
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__							(     _a	):






				def __init__(						self					:      int    ,     a					:      List[str]=0.0_1    ,     a					:      int=1_000   ):






							'''simple docstring'''







							lowerCAmelCase__      :      List[str]         =		p_stop
							lowerCAmelCase__      :      Tuple         =		max_length






				def __iter__(						self					:      List[Any]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      List[Any]         =		0
							lowerCAmelCase__      :      Union[str, Any]         =		False
							while not stop and count < self.max_length:
										yield count
										count += 1
										lowerCAmelCase__      :      int         =		random.random() < self.p_stop







class      A__							(     unittest.TestCase	):






				def 							_lowerCamelCase		(						self					:      Optional[int]    ,     a					:      str    ,     a					:      Dict    ,     a					:      Dict=False    ,     a					:      List[Any]=True   ):






							'''simple docstring'''







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






				def 							_lowerCamelCase		(						self					:      int   ):






							'''simple docstring'''







							lowerCAmelCase__      :      List[str]         =		BatchSampler(range(24   )    ,     batch_size=3    ,     drop_last=a   )
							lowerCAmelCase__      :      int         =		[
							    [[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(a    ,     a   )

							lowerCAmelCase__      :      Union[str, Any]         =		BatchSampler(range(24   )    ,     batch_size=3    ,     drop_last=a   )
							# Expected shouldn't change
							self.check_batch_sampler_shards(a    ,     a   )

							# Check the shards when the dataset is a round multiple of batch size but not total batch size.
							lowerCAmelCase__      :      Union[str, Any]         =		BatchSampler(range(21   )    ,     batch_size=3    ,     drop_last=a   )
							lowerCAmelCase__      :      List[Any]         =		[
							    [[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(a    ,     a   )

							lowerCAmelCase__      :      Dict         =		BatchSampler(range(21   )    ,     batch_size=3    ,     drop_last=a   )
							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(a    ,     a   )

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

							lowerCAmelCase__      :      List[str]         =		BatchSampler(range(22   )    ,     batch_size=3    ,     drop_last=a   )
							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(a    ,     a   )

							# 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__      :      Union[str, Any]         =		BatchSampler(range(20   )    ,     batch_size=3    ,     drop_last=a   )
							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(a    ,     a   )

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

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

							lowerCAmelCase__      :      List[str]         =		BatchSampler(range(2   )    ,     batch_size=3    ,     drop_last=a   )
							lowerCAmelCase__      :      Dict         =		[[], []]
							self.check_batch_sampler_shards(a    ,     a   )






				def 							_lowerCamelCase		(						self					:      Union[str, Any]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      str         =		BatchSampler(range(24   )    ,     batch_size=4    ,     drop_last=a   )
							lowerCAmelCase__      :      Optional[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(a    ,     a    ,     split_batches=a   )

							lowerCAmelCase__      :      List[str]         =		BatchSampler(range(24   )    ,     batch_size=4    ,     drop_last=a   )
							# Expected shouldn't change
							self.check_batch_sampler_shards(a    ,     a    ,     split_batches=a   )

							# Check the shards when the dataset is not a round multiple of batch size.
							lowerCAmelCase__      :      Union[str, Any]         =		BatchSampler(range(22   )    ,     batch_size=4    ,     drop_last=a   )
							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], [0, 1]],
							]
							self.check_batch_sampler_shards(a    ,     a    ,     split_batches=a   )

							lowerCAmelCase__      :      Optional[Any]         =		BatchSampler(range(22   )    ,     batch_size=4    ,     drop_last=a   )
							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(a    ,     a    ,     split_batches=a   )

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

							lowerCAmelCase__      :      Tuple         =		BatchSampler(range(21   )    ,     batch_size=4    ,     drop_last=a   )
							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(a    ,     a    ,     split_batches=a   )

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

							lowerCAmelCase__      :      Dict         =		BatchSampler(range(2   )    ,     batch_size=4    ,     drop_last=a   )
							lowerCAmelCase__      :      Dict         =		[[], []]
							self.check_batch_sampler_shards(a    ,     a    ,     split_batches=a   )






				def 							_lowerCamelCase		(						self					:      List[str]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      List[str]         =		BatchSampler(range(24   )    ,     batch_size=3    ,     drop_last=a   )
							lowerCAmelCase__      :      int         =		[
							    [[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(a    ,     a    ,     even_batches=a   )

							lowerCAmelCase__      :      Any         =		BatchSampler(range(24   )    ,     batch_size=3    ,     drop_last=a   )
							# Expected shouldn't change
							self.check_batch_sampler_shards(a    ,     a    ,     even_batches=a   )

							# Check the shards when the dataset is a round multiple of batch size but not total batch size.
							lowerCAmelCase__      :      Any         =		BatchSampler(range(21   )    ,     batch_size=3    ,     drop_last=a   )
							lowerCAmelCase__      :      List[str]         =		[
							    [[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(a    ,     a    ,     even_batches=a   )

							lowerCAmelCase__      :      Optional[Any]         =		BatchSampler(range(21   )    ,     batch_size=3    ,     drop_last=a   )
							lowerCAmelCase__      :      Tuple         =		[
							    [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
							    [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
							]
							self.check_batch_sampler_shards(a    ,     a    ,     even_batches=a   )

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

							lowerCAmelCase__      :      str         =		BatchSampler(range(22   )    ,     batch_size=3    ,     drop_last=a   )
							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(a    ,     a    ,     even_batches=a   )

							# 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=a   )
							lowerCAmelCase__      :      str         =		[
							    [[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(a    ,     a    ,     even_batches=a   )

							lowerCAmelCase__      :      Dict         =		BatchSampler(range(20   )    ,     batch_size=3    ,     drop_last=a   )
							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(a    ,     a    ,     even_batches=a   )

