'''simple docstring'''




from collections import OrderedDict
from typing import Mapping

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


UpperCamelCase__ :			Tuple												=				logging.get_logger(__name__)

UpperCamelCase__ :			Tuple												=				{
    """camembert-base""": """https://huggingface.co/camembert-base/resolve/main/config.json""",
    """umberto-commoncrawl-cased-v1""": (
        """https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json"""
    ),
    """umberto-wikipedia-uncased-v1""": (
        """https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json"""
    ),
}






class 		_UpperCamelCase						(							lowercase_					):


			'''simple docstring'''

			_A   :	int    					=	'''camembert'''

			def __init__(							self  :				Any							,  lowerCAmelCase__  :				Optional[int]=3_0_5_2_2							,  lowerCAmelCase__  :				List[Any]=7_6_8							,  lowerCAmelCase__  :				Dict=1_2							,  lowerCAmelCase__  :				str=1_2							,  lowerCAmelCase__  :				Union[str, Any]=3_0_7_2							,  lowerCAmelCase__  :				Tuple="gelu"							,  lowerCAmelCase__  :				Optional[int]=0.1							,  lowerCAmelCase__  :				str=0.1							,  lowerCAmelCase__  :				Tuple=5_1_2							,  lowerCAmelCase__  :				List[str]=2							,  lowerCAmelCase__  :				Dict=0.02							,  lowerCAmelCase__  :				int=1E-12							,  lowerCAmelCase__  :				List[Any]=1							,  lowerCAmelCase__  :				Optional[Any]=0							,  lowerCAmelCase__  :				Optional[Any]=2							,  lowerCAmelCase__  :				str="absolute"							,  lowerCAmelCase__  :				int=True							,  lowerCAmelCase__  :				List[Any]=None							,  **lowerCAmelCase__  :				Optional[Any]							,  ):





					"""simple docstring"""






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

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






class 		_UpperCamelCase						(							lowercase_					):


			'''simple docstring'''

			@property
			def UpperCamelCase__      (							self  :				str		):





					"""simple docstring"""






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




'''simple docstring'''




from sklearn.metrics import mean_squared_error

import datasets


UpperCamelCase__ :			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}
}
'''

UpperCamelCase__ :			List[str]												=				'''\
Mean Squared Error(MSE) is the average of the square of difference between the predicted
and actual values.
'''


UpperCamelCase__ :			Dict												=				'''
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  :				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 UpperCamelCase__      (							self  :				Optional[Any]		):





					"""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 UpperCamelCase__      (							self  :				List[Any]							,  lowerCAmelCase__  :				Any							,  lowerCAmelCase__  :				str							,  lowerCAmelCase__  :				List[Any]=None							,  lowerCAmelCase__  :				Optional[int]="uniform_average"							,  lowerCAmelCase__  :				int=True		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	str					 =	mean_squared_error(
					    lowerCAmelCase__							,  lowerCAmelCase__							,  sample_weight=lowerCAmelCase__							,  multioutput=lowerCAmelCase__							,  squared=lowerCAmelCase__		)

					return {"mse": mse}