





import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np

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


UpperCAmelCase	:	List[Any]		     =       logging.get_logger(__name__)







class 				__lowerCAmelCase       (    UpperCamelCase__):
      _lowercase				:					Optional[Any]						=			["""input_features"""]







      def __init__(    self		,      lowerCAmelCase__=8_0		,      lowerCAmelCase__=1_6_0_0_0		,      lowerCAmelCase__=1_6_0		,      lowerCAmelCase__=3_0		,      lowerCAmelCase__=4_0_0		,      lowerCAmelCase__=0.0		,      lowerCAmelCase__=False		,      **lowerCAmelCase__		,      )   ->						Tuple:



        '''simple docstring'''




        super().__init__(
            feature_size=lowerCAmelCase__		,      sampling_rate=lowerCAmelCase__		,      padding_value=lowerCAmelCase__		,      return_attention_mask=lowerCAmelCase__		,      **lowerCAmelCase__		,      )
        a__	:					Any										=n_fft
        a__	:					List[Any]										=hop_length
        a__	:					Any										=chunk_length
        a__	:					List[Any]										=chunk_length * sampling_rate
        a__	:					Tuple										=self.n_samples // hop_length
        a__	:					str										=sampling_rate
        a__	:					Dict										=mel_filter_bank(
            num_frequency_bins=1 + n_fft // 2		,      num_mel_filters=lowerCAmelCase__		,      min_frequency=0.0		,      max_frequency=80_00.0		,      sampling_rate=lowerCAmelCase__		,      norm="slaney"		,      mel_scale="slaney"		,      )







      def    _lowercase  (    self		,      lowerCAmelCase__							)   ->						np.ndarray:



        '''simple docstring'''




        a__	:					Tuple										=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		,      log_mel="log10"		,      )
        a__	:					List[Any]										=log_spec[:, :-1]
        a__	:					Optional[int]										=np.maximum(lowerCAmelCase__		,      log_spec.max() - 8.0							)
        a__	:					Optional[Any]										=(log_spec + 4.0) / 4.0
        return log_spec







      @staticmethod
      # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
      def    _lowercase  (    lowerCAmelCase__		,      lowerCAmelCase__		,      lowerCAmelCase__ = 0.0							)   ->						List[np.ndarray]:



        '''simple docstring'''




        if attention_mask is not None:
          a__	:					Union[str, Any]										=np.array(lowerCAmelCase__		,      np.intaa							)
          a__	:					str										=[]

          for vector, length in zip(lowerCAmelCase__		,      attention_mask.sum(-1							)							):
            a__	:					Union[str, Any]										=(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__	:					Tuple										=[(x - x.mean()) / np.sqrt(x.var() + 1E-7							) for x in input_values]

        return normed_input_values







      def __call__(    self		,      lowerCAmelCase__		,      lowerCAmelCase__ = True		,      lowerCAmelCase__ = None		,      lowerCAmelCase__ = None		,      lowerCAmelCase__ = None		,      lowerCAmelCase__ = "max_length"		,      lowerCAmelCase__ = None		,      lowerCAmelCase__ = None		,      lowerCAmelCase__ = None		,      **lowerCAmelCase__		,      )   ->						BatchFeature:



        '''simple docstring'''




        if sampling_rate is not None:
          if sampling_rate != self.sampling_rate:
            raise ValueError(
                F'''The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a'''
                F''' sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input'''
                F''' was sampled 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."							)

        a__	:					Union[str, Any]										=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}'''							)
        a__	:					List[Any]										=is_batched_numpy or (
            isinstance(lowerCAmelCase__		,      (list, tuple)							) and (isinstance(raw_speech[0]		,      (np.ndarray, tuple, list)							))
        )

        if is_batched:
          a__	:					List[str]										=[np.asarray([speech]		,      dtype=np.floataa							).T for speech in raw_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 raw_speech.dtype is np.dtype(np.floataa							):
          a__	:					Union[str, Any]										=raw_speech.astype(np.floataa							)

        # always return batch
        if not is_batched:
          a__	:					Union[str, Any]										=[np.asarray([raw_speech]							).T]

        a__	:					Optional[Any]										=BatchFeature({"input_features": raw_speech}							)

        # convert into correct format for padding

        a__	:					List[str]										=self.pad(
            lowerCAmelCase__		,      padding=lowerCAmelCase__		,      max_length=max_length if max_length else self.n_samples		,      truncation=lowerCAmelCase__		,      pad_to_multiple_of=lowerCAmelCase__		,      return_attention_mask=return_attention_mask or do_normalize		,      )

        # zero-mean and unit-variance normalization
        if do_normalize:
          a__	:					List[str]										=self.zero_mean_unit_var_norm(
              padded_inputs["input_features"]		,      attention_mask=padded_inputs["attention_mask"]		,      padding_value=self.padding_value		,      )
          a__	:					Tuple										=np.stack(padded_inputs["input_features"]		,      axis=0							)

        # make sure list is in array format
        a__	:					int										=padded_inputs.get("input_features"							).transpose(2		,      0		,      1							)

        a__	:					Optional[Any]										=[self._np_extract_fbank_features(lowerCAmelCase__							) for waveform in input_features[0]]

        if isinstance(input_features[0]		,      lowerCAmelCase__							):
          a__	:					Dict										=[np.asarray(lowerCAmelCase__		,      dtype=np.floataa							) for feature in input_features]
        else:
          a__	:					int										=input_features

        if return_attention_mask:
          # rescale from sample (48000) to feature (3000)
          a__	:					Optional[int]										=padded_inputs["attention_mask"][:, :: self.hop_length]

        if return_tensors is not None:
          a__	:					Optional[int]										=padded_inputs.convert_to_tensors(lowerCAmelCase__							)

        return padded_inputs







      def    _lowercase  (    self							)   ->						Dict[str, Any]:



        '''simple docstring'''




        a__	:					List[Any]										=copy.deepcopy(self.__dict__							)
        a__	:					Optional[int]										=self.__class__.__name__
        if "mel_filters" in output:
          del output["mel_filters"]
        return output













from math import pi
def      _A       (		SCREAMING_SNAKE_CASE     :   int       ,       SCREAMING_SNAKE_CASE     :   int						):





  """simple docstring"""



  return 2 * pi * radius * (angle / 360)


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






