



"""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  __SCREAMING_SNAKE_CASE			(   lowerCAmelCase_     ):


					'''simple docstring'''





					def __init__( self							:						int,						lowerCamelCase							:						int=0.01,						lowerCamelCase							:						Dict=1000  )->      Any:
								lowerCamelCase__	:	List[str]				=p_stop
								lowerCamelCase__	:	Optional[Any]				=max_length




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





class  __SCREAMING_SNAKE_CASE			(   unittest.TestCase     ):


					'''simple docstring'''





					def 	snake_case ( self							:						Optional[int],						lowerCamelCase							:						Optional[int],						lowerCamelCase							:						Any,						lowerCamelCase							:						str=False,						lowerCamelCase							:						List[Any]=True  )->      Tuple:
								lowerCamelCase__	:	int				=[
								    BatchSamplerShard(__UpperCamelCase,						2,						__UpperCamelCase,						split_batches=__UpperCamelCase,						even_batches=__UpperCamelCase  )
								    for i in range(2  )
								]
								lowerCamelCase__	:	str				=[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 	snake_case ( self							:						Union[str, Any]  )->      Union[str, Any]:
								# Check the shards when the dataset is a round multiple of total batch size.
								lowerCamelCase__	:	Union[str, Any]				=BatchSampler(range(24  ),						batch_size=3,						drop_last=__UpperCamelCase  )
								lowerCamelCase__	:	Dict				=[
								    [[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  )

								lowerCamelCase__	:	Union[str, Any]				=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.
								lowerCamelCase__	:	List[str]				=BatchSampler(range(21  ),						batch_size=3,						drop_last=__UpperCamelCase  )
								lowerCamelCase__	:	Tuple				=[
								    [[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  )

								lowerCamelCase__	:	Dict				=BatchSampler(range(21  ),						batch_size=3,						drop_last=__UpperCamelCase  )
								lowerCamelCase__	:	Any				=[
								    [[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.
								lowerCamelCase__	:	List[str]				=BatchSampler(range(22  ),						batch_size=3,						drop_last=__UpperCamelCase  )
								lowerCamelCase__	:	int				=[
								    [[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  )

								lowerCamelCase__	:	Any				=BatchSampler(range(22  ),						batch_size=3,						drop_last=__UpperCamelCase  )
								lowerCamelCase__	:	int				=[
								    [[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.
								lowerCamelCase__	:	Tuple				=BatchSampler(range(20  ),						batch_size=3,						drop_last=__UpperCamelCase  )
								lowerCamelCase__	:	Dict				=[
								    [[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  )

								lowerCamelCase__	:	List[Any]				=BatchSampler(range(20  ),						batch_size=3,						drop_last=__UpperCamelCase  )
								lowerCamelCase__	:	int				=[
								    [[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.
								lowerCamelCase__	:	Optional[Any]				=BatchSampler(range(2  ),						batch_size=3,						drop_last=__UpperCamelCase  )
								lowerCamelCase__	:	int				=[[[0, 1, 0]], [[1, 0, 1]]]
								self.check_batch_sampler_shards(__UpperCamelCase,						__UpperCamelCase  )

								lowerCamelCase__	:	Tuple				=BatchSampler(range(2  ),						batch_size=3,						drop_last=__UpperCamelCase  )
								lowerCamelCase__	:	Union[str, Any]				=[[], []]
								self.check_batch_sampler_shards(__UpperCamelCase,						__UpperCamelCase  )



					def 	snake_case ( self							:						Dict  )->      int:
								# Check the shards when the dataset is a round multiple of batch size.
								lowerCamelCase__	:	Tuple				=BatchSampler(range(24  ),						batch_size=4,						drop_last=__UpperCamelCase  )
								lowerCamelCase__	:	List[Any]				=[
								    [[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  )

								lowerCamelCase__	:	Optional[Any]				=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.
								lowerCamelCase__	:	List[Any]				=BatchSampler(range(22  ),						batch_size=4,						drop_last=__UpperCamelCase  )
								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(__UpperCamelCase,						__UpperCamelCase,						split_batches=__UpperCamelCase  )

								lowerCamelCase__	:	Any				=BatchSampler(range(22  ),						batch_size=4,						drop_last=__UpperCamelCase  )
								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(__UpperCamelCase,						__UpperCamelCase,						split_batches=__UpperCamelCase  )

