

'''simple docstring'''






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

import numpy as np

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


lowerCAmelCase__											=	logging.get_logger(__name__)






class 			lowercase_	(lowerCamelCase__      ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE    :  Any							=			['input_features']






  def __init__(      self   :     List[Any]					,lowercase__   :     Tuple=8_0					,lowercase__   :     List[Any]=1_6_0_0_0					,lowercase__   :     Optional[int]=1_6_0					,lowercase__   :     Dict=3_0					,lowercase__   :     Optional[Any]=4_0_0					,lowercase__   :     List[str]=0.0					,lowercase__   :     Optional[Any]=False					,**lowercase__   :     Optional[Any]					,):
   super().__init__(
       feature_size=lowercase__					,sampling_rate=lowercase__					,padding_value=lowercase__					,return_attention_mask=lowercase__					,**lowercase__					,)
   __lowercase							  = n_fft
   __lowercase							  = hop_length
   __lowercase							  = chunk_length
   __lowercase							  = chunk_length * sampling_rate
   __lowercase							  = self.n_samples // hop_length
   __lowercase							  = sampling_rate
   __lowercase							  = mel_filter_bank(
       num_frequency_bins=1 + n_fft // 2					,num_mel_filters=lowercase__					,min_frequency=0.0					,max_frequency=8_0_0_0.0					,sampling_rate=lowercase__					,norm='''slaney'''					,mel_scale='''slaney'''					,)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Union[str, Any]					,lowercase__   :     np.array			):
   __lowercase							  = spectrogram(
       lowercase__					,window_function(self.n_fft					,'''hann'''			)					,frame_length=self.n_fft					,hop_length=self.hop_length					,power=2.0					,mel_filters=self.mel_filters					,log_mel='''log10'''					,)
   __lowercase							  = log_spec[:, :-1]
   __lowercase							  = np.maximum(lowercase__					,log_spec.max() - 8.0			)
   __lowercase							  = (log_spec + 4.0) / 4.0
   return log_spec






  @staticmethod
  # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
  def 					SCREAMING_SNAKE_CASE      (      lowercase__   :     List[np.ndarray]					,lowercase__   :     List[np.ndarray]					,lowercase__   :     float = 0.0			):
   if attention_mask is not None:
    __lowercase							  = np.array(lowercase__					,np.intaa			)
    __lowercase							  = []

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

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

   return normed_input_values






  def __call__(      self   :     Optional[Any]					,lowercase__   :     Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]					,lowercase__   :     bool = True					,lowercase__   :     Optional[int] = None					,lowercase__   :     Optional[Union[str, TensorType]] = None					,lowercase__   :     Optional[bool] = None					,lowercase__   :     Optional[str] = "max_length"					,lowercase__   :     Optional[int] = None					,lowercase__   :     Optional[int] = None					,lowercase__   :     Optional[bool] = None					,**lowercase__   :     Optional[int]					,):

   if sampling_rate is not None:
    if sampling_rate != self.sampling_rate:
     raise ValueError(
         F"The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a"
         F" sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input"
         F" was sampled with {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.'''			)

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

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

   # always return batch
   if not is_batched:
    __lowercase							  = [np.asarray([raw_speech]			).T]

   __lowercase							  = BatchFeature({'''input_features''': raw_speech}			)

   # convert into correct format for padding

   __lowercase							  = self.pad(
       lowercase__					,padding=lowercase__					,max_length=max_length if max_length else self.n_samples					,truncation=lowercase__					,pad_to_multiple_of=lowercase__					,return_attention_mask=return_attention_mask or do_normalize					,)

   # zero-mean and unit-variance normalization
   if do_normalize:
    __lowercase							  = self.zero_mean_unit_var_norm(
        padded_inputs['''input_features''']					,attention_mask=padded_inputs['''attention_mask''']					,padding_value=self.padding_value					,)
    __lowercase							  = np.stack(padded_inputs['''input_features''']					,axis=0			)

   # make sure list is in array format
   __lowercase							  = padded_inputs.get('''input_features'''			).transpose(2					,0					,1			)

   __lowercase							  = [self._np_extract_fbank_features(lowercase__			) for waveform in input_features[0]]

   if isinstance(input_features[0]					,lowercase__			):
    __lowercase							  = [np.asarray(lowercase__					,dtype=np.floataa			) for feature in input_features]
   else:
    __lowercase							  = input_features

   if return_attention_mask:
    # rescale from sample (48000) to feature (3000)
    __lowercase							  = padded_inputs['''attention_mask'''][:, :: self.hop_length]

   if return_tensors is not None:
    __lowercase							  = padded_inputs.convert_to_tensors(lowercase__			)

   return padded_inputs






  def 					SCREAMING_SNAKE_CASE      (      self   :     str			):
   __lowercase							  = copy.deepcopy(self.__dict__			)
   __lowercase							  = self.__class__.__name__
   if "mel_filters" in output:
    del output["mel_filters"]
   return output





