






'''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_					(    lowerCAmelCase__				):




      """simple docstring"""




      def __init__( self			:		str     ,      UpperCAmelCase			:		List[str]=0.0_1     ,      UpperCAmelCase			:		str=1000		)  ->  List[Any]:


       '''simple docstring'''

       lowercase					:   Any				  =p_stop
       lowercase					:   int				  =max_length






      def __iter__( self			:		List[str]		)  ->  Optional[Any]:


       '''simple docstring'''

       lowercase					:   Optional[Any]				  =0
       lowercase					:   Dict				  =False
       while not stop and count < self.max_length:
        yield count
        count += 1
        lowercase					:   List[Any]				  =random.random() < self.p_stop





class UpperCAmelCase_					(    unittest.TestCase				):




      """simple docstring"""




      def 				A__      ( self			:		Any     ,      UpperCAmelCase			:		Optional[Any]     ,      UpperCAmelCase			:		List[Any]     ,      UpperCAmelCase			:		str=False     ,      UpperCAmelCase			:		Any=True		)  ->  Tuple:


       '''simple docstring'''

       lowercase					:   Tuple				  =[
           BatchSamplerShard(_lowerCamelCase     ,      2     ,      _lowerCamelCase     ,      split_batches=_lowerCamelCase     ,      even_batches=_lowerCamelCase		)
           for i in range(2		)
       ]
       lowercase					:   Tuple				  =[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 				A__      ( self			:		int		)  ->  Dict:


       '''simple docstring'''

       lowercase					:   str				  =BatchSampler(range(24		)     ,      batch_size=3     ,      drop_last=_lowerCamelCase		)
       lowercase					:   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(_lowerCamelCase     ,      _lowerCamelCase		)

       lowercase					:   Optional[Any]				  =BatchSampler(range(24		)     ,      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.
       lowercase					:   List[str]				  =BatchSampler(range(21		)     ,      batch_size=3     ,      drop_last=_lowerCamelCase		)
       lowercase					:   Optional[int]				  =[
           [[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(_lowerCamelCase     ,      _lowerCamelCase		)

       lowercase					:   str				  =BatchSampler(range(21		)     ,      batch_size=3     ,      drop_last=_lowerCamelCase		)
       lowercase					:   List[Any]				  =[
           [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
           [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
       ]
       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.
       lowercase					:   Dict				  =BatchSampler(range(22		)     ,      batch_size=3     ,      drop_last=_lowerCamelCase		)
       lowercase					:   List[str]				  =[
           [[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(_lowerCamelCase     ,      _lowerCamelCase		)

       lowercase					:   Dict				  =BatchSampler(range(22		)     ,      batch_size=3     ,      drop_last=_lowerCamelCase		)
       lowercase					:   int				  =[
           [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
           [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
       ]
       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.
       lowercase					:   List[Any]				  =BatchSampler(range(20		)     ,      batch_size=3     ,      drop_last=_lowerCamelCase		)
       lowercase					:   List[Any]				  =[
           [[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(_lowerCamelCase     ,      _lowerCamelCase		)

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

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

       lowercase					:   Any				  =BatchSampler(range(2		)     ,      batch_size=3     ,      drop_last=_lowerCamelCase		)
       lowercase					:   str				  =[[], []]
       self.check_batch_sampler_shards(_lowerCamelCase     ,      _lowerCamelCase		)






      def 				A__      ( self			:		Tuple		)  ->  Optional[int]:


       '''simple docstring'''

       lowercase					:   List[Any]				  =BatchSampler(range(24		)     ,      batch_size=4     ,      drop_last=_lowerCamelCase		)
       lowercase					:   Union[str, 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(_lowerCamelCase     ,      _lowerCamelCase     ,      split_batches=_lowerCamelCase		)

       lowercase					:   Tuple				  =BatchSampler(range(24		)     ,      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.
       lowercase					:   Dict				  =BatchSampler(range(22		)     ,      batch_size=4     ,      drop_last=_lowerCamelCase		)
       lowercase					:   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(_lowerCamelCase     ,      _lowerCamelCase     ,      split_batches=_lowerCamelCase		)

       lowercase					:   List[str]				  =BatchSampler(range(22		)     ,      batch_size=4     ,      drop_last=_lowerCamelCase		)
       lowercase					:   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(_lowerCamelCase     ,      _lowerCamelCase     ,      split_batches=_lowerCamelCase		)

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

       lowercase					:   Tuple				  =BatchSampler(range(21		)     ,      batch_size=4     ,      drop_last=_lowerCamelCase		)
       lowercase					:   List[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(_lowerCamelCase     ,      _lowerCamelCase     ,      split_batches=_lowerCamelCase		)

