



'''simple docstring'''






import warnings
from contextlib import contextmanager

from ....processing_utils import ProcessorMixin

class 				SCREAMING_SNAKE_CASE(							A__			):







	"""simple docstring"""






	lowerCamelCase__			 =       """MCTCTFeatureExtractor"""
	lowerCamelCase__			 =       """AutoTokenizer"""







	def __init__(						self			:     Dict		,			__snake_case			:     Optional[int]		,			__snake_case			:     List[str]		)			->		str:
								super().__init__(__snake_case		,			__snake_case		)
								UpperCAmelCase :       List[Any] 							=		self.feature_extractor
								UpperCAmelCase :       Union[str, Any] 							=		False







	def __call__(						self			:     Any		,			*__snake_case			:     List[str]		,			**__snake_case			:     Any		)			->		List[Any]:
								# For backward compatibility
								if self._in_target_context_manager:
															return self.current_processor(*__snake_case		,			**__snake_case		)

								if "raw_speech" in kwargs:
															warnings.warn('''Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'''		)
															UpperCAmelCase :       int 							=		kwargs.pop('''raw_speech'''		)
								else:
															UpperCAmelCase :       Union[str, Any] 							=		kwargs.pop('''audio'''		,			__snake_case		)
								UpperCAmelCase :       Optional[Any] 							=		kwargs.pop('''sampling_rate'''		,			__snake_case		)
								UpperCAmelCase :       Dict 							=		kwargs.pop('''text'''		,			__snake_case		)
								if len(__snake_case		) > 0:
															UpperCAmelCase :       Any 							=		args[0]
															UpperCAmelCase :       Optional[int] 							=		args[1:]

								if audio is None and text is None:
															raise ValueError('''You need to specify either an `audio` or `text` input to process.'''		)

								if audio is not None:
															UpperCAmelCase :       List[str] 							=		self.feature_extractor(__snake_case		,			*__snake_case		,			sampling_rate=__snake_case		,			**__snake_case		)
								if text is not None:
															UpperCAmelCase :       int 							=		self.tokenizer(__snake_case		,			**__snake_case		)

								if text is None:
															return inputs
								elif audio is None:
															return encodings
								else:
															UpperCAmelCase :       str 							=		encodings['''input_ids''']
															return inputs







	def      A	(						self			:     List[Any]		,			*__snake_case			:     List[Any]		,			**__snake_case			:     List[Any]		)			->		str:
								return self.tokenizer.batch_decode(*__snake_case		,			**__snake_case		)







	def      A	(						self			:     List[Any]		,			*__snake_case			:     int		,			**__snake_case			:     Optional[int]		)			->		Any:
								# For backward compatibility
								if self._in_target_context_manager:
															return self.current_processor.pad(*__snake_case		,			**__snake_case		)

								UpperCAmelCase :       List[Any] 							=		kwargs.pop('''input_features'''		,			__snake_case		)
								UpperCAmelCase :       Optional[Any] 							=		kwargs.pop('''labels'''		,			__snake_case		)
								if len(__snake_case		) > 0:
															UpperCAmelCase :       List[str] 							=		args[0]
															UpperCAmelCase :       List[Any] 							=		args[1:]

								if input_features is not None:
															UpperCAmelCase :       Tuple 							=		self.feature_extractor.pad(__snake_case		,			*__snake_case		,			**__snake_case		)
								if labels is not None:
															UpperCAmelCase :       Optional[int] 							=		self.tokenizer.pad(__snake_case		,			**__snake_case		)

								if labels is None:
															return input_features
								elif input_features is None:
															return labels
								else:
															UpperCAmelCase :       List[str] 							=		labels['''input_ids''']
															return input_features







	def      A	(						self			:     Union[str, Any]		,			*__snake_case			:     Optional[Any]		,			**__snake_case			:     Optional[int]		)			->		Optional[Any]:
								return self.tokenizer.decode(*__snake_case		,			**__snake_case		)







	@contextmanager
	def      A	(						self			:     Any		)			->		Optional[int]:
								warnings.warn(
								    '''`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '''
								    '''labels by using the argument `text` of the regular `__call__` method (either in the same call as '''
								    '''your audio inputs, or in a separate call.'''		)
								UpperCAmelCase :       Dict 							=		True
								UpperCAmelCase :       List[Any] 							=		self.tokenizer
								yield
								UpperCAmelCase :       Tuple 							=		self.feature_extractor
								UpperCAmelCase :       List[Any] 							=		False










'''simple docstring'''






from __future__ import annotations


def  snake_case_     (					_lowerCAmelCase  :						str   ,      _lowerCAmelCase  :						str  )      ->   bool:
							UpperCAmelCase :       str 							=		get_failure_array(_lowerCAmelCase  )

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

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


def  snake_case_     (					_lowerCAmelCase  :						str  )      ->   list[int]:
							UpperCAmelCase :       Optional[Any] 							=		[0]
							UpperCAmelCase :       str 							=		0
							UpperCAmelCase :       List[str] 							=		1
							while j < len(_lowerCAmelCase  ):
														if pattern[i] == pattern[j]:
																					i += 1
														elif i > 0:
																					UpperCAmelCase :       Union[str, Any] 							=		failure[i - 1]
																					continue
														j += 1
														failure.append(_lowerCAmelCase  )
							return failure


if __name__ == "__main__":
						# Test 1)
						UpperCamelCase__:   str								=			"abc1abc12"
						UpperCamelCase__:   str								=			"alskfjaldsabc1abc1abc12k23adsfabcabc"
						UpperCamelCase__:   Any								=			"alskfjaldsk23adsfabcabc"
						assert kmp(pattern, texta) and not kmp(pattern, texta)

						# Test 2)
						UpperCamelCase__:   Tuple								=			"ABABX"
						UpperCamelCase__:   Union[str, Any]								=			"ABABZABABYABABX"
						assert kmp(pattern, text)

						# Test 3)
						UpperCamelCase__:   Any								=			"AAAB"
						UpperCamelCase__:   str								=			"ABAAAAAB"
						assert kmp(pattern, text)

						# Test 4)
						UpperCamelCase__:   int								=			"abcdabcy"
						UpperCamelCase__:   Any								=			"abcxabcdabxabcdabcdabcy"
						assert kmp(pattern, text)

						# Test 5)
						UpperCamelCase__:   List[str]								=			"aabaabaaa"
						assert get_failure_array(pattern) == [0, 1, 0, 1, 2, 3, 4, 5, 2]





