
'''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 						__UpperCAmelCase      (					_lowerCamelCase						):


							def __init__(       self    ,   lowerCAmelCase_=0.01    ,   lowerCAmelCase_=10_00  ):





								"""simple docstring"""






								_snake_case   						=		p_stop
								_snake_case   						=		max_length




							def __iter__(       self  ):





								"""simple docstring"""






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


class 						__UpperCAmelCase      (					unittest.TestCase						):


							def  lowerCamelCase				(       self    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_=False    ,   lowerCAmelCase_=True  ):





								"""simple docstring"""






								_snake_case   						=		[
								    BatchSamplerShard(lowerCAmelCase_    ,   2    ,   lowerCAmelCase_    ,   split_batches=lowerCAmelCase_    ,   even_batches=lowerCAmelCase_  )
								    for i in range(2  )
								]
								_snake_case   						=		[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  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		BatchSampler(range(24  )    ,   batch_size=3    ,   drop_last=lowerCAmelCase_  )
								_snake_case   						=		[
								    [[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(lowerCAmelCase_    ,   lowerCAmelCase_  )

								_snake_case   						=		BatchSampler(range(24  )    ,   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.
								_snake_case   						=		BatchSampler(range(21  )    ,   batch_size=3    ,   drop_last=lowerCAmelCase_  )
								_snake_case   						=		[
								    [[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(lowerCAmelCase_    ,   lowerCAmelCase_  )

								_snake_case   						=		BatchSampler(range(21  )    ,   batch_size=3    ,   drop_last=lowerCAmelCase_  )
								_snake_case   						=		[
								    [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
								    [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
								]
								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.
								_snake_case   						=		BatchSampler(range(22  )    ,   batch_size=3    ,   drop_last=lowerCAmelCase_  )
								_snake_case   						=		[
								    [[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(lowerCAmelCase_    ,   lowerCAmelCase_  )

								_snake_case   						=		BatchSampler(range(22  )    ,   batch_size=3    ,   drop_last=lowerCAmelCase_  )
								_snake_case   						=		[
								    [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
								    [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
								]
								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.
								_snake_case   						=		BatchSampler(range(20  )    ,   batch_size=3    ,   drop_last=lowerCAmelCase_  )
								_snake_case   						=		[
								    [[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(lowerCAmelCase_    ,   lowerCAmelCase_  )

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

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

								_snake_case   						=		BatchSampler(range(2  )    ,   batch_size=3    ,   drop_last=lowerCAmelCase_  )
								_snake_case   						=		[[], []]
								self.check_batch_sampler_shards(lowerCAmelCase_    ,   lowerCAmelCase_  )


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		BatchSampler(range(24  )    ,   batch_size=4    ,   drop_last=lowerCAmelCase_  )
								_snake_case   						=		[
								    [[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(lowerCAmelCase_    ,   lowerCAmelCase_    ,   split_batches=lowerCAmelCase_  )

								_snake_case   						=		BatchSampler(range(24  )    ,   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.
								_snake_case   						=		BatchSampler(range(22  )    ,   batch_size=4    ,   drop_last=lowerCAmelCase_  )
								_snake_case   						=		[
								    [[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(lowerCAmelCase_    ,   lowerCAmelCase_    ,   split_batches=lowerCAmelCase_  )

								_snake_case   						=		BatchSampler(range(22  )    ,   batch_size=4    ,   drop_last=lowerCAmelCase_  )
								_snake_case   						=		[
								    [[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(lowerCAmelCase_    ,   lowerCAmelCase_    ,   split_batches=lowerCAmelCase_  )

								# Check the shards when the dataset is not a round multiple of batch size or num_processes.
								_snake_case   						=		BatchSampler(range(21  )    ,   batch_size=4    ,   drop_last=lowerCAmelCase_  )
								_snake_case   						=		[
								    [[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(lowerCAmelCase_    ,   lowerCAmelCase_    ,   split_batches=lowerCAmelCase_  )

								_snake_case   						=		BatchSampler(range(21  )    ,   batch_size=4    ,   drop_last=lowerCAmelCase_  )
								_snake_case   						=		[
								    [[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(lowerCAmelCase_    ,   lowerCAmelCase_    ,   split_batches=lowerCAmelCase_  )

