





from scipy.stats import pearsonr

import datasets


_lowercase		:  Optional[Any]			       ='''
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.
'''


_lowercase		:  Optional[Any]			       ='''
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
'''


_lowercase		:  Union[str, Any]			       ='''
@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   __a     (		self     :     Optional[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.pearsonr.html']       ,      )





					def   __a     (		self     :     int       ,      lowerCamelCase     :     List[Any]       ,      lowerCamelCase     :     Optional[Any]       ,      lowerCamelCase     :     Optional[Any]=False						):
							if return_pvalue:
									lowerCamelCase_	:     Optional[Any] 						=      pearsonr(UpperCamelCase__       ,      UpperCamelCase__						)
									return {"pearsonr": results[0], "p-value": results[1]}
							else:
									return {"pearsonr": float(pearsonr(UpperCamelCase__       ,      UpperCamelCase__						)[0]						)}

'''simple docstring'''






import itertools
import os
import random
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
   from transformers import WhisperFeatureExtractor

if is_torch_available():
   import torch

_UpperCAmelCase    :     List[Any]         =       random.Random()





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			int   ,		__snake_case							:			Dict=1.0   ,		__snake_case							:			List[Any]=None   ,		__snake_case							:			List[str]=None				):
     if rng is None:
          _A    		=  global_rng

     _A    		=  []
     for batch_idx in range(shape[0]				):
          values.append([]				)
          for _ in range(shape[1]				):
               values[-1].append(rng.random() * scale				)

     return values







@require_torch
@require_torchaudio
class lowercase_       (		unittest.TestCase			):







   """simple docstring"""

   def __init__(      self      :			Tuple, UpperCamelCase__      :			Tuple, UpperCamelCase__      :			Union[str, Any]=7, UpperCamelCase__      :			List[str]=4_00, UpperCamelCase__      :			Any=20_00, UpperCamelCase__      :			int=10, UpperCamelCase__      :			Tuple=1_60, UpperCamelCase__      :			Tuple=8, UpperCamelCase__      :			List[str]=0.0, UpperCamelCase__      :			Optional[Any]=40_00, UpperCamelCase__      :			List[str]=False, UpperCamelCase__      :			List[Any]=True, )  ->			Dict:
        _A    		=  parent
        _A    		=  batch_size
        _A    		=  min_seq_length
        _A    		=  max_seq_length
        _A    		=  (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
        _A    		=  padding_value
        _A    		=  sampling_rate
        _A    		=  return_attention_mask
        _A    		=  do_normalize
        _A    		=  feature_size
        _A    		=  chunk_length
        _A    		=  hop_length
   def 	__UpperCAmelCase   (      self      :			Tuple     )  ->			Optional[Any]:
        return {
            "feature_size": self.feature_size,
            "hop_length": self.hop_length,
            "chunk_length": self.chunk_length,
            "padding_value": self.padding_value,
            "sampling_rate": self.sampling_rate,
            "return_attention_mask": self.return_attention_mask,
            "do_normalize": self.do_normalize,
        }

   def 	__UpperCAmelCase   (      self      :			Optional[Any], UpperCamelCase__      :			List[Any]=False, UpperCamelCase__      :			str=False     )  ->			Any:
        def _flatten(UpperCamelCase__      :			List[str]     ):
             return list(itertools.chain(*UpperCamelCase__     )     )

        if equal_length:
             _A    		=  [floats_list((self.max_seq_length, self.feature_size)     ) for _ in range(self.batch_size     )]
        else:
             # make sure that inputs increase in size
             _A    		=  [
                 floats_list((x, self.feature_size)     )
                 for x in range(self.min_seq_length, self.max_seq_length, self.seq_length_diff     )
             ]
        if numpify:
             _A    		=  [np.asarray(UpperCamelCase__     ) for x in speech_inputs]
        return speech_inputs







@require_torch
@require_torchaudio
class lowercase_       (		_UpperCamelCase    ,    unittest.TestCase			):







   """simple docstring"""

