






from typing import Dict, Optional

import numpy as np

import datasets


__UpperCAmelCase     							=						"\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"

__UpperCAmelCase     							=						"\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"

__UpperCAmelCase     							=						"\\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   :							Union[str, Any]					,	__snake_case   :							Union[str, Any]					,	__snake_case   :							Optional[Any]					,	__snake_case   :							Any					,	__snake_case   :							Dict = None					,	__snake_case   :							Optional[Any] = False					,	):


					'''simple docstring'''


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

    # turn into Numpy arrays
					UpperCAmelCase_   :				int						=						np.array(lowerCamelCase_  )
					UpperCAmelCase_   :				int						=						np.array(lowerCamelCase_  )

					if reduce_labels:
										UpperCAmelCase_   :				List[str]						=						255
										UpperCAmelCase_   :				Any						=						label - 1
										UpperCAmelCase_   :				Union[str, Any]						=						255

					UpperCAmelCase_   :				Optional[Any]						=						label != ignore_index
					UpperCAmelCase_   :				Tuple						=						np.not_equal(lowerCamelCase_					,	lowerCamelCase_  )
					UpperCAmelCase_   :				Union[str, Any]						=						pred_label[mask]
					UpperCAmelCase_   :				Dict						=						np.array(lowerCamelCase_  )[mask]

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

					UpperCAmelCase_   :				Optional[Any]						=						np.histogram(lowerCamelCase_					,	bins=lowerCamelCase_					,	range=(0, num_labels - 1)  )[0]
					UpperCAmelCase_   :				Optional[int]						=						np.histogram(lowerCamelCase_					,	bins=lowerCamelCase_					,	range=(0, num_labels - 1)  )[0]
					UpperCAmelCase_   :				Tuple						=						np.histogram(lowerCamelCase_					,	bins=lowerCamelCase_					,	range=(0, num_labels - 1)  )[0]

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

					return area_intersect, area_union, area_pred_label, area_label




def 					lowercase__				(					__snake_case   :							str					,	__snake_case   :							Tuple					,	__snake_case   :							Any					,	__snake_case   :							int					,	__snake_case   :							int = None					,	__snake_case   :							Optional[int] = False					,	):


					'''simple docstring'''


					UpperCAmelCase_   :				str						=						np.zeros((num_labels,)					,	dtype=np.floataa  )
					UpperCAmelCase_   :				Any						=						np.zeros((num_labels,)					,	dtype=np.floataa  )
					UpperCAmelCase_   :				List[Any]						=						np.zeros((num_labels,)					,	dtype=np.floataa  )
					UpperCAmelCase_   :				Tuple						=						np.zeros((num_labels,)					,	dtype=np.floataa  )
					for result, gt_seg_map in zip(lowerCamelCase_					,	lowerCamelCase_  ):
										UpperCAmelCase_   :				List[str]						=						intersect_and_union(
										    lowerCamelCase_					,	lowerCamelCase_					,	lowerCamelCase_					,	lowerCamelCase_					,	lowerCamelCase_					,	lowerCamelCase_  )
										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   :							List[Any]					,	__snake_case   :							Tuple					,	__snake_case   :							Optional[Any]					,	__snake_case   :							Tuple					,	__snake_case   :							int = None					,	__snake_case   :							str = None					,	__snake_case   :							Any = False					,	):


					'''simple docstring'''


					UpperCAmelCase_   :				Union[str, Any]						=						total_intersect_and_union(
					    lowerCamelCase_					,	lowerCamelCase_					,	lowerCamelCase_					,	lowerCamelCase_					,	lowerCamelCase_					,	lowerCamelCase_  )

					# compute metrics
					UpperCAmelCase_   :				Tuple						=						{}

					UpperCAmelCase_   :				List[Any]						=						total_area_intersect.sum() / total_area_label.sum()
					UpperCAmelCase_   :				List[str]						=						total_area_intersect / total_area_union
					UpperCAmelCase_   :				Optional[Any]						=						total_area_intersect / total_area_label

