#!/usr/bin/env python
# -*- encoding: utf-8 -*-

from .amp_type import AMP_TYPE
from colossalai.context import Config
import torch.nn as nn
from torch.optim import Optimizer
from torch.nn.modules.loss import _Loss
from .torch_amp import convert_to_torch_amp
from .apex_amp import convert_to_apex_amp
from .naive_amp import convert_to_naive_amp


def convert_to_amp(model: nn.Module, optimizer: Optimizer, criterion: _Loss, mode: AMP_TYPE, amp_config: Config = None):
    """A helper function to wrap training components with Torch AMP modules.

    Args:
        param model (:class:`torch.nn.Module`): your model object.
        optimizer (:class:`torch.optim.Optimizer`): your optimizer object.
        criterion (:class:`torch.nn.modules.loss._Loss`): your loss function object.
        mode (:class:`colossalai.amp.AMP_TYPE`): amp mode.
        amp_config (:class:`colossalai.context.Config` or dict): configuration for different amp modes

    Returns:
        A tuple (model, optimizer, criterion).

    Note:
        ``amp_config`` may vary from different mode you choose. You should check the corresponding amp mode
        for more details about ``amp_config``.
        For ``apex_amp``, please check
        `apex_amp config <https://nvidia.github.io/apex/amp.html?highlight=apex%20amp>`_.
        For ``naive_amp``, please check
        `naive_amp config <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/amp/naive_amp/_fp16_optimizer.py#L42>`_.
        For ``torch_amp``, please check
        `torch_amp config <https://github.com/pytorch/pytorch/blob/master/torch/cuda/amp/grad_scaler.py#L97>`_.
    """
    assert isinstance(mode, AMP_TYPE), \
        f'expected the argument mode be AMP_TYPE, but got {type(mode)}'

    if amp_config is None:
        amp_config = Config()

    if mode == AMP_TYPE.TORCH:
        model, optimizer, criterion = convert_to_torch_amp(model, optimizer, criterion, amp_config)
    elif mode == AMP_TYPE.APEX:
        model, optimizer = convert_to_apex_amp(model, optimizer, amp_config)
    elif mode == AMP_TYPE.NAIVE:
        model, optimizer = convert_to_naive_amp(model, optimizer, amp_config)

    return model, optimizer, criterion