
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   						=     logging.getLogger(__name__)


def  A__ (       ):
   SCREAMING_SNAKE_CASE_	=      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=10,  help='''batch size'''						)
   parser.add_argument('''--tgt_len''',  type=__UpperCAmelCase,  default=1_28,  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=16_00,  help='''length of the retained previous heads'''						)
   parser.add_argument('''--clamp_len''',  type=__UpperCAmelCase,  default=10_00,  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.'''						)
   SCREAMING_SNAKE_CASE_	=      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()

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

   SCREAMING_SNAKE_CASE_	=      corpus.get_iterator('''valid''',  args.batch_size,  args.tgt_len,  device=__UpperCAmelCase,  ext_len=args.ext_len						)
   SCREAMING_SNAKE_CASE_	=      corpus.get_iterator('''test''',  args.batch_size,  args.tgt_len,  device=__UpperCAmelCase,  ext_len=args.ext_len						)

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

   ###############################################################################
   # Evaluation code
   ###############################################################################
   def evaluate(__lowerCamelCase						):
      # Turn on evaluation mode which disables dropout.
      model.eval()
      SCREAMING_SNAKE_CASE_	=      0, 0.0
      SCREAMING_SNAKE_CASE_	=      time.time()
      with torch.no_grad():
         SCREAMING_SNAKE_CASE_	=      None
         for idx, (data, target, seq_len) in enumerate(__UpperCAmelCase						):
            SCREAMING_SNAKE_CASE_	=      model(__UpperCAmelCase,  lm_labels=__UpperCAmelCase,  mems=__UpperCAmelCase						)
            SCREAMING_SNAKE_CASE_	=      ret
            SCREAMING_SNAKE_CASE_	=      loss.mean()
            total_loss += seq_len * loss.item()
            total_len += seq_len
         SCREAMING_SNAKE_CASE_	=      time.time() - start_time
      logger.info('''Time : {:.2f}s, {:.2f}ms/segment'''.format(__UpperCAmelCase,  10_00 * total_time / (idx + 1)						)						)
      return total_loss / total_len

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

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

   SCREAMING_SNAKE_CASE_	=      ''
   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_00						)
   logger.info(__UpperCAmelCase						)
   logger.info('''=''' * 1_00						)


if __name__ == "__main__":
   main()



"""simple docstring"""







from __future__ import annotations

import math
import random


def   __lowerCAmelCase(		__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase      ):






	"""simple docstring"""





	_lowercase			:     Optional[int]      					=				d * e - 1
	_lowercase			:     int      					=				0
	_lowercase			:     Union[str, Any]      					=				0
	while p == 0:
		_lowercase			:     int      					=				random.randint(2  ,n - 1      )
		_lowercase			:     str      					=				k
		while True:
			if t % 2 == 0:
				_lowercase			:     List[Any]      					=				t // 2
				_lowercase			:     int      					=				(g**t) % n
				_lowercase			:     Tuple      					=				math.gcd(x - 1  ,__UpperCAmelCase      )
				if x > 1 and y > 1:
					_lowercase			:     List[str]      					=				y
					_lowercase			:     str      					=				n // y
					break  # find the correct factors
			else:
				break  # t is not divisible by 2, break and choose another g
	return sorted([p, q]      )


if __name__ == "__main__":
						import doctest

						doctest.testmod()



