'''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 (       __lowerCAmelCase				):



		"""simple docstring"""







		def __init__(							self   ,    snake_case_=0.0_1   ,    snake_case_=1000					):

				'''simple docstring'''







				__UpperCAmelCase:					str		= p_stop
				__UpperCAmelCase:					Optional[int]		= max_length







		def __iter__(							self					):

				'''simple docstring'''







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




class    a (       unittest.TestCase				):



		"""simple docstring"""







		def    lowercase_  (							self   ,    snake_case_   ,    snake_case_   ,    snake_case_=False   ,    snake_case_=True					):

				'''simple docstring'''







				__UpperCAmelCase:					Optional[int]		= [
				    BatchSamplerShard(__UpperCamelCase   ,    2   ,    __UpperCamelCase   ,    split_batches=__UpperCamelCase   ,    even_batches=__UpperCamelCase					)
				    for i in range(2					)
				]
				__UpperCAmelCase:					Tuple		= [list(__UpperCamelCase					) for batch_sampler_shard in batch_sampler_shards]
				if not split_batches:
						self.assertListEqual([len(__UpperCamelCase					) for shard in batch_sampler_shards]   ,    [len(__UpperCamelCase					) for e in expected]					)
				self.assertListEqual(__UpperCamelCase   ,    __UpperCamelCase					)







		def    lowercase_  (							self					):

				'''simple docstring'''







				__UpperCAmelCase:					Optional[Any]		= BatchSampler(range(24					)   ,    batch_size=3   ,    drop_last=__UpperCamelCase					)
				__UpperCAmelCase:					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(__UpperCamelCase   ,    __UpperCamelCase					)

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

				# 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=__UpperCamelCase					)
				__UpperCAmelCase:					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(__UpperCamelCase   ,    __UpperCamelCase					)

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

				# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
				# num_processes batch.
				__UpperCAmelCase:					Dict		= BatchSampler(range(22					)   ,    batch_size=3   ,    drop_last=__UpperCamelCase					)
				__UpperCAmelCase:					int		= [
				    [[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(__UpperCamelCase   ,    __UpperCamelCase					)

				__UpperCAmelCase:					List[str]		= BatchSampler(range(22					)   ,    batch_size=3   ,    drop_last=__UpperCamelCase					)
				__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(__UpperCamelCase   ,    __UpperCamelCase					)

				# 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:					Optional[Any]		= BatchSampler(range(20					)   ,    batch_size=3   ,    drop_last=__UpperCamelCase					)
				__UpperCAmelCase:					Union[str, Any]		= [
				    [[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(__UpperCamelCase   ,    __UpperCamelCase					)

				__UpperCAmelCase:					Optional[Any]		= BatchSampler(range(20					)   ,    batch_size=3   ,    drop_last=__UpperCamelCase					)
				__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(__UpperCamelCase   ,    __UpperCamelCase					)

				# Check the shards when the dataset is very small.
				__UpperCAmelCase:					Any		= BatchSampler(range(2					)   ,    batch_size=3   ,    drop_last=__UpperCamelCase					)
				__UpperCAmelCase:					str		= [[[0, 1, 0]], [[1, 0, 1]]]
				self.check_batch_sampler_shards(__UpperCamelCase   ,    __UpperCamelCase					)

				__UpperCAmelCase:					Optional[int]		= BatchSampler(range(2					)   ,    batch_size=3   ,    drop_last=__UpperCamelCase					)
				__UpperCAmelCase:					Any		= [[], []]
				self.check_batch_sampler_shards(__UpperCamelCase   ,    __UpperCamelCase					)







		def    lowercase_  (							self					):

				'''simple docstring'''







				__UpperCAmelCase:					Dict		= BatchSampler(range(24					)   ,    batch_size=4   ,    drop_last=__UpperCamelCase					)
				__UpperCAmelCase:					Tuple		= [
				    [[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(__UpperCamelCase   ,    __UpperCamelCase   ,    split_batches=__UpperCamelCase					)

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

				# Check the shards when the dataset is not a round multiple of batch size.
				__UpperCAmelCase:					List[str]		= BatchSampler(range(22					)   ,    batch_size=4   ,    drop_last=__UpperCamelCase					)
				__UpperCAmelCase:					Union[str, Any]		= [
				    [[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(__UpperCamelCase   ,    __UpperCamelCase   ,    split_batches=__UpperCamelCase					)

