



'''simple docstring'''





import random
import unittest

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

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






class __A     (    A		):



 '''simple docstring'''







 def __init__(self					,  A=0.01					,  A=1_000			)       ->	int:


      """simple docstring"""



      _a           =    p_stop
      _a           =    max_length



 def __iter__(self			)       ->	str:


      """simple docstring"""



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






class __A     (    unittest.TestCase		):



 '''simple docstring'''







 def    a__    (self					,  A					,  A					,  A=False					,  A=True			)       ->	List[Any]:


      """simple docstring"""



      _a           =    [
          BatchSamplerShard(A					,  2					,  A					,  split_batches=A					,  even_batches=A			)
          for i in range(2			)
      ]
      _a           =    [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    a__    (self			)       ->	Tuple:


      """simple docstring"""



      _a           =    BatchSampler(range(24			)					,  batch_size=3					,  drop_last=A			)
      _a           =    [
          [[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			)

      _a           =    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.
      _a           =    BatchSampler(range(21			)					,  batch_size=3					,  drop_last=A			)
      _a           =    [
          [[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			)

      _a           =    BatchSampler(range(21			)					,  batch_size=3					,  drop_last=A			)
      _a           =    [
          [[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.
      _a           =    BatchSampler(range(22			)					,  batch_size=3					,  drop_last=A			)
      _a           =    [
          [[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			)

      _a           =    BatchSampler(range(22			)					,  batch_size=3					,  drop_last=A			)
      _a           =    [
          [[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.
      _a           =    BatchSampler(range(20			)					,  batch_size=3					,  drop_last=A			)
      _a           =    [
          [[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			)

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

      _a           =    BatchSampler(range(2			)					,  batch_size=3					,  drop_last=A			)
      _a           =    [[], []]
      self.check_batch_sampler_shards(A					,  A			)



 def    a__    (self			)       ->	Dict:


      """simple docstring"""



      _a           =    BatchSampler(range(24			)					,  batch_size=4					,  drop_last=A			)
      _a           =    [
          [[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			)

      _a           =    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.
      _a           =    BatchSampler(range(22			)					,  batch_size=4					,  drop_last=A			)
      _a           =    [
          [[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			)

      _a           =    BatchSampler(range(22			)					,  batch_size=4					,  drop_last=A			)
      _a           =    [
          [[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.
      _a           =    BatchSampler(range(21			)					,  batch_size=4					,  drop_last=A			)
      _a           =    [
          [[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			)

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

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



 def    a__    (self			)       ->	Optional[int]:


      """simple docstring"""



      _a           =    BatchSampler(range(24			)					,  batch_size=3					,  drop_last=A			)
      _a           =    [
          [[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			)

      _a           =    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.
      _a           =    BatchSampler(range(21			)					,  batch_size=3					,  drop_last=A			)
      _a           =    [
          [[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			)

      _a           =    BatchSampler(range(21			)					,  batch_size=3					,  drop_last=A			)
      _a           =    [
          [[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.
      _a           =    BatchSampler(range(22			)					,  batch_size=3					,  drop_last=A			)
      _a           =    [
          [[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			)

      _a           =    BatchSampler(range(22			)					,  batch_size=3					,  drop_last=A			)
      _a           =    [
          [[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.
      _a           =    BatchSampler(range(20			)					,  batch_size=3					,  drop_last=A			)
      _a           =    [
          [[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			)

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

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



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


      """simple docstring"""



      _a           =    BatchSampler(range(24			)					,  batch_size=4					,  drop_last=A			)
      _a           =    [
          [[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			)

      _a           =    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.
      _a           =    BatchSampler(range(22			)					,  batch_size=4					,  drop_last=A			)
      _a           =    [
          [[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			)

      _a           =    BatchSampler(range(22			)					,  batch_size=4					,  drop_last=A			)
      _a           =    [
          [[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.
      _a           =    BatchSampler(range(21			)					,  batch_size=4					,  drop_last=A			)
      _a           =    [
          [[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			)

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

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



 def    a__    (self			)       ->	Dict:


      """simple docstring"""



      _a           =    [[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 10, 11], [12, 13]]
      _a           =    [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    a__    (self					,  A					,  A					,  A					,  A=False					,  A=2					,  A=False			)       ->	Optional[Any]:


      """simple docstring"""



      random.seed(A			)
      _a           =    list(A			)

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

      _a           =    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
      _a           =    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			)

      _a           =    []
      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    a__    (self			)       ->	str:


      """simple docstring"""



      _a           =    42
      _a           =    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
      _a           =    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    a__    (self			)       ->	Union[str, Any]:


      """simple docstring"""



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



 def    a__    (self			)       ->	str:


      """simple docstring"""



      _a           =    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    a__    (self			)       ->	List[Any]:


      """simple docstring"""



      _a           =    DataLoader(list(range(16			)			)					,  batch_size=4			)
      _a           =    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    a__    (self			)       ->	Dict:


      """simple docstring"""



      _a           =    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    a__    (self			)       ->	Union[str, Any]:


      """simple docstring"""



      Accelerator()
      _a           =    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			)








'''simple docstring'''





from __future__ import annotations




def lowerCAmelCase    (__A):



     """simple docstring"""



     _a           =    2
     _a           =    []
     while i * i <= n:
          if n % i:
               i += 1
          else:
               n //= i
               factors.append(__A)
     if n > 1:
          factors.append(__A)
     return factors


if __name__ == "__main__":
  import doctest

  doctest.testmod()



