



"""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


lowerCAmelCase__              =							logging.get_logger(__name__)






class 			__snake_case			(      _lowercase):
  snake_case__							:  Dict         =		["audio_values", "audio_mask"]




  def __init__(					self	:		List[str]		,   __lowerCAmelCase	:		Optional[Any]=2_0_4_8		,   __lowerCAmelCase	:		Optional[int]=1		,   __lowerCAmelCase	:		Dict=[1_6, 1_6]		,   __lowerCAmelCase	:		Optional[Any]=1_2_8		,   __lowerCAmelCase	:		Optional[int]=4_4_1_0_0		,   __lowerCAmelCase	:		Optional[Any]=8_6		,   __lowerCAmelCase	:		Dict=2_0_4_8		,   __lowerCAmelCase	:		Tuple=0.0		,   **__lowerCAmelCase	:		Dict		,   ):






         """simple docstring"""






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

         _lowerCamelCase		:							str            =					spectrogram_length
         _lowerCamelCase		:							List[str]            =					num_channels
         _lowerCamelCase		:							List[str]            =					patch_size
         _lowerCamelCase		:							Optional[Any]            =					feature_size // self.patch_size[1]
         _lowerCamelCase		:							Any            =					n_fft
         _lowerCamelCase		:							int            =					sampling_rate // hop_length_to_sampling_rate
         _lowerCamelCase		:							Optional[int]            =					sampling_rate
         _lowerCamelCase		:							Optional[Any]            =					padding_value
         _lowerCamelCase		:							str            =					mel_filter_bank(
             num_frequency_bins=1 + n_fft // 2		,   num_mel_filters=__lowerCAmelCase		,   min_frequency=0.0		,   max_frequency=2_20_50.0		,   sampling_rate=__lowerCAmelCase		,   norm='''slaney'''		,   mel_scale='''slaney'''		,   ).T




  def 		SCREAMING_SNAKE_CASE							(					self	:		Optional[Any]		,   __lowerCAmelCase	:		np.array						):






         """simple docstring"""






         _lowerCamelCase		:							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.T		,   log_mel='''dB'''		,   db_range=80.0		,   )
         _lowerCamelCase		:							Union[str, Any]            =					log_spec[:, :-1]
         _lowerCamelCase		:							Optional[Any]            =					log_spec - 20.0
         _lowerCamelCase		:							List[Any]            =					np.clip(log_spec / 40.0		,   -2.0		,   0.0						) + 1.0
         return log_spec




  def __call__(					self	:		Optional[Any]		,   __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	:		List[str]		,   ):






         """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		:							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}'''						)
         _lowerCamelCase		:							List[str]            =					is_batched_numpy or (
             isinstance(__lowerCAmelCase		,   (list, tuple)						) and (isinstance(raw_speech[0]		,   (np.ndarray, tuple, list)						))
         )
         if is_batched:
                _lowerCamelCase		:							str            =					[np.asarray([speech]		,   dtype=np.floataa						).T for speech in raw_speech]
         elif not is_batched and not isinstance(__lowerCAmelCase		,   np.ndarray						):
                _lowerCamelCase		:							Any            =					np.asarray(__lowerCAmelCase		,   dtype=np.floataa						)
         elif isinstance(__lowerCAmelCase		,   np.ndarray						) and raw_speech.dtype is np.dtype(np.floataa						):
                _lowerCamelCase		:							Union[str, Any]            =					raw_speech.astype(np.floataa						)
         # always return batch
         if not is_batched:
                _lowerCamelCase		:							Tuple            =					[np.asarray([raw_speech]						).T]

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

         # Create audio attention mask
         _lowerCamelCase		:							List[Any]            =					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		:							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
                ]
                _lowerCamelCase		:							str            =					np.array(__lowerCAmelCase						).astype(np.floataa						)

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

         # return as BatchFeature
         if return_attention_mask:
                _lowerCamelCase		:							Union[str, Any]            =					{'''audio_values''': padded_audio_features, '''audio_mask''': audio_mask}
         else:
                _lowerCamelCase		:							Any            =					{'''audio_values''': padded_audio_features}

         _lowerCamelCase		:							Optional[int]            =					BatchFeature(data=__lowerCAmelCase		,   tensor_type=__lowerCAmelCase						)
         return encoded_inputs










__magic_name__   :  str        =							8.314_4598
def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase) ->			float:
      """simple docstring"""

      if temperature < 0:
            raise Exception('Temperature cannot be less than 0 K')
      if molar_mass <= 0:
            raise Exception('Molar mass cannot be less than or equal to 0 kg/mol')
      else:
            return (3 * UNIVERSAL_GAS_CONSTANT * temperature / molar_mass) ** 0.5


if __name__ == "__main__":
       import doctest

       # run doctest
       doctest.testmod()

       # example
       __magic_name__   :  List[Any]        =							300
       __magic_name__   :  Union[str, Any]        =							28
       __magic_name__   :  str        =							rms_speed_of_molecule(temperature, molar_mass)
       print(F'Vrms of Nitrogen gas at 300 K is {vrms} m/s')



