

'''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 TensorType, logging


snake_case_					  =	logging.get_logger(__name__)
class      SCREAMING_SNAKE_CASE__      (    _UpperCAmelCase				):
    _A         =							["input_values", "attention_mask"]




    def __init__( self    ,					lowercase__=1    ,					lowercase__=1_6000    ,					lowercase__=128    ,					lowercase__=1024    ,					lowercase__=0.0    ,					lowercase__=True    ,					lowercase__=-4.2677393    ,					lowercase__=4.5689974    ,					lowercase__=False    ,					**lowercase__    ,					):






          """simple docstring"""






          super().__init__(feature_size=lowercase__    ,					sampling_rate=lowercase__    ,					padding_value=lowercase__    ,					**lowercase__      )
          SCREAMING_SNAKE_CASE_				:      List[Any]					=	num_mel_bins
          SCREAMING_SNAKE_CASE_				:      Any					=	max_length
          SCREAMING_SNAKE_CASE_				:      int					=	do_normalize
          SCREAMING_SNAKE_CASE_				:      Any					=	mean
          SCREAMING_SNAKE_CASE_				:      str					=	std
          SCREAMING_SNAKE_CASE_				:      List[Any]					=	return_attention_mask




    def   __lowerCamelCase				( self    ,					lowercase__    ,					lowercase__    ,					):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      str					=	torch.from_numpy(lowercase__      ).unsqueeze(0      )
          SCREAMING_SNAKE_CASE_				:      Optional[int]					=	ta_kaldi.fbank(
              lowercase__    ,					htk_compat=lowercase__    ,					sample_frequency=self.sampling_rate    ,					use_energy=lowercase__    ,					window_type="hanning"    ,					num_mel_bins=self.num_mel_bins    ,					dither=0.0    ,					frame_shift=10    ,					)

          SCREAMING_SNAKE_CASE_				:      Optional[int]					=	fbank.shape[0]
          SCREAMING_SNAKE_CASE_				:      List[str]					=	max_length - n_frames

          # pad or truncate, depending on difference
          if difference > 0:
                SCREAMING_SNAKE_CASE_				:      Dict					=	torch.nn.ZeroPadad((0, 0, 0, difference)      )
                SCREAMING_SNAKE_CASE_				:      int					=	pad_module(lowercase__      )
          elif difference < 0:
                SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	fbank[0:max_length, :]

          SCREAMING_SNAKE_CASE_				:      List[Any]					=	fbank.numpy()

          return fbank




    def   __lowerCamelCase				( self    ,					lowercase__      ):






          """simple docstring"""






          return (input_values - (self.mean)) / (self.std * 2)







    def __call__( self    ,					lowercase__    ,					lowercase__ = None    ,					lowercase__ = None    ,					**lowercase__    ,					):






          """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."      )

          SCREAMING_SNAKE_CASE_				:      Dict					=	isinstance(lowercase__    ,					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}"      )
          SCREAMING_SNAKE_CASE_				:      Any					=	is_batched_numpy or (
              isinstance(lowercase__    ,					(list, tuple)      ) and (isinstance(raw_speech[0]    ,					(np.ndarray, tuple, list)      ))
          )

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

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

          # extract fbank features and pad/truncate to max_length
          SCREAMING_SNAKE_CASE_				:      Tuple					=	[self._extract_fbank_features(lowercase__    ,					max_length=self.max_length      ) for waveform in raw_speech]

          # convert into BatchFeature
          SCREAMING_SNAKE_CASE_				:      List[Any]					=	BatchFeature({"input_values": features}      )

          # make sure list is in array format
          SCREAMING_SNAKE_CASE_				:      List[Any]					=	padded_inputs.get("input_values"      )
          if isinstance(input_values[0]    ,					lowercase__      ):
                SCREAMING_SNAKE_CASE_				:      Optional[int]					=	[np.asarray(lowercase__    ,					dtype=np.floataa      ) for feature in input_values]

