



import random
import unittest

from torch.utils.data import BatchSampler, DataLoader, IterableDataset

from accelerate import Accelerator
from accelerate.data_loader import (
    BatchSamplerShard,
    DataLoaderDispatcher,
    DataLoaderShard,
    IterableDatasetShard,
    SkipBatchSampler,
    SkipDataLoader,
    skip_first_batches,
)




class 					snake_case__						(       lowerCAmelCase_     ):






     """simple docstring"""


     def __init__(       self  :  Tuple, _snake_case  :  List[str]=0.0_1, _snake_case  :  List[Any]=1_0_0_0					)   ->str:
            snake_case__							:	List[str]   	=	p_stop
            snake_case__							:	str   	=	max_length






     def __iter__(       self  :  Optional[int]					)   ->Dict:
            snake_case__							:	int   	=	0
            snake_case__							:	List[Any]   	=	False
            while not stop and count < self.max_length:
                   yield count
                   count += 1
                   snake_case__							:	Dict   	=	random.random() < self.p_stop




class 					snake_case__						(       unittest.TestCase     ):






     """simple docstring"""


     def    lowercase_   (       self  :  int, _snake_case  :  Tuple, _snake_case  :  Tuple, _snake_case  :  Optional[Any]=False, _snake_case  :  Any=True					)   ->Tuple:
            snake_case__							:	List[Any]   	=	[
                BatchSamplerShard(_snake_case, 2, _snake_case, split_batches=_snake_case, even_batches=_snake_case					)
                for i in range(2					)
            ]
            snake_case__							:	List[Any]   	=	[list(_snake_case					) for batch_sampler_shard in batch_sampler_shards]
            if not split_batches:
                   self.assertListEqual([len(_snake_case					) for shard in batch_sampler_shards], [len(_snake_case					) for e in expected]					)
            self.assertListEqual(_snake_case, _snake_case					)






     def    lowercase_   (       self  :  Tuple					)   ->Tuple:
            # Check the shards when the dataset is a round multiple of total batch size.
            snake_case__							:	List[str]   	=	BatchSampler(range(2_4					), batch_size=3, drop_last=_snake_case					)
            snake_case__							:	int   	=	[
                [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4], [1_8, 1_9, 2_0]],
                [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7], [2_1, 2_2, 2_3]],
            ]
            self.check_batch_sampler_shards(_snake_case, _snake_case					)

            snake_case__							:	Union[str, Any]   	=	BatchSampler(range(2_4					), batch_size=3, drop_last=_snake_case					)
            # Expected shouldn't change
            self.check_batch_sampler_shards(_snake_case, _snake_case					)

            # Check the shards when the dataset is a round multiple of batch size but not total batch size.
            snake_case__							:	List[Any]   	=	BatchSampler(range(2_1					), batch_size=3, drop_last=_snake_case					)
            snake_case__							:	List[Any]   	=	[
                [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4], [1_8, 1_9, 2_0]],
                [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7], [0, 1, 2]],
            ]
            self.check_batch_sampler_shards(_snake_case, _snake_case					)

            snake_case__							:	Optional[int]   	=	BatchSampler(range(2_1					), batch_size=3, drop_last=_snake_case					)
            snake_case__							:	List[Any]   	=	[
                [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4]],
                [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7]],
            ]
            self.check_batch_sampler_shards(_snake_case, _snake_case					)

            # Check the shards when the dataset is not a round multiple of batch size but has a multiple of
            # num_processes batch.
            snake_case__							:	int   	=	BatchSampler(range(2_2					), batch_size=3, drop_last=_snake_case					)
            snake_case__							:	Any   	=	[
                [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4], [1_8, 1_9, 2_0]],
                [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7], [2_1, 0, 1]],
            ]
            self.check_batch_sampler_shards(_snake_case, _snake_case					)

            snake_case__							:	List[str]   	=	BatchSampler(range(2_2					), batch_size=3, drop_last=_snake_case					)
            snake_case__							:	Tuple   	=	[
                [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4]],
                [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7]],
            ]
            self.check_batch_sampler_shards(_snake_case, _snake_case					)

            # Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
            # num_processes batch.
            snake_case__							:	Union[str, Any]   	=	BatchSampler(range(2_0					), batch_size=3, drop_last=_snake_case					)
            snake_case__							:	Optional[int]   	=	[
                [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4], [1_8, 1_9, 0]],
                [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7], [1, 2, 3]],
            ]
            self.check_batch_sampler_shards(_snake_case, _snake_case					)

            snake_case__							:	Optional[int]   	=	BatchSampler(range(2_0					), batch_size=3, drop_last=_snake_case					)
            snake_case__							:	Dict   	=	[
                [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4]],
                [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7]],
            ]
            self.check_batch_sampler_shards(_snake_case, _snake_case					)

