


"""simple docstring"""






import random
import unittest

from torch.utils.data import BatchSampler, DataLoader, IterableDataset

from accelerate import Accelerator
from accelerate.data_loader import (
    BatchSamplerShard,
    DataLoaderDispatcher,
    DataLoaderShard,
    IterableDatasetShard,
    SkipBatchSampler,
    SkipDataLoader,
    skip_first_batches,
)




class 							A_      (       _a ):


  def __init__(			self:						List[Any]							,__lowerCAmelCase:						Dict=0.01							,__lowerCAmelCase:						Optional[Any]=1_000    ):







     '''simple docstring'''







     _lowerCamelCase   :				Union[str, Any]							=     p_stop
     _lowerCamelCase   :				List[str]							=     max_length






  def __iter__(			self:						List[Any]    ):







     '''simple docstring'''







     _lowerCamelCase   :				int							=     0
     _lowerCamelCase   :				str							=     False
     while not stop and count < self.max_length:
        yield count
        count += 1
        _lowerCamelCase   :				Any							=     random.random() < self.p_stop




class 							A_      (       unittest.TestCase ):


  def 							_lowercase					(			self:						Optional[Any]							,__lowerCAmelCase:						Tuple							,__lowerCAmelCase:						Any							,__lowerCAmelCase:						Union[str, Any]=False							,__lowerCAmelCase:						Union[str, Any]=True    ):







     '''simple docstring'''







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


  def 							_lowercase					(			self:						List[Any]    ):







     '''simple docstring'''







     _lowerCamelCase   :				Tuple							=     BatchSampler(range(24    )							,batch_size=3							,drop_last=__lowerCAmelCase    )
     _lowerCamelCase   :				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(__lowerCAmelCase							,__lowerCAmelCase    )

     _lowerCamelCase   :				List[str]							=     BatchSampler(range(24    )							,batch_size=3							,drop_last=__lowerCAmelCase    )
     # Expected shouldn't change
     self.check_batch_sampler_shards(__lowerCAmelCase							,__lowerCAmelCase    )

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

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

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

     _lowerCamelCase   :				List[Any]							=     BatchSampler(range(22    )							,batch_size=3							,drop_last=__lowerCAmelCase    )
     _lowerCamelCase   :				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(__lowerCAmelCase							,__lowerCAmelCase    )

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

     _lowerCamelCase   :				Union[str, Any]							=     BatchSampler(range(20    )							,batch_size=3							,drop_last=__lowerCAmelCase    )
     _lowerCamelCase   :				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(__lowerCAmelCase							,__lowerCAmelCase    )

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

     _lowerCamelCase   :				int							=     BatchSampler(range(2    )							,batch_size=3							,drop_last=__lowerCAmelCase    )
     _lowerCamelCase   :				List[Any]							=     [[], []]
     self.check_batch_sampler_shards(__lowerCAmelCase							,__lowerCAmelCase    )


  def 							_lowercase					(			self:						Any    ):







     '''simple docstring'''







     _lowerCamelCase   :				Any							=     BatchSampler(range(24    )							,batch_size=4							,drop_last=__lowerCAmelCase    )
     _lowerCamelCase   :				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(__lowerCAmelCase							,__lowerCAmelCase							,split_batches=__lowerCAmelCase    )

     _lowerCamelCase   :				str							=     BatchSampler(range(24    )							,batch_size=4							,drop_last=__lowerCAmelCase    )
     # Expected shouldn't change
     self.check_batch_sampler_shards(__lowerCAmelCase							,__lowerCAmelCase							,split_batches=__lowerCAmelCase    )

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

     _lowerCamelCase   :				int							=     BatchSampler(range(22    )							,batch_size=4							,drop_last=__lowerCAmelCase    )
     _lowerCamelCase   :				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(__lowerCAmelCase							,__lowerCAmelCase							,split_batches=__lowerCAmelCase    )

     # Check the shards when the dataset is not a round multiple of batch size or num_processes.
     _lowerCamelCase   :				Tuple							=     BatchSampler(range(21    )							,batch_size=4							,drop_last=__lowerCAmelCase    )
     _lowerCamelCase   :				Optional[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(__lowerCAmelCase							,__lowerCAmelCase							,split_batches=__lowerCAmelCase    )

     _lowerCamelCase   :				Optional[Any]							=     BatchSampler(range(21    )							,batch_size=4							,drop_last=__lowerCAmelCase    )
     _lowerCamelCase   :				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(__lowerCAmelCase							,__lowerCAmelCase							,split_batches=__lowerCAmelCase    )

