


'''simple docstring'''




import random
import unittest

from torch.utils.data import BatchSampler, DataLoader, IterableDataset

from accelerate import Accelerator
from accelerate.data_loader import (
    BatchSamplerShard,
    DataLoaderDispatcher,
    DataLoaderShard,
    IterableDatasetShard,
    SkipBatchSampler,
    SkipDataLoader,
    skip_first_batches,
)

class 		UpperCAmelCase_      (  __lowercase		):





     def __init__( self       :      Union[str, Any]    ,      UpperCAmelCase__       :      Optional[Any]=0.01    ,      UpperCAmelCase__       :      List[Any]=1_0_0_0    )				->  Optional[Any]:
        lowerCAmelCase     =			p_stop
        lowerCAmelCase     =			max_length





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




class 		UpperCAmelCase_      (  unittest.TestCase		):





     def 							__UpperCAmelCase      ( self       :      Optional[int]    ,      UpperCAmelCase__       :      Any    ,      UpperCAmelCase__       :      Optional[Any]    ,      UpperCAmelCase__       :      int=False    ,      UpperCAmelCase__       :      List[str]=True    )				->  List[Any]:
        lowerCAmelCase     =			[
            BatchSamplerShard(UpperCAmelCase__    ,      2    ,      UpperCAmelCase__    ,      split_batches=UpperCAmelCase__    ,      even_batches=UpperCAmelCase__    )
            for i in range(2    )
        ]
        lowerCAmelCase     =			[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 							__UpperCAmelCase      ( self       :      List[str]    )				->  int:
        # Check the shards when the dataset is a round multiple of total batch size.
        lowerCAmelCase     =			BatchSampler(range(2_4    )    ,      batch_size=3    ,      drop_last=UpperCAmelCase__    )
        lowerCAmelCase     =			[
            [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4], [1_8, 1_9, 2_0]],
            [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7], [2_1, 2_2, 2_3]],
        ]
        self.check_batch_sampler_shards(UpperCAmelCase__    ,      UpperCAmelCase__    )

        lowerCAmelCase     =			BatchSampler(range(2_4    )    ,      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     =			BatchSampler(range(2_1    )    ,      batch_size=3    ,      drop_last=UpperCAmelCase__    )
        lowerCAmelCase     =			[
            [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4], [1_8, 1_9, 2_0]],
            [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7], [0, 1, 2]],
        ]
        self.check_batch_sampler_shards(UpperCAmelCase__    ,      UpperCAmelCase__    )

        lowerCAmelCase     =			BatchSampler(range(2_1    )    ,      batch_size=3    ,      drop_last=UpperCAmelCase__    )
        lowerCAmelCase     =			[
            [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4]],
            [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7]],
        ]
        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     =			BatchSampler(range(2_2    )    ,      batch_size=3    ,      drop_last=UpperCAmelCase__    )
        lowerCAmelCase     =			[
            [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4], [1_8, 1_9, 2_0]],
            [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7], [2_1, 0, 1]],
        ]
        self.check_batch_sampler_shards(UpperCAmelCase__    ,      UpperCAmelCase__    )

        lowerCAmelCase     =			BatchSampler(range(2_2    )    ,      batch_size=3    ,      drop_last=UpperCAmelCase__    )
        lowerCAmelCase     =			[
            [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4]],
            [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7]],
        ]
        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     =			BatchSampler(range(2_0    )    ,      batch_size=3    ,      drop_last=UpperCAmelCase__    )
        lowerCAmelCase     =			[
            [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4], [1_8, 1_9, 0]],
            [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7], [1, 2, 3]],
        ]
        self.check_batch_sampler_shards(UpperCAmelCase__    ,      UpperCAmelCase__    )

        lowerCAmelCase     =			BatchSampler(range(2_0    )    ,      batch_size=3    ,      drop_last=UpperCAmelCase__    )
        lowerCAmelCase     =			[
            [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4]],
            [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7]],
        ]
        self.check_batch_sampler_shards(UpperCAmelCase__    ,      UpperCAmelCase__    )

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

        lowerCAmelCase     =			BatchSampler(range(2    )    ,      batch_size=3    ,      drop_last=UpperCAmelCase__    )
        lowerCAmelCase     =			[[], []]
        self.check_batch_sampler_shards(UpperCAmelCase__    ,      UpperCAmelCase__    )





     def 							__UpperCAmelCase      ( self       :      Union[str, Any]    )				->  Any:
        # Check the shards when the dataset is a round multiple of batch size.
        lowerCAmelCase     =			BatchSampler(range(2_4    )    ,      batch_size=4    ,      drop_last=UpperCAmelCase__    )
        lowerCAmelCase     =			[
            [[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7], [2_0, 2_1]],
            [[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9], [2_2, 2_3]],
        ]
        self.check_batch_sampler_shards(UpperCAmelCase__    ,      UpperCAmelCase__    ,      split_batches=UpperCAmelCase__    )

