



'''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	(		__a):


 '''simple docstring'''




 def __init__(   self							,  lowerCAmelCase_=0.0_1							,  lowerCAmelCase_=1_0_0_0) ->      List[str]:



      """simple docstring"""







      a_       		=p_stop
      a_       		=max_length




 def __iter__(   self) ->      List[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 	UpperCAmelCase	(		unittest.TestCase):


 '''simple docstring'''




 def       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_=False							,  lowerCAmelCase_=True) ->      str:



      """simple docstring"""







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




 def       lowercase_  (   self) ->      Union[str, Any]:



      """simple docstring"""







      a_       		=BatchSampler(range(2_4)							,  batch_size=3							,  drop_last=lowerCAmelCase_)
      a_       		=[
          [[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(lowerCAmelCase_							,  lowerCAmelCase_)

      a_       		=BatchSampler(range(2_4)							,  batch_size=3							,  drop_last=lowerCAmelCase_)
      # Expected shouldn't change
      self.check_batch_sampler_shards(lowerCAmelCase_							,  lowerCAmelCase_)

      # Check the shards when the dataset is a round multiple of batch size but not total batch size.
      a_       		=BatchSampler(range(2_1)							,  batch_size=3							,  drop_last=lowerCAmelCase_)
      a_       		=[
          [[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(lowerCAmelCase_							,  lowerCAmelCase_)

      a_       		=BatchSampler(range(2_1)							,  batch_size=3							,  drop_last=lowerCAmelCase_)
      a_       		=[
          [[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(lowerCAmelCase_							,  lowerCAmelCase_)

      # 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(2_2)							,  batch_size=3							,  drop_last=lowerCAmelCase_)
      a_       		=[
          [[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(lowerCAmelCase_							,  lowerCAmelCase_)

      a_       		=BatchSampler(range(2_2)							,  batch_size=3							,  drop_last=lowerCAmelCase_)
      a_       		=[
          [[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(lowerCAmelCase_							,  lowerCAmelCase_)

      # 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(2_0)							,  batch_size=3							,  drop_last=lowerCAmelCase_)
      a_       		=[
          [[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(lowerCAmelCase_							,  lowerCAmelCase_)

      a_       		=BatchSampler(range(2_0)							,  batch_size=3							,  drop_last=lowerCAmelCase_)
      a_       		=[
          [[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(lowerCAmelCase_							,  lowerCAmelCase_)

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

      a_       		=BatchSampler(range(2)							,  batch_size=3							,  drop_last=lowerCAmelCase_)
      a_       		=[[], []]
      self.check_batch_sampler_shards(lowerCAmelCase_							,  lowerCAmelCase_)




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



      """simple docstring"""







      a_       		=BatchSampler(range(2_4)							,  batch_size=4							,  drop_last=lowerCAmelCase_)
      a_       		=[
          [[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(lowerCAmelCase_							,  lowerCAmelCase_							,  split_batches=lowerCAmelCase_)

      a_       		=BatchSampler(range(2_4)							,  batch_size=4							,  drop_last=lowerCAmelCase_)
      # Expected shouldn't change
      self.check_batch_sampler_shards(lowerCAmelCase_							,  lowerCAmelCase_							,  split_batches=lowerCAmelCase_)

      # Check the shards when the dataset is not a round multiple of batch size.
      a_       		=BatchSampler(range(2_2)							,  batch_size=4							,  drop_last=lowerCAmelCase_)
      a_       		=[
          [[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(lowerCAmelCase_							,  lowerCAmelCase_							,  split_batches=lowerCAmelCase_)

      a_       		=BatchSampler(range(2_2)							,  batch_size=4							,  drop_last=lowerCAmelCase_)
      a_       		=[
          [[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(lowerCAmelCase_							,  lowerCAmelCase_							,  split_batches=lowerCAmelCase_)

      # Check the shards when the dataset is not a round multiple of batch size or num_processes.
      a_       		=BatchSampler(range(2_1)							,  batch_size=4							,  drop_last=lowerCAmelCase_)
      a_       		=[
          [[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(lowerCAmelCase_							,  lowerCAmelCase_							,  split_batches=lowerCAmelCase_)

      a_       		=BatchSampler(range(2_1)							,  batch_size=4							,  drop_last=lowerCAmelCase_)
      a_       		=[
          [[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(lowerCAmelCase_							,  lowerCAmelCase_							,  split_batches=lowerCAmelCase_)

