




'''simple docstring'''




# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin






class      UpperCAmelCase_     (  __a       ,	__a     ):


	"""simple docstring"""


	snake_case									=		1







	@register_to_config
	def __init__(						self						, UpperCAmelCase_=20_00						, UpperCAmelCase_=0.1						, UpperCAmelCase_=20						, UpperCAmelCase_=1e-3				):
						snake_case_	=      None
						snake_case_	=      None
						snake_case_	=      None







	def  _lowercase    (						self						, UpperCAmelCase_						, UpperCAmelCase_ = None				):
						snake_case_	=      torch.linspace(1						, self.config.sampling_eps						, __UpperCamelCase						, device=__UpperCamelCase				)







	def  _lowercase    (						self						, UpperCAmelCase_						, UpperCAmelCase_						, UpperCAmelCase_						, UpperCAmelCase_=None				):
						if self.timesteps is None:
											raise ValueError(
											    "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"				)

						# TODO(Patrick) better comments + non-PyTorch
						# postprocess model score
						snake_case_	=      (
						    -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
						)
						snake_case_	=      torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff				)				)
						snake_case_	=      std.flatten()
						while len(std.shape				) < len(score.shape				):
											snake_case_	=      std.unsqueeze(-1				)
						snake_case_	=      -score / std

						# compute
						snake_case_	=      -1.0 / len(self.timesteps				)

						snake_case_	=      self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
						snake_case_	=      beta_t.flatten()
						while len(beta_t.shape				) < len(x.shape				):
											snake_case_	=      beta_t.unsqueeze(-1				)
						snake_case_	=      -0.5 * beta_t * x

						snake_case_	=      torch.sqrt(__UpperCamelCase				)
						snake_case_	=      drift - diffusion**2 * score
						snake_case_	=      x + drift * dt

						# add noise
						snake_case_	=      randn_tensor(x.shape						, layout=x.layout						, generator=__UpperCamelCase						, device=x.device						, dtype=x.dtype				)
						snake_case_	=      x_mean + diffusion * math.sqrt(-dt				) * noise

						return x, x_mean







	def __len__(						self				):
						return self.config.num_train_timesteps






import numpy as np

import datasets


SCREAMING_SNAKE_CASE		:     Optional[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"

SCREAMING_SNAKE_CASE		:     Dict         =	"\\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"

SCREAMING_SNAKE_CASE		:     List[str]         =	"\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				):





						def        A_							(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        A_							(self			,     __UpperCamelCase			,     __UpperCamelCase						)				->				int:
												# convert to numpy arrays
												UpperCamelCase_		:							int  =		np.array(__UpperCamelCase						)
												UpperCamelCase_		:							Union[str, Any]  =		np.array(__UpperCamelCase						)

												# 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
												UpperCamelCase_		:							str  =		X - np.mean(__UpperCamelCase						)
												UpperCamelCase_		:							Dict  =		np.cov(reference_distribution.T						)
												try:
																		UpperCamelCase_		:							str  =		np.linalg.inv(__UpperCamelCase						)
												except np.linalg.LinAlgError:
																		UpperCamelCase_		:							List[Any]  =		np.linalg.pinv(__UpperCamelCase						)
												UpperCamelCase_		:							Tuple  =		np.dot(__UpperCamelCase			,     __UpperCamelCase						)
												UpperCamelCase_		:							Tuple  =		np.dot(__UpperCamelCase			,     X_minus_mu.T						).diagonal()

												return {"mahalanobis": mahal_dist}






