# Copyright 2024 NVIDIA CORPORATION & AFFILIATES
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
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# SPDX-License-Identifier: Apache-2.0

from typing import Callable

import numpy as np
import torch
import torch.nn as nn
from torch.nn.utils.spectral_norm import SpectralNorm


class ResidualBlock(nn.Module):
    def __init__(self, fn: Callable):
        super().__init__()
        self.fn = fn

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return (self.fn(x) + x) / np.sqrt(2)


class SpectralConv1d(nn.Conv1d):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        SpectralNorm.apply(self, name="weight", n_power_iterations=1, dim=0, eps=1e-12)


class BatchNormLocal(nn.Module):
    def __init__(self, num_features: int, affine: bool = True, virtual_bs: int = 8, eps: float = 1e-5):
        super().__init__()
        self.virtual_bs = virtual_bs
        self.eps = eps
        self.affine = affine

        if self.affine:
            self.weight = nn.Parameter(torch.ones(num_features))
            self.bias = nn.Parameter(torch.zeros(num_features))

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        shape = x.size()

        # Reshape batch into groups.
        G = np.ceil(x.size(0) / self.virtual_bs).astype(int)
        x = x.view(G, -1, x.size(-2), x.size(-1))

        # Calculate stats.
        mean = x.mean([1, 3], keepdim=True)
        var = x.var([1, 3], keepdim=True, unbiased=False)
        x = (x - mean) / (torch.sqrt(var + self.eps))

        if self.affine:
            x = x * self.weight[None, :, None] + self.bias[None, :, None]

        return x.view(shape)


def make_block(channels: int, kernel_size: int) -> nn.Module:
    return nn.Sequential(
        SpectralConv1d(
            channels,
            channels,
            kernel_size=kernel_size,
            padding=kernel_size // 2,
            padding_mode="circular",
        ),
        BatchNormLocal(channels),
        nn.LeakyReLU(0.2, True),
    )


# Adapted from https://github.com/autonomousvision/stylegan-t/blob/main/networks/discriminator.py
class DiscHead(nn.Module):
    def __init__(self, channels: int, c_dim: int, cmap_dim: int = 64):
        super().__init__()
        self.channels = channels
        self.c_dim = c_dim
        self.cmap_dim = cmap_dim

        self.main = nn.Sequential(
            make_block(channels, kernel_size=1), ResidualBlock(make_block(channels, kernel_size=9))
        )

        if self.c_dim > 0:
            self.cmapper = nn.Linear(self.c_dim, cmap_dim)
            self.cls = SpectralConv1d(channels, cmap_dim, kernel_size=1, padding=0)
        else:
            self.cls = SpectralConv1d(channels, 1, kernel_size=1, padding=0)

    def forward(self, x: torch.Tensor, c: torch.Tensor) -> torch.Tensor:
        h = self.main(x)
        out = self.cls(h)

        if self.c_dim > 0:
            cmap = self.cmapper(c).unsqueeze(-1)
            out = (out * cmap).sum(1, keepdim=True) * (1 / np.sqrt(self.cmap_dim))

        return out