


'''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 		a__    (							a__  ):





							'''simple docstring'''






							def __init__(		self ,   lowerCamelCase_=0.01 ,   lowerCamelCase_=10_00       )	->							Dict:
														lowerCAmelCase__	       =     p_stop
														lowerCAmelCase__	       =     max_length







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




class 		a__    (							unittest.TestCase  ):





							'''simple docstring'''






							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_=False ,   lowerCamelCase_=True       )	->							str:
														lowerCAmelCase__	       =     [
														    BatchSamplerShard(lowerCamelCase_ ,   2 ,   lowerCamelCase_ ,   split_batches=lowerCamelCase_ ,   even_batches=lowerCamelCase_       )
														    for i in range(2       )
														]
														lowerCAmelCase__	       =     [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   __SCREAMING_SNAKE_CASE							(		self       )	->							Optional[int]:
														# Check the shards when the dataset is a round multiple of total batch size.
														lowerCAmelCase__	       =     BatchSampler(range(24       ) ,   batch_size=3 ,   drop_last=lowerCamelCase_       )
														lowerCAmelCase__	       =     [
														    [[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__	       =     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__	       =     BatchSampler(range(21       ) ,   batch_size=3 ,   drop_last=lowerCamelCase_       )
														lowerCAmelCase__	       =     [
														    [[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__	       =     BatchSampler(range(21       ) ,   batch_size=3 ,   drop_last=lowerCamelCase_       )
														lowerCAmelCase__	       =     [
														    [[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__	       =     BatchSampler(range(22       ) ,   batch_size=3 ,   drop_last=lowerCamelCase_       )
														lowerCAmelCase__	       =     [
														    [[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__	       =     BatchSampler(range(22       ) ,   batch_size=3 ,   drop_last=lowerCamelCase_       )
														lowerCAmelCase__	       =     [
														    [[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__	       =     BatchSampler(range(20       ) ,   batch_size=3 ,   drop_last=lowerCamelCase_       )
														lowerCAmelCase__	       =     [
														    [[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__	       =     BatchSampler(range(20       ) ,   batch_size=3 ,   drop_last=lowerCamelCase_       )
														lowerCAmelCase__	       =     [
														    [[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__	       =     BatchSampler(range(2       ) ,   batch_size=3 ,   drop_last=lowerCamelCase_       )
														lowerCAmelCase__	       =     [[[0, 1, 0]], [[1, 0, 1]]]
														self.check_batch_sampler_shards(lowerCamelCase_ ,   lowerCamelCase_       )

														lowerCAmelCase__	       =     BatchSampler(range(2       ) ,   batch_size=3 ,   drop_last=lowerCamelCase_       )
														lowerCAmelCase__	       =     [[], []]
														self.check_batch_sampler_shards(lowerCamelCase_ ,   lowerCamelCase_       )







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							str:
														# Check the shards when the dataset is a round multiple of batch size.
														lowerCAmelCase__	       =     BatchSampler(range(24       ) ,   batch_size=4 ,   drop_last=lowerCamelCase_       )
														lowerCAmelCase__	       =     [
														    [[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__	       =     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__	       =     BatchSampler(range(22       ) ,   batch_size=4 ,   drop_last=lowerCamelCase_       )
														lowerCAmelCase__	       =     [
														    [[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__	       =     BatchSampler(range(22       ) ,   batch_size=4 ,   drop_last=lowerCamelCase_       )
														lowerCAmelCase__	       =     [
														    [[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__	       =     BatchSampler(range(21       ) ,   batch_size=4 ,   drop_last=lowerCamelCase_       )
														lowerCAmelCase__	       =     [
														    [[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__	       =     BatchSampler(range(21       ) ,   batch_size=4 ,   drop_last=lowerCamelCase_       )
														lowerCAmelCase__	       =     [
														    [[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__	       =     BatchSampler(range(2       ) ,   batch_size=4 ,   drop_last=lowerCamelCase_       )
														lowerCAmelCase__	       =     [[[0, 1]], [[0, 1]]]
														self.check_batch_sampler_shards(lowerCamelCase_ ,   lowerCamelCase_ ,   split_batches=lowerCamelCase_       )