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

								_snake_case   						=		BatchSampler(range(2  )    ,   batch_size=4    ,   drop_last=lowerCAmelCase_  )
								_snake_case   						=		[[], []]
								self.check_batch_sampler_shards(lowerCAmelCase_    ,   lowerCAmelCase_    ,   split_batches=lowerCAmelCase_  )


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		BatchSampler(range(24  )    ,   batch_size=3    ,   drop_last=lowerCAmelCase_  )
								_snake_case   						=		[
								    [[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(lowerCAmelCase_    ,   lowerCAmelCase_    ,   even_batches=lowerCAmelCase_  )

								_snake_case   						=		BatchSampler(range(24  )    ,   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.
								_snake_case   						=		BatchSampler(range(21  )    ,   batch_size=3    ,   drop_last=lowerCAmelCase_  )
								_snake_case   						=		[
								    [[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(lowerCAmelCase_    ,   lowerCAmelCase_    ,   even_batches=lowerCAmelCase_  )

								_snake_case   						=		BatchSampler(range(21  )    ,   batch_size=3    ,   drop_last=lowerCAmelCase_  )
								_snake_case   						=		[
								    [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
								    [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
								]
								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.
								_snake_case   						=		BatchSampler(range(22  )    ,   batch_size=3    ,   drop_last=lowerCAmelCase_  )
								_snake_case   						=		[
								    [[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(lowerCAmelCase_    ,   lowerCAmelCase_    ,   even_batches=lowerCAmelCase_  )

								_snake_case   						=		BatchSampler(range(22  )    ,   batch_size=3    ,   drop_last=lowerCAmelCase_  )
								_snake_case   						=		[
								    [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
								    [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
								]
								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.
								_snake_case   						=		BatchSampler(range(20  )    ,   batch_size=3    ,   drop_last=lowerCAmelCase_  )
								_snake_case   						=		[
								    [[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(lowerCAmelCase_    ,   lowerCAmelCase_    ,   even_batches=lowerCAmelCase_  )

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

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

								_snake_case   						=		BatchSampler(range(2  )    ,   batch_size=3    ,   drop_last=lowerCAmelCase_  )
								_snake_case   						=		[[], []]
								self.check_batch_sampler_shards(lowerCAmelCase_    ,   lowerCAmelCase_    ,   even_batches=lowerCAmelCase_  )


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		BatchSampler(range(24  )    ,   batch_size=4    ,   drop_last=lowerCAmelCase_  )
								_snake_case   						=		[
								    [[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(lowerCAmelCase_    ,   lowerCAmelCase_    ,   split_batches=lowerCAmelCase_    ,   even_batches=lowerCAmelCase_  )

								_snake_case   						=		BatchSampler(range(24  )    ,   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.
								_snake_case   						=		BatchSampler(range(22  )    ,   batch_size=4    ,   drop_last=lowerCAmelCase_  )
								_snake_case   						=		[
								    [[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(lowerCAmelCase_    ,   lowerCAmelCase_    ,   split_batches=lowerCAmelCase_    ,   even_batches=lowerCAmelCase_  )

								_snake_case   						=		BatchSampler(range(22  )    ,   batch_size=4    ,   drop_last=lowerCAmelCase_  )
								_snake_case   						=		[
								    [[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(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.
								_snake_case   						=		BatchSampler(range(21  )    ,   batch_size=4    ,   drop_last=lowerCAmelCase_  )
								_snake_case   						=		[
								    [[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(lowerCAmelCase_    ,   lowerCAmelCase_    ,   split_batches=lowerCAmelCase_    ,   even_batches=lowerCAmelCase_  )

								_snake_case   						=		BatchSampler(range(21  )    ,   batch_size=4    ,   drop_last=lowerCAmelCase_  )
								_snake_case   						=		[
								    [[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(lowerCAmelCase_    ,   lowerCAmelCase_    ,   split_batches=lowerCAmelCase_    ,   even_batches=lowerCAmelCase_  )

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

								_snake_case   						=		BatchSampler(range(2  )    ,   batch_size=4    ,   drop_last=lowerCAmelCase_  )
								_snake_case   						=		[[], []]
								self.check_batch_sampler_shards(lowerCAmelCase_    ,   lowerCAmelCase_    ,   split_batches=lowerCAmelCase_    ,   even_batches=lowerCAmelCase_  )


