
from sklearn.metrics import mean_absolute_error

import datasets


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

lowerCamelCase_  :       Union[str, Any]					      =     """\
Mean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual
values.
"""


lowerCamelCase_  :       int					      =     """
Args:
    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Estimated target values.
    references: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Ground truth (correct) target values.
    sample_weight: array-like of shape (n_samples,), default=None
        Sample weights.
    multioutput: {\"raw_values\", \"uniform_average\"} or array-like of shape (n_outputs,), default=\"uniform_average\"
        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.

                 \"raw_values\" : Returns a full set of errors in case of multioutput input.

                 \"uniform_average\" : Errors of all outputs are averaged with uniform weight.

Returns:
    mae : mean absolute error.
        If multioutput is \"raw_values\", then mean absolute error is returned for each output separately. If multioutput is \"uniform_average\" or an ndarray of weights, then the weighted average of all output errors is returned.
        MAE output is non-negative floating point. The best value is 0.0.
Examples:

    >>> mae_metric = datasets.load_metric(\"mae\")
    >>> predictions = [2.5, 0.0, 2, 8]
    >>> references = [3, -0.5, 2, 7]
    >>> results = mae_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mae': 0.5}

    If you're using multi-dimensional lists, then set the config as follows :

    >>> mae_metric = datasets.load_metric(\"mae\", \"multilist\")
    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
    >>> references = [[0, 2], [-1, 2], [8, -5]]
    >>> results = mae_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mae': 0.75}
    >>> results = mae_metric.compute(predictions=predictions, references=references, multioutput='raw_values')
    >>> print(results)
    {'mae': array([0.5, 1. ])}
"""

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






			'''simple docstring'''

			def 					lowerCAmelCase__		(				self       :				Union[str, Any]					):
							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_absolute_error.html"""
							    ]  ,		)

			def 					lowerCAmelCase__		(				self       :				Optional[Any]					):
							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 					lowerCAmelCase__		(				self       :				List[Any]  ,		snake_case_       :				str  ,		snake_case_       :				Dict  ,		snake_case_       :				Dict=None  ,		snake_case_       :				Optional[Any]="uniform_average"					):
							UpperCamelCase_:							List[str]		=  mean_absolute_error(snake_case_  ,		snake_case_  ,		sample_weight=snake_case_  ,		multioutput=snake_case_					)

							return {"mae": mae_score}









import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest







class  __lowerCamelCase				(	__snake_case	):
      lowerCamelCase_	:      Tuple          =					(DDIMScheduler,)
      lowerCamelCase_	:      Union[str, Any]          =					(('eta', 0.0), ('num_inference_steps', 50))







      def 	lowerCAmelCase_    (     self							,  **lowerCamelCase   )						->       Dict:
        snake_case_       						=      {
            """num_train_timesteps""": 1000,
            """beta_start""": 0.0001,
            """beta_end""": 0.02,
            """beta_schedule""": """linear""",
            """clip_sample""": True,
        }

        config.update(**lowerCamelCase   )
        return config







      def 	lowerCAmelCase_    (     self							,  **lowerCamelCase   )						->       List[str]:
        snake_case_       						=      self.scheduler_classes[0]
        snake_case_       						=      self.get_scheduler_config(**lowerCamelCase   )
        snake_case_       						=      scheduler_class(**lowerCamelCase   )

        snake_case_						,				snake_case_       						=      10, 0.0

        snake_case_       						=      self.dummy_model()
        snake_case_       						=      self.dummy_sample_deter

        scheduler.set_timesteps(lowerCamelCase   )

        for t in scheduler.timesteps:
          snake_case_       						=      model(lowerCamelCase							,  lowerCamelCase   )
          snake_case_       						=      scheduler.step(lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase   ).prev_sample

        return sample







      def 	lowerCAmelCase_    (     self   )						->       List[Any]:
        for timesteps in [100, 500, 1000]:
          self.check_over_configs(num_train_timesteps=lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       Optional[int]:
        for steps_offset in [0, 1]:
          self.check_over_configs(steps_offset=lowerCamelCase   )

