

import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer





class 	snake_case_   (		__A					):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE     :   List[Any]									=				"Wav2Vec2FeatureExtractor"
			SCREAMING_SNAKE_CASE     :   Dict									=				"AutoTokenizer"







			def __init__(     self :				str							,   _UpperCamelCase :				Dict							,   _UpperCamelCase :				str							)      ->Dict:
									super().__init__(_UpperCamelCase							,   _UpperCamelCase							)
									snake_case_   =	self.feature_extractor
									snake_case_   =	False







			@classmethod
			def 				snake_case__(     cls :				Union[str, Any]							,   _UpperCamelCase :				str							,   **_UpperCamelCase :				List[str]							)      ->Dict:
									try:
															return super().from_pretrained(_UpperCamelCase							,   **_UpperCamelCase							)
									except OSError:
															warnings.warn(
															    f'''Loading a tokenizer inside {cls.__name__} from a config that does not'''
															    ''' include a `tokenizer_class` attribute is deprecated and will be '''
															    '''removed in v5. Please add `\'tokenizer_class\': \'Wav2Vec2CTCTokenizer\'`'''
															    ''' attribute to either your `config.json` or `tokenizer_config.json` '''
															    '''file to suppress this warning: '''							,   _UpperCamelCase							,   )

															snake_case_   =	WavaVecaFeatureExtractor.from_pretrained(_UpperCamelCase							,   **_UpperCamelCase							)
															snake_case_   =	WavaVecaCTCTokenizer.from_pretrained(_UpperCamelCase							,   **_UpperCamelCase							)

															return cls(feature_extractor=_UpperCamelCase							,   tokenizer=_UpperCamelCase							)







			def __call__(     self :				Dict							,   *_UpperCamelCase :				int							,   **_UpperCamelCase :				Dict							)      ->Tuple:
									# For backward compatibility
									if self._in_target_context_manager:
															return self.current_processor(*_UpperCamelCase							,   **_UpperCamelCase							)

									if "raw_speech" in kwargs:
															warnings.warn('''Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'''							)
															snake_case_   =	kwargs.pop('''raw_speech'''							)
									else:
															snake_case_   =	kwargs.pop('''audio'''							,   _UpperCamelCase							)
									snake_case_   =	kwargs.pop('''sampling_rate'''							,   _UpperCamelCase							)
									snake_case_   =	kwargs.pop('''text'''							,   _UpperCamelCase							)
									if len(_UpperCamelCase							) > 0:
															snake_case_   =	args[0]
															snake_case_   =	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:
															snake_case_   =	self.feature_extractor(_UpperCamelCase							,   *_UpperCamelCase							,   sampling_rate=_UpperCamelCase							,   **_UpperCamelCase							)
									if text is not None:
															snake_case_   =	self.tokenizer(_UpperCamelCase							,   **_UpperCamelCase							)

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







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

									snake_case_   =	kwargs.pop('''input_features'''							,   _UpperCamelCase							)
									snake_case_   =	kwargs.pop('''labels'''							,   _UpperCamelCase							)
									if len(_UpperCamelCase							) > 0:
															snake_case_   =	args[0]
															snake_case_   =	args[1:]

									if input_features is not None:
															snake_case_   =	self.feature_extractor.pad(_UpperCamelCase							,   *_UpperCamelCase							,   **_UpperCamelCase							)
									if labels is not None:
															snake_case_   =	self.tokenizer.pad(_UpperCamelCase							,   **_UpperCamelCase							)

									if labels is None:
															return input_features
									elif input_features is None:
															return labels
									else:
															snake_case_   =	labels['''input_ids''']
															return input_features







			def 				snake_case__(     self :				Union[str, Any]							,   *_UpperCamelCase :				str							,   **_UpperCamelCase :				Optional[int]							)      ->Optional[int]:
									return self.tokenizer.batch_decode(*_UpperCamelCase							,   **_UpperCamelCase							)







			def 				snake_case__(     self :				List[str]							,   *_UpperCamelCase :				Optional[Any]							,   **_UpperCamelCase :				str							)      ->Optional[Any]:
									return self.tokenizer.decode(*_UpperCamelCase							,   **_UpperCamelCase							)







			@contextmanager
			def 				snake_case__(     self :				str							)      ->List[Any]:
									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.'''							)
									snake_case_   =	True
									snake_case_   =	self.tokenizer
									yield
									snake_case_   =	self.feature_extractor
									snake_case_   =	False


from __future__ import annotations

from collections.abc import Generator


def   __SCREAMING_SNAKE_CASE					():
						snake_case_   =	{}
						snake_case_   =	2
						while True:
												snake_case_   =	factor_map.pop(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       )
												if factor:
																		snake_case_   =	factor + prime
																		while x in factor_map:
																								x += factor
																		snake_case_   =	factor
												else:
																		snake_case_   =	prime
																		yield prime
												prime += 1


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__ = 1E10       ):
						snake_case_   =	sieve()

						snake_case_   =	1
						while True:
												snake_case_   =	next(SCREAMING_SNAKE_CASE__       )
												if (2 * prime * n) > limit:
																		return n
												# Ignore the next prime as the reminder will be 2.
												next(SCREAMING_SNAKE_CASE__       )
												n += 2


if __name__ == "__main__":
					print(solution())