






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     _SCREAMING_SNAKE_CASE       (			_a      ):





	def __init__(		self				:					Dict ,  __lowerCamelCase				:					Any=0.01 ,  __lowerCamelCase				:					Optional[Any]=1_000	):
		UpperCamelCase     :Dict          =	p_stop
		UpperCamelCase     :Dict          =	max_length





	def __iter__(		self				:					List[str]	):
		UpperCamelCase     :Optional[Any]          =	0
		UpperCamelCase     :int          =	False
		while not stop and count < self.max_length:
			yield count
			count += 1
			UpperCamelCase     :List[Any]          =	random.random() < self.p_stop

class     _SCREAMING_SNAKE_CASE       (			unittest.TestCase      ):





	def 						_A (		self				:					List[Any] ,  __lowerCamelCase				:					Dict ,  __lowerCamelCase				:					Union[str, Any] ,  __lowerCamelCase				:					List[Any]=False ,  __lowerCamelCase				:					Optional[Any]=True	):
		UpperCamelCase     :str          =	[
		    BatchSamplerShard(__lowerCamelCase ,  2 ,  __lowerCamelCase ,  split_batches=__lowerCamelCase ,  even_batches=__lowerCamelCase	)
		    for i in range(2	)
		]
		UpperCamelCase     :Union[str, Any]          =	[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				:					List[str]	):
		# Check the shards when the dataset is a round multiple of total batch size.
		UpperCamelCase     :Optional[Any]          =	BatchSampler(range(24	) ,  batch_size=3 ,  drop_last=__lowerCamelCase	)
		UpperCamelCase     :Tuple          =	[
		    [[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     :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.
		UpperCamelCase     :Tuple          =	BatchSampler(range(21	) ,  batch_size=3 ,  drop_last=__lowerCamelCase	)
		UpperCamelCase     :Union[str, Any]          =	[
		    [[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     :Optional[Any]          =	BatchSampler(range(21	) ,  batch_size=3 ,  drop_last=__lowerCamelCase	)
		UpperCamelCase     :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 not a round multiple of batch size but has a multiple of
		# num_processes batch.
		UpperCamelCase     :Any          =	BatchSampler(range(22	) ,  batch_size=3 ,  drop_last=__lowerCamelCase	)
		UpperCamelCase     :Dict          =	[
		    [[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     :Union[str, Any]          =	BatchSampler(range(22	) ,  batch_size=3 ,  drop_last=__lowerCamelCase	)
		UpperCamelCase     :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 and has not a multiple of
		# num_processes batch.
		UpperCamelCase     :Union[str, Any]          =	BatchSampler(range(20	) ,  batch_size=3 ,  drop_last=__lowerCamelCase	)
		UpperCamelCase     :Optional[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	)

		UpperCamelCase     :Optional[Any]          =	BatchSampler(range(20	) ,  batch_size=3 ,  drop_last=__lowerCamelCase	)
		UpperCamelCase     :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	)

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

		UpperCamelCase     :Union[str, Any]          =	BatchSampler(range(2	) ,  batch_size=3 ,  drop_last=__lowerCamelCase	)
		UpperCamelCase     :Union[str, Any]          =	[[], []]
		self.check_batch_sampler_shards(__lowerCamelCase ,  __lowerCamelCase	)





	def 						_A (		self				:					Dict	):
		# Check the shards when the dataset is a round multiple of batch size.
		UpperCamelCase     :Any          =	BatchSampler(range(24	) ,  batch_size=4 ,  drop_last=__lowerCamelCase	)
		UpperCamelCase     :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	)

		UpperCamelCase     :List[str]          =	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     :Optional[int]          =	BatchSampler(range(22	) ,  batch_size=4 ,  drop_last=__lowerCamelCase	)
		UpperCamelCase     :Tuple          =	[
		    [[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     :Tuple          =	BatchSampler(range(22	) ,  batch_size=4 ,  drop_last=__lowerCamelCase	)
		UpperCamelCase     :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	)

		# Check the shards when the dataset is not a round multiple of batch size or num_processes.
		UpperCamelCase     :int          =	BatchSampler(range(21	) ,  batch_size=4 ,  drop_last=__lowerCamelCase	)
		UpperCamelCase     :Optional[int]          =	[
		    [[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     :Union[str, Any]          =	BatchSampler(range(21	) ,  batch_size=4 ,  drop_last=__lowerCamelCase	)
		UpperCamelCase     :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	)

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

		UpperCamelCase     :Dict          =	BatchSampler(range(2	) ,  batch_size=4 ,  drop_last=__lowerCamelCase	)
		UpperCamelCase     :int          =	[[], []]
		self.check_batch_sampler_shards(__lowerCamelCase ,  __lowerCamelCase ,  split_batches=__lowerCamelCase	)





	def 						_A (		self				:					Dict	):
		# Check the shards when the dataset is a round multiple of total batch size.
		UpperCamelCase     :str          =	BatchSampler(range(24	) ,  batch_size=3 ,  drop_last=__lowerCamelCase	)
		UpperCamelCase     :Optional[Any]          =	[
		    [[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     :Optional[Any]          =	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     :Union[str, Any]          =	BatchSampler(range(21	) ,  batch_size=3 ,  drop_last=__lowerCamelCase	)
		UpperCamelCase     :Any          =	[
		    [[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     :int          =	BatchSampler(range(21	) ,  batch_size=3 ,  drop_last=__lowerCamelCase	)
		UpperCamelCase     :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 ,  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     :Tuple          =	BatchSampler(range(22	) ,  batch_size=3 ,  drop_last=__lowerCamelCase	)
		UpperCamelCase     :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	)

