



"""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  __SCREAMING_SNAKE_CASE			(   lowerCAmelCase_     ):


					'''simple docstring'''





					def __init__( self							:						Any,						lowerCamelCase							:						Optional[Any]=0.01,						lowerCamelCase							:						Any=1000  )->      Any:
								lowerCamelCase__	:	int				=p_stop
								lowerCamelCase__	:	int				=max_length




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





class  __SCREAMING_SNAKE_CASE			(   unittest.TestCase     ):


					'''simple docstring'''





					def 	snake_case ( self							:						Optional[int],						lowerCamelCase							:						Tuple,						lowerCamelCase							:						Union[str, Any],						lowerCamelCase							:						str=False,						lowerCamelCase							:						Optional[Any]=True  )->      Union[str, Any]:
								lowerCamelCase__	:	Tuple				=[
								    BatchSamplerShard(lowerCamelCase,						2,						lowerCamelCase,						split_batches=lowerCamelCase,						even_batches=lowerCamelCase  )
								    for i in range(2  )
								]
								lowerCamelCase__	:	Any				=[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 	snake_case ( self							:						Tuple  )->      Any:
								# Check the shards when the dataset is a round multiple of total batch size.
								lowerCamelCase__	:	Optional[int]				=BatchSampler(range(24  ),						batch_size=3,						drop_last=lowerCamelCase  )
								lowerCamelCase__	:	List[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(lowerCamelCase,						lowerCamelCase  )

								lowerCamelCase__	:	Tuple				=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.
								lowerCamelCase__	:	List[Any]				=BatchSampler(range(21  ),						batch_size=3,						drop_last=lowerCamelCase  )
								lowerCamelCase__	:	List[str]				=[
								    [[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  )

								lowerCamelCase__	:	str				=BatchSampler(range(21  ),						batch_size=3,						drop_last=lowerCamelCase  )
								lowerCamelCase__	:	Union[str, Any]				=[
								    [[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.
								lowerCamelCase__	:	Dict				=BatchSampler(range(22  ),						batch_size=3,						drop_last=lowerCamelCase  )
								lowerCamelCase__	:	Optional[int]				=[
								    [[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  )

								lowerCamelCase__	:	List[str]				=BatchSampler(range(22  ),						batch_size=3,						drop_last=lowerCamelCase  )
								lowerCamelCase__	:	Optional[int]				=[
								    [[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.
								lowerCamelCase__	:	Optional[int]				=BatchSampler(range(20  ),						batch_size=3,						drop_last=lowerCamelCase  )
								lowerCamelCase__	:	int				=[
								    [[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  )

								lowerCamelCase__	:	List[Any]				=BatchSampler(range(20  ),						batch_size=3,						drop_last=lowerCamelCase  )
								lowerCamelCase__	:	str				=[
								    [[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.
								lowerCamelCase__	:	Optional[int]				=BatchSampler(range(2  ),						batch_size=3,						drop_last=lowerCamelCase  )
								lowerCamelCase__	:	Optional[int]				=[[[0, 1, 0]], [[1, 0, 1]]]
								self.check_batch_sampler_shards(lowerCamelCase,						lowerCamelCase  )

								lowerCamelCase__	:	Optional[Any]				=BatchSampler(range(2  ),						batch_size=3,						drop_last=lowerCamelCase  )
								lowerCamelCase__	:	Optional[Any]				=[[], []]
								self.check_batch_sampler_shards(lowerCamelCase,						lowerCamelCase  )



					def 	snake_case ( self							:						List[str]  )->      Union[str, Any]:
								# Check the shards when the dataset is a round multiple of batch size.
								lowerCamelCase__	:	List[Any]				=BatchSampler(range(24  ),						batch_size=4,						drop_last=lowerCamelCase  )
								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(lowerCamelCase,						lowerCamelCase,						split_batches=lowerCamelCase  )

								lowerCamelCase__	:	str				=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.
								lowerCamelCase__	:	Union[str, Any]				=BatchSampler(range(22  ),						batch_size=4,						drop_last=lowerCamelCase  )
								lowerCamelCase__	:	Any				=[
								    [[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  )

