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
)
__a:			Optional[int]	       =       logging.getLogger(__name__)
def       __UpperCamelCase	(       ):
 lowercase__    :   Dict						= 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.'''					)
 lowercase__    :   int						= parser.parse_args()

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

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

 lowercase__    :   str						= []
 lowercase__    :   int						= 0
 lowercase__    :   Union[str, Any]						= 1_0000
 lowercase__    :   Optional[int]						= time.time()
 for text in data:
  lowercase__    :   Dict						= F"""{bos} {text.strip()} {sep}"""
  lowercase__    :   Optional[Any]						= tokenizer.encode(lowercase__		,					add_special_tokens=lowercase__					)
  rslt.append(lowercase__					)

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

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


'''simple docstring'''

import json
import os
import unittest

from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class       UpperCAmelCase  (						a__   ,						unittest.TestCase ):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE 		=   OpenAIGPTTokenizer
       SCREAMING_SNAKE_CASE 		=   OpenAIGPTTokenizerFast
       SCREAMING_SNAKE_CASE 		=   True
       SCREAMING_SNAKE_CASE 		=   False

       def _lowerCAmelCase(						self			)  ->  Optional[int]:
        super().setUp()

        # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
        lowercase__    :   Optional[Any]						= [
            '''l''',
            '''o''',
            '''w''',
            '''e''',
            '''r''',
            '''s''',
            '''t''',
            '''i''',
            '''d''',
            '''n''',
            '''w</w>''',
            '''r</w>''',
            '''t</w>''',
            '''lo''',
            '''low''',
            '''er</w>''',
            '''low</w>''',
            '''lowest</w>''',
            '''newer</w>''',
            '''wider</w>''',
            '''<unk>''',
        ]
        lowercase__    :   Optional[int]						= dict(zip(__lowerCAmelCase     ,       range(len(__lowerCAmelCase			)			)			)			)
        lowercase__    :   Union[str, Any]						= ['''#version: 0.2''', '''l o''', '''lo w''', '''e r</w>''', '''''']

        lowercase__    :   Optional[int]						= os.path.join(self.tmpdirname     ,       VOCAB_FILES_NAMES['''vocab_file''']			)
        lowercase__    :   Optional[int]						= os.path.join(self.tmpdirname     ,       VOCAB_FILES_NAMES['''merges_file''']			)
        with open(self.vocab_file     ,       '''w'''			) as fp:
         fp.write(json.dumps(__lowerCAmelCase			)			)
        with open(self.merges_file     ,       '''w'''			) as fp:
         fp.write('''\n'''.join(__lowerCAmelCase			)			)

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  Dict:
        return "lower newer", "lower newer"

       def _lowerCAmelCase(						self			)  ->  List[Any]:
        lowercase__    :   int						= OpenAIGPTTokenizer(self.vocab_file     ,       self.merges_file			)

        lowercase__    :   List[Any]						= '''lower'''
        lowercase__    :   Any						= ['''low''', '''er</w>''']
        lowercase__    :   Union[str, Any]						= tokenizer.tokenize(__lowerCAmelCase			)
        self.assertListEqual(__lowerCAmelCase     ,       __lowerCAmelCase			)

        lowercase__    :   Tuple						= tokens + ['''<unk>''']
        lowercase__    :   Optional[Any]						= [14, 15, 20]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCAmelCase			)     ,       __lowerCAmelCase			)

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase=15			)  ->  Optional[int]:
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
         with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""			):
          lowercase__    :   List[str]						= self.rust_tokenizer_class.from_pretrained(__lowerCAmelCase     ,       **__lowerCAmelCase			)

          # Simple input
          lowercase__    :   List[str]						= '''This is a simple input'''
          lowercase__    :   Any						= ['''This is a simple input 1''', '''This is a simple input 2''']
          lowercase__    :   str						= ('''This is a simple input''', '''This is a pair''')
          lowercase__    :   str						= [
              ('''This is a simple input 1''', '''This is a simple input 2'''),
              ('''This is a simple pair 1''', '''This is a simple pair 2'''),
          ]

          # Simple input tests
          self.assertRaises(__lowerCAmelCase     ,       tokenizer_r.encode     ,       __lowerCAmelCase     ,       max_length=__lowerCAmelCase     ,       padding='''max_length'''			)

          # Simple input
          self.assertRaises(__lowerCAmelCase     ,       tokenizer_r.encode_plus     ,       __lowerCAmelCase     ,       max_length=__lowerCAmelCase     ,       padding='''max_length'''			)

          # Simple input
          self.assertRaises(
              __lowerCAmelCase     ,       tokenizer_r.batch_encode_plus     ,       __lowerCAmelCase     ,       max_length=__lowerCAmelCase     ,       padding='''max_length'''     ,       )

          # Pair input
          self.assertRaises(__lowerCAmelCase     ,       tokenizer_r.encode     ,       __lowerCAmelCase     ,       max_length=__lowerCAmelCase     ,       padding='''max_length'''			)

          # Pair input
          self.assertRaises(__lowerCAmelCase     ,       tokenizer_r.encode_plus     ,       __lowerCAmelCase     ,       max_length=__lowerCAmelCase     ,       padding='''max_length'''			)

          # Pair input
          self.assertRaises(
              __lowerCAmelCase     ,       tokenizer_r.batch_encode_plus     ,       __lowerCAmelCase     ,       max_length=__lowerCAmelCase     ,       padding='''max_length'''     ,       )


       def _lowerCAmelCase(						self			)  ->  Tuple:
        pass



@require_ftfy
@require_spacy
@require_tokenizers
class       UpperCAmelCase  (						a__ ):





       '''simple docstring'''



       pass

