






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       (  UpperCAmelCase__    ):






  """simple docstring"""





  def __init__(     self		,		_UpperCAmelCase=0.0_1		,		_UpperCAmelCase=1000 )			-> List[str]:
     UpperCamelCase_									=     p_stop
     UpperCamelCase_									=     max_length




  def __iter__(     self )			-> Tuple:
     UpperCamelCase_									=     0
     UpperCamelCase_									=     False
     while not stop and count < self.max_length:
        yield count
        count += 1
        UpperCamelCase_									=     random.random() < self.p_stop




class 						_a       (  unittest.TestCase    ):






  """simple docstring"""





  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase=False		,		_UpperCAmelCase=True )			-> Any:
     UpperCamelCase_									=     [
         BatchSamplerShard(_UpperCAmelCase		,		2		,		_UpperCAmelCase		,		split_batches=_UpperCAmelCase		,		even_batches=_UpperCAmelCase )
         for i in range(2 )
     ]
     UpperCamelCase_									=     [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 )			-> Optional[Any]:
     # Check the shards when the dataset is a round multiple of total batch size.
     UpperCamelCase_									=     BatchSampler(range(24 )		,		batch_size=3		,		drop_last=_UpperCAmelCase )
     UpperCamelCase_									=     [
         [[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 )

     UpperCamelCase_									=     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.
     UpperCamelCase_									=     BatchSampler(range(21 )		,		batch_size=3		,		drop_last=_UpperCAmelCase )
     UpperCamelCase_									=     [
         [[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 )

     UpperCamelCase_									=     BatchSampler(range(21 )		,		batch_size=3		,		drop_last=_UpperCAmelCase )
     UpperCamelCase_									=     [
         [[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.
     UpperCamelCase_									=     BatchSampler(range(22 )		,		batch_size=3		,		drop_last=_UpperCAmelCase )
     UpperCamelCase_									=     [
         [[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 )

     UpperCamelCase_									=     BatchSampler(range(22 )		,		batch_size=3		,		drop_last=_UpperCAmelCase )
     UpperCamelCase_									=     [
         [[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.
     UpperCamelCase_									=     BatchSampler(range(20 )		,		batch_size=3		,		drop_last=_UpperCAmelCase )
     UpperCamelCase_									=     [
         [[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 )

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

     UpperCamelCase_									=     BatchSampler(range(2 )		,		batch_size=3		,		drop_last=_UpperCAmelCase )
     UpperCamelCase_									=     [[], []]
     self.check_batch_sampler_shards(_UpperCAmelCase		,		_UpperCAmelCase )




  def    _UpperCAmelCase     (     self )			-> Any:
     # Check the shards when the dataset is a round multiple of batch size.
     UpperCamelCase_									=     BatchSampler(range(24 )		,		batch_size=4		,		drop_last=_UpperCAmelCase )
     UpperCamelCase_									=     [
         [[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 )

     UpperCamelCase_									=     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.
     UpperCamelCase_									=     BatchSampler(range(22 )		,		batch_size=4		,		drop_last=_UpperCAmelCase )
     UpperCamelCase_									=     [
         [[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 )

     UpperCamelCase_									=     BatchSampler(range(22 )		,		batch_size=4		,		drop_last=_UpperCAmelCase )
     UpperCamelCase_									=     [
         [[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.
     UpperCamelCase_									=     BatchSampler(range(21 )		,		batch_size=4		,		drop_last=_UpperCAmelCase )
     UpperCamelCase_									=     [
         [[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 )

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

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




  def    _UpperCAmelCase     (     self )			-> Dict:
     # Check the shards when the dataset is a round multiple of total batch size.
     UpperCamelCase_									=     BatchSampler(range(24 )		,		batch_size=3		,		drop_last=_UpperCAmelCase )
     UpperCamelCase_									=     [
         [[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 )

     UpperCamelCase_									=     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.
     UpperCamelCase_									=     BatchSampler(range(21 )		,		batch_size=3		,		drop_last=_UpperCAmelCase )
     UpperCamelCase_									=     [
         [[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 )

     UpperCamelCase_									=     BatchSampler(range(21 )		,		batch_size=3		,		drop_last=_UpperCAmelCase )
     UpperCamelCase_									=     [
         [[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.
     UpperCamelCase_									=     BatchSampler(range(22 )		,		batch_size=3		,		drop_last=_UpperCAmelCase )
     UpperCamelCase_									=     [
         [[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 )

