from sklearn.metrics import recall_score

import datasets


lowercase_     :   Any 				=				'\nRecall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation:\nRecall = TP / (TP + FN)\nWhere TP is the true positives and FN is the false negatives.\n'


lowercase_     :   Dict 				=				'\nArgs:\n- **predictions** (`list` of `int`): The predicted labels.\n- **references** (`list` of `int`): The ground truth labels.\n- **labels** (`list` of `int`): The set of labels to include when `average` is not set to `binary`, and their order when average is `None`. Labels present in the data can be excluded in this input, for example to calculate a multiclass average ignoring a majority negative class, while labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in y_true and y_pred are used in sorted order. Defaults to None.\n- **pos_label** (`int`): The class label to use as the \'positive class\' when calculating the recall. Defaults to `1`.\n- **average** (`string`): This parameter is required for multiclass/multilabel targets. If None, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `\'binary\'`.\n    - `\'binary\'`: Only report results for the class specified by `pos_label`. This is applicable only if the target labels and predictions are binary.\n    - `\'micro\'`: Calculate metrics globally by counting the total true positives, false negatives, and false positives.\n    - `\'macro\'`: Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.\n    - `\'weighted\'`: Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `\'macro\'` to account for label imbalance. Note that it can result in an F-score that is not between precision and recall.\n    - `\'samples\'`: Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).\n- **sample_weight** (`list` of `float`): Sample weights Defaults to `None`.\n- **zero_division** (): Sets the value to return when there is a zero division. Defaults to .\n    - `\'warn\'`: If there is a zero division, the return value is `0`, but warnings are also raised.\n    - `0`: If there is a zero division, the return value is `0`.\n    - `1`: If there is a zero division, the return value is `1`.\n\nReturns:\n- **recall** (`float`, or `array` of `float`): Either the general recall score, or the recall scores for individual classes, depending on the values input to `labels` and `average`. Minimum possible value is 0. Maximum possible value is 1. A higher recall means that more of the positive examples have been labeled correctly. Therefore, a higher recall is generally considered better.\n\nExamples:\n\n    Example 1-A simple example with some errors\n        >>> recall_metric = datasets.load_metric(\'recall\')\n        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1])\n        >>> print(results)\n        {\'recall\': 0.6666666666666666}\n\n    Example 2-The same example as Example 1, but with `pos_label=0` instead of the default `pos_label=1`.\n        >>> recall_metric = datasets.load_metric(\'recall\')\n        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], pos_label=0)\n        >>> print(results)\n        {\'recall\': 0.5}\n\n    Example 3-The same example as Example 1, but with `sample_weight` included.\n        >>> recall_metric = datasets.load_metric(\'recall\')\n        >>> sample_weight = [0.9, 0.2, 0.9, 0.3, 0.8]\n        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], sample_weight=sample_weight)\n        >>> print(results)\n        {\'recall\': 0.55}\n\n    Example 4-A multiclass example, using different averages.\n        >>> recall_metric = datasets.load_metric(\'recall\')\n        >>> predictions = [0, 2, 1, 0, 0, 1]\n        >>> references = [0, 1, 2, 0, 1, 2]\n        >>> results = recall_metric.compute(predictions=predictions, references=references, average=\'macro\')\n        >>> print(results)\n        {\'recall\': 0.3333333333333333}\n        >>> results = recall_metric.compute(predictions=predictions, references=references, average=\'micro\')\n        >>> print(results)\n        {\'recall\': 0.3333333333333333}\n        >>> results = recall_metric.compute(predictions=predictions, references=references, average=\'weighted\')\n        >>> print(results)\n        {\'recall\': 0.3333333333333333}\n        >>> results = recall_metric.compute(predictions=predictions, references=references, average=None)\n        >>> print(results)\n        {\'recall\': array([1., 0., 0.])}\n'


lowercase_     :   str 				=				'\n@article{scikit-learn, title={Scikit-learn: Machine Learning in {P}ython}, author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V. and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P. and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.}, journal={Journal of Machine Learning Research}, volume={12}, pages={2825--2830}, year={2011}\n'






