKE_CASE__     ,   bins=SCREAMING_SNAKE_CASE__     ,   range=(0, num_labels - 1)     )[0]
	__snake_case	:						Tuple						=				np.histogram(SCREAMING_SNAKE_CASE__     ,   bins=SCREAMING_SNAKE_CASE__     ,   range=(0, num_labels - 1)     )[0]
	__snake_case	:						Tuple						=				np.histogram(SCREAMING_SNAKE_CASE__     ,   bins=SCREAMING_SNAKE_CASE__     ,   range=(0, num_labels - 1)     )[0]

	__snake_case	:						Union[str, Any]						=				area_pred_label + area_label - area_intersect

	return area_intersect, area_union, area_pred_label, area_label






def 		lowercase							(     _snake_case						:      Union[str, Any]     ,   _snake_case						:      Optional[int]     ,   _snake_case						:      List[str]     ,   _snake_case						:      bool     ,   _snake_case						:      Optional[Dict[int, int]] = None     ,   _snake_case						:      bool = False     ,   )     ->int:
	"""simple docstring"""





	__snake_case	:						int						=				np.zeros((num_labels,)     ,   dtype=np.floataa     )
	__snake_case	:						List[str]						=				np.zeros((num_labels,)     ,   dtype=np.floataa     )
	__snake_case	:						Union[str, Any]						=				np.zeros((num_labels,)     ,   dtype=np.floataa     )
	__snake_case	:						Optional[int]						=				np.zeros((num_labels,)     ,   dtype=np.floataa     )
	for result, gt_seg_map in zip(SCREAMING_SNAKE_CASE__     ,   SCREAMING_SNAKE_CASE__     ):
		__snake_case	:						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 		lowercase							(     _snake_case						:      str     ,   _snake_case						:      Any     ,   _snake_case						:      str     ,   _snake_case						:      bool     ,   _snake_case						:      Optional[int] = None     ,   _snake_case						:      Optional[Dict[int, int]] = None     ,   _snake_case						:      bool = False     ,   )     ->int:
	"""simple docstring"""





	__snake_case	:						str						=				total_intersect_and_union(
	    SCREAMING_SNAKE_CASE__     ,   SCREAMING_SNAKE_CASE__     ,   SCREAMING_SNAKE_CASE__     ,   SCREAMING_SNAKE_CASE__     ,   SCREAMING_SNAKE_CASE__     ,   SCREAMING_SNAKE_CASE__     )

	# compute metrics
	__snake_case	:						Dict						=				{}

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

	__snake_case	:						str						=				np.nanmean(SCREAMING_SNAKE_CASE__     )
	__snake_case	:						List[str]						=				np.nanmean(SCREAMING_SNAKE_CASE__     )
	__snake_case	:						Tuple						=				all_acc
	__snake_case	:						int						=				iou
	__snake_case	:						List[str]						=				acc

	if nan_to_num is not None:
		__snake_case	:						List[Any]						=				{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 			_UpperCAmelCase (							datasets.Metric				):






		'''simple docstring'''

		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				,       a_				,       a_				,       a_				,       a_				,       a_ = None				,       a_ = None				,       a_ = False				,       ):





			'''simple docstring'''

			__snake_case	:						Optional[int]						=				mean_iou(
			    results=lowercase_				,       gt_seg_maps=lowercase_				,       num_labels=lowercase_				,       ignore_index=lowercase_				,       nan_to_num=lowercase_				,       label_map=lowercase_				,       reduce_labels=lowercase_				,       )
			return iou_result









# using dfs for finding eulerian path traversal




def 	a_      (    SCREAMING_SNAKE_CASE__					:      Any		,			SCREAMING_SNAKE_CASE__					:      List[str]		,			SCREAMING_SNAKE_CASE__					:      List[str]		,			SCREAMING_SNAKE_CASE__					:      Optional[int]=None     ):
       '''simple docstring'''





       _lowerCamelCase       :		Optional[Any]		=(path or []) + [u]
       for v in graph[u]:
              if visited_edge[u][v] is False:
                     _lowerCamelCase	,     _lowerCamelCase       :		Dict		=True, True
                     _lowerCamelCase       :		Optional[Any]		=dfs(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )
       return path




def 	a_      (    SCREAMING_SNAKE_CASE__					:      List[str]		,			SCREAMING_SNAKE_CASE__					:      Optional[Any]     ):
       '''simple docstring'''





       _lowerCamelCase       :		Union[str, Any]		=0
       _lowerCamelCase       :		Union[str, Any]		=-1
       for i in range(SCREAMING_SNAKE_CASE__     ):
              if i not in graph.keys():
                     continue
              if len(graph[i]     ) % 2 == 1:
                     odd_degree_nodes += 1
                     _lowerCamelCase       :		Tuple		=i
       if odd_degree_nodes == 0:
              return 1, odd_node
       if odd_degree_nodes == 2:
              return 2, odd_node
       return 3, odd_node




def 	a_      (    SCREAMING_SNAKE_CASE__					:      Union[str, Any]		,			SCREAMING_SNAKE_CASE__					:      Any     ):
       '''simple docstring'''





       _lowerCamelCase       :		Tuple		=[[False for _ in range(max_node + 1     )] for _ in range(max_node + 1     )]
       _lowerCamelCase	,     _lowerCamelCase       :		Optional[int]		=check_circuit_or_path(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )
       if check == 3:
              print('graph is not Eulerian'     )
              print('no path'     )
              return
       _lowerCamelCase       :		Any		=1
       if check == 2:
              _lowerCamelCase       :		Tuple		=odd_node
              print('graph has a Euler path'     )
       if check == 1:
              print('graph has a Euler cycle'     )
       _lowerCamelCase       :		int		=dfs(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )
       print(SCREAMING_SNAKE_CASE__     )




def 	a_      (    ):
       '''simple docstring'''





       _lowerCamelCase       :		Union[str, Any]		={1: [2, 3, 4], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [4]}
       _lowerCamelCase       :		str		={1: [2, 3, 4, 5], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [1, 4]}
       _lowerCamelCase       :		List[Any]		={1: [2, 3, 4], 2: [1, 3, 4], 3: [1, 2], 4: [1, 2, 5], 5: [4]}
       _lowerCamelCase       :		Any		={1: [2, 3], 2: [1, 3], 3: [1, 2]}
       _lowerCamelCase       :		Dict		={
           1: [],
           2: []
           # all degree is zero
       }
       _lowerCamelCase       :		str		=10
       check_euler(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )
       check_euler(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )
       check_euler(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )
       check_euler(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )
       check_euler(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )


if __name__ == "__main__":
 main()


