code: 
  
  
  
  '''simple docstring'''
  
  
  
  import argparse
  import logging
  
  import numpy as np
  import torch
  
  from transformers import AutoModelForCausalLM, AutoTokenizer
  
  
  logging.basicConfig(
      format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
      datefmt='%m/%d/%Y %H:%M:%S',
      level=logging.INFO,
  )
  a__						:			Dict										=			logging.getLogger(__name__)
  
  
  
  
  
  
  
  def _lowercase   (  __A       ):
  
  
  
   '''simple docstring'''
  
  
  
   np.random.seed(args.seed       )
   torch.manual_seed(args.seed       )
   if args.n_gpu > 0:
    torch.cuda.manual_seed_all(args.seed       )
  
  
  
  
  
  
  
  def _lowercase   (  ):
  
  
  
   '''simple docstring'''
  
  
  
   __UpperCamelCase				      =  argparse.ArgumentParser()
   parser.add_argument(
       """--model_name_or_path"""							,default=__A							,type=__A							,required=__A							,)
   parser.add_argument("""--prompt"""							,type=__A							,default=""""""       )
   parser.add_argument("""--length"""							,type=__A							,default=20       )
   parser.add_argument("""--stop_token"""							,type=__A							,default=__A							,help="""Token at which text generation is stopped"""       )
   parser.add_argument(
       """--temperature"""							,type=__A							,default=1.0							,help="""temperature of 1.0 has no effect, lower tend toward greedy sampling"""							,)
   parser.add_argument(
       """--repetition_penalty"""							,type=__A							,default=1.0							,help="""primarily useful for CTRL model; in that case, use 1.2"""       )
   parser.add_argument("""--k"""							,type=__A							,default=0       )
   parser.add_argument("""--penalty_alpha"""							,type=__A							,default=0.0       )
   parser.add_argument("""--p"""							,type=__A							,default=0.9       )
  
   parser.add_argument("""--prefix"""							,type=__A							,default=""""""							,help="""Text added prior to input."""       )
   parser.add_argument("""--padding_text"""							,type=__A							,default=""""""							,help="""Deprecated, the use of `--prefix` is preferred."""       )
   parser.add_argument("""--xlm_language"""							,type=__A							,default=""""""							,help="""Optional language when used with the XLM model."""       )
  
   parser.add_argument("""--seed"""							,type=__A							,default=42							,help="""random seed for initialization"""       )
   parser.add_argument("""--no_cuda"""							,action="""store_true"""							,help="""Avoid using CUDA when available"""       )
   parser.add_argument(
       """--fp16"""							,action="""store_true"""							,help="""Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"""							,)
   __UpperCamelCase				      =  parser.parse_args()
  
   __UpperCamelCase				      =  torch.device("""cuda""" if torch.cuda.is_available() and not args.no_cuda else """cpu"""       )
   __UpperCamelCase				      =  0 if args.no_cuda else torch.cuda.device_count()
  
   logger.warning(f"device: {args.device}, n_gpu: {args.n_gpu}, 16-bits training: {args.fpaa}"       )
  
   set_seed(__A       )
  
   # Initialize the model and tokenizer
   __UpperCamelCase				      =  AutoTokenizer.from_pretrained(args.model_name_or_path       )
   __UpperCamelCase				      =  AutoModelForCausalLM.from_pretrained(args.model_name_or_path       )
  
   # tokenizer = GPT2Tokenizer.from_pretrained(args.model_name_or_path)
   # model = OPTForCausalLM.from_pretrained(args.model_name_or_path)
   model.to(args.device       )
  
   if args.fpaa:
    model.half()
  
   logger.info(__A       )
   __UpperCamelCase				      =  args.prompt if args.prompt else input("""Model prompt >>> """       )
  
   __UpperCamelCase				      =  tokenizer(__A							,return_tensors="""pt"""							,add_special_tokens=__A       )
   __UpperCamelCase				      =  {key: value.to(args.device       ) for key, value in inputs.items()}
  
   __UpperCamelCase				      =  model.generate(
       **__A							,max_length=args.length + len(inputs["""input_ids"""][0]       )							,penalty_alpha=args.penalty_alpha							,top_k=args.k							,)
  
   __UpperCamelCase				      =  []
   for generated_sequence_idx, generated_sequence in enumerate(__A       ):
    print(f"=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==="       )
    __UpperCamelCase				      =  generated_sequence.tolist()
  
    # Decode text
    __UpperCamelCase				      =  tokenizer.decode(__A							,clean_up_tokenization_spaces=__A							,add_special_tokens=__A       )
  
    # Remove all text after the stop token
    __UpperCamelCase				      =  text[: text.find(args.stop_token       ) if args.stop_token else None]
  
    # Add the prompt at the beginning of the sequence. Remove the excess text that was used for pre-processing
    __UpperCamelCase				      =  (
        prompt_text + text[len(tokenizer.decode(inputs["""input_ids"""][0]							,clean_up_tokenization_spaces=__A       )       ) :]
    )
  
    generated_sequences.append(__A       )
    print(__A       )
  
   return generated_sequences
  
  
  if __name__ == "__main__":
        main()
  
  
  
  
  
  
style_context: 
  
  
  
  '''simple docstring'''
  
  
  
  a__						:			str										=			[chr(i) for i in range(3_2, 1_2_6)]
  a__						:			List[Any]										=			list(range(len(alphabets)))
  a__						:			Union[str, Any]										=			list(range(len(alphabets)))
  a__						:			Dict										=			list(range(len(alphabets)))
  a__						:			Union[str, Any]										=			list(reversed(range(len(alphabets))))
  a__						:			List[str]										=			[]
  a__						:			int										=			0
  
  
  
  
  
  
  
  def _lowercase   (  ):
  
  
  
   '''simple docstring'''
  
  
  
   global gear_one_pos
   global gear_two_pos
   global gear_three_pos
   __UpperCamelCase				      =  gear_one[0]
   gear_one.append(__A       )
   del gear_one[0]
   gear_one_pos += 1
   if gear_one_pos % int(len(__A       )       ) == 0:
    __UpperCamelCase				      =  gear_two[0]
    gear_two.append(__A       )
    del gear_two[0]
    gear_two_pos += 1
    if gear_two_pos % int(len(__A       )       ) == 0:
     __UpperCamelCase				      =  gear_three[0]
     gear_three.append(__A       )
     del gear_three[0]
     gear_three_pos += 1
  
  
  
  
  
  
  
  def _lowercase   (  __A       ):
  
  
  
   '''simple docstring'''
  
  
  
   __UpperCamelCase				      =  alphabets.index(__A       )
   __UpperCamelCase				      =  gear_one[target]
   __UpperCamelCase				      =  gear_two[target]
   __UpperCamelCase				      =  gear_three[target]
   __UpperCamelCase				      =  reflector[target]
   __UpperCamelCase				      =  gear_three.index(__A       )
   __UpperCamelCase				      =  gear_two.index(__A       )
   __UpperCamelCase				      =  gear_one.index(__A       )
   code.append(alphabets[target]       )
   rotator()
  
  
  if __name__ == "__main__":
   a__						:			Any										=			list(input('Type your message:\n'))
   while True:
    try:
     a__						:			List[Any]										=			int(input('Please set token:(must be only digits)\n'))
     break
    except Exception as error:
     print(error)
   for _ in range(token):
    rotator()
   for j in decode:
    engine(j)
   print('\n' + ''.join(code))
   print(
       f'''\nYour Token is {token} please write it down.\nIf you want to decode '''
       'this message again you should input same digits as token!'
   )
  
  
  
  
  
  