


'''simple docstring'''







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     :  List[Any]											=    logging.get_logger(__name__)
class 						UpperCamelCase__       (			__a						):



     """simple docstring"""






     __magic_name__           =     ['input_values', 'attention_mask']







     def __init__(			self   ,						snake_case__ = 1   ,						snake_case__ = 1_6000   ,						snake_case__ = 0.0   ,						snake_case__ = False   ,						snake_case__ = 80   ,						snake_case__ = 16   ,						snake_case__ = 64   ,						snake_case__ = "hann_window"   ,						snake_case__ = 1.0   ,						snake_case__ = 80   ,						snake_case__ = 7600   ,						snake_case__ = 1E-10   ,						snake_case__ = 2   ,						snake_case__ = True   ,						**snake_case__   ,						):

            '''simple docstring'''

            super().__init__(feature_size=_lowerCamelCase   ,						sampling_rate=_lowerCamelCase   ,						padding_value=_lowerCamelCase   ,						**_lowerCamelCase )
            _lowerCAmelCase     :				List[str]       =							do_normalize
            _lowerCAmelCase     :				str       =							return_attention_mask

            _lowerCAmelCase     :				Tuple       =							num_mel_bins
            _lowerCAmelCase     :				Any       =							hop_length
            _lowerCAmelCase     :				List[Any]       =							win_length
            _lowerCAmelCase     :				Union[str, Any]       =							win_function
            _lowerCAmelCase     :				Optional[Any]       =							frame_signal_scale
            _lowerCAmelCase     :				Optional[Any]       =							fmin
            _lowerCAmelCase     :				Dict       =							fmax
            _lowerCAmelCase     :				Dict       =							mel_floor
            _lowerCAmelCase     :				List[Any]       =							reduction_factor

            _lowerCAmelCase     :				Any       =							win_length * sampling_rate // 1000
            _lowerCAmelCase     :				List[str]       =							hop_length * sampling_rate // 1000
            _lowerCAmelCase     :				List[Any]       =							optimal_fft_length(self.sample_size )
            _lowerCAmelCase     :				Optional[Any]       =							(self.n_fft // 2) + 1

            _lowerCAmelCase     :				Tuple       =							window_function(window_length=self.sample_size   ,						name=self.win_function   ,						periodic=_lowerCamelCase )

            _lowerCAmelCase     :				Union[str, Any]       =							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'   ,						_lowerCamelCase   ,						)
            if reduction_factor != 2.0:
                   warnings.warn(
                       'The argument `reduction_factor` is deprecated and will be removed in version 4.30.0 of Transformers'   ,						_lowerCamelCase   ,						)







     @staticmethod
     # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
     def a						(			snake_case__   ,						snake_case__   ,						snake_case__ = 0.0 ):

            '''simple docstring'''

            if attention_mask is not None:
                   _lowerCAmelCase     :				Any       =							np.array(_lowerCamelCase   ,						np.intaa )
                   _lowerCAmelCase     :				str       =							[]

                   for vector, length in zip(_lowerCamelCase   ,						attention_mask.sum(-1 ) ):
                          _lowerCAmelCase     :				Tuple       =							(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7 )
                          if length < normed_slice.shape[0]:
                                 _lowerCAmelCase     :				Optional[Any]       =							padding_value

                          normed_input_values.append(_lowerCamelCase )
            else:
                   _lowerCAmelCase     :				List[str]       =							[(x - x.mean()) / np.sqrt(x.var() + 1E-7 ) for x in input_values]

            return normed_input_values







     def a						(			self   ,						snake_case__   ,						):

            '''simple docstring'''

            _lowerCAmelCase     :				List[Any]       =							spectrogram(
                _lowerCamelCase   ,						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   ,						snake_case__ = None   ,						snake_case__ = None   ,						snake_case__ = False   ,						snake_case__ = None   ,						snake_case__ = False   ,						snake_case__ = None   ,						snake_case__ = None   ,						snake_case__ = None   ,						snake_case__ = None   ,						**snake_case__   ,						):

