



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  lowerCamelCase__    (							lowerCamelCase__):




		'''simple docstring'''



		def __init__(self	,__lowerCamelCase=0.01	,__lowerCamelCase=10_00				)  ->    Union[str, Any]:

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[Any]				=  p_stop
						lowerCAmelCase__  :       Any				=  max_length



		def __iter__(self				)  ->    Optional[Any]:

						"""simple docstring"""






						lowerCAmelCase__  :       str				=  0
						lowerCAmelCase__  :       str				=  False
						while not stop and count < self.max_length:
										yield count
										count += 1
										lowerCAmelCase__  :       Union[str, Any]				=  random.random() < self.p_stop



class  lowerCamelCase__    (							unittest.TestCase):




		'''simple docstring'''



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase=False	,__lowerCamelCase=True				)  ->    Tuple:

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[Any]				=  [
						    BatchSamplerShard(__lowerCamelCase	,2	,__lowerCamelCase	,split_batches=__lowerCamelCase	,even_batches=__lowerCamelCase				)
						    for i in range(2				)
						]
						lowerCAmelCase__  :       List[str]				=  [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				)  ->    List[Any]:

						"""simple docstring"""






						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__  :       Optional[int]				=  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__  :       Union[str, Any]				=  BatchSampler(range(21				)	,batch_size=3	,drop_last=__lowerCamelCase				)
						lowerCAmelCase__  :       Union[str, Any]				=  [
						    [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
						    [[3, 4, 5], [9, 10, 11], [15, 16, 17], [0, 1, 2]],
						]
						self.check_batch_sampler_shards(__lowerCamelCase	,__lowerCamelCase				)

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

						lowerCAmelCase__  :       Tuple				=  BatchSampler(range(22				)	,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 and has not a multiple of
						# num_processes batch.
						lowerCAmelCase__  :       str				=  BatchSampler(range(20				)	,batch_size=3	,drop_last=__lowerCamelCase				)
						lowerCAmelCase__  :       Optional[Any]				=  [
						    [[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__  :       Optional[Any]				=  BatchSampler(range(20				)	,batch_size=3	,drop_last=__lowerCamelCase				)
						lowerCAmelCase__  :       List[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 very small.
						lowerCAmelCase__  :       Optional[int]				=  BatchSampler(range(2				)	,batch_size=3	,drop_last=__lowerCamelCase				)
						lowerCAmelCase__  :       Dict				=  [[[0, 1, 0]], [[1, 0, 1]]]
						self.check_batch_sampler_shards(__lowerCamelCase	,__lowerCamelCase				)

						lowerCAmelCase__  :       Any				=  BatchSampler(range(2				)	,batch_size=3	,drop_last=__lowerCamelCase				)
						lowerCAmelCase__  :       Optional[int]				=  [[], []]
						self.check_batch_sampler_shards(__lowerCamelCase	,__lowerCamelCase				)



		def 	lowerCAmelCase__   (self				)  ->    Dict:

						"""simple docstring"""






						lowerCAmelCase__  :       Tuple				=  BatchSampler(range(24				)	,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], [22, 23]],
						]
						self.check_batch_sampler_shards(__lowerCamelCase	,__lowerCamelCase	,split_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				)

						# 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__  :       Tuple				=  [
						    [[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[Any]				=  BatchSampler(range(22				)	,batch_size=4	,drop_last=__lowerCamelCase				)
						lowerCAmelCase__  :       List[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__  :       Optional[Any]				=  BatchSampler(range(21				)	,batch_size=4	,drop_last=__lowerCamelCase				)
						lowerCAmelCase__  :       int				=  [
						    [[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__  :       Optional[Any]				=  BatchSampler(range(21				)	,batch_size=4	,drop_last=__lowerCamelCase				)
						lowerCAmelCase__  :       List[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 very small.
						lowerCAmelCase__  :       Optional[int]				=  BatchSampler(range(2				)	,batch_size=4	,drop_last=__lowerCamelCase				)
						lowerCAmelCase__  :       Union[str, Any]				=  [[[0, 1]], [[0, 1]]]
						self.check_batch_sampler_shards(__lowerCamelCase	,__lowerCamelCase	,split_batches=__lowerCamelCase				)

						lowerCAmelCase__  :       int				=  BatchSampler(range(2				)	,batch_size=4	,drop_last=__lowerCamelCase				)
						lowerCAmelCase__  :       Tuple				=  [[], []]
						self.check_batch_sampler_shards(__lowerCamelCase	,__lowerCamelCase	,split_batches=__lowerCamelCase				)



		def 	lowerCAmelCase__   (self				)  ->    List[str]:

