

"""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    snake_case     (    __snake_case  ):

				def __init__(     self							:    Dict					,  UpperCamelCase__							:    List[str]=0.01					,  UpperCamelCase__							:    Optional[int]=1_0_0_0)->       Dict:



						'''simple docstring'''


						__lowerCAmelCase:	Dict   				=      p_stop
						__lowerCAmelCase:	Tuple   				=      max_length




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



						'''simple docstring'''


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





class    snake_case     (    unittest.TestCase  ):

				def 							lowercase_					(     self							:    str					,  UpperCamelCase__							:    Tuple					,  UpperCamelCase__							:    Optional[int]					,  UpperCamelCase__							:    str=False					,  UpperCamelCase__							:    Tuple=True)->       Optional[Any]:



						'''simple docstring'''


						__lowerCAmelCase:	Dict   				=      [
						    BatchSamplerShard(UpperCamelCase__					,  2					,  UpperCamelCase__					,  split_batches=UpperCamelCase__					,  even_batches=UpperCamelCase__)
						    for i in range(2)
						]
						__lowerCAmelCase:	Tuple   				=      [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 							lowercase_					(     self							:    Any)->       List[Any]:



						'''simple docstring'''


						__lowerCAmelCase:	Optional[Any]   				=      BatchSampler(range(2_4)					,  batch_size=3					,  drop_last=UpperCamelCase__)
						__lowerCAmelCase:	Tuple   				=      [
						    [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4], [1_8, 1_9, 2_0]],
						    [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7], [2_1, 2_2, 2_3]],
						]
						self.check_batch_sampler_shards(UpperCamelCase__					,  UpperCamelCase__)

						__lowerCAmelCase:	Optional[Any]   				=      BatchSampler(range(2_4)					,  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.
						__lowerCAmelCase:	List[str]   				=      BatchSampler(range(2_1)					,  batch_size=3					,  drop_last=UpperCamelCase__)
						__lowerCAmelCase:	Union[str, Any]   				=      [
						    [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4], [1_8, 1_9, 2_0]],
						    [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7], [0, 1, 2]],
						]
						self.check_batch_sampler_shards(UpperCamelCase__					,  UpperCamelCase__)

						__lowerCAmelCase:	Tuple   				=      BatchSampler(range(2_1)					,  batch_size=3					,  drop_last=UpperCamelCase__)
						__lowerCAmelCase:	Optional[int]   				=      [
						    [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4]],
						    [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7]],
						]
						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.
						__lowerCAmelCase:	int   				=      BatchSampler(range(2_2)					,  batch_size=3					,  drop_last=UpperCamelCase__)
						__lowerCAmelCase:	str   				=      [
						    [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4], [1_8, 1_9, 2_0]],
						    [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7], [2_1, 0, 1]],
						]
						self.check_batch_sampler_shards(UpperCamelCase__					,  UpperCamelCase__)

						__lowerCAmelCase:	int   				=      BatchSampler(range(2_2)					,  batch_size=3					,  drop_last=UpperCamelCase__)
						__lowerCAmelCase:	Dict   				=      [
						    [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4]],
						    [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7]],
						]
						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.
						__lowerCAmelCase:	List[Any]   				=      BatchSampler(range(2_0)					,  batch_size=3					,  drop_last=UpperCamelCase__)
						__lowerCAmelCase:	List[str]   				=      [
						    [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4], [1_8, 1_9, 0]],
						    [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7], [1, 2, 3]],
						]
						self.check_batch_sampler_shards(UpperCamelCase__					,  UpperCamelCase__)

						__lowerCAmelCase:	Optional[int]   				=      BatchSampler(range(2_0)					,  batch_size=3					,  drop_last=UpperCamelCase__)
						__lowerCAmelCase:	str   				=      [
						    [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4]],
						    [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7]],
						]
						self.check_batch_sampler_shards(UpperCamelCase__					,  UpperCamelCase__)

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

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

				def 							lowercase_					(     self							:    List[Any])->       Dict:



						'''simple docstring'''


						__lowerCAmelCase:	Dict   				=      BatchSampler(range(2_4)					,  batch_size=4					,  drop_last=UpperCamelCase__)
						__lowerCAmelCase:	Dict   				=      [
						    [[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7], [2_0, 2_1]],
						    [[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9], [2_2, 2_3]],
						]
						self.check_batch_sampler_shards(UpperCamelCase__					,  UpperCamelCase__					,  split_batches=UpperCamelCase__)

