"""EvoNormB0 (Batched) and EvoNormS0 (Sample) in PyTorch

An attempt at getting decent performing EvoNorms running in PyTorch.
While currently faster than other impl, still quite a ways off the built-in BN
in terms of memory usage and throughput.

Still very much a WIP, fiddling with buffer usage, in-place optimizations, and layouts.

Hacked together by Ross Wightman
"""

import torch
import torch.nn as nn


class EvoNormBatch2d(nn.Module):
    def __init__(self, num_features, momentum=0.1, nonlin=True, eps=1e-5):
        super(EvoNormBatch2d, self).__init__()
        self.momentum = momentum
        self.nonlin = nonlin
        self.eps = eps
        param_shape = (1, num_features, 1, 1)
        self.weight = nn.Parameter(torch.ones(param_shape), requires_grad=True)
        self.bias = nn.Parameter(torch.zeros(param_shape), requires_grad=True)
        if nonlin:
            self.v = nn.Parameter(torch.ones(param_shape), requires_grad=True)
        self.register_buffer('running_var', torch.ones(1, num_features, 1, 1))
        self.reset_parameters()

    def reset_parameters(self):
        nn.init.ones_(self.weight)
        nn.init.zeros_(self.bias)
        if self.nonlin:
            nn.init.ones_(self.v)

    def forward(self, x):
        assert x.dim() == 4, 'expected 4D input'
        x_type = x.dtype
        if self.training:
            var = x.var(dim=(0, 2, 3), unbiased=False, keepdim=True)
            self.running_var.copy_(self.momentum * var.detach() + (1 - self.momentum) * self.running_var)
        else:
            var = self.running_var.clone()

        if self.nonlin:
            v = self.v.to(dtype=x_type)
            d = (x * v) + x.var(dim=(2, 3), unbiased=False, keepdim=True).add_(self.eps).sqrt_().to(dtype=x_type)
            d = d.max(var.add_(self.eps).sqrt_().to(dtype=x_type))
            x = x / d
            return x.mul_(self.weight).add_(self.bias)
        else:
            return x.mul(self.weight).add_(self.bias)


class EvoNormSample2d(nn.Module):
    def __init__(self, num_features, nonlin=True, groups=8, eps=1e-5):
        super(EvoNormSample2d, self).__init__()
        self.nonlin = nonlin
        self.groups = groups
        self.eps = eps
        param_shape = (1, num_features, 1, 1)
        self.weight = nn.Parameter(torch.ones(param_shape), requires_grad=True)
        self.bias = nn.Parameter(torch.zeros(param_shape), requires_grad=True)
        if nonlin:
            self.v = nn.Parameter(torch.ones(param_shape), requires_grad=True)
        self.reset_parameters()

    def reset_parameters(self):
        nn.init.ones_(self.weight)
        nn.init.zeros_(self.bias)
        if self.nonlin:
            nn.init.ones_(self.v)

    def forward(self, x):
        assert x.dim() == 4, 'expected 4D input'
        B, C, H, W = x.shape
        assert C % self.groups == 0
        if self.nonlin:
            n = (x * self.v).sigmoid().reshape(B, self.groups, -1)
            x = x.reshape(B, self.groups, -1)
            x = n / x.var(dim=-1, unbiased=False, keepdim=True).add_(self.eps).sqrt_()
            x = x.reshape(B, C, H, W)
            return x.mul_(self.weight).add_(self.bias)
        else:
            return x.mul(self.weight).add_(self.bias)