import os
import unittest

from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
class 	__snake_case	( __lowerCAmelCase ,  unittest.TestCase		):





      '''simple docstring'''






      _snake_case    		=       LxmertTokenizer
      _snake_case    		=       LxmertTokenizerFast
      _snake_case    		=       True
      _snake_case    		=       True



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


          """simple docstring"""


          super().setUp()

          _lowerCamelCase     :		Any 						=						[
              """[UNK]""",
              """[CLS]""",
              """[SEP]""",
              """want""",
              """##want""",
              """##ed""",
              """wa""",
              """un""",
              """runn""",
              """##ing""",
              """,""",
              """low""",
              """lowest""",
          ]
          _lowerCamelCase     :		List[str] 						=						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 	_SCREAMING_SNAKE_CASE    (					self     :				str    ,  _UpperCamelCase     :				List[str])				->int:


          """simple docstring"""


          _lowerCamelCase     :		Any 						=						"""UNwant\u00E9d,running"""
          _lowerCamelCase     :		Optional[int] 						=						"""unwanted, running"""
          return input_text, output_text



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Dict)				->Optional[int]:


          """simple docstring"""


          _lowerCamelCase     :		List[str] 						=						self.tokenizer_class(self.vocab_file)

          _lowerCamelCase     :		Tuple 						=						tokenizer.tokenize("""UNwant\u00E9d,running""")
          self.assertListEqual(_UpperCamelCase    ,  ["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""])
          self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCamelCase)    ,  [7, 4, 5, 10, 8, 9])




      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[int])				->int:


          """simple docstring"""


          if not self.test_rust_tokenizer:
              return

          _lowerCamelCase     :		Tuple 						=						self.get_tokenizer()
          _lowerCamelCase     :		int 						=						self.get_rust_tokenizer()

          _lowerCamelCase     :		List[Any] 						=						"""I was born in 92000, and this is falsé."""

          _lowerCamelCase     :		Tuple 						=						tokenizer.tokenize(_UpperCamelCase)
          _lowerCamelCase     :		int 						=						rust_tokenizer.tokenize(_UpperCamelCase)
          self.assertListEqual(_UpperCamelCase    ,  _UpperCamelCase)

          _lowerCamelCase     :		str 						=						tokenizer.encode(_UpperCamelCase    ,  add_special_tokens=_UpperCamelCase)
          _lowerCamelCase     :		Optional[int] 						=						rust_tokenizer.encode(_UpperCamelCase    ,  add_special_tokens=_UpperCamelCase)
          self.assertListEqual(_UpperCamelCase    ,  _UpperCamelCase)

          _lowerCamelCase     :		List[Any] 						=						self.get_rust_tokenizer()
          _lowerCamelCase     :		Optional[Any] 						=						tokenizer.encode(_UpperCamelCase)
          _lowerCamelCase     :		Any 						=						rust_tokenizer.encode(_UpperCamelCase)
          self.assertListEqual(_UpperCamelCase    ,  _UpperCamelCase)





import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser







def 						A__  (					__A	):







    '''simple docstring'''
    _lowerCamelCase     :		Tuple 						=						{}
    _lowerCamelCase     :		List[Any] 						=						tokenizer(example["""content"""]   , truncation=__A	)["""input_ids"""]
    _lowerCamelCase     :		Tuple 						=						len(example["""content"""]	) / len(output["""input_ids"""]	)
    return output


lowerCAmelCase			:							int     =HfArgumentParser(PretokenizationArguments)
lowerCAmelCase			:							int     =parser.parse_args()
if args.num_workers is None:
    lowerCAmelCase			:							Any     =multiprocessing.cpu_count()
lowerCAmelCase			:							Optional[Any]     =AutoTokenizer.from_pretrained(args.tokenizer_dir)

lowerCAmelCase			:							str     =time.time()
lowerCAmelCase			:							Union[str, Any]     =load_dataset(args.dataset_name, split="train")
print(F"""Dataset loaded in {time.time()-t_start:.2f}s""")

lowerCAmelCase			:							Dict     =time.time()
lowerCAmelCase			:							Dict     =ds.map(
    tokenize,
    num_proc=args.num_workers,
    remove_columns=[
        "repo_name",
        "path",
        "copies",
        "size",
        "content",
        "license",
        "hash",
        "line_mean",
        "line_max",
        "alpha_frac",
        "autogenerated",
    ],
)
print(F"""Dataset tokenized in {time.time()-t_start:.2f}s""")

lowerCAmelCase			:							Tuple     =time.time()
ds.push_to_hub(args.tokenized_data_repo)
print(F"""Data pushed to the hub in {time.time()-t_start:.2f}s""")




