


'''simple docstring'''
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer
class      lowercase			(							A__      ):

		"""simple docstring"""

		_a									=       'Wav2Vec2FeatureExtractor'
		_a									=       'AutoTokenizer'




		def __init__(	self					,     UpperCamelCase_					,     UpperCamelCase_							):

							'''simple docstring'''


							super().__init__(UpperCamelCase_					,     UpperCamelCase_							)
							UpperCamelCase__     :Optional[Any]							      =				self.feature_extractor
							UpperCamelCase__     :str							      =				False




		@classmethod
		def 					lowerCAmelCase__       (	cls					,     UpperCamelCase_					,     **UpperCamelCase_							):

							'''simple docstring'''


							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_					,     )

												UpperCamelCase__     :Optional[int]							      =				WavaVecaFeatureExtractor.from_pretrained(UpperCamelCase_					,     **UpperCamelCase_							)
												UpperCamelCase__     :Optional[Any]							      =				WavaVecaCTCTokenizer.from_pretrained(UpperCamelCase_					,     **UpperCamelCase_							)

												return cls(feature_extractor=UpperCamelCase_					,     tokenizer=UpperCamelCase_							)




		def __call__(	self					,     *UpperCamelCase_					,     **UpperCamelCase_							):

							'''simple docstring'''


							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.'''							)
												UpperCamelCase__     :Any							      =				kwargs.pop('''raw_speech'''							)
							else:
												UpperCamelCase__     :Any							      =				kwargs.pop('''audio'''					,     UpperCamelCase_							)
							UpperCamelCase__     :Union[str, Any]							      =				kwargs.pop('''sampling_rate'''					,     UpperCamelCase_							)
							UpperCamelCase__     :Tuple							      =				kwargs.pop('''text'''					,     UpperCamelCase_							)
							if len(UpperCamelCase_							) > 0:
												UpperCamelCase__     :List[Any]							      =				args[0]
												UpperCamelCase__     :Tuple							      =				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(UpperCamelCase_					,     *UpperCamelCase_					,     sampling_rate=UpperCamelCase_					,     **UpperCamelCase_							)
							if text is not None:
												UpperCamelCase__     :List[Any]							      =				self.tokenizer(UpperCamelCase_					,     **UpperCamelCase_							)

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




		def 					lowerCAmelCase__       (	self					,     *UpperCamelCase_					,     **UpperCamelCase_							):

							'''simple docstring'''


							if self._in_target_context_manager:
												return self.current_processor.pad(*UpperCamelCase_					,     **UpperCamelCase_							)

							UpperCamelCase__     :Union[str, Any]							      =				kwargs.pop('''input_features'''					,     UpperCamelCase_							)
							UpperCamelCase__     :Optional[int]							      =				kwargs.pop('''labels'''					,     UpperCamelCase_							)
							if len(UpperCamelCase_							) > 0:
												UpperCamelCase__     :Any							      =				args[0]
												UpperCamelCase__     :Union[str, Any]							      =				args[1:]

							if input_features is not None:
												UpperCamelCase__     :str							      =				self.feature_extractor.pad(UpperCamelCase_					,     *UpperCamelCase_					,     **UpperCamelCase_							)
							if labels is not None:
												UpperCamelCase__     :Dict							      =				self.tokenizer.pad(UpperCamelCase_					,     **UpperCamelCase_							)

							if labels is None:
												return input_features
							elif input_features is None:
												return labels
							else:
												UpperCamelCase__     :Union[str, Any]							      =				labels['''input_ids''']
												return input_features




		def 					lowerCAmelCase__       (	self					,     *UpperCamelCase_					,     **UpperCamelCase_							):

							'''simple docstring'''


							return self.tokenizer.batch_decode(*UpperCamelCase_					,     **UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self					,     *UpperCamelCase_					,     **UpperCamelCase_							):

							'''simple docstring'''


							return self.tokenizer.decode(*UpperCamelCase_					,     **UpperCamelCase_							)




		@contextmanager
		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							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__     :Optional[int]							      =				True
							UpperCamelCase__     :Dict							      =				self.tokenizer
							yield
							UpperCamelCase__     :Union[str, Any]							      =				self.feature_extractor
							UpperCamelCase__     :int							      =				False



'''simple docstring'''
import sys

__snake_case							   =  (
    '''73167176531330624919225119674426574742355349194934'''
    '''96983520312774506326239578318016984801869478851843'''
    '''85861560789112949495459501737958331952853208805511'''
    '''12540698747158523863050715693290963295227443043557'''
    '''66896648950445244523161731856403098711121722383113'''
    '''62229893423380308135336276614282806444486645238749'''
    '''30358907296290491560440772390713810515859307960866'''
    '''70172427121883998797908792274921901699720888093776'''
    '''65727333001053367881220235421809751254540594752243'''
    '''52584907711670556013604839586446706324415722155397'''
    '''53697817977846174064955149290862569321978468622482'''
    '''83972241375657056057490261407972968652414535100474'''
    '''82166370484403199890008895243450658541227588666881'''
    '''16427171479924442928230863465674813919123162824586'''
    '''17866458359124566529476545682848912883142607690042'''
    '''24219022671055626321111109370544217506941658960408'''
    '''07198403850962455444362981230987879927244284909188'''
    '''84580156166097919133875499200524063689912560717606'''
    '''05886116467109405077541002256983155200055935729725'''
    '''71636269561882670428252483600823257530420752963450'''
)


def     a   (    __a     )						->			int:



					'''simple docstring'''






					UpperCamelCase__     :Tuple							      =				1
					for digit in s:
										product *= int(__a     )
					return product


def     a   (    __a = N     )						->			int:



					'''simple docstring'''






					UpperCamelCase__     :Tuple							      =				-sys.maxsize - 1
					UpperCamelCase__     :Dict							      =				n[:13]
					UpperCamelCase__     :Dict							      =				13
					while cur_index < len(__a     ) - 13:
										if int(n[cur_index]     ) >= int(substr[0]     ):
															UpperCamelCase__     :Dict							      =				substr[1:] + n[cur_index]
															cur_index += 1
										else:
															UpperCamelCase__     :List[str]							      =				max(__a				,      str_eval(__a     )     )
															UpperCamelCase__     :Union[str, Any]							      =				n[cur_index : cur_index + 13]
															cur_index += 13
					return largest_product


if __name__ == "__main__":
		print(F"""{solution() = }""")