   __lowerCAmelCase         =   WhisperFeatureExtractor if is_speech_available() else None
   def 	__UpperCAmelCase   (      self      :			Dict     )  ->			Optional[Any]:
        _A    		=  WhisperFeatureExtractionTester(self     )
   def 	__UpperCAmelCase   (      self      :			List[Any]     )  ->			int:
        _A    		=  self.feature_extraction_class(**self.feat_extract_dict     )

        with tempfile.TemporaryDirectory() as tmpdirname:
             _A    		=  feat_extract_first.save_pretrained(UpperCamelCase__     )[0]
             check_json_file_has_correct_format(UpperCamelCase__     )
             _A    		=  self.feature_extraction_class.from_pretrained(UpperCamelCase__     )

        _A    		=  feat_extract_first.to_dict()
        _A    		=  feat_extract_second.to_dict()
        _A    		=  feat_extract_first.mel_filters
        _A    		=  feat_extract_second.mel_filters
        self.assertTrue(np.allclose(UpperCamelCase__, UpperCamelCase__     )     )
        self.assertEqual(UpperCamelCase__, UpperCamelCase__     )
   def 	__UpperCAmelCase   (      self      :			Optional[int]     )  ->			List[Any]:
        _A    		=  self.feature_extraction_class(**self.feat_extract_dict     )

        with tempfile.TemporaryDirectory() as tmpdirname:
             _A    		=  os.path.join(UpperCamelCase__, 'feat_extract.json'     )
             feat_extract_first.to_json_file(UpperCamelCase__     )
             _A    		=  self.feature_extraction_class.from_json_file(UpperCamelCase__     )

        _A    		=  feat_extract_first.to_dict()
        _A    		=  feat_extract_second.to_dict()
        _A    		=  feat_extract_first.mel_filters
        _A    		=  feat_extract_second.mel_filters
        self.assertTrue(np.allclose(UpperCamelCase__, UpperCamelCase__     )     )
        self.assertEqual(UpperCamelCase__, UpperCamelCase__     )
   def 	__UpperCAmelCase   (      self      :			Union[str, Any]     )  ->			List[Any]:
        # Tests that all call wrap to encode_plus and batch_encode_plus
        _A    		=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
        # create three inputs of length 800, 1000, and 1200
        _A    		=  [floats_list((1, x)     )[0] for x in range(8_00, 14_00, 2_00     )]
        _A    		=  [np.asarray(UpperCamelCase__     ) for speech_input in speech_inputs]

        # Test feature size
        _A    		=  feature_extractor(UpperCamelCase__, padding='max_length', return_tensors='np'     ).input_features
        self.assertTrue(input_features.ndim == 3     )
        self.assertTrue(input_features.shape[-1] == feature_extractor.nb_max_frames     )
        self.assertTrue(input_features.shape[-2] == feature_extractor.feature_size     )

        # Test not batched input
        _A    		=  feature_extractor(speech_inputs[0], return_tensors='np'     ).input_features
        _A    		=  feature_extractor(np_speech_inputs[0], return_tensors='np'     ).input_features
        self.assertTrue(np.allclose(UpperCamelCase__, UpperCamelCase__, atol=1e-3     )     )

        # Test batched
        _A    		=  feature_extractor(UpperCamelCase__, return_tensors='np'     ).input_features
        _A    		=  feature_extractor(UpperCamelCase__, return_tensors='np'     ).input_features
        for enc_seq_a, enc_seq_a in zip(UpperCamelCase__, UpperCamelCase__     ):
             self.assertTrue(np.allclose(UpperCamelCase__, UpperCamelCase__, atol=1e-3     )     )

        # Test 2-D numpy arrays are batched.
        _A    		=  [floats_list((1, x)     )[0] for x in (8_00, 8_00, 8_00)]
        _A    		=  np.asarray(UpperCamelCase__     )
        _A    		=  feature_extractor(UpperCamelCase__, return_tensors='np'     ).input_features
        _A    		=  feature_extractor(UpperCamelCase__, return_tensors='np'     ).input_features
        for enc_seq_a, enc_seq_a in zip(UpperCamelCase__, UpperCamelCase__     ):
             self.assertTrue(np.allclose(UpperCamelCase__, UpperCamelCase__, atol=1e-3     )     )