'''simple docstring'''

import absl  # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk  # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy  # noqa: F401 # Here to have a nice missing dependency error message early on
import six  # noqa: F401 # Here to have a nice missing dependency error message early on
from rouge_score import rouge_scorer, scoring

import datasets


a_  :				str						= """\
@inproceedings{lin-2004-rouge,
    title = \"{ROUGE}: A Package for Automatic Evaluation of Summaries\",
    author = \"Lin, Chin-Yew\",
    booktitle = \"Text Summarization Branches Out\",
    month = jul,
    year = \"2004\",
    address = \"Barcelona, Spain\",
    publisher = \"Association for Computational Linguistics\",
    url = \"https://www.aclweb.org/anthology/W04-1013\",
    pages = \"74--81\",
}
"""

a_  :				int						= """\
ROUGE, or Recall-Oriented Understudy for Gisting Evaluation, is a set of metrics and a software package used for
evaluating automatic summarization and machine translation software in natural language processing.
The metrics compare an automatically produced summary or translation against a reference or a set of references (human-produced) summary or translation.

Note that ROUGE is case insensitive, meaning that upper case letters are treated the same way as lower case letters.

This metrics is a wrapper around Google Research reimplementation of ROUGE:
https://github.com/google-research/google-research/tree/master/rouge
"""

a_  :				Tuple						= """
Calculates average rouge scores for a list of hypotheses and references
Args:
    predictions: list of predictions to score. Each prediction
        should be a string with tokens separated by spaces.
    references: list of reference for each prediction. Each
        reference should be a string with tokens separated by spaces.
    rouge_types: A list of rouge types to calculate.
        Valid names:
        `\"rouge{n}\"` (e.g. `\"rouge1\"`, `\"rouge2\"`) where: {n} is the n-gram based scoring,
        `\"rougeL\"`: Longest common subsequence based scoring.
        `\"rougeLSum\"`: rougeLsum splits text using `\"\n\"`.
        See details in https://github.com/huggingface/datasets/issues/617
    use_stemmer: Bool indicating whether Porter stemmer should be used to strip word suffixes.
    use_aggregator: Return aggregates if this is set to True
Returns:
    rouge1: rouge_1 (precision, recall, f1),
    rouge2: rouge_2 (precision, recall, f1),
    rougeL: rouge_l (precision, recall, f1),
    rougeLsum: rouge_lsum (precision, recall, f1)
Examples:

    >>> rouge = datasets.load_metric('rouge')
    >>> predictions = [\"hello there\", \"general kenobi\"]
    >>> references = [\"hello there\", \"general kenobi\"]
    >>> results = rouge.compute(predictions=predictions, references=references)
    >>> print(list(results.keys()))
    ['rouge1', 'rouge2', 'rougeL', 'rougeLsum']
    >>> print(results[\"rouge1\"])
    AggregateScore(low=Score(precision=1.0, recall=1.0, fmeasure=1.0), mid=Score(precision=1.0, recall=1.0, fmeasure=1.0), high=Score(precision=1.0, recall=1.0, fmeasure=1.0))
    >>> print(results[\"rouge1\"].mid.fmeasure)
    1.0
"""





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





  """simple docstring"""




  def   snake_case		(    self     ):







   """simple docstring"""
   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/google-research/google-research/tree/master/rouge"]			,  reference_urls=[
           "https://en.wikipedia.org/wiki/ROUGE_(metric)",
           "https://github.com/google-research/google-research/tree/master/rouge",
       ]			,  )







  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase=None			,  UpperCamelCase=True			,  UpperCamelCase=False     ):







   """simple docstring"""
   if rouge_types is None:
    lowerCamelCase_      =  ["rouge1", "rouge2", "rougeL", "rougeLsum"]

   lowerCamelCase_      =  rouge_scorer.RougeScorer(rouge_types=UpperCamelCase			,  use_stemmer=UpperCamelCase     )
   if use_aggregator:
    lowerCamelCase_      =  scoring.BootstrapAggregator()
   else:
    lowerCamelCase_      =  []

   for ref, pred in zip(UpperCamelCase			,  UpperCamelCase     ):
    lowerCamelCase_      =  scorer.score(UpperCamelCase			,  UpperCamelCase     )
    if use_aggregator:
     aggregator.add_scores(UpperCamelCase     )
    else:
     scores.append(UpperCamelCase     )

   if use_aggregator:
    lowerCamelCase_      =  aggregator.aggregate()
   else:
    lowerCamelCase_      =  {}
    for key in scores[0]:
     lowerCamelCase_      =  [score[key] for score in scores]

   return result



