code: 
  
  import os
  import sys
  from pathlib import Path
  
  import pytest
  
  from datasets import Dataset, IterableDataset
  from datasets.distributed import split_dataset_by_node
  
  from .utils import execute_subprocess_async, get_torch_dist_unique_port, require_torch
  
  
  def 					__lowerCAmelCase			(  ):
  
  
  
  							"""simple docstring"""
  
  
  							_lowercase				=	Dataset.from_dict({"""i""": range(17					)}					)
  							_lowercase				=	len(_A					)
  							_lowercase				=	3
  							_lowercase				=	[
  							    split_dataset_by_node(_A ,rank=_A ,world_size=_A					) for rank in range(_A					)
  							]
  							assert sum(len(_A					) for ds in datasets_per_rank					) == full_size
  							assert len({tuple(x.values()					) for ds in datasets_per_rank for x in ds}					) == full_size
  
  
  def 					__lowerCAmelCase			(  ):
  
  
  
  							"""simple docstring"""
  
  
  							def gen():
  														return ({"i": i} for i in range(17					))
  
  							_lowercase				=	3
  							_lowercase				=	IterableDataset.from_generator(_A					)
  							_lowercase				=	len(list(_A					)					)
  							_lowercase				=	[
  							    split_dataset_by_node(_A ,rank=_A ,world_size=_A					) for rank in range(_A					)
  							]
  							assert sum(len(list(_A					)					) for ds in datasets_per_rank					) == full_size
  							assert len({tuple(x.values()					) for ds in datasets_per_rank for x in ds}					) == full_size
  
  
  @pytest.mark.parametrize("""shards_per_node""" ,[1, 2, 3]					)
  def 					__lowerCAmelCase			(  _A					):
  
  
  
  							"""simple docstring"""
  
  
  							def gen(_A					):
  														for shard in shards:
  																					yield from ({"i": i, "shard": shard} for i in range(17					))
  
  							_lowercase				=	3
  							_lowercase				=	shards_per_node * world_size
  							_lowercase				=	{"""shards""": [f'''shard_{shard_idx}.txt''' for shard_idx in range(_A					)]}
  							_lowercase				=	IterableDataset.from_generator(_A ,gen_kwargs=_A					)
  							_lowercase				=	len(list(_A					)					)
  							assert full_ds.n_shards == world_size * shards_per_node
  							_lowercase				=	[
  							    split_dataset_by_node(_A ,rank=_A ,world_size=_A					) for rank in range(_A					)
  							]
  							assert [ds.n_shards for ds in datasets_per_rank] == [shards_per_node] * world_size
  							assert sum(len(list(_A					)					) for ds in datasets_per_rank					) == full_size
  							assert len({tuple(x.values()					) for ds in datasets_per_rank for x in ds}					) == full_size
  
  
  def 					__lowerCAmelCase			(  ):
  
  
  
  							"""simple docstring"""
  
  
  							def gen():
  														return ({"i": i} for i in range(17					))
  
  							_lowercase				=	2
  							_lowercase				=	IterableDataset.from_generator(_A					)
  							_lowercase				=	len(list(_A					)					)
  
  							_lowercase				=	split_dataset_by_node(_A ,rank=0 ,world_size=_A					).shuffle(seed=42					)
  							assert len(list(_A					)					) == 1 + full_size // world_size
  							with pytest.raises(_A					):
  														split_dataset_by_node(_A ,rank=0 ,world_size=_A					).shuffle()
  
  							_lowercase				=	split_dataset_by_node(full_ds.shuffle(seed=42					) ,rank=0 ,world_size=_A					)
  							assert len(list(_A					)					) == 1 + full_size // world_size
  							with pytest.raises(_A					):
  														split_dataset_by_node(full_ds.shuffle() ,rank=0 ,world_size=_A					)
  
  
  @pytest.mark.parametrize("""streaming""" ,[False, True]					)
  @require_torch
  @pytest.mark.skipif(os.name == """nt""" ,reason="""execute_subprocess_async doesn't support windows"""					)
  @pytest.mark.integration
  def 					__lowerCAmelCase			(  _A					):
  
  
  
  							"""simple docstring"""
  
  
  							_lowercase				=	2
  							_lowercase				=	get_torch_dist_unique_port()
  							_lowercase				=	Path(_A					).resolve().parent / """distributed_scripts""" / """launch_torch_distributed.py"""
  							_lowercase				=	f'''
          -m torch.distributed.launch
          --nproc_per_node={nproc_per_node}
          --master_port={master_port}
          {test_script}
      '''.split()
  							_lowercase				=	f'''
          --streaming={streaming}
      '''.split()
  							_lowercase				=	[sys.executable] + distributed_args + args
  							execute_subprocess_async(_A ,env=os.environ.copy()					)
  
  
  
  
  
  
  
  @pytest.mark.parametrize(
      """nproc_per_node, num_workers""" ,[
          (2, 2),  # each node has 2 shards and each worker has 1 shards
          (3, 2),  # each node uses all the shards but skips examples, and each worker has 2 shards
      ] ,)
  @require_torch
  @pytest.mark.skipif(os.name == """nt""" ,reason="""execute_subprocess_async doesn't support windows"""					)
  @pytest.mark.integration
  def 					__lowerCAmelCase			(  _A ,_A					):
  
  
  
  							"""simple docstring"""
  
  
  							_lowercase				=	True
  							_lowercase				=	get_torch_dist_unique_port()
  							_lowercase				=	Path(_A					).resolve().parent / """distributed_scripts""" / """launch_torch_distributed.py"""
  							_lowercase				=	f'''
          -m torch.distributed.launch
          --nproc_per_node={nproc_per_node}
          --master_port={master_port}
          {test_script}
      '''.split()
  							_lowercase				=	f'''
          --streaming={streaming}
          --num_workers={num_workers}
      '''.split()
  							_lowercase				=	[sys.executable] + distributed_args + args
  							execute_subprocess_async(_A ,env=os.environ.copy()					)
  
  
  
style_context: def 					__lowerCAmelCase			(  _A					):
  
  
  
  							"""simple docstring"""
  
  
  							if len(_A					) <= 1:
  														return data
  							else:
  														return [
  														    *quick_sort([e for e in data[1:] if e <= data[0]]					),
  														    data[0],
  														    *quick_sort([e for e in data[1:] if e > data[0]]					),
  														]
  
  
  if __name__ == "__main__":
  				import doctest
  
  				doctest.testmod()
  
  
  