






"""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 _lowerCAmelCase						(    __UpperCAmelCase       ):







						def __init__(    self   ,				lowerCAmelCase_=0.01   ,				lowerCAmelCase_=1_0_0_0							)		->							int:
										_SCREAMING_SNAKE_CASE   :		Any       		=    p_stop
										_SCREAMING_SNAKE_CASE   :		Dict       		=    max_length







						def __iter__(    self							)		->							int:
										_SCREAMING_SNAKE_CASE   :		int       		=    0
										_SCREAMING_SNAKE_CASE   :		Any       		=    False
										while not stop and count < self.max_length:
														yield count
														count += 1
														_SCREAMING_SNAKE_CASE   :		Tuple       		=    random.random() < self.p_stop







class _lowerCAmelCase						(    unittest.TestCase       ):







						def 			A      (    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_=False   ,				lowerCAmelCase_=True							)		->							Optional[int]:
										_SCREAMING_SNAKE_CASE   :		int       		=    [
										    BatchSamplerShard(lowerCAmelCase_   ,				2   ,				lowerCAmelCase_   ,				split_batches=lowerCAmelCase_   ,				even_batches=lowerCAmelCase_							)
										    for i in range(2							)
										]
										_SCREAMING_SNAKE_CASE   :		Dict       		=    [list(lowerCAmelCase_							) for batch_sampler_shard in batch_sampler_shards]
										if not split_batches:
														self.assertListEqual([len(lowerCAmelCase_							) for shard in batch_sampler_shards]   ,				[len(lowerCAmelCase_							) for e in expected]							)
										self.assertListEqual(lowerCAmelCase_   ,				lowerCAmelCase_							)







						def 			A      (    self							)		->							Optional[int]:
										# Check the shards when the dataset is a round multiple of total batch size.
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    BatchSampler(range(2_4							)   ,				batch_size=3   ,				drop_last=lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		int       		=    [
										    [[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(lowerCAmelCase_   ,				lowerCAmelCase_							)

										_SCREAMING_SNAKE_CASE   :		Any       		=    BatchSampler(range(2_4							)   ,				batch_size=3   ,				drop_last=lowerCAmelCase_							)
										# Expected shouldn't change
										self.check_batch_sampler_shards(lowerCAmelCase_   ,				lowerCAmelCase_							)

										# Check the shards when the dataset is a round multiple of batch size but not total batch size.
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    BatchSampler(range(2_1							)   ,				batch_size=3   ,				drop_last=lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		List[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], [0, 1, 2]],
										]
										self.check_batch_sampler_shards(lowerCAmelCase_   ,				lowerCAmelCase_							)

										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    BatchSampler(range(2_1							)   ,				batch_size=3   ,				drop_last=lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		List[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(lowerCAmelCase_   ,				lowerCAmelCase_							)

										# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
										# num_processes batch.
										_SCREAMING_SNAKE_CASE   :		str       		=    BatchSampler(range(2_2							)   ,				batch_size=3   ,				drop_last=lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		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, 0, 1]],
										]
										self.check_batch_sampler_shards(lowerCAmelCase_   ,				lowerCAmelCase_							)

										_SCREAMING_SNAKE_CASE   :		Tuple       		=    BatchSampler(range(2_2							)   ,				batch_size=3   ,				drop_last=lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		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(lowerCAmelCase_   ,				lowerCAmelCase_							)

										# Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
										# num_processes batch.
										_SCREAMING_SNAKE_CASE   :		Dict       		=    BatchSampler(range(2_0							)   ,				batch_size=3   ,				drop_last=lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    [
										    [[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(lowerCAmelCase_   ,				lowerCAmelCase_							)

										_SCREAMING_SNAKE_CASE   :		List[str]       		=    BatchSampler(range(2_0							)   ,				batch_size=3   ,				drop_last=lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		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(lowerCAmelCase_   ,				lowerCAmelCase_							)

