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


lowercase_  : Tuple								=					logging.get_logger(__name__)






class 		_lowerCamelCase		( UpperCamelCase_     ):
    __a						=   ["input_values", "attention_mask"]
    def __init__(	self			,			lowerCAmelCase = 1			,			lowerCAmelCase = 16000			,			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			,			)							->       str:
     super().__init__(feature_size=lowerCAmelCase			,			sampling_rate=lowerCAmelCase			,			padding_value=lowerCAmelCase			,			**lowerCAmelCase )
     SCREAMING_SNAKE_CASE__: Tuple=   do_normalize
     SCREAMING_SNAKE_CASE__: Optional[Any]=   return_attention_mask

     SCREAMING_SNAKE_CASE__: Optional[int]=   num_mel_bins
     SCREAMING_SNAKE_CASE__: Union[str, Any]=   hop_length
     SCREAMING_SNAKE_CASE__: Optional[int]=   win_length
     SCREAMING_SNAKE_CASE__: Dict=   win_function
     SCREAMING_SNAKE_CASE__: str=   frame_signal_scale
     SCREAMING_SNAKE_CASE__: Optional[int]=   fmin
     SCREAMING_SNAKE_CASE__: Any=   fmax
     SCREAMING_SNAKE_CASE__: Union[str, Any]=   mel_floor
     SCREAMING_SNAKE_CASE__: Tuple=   reduction_factor

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

     SCREAMING_SNAKE_CASE__: List[str]=   window_function(window_length=self.sample_size			,			name=self.win_function			,			periodic=lowerCAmelCase )

     SCREAMING_SNAKE_CASE__: List[Any]=   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     UpperCamelCase_					(	lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase = 0.0 )							->       List[np.ndarray]:
     if attention_mask is not None:
      SCREAMING_SNAKE_CASE__: Any=   np.array(lowerCAmelCase			,			np.intaa )
      SCREAMING_SNAKE_CASE__: Optional[Any]=   []

      for vector, length in zip(lowerCAmelCase			,			attention_mask.sum(-1 ) ):
       SCREAMING_SNAKE_CASE__: str=   (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7 )
       if length < normed_slice.shape[0]:
        SCREAMING_SNAKE_CASE__: Optional[int]=   padding_value

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

     return normed_input_values
    def     UpperCamelCase_					(	self			,			lowerCAmelCase			,			)							->       np.ndarray:
     SCREAMING_SNAKE_CASE__: Tuple=   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			,			)							->       BatchFeature:
     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:
      SCREAMING_SNAKE_CASE__: str=   self._process_audio(
          lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase			,			**lowerCAmelCase			,			)
     else:
      SCREAMING_SNAKE_CASE__: int=   None

     if audio_target is not None:
      SCREAMING_SNAKE_CASE__: Union[str, Any]=   self._process_audio(
          lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase			,			**lowerCAmelCase			,			)

      if inputs is None:
       return inputs_target
      else:
       SCREAMING_SNAKE_CASE__: Tuple=   inputs_target['''input_values''']
       SCREAMING_SNAKE_CASE__: List[str]=   inputs_target.get('''attention_mask''' )
       if decoder_attention_mask is not None:
        SCREAMING_SNAKE_CASE__: Dict=   decoder_attention_mask

     return inputs
    def     UpperCamelCase_					(	self			,			lowerCAmelCase			,			lowerCAmelCase = False			,			lowerCAmelCase = False			,			lowerCAmelCase = None			,			lowerCAmelCase = False			,			lowerCAmelCase = None			,			lowerCAmelCase = None			,			lowerCAmelCase = None			,			**lowerCAmelCase			,			)							->       BatchFeature:
     SCREAMING_SNAKE_CASE__: Tuple=   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}' )
     SCREAMING_SNAKE_CASE__: int=   is_batched_numpy or (
         isinstance(lowerCAmelCase			,			(list, tuple) ) and (isinstance(speech[0]			,			(np.ndarray, tuple, list) ))
     )

     if is_batched:
      SCREAMING_SNAKE_CASE__: Optional[int]=   [np.asarray(lowerCAmelCase			,			dtype=np.floataa ) for speech in speech]
     elif not is_batched and not isinstance(lowerCAmelCase			,			np.ndarray ):
      SCREAMING_SNAKE_CASE__: Dict=   np.asarray(lowerCAmelCase			,			dtype=np.floataa )
     elif isinstance(lowerCAmelCase			,			np.ndarray ) and speech.dtype is np.dtype(np.floataa ):
      SCREAMING_SNAKE_CASE__: int=   speech.astype(np.floataa )

     # always return batch
     if not is_batched:
      SCREAMING_SNAKE_CASE__: List[str]=   [speech]

     # needed to make pad() work on spectrogram inputs
     SCREAMING_SNAKE_CASE__: List[str]=   self.feature_size

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

     SCREAMING_SNAKE_CASE__: Union[str, Any]=   self.pad(
         lowerCAmelCase			,			padding=lowerCAmelCase			,			max_length=lowerCAmelCase			,			truncation=lowerCAmelCase			,			pad_to_multiple_of=lowerCAmelCase			,			return_attention_mask=lowerCAmelCase			,			**lowerCAmelCase			,			)

     SCREAMING_SNAKE_CASE__: str=   feature_size_hack

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

     # convert attention_mask to correct format
     SCREAMING_SNAKE_CASE__: Union[str, Any]=   padded_inputs.get('''attention_mask''' )
     if attention_mask is not None:
      SCREAMING_SNAKE_CASE__: Optional[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:
      SCREAMING_SNAKE_CASE__: List[str]=   (
          attention_mask
          if self._get_padding_strategies(lowerCAmelCase			,			max_length=lowerCAmelCase ) is not PaddingStrategy.DO_NOT_PAD
          else None
      )
      SCREAMING_SNAKE_CASE__: Any=   self.zero_mean_unit_var_norm(
          padded_inputs['''input_values''']			,			attention_mask=lowerCAmelCase			,			padding_value=self.padding_value )

     if return_tensors is not None:
      SCREAMING_SNAKE_CASE__: List[str]=   padded_inputs.convert_to_tensors(lowerCAmelCase )

     return padded_inputs



    def     UpperCamelCase_					(	self )							->       Dict[str, Any]:
     SCREAMING_SNAKE_CASE__: Tuple=   super().to_dict()

     # Don't serialize these as they are derived from the other properties.
     SCREAMING_SNAKE_CASE__: List[str]=   ['''window''', '''mel_filters''', '''sample_size''', '''sample_stride''', '''n_fft''', '''n_freqs''']
     for name in names:
      if name in output:
       del output[name]

     return output



import math





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase				):

    if initial_intensity < 0:
        raise ValueError("The value of intensity cannot be negative"				)
        # handling of negative values of initial intensity
    if angle < 0 or angle > 360:
        raise ValueError("In Malus Law, the angle is in the range 0-360 degrees"				)
        # handling of values out of allowed range
    return initial_intensity * (math.cos(math.radians(__lowerCAmelCase				)				) ** 2)


if __name__ == "__main__":
     import doctest

     doctest.testmod(name='''malus_law''')



