
'''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 						__UpperCAmelCase      (					_lowerCamelCase						):


							def __init__(       self    ,   lowerCAmelCase_=0.01    ,   lowerCAmelCase_=10_00  ):





								"""simple docstring"""






								_snake_case   						=		p_stop
								_snake_case   						=		max_length




							def __iter__(       self  ):





								"""simple docstring"""






								_snake_case   						=		0
								_snake_case   						=		False
								while not stop and count < self.max_length:
									yield count
									count += 1
									_snake_case   						=		random.random() < self.p_stop


class 						__UpperCAmelCase      (					unittest.TestCase						):


							def  lowerCamelCase				(       self    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_=False    ,   lowerCAmelCase_=True  ):





								"""simple docstring"""






								_snake_case   						=		[
								    BatchSamplerShard(lowerCAmelCase_    ,   2    ,   lowerCAmelCase_    ,   split_batches=lowerCAmelCase_    ,   even_batches=lowerCAmelCase_  )
								    for i in range(2  )
								]
								_snake_case   						=		[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  ):





								"""simple docstring"""






								_snake_case   						=		BatchSampler(range(24  )    ,   batch_size=3    ,   drop_last=lowerCAmelCase_  )
								_snake_case   						=		[
								    [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
								    [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]],
								]
								self.check_batch_sampler_shards(lowerCAmelCase_    ,   lowerCAmelCase_  )

								_snake_case   						=		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.
								_snake_case   						=		BatchSampler(range(21  )    ,   batch_size=3    ,   drop_last=lowerCAmelCase_  )
								_snake_case   						=		[
								    [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
								    [[3, 4, 5], [9, 10, 11], [15, 16, 17], [0, 1, 2]],
								]
								self.check_batch_sampler_shards(lowerCAmelCase_    ,   lowerCAmelCase_  )

								_snake_case   						=		BatchSampler(range(21  )    ,   batch_size=3    ,   drop_last=lowerCAmelCase_  )
								_snake_case   						=		[
								    [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
								    [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
								]
								self.check_batch_sampler_shards(lowerCAmelCase_    ,   lowerCAmelCase_  )

								# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
								# num_processes batch.
								_snake_case   						=		BatchSampler(range(22  )    ,   batch_size=3    ,   drop_last=lowerCAmelCase_  )
								_snake_case   						=		[
								    [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
								    [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 0, 1]],
								]
								self.check_batch_sampler_shards(lowerCAmelCase_    ,   lowerCAmelCase_  )

								_snake_case   						=		BatchSampler(range(22  )    ,   batch_size=3    ,   drop_last=lowerCAmelCase_  )
								_snake_case   						=		[
								    [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
								    [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
								]
								self.check_batch_sampler_shards(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.
								_snake_case   						=		BatchSampler(range(20  )    ,   batch_size=3    ,   drop_last=lowerCAmelCase_  )
								_snake_case   						=		[
								    [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 0]],
								    [[3, 4, 5], [9, 10, 11], [15, 16, 17], [1, 2, 3]],
								]
								self.check_batch_sampler_shards(lowerCAmelCase_    ,   lowerCAmelCase_  )

								_snake_case   						=		BatchSampler(range(20  )    ,   batch_size=3    ,   drop_last=lowerCAmelCase_  )
								_snake_case   						=		[
								    [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
								    [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
								]
								self.check_batch_sampler_shards(lowerCAmelCase_    ,   lowerCAmelCase_  )

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

								_snake_case   						=		BatchSampler(range(2  )    ,   batch_size=3    ,   drop_last=lowerCAmelCase_  )
								_snake_case   						=		[[], []]
								self.check_batch_sampler_shards(lowerCAmelCase_    ,   lowerCAmelCase_  )


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		BatchSampler(range(24  )    ,   batch_size=4    ,   drop_last=lowerCAmelCase_  )
								_snake_case   						=		[
								    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
								    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23]],
								]
								self.check_batch_sampler_shards(lowerCAmelCase_    ,   lowerCAmelCase_    ,   split_batches=lowerCAmelCase_  )

								_snake_case   						=		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.
								_snake_case   						=		BatchSampler(range(22  )    ,   batch_size=4    ,   drop_last=lowerCAmelCase_  )
								_snake_case   						=		[
								    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
								    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [0, 1]],
								]
								self.check_batch_sampler_shards(lowerCAmelCase_    ,   lowerCAmelCase_    ,   split_batches=lowerCAmelCase_  )

								_snake_case   						=		BatchSampler(range(22  )    ,   batch_size=4    ,   drop_last=lowerCAmelCase_  )
								_snake_case   						=		[
								    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
								    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
								]
								self.check_batch_sampler_shards(lowerCAmelCase_    ,   lowerCAmelCase_    ,   split_batches=lowerCAmelCase_  )