          # normalization
          if self.do_normalize:
                SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	[self.normalize(lowercase__      ) for feature in input_values]

          if return_tensors is not None:
                SCREAMING_SNAKE_CASE_				:      List[Any]					=	padded_inputs.convert_to_tensors(lowercase__      )

          return padded_inputs




'''simple docstring'''






from sklearn.metrics import roc_auc_score

import datasets


snake_case_					  =	'\nThis metric computes the area under the curve (AUC) for the Receiver Operating Characteristic Curve (ROC). The return values represent how well the model used is predicting the correct classes, based on the input data. A score of `0.5` means that the model is predicting exactly at chance, i.e. the model\'s predictions are correct at the same rate as if the predictions were being decided by the flip of a fair coin or the roll of a fair die. A score above `0.5` indicates that the model is doing better than chance, while a score below `0.5` indicates that the model is doing worse than chance.\n\nThis metric has three separate use cases:\n    - binary: The case in which there are only two different label classes, and each example gets only one label. This is the default implementation.\n    - multiclass: The case in which there can be more than two different label classes, but each example still gets only one label.\n    - multilabel: The case in which there can be more than two different label classes, and each example can have more than one label.\n'

snake_case_					  =	'\nArgs:\n- references (array-like of shape (n_samples,) or (n_samples, n_classes)): Ground truth labels. Expects different input based on use case:\n    - binary: expects an array-like of shape (n_samples,)\n    - multiclass: expects an array-like of shape (n_samples,)\n    - multilabel: expects an array-like of shape (n_samples, n_classes)\n- prediction_scores (array-like of shape (n_samples,) or (n_samples, n_classes)): Model predictions. Expects different inputs based on use case:\n    - binary: expects an array-like of shape (n_samples,)\n    - multiclass: expects an array-like of shape (n_samples, n_classes)\n    - multilabel: expects an array-like of shape (n_samples, n_classes)\n- average (`str`): Type of average, and is ignored in the binary use case. Defaults to \'macro\'. Options are:\n    - `\'micro\'`: Calculates metrics globally by considering each element of the label indicator matrix as a label. Only works with the multilabel use case.\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 (i.e. the number of true instances for each label).\n    - `\'samples\'`: Calculate metrics for each instance, and find their average. Only works with the multilabel use case.\n    - `None`:  No average is calculated, and scores for each class are returned. Only works with the multilabels use case.\n- sample_weight (array-like of shape (n_samples,)): Sample weights. Defaults to None.\n- max_fpr (`float`): If not None, the standardized partial AUC over the range [0, `max_fpr`] is returned. Must be greater than `0` and less than or equal to `1`. Defaults to `None`. Note: For the multiclass use case, `max_fpr` should be either `None` or `1.0` as ROC AUC partial computation is not currently supported for `multiclass`.\n- multi_class (`str`): Only used for multiclass targets, where it is required. Determines the type of configuration to use. Options are:\n    - `\'ovr\'`: Stands for One-vs-rest. Computes the AUC of each class against the rest. This treats the multiclass case in the same way as the multilabel case. Sensitive to class imbalance even when `average == \'macro\'`, because class imbalance affects the composition of each of the \'rest\' groupings.\n    - `\'ovo\'`: Stands for One-vs-one. Computes the average AUC of all possible pairwise combinations of classes. Insensitive to class imbalance when `average == \'macro\'`.\n- labels (array-like of shape (n_classes,)): Only used for multiclass targets. List of labels that index the classes in\n    `prediction_scores`. If `None`, the numerical or lexicographical order of the labels in\n    `prediction_scores` is used. Defaults to `None`.\nReturns:\n    roc_auc (`float` or array-like of shape (n_classes,)): Returns array if in multilabel use case and `average=\'None\'`. Otherwise, returns `float`.\nExamples:\n    Example 1:\n        >>> roc_auc_score = datasets.load_metric("roc_auc")\n        >>> refs = [1, 0, 1, 1, 0, 0]\n        >>> pred_scores = [0.5, 0.2, 0.99, 0.3, 0.1, 0.7]\n        >>> results = roc_auc_score.compute(references=refs, prediction_scores=pred_scores)\n        >>> print(round(results[\'roc_auc\'], 2))\n        0.78\n\n    Example 2:\n        >>> roc_auc_score = datasets.load_metric("roc_auc", "multiclass")\n        >>> refs = [1, 0, 1, 2, 2, 0]\n        >>> pred_scores = [[0.3, 0.5, 0.2],\n        ...                 [0.7, 0.2, 0.1],\n        ...                 [0.005, 0.99, 0.005],\n        ...                 [0.2, 0.3, 0.5],\n        ...                 [0.1, 0.1, 0.8],\n        ...                 [0.1, 0.7, 0.2]]\n        >>> results = roc_auc_score.compute(references=refs, prediction_scores=pred_scores, multi_class=\'ovr\')\n        >>> print(round(results[\'roc_auc\'], 2))\n        0.85\n\n    Example 3:\n        >>> roc_auc_score = datasets.load_metric("roc_auc", "multilabel")\n        >>> refs = [[1, 1, 0],\n        ...         [1, 1, 0],\n        ...         [0, 1, 0],\n        ...         [0, 0, 1],\n        ...         [0, 1, 1],\n        ...         [1, 0, 1]]\n        >>> pred_scores = [[0.3, 0.5, 0.2],\n        ...                 [0.7, 0.2, 0.1],\n        ...                 [0.005, 0.99, 0.005],\n        ...                 [0.2, 0.3, 0.5],\n        ...                 [0.1, 0.1, 0.8],\n        ...                 [0.1, 0.7, 0.2]]\n        >>> results = roc_auc_score.compute(references=refs, prediction_scores=pred_scores, average=None)\n        >>> print([round(res, 2) for res in results[\'roc_auc\']])\n        [0.83, 0.38, 0.94]\n'

