


from scipy.stats import spearmanr

import datasets


_snake_case					:		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"

_snake_case					:		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"

_snake_case					:		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     a		(datasets.Metric				):




							"""simple docstring"""


							def    __snake_case (							self	:  Optional[int]    )     ->   Any:
														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    __snake_case (							self	:  Optional[Any]   ,     lowerCamelCase	:  Optional[int]   ,     lowerCamelCase	:  int   ,     lowerCamelCase	:  Tuple=False    )     ->   Dict:
														__snake_case		:      Optional[int]										=       spearmanr(lowerCamelCase   ,     lowerCamelCase    )
														if return_pvalue:
																					return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
														else:
																					return {"spearmanr": results[0]}





import math
from typing import Optional

import numpy as np

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


_snake_case					:		Union[str, Any]      =    logging.get_logger(__name__)

_snake_case					:		List[str]      =    {
    "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     a		(_lowerCAmelCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							Tuple					  =  "encodec"

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

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

							@property
							def    __snake_case (							self	:  Union[str, Any]    )     ->   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    __snake_case (							self	:  Optional[Any]    )     ->   int:
														__snake_case		:      Union[str, Any]										=       np.prod(self.upsampling_ratios    )
														return math.ceil(self.sampling_rate / hop_length    )






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

