
"""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_  					= logging.getLogger(__name__)
def       __UpperCAmelCase		(		)  ->							List[str]:
   _UpperCAmelCase 						=       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=1_28     ,	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=16_00     ,	help="length of the retained previous heads"					)
   parser.add_argument("--clamp_len"     ,	type=a_     ,	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=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."					)
   _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=a_					)
      ptvsd.wait_for_attach()

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

   _UpperCAmelCase 						=       corpus.get_iterator("valid"     ,	args.batch_size     ,	args.tgt_len     ,	device=a_     ,	ext_len=args.ext_len					)
   _UpperCAmelCase 						=       corpus.get_iterator("test"     ,	args.batch_size     ,	args.tgt_len     ,	device=a_     ,	ext_len=args.ext_len					)

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

   ###############################################################################
   # Evaluation code
   ###############################################################################
   def evaluate(a_      :		Optional[Any]					):
      # Turn on evaluation mode which disables dropout.
      model.eval()
      _UpperCAmelCase						,				_UpperCAmelCase 						=       0, 0.0
      _UpperCAmelCase 						=       time.time()
      with torch.no_grad():
         _UpperCAmelCase 						=       None
         for idx, (data, target, seq_len) in enumerate(a_					):
            _UpperCAmelCase 						=       model(a_     ,	lm_labels=a_     ,	mems=a_					)
            _UpperCAmelCase						,				_UpperCAmelCase						,				_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(a_     ,	10_00 * total_time / (idx + 1)					)					)
      return total_loss / total_len

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

   def format_log(a_      :		Optional[int]     ,	a_      :		Any					):
      _UpperCAmelCase 						=       "| {0} loss {1:5.2f} | {0} ppl {2:9.3f} ".format(a_     ,	a_     ,	math.exp(a_					)					)
      return log_str

   _UpperCAmelCase 						=       ""
   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("=" * 1_00					)
   logger.info(a_					)
   logger.info("=" * 1_00					)


if __name__ == "__main__":
  main()



"""simple docstring"""







def       __UpperCAmelCase		(		a_      :		list[int]					)  ->							int:

   # validate:
   if not isinstance(a_     ,	a_					) or not all(
       isinstance(a_     ,	a_					) and item >= 0 for item in citations					):
      raise ValueError("The citations should be a list of non negative integers."					)

   citations.sort()
   _UpperCAmelCase 						=       len(a_					)

   for i in range(a_					):
      if citations[len_citations - 1 - i] <= i:
         return i

   return len_citations


if __name__ == "__main__":
  import doctest

  doctest.testmod()

