




'''simple docstring'''





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       SCREAMING_SNAKE_CASE			( )						->							Optional[int]:
    UpperCAmelCase_											=  Dataset.from_dict({'''i''': range(17   )}   )
    UpperCAmelCase_											=  len(__snake_case   )
    UpperCAmelCase_											=  3
    UpperCAmelCase_											=  [
        split_dataset_by_node(__snake_case ,  rank=__snake_case ,  world_size=__snake_case   ) for rank in range(__snake_case   )
    ]
    assert sum(len(__snake_case   ) 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       SCREAMING_SNAKE_CASE			( )						->							Dict:
    def gen():
        return ({"i": i} for i in range(17   ))

    UpperCAmelCase_											=  3
    UpperCAmelCase_											=  IterableDataset.from_generator(__snake_case   )
    UpperCAmelCase_											=  len(list(__snake_case   )   )
    UpperCAmelCase_											=  [
        split_dataset_by_node(__snake_case ,  rank=__snake_case ,  world_size=__snake_case   ) for rank in range(__snake_case   )
    ]
    assert sum(len(list(__snake_case   )   ) 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       SCREAMING_SNAKE_CASE			( __UpperCamelCase    :		Optional[Any]   )						->							List[Any]:
    def gen(__UpperCamelCase    :		List[str]   ):
        for shard in shards:
            yield from ({"i": i, "shard": shard} for i in range(17   ))

    UpperCAmelCase_											=  3
    UpperCAmelCase_											=  shards_per_node * world_size
    UpperCAmelCase_											=  {'''shards''': [f'shard_{shard_idx}.txt' for shard_idx in range(__snake_case   )]}
    UpperCAmelCase_											=  IterableDataset.from_generator(__snake_case ,  gen_kwargs=__snake_case   )
    UpperCAmelCase_											=  len(list(__snake_case   )   )
    assert full_ds.n_shards == world_size * shards_per_node
    UpperCAmelCase_											=  [
        split_dataset_by_node(__snake_case ,  rank=__snake_case ,  world_size=__snake_case   ) for rank in range(__snake_case   )
    ]
    assert [ds.n_shards for ds in datasets_per_rank] == [shards_per_node] * world_size
    assert sum(len(list(__snake_case   )   ) 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       SCREAMING_SNAKE_CASE			( )						->							int:
    def gen():
        return ({"i": i} for i in range(17   ))

    UpperCAmelCase_											=  2
    UpperCAmelCase_											=  IterableDataset.from_generator(__snake_case   )
    UpperCAmelCase_											=  len(list(__snake_case   )   )

    UpperCAmelCase_											=  split_dataset_by_node(__snake_case ,  rank=0 ,  world_size=__snake_case   ).shuffle(seed=42   )
    assert len(list(__snake_case   )   ) == 1 + full_size // world_size
    with pytest.raises(__snake_case   ):
        split_dataset_by_node(__snake_case ,  rank=0 ,  world_size=__snake_case   ).shuffle()

    UpperCAmelCase_											=  split_dataset_by_node(full_ds.shuffle(seed=42   ) ,  rank=0 ,  world_size=__snake_case   )
    assert len(list(__snake_case   )   ) == 1 + full_size // world_size
    with pytest.raises(__snake_case   ):
        split_dataset_by_node(full_ds.shuffle() ,  rank=0 ,  world_size=__snake_case   )




@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       SCREAMING_SNAKE_CASE			( __UpperCamelCase    :		int   )						->							str:
    UpperCAmelCase_											=  2
    UpperCAmelCase_											=  get_torch_dist_unique_port()
    UpperCAmelCase_											=  Path(__snake_case   ).resolve().parent / '''distributed_scripts''' / '''launch_torch_distributed.py'''
    UpperCAmelCase_											=  f'\n        -m torch.distributed.launch\n        --nproc_per_node={nproc_per_node}\n        --master_port={master_port}\n        {test_script}\n    '.split()
    UpperCAmelCase_											=  f'\n        --streaming={streaming}\n    '.split()
    UpperCAmelCase_											=  [sys.executable] + distributed_args + args
    execute_subprocess_async(__snake_case ,  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       SCREAMING_SNAKE_CASE			( __UpperCamelCase    :		Dict ,  __UpperCamelCase    :		Optional[int]   )						->							Any:
    UpperCAmelCase_											=  True
    UpperCAmelCase_											=  get_torch_dist_unique_port()
    UpperCAmelCase_											=  Path(__snake_case   ).resolve().parent / '''distributed_scripts''' / '''launch_torch_distributed.py'''
    UpperCAmelCase_											=  f'\n        -m torch.distributed.launch\n        --nproc_per_node={nproc_per_node}\n        --master_port={master_port}\n        {test_script}\n    '.split()
    UpperCAmelCase_											=  f'\n        --streaming={streaming}\n        --num_workers={num_workers}\n    '.split()
    UpperCAmelCase_											=  [sys.executable] + distributed_args + args
    execute_subprocess_async(__snake_case ,  env=os.environ.copy()   )











def       SCREAMING_SNAKE_CASE			( )						->							List[Any]:
    UpperCAmelCase_											=  1

    while True:
        if (
            sorted(str(__UpperCamelCase   )   )
            == sorted(str(2 * i   )   )
            == sorted(str(3 * i   )   )
            == sorted(str(4 * i   )   )
            == sorted(str(5 * i   )   )
            == sorted(str(6 * i   )   )
        ):
            return i

        i += 1


if __name__ == "__main__":
 print(solution())





