



import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
				import torch

				from transformers.generation import DisjunctiveConstraint

@require_torch
class 				snake_case__(unittest.TestCase      ):

					"""simple docstring"""






					def 			snake_case					(							self :     Optional[int]					):
									# For consistency across different places the DisjunctiveConstraint is called,
									# dc.token_ids is a list of integers. It is also initialized only by integers.

									lowercase__					:   Optional[Any]													=					[[1, 2, 4], [1, 2, 3, 4]]
									lowercase__					:   int													=					DisjunctiveConstraint(_SCREAMING_SNAKE_CASE					)
									self.assertTrue(isinstance(dc.token_ids				,       _SCREAMING_SNAKE_CASE					)					)

									with self.assertRaises(_SCREAMING_SNAKE_CASE					):
													DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]]					)					)

									with self.assertRaises(_SCREAMING_SNAKE_CASE					):
													DisjunctiveConstraint([torch.LongTensor([1, 2, 4]					), torch.LongTensor([1, 2, 3, 4, 5]					)]					)







					def 			snake_case					(							self :     Optional[Any]					):
									# We can't have constraints that are complete subsets of another. This leads to a preverse
									# interpretation of "constraint fulfillment": does generating [1,2,3] fulfill the constraint?
									# It would mean that it generated [1,2] which fulfills it, but it's in the middle of potentially
									# fulfilling [1,2,3,4]. If we believe that [1,2,3] does fulfill the constraint, then the algorithm
									# will necessarily never reach [1,2,3,4], giving users a false sense of control (better to just not allow it).
									lowercase__					:   Tuple													=					[[1, 2], [1, 2, 3, 4]]

									with self.assertRaises(_SCREAMING_SNAKE_CASE					):
													DisjunctiveConstraint(_SCREAMING_SNAKE_CASE					)  # fails here







					def 			snake_case					(							self :     Optional[int]					):
									lowercase__					:   Union[str, Any]													=					[[1, 2, 3], [1, 2, 4]]

									lowercase__					:   Optional[int]													=					DisjunctiveConstraint(_SCREAMING_SNAKE_CASE					)

									lowercase__					:   List[Any]													=					dc.update(1					)
									lowercase__					:   Optional[Any]													=					stepped is True and completed is False and reset is False
									self.assertTrue(_SCREAMING_SNAKE_CASE					)
									self.assertTrue(not dc.completed					)
									self.assertTrue(dc.current_seq == [1]					)

									lowercase__					:   Any													=					dc.update(2					)
									lowercase__					:   List[str]													=					stepped is True and completed is False and reset is False
									self.assertTrue(_SCREAMING_SNAKE_CASE					)
									self.assertTrue(not dc.completed					)
									self.assertTrue(dc.current_seq == [1, 2]					)

									lowercase__					:   Tuple													=					dc.update(3					)
									lowercase__					:   Optional[Any]													=					stepped is True and completed is True and reset is False
									self.assertTrue(_SCREAMING_SNAKE_CASE					)
									self.assertTrue(dc.completed					)  # Completed!
									self.assertTrue(dc.current_seq == [1, 2, 3]					)







					def 			snake_case					(							self :     int					):
									lowercase__					:   List[Any]													=					[[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

									lowercase__					:   List[Any]													=					DisjunctiveConstraint(_SCREAMING_SNAKE_CASE					)

									lowercase__					:   str													=					dc.update(1					)
									self.assertTrue(not dc.completed					)
									self.assertTrue(dc.current_seq == [1]					)

									lowercase__					:   str													=					dc.update(2					)
									self.assertTrue(not dc.completed					)
									self.assertTrue(dc.current_seq == [1, 2]					)

									lowercase__					:   Optional[int]													=					dc.update(4					)
									self.assertTrue(not dc.completed					)
									self.assertTrue(dc.current_seq == [1, 2, 4]					)

									lowercase__					:   List[str]													=					dc.update(5					)
									self.assertTrue(dc.completed					)  # Completed!
									self.assertTrue(dc.current_seq == [1, 2, 4, 5]					)

									dc.reset()

									lowercase__					:   Optional[Any]													=					dc.update(1					)
									self.assertTrue(not dc.completed					)
									self.assertTrue(dc.remaining() == 3					)
									self.assertTrue(dc.current_seq == [1]					)

									lowercase__					:   str													=					dc.update(2					)
									self.assertTrue(not dc.completed					)
									self.assertTrue(dc.remaining() == 2					)
									self.assertTrue(dc.current_seq == [1, 2]					)

									lowercase__					:   int													=					dc.update(5					)
									self.assertTrue(dc.completed					)  # Completed!
									self.assertTrue(dc.remaining() == 0					)
									self.assertTrue(dc.current_seq == [1, 2, 5]					)











import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser


def  __lowerCamelCase (       lowerCamelCase__						):

				"""simple docstring"""


				lowercase__					:   Optional[Any]													=					{}
				lowercase__					:   Tuple													=					tokenizer(example["content"]					,			truncation=lowerCamelCase__						)["input_ids"]
				lowercase__					:   Optional[int]													=					len(example["content"]						) / len(output["input_ids"]						)
				return output


lowerCAmelCase__					  =  HfArgumentParser(PretokenizationArguments)
lowerCAmelCase__					  =  parser.parse_args()
if args.num_workers is None:
				lowerCAmelCase__					  =  multiprocessing.cpu_count()
lowerCAmelCase__					  =  AutoTokenizer.from_pretrained(args.tokenizer_dir)

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

lowerCAmelCase__					  =  time.time()
lowerCAmelCase__					  =  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__					  =  time.time()
ds.push_to_hub(args.tokenized_data_repo)
print(f'''Data pushed to the hub in {time.time()-t_start:.2f}s''')




