"""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					(		lowerCAmelCase_ ):


    def __init__(       self						:	List[str] ,					__lowerCAmelCase						:	Union[str, Any]=0.01 ,					__lowerCAmelCase						:	str=1000       ):
      _UpperCAmelCase    			=   p_stop
      _UpperCAmelCase    			=   max_length


    def __iter__(       self						:	Optional[Any]       ):
      _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 ):


    def   lowerCAmelCase_	(       self						:	Any ,					__lowerCAmelCase						:	List[Any] ,					__lowerCAmelCase						:	Union[str, Any] ,					__lowerCAmelCase						:	Optional[Any]=False ,					__lowerCAmelCase						:	Dict=True       ):
      _UpperCAmelCase    			=   [
          BatchSamplerShard(__lowerCAmelCase ,					2 ,					__lowerCAmelCase ,					split_batches=__lowerCAmelCase ,					even_batches=__lowerCAmelCase       )
          for i in range(2       )
      ]
      _UpperCAmelCase    			=   [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   lowerCAmelCase_	(       self						:	int       ):
      # Check the shards when the dataset is a round multiple of total batch size.
      _UpperCAmelCase    			=   BatchSampler(range(24       ) ,					batch_size=3 ,					drop_last=__lowerCAmelCase       )
      _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(__lowerCAmelCase ,					__lowerCAmelCase       )

      _UpperCAmelCase    			=   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.
      _UpperCAmelCase    			=   BatchSampler(range(21       ) ,					batch_size=3 ,					drop_last=__lowerCAmelCase       )
      _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(__lowerCAmelCase ,					__lowerCAmelCase       )

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

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

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

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


    def   lowerCAmelCase_	(       self						:	Any       ):
      # Check the shards when the dataset is a round multiple of batch size.
      _UpperCAmelCase    			=   BatchSampler(range(24       ) ,					batch_size=4 ,					drop_last=__lowerCAmelCase       )
      _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(__lowerCAmelCase ,					__lowerCAmelCase ,					split_batches=__lowerCAmelCase       )

      _UpperCAmelCase    			=   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.
      _UpperCAmelCase    			=   BatchSampler(range(22       ) ,					batch_size=4 ,					drop_last=__lowerCAmelCase       )
      _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(__lowerCAmelCase ,					__lowerCAmelCase ,					split_batches=__lowerCAmelCase       )

      _UpperCAmelCase    			=   BatchSampler(range(22       ) ,					batch_size=4 ,					drop_last=__lowerCAmelCase       )
      _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(__lowerCAmelCase ,					__lowerCAmelCase ,					split_batches=__lowerCAmelCase       )

      # 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=__lowerCAmelCase       )
      _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(__lowerCAmelCase ,					__lowerCAmelCase ,					split_batches=__lowerCAmelCase       )

      _UpperCAmelCase    			=   BatchSampler(range(21       ) ,					batch_size=4 ,					drop_last=__lowerCAmelCase       )
      _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(__lowerCAmelCase ,					__lowerCAmelCase ,					split_batches=__lowerCAmelCase       )

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

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


    def   lowerCAmelCase_	(       self						:	List[Any]       ):
      # Check the shards when the dataset is a round multiple of total batch size.
      _UpperCAmelCase    			=   BatchSampler(range(24       ) ,					batch_size=3 ,					drop_last=__lowerCAmelCase       )
      _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(__lowerCAmelCase ,					__lowerCAmelCase ,					even_batches=__lowerCAmelCase       )

      _UpperCAmelCase    			=   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.
      _UpperCAmelCase    			=   BatchSampler(range(21       ) ,					batch_size=3 ,					drop_last=__lowerCAmelCase       )
      _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(__lowerCAmelCase ,					__lowerCAmelCase ,					even_batches=__lowerCAmelCase       )

      _UpperCAmelCase    			=   BatchSampler(range(21       ) ,					batch_size=3 ,					drop_last=__lowerCAmelCase       )
      _UpperCAmelCase    			=   [
          [[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.
      _UpperCAmelCase    			=   BatchSampler(range(22       ) ,					batch_size=3 ,					drop_last=__lowerCAmelCase       )
      _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(__lowerCAmelCase ,					__lowerCAmelCase ,					even_batches=__lowerCAmelCase       )

      _UpperCAmelCase    			=   BatchSampler(range(22       ) ,					batch_size=3 ,					drop_last=__lowerCAmelCase       )
      _UpperCAmelCase    			=   [
          [[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.
      _UpperCAmelCase    			=   BatchSampler(range(20       ) ,					batch_size=3 ,					drop_last=__lowerCAmelCase       )
      _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(__lowerCAmelCase ,					__lowerCAmelCase ,					even_batches=__lowerCAmelCase       )

