# _schema.py

# === IMPORTS ===
import json
import os
from pathlib import Path
from typing import Any, Dict, List, Tuple

import matplotlib.pyplot as plt
import torch
import torch.nn as nn
import torch.nn.functional as F
from PIL import Image
from torch.utils.data import DataLoader, Dataset, Subset
from torchvision import transforms
from tqdm import tqdm

# markdown
"""# Configuration

Global settings for the Google -> Yandex GAN:
- Dataset path and image size
- Optimizer and training hyperparameters
- Device preference with safe CUDA compatibility checks
- GAN, L1, SSIM and edge reconstruction weights
- Output directories for checkpoints and generated samples"""
# code: ./src/config.py

# markdown
"""## Dataset

Google/Yandex paired image loader.

**Direction:**
- `google_img` is the generator input
- `yandex_img` is the target image from the same pair

**Returns:**
- Batch dict with `google_img`, `yandex_img`, `idx`"""
# code: ./src/dataloader.py

# code: ./src/test_dataloader.py

# markdown
"""## Model

Pix2pix-style GAN for Google -> Yandex map translation.

**Generator:**
- `GeneratorUNet`
- Input: Google image `(B, 3, H, W)`
- Output: generated Yandex image `(B, 3, H, W)`

**Discriminator:**
- `DiscriminatorPatchGAN`
- Input pair: `(google_img, yandex_img)`
- Learns to distinguish real pairs from `(google_img, fake_yandex)`

**Generator loss:**
- adversarial loss
- `lambda_L1 * L1(fake_yandex, yandex_img)`
- `lambda_SSIM * SSIMLoss(fake_yandex, yandex_img)`
- `lambda_edge * SobelEdgeLoss(fake_yandex, yandex_img)`

The generator uses bilinear upsampling followed by convolution to avoid
checkerboard artifacts from transposed convolutions."""
# code: ./src/model.py

# markdown
"""## Training

`GANTrainer` trains discriminator and generator alternately.

**Training step:**
1. Generate `fake_yandex = G(google_img)`
2. Train discriminator on real pair `(google_img, yandex_img)` and fake pair `(google_img, fake_yandex)`
3. Train generator against discriminator and paired Yandex target

**Checkpoint saving:**
- `best.pth`
- `epoch_N.pth`
- `final.pth`"""
# code: ./src/trainer.py

# markdown
"""## Analysis

Visualization helpers for generated samples and collected training metrics.

Training history plot contains:
1. Generator loss
2. Discriminator loss
3. L1 loss against the paired Yandex target
4. SSIM structure loss
5. Sobel edge loss
6. Best-checkpoint reconstruction score

The sample grid contains:
1. Google input
2. Generated Yandex
3. Original Yandex target"""
# code: ./src/analyze.py

# markdown
"""## Main Pipeline

Executes the full GAN workflow:
1. Create config
2. Build paired data loaders
3. Initialize Google -> Yandex GAN
4. Train with validation
5. Save checkpoints in `runs/checkpoints/`
6. Show loss plots and generated sample grid

This block is intentionally top-level, not wrapped in `main()`, so notebook
variables such as `model`, `trainer`, `train_loader`, `val_loader`, and
`training_analysis` remain available for debugging."""
# code: ./src/main.py

# # shell:
# !zip artefacts.zip runs/checkpoints/best.pth runs/images/training_history.png runs/images/generation_samples.png