						"""simple docstring"""






						lowerCAmelCase__  :       Tuple				=  BatchSampler(range(24				)	,batch_size=3	,drop_last=__lowerCamelCase				)
						lowerCAmelCase__  :       Optional[Any]				=  [
						    [[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__  :       Tuple				=  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__  :       List[str]				=  BatchSampler(range(21				)	,batch_size=3	,drop_last=__lowerCamelCase				)
						lowerCAmelCase__  :       Tuple				=  [
						    [[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__  :       Tuple				=  BatchSampler(range(21				)	,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 has a multiple of
						# num_processes batch.
						lowerCAmelCase__  :       List[str]				=  BatchSampler(range(22				)	,batch_size=3	,drop_last=__lowerCamelCase				)
						lowerCAmelCase__  :       Tuple				=  [
						    [[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__  :       Any				=  BatchSampler(range(22				)	,batch_size=3	,drop_last=__lowerCamelCase				)
						lowerCAmelCase__  :       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	,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__  :       Union[str, Any]				=  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[Any]				=  BatchSampler(range(20				)	,batch_size=3	,drop_last=__lowerCamelCase				)
						lowerCAmelCase__  :       List[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__  :       List[str]				=  BatchSampler(range(2				)	,batch_size=3	,drop_last=__lowerCamelCase				)
						lowerCAmelCase__  :       Any				=  [[[0, 1]], []]
						self.check_batch_sampler_shards(__lowerCamelCase	,__lowerCamelCase	,even_batches=__lowerCamelCase				)

						lowerCAmelCase__  :       Any				=  BatchSampler(range(2				)	,batch_size=3	,drop_last=__lowerCamelCase				)
						lowerCAmelCase__  :       Optional[int]				=  [[], []]
						self.check_batch_sampler_shards(__lowerCamelCase	,__lowerCamelCase	,even_batches=__lowerCamelCase				)



		def 	lowerCAmelCase__   (self				)  ->    Any:

						"""simple docstring"""






						lowerCAmelCase__  :       Union[str, 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	,even_batches=__lowerCamelCase				)

						lowerCAmelCase__  :       Union[str, Any]				=  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__  :       int				=  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]],
						]
						self.check_batch_sampler_shards(__lowerCamelCase	,__lowerCamelCase	,split_batches=__lowerCamelCase	,even_batches=__lowerCamelCase				)

						lowerCAmelCase__  :       Any				=  BatchSampler(range(22				)	,batch_size=4	,drop_last=__lowerCamelCase				)
						lowerCAmelCase__  :       List[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	,even_batches=__lowerCamelCase				)

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

						lowerCAmelCase__  :       List[Any]				=  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	,even_batches=__lowerCamelCase				)

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

						lowerCAmelCase__  :       List[Any]				=  BatchSampler(range(2				)	,batch_size=4	,drop_last=__lowerCamelCase				)
						lowerCAmelCase__  :       str				=  [[], []]
						self.check_batch_sampler_shards(__lowerCamelCase	,__lowerCamelCase	,split_batches=__lowerCamelCase	,even_batches=__lowerCamelCase				)



		def 	lowerCAmelCase__   (self				)  ->    Optional[Any]:

						"""simple docstring"""






						lowerCAmelCase__  :       List[str]				=  [[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 10, 11], [12, 13]]
						lowerCAmelCase__  :       Dict				=  [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				)  ->    Dict:

						"""simple docstring"""






						random.seed(__lowerCamelCase				)
						lowerCAmelCase__  :       int				=  list(__lowerCamelCase				)

						lowerCAmelCase__  :       Tuple				=  [
						    IterableDatasetShard(
						        __lowerCamelCase	,batch_size=__lowerCamelCase	,drop_last=__lowerCamelCase	,num_processes=__lowerCamelCase	,process_index=__lowerCamelCase	,split_batches=__lowerCamelCase	,)
						    for i in range(__lowerCamelCase				)
						]
						lowerCAmelCase__  :       List[Any]				=  []
						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__  :       str				=  batch_size // num_processes if split_batches else batch_size
						# All iterable dataset shard should have the same length, a round multiple of shard_batch_size
						lowerCAmelCase__  :       Optional[int]				=  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__  :       Optional[int]				=  []
						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				)  ->    List[str]:

						"""simple docstring"""






						lowerCAmelCase__  :       Tuple				=  42
						lowerCAmelCase__  :       Union[str, Any]				=  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__  :       Tuple				=  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				)  ->    Tuple:

						"""simple docstring"""






						lowerCAmelCase__  :       str				=  BatchSampler(range(16				)	,batch_size=4	,drop_last=__lowerCamelCase				)
						lowerCAmelCase__  :       Optional[Any]				=  SkipBatchSampler(__lowerCamelCase	,2				)
						self.assertListEqual(list(__lowerCamelCase				)	,[[8, 9, 10, 11], [12, 13, 14, 15]]				)



		def 	lowerCAmelCase__   (self				)  ->    Optional[Any]:

