





'''simple docstring'''






from typing import 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 ....file_utils import PaddingStrategy, TensorType
from ....utils import logging


snake_case_     =	logging.get_logger(__name__)


class   _lowercase						(							a				):
   _UpperCamelCase       		=   ["""input_features""", """attention_mask"""]


   def __init__( self    ,   _UpperCAmelCase=80    ,   _UpperCAmelCase=16_000    ,   _UpperCAmelCase=0.0    ,   _UpperCAmelCase=10    ,   _UpperCAmelCase=25    ,   _UpperCAmelCase="hamming_window"    ,   _UpperCAmelCase=32768.0    ,   _UpperCAmelCase=0.97    ,   _UpperCAmelCase=1.0    ,   _UpperCAmelCase=True    ,   _UpperCAmelCase=True    ,   _UpperCAmelCase=False    ,   **_UpperCAmelCase    ,   ):
       super().__init__(feature_size=_UpperCAmelCase    ,   sampling_rate=_UpperCAmelCase    ,   padding_value=_UpperCAmelCase    ,   **_UpperCAmelCase					)

       A       :       Any							=						feature_size
       A       :       int							=						sampling_rate
       A       :       Dict							=						padding_value
       A       :       str							=						hop_length
       A       :       str							=						win_length
       A       :       Dict							=						frame_signal_scale
       A       :       Optional[Any]							=						preemphasis_coeff
       A       :       List[Any]							=						mel_floor
       A       :       Optional[int]							=						normalize_means
       A       :       Optional[Any]							=						normalize_vars
       A       :       Tuple							=						win_function
       A       :       Union[str, Any]							=						return_attention_mask

       A       :       Union[str, Any]							=						win_length * sampling_rate // 1_000
       A       :       Tuple							=						hop_length * sampling_rate // 1_000

       A       :       Any							=						optimal_fft_length(self.sample_size					)
       A       :       List[Any]							=						(self.n_fft // 2) + 1


   def 					snake_case				( self    ,   _UpperCAmelCase					):
       if self.win_function == "hamming_window":
           A       :       int							=						window_function(window_length=self.sample_size    ,   name=self.win_function    ,   periodic=_UpperCAmelCase					)
       else:
           A       :       Optional[int]							=						window_function(window_length=self.sample_size    ,   name=self.win_function					)

       A       :       str							=						mel_filter_bank(
           num_frequency_bins=self.n_freqs    ,   num_mel_filters=self.feature_size    ,   min_frequency=0.0    ,   max_frequency=self.sampling_rate / 2.0    ,   sampling_rate=self.sampling_rate    ,   )

       A       :       List[str]							=						spectrogram(
           one_waveform * self.frame_signal_scale    ,   window=_UpperCAmelCase    ,   frame_length=self.sample_size    ,   hop_length=self.sample_stride    ,   fft_length=self.n_fft    ,   center=_UpperCAmelCase    ,   preemphasis=self.preemphasis_coeff    ,   mel_filters=_UpperCAmelCase    ,   mel_floor=self.mel_floor    ,   log_mel='''log'''    ,   )
       return msfc_features.T


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase					):
       # make sure we normalize float32 arrays
       if self.normalize_means:
           A       :       Dict							=						x[:input_length].mean(axis=0					)
           A       :       Optional[Any]							=						np.subtract(_UpperCAmelCase    ,   _UpperCAmelCase					)
       if self.normalize_vars:
           A       :       Tuple							=						x[:input_length].std(axis=0					)
           A       :       Optional[int]							=						np.divide(_UpperCAmelCase    ,   _UpperCAmelCase					)

       if input_length < x.shape[0]:
           A       :       Optional[int]							=						padding_value

       # make sure array is in float32
       A       :       Optional[Any]							=						x.astype(np.floataa					)

       return x


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase = None					):
       A       :       Any							=						attention_mask.sum(-1					) if attention_mask is not None else [x.shape[0] for x in input_features]
       return [self._normalize_one(_UpperCAmelCase    ,   _UpperCAmelCase    ,   self.padding_value					) for x, n in zip(_UpperCAmelCase    ,   _UpperCAmelCase					)]


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

       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       :       List[Any]							=						isinstance(_UpperCAmelCase    ,   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       :       Optional[int]							=						is_batched_numpy or (
           isinstance(_UpperCAmelCase    ,   (list, tuple)					) and (isinstance(raw_speech[0]    ,   (np.ndarray, tuple, list)					))
       )

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

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

       # extract fbank features
       A       :       Optional[Any]							=						[self._extract_mfsc_features(_UpperCAmelCase					) for one_waveform in raw_speech]

       # convert into correct format for padding
       A       :       List[Any]							=						BatchFeature({'''input_features''': features}					)

       A       :       int							=						self.pad(
           _UpperCAmelCase    ,   padding=_UpperCAmelCase    ,   max_length=_UpperCAmelCase    ,   truncation=_UpperCAmelCase    ,   pad_to_multiple_of=_UpperCAmelCase    ,   return_attention_mask=_UpperCAmelCase    ,   **_UpperCAmelCase    ,   )
       # make sure list is in array format
       A       :       Optional[Any]							=						padded_inputs.get('''input_features'''					)
       if isinstance(input_features[0]    ,   _UpperCAmelCase					):
           A       :       List[Any]							=						[np.asarray(_UpperCAmelCase    ,   dtype=np.floataa					) for feature in input_features]

       A       :       int							=						padded_inputs.get('''attention_mask'''					)
       if attention_mask is not None:
           A       :       List[str]							=						[np.asarray(_UpperCAmelCase    ,   dtype=np.intaa					) for array in attention_mask]

       if self.normalize_means or self.normalize_vars:
           A       :       List[Any]							=						(
               np.array(_UpperCAmelCase    ,   dtype=np.intaa					)
               if self._get_padding_strategies(_UpperCAmelCase    ,   max_length=_UpperCAmelCase					) is not PaddingStrategy.DO_NOT_PAD
               and padding
               else None
           )
           A       :       Optional[Any]							=						self.normalize(
               padded_inputs['''input_features''']    ,   attention_mask=_UpperCAmelCase					)

       if return_tensors is not None:
           A       :       str							=						padded_inputs.convert_to_tensors(_UpperCAmelCase					)

       return padded_inputs







'''simple docstring'''






from math import pi
def 							_lowerCamelCase(  UpperCamelCase__    : int    ,       UpperCamelCase__    : int		)		->  float:
    return 2 * pi * radius * (angle / 360)


if __name__ == "__main__":
     print(arc_length(90, 10))
