


"""simple docstring"""







from sklearn.metrics import mean_squared_error

import datasets


a__    :							int            =					'''\
@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__    :							Dict            =					'''\
Mean Squared Error(MSE) is the average of the square of difference between the predicted
and actual values.
'''


a__    :							Any            =					'''
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      :    Optional[Any]       )	->     List[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_squared_error.html"
						    ]      ,      )




	def   UpperCAmelCase_				(			self      :    Tuple       )	->     List[str]:
						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      :    Tuple      ,      UpperCAmelCase__      :    List[Any]      ,      UpperCAmelCase__      :    Optional[int]      ,      UpperCAmelCase__      :    Any=None      ,      UpperCAmelCase__      :    List[Any]="uniform_average"      ,      UpperCAmelCase__      :    Optional[Any]=True       )	->     Any:
						__SCREAMING_SNAKE_CASE						       =    mean_squared_error(
						    UpperCAmelCase__      ,      UpperCAmelCase__      ,      sample_weight=UpperCAmelCase__      ,      multioutput=UpperCAmelCase__      ,      squared=UpperCAmelCase__       )

						return {"mse": mse}




"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


a__    :							List[str]            =					{
    '''configuration_biogpt''': ['''BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BioGptConfig'''],
    '''tokenization_biogpt''': ['''BioGptTokenizer'''],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__    :							Union[str, Any]            =					[
		    '''BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''BioGptForCausalLM''',
		    '''BioGptForTokenClassification''',
		    '''BioGptForSequenceClassification''',
		    '''BioGptModel''',
		    '''BioGptPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
		from .tokenization_biogpt import BioGptTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_biogpt import (
				    BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BioGptForCausalLM,
				    BioGptForSequenceClassification,
				    BioGptForTokenClassification,
				    BioGptModel,
				    BioGptPreTrainedModel,
				)


else:
		import sys

		a__    :							Optional[int]            =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
