
'''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 			lowerCAmelCase (				__UpperCAmelCase			):




		def __init__(				self					,						UpperCamelCase=0.01					,						UpperCamelCase=1_000      ):
								_SCREAMING_SNAKE_CASE				= p_stop
								_SCREAMING_SNAKE_CASE				= max_length




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


class 			lowerCAmelCase (				unittest.TestCase			):




		def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase=False					,						UpperCamelCase=True      ):
								_SCREAMING_SNAKE_CASE				= [
								    BatchSamplerShard(UpperCamelCase					,						2					,						UpperCamelCase					,						split_batches=UpperCamelCase					,						even_batches=UpperCamelCase      )
								    for i in range(2      )
								]
								_SCREAMING_SNAKE_CASE				= [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      ):
								# Check the shards when the dataset is a round multiple of total batch size.
								_SCREAMING_SNAKE_CASE				= BatchSampler(range(24      )					,						batch_size=3					,						drop_last=UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= [
								    [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
								    [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]],
								]
								self.check_batch_sampler_shards(UpperCamelCase					,						UpperCamelCase      )

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

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

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

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

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

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

								_SCREAMING_SNAKE_CASE				= BatchSampler(range(2      )					,						batch_size=3					,						drop_last=UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= [[], []]
								self.check_batch_sampler_shards(UpperCamelCase					,						UpperCamelCase      )




		def     lowercase       (				self      ):
								# Check the shards when the dataset is a round multiple of batch size.
								_SCREAMING_SNAKE_CASE				= BatchSampler(range(24      )					,						batch_size=4					,						drop_last=UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= [
								    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
								    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23]],
								]
								self.check_batch_sampler_shards(UpperCamelCase					,						UpperCamelCase					,						split_batches=UpperCamelCase      )

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

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

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

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

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

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




		def     lowercase       (				self      ):
								# Check the shards when the dataset is a round multiple of total batch size.
								_SCREAMING_SNAKE_CASE				= BatchSampler(range(24      )					,						batch_size=3					,						drop_last=UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= [
								    [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
								    [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]],
								]
								self.check_batch_sampler_shards(UpperCamelCase					,						UpperCamelCase					,						even_batches=UpperCamelCase      )

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

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

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

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

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

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




		def     lowercase       (				self      ):
								# Check the shards when the dataset is a round multiple of batch size.
								_SCREAMING_SNAKE_CASE				= BatchSampler(range(24      )					,						batch_size=4					,						drop_last=UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= [
								    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
								    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23]],
								]
								self.check_batch_sampler_shards(UpperCamelCase					,						UpperCamelCase					,						split_batches=UpperCamelCase					,						even_batches=UpperCamelCase      )

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

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

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

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

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




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= [[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 10, 11], [12, 13]]
								_SCREAMING_SNAKE_CASE				= [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					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase=False					,						UpperCamelCase=2					,						UpperCamelCase=False      ):
								random.seed(UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= list(UpperCamelCase      )

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

								_SCREAMING_SNAKE_CASE				= batch_size // num_processes if split_batches else batch_size
								# All iterable dataset shard should have the same length, a round multiple of shard_batch_size
								_SCREAMING_SNAKE_CASE				= 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      )

								_SCREAMING_SNAKE_CASE				= []
								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      ):
								_SCREAMING_SNAKE_CASE				= 42
								_SCREAMING_SNAKE_CASE				= 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
								_SCREAMING_SNAKE_CASE				= 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      ):
								_SCREAMING_SNAKE_CASE				= BatchSampler(range(16      )					,						batch_size=4					,						drop_last=UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= SkipBatchSampler(UpperCamelCase					,						2      )
								self.assertListEqual(list(UpperCamelCase      )					,						[[8, 9, 10, 11], [12, 13, 14, 15]]      )




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




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= DataLoader(list(range(16      )      )					,						batch_size=4      )
								_SCREAMING_SNAKE_CASE				= 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      ):
								_SCREAMING_SNAKE_CASE				= 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      ):
								Accelerator()
								_SCREAMING_SNAKE_CASE				= 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 collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_snake_case				:   List[Any]     =				logging.get_logger(__name__)

_snake_case				:   int     =				{
    """YituTech/conv-bert-base""": """https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json""",
    """YituTech/conv-bert-medium-small""": (
        """https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json"""
    ),
    """YituTech/conv-bert-small""": """https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json""",
    # See all ConvBERT models at https://huggingface.co/models?filter=convbert
}


class 			lowerCAmelCase (				__UpperCAmelCase			):
		a							:					Tuple       =					"""convbert"""




		def __init__(				self					,						UpperCamelCase=30_522					,						UpperCamelCase=768					,						UpperCamelCase=12					,						UpperCamelCase=12					,						UpperCamelCase=3_072					,						UpperCamelCase="gelu"					,						UpperCamelCase=0.1					,						UpperCamelCase=0.1					,						UpperCamelCase=512					,						UpperCamelCase=2					,						UpperCamelCase=0.02					,						UpperCamelCase=1e-12					,						UpperCamelCase=1					,						UpperCamelCase=0					,						UpperCamelCase=2					,						UpperCamelCase=768					,						UpperCamelCase=2					,						UpperCamelCase=9					,						UpperCamelCase=1					,						UpperCamelCase=None					,						**UpperCamelCase					,						):
								super().__init__(
								    pad_token_id=UpperCamelCase					,						bos_token_id=UpperCamelCase					,						eos_token_id=UpperCamelCase					,						**UpperCamelCase					,						)

								_SCREAMING_SNAKE_CASE				= vocab_size
								_SCREAMING_SNAKE_CASE				= hidden_size
								_SCREAMING_SNAKE_CASE				= num_hidden_layers
								_SCREAMING_SNAKE_CASE				= num_attention_heads
								_SCREAMING_SNAKE_CASE				= intermediate_size
								_SCREAMING_SNAKE_CASE				= hidden_act
								_SCREAMING_SNAKE_CASE				= hidden_dropout_prob
								_SCREAMING_SNAKE_CASE				= attention_probs_dropout_prob
								_SCREAMING_SNAKE_CASE				= max_position_embeddings
								_SCREAMING_SNAKE_CASE				= type_vocab_size
								_SCREAMING_SNAKE_CASE				= initializer_range
								_SCREAMING_SNAKE_CASE				= layer_norm_eps
								_SCREAMING_SNAKE_CASE				= embedding_size
								_SCREAMING_SNAKE_CASE				= head_ratio
								_SCREAMING_SNAKE_CASE				= conv_kernel_size
								_SCREAMING_SNAKE_CASE				= num_groups
								_SCREAMING_SNAKE_CASE				= classifier_dropout


class 			lowerCAmelCase (				__UpperCAmelCase			):




		@property
		def     lowercase       (				self      ):
								if self.task == "multiple-choice":
														_SCREAMING_SNAKE_CASE				= {0: "batch", 1: "choice", 2: "sequence"}
								else:
														_SCREAMING_SNAKE_CASE				= {0: "batch", 1: "sequence"}
								return OrderedDict(
								    [
								        ("input_ids", dynamic_axis),
								        ("attention_mask", dynamic_axis),
								        ("token_type_ids", dynamic_axis),
								    ]      )