"""
Flux LoRA Training - Flow Matching with correct latent packing.
"""
from diffusers import FluxPipeline
from diffusers.optimization import get_scheduler
from peft import LoraConfig, get_peft_model
from accelerate import Accelerator
import torch
import torch.nn.functional as F
import webdataset as wds
from pathlib import Path
from PIL import Image
import io
import time
from torchvision import transforms

MODEL_NAME = "black-forest-labs/FLUX.1-schnell"
DATA_DIR = "/data0/datasets/processed/flux_train/shards"
OUTPUT_DIR = "/data0/checkpoints/flux_lora"
CACHE_DIR = "/data0/models"
BATCH_SIZE = 1
GRAD_ACCUM = 4
LR = 1e-4
MAX_STEPS = 50000
SAVE_STEPS = 5000
LORA_RANK = 128

Path(OUTPUT_DIR).mkdir(parents=True, exist_ok=True)

accelerator = Accelerator(
    mixed_precision="bf16",
    gradient_accumulation_steps=GRAD_ACCUM,
)

print("Loading Flux...")
pipe = FluxPipeline.from_pretrained(
    MODEL_NAME,
    torch_dtype=torch.bfloat16,
    cache_dir=CACHE_DIR,
)

transformer = pipe.transformer
vae = pipe.vae

vae.requires_grad_(False)
pipe.text_encoder.requires_grad_(False)
pipe.text_encoder_2.requires_grad_(False)

lora_config = LoraConfig(
    r=LORA_RANK,
    lora_alpha=LORA_RANK,
    target_modules=["to_q", "to_k", "to_v", "to_out.0"],
    lora_dropout=0.05,
)
transformer = get_peft_model(transformer, lora_config)
transformer.print_trainable_parameters()

optimizer = torch.optim.AdamW(transformer.parameters(), lr=LR, weight_decay=0.01)
lr_scheduler = get_scheduler("cosine", optimizer=optimizer, num_warmup_steps=500, num_training_steps=MAX_STEPS)

transform = transforms.Compose([
    transforms.Resize(1024, interpolation=transforms.InterpolationMode.LANCZOS),
    transforms.CenterCrop(1024),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5]),
])

tar_files = sorted(Path(DATA_DIR).glob("*.tar"))
print(f"Found {len(tar_files)} tar shards")


def preprocess(sample):
    try:
        img = sample["jpg"]
        if isinstance(img, bytes):
            img = Image.open(io.BytesIO(img)).convert("RGB")
        caption = sample.get("txt", b"")
        if isinstance(caption, bytes):
            caption = caption.decode("utf-8")
        return {"image": transform(img), "caption": caption}
    except:
        return None


def ignore_errors(exn):
    print(f"WebDataset error (skipping): {exn}")
    return True

dataset = (
    wds.WebDataset([str(f) for f in tar_files], shardshuffle=True, handler=ignore_errors)
    .shuffle(1000)
    .decode("pil", handler=ignore_errors)
    .map(preprocess)
    .select(lambda x: x is not None)
)
dataloader = torch.utils.data.DataLoader(dataset, batch_size=BATCH_SIZE, num_workers=4, pin_memory=True)

transformer, optimizer, dataloader, lr_scheduler = accelerator.prepare(
    transformer, optimizer, dataloader, lr_scheduler
)
vae.to(accelerator.device, dtype=torch.bfloat16)
pipe.text_encoder.to(accelerator.device, dtype=torch.bfloat16)
pipe.text_encoder_2.to(accelerator.device, dtype=torch.bfloat16)


def pack_latents(latents, batch_size, num_channels, height, width):
    latents = latents.view(batch_size, num_channels, height // 2, 2, width // 2, 2)
    latents = latents.permute(0, 2, 4, 1, 3, 5)
    latents = latents.reshape(batch_size, (height // 2) * (width // 2), num_channels * 4)
    return latents


def prepare_latent_image_ids(height, width, device, dtype):
    latent_image_ids = torch.zeros(height // 2, width // 2, 3)
    latent_image_ids[..., 1] = latent_image_ids[..., 1] + torch.arange(height // 2)[:, None]
    latent_image_ids[..., 2] = latent_image_ids[..., 2] + torch.arange(width // 2)[None, :]
    latent_image_ids = latent_image_ids.reshape(height // 2 * width // 2, 3)
    return latent_image_ids.to(device=device, dtype=dtype)


global_step = 0
t0 = time.time()
print(f"Starting training... Max steps: {MAX_STEPS}")

transformer.train()
while global_step < MAX_STEPS:
    for batch in dataloader:
        if global_step >= MAX_STEPS:
            break

        with accelerator.accumulate(transformer):
            images = batch["image"].to(accelerator.device, dtype=torch.bfloat16)
            captions = batch["caption"]
            bs = images.shape[0]

            with torch.no_grad():
                latents = vae.encode(images).latent_dist.sample()
                latents = (latents - vae.config.shift_factor) * vae.config.scaling_factor

                packed_latents = pack_latents(latents, bs, 16, 128, 128)
                latent_image_ids = prepare_latent_image_ids(128, 128, accelerator.device, torch.bfloat16)

                prompt_embeds, pooled_prompt_embeds, text_ids = pipe.encode_prompt(
                    prompt=captions if isinstance(captions, list) else [captions],
                    prompt_2=None,
                    device=accelerator.device,
                )

            noise = torch.randn_like(packed_latents)
            t = torch.rand(bs, device=accelerator.device, dtype=torch.bfloat16)
            t_expand = t.view(-1, 1, 1)

            noisy_latents = (1 - t_expand) * packed_latents + t_expand * noise
            timesteps = (t * 1000).to(dtype=packed_latents.dtype)

            model_pred = transformer(
                hidden_states=noisy_latents,
                timestep=timesteps,
                encoder_hidden_states=prompt_embeds,
                pooled_projections=pooled_prompt_embeds,
                txt_ids=text_ids,
                img_ids=latent_image_ids,
                return_dict=False,
            )[0]

            target = noise - packed_latents
            loss = F.mse_loss(model_pred, target)

            accelerator.backward(loss)
            if accelerator.sync_gradients:
                accelerator.clip_grad_norm_(transformer.parameters(), 1.0)
            optimizer.step()
            lr_scheduler.step()
            optimizer.zero_grad()

        if accelerator.sync_gradients:
            global_step += 1

            if global_step % 100 == 0:
                elapsed = time.time() - t0
                print(f"Step {global_step}/{MAX_STEPS} | Loss: {loss.item():.4f} | LR: {lr_scheduler.get_last_lr()[0]:.2e} | Time: {elapsed/3600:.1f}h")

            if global_step % SAVE_STEPS == 0:
                save_path = f"{OUTPUT_DIR}/checkpoint-{global_step}"
                accelerator.unwrap_model(transformer).save_pretrained(save_path)
                print(f"Saved: {save_path}")

final_path = f"{OUTPUT_DIR}/final"
accelerator.unwrap_model(transformer).save_pretrained(final_path)
print(f"Training complete! Saved to {final_path}")
print(f"Total time: {(time.time()-t0)/3600:.1f} hours")