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







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							int:
														# Check the shards when the dataset is a round multiple of total batch size.
														lowerCAmelCase__	       =     BatchSampler(range(24       ) ,   batch_size=3 ,   drop_last=lowerCamelCase_       )
														lowerCAmelCase__	       =     [
														    [[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__	       =     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__	       =     BatchSampler(range(21       ) ,   batch_size=3 ,   drop_last=lowerCamelCase_       )
														lowerCAmelCase__	       =     [
														    [[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__	       =     BatchSampler(range(21       ) ,   batch_size=3 ,   drop_last=lowerCamelCase_       )
														lowerCAmelCase__	       =     [
														    [[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__	       =     BatchSampler(range(22       ) ,   batch_size=3 ,   drop_last=lowerCamelCase_       )
														lowerCAmelCase__	       =     [
														    [[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__	       =     BatchSampler(range(22       ) ,   batch_size=3 ,   drop_last=lowerCamelCase_       )
														lowerCAmelCase__	       =     [
														    [[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__	       =     BatchSampler(range(20       ) ,   batch_size=3 ,   drop_last=lowerCamelCase_       )
														lowerCAmelCase__	       =     [
														    [[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__	       =     BatchSampler(range(20       ) ,   batch_size=3 ,   drop_last=lowerCamelCase_       )
														lowerCAmelCase__	       =     [
														    [[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__	       =     BatchSampler(range(2       ) ,   batch_size=3 ,   drop_last=lowerCamelCase_       )
														lowerCAmelCase__	       =     [[[0, 1]], []]
														self.check_batch_sampler_shards(lowerCamelCase_ ,   lowerCamelCase_ ,   even_batches=lowerCamelCase_       )

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







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Union[str, Any]:
														# Check the shards when the dataset is a round multiple of batch size.
														lowerCAmelCase__	       =     BatchSampler(range(24       ) ,   batch_size=4 ,   drop_last=lowerCamelCase_       )
														lowerCAmelCase__	       =     [
														    [[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__	       =     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__	       =     BatchSampler(range(22       ) ,   batch_size=4 ,   drop_last=lowerCamelCase_       )
														lowerCAmelCase__	       =     [
														    [[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__	       =     BatchSampler(range(22       ) ,   batch_size=4 ,   drop_last=lowerCamelCase_       )
														lowerCAmelCase__	       =     [
														    [[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__	       =     BatchSampler(range(21       ) ,   batch_size=4 ,   drop_last=lowerCamelCase_       )
														lowerCAmelCase__	       =     [
														    [[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__	       =     BatchSampler(range(21       ) ,   batch_size=4 ,   drop_last=lowerCamelCase_       )
														lowerCAmelCase__	       =     [
														    [[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__	       =     BatchSampler(range(2       ) ,   batch_size=4 ,   drop_last=lowerCamelCase_       )
														lowerCAmelCase__	       =     [[[0, 1]], []]
														self.check_batch_sampler_shards(lowerCamelCase_ ,   lowerCamelCase_ ,   split_batches=lowerCamelCase_ ,   even_batches=lowerCamelCase_       )

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







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Tuple:
														lowerCAmelCase__	       =     [[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 10, 11], [12, 13]]
														lowerCAmelCase__	       =     [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   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_=False ,   lowerCamelCase_=2 ,   lowerCamelCase_=False       )	->							Union[str, Any]:
														random.seed(lowerCamelCase_       )
														lowerCAmelCase__	       =     list(lowerCamelCase_       )

														lowerCAmelCase__	       =     [
														    IterableDatasetShard(
														        lowerCamelCase_ ,   batch_size=lowerCamelCase_ ,   drop_last=lowerCamelCase_ ,   num_processes=lowerCamelCase_ ,   process_index=lowerCamelCase_ ,   split_batches=lowerCamelCase_ ,   )
														    for i in range(lowerCamelCase_       )
														]
														lowerCAmelCase__	       =     []
														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__	       =     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__	       =     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__	       =     []
														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   __SCREAMING_SNAKE_CASE							(		self       )	->							Union[str, Any]:
														lowerCAmelCase__	       =     42
														lowerCAmelCase__	       =     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__	       =     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   __SCREAMING_SNAKE_CASE							(		self       )	->							Optional[int]:
														lowerCAmelCase__	       =     BatchSampler(range(16       ) ,   batch_size=4 ,   drop_last=lowerCamelCase_       )
														lowerCAmelCase__	       =     SkipBatchSampler(lowerCamelCase_ ,   2       )
														self.assertListEqual(list(lowerCamelCase_       ) ,   [[8, 9, 10, 11], [12, 13, 14, 15]]       )