        # Test truncation required
        _A    		=  [floats_list((1, x)     )[0] for x in range(2_00, (feature_extractor.n_samples + 5_00), 2_00     )]
        _A    		=  [np.asarray(UpperCamelCase__     ) for speech_input in speech_inputs]

        _A    		=  [x[: feature_extractor.n_samples] for x in speech_inputs]
        _A    		=  [np.asarray(UpperCamelCase__     ) for speech_input in speech_inputs_truncated]

        _A    		=  feature_extractor(UpperCamelCase__, return_tensors='np'     ).input_features
        _A    		=  feature_extractor(UpperCamelCase__, return_tensors='np'     ).input_features
        for enc_seq_a, enc_seq_a in zip(UpperCamelCase__, UpperCamelCase__     ):
             self.assertTrue(np.allclose(UpperCamelCase__, UpperCamelCase__, atol=1e-3     )     )
   def 	__UpperCAmelCase   (      self      :			Any     )  ->			Any:
        import torch

        _A    		=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
        _A    		=  np.random.rand(1_00, 32     ).astype(np.floataa     )
        _A    		=  np_speech_inputs.tolist()

        for inputs in [py_speech_inputs, np_speech_inputs]:
             _A    		=  feature_extractor.pad([{'input_features': inputs}], return_tensors='np'     )
             self.assertTrue(np_processed.input_features.dtype == np.floataa     )
             _A    		=  feature_extractor.pad([{'input_features': inputs}], return_tensors='pt'     )
             self.assertTrue(pt_processed.input_features.dtype == torch.floataa     )
   def 	__UpperCAmelCase   (      self      :			int, UpperCamelCase__      :			int     )  ->			Optional[Any]:
        _A    		=  load_dataset('hf-internal-testing/librispeech_asr_dummy', 'clean', split='validation'     )
        # automatic decoding with librispeech
        _A    		=  ds.sort('id'     ).select(range(UpperCamelCase__     )     )[:num_samples]['audio']

        return [x["array"] for x in speech_samples]
   def 	__UpperCAmelCase   (      self      :			Dict     )  ->			List[Any]:
        # fmt: off
        _A    		=  torch.tensor(
            [
                0.1_193, -0.0_946, -0.1_098, -0.0_196, 0.0_225, -0.0_690, -0.1_736, 0.0_951,
                0.0_971, -0.0_817, -0.0_702, 0.0_162, 0.0_260, 0.0_017, -0.0_192, -0.1_678,
                0.0_709, -0.1_867, -0.0_655, -0.0_274, -0.0_234, -0.1_884, -0.0_516, -0.0_554,
                -0.0_274, -0.1_425, -0.1_423, 0.0_837, 0.0_377, -0.0_854
            ]     )
        # fmt: on

        _A    		=  self._load_datasamples(1     )
        _A    		=  WhisperFeatureExtractor()
        _A    		=  feature_extractor(UpperCamelCase__, return_tensors='pt'     ).input_features
        self.assertEqual(input_features.shape, (1, 80, 30_00)     )
        self.assertTrue(torch.allclose(input_features[0, 0, :30], UpperCamelCase__, atol=1e-4     )     )

   def 	__UpperCAmelCase   (      self      :			int     )  ->			Any:
        _A    		=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
        _A    		=  self._load_datasamples(1     )[0]
        _A    		=  ((audio - audio.min()) / (audio.max() - audio.min())) * 6_55_35  # Rescale to [0, 65535] to show issue
        _A    		=  feat_extract.zero_mean_unit_var_norm([audio], attention_mask=UpperCamelCase__     )[0]

        self.assertTrue(np.all(np.mean(UpperCamelCase__     ) < 1e-3     )     )
        self.assertTrue(np.all(np.abs(np.var(UpperCamelCase__     ) - 1     ) < 1e-3     )     )



