


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__    :Dict       =				logging.get_logger(__name__)

class 						__a (							UpperCAmelCase     ):
		_a      :    Tuple           =      ['input_features', 'attention_mask']
		def __init__(     self     ,       _SCREAMING_SNAKE_CASE=80     ,       _SCREAMING_SNAKE_CASE=16000     ,       _SCREAMING_SNAKE_CASE=80     ,       _SCREAMING_SNAKE_CASE=0.0     ,       _SCREAMING_SNAKE_CASE=True     ,       _SCREAMING_SNAKE_CASE=True     ,       _SCREAMING_SNAKE_CASE=True     ,       **_SCREAMING_SNAKE_CASE     ,       )					->			Optional[Any]:




									"""simple docstring"""





									super().__init__(feature_size=_SCREAMING_SNAKE_CASE     ,       sampling_rate=_SCREAMING_SNAKE_CASE     ,       padding_value=_SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase											=     num_mel_bins
									_UpperCAmelCase											=     do_ceptral_normalize
									_UpperCAmelCase											=     normalize_means
									_UpperCAmelCase											=     normalize_vars
									_UpperCAmelCase											=     True
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       )					->			np.ndarray:




									"""simple docstring"""





									_UpperCAmelCase											=     waveform * (2**15)  # Kaldi compliance: 16-bit signed integers
									_UpperCAmelCase											=     torch.from_numpy(_SCREAMING_SNAKE_CASE		).unsqueeze(0		)
									_UpperCAmelCase											=     ta_kaldi.fbank(_SCREAMING_SNAKE_CASE     ,       num_mel_bins=self.num_mel_bins     ,       sample_frequency=self.sampling_rate		)
									return features.numpy()
		@staticmethod
		def       UpperCAmelCase__							(     _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE = True     ,       _SCREAMING_SNAKE_CASE = True     ,       _SCREAMING_SNAKE_CASE = 0.0     ,       )					->			np.ndarray:




									"""simple docstring"""





									if normalize_means:
																_UpperCAmelCase											=     x[:input_length].mean(axis=0		)
																_UpperCAmelCase											=     np.subtract(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)
									if normalize_vars:
																_UpperCAmelCase											=     x[:input_length].std(axis=0		)
																_UpperCAmelCase											=     np.divide(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)

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

									# make sure array is in float32
									_UpperCAmelCase											=     x.astype(np.floataa		)

									return x
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE = None		)					->			List[np.ndarray]:




									"""simple docstring"""





									_UpperCAmelCase											=     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     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE = False     ,       _SCREAMING_SNAKE_CASE = None     ,       _SCREAMING_SNAKE_CASE = False     ,       _SCREAMING_SNAKE_CASE = None     ,       _SCREAMING_SNAKE_CASE = None     ,       _SCREAMING_SNAKE_CASE = None     ,       _SCREAMING_SNAKE_CASE = None     ,       **_SCREAMING_SNAKE_CASE     ,       )					->			BatchFeature:




									"""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.'		)

									_UpperCAmelCase											=     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}'''		)
									_UpperCAmelCase											=     is_batched_numpy or (
									    isinstance(_SCREAMING_SNAKE_CASE     ,       (list, tuple)		) and (isinstance(raw_speech[0]     ,       (np.ndarray, tuple, list)		))
									)

									if is_batched:
																_UpperCAmelCase											=     [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		):
																_UpperCAmelCase											=     np.asarray(_SCREAMING_SNAKE_CASE     ,       dtype=np.floataa		)
									elif isinstance(_SCREAMING_SNAKE_CASE     ,       np.ndarray		) and raw_speech.dtype is np.dtype(np.floataa		):
																_UpperCAmelCase											=     raw_speech.astype(np.floataa		)

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

									# extract fbank features
									_UpperCAmelCase											=     [self._extract_fbank_features(_SCREAMING_SNAKE_CASE		) for waveform in raw_speech]

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

									_UpperCAmelCase											=     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
									_UpperCAmelCase											=     padded_inputs.get('input_features'		)
									if isinstance(input_features[0]     ,       _SCREAMING_SNAKE_CASE		):
																_UpperCAmelCase											=     [np.asarray(_SCREAMING_SNAKE_CASE     ,       dtype=np.floataa		) for feature in input_features]

									_UpperCAmelCase											=     padded_inputs.get('attention_mask'		)
									if attention_mask is not None:
																_UpperCAmelCase											=     [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:
																_UpperCAmelCase											=     (
																    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
																)
																_UpperCAmelCase											=     self.normalize(
																    padded_inputs['input_features']     ,       attention_mask=_SCREAMING_SNAKE_CASE		)

									if return_tensors is not None:
																_UpperCAmelCase											=     padded_inputs.convert_to_tensors(_SCREAMING_SNAKE_CASE		)

									return padded_inputs




from __future__ import annotations

def 			lowerCAmelCase__					(     a__:						str     ,    a__:						str			)    ->						bool:


							'''simple docstring'''





							_UpperCAmelCase											=     get_failure_array(a__			)

							# 2) Step through text searching for pattern
							_UpperCAmelCase						,			_UpperCAmelCase											=     0, 0  # index into text, pattern
							while i < len(a__			):
														if pattern[j] == text[i]:
																					if j == (len(a__			) - 1):
																												return True
																					j += 1

														# if this is a prefix in our pattern
														# just go back far enough to continue
														elif j > 0:
																					_UpperCAmelCase											=     failure[j - 1]
																					continue
														i += 1
							return False

def 			lowerCAmelCase__					(     a__:						str			)    ->						list[int]:


							'''simple docstring'''





							_UpperCAmelCase											=     [0]
							_UpperCAmelCase											=     0
							_UpperCAmelCase											=     1
							while j < len(a__			):
														if pattern[i] == pattern[j]:
																					i += 1
														elif i > 0:
																					_UpperCAmelCase											=     failure[i - 1]
																					continue
														j += 1
														failure.append(a__			)
							return failure


if __name__ == "__main__":
		# Test 1)
		lowerCAmelCase__    :List[Any]       =				'''abc1abc12'''
		lowerCAmelCase__    :Optional[int]       =				'''alskfjaldsabc1abc1abc12k23adsfabcabc'''
		lowerCAmelCase__    :int       =				'''alskfjaldsk23adsfabcabc'''
		assert kmp(pattern, texta) and not kmp(pattern, texta)

		# Test 2)
		lowerCAmelCase__    :List[str]       =				'''ABABX'''
		lowerCAmelCase__    :int       =				'''ABABZABABYABABX'''
		assert kmp(pattern, text)

		# Test 3)
		lowerCAmelCase__    :int       =				'''AAAB'''
		lowerCAmelCase__    :Any       =				'''ABAAAAAB'''
		assert kmp(pattern, text)

		# Test 4)
		lowerCAmelCase__    :Union[str, Any]       =				'''abcdabcy'''
		lowerCAmelCase__    :Any       =				'''abcxabcdabxabcdabcdabcy'''
		assert kmp(pattern, text)

		# Test 5)
		lowerCAmelCase__    :List[Any]       =				'''aabaabaaa'''
		assert get_failure_array(pattern) == [0, 1, 0, 1, 2, 3, 4, 5, 2]
