



"""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     :    int				,   __UpperCamelCase     :    int=0.0_1				,   __UpperCamelCase     :    Dict=1_0_0_0   )->Any:
							_UpperCAmelCase					=    p_stop
							_UpperCAmelCase					=    max_length



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



class 			_a (					unittest.TestCase):
			"""simple docstring"""



			def  lowercase__						(		self     :    Optional[int]				,   __UpperCamelCase     :    Optional[int]				,   __UpperCamelCase     :    Any				,   __UpperCamelCase     :    str=False				,   __UpperCamelCase     :    List[Any]=True   )->Tuple:
							_UpperCAmelCase					=    [
							    BatchSamplerShard(__UpperCamelCase				,   2				,   __UpperCamelCase				,   split_batches=__UpperCamelCase				,   even_batches=__UpperCamelCase   )
							    for i in range(2   )
							]
							_UpperCAmelCase					=    [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     :    Union[str, Any]   )->Union[str, Any]:
							# Check the shards when the dataset is a round multiple of total batch size.
							_UpperCAmelCase					=    BatchSampler(range(2_4   )				,   batch_size=3				,   drop_last=__UpperCamelCase   )
							_UpperCAmelCase					=    [
							    [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4], [1_8, 1_9, 2_0]],
							    [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7], [2_1, 2_2, 2_3]],
							]
							self.check_batch_sampler_shards(__UpperCamelCase				,   __UpperCamelCase   )

							_UpperCAmelCase					=    BatchSampler(range(2_4   )				,   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					=    BatchSampler(range(2_1   )				,   batch_size=3				,   drop_last=__UpperCamelCase   )
							_UpperCAmelCase					=    [
							    [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4], [1_8, 1_9, 2_0]],
							    [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7], [0, 1, 2]],
							]
							self.check_batch_sampler_shards(__UpperCamelCase				,   __UpperCamelCase   )

							_UpperCAmelCase					=    BatchSampler(range(2_1   )				,   batch_size=3				,   drop_last=__UpperCamelCase   )
							_UpperCAmelCase					=    [
							    [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4]],
							    [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7]],
							]
							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					=    BatchSampler(range(2_2   )				,   batch_size=3				,   drop_last=__UpperCamelCase   )
							_UpperCAmelCase					=    [
							    [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4], [1_8, 1_9, 2_0]],
							    [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7], [2_1, 0, 1]],
							]
							self.check_batch_sampler_shards(__UpperCamelCase				,   __UpperCamelCase   )

							_UpperCAmelCase					=    BatchSampler(range(2_2   )				,   batch_size=3				,   drop_last=__UpperCamelCase   )
							_UpperCAmelCase					=    [
							    [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4]],
							    [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7]],
							]
							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					=    BatchSampler(range(2_0   )				,   batch_size=3				,   drop_last=__UpperCamelCase   )
							_UpperCAmelCase					=    [
							    [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4], [1_8, 1_9, 0]],
							    [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7], [1, 2, 3]],
							]
							self.check_batch_sampler_shards(__UpperCamelCase				,   __UpperCamelCase   )

							_UpperCAmelCase					=    BatchSampler(range(2_0   )				,   batch_size=3				,   drop_last=__UpperCamelCase   )
							_UpperCAmelCase					=    [
							    [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4]],
							    [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7]],
							]
							self.check_batch_sampler_shards(__UpperCamelCase				,   __UpperCamelCase   )

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

							_UpperCAmelCase					=    BatchSampler(range(2   )				,   batch_size=3				,   drop_last=__UpperCamelCase   )
							_UpperCAmelCase					=    [[], []]
							self.check_batch_sampler_shards(__UpperCamelCase				,   __UpperCamelCase   )



			def  lowercase__						(		self     :    Dict   )->int:
							# Check the shards when the dataset is a round multiple of batch size.
							_UpperCAmelCase					=    BatchSampler(range(2_4   )				,   batch_size=4				,   drop_last=__UpperCamelCase   )
							_UpperCAmelCase					=    [
							    [[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7], [2_0, 2_1]],
							    [[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9], [2_2, 2_3]],
							]
							self.check_batch_sampler_shards(__UpperCamelCase				,   __UpperCamelCase				,   split_batches=__UpperCamelCase   )

