


'''simple docstring'''







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 						UpperCamelCase__       (			SCREAMING_SNAKE_CASE_						):



     """simple docstring"""






     def __init__(			self   ,						snake_case__=0.01   ,						snake_case__=1000 ):

            '''simple docstring'''

            _lowerCAmelCase     :				Union[str, Any]       =							p_stop
            _lowerCAmelCase     :				List[Any]       =							max_length







     def __iter__(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				int       =							0
            _lowerCAmelCase     :				Union[str, Any]       =							False
            while not stop and count < self.max_length:
                   yield count
                   count += 1
                   _lowerCAmelCase     :				str       =							random.random() < self.p_stop
class 						UpperCamelCase__       (			unittest.TestCase						):



     """simple docstring"""






     def a						(			self   ,						snake_case__   ,						snake_case__   ,						snake_case__=False   ,						snake_case__=True ):

            '''simple docstring'''

            _lowerCAmelCase     :				Union[str, Any]       =							[
                BatchSamplerShard(snake_case__   ,						2   ,						snake_case__   ,						split_batches=snake_case__   ,						even_batches=snake_case__ )
                for i in range(2 )
            ]
            _lowerCAmelCase     :				Dict       =							[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 a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Union[str, Any]       =							BatchSampler(range(24 )   ,						batch_size=3   ,						drop_last=snake_case__ )
            _lowerCAmelCase     :				List[str]       =							[
                [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
                [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]],
            ]
            self.check_batch_sampler_shards(snake_case__   ,						snake_case__ )

            _lowerCAmelCase     :				Tuple       =							BatchSampler(range(24 )   ,						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.
            _lowerCAmelCase     :				Dict       =							BatchSampler(range(21 )   ,						batch_size=3   ,						drop_last=snake_case__ )
            _lowerCAmelCase     :				str       =							[
                [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
                [[3, 4, 5], [9, 10, 11], [15, 16, 17], [0, 1, 2]],
            ]
            self.check_batch_sampler_shards(snake_case__   ,						snake_case__ )

            _lowerCAmelCase     :				Tuple       =							BatchSampler(range(21 )   ,						batch_size=3   ,						drop_last=snake_case__ )
            _lowerCAmelCase     :				Dict       =							[
                [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
                [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
            ]
            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.
            _lowerCAmelCase     :				Union[str, Any]       =							BatchSampler(range(22 )   ,						batch_size=3   ,						drop_last=snake_case__ )
            _lowerCAmelCase     :				List[Any]       =							[
                [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
                [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 0, 1]],
            ]
            self.check_batch_sampler_shards(snake_case__   ,						snake_case__ )

            _lowerCAmelCase     :				Optional[int]       =							BatchSampler(range(22 )   ,						batch_size=3   ,						drop_last=snake_case__ )
            _lowerCAmelCase     :				Optional[int]       =							[
                [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
                [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
            ]
            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.
            _lowerCAmelCase     :				Tuple       =							BatchSampler(range(20 )   ,						batch_size=3   ,						drop_last=snake_case__ )
            _lowerCAmelCase     :				Tuple       =							[
                [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 0]],
                [[3, 4, 5], [9, 10, 11], [15, 16, 17], [1, 2, 3]],
            ]
            self.check_batch_sampler_shards(snake_case__   ,						snake_case__ )

            _lowerCAmelCase     :				Union[str, Any]       =							BatchSampler(range(20 )   ,						batch_size=3   ,						drop_last=snake_case__ )
            _lowerCAmelCase     :				Union[str, Any]       =							[
                [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
                [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
            ]
            self.check_batch_sampler_shards(snake_case__   ,						snake_case__ )

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

            _lowerCAmelCase     :				Tuple       =							BatchSampler(range(2 )   ,						batch_size=3   ,						drop_last=snake_case__ )
            _lowerCAmelCase     :				List[str]       =							[[], []]
            self.check_batch_sampler_shards(snake_case__   ,						snake_case__ )







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Tuple       =							BatchSampler(range(24 )   ,						batch_size=4   ,						drop_last=snake_case__ )
            _lowerCAmelCase     :				List[str]       =							[
                [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
                [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23]],
            ]
            self.check_batch_sampler_shards(snake_case__   ,						snake_case__   ,						split_batches=snake_case__ )

