import os
from pathlib import Path

import pytest
import torch
import torch.nn as nn
from torch.optim import Adam
from torchvision import transforms
from torchvision.datasets import CIFAR10
from torchvision.models import resnet18

import colossalai
from colossalai.core import global_context as gpc
from colossalai.logging import get_dist_logger
from colossalai.testing import rerun_if_address_is_in_use, spawn
from colossalai.utils import get_dataloader

# Config
BATCH_SIZE = 2
NUM_CLASSES = 10

CONFIG = dict(parallel=dict(pipeline=dict(size=1), tensor=dict(size=1, mode=None)),
              clip_grad_norm=1.0,
              gradient_accumulation=4)


def run_no_pipeline(rank, world_size, port):

    # init dist env
    colossalai.launch(config=CONFIG, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')

    # build model
    model = resnet18(num_classes=10)

    # build dataloaders
    train_dataset = CIFAR10(root=Path(os.environ['DATA']),
                            download=True,
                            transform=transforms.Compose([
                                transforms.ToTensor(),
                                transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))
                            ]))
    train_dataloader = get_dataloader(dataset=train_dataset,
                                      shuffle=True,
                                      batch_size=BATCH_SIZE,
                                      pin_memory=True,
                                      drop_last=True)

    # build optimizer
    optimizer = Adam(model.parameters(), lr=0.001)
    criterion = nn.CrossEntropyLoss()

    engine, train_dataloader, *args = colossalai.initialize(model=model,
                                                            optimizer=optimizer,
                                                            criterion=criterion,
                                                            train_dataloader=train_dataloader)
    logger = get_dist_logger()
    rank = torch.distributed.get_rank()
    param_track = []
    grad_track = []
    next(model.parameters()).retain_grad()

    engine.train()
    step = 0
    for img, label in train_dataloader:
        engine.zero_grad()
        img = img.cuda()
        label = label.cuda()
        output = engine(img)
        loss = engine.criterion(output, label)
        engine.backward(loss)
        engine.step()

        # check
        param_track.append(next(model.parameters())[0].clone())
        grad_track.append(next(model.parameters()).grad[0].clone())
        step += 1
        if step == CONFIG['gradient_accumulation']:
            break

    assert not torch.all(grad_track[0] == grad_track[-1]), 'grad should be different in different iterations'
    assert torch.all(param_track[0] == param_track[1]) and not torch.all(param_track[0] == param_track[-1]), \
        'param should be the same in the first few iterations and only changed in the last iteration'

    gpc.destroy()
    torch.cuda.empty_cache()


@pytest.mark.dist
@rerun_if_address_is_in_use()
def test_engine():
    spawn(run_no_pipeline, 4)


if __name__ == '__main__':
    test_engine()