
'''simple docstring'''
import argparse
import logging
import math
import time

import torch

from transformers import TransfoXLCorpus, TransfoXLLMHeadModel


logging.basicConfig(
    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO
)
A__ :		Any					=logging.getLogger(__name__)







def 							A_  (    )					->							Any:





  """simple docstring"""

  __A		:  Union[str, Any]       				=		argparse.ArgumentParser(description="""PyTorch Transformer Language Model"""			)
  parser.add_argument("""--model_name"""      ,  type=__SCREAMING_SNAKE_CASE      ,  default="""transfo-xl-wt103"""      ,  help="""pretrained model name"""			)
  parser.add_argument(
      """--split"""      ,  type=__SCREAMING_SNAKE_CASE      ,  default="""test"""      ,  choices=["""all""", """valid""", """test"""]      ,  help="""which split to evaluate"""			)
  parser.add_argument("""--batch_size"""      ,  type=__SCREAMING_SNAKE_CASE      ,  default=10      ,  help="""batch size"""			)
  parser.add_argument("""--tgt_len"""      ,  type=__SCREAMING_SNAKE_CASE      ,  default=128      ,  help="""number of tokens to predict"""			)
  parser.add_argument("""--ext_len"""      ,  type=__SCREAMING_SNAKE_CASE      ,  default=0      ,  help="""length of the extended context"""			)
  parser.add_argument("""--mem_len"""      ,  type=__SCREAMING_SNAKE_CASE      ,  default=1600      ,  help="""length of the retained previous heads"""			)
  parser.add_argument("""--clamp_len"""      ,  type=__SCREAMING_SNAKE_CASE      ,  default=1000      ,  help="""max positional embedding index"""			)
  parser.add_argument("""--no_cuda"""      ,  action="""store_true"""      ,  help="""Do not use CUDA even though CUA is available"""			)
  parser.add_argument("""--work_dir"""      ,  type=__SCREAMING_SNAKE_CASE      ,  required=__SCREAMING_SNAKE_CASE      ,  help="""path to the work_dir"""			)
  parser.add_argument("""--no_log"""      ,  action="""store_true"""      ,  help="""do not log the eval result"""			)
  parser.add_argument("""--same_length"""      ,  action="""store_true"""      ,  help="""set same length attention with masking"""			)
  parser.add_argument("""--server_ip"""      ,  type=__SCREAMING_SNAKE_CASE      ,  default=""""""      ,  help="""Can be used for distant debugging."""			)
  parser.add_argument("""--server_port"""      ,  type=__SCREAMING_SNAKE_CASE      ,  default=""""""      ,  help="""Can be used for distant debugging."""			)
  __A		:  int       				=		parser.parse_args()
  assert args.ext_len >= 0, "extended context length must be non-negative"

  if args.server_ip and args.server_port:
    # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
    import ptvsd

    print("""Waiting for debugger attach"""			)
    ptvsd.enable_attach(address=(args.server_ip, args.server_port)      ,  redirect_output=__SCREAMING_SNAKE_CASE			)
    ptvsd.wait_for_attach()

  __A		:  List[str]       				=		torch.device("""cuda""" if torch.cuda.is_available() and not args.no_cuda else """cpu"""			)
  logger.info("""device: {}""".format(__SCREAMING_SNAKE_CASE			)			)

  # Load a pre-processed dataset
  # You can also build the corpus yourself using TransfoXLCorpus methods
  # The pre-processing involve computing word frequencies to prepare the Adaptive input and SoftMax
  # and tokenizing the dataset
  # The pre-processed corpus is a convertion (using the conversion script )
  __A		:  List[str]       				=		TransfoXLCorpus.from_pretrained(args.model_name			)

  __A		:  Optional[Any]       				=		corpus.get_iterator("""valid"""      ,  args.batch_size      ,  args.tgt_len      ,  device=__SCREAMING_SNAKE_CASE      ,  ext_len=args.ext_len			)
  __A		:  Any       				=		corpus.get_iterator("""test"""      ,  args.batch_size      ,  args.tgt_len      ,  device=__SCREAMING_SNAKE_CASE      ,  ext_len=args.ext_len			)

  # Load a pre-trained model
  __A		:  str       				=		TransfoXLLMHeadModel.from_pretrained(args.model_name			)
  model.to(__SCREAMING_SNAKE_CASE			)

  logger.info(
      """Evaluating with bsz {} tgt_len {} ext_len {} mem_len {} clamp_len {}""".format(
          args.batch_size      ,  args.tgt_len      ,  args.ext_len      ,  args.mem_len      ,  args.clamp_len			)			)

  model.reset_memory_length(args.mem_len			)
  if args.clamp_len > 0:
    __A		:  int       				=		args.clamp_len
  if args.same_length:
    __A		:  List[Any]       				=		True