        lowerCAmelCase     =			BatchSampler(range(2_4    )    ,      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     =			BatchSampler(range(2_2    )    ,      batch_size=4    ,      drop_last=UpperCAmelCase__    )
        lowerCAmelCase     =			[
            [[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7], [2_0, 2_1]],
            [[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9], [0, 1]],
        ]
        self.check_batch_sampler_shards(UpperCAmelCase__    ,      UpperCAmelCase__    ,      split_batches=UpperCAmelCase__    )

        lowerCAmelCase     =			BatchSampler(range(2_2    )    ,      batch_size=4    ,      drop_last=UpperCAmelCase__    )
        lowerCAmelCase     =			[
            [[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7]],
            [[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9]],
        ]
        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     =			BatchSampler(range(2_1    )    ,      batch_size=4    ,      drop_last=UpperCAmelCase__    )
        lowerCAmelCase     =			[
            [[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7], [2_0, 0]],
            [[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9], [1, 2]],
        ]
        self.check_batch_sampler_shards(UpperCAmelCase__    ,      UpperCAmelCase__    ,      split_batches=UpperCAmelCase__    )

        lowerCAmelCase     =			BatchSampler(range(2_1    )    ,      batch_size=4    ,      drop_last=UpperCAmelCase__    )
        lowerCAmelCase     =			[
            [[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7]],
            [[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9]],
        ]
        self.check_batch_sampler_shards(UpperCAmelCase__    ,      UpperCAmelCase__    ,      split_batches=UpperCAmelCase__    )

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

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





     def 							__UpperCAmelCase      ( self       :      str    )				->  int:
        # Check the shards when the dataset is a round multiple of total batch size.
        lowerCAmelCase     =			BatchSampler(range(2_4    )    ,      batch_size=3    ,      drop_last=UpperCAmelCase__    )
        lowerCAmelCase     =			[
            [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4], [1_8, 1_9, 2_0]],
            [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7], [2_1, 2_2, 2_3]],
        ]
        self.check_batch_sampler_shards(UpperCAmelCase__    ,      UpperCAmelCase__    ,      even_batches=UpperCAmelCase__    )

        lowerCAmelCase     =			BatchSampler(range(2_4    )    ,      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     =			BatchSampler(range(2_1    )    ,      batch_size=3    ,      drop_last=UpperCAmelCase__    )
        lowerCAmelCase     =			[
            [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4], [1_8, 1_9, 2_0]],
            [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7]],
        ]
        self.check_batch_sampler_shards(UpperCAmelCase__    ,      UpperCAmelCase__    ,      even_batches=UpperCAmelCase__    )

        lowerCAmelCase     =			BatchSampler(range(2_1    )    ,      batch_size=3    ,      drop_last=UpperCAmelCase__    )
        lowerCAmelCase     =			[
            [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4]],
            [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7]],
        ]
        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     =			BatchSampler(range(2_2    )    ,      batch_size=3    ,      drop_last=UpperCAmelCase__    )
        lowerCAmelCase     =			[
            [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4], [1_8, 1_9, 2_0]],
            [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7], [2_1]],
        ]
        self.check_batch_sampler_shards(UpperCAmelCase__    ,      UpperCAmelCase__    ,      even_batches=UpperCAmelCase__    )

        lowerCAmelCase     =			BatchSampler(range(2_2    )    ,      batch_size=3    ,      drop_last=UpperCAmelCase__    )
        lowerCAmelCase     =			[
            [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4]],
            [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7]],
        ]
        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     =			BatchSampler(range(2_0    )    ,      batch_size=3    ,      drop_last=UpperCAmelCase__    )
        lowerCAmelCase     =			[
            [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4], [1_8, 1_9]],
            [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7]],
        ]
        self.check_batch_sampler_shards(UpperCAmelCase__    ,      UpperCAmelCase__    ,      even_batches=UpperCAmelCase__    )

        lowerCAmelCase     =			BatchSampler(range(2_0    )    ,      batch_size=3    ,      drop_last=UpperCAmelCase__    )
        lowerCAmelCase     =			[
            [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4]],
            [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7]],
        ]
        self.check_batch_sampler_shards(UpperCAmelCase__    ,      UpperCAmelCase__    ,      even_batches=UpperCAmelCase__    )

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

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





     def 							__UpperCAmelCase      ( self       :      List[Any]    )				->  Tuple:
        # Check the shards when the dataset is a round multiple of batch size.
        lowerCAmelCase     =			BatchSampler(range(2_4    )    ,      batch_size=4    ,      drop_last=UpperCAmelCase__    )
        lowerCAmelCase     =			[
            [[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7], [2_0, 2_1]],
            [[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9], [2_2, 2_3]],
        ]
        self.check_batch_sampler_shards(UpperCAmelCase__    ,      UpperCAmelCase__    ,      split_batches=UpperCAmelCase__    ,      even_batches=UpperCAmelCase__    )

        lowerCAmelCase     =			BatchSampler(range(2_4    )    ,      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     =			BatchSampler(range(2_2    )    ,      batch_size=4    ,      drop_last=UpperCAmelCase__    )
        lowerCAmelCase     =			[
            [[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7], [2_0, 2_1]],
            [[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9]],
        ]
        self.check_batch_sampler_shards(UpperCAmelCase__    ,      UpperCAmelCase__    ,      split_batches=UpperCAmelCase__    ,      even_batches=UpperCAmelCase__    )

