# Copyright (c) OpenMMLab. All rights reserved.
import copy
import inspect
from typing import List, Union

import torch
import torch.nn as nn

from mmengine.config import Config, ConfigDict
from mmengine.device import is_npu_available, is_npu_support_full_precision
from mmengine.registry import OPTIM_WRAPPER_CONSTRUCTORS, OPTIMIZERS
from .optimizer_wrapper import OptimWrapper


def register_torch_optimizers() -> List[str]:
    """Register optimizers in ``torch.optim`` to the ``OPTIMIZERS`` registry.

    Returns:
        List[str]: A list of registered optimizers' name.
    """
    torch_optimizers = []
    for module_name in dir(torch.optim):
        if module_name.startswith('__'):
            continue
        _optim = getattr(torch.optim, module_name)
        if inspect.isclass(_optim) and issubclass(_optim,
                                                  torch.optim.Optimizer):
            OPTIMIZERS.register_module(module=_optim)
            torch_optimizers.append(module_name)
    return torch_optimizers


TORCH_OPTIMIZERS = register_torch_optimizers()


def register_torch_npu_optimizers() -> List[str]:
    """Register optimizers in ``torch npu`` to the ``OPTIMIZERS`` registry.

    Returns:
        List[str]: A list of registered optimizers' name.
    """
    if not is_npu_available():
        return []

    import torch_npu
    if not hasattr(torch_npu, 'optim'):
        return []

    torch_npu_optimizers = []
    for module_name in dir(torch_npu.optim):
        if module_name.startswith('__') or module_name in OPTIMIZERS:
            continue
        _optim = getattr(torch_npu.optim, module_name)
        if inspect.isclass(_optim) and issubclass(_optim,
                                                  torch.optim.Optimizer):
            OPTIMIZERS.register_module(module=_optim)
            torch_npu_optimizers.append(module_name)
    return torch_npu_optimizers


NPU_OPTIMIZERS = register_torch_npu_optimizers()


def register_dadaptation_optimizers() -> List[str]:
    """Register optimizers in ``dadaptation`` to the ``OPTIMIZERS`` registry.

    Returns:
        List[str]: A list of registered optimizers' name.
    """
    dadaptation_optimizers = []
    try:
        import dadaptation
    except ImportError:
        pass
    else:
        for module_name in ['DAdaptAdaGrad', 'DAdaptAdam', 'DAdaptSGD']:
            _optim = getattr(dadaptation, module_name)
            if inspect.isclass(_optim) and issubclass(_optim,
                                                      torch.optim.Optimizer):
                OPTIMIZERS.register_module(module=_optim)
                dadaptation_optimizers.append(module_name)
    return dadaptation_optimizers


DADAPTATION_OPTIMIZERS = register_dadaptation_optimizers()


def register_lion_optimizers() -> List[str]:
    """Register Lion optimizer to the ``OPTIMIZERS`` registry.

    Returns:
        List[str]: A list of registered optimizers' name.
    """
    optimizers = []
    try:
        from lion_pytorch import Lion
    except ImportError:
        pass
    else:
        OPTIMIZERS.register_module(module=Lion)
        optimizers.append('Lion')
    return optimizers


LION_OPTIMIZERS = register_lion_optimizers()


def register_sophia_optimizers() -> List[str]:
    """Register Sophia optimizer to the ``OPTIMIZERS`` registry.

    Returns:
        List[str]: A list of registered optimizers' name.
    """
    optimizers = []
    try:
        import Sophia
    except ImportError:
        pass
    else:
        for module_name in dir(Sophia):
            _optim = getattr(Sophia, module_name)
            if inspect.isclass(_optim) and issubclass(_optim,
                                                      torch.optim.Optimizer):
                OPTIMIZERS.register_module(module=_optim)
                optimizers.append(module_name)
    return optimizers


SOPHIA_OPTIMIZERS = register_sophia_optimizers()


def register_deepspeed_optimizers() -> List[str]:
    """Register optimizers in ``deepspeed`` to the ``OPTIMIZERS`` registry.

    Returns:
        List[str]: A list of registered optimizers' name.
    """
    deepspeed_optimizers = []
    try:
        import deepspeed  # noqa: F401
    except ImportError:
        pass
    else:
        from deepspeed.ops.adam import DeepSpeedCPUAdam, FusedAdam
        from deepspeed.ops.lamb import FusedLamb
        from deepspeed.runtime.fp16.onebit import (OnebitAdam, OnebitLamb,
                                                   ZeroOneAdam)

        OPTIMIZERS.register_module(module=DeepSpeedCPUAdam)
        deepspeed_optimizers.append('DeepSpeedCPUAdam')
        OPTIMIZERS.register_module(module=FusedAdam)
        deepspeed_optimizers.append('FusedAdam')
        OPTIMIZERS.register_module(module=FusedLamb)
        deepspeed_optimizers.append('FusedLamb')
        OPTIMIZERS.register_module(module=OnebitAdam)
        deepspeed_optimizers.append('OnebitAdam')
        OPTIMIZERS.register_module(module=OnebitLamb)
        deepspeed_optimizers.append('OnebitLamb')
        OPTIMIZERS.register_module(module=ZeroOneAdam)
        deepspeed_optimizers.append('ZeroOneAdam')

    return deepspeed_optimizers


DEEPSPEED_OPTIMIZERS = register_deepspeed_optimizers()


def build_optim_wrapper(model: nn.Module,
                        cfg: Union[dict, Config, ConfigDict]) -> OptimWrapper:
    """Build function of OptimWrapper.

    If ``constructor`` is set in the ``cfg``, this method will build an
    optimizer wrapper constructor, and use optimizer wrapper constructor to
    build the optimizer wrapper. If ``constructor`` is not set, the
    ``DefaultOptimWrapperConstructor`` will be used by default.

    Args:
        model (nn.Module): Model to be optimized.
        cfg (dict): Config of optimizer wrapper, optimizer constructor and
            optimizer.

    Returns:
        OptimWrapper: The built optimizer wrapper.
    """
    optim_wrapper_cfg = copy.deepcopy(cfg)
    constructor_type = optim_wrapper_cfg.pop('constructor',
                                             'DefaultOptimWrapperConstructor')
    paramwise_cfg = optim_wrapper_cfg.pop('paramwise_cfg', None)

    # Since the current generation of NPU(Ascend 910) only supports
    # mixed precision training, here we turn on mixed precision
    # to make the training normal
    if is_npu_available() and not is_npu_support_full_precision():
        optim_wrapper_cfg['type'] = 'AmpOptimWrapper'

    optim_wrapper_constructor = OPTIM_WRAPPER_CONSTRUCTORS.build(
        dict(
            type=constructor_type,
            optim_wrapper_cfg=optim_wrapper_cfg,
            paramwise_cfg=paramwise_cfg))
    optim_wrapper = optim_wrapper_constructor(model)
    return optim_wrapper