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		[[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 10, 11], [12, 13]]
								_snake_case   						=		[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], [12, 13]]  )
								self.assertListEqual(list(batch_sampler_shards[1]  )    ,   [[3, 4], [9, 10, 11]]  )


							def  lowerCamelCase				(       self    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_=False    ,   lowerCAmelCase_=2    ,   lowerCAmelCase_=False  ):





								"""simple docstring"""






								random.seed(lowerCAmelCase_  )
								_snake_case   						=		list(lowerCAmelCase_  )

								_snake_case   						=		[
								    IterableDatasetShard(
								        lowerCAmelCase_    ,   batch_size=lowerCAmelCase_    ,   drop_last=lowerCAmelCase_    ,   num_processes=lowerCAmelCase_    ,   process_index=lowerCAmelCase_    ,   split_batches=lowerCAmelCase_    ,   )
								    for i in range(lowerCAmelCase_  )
								]
								_snake_case   						=		[]
								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_  )  )

								_snake_case   						=		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
								_snake_case   						=		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  )

								_snake_case   						=		[]
								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  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		42
								_snake_case   						=		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
								_snake_case   						=		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  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		BatchSampler(range(16  )    ,   batch_size=4    ,   drop_last=lowerCAmelCase_  )
								_snake_case   						=		SkipBatchSampler(lowerCAmelCase_    ,   2  )
								self.assertListEqual(list(lowerCAmelCase_  )    ,   [[8, 9, 10, 11], [12, 13, 14, 15]]  )


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		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  ):





								"""simple docstring"""






								_snake_case   						=		DataLoader(list(range(16  )  )    ,   batch_size=4  )
								_snake_case   						=		skip_first_batches(lowerCAmelCase_    ,   num_batches=2  )
								self.assertListEqual([t.tolist() for t in new_dataloader]    ,   [[8, 9, 10, 11], [12, 13, 14, 15]]  )


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		DataLoaderShard(list(range(16  )  )    ,   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  lowerCamelCase				(       self  ):





								"""simple docstring"""






								Accelerator()
								_snake_case   						=		DataLoaderDispatcher(range(16  )    ,   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'''





from typing import Callable, Dict, Optional, Tuple

import torch
from torch import nn
from torch.distributions import (
    AffineTransform,
    Distribution,
    Independent,
    NegativeBinomial,
    Normal,
    StudentT,
    TransformedDistribution,
)




class        __A      ( UpperCamelCase__	):



    def __init__(self    :    int   ,			__a    :    Distribution   ,			__a    :    Dict=None   ,			__a    :    int=None   ,			__a    :    Any=0 ):
         UpperCAmelCase_           =   1.0 if scale is None else scale
         UpperCAmelCase_           =   0.0 if loc is None else loc

         super().__init__(__a   ,			[AffineTransform(loc=self.loc   ,			scale=self.scale   ,			event_dim=__a )] )



    @property
    def 					_lowercase						(self    :    Union[str, Any] ):
         return self.base_dist.mean * self.scale + self.loc



    @property
    def 					_lowercase						(self    :    List[Any] ):
         return self.base_dist.variance * self.scale**2






    @property
    def 					_lowercase						(self    :    List[Any] ):
         return self.variance.sqrt()




class        __A      ( nn.Module	):



    def __init__(self    :    Optional[int]   ,			__a    :    int   ,			__a    :    Dict[str, int]   ,			__a    :    Callable[..., Tuple[torch.Tensor]]   ,			**__a    :    List[str] ):
         super().__init__(**__a )
         UpperCAmelCase_           =   args_dim
         UpperCAmelCase_           =   nn.ModuleList([nn.Linear(__a   ,			__a ) for dim in args_dim.values()] )
         UpperCAmelCase_           =   domain_map






    def 					_lowercase						(self    :    List[str]   ,			__a    :    torch.Tensor ):
         UpperCAmelCase_           =   [proj(__a ) for proj in self.proj]

         return self.domain_map(*__a )




class        __A      ( nn.Module	):



    def __init__(self    :    Union[str, Any]   ,			__a    :    List[str] ):
         super().__init__()
         UpperCAmelCase_           =   function






    def 					_lowercase						(self    :    Optional[int]   ,			__a    :    List[str]   ,			*__a    :    Optional[int] ):
         return self.function(__a   ,			*__a )




