






"""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
)
a  :						str					       =							logging.getLogger(__name__)




def       snake_case__		(   )    ->Tuple:
 UpperCAmelCase__ 						=				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=1_0       ,	help="""batch size"""			)
 parser.add_argument("""--tgt_len"""       ,	type=lowerCAmelCase__       ,	default=1_2_8       ,	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=1_6_0_0       ,	help="""length of the retained previous heads"""			)
 parser.add_argument("""--clamp_len"""       ,	type=lowerCAmelCase__       ,	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=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."""			)
 UpperCAmelCase__ 						=				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()

 UpperCAmelCase__ 						=				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 )
 UpperCAmelCase__ 						=				TransfoXLCorpus.from_pretrained(args.model_name			)

 UpperCAmelCase__ 						=				corpus.get_iterator("""valid"""       ,	args.batch_size       ,	args.tgt_len       ,	device=lowerCAmelCase__       ,	ext_len=args.ext_len			)
 UpperCAmelCase__ 						=				corpus.get_iterator("""test"""       ,	args.batch_size       ,	args.tgt_len       ,	device=lowerCAmelCase__       ,	ext_len=args.ext_len			)

 # Load a pre-trained model
 UpperCAmelCase__ 						=				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:
  UpperCAmelCase__ 						=				args.clamp_len
 if args.same_length:
  UpperCAmelCase__ 						=				True

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

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

 def format_log(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE			):
  UpperCAmelCase__ 						=				'| {0} loss {1:5.2f} | {0} ppl {2:9.3f} '.format(lowerCAmelCase__       ,	lowerCAmelCase__       ,	math.exp(lowerCAmelCase__			)			)
  return log_str

 UpperCAmelCase__ 						=				''
 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("""=""" * 1_0_0			)
 logger.info(lowerCAmelCase__			)
 logger.info("""=""" * 1_0_0			)


if __name__ == "__main__":
 main()








"""simple docstring"""







import unittest
from timeit import timeit

def 							UpperCamelCase_       (       lowerCAmelCase__ :							int       ,  lowerCAmelCase__ :							int		)		->  int:

  """simple docstring"""




  lowerCAmelCase_  :							Any								=					first_num if first_num >= second_num else second_num
  lowerCAmelCase_  :							Union[str, Any]								=					max_num
  while (common_mult % first_num > 0) or (common_mult % second_num > 0):
    common_mult += max_num
  return common_mult

def 							UpperCamelCase_       (       lowerCAmelCase__ :							int       ,  lowerCAmelCase__ :							int		)		->  int:

  """simple docstring"""




  return b if a == 0 else greatest_common_divisor(b % a       ,  lowerCAmelCase__		)

def 							UpperCamelCase_       (       lowerCAmelCase__ :							int       ,  lowerCAmelCase__ :							int		)		->  int:

  """simple docstring"""




  return first_num // greatest_common_divisor(lowerCAmelCase__       ,  lowerCAmelCase__		) * second_num

def 							UpperCamelCase_       (       )		->  Dict:

  """simple docstring"""




  lowerCAmelCase_  :							str								=					(
      'from __main__ import least_common_multiple_slow, least_common_multiple_fast'
  )
  print(
      'least_common_multiple_slow():'       ,  timeit('least_common_multiple_slow(1000, 999)'       ,  setup=lowerCAmelCase__		)       ,  )
  print(
      'least_common_multiple_fast():'       ,  timeit('least_common_multiple_fast(1000, 999)'       ,  setup=lowerCAmelCase__		)       ,  )







class  UpperCamelCase__  (      unittest.TestCase    ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE    							=	(
          (10, 20),
          (13, 15),
          (4, 31),
          (10, 42),
          (43, 34),
          (5, 12),
          (12, 25),
          (10, 25),
          (6, 9),
      )
      _SCREAMING_SNAKE_CASE    							=	(20, 195, 124, 210, 1462, 60, 300, 50, 18)



      def 	SCREAMING_SNAKE_CASE__					( self							: List[str]			):
        for i, (first_num, second_num) in enumerate(self.test_inputs			):
          lowerCAmelCase_  :							Union[str, Any]								=					least_common_multiple_slow(SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_			)
          lowerCAmelCase_  :							Any								=					least_common_multiple_fast(SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_			)
          with self.subTest(i=SCREAMING_SNAKE_CASE_			):
            self.assertEqual(SCREAMING_SNAKE_CASE_       ,     self.expected_results[i]			)
            self.assertEqual(SCREAMING_SNAKE_CASE_       ,     self.expected_results[i]			)


if __name__ == "__main__":
     benchmark()
     unittest.main()