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







      def 	lowerCAmelCase_    (     self   )						->       Optional[Any]:
        for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]							,  [0.002, 0.02, 0.2, 2]   ):
          self.check_over_configs(beta_start=lowerCamelCase							,  beta_end=lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       Dict:
        for schedule in ["linear", "squaredcos_cap_v2"]:
          self.check_over_configs(beta_schedule=lowerCamelCase   )







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







      def 	lowerCAmelCase_    (     self   )						->       Union[str, Any]:
        for clip_sample in [True, False]:
          self.check_over_configs(clip_sample=lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       Optional[Any]:
        for timestep_spacing in ["trailing", "leading"]:
          self.check_over_configs(timestep_spacing=lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       Dict:
        for rescale_betas_zero_snr in [True, False]:
          self.check_over_configs(rescale_betas_zero_snr=lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       Optional[Any]:
        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 	lowerCAmelCase_    (     self   )						->       Dict:
        for t in [1, 10, 49]:
          self.check_over_forward(time_step=lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       List[Any]:
        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 	lowerCAmelCase_    (     self   )						->       Union[str, Any]:
        for t, eta in zip([1, 10, 49]							,  [0.0, 0.5, 1.0]   ):
          self.check_over_forward(time_step=lowerCamelCase							,  eta=lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       int:
        snake_case_       						=      self.scheduler_classes[0]
        snake_case_       						=      self.get_scheduler_config()
        snake_case_       						=      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_4771   )   ) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(980							,  960   ) - 0.3_2460   )   ) < 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_0979   )   ) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(999							,  998   ) - 0.02   )   ) < 1e-5







      def 	lowerCAmelCase_    (     self   )						->       Union[str, Any]:
        snake_case_       						=      self.full_loop()

        snake_case_       						=      torch.sum(torch.abs(lowerCamelCase   )   )
        snake_case_       						=      torch.mean(torch.abs(lowerCamelCase   )   )

        assert abs(result_sum.item() - 172.0067   ) < 1e-2
        assert abs(result_mean.item() - 0.22_3967   ) < 1e-3







      def 	lowerCAmelCase_    (     self   )						->       Optional[Any]:
        snake_case_       						=      self.full_loop(prediction_type="""v_prediction"""   )

        snake_case_       						=      torch.sum(torch.abs(lowerCamelCase   )   )
        snake_case_       						=      torch.mean(torch.abs(lowerCamelCase   )   )

        assert abs(result_sum.item() - 52.5302   ) < 1e-2
        assert abs(result_mean.item() - 0.0684   ) < 1e-3







      def 	lowerCAmelCase_    (     self   )						->       Optional[int]:
        # We specify different beta, so that the first alpha is 0.99
        snake_case_       						=      self.full_loop(set_alpha_to_one=lowerCamelCase							,  beta_start=0.01   )
        snake_case_       						=      torch.sum(torch.abs(lowerCamelCase   )   )
        snake_case_       						=      torch.mean(torch.abs(lowerCamelCase   )   )

        assert abs(result_sum.item() - 149.8295   ) < 1e-2
        assert abs(result_mean.item() - 0.1951   ) < 1e-3







      def 	lowerCAmelCase_    (     self   )						->       List[Any]:
        # We specify different beta, so that the first alpha is 0.99
        snake_case_       						=      self.full_loop(set_alpha_to_one=lowerCamelCase							,  beta_start=0.01   )
        snake_case_       						=      torch.sum(torch.abs(lowerCamelCase   )   )
        snake_case_       						=      torch.mean(torch.abs(lowerCamelCase   )   )

        assert abs(result_sum.item() - 149.0784   ) < 1e-2
        assert abs(result_mean.item() - 0.1941   ) < 1e-3