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

     _lowerCamelCase   :				List[str]							=     BatchSampler(range(2    )							,batch_size=4							,drop_last=__lowerCAmelCase    )
     _lowerCamelCase   :				Union[str, Any]							=     [[], []]
     self.check_batch_sampler_shards(__lowerCAmelCase							,__lowerCAmelCase							,split_batches=__lowerCAmelCase    )


  def 							_lowercase					(			self:						Optional[int]    ):







     '''simple docstring'''







     _lowerCamelCase   :				List[str]							=     BatchSampler(range(24    )							,batch_size=3							,drop_last=__lowerCAmelCase    )
     _lowerCamelCase   :				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(__lowerCAmelCase							,__lowerCAmelCase							,even_batches=__lowerCAmelCase    )

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

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

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

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

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

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

     _lowerCamelCase   :				Optional[int]							=     BatchSampler(range(20    )							,batch_size=3							,drop_last=__lowerCAmelCase    )
     _lowerCamelCase   :				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(__lowerCAmelCase							,__lowerCAmelCase							,even_batches=__lowerCAmelCase    )

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

     _lowerCamelCase   :				Optional[Any]							=     BatchSampler(range(2    )							,batch_size=3							,drop_last=__lowerCAmelCase    )
     _lowerCamelCase   :				Optional[Any]							=     [[], []]
     self.check_batch_sampler_shards(__lowerCAmelCase							,__lowerCAmelCase							,even_batches=__lowerCAmelCase    )


  def 							_lowercase					(			self:						Tuple    ):







     '''simple docstring'''







     _lowerCamelCase   :				Tuple							=     BatchSampler(range(24    )							,batch_size=4							,drop_last=__lowerCAmelCase    )
     _lowerCamelCase   :				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(__lowerCAmelCase							,__lowerCAmelCase							,split_batches=__lowerCAmelCase							,even_batches=__lowerCAmelCase    )

     _lowerCamelCase   :				List[Any]							=     BatchSampler(range(24    )							,batch_size=4							,drop_last=__lowerCAmelCase    )
     # Expected shouldn't change
     self.check_batch_sampler_shards(__lowerCAmelCase							,__lowerCAmelCase							,split_batches=__lowerCAmelCase							,even_batches=__lowerCAmelCase    )

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

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

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

     _lowerCamelCase   :				Union[str, Any]							=     BatchSampler(range(21    )							,batch_size=4							,drop_last=__lowerCAmelCase    )
     _lowerCamelCase   :				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(__lowerCAmelCase							,__lowerCAmelCase							,split_batches=__lowerCAmelCase							,even_batches=__lowerCAmelCase    )

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

     _lowerCamelCase   :				Union[str, Any]							=     BatchSampler(range(2    )							,batch_size=4							,drop_last=__lowerCAmelCase    )
     _lowerCamelCase   :				Union[str, Any]							=     [[], []]
     self.check_batch_sampler_shards(__lowerCAmelCase							,__lowerCAmelCase							,split_batches=__lowerCAmelCase							,even_batches=__lowerCAmelCase    )


  def 							_lowercase					(			self:						Dict    ):







     '''simple docstring'''







     _lowerCamelCase   :				Any							=     [[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 10, 11], [12, 13]]
     _lowerCamelCase   :				Any							=     [BatchSamplerShard(__lowerCAmelCase							,2							,__lowerCAmelCase							,even_batches=__lowerCAmelCase    ) for i in range(2    )]

     self.assertEqual(len(batch_sampler_shards[0]    )							,3    )
     self.assertEqual(len(batch_sampler_shards[1]    )							,2    )

     self.assertListEqual(list(batch_sampler_shards[0]    )							,[[0, 1, 2], [5, 6, 7, 8], [12, 13]]    )
     self.assertListEqual(list(batch_sampler_shards[1]    )							,[[3, 4], [9, 10, 11]]    )


  def 							_lowercase					(			self:						Any							,__lowerCAmelCase:						Optional[int]							,__lowerCAmelCase:						Tuple							,__lowerCAmelCase:						Optional[Any]							,__lowerCAmelCase:						Dict=False							,__lowerCAmelCase:						List[Any]=2							,__lowerCAmelCase:						int=False    ):







     '''simple docstring'''







     random.seed(__lowerCAmelCase    )
     _lowerCamelCase   :				Optional[int]							=     list(__lowerCAmelCase    )

     _lowerCamelCase   :				str							=     [
         IterableDatasetShard(
             __lowerCAmelCase							,batch_size=__lowerCAmelCase							,drop_last=__lowerCAmelCase							,num_processes=__lowerCAmelCase							,process_index=__lowerCAmelCase							,split_batches=__lowerCAmelCase							,)
         for i in range(__lowerCAmelCase    )
     ]
     _lowerCamelCase   :				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(__lowerCAmelCase    )
        iterable_dataset_lists.append(list(__lowerCAmelCase    )    )

