

'''simple docstring'''



from typing import Dict, Optional

import numpy as np

import datasets


__snake_case			:     Any										=      '\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'

__snake_case			:     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'

__snake_case			:     Tuple										=      '\\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 						__lowerCamelCase	(       __snake_case					:		int,		__snake_case					:		str,		__snake_case					:		List[str],		__snake_case					:		bool,		__snake_case					:		Optional[Dict[int, int]] = None,		__snake_case					:		bool = False,		)	->							Dict:






						"""simple docstring"""





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

    # turn into Numpy arrays
						A__					:       Any     =np.array(__snake_case	)
						A__					:       Union[str, Any]     =np.array(__snake_case	)

						if reduce_labels:
												A__					:       Optional[Any]     =255
												A__					:       Dict     =label - 1
												A__					:       str     =255

						A__					:       List[str]     =label != ignore_index
						A__					:       Tuple     =np.not_equal(__snake_case,		__snake_case	)
						A__					:       Tuple     =pred_label[mask]
						A__					:       Union[str, Any]     =np.array(__snake_case	)[mask]

						A__					:       Optional[Any]     =pred_label[pred_label == label]

						A__					:       Any     =np.histogram(__snake_case,		bins=__snake_case,		range=(0, num_labels - 1)	)[0]
						A__					:       List[Any]     =np.histogram(__snake_case,		bins=__snake_case,		range=(0, num_labels - 1)	)[0]
						A__					:       int     =np.histogram(__snake_case,		bins=__snake_case,		range=(0, num_labels - 1)	)[0]

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

						return area_intersect, area_union, area_pred_label, area_label
def 						__lowerCamelCase	(       __snake_case					:		int,		__snake_case					:		List[Any],		__snake_case					:		Any,		__snake_case					:		bool,		__snake_case					:		Optional[Dict[int, int]] = None,		__snake_case					:		bool = False,		)	->							str:






						"""simple docstring"""





						A__					:       Dict     =np.zeros((num_labels,),		dtype=np.floataa	)
						A__					:       Any     =np.zeros((num_labels,),		dtype=np.floataa	)
						A__					:       Tuple     =np.zeros((num_labels,),		dtype=np.floataa	)
						A__					:       Optional[Any]     =np.zeros((num_labels,),		dtype=np.floataa	)
						for result, gt_seg_map in zip(__snake_case,		__snake_case	):
												A__					:       List[str]     =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 						__lowerCamelCase	(       __snake_case					:		Union[str, Any],		__snake_case					:		int,		__snake_case					:		int,		__snake_case					:		bool,		__snake_case					:		Optional[int] = None,		__snake_case					:		Optional[Dict[int, int]] = None,		__snake_case					:		bool = False,		)	->							List[Any]:






						"""simple docstring"""





						A__					:       List[Any]     =total_intersect_and_union(
						    __snake_case,		__snake_case,		__snake_case,		__snake_case,		__snake_case,		__snake_case	)

						# compute metrics
						A__					:       Dict     ={}

						A__					:       Optional[int]     =total_area_intersect.sum() / total_area_label.sum()
						A__					:       Optional[int]     =total_area_intersect / total_area_union
						A__					:       Optional[Any]     =total_area_intersect / total_area_label

						A__					:       str     =np.nanmean(__snake_case	)
						A__					:       Optional[int]     =np.nanmean(__snake_case	)
						A__					:       Optional[int]     =all_acc
						A__					:       Union[str, Any]     =iou
						A__					:       int     =acc

						if nan_to_num is not None:
												A__					:       int     ={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 lowerCamelCase			(  datasets.Metric						):



			'''simple docstring'''






			def lowercase__						(				self	:						List[Any]			)      ->		Optional[Any]:





