






"""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
)
UpperCamelCase_						:					Optional[Any]      				=			logging.getLogger(__name__)



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

      __snake_case   :     Optional[Any]	      =torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu'''			)
      logger.info('''device: {}'''.format(a			)			)

      # 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   :     Optional[int]	      =corpus.get_iterator('''valid'''							,		args.batch_size							,		args.tgt_len							,		device=a							,		ext_len=args.ext_len			)
      __snake_case   :     int	      =corpus.get_iterator('''test'''							,		args.batch_size							,		args.tgt_len							,		device=a							,		ext_len=args.ext_len			)

      # Load a pre-trained model
      __snake_case   :     Tuple	      =TransfoXLLMHeadModel.from_pretrained(args.model_name			)
      model.to(a			)

      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   :     Union[str, Any]	      =True

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

      # Run on test data.
      if args.split == "all":
            __snake_case   :     int	      =evaluate(a			)
            __snake_case   :     List[Any]	      =evaluate(a			)
      elif args.split == "valid":
            __snake_case   :     int	      =evaluate(a			)
            __snake_case   :     Any	      =None
      elif args.split == "test":
            __snake_case   :     Tuple	      =evaluate(a			)
            __snake_case   :     Tuple	      =None

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

      __snake_case   :     Any	      =''
      if valid_loss is not None:
            log_str += format_log(a							,		'''valid'''			)
      if test_loss is not None:
            log_str += format_log(a							,		'''test'''			)

      logger.info('''=''' * 100			)
      logger.info(a			)
      logger.info('''=''' * 100			)


if __name__ == "__main__":
    main()








from math import sqrt


def 		_lowerCamelCase					(       UpperCamelCase				)      ->		list[int]:





  """simple docstring"""





  _lowercase:			Dict      =     [1]
  for i in range(2       ,  int(sqrt(UpperCamelCase				)				) + 1       ,  1				):
    if number % i == 0:
      values.append(UpperCamelCase				)
      if int(number // i				) != i:
        values.append(int(number // i				)				)
  return sorted(UpperCamelCase				)


def 		_lowerCamelCase					(       UpperCamelCase				)      ->		bool:





  """simple docstring"""





  return sum(factors(UpperCamelCase				)				) > n


def 		_lowerCamelCase					(       UpperCamelCase				)      ->		bool:





  """simple docstring"""





  _lowercase:			Dict      =     factors(UpperCamelCase				)
  _lowercase:			int      =     len(UpperCamelCase				)
  _lowercase:			Optional[int]      =     [[0 for i in range(number + 1				)] for j in range(r + 1				)]
  for i in range(r + 1				):
    _lowercase:			List[Any]      =     True

  for i in range(1       ,  number + 1				):
    _lowercase:			Optional[int]      =     False

  for i in range(1       ,  r + 1				):
    for j in range(1       ,  number + 1				):
      if j < values[i - 1]:
        _lowercase:			Optional[int]      =     subset[i - 1][j]
      else:
        _lowercase:			Optional[Any]      =     subset[i - 1][j] or subset[i - 1][j - values[i - 1]]

  return subset[r][number] != 0


def 		_lowerCamelCase					(       UpperCamelCase				)      ->		bool:





  """simple docstring"""





  return abundant(UpperCamelCase				) and not semi_perfect(UpperCamelCase				)


if __name__ == "__main__":
   import doctest

   doctest.testmod(verbose=True)
   for number in (69, 70, 71):
      print(f'''{number} is {'' if weird(number) else 'not '}weird.''')
