
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			(   _A					):






			'''simple docstring'''

			def __init__(    self	:    int  ,		snake_case_	:    Tuple=0.01  ,		snake_case_	:    Tuple=1000 ):
							UpperCamelCase_:							List[str]		=  p_stop
							UpperCamelCase_:							int		=  max_length




			def __iter__(    self	:    List[str] ):
							UpperCamelCase_:							List[Any]		=  0
							UpperCamelCase_:							Union[str, Any]		=  False
							while not stop and count < self.max_length:
											yield count
											count += 1
											UpperCamelCase_:							Tuple		=  random.random() < self.p_stop




class     _UpperCamelCase			(   unittest.TestCase					):






			'''simple docstring'''

			def  lowerCAmelCase__		(    self	:    str  ,		snake_case_	:    Tuple  ,		snake_case_	:    Tuple  ,		snake_case_	:    List[str]=False  ,		snake_case_	:    Dict=True ):
							UpperCamelCase_:							str		=  [
							    BatchSamplerShard(snake_case_  ,		2  ,		snake_case_  ,		split_batches=snake_case_  ,		even_batches=snake_case_ )
							    for i in range(2 )
							]
							UpperCamelCase_:							Dict		=  [list(snake_case_ ) for batch_sampler_shard in batch_sampler_shards]
							if not split_batches:
											self.assertListEqual([len(snake_case_ ) for shard in batch_sampler_shards]  ,		[len(snake_case_ ) for e in expected] )
							self.assertListEqual(snake_case_  ,		snake_case_ )

			def  lowerCAmelCase__		(    self	:    List[str] ):
							# Check the shards when the dataset is a round multiple of total batch size.
							UpperCamelCase_:							List[str]		=  BatchSampler(range(24 )  ,		batch_size=3  ,		drop_last=snake_case_ )
							UpperCamelCase_:							Union[str, Any]		=  [
							    [[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(snake_case_  ,		snake_case_ )

							UpperCamelCase_:							Dict		=  BatchSampler(range(24 )  ,		batch_size=3  ,		drop_last=snake_case_ )
							# Expected shouldn't change
							self.check_batch_sampler_shards(snake_case_  ,		snake_case_ )

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

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

							# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
							# num_processes batch.
							UpperCamelCase_:							List[Any]		=  BatchSampler(range(22 )  ,		batch_size=3  ,		drop_last=snake_case_ )
							UpperCamelCase_:							Any		=  [
							    [[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(snake_case_  ,		snake_case_ )

							UpperCamelCase_:							int		=  BatchSampler(range(22 )  ,		batch_size=3  ,		drop_last=snake_case_ )
							UpperCamelCase_:							Tuple		=  [
							    [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
							    [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
							]
							self.check_batch_sampler_shards(snake_case_  ,		snake_case_ )

							# 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_:							str		=  BatchSampler(range(20 )  ,		batch_size=3  ,		drop_last=snake_case_ )
							UpperCamelCase_:							str		=  [
							    [[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(snake_case_  ,		snake_case_ )

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

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

							UpperCamelCase_:							int		=  BatchSampler(range(2 )  ,		batch_size=3  ,		drop_last=snake_case_ )
							UpperCamelCase_:							str		=  [[], []]
							self.check_batch_sampler_shards(snake_case_  ,		snake_case_ )

			def  lowerCAmelCase__		(    self	:    Optional[int] ):
							# Check the shards when the dataset is a round multiple of batch size.
							UpperCamelCase_:							str		=  BatchSampler(range(24 )  ,		batch_size=4  ,		drop_last=snake_case_ )
							UpperCamelCase_:							str		=  [
							    [[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(snake_case_  ,		snake_case_  ,		split_batches=snake_case_ )

							UpperCamelCase_:							Dict		=  BatchSampler(range(24 )  ,		batch_size=4  ,		drop_last=snake_case_ )
							# Expected shouldn't change
							self.check_batch_sampler_shards(snake_case_  ,		snake_case_  ,		split_batches=snake_case_ )