					UpperCAmelCase_   :				Tuple						=						np.nanmean(lowerCamelCase_  )
					UpperCAmelCase_   :				Dict						=						np.nanmean(lowerCamelCase_  )
					UpperCAmelCase_   :				List[Any]						=						all_acc
					UpperCAmelCase_   :				int						=						iou
					UpperCAmelCase_   :				Union[str, Any]						=						acc

					if nan_to_num is not None:
										UpperCAmelCase_   :				Dict						=						{metric: np.nan_to_num(lowerCamelCase_					,	nan=lowerCamelCase_  ) 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  __UpperCAmelCase      (							self       )     ->							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  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase = None    ,		_UpperCamelCase = None    ,		_UpperCamelCase = False    ,		)     ->							List[Any]:
							UpperCAmelCase_   :				Any						=						mean_iou(
							    results=_UpperCamelCase    ,		gt_seg_maps=_UpperCamelCase    ,		num_labels=_UpperCamelCase    ,		ignore_index=_UpperCamelCase    ,		nan_to_num=_UpperCamelCase    ,		label_map=_UpperCamelCase    ,		reduce_labels=_UpperCamelCase    ,		)
							return iou_result














import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest



class   lowerCamelCase				(_snake_case      ):







		'''simple docstring'''



		_snake_case   :   Any							=  (DDPMParallelScheduler,)



		def  __UpperCAmelCase      (							self    ,		**_UpperCamelCase       )     ->							Any:
							UpperCAmelCase_   :				List[str]						=						{
							    'num_train_timesteps': 1_0_0_0,
							    'beta_start': 0.00_01,
							    'beta_end': 0.02,
							    'beta_schedule': 'linear',
							    'variance_type': 'fixed_small',
							    'clip_sample': True,
							}

							config.update(**_UpperCamelCase       )
							return config



		def  __UpperCAmelCase      (							self       )     ->							Any:
							for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
												self.check_over_configs(num_train_timesteps=_UpperCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							int:
							for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]    ,		[0.0_02, 0.02, 0.2, 2]       ):
												self.check_over_configs(beta_start=_UpperCamelCase    ,		beta_end=_UpperCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							Any:
							for schedule in ["linear", "squaredcos_cap_v2"]:
												self.check_over_configs(beta_schedule=_UpperCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							Optional[Any]:
							for variance in ["fixed_small", "fixed_large", "other"]:
												self.check_over_configs(variance_type=_UpperCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							Optional[int]:
							for clip_sample in [True, False]:
												self.check_over_configs(clip_sample=_UpperCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							Union[str, Any]:
							self.check_over_configs(thresholding=_UpperCamelCase       )
							for threshold in [0.5, 1.0, 2.0]:
												for prediction_type in ["epsilon", "sample", "v_prediction"]:
																	self.check_over_configs(
																	    thresholding=_UpperCamelCase    ,		prediction_type=_UpperCamelCase    ,		sample_max_value=_UpperCamelCase    ,		)



		def  __UpperCAmelCase      (							self       )     ->							Any:
							for prediction_type in ["epsilon", "sample", "v_prediction"]:
												self.check_over_configs(prediction_type=_UpperCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							Union[str, Any]:
							for t in [0, 5_0_0, 9_9_9]:
												self.check_over_forward(time_step=_UpperCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							Dict:
							UpperCAmelCase_   :				Dict						=						self.scheduler_classes[0]
							UpperCAmelCase_   :				int						=						self.get_scheduler_config()
							UpperCAmelCase_   :				Union[str, Any]						=						scheduler_class(**_UpperCamelCase       )

							assert torch.sum(torch.abs(scheduler._get_variance(0       ) - 0.0       )       ) < 1E-5
							assert torch.sum(torch.abs(scheduler._get_variance(4_8_7       ) - 0.0_09_79       )       ) < 1E-5
							assert torch.sum(torch.abs(scheduler._get_variance(9_9_9       ) - 0.02       )       ) < 1E-5



		def  __UpperCAmelCase      (							self       )     ->							Dict:
							UpperCAmelCase_   :				List[Any]						=						self.scheduler_classes[0]
							UpperCAmelCase_   :				Any						=						self.get_scheduler_config()
							UpperCAmelCase_   :				Union[str, Any]						=						scheduler_class(**_UpperCamelCase       )

