import pytest

from datasets import inspect_metric, list_metrics, load_metric




@pytest.fixture
def    lowerCAmelCase_ (     __A							)     ->				Union[str, Any]:







      '''simple docstring'''


      monkeypatch.setattr("datasets.utils.deprecation_utils._emitted_deprecation_warnings",  set()							)




@pytest.fixture
def    lowerCAmelCase_ (     __A							)     ->				Any:







      '''simple docstring'''


      class      A							:


         def __init__(self     :						Dict       ,  __UpperCAmelCase     :						List[Any]		)						->				Optional[int]:
               """simple docstring"""



               UpperCAmelCase__								=  metric_id





      class      A							:
         __UpperCAmelCase : Union[str, Any]      =			[MetricMock(UpperCAmelCase_     ) for metric_id in ['accuracy', 'mse', 'precision', 'codeparrot/apps_metric']]


         def 			lowercase_      (self     :						Optional[Any]		)						->				Union[str, Any]:
               """simple docstring"""



               return self._metrics




      monkeypatch.setattr("datasets.inspect.huggingface_hub",  HfhMock()							)







@pytest.mark.parametrize(
    "func, args",  [(load_metric, ("metrics/mse",)), (list_metrics, ()), (inspect_metric, ("metrics/mse", "tmp_path"))]							)
def    lowerCAmelCase_ (     __A,  __A,  __A,  __A,  __A							)     ->				Optional[int]:







      '''simple docstring'''


      if "tmp_path" in args:
            UpperCAmelCase__								=  tuple(arg if arg != "tmp_path" else tmp_path for arg in args							)
      with pytest.warns(__A,  match="https://huggingface.co/docs/evaluate"							):
            func(*__A							)











"""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()


