



'''simple docstring'''





import os
import re
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowercase_			=  logging.get_logger(__name__)
lowercase_			=  {"vocab_file": "spiece.model"}

lowercase_			=  {
    "vocab_file": {
        "google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/spiece.model",
        "google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/spiece.model",
    },
}

lowercase_			=  {
    "google/fnet-base": 512,
    "google/fnet-large": 512,
}

lowercase_			=  "▁"






class __A      (  A						):



 '''simple docstring'''







 __lowerCamelCase   :	Any										=	VOCAB_FILES_NAMES
 __lowerCamelCase   :	int										=	PRETRAINED_VOCAB_FILES_MAP
 __lowerCamelCase   :	Optional[int]										=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowerCamelCase   :	Tuple										=	['input_ids', 'token_type_ids']



 def __init__(self			,       A			,       A=False			,       A=True			,       A=True			,       A="<unk>"			,       A="[SEP]"			,       A="<pad>"			,       A="[CLS]"			,       A="[MASK]"			,       A = None			,       **A			,       )   ->					None:


      """simple docstring"""



      _a     =  (
          AddedToken(A			,       lstrip=A			,       rstrip=A			,       normalized=A	)
          if isinstance(A			,       A	)
          else mask_token
      )

      _a     =  {} if sp_model_kwargs is None else sp_model_kwargs

      super().__init__(
          do_lower_case=A			,       remove_space=A			,       keep_accents=A			,       unk_token=A			,       sep_token=A			,       pad_token=A			,       cls_token=A			,       mask_token=A			,       sp_model_kwargs=self.sp_model_kwargs			,       **A			,       )

      _a     =  do_lower_case
      _a     =  remove_space
      _a     =  keep_accents
      _a     =  vocab_file

      _a     =  spm.SentencePieceProcessor(**self.sp_model_kwargs	)
      self.sp_model.Load(A	)



 @property
 def  a__  (self	)   ->					List[str]:


      """simple docstring"""



      return len(self.sp_model	)



 def  a__  (self	)   ->					List[Any]:


      """simple docstring"""



      _a     =  {self.convert_ids_to_tokens(A	): i for i in range(self.vocab_size	)}
      vocab.update(self.added_tokens_encoder	)
      return vocab



 def __getstate__(self	)   ->					List[str]:


      """simple docstring"""



      _a     =  self.__dict__.copy()
      _a     =  None
      return state



 def __setstate__(self			,       A	)   ->					Optional[int]:


      """simple docstring"""



      _a     =  d

      # for backward compatibility
      if not hasattr(self			,       '''sp_model_kwargs'''	):
           _a     =  {}

      _a     =  spm.SentencePieceProcessor(**self.sp_model_kwargs	)
      self.sp_model.Load(self.vocab_file	)



 def  a__  (self			,       A	)   ->					Dict:


      """simple docstring"""



      if self.remove_space:
           _a     =  ''' '''.join(inputs.strip().split()	)
      else:
           _a     =  inputs
      _a     =  outputs.replace('''``'''			,       '''"'''	).replace('''\'\''''			,       '''"'''	)

      if not self.keep_accents:
           _a     =  unicodedata.normalize('''NFKD'''			,       A	)
           _a     =  ''''''.join([c for c in outputs if not unicodedata.combining(A	)]	)
      if self.do_lower_case:
           _a     =  outputs.lower()

      return outputs



 def  a__  (self			,       A	)   ->					List[str]:


      """simple docstring"""



      _a     =  self.preprocess_text(A	)
      _a     =  self.sp_model.encode(A			,       out_type=A	)
      _a     =  []
      for piece in pieces:
           if len(A	) > 1 and piece[-1] == str(''','''	) and piece[-2].isdigit():
                _a     =  self.sp_model.EncodeAsPieces(piece[:-1].replace(A			,       ''''''	)	)
                if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                     if len(cur_pieces[0]	) == 1:
                          _a     =  cur_pieces[1:]
                     else:
                          _a     =  cur_pieces[0][1:]
                cur_pieces.append(piece[-1]	)
                new_pieces.extend(A	)
           else:
                new_pieces.append(A	)

      return new_pieces



 def  a__  (self			,       A	)   ->					Dict:


