


'''simple docstring'''






from typing import 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 ....file_utils import PaddingStrategy, TensorType
from ....utils import logging


__A	:	Optional[Any]     =     logging.get_logger(__name__)
class 						__UpperCamelCase	(						__lowerCAmelCase							):
							lowercase			:		List[str]   		=  ["input_features", "attention_mask"]





							def __init__(  self		:Union[str, Any]  ,_UpperCamelCase		:Optional[int]=8_0  ,_UpperCamelCase		:int=1_6_0_0_0  ,_UpperCamelCase		:Optional[int]=0.0  ,_UpperCamelCase		:Dict=1_0  ,_UpperCamelCase		:Tuple=2_5  ,_UpperCamelCase		:List[Any]="hamming_window"  ,_UpperCamelCase		:Tuple=3_2_7_6_8.0  ,_UpperCamelCase		:Optional[int]=0.97  ,_UpperCamelCase		:int=1.0  ,_UpperCamelCase		:List[str]=True  ,_UpperCamelCase		:str=True  ,_UpperCamelCase		:str=False  ,**_UpperCamelCase		:Optional[int]  ,):
									super().__init__(feature_size=lowerCamelCase__  ,sampling_rate=lowerCamelCase__  ,padding_value=lowerCamelCase__  ,**lowerCamelCase__			)

									snake_case_		:							Union[str, Any]			       =						feature_size
									snake_case_		:							Any			       =						sampling_rate
									snake_case_		:							Optional[int]			       =						padding_value
									snake_case_		:							Tuple			       =						hop_length
									snake_case_		:							Dict			       =						win_length
									snake_case_		:							Optional[int]			       =						frame_signal_scale
									snake_case_		:							Optional[int]			       =						preemphasis_coeff
									snake_case_		:							Union[str, Any]			       =						mel_floor
									snake_case_		:							Optional[int]			       =						normalize_means
									snake_case_		:							Any			       =						normalize_vars
									snake_case_		:							Any			       =						win_function
									snake_case_		:							Optional[Any]			       =						return_attention_mask

									snake_case_		:							Any			       =						win_length * sampling_rate // 1_0_0_0
									snake_case_		:							Tuple			       =						hop_length * sampling_rate // 1_0_0_0

									snake_case_		:							Union[str, Any]			       =						optimal_fft_length(self.sample_size			)
									snake_case_		:							Tuple			       =						(self.n_fft // 2) + 1





							def 		a__   (  self		:Tuple  ,_UpperCamelCase		:np.array			):






									if self.win_function == "hamming_window":
											snake_case_		:							Tuple			       =						window_function(window_length=self.sample_size  ,name=self.win_function  ,periodic=lowerCamelCase__			)
									else:
											snake_case_		:							Union[str, Any]			       =						window_function(window_length=self.sample_size  ,name=self.win_function			)

									snake_case_		:							List[str]			       =						mel_filter_bank(
									    num_frequency_bins=self.n_freqs  ,num_mel_filters=self.feature_size  ,min_frequency=0.0  ,max_frequency=self.sampling_rate / 2.0  ,sampling_rate=self.sampling_rate  ,)

									snake_case_		:							int			       =						spectrogram(
									    one_waveform * self.frame_signal_scale  ,window=lowerCamelCase__  ,frame_length=self.sample_size  ,hop_length=self.sample_stride  ,fft_length=self.n_fft  ,center=lowerCamelCase__  ,preemphasis=self.preemphasis_coeff  ,mel_filters=lowerCamelCase__  ,mel_floor=self.mel_floor  ,log_mel="""log"""  ,)
									return msfc_features.T





							def 		a__   (  self		:Tuple  ,_UpperCamelCase		:Optional[Any]  ,_UpperCamelCase		:Tuple  ,_UpperCamelCase		:str			):






									if self.normalize_means:
											snake_case_		:							Union[str, Any]			       =						x[:input_length].mean(axis=0			)
											snake_case_		:							str			       =						np.subtract(lowerCamelCase__  ,lowerCamelCase__			)
									if self.normalize_vars:
											snake_case_		:							Any			       =						x[:input_length].std(axis=0			)
											snake_case_		:							List[Any]			       =						np.divide(lowerCamelCase__  ,lowerCamelCase__			)

									if input_length < x.shape[0]:
											snake_case_		:							int			       =						padding_value

