



'''simple docstring'''




from ...configuration_utils import PretrainedConfig
from ...utils import logging


a     :    List[str]      						=					logging.get_logger(__name__)

a     :    Optional[Any]      						=					{
    """caidas/swin2sr-classicalsr-x2-64""": (
        """https://huggingface.co/caidas/swin2sr-classicalsr-x2-64/resolve/main/config.json"""
    ),
}

class     UpperCamelCase_				(							__magic_name__    ):
	lowercase    =				'swin2sr'

	lowercase    =				{
	    'hidden_size': 'embed_dim',
	    'num_attention_heads': 'num_heads',
	    'num_hidden_layers': 'num_layers',
	}





	def __init__(						self	,	A=64	,	A=1	,	A=3	,	A=180	,	A=[6, 6, 6, 6, 6, 6]	,	A=[6, 6, 6, 6, 6, 6]	,	A=8	,	A=2.0	,	A=True	,	A=0.0	,	A=0.0	,	A=0.1	,	A="gelu"	,	A=False	,	A=0.0_2	,	A=1e-5	,	A=2	,	A=1.0	,	A="1conv"	,	A="pixelshuffle"	,	**A	,	)    ->    List[str]:
								super().__init__(**A					)

								UpperCAmelCase  :	List[str]							  =	image_size
								UpperCAmelCase  :	Dict							  =	patch_size
								UpperCAmelCase  :	List[Any]							  =	num_channels
								UpperCAmelCase  :	Union[str, Any]							  =	embed_dim
								UpperCAmelCase  :	Dict							  =	depths
								UpperCAmelCase  :	Dict							  =	len(A					)
								UpperCAmelCase  :	Dict							  =	num_heads
								UpperCAmelCase  :	str							  =	window_size
								UpperCAmelCase  :	Tuple							  =	mlp_ratio
								UpperCAmelCase  :	List[str]							  =	qkv_bias
								UpperCAmelCase  :	Any							  =	hidden_dropout_prob
								UpperCAmelCase  :	Union[str, Any]							  =	attention_probs_dropout_prob
								UpperCAmelCase  :	List[str]							  =	drop_path_rate
								UpperCAmelCase  :	Tuple							  =	hidden_act
								UpperCAmelCase  :	List[str]							  =	use_absolute_embeddings
								UpperCAmelCase  :	List[str]							  =	layer_norm_eps
								UpperCAmelCase  :	Dict							  =	initializer_range
								UpperCAmelCase  :	Any							  =	upscale
								UpperCAmelCase  :	Tuple							  =	img_range
								UpperCAmelCase  :	Tuple							  =	resi_connection
								UpperCAmelCase  :	Tuple							  =	upsampler






'''simple docstring'''




from sklearn.metrics import mean_squared_error

import datasets


a     :    Union[str, 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}
}
"""

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


a     :    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.

    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    ):





	def 				_lowercase(						self					)    ->    List[str]:
								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					)    ->    List[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 				_lowercase(						self	,	A	,	A	,	A=None	,	A="uniform_average"	,	A=True					)    ->    List[Any]:
								UpperCAmelCase  :	List[Any]							  =	mean_squared_error(
								    A	,	A	,	sample_weight=A	,	multioutput=A	,	squared=A					)

								return {"mse": mse}

