

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'''





		__UpperCAmelCase   :					Any													=     Dataset.from_dict({'''i''': range(17						)}						)
		__UpperCAmelCase   :					List[Any]													=     len(_SCREAMING_SNAKE_CASE						)
		__UpperCAmelCase   :					List[str]													=     3
		__UpperCAmelCase   :					List[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 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						))

		__UpperCAmelCase   :					Any													=     3
		__UpperCAmelCase   :					Optional[int]													=     IterableDataset.from_generator(_SCREAMING_SNAKE_CASE						)
		__UpperCAmelCase   :					Optional[int]													=     len(list(_SCREAMING_SNAKE_CASE						)						)
		__UpperCAmelCase   :					List[str]													=     [
		    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						(				_UpperCAmelCase							: Optional[Any]						):







		'''simple docstring'''





		def gen(_UpperCAmelCase							: Optional[int]						):
				for shard in shards:
						yield from ({"i": i, "shard": shard} for i in range(17						))

		__UpperCAmelCase   :					Optional[int]													=     3
		__UpperCAmelCase   :					Dict													=     shards_per_node * world_size
		__UpperCAmelCase   :					Dict													=     {"shards": [f'shard_{shard_idx}.txt' for shard_idx in range(_SCREAMING_SNAKE_CASE						)]}
		__UpperCAmelCase   :					Dict													=     IterableDataset.from_generator(_SCREAMING_SNAKE_CASE			,       gen_kwargs=_SCREAMING_SNAKE_CASE						)
		__UpperCAmelCase   :					Optional[Any]													=     len(list(_SCREAMING_SNAKE_CASE						)						)
		assert full_ds.n_shards == world_size * shards_per_node
		__UpperCAmelCase   :					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 [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						))

		__UpperCAmelCase   :					Union[str, Any]													=     2
		__UpperCAmelCase   :					Optional[int]													=     IterableDataset.from_generator(_SCREAMING_SNAKE_CASE						)
		__UpperCAmelCase   :					Union[str, Any]													=     len(list(_SCREAMING_SNAKE_CASE						)						)

		__UpperCAmelCase   :					Optional[int]													=     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()

		__UpperCAmelCase   :					Dict													=     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						(				_UpperCAmelCase							: List[str]						):







		'''simple docstring'''





		__UpperCAmelCase   :					str													=     2
		__UpperCAmelCase   :					List[str]													=     get_torch_dist_unique_port()
		__UpperCAmelCase   :					Tuple													=     Path(_SCREAMING_SNAKE_CASE						).resolve().parent / "distributed_scripts" / "launch_torch_distributed.py"
		__UpperCAmelCase   :					Optional[Any]													=     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[str]													=     f'\n        --streaming={streaming}\n    '.split()
		__UpperCAmelCase   :					Any													=     [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						(				_UpperCAmelCase							: int			,       _UpperCAmelCase							: Optional[Any]						):







		'''simple docstring'''





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










from __future__ import annotations

import random
from collections.abc import Iterable
from typing import Any, Generic, TypeVar

__A             =TypeVar("T", bound=bool)

class 		UpperCAmelCase__      (      Generic[T]       ):




			'''simple docstring'''






			def __init__(       self							:			List[Any]	,	a_							:			T			):
					'''simple docstring'''





					__UpperCAmelCase   :					T													=     value
					__UpperCAmelCase   :					RandomizedHeapNode[T] | None													=     None
					__UpperCAmelCase   :					RandomizedHeapNode[T] | None													=     None




			@property
			def 			snake_case__					(       self							:			Any			):
					'''simple docstring'''





					return self._value




			@staticmethod
			def 			snake_case__					(       a_							:			RandomizedHeapNode[T] | None	,	a_							:			RandomizedHeapNode[T] | None			):
					'''simple docstring'''





					if not roota:
							return roota

					if not roota:
							return roota

					if roota.value > roota.value:
							__UpperCAmelCase							, __UpperCAmelCase   :					int													=     roota, roota

					if random.choice([True, False]			):
							__UpperCAmelCase							, __UpperCAmelCase   :					Union[str, Any]													=     roota.right, roota.left

					__UpperCAmelCase   :					Optional[Any]													=     RandomizedHeapNode.merge(roota.left	,	a_			)

					return roota

class 		UpperCAmelCase__      (      Generic[T]       ):




			'''simple docstring'''






			def __init__(       self							:			Tuple	,	a_							:			Iterable[T] | None = ()			):
					'''simple docstring'''





					__UpperCAmelCase   :					RandomizedHeapNode[T] | None													=     None

					if data:
							for item in data:
									self.insert(a_			)




			def 			snake_case__					(       self							:			List[str]	,	a_							:			T			):
					'''simple docstring'''





					__UpperCAmelCase   :					Optional[Any]													=     RandomizedHeapNode.merge(self._root	,	RandomizedHeapNode(a_			)			)




			def 			snake_case__					(       self							:			Optional[int]			):
					'''simple docstring'''





					__UpperCAmelCase   :					Optional[Any]													=     self.top()

					if self._root is None:
							return None

					__UpperCAmelCase   :					Tuple													=     RandomizedHeapNode.merge(self._root.left	,	self._root.right			)

					return result




			def 			snake_case__					(       self							:			Any			):
					'''simple docstring'''





					if not self._root:
							raise IndexError('''Can\'t get top element for the empty heap.'''			)
					return self._root.value




			def 			snake_case__					(       self							:			Optional[Any]			):
					'''simple docstring'''





					__UpperCAmelCase   :					str													=     None




			def 			snake_case__					(       self							:			Tuple			):
					'''simple docstring'''





					__UpperCAmelCase   :					List[Any]													=     []
					while self:
							result.append(self.pop()			)

					return result




			def __bool__(       self							:			List[Any]			):
					'''simple docstring'''





					return self._root is not None


if __name__ == "__main__":
				import doctest

				doctest.testmod()