       # Check the shards when the dataset is very small.
       lowercase					:   Optional[Any]				  =BatchSampler(range(2		)     ,      batch_size=4     ,      drop_last=_lowerCamelCase		)
       lowercase					:   List[str]				  =[[[0, 1]], [[0, 1]]]
       self.check_batch_sampler_shards(_lowerCamelCase     ,      _lowerCamelCase     ,      split_batches=_lowerCamelCase		)

       lowercase					:   Union[str, Any]				  =BatchSampler(range(2		)     ,      batch_size=4     ,      drop_last=_lowerCamelCase		)
       lowercase					:   Dict				  =[[], []]
       self.check_batch_sampler_shards(_lowerCamelCase     ,      _lowerCamelCase     ,      split_batches=_lowerCamelCase		)






      def 				A__      ( self			:		Dict		)  ->  Dict:


       '''simple docstring'''

       lowercase					:   List[Any]				  =BatchSampler(range(24		)     ,      batch_size=3     ,      drop_last=_lowerCamelCase		)
       lowercase					:   Optional[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(_lowerCamelCase     ,      _lowerCamelCase     ,      even_batches=_lowerCamelCase		)

       lowercase					:   Optional[int]				  =BatchSampler(range(24		)     ,      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.
       lowercase					:   List[Any]				  =BatchSampler(range(21		)     ,      batch_size=3     ,      drop_last=_lowerCamelCase		)
       lowercase					:   int				  =[
           [[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(_lowerCamelCase     ,      _lowerCamelCase     ,      even_batches=_lowerCamelCase		)

       lowercase					:   Tuple				  =BatchSampler(range(21		)     ,      batch_size=3     ,      drop_last=_lowerCamelCase		)
       lowercase					:   Dict				  =[
           [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
           [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
       ]
       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.
       lowercase					:   Optional[Any]				  =BatchSampler(range(22		)     ,      batch_size=3     ,      drop_last=_lowerCamelCase		)
       lowercase					:   Union[str, Any]				  =[
           [[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(_lowerCamelCase     ,      _lowerCamelCase     ,      even_batches=_lowerCamelCase		)

       lowercase					:   int				  =BatchSampler(range(22		)     ,      batch_size=3     ,      drop_last=_lowerCamelCase		)
       lowercase					:   Any				  =[
           [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
           [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
       ]
       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.
       lowercase					:   Any				  =BatchSampler(range(20		)     ,      batch_size=3     ,      drop_last=_lowerCamelCase		)
       lowercase					:   Union[str, Any]				  =[
           [[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(_lowerCamelCase     ,      _lowerCamelCase     ,      even_batches=_lowerCamelCase		)

       lowercase					:   List[Any]				  =BatchSampler(range(20		)     ,      batch_size=3     ,      drop_last=_lowerCamelCase		)
       lowercase					:   Any				  =[
           [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
           [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
       ]
       self.check_batch_sampler_shards(_lowerCamelCase     ,      _lowerCamelCase     ,      even_batches=_lowerCamelCase		)

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

       lowercase					:   Optional[Any]				  =BatchSampler(range(2		)     ,      batch_size=3     ,      drop_last=_lowerCamelCase		)
       lowercase					:   Any				  =[[], []]
       self.check_batch_sampler_shards(_lowerCamelCase     ,      _lowerCamelCase     ,      even_batches=_lowerCamelCase		)






      def 				A__      ( self			:		Optional[int]		)  ->  Union[str, Any]:


       '''simple docstring'''

       lowercase					:   List[str]				  =BatchSampler(range(24		)     ,      batch_size=4     ,      drop_last=_lowerCamelCase		)
       lowercase					:   str				  =[
           [[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(_lowerCamelCase     ,      _lowerCamelCase     ,      split_batches=_lowerCamelCase     ,      even_batches=_lowerCamelCase		)

       lowercase					:   Tuple				  =BatchSampler(range(24		)     ,      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.
       lowercase					:   str				  =BatchSampler(range(22		)     ,      batch_size=4     ,      drop_last=_lowerCamelCase		)
       lowercase					:   Tuple				  =[
           [[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(_lowerCamelCase     ,      _lowerCamelCase     ,      split_batches=_lowerCamelCase     ,      even_batches=_lowerCamelCase		)

       lowercase					:   Dict				  =BatchSampler(range(22		)     ,      batch_size=4     ,      drop_last=_lowerCamelCase		)
       lowercase					:   Optional[Any]				  =[
           [[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(_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.
       lowercase					:   List[Any]				  =BatchSampler(range(21		)     ,      batch_size=4     ,      drop_last=_lowerCamelCase		)
       lowercase					:   Optional[Any]				  =[
           [[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(_lowerCamelCase     ,      _lowerCamelCase     ,      split_batches=_lowerCamelCase     ,      even_batches=_lowerCamelCase		)

       lowercase					:   Tuple				  =BatchSampler(range(21		)     ,      batch_size=4     ,      drop_last=_lowerCamelCase		)
       lowercase					:   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(_lowerCamelCase     ,      _lowerCamelCase     ,      split_batches=_lowerCamelCase     ,      even_batches=_lowerCamelCase		)