						__lowerCAmelCase:	List[str]   				=      BatchSampler(range(2_4)					,  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.
						__lowerCAmelCase:	Tuple   				=      BatchSampler(range(2_2)					,  batch_size=4					,  drop_last=UpperCamelCase__)
						__lowerCAmelCase:	Optional[Any]   				=      [
						    [[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7], [2_0, 2_1]],
						    [[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9], [0, 1]],
						]
						self.check_batch_sampler_shards(UpperCamelCase__					,  UpperCamelCase__					,  split_batches=UpperCamelCase__)

						__lowerCAmelCase:	List[Any]   				=      BatchSampler(range(2_2)					,  batch_size=4					,  drop_last=UpperCamelCase__)
						__lowerCAmelCase:	Tuple   				=      [
						    [[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7]],
						    [[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9]],
						]
						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.
						__lowerCAmelCase:	Union[str, Any]   				=      BatchSampler(range(2_1)					,  batch_size=4					,  drop_last=UpperCamelCase__)
						__lowerCAmelCase:	Optional[Any]   				=      [
						    [[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7], [2_0, 0]],
						    [[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9], [1, 2]],
						]
						self.check_batch_sampler_shards(UpperCamelCase__					,  UpperCamelCase__					,  split_batches=UpperCamelCase__)

						__lowerCAmelCase:	Any   				=      BatchSampler(range(2_1)					,  batch_size=4					,  drop_last=UpperCamelCase__)
						__lowerCAmelCase:	Dict   				=      [
						    [[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7]],
						    [[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9]],
						]
						self.check_batch_sampler_shards(UpperCamelCase__					,  UpperCamelCase__					,  split_batches=UpperCamelCase__)

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

						__lowerCAmelCase:	str   				=      BatchSampler(range(2)					,  batch_size=4					,  drop_last=UpperCamelCase__)
						__lowerCAmelCase:	Tuple   				=      [[], []]
						self.check_batch_sampler_shards(UpperCamelCase__					,  UpperCamelCase__					,  split_batches=UpperCamelCase__)

				def 							lowercase_					(     self							:    int)->       Dict:



						'''simple docstring'''


						__lowerCAmelCase:	List[Any]   				=      BatchSampler(range(2_4)					,  batch_size=3					,  drop_last=UpperCamelCase__)
						__lowerCAmelCase:	Dict   				=      [
						    [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4], [1_8, 1_9, 2_0]],
						    [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7], [2_1, 2_2, 2_3]],
						]
						self.check_batch_sampler_shards(UpperCamelCase__					,  UpperCamelCase__					,  even_batches=UpperCamelCase__)

						__lowerCAmelCase:	Union[str, Any]   				=      BatchSampler(range(2_4)					,  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.
						__lowerCAmelCase:	List[Any]   				=      BatchSampler(range(2_1)					,  batch_size=3					,  drop_last=UpperCamelCase__)
						__lowerCAmelCase:	Any   				=      [
						    [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4], [1_8, 1_9, 2_0]],
						    [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7]],
						]
						self.check_batch_sampler_shards(UpperCamelCase__					,  UpperCamelCase__					,  even_batches=UpperCamelCase__)

						__lowerCAmelCase:	int   				=      BatchSampler(range(2_1)					,  batch_size=3					,  drop_last=UpperCamelCase__)
						__lowerCAmelCase:	Tuple   				=      [
						    [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4]],
						    [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7]],
						]
						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.
						__lowerCAmelCase:	Tuple   				=      BatchSampler(range(2_2)					,  batch_size=3					,  drop_last=UpperCamelCase__)
						__lowerCAmelCase:	Dict   				=      [
						    [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4], [1_8, 1_9, 2_0]],
						    [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7], [2_1]],
						]
						self.check_batch_sampler_shards(UpperCamelCase__					,  UpperCamelCase__					,  even_batches=UpperCamelCase__)

						__lowerCAmelCase:	Dict   				=      BatchSampler(range(2_2)					,  batch_size=3					,  drop_last=UpperCamelCase__)
						__lowerCAmelCase:	Dict   				=      [
						    [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4]],
						    [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7]],
						]
						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.
						__lowerCAmelCase:	Optional[Any]   				=      BatchSampler(range(2_0)					,  batch_size=3					,  drop_last=UpperCamelCase__)
						__lowerCAmelCase:	str   				=      [
						    [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4], [1_8, 1_9]],
						    [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7]],
						]
						self.check_batch_sampler_shards(UpperCamelCase__					,  UpperCamelCase__					,  even_batches=UpperCamelCase__)

