
"""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()		)






import json
import logging
import os
import subprocess
from argparse import ArgumentParser


__A       =logging.getLogger(__name__)

def 			a		( ):







		'''simple docstring'''





		__UpperCAmelCase       :	Dict						=      ArgumentParser()
		__UpperCAmelCase				,   __UpperCAmelCase       :	int						=      parser.parse_known_args()
		for arg in unknown:
				if arg.startswith(('''-''', '''--''')		):
						parser.add_argument(arg.split('''='''		)[0]		)

		return parser.parse_args()

def 			a		( ):







		'''simple docstring'''





		__UpperCAmelCase       :	List[Any]						=      parse_args()
		__UpperCAmelCase       :	Tuple						=      88_88
		__UpperCAmelCase       :	Any						=      int(os.environ['''SM_NUM_GPUS''']		)
		__UpperCAmelCase       :	Optional[int]						=      json.loads(os.environ['''SM_HOSTS''']		)
		__UpperCAmelCase       :	int						=      len(_UpperCAmelCase		)
		__UpperCAmelCase       :	Optional[Any]						=      os.environ['''SM_CURRENT_HOST''']
		__UpperCAmelCase       :	List[str]						=      hosts.index(_UpperCAmelCase		)
		__UpperCAmelCase       :	Dict						=      '''INFO'''

		if num_nodes > 1:
				__UpperCAmelCase       :	Tuple						=      f'python -m torch.distributed.launch \\n                --nnodes={num_nodes}  \\n                --node_rank={rank}  \\n                --nproc_per_node={num_gpus}  \\n                --master_addr={hosts[0]}  \\n                --master_port={port} \\n                ./run_glue.py \\n                {"".join(["nested fstring" for parameter,value in args.__dict__.items()]		)}'
		else:
				__UpperCAmelCase       :	Dict						=      f'python -m torch.distributed.launch \\n            --nproc_per_node={num_gpus}  \\n            ./run_glue.py \\n            {"".join(["nested fstring" for parameter,value in args.__dict__.items()]		)}'
		try:
				subprocess.run(_UpperCAmelCase					,  shell=_UpperCAmelCase		)
		except Exception as e:
				logger.info(_UpperCAmelCase		)


if __name__ == "__main__":
				main()