            '''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:
                   _lowerCAmelCase     :				List[str]       =							self._process_audio(
                       _lowerCamelCase   ,						_lowerCamelCase   ,						_lowerCamelCase   ,						_lowerCamelCase   ,						_lowerCamelCase   ,						_lowerCamelCase   ,						_lowerCamelCase   ,						_lowerCamelCase   ,						**_lowerCamelCase   ,						)
            else:
                   _lowerCAmelCase     :				int       =							None

            if audio_target is not None:
                   _lowerCAmelCase     :				Optional[Any]       =							self._process_audio(
                       _lowerCamelCase   ,						_lowerCamelCase   ,						_lowerCamelCase   ,						_lowerCamelCase   ,						_lowerCamelCase   ,						_lowerCamelCase   ,						_lowerCamelCase   ,						_lowerCamelCase   ,						**_lowerCamelCase   ,						)

                   if inputs is None:
                          return inputs_target
                   else:
                          _lowerCAmelCase     :				List[Any]       =							inputs_target['input_values']
                          _lowerCAmelCase     :				Optional[int]       =							inputs_target.get('attention_mask' )
                          if decoder_attention_mask is not None:
                                 _lowerCAmelCase     :				Tuple       =							decoder_attention_mask

            return inputs







     def a						(			self   ,						snake_case__   ,						snake_case__ = False   ,						snake_case__ = False   ,						snake_case__ = None   ,						snake_case__ = False   ,						snake_case__ = None   ,						snake_case__ = None   ,						snake_case__ = None   ,						**snake_case__   ,						):

            '''simple docstring'''

            _lowerCAmelCase     :				Optional[Any]       =							isinstance(_lowerCamelCase   ,						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}' )
            _lowerCAmelCase     :				Union[str, Any]       =							is_batched_numpy or (
                isinstance(_lowerCamelCase   ,						(list, tuple) ) and (isinstance(speech[0]   ,						(np.ndarray, tuple, list) ))
            )

            if is_batched:
                   _lowerCAmelCase     :				Optional[Any]       =							[np.asarray(_lowerCamelCase   ,						dtype=np.floataa ) for speech in speech]
            elif not is_batched and not isinstance(_lowerCamelCase   ,						np.ndarray ):
                   _lowerCAmelCase     :				str       =							np.asarray(_lowerCamelCase   ,						dtype=np.floataa )
            elif isinstance(_lowerCamelCase   ,						np.ndarray ) and speech.dtype is np.dtype(np.floataa ):
                   _lowerCAmelCase     :				Any       =							speech.astype(np.floataa )

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

            # needed to make pad() work on spectrogram inputs
            _lowerCAmelCase     :				Any       =							self.feature_size

            # convert into correct format for padding
            if is_target:
                   _lowerCAmelCase     :				Optional[int]       =							[self._extract_mel_features(_lowerCamelCase ) for waveform in speech]
                   _lowerCAmelCase     :				Tuple       =							BatchFeature({'input_values': features} )
                   _lowerCAmelCase     :				Union[str, Any]       =							self.num_mel_bins
            else:
                   _lowerCAmelCase     :				Optional[Any]       =							BatchFeature({'input_values': speech} )

            _lowerCAmelCase     :				Any       =							self.pad(
                _lowerCamelCase   ,						padding=_lowerCamelCase   ,						max_length=_lowerCamelCase   ,						truncation=_lowerCamelCase   ,						pad_to_multiple_of=_lowerCamelCase   ,						return_attention_mask=_lowerCamelCase   ,						**_lowerCamelCase   ,						)

            _lowerCAmelCase     :				str       =							feature_size_hack

            # convert input values to correct format
            _lowerCAmelCase     :				Tuple       =							padded_inputs['input_values']
            if not isinstance(input_values[0]   ,						np.ndarray ):
                   _lowerCAmelCase     :				List[str]       =							[np.asarray(_lowerCamelCase   ,						dtype=np.floataa ) for array in input_values]
            elif (
                not isinstance(_lowerCamelCase   ,						np.ndarray )
                and isinstance(input_values[0]   ,						np.ndarray )
                and input_values[0].dtype is np.dtype(np.floataa )
            ):
                   _lowerCAmelCase     :				Optional[Any]       =							[array.astype(np.floataa ) for array in input_values]
            elif isinstance(_lowerCamelCase   ,						np.ndarray ) and input_values.dtype is np.dtype(np.floataa ):
                   _lowerCAmelCase     :				List[Any]       =							input_values.astype(np.floataa )

