



'''simple docstring'''




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


a     :    int      						=					logging.get_logger(__name__)

a     :    str      						=					{
    """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     UpperCamelCase_				(							__magic_name__    ):
	lowercase    =				'speech_to_text_2'
	lowercase    =				['past_key_values']
	lowercase    =				{'num_attention_heads': 'decoder_attention_heads', 'hidden_size': 'd_model'}





	def __init__(						self	,	A=10000	,	A=6	,	A=2048	,	A=4	,	A=0.0	,	A=True	,	A="relu"	,	A=256	,	A=0.1	,	A=0.0	,	A=0.0	,	A=0.0_2	,	A=2	,	A=True	,	A=1	,	A=0	,	A=2	,	A=1024	,	**A	,	)    ->    List[str]:
								UpperCAmelCase  :	Optional[int]							  =	vocab_size
								UpperCAmelCase  :	List[str]							  =	d_model
								UpperCAmelCase  :	Any							  =	decoder_ffn_dim
								UpperCAmelCase  :	Optional[int]							  =	decoder_layers
								UpperCAmelCase  :	Any							  =	decoder_attention_heads
								UpperCAmelCase  :	Optional[Any]							  =	dropout
								UpperCAmelCase  :	int							  =	attention_dropout
								UpperCAmelCase  :	Dict							  =	activation_dropout
								UpperCAmelCase  :	List[str]							  =	activation_function
								UpperCAmelCase  :	Tuple							  =	init_std
								UpperCAmelCase  :	List[str]							  =	decoder_layerdrop
								UpperCAmelCase  :	Optional[Any]							  =	use_cache
								UpperCAmelCase  :	Tuple							  =	decoder_layers
								UpperCAmelCase  :	Dict							  =	scale_embedding  # scale factor will be sqrt(d_model) if True
								UpperCAmelCase  :	int							  =	max_target_positions

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






'''simple docstring'''




from scipy.stats import pearsonr

import datasets


a     :    str      						=					"""
Pearson correlation coefficient and p-value for testing non-correlation.
The Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies on the assumption that each dataset is normally distributed. Like other correlation coefficients, this one varies between -1 and +1 with 0 implying no correlation. Correlations of -1 or +1 imply an exact linear relationship. Positive correlations imply that as x increases, so does y. Negative correlations imply that as x increases, y decreases.
The p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets.
"""


a     :    Dict      						=					"""
Args:
    predictions (`list` of `int`): Predicted class labels, as returned by a model.
    references (`list` of `int`): Ground truth labels.
    return_pvalue (`boolean`): If `True`, returns the p-value, along with the correlation coefficient. If `False`, returns only the correlation coefficient. Defaults to `False`.

Returns:
    pearsonr (`float`): Pearson correlation coefficient. Minimum possible value is -1. Maximum possible value is 1. Values of 1 and -1 indicate exact linear positive and negative relationships, respectively. A value of 0 implies no correlation.
    p-value (`float`): P-value, which roughly indicates the probability of an The p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets. Minimum possible value is 0. Maximum possible value is 1. Higher values indicate higher probabilities.

Examples:

    Example 1-A simple example using only predictions and references.
        >>> pearsonr_metric = datasets.load_metric(\"pearsonr\")
        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5])
        >>> print(round(results['pearsonr'], 2))
        -0.74

    Example 2-The same as Example 1, but that also returns the `p-value`.
        >>> pearsonr_metric = datasets.load_metric(\"pearsonr\")
        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5], return_pvalue=True)
        >>> print(sorted(list(results.keys())))
        ['p-value', 'pearsonr']
        >>> print(round(results['pearsonr'], 2))
        -0.74
        >>> print(round(results['p-value'], 2))
        0.15
"""


a     :    Optional[int]      						=					"""
@article{2020SciPy-NMeth,
author  = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and
      Haberland, Matt and Reddy, Tyler and Cournapeau, David and
      Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and
      Bright, Jonathan and {van der Walt}, St{\'e}fan J. and
      Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and
      Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and
      Kern, Robert and Larson, Eric and Carey, C J and
      Polat, Ilhan and Feng, Yu and Moore, Eric W. and
      {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and
      Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and
      Harris, Charles R. and Archibald, Anne M. and
      Ribeiro, Antonio H. and Pedregosa, Fabian and
      {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},
title   = {{{SciPy} 1.0: Fundamental Algorithms for Scientific
      Computing in Python}},
journal = {Nature Methods},
year    = {2020},
volume  = {17},
pages   = {261--272},
adsurl  = {https://rdcu.be/b08Wh},
doi = {10.1038/s41592-019-0686-2},
}
"""

@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION   ,  _KWARGS_DESCRIPTION    )
class     UpperCamelCase_				(							datasets.Metric    ):





	def 				_lowercase(						self					)    ->    Optional[int]:
								return datasets.MetricInfo(
								    description=_DESCRIPTION	,	citation=_CITATION	,	inputs_description=_KWARGS_DESCRIPTION	,	features=datasets.Features(
								        {
								            """predictions""": datasets.Value("""float"""					),
								            """references""": datasets.Value("""float"""					),
								        }					)	,	reference_urls=["""https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.pearsonr.html"""]	,	)






	def 				_lowercase(						self	,	A	,	A	,	A=False					)    ->    int:
								if return_pvalue:
															UpperCAmelCase  :	int							  =	pearsonr(A	,	A					)
															return {"pearsonr": results[0], "p-value": results[1]}
								else:
															return {"pearsonr": float(pearsonr(A	,	A					)[0]					)}