				__UpperCAmelCase:					Optional[int]		= BatchSampler(range(22					)   ,    batch_size=4   ,    drop_last=__UpperCamelCase					)
				__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(__UpperCamelCase   ,    __UpperCamelCase   ,    split_batches=__UpperCamelCase					)

				# 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=__UpperCamelCase					)
				__UpperCAmelCase:					int		= [
				    [[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(__UpperCamelCase   ,    __UpperCamelCase   ,    split_batches=__UpperCamelCase					)

				__UpperCAmelCase:					List[Any]		= BatchSampler(range(21					)   ,    batch_size=4   ,    drop_last=__UpperCamelCase					)
				__UpperCAmelCase:					Optional[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(__UpperCamelCase   ,    __UpperCamelCase   ,    split_batches=__UpperCamelCase					)

				# Check the shards when the dataset is very small.
				__UpperCAmelCase:					List[str]		= BatchSampler(range(2					)   ,    batch_size=4   ,    drop_last=__UpperCamelCase					)
				__UpperCAmelCase:					List[str]		= [[[0, 1]], [[0, 1]]]
				self.check_batch_sampler_shards(__UpperCamelCase   ,    __UpperCamelCase   ,    split_batches=__UpperCamelCase					)

				__UpperCAmelCase:					int		= BatchSampler(range(2					)   ,    batch_size=4   ,    drop_last=__UpperCamelCase					)
				__UpperCAmelCase:					Optional[int]		= [[], []]
				self.check_batch_sampler_shards(__UpperCamelCase   ,    __UpperCamelCase   ,    split_batches=__UpperCamelCase					)







		def    lowercase_  (							self					):

				'''simple docstring'''







				__UpperCAmelCase:					Tuple		= BatchSampler(range(24					)   ,    batch_size=3   ,    drop_last=__UpperCamelCase					)
				__UpperCAmelCase:					Optional[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(__UpperCamelCase   ,    __UpperCamelCase   ,    even_batches=__UpperCamelCase					)

				__UpperCAmelCase:					List[Any]		= BatchSampler(range(24					)   ,    batch_size=3   ,    drop_last=__UpperCamelCase					)
				# Expected shouldn't change
				self.check_batch_sampler_shards(__UpperCamelCase   ,    __UpperCamelCase   ,    even_batches=__UpperCamelCase					)

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

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

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

				__UpperCAmelCase:					Optional[Any]		= BatchSampler(range(22					)   ,    batch_size=3   ,    drop_last=__UpperCamelCase					)
				__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(__UpperCamelCase   ,    __UpperCamelCase   ,    even_batches=__UpperCamelCase					)

				# 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:					Optional[Any]		= BatchSampler(range(20					)   ,    batch_size=3   ,    drop_last=__UpperCamelCase					)
				__UpperCAmelCase:					str		= [
				    [[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(__UpperCamelCase   ,    __UpperCamelCase   ,    even_batches=__UpperCamelCase					)

				__UpperCAmelCase:					int		= BatchSampler(range(20					)   ,    batch_size=3   ,    drop_last=__UpperCamelCase					)
				__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(__UpperCamelCase   ,    __UpperCamelCase   ,    even_batches=__UpperCamelCase					)

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

				__UpperCAmelCase:					Optional[int]		= BatchSampler(range(2					)   ,    batch_size=3   ,    drop_last=__UpperCamelCase					)
				__UpperCAmelCase:					int		= [[], []]
				self.check_batch_sampler_shards(__UpperCamelCase   ,    __UpperCamelCase   ,    even_batches=__UpperCamelCase					)







		def    lowercase_  (							self					):

				'''simple docstring'''







				__UpperCAmelCase:					Union[str, Any]		= BatchSampler(range(24					)   ,    batch_size=4   ,    drop_last=__UpperCamelCase					)
				__UpperCAmelCase:					Dict		= [
				    [[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(__UpperCamelCase   ,    __UpperCamelCase   ,    split_batches=__UpperCamelCase   ,    even_batches=__UpperCamelCase					)

				__UpperCAmelCase:					Tuple		= BatchSampler(range(24					)   ,    batch_size=4   ,    drop_last=__UpperCamelCase					)
				# Expected shouldn't change
				self.check_batch_sampler_shards(__UpperCamelCase   ,    __UpperCamelCase   ,    split_batches=__UpperCamelCase   ,    even_batches=__UpperCamelCase					)

