
'''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 ):



    def __init__(	self      :       Optional[int],__A      :       Union[str, Any]=0.01,__A      :       str=1_0_0_0     ):
          _lowerCamelCase				:   Any								=   p_stop
          _lowerCamelCase				:   Tuple								=   max_length



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


class  UpperCAmelCase__						(       unittest.TestCase ):



    def   lowerCamelCase_  (	self      :       str,__A      :       List[str],__A      :       List[str],__A      :       Union[str, Any]=False,__A      :       Dict=True     ):
          _lowerCamelCase				:   Tuple								=   [
              BatchSamplerShard(__A,2,__A,split_batches=__A,even_batches=__A     )
              for i in range(2     )
          ]
          _lowerCamelCase				:   Dict								=   [list(__A     ) for batch_sampler_shard in batch_sampler_shards]
          if not split_batches:
                self.assertListEqual([len(__A     ) for shard in batch_sampler_shards],[len(__A     ) for e in expected]     )
          self.assertListEqual(__A,__A     )



    def   lowerCamelCase_  (	self      :       Any     ):
          # Check the shards when the dataset is a round multiple of total batch size.
          _lowerCamelCase				:   int								=   BatchSampler(range(2_4     ),batch_size=3,drop_last=__A     )
          _lowerCamelCase				:   Tuple								=   [
              [[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(__A,__A     )

          _lowerCamelCase				:   Any								=   BatchSampler(range(2_4     ),batch_size=3,drop_last=__A     )
          # Expected shouldn't change
          self.check_batch_sampler_shards(__A,__A     )

          # Check the shards when the dataset is a round multiple of batch size but not total batch size.
          _lowerCamelCase				:   int								=   BatchSampler(range(2_1     ),batch_size=3,drop_last=__A     )
          _lowerCamelCase				:   int								=   [
              [[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(__A,__A     )

          _lowerCamelCase				:   Union[str, Any]								=   BatchSampler(range(2_1     ),batch_size=3,drop_last=__A     )
          _lowerCamelCase				:   str								=   [
              [[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(__A,__A     )

          # Check the shards when the dataset is not a round multiple of batch size but has a multiple of
          # num_processes batch.
          _lowerCamelCase				:   Optional[int]								=   BatchSampler(range(2_2     ),batch_size=3,drop_last=__A     )
          _lowerCamelCase				:   List[Any]								=   [
              [[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(__A,__A     )

          _lowerCamelCase				:   Any								=   BatchSampler(range(2_2     ),batch_size=3,drop_last=__A     )
          _lowerCamelCase				:   Any								=   [
              [[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(__A,__A     )

          # 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				:   Any								=   BatchSampler(range(2_0     ),batch_size=3,drop_last=__A     )
          _lowerCamelCase				:   int								=   [
              [[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(__A,__A     )

          _lowerCamelCase				:   Tuple								=   BatchSampler(range(2_0     ),batch_size=3,drop_last=__A     )
          _lowerCamelCase				:   Tuple								=   [
              [[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(__A,__A     )

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

          _lowerCamelCase				:   Tuple								=   BatchSampler(range(2     ),batch_size=3,drop_last=__A     )
          _lowerCamelCase				:   Dict								=   [[], []]
          self.check_batch_sampler_shards(__A,__A     )



    def   lowerCamelCase_  (	self      :       Optional[Any]     ):
          # Check the shards when the dataset is a round multiple of batch size.
          _lowerCamelCase				:   Any								=   BatchSampler(range(2_4     ),batch_size=4,drop_last=__A     )
          _lowerCamelCase				:   Dict								=   [
              [[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(__A,__A,split_batches=__A     )

          _lowerCamelCase				:   Optional[Any]								=   BatchSampler(range(2_4     ),batch_size=4,drop_last=__A     )
          # Expected shouldn't change
          self.check_batch_sampler_shards(__A,__A,split_batches=__A     )

          # Check the shards when the dataset is not a round multiple of batch size.
          _lowerCamelCase				:   int								=   BatchSampler(range(2_2     ),batch_size=4,drop_last=__A     )
          _lowerCamelCase				:   List[str]								=   [
              [[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(__A,__A,split_batches=__A     )

          _lowerCamelCase				:   Union[str, Any]								=   BatchSampler(range(2_2     ),batch_size=4,drop_last=__A     )
          _lowerCamelCase				:   Any								=   [
              [[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(__A,__A,split_batches=__A     )

          # Check the shards when the dataset is not a round multiple of batch size or num_processes.
          _lowerCamelCase				:   Any								=   BatchSampler(range(2_1     ),batch_size=4,drop_last=__A     )
          _lowerCamelCase				:   Dict								=   [
              [[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(__A,__A,split_batches=__A     )

          _lowerCamelCase				:   List[Any]								=   BatchSampler(range(2_1     ),batch_size=4,drop_last=__A     )
          _lowerCamelCase				:   Tuple								=   [
              [[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(__A,__A,split_batches=__A     )

