






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 :   List[Any]									=len(a_   )
					__UpperCamelCase :   Dict									=3
					__UpperCamelCase :   Optional[int]									=[
					    split_dataset_by_node(a_				,rank=a_				,world_size=a_   ) for rank in range(a_   )
					]
					assert sum(len(a_   ) 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 :   Optional[int]									=3
					__UpperCamelCase :   Optional[Any]									=IterableDataset.from_generator(a_   )
					__UpperCamelCase :   int									=len(list(a_   )   )
					__UpperCamelCase :   int									=[
					    split_dataset_by_node(a_				,rank=a_				,world_size=a_   ) for rank in range(a_   )
					]
					assert sum(len(list(a_   )   ) 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	(			a_   )	->		Optional[int]:
					def gen(a_   ):
										for shard in shards:
															yield from ({"i": i, "shard": shard} for i in range(17   ))

					__UpperCamelCase :   List[Any]									=3
					__UpperCamelCase :   Dict									=shards_per_node * world_size
					__UpperCamelCase :   Tuple									={'shards': [F'shard_{shard_idx}.txt' for shard_idx in range(a_   )]}
					__UpperCamelCase :   int									=IterableDataset.from_generator(a_				,gen_kwargs=a_   )
					__UpperCamelCase :   Optional[Any]									=len(list(a_   )   )
					assert full_ds.n_shards == world_size * shards_per_node
					__UpperCamelCase :   Dict									=[
					    split_dataset_by_node(a_				,rank=a_				,world_size=a_   ) for rank in range(a_   )
					]
					assert [ds.n_shards for ds in datasets_per_rank] == [shards_per_node] * world_size
					assert sum(len(list(a_   )   ) 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	(			)	->		str:
					def gen():
										return ({"i": i} for i in range(17   ))

					__UpperCamelCase :   int									=2
					__UpperCamelCase :   Optional[Any]									=IterableDataset.from_generator(a_   )
					__UpperCamelCase :   Optional[Any]									=len(list(a_   )   )

					__UpperCamelCase :   Union[str, Any]									=split_dataset_by_node(a_				,rank=0				,world_size=a_   ).shuffle(seed=42   )
					assert len(list(a_   )   ) == 1 + full_size // world_size
					with pytest.raises(a_   ):
										split_dataset_by_node(a_				,rank=0				,world_size=a_   ).shuffle()

					__UpperCamelCase :   List[str]									=split_dataset_by_node(full_ds.shuffle(seed=42   )				,rank=0				,world_size=a_   )
					assert len(list(a_   )   ) == 1 + full_size // world_size
					with pytest.raises(a_   ):
										split_dataset_by_node(full_ds.shuffle()				,rank=0				,world_size=a_   )






@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	(			a_   )	->		Dict:
					__UpperCamelCase :   str									=2
					__UpperCamelCase :   str									=get_torch_dist_unique_port()
					__UpperCamelCase :   Dict									=Path(a_   ).resolve().parent / 'distributed_scripts' / 'launch_torch_distributed.py'
					__UpperCamelCase :   Tuple									=F'\n        -m torch.distributed.launch\n        --nproc_per_node={nproc_per_node}\n        --master_port={master_port}\n        {test_script}\n    '.split()
					__UpperCamelCase :   Optional[int]									=F'\n        --streaming={streaming}\n    '.split()
					__UpperCamelCase :   int									=[sys.executable] + distributed_args + args
					execute_subprocess_async(a_				,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	(			a_				,a_   )	->		int:
					__UpperCamelCase :   int									=True
					__UpperCamelCase :   Any									=get_torch_dist_unique_port()
					__UpperCamelCase :   str									=Path(a_   ).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 :   Any									=F'\n        --streaming={streaming}\n        --num_workers={num_workers}\n    '.split()
					__UpperCamelCase :   Tuple									=[sys.executable] + distributed_args + args
					execute_subprocess_async(a_				,env=os.environ.copy()   )









from __future__ import annotations




class 							__A	:





				"""simple docstring"""







				def __init__(					self				,    lowerCamelCase__ = 0       ):




									"""simple docstring"""

									__UpperCamelCase :   str									=value
									__UpperCamelCase :   Node | None									=None
									__UpperCamelCase :   Node | None									=None






def 				A	(			a_				,a_   )	->		Node | None:
					if treea is None:
										return treea
					if treea is None:
										return treea

					__UpperCamelCase :   Dict									=treea.value + treea.value
					__UpperCamelCase :   Any									=merge_two_binary_trees(treea.left				,treea.left   )
					__UpperCamelCase :   Union[str, Any]									=merge_two_binary_trees(treea.right				,treea.right   )
					return treea






def 				A	(			a_   )	->		None:
					if root:
										print(root.value   )
										print_preorder(root.left   )
										print_preorder(root.right   )


if __name__ == "__main__":
							A_			:List[str]	    =		Node(1)
							A_			:Dict	    =		Node(2)
							A_			:str	    =		Node(3)
							A_			:Union[str, Any]	    =		Node(4)

							A_			:Any	    =		Node(2)
							A_			:Tuple	    =		Node(4)
							A_			:Optional[Any]	    =		Node(6)
							A_			:Optional[Any]	    =		Node(9)
							A_			:str	    =		Node(5)

							print('''Tree1 is: ''')
							print_preorder(treea)
							print('''Tree2 is: ''')
							print_preorder(treea)
							A_			:Optional[Any]	    =		merge_two_binary_trees(treea, treea)
							print('''Merged Tree is: ''')
							print_preorder(merged_tree)