						"""simple docstring"""






						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 	lowerCAmelCase__   (self				)  ->    Optional[Any]:

						"""simple docstring"""






						lowerCAmelCase__  :       Tuple				=  DataLoader(list(range(16				)				)	,batch_size=4				)
						lowerCAmelCase__  :       List[Any]				=  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				)  ->    List[str]:

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[Any]				=  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				)  ->    Dict:

						"""simple docstring"""






						Accelerator()
						lowerCAmelCase__  :       int				=  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				)






from typing import Dict

import numpy as np
import torch

from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map






def 			lowerCAmelCase__	(					lowerCamelCase_	:	Dict[str, torch.Tensor]):





				'''simple docstring'''

				lowerCAmelCase__  :       List[Any]				=  []
				lowerCAmelCase__  :       int				=  []
				lowerCAmelCase__  :       Optional[int]				=  []

				for rt in rc.restypes:
								lowerCAmelCase__  :       int				=  rc.restype_name_to_atomaa_names[rc.restype_atoa[rt]]
								restype_atomaa_to_atomaa_list.append([(rc.atom_order[name] if name else 0) for name in atom_names])
								lowerCAmelCase__  :       Any				=  {name: i for i, name in enumerate(lowerCamelCase_)}
								restype_atomaa_to_atomaa_list.append(
								    [(atom_name_to_idxaa[name] if name in atom_name_to_idxaa else 0) for name in rc.atom_types])

								restype_atomaa_mask_list.append([(1.0 if name else 0.0) for name in atom_names])

				# Add dummy mapping for restype 'UNK'
				restype_atomaa_to_atomaa_list.append([0] * 14)
				restype_atomaa_to_atomaa_list.append([0] * 37)
				restype_atomaa_mask_list.append([0.0] * 14)

				lowerCAmelCase__  :       Optional[Any]				=  torch.tensor(
				    lowerCamelCase_ ,dtype=torch.intaa ,device=protein['''aatype'''].device ,)
				lowerCAmelCase__  :       Optional[int]				=  torch.tensor(
				    lowerCamelCase_ ,dtype=torch.intaa ,device=protein['''aatype'''].device ,)
				lowerCAmelCase__  :       List[str]				=  torch.tensor(
				    lowerCamelCase_ ,dtype=torch.floataa ,device=protein['''aatype'''].device ,)
				lowerCAmelCase__  :       Optional[Any]				=  protein['''aatype'''].to(torch.long)

				# create the mapping for (residx, atom14) --> atom37, i.e. an array
				# with shape (num_res, 14) containing the atom37 indices for this protein
				lowerCAmelCase__  :       Optional[int]				=  restype_atomaa_to_atomaa[protein_aatype]
				lowerCAmelCase__  :       List[str]				=  restype_atomaa_mask[protein_aatype]

				lowerCAmelCase__  :       Tuple				=  residx_atomaa_mask
				lowerCAmelCase__  :       Dict				=  residx_atomaa_to_atomaa.long()

				# create the gather indices for mapping back
				lowerCAmelCase__  :       Union[str, Any]				=  restype_atomaa_to_atomaa[protein_aatype]
				lowerCAmelCase__  :       int				=  residx_atomaa_to_atomaa.long()

				# create the corresponding mask
				lowerCAmelCase__  :       List[str]				=  torch.zeros([21, 37] ,dtype=torch.floataa ,device=protein['''aatype'''].device)
				for restype, restype_letter in enumerate(rc.restypes):
								lowerCAmelCase__  :       List[str]				=  rc.restype_atoa[restype_letter]
								lowerCAmelCase__  :       List[str]				=  rc.residue_atoms[restype_name]
								for atom_name in atom_names:
												lowerCAmelCase__  :       List[Any]				=  rc.atom_order[atom_name]
												lowerCAmelCase__  :       Any				=  1

				lowerCAmelCase__  :       int				=  restype_atomaa_mask[protein_aatype]
				lowerCAmelCase__  :       int				=  residx_atomaa_mask

				return protein






def 			lowerCAmelCase__	(					lowerCamelCase_	:	Dict[str, torch.Tensor]):





				'''simple docstring'''

				lowerCAmelCase__  :       Optional[Any]				=  tree_map(lambda lowerCamelCase_: torch.tensor(lowerCamelCase_ ,device=batch['''aatype'''].device) ,lowerCamelCase_ ,np.ndarray)
				lowerCAmelCase__  :       Union[str, Any]				=  tensor_tree_map(lambda lowerCamelCase_: np.array(lowerCamelCase_) ,make_atomaa_masks(lowerCamelCase_))
				return out

