






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   __SCREAMING_SNAKE_CASE(	a_	):
							def __init__(     self:      int       ,	UpperCamelCase:      List[str]=0.01       ,	UpperCamelCase:      Dict=10_00						)		->		List[Any]:
											snake_case__									=     p_stop
											snake_case__									=     max_length


							def __iter__(     self:      Optional[int]						)		->		Optional[int]:
											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   __SCREAMING_SNAKE_CASE(	unittest.TestCase	):
							def lowerCAmelCase_ (     self:      List[str]       ,	UpperCamelCase:      List[str]       ,	UpperCamelCase:      Optional[Any]       ,	UpperCamelCase:      Union[str, Any]=False       ,	UpperCamelCase:      Any=True						)		->		List[Any]:
											snake_case__									=     [
											    BatchSamplerShard(UpperCamelCase       ,	2       ,	UpperCamelCase       ,	split_batches=UpperCamelCase       ,	even_batches=UpperCamelCase						)
											    for i in range(2						)
											]
											snake_case__									=     [list(UpperCamelCase						) for batch_sampler_shard in batch_sampler_shards]
											if not split_batches:
															self.assertListEqual([len(UpperCamelCase						) for shard in batch_sampler_shards]       ,	[len(UpperCamelCase						) for e in expected]						)
											self.assertListEqual(UpperCamelCase       ,	UpperCamelCase						)
							def lowerCAmelCase_ (     self:      Optional[int]						)		->		List[Any]:
											# Check the shards when the dataset is a round multiple of total batch size.
											snake_case__									=     BatchSampler(range(24						)       ,	batch_size=3       ,	drop_last=UpperCamelCase						)
											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(UpperCamelCase       ,	UpperCamelCase						)

											snake_case__									=     BatchSampler(range(24						)       ,	batch_size=3       ,	drop_last=UpperCamelCase						)
											# Expected shouldn't change
											self.check_batch_sampler_shards(UpperCamelCase       ,	UpperCamelCase						)

											# 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=UpperCamelCase						)
											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(UpperCamelCase       ,	UpperCamelCase						)

											snake_case__									=     BatchSampler(range(21						)       ,	batch_size=3       ,	drop_last=UpperCamelCase						)
											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(UpperCamelCase       ,	UpperCamelCase						)

											# 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=UpperCamelCase						)
											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(UpperCamelCase       ,	UpperCamelCase						)

											snake_case__									=     BatchSampler(range(22						)       ,	batch_size=3       ,	drop_last=UpperCamelCase						)
											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(UpperCamelCase       ,	UpperCamelCase						)

											# 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=UpperCamelCase						)
											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(UpperCamelCase       ,	UpperCamelCase						)

											snake_case__									=     BatchSampler(range(20						)       ,	batch_size=3       ,	drop_last=UpperCamelCase						)
											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(UpperCamelCase       ,	UpperCamelCase						)

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

											snake_case__									=     BatchSampler(range(2						)       ,	batch_size=3       ,	drop_last=UpperCamelCase						)
											snake_case__									=     [[], []]
											self.check_batch_sampler_shards(UpperCamelCase       ,	UpperCamelCase						)
							def lowerCAmelCase_ (     self:      Tuple						)		->		Optional[int]:
											# Check the shards when the dataset is a round multiple of batch size.
											snake_case__									=     BatchSampler(range(24						)       ,	batch_size=4       ,	drop_last=UpperCamelCase						)
											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(UpperCamelCase       ,	UpperCamelCase       ,	split_batches=UpperCamelCase						)

											snake_case__									=     BatchSampler(range(24						)       ,	batch_size=4       ,	drop_last=UpperCamelCase						)
											# Expected shouldn't change
											self.check_batch_sampler_shards(UpperCamelCase       ,	UpperCamelCase       ,	split_batches=UpperCamelCase						)

											# Check the shards when the dataset is not a round multiple of batch size.
											snake_case__									=     BatchSampler(range(22						)       ,	batch_size=4       ,	drop_last=UpperCamelCase						)
											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(UpperCamelCase       ,	UpperCamelCase       ,	split_batches=UpperCamelCase						)

											snake_case__									=     BatchSampler(range(22						)       ,	batch_size=4       ,	drop_last=UpperCamelCase						)
											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(UpperCamelCase       ,	UpperCamelCase       ,	split_batches=UpperCamelCase						)