								# 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=__UpperCamelCase  )
								lowerCamelCase__	:	Any				=[
								    [[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  )

								lowerCamelCase__	:	List[str]				=BatchSampler(range(21  ),						batch_size=4,						drop_last=__UpperCamelCase  )
								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(__UpperCamelCase,						__UpperCamelCase,						split_batches=__UpperCamelCase  )

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

								lowerCamelCase__	:	Optional[int]				=BatchSampler(range(2  ),						batch_size=4,						drop_last=__UpperCamelCase  )
								lowerCamelCase__	:	int				=[[], []]
								self.check_batch_sampler_shards(__UpperCamelCase,						__UpperCamelCase,						split_batches=__UpperCamelCase  )



					def 	snake_case ( self							:						int  )->      Optional[Any]:
								# Check the shards when the dataset is a round multiple of total batch size.
								lowerCamelCase__	:	Any				=BatchSampler(range(24  ),						batch_size=3,						drop_last=__UpperCamelCase  )
								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(__UpperCamelCase,						__UpperCamelCase,						even_batches=__UpperCamelCase  )

								lowerCamelCase__	:	List[str]				=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.
								lowerCamelCase__	:	List[Any]				=BatchSampler(range(21  ),						batch_size=3,						drop_last=__UpperCamelCase  )
								lowerCamelCase__	:	int				=[
								    [[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  )

								lowerCamelCase__	:	str				=BatchSampler(range(21  ),						batch_size=3,						drop_last=__UpperCamelCase  )
								lowerCamelCase__	:	Any				=[
								    [[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.
								lowerCamelCase__	:	Dict				=BatchSampler(range(22  ),						batch_size=3,						drop_last=__UpperCamelCase  )
								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(__UpperCamelCase,						__UpperCamelCase,						even_batches=__UpperCamelCase  )

								lowerCamelCase__	:	List[Any]				=BatchSampler(range(22  ),						batch_size=3,						drop_last=__UpperCamelCase  )
								lowerCamelCase__	:	Optional[Any]				=[
								    [[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.
								lowerCamelCase__	:	Optional[int]				=BatchSampler(range(20  ),						batch_size=3,						drop_last=__UpperCamelCase  )
								lowerCamelCase__	:	Optional[int]				=[
								    [[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  )

								lowerCamelCase__	:	Union[str, Any]				=BatchSampler(range(20  ),						batch_size=3,						drop_last=__UpperCamelCase  )
								lowerCamelCase__	:	List[str]				=[
								    [[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.
								lowerCamelCase__	:	Tuple				=BatchSampler(range(2  ),						batch_size=3,						drop_last=__UpperCamelCase  )
								lowerCamelCase__	:	List[str]				=[[[0, 1]], []]
								self.check_batch_sampler_shards(__UpperCamelCase,						__UpperCamelCase,						even_batches=__UpperCamelCase  )

								lowerCamelCase__	:	List[str]				=BatchSampler(range(2  ),						batch_size=3,						drop_last=__UpperCamelCase  )
								lowerCamelCase__	:	int				=[[], []]
								self.check_batch_sampler_shards(__UpperCamelCase,						__UpperCamelCase,						even_batches=__UpperCamelCase  )



					def 	snake_case ( self							:						Any  )->      Tuple:
								# Check the shards when the dataset is a round multiple of batch size.
								lowerCamelCase__	:	List[Any]				=BatchSampler(range(24  ),						batch_size=4,						drop_last=__UpperCamelCase  )
								lowerCamelCase__	:	Tuple				=[
								    [[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  )

								lowerCamelCase__	:	List[str]				=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.
								lowerCamelCase__	:	Dict				=BatchSampler(range(22  ),						batch_size=4,						drop_last=__UpperCamelCase  )
								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(__UpperCamelCase,						__UpperCamelCase,						split_batches=__UpperCamelCase,						even_batches=__UpperCamelCase  )

								lowerCamelCase__	:	Dict				=BatchSampler(range(22  ),						batch_size=4,						drop_last=__UpperCamelCase  )
								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(__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.
								lowerCamelCase__	:	int				=BatchSampler(range(21  ),						batch_size=4,						drop_last=__UpperCamelCase  )
								lowerCamelCase__	:	List[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(__UpperCamelCase,						__UpperCamelCase,						split_batches=__UpperCamelCase,						even_batches=__UpperCamelCase  )

