

"""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       =						logging.getLogger(__name__)



def 			SCREAMING_SNAKE_CASE       (   ) ->	str:
 lowerCAmelCase__      :							Any    =							argparse.ArgumentParser(description="PyTorch Transformer Language Model"      )
 parser.add_argument("--model_name"				,				type=lowercase__				,				default="transfo-xl-wt103"				,				help="pretrained model name"      )
 parser.add_argument(
     "--split"				,				type=lowercase__				,				default="test"				,				choices=["all", "valid", "test"]				,				help="which split to evaluate"      )
 parser.add_argument("--batch_size"				,				type=lowercase__				,				default=1_0				,				help="batch size"      )
 parser.add_argument("--tgt_len"				,				type=lowercase__				,				default=1_2_8				,				help="number of tokens to predict"      )
 parser.add_argument("--ext_len"				,				type=lowercase__				,				default=0				,				help="length of the extended context"      )
 parser.add_argument("--mem_len"				,				type=lowercase__				,				default=1_6_0_0				,				help="length of the retained previous heads"      )
 parser.add_argument("--clamp_len"				,				type=lowercase__				,				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=lowercase__				,				required=lowercase__				,				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=lowercase__				,				default=""				,				help="Can be used for distant debugging."      )
 parser.add_argument("--server_port"				,				type=lowercase__				,				default=""				,				help="Can be used for distant debugging."      )
 lowerCAmelCase__      :							Optional[Any]    =							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=lowercase__      )
  ptvsd.wait_for_attach()

 lowerCAmelCase__      :							Union[str, Any]    =							torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu"      )
 logger.info("device: {}".format(lowercase__      )      )

 # 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__      :							Optional[Any]    =							TransfoXLCorpus.from_pretrained(args.model_name      )

 lowerCAmelCase__      :							Any    =							corpus.get_iterator("valid"				,				args.batch_size				,				args.tgt_len				,				device=lowercase__				,				ext_len=args.ext_len      )
 lowerCAmelCase__      :							int    =							corpus.get_iterator("test"				,				args.batch_size				,				args.tgt_len				,				device=lowercase__				,				ext_len=args.ext_len      )

 # Load a pre-trained model
 lowerCAmelCase__      :							Dict    =							TransfoXLLMHeadModel.from_pretrained(args.model_name      )
 model.to(lowercase__      )

 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__      :							List[Any]    =							args.clamp_len
 if args.same_length:
  lowerCAmelCase__      :							List[str]    =							True

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

 # Run on test data.
 if args.split == "all":
  lowerCAmelCase__      :							Dict    =							evaluate(lowercase__      )
  lowerCAmelCase__      :							Union[str, Any]    =							evaluate(lowercase__      )
 elif args.split == "valid":
  lowerCAmelCase__      :							int    =							evaluate(lowercase__      )
  lowerCAmelCase__      :							Union[str, Any]    =							None
 elif args.split == "test":
  lowerCAmelCase__      :							Dict    =							evaluate(lowercase__      )
  lowerCAmelCase__      :							Optional[Any]    =							None

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

 lowerCAmelCase__      :							Any    =							""
 if valid_loss is not None:
  log_str += format_log(lowercase__				,				"valid"      )
 if test_loss is not None:
  log_str += format_log(lowercase__				,				"test"      )

 logger.info("=" * 1_0_0      )
 logger.info(lowercase__      )
 logger.info("=" * 1_0_0      )


if __name__ == "__main__":
    main()









"""simple docstring"""

from itertools import product



def 			SCREAMING_SNAKE_CASE       (   lowercase__ = 1_5      ) ->	int:
 lowerCAmelCase__      :							float    =							0.0
 lowerCAmelCase__      :							float
 lowerCAmelCase__      :							int
 lowerCAmelCase__      :							int
 lowerCAmelCase__      :							int
 lowerCAmelCase__      :							int
 lowerCAmelCase__      :							tuple[int, ...]

 for series in product(range(2      )				,				repeat=lowercase__      ):
  lowerCAmelCase__      :							Optional[int]    =							series.count(1      )
  lowerCAmelCase__      :							Dict    =							num_turns - num_blue
  if num_red >= num_blue:
   continue
  lowerCAmelCase__      :							Dict    =							1.0
  for ind, col in enumerate(lowercase__				,				2      ):
   if col == 0:
    prob *= (ind - 1) / ind
   else:
    prob *= 1 / ind

  total_prob += prob

 return int(1 / total_prob      )


if __name__ == "__main__":
    print(f"{solution() = }")






