


"""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_					(   )				->     Tuple:
			_UpperCamelCase				:int				    =  Dataset.from_dict({'''i''': range(17		)}		)
			_UpperCamelCase				:Optional[Any]				    =  len(snake_case__		)
			_UpperCamelCase				:Tuple				    =  3
			_UpperCamelCase				:str				    =  [
			    split_dataset_by_node(snake_case__					,				rank=snake_case__					,				world_size=snake_case__		) for rank in range(snake_case__		)
			]
			assert sum(len(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_					(   )				->     Dict:







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

			_UpperCamelCase				:Dict				    =  3
			_UpperCamelCase				:List[Any]				    =  IterableDataset.from_generator(snake_case__		)
			_UpperCamelCase				:Optional[int]				    =  len(list(snake_case__		)		)
			_UpperCamelCase				:Union[str, Any]				    =  [
			    split_dataset_by_node(snake_case__					,				rank=snake_case__					,				world_size=snake_case__		) for rank in range(snake_case__		)
			]
			assert sum(len(list(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_					(   snake_case__		)				->     str:







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

			_UpperCamelCase				:Union[str, Any]				    =  3
			_UpperCamelCase				:Optional[int]				    =  shards_per_node * world_size
			_UpperCamelCase				:Optional[int]				    =  {'''shards''': [f"shard_{shard_idx}.txt" for shard_idx in range(snake_case__		)]}
			_UpperCamelCase				:str				    =  IterableDataset.from_generator(snake_case__					,				gen_kwargs=snake_case__		)
			_UpperCamelCase				:List[str]				    =  len(list(snake_case__		)		)
			assert full_ds.n_shards == world_size * shards_per_node
			_UpperCamelCase				:List[str]				    =  [
			    split_dataset_by_node(snake_case__					,				rank=snake_case__					,				world_size=snake_case__		) for rank in range(snake_case__		)
			]
			assert [ds.n_shards for ds in datasets_per_rank] == [shards_per_node] * world_size
			assert sum(len(list(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_					(   )				->     Tuple:







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

			_UpperCamelCase				:Dict				    =  2
			_UpperCamelCase				:Optional[int]				    =  IterableDataset.from_generator(snake_case__		)
			_UpperCamelCase				:int				    =  len(list(snake_case__		)		)

			_UpperCamelCase				:List[Any]				    =  split_dataset_by_node(snake_case__					,				rank=0					,				world_size=snake_case__		).shuffle(seed=42		)
			assert len(list(snake_case__		)		) == 1 + full_size // world_size
			with pytest.raises(snake_case__		):
						split_dataset_by_node(snake_case__					,				rank=0					,				world_size=snake_case__		).shuffle()

			_UpperCamelCase				:List[str]				    =  split_dataset_by_node(full_ds.shuffle(seed=42		)					,				rank=0					,				world_size=snake_case__		)
			assert len(list(snake_case__		)		) == 1 + full_size // world_size
			with pytest.raises(snake_case__		):
						split_dataset_by_node(full_ds.shuffle()					,				rank=0					,				world_size=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_					(   snake_case__		)				->     str:
			_UpperCamelCase				:List[Any]				    =  2
			_UpperCamelCase				:Dict				    =  get_torch_dist_unique_port()
			_UpperCamelCase				:Dict				    =  Path(snake_case__		).resolve().parent / '''distributed_scripts''' / '''launch_torch_distributed.py'''
			_UpperCamelCase				:str				    =  f"\n        -m torch.distributed.launch\n        --nproc_per_node={nproc_per_node}\n        --master_port={master_port}\n        {test_script}\n    ".split()
			_UpperCamelCase				:Dict				    =  f"\n        --streaming={streaming}\n    ".split()
			_UpperCamelCase				:int				    =  [sys.executable] + distributed_args + args
			execute_subprocess_async(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_					(   snake_case__					,				snake_case__		)				->     str:
			_UpperCamelCase				:Tuple				    =  True
			_UpperCamelCase				:Optional[Any]				    =  get_torch_dist_unique_port()
			_UpperCamelCase				:List[str]				    =  Path(snake_case__		).resolve().parent / '''distributed_scripts''' / '''launch_torch_distributed.py'''
			_UpperCamelCase				:Dict				    =  f"\n        -m torch.distributed.launch\n        --nproc_per_node={nproc_per_node}\n        --master_port={master_port}\n        {test_script}\n    ".split()
			_UpperCamelCase				:List[Any]				    =  f"\n        --streaming={streaming}\n        --num_workers={num_workers}\n    ".split()
			_UpperCamelCase				:Dict				    =  [sys.executable] + distributed_args + args
			execute_subprocess_async(snake_case__					,				env=os.environ.copy()		)





"""simple docstring"""






from __future__ import annotations

from enum import Enum






class     A(     lowerCamelCase__		):

		"""simple docstring"""





		A         =       24
		A         =       21
		A         =       18
		A         =       15
		A         =       12
		A         =       9
		A         =       6
		A         =       3
		A         =       2
		A         =       1
		A         =       -1
		A         =       -2
		A         =       -3
		A         =       -6
		A         =       -9
		A         =       -12
		A         =       -15
		A         =       -18
		A         =       -21
		A         =       -24






class     A(     lowerCamelCase__		):

		"""simple docstring"""





		A         =       8
		A         =       7
		A         =       6
		A         =       5
		A         =       4
		A         =       3
		A         =       2
		A         =       1





def 							A_					(   snake_case__					,				snake_case__					,				snake_case__					,				)				->     float:
			if isinstance(snake_case__					,				snake_case__		):
						_UpperCamelCase				:Tuple				    =  SIUnit[known_prefix.lower()]
			if isinstance(snake_case__					,				snake_case__		):
						_UpperCamelCase				:Optional[int]				    =  SIUnit[unknown_prefix.lower()]
			_UpperCamelCase				:float				    =  known_amount * (
			    10 ** (known_prefix.value - unknown_prefix.value)
			)
			return unknown_amount





def 							A_					(   snake_case__					,				snake_case__					,				snake_case__					,				)				->     float:
			if isinstance(snake_case__					,				snake_case__		):
						_UpperCamelCase				:Any				    =  BinaryUnit[known_prefix.lower()]
			if isinstance(snake_case__					,				snake_case__		):
						_UpperCamelCase				:Dict				    =  BinaryUnit[unknown_prefix.lower()]
			_UpperCamelCase				:float				    =  known_amount * (
			    2 ** ((known_prefix.value - unknown_prefix.value) * 10)
			)
			return unknown_amount


if __name__ == "__main__":
					import doctest

					doctest.testmod()

