



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  __magic_name__   (							A__						):






		def __init__(						self      :     Tuple				,		UpperCamelCase__      :     Optional[Any]=0.01				,		UpperCamelCase__      :     str=10_00      )				->					Optional[int]:
								'''simple docstring'''







								UpperCAmelCase         =     p_stop
								UpperCAmelCase         =     max_length







		def __iter__(						self      :     Dict      )				->					Optional[Any]:
								'''simple docstring'''







								UpperCAmelCase         =     0
								UpperCAmelCase         =     False
								while not stop and count < self.max_length:
														yield count
														count += 1
														UpperCAmelCase         =     random.random() < self.p_stop





class  __magic_name__   (							unittest.TestCase						):






		def SCREAMING_SNAKE_CASE_			(						self      :     Tuple				,		UpperCamelCase__      :     Optional[Any]				,		UpperCamelCase__      :     int				,		UpperCamelCase__      :     List[Any]=False				,		UpperCamelCase__      :     List[str]=True      )				->					str:
								'''simple docstring'''







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






		def SCREAMING_SNAKE_CASE_			(						self      :     Tuple      )				->					List[Any]:
								'''simple docstring'''







								UpperCAmelCase         =     BatchSampler(range(24      )				,		batch_size=3				,		drop_last=UpperCamelCase__      )
								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(UpperCamelCase__				,		UpperCamelCase__      )

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

								# 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=UpperCamelCase__      )
								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(UpperCamelCase__				,		UpperCamelCase__      )

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

								# 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=UpperCamelCase__      )
								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(UpperCamelCase__				,		UpperCamelCase__      )

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

								# 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=UpperCamelCase__      )
								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(UpperCamelCase__				,		UpperCamelCase__      )

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

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

								UpperCAmelCase         =     BatchSampler(range(2      )				,		batch_size=3				,		drop_last=UpperCamelCase__      )
								UpperCAmelCase         =     [[], []]
								self.check_batch_sampler_shards(UpperCamelCase__				,		UpperCamelCase__      )






		def SCREAMING_SNAKE_CASE_			(						self      :     List[str]      )				->					Any:
								'''simple docstring'''







								UpperCAmelCase         =     BatchSampler(range(24      )				,		batch_size=4				,		drop_last=UpperCamelCase__      )
								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(UpperCamelCase__				,		UpperCamelCase__				,		split_batches=UpperCamelCase__      )

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

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

								UpperCAmelCase         =     BatchSampler(range(22      )				,		batch_size=4				,		drop_last=UpperCamelCase__      )
								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(UpperCamelCase__				,		UpperCamelCase__				,		split_batches=UpperCamelCase__      )

								# 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=UpperCamelCase__      )
								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(UpperCamelCase__				,		UpperCamelCase__				,		split_batches=UpperCamelCase__      )

								UpperCAmelCase         =     BatchSampler(range(21      )				,		batch_size=4				,		drop_last=UpperCamelCase__      )
								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(UpperCamelCase__				,		UpperCamelCase__				,		split_batches=UpperCamelCase__      )

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

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






		def SCREAMING_SNAKE_CASE_			(						self      :     Dict      )				->					Optional[int]:
								'''simple docstring'''







								UpperCAmelCase         =     BatchSampler(range(24      )				,		batch_size=3				,		drop_last=UpperCamelCase__      )
								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(UpperCamelCase__				,		UpperCamelCase__				,		even_batches=UpperCamelCase__      )

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

								# 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=UpperCamelCase__      )
								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(UpperCamelCase__				,		UpperCamelCase__				,		even_batches=UpperCamelCase__      )

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

								# 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=UpperCamelCase__      )
								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(UpperCamelCase__				,		UpperCamelCase__				,		even_batches=UpperCamelCase__      )

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

								# 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=UpperCamelCase__      )
								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(UpperCamelCase__				,		UpperCamelCase__				,		even_batches=UpperCamelCase__      )

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

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

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






		def SCREAMING_SNAKE_CASE_			(						self      :     List[Any]      )				->					Dict:
								'''simple docstring'''







								UpperCAmelCase         =     BatchSampler(range(24      )				,		batch_size=4				,		drop_last=UpperCamelCase__      )
								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(UpperCamelCase__				,		UpperCamelCase__				,		split_batches=UpperCamelCase__				,		even_batches=UpperCamelCase__      )

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

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

								UpperCAmelCase         =     BatchSampler(range(22      )				,		batch_size=4				,		drop_last=UpperCamelCase__      )
								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(UpperCamelCase__				,		UpperCamelCase__				,		split_batches=UpperCamelCase__				,		even_batches=UpperCamelCase__      )

								# 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=UpperCamelCase__      )
								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(UpperCamelCase__				,		UpperCamelCase__				,		split_batches=UpperCamelCase__				,		even_batches=UpperCamelCase__      )

								UpperCAmelCase         =     BatchSampler(range(21      )				,		batch_size=4				,		drop_last=UpperCamelCase__      )
								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(UpperCamelCase__				,		UpperCamelCase__				,		split_batches=UpperCamelCase__				,		even_batches=UpperCamelCase__      )

