





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 			SCREAMING_SNAKE_CASE__							(    ):
		SCREAMING_SNAKE_CASE__								= Dataset.from_dict({"""i""": range(17      )}      )
		SCREAMING_SNAKE_CASE__								= len(snake_case__      )
		SCREAMING_SNAKE_CASE__								= 3
		SCREAMING_SNAKE_CASE__								= [
		    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 			SCREAMING_SNAKE_CASE__							(    ):






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

		SCREAMING_SNAKE_CASE__								= 3
		SCREAMING_SNAKE_CASE__								= IterableDataset.from_generator(snake_case__      )
		SCREAMING_SNAKE_CASE__								= len(list(snake_case__      )      )
		SCREAMING_SNAKE_CASE__								= [
		    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 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							List[str]      ):






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

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






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

		SCREAMING_SNAKE_CASE__								= 2
		SCREAMING_SNAKE_CASE__								= IterableDataset.from_generator(snake_case__      )
		SCREAMING_SNAKE_CASE__								= len(list(snake_case__      )      )

		SCREAMING_SNAKE_CASE__								= 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()

		SCREAMING_SNAKE_CASE__								= 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 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							List[Any]      ):
		SCREAMING_SNAKE_CASE__								= 2
		SCREAMING_SNAKE_CASE__								= get_torch_dist_unique_port()
		SCREAMING_SNAKE_CASE__								= Path(snake_case__      ).resolve().parent / """distributed_scripts""" / """launch_torch_distributed.py"""
		SCREAMING_SNAKE_CASE__								= f'''
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    '''.split()
		SCREAMING_SNAKE_CASE__								= f'''
        --streaming={streaming}
    '''.split()
		SCREAMING_SNAKE_CASE__								= [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 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							Optional[Any]   ,				UpperCamelCase__:							Tuple      ):
		SCREAMING_SNAKE_CASE__								= True
		SCREAMING_SNAKE_CASE__								= get_torch_dist_unique_port()
		SCREAMING_SNAKE_CASE__								= Path(snake_case__      ).resolve().parent / """distributed_scripts""" / """launch_torch_distributed.py"""
		SCREAMING_SNAKE_CASE__								= f'''
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    '''.split()
		SCREAMING_SNAKE_CASE__								= f'''
        --streaming={streaming}
        --num_workers={num_workers}
    '''.split()
		SCREAMING_SNAKE_CASE__								= [sys.executable] + distributed_args + args
		execute_subprocess_async(snake_case__   ,				env=os.environ.copy()      )






import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


_lowerCamelCase							=     500000

_lowerCamelCase			,    _lowerCamelCase							=     os.path.split(__file__)
_lowerCamelCase							=     os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))






@get_duration
def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							datasets.Dataset      ):
		SCREAMING_SNAKE_CASE__								= dataset.select(range(0   ,				len(UpperCamelCase__      )   ,				2      )      )






@get_duration
def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							datasets.Dataset      ):
		SCREAMING_SNAKE_CASE__								= dataset.sort("""numbers"""      )






@get_duration
def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							datasets.Dataset      ):
		SCREAMING_SNAKE_CASE__								= dataset.shuffle()






@get_duration
def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							datasets.Dataset      ):
		SCREAMING_SNAKE_CASE__								= dataset.train_test_split(0.1      )






@get_duration
def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							datasets.Dataset   ,				UpperCamelCase__:							Tuple=10      ):
		for shard_id in range(UpperCamelCase__      ):
				SCREAMING_SNAKE_CASE__								= dataset.shard(UpperCamelCase__   ,				UpperCamelCase__      )






def 			SCREAMING_SNAKE_CASE__							(    ):
		SCREAMING_SNAKE_CASE__								= {"""num examples""": SPEED_TEST_N_EXAMPLES}
		SCREAMING_SNAKE_CASE__								= (select, sort, shuffle, train_test_split, shard)
		with tempfile.TemporaryDirectory() as tmp_dir:
				print("""generating dataset"""      )
				SCREAMING_SNAKE_CASE__								= datasets.Features({"""text""": datasets.Value("""string"""      ), """numbers""": datasets.Value("""float32"""      )}      )
				SCREAMING_SNAKE_CASE__								= generate_example_dataset(
				    os.path.join(UpperCamelCase__   ,				"""dataset.arrow"""      )   ,				UpperCamelCase__   ,				num_examples=UpperCamelCase__      )
				print("""Functions"""      )
				for func in functions:
						print(func.__name__      )
						SCREAMING_SNAKE_CASE__								= func(UpperCamelCase__      )

		with open(UpperCamelCase__   ,				"""wb"""      ) as f:
				f.write(json.dumps(UpperCamelCase__      ).encode("""utf-8"""      )      )


if __name__ == "__main__":  # useful to run the profiler
	benchmark_indices_mapping()