
'''simple docstring'''



from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

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




@dataclass
class     UpperCAmelCase_	(						_SCREAMING_SNAKE_CASE					):






						'''simple docstring'''






						_lowercase						: torch.FloatTensor
						_lowercase						: torch.FloatTensor
						_lowercase						: Optional[torch.FloatTensor]										=  None






class     UpperCAmelCase_	(						_SCREAMING_SNAKE_CASE			,				_SCREAMING_SNAKE_CASE					):






						'''simple docstring'''






						_lowercase						: Optional[int]										=  2




						@register_to_config
						def __init__(   self       ,  _lowercase = 0.02       ,  _lowercase = 100       ,  _lowercase = 1.007       ,  _lowercase = 80       ,  _lowercase = 0.05       ,  _lowercase = 50       ,  ):

												"""simple docstring"""



												_lowerCAmelCase    		=					sigma_max

												# setable values
												_lowerCAmelCase    		=					None
												_lowerCAmelCase    		=					None
												_lowerCAmelCase    		=					None  # sigma(t_i)




						def    _lowercase      (   self       ,  _lowercase       ,  _lowercase = None ):

												"""simple docstring"""



												return sample




						def    _lowercase      (   self       ,  _lowercase       ,  _lowercase = None ):

												"""simple docstring"""



												_lowerCAmelCase    		=					num_inference_steps
												_lowerCAmelCase    		=					np.arange(0       ,  self.num_inference_steps )[::-1].copy()
												_lowerCAmelCase    		=					torch.from_numpy(_lowercase ).to(_lowercase )
												_lowerCAmelCase    		=					[
												    (
												        self.config.sigma_max**2
												        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
												    )
												    for i in self.timesteps
												]
												_lowerCAmelCase    		=					torch.tensor(_lowercase       ,  dtype=torch.floataa       ,  device=_lowercase )




						def    _lowercase      (   self       ,  _lowercase       ,  _lowercase       ,  _lowercase = None ):

												"""simple docstring"""



												if self.config.s_min <= sigma <= self.config.s_max:
																		_lowerCAmelCase    		=					min(self.config.s_churn / self.num_inference_steps       ,  2**0.5 - 1 )
												else:
																		_lowerCAmelCase    		=					0

												# sample eps ~ N(0, S_noise^2 * I)
												_lowerCAmelCase    		=					self.config.s_noise * randn_tensor(sample.shape       ,  generator=_lowercase ).to(sample.device )
												_lowerCAmelCase    		=					sigma + gamma * sigma
												_lowerCAmelCase    		=					sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

												return sample_hat, sigma_hat




						def    _lowercase      (   self       ,  _lowercase       ,  _lowercase       ,  _lowercase       ,  _lowercase       ,  _lowercase = True       ,  ):

												"""simple docstring"""



												_lowerCAmelCase    		=					sample_hat + sigma_hat * model_output
												_lowerCAmelCase    		=					(sample_hat - pred_original_sample) / sigma_hat
												_lowerCAmelCase    		=					sample_hat + (sigma_prev - sigma_hat) * derivative

												if not return_dict:
																		return (sample_prev, derivative)

												return KarrasVeOutput(
												    prev_sample=_lowercase       ,  derivative=_lowercase       ,  pred_original_sample=_lowercase )




						def    _lowercase      (   self       ,  _lowercase       ,  _lowercase       ,  _lowercase       ,  _lowercase       ,  _lowercase       ,  _lowercase       ,  _lowercase = True       ,  ):

												"""simple docstring"""



												_lowerCAmelCase    		=					sample_prev + sigma_prev * model_output
												_lowerCAmelCase    		=					(sample_prev - pred_original_sample) / sigma_prev
												_lowerCAmelCase    		=					sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

												if not return_dict:
																		return (sample_prev, derivative)

												return KarrasVeOutput(
												    prev_sample=_lowercase       ,  derivative=_lowercase       ,  pred_original_sample=_lowercase )




						def    _lowercase      (   self       ,  _lowercase       ,  _lowercase       ,  _lowercase ):

												"""simple docstring"""



												raise NotImplementedError()


'''simple docstring'''



from sklearn.metrics import mean_squared_error

import datasets


_lowercase          =						"""\
@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}
}
"""

_lowercase          =						"""\
Mean Squared Error(MSE) is the average of the square of difference between the predicted
and actual values.
"""


_lowercase          =						"""
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.

    squared : bool, default=True
        If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.

Returns:
    mse : mean squared error.
Examples:

    >>> mse_metric = datasets.load_metric(\"mse\")
    >>> predictions = [2.5, 0.0, 2, 8]
    >>> references = [3, -0.5, 2, 7]
    >>> results = mse_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mse': 0.375}
    >>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)
    >>> print(rmse_result)
    {'mse': 0.6123724356957945}

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

    >>> mse_metric = datasets.load_metric(\"mse\", \"multilist\")
    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
    >>> references = [[0, 2], [-1, 2], [8, -5]]
    >>> results = mse_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mse': 0.7083333333333334}
    >>> results = mse_metric.compute(predictions=predictions, references=references, multioutput='raw_values')
    >>> print(results) # doctest: +NORMALIZE_WHITESPACE
    {'mse': array([0.41666667, 1. ])}
"""




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






						'''simple docstring'''






						def    _lowercase      (   self ):

												"""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 ):

												"""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       ,  _lowercase       ,  _lowercase       ,  _lowercase=None       ,  _lowercase="uniform_average"       ,  _lowercase=True ):

												"""simple docstring"""



												_lowerCAmelCase    		=					mean_squared_error(
												    _lowercase       ,  _lowercase       ,  sample_weight=_lowercase       ,  multioutput=_lowercase       ,  squared=_lowercase )

												return {"mse": mse}
