




import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest







class        __lowerCAmelCase					(			__magic_name__					):

			"""simple docstring"""







			snake_case_  						=			(DDIMParallelScheduler,)
			snake_case_  						=			(('''eta''', 0.0), ('''num_inference_steps''', 50))


			def  lowercase_       ( self     ,		**lowerCamelCase__				)       -> Tuple:



							'''simple docstring'''
							__lowerCamelCase									=       {
							    'num_train_timesteps': 1_000,
							    'beta_start': 0.00_01,
							    'beta_end': 0.02,
							    'beta_schedule': 'linear',
							    'clip_sample': True,
							}

							config.update(**lowerCamelCase__				)
							return config


			def  lowercase_       ( self     ,		**lowerCamelCase__				)       -> Union[str, Any]:



							'''simple docstring'''
							__lowerCamelCase									=       self.scheduler_classes[0]
							__lowerCamelCase									=       self.get_scheduler_config(**lowerCamelCase__				)
							__lowerCamelCase									=       scheduler_class(**lowerCamelCase__				)

							__lowerCamelCase			,		__lowerCamelCase									=       10, 0.0

							__lowerCamelCase									=       self.dummy_model()
							__lowerCamelCase									=       self.dummy_sample_deter

							scheduler.set_timesteps(lowerCamelCase__				)

							for t in scheduler.timesteps:
											__lowerCamelCase									=       model(lowerCamelCase__     ,		lowerCamelCase__				)
											__lowerCamelCase									=       scheduler.step(lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__				).prev_sample

							return sample


			def  lowercase_       ( self				)       -> Optional[Any]:



							'''simple docstring'''
							for timesteps in [100, 500, 1_000]:
											self.check_over_configs(num_train_timesteps=lowerCamelCase__				)


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



							'''simple docstring'''
							for steps_offset in [0, 1]:
											self.check_over_configs(steps_offset=lowerCamelCase__				)

							__lowerCamelCase									=       self.scheduler_classes[0]
							__lowerCamelCase									=       self.get_scheduler_config(steps_offset=1				)
							__lowerCamelCase									=       scheduler_class(**lowerCamelCase__				)
							scheduler.set_timesteps(5				)
							assert torch.equal(scheduler.timesteps     ,		torch.LongTensor([801, 601, 401, 201, 1]				)				)


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



							'''simple docstring'''
							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=lowerCamelCase__     ,		beta_end=lowerCamelCase__				)


			def  lowercase_       ( self				)       -> List[Any]:



							'''simple docstring'''
							for schedule in ["linear", "squaredcos_cap_v2"]:
											self.check_over_configs(beta_schedule=lowerCamelCase__				)


			def  lowercase_       ( self				)       -> str:



							'''simple docstring'''
							for prediction_type in ["epsilon", "v_prediction"]:
											self.check_over_configs(prediction_type=lowerCamelCase__				)


			def  lowercase_       ( self				)       -> Any:



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


			def  lowercase_       ( self				)       -> List[str]:



							'''simple docstring'''
							for timestep_spacing in ["trailing", "leading"]:
											self.check_over_configs(timestep_spacing=lowerCamelCase__				)


			def  lowercase_       ( self				)       -> Optional[Any]:



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


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



							'''simple docstring'''
							self.check_over_configs(thresholding=lowerCamelCase__				)
							for threshold in [0.5, 1.0, 2.0]:
											for prediction_type in ["epsilon", "v_prediction"]:
															self.check_over_configs(
															    thresholding=lowerCamelCase__     ,		prediction_type=lowerCamelCase__     ,		sample_max_value=lowerCamelCase__     ,		)


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



							'''simple docstring'''
							for t in [1, 10, 49]:
											self.check_over_forward(time_step=lowerCamelCase__				)


			def  lowercase_       ( self				)       -> List[str]:



							'''simple docstring'''
							for t, num_inference_steps in zip([1, 10, 50]     ,		[10, 50, 500]				):
											self.check_over_forward(time_step=lowerCamelCase__     ,		num_inference_steps=lowerCamelCase__				)


			def  lowercase_       ( self				)       -> Optional[Any]:



