
import os
import unittest

from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
class        SCREAMING_SNAKE_CASE__					(     snake_case_							,unittest.TestCase ):





	'''simple docstring'''



	_UpperCAmelCase	:		Tuple          = LayoutLMTokenizer
	_UpperCAmelCase	:		Dict          = LayoutLMTokenizerFast
	_UpperCAmelCase	:		Tuple          = True
	_UpperCAmelCase	:		List[str]          = True



	def 			A							(  self     :   Optional[Any]     ):


				'''simple docstring'''


				super().setUp()

				_snake_case          =      [
				    '[UNK]',
				    '[CLS]',
				    '[SEP]',
				    'want',
				    '##want',
				    '##ed',
				    'wa',
				    'un',
				    'runn',
				    '##ing',
				    ',',
				    'low',
				    'lowest',
				]
				_snake_case          =      os.path.join(self.tmpdirname      ,  VOCAB_FILES_NAMES['vocab_file']     )
				with open(self.vocab_file      ,  'w'      ,  encoding='utf-8'     ) as vocab_writer:
							vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]     )     )



	def 			A							(  self     :   List[Any]      ,  **lowercase     :   Optional[int]     ):


				'''simple docstring'''


				return LayoutLMTokenizer.from_pretrained(self.tmpdirname      ,  **lowercase     )



	def 			A							(  self     :   Tuple      ,  lowercase     :   Tuple     ):


				'''simple docstring'''


				_snake_case          =      'UNwant\u00E9d,running'
				_snake_case          =      'unwanted, running'
				return input_text, output_text



	def 			A							(  self     :   List[str]     ):


				'''simple docstring'''


				_snake_case          =      self.tokenizer_class(self.vocab_file     )

				_snake_case          =      tokenizer.tokenize('UNwant\u00E9d,running'     )
				self.assertListEqual(lowercase      ,  ['un', '##want', '##ed', ',', 'runn', '##ing']     )
				self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase     )      ,  [7, 4, 5, 10, 8, 9]     )





	def 			A							(  self     :   Union[str, Any]     ):


				'''simple docstring'''


				pass

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	:    Tuple          =      logging.getLogger(__name__)






def 	a_		(	)			->  Any:
			_snake_case          =      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.'			)
			_snake_case          =      parser.parse_args()

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

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

			_snake_case          =      []
			_snake_case          =      0
			_snake_case          =      10_000
			_snake_case          =      time.time()
			for text in data:
						_snake_case          =      f'''{bos} {text.strip()} {sep}'''
						_snake_case          =      tokenizer.encode(__lowercase	, add_special_tokens=__lowercase			)
						rslt.append(__lowercase			)

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

			_snake_case          =      f'''{args.dump_file}.{args.tokenizer_name}.pickle'''
			_snake_case          =      tokenizer.vocab_size
			if vocab_size < (1 << 16):
						_snake_case          =      [np.uintaa(__lowercase			) for d in rslt]
			else:
						_snake_case          =      [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()