



"""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							:  Dict,  lowerCamelCase							:  List[Any]=0.01,  lowerCamelCase							:  Tuple=1000     )->					Union[str, Any]:
								lowerCamelCase__		:		Union[str, Any]			=p_stop
								lowerCamelCase__		:		List[Any]			=max_length




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





class        __SCREAMING_SNAKE_CASE	(      unittest.TestCase   ):


					'''simple docstring'''





					def 		snake_case       (       self							:  int,  lowerCamelCase							:  str,  lowerCamelCase							:  Optional[int],  lowerCamelCase							:  Optional[Any]=False,  lowerCamelCase							:  Union[str, Any]=True     )->					Optional[int]:
								lowerCamelCase__		:		str			=[
								    BatchSamplerShard(lowerCamelCase,  2,  lowerCamelCase,  split_batches=lowerCamelCase,  even_batches=lowerCamelCase     )
								    for i in range(2     )
								]
								lowerCamelCase__		:		Dict			=[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							:  Union[str, Any]     )->					Dict:
								# Check the shards when the dataset is a round multiple of total batch size.
								lowerCamelCase__		:		Tuple			=BatchSampler(range(24     ),  batch_size=3,  drop_last=lowerCamelCase     )
								lowerCamelCase__		:		Dict			=[
								    [[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__		:		Any			=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__		:		Optional[int]			=BatchSampler(range(21     ),  batch_size=3,  drop_last=lowerCamelCase     )
								lowerCamelCase__		:		List[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__		:		Dict			=BatchSampler(range(21     ),  batch_size=3,  drop_last=lowerCamelCase     )
								lowerCamelCase__		:		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__		:		Optional[Any]			=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, 0, 1]],
								]
								self.check_batch_sampler_shards(lowerCamelCase,  lowerCamelCase     )

								lowerCamelCase__		:		Tuple			=BatchSampler(range(22     ),  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 not a round multiple of batch size but and has not a multiple of
								# num_processes batch.
								lowerCamelCase__		:		Optional[Any]			=BatchSampler(range(20     ),  batch_size=3,  drop_last=lowerCamelCase     )
								lowerCamelCase__		:		Tuple			=[
								    [[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[int]			=BatchSampler(range(20     ),  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     )

								# Check the shards when the dataset is very small.
								lowerCamelCase__		:		Union[str, Any]			=BatchSampler(range(2     ),  batch_size=3,  drop_last=lowerCamelCase     )
								lowerCamelCase__		:		str			=[[[0, 1, 0]], [[1, 0, 1]]]
								self.check_batch_sampler_shards(lowerCamelCase,  lowerCamelCase     )

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



					def 		snake_case       (       self							:  int     )->					Tuple:
								# 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__		:		Tuple			=[
								    [[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__		:		Optional[int]			=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__		:		Optional[Any]			=BatchSampler(range(22     ),  batch_size=4,  drop_last=lowerCamelCase     )
								lowerCamelCase__		:		Optional[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__		:		Optional[int]			=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[int]			=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__		:		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[Any]			=BatchSampler(range(2     ),  batch_size=4,  drop_last=lowerCamelCase     )
								lowerCamelCase__		:		Dict			=[[[0, 1]], [[0, 1]]]
								self.check_batch_sampler_shards(lowerCamelCase,  lowerCamelCase,  split_batches=lowerCamelCase     )

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



					def 		snake_case       (       self							:  int     )->					List[str]:
								# Check the shards when the dataset is a round multiple of total batch size.
								lowerCamelCase__		:		Optional[Any]			=BatchSampler(range(24     ),  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, 22, 23]],
								]
								self.check_batch_sampler_shards(lowerCamelCase,  lowerCamelCase,  even_batches=lowerCamelCase     )

								lowerCamelCase__		:		str			=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__		:		Optional[int]			=[
								    [[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__		:		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 not a round multiple of batch size but has a multiple of
								# num_processes batch.
								lowerCamelCase__		:		Tuple			=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__		:		List[Any]			=BatchSampler(range(22     ),  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 not a round multiple of batch size but and has not a multiple of
								# num_processes batch.
								lowerCamelCase__		:		Optional[Any]			=BatchSampler(range(20     ),  batch_size=3,  drop_last=lowerCamelCase     )
								lowerCamelCase__		:		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__		:		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,  even_batches=lowerCamelCase     )

