from PIL import Image
import gradio as gr
from imagenet_en_cn import IMAGENET_1K_CLASSES
from huggingface_hub import hf_hub_download
import torch
torch.backends.cuda.matmul.allow_tf32 = True
torch.backends.cudnn.allow_tf32 = True
torch.set_float32_matmul_precision('high')
setattr(torch.nn.Linear, 'reset_parameters', lambda self: None)
setattr(torch.nn.LayerNorm, 'reset_parameters', lambda self: None)

import time
import argparse
from tokenizer_image.vq_model import VQ_models
from models.gpt import GPT_models
from models.generate import generate

device = "cuda"

model2ckpt = {
    "GPT-XL": ("vq_ds16_c2i.pt", "c2i_XL_384.pt", 384),
    "GPT-B": ("vq_ds16_c2i.pt", "c2i_B_256.pt", 256),
}

def load_model(args):
    ckpt_folder = "./"
    vq_ckpt, gpt_ckpt, image_size = model2ckpt[args.gpt_model]
    hf_hub_download(repo_id="FoundationVision/LlamaGen", filename=vq_ckpt, local_dir=ckpt_folder)
    hf_hub_download(repo_id="FoundationVision/LlamaGen", filename=gpt_ckpt, local_dir=ckpt_folder)
    # create and load model
    vq_model = VQ_models[args.vq_model](
        codebook_size=args.codebook_size,
        codebook_embed_dim=args.codebook_embed_dim)
    vq_model.to(device)
    vq_model.eval()
    checkpoint = torch.load(f"{ckpt_folder}{vq_ckpt}", map_location="cpu")
    vq_model.load_state_dict(checkpoint["model"])
    del checkpoint
    print(f"image tokenizer is loaded")

    # create and load gpt model
    precision = {'none': torch.float32, 'bf16': torch.bfloat16, 'fp16': torch.float16}[args.precision]
    latent_size = image_size // args.downsample_size
    gpt_model = GPT_models[args.gpt_model](
        vocab_size=args.codebook_size,
        block_size=latent_size ** 2,
        num_classes=args.num_classes,
        cls_token_num=args.cls_token_num,
        model_type=args.gpt_type,
    ).to(device=device, dtype=precision)
    
    checkpoint = torch.load(f"{ckpt_folder}{gpt_ckpt}", map_location="cpu")
    if args.from_fsdp: # fspd
        model_weight = checkpoint
    elif "model" in checkpoint:  # ddp
        model_weight = checkpoint["model"]
    elif "module" in checkpoint: # deepspeed
        model_weight = checkpoint["module"]
    elif "state_dict" in checkpoint:
        model_weight = checkpoint["state_dict"]
    else:
        raise Exception("please check model weight")
    # if 'freqs_cis' in model_weight:
    #     model_weight.pop('freqs_cis')
    gpt_model.load_state_dict(model_weight, strict=False)
    gpt_model.eval()
    del checkpoint
    print(f"gpt model is loaded")

    if args.compile:
        print(f"compiling the model...")
        gpt_model = torch.compile(
            gpt_model,
            mode="reduce-overhead",
            fullgraph=True
        ) # requires PyTorch 2.0 (optional)
    else:
        print(f"no need to compile model in demo") 

    return vq_model, gpt_model, image_size


def infer(cfg_scale, top_k, top_p, temperature, class_label, seed):
    n = 4
    latent_size = image_size // args.downsample_size
    # Labels to condition the model with (feel free to change):
    class_labels = [class_label for _ in range(n)]
    c_indices = torch.tensor(class_labels, device=device)
    qzshape = [len(class_labels), args.codebook_embed_dim, latent_size, latent_size]

    t1 = time.time()
    torch.manual_seed(seed)
    index_sample = generate(
        gpt_model, c_indices, latent_size ** 2,
        cfg_scale=cfg_scale, cfg_interval=args.cfg_interval,
        temperature=temperature, top_k=top_k,
        top_p=top_p, sample_logits=True, 
        )
    sampling_time = time.time() - t1
    print(f"gpt sampling takes about {sampling_time:.2f} seconds.")    

