






"""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      snake_case    ():







  '''simple docstring'''

  a	:     int							=  Dataset.from_dict({'i': range(1_7 )} )
  a	:     List[Any]							=  len(A_ )
  a	:     Optional[int]							=  3
  a	:     List[str]							=  [
      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      snake_case    ():







  '''simple docstring'''

  def gen():
    return ({"i": i} for i in range(1_7 ))

  a	:     Any							=  3
  a	:     Optional[Any]							=  IterableDataset.from_generator(A_ )
  a	:     List[str]							=  len(list(A_ ) )
  a	:     Dict							=  [
      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      snake_case    (A_ :List[Any] ):







  '''simple docstring'''

  def gen(A_ :List[str] ):
    for shard in shards:
      yield from ({"i": i, "shard": shard} for i in range(1_7 ))

  a	:     List[Any]							=  3
  a	:     int							=  shards_per_node * world_size
  a	:     Union[str, Any]							=  {'shards': [f'''shard_{shard_idx}.txt''' for shard_idx in range(A_ )]}
  a	:     Optional[Any]							=  IterableDataset.from_generator(A_      ,     gen_kwargs=A_ )
  a	:     List[Any]							=  len(list(A_ ) )
  assert full_ds.n_shards == world_size * shards_per_node
  a	:     Union[str, Any]							=  [
      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      snake_case    ():







  '''simple docstring'''

  def gen():
    return ({"i": i} for i in range(1_7 ))

  a	:     Union[str, Any]							=  2
  a	:     List[str]							=  IterableDataset.from_generator(A_ )
  a	:     Optional[int]							=  len(list(A_ ) )

  a	:     Dict							=  split_dataset_by_node(A_      ,     rank=0      ,     world_size=A_ ).shuffle(seed=4_2 )
  assert len(list(A_ ) ) == 1 + full_size // world_size
  with pytest.raises(A_ ):
    split_dataset_by_node(A_      ,     rank=0      ,     world_size=A_ ).shuffle()

  a	:     int							=  split_dataset_by_node(full_ds.shuffle(seed=4_2 )      ,     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      snake_case    (A_ :int ):







  '''simple docstring'''

  a	:     Optional[int]							=  2
  a	:     str							=  get_torch_dist_unique_port()
  a	:     Optional[Any]							=  Path(A_ ).resolve().parent / 'distributed_scripts' / 'launch_torch_distributed.py'
  a	:     Tuple							=  f'''
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    '''.split()
  a	:     Tuple							=  f'''
        --streaming={streaming}
    '''.split()
  a	:     Any							=  [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      snake_case    (A_ :str      ,     A_ :int ):







  '''simple docstring'''

  a	:     Union[str, Any]							=  True
  a	:     List[str]							=  get_torch_dist_unique_port()
  a	:     List[str]							=  Path(A_ ).resolve().parent / 'distributed_scripts' / 'launch_torch_distributed.py'
  a	:     List[Any]							=  f'''
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    '''.split()
  a	:     int							=  f'''
        --streaming={streaming}
        --num_workers={num_workers}
    '''.split()
  a	:     Optional[Any]							=  [sys.executable] + distributed_args + args
  execute_subprocess_async(A_      ,     env=os.environ.copy() )












"""simple docstring"""


from __future__ import annotations




def      snake_case    (A_ :int ):







  '''simple docstring'''

  assert (
      isinstance(A_      ,     A_ ) and mask > 0
  ), f'''mask needs to be positive integer, your input {mask}'''
  a	:     Any							=  []
  a	:     Union[str, Any]							=  mask

  while submask:
    all_submasks.append(A_ )
    a	:     Any							=  (submask - 1) & mask

  return all_submasks


if __name__ == "__main__":
   import doctest

   doctest.testmod()