								# Check the shards when the dataset is very small.
								lowerCamelCase__		:		Dict			=BatchSampler(range(2     ),  batch_size=3,  drop_last=lowerCamelCase     )
								lowerCamelCase__		:		Any			=[[[0, 1]], []]
								self.check_batch_sampler_shards(lowerCamelCase,  lowerCamelCase,  even_batches=lowerCamelCase     )

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



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

								lowerCamelCase__		:		Optional[Any]			=BatchSampler(range(22     ),  batch_size=4,  drop_last=lowerCamelCase     )
								lowerCamelCase__		:		int			=[
								    [[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__		:		List[str]			=BatchSampler(range(21     ),  batch_size=4,  drop_last=lowerCamelCase     )
								lowerCamelCase__		:		List[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__		:		Tuple			=BatchSampler(range(21     ),  batch_size=4,  drop_last=lowerCamelCase     )
								lowerCamelCase__		:		Dict			=[
								    [[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__		:		int			=BatchSampler(range(2     ),  batch_size=4,  drop_last=lowerCamelCase     )
								lowerCamelCase__		:		Optional[Any]			=[[[0, 1]], []]
								self.check_batch_sampler_shards(lowerCamelCase,  lowerCamelCase,  split_batches=lowerCamelCase,  even_batches=lowerCamelCase     )

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



					def 		snake_case       (       self							:  Tuple     )->					Any:
								lowerCamelCase__		:		Optional[Any]			=[[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 10, 11], [12, 13]]
								lowerCamelCase__		:		Optional[int]			=[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							:  Any,  lowerCamelCase							:  Any,  lowerCamelCase							:  List[str],  lowerCamelCase							:  int,  lowerCamelCase							:  Optional[int]=False,  lowerCamelCase							:  Tuple=2,  lowerCamelCase							:  Dict=False     )->					Optional[int]:
								random.seed(lowerCamelCase     )
								lowerCamelCase__		:		Any			=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__		:		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__		:		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__		:		Dict			=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__		:		Dict			=[]
								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							:  Tuple     )->					List[Any]:
								lowerCamelCase__		:		Union[str, Any]			=42
								lowerCamelCase__		:		Tuple			=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__		:		str			=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							:  Dict     )->					List[Any]:
								lowerCamelCase__		:		List[str]			=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							:  List[str]     )->					Union[str, Any]:
								lowerCamelCase__		:		Any			=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     )->					Optional[Any]:
								lowerCamelCase__		:		int			=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 		snake_case       (       self							:  int     )->					Optional[Any]:
								lowerCamelCase__		:		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 		snake_case       (       self							:  Optional[int]     )->					int:
								Accelerator()
								lowerCamelCase__		:		Union[str, 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 __future__ import annotations

_lowercase : Dict      						=						1.6_021E-19  # units = C






def      snake_case__   (		__lowerCamelCase				:    float							,		__lowerCamelCase				:    float							,		__lowerCamelCase				:    float							,		):
			"""simple docstring"""
			if (conductivity, electron_conc, mobility).count(0	) != 1:
						raise ValueError('''You cannot supply more or less than 2 values'''	)
			elif conductivity < 0:
						raise ValueError('''Conductivity cannot be negative'''	)
			elif electron_conc < 0:
						raise ValueError('''Electron concentration cannot be negative'''	)
			elif mobility < 0:
						raise ValueError('''mobility cannot be negative'''	)
			elif conductivity == 0:
						return (
						    "conductivity",
						    mobility * electron_conc * ELECTRON_CHARGE,
						)
			elif electron_conc == 0:
						return (
						    "electron_conc",
						    conductivity / (mobility * ELECTRON_CHARGE),
						)
			else:
						return (
						    "mobility",
						    conductivity / (electron_conc * ELECTRON_CHARGE),
						)


if __name__ == "__main__":
		import doctest

		doctest.testmod()




