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 test_split_dataset_by_node_map_style():
    full_ds = Dataset.from_dict({"i": range(17)})
    full_size = len(full_ds)
    world_size = 3
    datasets_per_rank = [
        split_dataset_by_node(full_ds, rank=rank, world_size=world_size) for rank in range(world_size)
    ]
    assert sum(len(ds) 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 test_split_dataset_by_node_iterable():
    def gen():
        return ({"i": i} for i in range(17))

    world_size = 3
    full_ds = IterableDataset.from_generator(gen)
    full_size = len(list(full_ds))
    datasets_per_rank = [
        split_dataset_by_node(full_ds, rank=rank, world_size=world_size) for rank in range(world_size)
    ]
    assert sum(len(list(ds)) 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 test_split_dataset_by_node_iterable_sharded(shards_per_node):
    def gen(shards):
        for shard in shards:
            yield from ({"i": i, "shard": shard} for i in range(17))

    world_size = 3
    num_shards = shards_per_node * world_size
    gen_kwargs = {"shards": [f"shard_{shard_idx}.txt" for shard_idx in range(num_shards)]}
    full_ds = IterableDataset.from_generator(gen, gen_kwargs=gen_kwargs)
    full_size = len(list(full_ds))
    assert full_ds.n_shards == world_size * shards_per_node
    datasets_per_rank = [
        split_dataset_by_node(full_ds, rank=rank, world_size=world_size) for rank in range(world_size)
    ]
    assert [ds.n_shards for ds in datasets_per_rank] == [shards_per_node] * world_size
    assert sum(len(list(ds)) 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("streaming", [False, True])
@require_torch
@pytest.mark.skipif(os.name == "nt", reason="execute_subprocess_async doesn't support windows")
@pytest.mark.integration
def test_torch_distributed_launch(streaming):
    nproc_per_node = 2
    master_port = get_torch_dist_unique_port()
    test_script = Path(__file__).resolve().parent / "distributed_scripts" / "test_torch_distributed_launch.py"
    distributed_args = f"""
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    """.split()
    args = f"""
        --streaming={streaming}
    """.split()
    cmd = [sys.executable] + distributed_args + args
    execute_subprocess_async(cmd, 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 test_torch_distributed_launch_streaming_with_num_workers(nproc_per_node, num_workers):
    streaming = True
    master_port = get_torch_dist_unique_port()
    test_script = Path(__file__).resolve().parent / "distributed_scripts" / "test_torch_distributed_launch.py"
    distributed_args = f"""
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    """.split()
    args = f"""
        --streaming={streaming}
        --num_workers={num_workers}
    """.split()
    cmd = [sys.executable] + distributed_args + args
    execute_subprocess_async(cmd, env=os.environ.copy())

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 test_split_dataset_by_node_map_style():
    full_ds = Dataset.from_dict({"i": range(17)})
    full_size = len(full_ds)
    world_size = 3
    datasets_per_rank = [
        split_dataset_by_node(full_ds, rank=rank, world_size=world_size) for rank in range(world_size)
    ]
    assert sum(len(ds) for ds in datasets_per_rank) == full_size
    assert len(set(tuple(x.values()) for ds in datasets_per_rank for x in ds)) == full_size


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

    world_size = 3
    full_ds = IterableDataset.from_generator(gen)
    full_size = len(list(full_ds))
    datasets_per_rank = [
        split_dataset_by_node(full_ds, rank=rank, world_size=world_size) for rank in range(world_size)
    ]
    assert sum(len(list(ds)) for ds in datasets_per_rank) == full_size
    assert len(set(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 test_split_dataset_by_node_iterable_sharded(shards_per_node):
    def gen(shards):
        for shard in shards:
            yield from ({"i": i, "shard": shard} for i in range(17))

    world_size = 3
    num_shards = shards_per_node * world_size
    gen_kwargs = {"shards": [f"shard_{shard_idx}.txt" for shard_idx in range(num_shards)]}
    full_ds = IterableDataset.from_generator(gen, gen_kwargs=gen_kwargs)
    full_size = len(list(full_ds))
    assert full_ds.n_shards == world_size * shards_per_node
    datasets_per_rank = [
        split_dataset_by_node(full_ds, rank=rank, world_size=world_size) for rank in range(world_size)
    ]
    assert [ds.n_shards for ds in datasets_per_rank] == [shards_per_node] * world_size
    assert sum(len(list(ds)) for ds in datasets_per_rank) == full_size
    assert len(set(tuple(x.values()) for ds in datasets_per_rank for x in ds)) == full_size


@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 test_torch_distributed_launch(streaming):
    nproc_per_node = 2
    master_port = get_torch_dist_unique_port()
    test_script = Path(__file__).resolve().parent / "distributed_scripts" / "test_torch_distributed_launch.py"
    distributed_args = f"""
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    """.split()
    args = f"""
        --streaming={streaming}
    """.split()
    cmd = [sys.executable] + distributed_args + args
    execute_subprocess_async(cmd, 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 test_torch_distributed_launch_streaming_with_num_workers(nproc_per_node, num_workers):
    streaming = True
    master_port = get_torch_dist_unique_port()
    test_script = Path(__file__).resolve().parent / "distributed_scripts" / "test_torch_distributed_launch.py"
    distributed_args = f"""
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    """.split()
    args = f"""
        --streaming={streaming}
        --num_workers={num_workers}
    """.split()
    cmd = [sys.executable] + distributed_args + args
    execute_subprocess_async(cmd, env=os.environ.copy())
