def 		snake_case__  (		lowercase = 998001				)     ->   List[str]:

   # fetches the next number
   for number in range(n - 1       ,							9999       ,							-1				):
      lowerCAmelCase_:       str         =  str(lowercase				)

      # checks whether 'str_number' is a palindrome.
      if str_number == str_number[::-1]:
         lowerCAmelCase_:       List[str]         =  999

         # if 'number' is a product of two 3-digit numbers
         # then number is the answer otherwise fetch next number.
         while divisor != 99:
            if (number % divisor == 0) and (len(str(number // divisor				)				) == 3.0):
               return number
            divisor -= 1
   raise ValueError("That number is larger than our acceptable range."				)


if __name__ == "__main__":
 print(f'''{solution() = }''')
from ctypes import c_float, sizeof
from enum import Enum
from typing import TYPE_CHECKING, Optional, Union


if TYPE_CHECKING:
 from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore

class 				_lowercase						(    UpperCAmelCase__ ):






    '''simple docstring'''
    SCREAMING_SNAKE_CASE:							Union[str, Any]        =						c_float




    @property
    def     _a						(						self   ):
       return sizeof(self.value   )





def 		snake_case__  (		lowercase       ,							lowercase       ,							lowercase = 0				):
   # < 0 is possible if using a dynamic axis
   if dimension <= 0:
      lowerCAmelCase_:       int         =  fixed_dimension

   dimension -= num_token_to_add
   return dimension
def 		snake_case__  (		lowercase       ,							lowercase				):
   return num_parameters * dtype.size







def 		snake_case__  (		lowercase				):
   from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore

   try:
      return AutoProcessor.from_pretrained(lowercase				)
   except (ValueError, OSError, KeyError):
      lowerCAmelCase_     ,  lowerCAmelCase_:       int         =  None, None
      try:
         lowerCAmelCase_:       Dict         =  AutoTokenizer.from_pretrained(lowercase				)
      except (OSError, KeyError):
         pass
      try:
         lowerCAmelCase_:       Optional[int]         =  AutoFeatureExtractor.from_pretrained(lowercase				)
      except (OSError, KeyError):
         pass

      if tokenizer is not None and feature_extractor is not None:
         raise ValueError(
             F'''Couldn\'t auto-detect preprocessor for {model_name}. Found both a tokenizer and a feature extractor.'''				)
      elif tokenizer is None and feature_extractor is None:
         return None
      elif tokenizer is not None:
         return tokenizer
      else:
         return feature_extractor