






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  lowerCAmelCase_       (				snake_case__					):




							def __init__(   self     :   Optional[int] ,							SCREAMING_SNAKE_CASE_     :   Optional[int]=0.01 ,							SCREAMING_SNAKE_CASE_     :   List[Any]=1_000	):
											lowerCAmelCase__      			=						p_stop
											lowerCAmelCase__      			=						max_length




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



class  lowerCAmelCase_       (				unittest.TestCase					):




							def 						__snake_case					(   self     :   Union[str, Any] ,							SCREAMING_SNAKE_CASE_     :   Union[str, Any] ,							SCREAMING_SNAKE_CASE_     :   Tuple ,							SCREAMING_SNAKE_CASE_     :   Tuple=False ,							SCREAMING_SNAKE_CASE_     :   int=True	):
											lowerCAmelCase__      			=						[
											    BatchSamplerShard(SCREAMING_SNAKE_CASE_ ,							2 ,							SCREAMING_SNAKE_CASE_ ,							split_batches=SCREAMING_SNAKE_CASE_ ,							even_batches=SCREAMING_SNAKE_CASE_	)
											    for i in range(2	)
											]
											lowerCAmelCase__      			=						[list(SCREAMING_SNAKE_CASE_	) for batch_sampler_shard in batch_sampler_shards]
											if not split_batches:
															self.assertListEqual([len(SCREAMING_SNAKE_CASE_	) for shard in batch_sampler_shards] ,							[len(SCREAMING_SNAKE_CASE_	) for e in expected]	)
											self.assertListEqual(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_	)




							def 						__snake_case					(   self     :   Optional[Any]	):
											# Check the shards when the dataset is a round multiple of total batch size.
											lowerCAmelCase__      			=						BatchSampler(range(24	) ,							batch_size=3 ,							drop_last=SCREAMING_SNAKE_CASE_	)
											lowerCAmelCase__      			=						[
											    [[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(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_	)

											lowerCAmelCase__      			=						BatchSampler(range(24	) ,							batch_size=3 ,							drop_last=SCREAMING_SNAKE_CASE_	)
											# Expected shouldn't change
											self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_	)

											# Check the shards when the dataset is a round multiple of batch size but not total batch size.
											lowerCAmelCase__      			=						BatchSampler(range(21	) ,							batch_size=3 ,							drop_last=SCREAMING_SNAKE_CASE_	)
											lowerCAmelCase__      			=						[
											    [[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(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_	)

											lowerCAmelCase__      			=						BatchSampler(range(21	) ,							batch_size=3 ,							drop_last=SCREAMING_SNAKE_CASE_	)
											lowerCAmelCase__      			=						[
											    [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
											    [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
											]
											self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_	)

											# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
											# num_processes batch.
											lowerCAmelCase__      			=						BatchSampler(range(22	) ,							batch_size=3 ,							drop_last=SCREAMING_SNAKE_CASE_	)
											lowerCAmelCase__      			=						[
											    [[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(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_	)

											lowerCAmelCase__      			=						BatchSampler(range(22	) ,							batch_size=3 ,							drop_last=SCREAMING_SNAKE_CASE_	)
											lowerCAmelCase__      			=						[
											    [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
											    [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
											]
											self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_	)

											# Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
											# num_processes batch.
											lowerCAmelCase__      			=						BatchSampler(range(20	) ,							batch_size=3 ,							drop_last=SCREAMING_SNAKE_CASE_	)
											lowerCAmelCase__      			=						[
											    [[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(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_	)

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

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

											lowerCAmelCase__      			=						BatchSampler(range(2	) ,							batch_size=3 ,							drop_last=SCREAMING_SNAKE_CASE_	)
											lowerCAmelCase__      			=						[[], []]
											self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_	)




							def 						__snake_case					(   self     :   List[str]	):
											# Check the shards when the dataset is a round multiple of batch size.
											lowerCAmelCase__      			=						BatchSampler(range(24	) ,							batch_size=4 ,							drop_last=SCREAMING_SNAKE_CASE_	)
											lowerCAmelCase__      			=						[
											    [[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(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_ ,							split_batches=SCREAMING_SNAKE_CASE_	)

											lowerCAmelCase__      			=						BatchSampler(range(24	) ,							batch_size=4 ,							drop_last=SCREAMING_SNAKE_CASE_	)
											# Expected shouldn't change
											self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_ ,							split_batches=SCREAMING_SNAKE_CASE_	)

											# Check the shards when the dataset is not a round multiple of batch size.
											lowerCAmelCase__      			=						BatchSampler(range(22	) ,							batch_size=4 ,							drop_last=SCREAMING_SNAKE_CASE_	)
											lowerCAmelCase__      			=						[
											    [[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(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_ ,							split_batches=SCREAMING_SNAKE_CASE_	)

											lowerCAmelCase__      			=						BatchSampler(range(22	) ,							batch_size=4 ,							drop_last=SCREAMING_SNAKE_CASE_	)
											lowerCAmelCase__      			=						[
											    [[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(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_ ,							split_batches=SCREAMING_SNAKE_CASE_	)

