
"""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  A__       (	__a):

		def __init__(      self       ,		_SCREAMING_SNAKE_CASE=0.01       ,		_SCREAMING_SNAKE_CASE=10_00 ):
			__lowerCAmelCase			:						List[Any]									=      p_stop
			__lowerCAmelCase			:						List[Any]									=      max_length







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




class  A__       (	unittest.TestCase):

		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE=False       ,		_SCREAMING_SNAKE_CASE=True ):
			__lowerCAmelCase			:						int									=      [
			    BatchSamplerShard(_SCREAMING_SNAKE_CASE       ,		2       ,		_SCREAMING_SNAKE_CASE       ,		split_batches=_SCREAMING_SNAKE_CASE       ,		even_batches=_SCREAMING_SNAKE_CASE )
			    for i in range(2 )
			]
			__lowerCAmelCase			:						Dict									=      [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 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						List[Any]									=      BatchSampler(range(24 )       ,		batch_size=3       ,		drop_last=_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						str									=      [
			    [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
			    [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]],
			]
			self.check_batch_sampler_shards(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE )

			__lowerCAmelCase			:						Optional[Any]									=      BatchSampler(range(24 )       ,		batch_size=3       ,		drop_last=_SCREAMING_SNAKE_CASE )
			# Expected shouldn't change
			self.check_batch_sampler_shards(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE )

			# Check the shards when the dataset is a round multiple of batch size but not total batch size.
			__lowerCAmelCase			:						Optional[int]									=      BatchSampler(range(21 )       ,		batch_size=3       ,		drop_last=_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						Tuple									=      [
			    [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
			    [[3, 4, 5], [9, 10, 11], [15, 16, 17], [0, 1, 2]],
			]
			self.check_batch_sampler_shards(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE )

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

			# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
			# num_processes batch.
			__lowerCAmelCase			:						Tuple									=      BatchSampler(range(22 )       ,		batch_size=3       ,		drop_last=_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						Tuple									=      [
			    [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
			    [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 0, 1]],
			]
			self.check_batch_sampler_shards(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE )

			__lowerCAmelCase			:						Union[str, Any]									=      BatchSampler(range(22 )       ,		batch_size=3       ,		drop_last=_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						Dict									=      [
			    [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
			    [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
			]
			self.check_batch_sampler_shards(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE )

			# Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
			# num_processes batch.
			__lowerCAmelCase			:						Any									=      BatchSampler(range(20 )       ,		batch_size=3       ,		drop_last=_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						Tuple									=      [
			    [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 0]],
			    [[3, 4, 5], [9, 10, 11], [15, 16, 17], [1, 2, 3]],
			]
			self.check_batch_sampler_shards(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE )

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

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

			__lowerCAmelCase			:						List[str]									=      BatchSampler(range(2 )       ,		batch_size=3       ,		drop_last=_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						Optional[Any]									=      [[], []]
			self.check_batch_sampler_shards(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE )

		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						Union[str, Any]									=      BatchSampler(range(24 )       ,		batch_size=4       ,		drop_last=_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						List[Any]									=      [
			    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
			    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23]],
			]
			self.check_batch_sampler_shards(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		split_batches=_SCREAMING_SNAKE_CASE )

			__lowerCAmelCase			:						Tuple									=      BatchSampler(range(24 )       ,		batch_size=4       ,		drop_last=_SCREAMING_SNAKE_CASE )
			# Expected shouldn't change
			self.check_batch_sampler_shards(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		split_batches=_SCREAMING_SNAKE_CASE )

			# Check the shards when the dataset is not a round multiple of batch size.
			__lowerCAmelCase			:						Optional[Any]									=      BatchSampler(range(22 )       ,		batch_size=4       ,		drop_last=_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						List[str]									=      [
			    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
			    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [0, 1]],
			]
			self.check_batch_sampler_shards(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		split_batches=_SCREAMING_SNAKE_CASE )

			__lowerCAmelCase			:						Dict									=      BatchSampler(range(22 )       ,		batch_size=4       ,		drop_last=_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						List[Any]									=      [
			    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
			    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
			]
			self.check_batch_sampler_shards(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		split_batches=_SCREAMING_SNAKE_CASE )

