



"""simple docstring"""
from math import ceil
from typing import List, Optional, Union

import numpy as np

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


A:   List[str]								=		logging.get_logger(__name__)



class       SCREAMING_SNAKE_CASE__  (					UpperCAmelCase__ ):
   __lowerCAmelCase  :   Optional[int]						=						['audio_values', 'audio_mask']

   def __init__(   self      ,     _SCREAMING_SNAKE_CASE=2048      ,     _SCREAMING_SNAKE_CASE=1      ,     _SCREAMING_SNAKE_CASE=[16, 16]      ,     _SCREAMING_SNAKE_CASE=128      ,     _SCREAMING_SNAKE_CASE=44100      ,     _SCREAMING_SNAKE_CASE=86      ,     _SCREAMING_SNAKE_CASE=2048      ,     _SCREAMING_SNAKE_CASE=0.0      ,     **_SCREAMING_SNAKE_CASE      ,     )  ->      str:





    '''simple docstring'''




    super().__init__(
        feature_size=_SCREAMING_SNAKE_CASE      ,     sampling_rate=_SCREAMING_SNAKE_CASE      ,     padding_value=_SCREAMING_SNAKE_CASE      ,     **_SCREAMING_SNAKE_CASE      ,     )

    UpperCAmelCase	:					Tuple	=	spectrogram_length
    UpperCAmelCase	:					List[str]	=	num_channels
    UpperCAmelCase	:					str	=	patch_size
    UpperCAmelCase	:					Any	=	feature_size // self.patch_size[1]
    UpperCAmelCase	:					Optional[Any]	=	n_fft
    UpperCAmelCase	:					Dict	=	sampling_rate // hop_length_to_sampling_rate
    UpperCAmelCase	:					Union[str, Any]	=	sampling_rate
    UpperCAmelCase	:					Dict	=	padding_value
    UpperCAmelCase	:					str	=	mel_filter_bank(
        num_frequency_bins=1 + n_fft // 2      ,     num_mel_filters=_SCREAMING_SNAKE_CASE      ,     min_frequency=0.0      ,     max_frequency=2_2050.0      ,     sampling_rate=_SCREAMING_SNAKE_CASE      ,     norm="""slaney"""      ,     mel_scale="""slaney"""      ,     ).T

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE						)  ->      np.ndarray:





    '''simple docstring'''




    UpperCAmelCase	:					Tuple	=	spectrogram(
        _SCREAMING_SNAKE_CASE      ,     window_function(self.n_fft      ,     """hann"""						)      ,     frame_length=self.n_fft      ,     hop_length=self.hop_length      ,     power=2.0      ,     mel_filters=self.mel_filters.T      ,     log_mel="""dB"""      ,     db_range=80.0      ,     )
    UpperCAmelCase	:					int	=	log_spec[:, :-1]
    UpperCAmelCase	:					Any	=	log_spec - 20.0
    UpperCAmelCase	:					List[Any]	=	np.clip(log_spec / 40.0      ,     -2.0      ,     0.0						) + 1.0
    return log_spec




   def __call__(   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE = None      ,     _SCREAMING_SNAKE_CASE = True      ,     _SCREAMING_SNAKE_CASE = None      ,     _SCREAMING_SNAKE_CASE = False      ,     _SCREAMING_SNAKE_CASE = False      ,     **_SCREAMING_SNAKE_CASE      ,     )  ->      BatchFeature:





    '''simple docstring'''




    if sampling_rate is not None:
     if sampling_rate != self.sampling_rate:
      raise ValueError(
          """This feature extractor is set to support sampling rate"""
          F" of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled"
          F" 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."""						)

    UpperCAmelCase	:					Union[str, Any]	=	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	:					int	=	is_batched_numpy or (
        isinstance(_SCREAMING_SNAKE_CASE      ,     (list, tuple)						) and (isinstance(raw_speech[0]      ,     (np.ndarray, tuple, list)						))
    )
    if is_batched:
     UpperCAmelCase	:					Optional[int]	=	[np.asarray([speech]      ,     dtype=np.floataa						).T for speech in raw_speech]
    elif not is_batched and not isinstance(_SCREAMING_SNAKE_CASE      ,     np.ndarray						):
     UpperCAmelCase	:					Any	=	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	:					str	=	raw_speech.astype(np.floataa						)
    # always return batch
    if not is_batched:
     UpperCAmelCase	:					Optional[Any]	=	[np.asarray([raw_speech]						).T]

