
"""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 					a__ ( ):

  """simple docstring"""







  _snake_case  :    int            = Dataset.from_dict({"i": range(17		)}		)
  _snake_case  :    Tuple            = len(a		)
  _snake_case  :    int            = 3
  _snake_case  :    Any            = [
      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 					a__ ( ):

  """simple docstring"""







  def gen():
    return ({"i": i} for i in range(17		))

  _snake_case  :    Union[str, Any]            = 3
  _snake_case  :    Optional[int]            = IterableDataset.from_generator(a		)
  _snake_case  :    Optional[int]            = len(list(a		)		)
  _snake_case  :    Optional[int]            = [
      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 					a__ ( a :					int		):

  """simple docstring"""







  def gen(a :					int		):
    for shard in shards:
      yield from ({"i": i, "shard": shard} for i in range(17		))

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

  """simple docstring"""







  def gen():
    return ({"i": i} for i in range(17		))

  _snake_case  :    Tuple            = 2
  _snake_case  :    str            = IterableDataset.from_generator(a		)
  _snake_case  :    List[Any]            = len(list(a		)		)

  _snake_case  :    Union[str, Any]            = 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()

  _snake_case  :    str            = 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 					a__ ( a :					str		):

  """simple docstring"""







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

  """simple docstring"""







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





class 	a__						:

       def __init__( self   :  int   ,				lowerCamelCase_   :  int     ):
            a_     :  Dict				      =     data
            a_     :  int
            a_     :  Node



def 			_a      (    __UpperCamelCase			):
     a_     :  int				      =     0
     a_     :  Optional[Any]				      =     x



def 			_a      (    __UpperCamelCase	,	__UpperCamelCase			):
     a_      ,      a_     :  Dict				      =     find_set(__UpperCamelCase			), find_set(__UpperCamelCase			)
     if x == y:
          return

     elif x.rank > y.rank:
          a_     :  Tuple				      =     x
     else:
          a_     :  Optional[Any]				      =     y
          if x.rank == y.rank:
               y.rank += 1



def 			_a      (    __UpperCamelCase			):
     if x != x.parent:
          a_     :  Tuple				      =     find_set(x.parent			)
     return x.parent



def 			_a      (    __UpperCamelCase			):
     a_     :  List[Any]				      =     ({0, 1, 2}, {3, 4, 5})
     for s in sets:
          if node.data in s:
               return s
     a_     :  Union[str, Any]				      =     F'''{node.data} is not in {sets}'''
     raise ValueError(__UpperCamelCase			)



def 			_a      (    ):
     a_     :  int				      =     [Node(__UpperCamelCase			) for i in range(6			)]
     for v in vertex:
          make_set(__UpperCamelCase			)

     union_set(vertex[0]	,	vertex[1]			)
     union_set(vertex[1]	,	vertex[2]			)
     union_set(vertex[3]	,	vertex[4]			)
     union_set(vertex[3]	,	vertex[5]			)

     for nodea in vertex:
          for nodea in vertex:
               if find_python_set(__UpperCamelCase			).isdisjoint(find_python_set(__UpperCamelCase			)			):
                    assert find_set(__UpperCamelCase			) != find_set(__UpperCamelCase			)
               else:
                    assert find_set(__UpperCamelCase			) == find_set(__UpperCamelCase			)


if __name__ == "__main__":
       test_disjoint_set()