											# 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=UpperCamelCase						)
											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(UpperCamelCase       ,	UpperCamelCase       ,	split_batches=UpperCamelCase						)

											snake_case__									=     BatchSampler(range(21						)       ,	batch_size=4       ,	drop_last=UpperCamelCase						)
											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(UpperCamelCase       ,	UpperCamelCase       ,	split_batches=UpperCamelCase						)

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

											snake_case__									=     BatchSampler(range(2						)       ,	batch_size=4       ,	drop_last=UpperCamelCase						)
											snake_case__									=     [[], []]
											self.check_batch_sampler_shards(UpperCamelCase       ,	UpperCamelCase       ,	split_batches=UpperCamelCase						)
							def lowerCAmelCase_ (     self:      Optional[Any]						)		->		Any:
											# Check the shards when the dataset is a round multiple of total batch size.
											snake_case__									=     BatchSampler(range(24						)       ,	batch_size=3       ,	drop_last=UpperCamelCase						)
											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(UpperCamelCase       ,	UpperCamelCase       ,	even_batches=UpperCamelCase						)

											snake_case__									=     BatchSampler(range(24						)       ,	batch_size=3       ,	drop_last=UpperCamelCase						)
											# Expected shouldn't change
											self.check_batch_sampler_shards(UpperCamelCase       ,	UpperCamelCase       ,	even_batches=UpperCamelCase						)

											# 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=UpperCamelCase						)
											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(UpperCamelCase       ,	UpperCamelCase       ,	even_batches=UpperCamelCase						)

											snake_case__									=     BatchSampler(range(21						)       ,	batch_size=3       ,	drop_last=UpperCamelCase						)
											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(UpperCamelCase       ,	UpperCamelCase       ,	even_batches=UpperCamelCase						)

											# 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=UpperCamelCase						)
											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(UpperCamelCase       ,	UpperCamelCase       ,	even_batches=UpperCamelCase						)

											snake_case__									=     BatchSampler(range(22						)       ,	batch_size=3       ,	drop_last=UpperCamelCase						)
											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(UpperCamelCase       ,	UpperCamelCase       ,	even_batches=UpperCamelCase						)

											# 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=UpperCamelCase						)
											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(UpperCamelCase       ,	UpperCamelCase       ,	even_batches=UpperCamelCase						)

											snake_case__									=     BatchSampler(range(20						)       ,	batch_size=3       ,	drop_last=UpperCamelCase						)
											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(UpperCamelCase       ,	UpperCamelCase       ,	even_batches=UpperCamelCase						)

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

											snake_case__									=     BatchSampler(range(2						)       ,	batch_size=3       ,	drop_last=UpperCamelCase						)
											snake_case__									=     [[], []]
											self.check_batch_sampler_shards(UpperCamelCase       ,	UpperCamelCase       ,	even_batches=UpperCamelCase						)
							def lowerCAmelCase_ (     self:      Tuple						)		->		Any:
											# Check the shards when the dataset is a round multiple of batch size.
											snake_case__									=     BatchSampler(range(24						)       ,	batch_size=4       ,	drop_last=UpperCamelCase						)
											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(UpperCamelCase       ,	UpperCamelCase       ,	split_batches=UpperCamelCase       ,	even_batches=UpperCamelCase						)

											snake_case__									=     BatchSampler(range(24						)       ,	batch_size=4       ,	drop_last=UpperCamelCase						)
											# Expected shouldn't change
											self.check_batch_sampler_shards(UpperCamelCase       ,	UpperCamelCase       ,	split_batches=UpperCamelCase       ,	even_batches=UpperCamelCase						)

											# Check the shards when the dataset is not a round multiple of batch size.
											snake_case__									=     BatchSampler(range(22						)       ,	batch_size=4       ,	drop_last=UpperCamelCase						)
											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(UpperCamelCase       ,	UpperCamelCase       ,	split_batches=UpperCamelCase       ,	even_batches=UpperCamelCase						)

											snake_case__									=     BatchSampler(range(22						)       ,	batch_size=4       ,	drop_last=UpperCamelCase						)
											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(UpperCamelCase       ,	UpperCamelCase       ,	split_batches=UpperCamelCase       ,	even_batches=UpperCamelCase						)

											# 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=UpperCamelCase						)
											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(UpperCamelCase       ,	UpperCamelCase       ,	split_batches=UpperCamelCase       ,	even_batches=UpperCamelCase						)

											snake_case__									=     BatchSampler(range(21						)       ,	batch_size=4       ,	drop_last=UpperCamelCase						)
											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(UpperCamelCase       ,	UpperCamelCase       ,	split_batches=UpperCamelCase       ,	even_batches=UpperCamelCase						)

