



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


_lowercase    	=		logging.get_logger(__name__)



class      _UpperCAmelCase      (							A__  ):
     UpperCamelCase__		     =					['''input_features''', '''attention_mask''']




     def __init__(					self			,    a__=8_0			,    a__=1_6_0_0_0			,    a__=8_0			,    a__=0.0			,    a__=True			,    a__=True			,    a__=True			,    **a__			,    ):
         super().__init__(feature_size=a__			,    sampling_rate=a__			,    padding_value=a__			,    **a__)
         A__					=       num_mel_bins
         A__					=       do_ceptral_normalize
         A__					=       normalize_means
         A__					=       normalize_vars
         A__					=       True




     def 		snake_case_		(					self			,    a__			,    ):
         A__					=       waveform * (2**1_5)  # Kaldi compliance: 16-bit signed integers
         A__					=       torch.from_numpy(a__).unsqueeze(0)
         A__					=       ta_kaldi.fbank(a__			,    num_mel_bins=self.num_mel_bins			,    sample_frequency=self.sampling_rate)
         return features.numpy()




     @staticmethod
     def 		snake_case_		(					a__			,    a__			,    a__ = True			,    a__ = True			,    a__ = 0.0			,    ):
         # make sure we normalize float32 arrays
         if normalize_means:
             A__					=       x[:input_length].mean(axis=0)
             A__					=       np.subtract(a__			,    a__)
         if normalize_vars:
             A__					=       x[:input_length].std(axis=0)
             A__					=       np.divide(a__			,    a__)

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

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

         return x




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




     def __call__(					self			,    a__			,    a__ = False			,    a__ = None			,    a__ = False			,    a__ = None			,    a__ = None			,    a__ = None			,    a__ = None			,    **a__			,    ):

         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.''')

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

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

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

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

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

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

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

         A__					=       padded_inputs.get('''attention_mask''')
         if attention_mask is not None:
             A__					=       [np.asarray(a__			,    dtype=np.intaa) for array in attention_mask]

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

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

         return padded_inputs





import re



def 		lowerCAmelCase__     (       UpperCamelCase_					:							str						)->	str:

    if len(re.findall('''[ATCG]'''			,							UpperCamelCase_						)						) != len(UpperCamelCase_						):
        raise ValueError('''Invalid Strand'''						)

    return dna.translate(dna.maketrans('''ATCG'''			,							'''TAGC'''						)						)


if __name__ == "__main__":
   import doctest

   doctest.testmod()
