'''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 				UpperCAmelCase (       __a ):




    '''simple docstring'''







    def __init__(       self    ,							lowercase__=0.0_1    ,							lowercase__=1_000 )							-> List[str]:
         SCREAMING_SNAKE_CASE					:			str    	=       p_stop
         SCREAMING_SNAKE_CASE					:			Any    	=       max_length






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






class 				UpperCAmelCase (       unittest.TestCase ):




    '''simple docstring'''







    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__    ,							lowercase__=False    ,							lowercase__=True )							-> str:
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       [
             BatchSamplerShard(lowerCAmelCase_    ,							2    ,							lowerCAmelCase_    ,							split_batches=lowerCAmelCase_    ,							even_batches=lowerCAmelCase_ )
             for i in range(2 )
         ]
         SCREAMING_SNAKE_CASE					:			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       _UpperCamelCase    (       self )							-> Union[str, Any]:
         SCREAMING_SNAKE_CASE					:			List[str]    	=       BatchSampler(range(24 )    ,							batch_size=3    ,							drop_last=lowerCAmelCase_ )
         SCREAMING_SNAKE_CASE					:			int    	=       [
             [[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_ )

         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       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.
         SCREAMING_SNAKE_CASE					:			Tuple    	=       BatchSampler(range(21 )    ,							batch_size=3    ,							drop_last=lowerCAmelCase_ )
         SCREAMING_SNAKE_CASE					:			Tuple    	=       [
             [[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_ )

         SCREAMING_SNAKE_CASE					:			List[Any]    	=       BatchSampler(range(21 )    ,							batch_size=3    ,							drop_last=lowerCAmelCase_ )
         SCREAMING_SNAKE_CASE					:			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_ )

         # 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					:			Any    	=       BatchSampler(range(22 )    ,							batch_size=3    ,							drop_last=lowerCAmelCase_ )
         SCREAMING_SNAKE_CASE					:			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(lowerCAmelCase_    ,							lowerCAmelCase_ )

         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       BatchSampler(range(22 )    ,							batch_size=3    ,							drop_last=lowerCAmelCase_ )
         SCREAMING_SNAKE_CASE					:			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.
         SCREAMING_SNAKE_CASE					:			Tuple    	=       BatchSampler(range(20 )    ,							batch_size=3    ,							drop_last=lowerCAmelCase_ )
         SCREAMING_SNAKE_CASE					:			Optional[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(lowerCAmelCase_    ,							lowerCAmelCase_ )

         SCREAMING_SNAKE_CASE					:			Tuple    	=       BatchSampler(range(20 )    ,							batch_size=3    ,							drop_last=lowerCAmelCase_ )
         SCREAMING_SNAKE_CASE					:			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.
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       BatchSampler(range(2 )    ,							batch_size=3    ,							drop_last=lowerCAmelCase_ )
         SCREAMING_SNAKE_CASE					:			int    	=       [[[0, 1, 0]], [[1, 0, 1]]]
         self.check_batch_sampler_shards(lowerCAmelCase_    ,							lowerCAmelCase_ )

         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       BatchSampler(range(2 )    ,							batch_size=3    ,							drop_last=lowerCAmelCase_ )
         SCREAMING_SNAKE_CASE					:			int    	=       [[], []]
         self.check_batch_sampler_shards(lowerCAmelCase_    ,							lowerCAmelCase_ )






    def       _UpperCamelCase    (       self )							-> Optional[int]:
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       BatchSampler(range(24 )    ,							batch_size=4    ,							drop_last=lowerCAmelCase_ )
         SCREAMING_SNAKE_CASE					:			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(lowerCAmelCase_    ,							lowerCAmelCase_    ,							split_batches=lowerCAmelCase_ )

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

         SCREAMING_SNAKE_CASE					:			List[Any]    	=       BatchSampler(range(22 )    ,							batch_size=4    ,							drop_last=lowerCAmelCase_ )
         SCREAMING_SNAKE_CASE					:			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_ )

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

         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       BatchSampler(range(21 )    ,							batch_size=4    ,							drop_last=lowerCAmelCase_ )
         SCREAMING_SNAKE_CASE					:			List[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.
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       BatchSampler(range(2 )    ,							batch_size=4    ,							drop_last=lowerCAmelCase_ )
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       [[[0, 1]], [[0, 1]]]
         self.check_batch_sampler_shards(lowerCAmelCase_    ,							lowerCAmelCase_    ,							split_batches=lowerCAmelCase_ )

