'''simple docstring'''

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:
  UpperCamelCase__  :						Any					=      None

UpperCamelCase__  :						Optional[Any]					=      logging.get_logger(__name__)
UpperCamelCase__  :						Dict					=      {'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

UpperCamelCase__  :						List[Any]					=      {
    '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',
    },
}

UpperCamelCase__  :						Any					=      {
    'google/fnet-base': 512,
    'google/fnet-large': 512,
}

UpperCamelCase__  :						Optional[Any]					=      '▁'


class 					_lowercase						(   lowerCAmelCase ):
      '''simple docstring'''


      UpperCAmelCase_   :   Tuple   				= VOCAB_FILES_NAMES
      UpperCAmelCase_   :   Dict   				= PRETRAINED_VOCAB_FILES_MAP
      UpperCAmelCase_   :   Tuple   				= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCAmelCase_   :   Union[str, Any]   				= ['''input_ids''', '''token_type_ids''']
      UpperCAmelCase_   :   Dict   				= FNetTokenizer




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



         '''simple docstring'''



         UpperCAmelCase__				:					Tuple 					=	(
             AddedToken(lowerCamelCase_       ,lstrip=lowerCamelCase_       ,rstrip=lowerCamelCase_       ,normalized=lowerCamelCase_				)
             if isinstance(lowerCamelCase_       ,lowerCamelCase_				)
             else mask_token
         )

         super().__init__(
             lowerCamelCase_       ,tokenizer_file=lowerCamelCase_       ,do_lower_case=lowerCamelCase_       ,remove_space=lowerCamelCase_       ,keep_accents=lowerCamelCase_       ,unk_token=lowerCamelCase_       ,sep_token=lowerCamelCase_       ,pad_token=lowerCamelCase_       ,cls_token=lowerCamelCase_       ,mask_token=lowerCamelCase_       ,**lowerCamelCase_       ,)

         UpperCAmelCase__				:					Optional[Any] 					=	do_lower_case
         UpperCAmelCase__				:					List[Any] 					=	remove_space
         UpperCAmelCase__				:					Any 					=	keep_accents
         UpperCAmelCase__				:					List[Any] 					=	vocab_file
         UpperCAmelCase__				:					Tuple 					=	False if not self.vocab_file else True




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_ = None				)      ->      List[int]:



         '''simple docstring'''



         UpperCAmelCase__				:					List[Any] 					=	[self.sep_token_id]
         UpperCAmelCase__				:					str 					=	[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 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_ = None				)      ->      List[int]:



         '''simple docstring'''



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



         '''simple docstring'''



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

         if os.path.abspath(self.vocab_file				) != os.path.abspath(lowerCamelCase_				):
            copyfile(self.vocab_file       ,lowerCamelCase_				)

         return (out_vocab_file,)





'''simple docstring'''

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__  :						Dict					=      logging.getLogger(__name__)







def __UpperCamelCase( ):
   '''simple docstring'''
   UpperCAmelCase__				:					Optional[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=_A	,      default='''data/dump.txt'''	,      help='''The path to the data.'''     )
   parser.add_argument('''--tokenizer_type'''	,      type=_A	,      default='''bert'''	,      choices=['''bert''', '''roberta''', '''gpt2''']     )
   parser.add_argument('''--tokenizer_name'''	,      type=_A	,      default='''bert-base-uncased'''	,      help='''The tokenizer to use.'''     )
   parser.add_argument('''--dump_file'''	,      type=_A	,      default='''data/dump'''	,      help='''The dump file prefix.'''     )
   UpperCAmelCase__				:					Optional[int] 					=	parser.parse_args()

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

   logger.info('''Start encoding'''     )
   logger.info(F'''{len(_A     )} examples to process.'''     )

   UpperCAmelCase__				:					List[str] 					=	[]
   UpperCAmelCase__				:					Union[str, Any] 					=	0
   UpperCAmelCase__				:					Optional[int] 					=	1_00_00
   UpperCAmelCase__				:					Tuple 					=	time.time()
   for text in data:
      UpperCAmelCase__				:					Any 					=	F'''{bos} {text.strip()} {sep}'''
      UpperCAmelCase__				:					int 					=	tokenizer.encode(_A	,      add_special_tokens=_A     )
      rslt.append(_A     )

      iter += 1
      if iter % interval == 0:
         UpperCAmelCase__				:					int 					=	time.time()
         logger.info(F'''{iter} examples processed. - {(end-start):.2f}s/{interval}expl'''     )
         UpperCAmelCase__				:					Optional[Any] 					=	time.time()
   logger.info('''Finished binarization'''     )
   logger.info(F'''{len(_A     )} examples processed.'''     )

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


if __name__ == "__main__":
  main()




