

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig


lowerCAmelCase_       	=	{
    '''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/config.json''',
    '''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/config.json''',
    '''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/config.json''',
    '''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/config.json''',
    '''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/config.json''',
    '''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/config.json''',
    '''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/config.json''',
    '''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/config.json''',
}





class 	snake_case_   (		__A					):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE     :   int									=				"albert"







			def __init__(     self :				Tuple							,   _UpperCamelCase :				List[str]=3_0_0_0_0							,   _UpperCamelCase :				Optional[Any]=1_2_8							,   _UpperCamelCase :				List[Any]=4_0_9_6							,   _UpperCamelCase :				int=1_2							,   _UpperCamelCase :				Union[str, Any]=1							,   _UpperCamelCase :				Any=6_4							,   _UpperCamelCase :				str=1_6_3_8_4							,   _UpperCamelCase :				str=1							,   _UpperCamelCase :				Union[str, Any]="gelu_new"							,   _UpperCamelCase :				Union[str, Any]=0							,   _UpperCamelCase :				Union[str, Any]=0							,   _UpperCamelCase :				Optional[Any]=5_1_2							,   _UpperCamelCase :				Any=2							,   _UpperCamelCase :				List[str]=0.02							,   _UpperCamelCase :				Dict=1e-12							,   _UpperCamelCase :				Tuple=0.1							,   _UpperCamelCase :				Tuple="absolute"							,   _UpperCamelCase :				int=0							,   _UpperCamelCase :				int=2							,   _UpperCamelCase :				Any=3							,   **_UpperCamelCase :				Dict							,   )      ->Dict:
									super().__init__(pad_token_id=_UpperCamelCase							,   bos_token_id=_UpperCamelCase							,   eos_token_id=_UpperCamelCase							,   **_UpperCamelCase							)

									snake_case_   =	vocab_size
									snake_case_   =	embedding_size
									snake_case_   =	hidden_size
									snake_case_   =	num_hidden_layers
									snake_case_   =	num_hidden_groups
									snake_case_   =	num_attention_heads
									snake_case_   =	inner_group_num
									snake_case_   =	hidden_act
									snake_case_   =	intermediate_size
									snake_case_   =	hidden_dropout_prob
									snake_case_   =	attention_probs_dropout_prob
									snake_case_   =	max_position_embeddings
									snake_case_   =	type_vocab_size
									snake_case_   =	initializer_range
									snake_case_   =	layer_norm_eps
									snake_case_   =	classifier_dropout_prob
									snake_case_   =	position_embedding_type






class 	snake_case_   (		__A					):







			'''simple docstring'''

			@property
			def 				snake_case__(     self :				Optional[int]							)      ->Mapping[str, Mapping[int, str]]:
									if self.task == "multiple-choice":
															snake_case_   =	{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
									else:
															snake_case_   =	{0: '''batch''', 1: '''sequence'''}
									return OrderedDict(
									    [
									        ('''input_ids''', dynamic_axis),
									        ('''attention_mask''', dynamic_axis),
									        ('''token_type_ids''', dynamic_axis),
									    ]							)


from sklearn.metrics import mean_squared_error

import datasets


lowerCAmelCase_       	=	'''\
@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_       	=	'''\
Mean Squared Error(MSE) is the average of the square of difference between the predicted
and actual values.
'''


lowerCAmelCase_       	=	'''
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					):







			'''simple docstring'''

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

									return {"mse": mse}