
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  lowercase__(				UpperCAmelCase							):



	"""simple docstring"""




	def __init__(       self  :			Union[str, Any]       ,							SCREAMING_SNAKE_CASE_  :			Tuple=0.01       ,							SCREAMING_SNAKE_CASE_  :			int=1_0_0_0       )       ->     Tuple:
					lowercase_       =					p_stop
					lowercase_       =					max_length




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

class  lowercase__(				unittest.TestCase							):



	"""simple docstring"""




	def 						_lowercase  (       self  :			Union[str, Any]       ,							SCREAMING_SNAKE_CASE_  :			List[str]       ,							SCREAMING_SNAKE_CASE_  :			int       ,							SCREAMING_SNAKE_CASE_  :			Dict=False       ,							SCREAMING_SNAKE_CASE_  :			Dict=True       )       ->     List[str]:
					lowercase_       =					[
					    BatchSamplerShard(SCREAMING_SNAKE_CASE_       ,							2       ,							SCREAMING_SNAKE_CASE_       ,							split_batches=SCREAMING_SNAKE_CASE_       ,							even_batches=SCREAMING_SNAKE_CASE_       )
					    for i in range(2       )
					]
					lowercase_       =					[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 						_lowercase  (       self  :			Dict       )       ->     Optional[Any]:
					# Check the shards when the dataset is a round multiple of total batch size.
					lowercase_       =					BatchSampler(range(2_4       )       ,							batch_size=3       ,							drop_last=SCREAMING_SNAKE_CASE_       )
					lowercase_       =					[
					    [[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(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       )

					lowercase_       =					BatchSampler(range(2_4       )       ,							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.
					lowercase_       =					BatchSampler(range(2_1       )       ,							batch_size=3       ,							drop_last=SCREAMING_SNAKE_CASE_       )
					lowercase_       =					[
					    [[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(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       )

					lowercase_       =					BatchSampler(range(2_1       )       ,							batch_size=3       ,							drop_last=SCREAMING_SNAKE_CASE_       )
					lowercase_       =					[
					    [[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(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.
					lowercase_       =					BatchSampler(range(2_2       )       ,							batch_size=3       ,							drop_last=SCREAMING_SNAKE_CASE_       )
					lowercase_       =					[
					    [[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(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       )

					lowercase_       =					BatchSampler(range(2_2       )       ,							batch_size=3       ,							drop_last=SCREAMING_SNAKE_CASE_       )
					lowercase_       =					[
					    [[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(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.
					lowercase_       =					BatchSampler(range(2_0       )       ,							batch_size=3       ,							drop_last=SCREAMING_SNAKE_CASE_       )
					lowercase_       =					[
					    [[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(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       )

					lowercase_       =					BatchSampler(range(2_0       )       ,							batch_size=3       ,							drop_last=SCREAMING_SNAKE_CASE_       )
					lowercase_       =					[
					    [[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(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       )

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

					lowercase_       =					BatchSampler(range(2       )       ,							batch_size=3       ,							drop_last=SCREAMING_SNAKE_CASE_       )
					lowercase_       =					[[], []]
					self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       )
	def 						_lowercase  (       self  :			Tuple       )       ->     List[str]:
					# Check the shards when the dataset is a round multiple of batch size.
					lowercase_       =					BatchSampler(range(2_4       )       ,							batch_size=4       ,							drop_last=SCREAMING_SNAKE_CASE_       )
					lowercase_       =					[
					    [[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(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							split_batches=SCREAMING_SNAKE_CASE_       )

					lowercase_       =					BatchSampler(range(2_4       )       ,							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.
					lowercase_       =					BatchSampler(range(2_2       )       ,							batch_size=4       ,							drop_last=SCREAMING_SNAKE_CASE_       )
					lowercase_       =					[
					    [[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(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							split_batches=SCREAMING_SNAKE_CASE_       )

					lowercase_       =					BatchSampler(range(2_2       )       ,							batch_size=4       ,							drop_last=SCREAMING_SNAKE_CASE_       )
					lowercase_       =					[
					    [[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(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.
					lowercase_       =					BatchSampler(range(2_1       )       ,							batch_size=4       ,							drop_last=SCREAMING_SNAKE_CASE_       )
					lowercase_       =					[
					    [[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(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							split_batches=SCREAMING_SNAKE_CASE_       )

					lowercase_       =					BatchSampler(range(2_1       )       ,							batch_size=4       ,							drop_last=SCREAMING_SNAKE_CASE_       )
					lowercase_       =					[
					    [[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(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							split_batches=SCREAMING_SNAKE_CASE_       )

