




from sklearn.metrics import mean_squared_error

import datasets


_lowerCamelCase      :						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}
}
"""

_lowerCamelCase      :						Optional[Any]							=  """\
Mean Squared Error(MSE) is the average of the square of difference between the predicted
and actual values.
"""


_lowerCamelCase      :						Tuple							=  """
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 			SCREAMING_SNAKE_CASE				(     self :      Optional[Any])		->Optional[int]:





           '''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 			SCREAMING_SNAKE_CASE				(     self :      Optional[int])		->int:





           '''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 			SCREAMING_SNAKE_CASE				(     self :      Tuple   ,					UpperCAmelCase__ :      Optional[int]   ,					UpperCAmelCase__ :      List[Any]   ,					UpperCAmelCase__ :      str=None   ,					UpperCAmelCase__ :      Optional[Any]="uniform_average"   ,					UpperCAmelCase__ :      Tuple=True)		->Optional[Any]:





           '''simple docstring'''

           A__ 					=       mean_squared_error(
               UpperCAmelCase__   ,					UpperCAmelCase__   ,					sample_weight=UpperCAmelCase__   ,					multioutput=UpperCAmelCase__   ,					squared=UpperCAmelCase__)

           return {"mse": mse}




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


__magic_name__  							=	logging.get_logger(__name__)

__magic_name__  							=	{
    '''microsoft/biogpt''': '''https://huggingface.co/microsoft/biogpt/resolve/main/config.json''',
    # See all BioGPT models at https://huggingface.co/models?filter=biogpt
}




class        _SCREAMING_SNAKE_CASE	(   __UpperCamelCase			):
      _A    :		Dict     						=  'biogpt'
      def __init__(      self		,						lowerCamelCase=4_23_84		,						lowerCamelCase=10_24		,						lowerCamelCase=24		,						lowerCamelCase=16		,						lowerCamelCase=40_96		,						lowerCamelCase="gelu"		,						lowerCamelCase=0.1		,						lowerCamelCase=0.1		,						lowerCamelCase=10_24		,						lowerCamelCase=0.0_2		,						lowerCamelCase=1e-12		,						lowerCamelCase=True		,						lowerCamelCase=True		,						lowerCamelCase=0.0		,						lowerCamelCase=0.0		,						lowerCamelCase=1		,						lowerCamelCase=0		,						lowerCamelCase=2		,						**lowerCamelCase		,						):
          snake_case__    =					vocab_size
          snake_case__    =					max_position_embeddings
          snake_case__    =					hidden_size
          snake_case__    =					num_hidden_layers
          snake_case__    =					num_attention_heads
          snake_case__    =					intermediate_size
          snake_case__    =					hidden_act
          snake_case__    =					hidden_dropout_prob
          snake_case__    =					attention_probs_dropout_prob
          snake_case__    =					initializer_range
          snake_case__    =					layer_norm_eps
          snake_case__    =					scale_embedding
          snake_case__    =					use_cache
          snake_case__    =					layerdrop
          snake_case__    =					activation_dropout
          super().__init__(pad_token_id=lowerCamelCase		,						bos_token_id=lowerCamelCase		,						eos_token_id=lowerCamelCase		,						**lowerCamelCase			)