											# Check the shards when the dataset is not a round multiple of batch size or num_processes.
											lowerCAmelCase__      			=						BatchSampler(range(21	) ,							batch_size=4 ,							drop_last=SCREAMING_SNAKE_CASE_	)
											lowerCAmelCase__      			=						[
											    [[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(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_ ,							split_batches=SCREAMING_SNAKE_CASE_	)

											lowerCAmelCase__      			=						BatchSampler(range(21	) ,							batch_size=4 ,							drop_last=SCREAMING_SNAKE_CASE_	)
											lowerCAmelCase__      			=						[
											    [[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(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_ ,							split_batches=SCREAMING_SNAKE_CASE_	)

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

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




							def 						__snake_case					(   self     :   Union[str, Any]	):
											# Check the shards when the dataset is a round multiple of total batch size.
											lowerCAmelCase__      			=						BatchSampler(range(24	) ,							batch_size=3 ,							drop_last=SCREAMING_SNAKE_CASE_	)
											lowerCAmelCase__      			=						[
											    [[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(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_ ,							even_batches=SCREAMING_SNAKE_CASE_	)

											lowerCAmelCase__      			=						BatchSampler(range(24	) ,							batch_size=3 ,							drop_last=SCREAMING_SNAKE_CASE_	)
											# Expected shouldn't change
											self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_ ,							even_batches=SCREAMING_SNAKE_CASE_	)

											# Check the shards when the dataset is a round multiple of batch size but not total batch size.
											lowerCAmelCase__      			=						BatchSampler(range(21	) ,							batch_size=3 ,							drop_last=SCREAMING_SNAKE_CASE_	)
											lowerCAmelCase__      			=						[
											    [[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(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_ ,							even_batches=SCREAMING_SNAKE_CASE_	)

											lowerCAmelCase__      			=						BatchSampler(range(21	) ,							batch_size=3 ,							drop_last=SCREAMING_SNAKE_CASE_	)
											lowerCAmelCase__      			=						[
											    [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
											    [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
											]
											self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_ ,							even_batches=SCREAMING_SNAKE_CASE_	)

											# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
											# num_processes batch.
											lowerCAmelCase__      			=						BatchSampler(range(22	) ,							batch_size=3 ,							drop_last=SCREAMING_SNAKE_CASE_	)
											lowerCAmelCase__      			=						[
											    [[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(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_ ,							even_batches=SCREAMING_SNAKE_CASE_	)

											lowerCAmelCase__      			=						BatchSampler(range(22	) ,							batch_size=3 ,							drop_last=SCREAMING_SNAKE_CASE_	)
											lowerCAmelCase__      			=						[
											    [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
											    [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
											]
											self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_ ,							even_batches=SCREAMING_SNAKE_CASE_	)

											# Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
											# num_processes batch.
											lowerCAmelCase__      			=						BatchSampler(range(20	) ,							batch_size=3 ,							drop_last=SCREAMING_SNAKE_CASE_	)
											lowerCAmelCase__      			=						[
											    [[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(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_ ,							even_batches=SCREAMING_SNAKE_CASE_	)

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

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

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




							def 						__snake_case					(   self     :   List[str]	):
											# Check the shards when the dataset is a round multiple of batch size.
											lowerCAmelCase__      			=						BatchSampler(range(24	) ,							batch_size=4 ,							drop_last=SCREAMING_SNAKE_CASE_	)
											lowerCAmelCase__      			=						[
											    [[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(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_ ,							split_batches=SCREAMING_SNAKE_CASE_ ,							even_batches=SCREAMING_SNAKE_CASE_	)

											lowerCAmelCase__      			=						BatchSampler(range(24	) ,							batch_size=4 ,							drop_last=SCREAMING_SNAKE_CASE_	)
											# Expected shouldn't change
											self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_ ,							split_batches=SCREAMING_SNAKE_CASE_ ,							even_batches=SCREAMING_SNAKE_CASE_	)

											# Check the shards when the dataset is not a round multiple of batch size.
											lowerCAmelCase__      			=						BatchSampler(range(22	) ,							batch_size=4 ,							drop_last=SCREAMING_SNAKE_CASE_	)
											lowerCAmelCase__      			=						[
											    [[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(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_ ,							split_batches=SCREAMING_SNAKE_CASE_ ,							even_batches=SCREAMING_SNAKE_CASE_	)

											lowerCAmelCase__      			=						BatchSampler(range(22	) ,							batch_size=4 ,							drop_last=SCREAMING_SNAKE_CASE_	)
											lowerCAmelCase__      			=						[
											    [[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(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_ ,							split_batches=SCREAMING_SNAKE_CASE_ ,							even_batches=SCREAMING_SNAKE_CASE_	)