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

										_SCREAMING_SNAKE_CASE   :		int       		=    BatchSampler(range(2							)   ,				batch_size=3   ,				drop_last=lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    [[], []]
										self.check_batch_sampler_shards(lowerCAmelCase_   ,				lowerCAmelCase_							)







						def 			A      (    self							)		->							Optional[Any]:
										# Check the shards when the dataset is a round multiple of batch size.
										_SCREAMING_SNAKE_CASE   :		Any       		=    BatchSampler(range(2_4							)   ,				batch_size=4   ,				drop_last=lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    [
										    [[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(lowerCAmelCase_   ,				lowerCAmelCase_   ,				split_batches=lowerCAmelCase_							)

										_SCREAMING_SNAKE_CASE   :		List[str]       		=    BatchSampler(range(2_4							)   ,				batch_size=4   ,				drop_last=lowerCAmelCase_							)
										# Expected shouldn't change
										self.check_batch_sampler_shards(lowerCAmelCase_   ,				lowerCAmelCase_   ,				split_batches=lowerCAmelCase_							)

										# Check the shards when the dataset is not a round multiple of batch size.
										_SCREAMING_SNAKE_CASE   :		str       		=    BatchSampler(range(2_2							)   ,				batch_size=4   ,				drop_last=lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		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], [0, 1]],
										]
										self.check_batch_sampler_shards(lowerCAmelCase_   ,				lowerCAmelCase_   ,				split_batches=lowerCAmelCase_							)

										_SCREAMING_SNAKE_CASE   :		Any       		=    BatchSampler(range(2_2							)   ,				batch_size=4   ,				drop_last=lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		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(lowerCAmelCase_   ,				lowerCAmelCase_   ,				split_batches=lowerCAmelCase_							)

										# Check the shards when the dataset is not a round multiple of batch size or num_processes.
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    BatchSampler(range(2_1							)   ,				batch_size=4   ,				drop_last=lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		int       		=    [
										    [[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(lowerCAmelCase_   ,				lowerCAmelCase_   ,				split_batches=lowerCAmelCase_							)

										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    BatchSampler(range(2_1							)   ,				batch_size=4   ,				drop_last=lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		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(lowerCAmelCase_   ,				lowerCAmelCase_   ,				split_batches=lowerCAmelCase_							)

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

										_SCREAMING_SNAKE_CASE   :		int       		=    BatchSampler(range(2							)   ,				batch_size=4   ,				drop_last=lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    [[], []]
										self.check_batch_sampler_shards(lowerCAmelCase_   ,				lowerCAmelCase_   ,				split_batches=lowerCAmelCase_							)







						def 			A      (    self							)		->							List[str]:
										# Check the shards when the dataset is a round multiple of total batch size.
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    BatchSampler(range(2_4							)   ,				batch_size=3   ,				drop_last=lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		int       		=    [
										    [[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(lowerCAmelCase_   ,				lowerCAmelCase_   ,				even_batches=lowerCAmelCase_							)

										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    BatchSampler(range(2_4							)   ,				batch_size=3   ,				drop_last=lowerCAmelCase_							)
										# Expected shouldn't change
										self.check_batch_sampler_shards(lowerCAmelCase_   ,				lowerCAmelCase_   ,				even_batches=lowerCAmelCase_							)

										# Check the shards when the dataset is a round multiple of batch size but not total batch size.
										_SCREAMING_SNAKE_CASE   :		Dict       		=    BatchSampler(range(2_1							)   ,				batch_size=3   ,				drop_last=lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		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]],
										]
										self.check_batch_sampler_shards(lowerCAmelCase_   ,				lowerCAmelCase_   ,				even_batches=lowerCAmelCase_							)

										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    BatchSampler(range(2_1							)   ,				batch_size=3   ,				drop_last=lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		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(lowerCAmelCase_   ,				lowerCAmelCase_   ,				even_batches=lowerCAmelCase_							)

										# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
										# num_processes batch.
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    BatchSampler(range(2_2							)   ,				batch_size=3   ,				drop_last=lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    [
										    [[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(lowerCAmelCase_   ,				lowerCAmelCase_   ,				even_batches=lowerCAmelCase_							)

										_SCREAMING_SNAKE_CASE   :		Tuple       		=    BatchSampler(range(2_2							)   ,				batch_size=3   ,				drop_last=lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		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(lowerCAmelCase_   ,				lowerCAmelCase_   ,				even_batches=lowerCAmelCase_							)

										# Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
										# num_processes batch.
										_SCREAMING_SNAKE_CASE   :		str       		=    BatchSampler(range(2_0							)   ,				batch_size=3   ,				drop_last=lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    [
										    [[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(lowerCAmelCase_   ,				lowerCAmelCase_   ,				even_batches=lowerCAmelCase_							)

										_SCREAMING_SNAKE_CASE   :		Tuple       		=    BatchSampler(range(2_0							)   ,				batch_size=3   ,				drop_last=lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		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(lowerCAmelCase_   ,				lowerCAmelCase_   ,				even_batches=lowerCAmelCase_							)

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

										_SCREAMING_SNAKE_CASE   :		Dict       		=    BatchSampler(range(2							)   ,				batch_size=3   ,				drop_last=lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		str       		=    [[], []]
										self.check_batch_sampler_shards(lowerCAmelCase_   ,				lowerCAmelCase_   ,				even_batches=lowerCAmelCase_							)







						def 			A      (    self							)		->							Tuple:
										# Check the shards when the dataset is a round multiple of batch size.
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    BatchSampler(range(2_4							)   ,				batch_size=4   ,				drop_last=lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		str       		=    [
										    [[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(lowerCAmelCase_   ,				lowerCAmelCase_   ,				split_batches=lowerCAmelCase_   ,				even_batches=lowerCAmelCase_							)

										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    BatchSampler(range(2_4							)   ,				batch_size=4   ,				drop_last=lowerCAmelCase_							)
										# Expected shouldn't change
										self.check_batch_sampler_shards(lowerCAmelCase_   ,				lowerCAmelCase_   ,				split_batches=lowerCAmelCase_   ,				even_batches=lowerCAmelCase_							)

										# Check the shards when the dataset is not a round multiple of batch size.
										_SCREAMING_SNAKE_CASE   :		str       		=    BatchSampler(range(2_2							)   ,				batch_size=4   ,				drop_last=lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		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]],
										]
										self.check_batch_sampler_shards(lowerCAmelCase_   ,				lowerCAmelCase_   ,				split_batches=lowerCAmelCase_   ,				even_batches=lowerCAmelCase_							)

										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    BatchSampler(range(2_2							)   ,				batch_size=4   ,				drop_last=lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		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(lowerCAmelCase_   ,				lowerCAmelCase_   ,				split_batches=lowerCAmelCase_   ,				even_batches=lowerCAmelCase_							)

										# Check the shards when the dataset is not a round multiple of batch size or num_processes.
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    BatchSampler(range(2_1							)   ,				batch_size=4   ,				drop_last=lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		int       		=    [
										    [[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(lowerCAmelCase_   ,				lowerCAmelCase_   ,				split_batches=lowerCAmelCase_   ,				even_batches=lowerCAmelCase_							)

										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    BatchSampler(range(2_1							)   ,				batch_size=4   ,				drop_last=lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		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(lowerCAmelCase_   ,				lowerCAmelCase_   ,				split_batches=lowerCAmelCase_   ,				even_batches=lowerCAmelCase_							)

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

										_SCREAMING_SNAKE_CASE   :		str       		=    BatchSampler(range(2							)   ,				batch_size=4   ,				drop_last=lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		Dict       		=    [[], []]
										self.check_batch_sampler_shards(lowerCAmelCase_   ,				lowerCAmelCase_   ,				split_batches=lowerCAmelCase_   ,				even_batches=lowerCAmelCase_							)







						def 			A      (    self							)		->							List[str]:
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    [[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 1_0, 1_1], [1_2, 1_3]]
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    [BatchSamplerShard(lowerCAmelCase_   ,				2   ,				lowerCAmelCase_   ,				even_batches=lowerCAmelCase_							) for i in range(2							)]

										self.assertEqual(len(batch_sampler_shards[0]							)   ,				3							)
										self.assertEqual(len(batch_sampler_shards[1]							)   ,				2							)

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







						def 			A      (    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_=False   ,				lowerCAmelCase_=2   ,				lowerCAmelCase_=False							)		->							Optional[Any]:
										random.seed(lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    list(lowerCAmelCase_							)