								lowerCamelCase__	:	List[str]				=BatchSampler(range(22  ),						batch_size=4,						drop_last=lowerCamelCase  )
								lowerCamelCase__	:	str				=[
								    [[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.
								lowerCamelCase__	:	Dict				=BatchSampler(range(21  ),						batch_size=4,						drop_last=lowerCamelCase  )
								lowerCamelCase__	:	List[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(lowerCamelCase,						lowerCamelCase,						split_batches=lowerCamelCase  )

								lowerCamelCase__	:	str				=BatchSampler(range(21  ),						batch_size=4,						drop_last=lowerCamelCase  )
								lowerCamelCase__	:	Tuple				=[
								    [[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.
								lowerCamelCase__	:	Any				=BatchSampler(range(2  ),						batch_size=4,						drop_last=lowerCamelCase  )
								lowerCamelCase__	:	Tuple				=[[[0, 1]], [[0, 1]]]
								self.check_batch_sampler_shards(lowerCamelCase,						lowerCamelCase,						split_batches=lowerCamelCase  )

								lowerCamelCase__	:	Union[str, Any]				=BatchSampler(range(2  ),						batch_size=4,						drop_last=lowerCamelCase  )
								lowerCamelCase__	:	Optional[int]				=[[], []]
								self.check_batch_sampler_shards(lowerCamelCase,						lowerCamelCase,						split_batches=lowerCamelCase  )



					def 	snake_case ( self							:						Optional[Any]  )->      Tuple:
								# Check the shards when the dataset is a round multiple of total batch size.
								lowerCamelCase__	:	Dict				=BatchSampler(range(24  ),						batch_size=3,						drop_last=lowerCamelCase  )
								lowerCamelCase__	:	Optional[int]				=[
								    [[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  )

								lowerCamelCase__	:	Any				=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.
								lowerCamelCase__	:	Optional[Any]				=BatchSampler(range(21  ),						batch_size=3,						drop_last=lowerCamelCase  )
								lowerCamelCase__	:	str				=[
								    [[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  )

								lowerCamelCase__	:	List[Any]				=BatchSampler(range(21  ),						batch_size=3,						drop_last=lowerCamelCase  )
								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(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.
								lowerCamelCase__	:	str				=BatchSampler(range(22  ),						batch_size=3,						drop_last=lowerCamelCase  )
								lowerCamelCase__	:	str				=[
								    [[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  )

								lowerCamelCase__	:	List[str]				=BatchSampler(range(22  ),						batch_size=3,						drop_last=lowerCamelCase  )
								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(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.
								lowerCamelCase__	:	int				=BatchSampler(range(20  ),						batch_size=3,						drop_last=lowerCamelCase  )
								lowerCamelCase__	:	List[str]				=[
								    [[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  )

								lowerCamelCase__	:	List[str]				=BatchSampler(range(20  ),						batch_size=3,						drop_last=lowerCamelCase  )
								lowerCamelCase__	:	Optional[Any]				=[
								    [[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.
								lowerCamelCase__	:	Dict				=BatchSampler(range(2  ),						batch_size=3,						drop_last=lowerCamelCase  )
								lowerCamelCase__	:	int				=[[[0, 1]], []]
								self.check_batch_sampler_shards(lowerCamelCase,						lowerCamelCase,						even_batches=lowerCamelCase  )

								lowerCamelCase__	:	str				=BatchSampler(range(2  ),						batch_size=3,						drop_last=lowerCamelCase  )
								lowerCamelCase__	:	int				=[[], []]
								self.check_batch_sampler_shards(lowerCamelCase,						lowerCamelCase,						even_batches=lowerCamelCase  )



					def 	snake_case ( self							:						Tuple  )->      List[str]:
								# Check the shards when the dataset is a round multiple of batch size.
								lowerCamelCase__	:	Tuple				=BatchSampler(range(24  ),						batch_size=4,						drop_last=lowerCamelCase  )
								lowerCamelCase__	:	Optional[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(lowerCamelCase,						lowerCamelCase,						split_batches=lowerCamelCase,						even_batches=lowerCamelCase  )

								lowerCamelCase__	:	List[str]				=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.
								lowerCamelCase__	:	Tuple				=BatchSampler(range(22  ),						batch_size=4,						drop_last=lowerCamelCase  )
								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]],
								]
								self.check_batch_sampler_shards(lowerCamelCase,						lowerCamelCase,						split_batches=lowerCamelCase,						even_batches=lowerCamelCase  )

