






"""simple docstring"""



import inspect
import os
import unittest

import torch

import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
    execute_subprocess_async,
    require_cpu,
    require_huggingface_suite,
    require_multi_gpu,
    require_single_gpu,
)
from accelerate.utils import patch_environment

@require_huggingface_suite
class        a     (   unittest.TestCase	):







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       inspect.getfile(accelerate.test_utils       )
      lowercase							    =       os.path.sep.join(
          mod_file.split(os.path.sep       )[:-1] + ['scripts', 'external_deps', 'test_metrics.py']       )

      from accelerate.test_utils.scripts.external_deps import test_metrics  # noqa: F401

      lowercase							    =       test_metrics







    @require_cpu
    def 			UpperCamelCase_      (			self       ):
      debug_launcher(self.test_metrics.main							,		num_processes=1       )







    @require_cpu
    def 			UpperCamelCase_      (			self       ):
      debug_launcher(self.test_metrics.main       )







    @require_single_gpu
    def 			UpperCamelCase_      (			self       ):
      self.test_metrics.main()







    @require_multi_gpu
    def 			UpperCamelCase_      (			self       ):
      print(F'Found {torch.cuda.device_count()} devices.'       )
      lowercase							    =       ['torchrun', F'--nproc_per_node={torch.cuda.device_count()}', self.test_file_path]
      with patch_environment(omp_num_threads=1       ):
        execute_subprocess_async(_lowerCamelCase							,		env=os.environ.copy()       )










"""simple docstring"""



import os
from argparse import ArgumentParser
from typing import List

import torch.utils.data

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node


_UpperCamelCase		:  List[Any]						=		4
_UpperCamelCase		:  Optional[Any]						=		3

class        a     (   a_	):
    pass


def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  List[str]    ):





  '''simple docstring'''





  for shard in shards:
    for i in range(__snake_case    ):
      yield {"i": i, "shard": shard}
def 						_SCREAMING_SNAKE_CASE (    ):





  '''simple docstring'''





  lowercase							    =       int(os.environ['RANK']    )
  lowercase							    =       int(os.environ['WORLD_SIZE']    )

  lowercase							    =       ArgumentParser()
  parser.add_argument('--streaming'		,					type=__snake_case    )
  parser.add_argument('--local_rank'		,					type=__snake_case    )
  parser.add_argument('--num_workers'		,					type=__snake_case		,					default=0    )
  lowercase							    =       parser.parse_args()
  lowercase							    =       args.streaming
  lowercase							    =       args.num_workers

  lowercase							    =       {'shards': [f'shard_{shard_idx}' for shard_idx in range(__snake_case    )]}
  lowercase							    =       IterableDataset.from_generator(__snake_case		,					gen_kwargs=__snake_case    )
  if not streaming:
    lowercase							    =       Dataset.from_list(list(__snake_case    )    )

  lowercase							    =       split_dataset_by_node(__snake_case		,					rank=__snake_case		,					world_size=__snake_case    )
  lowercase							    =       torch.utils.data.DataLoader(__snake_case		,					num_workers=__snake_case    )

  lowercase							    =       NUM_SHARDS * NUM_ITEMS_PER_SHARD
  lowercase							    =       full_size // world_size
  expected_local_size += int(rank < (full_size % world_size)    )

  lowercase							    =       sum(1 for _ in dataloader    )
  if local_size != expected_local_size:
    raise FailedTestError(f'local_size {local_size} != expected_local_size {expected_local_size}'    )


if __name__ == "__main__":
    main()