							'''simple docstring'''
							for t, eta in zip([1, 10, 49]     ,		[0.0, 0.5, 1.0]				):
											self.check_over_forward(time_step=lowerCamelCase__     ,		eta=lowerCamelCase__				)


			def  lowercase_       ( self				)       -> Optional[Any]:



							'''simple docstring'''
							__lowerCamelCase									=       self.scheduler_classes[0]
							__lowerCamelCase									=       self.get_scheduler_config()
							__lowerCamelCase									=       scheduler_class(**lowerCamelCase__				)

							assert torch.sum(torch.abs(scheduler._get_variance(0     ,		0				) - 0.0				)				) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(420     ,		400				) - 0.1_47_71				)				) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(980     ,		960				) - 0.3_24_60				)				) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(0     ,		0				) - 0.0				)				) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(487     ,		486				) - 0.0_09_79				)				) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(999     ,		998				) - 0.02				)				) < 1e-5


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



							'''simple docstring'''
							__lowerCamelCase									=       self.scheduler_classes[0]
							__lowerCamelCase									=       self.get_scheduler_config()
							__lowerCamelCase									=       scheduler_class(**lowerCamelCase__				)

							__lowerCamelCase			,		__lowerCamelCase									=       10, 0.0
							scheduler.set_timesteps(lowerCamelCase__				)

							__lowerCamelCase									=       self.dummy_model()
							__lowerCamelCase									=       self.dummy_sample_deter
							__lowerCamelCase									=       self.dummy_sample_deter + 0.1
							__lowerCamelCase									=       self.dummy_sample_deter - 0.1

							__lowerCamelCase									=       samplea.shape[0]
							__lowerCamelCase									=       torch.stack([samplea, samplea, samplea]     ,		dim=0				)
							__lowerCamelCase									=       torch.arange(lowerCamelCase__				)[0:3, None].repeat(1     ,		lowerCamelCase__				)

							__lowerCamelCase									=       model(samples.flatten(0     ,		1				)     ,		timesteps.flatten(0     ,		1				)				)
							__lowerCamelCase									=       scheduler.batch_step_no_noise(lowerCamelCase__     ,		timesteps.flatten(0     ,		1				)     ,		samples.flatten(0     ,		1				)     ,		lowerCamelCase__				)

							__lowerCamelCase									=       torch.sum(torch.abs(lowerCamelCase__				)				)
							__lowerCamelCase									=       torch.mean(torch.abs(lowerCamelCase__				)				)

							assert abs(result_sum.item() - 11_47.79_04				) < 1e-2
							assert abs(result_mean.item() - 0.49_82				) < 1e-3


			def  lowercase_       ( self				)       -> Optional[Any]:



							'''simple docstring'''
							__lowerCamelCase									=       self.full_loop()

							__lowerCamelCase									=       torch.sum(torch.abs(lowerCamelCase__				)				)
							__lowerCamelCase									=       torch.mean(torch.abs(lowerCamelCase__				)				)

							assert abs(result_sum.item() - 1_72.00_67				) < 1e-2
							assert abs(result_mean.item() - 0.22_39_67				) < 1e-3


			def  lowercase_       ( self				)       -> List[Any]:



							'''simple docstring'''
							__lowerCamelCase									=       self.full_loop(prediction_type='v_prediction'				)

							__lowerCamelCase									=       torch.sum(torch.abs(lowerCamelCase__				)				)
							__lowerCamelCase									=       torch.mean(torch.abs(lowerCamelCase__				)				)

							assert abs(result_sum.item() - 52.53_02				) < 1e-2
							assert abs(result_mean.item() - 0.06_84				) < 1e-3


			def  lowercase_       ( self				)       -> List[Any]:



							'''simple docstring'''
							# We specify different beta, so that the first alpha is 0.99
							__lowerCamelCase									=       self.full_loop(set_alpha_to_one=lowerCamelCase__     ,		beta_start=0.01				)
							__lowerCamelCase									=       torch.sum(torch.abs(lowerCamelCase__				)				)
							__lowerCamelCase									=       torch.mean(torch.abs(lowerCamelCase__				)				)

							assert abs(result_sum.item() - 1_49.82_95				) < 1e-2
							assert abs(result_mean.item() - 0.19_51				) < 1e-3






			def  lowercase_       ( self				)       -> Tuple:



							'''simple docstring'''
							# We specify different beta, so that the first alpha is 0.99
							__lowerCamelCase									=       self.full_loop(set_alpha_to_one=lowerCamelCase__     ,		beta_start=0.01				)
							__lowerCamelCase									=       torch.sum(torch.abs(lowerCamelCase__				)				)
							__lowerCamelCase									=       torch.mean(torch.abs(lowerCamelCase__				)				)

							assert abs(result_sum.item() - 1_49.07_84				) < 1e-2
							assert abs(result_mean.item() - 0.19_41				) < 1e-3








from sklearn.metrics import mean_squared_error

import datasets


__A								=       "\\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n journal={Journal of Machine Learning Research},\n volume={12},\n pages={2825--2830},\n year={2011}\n}\n"

__A								=       "\\nMean Squared Error(MSE) is the average of the square of difference between the predicted\nand actual values.\n"


__A								=       "\nArgs:\n    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Estimated target values.\n    references: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Ground truth (correct) target values.\n    sample_weight: array-like of shape (n_samples,), default=None\n        Sample weights.\n    multioutput: {\"raw_values\", \"uniform_average\"} or array-like of shape (n_outputs,), default=\"uniform_average\"\n        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.\n\n        \"raw_values\" : Returns a full set of errors in case of multioutput input.\n\n        \"uniform_average\" : Errors of all outputs are averaged with uniform weight.\n\n    squared : bool, default=True\n        If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.\n\nReturns:\n    mse : mean squared error.\nExamples:\n\n    >>> mse_metric = datasets.load_metric(\"mse\")\n    >>> predictions = [2.5, 0.0, 2, 8]\n    >>> references = [3, -0.5, 2, 7]\n    >>> results = mse_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'mse': 0.375}\n    >>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)\n    >>> print(rmse_result)\n    {'mse': 0.6123724356957945}\n\n    If you're using multi-dimensional lists, then set the config as follows :\n\n    >>> mse_metric = datasets.load_metric(\"mse\", \"multilist\")\n    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]\n    >>> references = [[0, 2], [-1, 2], [8, -5]]\n    >>> results = mse_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'mse': 0.7083333333333334}\n    >>> results = mse_metric.compute(predictions=predictions, references=references, multioutput='raw_values')\n    >>> print(results) # doctest: +NORMALIZE_WHITESPACE\n    {'mse': array([0.41666667, 1. ])}\n"







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

			"""simple docstring"""







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



							'''simple docstring'''
							return datasets.MetricInfo(
							    description=_DESCRIPTION     ,		citation=_CITATION     ,		inputs_description=_KWARGS_DESCRIPTION     ,		features=datasets.Features(self._get_feature_types()				)     ,		reference_urls=[
							        'https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html'
							    ]     ,		)


			def  lowercase_       ( self				)       -> Tuple:



							'''simple docstring'''
							if self.config_name == "multilist":
											return {
											    "predictions": datasets.Sequence(datasets.Value('float'				)				),
											    "references": datasets.Sequence(datasets.Value('float'				)				),
											}
							else:
											return {
											    "predictions": datasets.Value('float'				),
											    "references": datasets.Value('float'				),
											}






			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__=None     ,		lowerCamelCase__="uniform_average"     ,		lowerCamelCase__=True				)       -> List[Any]:



							'''simple docstring'''
							__lowerCamelCase									=       mean_squared_error(
							    lowerCamelCase__     ,		lowerCamelCase__     ,		sample_weight=lowerCamelCase__     ,		multioutput=lowerCamelCase__     ,		squared=lowerCamelCase__				)

							return {"mse": mse}