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

					lowercase_       =					BatchSampler(range(2       )       ,							batch_size=4       ,							drop_last=SCREAMING_SNAKE_CASE_       )
					lowercase_       =					[[], []]
					self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							split_batches=SCREAMING_SNAKE_CASE_       )
	def 						_lowercase  (       self  :			Dict       )       ->     List[Any]:
					# Check the shards when the dataset is a round multiple of total batch size.
					lowercase_       =					BatchSampler(range(2_4       )       ,							batch_size=3       ,							drop_last=SCREAMING_SNAKE_CASE_       )
					lowercase_       =					[
					    [[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(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							even_batches=SCREAMING_SNAKE_CASE_       )

					lowercase_       =					BatchSampler(range(2_4       )       ,							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.
					lowercase_       =					BatchSampler(range(2_1       )       ,							batch_size=3       ,							drop_last=SCREAMING_SNAKE_CASE_       )
					lowercase_       =					[
					    [[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(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							even_batches=SCREAMING_SNAKE_CASE_       )

					lowercase_       =					BatchSampler(range(2_1       )       ,							batch_size=3       ,							drop_last=SCREAMING_SNAKE_CASE_       )
					lowercase_       =					[
					    [[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(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.
					lowercase_       =					BatchSampler(range(2_2       )       ,							batch_size=3       ,							drop_last=SCREAMING_SNAKE_CASE_       )
					lowercase_       =					[
					    [[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(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							even_batches=SCREAMING_SNAKE_CASE_       )

					lowercase_       =					BatchSampler(range(2_2       )       ,							batch_size=3       ,							drop_last=SCREAMING_SNAKE_CASE_       )
					lowercase_       =					[
					    [[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(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.
					lowercase_       =					BatchSampler(range(2_0       )       ,							batch_size=3       ,							drop_last=SCREAMING_SNAKE_CASE_       )
					lowercase_       =					[
					    [[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(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							even_batches=SCREAMING_SNAKE_CASE_       )

					lowercase_       =					BatchSampler(range(2_0       )       ,							batch_size=3       ,							drop_last=SCREAMING_SNAKE_CASE_       )
					lowercase_       =					[
					    [[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(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							even_batches=SCREAMING_SNAKE_CASE_       )

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

					lowercase_       =					BatchSampler(range(2       )       ,							batch_size=3       ,							drop_last=SCREAMING_SNAKE_CASE_       )
					lowercase_       =					[[], []]
					self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							even_batches=SCREAMING_SNAKE_CASE_       )
	def 						_lowercase  (       self  :			int       )       ->     Optional[int]:
					# Check the shards when the dataset is a round multiple of batch size.
					lowercase_       =					BatchSampler(range(2_4       )       ,							batch_size=4       ,							drop_last=SCREAMING_SNAKE_CASE_       )
					lowercase_       =					[
					    [[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(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							split_batches=SCREAMING_SNAKE_CASE_       ,							even_batches=SCREAMING_SNAKE_CASE_       )

					lowercase_       =					BatchSampler(range(2_4       )       ,							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.
					lowercase_       =					BatchSampler(range(2_2       )       ,							batch_size=4       ,							drop_last=SCREAMING_SNAKE_CASE_       )
					lowercase_       =					[
					    [[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(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							split_batches=SCREAMING_SNAKE_CASE_       ,							even_batches=SCREAMING_SNAKE_CASE_       )

					lowercase_       =					BatchSampler(range(2_2       )       ,							batch_size=4       ,							drop_last=SCREAMING_SNAKE_CASE_       )
					lowercase_       =					[
					    [[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(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.
					lowercase_       =					BatchSampler(range(2_1       )       ,							batch_size=4       ,							drop_last=SCREAMING_SNAKE_CASE_       )
					lowercase_       =					[
					    [[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(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							split_batches=SCREAMING_SNAKE_CASE_       ,							even_batches=SCREAMING_SNAKE_CASE_       )

					lowercase_       =					BatchSampler(range(2_1       )       ,							batch_size=4       ,							drop_last=SCREAMING_SNAKE_CASE_       )
					lowercase_       =					[
					    [[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(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.
					lowercase_       =					BatchSampler(range(2       )       ,							batch_size=4       ,							drop_last=SCREAMING_SNAKE_CASE_       )
					lowercase_       =					[[[0, 1]], []]
					self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							split_batches=SCREAMING_SNAKE_CASE_       ,							even_batches=SCREAMING_SNAKE_CASE_       )

