



'''simple docstring'''




import math
from typing import Optional

import numpy as np

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


a     :    int      						=					logging.get_logger(__name__)

a     :    Union[str, Any]      						=					{
    """facebook/encodec_24khz""": """https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json""",
    """facebook/encodec_48khz""": """https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json""",
}

class     UpperCamelCase_				(							__magic_name__    ):
	lowercase    =				'encodec'





	def __init__(						self	,	A=[1.5, 3.0, 6.0, 12.0, 24.0]	,	A=24000	,	A=1	,	A=False	,	A=None	,	A=None	,	A=128	,	A=32	,	A=1	,	A=[8, 5, 4, 2]	,	A="weight_norm"	,	A=7	,	A=7	,	A=3	,	A=2	,	A=True	,	A="reflect"	,	A=2	,	A=2	,	A=1.0	,	A=1024	,	A=None	,	A=True	,	**A	,	)    ->    Optional[Any]:
								UpperCAmelCase  :	int							  =	target_bandwidths
								UpperCAmelCase  :	str							  =	sampling_rate
								UpperCAmelCase  :	Union[str, Any]							  =	audio_channels
								UpperCAmelCase  :	Optional[int]							  =	normalize
								UpperCAmelCase  :	Optional[Any]							  =	chunk_length_s
								UpperCAmelCase  :	Any							  =	overlap
								UpperCAmelCase  :	int							  =	hidden_size
								UpperCAmelCase  :	int							  =	num_filters
								UpperCAmelCase  :	Optional[int]							  =	num_residual_layers
								UpperCAmelCase  :	Optional[Any]							  =	upsampling_ratios
								UpperCAmelCase  :	Optional[int]							  =	norm_type
								UpperCAmelCase  :	Tuple							  =	kernel_size
								UpperCAmelCase  :	str							  =	last_kernel_size
								UpperCAmelCase  :	Optional[Any]							  =	residual_kernel_size
								UpperCAmelCase  :	Optional[int]							  =	dilation_growth_rate
								UpperCAmelCase  :	List[str]							  =	use_causal_conv
								UpperCAmelCase  :	Dict							  =	pad_mode
								UpperCAmelCase  :	Optional[Any]							  =	compress
								UpperCAmelCase  :	int							  =	num_lstm_layers
								UpperCAmelCase  :	Any							  =	trim_right_ratio
								UpperCAmelCase  :	Optional[Any]							  =	codebook_size
								UpperCAmelCase  :	Any							  =	codebook_dim if codebook_dim is not None else hidden_size
								UpperCAmelCase  :	List[str]							  =	use_conv_shortcut

								if self.norm_type not in ["weight_norm", "time_group_norm"]:
															raise ValueError(
															    f'''self.norm_type must be one of `"weight_norm"`, `"time_group_norm"`), got {self.norm_type}'''					)

								super().__init__(**A					)





	@property
	def 				_lowercase(						self					)    ->    Optional[int]:
								if self.chunk_length_s is None:
															return None
								else:
															return int(self.chunk_length_s * self.sampling_rate					)





	@property
	def 				_lowercase(						self					)    ->    Optional[int]:
								if self.chunk_length_s is None or self.overlap is None:
															return None
								else:
															return max(1	,	int((1.0 - self.overlap) * self.chunk_length					)					)





	@property
	def 				_lowercase(						self					)    ->    int:
								UpperCAmelCase  :	List[str]							  =	np.prod(self.upsampling_ratios					)
								return math.ceil(self.sampling_rate / hop_length					)






	@property
	def 				_lowercase(						self					)    ->    int:
								return int(1000 * self.target_bandwidths[-1] // (self.frame_rate * 10)					)






'''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]					)}

