





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_     ):


							def __init__(				self     :Tuple				,					__A     :Optional[Any]=0.0_1				,					__A     :Any=1000		) -> Dict:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   p_stop
									SCREAMING_SNAKE_CASE__								=   max_length


							def __iter__(				self     :str		) -> Optional[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   0
									SCREAMING_SNAKE_CASE__								=   False
									while not stop and count < self.max_length:
											yield count
											count += 1
											SCREAMING_SNAKE_CASE__								=   random.random() < self.p_stop




class 	UpperCamelCase_							(  unittest.TestCase     ):


							def   _snake_case	(				self     :Tuple				,					__A     :Union[str, Any]				,					__A     :Dict				,					__A     :str=False				,					__A     :str=True		) -> Union[str, Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   [
									    BatchSamplerShard(snake_case__				,					2				,					snake_case__				,					split_batches=snake_case__				,					even_batches=snake_case__		)
									    for i in range(2		)
									]
									SCREAMING_SNAKE_CASE__								=   [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   _snake_case	(				self     :Optional[int]		) -> List[str]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   BatchSampler(range(24		)				,					batch_size=3				,					drop_last=snake_case__		)
									SCREAMING_SNAKE_CASE__								=   [
									    [[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__		)

									SCREAMING_SNAKE_CASE__								=   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.
									SCREAMING_SNAKE_CASE__								=   BatchSampler(range(21		)				,					batch_size=3				,					drop_last=snake_case__		)
									SCREAMING_SNAKE_CASE__								=   [
									    [[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__		)

									SCREAMING_SNAKE_CASE__								=   BatchSampler(range(21		)				,					batch_size=3				,					drop_last=snake_case__		)
									SCREAMING_SNAKE_CASE__								=   [
									    [[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.
									SCREAMING_SNAKE_CASE__								=   BatchSampler(range(22		)				,					batch_size=3				,					drop_last=snake_case__		)
									SCREAMING_SNAKE_CASE__								=   [
									    [[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__		)

									SCREAMING_SNAKE_CASE__								=   BatchSampler(range(22		)				,					batch_size=3				,					drop_last=snake_case__		)
									SCREAMING_SNAKE_CASE__								=   [
									    [[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.
									SCREAMING_SNAKE_CASE__								=   BatchSampler(range(20		)				,					batch_size=3				,					drop_last=snake_case__		)
									SCREAMING_SNAKE_CASE__								=   [
									    [[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__		)

									SCREAMING_SNAKE_CASE__								=   BatchSampler(range(20		)				,					batch_size=3				,					drop_last=snake_case__		)
									SCREAMING_SNAKE_CASE__								=   [
									    [[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.
									SCREAMING_SNAKE_CASE__								=   BatchSampler(range(2		)				,					batch_size=3				,					drop_last=snake_case__		)
									SCREAMING_SNAKE_CASE__								=   [[[0, 1, 0]], [[1, 0, 1]]]
									self.check_batch_sampler_shards(snake_case__				,					snake_case__		)

									SCREAMING_SNAKE_CASE__								=   BatchSampler(range(2		)				,					batch_size=3				,					drop_last=snake_case__		)
									SCREAMING_SNAKE_CASE__								=   [[], []]
									self.check_batch_sampler_shards(snake_case__				,					snake_case__		)


							def   _snake_case	(				self     :Union[str, Any]		) -> Union[str, Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   BatchSampler(range(24		)				,					batch_size=4				,					drop_last=snake_case__		)
									SCREAMING_SNAKE_CASE__								=   [
									    [[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__		)

									SCREAMING_SNAKE_CASE__								=   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.
									SCREAMING_SNAKE_CASE__								=   BatchSampler(range(22		)				,					batch_size=4				,					drop_last=snake_case__		)
									SCREAMING_SNAKE_CASE__								=   [
									    [[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__		)

									SCREAMING_SNAKE_CASE__								=   BatchSampler(range(22		)				,					batch_size=4				,					drop_last=snake_case__		)
									SCREAMING_SNAKE_CASE__								=   [
									    [[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.
									SCREAMING_SNAKE_CASE__								=   BatchSampler(range(21		)				,					batch_size=4				,					drop_last=snake_case__		)
									SCREAMING_SNAKE_CASE__								=   [
									    [[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__		)

									SCREAMING_SNAKE_CASE__								=   BatchSampler(range(21		)				,					batch_size=4				,					drop_last=snake_case__		)
									SCREAMING_SNAKE_CASE__								=   [
									    [[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.
									SCREAMING_SNAKE_CASE__								=   BatchSampler(range(2		)				,					batch_size=4				,					drop_last=snake_case__		)
									SCREAMING_SNAKE_CASE__								=   [[[0, 1]], [[0, 1]]]
									self.check_batch_sampler_shards(snake_case__				,					snake_case__				,					split_batches=snake_case__		)

									SCREAMING_SNAKE_CASE__								=   BatchSampler(range(2		)				,					batch_size=4				,					drop_last=snake_case__		)
									SCREAMING_SNAKE_CASE__								=   [[], []]
									self.check_batch_sampler_shards(snake_case__				,					snake_case__				,					split_batches=snake_case__		)


							def   _snake_case	(				self     :Optional[Any]		) -> int:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   BatchSampler(range(24		)				,					batch_size=3				,					drop_last=snake_case__		)
									SCREAMING_SNAKE_CASE__								=   [
									    [[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__		)

