


"""simple docstring"""
def 						lowercase				(	_snake_case		: int = 1_000	)	->int:
	"""simple docstring"""





	__snake_case      :		Union[str, Any]         =							0
	for i in range(_snake_case	):
		if i % 3 == 0 or i % 5 == 0:
			result += i
	return result


if __name__ == "__main__":
			print(F'{solution() = }')











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
)
_UpperCAmelCase						:    Dict      =  logging.getLogger(__name__)



def    UpperCAmelCase__	(  ):
  lowercase     :int   						=    argparse.ArgumentParser(description="PyTorch Transformer Language Model" )
  parser.add_argument("--model_name",      type=lowerCamelCase,      default="transfo-xl-wt103",      help="pretrained model name" )
  parser.add_argument(
      "--split",      type=lowerCamelCase,      default="test",      choices=["all", "valid", "test"],      help="which split to evaluate" )
  parser.add_argument("--batch_size",      type=lowerCamelCase,      default=10,      help="batch size" )
  parser.add_argument("--tgt_len",      type=lowerCamelCase,      default=128,      help="number of tokens to predict" )
  parser.add_argument("--ext_len",      type=lowerCamelCase,      default=0,      help="length of the extended context" )
  parser.add_argument("--mem_len",      type=lowerCamelCase,      default=1600,      help="length of the retained previous heads" )
  parser.add_argument("--clamp_len",      type=lowerCamelCase,      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=lowerCamelCase,      required=lowerCamelCase,      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=lowerCamelCase,      default="",      help="Can be used for distant debugging." )
  parser.add_argument("--server_port",      type=lowerCamelCase,      default="",      help="Can be used for distant debugging." )
  lowercase     :Any   						=    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=lowerCamelCase )
    ptvsd.wait_for_attach()

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

  # 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 )
  lowercase     :Dict   						=    TransfoXLCorpus.from_pretrained(args.model_name )

  lowercase     :Optional[int]   						=    corpus.get_iterator("valid",      args.batch_size,      args.tgt_len,      device=lowerCamelCase,      ext_len=args.ext_len )
  lowercase     :Union[str, Any]   						=    corpus.get_iterator("test",      args.batch_size,      args.tgt_len,      device=lowerCamelCase,      ext_len=args.ext_len )

  # Load a pre-trained model
  lowercase     :List[Any]   						=    TransfoXLLMHeadModel.from_pretrained(args.model_name )
  model.to(lowerCamelCase )

  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:
    lowercase     :Tuple   						=    args.clamp_len
  if args.same_length:
    lowercase     :Tuple   						=    True

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

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

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

  lowercase     :Optional[int]   						=    ""
  if valid_loss is not None:
    log_str += format_log(lowerCamelCase,      "valid" )
  if test_loss is not None:
    log_str += format_log(lowerCamelCase,      "test" )

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


if __name__ == "__main__":
     main()