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION    ,     _KWARGS_DESCRIPTION    )
class 		_lowerCamelCase		(   datasets.Metric    ):
    def 						UpperCamelCase_				(  self   )       ->					Any:
     return datasets.MetricInfo(
         description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
             {
                 '''predictions''': datasets.Sequence(datasets.Value('''int32'''   )   ),
                 '''references''': datasets.Sequence(datasets.Value('''int32'''   )   ),
             }
             if self.config_name == '''multilabel'''
             else {
                 '''predictions''': datasets.Value('''int32'''   ),
                 '''references''': datasets.Value('''int32'''   ),
             }   ) , reference_urls=['''https://scikit-learn.org/stable/modules/generated/sklearn.metrics.recall_score.html'''] , )



    def 						UpperCamelCase_				(  self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase=None , lowerCAmelCase=1 , lowerCAmelCase="binary" , lowerCAmelCase=None , lowerCAmelCase="warn" , )       ->					List[str]:
     SCREAMING_SNAKE_CASE__:   Tuple=      recall_score(
         lowerCAmelCase , lowerCAmelCase , labels=lowerCAmelCase , pos_label=lowerCAmelCase , average=lowerCAmelCase , sample_weight=lowerCAmelCase , zero_division=lowerCAmelCase , )
     return {"recall": float(lowerCAmelCase   ) if score.size == 1 else score}


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

lowercase_     :   Any 				=				random.Random()







def 			A__      (	snake_case_ :		int       ,				snake_case_ :		Tuple=1.0       ,				snake_case_ :		Optional[Any]=None       ,				snake_case_ :		List[Any]=None      ):
 if rng is None:
  SCREAMING_SNAKE_CASE__:   Tuple=      global_rng

 SCREAMING_SNAKE_CASE__:   List[str]=      []
 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 		_lowerCamelCase		(   unittest.TestCase    ):
    def __init__(  self , lowerCAmelCase , lowerCAmelCase=7 , lowerCAmelCase=400 , lowerCAmelCase=2000 , lowerCAmelCase=10 , lowerCAmelCase=160 , lowerCAmelCase=8 , lowerCAmelCase=0.0 , lowerCAmelCase=4000 , lowerCAmelCase=False , lowerCAmelCase=True , )       ->					Tuple:
     SCREAMING_SNAKE_CASE__:   Any=      parent
     SCREAMING_SNAKE_CASE__:   Dict=      batch_size
     SCREAMING_SNAKE_CASE__:   List[Any]=      min_seq_length
     SCREAMING_SNAKE_CASE__:   Tuple=      max_seq_length
     SCREAMING_SNAKE_CASE__:   Optional[int]=      (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
     SCREAMING_SNAKE_CASE__:   str=      padding_value
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      sampling_rate
     SCREAMING_SNAKE_CASE__:   List[str]=      return_attention_mask
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      do_normalize
     SCREAMING_SNAKE_CASE__:   Optional[int]=      feature_size
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      chunk_length
     SCREAMING_SNAKE_CASE__:   List[Any]=      hop_length
    def 						UpperCamelCase_				(  self   )       ->					str:
     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 , lowerCAmelCase=False , lowerCAmelCase=False   )       ->					Optional[Any]:
     def _flatten(lowerCAmelCase   ):
      return list(itertools.chain(*lowerCAmelCase   )   )

