




import warnings
from typing import Any, Dict, List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


_lowerCamelCase	:		int							       =   logging.get_logger(__name__)


class 				UpperCamelCase_    ( UpperCAmelCase__     ):

    '''simple docstring'''






    UpperCAmelCase__							  =      ['''input_values''', '''attention_mask''']


    def __init__(			self      :	Optional[int]					, UpperCAmelCase__      :	int = 1					, UpperCAmelCase__      :	int = 16_000					, UpperCAmelCase__      :	float = 0.0					, UpperCAmelCase__      :	bool = False					, UpperCAmelCase__      :	int = 80					, UpperCAmelCase__      :	int = 16					, UpperCAmelCase__      :	int = 64					, UpperCAmelCase__      :	str = "hann_window"					, UpperCAmelCase__      :	float = 1.0					, UpperCAmelCase__      :	float = 80					, UpperCAmelCase__      :	float = 7_600					, UpperCAmelCase__      :	float = 1e-10					, UpperCAmelCase__      :	int = 2					, UpperCAmelCase__      :	bool = True					, **UpperCAmelCase__      :	int					, )						->int:





           '''simple docstring'''

           super().__init__(feature_size=UpperCAmelCase__					, sampling_rate=UpperCAmelCase__					, padding_value=UpperCAmelCase__					, **UpperCAmelCase__)
           A__       =    do_normalize
           A__       =    return_attention_mask

           A__       =    num_mel_bins
           A__       =    hop_length
           A__       =    win_length
           A__       =    win_function
           A__       =    frame_signal_scale
           A__       =    fmin
           A__       =    fmax
           A__       =    mel_floor
           A__       =    reduction_factor

           A__       =    win_length * sampling_rate // 1_000
           A__       =    hop_length * sampling_rate // 1_000
           A__       =    optimal_fft_length(self.sample_size)
           A__       =    (self.n_fft // 2) + 1

           A__       =    window_function(window_length=self.sample_size					, name=self.win_function					, periodic=UpperCAmelCase__)

           A__       =    mel_filter_bank(
               num_frequency_bins=self.n_freqs					, num_mel_filters=self.num_mel_bins					, min_frequency=self.fmin					, max_frequency=self.fmax					, sampling_rate=self.sampling_rate					, norm='''slaney'''					, mel_scale='''slaney'''					, )

           if frame_signal_scale != 1.0:
                  warnings.warn(
                      '''The argument `frame_signal_scale` is deprecated and will be removed in version 4.30.0 of Transformers'''					, UpperCAmelCase__					, )
           if reduction_factor != 2.0:
                  warnings.warn(
                      '''The argument `reduction_factor` is deprecated and will be removed in version 4.30.0 of Transformers'''					, UpperCAmelCase__					, )


    @staticmethod
    # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
    def        SCREAMING_SNAKE_CASE							(			UpperCAmelCase__      :	List[np.ndarray]					, UpperCAmelCase__      :	List[np.ndarray]					, UpperCAmelCase__      :	float = 0.0)						->List[np.ndarray]:





           '''simple docstring'''

           if attention_mask is not None:
                  A__       =    np.array(UpperCAmelCase__					, np.intaa)
                  A__       =    []

                  for vector, length in zip(UpperCAmelCase__					, attention_mask.sum(-1)):
                         A__       =    (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7)
                         if length < normed_slice.shape[0]:
                                A__       =    padding_value

                         normed_input_values.append(UpperCAmelCase__)
           else:
                  A__       =    [(x - x.mean()) / np.sqrt(x.var() + 1e-7) for x in input_values]

           return normed_input_values


    def        SCREAMING_SNAKE_CASE							(			self      :	Optional[Any]					, UpperCAmelCase__      :	np.ndarray					, )						->np.ndarray:





           '''simple docstring'''

           A__       =    spectrogram(
               UpperCAmelCase__					, window=self.window					, frame_length=self.sample_size					, hop_length=self.sample_stride					, fft_length=self.n_fft					, mel_filters=self.mel_filters					, mel_floor=self.mel_floor					, log_mel='''log10'''					, )
           return log_mel_spec.T


    def __call__(			self      :	Optional[int]					, UpperCAmelCase__      :	Optional[Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]] = None					, UpperCAmelCase__      :	Optional[Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]] = None					, UpperCAmelCase__      :	Union[bool, str, PaddingStrategy] = False					, UpperCAmelCase__      :	Optional[int] = None					, UpperCAmelCase__      :	bool = False					, UpperCAmelCase__      :	Optional[int] = None					, UpperCAmelCase__      :	Optional[bool] = None					, UpperCAmelCase__      :	Optional[Union[str, TensorType]] = None					, UpperCAmelCase__      :	Optional[int] = None					, **UpperCAmelCase__      :	int					, )						->BatchFeature:





           '''simple docstring'''

           if audio is None and audio_target is None:
                  raise ValueError('''You must provide either `audio` or `audio_target` values.''')