							# Check the shards when the dataset is not a round multiple of batch size.
							UpperCamelCase_:							Optional[int]		=  BatchSampler(range(22 )  ,		batch_size=4  ,		drop_last=snake_case_ )
							UpperCamelCase_:							int		=  [
							    [[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(snake_case_  ,		snake_case_  ,		split_batches=snake_case_ )

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

							# Check the shards when the dataset is not a round multiple of batch size or num_processes.
							UpperCamelCase_:							int		=  BatchSampler(range(21 )  ,		batch_size=4  ,		drop_last=snake_case_ )
							UpperCamelCase_:							Dict		=  [
							    [[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(snake_case_  ,		snake_case_  ,		split_batches=snake_case_ )

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

							# Check the shards when the dataset is very small.
							UpperCamelCase_:							Tuple		=  BatchSampler(range(2 )  ,		batch_size=4  ,		drop_last=snake_case_ )
							UpperCamelCase_:							Any		=  [[[0, 1]], [[0, 1]]]
							self.check_batch_sampler_shards(snake_case_  ,		snake_case_  ,		split_batches=snake_case_ )

							UpperCamelCase_:							Optional[int]		=  BatchSampler(range(2 )  ,		batch_size=4  ,		drop_last=snake_case_ )
							UpperCamelCase_:							Dict		=  [[], []]
							self.check_batch_sampler_shards(snake_case_  ,		snake_case_  ,		split_batches=snake_case_ )

			def  lowerCAmelCase__		(    self	:    Optional[int] ):
							# Check the shards when the dataset is a round multiple of total batch size.
							UpperCamelCase_:							Optional[int]		=  BatchSampler(range(24 )  ,		batch_size=3  ,		drop_last=snake_case_ )
							UpperCamelCase_:							Union[str, Any]		=  [
							    [[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(snake_case_  ,		snake_case_  ,		even_batches=snake_case_ )

							UpperCamelCase_:							Tuple		=  BatchSampler(range(24 )  ,		batch_size=3  ,		drop_last=snake_case_ )
							# Expected shouldn't change
							self.check_batch_sampler_shards(snake_case_  ,		snake_case_  ,		even_batches=snake_case_ )

							# Check the shards when the dataset is a round multiple of batch size but not total batch size.
							UpperCamelCase_:							str		=  BatchSampler(range(21 )  ,		batch_size=3  ,		drop_last=snake_case_ )
							UpperCamelCase_:							Optional[Any]		=  [
							    [[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(snake_case_  ,		snake_case_  ,		even_batches=snake_case_ )

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

							# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
							# num_processes batch.
							UpperCamelCase_:							Optional[int]		=  BatchSampler(range(22 )  ,		batch_size=3  ,		drop_last=snake_case_ )
							UpperCamelCase_:							List[str]		=  [
							    [[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(snake_case_  ,		snake_case_  ,		even_batches=snake_case_ )

							UpperCamelCase_:							List[Any]		=  BatchSampler(range(22 )  ,		batch_size=3  ,		drop_last=snake_case_ )
							UpperCamelCase_:							List[str]		=  [
							    [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
							    [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
							]
							self.check_batch_sampler_shards(snake_case_  ,		snake_case_  ,		even_batches=snake_case_ )

							# 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_:							List[Any]		=  BatchSampler(range(20 )  ,		batch_size=3  ,		drop_last=snake_case_ )
							UpperCamelCase_:							int		=  [
							    [[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(snake_case_  ,		snake_case_  ,		even_batches=snake_case_ )

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

							# Check the shards when the dataset is very small.
							UpperCamelCase_:							Tuple		=  BatchSampler(range(2 )  ,		batch_size=3  ,		drop_last=snake_case_ )
							UpperCamelCase_:							Optional[int]		=  [[[0, 1]], []]
							self.check_batch_sampler_shards(snake_case_  ,		snake_case_  ,		even_batches=snake_case_ )