            # Check the shards when the dataset is very small.
            snake_case__							:	Union[str, Any]   	=	BatchSampler(range(2					), batch_size=3, drop_last=_snake_case					)
            snake_case__							:	Optional[int]   	=	[[[0, 1, 0]], [[1, 0, 1]]]
            self.check_batch_sampler_shards(_snake_case, _snake_case					)

            snake_case__							:	Optional[int]   	=	BatchSampler(range(2					), batch_size=3, drop_last=_snake_case					)
            snake_case__							:	Optional[int]   	=	[[], []]
            self.check_batch_sampler_shards(_snake_case, _snake_case					)






     def    lowercase_   (       self  :  Optional[Any]					)   ->Optional[int]:
            # Check the shards when the dataset is a round multiple of batch size.
            snake_case__							:	int   	=	BatchSampler(range(2_4					), batch_size=4, drop_last=_snake_case					)
            snake_case__							:	Union[str, Any]   	=	[
                [[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7], [2_0, 2_1]],
                [[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9], [2_2, 2_3]],
            ]
            self.check_batch_sampler_shards(_snake_case, _snake_case, split_batches=_snake_case					)

            snake_case__							:	int   	=	BatchSampler(range(2_4					), batch_size=4, drop_last=_snake_case					)
            # Expected shouldn't change
            self.check_batch_sampler_shards(_snake_case, _snake_case, split_batches=_snake_case					)

            # Check the shards when the dataset is not a round multiple of batch size.
            snake_case__							:	Tuple   	=	BatchSampler(range(2_2					), batch_size=4, drop_last=_snake_case					)
            snake_case__							:	Tuple   	=	[
                [[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7], [2_0, 2_1]],
                [[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9], [0, 1]],
            ]
            self.check_batch_sampler_shards(_snake_case, _snake_case, split_batches=_snake_case					)

            snake_case__							:	int   	=	BatchSampler(range(2_2					), batch_size=4, drop_last=_snake_case					)
            snake_case__							:	List[str]   	=	[
                [[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7]],
                [[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9]],
            ]
            self.check_batch_sampler_shards(_snake_case, _snake_case, split_batches=_snake_case					)

            # Check the shards when the dataset is not a round multiple of batch size or num_processes.
            snake_case__							:	Optional[Any]   	=	BatchSampler(range(2_1					), batch_size=4, drop_last=_snake_case					)
            snake_case__							:	Any   	=	[
                [[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7], [2_0, 0]],
                [[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9], [1, 2]],
            ]
            self.check_batch_sampler_shards(_snake_case, _snake_case, split_batches=_snake_case					)

            snake_case__							:	Tuple   	=	BatchSampler(range(2_1					), batch_size=4, drop_last=_snake_case					)
            snake_case__							:	Any   	=	[
                [[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7]],
                [[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9]],
            ]
            self.check_batch_sampler_shards(_snake_case, _snake_case, split_batches=_snake_case					)

            # Check the shards when the dataset is very small.
            snake_case__							:	str   	=	BatchSampler(range(2					), batch_size=4, drop_last=_snake_case					)
            snake_case__							:	Any   	=	[[[0, 1]], [[0, 1]]]
            self.check_batch_sampler_shards(_snake_case, _snake_case, split_batches=_snake_case					)

            snake_case__							:	str   	=	BatchSampler(range(2					), batch_size=4, drop_last=_snake_case					)
            snake_case__							:	List[str]   	=	[[], []]
            self.check_batch_sampler_shards(_snake_case, _snake_case, split_batches=_snake_case					)






     def    lowercase_   (       self  :  Tuple					)   ->Tuple:
            # Check the shards when the dataset is a round multiple of total batch size.
            snake_case__							:	Dict   	=	BatchSampler(range(2_4					), batch_size=3, drop_last=_snake_case					)
            snake_case__							:	str   	=	[
                [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4], [1_8, 1_9, 2_0]],
                [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7], [2_1, 2_2, 2_3]],
            ]
            self.check_batch_sampler_shards(_snake_case, _snake_case, even_batches=_snake_case					)

            snake_case__							:	Union[str, Any]   	=	BatchSampler(range(2_4					), batch_size=3, drop_last=_snake_case					)
            # Expected shouldn't change
            self.check_batch_sampler_shards(_snake_case, _snake_case, even_batches=_snake_case					)

