'''simple docstring'''





import numpy as np

import datasets


_lowerCAmelCase	:int						=     '\nCompute the Mahalanobis Distance\n\nMahalonobis distance is the distance between a point and a distribution.\nAnd not between two distinct points. It is effectively a multivariate equivalent of the Euclidean distance.\nIt was introduced by Prof. P. C. Mahalanobis in 1936\nand has been used in various statistical applications ever since\n[source: https://www.machinelearningplus.com/statistics/mahalanobis-distance/]\n'

_lowerCAmelCase	:Optional[int]						=     '\\n@article{de2000mahalanobis,\n  title={The mahalanobis distance},\n  author={De Maesschalck, Roy and Jouan-Rimbaud, Delphine and Massart, D{\'e}sir{\'e} L},\n  journal={Chemometrics and intelligent laboratory systems},\n  volume={50},\n  number={1},\n  pages={1--18},\n  year={2000},\n  publisher={Elsevier}\n}\n'

_lowerCAmelCase	:Optional[int]						=     '\nArgs:\n    X: List of datapoints to be compared with the `reference_distribution`.\n    reference_distribution: List of datapoints from the reference distribution we want to compare to.\nReturns:\n    mahalanobis: The Mahalonobis distance for each datapoint in `X`.\nExamples:\n\n    >>> mahalanobis_metric = datasets.load_metric("mahalanobis")\n    >>> results = mahalanobis_metric.compute(reference_distribution=[[0, 1], [1, 0]], X=[[0, 1]])\n    >>> print(results)\n    {\'mahalanobis\': array([0.5])}\n'






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




    '''simple docstring'''







    def    _UpperCamelCase     (							self			)				->			str:
         return datasets.MetricInfo(
             description=_DESCRIPTION     ,				citation=_CITATION     ,				inputs_description=_KWARGS_DESCRIPTION     ,				features=datasets.Features(
                 {
                     'X': datasets.Sequence(datasets.Value('float'     ,				id='sequence'			)     ,				id='X'			),
                 }			)     ,				)






    def    _UpperCamelCase     (							self     ,				lowercase__     ,				lowercase__			)				->			str:
         SCREAMING_SNAKE_CASE       :      str     						=							np.array(lowercase__			)
         SCREAMING_SNAKE_CASE       :      Dict     						=							np.array(lowercase__			)

         # Assert that arrays are 2D
         if len(X.shape			) != 2:
              raise ValueError('Expected `X` to be a 2D vector'			)
         if len(reference_distribution.shape			) != 2:
              raise ValueError('Expected `reference_distribution` to be a 2D vector'			)
         if reference_distribution.shape[0] < 2:
              raise ValueError(
                  'Expected `reference_distribution` to be a 2D vector with more than one element in the first dimension'			)

         # Get mahalanobis distance for each prediction
         SCREAMING_SNAKE_CASE       :      List[str]     						=							X - np.mean(lowercase__			)
         SCREAMING_SNAKE_CASE       :      int     						=							np.cov(reference_distribution.T			)
         try:
              SCREAMING_SNAKE_CASE       :      Any     						=							np.linalg.inv(lowercase__			)
         except np.linalg.LinAlgError:
              SCREAMING_SNAKE_CASE       :      Union[str, Any]     						=							np.linalg.pinv(lowercase__			)
         SCREAMING_SNAKE_CASE       :      List[Any]     						=							np.dot(lowercase__     ,				lowercase__			)
         SCREAMING_SNAKE_CASE       :      Tuple     						=							np.dot(lowercase__     ,				X_minus_mu.T			).diagonal()

         return {"mahalanobis": mahal_dist}











import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest
class  _UpperCAmelCase   (			A__      ):

				"""simple docstring"""




				lowercase__											=			(IPNDMScheduler,)
				lowercase__											=			(("""num_inference_steps""", 50),)




				def 							lowercase__  (	self		:       Union[str, Any],    **lowerCamelCase		:       Tuple    ):

								'''simple docstring'''
								lowercase__  					=     {'''num_train_timesteps''': 1_000}
								config.update(**lowerCamelCase    )
								return config




				def 							lowercase__  (	self		:       Any,    lowerCamelCase		:       Any=0,    **lowerCamelCase		:       List[str]    ):

								'''simple docstring'''
								lowercase__  					=     dict(self.forward_default_kwargs    )
								lowercase__  					=     kwargs.pop('''num_inference_steps''',    lowerCamelCase    )
								lowercase__  					=     self.dummy_sample
								lowercase__  					=     0.1 * sample
								lowercase__  					=     [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

								for scheduler_class in self.scheduler_classes:
												lowercase__  					=     self.get_scheduler_config(**lowerCamelCase    )
												lowercase__  					=     scheduler_class(**lowerCamelCase    )
												scheduler.set_timesteps(lowerCamelCase    )
												# copy over dummy past residuals
												lowercase__  					=     dummy_past_residuals[:]

												if time_step is None:
																lowercase__  					=     scheduler.timesteps[len(scheduler.timesteps    ) // 2]

												with tempfile.TemporaryDirectory() as tmpdirname:
																scheduler.save_config(lowerCamelCase    )
																lowercase__  					=     scheduler_class.from_pretrained(lowerCamelCase    )
																new_scheduler.set_timesteps(lowerCamelCase    )
																# copy over dummy past residuals
																lowercase__  					=     dummy_past_residuals[:]

												lowercase__  					=     scheduler.step(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    **lowerCamelCase    ).prev_sample
												lowercase__  					=     new_scheduler.step(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    **lowerCamelCase    ).prev_sample

												assert torch.sum(torch.abs(output - new_output    )    ) < 1E-5, "Scheduler outputs are not identical"

												lowercase__  					=     scheduler.step(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    **lowerCamelCase    ).prev_sample
												lowercase__  					=     new_scheduler.step(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    **lowerCamelCase    ).prev_sample

												assert torch.sum(torch.abs(output - new_output    )    ) < 1E-5, "Scheduler outputs are not identical"




				def 							lowercase__  (	self		:       str    ):

