


"""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      __A     (snake_case__):





							'''simple docstring'''

							def __init__(				self							:      Any		,       UpperCAmelCase_							:      List[Any]=0.01		,       UpperCAmelCase_							:      Optional[int]=1_000       )					->Tuple:



														"""simple docstring"""



														snake_case_           =		p_stop
														snake_case_           =		max_length



							def __iter__(				self							:      List[Any]       )					->int:



														"""simple docstring"""



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





class      __A     (unittest.TestCase):





							'''simple docstring'''

							def 		lowerCAmelCase       (				self							:      Dict		,       UpperCAmelCase_							:      List[str]		,       UpperCAmelCase_							:      int		,       UpperCAmelCase_							:      Optional[int]=False		,       UpperCAmelCase_							:      List[str]=True       )					->List[Any]:



														"""simple docstring"""



														snake_case_           =		[
														    BatchSamplerShard(UpperCAmelCase_		,       2		,       UpperCAmelCase_		,       split_batches=UpperCAmelCase_		,       even_batches=UpperCAmelCase_       )
														    for i in range(2       )
														]
														snake_case_           =		[list(UpperCAmelCase_       ) for batch_sampler_shard in batch_sampler_shards]
														if not split_batches:
																					self.assertListEqual([len(UpperCAmelCase_       ) for shard in batch_sampler_shards]		,       [len(UpperCAmelCase_       ) for e in expected]       )
														self.assertListEqual(UpperCAmelCase_		,       UpperCAmelCase_       )



							def 		lowerCAmelCase       (				self							:      int       )					->Tuple:



														"""simple docstring"""



														snake_case_           =		BatchSampler(range(24       )		,       batch_size=3		,       drop_last=UpperCAmelCase_       )
														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(UpperCAmelCase_		,       UpperCAmelCase_       )

														snake_case_           =		BatchSampler(range(24       )		,       batch_size=3		,       drop_last=UpperCAmelCase_       )
														# Expected shouldn't change
														self.check_batch_sampler_shards(UpperCAmelCase_		,       UpperCAmelCase_       )

														# Check the shards when the dataset is a round multiple of batch size but not total batch size.
														snake_case_           =		BatchSampler(range(21       )		,       batch_size=3		,       drop_last=UpperCAmelCase_       )
														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(UpperCAmelCase_		,       UpperCAmelCase_       )

														snake_case_           =		BatchSampler(range(21       )		,       batch_size=3		,       drop_last=UpperCAmelCase_       )
														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(UpperCAmelCase_		,       UpperCAmelCase_       )

														# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
														# num_processes batch.
														snake_case_           =		BatchSampler(range(22       )		,       batch_size=3		,       drop_last=UpperCAmelCase_       )
														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(UpperCAmelCase_		,       UpperCAmelCase_       )

														snake_case_           =		BatchSampler(range(22       )		,       batch_size=3		,       drop_last=UpperCAmelCase_       )
														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(UpperCAmelCase_		,       UpperCAmelCase_       )

														# 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_           =		BatchSampler(range(20       )		,       batch_size=3		,       drop_last=UpperCAmelCase_       )
														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(UpperCAmelCase_		,       UpperCAmelCase_       )

														snake_case_           =		BatchSampler(range(20       )		,       batch_size=3		,       drop_last=UpperCAmelCase_       )
														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(UpperCAmelCase_		,       UpperCAmelCase_       )

														# Check the shards when the dataset is very small.
														snake_case_           =		BatchSampler(range(2       )		,       batch_size=3		,       drop_last=UpperCAmelCase_       )
														snake_case_           =		[[[0, 1, 0]], [[1, 0, 1]]]
														self.check_batch_sampler_shards(UpperCAmelCase_		,       UpperCAmelCase_       )

														snake_case_           =		BatchSampler(range(2       )		,       batch_size=3		,       drop_last=UpperCAmelCase_       )
														snake_case_           =		[[], []]
														self.check_batch_sampler_shards(UpperCAmelCase_		,       UpperCAmelCase_       )



							def 		lowerCAmelCase       (				self							:      List[Any]       )					->Tuple:



