





from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate

@dataclass(frozen=__lowercase    )
class   A							(						__lowercase    ):
      # `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
      _snake_case				  =field(default='''summarization'''		,   metadata={'''include_in_asdict_even_if_is_default''': True}    )
      _snake_case				  =Features({'''text''': Value('''string'''    )}    )
      _snake_case				  =Features({'''summary''': Value('''string'''    )}    )
      _snake_case				  ="text"
      _snake_case				  ="summary"



      @property
      def 						lowerCAmelCase__     (						self:    List[Any]							)    ->     Dict[str, str]:
         '''simple docstring'''

         return {self.text_column: "text", self.summary_column: "summary"}








import random
import unittest

from torch.utils.data import BatchSampler, DataLoader, IterableDataset

from accelerate import Accelerator
from accelerate.data_loader import (
    BatchSamplerShard,
    DataLoaderDispatcher,
    DataLoaderShard,
    IterableDatasetShard,
    SkipBatchSampler,
    SkipDataLoader,
    skip_first_batches,
)

class 						__a (							UpperCAmelCase     ):
		def __init__(     self     ,       _SCREAMING_SNAKE_CASE=0.01     ,       _SCREAMING_SNAKE_CASE=1000		)					->			str:




									"""simple docstring"""





									_UpperCAmelCase											=     p_stop
									_UpperCAmelCase											=     max_length






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




									"""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 						__a (							unittest.TestCase     ):
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE=False     ,       _SCREAMING_SNAKE_CASE=True		)					->			int:




									"""simple docstring"""





									_UpperCAmelCase											=     [
									    BatchSamplerShard(_SCREAMING_SNAKE_CASE     ,       2     ,       _SCREAMING_SNAKE_CASE     ,       split_batches=_SCREAMING_SNAKE_CASE     ,       even_batches=_SCREAMING_SNAKE_CASE		)
									    for i in range(2		)
									]
									_UpperCAmelCase											=     [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       UpperCAmelCase__							(     self		)					->			Optional[Any]:




									"""simple docstring"""





									_UpperCAmelCase											=     BatchSampler(range(24		)     ,       batch_size=3     ,       drop_last=_SCREAMING_SNAKE_CASE		)
									_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(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)

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

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

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

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

									_UpperCAmelCase											=     BatchSampler(range(2		)     ,       batch_size=3     ,       drop_last=_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase											=     [[], []]
									self.check_batch_sampler_shards(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)
		def       UpperCAmelCase__							(     self		)					->			Union[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase											=     BatchSampler(range(24		)     ,       batch_size=4     ,       drop_last=_SCREAMING_SNAKE_CASE		)
									_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(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       split_batches=_SCREAMING_SNAKE_CASE		)

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

									_UpperCAmelCase											=     BatchSampler(range(22		)     ,       batch_size=4     ,       drop_last=_SCREAMING_SNAKE_CASE		)
									_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(_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.
									_UpperCAmelCase											=     BatchSampler(range(21		)     ,       batch_size=4     ,       drop_last=_SCREAMING_SNAKE_CASE		)
									_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(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       split_batches=_SCREAMING_SNAKE_CASE		)

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

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

									_UpperCAmelCase											=     BatchSampler(range(2		)     ,       batch_size=4     ,       drop_last=_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase											=     [[], []]
									self.check_batch_sampler_shards(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       split_batches=_SCREAMING_SNAKE_CASE		)
		def       UpperCAmelCase__							(     self		)					->			Tuple:




									"""simple docstring"""





									_UpperCAmelCase											=     BatchSampler(range(24		)     ,       batch_size=3     ,       drop_last=_SCREAMING_SNAKE_CASE		)
									_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(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       even_batches=_SCREAMING_SNAKE_CASE		)

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

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

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

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

									_UpperCAmelCase											=     BatchSampler(range(2		)     ,       batch_size=3     ,       drop_last=_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase											=     [[], []]
									self.check_batch_sampler_shards(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       even_batches=_SCREAMING_SNAKE_CASE		)
		def       UpperCAmelCase__							(     self		)					->			Optional[Any]:




									"""simple docstring"""





									_UpperCAmelCase											=     BatchSampler(range(24		)     ,       batch_size=4     ,       drop_last=_SCREAMING_SNAKE_CASE		)
									_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(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       split_batches=_SCREAMING_SNAKE_CASE     ,       even_batches=_SCREAMING_SNAKE_CASE		)

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

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

									_UpperCAmelCase											=     BatchSampler(range(21		)     ,       batch_size=4     ,       drop_last=_SCREAMING_SNAKE_CASE		)
									_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(_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.
									_UpperCAmelCase											=     BatchSampler(range(2		)     ,       batch_size=4     ,       drop_last=_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase											=     [[[0, 1]], []]
									self.check_batch_sampler_shards(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       split_batches=_SCREAMING_SNAKE_CASE     ,       even_batches=_SCREAMING_SNAKE_CASE		)

									_UpperCAmelCase											=     BatchSampler(range(2		)     ,       batch_size=4     ,       drop_last=_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase											=     [[], []]
									self.check_batch_sampler_shards(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       split_batches=_SCREAMING_SNAKE_CASE     ,       even_batches=_SCREAMING_SNAKE_CASE		)
		def       UpperCAmelCase__							(     self		)					->			Optional[Any]:




									"""simple docstring"""





									_UpperCAmelCase											=     [[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 10, 11], [12, 13]]
									_UpperCAmelCase											=     [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       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE=False     ,       _SCREAMING_SNAKE_CASE=2     ,       _SCREAMING_SNAKE_CASE=False		)					->			Dict:




									"""simple docstring"""





									random.seed(_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase											=     list(_SCREAMING_SNAKE_CASE		)

									_UpperCAmelCase											=     [
									    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		)
									]
									_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(_SCREAMING_SNAKE_CASE		)
																iterable_dataset_lists.append(list(_SCREAMING_SNAKE_CASE		)		)

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

									_UpperCAmelCase											=     []
									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       UpperCAmelCase__							(     self		)					->			List[Any]:




									"""simple docstring"""





									_UpperCAmelCase											=     42
									_UpperCAmelCase											=     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
									_UpperCAmelCase											=     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       UpperCAmelCase__							(     self		)					->			str:




									"""simple docstring"""





									_UpperCAmelCase											=     BatchSampler(range(16		)     ,       batch_size=4     ,       drop_last=_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase											=     SkipBatchSampler(_SCREAMING_SNAKE_CASE     ,       2		)
									self.assertListEqual(list(_SCREAMING_SNAKE_CASE		)     ,       [[8, 9, 10, 11], [12, 13, 14, 15]]		)
		def       UpperCAmelCase__							(     self		)					->			Tuple:




									"""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       UpperCAmelCase__							(     self		)					->			int:




									"""simple docstring"""





									_UpperCAmelCase											=     DataLoader(list(range(16		)		)     ,       batch_size=4		)
									_UpperCAmelCase											=     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       UpperCAmelCase__							(     self		)					->			int:




									"""simple docstring"""





									_UpperCAmelCase											=     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       UpperCAmelCase__							(     self		)					->			Any:




									"""simple docstring"""





									Accelerator()
									_UpperCAmelCase											=     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		)