								lowerCamelCase__	:	Tuple				=BatchSampler(range(22  ),						batch_size=4,						drop_last=lowerCamelCase  )
								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(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.
								lowerCamelCase__	:	Optional[int]				=BatchSampler(range(21  ),						batch_size=4,						drop_last=lowerCamelCase  )
								lowerCamelCase__	:	str				=[
								    [[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  )

								lowerCamelCase__	:	int				=BatchSampler(range(21  ),						batch_size=4,						drop_last=lowerCamelCase  )
								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(lowerCamelCase,						lowerCamelCase,						split_batches=lowerCamelCase,						even_batches=lowerCamelCase  )

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

								lowerCamelCase__	:	Any				=BatchSampler(range(2  ),						batch_size=4,						drop_last=lowerCamelCase  )
								lowerCamelCase__	:	Any				=[[], []]
								self.check_batch_sampler_shards(lowerCamelCase,						lowerCamelCase,						split_batches=lowerCamelCase,						even_batches=lowerCamelCase  )



					def 	snake_case ( self							:						List[str]  )->      Dict:
								lowerCamelCase__	:	Dict				=[[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 10, 11], [12, 13]]
								lowerCamelCase__	:	List[Any]				=[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 	snake_case ( self							:						Optional[int],						lowerCamelCase							:						Any,						lowerCamelCase							:						Dict,						lowerCamelCase							:						Any,						lowerCamelCase							:						List[Any]=False,						lowerCamelCase							:						Optional[int]=2,						lowerCamelCase							:						Any=False  )->      Optional[int]:
								random.seed(lowerCamelCase  )
								lowerCamelCase__	:	int				=list(lowerCamelCase  )

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

								lowerCamelCase__	:	Union[str, Any]				=batch_size // num_processes if split_batches else batch_size
								# All iterable dataset shard should have the same length, a round multiple of shard_batch_size
								lowerCamelCase__	:	Any				=iterable_dataset_lists[0]
								for l in iterable_dataset_lists[1:]:
											self.assertEqual(len(lowerCamelCase  ),						len(lowerCamelCase  )  )
											self.assertTrue(len(lowerCamelCase  ) % shard_batch_size == 0  )

								lowerCamelCase__	:	Tuple				=[]
								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 	snake_case ( self							:						List[Any]  )->      Any:
								lowerCamelCase__	:	Optional[int]				=42
								lowerCamelCase__	:	List[str]				=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
								lowerCamelCase__	:	Optional[Any]				=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 	snake_case ( self							:						int  )->      List[Any]:
								lowerCamelCase__	:	Optional[Any]				=BatchSampler(range(16  ),						batch_size=4,						drop_last=lowerCamelCase  )
								lowerCamelCase__	:	List[Any]				=SkipBatchSampler(lowerCamelCase,						2  )
								self.assertListEqual(list(lowerCamelCase  ),						[[8, 9, 10, 11], [12, 13, 14, 15]]  )



					def 	snake_case ( self							:						int  )->      int:
								lowerCamelCase__	:	List[str]				=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 	snake_case ( self							:						Tuple  )->      Dict:
								lowerCamelCase__	:	Any				=DataLoader(list(range(16  )  ),						batch_size=4  )
								lowerCamelCase__	:	int				=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 	snake_case ( self							:						List[Any]  )->      Any:
								lowerCamelCase__	:	Dict				=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 	snake_case ( self							:						Optional[int]  )->      Tuple:
								Accelerator()
								lowerCamelCase__	:	List[Any]				=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 Optional, Tuple, Union

import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict

from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
from .modeling_flax_utils import FlaxModelMixin
from .unet_ad_blocks_flax import (
    FlaxCrossAttnDownBlockaD,
    FlaxDownBlockaD,
    FlaxUNetMidBlockaDCrossAttn,
)

@flax.struct.dataclass
class  __SCREAMING_SNAKE_CASE			(   lowerCAmelCase_     ):


					'''simple docstring'''





					_a         =							42
					_a         =							42





class  __SCREAMING_SNAKE_CASE			(   nn.Module     ):


					'''simple docstring'''