									# make sure array is in float32
									snake_case_		:							Any			       =						x.astype(np.floataa			)

									return x





							def 		a__   (  self		:Optional[Any]  ,_UpperCamelCase		:List[np.ndarray]  ,_UpperCamelCase		:Optional[np.ndarray] = None			):
									snake_case_		:							Tuple			       =						attention_mask.sum(-1			) if attention_mask is not None else [x.shape[0] for x in input_features]
									return [self._normalize_one(lowerCamelCase__  ,lowerCamelCase__  ,self.padding_value			) for x, n in zip(lowerCamelCase__  ,lowerCamelCase__			)]





							def __call__(  self		:Optional[Any]  ,_UpperCamelCase		:Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]  ,_UpperCamelCase		:Union[bool, str, PaddingStrategy] = False  ,_UpperCamelCase		:Optional[int] = None  ,_UpperCamelCase		:bool = False  ,_UpperCamelCase		:Optional[int] = None  ,_UpperCamelCase		:Optional[bool] = None  ,_UpperCamelCase		:Optional[Union[str, TensorType]] = None  ,_UpperCamelCase		:Optional[int] = None  ,**_UpperCamelCase		:str  ,):






									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 `raw_speech` 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."""			)

									snake_case_		:							List[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}'''			)
									snake_case_		:							str			       =						is_batched_numpy or (
									    isinstance(lowerCamelCase__  ,(list, tuple)			) and (isinstance(raw_speech[0]  ,(np.ndarray, tuple, list)			))
									)

									if is_batched:
											snake_case_		:							List[Any]			       =						[np.asarray(lowerCamelCase__  ,dtype=np.floataa			) for speech in raw_speech]
									elif not is_batched and not isinstance(lowerCamelCase__  ,np.ndarray			):
											snake_case_		:							Any			       =						np.asarray(lowerCamelCase__  ,dtype=np.floataa			)
									elif isinstance(lowerCamelCase__  ,np.ndarray			) and raw_speech.dtype is np.dtype(np.floataa			):
											snake_case_		:							Tuple			       =						raw_speech.astype(np.floataa			)

									# always return batch
									if not is_batched:
											snake_case_		:							Optional[Any]			       =						[raw_speech]

									# extract fbank features
									snake_case_		:							int			       =						[self._extract_mfsc_features(lowerCamelCase__			) for one_waveform in raw_speech]

									# convert into correct format for padding
									snake_case_		:							Union[str, Any]			       =						BatchFeature({"""input_features""": features}			)

									snake_case_		:							Dict			       =						self.pad(
									    lowerCamelCase__  ,padding=lowerCamelCase__  ,max_length=lowerCamelCase__  ,truncation=lowerCamelCase__  ,pad_to_multiple_of=lowerCamelCase__  ,return_attention_mask=lowerCamelCase__  ,**lowerCamelCase__  ,)
									# make sure list is in array format
									snake_case_		:							Union[str, Any]			       =						padded_inputs.get("""input_features"""			)
									if isinstance(input_features[0]  ,lowerCamelCase__			):
											snake_case_		:							Optional[int]			       =						[np.asarray(lowerCamelCase__  ,dtype=np.floataa			) for feature in input_features]

									snake_case_		:							Dict			       =						padded_inputs.get("""attention_mask"""			)
									if attention_mask is not None:
											snake_case_		:							Optional[Any]			       =						[np.asarray(lowerCamelCase__  ,dtype=np.intaa			) for array in attention_mask]

									if self.normalize_means or self.normalize_vars:
											snake_case_		:							List[Any]			       =						(
											    np.array(lowerCamelCase__  ,dtype=np.intaa			)
											    if self._get_padding_strategies(lowerCamelCase__  ,max_length=lowerCamelCase__			) is not PaddingStrategy.DO_NOT_PAD
											    and padding
											    else None
											)
											snake_case_		:							Optional[int]			       =						self.normalize(
											    padded_inputs["""input_features"""]  ,attention_mask=lowerCamelCase__			)

									if return_tensors is not None:
											snake_case_		:							Tuple			       =						padded_inputs.convert_to_tensors(lowerCamelCase__			)

									return padded_inputs



'''simple docstring'''






from abc import ABC, abstractmethod
from argparse import ArgumentParser
class 						__UpperCamelCase	(						lowercase__							):





							@staticmethod
							@abstractmethod
							def 		a__   (  _UpperCamelCase		:ArgumentParser			):
									raise NotImplementedError()





							@abstractmethod
							def 		a__   (  self		:Optional[Any]			):
									raise NotImplementedError()