														"""simple docstring"""



														snake_case_           =		BatchSampler(range(24       )		,       batch_size=4		,       drop_last=UpperCAmelCase_       )
														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(UpperCAmelCase_		,       UpperCAmelCase_		,       split_batches=UpperCAmelCase_       )

														snake_case_           =		BatchSampler(range(24       )		,       batch_size=4		,       drop_last=UpperCAmelCase_       )
														# Expected shouldn't change
														self.check_batch_sampler_shards(UpperCAmelCase_		,       UpperCAmelCase_		,       split_batches=UpperCAmelCase_       )

														# Check the shards when the dataset is not a round multiple of batch size.
														snake_case_           =		BatchSampler(range(22       )		,       batch_size=4		,       drop_last=UpperCAmelCase_       )
														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(UpperCAmelCase_		,       UpperCAmelCase_		,       split_batches=UpperCAmelCase_       )

														snake_case_           =		BatchSampler(range(22       )		,       batch_size=4		,       drop_last=UpperCAmelCase_       )
														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(UpperCAmelCase_		,       UpperCAmelCase_		,       split_batches=UpperCAmelCase_       )

														# Check the shards when the dataset is not a round multiple of batch size or num_processes.
														snake_case_           =		BatchSampler(range(21       )		,       batch_size=4		,       drop_last=UpperCAmelCase_       )
														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(UpperCAmelCase_		,       UpperCAmelCase_		,       split_batches=UpperCAmelCase_       )

														snake_case_           =		BatchSampler(range(21       )		,       batch_size=4		,       drop_last=UpperCAmelCase_       )
														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(UpperCAmelCase_		,       UpperCAmelCase_		,       split_batches=UpperCAmelCase_       )

														# Check the shards when the dataset is very small.
														snake_case_           =		BatchSampler(range(2       )		,       batch_size=4		,       drop_last=UpperCAmelCase_       )
														snake_case_           =		[[[0, 1]], [[0, 1]]]
														self.check_batch_sampler_shards(UpperCAmelCase_		,       UpperCAmelCase_		,       split_batches=UpperCAmelCase_       )

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



							def 		lowerCAmelCase       (				self							:      Optional[Any]       )					->Dict:



														"""simple docstring"""



														snake_case_           =		BatchSampler(range(24       )		,       batch_size=3		,       drop_last=UpperCAmelCase_       )
														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(UpperCAmelCase_		,       UpperCAmelCase_		,       even_batches=UpperCAmelCase_       )

														snake_case_           =		BatchSampler(range(24       )		,       batch_size=3		,       drop_last=UpperCAmelCase_       )
														# Expected shouldn't change
														self.check_batch_sampler_shards(UpperCAmelCase_		,       UpperCAmelCase_		,       even_batches=UpperCAmelCase_       )

														# Check the shards when the dataset is a round multiple of batch size but not total batch size.
														snake_case_           =		BatchSampler(range(21       )		,       batch_size=3		,       drop_last=UpperCAmelCase_       )
														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(UpperCAmelCase_		,       UpperCAmelCase_		,       even_batches=UpperCAmelCase_       )

														snake_case_           =		BatchSampler(range(21       )		,       batch_size=3		,       drop_last=UpperCAmelCase_       )
														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(UpperCAmelCase_		,       UpperCAmelCase_		,       even_batches=UpperCAmelCase_       )

														# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
														# num_processes batch.
														snake_case_           =		BatchSampler(range(22       )		,       batch_size=3		,       drop_last=UpperCAmelCase_       )
														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(UpperCAmelCase_		,       UpperCAmelCase_		,       even_batches=UpperCAmelCase_       )

														snake_case_           =		BatchSampler(range(22       )		,       batch_size=3		,       drop_last=UpperCAmelCase_       )
														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(UpperCAmelCase_		,       UpperCAmelCase_		,       even_batches=UpperCAmelCase_       )

