


"""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 __lowercase  (   ):
				SCREAMING_SNAKE_CASE__  				=					Dataset.from_dict({"i": range(17	)}	)
				SCREAMING_SNAKE_CASE__  				=					len(lowerCamelCase_	)
				SCREAMING_SNAKE_CASE__  				=					3
				SCREAMING_SNAKE_CASE__  				=					[
				    split_dataset_by_node(lowerCamelCase_    ,       rank=lowerCamelCase_    ,       world_size=lowerCamelCase_	) for rank in range(lowerCamelCase_	)
				]
				assert sum(len(lowerCamelCase_	) 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  (   ):
				def gen():
								return ({"i": i} for i in range(17	))

				SCREAMING_SNAKE_CASE__  				=					3
				SCREAMING_SNAKE_CASE__  				=					IterableDataset.from_generator(lowerCamelCase_	)
				SCREAMING_SNAKE_CASE__  				=					len(list(lowerCamelCase_	)	)
				SCREAMING_SNAKE_CASE__  				=					[
				    split_dataset_by_node(lowerCamelCase_    ,       rank=lowerCamelCase_    ,       world_size=lowerCamelCase_	) for rank in range(lowerCamelCase_	)
				]
				assert sum(len(list(lowerCamelCase_	)	) 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  (   lowerCamelCase_	:   Optional[int]	):
				def gen(lowerCamelCase_	:   str	):
								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(lowerCamelCase_	)]}
				SCREAMING_SNAKE_CASE__  				=					IterableDataset.from_generator(lowerCamelCase_    ,       gen_kwargs=lowerCamelCase_	)
				SCREAMING_SNAKE_CASE__  				=					len(list(lowerCamelCase_	)	)
				assert full_ds.n_shards == world_size * shards_per_node
				SCREAMING_SNAKE_CASE__  				=					[
				    split_dataset_by_node(lowerCamelCase_    ,       rank=lowerCamelCase_    ,       world_size=lowerCamelCase_	) for rank in range(lowerCamelCase_	)
				]
				assert [ds.n_shards for ds in datasets_per_rank] == [shards_per_node] * world_size
				assert sum(len(list(lowerCamelCase_	)	) 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  (   ):
				def gen():
								return ({"i": i} for i in range(17	))

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

				SCREAMING_SNAKE_CASE__  				=					split_dataset_by_node(lowerCamelCase_    ,       rank=0    ,       world_size=lowerCamelCase_	).shuffle(seed=42	)
				assert len(list(lowerCamelCase_	)	) == 1 + full_size // world_size
				with pytest.raises(lowerCamelCase_	):
								split_dataset_by_node(lowerCamelCase_    ,       rank=0    ,       world_size=lowerCamelCase_	).shuffle()

				SCREAMING_SNAKE_CASE__  				=					split_dataset_by_node(full_ds.shuffle(seed=42	)    ,       rank=0    ,       world_size=lowerCamelCase_	)
				assert len(list(lowerCamelCase_	)	) == 1 + full_size // world_size
				with pytest.raises(lowerCamelCase_	):
								split_dataset_by_node(full_ds.shuffle()    ,       rank=0    ,       world_size=lowerCamelCase_	)


@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  (   lowerCamelCase_	:   Tuple	):
				SCREAMING_SNAKE_CASE__  				=					2
				SCREAMING_SNAKE_CASE__  				=					get_torch_dist_unique_port()
				SCREAMING_SNAKE_CASE__  				=					Path(lowerCamelCase_	).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(lowerCamelCase_    ,       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  (   lowerCamelCase_	:   str    ,       lowerCamelCase_	:   List[str]	):
				SCREAMING_SNAKE_CASE__  				=					True
				SCREAMING_SNAKE_CASE__  				=					get_torch_dist_unique_port()
				SCREAMING_SNAKE_CASE__  				=					Path(lowerCamelCase_	).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(lowerCamelCase_    ,       env=os.environ.copy()	)




"""simple docstring"""






from __future__ import annotations

from collections.abc import Generator


def __lowercase  (   lowerCamelCase_	:   int	):
				if not isinstance(lowerCamelCase_    ,       lowerCamelCase_	) or n < 1:
								raise Exception("Sequence only defined for positive integers"	)

				yield n
				while n != 1:
								if n % 2 == 0:
												n //= 2
								else:
												SCREAMING_SNAKE_CASE__  				=					3 * n + 1
								yield n


def __lowercase  (   ):
				SCREAMING_SNAKE_CASE__  				=					43
				SCREAMING_SNAKE_CASE__  				=					tuple(collatz_sequence(lowerCamelCase_	)	)
				print(lowerCamelCase_	)
				print(F'''Collatz sequence from {n} took {len(lowerCamelCase_	)} steps.'''	)


if __name__ == "__main__":
	main()