            _lowerCAmelCase     :				Dict       =							BatchSampler(range(24 )   ,						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.
            _lowerCAmelCase     :				Any       =							BatchSampler(range(22 )   ,						batch_size=4   ,						drop_last=snake_case__ )
            _lowerCAmelCase     :				List[str]       =							[
                [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
                [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [0, 1]],
            ]
            self.check_batch_sampler_shards(snake_case__   ,						snake_case__   ,						split_batches=snake_case__ )

            _lowerCAmelCase     :				Any       =							BatchSampler(range(22 )   ,						batch_size=4   ,						drop_last=snake_case__ )
            _lowerCAmelCase     :				int       =							[
                [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
                [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
            ]
            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.
            _lowerCAmelCase     :				List[str]       =							BatchSampler(range(21 )   ,						batch_size=4   ,						drop_last=snake_case__ )
            _lowerCAmelCase     :				int       =							[
                [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 0]],
                [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [1, 2]],
            ]
            self.check_batch_sampler_shards(snake_case__   ,						snake_case__   ,						split_batches=snake_case__ )

            _lowerCAmelCase     :				Optional[Any]       =							BatchSampler(range(21 )   ,						batch_size=4   ,						drop_last=snake_case__ )
            _lowerCAmelCase     :				Optional[Any]       =							[
                [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
                [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
            ]
            self.check_batch_sampler_shards(snake_case__   ,						snake_case__   ,						split_batches=snake_case__ )

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

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







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				int       =							BatchSampler(range(24 )   ,						batch_size=3   ,						drop_last=snake_case__ )
            _lowerCAmelCase     :				Any       =							[
                [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
                [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]],
            ]
            self.check_batch_sampler_shards(snake_case__   ,						snake_case__   ,						even_batches=snake_case__ )

            _lowerCAmelCase     :				Tuple       =							BatchSampler(range(24 )   ,						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.
            _lowerCAmelCase     :				Optional[Any]       =							BatchSampler(range(21 )   ,						batch_size=3   ,						drop_last=snake_case__ )
            _lowerCAmelCase     :				Union[str, Any]       =							[
                [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
                [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
            ]
            self.check_batch_sampler_shards(snake_case__   ,						snake_case__   ,						even_batches=snake_case__ )

            _lowerCAmelCase     :				Optional[int]       =							BatchSampler(range(21 )   ,						batch_size=3   ,						drop_last=snake_case__ )
            _lowerCAmelCase     :				Union[str, Any]       =							[
                [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
                [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
            ]
            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.
            _lowerCAmelCase     :				int       =							BatchSampler(range(22 )   ,						batch_size=3   ,						drop_last=snake_case__ )
            _lowerCAmelCase     :				List[Any]       =							[
                [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
                [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21]],
            ]
            self.check_batch_sampler_shards(snake_case__   ,						snake_case__   ,						even_batches=snake_case__ )

            _lowerCAmelCase     :				Optional[Any]       =							BatchSampler(range(22 )   ,						batch_size=3   ,						drop_last=snake_case__ )
            _lowerCAmelCase     :				str       =							[
                [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
                [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
            ]
            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.
            _lowerCAmelCase     :				List[Any]       =							BatchSampler(range(20 )   ,						batch_size=3   ,						drop_last=snake_case__ )
            _lowerCAmelCase     :				Any       =							[
                [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19]],
                [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
            ]
            self.check_batch_sampler_shards(snake_case__   ,						snake_case__   ,						even_batches=snake_case__ )

            _lowerCAmelCase     :				str       =							BatchSampler(range(20 )   ,						batch_size=3   ,						drop_last=snake_case__ )
            _lowerCAmelCase     :				Union[str, Any]       =							[
                [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
                [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
            ]
            self.check_batch_sampler_shards(snake_case__   ,						snake_case__   ,						even_batches=snake_case__ )

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

            _lowerCAmelCase     :				Any       =							BatchSampler(range(2 )   ,						batch_size=3   ,						drop_last=snake_case__ )
            _lowerCAmelCase     :				Optional[Any]       =							[[], []]
            self.check_batch_sampler_shards(snake_case__   ,						snake_case__   ,						even_batches=snake_case__ )







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Optional[int]       =							BatchSampler(range(24 )   ,						batch_size=4   ,						drop_last=snake_case__ )
            _lowerCAmelCase     :				int       =							[
                [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
                [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23]],
            ]
            self.check_batch_sampler_shards(snake_case__   ,						snake_case__   ,						split_batches=snake_case__   ,						even_batches=snake_case__ )