														# 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_           =		BatchSampler(range(20       )		,       batch_size=3		,       drop_last=UpperCAmelCase_       )
														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(UpperCAmelCase_		,       UpperCAmelCase_		,       even_batches=UpperCAmelCase_       )

														snake_case_           =		BatchSampler(range(20       )		,       batch_size=3		,       drop_last=UpperCAmelCase_       )
														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(UpperCAmelCase_		,       UpperCAmelCase_		,       even_batches=UpperCAmelCase_       )

														# Check the shards when the dataset is very small.
														snake_case_           =		BatchSampler(range(2       )		,       batch_size=3		,       drop_last=UpperCAmelCase_       )
														snake_case_           =		[[[0, 1]], []]
														self.check_batch_sampler_shards(UpperCAmelCase_		,       UpperCAmelCase_		,       even_batches=UpperCAmelCase_       )

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



							def 		lowerCAmelCase       (				self							:      Tuple       )					->Tuple:



														"""simple docstring"""



														snake_case_           =		BatchSampler(range(24       )		,       batch_size=4		,       drop_last=UpperCAmelCase_       )
														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(UpperCAmelCase_		,       UpperCAmelCase_		,       split_batches=UpperCAmelCase_		,       even_batches=UpperCAmelCase_       )

														snake_case_           =		BatchSampler(range(24       )		,       batch_size=4		,       drop_last=UpperCAmelCase_       )
														# Expected shouldn't change
														self.check_batch_sampler_shards(UpperCAmelCase_		,       UpperCAmelCase_		,       split_batches=UpperCAmelCase_		,       even_batches=UpperCAmelCase_       )

														# Check the shards when the dataset is not a round multiple of batch size.
														snake_case_           =		BatchSampler(range(22       )		,       batch_size=4		,       drop_last=UpperCAmelCase_       )
														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(UpperCAmelCase_		,       UpperCAmelCase_		,       split_batches=UpperCAmelCase_		,       even_batches=UpperCAmelCase_       )

														snake_case_           =		BatchSampler(range(22       )		,       batch_size=4		,       drop_last=UpperCAmelCase_       )
														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(UpperCAmelCase_		,       UpperCAmelCase_		,       split_batches=UpperCAmelCase_		,       even_batches=UpperCAmelCase_       )

														# Check the shards when the dataset is not a round multiple of batch size or num_processes.
														snake_case_           =		BatchSampler(range(21       )		,       batch_size=4		,       drop_last=UpperCAmelCase_       )
														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(UpperCAmelCase_		,       UpperCAmelCase_		,       split_batches=UpperCAmelCase_		,       even_batches=UpperCAmelCase_       )

														snake_case_           =		BatchSampler(range(21       )		,       batch_size=4		,       drop_last=UpperCAmelCase_       )
														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(UpperCAmelCase_		,       UpperCAmelCase_		,       split_batches=UpperCAmelCase_		,       even_batches=UpperCAmelCase_       )

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

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



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



														"""simple docstring"""



														snake_case_           =		[[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 10, 11], [12, 13]]
														snake_case_           =		[BatchSamplerShard(UpperCAmelCase_		,       2		,       UpperCAmelCase_		,       even_batches=UpperCAmelCase_       ) 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 		lowerCAmelCase       (				self							:      Optional[int]		,       UpperCAmelCase_							:      Any		,       UpperCAmelCase_							:      Union[str, Any]		,       UpperCAmelCase_							:      Optional[int]		,       UpperCAmelCase_							:      List[str]=False		,       UpperCAmelCase_							:      List[Any]=2		,       UpperCAmelCase_							:      Tuple=False       )					->Tuple:



