





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__						:			Optional[int]									=logging.get_logger(__name__)

class       __A     (						A_    ):
				__A      =				['''input_features''']


				def __init__(		self   , UpperCAmelCase_=80   , UpperCAmelCase_=16000   , UpperCAmelCase_=160   , UpperCAmelCase_=30   , UpperCAmelCase_=400   , UpperCAmelCase_=0.0   , UpperCAmelCase_=False   , **UpperCAmelCase_   , ):
					super().__init__(
					    feature_size=UpperCAmelCase_   , sampling_rate=UpperCAmelCase_   , padding_value=UpperCAmelCase_   , return_attention_mask=UpperCAmelCase_   , **UpperCAmelCase_   , )
					lowerCamelCase	 =n_fft
					lowerCamelCase	 =hop_length
					lowerCamelCase	 =chunk_length
					lowerCamelCase	 =chunk_length * sampling_rate
					lowerCamelCase	 =self.n_samples // hop_length
					lowerCamelCase	 =sampling_rate
					lowerCamelCase	 =mel_filter_bank(
					    num_frequency_bins=1 + n_fft // 2   , num_mel_filters=UpperCAmelCase_   , min_frequency=0.0   , max_frequency=8000.0   , sampling_rate=UpperCAmelCase_   , norm="""slaney"""   , mel_scale="""slaney"""   , )


				def _snake_case       (		self   , UpperCAmelCase_							):
					lowerCamelCase	 =spectrogram(
					    UpperCAmelCase_   , 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"""   , )
					lowerCamelCase	 =log_spec[:, :-1]
					lowerCamelCase	 =np.maximum(UpperCAmelCase_   , log_spec.max() - 8.0							)
					lowerCamelCase	 =(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       (		UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_ = 0.0							):
					if attention_mask is not None:
						lowerCamelCase	 =np.array(UpperCAmelCase_   , np.intaa							)
						lowerCamelCase	 =[]

						for vector, length in zip(UpperCAmelCase_   , attention_mask.sum(-1							)							):
							lowerCamelCase	 =(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7							)
							if length < normed_slice.shape[0]:
								lowerCamelCase	 =padding_value

							normed_input_values.append(UpperCAmelCase_							)
					else:
						lowerCamelCase	 =[(x - x.mean()) / np.sqrt(x.var() + 1E-7							) for x in input_values]

					return normed_input_values


				def __call__(		self   , UpperCAmelCase_   , UpperCAmelCase_ = True   , UpperCAmelCase_ = None   , UpperCAmelCase_ = None   , UpperCAmelCase_ = None   , UpperCAmelCase_ = "max_length"   , UpperCAmelCase_ = None   , UpperCAmelCase_ = None   , UpperCAmelCase_ = None   , **UpperCAmelCase_   , ):

					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."""							)

					lowerCamelCase	 =isinstance(UpperCAmelCase_   , 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	 =is_batched_numpy or (
					    isinstance(UpperCAmelCase_   , (list, tuple)							) and (isinstance(raw_speech[0]   , (np.ndarray, tuple, list)							))
					)

					if is_batched:
						lowerCamelCase	 =[np.asarray([speech]   , dtype=np.floataa							).T for speech in raw_speech]
					elif not is_batched and not isinstance(UpperCAmelCase_   , np.ndarray							):
						lowerCamelCase	 =np.asarray(UpperCAmelCase_   , dtype=np.floataa							)
					elif isinstance(UpperCAmelCase_   , np.ndarray							) and raw_speech.dtype is np.dtype(np.floataa							):
						lowerCamelCase	 =raw_speech.astype(np.floataa							)

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

					lowerCamelCase	 =BatchFeature({"""input_features""": raw_speech}							)

					# convert into correct format for padding

					lowerCamelCase	 =self.pad(
					    UpperCAmelCase_   , padding=UpperCAmelCase_   , max_length=max_length if max_length else self.n_samples   , truncation=UpperCAmelCase_   , pad_to_multiple_of=UpperCAmelCase_   , return_attention_mask=return_attention_mask or do_normalize   , )

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

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

					lowerCamelCase	 =[self._np_extract_fbank_features(UpperCAmelCase_							) for waveform in input_features[0]]

					if isinstance(input_features[0]   , UpperCAmelCase_							):
						lowerCamelCase	 =[np.asarray(UpperCAmelCase_   , dtype=np.floataa							) for feature in input_features]
					else:
						lowerCamelCase	 =input_features

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

					if return_tensors is not None:
						lowerCamelCase	 =padded_inputs.convert_to_tensors(UpperCAmelCase_							)

					return padded_inputs


				def _snake_case       (		self							):
					lowerCamelCase	 =copy.deepcopy(self.__dict__							)
					lowerCamelCase	 =self.__class__.__name__
					if "mel_filters" in output:
						del output["mel_filters"]
					return output












def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase				)						->  list[str]:
				return [sentence[i : i + ngram_size] for i in range(len(_UpperCAmelCase				) - ngram_size + 1				)]


if __name__ == "__main__":
							from doctest import testmod

							testmod()