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Optional[int]:
														lowerCAmelCase__	       =     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   __SCREAMING_SNAKE_CASE							(		self       )	->							str:
														lowerCAmelCase__	       =     DataLoader(list(range(16       )       ) ,   batch_size=4       )
														lowerCAmelCase__	       =     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   __SCREAMING_SNAKE_CASE							(		self       )	->							Tuple:
														lowerCAmelCase__	       =     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   __SCREAMING_SNAKE_CASE							(		self       )	->							Optional[int]:
														Accelerator()
														lowerCAmelCase__	       =     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 math
from enum import Enum
from typing import Optional, Union

from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR

from .utils import logging


__UpperCAmelCase       =							logging.get_logger(__name__)




class 		a__    (							a__  ):





							'''simple docstring'''






							lowercase__      :       int     						=				"linear"
							lowercase__      :       Any     						=				"cosine"
							lowercase__      :       Optional[int]     						=				"cosine_with_restarts"
							lowercase__      :       Optional[Any]     						=				"polynomial"
							lowercase__      :       Tuple     						=				"constant"
							lowercase__      :       Optional[int]     						=				"constant_with_warmup"
							lowercase__      :       Optional[int]     						=				"piecewise_constant"


def  _snake_case       (					A			,   A = -1	)						->   Any:
							return LambdaLR(A			,   lambda A	: 1			,   last_epoch=A	)


def  _snake_case       (					A			,   A			,   A = -1	)						->   Optional[Any]:

							def lr_lambda(A	):
														if current_step < num_warmup_steps:
																					return float(A	) / float(max(1.0			,   A	)	)
														return 1.0

							return LambdaLR(A			,   A			,   last_epoch=A	)


def  _snake_case       (					A			,   A			,   A = -1	)						->   Union[str, Any]:
							lowerCAmelCase__	       =     {}
							lowerCAmelCase__	       =     step_rules.split(''','''	)
							for rule_str in rule_list[:-1]:
														lowerCAmelCase__       , lowerCAmelCase__	       =     rule_str.split(''':'''	)
														lowerCAmelCase__	       =     int(A	)
														lowerCAmelCase__	       =     float(A	)
														lowerCAmelCase__	       =     value
							lowerCAmelCase__	       =     float(rule_list[-1]	)

							def create_rules_function(A			,   A	):
														def rule_func(A	) -> float:
																					lowerCAmelCase__	       =     sorted(rules_dict.keys()	)
																					for i, sorted_step in enumerate(A	):
																												if steps < sorted_step:
																																			return rules_dict[sorted_steps[i]]
																					return last_lr_multiple

														return rule_func

							lowerCAmelCase__	       =     create_rules_function(A			,   A	)

							return LambdaLR(A			,   A			,   last_epoch=A	)


def  _snake_case       (					A			,   A			,   A			,   A=-1	)						->   Optional[int]:

							def lr_lambda(A	):
														if current_step < num_warmup_steps:
																					return float(A	) / float(max(1			,   A	)	)
														return max(
														    0.0			,   float(num_training_steps - current_step	) / float(max(1			,   num_training_steps - num_warmup_steps	)	)	)

							return LambdaLR(A			,   A			,   A	)


def  _snake_case       (					A			,   A			,   A			,   A = 0.5			,   A = -1	)						->   List[str]:

							def lr_lambda(A	):
														if current_step < num_warmup_steps:
																					return float(A	) / float(max(1			,   A	)	)
														lowerCAmelCase__	       =     float(current_step - num_warmup_steps	) / float(max(1			,   num_training_steps - num_warmup_steps	)	)
														return max(0.0			,   0.5 * (1.0 + math.cos(math.pi * float(A	) * 2.0 * progress	))	)