								lowerCamelCase__	:	Union[str, Any]				=BatchSampler(range(21  ),						batch_size=4,						drop_last=__UpperCamelCase  )
								lowerCamelCase__	:	Dict				=[
								    [[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.
								lowerCamelCase__	:	Any				=BatchSampler(range(2  ),						batch_size=4,						drop_last=__UpperCamelCase  )
								lowerCamelCase__	:	Optional[Any]				=[[[0, 1]], []]
								self.check_batch_sampler_shards(__UpperCamelCase,						__UpperCamelCase,						split_batches=__UpperCamelCase,						even_batches=__UpperCamelCase  )

								lowerCamelCase__	:	Optional[Any]				=BatchSampler(range(2  ),						batch_size=4,						drop_last=__UpperCamelCase  )
								lowerCamelCase__	:	Optional[int]				=[[], []]
								self.check_batch_sampler_shards(__UpperCamelCase,						__UpperCamelCase,						split_batches=__UpperCamelCase,						even_batches=__UpperCamelCase  )



					def 	snake_case ( self							:						Any  )->      Union[str, Any]:
								lowerCamelCase__	:	Dict				=[[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 10, 11], [12, 13]]
								lowerCamelCase__	:	Tuple				=[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 	snake_case ( self							:						str,						lowerCamelCase							:						Union[str, Any],						lowerCamelCase							:						Tuple,						lowerCamelCase							:						Dict,						lowerCamelCase							:						List[Any]=False,						lowerCamelCase							:						Dict=2,						lowerCamelCase							:						Dict=False  )->      int:
								random.seed(__UpperCamelCase  )
								lowerCamelCase__	:	str				=list(__UpperCamelCase  )

								lowerCamelCase__	:	Optional[int]				=[
								    IterableDatasetShard(
								        __UpperCamelCase,						batch_size=__UpperCamelCase,						drop_last=__UpperCamelCase,						num_processes=__UpperCamelCase,						process_index=__UpperCamelCase,						split_batches=__UpperCamelCase,						)
								    for i in range(__UpperCamelCase  )
								]
								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(__UpperCamelCase  )
											iterable_dataset_lists.append(list(__UpperCamelCase  )  )

								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__	:	Any				=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  )

								lowerCamelCase__	:	Dict				=[]
								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 	snake_case ( self							:						str  )->      int:
								lowerCamelCase__	:	Optional[Any]				=42
								lowerCamelCase__	:	Union[str, Any]				=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
								lowerCamelCase__	:	List[Any]				=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 	snake_case ( self							:						Optional[Any]  )->      List[Any]:
								lowerCamelCase__	:	Any				=BatchSampler(range(16  ),						batch_size=4,						drop_last=__UpperCamelCase  )
								lowerCamelCase__	:	List[str]				=SkipBatchSampler(__UpperCamelCase,						2  )
								self.assertListEqual(list(__UpperCamelCase  ),						[[8, 9, 10, 11], [12, 13, 14, 15]]  )



					def 	snake_case ( self							:						str  )->      Tuple:
								lowerCamelCase__	:	int				=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							:						List[Any]  )->      str:
								lowerCamelCase__	:	List[str]				=DataLoader(list(range(16  )  ),						batch_size=4  )
								lowerCamelCase__	:	str				=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 	snake_case ( self							:						Any  )->      Optional[int]:
								lowerCamelCase__	:	Dict				=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 	snake_case ( self							:						Any  )->      Tuple:
								Accelerator()
								lowerCamelCase__	:	Any				=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 __future__ import annotations

from collections.abc import Iterator

class  __SCREAMING_SNAKE_CASE			:


					'''simple docstring'''





					def __init__( self							:						str,						lowerCamelCase							:						int  )->      None:
								lowerCamelCase__	:	str				=value
								lowerCamelCase__	:	Node | None				=None
								lowerCamelCase__	:	Node | None				=None





class  __SCREAMING_SNAKE_CASE			:


					'''simple docstring'''





					def __init__( self							:						int,						lowerCamelCase							:						Node  )->      None:
								lowerCamelCase__	:	Any				=tree



					def 	snake_case ( self							:						str,						lowerCamelCase							:						Node | None  )->      int:
								if node is None:
											return 0
								return node.value + (
								    self.depth_first_search(node.left  ) + self.depth_first_search(node.right  )
								)




					def __iter__( self							:						Dict  )->      Iterator[int]:
								yield self.depth_first_search(self.tree  )


if __name__ == "__main__":
		import doctest

		doctest.testmod()