									'''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 lowercase__						(				self	:						Optional[int]		,      lowerCAmelCase_	:						Union[str, Any]		,      lowerCAmelCase_	:						Union[str, Any]		,      lowerCAmelCase_	:						int		,      lowerCAmelCase_	:						bool		,      lowerCAmelCase_	:						Optional[int] = None		,      lowerCAmelCase_	:						Optional[Dict[int, int]] = None		,      lowerCAmelCase_	:						bool = False		,      )      ->		List[str]:





									'''simple docstring'''
									A__					:       Tuple     =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




'''simple docstring'''



import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest



class lowerCamelCase			(  lowercase_						):



			'''simple docstring'''






			__snake_case        =	(CMStochasticIterativeScheduler,)
			__snake_case        =	10


			def lowercase__						(				self	:						List[str]		,      **lowerCAmelCase_	:						Dict			)      ->		Dict:





									'''simple docstring'''
									A__					:       int     ={
									    """num_train_timesteps""": 2_01,
									    """sigma_min""": 0.002,
									    """sigma_max""": 80.0,
									}

									config.update(**lowerCAmelCase_			)
									return config


			def lowercase__						(				self	:						Optional[Any]			)      ->		Optional[int]:





									'''simple docstring'''
									A__					:       Dict     =10

									A__					:       str     =self.get_scheduler_config()
									A__					:       Any     =self.scheduler_classes[0](**lowerCAmelCase_			)

									scheduler.set_timesteps(lowerCAmelCase_			)

									A__					:       List[Any]     =scheduler.timesteps[0]
									A__					:       Union[str, Any]     =scheduler.timesteps[1]

									A__					:       Optional[int]     =self.dummy_sample
									A__					:       Union[str, Any]     =0.1 * sample

									A__					:       Dict     =scheduler.step(lowerCAmelCase_		,      lowerCAmelCase_		,      lowerCAmelCase_			).prev_sample
									A__					:       int     =scheduler.step(lowerCAmelCase_		,      lowerCAmelCase_		,      lowerCAmelCase_			).prev_sample

									self.assertEqual(output_a.shape		,      sample.shape			)
									self.assertEqual(output_a.shape		,      output_a.shape			)


			def lowercase__						(				self	:						Optional[int]			)      ->		Optional[int]:





									'''simple docstring'''
									for timesteps in [10, 50, 1_00, 10_00]:
															self.check_over_configs(num_train_timesteps=lowerCAmelCase_			)


			def lowercase__						(				self	:						Tuple			)      ->		Optional[int]:





									'''simple docstring'''
									for clip_denoised in [True, False]:
															self.check_over_configs(clip_denoised=lowerCAmelCase_			)


			def lowercase__						(				self	:						List[Any]			)      ->		Dict:





									'''simple docstring'''
									A__					:       Union[str, Any]     =self.scheduler_classes[0]
									A__					:       Dict     =self.get_scheduler_config()
									A__					:       Any     =scheduler_class(**lowerCAmelCase_			)

									A__					:       int     =1
									scheduler.set_timesteps(lowerCAmelCase_			)
									A__					:       Any     =scheduler.timesteps

									A__					:       Optional[int]     =torch.manual_seed(0			)

									A__					:       List[Any]     =self.dummy_model()
									A__					:       Optional[int]     =self.dummy_sample_deter * scheduler.init_noise_sigma

									for i, t in enumerate(lowerCAmelCase_			):
															# 1. scale model input
															A__					:       Optional[Any]     =scheduler.scale_model_input(lowerCAmelCase_		,      lowerCAmelCase_			)

															# 2. predict noise residual
															A__					:       Union[str, Any]     =model(lowerCAmelCase_		,      lowerCAmelCase_			)

															# 3. predict previous sample x_t-1
															A__					:       Tuple     =scheduler.step(lowerCAmelCase_		,      lowerCAmelCase_		,      lowerCAmelCase_		,      generator=lowerCAmelCase_			).prev_sample