     UpperCamelCase_									=     BatchSampler(range(22 )		,		batch_size=3		,		drop_last=_UpperCAmelCase )
     UpperCamelCase_									=     [
         [[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.
     UpperCamelCase_									=     BatchSampler(range(20 )		,		batch_size=3		,		drop_last=_UpperCAmelCase )
     UpperCamelCase_									=     [
         [[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 )

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

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




  def    _UpperCAmelCase     (     self )			-> Optional[int]:
     # Check the shards when the dataset is a round multiple of batch size.
     UpperCamelCase_									=     BatchSampler(range(24 )		,		batch_size=4		,		drop_last=_UpperCAmelCase )
     UpperCamelCase_									=     [
         [[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 )

     UpperCamelCase_									=     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.
     UpperCamelCase_									=     BatchSampler(range(22 )		,		batch_size=4		,		drop_last=_UpperCAmelCase )
     UpperCamelCase_									=     [
         [[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 )

     UpperCamelCase_									=     BatchSampler(range(22 )		,		batch_size=4		,		drop_last=_UpperCAmelCase )
     UpperCamelCase_									=     [
         [[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.
     UpperCamelCase_									=     BatchSampler(range(21 )		,		batch_size=4		,		drop_last=_UpperCAmelCase )
     UpperCamelCase_									=     [
         [[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 )

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

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




  def    _UpperCAmelCase     (     self )			-> Optional[Any]:
     UpperCamelCase_									=     [[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 10, 11], [12, 13]]
     UpperCamelCase_									=     [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    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase=False		,		_UpperCAmelCase=2		,		_UpperCAmelCase=False )			-> int:
     random.seed(_UpperCAmelCase )
     UpperCamelCase_									=     list(_UpperCAmelCase )

     UpperCamelCase_									=     [
         IterableDatasetShard(
             _UpperCAmelCase		,		batch_size=_UpperCAmelCase		,		drop_last=_UpperCAmelCase		,		num_processes=_UpperCAmelCase		,		process_index=_UpperCAmelCase		,		split_batches=_UpperCAmelCase		,		)
         for i in range(_UpperCAmelCase )
     ]
     UpperCamelCase_									=     []
     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 ) )

     UpperCamelCase_									=     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
     UpperCamelCase_									=     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 )

     UpperCamelCase_									=     []
     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 )			-> Union[str, Any]:
     UpperCamelCase_									=     42
     UpperCamelCase_									=     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
     UpperCamelCase_									=     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 )			-> Union[str, Any]:
     UpperCamelCase_									=     BatchSampler(range(16 )		,		batch_size=4		,		drop_last=_UpperCAmelCase )
     UpperCamelCase_									=     SkipBatchSampler(_UpperCAmelCase		,		2 )
     self.assertListEqual(list(_UpperCAmelCase )		,		[[8, 9, 10, 11], [12, 13, 14, 15]] )




  def    _UpperCAmelCase     (     self )			-> Union[str, Any]:
     UpperCamelCase_									=     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    _UpperCAmelCase     (     self )			-> List[str]:
     UpperCamelCase_									=     DataLoader(list(range(16 ) )		,		batch_size=4 )
     UpperCamelCase_									=     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    _UpperCAmelCase     (     self )			-> Any:
     UpperCamelCase_									=     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    _UpperCAmelCase     (     self )			-> int:
     Accelerator()
     UpperCamelCase_									=     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 )













snake_case__      :	List[Any]       	=      """Tobias Carryer"""

from time import time


class 						_a       :






  """simple docstring"""





  def __init__(     self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase=int(time() ) )			-> Tuple:  # noqa: B008
     UpperCamelCase_									=     multiplier
     UpperCamelCase_									=     increment
     UpperCamelCase_									=     modulo
     UpperCamelCase_									=     seed




  def    _UpperCAmelCase     (     self )			-> Any:
     UpperCamelCase_									=     (self.multiplier * self.seed + self.increment) % self.modulo
     return self.seed


if __name__ == "__main__":
 # Show the LCG in action.
 snake_case__      :	List[Any]       	=      LinearCongruentialGenerator(1_6_6_4_5_2_5, 1_0_1_3_9_0_4_2_2_3, 2 << 3_1)
 while True:
  print(lcg.next_number())





