
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"""




  _lowerCAmelCase						=		Dataset.from_dict({'i': range(17    )}    )
  _lowerCAmelCase						=		len(__SCREAMING_SNAKE_CASE    )
  _lowerCAmelCase						=		3
  _lowerCAmelCase						=		[
      split_dataset_by_node(__SCREAMING_SNAKE_CASE		,				rank=__SCREAMING_SNAKE_CASE		,				world_size=__SCREAMING_SNAKE_CASE    ) for rank in range(__SCREAMING_SNAKE_CASE    )
  ]
  assert sum(len(__SCREAMING_SNAKE_CASE    ) 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    ))

  _lowerCAmelCase						=		3
  _lowerCAmelCase						=		IterableDataset.from_generator(__SCREAMING_SNAKE_CASE    )
  _lowerCAmelCase						=		len(list(__SCREAMING_SNAKE_CASE    )    )
  _lowerCAmelCase						=		[
      split_dataset_by_node(__SCREAMING_SNAKE_CASE		,				rank=__SCREAMING_SNAKE_CASE		,				world_size=__SCREAMING_SNAKE_CASE    ) for rank in range(__SCREAMING_SNAKE_CASE    )
  ]
  assert sum(len(list(__SCREAMING_SNAKE_CASE    )    ) 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  (					__SCREAMING_SNAKE_CASE					:  List[Any]    ):






  """simple docstring"""




  def gen(__SCREAMING_SNAKE_CASE					:  List[Any]    ):
    for shard in shards:
      yield from ({"i": i, "shard": shard} for i in range(17    ))

  _lowerCAmelCase						=		3
  _lowerCAmelCase						=		shards_per_node * world_size
  _lowerCAmelCase						=		{'shards': [f'''shard_{shard_idx}.txt''' for shard_idx in range(__SCREAMING_SNAKE_CASE    )]}
  _lowerCAmelCase						=		IterableDataset.from_generator(__SCREAMING_SNAKE_CASE		,				gen_kwargs=__SCREAMING_SNAKE_CASE    )
  _lowerCAmelCase						=		len(list(__SCREAMING_SNAKE_CASE    )    )
  assert full_ds.n_shards == world_size * shards_per_node
  _lowerCAmelCase						=		[
      split_dataset_by_node(__SCREAMING_SNAKE_CASE		,				rank=__SCREAMING_SNAKE_CASE		,				world_size=__SCREAMING_SNAKE_CASE    ) for rank in range(__SCREAMING_SNAKE_CASE    )
  ]
  assert [ds.n_shards for ds in datasets_per_rank] == [shards_per_node] * world_size
  assert sum(len(list(__SCREAMING_SNAKE_CASE    )    ) 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    ))

  _lowerCAmelCase						=		2
  _lowerCAmelCase						=		IterableDataset.from_generator(__SCREAMING_SNAKE_CASE    )
  _lowerCAmelCase						=		len(list(__SCREAMING_SNAKE_CASE    )    )

  _lowerCAmelCase						=		split_dataset_by_node(__SCREAMING_SNAKE_CASE		,				rank=0		,				world_size=__SCREAMING_SNAKE_CASE    ).shuffle(seed=42    )
  assert len(list(__SCREAMING_SNAKE_CASE    )    ) == 1 + full_size // world_size
  with pytest.raises(__SCREAMING_SNAKE_CASE    ):
    split_dataset_by_node(__SCREAMING_SNAKE_CASE		,				rank=0		,				world_size=__SCREAMING_SNAKE_CASE    ).shuffle()

  _lowerCAmelCase						=		split_dataset_by_node(full_ds.shuffle(seed=42    )		,				rank=0		,				world_size=__SCREAMING_SNAKE_CASE    )
  assert len(list(__SCREAMING_SNAKE_CASE    )    ) == 1 + full_size // world_size
  with pytest.raises(__SCREAMING_SNAKE_CASE    ):
    split_dataset_by_node(full_ds.shuffle()		,				rank=0		,				world_size=__SCREAMING_SNAKE_CASE    )



@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  (					__SCREAMING_SNAKE_CASE					:  Union[str, Any]    ):






  """simple docstring"""




  _lowerCAmelCase						=		2
  _lowerCAmelCase						=		get_torch_dist_unique_port()
  _lowerCAmelCase						=		Path(__SCREAMING_SNAKE_CASE    ).resolve().parent / 'distributed_scripts' / 'launch_torch_distributed.py'
  _lowerCAmelCase						=		f'''
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    '''.split()
  _lowerCAmelCase						=		f'''
        --streaming={streaming}
    '''.split()
  _lowerCAmelCase						=		[sys.executable] + distributed_args + args
  execute_subprocess_async(__SCREAMING_SNAKE_CASE		,				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  (					__SCREAMING_SNAKE_CASE					:  Tuple		,				__SCREAMING_SNAKE_CASE					:  List[str]    ):






  """simple docstring"""




  _lowerCAmelCase						=		True
  _lowerCAmelCase						=		get_torch_dist_unique_port()
  _lowerCAmelCase						=		Path(__SCREAMING_SNAKE_CASE    ).resolve().parent / 'distributed_scripts' / 'launch_torch_distributed.py'
  _lowerCAmelCase						=		f'''
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    '''.split()
  _lowerCAmelCase						=		f'''
        --streaming={streaming}
        --num_workers={num_workers}
    '''.split()
  _lowerCAmelCase						=		[sys.executable] + distributed_args + args
  execute_subprocess_async(__SCREAMING_SNAKE_CASE		,				env=os.environ.copy()    )


import abc
import copy
import dataclasses
from dataclasses import dataclass
from typing import ClassVar, Dict, Type, TypeVar

from ..features import Features


_UpperCamelCase:					List[Any]     =TypeVar('T', bound='TaskTemplate')





@dataclass(frozen=SCREAMING_SNAKE_CASE   )
class 						__lowercase(     abc.ABC   ):


       """simple docstring"""







       UpperCamelCase_				 =							42
       UpperCamelCase_				 =							42
       UpperCamelCase_				 =							42



       def SCREAMING_SNAKE_CASE_				(     self					:			T  ,      _lowerCAmelCase					:			Features       )					->   T:
         return copy.deepcopy(self       )



       @property
       def SCREAMING_SNAKE_CASE_				(     self					:			str       )					->   Features:
         return Features(**self.input_schema  ,      **self.label_schema       )



       @property
       @abc.abstractmethod
       def SCREAMING_SNAKE_CASE_				(     self					:			Dict       )					->   Dict[str, str]:
         raise NotImplementedError






       @classmethod
       def SCREAMING_SNAKE_CASE_				(     cls					:			Type[T]  ,      _lowerCAmelCase					:			dict       )					->   T:
         _lowerCAmelCase						=		{f.name for f in dataclasses.fields(cls       )}
         return cls(**{k: v for k, v in template_dict.items() if k in field_names}       )