							_UpperCAmelCase					=    BatchSampler(range(2_4   )				,   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					=    BatchSampler(range(2_2   )				,   batch_size=4				,   drop_last=__UpperCamelCase   )
							_UpperCAmelCase					=    [
							    [[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7], [2_0, 2_1]],
							    [[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9], [0, 1]],
							]
							self.check_batch_sampler_shards(__UpperCamelCase				,   __UpperCamelCase				,   split_batches=__UpperCamelCase   )

							_UpperCAmelCase					=    BatchSampler(range(2_2   )				,   batch_size=4				,   drop_last=__UpperCamelCase   )
							_UpperCAmelCase					=    [
							    [[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7]],
							    [[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9]],
							]
							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					=    BatchSampler(range(2_1   )				,   batch_size=4				,   drop_last=__UpperCamelCase   )
							_UpperCAmelCase					=    [
							    [[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7], [2_0, 0]],
							    [[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9], [1, 2]],
							]
							self.check_batch_sampler_shards(__UpperCamelCase				,   __UpperCamelCase				,   split_batches=__UpperCamelCase   )

							_UpperCAmelCase					=    BatchSampler(range(2_1   )				,   batch_size=4				,   drop_last=__UpperCamelCase   )
							_UpperCAmelCase					=    [
							    [[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7]],
							    [[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9]],
							]
							self.check_batch_sampler_shards(__UpperCamelCase				,   __UpperCamelCase				,   split_batches=__UpperCamelCase   )

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

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



			def  lowercase__						(		self     :    int   )->Optional[Any]:
							# Check the shards when the dataset is a round multiple of total batch size.
							_UpperCAmelCase					=    BatchSampler(range(2_4   )				,   batch_size=3				,   drop_last=__UpperCamelCase   )
							_UpperCAmelCase					=    [
							    [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4], [1_8, 1_9, 2_0]],
							    [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7], [2_1, 2_2, 2_3]],
							]
							self.check_batch_sampler_shards(__UpperCamelCase				,   __UpperCamelCase				,   even_batches=__UpperCamelCase   )

							_UpperCAmelCase					=    BatchSampler(range(2_4   )				,   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					=    BatchSampler(range(2_1   )				,   batch_size=3				,   drop_last=__UpperCamelCase   )
							_UpperCAmelCase					=    [
							    [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4], [1_8, 1_9, 2_0]],
							    [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7]],
							]
							self.check_batch_sampler_shards(__UpperCamelCase				,   __UpperCamelCase				,   even_batches=__UpperCamelCase   )

							_UpperCAmelCase					=    BatchSampler(range(2_1   )				,   batch_size=3				,   drop_last=__UpperCamelCase   )
							_UpperCAmelCase					=    [
							    [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4]],
							    [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7]],
							]
							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					=    BatchSampler(range(2_2   )				,   batch_size=3				,   drop_last=__UpperCamelCase   )
							_UpperCAmelCase					=    [
							    [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4], [1_8, 1_9, 2_0]],
							    [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7], [2_1]],
							]
							self.check_batch_sampler_shards(__UpperCamelCase				,   __UpperCamelCase				,   even_batches=__UpperCamelCase   )

							_UpperCAmelCase					=    BatchSampler(range(2_2   )				,   batch_size=3				,   drop_last=__UpperCamelCase   )
							_UpperCAmelCase					=    [
							    [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4]],
							    [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7]],
							]
							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					=    BatchSampler(range(2_0   )				,   batch_size=3				,   drop_last=__UpperCamelCase   )
							_UpperCAmelCase					=    [
							    [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4], [1_8, 1_9]],
							    [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7]],
							]
							self.check_batch_sampler_shards(__UpperCamelCase				,   __UpperCamelCase				,   even_batches=__UpperCamelCase   )

							_UpperCAmelCase					=    BatchSampler(range(2_0   )				,   batch_size=3				,   drop_last=__UpperCamelCase   )
							_UpperCAmelCase					=    [
							    [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4]],
							    [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7]],
							]
							self.check_batch_sampler_shards(__UpperCamelCase				,   __UpperCamelCase				,   even_batches=__UpperCamelCase   )