            # Check the shards when the dataset is a round multiple of batch size but not total batch size.
            snake_case__							:	str   	=	BatchSampler(range(2_1					), batch_size=3, drop_last=_snake_case					)
            snake_case__							:	Union[str, Any]   	=	[
                [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4], [1_8, 1_9, 2_0]],
                [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7]],
            ]
            self.check_batch_sampler_shards(_snake_case, _snake_case, even_batches=_snake_case					)

            snake_case__							:	Any   	=	BatchSampler(range(2_1					), batch_size=3, drop_last=_snake_case					)
            snake_case__							:	Optional[Any]   	=	[
                [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4]],
                [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7]],
            ]
            self.check_batch_sampler_shards(_snake_case, _snake_case, even_batches=_snake_case					)

            # Check the shards when the dataset is not a round multiple of batch size but has a multiple of
            # num_processes batch.
            snake_case__							:	List[str]   	=	BatchSampler(range(2_2					), batch_size=3, drop_last=_snake_case					)
            snake_case__							:	Optional[int]   	=	[
                [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4], [1_8, 1_9, 2_0]],
                [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7], [2_1]],
            ]
            self.check_batch_sampler_shards(_snake_case, _snake_case, even_batches=_snake_case					)

            snake_case__							:	Tuple   	=	BatchSampler(range(2_2					), batch_size=3, drop_last=_snake_case					)
            snake_case__							:	int   	=	[
                [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4]],
                [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7]],
            ]
            self.check_batch_sampler_shards(_snake_case, _snake_case, even_batches=_snake_case					)

            # Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
            # num_processes batch.
            snake_case__							:	Any   	=	BatchSampler(range(2_0					), batch_size=3, drop_last=_snake_case					)
            snake_case__							:	int   	=	[
                [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4], [1_8, 1_9]],
                [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7]],
            ]
            self.check_batch_sampler_shards(_snake_case, _snake_case, even_batches=_snake_case					)

            snake_case__							:	str   	=	BatchSampler(range(2_0					), batch_size=3, drop_last=_snake_case					)
            snake_case__							:	Optional[int]   	=	[
                [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4]],
                [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7]],
            ]
            self.check_batch_sampler_shards(_snake_case, _snake_case, even_batches=_snake_case					)

            # Check the shards when the dataset is very small.
            snake_case__							:	Union[str, Any]   	=	BatchSampler(range(2					), batch_size=3, drop_last=_snake_case					)
            snake_case__							:	Optional[int]   	=	[[[0, 1]], []]
            self.check_batch_sampler_shards(_snake_case, _snake_case, even_batches=_snake_case					)

            snake_case__							:	int   	=	BatchSampler(range(2					), batch_size=3, drop_last=_snake_case					)
            snake_case__							:	List[Any]   	=	[[], []]
            self.check_batch_sampler_shards(_snake_case, _snake_case, even_batches=_snake_case					)






     def    lowercase_   (       self  :  Optional[Any]					)   ->Union[str, Any]:
            # Check the shards when the dataset is a round multiple of batch size.
            snake_case__							:	List[Any]   	=	BatchSampler(range(2_4					), batch_size=4, drop_last=_snake_case					)
            snake_case__							:	int   	=	[
                [[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7], [2_0, 2_1]],
                [[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9], [2_2, 2_3]],
            ]
            self.check_batch_sampler_shards(_snake_case, _snake_case, split_batches=_snake_case, even_batches=_snake_case					)

            snake_case__							:	List[Any]   	=	BatchSampler(range(2_4					), batch_size=4, drop_last=_snake_case					)
            # Expected shouldn't change
            self.check_batch_sampler_shards(_snake_case, _snake_case, split_batches=_snake_case, even_batches=_snake_case					)

            # Check the shards when the dataset is not a round multiple of batch size.
            snake_case__							:	int   	=	BatchSampler(range(2_2					), batch_size=4, drop_last=_snake_case					)
            snake_case__							:	Any   	=	[
                [[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7], [2_0, 2_1]],
                [[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9]],
            ]
            self.check_batch_sampler_shards(_snake_case, _snake_case, split_batches=_snake_case, even_batches=_snake_case					)

            snake_case__							:	List[Any]   	=	BatchSampler(range(2_2					), batch_size=4, drop_last=_snake_case					)
            snake_case__							:	int   	=	[
                [[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7]],
                [[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9]],
            ]
            self.check_batch_sampler_shards(_snake_case, _snake_case, split_batches=_snake_case, even_batches=_snake_case					)

