





import warnings
from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType



class    __lowerCAmelCase			(     _a	):
			lowerCamelCase_	:	str			     = ['''image_processor''', '''tokenizer''']
			lowerCamelCase_	:	Dict			     = '''FlavaImageProcessor'''
			lowerCamelCase_	:	List[str]			     = ('''BertTokenizer''', '''BertTokenizerFast''')






			def __init__(self   ,				__magic_name__=None   ,				__magic_name__=None   ,				**__magic_name__       )	->  List[Any]:


							'''simple docstring'''



							snake_case_		:       Any           =    None
							if "feature_extractor" in kwargs:
											warnings.warn(
											    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
											    ''' instead.'''   ,				__magic_name__   ,				)
											snake_case_		:       Optional[Any]           =    kwargs.pop('''feature_extractor'''       )

							snake_case_		:       Optional[int]           =    image_processor if image_processor is not None else feature_extractor
							if image_processor is None:
											raise ValueError('''You need to specify an `image_processor`.'''       )
							if tokenizer is None:
											raise ValueError('''You need to specify a `tokenizer`.'''       )

							super().__init__(__magic_name__   ,				__magic_name__       )
							snake_case_		:       Tuple           =    self.image_processor






			def __call__(self   ,				__magic_name__ = None   ,				__magic_name__ = None   ,				__magic_name__ = True   ,				__magic_name__ = False   ,				__magic_name__ = False   ,				__magic_name__ = None   ,				__magic_name__ = 0   ,				__magic_name__ = None   ,				__magic_name__ = None   ,				__magic_name__ = None   ,				__magic_name__ = None   ,				__magic_name__ = None   ,				__magic_name__ = False   ,				__magic_name__ = False   ,				__magic_name__ = False   ,				__magic_name__ = False   ,				__magic_name__ = True   ,				__magic_name__ = None   ,				**__magic_name__   ,				)	->  Dict:


							'''simple docstring'''



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

							if text is not None:
											snake_case_		:       Optional[int]           =    self.tokenizer(
											    text=__magic_name__   ,				add_special_tokens=__magic_name__   ,				padding=__magic_name__   ,				truncation=__magic_name__   ,				max_length=__magic_name__   ,				stride=__magic_name__   ,				pad_to_multiple_of=__magic_name__   ,				return_token_type_ids=__magic_name__   ,				return_attention_mask=__magic_name__   ,				return_overflowing_tokens=__magic_name__   ,				return_special_tokens_mask=__magic_name__   ,				return_offsets_mapping=__magic_name__   ,				return_length=__magic_name__   ,				verbose=__magic_name__   ,				return_tensors=__magic_name__   ,				**__magic_name__   ,				)
							if images is not None:
											snake_case_		:       Any           =    self.image_processor(
											    __magic_name__   ,				return_image_mask=__magic_name__   ,				return_codebook_pixels=__magic_name__   ,				return_tensors=__magic_name__   ,				**__magic_name__   ,				)

							if text is not None and images is not None:
											encoding.update(__magic_name__       )
											return encoding
							elif text is not None:
											return encoding
							else:
											return BatchEncoding(data=dict(**__magic_name__       )   ,				tensor_type=__magic_name__       )






			def      lowerCamelCase    (self   ,				*__magic_name__   ,				**__magic_name__       )	->  Optional[Any]:


							'''simple docstring'''



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






			def      lowerCamelCase    (self   ,				*__magic_name__   ,				**__magic_name__       )	->  List[Any]:


							'''simple docstring'''



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






			@property
			def      lowerCamelCase    (self       )	->  Any:


							'''simple docstring'''



							snake_case_		:       Tuple           =    self.tokenizer.model_input_names
							snake_case_		:       List[Any]           =    self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )






			@property
			def      lowerCamelCase    (self       )	->  str:


							'''simple docstring'''



							warnings.warn(
							    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''   ,				__magic_name__   ,				)
							return self.image_processor_class






			@property
			def      lowerCamelCase    (self       )	->  List[str]:


							'''simple docstring'''



							warnings.warn(
							    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''   ,				__magic_name__   ,				)
							return self.image_processor



import sys

lowercase_   							=  (
    '73167176531330624919225119674426574742355349194934'
    '96983520312774506326239578318016984801869478851843'
    '85861560789112949495459501737958331952853208805511'
    '12540698747158523863050715693290963295227443043557'
    '66896648950445244523161731856403098711121722383113'
    '62229893423380308135336276614282806444486645238749'
    '30358907296290491560440772390713810515859307960866'
    '70172427121883998797908792274921901699720888093776'
    '65727333001053367881220235421809751254540594752243'
    '52584907711670556013604839586446706324415722155397'
    '53697817977846174064955149290862569321978468622482'
    '83972241375657056057490261407972968652414535100474'
    '82166370484403199890008895243450658541227588666881'
    '16427171479924442928230863465674813919123162824586'
    '17866458359124566529476545682848912883142607690042'
    '24219022671055626321111109370544217506941658960408'
    '07198403850962455444362981230987879927244284909188'
    '84580156166097919133875499200524063689912560717606'
    '05886116467109405077541002256983155200055935729725'
    '71636269561882670428252483600823257530420752963450'
)
def 	a (     A__      :  str = N	)       -> int:





      """simple docstring"""



      _lowercase      			=-sys.maxsize - 1
      for i in range(len(A__	) - 12	):
            _lowercase      			=1
            for j in range(13	):
                  product *= int(n[i + j]	)
            if product > largest_product:
                  _lowercase      			=product
      return largest_product


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


