

"""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_       :						str					=    logging.getLogger(__name__)





def 			_snake_case  (     ):
     A__				      =	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=128       , 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=1600       , help="""length of the retained previous heads"""      )
     parser.add_argument("""--clamp_len"""       , type=UpperCAmelCase_       , default=1000       , 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."""      )
     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=UpperCAmelCase_      )
          ptvsd.wait_for_attach()

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

     A__				      =	corpus.get_iterator("""valid"""       , args.batch_size       , args.tgt_len       , device=UpperCAmelCase_       , ext_len=args.ext_len      )
     A__				      =	corpus.get_iterator("""test"""       , args.batch_size       , args.tgt_len       , device=UpperCAmelCase_       , ext_len=args.ext_len      )

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

     ###############################################################################
     # Evaluation code
     ###############################################################################
     def evaluate(UpperCAmelCase_	:       Union[str, Any]      ):
          # 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(UpperCAmelCase_      ):
                    A__				      =	model(UpperCAmelCase_       , lm_labels=UpperCAmelCase_       , mems=UpperCAmelCase_      )
                    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(UpperCAmelCase_       , 1000 * total_time / (idx + 1)      )      )
          return total_loss / total_len

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

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

     A__				      =	""""""
     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("""=""" * 100      )
     logger.info(UpperCAmelCase_      )
     logger.info("""=""" * 100      )


if __name__ == "__main__":
 main()









"""simple docstring"""



from typing import List

from tokenizers import NormalizedString, PreTokenizedString, normalizers



class        a    :



    """simple docstring"""



    def __init__(  self:			List[Any]     ,    UpperCamelCase:			str							):






         """simple docstring"""


         A__				      =	vocab
         A__				      =	normalizers.BertNormalizer(
             clean_text=UpperCamelCase     ,    handle_chinese_chars=UpperCamelCase     ,    strip_accents=UpperCamelCase     ,    lowercase=UpperCamelCase     ,    )
         try:
              import rjieba
         except ImportError:
              raise ImportError(
                  """You need to install rjieba to use RoFormerTokenizer. """
                  """See https://pypi.org/project/rjieba/ for installation."""							)
         A__				      =	rjieba

    def 					UpperCamelCase						(  self:			Optional[Any]     ,    UpperCamelCase:			int     ,    UpperCamelCase:			NormalizedString							):






         """simple docstring"""


         A__				      =	[]

         # this code slice normalized_string is too slow (6s) but test_alignement_methods can pass
         for token, start, end in self.jieba.tokenize(str(UpperCamelCase							)     ,    hmm=UpperCamelCase							):
              if token in self.vocab:
                   splits.append(normalized_string[start:end]							)
              else:
                   A__				      =	self.normalizers.normalize_str(UpperCamelCase							).split()
                   for token in token_list:
                        if token:
                             A__				      =	start + len(UpperCamelCase							)
                             splits.append(normalized_string[start:end]							)
                             A__				      =	end

        # this code test_alignement_methods can't pass but fast (300ms)
        # for token in self.jieba.cut(str(normalized_string), False):
        #     if token in self.vocab:
        #         splits.append(NormalizedString(token))
        #     else:
        #         token_list = self.normalizers.normalize_str(token).split()
        #         for token in token_list:
        #             if token:
        #                 splits.append(NormalizedString(token))

         return splits





    def 					UpperCamelCase						(  self:			Tuple     ,    UpperCamelCase:			PreTokenizedString							):






         """simple docstring"""


         pretok.split(self.jieba_split							)