				# 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=__UpperCamelCase					)
				__UpperCAmelCase:					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(__UpperCamelCase   ,    __UpperCamelCase   ,    split_batches=__UpperCamelCase   ,    even_batches=__UpperCamelCase					)

				__UpperCAmelCase:					List[Any]		= BatchSampler(range(22					)   ,    batch_size=4   ,    drop_last=__UpperCamelCase					)
				__UpperCAmelCase:					Optional[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(__UpperCamelCase   ,    __UpperCamelCase   ,    split_batches=__UpperCamelCase   ,    even_batches=__UpperCamelCase					)

				# Check the shards when the dataset is not a round multiple of batch size or num_processes.
				__UpperCAmelCase:					Optional[Any]		= BatchSampler(range(21					)   ,    batch_size=4   ,    drop_last=__UpperCamelCase					)
				__UpperCAmelCase:					Optional[int]		= [
				    [[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(__UpperCamelCase   ,    __UpperCamelCase   ,    split_batches=__UpperCamelCase   ,    even_batches=__UpperCamelCase					)

				__UpperCAmelCase:					Dict		= BatchSampler(range(21					)   ,    batch_size=4   ,    drop_last=__UpperCamelCase					)
				__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(__UpperCamelCase   ,    __UpperCamelCase   ,    split_batches=__UpperCamelCase   ,    even_batches=__UpperCamelCase					)

				# Check the shards when the dataset is very small.
				__UpperCAmelCase:					Dict		= BatchSampler(range(2					)   ,    batch_size=4   ,    drop_last=__UpperCamelCase					)
				__UpperCAmelCase:					List[str]		= [[[0, 1]], []]
				self.check_batch_sampler_shards(__UpperCamelCase   ,    __UpperCamelCase   ,    split_batches=__UpperCamelCase   ,    even_batches=__UpperCamelCase					)

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







		def    lowercase_  (							self					):

				'''simple docstring'''







				__UpperCAmelCase:					Any		= [[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 10, 11], [12, 13]]
				__UpperCAmelCase:					Dict		= [BatchSamplerShard(__UpperCamelCase   ,    2   ,    __UpperCamelCase   ,    even_batches=__UpperCamelCase					) 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    lowercase_  (							self   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_=False   ,    snake_case_=2   ,    snake_case_=False					):

				'''simple docstring'''







				random.seed(__UpperCamelCase					)
				__UpperCAmelCase:					Optional[int]		= list(__UpperCamelCase					)

				__UpperCAmelCase:					Optional[int]		= [
				    IterableDatasetShard(
				        __UpperCamelCase   ,    batch_size=__UpperCamelCase   ,    drop_last=__UpperCamelCase   ,    num_processes=__UpperCamelCase   ,    process_index=__UpperCamelCase   ,    split_batches=__UpperCamelCase   ,    )
				    for i in range(__UpperCamelCase					)
				]
				__UpperCAmelCase:					List[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(__UpperCamelCase					)
						iterable_dataset_lists.append(list(__UpperCamelCase					)					)

				__UpperCAmelCase:					Optional[int]		= 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:					str		= iterable_dataset_lists[0]
				for l in iterable_dataset_lists[1:]:
						self.assertEqual(len(__UpperCamelCase					)   ,    len(__UpperCamelCase					)					)
						self.assertTrue(len(__UpperCamelCase					) % shard_batch_size == 0					)

				__UpperCAmelCase:					Tuple		= []
				for idx in range(0   ,    len(__UpperCamelCase					)   ,    __UpperCamelCase					):
						for l in iterable_dataset_lists:
								observed += l[idx : idx + shard_batch_size]

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







		def    lowercase_  (							self					):

				'''simple docstring'''







				__UpperCAmelCase:					List[Any]		= 42
				__UpperCAmelCase:					int		= RandomIterableDataset()

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

				# Edge case with a very small dataset
				__UpperCAmelCase:					List[str]		= RandomIterableDataset(max_length=2					)

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







		def    lowercase_  (							self					):

				'''simple docstring'''







				__UpperCAmelCase:					Dict		= BatchSampler(range(16					)   ,    batch_size=4   ,    drop_last=__UpperCamelCase					)
				__UpperCAmelCase:					Any		= SkipBatchSampler(__UpperCamelCase   ,    2					)
				self.assertListEqual(list(__UpperCamelCase					)   ,    [[8, 9, 10, 11], [12, 13, 14, 15]]					)







		def    lowercase_  (							self					):