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

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



			def  lowercase__						(		self     :    Any   )->Tuple:
							# Check the shards when the dataset is a round multiple of batch size.
							_UpperCAmelCase					=    BatchSampler(range(2_4   )				,   batch_size=4				,   drop_last=__UpperCamelCase   )
							_UpperCAmelCase					=    [
							    [[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7], [2_0, 2_1]],
							    [[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9], [2_2, 2_3]],
							]
							self.check_batch_sampler_shards(__UpperCamelCase				,   __UpperCamelCase				,   split_batches=__UpperCamelCase				,   even_batches=__UpperCamelCase   )

							_UpperCAmelCase					=    BatchSampler(range(2_4   )				,   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					=    BatchSampler(range(2_2   )				,   batch_size=4				,   drop_last=__UpperCamelCase   )
							_UpperCAmelCase					=    [
							    [[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7], [2_0, 2_1]],
							    [[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9]],
							]
							self.check_batch_sampler_shards(__UpperCamelCase				,   __UpperCamelCase				,   split_batches=__UpperCamelCase				,   even_batches=__UpperCamelCase   )

							_UpperCAmelCase					=    BatchSampler(range(2_2   )				,   batch_size=4				,   drop_last=__UpperCamelCase   )
							_UpperCAmelCase					=    [
							    [[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7]],
							    [[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9]],
							]
							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					=    BatchSampler(range(2_1   )				,   batch_size=4				,   drop_last=__UpperCamelCase   )
							_UpperCAmelCase					=    [
							    [[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7], [2_0]],
							    [[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9]],
							]
							self.check_batch_sampler_shards(__UpperCamelCase				,   __UpperCamelCase				,   split_batches=__UpperCamelCase				,   even_batches=__UpperCamelCase   )

							_UpperCAmelCase					=    BatchSampler(range(2_1   )				,   batch_size=4				,   drop_last=__UpperCamelCase   )
							_UpperCAmelCase					=    [
							    [[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7]],
							    [[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9]],
							]
							self.check_batch_sampler_shards(__UpperCamelCase				,   __UpperCamelCase				,   split_batches=__UpperCamelCase				,   even_batches=__UpperCamelCase   )

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

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



			def  lowercase__						(		self     :    Any   )->Union[str, Any]:
							_UpperCAmelCase					=    [[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 1_0, 1_1], [1_2, 1_3]]
							_UpperCAmelCase					=    [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], [1_2, 1_3]]   )
							self.assertListEqual(list(batch_sampler_shards[1]   )				,   [[3, 4], [9, 1_0, 1_1]]   )



			def  lowercase__						(		self     :    str				,   __UpperCamelCase     :    Union[str, Any]				,   __UpperCamelCase     :    Tuple				,   __UpperCamelCase     :    Dict				,   __UpperCamelCase     :    List[Any]=False				,   __UpperCamelCase     :    Dict=2				,   __UpperCamelCase     :    Dict=False   )->int:
							random.seed(__UpperCamelCase   )
							_UpperCAmelCase					=    list(__UpperCamelCase   )

							_UpperCAmelCase					=    [
							    IterableDatasetShard(
							        __UpperCamelCase				,   batch_size=__UpperCamelCase				,   drop_last=__UpperCamelCase				,   num_processes=__UpperCamelCase				,   process_index=__UpperCamelCase				,   split_batches=__UpperCamelCase				,   )
							    for i in range(__UpperCamelCase   )
							]
							_UpperCAmelCase					=    []
							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					=    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					=    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					=    []
							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     :    str   )->int:
							_UpperCAmelCase					=    4_2
							_UpperCAmelCase					=    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					=    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     :    Optional[Any]   )->List[Any]:
							_UpperCAmelCase					=    BatchSampler(range(1_6   )				,   batch_size=4				,   drop_last=__UpperCamelCase   )
							_UpperCAmelCase					=    SkipBatchSampler(__UpperCamelCase				,   2   )
							self.assertListEqual(list(__UpperCamelCase   )				,   [[8, 9, 1_0, 1_1], [1_2, 1_3, 1_4, 1_5]]   )