            # Check the shards when the dataset is not a round multiple of batch size or num_processes.
            snake_case__							:	Any   	=	BatchSampler(range(2_1					), batch_size=4, drop_last=_snake_case					)
            snake_case__							:	Tuple   	=	[
                [[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7], [2_0]],
                [[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9]],
            ]
            self.check_batch_sampler_shards(_snake_case, _snake_case, split_batches=_snake_case, even_batches=_snake_case					)

            snake_case__							:	List[Any]   	=	BatchSampler(range(2_1					), batch_size=4, drop_last=_snake_case					)
            snake_case__							:	Optional[int]   	=	[
                [[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7]],
                [[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9]],
            ]
            self.check_batch_sampler_shards(_snake_case, _snake_case, split_batches=_snake_case, even_batches=_snake_case					)

            # Check the shards when the dataset is very small.
            snake_case__							:	int   	=	BatchSampler(range(2					), batch_size=4, drop_last=_snake_case					)
            snake_case__							:	Any   	=	[[[0, 1]], []]
            self.check_batch_sampler_shards(_snake_case, _snake_case, split_batches=_snake_case, even_batches=_snake_case					)

            snake_case__							:	List[str]   	=	BatchSampler(range(2					), batch_size=4, drop_last=_snake_case					)
            snake_case__							:	List[Any]   	=	[[], []]
            self.check_batch_sampler_shards(_snake_case, _snake_case, split_batches=_snake_case, even_batches=_snake_case					)






     def    lowercase_   (       self  :  List[Any]					)   ->int:
            snake_case__							:	List[Any]   	=	[[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 1_0, 1_1], [1_2, 1_3]]
            snake_case__							:	List[str]   	=	[BatchSamplerShard(_snake_case, 2, _snake_case, even_batches=_snake_case					) for i in range(2					)]

            self.assertEqual(len(batch_sampler_shards[0]					), 3					)
            self.assertEqual(len(batch_sampler_shards[1]					), 2					)

            self.assertListEqual(list(batch_sampler_shards[0]					), [[0, 1, 2], [5, 6, 7, 8], [1_2, 1_3]]					)
            self.assertListEqual(list(batch_sampler_shards[1]					), [[3, 4], [9, 1_0, 1_1]]					)






     def    lowercase_   (       self  :  Union[str, Any], _snake_case  :  Tuple, _snake_case  :  List[Any], _snake_case  :  Optional[Any], _snake_case  :  List[Any]=False, _snake_case  :  List[Any]=2, _snake_case  :  Dict=False					)   ->Optional[int]:
            random.seed(_snake_case					)
            snake_case__							:	Tuple   	=	list(_snake_case					)

            snake_case__							:	Dict   	=	[
                IterableDatasetShard(
                    _snake_case, batch_size=_snake_case, drop_last=_snake_case, num_processes=_snake_case, process_index=_snake_case, split_batches=_snake_case, )
                for i in range(_snake_case					)
            ]
            snake_case__							:	Optional[int]   	=	[]
            for iterable_dataset_shard in iterable_dataset_shards:
                   # Since our random iterable dataset will be... random... we need to use a seed to get reproducible results.
                   random.seed(_snake_case					)
                   iterable_dataset_lists.append(list(_snake_case					)					)

            snake_case__							:	int   	=	batch_size // num_processes if split_batches else batch_size
            # All iterable dataset shard should have the same length, a round multiple of shard_batch_size
            snake_case__							:	Any   	=	iterable_dataset_lists[0]
            for l in iterable_dataset_lists[1:]:
                   self.assertEqual(len(_snake_case					), len(_snake_case					)					)
                   self.assertTrue(len(_snake_case					) % shard_batch_size == 0					)

            snake_case__							:	List[Any]   	=	[]
            for idx in range(0, len(_snake_case					), _snake_case					):
                   for l in iterable_dataset_lists:
                          observed += l[idx : idx + shard_batch_size]

            if not drop_last:
                   while len(_snake_case					) < len(_snake_case					):
                          reference += reference
            self.assertListEqual(_snake_case, reference[: len(_snake_case					)]					)






     def    lowercase_   (       self  :  Tuple					)   ->Union[str, Any]:
            snake_case__							:	List[Any]   	=	4_2
            snake_case__							:	Tuple   	=	RandomIterableDataset()

            self.check_iterable_dataset_shards(_snake_case, _snake_case, batch_size=4, drop_last=_snake_case, split_batches=_snake_case					)
            self.check_iterable_dataset_shards(_snake_case, _snake_case, batch_size=4, drop_last=_snake_case, split_batches=_snake_case					)
            self.check_iterable_dataset_shards(_snake_case, _snake_case, batch_size=4, drop_last=_snake_case, split_batches=_snake_case					)
            self.check_iterable_dataset_shards(_snake_case, _snake_case, batch_size=4, drop_last=_snake_case, split_batches=_snake_case					)

