import os
from PIL.Image import Image
from diffusers import StableDiffusionPipeline as SDP
from diffusers import AutoPipelineForText2Image as AP
from diffusers import DiffusionPipeline as DP
import torch
import json
import random

DEVICE = "mps" if torch.backends.mps.is_available() else \
        "cuda" if torch.cuda.is_available() else "cpu"

# Model 1
pipe = SDP.from_pretrained("nitrosocke/Ghibli-Diffusion",
                           torch_dtype=torch.float16).to(DEVICE)
ID_PREFIX = "nitrosocke"

# Model 2
# pipe = AP.from_pretrained("black-forest-labs/FLUX.1-dev",
#                           torch_dtype=torch.bfloat16).to(DEVICE)
# pipe.load_lora_weights('openfree/flux-chatgpt-ghibli-lora',
#                        weight_name='flux-chatgpt-ghibli-lora.safetensors')
# ID_PREFIX = "openfree"

# Model 3
# pipe = DP.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0",
#                           torch_dtype=torch.float16, variant="fp16",
#                           use_safetensors=True,).to(DEVICE)
# pipe.load_lora_weights("KappaNeuro/studio-ghibli-style")
# pipe.to(DEVICE)
# pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
# pipe.enable_model_cpu_offload()
# ID_PREFIX = "KappaNeuro"

NUM_IMAGES = 3
out_dir = f"data/{ID_PREFIX}"
os.makedirs(out_dir, exist_ok=True)

with open("metadata.jsonl", "r", encoding="utf-8") as fin, \
    open("ai_entries.jsonl", "w", encoding="utf-8") as fout, \
    open("pairs.jsonl", "a", encoding="utf-8") as pairs:
    for i, line in enumerate(fin):
        sample = json.loads(line)

        if sample["label"] != "real":
            continue

        description = sample["description"]
        real_id: str = sample["id"]
        aigen_id = real_id.replace("real", ID_PREFIX)
        prompt = f"ghibli style, {description}"
        seeds = [random.randrange(2**32) for _ in range(NUM_IMAGES)]
        gens = [torch.Generator(device=DEVICE).manual_seed(s) for s in seeds]

        images: list[Image] = pipe(prompt, num_images_per_prompt=NUM_IMAGES, generator=gens).images
        src_path = sample["image"]
        src_file: str = os.path.basename(src_path)
        file_noext = src_file.split(".")[0]

        for j, image in enumerate(images):
            img_id = f"{aigen_id}-{j}"
            dst_path = os.path.join(out_dir, f"{file_noext}_{j}.jpg")

            fout.write(json.dumps({
                "id": img_id,
                "image": dst_path,
                "label": ID_PREFIX,
                "description": description,
            }) + "\n")
            pairs.write(json.dumps({
                "real_image": src_path,
                "ai_image": dst_path,
                "description": description,
                "seed": seeds[j]
            }) + "\n")
            image.save(dst_path)