





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


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





class 				a			(      __lowercase			):
				SCREAMING_SNAKE_CASE__     :   int		      =     ['''input_features''']






				def __init__(  self			, _lowerCAmelCase=80			, _lowerCAmelCase=16000			, _lowerCAmelCase=160			, _lowerCAmelCase=30			, _lowerCAmelCase=400			, _lowerCAmelCase=0.0			, _lowerCAmelCase=False			, **_lowerCAmelCase			, ):

										"""simple docstring"""




										super().__init__(
										    feature_size=_lowerCAmelCase			, sampling_rate=_lowerCAmelCase			, padding_value=_lowerCAmelCase			, return_attention_mask=_lowerCAmelCase			, **_lowerCAmelCase			, )
										__SCREAMING_SNAKE_CASE:				str           =   n_fft
										__SCREAMING_SNAKE_CASE:				Any           =   hop_length
										__SCREAMING_SNAKE_CASE:				int           =   chunk_length
										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   chunk_length * sampling_rate
										__SCREAMING_SNAKE_CASE:				Any           =   self.n_samples // hop_length
										__SCREAMING_SNAKE_CASE:				Any           =   sampling_rate
										__SCREAMING_SNAKE_CASE:				str           =   mel_filter_bank(
										    num_frequency_bins=1 + n_fft // 2			, num_mel_filters=_lowerCAmelCase			, min_frequency=0.0			, max_frequency=8000.0			, sampling_rate=_lowerCAmelCase			, norm='''slaney'''			, mel_scale='''slaney'''			, )






				def 			snake_case_    (  self			, _lowerCAmelCase					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				str           =   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'''			, )
										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   log_spec[:, :-1]
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   np.maximum(_lowerCAmelCase			, log_spec.max() - 8.0					)
										__SCREAMING_SNAKE_CASE:				Tuple           =   (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 			snake_case_    (  _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase = 0.0					):

										"""simple docstring"""




										if attention_mask is not None:
																__SCREAMING_SNAKE_CASE:				int           =   np.array(_lowerCAmelCase			, np.intaa					)
																__SCREAMING_SNAKE_CASE:				List[str]           =   []

																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:				Union[str, Any]           =   padding_value

																						normed_input_values.append(_lowerCAmelCase					)
										else:
																__SCREAMING_SNAKE_CASE:				str           =   [(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			, ):

										"""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.'''					)

										__SCREAMING_SNAKE_CASE:				List[str]           =   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}"""					)
										__SCREAMING_SNAKE_CASE:				str           =   is_batched_numpy or (
										    isinstance(_lowerCAmelCase			, (list, tuple)					) and (isinstance(raw_speech[0]			, (np.ndarray, tuple, list)					))
										)

										if is_batched:
																__SCREAMING_SNAKE_CASE:				Any           =   [np.asarray([speech]			, dtype=np.floataa					).T for speech in raw_speech]
										elif not is_batched and not isinstance(_lowerCAmelCase			, np.ndarray					):
																__SCREAMING_SNAKE_CASE:				List[Any]           =   np.asarray(_lowerCAmelCase			, dtype=np.floataa					)
										elif isinstance(_lowerCAmelCase			, np.ndarray					) and raw_speech.dtype is np.dtype(np.floataa					):
																__SCREAMING_SNAKE_CASE:				str           =   raw_speech.astype(np.floataa					)

										# always return batch
										if not is_batched:
																__SCREAMING_SNAKE_CASE:				Any           =   [np.asarray([raw_speech]					).T]

										__SCREAMING_SNAKE_CASE:				str           =   BatchFeature({'''input_features''': raw_speech}					)

										# convert into correct format for padding

										__SCREAMING_SNAKE_CASE:				Optional[int]           =   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:
																__SCREAMING_SNAKE_CASE:				Any           =   self.zero_mean_unit_var_norm(
																    padded_inputs['''input_features''']			, attention_mask=padded_inputs['''attention_mask''']			, padding_value=self.padding_value			, )
																__SCREAMING_SNAKE_CASE:				List[Any]           =   np.stack(padded_inputs['''input_features''']			, axis=0					)

										# make sure list is in array format
										__SCREAMING_SNAKE_CASE:				List[str]           =   padded_inputs.get('''input_features'''					).transpose(2			, 0			, 1					)

										__SCREAMING_SNAKE_CASE:				str           =   [self._np_extract_fbank_features(_lowerCAmelCase					) for waveform in input_features[0]]

										if isinstance(input_features[0]			, _lowerCAmelCase					):
																__SCREAMING_SNAKE_CASE:				Tuple           =   [np.asarray(_lowerCAmelCase			, dtype=np.floataa					) for feature in input_features]
										else:
																__SCREAMING_SNAKE_CASE:				Any           =   input_features

										if return_attention_mask:
																# rescale from sample (48000) to feature (3000)
																__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   padded_inputs['''attention_mask'''][:, :: self.hop_length]

										if return_tensors is not None:
																__SCREAMING_SNAKE_CASE:				Any           =   padded_inputs.convert_to_tensors(_lowerCAmelCase					)

										return padded_inputs






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   copy.deepcopy(self.__dict__					)
										__SCREAMING_SNAKE_CASE:				int           =   self.__class__.__name__
										if "mel_filters" in output:
																del output["mel_filters"]
										return output










def 		lowerCAmelCase  (	UpperCamelCase__   :   int       ,			UpperCamelCase__   :   float       ,			UpperCamelCase__   :   float					)      ->    float:

						"""simple docstring"""





						return round(float(moles / volume					) * nfactor					)






def 		lowerCAmelCase  (	UpperCamelCase__   :   float       ,			UpperCamelCase__   :   float       ,			UpperCamelCase__   :   float					)      ->    float:

						"""simple docstring"""





						return round(float((moles * 0.08_21 * temperature) / (volume)					)					)






def 		lowerCAmelCase  (	UpperCamelCase__   :   float       ,			UpperCamelCase__   :   float       ,			UpperCamelCase__   :   float					)      ->    float:

						"""simple docstring"""





						return round(float((moles * 0.08_21 * temperature) / (pressure)					)					)






def 		lowerCAmelCase  (	UpperCamelCase__   :   float       ,			UpperCamelCase__   :   float       ,			UpperCamelCase__   :   float					)      ->    float:

						"""simple docstring"""





						return round(float((pressure * volume) / (0.08_21 * moles)					)					)


if __name__ == "__main__":
							import doctest

							doctest.testmod()