			# Check the shards when the dataset is not a round multiple of batch size or num_processes.
			__lowerCAmelCase			:						Dict									=      BatchSampler(range(21 )       ,		batch_size=4       ,		drop_last=_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						Union[str, Any]									=      [
			    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 0]],
			    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [1, 2]],
			]
			self.check_batch_sampler_shards(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		split_batches=_SCREAMING_SNAKE_CASE )

			__lowerCAmelCase			:						int									=      BatchSampler(range(21 )       ,		batch_size=4       ,		drop_last=_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						int									=      [
			    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
			    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
			]
			self.check_batch_sampler_shards(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		split_batches=_SCREAMING_SNAKE_CASE )

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

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

		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						Dict									=      BatchSampler(range(24 )       ,		batch_size=3       ,		drop_last=_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						Tuple									=      [
			    [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
			    [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]],
			]
			self.check_batch_sampler_shards(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		even_batches=_SCREAMING_SNAKE_CASE )

			__lowerCAmelCase			:						List[str]									=      BatchSampler(range(24 )       ,		batch_size=3       ,		drop_last=_SCREAMING_SNAKE_CASE )
			# Expected shouldn't change
			self.check_batch_sampler_shards(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		even_batches=_SCREAMING_SNAKE_CASE )

			# Check the shards when the dataset is a round multiple of batch size but not total batch size.
			__lowerCAmelCase			:						Optional[int]									=      BatchSampler(range(21 )       ,		batch_size=3       ,		drop_last=_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						Union[str, Any]									=      [
			    [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
			    [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
			]
			self.check_batch_sampler_shards(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		even_batches=_SCREAMING_SNAKE_CASE )

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

			# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
			# num_processes batch.
			__lowerCAmelCase			:						Optional[Any]									=      BatchSampler(range(22 )       ,		batch_size=3       ,		drop_last=_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						Union[str, Any]									=      [
			    [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
			    [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21]],
			]
			self.check_batch_sampler_shards(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		even_batches=_SCREAMING_SNAKE_CASE )

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

			# Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
			# num_processes batch.
			__lowerCAmelCase			:						str									=      BatchSampler(range(20 )       ,		batch_size=3       ,		drop_last=_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						Any									=      [
			    [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19]],
			    [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
			]
			self.check_batch_sampler_shards(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		even_batches=_SCREAMING_SNAKE_CASE )

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

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

			__lowerCAmelCase			:						int									=      BatchSampler(range(2 )       ,		batch_size=3       ,		drop_last=_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						Any									=      [[], []]
			self.check_batch_sampler_shards(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		even_batches=_SCREAMING_SNAKE_CASE )

		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						Any									=      BatchSampler(range(24 )       ,		batch_size=4       ,		drop_last=_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						int									=      [
			    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
			    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23]],
			]
			self.check_batch_sampler_shards(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		split_batches=_SCREAMING_SNAKE_CASE       ,		even_batches=_SCREAMING_SNAKE_CASE )

			__lowerCAmelCase			:						List[str]									=      BatchSampler(range(24 )       ,		batch_size=4       ,		drop_last=_SCREAMING_SNAKE_CASE )
			# Expected shouldn't change
			self.check_batch_sampler_shards(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		split_batches=_SCREAMING_SNAKE_CASE       ,		even_batches=_SCREAMING_SNAKE_CASE )

			# Check the shards when the dataset is not a round multiple of batch size.
			__lowerCAmelCase			:						Any									=      BatchSampler(range(22 )       ,		batch_size=4       ,		drop_last=_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						Dict									=      [
			    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
			    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
			]
			self.check_batch_sampler_shards(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		split_batches=_SCREAMING_SNAKE_CASE       ,		even_batches=_SCREAMING_SNAKE_CASE )

			__lowerCAmelCase			:						Dict									=      BatchSampler(range(22 )       ,		batch_size=4       ,		drop_last=_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						Optional[Any]									=      [
			    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
			    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
			]
			self.check_batch_sampler_shards(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		split_batches=_SCREAMING_SNAKE_CASE       ,		even_batches=_SCREAMING_SNAKE_CASE )

