






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   lowercase__   (  ):


					'''simple docstring'''


					UpperCAmelCase_					:   Dict   =		Dataset.from_dict({'i': range(17						)}						)
					UpperCAmelCase_					:   Dict   =		len(__snake_case						)
					UpperCAmelCase_					:   Optional[Any]   =		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   lowercase__   (  ):


					'''simple docstring'''


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

					UpperCAmelCase_					:   Optional[Any]   =		3
					UpperCAmelCase_					:   int   =		IterableDataset.from_generator(__snake_case						)
					UpperCAmelCase_					:   Dict   =		len(list(__snake_case						)						)
					UpperCAmelCase_					:   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   lowercase__   (  __snake_case					:							Union[str, Any]						):


					'''simple docstring'''


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

					UpperCAmelCase_					:   Dict   =		3
					UpperCAmelCase_					:   List[str]   =		shards_per_node * world_size
					UpperCAmelCase_					:   Optional[Any]   =		{'shards': [F"shard_{shard_idx}.txt" for shard_idx in range(__snake_case						)]}
					UpperCAmelCase_					:   Tuple   =		IterableDataset.from_generator(__snake_case  , gen_kwargs=__snake_case						)
					UpperCAmelCase_					:   str   =		len(list(__snake_case						)						)
					assert full_ds.n_shards == world_size * shards_per_node
					UpperCAmelCase_					:   Dict   =		[
					    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   lowercase__   (  ):


					'''simple docstring'''


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

					UpperCAmelCase_					:   List[str]   =		2
					UpperCAmelCase_					:   int   =		IterableDataset.from_generator(__snake_case						)
					UpperCAmelCase_					:   List[str]   =		len(list(__snake_case						)						)

					UpperCAmelCase_					:   Dict   =		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_					:   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   lowercase__   (  __snake_case					:							Optional[int]						):


					'''simple docstring'''


					UpperCAmelCase_					:   List[str]   =		2
					UpperCAmelCase_					:   List[Any]   =		get_torch_dist_unique_port()
					UpperCAmelCase_					:   Any   =		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_					:   Optional[Any]   =		F"\n        --streaming={streaming}\n    ".split()
					UpperCAmelCase_					:   str   =		[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   lowercase__   (  __snake_case					:							List[Any]  , __snake_case					:							Tuple						):


					'''simple docstring'''


					UpperCAmelCase_					:   Optional[Any]   =		True
					UpperCAmelCase_					:   int   =		get_torch_dist_unique_port()
					UpperCAmelCase_					:   Dict   =		Path(__snake_case						).resolve().parent / 'distributed_scripts' / 'launch_torch_distributed.py'
					UpperCAmelCase_					:   int   =		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        --num_workers={num_workers}\n    ".split()
					UpperCAmelCase_					:   Optional[Any]   =		[sys.executable] + distributed_args + args
					execute_subprocess_async(__snake_case  , env=os.environ.copy()						)














from __future__ import annotations



class 						lowerCamelCase   :







		'''simple docstring'''



		def __init__(							self       ,	_UpperCamelCase = 0     )			->							None:
							UpperCAmelCase_					:   int   =		value
							UpperCAmelCase_					:   Node | None   =		None
							UpperCAmelCase_					:   Node | None   =		None





def   lowercase__   (  __snake_case					:							Node | None  , __snake_case					:							Node | None						):


					'''simple docstring'''


					if treea is None:
										return treea
					if treea is None:
										return treea

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




def   lowercase__   (  __snake_case					:							Node | None						):


					'''simple docstring'''


					if root:
										print(root.value						)
										print_preorder(root.left						)
										print_preorder(root.right						)


if __name__ == "__main__":
		__UpperCAmelCase										=		Node(1)
		__UpperCAmelCase										=		Node(2)
		__UpperCAmelCase										=		Node(3)
		__UpperCAmelCase										=		Node(4)

		__UpperCAmelCase										=		Node(2)
		__UpperCAmelCase										=		Node(4)
		__UpperCAmelCase										=		Node(6)
		__UpperCAmelCase										=		Node(9)
		__UpperCAmelCase										=		Node(5)

		print('Tree1 is: ')
		print_preorder(treea)
		print('Tree2 is: ')
		print_preorder(treea)
		__UpperCAmelCase										=		merge_two_binary_trees(treea, treea)
		print('Merged Tree is: ')
		print_preorder(merged_tree)