							UpperCamelCase_:							Any		=  BatchSampler(range(2 )  ,		batch_size=3  ,		drop_last=snake_case_ )
							UpperCamelCase_:							Tuple		=  [[], []]
							self.check_batch_sampler_shards(snake_case_  ,		snake_case_  ,		even_batches=snake_case_ )

			def  lowerCAmelCase__		(    self	:    Any ):
							# Check the shards when the dataset is a round multiple of batch size.
							UpperCamelCase_:							int		=  BatchSampler(range(24 )  ,		batch_size=4  ,		drop_last=snake_case_ )
							UpperCamelCase_:							List[Any]		=  [
							    [[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(snake_case_  ,		snake_case_  ,		split_batches=snake_case_  ,		even_batches=snake_case_ )

							UpperCamelCase_:							Optional[int]		=  BatchSampler(range(24 )  ,		batch_size=4  ,		drop_last=snake_case_ )
							# Expected shouldn't change
							self.check_batch_sampler_shards(snake_case_  ,		snake_case_  ,		split_batches=snake_case_  ,		even_batches=snake_case_ )

							# Check the shards when the dataset is not a round multiple of batch size.
							UpperCamelCase_:							int		=  BatchSampler(range(22 )  ,		batch_size=4  ,		drop_last=snake_case_ )
							UpperCamelCase_:							Optional[int]		=  [
							    [[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(snake_case_  ,		snake_case_  ,		split_batches=snake_case_  ,		even_batches=snake_case_ )

							UpperCamelCase_:							Dict		=  BatchSampler(range(22 )  ,		batch_size=4  ,		drop_last=snake_case_ )
							UpperCamelCase_:							Union[str, Any]		=  [
							    [[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(snake_case_  ,		snake_case_  ,		split_batches=snake_case_  ,		even_batches=snake_case_ )

							# Check the shards when the dataset is not a round multiple of batch size or num_processes.
							UpperCamelCase_:							List[Any]		=  BatchSampler(range(21 )  ,		batch_size=4  ,		drop_last=snake_case_ )
							UpperCamelCase_:							Union[str, Any]		=  [
							    [[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(snake_case_  ,		snake_case_  ,		split_batches=snake_case_  ,		even_batches=snake_case_ )

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

							# Check the shards when the dataset is very small.
							UpperCamelCase_:							Any		=  BatchSampler(range(2 )  ,		batch_size=4  ,		drop_last=snake_case_ )
							UpperCamelCase_:							Union[str, Any]		=  [[[0, 1]], []]
							self.check_batch_sampler_shards(snake_case_  ,		snake_case_  ,		split_batches=snake_case_  ,		even_batches=snake_case_ )

							UpperCamelCase_:							Tuple		=  BatchSampler(range(2 )  ,		batch_size=4  ,		drop_last=snake_case_ )
							UpperCamelCase_:							List[Any]		=  [[], []]
							self.check_batch_sampler_shards(snake_case_  ,		snake_case_  ,		split_batches=snake_case_  ,		even_batches=snake_case_ )

			def  lowerCAmelCase__		(    self	:    str ):
							UpperCamelCase_:							Any		=  [[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 10, 11], [12, 13]]
							UpperCamelCase_:							Dict		=  [BatchSamplerShard(snake_case_  ,		2  ,		snake_case_  ,		even_batches=snake_case_ ) 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	:    str  ,		snake_case_	:    str  ,		snake_case_	:    Optional[int]  ,		snake_case_	:    Any  ,		snake_case_	:    Union[str, Any]=False  ,		snake_case_	:    int=2  ,		snake_case_	:    str=False ):
							random.seed(snake_case_ )
							UpperCamelCase_:							Union[str, Any]		=  list(snake_case_ )

