

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()					)



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()