			# Check the shards when the dataset is not a round multiple of batch size or num_processes.
			__lowerCAmelCase			:						str									=      BatchSampler(range(21 )       ,		batch_size=4       ,		drop_last=_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						Optional[Any]									=      [
			    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20]],
			    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
			]
			self.check_batch_sampler_shards(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		split_batches=_SCREAMING_SNAKE_CASE       ,		even_batches=_SCREAMING_SNAKE_CASE )

			__lowerCAmelCase			:						Tuple									=      BatchSampler(range(21 )       ,		batch_size=4       ,		drop_last=_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						Union[str, Any]									=      [
			    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
			    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
			]
			self.check_batch_sampler_shards(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		split_batches=_SCREAMING_SNAKE_CASE       ,		even_batches=_SCREAMING_SNAKE_CASE )

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

			__lowerCAmelCase			:						int									=      BatchSampler(range(2 )       ,		batch_size=4       ,		drop_last=_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						int									=      [[], []]
			self.check_batch_sampler_shards(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		split_batches=_SCREAMING_SNAKE_CASE       ,		even_batches=_SCREAMING_SNAKE_CASE )

		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						str									=      [[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 10, 11], [12, 13]]
			__lowerCAmelCase			:						str									=      [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 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE=False       ,		_SCREAMING_SNAKE_CASE=2       ,		_SCREAMING_SNAKE_CASE=False ):
			random.seed(_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						str									=      list(_SCREAMING_SNAKE_CASE )

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

			__lowerCAmelCase			:						str									=      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			:						List[str]									=      iterable_dataset_lists[0]
			for l in iterable_dataset_lists[1:]:
				self.assertEqual(len(_SCREAMING_SNAKE_CASE )       ,		len(_SCREAMING_SNAKE_CASE ) )
				self.assertTrue(len(_SCREAMING_SNAKE_CASE ) % shard_batch_size == 0 )

			__lowerCAmelCase			:						Optional[int]									=      []
			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 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						List[Any]									=      42
			__lowerCAmelCase			:						Dict									=      RandomIterableDataset()

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

			# Edge case with a very small dataset
			__lowerCAmelCase			:						Tuple									=      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 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						Optional[Any]									=      BatchSampler(range(16 )       ,		batch_size=4       ,		drop_last=_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						Union[str, Any]									=      SkipBatchSampler(_SCREAMING_SNAKE_CASE       ,		2 )
			self.assertListEqual(list(_SCREAMING_SNAKE_CASE )       ,		[[8, 9, 10, 11], [12, 13, 14, 15]] )

		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						Any									=      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 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						Union[str, Any]									=      DataLoader(list(range(16 ) )       ,		batch_size=4 )
			__lowerCAmelCase			:						int									=      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 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						Optional[Any]									=      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 							__lowerCamelCase							(      self ):
			Accelerator()
			__lowerCAmelCase			:						Any									=      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 )





'''simple docstring'''







from __future__ import annotations





def 						snake_case_							(_a	:							Dict    ,   _a	:							str    ,   _a	:							Optional[Any]    ,   _a	:							List[str]     ):  # noqa: E741
    while r - l > 1:
        UpperCAmelCase			       =		(l + r) // 2
        if v[m] >= key:
            UpperCAmelCase			       =		m
        else:
            UpperCAmelCase			       =		m  # noqa: E741
    return r





def 						snake_case_							(_a	:							list[int]     ):
    if len(_a     ) == 0:
        return 0

    UpperCAmelCase			       =		[0] * len(_a     )
    UpperCAmelCase			       =		1

    UpperCAmelCase			       =		v[0]

    for i in range(1    ,   len(_a     )     ):
        if v[i] < tail[0]:
            UpperCAmelCase			       =		v[i]
        elif v[i] > tail[length - 1]:
            UpperCAmelCase			       =		v[i]
            length += 1
        else:
            UpperCAmelCase			       =		v[i]

    return length


if __name__ == "__main__":
   import doctest

   doctest.testmod()




