





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


SCREAMING_SNAKE_CASE__							:      Optional[Any]         =    logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__							:      str         =    {
    """andreasmadsen/efficient_mlm_m0.40""": (
        """https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json"""
    ),
}







class 	__lowerCAmelCase      (       _UpperCamelCase	):
   _UpperCamelCase			:    List[str]  		=					"""roberta-prelayernorm"""





   def __init__(			self       ,      snake_case=50_265       ,      snake_case=768       ,      snake_case=12       ,      snake_case=12       ,      snake_case=3_072       ,      snake_case="gelu"       ,      snake_case=0.1       ,      snake_case=0.1       ,      snake_case=512       ,      snake_case=2       ,      snake_case=0.02       ,      snake_case=1E-12       ,      snake_case=1       ,      snake_case=0       ,      snake_case=2       ,      snake_case="absolute"       ,      snake_case=True       ,      snake_case=None       ,      **snake_case       ,      )					->	Tuple:
      """simple docstring"""





      super().__init__(pad_token_id=snake_case       ,      bos_token_id=snake_case       ,      eos_token_id=snake_case       ,      **snake_case   )

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







class 	__lowerCAmelCase      (       _UpperCamelCase	):





   @property
   def 						_snake_case			(			self   )					->	Mapping[str, Mapping[int, str]]:
      """simple docstring"""





      if self.task == "multiple-choice":
         a__						:    Optional[int]   		=	{0: "batch", 1: "choice", 2: "sequence"}
      else:
         a__						:    Optional[Any]   		=	{0: "batch", 1: "sequence"}
      return OrderedDict(
          [
              ("input_ids", dynamic_axis),
              ("attention_mask", dynamic_axis),
          ]   )








from sklearn.metrics import mean_squared_error

import datasets


SCREAMING_SNAKE_CASE__							:      List[str]         =    """\
@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}
}
"""

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


SCREAMING_SNAKE_CASE__							:      Optional[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 	__lowerCAmelCase      (       datasets.Metric	):





   def 						_snake_case			(			self   )					->	List[str]:
      """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 						_snake_case			(			self   )					->	Optional[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 						_snake_case			(			self       ,      snake_case       ,      snake_case       ,      snake_case=None       ,      snake_case="uniform_average"       ,      snake_case=True   )					->	Optional[Any]:
      """simple docstring"""





      a__						:    List[str]   		=	mean_squared_error(
          snake_case       ,      snake_case       ,      sample_weight=snake_case       ,      multioutput=snake_case       ,      squared=snake_case   )

      return {"mse": mse}