					lowercase_       =					BatchSampler(range(2       )       ,							batch_size=4       ,							drop_last=SCREAMING_SNAKE_CASE_       )
					lowercase_       =					[[], []]
					self.check_batch_sampler_shards(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							split_batches=SCREAMING_SNAKE_CASE_       ,							even_batches=SCREAMING_SNAKE_CASE_       )
	def 						_lowercase  (       self  :			Tuple       )       ->     str:
					lowercase_       =					[[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 1_0, 1_1], [1_2, 1_3]]
					lowercase_       =					[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], [1_2, 1_3]]       )
					self.assertListEqual(list(batch_sampler_shards[1]       )       ,							[[3, 4], [9, 1_0, 1_1]]       )
	def 						_lowercase  (       self  :			Any       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]       ,							SCREAMING_SNAKE_CASE_  :			Optional[Any]       ,							SCREAMING_SNAKE_CASE_  :			Union[str, Any]       ,							SCREAMING_SNAKE_CASE_  :			List[str]=False       ,							SCREAMING_SNAKE_CASE_  :			List[str]=2       ,							SCREAMING_SNAKE_CASE_  :			List[str]=False       )       ->     Any:
					random.seed(SCREAMING_SNAKE_CASE_       )
					lowercase_       =					list(SCREAMING_SNAKE_CASE_       )

					lowercase_       =					[
					    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_       )
					]
					lowercase_       =					[]
					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_       )       )

					lowercase_       =					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
					lowercase_       =					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       )

					lowercase_       =					[]
					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 						_lowercase  (       self  :			Any       )       ->     Union[str, Any]:
					lowercase_       =					4_2
					lowercase_       =					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
					lowercase_       =					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 						_lowercase  (       self  :			List[str]       )       ->     List[Any]:
					lowercase_       =					BatchSampler(range(1_6       )       ,							batch_size=4       ,							drop_last=SCREAMING_SNAKE_CASE_       )
					lowercase_       =					SkipBatchSampler(SCREAMING_SNAKE_CASE_       ,							2       )
					self.assertListEqual(list(SCREAMING_SNAKE_CASE_       )       ,							[[8, 9, 1_0, 1_1], [1_2, 1_3, 1_4, 1_5]]       )
	def 						_lowercase  (       self  :			Any       )       ->     Any:
					lowercase_       =					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  :			Any       )       ->     Optional[int]:
					lowercase_       =					DataLoader(list(range(1_6       )       )       ,							batch_size=4       )
					lowercase_       =					skip_first_batches(SCREAMING_SNAKE_CASE_       ,							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  :			Any       )       ->     Optional[int]:
					lowercase_       =					DataLoaderShard(list(range(1_6       )       )       ,							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 						_lowercase  (       self  :			Any       )       ->     Any:
					Accelerator()
					lowercase_       =					DataLoaderDispatcher(range(1_6       )       ,							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       )






import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING


__a  			=					logging.get_logger(__name__)
class  lowercase__(				UpperCAmelCase							):



	"""simple docstring"""




	a			:Union[str, Any]         =	'upernet'
	def __init__(       self  :			Dict       ,							SCREAMING_SNAKE_CASE_  :			Dict=None       ,							SCREAMING_SNAKE_CASE_  :			str=5_1_2       ,							SCREAMING_SNAKE_CASE_  :			Tuple=0.02       ,							SCREAMING_SNAKE_CASE_  :			Optional[Any]=[1, 2, 3, 6]       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]=True       ,							SCREAMING_SNAKE_CASE_  :			Tuple=0.4       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]=3_8_4       ,							SCREAMING_SNAKE_CASE_  :			str=2_5_6       ,							SCREAMING_SNAKE_CASE_  :			Dict=1       ,							SCREAMING_SNAKE_CASE_  :			Tuple=False       ,							SCREAMING_SNAKE_CASE_  :			str=2_5_5       ,							**SCREAMING_SNAKE_CASE_  :			str       ,							)       ->     int:
					super().__init__(**SCREAMING_SNAKE_CASE_       )

					if backbone_config is None:
									logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''       )
									lowercase_       =					CONFIG_MAPPING['''resnet'''](out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']       )
					elif isinstance(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ):
									lowercase_       =					backbone_config.get('''model_type'''       )
									lowercase_       =					CONFIG_MAPPING[backbone_model_type]
									lowercase_       =					config_class.from_dict(SCREAMING_SNAKE_CASE_       )

					lowercase_       =					backbone_config
					lowercase_       =					hidden_size
					lowercase_       =					initializer_range
					lowercase_       =					pool_scales
					lowercase_       =					use_auxiliary_head
					lowercase_       =					auxiliary_loss_weight
					lowercase_       =					auxiliary_in_channels
					lowercase_       =					auxiliary_channels
					lowercase_       =					auxiliary_num_convs
					lowercase_       =					auxiliary_concat_input
					lowercase_       =					loss_ignore_index




	def 						_lowercase  (       self  :			List[str]       )       ->     List[str]:
					lowercase_       =					copy.deepcopy(self.__dict__       )
					lowercase_       =					self.backbone_config.to_dict()
					lowercase_       =					self.__class__.model_type
					return output