									SCREAMING_SNAKE_CASE__								=   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.
									SCREAMING_SNAKE_CASE__								=   BatchSampler(range(21		)				,					batch_size=3				,					drop_last=snake_case__		)
									SCREAMING_SNAKE_CASE__								=   [
									    [[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__		)

									SCREAMING_SNAKE_CASE__								=   BatchSampler(range(21		)				,					batch_size=3				,					drop_last=snake_case__		)
									SCREAMING_SNAKE_CASE__								=   [
									    [[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.
									SCREAMING_SNAKE_CASE__								=   BatchSampler(range(22		)				,					batch_size=3				,					drop_last=snake_case__		)
									SCREAMING_SNAKE_CASE__								=   [
									    [[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__		)

									SCREAMING_SNAKE_CASE__								=   BatchSampler(range(22		)				,					batch_size=3				,					drop_last=snake_case__		)
									SCREAMING_SNAKE_CASE__								=   [
									    [[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.
									SCREAMING_SNAKE_CASE__								=   BatchSampler(range(20		)				,					batch_size=3				,					drop_last=snake_case__		)
									SCREAMING_SNAKE_CASE__								=   [
									    [[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__		)

									SCREAMING_SNAKE_CASE__								=   BatchSampler(range(20		)				,					batch_size=3				,					drop_last=snake_case__		)
									SCREAMING_SNAKE_CASE__								=   [
									    [[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.
									SCREAMING_SNAKE_CASE__								=   BatchSampler(range(2		)				,					batch_size=3				,					drop_last=snake_case__		)
									SCREAMING_SNAKE_CASE__								=   [[[0, 1]], []]
									self.check_batch_sampler_shards(snake_case__				,					snake_case__				,					even_batches=snake_case__		)

									SCREAMING_SNAKE_CASE__								=   BatchSampler(range(2		)				,					batch_size=3				,					drop_last=snake_case__		)
									SCREAMING_SNAKE_CASE__								=   [[], []]
									self.check_batch_sampler_shards(snake_case__				,					snake_case__				,					even_batches=snake_case__		)


							def   _snake_case	(				self     :Union[str, Any]		) -> str:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   BatchSampler(range(24		)				,					batch_size=4				,					drop_last=snake_case__		)
									SCREAMING_SNAKE_CASE__								=   [
									    [[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__		)

									SCREAMING_SNAKE_CASE__								=   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.
									SCREAMING_SNAKE_CASE__								=   BatchSampler(range(22		)				,					batch_size=4				,					drop_last=snake_case__		)
									SCREAMING_SNAKE_CASE__								=   [
									    [[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__		)

									SCREAMING_SNAKE_CASE__								=   BatchSampler(range(22		)				,					batch_size=4				,					drop_last=snake_case__		)
									SCREAMING_SNAKE_CASE__								=   [
									    [[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.
									SCREAMING_SNAKE_CASE__								=   BatchSampler(range(21		)				,					batch_size=4				,					drop_last=snake_case__		)
									SCREAMING_SNAKE_CASE__								=   [
									    [[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__		)

									SCREAMING_SNAKE_CASE__								=   BatchSampler(range(21		)				,					batch_size=4				,					drop_last=snake_case__		)
									SCREAMING_SNAKE_CASE__								=   [
									    [[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.
									SCREAMING_SNAKE_CASE__								=   BatchSampler(range(2		)				,					batch_size=4				,					drop_last=snake_case__		)
									SCREAMING_SNAKE_CASE__								=   [[[0, 1]], []]
									self.check_batch_sampler_shards(snake_case__				,					snake_case__				,					split_batches=snake_case__				,					even_batches=snake_case__		)

									SCREAMING_SNAKE_CASE__								=   BatchSampler(range(2		)				,					batch_size=4				,					drop_last=snake_case__		)
									SCREAMING_SNAKE_CASE__								=   [[], []]
									self.check_batch_sampler_shards(snake_case__				,					snake_case__				,					split_batches=snake_case__				,					even_batches=snake_case__		)


							def   _snake_case	(				self     :Tuple		) -> int:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   [[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 10, 11], [12, 13]]
									SCREAMING_SNAKE_CASE__								=   [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   _snake_case	(				self     :str				,					__A     :Optional[int]				,					__A     :Tuple				,					__A     :Dict				,					__A     :List[str]=False				,					__A     :Optional[Any]=2				,					__A     :Tuple=False		) -> Any:



									"""simple docstring"""



									random.seed(snake_case__		)
									SCREAMING_SNAKE_CASE__								=   list(snake_case__		)

									SCREAMING_SNAKE_CASE__								=   [
									    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__		)
									]
									SCREAMING_SNAKE_CASE__								=   []
									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__		)		)

									SCREAMING_SNAKE_CASE__								=   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
									SCREAMING_SNAKE_CASE__								=   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		)

									SCREAMING_SNAKE_CASE__								=   []
									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   _snake_case	(				self     :List[Any]		) -> Optional[int]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   42
									SCREAMING_SNAKE_CASE__								=   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
									SCREAMING_SNAKE_CASE__								=   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   _snake_case	(				self     :List[str]		) -> Any:



									"""simple docstring"""



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


							def   _snake_case	(				self     :Optional[Any]		) -> Any:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   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   _snake_case	(				self     :int		) -> Union[str, Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   DataLoader(list(range(16		)		)				,					batch_size=4		)
									SCREAMING_SNAKE_CASE__								=   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   _snake_case	(				self     :int		) -> Union[str, Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   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   _snake_case	(				self     :List[Any]		) -> int:



									"""simple docstring"""



									Accelerator()
									SCREAMING_SNAKE_CASE__								=   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))

