





from ...utils import logging
from ..ta.modeling_tf_ta import TFTaEncoderModel, TFTaForConditionalGeneration, TFTaModel
from .configuration_mta import MTaConfig


UpperCAmelCase_           =	logging.get_logger(__name__)

UpperCAmelCase_           =	"T5Config"




class 			__UpperCamelCase    (							A__    ):
     __A	:  str							  =   """mt5"""
     __A	:  Optional[Any]							  =   MTaConfig




class 			__UpperCamelCase    (							A__    ):
     __A	:  Tuple							  =   """mt5"""
     __A	:  List[str]							  =   MTaConfig




class 			__UpperCamelCase    (							A__    ):
     __A	:  Optional[Any]							  =   """mt5"""
     __A	:  int							  =   MTaConfig






from __future__ import annotations
def      A__       (    SCREAMING_SNAKE_CASE_   :      int						)  ->  bool:






   """simple docstring"""







   _UpperCAmelCase           =   str(SCREAMING_SNAKE_CASE_						)
   return len(SCREAMING_SNAKE_CASE_						) == 9 and set(SCREAMING_SNAKE_CASE_						) == set('''123456789'''						)
def      A__       (    )  ->  int | None:






   """simple docstring"""







   for base_num in range(99_99				,	49_99				,	-1						):
      _UpperCAmelCase           =   10_00_02 * base_num
      if is_9_pandigital(SCREAMING_SNAKE_CASE_						):
         return candidate

   for base_num in range(3_33				,	99				,	-1						):
      _UpperCAmelCase           =   1_00_20_03 * base_num
      if is_9_pandigital(SCREAMING_SNAKE_CASE_						):
         return candidate

   return None


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