         SCREAMING_SNAKE_CASE					:			List[str]    	=       BatchSampler(range(2 )    ,							batch_size=4    ,							drop_last=lowerCAmelCase_ )
         SCREAMING_SNAKE_CASE					:			str    	=       [[], []]
         self.check_batch_sampler_shards(lowerCAmelCase_    ,							lowerCAmelCase_    ,							split_batches=lowerCAmelCase_ )






    def       _UpperCamelCase    (       self )							-> Optional[Any]:
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       BatchSampler(range(24 )    ,							batch_size=3    ,							drop_last=lowerCAmelCase_ )
         SCREAMING_SNAKE_CASE					:			str    	=       [
             [[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_ )

         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       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.
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       BatchSampler(range(21 )    ,							batch_size=3    ,							drop_last=lowerCAmelCase_ )
         SCREAMING_SNAKE_CASE					:			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_ )

         SCREAMING_SNAKE_CASE					:			Tuple    	=       BatchSampler(range(21 )    ,							batch_size=3    ,							drop_last=lowerCAmelCase_ )
         SCREAMING_SNAKE_CASE					:			Dict    	=       [
             [[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.
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       BatchSampler(range(22 )    ,							batch_size=3    ,							drop_last=lowerCAmelCase_ )
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       [
             [[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_ )

         SCREAMING_SNAKE_CASE					:			Tuple    	=       BatchSampler(range(22 )    ,							batch_size=3    ,							drop_last=lowerCAmelCase_ )
         SCREAMING_SNAKE_CASE					:			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 not a round multiple of batch size but and has not a multiple of
         # num_processes batch.
         SCREAMING_SNAKE_CASE					:			str    	=       BatchSampler(range(20 )    ,							batch_size=3    ,							drop_last=lowerCAmelCase_ )
         SCREAMING_SNAKE_CASE					:			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(lowerCAmelCase_    ,							lowerCAmelCase_    ,							even_batches=lowerCAmelCase_ )

         SCREAMING_SNAKE_CASE					:			Dict    	=       BatchSampler(range(20 )    ,							batch_size=3    ,							drop_last=lowerCAmelCase_ )
         SCREAMING_SNAKE_CASE					:			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.
         SCREAMING_SNAKE_CASE					:			Tuple    	=       BatchSampler(range(2 )    ,							batch_size=3    ,							drop_last=lowerCAmelCase_ )
         SCREAMING_SNAKE_CASE					:			int    	=       [[[0, 1]], []]
         self.check_batch_sampler_shards(lowerCAmelCase_    ,							lowerCAmelCase_    ,							even_batches=lowerCAmelCase_ )

         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       BatchSampler(range(2 )    ,							batch_size=3    ,							drop_last=lowerCAmelCase_ )
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       [[], []]
         self.check_batch_sampler_shards(lowerCAmelCase_    ,							lowerCAmelCase_    ,							even_batches=lowerCAmelCase_ )






    def       _UpperCamelCase    (       self )							-> Dict:
         SCREAMING_SNAKE_CASE					:			List[str]    	=       BatchSampler(range(24 )    ,							batch_size=4    ,							drop_last=lowerCAmelCase_ )
         SCREAMING_SNAKE_CASE					:			Optional[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_ )

         SCREAMING_SNAKE_CASE					:			Union[str, 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.
         SCREAMING_SNAKE_CASE					:			int    	=       BatchSampler(range(22 )    ,							batch_size=4    ,							drop_last=lowerCAmelCase_ )
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       [
             [[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_ )

         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       BatchSampler(range(22 )    ,							batch_size=4    ,							drop_last=lowerCAmelCase_ )
         SCREAMING_SNAKE_CASE					:			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_    ,							even_batches=lowerCAmelCase_ )

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

         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       BatchSampler(range(21 )    ,							batch_size=4    ,							drop_last=lowerCAmelCase_ )
         SCREAMING_SNAKE_CASE					:			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(lowerCAmelCase_    ,							lowerCAmelCase_    ,							split_batches=lowerCAmelCase_    ,							even_batches=lowerCAmelCase_ )

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

         SCREAMING_SNAKE_CASE					:			List[Any]    	=       BatchSampler(range(2 )    ,							batch_size=4    ,							drop_last=lowerCAmelCase_ )
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       [[], []]
         self.check_batch_sampler_shards(lowerCAmelCase_    ,							lowerCAmelCase_    ,							split_batches=lowerCAmelCase_    ,							even_batches=lowerCAmelCase_ )






    def       _UpperCamelCase    (       self )							-> Tuple:
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       [[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 10, 11], [12, 13]]
         SCREAMING_SNAKE_CASE					:			str    	=       [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       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__=False    ,							lowercase__=2    ,							lowercase__=False )							-> int:
         random.seed(lowerCAmelCase_ )
         SCREAMING_SNAKE_CASE					:			Any    	=       list(lowerCAmelCase_ )