        lowerCAmelCase     =			BatchSampler(range(2_2    )    ,      batch_size=4    ,      drop_last=UpperCAmelCase__    )
        lowerCAmelCase     =			[
            [[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7]],
            [[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9]],
        ]
        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     =			BatchSampler(range(2_1    )    ,      batch_size=4    ,      drop_last=UpperCAmelCase__    )
        lowerCAmelCase     =			[
            [[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7], [2_0]],
            [[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9]],
        ]
        self.check_batch_sampler_shards(UpperCAmelCase__    ,      UpperCAmelCase__    ,      split_batches=UpperCAmelCase__    ,      even_batches=UpperCAmelCase__    )

        lowerCAmelCase     =			BatchSampler(range(2_1    )    ,      batch_size=4    ,      drop_last=UpperCAmelCase__    )
        lowerCAmelCase     =			[
            [[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7]],
            [[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9]],
        ]
        self.check_batch_sampler_shards(UpperCAmelCase__    ,      UpperCAmelCase__    ,      split_batches=UpperCAmelCase__    ,      even_batches=UpperCAmelCase__    )

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

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





     def 							__UpperCAmelCase      ( self       :      Optional[int]    )				->  str:
        lowerCAmelCase     =			[[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 1_0, 1_1], [1_2, 1_3]]
        lowerCAmelCase     =			[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], [1_2, 1_3]]    )
        self.assertListEqual(list(batch_sampler_shards[1]    )    ,      [[3, 4], [9, 1_0, 1_1]]    )





     def 							__UpperCAmelCase      ( self       :      Optional[Any]    ,      UpperCAmelCase__       :      str    ,      UpperCAmelCase__       :      Dict    ,      UpperCAmelCase__       :      Union[str, Any]    ,      UpperCAmelCase__       :      List[Any]=False    ,      UpperCAmelCase__       :      Tuple=2    ,      UpperCAmelCase__       :      int=False    )				->  Optional[int]:
        random.seed(UpperCAmelCase__    )
        lowerCAmelCase     =			list(UpperCAmelCase__    )

        lowerCAmelCase     =			[
            IterableDatasetShard(
                UpperCAmelCase__    ,      batch_size=UpperCAmelCase__    ,      drop_last=UpperCAmelCase__    ,      num_processes=UpperCAmelCase__    ,      process_index=UpperCAmelCase__    ,      split_batches=UpperCAmelCase__    ,      )
            for i in range(UpperCAmelCase__    )
        ]
        lowerCAmelCase     =			[]
        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     =			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     =			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     =			[]
        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 							__UpperCAmelCase      ( self       :      List[str]    )				->  int:
        lowerCAmelCase     =			4_2
        lowerCAmelCase     =			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     =			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 							__UpperCAmelCase      ( self       :      Tuple    )				->  Dict:
        lowerCAmelCase     =			BatchSampler(range(1_6    )    ,      batch_size=4    ,      drop_last=UpperCAmelCase__    )
        lowerCAmelCase     =			SkipBatchSampler(UpperCAmelCase__    ,      2    )
        self.assertListEqual(list(UpperCAmelCase__    )    ,      [[8, 9, 1_0, 1_1], [1_2, 1_3, 1_4, 1_5]]    )





     def 							__UpperCAmelCase      ( self       :      Union[str, Any]    )				->  Union[str, Any]:
        lowerCAmelCase     =			SkipDataLoader(list(range(1_6    )    )    ,      batch_size=4    ,      skip_batches=2    )
        self.assertListEqual([t.tolist() for t in dataloader]    ,      [[8, 9, 1_0, 1_1], [1_2, 1_3, 1_4, 1_5]]    )





     def 							__UpperCAmelCase      ( self       :      Tuple    )				->  List[str]:
        lowerCAmelCase     =			DataLoader(list(range(1_6    )    )    ,      batch_size=4    )
        lowerCAmelCase     =			skip_first_batches(UpperCAmelCase__    ,      num_batches=2    )
        self.assertListEqual([t.tolist() for t in new_dataloader]    ,      [[8, 9, 1_0, 1_1], [1_2, 1_3, 1_4, 1_5]]    )





     def 							__UpperCAmelCase      ( self       :      List[str]    )				->  List[Any]:
        lowerCAmelCase     =			DataLoaderShard(list(range(1_6    )    )    ,      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 							__UpperCAmelCase      ( self       :      Tuple    )				->  List[Any]:
        Accelerator()
        lowerCAmelCase     =			DataLoaderDispatcher(range(1_6    )    ,      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__    (		_A							):







	'''simple docstring'''





	snake_case_		    =     len(_A							)
	for i in range(_A							):
		for j in range(i + 1     ,	_A							):
			if numbers[j] < numbers[i]:
				snake_case_    ,      snake_case_		    =     numbers[j], numbers[i]
	return numbers


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