






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 PaddingStrategy, TensorType, logging


__UpperCAmelCase     							=						logging.get_logger(__name__)



class   lowerCamelCase				(_snake_case      ):







		'''simple docstring'''



		_snake_case   :   str							=  ['''input_features''', '''attention_mask''']



		def __init__(							self    ,		_UpperCamelCase=8_0    ,		_UpperCamelCase=1_6_0_0_0    ,		_UpperCamelCase=8_0    ,		_UpperCamelCase=0.0    ,		_UpperCamelCase=True    ,		_UpperCamelCase=True    ,		_UpperCamelCase=True    ,		**_UpperCamelCase    ,		)     ->							Any:
							super().__init__(feature_size=_UpperCamelCase    ,		sampling_rate=_UpperCamelCase    ,		padding_value=_UpperCamelCase    ,		**_UpperCamelCase       )
							UpperCAmelCase_   :				Any						=						num_mel_bins
							UpperCAmelCase_   :				str						=						do_ceptral_normalize
							UpperCAmelCase_   :				Union[str, Any]						=						normalize_means
							UpperCAmelCase_   :				List[Any]						=						normalize_vars
							UpperCAmelCase_   :				Optional[int]						=						True



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		)     ->							np.ndarray:
							UpperCAmelCase_   :				List[Any]						=						waveform * (2**1_5)  # Kaldi compliance: 16-bit signed integers
							UpperCAmelCase_   :				Union[str, Any]						=						torch.from_numpy(_UpperCamelCase       ).unsqueeze(0       )
							UpperCAmelCase_   :				Any						=						ta_kaldi.fbank(_UpperCamelCase    ,		num_mel_bins=self.num_mel_bins    ,		sample_frequency=self.sampling_rate       )
							return features.numpy()



		@staticmethod
		def  __UpperCAmelCase      (							_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase = True    ,		_UpperCamelCase = True    ,		_UpperCamelCase = 0.0    ,		)     ->							np.ndarray:
							# make sure we normalize float32 arrays
							if normalize_means:
												UpperCAmelCase_   :				List[str]						=						x[:input_length].mean(axis=0       )
												UpperCAmelCase_   :				List[str]						=						np.subtract(_UpperCamelCase    ,		_UpperCamelCase       )
							if normalize_vars:
												UpperCAmelCase_   :				Union[str, Any]						=						x[:input_length].std(axis=0       )
												UpperCAmelCase_   :				str						=						np.divide(_UpperCamelCase    ,		_UpperCamelCase       )

							if input_length < x.shape[0]:
												UpperCAmelCase_   :				List[Any]						=						padding_value

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

							return x



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase = None       )     ->							List[np.ndarray]:
							UpperCAmelCase_   :				int						=						attention_mask.sum(-1       ) if attention_mask is not None else [x.shape[0] for x in input_features]
							return [
							    self.utterance_cmvn(_UpperCamelCase    ,		_UpperCamelCase    ,		self.normalize_means    ,		self.normalize_vars    ,		self.padding_value       )
							    for x, n in zip(_UpperCamelCase    ,		_UpperCamelCase       )
							]



		def __call__(							self    ,		_UpperCamelCase    ,		_UpperCamelCase = False    ,		_UpperCamelCase = None    ,		_UpperCamelCase = False    ,		_UpperCamelCase = None    ,		_UpperCamelCase = None    ,		_UpperCamelCase = None    ,		_UpperCamelCase = None    ,		**_UpperCamelCase    ,		)     ->							BatchFeature:

							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.'       )

							UpperCAmelCase_   :				int						=						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}"       )
							UpperCAmelCase_   :				str						=						is_batched_numpy or (
							    isinstance(_UpperCamelCase    ,		(list, tuple)       ) and (isinstance(raw_speech[0]    ,		(np.ndarray, tuple, list)       ))
							)

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

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

							# extract fbank features
							UpperCAmelCase_   :				Union[str, Any]						=						[self._extract_fbank_features(_UpperCamelCase       ) for waveform in raw_speech]

							# convert into correct format for padding
							UpperCAmelCase_   :				Tuple						=						BatchFeature({'input_features': features}       )

							UpperCAmelCase_   :				Dict						=						self.pad(
							    _UpperCamelCase    ,		padding=_UpperCamelCase    ,		max_length=_UpperCamelCase    ,		truncation=_UpperCamelCase    ,		pad_to_multiple_of=_UpperCamelCase    ,		return_attention_mask=_UpperCamelCase    ,		**_UpperCamelCase    ,		)

							# make sure list is in array format
							UpperCAmelCase_   :				int						=						padded_inputs.get('input_features'       )
							if isinstance(input_features[0]    ,		_UpperCamelCase       ):
												UpperCAmelCase_   :				Dict						=						[np.asarray(_UpperCamelCase    ,		dtype=np.floataa       ) for feature in input_features]

							UpperCAmelCase_   :				Optional[int]						=						padded_inputs.get('attention_mask'       )
							if attention_mask is not None:
												UpperCAmelCase_   :				Optional[int]						=						[np.asarray(_UpperCamelCase    ,		dtype=np.intaa       ) for array in attention_mask]

							# Utterance-level cepstral mean and variance normalization
							if self.do_ceptral_normalize:
												UpperCAmelCase_   :				List[str]						=						(
												    np.array(_UpperCamelCase    ,		dtype=np.intaa       )
												    if self._get_padding_strategies(_UpperCamelCase    ,		max_length=_UpperCamelCase       ) is not PaddingStrategy.DO_NOT_PAD
												    else None
												)
												UpperCAmelCase_   :				str						=						self.normalize(
												    padded_inputs['input_features']    ,		attention_mask=_UpperCamelCase       )

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

							return padded_inputs














def 					lowercase__				(					__snake_case   :							int  ):


					'''simple docstring'''


					UpperCAmelCase_   :				Tuple						=						[1]

					UpperCAmelCase_	,  UpperCAmelCase_	,  UpperCAmelCase_   :				Tuple						=						0, 0, 0
					UpperCAmelCase_   :				Union[str, Any]						=						ugly_nums[ia] * 2
					UpperCAmelCase_   :				Tuple						=						ugly_nums[ia] * 3
					UpperCAmelCase_   :				Union[str, Any]						=						ugly_nums[ia] * 5

					for _ in range(1					,	__snake_case  ):
										UpperCAmelCase_   :				Tuple						=						min(__snake_case					,	__snake_case					,	__snake_case  )
										ugly_nums.append(__snake_case  )
										if next_num == next_a:
															ia += 1
															UpperCAmelCase_   :				Union[str, Any]						=						ugly_nums[ia] * 2
										if next_num == next_a:
															ia += 1
															UpperCAmelCase_   :				Any						=						ugly_nums[ia] * 3
										if next_num == next_a:
															ia += 1
															UpperCAmelCase_   :				List[str]						=						ugly_nums[ia] * 5
					return ugly_nums[-1]


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

		testmod(verbose=True)
		print(F'{ugly_numbers(200) = }')






