from typing import Optional, Tuple

import torch


def colo_model_optimizer_usage(optim) -> Tuple[int, int]:
    """Trace the optimizer memory usage

    Args:
        optim (ShardedOptimV2): an instance of ShardedOptimizer

    Returns:
        Tuple[int, int]: cuda/cpu memory usage in Byte
    """
    if optim is None:
        return 0, 0
    assert hasattr(optim, 'get_memory_usage'), f"{type(optim)} has no attr get_memory_usage()"
    return optim.get_memory_usage()


def colo_model_mem_usage(model: torch.nn.Module) -> Tuple[int, int]:
    """
    Trace the model memory usage.
    Args:
        model (torch.nn.Module): a torch model

    Returns:
        Tuple[int, int]: cuda memory usage in Byte, cpu memory usage in Byte
    """
    if model is None:
        return 0, 0

    def _get_tensor_mem_use(t: Optional[torch.Tensor]):
        if t is None:
            return 0, 0
        assert isinstance(t, torch.Tensor)
        _cpu_mem_usage, _cuda_mem_usage = 0, 0
        if t.device.type == 'cpu':
            _cpu_mem_usage += t.numel() * t.element_size()
        elif t.device.type == 'cuda':
            _cuda_mem_usage += t.numel() * t.element_size()
        return _cuda_mem_usage, _cpu_mem_usage

    cuda_mem_usage = 0
    cpu_mem_usage = 0
    for param in model.parameters():
        if hasattr(param, 'colo_attr'):
            t_cuda, t_cpu = param.colo_attr.get_memory_usage()
            cuda_mem_usage += t_cuda
            cpu_mem_usage += t_cpu
        else:
            t_cuda, t_cpu = _get_tensor_mem_use(param.data)
            cuda_mem_usage += t_cuda
            cpu_mem_usage += t_cpu
            t_cuda, t_cpu = _get_tensor_mem_use(param.grad)
            cuda_mem_usage += t_cuda
            cpu_mem_usage += t_cpu

    return cuda_mem_usage, cpu_mem_usage