





from functools import reduce

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





def       _UpperCamelCase       (a__				:str = N    ):






							"""simple docstring"""





							return max(
							    # mypy cannot properly interpret reduce
							    int(reduce(lambda a__				,     a__    : str(int(a__    ) * int(a__    )    )				,     n[i : i + 13]    )    )
							    for i in range(len(a__    ) - 12    )    )


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











import warnings

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase__     =	logging.get_logger(__name__)

UpperCamelCase__     =	{
    "xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/config.json",
    "xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/config.json",
}

class     __SCREAMING_SNAKE_CASE   (       _a    ):
							snake_case       : Any         =    """xlnet"""
							snake_case       : Optional[Any]         =    ["""mems"""]
							snake_case       : Any         =    {
							    """n_token""": """vocab_size""",  # Backward compatibility
							    """hidden_size""": """d_model""",
							    """num_attention_heads""": """n_head""",
							    """num_hidden_layers""": """n_layer""",
							}






							def __init__(  self					,	__lowerCAmelCase=32000					,	__lowerCAmelCase=1024					,	__lowerCAmelCase=24					,	__lowerCAmelCase=16					,	__lowerCAmelCase=4096					,	__lowerCAmelCase="gelu"					,	__lowerCAmelCase=True					,	__lowerCAmelCase="bi"					,	__lowerCAmelCase=0.02					,	__lowerCAmelCase=1E-12					,	__lowerCAmelCase=0.1					,	__lowerCAmelCase=512					,	__lowerCAmelCase=None					,	__lowerCAmelCase=True					,	__lowerCAmelCase=False					,	__lowerCAmelCase=False					,	__lowerCAmelCase=-1					,	__lowerCAmelCase=False					,	__lowerCAmelCase="last"					,	__lowerCAmelCase=True					,	__lowerCAmelCase="tanh"					,	__lowerCAmelCase=0.1					,	__lowerCAmelCase=5					,	__lowerCAmelCase=5					,	__lowerCAmelCase=5					,	__lowerCAmelCase=1					,	__lowerCAmelCase=2					,	**__lowerCAmelCase					,	):
														UpperCamelCase__			       =     vocab_size
														UpperCamelCase__			       =     d_model
														UpperCamelCase__			       =     n_layer
														UpperCamelCase__			       =     n_head
														if d_model % n_head != 0:
																					raise ValueError(f"""'d_model % n_head' ({d_model % n_head}) should be equal to 0"""					)
														if "d_head" in kwargs:
																					if kwargs["d_head"] != d_model // n_head:
																												raise ValueError(
																												    f"""`d_head` ({kwargs["d_head"]}) should be equal to `d_model // n_head` ({d_model // n_head})"""					)
														UpperCamelCase__			       =     d_model // n_head
														UpperCamelCase__			       =     ff_activation
														UpperCamelCase__			       =     d_inner
														UpperCamelCase__			       =     untie_r
														UpperCamelCase__			       =     attn_type

														UpperCamelCase__			       =     initializer_range
														UpperCamelCase__			       =     layer_norm_eps

														UpperCamelCase__			       =     dropout
														UpperCamelCase__			       =     mem_len
														UpperCamelCase__			       =     reuse_len
														UpperCamelCase__			       =     bi_data
														UpperCamelCase__			       =     clamp_len
														UpperCamelCase__			       =     same_length

														UpperCamelCase__			       =     summary_type
														UpperCamelCase__			       =     summary_use_proj
														UpperCamelCase__			       =     summary_activation
														UpperCamelCase__			       =     summary_last_dropout
														UpperCamelCase__			       =     start_n_top
														UpperCamelCase__			       =     end_n_top

														UpperCamelCase__			       =     bos_token_id
														UpperCamelCase__			       =     pad_token_id
														UpperCamelCase__			       =     eos_token_id

														if "use_cache" in kwargs:
																					warnings.warn(
																					    """The `use_cache` argument is deprecated and will be removed in a future version, use `use_mems_eval`"""
																					    """ instead."""					,	__lowerCAmelCase					,	)
																					UpperCamelCase__			       =     kwargs["""use_cache"""]

														UpperCamelCase__			       =     use_mems_eval
														UpperCamelCase__			       =     use_mems_train
														super().__init__(pad_token_id=__lowerCAmelCase					,	bos_token_id=__lowerCAmelCase					,	eos_token_id=__lowerCAmelCase					,	**__lowerCAmelCase					)






							@property
							def 			_lowerCamelCase						(  self					):
														logger.info(f"""The model {self.model_type} is one of the few models that has no sequence length limit."""					)
														return -1







							@max_position_embeddings.setter
							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase					):
														# Message copied from Transformer-XL documentation
														raise NotImplementedError(
														    f"""The model {self.model_type} is one of the few models that has no sequence length limit."""					)