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

     _lowerCamelCase   :				List[Any]							=     []
     for idx in range(0							,len(__lowerCAmelCase    )							,__lowerCAmelCase    ):
        for l in iterable_dataset_lists:
           observed += l[idx : idx + shard_batch_size]

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


  def 							_lowercase					(			self:						str    ):







     '''simple docstring'''







     _lowerCamelCase   :				Dict							=     42
     _lowerCamelCase   :				Union[str, Any]							=     RandomIterableDataset()

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

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

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


  def 							_lowercase					(			self:						str    ):







     '''simple docstring'''







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


  def 							_lowercase					(			self:						Optional[Any]    ):







     '''simple docstring'''







     _lowerCamelCase   :				Any							=     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:						Optional[int]    ):







     '''simple docstring'''







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


  def 							_lowercase					(			self:						int    ):







     '''simple docstring'''







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

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






  def 							_lowercase					(			self:						Tuple    ):







     '''simple docstring'''







     Accelerator()
     _lowerCamelCase   :				List[str]							=     DataLoaderDispatcher(range(16    )							,batch_size=4    )
     for idx, _ in enumerate(__lowerCAmelCase    ):
        self.assertEqual(dataloader.end_of_dataloader							,idx == 3    )

     # Test it also works on the second iteration
     for idx, _ in enumerate(__lowerCAmelCase    ):
        self.assertEqual(dataloader.end_of_dataloader							,idx == 3    )
from collections import OrderedDict
from typing import Mapping

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


_lowercase:	Optional[Any]       			=		logging.get_logger(__name__)

_lowercase:	Tuple       			=		{
    '''andreasmadsen/efficient_mlm_m0.40''': (
        '''https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json'''
    ),
}



class        lowerCamelCase__				(   UpperCAmelCase				):
			UpperCamelCase__    							="roberta-prelayernorm"





			def __init__(       self	:	int  ,  lowercase__	:	int=5_02_65  ,  lowercase__	:	str=7_68  ,  lowercase__	:	List[Any]=12  ,  lowercase__	:	Any=12  ,  lowercase__	:	Optional[Any]=30_72  ,  lowercase__	:	List[Any]="gelu"  ,  lowercase__	:	str=0.1  ,  lowercase__	:	List[Any]=0.1  ,  lowercase__	:	Tuple=5_12  ,  lowercase__	:	Optional[int]=2  ,  lowercase__	:	Dict=0.0_2  ,  lowercase__	:	Tuple=1e-12  ,  lowercase__	:	Optional[int]=1  ,  lowercase__	:	Optional[int]=0  ,  lowercase__	:	Union[str, Any]=2  ,  lowercase__	:	Any="absolute"  ,  lowercase__	:	Any=True  ,  lowercase__	:	Any=None  ,  **lowercase__	:	Optional[int]  ,  ):
										super().__init__(pad_token_id=lowercase__  ,  bos_token_id=lowercase__  ,  eos_token_id=lowercase__  ,  **lowercase__						)

										_lowerCAmelCase							=							vocab_size
										_lowerCAmelCase							=							hidden_size
										_lowerCAmelCase							=							num_hidden_layers
										_lowerCAmelCase							=							num_attention_heads
										_lowerCAmelCase							=							hidden_act
										_lowerCAmelCase							=							intermediate_size
										_lowerCAmelCase							=							hidden_dropout_prob
										_lowerCAmelCase							=							attention_probs_dropout_prob
										_lowerCAmelCase							=							max_position_embeddings
										_lowerCAmelCase							=							type_vocab_size
										_lowerCAmelCase							=							initializer_range
										_lowerCAmelCase							=							layer_norm_eps
										_lowerCAmelCase							=							position_embedding_type
										_lowerCAmelCase							=							use_cache
										_lowerCAmelCase							=							classifier_dropout







class        lowerCamelCase__				(   UpperCAmelCase				):





			@property
			def   SCREAMING_SNAKE_CASE__     (       self	:	List[str]						):
										if self.task == "multiple-choice":
																	_lowerCAmelCase							=							{0: 'batch', 1: 'choice', 2: 'sequence'}
										else:
																	_lowerCAmelCase							=							{0: 'batch', 1: 'sequence'}
										return OrderedDict(
										    [
										        ('input_ids', dynamic_axis),
										        ('attention_mask', dynamic_axis),
										    ]						)






