




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


lowerCAmelCase_						=			logging.get_logger(__name__)

lowerCAmelCase_						=			{
    'facebook/s2t-wav2vec2-large-en-de': (
        'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json'
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}
class 						_A (  _lowerCamelCase				):
  _UpperCamelCase       :      Tuple			=      '''speech_to_text_2'''
  _UpperCamelCase       :      Union[str, Any]			=      ['''past_key_values''']
  _UpperCamelCase       :      int			=      {'''num_attention_heads''': '''decoder_attention_heads''', '''hidden_size''': '''d_model'''}






  def __init__(  self		:     Union[str, Any]				,						_A		:     int=10_000				,						_A		:     int=6				,						_A		:     List[Any]=2_048				,						_A		:     Optional[int]=4				,						_A		:     str=0.0				,						_A		:     int=True				,						_A		:     List[Any]="relu"				,						_A		:     int=256				,						_A		:     Dict=0.1				,						_A		:     Tuple=0.0				,						_A		:     int=0.0				,						_A		:     Any=0.02				,						_A		:     Any=2				,						_A		:     int=True				,						_A		:     Union[str, Any]=1				,						_A		:     List[Any]=0				,						_A		:     Any=2				,						_A		:     int=1_024				,						**_A		:     Optional[Any]				,						)		->   Optional[int]:






       """simple docstring"""







       lowercase :       Union[str, Any]       =			vocab_size
       lowercase :       int       =			d_model
       lowercase :       Dict       =			decoder_ffn_dim
       lowercase :       Dict       =			decoder_layers
       lowercase :       Any       =			decoder_attention_heads
       lowercase :       Any       =			dropout
       lowercase :       Union[str, Any]       =			attention_dropout
       lowercase :       List[str]       =			activation_dropout
       lowercase :       int       =			activation_function
       lowercase :       Tuple       =			init_std
       lowercase :       Optional[Any]       =			decoder_layerdrop
       lowercase :       str       =			use_cache
       lowercase :       Optional[Any]       =			decoder_layers
       lowercase :       str       =			scale_embedding  # scale factor will be sqrt(d_model) if True
       lowercase :       Tuple       =			max_target_positions

       super().__init__(
           pad_token_id=_A				,						bos_token_id=_A				,						eos_token_id=_A				,						decoder_start_token_id=_A				,						**_A				,						)





from sklearn.metrics import mean_squared_error

import datasets


lowerCAmelCase_						=			'\\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n journal={Journal of Machine Learning Research},\n volume={12},\n pages={2825--2830},\n year={2011}\n}\n'

lowerCAmelCase_						=			'\\nMean Squared Error(MSE) is the average of the square of difference between the predicted\nand actual values.\n'


lowerCAmelCase_						=			'\nArgs:\n    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Estimated target values.\n    references: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Ground truth (correct) target values.\n    sample_weight: array-like of shape (n_samples,), default=None\n        Sample weights.\n    multioutput: {"raw_values", "uniform_average"} or array-like of shape (n_outputs,), default="uniform_average"\n        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.\n\n        "raw_values" : Returns a full set of errors in case of multioutput input.\n\n        "uniform_average" : Errors of all outputs are averaged with uniform weight.\n\n    squared : bool, default=True\n        If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.\n\nReturns:\n    mse : mean squared error.\nExamples:\n\n    >>> mse_metric = datasets.load_metric("mse")\n    >>> predictions = [2.5, 0.0, 2, 8]\n    >>> references = [3, -0.5, 2, 7]\n    >>> results = mse_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'mse\': 0.375}\n    >>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)\n    >>> print(rmse_result)\n    {\'mse\': 0.6123724356957945}\n\n    If you\'re using multi-dimensional lists, then set the config as follows :\n\n    >>> mse_metric = datasets.load_metric("mse", "multilist")\n    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]\n    >>> references = [[0, 2], [-1, 2], [8, -5]]\n    >>> results = mse_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'mse\': 0.7083333333333334}\n    >>> results = mse_metric.compute(predictions=predictions, references=references, multioutput=\'raw_values\')\n    >>> print(results) # doctest: +NORMALIZE_WHITESPACE\n    {\'mse\': array([0.41666667, 1. ])}\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION      ,       _KWARGS_DESCRIPTION				)
class 						_A (  datasets.Metric				):






  def  __a      (  self		:     List[Any]				)		->   Optional[Any]:






       """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  __a      (  self		:     List[Any]				)		->   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  __a      (  self		:     Any				,						_A		:     Dict				,						_A		:     Any				,						_A		:     Any=None				,						_A		:     Any="uniform_average"				,						_A		:     Optional[Any]=True				)		->   Dict:






       """simple docstring"""







       lowercase :       Any       =			mean_squared_error(
           _A				,						_A				,						sample_weight=_A				,						multioutput=_A				,						squared=_A				)

       return {"mse": mse}