from functools import partial
from typing import List, Tuple

import pytest
import torch
import torch.multiprocessing as mp

try:
    from diffusers import UNet2DModel
    MODELS = [UNet2DModel]
    HAS_REPO = True
except:
    MODELS = []
    HAS_REPO = False

from test_diffuser_utils import run_test

from colossalai.autochunk.autochunk_codegen import AUTOCHUNK_AVAILABLE

BATCH_SIZE = 2
SEQ_LENGTH = 5
HEIGHT = 224
WIDTH = 224
IN_CHANNELS = 3
LATENTS_SHAPE = (BATCH_SIZE, IN_CHANNELS, HEIGHT // 7, WIDTH // 7)


def get_data(shape: tuple) -> Tuple[List, List]:
    sample = torch.randn(shape)
    meta_args = [
        ("sample", sample),
    ]
    concrete_args = [("timestep", 50)]
    return meta_args, concrete_args


@pytest.mark.skipif(
    True,
    reason="not implemented",
)
@pytest.mark.skipif(
    not (AUTOCHUNK_AVAILABLE and HAS_REPO),
    reason="torch version is lower than 1.12.0",
)
@pytest.mark.parametrize("model", MODELS)
@pytest.mark.parametrize("shape", [LATENTS_SHAPE])
@pytest.mark.parametrize("max_memory", [64])
def test_evoformer_block(model, shape, max_memory):
    run_func = partial(
        run_test,
        max_memory=max_memory,
        model=model,
        data=get_data(shape),
        print_code=False,
        print_mem=False,
        print_progress=False,
    )
    mp.spawn(run_func, nprocs=1)


if __name__ == "__main__":
    run_test(
        rank=0,
        data=get_data(LATENTS_SHAPE),
        max_memory=64,
        model=UNet2DModel,
        print_code=False,
        print_mem=False,
        print_progress=False,
    )