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

       lowercase					:   Optional[int]				  =BatchSampler(range(2		)     ,      batch_size=4     ,      drop_last=_lowerCamelCase		)
       lowercase					:   Optional[Any]				  =[[], []]
       self.check_batch_sampler_shards(_lowerCamelCase     ,      _lowerCamelCase     ,      split_batches=_lowerCamelCase     ,      even_batches=_lowerCamelCase		)






      def 				A__      ( self			:		List[str]		)  ->  Dict:


       '''simple docstring'''

       lowercase					:   List[Any]				  =[[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 10, 11], [12, 13]]
       lowercase					:   Optional[Any]				  =[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], [12, 13]]		)
       self.assertListEqual(list(batch_sampler_shards[1]		)     ,      [[3, 4], [9, 10, 11]]		)






      def 				A__      ( self			:		Union[str, Any]     ,      UpperCAmelCase			:		List[str]     ,      UpperCAmelCase			:		Dict     ,      UpperCAmelCase			:		Optional[int]     ,      UpperCAmelCase			:		int=False     ,      UpperCAmelCase			:		Optional[int]=2     ,      UpperCAmelCase			:		Dict=False		)  ->  str:


       '''simple docstring'''

       random.seed(_lowerCamelCase		)
       lowercase					:   Any				  =list(_lowerCamelCase		)

       lowercase					:   Tuple				  =[
           IterableDatasetShard(
               _lowerCamelCase     ,      batch_size=_lowerCamelCase     ,      drop_last=_lowerCamelCase     ,      num_processes=_lowerCamelCase     ,      process_index=_lowerCamelCase     ,      split_batches=_lowerCamelCase     ,      )
           for i in range(_lowerCamelCase		)
       ]
       lowercase					:   str				  =[]
       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		)		)

       lowercase					:   Optional[Any]				  =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
       lowercase					:   List[Any]				  =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		)

       lowercase					:   Dict				  =[]
       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 				A__      ( self			:		str		)  ->  Optional[Any]:


       '''simple docstring'''

       lowercase					:   Optional[int]				  =42
       lowercase					:   int				  =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
       lowercase					:   List[str]				  =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 				A__      ( self			:		int		)  ->  List[str]:


       '''simple docstring'''

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






      def 				A__      ( self			:		Optional[int]		)  ->  Tuple:


       '''simple docstring'''

       lowercase					:   Tuple				  =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			:		Tuple		)  ->  Optional[Any]:


       '''simple docstring'''

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






      def 				A__      ( self			:		int		)  ->  Optional[Any]:


       '''simple docstring'''

       lowercase					:   Optional[Any]				  =DataLoaderShard(list(range(16		)		)     ,      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 				A__      ( self			:		Optional[int]		)  ->  str:


       '''simple docstring'''

       Accelerator()
       lowercase					:   int				  =DataLoaderDispatcher(range(16		)     ,      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 __future__ import annotations

SCREAMING_SNAKE_CASE						=	8.988E9  # units = N * m^s * C^-2


def 							lowercase_				(    __A :       float					,      __A :       float					,      __A :       float					,      __A :       float			)       ->      dict[str, float]:



 """simple docstring"""




 lowercase					:   Dict				  =abs(chargea * chargea			)

 if (force, chargea, chargea, distance).count(0			) != 1:
  raise ValueError('''One and only one argument must be 0'''			)
 if distance < 0:
  raise ValueError('''Distance cannot be negative'''			)
 if force == 0:
  lowercase					:   Union[str, Any]				  =COULOMBS_CONSTANT * charge_product / (distance**2)
  return {"force": force}
 elif chargea == 0:
  lowercase					:   int				  =abs(__A			) * (distance**2) / (COULOMBS_CONSTANT * chargea)
  return {"charge1": chargea}
 elif chargea == 0:
  lowercase					:   int				  =abs(__A			) * (distance**2) / (COULOMBS_CONSTANT * chargea)
  return {"charge2": chargea}
 elif distance == 0:
  lowercase					:   Tuple				  =(COULOMBS_CONSTANT * charge_product / abs(__A			)) ** 0.5
  return {"distance": distance}
 raise ValueError('''Exactly one argument must be 0'''			)


if __name__ == "__main__":
      import doctest

      doctest.testmod()