    t2 = time.time()
    samples = vq_model.decode_code(index_sample, qzshape) # output value is between [-1, 1]
    decoder_time = time.time() - t2
    print(f"decoder takes about {decoder_time:.2f} seconds.")
    # Convert to PIL.Image format:
    samples = samples.mul(127.5).add_(128.0).clamp_(0, 255).permute(0, 2, 3, 1).to("cpu", torch.uint8).numpy()
    samples = [Image.fromarray(sample) for sample in samples]
    return samples


parser = argparse.ArgumentParser()
parser.add_argument("--gpt-model", type=str, choices=list(GPT_models.keys()), default="GPT-XL")
parser.add_argument("--gpt-type", type=str, choices=['c2i', 't2i'], default="c2i", help="class-conditional or text-conditional")
parser.add_argument("--from-fsdp", action='store_true')
parser.add_argument("--cls-token-num", type=int, default=1, help="max token number of condition input")
parser.add_argument("--precision", type=str, default='bf16', choices=["none", "fp16", "bf16"]) 
parser.add_argument("--compile", action='store_true', default=False)
parser.add_argument("--vq-model", type=str, choices=list(VQ_models.keys()), default="VQ-16")
parser.add_argument("--codebook-size", type=int, default=16384, help="codebook size for vector quantization")
parser.add_argument("--codebook-embed-dim", type=int, default=8, help="codebook dimension for vector quantization")
parser.add_argument("--downsample-size", type=int, choices=[8, 16], default=16)
parser.add_argument("--num-classes", type=int, default=1000)
parser.add_argument("--cfg-scale", type=float, default=4.0)
parser.add_argument("--cfg-interval", type=float, default=-1)
parser.add_argument("--seed", type=int, default=0)
parser.add_argument("--top-k", type=int, default=2000,help="top-k value to sample with")
parser.add_argument("--temperature", type=float, default=1.0, help="temperature value to sample with")
parser.add_argument("--top-p", type=float, default=1.0, help="top-p value to sample with")
args = parser.parse_args()

vq_model, gpt_model, image_size = load_model(args)

with gr.Blocks() as demo:
    gr.Markdown("<h1 style='text-align: center'>Autoregressive Model Beats Diffusion: Llama for Scalable Image Generation</h1>")

    with gr.Tabs():
        with gr.TabItem('Generate'):
            with gr.Row():
                with gr.Column():
                    # with gr.Row():
                    #     image_size = gr.Radio(choices=[384], value=384, label='Peize Model Resolution')
                    with gr.Row():
                        i1k_class = gr.Dropdown(
                            list(IMAGENET_1K_CLASSES.values()),
                            value='Eskimo dog, husky [爱斯基摩犬,哈士奇]',
                            type="index", label='ImageNet-1K Class'
                        )
                    cfg_scale = gr.Slider(minimum=1, maximum=25, step=0.1, value=4.0, label='Classifier-free Guidance Scale')
                    top_k = gr.Slider(minimum=1, maximum=16384, step=1, value=4000, label='Top-K')
                    top_p = gr.Slider(minimum=0., maximum=1.0, step=0.1, value=1.0, label="Top-P")
                    temperature = gr.Slider(minimum=0., maximum=1.0, step=0.1, value=1.0, label='Temperature')
                    seed = gr.Slider(minimum=0, maximum=1000, step=1, value=42, label='Seed')
                    # seed = gr.Number(value=0, label='Seed')
                    button = gr.Button("Generate", variant="primary")
                with gr.Column():
                    output = gr.Gallery(label='Generated Images', height=700)
                    button.click(infer, inputs=[cfg_scale, top_k, top_p, temperature, i1k_class, seed], outputs=[output])
    demo.queue()
    demo.launch(debug=True)