
from functools import reduce

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







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


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


from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


lowercase  : List[str]						   =							logging.get_logger(__name__)

lowercase  : Tuple						   =							{
    """google/umt5-small""": """https://huggingface.co/google/umt5-small/resolve/main/config.json""",
    # See all umt5 models at https://huggingface.co/models?filter=umt5
}

class __snake_case		(				lowerCAmelCase					):
    _a		:						List[str]=    "umt5"
    _a		:						Optional[Any]=    ["past_key_values"]
    def __init__(  self		,snake_case=250112		,snake_case=512		,snake_case=64		,snake_case=1024		,snake_case=8		,snake_case=None		,snake_case=6		,snake_case=32		,snake_case=128		,snake_case=0.1		,snake_case=1e-6		,snake_case=1.0		,snake_case="gated-gelu"		,snake_case=True		,snake_case=True		,snake_case="T5Tokenizer"		,snake_case=True		,snake_case=0		,snake_case=1		,snake_case=0		,**snake_case		,):



           '''simple docstring'''







           super().__init__(
               is_encoder_decoder=snake_case		,tokenizer_class=snake_case		,tie_word_embeddings=snake_case		,pad_token_id=snake_case		,eos_token_id=snake_case		,decoder_start_token_id=snake_case		,**snake_case		,)
           lowercase							:					List[str]	     =				vocab_size
           lowercase							:					Optional[Any]	     =				d_model
           lowercase							:					int	     =				d_kv
           lowercase							:					List[Any]	     =				d_ff
           lowercase							:					Dict	     =				num_layers
           lowercase							:					int	     =				(
               num_decoder_layers if num_decoder_layers is not None else self.num_layers
           )  # default = symmetry
           lowercase							:					List[Any]	     =				num_heads
           lowercase							:					Optional[Any]	     =				relative_attention_num_buckets
           lowercase							:					Dict	     =				relative_attention_max_distance
           lowercase							:					Dict	     =				dropout_rate
           lowercase							:					Any	     =				layer_norm_epsilon
           lowercase							:					Any	     =				initializer_factor
           lowercase							:					Union[str, Any]	     =				feed_forward_proj
           lowercase							:					Optional[Any]	     =				use_cache

           lowercase							:					Dict	     =				self.feed_forward_proj.split("""-"""				)
           lowercase							:					List[Any]	     =				act_info[-1]
           lowercase							:					Any	     =				act_info[0] == """gated"""

           if len(snake_case				) > 1 and act_info[0] != "gated" or len(snake_case				) > 2:
                  raise ValueError(
                      f"`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."
                      """Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. """
                      """'gated-gelu' or 'relu'"""				)

           if feed_forward_proj == "gated-gelu":
                  lowercase							:					int	     =				"""gelu_new"""
    @property
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           return self.d_model
    @property
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           return self.num_heads
    @property
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           return self.num_layers






class __snake_case		(				lowerCAmelCase					):
    @property
    # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					int	     =				{
               """input_ids""": {0: """batch""", 1: """encoder_sequence"""},
               """attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
           }
           if self.use_past:
                  lowercase							:					List[Any]	     =				"""past_encoder_sequence + sequence"""
                  lowercase							:					Dict	     =				{0: """batch"""}
                  lowercase							:					Union[str, Any]	     =				{0: """batch""", 1: """past_decoder_sequence + sequence"""}
           else:
                  lowercase							:					Optional[Any]	     =				{0: """batch""", 1: """decoder_sequence"""}
                  lowercase							:					List[Any]	     =				{0: """batch""", 1: """decoder_sequence"""}

           if self.use_past:
                  self.fill_with_past_key_values_(snake_case		,direction="""inputs"""				)

           return common_inputs
    @property
    # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           return 13
    @property
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           return 5e-4
