



'''simple docstring'''





from typing import List, Optional, Union

import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


A__						:						Optional[Any]          =      logging.get_logger(__name__)



class        __snake_case		(  __SCREAMING_SNAKE_CASE		):
							_a       =				['''input_values''', '''attention_mask''']




							def __init__( self  : Any       ,       A_  : Optional[Any]=1       ,       A_  : List[str]=1_6_0_0_0       ,       A_  : List[Any]=1_2_8       ,       A_  : int=1_0_2_4       ,       A_  : int=0.0       ,       A_  : Tuple=True       ,       A_  : List[str]=-4.267_7393       ,       A_  : Tuple=4.568_9974       ,       A_  : Optional[Any]=False       ,       **A_  : Any       ,       ):
									super().__init__(feature_size=UpperCamelCase__       ,       sampling_rate=UpperCamelCase__       ,       padding_value=UpperCamelCase__       ,       **UpperCamelCase__)
									lowerCAmelCase_    :  Tuple             =				num_mel_bins
									lowerCAmelCase_    :  Union[str, Any]             =				max_length
									lowerCAmelCase_    :  List[Any]             =				do_normalize
									lowerCAmelCase_    :  str             =				mean
									lowerCAmelCase_    :  List[str]             =				std
									lowerCAmelCase_    :  Any             =				return_attention_mask




							def      UpperCAmelCase__    ( self  : Optional[int]       ,       A_  : np.ndarray       ,       A_  : int       ,       ):
									lowerCAmelCase_    :  int             =				torch.from_numpy(UpperCamelCase__).unsqueeze(0)
									lowerCAmelCase_    :  Dict             =				ta_kaldi.fbank(
									    UpperCamelCase__       ,       htk_compat=UpperCamelCase__       ,       sample_frequency=self.sampling_rate       ,       use_energy=UpperCamelCase__       ,       window_type='''hanning'''       ,       num_mel_bins=self.num_mel_bins       ,       dither=0.0       ,       frame_shift=1_0       ,       )

									lowerCAmelCase_    :  List[str]             =				fbank.shape[0]
									lowerCAmelCase_    :  Any             =				max_length - n_frames

									# pad or truncate, depending on difference
									if difference > 0:
											lowerCAmelCase_    :  Optional[Any]             =				torch.nn.ZeroPadad((0, 0, 0, difference))
											lowerCAmelCase_    :  Tuple             =				pad_module(UpperCamelCase__)
									elif difference < 0:
											lowerCAmelCase_    :  Optional[int]             =				fbank[0:max_length, :]

									lowerCAmelCase_    :  Any             =				fbank.numpy()

									return fbank




							def      UpperCAmelCase__    ( self  : Any       ,       A_  : np.ndarray):
									return (input_values - (self.mean)) / (self.std * 2)







							def __call__( self  : Union[str, Any]       ,       A_  : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]       ,       A_  : Optional[int] = None       ,       A_  : Optional[Union[str, TensorType]] = None       ,       **A_  : 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.''')

									lowerCAmelCase_    :  List[str]             =				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_    :  Any             =				is_batched_numpy or (
									    isinstance(UpperCamelCase__       ,       (list, tuple)) and (isinstance(raw_speech[0]       ,       (np.ndarray, tuple, list)))
									)

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

									# always return batch
									if not is_batched:
											lowerCAmelCase_    :  Tuple             =				[raw_speech]

									# extract fbank features and pad/truncate to max_length
									lowerCAmelCase_    :  Tuple             =				[self._extract_fbank_features(UpperCamelCase__       ,       max_length=self.max_length) for waveform in raw_speech]

									# convert into BatchFeature
									lowerCAmelCase_    :  Any             =				BatchFeature({'''input_values''': features})

									# make sure list is in array format
									lowerCAmelCase_    :  Tuple             =				padded_inputs.get('''input_values''')
									if isinstance(input_values[0]       ,       UpperCamelCase__):
											lowerCAmelCase_    :  List[str]             =				[np.asarray(UpperCamelCase__       ,       dtype=np.floataa) for feature in input_values]

									# normalization
									if self.do_normalize:
											lowerCAmelCase_    :  int             =				[self.normalize(UpperCamelCase__) for feature in input_values]

									if return_tensors is not None:
											lowerCAmelCase_    :  Union[str, Any]             =				padded_inputs.convert_to_tensors(UpperCamelCase__)

									return padded_inputs










from __future__ import annotations

A__						:						Tuple          =      '''Alexander Joslin'''


def     UpperCamelCase(						__UpperCamelCase							:	int				):

		if number <= 0:
				lowerCAmelCase_    :  str             =				f"""n must be greater than 0. Got n = {number}"""
				raise ValueError(__UpperCamelCase				)

		lowerCAmelCase_    :  str             =				[number + 1] * (number + 1)

		# starting position
		lowerCAmelCase_    :  List[Any]             =				0
		for i in range(1  ,__UpperCamelCase				):
				lowerCAmelCase_    :  int             =				min(table[i + 1]  ,table[i] + 1				)
				# check if out of bounds
				if i * 2 <= number:
						lowerCAmelCase_    :  Union[str, Any]             =				min(table[i * 2]  ,table[i] + 1				)
				# check if out of bounds
				if i * 3 <= number:
						lowerCAmelCase_    :  Union[str, Any]             =				min(table[i * 3]  ,table[i] + 1				)
		return table[number]


if __name__ == "__main__":
						import doctest

						doctest.testmod()