snake_case_					  =	'\\n@article{doi:10.1177/0272989X8900900307,\nauthor = {Donna Katzman McClish},\ntitle ={Analyzing a Portion of the ROC Curve},\njournal = {Medical Decision Making},\nvolume = {9},\nnumber = {3},\npages = {190-195},\nyear = {1989},\ndoi = {10.1177/0272989X8900900307},\n    note ={PMID: 2668680},\nURL = {https://doi.org/10.1177/0272989X8900900307},\neprint = {https://doi.org/10.1177/0272989X8900900307}\n}\n\n\n@article{10.1023/A:1010920819831,\nauthor = {Hand, David J. and Till, Robert J.},\ntitle = {A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems},\nyear = {2001},\nissue_date = {November 2001},\npublisher = {Kluwer Academic Publishers},\naddress = {USA},\nvolume = {45},\nnumber = {2},\nissn = {0885-6125},\nurl = {https://doi.org/10.1023/A:1010920819831},\ndoi = {10.1023/A:1010920819831},\njournal = {Mach. Learn.},\nmonth = {oct},\npages = {171–186},\nnumpages = {16},\nkeywords = {Gini index, AUC, error rate, ROC curve, receiver operating characteristic}\n}\n\n\n@article{scikit-learn,\ntitle={Scikit-learn: Machine Learning in {P}ython},\nauthor={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.},\njournal={Journal of Machine Learning Research},\nvolume={12},\npages={2825--2830},\nyear={2011}\n}\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION,_KWARGS_DESCRIPTION				)
class      SCREAMING_SNAKE_CASE__      (    datasets.Metric				):




    def   __lowerCamelCase				( self      ):






          """simple docstring"""






          return datasets.MetricInfo(
              description=_DESCRIPTION    ,					citation=_CITATION    ,					inputs_description=_KWARGS_DESCRIPTION    ,					features=datasets.Features(
                  {
                      "prediction_scores": datasets.Sequence(datasets.Value("float"      )      ),
                      "references": datasets.Value("int32"      ),
                  }
                  if self.config_name == "multiclass"
                  else {
                      "references": datasets.Sequence(datasets.Value("int32"      )      ),
                      "prediction_scores": datasets.Sequence(datasets.Value("float"      )      ),
                  }
                  if self.config_name == "multilabel"
                  else {
                      "references": datasets.Value("int32"      ),
                      "prediction_scores": datasets.Value("float"      ),
                  }      )    ,					reference_urls=["https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_auc_score.html"]    ,					)







    def   __lowerCamelCase				( self    ,					lowercase__    ,					lowercase__    ,					lowercase__="macro"    ,					lowercase__=None    ,					lowercase__=None    ,					lowercase__="raise"    ,					lowercase__=None    ,					):






          """simple docstring"""






          return {
              "roc_auc": roc_auc_score(
                  lowercase__    ,					lowercase__    ,					average=lowercase__    ,					sample_weight=lowercase__    ,					max_fpr=lowercase__    ,					multi_class=lowercase__    ,					labels=lowercase__    ,					)
          }

