import torch.nn as nn
from configs.basic.build_layer import build_conv_layer, build_norm_layer, build_activation_layer, build_padding_layer
from core.initialize import kaiming_init, constant_init
import copy

class ConvModule(nn.Module):
    """A conv block that bundles conv/norm/activation layers.

    This block simplifies the usage of convolution layers, which are commonly
    used with a norm layer (e.g., BatchNorm) and activation layer (e.g., ReLU).
    It is based upon three build methods: `build_conv_layer()`,
    `build_norm_layer()` and `build_activation_layer()`.

    Besides, we add some additional features in this module.
    1. Automatically set `bias` of the conv layer.
    2. Spectral norm is supported.
    3. More padding modes are supported. Before PyTorch 1.5, nn.Conv2d only
    supports zero and circular padding, and we add "reflect" padding mode.

    Args:
        in_channels (int): Number of channels in the input feature map.
            Same as that in ``nn._ConvNd``.
        out_channels (int): Number of channels produced by the convolution.
            Same as that in ``nn._ConvNd``.
        kernel_size (int | tuple[int]): Size of the convolving kernel.
            Same as that in ``nn._ConvNd``.
        stride (int | tuple[int]): Stride of the convolution.
            Same as that in ``nn._ConvNd``.
        padding (int | tuple[int]): Zero-padding added to both sides of
            the input. Same as that in ``nn._ConvNd``.
        dilation (int | tuple[int]): Spacing between kernel elements.
            Same as that in ``nn._ConvNd``.
        groups (int): Number of blocked connections from input channels to
            output channels. Same as that in ``nn._ConvNd``.
        bias (bool | str): If specified as `auto`, it will be decided by the
            norm_cfg. Bias will be set as True if `norm_cfg` is None, otherwise
            False. Default: "auto".
        conv_cfg (dict): Config dict for convolution layer. Default: None,
            which means using conv2d.
        norm_cfg (dict): Config dict for normalization layer. Default: None.
        act_cfg (dict): Config dict for activation layer.
            Default: dict(type='ReLU').
        inplace (bool): Whether to use inplace mode for activation.
            Default: True.
        with_spectral_norm (bool): Whether use spectral norm in conv module.
            Default: False.
        padding_mode (str): If the `padding_mode` has not been supported by
            current `Conv2d` in PyTorch, we will use our own padding layer
            instead. Currently, we support ['zeros', 'circular'] with official
            implementation and ['reflect'] with our own implementation.
            Default: 'zeros'.
        order (tuple[str]): The order of conv/norm/activation layers. It is a
            sequence of "conv", "norm" and "act". Common examples are
            ("conv", "norm", "act") and ("act", "conv", "norm").
            Default: ('conv', 'norm', 'act').
    """
    def __init__(self,
                 in_channels,
                 out_channels,
                 kernel_size,
                 stride=1,
                 padding=0,
                 dilation=1,
                 groups=1,
                 bias='auto',
                 conv_cfg=None,
                 norm_cfg=None,
                 act_cfg=dict(type='ReLU'),
                 inplace=True,
                 with_spectral_norm=False,
                 padding_mode='zeros',
                 order=('conv', 'norm', 'act')):
        super(ConvModule, self).__init__()
        assert conv_cfg is None or isinstance(conv_cfg, dict)
        assert norm_cfg is None or isinstance(norm_cfg, dict)
        assert act_cfg is None or isinstance(act_cfg, dict)
        official_padding_mode = ['zeros', 'circular']
        self.conv_cfg = copy.deepcopy(conv_cfg)
        self.norm_cfg = copy.deepcopy(norm_cfg)
        self.act_cfg = copy.deepcopy(act_cfg)
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.kernel_size = kernel_size
        self.stride = stride
        self.inplace = inplace
        self.with_spectral_norm = with_spectral_norm
        self.with_explicit_padding = padding_mode not in official_padding_mode
        self.order = order
        assert isinstance(self.order, tuple) and len(self.order) == 3
        assert set(order) == set(['conv', 'norm', 'act'])
        
        self.with_norm = norm_cfg is not None
        self.with_activation = act_cfg is not None
        
        # if the conv layer is before a norm layer, bias is unnecessary.
        if bias == 'auto':
            bias = not self.with_norm
        self.with_bias = bias

        if self.with_explicit_padding:
            pad_cfg = dict(type=padding_mode)
            self.padding_layer = build_padding_layer(pad_cfg, padding)
        
        conv_padding = 0 if self.with_explicit_padding else padding
        
        self.conv = build_conv_layer(
            conv_cfg,
            in_channels,
            out_channels,
            kernel_size,
            stride=stride,
            padding=conv_padding,
            dilation=dilation,
            groups=groups,
            bias=bias)
        
        if self.with_spectral_norm:
            self.conv = nn.utils.spectral_norm(self.conv)
            
        if self.with_norm:
            if order.index('norm') > order.index('conv'):
                norm_channels = out_channels
            else:
                norm_channels = in_channels
            self.norm_name, norm = build_norm_layer(norm_cfg, norm_channels)
            self.add_module(self.norm_name, norm)
            
        if self.with_activation:
            act_cfg_ = act_cfg.copy()
            # nn.Tanh has no 'inplace' argument
            if act_cfg_['type'] not in [
                    'Tanh', 'PReLU', 'Sigmoid', 'HSigmoid', 'Swish'
            ]:
                act_cfg_.setdefault('inplace', inplace)
            self.activate = build_activation_layer(act_cfg_)
            
        self.init_weights()
        
    @property
    def norm(self):
        if self.norm_name:
            return getattr(self, self.norm_name)
        else:
            return None
        
    def init_weights(self):
        if not hasattr(self.conv, 'init_weights'):
            if self.with_activation and self.act_cfg['type'] == 'LeakyReLU':
                nonlinearity = 'leaky_relu'
                a = self.act_cfg.get('negative_slope', 0.01)
            else:
                nonlinearity = 'relu'
                a = 0
            kaiming_init(self.conv, a=a, nonlinearity=nonlinearity)
        if self.with_norm:
            constant_init(self.norm, 1, bias=0)
    
    def forward(self, x, activate=True, norm=True):  
        for layer in self.order:
            if layer == 'conv':
                if self.with_explicit_padding:
                    x = self.padding_layer(x)
                x = self.conv(x)
            elif layer == 'norm' and norm and self.with_norm:
                x = self.norm(x)
            elif layer == 'act' and activate and self.with_activation:
                x = self.activate(x)

        return x