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

          _lowerCamelCase				:   Dict								=   BatchSampler(range(2     ),batch_size=4,drop_last=__A     )
          _lowerCamelCase				:   int								=   [[], []]
          self.check_batch_sampler_shards(__A,__A,split_batches=__A     )



    def   lowerCamelCase_  (	self      :       Dict     ):
          # Check the shards when the dataset is a round multiple of total batch size.
          _lowerCamelCase				:   List[Any]								=   BatchSampler(range(2_4     ),batch_size=3,drop_last=__A     )
          _lowerCamelCase				:   Tuple								=   [
              [[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(__A,__A,even_batches=__A     )

          _lowerCamelCase				:   str								=   BatchSampler(range(2_4     ),batch_size=3,drop_last=__A     )
          # Expected shouldn't change
          self.check_batch_sampler_shards(__A,__A,even_batches=__A     )

          # Check the shards when the dataset is a round multiple of batch size but not total batch size.
          _lowerCamelCase				:   List[str]								=   BatchSampler(range(2_1     ),batch_size=3,drop_last=__A     )
          _lowerCamelCase				:   Tuple								=   [
              [[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(__A,__A,even_batches=__A     )

          _lowerCamelCase				:   Tuple								=   BatchSampler(range(2_1     ),batch_size=3,drop_last=__A     )
          _lowerCamelCase				:   int								=   [
              [[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(__A,__A,even_batches=__A     )

          # 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(2_2     ),batch_size=3,drop_last=__A     )
          _lowerCamelCase				:   Optional[Any]								=   [
              [[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(__A,__A,even_batches=__A     )

          _lowerCamelCase				:   Optional[int]								=   BatchSampler(range(2_2     ),batch_size=3,drop_last=__A     )
          _lowerCamelCase				:   Optional[int]								=   [
              [[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(__A,__A,even_batches=__A     )

          # 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				:   Optional[Any]								=   BatchSampler(range(2_0     ),batch_size=3,drop_last=__A     )
          _lowerCamelCase				:   List[Any]								=   [
              [[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(__A,__A,even_batches=__A     )

          _lowerCamelCase				:   List[str]								=   BatchSampler(range(2_0     ),batch_size=3,drop_last=__A     )
          _lowerCamelCase				:   Union[str, Any]								=   [
              [[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(__A,__A,even_batches=__A     )

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

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



    def   lowerCamelCase_  (	self      :       Tuple     ):
          # Check the shards when the dataset is a round multiple of batch size.
          _lowerCamelCase				:   Any								=   BatchSampler(range(2_4     ),batch_size=4,drop_last=__A     )
          _lowerCamelCase				:   Any								=   [
              [[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(__A,__A,split_batches=__A,even_batches=__A     )

          _lowerCamelCase				:   Union[str, Any]								=   BatchSampler(range(2_4     ),batch_size=4,drop_last=__A     )
          # Expected shouldn't change
          self.check_batch_sampler_shards(__A,__A,split_batches=__A,even_batches=__A     )

          # Check the shards when the dataset is not a round multiple of batch size.
          _lowerCamelCase				:   Optional[Any]								=   BatchSampler(range(2_2     ),batch_size=4,drop_last=__A     )
          _lowerCamelCase				:   Any								=   [
              [[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(__A,__A,split_batches=__A,even_batches=__A     )

          _lowerCamelCase				:   List[Any]								=   BatchSampler(range(2_2     ),batch_size=4,drop_last=__A     )
          _lowerCamelCase				:   Any								=   [
              [[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(__A,__A,split_batches=__A,even_batches=__A     )

          # Check the shards when the dataset is not a round multiple of batch size or num_processes.
          _lowerCamelCase				:   str								=   BatchSampler(range(2_1     ),batch_size=4,drop_last=__A     )
          _lowerCamelCase				:   Optional[Any]								=   [
              [[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(__A,__A,split_batches=__A,even_batches=__A     )

          _lowerCamelCase				:   Optional[int]								=   BatchSampler(range(2_1     ),batch_size=4,drop_last=__A     )
          _lowerCamelCase				:   str								=   [
              [[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(__A,__A,split_batches=__A,even_batches=__A     )

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

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



    def   lowerCamelCase_  (	self      :       Tuple     ):
          _lowerCamelCase				:   str								=   [[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 1_0, 1_1], [1_2, 1_3]]
          _lowerCamelCase				:   List[str]								=   [BatchSamplerShard(__A,2,__A,even_batches=__A     ) 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   lowerCamelCase_  (	self      :       int,__A      :       List[str],__A      :       int,__A      :       List[str],__A      :       Optional[int]=False,__A      :       List[str]=2,__A      :       Optional[Any]=False     ):
          random.seed(__A     )
          _lowerCamelCase				:   str								=   list(__A     )

