




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				:		Optional[int]     =   logging.getLogger(__name__)

def 			_SCREAMING_SNAKE_CASE			(							):


      '''simple docstring'''



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

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

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

      lowerCamelCase_		     =    []
      lowerCamelCase_		     =    0
      lowerCamelCase_		     =    1_00_00
      lowerCamelCase_		     =    time.time()
      for text in data:
            lowerCamelCase_		     =    f"""{bos} {text.strip()} {sep}"""
            lowerCamelCase_		     =    tokenizer.encode(lowercase	,							add_special_tokens=lowercase     )
            rslt.append(lowercase     )

            iter += 1
            if iter % interval == 0:
                  lowerCamelCase_		     =    time.time()
                  logger.info(f"""{iter} examples processed. - {(end-start):.2f}s/{interval}expl"""     )
                  lowerCamelCase_		     =    time.time()
      logger.info('Finished binarization'     )
      logger.info(f"""{len(lowercase     )} examples processed."""     )

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


if __name__ == "__main__":
 main()









import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
 from .tokenization_fnet import FNetTokenizer
else:
 lowerCamelCase				:		List[Any]     =   None

lowerCamelCase				:		int     =   logging.get_logger(__name__)
lowerCamelCase				:		Optional[int]     =   {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

lowerCamelCase				:		Optional[int]     =   {
    "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",
    },
    "tokenizer_file": {
        "google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/tokenizer.json",
        "google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/tokenizer.json",
    },
}

lowerCamelCase				:		List[Any]     =   {
    "google/fnet-base": 512,
    "google/fnet-large": 512,
}

lowerCamelCase				:		Any     =   "▁"







class   A(						UpperCamelCase   ):



 '''simple docstring'''


 UpperCamelCase						   =						VOCAB_FILES_NAMES
 UpperCamelCase						   =						PRETRAINED_VOCAB_FILES_MAP
 UpperCamelCase						   =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCamelCase						   =						['''input_ids''', '''token_type_ids''']
 UpperCamelCase						   =						FNetTokenizer

 def __init__(				self					:  Optional[int]						,							A_					:  Any=None						,							A_					:  int=None						,							A_					:  int=False						,							A_					:  Optional[int]=True						,							A_					:  List[Any]=True						,							A_					:  Tuple="<unk>"						,							A_					:  Optional[int]="[SEP]"						,							A_					:  List[Any]="<pad>"						,							A_					:  Optional[int]="[CLS]"						,							A_					:  Optional[Any]="[MASK]"						,							**A_					:  Dict						,							)   ->	List[str]:

       """simple docstring"""






       lowerCamelCase_		     =    (
           AddedToken(A_						,							lstrip=A_						,							rstrip=A_						,							normalized=A_  )
           if isinstance(A_						,							A_  )
           else mask_token
       )

       super().__init__(
           A_						,							tokenizer_file=A_						,							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_						,							**A_						,							)

       lowerCamelCase_		     =    do_lower_case
       lowerCamelCase_		     =    remove_space
       lowerCamelCase_		     =    keep_accents
       lowerCamelCase_		     =    vocab_file
       lowerCamelCase_		     =    False if not self.vocab_file else True

 def 				a__ (				self					:  List[Any]						,							A_					:  List[int]						,							A_					:  Optional[List[int]] = None  )   ->	List[int]:

       """simple docstring"""






       lowerCamelCase_		     =    [self.sep_token_id]
       lowerCamelCase_		     =    [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					:  Tuple						,							A_					:  List[int]						,							A_					:  Optional[List[int]] = None  )   ->	List[int]:

       """simple docstring"""






       lowerCamelCase_		     =    [self.sep_token_id]
       lowerCamelCase_		     =    [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					:  Dict						,							A_					:  str						,							A_					:  Optional[str] = None  )   ->	Tuple[str]:

       """simple docstring"""






       if not os.path.isdir(A_  ):
             logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""  )
             return
       lowerCamelCase_		     =    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_  ):
             copyfile(self.vocab_file						,							A_  )

       return (out_vocab_file,)



