






"""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 			lowercase__			(      lowerCamelCase__    ):
							'''simple docstring'''







							def __init__(  self	,       snake_case=0.01	,       snake_case=1000						)			->							Union[str, Any]:
									_UpperCAmelCase							 =		p_stop
									_UpperCAmelCase							 =		max_length






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







class 			lowercase__			(      unittest.TestCase    ):
							'''simple docstring'''







							def        lowerCamelCase_   (  self	,       snake_case	,       snake_case	,       snake_case=False	,       snake_case=True						)			->							Tuple:
									_UpperCAmelCase							 =		[
									    BatchSamplerShard(__lowerCamelCase	,       2	,       __lowerCamelCase	,       split_batches=__lowerCamelCase	,       even_batches=__lowerCamelCase						)
									    for i in range(2						)
									]
									_UpperCAmelCase							 =		[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]:
									_UpperCAmelCase							 =		BatchSampler(range(24						)	,       batch_size=3	,       drop_last=__lowerCamelCase						)
									_UpperCAmelCase							 =		[
									    [[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						)

									_UpperCAmelCase							 =		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.
									_UpperCAmelCase							 =		BatchSampler(range(21						)	,       batch_size=3	,       drop_last=__lowerCamelCase						)
									_UpperCAmelCase							 =		[
									    [[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						)

									_UpperCAmelCase							 =		BatchSampler(range(21						)	,       batch_size=3	,       drop_last=__lowerCamelCase						)
									_UpperCAmelCase							 =		[
									    [[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.
									_UpperCAmelCase							 =		BatchSampler(range(22						)	,       batch_size=3	,       drop_last=__lowerCamelCase						)
									_UpperCAmelCase							 =		[
									    [[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						)

									_UpperCAmelCase							 =		BatchSampler(range(22						)	,       batch_size=3	,       drop_last=__lowerCamelCase						)
									_UpperCAmelCase							 =		[
									    [[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.
									_UpperCAmelCase							 =		BatchSampler(range(20						)	,       batch_size=3	,       drop_last=__lowerCamelCase						)
									_UpperCAmelCase							 =		[
									    [[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						)

									_UpperCAmelCase							 =		BatchSampler(range(20						)	,       batch_size=3	,       drop_last=__lowerCamelCase						)
									_UpperCAmelCase							 =		[
									    [[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.
									_UpperCAmelCase							 =		BatchSampler(range(2						)	,       batch_size=3	,       drop_last=__lowerCamelCase						)
									_UpperCAmelCase							 =		[[[0, 1, 0]], [[1, 0, 1]]]
									self.check_batch_sampler_shards(__lowerCamelCase	,       __lowerCamelCase						)

									_UpperCAmelCase							 =		BatchSampler(range(2						)	,       batch_size=3	,       drop_last=__lowerCamelCase						)
									_UpperCAmelCase							 =		[[], []]
									self.check_batch_sampler_shards(__lowerCamelCase	,       __lowerCamelCase						)






							def        lowerCamelCase_   (  self						)			->							Dict:
									_UpperCAmelCase							 =		BatchSampler(range(24						)	,       batch_size=4	,       drop_last=__lowerCamelCase						)
									_UpperCAmelCase							 =		[
									    [[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						)

									_UpperCAmelCase							 =		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.
									_UpperCAmelCase							 =		BatchSampler(range(22						)	,       batch_size=4	,       drop_last=__lowerCamelCase						)
									_UpperCAmelCase							 =		[
									    [[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						)

									_UpperCAmelCase							 =		BatchSampler(range(22						)	,       batch_size=4	,       drop_last=__lowerCamelCase						)
									_UpperCAmelCase							 =		[
									    [[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.
									_UpperCAmelCase							 =		BatchSampler(range(21						)	,       batch_size=4	,       drop_last=__lowerCamelCase						)
									_UpperCAmelCase							 =		[
									    [[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						)

									_UpperCAmelCase							 =		BatchSampler(range(21						)	,       batch_size=4	,       drop_last=__lowerCamelCase						)
									_UpperCAmelCase							 =		[
									    [[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.
									_UpperCAmelCase							 =		BatchSampler(range(2						)	,       batch_size=4	,       drop_last=__lowerCamelCase						)
									_UpperCAmelCase							 =		[[[0, 1]], [[0, 1]]]
									self.check_batch_sampler_shards(__lowerCamelCase	,       __lowerCamelCase	,       split_batches=__lowerCamelCase						)

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






							def        lowerCamelCase_   (  self						)			->							List[str]:
									_UpperCAmelCase							 =		BatchSampler(range(24						)	,       batch_size=3	,       drop_last=__lowerCamelCase						)
									_UpperCAmelCase							 =		[
									    [[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						)

									_UpperCAmelCase							 =		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.
									_UpperCAmelCase							 =		BatchSampler(range(21						)	,       batch_size=3	,       drop_last=__lowerCamelCase						)
									_UpperCAmelCase							 =		[
									    [[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						)

									_UpperCAmelCase							 =		BatchSampler(range(21						)	,       batch_size=3	,       drop_last=__lowerCamelCase						)
									_UpperCAmelCase							 =		[
									    [[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.
									_UpperCAmelCase							 =		BatchSampler(range(22						)	,       batch_size=3	,       drop_last=__lowerCamelCase						)
									_UpperCAmelCase							 =		[
									    [[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						)

