






'''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 PaddingStrategy, TensorType, logging


SCREAMING_SNAKE_CASE__											=     logging.get_logger(__name__)






class        a_	(    lowerCamelCase       ):
  lowercase  	=   ["""input_features""", """attention_mask"""]

  def __init__(   self    ,	_SCREAMING_SNAKE_CASE=80    ,	_SCREAMING_SNAKE_CASE=16000    ,	_SCREAMING_SNAKE_CASE=80    ,	_SCREAMING_SNAKE_CASE=0.0    ,	_SCREAMING_SNAKE_CASE=True    ,	_SCREAMING_SNAKE_CASE=True    ,	_SCREAMING_SNAKE_CASE=True    ,	**_SCREAMING_SNAKE_CASE    ,	)						->   Tuple:



        """simple docstring"""






        super().__init__(feature_size=_SCREAMING_SNAKE_CASE    ,	sampling_rate=_SCREAMING_SNAKE_CASE    ,	padding_value=_SCREAMING_SNAKE_CASE    ,	**_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      num_mel_bins
        UpperCamelCase       						=      do_ceptral_normalize
        UpperCamelCase       						=      normalize_means
        UpperCamelCase       						=      normalize_vars
        UpperCamelCase       						=      True

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	)						->   Any:



        """simple docstring"""






        UpperCamelCase       						=      waveform * (2**15)  # Kaldi compliance: 16-bit signed integers
        UpperCamelCase       						=      torch.from_numpy(_SCREAMING_SNAKE_CASE   ).unsqueeze(0   )
        UpperCamelCase       						=      ta_kaldi.fbank(_SCREAMING_SNAKE_CASE    ,	num_mel_bins=self.num_mel_bins    ,	sample_frequency=self.sampling_rate   )
        return features.numpy()

  @staticmethod
  def 						A__		(   _SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE = True    ,	_SCREAMING_SNAKE_CASE = True    ,	_SCREAMING_SNAKE_CASE = 0.0    ,	)						->   Optional[int]:



        """simple docstring"""






        if normalize_means:
              UpperCamelCase       						=      x[:input_length].mean(axis=0   )
              UpperCamelCase       						=      np.subtract(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )
        if normalize_vars:
              UpperCamelCase       						=      x[:input_length].std(axis=0   )
              UpperCamelCase       						=      np.divide(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )

        if input_length < x.shape[0]:
              UpperCamelCase       						=      padding_value

        # make sure array is in float32
        UpperCamelCase       						=      x.astype(np.floataa   )

        return x

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE = None   )						->   List[Any]:



        """simple docstring"""






        UpperCamelCase       						=      attention_mask.sum(-1   ) if attention_mask is not None else [x.shape[0] for x in input_features]
        return [
            self.utterance_cmvn(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	self.normalize_means    ,	self.normalize_vars    ,	self.padding_value   )
            for x, n in zip(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )
        ]


  def __call__(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE = False    ,	_SCREAMING_SNAKE_CASE = None    ,	_SCREAMING_SNAKE_CASE = False    ,	_SCREAMING_SNAKE_CASE = None    ,	_SCREAMING_SNAKE_CASE = None    ,	_SCREAMING_SNAKE_CASE = None    ,	_SCREAMING_SNAKE_CASE = None    ,	**_SCREAMING_SNAKE_CASE    ,	)						->   Optional[int]:



        """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."""   )

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

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

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

        # extract fbank features
        UpperCamelCase       						=      [self._extract_fbank_features(_SCREAMING_SNAKE_CASE   ) for waveform in raw_speech]

        # convert into correct format for padding
        UpperCamelCase       						=      BatchFeature({"""input_features""": features}   )

        UpperCamelCase       						=      self.pad(
            _SCREAMING_SNAKE_CASE    ,	padding=_SCREAMING_SNAKE_CASE    ,	max_length=_SCREAMING_SNAKE_CASE    ,	truncation=_SCREAMING_SNAKE_CASE    ,	pad_to_multiple_of=_SCREAMING_SNAKE_CASE    ,	return_attention_mask=_SCREAMING_SNAKE_CASE    ,	**_SCREAMING_SNAKE_CASE    ,	)

        # make sure list is in array format
        UpperCamelCase       						=      padded_inputs.get("""input_features"""   )
        if isinstance(input_features[0]    ,	_SCREAMING_SNAKE_CASE   ):
              UpperCamelCase       						=      [np.asarray(_SCREAMING_SNAKE_CASE    ,	dtype=np.floataa   ) for feature in input_features]

        UpperCamelCase       						=      padded_inputs.get("""attention_mask"""   )
        if attention_mask is not None:
              UpperCamelCase       						=      [np.asarray(_SCREAMING_SNAKE_CASE    ,	dtype=np.intaa   ) for array in attention_mask]

        # Utterance-level cepstral mean and variance normalization
        if self.do_ceptral_normalize:
              UpperCamelCase       						=      (
                  np.array(_SCREAMING_SNAKE_CASE    ,	dtype=np.intaa   )
                  if self._get_padding_strategies(_SCREAMING_SNAKE_CASE    ,	max_length=_SCREAMING_SNAKE_CASE   ) is not PaddingStrategy.DO_NOT_PAD
                  else None
              )
              UpperCamelCase       						=      self.normalize(
                  padded_inputs["""input_features"""]    ,	attention_mask=_SCREAMING_SNAKE_CASE   )

        if return_tensors is not None:
              UpperCamelCase       						=      padded_inputs.convert_to_tensors(_SCREAMING_SNAKE_CASE   )

        return padded_inputs








'''simple docstring'''

import fire

from utils import calculate_rouge, save_json







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase=None	,		**__UpperCamelCase					)->				int:
      UpperCamelCase       						=      [x.strip() for x in open(__UpperCamelCase					).readlines()]
      UpperCamelCase       						=      [x.strip() for x in open(__UpperCamelCase					).readlines()][: len(__UpperCamelCase					)]
      UpperCamelCase       						=      calculate_rouge(__UpperCamelCase	,		__UpperCamelCase	,		**__UpperCamelCase					)
      if save_path is not None:
            save_json(__UpperCamelCase	,		__UpperCamelCase	,		indent=__UpperCamelCase					)
      return metrics  # these print nicely


if __name__ == "__main__":
     fire.Fire(calculate_rouge_path)