					_a         =							42
					_a         =							(1_6, 3_2, 9_6, 2_5_6)
					_a         =							jnp.floataa



					def 	snake_case ( self							:						Tuple  )->      int:
								lowerCamelCase__	:	Tuple				=nn.Conv(
								    self.block_out_channels[0],						kernel_size=(3, 3),						padding=((1, 1), (1, 1)),						dtype=self.dtype,						)

								lowerCamelCase__	:	Dict				=[]
								for i in range(len(self.block_out_channels  ) - 1  ):
											lowerCamelCase__	:	Dict				=self.block_out_channels[i]
											lowerCamelCase__	:	Dict				=self.block_out_channels[i + 1]
											lowerCamelCase__	:	List[str]				=nn.Conv(
											    lowerCamelCase,						kernel_size=(3, 3),						padding=((1, 1), (1, 1)),						dtype=self.dtype,						)
											blocks.append(lowerCamelCase  )
											lowerCamelCase__	:	Optional[int]				=nn.Conv(
											    lowerCamelCase,						kernel_size=(3, 3),						strides=(2, 2),						padding=((1, 1), (1, 1)),						dtype=self.dtype,						)
											blocks.append(lowerCamelCase  )
								lowerCamelCase__	:	Any				=blocks

								lowerCamelCase__	:	Optional[int]				=nn.Conv(
								    self.conditioning_embedding_channels,						kernel_size=(3, 3),						padding=((1, 1), (1, 1)),						kernel_init=nn.initializers.zeros_init(),						bias_init=nn.initializers.zeros_init(),						dtype=self.dtype,						)




					def __call__( self							:						Any,						lowerCamelCase							:						int  )->      List[str]:
								lowerCamelCase__	:	Tuple				=self.conv_in(lowerCamelCase  )
								lowerCamelCase__	:	Dict				=nn.silu(lowerCamelCase  )

								for block in self.blocks:
											lowerCamelCase__	:	str				=block(lowerCamelCase  )
											lowerCamelCase__	:	List[str]				=nn.silu(lowerCamelCase  )

								lowerCamelCase__	:	Any				=self.conv_out(lowerCamelCase  )

								return embedding





@flax_register_to_config
class  __SCREAMING_SNAKE_CASE			(   nn.Module	,     lowerCAmelCase_	,     lowerCAmelCase_     ):


					'''simple docstring'''





					_a         =							3_2
					_a         =							4
					_a         =							(
					    "CrossAttnDownBlock2D",
					    "CrossAttnDownBlock2D",
					    "CrossAttnDownBlock2D",
					    "DownBlock2D",
					)
					_a         =							False
					_a         =							(3_2_0, 6_4_0, 1_2_8_0, 1_2_8_0)
					_a         =							2
					_a         =							8
					_a         =							None
					_a         =							1_2_8_0
					_a         =							0.0
					_a         =							False
					_a         =							jnp.floataa
					_a         =							True
					_a         =							0
					_a         =							"rgb"
					_a         =							(1_6, 3_2, 9_6, 2_5_6)



					def 	snake_case ( self							:						str,						lowerCamelCase							:						jax.random.KeyArray  )->      FrozenDict:
								# init input tensors
								lowerCamelCase__	:	int				=(1, self.in_channels, self.sample_size, self.sample_size)
								lowerCamelCase__	:	int				=jnp.zeros(lowerCamelCase,						dtype=jnp.floataa  )
								lowerCamelCase__	:	Union[str, Any]				=jnp.ones((1,),						dtype=jnp.intaa  )
								lowerCamelCase__	:	str				=jnp.zeros((1, 1, self.cross_attention_dim),						dtype=jnp.floataa  )
								lowerCamelCase__	:	Any				=(1, 3, self.sample_size * 8, self.sample_size * 8)
								lowerCamelCase__	:	Optional[Any]				=jnp.zeros(lowerCamelCase,						dtype=jnp.floataa  )

								lowerCamelCase__			,     lowerCamelCase__	:	List[Any]				=jax.random.split(lowerCamelCase  )
								lowerCamelCase__	:	Dict				={'''params''': params_rng, '''dropout''': dropout_rng}

								return self.init(lowerCamelCase,						lowerCamelCase,						lowerCamelCase,						lowerCamelCase,						lowerCamelCase  )["params"]



					def 	snake_case ( self							:						Any  )->      Tuple:
								lowerCamelCase__	:	Optional[int]				=self.block_out_channels
								lowerCamelCase__	:	Tuple				=block_out_channels[0] * 4

