






'''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[Any]  							=		logging.get_logger(__name__)
class 				lowercase       (							lowercase__      ):


    '''simple docstring'''

    lowerCAmelCase__      				=							["audio_values", "audio_mask"]

    def __init__(					self     :     List[str]				,     __lowerCamelCase     :     Tuple=2048				,     __lowerCamelCase     :     Union[str, Any]=1				,     __lowerCamelCase     :     Optional[int]=[16, 16]				,     __lowerCamelCase     :     List[Any]=128				,     __lowerCamelCase     :     List[Any]=44100				,     __lowerCamelCase     :     Any=86				,     __lowerCamelCase     :     Dict=2048				,     __lowerCamelCase     :     List[Any]=0.0				,     **__lowerCamelCase     :     Optional[int]				,     )							->							Any:

     '''simple docstring'''




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

     lowerCamelCase__  =     spectrogram_length
     lowerCamelCase__  =     num_channels
     lowerCamelCase__  =     patch_size
     lowerCamelCase__  =     feature_size // self.patch_size[1]
     lowerCamelCase__  =     n_fft
     lowerCamelCase__  =     sampling_rate // hop_length_to_sampling_rate
     lowerCamelCase__  =     sampling_rate
     lowerCamelCase__  =     padding_value
     lowerCamelCase__  =     mel_filter_bank(
         num_frequency_bins=1 + n_fft // 2				,     num_mel_filters=__lowerCamelCase				,     min_frequency=0.0				,     max_frequency=22050.0				,     sampling_rate=__lowerCamelCase				,     norm="slaney"				,     mel_scale="slaney"				,     ).T

    def    a__				(					self     :     str				,     __lowerCamelCase     :     np.array     )							->							Optional[Any]:

     '''simple docstring'''




     lowerCamelCase__  =     spectrogram(
         __lowerCamelCase				,     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				,     )
     lowerCamelCase__  =     log_spec[:, :-1]
     lowerCamelCase__  =     log_spec - 20.0
     lowerCamelCase__  =     np.clip(log_spec / 40.0				,     -2.0				,     0.0     ) + 1.0
     return log_spec




    def __call__(					self     :     Optional[int]				,     __lowerCamelCase     :     Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]				,     __lowerCamelCase     :     Optional[Union[str, TensorType]] = None				,     __lowerCamelCase     :     Optional[bool] = True				,     __lowerCamelCase     :     Optional[int] = None				,     __lowerCamelCase     :     bool = False				,     __lowerCamelCase     :     bool = False				,     **__lowerCamelCase     :     Tuple				,     )							->							Tuple:

     '''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."     )

     lowerCamelCase__  =     isinstance(__lowerCamelCase				,     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}'''     )
     lowerCamelCase__  =     is_batched_numpy or (
         isinstance(__lowerCamelCase				,     (list, tuple)     ) and (isinstance(raw_speech[0]				,     (np.ndarray, tuple, list)     ))
     )
     if is_batched:
      lowerCamelCase__  =     [np.asarray([speech]				,     dtype=np.floataa     ).T for speech in raw_speech]
     elif not is_batched and not isinstance(__lowerCamelCase				,     np.ndarray     ):
      lowerCamelCase__  =     np.asarray(__lowerCamelCase				,     dtype=np.floataa     )
     elif isinstance(__lowerCamelCase				,     np.ndarray     ) and raw_speech.dtype is np.dtype(np.floataa     ):
      lowerCamelCase__  =     raw_speech.astype(np.floataa     )
     # always return batch
     if not is_batched:
      lowerCamelCase__  =     [np.asarray([raw_speech]     ).T]

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

     # Create audio attention mask
     lowerCamelCase__  =     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:
      lowerCamelCase__  =     [
          (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
      ]
      lowerCamelCase__  =     np.array(__lowerCamelCase     ).astype(np.floataa     )

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

     # return as BatchFeature
     if return_attention_mask:
      lowerCamelCase__  =     {"audio_values": padded_audio_features, "audio_mask": audio_mask}
     else:
      lowerCamelCase__  =     {"audio_values": padded_audio_features}

     lowerCamelCase__  =     BatchFeature(data=__lowerCamelCase				,     tensor_type=__lowerCamelCase     )
     return encoded_inputs













'''simple docstring'''





from decimal import Decimal, getcontext
from math import ceil, factorial






def    lowerCamelCase_     (    lowercase__):

 if not isinstance(lowercase__				,     lowercase__):
  raise TypeError("Undefined for non-integers")
 elif precision < 1:
  raise ValueError("Undefined for non-natural numbers")

 lowerCamelCase__  =     precision
 lowerCamelCase__  =     ceil(precision / 14)
 lowerCamelCase__  =     426880 * Decimal(10005).sqrt()
 lowerCamelCase__  =     1
 lowerCamelCase__  =     13591409
 lowerCamelCase__  =     Decimal(lowercase__)
 for k in range(1				,     lowercase__):
  lowerCamelCase__  =     factorial(6 * k) // (factorial(3 * k) * factorial(lowercase__) ** 3)
  linear_term += 545140134
  exponential_term *= -262537412640768000
  partial_sum += Decimal(multinomial_term * linear_term) / exponential_term
 return str(constant_term / partial_sum)[:-1]


if __name__ == "__main__":
 __A							:							Dict  							=		50
 print(F"""The first {n} digits of pi is: {pi(n)}""")

