





'''simple docstring'''
from sklearn.metrics import recall_score

import datasets


lowerCamelCase			:				List[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"


lowerCamelCase			:				List[Any] 			=					"\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"


lowerCamelCase			:				int 			=					"\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 							A__   (	datasets.Metric  ):





  def 						A  (		self				:     Dict			)     ->   List[Any]:





       '''simple docstring'''






       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 						A  (		self				:     List[Any]							, _a				:     Dict							, _a				:     Union[str, Any]							, _a				:     Optional[Any]=None							, _a				:     List[str]=1							, _a				:     Any="binary"							, _a				:     Optional[int]=None							, _a				:     int="warn"							, )     ->   Optional[int]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =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}



'''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

lowerCamelCase			:				List[str] 			=					random.Random()


def 	_lowerCAmelCase (						_UpperCamelCase						:					Optional[Any]							,			_UpperCamelCase						:					List[str]=1.0							,			_UpperCamelCase						:					Optional[int]=None							,			_UpperCamelCase						:					Dict=None    )				->      Any:
     """simple docstring"""




     if rng is None:
          _SCREAMING_SNAKE_CASE							   =global_rng

     _SCREAMING_SNAKE_CASE							   =[]
     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 							A__   (	unittest.TestCase  ):





  def __init__(		self				:     List[Any]							, _a				:     Tuple							, _a				:     Dict=7							, _a				:     List[Any]=400							, _a				:     List[str]=2000							, _a				:     Optional[Any]=10							, _a				:     Dict=160							, _a				:     Tuple=8							, _a				:     Any=0.0							, _a				:     Optional[Any]=4000							, _a				:     List[Any]=False							, _a				:     Dict=True							, )     ->   Union[str, Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =parent
       _SCREAMING_SNAKE_CASE							   =batch_size
       _SCREAMING_SNAKE_CASE							   =min_seq_length
       _SCREAMING_SNAKE_CASE							   =max_seq_length
       _SCREAMING_SNAKE_CASE							   =(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
       _SCREAMING_SNAKE_CASE							   =padding_value
       _SCREAMING_SNAKE_CASE							   =sampling_rate
       _SCREAMING_SNAKE_CASE							   =return_attention_mask
       _SCREAMING_SNAKE_CASE							   =do_normalize
       _SCREAMING_SNAKE_CASE							   =feature_size
       _SCREAMING_SNAKE_CASE							   =chunk_length
       _SCREAMING_SNAKE_CASE							   =hop_length





  def 						A  (		self				:     Any			)     ->   Optional[Any]:





       '''simple docstring'''






       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 						A  (		self				:     Optional[Any]							, _a				:     Any=False							, _a				:     Union[str, Any]=False			)     ->   Optional[Any]:





       '''simple docstring'''






       def _flatten(_a				:     Union[str, Any]			):
            return list(itertools.chain(*_a			)			)

       if equal_length:
            _SCREAMING_SNAKE_CASE							   =[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							   =[
                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							   =[np.asarray(_a			) for x in speech_inputs]
       return speech_inputs







@require_torch
@require_torchaudio
class 							A__   (	A__					,       unittest.TestCase  ):
  A__					=    WhisperFeatureExtractor if is_speech_available() else None





  def 						A  (		self				:     Union[str, Any]			)     ->   Any:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =WhisperFeatureExtractionTester(self			)





  def 						A  (		self				:     str			)     ->   Tuple:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =self.feature_extraction_class(**self.feat_extract_dict			)

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

       _SCREAMING_SNAKE_CASE							   =feat_extract_first.to_dict()
       _SCREAMING_SNAKE_CASE							   =feat_extract_second.to_dict()
       _SCREAMING_SNAKE_CASE							   =feat_extract_first.mel_filters
       _SCREAMING_SNAKE_CASE							   =feat_extract_second.mel_filters
       self.assertTrue(np.allclose(_a							, _a			)			)
       self.assertEqual(_a							, _a			)





  def 						A  (		self				:     Tuple			)     ->   Any:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =self.feature_extraction_class(**self.feat_extract_dict			)