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






@require_torch
@require_torchaudio
class 		_lowerCamelCase		(   UpperCamelCase_    ,     unittest.TestCase    ):
    __a  =      WhisperFeatureExtractor if is_speech_available() else None
    def 						UpperCamelCase_				(  self   )       ->					Dict:
     SCREAMING_SNAKE_CASE__:   Any=      WhisperFeatureExtractionTester(self   )
    def 						UpperCamelCase_				(  self   )       ->					int:
     SCREAMING_SNAKE_CASE__:   List[str]=      self.feature_extraction_class(**self.feat_extract_dict   )

     with tempfile.TemporaryDirectory() as tmpdirname:
      SCREAMING_SNAKE_CASE__:   List[str]=      feat_extract_first.save_pretrained(lowerCAmelCase   )[0]
      check_json_file_has_correct_format(lowerCAmelCase   )
      SCREAMING_SNAKE_CASE__:   str=      self.feature_extraction_class.from_pretrained(lowerCAmelCase   )

     SCREAMING_SNAKE_CASE__:   int=      feat_extract_first.to_dict()
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      feat_extract_second.to_dict()
     SCREAMING_SNAKE_CASE__:   int=      feat_extract_first.mel_filters
     SCREAMING_SNAKE_CASE__:   Any=      feat_extract_second.mel_filters
     self.assertTrue(np.allclose(lowerCAmelCase , lowerCAmelCase   )   )
     self.assertEqual(lowerCAmelCase , lowerCAmelCase   )
    def 						UpperCamelCase_				(  self   )       ->					Tuple:
     SCREAMING_SNAKE_CASE__:   List[Any]=      self.feature_extraction_class(**self.feat_extract_dict   )

     with tempfile.TemporaryDirectory() as tmpdirname:
      SCREAMING_SNAKE_CASE__:   List[str]=      os.path.join(lowerCAmelCase , '''feat_extract.json'''   )
      feat_extract_first.to_json_file(lowerCAmelCase   )
      SCREAMING_SNAKE_CASE__:   Tuple=      self.feature_extraction_class.from_json_file(lowerCAmelCase   )

     SCREAMING_SNAKE_CASE__:   Tuple=      feat_extract_first.to_dict()
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      feat_extract_second.to_dict()
     SCREAMING_SNAKE_CASE__:   List[str]=      feat_extract_first.mel_filters
     SCREAMING_SNAKE_CASE__:   Optional[int]=      feat_extract_second.mel_filters
     self.assertTrue(np.allclose(lowerCAmelCase , lowerCAmelCase   )   )
     self.assertEqual(lowerCAmelCase , lowerCAmelCase   )
    def 						UpperCamelCase_				(  self   )       ->					Tuple:
     # Tests that all call wrap to encode_plus and batch_encode_plus
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
     # create three inputs of length 800, 1000, and 1200
     SCREAMING_SNAKE_CASE__:   str=      [floats_list((1, x)   )[0] for x in range(800 , 1400 , 200   )]
     SCREAMING_SNAKE_CASE__:   Tuple=      [np.asarray(lowerCAmelCase   ) for speech_input in speech_inputs]

     # Test feature size
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      feature_extractor(lowerCAmelCase , 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
     SCREAMING_SNAKE_CASE__:   List[str]=      feature_extractor(speech_inputs[0] , return_tensors='''np'''   ).input_features
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      feature_extractor(np_speech_inputs[0] , return_tensors='''np'''   ).input_features
     self.assertTrue(np.allclose(lowerCAmelCase , lowerCAmelCase , atol=1e-3   )   )

     # Test batched
     SCREAMING_SNAKE_CASE__:   Optional[int]=      feature_extractor(lowerCAmelCase , return_tensors='''np'''   ).input_features
     SCREAMING_SNAKE_CASE__:   List[Any]=      feature_extractor(lowerCAmelCase , return_tensors='''np'''   ).input_features
     for enc_seq_a, enc_seq_a in zip(lowerCAmelCase , lowerCAmelCase   ):
      self.assertTrue(np.allclose(lowerCAmelCase , lowerCAmelCase , atol=1e-3   )   )