    # Convert audio signals to log mel spectrograms, truncate by time axis
    UpperCAmelCase	:					str	=	[
        self._np_extract_fbank_features(waveform.squeeze()						).T[: self.spectrogram_length] for waveform in raw_speech
    ]
    if isinstance(audio_features[0]      ,     _SCREAMING_SNAKE_CASE						):
     UpperCAmelCase	:					List[Any]	=	[np.asarray(_SCREAMING_SNAKE_CASE      ,     dtype=np.floataa						) for feature in audio_features]

    # Create audio attention mask
    UpperCAmelCase	:					Dict	=	max(
        [ceil(feature.shape[0] / self.patch_size[0]						) * self.freq_len for feature in audio_features]						)  # The maximum number of audio patches in a batch
    if return_attention_mask:
     UpperCAmelCase	:					List[Any]	=	[
         (ceil(feature.shape[0] / self.patch_size[0]						) * self.freq_len) * [1]
         + (max_patch_len - ceil(feature.shape[0] / self.patch_size[0]						) * self.freq_len) * [0]
         for feature in audio_features
     ]
     UpperCAmelCase	:					Optional[int]	=	np.array(_SCREAMING_SNAKE_CASE						).astype(np.floataa						)

    # convert into correct format for padding
    UpperCAmelCase	:					Dict	=	max_patch_len // self.freq_len * self.patch_size[0]  # The maximum audio size in a batch
    UpperCAmelCase	:					Tuple	=	np.ones([len(_SCREAMING_SNAKE_CASE						), 1, max_time_len, self.feature_size]						).astype(np.floataa						)
    UpperCAmelCase	:					Dict	=	padded_audio_features * self.padding_value
    for i in range(len(_SCREAMING_SNAKE_CASE						)						):
     UpperCAmelCase	:					Dict	=	audio_features[i]
     UpperCAmelCase	:					Any	=	feature

    # return as BatchFeature
    if return_attention_mask:
     UpperCAmelCase	:					Optional[Any]	=	{"""audio_values""": padded_audio_features, """audio_mask""": audio_mask}
    else:
     UpperCAmelCase	:					int	=	{"""audio_values""": padded_audio_features}

    UpperCAmelCase	:					Dict	=	BatchFeature(data=_SCREAMING_SNAKE_CASE      ,     tensor_type=_SCREAMING_SNAKE_CASE						)
    return encoded_inputs





"""simple docstring"""
A:   int								=		range(2, 2_0 + 1)
A:   Any								=		[1_0**k for k in range(ks[-1] + 1)]
A:   dict[int, dict[int, list[list[int]]]]								=		{}

def       _snake_case						(						UpperCamelCase						:		Dict			,		UpperCamelCase						:		Any			,		UpperCamelCase						:		Any			,		UpperCamelCase						:		int						):
 UpperCAmelCase	:					List[str]	=	sum(a_i[j] for j in range(UpperCamelCase			,		len(UpperCamelCase						)						)						)
 UpperCAmelCase	:					str	=	sum(a_i[j] * base[j] for j in range(min(len(UpperCamelCase						)			,		UpperCamelCase						)						)						)

 UpperCAmelCase	,		UpperCAmelCase	:					str	=	0, 0
 UpperCAmelCase	:					Optional[Any]	=	n - i

 UpperCAmelCase	:					Optional[int]	=	memo.get(UpperCamelCase						)

 if sub_memo is not None:
  UpperCAmelCase	:					str	=	sub_memo.get(UpperCamelCase						)

  if jumps is not None and len(UpperCamelCase						) > 0:
   # find and make the largest jump without going over
   UpperCAmelCase	:					Tuple	=	-1
   for _k in range(len(UpperCamelCase						) - 1			,		-1			,		-1						):
    if jumps[_k][2] <= k and jumps[_k][1] <= max_dn:
     UpperCAmelCase	:					int	=	_k
     break

   if max_jump >= 0:
    UpperCAmelCase	,		UpperCAmelCase	,		UpperCAmelCase	:					Optional[Any]	=	jumps[max_jump]
    # since the difference between jumps is cached, add c
    UpperCAmelCase	:					List[str]	=	diff + c
    for j in range(min(UpperCamelCase			,		len(UpperCamelCase						)						)						):
     UpperCAmelCase	,		UpperCAmelCase	:					Union[str, Any]	=	divmod(UpperCamelCase			,		10						)
    if new_c > 0:
     add(UpperCamelCase			,		UpperCamelCase			,		UpperCamelCase						)