								# If `num_attention_heads` is not defined (which is the case for most models)
								# it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
								# The reason for this behavior is to correct for incorrectly named variables that were introduced
								# when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
								# Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
								# which is why we correct for the naming here.
								lowerCamelCase__	:	List[Any]				=self.num_attention_heads or self.attention_head_dim

								# input
								lowerCamelCase__	:	int				=nn.Conv(
								    block_out_channels[0],						kernel_size=(3, 3),						strides=(1, 1),						padding=((1, 1), (1, 1)),						dtype=self.dtype,						)

								# time
								lowerCamelCase__	:	str				=FlaxTimesteps(
								    block_out_channels[0],						flip_sin_to_cos=self.flip_sin_to_cos,						freq_shift=self.config.freq_shift  )
								lowerCamelCase__	:	Dict				=FlaxTimestepEmbedding(lowerCamelCase,						dtype=self.dtype  )

								lowerCamelCase__	:	List[Any]				=FlaxControlNetConditioningEmbedding(
								    conditioning_embedding_channels=block_out_channels[0],						block_out_channels=self.conditioning_embedding_out_channels,						)

								lowerCamelCase__	:	Dict				=self.only_cross_attention
								if isinstance(lowerCamelCase,						lowerCamelCase  ):
											lowerCamelCase__	:	int				=(only_cross_attention,) * len(self.down_block_types  )

								if isinstance(lowerCamelCase,						lowerCamelCase  ):
											lowerCamelCase__	:	List[str]				=(num_attention_heads,) * len(self.down_block_types  )

								# down
								lowerCamelCase__	:	Union[str, Any]				=[]
								lowerCamelCase__	:	Dict				=[]

								lowerCamelCase__	:	List[Any]				=block_out_channels[0]

								lowerCamelCase__	:	List[Any]				=nn.Conv(
								    lowerCamelCase,						kernel_size=(1, 1),						padding='''VALID''',						kernel_init=nn.initializers.zeros_init(),						bias_init=nn.initializers.zeros_init(),						dtype=self.dtype,						)
								controlnet_down_blocks.append(lowerCamelCase  )

								for i, down_block_type in enumerate(self.down_block_types  ):
											lowerCamelCase__	:	List[Any]				=output_channel
											lowerCamelCase__	:	str				=block_out_channels[i]
											lowerCamelCase__	:	Dict				=i == len(lowerCamelCase  ) - 1

											if down_block_type == "CrossAttnDownBlock2D":
														lowerCamelCase__	:	str				=FlaxCrossAttnDownBlockaD(
														    in_channels=lowerCamelCase,						out_channels=lowerCamelCase,						dropout=self.dropout,						num_layers=self.layers_per_block,						num_attention_heads=num_attention_heads[i],						add_downsample=not is_final_block,						use_linear_projection=self.use_linear_projection,						only_cross_attention=only_cross_attention[i],						dtype=self.dtype,						)
											else:
														lowerCamelCase__	:	List[Any]				=FlaxDownBlockaD(
														    in_channels=lowerCamelCase,						out_channels=lowerCamelCase,						dropout=self.dropout,						num_layers=self.layers_per_block,						add_downsample=not is_final_block,						dtype=self.dtype,						)

											down_blocks.append(lowerCamelCase  )

											for _ in range(self.layers_per_block  ):
														lowerCamelCase__	:	Any				=nn.Conv(
														    lowerCamelCase,						kernel_size=(1, 1),						padding='''VALID''',						kernel_init=nn.initializers.zeros_init(),						bias_init=nn.initializers.zeros_init(),						dtype=self.dtype,						)
														controlnet_down_blocks.append(lowerCamelCase  )

											if not is_final_block:
														lowerCamelCase__	:	Any				=nn.Conv(
														    lowerCamelCase,						kernel_size=(1, 1),						padding='''VALID''',						kernel_init=nn.initializers.zeros_init(),						bias_init=nn.initializers.zeros_init(),						dtype=self.dtype,						)
														controlnet_down_blocks.append(lowerCamelCase  )

								lowerCamelCase__	:	int				=down_blocks
								lowerCamelCase__	:	List[str]				=controlnet_down_blocks

