


"""simple docstring"""







import json
import os
import unittest

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin






class 				__A    (	_SCREAMING_SNAKE_CASE,   unittest.TestCase      ):






 """simple docstring"""




 __lowerCAmelCase			 =  XLMTokenizer
 __lowerCAmelCase			 =  False


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Union[str, Any]:
     super().setUp()

     # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
     a					=[
         '''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>''',
     ]
     a					=dict(zip(__A			,   range(len(__A						)						)						)						)
     a					=['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

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


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A						) ->					Dict:
     a					='''lower newer'''
     a					='''lower newer'''
     return input_text, output_text


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Optional[int]:
     a					=XLMTokenizer(self.vocab_file			,   self.merges_file						)

     a					='''lower'''
     a					=['''low''', '''er</w>''']
     a					=tokenizer.tokenize(__A						)
     self.assertListEqual(__A			,   __A						)

     a					=tokens + ['''<unk>''']
     a					=[14, 15, 20]
     self.assertListEqual(tokenizer.convert_tokens_to_ids(__A						)			,   __A						)





 @slow
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Optional[Any]:
     a					=XLMTokenizer.from_pretrained('''xlm-mlm-en-2048'''						)

     a					=tokenizer.encode('''sequence builders'''			,   add_special_tokens=__A						)
     a					=tokenizer.encode('''multi-sequence build'''			,   add_special_tokens=__A						)

     a					=tokenizer.build_inputs_with_special_tokens(__A						)
     a					=tokenizer.build_inputs_with_special_tokens(__A			,   __A						)

     assert encoded_sentence == [0] + text + [1]
     assert encoded_pair == [0] + text + [1] + text_a + [1]



"""simple docstring"""







import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser
def _A						(						lowercase		):







    """simple docstring"""
    a					={}
    a					=tokenizer(example['''content''']	,   truncation=lowercase		)['''input_ids''']
    a					=len(example['''content''']		) / len(output['''input_ids''']		)
    return output


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

lowerCamelCase_       :       Any      						=  time.time()
lowerCamelCase_       :       int      						=  load_dataset(args.dataset_name, split="""train""")
print(F'Dataset loaded in {time.time()-t_start:.2f}s')

lowerCamelCase_       :       List[str]      						=  time.time()
lowerCamelCase_       :       str      						=  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_       :       Union[str, Any]      						=  time.time()
ds.push_to_hub(args.tokenized_data_repo)





print(F'Data pushed to the hub in {time.time()-t_start:.2f}s')