							UpperCAmelCase_   :				Optional[Any]						=						len(_UpperCamelCase       )

							UpperCAmelCase_   :				Any						=						self.dummy_model()
							UpperCAmelCase_   :				List[Any]						=						self.dummy_sample_deter
							UpperCAmelCase_   :				Union[str, Any]						=						self.dummy_sample_deter + 0.1
							UpperCAmelCase_   :				str						=						self.dummy_sample_deter - 0.1

							UpperCAmelCase_   :				Tuple						=						samplea.shape[0]
							UpperCAmelCase_   :				Tuple						=						torch.stack([samplea, samplea, samplea]    ,		dim=0       )
							UpperCAmelCase_   :				str						=						torch.arange(_UpperCamelCase       )[0:3, None].repeat(1    ,		_UpperCamelCase       )

							UpperCAmelCase_   :				Any						=						model(samples.flatten(0    ,		1       )    ,		timesteps.flatten(0    ,		1       )       )
							UpperCAmelCase_   :				Dict						=						scheduler.batch_step_no_noise(_UpperCamelCase    ,		timesteps.flatten(0    ,		1       )    ,		samples.flatten(0    ,		1       )       )

							UpperCAmelCase_   :				List[Any]						=						torch.sum(torch.abs(_UpperCamelCase       )       )
							UpperCAmelCase_   :				Any						=						torch.mean(torch.abs(_UpperCamelCase       )       )

							assert abs(result_sum.item() - 11_53.18_33       ) < 1E-2
							assert abs(result_mean.item() - 0.50_05       ) < 1E-3



		def  __UpperCAmelCase      (							self       )     ->							Union[str, Any]:
							UpperCAmelCase_   :				List[str]						=						self.scheduler_classes[0]
							UpperCAmelCase_   :				List[Any]						=						self.get_scheduler_config()
							UpperCAmelCase_   :				Any						=						scheduler_class(**_UpperCamelCase       )

							UpperCAmelCase_   :				Optional[int]						=						len(_UpperCamelCase       )

							UpperCAmelCase_   :				Optional[int]						=						self.dummy_model()
							UpperCAmelCase_   :				Optional[int]						=						self.dummy_sample_deter
							UpperCAmelCase_   :				Union[str, Any]						=						torch.manual_seed(0       )

							for t in reversed(range(_UpperCamelCase       )       ):
												# 1. predict noise residual
												UpperCAmelCase_   :				Union[str, Any]						=						model(_UpperCamelCase    ,		_UpperCamelCase       )

												# 2. predict previous mean of sample x_t-1
												UpperCAmelCase_   :				str						=						scheduler.step(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		generator=_UpperCamelCase       ).prev_sample

												UpperCAmelCase_   :				str						=						pred_prev_sample

							UpperCAmelCase_   :				Optional[int]						=						torch.sum(torch.abs(_UpperCamelCase       )       )
							UpperCAmelCase_   :				Optional[int]						=						torch.mean(torch.abs(_UpperCamelCase       )       )

							assert abs(result_sum.item() - 2_58.96_06       ) < 1E-2
							assert abs(result_mean.item() - 0.33_72       ) < 1E-3



		def  __UpperCAmelCase      (							self       )     ->							int:
							UpperCAmelCase_   :				List[Any]						=						self.scheduler_classes[0]
							UpperCAmelCase_   :				Optional[int]						=						self.get_scheduler_config(prediction_type='v_prediction'       )
							UpperCAmelCase_   :				Tuple						=						scheduler_class(**_UpperCamelCase       )