            _lowerCAmelCase     :				str       =							BatchSampler(range(24 )   ,						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.
            _lowerCAmelCase     :				Optional[int]       =							BatchSampler(range(22 )   ,						batch_size=4   ,						drop_last=snake_case__ )
            _lowerCAmelCase     :				Optional[int]       =							[
                [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
                [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
            ]
            self.check_batch_sampler_shards(snake_case__   ,						snake_case__   ,						split_batches=snake_case__   ,						even_batches=snake_case__ )

            _lowerCAmelCase     :				Union[str, Any]       =							BatchSampler(range(22 )   ,						batch_size=4   ,						drop_last=snake_case__ )
            _lowerCAmelCase     :				Any       =							[
                [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
                [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
            ]
            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.
            _lowerCAmelCase     :				Union[str, Any]       =							BatchSampler(range(21 )   ,						batch_size=4   ,						drop_last=snake_case__ )
            _lowerCAmelCase     :				Any       =							[
                [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20]],
                [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
            ]
            self.check_batch_sampler_shards(snake_case__   ,						snake_case__   ,						split_batches=snake_case__   ,						even_batches=snake_case__ )

            _lowerCAmelCase     :				List[str]       =							BatchSampler(range(21 )   ,						batch_size=4   ,						drop_last=snake_case__ )
            _lowerCAmelCase     :				Any       =							[
                [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
                [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
            ]
            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.
            _lowerCAmelCase     :				Optional[Any]       =							BatchSampler(range(2 )   ,						batch_size=4   ,						drop_last=snake_case__ )
            _lowerCAmelCase     :				List[Any]       =							[[[0, 1]], []]
            self.check_batch_sampler_shards(snake_case__   ,						snake_case__   ,						split_batches=snake_case__   ,						even_batches=snake_case__ )

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







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				List[str]       =							[[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 10, 11], [12, 13]]
            _lowerCAmelCase     :				Tuple       =							[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], [12, 13]] )
            self.assertListEqual(list(batch_sampler_shards[1] )   ,						[[3, 4], [9, 10, 11]] )







     def a						(			self   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__=False   ,						snake_case__=2   ,						snake_case__=False ):

            '''simple docstring'''

            random.seed(snake_case__ )
            _lowerCAmelCase     :				Optional[Any]       =							list(snake_case__ )

            _lowerCAmelCase     :				List[str]       =							[
                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__ )
            ]
            _lowerCAmelCase     :				Tuple       =							[]
            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__ ) )

            _lowerCAmelCase     :				Dict       =							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
            _lowerCAmelCase     :				List[str]       =							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 )

            _lowerCAmelCase     :				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 a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Tuple       =							42
            _lowerCAmelCase     :				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
            _lowerCAmelCase     :				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 a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Optional[Any]       =							BatchSampler(range(16 )   ,						batch_size=4   ,						drop_last=snake_case__ )
            _lowerCAmelCase     :				Optional[Any]       =							SkipBatchSampler(snake_case__   ,						2 )
            self.assertListEqual(list(snake_case__ )   ,						[[8, 9, 10, 11], [12, 13, 14, 15]] )







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				List[Any]       =							SkipDataLoader(list(range(16 ) )   ,						batch_size=4   ,						skip_batches=2 )
            self.assertListEqual([t.tolist() for t in dataloader]   ,						[[8, 9, 10, 11], [12, 13, 14, 15]] )







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				str       =							DataLoader(list(range(16 ) )   ,						batch_size=4 )
            _lowerCAmelCase     :				Optional[Any]       =							skip_first_batches(snake_case__   ,						num_batches=2 )
            self.assertListEqual([t.tolist() for t in new_dataloader]   ,						[[8, 9, 10, 11], [12, 13, 14, 15]] )







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Tuple       =							DataLoaderShard(list(range(16 ) )   ,						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 a						(			self ):

            '''simple docstring'''

            Accelerator()
            _lowerCAmelCase     :				List[Any]       =							DataLoaderDispatcher(range(16 )   ,						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       lowercase					(_A       ):







       """simple docstring"""







       _lowerCAmelCase     :				Union[str, Any]       =							len(_A       )
       for i in range(length - 1       ):
              _lowerCAmelCase     :				List[Any]       =							i
              for k in range(i + 1 ,    _A       ):
                     if collection[k] < collection[least]:
                            _lowerCAmelCase     :				List[str]       =							k
              if least != i:
                     _lowerCAmelCase      ,  _lowerCAmelCase     :				Union[str, Any]       =							(collection[i], collection[least])
       return collection


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

