






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_	:	Tuple				=				logging.getLogger(__name__)

def 						snake_case      () ->       List[str]:
  UpperCamelCase_:      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=1_0			,					help='batch size'						)
  parser.add_argument('--tgt_len'			,					type=UpperCAmelCase__			,					default=1_2_8			,					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_6_0_0			,					help='length of the retained previous heads'						)
  parser.add_argument('--clamp_len'			,					type=UpperCAmelCase__			,					default=1_0_0_0			,					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.'						)
  UpperCamelCase_:      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()

  UpperCamelCase_:      Dict					   =       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 )
  UpperCamelCase_:      List[str]					   =       TransfoXLCorpus.from_pretrained(args.model_name						)

  UpperCamelCase_:      Optional[int]					   =       corpus.get_iterator('valid'			,					args.batch_size			,					args.tgt_len			,					device=UpperCAmelCase__			,					ext_len=args.ext_len						)
  UpperCamelCase_:      Any					   =       corpus.get_iterator('test'			,					args.batch_size			,					args.tgt_len			,					device=UpperCAmelCase__			,					ext_len=args.ext_len						)

  # Load a pre-trained model
  UpperCamelCase_:      Tuple					   =       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:
    UpperCamelCase_:      str					   =       args.clamp_len
  if args.same_length:
    UpperCamelCase_:      Any					   =       True

  ###############################################################################
  # Evaluation code
  ###############################################################################
  def evaluate(UpperCAmelCase__						):
    # Turn on evaluation mode which disables dropout.
    model.eval()
    UpperCamelCase_   ,UpperCamelCase_:      List[Any]					   =       0, 0.0
    UpperCamelCase_:      Tuple					   =       time.time()
    with torch.no_grad():
      UpperCamelCase_:      List[str]					   =       None
      for idx, (data, target, seq_len) in enumerate(UpperCAmelCase__						):
        UpperCamelCase_:      Tuple					   =       model(UpperCAmelCase__			,					lm_labels=UpperCAmelCase__			,					mems=UpperCAmelCase__						)
        UpperCamelCase_   ,UpperCamelCase_   ,UpperCamelCase_:      str					   =       ret
        UpperCamelCase_:      Tuple					   =       loss.mean()
        total_loss += seq_len * loss.item()
        total_len += seq_len
      UpperCamelCase_:      str					   =       time.time() - start_time
    logger.info('Time : {:.2f}s, {:.2f}ms/segment'.format(UpperCAmelCase__			,					1_0_0_0 * total_time / (idx + 1)						)						)
    return total_loss / total_len

  # Run on test data.
  if args.split == "all":
    UpperCamelCase_:      Optional[int]					   =       evaluate(UpperCAmelCase__						)
    UpperCamelCase_:      List[str]					   =       evaluate(UpperCAmelCase__						)
  elif args.split == "valid":
    UpperCamelCase_:      Optional[int]					   =       evaluate(UpperCAmelCase__						)
    UpperCamelCase_:      Optional[int]					   =       None
  elif args.split == "test":
    UpperCamelCase_:      Optional[Any]					   =       evaluate(UpperCAmelCase__						)
    UpperCamelCase_:      List[str]					   =       None

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

  UpperCamelCase_:      Any					   =       ''
  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('=' * 1_0_0						)
  logger.info(UpperCAmelCase__						)
  logger.info('=' * 1_0_0						)


if __name__ == "__main__":
   main()







def 						snake_case      (UpperCAmelCase__ = 4_0_0_0_0_0_0						) ->       int:

  if n <= 1:
    return 0
  UpperCamelCase_:      Dict					   =       0
  UpperCamelCase_:      Union[str, Any]					   =       2
  UpperCamelCase_:      int					   =       0
  while 4 * b + a <= n:
    UpperCamelCase_   ,UpperCamelCase_:      List[str]					   =       b, 4 * b + a
    count += a
  return count + b


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