											# Check the shards when the dataset is not a round multiple of batch size or num_processes.
											lowerCAmelCase__      			=						BatchSampler(range(21	) ,							batch_size=4 ,							drop_last=SCREAMING_SNAKE_CASE_	)
											lowerCAmelCase__      			=						[
											    [[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(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_ ,							split_batches=SCREAMING_SNAKE_CASE_ ,							even_batches=SCREAMING_SNAKE_CASE_	)

											lowerCAmelCase__      			=						BatchSampler(range(21	) ,							batch_size=4 ,							drop_last=SCREAMING_SNAKE_CASE_	)
											lowerCAmelCase__      			=						[
											    [[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(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_ ,							split_batches=SCREAMING_SNAKE_CASE_ ,							even_batches=SCREAMING_SNAKE_CASE_	)

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

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




							def 						__snake_case					(   self     :   Optional[Any]	):
											lowerCAmelCase__      			=						[[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 10, 11], [12, 13]]
											lowerCAmelCase__      			=						[BatchSamplerShard(SCREAMING_SNAKE_CASE_ ,							2 ,							SCREAMING_SNAKE_CASE_ ,							even_batches=SCREAMING_SNAKE_CASE_	) 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 						__snake_case					(   self     :   List[Any] ,							SCREAMING_SNAKE_CASE_     :   List[str] ,							SCREAMING_SNAKE_CASE_     :   List[str] ,							SCREAMING_SNAKE_CASE_     :   int ,							SCREAMING_SNAKE_CASE_     :   Union[str, Any]=False ,							SCREAMING_SNAKE_CASE_     :   int=2 ,							SCREAMING_SNAKE_CASE_     :   List[Any]=False	):
											random.seed(SCREAMING_SNAKE_CASE_	)
											lowerCAmelCase__      			=						list(SCREAMING_SNAKE_CASE_	)

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

											lowerCAmelCase__      			=						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
											lowerCAmelCase__      			=						iterable_dataset_lists[0]
											for l in iterable_dataset_lists[1:]:
															self.assertEqual(len(SCREAMING_SNAKE_CASE_	) ,							len(SCREAMING_SNAKE_CASE_	)	)
															self.assertTrue(len(SCREAMING_SNAKE_CASE_	) % shard_batch_size == 0	)

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

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




							def 						__snake_case					(   self     :   Optional[int]	):
											lowerCAmelCase__      			=						42
											lowerCAmelCase__      			=						RandomIterableDataset()

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

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

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




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




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




							def 						__snake_case					(   self     :   Tuple	):
											lowerCAmelCase__      			=						DataLoaderShard(list(range(16	)	) ,							batch_size=4	)
											for idx, _ in enumerate(SCREAMING_SNAKE_CASE_	):
															self.assertEqual(dataloader.end_of_dataloader ,							idx == 3	)

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




							def 						__snake_case					(   self     :   str	):
											Accelerator()
											lowerCAmelCase__      			=						DataLoaderDispatcher(range(16	) ,							batch_size=4	)
											for idx, _ in enumerate(SCREAMING_SNAKE_CASE_	):
															self.assertEqual(dataloader.end_of_dataloader ,							idx == 3	)

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










from typing import TYPE_CHECKING

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


_UpperCAmelCase			:		str   				=    {
    "configuration_blenderbot_small": [
        "BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BlenderbotSmallConfig",
        "BlenderbotSmallOnnxConfig",
    ],
    "tokenization_blenderbot_small": ["BlenderbotSmallTokenizer"],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_UpperCAmelCase			:		List[str]   				=    ["BlenderbotSmallTokenizerFast"]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_UpperCAmelCase			:		List[str]   				=    [
			    "BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "BlenderbotSmallForCausalLM",
			    "BlenderbotSmallForConditionalGeneration",
			    "BlenderbotSmallModel",
			    "BlenderbotSmallPreTrainedModel",
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_UpperCAmelCase			:		Dict   				=    [
			    "TFBlenderbotSmallForConditionalGeneration",
			    "TFBlenderbotSmallModel",
			    "TFBlenderbotSmallPreTrainedModel",
			]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_UpperCAmelCase			:		List[str]   				=    [
			    "FlaxBlenderbotSmallForConditionalGeneration",
			    "FlaxBlenderbotSmallModel",
			    "FlaxBlenderbotSmallPreTrainedModel",
			]

if TYPE_CHECKING:
			from .configuration_blenderbot_small import (
			    BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    BlenderbotSmallConfig,
			    BlenderbotSmallOnnxConfig,
			)
			from .tokenization_blenderbot_small import BlenderbotSmallTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_blenderbot_small_fast import BlenderbotSmallTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_blenderbot_small import (
						    BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST,
						    BlenderbotSmallForCausalLM,
						    BlenderbotSmallForConditionalGeneration,
						    BlenderbotSmallModel,
						    BlenderbotSmallPreTrainedModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_blenderbot_small import (
						    TFBlenderbotSmallForConditionalGeneration,
						    TFBlenderbotSmallModel,
						    TFBlenderbotSmallPreTrainedModel,
						)

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_blenderbot_small import (
						    FlaxBlenderbotSmallForConditionalGeneration,
						    FlaxBlenderbotSmallModel,
						    FlaxBlenderbotSmallPreTrainedModel,
						)

else:
			import sys

			_UpperCAmelCase			:		int   				=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





