


"""simple docstring"""


from __future__ import annotations



class 			lowerCamelCase__						:







						def __init__(							self   :    Tuple			,			_lowercase   :    str			,			_lowercase   :    str		):
								A       , A					       = text, pattern
								A       , A					       = len(_lowercase		), len(_lowercase		)







						def __a						(							self   :    List[str]			,			_lowercase   :    str		):

								for i in range(self.patLen - 1			,			-1			,			-1		):
										if char == self.pattern[i]:
												return i
								return -1







						def __a						(							self   :    Union[str, Any]			,			_lowercase   :    int		):

								for i in range(self.patLen - 1			,			-1			,			-1		):
										if self.pattern[i] != self.text[current_pos + i]:
												return current_pos + i
								return -1







						def __a						(							self   :    Union[str, Any]		):
								# searches pattern in text and returns index positions
								A					       = []
								for i in range(self.textLen - self.patLen + 1		):
										A					       = self.mismatch_in_text(_lowercase		)
										if mismatch_index == -1:
												positions.append(_lowercase		)
										else:
												A					       = self.match_in_pattern(self.text[mismatch_index]		)
												A					       = (
												    mismatch_index - match_index
												)  # shifting index lgtm [py/multiple-definition]
								return positions

UpperCamelCase						:				Any             =      "ABAABA"
UpperCamelCase						:				int             =      "AB"
UpperCamelCase						:				str             =      BoyerMooreSearch(text, pattern)
UpperCamelCase						:				List[str]             =      bms.bad_character_heuristic()

if len(positions) == 0:
	print("No match found")
else:
	print("Pattern found in following positions: ")
	print(positions)









"""simple docstring"""


from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding



class 			lowerCamelCase__						(   UpperCAmelCase_			):
						lowerCAmelCase       =     """ClapFeatureExtractor"""
						lowerCAmelCase       =     ("""RobertaTokenizer""", """RobertaTokenizerFast""")







						def __init__(							self   :    Dict			,			_lowercase   :    List[str]			,			_lowercase   :    Optional[int]		):
								super().__init__(_lowercase			,			_lowercase		)







						def __call__(							self   :    Optional[int]			,			_lowercase   :    List[Any]=None			,			_lowercase   :    Dict=None			,			_lowercase   :    str=None			,			**_lowercase   :    Union[str, Any]		):
								A					       = kwargs.pop('sampling_rate'			,			_lowercase		)

								if text is None and audios is None:
										raise ValueError('You have to specify either text or audios. Both cannot be none.'		)

								if text is not None:
										A					       = self.tokenizer(_lowercase			,			return_tensors=_lowercase			,			**_lowercase		)

								if audios is not None:
										A					       = self.feature_extractor(
										    _lowercase			,			sampling_rate=_lowercase			,			return_tensors=_lowercase			,			**_lowercase		)

								if text is not None and audios is not None:
										A					       = audio_features.input_features
										return encoding
								elif text is not None:
										return encoding
								else:
										return BatchEncoding(data=dict(**_lowercase		)			,			tensor_type=_lowercase		)







						def __a						(							self   :    List[Any]			,			*_lowercase   :    Dict			,			**_lowercase   :    Optional[int]		):
								return self.tokenizer.batch_decode(*_lowercase			,			**_lowercase		)







						def __a						(							self   :    List[str]			,			*_lowercase   :    str			,			**_lowercase   :    Optional[int]		):
								return self.tokenizer.decode(*_lowercase			,			**_lowercase		)







						@property
						def __a						(							self   :    Tuple		):
								A					       = self.tokenizer.model_input_names
								A					       = self.feature_extractor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names		)		)





