

"""simple docstring"""





def   _A  (  UpperCamelCase_					:	int)     ->						Union[str, Any]:
   '''simple docstring'''


   __lowercase								=						int(SCREAMING_SNAKE_CASE__)
   if n_element < 1:
      __lowercase								=						ValueError("a should be a positive number")
      raise my_error

   __lowercase								=						[1]
   __lowercase		,__lowercase		,__lowercase								=						(0, 0, 0)
   __lowercase								=						1
   while index < n_element:
      while hamming_list[i] * 2 <= hamming_list[-1]:
         i += 1
      while hamming_list[j] * 3 <= hamming_list[-1]:
         j += 1
      while hamming_list[k] * 5 <= hamming_list[-1]:
         k += 1
      hamming_list.append(
          min(hamming_list[i] * 2,      hamming_list[j] * 3,      hamming_list[k] * 5))
      index += 1
   return hamming_list


if __name__ == "__main__":
  _a							=							input('Enter the last number (nth term) of the Hamming Number Series: ')
  print('Formula of Hamming Number Series => 2^i * 3^j * 5^k')
  _a							=							hamming(int(n))
  print('-----------------------------------------------------')
  print(F"The list with nth numbers is: {hamming_numbers}")
  print('-----------------------------------------------------')







"""simple docstring"""





import fire

from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer




def   _A  (  UpperCamelCase_					:	str,      UpperCamelCase_					:	str,      **UpperCamelCase_					:	Optional[int])     ->						Tuple:
   '''simple docstring'''


   __lowercase								=						AutoConfig.from_pretrained(UpperCamelCase_,      **UpperCamelCase_)
   __lowercase								=						AutoModelForSeqaSeqLM.from_config(UpperCamelCase_)
   model.save_pretrained(UpperCamelCase_)
   AutoTokenizer.from_pretrained(UpperCamelCase_).save_pretrained(UpperCamelCase_)
   return model


if __name__ == "__main__":
   fire.Fire(save_randomly_initialized_version)