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

         SCREAMING_SNAKE_CASE					:			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
         SCREAMING_SNAKE_CASE					:			List[str]    	=       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 )

         SCREAMING_SNAKE_CASE					:			Dict    	=       []
         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       _UpperCamelCase    (       self )							-> List[str]:
         SCREAMING_SNAKE_CASE					:			str    	=       42
         SCREAMING_SNAKE_CASE					:			Tuple    	=       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
         SCREAMING_SNAKE_CASE					:			Dict    	=       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       _UpperCamelCase    (       self )							-> Any:
         SCREAMING_SNAKE_CASE					:			int    	=       BatchSampler(range(16 )    ,							batch_size=4    ,							drop_last=lowerCAmelCase_ )
         SCREAMING_SNAKE_CASE					:			int    	=       SkipBatchSampler(lowerCAmelCase_    ,							2 )
         self.assertListEqual(list(lowerCAmelCase_ )    ,							[[8, 9, 10, 11], [12, 13, 14, 15]] )






    def       _UpperCamelCase    (       self )							-> List[Any]:
         SCREAMING_SNAKE_CASE					:			List[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       _UpperCamelCase    (       self )							-> Optional[Any]:
         SCREAMING_SNAKE_CASE					:			int    	=       DataLoader(list(range(16 ) )    ,							batch_size=4 )
         SCREAMING_SNAKE_CASE					:			Dict    	=       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       _UpperCamelCase    (       self )							-> int:
         SCREAMING_SNAKE_CASE					:			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       _UpperCamelCase    (       self )							-> int:
         Accelerator()
         SCREAMING_SNAKE_CASE					:			int    	=       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 )




'''simple docstring'''





from __future__ import annotations

from statistics import mean


def     __lowerCAmelCase						(       a_       ,      a_       ,      a_		)      ->	list[int]:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       [0] * no_of_processes
     SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       [0] * no_of_processes

     # Initialize remaining_time to waiting_time.

     for i in range(a_		):
          SCREAMING_SNAKE_CASE					:			Tuple    	=       burst_time[i]
     SCREAMING_SNAKE_CASE					:			list[int]    	=       []

     SCREAMING_SNAKE_CASE					:			List[Any]    	=       0
     SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       0

     # When processes are not completed,
     # A process whose arrival time has passed \
     # and has remaining execution time is put into the ready_process.
     # The shortest process in the ready_process, target_process is executed.

     while completed != no_of_processes:
          SCREAMING_SNAKE_CASE					:			Optional[int]    	=       []
          SCREAMING_SNAKE_CASE					:			Dict    	=       -1

          for i in range(a_		):
               if (arrival_time[i] <= total_time) and (remaining_time[i] > 0):
                    ready_process.append(a_		)

          if len(a_		) > 0:
               SCREAMING_SNAKE_CASE					:			Dict    	=       ready_process[0]
               for i in ready_process:
                    if remaining_time[i] < remaining_time[target_process]:
                         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       i
               total_time += burst_time[target_process]
               completed += 1
               SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       0
               SCREAMING_SNAKE_CASE					:			Tuple    	=       (
                   total_time - arrival_time[target_process] - burst_time[target_process]
               )
          else:
               total_time += 1

     return waiting_time


def     __lowerCAmelCase						(       a_       ,      a_       ,      a_		)      ->	list[int]:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			List[Any]    	=       [0] * no_of_processes
     for i in range(a_		):
          SCREAMING_SNAKE_CASE					:			List[Any]    	=       burst_time[i] + waiting_time[i]
     return turn_around_time


if __name__ == "__main__":
 print("""[TEST CASE 01]""")

 _lowerCAmelCase  :Optional[int]  				=    4
 _lowerCAmelCase  :Optional[int]  				=    [2, 5, 3, 7]
 _lowerCAmelCase  :List[str]  				=    [0, 0, 0, 0]
 _lowerCAmelCase  :Union[str, Any]  				=    calculate_waitingtime(arrival_time, burst_time, no_of_processes)
 _lowerCAmelCase  :Any  				=    calculate_turnaroundtime(
     burst_time, no_of_processes, waiting_time
 )

 # Printing the Result
 print("""PID\tBurst Time\tArrival Time\tWaiting Time\tTurnaround Time""")
 for i, process_id in enumerate(list(range(1, 5))):
  print(
      f"""{process_id}\t{burst_time[i]}\t\t\t{arrival_time[i]}\t\t\t\t"""
      f"""{waiting_time[i]}\t\t\t\t{turn_around_time[i]}"""
  )
 print(f"""\nAverage waiting time = {mean(waiting_time):.5f}""")
 print(f"""Average turnaround time = {mean(turn_around_time):.5f}""")



