






'''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 PaddingStrategy, TensorType, logging


__lowerCAmelCase       		=      logging.get_logger(__name__)





class 	SCREAMING_SNAKE_CASE					(					SCREAMING_SNAKE_CASE_       ):
			snake_case__  =				["input_features", "attention_mask"]






			def __init__( self      :			Union[str, Any]  ,						__SCREAMING_SNAKE_CASE      :			Any=80  ,						__SCREAMING_SNAKE_CASE      :			Union[str, Any]=1_6000  ,						__SCREAMING_SNAKE_CASE      :			Dict=80  ,						__SCREAMING_SNAKE_CASE      :			Dict=0.0  ,						__SCREAMING_SNAKE_CASE      :			Optional[int]=True  ,						__SCREAMING_SNAKE_CASE      :			Tuple=True  ,						__SCREAMING_SNAKE_CASE      :			List[Any]=True  ,						**__SCREAMING_SNAKE_CASE      :			Union[str, Any]  ,						)			->			Tuple:
							super().__init__(feature_size=__SCREAMING_SNAKE_CASE  ,						sampling_rate=__SCREAMING_SNAKE_CASE  ,						padding_value=__SCREAMING_SNAKE_CASE  ,						**__SCREAMING_SNAKE_CASE					)
							a_		:  Union[str, Any]											=      num_mel_bins
							a_		:  str											=      do_ceptral_normalize
							a_		:  int											=      normalize_means
							a_		:  Optional[int]											=      normalize_vars
							a_		:  Dict											=      True






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






			@staticmethod
			def 			SCREAMING_SNAKE_CASE      ( __SCREAMING_SNAKE_CASE      :			np.ndarray  ,						__SCREAMING_SNAKE_CASE      :			int  ,						__SCREAMING_SNAKE_CASE      :			Optional[bool] = True  ,						__SCREAMING_SNAKE_CASE      :			Optional[bool] = True  ,						__SCREAMING_SNAKE_CASE      :			float = 0.0  ,						)			->			np.ndarray:
							# make sure we normalize float32 arrays
							if normalize_means:
											a_		:  Any											=      x[:input_length].mean(axis=0					)
											a_		:  Any											=      np.subtract(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					)
							if normalize_vars:
											a_		:  Dict											=      x[:input_length].std(axis=0					)
											a_		:  List[Any]											=      np.divide(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					)

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

							# make sure array is in float32
							a_		:  Union[str, Any]											=      x.astype(np.floataa					)

							return x






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







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

							a_		:  Dict											=      isinstance(__SCREAMING_SNAKE_CASE  ,						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}'					)
							a_		:  int											=      is_batched_numpy or (
							    isinstance(__SCREAMING_SNAKE_CASE  ,						(list, tuple)					) and (isinstance(raw_speech[0]  ,						(np.ndarray, tuple, list)					))
							)

							if is_batched:
											a_		:  Dict											=      [np.asarray(__SCREAMING_SNAKE_CASE  ,						dtype=np.floataa					) for speech in raw_speech]
							elif not is_batched and not isinstance(__SCREAMING_SNAKE_CASE  ,						np.ndarray					):
											a_		:  Optional[int]											=      np.asarray(__SCREAMING_SNAKE_CASE  ,						dtype=np.floataa					)
							elif isinstance(__SCREAMING_SNAKE_CASE  ,						np.ndarray					) and raw_speech.dtype is np.dtype(np.floataa					):
											a_		:  Dict											=      raw_speech.astype(np.floataa					)

							# always return batch
							if not is_batched:
											a_		:  List[Any]											=      [raw_speech]

							# extract fbank features
							a_		:  int											=      [self._extract_fbank_features(__SCREAMING_SNAKE_CASE					) for waveform in raw_speech]

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

							a_		:  int											=      self.pad(
							    __SCREAMING_SNAKE_CASE  ,						padding=__SCREAMING_SNAKE_CASE  ,						max_length=__SCREAMING_SNAKE_CASE  ,						truncation=__SCREAMING_SNAKE_CASE  ,						pad_to_multiple_of=__SCREAMING_SNAKE_CASE  ,						return_attention_mask=__SCREAMING_SNAKE_CASE  ,						**__SCREAMING_SNAKE_CASE  ,						)

							# make sure list is in array format
							a_		:  Optional[Any]											=      padded_inputs.get('''input_features'''					)
							if isinstance(input_features[0]  ,						__SCREAMING_SNAKE_CASE					):
											a_		:  str											=      [np.asarray(__SCREAMING_SNAKE_CASE  ,						dtype=np.floataa					) for feature in input_features]

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

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

							if return_tensors is not None:
											a_		:  Optional[int]											=      padded_inputs.convert_to_tensors(__SCREAMING_SNAKE_CASE					)

							return padded_inputs












'''simple docstring'''







def 		_UpperCAmelCase   (							__A							:   str						,		__A							:   str      ):

				def get_matched_characters(__A							:   str						,		__A							:   str      ) -> str:
								a_		:  Union[str, Any]											=      []
								a_		:  int											=      min(len(_stra      )						,		len(_stra      )      ) // 2
								for i, l in enumerate(_stra      ):
												a_		:  Any											=      int(max(0						,		i - limit      )      )
												a_		:  Union[str, Any]											=      int(min(i + limit + 1						,		len(_stra      )      )      )
												if l in _stra[left:right]:
																matched.append(__A      )
																a_		:  Any											=      f'{_stra[0:_stra.index(__A      )]} {_stra[_stra.index(__A      ) + 1:]}'

								return "".join(__A      )

				# matching characters
				a_		:  Optional[Any]											=      get_matched_characters(__A						,		__A      )
				a_		:  int											=      get_matched_characters(__A						,		__A      )
				a_		:  Any											=      len(__A      )

				# transposition
				a_		:  List[Any]											=      (
				    len([(ca, ca) for ca, ca in zip(__A						,		__A      ) if ca != ca]      ) // 2
				)

				if not match_count:
								a_		:  Dict											=      0.0
				else:
								a_		:  Optional[int]											=      (
								    1
								    / 3
								    * (
								        match_count / len(__A      )
								        + match_count / len(__A      )
								        + (match_count - transpositions) / match_count
								    )
								)

				# common prefix up to 4 characters
				a_		:  List[str]											=      0
				for ca, ca in zip(stra[:4]						,		stra[:4]      ):
								if ca == ca:
												prefix_len += 1
								else:
												break

				return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		print(jaro_winkler('hello', 'world'))