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

								_snake_case   						=		BatchSampler(range(21  )    ,   batch_size=4    ,   drop_last=lowerCAmelCase_  )
								_snake_case   						=		[
								    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
								    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
								]
								self.check_batch_sampler_shards(lowerCAmelCase_    ,   lowerCAmelCase_    ,   split_batches=lowerCAmelCase_  )

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

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


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		BatchSampler(range(24  )    ,   batch_size=3    ,   drop_last=lowerCAmelCase_  )
								_snake_case   						=		[
								    [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
								    [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]],
								]
								self.check_batch_sampler_shards(lowerCAmelCase_    ,   lowerCAmelCase_    ,   even_batches=lowerCAmelCase_  )

								_snake_case   						=		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.
								_snake_case   						=		BatchSampler(range(21  )    ,   batch_size=3    ,   drop_last=lowerCAmelCase_  )
								_snake_case   						=		[
								    [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
								    [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
								]
								self.check_batch_sampler_shards(lowerCAmelCase_    ,   lowerCAmelCase_    ,   even_batches=lowerCAmelCase_  )

								_snake_case   						=		BatchSampler(range(21  )    ,   batch_size=3    ,   drop_last=lowerCAmelCase_  )
								_snake_case   						=		[
								    [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
								    [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
								]
								self.check_batch_sampler_shards(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.
								_snake_case   						=		BatchSampler(range(22  )    ,   batch_size=3    ,   drop_last=lowerCAmelCase_  )
								_snake_case   						=		[
								    [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
								    [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21]],
								]
								self.check_batch_sampler_shards(lowerCAmelCase_    ,   lowerCAmelCase_    ,   even_batches=lowerCAmelCase_  )

								_snake_case   						=		BatchSampler(range(22  )    ,   batch_size=3    ,   drop_last=lowerCAmelCase_  )
								_snake_case   						=		[
								    [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
								    [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
								]
								self.check_batch_sampler_shards(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.
								_snake_case   						=		BatchSampler(range(20  )    ,   batch_size=3    ,   drop_last=lowerCAmelCase_  )
								_snake_case   						=		[
								    [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19]],
								    [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
								]
								self.check_batch_sampler_shards(lowerCAmelCase_    ,   lowerCAmelCase_    ,   even_batches=lowerCAmelCase_  )

								_snake_case   						=		BatchSampler(range(20  )    ,   batch_size=3    ,   drop_last=lowerCAmelCase_  )
								_snake_case   						=		[
								    [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
								    [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
								]
								self.check_batch_sampler_shards(lowerCAmelCase_    ,   lowerCAmelCase_    ,   even_batches=lowerCAmelCase_  )

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

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


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		BatchSampler(range(24  )    ,   batch_size=4    ,   drop_last=lowerCAmelCase_  )
								_snake_case   						=		[
								    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
								    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23]],
								]
								self.check_batch_sampler_shards(lowerCAmelCase_    ,   lowerCAmelCase_    ,   split_batches=lowerCAmelCase_    ,   even_batches=lowerCAmelCase_  )

								_snake_case   						=		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.
								_snake_case   						=		BatchSampler(range(22  )    ,   batch_size=4    ,   drop_last=lowerCAmelCase_  )
								_snake_case   						=		[
								    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
								    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
								]
								self.check_batch_sampler_shards(lowerCAmelCase_    ,   lowerCAmelCase_    ,   split_batches=lowerCAmelCase_    ,   even_batches=lowerCAmelCase_  )

								_snake_case   						=		BatchSampler(range(22  )    ,   batch_size=4    ,   drop_last=lowerCAmelCase_  )
								_snake_case   						=		[
								    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
								    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
								]
								self.check_batch_sampler_shards(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.
								_snake_case   						=		BatchSampler(range(21  )    ,   batch_size=4    ,   drop_last=lowerCAmelCase_  )
								_snake_case   						=		[
								    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20]],
								    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
								]
								self.check_batch_sampler_shards(lowerCAmelCase_    ,   lowerCAmelCase_    ,   split_batches=lowerCAmelCase_    ,   even_batches=lowerCAmelCase_  )

								_snake_case   						=		BatchSampler(range(21  )    ,   batch_size=4    ,   drop_last=lowerCAmelCase_  )
								_snake_case   						=		[
								    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
								    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
								]
								self.check_batch_sampler_shards(lowerCAmelCase_    ,   lowerCAmelCase_    ,   split_batches=lowerCAmelCase_    ,   even_batches=lowerCAmelCase_  )

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

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


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		[[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 10, 11], [12, 13]]
								_snake_case   						=		[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  ):





								"""simple docstring"""






								random.seed(lowerCAmelCase_  )
								_snake_case   						=		list(lowerCAmelCase_  )

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

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

								_snake_case   						=		[]
								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  ):