            # convert attention_mask to correct format
            _lowerCAmelCase     :				Union[str, Any]       =							padded_inputs.get('attention_mask' )
            if attention_mask is not None:
                   _lowerCAmelCase     :				Tuple       =							[np.asarray(_lowerCamelCase   ,						dtype=np.intaa ) for array in attention_mask]

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

            if return_tensors is not None:
                   _lowerCAmelCase     :				Any       =							padded_inputs.convert_to_tensors(_lowerCamelCase )

            return padded_inputs







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Tuple       =							super().to_dict()

            # Don't serialize these as they are derived from the other properties.
            _lowerCAmelCase     :				List[Any]       =							['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'''

import math
import sys



def snake_case_      (					__snake_case  :							str)			->					str:
   lowerCAmelCase_			=      ''''''
   try:
      with open(__snake_case	,   '''rb''') as binary_file:
         lowerCAmelCase_			=      binary_file.read()
      for dat in data:
         lowerCAmelCase_			=      F'''{dat:08b}'''
         result += curr_byte
      return result
   except OSError:
      print('''File not accessible''')
      sys.exit()



def snake_case_      (					__snake_case  :							str)			->					str:
   lowerCAmelCase_			=      {'''0''': '''0''', '''1''': '''1'''}
   lowerCAmelCase_	,lowerCAmelCase_			=      '''''', ''''''
   lowerCAmelCase_			=      len(__snake_case)

   for i in range(len(__snake_case)):
      curr_string += data_bits[i]
      if curr_string not in lexicon:
         continue

      lowerCAmelCase_			=      lexicon[curr_string]
      result += last_match_id
      lowerCAmelCase_			=      last_match_id + '''0'''

      if math.loga(__snake_case).is_integer():
         lowerCAmelCase_			=      {}
         for curr_key in list(__snake_case):
            lowerCAmelCase_			=      lexicon.pop(__snake_case)
         lowerCAmelCase_			=      new_lex

      lowerCAmelCase_			=      last_match_id + '''1'''
      index += 1
      lowerCAmelCase_			=      ''''''
   return result



def snake_case_      (					__snake_case  :							str	,   __snake_case  :							str)			->					None:
   lowerCAmelCase_			=      8
   try:
      with open(__snake_case	,   '''wb''') as opened_file:
         lowerCAmelCase_			=      [
             to_write[i : i + byte_length]
             for i in range(0	,   len(__snake_case)	,   __snake_case)
         ]

         if len(result_byte_array[-1]) % byte_length == 0:
            result_byte_array.append('''10000000''')
         else:
            result_byte_array[-1] += "1" + "0" * (
                byte_length - len(result_byte_array[-1]) - 1
            )

         for elem in result_byte_array[:-1]:
            opened_file.write(int(__snake_case	,   2).to_bytes(1	,   byteorder='''big'''))
   except OSError:
      print('''File not accessible''')
      sys.exit()



def snake_case_      (					__snake_case  :							str)			->					str:
   lowerCAmelCase_			=      0
   for letter in data_bits:
      if letter == "1":
         break
      counter += 1

   lowerCAmelCase_			=      data_bits[counter:]
   lowerCAmelCase_			=      data_bits[counter + 1 :]
   return data_bits



def snake_case_      (					__snake_case  :							str	,   __snake_case  :							str)			->					None:
   lowerCAmelCase_			=      read_file_binary(__snake_case)
   lowerCAmelCase_			=      remove_prefix(__snake_case)
   lowerCAmelCase_			=      decompress_data(__snake_case)
   write_file_binary(__snake_case	,   __snake_case)


if __name__ == "__main__":
 compress(sys.argv[1], sys.argv[2])