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

							lowerCAmelCase__      :      Dict         =		BatchSampler(range(2   )    ,     batch_size=3    ,     drop_last=a   )
							lowerCAmelCase__      :      Optional[int]         =		[[], []]
							self.check_batch_sampler_shards(a    ,     a    ,     even_batches=a   )






				def 							_lowerCamelCase		(						self					:      Union[str, Any]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      str         =		BatchSampler(range(24   )    ,     batch_size=4    ,     drop_last=a   )
							lowerCAmelCase__      :      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(a    ,     a    ,     split_batches=a    ,     even_batches=a   )

							lowerCAmelCase__      :      Optional[Any]         =		BatchSampler(range(24   )    ,     batch_size=4    ,     drop_last=a   )
							# Expected shouldn't change
							self.check_batch_sampler_shards(a    ,     a    ,     split_batches=a    ,     even_batches=a   )

							# Check the shards when the dataset is not a round multiple of batch size.
							lowerCAmelCase__      :      int         =		BatchSampler(range(22   )    ,     batch_size=4    ,     drop_last=a   )
							lowerCAmelCase__      :      str         =		[
							    [[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(a    ,     a    ,     split_batches=a    ,     even_batches=a   )

							lowerCAmelCase__      :      List[str]         =		BatchSampler(range(22   )    ,     batch_size=4    ,     drop_last=a   )
							lowerCAmelCase__      :      str         =		[
							    [[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(a    ,     a    ,     split_batches=a    ,     even_batches=a   )

							# 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=a   )
							lowerCAmelCase__      :      Optional[int]         =		[
							    [[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(a    ,     a    ,     split_batches=a    ,     even_batches=a   )

							lowerCAmelCase__      :      Any         =		BatchSampler(range(21   )    ,     batch_size=4    ,     drop_last=a   )
							lowerCAmelCase__      :      Optional[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(a    ,     a    ,     split_batches=a    ,     even_batches=a   )

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

							lowerCAmelCase__      :      str         =		BatchSampler(range(2   )    ,     batch_size=4    ,     drop_last=a   )
							lowerCAmelCase__      :      Optional[int]         =		[[], []]
							self.check_batch_sampler_shards(a    ,     a    ,     split_batches=a    ,     even_batches=a   )






				def 							_lowerCamelCase		(						self					:      Optional[Any]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Union[str, Any]         =		[[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 10, 11], [12, 13]]
							lowerCAmelCase__      :      List[str]         =		[BatchSamplerShard(a    ,     2    ,     a    ,     even_batches=a   ) 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					:      Any    ,     a					:      Union[str, Any]    ,     a					:      str    ,     a					:      List[str]    ,     a					:      Optional[Any]=False    ,     a					:      List[Any]=2    ,     a					:      str=False   ):






							'''simple docstring'''







							random.seed(a   )
							lowerCAmelCase__      :      List[str]         =		list(a   )

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

							lowerCAmelCase__      :      Tuple         =		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__      :      str         =		iterable_dataset_lists[0]
							for l in iterable_dataset_lists[1:]:
										self.assertEqual(len(a   )    ,     len(a   )   )
										self.assertTrue(len(a   ) % shard_batch_size == 0   )

							lowerCAmelCase__      :      Tuple         =		[]
							for idx in range(0    ,     len(a   )    ,     a   ):
										for l in iterable_dataset_lists:
													observed += l[idx : idx + shard_batch_size]

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






				def 							_lowerCamelCase		(						self					:      Tuple   ):






							'''simple docstring'''







							lowerCAmelCase__      :      List[Any]         =		42
							lowerCAmelCase__      :      int         =		RandomIterableDataset()

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

							# Edge case with a very small dataset
							lowerCAmelCase__      :      Union[str, Any]         =		RandomIterableDataset(max_length=2   )

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






				def 							_lowerCamelCase		(						self					:      Optional[int]   ):






							'''simple docstring'''







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






				def 							_lowerCamelCase		(						self					:      Tuple   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Union[str, 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 							_lowerCamelCase		(						self					:      int   ):






							'''simple docstring'''







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






				def 							_lowerCamelCase		(						self					:      Union[str, Any]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Optional[Any]         =		DataLoaderShard(list(range(16   )   )    ,     batch_size=4   )
							for idx, _ in enumerate(a   ):
										self.assertEqual(dataloader.end_of_dataloader    ,     idx == 3   )

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






				def 							_lowerCamelCase		(						self					:      List[Any]   ):






							'''simple docstring'''







							Accelerator()
							lowerCAmelCase__      :      Optional[Any]         =		DataLoaderDispatcher(range(16   )    ,     batch_size=4   )
							for idx, _ in enumerate(a   ):
										self.assertEqual(dataloader.end_of_dataloader    ,     idx == 3   )

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



import random
from typing import Any



def 				UpperCamelCase_(      lowerCamelCase_ )    ->						list[Any]:
    for _ in range(len(lowerCamelCase_ ) ):
        _lowercase       :							Optional[int]      		=		random.randint(0							, len(lowerCamelCase_ ) - 1 )
        _lowercase       :							str      		=		random.randint(0							, len(lowerCamelCase_ ) - 1 )
        _lowercase		,				_lowercase       :							Optional[int]      		=		data[b], data[a]
    return data


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE       :      str												= [0, 1, 2, 3, 4, 5, 6, 7]
 SCREAMING_SNAKE_CASE       :      int												= ["python", "says", "hello", "!"]
 print("Fisher-Yates Shuffle:")
 print("List", integers, strings)
 print("FY Shuffle", fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))