				'''simple docstring'''







				__UpperCAmelCase:					List[str]		= 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    lowercase_  (							self					):

				'''simple docstring'''







				__UpperCAmelCase:					Tuple		= DataLoader(list(range(16					)					)   ,    batch_size=4					)
				__UpperCAmelCase:					Union[str, Any]		= skip_first_batches(__UpperCamelCase   ,    num_batches=2					)
				self.assertListEqual([t.tolist() for t in new_dataloader]   ,    [[8, 9, 10, 11], [12, 13, 14, 15]]					)







		def    lowercase_  (							self					):

				'''simple docstring'''







				__UpperCAmelCase:					Optional[Any]		= DataLoaderShard(list(range(16					)					)   ,    batch_size=4					)
				for idx, _ in enumerate(__UpperCamelCase					):
						self.assertEqual(dataloader.end_of_dataloader   ,    idx == 3					)

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







		def    lowercase_  (							self					):

				'''simple docstring'''







				Accelerator()
				__UpperCAmelCase:					int		= DataLoaderDispatcher(range(16					)   ,    batch_size=4					)
				for idx, _ in enumerate(__UpperCamelCase					):
						self.assertEqual(dataloader.end_of_dataloader   ,    idx == 3					)

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






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE_	   =			{
    'configuration_electra': ['ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ElectraConfig', 'ElectraOnnxConfig'],
    'tokenization_electra': ['ElectraTokenizer'],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    SCREAMING_SNAKE_CASE_	   =			['ElectraTokenizerFast']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    SCREAMING_SNAKE_CASE_	   =			[
        'ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST',
        'ElectraForCausalLM',
        'ElectraForMaskedLM',
        'ElectraForMultipleChoice',
        'ElectraForPreTraining',
        'ElectraForQuestionAnswering',
        'ElectraForSequenceClassification',
        'ElectraForTokenClassification',
        'ElectraModel',
        'ElectraPreTrainedModel',
        'load_tf_weights_in_electra',
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    SCREAMING_SNAKE_CASE_	   =			[
        'TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST',
        'TFElectraForMaskedLM',
        'TFElectraForMultipleChoice',
        'TFElectraForPreTraining',
        'TFElectraForQuestionAnswering',
        'TFElectraForSequenceClassification',
        'TFElectraForTokenClassification',
        'TFElectraModel',
        'TFElectraPreTrainedModel',
    ]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    SCREAMING_SNAKE_CASE_	   =			[
        'FlaxElectraForCausalLM',
        'FlaxElectraForMaskedLM',
        'FlaxElectraForMultipleChoice',
        'FlaxElectraForPreTraining',
        'FlaxElectraForQuestionAnswering',
        'FlaxElectraForSequenceClassification',
        'FlaxElectraForTokenClassification',
        'FlaxElectraModel',
        'FlaxElectraPreTrainedModel',
    ]


if TYPE_CHECKING:
    from .configuration_electra import ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP, ElectraConfig, ElectraOnnxConfig
    from .tokenization_electra import ElectraTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_electra_fast import ElectraTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_electra import (
            ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
            ElectraForCausalLM,
            ElectraForMaskedLM,
            ElectraForMultipleChoice,
            ElectraForPreTraining,
            ElectraForQuestionAnswering,
            ElectraForSequenceClassification,
            ElectraForTokenClassification,
            ElectraModel,
            ElectraPreTrainedModel,
            load_tf_weights_in_electra,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_electra import (
            TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFElectraForMaskedLM,
            TFElectraForMultipleChoice,
            TFElectraForPreTraining,
            TFElectraForQuestionAnswering,
            TFElectraForSequenceClassification,
            TFElectraForTokenClassification,
            TFElectraModel,
            TFElectraPreTrainedModel,
        )

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_electra import (
            FlaxElectraForCausalLM,
            FlaxElectraForMaskedLM,
            FlaxElectraForMultipleChoice,
            FlaxElectraForPreTraining,
            FlaxElectraForQuestionAnswering,
            FlaxElectraForSequenceClassification,
            FlaxElectraForTokenClassification,
            FlaxElectraModel,
            FlaxElectraPreTrainedModel,
        )

else:
    import sys

    SCREAMING_SNAKE_CASE_	   =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)