from contextlib import nullcontext
from typing import Optional

import torch
import torch.distributed as dist

import colossalai
from colossalai.booster import Booster
from colossalai.booster.plugin import GeminiPlugin
from colossalai.fx import is_compatible_with_meta
from colossalai.lazy.lazy_init import LazyInitContext
from colossalai.nn.optimizer import HybridAdam
from colossalai.tensor.colo_parameter import ColoParameter
from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn
from tests.kit.model_zoo import model_zoo


def run_fn(init_method, model_fn, data_gen_fn, output_transform_fn) -> Optional[str]:
    try:
        if init_method == 'lazy':
            ctx = LazyInitContext()
        else:
            ctx = nullcontext()
        plugin = GeminiPlugin(max_norm=1.0, initial_scale=2**5)
        booster = Booster(plugin=plugin)
        with ctx:
            model = model_fn()
        optimizer = HybridAdam(model.parameters(), lr=1e-3)
        criterion = lambda x: x.mean()
        data = data_gen_fn()

        data = {
            k: v.to('cuda') if torch.is_tensor(v) or 'Tensor' in v.__class__.__name__ else v for k, v in data.items()
        }

        model, optimizer, criterion, _, _ = booster.boost(model, optimizer, criterion)

        for n, p in model.named_parameters():
            assert isinstance(p, ColoParameter), f'{n} is not a ColoParameter'

        output = model(**data)
        output = output_transform_fn(output)
        output_key = list(output.keys())[0]
        loss = criterion(output[output_key])

        booster.backward(loss, optimizer)
        optimizer.step()

    except Exception as e:
        # raise e
        return repr(e)


# TODO(ver217): CI does not support lazy now
# @parameterize('init_method', ['lazy', 'none', 'colo'])


@parameterize('subset', ['torchvision', 'transformers', 'diffusers'])
@parameterize('init_method', ['none'])
def check_gemini_plugin(subset: str, init_method: str = 'none', early_stop: bool = True):
    """check gemini plugin over model zoo

    Args:
        early_stop (bool, optional): Whether to stop when getting the first error. Defaults to True.
    """
    is_support_meta = is_compatible_with_meta()
    if not is_support_meta and init_method == 'lazy':
        return

    passed_models = []
    failed_info = {}    # (model_name, error) pair

    for name, (model_fn, data_gen_fn, output_transform_fn, _, _) in model_zoo.get_sub_registry(subset).items():
        # These models lead to CUDA error
        if name in ('diffusers_auto_encoder_kl', 'diffusers_vq_model', 'diffusers_unet2d_model', 'timm_resmlp',
                    'timm_gmixer_12_224', 'timm_gmlp_b16_224', 'timm_mixer_b16_224', 'timm_convnext',
                    'torchvision_convnext_base'):
            continue
        # These models are not compatible with gemini
        if name in [
                'timm_convit',
                'timm_dm_nfnet',
                'torchvision_vit_b_16',
                'transformers_t5',
                'transformers_t5_for_conditional_generation',
                'transformers_t5_encoder_model',    # does not support apex rmsnorm
                'transformers_chatglm',
                'transformers_sam',
                'transformers_vit'
        ]:
            continue

        if init_method == 'lazy' and name in [
                'timm_convmixer', 'timm_vision_transformer', 'timm_deit', 'timm_deit3', 'timm_inception_v3',
                'timm_tnt_b_patch16_224', 'timm_rexnet', 'torchvision_densenet121', 'torchvision_efficientnet_b0',
                'torchvision_mobilenet_v2', 'torchvision_mnasnet0_5', 'torchvision_regnet_x_16gf',
                'torchvision_shufflenet_v2_x0_5', 'torchvision_efficientnet_v2_s'
        ]:
            continue
        err = run_fn(init_method, model_fn, data_gen_fn, output_transform_fn)

        if err is None:
            passed_models.append(name)
        else:
            failed_info[name] = err
            if early_stop:
                break

    if dist.get_rank() == 0:
        print(f'Init method: {init_method}')
        print(f'Passed models({len(passed_models)}): {passed_models}\n\n')
        print(f'Failed models({len(failed_info)}): {list(failed_info.keys())}\n\n')
    assert len(failed_info) == 0, '\n'.join([f'{k}: {v}' for k, v in failed_info.items()])


def run_dist(rank, world_size, port, early_stop: bool = True):
    # init dist env
    colossalai.launch(config=dict(), rank=rank, world_size=world_size, port=port, host='localhost')
    check_gemini_plugin(early_stop=early_stop)


@rerun_if_address_is_in_use()
def test_gemini_plugin(early_stop: bool = True):
    spawn(run_dist, 4, early_stop=early_stop)


if __name__ == '__main__':
    test_gemini_plugin(early_stop=False)