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

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




 def       lowercase_  (   self) ->      Optional[Any]:



      """simple docstring"""







      a_       		=BatchSampler(range(2_4)							,  batch_size=3							,  drop_last=lowerCAmelCase_)
      a_       		=[
          [[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(lowerCAmelCase_							,  lowerCAmelCase_							,  even_batches=lowerCAmelCase_)

      a_       		=BatchSampler(range(2_4)							,  batch_size=3							,  drop_last=lowerCAmelCase_)
      # Expected shouldn't change
      self.check_batch_sampler_shards(lowerCAmelCase_							,  lowerCAmelCase_							,  even_batches=lowerCAmelCase_)

      # Check the shards when the dataset is a round multiple of batch size but not total batch size.
      a_       		=BatchSampler(range(2_1)							,  batch_size=3							,  drop_last=lowerCAmelCase_)
      a_       		=[
          [[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(lowerCAmelCase_							,  lowerCAmelCase_							,  even_batches=lowerCAmelCase_)

      a_       		=BatchSampler(range(2_1)							,  batch_size=3							,  drop_last=lowerCAmelCase_)
      a_       		=[
          [[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(lowerCAmelCase_							,  lowerCAmelCase_							,  even_batches=lowerCAmelCase_)

      # 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(2_2)							,  batch_size=3							,  drop_last=lowerCAmelCase_)
      a_       		=[
          [[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(lowerCAmelCase_							,  lowerCAmelCase_							,  even_batches=lowerCAmelCase_)

      a_       		=BatchSampler(range(2_2)							,  batch_size=3							,  drop_last=lowerCAmelCase_)
      a_       		=[
          [[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(lowerCAmelCase_							,  lowerCAmelCase_							,  even_batches=lowerCAmelCase_)

      # 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(2_0)							,  batch_size=3							,  drop_last=lowerCAmelCase_)
      a_       		=[
          [[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(lowerCAmelCase_							,  lowerCAmelCase_							,  even_batches=lowerCAmelCase_)

      a_       		=BatchSampler(range(2_0)							,  batch_size=3							,  drop_last=lowerCAmelCase_)
      a_       		=[
          [[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(lowerCAmelCase_							,  lowerCAmelCase_							,  even_batches=lowerCAmelCase_)

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

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




 def       lowercase_  (   self) ->      Dict:



      """simple docstring"""







      a_       		=BatchSampler(range(2_4)							,  batch_size=4							,  drop_last=lowerCAmelCase_)
      a_       		=[
          [[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(lowerCAmelCase_							,  lowerCAmelCase_							,  split_batches=lowerCAmelCase_							,  even_batches=lowerCAmelCase_)

      a_       		=BatchSampler(range(2_4)							,  batch_size=4							,  drop_last=lowerCAmelCase_)
      # Expected shouldn't change
      self.check_batch_sampler_shards(lowerCAmelCase_							,  lowerCAmelCase_							,  split_batches=lowerCAmelCase_							,  even_batches=lowerCAmelCase_)

      # Check the shards when the dataset is not a round multiple of batch size.
      a_       		=BatchSampler(range(2_2)							,  batch_size=4							,  drop_last=lowerCAmelCase_)
      a_       		=[
          [[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(lowerCAmelCase_							,  lowerCAmelCase_							,  split_batches=lowerCAmelCase_							,  even_batches=lowerCAmelCase_)

      a_       		=BatchSampler(range(2_2)							,  batch_size=4							,  drop_last=lowerCAmelCase_)
      a_       		=[
          [[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(lowerCAmelCase_							,  lowerCAmelCase_							,  split_batches=lowerCAmelCase_							,  even_batches=lowerCAmelCase_)

      # Check the shards when the dataset is not a round multiple of batch size or num_processes.
      a_       		=BatchSampler(range(2_1)							,  batch_size=4							,  drop_last=lowerCAmelCase_)
      a_       		=[
          [[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(lowerCAmelCase_							,  lowerCAmelCase_							,  split_batches=lowerCAmelCase_							,  even_batches=lowerCAmelCase_)

      a_       		=BatchSampler(range(2_1)							,  batch_size=4							,  drop_last=lowerCAmelCase_)
      a_       		=[
          [[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(lowerCAmelCase_							,  lowerCAmelCase_							,  split_batches=lowerCAmelCase_							,  even_batches=lowerCAmelCase_)