									_UpperCAmelCase							 =		BatchSampler(range(22						)	,       batch_size=3	,       drop_last=__lowerCamelCase						)
									_UpperCAmelCase							 =		[
									    [[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.
									_UpperCAmelCase							 =		BatchSampler(range(20						)	,       batch_size=3	,       drop_last=__lowerCamelCase						)
									_UpperCAmelCase							 =		[
									    [[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						)

									_UpperCAmelCase							 =		BatchSampler(range(20						)	,       batch_size=3	,       drop_last=__lowerCamelCase						)
									_UpperCAmelCase							 =		[
									    [[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.
									_UpperCAmelCase							 =		BatchSampler(range(2						)	,       batch_size=3	,       drop_last=__lowerCamelCase						)
									_UpperCAmelCase							 =		[[[0, 1]], []]
									self.check_batch_sampler_shards(__lowerCamelCase	,       __lowerCamelCase	,       even_batches=__lowerCamelCase						)

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






							def        lowerCamelCase_   (  self						)			->							Any:
									_UpperCAmelCase							 =		BatchSampler(range(24						)	,       batch_size=4	,       drop_last=__lowerCamelCase						)
									_UpperCAmelCase							 =		[
									    [[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						)

									_UpperCAmelCase							 =		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.
									_UpperCAmelCase							 =		BatchSampler(range(22						)	,       batch_size=4	,       drop_last=__lowerCamelCase						)
									_UpperCAmelCase							 =		[
									    [[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						)

									_UpperCAmelCase							 =		BatchSampler(range(22						)	,       batch_size=4	,       drop_last=__lowerCamelCase						)
									_UpperCAmelCase							 =		[
									    [[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.
									_UpperCAmelCase							 =		BatchSampler(range(21						)	,       batch_size=4	,       drop_last=__lowerCamelCase						)
									_UpperCAmelCase							 =		[
									    [[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						)

									_UpperCAmelCase							 =		BatchSampler(range(21						)	,       batch_size=4	,       drop_last=__lowerCamelCase						)
									_UpperCAmelCase							 =		[
									    [[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.
									_UpperCAmelCase							 =		BatchSampler(range(2						)	,       batch_size=4	,       drop_last=__lowerCamelCase						)
									_UpperCAmelCase							 =		[[[0, 1]], []]
									self.check_batch_sampler_shards(__lowerCamelCase	,       __lowerCamelCase	,       split_batches=__lowerCamelCase	,       even_batches=__lowerCamelCase						)

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






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

									_UpperCAmelCase							 =		[
									    IterableDatasetShard(
									        __lowerCamelCase	,       batch_size=__lowerCamelCase	,       drop_last=__lowerCamelCase	,       num_processes=__lowerCamelCase	,       process_index=__lowerCamelCase	,       split_batches=__lowerCamelCase	,       )
									    for i in range(__lowerCamelCase						)
									]
									_UpperCAmelCase							 =		[]
									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						)						)

									_UpperCAmelCase							 =		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
									_UpperCAmelCase							 =		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						)

									_UpperCAmelCase							 =		[]
									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]:
									_UpperCAmelCase							 =		42
									_UpperCAmelCase							 =		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
									_UpperCAmelCase							 =		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:
									_UpperCAmelCase							 =		BatchSampler(range(16						)	,       batch_size=4	,       drop_last=__lowerCamelCase						)
									_UpperCAmelCase							 =		SkipBatchSampler(__lowerCamelCase	,       2						)
									self.assertListEqual(list(__lowerCamelCase						)	,       [[8, 9, 10, 11], [12, 13, 14, 15]]						)






							def        lowerCamelCase_   (  self						)			->							Optional[Any]:
									_UpperCAmelCase							 =		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]:
									_UpperCAmelCase							 =		DataLoader(list(range(16						)						)	,       batch_size=4						)
									_UpperCAmelCase							 =		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]:
									_UpperCAmelCase							 =		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:
									Accelerator()
									_UpperCAmelCase							 =		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"""





import argparse
import logging
import pickle
from collections import Counter


logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
lowercase  							=					logging.getLogger(__name__)

if __name__ == "__main__":
					lowercase  							=					argparse.ArgumentParser(
					    description='''Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)'''
					)
					parser.add_argument(
					    '''--data_file''', type=str, default='''data/dump.bert-base-uncased.pickle''', help='''The binarized dataset.'''
					)
					parser.add_argument(
					    '''--token_counts_dump''', type=str, default='''data/token_counts.bert-base-uncased.pickle''', help='''The dump file.'''
					)
					parser.add_argument('''--vocab_size''', default=3_05_22, type=int)
					lowercase  							=					parser.parse_args()

					logger.info(F'''Loading data from {args.data_file}''')
					with open(args.data_file, '''rb''') as fp:
										lowercase  							=					pickle.load(fp)

					logger.info('''Counting occurrences for MLM.''')
					lowercase  							=					Counter()
					for tk_ids in data:
										counter.update(tk_ids)
					lowercase  							=					[0] * args.vocab_size
					for k, v in counter.items():
										lowercase  							=					v

					logger.info(F'''Dump to {args.token_counts_dump}''')
					with open(args.token_counts_dump, '''wb''') as handle:
										pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)