							UpperCamelCase_:							Optional[int]		=  [
							    IterableDatasetShard(
							        snake_case_  ,		batch_size=snake_case_  ,		drop_last=snake_case_  ,		num_processes=snake_case_  ,		process_index=snake_case_  ,		split_batches=snake_case_  ,		)
							    for i in range(snake_case_ )
							]
							UpperCamelCase_:							Optional[Any]		=  []
							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(snake_case_ )
											iterable_dataset_lists.append(list(snake_case_ ) )

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

							UpperCamelCase_:							Dict		=  []
							for idx in range(0  ,		len(snake_case_ )  ,		snake_case_ ):
											for l in iterable_dataset_lists:
															observed += l[idx : idx + shard_batch_size]

							if not drop_last:
											while len(snake_case_ ) < len(snake_case_ ):
															reference += reference
							self.assertListEqual(snake_case_  ,		reference[: len(snake_case_ )] )

			def  lowerCAmelCase__		(    self	:    List[Any] ):
							UpperCamelCase_:							Union[str, Any]		=  42
							UpperCamelCase_:							Optional[int]		=  RandomIterableDataset()

							self.check_iterable_dataset_shards(snake_case_  ,		snake_case_  ,		batch_size=4  ,		drop_last=snake_case_  ,		split_batches=snake_case_ )
							self.check_iterable_dataset_shards(snake_case_  ,		snake_case_  ,		batch_size=4  ,		drop_last=snake_case_  ,		split_batches=snake_case_ )
							self.check_iterable_dataset_shards(snake_case_  ,		snake_case_  ,		batch_size=4  ,		drop_last=snake_case_  ,		split_batches=snake_case_ )
							self.check_iterable_dataset_shards(snake_case_  ,		snake_case_  ,		batch_size=4  ,		drop_last=snake_case_  ,		split_batches=snake_case_ )

							# Edge case with a very small dataset
							UpperCamelCase_:							Any		=  RandomIterableDataset(max_length=2 )

							self.check_iterable_dataset_shards(snake_case_  ,		snake_case_  ,		batch_size=4  ,		drop_last=snake_case_  ,		split_batches=snake_case_ )
							self.check_iterable_dataset_shards(snake_case_  ,		snake_case_  ,		batch_size=4  ,		drop_last=snake_case_  ,		split_batches=snake_case_ )
							self.check_iterable_dataset_shards(snake_case_  ,		snake_case_  ,		batch_size=4  ,		drop_last=snake_case_  ,		split_batches=snake_case_ )
							self.check_iterable_dataset_shards(snake_case_  ,		snake_case_  ,		batch_size=4  ,		drop_last=snake_case_  ,		split_batches=snake_case_ )

			def  lowerCAmelCase__		(    self	:    str ):
							UpperCamelCase_:							Optional[Any]		=  BatchSampler(range(16 )  ,		batch_size=4  ,		drop_last=snake_case_ )
							UpperCamelCase_:							int		=  SkipBatchSampler(snake_case_  ,		2 )
							self.assertListEqual(list(snake_case_ )  ,		[[8, 9, 10, 11], [12, 13, 14, 15]] )

			def  lowerCAmelCase__		(    self	:    List[Any] ):
							UpperCamelCase_:							Tuple		=  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	:    int ):
							UpperCamelCase_:							int		=  DataLoader(list(range(16 ) )  ,		batch_size=4 )
							UpperCamelCase_:							str		=  skip_first_batches(snake_case_  ,		num_batches=2 )
							self.assertListEqual([t.tolist() for t in new_dataloader]  ,		[[8, 9, 10, 11], [12, 13, 14, 15]] )

			def  lowerCAmelCase__		(    self	:    int ):
							UpperCamelCase_:							int		=  DataLoaderShard(list(range(16 ) )  ,		batch_size=4 )
							for idx, _ in enumerate(snake_case_ ):
											self.assertEqual(dataloader.end_of_dataloader  ,		idx == 3 )

