

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__      =		logging.getLogger(__name__)



def  lowercase    (				)					->       Union[str, Any]:
 _snake_case      :					Tuple					=   argparse.ArgumentParser(description="""PyTorch Transformer Language Model"""			)
 parser.add_argument("""--model_name"""			,    type=__UpperCamelCase			,    default="""transfo-xl-wt103"""			,    help="""pretrained model name"""			)
 parser.add_argument(
     """--split"""			,    type=__UpperCamelCase			,    default="""test"""			,    choices=["""all""", """valid""", """test"""]			,    help="""which split to evaluate"""			)
 parser.add_argument("""--batch_size"""			,    type=__UpperCamelCase			,    default=10			,    help="""batch size"""			)
 parser.add_argument("""--tgt_len"""			,    type=__UpperCamelCase			,    default=128			,    help="""number of tokens to predict"""			)
 parser.add_argument("""--ext_len"""			,    type=__UpperCamelCase			,    default=0			,    help="""length of the extended context"""			)
 parser.add_argument("""--mem_len"""			,    type=__UpperCamelCase			,    default=1_600			,    help="""length of the retained previous heads"""			)
 parser.add_argument("""--clamp_len"""			,    type=__UpperCamelCase			,    default=1_000			,    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=__UpperCamelCase			,    required=__UpperCamelCase			,    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=__UpperCamelCase			,    default=""""""			,    help="""Can be used for distant debugging."""			)
 parser.add_argument("""--server_port"""			,    type=__UpperCamelCase			,    default=""""""			,    help="""Can be used for distant debugging."""			)
 _snake_case      :					Optional[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=__UpperCamelCase			)
  ptvsd.wait_for_attach()

 _snake_case      :					Any					=   torch.device("""cuda""" if torch.cuda.is_available() and not args.no_cuda else """cpu"""			)
 logger.info("""device: {}""".format(__UpperCamelCase			)			)

 # 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 )
 _snake_case      :					Optional[Any]					=   TransfoXLCorpus.from_pretrained(args.model_name			)

 _snake_case      :					Any					=   corpus.get_iterator("""valid"""			,    args.batch_size			,    args.tgt_len			,    device=__UpperCamelCase			,    ext_len=args.ext_len			)
 _snake_case      :					Any					=   corpus.get_iterator("""test"""			,    args.batch_size			,    args.tgt_len			,    device=__UpperCamelCase			,    ext_len=args.ext_len			)

 # Load a pre-trained model
 _snake_case      :					Dict					=   TransfoXLLMHeadModel.from_pretrained(args.model_name			)
 model.to(__UpperCamelCase			)

 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:
  _snake_case      :					Union[str, Any]					=   args.clamp_len
 if args.same_length:
  _snake_case      :					Optional[Any]					=   True

 ###############################################################################
 # Evaluation code
 ###############################################################################
 def evaluate(SCREAMING_SNAKE_CASE__    :		List[Any]			):
  # Turn on evaluation mode which disables dropout.
  model.eval()
  _snake_case		,      _snake_case      :					str					=   0, 0.0
  _snake_case      :					Union[str, Any]					=   time.time()
  with torch.no_grad():
   _snake_case      :					Optional[int]					=   None
   for idx, (data, target, seq_len) in enumerate(__UpperCamelCase			):
    _snake_case      :					str					=   model(__UpperCamelCase			,    lm_labels=__UpperCamelCase			,    mems=__UpperCamelCase			)
    _snake_case		,      _snake_case		,      _snake_case      :					int					=   ret
    _snake_case      :					List[Any]					=   loss.mean()
    total_loss += seq_len * loss.item()
    total_len += seq_len
   _snake_case      :					str					=   time.time() - start_time
  logger.info("""Time : {:.2f}s, {:.2f}ms/segment""".format(__UpperCamelCase			,    1_000 * total_time / (idx + 1)			)			)
  return total_loss / total_len

 # Run on test data.
 if args.split == "all":
  _snake_case      :					Optional[Any]					=   evaluate(__UpperCamelCase			)
  _snake_case      :					Optional[int]					=   evaluate(__UpperCamelCase			)
 elif args.split == "valid":
  _snake_case      :					Optional[int]					=   evaluate(__UpperCamelCase			)
  _snake_case      :					Optional[int]					=   None
 elif args.split == "test":
  _snake_case      :					Union[str, Any]					=   evaluate(__UpperCamelCase			)
  _snake_case      :					Any					=   None

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

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

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


if __name__ == "__main__":
  main()





def  lowercase    (				SCREAMING_SNAKE_CASE__    :		Any			)					->       Optional[int]:
 _snake_case      :					Any					=   [701, 301, 132, 57, 23, 10, 4, 1]
 for gap in gaps:
  for i in range(SCREAMING_SNAKE_CASE__			,    len(SCREAMING_SNAKE_CASE__			)			):
   _snake_case      :					List[Any]					=   collection[i]
   _snake_case      :					List[str]					=   i
   while j >= gap and collection[j - gap] > insert_value:
    _snake_case      :					int					=   collection[j - gap]
    j -= gap
   if j != i:
    _snake_case      :					Tuple					=   insert_value
 return collection


if __name__ == "__main__":
  from doctest import testmod

  testmod()
  a__      =		input("""Enter numbers separated by a comma:\n""").strip()
  a__      =		[int(item) for item in user_input.split(""",""")]
  print(shell_sort(unsorted))