														"""simple docstring"""



														random.seed(UpperCAmelCase_       )
														snake_case_           =		list(UpperCAmelCase_       )

														snake_case_           =		[
														    IterableDatasetShard(
														        UpperCAmelCase_		,       batch_size=UpperCAmelCase_		,       drop_last=UpperCAmelCase_		,       num_processes=UpperCAmelCase_		,       process_index=UpperCAmelCase_		,       split_batches=UpperCAmelCase_		,       )
														    for i in range(UpperCAmelCase_       )
														]
														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(UpperCAmelCase_       )
																					iterable_dataset_lists.append(list(UpperCAmelCase_       )       )

														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
														snake_case_           =		iterable_dataset_lists[0]
														for l in iterable_dataset_lists[1:]:
																					self.assertEqual(len(UpperCAmelCase_       )		,       len(UpperCAmelCase_       )       )
																					self.assertTrue(len(UpperCAmelCase_       ) % shard_batch_size == 0       )

														snake_case_           =		[]
														for idx in range(0		,       len(UpperCAmelCase_       )		,       UpperCAmelCase_       ):
																					for l in iterable_dataset_lists:
																												observed += l[idx : idx + shard_batch_size]

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



							def 		lowerCAmelCase       (				self							:      Any       )					->Dict:



														"""simple docstring"""



														snake_case_           =		42
														snake_case_           =		RandomIterableDataset()

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

														# Edge case with a very small dataset
														snake_case_           =		RandomIterableDataset(max_length=2       )

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



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



														"""simple docstring"""



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



							def 		lowerCAmelCase       (				self							:      List[Any]       )					->Dict:



														"""simple docstring"""



														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 		lowerCAmelCase       (				self							:      int       )					->Optional[Any]:



														"""simple docstring"""



														snake_case_           =		DataLoader(list(range(16       )       )		,       batch_size=4       )
														snake_case_           =		skip_first_batches(UpperCAmelCase_		,       num_batches=2       )
														self.assertListEqual([t.tolist() for t in new_dataloader]		,       [[8, 9, 10, 11], [12, 13, 14, 15]]       )



							def 		lowerCAmelCase       (				self							:      int       )					->Any:



														"""simple docstring"""



														snake_case_           =		DataLoaderShard(list(range(16       )       )		,       batch_size=4       )
														for idx, _ in enumerate(UpperCAmelCase_       ):
																					self.assertEqual(dataloader.end_of_dataloader		,       idx == 3       )

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



							def 		lowerCAmelCase       (				self							:      str       )					->List[str]:



														"""simple docstring"""



														Accelerator()
														snake_case_           =		DataLoaderDispatcher(range(16       )		,       batch_size=4       )
														for idx, _ in enumerate(UpperCAmelCase_       ):
																					self.assertEqual(dataloader.end_of_dataloader		,       idx == 3       )

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







"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__SCREAMING_SNAKE_CASE							:    Optional[Any]		=    {
    'configuration_megatron_bert': ['MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegatronBertConfig'],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__SCREAMING_SNAKE_CASE							:    Tuple		=    [
				    'MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'MegatronBertForCausalLM',
				    'MegatronBertForMaskedLM',
				    'MegatronBertForMultipleChoice',
				    'MegatronBertForNextSentencePrediction',
				    'MegatronBertForPreTraining',
				    'MegatronBertForQuestionAnswering',
				    'MegatronBertForSequenceClassification',
				    'MegatronBertForTokenClassification',
				    'MegatronBertModel',
				    'MegatronBertPreTrainedModel',
				]

if TYPE_CHECKING:
				from .configuration_megatron_bert import MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, MegatronBertConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_megatron_bert import (
								    MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    MegatronBertForCausalLM,
								    MegatronBertForMaskedLM,
								    MegatronBertForMultipleChoice,
								    MegatronBertForNextSentencePrediction,
								    MegatronBertForPreTraining,
								    MegatronBertForQuestionAnswering,
								    MegatronBertForSequenceClassification,
								    MegatronBertForTokenClassification,
								    MegatronBertModel,
								    MegatronBertPreTrainedModel,
								)

else:
				import sys

				__SCREAMING_SNAKE_CASE							:    int		=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