							return LambdaLR(A			,   A			,   A	)


def  _snake_case       (					A			,   A			,   A			,   A = 1			,   A = -1	)						->   Union[str, Any]:

							def lr_lambda(A	):
														if current_step < num_warmup_steps:
																					return float(A	) / float(max(1			,   A	)	)
														lowerCAmelCase__	       =     float(current_step - num_warmup_steps	) / float(max(1			,   num_training_steps - num_warmup_steps	)	)
														if progress >= 1.0:
																					return 0.0
														return max(0.0			,   0.5 * (1.0 + math.cos(math.pi * ((float(A	) * progress) % 1.0)	))	)

							return LambdaLR(A			,   A			,   A	)


def  _snake_case       (					A			,   A			,   A			,   A=1E-7			,   A=1.0			,   A=-1	)						->   Union[str, Any]:
							lowerCAmelCase__	       =     optimizer.defaults['''lr''']
							if not (lr_init > lr_end):
														raise ValueError(F"""lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})"""	)

							def lr_lambda(A	):
														if current_step < num_warmup_steps:
																					return float(A	) / float(max(1			,   A	)	)
														elif current_step > num_training_steps:
																					return lr_end / lr_init  # as LambdaLR multiplies by lr_init
														else:
																					lowerCAmelCase__	       =     lr_init - lr_end
																					lowerCAmelCase__	       =     num_training_steps - num_warmup_steps
																					lowerCAmelCase__	       =     1 - (current_step - num_warmup_steps) / decay_steps
																					lowerCAmelCase__	       =     lr_range * pct_remaining**power + lr_end
																					return decay / lr_init  # as LambdaLR multiplies by lr_init

							return LambdaLR(A			,   A			,   A	)


__UpperCAmelCase       =							{
    SchedulerType.LINEAR: get_linear_schedule_with_warmup,
    SchedulerType.COSINE: get_cosine_schedule_with_warmup,
    SchedulerType.COSINE_WITH_RESTARTS: get_cosine_with_hard_restarts_schedule_with_warmup,
    SchedulerType.POLYNOMIAL: get_polynomial_decay_schedule_with_warmup,
    SchedulerType.CONSTANT: get_constant_schedule,
    SchedulerType.CONSTANT_WITH_WARMUP: get_constant_schedule_with_warmup,
    SchedulerType.PIECEWISE_CONSTANT: get_piecewise_constant_schedule,
}






def  _snake_case       (					A			,   A			,   A = None			,   A = None			,   A = None			,   A = 1			,   A = 1.0			,   A = -1			,   )						->   int:
							lowerCAmelCase__	       =     SchedulerType(A	)
							lowerCAmelCase__	       =     TYPE_TO_SCHEDULER_FUNCTION[name]
							if name == SchedulerType.CONSTANT:
														return schedule_func(A			,   last_epoch=A	)

							if name == SchedulerType.PIECEWISE_CONSTANT:
														return schedule_func(A			,   step_rules=A			,   last_epoch=A	)

							# All other schedulers require `num_warmup_steps`
							if num_warmup_steps is None:
														raise ValueError(F"""{name} requires `num_warmup_steps`, please provide that argument."""	)

							if name == SchedulerType.CONSTANT_WITH_WARMUP:
														return schedule_func(A			,   num_warmup_steps=A			,   last_epoch=A	)

							# All other schedulers require `num_training_steps`
							if num_training_steps is None:
														raise ValueError(F"""{name} requires `num_training_steps`, please provide that argument."""	)

							if name == SchedulerType.COSINE_WITH_RESTARTS:
														return schedule_func(
														    A			,   num_warmup_steps=A			,   num_training_steps=A			,   num_cycles=A			,   last_epoch=A			,   )

							if name == SchedulerType.POLYNOMIAL:
														return schedule_func(
														    A			,   num_warmup_steps=A			,   num_training_steps=A			,   power=A			,   last_epoch=A			,   )

							return schedule_func(
							    A			,   num_warmup_steps=A			,   num_training_steps=A			,   last_epoch=A	)