



'''simple docstring'''





def lowerCAmelCase  (__A = 100):



     """simple docstring"""



     _a     =  [[0 for _ in range(__A)] for _ in range(m + 1)]
     for i in range(m + 1):
          _a     =  1

     for n in range(m + 1):
          for k in range(1  ,     __A):
               memo[n][k] += memo[n][k - 1]
               if n > k:
                    memo[n][k] += memo[n - k - 1][k]

     return memo[m][m - 1] - 1


if __name__ == "__main__":
    print(solution(int(input("Enter a number: ").strip())))








'''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
)
lowercase_			=  logging.getLogger(__name__)




def lowerCAmelCase  ():



     """simple docstring"""



     _a     =  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.''')
     _a     =  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()

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

     _a     =  corpus.get_iterator('''valid'''  ,     args.batch_size  ,     args.tgt_len  ,     device=__A  ,     ext_len=args.ext_len)
     _a     =  corpus.get_iterator('''test'''  ,     args.batch_size  ,     args.tgt_len  ,     device=__A  ,     ext_len=args.ext_len)

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

     ###############################################################################
     # Evaluation code
     ###############################################################################
     def evaluate(__A):
          # Turn on evaluation mode which disables dropout.
          model.eval()
          _a  ,   _a     =  0, 0.0
          _a     =  time.time()
          with torch.no_grad():
               _a     =  None
               for idx, (data, target, seq_len) in enumerate(__A):
                    _a     =  model(__A  ,     lm_labels=__A  ,     mems=__A)
                    _a  ,   _a  ,   _a     =  ret
                    _a     =  loss.mean()
                    total_loss += seq_len * loss.item()
                    total_len += seq_len
               _a     =  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":
          _a     =  evaluate(__A)
          _a     =  evaluate(__A)
     elif args.split == "valid":
          _a     =  evaluate(__A)
          _a     =  None
     elif args.split == "test":
          _a     =  evaluate(__A)
          _a     =  None

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

     _a     =  ''''''
     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()