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

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






		def SCREAMING_SNAKE_CASE_			(						self      :     Tuple      )				->					Union[str, Any]:
								'''simple docstring'''







								UpperCAmelCase         =     [[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 10, 11], [12, 13]]
								UpperCAmelCase         =     [BatchSamplerShard(UpperCamelCase__				,		2				,		UpperCamelCase__				,		even_batches=UpperCamelCase__      ) 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 SCREAMING_SNAKE_CASE_			(						self      :     int				,		UpperCamelCase__      :     List[Any]				,		UpperCamelCase__      :     Optional[int]				,		UpperCamelCase__      :     Tuple				,		UpperCamelCase__      :     Union[str, Any]=False				,		UpperCamelCase__      :     int=2				,		UpperCamelCase__      :     str=False      )				->					Optional[int]:
								'''simple docstring'''







								random.seed(UpperCamelCase__      )
								UpperCAmelCase         =     list(UpperCamelCase__      )

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

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

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

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






		def SCREAMING_SNAKE_CASE_			(						self      :     List[str]      )				->					int:
								'''simple docstring'''







								UpperCAmelCase         =     42
								UpperCAmelCase         =     RandomIterableDataset()

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

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

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






		def SCREAMING_SNAKE_CASE_			(						self      :     Tuple      )				->					Dict:
								'''simple docstring'''







								UpperCAmelCase         =     BatchSampler(range(16      )				,		batch_size=4				,		drop_last=UpperCamelCase__      )
								UpperCAmelCase         =     SkipBatchSampler(UpperCamelCase__				,		2      )
								self.assertListEqual(list(UpperCamelCase__      )				,		[[8, 9, 10, 11], [12, 13, 14, 15]]      )






		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[Any]      )				->					Optional[int]:
								'''simple docstring'''







								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 SCREAMING_SNAKE_CASE_			(						self      :     Union[str, Any]      )				->					Any:
								'''simple docstring'''







								UpperCAmelCase         =     DataLoader(list(range(16      )      )				,		batch_size=4      )
								UpperCAmelCase         =     skip_first_batches(UpperCamelCase__				,		num_batches=2      )
								self.assertListEqual([t.tolist() for t in new_dataloader]				,		[[8, 9, 10, 11], [12, 13, 14, 15]]      )






		def SCREAMING_SNAKE_CASE_			(						self      :     Union[str, Any]      )				->					Optional[Any]:
								'''simple docstring'''







								UpperCAmelCase         =     DataLoaderShard(list(range(16      )      )				,		batch_size=4      )
								for idx, _ in enumerate(UpperCamelCase__      ):
														self.assertEqual(dataloader.end_of_dataloader				,		idx == 3      )

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







		def SCREAMING_SNAKE_CASE_			(						self      :     Any      )				->					str:
								'''simple docstring'''







								Accelerator()
								UpperCAmelCase         =     DataLoaderDispatcher(range(16      )				,		batch_size=4      )
								for idx, _ in enumerate(UpperCamelCase__      ):
														self.assertEqual(dataloader.end_of_dataloader				,		idx == 3      )

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








import tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
		import torch





@require_torch
@require_optimum
@slow
class  __magic_name__   (							unittest.TestCase						):






		def SCREAMING_SNAKE_CASE_			(						self      :     Tuple      )				->					Optional[int]:
								'''simple docstring'''







								UpperCAmelCase         =     "hf-internal-testing/tiny-random-t5"
								UpperCAmelCase         =     AutoTokenizer.from_pretrained(UpperCamelCase__      )
								UpperCAmelCase         =     AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase__      )

								UpperCAmelCase         =     tokenizer("This is me"				,		return_tensors="pt"      )

								UpperCAmelCase         =     model.to_bettertransformer()

								self.assertTrue(any("BetterTransformer" in mod.__class__.__name__ for _, mod in model.named_modules()      )      )

								UpperCAmelCase         =     model.generate(**UpperCamelCase__      )

								UpperCAmelCase         =     model.reverse_bettertransformer()

								self.assertFalse(any("BetterTransformer" in mod.__class__.__name__ for _, mod in model.named_modules()      )      )

								with tempfile.TemporaryDirectory() as tmpdirname:
														model.save_pretrained(UpperCamelCase__      )

														UpperCAmelCase         =     AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase__      )

														self.assertFalse(
														    any("BetterTransformer" in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()      )      )

														UpperCAmelCase         =     model_reloaded.generate(**UpperCamelCase__      )
														self.assertTrue(torch.allclose(UpperCamelCase__				,		UpperCamelCase__      )      )







		def SCREAMING_SNAKE_CASE_			(						self      :     List[str]      )				->					Optional[int]:
								'''simple docstring'''







								UpperCAmelCase         =     "hf-internal-testing/tiny-random-t5"
								UpperCAmelCase         =     AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase__      )

								UpperCAmelCase         =     model.to_bettertransformer()

								with tempfile.TemporaryDirectory() as tmpdirname:
														with self.assertRaises(UpperCamelCase__      ):
																				model.save_pretrained(UpperCamelCase__      )

														UpperCAmelCase         =     model.reverse_bettertransformer()
														model.save_pretrained(UpperCamelCase__      )



