


"""simple docstring"""




from scipy.stats import spearmanr

import datasets


__magic_name__						: Optional[Any]    =				'\nThe Spearman rank-order correlation coefficient is a measure of the\nrelationship between two datasets. Like other correlation coefficients,\nthis one varies between -1 and +1 with 0 implying no correlation.\nPositive correlations imply that as data in dataset x increases, so\ndoes data in dataset y. Negative correlations imply that as x increases,\ny decreases. Correlations of -1 or +1 imply an exact monotonic relationship.\n\nUnlike the Pearson correlation, the Spearman correlation does not\nassume that both datasets are normally distributed.\n\nThe p-value roughly indicates the probability of an uncorrelated system\nproducing datasets that have a Spearman correlation at least as extreme\nas the one computed from these datasets. The p-values are not entirely\nreliable but are probably reasonable for datasets larger than 500 or so.\n'

__magic_name__						: Dict    =				'\nArgs:\n    predictions (`List[float]`): Predicted labels, as returned by a model.\n    references (`List[float]`): Ground truth labels.\n    return_pvalue (`bool`): If `True`, returns the p-value. If `False`, returns\n            only the spearmanr score. Defaults to `False`.\nReturns:\n    spearmanr (`float`): Spearman correlation coefficient.\n    p-value (`float`): p-value. **Note**: is only returned if `return_pvalue=True` is input.\nExamples:\n    Example 1:\n        >>> spearmanr_metric = datasets.load_metric("spearmanr")\n        >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5], predictions=[10, 9, 2.5, 6, 4])\n        >>> print(results)\n        {\'spearmanr\': -0.7}\n\n    Example 2:\n        >>> spearmanr_metric = datasets.load_metric("spearmanr")\n        >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5],\n        ...                                     predictions=[10, 9, 2.5, 6, 4],\n        ...                                     return_pvalue=True)\n        >>> print(results[\'spearmanr\'])\n        -0.7\n        >>> print(round(results[\'spearmanr_pvalue\'], 2))\n        0.19\n'

__magic_name__						: Optional[Any]    =				R'\\n@book{kokoska2000crc,\n  title={CRC standard probability and statistics tables and formulae},\n  author={Kokoska, Stephen and Zwillinger, Daniel},\n  year={2000},\n  publisher={Crc Press}\n}\n@article{2020SciPy-NMeth,\n  author  = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and\n            Haberland, Matt and Reddy, Tyler and Cournapeau, David and\n            Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and\n            Bright, Jonathan and {van der Walt}, St{\'e}fan J. and\n            Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and\n            Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and\n            Kern, Robert and Larson, Eric and Carey, C J and\n            Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and\n            {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and\n            Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and\n            Harris, Charles R. and Archibald, Anne M. and\n            Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and\n            {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},\n  title   = {{{SciPy} 1.0: Fundamental Algorithms for Scientific\n            Computing in Python}},\n  journal = {Nature Methods},\n  year    = {2020},\n  volume  = {17},\n  pages   = {261--272},\n  adsurl  = {https://rdcu.be/b08Wh},\n  doi     = {10.1038/s41592-019-0686-2},\n}\n'




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




							def   __a   (     self:    List[str]   ):
										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.spearmanr.html"""]     ,							)




							def   __a   (     self:    Optional[int]     ,							A_:    List[str]     ,							A_:    str     ,							A_:    str=False   ):
										__lowerCamelCase          =      spearmanr(A_     ,							A_   )
										if return_pvalue:
													return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
										else:
													return {"spearmanr": results[0]}








"""simple docstring"""




import math
from typing import Optional

import numpy as np

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


__magic_name__						: List[str]    =				logging.get_logger(__name__)

__magic_name__						: Tuple    =				{
    '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 			__snake_case   (lowerCamelCase  ):
							__a					 =       '''encodec'''




							def __init__(     self:    Union[str, Any]     ,							A_:    Union[str, Any]=[1.5, 3.0, 6.0, 12.0, 24.0]     ,							A_:    Any=2_40_00     ,							A_:    List[Any]=1     ,							A_:    Optional[Any]=False     ,							A_:    Optional[int]=None     ,							A_:    int=None     ,							A_:    List[str]=1_28     ,							A_:    Union[str, Any]=32     ,							A_:    List[str]=1     ,							A_:    Dict=[8, 5, 4, 2]     ,							A_:    List[Any]="weight_norm"     ,							A_:    Any=7     ,							A_:    List[Any]=7     ,							A_:    Tuple=3     ,							A_:    List[str]=2     ,							A_:    Optional[Any]=True     ,							A_:    Optional[int]="reflect"     ,							A_:    Dict=2     ,							A_:    Union[str, Any]=2     ,							A_:    Union[str, Any]=1.0     ,							A_:    List[str]=10_24     ,							A_:    str=None     ,							A_:    List[str]=True     ,							**A_:    int     ,							):
										__lowerCamelCase          =      target_bandwidths
										__lowerCamelCase          =      sampling_rate
										__lowerCamelCase          =      audio_channels
										__lowerCamelCase          =      normalize
										__lowerCamelCase          =      chunk_length_s
										__lowerCamelCase          =      overlap
										__lowerCamelCase          =      hidden_size
										__lowerCamelCase          =      num_filters
										__lowerCamelCase          =      num_residual_layers
										__lowerCamelCase          =      upsampling_ratios
										__lowerCamelCase          =      norm_type
										__lowerCamelCase          =      kernel_size
										__lowerCamelCase          =      last_kernel_size
										__lowerCamelCase          =      residual_kernel_size
										__lowerCamelCase          =      dilation_growth_rate
										__lowerCamelCase          =      use_causal_conv
										__lowerCamelCase          =      pad_mode
										__lowerCamelCase          =      compress
										__lowerCamelCase          =      num_lstm_layers
										__lowerCamelCase          =      trim_right_ratio
										__lowerCamelCase          =      codebook_size
										__lowerCamelCase          =      codebook_dim if codebook_dim is not None else hidden_size
										__lowerCamelCase          =      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   __a   (     self:    Union[str, Any]   ):
										if self.chunk_length_s is None:
													return None
										else:
													return int(self.chunk_length_s * self.sampling_rate   )




							@property
							def   __a   (     self:    List[Any]   ):
										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   __a   (     self:    List[Any]   ):
										__lowerCamelCase          =      np.prod(self.upsampling_ratios   )
										return math.ceil(self.sampling_rate / hop_length   )




							@property
							def   __a   (     self:    List[Any]   ):
										return int(10_00 * self.target_bandwidths[-1] // (self.frame_rate * 10)   )




