import torch
import torch.nn as nn
import torch.nn.functional as F

from .padding import get_padding, get_padding_value, pad_same


def get_weight(module):
    std, mean = torch.std_mean(module.weight, dim=[1, 2, 3], keepdim=True, unbiased=False)
    weight = (module.weight - mean) / (std + module.eps)
    return weight


class StdConv2d(nn.Conv2d):
    """Conv2d with Weight Standardization. Used for BiT ResNet-V2 models.

    Paper: `Micro-Batch Training with Batch-Channel Normalization and Weight Standardization` -
        https://arxiv.org/abs/1903.10520v2
    """
    def __init__(
            self, in_channel, out_channels, kernel_size, stride=1, padding=None, dilation=1,
            groups=1, bias=False, eps=1e-5):
        if padding is None:
            padding = get_padding(kernel_size, stride, dilation)
        super().__init__(
            in_channel, out_channels, kernel_size, stride=stride,
            padding=padding, dilation=dilation, groups=groups, bias=bias)
        self.eps = eps

    def get_weight(self):
        std, mean = torch.std_mean(self.weight, dim=[1, 2, 3], keepdim=True, unbiased=False)
        weight = (self.weight - mean) / (std + self.eps)
        return weight

    def forward(self, x):
        x = F.conv2d(x, self.get_weight(), self.bias, self.stride, self.padding, self.dilation, self.groups)
        return x


class StdConv2dSame(nn.Conv2d):
    """Conv2d with Weight Standardization. TF compatible SAME padding. Used for ViT Hybrid model.

    Paper: `Micro-Batch Training with Batch-Channel Normalization and Weight Standardization` -
        https://arxiv.org/abs/1903.10520v2
    """
    def __init__(
            self, in_channel, out_channels, kernel_size, stride=1, padding='SAME', dilation=1,
            groups=1, bias=False, eps=1e-5):
        padding, is_dynamic = get_padding_value(padding, kernel_size, stride=stride, dilation=dilation)
        super().__init__(
            in_channel, out_channels, kernel_size, stride=stride, padding=padding, dilation=dilation,
            groups=groups, bias=bias)
        self.same_pad = is_dynamic
        self.eps = eps

    def get_weight(self):
        std, mean = torch.std_mean(self.weight, dim=[1, 2, 3], keepdim=True, unbiased=False)
        weight = (self.weight - mean) / (std + self.eps)
        return weight

    def forward(self, x):
        if self.same_pad:
            x = pad_same(x, self.kernel_size, self.stride, self.dilation)
        x = F.conv2d(x, self.get_weight(), self.bias, self.stride, self.padding, self.dilation, self.groups)
        return x


class ScaledStdConv2d(nn.Conv2d):
    """Conv2d layer with Scaled Weight Standardization.

    Paper: `Characterizing signal propagation to close the performance gap in unnormalized ResNets` -
        https://arxiv.org/abs/2101.08692

    NOTE: the operations used in this impl differ slightly from the DeepMind Haiku impl. The impact is minor.
    """

    def __init__(
            self, in_channels, out_channels, kernel_size, stride=1, padding=None, dilation=1, groups=1,
            bias=True, gamma=1.0, eps=1e-5, use_layernorm=False):
        if padding is None:
            padding = get_padding(kernel_size, stride, dilation)
        super().__init__(
            in_channels, out_channels, kernel_size, stride=stride, padding=padding, dilation=dilation,
            groups=groups, bias=bias)
        self.gain = nn.Parameter(torch.ones(self.out_channels, 1, 1, 1))
        self.scale = gamma * self.weight[0].numel() ** -0.5  # gamma * 1 / sqrt(fan-in)
        self.eps = eps ** 2 if use_layernorm else eps
        self.use_layernorm = use_layernorm  # experimental, slightly faster/less GPU memory to hijack LN kernel

    def get_weight(self):
        if self.use_layernorm:
            weight = self.scale * F.layer_norm(self.weight, self.weight.shape[1:], eps=self.eps)
        else:
            std, mean = torch.std_mean(self.weight, dim=[1, 2, 3], keepdim=True, unbiased=False)
            weight = self.scale * (self.weight - mean) / (std + self.eps)
        return self.gain * weight

    def forward(self, x):
        return F.conv2d(x, self.get_weight(), self.bias, self.stride, self.padding, self.dilation, self.groups)


class ScaledStdConv2dSame(nn.Conv2d):
    """Conv2d layer with Scaled Weight Standardization and Tensorflow-like SAME padding support

    Paper: `Characterizing signal propagation to close the performance gap in unnormalized ResNets` -
        https://arxiv.org/abs/2101.08692

    NOTE: the operations used in this impl differ slightly from the DeepMind Haiku impl. The impact is minor.
    """

    def __init__(
            self, in_channels, out_channels, kernel_size, stride=1, padding='SAME', dilation=1, groups=1,
            bias=True, gamma=1.0, eps=1e-5, use_layernorm=False):
        padding, is_dynamic = get_padding_value(padding, kernel_size, stride=stride, dilation=dilation)
        super().__init__(
            in_channels, out_channels, kernel_size, stride=stride, padding=padding, dilation=dilation,
            groups=groups, bias=bias)
        self.gain = nn.Parameter(torch.ones(self.out_channels, 1, 1, 1))
        self.scale = gamma * self.weight[0].numel() ** -0.5
        self.same_pad = is_dynamic
        self.eps = eps ** 2 if use_layernorm else eps
        self.use_layernorm = use_layernorm  # experimental, slightly faster/less GPU memory to hijack LN kernel

    # NOTE an alternate formulation to consider, closer to DeepMind Haiku impl but doesn't seem
    # to make much numerical difference (+/- .002 to .004) in top-1 during eval.
    # def get_weight(self):
    #         var, mean = torch.var_mean(self.weight, dim=[1, 2, 3], keepdim=True, unbiased=False)
    #         scale = torch.rsqrt((self.weight[0].numel() * var).clamp_(self.eps)) * self.gain
    #         weight = (self.weight - mean) * scale
    #     return self.gain * weight

    def get_weight(self):
        if self.use_layernorm:
            weight = self.scale * F.layer_norm(self.weight, self.weight.shape[1:], eps=self.eps)
        else:
            std, mean = torch.std_mean(self.weight, dim=[1, 2, 3], keepdim=True, unbiased=False)
            weight = self.scale * (self.weight - mean) / (std + self.eps)
        return self.gain * weight

    def forward(self, x):
        if self.same_pad:
            x = pad_same(x, self.kernel_size, self.stride, self.dilation)
        return F.conv2d(x, self.get_weight(), self.bias, self.stride, self.padding, self.dilation, self.groups)