  else:
   UpperCAmelCase	:					int	=	[]
 else:
  UpperCAmelCase	:					List[str]	=	{c: []}
  UpperCAmelCase	:					str	=	sub_memo

 if dn >= max_dn or c + diff >= base[k]:
  return diff, dn

 if k > ks[0]:
  while True:
   # keep doing smaller jumps
   UpperCAmelCase	,		UpperCAmelCase	:					List[str]	=	next_term(UpperCamelCase			,		k - 1			,		i + dn			,		UpperCamelCase						)
   diff += _diff
   dn += terms_jumped

   if dn >= max_dn or c + diff >= base[k]:
    break
 else:
  # would be too small a jump, just compute sequential terms instead
  UpperCAmelCase	,		UpperCAmelCase	:					int	=	compute(UpperCamelCase			,		UpperCamelCase			,		i + dn			,		UpperCamelCase						)
  diff += _diff
  dn += terms_jumped

 UpperCAmelCase	:					Dict	=	sub_memo[c]

 # keep jumps sorted by # of terms skipped
 UpperCAmelCase	:					str	=	0
 while j < len(UpperCamelCase						):
  if jumps[j][1] > dn:
   break
  j += 1

 # cache the jump for this value digitsum(b) and c
 sub_memo[c].insert(UpperCamelCase			,		(diff, dn, k)						)
 return (diff, dn)

def       _snake_case						(						UpperCamelCase						:		int			,		UpperCamelCase						:		Tuple			,		UpperCamelCase						:		Union[str, Any]			,		UpperCamelCase						:		Any						):
 if i >= n:
  return 0, i
 if k > len(UpperCamelCase						):
  a_i.extend([0 for _ in range(k - len(UpperCamelCase						)						)]						)

 # note: a_i -> b * 10^k + c
 # ds_b -> digitsum(b)
 # ds_c -> digitsum(c)
 UpperCAmelCase	:					List[str]	=	i
 UpperCAmelCase	,		UpperCAmelCase	,		UpperCAmelCase	:					Optional[int]	=	0, 0, 0
 for j in range(len(UpperCamelCase						)						):
  if j >= k:
   ds_b += a_i[j]
  else:
   ds_c += a_i[j]

 while i < n:
  i += 1
  UpperCAmelCase	:					Optional[int]	=	ds_c + ds_b
  diff += addend
  UpperCAmelCase	:					str	=	0
  for j in range(UpperCamelCase						):
   UpperCAmelCase	:					Any	=	a_i[j] + addend
   UpperCAmelCase	,		UpperCAmelCase	:					Any	=	divmod(UpperCamelCase			,		10						)

   ds_c += a_i[j]

  if addend > 0:
   break

 if addend > 0:
  add(UpperCamelCase			,		UpperCamelCase			,		UpperCamelCase						)
 return diff, i - start_i

def       _snake_case						(						UpperCamelCase						:		Union[str, Any]			,		UpperCamelCase						:		str			,		UpperCamelCase						:		Optional[int]						):
 for j in range(UpperCamelCase			,		len(UpperCamelCase						)						):
  UpperCAmelCase	:					Optional[int]	=	digits[j] + addend
  if s >= 10:
   UpperCAmelCase	,		UpperCAmelCase	:					int	=	divmod(UpperCamelCase			,		10						)
   UpperCAmelCase	:					str	=	addend // 10 + quotient
  else:
   UpperCAmelCase	:					Any	=	s
   UpperCAmelCase	:					Union[str, Any]	=	addend // 10

  if addend == 0:
   break

 while addend > 0:
  UpperCAmelCase	,		UpperCAmelCase	:					Any	=	divmod(UpperCamelCase			,		10						)
  digits.append(UpperCamelCase						)

def       _snake_case						(						UpperCamelCase						:		int = 10**15						):
 UpperCAmelCase	:					Dict	=	[1]
 UpperCAmelCase	:					int	=	1
 UpperCAmelCase	:					Tuple	=	0
 while True:
  UpperCAmelCase	,		UpperCAmelCase	:					Tuple	=	next_term(UpperCamelCase			,		20			,		i + dn			,		UpperCamelCase						)
  dn += terms_jumped
  if dn == n - i:
   break

 UpperCAmelCase	:					Any	=	0
 for j in range(len(UpperCamelCase						)						):
  a_n += digits[j] * 10**j
 return a_n


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