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

											snake_case__									=     BatchSampler(range(2						)       ,	batch_size=4       ,	drop_last=UpperCamelCase						)
											snake_case__									=     [[], []]
											self.check_batch_sampler_shards(UpperCamelCase       ,	UpperCamelCase       ,	split_batches=UpperCamelCase       ,	even_batches=UpperCamelCase						)
							def lowerCAmelCase_ (     self:      Union[str, Any]						)		->		str:
											snake_case__									=     [[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 10, 11], [12, 13]]
											snake_case__									=     [BatchSamplerShard(UpperCamelCase       ,	2       ,	UpperCamelCase       ,	even_batches=UpperCamelCase						) 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:      Tuple       ,	UpperCamelCase:      Any       ,	UpperCamelCase:      Optional[Any]       ,	UpperCamelCase:      Any       ,	UpperCamelCase:      str=False       ,	UpperCamelCase:      Optional[Any]=2       ,	UpperCamelCase:      List[Any]=False						)		->		Tuple:
											random.seed(UpperCamelCase						)
											snake_case__									=     list(UpperCamelCase						)

											snake_case__									=     [
											    IterableDatasetShard(
											        UpperCamelCase       ,	batch_size=UpperCamelCase       ,	drop_last=UpperCamelCase       ,	num_processes=UpperCamelCase       ,	process_index=UpperCamelCase       ,	split_batches=UpperCamelCase       ,	)
											    for i in range(UpperCamelCase						)
											]
											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(UpperCamelCase						)
															iterable_dataset_lists.append(list(UpperCamelCase						)						)

											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(UpperCamelCase						)       ,	len(UpperCamelCase						)						)
															self.assertTrue(len(UpperCamelCase						) % shard_batch_size == 0						)

											snake_case__									=     []
											for idx in range(0       ,	len(UpperCamelCase						)       ,	UpperCamelCase						):
															for l in iterable_dataset_lists:
																			observed += l[idx : idx + shard_batch_size]

											if not drop_last:
															while len(UpperCamelCase						) < len(UpperCamelCase						):
																			reference += reference
											self.assertListEqual(UpperCamelCase       ,	reference[: len(UpperCamelCase						)]						)
							def lowerCAmelCase_ (     self:      Dict						)		->		Optional[Any]:
											snake_case__									=     42
											snake_case__									=     RandomIterableDataset()

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

											# Edge case with a very small dataset
											snake_case__									=     RandomIterableDataset(max_length=2						)

											self.check_iterable_dataset_shards(UpperCamelCase       ,	UpperCamelCase       ,	batch_size=4       ,	drop_last=UpperCamelCase       ,	split_batches=UpperCamelCase						)
											self.check_iterable_dataset_shards(UpperCamelCase       ,	UpperCamelCase       ,	batch_size=4       ,	drop_last=UpperCamelCase       ,	split_batches=UpperCamelCase						)
											self.check_iterable_dataset_shards(UpperCamelCase       ,	UpperCamelCase       ,	batch_size=4       ,	drop_last=UpperCamelCase       ,	split_batches=UpperCamelCase						)
											self.check_iterable_dataset_shards(UpperCamelCase       ,	UpperCamelCase       ,	batch_size=4       ,	drop_last=UpperCamelCase       ,	split_batches=UpperCamelCase						)
							def lowerCAmelCase_ (     self:      List[Any]						)		->		List[str]:
											snake_case__									=     BatchSampler(range(16						)       ,	batch_size=4       ,	drop_last=UpperCamelCase						)
											snake_case__									=     SkipBatchSampler(UpperCamelCase       ,	2						)
											self.assertListEqual(list(UpperCamelCase						)       ,	[[8, 9, 10, 11], [12, 13, 14, 15]]						)
							def lowerCAmelCase_ (     self:      List[Any]						)		->		Union[str, Any]:
											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:      List[str]						)		->		Dict:
											snake_case__									=     DataLoader(list(range(16						)						)       ,	batch_size=4						)
											snake_case__									=     skip_first_batches(UpperCamelCase       ,	num_batches=2						)
											self.assertListEqual([t.tolist() for t in new_dataloader]       ,	[[8, 9, 10, 11], [12, 13, 14, 15]]						)
							def lowerCAmelCase_ (     self:      Any						)		->		Dict:
											snake_case__									=     DataLoaderShard(list(range(16						)						)       ,	batch_size=4						)
											for idx, _ in enumerate(UpperCamelCase						):
															self.assertEqual(dataloader.end_of_dataloader       ,	idx == 3						)