     # Test 2-D numpy arrays are batched.
     SCREAMING_SNAKE_CASE__:   Optional[int]=      [floats_list((1, x)   )[0] for x in (800, 800, 800)]
     SCREAMING_SNAKE_CASE__:   Any=      np.asarray(lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   List[str]=      feature_extractor(lowerCAmelCase , return_tensors='''np'''   ).input_features
     SCREAMING_SNAKE_CASE__:   List[Any]=      feature_extractor(lowerCAmelCase , return_tensors='''np'''   ).input_features
     for enc_seq_a, enc_seq_a in zip(lowerCAmelCase , lowerCAmelCase   ):
      self.assertTrue(np.allclose(lowerCAmelCase , lowerCAmelCase , atol=1e-3   )   )

     # Test truncation required
     SCREAMING_SNAKE_CASE__:   str=      [floats_list((1, x)   )[0] for x in range(200 , (feature_extractor.n_samples + 500) , 200   )]
     SCREAMING_SNAKE_CASE__:   str=      [np.asarray(lowerCAmelCase   ) for speech_input in speech_inputs]

     SCREAMING_SNAKE_CASE__:   str=      [x[: feature_extractor.n_samples] for x in speech_inputs]
     SCREAMING_SNAKE_CASE__:   int=      [np.asarray(lowerCAmelCase   ) for speech_input in speech_inputs_truncated]

     SCREAMING_SNAKE_CASE__:   Dict=      feature_extractor(lowerCAmelCase , return_tensors='''np'''   ).input_features
     SCREAMING_SNAKE_CASE__:   List[str]=      feature_extractor(lowerCAmelCase , return_tensors='''np'''   ).input_features
     for enc_seq_a, enc_seq_a in zip(lowerCAmelCase , lowerCAmelCase   ):
      self.assertTrue(np.allclose(lowerCAmelCase , lowerCAmelCase , atol=1e-3   )   )
    def 						UpperCamelCase_				(  self   )       ->					Dict:
     import torch

     SCREAMING_SNAKE_CASE__:   List[Any]=      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
     SCREAMING_SNAKE_CASE__:   Dict=      np.random.rand(100 , 32   ).astype(np.floataa   )
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      np_speech_inputs.tolist()

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

     return [x["array"] for x in speech_samples]
    def 						UpperCamelCase_				(  self   )       ->					int:
     # fmt: off
     SCREAMING_SNAKE_CASE__:   Dict=      torch.tensor(
         [
             0.1193, -0.0946, -0.1098, -0.0196, 0.0225, -0.0690, -0.1736, 0.0951,
             0.0971, -0.0817, -0.0702, 0.0162, 0.0260, 0.0017, -0.0192, -0.1678,
             0.0709, -0.1867, -0.0655, -0.0274, -0.0234, -0.1884, -0.0516, -0.0554,
             -0.0274, -0.1425, -0.1423, 0.0837, 0.0377, -0.0854
         ]   )
     # fmt: on

     SCREAMING_SNAKE_CASE__:   str=      self._load_datasamples(1   )
     SCREAMING_SNAKE_CASE__:   Dict=      WhisperFeatureExtractor()
     SCREAMING_SNAKE_CASE__:   Optional[int]=      feature_extractor(lowerCAmelCase , return_tensors='''pt'''   ).input_features
     self.assertEqual(input_features.shape , (1, 80, 3000)   )
     self.assertTrue(torch.allclose(input_features[0, 0, :30] , lowerCAmelCase , atol=1e-4   )   )



    def 						UpperCamelCase_				(  self   )       ->					Optional[Any]:
     SCREAMING_SNAKE_CASE__:   List[str]=      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
     SCREAMING_SNAKE_CASE__:   Dict=      self._load_datasamples(1   )[0]
     SCREAMING_SNAKE_CASE__:   str=      ((audio - audio.min()) / (audio.max() - audio.min())) * 65535  # Rescale to [0, 65535] to show issue
     SCREAMING_SNAKE_CASE__:   Optional[int]=      feat_extract.zero_mean_unit_var_norm([audio] , attention_mask=lowerCAmelCase   )[0]

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

