'''simple docstring'''




from math import ceil


def        lowerCAmelCase_     (			_lowerCamelCase:     Optional[int] ,   _lowerCamelCase:     List[str]      ):
		__SCREAMING_SNAKE_CASE :	Optional[int]					 =	list(range(0 ,   _lowerCamelCase      )      )

		__SCREAMING_SNAKE_CASE :	List[Any]					 =	[item for sublist in list(device_map.values()      ) for item in sublist]

		# Duplicate check
		__SCREAMING_SNAKE_CASE :	Dict					 =	[]
		for i in device_map_blocks:
				if device_map_blocks.count(_lowerCamelCase      ) > 1 and i not in duplicate_blocks:
						duplicate_blocks.append(_lowerCamelCase      )
    # Missing blocks
		__SCREAMING_SNAKE_CASE :	List[Any]					 =	[i for i in blocks if i not in device_map_blocks]
		__SCREAMING_SNAKE_CASE :	Any					 =	[i for i in device_map_blocks if i not in blocks]

		if len(_lowerCamelCase      ) != 0:
				raise ValueError(
				    """Duplicate attention blocks specified in device_map. Attention blocks must be specified to one device."""
				    """ These attention blocks were specified more than once: """ + str(_lowerCamelCase      )      )
		if len(_lowerCamelCase      ) != 0:
				raise ValueError(
				    """There are attention blocks for this model that are not specified in the device_map. Add these attention """
				    """blocks to a device on the device_map: """ + str(_lowerCamelCase      )      )
		if len(_lowerCamelCase      ) != 0:
				raise ValueError(
				    """The device_map contains more attention blocks than this model has. Remove these from the device_map:"""
				    + str(_lowerCamelCase      )      )


def        lowerCAmelCase_     (			_lowerCamelCase:     Union[str, Any] ,   _lowerCamelCase:     Optional[int]      ):
		__SCREAMING_SNAKE_CASE :	List[str]					 =	list(range(_lowerCamelCase      )      )
		__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	int(ceil(n_layers / len(_lowerCamelCase      )      )      )
		__SCREAMING_SNAKE_CASE :	Dict					 =	[layers[i : i + n_blocks] for i in range(0 ,   _lowerCamelCase ,   _lowerCamelCase      )]

		return dict(zip(_lowerCamelCase ,   _lowerCamelCase      )      )




'''simple docstring'''




from sklearn.metrics import mean_squared_error

import datasets


UpperCamelCase__ :			Optional[Any]												=				'''\
@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}
}
'''

UpperCamelCase__ :			List[str]												=				'''\
Mean Squared Error(MSE) is the average of the square of difference between the predicted
and actual values.
'''


UpperCamelCase__ :			Dict												=				'''
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 UpperCamelCase__      (							self  :				str		):





					"""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 UpperCamelCase__      (							self  :				Optional[Any]		):





					"""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 UpperCamelCase__      (							self  :				List[Any]							,  lowerCAmelCase__  :				Any							,  lowerCAmelCase__  :				str							,  lowerCAmelCase__  :				List[Any]=None							,  lowerCAmelCase__  :				Optional[int]="uniform_average"							,  lowerCAmelCase__  :				int=True		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	str					 =	mean_squared_error(
					    lowerCAmelCase__							,  lowerCAmelCase__							,  sample_weight=lowerCAmelCase__							,  multioutput=lowerCAmelCase__							,  squared=lowerCAmelCase__		)

					return {"mse": mse}