




'''simple docstring'''


import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest





class    __lowercase						(  __A ):
   _a							=      (UnCLIPScheduler,)







   def 		UpperCamelCase__			(	self      ,							**UpperCamelCase      )				->    Optional[int]:
     __a			=					{
         'num_train_timesteps': 1000,
         'variance_type': 'fixed_small_log',
         'clip_sample': True,
         'clip_sample_range': 1.0,
         'prediction_type': 'epsilon',
     }

     config.update(**UpperCamelCase__      )
     return config







   def 		UpperCamelCase__			(	self      )				->    Dict:
     for timesteps in [1, 5, 100, 1000]:
       self.check_over_configs(num_train_timesteps=UpperCamelCase__      )







   def 		UpperCamelCase__			(	self      )				->    Optional[Any]:
     for variance in ["fixed_small_log", "learned_range"]:
       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      )				->    str:
     for clip_sample_range in [1, 5, 10, 20]:
       self.check_over_configs(clip_sample_range=UpperCamelCase__      )







   def 		UpperCamelCase__			(	self      )				->    int:
     for prediction_type in ["epsilon", "sample"]:
       self.check_over_configs(prediction_type=UpperCamelCase__      )







   def 		UpperCamelCase__			(	self      )				->    Optional[int]:
     for time_step in [0, 500, 999]:
       for prev_timestep in [None, 5, 100, 250, 500, 750]:
         if prev_timestep is not None and prev_timestep >= time_step:
           continue

         self.check_over_forward(time_step=UpperCamelCase__      ,							prev_timestep=UpperCamelCase__      )







   def 		UpperCamelCase__			(	self      )				->    Any:
     __a			=					self.scheduler_classes[0]
     __a			=					self.get_scheduler_config(variance_type='fixed_small_log'      )
     __a			=					scheduler_class(**UpperCamelCase__      )

     assert torch.sum(torch.abs(scheduler._get_variance(0      ) - 1.0000e-10      )      ) < 1e-5
     assert torch.sum(torch.abs(scheduler._get_variance(487      ) - 0.0_549_625      )      ) < 1e-5
     assert torch.sum(torch.abs(scheduler._get_variance(999      ) - 0.9_994_987      )      ) < 1e-5







   def 		UpperCamelCase__			(	self      )				->    str:
     __a			=					self.scheduler_classes[0]
     __a			=					self.get_scheduler_config(variance_type='learned_range'      )
     __a			=					scheduler_class(**UpperCamelCase__      )

     __a			=					0.5

     assert scheduler._get_variance(1      ,							predicted_variance=UpperCamelCase__      ) - -10.1_712_790 < 1e-5
     assert scheduler._get_variance(487      ,							predicted_variance=UpperCamelCase__      ) - -5.7_998_052 < 1e-5
     assert scheduler._get_variance(999      ,							predicted_variance=UpperCamelCase__      ) - -0.0_010_011 < 1e-5







   def 		UpperCamelCase__			(	self      )				->    Any:
     __a			=					self.scheduler_classes[0]
     __a			=					self.get_scheduler_config()
     __a			=					scheduler_class(**UpperCamelCase__      )

     __a			=					scheduler.timesteps

     __a			=					self.dummy_model()
     __a			=					self.dummy_sample_deter
     __a			=					torch.manual_seed(0      )

     for i, t in enumerate(UpperCamelCase__      ):
       # 1. predict noise residual
       __a			=					model(UpperCamelCase__      ,							UpperCamelCase__      )

       # 2. predict previous mean of sample x_t-1
       __a			=					scheduler.step(UpperCamelCase__      ,							UpperCamelCase__      ,							UpperCamelCase__      ,							generator=UpperCamelCase__      ).prev_sample

       __a			=					pred_prev_sample

     __a			=					torch.sum(torch.abs(UpperCamelCase__      )      )
     __a			=					torch.mean(torch.abs(UpperCamelCase__      )      )

     assert abs(result_sum.item() - 252.268_2495      ) < 1e-2
     assert abs(result_mean.item() - 0.3_284_743      ) < 1e-3







   def 		UpperCamelCase__			(	self      )				->    List[Any]:
     __a			=					self.scheduler_classes[0]
     __a			=					self.get_scheduler_config()
     __a			=					scheduler_class(**UpperCamelCase__      )

     scheduler.set_timesteps(25      )

     __a			=					scheduler.timesteps

     __a			=					self.dummy_model()
     __a			=					self.dummy_sample_deter
     __a			=					torch.manual_seed(0      )

     for i, t in enumerate(UpperCamelCase__      ):
       # 1. predict noise residual
       __a			=					model(UpperCamelCase__      ,							UpperCamelCase__      )

       if i + 1 == timesteps.shape[0]:
         __a			=					None
       else:
         __a			=					timesteps[i + 1]

       # 2. predict previous mean of sample x_t-1
       __a			=					scheduler.step(
           UpperCamelCase__      ,							UpperCamelCase__      ,							UpperCamelCase__      ,							prev_timestep=UpperCamelCase__      ,							generator=UpperCamelCase__      ).prev_sample

       __a			=					pred_prev_sample

     __a			=					torch.sum(torch.abs(UpperCamelCase__      )      )
     __a			=					torch.mean(torch.abs(UpperCamelCase__      )      )

     assert abs(result_sum.item() - 258.204_4983      ) < 1e-2
     assert abs(result_mean.item() - 0.3_362_038      ) < 1e-3







   def 		UpperCamelCase__			(	self      )				->    Optional[Any]:
     pass







   def 		UpperCamelCase__			(	self      )				->    Any:
     pass









'''simple docstring'''


import numpy as np

import datasets


UpperCAmelCase_ 		=      "\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"

UpperCAmelCase_ 		=      "\\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"

UpperCAmelCase_ 		=      "\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    __lowercase						(  datasets.Metric ):







   def 		UpperCamelCase__			(	self      )				->    Optional[Any]:
     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      ,							UpperCamelCase      ,							UpperCamelCase      )				->    Optional[int]:
     # convert to numpy arrays
     __a			=					np.array(UpperCamelCase      )
     __a			=					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
     __a			=					X - np.mean(UpperCamelCase      )
     __a			=					np.cov(reference_distribution.T      )
     try:
       __a			=					np.linalg.inv(UpperCamelCase      )
     except np.linalg.LinAlgError:
       __a			=					np.linalg.pinv(UpperCamelCase      )
     __a			=					np.dot(UpperCamelCase      ,							UpperCamelCase      )
     __a			=					np.dot(UpperCamelCase      ,							X_minus_mu.T      ).diagonal()

     return {"mahalanobis": mahal_dist}



