



"""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
)
SCREAMING_SNAKE_CASE				:     Union[str, Any]          =	logging.getLogger(__name__)






def 	__UpperCAmelCase					(       )							->	str:






   """simple docstring"""


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

   _lowerCAmelCase					     =		torch.device("""cuda""" if torch.cuda.is_available() and not args.no_cuda else """cpu"""   )
   logger.info("""device: {}""".format(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 )
   _lowerCAmelCase					     =		TransfoXLCorpus.from_pretrained(args.model_name   )

   _lowerCAmelCase					     =		corpus.get_iterator("""valid"""   ,	args.batch_size   ,	args.tgt_len   ,	device=snake_case_   ,	ext_len=args.ext_len   )
   _lowerCAmelCase					     =		corpus.get_iterator("""test"""   ,	args.batch_size   ,	args.tgt_len   ,	device=snake_case_   ,	ext_len=args.ext_len   )

   # Load a pre-trained model
   _lowerCAmelCase					     =		TransfoXLLMHeadModel.from_pretrained(args.model_name   )
   model.to(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:
      _lowerCAmelCase					     =		args.clamp_len
   if args.same_length:
      _lowerCAmelCase					     =		True

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

   # Run on test data.
   if args.split == "all":
      _lowerCAmelCase					     =		evaluate(snake_case_   )
      _lowerCAmelCase					     =		evaluate(snake_case_   )
   elif args.split == "valid":
      _lowerCAmelCase					     =		evaluate(snake_case_   )
      _lowerCAmelCase					     =		None
   elif args.split == "test":
      _lowerCAmelCase					     =		evaluate(snake_case_   )
      _lowerCAmelCase					     =		None

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

   _lowerCAmelCase					     =		""""""
   if valid_loss is not None:
      log_str += format_log(snake_case_   ,	"""valid"""   )
   if test_loss is not None:
      log_str += format_log(snake_case_   ,	"""test"""   )

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


if __name__ == "__main__":
  main()




"""simple docstring"""





def 	__UpperCAmelCase					(       snake_case_  :  Optional[int]   ,	snake_case_  :  List[str]   )							->	Optional[int]:






   """simple docstring"""


   if n < 0:
      return 0
   # table[i] represents the number of ways to get to amount i
   _lowerCAmelCase					     =		[0] * (n + 1)

   # There is exactly 1 way to get to zero(You pick no coins).
   _lowerCAmelCase					     =		1

   # Pick all coins one by one and update table[] values
   # after the index greater than or equal to the value of the
   # picked coin
   for coin_val in s:
      for j in range(snake_case_   ,	n + 1   ):
         table[j] += table[j - coin_val]

   return table[n]


if __name__ == "__main__":
  import doctest

  doctest.testmod()