# Adapted from Open-Sora-Plan

# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# --------------------------------------------------------
# References:
# Open-Sora-Plan: https://github.com/PKU-YuanGroup/Open-Sora-Plan
# --------------------------------------------------------

import torch
import torch.nn as nn

from .block import Block


class GroupNorm(Block):
    def __init__(self, num_channels, num_groups=32, eps=1e-6, *args, **kwargs) -> None:
        super().__init__(*args, **kwargs)
        self.norm = torch.nn.GroupNorm(num_groups=num_groups, num_channels=num_channels, eps=1e-6, affine=True)

    def forward(self, x):
        return self.norm(x)


def Normalize(in_channels, num_groups=32):
    return torch.nn.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True)


class ActNorm(nn.Module):
    def __init__(self, num_features, logdet=False, affine=True, allow_reverse_init=False):
        assert affine
        super().__init__()
        self.logdet = logdet
        self.loc = nn.Parameter(torch.zeros(1, num_features, 1, 1))
        self.scale = nn.Parameter(torch.ones(1, num_features, 1, 1))
        self.allow_reverse_init = allow_reverse_init

        self.register_buffer("initialized", torch.tensor(0, dtype=torch.uint8))

    def initialize(self, input):
        with torch.no_grad():
            flatten = input.permute(1, 0, 2, 3).contiguous().view(input.shape[1], -1)
            mean = flatten.mean(1).unsqueeze(1).unsqueeze(2).unsqueeze(3).permute(1, 0, 2, 3)
            std = flatten.std(1).unsqueeze(1).unsqueeze(2).unsqueeze(3).permute(1, 0, 2, 3)

            self.loc.data.copy_(-mean)
            self.scale.data.copy_(1 / (std + 1e-6))

    def forward(self, input, reverse=False):
        if reverse:
            return self.reverse(input)
        if len(input.shape) == 2:
            input = input[:, :, None, None]
            squeeze = True
        else:
            squeeze = False

        _, _, height, width = input.shape

        if self.training and self.initialized.item() == 0:
            self.initialize(input)
            self.initialized.fill_(1)

        h = self.scale * (input + self.loc)

        if squeeze:
            h = h.squeeze(-1).squeeze(-1)

        if self.logdet:
            log_abs = torch.log(torch.abs(self.scale))
            logdet = height * width * torch.sum(log_abs)
            logdet = logdet * torch.ones(input.shape[0]).to(input)
            return h, logdet

        return h

    def reverse(self, output):
        if self.training and self.initialized.item() == 0:
            if not self.allow_reverse_init:
                raise RuntimeError(
                    "Initializing ActNorm in reverse direction is "
                    "disabled by default. Use allow_reverse_init=True to enable."
                )
            else:
                self.initialize(output)
                self.initialized.fill_(1)

        if len(output.shape) == 2:
            output = output[:, :, None, None]
            squeeze = True
        else:
            squeeze = False

        h = output / self.scale - self.loc

        if squeeze:
            h = h.squeeze(-1).squeeze(-1)
        return h