           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 audio 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.''')

           if audio is not None:
                  A__       =    self._process_audio(
                      UpperCAmelCase__					, UpperCAmelCase__					, UpperCAmelCase__					, UpperCAmelCase__					, UpperCAmelCase__					, UpperCAmelCase__					, UpperCAmelCase__					, UpperCAmelCase__					, **UpperCAmelCase__					, )
           else:
                  A__       =    None

           if audio_target is not None:
                  A__       =    self._process_audio(
                      UpperCAmelCase__					, UpperCAmelCase__					, UpperCAmelCase__					, UpperCAmelCase__					, UpperCAmelCase__					, UpperCAmelCase__					, UpperCAmelCase__					, UpperCAmelCase__					, **UpperCAmelCase__					, )

                  if inputs is None:
                         return inputs_target
                  else:
                         A__       =    inputs_target['''input_values''']
                         A__       =    inputs_target.get('''attention_mask''')
                         if decoder_attention_mask is not None:
                                A__       =    decoder_attention_mask

           return inputs


    def        SCREAMING_SNAKE_CASE							(			self      :	List[str]					, UpperCAmelCase__      :	Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]					, UpperCAmelCase__      :	bool = False					, UpperCAmelCase__      :	Union[bool, str, PaddingStrategy] = False					, UpperCAmelCase__      :	Optional[int] = None					, UpperCAmelCase__      :	bool = False					, UpperCAmelCase__      :	Optional[int] = None					, UpperCAmelCase__      :	Optional[bool] = None					, UpperCAmelCase__      :	Optional[Union[str, TensorType]] = None					, **UpperCAmelCase__      :	int					, )						->BatchFeature:





           '''simple docstring'''

           A__       =    isinstance(UpperCAmelCase__					, np.ndarray) and len(speech.shape) > 1
           if is_batched_numpy and len(speech.shape) > 2:
                  raise ValueError(f"""Only mono-channel audio is supported for input to {self}""")
           A__       =    is_batched_numpy or (
               isinstance(UpperCAmelCase__					, (list, tuple)) and (isinstance(speech[0]					, (np.ndarray, tuple, list)))
           )

           if is_batched:
                  A__       =    [np.asarray(UpperCAmelCase__					, dtype=np.floataa) for speech in speech]
           elif not is_batched and not isinstance(UpperCAmelCase__					, np.ndarray):
                  A__       =    np.asarray(UpperCAmelCase__					, dtype=np.floataa)
           elif isinstance(UpperCAmelCase__					, np.ndarray) and speech.dtype is np.dtype(np.floataa):
                  A__       =    speech.astype(np.floataa)

           # always return batch
           if not is_batched:
                  A__       =    [speech]

           # needed to make pad() work on spectrogram inputs
           A__       =    self.feature_size

           # convert into correct format for padding
           if is_target:
                  A__       =    [self._extract_mel_features(UpperCAmelCase__) for waveform in speech]
                  A__       =    BatchFeature({'''input_values''': features})
                  A__       =    self.num_mel_bins
           else:
                  A__       =    BatchFeature({'''input_values''': speech})

           A__       =    self.pad(
               UpperCAmelCase__					, padding=UpperCAmelCase__					, max_length=UpperCAmelCase__					, truncation=UpperCAmelCase__					, pad_to_multiple_of=UpperCAmelCase__					, return_attention_mask=UpperCAmelCase__					, **UpperCAmelCase__					, )

           A__       =    feature_size_hack

           # convert input values to correct format
           A__       =    padded_inputs['''input_values''']
           if not isinstance(input_values[0]					, np.ndarray):
                  A__       =    [np.asarray(UpperCAmelCase__					, dtype=np.floataa) for array in input_values]
           elif (
               not isinstance(UpperCAmelCase__					, np.ndarray)
               and isinstance(input_values[0]					, np.ndarray)
               and input_values[0].dtype is np.dtype(np.floataa)
           ):
                  A__       =    [array.astype(np.floataa) for array in input_values]
           elif isinstance(UpperCAmelCase__					, np.ndarray) and input_values.dtype is np.dtype(np.floataa):
                  A__       =    input_values.astype(np.floataa)

           # convert attention_mask to correct format
           A__       =    padded_inputs.get('''attention_mask''')
           if attention_mask is not None:
                  A__       =    [np.asarray(UpperCAmelCase__					, dtype=np.intaa) for array in attention_mask]