								'''simple docstring'''
								pass




				def 							lowercase__  (	self		:       Dict,    lowerCamelCase		:       Optional[int]=0,    **lowerCamelCase		:       Dict    ):

								'''simple docstring'''
								lowercase__  					=     dict(self.forward_default_kwargs    )
								lowercase__  					=     kwargs.pop('''num_inference_steps''',    lowerCamelCase    )
								lowercase__  					=     self.dummy_sample
								lowercase__  					=     0.1 * sample
								lowercase__  					=     [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

								for scheduler_class in self.scheduler_classes:
												lowercase__  					=     self.get_scheduler_config()
												lowercase__  					=     scheduler_class(**lowerCamelCase    )
												scheduler.set_timesteps(lowerCamelCase    )

												# copy over dummy past residuals (must be after setting timesteps)
												lowercase__  					=     dummy_past_residuals[:]

												if time_step is None:
																lowercase__  					=     scheduler.timesteps[len(scheduler.timesteps    ) // 2]

												with tempfile.TemporaryDirectory() as tmpdirname:
																scheduler.save_config(lowerCamelCase    )
																lowercase__  					=     scheduler_class.from_pretrained(lowerCamelCase    )
																# copy over dummy past residuals
																new_scheduler.set_timesteps(lowerCamelCase    )

																# copy over dummy past residual (must be after setting timesteps)
																lowercase__  					=     dummy_past_residuals[:]

												lowercase__  					=     scheduler.step(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    **lowerCamelCase    ).prev_sample
												lowercase__  					=     new_scheduler.step(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    **lowerCamelCase    ).prev_sample

												assert torch.sum(torch.abs(output - new_output    )    ) < 1E-5, "Scheduler outputs are not identical"

												lowercase__  					=     scheduler.step(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    **lowerCamelCase    ).prev_sample
												lowercase__  					=     new_scheduler.step(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    **lowerCamelCase    ).prev_sample

												assert torch.sum(torch.abs(output - new_output    )    ) < 1E-5, "Scheduler outputs are not identical"




				def 							lowercase__  (	self		:       int,    **lowerCamelCase		:       List[str]    ):

								'''simple docstring'''
								lowercase__  					=     self.scheduler_classes[0]
								lowercase__  					=     self.get_scheduler_config(**lowerCamelCase    )
								lowercase__  					=     scheduler_class(**lowerCamelCase    )

								lowercase__  					=     10
								lowercase__  					=     self.dummy_model()
								lowercase__  					=     self.dummy_sample_deter
								scheduler.set_timesteps(lowerCamelCase    )

								for i, t in enumerate(scheduler.timesteps    ):
												lowercase__  					=     model(lowerCamelCase,    lowerCamelCase    )
												lowercase__  					=     scheduler.step(lowerCamelCase,    lowerCamelCase,    lowerCamelCase    ).prev_sample

								for i, t in enumerate(scheduler.timesteps    ):
												lowercase__  					=     model(lowerCamelCase,    lowerCamelCase    )
												lowercase__  					=     scheduler.step(lowerCamelCase,    lowerCamelCase,    lowerCamelCase    ).prev_sample

								return sample




				def 							lowercase__  (	self		:       List[Any]    ):

								'''simple docstring'''
								lowercase__  					=     dict(self.forward_default_kwargs    )

								lowercase__  					=     kwargs.pop('''num_inference_steps''',    lowerCamelCase    )

								for scheduler_class in self.scheduler_classes:
												lowercase__  					=     self.get_scheduler_config()
												lowercase__  					=     scheduler_class(**lowerCamelCase    )

												lowercase__  					=     self.dummy_sample
												lowercase__  					=     0.1 * sample

												if num_inference_steps is not None and hasattr(lowerCamelCase,    '''set_timesteps'''    ):
																scheduler.set_timesteps(lowerCamelCase    )
												elif num_inference_steps is not None and not hasattr(lowerCamelCase,    '''set_timesteps'''    ):
																lowercase__  					=     num_inference_steps

												# copy over dummy past residuals (must be done after set_timesteps)
												lowercase__  					=     [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
												lowercase__  					=     dummy_past_residuals[:]

												lowercase__  					=     scheduler.timesteps[5]
												lowercase__  					=     scheduler.timesteps[6]

												lowercase__  					=     scheduler.step(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    **lowerCamelCase    ).prev_sample
												lowercase__  					=     scheduler.step(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    **lowerCamelCase    ).prev_sample

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

												lowercase__  					=     scheduler.step(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    **lowerCamelCase    ).prev_sample
												lowercase__  					=     scheduler.step(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    **lowerCamelCase    ).prev_sample

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




				def 							lowercase__  (	self		:       Dict    ):

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




				def 							lowercase__  (	self		:       str    ):

								'''simple docstring'''
								for t, num_inference_steps in zip([1, 5, 10],    [10, 50, 100]    ):
												self.check_over_forward(num_inference_steps=lowerCamelCase,    time_step=lowerCamelCase    )




				def 							lowercase__  (	self		:       List[str]    ):

								'''simple docstring'''
								lowercase__  					=     self.full_loop()
								lowercase__  					=     torch.mean(torch.abs(lowerCamelCase    )    )

								assert abs(result_mean.item() - 2_540_529    ) < 10