							# Test it also works on the second iteration
							for idx, _ in enumerate(snake_case_ ):
											self.assertEqual(dataloader.end_of_dataloader  ,		idx == 3 )




			def  lowerCAmelCase__		(    self	:    Any ):
							Accelerator()
							UpperCamelCase_:							Union[str, Any]		=  DataLoaderDispatcher(range(16 )  ,		batch_size=4 )
							for idx, _ in enumerate(snake_case_ ):
											self.assertEqual(dataloader.end_of_dataloader  ,		idx == 3 )

							# Test it also works on the second iteration
							for idx, _ in enumerate(snake_case_ ):
											self.assertEqual(dataloader.end_of_dataloader  ,		idx == 3 )








import unittest

from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_rjieba
@require_tokenizers
class     _UpperCamelCase			(   _A       ,    unittest.TestCase					):






			'''simple docstring'''

			__UpperCamelCase :  Union[str, Any]    =	RoFormerTokenizer
			__UpperCamelCase :  Union[str, Any]    =	RoFormerTokenizerFast
			__UpperCamelCase :  Optional[Any]    =	True
			__UpperCamelCase :  Tuple    =	True

			def  lowerCAmelCase__		(    self	:    Optional[Any] ):
							super().setUp()

			def  lowerCAmelCase__		(    self	:    List[str]  ,		**snake_case_	:    Union[str, Any] ):
							return self.tokenizer_class.from_pretrained("""junnyu/roformer_chinese_base"""  ,		**snake_case_ )

			def  lowerCAmelCase__		(    self	:    Dict  ,		**snake_case_	:    Union[str, Any] ):
							return self.rust_tokenizer_class.from_pretrained("""junnyu/roformer_chinese_base"""  ,		**snake_case_ )

			def  lowerCAmelCase__		(    self	:    Any ):
							UpperCamelCase_:							int		=  """永和服装饰品有限公司,今天天气非常好"""
							UpperCamelCase_:							int		=  """永和 服装 饰品 有限公司 , 今 天 天 气 非常 好"""
							return input_text, output_text

			def  lowerCAmelCase__		(    self	:    Dict ):
							UpperCamelCase_:							Dict		=  self.get_tokenizer()
							UpperCamelCase_,				UpperCamelCase_:							Optional[int]		=  self.get_chinese_input_output_texts()
							UpperCamelCase_:							Optional[int]		=  tokenizer.tokenize(snake_case_ )

							self.assertListEqual(snake_case_  ,		output_text.split() )

							UpperCamelCase_:							Dict		=  tokens + [tokenizer.unk_token]
							UpperCamelCase_:							int		=  [2_2943, 2_1332, 3_4431, 4_5904, 117, 306, 1231, 1231, 2653, 3_3994, 1266, 100]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case_ )  ,		snake_case_ )

			def  lowerCAmelCase__		(    self	:    Dict ):
							UpperCamelCase_:							Optional[int]		=  self.get_rust_tokenizer()
							UpperCamelCase_,				UpperCamelCase_:							Any		=  self.get_chinese_input_output_texts()
							UpperCamelCase_:							int		=  tokenizer.tokenize(snake_case_ )
							self.assertListEqual(snake_case_  ,		output_text.split() )
							UpperCamelCase_:							Union[str, Any]		=  tokens + [tokenizer.unk_token]
							UpperCamelCase_:							Optional[Any]		=  [2_2943, 2_1332, 3_4431, 4_5904, 117, 306, 1231, 1231, 2653, 3_3994, 1266, 100]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case_ )  ,		snake_case_ )

			def  lowerCAmelCase__		(    self	:    Dict ):
							pass

			def  lowerCAmelCase__		(    self	:    Tuple ):
							pass




			def  lowerCAmelCase__		(    self	:    Union[str, Any] ):
							pass