								"""simple docstring"""






								_snake_case   						=		42
								_snake_case   						=		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
								_snake_case   						=		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  ):





								"""simple docstring"""






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


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		SkipDataLoader(list(range(16  )  )    ,   batch_size=4    ,   skip_batches=2  )
								self.assertListEqual([t.tolist() for t in dataloader]    ,   [[8, 9, 10, 11], [12, 13, 14, 15]]  )


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		DataLoader(list(range(16  )  )    ,   batch_size=4  )
								_snake_case   						=		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  ):





								"""simple docstring"""






								_snake_case   						=		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  ):





								"""simple docstring"""






								Accelerator()
								_snake_case   						=		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  )



import argparse
import os
import shutil

import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer


def 						UpperCAmelCase			(	a_						)		->	List[str]:







				"""simple docstring"""



				__A   			= args.pruning_method
				__A   			= args.threshold

				__A   			= args.model_name_or_path.rstrip("/"						)
				__A   			= args.target_model_path

				print(F'''Load fine-pruned model from {model_name_or_path}'''						)
				__A   			= torch.load(os.path.join(a_		,   "pytorch_model.bin"						)						)
				__A   			= {}

				for name, tensor in model.items():
								if "embeddings" in name or "LayerNorm" in name or "pooler" in name:
												__A   			= tensor
												print(F'''Copied layer {name}'''						)
								elif "classifier" in name or "qa_output" in name:
												__A   			= tensor
												print(F'''Copied layer {name}'''						)
								elif "bias" in name:
												__A   			= tensor
												print(F'''Copied layer {name}'''						)
								else:
												if pruning_method == "magnitude":
																__A   			= MagnitudeBinarizer.apply(inputs=a_		,   threshold=a_						)
																__A   			= tensor * mask
																print(F'''Pruned layer {name}'''						)
												elif pruning_method == "topK":
																if "mask_scores" in name:
																				continue
																__A   			= name[:-6]
																__A   			= model[F'''{prefix_}mask_scores''']
																__A   			= TopKBinarizer.apply(a_		,   a_						)
																__A   			= tensor * mask
																print(F'''Pruned layer {name}'''						)
												elif pruning_method == "sigmoied_threshold":
																if "mask_scores" in name:
																				continue
																__A   			= name[:-6]
																__A   			= model[F'''{prefix_}mask_scores''']
																__A   			= ThresholdBinarizer.apply(a_		,   a_		,   a_						)
																__A   			= tensor * mask
																print(F'''Pruned layer {name}'''						)
												elif pruning_method == "l0":
																if "mask_scores" in name:
																				continue
																__A   			= name[:-6]
																__A   			= model[F'''{prefix_}mask_scores''']
																__A			,							__A   			= -0.1, 1.1
																__A   			= torch.sigmoid(a_						)
																__A   			= s * (r - l) + l
																__A   			= s_bar.clamp(min=0.0		,   max=1.0						)
																__A   			= tensor * mask
																print(F'''Pruned layer {name}'''						)
												else:
																raise ValueError("Unknown pruning method"						)

				if target_model_path is None:
								__A   			= os.path.join(
								    os.path.dirname(a_						)		,   F'''bertarized_{os.path.basename(a_						)}'''						)

				if not os.path.isdir(a_						):
								shutil.copytree(a_		,   a_						)
								print(F'''\nCreated folder {target_model_path}'''						)

				torch.save(a_		,   os.path.join(a_		,   "pytorch_model.bin"						)						)
				print("\nPruned model saved! See you later!"						)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE							:Tuple							 =				argparse.ArgumentParser()

					parser.add_argument(
					    '--pruning_method',
					    choices=['l0', 'magnitude', 'topK', 'sigmoied_threshold'],
					    type=str,
					    required=True,
					    help=(
					        'Pruning Method (l0 = L0 regularization, magnitude = Magnitude pruning, topK = Movement pruning,'
					        ' sigmoied_threshold = Soft movement pruning)'
					    ),
					)
					parser.add_argument(
					    '--threshold',
					    type=float,
					    required=False,
					    help=(
					        'For `magnitude` and `topK`, it is the level of remaining weights (in %) in the fine-pruned model.'
					        'For `sigmoied_threshold`, it is the threshold \tau against which the (sigmoied) scores are compared.'
					        'Not needed for `l0`'
					    ),
					)
					parser.add_argument(
					    '--model_name_or_path',
					    type=str,
					    required=True,
					    help='Folder containing the model that was previously fine-pruned',
					)
					parser.add_argument(
					    '--target_model_path',
					    default=None,
					    type=str,
					    required=False,
					    help='Folder containing the model that was previously fine-pruned',
					)

					SCREAMING_SNAKE_CASE							:str							 =				parser.parse_args()

					main(args)