										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    [
										    IterableDatasetShard(
										        lowerCAmelCase_   ,				batch_size=lowerCAmelCase_   ,				drop_last=lowerCAmelCase_   ,				num_processes=lowerCAmelCase_   ,				process_index=lowerCAmelCase_   ,				split_batches=lowerCAmelCase_   ,				)
										    for i in range(lowerCAmelCase_							)
										]
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    []
										for iterable_dataset_shard in iterable_dataset_shards:
														# Since our random iterable dataset will be... random... we need to use a seed to get reproducible results.
														random.seed(lowerCAmelCase_							)
														iterable_dataset_lists.append(list(lowerCAmelCase_							)							)

										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    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
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    iterable_dataset_lists[0]
										for l in iterable_dataset_lists[1:]:
														self.assertEqual(len(lowerCAmelCase_							)   ,				len(lowerCAmelCase_							)							)
														self.assertTrue(len(lowerCAmelCase_							) % shard_batch_size == 0							)

										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    []
										for idx in range(0   ,				len(lowerCAmelCase_							)   ,				lowerCAmelCase_							):
														for l in iterable_dataset_lists:
																		observed += l[idx : idx + shard_batch_size]

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







						def 			A      (    self							)		->							str:
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    4_2
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    RandomIterableDataset()

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

										# Edge case with a very small dataset
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    RandomIterableDataset(max_length=2							)

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







						def 			A      (    self							)		->							int:
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    BatchSampler(range(1_6							)   ,				batch_size=4   ,				drop_last=lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    SkipBatchSampler(lowerCAmelCase_   ,				2							)
										self.assertListEqual(list(lowerCAmelCase_							)   ,				[[8, 9, 1_0, 1_1], [1_2, 1_3, 1_4, 1_5]]							)







						def 			A      (    self							)		->							Any:
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    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 			A      (    self							)		->							List[Any]:
										_SCREAMING_SNAKE_CASE   :		Dict       		=    DataLoader(list(range(1_6							)							)   ,				batch_size=4							)
										_SCREAMING_SNAKE_CASE   :		Any       		=    skip_first_batches(lowerCAmelCase_   ,				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 			A      (    self							)		->							str:
										_SCREAMING_SNAKE_CASE   :		Any       		=    DataLoaderShard(list(range(1_6							)							)   ,				batch_size=4							)
										for idx, _ in enumerate(lowerCAmelCase_							):
														self.assertEqual(dataloader.end_of_dataloader   ,				idx == 3							)

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







						def 			A      (    self							)		->							Optional[int]:
										Accelerator()
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    DataLoaderDispatcher(range(1_6							)   ,				batch_size=4							)
										for idx, _ in enumerate(lowerCAmelCase_							):
														self.assertEqual(dataloader.end_of_dataloader   ,				idx == 3							)

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









"""simple docstring"""
def 				lowercase__		(					lowerCamelCase,						lowerCamelCase       ):
				return abs(lowerCamelCase       ) if a == 0 else greatest_common_divisor(b % a,						lowerCamelCase       )





def 				lowercase__		(					lowerCamelCase,						lowerCamelCase       ):
				while y:  # --> when y=0 then loop will terminate and return x as final GCD.
								_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    y, x % y
				return abs(lowerCamelCase       )





def 				lowercase__		(					):
				try:
								_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    input('Enter two integers separated by comma (,): '       ).split(','       )
								_SCREAMING_SNAKE_CASE   :		Dict       		=    int(nums[0]       )
								_SCREAMING_SNAKE_CASE   :		Dict       		=    int(nums[1]       )
								print(
								    f"""greatest_common_divisor({num_a}, {num_a}) = """
								    f"""{greatest_common_divisor(lowerCamelCase,						lowerCamelCase       )}"""       )
								print(f"""By iterative gcd({num_a}, {num_a}) = {gcd_by_iterative(lowerCamelCase,						lowerCamelCase       )}"""       )
				except (IndexError, UnboundLocalError, ValueError):
								print('Wrong input'       )


if __name__ == "__main__":
			main()

