


'''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 json
from typing import List, Optional, Tuple

from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokenization_utils import JiebaPreTokenizer


__UpperCAmelCase       =							logging.get_logger(__name__)

__UpperCAmelCase       =							{'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

__UpperCAmelCase       =							{
    '''vocab_file''': {
        '''junnyu/roformer_chinese_small''': '''https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/vocab.txt''',
        '''junnyu/roformer_chinese_base''': '''https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/vocab.txt''',
        '''junnyu/roformer_chinese_char_small''': (
            '''https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/vocab.txt'''
        ),
        '''junnyu/roformer_chinese_char_base''': (
            '''https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/vocab.txt'''
        ),
        '''junnyu/roformer_small_discriminator''': (
            '''https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/vocab.txt'''
        ),
        '''junnyu/roformer_small_generator''': (
            '''https://huggingface.co/junnyu/roformer_small_generator/resolve/main/vocab.txt'''
        ),
    }
}

__UpperCAmelCase       =							{
    '''junnyu/roformer_chinese_small''': 1_536,
    '''junnyu/roformer_chinese_base''': 1_536,
    '''junnyu/roformer_chinese_char_small''': 512,
    '''junnyu/roformer_chinese_char_base''': 512,
    '''junnyu/roformer_small_discriminator''': 128,
    '''junnyu/roformer_small_generator''': 128,
}


__UpperCAmelCase       =							{
    '''junnyu/roformer_chinese_small''': {'''do_lower_case''': True},
    '''junnyu/roformer_chinese_base''': {'''do_lower_case''': True},
    '''junnyu/roformer_chinese_char_small''': {'''do_lower_case''': True},
    '''junnyu/roformer_chinese_char_base''': {'''do_lower_case''': True},
    '''junnyu/roformer_small_discriminator''': {'''do_lower_case''': True},
    '''junnyu/roformer_small_generator''': {'''do_lower_case''': True},
}




class 		a__    (							a__  ):





							'''simple docstring'''






							lowercase__      :       int     						=				VOCAB_FILES_NAMES
							lowercase__      :       Optional[int]     						=				PRETRAINED_VOCAB_FILES_MAP
							lowercase__      :       Any     						=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowercase__      :       Union[str, Any]     						=				PRETRAINED_INIT_CONFIGURATION
							lowercase__      :       Tuple     						=				RoFormerTokenizer







							def __init__(		self ,   lowerCamelCase_=None ,   lowerCamelCase_=None ,   lowerCamelCase_=True ,   lowerCamelCase_="[UNK]" ,   lowerCamelCase_="[SEP]" ,   lowerCamelCase_="[PAD]" ,   lowerCamelCase_="[CLS]" ,   lowerCamelCase_="[MASK]" ,   lowerCamelCase_=True ,   lowerCamelCase_=None ,   **lowerCamelCase_ ,   )	->							Tuple:
														super().__init__(
														    lowerCamelCase_ ,   tokenizer_file=lowerCamelCase_ ,   do_lower_case=lowerCamelCase_ ,   unk_token=lowerCamelCase_ ,   sep_token=lowerCamelCase_ ,   pad_token=lowerCamelCase_ ,   cls_token=lowerCamelCase_ ,   mask_token=lowerCamelCase_ ,   tokenize_chinese_chars=lowerCamelCase_ ,   strip_accents=lowerCamelCase_ ,   **lowerCamelCase_ ,   )

														lowerCAmelCase__	       =     json.loads(self.backend_tokenizer.normalizer.__getstate__()       )
														if (
														    pre_tok_state.get('''lowercase''' ,   lowerCamelCase_       ) != do_lower_case
														    or pre_tok_state.get('''strip_accents''' ,   lowerCamelCase_       ) != strip_accents
														):
																					lowerCAmelCase__	       =     getattr(lowerCamelCase_ ,   pre_tok_state.pop('''type'''       )       )
																					lowerCAmelCase__	       =     do_lower_case
																					lowerCAmelCase__	       =     strip_accents
																					lowerCAmelCase__	       =     pre_tok_class(**lowerCamelCase_       )

														lowerCAmelCase__	       =     do_lower_case







							def __getstate__(		self       )	->							Any:
														lowerCAmelCase__	       =     self.__dict__.copy()
														lowerCAmelCase__	       =     BertPreTokenizer()
														return state







							def __setstate__(		self ,   lowerCamelCase_       )	->							List[Any]:
														lowerCAmelCase__	       =     d
														lowerCAmelCase__	       =     self.__dict__['''_tokenizer'''].get_vocab()
														lowerCAmelCase__	       =     PreTokenizer.custom(JiebaPreTokenizer(lowerCamelCase_       )       )







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_=None       )	->							Union[str, Any]:
														lowerCAmelCase__	       =     [self.cls_token_id] + token_ids_a + [self.sep_token_id]

														if token_ids_a:
																					output += token_ids_a + [self.sep_token_id]

														return output







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_ = None       )	->							List[int]:
														lowerCAmelCase__	       =     [self.sep_token_id]
														lowerCAmelCase__	       =     [self.cls_token_id]
														if token_ids_a is None:
																					return len(cls + token_ids_a + sep       ) * [0]
														return len(cls + token_ids_a + sep       ) * [0] + len(token_ids_a + sep       ) * [1]







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_ = None       )	->							Tuple[str]:
														lowerCAmelCase__	       =     self._tokenizer.model.save(lowerCamelCase_ ,   name=lowerCamelCase_       )
														return tuple(lowerCamelCase_       )







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_=None ,   lowerCamelCase_=None ,   lowerCamelCase_=False ,   **lowerCamelCase_ ,   )	->							Union[str, Any]:
														lowerCAmelCase__	       =     BertPreTokenizer()
														return super().save_pretrained(lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_ ,   **lowerCamelCase_       )