		UpperCamelCase     :Optional[Any]          =	BatchSampler(range(22	) ,  batch_size=3 ,  drop_last=__lowerCamelCase	)
		UpperCamelCase     :Union[str, 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.
		UpperCamelCase     :List[Any]          =	BatchSampler(range(20	) ,  batch_size=3 ,  drop_last=__lowerCamelCase	)
		UpperCamelCase     :Tuple          =	[
		    [[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     :str          =	BatchSampler(range(20	) ,  batch_size=3 ,  drop_last=__lowerCamelCase	)
		UpperCamelCase     :Tuple          =	[
		    [[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     :Union[str, Any]          =	BatchSampler(range(2	) ,  batch_size=3 ,  drop_last=__lowerCamelCase	)
		UpperCamelCase     :Tuple          =	[[[0, 1]], []]
		self.check_batch_sampler_shards(__lowerCamelCase ,  __lowerCamelCase ,  even_batches=__lowerCamelCase	)

		UpperCamelCase     :Optional[Any]          =	BatchSampler(range(2	) ,  batch_size=3 ,  drop_last=__lowerCamelCase	)
		UpperCamelCase     :Optional[int]          =	[[], []]
		self.check_batch_sampler_shards(__lowerCamelCase ,  __lowerCamelCase ,  even_batches=__lowerCamelCase	)





	def 						_A (		self				:					List[Any]	):
		# Check the shards when the dataset is a round multiple of batch size.
		UpperCamelCase     :Optional[int]          =	BatchSampler(range(24	) ,  batch_size=4 ,  drop_last=__lowerCamelCase	)
		UpperCamelCase     :int          =	[
		    [[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     :Any          =	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     :int          =	BatchSampler(range(22	) ,  batch_size=4 ,  drop_last=__lowerCamelCase	)
		UpperCamelCase     :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]],
		]
		self.check_batch_sampler_shards(__lowerCamelCase ,  __lowerCamelCase ,  split_batches=__lowerCamelCase ,  even_batches=__lowerCamelCase	)

		UpperCamelCase     :Union[str, Any]          =	BatchSampler(range(22	) ,  batch_size=4 ,  drop_last=__lowerCamelCase	)
		UpperCamelCase     :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.
		UpperCamelCase     :str          =	BatchSampler(range(21	) ,  batch_size=4 ,  drop_last=__lowerCamelCase	)
		UpperCamelCase     :List[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	)

		UpperCamelCase     :int          =	BatchSampler(range(21	) ,  batch_size=4 ,  drop_last=__lowerCamelCase	)
		UpperCamelCase     :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 ,  even_batches=__lowerCamelCase	)

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

		UpperCamelCase     :List[str]          =	BatchSampler(range(2	) ,  batch_size=4 ,  drop_last=__lowerCamelCase	)
		UpperCamelCase     :Tuple          =	[[], []]
		self.check_batch_sampler_shards(__lowerCamelCase ,  __lowerCamelCase ,  split_batches=__lowerCamelCase ,  even_batches=__lowerCamelCase	)





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

		UpperCamelCase     :Union[str, Any]          =	[
		    IterableDatasetShard(
		        __lowerCamelCase ,  batch_size=__lowerCamelCase ,  drop_last=__lowerCamelCase ,  num_processes=__lowerCamelCase ,  process_index=__lowerCamelCase ,  split_batches=__lowerCamelCase ,  )
		    for i in range(__lowerCamelCase	)
		]
		UpperCamelCase     :Union[str, Any]          =	[]
		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     :List[str]          =	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     :Dict          =	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     :List[str]          =	[]
		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				:					int	):
		UpperCamelCase     :Optional[int]          =	42
		UpperCamelCase     :Optional[Any]          =	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     :Optional[int]          =	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				:					Tuple	):
		UpperCamelCase     :Union[str, Any]          =	BatchSampler(range(16	) ,  batch_size=4 ,  drop_last=__lowerCamelCase	)
		UpperCamelCase     :Any          =	SkipBatchSampler(__lowerCamelCase ,  2	)
		self.assertListEqual(list(__lowerCamelCase	) ,  [[8, 9, 10, 11], [12, 13, 14, 15]]	)





	def 						_A (		self				:					Optional[Any]	):
		UpperCamelCase     :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				:					int	):
		UpperCamelCase     :Dict          =	DataLoader(list(range(16	)	) ,  batch_size=4	)
		UpperCamelCase     :Union[str, 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				:					Any	):
		UpperCamelCase     :Union[str, 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				:					List[str]	):
		Accelerator()
		UpperCamelCase     :Optional[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 typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_lowerCAmelCase    :List[Any]     =					{'configuration_opt': ['OPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'OPTConfig']}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase    :Any     =					[
      'OPT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'OPTForCausalLM',
      'OPTModel',
      'OPTPreTrainedModel',
      'OPTForSequenceClassification',
      'OPTForQuestionAnswering',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase    :Optional[int]     =					['TFOPTForCausalLM', 'TFOPTModel', 'TFOPTPreTrainedModel']

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase    :Any     =					[
      'FlaxOPTForCausalLM',
      'FlaxOPTModel',
      'FlaxOPTPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_opt import OPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPTConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_opt import (
        OPT_PRETRAINED_MODEL_ARCHIVE_LIST,
        OPTForCausalLM,
        OPTForQuestionAnswering,
        OPTForSequenceClassification,
        OPTModel,
        OPTPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel

else:
  import sys

  _lowerCAmelCase    :int     =					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


