





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      (   ):
       _UpperCamelCase														=    Dataset.from_dict({'''i''': range(17       )}       )
       _UpperCamelCase														=    len(SCREAMING_SNAKE_CASE__       )
       _UpperCamelCase														=    3
       _UpperCamelCase														=    [
           split_dataset_by_node(SCREAMING_SNAKE_CASE__     ,							rank=SCREAMING_SNAKE_CASE__     ,							world_size=SCREAMING_SNAKE_CASE__       ) for rank in range(SCREAMING_SNAKE_CASE__       )
       ]
       assert sum(len(SCREAMING_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      (   ):
       def gen():
              return ({"i": i} for i in range(17       ))

       _UpperCamelCase														=    3
       _UpperCamelCase														=    IterableDataset.from_generator(SCREAMING_SNAKE_CASE__       )
       _UpperCamelCase														=    len(list(SCREAMING_SNAKE_CASE__       )       )
       _UpperCamelCase														=    [
           split_dataset_by_node(SCREAMING_SNAKE_CASE__     ,							rank=SCREAMING_SNAKE_CASE__     ,							world_size=SCREAMING_SNAKE_CASE__       ) for rank in range(SCREAMING_SNAKE_CASE__       )
       ]
       assert sum(len(list(SCREAMING_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      (   lowerCAmelCase       ):
       def gen(lowerCAmelCase       ):
              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(SCREAMING_SNAKE_CASE__       )]}
       _UpperCamelCase														=    IterableDataset.from_generator(SCREAMING_SNAKE_CASE__     ,							gen_kwargs=SCREAMING_SNAKE_CASE__       )
       _UpperCamelCase														=    len(list(SCREAMING_SNAKE_CASE__       )       )
       assert full_ds.n_shards == world_size * shards_per_node
       _UpperCamelCase														=    [
           split_dataset_by_node(SCREAMING_SNAKE_CASE__     ,							rank=SCREAMING_SNAKE_CASE__     ,							world_size=SCREAMING_SNAKE_CASE__       ) for rank in range(SCREAMING_SNAKE_CASE__       )
       ]
       assert [ds.n_shards for ds in datasets_per_rank] == [shards_per_node] * world_size
       assert sum(len(list(SCREAMING_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      (   ):
       def gen():
              return ({"i": i} for i in range(17       ))

       _UpperCamelCase														=    2
       _UpperCamelCase														=    IterableDataset.from_generator(SCREAMING_SNAKE_CASE__       )
       _UpperCamelCase														=    len(list(SCREAMING_SNAKE_CASE__       )       )

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

       _UpperCamelCase														=    split_dataset_by_node(full_ds.shuffle(seed=42       )     ,							rank=0     ,							world_size=SCREAMING_SNAKE_CASE__       )
       assert len(list(SCREAMING_SNAKE_CASE__       )       ) == 1 + full_size // world_size
       with pytest.raises(SCREAMING_SNAKE_CASE__       ):
              split_dataset_by_node(full_ds.shuffle()     ,							rank=0     ,							world_size=SCREAMING_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      (   lowerCAmelCase       ):
       _UpperCamelCase														=    2
       _UpperCamelCase														=    get_torch_dist_unique_port()
       _UpperCamelCase														=    Path(SCREAMING_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(SCREAMING_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      (   lowerCAmelCase     ,							lowerCAmelCase       ):
       _UpperCamelCase														=    True
       _UpperCamelCase														=    get_torch_dist_unique_port()
       _UpperCamelCase														=    Path(SCREAMING_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(SCREAMING_SNAKE_CASE__     ,							env=os.environ.copy()       )










_A									=		{
    0: 'underwear',
    1: 'pants',
    2: 'belt',
    3: 'suit',
    4: 'shoe',
    5: 'socks',
    6: 'shirt',
    7: 'tie',
    8: 'watch',
}

_A									=		[[1, 4], [2, 4], [3], [], [], [4], [2, 7], [3], []]

_A									=		[0 for x in range(len(graph))]
_A									=		[]

def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   Optional[Any]     ,	SCREAMING_SNAKE_CASE__     :   Tuple							):
     __UpperCamelCase       				=1
     while stack:
          __UpperCamelCase       				=stack.pop()
          print(SCREAMING_SNAKE_CASE__     ,	clothes[current_clothing]							)
          order += 1

def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   Any     ,	SCREAMING_SNAKE_CASE__     :   int     ,	SCREAMING_SNAKE_CASE__     :   Tuple							):
     __UpperCamelCase       				=1
     for v in graph[u]:
          if not visited[v]:
               depth_first_search(SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__							)

     stack.append(SCREAMING_SNAKE_CASE__							)

def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   int     ,	SCREAMING_SNAKE_CASE__     :   List[str]							):
     for v in range(len(SCREAMING_SNAKE_CASE__							)							):
          if not visited[v]:
               depth_first_search(SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__							)


if __name__ == "__main__":
   topological_sort(graph, visited)
   print(stack)
   print_stack(stack, clothes)





