



"""simple docstring"""







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_   						=   logging.get_logger(__name__)
class        __A       (	A_    ):







		'''simple docstring'''





		lowerCAmelCase			:					Tuple   	=				["input_features"]







		def __init__(	self						:  Optional[Any]			,_snake_case						:  List[Any]=80			,_snake_case						:  Dict=16_000			,_snake_case						:  Any=160			,_snake_case						:  Tuple=30			,_snake_case						:  List[str]=400			,_snake_case						:  Dict=0.0			,_snake_case						:  List[Any]=False			,**_snake_case						:  str			,)					->		Any:






				"""simple docstring"""



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







		def 					UpperCAmelCase    (	self						:  Optional[Any]			,_snake_case						:  np.array						)					->		np.ndarray:






				"""simple docstring"""



				lowercase__   :    List[str]				=			spectrogram(
				    _snake_case			,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'''			,)
				lowercase__   :    Tuple				=			log_spec[:, :-1]
				lowercase__   :    Optional[Any]				=			np.maximum(_snake_case			,log_spec.max() - 8.0						)
				lowercase__   :    Optional[int]				=			(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 					UpperCAmelCase    (	_snake_case						:  List[np.ndarray]			,_snake_case						:  List[np.ndarray]			,_snake_case						:  float = 0.0						)					->		List[np.ndarray]:






				"""simple docstring"""



				if attention_mask is not None:
						lowercase__   :    Optional[Any]				=			np.array(_snake_case			,np.intaa						)
						lowercase__   :    Tuple				=			[]

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

								normed_input_values.append(_snake_case						)
				else:
						lowercase__   :    Optional[Any]				=			[(x - x.mean()) / np.sqrt(x.var() + 1e-7						) for x in input_values]

				return normed_input_values







		def __call__(	self						:  Optional[int]			,_snake_case						:  Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]			,_snake_case						:  bool = True			,_snake_case						:  Optional[int] = None			,_snake_case						:  Optional[Union[str, TensorType]] = None			,_snake_case						:  Optional[bool] = None			,_snake_case						:  Optional[str] = "max_length"			,_snake_case						:  Optional[int] = None			,_snake_case						:  Optional[int] = None			,_snake_case						:  Optional[bool] = None			,**_snake_case						:  Tuple			,)					->		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.'''						)

				lowercase__   :    Dict				=			isinstance(_snake_case			,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}"""						)
				lowercase__   :    List[Any]				=			is_batched_numpy or (
				    isinstance(_snake_case			,(list, tuple)						) and (isinstance(raw_speech[0]			,(np.ndarray, tuple, list)						))
				)

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

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

				lowercase__   :    int				=			BatchFeature({'''input_features''': raw_speech}						)

				# convert into correct format for padding

				lowercase__   :    Optional[Any]				=			self.pad(
				    _snake_case			,padding=_snake_case			,max_length=max_length if max_length else self.n_samples			,truncation=_snake_case			,pad_to_multiple_of=_snake_case			,return_attention_mask=return_attention_mask or do_normalize			,)

				# zero-mean and unit-variance normalization
				if do_normalize:
						lowercase__   :    int				=			self.zero_mean_unit_var_norm(
						    padded_inputs['''input_features''']			,attention_mask=padded_inputs['''attention_mask''']			,padding_value=self.padding_value			,)
						lowercase__   :    Dict				=			np.stack(padded_inputs['''input_features''']			,axis=0						)

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

				lowercase__   :    Optional[int]				=			[self._np_extract_fbank_features(_snake_case						) for waveform in input_features[0]]

				if isinstance(input_features[0]			,_snake_case						):
						lowercase__   :    Optional[int]				=			[np.asarray(_snake_case			,dtype=np.floataa						) for feature in input_features]
				else:
						lowercase__   :    Tuple				=			input_features

				if return_attention_mask:
						# rescale from sample (48000) to feature (3000)
						lowercase__   :    Tuple				=			padded_inputs['''attention_mask'''][:, :: self.hop_length]

				if return_tensors is not None:
						lowercase__   :    int				=			padded_inputs.convert_to_tensors(_snake_case						)

				return padded_inputs







		def 					UpperCAmelCase    (	self						:  Optional[int]						)					->		Dict[str, Any]:






				"""simple docstring"""



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





"""simple docstring"""







import unittest

from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script
@require_cpu
class        __A       (	unittest.TestCase    ):







		'''simple docstring'''





		def 					UpperCAmelCase    (	self						:  Optional[int]						)					->		str:






				"""simple docstring"""



				debug_launcher(test_script.main						)







		def 					UpperCAmelCase    (	self						:  Dict						)					->		Tuple:






				"""simple docstring"""



				debug_launcher(test_ops.main						)