class        __A      :
    a__       :						type
    a__       :						int
    a__       :						Dict[str, int]



    def __init__(self    :    List[Any]   ,			__a    :    int = 1 ):
         UpperCAmelCase_           =   dim
         UpperCAmelCase_           =   {k: dim * self.args_dim[k] for k in self.args_dim}



    def 					_lowercase						(self    :    Any   ,			__a    :    Any ):
         if self.dim == 1:
              return self.distribution_class(*__a )
         else:
              return Independent(self.distribution_class(*__a )   ,			1 )



    def 					_lowercase						(self    :    List[str]   ,			__a    :    Union[str, Any]   ,			__a    :    Optional[torch.Tensor] = None   ,			__a    :    Optional[torch.Tensor] = None   ,			):
         UpperCAmelCase_           =   self._base_distribution(__a )
         if loc is None and scale is None:
              return distr
         else:
              return AffineTransformed(__a   ,			loc=__a   ,			scale=__a   ,			event_dim=self.event_dim )



    @property
    def 					_lowercase						(self    :    Any ):
         return () if self.dim == 1 else (self.dim,)



    @property
    def 					_lowercase						(self    :    Dict ):
         return len(self.event_shape )



    @property
    def 					_lowercase						(self    :    Tuple ):
         return 0.0



    def 					_lowercase						(self    :    List[str]   ,			__a    :    int ):
         return ParameterProjection(
             in_features=__a   ,			args_dim=self.args_dim   ,			domain_map=LambdaLayer(self.domain_map )   ,			)



    def 					_lowercase						(self    :    Optional[int]   ,			*__a    :    torch.Tensor ):
         raise NotImplementedError()






    @staticmethod
    def 					_lowercase						(__a    :    torch.Tensor ):
         return (x + torch.sqrt(torch.square(__a ) + 4.0 )) / 2.0




class        __A      ( UpperCamelCase__	):
    a__       :						Dict[str, int]          =  {"df": 1, "loc": 1, "scale": 1}
    a__       :						type          =  StudentT



    @classmethod
    def 					_lowercase						(cls    :    Union[str, Any]   ,			__a    :    torch.Tensor   ,			__a    :    torch.Tensor   ,			__a    :    torch.Tensor ):
         UpperCAmelCase_           =   cls.squareplus(__a ).clamp_min(torch.finfo(scale.dtype ).eps )
         UpperCAmelCase_           =   2.0 + cls.squareplus(__a )
         return df.squeeze(-1 ), loc.squeeze(-1 ), scale.squeeze(-1 )




class        __A      ( UpperCamelCase__	):
    a__       :						Dict[str, int]          =  {"loc": 1, "scale": 1}
    a__       :						type          =  Normal



    @classmethod
    def 					_lowercase						(cls    :    Tuple   ,			__a    :    torch.Tensor   ,			__a    :    torch.Tensor ):
         UpperCAmelCase_           =   cls.squareplus(__a ).clamp_min(torch.finfo(scale.dtype ).eps )
         return loc.squeeze(-1 ), scale.squeeze(-1 )




class        __A      ( UpperCamelCase__	):
    a__       :						Dict[str, int]          =  {"total_count": 1, "logits": 1}
    a__       :						type          =  NegativeBinomial



    @classmethod
    def 					_lowercase						(cls    :    Optional[Any]   ,			__a    :    torch.Tensor   ,			__a    :    torch.Tensor ):
         UpperCAmelCase_           =   cls.squareplus(__a )
         return total_count.squeeze(-1 ), logits.squeeze(-1 )



    def 					_lowercase						(self    :    List[str]   ,			__a    :    str ):
         UpperCAmelCase_    , UpperCAmelCase_           =   distr_args
         if self.dim == 1:
              return self.distribution_class(total_count=__a   ,			logits=__a )
         else:
              return Independent(self.distribution_class(total_count=__a   ,			logits=__a )   ,			1 )






    def 					_lowercase						(self    :    Optional[Any]   ,			__a    :    int   ,			__a    :    Optional[torch.Tensor] = None   ,			__a    :    Optional[torch.Tensor] = None ):
         UpperCAmelCase_    , UpperCAmelCase_           =   distr_args

         if scale is not None:
              # See scaling property of Gamma.
              logits += scale.log()

         return self._base_distribution((total_count, logits) )