								# mid
								lowerCamelCase__	:	Tuple				=block_out_channels[-1]
								lowerCamelCase__	:	List[Any]				=FlaxUNetMidBlockaDCrossAttn(
								    in_channels=lowerCamelCase,						dropout=self.dropout,						num_attention_heads=num_attention_heads[-1],						use_linear_projection=self.use_linear_projection,						dtype=self.dtype,						)

								lowerCamelCase__	:	List[str]				=nn.Conv(
								    lowerCamelCase,						kernel_size=(1, 1),						padding='''VALID''',						kernel_init=nn.initializers.zeros_init(),						bias_init=nn.initializers.zeros_init(),						dtype=self.dtype,						)




					def __call__( self							:						int,						lowerCamelCase							:						List[Any],						lowerCamelCase							:						Tuple,						lowerCamelCase							:						Union[str, Any],						lowerCamelCase							:						str,						lowerCamelCase							:						float = 1.0,						lowerCamelCase							:						bool = True,						lowerCamelCase							:						bool = False,						)->      Union[FlaxControlNetOutput, Tuple]:
								lowerCamelCase__	:	int				=self.controlnet_conditioning_channel_order
								if channel_order == "bgr":
											lowerCamelCase__	:	int				=jnp.flip(lowerCamelCase,						axis=1  )

								# 1. time
								if not isinstance(lowerCamelCase,						jnp.ndarray  ):
											lowerCamelCase__	:	Any				=jnp.array([timesteps],						dtype=jnp.intaa  )
								elif isinstance(lowerCamelCase,						jnp.ndarray  ) and len(timesteps.shape  ) == 0:
											lowerCamelCase__	:	List[str]				=timesteps.astype(dtype=jnp.floataa  )
											lowerCamelCase__	:	int				=jnp.expand_dims(lowerCamelCase,						0  )

								lowerCamelCase__	:	Optional[Any]				=self.time_proj(lowerCamelCase  )
								lowerCamelCase__	:	Optional[Any]				=self.time_embedding(lowerCamelCase  )

								# 2. pre-process
								lowerCamelCase__	:	Optional[int]				=jnp.transpose(lowerCamelCase,						(0, 2, 3, 1)  )
								lowerCamelCase__	:	Dict				=self.conv_in(lowerCamelCase  )

								lowerCamelCase__	:	List[str]				=jnp.transpose(lowerCamelCase,						(0, 2, 3, 1)  )
								lowerCamelCase__	:	int				=self.controlnet_cond_embedding(lowerCamelCase  )
								sample += controlnet_cond

								# 3. down
								lowerCamelCase__	:	Union[str, Any]				=(sample,)
								for down_block in self.down_blocks:
											if isinstance(lowerCamelCase,						lowerCamelCase  ):
														lowerCamelCase__			,     lowerCamelCase__	:	Dict				=down_block(lowerCamelCase,						lowerCamelCase,						lowerCamelCase,						deterministic=not train  )
											else:
														lowerCamelCase__			,     lowerCamelCase__	:	Tuple				=down_block(lowerCamelCase,						lowerCamelCase,						deterministic=not train  )
											down_block_res_samples += res_samples

								# 4. mid
								lowerCamelCase__	:	Optional[int]				=self.mid_block(lowerCamelCase,						lowerCamelCase,						lowerCamelCase,						deterministic=not train  )

								# 5. contronet blocks
								lowerCamelCase__	:	Optional[Any]				=()
								for down_block_res_sample, controlnet_block in zip(lowerCamelCase,						self.controlnet_down_blocks  ):
											lowerCamelCase__	:	Union[str, Any]				=controlnet_block(lowerCamelCase  )
											controlnet_down_block_res_samples += (down_block_res_sample,)

								lowerCamelCase__	:	List[str]				=controlnet_down_block_res_samples

								lowerCamelCase__	:	List[str]				=self.controlnet_mid_block(lowerCamelCase  )

								# 6. scaling
								lowerCamelCase__	:	Union[str, Any]				=[sample * conditioning_scale for sample in down_block_res_samples]
								mid_block_res_sample *= conditioning_scale

								if not return_dict:
											return (down_block_res_samples, mid_block_res_sample)

								return FlaxControlNetOutput(
								    down_block_res_samples=lowerCamelCase,						mid_block_res_sample=lowerCamelCase  )