															A__					:       Dict     =pred_prev_sample

									A__					:       Union[str, Any]     =torch.sum(torch.abs(lowerCAmelCase_			)			)
									A__					:       Optional[Any]     =torch.mean(torch.abs(lowerCAmelCase_			)			)

									assert abs(result_sum.item() - 192.7614			) < 1e-2
									assert abs(result_mean.item() - 0.2510			) < 1e-3


			def lowercase__						(				self	:						Dict			)      ->		Union[str, Any]:





									'''simple docstring'''
									A__					:       Dict     =self.scheduler_classes[0]
									A__					:       Dict     =self.get_scheduler_config()
									A__					:       Tuple     =scheduler_class(**lowerCAmelCase_			)

									A__					:       Tuple     =[1_06, 0]
									scheduler.set_timesteps(timesteps=lowerCAmelCase_			)
									A__					:       List[Any]     =scheduler.timesteps

									A__					:       Optional[Any]     =torch.manual_seed(0			)

									A__					:       int     =self.dummy_model()
									A__					:       int     =self.dummy_sample_deter * scheduler.init_noise_sigma

									for t in timesteps:
															# 1. scale model input
															A__					:       Any     =scheduler.scale_model_input(lowerCAmelCase_		,      lowerCAmelCase_			)

															# 2. predict noise residual
															A__					:       List[str]     =model(lowerCAmelCase_		,      lowerCAmelCase_			)

															# 3. predict previous sample x_t-1
															A__					:       Dict     =scheduler.step(lowerCAmelCase_		,      lowerCAmelCase_		,      lowerCAmelCase_		,      generator=lowerCAmelCase_			).prev_sample

															A__					:       Union[str, Any]     =pred_prev_sample

									A__					:       Union[str, Any]     =torch.sum(torch.abs(lowerCAmelCase_			)			)
									A__					:       List[Any]     =torch.mean(torch.abs(lowerCAmelCase_			)			)

									assert abs(result_sum.item() - 347.6357			) < 1e-2
									assert abs(result_mean.item() - 0.4527			) < 1e-3


			def lowercase__						(				self	:						Optional[Any]			)      ->		Dict:





									'''simple docstring'''
									A__					:       Optional[Any]     =self.scheduler_classes[0]
									A__					:       Union[str, Any]     =self.get_scheduler_config()
									A__					:       List[Any]     =scheduler_class(**lowerCAmelCase_			)

									A__					:       Tuple     =[39, 30, 12, 15, 0]

									with self.assertRaises(lowerCAmelCase_		,      msg="""`timesteps` must be in descending order."""			):
															scheduler.set_timesteps(timesteps=lowerCAmelCase_			)


			def lowercase__						(				self	:						Union[str, Any]			)      ->		List[str]:





									'''simple docstring'''
									A__					:       Union[str, Any]     =self.scheduler_classes[0]
									A__					:       List[str]     =self.get_scheduler_config()
									A__					:       Tuple     =scheduler_class(**lowerCAmelCase_			)

									A__					:       Dict     =[39, 30, 12, 1, 0]
									A__					:       int     =len(lowerCAmelCase_			)

									with self.assertRaises(lowerCAmelCase_		,      msg="""Can only pass one of `num_inference_steps` or `timesteps`."""			):
															scheduler.set_timesteps(num_inference_steps=lowerCAmelCase_		,      timesteps=lowerCAmelCase_			)



			def lowercase__						(				self	:						Any			)      ->		Optional[int]:





									'''simple docstring'''
									A__					:       Optional[Any]     =self.scheduler_classes[0]
									A__					:       Any     =self.get_scheduler_config()
									A__					:       Optional[int]     =scheduler_class(**lowerCAmelCase_			)

									A__					:       List[str]     =[scheduler.config.num_train_timesteps]

									with self.assertRaises(
									    lowerCAmelCase_		,      msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"""		,      ):
															scheduler.set_timesteps(timesteps=lowerCAmelCase_			)