            # Edge case with a very small dataset
            snake_case__							:	Optional[Any]   	=	RandomIterableDataset(max_length=2					)

            self.check_iterable_dataset_shards(_snake_case, _snake_case, batch_size=4, drop_last=_snake_case, split_batches=_snake_case					)
            self.check_iterable_dataset_shards(_snake_case, _snake_case, batch_size=4, drop_last=_snake_case, split_batches=_snake_case					)
            self.check_iterable_dataset_shards(_snake_case, _snake_case, batch_size=4, drop_last=_snake_case, split_batches=_snake_case					)
            self.check_iterable_dataset_shards(_snake_case, _snake_case, batch_size=4, drop_last=_snake_case, split_batches=_snake_case					)






     def    lowercase_   (       self  :  str					)   ->Optional[int]:
            snake_case__							:	List[str]   	=	BatchSampler(range(1_6					), batch_size=4, drop_last=_snake_case					)
            snake_case__							:	List[Any]   	=	SkipBatchSampler(_snake_case, 2					)
            self.assertListEqual(list(_snake_case					), [[8, 9, 1_0, 1_1], [1_2, 1_3, 1_4, 1_5]]					)






     def    lowercase_   (       self  :  List[str]					)   ->str:
            snake_case__							:	Optional[Any]   	=	SkipDataLoader(list(range(1_6					)					), batch_size=4, skip_batches=2					)
            self.assertListEqual([t.tolist() for t in dataloader], [[8, 9, 1_0, 1_1], [1_2, 1_3, 1_4, 1_5]]					)






     def    lowercase_   (       self  :  Optional[Any]					)   ->Union[str, Any]:
            snake_case__							:	Optional[Any]   	=	DataLoader(list(range(1_6					)					), batch_size=4					)
            snake_case__							:	Union[str, Any]   	=	skip_first_batches(_snake_case, num_batches=2					)
            self.assertListEqual([t.tolist() for t in new_dataloader], [[8, 9, 1_0, 1_1], [1_2, 1_3, 1_4, 1_5]]					)






     def    lowercase_   (       self  :  Optional[int]					)   ->Any:
            snake_case__							:	List[Any]   	=	DataLoaderShard(list(range(1_6					)					), batch_size=4					)
            for idx, _ in enumerate(_snake_case					):
                   self.assertEqual(dataloader.end_of_dataloader, idx == 3					)

            # Test it also works on the second iteration
            for idx, _ in enumerate(_snake_case					):
                   self.assertEqual(dataloader.end_of_dataloader, idx == 3					)






     def    lowercase_   (       self  :  Tuple					)   ->Any:
            Accelerator()
            snake_case__							:	List[str]   	=	DataLoaderDispatcher(range(1_6					), batch_size=4					)
            for idx, _ in enumerate(_snake_case					):
                   self.assertEqual(dataloader.end_of_dataloader, idx == 3					)

            # Test it also works on the second iteration
            for idx, _ in enumerate(_snake_case					):
                   self.assertEqual(dataloader.end_of_dataloader, idx == 3					)












"""simple docstring"""






def      _snake_case			(				lowercase__		):
				stooge(lowercase__							,    0							,    len(lowercase__		) - 1		)
				return arr







def      _snake_case			(				lowercase__							,    lowercase__							,    lowercase__		):
				if i >= h:
								return

				# If first element is smaller than the last then swap them
				if arr[i] > arr[h]:
								_lowerCamelCase, _lowerCamelCase     :			Optional[Any]    =    arr[h], arr[i]

				# If there are more than 2 elements in the array
				if h - i + 1 > 2:
								_lowerCamelCase     :			Union[str, Any]    =    (int)((h - i + 1) / 3		)

								# Recursively sort first 2/3 elements
								stooge(lowercase__							,    lowercase__							,    (h - t)		)

								# Recursively sort last 2/3 elements
								stooge(lowercase__							,    i + t							,    (lowercase__)		)

								# Recursively sort first 2/3 elements
								stooge(lowercase__							,    lowercase__							,    (h - t)		)


if __name__ == "__main__":
			lowercase__     						=						input("""Enter numbers separated by a comma:\n""").strip()
			lowercase__     						=						[int(item) for item in user_input.split(""",""")]
			print(stooge_sort(unsorted))