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


							def lowerCAmelCase_ (     self:      Optional[int]						)		->		Tuple:
											Accelerator()
											snake_case__									=     DataLoaderDispatcher(range(16						)       ,	batch_size=4						)
											for idx, _ in enumerate(UpperCamelCase						):
															self.assertEqual(dataloader.end_of_dataloader       ,	idx == 3						)

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












from typing import Optional, Tuple, Union

import torch
from einops import rearrange, reduce

from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput


__UpperCamelCase					:		Optional[int]	  =						8
def      a_ (      _A      ,   _A=BITS  )			->			List[Any]:







				"""simple docstring"""
				snake_case__									=     x.device

				snake_case__									=     (x * 255).int().clamp(0      ,   255  )

				snake_case__									=     2 ** torch.arange(bits - 1      ,   -1      ,   -1      ,   device=_A  )
				snake_case__									=     rearrange(_A      ,   'd -> d 1 1'  )
				snake_case__									=     rearrange(_A      ,   'b c h w -> b c 1 h w'  )

				snake_case__									=     ((x & mask) != 0).float()
				snake_case__									=     rearrange(_A      ,   'b c d h w -> b (c d) h w'  )
				snake_case__									=     bits * 2 - 1
				return bits
def      a_ (      _A      ,   _A=BITS  )			->			List[str]:







				"""simple docstring"""
				snake_case__									=     x.device

				snake_case__									=     (x > 0).int()
				snake_case__									=     2 ** torch.arange(bits - 1      ,   -1      ,   -1      ,   device=_A      ,   dtype=torch.intaa  )

				snake_case__									=     rearrange(_A      ,   'd -> d 1 1'  )
				snake_case__									=     rearrange(_A      ,   'b (c d) h w -> b c d h w'      ,   d=8  )
				snake_case__									=     reduce(x * mask      ,   'b c d h w -> b c h w'      ,   'sum'  )
				return (dec / 255).clamp(0.0      ,   1.0  )
def      a_ (      self      ,   _A      ,   _A      ,   _A      ,   _A = 0.0      ,   _A = True      ,   _A=None      ,   _A = True      ,   )			->			Union[DDIMSchedulerOutput, Tuple]:







				"""simple docstring"""
				if self.num_inference_steps is None:
								raise ValueError(
								    'Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'  )

				# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
				# Ideally, read DDIM paper in-detail understanding

				# Notation (<variable name> -> <name in paper>
				# - pred_noise_t -> e_theta(x_t, t)
				# - pred_original_sample -> f_theta(x_t, t) or x_0
				# - std_dev_t -> sigma_t
				# - eta -> η
				# - pred_sample_direction -> "direction pointing to x_t"
				# - pred_prev_sample -> "x_t-1"

				# 1. get previous step value (=t-1)
				snake_case__									=     timestep - self.config.num_train_timesteps // self.num_inference_steps

				# 2. compute alphas, betas
				snake_case__									=     self.alphas_cumprod[timestep]
				snake_case__									=     self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

				snake_case__									=     1 - alpha_prod_t

				# 3. compute predicted original sample from predicted noise also called
				# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
				snake_case__									=     (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

				# 4. Clip "predicted x_0"
				snake_case__									=     self.bit_scale
				if self.config.clip_sample:
								snake_case__									=     torch.clamp(_A      ,   -scale      ,   _A  )

				# 5. compute variance: "sigma_t(η)" -> see formula (16)
				# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
				snake_case__									=     self._get_variance(_A      ,   _A  )
				snake_case__									=     eta * variance ** 0.5

				if use_clipped_model_output:
								# the model_output is always re-derived from the clipped x_0 in Glide
								snake_case__									=     (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5

				# 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
				snake_case__									=     (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

				# 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
				snake_case__									=     alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

				if eta > 0:
								# randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072
								snake_case__									=     model_output.device if torch.is_tensor(_A  ) else 'cpu'
								snake_case__									=     torch.randn(model_output.shape      ,   dtype=model_output.dtype      ,   generator=_A  ).to(_A  )
								snake_case__									=     self._get_variance(_A      ,   _A  ) ** 0.5 * eta * noise

								snake_case__									=     prev_sample + variance

				if not return_dict:
								return (prev_sample,)

				return DDIMSchedulerOutput(prev_sample=_A      ,   pred_original_sample=_A  )
def      a_ (      self      ,   _A      ,   _A      ,   _A      ,   _A="epsilon"      ,   _A=None      ,   _A = True      ,   )			->			Union[DDPMSchedulerOutput, Tuple]:







				"""simple docstring"""
				snake_case__									=     timestep

				if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
								snake_case__							,						snake_case__									=     torch.split(_A      ,   sample.shape[1]      ,   dim=1  )
				else:
								snake_case__									=     None

				# 1. compute alphas, betas
				snake_case__									=     self.alphas_cumprod[t]
				snake_case__									=     self.alphas_cumprod[t - 1] if t > 0 else self.one
				snake_case__									=     1 - alpha_prod_t
				snake_case__									=     1 - alpha_prod_t_prev

				# 2. compute predicted original sample from predicted noise also called
				# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
				if prediction_type == "epsilon":
								snake_case__									=     (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
				elif prediction_type == "sample":
								snake_case__									=     model_output
				else:
								raise ValueError(f'''Unsupported prediction_type {prediction_type}.'''  )

				# 3. Clip "predicted x_0"
				snake_case__									=     self.bit_scale
				if self.config.clip_sample:
								snake_case__									=     torch.clamp(_A      ,   -scale      ,   _A  )

				# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
				# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
				snake_case__									=     (alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
				snake_case__									=     self.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

				# 5. Compute predicted previous sample µ_t
				# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
				snake_case__									=     pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

				# 6. Add noise
				snake_case__									=     0
				if t > 0:
								snake_case__									=     torch.randn(
								    model_output.size()      ,   dtype=model_output.dtype      ,   layout=model_output.layout      ,   generator=_A  ).to(model_output.device  )
								snake_case__									=     (self._get_variance(_A      ,   predicted_variance=_A  ) ** 0.5) * noise

				snake_case__									=     pred_prev_sample + variance

				if not return_dict:
								return (pred_prev_sample,)

				return DDPMSchedulerOutput(prev_sample=_A      ,   pred_original_sample=_A  )


class   __SCREAMING_SNAKE_CASE(	a_	):
							def __init__(     self:      Dict       ,	UpperCamelCase:      UNetaDConditionModel       ,	UpperCamelCase:      Union[DDIMScheduler, DDPMScheduler]       ,	UpperCamelCase:      Optional[float] = 1.0       ,	)		->		Union[str, Any]:
											super().__init__()
											snake_case__									=     bit_scale
											snake_case__									=     (
											    ddim_bit_scheduler_step if isinstance(UpperCamelCase       ,	UpperCamelCase						) else ddpm_bit_scheduler_step
											)

											self.register_modules(unet=UpperCamelCase       ,	scheduler=UpperCamelCase						)


							@torch.no_grad()
							def __call__(     self:      Dict       ,	UpperCamelCase:      Optional[int] = 2_56       ,	UpperCamelCase:      Optional[int] = 2_56       ,	UpperCamelCase:      Optional[int] = 50       ,	UpperCamelCase:      Optional[torch.Generator] = None       ,	UpperCamelCase:      Optional[int] = 1       ,	UpperCamelCase:      Optional[str] = "pil"       ,	UpperCamelCase:      bool = True       ,	**UpperCamelCase:      int       ,	)		->		Union[Tuple, ImagePipelineOutput]:
											snake_case__									=     torch.randn(
											    (batch_size, self.unet.config.in_channels, height, width)       ,	generator=UpperCamelCase       ,	)
											snake_case__									=     decimal_to_bits(UpperCamelCase						) * self.bit_scale
											snake_case__									=     latents.to(self.device						)

											self.scheduler.set_timesteps(UpperCamelCase						)

											for t in self.progress_bar(self.scheduler.timesteps						):
															# predict the noise residual
															snake_case__									=     self.unet(UpperCamelCase       ,	UpperCamelCase						).sample

															# compute the previous noisy sample x_t -> x_t-1
															snake_case__									=     self.scheduler.step(UpperCamelCase       ,	UpperCamelCase       ,	UpperCamelCase						).prev_sample

											snake_case__									=     bits_to_decimal(UpperCamelCase						)

											if output_type == "pil":
															snake_case__									=     self.numpy_to_pil(UpperCamelCase						)

											if not return_dict:
															return (image,)

											return ImagePipelineOutput(images=UpperCamelCase						)




