



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__       =			logging.getLogger(__name__)


def 						_UpperCAmelCase							( ):





					"""simple docstring"""

					_lowerCAmelCase         =       argparse.ArgumentParser(description="""PyTorch Transformer Language Model"""     )
					parser.add_argument("""--model_name"""      ,		type=snake_case      ,		default="""transfo-xl-wt103"""      ,		help="""pretrained model name"""     )
					parser.add_argument(
					    """--split"""      ,		type=snake_case      ,		default="""test"""      ,		choices=["""all""", """valid""", """test"""]      ,		help="""which split to evaluate"""     )
					parser.add_argument("""--batch_size"""      ,		type=snake_case      ,		default=10      ,		help="""batch size"""     )
					parser.add_argument("""--tgt_len"""      ,		type=snake_case      ,		default=1_28      ,		help="""number of tokens to predict"""     )
					parser.add_argument("""--ext_len"""      ,		type=snake_case      ,		default=0      ,		help="""length of the extended context"""     )
					parser.add_argument("""--mem_len"""      ,		type=snake_case      ,		default=16_00      ,		help="""length of the retained previous heads"""     )
					parser.add_argument("""--clamp_len"""      ,		type=snake_case      ,		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=snake_case      ,		required=snake_case      ,		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=snake_case      ,		default=""""""      ,		help="""Can be used for distant debugging."""     )
					parser.add_argument("""--server_port"""      ,		type=snake_case      ,		default=""""""      ,		help="""Can be used for distant debugging."""     )
					_lowerCAmelCase         =       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=snake_case     )
										ptvsd.wait_for_attach()

					_lowerCAmelCase         =       torch.device("""cuda""" if torch.cuda.is_available() and not args.no_cuda else """cpu"""     )
					logger.info("""device: {}""".format(snake_case     )     )

					# 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         =       TransfoXLCorpus.from_pretrained(args.model_name     )

					_lowerCAmelCase         =       corpus.get_iterator("""valid"""      ,		args.batch_size      ,		args.tgt_len      ,		device=snake_case      ,		ext_len=args.ext_len     )
					_lowerCAmelCase         =       corpus.get_iterator("""test"""      ,		args.batch_size      ,		args.tgt_len      ,		device=snake_case      ,		ext_len=args.ext_len     )

					# Load a pre-trained model
					_lowerCAmelCase         =       TransfoXLLMHeadModel.from_pretrained(args.model_name     )
					model.to(snake_case     )

					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         =       args.clamp_len
					if args.same_length:
										_lowerCAmelCase         =       True

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

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

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

					_lowerCAmelCase         =       """"""
					if valid_loss is not None:
										log_str += format_log(snake_case      ,		"""valid"""     )
					if test_loss is not None:
										log_str += format_log(snake_case      ,		"""test"""     )

					logger.info("""=""" * 1_00     )
					logger.info(snake_case     )
					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()

