
'''simple docstring'''




from typing import List, Optional, Union

import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


lowercase			:				Optional[int]    		=			logging.get_logger(__name__)





class  A				(							__snake_case						):
     __magic_name__						=		['''input_features''', '''attention_mask''']





     def __init__(							self						,			SCREAMING_SNAKE_CASE=80						,			SCREAMING_SNAKE_CASE=16000						,			SCREAMING_SNAKE_CASE=80						,			SCREAMING_SNAKE_CASE=0.0						,			SCREAMING_SNAKE_CASE=True						,			SCREAMING_SNAKE_CASE=True						,			SCREAMING_SNAKE_CASE=True						,			**SCREAMING_SNAKE_CASE						,			) ->				int:






         """simple docstring"""






         super().__init__(feature_size=SCREAMING_SNAKE_CASE						,			sampling_rate=SCREAMING_SNAKE_CASE						,			padding_value=SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE	)
         A       :   Optional[int]					=						num_mel_bins
         A       :   Tuple					=						do_ceptral_normalize
         A       :   Dict					=						normalize_means
         A       :   List[Any]					=						normalize_vars
         A       :   List[str]					=						True





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			) ->				np.ndarray:






         """simple docstring"""






         A       :   List[Any]					=						waveform * (2**15)  # Kaldi compliance: 16-bit signed integers
         A       :   Any					=						torch.from_numpy(SCREAMING_SNAKE_CASE	).unsqueeze(0	)
         A       :   Any					=						ta_kaldi.fbank(SCREAMING_SNAKE_CASE						,			num_mel_bins=self.num_mel_bins						,			sample_frequency=self.sampling_rate	)
         return features.numpy()





     @staticmethod
     def 				__lowerCAmelCase							(							SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE = True						,			SCREAMING_SNAKE_CASE = True						,			SCREAMING_SNAKE_CASE = 0.0						,			) ->				np.ndarray:






         """simple docstring"""






         if normalize_means:
             A       :   Dict					=						x[:input_length].mean(axis=0	)
             A       :   Optional[Any]					=						np.subtract(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)
         if normalize_vars:
             A       :   str					=						x[:input_length].std(axis=0	)
             A       :   int					=						np.divide(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)

         if input_length < x.shape[0]:
             A       :   List[str]					=						padding_value

         # make sure array is in float32
         A       :   Tuple					=						x.astype(np.floataa	)

         return x





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE = None	) ->				List[np.ndarray]:






         """simple docstring"""






         A       :   List[Any]					=						attention_mask.sum(-1	) if attention_mask is not None else [x.shape[0] for x in input_features]
         return [
             self.utterance_cmvn(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			self.normalize_means						,			self.normalize_vars						,			self.padding_value	)
             for x, n in zip(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)
         ]





     def __call__(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE = False						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = False						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = None						,			**SCREAMING_SNAKE_CASE						,			) ->				BatchFeature:






         """simple docstring"""






         if sampling_rate is not None:
             if sampling_rate != self.sampling_rate:
                 raise ValueError(
                     F'The model corresponding to this feature extractor: {self} was trained using a sampling rate of'
                     F' {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with'
                     F' {self.sampling_rate} and not {sampling_rate}.'	)
         else:
             logger.warning(
                 '''It is strongly recommended to pass the `sampling_rate` argument to this function. '''
                 '''Failing to do so can result in silent errors that might be hard to debug.'''	)

         A       :   List[Any]					=						isinstance(SCREAMING_SNAKE_CASE						,			np.ndarray	) and len(raw_speech.shape	) > 1
         if is_batched_numpy and len(raw_speech.shape	) > 2:
             raise ValueError(F'Only mono-channel audio is supported for input to {self}'	)
         A       :   Tuple					=						is_batched_numpy or (
             isinstance(SCREAMING_SNAKE_CASE						,			(list, tuple)	) and (isinstance(raw_speech[0]						,			(np.ndarray, tuple, list)	))
         )

         if is_batched:
             A       :   Union[str, Any]					=						[np.asarray(SCREAMING_SNAKE_CASE						,			dtype=np.floataa	) for speech in raw_speech]
         elif not is_batched and not isinstance(SCREAMING_SNAKE_CASE						,			np.ndarray	):
             A       :   Union[str, Any]					=						np.asarray(SCREAMING_SNAKE_CASE						,			dtype=np.floataa	)
         elif isinstance(SCREAMING_SNAKE_CASE						,			np.ndarray	) and raw_speech.dtype is np.dtype(np.floataa	):
             A       :   Optional[int]					=						raw_speech.astype(np.floataa	)

