




import math

import tensorflow as tf
from packaging import version







def      lowerCamelCase_						(			UpperCamelCase__							: Any )	->			List[str]:





				"""simple docstring"""


				__lowerCamelCase									=       tf.convert_to_tensor(UpperCamelCase__ )
				__lowerCamelCase									=       0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0 )					,	x.dtype ) ))

				return x * cdf







def      lowerCamelCase_						(			UpperCamelCase__							: Optional[int] )	->			Union[str, Any]:





				"""simple docstring"""


				__lowerCamelCase									=       tf.convert_to_tensor(UpperCamelCase__ )
				__lowerCamelCase									=       tf.cast(math.pi					,	x.dtype )
				__lowerCamelCase									=       tf.cast(0.04_47_15					,	x.dtype )
				__lowerCamelCase									=       0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi ) * (x + coeff * tf.pow(UpperCamelCase__					,	3 )) ))

				return x * cdf







def      lowerCamelCase_						(			UpperCamelCase__							: Optional[Any] )	->			Optional[Any]:





				"""simple docstring"""


				__lowerCamelCase									=       tf.convert_to_tensor(UpperCamelCase__ )

				return x * tf.tanh(tf.math.softplus(UpperCamelCase__ ) )







def      lowerCamelCase_						(			UpperCamelCase__							: int )	->			Optional[int]:





				"""simple docstring"""


				__lowerCamelCase									=       tf.convert_to_tensor(UpperCamelCase__ )
				__lowerCamelCase									=       tf.cast(0.04_47_15					,	x.dtype )
				__lowerCamelCase									=       tf.cast(0.79_78_84_56_08					,	x.dtype )

				return 0.5 * x * (1.0 + tf.tanh(x * coeffa * (1.0 + coeffa * x * x) ))







def      lowerCamelCase_						(			UpperCamelCase__							: Optional[Any] )	->			List[str]:





				"""simple docstring"""


				__lowerCamelCase									=       tf.convert_to_tensor(UpperCamelCase__ )
				__lowerCamelCase									=       tf.cast(1.7_02					,	x.dtype )
				return x * tf.math.sigmoid(coeff * x )







def      lowerCamelCase_						(			UpperCamelCase__							: str )	->			Tuple:





				"""simple docstring"""


				return tf.clip_by_value(_gelu(UpperCamelCase__ )					,	-10					,	10 )







def      lowerCamelCase_						(			UpperCamelCase__							: str					,	UpperCamelCase__							: List[Any]=-1 )	->			Tuple:





				"""simple docstring"""


				__lowerCamelCase			,		__lowerCamelCase									=       tf.split(UpperCamelCase__					,	2					,	axis=UpperCamelCase__ )
				return a * tf.math.sigmoid(UpperCamelCase__ )


if version.parse(tf.version.VERSION) >= version.parse("2.4"):







					def      lowerCamelCase_						(			UpperCamelCase__							: int )	->			int:





									"""simple docstring"""


									return tf.keras.activations.gelu(UpperCamelCase__					,	approximate=UpperCamelCase__ )

					__A								=       tf.keras.activations.gelu


					__A								=       approximate_gelu_wrap
else:
					__A								=       _gelu
					__A								=       _gelu_new


__A								=       {
    "gelu": gelu,
    "gelu_10": gelu_aa,
    "gelu_fast": gelu_fast,
    "gelu_new": gelu_new,
    "glu": glu,
    "mish": mish,
    "quick_gelu": quick_gelu,
    "relu": tf.keras.activations.relu,
    "sigmoid": tf.keras.activations.sigmoid,
    "silu": tf.keras.activations.swish,
    "swish": tf.keras.activations.swish,
    "tanh": tf.keras.activations.tanh,
}







def      lowerCamelCase_						(			UpperCamelCase__							: Union[str, Any] )	->			int:





				"""simple docstring"""


				if activation_string in ACTaFN:
								return ACTaFN[activation_string]
				else:
								raise KeyError(F"""function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys() )}""" )








from sklearn.metrics import mean_squared_error

import datasets


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

__A								=       "\\nMean Squared Error(MSE) is the average of the square of difference between the predicted\nand actual values.\n"


__A								=       "\nArgs:\n    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Estimated target values.\n    references: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Ground truth (correct) target values.\n    sample_weight: array-like of shape (n_samples,), default=None\n        Sample weights.\n    multioutput: {\"raw_values\", \"uniform_average\"} or array-like of shape (n_outputs,), default=\"uniform_average\"\n        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.\n\n        \"raw_values\" : Returns a full set of errors in case of multioutput input.\n\n        \"uniform_average\" : Errors of all outputs are averaged with uniform weight.\n\n    squared : bool, default=True\n        If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.\n\nReturns:\n    mse : mean squared error.\nExamples:\n\n    >>> mse_metric = datasets.load_metric(\"mse\")\n    >>> predictions = [2.5, 0.0, 2, 8]\n    >>> references = [3, -0.5, 2, 7]\n    >>> results = mse_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'mse': 0.375}\n    >>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)\n    >>> print(rmse_result)\n    {'mse': 0.6123724356957945}\n\n    If you're using multi-dimensional lists, then set the config as follows :\n\n    >>> mse_metric = datasets.load_metric(\"mse\", \"multilist\")\n    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]\n    >>> references = [[0, 2], [-1, 2], [8, -5]]\n    >>> results = mse_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'mse': 0.7083333333333334}\n    >>> results = mse_metric.compute(predictions=predictions, references=references, multioutput='raw_values')\n    >>> print(results) # doctest: +NORMALIZE_WHITESPACE\n    {'mse': array([0.41666667, 1. ])}\n"







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

			"""simple docstring"""







			def  lowercase_       ( self				)       -> Union[str, Any]:



							'''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				)       -> Tuple:



							'''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     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__=None     ,		lowerCamelCase__="uniform_average"     ,		lowerCamelCase__=True				)       -> List[Any]:



							'''simple docstring'''
							__lowerCamelCase									=       mean_squared_error(
							    lowerCamelCase__     ,		lowerCamelCase__     ,		sample_weight=lowerCamelCase__     ,		multioutput=lowerCamelCase__     ,		squared=lowerCamelCase__				)

							return {"mse": mse}


