"""Standalone PyTorch definition for the Ermolaev CUT generator.

The generator structure is derived from the official CUT implementation:
https://github.com/taesungp/contrastive-unpaired-translation
"""

from __future__ import annotations

import functools
import torch
from torch import nn


class ResnetBlock(nn.Module):
    def __init__(self, dim: int):
        super().__init__()
        norm = functools.partial(nn.InstanceNorm2d, affine=False, track_running_stats=False)
        self.conv_block = nn.Sequential(
            nn.ReflectionPad2d(1), nn.Conv2d(dim, dim, 3, bias=True), norm(dim), nn.ReLU(True),
            nn.ReflectionPad2d(1), nn.Conv2d(dim, dim, 3, bias=True), norm(dim),
        )

    def forward(self, x):
        return x + self.conv_block(x)


class ErmolaevGenerator(nn.Module):
    """3-channel, 6-block CUT ResNet generator with 32 base filters."""

    def __init__(self):
        super().__init__()
        norm = functools.partial(nn.InstanceNorm2d, affine=False, track_running_stats=False)
        layers = [
            nn.ReflectionPad2d(3), nn.Conv2d(3, 32, 7, bias=True), norm(32), nn.ReLU(True),
            nn.Conv2d(32, 64, 3, stride=2, padding=1, bias=True), norm(64), nn.ReLU(True),
            nn.Conv2d(64, 128, 3, stride=2, padding=1, bias=True), norm(128), nn.ReLU(True),
        ]
        layers.extend(ResnetBlock(128) for _ in range(6))
        layers.extend([
            nn.ConvTranspose2d(128, 64, 3, stride=2, padding=1, output_padding=1, bias=True),
            norm(64), nn.ReLU(True),
            nn.ConvTranspose2d(64, 32, 3, stride=2, padding=1, output_padding=1, bias=True),
            norm(32), nn.ReLU(True),
            nn.ReflectionPad2d(3), nn.Conv2d(32, 3, 7), nn.Tanh(),
        ])
        self.model = nn.Sequential(*layers)

    def forward(self, image):
        return self.model(image)


def load_generator(weights_path, device="cpu"):
    model = ErmolaevGenerator()
    state = torch.load(weights_path, map_location="cpu", weights_only=True)
    model.load_state_dict(state, strict=True)
    return model.to(device).eval()