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

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


    def   lowerCAmelCase_	(       self						:	Any       ):
      # Check the shards when the dataset is a round multiple of batch size.
      _UpperCAmelCase    			=   BatchSampler(range(24       ) ,					batch_size=4 ,					drop_last=__lowerCAmelCase       )
      _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(__lowerCAmelCase ,					__lowerCAmelCase ,					split_batches=__lowerCAmelCase ,					even_batches=__lowerCAmelCase       )

      _UpperCAmelCase    			=   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.
      _UpperCAmelCase    			=   BatchSampler(range(22       ) ,					batch_size=4 ,					drop_last=__lowerCAmelCase       )
      _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(__lowerCAmelCase ,					__lowerCAmelCase ,					split_batches=__lowerCAmelCase ,					even_batches=__lowerCAmelCase       )

      _UpperCAmelCase    			=   BatchSampler(range(22       ) ,					batch_size=4 ,					drop_last=__lowerCAmelCase       )
      _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(__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.
      _UpperCAmelCase    			=   BatchSampler(range(21       ) ,					batch_size=4 ,					drop_last=__lowerCAmelCase       )
      _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(__lowerCAmelCase ,					__lowerCAmelCase ,					split_batches=__lowerCAmelCase ,					even_batches=__lowerCAmelCase       )

      _UpperCAmelCase    			=   BatchSampler(range(21       ) ,					batch_size=4 ,					drop_last=__lowerCAmelCase       )
      _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(__lowerCAmelCase ,					__lowerCAmelCase ,					split_batches=__lowerCAmelCase ,					even_batches=__lowerCAmelCase       )

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

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


    def   lowerCAmelCase_	(       self						:	Optional[Any]       ):
      _UpperCAmelCase    			=   [[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 10, 11], [12, 13]]
      _UpperCAmelCase    			=   [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   lowerCAmelCase_	(       self						:	Tuple ,					__lowerCAmelCase						:	Any ,					__lowerCAmelCase						:	Optional[Any] ,					__lowerCAmelCase						:	str ,					__lowerCAmelCase						:	Any=False ,					__lowerCAmelCase						:	Dict=2 ,					__lowerCAmelCase						:	Optional[Any]=False       ):
      random.seed(__lowerCAmelCase       )
      _UpperCAmelCase    			=   list(__lowerCAmelCase       )

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

      _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(__lowerCAmelCase       ) ,					len(__lowerCAmelCase       )       )
        self.assertTrue(len(__lowerCAmelCase       ) % shard_batch_size == 0       )

      _UpperCAmelCase    			=   []
      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   lowerCAmelCase_	(       self						:	Optional[Any]       ):
      _UpperCAmelCase    			=   42
      _UpperCAmelCase    			=   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
      _UpperCAmelCase    			=   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   lowerCAmelCase_	(       self						:	Optional[int]       ):
      _UpperCAmelCase    			=   BatchSampler(range(16       ) ,					batch_size=4 ,					drop_last=__lowerCAmelCase       )
      _UpperCAmelCase    			=   SkipBatchSampler(__lowerCAmelCase ,					2       )
      self.assertListEqual(list(__lowerCAmelCase       ) ,					[[8, 9, 10, 11], [12, 13, 14, 15]]       )


    def   lowerCAmelCase_	(       self						:	int       ):
      _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   lowerCAmelCase_	(       self						:	List[Any]       ):
      _UpperCAmelCase    			=   DataLoader(list(range(16       )       ) ,					batch_size=4       )
      _UpperCAmelCase    			=   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   lowerCAmelCase_	(       self						:	Tuple       ):
      _UpperCAmelCase    			=   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   lowerCAmelCase_	(       self						:	Optional[Any]       ):
      Accelerator()
      _UpperCAmelCase    			=   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 typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


UpperCAmelCase__			=    {
    """configuration_gpt_bigcode""": ["""GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTBigCodeConfig"""],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase__			=    [
        """GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """GPTBigCodeForSequenceClassification""",
        """GPTBigCodeForTokenClassification""",
        """GPTBigCodeForCausalLM""",
        """GPTBigCodeModel""",
        """GPTBigCodePreTrainedModel""",
    ]

if TYPE_CHECKING:
    from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_gpt_bigcode import (
            GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
            GPTBigCodeForCausalLM,
            GPTBigCodeForSequenceClassification,
            GPTBigCodeForTokenClassification,
            GPTBigCodeModel,
            GPTBigCodePreTrainedModel,
        )


else:
    import sys

    UpperCAmelCase__			=    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
