






'''simple docstring'''





from sklearn.metrics import mean_squared_error

import datasets


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


_UpperCamelCase						:						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  snake_case__      (					datasets.Metric):




					def 		A      (  self  :		Union[str, Any]      )    ->							Tuple:
								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 		A      (  self  :		List[Any]      )    ->							int:
								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 		A      (  self  :		Optional[Any]				, _A  :		Optional[int]				, _A  :		List[str]				, _A  :		str=None				, _A  :		str="uniform_average"				, _A  :		int=True      )    ->							Dict:
								UpperCAmelCase_      :       str          =       mean_squared_error(
								    snake_case__				, snake_case__				, sample_weight=snake_case__				, multioutput=snake_case__				, squared=snake_case__      )

								return {"mse": mse}










"""simple docstring"""



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


_lowerCAmelCase	:	Any				=      logging.get_logger(__name__)

_lowerCAmelCase	:	Union[str, Any]				=      {
    # See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}




class 			lowerCAmelCase__			(    __magic_name__   ):
   SCREAMING_SNAKE_CASE_    ='''megatron-bert'''
   def __init__(		self      :	Tuple							,			snake_case__      :	List[Any]=2_9_0_5_6							,			snake_case__      :	Any=1_0_2_4							,			snake_case__      :	Tuple=2_4							,			snake_case__      :	Optional[Any]=1_6							,			snake_case__      :	List[str]=4_0_9_6							,			snake_case__      :	Union[str, Any]="gelu"							,			snake_case__      :	int=0.1							,			snake_case__      :	Dict=0.1							,			snake_case__      :	str=5_1_2							,			snake_case__      :	Optional[Any]=2							,			snake_case__      :	Any=0.02							,			snake_case__      :	Optional[Any]=1e-12							,			snake_case__      :	str=0							,			snake_case__      :	Optional[Any]="absolute"							,			snake_case__      :	Optional[Any]=True							,			**snake_case__      :	Any							,			):


        '''simple docstring'''
        super().__init__(pad_token_id=snake_case__							,			**snake_case__							)

        UpperCAmelCase__						:    Union[str, Any]							=							vocab_size
        UpperCAmelCase__						:    Union[str, Any]							=							hidden_size
        UpperCAmelCase__						:    Optional[Any]							=							num_hidden_layers
        UpperCAmelCase__						:    Tuple							=							num_attention_heads
        UpperCAmelCase__						:    List[Any]							=							hidden_act
        UpperCAmelCase__						:    Optional[int]							=							intermediate_size
        UpperCAmelCase__						:    Dict							=							hidden_dropout_prob
        UpperCAmelCase__						:    str							=							attention_probs_dropout_prob
        UpperCAmelCase__						:    str							=							max_position_embeddings
        UpperCAmelCase__						:    List[str]							=							type_vocab_size
        UpperCAmelCase__						:    Tuple							=							initializer_range
        UpperCAmelCase__						:    str							=							layer_norm_eps
        UpperCAmelCase__						:    List[Any]							=							position_embedding_type
        UpperCAmelCase__						:    Dict							=							use_cache