						__lowerCAmelCase:	List[Any]   				=      BatchSampler(range(2_0)					,  batch_size=3					,  drop_last=UpperCamelCase__)
						__lowerCAmelCase:	Optional[Any]   				=      [
						    [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4]],
						    [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7]],
						]
						self.check_batch_sampler_shards(UpperCamelCase__					,  UpperCamelCase__					,  even_batches=UpperCamelCase__)

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

						__lowerCAmelCase:	Dict   				=      BatchSampler(range(2)					,  batch_size=3					,  drop_last=UpperCamelCase__)
						__lowerCAmelCase:	Tuple   				=      [[], []]
						self.check_batch_sampler_shards(UpperCamelCase__					,  UpperCamelCase__					,  even_batches=UpperCamelCase__)

				def 							lowercase_					(     self							:    int)->       Tuple:



						'''simple docstring'''


						__lowerCAmelCase:	List[Any]   				=      BatchSampler(range(2_4)					,  batch_size=4					,  drop_last=UpperCamelCase__)
						__lowerCAmelCase:	Optional[int]   				=      [
						    [[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7], [2_0, 2_1]],
						    [[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9], [2_2, 2_3]],
						]
						self.check_batch_sampler_shards(UpperCamelCase__					,  UpperCamelCase__					,  split_batches=UpperCamelCase__					,  even_batches=UpperCamelCase__)

						__lowerCAmelCase:	List[Any]   				=      BatchSampler(range(2_4)					,  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.
						__lowerCAmelCase:	str   				=      BatchSampler(range(2_2)					,  batch_size=4					,  drop_last=UpperCamelCase__)
						__lowerCAmelCase:	Optional[int]   				=      [
						    [[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7], [2_0, 2_1]],
						    [[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9]],
						]
						self.check_batch_sampler_shards(UpperCamelCase__					,  UpperCamelCase__					,  split_batches=UpperCamelCase__					,  even_batches=UpperCamelCase__)

						__lowerCAmelCase:	int   				=      BatchSampler(range(2_2)					,  batch_size=4					,  drop_last=UpperCamelCase__)
						__lowerCAmelCase:	str   				=      [
						    [[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7]],
						    [[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9]],
						]
						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.
						__lowerCAmelCase:	Dict   				=      BatchSampler(range(2_1)					,  batch_size=4					,  drop_last=UpperCamelCase__)
						__lowerCAmelCase:	List[str]   				=      [
						    [[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7], [2_0]],
						    [[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9]],
						]
						self.check_batch_sampler_shards(UpperCamelCase__					,  UpperCamelCase__					,  split_batches=UpperCamelCase__					,  even_batches=UpperCamelCase__)

						__lowerCAmelCase:	Tuple   				=      BatchSampler(range(2_1)					,  batch_size=4					,  drop_last=UpperCamelCase__)
						__lowerCAmelCase:	Optional[Any]   				=      [
						    [[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7]],
						    [[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9]],
						]
						self.check_batch_sampler_shards(UpperCamelCase__					,  UpperCamelCase__					,  split_batches=UpperCamelCase__					,  even_batches=UpperCamelCase__)

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

						__lowerCAmelCase:	str   				=      BatchSampler(range(2)					,  batch_size=4					,  drop_last=UpperCamelCase__)
						__lowerCAmelCase:	Union[str, Any]   				=      [[], []]
						self.check_batch_sampler_shards(UpperCamelCase__					,  UpperCamelCase__					,  split_batches=UpperCamelCase__					,  even_batches=UpperCamelCase__)

				def 							lowercase_					(     self							:    Union[str, Any])->       Union[str, Any]:



						'''simple docstring'''


						__lowerCAmelCase:	int   				=      [[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 1_0, 1_1], [1_2, 1_3]]
						__lowerCAmelCase:	Tuple   				=      [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], [1_2, 1_3]])
						self.assertListEqual(list(batch_sampler_shards[1])					,  [[3, 4], [9, 1_0, 1_1]])

				def 							lowercase_					(     self							:    List[Any]					,  UpperCamelCase__							:    Union[str, Any]					,  UpperCamelCase__							:    List[Any]					,  UpperCamelCase__							:    Tuple					,  UpperCamelCase__							:    Optional[Any]=False					,  UpperCamelCase__							:    str=2					,  UpperCamelCase__							:    Optional[int]=False)->       Union[str, Any]:



						'''simple docstring'''


						random.seed(UpperCamelCase__)
						__lowerCAmelCase:	Union[str, Any]   				=      list(UpperCamelCase__)

						__lowerCAmelCase:	List[str]   				=      [
						    IterableDatasetShard(
						        UpperCamelCase__					,  batch_size=UpperCamelCase__					,  drop_last=UpperCamelCase__					,  num_processes=UpperCamelCase__					,  process_index=UpperCamelCase__					,  split_batches=UpperCamelCase__					,  )
						    for i in range(UpperCamelCase__)
						]
						__lowerCAmelCase:	Tuple   				=      []
						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__))

						__lowerCAmelCase:	Optional[int]   				=      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:	int   				=      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)

						__lowerCAmelCase:	Optional[int]   				=      []
						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 							lowercase_					(     self							:    int)->       Any:



						'''simple docstring'''


						__lowerCAmelCase:	List[Any]   				=      4_2
						__lowerCAmelCase:	Optional[Any]   				=      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
						__lowerCAmelCase:	int   				=      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 							lowercase_					(     self							:    Dict)->       Any:



						'''simple docstring'''


						__lowerCAmelCase:	Any   				=      BatchSampler(range(1_6)					,  batch_size=4					,  drop_last=UpperCamelCase__)
						__lowerCAmelCase:	int   				=      SkipBatchSampler(UpperCamelCase__					,  2)
						self.assertListEqual(list(UpperCamelCase__)					,  [[8, 9, 1_0, 1_1], [1_2, 1_3, 1_4, 1_5]])

				def 							lowercase_					(     self							:    Optional[Any])->       Dict:



						'''simple docstring'''


						__lowerCAmelCase:	List[Any]   				=      SkipDataLoader(list(range(1_6))					,  batch_size=4					,  skip_batches=2)
						self.assertListEqual([t.tolist() for t in dataloader]					,  [[8, 9, 1_0, 1_1], [1_2, 1_3, 1_4, 1_5]])

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



						'''simple docstring'''


						__lowerCAmelCase:	List[str]   				=      DataLoader(list(range(1_6))					,  batch_size=4)
						__lowerCAmelCase:	str   				=      skip_first_batches(UpperCamelCase__					,  num_batches=2)
						self.assertListEqual([t.tolist() for t in new_dataloader]					,  [[8, 9, 1_0, 1_1], [1_2, 1_3, 1_4, 1_5]])

				def 							lowercase_					(     self							:    str)->       str:



						'''simple docstring'''


						__lowerCAmelCase:	Any   				=      DataLoaderShard(list(range(1_6))					,  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 							lowercase_					(     self							:    str)->       int:



						'''simple docstring'''


						Accelerator()
						__lowerCAmelCase:	List[str]   				=      DataLoaderDispatcher(range(1_6)					,  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)





"""simple docstring"""


from typing import TYPE_CHECKING

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


__A						       =      {
    "configuration_speecht5": [
        "SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP",
        "SpeechT5Config",
        "SpeechT5HifiGanConfig",
    ],
    "feature_extraction_speecht5": ["SpeechT5FeatureExtractor"],
    "processing_speecht5": ["SpeechT5Processor"],
}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A						       =      ["SpeechT5Tokenizer"]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A						       =      [
			    "SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "SpeechT5ForSpeechToText",
			    "SpeechT5ForSpeechToSpeech",
			    "SpeechT5ForTextToSpeech",
			    "SpeechT5Model",
			    "SpeechT5PreTrainedModel",
			    "SpeechT5HifiGan",
			]

if TYPE_CHECKING:
			from .configuration_speechta import (
			    SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
			    SpeechTaConfig,
			    SpeechTaHifiGanConfig,
			)
			from .feature_extraction_speechta import SpeechTaFeatureExtractor
			from .processing_speechta import SpeechTaProcessor

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_speechta import SpeechTaTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_speechta import (
						    SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
						    SpeechTaForSpeechToSpeech,
						    SpeechTaForSpeechToText,
						    SpeechTaForTextToSpeech,
						    SpeechTaHifiGan,
						    SpeechTaModel,
						    SpeechTaPreTrainedModel,
						)

else:
			import sys

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


