


import argparse
import logging
import pickle
import random
import time

import numpy as np

from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer


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	( ):
  A_      :				str		     =					argparse.ArgumentParser(
      description='''Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids).'''       )
  parser.add_argument('''--file_path''' ,			type=SCREAMING_SNAKE_CASE ,			default='''data/dump.txt''' ,			help='''The path to the data.'''       )
  parser.add_argument('''--tokenizer_type''' ,			type=SCREAMING_SNAKE_CASE ,			default='''bert''' ,			choices=['''bert''', '''roberta''', '''gpt2''']       )
  parser.add_argument('''--tokenizer_name''' ,			type=SCREAMING_SNAKE_CASE ,			default='''bert-base-uncased''' ,			help='''The tokenizer to use.'''       )
  parser.add_argument('''--dump_file''' ,			type=SCREAMING_SNAKE_CASE ,			default='''data/dump''' ,			help='''The dump file prefix.'''       )
  A_      :				Dict		     =					parser.parse_args()

  logger.info(f'''Loading Tokenizer ({args.tokenizer_name})'''       )
  if args.tokenizer_type == "bert":
    A_      :				Optional[int]		     =					BertTokenizer.from_pretrained(args.tokenizer_name       )
    A_      :				Tuple		     =					tokenizer.special_tokens_map['''cls_token''']  # `[CLS]`
    A_      :				List[str]		     =					tokenizer.special_tokens_map['''sep_token''']  # `[SEP]`
  elif args.tokenizer_type == "roberta":
    A_      :				Dict		     =					RobertaTokenizer.from_pretrained(args.tokenizer_name       )
    A_      :				str		     =					tokenizer.special_tokens_map['''cls_token''']  # `<s>`
    A_      :				str		     =					tokenizer.special_tokens_map['''sep_token''']  # `</s>`
  elif args.tokenizer_type == "gpt2":
    A_      :				Union[str, Any]		     =					GPTaTokenizer.from_pretrained(args.tokenizer_name       )
    A_      :				Tuple		     =					tokenizer.special_tokens_map['''bos_token''']  # `<|endoftext|>`
    A_      :				Tuple		     =					tokenizer.special_tokens_map['''eos_token''']  # `<|endoftext|>`

  logger.info(f'''Loading text from {args.file_path}'''       )
  with open(args.file_path ,			'''r''' ,			encoding='''utf8'''       ) as fp:
    A_      :				Any		     =					fp.readlines()

  logger.info('''Start encoding'''       )
  logger.info(f'''{len(SCREAMING_SNAKE_CASE       )} examples to process.'''       )

  A_      :				Optional[Any]		     =					[]
  A_      :				List[Any]		     =					0
  A_      :				Union[str, Any]		     =					10_000
  A_      :				Tuple		     =					time.time()
  for text in data:
    A_      :				Any		     =					f'''{bos} {text.strip()} {sep}'''
    A_      :				int		     =					tokenizer.encode(SCREAMING_SNAKE_CASE ,			add_special_tokens=SCREAMING_SNAKE_CASE       )
    rslt.append(SCREAMING_SNAKE_CASE       )

    iter += 1
    if iter % interval == 0:
      A_      :				Tuple		     =					time.time()
      logger.info(f'''{iter} examples processed. - {(end-start):.2f}s/{interval}expl'''       )
      A_      :				List[str]		     =					time.time()
  logger.info('''Finished binarization'''       )
  logger.info(f'''{len(SCREAMING_SNAKE_CASE       )} examples processed.'''       )

  A_      :				Optional[Any]		     =					f'''{args.dump_file}.{args.tokenizer_name}.pickle'''
  A_      :				Tuple		     =					tokenizer.vocab_size
  if vocab_size < (1 << 16):
    A_      :				str		     =					[np.uintaa(SCREAMING_SNAKE_CASE       ) for d in rslt]
  else:
    A_      :				Any		     =					[np.intaa(SCREAMING_SNAKE_CASE       ) for d in rslt]
  random.shuffle(rslt_       )
  logger.info(f'''Dump to {dp_file}'''       )
  with open(SCREAMING_SNAKE_CASE ,			'''wb'''       ) as handle:
    pickle.dump(rslt_ ,			SCREAMING_SNAKE_CASE ,			protocol=pickle.HIGHEST_PROTOCOL       )


if __name__ == "__main__":
     main()








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


UpperCamelCase								=	{
    """configuration_nezha""": ["""NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """NezhaConfig"""],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase								=	[
         """NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """NezhaForNextSentencePrediction""",
         """NezhaForMaskedLM""",
         """NezhaForPreTraining""",
         """NezhaForMultipleChoice""",
         """NezhaForQuestionAnswering""",
         """NezhaForSequenceClassification""",
         """NezhaForTokenClassification""",
         """NezhaModel""",
         """NezhaPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_nezha import NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP, NezhaConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_nezha import (
              NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST,
              NezhaForMaskedLM,
              NezhaForMultipleChoice,
              NezhaForNextSentencePrediction,
              NezhaForPreTraining,
              NezhaForQuestionAnswering,
              NezhaForSequenceClassification,
              NezhaForTokenClassification,
              NezhaModel,
              NezhaPreTrainedModel,
          )


else:
     import sys

     UpperCamelCase								=	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