      """simple docstring"""



      return self.sp_model.PieceToId(A	)



 def  a__  (self			,       A	)   ->					Union[str, Any]:


      """simple docstring"""



      return self.sp_model.IdToPiece(A	)



 def  a__  (self			,       A	)   ->					Any:


      """simple docstring"""



      _a     =  []
      _a     =  ''''''
      _a     =  False
      for token in tokens:
           # make sure that special tokens are not decoded using sentencepiece model
           if token in self.all_special_tokens:
                if not prev_is_special:
                     out_string += " "
                out_string += self.sp_model.decode(A	) + token
                _a     =  True
                _a     =  []
           else:
                current_sub_tokens.append(A	)
                _a     =  False
      out_string += self.sp_model.decode(A	)
      return out_string.strip()



 def  a__  (self			,       A			,       A = False			,       A = None			,       A = True			,       **A			,       )   ->					str:


      """simple docstring"""



      _a     =  kwargs.pop('''use_source_tokenizer'''			,       A	)

      _a     =  self.convert_ids_to_tokens(A			,       skip_special_tokens=A	)

      # To avoid mixing byte-level and unicode for byte-level BPT
      # we need to build string separately for added tokens and byte-level tokens
      # cf. https://github.com/huggingface/transformers/issues/1133
      _a     =  []
      _a     =  []
      for token in filtered_tokens:
           if skip_special_tokens and token in self.all_special_ids:
                continue
           if token in self.added_tokens_encoder:
                if current_sub_text:
                     sub_texts.append(self.convert_tokens_to_string(A	)	)
                     _a     =  []
                sub_texts.append(A	)
           else:
                current_sub_text.append(A	)
      if current_sub_text:
           sub_texts.append(self.convert_tokens_to_string(A	)	)

      # Mimic the behavior of the Rust tokenizer:
      # No space after <unk>
      if spaces_between_special_tokens:
           _a     =  re.sub(R'''(<unk>) '''			,       R'''\1'''			,       ''' '''.join(A	)	)
      else:
           _a     =  ''''''.join(A	)

      _a     =  (
          clean_up_tokenization_spaces
          if clean_up_tokenization_spaces is not None
          else self.clean_up_tokenization_spaces
      )
      if clean_up_tokenization_spaces:
           _a     =  self.clean_up_tokenization(A	)
           return clean_text
      else:
           return text



 def  a__  (self			,       A			,       A = None	)   ->					List[int]:


      """simple docstring"""



      _a     =  [self.sep_token_id]
      _a     =  [self.cls_token_id]
      if token_ids_a is None:
           return cls + token_ids_a + sep
      return cls + token_ids_a + sep + token_ids_a + sep



 def  a__  (self			,       A			,       A = None			,       A = False	)   ->					List[int]:


      """simple docstring"""



      if already_has_special_tokens:
           return super().get_special_tokens_mask(
               token_ids_a=A			,       token_ids_a=A			,       already_has_special_tokens=A	)

      if token_ids_a is not None:
           return [1] + ([0] * len(A	)) + [1] + ([0] * len(A	)) + [1]
      return [1] + ([0] * len(A	)) + [1]



 def  a__  (self			,       A			,       A = None	)   ->					List[int]:


      """simple docstring"""



      _a     =  [self.sep_token_id]
      _a     =  [self.cls_token_id]

      if token_ids_a is None:
           return len(cls + token_ids_a + sep	) * [0]
      return len(cls + token_ids_a + sep	) * [0] + len(token_ids_a + sep	) * [1]



 def  a__  (self			,       A			,       A = None	)   ->					Tuple[str]:


      """simple docstring"""



      if not os.path.isdir(A	):
           logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''	)
           return
      _a     =  os.path.join(
          A			,       (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']	)

      if os.path.abspath(self.vocab_file	) != os.path.abspath(A	) and os.path.isfile(self.vocab_file	):
           copyfile(self.vocab_file			,       A	)
      elif not os.path.isfile(self.vocab_file	):
           with open(A			,       '''wb'''	) as fi:
                _a     =  self.sp_model.serialized_model_proto()
                fi.write(A	)

      return (out_vocab_file,)








'''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()