         # always return batch
         if not is_batched:
             A       :   Any					=						[raw_speech]

         # extract fbank features
         A       :   List[str]					=						[self._extract_fbank_features(SCREAMING_SNAKE_CASE	) for waveform in raw_speech]

         # convert into correct format for padding
         A       :   str					=						BatchFeature({'''input_features''': features}	)

         A       :   Union[str, Any]					=						self.pad(
             SCREAMING_SNAKE_CASE						,			padding=SCREAMING_SNAKE_CASE						,			max_length=SCREAMING_SNAKE_CASE						,			truncation=SCREAMING_SNAKE_CASE						,			pad_to_multiple_of=SCREAMING_SNAKE_CASE						,			return_attention_mask=SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE						,			)

         # make sure list is in array format
         A       :   List[str]					=						padded_inputs.get('''input_features'''	)
         if isinstance(input_features[0]						,			SCREAMING_SNAKE_CASE	):
             A       :   str					=						[np.asarray(SCREAMING_SNAKE_CASE						,			dtype=np.floataa	) for feature in input_features]

         A       :   Union[str, Any]					=						padded_inputs.get('''attention_mask'''	)
         if attention_mask is not None:
             A       :   Union[str, Any]					=						[np.asarray(SCREAMING_SNAKE_CASE						,			dtype=np.intaa	) for array in attention_mask]

         # Utterance-level cepstral mean and variance normalization
         if self.do_ceptral_normalize:
             A       :   Dict					=						(
                 np.array(SCREAMING_SNAKE_CASE						,			dtype=np.intaa	)
                 if self._get_padding_strategies(SCREAMING_SNAKE_CASE						,			max_length=SCREAMING_SNAKE_CASE	) is not PaddingStrategy.DO_NOT_PAD
                 else None
             )
             A       :   List[Any]					=						self.normalize(
                 padded_inputs['''input_features''']						,			attention_mask=SCREAMING_SNAKE_CASE	)

         if return_tensors is not None:
             A       :   int					=						padded_inputs.convert_to_tensors(SCREAMING_SNAKE_CASE	)

         return padded_inputs






'''simple docstring'''




from scipy.stats import pearsonr

import datasets


lowercase			:				Optional[int]    		=			'\nPearson correlation coefficient and p-value for testing non-correlation.\nThe 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.\nThe 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.\n'


lowercase			:				Optional[Any]    		=			'\nArgs:\n    predictions (`list` of `int`): Predicted class labels, as returned by a model.\n    references (`list` of `int`): Ground truth labels.\n    return_pvalue (`boolean`): If `True`, returns the p-value, along with the correlation coefficient. If `False`, returns only the correlation coefficient. Defaults to `False`.\n\nReturns:\n    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.\n    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.\n\nExamples:\n\n    Example 1-A simple example using only predictions and references.\n        >>> pearsonr_metric = datasets.load_metric("pearsonr")\n        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5])\n        >>> print(round(results[\'pearsonr\'], 2))\n        -0.74\n\n    Example 2-The same as Example 1, but that also returns the `p-value`.\n        >>> pearsonr_metric = datasets.load_metric("pearsonr")\n        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5], return_pvalue=True)\n        >>> print(sorted(list(results.keys())))\n        [\'p-value\', \'pearsonr\']\n        >>> print(round(results[\'pearsonr\'], 2))\n        -0.74\n        >>> print(round(results[\'p-value\'], 2))\n        0.15\n'


lowercase			:				str    		=			'\n@article{2020SciPy-NMeth,\nauthor  = {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, Ilhan 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, Antonio H. and Pedregosa, Fabian and\n      {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},\ntitle   = {{{SciPy} 1.0: Fundamental Algorithms for Scientific\n      Computing in Python}},\njournal = {Nature Methods},\nyear    = {2020},\nvolume  = {17},\npages   = {261--272},\nadsurl  = {https://rdcu.be/b08Wh},\ndoi = {10.1038/s41592-019-0686-2},\n}\n'





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





     def 				__lowerCAmelCase							(							self	) ->				List[str]:






         """simple docstring"""






         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 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE=False	) ->				Union[str, Any]:






         """simple docstring"""






         if return_pvalue:
             A       :   Union[str, Any]					=						pearsonr(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)
             return {"pearsonr": results[0], "p-value": results[1]}
         else:
             return {"pearsonr": float(pearsonr(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)[0]	)}