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

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




 def       lowercase_  (   self) ->      Tuple:



      """simple docstring"""







      a_       		=[[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 1_0, 1_1], [1_2, 1_3]]
      a_       		=[BatchSamplerShard(lowerCAmelCase_							,  2							,  lowerCAmelCase_							,  even_batches=lowerCAmelCase_) 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       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_=False							,  lowerCAmelCase_=2							,  lowerCAmelCase_=False) ->      int:



      """simple docstring"""







      random.seed(lowerCAmelCase_)
      a_       		=list(lowerCAmelCase_)

      a_       		=[
          IterableDatasetShard(
              lowerCAmelCase_							,  batch_size=lowerCAmelCase_							,  drop_last=lowerCAmelCase_							,  num_processes=lowerCAmelCase_							,  process_index=lowerCAmelCase_							,  split_batches=lowerCAmelCase_							,  )
          for i in range(lowerCAmelCase_)
      ]
      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(lowerCAmelCase_)
           iterable_dataset_lists.append(list(lowerCAmelCase_))

      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(lowerCAmelCase_)							,  len(lowerCAmelCase_))
           self.assertTrue(len(lowerCAmelCase_) % shard_batch_size == 0)

      a_       		=[]
      for idx in range(0							,  len(lowerCAmelCase_)							,  lowerCAmelCase_):
           for l in iterable_dataset_lists:
                observed += l[idx : idx + shard_batch_size]

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




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



      """simple docstring"""







      a_       		=4_2
      a_       		=RandomIterableDataset()

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

      # Edge case with a very small dataset
      a_       		=RandomIterableDataset(max_length=2)

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




 def       lowercase_  (   self) ->      Any:



      """simple docstring"""







      a_       		=BatchSampler(range(1_6)							,  batch_size=4							,  drop_last=lowerCAmelCase_)
      a_       		=SkipBatchSampler(lowerCAmelCase_							,  2)
      self.assertListEqual(list(lowerCAmelCase_)							,  [[8, 9, 1_0, 1_1], [1_2, 1_3, 1_4, 1_5]])




 def       lowercase_  (   self) ->      List[Any]:



      """simple docstring"""







      a_       		=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       lowercase_  (   self) ->      Optional[Any]:



      """simple docstring"""







      a_       		=DataLoader(list(range(1_6))							,  batch_size=4)
      a_       		=skip_first_batches(lowerCAmelCase_							,  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       lowercase_  (   self) ->      int:



      """simple docstring"""







      a_       		=DataLoaderShard(list(range(1_6))							,  batch_size=4)
      for idx, _ in enumerate(lowerCAmelCase_):
           self.assertEqual(dataloader.end_of_dataloader							,  idx == 3)

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




 def       lowercase_  (   self) ->      int:



      """simple docstring"""







      Accelerator()
      a_       		=DataLoaderDispatcher(range(1_6)							,  batch_size=4)
      for idx, _ in enumerate(lowerCAmelCase_):
           self.assertEqual(dataloader.end_of_dataloader							,  idx == 3)

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







'''simple docstring'''


from typing import Any

import numpy as np






def  UpperCAmelCase_       (		lowercase__       ):


     '''simple docstring'''







     return np.array_equal(lowercase__       ,	matrix.conjugate().T       )






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ):


     '''simple docstring'''







     a_       		=v.conjugate().T
     a_       		=v_star.dot(lowercase__       )
     assert isinstance(lowercase__       ,	np.ndarray       )
     return (v_star_dot.dot(lowercase__       )) / (v_star.dot(lowercase__       ))






def  UpperCAmelCase_       (		):


     '''simple docstring'''







     a_       		=np.array([[2, 2 + 1j, 4], [2 - 1j, 3, 1j], [4, -1j, 1]]       )
     a_       		=np.array([[1], [2], [3]]       )
     assert is_hermitian(lowercase__       ), F"""{a} is not hermitian."""
     print(rayleigh_quotient(lowercase__       ,	lowercase__       )       )

     a_       		=np.array([[1, 2, 4], [2, 3, -1], [4, -1, 1]]       )
     assert is_hermitian(lowercase__       ), F"""{a} is not hermitian."""
     assert rayleigh_quotient(lowercase__       ,	lowercase__       ) == float(3       )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  tests()