  ###############################################################################
  # Evaluation code
  ###############################################################################
  def evaluate(__SCREAMING_SNAKE_CASE    :	Optional[int]			):
    # Turn on evaluation mode which disables dropout.
    model.eval()
    __A				,       __A		:  Union[str, Any]       				=		0, 0.0
    __A		:  str       				=		time.time()
    with torch.no_grad():
      __A		:  Dict       				=		None
      for idx, (data, target, seq_len) in enumerate(__SCREAMING_SNAKE_CASE			):
        __A		:  List[str]       				=		model(__SCREAMING_SNAKE_CASE      ,  lm_labels=__SCREAMING_SNAKE_CASE      ,  mems=__SCREAMING_SNAKE_CASE			)
        __A				,       __A				,       __A		:  Optional[Any]       				=		ret
        __A		:  List[Any]       				=		loss.mean()
        total_loss += seq_len * loss.item()
        total_len += seq_len
      __A		:  str       				=		time.time() - start_time
    logger.info("""Time : {:.2f}s, {:.2f}ms/segment""".format(__SCREAMING_SNAKE_CASE      ,  1000 * total_time / (idx + 1)			)			)
    return total_loss / total_len

  # Run on test data.
  if args.split == "all":
    __A		:  Tuple       				=		evaluate(__SCREAMING_SNAKE_CASE			)
    __A		:  Union[str, Any]       				=		evaluate(__SCREAMING_SNAKE_CASE			)
  elif args.split == "valid":
    __A		:  Dict       				=		evaluate(__SCREAMING_SNAKE_CASE			)
    __A		:  Dict       				=		None
  elif args.split == "test":
    __A		:  Union[str, Any]       				=		evaluate(__SCREAMING_SNAKE_CASE			)
    __A		:  Tuple       				=		None

  def format_log(__SCREAMING_SNAKE_CASE    :	Optional[int]      ,  __SCREAMING_SNAKE_CASE    :	str			):
    __A		:  Optional[int]       				=		"""| {0} loss {1:5.2f} | {0} ppl {2:9.3f} """.format(__SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE      ,  math.exp(__SCREAMING_SNAKE_CASE			)			)
    return log_str

  __A		:  str       				=		""""""
  if valid_loss is not None:
    log_str += format_log(__SCREAMING_SNAKE_CASE      ,  """valid"""			)
  if test_loss is not None:
    log_str += format_log(__SCREAMING_SNAKE_CASE      ,  """test"""			)

  logger.info("""=""" * 100			)
  logger.info(__SCREAMING_SNAKE_CASE			)
  logger.info("""=""" * 100			)


if __name__ == "__main__":
     main()





'''simple docstring'''
A__ :		Optional[Any]					=[chr(i) for i in range(32, 126)]
A__ :		Tuple					=list(range(len(alphabets)))
A__ :		int					=list(range(len(alphabets)))
A__ :		Optional[Any]					=list(range(len(alphabets)))
A__ :		Any					=list(reversed(range(len(alphabets))))
A__ :		Optional[int]					=[]
A__ :		Tuple					=0







def 							A_  (    )					->							Any:





  """simple docstring"""

  global gear_one_pos
  global gear_two_pos
  global gear_three_pos
  __A		:  Any       				=		gear_one[0]
  gear_one.append(__SCREAMING_SNAKE_CASE			)
  del gear_one[0]
  gear_one_pos += 1
  if gear_one_pos % int(len(__SCREAMING_SNAKE_CASE			)			) == 0:
    __A		:  Any       				=		gear_two[0]
    gear_two.append(__SCREAMING_SNAKE_CASE			)
    del gear_two[0]
    gear_two_pos += 1
    if gear_two_pos % int(len(__SCREAMING_SNAKE_CASE			)			) == 0:
      __A		:  str       				=		gear_three[0]
      gear_three.append(__SCREAMING_SNAKE_CASE			)
      del gear_three[0]
      gear_three_pos += 1







def 							A_  (    __SCREAMING_SNAKE_CASE    :	str			)					->							List[str]:





  """simple docstring"""

  __A		:  Union[str, Any]       				=		alphabets.index(__SCREAMING_SNAKE_CASE			)
  __A		:  Dict       				=		gear_one[target]
  __A		:  int       				=		gear_two[target]
  __A		:  List[str]       				=		gear_three[target]
  __A		:  List[Any]       				=		reflector[target]
  __A		:  List[Any]       				=		gear_three.index(__SCREAMING_SNAKE_CASE			)
  __A		:  Tuple       				=		gear_two.index(__SCREAMING_SNAKE_CASE			)
  __A		:  List[str]       				=		gear_one.index(__SCREAMING_SNAKE_CASE			)
  code.append(alphabets[target]			)
  rotator()


if __name__ == "__main__":
     A__ :		int					=list(input('Type your message:\n'))
     while True:
          try:
               A__ :		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!'
     )



