


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 	lowerCamelCase_       (			):




		"""simple docstring"""






		lowerCAmelCase__  :     Optional[int]       	=   Dataset.from_dict({'''i''': range(17			)}			)
		lowerCAmelCase__  :     Optional[Any]       	=   len(__SCREAMING_SNAKE_CASE			)
		lowerCAmelCase__  :     Tuple       	=   3
		lowerCAmelCase__  :     Optional[int]       	=   [
		    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 	lowerCamelCase_       (			):




		"""simple docstring"""






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

		lowerCAmelCase__  :     Dict       	=   3
		lowerCAmelCase__  :     Union[str, Any]       	=   IterableDataset.from_generator(__SCREAMING_SNAKE_CASE			)
		lowerCAmelCase__  :     int       	=   len(list(__SCREAMING_SNAKE_CASE			)			)
		lowerCAmelCase__  :     int       	=   [
		    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 	lowerCamelCase_       (			_a			):




		"""simple docstring"""






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

		lowerCAmelCase__  :     Tuple       	=   3
		lowerCAmelCase__  :     Optional[Any]       	=   shards_per_node * world_size
		lowerCAmelCase__  :     int       	=   {'''shards''': [f'shard_{shard_idx}.txt' for shard_idx in range(__SCREAMING_SNAKE_CASE			)]}
		lowerCAmelCase__  :     List[str]       	=   IterableDataset.from_generator(__SCREAMING_SNAKE_CASE      ,  gen_kwargs=__SCREAMING_SNAKE_CASE			)
		lowerCAmelCase__  :     List[str]       	=   len(list(__SCREAMING_SNAKE_CASE			)			)
		assert full_ds.n_shards == world_size * shards_per_node
		lowerCAmelCase__  :     Optional[Any]       	=   [
		    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 	lowerCamelCase_       (			):




		"""simple docstring"""






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

		lowerCAmelCase__  :     int       	=   2
		lowerCAmelCase__  :     int       	=   IterableDataset.from_generator(__SCREAMING_SNAKE_CASE			)
		lowerCAmelCase__  :     Tuple       	=   len(list(__SCREAMING_SNAKE_CASE			)			)

		lowerCAmelCase__  :     Union[str, Any]       	=   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__  :     Union[str, Any]       	=   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 	lowerCamelCase_       (			_a			):




		"""simple docstring"""






		lowerCAmelCase__  :     Any       	=   2
		lowerCAmelCase__  :     Union[str, Any]       	=   get_torch_dist_unique_port()
		lowerCAmelCase__  :     List[Any]       	=   Path(__SCREAMING_SNAKE_CASE			).resolve().parent / '''distributed_scripts''' / '''launch_torch_distributed.py'''
		lowerCAmelCase__  :     List[str]       	=   f'\n        -m torch.distributed.launch\n        --nproc_per_node={nproc_per_node}\n        --master_port={master_port}\n        {test_script}\n    '.split()
		lowerCAmelCase__  :     int       	=   f'\n        --streaming={streaming}\n    '.split()
		lowerCAmelCase__  :     Optional[int]       	=   [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 	lowerCamelCase_       (			_a      ,  _a			):




		"""simple docstring"""






		lowerCAmelCase__  :     int       	=   True
		lowerCAmelCase__  :     str       	=   get_torch_dist_unique_port()
		lowerCAmelCase__  :     Dict       	=   Path(__SCREAMING_SNAKE_CASE			).resolve().parent / '''distributed_scripts''' / '''launch_torch_distributed.py'''
		lowerCAmelCase__  :     List[str]       	=   f'\n        -m torch.distributed.launch\n        --nproc_per_node={nproc_per_node}\n        --master_port={master_port}\n        {test_script}\n    '.split()
		lowerCAmelCase__  :     Any       	=   f'\n        --streaming={streaming}\n        --num_workers={num_workers}\n    '.split()
		lowerCAmelCase__  :     Dict       	=   [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}       )
