


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


snake_case				:    Any       				=     logging.get_logger(__name__)







class 		_snake_case  (      _snake_case							):
 SCREAMING_SNAKE_CASE__      =       ['input_values', 'attention_mask']




 def __init__(		self					,  _lowerCamelCase = 1					,  _lowerCamelCase = 1_6000					,  _lowerCamelCase = 0.0					,  _lowerCamelCase = False					,  _lowerCamelCase = 80					,  _lowerCamelCase = 16					,  _lowerCamelCase = 64					,  _lowerCamelCase = "hann_window"					,  _lowerCamelCase = 1.0					,  _lowerCamelCase = 80					,  _lowerCamelCase = 7600					,  _lowerCamelCase = 1e-10					,  _lowerCamelCase = 2					,  _lowerCamelCase = True					,  **_lowerCamelCase					,  ):
       super().__init__(feature_size=_lowerCamelCase					,  sampling_rate=_lowerCamelCase					,  padding_value=_lowerCamelCase					,  **_lowerCamelCase    )
       a						:Union[str, Any] 							=       do_normalize
       a						:List[Any] 							=       return_attention_mask

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

       a						:Union[str, Any] 							=       win_length * sampling_rate // 1000
       a						:Dict 							=       hop_length * sampling_rate // 1000
       a						:Any 							=       optimal_fft_length(self.sample_size    )
       a						:List[Any] 							=       (self.n_fft // 2) + 1

       a						:Any 							=       window_function(window_length=self.sample_size					,  name=self.win_function					,  periodic=_lowerCamelCase    )

       a						:str 							=       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        SCREAMING_SNAKE_CASE__				(		_lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase = 0.0    ):
       if attention_mask is not None:
             a						:List[Any] 							=       np.array(_lowerCamelCase					,  np.intaa    )
             a						:List[str] 							=       []

             for vector, length in zip(_lowerCamelCase					,  attention_mask.sum(-1    )    ):
                   a						:Dict 							=       (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7    )
                   if length < normed_slice.shape[0]:
                         a						:Union[str, Any] 							=       padding_value

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

       return normed_input_values




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  ):
       a						:Union[str, 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					,  _lowerCamelCase = None					,  _lowerCamelCase = None					,  _lowerCamelCase = False					,  _lowerCamelCase = None					,  _lowerCamelCase = False					,  _lowerCamelCase = None					,  _lowerCamelCase = None					,  _lowerCamelCase = None					,  _lowerCamelCase = None					,  **_lowerCamelCase					,  ):
       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						:Optional[Any] 							=       self._process_audio(
                 _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  **_lowerCamelCase					,  )
       else:
             a						:int 							=       None

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

             if inputs is None:
                   return inputs_target
             else:
                   a						:Optional[Any] 							=       inputs_target['''input_values''']
                   a						:Union[str, Any] 							=       inputs_target.get('''attention_mask'''    )
                   if decoder_attention_mask is not None:
                         a						:str 							=       decoder_attention_mask

       return inputs




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase = False					,  _lowerCamelCase = False					,  _lowerCamelCase = None					,  _lowerCamelCase = False					,  _lowerCamelCase = None					,  _lowerCamelCase = None					,  _lowerCamelCase = None					,  **_lowerCamelCase					,  ):
       a						:Optional[int] 							=       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}'''    )
       a						:List[Any] 							=       is_batched_numpy or (
           isinstance(_lowerCamelCase					,  (list, tuple)    ) and (isinstance(speech[0]					,  (np.ndarray, tuple, list)    ))
       )

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

       # always return batch
       if not is_batched:
             a						:List[Any] 							=       [speech]

       # needed to make pad() work on spectrogram inputs
       a						:Optional[int] 							=       self.feature_size

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

       a						:Optional[int] 							=       self.pad(
           _lowerCamelCase					,  padding=_lowerCamelCase					,  max_length=_lowerCamelCase					,  truncation=_lowerCamelCase					,  pad_to_multiple_of=_lowerCamelCase					,  return_attention_mask=_lowerCamelCase					,  **_lowerCamelCase					,  )

       a						:List[str] 							=       feature_size_hack

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

       # convert attention_mask to correct format
       a						:Any 							=       padded_inputs.get('''attention_mask'''    )
       if attention_mask is not None:
             a						:Union[str, Any] 							=       [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:
             a						:Union[str, Any] 							=       (
                 attention_mask
                 if self._get_padding_strategies(_lowerCamelCase					,  max_length=_lowerCamelCase    ) is not PaddingStrategy.DO_NOT_PAD
                 else None
             )
             a						: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:
             a						:Any 							=       padded_inputs.convert_to_tensors(_lowerCamelCase    )

       return padded_inputs




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:List[str] 							=       super().to_dict()

       # Don't serialize these as they are derived from the other properties.
       a						:Tuple 							=       ['''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 os

__snake_case    						={"""I""": 1, """V""": 5, """X""": 10, """L""": 50, """C""": 100, """D""": 500, """M""": 1_000}





def 	a_					( lowerCamelCase	:     str				):
   lowerCAmelCase     =			0

   lowerCAmelCase     =			0
   while index < len(lowerCamelCase				) - 1:
      lowerCAmelCase     =			SYMBOLS[numerals[index]]
      lowerCAmelCase     =			SYMBOLS[numerals[index + 1]]
      if current_value < next_value:
         total_value -= current_value
      else:
         total_value += current_value
      index += 1
   total_value += SYMBOLS[numerals[index]]

   return total_value





def 	a_					( lowerCamelCase	:     int				):
   lowerCAmelCase     =			''

   lowerCAmelCase     =			num // 1000
   numerals += m_count * "M"
   num %= 1000

   lowerCAmelCase     =			num // 100
   if c_count == 9:
      numerals += "CM"
      c_count -= 9
   elif c_count == 4:
      numerals += "CD"
      c_count -= 4
   if c_count >= 5:
      numerals += "D"
      c_count -= 5
   numerals += c_count * "C"
   num %= 100

   lowerCAmelCase     =			num // 10
   if x_count == 9:
      numerals += "XC"
      x_count -= 9
   elif x_count == 4:
      numerals += "XL"
      x_count -= 4
   if x_count >= 5:
      numerals += "L"
      x_count -= 5
   numerals += x_count * "X"
   num %= 10

   if num == 9:
      numerals += "IX"
      num -= 9
   elif num == 4:
      numerals += "IV"
      num -= 4
   if num >= 5:
      numerals += "V"
      num -= 5
   numerals += num * "I"

   return numerals





def 	a_					( lowerCamelCase	:     str = "/p089_roman.txt"				):
   lowerCAmelCase     =			0

   with open(os.path.dirname(lowerCamelCase				) + roman_numerals_filename				) as filea:
      lowerCAmelCase     =			filea.readlines()

   for line in lines:
      lowerCAmelCase     =			line.strip()
      lowerCAmelCase     =			parse_roman_numerals(lowerCamelCase				)
      lowerCAmelCase     =			generate_roman_numerals(lowerCamelCase				)
      savings += len(lowerCamelCase				) - len(lowerCamelCase				)

   return savings


if __name__ == "__main__":
       print(F'''{solution() = }''')