							UpperCAmelCase_   :				Tuple						=						len(_UpperCamelCase       )

							UpperCAmelCase_   :				Tuple						=						self.dummy_model()
							UpperCAmelCase_   :				List[str]						=						self.dummy_sample_deter
							UpperCAmelCase_   :				Any						=						torch.manual_seed(0       )

							for t in reversed(range(_UpperCamelCase       )       ):
												# 1. predict noise residual
												UpperCAmelCase_   :				Optional[Any]						=						model(_UpperCamelCase    ,		_UpperCamelCase       )

												# 2. predict previous mean of sample x_t-1
												UpperCAmelCase_   :				Any						=						scheduler.step(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		generator=_UpperCamelCase       ).prev_sample

												UpperCAmelCase_   :				List[str]						=						pred_prev_sample

							UpperCAmelCase_   :				Optional[int]						=						torch.sum(torch.abs(_UpperCamelCase       )       )
							UpperCAmelCase_   :				Any						=						torch.mean(torch.abs(_UpperCamelCase       )       )

							assert abs(result_sum.item() - 2_02.02_96       ) < 1E-2
							assert abs(result_mean.item() - 0.26_31       ) < 1E-3



		def  __UpperCAmelCase      (							self       )     ->							List[Any]:
							UpperCAmelCase_   :				Any						=						self.scheduler_classes[0]
							UpperCAmelCase_   :				Optional[Any]						=						self.get_scheduler_config()
							UpperCAmelCase_   :				str						=						scheduler_class(**_UpperCamelCase       )

							UpperCAmelCase_   :				Optional[int]						=						[1_0_0, 8_7, 5_0, 1, 0]

							scheduler.set_timesteps(timesteps=_UpperCamelCase       )

							UpperCAmelCase_   :				Tuple						=						scheduler.timesteps

							for i, timestep in enumerate(_UpperCamelCase       ):
												if i == len(_UpperCamelCase       ) - 1:
																	UpperCAmelCase_   :				List[str]						=						-1
												else:
																	UpperCAmelCase_   :				int						=						timesteps[i + 1]

												UpperCAmelCase_   :				int						=						scheduler.previous_timestep(_UpperCamelCase       )
												UpperCAmelCase_   :				Optional[Any]						=						prev_t.item()

												self.assertEqual(_UpperCamelCase    ,		_UpperCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							List[Any]:
							UpperCAmelCase_   :				str						=						self.scheduler_classes[0]
							UpperCAmelCase_   :				Tuple						=						self.get_scheduler_config()
							UpperCAmelCase_   :				Union[str, Any]						=						scheduler_class(**_UpperCamelCase       )

							UpperCAmelCase_   :				Optional[int]						=						[1_0_0, 8_7, 5_0, 5_1, 0]

							with self.assertRaises(_UpperCamelCase    ,		msg='`custom_timesteps` must be in descending order.'       ):
												scheduler.set_timesteps(timesteps=_UpperCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							Tuple:
							UpperCAmelCase_   :				Any						=						self.scheduler_classes[0]
							UpperCAmelCase_   :				List[str]						=						self.get_scheduler_config()
							UpperCAmelCase_   :				List[str]						=						scheduler_class(**_UpperCamelCase       )

							UpperCAmelCase_   :				Tuple						=						[1_0_0, 8_7, 5_0, 1, 0]
							UpperCAmelCase_   :				Optional[Any]						=						len(_UpperCamelCase       )

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



		def  __UpperCAmelCase      (							self       )     ->							Dict:
							UpperCAmelCase_   :				List[str]						=						self.scheduler_classes[0]
							UpperCAmelCase_   :				Any						=						self.get_scheduler_config()
							UpperCAmelCase_   :				Optional[Any]						=						scheduler_class(**_UpperCamelCase       )

							UpperCAmelCase_   :				Dict						=						[scheduler.config.num_train_timesteps]

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






