

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
)
lowerCAmelCase   :      int     =     logging.getLogger(__name__)

def       A_     (							):

      """simple docstring"""



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

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

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

      SCREAMING_SNAKE_CASE_ :     List[Any]							=   []
      SCREAMING_SNAKE_CASE_ :     List[str]							=   0
      SCREAMING_SNAKE_CASE_ :     str							=   1_0_0_0_0
      SCREAMING_SNAKE_CASE_ :     int							=   time.time()
      for text in data:
            SCREAMING_SNAKE_CASE_ :     Dict							=   f"{bos} {text.strip()} {sep}"
            SCREAMING_SNAKE_CASE_ :     Optional[int]							=   tokenizer.encode(__lowerCAmelCase    ,      add_special_tokens=__lowerCAmelCase     )
            rslt.append(__lowerCAmelCase     )

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

      SCREAMING_SNAKE_CASE_ :     List[Any]							=   f"{args.dump_file}.{args.tokenizer_name}.pickle"
      SCREAMING_SNAKE_CASE_ :     Dict							=   tokenizer.vocab_size
      if vocab_size < (1 << 1_6):
            SCREAMING_SNAKE_CASE_ :     Dict							=   [np.uintaa(__lowerCAmelCase     ) for d in rslt]
      else:
            SCREAMING_SNAKE_CASE_ :     Dict							=   [np.intaa(__lowerCAmelCase     ) for d in rslt]
      random.shuffle(rslt_     )
      logger.info(f"Dump to {dp_file}"     )
      with open(__lowerCAmelCase    ,      'wb'     ) as handle:
            pickle.dump(rslt_    ,      __lowerCAmelCase    ,      protocol=pickle.HIGHEST_PROTOCOL     )


if __name__ == "__main__":
    main()










import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer


snake_case							:       Dict     			=      logging.get_logger(__name__)

snake_case							:       Dict     			=      {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

snake_case							:       List[Any]     			=      {
    '''vocab_file''': {
        '''distilbert-base-uncased''': '''https://huggingface.co/distilbert-base-uncased/resolve/main/vocab.txt''',
        '''distilbert-base-uncased-distilled-squad''': (
            '''https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/vocab.txt'''
        ),
        '''distilbert-base-cased''': '''https://huggingface.co/distilbert-base-cased/resolve/main/vocab.txt''',
        '''distilbert-base-cased-distilled-squad''': (
            '''https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/vocab.txt'''
        ),
        '''distilbert-base-german-cased''': '''https://huggingface.co/distilbert-base-german-cased/resolve/main/vocab.txt''',
        '''distilbert-base-multilingual-cased''': (
            '''https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''distilbert-base-uncased''': '''https://huggingface.co/distilbert-base-uncased/resolve/main/tokenizer.json''',
        '''distilbert-base-uncased-distilled-squad''': (
            '''https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/tokenizer.json'''
        ),
        '''distilbert-base-cased''': '''https://huggingface.co/distilbert-base-cased/resolve/main/tokenizer.json''',
        '''distilbert-base-cased-distilled-squad''': (
            '''https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/tokenizer.json'''
        ),
        '''distilbert-base-german-cased''': (
            '''https://huggingface.co/distilbert-base-german-cased/resolve/main/tokenizer.json'''
        ),
        '''distilbert-base-multilingual-cased''': (
            '''https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/tokenizer.json'''
        ),
    },
}

snake_case							:       int     			=      {
    '''distilbert-base-uncased''': 5_12,
    '''distilbert-base-uncased-distilled-squad''': 5_12,
    '''distilbert-base-cased''': 5_12,
    '''distilbert-base-cased-distilled-squad''': 5_12,
    '''distilbert-base-german-cased''': 5_12,
    '''distilbert-base-multilingual-cased''': 5_12,
}


snake_case							:       Union[str, Any]     			=      {
    '''distilbert-base-uncased''': {'''do_lower_case''': True},
    '''distilbert-base-uncased-distilled-squad''': {'''do_lower_case''': True},
    '''distilbert-base-cased''': {'''do_lower_case''': False},
    '''distilbert-base-cased-distilled-squad''': {'''do_lower_case''': False},
    '''distilbert-base-german-cased''': {'''do_lower_case''': False},
    '''distilbert-base-multilingual-cased''': {'''do_lower_case''': False},
}







class        snake_case_   (lowerCamelCase_      ):
					UpperCAmelCase__					:    Any          =			VOCAB_FILES_NAMES
					UpperCAmelCase__					:    str          =			PRETRAINED_VOCAB_FILES_MAP
					UpperCAmelCase__					:    int          =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					UpperCAmelCase__					:    Tuple          =			PRETRAINED_INIT_CONFIGURATION
					UpperCAmelCase__					:    Optional[Any]          =			['''input_ids''', '''attention_mask''']
					UpperCAmelCase__					:    Optional[int]          =			DistilBertTokenizer
					def __init__(      self   :Dict			,__snake_case   :Dict=None			,__snake_case   :Optional[Any]=None			,__snake_case   :Optional[Any]=True			,__snake_case   :List[Any]="[UNK]"			,__snake_case   :str="[SEP]"			,__snake_case   :List[Any]="[PAD]"			,__snake_case   :Tuple="[CLS]"			,__snake_case   :Optional[int]="[MASK]"			,__snake_case   :Dict=True			,__snake_case   :Dict=None			,**__snake_case   :List[Any]			,)					->    Optional[int]:
												super().__init__(
												    __snake_case			,tokenizer_file=__snake_case			,do_lower_case=__snake_case			,unk_token=__snake_case			,sep_token=__snake_case			,pad_token=__snake_case			,cls_token=__snake_case			,mask_token=__snake_case			,tokenize_chinese_chars=__snake_case			,strip_accents=__snake_case			,**__snake_case			,)

												a__             =     json.loads(self.backend_tokenizer.normalizer.__getstate__()      )
												if (
												    normalizer_state.get('lowercase'			,__snake_case      ) != do_lower_case
												    or normalizer_state.get('strip_accents'			,__snake_case      ) != strip_accents
												    or normalizer_state.get('handle_chinese_chars'			,__snake_case      ) != tokenize_chinese_chars
												):
																			a__             =     getattr(__snake_case			,normalizer_state.pop('type'      )      )
																			a__             =     do_lower_case
																			a__             =     strip_accents
																			a__             =     tokenize_chinese_chars
																			a__             =     normalizer_class(**__snake_case      )

												a__             =     do_lower_case
					def  lowerCamelCase__(      self   :Any			,__snake_case   :List[str]			,__snake_case   :int=None      )					->    Dict:
												a__             =     [self.cls_token_id] + token_ids_a + [self.sep_token_id]

												if token_ids_a:
																			output += token_ids_a + [self.sep_token_id]

												return output
					def  lowerCamelCase__(      self   :List[str]			,__snake_case   :List[int]			,__snake_case   :Optional[List[int]] = None      )					->    List[int]:
												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  lowerCamelCase__(      self   :Union[str, Any]			,__snake_case   :str			,__snake_case   :Optional[str] = None      )					->    Tuple[str]:
												a__             =     self._tokenizer.model.save(__snake_case			,name=__snake_case      )
												return tuple(__snake_case      )