			def  lowercase__						(		self     :    str   )->Tuple:
							_UpperCAmelCase					=    SkipDataLoader(list(range(1_6   )   )				,   batch_size=4				,   skip_batches=2   )
							self.assertListEqual([t.tolist() for t in dataloader]				,   [[8, 9, 1_0, 1_1], [1_2, 1_3, 1_4, 1_5]]   )



			def  lowercase__						(		self     :    List[Any]   )->str:
							_UpperCAmelCase					=    DataLoader(list(range(1_6   )   )				,   batch_size=4   )
							_UpperCAmelCase					=    skip_first_batches(__UpperCamelCase				,   num_batches=2   )
							self.assertListEqual([t.tolist() for t in new_dataloader]				,   [[8, 9, 1_0, 1_1], [1_2, 1_3, 1_4, 1_5]]   )



			def  lowercase__						(		self     :    Any   )->Optional[int]:
							_UpperCAmelCase					=    DataLoaderShard(list(range(1_6   )   )				,   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     :    Any   )->Tuple:
							Accelerator()
							_UpperCAmelCase					=    DataLoaderDispatcher(range(1_6   )				,   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"""


import math_equivalence  # From: git+https://github.com/hendrycks/math.git

import datasets


__A   :							Any										=   "\\n@article{hendrycksmath2021,\n  title={Measuring Mathematical Problem Solving With the MATH Dataset},\n  author={Dan Hendrycks\n    and Collin Burns\n    and Saurav Kadavath\n    and Akul Arora\n    and Steven Basart\n    and Eric Tang\n    and Dawn Song\n    and Jacob Steinhardt},\n  journal={arXiv preprint arXiv:2103.03874},\n  year={2021}\n}\n"


__A   :							Optional[Any]										=   "\\nThis metric is used to assess performance on the Mathematics Aptitude Test of Heuristics (MATH) dataset.\nIt first canonicalizes the inputs (e.g., converting \"1/2\" to \"\\frac{1}{2}\") and then computes accuracy.\n"


__A   :							Tuple										=   r"\nCalculates accuracy after canonicalizing inputs.\n\nArgs:\n    predictions: list of predictions to score. Each prediction\n        is a string that contains natural language and LaTex.\n    references: list of reference for each prediction. Each\n        reference is a string that contains natural language\n        and LaTex.\nReturns:\n    accuracy: accuracy after canonicalizing inputs\n        (e.g., converting \"1/2\" to \"\\frac{1}{2}\")\n\nExamples:\n    >>> metric = datasets.load_metric(\"competition_math\")\n    >>> results = metric.compute(references=[\"\\frac{1}{2}\"], predictions=[\"1/2\"])\n    >>> print(results)\n    {'accuracy': 1.0}\n"
@datasets.utils.file_utils.add_end_docstrings(_DESCRIPTION			,							_KWARGS_DESCRIPTION)
class 			_a (					datasets.Metric):
			"""simple docstring"""



			def  lowercase__						(		self     :    List[Any]   )->Dict:
							return datasets.MetricInfo(
							    description=_DESCRIPTION				,   citation=_CITATION				,   inputs_description=_KWARGS_DESCRIPTION				,   features=datasets.Features(
							        {
							            '''predictions''': datasets.Value('''string'''   ),
							            '''references''': datasets.Value('''string'''   ),
							        }   )				,   homepage='''https://github.com/hendrycks/math'''				,   codebase_urls=['''https://github.com/hendrycks/math''']				,   )



			def  lowercase__						(		self     :    int				,   __UpperCamelCase     :    List[str]				,   __UpperCamelCase     :    Union[str, Any]   )->Tuple:
							_UpperCAmelCase					=    0.0
							for i, j in zip(__UpperCamelCase				,   __UpperCamelCase   ):
											n_correct += 1.0 if math_equivalence.is_equiv(__UpperCamelCase				,   __UpperCamelCase   ) else 0.0
							_UpperCAmelCase					=    n_correct / len(__UpperCamelCase   )
							return {
							    "accuracy": accuracy,
							}