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

          _lowerCamelCase				:   Union[str, 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				:   Any								=   iterable_dataset_lists[0]
          for l in iterable_dataset_lists[1:]:
                self.assertEqual(len(__A     ),len(__A     )     )
                self.assertTrue(len(__A     ) % shard_batch_size == 0     )

          _lowerCamelCase				:   Optional[int]								=   []
          for idx in range(0,len(__A     ),__A     ):
                for l in iterable_dataset_lists:
                      observed += l[idx : idx + shard_batch_size]

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



    def   lowerCamelCase_  (	self      :       int     ):
          _lowerCamelCase				:   int								=   4_2
          _lowerCamelCase				:   Optional[int]								=   RandomIterableDataset()

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

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

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



    def   lowerCamelCase_  (	self      :       List[Any]     ):
          _lowerCamelCase				:   List[Any]								=   BatchSampler(range(1_6     ),batch_size=4,drop_last=__A     )
          _lowerCamelCase				:   Tuple								=   SkipBatchSampler(__A,2     )
          self.assertListEqual(list(__A     ),[[8, 9, 1_0, 1_1], [1_2, 1_3, 1_4, 1_5]]     )



    def   lowerCamelCase_  (	self      :       Tuple     ):
          _lowerCamelCase				:   str								=   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   lowerCamelCase_  (	self      :       Union[str, Any]     ):
          _lowerCamelCase				:   Union[str, Any]								=   DataLoader(list(range(1_6     )     ),batch_size=4     )
          _lowerCamelCase				:   List[str]								=   skip_first_batches(__A,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   lowerCamelCase_  (	self      :       str     ):
          _lowerCamelCase				:   Optional[int]								=   DataLoaderShard(list(range(1_6     )     ),batch_size=4     )
          for idx, _ in enumerate(__A     ):
                self.assertEqual(dataloader.end_of_dataloader,idx == 3     )

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



    def   lowerCamelCase_  (	self      :       Dict     ):
          Accelerator()
          _lowerCamelCase				:   List[Any]								=   DataLoaderDispatcher(range(1_6     ),batch_size=4     )
          for idx, _ in enumerate(__A     ):
                self.assertEqual(dataloader.end_of_dataloader,idx == 3     )

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

'''simple docstring'''






from __future__ import annotations







def 		A_       (	_lowerCAmelCase   :    list[int]				,	_lowerCAmelCase   :    int				,	_lowerCAmelCase   :    int				,	_lowerCAmelCase   :    int							):



      """simple docstring"""




      if (direction == 1 and array[indexa] > array[indexa]) or (
          direction == 0 and array[indexa] < array[indexa]
      ):
            _lowerCamelCase						,		_lowerCamelCase				:   List[Any]								=   array[indexa], array[indexa]







def 		A_       (	_lowerCAmelCase   :    list[int]				,	_lowerCAmelCase   :    int				,	_lowerCAmelCase   :    int				,	_lowerCAmelCase   :    int							):



      """simple docstring"""




      if length > 1:
            _lowerCamelCase				:   Optional[int]								=   int(length / 2							)
            for i in range(_lowerCAmelCase				,	low + middle							):
                  comp_and_swap(_lowerCAmelCase				,	_lowerCAmelCase				,	i + middle				,	_lowerCAmelCase							)
            bitonic_merge(_lowerCAmelCase				,	_lowerCAmelCase				,	_lowerCAmelCase				,	_lowerCAmelCase							)
            bitonic_merge(_lowerCAmelCase				,	low + middle				,	_lowerCAmelCase				,	_lowerCAmelCase							)







def 		A_       (	_lowerCAmelCase   :    list[int]				,	_lowerCAmelCase   :    int				,	_lowerCAmelCase   :    int				,	_lowerCAmelCase   :    int							):



      """simple docstring"""




      if length > 1:
            _lowerCamelCase				:   Optional[Any]								=   int(length / 2							)
            bitonic_sort(_lowerCAmelCase				,	_lowerCAmelCase				,	_lowerCAmelCase				,	1							)
            bitonic_sort(_lowerCAmelCase				,	low + middle				,	_lowerCAmelCase				,	0							)
            bitonic_merge(_lowerCAmelCase				,	_lowerCAmelCase				,	_lowerCAmelCase				,	_lowerCAmelCase							)


if __name__ == "__main__":
 UpperCAmelCase_       :   Any   =       input('Enter numbers separated by a comma:\n').strip()
 UpperCAmelCase_       :   Optional[int]   =       [int(item.strip()) for item in user_input.split(',')]

 bitonic_sort(unsorted, 0, len(unsorted), 1)
 print('\nSorted array in ascending order is: ', end='')
 print(*unsorted, sep=', ')

 bitonic_merge(unsorted, 0, len(unsorted), 0)
 print('Sorted array in descending order is: ', end='')
 print(*unsorted, sep=', ')