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						:			str     		=	logging.get_logger(__name__)





class    lowercase__		(  a__):
						UpperCamelCase_	=						["""input_features""", """attention_mask"""]


						def __init__(			self      :							Optional[Any]       ,						UpperCamelCase__      :							Any=80       ,						UpperCamelCase__      :							Union[str, Any]=1_6000       ,						UpperCamelCase__      :							Optional[Any]=80       ,						UpperCamelCase__      :							List[str]=0.0       ,						UpperCamelCase__      :							List[Any]=True       ,						UpperCamelCase__      :							int=True       ,						UpperCamelCase__      :							int=True       ,						**UpperCamelCase__      :							Tuple       ,						):





												'''simple docstring'''





												super().__init__(feature_size=_UpperCAmelCase       ,						sampling_rate=_UpperCAmelCase       ,						padding_value=_UpperCAmelCase       ,						**_UpperCAmelCase		)
												SCREAMING_SNAKE_CASE				:       str     							=				num_mel_bins
												SCREAMING_SNAKE_CASE				:       Optional[Any]     							=				do_ceptral_normalize
												SCREAMING_SNAKE_CASE				:       Optional[Any]     							=				normalize_means
												SCREAMING_SNAKE_CASE				:       int     							=				normalize_vars
												SCREAMING_SNAKE_CASE				:       int     							=				True


						def 			__A			(			self      :							Union[str, Any]       ,						UpperCamelCase__      :							Tuple       ,						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE				:       Any     							=				waveform * (2**15)  # Kaldi compliance: 16-bit signed integers
												SCREAMING_SNAKE_CASE				:       Union[str, Any]     							=				torch.from_numpy(_UpperCAmelCase		).unsqueeze(0		)
												SCREAMING_SNAKE_CASE				:       List[str]     							=				ta_kaldi.fbank(_UpperCAmelCase       ,						num_mel_bins=self.num_mel_bins       ,						sample_frequency=self.sampling_rate		)
												return features.numpy()


						@staticmethod
						def 			__A			(			UpperCamelCase__      :							Dict       ,						UpperCamelCase__      :							int       ,						UpperCamelCase__      :							Dict = True       ,						UpperCamelCase__      :							List[Any] = True       ,						UpperCamelCase__      :							Any = 0.0       ,						):





												'''simple docstring'''





												if normalize_means:
																		SCREAMING_SNAKE_CASE				:       Any     							=				x[:input_length].mean(axis=0		)
																		SCREAMING_SNAKE_CASE				:       Dict     							=				np.subtract(_UpperCAmelCase       ,						_UpperCAmelCase		)
												if normalize_vars:
																		SCREAMING_SNAKE_CASE				:       Optional[Any]     							=				x[:input_length].std(axis=0		)
																		SCREAMING_SNAKE_CASE				:       List[Any]     							=				np.divide(_UpperCAmelCase       ,						_UpperCAmelCase		)

												if input_length < x.shape[0]:
																		SCREAMING_SNAKE_CASE				:       Dict     							=				padding_value

												# make sure array is in float32
												SCREAMING_SNAKE_CASE				:       List[str]     							=				x.astype(np.floataa		)

												return x


						def 			__A			(			self      :							List[Any]       ,						UpperCamelCase__      :							int       ,						UpperCamelCase__      :							int = None		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE				:       Union[str, Any]     							=				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      :							List[str]       ,						UpperCamelCase__      :							Union[str, Any]       ,						UpperCamelCase__      :							str = False       ,						UpperCamelCase__      :							Dict = None       ,						UpperCamelCase__      :							Dict = False       ,						UpperCamelCase__      :							Optional[Any] = None       ,						UpperCamelCase__      :							List[str] = None       ,						UpperCamelCase__      :							Optional[int] = None       ,						UpperCamelCase__      :							Dict = None       ,						**UpperCamelCase__      :							Any       ,						):





												'''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} 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.'''		)

												SCREAMING_SNAKE_CASE				:       Tuple     							=				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}"""		)
												SCREAMING_SNAKE_CASE				:       Any     							=				is_batched_numpy or (
												    isinstance(_UpperCAmelCase       ,						(list, tuple)		) and (isinstance(raw_speech[0]       ,						(np.ndarray, tuple, list)		))
												)

												if is_batched:
																		SCREAMING_SNAKE_CASE				:       Union[str, Any]     							=				[np.asarray(_UpperCAmelCase       ,						dtype=np.floataa		) for speech in raw_speech]
												elif not is_batched and not isinstance(_UpperCAmelCase       ,						np.ndarray		):
																		SCREAMING_SNAKE_CASE				:       Any     							=				np.asarray(_UpperCAmelCase       ,						dtype=np.floataa		)
												elif isinstance(_UpperCAmelCase       ,						np.ndarray		) and raw_speech.dtype is np.dtype(np.floataa		):
																		SCREAMING_SNAKE_CASE				:       Optional[Any]     							=				raw_speech.astype(np.floataa		)

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

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

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

												SCREAMING_SNAKE_CASE				:       List[Any]     							=				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
												SCREAMING_SNAKE_CASE				:       Optional[int]     							=				padded_inputs.get('''input_features'''		)
												if isinstance(input_features[0]       ,						_UpperCAmelCase		):
																		SCREAMING_SNAKE_CASE				:       Tuple     							=				[np.asarray(_UpperCAmelCase       ,						dtype=np.floataa		) for feature in input_features]

												SCREAMING_SNAKE_CASE				:       Any     							=				padded_inputs.get('''attention_mask'''		)
												if attention_mask is not None:
																		SCREAMING_SNAKE_CASE				:       Union[str, Any]     							=				[np.asarray(_UpperCAmelCase       ,						dtype=np.intaa		) for array in attention_mask]

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

												if return_tensors is not None:
																		SCREAMING_SNAKE_CASE				:       Optional[Any]     							=				padded_inputs.convert_to_tensors(_UpperCAmelCase		)

												return padded_inputs












'''simple docstring'''




import math
import sys






def 			_lowerCAmelCase     (    __snake_case    :					int     )	->	int:
     if number != int(__snake_case     ):
          raise ValueError('the value of input must be a natural number'     )
     if number < 0:
          raise ValueError('the value of input must not be a negative number'     )
     if number == 0:
          return 1
     __A		:					str						=			[-1] * (number + 1)
     __A		:					Dict						=			0
     for i in range(1					,				number + 1     ):
          __A		:					int						=			sys.maxsize
          __A		:					int						=			int(math.sqrt(__snake_case     )     )
          for j in range(1					,				root + 1     ):
               __A		:					str						=			1 + answers[i - (j**2)]
               __A		:					Dict						=			min(__snake_case					,				__snake_case     )
          __A		:					Union[str, Any]						=			answer
     return answers[number]


if __name__ == "__main__":
  import doctest

  doctest.testmod()