


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 			_a  (				):







 """simple docstring"""



 UpperCamelCase__					:    Tuple          =   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.'''     )
 UpperCamelCase__					:    Any          =   parser.parse_args()

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

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

 UpperCamelCase__					:    Optional[int]          =   []
 UpperCamelCase__					:    Tuple          =   0
 UpperCamelCase__					:    int          =   10000
 UpperCamelCase__					:    List[Any]          =   time.time()
 for text in data:
  UpperCamelCase__					:    int          =   F"{bos} {text.strip()} {sep}"
  UpperCamelCase__					:    List[str]          =   tokenizer.encode(SCREAMING_SNAKE_CASE    , add_special_tokens=SCREAMING_SNAKE_CASE     )
  rslt.append(SCREAMING_SNAKE_CASE     )

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

 UpperCamelCase__					:    List[Any]          =   F"{args.dump_file}.{args.tokenizer_name}.pickle"
 UpperCamelCase__					:    Any          =   tokenizer.vocab_size
 if vocab_size < (1 << 16):
  UpperCamelCase__					:    Optional[Any]          =   [np.uintaa(SCREAMING_SNAKE_CASE     ) for d in rslt]
 else:
  UpperCamelCase__					:    List[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()






import unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class       __magic_name__	(     __lowerCAmelCase					,				unittest.TestCase):
     A:		Optional[int]			=      BarthezTokenizer
     A:		Optional[int]			=      BarthezTokenizerFast
     A:		str			=      True
     A:		Optional[int]			=      True


     def      UpperCAmelCase__	(      self							:     List[Any]			)  ->      str:





      '''simple docstring'''






      super().setUp()

      UpperCamelCase__					:    Optional[Any]          =   BarthezTokenizerFast.from_pretrained('''moussaKam/mbarthez'''			)
      tokenizer.save_pretrained(self.tmpdirname			)
      tokenizer.save_pretrained(self.tmpdirname							,	legacy_format=lowerCamelCase__			)
      UpperCamelCase__					:    int          =   tokenizer


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





      '''simple docstring'''






      UpperCamelCase__					:    Optional[int]          =   '''<pad>'''
      UpperCamelCase__					:    List[str]          =   1

      self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCamelCase__			)							,	lowerCamelCase__			)
      self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCamelCase__			)							,	lowerCamelCase__			)


     def      UpperCAmelCase__	(      self							:     int			)  ->      Tuple:





      '''simple docstring'''






      UpperCamelCase__					:    int          =   list(self.get_tokenizer().get_vocab().keys()			)

      self.assertEqual(vocab_keys[0]							,	'''<s>'''			)
      self.assertEqual(vocab_keys[1]							,	'''<pad>'''			)
      self.assertEqual(vocab_keys[-1]							,	'''<mask>'''			)
      self.assertEqual(len(lowerCamelCase__			)							,	101122			)


     def      UpperCAmelCase__	(      self							:     Optional[Any]			)  ->      str:





      '''simple docstring'''






      self.assertEqual(self.get_tokenizer().vocab_size							,	101122			)


     @require_torch
     def      UpperCAmelCase__	(      self							:     Dict			)  ->      Tuple:





      '''simple docstring'''






      UpperCamelCase__					:    Dict          =   ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
      UpperCamelCase__					:    Any          =   [0, 57, 3018, 70307, 91, 2]

      UpperCamelCase__					:    Optional[int]          =   self.tokenizer(
          lowerCamelCase__							,	max_length=len(lowerCamelCase__			)							,	padding=lowerCamelCase__							,	truncation=lowerCamelCase__							,	return_tensors='''pt'''			)
      self.assertIsInstance(lowerCamelCase__							,	lowerCamelCase__			)

      self.assertEqual((2, 6)							,	batch.input_ids.shape			)
      self.assertEqual((2, 6)							,	batch.attention_mask.shape			)
      UpperCamelCase__					:    List[Any]          =   batch.input_ids.tolist()[0]
      self.assertListEqual(lowerCamelCase__							,	lowerCamelCase__			)


     def      UpperCAmelCase__	(      self							:     str			)  ->      List[Any]:





      '''simple docstring'''






      if not self.test_rust_tokenizer:
       return

      UpperCamelCase__					:    List[Any]          =   self.get_tokenizer()
      UpperCamelCase__					:    Optional[int]          =   self.get_rust_tokenizer()

      UpperCamelCase__					:    Any          =   '''I was born in 92000, and this is falsé.'''

      UpperCamelCase__					:    List[str]          =   tokenizer.tokenize(lowerCamelCase__			)
      UpperCamelCase__					:    Tuple          =   rust_tokenizer.tokenize(lowerCamelCase__			)
      self.assertListEqual(lowerCamelCase__							,	lowerCamelCase__			)

      UpperCamelCase__					:    Tuple          =   tokenizer.encode(lowerCamelCase__							,	add_special_tokens=lowerCamelCase__			)
      UpperCamelCase__					:    str          =   rust_tokenizer.encode(lowerCamelCase__							,	add_special_tokens=lowerCamelCase__			)
      self.assertListEqual(lowerCamelCase__							,	lowerCamelCase__			)

      UpperCamelCase__					:    Tuple          =   self.get_rust_tokenizer()
      UpperCamelCase__					:    Optional[int]          =   tokenizer.encode(lowerCamelCase__			)
      UpperCamelCase__					:    str          =   rust_tokenizer.encode(lowerCamelCase__			)
      self.assertListEqual(lowerCamelCase__							,	lowerCamelCase__			)


     @slow
     def      UpperCAmelCase__	(      self							:     List[str]			)  ->      Dict:





      '''simple docstring'''






      UpperCamelCase__					:    Optional[int]          =   {'''input_ids''': [[0, 490, 14328, 4507, 354, 47, 43669, 95, 25, 78117, 20215, 19779, 190, 22, 400, 4, 35343, 80310, 603, 86, 24937, 105, 33438, 94762, 196, 39642, 7, 15, 15933, 173, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 10534, 87, 25, 66, 3358, 196, 55289, 8, 82961, 81, 2204, 75203, 7, 15, 763, 12956, 216, 178, 14328, 9595, 1377, 69693, 7, 448, 71021, 196, 18106, 1437, 13974, 108, 9083, 4, 49315, 7, 39, 86, 1326, 2793, 46333, 4, 448, 196, 74588, 7, 49315, 7, 39, 21, 822, 38470, 74, 21, 66723, 62480, 8, 22050, 5, 2]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
      # fmt: on

      # moussaKam/mbarthez is a french model. So we also use french texts.
      UpperCamelCase__					:    int          =   [
          '''Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '''
          '''utilisé principalement dans le domaine du traitement automatique des langues (TAL).''',
          '''À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '''
          '''pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '''
          '''telles que la traduction et la synthèse de texte.''',
      ]

      self.tokenizer_integration_test_util(
          expected_encoding=lowerCamelCase__							,	model_name='''moussaKam/mbarthez'''							,	revision='''c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6'''							,	sequences=lowerCamelCase__							,	)