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

       _SCREAMING_SNAKE_CASE							   =feat_extract_first.to_dict()
       _SCREAMING_SNAKE_CASE							   =feat_extract_second.to_dict()
       _SCREAMING_SNAKE_CASE							   =feat_extract_first.mel_filters
       _SCREAMING_SNAKE_CASE							   =feat_extract_second.mel_filters
       self.assertTrue(np.allclose(_a							, _a			)			)
       self.assertEqual(_a							, _a			)





  def 						A  (		self				:     List[Any]			)     ->   Union[str, Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
       # create three inputs of length 800, 1000, and 1200
       _SCREAMING_SNAKE_CASE							   =[floats_list((1, x)			)[0] for x in range(800							, 1400							, 200			)]
       _SCREAMING_SNAKE_CASE							   =[np.asarray(_a			) for speech_input in speech_inputs]

       # Test feature size
       _SCREAMING_SNAKE_CASE							   =feature_extractor(_a							, 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							   =feature_extractor(speech_inputs[0]							, return_tensors='np'			).input_features
       _SCREAMING_SNAKE_CASE							   =feature_extractor(np_speech_inputs[0]							, return_tensors='np'			).input_features
       self.assertTrue(np.allclose(_a							, _a							, atol=1e-3			)			)

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

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

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

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

       _SCREAMING_SNAKE_CASE							   =feature_extractor(_a							, return_tensors='np'			).input_features
       _SCREAMING_SNAKE_CASE							   =feature_extractor(_a							, return_tensors='np'			).input_features
       for enc_seq_a, enc_seq_a in zip(_a							, _a			):
            self.assertTrue(np.allclose(_a							, _a							, atol=1e-3			)			)





  def 						A  (		self				:     Any			)     ->   List[Any]:





       '''simple docstring'''






       import torch

       _SCREAMING_SNAKE_CASE							   =self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
       _SCREAMING_SNAKE_CASE							   =np.random.rand(100							, 32			).astype(np.floataa			)
       _SCREAMING_SNAKE_CASE							   =np_speech_inputs.tolist()

       for inputs in [py_speech_inputs, np_speech_inputs]:
            _SCREAMING_SNAKE_CASE							   =feature_extractor.pad([{'input_features': inputs}]							, return_tensors='np'			)
            self.assertTrue(np_processed.input_features.dtype == np.floataa			)
            _SCREAMING_SNAKE_CASE							   =feature_extractor.pad([{'input_features': inputs}]							, return_tensors='pt'			)
            self.assertTrue(pt_processed.input_features.dtype == torch.floataa			)





  def 						A  (		self				:     Tuple							, _a				:     str			)     ->   Optional[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =load_dataset('hf-internal-testing/librispeech_asr_dummy'							, 'clean'							, split='validation'			)
       # automatic decoding with librispeech
       _SCREAMING_SNAKE_CASE							   =ds.sort('id'			).select(range(_a			)			)[:num_samples]['audio']

       return [x["array"] for x in speech_samples]





  def 						A  (		self				:     List[Any]			)     ->   Optional[int]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =torch.tensor(
           [
               0.11_93, -0.09_46, -0.10_98, -0.01_96, 0.02_25, -0.06_90, -0.17_36, 0.09_51,
               0.09_71, -0.08_17, -0.07_02, 0.01_62, 0.02_60, 0.00_17, -0.01_92, -0.16_78,
               0.07_09, -0.18_67, -0.06_55, -0.02_74, -0.02_34, -0.18_84, -0.05_16, -0.05_54,
               -0.02_74, -0.14_25, -0.14_23, 0.08_37, 0.03_77, -0.08_54
           ]			)
       # fmt: on

       _SCREAMING_SNAKE_CASE							   =self._load_datasamples(1			)
       _SCREAMING_SNAKE_CASE							   =WhisperFeatureExtractor()
       _SCREAMING_SNAKE_CASE							   =feature_extractor(_a							, return_tensors='pt'			).input_features
       self.assertEqual(input_features.shape							, (1, 80, 3000)			)
       self.assertTrue(torch.allclose(input_features[0, 0, :30]							, _a							, atol=1e-4			)			)





  def 						A  (		self				:     Union[str, Any]			)     ->   Tuple:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
       _SCREAMING_SNAKE_CASE							   =self._load_datasamples(1			)[0]
       _SCREAMING_SNAKE_CASE							   =((audio - audio.min()) / (audio.max() - audio.min())) * 6_5535  # Rescale to [0, 65535] to show issue
       _SCREAMING_SNAKE_CASE							   =feat_extract.zero_mean_unit_var_norm([audio]							, attention_mask=_a			)[0]

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