           # zero-mean and unit-variance normalization
           if not is_target and self.do_normalize:
                  A__       =    (
                      attention_mask
                      if self._get_padding_strategies(UpperCAmelCase__					, max_length=UpperCAmelCase__) is not PaddingStrategy.DO_NOT_PAD
                      else None
                  )
                  A__       =    self.zero_mean_unit_var_norm(
                      padded_inputs['''input_values''']					, attention_mask=UpperCAmelCase__					, padding_value=self.padding_value)

           if return_tensors is not None:
                  A__       =    padded_inputs.convert_to_tensors(UpperCAmelCase__)

           return padded_inputs





    def        SCREAMING_SNAKE_CASE							(			self      :	int)						->Dict[str, Any]:





           '''simple docstring'''

           A__       =    super().to_dict()

           # Don't serialize these as they are derived from the other properties.
           A__       =    ['''window''', '''mel_filters''', '''sample_size''', '''sample_stride''', '''n_fft''', '''n_freqs''']
           for name in names:
                  if name in output:
                         del output[name]

           return output



"""simple docstring"""






from jiwer import compute_measures

import datasets


__A			   = "\\n@inproceedings{inproceedings,\n    author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n    year = {2004},\n    month = {01},\n    pages = {},\n    title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}\n}\n"

__A			   = "\\nWord error rate (WER) is a common metric of the performance of an automatic speech recognition system.\n\nThe general difficulty of measuring performance lies in the fact that the recognized word sequence can have a different length from the reference word sequence (supposedly the correct one). The WER is derived from the Levenshtein distance, working at the word level instead of the phoneme level. The WER is a valuable tool for comparing different systems as well as for evaluating improvements within one system. This kind of measurement, however, provides no details on the nature of translation errors and further work is therefore required to identify the main source(s) of error and to focus any research effort.\n\nThis problem is solved by first aligning the recognized word sequence with the reference (spoken) word sequence using dynamic string alignment. Examination of this issue is seen through a theory called the power law that states the correlation between perplexity and word error rate.\n\nWord error rate can then be computed as:\n\nWER = (S + D + I) / N = (S + D + I) / (S + D + C)\n\nwhere\n\nS is the number of substitutions,\nD is the number of deletions,\nI is the number of insertions,\nC is the number of correct words,\nN is the number of words in the reference (N=S+D+C).\n\nThis value indicates the average number of errors per reference word. The lower the value, the better the\nperformance of the ASR system with a WER of 0 being a perfect score.\n"

__A			   = "\nCompute WER score of transcribed segments against references.\n\nArgs:\n    references: List of references for each speech input.\n    predictions: List of transcriptions to score.\n    concatenate_texts (bool, default=False): Whether to concatenate all input texts or compute WER iteratively.\n\nReturns:\n    (float): the word error rate\n\nExamples:\n\n    >>> predictions = [\"this is the prediction\", \"there is an other sample\"]\n    >>> references = [\"this is the reference\", \"there is another one\"]\n    >>> wer = datasets.load_metric(\"wer\")\n    >>> wer_score = wer.compute(predictions=predictions, references=references)\n    >>> print(wer_score)\n    0.5\n"


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



    """simple docstring"""

    def _snake_case			(   self							):
       return datasets.MetricInfo(
           description=_DESCRIPTION    ,			citation=_CITATION    ,			inputs_description=_KWARGS_DESCRIPTION    ,			features=datasets.Features(
               {
                   '''predictions''': datasets.Value('''string'''    ,			id='''sequence'''							),
                   '''references''': datasets.Value('''string'''    ,			id='''sequence'''							),
               }							)    ,			codebase_urls=['''https://github.com/jitsi/jiwer/''']    ,			reference_urls=[
               '''https://en.wikipedia.org/wiki/Word_error_rate''',
           ]    ,			)






    def _snake_case			(   self    ,			_UpperCAmelCase=None    ,			_UpperCAmelCase=None    ,			_UpperCAmelCase=False							):
       if concatenate_texts:
          return compute_measures(_UpperCAmelCase    ,			_UpperCAmelCase							)["wer"]
       else:
          lowercase__:   Dict					=      0
          lowercase__:   Union[str, Any]					=      0
          for prediction, reference in zip(_UpperCAmelCase    ,			_UpperCAmelCase							):
             lowercase__:   Tuple					=      compute_measures(_UpperCAmelCase    ,			_UpperCAmelCase							)
             incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
             total += measures["substitutions"] + measures["deletions"] + measures["hits"]
          return incorrect / total
